From 88398822ab9f084796b0e67e37784500fc3e26ba Mon Sep 17 00:00:00 2001
From: Jean-Denis Giguere <jean-denis@anagraph.io>
Date: Thu, 7 Feb 2019 09:27:52 -0500
Subject: [PATCH 001/380] Build instructions documentation

---
 README.md | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/README.md b/README.md
index 8644f40a5..f592c2126 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,43 @@ Please see the [Getting Started](http://rasterframes.io/getting-started.html) se
 * [List of available UDFs](http://rasterframes.io/latest/api/index.html#astraea.spark.rasterframes.RasterFunctions)
 * [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/) 
 
+## Build instruction
+
+First, you will need [sbt](https://www.scala-sbt.org/).
+
+Download the source from GitHub.
+
+```bash
+git clone https://github.com/locationtech/rasterframes.git
+cd rasterframes
+```
+
+To publish to hyou Ivy local repository:
+
+```bash
+sbt publishLocal
+```
+
+You can run tests with
+
+```bash
+sbt test
+```
+
+and integration tests
+
+```bash
+sbt it:test
+```
+
+The documentation may be built with
+
+```bash
+sbt makeSite
+```
+
+The `pyrasteframes` build instructions are located (pyrasterframes/python/README.rst)[pyrasterframes/python/README.rst]
+
 ## Copyright and License
 
 RasterFrames is released under the Apache 2.0 License, copyright Astraea, Inc. 2017-2018.

From 1d854ffe4bd6e91149f477ab9b94e99d874d48f1 Mon Sep 17 00:00:00 2001
From: Jean-Denis Giguere <jean-denis@anagraph.io>
Date: Thu, 7 Feb 2019 09:30:56 -0500
Subject: [PATCH 002/380] Build instructions documentation - fix link syntax

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f592c2126..9ad3c9c98 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ The documentation may be built with
 sbt makeSite
 ```
 
-The `pyrasteframes` build instructions are located (pyrasterframes/python/README.rst)[pyrasterframes/python/README.rst]
+The `pyrasteframes` build instructions are located [pyrasterframes/python/README.rst](pyrasterframes/python/README.rst)
 
 ## Copyright and License
 

From fb93767517592305df9455d1c459649406f64960 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 13 Mar 2019 13:52:24 -0400
Subject: [PATCH 003/380] Collecting all Astraea-specific build overrides into
 a separate sbt file. Due to sbt evaluating build scripts in alphabetical
 order we can use a `x-` prefix for the filename and ensure the settings in it
 take precidence over the standard settings, while still maintaining
 syncronization/parity with the standard branches.

---
 project/ProjectPlugin.scala | 10 +---------
 version.sbt                 |  2 +-
 x-astraea.sbt               | 10 ++++++++++
 3 files changed, 12 insertions(+), 10 deletions(-)
 create mode 100644 x-astraea.sbt

diff --git a/project/ProjectPlugin.scala b/project/ProjectPlugin.scala
index 0a8df52af..ee6359d1c 100644
--- a/project/ProjectPlugin.scala
+++ b/project/ProjectPlugin.scala
@@ -40,15 +40,7 @@ object ProjectPlugin extends AutoPlugin {
     rfSparkVersion in ThisBuild := "2.3.2" ,
     rfGeoTrellisVersion in ThisBuild := "2.1.0",
     rfGeoMesaVersion in ThisBuild := "2.1.0",
-
-    credentials += Credentials(Path.userHome / ".sbt" / ".credentials"),
-    publishTo := {
-      val base = "https://s22s.mycloudrepo.io/repositories"
-      if (isSnapshot.value)
-        Some("Astraea Internal Snapshots" at s"$base/snapshots/")
-      else
-        Some("Astraea Internal Releases" at s"$base/releases/")
-    },
+    publishTo := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
     publishArtifact in Test := false,
diff --git a/version.sbt b/version.sbt
index 6f256b13e..d8a83a96c 100644
--- a/version.sbt
+++ b/version.sbt
@@ -1 +1 @@
-version in ThisBuild := "0.8.0-astraea-SNAPSHOT"
+version in ThisBuild := "0.8.0-SNAPSHOT"
diff --git a/x-astraea.sbt b/x-astraea.sbt
new file mode 100644
index 000000000..f3b51a14c
--- /dev/null
+++ b/x-astraea.sbt
@@ -0,0 +1,10 @@
+// Internal Astraea-specific overides
+version in ThisBuild := "0.8.0-astraea-SNAPSHOT"
+credentials in ThisBuild += Credentials(Path.userHome / ".sbt" / ".credentials")
+publishTo := {
+  val base = "https://s22s.mycloudrepo.io/repositories"
+  if (isSnapshot.value)
+    Some("Astraea Internal Snapshots" at s"$base/snapshots/")
+  else
+    Some("Astraea Internal Releases" at s"$base/releases/")
+}
\ No newline at end of file

From 76badd7831d508f70f029a8ddadf6b17e08b43b0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 19 Mar 2019 14:30:02 -0400
Subject: [PATCH 004/380] Fixes to override capability.

---
 project/ProjectPlugin.scala |  2 +-
 x-astraea.sbt               | 14 +++++++++++---
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/project/ProjectPlugin.scala b/project/ProjectPlugin.scala
index ee6359d1c..42d3696c7 100644
--- a/project/ProjectPlugin.scala
+++ b/project/ProjectPlugin.scala
@@ -40,7 +40,7 @@ object ProjectPlugin extends AutoPlugin {
     rfSparkVersion in ThisBuild := "2.3.2" ,
     rfGeoTrellisVersion in ThisBuild := "2.1.0",
     rfGeoMesaVersion in ThisBuild := "2.1.0",
-    publishTo := sonatypePublishTo.value,
+    publishTo in ThisBuild := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
     publishArtifact in Test := false,
diff --git a/x-astraea.sbt b/x-astraea.sbt
index f3b51a14c..0e2aac86d 100644
--- a/x-astraea.sbt
+++ b/x-astraea.sbt
@@ -1,10 +1,18 @@
 // Internal Astraea-specific overides
-version in ThisBuild := "0.8.0-astraea-SNAPSHOT"
-credentials in ThisBuild += Credentials(Path.userHome / ".sbt" / ".credentials")
+ThisBuild / version := "0.8.0-astraea-SNAPSHOT"
+ThisBuild / credentials += Credentials(Path.userHome / ".sbt" / ".credentials")
 publishTo := {
   val base = "https://s22s.mycloudrepo.io/repositories"
   if (isSnapshot.value)
     Some("Astraea Internal Snapshots" at s"$base/snapshots/")
   else
     Some("Astraea Internal Releases" at s"$base/releases/")
-}
\ No newline at end of file
+}
+
+// Coudn't figure out why we have to call all these out explicitly.
+// The above should have been sufficient based on my understanding of
+// the precidence rules in sbt.
+LocalProject("core") / publishTo := publishTo.value
+LocalProject("datasource") / publishTo := publishTo.value
+LocalProject("pyrasterframes") / publishTo := publishTo.value
+LocalProject("experimental") / publishTo := publishTo.value
\ No newline at end of file

From c92323fda69d6de31b3a2149a70fb7f3b324a82a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 25 Mar 2019 11:31:33 -0400
Subject: [PATCH 005/380] Expose python expm1 function

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/tut/reference.md                          | 2 +-
 pyrasterframes/python/pyrasterframes/rasterfunctions.py | 2 +-
 pyrasterframes/python/tests/PyRasterFramesTests.py      | 1 +
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 47a9b3384..d264d900d 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -547,7 +547,7 @@ _Python_:
 
 _SQL_: `rf_log1p`
 
-Performs natural logarithm of cell values plus one. Invers of @ref:[`expm1`](reference.md#expm1).
+Performs natural logarithm of cell values plus one. Inverse of @ref:[`expm1`](reference.md#expm1).
 
 ### Tile Statistics
 
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index b035bb253..589b256d8 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -243,7 +243,7 @@ def _(data_tile, mask_tile, mask_value):
     'exp': 'Performs cell-wise exponential',
     'exp2': 'Compute 2 to the power of cell values',
     'exp10': 'Compute 10 to the power of cell values',
-    'exp1m': 'Performs cell-wise exponential, then subtract one',
+    'expm1': 'Performs cell-wise exponential, then subtract one',
     'resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
 
     # ------- JTS functions -------
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 512809efa..b47e25577 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -101,6 +101,7 @@ def test_general(self):
             .withColumn('ascii', render_ascii(self.tileCol)) \
             .withColumn('log', log(self.tileCol)) \
             .withColumn('exp', exp(self.tileCol)) \
+            .withColumn('expm1', expm1(self.tileCol)) \
             .withColumn('round', round(self.tileCol))
 
         df.show()

From cd622a0ab814538a475345b854069b90772fd23b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 26 Mar 2019 10:54:51 -0400
Subject: [PATCH 006/380] Setting version to 0.8.0-SNAPSHOT

---
 version.sbt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/version.sbt b/version.sbt
index eb883072c..d8a83a96c 100644
--- a/version.sbt
+++ b/version.sbt
@@ -1 +1 @@
-version in ThisBuild := "0.8.0-RC4"
+version in ThisBuild := "0.8.0-SNAPSHOT"

From 0159a672f9497ed7513511618408b39145e6c5e0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 13 Mar 2019 13:52:24 -0400
Subject: [PATCH 007/380] Initial, change minimized integration of
 geotrellis-gdal.

---
 core/build.sbt                                |  2 +-
 .../spark/rasterframes/ref/RasterSource.scala | 52 +++++++++++++++++++
 .../astraea/spark/rasterframes/TestData.scala |  6 +--
 .../rasterframes/ref/RasterRefSpec.scala      |  2 +-
 x-astraea.sbt                                 | 10 ++++
 5 files changed, 67 insertions(+), 5 deletions(-)
 create mode 100644 x-astraea.sbt

diff --git a/core/build.sbt b/core/build.sbt
index eb5164045..830ec2d2f 100644
--- a/core/build.sbt
+++ b/core/build.sbt
@@ -6,7 +6,7 @@ libraryDependencies ++= Seq(
   "com.chuusai" %% "shapeless" % "2.3.2",
   "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
   "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-
+  "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.7.4-local.1",
   spark("core").value % Provided,
   spark("mllib").value % Provided,
   spark("sql").value % Provided,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 9dc9bd55e..83acf8103 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -134,8 +134,10 @@ object RasterSource extends LazyLogging {
 
   private def _logger = logger
 
+
   def apply(source: URI, callback: Option[ReadCallback] = None): RasterSource =
     source.getScheme match {
+      case GDALRasterSource() ⇒ GDALRasterSource(source, callback)
       case "http" | "https" ⇒ HttpGeoTiffRasterSource(source, callback)
       case "file" ⇒ FileGeoTiffRasterSource(source, callback)
       case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" ⇒
@@ -209,6 +211,56 @@ object RasterSource extends LazyLogging {
     }
   }
 
+  case class GDALRasterSource(source: URI, callback: Option[ReadCallback]) extends RasterSource with URIRasterSource {
+    import geotrellis.contrib.vlm.gdal.{GDALRasterSource ⇒ VLMRasterSource}
+
+    @transient
+    private lazy val gdal = {
+      val cleaned = source.toASCIIString.replace("gdal+", "")
+      VLMRasterSource(cleaned)
+    }
+
+    override def crs: CRS = gdal.crs
+
+    override def extent: Extent = gdal.extent
+
+    // TODO: See if dates are available in gdal.
+    // Maybe useful: gdal.dataset.getMetadata_Dict
+    override def timestamp: Option[ZonedDateTime] = None
+
+    override def cellType: CellType = gdal.cellType
+
+    override def bandCount: Int = gdal.bandCount
+
+    override def cols: Int = gdal.cols
+
+    override def rows: Int = gdal.rows
+
+    override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] =
+      if (bandCount == 1)
+        Left(gdal.read(extent, Seq(0)).get.mapTile(_.band(0)))
+      else
+        Right(gdal.read(extent).get)
+
+    override def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
+      val grid = gdal.gridBounds
+
+      val tiled = grid.split(256, 256).toTraversable
+
+      if (bandCount == 1)
+        Left(gdal.readBounds(tiled, Seq(0)).map(_.mapTile(_.band(0))).toSeq)
+      else
+        Right(gdal.readBounds(tiled).toSeq)
+    }
+
+    override def nativeLayout: Option[TileLayout] =
+      Some(TileLayout(1, 1, cols, rows))
+  }
+
+  object GDALRasterSource {
+    def unapply(scheme: String): Boolean = scheme.startsWith("gdal+")
+  }
+
   trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
     protected def rangeReader: RangeReader
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index 29c06849e..db59dfb58 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -123,13 +123,13 @@ trait TestData {
     rf.toTileLayerRDD(rf.tileColumns.head).left.get
   }
 
-  private val baseCOG = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_%s.TIF"
+  private val baseCOG = "gdal+https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_%s.TIF"
   lazy val remoteCOGSingleband1 = URI.create(baseCOG.format("B1"))
   lazy val remoteCOGSingleband2 = URI.create(baseCOG.format("B2"))
 
-  lazy val remoteCOGMultiband =  URI.create("https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
+  lazy val remoteCOGMultiband =  URI.create("gdal+https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
 
-  lazy val remoteMODIS = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
+  lazy val remoteMODIS = URI.create("gdal+https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
 
   object JTS {
     val fact = new GeometryFactory()
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index 4efe2b474..dbf0327cd 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -150,8 +150,8 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         assert(counter.reads === 0)
         assert(subRaster.tile.statistics.map(_.dataCells) === Some(subRaster.cols * subRaster.rows))
-        assert(counter.reads > 0)
         println(counter)
+        assert(counter.reads > 0)
       }
     }
 
diff --git a/x-astraea.sbt b/x-astraea.sbt
new file mode 100644
index 000000000..f3b51a14c
--- /dev/null
+++ b/x-astraea.sbt
@@ -0,0 +1,10 @@
+// Internal Astraea-specific overides
+version in ThisBuild := "0.8.0-astraea-SNAPSHOT"
+credentials in ThisBuild += Credentials(Path.userHome / ".sbt" / ".credentials")
+publishTo := {
+  val base = "https://s22s.mycloudrepo.io/repositories"
+  if (isSnapshot.value)
+    Some("Astraea Internal Snapshots" at s"$base/snapshots/")
+  else
+    Some("Astraea Internal Releases" at s"$base/releases/")
+}
\ No newline at end of file

From d67cedd16895cab94d665d88d315c16f97f717a6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 2 Jan 2019 16:10:42 -0500
Subject: [PATCH 008/380] Added basic read callback support.

---
 .../spark/rasterframes/ref/LayerSpace.scala   | 80 -------------------
 .../spark/rasterframes/ref/RasterSource.scala | 22 ++++-
 .../rasterframes/ref/RasterRefSpec.scala      | 10 +--
 3 files changed, 22 insertions(+), 90 deletions(-)
 delete mode 100644 core/src/main/scala/astraea/spark/rasterframes/ref/LayerSpace.scala

diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/LayerSpace.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/LayerSpace.scala
deleted file mode 100644
index 25a9d050c..000000000
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/LayerSpace.scala
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package astraea.spark.rasterframes.ref
-
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import geotrellis.proj4.CRS
-import geotrellis.raster._
-import geotrellis.raster.resample.ResampleMethod
-import geotrellis.spark.tiling.LayoutDefinition
-import geotrellis.spark.{SpatialKey, _}
-
-
-/**
- * NB: This package is only a temporary home for this.
- *
- * @since 9/5/18
- */
-case class LayerSpace(
-  crs: CRS,
-  cellType: CellType,
-  layout: LayoutDefinition,
-  resampleMethod: ResampleMethod = ResampleMethod.DEFAULT
-) {
-
-  def reproject(dest: CRS): LayerSpace = {
-    copy(
-      crs = dest,
-      layout = layout.copy(extent = layout.extent.reproject(crs, dest))
-    )
-  }
-
-  def asTileLayerMetadata: TileLayerMetadata[SpatialKey] = {
-    val bounds = KeyBounds(
-      SpatialKey(0, 0),
-      SpatialKey(layout.layoutCols - 1, layout.layoutRows - 1)
-    )
-    TileLayerMetadata(cellType, layout, layout.extent, crs, bounds)
-  }
-}
-
-object LayerSpace {
-
-  private[rasterframes]
-  def defaultLayout(prt: ProjectedRasterTile): LayoutDefinition =
-    LayoutDefinition(prt.extent, TileLayout(1, 1, prt.cols, prt.rows))
-
-  def from(rs: RasterSource): LayerSpace = new LayerSpace(
-    rs.crs, rs.cellType, LayoutDefinition(rs.extent, rs.nativeLayout
-      .getOrElse(TileLayout(1, 1, rs.cols, rs.rows))
-    )
-  )
-
-  def from(rr: RasterRef): LayerSpace = new LayerSpace(
-    rr.crs, rr.cellType, RasterRef.defaultLayout(rr)
-  )
-
-  def from(prt: ProjectedRasterTile): LayerSpace = new LayerSpace(
-    prt.crs, prt.cellType, defaultLayout(prt)
-  )
-
-}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 83acf8103..c71417a83 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -213,6 +213,7 @@ object RasterSource extends LazyLogging {
 
   case class GDALRasterSource(source: URI, callback: Option[ReadCallback]) extends RasterSource with URIRasterSource {
     import geotrellis.contrib.vlm.gdal.{GDALRasterSource ⇒ VLMRasterSource}
+    import GDALRasterSource.MAX_SIZE
 
     @transient
     private lazy val gdal = {
@@ -236,16 +237,27 @@ object RasterSource extends LazyLogging {
 
     override def rows: Int = gdal.rows
 
-    override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] =
+    override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = {
+
+      callback.foreach { cb ⇒
+        val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
+        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
+      }
+
       if (bandCount == 1)
         Left(gdal.read(extent, Seq(0)).get.mapTile(_.band(0)))
       else
         Right(gdal.read(extent).get)
+    }
 
     override def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
       val grid = gdal.gridBounds
 
-      val tiled = grid.split(256, 256).toTraversable
+      callback.foreach { cb ⇒
+        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
+      }
+
+      val tiled = grid.split(MAX_SIZE, MAX_SIZE).toTraversable
 
       if (bandCount == 1)
         Left(gdal.readBounds(tiled, Seq(0)).map(_.mapTile(_.band(0))).toSeq)
@@ -253,11 +265,13 @@ object RasterSource extends LazyLogging {
         Right(gdal.readBounds(tiled).toSeq)
     }
 
-    override def nativeLayout: Option[TileLayout] =
-      Some(TileLayout(1, 1, cols, rows))
+    override def nativeLayout: Option[TileLayout] = {
+      Some(TileLayout(cols / MAX_SIZE + 1, rows / MAX_SIZE + 1, math.min(cols, MAX_SIZE), math.min(rows, MAX_SIZE)))
+    }
   }
 
   object GDALRasterSource {
+    final val MAX_SIZE = 256
     def unapply(scheme: String): Boolean = scheme.startsWith("gdal+")
   }
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index dbf0327cd..ad29e0e0e 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -26,7 +26,7 @@ import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.expressions.accessors._
 import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
-import geotrellis.raster.Tile
+import geotrellis.raster.{GridBounds, Tile}
 import geotrellis.vector.Extent
 
 /**
@@ -36,6 +36,7 @@ import geotrellis.vector.Extent
  */
 //noinspection TypeAnnotation
 class RasterRefSpec extends TestEnvironment with TestData {
+
   def sub(e: Extent) = {
     val c = e.center
     val w = e.width
@@ -44,7 +45,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
   }
 
   trait Fixture {
-    val counter = new ReadMonitor
+    val counter = new ReadMonitor(false)
     val src = RasterSource(remoteCOGSingleband1, Some(counter))
     val fullRaster = RasterRef(src)
     val subExtent = sub(src.extent)
@@ -94,7 +95,6 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         val ds = Seq((1, RasterRefTile(fullRaster): Tile)).toDF("index", "ref")
         val dims = ds.select(GetDimensions($"ref"))
-        println(counter)
         assert(dims.count() === 1)
         assert(dims.first() !== null)
       }
@@ -103,7 +103,6 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         val ds = Seq((1, RasterRefTile(subRaster): Tile)).toDF("index", "ref")
         val dims = ds.select(GetDimensions($"ref"))
-        println(counter)
         assert(dims.count() === 1)
         assert(dims.first() !== null)
       }
@@ -150,8 +149,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         assert(counter.reads === 0)
         assert(subRaster.tile.statistics.map(_.dataCells) === Some(subRaster.cols * subRaster.rows))
-        println(counter)
-        assert(counter.reads > 0)
+         assert(counter.reads > 0)
       }
     }
 

From e39e17ec8cdef9b2e91527ff9e15cd8e172a216b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 3 Jan 2019 10:36:31 -0500
Subject: [PATCH 009/380] Misc updates to prototype gdal connector.

---
 .scalafmt.conf                                |   6 +-
 .../spark/rasterframes/ref/RasterSource.scala | 168 ++++++++++--------
 2 files changed, 96 insertions(+), 78 deletions(-)

diff --git a/.scalafmt.conf b/.scalafmt.conf
index 4d09e93c7..ca5e10394 100644
--- a/.scalafmt.conf
+++ b/.scalafmt.conf
@@ -1,10 +1,7 @@
 maxColumn = 138
 continuationIndent.defnSite = 2
-continuationIndent.callSite = 2
-continuationIndent.extendSite = 2
 binPack.parentConstructors = true
 binPack.literalArgumentLists = false
-binPack.unsafeCallSite = true
 newlines.penalizeSingleSelectMultiArgList = false
 newlines.sometimesBeforeColonInMethodReturnType = false
 align.openParenCallSite = false
@@ -16,5 +13,4 @@ rewriteTokens {
 }
 optIn.selfAnnotationNewline = false
 optIn.breakChainOnFirstMethodDot = true
-optIn.configStyleArguments = false
-importSelectors = BinPack
+importSelectors = BinPack
\ No newline at end of file
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index c71417a83..e4cf4c4a1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -86,17 +86,17 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
     val extents = nativeTiling
     if (bandCount == 1) {
       val rasters = for {
-        extent ← extents
+        extent <- extents
         rr = RasterRef(this, Some(extent))
         tile: Tile = RasterRefTile(rr)
       } yield Raster(tile, extent)
       Left(rasters)
-    }
-    else {
+    } else {
       // Need to figure this out.
-      RasterSource._logger.warn("Lazy reading is not available for multiband images. Performing eager read.")
+      RasterSource._logger.warn(
+        "Lazy reading is not available for multiband images. Performing eager read.")
       val rasters = for {
-        extent ← extents
+        extent <- extents
         raster = this.read(extent).right.get
       } yield raster
       Right(rasters)
@@ -114,14 +114,15 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
   def tileContext: TileContext = TileContext(extent, crs)
 
   def nativeTiling: Seq[Extent] = {
-    nativeLayout.map { tileLayout ⇒
-      val layout = LayoutDefinition(extent, tileLayout)
-      val transform = layout.mapTransform
-      for {
-        col ← 0 until tileLayout.layoutCols
-        row ← 0 until tileLayout.layoutRows
-      } yield transform(col, row)
-    }
+    nativeLayout
+      .map { tileLayout =>
+        val layout = LayoutDefinition(extent, tileLayout)
+        val transform = layout.mapTransform
+        for {
+          col <- 0 until tileLayout.layoutCols
+          row <- 0 until tileLayout.layoutRows
+        } yield transform(col, row)
+      }
       .getOrElse(Seq(extent))
   }
 }
@@ -134,24 +135,22 @@ object RasterSource extends LazyLogging {
 
   private def _logger = logger
 
-
   def apply(source: URI, callback: Option[ReadCallback] = None): RasterSource =
     source.getScheme match {
-      case GDALRasterSource() ⇒ GDALRasterSource(source, callback)
-      case "http" | "https" ⇒ HttpGeoTiffRasterSource(source, callback)
-      case "file" ⇒ FileGeoTiffRasterSource(source, callback)
-      case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" ⇒
+      case GDALRasterSource()                        => GDALRasterSource(source, callback)
+      case "http" | "https"                          => HttpGeoTiffRasterSource(source, callback)
+      case "file"                                    => FileGeoTiffRasterSource(source, callback)
+      case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" =>
         // TODO: How can we get the active hadoop configuration
         // TODO: without having to pass it through?
-        val config = () ⇒ new Configuration()
+        val config = () => new Configuration()
         HadoopGeoTiffRasterSource(source, config, callback)
-      case "s3" ⇒
-        val client = () ⇒ S3Client.DEFAULT
+      case "s3" =>
+        val client = () => S3Client.DEFAULT
         S3GeoTiffRasterSource(source, client, callback)
-      case s ⇒ throw new UnsupportedOperationException(s"Scheme '$s' not supported")
+      case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
     }
 
-
   case class SimpleGeoTiffInfo(
     cellType: CellType,
     extent: Extent,
@@ -165,13 +164,21 @@ object RasterSource extends LazyLogging {
 
   object SimpleGeoTiffInfo {
     def apply(info: GeoTiffReader.GeoTiffInfo): SimpleGeoTiffInfo =
-      SimpleGeoTiffInfo(info.cellType, info.extent, info.rasterExtent, info.crs, info.tags, info.segmentLayout, info.bandCount, info.noDataValue)
+      SimpleGeoTiffInfo(
+        info.cellType,
+        info.extent,
+        info.rasterExtent,
+        info.crs,
+        info.tags,
+        info.segmentLayout,
+        info.bandCount,
+        info.noDataValue)
   }
 
   // According to https://goo.gl/2z8xx9 the GeoTIFF date format is 'YYYY:MM:DD HH:MM:SS'
   private val dateFormat = DateTimeFormatter.ofPattern("yyyy:MM:dd HH:mm:ss")
 
-  trait URIRasterSource { _: RasterSource ⇒
+  trait URIRasterSource { _: RasterSource =>
     def source: URI
 
     abstract override def toString: String = {
@@ -211,9 +218,10 @@ object RasterSource extends LazyLogging {
     }
   }
 
-  case class GDALRasterSource(source: URI, callback: Option[ReadCallback]) extends RasterSource with URIRasterSource {
-    import geotrellis.contrib.vlm.gdal.{GDALRasterSource ⇒ VLMRasterSource}
+  case class GDALRasterSource(source: URI, callback: Option[ReadCallback])
+      extends RasterSource with URIRasterSource {
     import GDALRasterSource.MAX_SIZE
+    import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 
     @transient
     private lazy val gdal = {
@@ -227,7 +235,10 @@ object RasterSource extends LazyLogging {
 
     // TODO: See if dates are available in gdal.
     // Maybe useful: gdal.dataset.getMetadata_Dict
-    override def timestamp: Option[ZonedDateTime] = None
+    override def timestamp: Option[ZonedDateTime] = {
+      val meta = gdal.dataset.getMetadata_Dict
+      dateFromMetadata(meta)
+    }
 
     override def cellType: CellType = gdal.cellType
 
@@ -239,7 +250,7 @@ object RasterSource extends LazyLogging {
 
     override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = {
 
-      callback.foreach { cb ⇒
+      callback.foreach { cb =>
         val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
         cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
       }
@@ -253,7 +264,7 @@ object RasterSource extends LazyLogging {
     override def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
       val grid = gdal.gridBounds
 
-      callback.foreach { cb ⇒
+      callback.foreach { cb =>
         cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
       }
 
@@ -266,7 +277,12 @@ object RasterSource extends LazyLogging {
     }
 
     override def nativeLayout: Option[TileLayout] = {
-      Some(TileLayout(cols / MAX_SIZE + 1, rows / MAX_SIZE + 1, math.min(cols, MAX_SIZE), math.min(rows, MAX_SIZE)))
+      Some(
+        TileLayout(
+          cols / MAX_SIZE + 1,
+          rows / MAX_SIZE + 1,
+          math.min(cols, MAX_SIZE),
+          math.min(rows, MAX_SIZE)))
     }
   }
 
@@ -281,13 +297,13 @@ object RasterSource extends LazyLogging {
     private def realInfo =
       GeoTiffReader.readGeoTiffInfo(rangeReader, streaming = true, withOverviews = false)
 
-    private lazy val tiffInfo = SimpleGeoTiffInfo(realInfo)
+    protected lazy val tiffInfo = SimpleGeoTiffInfo(realInfo)
 
     def crs: CRS = tiffInfo.crs
 
     def extent: Extent = tiffInfo.extent
 
-    def timestamp: Option[ZonedDateTime] = resolveDate
+    def timestamp: Option[ZonedDateTime] = dateFromMetadata(tiffInfo.tags.headTags)
 
     override def cols: Int = tiffInfo.rasterExtent.cols
 
@@ -305,16 +321,6 @@ object RasterSource extends LazyLogging {
       else None
     }
 
-    // TODO: Determine if this is the correct way to  handle time.
-    protected def resolveDate: Option[ZonedDateTime] = {
-      tiffInfo.tags.headTags
-        .get(Tags.TIFFTAG_DATETIME)
-        .flatMap(ds ⇒ Try({
-          logger.debug("Parsing header date: " + ds)
-          ZonedDateTime.parse(ds, dateFormat)
-        }).toOption)
-    }
-
     def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = {
       val info = realInfo
       if (bandCount == 1) {
@@ -328,8 +334,7 @@ object RasterSource extends LazyLogging {
           List.empty
         )
         Left(gt.crop(extent).raster)
-      }
-      else {
+      } else {
         val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
         val gt = new MultibandGeoTiff(
           geoTiffTile,
@@ -353,18 +358,17 @@ object RasterSource extends LazyLogging {
       if (info.bandCount == 1) {
         val geotile = GeoTiffReader.geoTiffSinglebandTile(info)
 
-        val rows = windows.map(gb ⇒ {
+        val rows = windows.map(gb => {
           val tile = geotile.crop(gb)
           val extent = re.extentFor(gb, clamp = false)
           Raster(tile, extent)
         })
 
         Left(rows.toSeq)
-      }
-      else {
+      } else {
         val geotile = GeoTiffReader.geoTiffMultibandTile(info)
 
-        val rows = windows.map(gb ⇒ {
+        val rows = windows.map(gb => {
           val tile = geotile.crop(gb)
           val extent = re.extentFor(gb, clamp = false)
           Raster(tile, extent)
@@ -375,57 +379,73 @@ object RasterSource extends LazyLogging {
     }
   }
 
-  case class FileGeoTiffRasterSource(source: URI, callback: Option[ReadCallback]) extends RangeReaderRasterSource
-    with URIRasterSource with URIRasterSourceDebugString { self ⇒
+  case class FileGeoTiffRasterSource(source: URI, callback: Option[ReadCallback])
+      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
     protected lazy val rangeReader = {
       val base = FileRangeReader(source.getPath)
       // TODO: DRY
-      callback.map(cb ⇒ ReportingRangeReader(base, cb, self)).getOrElse(base)
+      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
     }
   }
 
-  case class HadoopGeoTiffRasterSource(source: URI, config: () ⇒ Configuration, callback: Option[ReadCallback]) extends RangeReaderRasterSource
-    with URIRasterSource with URIRasterSourceDebugString { self ⇒
+  case class HadoopGeoTiffRasterSource(
+    source: URI,
+    config: () => Configuration,
+    callback: Option[ReadCallback])
+      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
     protected lazy val rangeReader = {
       val base = HdfsRangeReader(new Path(source.getPath), config())
-      callback.map(cb ⇒ ReportingRangeReader(base, cb, self)).getOrElse(base)
+      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
     }
   }
 
-  case class S3GeoTiffRasterSource(source: URI, client: () ⇒ S3Client, callback: Option[ReadCallback]) extends RangeReaderRasterSource
-    with URIRasterSource with URIRasterSourceDebugString { self ⇒
+  case class S3GeoTiffRasterSource(
+    source: URI,
+    client: () => S3Client,
+    callback: Option[ReadCallback])
+      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
     protected lazy val rangeReader = {
       val base = S3RangeReader(source, client())
-      callback.map(cb ⇒ ReportingRangeReader(base, cb, self)).getOrElse(base)
+      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
     }
   }
 
-  case class HttpGeoTiffRasterSource(source: URI, callback: Option[ReadCallback]) extends RangeReaderRasterSource
-    with URIRasterSource with URIRasterSourceDebugString { self ⇒
+  case class HttpGeoTiffRasterSource(source: URI, callback: Option[ReadCallback])
+      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
 
     @transient
     protected lazy val rangeReader = {
       val base = HttpRangeReader(source)
-      callback.map(cb ⇒ ReportingRangeReader(base, cb, self)).getOrElse(base)
+      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
     }
 
-    override protected def resolveDate: Option[ZonedDateTime] = {
-      super.resolveDate
+    override def timestamp: Option[ZonedDateTime] =
+      dateFromMetadata(tiffInfo.tags.headTags)
         .orElse {
           val hrr = rangeReader match {
-            case h: HttpRangeReader ⇒ h
-            case ReportingRangeReader(h: HttpRangeReader, _, _) ⇒ h
+            case h: HttpRangeReader                             => h
+            case ReportingRangeReader(h: HttpRangeReader, _, _) => h
           }
-          hrr.response.headers.get("Last-Modified")
+          hrr.response.headers
+            .get("Last-Modified")
             .flatMap(_.headOption)
-            .flatMap(s ⇒ Try(ZonedDateTime.parse(s, DateTimeFormatter.RFC_1123_DATE_TIME)).toOption)
+            .flatMap(s =>
+              Try(ZonedDateTime.parse(s, DateTimeFormatter.RFC_1123_DATE_TIME)).toOption)
         }
-    }
   }
 
+  private[ref] def dateFromMetadata(meta: Map[String, String]): Option[ZonedDateTime] =
+    meta
+      .get(Tags.TIFFTAG_DATETIME)
+      .flatMap(ds =>
+        Try({
+          logger.debug("Parsing header date: " + ds)
+          ZonedDateTime.parse(ds, dateFormat)
+        }).toOption)
+
   /** Trait for registering a callback for logging or monitoring range reads.
    * NB: the callback will be invoked from within a Spark task, and therefore
    * is serialized along with its closure to executors. */
@@ -433,7 +453,11 @@ object RasterSource extends LazyLogging {
     def readRange(source: RasterSource, start: Long, length: Int): Unit
   }
 
-  private case class ReportingRangeReader(delegate: RangeReader, callback: ReadCallback, parent: RasterSource) extends RangeReader {
+  private case class ReportingRangeReader(
+    delegate: RangeReader,
+    callback: ReadCallback,
+    parent: RasterSource)
+      extends RangeReader {
     override def totalLength: Long = delegate.totalLength
 
     override protected def readClippedRange(start: Long, length: Int): Array[Byte] = {
@@ -442,8 +466,7 @@ object RasterSource extends LazyLogging {
     }
   }
 
-  trait URIRasterSourceDebugString {
-    _: RangeReaderRasterSource with URIRasterSource with Product ⇒
+  trait URIRasterSourceDebugString { _: RangeReaderRasterSource with URIRasterSource with Product =>
     def toDebugString: String = {
       val buf = new StringBuilder()
       buf.append(productPrefix)
@@ -464,5 +487,4 @@ object RasterSource extends LazyLogging {
       buf.toString
     }
   }
-
-}
\ No newline at end of file
+}

From fd1e557b8f5dbdaaa718943a7e19ab2722e24b0a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 13 Mar 2019 14:51:35 -0400
Subject: [PATCH 010/380] Updated geotrellis contrib version.

---
 core/build.sbt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/build.sbt b/core/build.sbt
index 830ec2d2f..d65a95cab 100644
--- a/core/build.sbt
+++ b/core/build.sbt
@@ -2,11 +2,13 @@ enablePlugins(BuildInfoPlugin)
 
 moduleName := "rasterframes"
 
+resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis"
+
 libraryDependencies ++= Seq(
   "com.chuusai" %% "shapeless" % "2.3.2",
   "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
   "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-  "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.7.4-local.1",
+  "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
   spark("core").value % Provided,
   spark("mllib").value % Provided,
   spark("sql").value % Provided,

From abd1ac4c287460f59fdd9db2a3700c3fb42bd84a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 13 Mar 2019 16:36:09 -0400
Subject: [PATCH 011/380] Bunch of sbt build cleanup.

---
 .circleci/config.yml                          |   1 +
 build.sbt                                     |  64 ++++++++-
 core/build.sbt                                |  37 ------
 .../extensions/RasterFrameMethods.scala       |   4 +-
 .../spark/rasterframes/ref/RasterSource.scala |   1 +
 .../spark/rasterframes/util/package.scala     |   3 +-
 datasource/build.sbt                          |  35 -----
 docs/build.sbt                                |   1 -
 experimental/build.sbt                        |  12 --
 project/RFAssemblyPlugin.scala                |  56 ++++++++
 ...jectPlugin.scala => RFProjectPlugin.scala} | 124 ++++--------------
 project/RFReleasePlugin.scala                 |  71 ++++++++++
 project/plugins.sbt                           |  14 +-
 project/project/plugins.sbt                   |   1 +
 14 files changed, 228 insertions(+), 196 deletions(-)
 delete mode 100644 core/build.sbt
 delete mode 100644 datasource/build.sbt
 delete mode 100644 experimental/build.sbt
 create mode 100644 project/RFAssemblyPlugin.scala
 rename project/{ProjectPlugin.scala => RFProjectPlugin.scala} (51%)
 create mode 100644 project/RFReleasePlugin.scala
 create mode 100644 project/project/plugins.sbt

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 32e5a1e18..fe3a6f3fc 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -25,6 +25,7 @@ _restore_cache: &restore_cache
 _save_cache: &save_cache
   key: v2-dependencies--{{ checksum "build.sbt" }}
   paths:
+    - ~/.cache/coursier
     - ~/.ivy2/cache
     - ~/.sbt
     - ~/.rf_cache
diff --git a/build.sbt b/build.sbt
index 05aed4e8d..ba8885577 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,3 +1,4 @@
+import sbt.Keys.resolvers
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
@@ -5,8 +6,8 @@ lazy val root = project
   .in(file("."))
   .withId("RasterFrames")
   .aggregate(core, datasource, pyrasterframes, experimental)
+  .enablePlugins(RFReleasePlugin)
   .settings(publish / skip := true)
-  .settings(releaseSettings)
 
 lazy val deployment = project
   .dependsOn(root)
@@ -15,18 +16,65 @@ lazy val deployment = project
 lazy val IntegrationTest = config("it") extend Test
 
 lazy val core = project
+  .enablePlugins(BuildInfoPlugin)
   .configs(IntegrationTest)
   .settings(inConfig(IntegrationTest)(Defaults.testSettings))
   .settings(Defaults.itSettings)
   .disablePlugins(SparkPackagePlugin)
+  .settings(
+    moduleName := "rasterframes",
+    resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
+    libraryDependencies ++= Seq(
+      "com.chuusai" %% "shapeless" % "2.3.2",
+      "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
+      "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
+      //"com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.7.4-local.1",
+      spark("core").value % Provided,
+      spark("mllib").value % Provided,
+      spark("sql").value % Provided,
+      geotrellis("spark").value,
+      geotrellis("raster").value,
+      geotrellis("s3").value,
+      geotrellis("spark-testkit").value % Test excludeAll (
+        ExclusionRule(organization = "org.scalastic"),
+        ExclusionRule(organization = "org.scalatest")
+      ),
+      scalaTest
+    ),
+
+    buildInfoKeys ++= Seq[BuildInfoKey](
+      name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
+    ),
+    buildInfoPackage := "astraea.spark.rasterframes",
+    buildInfoObject := "RFBuildInfo",
+    buildInfoOptions := Seq(
+      BuildInfoOption.ToMap,
+      BuildInfoOption.BuildTime
+    )
+  )
 
 lazy val pyrasterframes = project
   .dependsOn(core, datasource, experimental)
-  .settings(assemblySettings)
+  .enablePlugins(RFAssemblyPlugin)
 
 lazy val datasource = project
   .dependsOn(core % "test->test;compile->compile")
   .disablePlugins(SparkPackagePlugin)
+  .settings(
+    moduleName := "rasterframes-datasource",
+    libraryDependencies ++= Seq(
+      geotrellis("s3").value,
+      spark("core").value % Provided,
+      spark("mllib").value % Provided,
+      spark("sql").value % Provided
+    ),
+    initialCommands in console := (initialCommands in console).value + 
+      """
+        |import astraea.spark.rasterframes.datasource.geotrellis._
+        |import astraea.spark.rasterframes.datasource.geotiff._
+        |""".stripMargin
+  )
 
 lazy val experimental = project
   .configs(IntegrationTest)
@@ -34,6 +82,18 @@ lazy val experimental = project
   .dependsOn(core % "test->test;it->test;compile->compile")
   .dependsOn(datasource % "test->test;it->test;compile->compile")
   .disablePlugins(SparkPackagePlugin)
+  .settings(
+    moduleName := "rasterframes-experimental",
+    libraryDependencies ++= Seq(
+      geotrellis("s3").value,
+      spark("core").value % Provided,
+      spark("mllib").value % Provided,
+      spark("sql").value % Provided
+    ),
+    fork in IntegrationTest := true,
+    javaOptions in IntegrationTest := Seq("-Xmx2G"),
+    parallelExecution in IntegrationTest := false
+  )
 
 lazy val docs = project
   .dependsOn(core, datasource)
diff --git a/core/build.sbt b/core/build.sbt
deleted file mode 100644
index d65a95cab..000000000
--- a/core/build.sbt
+++ /dev/null
@@ -1,37 +0,0 @@
-enablePlugins(BuildInfoPlugin)
-
-moduleName := "rasterframes"
-
-resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis"
-
-libraryDependencies ++= Seq(
-  "com.chuusai" %% "shapeless" % "2.3.2",
-  "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
-  "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-  "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
-  spark("core").value % Provided,
-  spark("mllib").value % Provided,
-  spark("sql").value % Provided,
-  geotrellis("spark").value,
-  geotrellis("raster").value,
-  geotrellis("s3").value,
-  geotrellis("spark-testkit").value % Test excludeAll (
-    ExclusionRule(organization = "org.scalastic"),
-    ExclusionRule(organization = "org.scalatest")
-  ),
-  scalaTest
-)
-
-buildInfoKeys ++= Seq[BuildInfoKey](
-  name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
-)
-
-buildInfoPackage := "astraea.spark.rasterframes"
-
-buildInfoObject := "RFBuildInfo"
-
-buildInfoOptions := Seq(
-  BuildInfoOption.ToMap,
-  BuildInfoOption.BuildTime
-)
-
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala
index e83e55fd3..7e0130010 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala
@@ -26,7 +26,7 @@ import geotrellis.raster.{MultibandTile, ProjectedRaster, Tile, TileLayout}
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.spark.tiling.{LayoutDefinition, Tiler}
-import geotrellis.util.{LazyLogging, MethodExtensions}
+import geotrellis.util.MethodExtensions
 import geotrellis.vector.ProjectedExtent
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
@@ -35,6 +35,8 @@ import org.apache.spark.sql.types.{Metadata, TimestampType}
 import spray.json._
 import astraea.spark.rasterframes.encoders.StandardEncoders._
 import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import com.typesafe.scalalogging.LazyLogging
+
 import scala.reflect.runtime.universe._
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index e4cf4c4a1..b3cb72757 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -284,6 +284,7 @@ object RasterSource extends LazyLogging {
           math.min(cols, MAX_SIZE),
           math.min(rows, MAX_SIZE)))
     }
+    override def tags: Option[Tags] = Some(Tags(gdal.dataset.getMetadata_Dict, List.empty))
   }
 
   object GDALRasterSource {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
index 02a365cea..0581342e1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
@@ -19,6 +19,7 @@
 
 package astraea.spark.rasterframes
 
+import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.raster
 import geotrellis.raster.{CellGrid, Tile, isNoData}
@@ -30,7 +31,7 @@ import geotrellis.raster.merge.TileMergeMethods
 import geotrellis.raster.prototype.TilePrototypeMethods
 import geotrellis.spark.Bounds
 import geotrellis.spark.tiling.TilerKeyMethods
-import geotrellis.util.{ByteReader, GetComponent, LazyLogging}
+import geotrellis.util.{ByteReader, GetComponent}
 import org.apache.spark.sql.catalyst.expressions.{Expression, NamedExpression}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
diff --git a/datasource/build.sbt b/datasource/build.sbt
deleted file mode 100644
index b42fe3d3f..000000000
--- a/datasource/build.sbt
+++ /dev/null
@@ -1,35 +0,0 @@
-moduleName := "rasterframes-datasource"
-
-libraryDependencies ++= Seq(
-  geotrellis("s3").value,
-  spark("core").value % Provided,
-  spark("mllib").value % Provided,
-  spark("sql").value % Provided
-)
-
-initialCommands in console := """
-    |import astraea.spark.rasterframes._
-    |import geotrellis.raster._
-    |import geotrellis.spark.io.kryo.KryoRegistrator
-    |import org.apache.spark.serializer.KryoSerializer
-    |import org.apache.spark.sql._
-    |import org.apache.spark.sql.functions._
-    |import astraea.spark.rasterframes.datasource.geotrellis._
-    |import astraea.spark.rasterframes.datasource.geotiff._
-    |implicit val spark = SparkSession.builder()
-    |    .master("local[*]")
-    |    .appName(getClass.getName)
-    |    .config("spark.serializer", classOf[KryoSerializer].getName)
-    |    .config("spark.kryoserializer.buffer.max", "500m")
-    |    .config("spark.kryo.registrationRequired", "false")
-    |    .config("spark.kryo.registrator", classOf[KryoRegistrator].getName)
-    |    .getOrCreate()
-    |    .withRasterFrames
-    |spark.sparkContext.setLogLevel("ERROR")
-    |import spark.implicits._
-    |
-    |""".stripMargin
-
-cleanupCommands in console := """
-    |spark.stop()
-    |""".stripMargin
\ No newline at end of file
diff --git a/docs/build.sbt b/docs/build.sbt
index 3025e7c75..d7bb66c61 100644
--- a/docs/build.sbt
+++ b/docs/build.sbt
@@ -9,7 +9,6 @@ libraryDependencies ++= Seq(
   spark("sql").value % Tut
 )
 
-git.remoteRepo := "git@github.com:locationtech/rasterframes.git"
 apiURL := Some(url("http://rasterframes.io/latest/api"))
 autoAPIMappings := true
 ghpagesNoJekyll := true
diff --git a/experimental/build.sbt b/experimental/build.sbt
deleted file mode 100644
index 2b5168fbc..000000000
--- a/experimental/build.sbt
+++ /dev/null
@@ -1,12 +0,0 @@
-moduleName := "rasterframes-experimental"
-
-libraryDependencies ++= Seq(
-  geotrellis("s3").value,
-  spark("core").value % Provided,
-  spark("mllib").value % Provided,
-  spark("sql").value % Provided
-)
-
-fork in IntegrationTest := true
-javaOptions in IntegrationTest := Seq("-Xmx2G")
-parallelExecution in IntegrationTest := false
diff --git a/project/RFAssemblyPlugin.scala b/project/RFAssemblyPlugin.scala
new file mode 100644
index 000000000..d3fce3c68
--- /dev/null
+++ b/project/RFAssemblyPlugin.scala
@@ -0,0 +1,56 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+import sbt.Keys._
+import sbt._
+import sbtassembly.AssemblyPlugin.autoImport._
+
+object RFAssemblyPlugin extends AutoPlugin {
+  override def trigger: PluginTrigger = noTrigger
+  override def projectSettings = Seq(
+    test in assembly := {},
+    assemblyMergeStrategy in assembly := {
+      case "logback.xml" ⇒ MergeStrategy.singleOrError
+      case "git.properties" ⇒ MergeStrategy.discard
+      case x if Assembly.isConfigFile(x) ⇒ MergeStrategy.concat
+      case PathList(ps @ _*) if Assembly.isReadme(ps.last) || Assembly.isLicenseFile(ps.last) ⇒
+        MergeStrategy.rename
+      case PathList("META-INF", xs @ _*) ⇒
+        xs map {_.toLowerCase} match {
+          case "manifest.mf" :: Nil | "index.list" :: Nil | "dependencies" :: Nil ⇒
+            MergeStrategy.discard
+          case ps @ x :: _ if ps.last.endsWith(".sf") || ps.last.endsWith(".dsa") ⇒
+            MergeStrategy.discard
+          case "plexus" :: _ ⇒
+            MergeStrategy.discard
+          case "services" :: _ ⇒
+            MergeStrategy.filterDistinctLines
+          case "spring.schemas" :: Nil | "spring.handlers" :: Nil ⇒
+            MergeStrategy.filterDistinctLines
+          case "maven" :: rest if rest.lastOption.exists(_.startsWith("pom")) ⇒
+            MergeStrategy.discard
+          case _ ⇒ MergeStrategy.deduplicate
+        }
+
+      case _ ⇒ MergeStrategy.deduplicate
+    }
+  )
+}
diff --git a/project/ProjectPlugin.scala b/project/RFProjectPlugin.scala
similarity index 51%
rename from project/ProjectPlugin.scala
rename to project/RFProjectPlugin.scala
index 4f575edb2..5388e5518 100644
--- a/project/ProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -1,32 +1,44 @@
+import com.typesafe.sbt.{GitPlugin, SbtGit}
+import com.typesafe.sbt.SbtGit.git
 import sbt.Keys._
 import sbt._
-import sbtassembly.AssemblyKeys.assembly
-import sbtassembly.AssemblyPlugin.autoImport._
-import sbtrelease.ReleasePlugin.autoImport.ReleaseTransformations._
-import sbtrelease.ReleasePlugin.autoImport._
-
-import com.servicerocket.sbt.release.git.flow.Steps._
-import com.typesafe.sbt.sbtghpages.GhpagesPlugin.autoImport._
-import com.typesafe.sbt.site.SitePlugin.autoImport._
 import xerial.sbt.Sonatype.autoImport._
 
 /**
  * @since 8/20/17
  */
-object ProjectPlugin extends AutoPlugin {
+object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
+  override def requires = GitPlugin
+  object autoImport {
+    val rfSparkVersion = settingKey[String]("Apache Spark version")
+    val rfGeoTrellisVersion = settingKey[String]("GeoTrellis version")
+    val rfGeoMesaVersion = settingKey[String]("GeoMesa version")
+
+    def geotrellis(module: String) = Def.setting {
+      "org.locationtech.geotrellis" %% s"geotrellis-$module" % rfGeoTrellisVersion.value
+    }
+    def spark(module: String) = Def.setting {
+      "org.apache.spark" %% s"spark-$module" % rfSparkVersion.value
+    }
+
+    val scalaTest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
+  }
   import autoImport._
 
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
-    //organization := "io.astraea",
     organizationName := "LocationTech RasterFrames",
     startYear := Some(2017),
     homepage := Some(url("http://rasterframes.io")),
+    git.remoteRepo := "git@github.com:locationtech/rasterframes.git",
     scmInfo := Some(ScmInfo(url("https://github.com/locationtech/rasterframes"), "git@github.com:locationtech/rasterframes.git")),
     description := "RasterFrames brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis",
     licenses += ("Apache-2.0", url("https://www.apache.org/licenses/LICENSE-2.0.html")),
     scalaVersion := "2.11.12",
+    rfSparkVersion in ThisBuild := "2.3.2" ,
+    rfGeoTrellisVersion in ThisBuild := "2.1.0",
+    rfGeoMesaVersion in ThisBuild := "2.1.1",
     scalacOptions ++= Seq("-feature", "-deprecation"),
     scalacOptions in (Compile, doc) ++= Seq("-no-link-warnings"),
     javacOptions ++= Seq("-source", "1.8", "-target", "1.8"),
@@ -37,9 +49,6 @@ object ProjectPlugin extends AutoPlugin {
       "Open Source Geospatial Foundation Repository" at "http://download.osgeo.org/webdav/geotools/"
     ),
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
-    rfSparkVersion in ThisBuild := "2.3.2" ,
-    rfGeoTrellisVersion in ThisBuild := "2.1.0",
-    rfGeoMesaVersion in ThisBuild := "2.1.0",
     publishTo := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
@@ -82,89 +91,14 @@ object ProjectPlugin extends AutoPlugin {
         |import geotrellis.raster._
         |import geotrellis.spark._
         |import astraea.spark.rasterframes._
-        |implicit val spark = SparkSession.builder().master("local[*]").getOrCreate().withRasterFrames
+        |implicit val spark = SparkSession.builder()
+        |  .master("local[*]")
+        |  .withKryoSerialization
+        |  .getOrCreate()
+        |  .withRasterFrames
+        |spark.sparkContext.setLogLevel("ERROR")
+        |import spark.implicits._
       """.stripMargin.trim,
     cleanupCommands in console := "spark.stop()"
   )
-
-  object autoImport {
-    val rfSparkVersion = settingKey[String]("Apache Spark version")
-    val rfGeoTrellisVersion = settingKey[String]("GeoTrellis version")
-    val rfGeoMesaVersion = settingKey[String]("GeoMesa version")
-
-    def geotrellis(module: String) = Def.setting {
-      "org.locationtech.geotrellis" %% s"geotrellis-$module" % rfGeoTrellisVersion.value
-    }
-    def spark(module: String) = Def.setting {
-      "org.apache.spark" %% s"spark-$module" % rfSparkVersion.value
-    }
-
-    val scalaTest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
-
-    def assemblySettings: Seq[Def.Setting[_]] = Seq(
-      test in assembly := {},
-      assemblyMergeStrategy in assembly := {
-        case "logback.xml" ⇒ MergeStrategy.singleOrError
-        case "git.properties" ⇒ MergeStrategy.discard
-        case x if Assembly.isConfigFile(x) ⇒ MergeStrategy.concat
-        case PathList(ps @ _*) if Assembly.isReadme(ps.last) || Assembly.isLicenseFile(ps.last) ⇒
-          MergeStrategy.rename
-        case PathList("META-INF", xs @ _*) ⇒
-          xs map {_.toLowerCase} match {
-            case "manifest.mf" :: Nil | "index.list" :: Nil | "dependencies" :: Nil ⇒
-              MergeStrategy.discard
-            case ps @ _ :: _ if ps.last.endsWith(".sf") || ps.last.endsWith(".dsa") ⇒
-              MergeStrategy.discard
-            case "plexus" :: _ ⇒
-              MergeStrategy.discard
-            case "services" :: _ ⇒
-              MergeStrategy.filterDistinctLines
-            case "spring.schemas" :: Nil | "spring.handlers" :: Nil ⇒
-              MergeStrategy.filterDistinctLines
-            case "maven" :: rest if rest.lastOption.exists(_.startsWith("pom")) ⇒
-              MergeStrategy.discard
-            case _ ⇒ MergeStrategy.deduplicate
-          }
-
-        case _ ⇒ MergeStrategy.deduplicate
-      }
-    )
-
-    def releaseSettings: Seq[Def.Setting[_]] = {
-      val buildSite: State ⇒ State = releaseStepTask(makeSite in LocalProject("docs"))
-      val publishSite: State ⇒ State = releaseStepTask(ghpagesPushSite in LocalProject("docs"))
-      Seq(
-        releaseIgnoreUntrackedFiles := true,
-        releaseTagName := s"${version.value}",
-        releaseProcess := Seq[ReleaseStep](
-          checkSnapshotDependencies,
-          checkGitFlowExists,
-          inquireVersions,
-          runTest,
-          gitFlowReleaseStart,
-          setReleaseVersion,
-          buildSite,
-          publishSite,
-          commitReleaseVersion,
-          tagRelease,
-          releaseStepCommand("publishSigned"),
-          releaseStepCommand("sonatypeReleaseAll"),
-          gitFlowReleaseFinish,
-          setNextVersion,
-          commitNextVersion
-        ),
-        commands += Command.command("bumpVersion"){ st ⇒
-          val extracted = Project.extract(st)
-          val ver = extracted.get(version)
-          val nextFun = extracted.runTask(releaseNextVersion, st)._2
-
-          val nextVersion = nextFun(ver)
-
-          val file = extracted.get(releaseVersionFile)
-          IO.writeLines(file, Seq(s"""version in ThisBuild := "$nextVersion""""))
-          extracted.appendWithSession(Seq(version := nextVersion), st)
-        }
-      )
-    }
-  }
 }
diff --git a/project/RFReleasePlugin.scala b/project/RFReleasePlugin.scala
new file mode 100644
index 000000000..8d439927b
--- /dev/null
+++ b/project/RFReleasePlugin.scala
@@ -0,0 +1,71 @@
+
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+import sbt.Keys._
+import sbt._
+import sbtrelease.ReleasePlugin.autoImport.ReleaseTransformations._
+import sbtrelease.ReleasePlugin.autoImport._
+import com.servicerocket.sbt.release.git.flow.Steps._
+import com.typesafe.sbt.sbtghpages.GhpagesPlugin
+import com.typesafe.sbt.sbtghpages.GhpagesPlugin.autoImport.ghpagesPushSite
+import com.typesafe.sbt.site.SitePlugin
+import com.typesafe.sbt.site.SitePlugin.autoImport.makeSite
+object RFReleasePlugin extends AutoPlugin {
+  override def trigger: PluginTrigger = noTrigger
+  override def requires = RFProjectPlugin && SitePlugin && GhpagesPlugin
+  override def projectSettings = {
+    val buildSite: State ⇒ State = releaseStepTask(makeSite in LocalProject("docs"))
+    val publishSite: State ⇒ State = releaseStepTask(ghpagesPushSite in LocalProject("docs"))
+    Seq(
+      releaseIgnoreUntrackedFiles := true,
+      releaseTagName := s"${version.value}",
+      releaseProcess := Seq[ReleaseStep](
+        checkSnapshotDependencies,
+        checkGitFlowExists,
+        inquireVersions,
+        runTest,
+        gitFlowReleaseStart,
+        setReleaseVersion,
+        buildSite,
+        publishSite,
+        commitReleaseVersion,
+        tagRelease,
+        releaseStepCommand("publishSigned"),
+        releaseStepCommand("sonatypeReleaseAll"),
+        gitFlowReleaseFinish,
+        setNextVersion,
+        commitNextVersion
+      ),
+      commands += Command.command("bumpVersion"){ st ⇒
+        val extracted = Project.extract(st)
+        val ver = extracted.get(version)
+        val nextFun = extracted.runTask(releaseNextVersion, st)._2
+
+        val nextVersion = nextFun(ver)
+
+        val file = extracted.get(releaseVersionFile)
+        IO.writeLines(file, Seq(s"""version in ThisBuild := "$nextVersion""""))
+        extracted.appendWithSession(Seq(version := nextVersion), st)
+      }
+    )
+  }
+}
diff --git a/project/plugins.sbt b/project/plugins.sbt
index a3fd9b0e9..5aa78c4dc 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -1,30 +1,20 @@
 logLevel := sbt.Level.Error
 
 resolvers += Resolver.bintrayIvyRepo("s22s", "sbt-plugins")
-
+//addSbtCoursier
 addSbtPlugin("com.servicerocket" % "sbt-git-flow" % "0.1.3-astraea.1")
-
 addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.6")
-
 addSbtPlugin("com.eed3si9n" % "sbt-buildinfo" % "0.7.0")
-
 addSbtPlugin("de.heikoseeberger" % "sbt-header" % "3.0.2")
-
 addSbtPlugin("org.tpolecat" % "tut-plugin" % "0.6.10")
-
 addSbtPlugin("com.typesafe.sbt" % "sbt-ghpages" % "0.6.2")
-
 addSbtPlugin("com.typesafe.sbt" % "sbt-site" % "1.3.2")
 addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.0")
-
 addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.4")
-
 addSbtPlugin("org.xerial.sbt" % "sbt-sonatype" % "2.1")
-
 addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")
-
 addSbtPlugin("org.spark-packages" % "sbt-spark-package" % "0.2.7-astraea.1")
-
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.4.1")
 addSbtPlugin("net.vonbuchholtz" % "sbt-dependency-check" % "0.2.10")
 
+
diff --git a/project/project/plugins.sbt b/project/project/plugins.sbt
new file mode 100644
index 000000000..9c5ecac47
--- /dev/null
+++ b/project/project/plugins.sbt
@@ -0,0 +1 @@
+addSbtPlugin("io.get-coursier" % "sbt-coursier" % "1.1.0-M11")
\ No newline at end of file

From 6768790178042fdc694a4f3c5274e00f9bfcdd16 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 14 Mar 2019 13:16:36 -0400
Subject: [PATCH 012/380] Upgraded multiple base libraries: * GeoTrellis 2.2.0
 * GeoMesa 2.2.1 * JTS 1.16.0

---
 build.sbt                                     |  12 +-
 .../spark/rasterframes/RasterFunctions.scala  |   2 +-
 .../spark/rasterframes/StandardColumns.scala  |   2 +-
 .../encoders/EnvelopeEncoder.scala            |   2 +-
 .../encoders/StandardEncoders.scala           |   2 +-
 .../encoders/StandardSerializers.scala        |   2 +-
 .../expressions/SpatialRelation.scala         |   2 +-
 .../expressions/accessors/GetEnvelope.scala   |   2 +-
 .../transformers/BoundsToGeometry.scala       |   2 +-
 .../transformers/ReprojectGeometry.scala      |   2 +-
 .../extensions/ProjectedRasterMethods.scala   |   4 +-
 .../extensions/RFSpatialColumnMethods.scala   |   2 +-
 .../rasterframes/functions/package.scala      |   2 +-
 .../spark/rasterframes/jts/Implicits.scala    |   2 +-
 .../jts/ReprojectionTransformer.scala         |   4 +-
 .../rasterframes/ref/HttpRangeReader.scala    |   4 +-
 .../spark/rasterframes/ref/RasterSource.scala |  28 +++-
 .../rasterframes/rules/SpatialFilters.scala   |   2 +-
 .../util/ZeroSevenCompatibilityKit.scala      |   2 +-
 .../astraea/spark/rasterframes/JTSSpec.scala  |   2 +-
 .../rasterframes/ReprojectGeometrySpec.scala  |   2 +-
 .../spark/rasterframes/SpatialKeySpec.scala   |   2 +-
 .../astraea/spark/rasterframes/TestData.scala |   2 +-
 .../spark/rasterframes/TestEnvironment.scala  |   4 +-
 .../encoders/CatalystSerializerSpec.scala     |   2 +-
 .../rasterframes/encoders/EncodingSpec.scala  |   2 +-
 .../datasource/geotiff/GeoTiffRelation.scala  |   1 +
 .../geotrellis/GeoTrellisRelation.scala       |   7 +-
 docs/src/main/tut/release-notes.md            |   7 +-
 .../datasource/ReadTilesExpression.scala      |   2 +-
 .../datasource/awspds/L8CatalogRelation.scala |   2 +-
 .../experimental/datasource/geojson/DOM.scala |   4 +-
 .../geojson/GeoJsonDataSource.scala           |   2 +-
 .../experimental/slippy/SlippyExport.scala    | 157 ------------------
 project/RFProjectPlugin.scala                 |   5 -
 project/plugins.sbt                           |   2 +-
 .../spark/rasterframes/py/PyRFContext.scala   |   6 +-
 37 files changed, 80 insertions(+), 211 deletions(-)
 delete mode 100644 experimental/src/main/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExport.scala

diff --git a/build.sbt b/build.sbt
index ba8885577..09e9d4dae 100644
--- a/build.sbt
+++ b/build.sbt
@@ -2,6 +2,11 @@ import sbt.Keys.resolvers
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
+// NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
+rfSparkVersion in ThisBuild := "2.3.2"
+rfGeoTrellisVersion in ThisBuild := "2.2.0"
+rfGeoMesaVersion in ThisBuild := "2.2.1"
+
 lazy val root = project
   .in(file("."))
   .withId("RasterFrames")
@@ -26,10 +31,10 @@ lazy val core = project
     resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
     libraryDependencies ++= Seq(
       "com.chuusai" %% "shapeless" % "2.3.2",
+      "org.locationtech.jts" % "jts-core" % "1.16.0",
       "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
       "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-      //"com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
-      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.7.4-local.1",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
       spark("core").value % Provided,
       spark("mllib").value % Provided,
       spark("sql").value % Provided,
@@ -42,7 +47,6 @@ lazy val core = project
       ),
       scalaTest
     ),
-
     buildInfoKeys ++= Seq[BuildInfoKey](
       name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
     ),
@@ -69,7 +73,7 @@ lazy val datasource = project
       spark("mllib").value % Provided,
       spark("sql").value % Provided
     ),
-    initialCommands in console := (initialCommands in console).value + 
+    initialCommands in console := (initialCommands in console).value +
       """
         |import astraea.spark.rasterframes.datasource.geotrellis._
         |import astraea.spark.rasterframes.datasource.geotiff._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
index ff08dd44c..906f2a38d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
@@ -28,7 +28,7 @@ import astraea.spark.rasterframes.expressions.tilestats._
 import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics}
 import astraea.spark.rasterframes.{functions => F}
-import com.vividsolutions.jts.geom.{Envelope, Geometry}
+import org.locationtech.jts.geom.{Envelope, Geometry}
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.{CellType, Tile}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
index 340b17198..d08df5d3c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
@@ -5,7 +5,7 @@ import java.sql.Timestamp
 import geotrellis.raster.{Tile, TileFeature}
 import geotrellis.spark.{SpatialKey, TemporalKey}
 import org.apache.spark.sql.functions.col
-import com.vividsolutions.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
+import org.locationtech.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
 import geotrellis.proj4.CRS
 import geotrellis.vector.Extent
 import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
index 5888a1974..e8f168db5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
@@ -1,6 +1,6 @@
 package astraea.spark.rasterframes.encoders
 
-import com.vividsolutions.jts.geom.Envelope
+import org.locationtech.jts.geom.Envelope
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.GetColumnByOrdinal
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala
index 625eea1cd..d0a11ab78 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala
@@ -24,7 +24,7 @@ import java.sql.Timestamp
 
 import astraea.spark.rasterframes.model._
 import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
-import com.vividsolutions.jts.geom.Envelope
+import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
 import geotrellis.raster.{CellSize, CellType, Tile, TileLayout}
 import geotrellis.spark.tiling.LayoutDefinition
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
index aaff5c534..11a236b10 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
@@ -22,7 +22,7 @@
 package astraea.spark.rasterframes.encoders
 import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
 import astraea.spark.rasterframes.util.CRSParser
-import com.vividsolutions.jts.geom.Envelope
+import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.spark._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
index e994c8a64..18f93dd87 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
@@ -20,7 +20,7 @@
 package astraea.spark.rasterframes.expressions
 
 import astraea.spark.rasterframes.expressions.SpatialRelation.RelationPredicate
-import com.vividsolutions.jts.geom._
+import org.locationtech.jts.geom._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
index 551f64eb0..b082f14b0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
@@ -22,7 +22,7 @@
 package astraea.spark.rasterframes.expressions.accessors
 
 import astraea.spark.rasterframes.encoders.EnvelopeEncoder
-import com.vividsolutions.jts.geom.{Envelope, Geometry}
+import org.locationtech.jts.geom.{Envelope, Geometry}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, UnaryExpression}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala
index 9d6a8c652..b928e9971 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala
@@ -24,7 +24,7 @@ package astraea.spark.rasterframes.expressions.transformers
 import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.row
-import com.vividsolutions.jts.geom.{Envelope, Geometry}
+import org.locationtech.jts.geom.{Envelope, Geometry}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 7e78c5942..6958d2ab8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -25,7 +25,7 @@ import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.encoders.{CatalystSerializer, serialized_literal}
 import astraea.spark.rasterframes.jts.ReprojectionTransformer
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala
index 96709ef10..df1f6a893 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala
@@ -92,7 +92,7 @@ abstract class ProjectedRasterMethods[T <: CellGrid: WithMergeMethods: WithProto
    */
   def toTileLayerRDD(tileCols: Int,
                      tileRows: Int)(implicit spark: SparkSession): XTileLayerRDD[SpatialKey] = {
-    val layout = LayoutDefinition(self.rasterExtent, tileCols, tileRows)
+    val layout = LayoutDefinition(self.raster.rasterExtent, tileCols, tileRows)
     val kb = KeyBounds(SpatialKey(0, 0), SpatialKey(layout.layoutCols - 1, layout.layoutRows - 1))
     val tlm = TileLayerMetadata(self.tile.cellType, layout, self.extent, self.crs, kb)
 
@@ -115,7 +115,7 @@ abstract class ProjectedRasterMethods[T <: CellGrid: WithMergeMethods: WithProto
    * @param spark [[SparkSession]] in which to create RDD
    */
   def toTileLayerRDD(tileCols: Int, tileRows: Int, timestamp: ZonedDateTime)(implicit spark: SparkSession): XTileLayerRDD[SpaceTimeKey] = {
-    val layout = LayoutDefinition(self.rasterExtent, tileCols, tileRows)
+    val layout = LayoutDefinition(self.raster.rasterExtent, tileCols, tileRows)
     val kb = KeyBounds(SpaceTimeKey(0, 0, timestamp), SpaceTimeKey(layout.layoutCols - 1, layout.layoutRows - 1, timestamp))
     val tlm = TileLayerMetadata(self.tile.cellType, layout, self.extent, self.crs, kb)
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
index af744f5f4..71204c8ec 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
@@ -21,7 +21,7 @@ package astraea.spark.rasterframes.extensions
 
 import astraea.spark.rasterframes.util._
 import astraea.spark.rasterframes.{RasterFrame, StandardColumns}
-import com.vividsolutions.jts.geom.{Point, Polygon}
+import org.locationtech.jts.geom.{Point, Polygon}
 import geotrellis.proj4.LatLng
 import geotrellis.spark.SpatialKey
 import geotrellis.spark.tiling.MapKeyTransform
diff --git a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
index 060b08fa3..2594a6590 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
@@ -18,7 +18,7 @@ package astraea.spark.rasterframes
 import astraea.spark.rasterframes.expressions.aggstats._
 import astraea.spark.rasterframes.jts.ReprojectionTransformer
 import astraea.spark.rasterframes.util.CRSParser
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import geotrellis.raster.mapalgebra.local._
 import geotrellis.raster.{Tile, _}
 import geotrellis.vector.Extent
diff --git a/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala b/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala
index e257ebfa5..86d4dd73b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala
@@ -23,7 +23,7 @@ import java.sql.{Date, Timestamp}
 import java.time.{LocalDate, ZonedDateTime}
 
 import astraea.spark.rasterframes.expressions.SpatialRelation.{Contains, Intersects}
-import com.vividsolutions.jts.geom._
+import org.locationtech.jts.geom._
 import geotrellis.util.MethodExtensions
 import geotrellis.vector.{Point ⇒ gtPoint}
 import org.apache.spark.sql.{Column, TypedColumn}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala b/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala
index 1d583c739..4946001c8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala
@@ -20,8 +20,8 @@
 
 package astraea.spark.rasterframes.jts
 
-import com.vividsolutions.jts.geom.{CoordinateSequence, Geometry}
-import com.vividsolutions.jts.geom.util.GeometryTransformer
+import org.locationtech.jts.geom.{CoordinateSequence, Geometry}
+import org.locationtech.jts.geom.util.GeometryTransformer
 import geotrellis.proj4.CRS
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala
index b0675e8f4..f85f5b398 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala
@@ -22,10 +22,12 @@
 package astraea.spark.rasterframes.ref
 
 
-import geotrellis.util.{LazyLogging, RangeReader}
+import geotrellis.util.RangeReader
 import scalaj.http.{Http, HttpResponse}
 import java.net.{URI, URL}
 
+import com.typesafe.scalalogging.LazyLogging
+
 import scala.util.Try
 
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index b3cb72757..3f1f662c8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -46,7 +46,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
-
+import scala.collection.JavaConverters._
 import scala.util.Try
 
 /**
@@ -148,6 +148,9 @@ object RasterSource extends LazyLogging {
       case "s3" =>
         val client = () => S3Client.DEFAULT
         S3GeoTiffRasterSource(source, client, callback)
+      case s if s.startsWith("gdal+") =>
+        val cleaned = new URI(source.toASCIIString.replace("gdal+", ""))
+        apply(cleaned, callback)
       case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
     }
 
@@ -233,11 +236,16 @@ object RasterSource extends LazyLogging {
 
     override def extent: Extent = gdal.extent
 
+    private def metadata = gdal.dataset
+      .GetMetadata_Dict()
+      .asInstanceOf[java.util.Dictionary[String, String]]
+      .asScala
+      .toMap
+
     // TODO: See if dates are available in gdal.
     // Maybe useful: gdal.dataset.getMetadata_Dict
     override def timestamp: Option[ZonedDateTime] = {
-      val meta = gdal.dataset.getMetadata_Dict
-      dateFromMetadata(meta)
+      dateFromMetadata(metadata)
     }
 
     override def cellType: CellType = gdal.cellType
@@ -284,12 +292,22 @@ object RasterSource extends LazyLogging {
           math.min(cols, MAX_SIZE),
           math.min(rows, MAX_SIZE)))
     }
-    override def tags: Option[Tags] = Some(Tags(gdal.dataset.getMetadata_Dict, List.empty))
+    override def tags: Option[Tags] = Some(Tags(metadata, List.empty))
   }
 
   object GDALRasterSource {
     final val MAX_SIZE = 256
-    def unapply(scheme: String): Boolean = scheme.startsWith("gdal+")
+    @transient
+    lazy val hasGDAL: Boolean = try {
+      org.gdal.gdal.gdal.AllRegister()
+      true
+    }
+    catch {
+      case _: UnsatisfiedLinkError =>
+        logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
+        false
+    }
+    def unapply(scheme: String): Boolean = scheme.startsWith("gdal+") && hasGDAL
   }
 
   trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala b/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala
index 1a1128150..e2cae5734 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala
@@ -20,7 +20,7 @@
 
 package astraea.spark.rasterframes.rules
 
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import org.apache.spark.sql.sources.Filter
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
index bbb23a282..16d3ba271 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -29,7 +29,7 @@ import astraea.spark.rasterframes.expressions.tilestats._
 import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics}
 import astraea.spark.rasterframes.{functions => F}
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.{CellType, Tile}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
index 52def8620..0580e5ba1 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
@@ -19,7 +19,7 @@
 
 package astraea.spark.rasterframes
 
-import com.vividsolutions.jts.geom._
+import org.locationtech.jts.geom._
 import geotrellis.proj4.{LatLng, Sinusoidal, WebMercator}
 import geotrellis.vector.{Point ⇒ GTPoint}
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala
index 39ea3b1c1..b1b5b8862 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala
@@ -21,7 +21,7 @@
 
 package astraea.spark.rasterframes
 
-import com.vividsolutions.jts.geom._
+import org.locationtech.jts.geom._
 import geotrellis.proj4.{CRS, LatLng, Sinusoidal, WebMercator}
 import org.apache.spark.sql.Encoders
 import org.scalatest.{FunSpec, Matchers}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
index 065e9a5ed..900ad1bb4 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
@@ -19,7 +19,7 @@
 
 package astraea.spark.rasterframes
 
-import com.vividsolutions.jts.geom.Polygon
+import org.locationtech.jts.geom.Polygon
 import geotrellis.proj4.LatLng
 import geotrellis.vector.Point
 import org.locationtech.geomesa.curve.Z2SFC
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index db59dfb58..2502857b2 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -26,7 +26,7 @@ import astraea.spark.rasterframes.expressions.tilestats.NoDataCells
 import astraea.spark.rasterframes.model.TileContext
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.{functions => F}
-import com.vividsolutions.jts.geom.{Coordinate, GeometryFactory}
+import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
 import geotrellis.proj4.{CRS, LatLng}
 import geotrellis.raster
 import geotrellis.raster._
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala b/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
index aaf173014..c8d244be9 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
@@ -23,9 +23,9 @@ import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.st
 import astraea.spark.rasterframes.ref.RasterSource
 import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
 import astraea.spark.rasterframes.util.toParquetFriendlyColumnName
-import com.vividsolutions.jts.geom.Geometry
+import com.typesafe.scalalogging.LazyLogging
+import org.locationtech.jts.geom.Geometry
 import geotrellis.spark.testkit.{TestEnvironment => GeoTrellisTestEnvironment}
-import geotrellis.util.LazyLogging
 import org.apache.spark.SparkContext
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions.col
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
index c489b8d7b..934e7687c 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -29,7 +29,7 @@ import astraea.spark.rasterframes.{TestData, TestEnvironment}
 import geotrellis.proj4._
 import geotrellis.raster.{CellSize, CellType, TileLayout, UShortUserDefinedNoDataCellType}
 import geotrellis.spark.tiling.LayoutDefinition
-import geotrellis.spark.{Bounds, KeyBounds, SpaceTimeKey, SpatialKey, TileLayerMetadata}
+import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.scalatest.Assertion
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala
index a0c0bad0e..4875005ae 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala
@@ -26,7 +26,7 @@ import java.net.URI
 
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import com.vividsolutions.jts.geom.Envelope
+import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4._
 import geotrellis.raster.{CellType, Tile, TileFeature}
 import geotrellis.spark.{SpaceTimeKey, SpatialKey, TemporalProjectedExtent, TileLayerMetadata}
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
index 8503171c8..9deafe69d 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
@@ -24,6 +24,7 @@ import java.net.URI
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.util._
+import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.spark._
 import geotrellis.spark.io._
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
index 918f43015..5f5cb6de5 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
@@ -28,18 +28,19 @@ import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisRelation.{C, TileFeatureData}
 import astraea.spark.rasterframes.datasource.geotrellis.TileFeatureSupport._
 import astraea.spark.rasterframes.rules.splitFilters
-import astraea.spark.rasterframes.rules.SpatialFilters.{Contains ⇒ sfContains, Intersects ⇒ sfIntersects}
+import astraea.spark.rasterframes.rules.SpatialFilters.{Contains => sfContains, Intersects => sfIntersects}
 import astraea.spark.rasterframes.rules.SpatialRelationReceiver
 import astraea.spark.rasterframes.rules.TemporalFilters.{BetweenDates, BetweenTimes}
 import astraea.spark.rasterframes.util.SubdivideSupport._
 import astraea.spark.rasterframes.util._
-import com.vividsolutions.jts.geom
+import com.typesafe.scalalogging.LazyLogging
+import org.locationtech.jts.geom
 import geotrellis.raster.{CellGrid, MultibandTile, Tile, TileFeature}
 import geotrellis.spark.io._
 import geotrellis.spark.io.avro.AvroRecordCodec
 import geotrellis.spark.util.KryoWrapper
 import geotrellis.spark.{LayerId, Metadata, SpatialKey, TileLayerMetadata, _}
-import geotrellis.util.{LazyLogging, _}
+import geotrellis.util._
 import geotrellis.vector._
 import org.apache.avro.Schema
 import org.apache.avro.generic.GenericRecord
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index c043ac772..4b8738fb1 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -4,12 +4,17 @@
 
 ### 0.8.0
 
+<<<<<<< HEAD
 * Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`, `resample`.
+=======
+* Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.2.0, GeoMesa 2.2.1, JTS 1.16.0.
+* _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
+>>>>>>> Upgraded multiple base libraries:
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
 * _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * _Breaking_: In Scala and SQL, `..._scalar` functions (e.g. `local_add_scalar`) have been removed. Non-scalar forms now dynamically detect type of right hand side.
-* _Breaking_: `tileToArray` has been replaced with `tile_to_array_double` and `tile_to_array_int`. 
+* _Breaking_: `tileToArray` has been replaced with `tile_to_array_double` and `tile_to_array_int`.
 * Added `render_matrix` debugging function.
 * _Breaking_: renamed `agg_histogram` to `agg_approx_histogram`, `local_agg_stats` to `agg_local_stats`, `local_agg_max` to `agg_local_max`, `local_agg_min` to `agg_local_min`, `local_agg_mean` to `agg_local_mean`, `local_agg_data_cells` to `agg_local_data_cells`, `local_agg_no_data_cells` to `agg_local_no_data_cells`.
 * _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `agg_stats` instead.
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
index 1d00be0d5..ac13e4b20 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -146,7 +146,7 @@ case class ReadTilesExpression(children: Seq[Expression]) extends Expression
     rowMajor.map(row ⇒ {
       val serializedTiles = row.map {
         case null ⇒ null
-        case t ⇒ TileType.serialize(t)
+        case t ⇒ TileType.serialize(t.tile)
       }
 
       val firstBandExt = row
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
index f5d3796b4..556d47cc4 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
@@ -21,7 +21,7 @@
 package astraea.spark.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
-import com.vividsolutions.jts.geom.Envelope
+import org.locationtech.jts.geom.Envelope
 import org.apache.hadoop.fs.{Path ⇒ HadoopPath}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala
index 2dbcb7f0c..c0551c797 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala
@@ -20,8 +20,8 @@
 
 package astraea.spark.rasterframes.experimental.datasource.geojson
 
-import com.vividsolutions.jts.geom.{Envelope, Geometry}
-import com.vividsolutions.jts.io.geojson.{GeoJsonReader, GeoJsonWriter}
+import org.locationtech.jts.geom.{Envelope, Geometry}
+import org.locationtech.jts.io.geojson.{GeoJsonReader, GeoJsonWriter}
 import geotrellis.vector.Extent
 import spray.json._
 import spray.json.DefaultJsonProtocol._
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
index f042fbd1c..418034147 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
@@ -21,7 +21,7 @@
 package astraea.spark.rasterframes.experimental.datasource.geojson
 
 import astraea.spark.rasterframes.experimental.datasource.geojson.DOM._
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.jts.JTSTypes
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExport.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExport.scala
deleted file mode 100644
index 0af839ffa..000000000
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExport.scala
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea. Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- *
- */
-
-package astraea.spark.rasterframes.experimental.slippy
-
-import java.io.PrintStream
-import java.net.URI
-
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.util._
-import geotrellis.proj4.{LatLng, WebMercator}
-import geotrellis.raster._
-import geotrellis.raster.io.geotiff._
-import geotrellis.raster.io.geotiff.tags.codes.ColorSpace
-import geotrellis.raster.resample.Bilinear
-import geotrellis.spark._
-import geotrellis.spark.io.slippy.HadoopSlippyTileWriter
-import geotrellis.spark.pyramid.Pyramid
-import geotrellis.spark.tiling.ZoomedLayoutScheme
-import geotrellis.util.MethodExtensions
-import org.apache.commons.lang3.text.StrSubstitutor
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.spark.annotation.Experimental
-
-import scala.io.Source
-
-/**
- * Experimental support for exporting a RasterFrame into Slippy map format.
- *
- * @since 4/10/18
- */
-@Experimental
-trait SlippyExport extends MethodExtensions[RasterFrame]{
-  /**
-   * Export GeoTiff tiles in a slippy map directory structure; for debugging purposes only.
-   * NB: Temporal components are ignored blindly.
-   */
-  @Experimental
-  def exportGeoTiffTiles(dest: URI): Unit = {
-    val spark = self.sparkSession
-    implicit val sc = spark.sparkContext
-
-    val tlm = self.tileLayerMetadata.merge
-    val crs = tlm.crs
-    val mapTransform = tlm.mapTransform
-
-    val writer = new HadoopSlippyTileWriter[MultibandTile](dest.toASCIIString, "tiff")({ (key, tile) =>
-      val extent = mapTransform(key)
-      // If we have exactly 3 columns, we assume RGB color space.
-      val opts = GeoTiffOptions.DEFAULT
-        .mapWhen(_ ⇒ tile.bands.lengthCompare(3) == 0,
-          _.copy(colorSpace = ColorSpace.RGB)
-        )
-      MultibandGeoTiff(tile, extent, crs, opts).toByteArray
-    })
-
-    val tlrdd: MultibandTileLayerRDD[SpatialKey] = self.toMultibandTileLayerRDD(self.tileColumns: _*) match {
-      case Left(spatial) ⇒ spatial
-      case Right(origRDD) ⇒
-        val newBounds = origRDD.metadata.bounds.flatMap[SpatialKey] {
-          bounds => KeyBounds(bounds.minKey.spatialKey, bounds.maxKey.spatialKey)
-        }
-        val newMD = origRDD.metadata.copy(bounds = newBounds)
-        val rdd = origRDD.map { case (k, v) ⇒ (k.spatialKey, v)}
-        ContextRDD(rdd, newMD)
-    }
-
-    writer.write(0, tlrdd)
-  }
-
-  /**
-   * Export tiles as a slippy map. For debugging purposes only.
-   * NB: Temporal components are ignored blindly.
-   * @param dest URI for Hadoop supported storage endpoint (e.g. 'file://', 'hdfs://', etc.).
-   * @param colorMap Optional color map to use for rendering tiles in non-RGB RasterFrames.
-   */
-  @Experimental
-  def exportSlippyMap(dest: URI, renderer: MultibandRender.Profile = MultibandRender.Default): Unit = {
-    val spark = self.sparkSession
-    implicit val sc = spark.sparkContext
-
-    val tileDirName = "rf-tiles"
-
-
-    val inputRDD: MultibandTileLayerRDD[SpatialKey] = self.toMultibandTileLayerRDD(self.tileColumns: _*) match {
-      case Left(spatial) ⇒ spatial
-      case Right(origRDD) ⇒
-        val newBounds = origRDD.metadata.bounds.flatMap[SpatialKey] {
-          bounds => KeyBounds(bounds.minKey.spatialKey, bounds.maxKey.spatialKey)
-        }
-        val newMD = origRDD.metadata.copy(bounds = newBounds)
-        val rdd = origRDD.map { case (k, v) ⇒ (k.spatialKey, v)}
-        ContextRDD(rdd, newMD)
-    }
-
-    val layoutScheme = ZoomedLayoutScheme(WebMercator, tileSize = 256)
-
-    val (zoom, reprojected) = inputRDD.reproject(WebMercator, layoutScheme, Bilinear)
-    val writer = new HadoopSlippyTileWriter[MultibandTile](dest.toASCIIString + "/" + tileDirName, "png")({ (_, tile) =>
-      //require(tile.bandCount >= 3 || renderer.isInstanceOf[ColorRampProfile],
-      //  "Single-band and dual-band RasterFrames require a ColorRampProfile for rendering")
-      val png = renderer.render(tile)
-      png.bytes
-    })
-
-    val center = reprojected.metadata.extent.center
-
-    SlippyExport.writeHtml(dest, sc.hadoopConfiguration, Map(
-      "maxZoom" -> zoom.toString,
-      "id" -> tileDirName,
-      "viewLat" -> center.y.toString,
-      "viewLon" -> center.x.toString
-    ))
-
-    // Pyramiding up the zoom levels, write our tiles out to the local file system.
-    Pyramid.upLevels(reprojected, layoutScheme, zoom, Bilinear) { (rdd, z) =>
-      writer.write(z, rdd)
-    }
-  }
-}
-
-object SlippyExport {
-  import scala.collection.JavaConverters._
-  implicit class RasterFrameHasSlippy(val self: RasterFrame) extends SlippyExport
-  private def writeHtml(dest: URI, conf: Configuration, subs: Map[String, String]): Unit = {
-    val rawLines = Source.fromInputStream(getClass.getResourceAsStream("/slippy.html")).getLines()
-
-    val subst = new StrSubstitutor(subs.asJava)
-
-    val fs = FileSystem.get(dest, conf)
-    withResource(fs.create(new Path(new Path(dest), "index.html"), true)) { hout ⇒
-      val out = new PrintStream(hout, true ,"UTF-8")
-      for(line ← rawLines) {
-        out.println(subst.replace(line))
-      }
-    }
-  }
-}
-
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 5388e5518..71694980d 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -24,7 +24,6 @@ object RFProjectPlugin extends AutoPlugin {
 
     val scalaTest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
   }
-  import autoImport._
 
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
@@ -36,9 +35,6 @@ object RFProjectPlugin extends AutoPlugin {
     description := "RasterFrames brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis",
     licenses += ("Apache-2.0", url("https://www.apache.org/licenses/LICENSE-2.0.html")),
     scalaVersion := "2.11.12",
-    rfSparkVersion in ThisBuild := "2.3.2" ,
-    rfGeoTrellisVersion in ThisBuild := "2.1.0",
-    rfGeoMesaVersion in ThisBuild := "2.1.1",
     scalacOptions ++= Seq("-feature", "-deprecation"),
     scalacOptions in (Compile, doc) ++= Seq("-no-link-warnings"),
     javacOptions ++= Seq("-source", "1.8", "-target", "1.8"),
@@ -48,7 +44,6 @@ object RFProjectPlugin extends AutoPlugin {
       "boundless-releases" at "https://repo.boundlessgeo.com/main/",
       "Open Source Geospatial Foundation Repository" at "http://download.osgeo.org/webdav/geotools/"
     ),
-    // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
     publishTo := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 5aa78c4dc..1ac9bcfa4 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -1,7 +1,7 @@
 logLevel := sbt.Level.Error
 
 resolvers += Resolver.bintrayIvyRepo("s22s", "sbt-plugins")
-//addSbtCoursier
+addSbtCoursier
 addSbtPlugin("com.servicerocket" % "sbt-git-flow" % "0.1.3-astraea.1")
 addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.6")
 addSbtPlugin("com.eed3si9n" % "sbt-buildinfo" % "0.7.0")
diff --git a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
index b80a8a3f8..d4a8967c0 100644
--- a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
@@ -20,7 +20,7 @@ package astraea.spark.rasterframes.py
 
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.util.CRSParser
-import com.vividsolutions.jts.geom.Geometry
+import org.locationtech.jts.geom.Geometry
 import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
@@ -171,11 +171,11 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   // return toRaster, get just the tile, and make an array out of it
   def toIntRaster(df: DataFrame, colname: String, cols: Int, rows: Int): Array[Int] = {
-    df.asRF.toRaster(df.col(colname), cols, rows).toArray()
+    df.asRF.toRaster(df.col(colname), cols, rows).tile.toArray()
   }
 
   def toDoubleRaster(df: DataFrame, colname: String, cols: Int, rows: Int): Array[Double] = {
-    df.asRF.toRaster(df.col(colname), cols, rows).toArrayDouble()
+    df.asRF.toRaster(df.col(colname), cols, rows).tile.toArrayDouble()
   }
 
   def tileLayerMetadata(df: DataFrame): String =

From 711642043ce710584746ac59b3ae94a20cee13cc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 14 Mar 2019 15:24:08 -0400
Subject: [PATCH 013/380] Added ability to control use of GDAL bindings via
 config/command line. Instrumented test against RasterSourceIT.

---
 .../rasterframes/ref/RasterSourceIT.scala     | 39 ++++++----
 core/src/main/resources/reference.conf        | 12 ++++
 .../astraea/spark/rasterframes/package.scala  |  9 ++-
 .../spark/rasterframes/ref/RasterSource.scala | 15 ++--
 .../spark/rasterframes/util/package.scala     | 11 +--
 .../slippy/SlippyExportDriver.scala           | 72 -------------------
 6 files changed, 56 insertions(+), 102 deletions(-)
 delete mode 100644 experimental/src/it/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExportDriver.scala

diff --git a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
index 6f9069183..f01f333f7 100644
--- a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
@@ -23,12 +23,10 @@ package astraea.spark.rasterframes.ref
 
 import java.net.URI
 
-import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
-import astraea.spark.rasterframes.ref.RasterSource.FileGeoTiffRasterSource
+import astraea.spark.rasterframes
+import astraea.spark.rasterframes.util.time
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
-import geotrellis.raster.io.geotiff.GeoTiff
 import geotrellis.vector.Extent
-import org.apache.spark.sql.rf.RasterSourceUDT
 
 /**
  *
@@ -45,17 +43,28 @@ class RasterSourceIT extends TestEnvironment with TestData {
 
   describe("RasterSource.readAll") {
     it("should return consistently ordered tiles across bands for a given scene") {
-      // These specific scenes exhibit the problem where we see different subtile segment ordering across the bands of a given scene.
-      val rURI = new URI("https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/016/034/LC08_L1TP_016034_20181003_20181003_01_RT/LC08_L1TP_016034_20181003_20181003_01_RT_B4.TIF")
-      val bURI = new URI("https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/016/034/LC08_L1TP_016034_20181003_20181003_01_RT/LC08_L1TP_016034_20181003_20181003_01_RT_B2.TIF")
-
-      val red = RasterSource(rURI).readAll().left.get
-      val blue = RasterSource(bURI).readAll().left.get
-
-      red should not be empty
-      red.size should equal(blue.size)
-
-      red.map(_.dimensions) should contain theSameElementsAs blue.map(_.dimensions)
+      time(s"two band comparison prefer-gdal=${ rasterframes.rfConfig.getBoolean("prefer-gdal")}") {
+        // These specific scenes exhibit the problem where we see different subtile segment ordering across the bands of a given scene.
+        val rURI = new URI(
+          "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/016/034/LC08_L1TP_016034_20181003_20181003_01_RT/LC08_L1TP_016034_20181003_20181003_01_RT_B4.TIF")
+        val bURI = new URI(
+          "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/016/034/LC08_L1TP_016034_20181003_20181003_01_RT/LC08_L1TP_016034_20181003_20181003_01_RT_B2.TIF")
+        val red = time("read B4") {
+          RasterSource(rURI).readAll().left.get
+        }
+        val blue = time("read B2") {
+          RasterSource(bURI).readAll().left.get
+        }
+        time("test empty") {
+          red should not be empty
+        }
+        time("compare sizes") {
+          red.size should equal(blue.size)
+        }
+        time("compare dimensions") {
+          red.map(_.dimensions) should contain theSameElementsAs blue.map(_.dimensions)
+        }
+      }
     }
   }
 }
diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 980088e28..f082a02ef 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -1,3 +1,15 @@
 rasterframes {
   nominal-tile-size: 256
+  prefer-gdal: true
+}
+
+gdal {
+  settings {
+    options {
+      // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
+      CPL_DEBUG = "OFF"
+    }
+    // set this to `false` if CPL_DEBUG is `ON`
+    useExceptions: true
+  }
 }
\ No newline at end of file
diff --git a/core/src/main/scala/astraea/spark/rasterframes/package.scala b/core/src/main/scala/astraea/spark/rasterframes/package.scala
index 7b360ed25..7bb56d402 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/package.scala
@@ -32,7 +32,7 @@ import scala.reflect.runtime.universe._
 
 /**
  *  Module providing support for RasterFrames.
- * `import astraea.spark.rasterframes._`., and then call `rfInit(SQLContext)`.
+ * `import astraea.spark.rasterframes._`.
  *
  * @since 7/18/17
  */
@@ -45,9 +45,13 @@ package object rasterframes extends StandardColumns
   with DataFrameFunctions.Library
   with LazyLogging {
 
+  @transient
+  private[rasterframes]
+  val rfConfig = ConfigFactory.load().getConfig("rasterframes")
+
   /** The generally expected tile size, as defined by configuration property `rasterframes.nominal-tile-size`.*/
   @transient
-  final val NOMINAL_TILE_SIZE: Int = ConfigFactory.load().getInt("rasterframes.nominal-tile-size")
+  final val NOMINAL_TILE_SIZE: Int = rfConfig.getInt("nominal-tile-size")
 
   /**
    * Initialization injection point. Must be called before any RasterFrame
@@ -123,5 +127,4 @@ package object rasterframes extends StandardColumns
       override val selfType: TypeTag[SpaceTimeKey] = implicitly
     }
   }
-
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 3f1f662c8..50dd70cd0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -223,7 +223,6 @@ object RasterSource extends LazyLogging {
 
   case class GDALRasterSource(source: URI, callback: Option[ReadCallback])
       extends RasterSource with URIRasterSource {
-    import GDALRasterSource.MAX_SIZE
     import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 
     @transient
@@ -276,7 +275,7 @@ object RasterSource extends LazyLogging {
         cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
       }
 
-      val tiled = grid.split(MAX_SIZE, MAX_SIZE).toTraversable
+      val tiled = grid.split(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE).toTraversable
 
       if (bandCount == 1)
         Left(gdal.readBounds(tiled, Seq(0)).map(_.mapTile(_.band(0))).toSeq)
@@ -287,16 +286,16 @@ object RasterSource extends LazyLogging {
     override def nativeLayout: Option[TileLayout] = {
       Some(
         TileLayout(
-          cols / MAX_SIZE + 1,
-          rows / MAX_SIZE + 1,
-          math.min(cols, MAX_SIZE),
-          math.min(rows, MAX_SIZE)))
+          cols / NOMINAL_TILE_SIZE + 1,
+          rows / NOMINAL_TILE_SIZE + 1,
+          math.min(cols, NOMINAL_TILE_SIZE),
+          math.min(rows, NOMINAL_TILE_SIZE)))
     }
     override def tags: Option[Tags] = Some(Tags(metadata, List.empty))
   }
 
   object GDALRasterSource {
-    final val MAX_SIZE = 256
+    private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
     @transient
     lazy val hasGDAL: Boolean = try {
       org.gdal.gdal.gdal.AllRegister()
@@ -307,7 +306,7 @@ object RasterSource extends LazyLogging {
         logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
         false
     }
-    def unapply(scheme: String): Boolean = scheme.startsWith("gdal+") && hasGDAL
+    def unapply(scheme: String): Boolean = (preferGdal || scheme.startsWith("gdal+")) && hasGDAL
   }
 
   trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
index 0581342e1..b0e50825b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
@@ -19,16 +19,15 @@
 
 package astraea.spark.rasterframes
 
-import com.typesafe.scalalogging.LazyLogging
+import com.typesafe.scalalogging.Logger
 import geotrellis.proj4.CRS
-import geotrellis.raster
-import geotrellis.raster.{CellGrid, Tile, isNoData}
 import geotrellis.raster.crop.TileCropMethods
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.mask.TileMaskMethods
 import geotrellis.raster.merge.TileMergeMethods
 import geotrellis.raster.prototype.TilePrototypeMethods
+import geotrellis.raster.{CellGrid, Tile, isNoData}
 import geotrellis.spark.Bounds
 import geotrellis.spark.tiling.TilerKeyMethods
 import geotrellis.util.{ByteReader, GetComponent}
@@ -38,6 +37,7 @@ import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.sql.{Column, DataFrame, SQLContext}
+import org.slf4j.LoggerFactory
 import spire.syntax.cfor._
 
 import scala.Boolean.box
@@ -47,7 +47,10 @@ import scala.Boolean.box
  *
  * @since 12/18/17
  */
-package object util extends LazyLogging {
+package object util {
+  @transient
+  protected lazy val logger: Logger =
+    Logger(LoggerFactory.getLogger("astraea.spark.rasterframes"))
 
   import reflect.ClassTag
   import reflect.runtime.universe._
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExportDriver.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExportDriver.scala
deleted file mode 100644
index 64e3c43f4..000000000
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/slippy/SlippyExportDriver.scala
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea. Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- *
- */
-
-package astraea.spark.rasterframes.experimental.slippy
-
-import java.io.File
-
-import astraea.spark.rasterframes._
-import geotrellis.raster._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import org.apache.spark.sql.SparkSession
-import SlippyExport._
-import astraea.spark.rasterframes.util.MultibandRender
-
-object SlippyExportDriver {
-  def main(args: Array[String]): Unit = {
-
-    implicit val spark = SparkSession
-      .builder()
-      .master("local[*]")
-      .appName("RasterFrames")
-      .getOrCreate()
-      .withRasterFrames
-
-    def mergeBands = {
-      val bands: Seq[SinglebandGeoTiff] = for (i ← 1 to 3) yield {
-        TestData.l8Sample(i)
-      }
-
-      val mtile = MultibandTile(bands.map(_.tile))
-
-      val pr = ProjectedRaster(mtile, bands.head.extent, bands.head.crs)
-
-      implicit val bandCount = PairRDDConverter.forSpatialMultiband(bands.length)
-
-      val rf = pr.toRF(64, 64)
-
-      //rf.exportGeoTiffTiles(new File("target/slippy-tiff").toURI)
-
-      rf.exportSlippyMap(new File("target/slippy-1/").toURI)
-    }
-
-   def multiband = {
-     implicit val bandCount = PairRDDConverter.forSpatialMultiband(3)
-     val rf = TestData.rgbCogSample.projectedRaster.toRF(256, 256)
-     import astraea.spark.rasterframes.datasource.geotiff._
-     //val rf = spark.read.geotiff.loadRF(getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI)
-     println(rf.tileLayerMetadata.merge.toString)
-     rf.exportSlippyMap(new File("target/slippy-2/").toURI, MultibandRender.Landsat8NaturalColor)
-    }
-
-    multiband
-    //mergeBands
-  }
-}

From b3050e9d426e195472fdc88595f7e6f4cbf1f3e7 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 27 Mar 2019 13:48:52 -0400
Subject: [PATCH 014/380] Support for GeoMesa shapely UDT in pyrasterframes

---
 .../python/geomesa_pyspark/types.py           | 62 ++++++++++++-----
 pyrasterframes/python/setup.py                |  5 +-
 .../python/tests/PyRasterFramesTests.py       | 69 +++++++++++++++----
 3 files changed, 104 insertions(+), 32 deletions(-)

diff --git a/pyrasterframes/python/geomesa_pyspark/types.py b/pyrasterframes/python/geomesa_pyspark/types.py
index f70f010c7..5f1d0a110 100644
--- a/pyrasterframes/python/geomesa_pyspark/types.py
+++ b/pyrasterframes/python/geomesa_pyspark/types.py
@@ -9,15 +9,17 @@
    http://www.opensource.org/licenses/apache2.0.php.
 + ***********************************************************************/"""
 
-from pyspark.sql.types import UserDefinedType
-from pyspark.sql import Row
-from pyspark.sql.types import *
-from pyrasterframes.context import RFContext
+from pyspark.sql.types import UserDefinedType, StructField, BinaryType, StructType
+from shapely import wkb
+from shapely.geometry import LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon
+from shapely.geometry.base import BaseGeometry
+from shapely.geometry.collection import GeometryCollection
+
+
+class ShapelyGeometryUDT(UserDefinedType):
 
-class GeometryUDT(UserDefinedType):
     @classmethod
-    def sqlType(self):
-        #    return StructField("wkb", BinaryType(), False)
+    def sqlType(cls):
         return StructType([StructField("wkb", BinaryType(), True)])
 
     @classmethod
@@ -29,40 +31,64 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.jts.' + cls.__name__
 
     def serialize(self, obj):
-        if (obj is None): return None
-        return Row(obj.toBytes)
+        return [_serialize_to_wkb(obj)]
 
     def deserialize(self, datum):
-        return RFContext._jvm_mirror().generate_geometry(datum[0])
+        return _deserialize_from_wkb(datum[0])
 
 
-class PointUDT(GeometryUDT):
+class PointUDT(ShapelyGeometryUDT):
     pass
 
 
-class LineStringUDT(GeometryUDT):
+class LineStringUDT(ShapelyGeometryUDT):
     pass
 
 
-class PolygonUDT(GeometryUDT):
+class PolygonUDT(ShapelyGeometryUDT):
     pass
 
 
-class MultiPointUDT(GeometryUDT):
+class MultiPointUDT(ShapelyGeometryUDT):
     pass
 
 
-class MultiLineStringUDT(GeometryUDT):
+class MultiLineStringUDT(ShapelyGeometryUDT):
     pass
 
 
-class MultiPolygonUDT(GeometryUDT):
+class MultiPolygonUDT(ShapelyGeometryUDT):
     pass
 
 
-class GeometryUDT(GeometryUDT):
+class GeometryUDT(ShapelyGeometryUDT):
     pass
 
 
-class GeometryCollectionUDT(GeometryUDT):
+class GeometryCollectionUDT(ShapelyGeometryUDT):
     pass
+
+
+def _serialize_to_wkb(data):
+    if isinstance(data, BaseGeometry):
+        return bytearray(data.wkb)  # bytearray(...) needed for Python 2 compat.
+    return None
+
+
+def _deserialize_from_wkb(data):
+    if data is None:
+        return None
+    return wkb.loads(bytes(data))  # bytes(...) needed for Python 2 compat.
+
+
+_deserialize_from_wkb.__safe_for_unpickling__ = True
+
+# inject some PySpark constructs into Shapely's geometry types
+Point.__UDT__ = PointUDT()
+MultiPoint.__UDT__ = MultiPointUDT()
+LineString.__UDT__ = LineStringUDT()
+MultiLineString.__UDT__ = MultiLineStringUDT()
+Polygon.__UDT__ = PolygonUDT()
+MultiPolygon.__UDT__ = MultiPolygonUDT()
+BaseGeometry.__UDT__ = GeometryUDT()
+GeometryCollection.__UDT__ = GeometryCollectionUDT()
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index b24dfa3ad..4119ce757 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -118,6 +118,8 @@ def run(self):
     license='Apache 2',
     setup_requires=['pytest-runner', pyspark_ver, 'pathlib'],
     install_requires=[
+        'pytz', 'shapely',
+        # 'geomesa-pyspark'
         # pyspark_ver,
         # 'pathlib'
     ],
@@ -125,7 +127,8 @@ def run(self):
         pyspark_ver,
         'pytest==3.4.2',
         'pypandoc',
-        'numpy>=1.7'
+        'numpy>=1.7',
+        'pandas',
     ],
     test_suite="pytest-runner",
     packages=find_packages(exclude=['tests', 'examples']),
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index b47e25577..984943ae4 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -216,19 +216,62 @@ def test_resample(self):
         self.assertTrue(result == 1)  # short hand for all values are true
 
 
-def suite():
-    functionTests = unittest.TestSuite()
-    functionTests.addTest(RasterFunctionsTest('test_identify_columns'))
-    functionTests.addTest(RasterFunctionsTest('test_tile_operations'))
-    functionTests.addTest(RasterFunctionsTest('test_general'))
-    functionTests.addTest(RasterFunctionsTest('test_rasterize'))
-    functionTests.addTest(RasterFunctionsTest('test_reproject'))
-    functionTests.addTest(RasterFunctionsTest('test_aggregations'))
-    functionTests.addTest(RasterFunctionsTest('test_explode'))
-    functionTests.addTest(RasterFunctionsTest('test_sql'))
-    functionTests.addTest(RasterFunctionsTest('test_maskByValue'))
-    functionTests.addTest(RasterFunctionsTest('test_resample'))
-    return functionTests
+    def test_geomesa_pyspark(self):
+        from pyspark.sql.functions import lit, udf
+        import shapely
+        import pandas as pd
+        import numpy.testing
+
+        pandas_df = pd.DataFrame({
+            'eye': ['a', 'b', 'c', 'd'],
+            'x': [0.0, 1.0, 2.0, 3.0],
+            'y': [-4.0, -3.0, -2.0, -1.0],
+        })
+        df = self.spark.createDataFrame(pandas_df)
+        df = df.withColumn("point_geom",
+                           st_point(df.x, df.y)
+                           )
+        df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
+
+        # Use python shapely UDT in a UDF
+        @udf("double")
+        def area_fn(g):
+            return g.area
+
+        @udf("double")
+        def length_fn(g):
+            return g.length
+
+        df = df.withColumn("poly_area", area_fn(df.poly_geom))
+        df = df.withColumn("poly_len", length_fn(df.poly_geom))
+
+
+        # Collect to python driver in shapely UDT
+        pandas_df_out = df.toPandas()
+
+        # Confirm we get a shapely type back from st_* function
+        assert(type(pandas_df_out.poly_geom.iloc[0]) is shapely.geometry.Polygon)
+
+        # And our spark-side manipulations were correct
+        xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == pandas_df.x
+        self.assertTrue(all(xs_correct))
+        centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
+                                                      g.centroid.coords[0][1]).tolist()
+        numpy.testing.assert_almost_equal(centroid_ys, pandas_df.y.tolist())
+
+        # Including from UDF's
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.area).values,
+            pandas_df_out.poly_area.values
+        )
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.length).values,
+            pandas_df_out.poly_len.values
+        )
 
 
+def suite():
+    function_tests = unittest.TestSuite()
+    return function_tests
+
 unittest.TextTestRunner().run(suite())

From 1f37070f2f3d6af842c6f4b7ee0bdb20cc1d5997 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 27 Mar 2019 17:21:32 -0400
Subject: [PATCH 015/380] Clean up some geomesa_pyspark tests; expand to test
 UDF returning geometry UDT

---
 pyrasterframes/python/setup.py                |  1 -
 .../python/tests/PyRasterFramesTests.py       | 22 ++++++++++++++++---
 2 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index 4119ce757..a0f318008 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -119,7 +119,6 @@ def run(self):
     setup_requires=['pytest-runner', pyspark_ver, 'pathlib'],
     install_requires=[
         'pytz', 'shapely',
-        # 'geomesa-pyspark'
         # pyspark_ver,
         # 'pathlib'
     ],
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 984943ae4..07dc008f9 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -3,6 +3,7 @@
 from pyspark.sql.functions import *
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
+from geomesa_pyspark.types import *
 from pathlib import Path
 import os
 import unittest
@@ -217,7 +218,7 @@ def test_resample(self):
 
 
     def test_geomesa_pyspark(self):
-        from pyspark.sql.functions import lit, udf
+        from pyspark.sql.functions import lit, udf, sum
         import shapely
         import pandas as pd
         import numpy.testing
@@ -245,16 +246,31 @@ def length_fn(g):
         df = df.withColumn("poly_area", area_fn(df.poly_geom))
         df = df.withColumn("poly_len", length_fn(df.poly_geom))
 
+        # Return UDT in a UDF!
+        def some_point(g):
+            return g.representative_point()
+
+        some_point_udf = udf(some_point, PointUDT())
+
+        df = df.withColumn("any_point", some_point_udf(df.poly_geom))
+        # spark-side UDF/UDT are correct
+        intersect_total = df.agg(sum(
+            st_intersects(df.poly_geom, df.any_point).astype('double')
+        ).alias('s')).collect()[0].s
+        self.assertTrue(intersect_total == df.count())
+
 
         # Collect to python driver in shapely UDT
         pandas_df_out = df.toPandas()
 
-        # Confirm we get a shapely type back from st_* function
-        assert(type(pandas_df_out.poly_geom.iloc[0]) is shapely.geometry.Polygon)
+        # Confirm we get a shapely type back from st_* function and UDF
+        self.assertIsInstance(pandas_df_out.poly_geom.iloc[0], shapely.geometry.Polygon)
+        self.assertIsInstance(pandas_df_out.any_point.iloc[0], shapely.geometry.Point)
 
         # And our spark-side manipulations were correct
         xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == pandas_df.x
         self.assertTrue(all(xs_correct))
+
         centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
                                                       g.centroid.coords[0][1]).tolist()
         numpy.testing.assert_almost_equal(centroid_ys, pandas_df.y.tolist())

From acda27a105efb7a6edf913fa09f756b8c3f9a449 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 28 Mar 2019 11:16:04 -0400
Subject: [PATCH 016/380] Update release notes for recently merged work

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/tut/release-notes.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index c043ac772..174f02aee 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -4,7 +4,9 @@
 
 ### 0.8.0
 
-* Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`, `resample`.
+* Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`.
+* Support python-side `shapely` geometry User-Defined Type.
+* SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
 * _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.

From 7283c3c31caee539104faff57c4abc49d6e77f44 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 14 Mar 2019 16:06:38 -0400
Subject: [PATCH 017/380] Removed use of Either in RasterSource and always
 return MBT. (+1 squashed commit) Build and test tweaks.

---
 .gitignore                                    |   2 +
 build.sbt                                     |   6 +-
 .../rasterframes/ref/RasterSourceIT.scala     |   4 +-
 .../transformers/RasterSourceToTiles.scala    |   8 +-
 .../spark/rasterframes/ref/RasterRef.scala    |   2 +-
 .../spark/rasterframes/ref/RasterSource.scala | 130 +++++-------------
 core/src/test/resources/application.conf      |  10 ++
 .../astraea/spark/rasterframes/TestData.scala |   6 +-
 .../rasterframes/TileAssemblerSpec.scala      |   4 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |  23 ++--
 docs/src/main/tut/getting-started.md          |   7 +
 project/RFProjectPlugin.scala                 |   2 +-
 12 files changed, 81 insertions(+), 123 deletions(-)
 create mode 100644 core/src/test/resources/application.conf

diff --git a/.gitignore b/.gitignore
index ca41e7212..ff43c9712 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,3 +25,5 @@ metastore_db
 tour/jars
 tour/*.tiff
 scoverage-report*
+
+zz-*
diff --git a/build.sbt b/build.sbt
index 09e9d4dae..91db148a1 100644
--- a/build.sbt
+++ b/build.sbt
@@ -3,9 +3,9 @@ addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
 // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
-rfSparkVersion in ThisBuild := "2.3.2"
-rfGeoTrellisVersion in ThisBuild := "2.2.0"
-rfGeoMesaVersion in ThisBuild := "2.2.1"
+ThisBuild / rfSparkVersion := "2.3.2"
+ThisBuild / rfGeoTrellisVersion := "2.2.0"
+ThisBuild / rfGeoMesaVersion := "2.2.1"
 
 lazy val root = project
   .in(file("."))
diff --git a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
index f01f333f7..d5283ae2d 100644
--- a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
@@ -50,10 +50,10 @@ class RasterSourceIT extends TestEnvironment with TestData {
         val bURI = new URI(
           "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/016/034/LC08_L1TP_016034_20181003_20181003_01_RT/LC08_L1TP_016034_20181003_20181003_01_RT_B2.TIF")
         val red = time("read B4") {
-          RasterSource(rURI).readAll().left.get
+          RasterSource(rURI).readAll()
         }
         val blue = time("read B2") {
-          RasterSource(bURI).readAll().left.get
+          RasterSource(bURI).readAll()
         }
         time("test empty") {
           red should not be empty
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
index 2b1caa3ba..b26c0f944 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
@@ -59,12 +59,14 @@ case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean)
       val refs = children.map { child ⇒
         val src = RasterSourceType.deserialize(child.eval(input))
         val tiles = if (applyTiling) src.readAll() else {
-          src.read(src.extent).right.map(Seq(_)).left.map(Seq(_))
+          Seq(src.read(src.extent))
         }
 
-        require(tiles.isLeft, "Multiband tiles are not yet supported")
+        tiles.headOption.foreach(r => {
+          require(r.tile.bandCount <= 1, "Multiband tiles are not yet supported")
+        })
 
-        tiles.left.get
+        tiles.map(_.mapTile(_.band(0)))
       }
       refs.transpose.map(ts ⇒ InternalRow(ts.map(r ⇒ r.tile.toInternalRow): _*))
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
index ff176765c..ff6564807 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
@@ -54,7 +54,7 @@ case class RasterRef(source: RasterSource, subextent: Option[Extent])
   protected lazy val realizedTile: Tile = {
     require(source.bandCount == 1, "Expected singleband tile")
     RasterRef.log.trace(s"Fetching $srcExtent from $source")
-    source.read(srcExtent).left.get.tile
+    source.read(srcExtent).tile.band(0)
   }
 
   /** Splits this tile into smaller tiles based on the reported
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 50dd70cd0..3337e081b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -26,14 +26,13 @@ import java.time.format.DateTimeFormatter
 
 import astraea.spark.rasterframes.NOMINAL_TILE_SIZE
 import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.util.GeoTiffInfoSupport
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, MultibandGeoTiff, SinglebandGeoTiff, Tags}
+import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, MultibandGeoTiff, Tags}
 import geotrellis.raster.split.Split
 import geotrellis.spark.io.hadoop.HdfsRangeReader
 import geotrellis.spark.io.s3.S3Client
@@ -46,6 +45,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
+
 import scala.collection.JavaConverters._
 import scala.util.Try
 
@@ -68,40 +68,9 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def tags: Option[Tags]
 
-  def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]]
-
-  /** Reads the given extent as a single multiband raster. */
-  def readMultiband(extent: Extent): Raster[MultibandTile] =
-    read(extent).fold(r => {
-      r.copy(tile = MultibandTile(r.tile))
-    }, identity)
-
-  def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]]
-  def readAllMultiband(): Seq[Raster[MultibandTile]] =
-    readAll().fold(_.map(r => {
-      r.copy(tile = MultibandTile(r.tile))
-    }), identity)
-
-  def readAllLazy(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
-    val extents = nativeTiling
-    if (bandCount == 1) {
-      val rasters = for {
-        extent <- extents
-        rr = RasterRef(this, Some(extent))
-        tile: Tile = RasterRefTile(rr)
-      } yield Raster(tile, extent)
-      Left(rasters)
-    } else {
-      // Need to figure this out.
-      RasterSource._logger.warn(
-        "Lazy reading is not available for multiband images. Performing eager read.")
-      val rasters = for {
-        extent <- extents
-        raster = this.read(extent).right.get
-      } yield raster
-      Right(rasters)
-    }
-  }
+  def read(extent: Extent): Raster[MultibandTile]
+
+  def readAll(): Seq[Raster[MultibandTile]]
 
   def nativeLayout: Option[TileLayout]
 
@@ -204,9 +173,8 @@ object RasterSource extends LazyLogging {
 
     override def tags: Option[Tags] = None
 
-    override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = Left(
-      Raster(tile.crop(rasterExtent.gridBoundsFor(extent, false)), extent)
-    )
+    override def read(extent: Extent): Raster[MultibandTile] =
+      Raster(MultibandTile(tile.crop(rasterExtent.gridBoundsFor(extent, false))), extent)
 
     override def nativeLayout: Option[TileLayout] = Some(
       TileLayout(
@@ -216,9 +184,8 @@ object RasterSource extends LazyLogging {
         tileRows = NOMINAL_TILE_SIZE)
     )
 
-    def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
-      Left(Raster(tile, extent).split(nativeLayout.get, Split.Options(false, false)).toSeq)
-    }
+    def readAll(): Seq[Raster[MultibandTile]] =
+      Raster(MultibandTile(tile), extent).split(nativeLayout.get, Split.Options(false, false)).toSeq
   }
 
   case class GDALRasterSource(source: URI, callback: Option[ReadCallback])
@@ -255,20 +222,15 @@ object RasterSource extends LazyLogging {
 
     override def rows: Int = gdal.rows
 
-    override def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = {
-
+    override def read(extent: Extent): Raster[MultibandTile] = {
       callback.foreach { cb =>
         val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
         cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
       }
-
-      if (bandCount == 1)
-        Left(gdal.read(extent, Seq(0)).get.mapTile(_.band(0)))
-      else
-        Right(gdal.read(extent).get)
+      gdal.read(extent).get
     }
 
-    override def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
+    override def readAll(): Seq[Raster[MultibandTile]] = {
       val grid = gdal.gridBounds
 
       callback.foreach { cb =>
@@ -277,10 +239,7 @@ object RasterSource extends LazyLogging {
 
       val tiled = grid.split(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE).toTraversable
 
-      if (bandCount == 1)
-        Left(gdal.readBounds(tiled, Seq(0)).map(_.mapTile(_.band(0))).toSeq)
-      else
-        Right(gdal.readBounds(tiled).toSeq)
+      gdal.readBounds(tiled).toSeq
     }
 
     override def nativeLayout: Option[TileLayout] = {
@@ -339,61 +298,36 @@ object RasterSource extends LazyLogging {
       else None
     }
 
-    def read(extent: Extent): Either[Raster[Tile], Raster[MultibandTile]] = {
+    def read(extent: Extent): Raster[MultibandTile] = {
       val info = realInfo
-      if (bandCount == 1) {
-        val geoTiffTile = GeoTiffReader.geoTiffSinglebandTile(info)
-        val gt = new SinglebandGeoTiff(
-          geoTiffTile,
-          info.extent,
-          info.crs,
-          info.tags,
-          info.options,
-          List.empty
-        )
-        Left(gt.crop(extent).raster)
-      } else {
-        val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
-        val gt = new MultibandGeoTiff(
-          geoTiffTile,
-          info.extent,
-          info.crs,
-          info.tags,
-          info.options,
-          List.empty
-        )
-        Right(gt.crop(extent).raster)
-      }
+      val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
+      val gt = new MultibandGeoTiff(
+        geoTiffTile,
+        info.extent,
+        info.crs,
+        info.tags,
+        info.options,
+        List.empty
+      )
+      gt.crop(extent).raster
     }
 
-    def readAll(): Either[Seq[Raster[Tile]], Seq[Raster[MultibandTile]]] = {
+    def readAll(): Seq[Raster[MultibandTile]] = {
       val info = realInfo
 
       // Thanks to @pomadchin for showing us how to do this :-)
       val windows = info.segmentLayout.listWindows(NOMINAL_TILE_SIZE)
       val re = info.rasterExtent
 
-      if (info.bandCount == 1) {
-        val geotile = GeoTiffReader.geoTiffSinglebandTile(info)
+      val geotile = GeoTiffReader.geoTiffMultibandTile(info)
 
-        val rows = windows.map(gb => {
-          val tile = geotile.crop(gb)
-          val extent = re.extentFor(gb, clamp = false)
-          Raster(tile, extent)
-        })
+      val rows = windows.map(gb => {
+        val tile = geotile.crop(gb)
+        val extent = re.extentFor(gb, clamp = false)
+        Raster(tile, extent)
+      })
 
-        Left(rows.toSeq)
-      } else {
-        val geotile = GeoTiffReader.geoTiffMultibandTile(info)
-
-        val rows = windows.map(gb => {
-          val tile = geotile.crop(gb)
-          val extent = re.extentFor(gb, clamp = false)
-          Raster(tile, extent)
-        })
-
-        Right(rows.toSeq)
-      }
+      rows.toSeq
     }
   }
 
diff --git a/core/src/test/resources/application.conf b/core/src/test/resources/application.conf
new file mode 100644
index 000000000..d2283141c
--- /dev/null
+++ b/core/src/test/resources/application.conf
@@ -0,0 +1,10 @@
+gdal {
+  settings {
+    options {
+      // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
+      CPL_DEBUG = "ON"
+    }
+    // set this to `false` if CPL_DEBUG is `ON`
+    useExceptions: false
+  }
+}
\ No newline at end of file
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index 2502857b2..b6e7da48b 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -123,13 +123,13 @@ trait TestData {
     rf.toTileLayerRDD(rf.tileColumns.head).left.get
   }
 
-  private val baseCOG = "gdal+https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_%s.TIF"
+  private val baseCOG = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_%s.TIF"
   lazy val remoteCOGSingleband1 = URI.create(baseCOG.format("B1"))
   lazy val remoteCOGSingleband2 = URI.create(baseCOG.format("B2"))
 
-  lazy val remoteCOGMultiband =  URI.create("gdal+https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
+  lazy val remoteCOGMultiband =  URI.create("https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
 
-  lazy val remoteMODIS = URI.create("gdal+https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
+  lazy val remoteMODIS = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
 
   object JTS {
     val fact = new GeometryFactory()
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala
index 29eff421f..15ee82860 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala
@@ -134,9 +134,9 @@ object TileAssemblerSpec extends  LazyLogging {
   implicit class WithToDF(val rs: RasterSource) {
     def toDF(implicit spark: SparkSession): DataFrame = {
       import spark.implicits._
-      rs.readAll().left.get
+      rs.readAll()
         .zipWithIndex
-        .map { case (r, i) ⇒ (i, r.extent, r.tile) }
+        .map { case (r, i) ⇒ (i, r.extent, r.tile.band(0)) }
         .toDF("spatial_index", "extent", "tile")
         .repartition($"spatial_index")
         .forceCache
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 987f253f3..993c1381d 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -22,12 +22,14 @@
 package astraea.spark.rasterframes.ref
 
 import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
-import astraea.spark.rasterframes.ref.RasterSource.FileGeoTiffRasterSource
+import astraea.spark.rasterframes.ref.RasterSource.{FileGeoTiffRasterSource, GDALRasterSource}
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
 import geotrellis.raster.io.geotiff.GeoTiff
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
 
+import scala.language.reflectiveCalls
+
 /**
  *
  *
@@ -61,13 +63,13 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     it("should read sub-tile") {
       withClue("remoteCOGSingleband") {
         val src = RasterSource(remoteCOGSingleband1)
-        val Left(raster) = src.read(sub(src.extent))
+        val raster = src.read(sub(src.extent))
         assert(raster.size > 0 && raster.size < src.size)
       }
       withClue("remoteCOGMultiband") {
         val src = RasterSource(remoteCOGMultiband)
         //println("CoG size", src.size, src.dimensions)
-        val Right(raster) = src.read(sub(src.extent))
+        val raster = src.read(sub(src.extent))
         //println("Subtile size", raster.size, raster.dimensions)
         assert(raster.size > 0 && raster.size < src.size)
       }
@@ -104,22 +106,21 @@ class RasterSourceSpec extends TestEnvironment with TestData {
 
     it("should cache headers")(new Fixture {
       val e = src.extent
-      assert(counter.reads === 1)
+      val startReads = counter.reads
 
       val c = src.crs
       val e2 = src.extent
       val ct = src.cellType
-      assert(counter.reads === 1)
+      counter.reads should be(startReads)
     })
 
     it("should Spark serialize caching")(new Fixture {
 
       import spark.implicits._
 
-      assert(src.isInstanceOf[FileGeoTiffRasterSource])
+      val origType = src.getClass
 
       val e = src.extent
-      assert(counter.reads === 1)
 
       val df = Seq(src, src, src).toDS.repartition(3)
       val src2 = df.collect()(1)
@@ -128,8 +129,10 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       val ct = src2.cellType
 
       src2 match {
-        case fs: FileGeoTiffRasterSource ⇒
-          fs.callback match {
+        case _: GDALRasterSource => ()
+        case s if s.getClass == origType ⇒
+          val t = s.asInstanceOf[RasterSource { def callback: Option[ReadMonitor] }]
+          t.callback match {
             case Some(cb: ReadMonitor) ⇒ assert(cb.reads === 1)
             case o ⇒ fail(s"Expected '$o' to be a ReadMonitor")
           }
@@ -142,7 +145,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     it("should read all tiles") {
       val src = RasterSource(remoteMODIS)
 
-      val subrasters = src.readAll().left.get
+      val subrasters = src.readAll()
 
       val collected = subrasters.map(_.extent).reduceLeft(_.combine(_))
 
diff --git a/docs/src/main/tut/getting-started.md b/docs/src/main/tut/getting-started.md
index 413131e84..b16dac563 100644
--- a/docs/src/main/tut/getting-started.md
+++ b/docs/src/main/tut/getting-started.md
@@ -44,6 +44,13 @@ It assumes that SparkSQL 2.3.x is available in the runtime classpath. Here's how
   version="2.3.2"
 }
 
+
+Until GeoTrellis 3.0 is released, the following artifact resolver needs to be added to your build.
+
+```scala
+resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis"
+```
+  
 @@@ note
 Most of the following examples are shown using the Spark DataFrames API. However, many could also be rewritten to use the Spark SQL API instead. We hope to add more examples in that form in the future.
 @@@
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 71694980d..716d70fdd 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -44,7 +44,7 @@ object RFProjectPlugin extends AutoPlugin {
       "boundless-releases" at "https://repo.boundlessgeo.com/main/",
       "Open Source Geospatial Foundation Repository" at "http://download.osgeo.org/webdav/geotools/"
     ),
-    publishTo := sonatypePublishTo.value,
+    publishTo in ThisBuild := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
     publishArtifact in Test := false,

From 801ca0a3ee4f3328c26247cd01233e6c092c0303 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 27 Mar 2019 11:54:09 -0400
Subject: [PATCH 018/380] Removed `timestamp` from `RasterSource`.

---
 .../astraea/spark/rasterframes/ref/RasterSource.scala  | 10 ----------
 .../spark/rasterframes/ref/RasterSourceSpec.scala      |  2 +-
 2 files changed, 1 insertion(+), 11 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 3337e081b..be27197b5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -60,8 +60,6 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def extent: Extent
 
-  def timestamp: Option[ZonedDateTime]
-
   def cellType: CellType
 
   def bandCount: Int
@@ -165,8 +163,6 @@ object RasterSource extends LazyLogging {
 
     override def cols: Int = tile.cols
 
-    override def timestamp: Option[ZonedDateTime] = None
-
     override def cellType: CellType = tile.cellType
 
     override def bandCount: Int = 1
@@ -208,12 +204,6 @@ object RasterSource extends LazyLogging {
       .asScala
       .toMap
 
-    // TODO: See if dates are available in gdal.
-    // Maybe useful: gdal.dataset.getMetadata_Dict
-    override def timestamp: Option[ZonedDateTime] = {
-      dateFromMetadata(metadata)
-    }
-
     override def cellType: CellType = gdal.cellType
 
     override def bandCount: Int = gdal.bandCount
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 993c1381d..1559c3c67 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -22,7 +22,7 @@
 package astraea.spark.rasterframes.ref
 
 import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
-import astraea.spark.rasterframes.ref.RasterSource.{FileGeoTiffRasterSource, GDALRasterSource}
+import astraea.spark.rasterframes.ref.RasterSource.GDALRasterSource
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
 import geotrellis.raster.io.geotiff.GeoTiff
 import geotrellis.vector.Extent

From 3bf334597264ff75a4706e0c90d7f18c68c29eaa Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 28 Mar 2019 11:31:20 -0400
Subject: [PATCH 019/380] Incremental reimplementation of RasterSource reading
 to depend on geotrellis-contrib version. Linting fixes/cleanup.

---
 .../bench/MultibandRenderBench.scala          |   2 +-
 .../spark/rasterframes/PairRDDConverter.scala |   2 +-
 .../spark/rasterframes/RasterFunctions.scala  |   3 +-
 .../spark/rasterframes/StandardColumns.scala  |  11 +-
 .../TemporalProjectedExtentEncoder.scala      |   6 +-
 .../expressions/DynamicExtractors.scala       |   2 +-
 .../expressions/TileAssembler.scala           |   2 +-
 .../aggstats/LocalTileOpAggregate.scala       |   5 +-
 .../expressions/localops/Add.scala            |   3 +-
 .../expressions/localops/Resample.scala       |   3 +-
 .../expressions/tilestats/TileMean.scala      |   1 -
 .../RasterSourceToRasterRefs.scala            |   6 +-
 .../transformers/RasterSourceToTiles.scala    |   5 -
 .../extensions/ContextRDDMethods.scala        |   4 +-
 .../rasterframes/extensions/Implicits.scala   |   1 -
 .../extensions/SQLContextMethods.scala        |   2 +-
 .../rasterframes/functions/package.scala      |   4 +-
 .../rasterframes/model/TileDimensions.scala   |   6 +-
 .../astraea/spark/rasterframes/package.scala  |   3 +-
 .../spark/rasterframes/ref/RasterRef.scala    |  20 +-
 .../spark/rasterframes/ref/RasterSource.scala | 195 ++++++++++--------
 .../rasterframes/stats/CellHistogram.scala    |   4 +-
 .../tiles/ProjectedRasterTile.scala           |   3 +-
 .../util/GeoTiffInfoSupport.scala             |   2 +-
 .../rasterframes/util/ReadAccumulator.scala   |   2 +-
 .../org/apache/spark/sql/rf/TileUDT.scala     |   1 -
 .../apache/spark/sql/rf/VersionShims.scala    |   8 +-
 .../rasterframes/RasterFunctionsSpec.scala    |   6 +-
 .../astraea/spark/rasterframes/TestData.scala |   4 +-
 .../spark/rasterframes/TileStatsSpec.scala    |   8 +-
 .../rasterframes/ref/RasterRefSpec.scala      |   4 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |  26 ++-
 .../scala/examples/CreatingRasterFrames.scala |   6 +-
 core/src/test/scala/examples/Exporting.scala  |   6 +-
 core/src/test/scala/examples/Masking.scala    |   5 +-
 core/src/test/scala/examples/MeanValue.scala  |   1 -
 .../examples/NaturalColorComposite.scala      |   5 +-
 core/src/test/scala/examples/Scratch.scala    |   9 +-
 .../geotiff/GeoTiffCollectionRelation.scala   |   1 -
 .../geotrellis/GeoTrellisCatalog.scala        |   4 +-
 .../datasource/geotrellis/package.scala       |   4 +-
 .../rasterframes/datasource/package.scala     |   2 -
 .../geotrellis/GeoTrellisCatalogSpec.scala    |   2 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala |   2 +-
 .../geotrellis/TileFeatureSupportSpec.scala   |   2 -
 .../src/test/scala/examples/Creating.scala    |   3 -
 .../datasource/awspds/L8RelationTest.scala    |   1 -
 .../datasource/awspds/L8Relation.scala        |   1 -
 project/RFProjectPlugin.scala                 |   7 +-
 project/RFReleasePlugin.scala                 |  42 +++-
 project/plugins.sbt                           |   1 -
 51 files changed, 237 insertions(+), 221 deletions(-)

diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala b/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala
index 383710205..4156af6a7 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala
+++ b/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala
@@ -24,7 +24,7 @@ import java.util.concurrent.TimeUnit
 
 import astraea.spark.rasterframes.util.MultibandRender.Landsat8NaturalColor
 import geotrellis.raster._
-import geotrellis.raster.io.geotiff.{GeoTiff, MultibandGeoTiff}
+import geotrellis.raster.io.geotiff.MultibandGeoTiff
 import org.apache.commons.io.IOUtils
 import org.openjdk.jmh.annotations._
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala b/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
index 83686dbfd..6f7f5574c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
@@ -2,7 +2,7 @@ package astraea.spark.rasterframes
 
 import astraea.spark.rasterframes.util._
 import geotrellis.raster.{MultibandTile, Tile, TileFeature}
-import geotrellis.spark.{SpaceTimeKey, SpatialKey, TemporalKey}
+import geotrellis.spark.{SpaceTimeKey, SpatialKey}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.rf.TileUDT
diff --git a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
index 906f2a38d..db22791bb 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
@@ -28,13 +28,13 @@ import astraea.spark.rasterframes.expressions.tilestats._
 import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics}
 import astraea.spark.rasterframes.{functions => F}
-import org.locationtech.jts.geom.{Envelope, Geometry}
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
+import org.locationtech.jts.geom.{Envelope, Geometry}
 
 /**
  * UDFs for working with Tiles in Spark DataFrames.
@@ -43,7 +43,6 @@ import org.apache.spark.sql.functions._
  */
 trait RasterFunctions {
   import util._
-  import PrimitiveEncoders._
 
   // format: off
   /** Create a row for each cell in Tile. */
diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
index d08df5d3c..fe63b092b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
@@ -2,13 +2,13 @@ package astraea.spark.rasterframes
 
 import java.sql.Timestamp
 
-import geotrellis.raster.{Tile, TileFeature}
+import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import geotrellis.proj4.CRS
+import geotrellis.raster.Tile
 import geotrellis.spark.{SpatialKey, TemporalKey}
+import geotrellis.vector.Extent
 import org.apache.spark.sql.functions.col
 import org.locationtech.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
-import geotrellis.proj4.CRS
-import geotrellis.vector.Extent
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 
 /**
  * Constants identifying column in most RasterFrames.
@@ -25,7 +25,6 @@ trait StandardColumns {
   /** Default RasterFrame timestamp column name */
   val TIMESTAMP_COLUMN = col("timestamp").as[Timestamp]
 
-
   /** Default RasterFrame column name for an tile bounds value. */
   // This is a `def` because `PolygonUDT` needs to be initialized first.
   def BOUNDS_COLUMN = col("bounds").as[jtsPolygon]
@@ -47,7 +46,7 @@ trait StandardColumns {
   // This is a `def` because `TileUDT` needs to be initialized first.
   def TILE_COLUMN = col("tile").as[Tile]
 
-  /** Default RasterFrame [[TileFeature.data]] column name. */
+  /** Default RasterFrame `TileFeature.data` column name. */
   val TILE_FEATURE_DATA_COLUMN = col("tile_data")
 
   /** Default GeoTiff tags column. */
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
index 5e44bd7fe..310ba8971 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
@@ -19,16 +19,14 @@
 
 package astraea.spark.rasterframes.encoders
 
-import java.time.ZonedDateTime
-
 import astraea.spark.rasterframes._
 import geotrellis.spark.TemporalProjectedExtent
-import geotrellis.vector.ProjectedExtent
 import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 
 /**
- * Custom encoder for [[ProjectedExtent]]. Necessary because [[geotrellis.proj4.CRS]] within [[ProjectedExtent]] isn't a case class, and [[ZonedDateTime]] doesn't have a natural encoder.
+ * Custom encoder for `TemporalProjectedExtent`. Necessary because `geotrellis.proj4.CRS` within
+ * `ProjectedExtent` isn't a case class, and `ZonedDateTime` doesn't have a natural encoder.
  *
  * @since 8/2/17
  */
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
index 1dabc8201..b551df4c5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
@@ -106,7 +106,7 @@ object DynamicExtractors {
   lazy val intArgExtractor: PartialFunction[DataType, Any => IntegerArg] = {
     case _: IntegerType | _: ByteType | _: ShortType => {
       case i: Int   => IntegerArg(i)
-      case b: Byte  => IntegerArg(b)
+      case b: Byte  => IntegerArg(b.toInt)
       case s: Short => IntegerArg(s.toInt)
       case c: Char  => IntegerArg(c.toInt)
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
index c3a32267f..df633fe2c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
@@ -118,7 +118,7 @@ case class TileAssembler(
     val cells = Array.ofDim[Double](length)
     result.get(cells)
     val (tileCols, tileRows) = buffer.tileSize
-    val tile = ArrayTile(cells, tileCols, tileRows)
+    val tile = ArrayTile(cells, tileCols.toInt, tileRows.toInt)
     TileType.serialize(tile)
   }
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
index 7a5032176..d3247e964 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
@@ -25,15 +25,14 @@ import astraea.spark.rasterframes.expressions.accessors.ExtractTile
 import astraea.spark.rasterframes.functions.safeBinaryOp
 import astraea.spark.rasterframes.util.DataBiasedOp.{BiasedMax, BiasedMin}
 import geotrellis.raster.Tile
-import geotrellis.raster.mapalgebra.local
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
-import org.apache.spark.sql.{Column, Row, TypedColumn}
-import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
+import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{Column, Row, TypedColumn}
 
 /**
  * Aggregation function for applying a [[LocalTileBinaryOp]] pairwise across all tiles. Assumes Monoid algebra.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala
index d7f1a7867..d516a6723 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala
@@ -22,11 +22,10 @@
 package astraea.spark.rasterframes.expressions.localops
 
 import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
 import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
-import astraea.spark.rasterframes.expressions.{BinaryLocalRasterOp, DynamicExtractors}
 import astraea.spark.rasterframes.util.DataBiasedOp.BiasedAdd
 import geotrellis.raster.Tile
-import org.apache.spark.sql.rf._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala
index fd2ae2f29..7925f24a8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala
@@ -22,11 +22,10 @@
 package astraea.spark.rasterframes.expressions.localops
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
 import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
 import geotrellis.raster.Tile
 import geotrellis.raster.resample.NearestNeighbor
-import org.apache.spark.sql.rf._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala
index e23e68c08..30d6bc9cd 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala
@@ -22,7 +22,6 @@
 package astraea.spark.rasterframes.expressions.tilestats
 
 import astraea.spark.rasterframes.expressions.{NullToValue, UnaryRasterOp}
-import astraea.spark.rasterframes.functions.safeEval
 import astraea.spark.rasterframes.model.TileContext
 import geotrellis.raster.{Tile, isData}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 2581f8be5..fe8e2b5ee 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -21,6 +21,7 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
+import astraea.spark.rasterframes.NOMINAL_TILE_DIMS
 import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.ref.RasterRef
@@ -58,7 +59,10 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], applyTiling: Bool
     try {
       val refs = children.map { child ⇒
         val src = RasterSourceType.deserialize(child.eval(input))
-        if (applyTiling) src.nativeTiling.map(e ⇒ RasterRef(src, Some(e))) else Seq(RasterRef(src))
+        if (applyTiling) src
+          .layoutExtents(NOMINAL_TILE_DIMS)
+          .map(e ⇒ RasterRef(src, Some(e)))
+        else Seq(RasterRef(src))
       }
       refs.transpose.map(ts ⇒ InternalRow(ts.map(_.toInternalRow): _*))
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
index b26c0f944..64b1c9f6e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
@@ -61,11 +61,6 @@ case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean)
         val tiles = if (applyTiling) src.readAll() else {
           Seq(src.read(src.extent))
         }
-
-        tiles.headOption.foreach(r => {
-          require(r.tile.bandCount <= 1, "Multiband tiles are not yet supported")
-        })
-
         tiles.map(_.mapTile(_.band(0)))
       }
       refs.transpose.map(ts ⇒ InternalRow(ts.map(r ⇒ r.tile.toInternalRow): _*))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala
index ef0c901f8..d291ae9d4 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala
@@ -21,7 +21,7 @@ import astraea.spark.rasterframes.StandardColumns._
 import astraea.spark.rasterframes.extensions.Implicits._
 import astraea.spark.rasterframes.util._
 import astraea.spark.rasterframes.{PairRDDConverter, RasterFrame}
-import geotrellis.raster.{CellGrid, Tile}
+import geotrellis.raster.{CellGrid}
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.util.MethodExtensions
@@ -47,7 +47,7 @@ abstract class SpatialContextRDDMethods[T <: CellGrid](implicit spark: SparkSess
 }
 
 /**
- * Extension method on `ContextRDD`-shaped [[Tile]] RDDs keyed with [[SpaceTimeKey]], with appropriate context bounds to create a RasterFrame.
+ * Extension method on `ContextRDD`-shaped `Tile` RDDs keyed with [[SpaceTimeKey]], with appropriate context bounds to create a RasterFrame.
  * @since 9/11/17
  */
 abstract class SpatioTemporalContextRDDMethods[T <: CellGrid](
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala
index 8fdda51a0..bd8bb6d1c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala
@@ -24,7 +24,6 @@ import astraea.spark.rasterframes.util.{WithMergeMethods, WithPrototypeMethods}
 import geotrellis.raster._
 import geotrellis.spark.{Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
-import org.apache.hadoop.conf.{Configuration => HadoopConfiguration}
 import org.apache.spark.SparkConf
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala
index bcd2b31c4..f46f7f0e7 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala
@@ -20,7 +20,7 @@
 package astraea.spark.rasterframes.extensions
 
 import geotrellis.util.MethodExtensions
-import org.apache.spark.sql.{SQLContext, rf}
+import org.apache.spark.sql.SQLContext
 
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
index 2594a6590..0ff2be2ee 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
@@ -15,14 +15,12 @@
  */
 package astraea.spark.rasterframes
 
-import astraea.spark.rasterframes.expressions.aggstats._
 import astraea.spark.rasterframes.jts.ReprojectionTransformer
 import astraea.spark.rasterframes.util.CRSParser
-import org.locationtech.jts.geom.Geometry
-import geotrellis.raster.mapalgebra.local._
 import geotrellis.raster.{Tile, _}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.SQLContext
+import org.locationtech.jts.geom.Geometry
 
 /**
  * Module utils.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala b/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala
index 2f7f579ba..0e0e56e51 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala
@@ -21,8 +21,8 @@
 
 package astraea.spark.rasterframes.model
 
+import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import geotrellis.raster.Grid
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{ShortType, StructField, StructType}
@@ -49,8 +49,8 @@ object TileDimensions {
     )
 
     override protected def from[R](t: R, io: CatalystIO[R]): TileDimensions = TileDimensions(
-      io.getShort(t, 0),
-      io.getShort(t, 1)
+      io.getShort(t, 0).toInt,
+      io.getShort(t, 1).toInt
     )
   }
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/package.scala b/core/src/main/scala/astraea/spark/rasterframes/package.scala
index 7bb56d402..dafba71be 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/package.scala
@@ -17,6 +17,7 @@
 package astraea.spark
 
 import astraea.spark.rasterframes.encoders.StandardEncoders
+import astraea.spark.rasterframes.model.TileDimensions
 import astraea.spark.rasterframes.util.ZeroSevenCompatibilityKit
 import com.typesafe.config.ConfigFactory
 import com.typesafe.scalalogging.LazyLogging
@@ -27,7 +28,6 @@ import org.apache.spark.sql._
 import org.locationtech.geomesa.spark.jts.DataFrameFunctions
 import shapeless.tag.@@
 
-import scala.language.higherKinds
 import scala.reflect.runtime.universe._
 
 /**
@@ -52,6 +52,7 @@ package object rasterframes extends StandardColumns
   /** The generally expected tile size, as defined by configuration property `rasterframes.nominal-tile-size`.*/
   @transient
   final val NOMINAL_TILE_SIZE: Int = rfConfig.getInt("nominal-tile-size")
+  final val NOMINAL_TILE_DIMS: TileDimensions = TileDimensions(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE)
 
   /**
    * Initialization injection point. Must be called before any RasterFrame
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
index ff6564807..aec6b3b86 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
@@ -21,13 +21,12 @@
 
 package astraea.spark.rasterframes.ref
 
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
-import geotrellis.raster.{CellType, GridBounds, Tile, TileLayout}
-import geotrellis.spark.tiling.LayoutDefinition
+import geotrellis.raster.{CellType, GridBounds, Tile}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
@@ -56,15 +55,6 @@ case class RasterRef(source: RasterSource, subextent: Option[Extent])
     RasterRef.log.trace(s"Fetching $srcExtent from $source")
     source.read(srcExtent).tile.band(0)
   }
-
-  /** Splits this tile into smaller tiles based on the reported
-   * internal structure of the backing format. May return a single item.*/
-  def tileToNative: Seq[RasterRef] = {
-    val ex = this.extent
-    this.source.nativeTiling
-      .filter(_ intersects ex)
-      .map(e ⇒ RasterRef(this.source, Option(e)))
-  }
 }
 
 object RasterRef extends LazyLogging {
@@ -74,12 +64,6 @@ object RasterRef extends LazyLogging {
   /** Constructor for when data extent cover whole raster. */
   def apply(source: RasterSource): RasterRef = RasterRef(source, None)
 
-  private[rasterframes]
-  def defaultLayout(rr: RasterRef): LayoutDefinition =
-    LayoutDefinition(rr.extent, rr.source.nativeLayout
-      .getOrElse(TileLayout(1, 1, rr.cols, rr.rows))
-    )
-
   case class RasterRefTile(rr: RasterRef) extends ProjectedRasterTile {
     val extent: Extent = rr.extent
     val crs: CRS = rr.crs
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index be27197b5..3e5657f0c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -24,16 +24,16 @@ import java.net.URI
 import java.time.ZonedDateTime
 import java.time.format.DateTimeFormatter
 
-import astraea.spark.rasterframes.NOMINAL_TILE_SIZE
-import astraea.spark.rasterframes.model.TileContext
+import astraea.spark.rasterframes.NOMINAL_TILE_DIMS
+import astraea.spark.rasterframes.model.{TileContext, TileDimensions}
+import astraea.spark.rasterframes.ref.RasterSource.SINGLEBAND
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.util.GeoTiffInfoSupport
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, MultibandGeoTiff, Tags}
-import geotrellis.raster.split.Split
+import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, Tags}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
 import geotrellis.spark.io.s3.S3Client
 import geotrellis.spark.io.s3.util.S3RangeReader
@@ -66,11 +66,17 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def tags: Option[Tags]
 
-  def read(extent: Extent): Raster[MultibandTile]
+  def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
+    readBounds(Seq(bounds), bands).next()
 
-  def readAll(): Seq[Raster[MultibandTile]]
+  def read(extent: Extent, bands: Seq[Int] = SINGLEBAND): Raster[MultibandTile] =
+    read(rasterExtent.gridBoundsFor(extent, clamp = true), bands)
 
-  def nativeLayout: Option[TileLayout]
+  def readAll(
+    dims: TileDimensions = NOMINAL_TILE_DIMS,
+    bands: Seq[Int] = SINGLEBAND): Seq[Raster[MultibandTile]] = layoutBounds(dims).map(read(_, bands))
+
+  protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]]
 
   def rasterExtent = RasterExtent(extent, cols, rows)
 
@@ -80,28 +86,47 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def tileContext: TileContext = TileContext(extent, crs)
 
-  def nativeTiling: Seq[Extent] = {
-    nativeLayout
-      .map { tileLayout =>
-        val layout = LayoutDefinition(extent, tileLayout)
-        val transform = layout.mapTransform
-        for {
-          col <- 0 until tileLayout.layoutCols
-          row <- 0 until tileLayout.layoutRows
-        } yield transform(col, row)
-      }
-      .getOrElse(Seq(extent))
+  def tileLayout(dims: TileDimensions): TileLayout = {
+    require(dims.cols > 0 && dims.rows > 0, "Non-zero tile sizes")
+    TileLayout(
+      layoutCols = math.ceil(this.cols.toDouble / dims.cols).toInt,
+      layoutRows = math.ceil(this.rows.toDouble / dims.rows).toInt,
+      tileCols = dims.cols,
+      tileRows = dims.rows
+    )
+  }
+
+  def layoutExtents(dims: TileDimensions): Seq[Extent] = {
+    val tl = tileLayout(dims)
+    val layout = LayoutDefinition(extent, tl)
+    val transform = layout.mapTransform
+    for {
+      col <- 0 until tl.layoutCols
+      row <- 0 until tl.layoutRows
+    } yield transform(col, row)
+  }
+
+  def layoutBounds(dims: TileDimensions): Seq[GridBounds] = {
+    gridBounds.split(dims.cols, dims.rows).toSeq
+//    val tl = tileLayout(dims)
+//    for {
+//      col <- 0 until tl.layoutCols
+//      row <- 0 until tl.layoutRows
+//      cmin = math.min(col * tl.tileCols, cols)
+//      cmax = math.min((col + 1) * tl.tileCols, cols)
+//      rmin = math.min(row * tl.tileRows, cols)
+//      rmax = math.min((row + 1) * tl.tileRows, rows)
+//    } yield GridBounds(cmin, rmin, cmax, rmax)
   }
 }
 
 object RasterSource extends LazyLogging {
+  final val SINGLEBAND = Seq(0)
   implicit def rsEncoder: ExpressionEncoder[RasterSource] = {
     RasterSourceUDT // Makes sure UDT is registered first
     ExpressionEncoder()
   }
 
-  private def _logger = logger
-
   def apply(source: URI, callback: Option[ReadCallback] = None): RasterSource =
     source.getScheme match {
       case GDALRasterSource()                        => GDALRasterSource(source, callback)
@@ -169,19 +194,12 @@ object RasterSource extends LazyLogging {
 
     override def tags: Option[Tags] = None
 
-    override def read(extent: Extent): Raster[MultibandTile] =
-      Raster(MultibandTile(tile.crop(rasterExtent.gridBoundsFor(extent, false))), extent)
-
-    override def nativeLayout: Option[TileLayout] = Some(
-      TileLayout(
-        layoutCols = math.ceil(this.cols.toDouble / NOMINAL_TILE_SIZE).toInt,
-        layoutRows = math.ceil(this.rows.toDouble / NOMINAL_TILE_SIZE).toInt,
-        tileCols = NOMINAL_TILE_SIZE,
-        tileRows = NOMINAL_TILE_SIZE)
-    )
-
-    def readAll(): Seq[Raster[MultibandTile]] =
-      Raster(MultibandTile(tile), extent).split(nativeLayout.get, Split.Options(false, false)).toSeq
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+      bounds.map(b => {
+        val subext = rasterExtent.extentFor(b)
+        Raster(MultibandTile(tile.crop(b)), subext)
+      }).toIterator
+    }
   }
 
   case class GDALRasterSource(source: URI, callback: Option[ReadCallback])
@@ -212,35 +230,29 @@ object RasterSource extends LazyLogging {
 
     override def rows: Int = gdal.rows
 
-    override def read(extent: Extent): Raster[MultibandTile] = {
-      callback.foreach { cb =>
-        val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
-        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
-      }
-      gdal.read(extent).get
-    }
-
-    override def readAll(): Seq[Raster[MultibandTile]] = {
-      val grid = gdal.gridBounds
+//    override def read(extent: Extent): Raster[MultibandTile] = {
+//      callback.foreach { cb =>
+//        val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
+//        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
+//      }
+//      gdal.read(extent).get
+//    }
+//
+//    override def readAll(): Seq[Raster[MultibandTile]] = {
+//      val grid = gdal.gridBounds
+//
+//      callback.foreach { cb =>
+//        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
+//      }
+//
+//      val tiled = grid.split(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE).toTraversable
+//
+//      gdal.readBounds(tiled).toSeq
+//    }
 
-      callback.foreach { cb =>
-        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
-      }
-
-      val tiled = grid.split(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE).toTraversable
-
-      gdal.readBounds(tiled).toSeq
-    }
-
-    override def nativeLayout: Option[TileLayout] = {
-      Some(
-        TileLayout(
-          cols / NOMINAL_TILE_SIZE + 1,
-          rows / NOMINAL_TILE_SIZE + 1,
-          math.min(cols, NOMINAL_TILE_SIZE),
-          math.min(rows, NOMINAL_TILE_SIZE)))
-    }
     override def tags: Option[Tags] = Some(Tags(metadata, List.empty))
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
+      gdal.readBounds(bounds, bands)
   }
 
   object GDALRasterSource {
@@ -288,37 +300,48 @@ object RasterSource extends LazyLogging {
       else None
     }
 
-    def read(extent: Extent): Raster[MultibandTile] = {
+    override protected def readBounds(bounds: Traversable[GridBounds],
+      bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
       val info = realInfo
       val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
-      val gt = new MultibandGeoTiff(
-        geoTiffTile,
-        info.extent,
-        info.crs,
-        info.tags,
-        info.options,
-        List.empty
-      )
-      gt.crop(extent).raster
+      val intersectingBounds = bounds.flatMap(_.intersection(this)).toSeq
+      geoTiffTile.crop(intersectingBounds, bands.toArray).map { case (gb, tile) =>
+        Raster(tile, rasterExtent.extentFor(gb, clamp = true))
+      }
     }
 
-    def readAll(): Seq[Raster[MultibandTile]] = {
-      val info = realInfo
-
-      // Thanks to @pomadchin for showing us how to do this :-)
-      val windows = info.segmentLayout.listWindows(NOMINAL_TILE_SIZE)
-      val re = info.rasterExtent
+//    def read(extent: Extent): Raster[MultibandTile] = {
+//      val info = realInfo
+//      val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
+//      val gt = new MultibandGeoTiff(
+//        geoTiffTile,
+//        info.extent,
+//        info.crs,
+//        info.tags,
+//        info.options,
+//        List.empty
+//      )
+//      gt.crop(extent).raster
+//    }
+//
+//    def readAll(): Seq[Raster[MultibandTile]] = {
+//      val info = realInfo
+//
+//      // Thanks to @pomadchin for showing us how to do this :-)
+//      val windows = info.segmentLayout.listWindows(NOMINAL_TILE_SIZE)
+//      val re = info.rasterExtent
+//
+//      val geotile = GeoTiffReader.geoTiffMultibandTile(info)
+//
+//      val rows = windows.map(gb => {
+//        val tile = geotile.crop(gb)
+//        val extent = re.extentFor(gb, clamp = false)
+//        Raster(tile, extent)
+//      })
+//
+//      rows.toSeq
+//    }
 
-      val geotile = GeoTiffReader.geoTiffMultibandTile(info)
-
-      val rows = windows.map(gb => {
-        val tile = geotile.crop(gb)
-        val extent = re.extentFor(gb, clamp = false)
-        Raster(tile, extent)
-      })
-
-      rows.toSeq
-    }
   }
 
   case class FileGeoTiffRasterSource(source: URI, callback: Option[ReadCallback])
diff --git a/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala b/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala
index efc4908db..c32d6100f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala
@@ -160,13 +160,13 @@ object CellHistogram {
     }
     else {
       val h = tile.histogram
-      h.binCounts().map(p ⇒ Bin(p._1, p._2))
+      h.binCounts().map(p ⇒ Bin(p._1.toDouble, p._2))
     }
     CellHistogram(bins)
   }
 
   def apply(hist: GTHistogram[Int]): CellHistogram = {
-    CellHistogram(hist.binCounts().map(p ⇒ Bin(p._1, p._2)))
+    CellHistogram(hist.binCounts().map(p ⇒ Bin(p._1.toDouble, p._2)))
   }
   def apply(hist: GTHistogram[Double])(implicit ev: DummyImplicit): CellHistogram = {
     CellHistogram(hist.binCounts().map(p ⇒ Bin(p._1, p._2)))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
index a9551dd13..539f46fd2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
@@ -21,8 +21,8 @@
 
 package astraea.spark.rasterframes.tiles
 
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import astraea.spark.rasterframes.model.TileContext
 import astraea.spark.rasterframes.ref.ProjectedRasterLike
 import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
@@ -30,7 +30,6 @@ import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster.{CellType, ProjectedRaster, Tile}
 import geotrellis.vector.{Extent, ProjectedExtent}
-import org.apache.spark.sql.Encoder
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala b/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala
index 724d7eaeb..add36a914 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala
@@ -37,7 +37,7 @@ trait GeoTiffInfoSupport {
   val MAX_SIZE = 256
   private def defaultLayout(cols: Int, rows: Int): TileLayout = {
     def divs(cells: Int) = {
-      val layoutDivs = math.ceil(cells / MAX_SIZE.toFloat)
+      val layoutDivs = math.ceil(cells / MAX_SIZE.toDouble)
       val tileDivs = math.ceil(cells / layoutDivs)
       (layoutDivs.toInt, tileDivs.toInt)
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala b/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala
index f9cdfb48e..e83339f2c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala
@@ -35,7 +35,7 @@ import org.apache.spark.util.LongAccumulator
 case class ReadAccumulator(reads: () ⇒ LongAccumulator, bytes: () ⇒ LongAccumulator) extends ReadCallback {
   override def readRange(source: RasterSource, start: Long, length: Int): Unit = {
     reads().add(1)
-    bytes().add(length)
+    bytes().add(length.toLong)
   }
   override def toString: String =
     s"${productPrefix}(reads=${reads().value}, bytes=${bytes().value})"
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index 75ac0f7cf..643950445 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -74,7 +74,6 @@ case object TileUDT  {
   final val typeName: String = "tile"
 
   implicit def tileSerializer: CatalystSerializer[Tile] = new CatalystSerializer[Tile] {
-    import scala.language.reflectiveCalls
 
     override def schema: StructType = StructType(Seq(
       StructField("cell_context", CatalystSerializer[TileDataContext].schema, false),
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/VersionShims.scala b/core/src/main/scala/org/apache/spark/sql/rf/VersionShims.scala
index b9eb96981..81418d466 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/VersionShims.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/VersionShims.scala
@@ -1,18 +1,18 @@
 package org.apache.spark.sql.rf
 
-import java.lang.reflect.{Constructor, Method}
+import java.lang.reflect.Constructor
 
 import org.apache.spark.sql.catalyst.FunctionIdentifier
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.{FunctionBuilder, expressionInfo}
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder
 import org.apache.spark.sql.catalyst.catalog.CatalogTable
-import org.apache.spark.sql.{AnalysisException, DataFrame, Dataset, SQLContext}
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, BinaryExpression, Expression, ExpressionDescription, ExpressionInfo, RuntimeReplaceable, ScalaUDF}
 import org.apache.spark.sql.catalyst.expressions.objects.{Invoke, InvokeLike}
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, ExpressionDescription, ExpressionInfo}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.{AnalysisException, DataFrame, Dataset, SQLContext}
 
 import scala.reflect._
 import scala.util.{Failure, Success, Try}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala
index da2ab9c56..3e0aa841e 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala
@@ -27,9 +27,9 @@ import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import geotrellis.proj4.LatLng
 import geotrellis.raster
 import geotrellis.raster.testkit.RasterMatchers
-import geotrellis.raster.{ArrayTile, BitCellType, ByteUserDefinedNoDataCellType, DoubleConstantNoDataCellType, ShortConstantNoDataCellType, Tile, UByteConstantNoDataCellType}
+import geotrellis.raster.{ArrayTile, ByteUserDefinedNoDataCellType, DoubleConstantNoDataCellType, ShortConstantNoDataCellType, Tile, UByteConstantNoDataCellType}
 import geotrellis.vector.Extent
-import org.apache.spark.sql.{AnalysisException, Encoders}
+import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
 import org.scalatest.{FunSpec, Matchers}
 
@@ -58,7 +58,7 @@ class RasterFunctionsSpec extends FunSpec
   lazy val randDoubleNDTile  = TestData.injectND(numND)(randDoubleTile)
   lazy val randPositiveDoubleTile = TestData.projectedRasterTile(cols, rows, scala.util.Random.nextDouble() + 1e-6, extent, crs, DoubleConstantNoDataCellType)
 
-  val expectedRandNoData: Long = numND * tileCount
+  val expectedRandNoData: Long = numND * tileCount.toLong
   val expectedRandData: Long = cols * rows * tileCount - expectedRandNoData
   lazy val randNDTilesWithNull = Seq.fill[Tile](tileCount)(injectND(numND)(
     TestData.randomTile(cols, rows, UByteConstantNoDataCellType)
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index b6e7da48b..0c4045f8a 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -23,10 +23,7 @@ import java.nio.file.Paths
 import java.time.ZonedDateTime
 
 import astraea.spark.rasterframes.expressions.tilestats.NoDataCells
-import astraea.spark.rasterframes.model.TileContext
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import astraea.spark.rasterframes.{functions => F}
-import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
 import geotrellis.proj4.{CRS, LatLng}
 import geotrellis.raster
 import geotrellis.raster._
@@ -38,6 +35,7 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SparkSession
+import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
 
 import scala.reflect.ClassTag
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala
index 781b8290d..939ae1a46 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala
@@ -19,13 +19,11 @@
 
 package astraea.spark.rasterframes
 
-import astraea.spark.rasterframes.TestData.randomTile
-import astraea.spark.rasterframes.TestData.fracTile
-import astraea.spark.rasterframes.expressions.aggstats.LocalMeanAggregate
+import astraea.spark.rasterframes.TestData.{fracTile, randomTile}
 import astraea.spark.rasterframes.stats.CellHistogram
 import geotrellis.raster._
-import geotrellis.spark._
 import geotrellis.raster.mapalgebra.local.{Max, Min}
+import geotrellis.spark._
 import org.apache.spark.sql.functions._
 
 /**
@@ -35,8 +33,8 @@ import org.apache.spark.sql.functions._
  * @since 9/18/17
  */
 class TileStatsSpec extends TestEnvironment with TestData {
-  import sqlContext.implicits._
   import TestData.injectND
+  import sqlContext.implicits._
 
   describe("computing statistics over tiles") {
     //import org.apache.spark.sql.execution.debug._
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index ad29e0e0e..839ae1792 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -23,10 +23,10 @@ package astraea.spark.rasterframes.ref
 
 import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.expressions.accessors._
+import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
-import geotrellis.raster.{GridBounds, Tile}
+import geotrellis.raster.Tile
 import geotrellis.vector.Extent
 
 /**
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 1559c3c67..86bd29251 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -22,9 +22,9 @@
 package astraea.spark.rasterframes.ref
 
 import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
+import astraea.spark.rasterframes.model.TileDimensions
 import astraea.spark.rasterframes.ref.RasterSource.GDALRasterSource
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
-import geotrellis.raster.io.geotiff.GeoTiff
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
 
@@ -47,6 +47,22 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     it("should identify as UDT") {
       assert(new RasterSourceUDT() === new RasterSourceUDT())
     }
+    val rs = RasterSource(getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI)
+    it("should provide a tile layout") {
+      val layout = rs.tileLayout(TileDimensions(64, 64))
+      layout.totalCols shouldBe >= (rs.cols.toLong)
+      layout.totalRows shouldBe >= (rs.rows.toLong)
+    }
+    it("should compute nominal tile layout bounds") {
+      val bounds = rs.layoutBounds(TileDimensions(64, 64))
+      val agg = bounds.reduce(_ combine _)
+      agg should be (rs.gridBounds)
+    }
+    it("should compute nominal tile layout extents") {
+      val extents = rs.layoutExtents(TileDimensions(64, 64))
+      val agg = extents.reduce(_ combine _)
+      agg should be (rs.extent)
+    }
   }
 
   describe("HTTP RasterSource") {
@@ -158,10 +174,10 @@ class RasterSourceSpec extends TestEnvironment with TestData {
 
       assert(totalCells === src.size)
 
-      subrasters.zipWithIndex.foreach{case (r, i) ⇒
-        // TODO: how to test?
-        GeoTiff(r, src.crs).write(s"target/$i.tiff")
-      }
+//      subrasters.zipWithIndex.foreach{case (r, i) ⇒
+//        // TODO: how to test?
+//        GeoTiff(r, src.crs).write(s"target/$i.tiff")
+//      }
     }
   }
 }
diff --git a/core/src/test/scala/examples/CreatingRasterFrames.scala b/core/src/test/scala/examples/CreatingRasterFrames.scala
index f7a69043a..311de3dbc 100644
--- a/core/src/test/scala/examples/CreatingRasterFrames.scala
+++ b/core/src/test/scala/examples/CreatingRasterFrames.scala
@@ -35,11 +35,9 @@ object CreatingRasterFrames extends App {
 
   import astraea.spark.rasterframes._
   import geotrellis.raster._
-  import geotrellis.raster.render._
-  import geotrellis.spark.io._
   import geotrellis.raster.io.geotiff.SinglebandGeoTiff
+  import geotrellis.spark.io._
   import org.apache.spark.sql._
-  import org.apache.spark.sql.functions._
 
 //  Next, initialize the `SparkSession`, and call the `withRasterFrames` method on it:
 
@@ -47,8 +45,6 @@ object CreatingRasterFrames extends App {
     master("local[*]").appName("RasterFrames").
     getOrCreate().
     withRasterFrames
-
-  import spark.implicits._
   spark.sparkContext.setLogLevel("ERROR")
 
 //  ## From `ProjectedExtent`
diff --git a/core/src/test/scala/examples/Exporting.scala b/core/src/test/scala/examples/Exporting.scala
index 247e93944..79af79fd0 100644
--- a/core/src/test/scala/examples/Exporting.scala
+++ b/core/src/test/scala/examples/Exporting.scala
@@ -22,11 +22,9 @@ import java.nio.file.Files
 
 import astraea.spark.rasterframes._
 import geotrellis.raster._
+import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.{GeoTiff, SinglebandGeoTiff}
 import geotrellis.spark.{LayerId, SpatialKey}
-import geotrellis.spark.io.LayerWriter
-import geotrellis.spark.io.file.{FileAttributeStore, FileLayerWriter}
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import spray.json.JsValue
@@ -76,8 +74,6 @@ object Exporting extends App {
   //
   //
   //    Let's assume we have a RasterFrame we've done some fancy processing on:
-
-  import geotrellis.raster.equalization._
   val equalizer = udf((t: Tile) => t.equalize())
   val equalized = rf.withColumn("equalized", equalizer($"tile")).asRF
 
diff --git a/core/src/test/scala/examples/Masking.scala b/core/src/test/scala/examples/Masking.scala
index bc9c59213..00169b622 100644
--- a/core/src/test/scala/examples/Masking.scala
+++ b/core/src/test/scala/examples/Masking.scala
@@ -2,11 +2,10 @@ package examples
 
 import astraea.spark.rasterframes._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import org.apache.spark.sql._
-import geotrellis.raster.{mask => _, _}
 import geotrellis.raster.render._
+import geotrellis.raster.{mask => _, _}
+import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
-import astraea.spark.rasterframes.stats.{CellHistogram=>CH}
 
 object Masking extends App {
 
diff --git a/core/src/test/scala/examples/MeanValue.scala b/core/src/test/scala/examples/MeanValue.scala
index d2190a241..ca0576e02 100644
--- a/core/src/test/scala/examples/MeanValue.scala
+++ b/core/src/test/scala/examples/MeanValue.scala
@@ -22,7 +22,6 @@ package examples
 import astraea.spark.rasterframes._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.functions._
 
 /**
  * Compute the cell mean value of an image.
diff --git a/core/src/test/scala/examples/NaturalColorComposite.scala b/core/src/test/scala/examples/NaturalColorComposite.scala
index 3dee4092c..1a3e212ac 100644
--- a/core/src/test/scala/examples/NaturalColorComposite.scala
+++ b/core/src/test/scala/examples/NaturalColorComposite.scala
@@ -20,11 +20,8 @@
 package examples
 
 
-import java.nio.file.{CopyOption, StandardCopyOption}
-
+import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster.{MultibandTile, UByteConstantNoDataCellType}
-import geotrellis.raster.io.geotiff.{GeoTiff, SinglebandGeoTiff}
-import geotrellis.raster.render._
 
 /**
  *
diff --git a/core/src/test/scala/examples/Scratch.scala b/core/src/test/scala/examples/Scratch.scala
index fca8c4785..565b4c31c 100644
--- a/core/src/test/scala/examples/Scratch.scala
+++ b/core/src/test/scala/examples/Scratch.scala
@@ -20,8 +20,6 @@
 package examples
 
 import astraea.spark.rasterframes._
-import geotrellis.spark.io.kryo.KryoRegistrator
-import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.sql._
 
 /**
@@ -33,14 +31,11 @@ object Scratch extends App {
   implicit val spark = SparkSession.builder()
     .master("local[*]")
     .appName(getClass.getName)
-    .config("spark.serializer", classOf[KryoSerializer].getName)
-    .config("spark.kryoserializer.buffer.max", "500m")
-    .config("spark.kryo.registrationRequired", "false")
-    .config("spark.kryo.registrator", classOf[KryoRegistrator].getName)
+    .withKryoSerialization
     .getOrCreate()
     .withRasterFrames
 
-  import spark.implicits._
+  // import spark.implicits._
 
   // Your Spark code here.....
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 2f69d4425..d9f6e5e05 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -31,7 +31,6 @@ import geotrellis.spark.io.hadoop.HadoopGeoTiffRDD
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.hadoop.fs.Path
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.jts.JTSTypes
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.sources.{BaseRelation, PrunedScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
index 5af7f1e3f..36edf16a7 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
@@ -21,16 +21,14 @@ package astraea.spark.rasterframes.datasource.geotrellis
 
 import java.net.URI
 
-import astraea.spark.rasterframes
 import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisCatalog.GeoTrellisCatalogRelation
-import astraea.spark.rasterframes.util.time
 import geotrellis.spark.io.AttributeStore
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.rf.VersionShims
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.StructType
 import spray.json.DefaultJsonProtocol._
 import spray.json._
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
index 545c1f236..5f3304d05 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
@@ -79,12 +79,12 @@ package object geotrellis extends DataSourceOptions {
   implicit class GeoTrellisReaderWithRF(val reader: GeoTrellisRasterFrameReader) {
     def withTileSubdivisions(divs: Int): GeoTrellisRasterFrameReader =
       tag[GeoTrellisRasterFrameReaderTag][DataFrameReader](
-        reader.option(TILE_SUBDIVISIONS_PARAM, divs)
+        reader.option(TILE_SUBDIVISIONS_PARAM, divs.toLong)
       )
 
     def withNumPartitions(partitions: Int): GeoTrellisRasterFrameReader =
       tag[GeoTrellisRasterFrameReaderTag][DataFrameReader](
-        reader.option(NUM_PARTITIONS_PARAM, partitions)
+        reader.option(NUM_PARTITIONS_PARAM, partitions.toLong)
       )
 
     def loadRF(uri: URI, id: LayerId): RasterFrame =
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala
index 6962e9a11..970244883 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala
@@ -21,8 +21,6 @@ package astraea.spark.rasterframes
 
 import java.net.URI
 
-import org.apache.spark.sql.sources.{And, Filter}
-
 import scala.util.Try
 
 /**
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
index e69a5414a..02e86086e 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
@@ -21,7 +21,7 @@ package astraea.spark.rasterframes.datasource.geotrellis
 import java.io.File
 
 import astraea.spark.rasterframes._
-import geotrellis.proj4.{CRS, LatLng, Sinusoidal}
+import geotrellis.proj4.LatLng
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.spark.io.index.ZCurveKeyIndexMethod
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 009382639..68225cc3a 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -40,7 +40,7 @@ import geotrellis.vector._
 import org.apache.avro.generic._
 import org.apache.avro.{Schema, SchemaBuilder}
 import org.apache.hadoop.fs.FileUtil
-import org.apache.spark.sql.functions.{udf ⇒ sparkUdf}
+import org.apache.spark.sql.functions.{udf => sparkUdf}
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.storage.StorageLevel
 import org.scalatest.{BeforeAndAfterAll, Inspectors}
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
index 5d475f263..dbff2225a 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
@@ -151,8 +151,6 @@ class TileFeatureSupportSpec extends TestEnvironment
 
 object TileFeatureSupportSpec {
 
-  import scala.language.implicitConversions
-
   implicit class RichRandom(val rnd: scala.util.Random) extends AnyVal {
     def nextDouble(max: Double): Double = (rnd.nextInt * max) / Int.MaxValue.toDouble
     def nextOrderedPair(max:Double): (Double,Double) = (nextDouble(max),nextDouble(max)) match {
diff --git a/datasource/src/test/scala/examples/Creating.scala b/datasource/src/test/scala/examples/Creating.scala
index 174bbaa3f..a6e691ac1 100644
--- a/datasource/src/test/scala/examples/Creating.scala
+++ b/datasource/src/test/scala/examples/Creating.scala
@@ -22,8 +22,6 @@ package examples
 import java.io.File
 import java.nio.file.Files
 
-import geotrellis.raster.{Raster, Tile}
-
 /**
  * Examples of creating RasterFrames
  *
@@ -58,7 +56,6 @@ And, ss is standard Spark SQL practice, we import additional DataFrame support:
 */
 
 // tut:silent
-import spark.implicits._
 // tut:invisible
 spark.sparkContext.setLogLevel("ERROR")
 
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
index 688866a93..d37a7fd42 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
@@ -21,7 +21,6 @@
 package astraea.spark.rasterframes.experimental.datasource.awspds
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.util.ReadAccumulator
 import org.apache.spark.sql.DataFrame
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 845f5aad3..aa2d33353 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -22,7 +22,6 @@ package astraea.spark.rasterframes.experimental.datasource.awspds
 
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.encoders.CatalystSerializer
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.experimental.datasource.awspds.L8Relation.Bands
 import astraea.spark.rasterframes.expressions.transformers.{RasterSourceToRasterRefs, URIToRasterSource}
 import astraea.spark.rasterframes.ref.RasterRef
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 716d70fdd..455629a67 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -35,7 +35,12 @@ object RFProjectPlugin extends AutoPlugin {
     description := "RasterFrames brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis",
     licenses += ("Apache-2.0", url("https://www.apache.org/licenses/LICENSE-2.0.html")),
     scalaVersion := "2.11.12",
-    scalacOptions ++= Seq("-feature", "-deprecation"),
+    scalacOptions ++= Seq(
+      "-feature",
+      "-deprecation",
+      "-Ywarn-dead-code",
+      "-Ywarn-unused-import"
+    ),
     scalacOptions in (Compile, doc) ++= Seq("-no-link-warnings"),
     javacOptions ++= Seq("-source", "1.8", "-target", "1.8"),
     cancelable in Global := true,
diff --git a/project/RFReleasePlugin.scala b/project/RFReleasePlugin.scala
index 8d439927b..7eef23231 100644
--- a/project/RFReleasePlugin.scala
+++ b/project/RFReleasePlugin.scala
@@ -24,11 +24,13 @@ import sbt.Keys._
 import sbt._
 import sbtrelease.ReleasePlugin.autoImport.ReleaseTransformations._
 import sbtrelease.ReleasePlugin.autoImport._
-import com.servicerocket.sbt.release.git.flow.Steps._
 import com.typesafe.sbt.sbtghpages.GhpagesPlugin
 import com.typesafe.sbt.sbtghpages.GhpagesPlugin.autoImport.ghpagesPushSite
 import com.typesafe.sbt.site.SitePlugin
 import com.typesafe.sbt.site.SitePlugin.autoImport.makeSite
+import scala.sys.process.{Process => SProcess}
+
+/** Release process support. */
 object RFReleasePlugin extends AutoPlugin {
   override def trigger: PluginTrigger = noTrigger
   override def requires = RFProjectPlugin && SitePlugin && GhpagesPlugin
@@ -42,18 +44,20 @@ object RFReleasePlugin extends AutoPlugin {
         checkSnapshotDependencies,
         checkGitFlowExists,
         inquireVersions,
+        runClean,
         runTest,
         gitFlowReleaseStart,
         setReleaseVersion,
         buildSite,
-        publishSite,
         commitReleaseVersion,
         tagRelease,
         releaseStepCommand("publishSigned"),
         releaseStepCommand("sonatypeReleaseAll"),
+        publishSite,
         gitFlowReleaseFinish,
         setNextVersion,
-        commitNextVersion
+        commitNextVersion,
+        remindMeToPush
       ),
       commands += Command.command("bumpVersion"){ st ⇒
         val extracted = Project.extract(st)
@@ -68,4 +72,36 @@ object RFReleasePlugin extends AutoPlugin {
       }
     )
   }
+
+  def releaseVersion(state: State): String =
+    state.get(ReleaseKeys.versions).map(_._1).getOrElse {
+      sys.error("No versions are set! Was this release part executed before inquireVersions?")
+    }
+
+  val gitFlowReleaseStart = ReleaseStep(state ⇒ {
+    val version = releaseVersion(state)
+    SProcess(Seq("git", "flow", "release", "start", version)).!
+    state
+  })
+
+  val gitFlowReleaseFinish = ReleaseStep(state ⇒ {
+    val version = releaseVersion(state)
+    SProcess(Seq("git", "flow", "release", "finish", "-n", s"$version")).!
+    state
+  })
+
+  val remindMeToPush = ReleaseStep(state ⇒ {
+    state.log.warn("Don't forget to git push master AND develop!")
+    state
+  })
+
+  val checkGitFlowExists = ReleaseStep(state => {
+    SProcess(Seq("command", "-v", "git-flow")).!! match {
+      case "" => sys.error("git-flow is required for release. See https://github.com/nvie/gitflow for installation instructions.")
+      case _ => SProcess(Seq("git", "flow", "init", "-d")).! match {
+        case 0 => state
+        case e => sys.error(s"git-flow init failed with error code $e")
+      }
+    }
+  })
 }
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 1ac9bcfa4..75617f14d 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -2,7 +2,6 @@ logLevel := sbt.Level.Error
 
 resolvers += Resolver.bintrayIvyRepo("s22s", "sbt-plugins")
 addSbtCoursier
-addSbtPlugin("com.servicerocket" % "sbt-git-flow" % "0.1.3-astraea.1")
 addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.6")
 addSbtPlugin("com.eed3si9n" % "sbt-buildinfo" % "0.7.0")
 addSbtPlugin("de.heikoseeberger" % "sbt-header" % "3.0.2")

From e84de49ab13419f92e3d8f8e6443184890467ce6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 28 Mar 2019 13:46:40 -0400
Subject: [PATCH 020/380] Remove read callbacks due to incompatibility with
 GeoTrellis RasterSource.

---
 .../rasterframes/bench/RasterRefBench.scala   |  46 +----
 .../transformers/URIToRasterSource.scala      |  11 +-
 .../spark/rasterframes/ref/RasterSource.scala | 174 +++---------------
 .../rasterframes/util/RFKryoRegistrator.scala |   1 -
 .../rasterframes/util/ReadAccumulator.scala   |  50 -----
 core/src/test/resources/application.conf      |   6 +-
 .../spark/rasterframes/TestEnvironment.scala  |  13 --
 .../rasterframes/ref/RasterRefSpec.scala      |   9 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |  49 -----
 .../datasource/awspds/L8RelationTest.scala    |   2 -
 .../datasource/awspds/L8DataSource.scala      |   8 +-
 .../datasource/awspds/L8Relation.scala        |   7 +-
 project/plugins.sbt                           |   1 +
 13 files changed, 40 insertions(+), 337 deletions(-)
 delete mode 100644 core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala

diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala
index c68c826e8..a2a9104bf 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala
@@ -24,11 +24,9 @@ package astraea.spark.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.expressions.transformers.RasterSourceToTiles
 import astraea.spark.rasterframes.ref.RasterSource
-import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
 import org.openjdk.jmh.annotations._
@@ -48,18 +46,8 @@ class RasterRefBench  extends SparkEnv with LazyLogging {
 
   @Setup(Level.Trial)
   def setupData(): Unit = {
-    val watcher = new ReadCallback {
-      var count: Long = 0
-      var calls: Int = 0
-      override def readRange(source: RasterSource, start: Long, length: Int): Unit = {
-        calls += 1
-        count += length
-        logger.debug("%4d -- %,d bytes".format(calls, count))
-      }
-    }
-
-    val r1 = RasterSource(remoteCOGSingleband1, Some(watcher))
-    val r2 = RasterSource(remoteCOGSingleband2, Some(watcher))
+    val r1 = RasterSource(remoteCOGSingleband1)
+    val r2 = RasterSource(remoteCOGSingleband2)
     singleDF = Seq((r1, r2)).toDF("B1", "B2")
       .select(RasterSourceToTiles(false, $"B1", $"B2"))
 
@@ -98,32 +86,4 @@ class RasterRefBench  extends SparkEnv with LazyLogging {
     singleDF.select(agg_stats(normalized_difference($"B1", $"B2"))).collect()
   }
 
-}
-
-object RasterRefBench {
-
-//  import org.openjdk.jmh.runner.RunnerException
-//  import org.openjdk.jmh.runner.options.OptionsBuilder
-//
-//  @throws[RunnerException]
-  def main(args: Array[String]): Unit = {
-
-  val thing = new RasterRefBench()
-  thing.setupData()
-  rasterframes.util.time("compute stats expanded") {
-    thing.computeStatsSingle()
-  }
-
-  rasterframes.util.time("compute stats single") {
-    thing.computeStatsExpanded()
-  }
-
-  //    val opt = new OptionsBuilder()
-//      .include(classOf[RasterRefBench].getSimpleName)
-//      .threads(4)
-//      .forks(5)
-//      .build()
-//
-//    new Runner(opt).run()
-  }
-}
+}
\ No newline at end of file
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala
index 0821e43db..a10a6db7f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -23,7 +23,6 @@ package astraea.spark.rasterframes.expressions.transformers
 
 import java.net.URI
 
-import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
 import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
@@ -40,7 +39,7 @@ import org.apache.spark.unsafe.types.UTF8String
  *
  * @since 5/4/18
  */
-case class URIToRasterSource(override val child: Expression, accumulator: Option[ReadCallback])
+case class URIToRasterSource(override val child: Expression)
   extends UnaryExpression with ExpectsInputTypes with CodegenFallback with LazyLogging {
 
   override def nodeName: String = "uri_to_raster_source"
@@ -52,16 +51,12 @@ case class URIToRasterSource(override val child: Expression, accumulator: Option
   override protected def nullSafeEval(input: Any): Any =  {
     val uriString = input.asInstanceOf[UTF8String].toString
     val uri = URI.create(uriString)
-    val ref = RasterSource(uri, accumulator)
+    val ref = RasterSource(uri)
     RasterSourceUDT.serialize(ref)
   }
 }
 
 object URIToRasterSource {
   def apply(rasterURI: Column): TypedColumn[Any, RasterRef] =
-    new Column(new URIToRasterSource(rasterURI.expr, None)).as[RasterRef]
-  def apply(rasterURI: Column, accumulator: ReadCallback): TypedColumn[Any, RasterRef] =
-    new Column(new URIToRasterSource(rasterURI.expr, Option(accumulator))).as[RasterRef]
-  def apply(rasterURI: Column, accumulator: Option[ReadCallback]): TypedColumn[Any, RasterRef] =
-    new Column(new URIToRasterSource(rasterURI.expr, accumulator)).as[RasterRef]
+    new Column(new URIToRasterSource(rasterURI.expr)).as[RasterRef]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 3e5657f0c..4c9d0a0fd 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -21,8 +21,6 @@
 package astraea.spark.rasterframes.ref
 
 import java.net.URI
-import java.time.ZonedDateTime
-import java.time.format.DateTimeFormatter
 
 import astraea.spark.rasterframes.NOMINAL_TILE_DIMS
 import astraea.spark.rasterframes.model.{TileContext, TileDimensions}
@@ -47,7 +45,6 @@ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
 
 import scala.collection.JavaConverters._
-import scala.util.Try
 
 /**
  * Abstraction over fetching geospatial raster data.
@@ -108,15 +105,6 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def layoutBounds(dims: TileDimensions): Seq[GridBounds] = {
     gridBounds.split(dims.cols, dims.rows).toSeq
-//    val tl = tileLayout(dims)
-//    for {
-//      col <- 0 until tl.layoutCols
-//      row <- 0 until tl.layoutRows
-//      cmin = math.min(col * tl.tileCols, cols)
-//      cmax = math.min((col + 1) * tl.tileCols, cols)
-//      rmin = math.min(row * tl.tileRows, cols)
-//      rmax = math.min((row + 1) * tl.tileRows, rows)
-//    } yield GridBounds(cmin, rmin, cmax, rmax)
   }
 }
 
@@ -127,22 +115,22 @@ object RasterSource extends LazyLogging {
     ExpressionEncoder()
   }
 
-  def apply(source: URI, callback: Option[ReadCallback] = None): RasterSource =
+  def apply(source: URI): RasterSource =
     source.getScheme match {
-      case GDALRasterSource()                        => GDALRasterSource(source, callback)
-      case "http" | "https"                          => HttpGeoTiffRasterSource(source, callback)
-      case "file"                                    => FileGeoTiffRasterSource(source, callback)
+      case GDALRasterSource()                        => GDALRasterSource(source)
+      case "http" | "https"                          => HttpGeoTiffRasterSource(source)
+      case "file"                                    => FileGeoTiffRasterSource(source)
       case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" =>
         // TODO: How can we get the active hadoop configuration
         // TODO: without having to pass it through?
         val config = () => new Configuration()
-        HadoopGeoTiffRasterSource(source, config, callback)
+        HadoopGeoTiffRasterSource(source, config)
       case "s3" =>
         val client = () => S3Client.DEFAULT
-        S3GeoTiffRasterSource(source, client, callback)
+        S3GeoTiffRasterSource(source, client)
       case s if s.startsWith("gdal+") =>
         val cleaned = new URI(source.toASCIIString.replace("gdal+", ""))
-        apply(cleaned, callback)
+        apply(cleaned)
       case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
     }
 
@@ -170,9 +158,6 @@ object RasterSource extends LazyLogging {
         info.noDataValue)
   }
 
-  // According to https://goo.gl/2z8xx9 the GeoTIFF date format is 'YYYY:MM:DD HH:MM:SS'
-  private val dateFormat = DateTimeFormatter.ofPattern("yyyy:MM:dd HH:mm:ss")
-
   trait URIRasterSource { _: RasterSource =>
     def source: URI
 
@@ -202,14 +187,19 @@ object RasterSource extends LazyLogging {
     }
   }
 
-  case class GDALRasterSource(source: URI, callback: Option[ReadCallback])
+  case class GDALRasterSource(source: URI)
       extends RasterSource with URIRasterSource {
     import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 
     @transient
     private lazy val gdal = {
       val cleaned = source.toASCIIString.replace("gdal+", "")
-      VLMRasterSource(cleaned)
+      // VSIPath doesn't like "file:/path..."
+      val tweaked = if (cleaned.matches("^file:/[^/].*"))
+        cleaned.replace("file:", "")
+      else cleaned
+
+      VLMRasterSource(tweaked)
     }
 
     override def crs: CRS = gdal.crs
@@ -230,29 +220,10 @@ object RasterSource extends LazyLogging {
 
     override def rows: Int = gdal.rows
 
-//    override def read(extent: Extent): Raster[MultibandTile] = {
-//      callback.foreach { cb =>
-//        val grid = rasterExtent.gridBoundsFor(extent, clamp = false)
-//        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
-//      }
-//      gdal.read(extent).get
-//    }
-//
-//    override def readAll(): Seq[Raster[MultibandTile]] = {
-//      val grid = gdal.gridBounds
-//
-//      callback.foreach { cb =>
-//        cb.readRange(this, 0, grid.size.toInt * cellType.bytes * bandCount)
-//      }
-//
-//      val tiled = grid.split(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE).toTraversable
-//
-//      gdal.readBounds(tiled).toSeq
-//    }
-
     override def tags: Option[Tags] = Some(Tags(metadata, List.empty))
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
       gdal.readBounds(bounds, bands)
+    }
   }
 
   object GDALRasterSource {
@@ -282,8 +253,6 @@ object RasterSource extends LazyLogging {
 
     def extent: Extent = tiffInfo.extent
 
-    def timestamp: Option[ZonedDateTime] = dateFromMetadata(tiffInfo.tags.headTags)
-
     override def cols: Int = tiffInfo.rasterExtent.cols
 
     override def rows: Int = tiffInfo.rasterExtent.rows
@@ -309,126 +278,33 @@ object RasterSource extends LazyLogging {
         Raster(tile, rasterExtent.extentFor(gb, clamp = true))
       }
     }
-
-//    def read(extent: Extent): Raster[MultibandTile] = {
-//      val info = realInfo
-//      val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
-//      val gt = new MultibandGeoTiff(
-//        geoTiffTile,
-//        info.extent,
-//        info.crs,
-//        info.tags,
-//        info.options,
-//        List.empty
-//      )
-//      gt.crop(extent).raster
-//    }
-//
-//    def readAll(): Seq[Raster[MultibandTile]] = {
-//      val info = realInfo
-//
-//      // Thanks to @pomadchin for showing us how to do this :-)
-//      val windows = info.segmentLayout.listWindows(NOMINAL_TILE_SIZE)
-//      val re = info.rasterExtent
-//
-//      val geotile = GeoTiffReader.geoTiffMultibandTile(info)
-//
-//      val rows = windows.map(gb => {
-//        val tile = geotile.crop(gb)
-//        val extent = re.extentFor(gb, clamp = false)
-//        Raster(tile, extent)
-//      })
-//
-//      rows.toSeq
-//    }
-
   }
 
-  case class FileGeoTiffRasterSource(source: URI, callback: Option[ReadCallback])
+  case class FileGeoTiffRasterSource(source: URI)
       extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
-    protected lazy val rangeReader = {
-      val base = FileRangeReader(source.getPath)
-      // TODO: DRY
-      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
-    }
+    protected lazy val rangeReader = FileRangeReader(source.getPath)
   }
 
   case class HadoopGeoTiffRasterSource(
-    source: URI,
-    config: () => Configuration,
-    callback: Option[ReadCallback])
+    source: URI, config: () => Configuration)
       extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
-    protected lazy val rangeReader = {
-      val base = HdfsRangeReader(new Path(source.getPath), config())
-      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
-    }
+    protected lazy val rangeReader = HdfsRangeReader(new Path(source.getPath), config())
   }
 
   case class S3GeoTiffRasterSource(
-    source: URI,
-    client: () => S3Client,
-    callback: Option[ReadCallback])
+    source: URI, client: () => S3Client)
       extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
-    protected lazy val rangeReader = {
-      val base = S3RangeReader(source, client())
-      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
-    }
+    protected lazy val rangeReader = S3RangeReader(source, client())
   }
 
-  case class HttpGeoTiffRasterSource(source: URI, callback: Option[ReadCallback])
+  case class HttpGeoTiffRasterSource(source: URI)
       extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
 
     @transient
-    protected lazy val rangeReader = {
-      val base = HttpRangeReader(source)
-      callback.map(cb => ReportingRangeReader(base, cb, self)).getOrElse(base)
-    }
-
-    override def timestamp: Option[ZonedDateTime] =
-      dateFromMetadata(tiffInfo.tags.headTags)
-        .orElse {
-          val hrr = rangeReader match {
-            case h: HttpRangeReader                             => h
-            case ReportingRangeReader(h: HttpRangeReader, _, _) => h
-          }
-          hrr.response.headers
-            .get("Last-Modified")
-            .flatMap(_.headOption)
-            .flatMap(s =>
-              Try(ZonedDateTime.parse(s, DateTimeFormatter.RFC_1123_DATE_TIME)).toOption)
-        }
-  }
-
-  private[ref] def dateFromMetadata(meta: Map[String, String]): Option[ZonedDateTime] =
-    meta
-      .get(Tags.TIFFTAG_DATETIME)
-      .flatMap(ds =>
-        Try({
-          logger.debug("Parsing header date: " + ds)
-          ZonedDateTime.parse(ds, dateFormat)
-        }).toOption)
-
-  /** Trait for registering a callback for logging or monitoring range reads.
-   * NB: the callback will be invoked from within a Spark task, and therefore
-   * is serialized along with its closure to executors. */
-  trait ReadCallback extends Serializable {
-    def readRange(source: RasterSource, start: Long, length: Int): Unit
-  }
-
-  private case class ReportingRangeReader(
-    delegate: RangeReader,
-    callback: ReadCallback,
-    parent: RasterSource)
-      extends RangeReader {
-    override def totalLength: Long = delegate.totalLength
-
-    override protected def readClippedRange(start: Long, length: Int): Array[Byte] = {
-      callback.readRange(parent, start, length)
-      delegate.readRange(start, length)
-    }
+    protected lazy val rangeReader = HttpRangeReader(source)
   }
 
   trait URIRasterSourceDebugString { _: RangeReaderRasterSource with URIRasterSource with Product =>
@@ -446,8 +322,6 @@ object RasterSource extends LazyLogging {
       buf.append(crs)
       buf.append(", extent=")
       buf.append(extent)
-      buf.append(", timestamp=")
-      buf.append(timestamp)
       buf.append(")")
       buf.toString
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala b/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
index 58fb62121..ae8bf03f9 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
@@ -36,7 +36,6 @@ import com.esotericsoftware.kryo.Kryo
 class RFKryoRegistrator extends geotrellis.spark.io.kryo.KryoRegistrator {
   override def registerClasses(kryo: Kryo): Unit = {
     super.registerClasses(kryo)
-    kryo.register(classOf[ReadCallback])
     kryo.register(classOf[RasterSource])
     kryo.register(classOf[RasterRef])
     kryo.register(classOf[RasterRefTile])
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala b/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala
deleted file mode 100644
index e83339f2c..000000000
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ReadAccumulator.scala
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package astraea.spark.rasterframes.util
-
-import astraea.spark.rasterframes.ref.RasterSource
-import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
-import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.SparkContext
-import org.apache.spark.util.LongAccumulator
-
-/**
- * Support for keeping counts of read operations from RasterSource-s
- *
- * @since 9/3/18
- */
-case class ReadAccumulator(reads: () ⇒ LongAccumulator, bytes: () ⇒ LongAccumulator) extends ReadCallback {
-  override def readRange(source: RasterSource, start: Long, length: Int): Unit = {
-    reads().add(1)
-    bytes().add(length.toLong)
-  }
-  override def toString: String =
-    s"${productPrefix}(reads=${reads().value}, bytes=${bytes().value})"
-}
-
-object ReadAccumulator extends LazyLogging {
-  def apply(sc: SparkContext, prefix: String): ReadAccumulator = this.synchronized {
-    val reads = sc.longAccumulator(prefix + ".reads")
-    val bytes = sc.longAccumulator(prefix + ".bytes")
-    new ReadAccumulator(() ⇒ reads, () ⇒ bytes)
-  }
-}
\ No newline at end of file
diff --git a/core/src/test/resources/application.conf b/core/src/test/resources/application.conf
index d2283141c..b274441dd 100644
--- a/core/src/test/resources/application.conf
+++ b/core/src/test/resources/application.conf
@@ -2,9 +2,11 @@ gdal {
   settings {
     options {
       // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
-      CPL_DEBUG = "ON"
+      CPL_DEBUG = "OFF"
+      // TIFF_USE_OVR = "NO"
+      // GDAL_TIFF_INTERNAL_MASK = "YES"
     }
     // set this to `false` if CPL_DEBUG is `ON`
-    useExceptions: false
+    useExceptions: true
   }
 }
\ No newline at end of file
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala b/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
index c8d244be9..84d2e3fe2 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
@@ -20,8 +20,6 @@ package astraea.spark.rasterframes
 import java.nio.file.{Files, Paths}
 
 import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.stringEnc
-import astraea.spark.rasterframes.ref.RasterSource
-import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
 import astraea.spark.rasterframes.util.toParquetFriendlyColumnName
 import com.typesafe.scalalogging.LazyLogging
 import org.locationtech.jts.geom.Geometry
@@ -95,16 +93,5 @@ trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
 }
 
 object TestEnvironment {
-  case class ReadMonitor(ignoreHeader: Boolean = true) extends ReadCallback with LazyLogging {
-    var reads: Int = 0
-    var total: Long = 0
-    override def readRange(source: RasterSource, start: Long, length: Int): Unit = {
-      logger.trace(s"Reading $length at $start from $source")
-      // Ignore header reads
-      if(!ignoreHeader || start > 0) reads += 1
-      total += length
-    }
 
-    override def toString: String = s"$productPrefix(reads=$reads, total=$total)"
-  }
 }
\ No newline at end of file
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index 839ae1792..4f7b77563 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.ref
 
-import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.expressions.accessors._
 import astraea.spark.rasterframes.expressions.transformers._
@@ -45,8 +44,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
   }
 
   trait Fixture {
-    val counter = new ReadMonitor(false)
-    val src = RasterSource(remoteCOGSingleband1, Some(counter))
+    val src = RasterSource(remoteCOGSingleband1)
     val fullRaster = RasterRef(src)
     val subExtent = sub(src.extent)
     val subRaster = RasterRef(src, Option(subExtent))
@@ -134,22 +132,18 @@ class RasterRefSpec extends TestEnvironment with TestData {
     it("should delay reading") {
       new Fixture {
         assert(subRaster.cellType === src.cellType)
-        assert(counter.reads === 0)
       }
     }
     it("should support subextents") {
       new Fixture {
         assert(subRaster.cols.toDouble === src.cols * 0.01 +- 2.0)
         assert(subRaster.rows.toDouble === src.rows * 0.01 +- 2.0)
-        assert(counter.reads === 0)
         //subRaster.tile.rescale(0, 255).renderPng().write("target/foo1.png")
       }
     }
     it("should be realizable") {
       new Fixture {
-        assert(counter.reads === 0)
         assert(subRaster.tile.statistics.map(_.dataCells) === Some(subRaster.cols * subRaster.rows))
-         assert(counter.reads > 0)
       }
     }
 
@@ -187,6 +181,5 @@ class RasterRefSpec extends TestEnvironment with TestData {
         assert(refs.count() > 1)
       }
     }
-
   }
 }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 86bd29251..18f89dbf4 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -21,15 +21,11 @@
 
 package astraea.spark.rasterframes.ref
 
-import astraea.spark.rasterframes.TestEnvironment.ReadMonitor
 import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.ref.RasterSource.GDALRasterSource
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
 
-import scala.language.reflectiveCalls
-
 /**
  *
  *
@@ -112,51 +108,6 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     }
   }
 
-  describe("Caching") {
-    val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toUri
-
-    trait Fixture {
-      val counter = ReadMonitor(false)
-      val src = RasterSource(localSrc, Some(counter))
-    }
-
-    it("should cache headers")(new Fixture {
-      val e = src.extent
-      val startReads = counter.reads
-
-      val c = src.crs
-      val e2 = src.extent
-      val ct = src.cellType
-      counter.reads should be(startReads)
-    })
-
-    it("should Spark serialize caching")(new Fixture {
-
-      import spark.implicits._
-
-      val origType = src.getClass
-
-      val e = src.extent
-
-      val df = Seq(src, src, src).toDS.repartition(3)
-      val src2 = df.collect()(1)
-
-      val e2 = src2.extent
-      val ct = src2.cellType
-
-      src2 match {
-        case _: GDALRasterSource => ()
-        case s if s.getClass == origType ⇒
-          val t = s.asInstanceOf[RasterSource { def callback: Option[ReadMonitor] }]
-          t.callback match {
-            case Some(cb: ReadMonitor) ⇒ assert(cb.reads === 1)
-            case o ⇒ fail(s"Expected '$o' to be a ReadMonitor")
-          }
-        case o ⇒ fail(s"Expected '$o' to be FileGeoTiffRasterSource")
-      }
-    })
-  }
-
   describe("RasterSourceToTiles Expression") {
     it("should read all tiles") {
       val src = RasterSource(remoteMODIS)
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
index d37a7fd42..6f7d7e17d 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
@@ -43,7 +43,6 @@ class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeA
   override protected def beforeAll(): Unit = {
     val l8 = spark.read
       .format(L8DataSource.SHORT_NAME)
-      .option(L8DataSource.ACCUMULATORS, true)
       .load()
     l8.createOrReplaceTempView("l8")
     scenes = sql(query).cache()
@@ -58,7 +57,6 @@ class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeA
     it("should count tiles") {
       val l8 = spark.read
         .format(L8DataSource.SHORT_NAME)
-        .option(L8DataSource.ACCUMULATORS, true)
         .option(L8DataSource.USE_TILING, true)
         .load()
       l8.createOrReplaceTempView("l82")
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala
index 9933ec256..9a7e4aab7 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala
@@ -20,7 +20,6 @@
 
 package astraea.spark.rasterframes.experimental.datasource.awspds
 
-import astraea.spark.rasterframes.util.ReadAccumulator
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
 
@@ -34,16 +33,11 @@ class L8DataSource extends DataSourceRegister with RelationProvider {
 
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
     val useTiling = parameters.get(L8DataSource.USE_TILING).exists(_.toBoolean)
-    val accumulators = parameters
-      .get(L8DataSource.ACCUMULATORS)
-      .filter(_.toBoolean)
-      .map(_ ⇒ ReadAccumulator(sqlContext.sparkContext, L8DataSource.SHORT_NAME))
-    L8Relation(sqlContext, useTiling, accumulators)
+    L8Relation(sqlContext, useTiling)
   }
 }
 
 object L8DataSource {
   final val SHORT_NAME = "awsl8"
   final val USE_TILING = "useTiling"
-  final val ACCUMULATORS = "accumulators"
 }
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
index aa2d33353..0672a52d9 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -23,9 +23,8 @@ package astraea.spark.rasterframes.experimental.datasource.awspds
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.experimental.datasource.awspds.L8Relation.Bands
-import astraea.spark.rasterframes.expressions.transformers.{RasterSourceToRasterRefs, URIToRasterSource}
+import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.ref.RasterRef
-import astraea.spark.rasterframes.ref.RasterSource.ReadCallback
 import astraea.spark.rasterframes.rules.SpatialFilters.{Contains, Intersects}
 import astraea.spark.rasterframes.rules._
 import astraea.spark.rasterframes.util._
@@ -41,7 +40,7 @@ import org.apache.spark.sql.{Column, Row, SQLContext}
  *
  * @since 8/21/18
  */
-case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, accumulator: Option[ReadCallback], filters: Seq[Filter] = Seq.empty)
+case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[Filter] = Seq.empty)
   extends BaseRelation with PrunedFilteredScan with SpatialRelationReceiver[L8Relation] with LazyLogging {
   override def schema: StructType = L8Relation.schema
 
@@ -101,7 +100,7 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, accumulator: O
 
     val df = {
       // NB: We assume that `nativeTiling` preserves the band names.
-      val expanded = RasterSourceToRasterRefs(useTiling, bands.map(b ⇒ URIToRasterSource(l8_band_url(b), accumulator).as(b)): _*)
+      val expanded = RasterSourceToRasterRefs(useTiling, bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b)): _*)
       filtered.select(nonTile :+ expanded: _*)
     }
 
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 75617f14d..88fade4bd 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -15,5 +15,6 @@ addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")
 addSbtPlugin("org.spark-packages" % "sbt-spark-package" % "0.2.7-astraea.1")
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.4.1")
 addSbtPlugin("net.vonbuchholtz" % "sbt-dependency-check" % "0.2.10")
+addSbtPlugin("com.github.gseitz" %% "sbt-release" % "1.0.9")
 
 

From 99d8e9d372167201c380415b92c85d6a75347769 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 28 Mar 2019 14:33:35 -0400
Subject: [PATCH 021/380] Replaced all but HDFS RasterSource with
 geotrellis-contrib versions.

---
 .../spark/rasterframes/ref/RasterSource.scala | 161 +++++++++---------
 .../rasterframes/util/RFKryoRegistrator.scala |   7 +-
 2 files changed, 84 insertions(+), 84 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 4c9d0a0fd..516b6f719 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -28,15 +28,16 @@ import astraea.spark.rasterframes.ref.RasterSource.SINGLEBAND
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.util.GeoTiffInfoSupport
 import com.typesafe.scalalogging.LazyLogging
+import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
+import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
+import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, Tags}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
-import geotrellis.spark.io.s3.S3Client
-import geotrellis.spark.io.s3.util.S3RangeReader
 import geotrellis.spark.tiling.LayoutDefinition
-import geotrellis.util.{FileRangeReader, RangeReader}
+import geotrellis.util.RangeReader
 import geotrellis.vector.Extent
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -61,7 +62,7 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def bandCount: Int
 
-  def tags: Option[Tags]
+  def tags: Tags
 
   def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
     readBounds(Seq(bounds), bands).next()
@@ -69,9 +70,8 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
   def read(extent: Extent, bands: Seq[Int] = SINGLEBAND): Raster[MultibandTile] =
     read(rasterExtent.gridBoundsFor(extent, clamp = true), bands)
 
-  def readAll(
-    dims: TileDimensions = NOMINAL_TILE_DIMS,
-    bands: Seq[Int] = SINGLEBAND): Seq[Raster[MultibandTile]] = layoutBounds(dims).map(read(_, bands))
+  def readAll(dims: TileDimensions = NOMINAL_TILE_DIMS, bands: Seq[Int] = SINGLEBAND): Seq[Raster[MultibandTile]] =
+    layoutBounds(dims).map(read(_, bands))
 
   protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]]
 
@@ -110,29 +110,27 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
 object RasterSource extends LazyLogging {
   final val SINGLEBAND = Seq(0)
+  final val EMPTY_TAGS = Tags(Map.empty, List.empty)
+
   implicit def rsEncoder: ExpressionEncoder[RasterSource] = {
     RasterSourceUDT // Makes sure UDT is registered first
     ExpressionEncoder()
   }
 
-  def apply(source: URI): RasterSource =
-    source.getScheme match {
-      case GDALRasterSource()                        => GDALRasterSource(source)
-      case "http" | "https"                          => HttpGeoTiffRasterSource(source)
-      case "file"                                    => FileGeoTiffRasterSource(source)
-      case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" =>
-        // TODO: How can we get the active hadoop configuration
-        // TODO: without having to pass it through?
-        val config = () => new Configuration()
-        HadoopGeoTiffRasterSource(source, config)
-      case "s3" =>
-        val client = () => S3Client.DEFAULT
-        S3GeoTiffRasterSource(source, client)
-      case s if s.startsWith("gdal+") =>
-        val cleaned = new URI(source.toASCIIString.replace("gdal+", ""))
-        apply(cleaned)
-      case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
-    }
+  def apply(source: URI): RasterSource = source.getScheme match {
+    case GDALRasterSource()                        => GDALRasterSource(source)
+    case "file" | "http" | "https" | "s3"          =>
+      DelegatingRasterSource(source, GeoTiffRasterSource(source.toASCIIString))
+    case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" =>
+      // TODO: How can we get the active hadoop configuration
+      // TODO: without having to pass it through?
+      val config = () => new Configuration()
+      HadoopGeoTiffRasterSource(source, config)
+    case s if s.startsWith("gdal+") =>
+      val cleaned = new URI(source.toASCIIString.replace("gdal+", ""))
+      apply(cleaned)
+    case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
+  }
 
   case class SimpleGeoTiffInfo(
     cellType: CellType,
@@ -166,6 +164,28 @@ object RasterSource extends LazyLogging {
     }
   }
 
+  /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
+  case class DelegatingRasterSource(source: URI, delegate: GTRasterSource) extends RasterSource with URIRasterSource {
+    override def cols: Int = delegate.cols
+    override def rows: Int = delegate.rows
+    override def crs: CRS = delegate.crs
+    override def extent: Extent = delegate.extent
+    override def cellType: CellType = delegate.cellType
+    override def bandCount: Int = delegate.bandCount
+    override def tags: Tags = delegate match {
+      case rs: GeoTiffRasterSource => rs.tiff.tags
+      case _                       => EMPTY_TAGS
+    }
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
+      delegate.readBounds(bounds, bands)
+    override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
+      delegate.read(bounds, bands)
+        .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
+    override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
+      delegate.read(extent, bands)
+        .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
+  }
+
   case class InMemoryRasterSource(tile: Tile, extent: Extent, crs: CRS) extends RasterSource {
     def this(prt: ProjectedRasterTile) = this(prt, prt.extent, prt.crs)
 
@@ -177,27 +197,29 @@ object RasterSource extends LazyLogging {
 
     override def bandCount: Int = 1
 
-    override def tags: Option[Tags] = None
+    override def tags: Tags = EMPTY_TAGS
 
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
-      bounds.map(b => {
-        val subext = rasterExtent.extentFor(b)
-        Raster(MultibandTile(tile.crop(b)), subext)
-      }).toIterator
+    override protected def readBounds(
+      bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+      bounds
+        .map(b => {
+          val subext = rasterExtent.extentFor(b)
+          Raster(MultibandTile(tile.crop(b)), subext)
+        })
+        .toIterator
     }
   }
 
-  case class GDALRasterSource(source: URI)
-      extends RasterSource with URIRasterSource {
-    import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
+  case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSource {
 
     @transient
     private lazy val gdal = {
       val cleaned = source.toASCIIString.replace("gdal+", "")
-      // VSIPath doesn't like "file:/path..."
-      val tweaked = if (cleaned.matches("^file:/[^/].*"))
-        cleaned.replace("file:", "")
-      else cleaned
+      // VSIPath doesn't like single slash "file:/path..."
+      val tweaked =
+        if (cleaned.matches("^file:/[^/].*"))
+          cleaned.replace("file:", "")
+        else cleaned
 
       VLMRasterSource(tweaked)
     }
@@ -206,11 +228,12 @@ object RasterSource extends LazyLogging {
 
     override def extent: Extent = gdal.extent
 
-    private def metadata = gdal.dataset
-      .GetMetadata_Dict()
-      .asInstanceOf[java.util.Dictionary[String, String]]
-      .asScala
-      .toMap
+    private def metadata =
+      gdal.dataset
+        .GetMetadata_Dict()
+        .asInstanceOf[java.util.Dictionary[String, String]]
+        .asScala
+        .toMap
 
     override def cellType: CellType = gdal.cellType
 
@@ -220,24 +243,27 @@ object RasterSource extends LazyLogging {
 
     override def rows: Int = gdal.rows
 
-    override def tags: Option[Tags] = Some(Tags(metadata, List.empty))
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+    override def tags: Tags = Tags(metadata, List.empty)
+
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
       gdal.readBounds(bounds, bands)
-    }
   }
 
   object GDALRasterSource {
+
+    /** Determine if we should prefer GDAL for all types. */
     private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
     @transient
     lazy val hasGDAL: Boolean = try {
       org.gdal.gdal.gdal.AllRegister()
       true
-    }
-    catch {
+    } catch {
       case _: UnsatisfiedLinkError =>
         logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
         false
     }
+
+    /** Extractor for determining if a scheme indicates GDAL preference.  */
     def unapply(scheme: String): Boolean = (preferGdal || scheme.startsWith("gdal+")) && hasGDAL
   }
 
@@ -261,52 +287,25 @@ object RasterSource extends LazyLogging {
 
     def bandCount: Int = tiffInfo.bandCount
 
-    override def tags: Option[Tags] = Option(tiffInfo.tags)
-
-    def nativeLayout: Option[TileLayout] = {
-      if (tiffInfo.segmentLayout.isTiled)
-        Some(tiffInfo.segmentLayout.tileLayout)
-      else None
-    }
+    override def tags: Tags = tiffInfo.tags
 
-    override protected def readBounds(bounds: Traversable[GridBounds],
-      bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
       val info = realInfo
       val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
       val intersectingBounds = bounds.flatMap(_.intersection(this)).toSeq
-      geoTiffTile.crop(intersectingBounds, bands.toArray).map { case (gb, tile) =>
-        Raster(tile, rasterExtent.extentFor(gb, clamp = true))
+      geoTiffTile.crop(intersectingBounds, bands.toArray).map {
+        case (gb, tile) =>
+          Raster(tile, rasterExtent.extentFor(gb, clamp = true))
       }
     }
   }
 
-  case class FileGeoTiffRasterSource(source: URI)
-      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
-    @transient
-    protected lazy val rangeReader = FileRangeReader(source.getPath)
-  }
-
-  case class HadoopGeoTiffRasterSource(
-    source: URI, config: () => Configuration)
+  case class HadoopGeoTiffRasterSource(source: URI, config: () => Configuration)
       extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
     @transient
     protected lazy val rangeReader = HdfsRangeReader(new Path(source.getPath), config())
   }
 
-  case class S3GeoTiffRasterSource(
-    source: URI, client: () => S3Client)
-      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
-    @transient
-    protected lazy val rangeReader = S3RangeReader(source, client())
-  }
-
-  case class HttpGeoTiffRasterSource(source: URI)
-      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
-
-    @transient
-    protected lazy val rangeReader = HttpRangeReader(source)
-  }
-
   trait URIRasterSourceDebugString { _: RangeReaderRasterSource with URIRasterSource with Product =>
     def toDebugString: String = {
       val buf = new StringBuilder()
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala b/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
index ae8bf03f9..5f64d9954 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
@@ -39,10 +39,11 @@ class RFKryoRegistrator extends geotrellis.spark.io.kryo.KryoRegistrator {
     kryo.register(classOf[RasterSource])
     kryo.register(classOf[RasterRef])
     kryo.register(classOf[RasterRefTile])
-    kryo.register(classOf[FileGeoTiffRasterSource])
+    kryo.register(classOf[DelegatingRasterSource])
+    kryo.register(classOf[InMemoryRasterSource])
     kryo.register(classOf[HadoopGeoTiffRasterSource])
-    kryo.register(classOf[S3GeoTiffRasterSource])
-    kryo.register(classOf[HttpGeoTiffRasterSource])
+    kryo.register(classOf[GDALRasterSource])
+    kryo.register(classOf[SimpleGeoTiffInfo])
     kryo.register(classOf[geotrellis.raster.io.geotiff.reader.GeoTiffReader.GeoTiffInfo])
   }
 }

From bda1a066248a35955ea6c7e2e57204a0df1e2627 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 1 Apr 2019 11:23:35 -0400
Subject: [PATCH 022/380] Misc refinements and addition of JP2 test.

---
 build.sbt                                     |   2 +-
 .../spark/rasterframes/ref/RasterSource.scala |  72 +++++++++++-------
 core/src/test/resources/B01.jp2               | Bin 0 -> 3759959 bytes
 .../astraea/spark/rasterframes/TestData.scala |  11 +--
 .../rasterframes/ref/RasterSourceSpec.scala   |   7 ++
 5 files changed, 59 insertions(+), 33 deletions(-)
 create mode 100644 core/src/test/resources/B01.jp2

diff --git a/build.sbt b/build.sbt
index 91db148a1..1b28e9604 100644
--- a/build.sbt
+++ b/build.sbt
@@ -34,7 +34,7 @@ lazy val core = project
       "org.locationtech.jts" % "jts-core" % "1.16.0",
       "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
       "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.9.0",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.10.0",
       spark("core").value % Provided,
       spark("mllib").value % Provided,
       spark("sql").value % Provided,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 516b6f719..6083473d1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -117,19 +117,55 @@ object RasterSource extends LazyLogging {
     ExpressionEncoder()
   }
 
-  def apply(source: URI): RasterSource = source.getScheme match {
-    case GDALRasterSource()                        => GDALRasterSource(source)
-    case "file" | "http" | "https" | "s3"          =>
-      DelegatingRasterSource(source, GeoTiffRasterSource(source.toASCIIString))
-    case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" =>
+  def apply(source: URI): RasterSource = source match {
+    case IsGDAL()                        => GDALRasterSource(source)
+    case IsHadoopGeoTiff() =>
       // TODO: How can we get the active hadoop configuration
       // TODO: without having to pass it through?
       val config = () => new Configuration()
       HadoopGeoTiffRasterSource(source, config)
-    case s if s.startsWith("gdal+") =>
-      val cleaned = new URI(source.toASCIIString.replace("gdal+", ""))
-      apply(cleaned)
-    case s => throw new UnsupportedOperationException(s"Scheme '$s' not supported")
+    case IsDefaultGeoTiff() =>
+      DelegatingRasterSource(source, GeoTiffRasterSource(source.toASCIIString))
+    case s => throw new UnsupportedOperationException(s"Reading '$s' not supported")
+  }
+
+  object IsGDAL {
+    /** Determine if we should prefer GDAL for all types. */
+    private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
+    @transient
+    lazy val hasGDAL: Boolean = try {
+      org.gdal.gdal.gdal.AllRegister()
+      true
+    } catch {
+      case _: UnsatisfiedLinkError =>
+        logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
+        false
+    }
+
+    val gdalOnlyExtensions = Seq(".jp2")
+    def gdalOnly(source: URI): Boolean =
+      if(gdalOnlyExtensions.exists(source.getPath.endsWith)) {
+        require(hasGDAL, s"Can only read $source if GDAL is available")
+        true
+      } else false
+
+    /** Extractor for determining if a scheme indicates GDAL preference.  */
+    def unapply(source: URI): Boolean =
+      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal+")) && hasGDAL)
+  }
+
+  object IsDefaultGeoTiff {
+    def unapply(source: URI): Boolean = source.getScheme match {
+      case "file" | "http" | "https" | "s3" => true
+      case _                                => false
+    }
+  }
+
+  object IsHadoopGeoTiff {
+    def unapply(source: URI): Boolean = source.getScheme match {
+      case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" => true
+      case _                                         => false
+    }
   }
 
   case class SimpleGeoTiffInfo(
@@ -249,24 +285,6 @@ object RasterSource extends LazyLogging {
       gdal.readBounds(bounds, bands)
   }
 
-  object GDALRasterSource {
-
-    /** Determine if we should prefer GDAL for all types. */
-    private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
-    @transient
-    lazy val hasGDAL: Boolean = try {
-      org.gdal.gdal.gdal.AllRegister()
-      true
-    } catch {
-      case _: UnsatisfiedLinkError =>
-        logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
-        false
-    }
-
-    /** Extractor for determining if a scheme indicates GDAL preference.  */
-    def unapply(scheme: String): Boolean = (preferGdal || scheme.startsWith("gdal+")) && hasGDAL
-  }
-
   trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
     protected def rangeReader: RangeReader
 
diff --git a/core/src/test/resources/B01.jp2 b/core/src/test/resources/B01.jp2
new file mode 100644
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diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index 0c4045f8a..01911d884 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -18,7 +18,7 @@
 
 package astraea.spark.rasterframes
 
-import java.net.URI
+import java.net.{URI, URL}
 import java.nio.file.Paths
 import java.time.ZonedDateTime
 
@@ -122,12 +122,13 @@ trait TestData {
   }
 
   private val baseCOG = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_%s.TIF"
-  lazy val remoteCOGSingleband1 = URI.create(baseCOG.format("B1"))
-  lazy val remoteCOGSingleband2 = URI.create(baseCOG.format("B2"))
+  lazy val remoteCOGSingleband1: URI = URI.create(baseCOG.format("B1"))
+  lazy val remoteCOGSingleband2: URI = URI.create(baseCOG.format("B2"))
 
-  lazy val remoteCOGMultiband =  URI.create("https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
+  lazy val remoteCOGMultiband: URI =  URI.create("https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
 
-  lazy val remoteMODIS = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
+  lazy val remoteMODIS: URI = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
+  lazy val localSentinel: URL = getClass.getResource("/B01.jp2")
 
   object JTS {
     val fact = new GeometryFactory()
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 18f89dbf4..ace7df349 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -131,4 +131,11 @@ class RasterSourceSpec extends TestEnvironment with TestData {
 //      }
     }
   }
+
+  describe("GDAL support") {
+    it("should read JPG2000 scene") {
+      val src = RasterSource(localSentinel.toURI)
+      src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
+    }
+  }
 }

From 4110ae8b008f929f04b29bbfd5880c7ce48b5ab1 Mon Sep 17 00:00:00 2001
From: Phil Varner <pvarner@astrea.io>
Date: Mon, 1 Apr 2019 17:21:59 -0400
Subject: [PATCH 023/380] CircleCI build configuration (+5 squashed commits)
 Squashed commits: [1dd3d111] remove circleci files [cc2a261e] circleci fixes
 [9b5bcaef] add dockerhub auth for circlci container [87161ea0] add s22s to
 circleci container name [c73780c5] add gdal to circleci container

---
 .../scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index ace7df349..0f7738fb9 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -133,7 +133,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
   }
 
   describe("GDAL support") {
-    it("should read JPG2000 scene") {
+    it("should read JPEG2000 scene") {
       val src = RasterSource(localSentinel.toURI)
       src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
     }

From c6c922791432baedc7844908341867b6e85c798a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Apr 2019 11:28:07 -0400
Subject: [PATCH 024/380] Added better caching of header info in RasterSource.

---
 .../spark/rasterframes/ref/RasterSource.scala | 36 ++++++++++++++-----
 1 file changed, 27 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 6083473d1..2671876f2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -169,12 +169,13 @@ object RasterSource extends LazyLogging {
   }
 
   case class SimpleGeoTiffInfo(
+    cols: Int,
+    rows: Int,
     cellType: CellType,
     extent: Extent,
     rasterExtent: RasterExtent,
     crs: CRS,
     tags: Tags,
-    segmentLayout: GeoTiffSegmentLayout,
     bandCount: Int,
     noDataValue: Option[Double]
   )
@@ -182,12 +183,13 @@ object RasterSource extends LazyLogging {
   object SimpleGeoTiffInfo {
     def apply(info: GeoTiffReader.GeoTiffInfo): SimpleGeoTiffInfo =
       SimpleGeoTiffInfo(
+        info.segmentLayout.totalCols,
+        info.segmentLayout.totalRows,
         info.cellType,
         info.extent,
         info.rasterExtent,
         info.crs,
         info.tags,
-        info.segmentLayout,
         info.bandCount,
         info.noDataValue)
   }
@@ -202,16 +204,32 @@ object RasterSource extends LazyLogging {
 
   /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
   case class DelegatingRasterSource(source: URI, delegate: GTRasterSource) extends RasterSource with URIRasterSource {
-    override def cols: Int = delegate.cols
-    override def rows: Int = delegate.rows
-    override def crs: CRS = delegate.crs
-    override def extent: Extent = delegate.extent
-    override def cellType: CellType = delegate.cellType
-    override def bandCount: Int = delegate.bandCount
-    override def tags: Tags = delegate match {
+    // This helps reduce header reads between serializations
+    lazy val info: SimpleGeoTiffInfo = {
+      SimpleGeoTiffInfo(
+        delegate.cols,
+        delegate.rows,
+        delegate.cellType,
+        delegate.extent,
+        delegate.rasterExtent,
+        delegate.crs,
+        fetchTags,
+        delegate.bandCount,
+        None
+      )
+    }
+
+    override def cols: Int = info.cols
+    override def rows: Int = info.rows
+    override def crs: CRS = info.crs
+    override def extent: Extent = info.extent
+    override def cellType: CellType = info.cellType
+    override def bandCount: Int = info.bandCount
+    private def fetchTags: Tags = delegate match {
       case rs: GeoTiffRasterSource => rs.tiff.tags
       case _                       => EMPTY_TAGS
     }
+    override def tags: Tags = info.tags
     override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
       delegate.readBounds(bounds, bands)
     override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =

From 94c968be4fa2473e4b940979908d567cd9daea9a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Apr 2019 13:03:37 -0400
Subject: [PATCH 025/380] Experiment in delayed evaluation/parsing of CRS
 specification.

---
 .../rasterframes/encoders/CRSEncoder.scala    |  4 +-
 .../encoders/StandardSerializers.scala        |  4 +-
 .../rasterframes/functions/package.scala      |  6 +--
 .../spark/rasterframes/model/LazyCRS.scala    | 52 +++++++++++++++++++
 .../spark/rasterframes/ref/RasterSource.scala |  2 +-
 .../spark/rasterframes/util/package.scala     | 12 +----
 .../spark/rasterframes/py/PyRFContext.scala   |  6 +--
 7 files changed, 64 insertions(+), 22 deletions(-)
 create mode 100644 core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala

diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala
index b6a188d71..d043188cd 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala
@@ -19,7 +19,7 @@
 
 package astraea.spark.rasterframes.encoders
 
-import astraea.spark.rasterframes.util.CRSParser
+import astraea.spark.rasterframes.model.LazyCRS
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 
@@ -33,5 +33,5 @@ object CRSEncoder {
     "crsProj4", "toProj4String", (CRSEncoder.getClass, "fromString")
   )
   // Not sure why this delegate is necessary, but doGenCode fails without it.
-  def fromString(str: String): CRS = CRSParser(str)
+  def fromString(str: String): CRS = LazyCRS(str)
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
index 11a236b10..2d7aee8ad 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
@@ -21,7 +21,7 @@
 
 package astraea.spark.rasterframes.encoders
 import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
-import astraea.spark.rasterframes.util.CRSParser
+import astraea.spark.rasterframes.model.LazyCRS
 import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
 import geotrellis.raster._
@@ -77,7 +77,7 @@ trait StandardSerializers {
       )
     )
     override def from[R](row: R, io: CatalystIO[R]): CRS =
-      CRSParser(io.getString(row, 0))
+      LazyCRS(io.getString(row, 0))
   }
 
   implicit val cellTypeSerializer: CatalystSerializer[CellType] = new CatalystSerializer[CellType] {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
index 0ff2be2ee..a5c4547d7 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
@@ -16,7 +16,7 @@
 package astraea.spark.rasterframes
 
 import astraea.spark.rasterframes.jts.ReprojectionTransformer
-import astraea.spark.rasterframes.util.CRSParser
+import astraea.spark.rasterframes.model.LazyCRS
 import geotrellis.raster.{Tile, _}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.SQLContext
@@ -126,8 +126,8 @@ package object functions {
   /** Reporjects a geometry column from one CRS to another, where CRS are defined in Proj4 format. */
   private[rasterframes] val reprojectGeometryCRSName: (Geometry, String, String) ⇒ Geometry =
     (sourceGeom, srcName, dstName) ⇒ {
-      val src = CRSParser(srcName)
-      val dst = CRSParser(dstName)
+      val src = LazyCRS(srcName)
+      val dst = LazyCRS(dstName)
       val trans = new ReprojectionTransformer(src, dst)
       trans.transform(sourceGeom)
     }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala b/core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala
new file mode 100644
index 000000000..5ff441d09
--- /dev/null
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala
@@ -0,0 +1,52 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.model
+import astraea.spark.rasterframes.model.LazyCRS.EncodedCRS
+import geotrellis.proj4.CRS
+import org.locationtech.proj4j.CoordinateReferenceSystem
+
+class LazyCRS(encoded: EncodedCRS) extends CRS {
+  private lazy val delegate = LazyCRS.mapper(encoded)
+  override def proj4jCrs: CoordinateReferenceSystem = delegate.proj4jCrs
+  override def toProj4String: String =
+    if (encoded.startsWith("+proj")) encoded
+    else delegate.toProj4String
+}
+
+object LazyCRS {
+  trait ValidatedCRS
+  type EncodedCRS = String with ValidatedCRS
+
+  private val mapper: PartialFunction[String, CRS] = {
+    case e if e.toUpperCase().startsWith("EPSG")   => CRS.fromName(e) //not case-sensitive
+    case p if p.startsWith("+proj")                => CRS.fromString(p) // case sensitive
+    case w if w.toUpperCase().startsWith("GEOGCS") => CRS.fromWKT(w) //only case-sensitive inside double quotes
+  }
+
+  def apply(value: String): CRS = {
+    if (mapper.isDefinedAt(value)) {
+      new LazyCRS(value.asInstanceOf[EncodedCRS])
+    }
+    else throw new IllegalArgumentException(
+      "crs string must be either EPSG code, +proj string, or OGC WKT")
+  }
+}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 2671876f2..841bcffe7 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -33,8 +33,8 @@ import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
 import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
 import geotrellis.proj4.CRS
 import geotrellis.raster._
+import geotrellis.raster.io.geotiff.Tags
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.io.geotiff.{GeoTiffSegmentLayout, Tags}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.util.RangeReader
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
index b0e50825b..eef449c76 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
@@ -20,7 +20,6 @@
 package astraea.spark.rasterframes
 
 import com.typesafe.scalalogging.Logger
-import geotrellis.proj4.CRS
 import geotrellis.raster.crop.TileCropMethods
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
@@ -92,16 +91,7 @@ package object util {
   def opName(op: LocalTileBinaryOp) =
     op.getClass.getSimpleName.replace("$", "").toLowerCase
 
-  object CRSParser {
-    def apply(value: String): CRS = {
-      value match {
-        case e if e.toUpperCase().startsWith("EPSG") => CRS.fromName(e)  //not case-sensitive
-        case p if p.startsWith("+proj") => CRS.fromString(p)  // case sensitive
-        case w if w.toUpperCase().startsWith("GEOGCS") => CRS.fromWKT(w)  //only case-sensitive inside double quotes
-        case _ ⇒ throw new IllegalArgumentException("crs string must be either EPSG code, +proj string, or OGC WKT")
-      }
-    }
-  }
+
 
   implicit class WithCombine[T](left: Option[T]) {
     def combine[A, R >: A](a: A)(f: (T, A) ⇒ R): R = left.map(f(_, a)).getOrElse(a)
diff --git a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
index d4a8967c0..2d37ec215 100644
--- a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
@@ -19,7 +19,7 @@
 package astraea.spark.rasterframes.py
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.util.CRSParser
+import astraea.spark.rasterframes.model.LazyCRS
 import org.locationtech.jts.geom.Geometry
 import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
 import geotrellis.spark.io._
@@ -193,8 +193,8 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   def withSpatialIndex(df: DataFrame): RasterFrame = df.asRF.withSpatialIndex()
 
   def reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
-    val src = CRSParser(srcName)
-    val dst = CRSParser(dstName)
+    val src = LazyCRS(srcName)
+    val dst = LazyCRS(dstName)
     reproject_geometry(geometryCol, src, dst)
   }
 

From 2cc3570f27035cefcfe376e400794a6ed6b42a66 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 3 Apr 2019 16:24:12 -0400
Subject: [PATCH 026/380] Upgraded to latest geotrellis-contrib-gdal.

---
 build.sbt                                     |  3 +-
 .../spark/rasterframes/ref/RasterSource.scala | 30 +++++++++++--------
 .../astraea/spark/rasterframes/TestData.scala |  1 +
 .../rasterframes/ref/RasterSourceSpec.scala   |  6 ++--
 4 files changed, 23 insertions(+), 17 deletions(-)

diff --git a/build.sbt b/build.sbt
index 1b28e9604..5f195bbca 100644
--- a/build.sbt
+++ b/build.sbt
@@ -34,7 +34,8 @@ lazy val core = project
       "org.locationtech.jts" % "jts-core" % "1.16.0",
       "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
       "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.10.0",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.10.2",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "0.10.2",
       spark("core").value % Provided,
       spark("mllib").value % Provided,
       spark("sql").value % Provided,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 841bcffe7..b85dadcc9 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -27,6 +27,7 @@ import astraea.spark.rasterframes.model.{TileContext, TileDimensions}
 import astraea.spark.rasterframes.ref.RasterSource.SINGLEBAND
 import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.util.GeoTiffInfoSupport
+import com.azavea.gdal.GDALWarp
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
@@ -45,8 +46,6 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
 
-import scala.collection.JavaConverters._
-
 /**
  * Abstraction over fetching geospatial raster data.
  *
@@ -125,7 +124,7 @@ object RasterSource extends LazyLogging {
       val config = () => new Configuration()
       HadoopGeoTiffRasterSource(source, config)
     case IsDefaultGeoTiff() =>
-      DelegatingRasterSource(source, GeoTiffRasterSource(source.toASCIIString))
+      DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))
     case s => throw new UnsupportedOperationException(s"Reading '$s' not supported")
   }
 
@@ -134,7 +133,7 @@ object RasterSource extends LazyLogging {
     private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
     @transient
     lazy val hasGDAL: Boolean = try {
-      org.gdal.gdal.gdal.AllRegister()
+      val _ = new GDALWarp()
       true
     } catch {
       case _: UnsatisfiedLinkError =>
@@ -142,9 +141,9 @@ object RasterSource extends LazyLogging {
         false
     }
 
-    val gdalOnlyExtensions = Seq(".jp2")
+    val gdalOnlyExtensions = Seq(".jp2", ".mrf", ".hdf")
     def gdalOnly(source: URI): Boolean =
-      if(gdalOnlyExtensions.exists(source.getPath.endsWith)) {
+      if(gdalOnlyExtensions.exists(source.getPath.toLowerCase.endsWith)) {
         require(hasGDAL, s"Can only read $source if GDAL is available")
         true
       } else false
@@ -203,7 +202,17 @@ object RasterSource extends LazyLogging {
   }
 
   /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
-  case class DelegatingRasterSource(source: URI, delegate: GTRasterSource) extends RasterSource with URIRasterSource {
+  case class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
+    @transient
+    lazy val delegate = delegateBuilder()
+
+    // Bad, bad, bad?
+    override def equals(obj: Any): Boolean = obj match {
+      case drs: DelegatingRasterSource => drs.source == source
+      case _ => false
+    }
+    override def hashCode(): Int = source.hashCode()
+
     // This helps reduce header reads between serializations
     lazy val info: SimpleGeoTiffInfo = {
       SimpleGeoTiffInfo(
@@ -282,12 +291,7 @@ object RasterSource extends LazyLogging {
 
     override def extent: Extent = gdal.extent
 
-    private def metadata =
-      gdal.dataset
-        .GetMetadata_Dict()
-        .asInstanceOf[java.util.Dictionary[String, String]]
-        .asScala
-        .toMap
+    private def metadata = Map.empty[String, String]
 
     override def cellType: CellType = gdal.cellType
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index 01911d884..8a208c374 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -128,6 +128,7 @@ trait TestData {
   lazy val remoteCOGMultiband: URI =  URI.create("https://s3-us-west-2.amazonaws.com/radiant-nasa-iserv/2014/02/14/IP0201402141023382027S03100E/IP0201402141023382027S03100E-COG.tif")
 
   lazy val remoteMODIS: URI = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
+  lazy val remoteL8: URI = URI.create("https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/017/033/LC08_L1TP_017033_20181010_20181030_01_T1/LC08_L1TP_017033_20181010_20181030_01_T1_B4.TIF")
   lazy val localSentinel: URL = getClass.getResource("/B01.jp2")
 
   object JTS {
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 0f7738fb9..83eb4f729 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -45,17 +45,17 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     }
     val rs = RasterSource(getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI)
     it("should provide a tile layout") {
-      val layout = rs.tileLayout(TileDimensions(64, 64))
+      val layout = rs.tileLayout(TileDimensions(62, 61))
       layout.totalCols shouldBe >= (rs.cols.toLong)
       layout.totalRows shouldBe >= (rs.rows.toLong)
     }
     it("should compute nominal tile layout bounds") {
-      val bounds = rs.layoutBounds(TileDimensions(64, 64))
+      val bounds = rs.layoutBounds(TileDimensions(65, 60))
       val agg = bounds.reduce(_ combine _)
       agg should be (rs.gridBounds)
     }
     it("should compute nominal tile layout extents") {
-      val extents = rs.layoutExtents(TileDimensions(64, 64))
+      val extents = rs.layoutExtents(TileDimensions(63, 63))
       val agg = extents.reduce(_ combine _)
       agg should be (rs.extent)
     }

From 98bfe915904f8c9a776ea3ee02d3050528316348 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 4 Apr 2019 11:41:58 -0400
Subject: [PATCH 027/380] Moved JP2 test to integration test configuration.

---
 .../astraea/spark/rasterframes/ref/RasterSourceIT.scala    | 7 +++++++
 .../astraea/spark/rasterframes/ref/RasterSourceSpec.scala  | 7 -------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
index d5283ae2d..e32a8caa7 100644
--- a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
@@ -67,4 +67,11 @@ class RasterSourceIT extends TestEnvironment with TestData {
       }
     }
   }
+
+  describe("GDAL support") {
+    it("should read JPEG2000 scene") {
+      val src = RasterSource(localSentinel.toURI)
+      src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
+    }
+  }
 }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 83eb4f729..141eac48f 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -131,11 +131,4 @@ class RasterSourceSpec extends TestEnvironment with TestData {
 //      }
     }
   }
-
-  describe("GDAL support") {
-    it("should read JPEG2000 scene") {
-      val src = RasterSource(localSentinel.toURI)
-      src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
-    }
-  }
 }

From c68748e4321bc0b0cc06489196fe77c855685292 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 5 Apr 2019 15:14:27 -0400
Subject: [PATCH 028/380] Renamed `withBounds` to `withGeometry`. Added
 `withExtent`.

---
 .../spark/rasterframes/StandardColumns.scala  |  5 ++--
 .../extensions/RFSpatialColumnMethods.scala   | 30 +++++++++++++------
 .../rasterframes/util/debug/package.scala     |  2 +-
 .../rasterframes/GeometryOperationsSpec.scala |  2 +-
 .../astraea/spark/rasterframes/JTSSpec.scala  | 30 +++++++++----------
 .../spark/rasterframes/SpatialKeySpec.scala   |  2 +-
 .../geotiff/GeoTiffCollectionRelation.scala   |  2 +-
 .../geotrellis/GeoTrellisRelation.scala       |  2 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala | 26 ++++++++--------
 docs/src/main/tut/release-notes.md            |  2 ++
 .../datasource/awspds/PDSFields.scala         |  2 +-
 .../spark/rasterframes/py/PyRFContext.scala   |  2 +-
 12 files changed, 60 insertions(+), 47 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
index d08df5d3c..32c380c05 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
@@ -25,10 +25,9 @@ trait StandardColumns {
   /** Default RasterFrame timestamp column name */
   val TIMESTAMP_COLUMN = col("timestamp").as[Timestamp]
 
-
-  /** Default RasterFrame column name for an tile bounds value. */
+  /** Default RasterFrame column name for an tile extent as geometry value. */
   // This is a `def` because `PolygonUDT` needs to be initialized first.
-  def BOUNDS_COLUMN = col("bounds").as[jtsPolygon]
+  def GEOMETRY_COLUMN = col("geometry").as[jtsPolygon]
 
   /** Default RasterFrame column name for the center coordinates of the tile's bounds. */
   // This is a `def` because `PointUDT` needs to be initialized first.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
index 71204c8ec..46ee83205 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
@@ -21,13 +21,14 @@ package astraea.spark.rasterframes.extensions
 
 import astraea.spark.rasterframes.util._
 import astraea.spark.rasterframes.{RasterFrame, StandardColumns}
-import org.locationtech.jts.geom.{Point, Polygon}
+import org.locationtech.jts.geom.Point
 import geotrellis.proj4.LatLng
 import geotrellis.spark.SpatialKey
 import geotrellis.spark.tiling.MapKeyTransform
 import geotrellis.util.MethodExtensions
+import geotrellis.vector.Extent
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.functions.{asc, udf ⇒ sparkUdf}
+import org.apache.spark.sql.functions.{asc, udf => sparkUdf}
 import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 import org.locationtech.geomesa.curve.Z2SFC
 
@@ -43,28 +44,39 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
   /** Returns the key-space to map-space coordinate transform. */
   def mapTransform: MapKeyTransform = self.tileLayerMetadata.merge.mapTransform
 
-  private def keyCol2Bounds: Row ⇒ Polygon = {
+  private def keyCol2Extent: Row ⇒ Extent = {
     val transform = self.sparkSession.sparkContext.broadcast(mapTransform)
-    (r: Row) ⇒ transform.value.keyToExtent(SpatialKey(r.getInt(0), r.getInt(1))).jtsGeom
+    r ⇒ transform.value.keyToExtent(SpatialKey(r.getInt(0), r.getInt(1)))
   }
 
   private def keyCol2LatLng: Row ⇒ (Double, Double) = {
     val transform = self.sparkSession.sparkContext.broadcast(mapTransform)
     val crs = self.tileLayerMetadata.merge.crs
-    (r: Row) ⇒ {
+    r ⇒ {
       val center = transform.value.keyToExtent(SpatialKey(r.getInt(0), r.getInt(1))).center.reproject(crs, LatLng)
       (center.x, center.y)
     }
   }
 
+  /**
+    * Append a column containing the extent of the row's spatial key.
+    * Coordinates are in native CRS.
+    * @param colName name of column to append. Defaults to "extent"
+    * @return updated RasterFrame
+    */
+  def withExtent(colName: String = EXTENT_COLUMN.columnName): RasterFrame = {
+    val key2Extent = sparkUdf(keyCol2Extent)
+    self.withColumn(colName, key2Extent(self.spatialKeyColumn)).certify
+  }
+
   /**
    * Append a column containing the bounds of the row's spatial key.
    * Coordinates are in native CRS.
-   * @param colName name of column to append. Defaults to "bounds"
+   * @param colName name of column to append. Defaults to "geometry"
    * @return updated RasterFrame
    */
-  def withBounds(colName: String = BOUNDS_COLUMN.columnName): RasterFrame = {
-    val key2Bounds = sparkUdf(keyCol2Bounds)
+  def withGeometry(colName: String = GEOMETRY_COLUMN.columnName): RasterFrame = {
+    val key2Bounds = sparkUdf(keyCol2Extent andThen (_.jtsGeom))
     self.withColumn(colName, key2Bounds(self.spatialKeyColumn)).certify
   }
 
@@ -75,7 +87,7 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
    * @return updated RasterFrame
    */
   def withCenter(colName: String = CENTER_COLUMN.columnName): RasterFrame = {
-    val key2Center = sparkUdf(keyCol2Bounds andThen (_.getCentroid))
+    val key2Center = sparkUdf(keyCol2Extent andThen (_.center.jtsGeom))
     self.withColumn(colName, key2Center(self.spatialKeyColumn).as[Point]).certify
   }
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala b/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala
index 53b4b6aee..a7703449a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala
@@ -46,7 +46,7 @@ package object debug {
       import spray.json.DefaultJsonProtocol._
 
       val features = self
-        .select(BOUNDS_COLUMN, SPATIAL_KEY_COLUMN)
+        .select(GEOMETRY_COLUMN, SPATIAL_KEY_COLUMN)
         .collect()
         .map{ case (p, s) ⇒ Feature(Geometry(p).reproject(self.crs, LatLng), Map("col" -> s.col, "row" -> s.row)) }
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
index 28d0bcb94..d885009aa 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
@@ -44,7 +44,7 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
   describe("Geometery operations") {
     import spark.implicits._
     it("should rasterize geometry") {
-      val rf = l8Sample(1).projectedRaster.toRF.withBounds()
+      val rf = l8Sample(1).projectedRaster.toRF.withGeometry()
 
       val features = geoJson.parseGeoJson[JsonFeatureCollection].getAllPolygonFeatures[JsObject]()
       val df = features.map(f ⇒ (
diff --git a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
index 0580e5ba1..15fac8010 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
@@ -30,7 +30,7 @@ import geotrellis.vector.{Point ⇒ GTPoint}
  */
 class JTSSpec extends TestEnvironment with TestData with StandardColumns {
   describe("JTS interop") {
-    val rf = l8Sample(1).projectedRaster.toRF(10, 10).withBounds()
+    val rf = l8Sample(1).projectedRaster.toRF(10, 10).withGeometry()
     it("should allow joining and filtering of tiles based on points") {
       import spark.implicits._
 
@@ -43,27 +43,27 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
       val locs = coords.toDF("id", "point")
       withClue("join with point column") {
-        assert(rf.join(locs, st_contains(BOUNDS_COLUMN, $"point")).count === coords.length)
-        assert(rf.join(locs, st_intersects(BOUNDS_COLUMN, $"point")).count === coords.length)
+        assert(rf.join(locs, st_contains(GEOMETRY_COLUMN, $"point")).count === coords.length)
+        assert(rf.join(locs, st_intersects(GEOMETRY_COLUMN, $"point")).count === coords.length)
       }
 
       withClue("point literal") {
         val point = coords.head._2
-        assert(rf.filter(st_contains(BOUNDS_COLUMN, geomLit(point))).count === 1)
-        assert(rf.filter(st_intersects(BOUNDS_COLUMN, geomLit(point))).count === 1)
-        assert(rf.filter(BOUNDS_COLUMN intersects point).count === 1)
-        assert(rf.filter(BOUNDS_COLUMN intersects GTPoint(point)).count === 1)
-        assert(rf.filter(BOUNDS_COLUMN containsGeom point).count === 1)
+        assert(rf.filter(st_contains(GEOMETRY_COLUMN, geomLit(point))).count === 1)
+        assert(rf.filter(st_intersects(GEOMETRY_COLUMN, geomLit(point))).count === 1)
+        assert(rf.filter(GEOMETRY_COLUMN intersects point).count === 1)
+        assert(rf.filter(GEOMETRY_COLUMN intersects GTPoint(point)).count === 1)
+        assert(rf.filter(GEOMETRY_COLUMN containsGeom point).count === 1)
       }
 
       withClue("exercise predicates") {
         val point = geomLit(coords.head._2)
-        assert(rf.filter(st_covers(BOUNDS_COLUMN, point)).count === 1)
-        assert(rf.filter(st_crosses(BOUNDS_COLUMN, point)).count === 0)
-        assert(rf.filter(st_disjoint(BOUNDS_COLUMN, point)).count === rf.count - 1)
-        assert(rf.filter(st_overlaps(BOUNDS_COLUMN, point)).count === 0)
-        assert(rf.filter(st_touches(BOUNDS_COLUMN, point)).count === 0)
-        assert(rf.filter(st_within(BOUNDS_COLUMN, point)).count === 0)
+        assert(rf.filter(st_covers(GEOMETRY_COLUMN, point)).count === 1)
+        assert(rf.filter(st_crosses(GEOMETRY_COLUMN, point)).count === 0)
+        assert(rf.filter(st_disjoint(GEOMETRY_COLUMN, point)).count === rf.count - 1)
+        assert(rf.filter(st_overlaps(GEOMETRY_COLUMN, point)).count === 0)
+        assert(rf.filter(st_touches(GEOMETRY_COLUMN, point)).count === 0)
+        assert(rf.filter(st_within(GEOMETRY_COLUMN, point)).count === 0)
       }
     }
 
@@ -80,7 +80,7 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
     it("should provide a means of getting a bounding box") {
       import spark.implicits._
-      val boxed = rf.select(BOUNDS_COLUMN, envelope(BOUNDS_COLUMN) as "env")
+      val boxed = rf.select(GEOMETRY_COLUMN, envelope(GEOMETRY_COLUMN) as "env")
       assert(boxed.select($"env".as[Envelope]).first.getArea > 0)
       assert(boxed.toDF("bounds", "bbox").select("bbox.*").schema.length === 4)
     }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
index 900ad1bb4..14b55db90 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
@@ -41,7 +41,7 @@ class SpatialKeySpec extends TestEnvironment with TestData {
 
     it("should add an extent column") {
       val expected = raster.extent.jtsGeom
-      val result = rf.withBounds().select($"bounds".as[Polygon]).first
+      val result = rf.withGeometry().select($"bounds".as[Polygon]).first
       assert(result === expected)
     }
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 2f69d4425..e0215f5f7 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -86,7 +86,7 @@ object GeoTiffCollectionRelation {
   object Cols {
     lazy val PATH = "path"
     lazy val CRS = "crs"
-    lazy val EX = BOUNDS_COLUMN.columnName
+    lazy val EX = GEOMETRY_COLUMN.columnName
     lazy val TL = TILE_COLUMN.columnName
   }
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
index 5f5cb6de5..8a171af4e 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
@@ -349,6 +349,6 @@ object GeoTrellisRelation {
     lazy val TS = TIMESTAMP_COLUMN.columnName
     lazy val TL = TILE_COLUMN.columnName
     lazy val TF = TILE_FEATURE_DATA_COLUMN.columnName
-    lazy val EX = BOUNDS_COLUMN.columnName
+    lazy val EX = GEOMETRY_COLUMN.columnName
   }
 }
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 009382639..f788d0a8a 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -151,7 +151,7 @@ class GeoTrellisDataSourceSpec
     }
 
     it("should invoke Encoder[Extent]") {
-      val df = layerReader.loadRF(layer).withBounds()
+      val df = layerReader.loadRF(layer).withGeometry()
       assert(df.count > 0)
       assert(df.first.length === 5)
       assert(df.first.getAs[Extent](2) !== null)
@@ -285,7 +285,7 @@ class GeoTrellisDataSourceSpec
     it("should support extent against a geometry literal") {
       val df: DataFrame = layerReader
         .loadRF(layer)
-        .where(BOUNDS_COLUMN intersects pt1)
+        .where(GEOMETRY_COLUMN intersects pt1)
 
       assert(numFilters(df) === 1)
 
@@ -296,7 +296,7 @@ class GeoTrellisDataSourceSpec
     it("should support query with multiple geometry types") {
       // Mostly just testing that these evaluate without catalyst type errors.
       forEvery(JTS.all) { g ⇒
-        val query = layerReader.loadRF(layer).where(BOUNDS_COLUMN.intersects(g))
+        val query = layerReader.loadRF(layer).where(GEOMETRY_COLUMN.intersects(g))
           .persist(StorageLevel.OFF_HEAP)
         assert(query.count() === 0)
       }
@@ -309,7 +309,7 @@ class GeoTrellisDataSourceSpec
 
       val df = layerReader
         .loadRF(layer)
-        .where(st_intersects(BOUNDS_COLUMN, mkPtFcn(SPATIAL_KEY_COLUMN)))
+        .where(st_intersects(GEOMETRY_COLUMN, mkPtFcn(SPATIAL_KEY_COLUMN)))
 
       assert(numFilters(df) === 0)
 
@@ -360,8 +360,8 @@ class GeoTrellisDataSourceSpec
         val df = layerReader
           .loadRF(layer)
           .where(
-            ((BOUNDS_COLUMN intersects pt1) ||
-              (BOUNDS_COLUMN intersects pt2)) &&
+            ((GEOMETRY_COLUMN intersects pt1) ||
+              (GEOMETRY_COLUMN intersects pt2)) &&
               (TIMESTAMP_COLUMN === Timestamp.valueOf(now.toLocalDateTime))
           )
 
@@ -374,7 +374,7 @@ class GeoTrellisDataSourceSpec
       withClue("partially nested") {
         val df = layerReader
           .loadRF(layer)
-          .where((BOUNDS_COLUMN intersects pt1) || (BOUNDS_COLUMN intersects pt2))
+          .where((GEOMETRY_COLUMN intersects pt1) || (GEOMETRY_COLUMN intersects pt2))
           .where(TIMESTAMP_COLUMN === Timestamp.valueOf(now.toLocalDateTime))
 
         assert(numFilters(df) === 1)
@@ -388,7 +388,7 @@ class GeoTrellisDataSourceSpec
       withClue("intersects first") {
         val df = layerReader
           .loadRF(layer)
-          .where(BOUNDS_COLUMN intersects pt1)
+          .where(GEOMETRY_COLUMN intersects pt1)
           .where(TIMESTAMP_COLUMN betweenTimes(now.minusDays(1), now.plusDays(1)))
 
         assert(numFilters(df) == 1)
@@ -397,7 +397,7 @@ class GeoTrellisDataSourceSpec
         val df = layerReader
           .loadRF(layer)
           .where(TIMESTAMP_COLUMN betweenTimes(now.minusDays(1), now.plusDays(1)))
-          .where(BOUNDS_COLUMN intersects pt1)
+          .where(GEOMETRY_COLUMN intersects pt1)
 
         assert(numFilters(df) == 1)
       }
@@ -418,8 +418,8 @@ class GeoTrellisDataSourceSpec
     it("should handle renamed spatial filter columns") {
       val df = layerReader
         .loadRF(layer)
-        .where(BOUNDS_COLUMN intersects region.jtsGeom)
-        .withColumnRenamed(BOUNDS_COLUMN.columnName, "foobar")
+        .where(GEOMETRY_COLUMN intersects region.jtsGeom)
+        .withColumnRenamed(GEOMETRY_COLUMN.columnName, "foobar")
 
       assert(numFilters(df) === 1)
       assert(df.count > 0, df.printSchema)
@@ -428,8 +428,8 @@ class GeoTrellisDataSourceSpec
     it("should handle dropped spatial filter columns") {
       val df = layerReader
         .loadRF(layer)
-        .where(BOUNDS_COLUMN intersects region.jtsGeom)
-        .drop(BOUNDS_COLUMN)
+        .where(GEOMETRY_COLUMN intersects region.jtsGeom)
+        .drop(GEOMETRY_COLUMN)
 
       assert(numFilters(df) === 1)
     }
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index e32a02e82..bf51f640b 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -19,6 +19,8 @@
 * _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `agg_stats` instead.
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `astraea.spark.rasterframes.model`.
+* _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
+* Added `RasterFrame.withExtent` extension method.
    
 ## 0.7.x
 
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala
index 96eb55a7e..c0e531651 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala
@@ -43,7 +43,7 @@ trait PDSFields {
   final val PROC_LEVEL = StructField("processing_level", StringType, false)
   final val PATH = StructField("path", ShortType, false)
   final val ROW = StructField("row", ShortType, false)
-  final val BOUNDS = StructField(BOUNDS_COLUMN.columnName, JTSTypes.GeometryTypeInstance, false)
+  final val BOUNDS = StructField(GEOMETRY_COLUMN.columnName, JTSTypes.GeometryTypeInstance, false)
   final val ASSETS = StructField("assets", DataTypes.createMapType(StringType, StringType))
   final def BOUNDS_WGS84 = StructField(
     "bounds_wgs84", StandardEncoders.envelopeEncoder.schema, false
diff --git a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
index d4a8967c0..173d00527 100644
--- a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
@@ -184,7 +184,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def spatialJoin(df: DataFrame, right: DataFrame): RasterFrame = df.asRF.spatialJoin(right.asRF)
 
-  def withBounds(df: DataFrame): RasterFrame = df.asRF.withBounds()
+  def withBounds(df: DataFrame): RasterFrame = df.asRF.withGeometry()
 
   def withCenter(df: DataFrame): RasterFrame = df.asRF.withCenter()
 

From 6abd0010db035cced7d6944f5927e7f638d7a30f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 8 Apr 2019 16:50:11 -0400
Subject: [PATCH 029/380] Test fixes associated with changing `bounds` to
 `geometry`.

---
 .../spark/rasterframes/GeometryOperationsSpec.scala      | 6 +++---
 .../astraea/spark/rasterframes/SpatialKeySpec.scala      | 5 +----
 .../datasource/geotrellis/GeoTrellisDataSourceSpec.scala | 2 +-
 .../experimental/datasource/awspds/L8RelationTest.scala  | 4 ++--
 pyrasterframes/build.sbt                                 | 9 +++++++++
 pyrasterframes/python/tests/PyRasterFramesTests.py       | 4 ++--
 6 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
index d885009aa..51cd6c20a 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
@@ -50,7 +50,7 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
       val df = features.map(f ⇒ (
         f.geom.reproject(LatLng, rf.crs).jtsGeom,
         f.data.fields("id").asInstanceOf[JsNumber].value.intValue()
-      )).toDF("geom", "id")
+      )).toDF("geom", "__fid__")
 
       val toRasterize = rf.crossJoin(df)
 
@@ -58,7 +58,7 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
 
       val (cols, rows) = tlm.layout.tileLayout.tileDimensions
 
-      val rasterized = toRasterize.withColumn("rasterized", rasterize($"geom", $"bounds", $"id", cols, rows))
+      val rasterized = toRasterize.withColumn("rasterized", rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
 
       assert(rasterized.count() === df.count() * rf.count())
       assert(rasterized.select(tile_dimensions($"rasterized")).distinct().count() === 1)
@@ -67,7 +67,7 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
 
 
       toRasterize.createOrReplaceTempView("stuff")
-      val viaSQL = sql(s"select rf_rasterize(geom, bounds, id, $cols, $rows) as rasterized from stuff")
+      val viaSQL = sql(s"select rf_rasterize(geom, geometry, __fid__, $cols, $rows) as rasterized from stuff")
       assert(viaSQL.select(agg_data_cells($"rasterized")).first === pixelCount)
 
       //rasterized.select($"rasterized".as[Tile]).foreach(t ⇒ t.renderPng(ColorMaps.IGBP).write("target/" + t.hashCode() + ".png"))
diff --git a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
index 14b55db90..01af2b4d2 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
@@ -19,7 +19,6 @@
 
 package astraea.spark.rasterframes
 
-import org.locationtech.jts.geom.Polygon
 import geotrellis.proj4.LatLng
 import geotrellis.vector.Point
 import org.locationtech.geomesa.curve.Z2SFC
@@ -41,7 +40,7 @@ class SpatialKeySpec extends TestEnvironment with TestData {
 
     it("should add an extent column") {
       val expected = raster.extent.jtsGeom
-      val result = rf.withGeometry().select($"bounds".as[Polygon]).first
+      val result = rf.withGeometry().select(GEOMETRY_COLUMN).first
       assert(result === expected)
     }
 
@@ -64,6 +63,4 @@ class SpatialKeySpec extends TestEnvironment with TestData {
       assert(result === expected)
     }
   }
-  // This is to avoid an IntelliJ error
-  protected def withFixture(test: Any) = ???
 }
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 07de32c53..14a54cf6f 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -408,7 +408,7 @@ class GeoTrellisDataSourceSpec
           .loadRF(layer)
           .where($"timestamp" >= Timestamp.valueOf(now.minusDays(1).toLocalDateTime))
           .where($"timestamp" <= Timestamp.valueOf(now.plusDays(1).toLocalDateTime))
-          .where(st_intersects($"bounds", geomLit(pt1.jtsGeom)))
+          .where(st_intersects(GEOMETRY_COLUMN, geomLit(pt1.jtsGeom)))
 
         assert(numFilters(df) == 1)
       }
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
index 6f7d7e17d..b014dae7a 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
@@ -32,10 +32,10 @@ class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeA
   private var scenes: DataFrame = _
 
   val query =  """
-            |SELECT bounds, timestamp, B1, B2
+            |SELECT geometry, timestamp, B1, B2
             |FROM l8
             |WHERE
-            |  st_intersects(bounds, st_geomFromText('LINESTRING (-39.551 -7.1881, -72.2461 -45.7062)')) AND
+            |  st_intersects(geometry, st_geomFromText('LINESTRING (-39.551 -7.1881, -72.2461 -45.7062)')) AND
             |  timestamp > to_timestamp('2017-11-01') AND
             |  timestamp <= to_timestamp('2017-11-03')
           """.stripMargin
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index c4284836e..f2bacfaad 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -110,6 +110,8 @@ val pysparkCmd = taskKey[Unit]("Builds pyspark package and emits command string
 
 lazy val pyTest = taskKey[Int]("Run pyrasterframes tests. Return result code.")
 
+lazy val pyTestQuick = taskKey[Int]("Run pyrasterframes tests, skipping build and assembly. Return result code.")
+
 lazy val pyExamples = taskKey[Unit]("Run pyrasterframes examples.")
 
 lazy val pyWheel = taskKey[Unit]("Creates a Python .whl file")
@@ -158,6 +160,13 @@ pyTest := {
   Process("python setup.py test", wd).!
 }
 
+pyTestQuick := {
+    val s = streams.value
+  s.log.info("Running python tests...")
+  val wd = pythonSource.value
+  Process("python setup.py test", wd).!
+}
+
 Test / executeTests := {
   val standard = (Test / executeTests).value
   standard.overall match {
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 07dc008f9..10513f7d4 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -98,7 +98,7 @@ def test_general(self):
             .withColumn('mean', tile_mean(self.tileCol)) \
             .withColumn('sum', tile_sum(self.tileCol)) \
             .withColumn('stats', tile_stats(self.tileCol)) \
-            .withColumn('envelope', envelope('bounds')) \
+            .withColumn('envelope', envelope('geometry')) \
             .withColumn('ascii', render_ascii(self.tileCol)) \
             .withColumn('log', log(self.tileCol)) \
             .withColumn('exp', exp(self.tileCol)) \
@@ -109,7 +109,7 @@ def test_general(self):
 
     def test_rasterize(self):
         # NB: This test just makes sure rasterize runs, not that the results are correct.
-        withRaster = self.rf.withColumn('rasterize', rasterize('bounds', 'bounds', lit(42), 10, 10))
+        withRaster = self.rf.withColumn('rasterize', rasterize('geometry', 'geometry', lit(42), 10, 10))
         withRaster.show()
 
     def test_reproject(self):

From e09d1db34e0dadbc13ec51da85aa2b6d817b78af Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 9 Apr 2019 11:19:22 -0400
Subject: [PATCH 030/380] Renamed `GeometryToBounds` to  `GeometryToExtent`.

---
 .../rasterframes/expressions/accessors/GetEnvelope.scala    | 2 +-
 .../{GeometryToBounds.scala => GeometryToExtent.scala}      | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)
 rename core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/{GeometryToBounds.scala => GeometryToExtent.scala} (93%)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
index b082f14b0..ce6ef4504 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
@@ -36,7 +36,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  *
  * @since 2/22/18
  */
-@deprecated("Replace usages of this with GeometryToBounds", "11/4/2018")
+@deprecated("Replace usages of this with GeometryToExtent", "11/4/2018")
 case class GetEnvelope(child: Expression) extends UnaryExpression with CodegenFallback {
 
   override def nodeName: String = "envelope"
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToBounds.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToBounds.scala
rename to core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
index 4e08ad9ea..69cd3f7cb 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToBounds.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  *
  * @since 8/24/18
  */
-case class GeometryToBounds(child: Expression) extends UnaryExpression with CodegenFallback {
+case class GeometryToExtent(child: Expression) extends UnaryExpression with CodegenFallback {
   override def nodeName: String = "geometry_bounds"
 
   override def dataType: DataType = CatalystSerializer[Extent].schema
@@ -58,9 +58,9 @@ case class GeometryToBounds(child: Expression) extends UnaryExpression with Code
   }
 }
 
-object GeometryToBounds {
+object GeometryToExtent {
   import astraea.spark.rasterframes.encoders.StandardEncoders._
 
   def apply(bounds: Column): TypedColumn[Any, Extent] =
-    new Column(new GeometryToBounds(bounds.expr)).as[Extent]
+    new Column(new GeometryToExtent(bounds.expr)).as[Extent]
 }
\ No newline at end of file

From 5794587572f49e2d29bdc411336d8378f5507783 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 9 Apr 2019 10:32:13 -0400
Subject: [PATCH 031/380] Initial attempt converting pyrasterframes TileUDT to
 numpy; only works for float64 type

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../python/pyrasterframes/context.py          |  7 +-
 .../python/pyrasterframes/rasterfunctions.py  | 10 +--
 pyrasterframes/python/pyrasterframes/types.py | 90 ++++++++++++++-----
 .../python/tests/PyRasterFramesTests.py       | 49 +++++++++-
 .../spark/rasterframes/py/PyRFContext.scala   | 15 +++-
 5 files changed, 139 insertions(+), 32 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/python/pyrasterframes/context.py
index 9f238c225..13ed223a8 100644
--- a/pyrasterframes/python/pyrasterframes/context.py
+++ b/pyrasterframes/python/pyrasterframes/context.py
@@ -28,7 +28,7 @@ def lookup(self, function_name):
     @staticmethod
     def active():
         """
-        Get the active Pythono RFContext and throw an error if it is not enabled for RasterFrames.
+        Get the active Python RFContext and throw an error if it is not enabled for RasterFrames.
         """
         sc = SparkContext._active_spark_context
         if not hasattr(sc, '_rf_context'):
@@ -36,6 +36,11 @@ def active():
                 "RasterFrames have not been enabled for the active session. Call 'SparkSession.withRasterFrames()'.")
         return sc._rf_context
 
+    @staticmethod
+    def call(name, *args):
+        f = RFContext.active().lookup(name)
+        return f(*args)
+
     @staticmethod
     def _jvm_mirror():
         """
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 589b256d8..f63637321 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -14,14 +14,9 @@
 THIS_MODULE = 'pyrasterframes'
 
 
-def _context_call(name, *args):
-    f = RFContext.active().lookup(name)
-    return f(*args)
-
-
 def _celltype(cellTypeStr):
     """ Convert the string cell type to the expected CellType object."""
-    return _context_call('cell_type', cellTypeStr)
+    return RFContext.call('cell_type', cellTypeStr)
 
 
 def _create_assembleTile():
@@ -154,7 +149,7 @@ def _(data_tile, mask_tile, mask_value):
 _rf_unique_functions = {
     'array_to_tile': _create_arrayToTile(),
     'assemble_tile': _create_assembleTile(),
-    'cell_types': lambda: _context_call('cell_types'),
+    'cell_types': lambda: RFContext.call('cell_types'),
     'convert_cell_type': _create_convertCellType(),
     'explode_tiles': _create_explode_tiles(),
     'explode_tiles_sample': _create_explode_tiles_sample(),
@@ -235,6 +230,7 @@ def _(data_tile, mask_tile, mask_value):
     'local_greater_equal': 'Cellwise greater than or equal to comparison between two tiles',
     'local_equal': 'Cellwise equality comparison between two tiles',
     'local_unequal': 'Cellwise inequality comparison between two tiles',
+    'render_matrix': 'Render matrix as a formatted string',
     'round': 'Round cell values to the nearest integer without changing the cell type',
     'log': 'Performs cell-wise natural logarithm',
     'log10': 'Performs cell-wise logartithm with base 10',
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index b82cfb70c..693752db7 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -12,13 +12,14 @@ class here provides the PyRasterFrames entry point.
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
 from .context import RFContext
+import numpy
 
 __all__ = ['RasterFrame', 'TileUDT', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
 
 
 class RasterFrame(DataFrame):
     def __init__(self, jdf, spark_session):
-        DataFrame.__init__(self, jdf, spark_session)
+        DataFrame.__init__(self, jdf, spark_session._wrapped)
         self._jrfctx = spark_session.rasterframes._jrfctx
 
     def tileColumns(self):
@@ -130,27 +131,44 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.RasterSourceUDT'
 
     def serialize(self, obj):
-        if (obj is None): return None
+        # Not yet implemented. Kryo serialized bytes?
         return None
 
     def deserialize(self, datum):
-        return None
+        bytes(datum[0])
+
 
 class TileUDT(UserDefinedType):
     @classmethod
-    def sqlType(self):
+    def sqlType(cls):
+        """
+        Mirrors `schema` in scala companion object org.apache.spark.sql.rf.TileUDT
+        """
         return StructType([
-            StructField("cell_type", StringType(), False),
-            StructField("cols", ShortType(), False),
-            StructField("rows", ShortType(), False),
-            StructField("cells", BinaryType(), True),
-            StructField("ref", StructType([
-              StructField("source", RasterSourceUDT(), False),
-              StructField("subextent", StructType([
-                  StructField("xmin", DoubleType(), False),
-                  StructField("ymin", DoubleType(), False),
-                  StructField("xmax", DoubleType(), False),
-                  StructField("ymax", DoubleType(), False)]), True)]), True)])
+            StructField("cell_context", StructType([
+                StructField("cell_type", StructType([
+                    StructField("cellTypeName", StringType(), False)
+                    ]), False),
+                # ], False),  # life wood be ez if string dough
+                StructField("dimensions", StructType([
+                    StructField("cols", ShortType(), False),
+                    StructField("rows", ShortType(), False)
+                ]), False),
+            ]), False),
+            StructField("cell_data", StructType([
+                StructField("cells", BinaryType(), True),
+                StructField("ref", StructType([
+                    StructField("source", RasterSourceUDT(), False),
+                    StructField("subextent", StructType([
+                      StructField("xmin", DoubleType(), False),
+                      StructField("ymin", DoubleType(), False),
+                      StructField("xmax", DoubleType(), False),
+                      StructField("ymax", DoubleType(), False)
+
+                    ]), True)
+                ]), True)
+            ]), False)
+        ])
 
     @classmethod
     def module(cls):
@@ -160,16 +178,46 @@ def module(cls):
     def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
-    # NB: These will need implementations if UDFs are to be supported,
-    # preferably in numpy arrays.
-    def serialize(self, obj):
-        if (obj is None): return None
-        return None
+    def serialize(self, masked_array):
+        return_val = [
+            # cell_context
+            [
+                [masked_array.dtype.name],
+                [masked_array.shape[1], masked_array.shape[0]]
+            ],
+            # cell_data
+            [
+                    # cells
+                    RFContext.call('list_to_bytearray', masked_array.flatten().tolist(), masked_array.shape[1], masked_array.shape[0]),
+                    # ref -- TODO implement
+                    None
+            ]
+        ]
+        return return_val
 
     def deserialize(self, datum):
-        return None
+        """
+
+        :param datum:
+        :return: A Tile object from row data.
+        """
+        cell_type = datum.cell_context.cell_type.cellTypeName
+        cols = datum.cell_context.dimensions.cols
+        rows = datum.cell_context.dimensions.rows
+        cell_data_bytes = bytes(datum.cell_data.cells)
+        cell_value_list = list(RFContext.call('bytearray_to_list', cell_data_bytes, cell_type, cols, rows))
+
+        ma = MaskedArray(
+            numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type),
+            numpy.zeros((rows, cols))
+        )
+        return ma
+
+    deserialize.__safe_for_unpickling__ = True
 
 
+from numpy.ma import MaskedArray
+MaskedArray.__UDT__ = TileUDT()
 
 class TileExploder(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 07dc008f9..9951fb10a 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,5 +1,5 @@
 
-from pyspark.sql import SparkSession, Column
+from pyspark.sql import SparkSession, Column, SQLContext
 from pyspark.sql.functions import *
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
@@ -138,7 +138,7 @@ def test_sql(self):
 
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims',  tile_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims', tile_dimensions(self.tileCol)).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
@@ -285,9 +285,54 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
+    def test_tile_udt(self):
+        import pandas as pd
+        import numpy as np
+        from numpy.ma import MaskedArray
+        self.assertIsInstance(self.rf.sql_ctx, SQLContext)
+
+        # Try to create a tile from numpy.ma.MaskedArray
+        to_spark = pd.DataFrame({
+            't': [MaskedArray(np.random.randn(10, 10), np.zeros((10, 10))) for _ in range(3)],
+            'b': ['a', 'b', 'c'],
+            'c': [1, 2, 4],
+        })
+        rf_maybe = self.spark.createDataFrame(to_spark)
+        print("Type of dataframe: ", type(rf_maybe))
+        rf_maybe.printSchema()
+        print(rf_maybe.toPandas())
+
+        # Try to do something with it.
+        sums = to_spark.t.apply(lambda a: a.sum()).tolist()
+        maybe_sums = rf_maybe.select(tile_sum(rf_maybe.t).alias('tsum'))
+        print("Schema of tile sum")
+        maybe_sums.printSchema()
+
+        maybe_sums = [r.tsum for r in maybe_sums.collect()]
+        np.testing.assert_almost_equal(maybe_sums, sums, 12)
+
+
+        # Test round trip for an array
+        simple_array = MaskedArray(np.array([[1, 2], [3, 4]]).astype('float64'), np.zeros((2, 2)))
+        to_spark_2 = pd.DataFrame({
+            't': [simple_array]
+        })
+
+        rf_maybe_2 = self.spark.createDataFrame(to_spark_2)
+        print("RasterFrame `show`:")
+        rf_maybe_2.select(render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
+
+        pd_2 = rf_maybe_2.toPandas()
+        array_back_2 = pd_2.iloc[0].t
+        print("Array collected from toPandas output", array_back_2, sep='\n')
+
+        self.assertIsInstance(array_back_2, MaskedArray)
+        np.testing.assert_equal(array_back_2, simple_array)
+
 
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests
 
+
 unittest.TextTestRunner().run(suite())
diff --git a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
index b80a8a3f8..32b0eb46c 100644
--- a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
@@ -21,7 +21,7 @@ package astraea.spark.rasterframes.py
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.util.CRSParser
 import com.vividsolutions.jts.geom.Geometry
-import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
+import geotrellis.raster.{ArrayTile, CellType, MultibandTile, Tile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
@@ -102,6 +102,19 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     astraea.spark.rasterframes.functions.cellTypes().asJava
   }
 
+  def list_to_bytearray(l: java.util.ArrayList[Double], c: Int, r: Int): Array[Byte] = {
+    geotrellis.raster.ArrayTile(l.asScala.toArray, c, r).toBytes()
+  }
+
+  def bytearray_to_list(bytes: Array[Byte], cell_type_name: String, cols: Int, rows: Int): java.util.List[Double] = {
+    geotrellis.raster.ArrayTile.fromBytes(
+      bytes,
+      geotrellis.raster.CellType.fromName(cell_type_name),
+      cols,
+      rows
+    ).toListDouble.asJava
+  }
+
   /** DESERIALIZATION **/
 
   def generate_tile(cellType: String, cols: Int, rows: Int, bytes: Array[Byte]): ArrayTile = {

From f3d4b5a242febb81314cbf592bfb9715ad8273ad Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 9 Apr 2019 13:02:13 -0400
Subject: [PATCH 032/380] Python 2 compat in test print

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/tests/PyRasterFramesTests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 9951fb10a..edaa6550a 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -324,7 +324,7 @@ def test_tile_udt(self):
 
         pd_2 = rf_maybe_2.toPandas()
         array_back_2 = pd_2.iloc[0].t
-        print("Array collected from toPandas output", array_back_2, sep='\n')
+        print("Array collected from toPandas output\n", array_back_2)
 
         self.assertIsInstance(array_back_2, MaskedArray)
         np.testing.assert_equal(array_back_2, simple_array)

From ff6d289d9e0bd22f903ef1527fbad9c0b36382e8 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 9 Apr 2019 13:43:56 -0400
Subject: [PATCH 033/380] Tweaks for python 2 compatibility

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/pyrasterframes/types.py | 4 ++--
 pyrasterframes/python/setup.py                | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index 693752db7..69968bb55 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -188,7 +188,7 @@ def serialize(self, masked_array):
             # cell_data
             [
                     # cells
-                    RFContext.call('list_to_bytearray', masked_array.flatten().tolist(), masked_array.shape[1], masked_array.shape[0]),
+                    bytearray(RFContext.call('list_to_bytearray', masked_array.flatten().tolist(), masked_array.shape[1], masked_array.shape[0])),
                     # ref -- TODO implement
                     None
             ]
@@ -204,7 +204,7 @@ def deserialize(self, datum):
         cell_type = datum.cell_context.cell_type.cellTypeName
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
-        cell_data_bytes = bytes(datum.cell_data.cells)
+        cell_data_bytes = datum.cell_data.cells
         cell_value_list = list(RFContext.call('bytearray_to_list', cell_data_bytes, cell_type, cols, rows))
 
         ma = MaskedArray(
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index a0f318008..cf7a35f6d 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -118,16 +118,16 @@ def run(self):
     license='Apache 2',
     setup_requires=['pytest-runner', pyspark_ver, 'pathlib'],
     install_requires=[
-        'pytz', 'shapely',
-        # pyspark_ver,
-        # 'pathlib'
+        'pytz',
+        'shapely',
+        'numpy>=1.7',
     ],
     tests_require=[
         pyspark_ver,
         'pytest==3.4.2',
         'pypandoc',
-        'numpy>=1.7',
         'pandas',
+        # 'numpy>=1.7',
     ],
     test_suite="pytest-runner",
     packages=find_packages(exclude=['tests', 'examples']),

From bda1fffa0fa0375b560b9fbc6272b57e93e684bd Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 9 Apr 2019 14:33:00 -0400
Subject: [PATCH 034/380] Added `st_extent`.

---
 .../scala/astraea/spark/rasterframes/RasterFunctions.scala | 5 +++++
 .../spark/rasterframes/expressions/SpatialRelation.scala   | 7 +++++++
 .../astraea/spark/rasterframes/expressions/package.scala   | 1 +
 .../astraea/spark/rasterframes/functions/package.scala     | 1 -
 .../test/scala/astraea/spark/rasterframes/JTSSpec.scala    | 7 ++++---
 docs/src/main/tut/release-notes.md                         | 1 +
 pyrasterframes/python/pyrasterframes/rasterfunctions.py    | 1 +
 7 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
index db22791bb..cc4708cc0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
@@ -31,6 +31,7 @@ import astraea.spark.rasterframes.{functions => F}
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.{CellType, Tile}
+import geotrellis.vector.Extent
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
@@ -60,8 +61,12 @@ trait RasterFunctions {
   def tile_dimensions(col: Column): Column = GetDimensions(col)
 
   /** Extracts the bounding box of a geometry as a JTS envelope. */
+  @deprecated("Replace usages of this with `st_extent`", "11/4/2018")
   def envelope(col: Column): TypedColumn[Any, Envelope] = GetEnvelope(col)
 
+  /** Extracts the bounding box of a geometry as an Extent */
+  def st_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
+
   /** Flattens Tile into a double array. */
   def tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
index 18f93dd87..637fab10a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
@@ -19,13 +19,17 @@
 
 package astraea.spark.rasterframes.expressions
 
+import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.SpatialRelation.RelationPredicate
+import geotrellis.vector.Extent
 import org.locationtech.jts.geom._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ScalaUDF, _}
 import org.apache.spark.sql.jts.AbstractGeometryUDT
+import org.apache.spark.sql.rf.WithTypeConformity
 import org.apache.spark.sql.types._
 import org.locationtech.geomesa.spark.jts.udf.SpatialRelationFunctions._
 
@@ -43,6 +47,9 @@ abstract class SpatialRelation extends BinaryExpression
       case r: InternalRow ⇒
         expr.dataType match {
           case udt: AbstractGeometryUDT[_] ⇒ udt.deserialize(r)
+          case dt if dt.conformsTo(CatalystSerializer[Extent].schema) =>
+            val extent = r.to[Extent]
+            extent.jtsGeom
         }
     }
   }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
index e4c0bcc00..52510d3ef 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
@@ -66,6 +66,7 @@ package object expressions {
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
     registry.registerExpression[GetDimensions]("rf_tile_dimensions")
     registry.registerExpression[BoundsToGeometry]("rf_bounds_geometry")
+    registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
index a5c4547d7..6805a99d1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
@@ -133,7 +133,6 @@ package object functions {
     }
 
   def register(sqlContext: SQLContext): Unit = {
-
     sqlContext.udf.register("rf_make_constant_tile", makeConstantTile)
     sqlContext.udf.register("rf_tile_zeros", tileZeros)
     sqlContext.udf.register("rf_tile_ones", tileOnes)
diff --git a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
index 15fac8010..a017e9a2e 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
@@ -21,7 +21,8 @@ package astraea.spark.rasterframes
 
 import org.locationtech.jts.geom._
 import geotrellis.proj4.{LatLng, Sinusoidal, WebMercator}
-import geotrellis.vector.{Point ⇒ GTPoint}
+import geotrellis.vector.{Extent, Point => GTPoint}
+import org.apache.spark.sql.Column
 
 /**
  * Test rig for operations providing interop with JTS types.
@@ -80,8 +81,8 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
     it("should provide a means of getting a bounding box") {
       import spark.implicits._
-      val boxed = rf.select(GEOMETRY_COLUMN, envelope(GEOMETRY_COLUMN) as "env")
-      assert(boxed.select($"env".as[Envelope]).first.getArea > 0)
+      val boxed = rf.select(GEOMETRY_COLUMN, st_extent(GEOMETRY_COLUMN) as "extent")
+      assert(boxed.select($"extent".as[Extent]).first.area > 0)
       assert(boxed.toDF("bounds", "bbox").select("bbox.*").schema.length === 4)
     }
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index bf51f640b..a448ffa60 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -21,6 +21,7 @@
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `astraea.spark.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
 * Added `RasterFrame.withExtent` extension method.
+* Added `st_extent` and deprecated `envelope`.
    
 ## 0.7.x
 
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 589b256d8..7c015b8d9 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -245,6 +245,7 @@ def _(data_tile, mask_tile, mask_value):
     'exp10': 'Compute 10 to the power of cell values',
     'expm1': 'Performs cell-wise exponential, then subtract one',
     'resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
+    'st_extent': 'Compute the extent/bbox of a Geometry',
 
     # ------- JTS functions -------
     # spatial constructors

From c1cce3d73d26d6df926534f445bb19510b91205b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 9 Apr 2019 16:32:18 -0400
Subject: [PATCH 035/380] Renamed `bounds_geometry` to `extent_geometry`.

---
 .../astraea/spark/rasterframes/RasterFunctions.scala      | 2 +-
 .../astraea/spark/rasterframes/expressions/package.scala  | 2 +-
 .../{BoundsToGeometry.scala => ExtentToGeometry.scala}    | 8 ++++----
 .../rasterframes/util/ZeroSevenCompatibilityKit.scala     | 4 ++--
 docs/src/main/tut/release-notes.md                        | 2 +-
 .../experimental/datasource/awspds/L8Relation.scala       | 2 +-
 pyrasterframes/python/pyrasterframes/rasterfunctions.py   | 1 +
 7 files changed, 11 insertions(+), 10 deletions(-)
 rename core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/{BoundsToGeometry.scala => ExtentToGeometry.scala} (91%)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
index cc4708cc0..622d68f14 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
@@ -101,7 +101,7 @@ trait RasterFunctions {
     SetCellType(col, cellTypeName)
 
   /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
-  def bounds_geometry(bounds: Column): TypedColumn[Any, Geometry] = BoundsToGeometry(bounds)
+  def extent_geometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
 
   /** Assign a `NoData` value to the Tiles. */
   def with_no_data(col: Column, nodata: Double): TypedColumn[Any, Tile] = withAlias("with_no_data", col)(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
index 52510d3ef..b2a44cdd6 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
@@ -65,7 +65,7 @@ package object expressions {
     registry.registerExpression[GetCellType]("rf_cell_type")
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
     registry.registerExpression[GetDimensions]("rf_tile_dimensions")
-    registry.registerExpression[BoundsToGeometry]("rf_bounds_geometry")
+    registry.registerExpression[ExtentToGeometry]("rf_extent_geometry")
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala
rename to core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
index b928e9971..422ba821f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/BoundsToGeometry.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
@@ -40,8 +40,8 @@ import org.locationtech.geomesa.spark.jts.encoders.SpatialEncoders
  *
  * @since 8/24/18
  */
-case class BoundsToGeometry(child: Expression) extends UnaryExpression with CodegenFallback {
-    override def nodeName: String = "bounds_geometry"
+case class ExtentToGeometry(child: Expression) extends UnaryExpression with CodegenFallback {
+    override def nodeName: String = "extent_geometry"
 
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
 
@@ -71,7 +71,7 @@ case class BoundsToGeometry(child: Expression) extends UnaryExpression with Code
   }
 }
 
-object BoundsToGeometry extends SpatialEncoders {
+object ExtentToGeometry extends SpatialEncoders {
   def apply(bounds: Column): TypedColumn[Any, Geometry] =
-    new Column(new BoundsToGeometry(bounds.expr)).as[Geometry]
+    new Column(new ExtentToGeometry(bounds.expr)).as[Geometry]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
index 16d3ba271..098c7ea3f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -101,7 +101,7 @@ object ZeroSevenCompatibilityKit {
 
     /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
     @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def boundsGeometry(bounds: Column): TypedColumn[Any, Geometry] = BoundsToGeometry(bounds)
+    def boundsGeometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
 
     /** Assign a `NoData` value to the Tiles. */
     @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
@@ -331,7 +331,7 @@ object ZeroSevenCompatibilityKit {
     registry.registerFunc("rf_cellType", ub(GetCellType.apply))
     registry.registerFunc("rf_convertCellType", bb(SetCellType.apply))
     registry.registerFunc("rf_tileDimensions", ub(GetDimensions.apply))
-    registry.registerFunc("rf_boundsGeometry", ub(BoundsToGeometry.apply))
+    registry.registerFunc("rf_boundsGeometry", ub(ExtentToGeometry.apply))
     registry.registerFunc("rf_localAdd", bb(Add.apply))
     registry.registerFunc("rf_localSubtract", bb(Subtract.apply))
     registry.registerFunc("rf_localMultiply", bb(Multiply.apply))
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index a448ffa60..9fff73799 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -21,7 +21,7 @@
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `astraea.spark.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
 * Added `RasterFrame.withExtent` extension method.
-* Added `st_extent` and deprecated `envelope`.
+* Added `st_extent` and deprecated `envelope`. Renamed `bounds_geometry` to `extent_geometry`.
    
 ## 0.7.x
 
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 0672a52d9..23075dc86 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -88,7 +88,7 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
       .format(L8CatalogDataSource.SHORT_NAME)
       .load()
       .withColumnRenamed(PDSFields.ACQUISITION_DATE.name, PDSFields.TIMESTAMP.name)
-      .withColumn(PDSFields.BOUNDS.name, bounds_geometry(col(PDSFields.BOUNDS_WGS84.name)))
+      .withColumn(PDSFields.BOUNDS.name, extent_geometry(col(PDSFields.BOUNDS_WGS84.name)))
       .drop(PDSFields.BOUNDS_WGS84.name)
 
     val filtered = aggFilters
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 7c015b8d9..ddc8c19f0 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -246,6 +246,7 @@ def _(data_tile, mask_tile, mask_value):
     'expm1': 'Performs cell-wise exponential, then subtract one',
     'resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
     'st_extent': 'Compute the extent/bbox of a Geometry',
+    'extent_geometry': 'Convert the given extent/bbox to a polygon',
 
     # ------- JTS functions -------
     # spatial constructors

From 8ef4c7288d3c049390521eb2de7e4758c9c4cc22 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 8 Apr 2019 15:31:44 -0400
Subject: [PATCH 036/380] Initial skeleton of RasterSourceRelation.

---
 .../spark/rasterframes/StandardColumns.scala  |  3 +
 .../expressions/TileAssembler.scala           |  4 +-
 .../aggstats/CellStatsAggregate.scala         |  4 +-
 .../aggstats/HistogramAggregate.scala         |  3 +-
 .../aggstats/LocalCountAggregate.scala        |  4 +-
 .../aggstats/LocalMeanAggregate.scala         |  3 +-
 .../aggstats/LocalStatsAggregate.scala        |  3 +-
 .../aggstats/LocalTileOpAggregate.scala       |  4 +-
 .../transformers/RasterSourceToTiles.scala    |  3 +-
 .../astraea/spark/rasterframes/package.scala  |  4 +
 .../tiles/ProjectedRasterTile.scala           |  2 +-
 .../astraea/spark/rasterframes/TestData.scala |  3 +
 .../spark/rasterframes/TileUDTSpec.scala      |  1 -
 ...pache.spark.sql.sources.DataSourceRegister |  5 +-
 .../geotiff/GeoTiffCollectionRelation.scala   | 11 ---
 ...ltSource.scala => GeoTiffDataSource.scala} | 14 ++--
 .../datasource/geotiff/package.scala          |  4 +-
 ....scala => GeoTrellisLayerDataSource.scala} |  6 +-
 .../datasource/geotrellis/package.scala       |  2 +-
 .../rastersource/RasterSourceDataSource.scala | 58 +++++++++++++
 .../rastersource/RasterSourceRelation.scala   | 67 +++++++++++++++
 .../datasource/rastersource/package.scala     | 55 +++++++++++++
 .../geotiff/GeoTiffDataSourceSpec.scala       |  4 -
 .../RasterSourceDataSourceSpec.scala          | 82 +++++++++++++++++++
 .../datasource/ReadTilesExpression.scala      |  3 +-
 25 files changed, 298 insertions(+), 54 deletions(-)
 rename datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/{DefaultSource.scala => GeoTiffDataSource.scala} (90%)
 rename datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/{DefaultSource.scala => GeoTrellisLayerDataSource.scala} (96%)
 create mode 100644 datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
 create mode 100644 datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
 create mode 100644 datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
 create mode 100644 datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala

diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
index 50a516753..dd488ce6f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
@@ -57,6 +57,9 @@ trait StandardColumns {
 
   /** Default teil column index column for the cells of exploded tiles. */
   val ROW_INDEX_COLUMN = col("row_index").as[Int]
+
+  /** URI/URL/S3 path to raster. */
+  val PATH_COLUMN = col("path").as[String]
 }
 
 object StandardColumns extends StandardColumns
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
index df633fe2c..1f4ef5b0e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
@@ -29,10 +29,10 @@ import geotrellis.raster.{DataType => _, _}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.aggregate.{ImperativeAggregate, TypedImperativeAggregate}
 import org.apache.spark.sql.catalyst.expressions.{Expression, ImplicitCastInputTypes}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
 import spire.syntax.cfor._
+import astraea.spark.rasterframes.TileType
 
 /**
  * Aggregator for reassembling tiles from from exploded form
@@ -53,8 +53,6 @@ case class TileAssembler(
 
   override def inputTypes = Seq(ShortType, ShortType, DoubleType, ShortType, ShortType)
 
-  private val TileType = new TileUDT()
-
   override def prettyName: String = "assemble_tiles"
 
   override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): ImperativeAggregate =
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala
index cfcde38a5..86e2822b2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala
@@ -23,12 +23,12 @@ package astraea.spark.rasterframes.expressions.aggstats
 
 import astraea.spark.rasterframes.expressions.accessors.ExtractTile
 import astraea.spark.rasterframes.stats.CellStatistics
+import astraea.spark.rasterframes.TileType
 import geotrellis.raster.{Tile, _}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{DataType, _}
 import org.apache.spark.sql.{Column, Row, TypedColumn}
 
@@ -40,8 +40,6 @@ import org.apache.spark.sql.{Column, Row, TypedColumn}
 case class CellStatsAggregate() extends UserDefinedAggregateFunction {
   import CellStatsAggregate.C
   // TODO: rewrite as a DeclarativeAggregate
-  private val TileType = new TileUDT()
-
   override def inputSchema: StructType = StructType(StructField("value", TileType) :: Nil)
 
   override def dataType: DataType = StructType(Seq(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala
index 7920415da..8e74a8eae 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala
@@ -33,9 +33,9 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression,
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
+import astraea.spark.rasterframes.TileType
 
 /**
  * Histogram aggregation function for a full column of tiles.
@@ -45,7 +45,6 @@ import org.apache.spark.sql.{Column, Row, TypedColumn}
 case class HistogramAggregate(numBuckets: Int) extends UserDefinedAggregateFunction {
   def this() = this(StreamingHistogram.DEFAULT_NUM_BUCKETS)
   // TODO: rewrite as TypedAggregateExpression or similar.
-  private val TileType = new TileUDT()
 
   override def inputSchema: StructType = StructType(StructField("value", TileType) :: Nil)
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala
index f427d9ee3..332895b5f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala
@@ -29,9 +29,9 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression,
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, Row, TypedColumn}
+import astraea.spark.rasterframes.TileType
 
 /**
  * Catalyst aggregate function that counts `NoData` values in a cell-wise fashion.
@@ -47,8 +47,6 @@ class LocalCountAggregate(isData: Boolean) extends UserDefinedAggregateFunction
 
   private val add = safeBinaryOp(Add.apply(_: Tile, _: Tile))
 
-  private val TileType = new TileUDT()
-
   override def dataType: DataType = TileType
 
   override def inputSchema: StructType = StructType(Seq(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
index bab1eba20..be0722e32 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
@@ -27,16 +27,15 @@ import astraea.spark.rasterframes.expressions.transformers.SetCellType
 import geotrellis.raster.Tile
 import geotrellis.raster.mapalgebra.local
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, ExpressionDescription, If, IsNull, Literal}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import astraea.spark.rasterframes.TileType
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes a new tile contining the mean cell values across all tiles in column.",
   note = "All tiles in the column must be the same size."
 )
 case class LocalMeanAggregate(child: Expression) extends UnaryRasterAggregate {
-  private val TileType = new TileUDT()
 
   override def dataType: DataType = TileType
   override def nodeName: String = "agg_local_mean"
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
index 8df684a25..c5bdb47e0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
@@ -34,6 +34,7 @@ import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAg
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
+import astraea.spark.rasterframes.TileType
 
 
 /**
@@ -44,8 +45,6 @@ import org.apache.spark.sql.{Column, Row, TypedColumn}
 class LocalStatsAggregate() extends UserDefinedAggregateFunction {
   import LocalStatsAggregate.C
 
-  private val TileType = new TileUDT()
-
   override def inputSchema: StructType = StructType(Seq(
     StructField("value", TileType, true)
   ))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
index d3247e964..a6a2de5a0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
@@ -21,6 +21,7 @@
 
 package astraea.spark.rasterframes.expressions.aggstats
 
+import astraea.spark.rasterframes.TileType
 import astraea.spark.rasterframes.expressions.accessors.ExtractTile
 import astraea.spark.rasterframes.functions.safeBinaryOp
 import astraea.spark.rasterframes.util.DataBiasedOp.{BiasedMax, BiasedMin}
@@ -30,7 +31,6 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression,
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
 
@@ -43,8 +43,6 @@ class LocalTileOpAggregate(op: LocalTileBinaryOp) extends UserDefinedAggregateFu
 
   private val safeOp = safeBinaryOp(op.apply(_: Tile, _: Tile))
 
-  private val TileType = new TileUDT()
-
   override def inputSchema: StructType = StructType(Seq(
     StructField("value", TileType, true)
   ))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
index 64b1c9f6e..75aa0942f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 
 import scala.util.control.NonFatal
+import astraea.spark.rasterframes.TileType
 
 /**
  * Accepts RasterRef and generates one or more RasterRef instances representing the
@@ -43,8 +44,6 @@ case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean)
   with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
 
   private val RasterSourceType = new RasterSourceUDT()
-  private val TileType = new TileUDT()
-
   override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
   override def nodeName: String = "raster_source_to_tile"
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/package.scala b/core/src/main/scala/astraea/spark/rasterframes/package.scala
index dafba71be..34ae94624 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/package.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/package.scala
@@ -25,6 +25,7 @@ import geotrellis.raster.{Tile, TileFeature}
 import geotrellis.spark.{ContextRDD, Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.rf.TileUDT
 import org.locationtech.geomesa.spark.jts.DataFrameFunctions
 import shapeless.tag.@@
 
@@ -84,6 +85,9 @@ package object rasterframes extends StandardColumns
     rasterframes.rules.register(sqlContext)
   }
 
+  /** TileUDT type reference. */
+  def TileType = new TileUDT()
+
   /**
    * A RasterFrame is just a DataFrame with certain invariants, enforced via the methods that create and transform them:
    *   1. One column is a [[geotrellis.spark.SpatialKey]] or [[geotrellis.spark.SpaceTimeKey]]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
index 539f46fd2..34003fb31 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
@@ -33,6 +33,7 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
+import astraea.spark.rasterframes.TileType
 
 /**
  * A Tile that's also like a ProjectedRaster, with delayed evaluation support.
@@ -62,7 +63,6 @@ object ProjectedRasterTile {
   }
 
   implicit val serializer: CatalystSerializer[ProjectedRasterTile] = new CatalystSerializer[ProjectedRasterTile] {
-    val TileType =  new TileUDT()
     override def schema: StructType = StructType(Seq(
       StructField("tile_context", CatalystSerializer[TileContext].schema, false),
       StructField("tile", TileType, false))
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
index 8a208c374..c5097b53b 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
@@ -130,6 +130,9 @@ trait TestData {
   lazy val remoteMODIS: URI = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
   lazy val remoteL8: URI = URI.create("https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/017/033/LC08_L1TP_017033_20181010_20181030_01_T1/LC08_L1TP_017033_20181010_20181030_01_T1_B4.TIF")
   lazy val localSentinel: URL = getClass.getResource("/B01.jp2")
+  lazy val cogPath: URI = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
+  lazy val nonCogPath: URI = getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI
+  lazy val l8samplePath: URI = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI
 
   object JTS {
     val fact = new GeometryFactory()
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala
index b83b94486..548aaf192 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala
@@ -38,7 +38,6 @@ class TileUDTSpec extends TestEnvironment with TestData with Inspectors {
 
   spark.version
   val tileEncoder: ExpressionEncoder[Tile] = ExpressionEncoder()
-  val TileType = new TileUDT()
   implicit val ser = TileUDT.tileSerializer
 
   describe("TileUDT") {
diff --git a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index 26a271f13..051aad71c 100644
--- a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,3 +1,4 @@
-astraea.spark.rasterframes.datasource.geotiff.DefaultSource
-astraea.spark.rasterframes.datasource.geotrellis.DefaultSource
+astraea.spark.rasterframes.datasource.geotiff.GeoTiffDataSource
+astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource
 astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisCatalog
+astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index c23ed579d..76868d055 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -36,20 +36,12 @@ import org.apache.spark.sql.sources.{BaseRelation, PrunedScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
 
-/**
- *
- *
- * @since 7/31/18
- */
 case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount: Int) extends BaseRelation with PrunedScan {
 
   override def schema: StructType = StructType(Seq(
     StructField(Cols.PATH, StringType, false),
     StructField(EXTENT_COLUMN.columnName, CatalystSerializer[Extent].schema, nullable = true),
     StructField(CRS_COLUMN.columnName, CatalystSerializer[CRS].schema, false)
-//    StructField(METADATA_COLUMN.columnName,
-//      DataTypes.createMapType(StringType, StringType, false)
-//    )
   ) ++ (
     if(bandCount == 1) Seq(StructField(Cols.TL, new TileUDT, false))
     else for(b ← 1 to bandCount) yield StructField(Cols.TL + "_" + b, new TileUDT, nullable = true)
@@ -62,8 +54,6 @@ case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount
 
     val columnIndexes = requiredColumns.map(schema.fieldIndex)
 
-
-
     HadoopGeoTiffRDD.multiband(new Path(uri.toASCIIString), keyer, HadoopGeoTiffRDD.Options.DEFAULT)
       .map { case ((path, pe), mbt) ⇒
         val entries = columnIndexes.map {
@@ -77,7 +67,6 @@ case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount
         }
         Row(entries: _*)
       }
-
   }
 }
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/DefaultSource.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
similarity index 90%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/DefaultSource.scala
rename to datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 74acbbc98..ae6477adc 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/DefaultSource.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -34,10 +34,10 @@ import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
  * Spark SQL data source over GeoTIFF files.
  * @since 1/14/18
  */
-class DefaultSource extends DataSourceRegister
+class GeoTiffDataSource extends DataSourceRegister
   with RelationProvider with CreatableRelationProvider
   with DataSourceOptions with LazyLogging {
-  def shortName() = DefaultSource.SHORT_NAME
+  def shortName() = GeoTiffDataSource.SHORT_NAME
 
   def path(parameters: Map[String, String]) =
     uriParam(PATH_PARAM, parameters)
@@ -50,7 +50,7 @@ class DefaultSource extends DataSourceRegister
     val p = pathO.get
 
     if(p.getPath.contains("*")) {
-      val bandCount = parameters.get(DefaultSource.BAND_COUNT_PARAM).map(_.toInt).getOrElse(1)
+      val bandCount = parameters.get(GeoTiffDataSource.BAND_COUNT_PARAM).map(_.toInt).getOrElse(1)
       GeoTiffCollectionRelation(sqlContext, p, bandCount)
     }
     else GeoTiffRelation(sqlContext, p)
@@ -90,8 +90,8 @@ class DefaultSource extends DataSourceRegister
       (c, r)
     }
 
-    val cols = numParam(DefaultSource.IMAGE_WIDTH_PARAM, parameters).getOrElse(fullResCols)
-    val rows = numParam(DefaultSource.IMAGE_HEIGHT_PARAM, parameters).getOrElse(fullResRows)
+    val cols = numParam(GeoTiffDataSource.IMAGE_WIDTH_PARAM, parameters).getOrElse(fullResCols)
+    val rows = numParam(GeoTiffDataSource.IMAGE_HEIGHT_PARAM, parameters).getOrElse(fullResRows)
 
     require(cols <= Int.MaxValue && rows <= Int.MaxValue, s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
 
@@ -108,7 +108,7 @@ class DefaultSource extends DataSourceRegister
       case _ ⇒ ColorSpace.BlackIsZero
     }
 
-    val compress = parameters.get(DefaultSource.COMPRESS_PARAM).map(_.toBoolean).getOrElse(false)
+    val compress = parameters.get(GeoTiffDataSource.COMPRESS_PARAM).map(_.toBoolean).getOrElse(false)
     val options = GeoTiffOptions(Tiled, if (compress) DeflateCompression else NoCompression, colorSpace)
     val tags = Tags(
       RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
@@ -122,7 +122,7 @@ class DefaultSource extends DataSourceRegister
   }
 }
 
-object DefaultSource {
+object GeoTiffDataSource {
   final val SHORT_NAME = "geotiff"
   final val PATH_PARAM = "path"
   final val IMAGE_WIDTH_PARAM = "imageWidth"
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala
index 6e607c3b4..ab5a0073b 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala
@@ -40,11 +40,11 @@ package object geotiff {
   /** Adds `geotiff` format specifier to `DataFrameReader`. */
   implicit class DataFrameReaderHasGeoTiffFormat(val reader: DataFrameReader) {
     def geotiff: GeoTiffRasterFrameReader =
-      tag[GeoTiffRasterFrameReaderTag][DataFrameReader](reader.format(DefaultSource.SHORT_NAME))
+      tag[GeoTiffRasterFrameReaderTag][DataFrameReader](reader.format(GeoTiffDataSource.SHORT_NAME))
   }
 
   implicit class DataFrameWriterHasGeoTiffFormat[T](val writer: DataFrameWriter[T]) {
-    def geotiff: DataFrameWriter[T] = writer.format(DefaultSource.SHORT_NAME)
+    def geotiff: DataFrameWriter[T] = writer.format(GeoTiffDataSource.SHORT_NAME)
   }
 
   /** Adds `loadRF` to appropriately tagged `DataFrameReader` */
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/DefaultSource.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
similarity index 96%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/DefaultSource.scala
rename to datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
index 63e88e25a..d26cc8e41 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/DefaultSource.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
@@ -37,9 +37,9 @@ import scala.util.Try
  * DataSource over a GeoTrellis layer store.
  */
 @Experimental
-class DefaultSource extends DataSourceRegister
+class GeoTrellisLayerDataSource extends DataSourceRegister
   with RelationProvider with CreatableRelationProvider with DataSourceOptions {
-  def shortName(): String = DefaultSource.SHORT_NAME
+  def shortName(): String = GeoTrellisLayerDataSource.SHORT_NAME
 
   /**
    * Create a GeoTrellis data source.
@@ -110,6 +110,6 @@ class DefaultSource extends DataSourceRegister
   }
 }
 
-object DefaultSource {
+object GeoTrellisLayerDataSource {
   final val SHORT_NAME = "geotrellis"
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
index 5f3304d05..15e54ff44 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
@@ -21,7 +21,7 @@ package astraea.spark.rasterframes.datasource
 import java.net.URI
 
 import _root_.geotrellis.spark.LayerId
-import astraea.spark.rasterframes.datasource.geotrellis.DefaultSource._
+import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource._
 import astraea.spark.rasterframes.{RasterFrame, _}
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions.col
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
new file mode 100644
index 000000000..e942ac100
--- /dev/null
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -0,0 +1,58 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.datasource.rastersource
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+
+class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
+  override def shortName(): String = RasterSourceDataSource.SHORT_NAME
+  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
+    val bandCount = parameters
+      .get(RasterSourceDataSource.BAND_COUNT_PARAM)
+      .map(_.toInt)
+      .getOrElse(1)
+
+    val files = RasterSourceDataSource.filePaths(parameters)
+
+    RasterSourceRelation(sqlContext, files, bandCount)
+  }
+}
+object RasterSourceDataSource {
+  final val SHORT_NAME = "rastersource"
+  final val PATH_PARAM = "path"
+  final val PATHS_PARAM = "paths"
+  final val BAND_COUNT_PARAM = "bandCount"
+
+  private[rastersource]
+  def filePaths(parameters: Map[String, String]): Seq[String] =
+    (
+      parameters
+        .get(RasterSourceDataSource.PATHS_PARAM)
+        .toSeq
+        .flatMap(_.split(Array('\n','\r'))) ++
+        parameters
+          .get(RasterSourceDataSource.PATH_PARAM)
+          .toSeq
+      )
+      .filter(_.nonEmpty)
+
+}
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
new file mode 100644
index 000000000..384685554
--- /dev/null
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -0,0 +1,67 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.datasource.rastersource
+
+import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.expressions.accessors.{GetCRS, GetExtent}
+import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
+import astraea.spark.rasterframes.util._
+import geotrellis.proj4.CRS
+import geotrellis.vector.Extent
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.sources.{BaseRelation, TableScan}
+import org.apache.spark.sql.types.{StringType, StructField, StructType}
+import org.apache.spark.sql.{Row, SQLContext}
+
+case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandCount: Int) extends BaseRelation with TableScan {
+  override def schema: StructType = if (bandCount == 1) RasterSourceRelation.schema
+  else {
+    val fields = RasterSourceRelation.schema.fields
+    StructType(
+      fields.dropRight(1) ++ {
+        val base = fields.last
+        for (b <- 1 to bandCount) yield base.copy(name = base.name + "_" + b)
+      }
+    )
+  }
+  override def buildScan(): RDD[Row] = {
+    import sqlContext.implicits._
+    paths.toDF("path")
+      .withColumn("__rr", RasterSourceToRasterRefs(URIToRasterSource($"path")))
+      .select(
+        PATH_COLUMN,
+        GetExtent($"__rr") as EXTENT_COLUMN.columnName,
+        GetCRS($"__rr") as CRS_COLUMN.columnName,
+        RasterRefToTile($"__rr") as TILE_COLUMN.columnName
+      )
+      .rdd
+  }
+}
+object RasterSourceRelation {
+  def schema: StructType = StructType(Seq(
+    StructField(PATH_COLUMN.columnName, StringType, false),
+    StructField(EXTENT_COLUMN.columnName, CatalystSerializer[Extent].schema, nullable = true),
+    StructField(CRS_COLUMN.columnName, CatalystSerializer[CRS].schema, false),
+    StructField(TILE_COLUMN.columnName, TileType, true)
+  ))
+}
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
new file mode 100644
index 000000000..ccb556c42
--- /dev/null
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
@@ -0,0 +1,55 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.datasource
+import java.net.URI
+
+import org.apache.spark.sql.DataFrameReader
+import shapeless.tag
+import shapeless.tag.@@
+package object rastersource {
+
+  trait RasterSourceDataFrameReaderTag
+  type RasterSourceDataFrameReader = DataFrameReader @@ RasterSourceDataFrameReaderTag
+
+  /** Adds `rastersource` format specifier to `DataFrameReader`. */
+  implicit class DataFrameReaderHasRasterSourceFormat(val reader: DataFrameReader) {
+    def rastersource: RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.format(RasterSourceDataSource.SHORT_NAME))
+  }
+
+  /** Adds option methods relevant to RasterSourceDataSource. */
+  implicit class RasterSourceDataFrameReaderHasOptions(val reader: RasterSourceDataFrameReader) {
+    /** Set the number of bands to provide columns for. */
+    def withBandCount(bandCount: Int): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.BAND_COUNT_PARAM, bandCount))
+
+    def from(paths: Seq[String]): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.PATHS_PARAM, paths.mkString("\n"))
+      )
+
+    def from(uris: Seq[URI])(implicit d: DummyImplicit): RasterSourceDataFrameReader =
+      from(uris.map(_.toASCIIString))
+  }
+}
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 3bdeecd81..54ee09412 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -29,10 +29,6 @@ import org.apache.spark.sql.functions._
 class GeoTiffDataSourceSpec
     extends TestEnvironment with TestData {
 
-  val cogPath = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
-  val nonCogPath = getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI
-  val l8samplePath = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI
-
   describe("GeoTiff reading") {
 
     it("should read sample GeoTiff") {
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
new file mode 100644
index 000000000..cc7570680
--- /dev/null
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -0,0 +1,82 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.datasource.rastersource
+import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource.{PATHS_PARAM, PATH_PARAM}
+import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.util._
+
+class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
+  describe("DataSource parameter processing") {
+    it("should handle single `path`") {
+      val p = Map(PATH_PARAM -> "/usr/local/foo/bar.tif")
+      RasterSourceDataSource.filePaths(p) should be (p.values.toSeq)
+    }
+    it("should handle single `paths`") {
+      val p = Map(PATHS_PARAM -> "/usr/local/foo/bar.tif")
+      RasterSourceDataSource.filePaths(p) should be (p.values.toSeq)
+    }
+    it("should handle multiple `paths`") {
+      val expected = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
+      val p = Map(PATHS_PARAM -> expected.mkString("\n\r", "\n\n", "\r"))
+      RasterSourceDataSource.filePaths(p) should be (expected)
+    }
+    it("should handle both `path` and `paths`") {
+      val expected1 = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
+      val expected2 = "/usr/local/barf/baz.tif"
+      val p = Map(PATHS_PARAM -> expected1.mkString("\n"), PATH_PARAM -> expected2)
+      RasterSourceDataSource.filePaths(p) should be (expected1 :+ expected2)
+    }
+  }
+
+  describe("RasterSource as relation reading") {
+    it("should default to a single band schema") {
+      val df = spark.read.rastersource.load(l8samplePath.toASCIIString)
+      val tcols = df.tileColumns
+      tcols.length should be(1)
+      tcols.map(_.columnName) should contain("tile")
+    }
+    it("should support a multiband schema") {
+      val df = spark.read
+        .rastersource
+        .withBandCount(3)
+        .load(cogPath.toASCIIString)
+      val tcols = df.tileColumns
+      tcols.length should be(3)
+      tcols.map(_.columnName) should contain allElementsOf Seq("tile_1", "tile_2", "tile_3")
+    }
+    it("should read a single file") {
+      val df = spark.read.rastersource.load(l8samplePath.toASCIIString)
+      df.printSchema()
+      df.show(false)
+      fail()
+    }
+    it("should read a multiple files with one band") {
+      val df = spark.read.rastersource
+        .from(Seq(cogPath, l8samplePath, nonCogPath))
+        .load()
+      df.printSchema()
+      df.show(false)
+      fail()
+    }
+
+  }
+}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
index ac13e4b20..b5b5fdf5e 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -42,6 +42,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types._
+import astraea.spark.rasterframes.TileType
 
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal
@@ -52,8 +53,6 @@ import org.apache.spark.sql.types._
 case class ReadTilesExpression(children: Seq[Expression]) extends Expression
   with Generator with CodegenFallback with DownloadSupport with LazyLogging {
 
-  private val TileType = new TileUDT()
-
   override def nodeName: String = "download_tiles"
 
   def inputTypes = Seq.fill(children.length)(StringType)

From 598f74ee0e47aaede35adabb5ce32bc17ace5516 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Apr 2019 16:29:38 -0400
Subject: [PATCH 037/380] Created a less verbose way of getting a schema from
 an implicit CatalystSerializer.

---
 .../encoders/CatalystSerializer.scala         |  2 ++
 .../encoders/CellTypeEncoder.scala            |  4 +--
 .../encoders/EnvelopeEncoder.scala            |  4 +--
 .../encoders/StandardSerializers.scala        | 26 +++++++++----------
 .../expressions/DynamicExtractors.scala       | 15 ++++++-----
 .../expressions/SpatialRelation.scala         |  3 +--
 .../expressions/accessors/GetCRS.scala        |  3 +--
 .../expressions/accessors/GetCellType.scala   |  3 +--
 .../expressions/accessors/GetDimensions.scala |  6 ++---
 .../expressions/accessors/GetExtent.scala     |  3 +--
 .../accessors/GetTileContext.scala            |  3 +--
 .../transformers/ExtentToGeometry.scala       |  5 ++--
 .../transformers/GeometryToExtent.scala       |  5 ++--
 .../RasterSourceToRasterRefs.scala            | 23 ++++++++--------
 .../transformers/SetCellType.scala            |  3 +--
 .../rasterframes/model/CellContext.scala      |  5 ++--
 .../spark/rasterframes/model/Cells.scala      |  3 ++-
 .../rasterframes/model/TileContext.scala      |  5 ++--
 .../rasterframes/model/TileDataContext.scala  |  4 +--
 .../spark/rasterframes/ref/RasterRef.scala    |  3 ++-
 .../tiles/ProjectedRasterTile.scala           |  6 ++---
 .../apache/spark/sql/rf/RasterSourceUDT.scala |  2 +-
 .../org/apache/spark/sql/rf/TileUDT.scala     |  6 ++---
 .../astraea/spark/rasterframes/JTSSpec.scala  |  1 -
 .../geotiff/GeoTiffCollectionRelation.scala   | 10 +++----
 .../datasource/geotiff/GeoTiffRelation.scala  | 14 +++++-----
 .../datasource/ReadTilesExpression.scala      | 10 +++----
 .../datasource/awspds/L8Relation.scala        |  8 +++---
 28 files changed, 90 insertions(+), 95 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
index 3f09e1f38..39e933cfc 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
@@ -50,6 +50,8 @@ trait CatalystSerializer[T] extends Serializable {
 object CatalystSerializer extends StandardSerializers {
   def apply[T: CatalystSerializer]: CatalystSerializer[T] = implicitly
 
+  def schemaOf[T: CatalystSerializer]: StructType = apply[T].schema
+
   /**
    * For some reason `Row` and `InternalRow` share no common base type. Instead of using
    * structural types (which use reflection), this typeclass is used to normalize access
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala
index 953c2ed65..41d9a6ec5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.VersionShims.InvokeSafely
 import org.apache.spark.sql.types.{ObjectType, StringType}
 import org.apache.spark.unsafe.types.UTF8String
-
+import CatalystSerializer._
 import scala.reflect.classTag
 
 /**
@@ -41,7 +41,7 @@ object CellTypeEncoder {
     import org.apache.spark.sql.catalyst.expressions._
     import org.apache.spark.sql.catalyst.expressions.objects._
     val ctType = ScalaReflection.dataTypeFor[DataType]
-    val schema = CatalystSerializer[CellType].schema
+    val schema = schemaOf[CellType]
     val inputObject = BoundReference(0, ctType, nullable = false)
 
     val intermediateType = ObjectType(classOf[String])
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
index e8f168db5..9cc947e49 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
@@ -8,7 +8,7 @@ import org.apache.spark.sql.catalyst.expressions.objects.NewInstance
 import org.apache.spark.sql.catalyst.expressions.{BoundReference, CreateNamedStruct, Literal}
 import org.apache.spark.sql.rf.VersionShims.InvokeSafely
 import org.apache.spark.sql.types._
-
+import CatalystSerializer._
 import scala.reflect.classTag
 
 /**
@@ -18,7 +18,7 @@ import scala.reflect.classTag
  */
 object EnvelopeEncoder {
 
-  val schema = CatalystSerializer[Envelope].schema
+  val schema = schemaOf[Envelope]
 
   val dataType: DataType = ScalaReflection.dataTypeFor[Envelope]
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
index 2d7aee8ad..a35e14c9e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
@@ -20,15 +20,15 @@
  */
 
 package astraea.spark.rasterframes.encoders
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.model.LazyCRS
-import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.spark._
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.vector._
 import org.apache.spark.sql.types._
+import org.locationtech.jts.geom.Envelope
 
 /** Collection of CatalystSerializers for third-party types. */
 trait StandardSerializers {
@@ -93,8 +93,8 @@ trait StandardSerializers {
 
   implicit val projectedExtentSerializer: CatalystSerializer[ProjectedExtent] = new CatalystSerializer[ProjectedExtent] {
     override def schema: StructType = StructType(Seq(
-      StructField("extent", CatalystSerializer[Extent].schema, false),
-      StructField("crs", CatalystSerializer[CRS].schema, false)
+      StructField("extent", schemaOf[Extent], false),
+      StructField("crs", schemaOf[CRS], false)
     ))
 
     override protected def to[R](t: ProjectedExtent, io: CatalystSerializer.CatalystIO[R]): R = io.create(
@@ -187,8 +187,8 @@ trait StandardSerializers {
 
   implicit val layoutDefinitionSerializer = new CatalystSerializer[LayoutDefinition] {
     override def schema: StructType = StructType(Seq(
-      StructField("extent", CatalystSerializer[Extent].schema, true),
-      StructField("tileLayout", CatalystSerializer[TileLayout].schema, true)
+      StructField("extent", schemaOf[Extent], true),
+      StructField("tileLayout", schemaOf[TileLayout], true)
     ))
 
     override protected def to[R](t: LayoutDefinition, io: CatalystIO[R]): R = io.create(
@@ -204,8 +204,8 @@ trait StandardSerializers {
 
   implicit def boundsSerializer[T: CatalystSerializer]: CatalystSerializer[KeyBounds[T]] = new CatalystSerializer[KeyBounds[T]] {
     override def schema: StructType = StructType(Seq(
-      StructField("minKey", CatalystSerializer[T].schema, true),
-      StructField("maxKey", CatalystSerializer[T].schema, true)
+      StructField("minKey", schemaOf[T], true),
+      StructField("maxKey", schemaOf[T], true)
     ))
 
     override protected def to[R](t: KeyBounds[T], io: CatalystIO[R]): R = io.create(
@@ -221,11 +221,11 @@ trait StandardSerializers {
 
   def tileLayerMetadataSerializer[T: CatalystSerializer]: CatalystSerializer[TileLayerMetadata[T]] = new CatalystSerializer[TileLayerMetadata[T]] {
     override def schema: StructType = StructType(Seq(
-      StructField("cellType", CatalystSerializer[CellType].schema, false),
-      StructField("layout", CatalystSerializer[LayoutDefinition].schema, false),
-      StructField("extent", CatalystSerializer[Extent].schema, false),
-      StructField("crs", CatalystSerializer[CRS].schema, false),
-      StructField("bounds", CatalystSerializer[KeyBounds[T]].schema, false)
+      StructField("cellType", schemaOf[CellType], false),
+      StructField("layout", schemaOf[LayoutDefinition], false),
+      StructField("extent", schemaOf[Extent], false),
+      StructField("crs", schemaOf[CRS], false),
+      StructField("bounds", schemaOf[KeyBounds[T]], false)
     ))
 
     override protected def to[R](t: TileLayerMetadata[T], io: CatalystIO[R]): R = io.create(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
index b551df4c5..f8a41054c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
@@ -20,7 +20,6 @@
  */
 
 package astraea.spark.rasterframes.expressions
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.model.TileContext
 import astraea.spark.rasterframes.ref.{ProjectedRasterLike, RasterRef, RasterSource}
@@ -31,14 +30,14 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.rf.{TileUDT, _}
 import org.apache.spark.sql.types._
 
-private[expressions]
+private[rasterframes]
 object DynamicExtractors {
   /** Partial function for pulling a tile and its contesxt from an input row. */
   lazy val tileExtractor: PartialFunction[DataType, InternalRow => (Tile, Option[TileContext])] = {
     case _: TileUDT =>
       (row: InternalRow) =>
         (row.to[Tile](TileUDT.tileSerializer), None)
-    case t if t.conformsTo(CatalystSerializer[ProjectedRasterTile].schema) =>
+    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
       (row: InternalRow) => {
         val prt = row.to[ProjectedRasterTile]
         (prt, Some(TileContext(prt)))
@@ -48,7 +47,7 @@ object DynamicExtractors {
   lazy val rowTileExtractor: PartialFunction[DataType, Row => (Tile, Option[TileContext])] = {
     case _: TileUDT =>
       (row: Row) =>  (row.to[Tile](TileUDT.tileSerializer), None)
-    case t if t.conformsTo(CatalystSerializer[ProjectedRasterTile].schema) =>
+    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
       (row: Row) => {
         val prt = row.to[ProjectedRasterTile]
         (prt, Some(TileContext(prt)))
@@ -59,9 +58,9 @@ object DynamicExtractors {
   lazy val projectedRasterLikeExtractor: PartialFunction[DataType, InternalRow ⇒ ProjectedRasterLike] = {
     case _: RasterSourceUDT ⇒
       (row: InternalRow) ⇒ row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
-    case t if t.conformsTo(CatalystSerializer[ProjectedRasterTile].schema) =>
+    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
       (row: InternalRow) => row.to[ProjectedRasterTile]
-    case t if t.conformsTo(CatalystSerializer[RasterRef].schema) =>
+    case t if t.conformsTo(schemaOf[RasterRef]) =>
       (row: InternalRow) ⇒ row.to[RasterRef]
   }
 
@@ -71,8 +70,10 @@ object DynamicExtractors {
       (row: InternalRow) ⇒ row.to[Tile](TileUDT.tileSerializer)
     case _: RasterSourceUDT ⇒
       (row: InternalRow) ⇒ row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
-    case t if t.conformsTo(CatalystSerializer[RasterRef].schema) ⇒
+    case t if t.conformsTo(schemaOf[RasterRef]) ⇒
       (row: InternalRow) ⇒ row.to[RasterRef]
+    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
+      (row: InternalRow) => row.to[ProjectedRasterTile]
   }
 
   sealed trait TileOrNumberArg
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
index 637fab10a..db40c8cab 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
@@ -19,7 +19,6 @@
 
 package astraea.spark.rasterframes.expressions
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.SpatialRelation.RelationPredicate
 import geotrellis.vector.Extent
@@ -47,7 +46,7 @@ abstract class SpatialRelation extends BinaryExpression
       case r: InternalRow ⇒
         expr.dataType match {
           case udt: AbstractGeometryUDT[_] ⇒ udt.deserialize(r)
-          case dt if dt.conformsTo(CatalystSerializer[Extent].schema) =>
+          case dt if dt.conformsTo(schemaOf[Extent]) =>
             val extent = r.to[Extent]
             extent.jtsGeom
         }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala
index 1a6d29df0..9f3e0bfff 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.encoders.StandardEncoders.crsEncoder
 import astraea.spark.rasterframes.expressions.OnTileContextExpression
@@ -39,7 +38,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  * @since 9/9/18
  */
 case class GetCRS(child: Expression) extends OnTileContextExpression with CodegenFallback {
-  override def dataType: DataType = CatalystSerializer[CRS].schema
+  override def dataType: DataType = schemaOf[CRS]
   override def nodeName: String = "crs"
   override def eval(ctx: TileContext): InternalRow = ctx.crs.toInternalRow
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala
index eeb521e4b..35f35e7ed 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.OnCellGridExpression
 import geotrellis.raster.{CellGrid, CellType}
@@ -38,7 +37,7 @@ case class GetCellType(child: Expression) extends OnCellGridExpression with Code
 
   override def nodeName: String = "cell_type"
 
-  def dataType: DataType = CatalystSerializer[CellType].schema
+  def dataType: DataType = schemaOf[CellType]
   /** Implemented by subtypes to process incoming ProjectedRasterLike entity. */
   override def eval(cg: CellGrid): Any = cg.cellType.toInternalRow
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala
index 3589dbc1b..099e74834 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.OnCellGridExpression
 import astraea.spark.rasterframes.model.TileDimensions
@@ -34,11 +33,10 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
  * Extract a Tile's dimensions
  * @since 12/21/17
  */
-case class GetDimensions(child: Expression) extends OnCellGridExpression
-  with CodegenFallback {
+case class GetDimensions(child: Expression) extends OnCellGridExpression with CodegenFallback {
   override def nodeName: String = "tile_dimensions"
 
-  def dataType = CatalystSerializer[TileDimensions].schema
+  def dataType = schemaOf[TileDimensions]
 
   override def eval(grid: CellGrid): Any = TileDimensions(grid.cols, grid.rows).toInternalRow
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala
index c3e664887..653cf06a8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.encoders.StandardEncoders.extentEncoder
 import astraea.spark.rasterframes.expressions.OnTileContextExpression
@@ -39,7 +38,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  * @since 9/10/18
  */
 case class GetExtent(child: Expression) extends OnTileContextExpression with CodegenFallback {
-  override def dataType: DataType = CatalystSerializer[Extent].schema
+  override def dataType: DataType = schemaOf[Extent]
   override def nodeName: String = "extent"
   override def eval(ctx: TileContext): InternalRow = ctx.extent.toInternalRow
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala
index 98b7eb401..d169a1b93 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala
@@ -20,7 +20,6 @@
  */
 
 package astraea.spark.rasterframes.expressions.accessors
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.UnaryRasterOp
 import astraea.spark.rasterframes.model.TileContext
@@ -31,7 +30,7 @@ import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
 
 case class GetTileContext(child: Expression) extends UnaryRasterOp with CodegenFallback {
-  override def dataType: DataType = CatalystSerializer[TileContext].schema
+  override def dataType: DataType = schemaOf[TileContext]
 
   override def nodeName: String = "get_tile_context"
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any =
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
index 422ba821f..d57d0c0fd 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.row
 import org.locationtech.jts.geom.{Envelope, Geometry}
@@ -45,8 +44,8 @@ case class ExtentToGeometry(child: Expression) extends UnaryExpression with Code
 
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
 
-  private val envSchema = CatalystSerializer[Envelope].schema
-  private val extSchema = CatalystSerializer[Extent].schema
+  private val envSchema = schemaOf[Envelope]
+  private val extSchema = schemaOf[Extent]
 
   override def checkInputDataTypes(): TypeCheckResult = {
     child.dataType match {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
index 69cd3f7cb..be1f5296b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
@@ -40,7 +39,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
 case class GeometryToExtent(child: Expression) extends UnaryExpression with CodegenFallback {
   override def nodeName: String = "geometry_bounds"
 
-  override def dataType: DataType = CatalystSerializer[Extent].schema
+  override def dataType: DataType = schemaOf[Extent]
 
   override def checkInputDataTypes(): TypeCheckResult = {
     child.dataType match {
@@ -54,7 +53,7 @@ case class GeometryToExtent(child: Expression) extends UnaryExpression with Code
   override protected def nullSafeEval(input: Any): Any = {
     val geom = JTSTypes.GeometryTypeInstance.deserialize(input)
     val extent = Extent(geom.getEnvelopeInternal)
-    CatalystSerializer[Extent].toInternalRow(extent)
+    extent.toInternalRow
   }
 }
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index fe8e2b5ee..8ef4f6cb4 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -21,9 +21,8 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.NOMINAL_TILE_DIMS
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
+import astraea.spark.rasterframes.model.TileDimensions
 import astraea.spark.rasterframes.ref.RasterRef
 import astraea.spark.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
@@ -42,11 +41,11 @@ import scala.util.control.NonFatal
  *
  * @since 9/6/18
  */
-case class RasterSourceToRasterRefs(children: Seq[Expression], applyTiling: Boolean) extends Expression
+case class RasterSourceToRasterRefs(children: Seq[Expression], subtileDims: Option[TileDimensions] = None) extends Expression
   with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
 
   private val RasterSourceType = new RasterSourceUDT()
-  private val rasterRefSchema = CatalystSerializer[RasterRef].schema
+  private val rasterRefSchema = schemaOf[RasterRef]
 
   override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
   override def nodeName: String = "raster_source_to_raster_ref"
@@ -59,10 +58,12 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], applyTiling: Bool
     try {
       val refs = children.map { child ⇒
         val src = RasterSourceType.deserialize(child.eval(input))
-        if (applyTiling) src
-          .layoutExtents(NOMINAL_TILE_DIMS)
-          .map(e ⇒ RasterRef(src, Some(e)))
-        else Seq(RasterRef(src))
+        subtileDims.map(dims =>
+          src
+            .layoutExtents(dims)
+            .map(e ⇒ RasterRef(src, Some(e)))
+        )
+        .getOrElse( Seq(RasterRef(src)))
       }
       refs.transpose.map(ts ⇒ InternalRow(ts.map(_.toInternalRow): _*))
     }
@@ -75,7 +76,7 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], applyTiling: Bool
 }
 
 object RasterSourceToRasterRefs {
-  def apply(rrs: Column*): TypedColumn[Any, RasterRef] = apply(true, rrs: _*)
-  def apply(applyTiling: Boolean, rrs: Column*): TypedColumn[Any, RasterRef] =
-    new Column(new RasterSourceToRasterRefs(rrs.map(_.expr), applyTiling)).as[RasterRef]
+  def apply(rrs: Column*): TypedColumn[Any, RasterRef] = apply(None, rrs: _*)
+  def apply(subtileDims: Option[TileDimensions], rrs: Column*): TypedColumn[Any, RasterRef] =
+    new Column(new RasterSourceToRasterRefs(rrs.map(_.expr), subtileDims)).as[RasterRef]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala
index 96fcd4288..d335746eb 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala
@@ -21,7 +21,6 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.encoders.StandardEncoders._
 import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
@@ -50,7 +49,7 @@ case class SetCellType(tile: Expression, cellType: Expression)
   override def nodeName: String = "set_cell_type"
   override def dataType: DataType = left.dataType
 
-  private val ctSchema = CatalystSerializer[CellType].schema
+  private val ctSchema = schemaOf[CellType]
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (!tileExtractor.isDefinedAt(left.dataType))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala b/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
index cac2903dd..efc632c0b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
@@ -23,13 +23,14 @@ package astraea.spark.rasterframes.model
 import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{ShortType, StructField, StructType}
+import CatalystSerializer._
 
 case class CellContext(tile_context: TileContext, tile_data_context: TileDataContext, col_index: Short, row_index: Short)
 object CellContext {
   implicit val serializer: CatalystSerializer[CellContext] = new CatalystSerializer[CellContext] {
     override def schema: StructType = StructType(Seq(
-      StructField("tile_context", CatalystSerializer[TileContext].schema, false),
-      StructField("tile_data_context", CatalystSerializer[TileDataContext].schema, false),
+      StructField("tile_context", schemaOf[TileContext], false),
+      StructField("tile_data_context", schemaOf[TileDataContext], false),
       StructField("col_index", ShortType, false),
       StructField("row_index", ShortType, false)
     ))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala b/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
index acf847e45..03500c483 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
@@ -26,6 +26,7 @@ import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
 import geotrellis.raster.{ArrayTile, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
+import CatalystSerializer._
 
 /** Represents the union of binary cell datas or a reference to the data.*/
 case class Cells(data: Either[Array[Byte], RasterRef]) {
@@ -53,7 +54,7 @@ object Cells {
   implicit def cellsSerializer: CatalystSerializer[Cells] = new CatalystSerializer[Cells] {
     override def schema: StructType = StructType(Seq(
       StructField("cells", BinaryType, true),
-      StructField("ref", CatalystSerializer[RasterRef].schema, true)
+      StructField("ref", schemaOf[RasterRef], true)
     ))
     override protected def to[R](t: Cells, io: CatalystSerializer.CatalystIO[R]): R = io.create(
       t.data.left.getOrElse(null),
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala b/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala
index f5d49524c..e6d6a5565 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala
@@ -27,6 +27,7 @@ import geotrellis.raster.Tile
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{StructField, StructType}
+import CatalystSerializer._
 
 case class TileContext(extent: Extent, crs: CRS) {
   def toProjectRasterTile(t: Tile): ProjectedRasterTile = ProjectedRasterTile(t, extent, crs)
@@ -39,8 +40,8 @@ object TileContext {
   }
   implicit val serializer: CatalystSerializer[TileContext] = new CatalystSerializer[TileContext] {
     override def schema: StructType = StructType(Seq(
-      StructField("extent", CatalystSerializer[Extent].schema, false),
-      StructField("crs", CatalystSerializer[CRS].schema, false)
+      StructField("extent", schemaOf[Extent], false),
+      StructField("crs", schemaOf[CRS], false)
     ))
     override protected def to[R](t: TileContext, io: CatalystSerializer.CatalystIO[R]): R = io.create(
       io.to(t.extent),
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala b/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
index 121f8b845..dcba073f6 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
@@ -41,8 +41,8 @@ object TileDataContext {
 
   implicit val serializer: CatalystSerializer[TileDataContext] = new CatalystSerializer[TileDataContext] {
     override def schema: StructType =  StructType(Seq(
-      StructField("cell_type", CatalystSerializer[CellType].schema, false),
-      StructField("dimensions", CatalystSerializer[TileDimensions].schema, false)
+      StructField("cell_type", schemaOf[CellType], false),
+      StructField("dimensions", schemaOf[TileDimensions], false)
     ))
 
     override protected def to[R](t: TileDataContext, io: CatalystIO[R]): R = io.create(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
index aec6b3b86..6a1206722 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
@@ -31,6 +31,7 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
 import org.apache.spark.sql.types.{StructField, StructType}
+import CatalystSerializer._
 
 /**
  * A delayed-read projected raster implementation.
@@ -82,7 +83,7 @@ object RasterRef extends LazyLogging {
     val rsType = new RasterSourceUDT()
     override def schema: StructType = StructType(Seq(
       StructField("source", rsType, false),
-      StructField("subextent", CatalystSerializer[Extent].schema, true)
+      StructField("subextent", schemaOf[Extent], true)
     ))
 
     override def to[R](t: RasterRef, io: CatalystIO[R]): R = io.create(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
index 34003fb31..35ee3a387 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
@@ -21,7 +21,8 @@
 
 package astraea.spark.rasterframes.tiles
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import astraea.spark.rasterframes.TileType
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import astraea.spark.rasterframes.model.TileContext
 import astraea.spark.rasterframes.ref.ProjectedRasterLike
@@ -33,7 +34,6 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
-import astraea.spark.rasterframes.TileType
 
 /**
  * A Tile that's also like a ProjectedRaster, with delayed evaluation support.
@@ -64,7 +64,7 @@ object ProjectedRasterTile {
 
   implicit val serializer: CatalystSerializer[ProjectedRasterTile] = new CatalystSerializer[ProjectedRasterTile] {
     override def schema: StructType = StructType(Seq(
-      StructField("tile_context", CatalystSerializer[TileContext].schema, false),
+      StructField("tile_context", schemaOf[TileContext], false),
       StructField("tile", TileType, false))
     )
 
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index 2dee38a6e..be61615ce 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -44,7 +44,7 @@ class RasterSourceUDT extends UserDefinedType[RasterSource] {
 
   def userClass: Class[RasterSource] = classOf[RasterSource]
 
-  override def sqlType: DataType = CatalystSerializer[RasterSource].schema
+  override def sqlType: DataType = schemaOf[RasterSource]
 
   override def serialize(obj: RasterSource): InternalRow =
     Option(obj)
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index 643950445..aa2b24779 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -44,7 +44,7 @@ class TileUDT extends UserDefinedType[Tile] {
 
   def userClass: Class[Tile] = classOf[Tile]
 
-  def sqlType: StructType = CatalystSerializer[Tile].schema
+  def sqlType: StructType = schemaOf[Tile]
 
   override def serialize(obj: Tile): InternalRow =
     Option(obj)
@@ -76,8 +76,8 @@ case object TileUDT  {
   implicit def tileSerializer: CatalystSerializer[Tile] = new CatalystSerializer[Tile] {
 
     override def schema: StructType = StructType(Seq(
-      StructField("cell_context", CatalystSerializer[TileDataContext].schema, false),
-      StructField("cell_data", CatalystSerializer[Cells].schema, false)
+      StructField("cell_context", schemaOf[TileDataContext], false),
+      StructField("cell_data", schemaOf[Cells], false)
     ))
 
     override def to[R](t: Tile, io: CatalystIO[R]): R = io.create(
diff --git a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
index a017e9a2e..df8027a3a 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
@@ -22,7 +22,6 @@ package astraea.spark.rasterframes
 import org.locationtech.jts.geom._
 import geotrellis.proj4.{LatLng, Sinusoidal, WebMercator}
 import geotrellis.vector.{Extent, Point => GTPoint}
-import org.apache.spark.sql.Column
 
 /**
  * Test rig for operations providing interop with JTS types.
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 76868d055..910874e06 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -24,7 +24,7 @@ import java.net.URI
 
 import astraea.spark.rasterframes._
 import astraea.spark.rasterframes.datasource.geotiff.GeoTiffCollectionRelation.Cols
-import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.util._
 import geotrellis.proj4.CRS
 import geotrellis.spark.io.hadoop.HadoopGeoTiffRDD
@@ -40,8 +40,8 @@ case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount
 
   override def schema: StructType = StructType(Seq(
     StructField(Cols.PATH, StringType, false),
-    StructField(EXTENT_COLUMN.columnName, CatalystSerializer[Extent].schema, nullable = true),
-    StructField(CRS_COLUMN.columnName, CatalystSerializer[CRS].schema, false)
+    StructField(EXTENT_COLUMN.columnName, schemaOf[Extent], nullable = true),
+    StructField(CRS_COLUMN.columnName, schemaOf[CRS], false)
   ) ++ (
     if(bandCount == 1) Seq(StructField(Cols.TL, new TileUDT, false))
     else for(b ← 1 to bandCount) yield StructField(Cols.TL + "_" + b, new TileUDT, nullable = true)
@@ -58,8 +58,8 @@ case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount
       .map { case ((path, pe), mbt) ⇒
         val entries = columnIndexes.map {
           case 0 ⇒ path
-          case 1 ⇒ CatalystSerializer[Extent].toRow(pe.extent)
-          case 2 ⇒ CatalystSerializer[CRS].toRow(pe.crs)
+          case 1 ⇒ pe.extent.toRow
+          case 2 ⇒ pe.crs.toRow
           case i if i > 2 ⇒ {
             if(bandCount == 1 && mbt.bandCount > 2) mbt.color()
             else mbt.band(i - 3)
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
index 9deafe69d..2bc42893c 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
@@ -22,7 +22,7 @@ package astraea.spark.rasterframes.datasource.geotiff
 import java.net.URI
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
@@ -67,8 +67,8 @@ case class GeoTiffRelation(sqlContext: SQLContext, uri: URI) extends BaseRelatio
 
     StructType(Seq(
       StructField(SPATIAL_KEY_COLUMN.columnName, skSchema, nullable = false, skMetadata),
-      StructField(EXTENT_COLUMN.columnName, CatalystSerializer[Extent].schema, nullable = true),
-      StructField(CRS_COLUMN.columnName, CatalystSerializer[CRS].schema, nullable = true),
+      StructField(EXTENT_COLUMN.columnName, schemaOf[Extent], nullable = true),
+      StructField(CRS_COLUMN.columnName, schemaOf[CRS], nullable = true),
       StructField(METADATA_COLUMN.columnName,
         DataTypes.createMapType(StringType, StringType, false)
       )
@@ -86,8 +86,7 @@ case class GeoTiffRelation(sqlContext: SQLContext, uri: URI) extends BaseRelatio
     val trans = tlm.mapTransform
     val metadata = info.tags.headTags
 
-    val extSer = CatalystSerializer[Extent]
-    val encodedCRS = CatalystSerializer[CRS].toRow(tlm.crs)
+    val encodedCRS = tlm.crs.toRow
 
     if(info.segmentLayout.isTiled) {
       // TODO: Figure out how to do tile filtering via the range reader.
@@ -99,7 +98,7 @@ case class GeoTiffRelation(sqlContext: SQLContext, uri: URI) extends BaseRelatio
           val gb = trans.extentToBounds(pe.extent)
           val entries = columnIndexes.map {
             case 0 => SpatialKey(gb.colMin, gb.rowMin)
-            case 1 => extSer.toRow(pe.extent)
+            case 1 => pe.extent.toRow
             case 2 => encodedCRS
             case 3 => metadata
             case n => tiles.band(n - 4)
@@ -112,12 +111,11 @@ case class GeoTiffRelation(sqlContext: SQLContext, uri: URI) extends BaseRelatio
       val geotiff = HadoopGeoTiffReader.readMultiband(new Path(uri))
       val rdd = sqlContext.sparkContext.makeRDD(Seq((geotiff.projectedExtent, Shims.toArrayTile(geotiff.tile))))
 
-
       rdd.tileToLayout(tlm)
         .map { case (sk, tiles) ⇒
           val entries = columnIndexes.map {
             case 0 => sk
-            case 1 => extSer.toRow(trans.keyToExtent(sk))
+            case 1 => trans.keyToExtent(sk).toRow
             case 2 => encodedCRS
             case 3 => metadata
             case n => tiles.band(n - 4)
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
index b5b5fdf5e..24025e23c 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -22,13 +22,13 @@ package astraea.spark.rasterframes.experimental.datasource
 
 import java.net.URI
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.TileType
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.ref.HttpRangeReader
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
-import geotrellis.raster.{ProjectedRaster, Tile}
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
+import geotrellis.raster.{ProjectedRaster, Tile}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
 import geotrellis.spark.io.s3.S3Client
 import geotrellis.spark.io.s3.util.S3RangeReader
@@ -40,9 +40,7 @@ import org.apache.spark.sql.Column
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
-import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types._
-import astraea.spark.rasterframes.TileType
 
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal
@@ -58,8 +56,8 @@ case class ReadTilesExpression(children: Seq[Expression]) extends Expression
   def inputTypes = Seq.fill(children.length)(StringType)
 
   override def elementSchema: StructType = StructType(Seq(
-    StructField("crs", CatalystSerializer[CRS].schema, true),
-    StructField("extent", CatalystSerializer[Extent].schema, true)
+    StructField("crs", schemaOf[CRS], true),
+    StructField("extent", schemaOf[Extent], true)
   ) ++
     children
       .zipWithIndex
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 23075dc86..245b560c9 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -21,7 +21,7 @@
 package astraea.spark.rasterframes.experimental.datasource.awspds
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.experimental.datasource.awspds.L8Relation.Bands
 import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.ref.RasterRef
@@ -100,7 +100,9 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
 
     val df = {
       // NB: We assume that `nativeTiling` preserves the band names.
-      val expanded = RasterSourceToRasterRefs(useTiling, bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b)): _*)
+      val expanded = RasterSourceToRasterRefs(
+        if (useTiling) Some(NOMINAL_TILE_DIMS) else None,
+        bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b)): _*)
       filtered.select(nonTile :+ expanded: _*)
     }
 
@@ -126,7 +128,7 @@ object L8Relation extends PDSFields {
         case ACQUISITION_DATE ⇒ ACQUISITION_DATE.copy(name = StandardColumns.TIMESTAMP_COLUMN.columnName)
         case s if s.name == BOUNDS_WGS84.name ⇒ BOUNDS
         case s if s != DOWNLOAD_URL ⇒ s
-      } ++ L8Relation.Bands.values.toSeq.map(b ⇒ StructField(b.toString, CatalystSerializer[RasterRef].schema, true))
+      } ++ L8Relation.Bands.values.toSeq.map(b ⇒ StructField(b.toString, schemaOf[RasterRef], true))
     )
   }
 }

From be706aedf53f614adae6173983c39b96ed65de6c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Apr 2019 16:30:29 -0400
Subject: [PATCH 038/380] Provided means of specifying tile size via
 RasterSourceDataSource.

---
 .../spark/rasterframes/StandardColumns.scala  |  5 ++-
 .../transformers/RasterRefToTile.scala        | 14 ++++----
 .../extensions/DataFrameMethods.scala         | 33 +++----------------
 .../spark/rasterframes/ref/RasterSource.scala | 20 ++---------
 .../rasterframes/ref/RasterRefSpec.scala      |  2 +-
 .../rasterframes/ref/RasterSourceSpec.scala   | 11 ++++---
 .../rastersource/RasterSourceDataSource.scala | 33 ++++++++++++-------
 .../rastersource/RasterSourceRelation.scala   | 17 ++++------
 .../datasource/rastersource/package.scala     | 12 +++++--
 .../RasterSourceDataSourceSpec.scala          | 31 ++++++++++-------
 10 files changed, 81 insertions(+), 97 deletions(-)

diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
index dd488ce6f..e327e871a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
@@ -6,7 +6,7 @@ import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 import geotrellis.proj4.CRS
 import geotrellis.raster.Tile
 import geotrellis.spark.{SpatialKey, TemporalKey}
-import geotrellis.vector.Extent
+import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.functions.col
 import org.locationtech.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
 
@@ -36,6 +36,9 @@ trait StandardColumns {
   /** Default Extent column name. */
   def EXTENT_COLUMN = col("extent").as[Extent]
 
+  /** Default ProjectedExtent column name. */
+  def PROJECTED_EXTENT_COLUMN = col("proj_extent").as[ProjectedExtent]
+
   /** Default CRS column name. */
   def CRS_COLUMN = col("crs").as[CRS]
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala
index c3aa3f337..4a510fb37 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala
@@ -21,12 +21,11 @@
 
 package astraea.spark.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.row
 import astraea.spark.rasterframes.ref.RasterRef
+import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import com.typesafe.scalalogging.LazyLogging
-import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
 import org.apache.spark.sql.rf._
@@ -43,19 +42,18 @@ case class RasterRefToTile(child: Expression) extends UnaryExpression
 
   override def nodeName: String = "raster_ref_to_tile"
 
-  override def inputTypes = Seq(CatalystSerializer[RasterRef].schema)
+  override def inputTypes = Seq(schemaOf[RasterRef])
 
-  override def dataType: DataType = new TileUDT
+  override def dataType: DataType = schemaOf[ProjectedRasterTile]
 
   override protected def nullSafeEval(input: Any): Any = {
     implicit val ser = TileUDT.tileSerializer
     val ref = row(input).to[RasterRef]
-    (ref.tile: Tile).toInternalRow
+    ref.tile.toInternalRow
   }
 }
 
 object RasterRefToTile {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
-  def apply(rr: Column): TypedColumn[Any, Tile] =
-    new Column(RasterRefToTile(rr.expr)).as[Tile]
+  def apply(rr: Column): TypedColumn[Any, ProjectedRasterTile] =
+  new Column(RasterRefToTile(rr.expr)).as[ProjectedRasterTile]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
index ca38322ac..b8bad84ea 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
@@ -25,11 +25,12 @@ import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
 import spray.json.JsonFormat
 import astraea.spark.rasterframes.encoders.StandardEncoders._
+import astraea.spark.rasterframes.expressions.DynamicExtractors
+
 import scala.util.Try
 
 /**
@@ -82,15 +83,15 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     fetchMetadataValue(column, _.metadata.getString(SPATIAL_ROLE_KEY))
 
   /** Get the columns that are of type `Tile` */
-  def tileColumns: Seq[TypedColumn[Any, Tile]] =
+  def tileColumns: Seq[Column] =
     self.schema.fields
-      .filter(_.dataType.typeName.equalsIgnoreCase(TileUDT.typeName))
+      .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
       .map(f ⇒ col(f.name).as[Tile])
 
   /** Get the columns that are not of type `Tile` */
   def notTileColumns: Seq[Column] =
     self.schema.fields
-      .filterNot(_.dataType.typeName.equalsIgnoreCase(TileUDT.typeName))
+      .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
       .map(f ⇒ col(f.name))
 
   /** Get the spatial column. */
@@ -190,30 +191,6 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
       .setTemporalColumnRole(temporalKey)
       .asRF
 
-//  @throws[IllegalArgumentException]
-//  def asRF(space: LayerSpace): RasterFrame = {
-//    require(tileColumns.isEmpty, "This method doesn't yet support existing tile columns")
-//    // We have two use cases to consider: This is already a rasterframe and we need to
-//    // reproject it. If we have RasterRefs then we reproject those
-//    val (refFields, otherFields) = self.schema.fields
-//        .partition(_.dataType.typeName.equalsIgnoreCase(RasterRefUDT.typeName))
-//
-//    val refCols = refFields.map(f ⇒ self(f.name).as[RasterRef])
-//    val otherCols = otherFields.map(f ⇒ self(f.name))
-//
-//    // Reproject tile into layer space
-//    val projected = self.select(otherCols :+ ProjectIntoLayer(refCols, space): _*)
-//
-//    // Lastly, convert cell type as desired
-//    val tileCols = projected.tileColumns.map(c ⇒ convert_cell_type(c, space.cell_type).as(c.columnName))
-//    val remCols = projected.notTileColumns
-//
-//    val layer = projected.select(remCols ++ tileCols: _*)
-//
-//    val tlm = space.asTileLayerMetadata
-//    layer.setSpatialColumnRole(SPATIAL_KEY_COLUMN, tlm).asRF
-//  }
-
   /**
    * Converts [[DataFrame]] to a RasterFrame if the following constraints are fulfilled:
    *
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index b85dadcc9..45e708206 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -37,7 +37,6 @@ import geotrellis.raster._
 import geotrellis.raster.io.geotiff.Tags
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.spark.io.hadoop.HdfsRangeReader
-import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.util.RangeReader
 import geotrellis.vector.Extent
 import org.apache.hadoop.conf.Configuration
@@ -82,24 +81,9 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 
   def tileContext: TileContext = TileContext(extent, crs)
 
-  def tileLayout(dims: TileDimensions): TileLayout = {
-    require(dims.cols > 0 && dims.rows > 0, "Non-zero tile sizes")
-    TileLayout(
-      layoutCols = math.ceil(this.cols.toDouble / dims.cols).toInt,
-      layoutRows = math.ceil(this.rows.toDouble / dims.rows).toInt,
-      tileCols = dims.cols,
-      tileRows = dims.rows
-    )
-  }
-
   def layoutExtents(dims: TileDimensions): Seq[Extent] = {
-    val tl = tileLayout(dims)
-    val layout = LayoutDefinition(extent, tl)
-    val transform = layout.mapTransform
-    for {
-      col <- 0 until tl.layoutCols
-      row <- 0 until tl.layoutRows
-    } yield transform(col, row)
+    val re = rasterExtent
+    layoutBounds(dims).map(re.rasterExtentFor).map(_.extent)
   }
 
   def layoutBounds(dims: TileDimensions): Seq[GridBounds] = {
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index 4f7b77563..f1af549fe 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -177,7 +177,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         import spark.implicits._
         val df = Seq(src).toDF("src")
-        val refs = df.select(RasterSourceToRasterRefs(true, $"src"))
+        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), $"src"))
         assert(refs.count() > 1)
       }
     }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 141eac48f..60fac1206 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -44,11 +44,6 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       assert(new RasterSourceUDT() === new RasterSourceUDT())
     }
     val rs = RasterSource(getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI)
-    it("should provide a tile layout") {
-      val layout = rs.tileLayout(TileDimensions(62, 61))
-      layout.totalCols shouldBe >= (rs.cols.toLong)
-      layout.totalRows shouldBe >= (rs.rows.toLong)
-    }
     it("should compute nominal tile layout bounds") {
       val bounds = rs.layoutBounds(TileDimensions(65, 60))
       val agg = bounds.reduce(_ combine _)
@@ -59,6 +54,12 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       val agg = extents.reduce(_ combine _)
       agg should be (rs.extent)
     }
+    it("should reassemble correct grid from extents") {
+      val dims = TileDimensions(63, 63)
+      val ext = rs.layoutExtents(dims).head
+      val bounds = rs.layoutBounds(dims).head
+      rs.rasterExtent.gridBoundsFor(ext, false) should be (bounds)
+    }
   }
 
   describe("HTTP RasterSource") {
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index e942ac100..7e05ffa6f 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -20,20 +20,18 @@
  */
 
 package astraea.spark.rasterframes.datasource.rastersource
+import astraea.spark.rasterframes.model.TileDimensions
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
 
 class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
-  override def shortName(): String = RasterSourceDataSource.SHORT_NAME
+  import RasterSourceDataSource._
+  override def shortName(): String = SHORT_NAME
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
-    val bandCount = parameters
-      .get(RasterSourceDataSource.BAND_COUNT_PARAM)
-      .map(_.toInt)
-      .getOrElse(1)
-
-    val files = RasterSourceDataSource.filePaths(parameters)
-
-    RasterSourceRelation(sqlContext, files, bandCount)
+    val bandCount = parameters.bandCount
+    val files = parameters.filePaths
+    val tiling = parameters.tileDims
+    RasterSourceRelation(sqlContext, files, bandCount, tiling)
   }
 }
 object RasterSourceDataSource {
@@ -41,12 +39,13 @@ object RasterSourceDataSource {
   final val PATH_PARAM = "path"
   final val PATHS_PARAM = "paths"
   final val BAND_COUNT_PARAM = "bandCount"
+  final val TILE_DIMS_PARAM = "tileDimensions"
 
   private[rastersource]
-  def filePaths(parameters: Map[String, String]): Seq[String] =
-    (
+  implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
+    def filePaths: Seq[String] = (
       parameters
-        .get(RasterSourceDataSource.PATHS_PARAM)
+        .get(PATHS_PARAM)
         .toSeq
         .flatMap(_.split(Array('\n','\r'))) ++
         parameters
@@ -55,4 +54,14 @@ object RasterSourceDataSource {
       )
       .filter(_.nonEmpty)
 
+    def tileDims: Option[TileDimensions] =
+      parameters.get(TILE_DIMS_PARAM)
+      .map(_.split(',').map(_.trim.toInt))
+      .map { case Array(cols, rows) => TileDimensions(cols, rows)}
+
+    def bandCount: Int = parameters
+      .get(BAND_COUNT_PARAM)
+      .map(_.toInt)
+      .getOrElse(1)
+  }
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index 384685554..ec6980500 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -22,18 +22,17 @@
 package astraea.spark.rasterframes.datasource.rastersource
 
 import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer
-import astraea.spark.rasterframes.expressions.accessors.{GetCRS, GetExtent}
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
+import astraea.spark.rasterframes.model.TileDimensions
+import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import astraea.spark.rasterframes.util._
-import geotrellis.proj4.CRS
-import geotrellis.vector.Extent
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
 
-case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandCount: Int) extends BaseRelation with TableScan {
+case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandCount: Int, subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
   override def schema: StructType = if (bandCount == 1) RasterSourceRelation.schema
   else {
     val fields = RasterSourceRelation.schema.fields
@@ -47,11 +46,9 @@ case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], band
   override def buildScan(): RDD[Row] = {
     import sqlContext.implicits._
     paths.toDF("path")
-      .withColumn("__rr", RasterSourceToRasterRefs(URIToRasterSource($"path")))
+      .withColumn("__rr", RasterSourceToRasterRefs(subtileDims, URIToRasterSource($"path")))
       .select(
         PATH_COLUMN,
-        GetExtent($"__rr") as EXTENT_COLUMN.columnName,
-        GetCRS($"__rr") as CRS_COLUMN.columnName,
         RasterRefToTile($"__rr") as TILE_COLUMN.columnName
       )
       .rdd
@@ -60,8 +57,6 @@ case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], band
 object RasterSourceRelation {
   def schema: StructType = StructType(Seq(
     StructField(PATH_COLUMN.columnName, StringType, false),
-    StructField(EXTENT_COLUMN.columnName, CatalystSerializer[Extent].schema, nullable = true),
-    StructField(CRS_COLUMN.columnName, CatalystSerializer[CRS].schema, false),
-    StructField(TILE_COLUMN.columnName, TileType, true)
+    StructField(TILE_COLUMN.columnName, schemaOf[ProjectedRasterTile], true)
   ))
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
index ccb556c42..421d241cf 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
@@ -44,11 +44,19 @@ package object rastersource {
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.BAND_COUNT_PARAM, bandCount))
 
-    def from(paths: Seq[String]): RasterSourceDataFrameReader =
+    def withTileDimensions(cols: Int, rows: Int): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.TILE_DIMS_PARAM, s"$cols,$rows")
+      )
+
+    def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
-        reader.option(RasterSourceDataSource.PATHS_PARAM, paths.mkString("\n"))
+        reader.option(RasterSourceDataSource.PATHS_PARAM, newlineDelimPaths)
       )
 
+    def from(paths: Seq[String]): RasterSourceDataFrameReader =
+      from(paths.mkString("\n"))
+
     def from(uris: Seq[URI])(implicit d: DummyImplicit): RasterSourceDataFrameReader =
       from(uris.map(_.toASCIIString))
   }
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index cc7570680..099c501ee 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -20,30 +20,38 @@
  */
 
 package astraea.spark.rasterframes.datasource.rastersource
-import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource.{PATHS_PARAM, PATH_PARAM}
+import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource._
 import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.model.TileDimensions
 import astraea.spark.rasterframes.util._
 
 class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
+  import spark.implicits._
+
   describe("DataSource parameter processing") {
     it("should handle single `path`") {
       val p = Map(PATH_PARAM -> "/usr/local/foo/bar.tif")
-      RasterSourceDataSource.filePaths(p) should be (p.values.toSeq)
+      p.filePaths should be (p.values.toSeq)
     }
+
     it("should handle single `paths`") {
       val p = Map(PATHS_PARAM -> "/usr/local/foo/bar.tif")
-      RasterSourceDataSource.filePaths(p) should be (p.values.toSeq)
+      p.filePaths should be (p.values.toSeq)
     }
     it("should handle multiple `paths`") {
       val expected = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val p = Map(PATHS_PARAM -> expected.mkString("\n\r", "\n\n", "\r"))
-      RasterSourceDataSource.filePaths(p) should be (expected)
+      p.filePaths should be (expected)
     }
     it("should handle both `path` and `paths`") {
       val expected1 = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val expected2 = "/usr/local/barf/baz.tif"
       val p = Map(PATHS_PARAM -> expected1.mkString("\n"), PATH_PARAM -> expected2)
-      RasterSourceDataSource.filePaths(p) should be (expected1 :+ expected2)
+      p.filePaths should be (expected1 :+ expected2)
+    }
+    it("should parse tile dimensions") {
+      val p = Map(TILE_DIMS_PARAM -> "4, 5")
+      p.tileDims should be (Some(TileDimensions(4, 5)))
     }
   }
 
@@ -64,19 +72,20 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       tcols.map(_.columnName) should contain allElementsOf Seq("tile_1", "tile_2", "tile_3")
     }
     it("should read a single file") {
-      val df = spark.read.rastersource.load(l8samplePath.toASCIIString)
-      df.printSchema()
-      df.show(false)
-      fail()
+      val df = spark.read.rastersource
+        .withTileDimensions(128, 128)
+        .load(l8samplePath.toASCIIString)
+      df.count() should be(4)
+      df.select(tile_dimensions($"tile")).show(false)
+      df.select("path").distinct().count() should be(1)
     }
     it("should read a multiple files with one band") {
       val df = spark.read.rastersource
         .from(Seq(cogPath, l8samplePath, nonCogPath))
+        .withTileDimensions(128, 128)
         .load()
       df.printSchema()
       df.show(false)
-      fail()
     }
-
   }
 }

From e4de5c112897602b26f5bb12fc199dfcf53040c9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 15 Apr 2019 09:56:01 -0400
Subject: [PATCH 039/380] Added multiband support to RasterSourceRelation.

---
 bench/src/main/resources/log4j.properties     |   2 +-
 .../rasterframes/bench/TileEncodeBench.scala  |   2 +-
 core/src/it/resources/log4j.properties        |   2 +-
 .../encoders/CatalystSerializer.scala         |  20 +--
 .../encoders/StandardSerializers.scala        |   4 +-
 .../expressions/DynamicExtractors.scala       |  14 +-
 .../aggstats/LocalStatsAggregate.scala        |   1 -
 .../RasterSourceToRasterRefs.scala            |  30 +++--
 .../spark/rasterframes/ref/RasterRef.scala    |  20 ++-
 .../util/ZeroSevenCompatibilityKit.scala      | 122 +++++++++---------
 .../encoders/CatalystSerializerSpec.scala     |   2 +-
 .../rasterframes/ref/RasterRefSpec.scala      |  10 +-
 .../rastersource/RasterSourceDataSource.scala |  14 +-
 .../rastersource/RasterSourceRelation.scala   |  56 ++++----
 .../datasource/rastersource/package.scala     |   6 +-
 .../RasterSourceDataSourceSpec.scala          |  42 +++++-
 .../datasource/awspds/L8Relation.scala        |   6 +-
 17 files changed, 202 insertions(+), 151 deletions(-)

diff --git a/bench/src/main/resources/log4j.properties b/bench/src/main/resources/log4j.properties
index f8cb2c5b9..c6ede28bb 100644
--- a/bench/src/main/resources/log4j.properties
+++ b/bench/src/main/resources/log4j.properties
@@ -18,7 +18,7 @@
 #
 
 # Set everything to be logged to the console
-log4j.rootCategory=TRACE, FILE
+log4j.rootCategory=INFO, FILE
 log4j.appender.FILE=org.apache.log4j.FileAppender
 
 log4j.appender.FILE.File=target/jmh-log.out
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala b/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala
index 7f25235ae..5887aafc5 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala
+++ b/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala
@@ -52,7 +52,7 @@ class TileEncodeBench extends SparkEnv {
     cellTypeName match {
       case "rasterRef" ⇒
         val baseCOG = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_B1.TIF"
-        tile = RasterRefTile(RasterRef(RasterSource(URI.create(baseCOG)), Some(Extent(253785.0, 3235185.0, 485115.0, 3471015.0))))
+        tile = RasterRefTile(RasterRef(RasterSource(URI.create(baseCOG)), 0, Some(Extent(253785.0, 3235185.0, 485115.0, 3471015.0))))
       case _ ⇒
         tile = randomTile(tileSize, tileSize, cellTypeName)
     }
diff --git a/core/src/it/resources/log4j.properties b/core/src/it/resources/log4j.properties
index 378ae8e61..4a0646435 100644
--- a/core/src/it/resources/log4j.properties
+++ b/core/src/it/resources/log4j.properties
@@ -38,7 +38,7 @@ log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
 log4j.logger.astraea.spark.rasterframes=DEBUG
-log4j.logger.astraea.spark.rasterframes.ref=TRACE
+log4j.logger.astraea.spark.rasterframes.ref=INFO
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
index 39e933cfc..1e94ebfed 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
@@ -63,8 +63,8 @@ object CatalystSerializer extends StandardSerializers {
     def create(values: Any*): R
     def to[T: CatalystSerializer](t: T): R = CatalystSerializer[T].to(t, this)
     def toSeq[T: CatalystSerializer](t: Seq[T]): AnyRef
-    def get[T: CatalystSerializer](d: R, ordinal: Int): T
-    def getSeq[T: CatalystSerializer](d: R, ordinal: Int): Seq[T]
+    def get[T >: Null: CatalystSerializer](d: R, ordinal: Int): T
+    def getSeq[T >: Null: CatalystSerializer](d: R, ordinal: Int): Seq[T]
     def isNullAt(d: R, ordinal: Int): Boolean
     def getBoolean(d: R, ordinal: Int): Boolean
     def getByte(d: R, ordinal: Int): Byte
@@ -93,14 +93,14 @@ object CatalystSerializer extends StandardSerializers {
       override def getString(d: R, ordinal: Int): String = d.getString(ordinal)
       override def getByteArray(d: R, ordinal: Int): Array[Byte] =
         d.get(ordinal).asInstanceOf[Array[Byte]]
-      override def get[T: CatalystSerializer](d: R, ordinal: Int): T = {
+      override def get[T >: Null: CatalystSerializer](d: R, ordinal: Int): T = {
         d.getAs[Any](ordinal) match {
           case r: Row => r.to[T]
           case o => o.asInstanceOf[T]
         }
       }
       override def toSeq[T: CatalystSerializer](t: Seq[T]): AnyRef = t.map(_.toRow)
-      override def getSeq[T: CatalystSerializer](d: R, ordinal: Int): Seq[T] =
+      override def getSeq[T >: Null: CatalystSerializer](d: R, ordinal: Int): Seq[T] =
         d.getSeq[Row](ordinal).map(_.to[T])
       override def encode(str: String): String = str
     }
@@ -120,7 +120,7 @@ object CatalystSerializer extends StandardSerializers {
       override def getDouble(d: InternalRow, ordinal: Int): Double = d.getDouble(ordinal)
       override def getString(d: InternalRow, ordinal: Int): String = d.getString(ordinal)
       override def getByteArray(d: InternalRow, ordinal: Int): Array[Byte] = d.getBinary(ordinal)
-      override def get[T: CatalystSerializer](d: InternalRow, ordinal: Int): T = {
+      override def get[T >: Null: CatalystSerializer](d: InternalRow, ordinal: Int): T = {
         val ser = CatalystSerializer[T]
         val struct = d.getStruct(ordinal, ser.schema.size)
         struct.to[T]
@@ -129,7 +129,7 @@ object CatalystSerializer extends StandardSerializers {
       override def toSeq[T: CatalystSerializer](t: Seq[T]): ArrayData =
         ArrayData.toArrayData(t.map(_.toInternalRow).toArray)
 
-      override def getSeq[T: CatalystSerializer](d: InternalRow, ordinal: Int): Seq[T] = {
+      override def getSeq[T >: Null: CatalystSerializer](d: InternalRow, ordinal: Int): Seq[T] = {
         val ad = d.getArray(ordinal)
         val result = Array.ofDim[Any](ad.numElements()).asInstanceOf[Array[T]]
         ad.foreach(
@@ -143,15 +143,15 @@ object CatalystSerializer extends StandardSerializers {
   }
 
   implicit class WithToRow[T: CatalystSerializer](t: T) {
-    def toInternalRow: InternalRow = CatalystSerializer[T].toInternalRow(t)
-    def toRow: Row = CatalystSerializer[T].toRow(t)
+    def toInternalRow: InternalRow = if (t == null) null else CatalystSerializer[T].toInternalRow(t)
+    def toRow: Row = if (t == null) null else CatalystSerializer[T].toRow(t)
   }
 
   implicit class WithFromInternalRow(val r: InternalRow) extends AnyVal {
-    def to[T: CatalystSerializer]: T = CatalystSerializer[T].fromInternalRow(r)
+    def to[T >: Null: CatalystSerializer]: T = if (r == null) null else CatalystSerializer[T].fromInternalRow(r)
   }
 
   implicit class WithFromRow(val r: Row) extends AnyVal {
-    def to[T: CatalystSerializer]: T = CatalystSerializer[T].fromRow(r)
+    def to[T >: Null: CatalystSerializer]: T = CatalystSerializer[T].fromRow(r)
   }
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
index a35e14c9e..5d63f1db5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
@@ -202,7 +202,7 @@ trait StandardSerializers {
     )
   }
 
-  implicit def boundsSerializer[T: CatalystSerializer]: CatalystSerializer[KeyBounds[T]] = new CatalystSerializer[KeyBounds[T]] {
+  implicit def boundsSerializer[T >: Null: CatalystSerializer]: CatalystSerializer[KeyBounds[T]] = new CatalystSerializer[KeyBounds[T]] {
     override def schema: StructType = StructType(Seq(
       StructField("minKey", schemaOf[T], true),
       StructField("maxKey", schemaOf[T], true)
@@ -219,7 +219,7 @@ trait StandardSerializers {
     )
   }
 
-  def tileLayerMetadataSerializer[T: CatalystSerializer]: CatalystSerializer[TileLayerMetadata[T]] = new CatalystSerializer[TileLayerMetadata[T]] {
+  def tileLayerMetadataSerializer[T >: Null: CatalystSerializer]: CatalystSerializer[TileLayerMetadata[T]] = new CatalystSerializer[TileLayerMetadata[T]] {
     override def schema: StructType = StructType(Seq(
       StructField("cellType", schemaOf[CellType], false),
       StructField("layout", schemaOf[LayoutDefinition], false),
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
index f8a41054c..8c5a8991d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
@@ -57,21 +57,21 @@ object DynamicExtractors {
   /** Partial function for pulling a ProjectedRasterLike an input row. */
   lazy val projectedRasterLikeExtractor: PartialFunction[DataType, InternalRow ⇒ ProjectedRasterLike] = {
     case _: RasterSourceUDT ⇒
-      (row: InternalRow) ⇒ row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
+      (row: InternalRow) => row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
     case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
       (row: InternalRow) => row.to[ProjectedRasterTile]
     case t if t.conformsTo(schemaOf[RasterRef]) =>
-      (row: InternalRow) ⇒ row.to[RasterRef]
+      (row: InternalRow) => row.to[RasterRef]
   }
 
   /** Partial function for pulling a CellGrid from an input row. */
   lazy val gridExtractor: PartialFunction[DataType, InternalRow ⇒ CellGrid] = {
-    case _: TileUDT ⇒
-      (row: InternalRow) ⇒ row.to[Tile](TileUDT.tileSerializer)
-    case _: RasterSourceUDT ⇒
-      (row: InternalRow) ⇒ row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
+    case _: TileUDT =>
+      (row: InternalRow) => row.to[Tile](TileUDT.tileSerializer)
+    case _: RasterSourceUDT =>
+      (row: InternalRow) => row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
     case t if t.conformsTo(schemaOf[RasterRef]) ⇒
-      (row: InternalRow) ⇒ row.to[RasterRef]
+      (row: InternalRow) => row.to[RasterRef]
     case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
       (row: InternalRow) => row.to[ProjectedRasterTile]
   }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
index c5bdb47e0..dbe8a5b3f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
@@ -31,7 +31,6 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression,
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
-import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
 import astraea.spark.rasterframes.TileType
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 8ef4f6cb4..c0a9c31c8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -23,9 +23,10 @@ package astraea.spark.rasterframes.expressions.transformers
 
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.ref.RasterRef
+import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
 import astraea.spark.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
+import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
@@ -41,7 +42,7 @@ import scala.util.control.NonFatal
  *
  * @since 9/6/18
  */
-case class RasterSourceToRasterRefs(children: Seq[Expression], subtileDims: Option[TileDimensions] = None) extends Expression
+case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions] = None) extends Expression
   with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
 
   private val RasterSourceType = new RasterSourceUDT()
@@ -50,9 +51,16 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], subtileDims: Opti
   override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
   override def nodeName: String = "raster_source_to_raster_ref"
 
-  override def elementSchema: StructType = StructType(
-    children.map(e ⇒ StructField(e.name, rasterRefSchema, false))
-  )
+  override def elementSchema: StructType = StructType(for {
+    child <- children
+    band <- bandIndexes
+    name = child.name + (if (bandIndexes.length > 1) "_b" + band else "")
+  } yield StructField(name, rasterRefSchema, true))
+
+  private def band2ref(src: RasterSource, e: Option[Extent])(b: Int): RasterRef =
+    if (b < src.bandCount)
+      RasterRef(src, b, e)
+    else null
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
     try {
@@ -61,11 +69,11 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], subtileDims: Opti
         subtileDims.map(dims =>
           src
             .layoutExtents(dims)
-            .map(e ⇒ RasterRef(src, Some(e)))
+            .map(e ⇒ bandIndexes.map(band2ref(src, Some(e))))
         )
-        .getOrElse( Seq(RasterRef(src)))
+        .getOrElse(Seq(bandIndexes.map(band2ref(src, None))))
       }
-      refs.transpose.map(ts ⇒ InternalRow(ts.map(_.toInternalRow): _*))
+      refs.transpose.map(ts ⇒ InternalRow(ts.flatMap(_.map(_.toInternalRow)): _*))
     }
     catch {
       case NonFatal(ex) ⇒
@@ -76,7 +84,7 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], subtileDims: Opti
 }
 
 object RasterSourceToRasterRefs {
-  def apply(rrs: Column*): TypedColumn[Any, RasterRef] = apply(None, rrs: _*)
-  def apply(subtileDims: Option[TileDimensions], rrs: Column*): TypedColumn[Any, RasterRef] =
-    new Column(new RasterSourceToRasterRefs(rrs.map(_.expr), subtileDims)).as[RasterRef]
+  def apply(rrs: Column*): TypedColumn[Any, RasterRef] = apply(None, Seq(0), rrs: _*)
+  def apply(subtileDims: Option[TileDimensions], bandIndexes: Seq[Int], rrs: Column*): TypedColumn[Any, RasterRef] =
+    new Column(new RasterSourceToRasterRefs(rrs.map(_.expr), bandIndexes, subtileDims)).as[RasterRef]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
index 6a1206722..888f09274 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
@@ -30,7 +30,7 @@ import geotrellis.raster.{CellType, GridBounds, Tile}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
-import org.apache.spark.sql.types.{StructField, StructType}
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import CatalystSerializer._
 
 /**
@@ -38,7 +38,7 @@ import CatalystSerializer._
  *
  * @since 8/21/18
  */
-case class RasterRef(source: RasterSource, subextent: Option[Extent])
+case class RasterRef(source: RasterSource, bandIndex: Int, subextent: Option[Extent])
   extends ProjectedRasterLike {
   def crs: CRS = source.crs
   def extent: Extent = subextent.getOrElse(source.extent)
@@ -52,19 +52,14 @@ case class RasterRef(source: RasterSource, subextent: Option[Extent])
   protected def srcExtent: Extent = extent
 
   protected lazy val realizedTile: Tile = {
-    require(source.bandCount == 1, "Expected singleband tile")
-    RasterRef.log.trace(s"Fetching $srcExtent from $source")
-    source.read(srcExtent).tile.band(0)
+    RasterRef.log.trace(s"Fetching $srcExtent from band $bandIndex of $source")
+    source.read(srcExtent, Seq(bandIndex)).tile.band(0)
   }
 }
 
 object RasterRef extends LazyLogging {
   private val log = logger
 
-
-  /** Constructor for when data extent cover whole raster. */
-  def apply(source: RasterSource): RasterRef = RasterRef(source, None)
-
   case class RasterRefTile(rr: RasterRef) extends ProjectedRasterTile {
     val extent: Extent = rr.extent
     val crs: CRS = rr.crs
@@ -83,18 +78,21 @@ object RasterRef extends LazyLogging {
     val rsType = new RasterSourceUDT()
     override def schema: StructType = StructType(Seq(
       StructField("source", rsType, false),
+      StructField("band_index", IntegerType, false),
       StructField("subextent", schemaOf[Extent], true)
     ))
 
     override def to[R](t: RasterRef, io: CatalystIO[R]): R = io.create(
       io.to(t.source)(RasterSourceUDT.rasterSourceSerializer),
+      t.bandIndex,
       t.subextent.map(io.to[Extent]).orNull
     )
 
     override def from[R](row: R, io: CatalystIO[R]): RasterRef = RasterRef(
       io.get[RasterSource](row, 0)(RasterSourceUDT.rasterSourceSerializer),
-      if (io.isNullAt(row, 1)) None
-      else Option(io.get[Extent](row, 1))
+      io.getInt(row, 1),
+      if (io.isNullAt(row, 2)) None
+      else Option(io.get[Extent](row, 2))
     )
   }
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
index 098c7ea3f..fb0c14946 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -51,175 +51,175 @@ object ZeroSevenCompatibilityKit {
     private val delegate = new astraea.spark.rasterframes.RasterFunctions {}
     // format: off
     /** Create a row for each cell in Tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def explodeTiles(cols: Column*): Column = delegate.explode_tiles(cols: _*)
 
     /** Create a row for each cell in Tile with random sampling and optional seed. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def explodeTilesSample(sampleFraction: Double, seed: Option[Long], cols: Column*): Column =
       ExplodeTiles(sampleFraction, seed, cols)
 
     /** Create a row for each cell in Tile with random sampling (no seed). */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def explodeTilesSample(sampleFraction: Double, cols: Column*): Column =
       ExplodeTiles(sampleFraction, None, cols)
 
     /** Query the number of (cols, rows) in a Tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileDimensions(col: Column): Column = GetDimensions(col)
 
     @Experimental
     /** Convert array in `arrayCol` into a Tile of dimensions `cols` and `rows`*/
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def arrayToTile(arrayCol: Column, cols: Int, rows: Int) = withAlias("array_to_tile", arrayCol)(
       udf[Tile, AnyRef](F.arrayToTile(cols, rows)).apply(arrayCol)
     )
 
     /** Create a Tile from a column of cell data with location indexes and preform cell conversion. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def assembleTile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Int, tileRows: Int, ct: CellType): TypedColumn[Any, Tile] =
       convertCellType(TileAssembler(columnIndex, rowIndex, cellData, lit(tileCols), lit(tileRows)), ct).as(cellData.columnName).as[Tile]
 
     /** Create a Tile from  a column of cell data with location indexes. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def assembleTile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Column, tileRows: Column): TypedColumn[Any, Tile] =
       TileAssembler(columnIndex, rowIndex, cellData, tileCols, tileRows)
 
     /** Extract the Tile's cell type */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def cellType(col: Column): TypedColumn[Any, CellType] = GetCellType(col)
 
     /** Change the Tile's cell type */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def convertCellType(col: Column, cellType: CellType): TypedColumn[Any, Tile] =
       SetCellType(col, cellType)
 
     /** Change the Tile's cell type */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def convertCellType(col: Column, cellTypeName: String): TypedColumn[Any, Tile] =
       SetCellType(col, cellTypeName)
 
     /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def boundsGeometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
 
     /** Assign a `NoData` value to the Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def withNoData(col: Column, nodata: Double) = withAlias("withNoData", col)(
       udf[Tile, Tile](F.withNoData(nodata)).apply(col)
     ).as[Tile]
 
     /**  Compute the full column aggregate floating point histogram. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def aggHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.agg_approx_histogram(col)
 
     /** Compute the full column aggregate floating point statistics. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def aggStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.agg_stats(col)
 
     /** Computes the column aggregate mean. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def aggMean(col: Column) = CellMeanAggregate(col)
 
     /** Computes the number of non-NoData cells in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def aggDataCells(col: Column): TypedColumn[Any, Long] = delegate.agg_data_cells(col)
 
     /** Computes the number of NoData cells in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def aggNoDataCells(col: Column): TypedColumn[Any, Long] = delegate.agg_no_data_cells(col)
 
     /** Compute the Tile-wise mean */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileMean(col: Column): TypedColumn[Any, Double] = delegate.tile_mean(col)
 
     /** Compute the Tile-wise sum */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileSum(col: Column): TypedColumn[Any, Double] = delegate.tile_sum(col)
 
     /** Compute the minimum cell value in tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileMin(col: Column): TypedColumn[Any, Double] = delegate.tile_min(col)
 
     /** Compute the maximum cell value in tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileMax(col: Column): TypedColumn[Any, Double] = delegate.tile_max(col)
 
     /** Compute TileHistogram of Tile values. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.tile_histogram(col)
 
     /** Compute statistics of Tile values. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.tile_stats(col)
 
     /** Counts the number of non-NoData cells per Tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def dataCells(tile: Column): TypedColumn[Any, Long] = delegate.data_cells(tile)
 
     /** Counts the number of NoData cells per Tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def noDataCells(tile: Column): TypedColumn[Any, Long] = delegate.no_data_cells(tile)
 
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def isNoDataTile(tile: Column): TypedColumn[Any, Boolean] = delegate.is_no_data_tile(tile)
 
     /** Compute cell-local aggregate descriptive statistics for a column of Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggStats(col: Column): Column = delegate.agg_local_stats(col)
 
     /** Compute the cell-wise/local max operation between Tiles in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggMax(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_max(col)
 
     /** Compute the cellwise/local min operation between Tiles in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggMin(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_min(col)
 
     /** Compute the cellwise/local mean operation between Tiles in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggMean(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_mean(col)
 
     /** Compute the cellwise/local count of non-NoData cells for all Tiles in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggDataCells(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_data_cells(col)
 
     /** Compute the cellwise/local count of NoData cells for all Tiles in a column. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAggNoDataCells(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_no_data_cells(col)
 
     /** Cellwise addition between two Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAdd(left: Column, right: Column): Column = delegate.local_add(left, right)
 
     /** Cellwise addition of a scalar to a tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAddScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_add(tileCol, value)
 
     /** Cellwise subtraction between two Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localSubtract(left: Column, right: Column): Column = delegate.local_subtract(left, right)
 
     /** Cellwise subtraction of a scalar from a tile. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localSubtractScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_subtract(tileCol, value)
     /** Cellwise multiplication between two Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localMultiply(left: Column, right: Column): Column = delegate.local_multiply(left, right)
 
     /** Cellwise multiplication of a tile by a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localMultiplyScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_multiply(tileCol, value)
 
     /** Cellwise division between two Tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localDivide(left: Column, right: Column): Column = delegate.local_divide(left, right)
 
     /** Cellwise division of a tile by a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localDivideScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_divide(tileCol, value)
     /** Perform an arbitrary GeoTrellis `LocalTileBinaryOp` between two Tile columns. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAlgebra(op: LocalTileBinaryOp, left: Column, right: Column):
     TypedColumn[Any, Tile] =
       withAlias(opName(op), left, right)(
@@ -227,93 +227,93 @@ object ZeroSevenCompatibilityKit {
       ).as[Tile]
 
     /** Compute the normalized difference of two tile columns */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def normalizedDifference(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.normalized_difference(left, right)
 
     /** Constructor for constant tile column */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def makeConstantTile(value: Number, cols: Int, rows: Int, cellType: String): TypedColumn[Any, Tile] =
       udf(() => F.makeConstantTile(value, cols, rows, cellType)).apply().as(s"constant_$cellType").as[Tile]
 
     /** Alias for column of constant tiles of zero */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileZeros(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
       udf(() => F.tileZeros(cols, rows, cellType)).apply().as(s"zeros_$cellType").as[Tile]
 
     /** Alias for column of constant tiles of one */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def tileOnes(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
       udf(() => F.tileOnes(cols, rows, cellType)).apply().as(s"ones_$cellType").as[Tile]
 
     /** Where the mask tile equals the mask value, replace values in the source tile with NODATA */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def maskByValue(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
       delegate.mask_by_value(sourceTile, maskTile, maskValue)
 
     /** Where the mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def inverseMask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
       delegate.inverse_mask(sourceTile, maskTile)
 
     /** Reproject a column of geometry from one CRS to another. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def reprojectGeometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
       delegate.reproject_geometry(sourceGeom, srcCRS, dstCRS)
 
     /** Render Tile as ASCII string for debugging purposes. */
     @Experimental
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def renderAscii(col: Column): TypedColumn[Any, String] = delegate.render_ascii(col)
 
     /** Cellwise less than value comparison between two tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localLess(left: Column, right: Column): TypedColumn[Any, Tile] =
       delegate.local_less(left, right)
 
 
     /** Cellwise less than value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localLessScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_less(tileCol, value)
 
     /** Cellwise less than or equal to value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localLessEqual(left: Column, right: Column): TypedColumn[Any, Tile]  = delegate.local_less_equal(left, right)
 
     /** Cellwise less than or equal to value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localLessEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_less_equal(tileCol, value)
 
     /** Cellwise greater than value comparison between two tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localGreater(left: Column, right: Column): TypedColumn[Any, Tile] =
       delegate.local_greater(left, right)
 
     /** Cellwise greater than value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localGreaterScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_greater(tileCol, value)
 
     /** Cellwise greater than or equal to value comparison between two tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localGreaterEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_greater_equal(left, right)
 
     /** Cellwise greater than or equal to value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localGreaterEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_greater_equal(tileCol, value)
 
     /** Cellwise equal to value comparison between two tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_equal(left, right)
 
     /** Cellwise equal to value comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_equal(tileCol, value)
 
     /** Cellwise inequality comparison between two tiles. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localUnequal(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_unequal(left, right)
 
     /** Cellwise inequality comparison between a tile and a scalar. */
-    @deprecated("Part of 0.7.x compatility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
+    @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localUnequalScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_unequal(tileCol, value)
   }
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
index 934e7687c..1bfaef3c1 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -103,7 +103,7 @@ class CatalystSerializerSpec extends TestEnvironment with TestData {
 
     it("should serialize RasterRef") {
       val src = RasterSource(remoteCOGSingleband1)
-      val value = RasterRef(src, Some(src.extent.buffer(-3.0)))
+      val value = RasterRef(src, 0, Some(src.extent.buffer(-3.0)))
       assertConsistent(value)
       assertInvertable(value)
     }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
index f1af549fe..b50750fa6 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
@@ -27,6 +27,7 @@ import astraea.spark.rasterframes.expressions.transformers._
 import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
 import geotrellis.raster.Tile
 import geotrellis.vector.Extent
+import org.apache.spark.sql.Encoders
 
 /**
  *
@@ -45,13 +46,14 @@ class RasterRefSpec extends TestEnvironment with TestData {
 
   trait Fixture {
     val src = RasterSource(remoteCOGSingleband1)
-    val fullRaster = RasterRef(src)
+    val fullRaster = RasterRef(src, 0, None)
     val subExtent = sub(src.extent)
-    val subRaster = RasterRef(src, Option(subExtent))
+    val subRaster = RasterRef(src, 0, Some(subExtent))
   }
 
   import spark.implicits._
 
+  implicit val enc = Encoders.tuple(Encoders.scalaInt, RasterRef.rrEncoder)
   describe("GetCRS Expression") {
     it("should read from RasterRef") {
       new Fixture {
@@ -168,7 +170,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         import spark.implicits._
         val df = Seq(src).toDF("src")
-        val refs = df.select(RasterSourceToRasterRefs($"src"))
+        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), Seq(0), $"src"))
         assert(refs.count() > 1)
       }
     }
@@ -177,7 +179,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
       new Fixture {
         import spark.implicits._
         val df = Seq(src).toDF("src")
-        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), $"src"))
+        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), Seq(0), $"src"))
         assert(refs.count() > 1)
       }
     }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index 7e05ffa6f..0829a2b48 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -28,17 +28,17 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   import RasterSourceDataSource._
   override def shortName(): String = SHORT_NAME
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
-    val bandCount = parameters.bandCount
+    val inexes = parameters.bandIndexes
     val files = parameters.filePaths
     val tiling = parameters.tileDims
-    RasterSourceRelation(sqlContext, files, bandCount, tiling)
+    RasterSourceRelation(sqlContext, files, inexes, tiling)
   }
 }
 object RasterSourceDataSource {
   final val SHORT_NAME = "rastersource"
   final val PATH_PARAM = "path"
   final val PATHS_PARAM = "paths"
-  final val BAND_COUNT_PARAM = "bandCount"
+  final val BAND_INDEXES_PARAM = "bandIndexes"
   final val TILE_DIMS_PARAM = "tileDimensions"
 
   private[rastersource]
@@ -59,9 +59,9 @@ object RasterSourceDataSource {
       .map(_.split(',').map(_.trim.toInt))
       .map { case Array(cols, rows) => TileDimensions(cols, rows)}
 
-    def bandCount: Int = parameters
-      .get(BAND_COUNT_PARAM)
-      .map(_.toInt)
-      .getOrElse(1)
+    def bandIndexes: Seq[Int] = parameters
+      .get(BAND_INDEXES_PARAM)
+      .map(_.split(',').map(_.trim.toInt).toSeq)
+      .getOrElse(Seq(0))
   }
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index ec6980500..6491d12fc 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -22,6 +22,7 @@
 package astraea.spark.rasterframes.datasource.rastersource
 
 import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
 import astraea.spark.rasterframes.encoders.CatalystSerializer._
 import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
 import astraea.spark.rasterframes.model.TileDimensions
@@ -32,31 +33,42 @@ import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
 
-case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandCount: Int, subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
-  override def schema: StructType = if (bandCount == 1) RasterSourceRelation.schema
-  else {
-    val fields = RasterSourceRelation.schema.fields
-    StructType(
-      fields.dropRight(1) ++ {
-        val base = fields.last
-        for (b <- 1 to bandCount) yield base.copy(name = base.name + "_" + b)
-      }
-    )
-  }
+/**
+  * Constructs a Spark Relation over one or more RasterSource paths.
+  * @param sqlContext
+  * @param paths list of URIs to fetch rastefrom.
+  * @param bandIndexes band indexes to fetch
+  * @param subtileDims how big to tile/subdivide rasters info
+  */
+case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
+
+  override def schema: StructType = StructType(Seq(
+    StructField(PATH_COLUMN.columnName, StringType, false)
+  ) ++ {
+    val tileSchema = schemaOf[ProjectedRasterTile]
+    for {
+      name <- bandNames(bandIndexes)
+    } yield StructField(name, tileSchema, true)
+  })
+
   override def buildScan(): RDD[Row] = {
     import sqlContext.implicits._
-    paths.toDF("path")
-      .withColumn("__rr", RasterSourceToRasterRefs(subtileDims, URIToRasterSource($"path")))
-      .select(
-        PATH_COLUMN,
-        RasterRefToTile($"__rr") as TILE_COLUMN.columnName
-      )
-      .rdd
+    val names = bandNames(bandIndexes)
+    val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, URIToRasterSource($"path"))
+    val refsToTiles = names.map(n => RasterRefToTile($"$n") as n)
+
+    val df = paths.toDF("path")
+      .select(PATH_COLUMN, refs as names)
+      .select(PATH_COLUMN +: refsToTiles: _*)
+    df.rdd
   }
 }
 object RasterSourceRelation {
-  def schema: StructType = StructType(Seq(
-    StructField(PATH_COLUMN.columnName, StringType, false),
-    StructField(TILE_COLUMN.columnName, schemaOf[ProjectedRasterTile], true)
-  ))
+  private def bandNames(bandIndexes: Seq[Int]): Seq[String] = bandIndexes match {
+    case Seq() => Seq.empty
+    case Seq(0) => Seq(TILE_COLUMN.columnName)
+    case s =>
+      val basename = TILE_COLUMN.columnName
+      s.map(n => basename + "_b" + n)
+  }
 }
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
index 421d241cf..8219b8f7c 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
@@ -39,10 +39,10 @@ package object rastersource {
 
   /** Adds option methods relevant to RasterSourceDataSource. */
   implicit class RasterSourceDataFrameReaderHasOptions(val reader: RasterSourceDataFrameReader) {
-    /** Set the number of bands to provide columns for. */
-    def withBandCount(bandCount: Int): RasterSourceDataFrameReader =
+    /** Set the zero-based band indexes to read. Defaults to Seq(0). */
+    def withBandIndexes(bandIndexes: Int*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
-        reader.option(RasterSourceDataSource.BAND_COUNT_PARAM, bandCount))
+        reader.option(RasterSourceDataSource.BAND_INDEXES_PARAM, bandIndexes.mkString(",")))
 
     def withTileDimensions(cols: Int, rows: Int): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 099c501ee..dd4aec09c 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -65,18 +65,34 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     it("should support a multiband schema") {
       val df = spark.read
         .rastersource
-        .withBandCount(3)
+        .withBandIndexes(0, 1, 2)
         .load(cogPath.toASCIIString)
       val tcols = df.tileColumns
       tcols.length should be(3)
-      tcols.map(_.columnName) should contain allElementsOf Seq("tile_1", "tile_2", "tile_3")
+      tcols.map(_.columnName) should contain allElementsOf Seq("tile_b0", "tile_b1", "tile_b2")
+    }
+    it("should read a multiband file") {
+      val df = spark.read
+        .rastersource
+        .withBandIndexes(0, 1, 2)
+        .load(cogPath.toASCIIString)
+        .cache()
+      df.schema.size should be (4)
+      // Test (roughly) we have three distinct but compabible bands
+      val stats = df.agg(agg_stats($"tile_b0") as "s0", agg_stats($"tile_b1") as "s1", agg_stats($"tile_b2") as "s2")
+      stats.select($"s0.data_cells" === $"s1.data_cells").as[Boolean].first() should be(true)
+      stats.select($"s0.data_cells" === $"s2.data_cells").as[Boolean].first() should be(true)
+      stats.select($"s0.mean" =!= $"s1.mean").as[Boolean].first() should be(true)
+      stats.select($"s0.mean" =!= $"s2.mean").as[Boolean].first() should be(true)
     }
     it("should read a single file") {
       val df = spark.read.rastersource
         .withTileDimensions(128, 128)
         .load(l8samplePath.toASCIIString)
       df.count() should be(4)
-      df.select(tile_dimensions($"tile")).show(false)
+      df.select(tile_dimensions($"tile").as[TileDimensions]).collect() should contain allElementsOf
+        Seq(TileDimensions(128, 128), TileDimensions(59, 128), TileDimensions(128, 42), TileDimensions(59, 42))
+
       df.select("path").distinct().count() should be(1)
     }
     it("should read a multiple files with one band") {
@@ -84,8 +100,24 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .from(Seq(cogPath, l8samplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .load()
-      df.printSchema()
-      df.show(false)
+      df.select("path").distinct().count() should be(3)
+      df.schema.size should be(2)
+    }
+
+    it("should read a multiple files with heterogeneous bands") {
+      val df = spark.read.rastersource
+        .from(Seq(cogPath, l8samplePath, nonCogPath))
+        .withTileDimensions(128, 128)
+        .withBandIndexes(0, 1, 2, 3)
+        .load()
+        .cache()
+      df.select("path").distinct().count() should be(3)
+      df.schema.size should be(5)
+
+      df.select($"tile_b0").count() should be (df.select($"tile_b0").na.drop.count())
+      df.select($"tile_b1").na.drop.count() shouldBe <(df.count())
+      df.select($"tile_b1").na.drop.count() should be (df.select($"tile_b2").na.drop.count())
+      df.select($"tile_b3").na.drop.count() should be (0)
     }
   }
 }
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 245b560c9..8ad088075 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -99,10 +99,10 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
     val nonTile = other.map(col)
 
     val df = {
+      val dims = if (useTiling) Some(NOMINAL_TILE_DIMS) else None
+      val sources = bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b))
       // NB: We assume that `nativeTiling` preserves the band names.
-      val expanded = RasterSourceToRasterRefs(
-        if (useTiling) Some(NOMINAL_TILE_DIMS) else None,
-        bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b)): _*)
+      val expanded = RasterSourceToRasterRefs(dims, Seq(0), sources: _*)
       filtered.select(nonTile :+ expanded: _*)
     }
 

From 23107da950b57746bd4c45a771218b8b4216129a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 16 Apr 2019 16:50:04 -0400
Subject: [PATCH 040/380] Fixed bug in selecting "notTileColumns".

---
 core/src/main/resources/reference.conf                      | 6 +++---
 .../spark/rasterframes/extensions/DataFrameMethods.scala    | 2 +-
 .../scala/astraea/spark/rasterframes/RasterFrameSpec.scala  | 5 ++++-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 896bbc435..a73c761ee 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -1,6 +1,6 @@
 rasterframes {
-  nominal-tile-size: 256
-  prefer-gdal: false
+  nominal-tile-size = 256
+  prefer-gdal = false
 }
 
 gdal {
@@ -10,6 +10,6 @@ gdal {
       CPL_DEBUG = "OFF"
     }
     // set this to `false` if CPL_DEBUG is `ON`
-    useExceptions: true
+    useExceptions = true
   }
 }
\ No newline at end of file
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
index b8bad84ea..ae514396b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
@@ -91,7 +91,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   /** Get the columns that are not of type `Tile` */
   def notTileColumns: Seq[Column] =
     self.schema.fields
-      .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
+      .filter(f => !DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
       .map(f ⇒ col(f.name))
 
   /** Get the spatial column. */
diff --git a/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala
index 984da98e7..dcdb7b6e5 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala
@@ -180,7 +180,10 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       // Should use left's key column names
       assert(joined.spatialKeyColumn.columnName === left.spatialKeyColumn.columnName)
       assert(joined.temporalKeyColumn.map(_.columnName) === left.temporalKeyColumn.map(_.columnName))
-
+      assert(joined.tileColumns.size === 2)
+      assert(joined.notTileColumns.size === 2)
+      assert(joined.tileColumns.toSet === joined.tileColumns.toSet)
+      assert(joined.tileColumns.toSet !== joined.notTileColumns.toSet)
     }
 
     it("should convert a GeoTiff to RasterFrame") {

From 03a39e4d960103a35d3d8743102a15ee28aab946 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 17 Apr 2019 09:48:46 -0400
Subject: [PATCH 041/380] Enabled gdal by default.

---
 core/src/main/resources/reference.conf | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index a73c761ee..bf5ef40ee 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -1,6 +1,6 @@
 rasterframes {
   nominal-tile-size = 256
-  prefer-gdal = false
+  prefer-gdal = true
 }
 
 gdal {

From 930a49ee96d00840b6074fbebc5ff9bb0f023eec Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 18 Apr 2019 11:16:28 -0400
Subject: [PATCH 042/380] Switching the datamodel schema to use camel case,
 matching case class definition, due to the fact that `newMapEncoder` doesn't
 parameterize over key/value types in a way that allows the encoder to be
 passed in. Also added test that found extent discrepency between JVM and GDAL
 readers.

---
 .../RasterSourceToRasterRefs.scala            |  4 +--
 .../rasterframes/model/CellContext.scala      | 18 +++++-----
 .../spark/rasterframes/model/Cells.scala      |  2 +-
 .../rasterframes/model/TileDataContext.scala  |  6 ++--
 .../spark/rasterframes/ref/RasterRef.scala    |  2 +-
 .../spark/rasterframes/ref/RasterSource.scala | 28 ++++++++-------
 .../rasterframes/tiles/InternalRowTile.scala  |  2 +-
 .../tiles/ProjectedRasterTile.scala           |  2 +-
 .../encoders/CatalystSerializerSpec.scala     |  1 +
 .../rasterframes/ref/GDALvsJVMTest.scala      | 36 +++++++++++++++++++
 .../rasterframes/ref/RasterSourceSpec.scala   | 19 ++++++++++
 11 files changed, 89 insertions(+), 31 deletions(-)
 create mode 100644 core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala

diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index c0a9c31c8..442a1fc23 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -58,9 +58,7 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
   } yield StructField(name, rasterRefSchema, true))
 
   private def band2ref(src: RasterSource, e: Option[Extent])(b: Int): RasterRef =
-    if (b < src.bandCount)
-      RasterRef(src, b, e)
-    else null
+    if (b < src.bandCount) RasterRef(src, b, e) else null
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
     try {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala b/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
index efc632c0b..84a723686 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
@@ -25,20 +25,20 @@ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{ShortType, StructField, StructType}
 import CatalystSerializer._
 
-case class CellContext(tile_context: TileContext, tile_data_context: TileDataContext, col_index: Short, row_index: Short)
+case class CellContext(tileContext: TileContext, tileDataContext: TileDataContext, colIndex: Short, rowIndex: Short)
 object CellContext {
   implicit val serializer: CatalystSerializer[CellContext] = new CatalystSerializer[CellContext] {
     override def schema: StructType = StructType(Seq(
-      StructField("tile_context", schemaOf[TileContext], false),
-      StructField("tile_data_context", schemaOf[TileDataContext], false),
-      StructField("col_index", ShortType, false),
-      StructField("row_index", ShortType, false)
+      StructField("tileContext", schemaOf[TileContext], false),
+      StructField("tileDataContext", schemaOf[TileDataContext], false),
+      StructField("colIndex", ShortType, false),
+      StructField("rowIndex", ShortType, false)
     ))
     override protected def to[R](t: CellContext, io: CatalystSerializer.CatalystIO[R]): R = io.create(
-      io.to(t.tile_context),
-      io.to(t.tile_data_context),
-      t.col_index,
-      t.row_index
+      io.to(t.tileContext),
+      io.to(t.tileDataContext),
+      t.colIndex,
+      t.rowIndex
     )
     override protected def from[R](t: R, io: CatalystSerializer.CatalystIO[R]): CellContext = CellContext(
       io.get[TileContext](t, 0),
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala b/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
index 03500c483..b4ce7509c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
@@ -34,7 +34,7 @@ case class Cells(data: Either[Array[Byte], RasterRef]) {
   /** Convert cells into either a RasterRefTile or an ArrayTile. */
   def toTile(ctx: TileDataContext): Tile = {
     data.fold(
-      bytes => ArrayTile.fromBytes(bytes, ctx.cell_type, ctx.dimensions.cols, ctx.dimensions.rows),
+      bytes => ArrayTile.fromBytes(bytes, ctx.cellType, ctx.dimensions.cols, ctx.dimensions.rows),
       ref => RasterRefTile(ref)
     )
   }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala b/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
index dcba073f6..114b9f620 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{StructField, StructType}
 
 /** Encapsulates all information about a tile aside from actual cell values. */
-case class TileDataContext(cell_type: CellType, dimensions: TileDimensions)
+case class TileDataContext(cellType: CellType, dimensions: TileDimensions)
 object TileDataContext {
 
   /** Extracts the TileDataContext from a Tile. */
@@ -41,12 +41,12 @@ object TileDataContext {
 
   implicit val serializer: CatalystSerializer[TileDataContext] = new CatalystSerializer[TileDataContext] {
     override def schema: StructType =  StructType(Seq(
-      StructField("cell_type", schemaOf[CellType], false),
+      StructField("cellType", schemaOf[CellType], false),
       StructField("dimensions", schemaOf[TileDimensions], false)
     ))
 
     override protected def to[R](t: TileDataContext, io: CatalystIO[R]): R = io.create(
-      io.to(t.cell_type),
+      io.to(t.cellType),
       io.to(t.dimensions)
     )
     override protected def from[R](t: R, io: CatalystIO[R]): TileDataContext = TileDataContext(
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
index 888f09274..4423900e5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
@@ -78,7 +78,7 @@ object RasterRef extends LazyLogging {
     val rsType = new RasterSourceUDT()
     override def schema: StructType = StructType(Seq(
       StructField("source", rsType, false),
-      StructField("band_index", IntegerType, false),
+      StructField("bandIndex", IntegerType, false),
       StructField("subextent", schemaOf[Extent], true)
     ))
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
index 45e708206..0f70f7b38 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
@@ -101,18 +101,18 @@ object RasterSource extends LazyLogging {
   }
 
   def apply(source: URI): RasterSource = source match {
-    case IsGDAL()                        => GDALRasterSource(source)
+    case IsGDAL()          => GDALRasterSource(source)
     case IsHadoopGeoTiff() =>
       // TODO: How can we get the active hadoop configuration
       // TODO: without having to pass it through?
       val config = () => new Configuration()
       HadoopGeoTiffRasterSource(source, config)
-    case IsDefaultGeoTiff() =>
-      DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))
-    case s => throw new UnsupportedOperationException(s"Reading '$s' not supported")
+    case IsDefaultGeoTiff() => JVMGeoTiffRasterSource(source)
+    case s                  => throw new UnsupportedOperationException(s"Reading '$s' not supported")
   }
 
   object IsGDAL {
+
     /** Determine if we should prefer GDAL for all types. */
     private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
     @transient
@@ -127,7 +127,7 @@ object RasterSource extends LazyLogging {
 
     val gdalOnlyExtensions = Seq(".jp2", ".mrf", ".hdf")
     def gdalOnly(source: URI): Boolean =
-      if(gdalOnlyExtensions.exists(source.getPath.toLowerCase.endsWith)) {
+      if (gdalOnlyExtensions.exists(source.getPath.toLowerCase.endsWith)) {
         require(hasGDAL, s"Can only read $source if GDAL is available")
         true
       } else false
@@ -174,7 +174,8 @@ object RasterSource extends LazyLogging {
         info.crs,
         info.tags,
         info.bandCount,
-        info.noDataValue)
+        info.noDataValue
+      )
   }
 
   trait URIRasterSource { _: RasterSource =>
@@ -186,14 +187,14 @@ object RasterSource extends LazyLogging {
   }
 
   /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
-  case class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
+  abstract class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
     @transient
     lazy val delegate = delegateBuilder()
 
     // Bad, bad, bad?
     override def equals(obj: Any): Boolean = obj match {
       case drs: DelegatingRasterSource => drs.source == source
-      case _ => false
+      case _                           => false
     }
     override def hashCode(): Int = source.hashCode()
 
@@ -226,13 +227,17 @@ object RasterSource extends LazyLogging {
     override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
       delegate.readBounds(bounds, bands)
     override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
-      delegate.read(bounds, bands)
+      delegate
+        .read(bounds, bands)
         .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
     override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
-      delegate.read(extent, bands)
+      delegate
+        .read(extent, bands)
         .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
   }
 
+  case class JVMGeoTiffRasterSource(source: URI) extends DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))
+
   case class InMemoryRasterSource(tile: Tile, extent: Extent, crs: CRS) extends RasterSource {
     def this(prt: ProjectedRasterTile) = this(prt, prt.extent, prt.crs)
 
@@ -246,8 +251,7 @@ object RasterSource extends LazyLogging {
 
     override def tags: Tags = EMPTY_TAGS
 
-    override protected def readBounds(
-      bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
       bounds
         .map(b => {
           val subext = rasterExtent.extentFor(b)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala
index 021f0946a..4dd5c9248 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala
@@ -64,7 +64,7 @@ class InternalRowTile(val mem: InternalRow) extends DelegatingTile {
 
 
   /** Retrieve the cell type from the internal encoding. */
-  override def cellType: CellType = cellContext.cell_type
+  override def cellType: CellType = cellContext.cellType
 
   /** Retrieve the number of columns from the internal encoding. */
   override def cols: Int = cellContext.dimensions.cols
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
index 35ee3a387..4893f667f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
@@ -64,7 +64,7 @@ object ProjectedRasterTile {
 
   implicit val serializer: CatalystSerializer[ProjectedRasterTile] = new CatalystSerializer[ProjectedRasterTile] {
     override def schema: StructType = StructType(Seq(
-      StructField("tile_context", schemaOf[TileContext], false),
+      StructField("tileContext", schemaOf[TileContext], false),
       StructField("tile", TileType, false))
     )
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
index 1bfaef3c1..ad285b94a 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -102,6 +102,7 @@ class CatalystSerializerSpec extends TestEnvironment with TestData {
     }
 
     it("should serialize RasterRef") {
+      // TODO: Decide if RasterRef should be encoded 'flat', non-'flat', or depends
       val src = RasterSource(remoteCOGSingleband1)
       val value = RasterRef(src, 0, Some(src.extent.buffer(-3.0)))
       assertConsistent(value)
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala
new file mode 100644
index 000000000..24ae1b896
--- /dev/null
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala
@@ -0,0 +1,36 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package astraea.spark.rasterframes.ref
+import astraea.spark.rasterframes.{TestData, TestEnvironment}
+import geotrellis.contrib.vlm.gdal.GDALRasterSource
+import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
+
+class GDALvsJVMTest extends TestEnvironment with TestData {
+  describe("GDAL RasterSource vs JVM RasterSource") {
+    it("should compute the same extent") {
+      val imgPath = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI.getPath
+      val jvm = GeoTiffRasterSource(imgPath)
+      val gdal = GDALRasterSource(imgPath)
+      gdal.extent should be (jvm.extent)
+    }
+  }
+}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 60fac1206..0532894d7 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -22,6 +22,7 @@
 package astraea.spark.rasterframes.ref
 
 import astraea.spark.rasterframes.model.TileDimensions
+import astraea.spark.rasterframes.ref.RasterSource.{GDALRasterSource, JVMGeoTiffRasterSource}
 import astraea.spark.rasterframes.{TestData, TestEnvironment}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
@@ -108,6 +109,24 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       assert(!src.extent.isEmpty)
     }
   }
+  describe("GDAL Rastersource") {
+    val gdal = GDALRasterSource(l8samplePath)
+    val jvm = JVMGeoTiffRasterSource(l8samplePath)
+    it("should compute the same metadata as JVM RasterSource") {
+
+      gdal.cellType should be (jvm.cellType)
+
+    }
+    it("should compute the same dimensions as JVM RasterSource") {
+      val dims = TileDimensions(128, 128)
+      gdal.extent should be (jvm.extent)
+      gdal.rasterExtent should be (jvm.rasterExtent)
+      gdal.cellSize should be (jvm.cellSize)
+      gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
+      gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
+
+    }
+  }
 
   describe("RasterSourceToTiles Expression") {
     it("should read all tiles") {

From 38034c15d3c17102d326491e24cc215246d74fa7 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 22 Apr 2019 16:34:18 -0400
Subject: [PATCH 043/380] Tweaks to tests affected by
 https://github.com/OSGeo/gdal/issues/1468

---
 build.sbt                                     |  4 +--
 .../encoders/CatalystSerializer.scala         |  2 +-
 .../rasterframes/ref/GDALvsJVMTest.scala      | 36 -------------------
 .../rasterframes/ref/RasterSourceSpec.scala   |  4 +--
 .../RasterSourceDataSourceSpec.scala          | 12 ++++---
 5 files changed, 13 insertions(+), 45 deletions(-)
 delete mode 100644 core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala

diff --git a/build.sbt b/build.sbt
index 5f195bbca..bcad782de 100644
--- a/build.sbt
+++ b/build.sbt
@@ -34,8 +34,8 @@ lazy val core = project
       "org.locationtech.jts" % "jts-core" % "1.16.0",
       "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
       "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "0.10.2",
-      "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "0.10.2",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0",
+      "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0",
       spark("core").value % Provided,
       spark("mllib").value % Provided,
       spark("sql").value % Provided,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
index 1e94ebfed..9dae2a0f0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
@@ -152,6 +152,6 @@ object CatalystSerializer extends StandardSerializers {
   }
 
   implicit class WithFromRow(val r: Row) extends AnyVal {
-    def to[T >: Null: CatalystSerializer]: T = CatalystSerializer[T].fromRow(r)
+    def to[T >: Null: CatalystSerializer]: T = if (r == null) null else CatalystSerializer[T].fromRow(r)
   }
 }
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala
deleted file mode 100644
index 24ae1b896..000000000
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/GDALvsJVMTest.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2019 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package astraea.spark.rasterframes.ref
-import astraea.spark.rasterframes.{TestData, TestEnvironment}
-import geotrellis.contrib.vlm.gdal.GDALRasterSource
-import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
-
-class GDALvsJVMTest extends TestEnvironment with TestData {
-  describe("GDAL RasterSource vs JVM RasterSource") {
-    it("should compute the same extent") {
-      val imgPath = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI.getPath
-      val jvm = GeoTiffRasterSource(imgPath)
-      val gdal = GDALRasterSource(imgPath)
-      gdal.extent should be (jvm.extent)
-    }
-  }
-}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 0532894d7..127f55be8 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -110,8 +110,8 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     }
   }
   describe("GDAL Rastersource") {
-    val gdal = GDALRasterSource(l8samplePath)
-    val jvm = JVMGeoTiffRasterSource(l8samplePath)
+    val gdal = GDALRasterSource(cogPath)
+    val jvm = JVMGeoTiffRasterSource(cogPath)
     it("should compute the same metadata as JVM RasterSource") {
 
       gdal.cellType should be (jvm.cellType)
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index dd4aec09c..fe035a84b 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -86,12 +86,16 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       stats.select($"s0.mean" =!= $"s2.mean").as[Boolean].first() should be(true)
     }
     it("should read a single file") {
+      // Image is 1028 x 989 -> 9 x 8 tiles
       val df = spark.read.rastersource
         .withTileDimensions(128, 128)
-        .load(l8samplePath.toASCIIString)
-      df.count() should be(4)
-      df.select(tile_dimensions($"tile").as[TileDimensions]).collect() should contain allElementsOf
-        Seq(TileDimensions(128, 128), TileDimensions(59, 128), TileDimensions(128, 42), TileDimensions(59, 42))
+        .load(cogPath.toASCIIString)
+
+      df.count() should be(math.ceil(1028.0 / 128).toInt * math.ceil(989.0 / 128).toInt)
+
+      val dims = df.select(tile_dimensions($"tile").as[TileDimensions]).distinct().collect()
+      dims should contain allElementsOf
+        Seq(TileDimensions(4,128), TileDimensions(128,128), TileDimensions(128,93), TileDimensions(4,93))
 
       df.select("path").distinct().count() should be(1)
     }

From 27e348bef3807b3458a3299345b16bcbab931fed Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 22 Apr 2019 17:13:46 -0400
Subject: [PATCH 044/380] Separated out dependency management into separate
 plugin.

---
 build.sbt                          | 21 ++++-------
 project/RFDependenciesPlugin.scala | 58 ++++++++++++++++++++++++++++++
 project/RFProjectPlugin.scala      | 14 --------
 3 files changed, 65 insertions(+), 28 deletions(-)
 create mode 100644 project/RFDependenciesPlugin.scala

diff --git a/build.sbt b/build.sbt
index bcad782de..296370220 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,12 +1,6 @@
-import sbt.Keys.resolvers
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
-// NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
-ThisBuild / rfSparkVersion := "2.3.2"
-ThisBuild / rfGeoTrellisVersion := "2.2.0"
-ThisBuild / rfGeoMesaVersion := "2.2.1"
-
 lazy val root = project
   .in(file("."))
   .withId("RasterFrames")
@@ -28,14 +22,13 @@ lazy val core = project
   .disablePlugins(SparkPackagePlugin)
   .settings(
     moduleName := "rasterframes",
-    resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
     libraryDependencies ++= Seq(
-      "com.chuusai" %% "shapeless" % "2.3.2",
-      "org.locationtech.jts" % "jts-core" % "1.16.0",
-      "org.locationtech.geomesa" %% "geomesa-z3" % rfGeoMesaVersion.value,
-      "org.locationtech.geomesa" %% "geomesa-spark-jts" % rfGeoMesaVersion.value exclude("jgridshift", "jgridshift"),
-      "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0",
-      "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0",
+      shapeless,
+      `jts-core`,
+      geomesa("z3").value,
+      geomesa("spark-jts").value,
+      `geotrellis-contrib-vlm`,
+      `geotrellis-contrib-gdal`,
       spark("core").value % Provided,
       spark("mllib").value % Provided,
       spark("sql").value % Provided,
@@ -46,7 +39,7 @@ lazy val core = project
         ExclusionRule(organization = "org.scalastic"),
         ExclusionRule(organization = "org.scalatest")
       ),
-      scalaTest
+      scalatest
     ),
     buildInfoKeys ++= Seq[BuildInfoKey](
       name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
new file mode 100644
index 000000000..4ad610cf1
--- /dev/null
+++ b/project/RFDependenciesPlugin.scala
@@ -0,0 +1,58 @@
+
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+import sbt.Keys.resolvers
+import sbt._
+
+object RFDependenciesPlugin extends AutoPlugin {
+  override def trigger: PluginTrigger = allRequirements
+  object autoImport {
+    val rfSparkVersion = settingKey[String]("Apache Spark version")
+    val rfGeoTrellisVersion = settingKey[String]("GeoTrellis version")
+    val rfGeoMesaVersion = settingKey[String]("GeoMesa version")
+
+    def geotrellis(module: String) = Def.setting {
+      "org.locationtech.geotrellis" %% s"geotrellis-$module" % rfGeoTrellisVersion.value
+    }
+    def spark(module: String) = Def.setting {
+      "org.apache.spark" %% s"spark-$module" % rfSparkVersion.value
+    }
+    def geomesa(module: String) = Def.setting {
+      "org.locationtech.geomesa" %% s"geomesa-$module" % rfGeoMesaVersion.value
+    }
+
+    val scalatest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
+    val shapeless = "com.chuusai" %% "shapeless" % "2.3.2"
+    val `jts-core` = "org.locationtech.jts" % "jts-core" % "1.16.0"
+    val `geotrellis-contrib-vlm` = "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0"
+    val `geotrellis-contrib-gdal` = "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0"
+  }
+  import autoImport._
+
+  override def projectSettings = Seq(
+    resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
+    // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
+    rfSparkVersion := "2.3.2",
+    rfGeoTrellisVersion := "2.2.0",
+    rfGeoMesaVersion := "2.2.1"
+  )
+}
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 455629a67..7ca0e1985 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -10,20 +10,6 @@ import xerial.sbt.Sonatype.autoImport._
 object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = GitPlugin
-  object autoImport {
-    val rfSparkVersion = settingKey[String]("Apache Spark version")
-    val rfGeoTrellisVersion = settingKey[String]("GeoTrellis version")
-    val rfGeoMesaVersion = settingKey[String]("GeoMesa version")
-
-    def geotrellis(module: String) = Def.setting {
-      "org.locationtech.geotrellis" %% s"geotrellis-$module" % rfGeoTrellisVersion.value
-    }
-    def spark(module: String) = Def.setting {
-      "org.apache.spark" %% s"spark-$module" % rfSparkVersion.value
-    }
-
-    val scalaTest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
-  }
 
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",

From 1b6f23a8e2c7654d24638bf0725f53799d320004 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 23 Apr 2019 15:46:11 -0400
Subject: [PATCH 045/380] Added check to see if GDAL is available before
 running GDAL-dependent test.

---
 core/src/main/resources/reference.conf        |  7 +++--
 .../rasterframes/ref/RasterSourceSpec.scala   | 30 +++++++++----------
 2 files changed, 20 insertions(+), 17 deletions(-)

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index bf5ef40ee..00473ae15 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -3,11 +3,14 @@ rasterframes {
   prefer-gdal = true
 }
 
-gdal {
+vlm.gdal {
   settings {
     options {
       // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
-      CPL_DEBUG = "OFF"
+      //CPL_DEBUG = "OFF"
+      AWS_REQUEST_PAYER = "requester"
+      GDAL_DISABLE_READDIR_ON_OPEN = "YES"
+      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2"
     }
     // set this to `false` if CPL_DEBUG is `ON`
     useExceptions = true
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 127f55be8..883184ccd 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -109,22 +109,22 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       assert(!src.extent.isEmpty)
     }
   }
-  describe("GDAL Rastersource") {
-    val gdal = GDALRasterSource(cogPath)
-    val jvm = JVMGeoTiffRasterSource(cogPath)
-    it("should compute the same metadata as JVM RasterSource") {
-
-      gdal.cellType should be (jvm.cellType)
-
-    }
-    it("should compute the same dimensions as JVM RasterSource") {
-      val dims = TileDimensions(128, 128)
-      gdal.extent should be (jvm.extent)
-      gdal.rasterExtent should be (jvm.rasterExtent)
-      gdal.cellSize should be (jvm.cellSize)
-      gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
-      gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
 
+  if(RasterSource.IsGDAL.hasGDAL) {
+    describe("GDAL Rastersource") {
+      val gdal = GDALRasterSource(cogPath)
+      val jvm = JVMGeoTiffRasterSource(cogPath)
+      it("should compute the same metadata as JVM RasterSource") {
+        gdal.cellType should be(jvm.cellType)
+      }
+      it("should compute the same dimensions as JVM RasterSource") {
+        val dims = TileDimensions(128, 128)
+        gdal.extent should be(jvm.extent)
+        gdal.rasterExtent should be(jvm.rasterExtent)
+        gdal.cellSize should be(jvm.cellSize)
+        gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
+        gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
+      }
     }
   }
 

From 3db1cb12720bf2d6d5746af97d0fb99bf1740aa5 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 23 Apr 2019 20:19:24 -0400
Subject: [PATCH 046/380] Changed root package from
 `astraea.spark.rasterframes` to `org.locationtech.rasterframes`.

---
 README.md                                     |   2 +-
 bench/src/main/resources/log4j.properties     |   2 +-
 .../bench/BinaryTileOpBench.scala             |   7 +-
 .../bench/CatalystSerializerBench.scala       |   5 +-
 .../bench/MultibandRenderBench.scala          |   5 +-
 .../rasterframes/bench/RasterRefBench.scala   |  11 +-
 .../rasterframes/bench/SparkEnv.scala         |   8 +-
 .../bench/StatsComputeBench.scala             |  10 +-
 .../bench/TileAssembleBench.scala             |   7 +-
 .../bench/TileCellScanBench.scala             |   8 +-
 .../rasterframes/bench/TileEncodeBench.scala  |  11 +-
 .../rasterframes/bench/TileExplodeBench.scala |   8 +-
 .../rasterframes/bench/package.scala          |   6 +-
 build.sbt                                     |   6 +-
 core/src/it/resources/log4j.properties        |   4 +-
 .../rasterframes/ref/RasterSourceIT.scala     |   9 +-
 .../spark/rasterframes/MetadataKeys.scala     |  13 ---
 .../rules/SpatialUDFSubstitutionRules.scala   |  21 ----
 .../rasterframes/tiles/DelegatingTile.scala   | 109 ------------------
 .../spark/sql/rf/FilterTranslator.scala       |   5 +-
 .../apache/spark/sql/rf/RasterSourceUDT.scala |   8 +-
 .../org/apache/spark/sql/rf/TileUDT.scala     |  10 +-
 .../rasterframes/MetadataKeys.scala           |  34 ++++++
 .../rasterframes/PairRDDConverter.scala       |  25 +++-
 .../rasterframes/RasterFunctions.scala        |  29 ++---
 .../rasterframes/StandardColumns.scala        |  25 +++-
 .../rasterframes/encoders/CRSEncoder.scala    |   9 +-
 .../encoders/CatalystSerializer.scala         |   6 +-
 .../encoders/CatalystSerializerEncoder.scala  |   3 +-
 .../encoders/CellTypeEncoder.scala            |   6 +-
 .../encoders/DelegatingSubfieldEncoder.scala  |   6 +-
 .../encoders/EnvelopeEncoder.scala            |  23 +++-
 .../encoders/ProjectedExtentEncoder.scala     |   8 +-
 .../encoders/SparkBasicEncoders.scala         |   6 +-
 .../encoders/StandardEncoders.scala           |  11 +-
 .../encoders/StandardSerializers.scala        |   8 +-
 .../encoders/StringBackedEncoder.scala        |   6 +-
 .../TemporalProjectedExtentEncoder.scala      |   8 +-
 .../encoders/TileLayerMetadataEncoder.scala   |   8 +-
 .../rasterframes/encoders/URIEncoder.scala    |   6 +-
 .../rasterframes/encoders/package.scala       |   6 +-
 .../expressions/BinaryLocalRasterOp.scala     |   6 +-
 .../expressions/BinaryRasterOp.scala          |   7 +-
 .../expressions/DynamicExtractors.scala       |  12 +-
 .../expressions/NullToValue.scala             |   3 +-
 .../expressions/OnCellGridExpression.scala    |   6 +-
 .../expressions/OnTileContextExpression.scala |   8 +-
 .../expressions/SpatialRelation.scala         |  10 +-
 .../expressions/TileAssembler.scala           |  10 +-
 .../expressions/UnaryLocalRasterOp.scala      |   6 +-
 .../expressions/UnaryRasterAggregate.scala    |   5 +-
 .../expressions/UnaryRasterOp.scala           |   7 +-
 .../expressions/accessors/ExtractTile.scala   |  14 +--
 .../expressions/accessors/GetCRS.scala        |  10 +-
 .../expressions/accessors/GetCellType.scala   |   8 +-
 .../expressions/accessors/GetDimensions.scala |   8 +-
 .../expressions/accessors/GetEnvelope.scala   |   6 +-
 .../expressions/accessors/GetExtent.scala     |  10 +-
 .../accessors/GetTileContext.scala            |  12 +-
 .../aggstats/CellCountAggregate.scala         |   8 +-
 .../aggstats/CellMeanAggregate.scala          |   8 +-
 .../aggstats/CellStatsAggregate.scala         |  10 +-
 .../aggstats/HistogramAggregate.scala         |  12 +-
 .../aggstats/LocalCountAggregate.scala        |  10 +-
 .../aggstats/LocalMeanAggregate.scala         |  12 +-
 .../aggstats/LocalStatsAggregate.scala        |  12 +-
 .../aggstats/LocalTileOpAggregate.scala       |  12 +-
 .../expressions/generators/ExplodeTiles.scala |   8 +-
 .../expressions/localops/Add.scala            |  11 +-
 .../expressions/localops/Divide.scala         |   6 +-
 .../expressions/localops/Equal.scala          |   6 +-
 .../expressions/localops/Exp.scala            |   6 +-
 .../expressions/localops/Greater.scala        |   6 +-
 .../expressions/localops/GreaterEqual.scala   |   6 +-
 .../expressions/localops/Less.scala           |   6 +-
 .../expressions/localops/LessEqual.scala      |   6 +-
 .../expressions/localops/Log.scala            |   6 +-
 .../expressions/localops/Multiply.scala       |   6 +-
 .../localops/NormalizedDifference.scala       |   9 +-
 .../expressions/localops/Resample.scala       |   8 +-
 .../expressions/localops/Round.scala          |   6 +-
 .../expressions/localops/Subtract.scala       |   7 +-
 .../expressions/localops/Unequal.scala        |   6 +-
 .../rasterframes/expressions/package.scala    |  22 ++--
 .../expressions/tilestats/DataCells.scala     |   9 +-
 .../expressions/tilestats/IsNoDataTile.scala  |   9 +-
 .../expressions/tilestats/NoDataCells.scala   |   8 +-
 .../expressions/tilestats/Sum.scala           |   9 +-
 .../expressions/tilestats/TileHistogram.scala |   9 +-
 .../expressions/tilestats/TileMax.scala       |   8 +-
 .../expressions/tilestats/TileMean.scala      |   8 +-
 .../expressions/tilestats/TileMin.scala       |   8 +-
 .../expressions/tilestats/TileStats.scala     |   9 +-
 .../transformers/DebugRender.scala            |  11 +-
 .../transformers/ExtentToGeometry.scala       |   6 +-
 .../transformers/GeometryToExtent.scala       |   6 +-
 .../expressions/transformers/Mask.scala       |  11 +-
 .../transformers/RasterRefToTile.scala        |  10 +-
 .../RasterSourceToRasterRefs.scala            |  11 +-
 .../transformers/RasterSourceToTiles.scala    |   8 +-
 .../transformers/ReprojectGeometry.scala      |  11 +-
 .../transformers/SetCellType.scala            |  10 +-
 .../transformers/TileToArrayDouble.scala      |   9 +-
 .../transformers/TileToArrayInt.scala         |   9 +-
 .../transformers/URIToRasterSource.scala      |   5 +-
 .../extensions/ContextRDDMethods.scala        |  38 +++---
 .../extensions/DataFrameMethods.scala         |  50 ++++----
 .../rasterframes/extensions/Implicits.scala   |  16 +--
 .../rasterframes/extensions/KryoMethods.scala |   4 +-
 .../extensions/MetadataBuilderMethods.scala   |  12 +-
 .../extensions/MetadataMethods.scala          |  26 +++--
 .../extensions/ProjectedRasterMethods.scala   |  28 ++++-
 .../extensions/RFSpatialColumnMethods.scala   |  11 +-
 .../extensions/RasterFrameMethods.scala       |  34 +++---
 .../extensions/SQLContextMethods.scala        |   8 +-
 .../extensions/SparkSessionMethods.scala      |   8 +-
 .../rasterframes/functions/package.scala      |  30 ++---
 .../rasterframes/jts/Implicits.scala          |  10 +-
 .../jts/ReprojectionTransformer.scala         |   5 +-
 .../rasterframes/ml/NoDataFilter.scala        |  11 +-
 .../rasterframes/ml/Parameters.scala          |   6 +-
 .../rasterframes/ml/TileColumnSupport.scala   |   5 +-
 .../rasterframes/ml/TileExploder.scala        |  10 +-
 .../rasterframes/model/CellContext.scala      |   5 +-
 .../rasterframes/model/Cells.scala            |  11 +-
 .../rasterframes/model/LazyCRS.scala          |   5 +-
 .../rasterframes/model/TileContext.scala      |   9 +-
 .../rasterframes/model/TileDataContext.scala  |   8 +-
 .../rasterframes/model/TileDimensions.scala   |   6 +-
 .../rasterframes/rasterframes.scala}          |  44 ++++---
 .../rasterframes/ref/HttpRangeReader.scala    |  26 +++--
 .../ref/ProjectedRasterLike.scala             |   4 +-
 .../rasterframes/ref/RasterRef.scala          |  11 +-
 .../rasterframes/ref/RasterSource.scala       |  18 +--
 .../rules/SpatialFilterPushdownRules.scala    |   5 +-
 .../rasterframes/rules/SpatialFilters.scala   |   5 +-
 .../rules/SpatialRelationReceiver.scala       |   5 +-
 .../rules/SpatialUDFSubstitutionRules.scala   |  42 +++++++
 .../rasterframes/rules/TemporalFilters.scala  |   5 +-
 .../rasterframes/rules/package.scala          |  23 +++-
 .../rasterframes/stats/CellHistogram.scala    |   8 +-
 .../rasterframes/stats/CellStatistics.scala   |   8 +-
 .../stats/LocalCellStatistics.scala           |   3 +-
 .../rasterframes/tiles/InternalRowTile.scala  |  11 +-
 .../tiles/ProjectedRasterTile.scala           |  19 +--
 .../rasterframes/util/DataBiasedOp.scala      |   2 +-
 .../util/GeoTiffInfoSupport.scala             |   5 +-
 .../rasterframes/util/KryoSupport.scala       |   4 +-
 .../rasterframes/util/MultibandRender.scala   |   5 +-
 .../rasterframes/util/RFKryoRegistrator.scala |  11 +-
 .../rasterframes/util/SubdivideSupport.scala  |   5 +-
 .../util/ZeroSevenCompatibilityKit.scala      |  25 ++--
 .../rasterframes/util/debug/package.scala     |   7 +-
 .../rasterframes/util/package.scala           |   8 +-
 core/src/test/resources/log4j.properties      |   4 +-
 .../rasterframes/ml/TileExploderSpec.scala    |  26 -----
 .../test/scala/examples/Classification.scala  |   5 +-
 core/src/test/scala/examples/Clustering.scala |   4 +-
 .../scala/examples/CreatingRasterFrames.scala |   2 +-
 core/src/test/scala/examples/Exporting.scala  |   2 +-
 .../test/scala/examples/LocalArithmetic.scala |   2 +-
 core/src/test/scala/examples/Masking.scala    |   2 +-
 core/src/test/scala/examples/MeanValue.scala  |   2 +-
 core/src/test/scala/examples/NDVI.scala       |   2 +-
 core/src/test/scala/examples/Scratch.scala    |   2 +-
 core/src/test/scala/examples/Tour.scala       |   4 +-
 .../rasterframes/ExplodeSpec.scala            |   6 +-
 .../rasterframes/ExtensionMethodSpec.scala    |  13 ++-
 .../rasterframes/GeometryOperationsSpec.scala |   5 +-
 .../locationtech}/rasterframes/JTSSpec.scala  |   8 +-
 .../rasterframes/MetadataSpec.scala           |  23 +++-
 .../rasterframes/RasterFrameSpec.scala        |  25 +++-
 .../rasterframes/RasterFunctionsSpec.scala    |  12 +-
 .../rasterframes/ReprojectGeometrySpec.scala  |   4 +-
 .../rasterframes/SpatialEncodingSpec.scala    |   6 +-
 .../rasterframes/SpatialKeySpec.scala         |   6 +-
 .../locationtech}/rasterframes/TestData.scala |  11 +-
 .../rasterframes/TestEnvironment.scala        |  15 ++-
 .../rasterframes/TileAssemblerSpec.scala      |  12 +-
 .../rasterframes/TileStatsSpec.scala          |  12 +-
 .../rasterframes/TileUDTSpec.scala            |  12 +-
 .../encoders/CatalystSerializerSpec.scala     |  14 ++-
 .../rasterframes/encoders/EncodingSpec.scala  |   7 +-
 .../rasterframes/ml/NoDataFilterSpec.scala    |   9 +-
 .../rasterframes/ml/TileExploderSpec.scala    |  48 ++++++++
 .../rasterframes/ref/RasterRefSpec.scala      |  13 ++-
 .../rasterframes/ref/RasterSourceSpec.scala   |  11 +-
 ...pache.spark.sql.sources.DataSourceRegister |   8 +-
 .../datasource/DataSourceOptions.scala        |   5 +-
 .../geotiff/GeoTiffCollectionRelation.scala   |  13 ++-
 .../geotiff/GeoTiffDataSource.scala           |  12 +-
 .../datasource/geotiff/GeoTiffRelation.scala  |  12 +-
 .../datasource/geotiff/package.scala          |  16 +--
 .../geotrellis/GeoTrellisCatalog.scala        |   8 +-
 .../GeoTrellisLayerDataSource.scala           |  10 +-
 .../geotrellis/GeoTrellisRelation.scala       |  24 ++--
 .../datasource/geotrellis/Layer.scala         |  10 +-
 .../datasource/geotrellis/MergeableData.scala |   6 +-
 .../geotrellis/TileFeatureSupport.scala       |   8 +-
 .../datasource/geotrellis/package.scala       |  24 ++--
 .../rasterframes/datasource/package.scala     |   6 +-
 .../rastersource/RasterSourceDataSource.scala |   5 +-
 .../rastersource/RasterSourceRelation.scala   |  16 +--
 .../datasource/rastersource/package.scala     |   3 +-
 .../src/test/scala/examples/Creating.scala    |   6 +-
 .../GeoTiffCollectionDataSourceSpec.scala     |   8 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       |   9 +-
 .../geotrellis/GeoTrellisCatalogSpec.scala    |   9 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala |  15 ++-
 .../geotrellis/TileFeatureSupportSpec.scala   |  13 ++-
 .../RasterSourceDataSourceSpec.scala          |  10 +-
 .../src/it/resources/log4j.properties         |   2 +-
 .../awspds/L8CatalogRelationTest.scala        |   6 +-
 .../datasource/awspds/L8RelationTest.scala    |   5 +-
 .../awspds/MODISCatalogRelationTest.scala     |   6 +-
 ...pache.spark.sql.sources.DataSourceRegister |   8 +-
 .../datasource/DownloadExpression.scala       |  11 +-
 .../datasource/DownloadSupport.scala          |   5 +-
 .../datasource/ReadTilesExpression.scala      |  11 +-
 .../awspds/CachedDatasetRelation.scala        |   6 +-
 .../awspds/L8CatalogDataSource.scala          |   6 +-
 .../datasource/awspds/L8CatalogRelation.scala |   7 +-
 .../datasource/awspds/L8DataSource.scala      |   5 +-
 .../datasource/awspds/L8Relation.scala        |  21 ++--
 .../awspds/MODISCatalogDataSource.scala       |  11 +-
 .../awspds/MODISCatalogRelation.scala         |   5 +-
 .../datasource/awspds/PDSFields.scala         |  11 +-
 .../awspds/ResourceCacheSupport.scala         |   9 +-
 .../datasource/awspds/package.scala           |   8 +-
 .../experimental/datasource/geojson/DOM.scala |   5 +-
 .../geojson/GeoJsonDataSource.scala           |   7 +-
 .../datasource/geojson/package.scala          |   3 +-
 .../experimental/datasource/package.scala     |   5 +-
 .../geojson/GeoJsonDataSourceTest.scala       |   8 +-
 project/RFDependenciesPlugin.scala            |   8 +-
 project/RFProjectPlugin.scala                 |  23 ++--
 pyrasterframes/python/examples/__init__.py    |   2 +-
 .../python/pyrasterframes/context.py          |   2 +-
 pyrasterframes/python/pyrasterframes/types.py |   4 +-
 .../python/tests/PyRasterFramesTests.py       |   2 +-
 .../rasterframes/py/PyRFContext.scala         |  13 ++-
 241 files changed, 1432 insertions(+), 1098 deletions(-)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/BinaryTileOpBench.scala (92%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/CatalystSerializerBench.scala (91%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/MultibandRenderBench.scala (91%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/RasterRefBench.scala (89%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/SparkEnv.scala (90%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/StatsComputeBench.scala (90%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/TileAssembleBench.scala (94%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/TileCellScanBench.scala (91%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/TileEncodeBench.scala (87%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/TileExplodeBench.scala (91%)
 rename bench/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/bench/package.scala (93%)
 rename core/src/it/scala/{astraea/spark => org/locationtech}/rasterframes/ref/RasterSourceIT.scala (91%)
 delete mode 100644 core/src/main/scala/astraea/spark/rasterframes/MetadataKeys.scala
 delete mode 100644 core/src/main/scala/astraea/spark/rasterframes/rules/SpatialUDFSubstitutionRules.scala
 delete mode 100644 core/src/main/scala/astraea/spark/rasterframes/tiles/DelegatingTile.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/PairRDDConverter.scala (87%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/RasterFunctions.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/StandardColumns.scala (73%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/CRSEncoder.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/CatalystSerializer.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/CatalystSerializerEncoder.scala (98%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/CellTypeEncoder.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/DelegatingSubfieldEncoder.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/EnvelopeEncoder.scala (64%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/ProjectedExtentEncoder.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/SparkBasicEncoders.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/StandardEncoders.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/StandardSerializers.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/StringBackedEncoder.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/TemporalProjectedExtentEncoder.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/TileLayerMetadataEncoder.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/URIEncoder.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/package.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/BinaryLocalRasterOp.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/BinaryRasterOp.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/DynamicExtractors.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/NullToValue.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/OnCellGridExpression.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/OnTileContextExpression.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/SpatialRelation.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/TileAssembler.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/UnaryLocalRasterOp.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/UnaryRasterAggregate.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/UnaryRasterOp.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/ExtractTile.scala (77%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetCRS.scala (82%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetCellType.scala (85%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetDimensions.scala (84%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetEnvelope.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetExtent.scala (82%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/accessors/GetTileContext.scala (78%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/CellCountAggregate.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/CellMeanAggregate.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/CellStatsAggregate.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/HistogramAggregate.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/LocalCountAggregate.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/LocalMeanAggregate.scala (85%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/LocalStatsAggregate.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/generators/ExplodeTiles.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Add.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Divide.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Equal.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Exp.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Greater.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/GreaterEqual.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Less.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/LessEqual.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Log.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Multiply.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/NormalizedDifference.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Resample.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Round.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Subtract.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/localops/Unequal.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/package.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/DataCells.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/IsNoDataTile.scala (85%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/NoDataCells.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/Sum.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/TileHistogram.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/TileMax.scala (87%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/TileMean.scala (87%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/TileMin.scala (87%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/tilestats/TileStats.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/DebugRender.scala (88%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/ExtentToGeometry.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/GeometryToExtent.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/Mask.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/RasterRefToTile.scala (85%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/RasterSourceToTiles.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/ReprojectGeometry.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/SetCellType.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/TileToArrayDouble.scala (86%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/TileToArrayInt.scala (83%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/expressions/transformers/URIToRasterSource.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/ContextRDDMethods.scala (70%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/DataFrameMethods.scala (84%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/Implicits.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/KryoMethods.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/MetadataBuilderMethods.scala (83%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/MetadataMethods.scala (57%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/ProjectedRasterMethods.scala (84%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/RFSpatialColumnMethods.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/RasterFrameMethods.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/SQLContextMethods.scala (85%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/extensions/SparkSessionMethods.scala (83%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/functions/package.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/jts/Implicits.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/jts/ReprojectionTransformer.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ml/NoDataFilter.scala (88%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ml/Parameters.scala (89%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ml/TileColumnSupport.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ml/TileExploder.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/CellContext.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/Cells.scala (88%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/LazyCRS.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/TileContext.scala (88%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/TileDataContext.scala (88%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/model/TileDimensions.scala (90%)
 rename core/src/main/scala/{astraea/spark/rasterframes/package.scala => org/locationtech/rasterframes/rasterframes.scala} (83%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ref/HttpRangeReader.scala (87%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ref/ProjectedRasterLike.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ref/RasterRef.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/ref/RasterSource.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/rules/SpatialFilterPushdownRules.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/rules/SpatialFilters.scala (90%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/rules/SpatialRelationReceiver.scala (92%)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/rules/TemporalFilters.scala (91%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/rules/package.scala (59%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/stats/CellHistogram.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/stats/CellStatistics.scala (93%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/stats/LocalCellStatistics.scala (94%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/tiles/InternalRowTile.scala (96%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/tiles/ProjectedRasterTile.scala (83%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/DataBiasedOp.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/GeoTiffInfoSupport.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/KryoSupport.scala (95%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/MultibandRender.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/RFKryoRegistrator.scala (81%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/SubdivideSupport.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/ZeroSevenCompatibilityKit.scala (97%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/debug/package.scala (92%)
 rename core/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/util/package.scala (97%)
 delete mode 100644 core/src/test/scala/astraea/spark/rasterframes/ml/TileExploderSpec.scala
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ExplodeSpec.scala (98%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ExtensionMethodSpec.scala (92%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/GeometryOperationsSpec.scala (95%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/JTSSpec.scala (97%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/MetadataSpec.scala (63%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/RasterFrameSpec.scala (94%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/RasterFunctionsSpec.scala (98%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ReprojectGeometrySpec.scala (98%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/SpatialEncodingSpec.scala (90%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/SpatialKeySpec.scala (94%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/TestData.scala (97%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/TestEnvironment.scala (91%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/TileAssemblerSpec.scala (94%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/TileStatsSpec.scala (97%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/TileUDTSpec.scala (92%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/CatalystSerializerSpec.scala (90%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/encoders/EncodingSpec.scala (95%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ml/NoDataFilterSpec.scala (86%)
 create mode 100644 core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ref/RasterRefSpec.scala (94%)
 rename core/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/ref/RasterSourceSpec.scala (94%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/DataSourceOptions.scala (90%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala (87%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/GeoTiffDataSource.scala (95%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/GeoTiffRelation.scala (93%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/package.scala (90%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala (93%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala (95%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala (94%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/Layer.scala (83%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/MergeableData.scala (93%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/TileFeatureSupport.scala (95%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/package.scala (89%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/package.scala (90%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/rastersource/RasterSourceDataSource.scala (94%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/rastersource/RasterSourceRelation.scala (80%)
 rename datasource/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/rastersource/package.scala (98%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala (85%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala (92%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala (90%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala (97%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala (94%)
 rename datasource/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala (94%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/DownloadExpression.scala (87%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/DownloadSupport.scala (93%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/ReadTilesExpression.scala (94%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala (91%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala (93%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala (92%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8DataSource.scala (90%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8Relation.scala (88%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala (93%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala (96%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/PDSFields.scala (86%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala (92%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/package.scala (85%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/geojson/DOM.scala (96%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala (95%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/geojson/package.scala (94%)
 rename experimental/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/package.scala (95%)
 rename experimental/src/test/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala (90%)
 rename pyrasterframes/src/main/scala/{astraea/spark => org/locationtech}/rasterframes/py/PyRFContext.scala (95%)

diff --git a/README.md b/README.md
index f06072f02..f9572feb9 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ Please see the [Getting Started](http://rasterframes.io/getting-started.html) se
 
 * [Users' Manual](http://rasterframes.io/)
 * [API Documentation](http://rasterframes.io/latest/api/index.html) 
-* [List of available UDFs](http://rasterframes.io/latest/api/index.html#astraea.spark.rasterframes.RasterFunctions)
+* [List of available UDFs](http://rasterframes.io/latest/api/index.html#org.locationtech.rasterframes.RasterFunctions)
 * [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/) 
 
 ## Build instruction
diff --git a/bench/src/main/resources/log4j.properties b/bench/src/main/resources/log4j.properties
index c6ede28bb..9ed1d66ca 100644
--- a/bench/src/main/resources/log4j.properties
+++ b/bench/src/main/resources/log4j.properties
@@ -36,7 +36,7 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.astraea.spark=DEBUG
+log4j.logger.org.locationtech=DEBUG
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
 log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/BinaryTileOpBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/BinaryTileOpBench.scala
similarity index 92%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/BinaryTileOpBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/BinaryTileOpBench.scala
index 133d93356..dce7a7715 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/BinaryTileOpBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/BinaryTileOpBench.scala
@@ -19,11 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
+
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes.expressions.localops._
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes.expressions.localops._
+import org.locationtech.rasterframes._
 import geotrellis.raster.Tile
 import geotrellis.raster.mapalgebra.{local => gt}
 import org.apache.spark.sql._
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/CatalystSerializerBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
similarity index 91%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/CatalystSerializerBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
index b4abad9bf..aff447ac4 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/CatalystSerializerBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
@@ -19,15 +19,16 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, StandardEncoders}
+import org.locationtech.rasterframes.encoders.CatalystSerializer
 import geotrellis.proj4.{CRS, LatLng, Sinusoidal}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, StandardEncoders}
 import org.openjdk.jmh.annotations._
 
 @BenchmarkMode(Array(Mode.AverageTime))
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/MultibandRenderBench.scala
similarity index 91%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/MultibandRenderBench.scala
index 4156af6a7..8636a6a80 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/MultibandRenderBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/MultibandRenderBench.scala
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
+
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes.util.MultibandRender.Landsat8NaturalColor
+import org.locationtech.rasterframes.util.MultibandRender.Landsat8NaturalColor
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.MultibandGeoTiff
 import org.apache.commons.io.IOUtils
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
similarity index 89%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index a2a9104bf..c16875adb 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,16 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
-
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.transformers.RasterSourceToTiles
-import astraea.spark.rasterframes.ref.RasterSource
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.transformers.RasterSourceToTiles
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
+import org.locationtech.rasterframes.ref.RasterSource
 import org.openjdk.jmh.annotations._
 /**
  *
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/SparkEnv.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
similarity index 90%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/SparkEnv.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
index 8b718479a..817b01ef7 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/SparkEnv.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,11 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql.SparkSession
 import org.openjdk.jmh.annotations.{Level, TearDown}
 
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/StatsComputeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
similarity index 90%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/StatsComputeBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
index c9aa7eef4..718d19da4 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/StatsComputeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.stats.CellHistogram
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.stats.CellHistogram
 import org.apache.spark.sql._
 import org.openjdk.jmh.annotations._
 
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileAssembleBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
similarity index 94%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/TileAssembleBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
index 8fe31ef0d..66104420a 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileAssembleBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
+
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster.ByteConstantNoDataCellType
 import org.apache.spark.sql._
 import org.openjdk.jmh.annotations._
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileCellScanBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
similarity index 91%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/TileCellScanBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
index 64ee8716e..73e7c8889 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileCellScanBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,15 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes.tiles.InternalRowTile
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.rf.TileUDT
+import org.locationtech.rasterframes.tiles.InternalRowTile
 import org.openjdk.jmh.annotations._
 
 @BenchmarkMode(Array(Mode.AverageTime))
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
similarity index 87%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
index 5887aafc5..d0ffd765c 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileEncodeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,19 +15,22 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
 import java.net.URI
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
-import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.ref.RasterRef
 import geotrellis.raster.Tile
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.openjdk.jmh.annotations._
 
 @BenchmarkMode(Array(Mode.AverageTime))
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileExplodeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
similarity index 91%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/TileExplodeBench.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
index ebd4f169c..4aba17771 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/TileExplodeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes.bench
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.openjdk.jmh.annotations._
diff --git a/bench/src/main/scala/astraea/spark/rasterframes/bench/package.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
similarity index 93%
rename from bench/src/main/scala/astraea/spark/rasterframes/bench/package.scala
rename to bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
index 525c86734..dfdc71e3b 100644
--- a/bench/src/main/scala/astraea/spark/rasterframes/bench/package.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import java.net.URI
 
diff --git a/build.sbt b/build.sbt
index 296370220..0e21c7f37 100644
--- a/build.sbt
+++ b/build.sbt
@@ -44,7 +44,7 @@ lazy val core = project
     buildInfoKeys ++= Seq[BuildInfoKey](
       name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
     ),
-    buildInfoPackage := "astraea.spark.rasterframes",
+    buildInfoPackage := "org.locationtech.rasterframes",
     buildInfoObject := "RFBuildInfo",
     buildInfoOptions := Seq(
       BuildInfoOption.ToMap,
@@ -69,8 +69,8 @@ lazy val datasource = project
     ),
     initialCommands in console := (initialCommands in console).value +
       """
-        |import astraea.spark.rasterframes.datasource.geotrellis._
-        |import astraea.spark.rasterframes.datasource.geotiff._
+        |import org.locationtech.rasterframes.datasource.geotrellis._
+        |import org.locationtech.rasterframes.datasource.geotiff._
         |""".stripMargin
   )
 
diff --git a/core/src/it/resources/log4j.properties b/core/src/it/resources/log4j.properties
index 4a0646435..8a20b7b7e 100644
--- a/core/src/it/resources/log4j.properties
+++ b/core/src/it/resources/log4j.properties
@@ -37,8 +37,8 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.astraea.spark.rasterframes=DEBUG
-log4j.logger.astraea.spark.rasterframes.ref=INFO
+log4j.logger.org.locationtech.rasterframes=DEBUG
+log4j.logger.org.locationtech.rasterframes.ref=INFO
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
similarity index 91%
rename from core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
rename to core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index e32a8caa7..591332a5f 100644
--- a/core/src/it/scala/astraea/spark/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
-import astraea.spark.rasterframes
-import astraea.spark.rasterframes.util.time
-import astraea.spark.rasterframes.{TestData, TestEnvironment}
+import org.locationtech.rasterframes.util.time
+import org.locationtech.rasterframes.TestData
 import geotrellis.vector.Extent
+import org.locationtech.rasterframes
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  *
diff --git a/core/src/main/scala/astraea/spark/rasterframes/MetadataKeys.scala b/core/src/main/scala/astraea/spark/rasterframes/MetadataKeys.scala
deleted file mode 100644
index 2b4948798..000000000
--- a/core/src/main/scala/astraea/spark/rasterframes/MetadataKeys.scala
+++ /dev/null
@@ -1,13 +0,0 @@
-package astraea.spark.rasterframes
-
-/**
- *
- * @since 2/19/18
- */
-trait MetadataKeys {
-  /** Key under which ContextRDD metadata is stored. */
-  private[rasterframes] val CONTEXT_METADATA_KEY = "_context"
-
-  /** Key under which RasterFrame role a column plays. */
-  private[rasterframes] val SPATIAL_ROLE_KEY = "_stRole"
-}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialUDFSubstitutionRules.scala b/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialUDFSubstitutionRules.scala
deleted file mode 100644
index c0f985b0b..000000000
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialUDFSubstitutionRules.scala
+++ /dev/null
@@ -1,21 +0,0 @@
-package astraea.spark.rasterframes.rules
-
-import astraea.spark.rasterframes.expressions.SpatialRelation
-import org.apache.spark.sql.catalyst.expressions.ScalaUDF
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.catalyst.rules.Rule
-
-/**
- * Swaps out spatial relation UDFs for expression forms.
- *
- * @since 2/19/18
- */
-object SpatialUDFSubstitutionRules extends Rule[LogicalPlan] {
-  def apply(plan: LogicalPlan): LogicalPlan = {
-    plan.transform {
-      case q: LogicalPlan ⇒ q.transformExpressions {
-        case s: ScalaUDF ⇒ SpatialRelation.fromUDF(s).getOrElse(s)
-      }
-    }
-  }
-}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/DelegatingTile.scala b/core/src/main/scala/astraea/spark/rasterframes/tiles/DelegatingTile.scala
deleted file mode 100644
index 8f0a910c7..000000000
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/DelegatingTile.scala
+++ /dev/null
@@ -1,109 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package astraea.spark.rasterframes.tiles
-
-import geotrellis.raster._
-
-/**
- * A tile that wraps another tile. Originally intended for delayed reading, but useful in other special use cases.
- *
- * @since 8/22/18
- */
-trait DelegatingTile extends Tile {
-  protected def delegate: Tile
-
-  def cellType: CellType =
-    delegate.cellType
-
-  def cols: Int =
-    delegate.cols
-
-  def rows: Int =
-    delegate.rows
-
-  def mutable: MutableArrayTile =
-    delegate.mutable
-
-  def convert(cellType: CellType): Tile =
-    delegate.convert(cellType)
-
-  override def withNoData(noDataValue: Option[Double]): Tile =
-    delegate.withNoData(noDataValue)
-
-  def interpretAs(newCellType: CellType): Tile =
-    delegate.interpretAs(newCellType)
-
-  def get(col: Int, row: Int): Int =
-    delegate.get(col, row)
-
-  def getDouble(col: Int, row: Int): Double =
-    delegate.getDouble(col, row)
-
-  def toArrayTile(): ArrayTile =
-    delegate.toArrayTile()
-
-  def toArray(): Array[Int] =
-    delegate.toArray()
-
-  def toArrayDouble(): Array[Double] =
-    delegate.toArrayDouble()
-
-  def toBytes(): Array[Byte] =
-    delegate.toBytes()
-
-  def foreach(f: Int ⇒ Unit): Unit =
-    delegate.foreach(f)
-
-  def foreachDouble(f: Double ⇒ Unit): Unit =
-    delegate.foreachDouble(f)
-
-  def map(f: Int ⇒ Int): Tile =
-    delegate.map(f)
-
-  def combine(r2: Tile)(f: (Int, Int) ⇒ Int): Tile = (delegate, r2) match {
-    // Hack until https://github.com/locationtech/geotrellis/issues/2792
-    case (del: ArrayTile, r2: DelegatingTile) ⇒ del.combine(r2.toArrayTile())(f)
-    case _ ⇒ delegate.combine(r2)(f)
-  }
-
-  def combineDouble(r2: Tile)(f: (Double, Double) ⇒ Double): Tile = (delegate, r2) match {
-    // Hack until https://github.com/locationtech/geotrellis/issues/2792
-    case (del: ArrayTile, r2: DelegatingTile) ⇒ del.combineDouble(r2.toArrayTile())(f)
-    case _ ⇒ delegate.combineDouble(r2)(f)
-  }
-
-  def mapDouble(f: Double ⇒ Double): Tile =
-    delegate.mapDouble(f)
-
-  def foreachIntVisitor(visitor: IntTileVisitor): Unit =
-    delegate.foreachIntVisitor(visitor)
-
-  def foreachDoubleVisitor(visitor: DoubleTileVisitor): Unit =
-    delegate.foreachDoubleVisitor(visitor)
-
-  def mapIntMapper(mapper: IntTileMapper): Tile =
-    delegate.mapIntMapper(mapper)
-
-  def mapDoubleMapper(mapper: DoubleTileMapper): Tile =
-    delegate.mapDoubleMapper(mapper)
-
-}
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/FilterTranslator.scala b/core/src/main/scala/org/apache/spark/sql/rf/FilterTranslator.scala
index edecc44b5..6433ef8d3 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/FilterTranslator.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/FilterTranslator.scala
@@ -19,8 +19,8 @@ package org.apache.spark.sql.rf
 
 import java.sql.{Date, Timestamp}
 
-import astraea.spark.rasterframes.expressions.SpatialRelation.{Contains, Intersects}
-import astraea.spark.rasterframes.rules._
+import org.locationtech.rasterframes.expressions.SpatialRelation.{Contains, Intersects}
+import org.locationtech.rasterframes.rules._
 import org.apache.spark.sql.catalyst.CatalystTypeConverters.{convertToScala, createToScalaConverter}
 import org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.expressions.{Attribute, EmptyRow, Expression, Literal}
@@ -30,6 +30,7 @@ import org.apache.spark.sql.sources.Filter
 import org.apache.spark.sql.types.{DateType, StringType, TimestampType}
 import org.apache.spark.unsafe.types.UTF8String
 import org.locationtech.geomesa.spark.jts.rules.GeometryLiteral
+import org.locationtech.rasterframes.rules.{SpatialFilters, TemporalFilters}
 
 /**
  * This is a copy of [[org.apache.spark.sql.execution.datasources.DataSourceStrategy.translateFilter]], modified to add our spatial predicates.
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index be61615ce..fba25bf83 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -23,12 +23,12 @@ package org.apache.spark.sql.rf
 
 import java.nio.ByteBuffer
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.ref.RasterSource
-import astraea.spark.rasterframes.util.KryoSupport
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{DataType, UDTRegistration, UserDefinedType, _}
+import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.ref.RasterSource
+import org.locationtech.rasterframes.util.KryoSupport
 
 /**
  * Catalyst representation of a RasterSource.
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index aa2b24779..64507d578 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -20,14 +20,14 @@
  */
 
 package org.apache.spark.sql.rf
-
-import astraea.spark.rasterframes.encoders.CatalystSerializer
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.model.{Cells, TileDataContext}
-import astraea.spark.rasterframes.tiles.InternalRowTile
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.model.TileDataContext
 import geotrellis.raster._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{DataType, _}
+import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.model.{Cells, TileDataContext}
+import org.locationtech.rasterframes.tiles.InternalRowTile
 
 
 /**
diff --git a/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
new file mode 100644
index 000000000..c700c30d7
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
@@ -0,0 +1,34 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
+
+/**
+ *
+ * @since 2/19/18
+ */
+trait MetadataKeys {
+  /** Key under which ContextRDD metadata is stored. */
+  private[rasterframes] val CONTEXT_METADATA_KEY = "_context"
+
+  /** Key under which RasterFrame role a column plays. */
+  private[rasterframes] val SPATIAL_ROLE_KEY = "_stRole"
+}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala b/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
similarity index 87%
rename from core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
rename to core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
index 6f7f5574c..0b3caf09e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
@@ -1,6 +1,27 @@
-package astraea.spark.rasterframes
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
 
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 import geotrellis.raster.{MultibandTile, Tile, TileFeature}
 import geotrellis.spark.{SpaceTimeKey, SpatialKey}
 import org.apache.spark.rdd.RDD
diff --git a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
rename to core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 622d68f14..acd660e95 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,27 +15,28 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
-
-import astraea.spark.rasterframes.expressions.TileAssembler
-import astraea.spark.rasterframes.expressions.accessors._
-import astraea.spark.rasterframes.expressions.aggstats._
-import astraea.spark.rasterframes.expressions.generators._
-import astraea.spark.rasterframes.expressions.localops._
-import astraea.spark.rasterframes.expressions.tilestats._
-import astraea.spark.rasterframes.expressions.transformers._
-import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics}
-import astraea.spark.rasterframes.{functions => F}
+package org.locationtech.rasterframes
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import geotrellis.raster.{CellType, Tile}
 import geotrellis.vector.Extent
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
+import org.apache.spark.sql.functions.{lit, udf}
+import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.jts.geom.{Envelope, Geometry}
+import org.locationtech.rasterframes.expressions.TileAssembler
+import org.locationtech.rasterframes.expressions.accessors._
+import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.generators._
+import org.locationtech.rasterframes.expressions.localops._
+import org.locationtech.rasterframes.expressions.tilestats._
+import org.locationtech.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.stats._
+import org.locationtech.rasterframes.{functions => F}
 
 /**
  * UDFs for working with Tiles in Spark DataFrames.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
similarity index 73%
rename from core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
rename to core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
index e327e871a..a53415f2a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
@@ -1,14 +1,35 @@
-package astraea.spark.rasterframes
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
 
 import java.sql.Timestamp
 
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 import geotrellis.proj4.CRS
 import geotrellis.raster.Tile
 import geotrellis.spark.{SpatialKey, TemporalKey}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.functions.col
 import org.locationtech.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
+import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 
 /**
  * Constants identifying column in most RasterFrames.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
index d043188cd..235721753 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CRSEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
-
-import astraea.spark.rasterframes.model.LazyCRS
+package org.locationtech.rasterframes.encoders
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.locationtech.rasterframes.model.LazyCRS
 
 /**
  * Custom encoder for GT `CRS`.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
index 9dae2a0f0..b9788327b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,9 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import CatalystSerializer.CatalystIO
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.ArrayData
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializerEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializerEncoder.scala
similarity index 98%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializerEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializerEncoder.scala
index 27e452329..792b74165 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CatalystSerializerEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializerEncoder.scala
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
+
 import org.apache.spark.sql.catalyst.analysis.GetColumnByOrdinal
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions._
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
index 41d9a6ec5..0c7a23ed4 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/CellTypeEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import geotrellis.raster.{CellType, DataType}
 import org.apache.spark.sql.catalyst.ScalaReflection
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/DelegatingSubfieldEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/DelegatingSubfieldEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
index 9b984b8ad..d728521c7 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/DelegatingSubfieldEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{GetColumnByOrdinal, UnresolvedAttribute, UnresolvedExtractValue}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/EnvelopeEncoder.scala
similarity index 64%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/EnvelopeEncoder.scala
index 9cc947e49..50d66f3e0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/EnvelopeEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/EnvelopeEncoder.scala
@@ -1,4 +1,25 @@
-package astraea.spark.rasterframes.encoders
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.encoders
 
 import org.locationtech.jts.geom.Envelope
 import org.apache.spark.sql.catalyst.ScalaReflection
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/ProjectedExtentEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/ProjectedExtentEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
index 0599f9848..d3d1c7552 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/ProjectedExtentEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,11 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.vector.ProjectedExtent
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/SparkBasicEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/SparkBasicEncoders.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
index 670d2e217..6dbf8b447 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/SparkBasicEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import org.apache.spark.sql.{Encoder, Encoders}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
index d0a11ab78..83eb51c21 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,15 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import java.net.URI
 import java.sql.Timestamp
 
-import astraea.spark.rasterframes.model._
-import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
+import org.locationtech.rasterframes.model._
+import org.locationtech.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
 import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
 import geotrellis.raster.{CellSize, CellType, Tile, TileLayout}
@@ -32,6 +34,7 @@ import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TemporalKey, Tempo
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.locationtech.geomesa.spark.jts.encoders.SpatialEncoders
+import org.locationtech.rasterframes.model.{CellContext, Cells, TileContext, TileDataContext}
 
 import scala.reflect.runtime.universe._
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
index 5d63f1db5..36830c4c0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
@@ -19,9 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.encoders
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.model.LazyCRS
+package org.locationtech.rasterframes.encoders
+
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.spark._
@@ -29,6 +29,8 @@ import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.vector._
 import org.apache.spark.sql.types._
 import org.locationtech.jts.geom.Envelope
+import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
+import org.locationtech.rasterframes.model.LazyCRS
 
 /** Collection of CatalystSerializers for third-party types. */
 trait StandardSerializers {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/StringBackedEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/StringBackedEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
index 8dc950b4b..ebce11784 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/StringBackedEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.GetColumnByOrdinal
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
index 310ba8971..22ad3a95f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,11 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.spark.TemporalProjectedExtent
 import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/TileLayerMetadataEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/TileLayerMetadataEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
index c2ed1bbd4..39f7bce3e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/TileLayerMetadataEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import geotrellis.spark.{KeyBounds, TileLayerMetadata}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
@@ -31,7 +33,7 @@ import scala.reflect.runtime.universe._
  * @since 7/21/17
  */
 object TileLayerMetadataEncoder {
-  import astraea.spark.rasterframes._
+  import org.locationtech.rasterframes._
 
   private def fieldEncoders = Seq[(String, ExpressionEncoder[_])](
     "cellType" -> cellTypeEncoder,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/URIEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/URIEncoder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
index d50cd7803..0d603478e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/URIEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import java.net.URI
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/encoders/package.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/encoders/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
index 678bbfcd1..3992667ef 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/encoders/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import org.apache.spark.sql.Column
 import org.apache.spark.sql.catalyst.expressions.Literal
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryLocalRasterOp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryLocalRasterOp.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryLocalRasterOp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryLocalRasterOp.scala
index 3fac44c65..bd55345fa 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryLocalRasterOp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryLocalRasterOp.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryRasterOp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryRasterOp.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryRasterOp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryRasterOp.scala
index 02f8fc29e..690658064 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/BinaryRasterOp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/BinaryRasterOp.scala
@@ -19,9 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
-import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
+package org.locationtech.rasterframes.expressions
+
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
index 8c5a8991d..1f263dcea 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
@@ -19,16 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.ref.{ProjectedRasterLike, RasterRef, RasterSource}
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+package org.locationtech.rasterframes.expressions
+
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.ref.{ProjectedRasterLike, RasterRef}
 import geotrellis.raster.{CellGrid, Tile}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.rf.{TileUDT, _}
 import org.apache.spark.sql.types._
+import org.locationtech.rasterframes.model.TileContext
+import org.locationtech.rasterframes.ref.{ProjectedRasterLike, RasterRef, RasterSource}
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 private[rasterframes]
 object DynamicExtractors {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/NullToValue.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/NullToValue.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/NullToValue.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/NullToValue.scala
index edc52fcf7..8bc98c1e2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/NullToValue.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/NullToValue.scala
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.UnaryExpression
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/OnCellGridExpression.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/OnCellGridExpression.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
index b856ae2be..ec2489ed2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/OnCellGridExpression.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,9 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
 import geotrellis.raster.CellGrid
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/OnTileContextExpression.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/OnTileContextExpression.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
index a8797ae49..92044348a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/OnTileContextExpression.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,14 +19,14 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
 import org.apache.spark.sql.catalyst.expressions.UnaryExpression
+import org.locationtech.rasterframes.model.TileContext
 
 /**
  * Implements boilerplate for subtype expressions processing TileUDT (when ProjectedRasterTile), RasterSourceUDT, and
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
index db40c8cab..548651aca 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.SpatialRelation.RelationPredicate
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.SpatialRelation.RelationPredicate
 import geotrellis.vector.Extent
 import org.locationtech.jts.geom._
 import org.apache.spark.sql.catalyst.InternalRow
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
index 1f4ef5b0e..fe284546a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/TileAssembler.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
@@ -19,12 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
 import java.nio.ByteBuffer
 
-import astraea.spark.rasterframes.expressions.TileAssembler.TileBuffer
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.expressions.TileAssembler.TileBuffer
+import org.locationtech.rasterframes.util._
 import geotrellis.raster.{DataType => _, _}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.aggregate.{ImperativeAggregate, TypedImperativeAggregate}
@@ -32,7 +32,7 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, ImplicitCastInputT
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
 import spire.syntax.cfor._
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 /**
  * Aggregator for reassembling tiles from from exploded form
@@ -125,7 +125,7 @@ case class TileAssembler(
 }
 
 object TileAssembler {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
 
   def apply(
     columnIndex: Column,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryLocalRasterOp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryLocalRasterOp.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryLocalRasterOp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryLocalRasterOp.scala
index 049e6d9a1..46969c226 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryLocalRasterOp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryLocalRasterOp.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
+package org.locationtech.rasterframes.expressions
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterAggregate.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterAggregate.scala
index a28ae6753..cfea46ebe 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterAggregate.scala
@@ -19,8 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
-import astraea.spark.rasterframes.expressions.DynamicExtractors.rowTileExtractor
+package org.locationtech.rasterframes.expressions
+
+import org.locationtech.rasterframes.expressions.DynamicExtractors.rowTileExtractor
 import geotrellis.raster.Tile
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterOp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterOp.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterOp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterOp.scala
index f21dc4bb5..8d2b532c8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/UnaryRasterOp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/UnaryRasterOp.scala
@@ -19,13 +19,14 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions
+
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
 import org.apache.spark.sql.catalyst.expressions.UnaryExpression
+import org.locationtech.rasterframes.model.TileContext
 
 /** Boilerplate for expressions operating on a single Tile-like . */
 trait UnaryRasterOp extends UnaryExpression {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/ExtractTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
similarity index 77%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/ExtractTile.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
index 7cb7ba3b1..cc01b1f8f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/ExtractTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
@@ -19,19 +19,19 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.tiles.InternalRowTile
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile.ConcreteProjectedRasterTile
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile.ConcreteProjectedRasterTile
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
+import org.locationtech.rasterframes.tiles.InternalRowTile
 
 /** Expression to extract at tile from several types that contain tiles.*/
 case class ExtractTile(child: Expression) extends UnaryRasterOp with CodegenFallback {
@@ -47,7 +47,7 @@ case class ExtractTile(child: Expression) extends UnaryRasterOp with CodegenFall
 }
 
 object ExtractTile {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
   def apply(input: Column): TypedColumn[Any, Tile] =
     new Column(new ExtractTile(input.expr)).as[Tile]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
similarity index 82%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
index 9f3e0bfff..1f1a9d926 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
@@ -19,18 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.encoders.StandardEncoders.crsEncoder
-import astraea.spark.rasterframes.expressions.OnTileContextExpression
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.StandardEncoders.crsEncoder
+import org.locationtech.rasterframes.expressions.OnTileContextExpression
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 /**
  * Expression to extract the CRS out of a RasterRef or ProjectedRasterTile column.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
similarity index 85%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
index 35f35e7ed..b3debf12d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetCellType.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.OnCellGridExpression
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.OnCellGridExpression
 import geotrellis.raster.{CellGrid, CellType}
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
@@ -43,7 +43,7 @@ case class GetCellType(child: Expression) extends OnCellGridExpression with Code
 }
 
 object GetCellType {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
   def apply(col: Column): TypedColumn[Any, CellType] =
     new Column(new GetCellType(col.expr)).as[CellType]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
similarity index 84%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
index 099e74834..1ba0b5995 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetDimensions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
@@ -19,15 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.OnCellGridExpression
-import astraea.spark.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.OnCellGridExpression
 import geotrellis.raster.CellGrid
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.locationtech.rasterframes.model.TileDimensions
 
 /**
  * Extract a Tile's dimensions
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetEnvelope.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetEnvelope.scala
index ce6ef4504..d0c14491b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetEnvelope.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetEnvelope.scala
@@ -19,9 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.EnvelopeEncoder
 import org.locationtech.jts.geom.{Envelope, Geometry}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
@@ -30,6 +29,7 @@ import org.apache.spark.sql.jts.AbstractGeometryUDT
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.encoders.EnvelopeEncoder
 
 /**
  * Extracts the bounding box (envelope) of arbitrary JTS Geometry.
@@ -60,7 +60,7 @@ case class GetEnvelope(child: Expression) extends UnaryExpression with CodegenFa
 }
 
 object GetEnvelope {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
   def apply(col: Column): TypedColumn[Any, Envelope] =
     new GetEnvelope(col.expr).asColumn.as[Envelope]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
similarity index 82%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
index 653cf06a8..9cfdea016 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
@@ -19,18 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
+package org.locationtech.rasterframes.expressions.accessors
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.encoders.StandardEncoders.extentEncoder
-import astraea.spark.rasterframes.expressions.OnTileContextExpression
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.StandardEncoders.extentEncoder
+import org.locationtech.rasterframes.expressions.OnTileContextExpression
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 /**
  * Expression to extract the Extent out of a RasterRef or ProjectedRasterTile column.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetTileContext.scala
similarity index 78%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetTileContext.scala
index d169a1b93..6c9a3538a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/accessors/GetTileContext.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetTileContext.scala
@@ -19,15 +19,17 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.accessors
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions.accessors
+
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
 
 case class GetTileContext(child: Expression) extends UnaryRasterOp with CodegenFallback {
   override def dataType: DataType = schemaOf[TileContext]
@@ -38,7 +40,7 @@ case class GetTileContext(child: Expression) extends UnaryRasterOp with CodegenF
 }
 
 object GetTileContext {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.tileContextEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.tileContextEncoder
 
   def apply(input: Column): TypedColumn[Any, TileContext] =
     new Column(new GetTileContext(input.expr)).as[TileContext]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellCountAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
index 0a4424665..84d938973 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.UnaryRasterAggregate
-import astraea.spark.rasterframes.expressions.tilestats.{DataCells, NoDataCells}
+import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
+import org.locationtech.rasterframes.expressions.tilestats.{DataCells, NoDataCells}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, _}
 import org.apache.spark.sql.types.{LongType, Metadata}
@@ -64,7 +64,7 @@ abstract class CellCountAggregate(isData: Boolean) extends UnaryRasterAggregate
 }
 
 object CellCountAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
 
   @ExpressionDescription(
     usage = "_FUNC_(tile) - Count the total data (non-no-data) cells in a tile column.",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellMeanAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
index 846f169cb..ff89e75ef 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.UnaryRasterAggregate
-import astraea.spark.rasterframes.expressions.tilestats.{DataCells, Sum}
+import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
+import org.locationtech.rasterframes.expressions.tilestats.{DataCells, Sum}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, _}
 import org.apache.spark.sql.types.{DoubleType, LongType, Metadata}
@@ -79,7 +79,7 @@ case class CellMeanAggregate(child: Expression) extends UnaryRasterAggregate {
 }
 
 object CellMeanAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
   /** Computes the column aggregate mean. */
   def apply(tile: Column): TypedColumn[Any, Double] =
     new Column(new CellMeanAggregate(tile.expr).toAggregateExpression()).as[Double]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
index 86e2822b2..96f7c6172 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/CellStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.stats.CellStatistics
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.stats.CellStatistics
+import org.locationtech.rasterframes.TileType
 import geotrellis.raster.{Tile, _}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
 import org.apache.spark.sql.catalyst.expressions.{ExprId, Expression, ExpressionDescription, NamedExpression}
@@ -120,7 +120,7 @@ case class CellStatsAggregate() extends UserDefinedAggregateFunction {
 }
 
 object CellStatsAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.cellStatsEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.cellStatsEncoder
 
   def apply(col: Column): TypedColumn[Any, CellStatistics] =
     new Column(new CellStatsAggregateUDAF(col.expr))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
index 8e74a8eae..f74c3bb54 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/HistogramAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
@@ -19,13 +19,13 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
 import java.nio.ByteBuffer
 
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.functions.safeEval
-import astraea.spark.rasterframes.stats.CellHistogram
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.functions.safeEval
+import org.locationtech.rasterframes.stats.CellHistogram
 import geotrellis.raster.Tile
 import geotrellis.raster.histogram.{Histogram, StreamingHistogram}
 import geotrellis.spark.util.KryoSerializer
@@ -35,7 +35,7 @@ import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 /**
  * Histogram aggregation function for a full column of tiles.
@@ -95,7 +95,7 @@ case class HistogramAggregate(numBuckets: Int) extends UserDefinedAggregateFunct
 }
 
 object HistogramAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.cellHistEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.cellHistEncoder
 
   def apply(col: Column): TypedColumn[Any, CellHistogram] =
     new Column(new HistogramAggregateUDAF(col.expr))
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
index 332895b5f..76c73c5a1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.functions.safeBinaryOp
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.functions.safeBinaryOp
 import geotrellis.raster.mapalgebra.local.{Add, Defined, Undefined}
 import geotrellis.raster.{IntConstantNoDataCellType, Tile}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
@@ -31,7 +31,7 @@ import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, Row, TypedColumn}
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 /**
  * Catalyst aggregate function that counts `NoData` values in a cell-wise fashion.
@@ -81,7 +81,7 @@ class LocalCountAggregate(isData: Boolean) extends UserDefinedAggregateFunction
   override def evaluate(buffer: Row): Tile = buffer.getAs[Tile](0)
 }
 object LocalCountAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
   @ExpressionDescription(
     usage = "_FUNC_(tile) - Compute cell-wise count of non-no-data values."
   )
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
similarity index 85%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
index be0722e32..9a127f22d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
@@ -19,17 +19,17 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.UnaryRasterAggregate
-import astraea.spark.rasterframes.expressions.localops.{Add => AddTiles, Divide => DivideTiles}
-import astraea.spark.rasterframes.expressions.transformers.SetCellType
+import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
+import org.locationtech.rasterframes.expressions.localops.{Add => AddTiles, Divide => DivideTiles}
+import org.locationtech.rasterframes.expressions.transformers.SetCellType
 import geotrellis.raster.Tile
 import geotrellis.raster.mapalgebra.local
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, ExpressionDescription, If, IsNull, Literal}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes a new tile contining the mean cell values across all tiles in column.",
@@ -73,7 +73,7 @@ case class LocalMeanAggregate(child: Expression) extends UnaryRasterAggregate {
   override lazy val evaluateExpression: Expression = DivideTiles(sum, count)
 }
 object LocalMeanAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
 
   def apply(tile: Column): TypedColumn[Any, Tile] =
     new Column(new LocalMeanAggregate(tile.expr).toAggregateExpression()).as[Tile]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
index dbe8a5b3f..387250f55 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
@@ -19,12 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.functions.safeBinaryOp
-import astraea.spark.rasterframes.stats.LocalCellStatistics
-import astraea.spark.rasterframes.util.DataBiasedOp.{BiasedAdd, BiasedMax, BiasedMin}
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.functions.safeBinaryOp
+import org.locationtech.rasterframes.stats.LocalCellStatistics
+import org.locationtech.rasterframes.util.DataBiasedOp.{BiasedAdd, BiasedMax, BiasedMin}
 import geotrellis.raster.mapalgebra.local._
 import geotrellis.raster.{DoubleConstantNoDataCellType, IntConstantNoDataCellType, IntUserDefinedNoDataCellType, Tile}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
@@ -33,7 +33,7 @@ import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, TypedColumn}
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
index a6a2de5a0..dcfb51881 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
@@ -19,12 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggstats
 
-import astraea.spark.rasterframes.TileType
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.functions.safeBinaryOp
-import astraea.spark.rasterframes.util.DataBiasedOp.{BiasedMax, BiasedMin}
+import org.locationtech.rasterframes.TileType
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.functions.safeBinaryOp
+import org.locationtech.rasterframes.util.DataBiasedOp.{BiasedMax, BiasedMin}
 import geotrellis.raster.Tile
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateFunction, AggregateMode, Complete}
@@ -72,7 +72,7 @@ class LocalTileOpAggregate(op: LocalTileBinaryOp) extends UserDefinedAggregateFu
 }
 
 object LocalTileOpAggregate {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
 
   @ExpressionDescription(
     usage = "_FUNC_(tile) - Compute cell-wise minimum value from a tile column."
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/generators/ExplodeTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/generators/ExplodeTiles.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
index e39ca1814..7a5e6539a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/generators/ExplodeTiles.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.generators
+package org.locationtech.rasterframes.expressions.generators
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.util._
 import geotrellis.raster._
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
index d516a6723..59bba32f6 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Add.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
@@ -19,18 +19,19 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
-import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
-import astraea.spark.rasterframes.util.DataBiasedOp.BiasedAdd
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.functions.lit
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 
 @ExpressionDescription(
   usage = "_FUNC_(tile, rhs) - Performs cell-wise addition between two tiles or a tile and a scalar.",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Divide.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Divide.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
index 37aa4ab6c..27db49b5c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Divide.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Equal.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Equal.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
index 610b8beff..5d540db3a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Equal.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Exp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Exp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
index 40d34ee06..b6d5993fc 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Exp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.{UnaryLocalRasterOp, fpTile}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.{UnaryLocalRasterOp, fpTile}
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Greater.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Greater.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
index f78022972..a9cce3235 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Greater.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
@@ -18,10 +18,10 @@
  * SPDX-License-Identifier: Apache-2.0
  *
  */
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/GreaterEqual.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/GreaterEqual.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
index bf43ceca5..8d363ca62 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/GreaterEqual.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Less.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Less.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
index 4f8d4ad7b..25e053ee8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Less.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
@@ -18,10 +18,10 @@
  * SPDX-License-Identifier: Apache-2.0
  *
  */
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/LessEqual.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/LessEqual.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
index 983ac7c0d..51372c0ac 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/LessEqual.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Log.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Log.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
index e2da78ce1..1130e16a9 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Log.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.{UnaryLocalRasterOp, fpTile}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.{UnaryLocalRasterOp, fpTile}
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Multiply.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Multiply.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
index 7ed7c76b8..fabbcaa7a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Multiply.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/NormalizedDifference.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/NormalizedDifference.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
index 5760582d6..b6894f90d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/NormalizedDifference.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
-import astraea.spark.rasterframes.expressions.fpTile
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryRasterOp
+package org.locationtech.rasterframes.expressions.localops
+
+import org.locationtech.rasterframes.expressions.fpTile
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
index 7925f24a8..2e813a1fa 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Resample.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
-import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
 import geotrellis.raster.Tile
 import geotrellis.raster.resample.NearestNeighbor
 import org.apache.spark.sql.catalyst.InternalRow
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Round.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Round.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
index 010666e17..ed04647de 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Round.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.{NullToValue, UnaryLocalRasterOp}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryLocalRasterOp}
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Subtract.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Subtract.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
index 203bb578d..ef7925dd0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Subtract.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
@@ -19,9 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+package org.locationtech.rasterframes.expressions.localops
+
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Unequal.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Unequal.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
index f3342b9c6..b21334861 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/localops/Unequal.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.localops
+package org.locationtech.rasterframes.expressions.localops
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index b2a44cdd6..f0a7ad653 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,22 +15,26 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
-import astraea.spark.rasterframes.expressions.accessors._
-import astraea.spark.rasterframes.expressions.aggstats._
-import astraea.spark.rasterframes.expressions.generators._
-import astraea.spark.rasterframes.expressions.localops._
-import astraea.spark.rasterframes.expressions.tilestats._
-import astraea.spark.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.expressions.accessors._
+import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.generators._
+import org.locationtech.rasterframes.expressions.localops._
+import org.locationtech.rasterframes.expressions.tilestats._
+import org.locationtech.rasterframes.expressions.transformers._
 import geotrellis.raster.{DoubleConstantNoDataCellType, Tile}
 import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection}
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
 import org.apache.spark.sql.rf.VersionShims._
 import org.apache.spark.sql.{SQLContext, rf}
+import org.locationtech.rasterframes.expressions.aggstats.CellCountAggregate.DataCells
+import org.locationtech.rasterframes.expressions.localops._
 
 import scala.util.Try
 import scala.reflect.runtime.universe._
@@ -98,7 +102,7 @@ package object expressions {
     registry.registerExpression[TileMean]("rf_tile_mean")
     registry.registerExpression[TileStats]("rf_tile_stats")
     registry.registerExpression[TileHistogram]("rf_tile_histogram")
-    registry.registerExpression[CellCountAggregate.DataCells]("rf_agg_data_cells")
+    registry.registerExpression[DataCells]("rf_agg_data_cells")
     registry.registerExpression[CellCountAggregate.NoDataCells]("rf_agg_no_data_cells")
     registry.registerExpression[CellStatsAggregate.CellStatsAggregateUDAF]("rf_agg_stats")
     registry.registerExpression[HistogramAggregate.HistogramAggregateUDAF]("rf_agg_approx_histogram")
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/DataCells.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/DataCells.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
index a7d49c4ae..f62c88acc 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/DataCells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
-import astraea.spark.rasterframes.expressions.{UnaryRasterOp, NullToValue}
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions.tilestats
+
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster._
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.{DataType, LongType}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Counts the number of non-no-data cells in a tile",
@@ -46,7 +47,7 @@ case class DataCells(child: Expression) extends UnaryRasterOp
   override def na: Any = 0L
 }
 object DataCells {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
   def apply(tile: Column): TypedColumn[Any, Long] =
     new Column(DataCells(tile.expr)).as[Long]
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/IsNoDataTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
similarity index 85%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/IsNoDataTile.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
index 7b360a07c..08eb0ec76 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/IsNoDataTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
-import astraea.spark.rasterframes.expressions.{NullToValue, UnaryRasterOp}
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions.tilestats
+
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster._
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.{BooleanType, DataType}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Produces `true` if all the cells in a given tile are no-data",
@@ -46,7 +47,7 @@ case class IsNoDataTile(child: Expression) extends UnaryRasterOp
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = tile.isNoDataTile
 }
 object IsNoDataTile {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.boolEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.boolEnc
   def apply(tile: Column): TypedColumn[Any, Boolean] =
     new Column(IsNoDataTile(tile.expr)).as[Boolean]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/NoDataCells.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/NoDataCells.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
index 89c2ae10b..379af473a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/NoDataCells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
@@ -19,15 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.{UnaryRasterOp, NullToValue}
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster._
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.{DataType, LongType}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Counts the number of no-data cells in a tile",
@@ -47,7 +47,7 @@ case class NoDataCells(child: Expression) extends UnaryRasterOp
   override def na: Any = 0L
 }
 object NoDataCells {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.longEnc
   def apply(tile: Column): TypedColumn[Any, Long] =
     new Column(NoDataCells(tile.expr)).as[Long]
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/Sum.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/Sum.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
index cfa10666b..bac6af716 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/Sum.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions.tilestats
+
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import geotrellis.raster._
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes the sum of all the cells in a tile..",
@@ -45,7 +46,7 @@ case class Sum(child: Expression) extends UnaryRasterOp with CodegenFallback {
 }
 
 object Sum {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
   def apply(tile: Column): TypedColumn[Any, Double] =
     new Column(Sum(tile.expr)).as[Double]
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileHistogram.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileHistogram.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
index d7fe7d0c1..4ca25f0c9 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileHistogram.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
@@ -19,17 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.stats.CellHistogram
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.stats.CellHistogram
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes per-tile histogram.",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMax.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
similarity index 87%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMax.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
index 0e2595b2a..5f387cb89 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMax.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
@@ -19,15 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.{NullToValue, UnaryRasterOp}
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster.{Tile, isData}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Determines the maximum cell value.",
@@ -47,7 +47,7 @@ case class TileMax(child: Expression) extends UnaryRasterOp
   override def na: Any = Double.MinValue
 }
 object TileMax {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
 
   def apply(tile: Column): TypedColumn[Any, Double] =
     new Column(TileMax(tile.expr)).as[Double]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
similarity index 87%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
index 30d6bc9cd..4bd0ba611 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMean.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
@@ -19,15 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.{NullToValue, UnaryRasterOp}
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster.{Tile, isData}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes the mean cell value of a tile.",
@@ -47,7 +47,7 @@ case class TileMean(child: Expression) extends UnaryRasterOp
   override def na: Any = Double.NaN
 }
 object TileMean {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
 
   def apply(tile: Column): TypedColumn[Any, Double] =
     new Column(TileMean(tile.expr)).as[Double]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMin.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
similarity index 87%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMin.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
index 4d2edc9b3..bbc691b4a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileMin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
@@ -19,15 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.{NullToValue, UnaryRasterOp}
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryRasterOp}
 import geotrellis.raster.{Tile, isData}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.{DataType, DoubleType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Determines the minimum cell value.",
@@ -47,7 +47,7 @@ case class TileMin(child: Expression) extends UnaryRasterOp
   override def na: Any = Double.MaxValue
 }
 object TileMin {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.doubleEnc
 
   def apply(tile: Column): TypedColumn[Any, Double] =
     new Column(TileMin(tile.expr)).as[Double]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileStats.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileStats.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
index 015f048e8..225fbfcb7 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/tilestats/TileStats.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
@@ -19,17 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.tilestats
+package org.locationtech.rasterframes.expressions.tilestats
 
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.stats.CellStatistics
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.stats.CellStatistics
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Computes per-tile descriptive statistics.",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/DebugRender.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
similarity index 88%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/DebugRender.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
index c26cc6b51..f13551d7e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/DebugRender.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.util.TileAsMatrix
+package org.locationtech.rasterframes.expressions.transformers
+
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.util.TileAsMatrix
 import geotrellis.raster.Tile
 import geotrellis.raster.render.ascii.AsciiArtEncoder
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
@@ -30,6 +30,7 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescript
 import org.apache.spark.sql.types.{DataType, StringType}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.unsafe.types.UTF8String
+import org.locationtech.rasterframes.model.TileContext
 
 abstract class DebugRender(asciiArt: Boolean) extends UnaryRasterOp
    with CodegenFallback with Serializable {
@@ -45,7 +46,7 @@ abstract class DebugRender(asciiArt: Boolean) extends UnaryRasterOp
 }
 
 object DebugRender {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.stringEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.stringEnc
 
   @ExpressionDescription(
     usage = "_FUNC_(tile) - Coverts the contents of the given tile an ASCII art string rendering",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
index d57d0c0fd..b02a216ec 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ExtentToGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.row
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.row
 import org.locationtech.jts.geom.{Envelope, Geometry}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
index be1f5296b..cac726a8f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/GeometryToExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
@@ -19,9 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
@@ -58,7 +58,7 @@ case class GeometryToExtent(child: Expression) extends UnaryExpression with Code
 }
 
 object GeometryToExtent {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
 
   def apply(bounds: Column): TypedColumn[Any, Extent] =
     new Column(new GeometryToExtent(bounds.expr)).as[Extent]
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/Mask.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/Mask.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
index 03e81efc2..9fb333838 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/Mask.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.DynamicExtractors._
-import astraea.spark.rasterframes.expressions.row
+package org.locationtech.rasterframes.expressions.transformers
+
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.DynamicExtractors._
+import org.locationtech.rasterframes.expressions.row
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster
 import geotrellis.raster.Tile
@@ -81,7 +82,7 @@ abstract class Mask(val left: Expression, val middle: Expression, val right: Exp
   }
 }
 object Mask {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
+  import org.locationtech.rasterframes.encoders.StandardEncoders.singlebandTileEncoder
 
   @ExpressionDescription(
     usage = "_FUNC_(target, mask) - Generate a tile with the values from the data tile, but where cells in the masking tile contain NODATA, replace the data value with NODATA.",
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterRefToTile.scala
similarity index 85%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterRefToTile.scala
index 4a510fb37..f0c82c6de 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterRefToTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterRefToTile.scala
@@ -19,18 +19,18 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.row
-import astraea.spark.rasterframes.ref.RasterRef
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.row
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
  * Realizes a RasterRef into a Tile.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 442a1fc23..0bdfa1ee3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -19,12 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
@@ -33,6 +32,8 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
 import scala.util.control.NonFatal
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
index 75aa0942f..351c25783 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
@@ -19,10 +19,10 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.Column
 import org.apache.spark.sql.catalyst.InternalRow
@@ -32,7 +32,7 @@ import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 
 import scala.util.control.NonFatal
-import astraea.spark.rasterframes.TileType
+import org.locationtech.rasterframes.TileType
 
 /**
  * Accepts RasterRef and generates one or more RasterRef instances representing the
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 6958d2ab8..42a304bb3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -19,12 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, serialized_literal}
-import astraea.spark.rasterframes.jts.ReprojectionTransformer
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.serialized_literal
 import org.locationtech.jts.geom.Geometry
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.InternalRow
@@ -33,6 +32,8 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.jts.JTSTypes
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.jts.ReprojectionTransformer
 
 /**
   *
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
index d335746eb..772432d1f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/SetCellType.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
@@ -19,12 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.encoders.StandardEncoders._
-import astraea.spark.rasterframes.expressions.DynamicExtractors.tileExtractor
-import astraea.spark.rasterframes.expressions.row
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.StandardEncoders._
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes.expressions.row
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayDouble.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
similarity index 86%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayDouble.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
index 02a4bc4e8..7e3ba0a82 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayDouble.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
@@ -19,15 +19,16 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
+package org.locationtech.rasterframes.expressions.transformers
+
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.util.ArrayData
 import org.apache.spark.sql.types.{DataType, DataTypes, DoubleType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Coverts the contents of the given tile to an array of double floating-point values",
@@ -43,7 +44,7 @@ case class TileToArrayDouble(child: Expression) extends UnaryRasterOp with Codeg
   }
 }
 object TileToArrayDouble {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
   def apply(tile: Column): TypedColumn[Any, Array[Double]] =
     new Column(TileToArrayDouble(tile.expr)).as[Array[Double]]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayInt.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
similarity index 83%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayInt.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
index 31ad81516..3b1c697b3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/TileToArrayInt.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
@@ -19,16 +19,17 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
-import astraea.spark.rasterframes.expressions.UnaryRasterOp
-import astraea.spark.rasterframes.model.TileContext
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.util.ArrayData
 import org.apache.spark.sql.types.{DataType, DataTypes, IntegerType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Coverts the contents of the given tile to an array of integer values",
@@ -44,7 +45,7 @@ case class TileToArrayInt(child: Expression) extends UnaryRasterOp with CodegenF
   }
 }
 object TileToArrayInt {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
   def apply(tile: Column): TypedColumn[Any, Array[Int]] =
     new Column(TileToArrayInt(tile.expr)).as[Array[Int]]
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index a10a6db7f..e5bd7904b 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.transformers
 
 import java.net.URI
 
-import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.ref.RasterRef
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
@@ -31,6 +31,7 @@ import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StringType}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.unsafe.types.UTF8String
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
similarity index 70%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
index d291ae9d4..975d26b6a 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/ContextRDDMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
@@ -1,32 +1,38 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
-import astraea.spark.rasterframes.PairRDDConverter._
-import astraea.spark.rasterframes.StandardColumns._
-import astraea.spark.rasterframes.extensions.Implicits._
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.{PairRDDConverter, RasterFrame}
-import geotrellis.raster.{CellGrid}
+import org.locationtech.rasterframes.PairRDDConverter._
+import org.locationtech.rasterframes.StandardColumns._
+import Implicits._
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{PairRDDConverter, RasterFrame}
+import geotrellis.raster.CellGrid
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.util.MethodExtensions
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SparkSession
+import org.locationtech.rasterframes.PairRDDConverter
 
 /**
  * Extension method on `ContextRDD`-shaped RDDs with appropriate context bounds to create a RasterFrame.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
similarity index 84%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index ae514396b..ad9bc8bf1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -1,24 +1,29 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
-import astraea.spark.rasterframes.StandardColumns._
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.{MetadataKeys, RasterFrame}
+import org.locationtech.rasterframes.StandardColumns._
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.RasterFrame
 import geotrellis.raster.Tile
 import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
@@ -28,8 +33,9 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
 import spray.json.JsonFormat
-import astraea.spark.rasterframes.encoders.StandardEncoders._
-import astraea.spark.rasterframes.expressions.DynamicExtractors
+import org.locationtech.rasterframes.encoders.StandardEncoders._
+import org.locationtech.rasterframes.expressions.DynamicExtractors
+import org.locationtech.rasterframes.MetadataKeys
 
 import scala.util.Try
 
@@ -45,7 +51,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     attr.name == column.columnName || attr.semanticEquals(column.expr)
 
   /** Map over the Attribute representation of Columns, modifying the one matching `column` with `op`. */
-  private[astraea] def mapColumnAttribute(column: Column, op: Attribute ⇒  Attribute): DF = {
+  private[rasterframes] def mapColumnAttribute(column: Column, op: Attribute ⇒  Attribute): DF = {
     val analyzed = self.queryExecution.analyzed.output
     val selects = selector(column)
     val attrs = analyzed.map { attr ⇒
@@ -54,31 +60,31 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     self.select(attrs.map(a ⇒ new Column(a)): _*).asInstanceOf[DF]
   }
 
-  private[astraea] def addColumnMetadata(column: Column, op: MetadataBuilder ⇒ MetadataBuilder): DF = {
+  private[rasterframes] def addColumnMetadata(column: Column, op: MetadataBuilder ⇒ MetadataBuilder): DF = {
     mapColumnAttribute(column, attr ⇒ {
       val md = new MetadataBuilder().withMetadata(attr.metadata)
       attr.withMetadata(op(md).build)
     })
   }
 
-  private[astraea] def fetchMetadataValue[D](column: Column, reader: (Attribute) ⇒ D): Option[D] = {
+  private[rasterframes] def fetchMetadataValue[D](column: Column, reader: (Attribute) ⇒ D): Option[D] = {
     val analyzed = self.queryExecution.analyzed.output
     analyzed.find(selector(column)).map(reader)
   }
 
-  private[astraea]
+  private[rasterframes]
   def setSpatialColumnRole[K: SpatialComponent: JsonFormat](
     column: Column, md: TileLayerMetadata[K]): DF =
     addColumnMetadata(column,
       _.attachContext(md.asColumnMetadata).tagSpatialKey
     )
 
-  private[astraea]
+  private[rasterframes]
   def setTemporalColumnRole(column: Column): DF =
     addColumnMetadata(column, _.tagTemporalKey)
 
   /** Get the role tag the column plays in the RasterFrame, if any. */
-  private[astraea]
+  private[rasterframes]
   def getColumnRole(column: Column): Option[String] =
     fetchMetadataValue(column, _.metadata.getString(SPATIAL_ROLE_KEY))
 
@@ -219,5 +225,5 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
 
   /** Internal method for slapping the RasterFreame seal of approval on a DataFrame.
    * Only call if if you are sure it has a spatial key and tile columns and TileLayerMetadata. */
-  private[astraea] def certify = certifyRasterframe(self)
+  private[rasterframes] def certify = certifyRasterframe(self)
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
index bd8bb6d1c..d4f3de292 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
-import astraea.spark.rasterframes.RasterFrame
-import astraea.spark.rasterframes.util.{WithMergeMethods, WithPrototypeMethods}
+import org.locationtech.rasterframes.RasterFrame
+import org.locationtech.rasterframes.util.{WithMergeMethods, WithPrototypeMethods}
 import geotrellis.raster._
 import geotrellis.spark.{Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
@@ -61,17 +63,17 @@ trait Implicits {
     val self: RDD[(SpaceTimeKey, T)] with Metadata[TileLayerMetadata[SpaceTimeKey]]
   )(implicit spark: SparkSession) extends SpatioTemporalContextRDDMethods[T]
 
-  private[astraea]
+  private[rasterframes]
   implicit class WithMetadataMethods[R: JsonFormat](val self: R)
       extends MetadataMethods[R]
 
-  private[astraea]
+  private[rasterframes]
   implicit class WithMetadataAppendMethods(val self: SMetadata)
       extends MethodExtensions[SMetadata] {
     def append = new MetadataBuilder().withMetadata(self)
   }
 
-  private[astraea]
+  private[rasterframes]
   implicit class WithMetadataBuilderMethods(val self: MetadataBuilder)
       extends MetadataBuilderMethods
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/KryoMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/KryoMethods.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/KryoMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/KryoMethods.scala
index 52ed69557..7b291d7d6 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/KryoMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/KryoMethods.scala
@@ -19,12 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.extensions
-import astraea.spark.rasterframes.util.RFKryoRegistrator
+package org.locationtech.rasterframes.extensions
 import geotrellis.util.MethodExtensions
 import org.apache.spark.SparkConf
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.sql.SparkSession
+import org.locationtech.rasterframes.util.RFKryoRegistrator
 
 object KryoMethods {
   val kryoProperties = Map("spark.serializer" -> classOf[KryoSerializer].getName,
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataBuilderMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
similarity index 83%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataBuilderMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
index 491c30b4d..ba0f78c28 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataBuilderMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
-import astraea.spark.rasterframes.{MetadataKeys, StandardColumns}
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.types.{Metadata, MetadataBuilder}
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{MetadataKeys, StandardColumns}
 
 /**
  * Convenience to deal with boilerplate associated with adding
@@ -30,7 +32,7 @@ import astraea.spark.rasterframes.util._
  *
  * @since 12/21/17
  */
-private[astraea]
+private[rasterframes]
 abstract class MetadataBuilderMethods extends MethodExtensions[MetadataBuilder] with MetadataKeys with StandardColumns {
   def attachContext(md: Metadata) = self.putMetadata(CONTEXT_METADATA_KEY, md)
   def tagSpatialKey = self.putString(SPATIAL_ROLE_KEY, SPATIAL_KEY_COLUMN.columnName)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
similarity index 57%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
index e1a886e60..18374fade 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/MetadataMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
@@ -1,20 +1,26 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
+
 import geotrellis.util.MethodExtensions
 import spray.json.{JsObject, JsonFormat}
 import org.apache.spark.sql.types.{Metadata ⇒ SQLMetadata}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
similarity index 84%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
index df1f6a893..8edf30b14 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/ProjectedRasterMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
@@ -1,15 +1,37 @@
-package astraea.spark.rasterframes.extensions
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.extensions
 
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.{PairRDDConverter, RasterFrame, StandardColumns}
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{RasterFrame, StandardColumns}
 import geotrellis.raster.{CellGrid, ProjectedRaster}
 import geotrellis.spark._
 import geotrellis.spark.tiling._
 import geotrellis.util.MethodExtensions
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SparkSession
+import org.locationtech.rasterframes.{PairRDDConverter, StandardColumns}
 
 import scala.reflect.runtime.universe._
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
index 46ee83205..6cdc0cc62 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/RFSpatialColumnMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.{RasterFrame, StandardColumns}
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.RasterFrame
 import org.locationtech.jts.geom.Point
 import geotrellis.proj4.LatLng
 import geotrellis.spark.SpatialKey
@@ -31,6 +33,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.functions.{asc, udf => sparkUdf}
 import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 import org.locationtech.geomesa.curve.Z2SFC
+import org.locationtech.rasterframes.StandardColumns
 
 /**
  * RasterFrame extension methods associated with adding spatially descriptive columns.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
index 7e0130010..1338af7c3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/RasterFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
@@ -1,25 +1,30 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.{MetadataKeys, RasterFrame}
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.RasterFrame
 import geotrellis.proj4.CRS
 import geotrellis.raster.resample.{NearestNeighbor, ResampleMethod}
 import geotrellis.raster.{MultibandTile, ProjectedRaster, Tile, TileLayout}
@@ -33,9 +38,10 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{Metadata, TimestampType}
 import spray.json._
-import astraea.spark.rasterframes.encoders.StandardEncoders._
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import org.locationtech.rasterframes.encoders.StandardEncoders._
+import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 import com.typesafe.scalalogging.LazyLogging
+import org.locationtech.rasterframes.MetadataKeys
 
 import scala.reflect.runtime.universe._
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
similarity index 85%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
index f46f7f0e7..a9abb0d80 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/SQLContextMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.SQLContext
@@ -30,7 +32,7 @@ import org.apache.spark.sql.SQLContext
  */
 trait SQLContextMethods extends MethodExtensions[SQLContext] {
   def withRasterFrames: SQLContext = {
-    astraea.spark.rasterframes.initRF(self)
+    org.locationtech.rasterframes.initRF(self)
     self
   }
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/extensions/SparkSessionMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
similarity index 83%
rename from core/src/main/scala/astraea/spark/rasterframes/extensions/SparkSessionMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
index 9447e812e..92bcfd871 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/extensions/SparkSessionMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.extensions
+package org.locationtech.rasterframes.extensions
 
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.SparkSession
@@ -29,7 +31,7 @@ import org.apache.spark.sql.SparkSession
  */
 trait SparkSessionMethods extends MethodExtensions[SparkSession] {
   def withRasterFrames: SparkSession = {
-    astraea.spark.rasterframes.initRF(self.sqlContext)
+    org.locationtech.rasterframes.initRF(self.sqlContext)
     self
   }
 }
diff --git a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 6805a99d1..19b2d68c4 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -1,26 +1,30 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes
-
-import astraea.spark.rasterframes.jts.ReprojectionTransformer
-import astraea.spark.rasterframes.model.LazyCRS
+package org.locationtech.rasterframes
 import geotrellis.raster.{Tile, _}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.SQLContext
 import org.locationtech.jts.geom.Geometry
+import org.locationtech.rasterframes.jts.ReprojectionTransformer
+import org.locationtech.rasterframes.model.LazyCRS
 
 /**
  * Module utils.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala
rename to core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
index 86d4dd73b..51cd4c390 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/jts/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,21 +15,23 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.jts
+package org.locationtech.rasterframes.jts
 
 import java.sql.{Date, Timestamp}
 import java.time.{LocalDate, ZonedDateTime}
 
-import astraea.spark.rasterframes.expressions.SpatialRelation.{Contains, Intersects}
+import org.locationtech.rasterframes.expressions.SpatialRelation.{Contains, Intersects}
 import org.locationtech.jts.geom._
 import geotrellis.util.MethodExtensions
 import geotrellis.vector.{Point ⇒ gtPoint}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.functions._
 import org.locationtech.geomesa.spark.jts.DataFrameFunctions.SpatialConstructors
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 
 /**
  * Extension methods on typed columns allowing for DSL-like queries over JTS types.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala b/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala
rename to core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
index 4946001c8..86f5e9345 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/jts/ReprojectionTransformer.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.jts
+package org.locationtech.rasterframes.jts
 
 import org.locationtech.jts.geom.{CoordinateSequence, Geometry}
 import org.locationtech.jts.geom.util.GeometryTransformer
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ml/NoDataFilter.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
similarity index 88%
rename from core/src/main/scala/astraea/spark/rasterframes/ml/NoDataFilter.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
index dfe9499a3..735b3964c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ml/NoDataFilter.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,22 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.ml
+package org.locationtech.rasterframes.ml
 
-import astraea.spark.rasterframes.ml.Parameters.HasInputCols
+import org.locationtech.rasterframes.ml.Parameters.HasInputCols
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
 import org.apache.spark.sql.Dataset
 import org.apache.spark.sql.types.StructType
 import java.util.ArrayList
+
+import org.locationtech.rasterframes.ml.Parameters.HasInputCols
+
 import scala.collection.JavaConversions._
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ml/Parameters.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
similarity index 89%
rename from core/src/main/scala/astraea/spark/rasterframes/ml/Parameters.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
index 4bc2fd476..79e7ef1aa 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ml/Parameters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.ml
+package org.locationtech.rasterframes.ml
 
 import org.apache.spark.ml.param.{Params, StringArrayParam}
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ml/TileColumnSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/ml/TileColumnSupport.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
index eccc8f00e..1cdeaaa02 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ml/TileColumnSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.ml
+package org.locationtech.rasterframes.ml
 
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ml/TileExploder.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/ml/TileExploder.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
index d52b82d35..cf53407f1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ml/TileExploder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,18 +15,20 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.ml
+package org.locationtech.rasterframes.ml
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
 import org.apache.spark.sql.Dataset
 import org.apache.spark.sql.functions.col
 import org.apache.spark.sql.types._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 
 /**
  * SparkML Transformer for expanding tiles into single cell rows with
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala b/core/src/main/scala/org/locationtech/rasterframes/model/CellContext.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/CellContext.scala
index 84a723686..95a2e1bf0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/CellContext.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/CellContext.scala
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.model
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+package org.locationtech.rasterframes.model
+
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{ShortType, StructField, StructType}
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import CatalystSerializer._
 
 case class CellContext(tileContext: TileContext, tileDataContext: TileDataContext, colIndex: Short, rowIndex: Short)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
similarity index 88%
rename from core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
index b4ce7509c..1c7840511 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.model
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
-import astraea.spark.rasterframes.ref.RasterRef
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
+package org.locationtech.rasterframes.model
+
 import geotrellis.raster.{ArrayTile, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
-import CatalystSerializer._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 
 /** Represents the union of binary cell datas or a reference to the data.*/
 case class Cells(data: Either[Array[Byte], RasterRef]) {
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
index 5ff441d09..d01d49317 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/LazyCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
@@ -19,8 +19,9 @@
  *
  */
 
-package astraea.spark.rasterframes.model
-import astraea.spark.rasterframes.model.LazyCRS.EncodedCRS
+package org.locationtech.rasterframes.model
+
+import LazyCRS.EncodedCRS
 import geotrellis.proj4.CRS
 import org.locationtech.proj4j.CoordinateReferenceSystem
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala b/core/src/main/scala/org/locationtech/rasterframes/model/TileContext.scala
similarity index 88%
rename from core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/TileContext.scala
index e6d6a5565..912e1d81e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileContext.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/TileContext.scala
@@ -19,15 +19,16 @@
  *
  */
 
-package astraea.spark.rasterframes.model
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+package org.locationtech.rasterframes.model
+
 import geotrellis.proj4.CRS
 import geotrellis.raster.Tile
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{StructField, StructType}
-import CatalystSerializer._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 case class TileContext(extent: Extent, crs: CRS) {
   def toProjectRasterTile(t: Tile): ProjectedRasterTile = ProjectedRasterTile(t, extent, crs)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala b/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
similarity index 88%
rename from core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
index 114b9f620..bbf49705d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileDataContext.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
@@ -19,12 +19,14 @@
  *
  */
 
-package astraea.spark.rasterframes.model
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
+package org.locationtech.rasterframes.model
+
+import org.locationtech.rasterframes.encoders.CatalystSerializerEncoder
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{StructField, StructType}
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 
 /** Encapsulates all information about a tile aside from actual cell values. */
 case class TileDataContext(cellType: CellType, dimensions: TileDimensions)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala b/core/src/main/scala/org/locationtech/rasterframes/model/TileDimensions.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala
rename to core/src/main/scala/org/locationtech/rasterframes/model/TileDimensions.scala
index 0e0e56e51..e419ac668 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/model/TileDimensions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/TileDimensions.scala
@@ -19,13 +19,13 @@
  *
  */
 
-package astraea.spark.rasterframes.model
+package org.locationtech.rasterframes.model
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
+import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
 import geotrellis.raster.Grid
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{ShortType, StructField, StructType}
+import org.locationtech.rasterframes.encoders.CatalystSerializer
 
 /**
  * Typed wrapper for tile size information.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/package.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
similarity index 83%
rename from core/src/main/scala/astraea/spark/rasterframes/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index 34ae94624..83f4917a0 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -1,46 +1,44 @@
 /*
- * Copyright 2017 Astraea, Inc.
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark
-
-import astraea.spark.rasterframes.encoders.StandardEncoders
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.util.ZeroSevenCompatibilityKit
+package org.locationtech
 import com.typesafe.config.ConfigFactory
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster.{Tile, TileFeature}
 import geotrellis.spark.{ContextRDD, Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql._
 import org.apache.spark.sql.rf.TileUDT
+import org.apache.spark.sql.{DataFrame, SQLContext, rf}
 import org.locationtech.geomesa.spark.jts.DataFrameFunctions
+import org.locationtech.rasterframes.encoders.StandardEncoders
+import org.locationtech.rasterframes.extensions.Implicits
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.util.ZeroSevenCompatibilityKit
 import shapeless.tag.@@
-
 import scala.reflect.runtime.universe._
 
-/**
- *  Module providing support for RasterFrames.
- * `import astraea.spark.rasterframes._`.
- *
- * @since 7/18/17
- */
 package object rasterframes extends StandardColumns
   with RasterFunctions
   with ZeroSevenCompatibilityKit.RasterFunctions
-  with rasterframes.extensions.Implicits
+  with Implicits
   with rasterframes.jts.Implicits
   with StandardEncoders
   with DataFrameFunctions.Library
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
similarity index 87%
rename from core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
index f85f5b398..ed269f20f 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/HttpRangeReader.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
@@ -1,17 +1,22 @@
 /*
- * Copyright 2016 Azavea
+ * This software is licensed under the Apache 2 license, quoted below.
  *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
+ * Copyright 2019 Astraea, Inc.
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
  *
  * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
 // NB: Copied from
@@ -19,8 +24,7 @@
 // TODO: Submit PR with changes/fixes.
 //package geotrellis.spark.io.http.util
 
-package astraea.spark.rasterframes.ref
-
+package org.locationtech.rasterframes.ref
 
 import geotrellis.util.RangeReader
 import scalaj.http.{Http, HttpResponse}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/ProjectedRasterLike.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/ref/ProjectedRasterLike.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
index 7c5b2729d..f716c2f19 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/ProjectedRasterLike.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,7 +19,7 @@
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
 import geotrellis.proj4.CRS
 import geotrellis.raster.CellGrid
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
index 4423900e5..6833b5bf8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,11 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.raster.{CellType, GridBounds, Tile}
@@ -31,7 +28,9 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
-import CatalystSerializer._
+import org.locationtech.rasterframes.encoders.CatalystSerializer.{CatalystIO, _}
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
  * A delayed-read projected raster implementation.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
rename to core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 0f70f7b38..c266ab1d9 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,18 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
-import astraea.spark.rasterframes.NOMINAL_TILE_DIMS
-import astraea.spark.rasterframes.model.{TileContext, TileDimensions}
-import astraea.spark.rasterframes.ref.RasterSource.SINGLEBAND
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import astraea.spark.rasterframes.util.GeoTiffInfoSupport
+import org.locationtech.rasterframes.NOMINAL_TILE_DIMS
+import org.locationtech.rasterframes.model.TileDimensions
+import RasterSource.SINGLEBAND
 import com.azavea.gdal.GDALWarp
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
@@ -44,6 +43,9 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
+import org.locationtech.rasterframes.model.{TileContext, TileDimensions}
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+import org.locationtech.rasterframes.util.GeoTiffInfoSupport
 
 /**
  * Abstraction over fetching geospatial raster data.
@@ -114,7 +116,7 @@ object RasterSource extends LazyLogging {
   object IsGDAL {
 
     /** Determine if we should prefer GDAL for all types. */
-    private val preferGdal: Boolean = astraea.spark.rasterframes.rfConfig.getBoolean("prefer-gdal")
+    private val preferGdal: Boolean = org.locationtech.rasterframes.rfConfig.getBoolean("prefer-gdal")
     @transient
     lazy val hasGDAL: Boolean = try {
       val _ = new GDALWarp()
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilterPushdownRules.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilterPushdownRules.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
index d61640748..8d5f914bd 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilterPushdownRules.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.rules
+package org.locationtech.rasterframes.rules
 
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.Rule
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
similarity index 90%
rename from core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
index e2cae5734..8b1c61395 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialFilters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.rules
+package org.locationtech.rasterframes.rules
 
 import org.locationtech.jts.geom.Geometry
 import org.apache.spark.sql.sources.Filter
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialRelationReceiver.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/rules/SpatialRelationReceiver.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
index 36c681d14..b5cc03f43 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/SpatialRelationReceiver.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.rules
+package org.locationtech.rasterframes.rules
 
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.sources.{BaseRelation, Filter}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
new file mode 100644
index 000000000..0407a7a33
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
@@ -0,0 +1,42 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.rules
+
+import org.locationtech.rasterframes.expressions.SpatialRelation
+import org.apache.spark.sql.catalyst.expressions.ScalaUDF
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.rules.Rule
+
+/**
+ * Swaps out spatial relation UDFs for expression forms.
+ *
+ * @since 2/19/18
+ */
+object SpatialUDFSubstitutionRules extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = {
+    plan.transform {
+      case q: LogicalPlan ⇒ q.transformExpressions {
+        case s: ScalaUDF ⇒ SpatialRelation.fromUDF(s).getOrElse(s)
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/TemporalFilters.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
similarity index 91%
rename from core/src/main/scala/astraea/spark/rasterframes/rules/TemporalFilters.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
index 51ea9ddd7..cf41c9b54 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/TemporalFilters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.rules
+package org.locationtech.rasterframes.rules
 
 import java.sql.{Date, Timestamp}
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/rules/package.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/package.scala
similarity index 59%
rename from core/src/main/scala/astraea/spark/rasterframes/rules/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/rules/package.scala
index ff4755a86..0f028e14e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/rules/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/package.scala
@@ -1,4 +1,25 @@
-package astraea.spark.rasterframes
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
 
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
diff --git a/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala b/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala
rename to core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
index c32d6100f..861b3876d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/stats/CellHistogram.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.stats
-import astraea.spark.rasterframes.encoders.StandardEncoders
+package org.locationtech.rasterframes.stats
+
 import geotrellis.raster.Tile
 import geotrellis.raster.histogram.{Histogram => GTHistogram}
 import org.apache.spark.sql.types._
+import org.locationtech.rasterframes.encoders.StandardEncoders
 
 import scala.collection.mutable.{ListBuffer => MutableListBuffer}
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/stats/CellStatistics.scala b/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
similarity index 93%
rename from core/src/main/scala/astraea/spark/rasterframes/stats/CellStatistics.scala
rename to core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
index e1ba03b60..06dec77a3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/stats/CellStatistics.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.stats
-import astraea.spark.rasterframes.encoders.StandardEncoders
+package org.locationtech.rasterframes.stats
+
 import geotrellis.raster.Tile
 import org.apache.spark.sql.types.StructType
+import org.locationtech.rasterframes.encoders.StandardEncoders
 
 /**
  * Container for computed statistics over cells.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/stats/LocalCellStatistics.scala b/core/src/main/scala/org/locationtech/rasterframes/stats/LocalCellStatistics.scala
similarity index 94%
rename from core/src/main/scala/astraea/spark/rasterframes/stats/LocalCellStatistics.scala
rename to core/src/main/scala/org/locationtech/rasterframes/stats/LocalCellStatistics.scala
index 685722f62..39c0671f8 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/stats/LocalCellStatistics.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/stats/LocalCellStatistics.scala
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.stats
+package org.locationtech.rasterframes.stats
+
 import geotrellis.raster.Tile
 
 case class LocalCellStatistics(count: Tile, min: Tile, max: Tile, mean: Tile, variance: Tile)
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
similarity index 96%
rename from core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala
rename to core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
index 4dd5c9248..563aaf0e3 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/InternalRowTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,14 +19,15 @@
  *
  */
 
-package astraea.spark.rasterframes.tiles
+package org.locationtech.rasterframes.tiles
 
 import java.nio.ByteBuffer
 
-import astraea.spark.rasterframes.encoders.CatalystSerializer.CatalystIO
-import astraea.spark.rasterframes.model.{Cells, TileDataContext}
-import geotrellis.raster._
+import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
+import org.locationtech.rasterframes.model.TileDataContext
+import geotrellis.raster.{DelegatingTile, _}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.locationtech.rasterframes.model.{Cells, TileDataContext}
 
 /**
  * Wrapper around a `Tile` encoded in a Catalyst `InternalRow`, for the purpose
diff --git a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
similarity index 83%
rename from core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
rename to core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
index 4893f667f..5543038d5 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,21 +19,22 @@
  *
  */
 
-package astraea.spark.rasterframes.tiles
+package org.locationtech.rasterframes.tiles
 
-import astraea.spark.rasterframes.TileType
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
-import astraea.spark.rasterframes.model.TileContext
-import astraea.spark.rasterframes.ref.ProjectedRasterLike
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.TileType
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.encoders.CatalystSerializerEncoder
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.raster.{CellType, ProjectedRaster, Tile}
+import geotrellis.raster.{CellType, DelegatingTile, ProjectedRaster, Tile}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
+import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+import org.locationtech.rasterframes.model.TileContext
+import org.locationtech.rasterframes.ref.ProjectedRasterLike
 
 /**
  * A Tile that's also like a ProjectedRaster, with delayed evaluation support.
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/DataBiasedOp.scala b/core/src/main/scala/org/locationtech/rasterframes/util/DataBiasedOp.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/DataBiasedOp.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/DataBiasedOp.scala
index c2e2578a3..83e5fe76c 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/DataBiasedOp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/DataBiasedOp.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
 import geotrellis.raster
 import geotrellis.raster.isNoData
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
index add36a914..956d27cf1 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/GeoTiffInfoSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
+
 import geotrellis.raster.TileLayout
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader.GeoTiffInfo
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/KryoSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
similarity index 95%
rename from core/src/main/scala/astraea/spark/rasterframes/util/KryoSupport.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
index b20aa7851..96f865bba 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/KryoSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,7 +19,7 @@
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
 import java.nio.ByteBuffer
 
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/MultibandRender.scala b/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/MultibandRender.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
index 764d049ee..4493e4eb6 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/MultibandRender.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
 import geotrellis.raster._
 import geotrellis.raster.render.{ColorRamp, Png}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
similarity index 81%
rename from core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
index 5f64d9954..40a038b7d 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/RFKryoRegistrator.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,12 +19,13 @@
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
-import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
-import astraea.spark.rasterframes.ref.RasterSource._
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.ref.RasterSource._
 import com.esotericsoftware.kryo.Kryo
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
 
 /**
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/SubdivideSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/SubdivideSupport.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
index 162614651..3cefa640e 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/SubdivideSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
 import geotrellis.raster.crop.Crop
 import geotrellis.raster.{CellGrid, TileLayout}
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index fb0c14946..ac3842cfc 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -19,16 +19,17 @@
  *
  */
 
-package astraea.spark.rasterframes.util
-import astraea.spark.rasterframes.expressions.TileAssembler
-import astraea.spark.rasterframes.expressions.accessors._
-import astraea.spark.rasterframes.expressions.aggstats._
-import astraea.spark.rasterframes.expressions.generators._
-import astraea.spark.rasterframes.expressions.localops._
-import astraea.spark.rasterframes.expressions.tilestats._
-import astraea.spark.rasterframes.expressions.transformers._
-import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics}
-import astraea.spark.rasterframes.{functions => F}
+package org.locationtech.rasterframes.util
+
+import org.locationtech.rasterframes.expressions.TileAssembler
+import org.locationtech.rasterframes.expressions.accessors._
+import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.generators._
+import org.locationtech.rasterframes.expressions.localops._
+import org.locationtech.rasterframes.expressions.tilestats._
+import org.locationtech.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.stats._
+import org.locationtech.rasterframes.{functions => F}
 import org.locationtech.jts.geom.Geometry
 import geotrellis.proj4.CRS
 import geotrellis.raster.mapalgebra.local.LocalTileBinaryOp
@@ -45,10 +46,10 @@ import org.apache.spark.sql.{Column, SQLContext, TypedColumn, rf}
  * @since 4/3/17
  */
 object ZeroSevenCompatibilityKit {
-  import astraea.spark.rasterframes.encoders.StandardEncoders._
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
 
   trait RasterFunctions {
-    private val delegate = new astraea.spark.rasterframes.RasterFunctions {}
+    private val delegate = new org.locationtech.rasterframes.RasterFunctions {}
     // format: off
     /** Create a row for each cell in Tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
similarity index 92%
rename from core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
index a7703449a..447c4d908 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/debug/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.util
+package org.locationtech.rasterframes.util
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.proj4.LatLng
 import geotrellis.vector.{Feature, Geometry}
 import geotrellis.vector.io.json.JsonFeatureCollection
diff --git a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
similarity index 97%
rename from core/src/main/scala/astraea/spark/rasterframes/util/package.scala
rename to core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index eef449c76..6b492d4b2 100644
--- a/core/src/main/scala/astraea/spark/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import com.typesafe.scalalogging.Logger
 import geotrellis.raster.crop.TileCropMethods
@@ -49,7 +51,7 @@ import scala.Boolean.box
 package object util {
   @transient
   protected lazy val logger: Logger =
-    Logger(LoggerFactory.getLogger("astraea.spark.rasterframes"))
+    Logger(LoggerFactory.getLogger("org.locationtech.rasterframes"))
 
   import reflect.ClassTag
   import reflect.runtime.universe._
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index 378ae8e61..4c714c094 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -37,8 +37,8 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.astraea.spark.rasterframes=DEBUG
-log4j.logger.astraea.spark.rasterframes.ref=TRACE
+log4j.logger.org.locationtech.rasterframes=DEBUG
+log4j.logger.org.locationtech.rasterframes.ref=TRACE
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ml/TileExploderSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ml/TileExploderSpec.scala
deleted file mode 100644
index 8883045e1..000000000
--- a/core/src/test/scala/astraea/spark/rasterframes/ml/TileExploderSpec.scala
+++ /dev/null
@@ -1,26 +0,0 @@
-package astraea.spark.rasterframes.ml
-
-import astraea.spark.rasterframes.{TestData, TestEnvironment}
-import geotrellis.raster.Tile
-import org.apache.spark.sql.functions.lit
-/**
- *
- * @since 2/16/18
- */
-class TileExploderSpec extends TestEnvironment with TestData {
-  describe("Tile explode transformer") {
-    it("should explode tiles") {
-      import spark.implicits._
-      val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2").withColumn("other", lit("stuff"))
-
-      val exploder = new TileExploder()
-      val newSchema = exploder.transformSchema(df.schema)
-
-      val exploded = exploder.transform(df)
-      assert(newSchema === exploded.schema)
-      assert(exploded.columns.length === 5)
-      assert(exploded.count() === 9)
-      write(exploded)
-    }
-  }
-}
diff --git a/core/src/test/scala/examples/Classification.scala b/core/src/test/scala/examples/Classification.scala
index 4aebcf742..4d6579518 100644
--- a/core/src/test/scala/examples/Classification.scala
+++ b/core/src/test/scala/examples/Classification.scala
@@ -19,8 +19,8 @@
 
 package examples
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.ml.{NoDataFilter, TileExploder}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.ml.TileExploder
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
@@ -30,6 +30,7 @@ import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 import org.apache.spark.ml.feature.VectorAssembler
 import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
 import org.apache.spark.sql._
+import org.locationtech.rasterframes.ml.{NoDataFilter, TileExploder}
 
 object Classification extends App {
 
diff --git a/core/src/test/scala/examples/Clustering.scala b/core/src/test/scala/examples/Clustering.scala
index 2f8d4ce1f..6fc64f0e7 100644
--- a/core/src/test/scala/examples/Clustering.scala
+++ b/core/src/test/scala/examples/Clustering.scala
@@ -19,8 +19,7 @@
 
 package examples
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.ml.TileExploder
+import org.locationtech.rasterframes._
 import geotrellis.raster.ByteConstantNoDataCellType
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
@@ -28,6 +27,7 @@ import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.clustering.{KMeans, KMeansModel}
 import org.apache.spark.ml.feature.VectorAssembler
 import org.apache.spark.sql._
+import org.locationtech.rasterframes.ml.TileExploder
 
 object Clustering extends App {
 
diff --git a/core/src/test/scala/examples/CreatingRasterFrames.scala b/core/src/test/scala/examples/CreatingRasterFrames.scala
index 311de3dbc..a1e7e0536 100644
--- a/core/src/test/scala/examples/CreatingRasterFrames.scala
+++ b/core/src/test/scala/examples/CreatingRasterFrames.scala
@@ -33,7 +33,7 @@ object CreatingRasterFrames extends App {
 //
 //  First, some standard `import`s:
 
-  import astraea.spark.rasterframes._
+  import org.locationtech.rasterframes._
   import geotrellis.raster._
   import geotrellis.raster.io.geotiff.SinglebandGeoTiff
   import geotrellis.spark.io._
diff --git a/core/src/test/scala/examples/Exporting.scala b/core/src/test/scala/examples/Exporting.scala
index 79af79fd0..e78e7cd3f 100644
--- a/core/src/test/scala/examples/Exporting.scala
+++ b/core/src/test/scala/examples/Exporting.scala
@@ -20,7 +20,7 @@
 package examples
 import java.nio.file.Files
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster.render._
diff --git a/core/src/test/scala/examples/LocalArithmetic.scala b/core/src/test/scala/examples/LocalArithmetic.scala
index ddf666e96..99970f560 100644
--- a/core/src/test/scala/examples/LocalArithmetic.scala
+++ b/core/src/test/scala/examples/LocalArithmetic.scala
@@ -19,7 +19,7 @@
 
 package examples
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.spark.io.kryo.KryoRegistrator
 import org.apache.spark.serializer.KryoSerializer
diff --git a/core/src/test/scala/examples/Masking.scala b/core/src/test/scala/examples/Masking.scala
index 00169b622..1d93b913c 100644
--- a/core/src/test/scala/examples/Masking.scala
+++ b/core/src/test/scala/examples/Masking.scala
@@ -1,6 +1,6 @@
 package examples
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster.render._
 import geotrellis.raster.{mask => _, _}
diff --git a/core/src/test/scala/examples/MeanValue.scala b/core/src/test/scala/examples/MeanValue.scala
index ca0576e02..192685b2d 100644
--- a/core/src/test/scala/examples/MeanValue.scala
+++ b/core/src/test/scala/examples/MeanValue.scala
@@ -19,7 +19,7 @@
 
 package examples
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import org.apache.spark.sql.SparkSession
 
diff --git a/core/src/test/scala/examples/NDVI.scala b/core/src/test/scala/examples/NDVI.scala
index 971dfd8d4..0173c7807 100644
--- a/core/src/test/scala/examples/NDVI.scala
+++ b/core/src/test/scala/examples/NDVI.scala
@@ -20,7 +20,7 @@
 package examples
 import java.nio.file.{Files, Paths}
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster._
 import geotrellis.raster.render._
 import geotrellis.raster.io.geotiff.{GeoTiff, SinglebandGeoTiff}
diff --git a/core/src/test/scala/examples/Scratch.scala b/core/src/test/scala/examples/Scratch.scala
index 565b4c31c..151fc2b34 100644
--- a/core/src/test/scala/examples/Scratch.scala
+++ b/core/src/test/scala/examples/Scratch.scala
@@ -19,7 +19,7 @@
 
 package examples
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql._
 
 /**
diff --git a/core/src/test/scala/examples/Tour.scala b/core/src/test/scala/examples/Tour.scala
index d69cb5a1c..208e82564 100644
--- a/core/src/test/scala/examples/Tour.scala
+++ b/core/src/test/scala/examples/Tour.scala
@@ -23,12 +23,12 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import geotrellis.raster.io.geotiff._
 import geotrellis.raster.{ByteConstantNoDataCellType, Tile}
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.ml.TileExploder
+import org.locationtech.rasterframes._
 import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.clustering.KMeans
 import org.apache.spark.ml.feature.VectorAssembler
+import org.locationtech.rasterframes.ml.TileExploder
 
 /**
  * Example tour of some general features in RasterFrames
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
similarity index 98%
rename from core/src/test/scala/astraea/spark/rasterframes/ExplodeSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index a06b6444b..f703e58d6 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import geotrellis.raster._
 import geotrellis.raster.resample.NearestNeighbor
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
similarity index 92%
rename from core/src/test/scala/astraea/spark/rasterframes/ExtensionMethodSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 81c2d9202..b9810b88e 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,16 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes
-import astraea.spark.rasterframes.util.SubdivideSupport._
+package org.locationtech.rasterframes
+
 import geotrellis.proj4.LatLng
 import geotrellis.raster.{ByteCellType, GridBounds, TileLayout}
+import geotrellis.spark.tiling.{CRSWorldExtent, LayoutDefinition}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
-import geotrellis.spark.tiling.LayoutDefinition
-import geotrellis.spark.tiling.CRSWorldExtent
+import org.locationtech.rasterframes.util.SubdivideSupport
 
 /**
  * Tests miscellaneous extension methods.
@@ -32,7 +33,7 @@ import geotrellis.spark.tiling.CRSWorldExtent
  * @since 3/20/18
  */
 //noinspection ScalaUnusedSymbol
-class ExtensionMethodSpec extends TestEnvironment with TestData {
+class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSupport {
   lazy val rf = sampleTileLayerRDD.toRF
 
   describe("DataFrame exention methods") {
diff --git a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
similarity index 95%
rename from core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
index 51cd6c20a..28bf5f3a2 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import java.nio.file.{Files, Paths}
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
similarity index 97%
rename from core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
index df8027a3a..1d5442c51 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
-import org.locationtech.jts.geom._
 import geotrellis.proj4.{LatLng, Sinusoidal, WebMercator}
 import geotrellis.vector.{Extent, Point => GTPoint}
+import org.locationtech.jts.geom._
 
 /**
  * Test rig for operations providing interop with JTS types.
diff --git a/core/src/test/scala/astraea/spark/rasterframes/MetadataSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
similarity index 63%
rename from core/src/test/scala/astraea/spark/rasterframes/MetadataSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
index 4960f7e65..2a6fedb3f 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/MetadataSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
@@ -1,4 +1,25 @@
-package astraea.spark.rasterframes
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
 
 import org.apache.spark.sql.types.MetadataBuilder
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
similarity index 94%
rename from core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
index dcdb7b6e5..f3e41bc9a 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
@@ -1,11 +1,32 @@
 
 
-package astraea.spark.rasterframes
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
 
 import java.sql.Timestamp
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 import geotrellis.proj4.LatLng
 import geotrellis.raster.render.{ColorMap, ColorRamp}
 import geotrellis.raster.{ProjectedRaster, Tile, TileFeature, TileLayout, UByteCellType}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
similarity index 98%
rename from core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 3e0aa841e..6571d9c25 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -19,11 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes
-import astraea.spark.rasterframes.TestData.injectND
-import astraea.spark.rasterframes.expressions.accessors.ExtractTile
-import astraea.spark.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+package org.locationtech.rasterframes
+
 import geotrellis.proj4.LatLng
 import geotrellis.raster
 import geotrellis.raster.testkit.RasterMatchers
@@ -31,6 +28,9 @@ import geotrellis.raster.{ArrayTile, ByteUserDefinedNoDataCellType, DoubleConsta
 import geotrellis.vector.Extent
 import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.stats._
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.scalatest.{FunSpec, Matchers}
 
 class RasterFunctionsSpec extends FunSpec
@@ -60,7 +60,7 @@ class RasterFunctionsSpec extends FunSpec
 
   val expectedRandNoData: Long = numND * tileCount.toLong
   val expectedRandData: Long = cols * rows * tileCount - expectedRandNoData
-  lazy val randNDTilesWithNull = Seq.fill[Tile](tileCount)(injectND(numND)(
+  lazy val randNDTilesWithNull = Seq.fill[Tile](tileCount)(TestData.injectND(numND)(
     TestData.randomTile(cols, rows, UByteConstantNoDataCellType)
   )).map(ProjectedRasterTile(_, extent, crs)) :+ null
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
similarity index 98%
rename from core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index b1b5b8862..72bd5e960 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
-import org.locationtech.jts.geom._
 import geotrellis.proj4.{CRS, LatLng, Sinusoidal, WebMercator}
 import org.apache.spark.sql.Encoders
+import org.locationtech.jts.geom._
 import org.scalatest.{FunSpec, Matchers}
 
 /**
diff --git a/core/src/test/scala/astraea/spark/rasterframes/SpatialEncodingSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
similarity index 90%
rename from core/src/test/scala/astraea/spark/rasterframes/SpatialEncodingSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
index 137cd9058..5de668fc6 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/SpatialEncodingSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 /**
  * Tests type encoding for spatial types.
diff --git a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
similarity index 94%
rename from core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
index 01af2b4d2..0a3246e5c 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import geotrellis.proj4.LatLng
 import geotrellis.vector.Point
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
similarity index 97%
rename from core/src/test/scala/astraea/spark/rasterframes/TestData.scala
rename to core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index c5097b53b..eb8b24c3e 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright (c) 2017. Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -14,16 +14,18 @@
  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
  * License for the specific language governing permissions and limitations under
  * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import java.net.{URI, URL}
 import java.nio.file.Paths
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes.expressions.tilestats.NoDataCells
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+import org.locationtech.rasterframes.expressions.tilestats.NoDataCells
 import geotrellis.proj4.{CRS, LatLng}
 import geotrellis.raster
 import geotrellis.raster._
@@ -36,6 +38,7 @@ import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SparkSession
 import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 import scala.reflect.ClassTag
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
similarity index 91%
rename from core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
rename to core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
index 84d2e3fe2..fb2ca5246 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright (c) 2017. Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -14,23 +14,25 @@
  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
  * License for the specific language governing permissions and limitations under
  * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import java.nio.file.{Files, Paths}
 
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.stringEnc
-import astraea.spark.rasterframes.util.toParquetFriendlyColumnName
 import com.typesafe.scalalogging.LazyLogging
-import org.locationtech.jts.geom.Geometry
 import geotrellis.spark.testkit.{TestEnvironment => GeoTrellisTestEnvironment}
 import org.apache.spark.SparkContext
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions.col
 import org.apache.spark.sql.types.StructType
+import org.locationtech.jts.geom.Geometry
 import org.scalactic.Tolerance
 import org.scalatest._
 import org.scalatest.matchers.{MatchResult, Matcher}
+import org.locationtech.rasterframes.util._
 
 trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
   with Matchers with Inspectors with Tolerance with LazyLogging {
@@ -42,7 +44,7 @@ trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
 
   lazy val sqlContext: SQLContext = {
     val session = SparkSession.builder.config(_sc.getConf).getOrCreate()
-    astraea.spark.rasterframes.WithSQLContextMethods(session.sqlContext).withRasterFrames
+    org.locationtech.rasterframes.WithSQLContextMethods(session.sqlContext).withRasterFrames
   }
 
   lazy val sql: String ⇒ DataFrame = sqlContext.sql
@@ -84,6 +86,7 @@ trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
   def matchGeom(g: Geometry, tolerance: Double) = new GeometryMatcher(g, tolerance)
 
   def checkDocs(name: String): Unit = {
+    import spark.implicits._
     val docs = sql(s"DESCRIBE FUNCTION EXTENDED $name").as[String].collect().mkString("\n")
     docs should include(name)
     docs shouldNot include("not found")
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
similarity index 94%
rename from core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index 15ee82860..f2d285e67 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,16 +19,16 @@
  *
  */
 
-package astraea.spark.rasterframes
-import astraea.spark.rasterframes.ref.RasterSource
-import astraea.spark.rasterframes.ref.RasterSource.InMemoryRasterSource
+package org.locationtech.rasterframes
+
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.LatLng
 import geotrellis.raster._
 import geotrellis.raster.render.ColorRamps
 import geotrellis.vector.Extent
-import org.apache.spark.sql._
-import org.apache.spark.sql.{functions => F}
+import org.apache.spark.sql.{functions => F, _}
+import org.locationtech.rasterframes.ref.RasterSource
+import org.locationtech.rasterframes.ref.RasterSource.InMemoryRasterSource
 
 /**
  *
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
similarity index 97%
rename from core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index 939ae1a46..e57069d23 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,16 +15,18 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
-import astraea.spark.rasterframes.TestData.{fracTile, randomTile}
-import astraea.spark.rasterframes.stats.CellHistogram
 import geotrellis.raster._
 import geotrellis.raster.mapalgebra.local.{Max, Min}
 import geotrellis.spark._
 import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.TestData.randomTile
+import org.locationtech.rasterframes.stats.CellHistogram
 
 /**
  * Test rig associated with computing statistics and other descriptive
@@ -71,7 +73,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
     }
 
     // tiles defined for the next few tests
-    val tile1 = fracTile(10, 10, 5)
+    val tile1 = TestData.fracTile(10, 10, 5)
     val tile2 = ArrayTile(Array(-5, -4, -3, -2, -1, 0, 1, 2, 3), 3, 3)
     val tile3 = randomTile(255, 255, IntCellType)
 
diff --git a/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
similarity index 92%
rename from core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
index 548aaf192..35e0021b7 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/TileUDTSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
@@ -3,7 +3,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -17,15 +17,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
-
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.functions.cellTypes
+package org.locationtech.rasterframes
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.scalatest.Inspectors
 
 /**
@@ -42,7 +42,7 @@ class TileUDTSpec extends TestEnvironment with TestData with Inspectors {
 
   describe("TileUDT") {
     val tileSizes = Seq(2, 64, 128, 222, 511)
-    val ct = cellTypes().filter(_ != "bool")
+    val ct = functions.cellTypes().filter(_ != "bool")
 
     def forEveryConfig(test: Tile ⇒ Unit): Unit = {
       forEvery(tileSizes.combinations(2).toSeq) { case Seq(cols, rows) ⇒
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
similarity index 90%
rename from core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
index ad285b94a..39b998201 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -19,19 +19,23 @@
  *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
+
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes.encoders.StandardEncoders._
-import astraea.spark.rasterframes.model.{CellContext, TileContext, TileDataContext, TileDimensions}
-import astraea.spark.rasterframes.ref.{RasterRef, RasterSource}
-import astraea.spark.rasterframes.{TestData, TestEnvironment}
+import org.locationtech.rasterframes.encoders.StandardEncoders._
+import org.locationtech.rasterframes.model.{CellContext, TileDataContext, TileDimensions}
+import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.TestData
 import geotrellis.proj4._
 import geotrellis.raster.{CellSize, CellType, TileLayout, UShortUserDefinedNoDataCellType}
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.model.{CellContext, TileContext, TileDataContext, TileDimensions}
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.scalatest.Assertion
 
 class CatalystSerializerSpec extends TestEnvironment with TestData {
diff --git a/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
similarity index 95%
rename from core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
index 4875005ae..3139df995 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/encoders/EncodingSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
@@ -19,13 +19,12 @@
  *
  */
 
-package astraea.spark.rasterframes.encoders
+package org.locationtech.rasterframes.encoders
 
 import java.io.File
 import java.net.URI
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+import org.locationtech.rasterframes._
 import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4._
 import geotrellis.raster.{CellType, Tile, TileFeature}
@@ -34,6 +33,8 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.rf.TileUDT
+import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
  * Test rig for encoding GT types into Catalyst types.
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ml/NoDataFilterSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
similarity index 86%
rename from core/src/test/scala/astraea/spark/rasterframes/ml/NoDataFilterSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
index 17a0f25d4..4270a5314 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ml/NoDataFilterSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.ml
+package org.locationtech.rasterframes.ml
 
 import java.nio.file.Files
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.BeforeAndAfter
 
 /**
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
new file mode 100644
index 000000000..31e816d9a
--- /dev/null
+++ b/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
@@ -0,0 +1,48 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ml
+
+import org.locationtech.rasterframes.TestData
+import geotrellis.raster.Tile
+import org.apache.spark.sql.functions.lit
+import org.locationtech.rasterframes.TestEnvironment
+/**
+ *
+ * @since 2/16/18
+ */
+class TileExploderSpec extends TestEnvironment with TestData {
+  describe("Tile explode transformer") {
+    it("should explode tiles") {
+      import spark.implicits._
+      val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2").withColumn("other", lit("stuff"))
+
+      val exploder = new TileExploder()
+      val newSchema = exploder.transformSchema(df.schema)
+
+      val exploded = exploder.transform(df)
+      assert(newSchema === exploded.schema)
+      assert(exploded.columns.length === 5)
+      assert(exploded.count() === 9)
+      write(exploded)
+    }
+  }
+}
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
similarity index 94%
rename from core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index b50750fa6..59e5c81f5 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,15 +19,16 @@
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.accessors._
-import astraea.spark.rasterframes.expressions.transformers._
-import astraea.spark.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.accessors._
+import org.locationtech.rasterframes.expressions.transformers._
+import RasterRef.RasterRefTile
 import geotrellis.raster.Tile
 import geotrellis.vector.Extent
 import org.apache.spark.sql.Encoders
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  *
diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
similarity index 94%
rename from core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 883184ccd..18d158eeb 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,13 +19,14 @@
  *
  */
 
-package astraea.spark.rasterframes.ref
+package org.locationtech.rasterframes.ref
 
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.ref.RasterSource.{GDALRasterSource, JVMGeoTiffRasterSource}
-import astraea.spark.rasterframes.{TestData, TestEnvironment}
+import RasterSource.{GDALRasterSource, JVMGeoTiffRasterSource}
+import org.locationtech.rasterframes.TestData
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
+import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.model.TileDimensions
 
 /**
  *
diff --git a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index 051aad71c..d60fa152d 100644
--- a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,4 +1,4 @@
-astraea.spark.rasterframes.datasource.geotiff.GeoTiffDataSource
-astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource
-astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisCatalog
-astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource
+org.locationtech.rasterframes.datasource.geotiff.GeoTiffDataSource
+org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource
+org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisCatalog
+org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/DataSourceOptions.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
similarity index 90%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/DataSourceOptions.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
index 5baa8d67f..99063c9d0 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/DataSourceOptions.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.datasource
+package org.locationtech.rasterframes.datasource
 
 /**
  * Key constants associated with DataFrameReader options for certain DataSource implementations.
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
similarity index 87%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 910874e06..876895d88 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.datasource.geotiff
+package org.locationtech.rasterframes.datasource.geotiff
 
 import java.net.URI
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.geotiff.GeoTiffCollectionRelation.Cols
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.util._
 import geotrellis.proj4.CRS
 import geotrellis.spark.io.hadoop.HadoopGeoTiffRDD
 import geotrellis.vector.{Extent, ProjectedExtent}
@@ -35,6 +32,10 @@ import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.sources.{BaseRelation, PrunedScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.geotiff.GeoTiffCollectionRelation.Cols
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.util._
 
 case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount: Int) extends BaseRelation with PrunedScan {
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
similarity index 95%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index ae6477adc..885464df6 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotiff
+package org.locationtech.rasterframes.datasource.geotiff
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.datasource._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.datasource._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.apache.spark.sql.types.LongType
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
similarity index 93%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
index 2bc42893c..539bcb84d 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,15 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotiff
+package org.locationtech.rasterframes.datasource.geotiff
 
 import java.net.URI
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.spark._
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
similarity index 90%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index ab5a0073b..1c6c2bd0d 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,22 +15,18 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource
-
+package org.locationtech.rasterframes.datasource
 import java.net.URI
 
-import astraea.spark.rasterframes._
 import org.apache.spark.sql.{DataFrameReader, DataFrameWriter}
-import shapeless.tag
+import org.locationtech.rasterframes._
 import shapeless.tag.@@
+import shapeless.tag
 
-/**
- * Extension methods enabled by this module.
- *
- * @since 1/16/18
- */
 package object geotiff {
   /** Tagged type construction for enabling type-safe extension methods for loading
    * a RasterFrame in expected form. */
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
similarity index 93%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
index 36edf16a7..ed4dfe099 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,14 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.net.URI
 
-import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisCatalog.GeoTrellisCatalogRelation
 import geotrellis.spark.io.AttributeStore
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
@@ -30,6 +31,7 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.rf.VersionShims
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
+import org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisCatalog.GeoTrellisCatalogRelation
 import spray.json.DefaultJsonProtocol._
 import spray.json._
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
similarity index 95%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
index d26cc8e41..18f2248a4 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.net.URI
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.DataSourceOptions
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.DataSourceOptions
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.spark.io.index.ZCurveKeyIndexMethod
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
similarity index 94%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
index 8a171af4e..ddf8a9c74 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017-2018 Azavea & Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,24 +15,25 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.io.UnsupportedEncodingException
 import java.net.URI
 import java.sql.{Date, Timestamp}
 import java.time.{ZoneOffset, ZonedDateTime}
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisRelation.{C, TileFeatureData}
-import astraea.spark.rasterframes.datasource.geotrellis.TileFeatureSupport._
-import astraea.spark.rasterframes.rules.splitFilters
-import astraea.spark.rasterframes.rules.SpatialFilters.{Contains => sfContains, Intersects => sfIntersects}
-import astraea.spark.rasterframes.rules.SpatialRelationReceiver
-import astraea.spark.rasterframes.rules.TemporalFilters.{BetweenDates, BetweenTimes}
-import astraea.spark.rasterframes.util.SubdivideSupport._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.geotrellis.TileFeatureSupport._
+import org.locationtech.rasterframes.rules.splitFilters
+import org.locationtech.rasterframes.rules.SpatialFilters.{Contains => sfContains, Intersects => sfIntersects}
+import org.locationtech.rasterframes.rules.SpatialRelationReceiver
+import org.locationtech.rasterframes.rules.TemporalFilters.{BetweenDates, BetweenTimes}
+import org.locationtech.rasterframes.util.SubdivideSupport._
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import org.locationtech.jts.geom
 import geotrellis.raster.{CellGrid, MultibandTile, Tile, TileFeature}
@@ -50,6 +51,7 @@ import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Row, SQLContext, sources}
+import org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisRelation.{C, TileFeatureData}
 
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe._
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/Layer.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
similarity index 83%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/Layer.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
index a659de2ff..0fc7e28e6 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/Layer.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,16 +15,18 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.net.URI
 
-import astraea.spark.rasterframes
-import astraea.spark.rasterframes.encoders.DelegatingSubfieldEncoder
+import org.locationtech.rasterframes.encoders.DelegatingSubfieldEncoder
 import geotrellis.spark.LayerId
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.locationtech.rasterframes
 
 /**
  *   /** Connector between a GT `LayerId` and the path in which it lives. */
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/MergeableData.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
similarity index 93%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/MergeableData.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
index 73d537866..71ff81796 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/MergeableData.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 trait MergeableData[D] {
   def merge(l:D, r:D): D
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupport.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
similarity index 95%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
index 6691d6d51..8ef009f26 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,11 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 import geotrellis.raster.crop.{Crop, TileCropMethods}
 import geotrellis.raster.mask.TileMaskMethods
 import geotrellis.raster.merge.TileMergeMethods
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
similarity index 89%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
index 15e54ff44..5288cbe39 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/geotrellis/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,24 +15,20 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource
+package org.locationtech.rasterframes.datasource
 import java.net.URI
 
-import _root_.geotrellis.spark.LayerId
-import astraea.spark.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource._
-import astraea.spark.rasterframes.{RasterFrame, _}
 import org.apache.spark.sql._
-import org.apache.spark.sql.functions.col
-import shapeless.tag
+import org.apache.spark.sql.functions._
+import _root_.geotrellis.spark.LayerId
+import org.locationtech.rasterframes._
 import shapeless.tag.@@
+import shapeless.tag
 
-/**
- * Extension methods for literate and type-safe loading of geotrellis layers.
- *
- * @since 1/12/18
- */
 package object geotrellis extends DataSourceOptions {
   implicit val layerEncoder = Layer.layerEncoder
 
@@ -56,12 +52,12 @@ package object geotrellis extends DataSourceOptions {
       reader.format("geotrellis-catalog").load(base.toASCIIString)
 
     def geotrellis: GeoTrellisRasterFrameReader =
-      tag[GeoTrellisRasterFrameReaderTag][DataFrameReader](reader.format(SHORT_NAME))
+      tag[GeoTrellisRasterFrameReaderTag][DataFrameReader](reader.format(GeoTrellisLayerDataSource.SHORT_NAME))
   }
 
   implicit class DataFrameWriterHasGeotrellisFormat[T](val writer: DataFrameWriter[T]) {
     def geotrellis: GeoTrellisRasterFrameWriter[T] =
-      tag[GeoTrellisRasterFrameWriterTag][DataFrameWriter[T]](writer.format(SHORT_NAME))
+      tag[GeoTrellisRasterFrameWriterTag][DataFrameWriter[T]](writer.format(GeoTrellisLayerDataSource.SHORT_NAME))
   }
 
   implicit class GeoTrellisWriterAddLayer[T](val writer: GeoTrellisRasterFrameWriter[T]) {
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
similarity index 90%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
index 970244883..1af62bcd5 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,9 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes
+package org.locationtech.rasterframes
 
 import java.net.URI
 
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
similarity index 94%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index 0829a2b48..41b1ef76f 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -19,10 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.datasource.rastersource
-import astraea.spark.rasterframes.model.TileDimensions
+package org.locationtech.rasterframes.datasource.rastersource
+
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+import org.locationtech.rasterframes.model.TileDimensions
 
 class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   import RasterSourceDataSource._
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
similarity index 80%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index 6491d12fc..a063443dd 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -19,19 +19,19 @@
  *
  */
 
-package astraea.spark.rasterframes.datasource.rastersource
+package org.locationtech.rasterframes.datasource.rastersource
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
+import org.locationtech.rasterframes.util._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
diff --git a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
similarity index 98%
rename from datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
index 8219b8f7c..69720d6d9 100644
--- a/datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.datasource
+package org.locationtech.rasterframes.datasource
+
 import java.net.URI
 
 import org.apache.spark.sql.DataFrameReader
diff --git a/datasource/src/test/scala/examples/Creating.scala b/datasource/src/test/scala/examples/Creating.scala
index a6e691ac1..fed1fac4a 100644
--- a/datasource/src/test/scala/examples/Creating.scala
+++ b/datasource/src/test/scala/examples/Creating.scala
@@ -37,7 +37,7 @@ There are a couple of setup steps necessary anytime you want to work with Raster
 
 */
 // tut:silent
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql._
 
 
@@ -72,7 +72,7 @@ designed for this purpose.
 First add the following import:
 */
 
-import astraea.spark.rasterframes.datasource.geotiff._
+import org.locationtech.rasterframes.datasource.geotiff._
 /*
 (This is what adds the `.geotiff` method to `spark.read` below.)
 
@@ -121,7 +121,7 @@ Before we show how all of this works we need to have a GeoTrellis layer to work
 constructed above.
 
  */
-import astraea.spark.rasterframes.datasource.geotrellis._
+import org.locationtech.rasterframes.datasource.geotrellis._
 
 val base = Files.createTempDirectory("rf-").toUri
 val layer = Layer(base, "sample", 0)
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
similarity index 85%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
index 1d7237c5b..bbd173e01 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
-package astraea.spark.rasterframes.datasource.geotiff
+package org.locationtech.rasterframes.datasource.geotiff
 
 import java.io.{File, FilenameFilter}
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  * @since 1/14/18
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
similarity index 92%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 54ee09412..25e69c067 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes.datasource.geotiff
+package org.locationtech.rasterframes.datasource.geotiff
 
 import java.nio.file.Paths
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  * @since 1/14/18
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
similarity index 90%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
index 02e86086e..70e55d9df 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,20 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.io.File
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.proj4.LatLng
 import geotrellis.spark._
 import geotrellis.spark.io._
 import geotrellis.spark.io.index.ZCurveKeyIndexMethod
 import org.apache.hadoop.fs.FileUtil
+import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.BeforeAndAfter
 
 /**
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
similarity index 97%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 14a54cf6f..117886c9c 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017-2018 Azavea & Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,19 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
 import java.io.File
 import java.sql.Timestamp
 import java.time.ZonedDateTime
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.DataSourceOptions
-import astraea.spark.rasterframes.rules._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.DataSourceOptions
+import org.locationtech.rasterframes.rules._
+import org.locationtech.rasterframes.util._
 import geotrellis.proj4.LatLng
 import geotrellis.raster._
 import geotrellis.raster.resample.NearestNeighbor
@@ -43,6 +45,7 @@ import org.apache.hadoop.fs.FileUtil
 import org.apache.spark.sql.functions.{udf => sparkUdf}
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.storage.StorageLevel
+import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.{BeforeAndAfterAll, Inspectors}
 
 import scala.math.{max, min}
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
similarity index 94%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
index dbff2225a..c953d94c3 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
 
-package astraea.spark.rasterframes.datasource.geotrellis
+package org.locationtech.rasterframes.datasource.geotrellis
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.geotrellis.TileFeatureSupport._
-import astraea.spark.rasterframes.util.{WithCropMethods, WithMaskMethods, WithMergeMethods, WithPrototypeMethods}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.geotrellis.TileFeatureSupport._
+import org.locationtech.rasterframes.util.{WithCropMethods, WithMaskMethods, WithMergeMethods, WithPrototypeMethods}
 import geotrellis.proj4.LatLng
 import geotrellis.raster.crop.Crop
 import geotrellis.raster.rasterize.Rasterizer
@@ -32,6 +34,7 @@ import geotrellis.spark.tiling._
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
+import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.BeforeAndAfter
 
 import scala.reflect.ClassTag
diff --git a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
similarity index 94%
rename from datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index fe035a84b..42765f6e6 100644
--- a/datasource/src/test/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -19,11 +19,11 @@
  *
  */
 
-package astraea.spark.rasterframes.datasource.rastersource
-import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource._
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.util._
+package org.locationtech.rasterframes.datasource.rastersource
+import org.locationtech.rasterframes.{TestEnvironment, _}
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource._
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.util._
 
 class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
   import spark.implicits._
diff --git a/experimental/src/it/resources/log4j.properties b/experimental/src/it/resources/log4j.properties
index 6a7d4154e..5cc16f4db 100644
--- a/experimental/src/it/resources/log4j.properties
+++ b/experimental/src/it/resources/log4j.properties
@@ -35,7 +35,7 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.astraea.spark.rasterframes=DEBUG
+log4j.logger.org.locationtech.rasterframes=DEBUG
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index e86376246..448bacca8 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -18,13 +18,13 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import java.net.URL
 
-import astraea.spark.rasterframes.TestEnvironment
 import org.apache.spark.sql.functions._
-import astraea.spark.rasterframes.experimental.datasource._
+import org.locationtech.rasterframes.experimental.datasource._
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  * Test rig for L8 catalog stuff.
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
index b014dae7a..780ea9195 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
@@ -18,10 +18,11 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql.DataFrame
+import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
 /**
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
index eb2771e6f..e882e4edf 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
+++ b/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
@@ -18,13 +18,13 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 import java.net.URL
 import java.sql.Timestamp
 
-import astraea.spark.rasterframes.TestEnvironment
-import astraea.spark.rasterframes.experimental.datasource._
+import org.locationtech.rasterframes.experimental.datasource._
 import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  * Test rig for MODIS catalog stuff.
diff --git a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index 99180fa88..3db32938c 100644
--- a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,4 +1,4 @@
-astraea.spark.rasterframes.experimental.datasource.geojson.GeoJsonDataSource
-astraea.spark.rasterframes.experimental.datasource.awspds.L8CatalogDataSource
-astraea.spark.rasterframes.experimental.datasource.awspds.MODISCatalogDataSource
-astraea.spark.rasterframes.experimental.datasource.awspds.L8DataSource
+org.locationtech.rasterframes.experimental.datasource.geojson.GeoJsonDataSource
+org.locationtech.rasterframes.experimental.datasource.awspds.L8CatalogDataSource
+org.locationtech.rasterframes.experimental.datasource.awspds.MODISCatalogDataSource
+org.locationtech.rasterframes.experimental.datasource.awspds.L8DataSource
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
similarity index 87%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadExpression.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
index 32d55645b..4721a1fc8 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.experimental.datasource
 
 import java.net.URI
 
-import astraea.spark.rasterframes.util.GeoTiffInfoSupport
+import org.locationtech.rasterframes.util.GeoTiffInfoSupport
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -32,7 +33,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, Generator, GenericInternalRow, UnaryExpression}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 
 /**
  * Downloads data from URL and stores it in a column.
@@ -63,7 +64,7 @@ case class DownloadExpression(override val child: Expression, colPrefix: String)
 }
 
 object DownloadExpression {
-  import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
 
   def apply(urlColumn: Column): TypedColumn[Any, Array[Byte]] =
     new Column(
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadSupport.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
similarity index 93%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadSupport.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
index 9d4e6e7f5..a8db278ea 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/DownloadSupport.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.experimental.datasource
 
 import java.io._
 import java.net
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
similarity index 94%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
index 24025e23c..b5d920c86 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,16 +15,16 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.experimental.datasource
 
 import java.net.URI
 
-import astraea.spark.rasterframes.TileType
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.ref.HttpRangeReader
+import org.locationtech.rasterframes.TileType
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
@@ -41,6 +41,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
 import org.apache.spark.sql.types._
+import org.locationtech.rasterframes.ref.HttpRangeReader
 
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
similarity index 91%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
index 46dc23feb..1ead777e1 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,14 +19,14 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.sources.BaseRelation
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.util._
 
 /**
  * Mix-in for a data source that is cached as a parquet file.
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
similarity index 93%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
index 7262c2d97..ee3d8c7e3 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,11 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
-
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import java.io.FileNotFoundException
 import java.net.URI
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
similarity index 92%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
index 556d47cc4..873198713 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
 import org.locationtech.jts.geom.Envelope
@@ -42,7 +43,7 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
   protected def cacheFile: HadoopPath = sceneListPath.suffix(".parquet")
 
   protected def constructDataset: Dataset[Row] = {
-    import astraea.spark.rasterframes.encoders.StandardEncoders.envelopeEncoder
+    import org.locationtech.rasterframes.encoders.StandardEncoders.envelopeEncoder
     import sqlContext.implicits._
     logger.debug("Parsing " + sceneListPath)
     sqlContext.read
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
similarity index 90%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
index 9a7e4aab7..c57d34d0a 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8DataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
similarity index 88%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 8ad088075..f92dc36f4 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,25 +15,26 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.experimental.datasource.awspds.L8Relation.Bands
-import astraea.spark.rasterframes.expressions.transformers._
-import astraea.spark.rasterframes.ref.RasterRef
-import astraea.spark.rasterframes.rules.SpatialFilters.{Contains, Intersects}
-import astraea.spark.rasterframes.rules._
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.experimental.datasource.awspds.L8Relation.Bands
+import org.locationtech.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.rules.SpatialFilters.{Contains, Intersects}
+import org.locationtech.rasterframes.rules._
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, SQLContext}
+import org.locationtech.rasterframes.ref.RasterRef
 
 /**
  * Spark relation over AWS PDS Landsat 8 collection.
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
similarity index 93%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
index 84d461ad8..ffdb84f03 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,18 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import java.net.URI
 import java.time.LocalDate
 import java.time.temporal.ChronoUnit
 
-import astraea.spark.rasterframes.util.withResource
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes.util.withResource
+import org.locationtech.rasterframes._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
 import org.apache.hadoop.io.IOUtils
@@ -55,7 +56,7 @@ class MODISCatalogDataSource extends DataSourceRegister with RelationProvider wi
     require(parameters.get("path").isEmpty, "MODISCatalogDataSource doesn't support specifying a path. Please use `load()`.")
 
     sqlContext.withRasterFrames
-    astraea.spark.rasterframes.experimental.datasource.register(sqlContext)
+    org.locationtech.rasterframes.experimental.datasource.register(sqlContext)
 
     val start = parameters.get("start").map(LocalDate.parse).getOrElse(LocalDate.of(2013, 1, 1))
     val end = parameters.get("end").map(LocalDate.parse).getOrElse(LocalDate.now().minusDays(7))
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
similarity index 96%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
index bb1d8e70d..3419d9a81 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.hadoop.fs.{Path ⇒ HadoopPath}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
similarity index 86%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
index c0e531651..35c3ede44 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/PDSFields.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,14 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import astraea.spark.rasterframes.StandardColumns._
-import astraea.spark.rasterframes.util._
-import astraea.spark.rasterframes.encoders.StandardEncoders
+import org.locationtech.rasterframes.StandardColumns._
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.encoders.StandardEncoders
 import org.apache.spark.sql.jts.JTSTypes
 import org.apache.spark.sql.types._
 
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
similarity index 92%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
index 4e2e741d6..bfbecd013 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,16 +15,17 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import java.net.URI
 import java.time.{Duration, Instant}
 
-import astraea.spark.rasterframes.experimental.datasource.DownloadSupport
-import astraea.spark.rasterframes.util._
+import org.locationtech.rasterframes.experimental.datasource.DownloadSupport
+import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.commons.io.FilenameUtils
 import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
similarity index 85%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/package.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
index 60b1169fc..0afc2ecc1 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,15 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.experimental.datasource
+
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
-import astraea.spark.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 
 /**
  * Module support.
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
similarity index 96%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
index c0551c797..9cac6e156 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/DOM.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,10 +15,11 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.geojson
+package org.locationtech.rasterframes.experimental.datasource.geojson
 
 import org.locationtech.jts.geom.{Envelope, Geometry}
 import org.locationtech.jts.io.geojson.{GeoJsonReader, GeoJsonWriter}
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
similarity index 95%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
index 418034147..f84a18c5d 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,12 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.geojson
+package org.locationtech.rasterframes.experimental.datasource.geojson
 
-import astraea.spark.rasterframes.experimental.datasource.geojson.DOM._
+import org.locationtech.rasterframes.experimental.datasource.geojson.DOM._
 import org.locationtech.jts.geom.Geometry
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala
similarity index 94%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/package.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala
index 262c255d1..0ffda39a0 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/geojson/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.experimental.datasource
+
 import org.apache.spark.sql.DataFrameReader
 
 /**
diff --git a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
similarity index 95%
rename from experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/package.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
index 4e6129be4..137d7cc32 100644
--- a/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,7 +19,8 @@
  *
  */
 
-package astraea.spark.rasterframes.experimental
+package org.locationtech.rasterframes.experimental
+
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.rf.VersionShims._
diff --git a/experimental/src/test/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
similarity index 90%
rename from experimental/src/test/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
rename to experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
index 425d91ab8..4ecf766d7 100644
--- a/experimental/src/test/scala/astraea/spark/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
+++ b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea. Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,13 +15,13 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
  *
  */
 
-package astraea.spark.rasterframes.experimental.datasource.geojson
-
-import astraea.spark.rasterframes.TestEnvironment
+package org.locationtech.rasterframes.experimental.datasource.geojson
 import org.apache.spark.sql.types.{LongType, MapType}
+import org.locationtech.rasterframes.TestEnvironment
 
 /**
  * Test rig for GeoJsonRelation.
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index 4ad610cf1..8beeafa2d 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -49,7 +49,13 @@ object RFDependenciesPlugin extends AutoPlugin {
   import autoImport._
 
   override def projectSettings = Seq(
-    resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
+    resolvers ++= Seq(
+      "locationtech-releases" at "https://repo.locationtech.org/content/groups/releases",
+      "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis",
+      "boundless-releases" at "https://repo.boundlessgeo.com/main/",
+      "Open Source Geospatial Foundation Repository" at "http://download.osgeo.org/webdav/geotools/"
+    ),
+
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
     rfSparkVersion := "2.3.2",
     rfGeoTrellisVersion := "2.2.0",
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 7ca0e1985..d41451017 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -30,11 +30,6 @@ object RFProjectPlugin extends AutoPlugin {
     scalacOptions in (Compile, doc) ++= Seq("-no-link-warnings"),
     javacOptions ++= Seq("-source", "1.8", "-target", "1.8"),
     cancelable in Global := true,
-    resolvers ++= Seq(
-      "locationtech-releases" at "https://repo.locationtech.org/content/groups/releases",
-      "boundless-releases" at "https://repo.boundlessgeo.com/main/",
-      "Open Source Geospatial Foundation Repository" at "http://download.osgeo.org/webdav/geotools/"
-    ),
     publishTo in ThisBuild := sonatypePublishTo.value,
     publishMavenStyle := true,
     publishArtifact in (Compile, packageDoc) := true,
@@ -47,26 +42,26 @@ object RFProjectPlugin extends AutoPlugin {
       Developer(
         id = "metasim",
         name = "Simeon H.K. Fitch",
-        email = "fitch@astraea.io",
-        url = url("http://www.astraea.io")
+        email = "fitch@astraea.earth",
+        url = url("http://www.astraea.earth")
       ),
       Developer(
         id = "mteldridge",
         name = "Matt Eldridge",
-        email = "meldridge@astraea.io",
-        url = url("http://www.astraea.io")
+        email = "meldridge@astraea.earth",
+        url = url("http://www.astraea.earth")
       ),
       Developer(
         id = "bguseman",
         name = "Ben Guseman",
-        email = "bguseman@astraea.io",
-        url = url("http://www.astraea.io")
+        email = "bguseman@astraea.earth",
+        url = url("http://www.astraea.earth")
       ),
       Developer(
         id = "vpipkt",
         name = "Jason Brown",
-        email = "jbrown@astraea.io",
-        url = url("http://www.astraea.io")
+        email = "jbrown@astraea.earth",
+        url = url("http://www.astraea.earth")
       )
     ),
     initialCommands in console :=
@@ -76,7 +71,7 @@ object RFProjectPlugin extends AutoPlugin {
         |import org.apache.spark.sql.functions._
         |import geotrellis.raster._
         |import geotrellis.spark._
-        |import astraea.spark.rasterframes._
+        |import org.locationtech.rasterframes._
         |implicit val spark = SparkSession.builder()
         |  .master("local[*]")
         |  .withKryoSerialization
diff --git a/pyrasterframes/python/examples/__init__.py b/pyrasterframes/python/examples/__init__.py
index b084e150d..3a0b73092 100644
--- a/pyrasterframes/python/examples/__init__.py
+++ b/pyrasterframes/python/examples/__init__.py
@@ -15,7 +15,7 @@ def example_session():
         .config('spark.executor.extraClassPath', pyJar)
         .config("spark.ui.enabled", "false")
         .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-        .config("spark.kryo.registrator", "astraea.spark.rasterframes.util.RFKryoRegistrator")
+        .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator")
         .config("spark.kryoserializer.buffer.max", "500m")
         .getOrCreate())
 
diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/python/pyrasterframes/context.py
index 9f238c225..80a2e120d 100644
--- a/pyrasterframes/python/pyrasterframes/context.py
+++ b/pyrasterframes/python/pyrasterframes/context.py
@@ -16,7 +16,7 @@ def __init__(self, spark_session):
         self._gateway = spark_session.sparkContext._gateway
         self._jvm = self._gateway.jvm
         jsess = self._spark_session._jsparkSession
-        self._jrfctx = self._jvm.astraea.spark.rasterframes.py.PyRFContext(jsess)
+        self._jrfctx = self._jvm.org.locationtech.rasterframes.py.PyRFContext(jsess)
 
     def list_to_seq(self, py_list):
         conv = self.lookup('listToSeq')
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index b82cfb70c..21056f39a 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -177,7 +177,7 @@ class TileExploder(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     def __init__(self):
         super(TileExploder, self).__init__()
-        self._java_obj = self._new_java_obj("astraea.spark.rasterframes.ml.TileExploder", self.uid)
+        self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.TileExploder", self.uid)
 
 class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
@@ -185,6 +185,6 @@ class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     def __init__(self):
         super(NoDataFilter, self).__init__()
-        self._java_obj = self._new_java_obj("astraea.spark.rasterframes.ml.NoDataFilter", self.uid)
+        self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.NoDataFilter", self.uid)
     def setInputCols(self, values):
         self._java_obj.setInputCols(values)
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 10513f7d4..d0c531533 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -38,7 +38,7 @@ def setUpClass(cls):
             .config('spark.driver.extraClassPath', jarpath)
             .config('spark.executor.extraClassPath', jarpath)
             .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-            .config("spark.kryo.registrator", "astraea.spark.rasterframes.util.RFKryoRegistrator")
+            .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator")
             .config("spark.kryoserializer.buffer.max", "500m")
             .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
diff --git a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
similarity index 95%
rename from pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
rename to pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index cb28f87a7..7a3583672 100644
--- a/pyrasterframes/src/main/scala/astraea/spark/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2017-2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,17 +15,20 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
+ * SPDX-License-Identifier: Apache-2.0
+ *
  */
-package astraea.spark.rasterframes.py
+package org.locationtech.rasterframes.py
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.model.LazyCRS
+import org.locationtech.rasterframes._
 import org.locationtech.jts.geom.Geometry
 import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
 import org.locationtech.geomesa.spark.jts.util.WKBUtils
+import org.locationtech.rasterframes.RasterFunctions
+import org.locationtech.rasterframes.model.LazyCRS
 import spray.json._
 
 import scala.collection.JavaConverters._
@@ -99,7 +102,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     * @return Java List of String, which py4j can interpret as a python `list`
     */
   def cell_types = {
-    astraea.spark.rasterframes.functions.cellTypes().asJava
+    org.locationtech.rasterframes.functions.cellTypes().asJava
   }
 
   /** DESERIALIZATION **/

From 4344e217ca154a08439ccd57cdcf28d7d85fe6bc Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 24 Apr 2019 10:35:14 -0400
Subject: [PATCH 047/380] Add check for hasGDAL to RasterSourceSpec

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/ref/RasterSourceSpec.scala   | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
index 127f55be8..883184ccd 100644
--- a/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/astraea/spark/rasterframes/ref/RasterSourceSpec.scala
@@ -109,22 +109,22 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       assert(!src.extent.isEmpty)
     }
   }
-  describe("GDAL Rastersource") {
-    val gdal = GDALRasterSource(cogPath)
-    val jvm = JVMGeoTiffRasterSource(cogPath)
-    it("should compute the same metadata as JVM RasterSource") {
-
-      gdal.cellType should be (jvm.cellType)
-
-    }
-    it("should compute the same dimensions as JVM RasterSource") {
-      val dims = TileDimensions(128, 128)
-      gdal.extent should be (jvm.extent)
-      gdal.rasterExtent should be (jvm.rasterExtent)
-      gdal.cellSize should be (jvm.cellSize)
-      gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
-      gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
 
+  if(RasterSource.IsGDAL.hasGDAL) {
+    describe("GDAL Rastersource") {
+      val gdal = GDALRasterSource(cogPath)
+      val jvm = JVMGeoTiffRasterSource(cogPath)
+      it("should compute the same metadata as JVM RasterSource") {
+        gdal.cellType should be(jvm.cellType)
+      }
+      it("should compute the same dimensions as JVM RasterSource") {
+        val dims = TileDimensions(128, 128)
+        gdal.extent should be(jvm.extent)
+        gdal.rasterExtent should be(jvm.rasterExtent)
+        gdal.cellSize should be(jvm.cellSize)
+        gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
+        gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
+      }
     }
   }
 

From c8d49a136f1b6a12ca967781699bd977b85b4dbe Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 24 Apr 2019 11:36:27 -0400
Subject: [PATCH 048/380] Documentation updates.

---
 .../rasterframes/bench/RasterRefBench.scala   |  11 +-
 .../rasterframes/bench/SparkEnv.scala         |   2 +-
 .../bench/StatsComputeBench.scala             |   2 +-
 .../bench/TileAssembleBench.scala             |   2 +-
 .../bench/TileCellScanBench.scala             |   2 +-
 .../rasterframes/bench/TileEncodeBench.scala  |   2 +-
 .../rasterframes/bench/TileExplodeBench.scala |   2 +-
 .../rasterframes/bench/package.scala          |   6 +-
 .../rasterframes/MetadataKeys.scala           |   2 +-
 .../rasterframes/RasterFunctions.scala        |   2 +-
 .../rasterframes/encoders/CRSEncoder.scala    |   2 +-
 .../encoders/CatalystSerializer.scala         |   2 +-
 .../encoders/CellTypeEncoder.scala            |   2 +-
 .../encoders/DelegatingSubfieldEncoder.scala  |   2 +-
 .../encoders/ProjectedExtentEncoder.scala     |   2 +-
 .../encoders/SparkBasicEncoders.scala         |   2 +-
 .../encoders/StandardEncoders.scala           |   2 +-
 .../encoders/StringBackedEncoder.scala        |   2 +-
 .../TemporalProjectedExtentEncoder.scala      |   2 +-
 .../encoders/TileLayerMetadataEncoder.scala   |   2 +-
 .../rasterframes/encoders/URIEncoder.scala    |   2 +-
 .../rasterframes/encoders/package.scala       |   2 +-
 .../expressions/OnCellGridExpression.scala    |   2 +-
 .../expressions/OnTileContextExpression.scala |   2 +-
 .../expressions/SpatialRelation.scala         |   2 +-
 .../expressions/localops/Add.scala            |   7 +-
 .../rasterframes/expressions/package.scala    |   2 +-
 .../extensions/ContextRDDMethods.scala        |   2 +-
 .../extensions/DataFrameMethods.scala         |   2 +-
 .../rasterframes/extensions/Implicits.scala   |   2 +-
 .../extensions/MetadataBuilderMethods.scala   |   2 +-
 .../extensions/MetadataMethods.scala          |   2 +-
 .../extensions/RFSpatialColumnMethods.scala   |   2 +-
 .../extensions/RasterFrameMethods.scala       |   2 +-
 .../extensions/SQLContextMethods.scala        |   2 +-
 .../extensions/SparkSessionMethods.scala      |   2 +-
 .../rasterframes/functions/package.scala      |   2 +-
 .../rasterframes/jts/Implicits.scala          |   2 +-
 .../jts/ReprojectionTransformer.scala         |   2 +-
 .../rasterframes/ml/NoDataFilter.scala        |   2 +-
 .../rasterframes/ml/Parameters.scala          |   2 +-
 .../rasterframes/ml/TileColumnSupport.scala   |   2 +-
 .../rasterframes/ml/TileExploder.scala        |   2 +-
 .../rasterframes/rasterframes.scala           |   2 +-
 .../rasterframes/ref/HttpRangeReader.scala    | 129 ------------------
 .../ref/ProjectedRasterLike.scala             |   2 +-
 .../rasterframes/ref/RasterRef.scala          |   2 +-
 .../rasterframes/ref/RasterSource.scala       |   2 +-
 .../rules/SpatialFilterPushdownRules.scala    |   2 +-
 .../rasterframes/rules/SpatialFilters.scala   |   2 +-
 .../rules/SpatialRelationReceiver.scala       |   2 +-
 .../rules/SpatialUDFSubstitutionRules.scala   |   2 +-
 .../rasterframes/rules/TemporalFilters.scala  |   2 +-
 .../rasterframes/stats/CellHistogram.scala    |   2 +-
 .../rasterframes/stats/CellStatistics.scala   |   2 +-
 .../tiles/FixedDelegatingTile.scala           |  40 ++++++
 .../rasterframes/tiles/InternalRowTile.scala  |   6 +-
 .../tiles/ProjectedRasterTile.scala           |  13 +-
 .../util/GeoTiffInfoSupport.scala             |   2 +-
 .../rasterframes/util/KryoSupport.scala       |   2 +-
 .../rasterframes/util/MultibandRender.scala   |   2 +-
 .../rasterframes/util/RFKryoRegistrator.scala |   2 +-
 .../rasterframes/util/SubdivideSupport.scala  |   2 +-
 .../rasterframes/util/debug/package.scala     |   2 +-
 .../rasterframes/util/package.scala           |   2 +-
 core/src/test/resources/log4j.properties      |   2 +-
 .../rasterframes/ExplodeSpec.scala            |   2 +-
 .../rasterframes/ExtensionMethodSpec.scala    |   2 +-
 .../rasterframes/GeometryOperationsSpec.scala |   2 +-
 .../locationtech/rasterframes/JTSSpec.scala   |   2 +-
 .../rasterframes/MetadataSpec.scala           |   2 +-
 .../rasterframes/SpatialEncodingSpec.scala    |   2 +-
 .../rasterframes/SpatialKeySpec.scala         |   2 +-
 .../locationtech/rasterframes/TestData.scala  |   2 +-
 .../rasterframes/TestEnvironment.scala        |   2 +-
 .../rasterframes/TileAssemblerSpec.scala      |   2 +-
 .../rasterframes/TileStatsSpec.scala          |   4 +-
 .../rasterframes/TileUDTSpec.scala            |   4 +-
 .../rasterframes/ml/NoDataFilterSpec.scala    |   2 +-
 .../rasterframes/ml/TileExploderSpec.scala    |   2 +-
 .../rasterframes/ref/RasterRefSpec.scala      |   2 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |   2 +-
 .../datasource/DataSourceOptions.scala        |   2 +-
 .../geotiff/GeoTiffCollectionRelation.scala   |   2 +-
 .../geotiff/GeoTiffDataSource.scala           |   2 +-
 .../datasource/geotiff/GeoTiffRelation.scala  |   2 +-
 .../datasource/geotiff/package.scala          |   2 +-
 .../geotrellis/GeoTrellisCatalog.scala        |   2 +-
 .../GeoTrellisLayerDataSource.scala           |   2 +-
 .../geotrellis/GeoTrellisRelation.scala       |   2 +-
 .../datasource/geotrellis/Layer.scala         |   2 +-
 .../datasource/geotrellis/MergeableData.scala |   2 +-
 .../geotrellis/TileFeatureSupport.scala       |   2 +-
 .../datasource/geotrellis/package.scala       |   2 +-
 .../rasterframes/datasource/package.scala     |   2 +-
 .../GeoTiffCollectionDataSourceSpec.scala     |   2 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       |   2 +-
 .../geotrellis/GeoTrellisCatalogSpec.scala    |   2 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala |   2 +-
 .../geotrellis/TileFeatureSupportSpec.scala   |   2 +-
 docs/src/main/tut/apps/geotrellis-ops.md      |   2 +-
 docs/src/main/tut/apps/ndvi.md                |   2 +-
 docs/src/main/tut/creating-rasterframes.md    |   6 +-
 docs/src/main/tut/exporting-rasterframes.md   |   8 +-
 docs/src/main/tut/ml/classification.md        |   4 +-
 docs/src/main/tut/ml/clustering.md            |   4 +-
 docs/src/main/tut/ml/statistics.md            |   2 +-
 docs/src/main/tut/reference.md                |   2 +-
 docs/src/main/tut/release-notes.md            |   9 +-
 .../datasource/DownloadExpression.scala       |   2 +-
 .../datasource/DownloadSupport.scala          |   2 +-
 .../datasource/ReadTilesExpression.scala      |   2 +-
 .../awspds/CachedDatasetRelation.scala        |   2 +-
 .../awspds/L8CatalogDataSource.scala          |   2 +-
 .../datasource/awspds/L8CatalogRelation.scala |   2 +-
 .../datasource/awspds/L8DataSource.scala      |   2 +-
 .../datasource/awspds/L8Relation.scala        |   2 +-
 .../awspds/MODISCatalogDataSource.scala       |   2 +-
 .../awspds/MODISCatalogRelation.scala         |   2 +-
 .../datasource/awspds/PDSFields.scala         |   2 +-
 .../awspds/ResourceCacheSupport.scala         |   2 +-
 .../datasource/awspds/package.scala           |   2 +-
 .../experimental/datasource/geojson/DOM.scala |   2 +-
 .../geojson/GeoJsonDataSource.scala           |   2 +-
 .../experimental/datasource/package.scala     |   2 +-
 .../geojson/GeoJsonDataSourceTest.scala       |   2 +-
 pyrasterframes/python/.gitignore              |   3 +
 .../rasterframes/py/PyRFContext.scala         |   2 +-
 128 files changed, 195 insertions(+), 285 deletions(-)
 delete mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/tiles/FixedDelegatingTile.scala
 create mode 100644 pyrasterframes/python/.gitignore

diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index c16875adb..a2a9104bf 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,15 +19,16 @@
  *
  */
 
-package org.locationtech.rasterframes.bench
+package astraea.spark.rasterframes.bench
+
 
 import java.util.concurrent.TimeUnit
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.expressions.transformers.RasterSourceToTiles
+import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.expressions.transformers.RasterSourceToTiles
+import astraea.spark.rasterframes.ref.RasterSource
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
-import org.locationtech.rasterframes.ref.RasterSource
 import org.openjdk.jmh.annotations._
 /**
  *
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
index 817b01ef7..d3691f800 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/SparkEnv.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
index 718d19da4..b686fd6fa 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
index 66104420a..bf50ed334 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
index 73e7c8889..350ac811a 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileCellScanBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
index d0ffd765c..a4b0a2595 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileEncodeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
index 4aba17771..cd8004487 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
index dfdc71e3b..f047336e3 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -15,8 +15,6 @@
  * License for the specific language governing permissions and limitations under
  * the License.
  *
- * SPDX-License-Identifier: Apache-2.0
- *
  */
 
 package org.locationtech.rasterframes
@@ -31,7 +29,7 @@ import geotrellis.raster.{ArrayTile, CellType, NODATA, Tile, isNoData}
  * @author sfitch 
  * @since 10/4/17
  */
-package object bench {
+package object rasterframes {
   val rnd = new scala.util.Random(42)
 
   /** Construct a tile of given size and cell type populated with random values. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
index c700c30d7..d3f90a502 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index acd660e95..220e89683 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
index 235721753..39ed8d6f3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CRSEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
index b9788327b..921d0bb67 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
index 0c7a23ed4..ea01d4143 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CellTypeEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
index d728521c7..cf4c2e5ac 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/DelegatingSubfieldEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
index d3d1c7552..f5b078159 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/ProjectedExtentEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
index 6dbf8b447..7ec8121b5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
index 83eb51c21..f9e8d428f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
index ebce11784..2ec265ccc 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StringBackedEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
index 22ad3a95f..f69f7f160 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/TemporalProjectedExtentEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
index 39f7bce3e..2f59ea451 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/TileLayerMetadataEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
index 0d603478e..bbbcf25ea 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/URIEncoder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
index 3992667ef..d2ebf0c84 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
index ec2489ed2..05d56f7d1 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnCellGridExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
index 92044348a..78ebd1f5b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/OnTileContextExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
index 548651aca..34e44b082 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
index 59bba32f6..2885f6816 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
@@ -21,17 +21,16 @@
 
 package org.locationtech.rasterframes.expressions.localops
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
-import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
-import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.functions.lit
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 
 @ExpressionDescription(
   usage = "_FUNC_(tile, rhs) - Performs cell-wise addition between two tiles or a tile and a scalar.",
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index f0a7ad653..cc3110144 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
index 975d26b6a..d7aa0a665 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index ad9bc8bf1..58b1e7e56 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
index d4f3de292..f97a5dae0 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
index ba0f78c28..fc2401bb5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataBuilderMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
index 18374fade..5d96abdf4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/MetadataMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
index 6cdc0cc62..c7c46920b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
index 1338af7c3..d37404e85 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
index a9abb0d80..4a6df34cc 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SQLContextMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
index 92bcfd871..a726b4052 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SparkSessionMethods.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 19b2d68c4..e404657c4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
index 51cd4c390..358fdc258 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/jts/Implicits.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala b/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
index 86f5e9345..c4751cb3c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/jts/ReprojectionTransformer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
index 735b3964c..5cd9e780e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/NoDataFilter.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
index 79e7ef1aa..4d273a7f9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/Parameters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
index 1cdeaaa02..d261f7e91 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/TileColumnSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
index cf53407f1..c3ff11583 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index 83f4917a0..8a7ac0fc2 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
deleted file mode 100644
index ed269f20f..000000000
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/HttpRangeReader.scala
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2019 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-// NB: Copied from
-// https://github.com/locationtech/geotrellis/blob/835200c0a6031ddc09fca218ff082cefc9b116c6/spark/src/main/scala/geotrellis/spark/io/http/util/HttpRangeReader.scala
-// TODO: Submit PR with changes/fixes.
-//package geotrellis.spark.io.http.util
-
-package org.locationtech.rasterframes.ref
-
-import geotrellis.util.RangeReader
-import scalaj.http.{Http, HttpResponse}
-import java.net.{URI, URL}
-
-import com.typesafe.scalalogging.LazyLogging
-
-import scala.util.Try
-
-
-/**
- * This class extends [[RangeReader]] by reading chunks out of a GeoTiff at the
- * specified HTTP location.
- *
- * @param url: A [[URL]] pointing to the desired GeoTiff.
- */
-class HttpRangeReader(url: URL, useHeadRequest: Boolean) extends RangeReader with LazyLogging {
-
-  val request = Http(url.toString)
-
-  lazy val response: HttpResponse[String] = if(useHeadRequest) {
-    request.method("HEAD").asString
-  }
-  else {
-    request.method("GET").execute { is => "" }
-  }
-
-  lazy val totalLength: Long = {
-    /**
-     * "The Accept-Ranges response HTTP header is a marker used by the server
-     *  to advertise its support of partial requests. The value of this field
-     *  indicates the unit that can be used to define a range."
-     * https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Accept-Ranges
-     */
-    require(response.header("Accept-Ranges").contains("bytes"), "Server doesn't support ranged byte reads")
-
-    val contentLength = response
-        .header("Content-Length")
-        .flatMap({ cl => Try(cl.toLong).toOption }) match {
-          case Some(num) => num
-          case None => -1L
-        }
-
-    require(contentLength > 0,
-      "Server didn't provide (required) \"Content-Length\" headers, unable to do range-based read")
-
-    contentLength
-  }
-
-  def readClippedRange(start: Long, length: Int): Array[Byte] = {
-    //println("Range read", s"$start-${start + length} ($length bytes)")
-
-    val res = request
-      .method("GET")
-      .header("Range", s"bytes=${start}-${start + length}")
-      .asBytes
-
-    /**
-     * "If the byte-range-set is unsatisfiable, the server SHOULD return
-     *  a response with a status of 416 (Requested range not satisfiable).
-     *  Otherwise, the server SHOULD return a response with a status of 206
-     *  (Partial Content) containing the satisfiable ranges of the entity-body."
-     * https://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html
-     */
-    require(res.code != 416,
-      "Server unable to generate the byte range between ${start} and ${start + length}")
-
-    if (res.code != 206) logger.info("Server responded to range request with HTTP code other than PARTIAL_RESPONSE (206)")
-
-    res.body
-  }
-
-}
-
-/** The companion object of [[HttpRangeReader]] */
-object HttpRangeReader {
-
-  def apply(address: String): HttpRangeReader = apply(new URL(address))
-
-  def apply(uri: URI): HttpRangeReader = apply(uri.toURL)
-
-  /**
-   * Returns a new instance of HttpRangeReader.
-   *
-   * @param url: A [[URL]] pointing to the desired GeoTiff.
-   * @return A new instance of HttpRangeReader.
-   */
-  def apply(url: URL): HttpRangeReader = new HttpRangeReader(url, true)
-
-  /**
-   * Returns a new instance of HttpRangeReader which does not use HEAD
-   * to determine the totalLength.
-   *
-   * @param url: A [[URL]] pointing to the desired GeoTiff.
-   * @return A new instance of HttpRangeReader.
-   */
-  def withoutHeadRequest(url: URL): HttpRangeReader = new HttpRangeReader(url, false)
-
-  def withoutHeadRequest(address: String): HttpRangeReader = withoutHeadRequest(new URL(address))
-
-  def withoutHeadRequest(uri: URI): HttpRangeReader = withoutHeadRequest(uri.toURL)
-}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
index f716c2f19..515c47d12 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/ProjectedRasterLike.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
index 6833b5bf8..634695595 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index c266ab1d9..7cba2a590 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
index 8d5f914bd..3b3e54d6f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilterPushdownRules.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
index 8b1c61395..cf731b658 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialFilters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
index b5cc03f43..403d122ea 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialRelationReceiver.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
index 0407a7a33..e2ee23b1f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
index cf41c9b54..5315b63b7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/TemporalFilters.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala b/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
index 861b3876d..be3d547a3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/stats/CellHistogram.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala b/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
index 06dec77a3..ea371666d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/stats/CellStatistics.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/FixedDelegatingTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/FixedDelegatingTile.scala
new file mode 100644
index 000000000..52bfa5c1d
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/FixedDelegatingTile.scala
@@ -0,0 +1,40 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.tiles
+import geotrellis.raster.{ArrayTile, DelegatingTile, Tile}
+
+/**
+ * Temporary workaroud for https://github.com/locationtech/geotrellis/issues/2907
+ *
+ * @since 8/22/18
+ */
+trait FixedDelegatingTile extends DelegatingTile {
+  override def combine(r2: Tile)(f: (Int, Int) ⇒ Int): Tile = (delegate, r2) match {
+    case (del: ArrayTile, r2: DelegatingTile) ⇒ del.combine(r2.toArrayTile())(f)
+    case _ ⇒ delegate.combine(r2)(f)
+  }
+
+  override def combineDouble(r2: Tile)(f: (Double, Double) ⇒ Double): Tile = (delegate, r2) match {
+    case (del: ArrayTile, r2: DelegatingTile) ⇒ del.combineDouble(r2.toArrayTile())(f)
+    case _ ⇒ delegate.combineDouble(r2)(f)
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
index 563aaf0e3..9d18eb586 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -25,7 +25,7 @@ import java.nio.ByteBuffer
 
 import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
 import org.locationtech.rasterframes.model.TileDataContext
-import geotrellis.raster.{DelegatingTile, _}
+import geotrellis.raster._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.locationtech.rasterframes.model.{Cells, TileDataContext}
 
@@ -39,7 +39,7 @@ import org.locationtech.rasterframes.model.{Cells, TileDataContext}
  *
  * @since 11/29/17
  */
-class InternalRowTile(val mem: InternalRow) extends DelegatingTile {
+class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
   import InternalRowTile._
 
   /** @group COPIES */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
index 5543038d5..59a9f24cb 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -21,27 +21,26 @@
 
 package org.locationtech.rasterframes.tiles
 
-import org.locationtech.rasterframes.TileType
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.encoders.CatalystSerializerEncoder
-import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.raster.{CellType, DelegatingTile, ProjectedRaster, Tile}
+import geotrellis.raster.{CellType, ProjectedRaster, Tile}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.TileUDT
 import org.apache.spark.sql.types.{StructField, StructType}
+import org.locationtech.rasterframes.TileType
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import org.locationtech.rasterframes.model.TileContext
 import org.locationtech.rasterframes.ref.ProjectedRasterLike
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 
 /**
  * A Tile that's also like a ProjectedRaster, with delayed evaluation support.
  *
  * @since 9/5/18
  */
-trait ProjectedRasterTile extends DelegatingTile with ProjectedRasterLike {
+trait ProjectedRasterTile extends FixedDelegatingTile with ProjectedRasterLike {
   def extent: Extent
   def crs: CRS
   def projectedExtent: ProjectedExtent = ProjectedExtent(extent, crs)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
index 956d27cf1..e24bb8175 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/GeoTiffInfoSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
index 96f865bba..26754b91d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/KryoSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala b/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
index 4493e4eb6..b576f1e67 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/MultibandRender.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
index 40a038b7d..7fea0a93e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala b/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
index 3cefa640e..24ee2ce2d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/SubdivideSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
index 447c4d908..2d0c8c780 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index 6b492d4b2..f723224f9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index 4c714c094..db5aa310a 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -38,7 +38,7 @@ log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
 log4j.logger.org.locationtech.rasterframes=DEBUG
-log4j.logger.org.locationtech.rasterframes.ref=TRACE
+log4j.logger.org.locationtech.rasterframes.ref=DEBUG
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index f703e58d6..ac09b86c9 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index b9810b88e..dd497d0fa 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
index 28bf5f3a2..8e50935de 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
index 1d5442c51..cce1eba64 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
index 2a6fedb3f..244b9ab3d 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
index 5de668fc6..fe48aa4b2 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
index 0a3246e5c..773c28877 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index eb8b24c3e..13f93cd6c 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
index fb2ca5246..78ead237e 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index f2d285e67..2759323fb 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index e57069d23..a2fc4baf1 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -284,7 +284,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       val minTile = dsNd.select(agg_local_min($"tiles")).first()
       assert(minTile.toArray() === completeTile.toArray())
 
-            val maxTile = dsNd.select(agg_local_max($"tiles")).first()
+      val maxTile = dsNd.select(agg_local_max($"tiles")).first()
       assert(maxTile.toArray() === completeTile.toArray())
 
       val meanTile = dsNd.select(agg_local_mean($"tiles")).first()
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
index 35e0021b7..ceac41f64 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
@@ -1,9 +1,7 @@
-
-
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
index 4270a5314..1d4dbc4f6 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ml/NoDataFilterSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
index 31e816d9a..2d9e2d04c 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ml/TileExploderSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index 59e5c81f5..ca14400a1 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 18d158eeb..7fbc274f4 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
index 99063c9d0..d620dd4fd 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/DataSourceOptions.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 876895d88..3c3151783 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 885464df6..b0904caa6 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
index 539bcb84d..d16d69fb1 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index 1c6c2bd0d..39fb16ca1 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
index ed4dfe099..11edc1d5f 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalog.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
index 18f2248a4..8662fdc64 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
index ddf8a9c74..343f4683d 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017-2019 Astraea, Inc. & Azavea
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
index 0fc7e28e6..9f90c96fd 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/Layer.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
index 71ff81796..34bd6536b 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/MergeableData.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
index 8ef009f26..67ea65510 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
index 5288cbe39..bb4cc4840 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
index 1af62bcd5..9a649bb94 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
index bbd173e01..0ed2deefa 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 25e69c067..d221acab9 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
index 70e55d9df..042fa4c3b 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 117886c9c..70d70f0d7 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017-2019 Azavea & Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
index c953d94c3..0cf7e358c 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/TileFeatureSupportSpec.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/docs/src/main/tut/apps/geotrellis-ops.md b/docs/src/main/tut/apps/geotrellis-ops.md
index 81a97a3fa..edc5302c7 100644
--- a/docs/src/main/tut/apps/geotrellis-ops.md
+++ b/docs/src/main/tut/apps/geotrellis-ops.md
@@ -1,7 +1,7 @@
 # GeoTrellis Operations
 
 ```tut:invisible
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster._
 import geotrellis.raster.render._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
diff --git a/docs/src/main/tut/apps/ndvi.md b/docs/src/main/tut/apps/ndvi.md
index a9fb72a3e..5ce1d1246 100644
--- a/docs/src/main/tut/apps/ndvi.md
+++ b/docs/src/main/tut/apps/ndvi.md
@@ -1,7 +1,7 @@
 # Computing NDVI
 
 ```tut:invisible
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster._
 import geotrellis.raster.render._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
diff --git a/docs/src/main/tut/creating-rasterframes.md b/docs/src/main/tut/creating-rasterframes.md
index 7e7ab2c66..75b65f869 100644
--- a/docs/src/main/tut/creating-rasterframes.md
+++ b/docs/src/main/tut/creating-rasterframes.md
@@ -7,7 +7,7 @@ There are a couple of setup steps necessary anytime you want to work with Raster
 
 
 ```tut:silent
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import org.apache.spark.sql._
 ```
 
@@ -46,7 +46,7 @@ First add the following import:
 
 
 ```tut:silent
-import astraea.spark.rasterframes.datasource.geotiff._
+import org.locationtech.rasterframes.datasource.geotiff._
 import java.io.File
 ```
 
@@ -95,7 +95,7 @@ Before we show how all of this works we need to have a GeoTrellis layer to work
 
 
 ```tut:silent
-import astraea.spark.rasterframes.datasource.geotrellis._
+import org.locationtech.rasterframes.datasource.geotrellis._
 import java.nio.file.Files
 
 val base = Files.createTempDirectory("rf-").toUri
diff --git a/docs/src/main/tut/exporting-rasterframes.md b/docs/src/main/tut/exporting-rasterframes.md
index 2015943f5..5012f1019 100644
--- a/docs/src/main/tut/exporting-rasterframes.md
+++ b/docs/src/main/tut/exporting-rasterframes.md
@@ -1,7 +1,7 @@
 # Exporting&nbsp;RasterFrames
 
 ```tut:invisible
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.spark._
 import geotrellis.raster._
 import geotrellis.raster.render._
@@ -183,8 +183,8 @@ showType(rf.toTileLayerRDD($"tile".as[Tile]))
 spark.stop()
 ```
 
-[rfInit]: astraea.spark.rasterframes.package#rfInit%28SQLContext%29:Unit
+[rfInit]: org.locationtech.rasterframes.package#rfInit%28SQLContext%29:Unit
 [rdd]: org.apache.spark.sql.Dataset#frdd:org.apache.spark.rdd.RDD[T]
-[toTileLayerRDD]: astraea.spark.rasterframes.RasterFrameMethods#toTileLayerRDD%28tileCol:RasterFrameMethods.this.TileColumn%29:Either[geotrellis.spark.TileLayerRDD[geotrellis.spark.SpatialKey],geotrellis.spark.TileLayerRDD[geotrellis.spark.SpaceTimeKey]]
-[tile_to_array_int]: astraea.spark.rasterframes.ColumnFunctions#tile_to_array_int
+[toTileLayerRDD]: org.locationtech.rasterframes.RasterFrameMethods#toTileLayerRDD%28tileCol:RasterFrameMethods.this.TileColumn%29:Either[geotrellis.spark.TileLayerRDD[geotrellis.spark.SpatialKey],geotrellis.spark.TileLayerRDD[geotrellis.spark.SpaceTimeKey]]
+[tile_to_array_int]: org.locationtech.rasterframes.ColumnFunctions#tile_to_array_int
 
diff --git a/docs/src/main/tut/ml/classification.md b/docs/src/main/tut/ml/classification.md
index 55c8ced98..b62940a7b 100644
--- a/docs/src/main/tut/ml/classification.md
+++ b/docs/src/main/tut/ml/classification.md
@@ -9,8 +9,8 @@ SparkML.
 First some setup:
 
 ```tut:silent
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.ml.{NoDataFilter, TileExploder}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.ml.{NoDataFilter, TileExploder}
 import geotrellis.raster._
 import geotrellis.raster.render._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
diff --git a/docs/src/main/tut/ml/clustering.md b/docs/src/main/tut/ml/clustering.md
index a3e803ace..fc63eabdb 100644
--- a/docs/src/main/tut/ml/clustering.md
+++ b/docs/src/main/tut/ml/clustering.md
@@ -7,8 +7,8 @@ In this example we will do some simple cell clustering based on multiband imager
 First some setup:
 
 ```tut:silent
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.ml.TileExploder
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.ml.TileExploder
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.raster._
 import geotrellis.raster.render._
diff --git a/docs/src/main/tut/ml/statistics.md b/docs/src/main/tut/ml/statistics.md
index 3ff086ad1..6249eef73 100644
--- a/docs/src/main/tut/ml/statistics.md
+++ b/docs/src/main/tut/ml/statistics.md
@@ -1,7 +1,7 @@
 # Raster Statistics
 
 ```tut:invisible
-import astraea.spark.rasterframes._
+import org.locationtech.rasterframes._
 import geotrellis.raster._
 import geotrellis.raster.render._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index d264d900d..fe9b91851 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -941,6 +941,6 @@ Pretty print the tile values as plain text.
 
 
 
-[RasterFunctions]: astraea.spark.rasterframes.RasterFunctions
+[RasterFunctions]: org.locationtech.rasterframes.RasterFunctions
 [scaladoc]: latest/api/index.html
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 9fff73799..f8fb23350 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -4,7 +4,8 @@
 
 ### 0.8.0
 
-* Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.2.0, GeoMesa 2.2.1, JTS 1.16.0.
+* _Breaking_: Root package changed from `astraea.spark.rasterframes` to `org.locationtech.rasterframes`.
+* Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
 * Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`, `resample`.
 * Support python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
 * SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
@@ -18,7 +19,7 @@
 * _Breaking_: renamed `agg_histogram` to `agg_approx_histogram`, `local_agg_stats` to `agg_local_stats`, `local_agg_max` to `agg_local_max`, `local_agg_min` to `agg_local_min`, `local_agg_mean` to `agg_local_mean`, `local_agg_data_cells` to `agg_local_data_cells`, `local_agg_no_data_cells` to `agg_local_no_data_cells`.
 * _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `agg_stats` instead.
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
-* _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `astraea.spark.rasterframes.model`.
+* _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `org.locationtech.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
 * Added `RasterFrame.withExtent` extension method.
 * Added `st_extent` and deprecated `envelope`. Renamed `bounds_geometry` to `extent_geometry`.
@@ -53,8 +54,8 @@
 * Added [experimental DataSource implementations](https://github.com/locationtech/rasterframes/tree/develop/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds) for [MODIS](https://registry.opendata.aws/modis/) and [Landsat 8](https://registry.opendata.aws/landsat-8/) catalogs on AWS PDS.   
 * _Change_: Default interpoation for `toRaster` and `toMultibandRaster` has been changed from `Bilinear` to `NearestNeighbor`.
 * _Breaking_: Renamed/moved `astraea.spark.rasterframes.functions.CellStatsAggregateFunction.Statistics` to
-`astraea.spark.rasterframes.stats.CellStatistics`.
-* _Breaking_: `HistogramAggregateFunction` now generates the new type `astraea.spark.rasterframes.stats.CellHistogram`.
+`org.locationtech.rasterframes.stats.CellStatistics`.
+* _Breaking_: `HistogramAggregateFunction` now generates the new type `org.locationtech.rasterframes.stats.CellHistogram`.
 * _Breaking_: `box2D` renamed `envelope`.
 
 ## 0.6.x
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
index 4721a1fc8..0a39071ff 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
index a8db278ea..f5e0dff64 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
index b5d920c86..875d30c1a 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
index 1ead777e1..37be5335e 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2089 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
index ee3d8c7e3..47ff0afab 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
index 873198713..3f5c9a142 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
index c57d34d0a..5a6c25b6e 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
index f92dc36f4..4c8077da6 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
index ffdb84f03..d1c886755 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
index 3419d9a81..32ea9ce87 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
index 35c3ede44..f615bbbe2 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
index bfbecd013..f203a187f 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
index 0afc2ecc1..ccf1331ff 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
index 9cac6e156..1122f2bc7 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
index f84a18c5d..e2cb93a91 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
index 137d7cc32..57e8e8f53 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
index 4ecf766d7..8edab293f 100644
--- a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
+++ b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2018 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
diff --git a/pyrasterframes/python/.gitignore b/pyrasterframes/python/.gitignore
new file mode 100644
index 000000000..d43a8f7ce
--- /dev/null
+++ b/pyrasterframes/python/.gitignore
@@ -0,0 +1,3 @@
+.coverage
+htmlcov
+
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 7a3583672..3ea7ab4a0 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2019 Astraea, Inc.
+ * Copyright 2017-2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of

From a23fa6ad09239268d5fcd27e77b17b19713ec79e Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 24 Apr 2019 14:08:00 -0400
Subject: [PATCH 049/380] Documentation updates.

---
 docs/src/main/tut/release-notes.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index f8fb23350..307e1224d 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -4,7 +4,7 @@
 
 ### 0.8.0
 
-* _Breaking_: Root package changed from `astraea.spark.rasterframes` to `org.locationtech.rasterframes`.
+* _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
 * Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`, `resample`.
 * Support python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
@@ -114,7 +114,7 @@
 ### 0.5.9
 
 * Ported to sbt 1.0.3
-* Added sbt-generated `astraea.spark.rasterframes.RFBuildInfo`
+* Added sbt-generated `org.locationtech.rasterframes.RFBuildInfo`
 * Fixed bug in computing `aggMean` when one or more tiles are `null` 
 * Deprecated `rfIinit` in favor of `SparkSession.withRasterFrames` or `SQLContext.withRasterFrames` extension methods
 

From e31710cffc982ffc792fc316815c6f95fea78f2c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 24 Apr 2019 16:25:47 -0400
Subject: [PATCH 050/380] Prefixed all columnar functions with `rf_` to avoid
 namespace collisions when using Python `*` or Scala `_` imports.

---
 .../rasterframes/bench/RasterRefBench.scala   |  10 +-
 .../bench/StatsComputeBench.scala             |  10 +-
 .../bench/TileAssembleBench.scala             |   4 +-
 .../rasterframes/bench/TileExplodeBench.scala |   4 +-
 .../rasterframes/RasterFunctions.scala        | 176 ++++++-----
 .../expressions/accessors/GetCellType.scala   |   2 +-
 .../expressions/accessors/GetDimensions.scala |   2 +-
 .../aggstats/CellCountAggregate.scala         |   8 +-
 .../aggstats/CellMeanAggregate.scala          |   4 +-
 .../aggstats/CellStatsAggregate.scala         |   4 +-
 .../aggstats/HistogramAggregate.scala         |   4 +-
 .../aggstats/LocalCountAggregate.scala        |   8 +-
 .../aggstats/LocalMeanAggregate.scala         |   2 +-
 .../aggstats/LocalStatsAggregate.scala        |   4 +-
 .../aggstats/LocalTileOpAggregate.scala       |   4 +-
 .../expressions/generators/ExplodeTiles.scala |   2 +-
 .../expressions/localops/Add.scala            |   2 +-
 .../expressions/localops/Divide.scala         |   2 +-
 .../expressions/localops/Equal.scala          |   2 +-
 .../expressions/localops/Exp.scala            |   8 +-
 .../expressions/localops/Greater.scala        |   2 +-
 .../expressions/localops/GreaterEqual.scala   |   2 +-
 .../expressions/localops/Less.scala           |   2 +-
 .../expressions/localops/LessEqual.scala      |   2 +-
 .../expressions/localops/Log.scala            |   6 +-
 .../expressions/localops/Multiply.scala       |   2 +-
 .../localops/NormalizedDifference.scala       |   2 +-
 .../expressions/localops/Resample.scala       |   2 +-
 .../expressions/localops/Round.scala          |   4 +-
 .../expressions/localops/Subtract.scala       |   2 +-
 .../expressions/localops/Unequal.scala        |   2 +-
 .../rasterframes/expressions/package.scala    |   2 +-
 .../expressions/tilestats/DataCells.scala     |   2 +-
 .../expressions/tilestats/IsNoDataTile.scala  |   2 +-
 .../expressions/tilestats/NoDataCells.scala   |   2 +-
 .../expressions/tilestats/Sum.scala           |   2 +-
 .../expressions/tilestats/TileHistogram.scala |   2 +-
 .../expressions/tilestats/TileMax.scala       |   2 +-
 .../expressions/tilestats/TileMean.scala      |   2 +-
 .../expressions/tilestats/TileMin.scala       |   2 +-
 .../expressions/tilestats/TileStats.scala     |   2 +-
 .../transformers/DebugRender.scala            |   4 +-
 .../transformers/ExtentToGeometry.scala       |   2 +-
 .../expressions/transformers/Mask.scala       |   6 +-
 .../transformers/ReprojectGeometry.scala      |   2 +-
 .../transformers/TileToArrayDouble.scala      |   2 +-
 .../transformers/TileToArrayInt.scala         |   2 +-
 .../rasterframes/ml/TileExploder.scala        |   2 +-
 .../util/ZeroSevenCompatibilityKit.scala      |  96 +++---
 .../test/scala/examples/Classification.scala  |   4 +-
 core/src/test/scala/examples/Clustering.scala |   2 +-
 core/src/test/scala/examples/Exporting.scala  |  14 +-
 .../test/scala/examples/LocalArithmetic.scala |   8 +-
 core/src/test/scala/examples/Masking.scala    |   4 +-
 core/src/test/scala/examples/MeanValue.scala  |   2 +-
 core/src/test/scala/examples/Tour.scala       |  16 +-
 .../rasterframes/ExplodeSpec.scala            |  32 +-
 .../rasterframes/GeometryOperationsSpec.scala |   8 +-
 .../locationtech/rasterframes/JTSSpec.scala   |   8 +-
 .../rasterframes/RasterFrameSpec.scala        |   2 +-
 .../rasterframes/RasterFunctionsSpec.scala    | 278 +++++++++---------
 .../rasterframes/ReprojectGeometrySpec.scala  |  12 +-
 .../rasterframes/TileAssemblerSpec.scala      |  21 +-
 .../rasterframes/TileStatsSpec.scala          |  60 ++--
 .../geotiff/GeoTiffDataSource.scala           |   4 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       |   2 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala |  12 +-
 .../RasterSourceDataSourceSpec.scala          |   4 +-
 docs/src/main/tut/apps/geotrellis-ops.md      |   6 +-
 docs/src/main/tut/apps/ndvi.md                |   2 +-
 docs/src/main/tut/exporting-rasterframes.md   |   8 +-
 docs/src/main/tut/ml/classification.md        |   4 +-
 docs/src/main/tut/ml/clustering.md            |   2 +-
 docs/src/main/tut/ml/statistics.md            |  19 +-
 docs/src/main/tut/release-notes.md            |  21 +-
 .../datasource/awspds/L8Relation.scala        |   2 +-
 pyrasterframes/python/examples/Clustering.py  |   2 +-
 pyrasterframes/python/examples/MeanValue.py   |   2 +-
 pyrasterframes/python/examples/NDVI.py        |   2 +-
 .../python/pyrasterframes/__init__.py         |   1 +
 .../python/pyrasterframes/context.py          |   2 +-
 .../python/pyrasterframes/rasterfunctions.py  | 224 +++++++-------
 .../python/tests/PyRasterFramesTests.py       |  96 +++---
 .../rasterframes/py/PyRFContext.scala         |  82 +++---
 84 files changed, 690 insertions(+), 704 deletions(-)

diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index a2a9104bf..54386e2ef 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -58,7 +58,7 @@ class RasterRefBench  extends SparkEnv with LazyLogging {
   @Benchmark
   def computeDifferenceExpanded() = {
     expandedDF
-      .select(normalized_difference($"B1", $"B2"))
+      .select(rf_normalized_difference($"B1", $"B2"))
       .cache()
       .count()
   }
@@ -66,24 +66,24 @@ class RasterRefBench  extends SparkEnv with LazyLogging {
   @Benchmark
   def computeDifferenceSingle() = {
     singleDF
-      .select(normalized_difference($"B1", $"B2"))
+      .select(rf_normalized_difference($"B1", $"B2"))
       .cache()
       .count()
   }
 
   @Benchmark
   def computeStatsSingle() = {
-    singleDF.select(agg_stats($"B1")).collect()
+    singleDF.select(rf_agg_stats($"B1")).collect()
   }
 
   @Benchmark
   def computeStatsExpanded() = {
-    expandedDF.select(agg_stats($"B1")).collect()
+    expandedDF.select(rf_agg_stats($"B1")).collect()
   }
 
   @Benchmark
   def computeDifferenceStats() = {
-    singleDF.select(agg_stats(normalized_difference($"B1", $"B2"))).collect()
+    singleDF.select(rf_agg_stats(rf_normalized_difference($"B1", $"B2"))).collect()
   }
 
 }
\ No newline at end of file
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
index b686fd6fa..2ebc3efc0 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/StatsComputeBench.scala
@@ -59,26 +59,26 @@ class StatsComputeBench extends SparkEnv {
 
 //  @Benchmark
 //  def computeStats(): Array[CellStatistics] = {
-//    tiles.select(agg_stats($"tile")).collect()
+//    tiles.select(rf_agg_stats($"tile")).collect()
 //  }
 
   @Benchmark
   def computeHistogram(): Array[CellHistogram] = {
-    tiles.select(agg_approx_histogram($"tile")).collect()
+    tiles.select(rf_agg_approx_histogram($"tile")).collect()
   }
 
 //  @Benchmark
 //  def extractMean(): Array[Double] = {
-//    tiles.select(agg_stats($"tile").getField("mean")).map(_.getDouble(0)).collect()
+//    tiles.select(rf_agg_stats($"tile").getField("mean")).map(_.getDouble(0)).collect()
 //  }
 //
 //  @Benchmark
 //  def directMean(): Array[Double] = {
-//    tiles.repartition(10).select(agg_mean($"tile")).collect()
+//    tiles.repartition(10).select(rf_agg_mean($"tile")).collect()
 //  }
 
 //  @Benchmark
 //  def computeCounts() = {
-//    tiles.toDF("tile").select(data_cells($"tile") as "counts").agg(sum($"counts")).collect()
+//    tiles.toDF("tile").select(rf_data_cells($"tile") as "counts").agg(sum($"counts")).collect()
 //  }
 }
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
index bf50ed334..8b33af369 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileAssembleBench.scala
@@ -45,7 +45,7 @@ class TileAssembleBench extends SparkEnv {
   var cells1: DataFrame = _
   var cells2: DataFrame = _
 
-  val assembler = assemble_tile(
+  val assembler = rf_assemble_tile(
     $"column_index", $"row_index", $"tile",
     tileSize, tileSize, cellType
   )
@@ -54,7 +54,7 @@ class TileAssembleBench extends SparkEnv {
   def setupData(): Unit = {
     cells1 = Seq.fill(numTiles)(randomTile(tileSize, tileSize, cellType.name)).zipWithIndex
       .toDF("tile", "id")
-      .select($"id", explode_tiles($"tile"))
+      .select($"id", rf_explode_tiles($"tile"))
       .repartition(4, $"id")
       .cache()
 
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
index cd8004487..7f3352f69 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/TileExplodeBench.scala
@@ -58,11 +58,11 @@ class TileExplodeBench extends SparkEnv {
 
   @Benchmark
   def arrayExplode() = {
-    tiles.select(posexplode(tile_to_array_double($"tile"))).count()
+    tiles.select(posexplode(rf_tile_to_array_double($"tile"))).count()
   }
 
   @Benchmark
   def tileExplode() = {
-    tiles.select(explode_tiles($"tile")).count()
+    tiles.select(rf_explode_tiles($"tile")).count()
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 220e89683..976be8330 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -27,7 +27,7 @@ import geotrellis.vector.Extent
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.functions.{lit, udf}
 import org.apache.spark.sql.{Column, TypedColumn}
-import org.locationtech.jts.geom.{Envelope, Geometry}
+import org.locationtech.jts.geom.Geometry
 import org.locationtech.rasterframes.expressions.TileAssembler
 import org.locationtech.rasterframes.expressions.accessors._
 import org.locationtech.rasterframes.expressions.aggstats._
@@ -48,311 +48,307 @@ trait RasterFunctions {
 
   // format: off
   /** Create a row for each cell in Tile. */
-  def explode_tiles(cols: Column*): Column = explode_tiles_sample(1.0, None, cols: _*)
+  def rf_explode_tiles(cols: Column*): Column = rf_explode_tiles_sample(1.0, None, cols: _*)
 
   /** Create a row for each cell in Tile with random sampling and optional seed. */
-  def explode_tiles_sample(sampleFraction: Double, seed: Option[Long], cols: Column*): Column =
+  def rf_explode_tiles_sample(sampleFraction: Double, seed: Option[Long], cols: Column*): Column =
     ExplodeTiles(sampleFraction, seed, cols)
 
   /** Create a row for each cell in Tile with random sampling (no seed). */
-  def explode_tiles_sample(sampleFraction: Double, cols: Column*): Column =
+  def rf_explode_tiles_sample(sampleFraction: Double, cols: Column*): Column =
     ExplodeTiles(sampleFraction, None, cols)
 
   /** Query the number of (cols, rows) in a Tile. */
-  def tile_dimensions(col: Column): Column = GetDimensions(col)
-
-  /** Extracts the bounding box of a geometry as a JTS envelope. */
-  @deprecated("Replace usages of this with `st_extent`", "11/4/2018")
-  def envelope(col: Column): TypedColumn[Any, Envelope] = GetEnvelope(col)
+  def rf_tile_dimensions(col: Column): Column = GetDimensions(col)
 
   /** Extracts the bounding box of a geometry as an Extent */
-  def st_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
+  def rf_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
 
   /** Flattens Tile into a double array. */
-  def tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
+  def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
 
   /** Flattens Tile into an integer array. */
-  def tile_to_array_int(col: Column): TypedColumn[Any, Array[Double]] =
+  def rf_tile_to_array_int(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
 
   @Experimental
   /** Convert array in `arrayCol` into a Tile of dimensions `cols` and `rows`*/
-  def array_to_tile(arrayCol: Column, cols: Int, rows: Int) = withAlias("array_to_tile", arrayCol)(
+  def rf_array_to_tile(arrayCol: Column, cols: Int, rows: Int) = withAlias("rf_array_to_tile", arrayCol)(
     udf[Tile, AnyRef](F.arrayToTile(cols, rows)).apply(arrayCol)
   )
 
   /** Create a Tile from a column of cell data with location indexes and preform cell conversion. */
-  def assemble_tile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Int, tileRows: Int, ct: CellType): TypedColumn[Any, Tile] =
-    convert_cell_type(TileAssembler(columnIndex, rowIndex, cellData, lit(tileCols), lit(tileRows)), ct).as(cellData.columnName).as[Tile](singlebandTileEncoder)
+  def rf_assemble_tile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Int, tileRows: Int, ct: CellType): TypedColumn[Any, Tile] =
+    rf_convert_cell_type(TileAssembler(columnIndex, rowIndex, cellData, lit(tileCols), lit(tileRows)), ct).as(cellData.columnName).as[Tile](singlebandTileEncoder)
 
   /** Create a Tile from  a column of cell data with location indexes. */
-  def assemble_tile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Column, tileRows: Column): TypedColumn[Any, Tile] =
+  def rf_assemble_tile(columnIndex: Column, rowIndex: Column, cellData: Column, tileCols: Column, tileRows: Column): TypedColumn[Any, Tile] =
     TileAssembler(columnIndex, rowIndex, cellData, tileCols, tileRows)
 
   /** Extract the Tile's cell type */
-  def cell_type(col: Column): TypedColumn[Any, CellType] = GetCellType(col)
+  def rf_cell_type(col: Column): TypedColumn[Any, CellType] = GetCellType(col)
 
   /** Change the Tile's cell type */
-  def convert_cell_type(col: Column, cellType: CellType): TypedColumn[Any, Tile] =
+  def rf_convert_cell_type(col: Column, cellType: CellType): TypedColumn[Any, Tile] =
     SetCellType(col, cellType)
 
   /** Change the Tile's cell type */
-  def convert_cell_type(col: Column, cellTypeName: String): TypedColumn[Any, Tile] =
+  def rf_convert_cell_type(col: Column, cellTypeName: String): TypedColumn[Any, Tile] =
     SetCellType(col, cellTypeName)
 
   /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
-  def extent_geometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
+  def rf_extent_geometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
 
   /** Assign a `NoData` value to the Tiles. */
-  def with_no_data(col: Column, nodata: Double): TypedColumn[Any, Tile] = withAlias("with_no_data", col)(
+  def rf_with_no_data(col: Column, nodata: Double): TypedColumn[Any, Tile] = withAlias("rf_with_no_data", col)(
     udf[Tile, Tile](F.withNoData(nodata)).apply(col)
   ).as[Tile]
 
   /**  Compute the full column aggregate floating point histogram. */
-  def agg_approx_histogram(col: Column): TypedColumn[Any, CellHistogram] =
+  def rf_agg_approx_histogram(col: Column): TypedColumn[Any, CellHistogram] =
     HistogramAggregate(col)
 
   /** Compute the full column aggregate floating point statistics. */
-  def agg_stats(col: Column): TypedColumn[Any, CellStatistics] =
+  def rf_agg_stats(col: Column): TypedColumn[Any, CellStatistics] =
     CellStatsAggregate(col)
 
   /** Computes the column aggregate mean. */
-  def agg_mean(col: Column) = CellMeanAggregate(col)
+  def rf_agg_mean(col: Column) = CellMeanAggregate(col)
 
   /** Computes the number of non-NoData cells in a column. */
-  def agg_data_cells(col: Column): TypedColumn[Any, Long] = CellCountAggregate.DataCells(col)
+  def rf_agg_data_cells(col: Column): TypedColumn[Any, Long] = CellCountAggregate.DataCells(col)
 
   /** Computes the number of NoData cells in a column. */
-  def agg_no_data_cells(col: Column): TypedColumn[Any, Long] = CellCountAggregate.NoDataCells(col)
+  def rf_agg_no_data_cells(col: Column): TypedColumn[Any, Long] = CellCountAggregate.NoDataCells(col)
 
   /** Compute the Tile-wise mean */
-  def tile_mean(col: Column): TypedColumn[Any, Double] =
+  def rf_tile_mean(col: Column): TypedColumn[Any, Double] =
     TileMean(col)
 
   /** Compute the Tile-wise sum */
-  def tile_sum(col: Column): TypedColumn[Any, Double] =
+  def rf_tile_sum(col: Column): TypedColumn[Any, Double] =
     Sum(col)
 
   /** Compute the minimum cell value in tile. */
-  def tile_min(col: Column): TypedColumn[Any, Double] =
+  def rf_tile_min(col: Column): TypedColumn[Any, Double] =
     TileMin(col)
 
   /** Compute the maximum cell value in tile. */
-  def tile_max(col: Column): TypedColumn[Any, Double] =
+  def rf_tile_max(col: Column): TypedColumn[Any, Double] =
     TileMax(col)
 
   /** Compute TileHistogram of Tile values. */
-  def tile_histogram(col: Column): TypedColumn[Any, CellHistogram] =
+  def rf_tile_histogram(col: Column): TypedColumn[Any, CellHistogram] =
     TileHistogram(col)
 
   /** Compute statistics of Tile values. */
-  def tile_stats(col: Column): TypedColumn[Any, CellStatistics] =
+  def rf_tile_stats(col: Column): TypedColumn[Any, CellStatistics] =
     TileStats(col)
 
   /** Counts the number of non-NoData cells per Tile. */
-  def data_cells(tile: Column): TypedColumn[Any, Long] =
+  def rf_data_cells(tile: Column): TypedColumn[Any, Long] =
     DataCells(tile)
 
   /** Counts the number of NoData cells per Tile. */
-  def no_data_cells(tile: Column): TypedColumn[Any, Long] =
+  def rf_no_data_cells(tile: Column): TypedColumn[Any, Long] =
     NoDataCells(tile)
 
-  def is_no_data_tile(tile: Column): TypedColumn[Any, Boolean] =
+  def rf_is_no_data_tile(tile: Column): TypedColumn[Any, Boolean] =
     IsNoDataTile(tile)
 
   /** Compute cell-local aggregate descriptive statistics for a column of Tiles. */
-  def agg_local_stats(col: Column) =
+  def rf_agg_local_stats(col: Column) =
     LocalStatsAggregate(col)
 
   /** Compute the cell-wise/local max operation between Tiles in a column. */
-  def agg_local_max(col: Column): TypedColumn[Any, Tile] = LocalTileOpAggregate.LocalMaxUDAF(col)
+  def rf_agg_local_max(col: Column): TypedColumn[Any, Tile] = LocalTileOpAggregate.LocalMaxUDAF(col)
 
   /** Compute the cellwise/local min operation between Tiles in a column. */
-  def agg_local_min(col: Column): TypedColumn[Any, Tile] = LocalTileOpAggregate.LocalMinUDAF(col)
+  def rf_agg_local_min(col: Column): TypedColumn[Any, Tile] = LocalTileOpAggregate.LocalMinUDAF(col)
 
   /** Compute the cellwise/local mean operation between Tiles in a column. */
-  def agg_local_mean(col: Column): TypedColumn[Any, Tile] = LocalMeanAggregate(col)
+  def rf_agg_local_mean(col: Column): TypedColumn[Any, Tile] = LocalMeanAggregate(col)
 
   /** Compute the cellwise/local count of non-NoData cells for all Tiles in a column. */
-  def agg_local_data_cells(col: Column): TypedColumn[Any, Tile] = LocalCountAggregate.LocalDataCellsUDAF(col)
+  def rf_agg_local_data_cells(col: Column): TypedColumn[Any, Tile] = LocalCountAggregate.LocalDataCellsUDAF(col)
 
   /** Compute the cellwise/local count of NoData cells for all Tiles in a column. */
-  def agg_local_no_data_cells(col: Column): TypedColumn[Any, Tile] = LocalCountAggregate.LocalNoDataCellsUDAF(col)
+  def rf_agg_local_no_data_cells(col: Column): TypedColumn[Any, Tile] = LocalCountAggregate.LocalNoDataCellsUDAF(col)
 
   /** Cellwise addition between two Tiles or Tile and scalar column. */
-  def local_add(left: Column, right: Column): TypedColumn[Any, Tile] = Add(left, right)
+  def rf_local_add(left: Column, right: Column): TypedColumn[Any, Tile] = Add(left, right)
 
   /** Cellwise addition of a scalar value to a tile. */
-  def local_add[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Add(tileCol, value)
+  def rf_local_add[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Add(tileCol, value)
 
   /** Cellwise subtraction between two Tiles. */
-  def local_subtract(left: Column, right: Column): TypedColumn[Any, Tile] = Subtract(left, right)
+  def rf_local_subtract(left: Column, right: Column): TypedColumn[Any, Tile] = Subtract(left, right)
 
   /** Cellwise subtraction of a scalar value from a tile. */
-  def local_subtract[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile]  = Subtract(tileCol, value)
+  def rf_local_subtract[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile]  = Subtract(tileCol, value)
 
   /** Cellwise multiplication between two Tiles. */
-  def local_multiply(left: Column, right: Column): TypedColumn[Any, Tile] = Multiply(left, right)
+  def rf_local_multiply(left: Column, right: Column): TypedColumn[Any, Tile] = Multiply(left, right)
 
   /** Cellwise multiplication of a tile by a scalar value. */
-  def local_multiply[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Multiply(tileCol, value)
+  def rf_local_multiply[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Multiply(tileCol, value)
 
   /** Cellwise division between two Tiles. */
-  def local_divide(left: Column, right: Column): TypedColumn[Any, Tile] = Divide(left, right)
+  def rf_local_divide(left: Column, right: Column): TypedColumn[Any, Tile] = Divide(left, right)
 
   /** Cellwise division of a tile by a scalar value. */
-  def local_divide[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Divide(tileCol, value)
+  def rf_local_divide[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Divide(tileCol, value)
 
   /** Perform an arbitrary GeoTrellis `LocalTileBinaryOp` between two Tile columns. */
-  def local_algebra(op: LocalTileBinaryOp, left: Column, right: Column):
+  def rf_local_algebra(op: LocalTileBinaryOp, left: Column, right: Column):
   TypedColumn[Any, Tile] =
     withAlias(opName(op), left, right)(
       udf[Tile, Tile, Tile](op.apply).apply(left, right)
     ).as[Tile]
 
   /** Compute the normalized difference of two tile columns */
-  def normalized_difference(left: Column, right: Column) =
+  def rf_normalized_difference(left: Column, right: Column) =
     NormalizedDifference(left, right)
 
   /** Constructor for constant tile column */
-  def make_constant_tile(value: Number, cols: Int, rows: Int, cellType: String): TypedColumn[Any, Tile] =
+  def rf_make_constant_tile(value: Number, cols: Int, rows: Int, cellType: String): TypedColumn[Any, Tile] =
     udf(() => F.makeConstantTile(value, cols, rows, cellType)).apply().as(s"constant_$cellType").as[Tile]
 
   /** Alias for column of constant tiles of zero */
-  def tile_zeros(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
+  def rf_tile_zeros(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
     udf(() => F.tileZeros(cols, rows, cellType)).apply().as(s"zeros_$cellType").as[Tile]
 
   /** Alias for column of constant tiles of one */
-  def tile_ones(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
+  def rf_tile_ones(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
     udf(() => F.tileOnes(cols, rows, cellType)).apply().as(s"ones_$cellType").as[Tile]
 
-  /** Where the mask tile contains NODATA, replace values in the source tile with NODATA */
-  def mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
+  /** Where the rf_mask tile contains NODATA, replace values in the source tile with NODATA */
+  def rf_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
     Mask.MaskByDefined(sourceTile, maskTile)
 
-  /** Where the mask tile equals the mask value, replace values in the source tile with NODATA */
-  def mask_by_value(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
+  /** Where the rf_mask tile equals the rf_mask value, replace values in the source tile with NODATA */
+  def rf_mask_by_value(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
     Mask.MaskByValue(sourceTile, maskTile, maskValue)
 
-  /** Where the mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
-  def inverse_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
+  /** Where the rf_mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
+  def rf_inverse_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
     Mask.InverseMaskByDefined(sourceTile, maskTile)
 
   /** Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value. */
-  def rasterize(geometry: Column, bounds: Column, value: Column, cols: Int, rows: Int): TypedColumn[Any, Tile] =
-    withAlias("rasterize", geometry)(
+  def rf_rasterize(geometry: Column, bounds: Column, value: Column, cols: Int, rows: Int): TypedColumn[Any, Tile] =
+    withAlias("rf_rasterize", geometry)(
       udf(F.rasterize(_: Geometry, _: Geometry, _: Int, cols, rows)).apply(geometry, bounds, value)
     ).as[Tile]
 
   /** Reproject a column of geometry from one CRS to another. */
-  def reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
+  def rf_reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRSCol)
 
   /** Reproject a column of geometry from one CRS to another. */
-  def reproject_geometry(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
+  def rf_reproject_geometry(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRSCol, dstCRS)
 
   /** Reproject a column of geometry from one CRS to another. */
-  def reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
+  def rf_reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRS)
 
   /** Render Tile as ASCII string, for debugging purposes. */
-  def render_ascii(col: Column): TypedColumn[Any, String] =
+  def rf_render_ascii(col: Column): TypedColumn[Any, String] =
     DebugRender.RenderAscii(col)
 
   /** Render Tile cell values as numeric values, for debugging purposes. */
-  def render_matrix(col: Column): TypedColumn[Any, String] =
+  def rf_render_matrix(col: Column): TypedColumn[Any, String] =
     DebugRender.RenderMatrix(col)
 
   /** Cellwise less than value comparison between two tiles. */
-  def local_less(left: Column, right: Column): TypedColumn[Any, Tile] =
+  def rf_local_less(left: Column, right: Column): TypedColumn[Any, Tile] =
     Less(left, right)
 
   /** Cellwise less than value comparison between a tile and a scalar. */
-  def local_less[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_less[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     Less(tileCol, value)
 
   /** Cellwise less than or equal to value comparison between a tile and a scalar. */
-  def local_less_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
+  def rf_local_less_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
     LessEqual(left, right)
 
   /** Cellwise less than or equal to value comparison between a tile and a scalar. */
-  def local_less_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_less_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     LessEqual(tileCol, value)
 
   /** Cellwise greater than value comparison between two tiles. */
-  def local_greater(left: Column, right: Column): TypedColumn[Any, Tile] =
+  def rf_local_greater(left: Column, right: Column): TypedColumn[Any, Tile] =
     Greater(left, right)
 
   /** Cellwise greater than value comparison between a tile and a scalar. */
-  def local_greater[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_greater[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     Greater(tileCol, value)
 
   /** Cellwise greater than or equal to value comparison between two tiles. */
-  def local_greater_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
+  def rf_local_greater_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
     GreaterEqual(left, right)
 
   /** Cellwise greater than or equal to value comparison between a tile and a scalar. */
-  def local_greater_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_greater_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     GreaterEqual(tileCol, value)
 
   /** Cellwise equal to value comparison between two tiles. */
-  def local_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
+  def rf_local_equal(left: Column, right: Column): TypedColumn[Any, Tile]  =
     Equal(left, right)
 
   /** Cellwise equal to value comparison between a tile and a scalar. */
-  def local_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_equal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     Equal(tileCol, value)
 
   /** Cellwise inequality comparison between two tiles. */
-  def local_unequal(left: Column, right: Column): TypedColumn[Any, Tile]  =
+  def rf_local_unequal(left: Column, right: Column): TypedColumn[Any, Tile]  =
     Unequal(left, right)
 
   /** Cellwise inequality comparison between a tile and a scalar. */
-  def local_unequal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
+  def rf_local_unequal[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] =
     Unequal(tileCol, value)
 
   /** Round cell values to nearest integer without chaning cell type. */
-  def round(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_round(tileCol: Column): TypedColumn[Any, Tile] =
     Round(tileCol)
 
   /** Take natural logarithm of cell values. */
-  def log(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_log(tileCol: Column): TypedColumn[Any, Tile] =
     Log(tileCol)
 
   /** Take base 10 logarithm of cell values. */
-  def log10(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_log10(tileCol: Column): TypedColumn[Any, Tile] =
     Log10(tileCol)
 
   /** Take base 2 logarithm of cell values. */
-  def log2(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_log2(tileCol: Column): TypedColumn[Any, Tile] =
     Log2(tileCol)
 
   /** Natural logarithm of one plus cell values. */
-  def log1p(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_log1p(tileCol: Column): TypedColumn[Any, Tile] =
       Log1p(tileCol)
 
   /** Exponential of cell values */
-  def exp(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_exp(tileCol: Column): TypedColumn[Any, Tile] =
     Exp(tileCol)
 
   /** Ten to the power of cell values */
-  def exp10(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_exp10(tileCol: Column): TypedColumn[Any, Tile] =
     Exp10(tileCol)
 
   /** Two to the power of cell values */
-  def exp2(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_exp2(tileCol: Column): TypedColumn[Any, Tile] =
     Exp2(tileCol)
 
   /** Exponential of cell values, less one*/
-  def expm1(tileCol: Column): TypedColumn[Any, Tile] =
+  def rf_expm1(tileCol: Column): TypedColumn[Any, Tile] =
     ExpM1(tileCol)
 
   /** Resample tile using nearest-neighbor */
-  def resample[T: Numeric](tileCol: Column, value: T) = Resample(tileCol, value)
+  def rf_resample[T: Numeric](tileCol: Column, value: T) = Resample(tileCol, value)
 
   /** Resample tile using nearest-neighbor */
-  def resample(tileCol: Column, column2: Column) = Resample(tileCol, column2)
+  def rf_resample(tileCol: Column, column2: Column) = Resample(tileCol, column2)
 
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
index b3debf12d..869835c5f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCellType.scala
@@ -35,7 +35,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  */
 case class GetCellType(child: Expression) extends OnCellGridExpression with CodegenFallback {
 
-  override def nodeName: String = "cell_type"
+  override def nodeName: String = "rf_cell_type"
 
   def dataType: DataType = schemaOf[CellType]
   /** Implemented by subtypes to process incoming ProjectedRasterLike entity. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
index 1ba0b5995..f12bbd77d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
@@ -34,7 +34,7 @@ import org.locationtech.rasterframes.model.TileDimensions
  * @since 12/21/17
  */
 case class GetDimensions(child: Expression) extends OnCellGridExpression with CodegenFallback {
-  override def nodeName: String = "tile_dimensions"
+  override def nodeName: String = "rf_tile_dimensions"
 
   def dataType = schemaOf[TileDimensions]
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
index 84d938973..1e9e7e5e1 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
@@ -47,8 +47,8 @@ abstract class CellCountAggregate(isData: Boolean) extends UnaryRasterAggregate
   )
 
   private def CellTest =
-    if (isData) tileOpAsExpression("data_cells", DataCells.op)
-    else tileOpAsExpression("no_data_cells", NoDataCells.op)
+    if (isData) tileOpAsExpression("rf_data_cells", DataCells.op)
+    else tileOpAsExpression("rf_no_data_cells", NoDataCells.op)
 
   val updateExpressions = Seq(
     If(IsNull(child), count, Add(count, CellTest(child)))
@@ -77,7 +77,7 @@ object CellCountAggregate {
        92384753"""
   )
   case class DataCells(child: Expression) extends CellCountAggregate(true) {
-    override def nodeName: String = "agg_data_cells"
+    override def nodeName: String = "rf_agg_data_cells"
   }
   object DataCells {
     def apply(tile: Column): TypedColumn[Any, Long] =
@@ -94,7 +94,7 @@ object CellCountAggregate {
        23584"""
   )
   case class NoDataCells(child: Expression) extends CellCountAggregate(false) {
-    override def nodeName: String = "agg_no_data_cells"
+    override def nodeName: String = "rf_agg_no_data_cells"
   }
   object NoDataCells {
     def apply(tile: Column): TypedColumn[Any, Long] =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
index ff89e75ef..a77c3ee21 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
          ....
   """)
 case class CellMeanAggregate(child: Expression) extends UnaryRasterAggregate {
-  override def nodeName: String = "agg_mean"
+  override def nodeName: String = "rf_agg_mean"
 
   private lazy val sum =
     AttributeReference("sum", DoubleType, false, Metadata.empty)()
@@ -58,7 +58,7 @@ case class CellMeanAggregate(child: Expression) extends UnaryRasterAggregate {
   // Cant' figure out why we can't just use the Expression directly
   // this is necessary to properly handle null rows. For example,
   // if we use `tilestats.Sum` directly, we get an NPE when the stage is executed.
-  private val DataCellCounts = tileOpAsExpression("data_cells", DataCells.op)
+  private val DataCellCounts = tileOpAsExpression("rf_data_cells", DataCells.op)
   private val SumCells = tileOpAsExpression("sum_cells", Sum.op)
 
   override val updateExpressions = Seq(
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
index 96f7c6172..84ea9632b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
@@ -124,7 +124,7 @@ object CellStatsAggregate {
 
   def apply(col: Column): TypedColumn[Any, CellStatistics] =
     new Column(new CellStatsAggregateUDAF(col.expr))
-      .as(s"agg_stats($col)") // node renaming in class doesn't seem to propogate
+      .as(s"rf_agg_stats($col)") // node renaming in class doesn't seem to propogate
       .as[CellStatistics]
 
   /** Adapter hack to allow UserDefinedAggregateFunction to be referenced as an expression. */
@@ -145,7 +145,7 @@ object CellStatsAggregate {
   class CellStatsAggregateUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId)
     extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new CellStatsAggregate()), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_stats"
+    override def nodeName: String = "rf_agg_stats"
   }
   object CellStatsAggregateUDAF {
     def apply(child: Expression): CellStatsAggregateUDAF = new CellStatsAggregateUDAF(child)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
index f74c3bb54..cab065e58 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
@@ -99,7 +99,7 @@ object HistogramAggregate {
 
   def apply(col: Column): TypedColumn[Any, CellHistogram] =
     new Column(new HistogramAggregateUDAF(col.expr))
-      .as(s"agg_approx_histogram($col)") // node renaming in class doesn't seem to propogate
+      .as(s"rf_agg_approx_histogram($col)") // node renaming in class doesn't seem to propogate
       .as[CellHistogram]
 
   /** Adapter hack to allow UserDefinedAggregateFunction to be referenced as an expression. */
@@ -116,7 +116,7 @@ object HistogramAggregate {
   class HistogramAggregateUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId)
     extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new HistogramAggregate()), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_approx_histogram"
+    override def nodeName: String = "rf_agg_approx_histogram"
   }
   object HistogramAggregateUDAF {
     def apply(child: Expression): HistogramAggregateUDAF = new HistogramAggregateUDAF(child)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
index 76c73c5a1..8300cfd04 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
@@ -87,13 +87,13 @@ object LocalCountAggregate {
   )
   class LocalDataCellsUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId) extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new LocalCountAggregate(true)), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_local_data_cells"
+    override def nodeName: String = "rf_agg_local_data_cells"
   }
   object LocalDataCellsUDAF {
     def apply(child: Expression): LocalDataCellsUDAF = new LocalDataCellsUDAF(child)
     def apply(tile: Column): TypedColumn[Any, Tile] =
       new Column(new LocalDataCellsUDAF(tile.expr))
-        .as(s"agg_local_data_cells($tile)")
+        .as(s"rf_agg_local_data_cells($tile)")
         .as[Tile]
   }
 
@@ -102,13 +102,13 @@ object LocalCountAggregate {
   )
   class LocalNoDataCellsUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId) extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new LocalCountAggregate(false)), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_local_no_data_cells"
+    override def nodeName: String = "rf_agg_local_no_data_cells"
   }
   object LocalNoDataCellsUDAF {
     def apply(child: Expression): LocalNoDataCellsUDAF = new LocalNoDataCellsUDAF(child)
     def apply(tile: Column): TypedColumn[Any, Tile] =
       new Column(new LocalNoDataCellsUDAF(tile.expr))
-        .as(s"agg_local_no_data_cells($tile)")
+        .as(s"rf_agg_local_no_data_cells($tile)")
         .as[Tile]
   }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
index 9a127f22d..face1b8a8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
@@ -38,7 +38,7 @@ import org.locationtech.rasterframes.TileType
 case class LocalMeanAggregate(child: Expression) extends UnaryRasterAggregate {
 
   override def dataType: DataType = TileType
-  override def nodeName: String = "agg_local_mean"
+  override def nodeName: String = "rf_agg_local_mean"
 
   private lazy val count =
     AttributeReference("count", TileType, true)()
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
index 387250f55..65267ebc6 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
@@ -147,7 +147,7 @@ object LocalStatsAggregate {
 
   def apply(col: Column): TypedColumn[Any, LocalCellStatistics] =
     new Column(LocalStatsAggregateUDAF(col.expr))
-      .as(s"agg_local_stats($col)")
+      .as(s"rf_agg_local_stats($col)")
       .as[LocalCellStatistics]
 
   /** Adapter hack to allow UserDefinedAggregateFunction to be referenced as an expression. */
@@ -164,7 +164,7 @@ object LocalStatsAggregate {
   class LocalStatsAggregateUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId)
     extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new LocalStatsAggregate()), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_local_stats"
+    override def nodeName: String = "rf_agg_local_stats"
   }
   object LocalStatsAggregateUDAF {
     def apply(child: Expression): LocalStatsAggregateUDAF = new LocalStatsAggregateUDAF(child)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
index dcfb51881..8aacffa9f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
@@ -79,7 +79,7 @@ object LocalTileOpAggregate {
   )
   class LocalMinUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId) extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new LocalTileOpAggregate(BiasedMin)), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_local_min"
+    override def nodeName: String = "rf_agg_local_min"
   }
   object LocalMinUDAF {
     def apply(child: Expression): LocalMinUDAF = new LocalMinUDAF(child)
@@ -91,7 +91,7 @@ object LocalTileOpAggregate {
   )
   class LocalMaxUDAF(aggregateFunction: AggregateFunction, mode: AggregateMode, isDistinct: Boolean, resultId: ExprId) extends AggregateExpression(aggregateFunction, mode, isDistinct, resultId) {
     def this(child: Expression) = this(ScalaUDAF(Seq(ExtractTile(child)), new LocalTileOpAggregate(BiasedMax)), Complete, false, NamedExpression.newExprId)
-    override def nodeName: String = "agg_local_max"
+    override def nodeName: String = "rf_agg_local_max"
   }
   object LocalMaxUDAF {
     def apply(child: Expression): LocalMaxUDAF = new LocalMaxUDAF(child)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
index 7a5e6539a..bd2a4689a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/ExplodeTiles.scala
@@ -43,7 +43,7 @@ case class ExplodeTiles(
   extends Expression with Generator with CodegenFallback {
 
   def this(children: Seq[Expression]) = this(1.0, None, children)
-  override def nodeName: String = "explode_tiles"
+  override def nodeName: String = "rf_explode_tiles"
 
   override def elementSchema: StructType = {
     val names =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
index 2885f6816..e0b6b6714 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
@@ -47,7 +47,7 @@ import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 )
 case class Add(left: Expression, right: Expression) extends BinaryLocalRasterOp
   with CodegenFallback {
-  override val nodeName: String = "local_add"
+  override val nodeName: String = "rf_local_add"
   override protected def op(left: Tile, right: Tile): Tile = BiasedAdd(left, right)
   override protected def op(left: Tile, right: Double): Tile = BiasedAdd(left, right)
   override protected def op(left: Tile, right: Int): Tile = BiasedAdd(left, right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
index 27db49b5c..2b5f7d112 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Divide.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Divide(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "local_divide"
+  override val nodeName: String = "rf_local_divide"
   override protected def op(left: Tile, right: Tile): Tile = left.localDivide(right)
   override protected def op(left: Tile, right: Double): Tile = left.localDivide(right)
   override protected def op(left: Tile, right: Int): Tile = left.localDivide(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
index 5d540db3a..a9e809b47 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Equal.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Equal(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_equal"
+  override val nodeName: String = "rf_local_equal"
   override protected def op(left: Tile, right: Tile): Tile = left.localEqual(right)
   override protected def op(left: Tile, right: Double): Tile = left.localEqual(right)
   override protected def op(left: Tile, right: Int): Tile = left.localEqual(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
index b6d5993fc..ca9905e29 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Exp.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Exp(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "exp"
+  override val nodeName: String = "rf_exp"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localPowValue(math.E)
 
@@ -63,7 +63,7 @@ object Exp {
        ..."""
 )
 case class Exp10(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "log10"
+  override val nodeName: String = "rf_log10"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localPowValue(10.0)
 
@@ -84,7 +84,7 @@ object Exp10 {
        ..."""
 )
 case class Exp2(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "exp2"
+  override val nodeName: String = "rf_exp2"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localPowValue(2.0)
 
@@ -105,7 +105,7 @@ object Exp2{
        ..."""
 )
 case class ExpM1(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "expm1"
+  override val nodeName: String = "rf_expm1"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localPowValue(math.E).localSubtract(1.0)
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
index a9cce3235..ad9b8bf03 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Greater.scala
@@ -40,7 +40,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Greater(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_greater"
+  override val nodeName: String = "rf_local_greater"
   override protected def op(left: Tile, right: Tile): Tile = left.localGreater(right)
   override protected def op(left: Tile, right: Double): Tile = left.localGreater(right)
   override protected def op(left: Tile, right: Int): Tile = left.localGreater(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
index 8d363ca62..725898ca5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/GreaterEqual.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class GreaterEqual(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_greater_equal"
+  override val nodeName: String = "rf_local_greater_equal"
   override protected def op(left: Tile, right: Tile): Tile = left.localGreaterOrEqual(right)
   override protected def op(left: Tile, right: Double): Tile = left.localGreaterOrEqual(right)
   override protected def op(left: Tile, right: Int): Tile = left.localGreaterOrEqual(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
index 25e053ee8..a80d628f7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Less.scala
@@ -40,7 +40,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Less(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_less"
+  override val nodeName: String = "rf_local_less"
   override protected def op(left: Tile, right: Tile): Tile = left.localLess(right)
   override protected def op(left: Tile, right: Double): Tile = left.localLess(right)
   override protected def op(left: Tile, right: Int): Tile = left.localLess(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
index 51372c0ac..b9361610b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/LessEqual.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class LessEqual(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_less_equal"
+  override val nodeName: String = "rf_local_less_equal"
   override protected def op(left: Tile, right: Tile): Tile = left.localLessOrEqual(right)
   override protected def op(left: Tile, right: Double): Tile = left.localLessOrEqual(right)
   override protected def op(left: Tile, right: Int): Tile = left.localLessOrEqual(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
index 1130e16a9..8e8d8a011 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Log.scala
@@ -63,7 +63,7 @@ object Log {
        ..."""
 )
 case class Log10(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "log10"
+  override val nodeName: String = "rf_log10"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localLog10()
 
@@ -84,7 +84,7 @@ object Log10 {
        ..."""
 )
 case class Log2(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "log2"
+  override val nodeName: String = "rf_log2"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localLog() / math.log(2.0)
 
@@ -105,7 +105,7 @@ object Log2{
        ..."""
 )
 case class Log1p(child: Expression) extends UnaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "log1p"
+  override val nodeName: String = "rf_log1p"
 
   override protected def op(tile: Tile): Tile = fpTile(tile).localAdd(1.0).localLog()
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
index fabbcaa7a..784771906 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Multiply.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Multiply(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "local_multiply"
+  override val nodeName: String = "rf_local_multiply"
   override protected def op(left: Tile, right: Tile): Tile = left.localMultiply(right)
   override protected def op(left: Tile, right: Double): Tile = left.localMultiply(right)
   override protected def op(left: Tile, right: Int): Tile = left.localMultiply(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
index b6894f90d..079fd7273 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
@@ -42,7 +42,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
        ..."""
 )
 case class NormalizedDifference(left: Expression, right: Expression) extends BinaryRasterOp with CodegenFallback {
-  override val nodeName: String = "normalized_difference"
+  override val nodeName: String = "rf_normalized_difference"
   override protected def op(left: Tile, right: Tile): Tile = {
     val diff = fpTile(left.localSubtract(right))
     val sum = fpTile(left.localAdd(right))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
index 2e813a1fa..6e752dfbc 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Resample.scala
@@ -47,7 +47,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
 )
 case class Resample(left: Expression, right: Expression) extends BinaryLocalRasterOp
   with CodegenFallback {
-  override val nodeName: String = "resample"
+  override val nodeName: String = "rf_resample"
   override protected def op(left: Tile, right: Tile): Tile = left.resample(right.cols, right.rows, NearestNeighbor)
   override protected def op(left: Tile, right: Double): Tile = left.resample((left.cols * right).toInt,
                                                                              (left.rows * right).toInt, NearestNeighbor)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
index ed04647de..0ca22cb8f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
   usage = "_FUNC_(tile) - Round cell values to the nearest integer without changing the cell type.",
   arguments = """
   Arguments:
-    * tile - tile column to round""",
+    * tile - tile column to rf_round""",
   examples = """
   Examples:
     > SELECT  _FUNC_(tile);
@@ -40,7 +40,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
 )
 case class Round(child: Expression) extends UnaryLocalRasterOp
   with NullToValue with CodegenFallback {
-  override def nodeName: String = "round"
+  override def nodeName: String = "rf_round"
   override def na: Any = null
   override protected def op(child: Tile): Tile = child.localRound()
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
index ef7925dd0..c09a7ea47 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Subtract.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Subtract(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback {
-  override val nodeName: String = "local_subtract"
+  override val nodeName: String = "rf_local_subtract"
   override protected def op(left: Tile, right: Tile): Tile = left.localSubtract(right)
   override protected def op(left: Tile, right: Double): Tile = left.localSubtract(right)
   override protected def op(left: Tile, right: Int): Tile = left.localSubtract(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
index b21334861..48a1e3963 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Unequal.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
        ..."""
 )
 case class Unequal(left: Expression, right: Expression) extends BinaryLocalRasterOp with CodegenFallback  {
-  override val nodeName: String = "local_unequal"
+  override val nodeName: String = "rf_local_unequal"
   override protected def op(left: Tile, right: Tile): Tile = left.localUnequal(right)
   override protected def op(left: Tile, right: Double): Tile = left.localUnequal(right)
   override protected def op(left: Tile, right: Int): Tile = left.localUnequal(right)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index cc3110144..d426af4fd 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -70,7 +70,7 @@ package object expressions {
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
     registry.registerExpression[GetDimensions]("rf_tile_dimensions")
     registry.registerExpression[ExtentToGeometry]("rf_extent_geometry")
-    registry.registerExpression[GeometryToExtent]("st_extent")
+    registry.registerExpression[GeometryToExtent]("rf_extent")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
index f62c88acc..a18148db3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/DataCells.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class DataCells(child: Expression) extends UnaryRasterOp
   with CodegenFallback with NullToValue {
-  override def nodeName: String = "data_cells"
+  override def nodeName: String = "rf_data_cells"
   override def dataType: DataType = LongType
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = DataCells.op(tile)
   override def na: Any = 0L
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
index 08eb0ec76..fd855cd39 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/IsNoDataTile.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class IsNoDataTile(child: Expression) extends UnaryRasterOp
   with CodegenFallback with NullToValue {
-  override def nodeName: String = "is_no_data_tile"
+  override def nodeName: String = "rf_is_no_data_tile"
   override def na: Any = true
   override def dataType: DataType = BooleanType
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = tile.isNoDataTile
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
index 379af473a..cf47ba14e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/NoDataCells.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class NoDataCells(child: Expression) extends UnaryRasterOp
   with CodegenFallback with NullToValue {
-  override def nodeName: String = "no_data_cells"
+  override def nodeName: String = "rf_no_data_cells"
   override def dataType: DataType = LongType
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = NoDataCells.op(tile)
   override def na: Any = 0L
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
index bac6af716..825f88d1d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
@@ -40,7 +40,7 @@ import org.locationtech.rasterframes.model.TileContext
        2135.34"""
 )
 case class Sum(child: Expression) extends UnaryRasterOp with CodegenFallback {
-  override def nodeName: String = "tile_sum"
+  override def nodeName: String = "rf_tile_sum"
   override def dataType: DataType = DoubleType
   override protected def eval(tile: Tile,  ctx: Option[TileContext]): Any = Sum.op(tile)
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
index 4ca25f0c9..96e3d3dcc 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileHistogram.scala
@@ -44,7 +44,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class TileHistogram(child: Expression) extends UnaryRasterOp
   with CodegenFallback {
-  override def nodeName: String = "tile_histogram"
+  override def nodeName: String = "rf_tile_histogram"
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any =
     TileHistogram.converter(TileHistogram.op(tile))
   override def dataType: DataType = CellHistogram.schema
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
index 5f387cb89..3204f4aaf 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMax.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class TileMax(child: Expression) extends UnaryRasterOp
   with NullToValue with CodegenFallback {
-  override def nodeName: String = "tile_max"
+  override def nodeName: String = "rf_tile_max"
   override protected def eval(tile: Tile,  ctx: Option[TileContext]): Any = TileMax.op(tile)
   override def dataType: DataType = DoubleType
   override def na: Any = Double.MinValue
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
index 4bd0ba611..92c833f98 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMean.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class TileMean(child: Expression) extends UnaryRasterOp
   with NullToValue with CodegenFallback {
-  override def nodeName: String = "tile_mean"
+  override def nodeName: String = "rf_tile_mean"
   override protected def eval(tile: Tile,  ctx: Option[TileContext]): Any = TileMean.op(tile)
   override def dataType: DataType = DoubleType
   override def na: Any = Double.NaN
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
index bbc691b4a..71fa0194a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileMin.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class TileMin(child: Expression) extends UnaryRasterOp
   with NullToValue with CodegenFallback {
-  override def nodeName: String = "tile_min"
+  override def nodeName: String = "rf_tile_min"
   override protected def eval(tile: Tile,  ctx: Option[TileContext]): Any = TileMin.op(tile)
   override def dataType: DataType = DoubleType
   override def na: Any = Double.MaxValue
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
index 225fbfcb7..fac6d330e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/TileStats.scala
@@ -44,7 +44,7 @@ import org.locationtech.rasterframes.model.TileContext
 )
 case class TileStats(child: Expression) extends UnaryRasterOp
   with CodegenFallback {
-  override def nodeName: String = "tile_stats"
+  override def nodeName: String = "rf_tile_stats"
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any =
     TileStats.converter(TileStats.op(tile).orNull)
   override def dataType: DataType = CellStatistics.schema
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
index f13551d7e..babb9c7b7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/DebugRender.scala
@@ -55,7 +55,7 @@ object DebugRender {
     * tile - tile to render"""
   )
   case class RenderAscii(child: Expression) extends DebugRender(true) {
-    override def nodeName: String = "render_ascii"
+    override def nodeName: String = "rf_render_ascii"
   }
   object RenderAscii {
     def apply(tile: Column): TypedColumn[Any, String] =
@@ -69,7 +69,7 @@ object DebugRender {
     * tile - tile to render"""
   )
   case class RenderMatrix(child: Expression) extends DebugRender(false) {
-    override def nodeName: String = "render_matrix"
+    override def nodeName: String = "rf_render_matrix"
   }
   object RenderMatrix {
     def apply(tile: Column): TypedColumn[Any, String] =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
index b02a216ec..7303baa29 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
@@ -40,7 +40,7 @@ import org.locationtech.geomesa.spark.jts.encoders.SpatialEncoders
  * @since 8/24/18
  */
 case class ExtentToGeometry(child: Expression) extends UnaryExpression with CodegenFallback {
-    override def nodeName: String = "extent_geometry"
+    override def nodeName: String = "rf_extent_geometry"
 
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
index 9fb333838..ac755cb0b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
@@ -97,7 +97,7 @@ object Mask {
   )
   case class MaskByDefined(target: Expression, mask: Expression)
     extends Mask(target, mask, Literal(0), false) {
-    override def nodeName: String = "mask"
+    override def nodeName: String = "rf_mask"
   }
   object MaskByDefined {
     def apply(targetTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
@@ -117,7 +117,7 @@ object Mask {
   )
   case class InverseMaskByDefined(leftTile: Expression, rightTile: Expression)
     extends Mask(leftTile, rightTile, Literal(0), true) {
-    override def nodeName: String = "inverse_mask"
+    override def nodeName: String = "rf_inverse_mask"
   }
   object InverseMaskByDefined {
     def apply(srcTile: Column, maskingTile: Column): TypedColumn[Any, Tile] =
@@ -137,7 +137,7 @@ object Mask {
   )
   case class MaskByValue(leftTile: Expression, rightTile: Expression, maskValue: Expression)
     extends Mask(leftTile, rightTile, maskValue, false) {
-    override def nodeName: String = "mask_by_value"
+    override def nodeName: String = "rf_mask_by_value"
   }
   object MaskByValue {
     def apply(srcTile: Column, maskingTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 42a304bb3..83a5d876a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -43,7 +43,7 @@ import org.locationtech.rasterframes.jts.ReprojectionTransformer
 case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: Expression) extends Expression
   with CodegenFallback with ExpectsInputTypes {
 
-  override def nodeName: String = "reproject_geometry"
+  override def nodeName: String = "rf_reproject_geometry"
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
   override def nullable: Boolean = geometry.nullable || srcCRS.nullable || dstCRS.nullable
   override def children: Seq[Expression] = Seq(geometry, srcCRS, dstCRS)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
index 7e3ba0a82..5d7786f1c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayDouble.scala
@@ -37,7 +37,7 @@ import org.locationtech.rasterframes.model.TileContext
     * tile - tile to convert"""
 )
 case class TileToArrayDouble(child: Expression) extends UnaryRasterOp with CodegenFallback {
-  override def nodeName: String = "tile_to_array_double"
+  override def nodeName: String = "rf_tile_to_array_double"
   override def dataType: DataType = DataTypes.createArrayType(DoubleType, false)
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = {
     ArrayData.toArrayData(tile.toArrayDouble())
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
index 3b1c697b3..c299d57c7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/TileToArrayInt.scala
@@ -38,7 +38,7 @@ import org.locationtech.rasterframes.model.TileContext
     * tile - tile to convert"""
 )
 case class TileToArrayInt(child: Expression) extends UnaryRasterOp with CodegenFallback {
-  override def nodeName: String = "tile_to_array_int"
+  override def nodeName: String = "rf_tile_to_array_int"
   override def dataType: DataType = DataTypes.createArrayType(IntegerType, false)
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = {
     ArrayData.toArrayData(tile.toArray())
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
index c3ff11583..38f978231 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ml/TileExploder.scala
@@ -58,7 +58,7 @@ class TileExploder(override val uid: String) extends Transformer
     val (tiles, nonTiles) = selectTileAndNonTileFields(dataset.schema)
     val tileCols = tiles.map(f ⇒ col(f.name))
     val nonTileCols = nonTiles.map(f ⇒ col(f.name))
-    val exploder = explode_tiles(tileCols: _*)
+    val exploder = rf_explode_tiles(tileCols: _*)
     dataset.select(nonTileCols :+ exploder: _*)
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index ac3842cfc..083a58944 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -53,7 +53,7 @@ object ZeroSevenCompatibilityKit {
     // format: off
     /** Create a row for each cell in Tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def explodeTiles(cols: Column*): Column = delegate.explode_tiles(cols: _*)
+    def explodeTiles(cols: Column*): Column = delegate.rf_explode_tiles(cols: _*)
 
     /** Create a row for each cell in Tile with random sampling and optional seed. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
@@ -72,7 +72,7 @@ object ZeroSevenCompatibilityKit {
     @Experimental
     /** Convert array in `arrayCol` into a Tile of dimensions `cols` and `rows`*/
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def arrayToTile(arrayCol: Column, cols: Int, rows: Int) = withAlias("array_to_tile", arrayCol)(
+    def arrayToTile(arrayCol: Column, cols: Int, rows: Int) = withAlias("rf_array_to_tile", arrayCol)(
       udf[Tile, AnyRef](F.arrayToTile(cols, rows)).apply(arrayCol)
     )
 
@@ -112,11 +112,11 @@ object ZeroSevenCompatibilityKit {
 
     /**  Compute the full column aggregate floating point histogram. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def aggHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.agg_approx_histogram(col)
+    def aggHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.rf_agg_approx_histogram(col)
 
     /** Compute the full column aggregate floating point statistics. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def aggStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.agg_stats(col)
+    def aggStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.rf_agg_stats(col)
 
     /** Computes the column aggregate mean. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
@@ -124,101 +124,101 @@ object ZeroSevenCompatibilityKit {
 
     /** Computes the number of non-NoData cells in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def aggDataCells(col: Column): TypedColumn[Any, Long] = delegate.agg_data_cells(col)
+    def aggDataCells(col: Column): TypedColumn[Any, Long] = delegate.rf_agg_data_cells(col)
 
     /** Computes the number of NoData cells in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def aggNoDataCells(col: Column): TypedColumn[Any, Long] = delegate.agg_no_data_cells(col)
+    def aggNoDataCells(col: Column): TypedColumn[Any, Long] = delegate.rf_agg_no_data_cells(col)
 
     /** Compute the Tile-wise mean */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileMean(col: Column): TypedColumn[Any, Double] = delegate.tile_mean(col)
+    def tileMean(col: Column): TypedColumn[Any, Double] = delegate.rf_tile_mean(col)
 
     /** Compute the Tile-wise sum */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileSum(col: Column): TypedColumn[Any, Double] = delegate.tile_sum(col)
+    def tileSum(col: Column): TypedColumn[Any, Double] = delegate.rf_tile_sum(col)
 
     /** Compute the minimum cell value in tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileMin(col: Column): TypedColumn[Any, Double] = delegate.tile_min(col)
+    def tileMin(col: Column): TypedColumn[Any, Double] = delegate.rf_tile_min(col)
 
     /** Compute the maximum cell value in tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileMax(col: Column): TypedColumn[Any, Double] = delegate.tile_max(col)
+    def tileMax(col: Column): TypedColumn[Any, Double] = delegate.rf_tile_max(col)
 
     /** Compute TileHistogram of Tile values. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.tile_histogram(col)
+    def tileHistogram(col: Column): TypedColumn[Any, CellHistogram] = delegate.rf_tile_histogram(col)
 
     /** Compute statistics of Tile values. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def tileStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.tile_stats(col)
+    def tileStats(col: Column): TypedColumn[Any, CellStatistics] = delegate.rf_tile_stats(col)
 
     /** Counts the number of non-NoData cells per Tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def dataCells(tile: Column): TypedColumn[Any, Long] = delegate.data_cells(tile)
+    def dataCells(tile: Column): TypedColumn[Any, Long] = delegate.rf_data_cells(tile)
 
     /** Counts the number of NoData cells per Tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def noDataCells(tile: Column): TypedColumn[Any, Long] = delegate.no_data_cells(tile)
+    def noDataCells(tile: Column): TypedColumn[Any, Long] = delegate.rf_no_data_cells(tile)
 
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def isNoDataTile(tile: Column): TypedColumn[Any, Boolean] = delegate.is_no_data_tile(tile)
+    def isNoDataTile(tile: Column): TypedColumn[Any, Boolean] = delegate.rf_is_no_data_tile(tile)
 
     /** Compute cell-local aggregate descriptive statistics for a column of Tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggStats(col: Column): Column = delegate.agg_local_stats(col)
+    def localAggStats(col: Column): Column = delegate.rf_agg_local_stats(col)
 
     /** Compute the cell-wise/local max operation between Tiles in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggMax(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_max(col)
+    def localAggMax(col: Column): TypedColumn[Any, Tile] = delegate.rf_agg_local_max(col)
 
     /** Compute the cellwise/local min operation between Tiles in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggMin(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_min(col)
+    def localAggMin(col: Column): TypedColumn[Any, Tile] = delegate.rf_agg_local_min(col)
 
     /** Compute the cellwise/local mean operation between Tiles in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggMean(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_mean(col)
+    def localAggMean(col: Column): TypedColumn[Any, Tile] = delegate.rf_agg_local_mean(col)
 
     /** Compute the cellwise/local count of non-NoData cells for all Tiles in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggDataCells(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_data_cells(col)
+    def localAggDataCells(col: Column): TypedColumn[Any, Tile] = delegate.rf_agg_local_data_cells(col)
 
     /** Compute the cellwise/local count of NoData cells for all Tiles in a column. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAggNoDataCells(col: Column): TypedColumn[Any, Tile] = delegate.agg_local_no_data_cells(col)
+    def localAggNoDataCells(col: Column): TypedColumn[Any, Tile] = delegate.rf_agg_local_no_data_cells(col)
 
     /** Cellwise addition between two Tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAdd(left: Column, right: Column): Column = delegate.local_add(left, right)
+    def localAdd(left: Column, right: Column): Column = delegate.rf_local_add(left, right)
 
     /** Cellwise addition of a scalar to a tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localAddScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_add(tileCol, value)
+    def localAddScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_add(tileCol, value)
 
     /** Cellwise subtraction between two Tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localSubtract(left: Column, right: Column): Column = delegate.local_subtract(left, right)
+    def localSubtract(left: Column, right: Column): Column = delegate.rf_local_subtract(left, right)
 
     /** Cellwise subtraction of a scalar from a tile. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localSubtractScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_subtract(tileCol, value)
+    def localSubtractScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_subtract(tileCol, value)
     /** Cellwise multiplication between two Tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localMultiply(left: Column, right: Column): Column = delegate.local_multiply(left, right)
+    def localMultiply(left: Column, right: Column): Column = delegate.rf_local_multiply(left, right)
 
     /** Cellwise multiplication of a tile by a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localMultiplyScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_multiply(tileCol, value)
+    def localMultiplyScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_multiply(tileCol, value)
 
     /** Cellwise division between two Tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localDivide(left: Column, right: Column): Column = delegate.local_divide(left, right)
+    def localDivide(left: Column, right: Column): Column = delegate.rf_local_divide(left, right)
 
     /** Cellwise division of a tile by a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localDivideScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_divide(tileCol, value)
+    def localDivideScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_divide(tileCol, value)
     /** Perform an arbitrary GeoTrellis `LocalTileBinaryOp` between two Tile columns. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localAlgebra(op: LocalTileBinaryOp, left: Column, right: Column):
@@ -229,7 +229,7 @@ object ZeroSevenCompatibilityKit {
 
     /** Compute the normalized difference of two tile columns */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def normalizedDifference(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.normalized_difference(left, right)
+    def normalizedDifference(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.rf_normalized_difference(left, right)
 
     /** Constructor for constant tile column */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
@@ -246,76 +246,76 @@ object ZeroSevenCompatibilityKit {
     def tileOnes(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
       udf(() => F.tileOnes(cols, rows, cellType)).apply().as(s"ones_$cellType").as[Tile]
 
-    /** Where the mask tile equals the mask value, replace values in the source tile with NODATA */
+    /** Where the rf_mask tile equals the rf_mask value, replace values in the source tile with NODATA */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def maskByValue(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
-      delegate.mask_by_value(sourceTile, maskTile, maskValue)
+      delegate.rf_mask_by_value(sourceTile, maskTile, maskValue)
 
-    /** Where the mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
+    /** Where the rf_mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def inverseMask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
-      delegate.inverse_mask(sourceTile, maskTile)
+      delegate.rf_inverse_mask(sourceTile, maskTile)
 
     /** Reproject a column of geometry from one CRS to another. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def reprojectGeometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
-      delegate.reproject_geometry(sourceGeom, srcCRS, dstCRS)
+      delegate.rf_reproject_geometry(sourceGeom, srcCRS, dstCRS)
 
     /** Render Tile as ASCII string for debugging purposes. */
     @Experimental
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def renderAscii(col: Column): TypedColumn[Any, String] = delegate.render_ascii(col)
+    def renderAscii(col: Column): TypedColumn[Any, String] = delegate.rf_render_ascii(col)
 
     /** Cellwise less than value comparison between two tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localLess(left: Column, right: Column): TypedColumn[Any, Tile] =
-      delegate.local_less(left, right)
+      delegate.rf_local_less(left, right)
 
 
     /** Cellwise less than value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localLessScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_less(tileCol, value)
+    def localLessScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_less(tileCol, value)
 
     /** Cellwise less than or equal to value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localLessEqual(left: Column, right: Column): TypedColumn[Any, Tile]  = delegate.local_less_equal(left, right)
+    def localLessEqual(left: Column, right: Column): TypedColumn[Any, Tile]  = delegate.rf_local_less_equal(left, right)
 
     /** Cellwise less than or equal to value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localLessEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_less_equal(tileCol, value)
+    def localLessEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_less_equal(tileCol, value)
 
     /** Cellwise greater than value comparison between two tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def localGreater(left: Column, right: Column): TypedColumn[Any, Tile] =
-      delegate.local_greater(left, right)
+      delegate.rf_local_greater(left, right)
 
     /** Cellwise greater than value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localGreaterScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_greater(tileCol, value)
+    def localGreaterScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_greater(tileCol, value)
 
     /** Cellwise greater than or equal to value comparison between two tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localGreaterEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_greater_equal(left, right)
+    def localGreaterEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.rf_local_greater_equal(left, right)
 
     /** Cellwise greater than or equal to value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localGreaterEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_greater_equal(tileCol, value)
+    def localGreaterEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_greater_equal(tileCol, value)
 
     /** Cellwise equal to value comparison between two tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_equal(left, right)
+    def localEqual(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.rf_local_equal(left, right)
 
     /** Cellwise equal to value comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_equal(tileCol, value)
+    def localEqualScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_equal(tileCol, value)
 
     /** Cellwise inequality comparison between two tiles. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localUnequal(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.local_unequal(left, right)
+    def localUnequal(left: Column, right: Column): TypedColumn[Any, Tile] = delegate.rf_local_unequal(left, right)
 
     /** Cellwise inequality comparison between a tile and a scalar. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
-    def localUnequalScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.local_unequal(tileCol, value)
+    def localUnequalScalar[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = delegate.rf_local_unequal(tileCol, value)
   }
 
   def register(sqlContext: SQLContext): Unit = {
diff --git a/core/src/test/scala/examples/Classification.scala b/core/src/test/scala/examples/Classification.scala
index 4d6579518..ae87aa6cf 100644
--- a/core/src/test/scala/examples/Classification.scala
+++ b/core/src/test/scala/examples/Classification.scala
@@ -73,7 +73,7 @@ object Classification extends App {
     .toRF(tileSize, tileSize, targetCol)
 
   // Take a peek at what kind of label data we have to work with.
-  target.select(agg_stats(target(targetCol))).show
+  target.select(rf_agg_stats(target(targetCol))).show
 
   val abt = joinedRF.spatialJoin(target)
 
@@ -141,7 +141,7 @@ object Classification extends App {
   val tlm = joinedRF.tileLayerMetadata.left.get
 
   val retiled = scored.groupBy($"spatial_key").agg(
-    assemble_tile(
+    rf_assemble_tile(
       $"column_index", $"row_index", $"prediction",
       tlm.tileCols, tlm.tileRows, IntConstantNoDataCellType
     )
diff --git a/core/src/test/scala/examples/Clustering.scala b/core/src/test/scala/examples/Clustering.scala
index 6fc64f0e7..c5e08ca1b 100644
--- a/core/src/test/scala/examples/Clustering.scala
+++ b/core/src/test/scala/examples/Clustering.scala
@@ -89,7 +89,7 @@ object Clustering extends App {
   val tlm = joinedRF.tileLayerMetadata.left.get
 
   val retiled = clustered.groupBy($"spatial_key").agg(
-    assemble_tile(
+    rf_assemble_tile(
       $"column_index", $"row_index", $"prediction",
       tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
   )
diff --git a/core/src/test/scala/examples/Exporting.scala b/core/src/test/scala/examples/Exporting.scala
index e78e7cd3f..71ebb7faf 100644
--- a/core/src/test/scala/examples/Exporting.scala
+++ b/core/src/test/scala/examples/Exporting.scala
@@ -52,17 +52,17 @@ object Exporting extends App {
   //  The @scaladoc[`tile_to_array`][tile_to_array] column function requires a type parameter to indicate the array element
   //  type you would like used. The following types may be used: `Int`, `Double`, `Byte`, `Short`, `Float`
 
-  val withArrays = rf.withColumn("tileData", tile_to_array_int($"tile")).drop("tile")
+  val withArrays = rf.withColumn("tileData", rf_tile_to_array_int($"tile")).drop("tile")
   withArrays.show(5, 40)
 
   //  You can convert the data back to an array, but you have to specify the target tile dimensions.
 
-  val tileBack = withArrays.withColumn("tileAgain", array_to_tile($"tileData", 128, 128))
+  val tileBack = withArrays.withColumn("tileAgain", rf_array_to_tile($"tileData", 128, 128))
   tileBack.drop("tileData").show(5, 40)
 
   //  Note that the created tile will not have a `NoData` value associated with it. Here's how you can do that:
 
-  val tileBackAgain = withArrays.withColumn("tileAgain", with_no_data(array_to_tile($"tileData", 128, 128), 3))
+  val tileBackAgain = withArrays.withColumn("tileAgain", rf_with_no_data(rf_array_to_tile($"tileData", 128, 128), 3))
   tileBackAgain.drop("tileData").show(5, 50)
 
   //  ## Writing to Parquet
@@ -78,8 +78,8 @@ object Exporting extends App {
   val equalized = rf.withColumn("equalized", equalizer($"tile")).asRF
 
   equalized.printSchema
-  equalized.select(agg_stats($"tile")).show(false)
-  equalized.select(agg_stats($"equalized")).show(false)
+  equalized.select(rf_agg_stats($"tile")).show(false)
+  equalized.select(rf_agg_stats($"equalized")).show(false)
 
 
   //  We write it out just like any other DataFrame, including the ability to specify partitioning:
@@ -98,8 +98,8 @@ object Exporting extends App {
   val rf2 = spark.read.parquet(filePath)
 
   rf2.printSchema
-  equalized.select(agg_stats($"tile")).show(false)
-  equalized.select(agg_stats($"equalized")).show(false)
+  equalized.select(rf_agg_stats($"tile")).show(false)
+  equalized.select(rf_agg_stats($"equalized")).show(false)
 
   //  ## Converting to `RDD` and `TileLayerRDD`
   //
diff --git a/core/src/test/scala/examples/LocalArithmetic.scala b/core/src/test/scala/examples/LocalArithmetic.scala
index 99970f560..26c9bf6d4 100644
--- a/core/src/test/scala/examples/LocalArithmetic.scala
+++ b/core/src/test/scala/examples/LocalArithmetic.scala
@@ -52,12 +52,12 @@ object LocalArithmetic extends App {
     map { case (b, t) ⇒ t.projectedRaster.toRF(s"band_$b") }.
     reduce(_ spatialJoin _)
 
-  val addRF = joinedRF.withColumn("1+2", local_add(joinedRF("band_1"), joinedRF("band_2"))).asRF
-  val divideRF = joinedRF.withColumn("1/2", local_divide(joinedRF("band_1"), joinedRF("band_2"))).asRF
+  val addRF = joinedRF.withColumn("1+2", rf_local_add(joinedRF("band_1"), joinedRF("band_2"))).asRF
+  val divideRF = joinedRF.withColumn("1/2", rf_local_divide(joinedRF("band_1"), joinedRF("band_2"))).asRF
 
   addRF.select("1+2").collect().apply(0) .getClass
 
-  val raster = divideRF.select(tile_sum(divideRF("1/2")),
-    tile_sum(joinedRF("band_1")), tile_sum(joinedRF("band_2")))
+  val raster = divideRF.select(rf_tile_sum(divideRF("1/2")),
+    rf_tile_sum(joinedRF("band_1")), rf_tile_sum(joinedRF("band_2")))
   raster.show(1)
 }
\ No newline at end of file
diff --git a/core/src/test/scala/examples/Masking.scala b/core/src/test/scala/examples/Masking.scala
index 1d93b913c..4470e8871 100644
--- a/core/src/test/scala/examples/Masking.scala
+++ b/core/src/test/scala/examples/Masking.scala
@@ -33,9 +33,9 @@ object Masking extends App {
 
   val withMaskedTile = joinedRF.withColumn("maskTile", threshold(joinedRF("band_1"))).asRF
 
-  withMaskedTile.select(no_data_cells(withMaskedTile("maskTile"))).show()
+  withMaskedTile.select(rf_no_data_cells(withMaskedTile("maskTile"))).show()
 
-  val masked = withMaskedTile.withColumn("masked", mask(joinedRF("band_2"), joinedRF("maskTile"))).asRF
+  val masked = withMaskedTile.withColumn("masked", rf_mask(joinedRF("band_2"), joinedRF("maskTile"))).asRF
 
   val maskRF = masked.toRaster(masked("masked"), 466, 428)
   val b2 = masked.toRaster(masked("band_2"), 466, 428)
diff --git a/core/src/test/scala/examples/MeanValue.scala b/core/src/test/scala/examples/MeanValue.scala
index 192685b2d..5a2571404 100644
--- a/core/src/test/scala/examples/MeanValue.scala
+++ b/core/src/test/scala/examples/MeanValue.scala
@@ -44,7 +44,7 @@ object MeanValue extends App {
   rf.printSchema
 
   val tileCol = rf("tile")
-  rf.agg(agg_no_data_cells(tileCol), agg_data_cells(tileCol), agg_mean(tileCol)).show(false)
+  rf.agg(rf_agg_no_data_cells(tileCol), rf_agg_data_cells(tileCol), rf_agg_mean(tileCol)).show(false)
 
   spark.stop()
 }
diff --git a/core/src/test/scala/examples/Tour.scala b/core/src/test/scala/examples/Tour.scala
index 208e82564..8729c7d51 100644
--- a/core/src/test/scala/examples/Tour.scala
+++ b/core/src/test/scala/examples/Tour.scala
@@ -57,16 +57,16 @@ object Tour extends App {
   rf.show(8, false)
 
   // Confirm we have equally sized tiles
-  rf.select(tile_dimensions($"tile")).distinct().show()
+  rf.select(rf_tile_dimensions($"tile")).distinct().show()
 
   // Count the number of no-data cells
-  rf.select(agg_no_data_cells($"tile")).show(false)
+  rf.select(rf_agg_no_data_cells($"tile")).show(false)
 
   // Compute per-tile statistics
-  rf.select(tile_stats($"tile")).show(8, false)
+  rf.select(rf_tile_stats($"tile")).show(8, false)
 
   // Compute some aggregate stats over all cells
-  rf.select(agg_stats($"tile")).show(false)
+  rf.select(rf_agg_stats($"tile")).show(false)
 
   // Create a Spark UDT to perform contrast adjustment via GeoTrellis
   val contrast = udf((t: Tile) ⇒ t.sigmoidal(0.2, 10))
@@ -75,14 +75,14 @@ object Tour extends App {
   val withAdjusted = rf.withColumn("adjusted", contrast($"tile")).asRF
 
   // Show the stats for the adjusted version
-  withAdjusted.select(agg_stats($"adjusted")).show(false)
+  withAdjusted.select(rf_agg_stats($"adjusted")).show(false)
 
   // Reassemble into a raster and save to a file
   val raster = withAdjusted.toRaster($"adjusted", 774, 500)
   GeoTiff(raster).write("contrast-adjusted.tiff")
 
   // Perform some arbitrary local ops between columns and render
-  val withOp = withAdjusted.withColumn("op", local_subtract($"tile", $"adjusted")).asRF
+  val withOp = withAdjusted.withColumn("op", rf_local_subtract($"tile", $"adjusted")).asRF
   val raster2 = withOp.toRaster($"op", 774, 500)
   GeoTiff(raster2).write("with-op.tiff")
 
@@ -91,7 +91,7 @@ object Tour extends App {
   val k = 4
 
   // SparkML doesn't like NoData/NaN values, so we set the no-data value to something less offensive
-  val forML = rf.select(rf.spatialKeyColumn, with_no_data($"tile", 99999) as "tile").asRF
+  val forML = rf.select(rf.spatialKeyColumn, rf_with_no_data($"tile", 99999) as "tile").asRF
 
   // First we instantiate the transformer that converts tile rows into cell rows.
   val exploder = new TileExploder()
@@ -123,7 +123,7 @@ object Tour extends App {
 
   // RasterFrames provides a special aggregation function for assembling tiles from cells with column/row indexes
   val retiled = clusteredCells.groupBy(forML.spatialKeyColumn).agg(
-    assemble_tile($"column_index", $"row_index", $"prediction", tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
+    rf_assemble_tile($"column_index", $"row_index", $"prediction", tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
   )
 
   val clusteredRF = retiled.asRF($"spatial_key", tlm)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index ac09b86c9..15701b166 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -52,7 +52,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
       assert(query2.columns.length === 4)
 
       val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2")
-      val exploded = df.select(explode_tiles($"tile1", $"tile2"))
+      val exploded = df.select(rf_explode_tiles($"tile1", $"tile2"))
       //exploded.printSchema()
       assert(exploded.columns.length === 4)
       assert(exploded.count() === 9)
@@ -61,17 +61,17 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
     it("should explode tiles with random sampling") {
       val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2")
-      val exploded = df.select(explode_tiles_sample(0.5, $"tile1", $"tile2"))
+      val exploded = df.select(rf_explode_tiles_sample(0.5, $"tile1", $"tile2"))
       assert(exploded.columns.length === 4)
       assert(exploded.count() < 9)
     }
 
     it("should handle null tiles") {
       val df = Seq[Tile](null, byteArrayTile, null, byteArrayTile, null).toDF("tile1")
-      val exploded = df.select(explode_tiles($"tile1"))
+      val exploded = df.select(rf_explode_tiles($"tile1"))
       assert(exploded.count === byteArrayTile.size * 2)
       val df2 = Seq[(Tile, Tile)]((byteArrayTile, null), (null, byteArrayTile), (byteArrayTile, byteArrayTile)).toDF("tile1", "tile2")
-      val exploded2 = df2.select(explode_tiles($"tile1", $"tile2"))
+      val exploded2 = df2.select(rf_explode_tiles($"tile1", $"tile2"))
       assert(exploded2.count === byteArrayTile.size * 3)
     }
 
@@ -79,7 +79,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
       // Create a tile with a single (wierd) no-data value
       val tile: Tile = UShortArrayTile(rangeArray(9, _.toShort), 3, 3, 5.toShort)
       val cells = Seq(tile).toDF("tile")
-        .select(explode_tiles($"tile"))
+        .select(rf_explode_tiles($"tile"))
         .select($"tile".as[Double])
         .collect()
 
@@ -89,7 +89,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
     it("should handle user-defined NoData values in tile sampler") {
       val tiles = allTileTypes.filter(t ⇒ !t.isInstanceOf[BitArrayTile]).map(_.withNoData(Some(3)))
       val cells = tiles.toDF("tile")
-        .select(explode_tiles($"tile"))
+        .select(rf_explode_tiles($"tile"))
         .select($"tile".as[Double])
         .collect()
       cells.count(_.isNaN) should be(tiles.size)
@@ -105,33 +105,34 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
       val tile = FloatConstantTile(1.1f, 10, 10, FloatCellType)
       val df = Seq[Tile](tile).toDF("tile")
-      val arrayDF = df.select(tile_to_array_double($"tile").as[Array[Double]])
+      val arrayDF = df.select(rf_tile_to_array_double($"tile").as[Array[Double]])
       arrayDF.first().sum should be (110.0 +- 0.0001)
     }
 
     it("should convert an array into a tile") {
       val tile = FloatConstantTile(1.1f, 10, 10, FloatCellType)
       val df = Seq[Tile](tile, null).toDF("tile")
-      val arrayDF = df.withColumn("tileArray", tile_to_array_double($"tile"))
+      val arrayDF = df.withColumn("tileArray", rf_tile_to_array_double($"tile"))
 
-      val back = arrayDF.withColumn("backToTile", array_to_tile($"tileArray", 10, 10))
+      val back = arrayDF.withColumn("backToTile", rf_array_to_tile($"tileArray", 10, 10))
 
       val result = back.select($"backToTile".as[Tile]).first
 
       assert(result.toArrayDouble() === tile.toArrayDouble())
 
-      val hasNoData = back.withColumn("with_no_data", with_no_data($"backToTile", 0))
+      val hasNoData = back.withColumn("withNoData", rf_with_no_data($"backToTile", 0))
 
-      val result2 = hasNoData.select($"with_no_data".as[Tile]).first
+      val result2 = hasNoData.select($"withNoData".as[Tile]).first
 
       assert(result2.cellType.asInstanceOf[UserDefinedNoData[_]].noDataValue === 0)
     }
 
     it("should reassemble single exploded tile") {
       val df = Seq[Tile](byteArrayTile).toDF("tile")
-        .select(explode_tiles($"tile"))
+        .select(rf_explode_tiles($"tile"))
 
-      val assembled = df.agg(assemble_tile(
+      val assembled = df.agg(
+        rf_assemble_tile(
         COLUMN_INDEX_COLUMN,
         ROW_INDEX_COLUMN,
         TILE_COLUMN,
@@ -146,12 +147,13 @@ class ExplodeSpec extends TestEnvironment with TestData {
       val image = sampleSmallGeoTiff
       val tinyTiles = image.projectedRaster.toRF(10, 10)
 
-      val exploded = tinyTiles.select(tinyTiles.spatialKeyColumn, explode_tiles(tinyTiles.tileColumns.head))
+      val exploded = tinyTiles.select(tinyTiles.spatialKeyColumn, rf_explode_tiles(tinyTiles.tileColumns.head))
 
       //exploded.printSchema()
 
       val assembled = exploded.groupBy(tinyTiles.spatialKeyColumn)
-        .agg(assemble_tile(
+        .agg(
+          rf_assemble_tile(
           COLUMN_INDEX_COLUMN,
           ROW_INDEX_COLUMN,
           TILE_COLUMN,
diff --git a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
index 8e50935de..ea26594f7 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
@@ -59,17 +59,17 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
 
       val (cols, rows) = tlm.layout.tileLayout.tileDimensions
 
-      val rasterized = toRasterize.withColumn("rasterized", rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
+      val rasterized = toRasterize.withColumn("rasterized", rf_rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
 
       assert(rasterized.count() === df.count() * rf.count())
-      assert(rasterized.select(tile_dimensions($"rasterized")).distinct().count() === 1)
-      val pixelCount = rasterized.select(agg_data_cells($"rasterized")).first()
+      assert(rasterized.select(rf_tile_dimensions($"rasterized")).distinct().count() === 1)
+      val pixelCount = rasterized.select(rf_agg_data_cells($"rasterized")).first()
       assert(pixelCount < cols * rows)
 
 
       toRasterize.createOrReplaceTempView("stuff")
       val viaSQL = sql(s"select rf_rasterize(geom, geometry, __fid__, $cols, $rows) as rasterized from stuff")
-      assert(viaSQL.select(agg_data_cells($"rasterized")).first === pixelCount)
+      assert(viaSQL.select(rf_agg_data_cells($"rasterized")).first === pixelCount)
 
       //rasterized.select($"rasterized".as[Tile]).foreach(t ⇒ t.renderPng(ColorMaps.IGBP).write("target/" + t.hashCode() + ".png"))
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
index cce1eba64..a9da6cbcb 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
@@ -82,7 +82,7 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
     it("should provide a means of getting a bounding box") {
       import spark.implicits._
-      val boxed = rf.select(GEOMETRY_COLUMN, st_extent(GEOMETRY_COLUMN) as "extent")
+      val boxed = rf.select(GEOMETRY_COLUMN, rf_extent(GEOMETRY_COLUMN) as "extent")
       assert(boxed.select($"extent".as[Extent]).first.area > 0)
       assert(boxed.toDF("bounds", "bbox").select("bbox.*").schema.length === 4)
     }
@@ -110,9 +110,9 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
       val df = Seq((latLng, webMercator)).toDF("ll", "wm")
 
       val rp = df.select(
-        reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
-        reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
-        reproject_geometry(reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+        rf_reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
+        rf_reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
+        rf_reproject_geometry(rf_reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
       ).as[(Geometry, Geometry, Geometry)]
 
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
index f3e41bc9a..a23cfb771 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
@@ -87,7 +87,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       assert(rf.schema.head.metadata.json.contains("tileLayout"))
 
       assert(
-        rf.select(tile_dimensions($"tile"))
+        rf.select(rf_tile_dimensions($"tile"))
           .as[Tuple1[(Int, Int)]]
           .map(_._1)
           .collect()
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 6571d9c25..a7e94c2d2 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -71,46 +71,46 @@ class RasterFunctionsSpec extends FunSpec
     it("should local_add") {
       val df = Seq((one, two)).toDF("one", "two")
 
-      val maybeThree = df.select(local_add($"one", $"two")).as[ProjectedRasterTile]
+      val maybeThree = df.select(rf_local_add($"one", $"two")).as[ProjectedRasterTile]
       assertEqual(maybeThree.first(), three)
 
       assertEqual(df.selectExpr("rf_local_add(one, two)").as[ProjectedRasterTile].first(), three)
 
-      val maybeThreeTile = df.select(local_add(ExtractTile($"one"), ExtractTile($"two"))).as[Tile]
+      val maybeThreeTile = df.select(rf_local_add(ExtractTile($"one"), ExtractTile($"two"))).as[Tile]
       assertEqual(maybeThreeTile.first(), three.toArrayTile())
       checkDocs("rf_local_add")
     }
 
-    it("should local_subtract") {
+    it("should rf_local_subtract") {
       val df = Seq((three, two)).toDF("three", "two")
-      val maybeOne = df.select(local_subtract($"three", $"two")).as[ProjectedRasterTile]
+      val maybeOne = df.select(rf_local_subtract($"three", $"two")).as[ProjectedRasterTile]
       assertEqual(maybeOne.first(), one)
 
       assertEqual(df.selectExpr("rf_local_subtract(three, two)").as[ProjectedRasterTile].first(), one)
 
       val maybeOneTile =
-        df.select(local_subtract(ExtractTile($"three"), ExtractTile($"two"))).as[Tile]
+        df.select(rf_local_subtract(ExtractTile($"three"), ExtractTile($"two"))).as[Tile]
       assertEqual(maybeOneTile.first(), one.toArrayTile())
       checkDocs("rf_local_subtract")
     }
 
-    it("should local_multiply") {
+    it("should rf_local_multiply") {
       val df = Seq((three, two)).toDF("three", "two")
 
-      val maybeSix = df.select(local_multiply($"three", $"two")).as[ProjectedRasterTile]
+      val maybeSix = df.select(rf_local_multiply($"three", $"two")).as[ProjectedRasterTile]
       assertEqual(maybeSix.first(), six)
 
       assertEqual(df.selectExpr("rf_local_multiply(three, two)").as[ProjectedRasterTile].first(), six)
 
       val maybeSixTile =
-        df.select(local_multiply(ExtractTile($"three"), ExtractTile($"two"))).as[Tile]
+        df.select(rf_local_multiply(ExtractTile($"three"), ExtractTile($"two"))).as[Tile]
       assertEqual(maybeSixTile.first(), six.toArrayTile())
       checkDocs("rf_local_multiply")
     }
 
-    it("should local_divide") {
+    it("should rf_local_divide") {
       val df = Seq((six, two)).toDF("six", "two")
-      val maybeThree = df.select(local_divide($"six", $"two")).as[ProjectedRasterTile]
+      val maybeThree = df.select(rf_local_divide($"six", $"two")).as[ProjectedRasterTile]
       assertEqual(maybeThree.first(), three)
 
       assertEqual(df.selectExpr("rf_local_divide(six, two)").as[ProjectedRasterTile].first(), three)
@@ -119,138 +119,138 @@ class RasterFunctionsSpec extends FunSpec
         .as[ProjectedRasterTile].first(), six)
 
       val maybeThreeTile =
-        df.select(local_divide(ExtractTile($"six"), ExtractTile($"two"))).as[Tile]
+        df.select(rf_local_divide(ExtractTile($"six"), ExtractTile($"two"))).as[Tile]
       assertEqual(maybeThreeTile.first(), three.toArrayTile())
       checkDocs("rf_local_divide")
     }
   }
 
   describe("scalar tile operations") {
-    it("should local_add") {
+    it("should rf_local_add") {
       val df = Seq(one).toDF("one")
-      val maybeThree = df.select(local_add($"one", 2)).as[ProjectedRasterTile]
+      val maybeThree = df.select(rf_local_add($"one", 2)).as[ProjectedRasterTile]
       assertEqual(maybeThree.first(), three)
 
-      val maybeThreeD = df.select(local_add($"one", 2.1)).as[ProjectedRasterTile]
+      val maybeThreeD = df.select(rf_local_add($"one", 2.1)).as[ProjectedRasterTile]
       assertEqual(maybeThreeD.first(), three.convert(DoubleConstantNoDataCellType).localAdd(0.1))
 
-      val maybeThreeTile = df.select(local_add(ExtractTile($"one"), 2)).as[Tile]
+      val maybeThreeTile = df.select(rf_local_add(ExtractTile($"one"), 2)).as[Tile]
       assertEqual(maybeThreeTile.first(), three.toArrayTile())
     }
 
-    it("should local_subtract") {
+    it("should rf_local_subtract") {
       val df = Seq(three).toDF("three")
 
-      val maybeOne = df.select(local_subtract($"three", 2)).as[ProjectedRasterTile]
+      val maybeOne = df.select(rf_local_subtract($"three", 2)).as[ProjectedRasterTile]
       assertEqual(maybeOne.first(), one)
 
-      val maybeOneD = df.select(local_subtract($"three", 2.0)).as[ProjectedRasterTile]
+      val maybeOneD = df.select(rf_local_subtract($"three", 2.0)).as[ProjectedRasterTile]
       assertEqual(maybeOneD.first(), one)
 
-      val maybeOneTile = df.select(local_subtract(ExtractTile($"three"), 2)).as[Tile]
+      val maybeOneTile = df.select(rf_local_subtract(ExtractTile($"three"), 2)).as[Tile]
       assertEqual(maybeOneTile.first(), one.toArrayTile())
     }
 
-    it("should local_multiply") {
+    it("should rf_local_multiply") {
       val df = Seq(three).toDF("three")
 
-      val maybeSix = df.select(local_multiply($"three", 2)).as[ProjectedRasterTile]
+      val maybeSix = df.select(rf_local_multiply($"three", 2)).as[ProjectedRasterTile]
       assertEqual(maybeSix.first(), six)
 
-      val maybeSixD = df.select(local_multiply($"three", 2.0)).as[ProjectedRasterTile]
+      val maybeSixD = df.select(rf_local_multiply($"three", 2.0)).as[ProjectedRasterTile]
       assertEqual(maybeSixD.first(), six)
 
-      val maybeSixTile = df.select(local_multiply(ExtractTile($"three"), 2)).as[Tile]
+      val maybeSixTile = df.select(rf_local_multiply(ExtractTile($"three"), 2)).as[Tile]
       assertEqual(maybeSixTile.first(), six.toArrayTile())
     }
 
-    it("should local_divide") {
+    it("should rf_local_divide") {
       val df = Seq(six).toDF("six")
 
-      val maybeThree = df.select(local_divide($"six", 2)).as[ProjectedRasterTile]
+      val maybeThree = df.select(rf_local_divide($"six", 2)).as[ProjectedRasterTile]
       assertEqual(maybeThree.first(), three)
 
-      val maybeThreeD = df.select(local_divide($"six", 2.0)).as[ProjectedRasterTile]
+      val maybeThreeD = df.select(rf_local_divide($"six", 2.0)).as[ProjectedRasterTile]
       assertEqual(maybeThreeD.first(), three)
 
-      val maybeThreeTile = df.select(local_divide(ExtractTile($"six"), 2)).as[Tile]
+      val maybeThreeTile = df.select(rf_local_divide(ExtractTile($"six"), 2)).as[Tile]
       assertEqual(maybeThreeTile.first(), three.toArrayTile())
     }
   }
 
   describe("tile comparison relations") {
-    it("should evaluate local_less") {
+    it("should evaluate rf_local_less") {
       val df = Seq((two, three, six)).toDF("two", "three", "six")
-      df.select(tile_sum(local_less($"two", 6))).first() should be(100.0)
-      df.select(tile_sum(local_less($"two", 1.9))).first() should be(0.0)
-      df.select(tile_sum(local_less($"two", 2))).first() should be(0.0)
-      df.select(tile_sum(local_less($"three", $"two"))).first() should be(0.0)
-      df.select(tile_sum(local_less($"three", $"three"))).first() should be(0.0)
-      df.select(tile_sum(local_less($"three", $"six"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less($"two", 6))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less($"two", 1.9))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less($"two", 2))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less($"three", $"two"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less($"three", $"three"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less($"three", $"six"))).first() should be(100.0)
 
       df.selectExpr("rf_tile_sum(rf_local_less(two, 6))").as[Double].first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_less(three, three))").as[Double].first() should be(0.0)
       checkDocs("rf_local_less")
     }
 
-    it("should evaluate local_less_equal") {
+    it("should evaluate rf_local_less_equal") {
       val df = Seq((two, three, six)).toDF("two", "three", "six")
-      df.select(tile_sum(local_less_equal($"two", 6))).first() should be(100.0)
-      df.select(tile_sum(local_less_equal($"two", 1.9))).first() should be(0.0)
-      df.select(tile_sum(local_less_equal($"two", 2))).first() should be(100.0)
-      df.select(tile_sum(local_less_equal($"three", $"two"))).first() should be(0.0)
-      df.select(tile_sum(local_less_equal($"three", $"three"))).first() should be(100.0)
-      df.select(tile_sum(local_less_equal($"three", $"six"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"two", 6))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"two", 1.9))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"two", 2))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"three", $"two"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"three", $"three"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_less_equal($"three", $"six"))).first() should be(100.0)
 
       df.selectExpr("rf_tile_sum(rf_local_less_equal(two, 6))").as[Double].first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_less_equal(three, three))").as[Double].first() should be(100.0)
       checkDocs("rf_local_less_equal")
     }
 
-    it("should evaluate local_greater") {
+    it("should evaluate rf_local_greater") {
       val df = Seq((two, three, six)).toDF("two", "three", "six")
-      df.select(tile_sum(local_greater($"two", 6))).first() should be(0.0)
-      df.select(tile_sum(local_greater($"two", 1.9))).first() should be(100.0)
-      df.select(tile_sum(local_greater($"two", 2))).first() should be(0.0)
-      df.select(tile_sum(local_greater($"three", $"two"))).first() should be(100.0)
-      df.select(tile_sum(local_greater($"three", $"three"))).first() should be(0.0)
-      df.select(tile_sum(local_greater($"three", $"six"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater($"two", 6))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater($"two", 1.9))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater($"two", 2))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater($"three", $"two"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater($"three", $"three"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater($"three", $"six"))).first() should be(0.0)
 
       df.selectExpr("rf_tile_sum(rf_local_greater(two, 1.9))").as[Double].first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_greater(three, three))").as[Double].first() should be(0.0)
       checkDocs("rf_local_greater")
     }
 
-    it("should evaluate local_greater_equal") {
+    it("should evaluate rf_local_greater_equal") {
       val df = Seq((two, three, six)).toDF("two", "three", "six")
-      df.select(tile_sum(local_greater_equal($"two", 6))).first() should be(0.0)
-      df.select(tile_sum(local_greater_equal($"two", 1.9))).first() should be(100.0)
-      df.select(tile_sum(local_greater_equal($"two", 2))).first() should be(100.0)
-      df.select(tile_sum(local_greater_equal($"three", $"two"))).first() should be(100.0)
-      df.select(tile_sum(local_greater_equal($"three", $"three"))).first() should be(100.0)
-      df.select(tile_sum(local_greater_equal($"three", $"six"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"two", 6))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"two", 1.9))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"two", 2))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"three", $"two"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"three", $"three"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_greater_equal($"three", $"six"))).first() should be(0.0)
       df.selectExpr("rf_tile_sum(rf_local_greater_equal(two, 1.9))").as[Double].first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_greater_equal(three, three))").as[Double].first() should be(100.0)
       checkDocs("rf_local_greater_equal")
     }
 
-    it("should evaluate local_equal") {
+    it("should evaluate rf_local_equal") {
       val df = Seq((two, three, three)).toDF("two", "threeA", "threeB")
-      df.select(tile_sum(local_equal($"two", 2))).first() should be(100.0)
-      df.select(tile_sum(local_equal($"two", 2.1))).first() should be(0.0)
-      df.select(tile_sum(local_equal($"two", $"threeA"))).first() should be(0.0)
-      df.select(tile_sum(local_equal($"threeA", $"threeB"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_equal($"two", 2))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_equal($"two", 2.1))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_equal($"two", $"threeA"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_equal($"threeA", $"threeB"))).first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_equal(two, 1.9))").as[Double].first() should be(0.0)
       df.selectExpr("rf_tile_sum(rf_local_equal(threeA, threeB))").as[Double].first() should be(100.0)
       checkDocs("rf_local_equal")
     }
 
-    it("should evaluate local_unequal") {
+    it("should evaluate rf_local_unequal") {
       val df = Seq((two, three, three)).toDF("two", "threeA", "threeB")
-      df.select(tile_sum(local_unequal($"two", 2))).first() should be(0.0)
-      df.select(tile_sum(local_unequal($"two", 2.1))).first() should be(100.0)
-      df.select(tile_sum(local_unequal($"two", $"threeA"))).first() should be(100.0)
-      df.select(tile_sum(local_unequal($"threeA", $"threeB"))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_unequal($"two", 2))).first() should be(0.0)
+      df.select(rf_tile_sum(rf_local_unequal($"two", 2.1))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_unequal($"two", $"threeA"))).first() should be(100.0)
+      df.select(rf_tile_sum(rf_local_unequal($"threeA", $"threeB"))).first() should be(0.0)
       df.selectExpr("rf_tile_sum(rf_local_unequal(two, 1.9))").as[Double].first() should be(100.0)
       df.selectExpr("rf_tile_sum(rf_local_unequal(threeA, threeB))").as[Double].first() should be(0.0)
       checkDocs("rf_local_unequal")
@@ -260,10 +260,10 @@ class RasterFunctionsSpec extends FunSpec
   describe("per-tile stats") {
     it("should compute data cell counts") {
       val df = Seq(TestData.injectND(numND)(two)).toDF("two")
-      df.select(data_cells($"two")).first() shouldBe (cols * rows - numND).toLong
+      df.select(rf_data_cells($"two")).first() shouldBe (cols * rows - numND).toLong
 
       val df2 = randNDTilesWithNull.toDF("tile")
-      df2.select(data_cells($"tile") as "cells")
+      df2.select(rf_data_cells($"tile") as "cells")
         .agg(sum("cells"))
         .as[Long]
         .first() should be (expectedRandData)
@@ -272,10 +272,10 @@ class RasterFunctionsSpec extends FunSpec
     }
     it("should compute no-data cell counts") {
       val df = Seq(TestData.injectND(numND)(two)).toDF("two")
-      df.select(no_data_cells($"two")).first() should be(numND)
+      df.select(rf_no_data_cells($"two")).first() should be(numND)
 
       val df2 = randNDTilesWithNull.toDF("tile")
-      df2.select(no_data_cells($"tile") as "cells")
+      df2.select(rf_no_data_cells($"tile") as "cells")
         .agg(sum("cells"))
         .as[Long]
         .first() should be (expectedRandNoData)
@@ -284,15 +284,15 @@ class RasterFunctionsSpec extends FunSpec
     }
     it("should detect no-data tiles") {
       val df = Seq(nd).toDF("nd")
-      df.select(is_no_data_tile($"nd")).first() should be(true)
+      df.select(rf_is_no_data_tile($"nd")).first() should be(true)
       val df2 = Seq(two).toDF("not_nd")
-      df2.select(is_no_data_tile($"not_nd")).first() should be(false)
+      df2.select(rf_is_no_data_tile($"not_nd")).first() should be(false)
       checkDocs("rf_is_no_data_tile")
     }
     it("should find the minimum cell value") {
       val min = randNDTile.toArray().filter(c => raster.isData(c)).min.toDouble
       val df = Seq(randNDTile).toDF("rand")
-      df.select(tile_min($"rand")).first() should be(min)
+      df.select(rf_tile_min($"rand")).first() should be(min)
       df.selectExpr("rf_tile_min(rand)").as[Double].first() should be(min)
       checkDocs("rf_tile_min")
     }
@@ -300,7 +300,7 @@ class RasterFunctionsSpec extends FunSpec
     it("should find the maximum cell value") {
       val max = randNDTile.toArray().filter(c => raster.isData(c)).max.toDouble
       val df = Seq(randNDTile).toDF("rand")
-      df.select(tile_max($"rand")).first() should be(max)
+      df.select(rf_tile_max($"rand")).first() should be(max)
       df.selectExpr("rf_tile_max(rand)").as[Double].first() should be(max)
       checkDocs("rf_tile_max")
     }
@@ -308,7 +308,7 @@ class RasterFunctionsSpec extends FunSpec
       val values = randNDTile.toArray().filter(c => raster.isData(c))
       val mean = values.sum.toDouble / values.length
       val df = Seq(randNDTile).toDF("rand")
-      df.select(tile_mean($"rand")).first() should be(mean)
+      df.select(rf_tile_mean($"rand")).first() should be(mean)
       df.selectExpr("rf_tile_mean(rand)").as[Double].first() should be(mean)
       checkDocs("rf_tile_mean")
     }
@@ -317,7 +317,7 @@ class RasterFunctionsSpec extends FunSpec
       val values = randNDTile.toArray().filter(c => raster.isData(c))
       val mean = values.sum.toDouble / values.length
       val df = Seq(randNDTile).toDF("rand")
-      val stats = df.select(tile_stats($"rand")).first()
+      val stats = df.select(rf_tile_stats($"rand")).first()
       stats.mean should be (mean +- 0.00001)
 
       val stats2 = df.selectExpr("rf_tile_stats(rand) as stats")
@@ -325,16 +325,16 @@ class RasterFunctionsSpec extends FunSpec
         .first()
       stats2 should be (stats)
 
-      df.select(tile_stats($"rand") as "stats")
+      df.select(rf_tile_stats($"rand") as "stats")
         .select($"stats.mean").as[Double]
         .first() should be(mean +- 0.00001)
       df.selectExpr("rf_tile_stats(rand) as stats")
-        .select($"stats.no_data_cells").as[Long]
+        .select($"stats.rf_no_data_cells").as[Long]
         .first() should be <= (cols * rows - numND).toLong
 
       val df2 = randNDTilesWithNull.toDF("tile")
       df2
-        .select(tile_stats($"tile")("data_cells") as "cells")
+        .select(rf_tile_stats($"tile")("rf_data_cells") as "cells")
         .agg(sum("cells"))
         .as[Long]
         .first() should be (expectedRandData)
@@ -344,7 +344,7 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should compute the tile histogram") {
       val df = Seq(randNDTile).toDF("rand")
-      val h1 = df.select(tile_histogram($"rand")).first()
+      val h1 = df.select(rf_tile_histogram($"rand")).first()
 
       val h2 = df.selectExpr("rf_tile_histogram(rand) as hist")
         .select($"hist".as[CellHistogram])
@@ -359,14 +359,14 @@ class RasterFunctionsSpec extends FunSpec
   describe("aggregate statistics") {
     it("should count data cells") {
       val df = randNDTilesWithNull.filter(_ != null).toDF("tile")
-      df.select(agg_data_cells($"tile")).first() should be (expectedRandData)
+      df.select(rf_agg_data_cells($"tile")).first() should be (expectedRandData)
       df.selectExpr("rf_agg_data_cells(tile)").as[Long].first() should be (expectedRandData)
 
       checkDocs("rf_agg_data_cells")
     }
     it("should count no-data cells") {
       val df = randNDTilesWithNull.toDF("tile")
-      df.select(agg_no_data_cells($"tile")).first() should be (expectedRandNoData)
+      df.select(rf_agg_no_data_cells($"tile")).first() should be (expectedRandNoData)
       df.selectExpr("rf_agg_no_data_cells(tile)").as[Long].first() should be (expectedRandNoData)
       checkDocs("rf_agg_no_data_cells")
     }
@@ -375,12 +375,12 @@ class RasterFunctionsSpec extends FunSpec
       val df = randNDTilesWithNull.toDF("tile")
 
       df
-        .select(agg_stats($"tile") as "stats")
-        .select("stats.data_cells", "stats.no_data_cells")
+        .select(rf_agg_stats($"tile") as "stats")
+        .select("stats.rf_data_cells", "stats.rf_no_data_cells")
         .as[(Long, Long)]
         .first() should be ((expectedRandData, expectedRandNoData))
       df.selectExpr("rf_agg_stats(tile) as stats")
-        .select("stats.data_cells")
+        .select("stats.rf_data_cells")
         .as[Long]
         .first() should be (expectedRandData)
 
@@ -389,7 +389,7 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should compute a aggregate histogram") {
       val df = randNDTilesWithNull.toDF("tile")
-      val hist1 = df.select(agg_approx_histogram($"tile")).first()
+      val hist1 = df.select(rf_agg_approx_histogram($"tile")).first()
       val hist2 = df.selectExpr("rf_agg_approx_histogram(tile) as hist")
         .select($"hist".as[CellHistogram])
         .first()
@@ -399,7 +399,7 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should compute local statistics") {
       val df = randNDTilesWithNull.toDF("tile")
-      val stats1 = df.select(agg_local_stats($"tile"))
+      val stats1 = df.select(rf_agg_local_stats($"tile"))
         .first()
       val stats2 = df.selectExpr("rf_agg_local_stats(tile) as stats")
           .select($"stats".as[LocalCellStatistics])
@@ -411,21 +411,21 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should compute local min") {
       val df = Seq(two, three, one, six).toDF("tile")
-      df.select(agg_local_min($"tile")).first() should be(one.toArrayTile())
+      df.select(rf_agg_local_min($"tile")).first() should be(one.toArrayTile())
       df.selectExpr("rf_agg_local_min(tile)").as[Tile].first() should be(one.toArrayTile())
       checkDocs("rf_agg_local_min")
     }
 
     it("should compute local max") {
       val df = Seq(two, three, one, six).toDF("tile")
-      df.select(agg_local_max($"tile")).first() should be(six.toArrayTile())
+      df.select(rf_agg_local_max($"tile")).first() should be(six.toArrayTile())
       df.selectExpr("rf_agg_local_max(tile)").as[Tile].first() should be(six.toArrayTile())
       checkDocs("rf_agg_local_max")
     }
 
     it("should compute local data cell counts") {
       val df = Seq(two, randNDTile, nd).toDF("tile")
-      val t1 = df.select(agg_local_data_cells($"tile")).first()
+      val t1 = df.select(rf_agg_local_data_cells($"tile")).first()
       val t2 = df.selectExpr("rf_agg_local_data_cells(tile) as cnt").select($"cnt".as[Tile]).first()
       t1 should be (t2)
       checkDocs("rf_agg_local_data_cells")
@@ -433,20 +433,20 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should compute local no-data cell counts") {
       val df = Seq(two, randNDTile, nd).toDF("tile")
-      val t1 = df.select(agg_local_no_data_cells($"tile")).first()
+      val t1 = df.select(rf_agg_local_no_data_cells($"tile")).first()
       val t2 = df.selectExpr("rf_agg_local_no_data_cells(tile) as cnt").select($"cnt".as[Tile]).first()
       t1 should be (t2)
-      val t3 = df.select(local_add(agg_local_data_cells($"tile"), agg_local_no_data_cells($"tile"))).first()
+      val t3 = df.select(rf_local_add(rf_agg_local_data_cells($"tile"), rf_agg_local_no_data_cells($"tile"))).first()
       t3 should be(three.toArrayTile())
       checkDocs("rf_agg_local_no_data_cells")
     }
   }
 
   describe("analytical transformations") {
-    it("should compute normalized_difference") {
+    it("should compute rf_normalized_difference") {
       val df = Seq((three, two)).toDF("three", "two")
 
-      df.select(tile_to_array_double(normalized_difference($"three", $"two")))
+      df.select(rf_tile_to_array_double(rf_normalized_difference($"three", $"two")))
         .first()
         .forall(_ == 0.2) shouldBe true
 
@@ -458,64 +458,64 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_normalized_difference")
     }
 
-    it("should mask one tile against another") {
+    it("should rf_mask one tile against another") {
       val df = Seq[Tile](randTile).toDF("tile")
 
-      val withMask = df.withColumn("mask",
-        convert_cell_type(
-          local_greater($"tile", 50),
+      val withMask = df.withColumn("rf_mask",
+        rf_convert_cell_type(
+          rf_local_greater($"tile", 50),
           "uint8")
       )
 
       val withMasked = withMask.withColumn("masked",
-        mask($"tile", $"mask"))
+        rf_mask($"tile", $"rf_mask"))
 
-      val result = withMasked.agg(agg_no_data_cells($"tile") < agg_no_data_cells($"masked")).as[Boolean]
+      val result = withMasked.agg(rf_agg_no_data_cells($"tile") < rf_agg_no_data_cells($"masked")).as[Boolean]
 
       result.first() should be(true)
 
       checkDocs("rf_mask")
     }
 
-    it("should inverse mask one tile against another") {
+    it("should inverse rf_mask one tile against another") {
       val df = Seq[Tile](randTile).toDF("tile")
 
-      val baseND = df.select(agg_no_data_cells($"tile")).first()
+      val baseND = df.select(rf_agg_no_data_cells($"tile")).first()
 
-      val withMask = df.withColumn("mask",
-        convert_cell_type(
-          local_greater($"tile", 50),
+      val withMask = df.withColumn("rf_mask",
+        rf_convert_cell_type(
+          rf_local_greater($"tile", 50),
           "uint8"
         )
       )
 
       val withMasked = withMask
-        .withColumn("masked", mask($"tile", $"mask"))
-        .withColumn("inv_masked", inverse_mask($"tile", $"mask"))
+        .withColumn("masked", rf_mask($"tile", $"rf_mask"))
+        .withColumn("inv_masked", rf_inverse_mask($"tile", $"rf_mask"))
 
-      val result = withMasked.agg(agg_no_data_cells($"masked") + agg_no_data_cells($"inv_masked")).as[Long]
+      val result = withMasked.agg(rf_agg_no_data_cells($"masked") + rf_agg_no_data_cells($"inv_masked")).as[Long]
 
       result.first() should be(tileSize + baseND)
 
       checkDocs("rf_inverse_mask")
     }
 
-    it("should mask tile by another identified by specified value") {
+    it("should rf_mask tile by another identified by specified value") {
       val df = Seq[Tile](randTile).toDF("tile")
       val mask_value = 4
 
-      val withMask = df.withColumn("mask",
-        local_multiply(convert_cell_type(
-          local_greater($"tile", 50),
+      val withMask = df.withColumn("rf_mask",
+        rf_local_multiply(rf_convert_cell_type(
+          rf_local_greater($"tile", 50),
           "uint8"),
           lit(mask_value)
         )
       )
 
       val withMasked = withMask.withColumn("masked",
-        mask_by_value($"tile", $"mask", lit(mask_value)))
+        rf_mask_by_value($"tile", $"rf_mask", lit(mask_value)))
 
-      val result = withMasked.agg(agg_no_data_cells($"tile") < agg_no_data_cells($"masked")).as[Boolean]
+      val result = withMasked.agg(rf_agg_no_data_cells($"tile") < rf_agg_no_data_cells($"masked")).as[Boolean]
 
       result.first() should be(true)
       checkDocs("rf_mask_by_value")
@@ -523,7 +523,7 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should render ascii art") {
       val df = Seq[Tile](ProjectedRasterTile(TestData.l8Labels)).toDF("tile")
-      val r1 = df.select(render_ascii($"tile"))
+      val r1 = df.select(rf_render_ascii($"tile"))
       val r2 = df.selectExpr("rf_render_ascii(tile)").as[String]
       r1.first() should be(r2.first())
       checkDocs("rf_render_ascii")
@@ -531,7 +531,7 @@ class RasterFunctionsSpec extends FunSpec
 
     it("should render cells as matrix") {
       val df = Seq(randDoubleNDTile).toDF("tile")
-      val r1 = df.select(render_matrix($"tile"))
+      val r1 = df.select(rf_render_matrix($"tile"))
       val r2 = df.selectExpr("rf_render_matrix(tile)").as[String]
       r1.first() should be(r2.first())
       checkDocs("rf_render_matrix")
@@ -545,9 +545,9 @@ class RasterFunctionsSpec extends FunSpec
 
       val df = Seq((three_plus, three_less, three)).toDF("three_plus", "three_less", "three")
 
-      assertEqual(df.select(round($"three")).as[ProjectedRasterTile].first(), three)
-      assertEqual(df.select(round($"three_plus")).as[ProjectedRasterTile].first(), three_double)
-      assertEqual(df.select(round($"three_less")).as[ProjectedRasterTile].first(), three_double)
+      assertEqual(df.select(rf_round($"three")).as[ProjectedRasterTile].first(), three)
+      assertEqual(df.select(rf_round($"three_plus")).as[ProjectedRasterTile].first(), three_double)
+      assertEqual(df.select(rf_round($"three_less")).as[ProjectedRasterTile].first(), three_double)
 
       assertEqual(df.selectExpr("rf_round(three)").as[ProjectedRasterTile].first(), three)
       assertEqual(df.selectExpr("rf_round(three_plus)").as[ProjectedRasterTile].first(), three_double)
@@ -557,26 +557,26 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should take logarithms positive cell values"){
-      // log10 1000 == 3
+      // rf_log10 1000 == 3
       val thousand = TestData.projectedRasterTile(cols, rows, 1000, extent, crs, ShortConstantNoDataCellType)
       val threesDouble = TestData.projectedRasterTile(cols, rows, 3.0, extent, crs, DoubleConstantNoDataCellType)
       val zerosDouble = TestData.projectedRasterTile(cols, rows, 0.0, extent, crs, DoubleConstantNoDataCellType)
 
       val df1 = Seq(thousand).toDF("tile")
-      assertEqual(df1.select(log10($"tile")).as[ProjectedRasterTile].first(), threesDouble)
+      assertEqual(df1.select(rf_log10($"tile")).as[ProjectedRasterTile].first(), threesDouble)
 
-      // ln random tile == log10 random tile / log10(e); random tile square to ensure all positive cell values
+      // ln random tile == rf_log10 random tile / rf_log10(e); random tile square to ensure all positive cell values
       val df2 = Seq(randPositiveDoubleTile).toDF("tile")
       val log10e = math.log10(math.E)
-      assertEqual(df2.select(log($"tile")).as[ProjectedRasterTile].first(),
-                  df2.select(log10($"tile")).as[ProjectedRasterTile].first() / log10e)
+      assertEqual(df2.select(rf_log($"tile")).as[ProjectedRasterTile].first(),
+                  df2.select(rf_log10($"tile")).as[ProjectedRasterTile].first() / log10e)
 
       lazy val maybeZeros = df2
         .selectExpr(s"rf_local_subtract(rf_log(tile), rf_local_divide(rf_log10(tile), ${log10e}))")
         .as[ProjectedRasterTile].first()
       assertEqual(maybeZeros, zerosDouble)
 
-      // log1p for zeros should be ln(1)
+      // rf_log1p for zeros should be ln(1)
       val ln1 = math.log1p(0.0)
       val df3 = Seq(zero).toDF("tile")
       val maybeLn1 = df3.selectExpr(s"rf_log1p(tile)").as[ProjectedRasterTile].first()
@@ -594,42 +594,42 @@ class RasterFunctionsSpec extends FunSpec
 
       // tile zeros ==> -Infinity
       val df_0 = Seq(zero).toDF("tile")
-      assertEqual(df_0.select(log($"tile")).as[ProjectedRasterTile].first(), ni_float)
-      assertEqual(df_0.select(log10($"tile")).as[ProjectedRasterTile].first(), ni_float)
-      assertEqual(df_0.select(log2($"tile")).as[ProjectedRasterTile].first(), ni_float)
-      // log1p of zeros should be 0.
-      assertEqual(df_0.select(log1p($"tile")).as[ProjectedRasterTile].first(), zero_float)
+      assertEqual(df_0.select(rf_log($"tile")).as[ProjectedRasterTile].first(), ni_float)
+      assertEqual(df_0.select(rf_log10($"tile")).as[ProjectedRasterTile].first(), ni_float)
+      assertEqual(df_0.select(rf_log2($"tile")).as[ProjectedRasterTile].first(), ni_float)
+      // rf_log1p of zeros should be 0.
+      assertEqual(df_0.select(rf_log1p($"tile")).as[ProjectedRasterTile].first(), zero_float)
 
       // tile negative values ==> NaN
       assert(df_0.selectExpr("rf_log(rf_local_subtract(tile, 42))").as[ProjectedRasterTile].first().isNoDataTile)
       assert(df_0.selectExpr("rf_log2(rf_local_subtract(tile, 42))").as[ProjectedRasterTile].first().isNoDataTile)
-      assert(df_0.select(log1p(local_subtract($"tile", 42))).as[ProjectedRasterTile].first().isNoDataTile)
-      assert(df_0.select(log10(local_subtract($"tile", lit(0.01)))).as[ProjectedRasterTile].first().isNoDataTile)
+      assert(df_0.select(rf_log1p(rf_local_subtract($"tile", 42))).as[ProjectedRasterTile].first().isNoDataTile)
+      assert(df_0.select(rf_log10(rf_local_subtract($"tile", lit(0.01)))).as[ProjectedRasterTile].first().isNoDataTile)
 
     }
 
     it("should take exponential") {
       val df = Seq(six).toDF("tile")
 
-      // exp inverses log
+      // rf_exp inverses rf_log
       assertEqual(
-        df.select(exp(log($"tile"))).as[ProjectedRasterTile].first(),
+        df.select(rf_exp(rf_log($"tile"))).as[ProjectedRasterTile].first(),
         six
       )
 
       // base 2
       assertEqual(
-        df.select(exp2(log2($"tile"))).as[ProjectedRasterTile].first(),
+        df.select(rf_exp2(rf_log2($"tile"))).as[ProjectedRasterTile].first(),
         six)
 
       // base 10
       assertEqual(
-        df.select(exp10(log10($"tile"))).as[ProjectedRasterTile].first(),
+        df.select(rf_exp10(rf_log10($"tile"))).as[ProjectedRasterTile].first(),
         six)
 
       // plus/minus 1
       assertEqual(
-        df.select(expm1(log1p($"tile"))).as[ProjectedRasterTile].first(),
+        df.select(rf_expm1(rf_log1p($"tile"))).as[ProjectedRasterTile].first(),
         six)
 
       // SQL
@@ -647,7 +647,7 @@ class RasterFunctionsSpec extends FunSpec
         df.selectExpr("rf_exp2(rf_log2(tile))").as[ProjectedRasterTile].first(),
         six)
 
-      // SQL expm1
+      // SQL rf_expm1
       assertEqual(
         df.selectExpr("rf_expm1(rf_log1p(tile))").as[ProjectedRasterTile].first(),
         six)
@@ -678,11 +678,11 @@ class RasterFunctionsSpec extends FunSpec
 
     def df = Seq(lowRes).toDF("tile")
 
-    val maybeUp = df.select(resample($"tile", lit(2))).as[ProjectedRasterTile].first()
+    val maybeUp = df.select(rf_resample($"tile", lit(2))).as[ProjectedRasterTile].first()
     assertEqual(maybeUp, upsampled)
 
     def df2 = Seq((lowRes, fourByFour)).toDF("tile1", "tile2")
-    val maybeUpShape = df2.select(resample($"tile1", $"tile2")).as[ProjectedRasterTile].first()
+    val maybeUpShape = df2.select(rf_resample($"tile1", $"tile2")).as[ProjectedRasterTile].first()
     assertEqual(maybeUpShape, upsampled)
 
     // Downsample by double argument < 1
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index 72bd5e960..c227944e0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -60,9 +60,9 @@ class ReprojectGeometrySpec extends FunSpec
         val df = Seq((latLng, webMercator)).toDF("ll", "wm")
 
         val rp = df.select(
-          reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
-          reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
-          reproject_geometry(reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+          rf_reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
+          rf_reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
+          rf_reproject_geometry(rf_reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
         ).as[(Geometry, Geometry, Geometry)]
 
 
@@ -79,9 +79,9 @@ class ReprojectGeometrySpec extends FunSpec
         val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
 
         val rp = df.select(
-          reproject_geometry($"ll", $"llCRS", WebMercator) as "wm2",
-          reproject_geometry($"wm", WebMercator, $"llCRS") as "ll2",
-          reproject_geometry(reproject_geometry($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+          rf_reproject_geometry($"ll", $"llCRS", WebMercator) as "wm2",
+          rf_reproject_geometry($"wm", WebMercator, $"llCRS") as "ll2",
+          rf_reproject_geometry(rf_reproject_geometry($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
         ).as[(Geometry, Geometry, Geometry)]
 
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index 2759323fb..d484ff915 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -46,11 +46,11 @@ class TileAssemblerSpec extends TestEnvironment {
       val ct = rf.tileLayerMetadata.merge.cellType
       val (tileCols, tileRows) = rf.tileLayerMetadata.merge.tileLayout.tileDimensions
 
-      val exploded = rf.select($"spatial_key", explode_tiles($"tile"))
+      val exploded = rf.select($"spatial_key", rf_explode_tiles($"tile"))
 
       val assembled = exploded
         .groupBy($"spatial_key")
-        .agg(assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN, $"tile", tileCols, tileRows, ct))
+        .agg(rf_assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN, $"tile", tileCols, tileRows, ct))
 
 
       assert(
@@ -65,13 +65,14 @@ class TileAssemblerSpec extends TestEnvironment {
       val sceneSize = (260, 257)
       val rs = InMemoryRasterSource(TestData.randomTile(sceneSize._1, sceneSize._2, ByteConstantNoDataCellType), Extent(10, 20, 30, 40), LatLng)
       val df = rs.toDF
-      val exploded = df.select($"spatial_index", $"extent", tile_dimensions($"tile") as "tile_dimensions", explode_tiles($"tile"))
+      val exploded = df.select($"spatial_index", $"extent", rf_tile_dimensions($"tile") as "rf_tile_dimensions", rf_explode_tiles($"tile"))
 
       val assembled = exploded
-        .groupBy($"spatial_index", $"extent", $"tile_dimensions")
+        .groupBy($"spatial_index", $"extent", $"rf_tile_dimensions")
         .agg(
-          convert_cell_type(assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN,
-            $"tile", $"tile_dimensions.cols", $"tile_dimensions.rows"), rs.cellType) as "tile"
+          rf_convert_cell_type(
+            rf_assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN,
+            $"tile", $"rf_tile_dimensions.cols", $"rf_tile_dimensions.rows"), rs.cellType) as "tile"
         )
 
       assert(
@@ -89,7 +90,7 @@ class TileAssemblerSpec extends TestEnvironment {
 
       val exploded = util.time("exploded") {
         df
-          .select($"spatial_index", explode_tiles($"tile"))
+          .select($"spatial_index", rf_explode_tiles($"tile"))
           .forceCache
       }
 
@@ -98,7 +99,7 @@ class TileAssemblerSpec extends TestEnvironment {
       val assembled = util.time("assembled") {
         exploded
           .groupBy($"spatial_index")
-          .agg(assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN,
+          .agg(rf_assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN,
             $"tile", 256, 256,
             UShortUserDefinedNoDataCellType(32767)))
           .forceCache
@@ -111,8 +112,8 @@ class TileAssemblerSpec extends TestEnvironment {
 
       assert(assembled.count() === df.count())
 
-      val expected = df.select(agg_stats($"tile")).first()
-      val result = assembled.select(agg_stats($"tile")).first()
+      val expected = df.select(rf_agg_stats($"tile")).first()
+      val result = assembled.select(rf_agg_stats($"tile")).first()
 
       assert(result.copy(no_data_cells = expected.no_data_cells) === expected)
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index a2fc4baf1..3ddbc52ec 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -43,7 +43,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
     it("should report dimensions") {
       val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2")
 
-      val dims = df.select(tile_dimensions($"tile1") as "dims").select("dims.*")
+      val dims = df.select(rf_tile_dimensions($"tile1") as "dims").select("dims.*")
 
       assert(dims.as[(Int, Int)].first() === (3, 3))
       assert(dims.schema.head.name === "cols")
@@ -67,7 +67,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       forEvery(ct) { c =>
         val expected = CellType.fromName(c)
         val tile = randomTile(5, 5, expected)
-        val result = Seq(tile).toDF("tile").select(cell_type($"tile")).first()
+        val result = Seq(tile).toDF("tile").select(rf_cell_type($"tile")).first()
         result should be(expected)
       }
     }
@@ -80,7 +80,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
     it("should compute accurate item counts") {
       val ds = Seq[Tile](tile1, tile2, tile3).toDF("tiles")
       val checkedValues = Seq[Double](0, 4, 7, 13, 26)
-      val result = checkedValues.map(x => ds.select(tile_histogram($"tiles")).first().itemCount(x))
+      val result = checkedValues.map(x => ds.select(rf_tile_histogram($"tiles")).first().itemCount(x))
       forEvery(checkedValues) { x =>
         assert((x == 0 && result.head == 4) || result.contains(x - 1))
       }
@@ -89,7 +89,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
     it("Should compute quantiles") {
       val ds = Seq[Tile](tile1, tile2, tile3).toDF("tiles")
       val numBreaks = 5
-      val breaks = ds.select(tile_histogram($"tiles")).map(_.quantileBreaks(numBreaks)).collect()
+      val breaks = ds.select(rf_tile_histogram($"tiles")).map(_.quantileBreaks(numBreaks)).collect()
       assert(breaks(1).length === numBreaks)
       assert(breaks(0).apply(2) == 25)
       assert(breaks(1).max <= 3 && breaks.apply(1).min >= -5)
@@ -101,7 +101,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       ds.createOrReplaceTempView("tmp")
 
       withClue("max") {
-        val max = ds.agg(agg_local_max($"tiles"))
+        val max = ds.agg(rf_agg_local_max($"tiles"))
         val expected = Max(byteArrayTile, byteConstantTile)
         write(max)
         assert(max.as[Tile].first() === expected)
@@ -112,7 +112,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       }
 
       withClue("min") {
-        val min = ds.agg(agg_local_min($"tiles"))
+        val min = ds.agg(rf_agg_local_min($"tiles"))
         val expected = Min(byteArrayTile, byteConstantTile)
         write(min)
         assert(min.as[Tile].first() === Min(byteArrayTile, byteConstantTile))
@@ -127,11 +127,11 @@ class TileStatsSpec extends TestEnvironment with TestData {
       withClue("mean") {
 
         val ds = Seq.fill[Tile](3)(randomTile(5, 5, FloatConstantNoDataCellType)).toDS()
-        val means1 = ds.select(tile_stats($"value")).map(_.mean).collect
-        val means2 = ds.select(tile_mean($"value")).collect
+        val means1 = ds.select(rf_tile_stats($"value")).map(_.mean).collect
+        val means2 = ds.select(rf_tile_mean($"value")).collect
         // Compute the mean manually, knowing we're not dealing with no-data values.
         val means =
-          ds.select(tile_to_array_double($"value")).map(a => a.sum / a.length).collect
+          ds.select(rf_tile_to_array_double($"value")).map(a => a.sum / a.length).collect
 
         forAll(means.zip(means1)) { case (l, r) => assert(l === r +- 1e-6) }
         forAll(means.zip(means2)) { case (l, r) => assert(l === r +- 1e-6) }
@@ -139,7 +139,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       withClue("sum") {
         val rf = l8Sample(1).projectedRaster.toRF
         val expected = 309149454 // computed with rasterio
-        val result = rf.agg(sum(tile_sum($"tile"))).collect().head.getDouble(0)
+        val result = rf.agg(sum(rf_tile_sum($"tile"))).collect().head.getDouble(0)
         logger.info(s"L8 sample band 1 grand total: ${result}")
         assert(result === expected)
       }
@@ -149,7 +149,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
       val ds = Seq.fill[Tile](3)(randomTile(5, 5, FloatCellType)).toDF("tiles")
       ds.createOrReplaceTempView("tmp")
 
-      val r1 = ds.select(tile_histogram($"tiles"))
+      val r1 = ds.select(rf_tile_histogram($"tiles"))
       assert(r1.first.totalCount === 5 * 5)
       write(r1)
       val r2 = sql("select hist.* from (select rf_tile_histogram(tiles) as hist from tmp)").as[CellHistogram]
@@ -179,7 +179,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
         .fill[Tile](rows)(randomTile(tileSize, tileSize, FloatConstantNoDataCellType))
         .toDF("tiles")
       ds.createOrReplaceTempView("tmp")
-      val agg = ds.select(agg_approx_histogram($"tiles"))
+      val agg = ds.select(rf_agg_approx_histogram($"tiles"))
 
       val histArray = agg.collect()
       histArray.length should be (1)
@@ -198,21 +198,21 @@ class TileStatsSpec extends TestEnvironment with TestData {
 
     it("should compute aggregate mean") {
       val ds = (Seq.fill[Tile](10)(randomTile(5, 5, FloatCellType)) :+ null).toDF("tiles")
-      val agg = ds.select(agg_mean($"tiles"))
-      val stats = ds.select(agg_stats($"tiles") as "stats").select($"stats.mean".as[Double])
+      val agg = ds.select(rf_agg_mean($"tiles"))
+      val stats = ds.select(rf_agg_stats($"tiles") as "stats").select($"stats.mean".as[Double])
       assert(agg.first() === stats.first())
     }
 
     it("should compute aggregate statistics") {
       val ds = Seq.fill[Tile](10)(randomTile(5, 5, FloatConstantNoDataCellType)).toDF("tiles")
 
-      val exploded = ds.select(explode_tiles($"tiles"))
+      val exploded = ds.select(rf_explode_tiles($"tiles"))
       val (mean, vrnc) = exploded.agg(avg($"tiles"), var_pop($"tiles")).as[(Double, Double)].first
 
-      val stats = ds.select(agg_stats($"tiles") as "stats") ///.as[(Long, Double, Double, Double, Double)]
+      val stats = ds.select(rf_agg_stats($"tiles") as "stats") ///.as[(Long, Double, Double, Double, Double)]
       //stats.printSchema()
       noException shouldBe thrownBy {
-        ds.select(agg_stats($"tiles")).collect()
+        ds.select(rf_agg_stats($"tiles")).collect()
       }
 
       val agg = stats.select($"stats.variance".as[Double])
@@ -221,9 +221,9 @@ class TileStatsSpec extends TestEnvironment with TestData {
 
       ds.createOrReplaceTempView("tmp")
       val agg2 = sql("select stats.* from (select rf_agg_stats(tiles) as stats from tmp)")
-      assert(agg2.first().getAs[Long]("data_cells") === 250L)
+      assert(agg2.first().getAs[Long]("rf_data_cells") === 250L)
 
-      val agg3 = ds.agg(agg_stats($"tiles") as "stats").select($"stats.mean".as[Double])
+      val agg3 = ds.agg(rf_agg_stats($"tiles") as "stats").select($"stats.mean".as[Double])
       assert(mean === agg3.first())
     }
 
@@ -236,7 +236,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
         .map(injectND(2)) :+ null).toDF("tiles")
       ds.createOrReplaceTempView("tmp")
 
-      val agg = ds.select(agg_local_stats($"tiles") as "stats")
+      val agg = ds.select(rf_agg_local_stats($"tiles") as "stats")
       val stats = agg.select("stats.*")
 
       //printStatsRows(stats)
@@ -269,25 +269,25 @@ class TileStatsSpec extends TestEnvironment with TestData {
       val dsNd = (Seq.fill(20)(completeTile) :+ incompleteTile :+ null).toDF("tiles")
 
       // counted everything properly
-      val countTile = ds.select(agg_local_data_cells($"tiles")).first()
+      val countTile = ds.select(rf_agg_local_data_cells($"tiles")).first()
       forAll(countTile.toArray())(i => assert(i === 20))
 
-      val countArray = dsNd.select(agg_local_data_cells($"tiles")).first().toArray()
+      val countArray = dsNd.select(rf_agg_local_data_cells($"tiles")).first().toArray()
       val expectedCount =
         (completeTile.localDefined().toArray zip incompleteTile.localDefined().toArray()).toSeq.map(
             pr => pr._1 * 20 + pr._2)
       assert(countArray === expectedCount)
 
-      val countNodataArray = dsNd.select(agg_local_no_data_cells($"tiles")).first().toArray
+      val countNodataArray = dsNd.select(rf_agg_local_no_data_cells($"tiles")).first().toArray
       assert(countNodataArray === incompleteTile.localUndefined().toArray)
 
-      val minTile = dsNd.select(agg_local_min($"tiles")).first()
+      val minTile = dsNd.select(rf_agg_local_min($"tiles")).first()
       assert(minTile.toArray() === completeTile.toArray())
 
-      val maxTile = dsNd.select(agg_local_max($"tiles")).first()
+      val maxTile = dsNd.select(rf_agg_local_max($"tiles")).first()
       assert(maxTile.toArray() === completeTile.toArray())
 
-      val meanTile = dsNd.select(agg_local_mean($"tiles")).first()
+      val meanTile = dsNd.select(rf_agg_local_mean($"tiles")).first()
       assert(meanTile.toArray() === completeTile.toArray())
     }
   }
@@ -300,20 +300,20 @@ class TileStatsSpec extends TestEnvironment with TestData {
       .map(injectND(nds)) :+ null).toDF("tiles")
 
     it("should count cells by NoData state") {
-      val counts = tiles.select(no_data_cells($"tiles")).collect().dropRight(1)
+      val counts = tiles.select(rf_no_data_cells($"tiles")).collect().dropRight(1)
       forEvery(counts)(c => assert(c === nds))
-      val counts2 = tiles.select(data_cells($"tiles")).collect().dropRight(1)
+      val counts2 = tiles.select(rf_data_cells($"tiles")).collect().dropRight(1)
       forEvery(counts2)(c => assert(c === tsize * tsize - nds))
     }
 
     it("should detect all NoData tiles") {
-      val ndCount = tiles.select("*").where(is_no_data_tile($"tiles")).count()
+      val ndCount = tiles.select("*").where(rf_is_no_data_tile($"tiles")).count()
       ndCount should be(1)
 
       val ndTiles =
         (Seq.fill[Tile](count)(ArrayTile.empty(UByteConstantNoDataCellType, tsize, tsize)) :+ null)
           .toDF("tiles")
-      val ndCount2 = ndTiles.select("*").where(is_no_data_tile($"tiles")).count()
+      val ndCount2 = ndTiles.select("*").where(rf_is_no_data_tile($"tiles")).count()
       ndCount2 should be(count + 1)
     }
   }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index b0904caa6..a9c1bdaca 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -78,14 +78,14 @@ class GeoTiffDataSource extends DataSourceRegister
       val c = rf
         .where(SPATIAL_KEY_COLUMN("row") === sk.row)
         .agg(
-          F.sum(tile_dimensions(tc)("cols") cast(LongType))
+          F.sum(rf_tile_dimensions(tc)("cols") cast(LongType))
         ).first()
         .getLong(0)
 
       val r = rf
         .where(SPATIAL_KEY_COLUMN("col") === sk.col)
         .agg(
-          F.sum(tile_dimensions(tc)("rows") cast(LongType))
+          F.sum(rf_tile_dimensions(tc)("rows") cast(LongType))
         ).first()
         .getLong(0)
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index d221acab9..c80011438 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -86,7 +86,7 @@ class GeoTiffDataSourceSpec
       val rf = spark.read.geotiff.loadRF(l8samplePath)
       val expected = 309149454 // computed with rasterio
       val result = rf.agg(
-        sum(tile_sum(rf("tile")))
+        sum(rf_tile_sum(rf("tile")))
       ).collect().head.getDouble(0)
 
       assert(result === expected)
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 70d70f0d7..d8f8d8684 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -132,7 +132,7 @@ class GeoTrellisDataSourceSpec
 
     it("used produce tile UDT that we can manipulate") {
       val df = layerReader.loadRF(layer)
-        .select(SPATIAL_KEY_COLUMN, tile_stats(TILE_COLUMN))
+        .select(SPATIAL_KEY_COLUMN, rf_tile_stats(TILE_COLUMN))
       assert(df.count() > 0)
     }
 
@@ -188,7 +188,7 @@ class GeoTrellisDataSourceSpec
         .withTileSubdivisions(param)
         .loadRF(layer)
 
-      val dims = df.select(tile_dimensions(df.tileColumns.head)("cols"), tile_dimensions(df.tileColumns.head)("rows")).first()
+      val dims = df.select(rf_tile_dimensions(df.tileColumns.head)("cols"), rf_tile_dimensions(df.tileColumns.head)("rows")).first()
       assert(dims.getAs[Int](0) === tileSize / param)
       assert(dims.getAs[Int](1) === tileSize / param)
 
@@ -201,7 +201,7 @@ class GeoTrellisDataSourceSpec
         .withTileSubdivisions(param)
         .loadRF(tfLayer)
 
-      val dims = rf.select(tile_dimensions(rf.tileColumns.head)("cols"), tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / param)
       assert(dims.getAs[Int](1) === tileSize / param)
@@ -223,7 +223,7 @@ class GeoTrellisDataSourceSpec
 
       // is it subdivided?
       assert(rf.count === testRdd.count * subParam * subParam)
-      val dims = rf.select(tile_dimensions(rf.tileColumns.head)("cols"), tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
@@ -451,7 +451,7 @@ class GeoTrellisDataSourceSpec
 
       assert(rf.count === testRdd.count * subParam * subParam)
 
-      val dims = rf.select(tile_dimensions(rf.tileColumns.head)("cols"), tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
@@ -466,7 +466,7 @@ class GeoTrellisDataSourceSpec
 
       // is it subdivided?
       assert(rf.count === testRdd.count * subParam * subParam)
-      val dims = rf.select(tile_dimensions(rf.tileColumns.head)("cols"), tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 42765f6e6..8ce677ed7 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -79,7 +79,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .cache()
       df.schema.size should be (4)
       // Test (roughly) we have three distinct but compabible bands
-      val stats = df.agg(agg_stats($"tile_b0") as "s0", agg_stats($"tile_b1") as "s1", agg_stats($"tile_b2") as "s2")
+      val stats = df.agg(rf_agg_stats($"tile_b0") as "s0", rf_agg_stats($"tile_b1") as "s1", rf_agg_stats($"tile_b2") as "s2")
       stats.select($"s0.data_cells" === $"s1.data_cells").as[Boolean].first() should be(true)
       stats.select($"s0.data_cells" === $"s2.data_cells").as[Boolean].first() should be(true)
       stats.select($"s0.mean" =!= $"s1.mean").as[Boolean].first() should be(true)
@@ -93,7 +93,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       df.count() should be(math.ceil(1028.0 / 128).toInt * math.ceil(989.0 / 128).toInt)
 
-      val dims = df.select(tile_dimensions($"tile").as[TileDimensions]).distinct().collect()
+      val dims = df.select(rf_tile_dimensions($"tile").as[TileDimensions]).distinct().collect()
       dims should contain allElementsOf
         Seq(TileDimensions(4,128), TileDimensions(128,128), TileDimensions(128,93), TileDimensions(4,93))
 
diff --git a/docs/src/main/tut/apps/geotrellis-ops.md b/docs/src/main/tut/apps/geotrellis-ops.md
index edc5302c7..56ccbbabb 100644
--- a/docs/src/main/tut/apps/geotrellis-ops.md
+++ b/docs/src/main/tut/apps/geotrellis-ops.md
@@ -26,15 +26,15 @@ Here's an example creating a UDFs to invoke the `equalize` transformation on eac
 import geotrellis.raster.equalization._
 val equalizer = udf((t: Tile) => t.equalize())
 val equalized = rf.select(equalizer($"tile") as "equalized")
-equalized.select(tile_mean($"equalized") as "equalizedMean").show(5, false)
+equalized.select(rf_tile_mean($"equalized") as "equalizedMean").show(5, false)
 ```
 
 Here's an example downsampling a tile and rendering each tile as a matrix of numerical values.
 
 ```tut  
 val downsample = udf((t: Tile) => t.resample(4, 4))
-val downsampled = rf.where(no_data_cells($"tile") === 0).select(downsample($"tile") as "minime")
-downsampled.select(tile_to_array_double($"minime") as "cell_values").limit(2).show(false)
+val downsampled = rf.where(rf_no_data_cells($"tile") === 0).select(downsample($"tile") as "minime")
+downsampled.select(rf_tile_to_array_double($"minime") as "cell_values").limit(2).show(false)
 ```
 
 
diff --git a/docs/src/main/tut/apps/ndvi.md b/docs/src/main/tut/apps/ndvi.md
index 5ce1d1246..85c59f2b2 100644
--- a/docs/src/main/tut/apps/ndvi.md
+++ b/docs/src/main/tut/apps/ndvi.md
@@ -30,7 +30,7 @@ def redBand = SinglebandGeoTiff("../core/src/test/resources/L8-B4-Elkton-VA.tiff
 def nirBand = SinglebandGeoTiff("../core/src/test/resources/L8-B5-Elkton-VA.tiff").projectedRaster.toRF("nir_band")
 
 // We use `asRF` to indicate we know the structure still conforms to RasterFrame constraints
-val rf = redBand.spatialJoin(nirBand).withColumn("ndvi", normalized_difference($"red_band", $"nir_band")).asRF
+val rf = redBand.spatialJoin(nirBand).withColumn("ndvi", rf_normalized_difference($"red_band", $"nir_band")).asRF
 
 val pr = rf.toRaster($"ndvi", 466, 428)
 
diff --git a/docs/src/main/tut/exporting-rasterframes.md b/docs/src/main/tut/exporting-rasterframes.md
index 5012f1019..b2b806fbd 100644
--- a/docs/src/main/tut/exporting-rasterframes.md
+++ b/docs/src/main/tut/exporting-rasterframes.md
@@ -27,11 +27,11 @@ The cell values within a `Tile` are encoded internally as an array. There may be
 where the additional context provided by the `Tile` construct is no longer needed and one would
 prefer to work with the underlying array data.
 
-The @scaladoc[`tile_to_array_int`][tile_to_array_int] column function requires a type parameter to indicate the array element
-type you would like used. The following types may be used: `Int`, `Double`, `Byte`, `Short`, `Float`
+The @scaladoc[`rf_tile_to_array_int`][rf_tile_to_array_int] and @scaladoc[`rf_tile_to_array_double`][rf_tile_to_array_double]
+provide this facility.
 
 ```tut
-val withArrays = rf.withColumn("tileData", tile_to_array_int($"tile")).drop("tile")
+val withArrays = rf.withColumn("tileData", rf_tile_to_array_int($"tile")).drop("tile")
 withArrays.show(5, 40)
 ```
 
@@ -45,7 +45,7 @@ tileBack.drop("tileData").show(5, 40)
 Note that the created tile will not have a `NoData` value associated with it. Here's how you can do that:
 
 ```tut
-val tileBackAgain = withArrays.withColumn("tileAgain", withNoData(arrayToTile($"tileData", 128, 128), 3))
+val tileBackAgain = withArrays.withColumn("tileAgain", rf_with_no_data(rf_array_to_tile($"tileData", 128, 128), 3))
 tileBackAgain.drop("tileData").show(5, 50)
 ```
 
diff --git a/docs/src/main/tut/ml/classification.md b/docs/src/main/tut/ml/classification.md
index b62940a7b..fb50e5664 100644
--- a/docs/src/main/tut/ml/classification.md
+++ b/docs/src/main/tut/ml/classification.md
@@ -78,7 +78,7 @@ val target = readTiff(filenamePattern.format("Labels")).
 Take a peek at what kind of label data we have to work with.
 
 ```tut
-target.select(agg_stats(target(targetCol))).show
+target.select(rf_agg_stats(target(targetCol))).show
 ```
 
 Join the target label RasterFrame with the band tiles to create our analytics base table
@@ -186,7 +186,7 @@ First, we get the DataFrame back into RasterFrame form:
 val tlm = joinedRF.tileLayerMetadata.left.get
 
 val retiled = scored.groupBy($"spatial_key").agg(
-  assemble_tile(
+  rf_assemble_tile(
     $"column_index", $"row_index", $"prediction",
     tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType
   )
diff --git a/docs/src/main/tut/ml/clustering.md b/docs/src/main/tut/ml/clustering.md
index fc63eabdb..36de19652 100644
--- a/docs/src/main/tut/ml/clustering.md
+++ b/docs/src/main/tut/ml/clustering.md
@@ -119,7 +119,7 @@ First, we get the DataFrame back into RasterFrame form:
 val tlm = joinedRF.tileLayerMetadata.left.get
 
 val retiled = clustered.groupBy($"spatial_key").agg(
-  assemble_tile(
+  rf_assemble_tile(
     $"column_index", $"row_index", $"prediction",
     tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType
   )
diff --git a/docs/src/main/tut/ml/statistics.md b/docs/src/main/tut/ml/statistics.md
index 6249eef73..94611be0b 100644
--- a/docs/src/main/tut/ml/statistics.md
+++ b/docs/src/main/tut/ml/statistics.md
@@ -28,7 +28,7 @@ RasterFrames has a number of extension methods and columnar functions for perfor
 Get the nominal tile dimensions. Depending on the tiling there may be some tiles with different sizes on the edges.
 
 ```tut
-rf.select(rf.spatialKeyColumn, tile_dimensions($"tile")).show(3)
+rf.select(rf.spatialKeyColumn, rf_tile_dimensions($"tile")).show(3)
 ```
 
 ### Descriptive Statistics
@@ -38,16 +38,15 @@ rf.select(rf.spatialKeyColumn, tile_dimensions($"tile")).show(3)
 Count the numer of `NoData` and non-`NoData` cells in each tile.
 
 ```tut
-rf.select(rf.spatialKeyColumn, no_data_cells($"tile"), data_cells($"tile")).show(3)
+rf.select(rf.spatialKeyColumn, rf_no_data_cells($"tile"), rf_data_cells($"tile")).show(3)
 ```
 
 #### Tile Mean
 
-Compute the mean value in each tile. Use `tileMean` for integral cell types, and `tileMeanDouble` for floating point
-cell types.
+Compute the mean value in each tile.
  
 ```tut
-rf.select(rf.spatialKeyColumn, tile_mean($"tile")).show(3)
+rf.select(rf.spatialKeyColumn, rf_tile_mean($"tile")).show(3)
 ```
 
 #### Tile Summary Statistics
@@ -56,7 +55,7 @@ Compute a suite of summary statistics for each tile. Use `tile_stats` for integr
 for floating point cell types.
 
 ```tut
-rf.withColumn("stats", tile_stats($"tile")).select(rf.spatialKeyColumn, $"stats.*").show(3)
+rf.withColumn("stats", rf_tile_stats($"tile")).select(rf.spatialKeyColumn, $"stats.*").show(3)
 ```
 
 ### Histogram
@@ -66,22 +65,22 @@ The `tile_histogram` function computes a histogram over the data in each tile.
 In this example we compute quantile breaks.
 
 ```tut
-rf.select(tile_histogram($"tile")).map(_.quantileBreaks(5)).show(5, false)
+rf.select(rf_tile_histogram($"tile")).map(_.quantileBreaks(5)).show(5, false)
 ```
 
 ## Aggregate Statistics
 
-The `agg_stats` function computes the same summary statistics as `tile_stats`, but aggregates them over the whole 
+The `rf_agg_stats` function computes the same summary statistics as `rf_tile_stats`, but aggregates them over the whole 
 RasterFrame.
 
 ```tut
-rf.select(agg_stats($"tile")).show()
+rf.select(rf_agg_stats($"tile")).show()
 ```
 
 A more involved example: extract bin counts from a computed `Histogram`.
 
 ```tut
-rf.select(agg_approx_histogram($"tile")).
+rf.select(rf_agg_approx_histogram($"tile")).
   map(h => for(v <- h.labels) yield(v, h.itemCount(v))).
   select(explode($"value") as "counts").
   select("counts._1", "counts._2").
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 307e1224d..9e5698c36 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -4,25 +4,26 @@
 
 ### 0.8.0
 
-* _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
-* Added new tile functions `round`, `log`, `log10`, `log2`, `log1p`, `exp`, `exp10`, `exp2`, `expm1`, `resample`, `resample`.
+* Added new tile functions `rf_round`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.
 * Support python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
 * SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
-* _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
-* _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
+* Added `rf_render_matrix` debugging function.
+* Added `RasterFrame.withExtent` extension method.
+* Added `rf_extent` and removed `envelope`. 
+* _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
+* _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
+* _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, with an `rf_` prefix, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
 * _Breaking_: In Scala and SQL, `..._scalar` functions (e.g. `local_add_scalar`) have been removed. Non-scalar forms now dynamically detect type of right hand side.
-* _Breaking_: `tileToArray` has been replaced with `tile_to_array_double` and `tile_to_array_int`.
-* Added `render_matrix` debugging function.
-* _Breaking_: renamed `agg_histogram` to `agg_approx_histogram`, `local_agg_stats` to `agg_local_stats`, `local_agg_max` to `agg_local_max`, `local_agg_min` to `agg_local_min`, `local_agg_mean` to `agg_local_mean`, `local_agg_data_cells` to `agg_local_data_cells`, `local_agg_no_data_cells` to `agg_local_no_data_cells`.
-* _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `agg_stats` instead.
+* _Breaking_: `tileToArray` has been replaced with `_tile_to_array_double` and `_tile_to_array_int`.
+* _Breaking_: Renamed `bounds_geometry` to `rf_extent_geometry`.
+* _Breaking_: renamed `agg_histogram` to `rf_agg_approx_histogram`, `local_agg_stats` to `rf_agg_local_stats`, `local_agg_max` to `rf_agg_local_max`, `local_agg_min` to `rf_agg_local_min`, `local_agg_mean` to `rf_agg_local_mean`, `local_agg_data_cells` to `rf_agg_local_data_cells`, `local_agg_no_data_cells` to `rf_agg_local_no_data_cells`.
+* _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `rf_agg_stats` instead.
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `org.locationtech.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
-* Added `RasterFrame.withExtent` extension method.
-* Added `st_extent` and deprecated `envelope`. Renamed `bounds_geometry` to `extent_geometry`.
    
 ## 0.7.x
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 4c8077da6..3c734be2c 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -89,7 +89,7 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
       .format(L8CatalogDataSource.SHORT_NAME)
       .load()
       .withColumnRenamed(PDSFields.ACQUISITION_DATE.name, PDSFields.TIMESTAMP.name)
-      .withColumn(PDSFields.BOUNDS.name, extent_geometry(col(PDSFields.BOUNDS_WGS84.name)))
+      .withColumn(PDSFields.BOUNDS.name, rf_extent_geometry(col(PDSFields.BOUNDS_WGS84.name)))
       .drop(PDSFields.BOUNDS_WGS84.name)
 
     val filtered = aggFilters
diff --git a/pyrasterframes/python/examples/Clustering.py b/pyrasterframes/python/examples/Clustering.py
index 8566d6276..84e30c027 100644
--- a/pyrasterframes/python/examples/Clustering.py
+++ b/pyrasterframes/python/examples/Clustering.py
@@ -72,7 +72,7 @@
 layout = tlm['layoutDefinition']['tileLayout']
 
 retiled = clustered.groupBy('spatial_key').agg(
-    assemble_tile('column_index', 'row_index', 'prediction',
+    rf_assemble_tile('column_index', 'row_index', 'prediction',
         layout['tileCols'], layout['tileRows'], 'int8')
 )
 
diff --git a/pyrasterframes/python/examples/MeanValue.py b/pyrasterframes/python/examples/MeanValue.py
index 2c7308c43..2f02f0bac 100644
--- a/pyrasterframes/python/examples/MeanValue.py
+++ b/pyrasterframes/python/examples/MeanValue.py
@@ -21,5 +21,5 @@
 
 #py_mv_find_mean
 tileCol = 'tile'
-rf.agg(agg_no_data_cells(tileCol), agg_data_cells(tileCol), agg_mean(tileCol)).show(5, False)
+rf.agg(rf_agg_no_data_cells(tileCol), rf_agg_data_cells(tileCol), rf_agg_mean(tileCol)).show(5, False)
 #py_mv_find_mean
\ No newline at end of file
diff --git a/pyrasterframes/python/examples/NDVI.py b/pyrasterframes/python/examples/NDVI.py
index f98619d27..fc6594bb6 100644
--- a/pyrasterframes/python/examples/NDVI.py
+++ b/pyrasterframes/python/examples/NDVI.py
@@ -22,7 +22,7 @@
 
 #py_ndvi_column
 rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
-    .withColumn("ndvi", normalized_difference('red_band', 'nir_band'))
+    .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
 rf.printSchema()
 rf.show(20)
 #py_ndvi_column
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index b886bfabd..fe3f1b9ba 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -52,6 +52,7 @@ def _convertDF(df, sp_key = None, metadata = None):
 # TODO: make sure this supports **options
 DataFrameReader.geotiff = lambda df_reader, path: _reader(df_reader, "geotiff", path)
 DataFrameReader.geotrellis = lambda df_reader, path: _reader(df_reader, "geotrellis", path)
+DataFrameReader.rastersource = lambda df_reader, path: _reader(df_reader, "rastersource", path)
 
 
 
diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/python/pyrasterframes/context.py
index 80a2e120d..f6d307475 100644
--- a/pyrasterframes/python/pyrasterframes/context.py
+++ b/pyrasterframes/python/pyrasterframes/context.py
@@ -19,7 +19,7 @@ def __init__(self, spark_session):
         self._jrfctx = self._jvm.org.locationtech.rasterframes.py.PyRFContext(jsess)
 
     def list_to_seq(self, py_list):
-        conv = self.lookup('listToSeq')
+        conv = self.lookup('_listToSeq')
         return conv(py_list)
 
     def lookup(self, function_name):
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index ddc8c19f0..d23213a2a 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -21,15 +21,15 @@ def _context_call(name, *args):
 
 def _celltype(cellTypeStr):
     """ Convert the string cell type to the expected CellType object."""
-    return _context_call('cell_type', cellTypeStr)
+    return _context_call('rf_cell_type', cellTypeStr)
 
 
 def _create_assembleTile():
     """ Create a function mapping to the Scala implementation."""
     def _(colIndex, rowIndex, cellData, numCols, numRows, cellType):
-        jfcn = RFContext.active().lookup('assemble_tile')
+        jfcn = RFContext.active().lookup('rf_assemble_tile')
         return Column(jfcn(_to_java_column(colIndex), _to_java_column(rowIndex), _to_java_column(cellData), numCols, numRows, _celltype(cellType)))
-    _.__name__ = 'assemble_tile'
+    _.__name__ = 'rf_assemble_tile'
     _.__doc__ = "Create a Tile from  a column of cell data with location indices"
     _.__module__ = THIS_MODULE
     return _
@@ -38,9 +38,9 @@ def _(colIndex, rowIndex, cellData, numCols, numRows, cellType):
 def _create_arrayToTile():
     """ Create a function mapping to the Scala implementation."""
     def _(arrayCol, numCols, numRows):
-        jfcn = RFContext.active().lookup('array_to_tile')
+        jfcn = RFContext.active().lookup('rf_array_to_tile')
         return Column(jfcn(_to_java_column(arrayCol), numCols, numRows))
-    _.__name__ = 'array_to_tile'
+    _.__name__ = 'rf_array_to_tile'
     _.__doc__ = "Convert array in `arrayCol` into a Tile of dimensions `numCols` and `numRows'"
     _.__module__ = THIS_MODULE
     return _
@@ -49,9 +49,9 @@ def _(arrayCol, numCols, numRows):
 def _create_convertCellType():
     """ Create a function mapping to the Scala implementation."""
     def _(tileCol, cellType):
-        jfcn = RFContext.active().lookup('convert_cell_type')
+        jfcn = RFContext.active().lookup('rf_convert_cell_type')
         return Column(jfcn(_to_java_column(tileCol), _celltype(cellType)))
-    _.__name__ = 'convert_cell_type'
+    _.__name__ = 'rf_convert_cell_type'
     _.__doc__ = "Convert the numeric type of the Tiles in `tileCol`"
     _.__module__ = THIS_MODULE
     return _
@@ -60,9 +60,9 @@ def _(tileCol, cellType):
 def _create_makeConstantTile():
     """ Create a function mapping to the Scala implementation."""
     def _(value, cols, rows, cellType):
-        jfcn = RFContext.active().lookup('make_constant_tile')
+        jfcn = RFContext.active().lookup('rf_make_constant_tile')
         return Column(jfcn(value, cols, rows, cellType))
-    _.__name__ = 'make_constant_tile'
+    _.__name__ = 'rf_make_constant_tile'
     _.__doc__ = "Constructor for constant tile column"
     _.__module__ = THIS_MODULE
     return _
@@ -71,9 +71,9 @@ def _(value, cols, rows, cellType):
 def _create_tileZeros():
     """ Create a function mapping to the Scala implementation."""
     def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('tile_zeros')
+        jfcn = RFContext.active().lookup('rf_tile_zeros')
         return Column(jfcn(cols, rows, cellType))
-    _.__name__ = 'tile_zeros'
+    _.__name__ = 'rf_tile_zeros'
     _.__doc__ = "Create column of constant tiles of zero"
     _.__module__ = THIS_MODULE
     return _
@@ -82,31 +82,31 @@ def _(cols, rows, cellType = 'float64'):
 def _create_tileOnes():
     """ Create a function mapping to the Scala implementation."""
     def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('tile_ones')
+        jfcn = RFContext.active().lookup('rf_tile_ones')
         return Column(jfcn(cols, rows, cellType))
-    _.__name__ = 'tile_ones'
+    _.__name__ = 'rf_tile_ones'
     _.__doc__ = "Create column of constant tiles of one"
     _.__module__ = THIS_MODULE
     return _
 
 
 def _create_rasterize():
-    """ Create a function mapping to the Scala rasterize function. """
+    """ Create a function mapping to the Scala rf_rasterize function. """
     def _(geometryCol, boundsCol, valueCol, numCols, numRows):
-        jfcn = RFContext.active().lookup('rasterize')
+        jfcn = RFContext.active().lookup('rf_rasterize')
         return Column(jfcn(_to_java_column(geometryCol), _to_java_column(boundsCol), _to_java_column(valueCol), numCols, numRows))
-    _.__name__ = 'rasterize'
+    _.__name__ = 'rf_rasterize'
     _.__doc__ = 'Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value.'
     _.__module__ = THIS_MODULE
     return _
 
 
 def _create_reproject_geometry():
-    """ Create a function mapping to the Scala reproject_geometry function. """
+    """ Create a function mapping to the Scala rf_reproject_geometry function. """
     def _(geometryCol, srcCRSName, dstCRSName):
-        jfcn = RFContext.active().lookup('reproject_geometry')
+        jfcn = RFContext.active().lookup('rf_reproject_geometry')
         return Column(jfcn(_to_java_column(geometryCol), srcCRSName, dstCRSName))
-    _.__name__ = 'reproject_geometry'
+    _.__name__ = 'rf_reproject_geometry'
     _.__doc__ = """Reproject a column of geometry given the CRS names of the source and destination.
 Currently supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
 An example of a valid CRS name is EPSG:3005.
@@ -116,137 +116,137 @@ def _(geometryCol, srcCRSName, dstCRSName):
 
 
 def _create_explode_tiles():
-    """ Create a function mapping to Scala explode_tiles function """
+    """ Create a function mapping to Scala rf_explode_tiles function """
     def _(*args):
-        jfcn = RFContext.active().lookup('explode_tiles')
+        jfcn = RFContext.active().lookup('rf_explode_tiles')
         jcols = [_to_java_column(arg) for arg in args]
         return Column(jfcn(RFContext.active().list_to_seq(jcols)))
-    _.__name__ = 'explode_tiles'
+    _.__name__ = 'rf_explode_tiles'
     _.__doc__ = 'Create a row for each cell in Tile.'
     _.__module__ = THIS_MODULE
     return _
 
 
 def _create_explode_tiles_sample():
-    """ Create a function mapping to Scala explode_tiles_sample function"""
+    """ Create a function mapping to Scala rf_explode_tiles_sample function"""
     def _(sample_frac, seed, *tile_cols):
-        jfcn = RFContext.active().lookup('explode_tiles_sample')
+        jfcn = RFContext.active().lookup('rf_explode_tiles_sample')
         jcols = [_to_java_column(arg) for arg in tile_cols]
         return Column(jfcn(sample_frac, seed, RFContext.active().list_to_seq(jcols)))
 
-    _.__name__ = 'explode_tiles_sample'
+    _.__name__ = 'rf_explode_tiles_sample'
     _.__doc__ = 'Create a row for a sample of cells in Tile columns.'
     _.__module__ = THIS_MODULE
     return _
 
 
 def _create_maskByValue():
-    """ Create a function mapping to Scala mask_by_value function """
+    """ Create a function mapping to Scala rf_mask_by_value function """
     def _(data_tile, mask_tile, mask_value):
-        jfcn = RFContext.active().lookup('mask_by_value')
+        jfcn = RFContext.active().lookup('rf_mask_by_value')
         return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
-    _.__name__ = 'mask_by_value'
+    _.__name__ = 'rf_mask_by_value'
     _.__doc__ = 'Generate a tile with the values from the data tile, but where cells in the masking tile contain the masking value, replace the data value with NODATA.'
     _.__module__ = THIS_MODULE
     return _
 
 
 _rf_unique_functions = {
-    'array_to_tile': _create_arrayToTile(),
-    'assemble_tile': _create_assembleTile(),
-    'cell_types': lambda: _context_call('cell_types'),
-    'convert_cell_type': _create_convertCellType(),
-    'explode_tiles': _create_explode_tiles(),
-    'explode_tiles_sample': _create_explode_tiles_sample(),
-    'make_constant_tile': _create_makeConstantTile(),
-    'mask_by_value': _create_maskByValue(),
-    'rasterize': _create_rasterize(),
-    'reproject_geometry': _create_reproject_geometry(),
-    'tile_ones': _create_tileOnes(),
-    'tile_zeros': _create_tileZeros(),
+    'rf_array_to_tile': _create_arrayToTile(),
+    'rf_assemble_tile': _create_assembleTile(),
+    'rf_cell_types': lambda: _context_call('rf_cell_types'),
+    'rf_convert_cell_type': _create_convertCellType(),
+    'rf_explode_tiles': _create_explode_tiles(),
+    'rf_explode_tiles_sample': _create_explode_tiles_sample(),
+    'rf_make_constant_tile': _create_makeConstantTile(),
+    'rf_mask_by_value': _create_maskByValue(),
+    'rf_rasterize': _create_rasterize(),
+    'rf_reproject_geometry': _create_reproject_geometry(),
+    'rf_tile_ones': _create_tileOnes(),
+    'rf_tile_zeros': _create_tileZeros(),
 }
 
 
 _rf_column_scalar_functions = {
-    'with_no_data': 'Assign a `NoData` value to the Tiles in the given Column.',
-    'local_add_scalar': 'Add a scalar to a Tile',
-    'local_add_scalar_int': 'Add a scalar to a Tile',
-    'local_subtract_scalar': 'Subtract a scalar from a Tile',
-    'local_subtract_scalar_int': 'Subtract a scalar from a Tile',
-    'local_multiply_scalar': 'Multiply a Tile by a scalar',
-    'local_multiply_scalar_int': 'Multiply a Tile by a scalar',
-    'local_divide_scalar': 'Divide a Tile by a scalar',
-    'local_divide_scalar_int': 'Divide a Tile by a scalar',
-    'local_less_scalar': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
-    'local_less_scalar_int': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
-    'local_less_equal_scalar': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
-    'local_less_equal_scalar_int': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
-    'local_greater_scalar': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
-    'local_greater_scalar_int': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
-    'local_greater_equal_scalar': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
-    'local_greater_equal_scalar_int': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
-    'local_equal_scalar': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
-    'local_equal_scalar_int': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
-    'local_unequal_scalar': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
-    'local_unequal_scalar_int': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
+    'rf_with_no_data': 'Assign a `NoData` value to the Tiles in the given Column.',
+    'rf_local_add_double': 'Add a scalar to a Tile',
+    'rf_local_add_int': 'Add a scalar to a Tile',
+    'rf_local_subtract_double': 'Subtract a scalar from a Tile',
+    'rf_local_subtract_int': 'Subtract a scalar from a Tile',
+    'rf_local_multiply_double': 'Multiply a Tile by a scalar',
+    'rf_local_multiply_int': 'Multiply a Tile by a scalar',
+    'rf_local_divide_double': 'Divide a Tile by a scalar',
+    'rf_local_divide_int': 'Divide a Tile by a scalar',
+    'rf_local_less_double': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
+    'rf_local_less_int': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
+    'rf_local_less_equal_double': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
+    'rf_local_less_equal_int': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
+    'rf_local_greater_double': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
+    'rf_local_greater_int': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
+    'rf_local_greater_equal_double': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
+    'rf_local_greater_equal_int': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
+    'rf_local_equal_double': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
+    'rf_local_equal_int': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
+    'rf_local_unequal_double': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
+    'rf_local_unequal_int': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
 }
 
 
 _rf_column_functions = {
     # ------- RasterFrames functions -------
-    'tile_dimensions': 'Query the number of (cols, rows) in a Tile.',
+    'rf_tile_dimensions': 'Query the number of (cols, rows) in a Tile.',
     'envelope': 'Extracts the bounding box (envelope) of the geometry.',
-    'tile_to_int_array': 'Flattens Tile into an array of integers. Deprecated in favor of `tile_to_array_int`.',
-    'tile_to_double_array': 'Flattens Tile into an array of doubles. Deprecated in favor of `tile_to_array_double`',
-    'tile_to_array_int': 'Flattens Tile into an array of integers.',
-    'tile_to_array_double': 'Flattens Tile into an array of doubles.',
-    'cell_type': 'Extract the Tile\'s cell type',
-    'is_no_data_tile': 'Report if the Tile is entirely NODDATA cells',
-    'agg_approx_histogram': 'Compute the full column aggregate floating point histogram',
-    'agg_stats': 'Compute the full column aggregate floating point statistics',
-    'agg_mean': 'Computes the column aggregate mean',
-    'agg_data_cells': 'Computes the number of non-NoData cells in a column',
-    'agg_no_data_cells': 'Computes the number of NoData cells in a column',
-    'tile_histogram': 'Compute the Tile-wise histogram',
-    'tile_mean': 'Compute the Tile-wise mean',
-    'tile_sum': 'Compute the Tile-wise sum',
-    'tile_min': 'Compute the Tile-wise minimum',
-    'tile_max': 'Compute the Tile-wise maximum',
-    'tile_stats': 'Compute the Tile-wise floating point statistics',
-    'render_ascii': 'Render ASCII art of tile',
-    'no_data_cells': 'Count of NODATA cells',
-    'data_cells': 'Count of cells with valid data',
-    'local_add': 'Add two Tiles',
-    'local_subtract': 'Subtract two Tiles',
-    'local_multiply': 'Multiply two Tiles',
-    'local_divide': 'Divide two Tiles',
-    'normalized_difference': 'Compute the normalized difference of two tiles',
+    'tile_to_int_array': 'Flattens Tile into an array of integers. Deprecated in favor of `rf_tile_to_array_int`.',
+    'tile_to_double_array': 'Flattens Tile into an array of doubles. Deprecated in favor of `rf_tile_to_array_double`',
+    'rf_tile_to_array_int': 'Flattens Tile into an array of integers.',
+    'rf_tile_to_array_double': 'Flattens Tile into an array of doubles.',
+    'rf_cell_type': 'Extract the Tile\'s cell type',
+    'rf_is_no_data_tile': 'Report if the Tile is entirely NODDATA cells',
+    'rf_agg_approx_histogram': 'Compute the full column aggregate floating point histogram',
+    'rf_agg_stats': 'Compute the full column aggregate floating point statistics',
+    'rf_agg_mean': 'Computes the column aggregate mean',
+    'rf_agg_data_cells': 'Computes the number of non-NoData cells in a column',
+    'rf_agg_no_data_cells': 'Computes the number of NoData cells in a column',
+    'rf_tile_histogram': 'Compute the Tile-wise histogram',
+    'rf_tile_mean': 'Compute the Tile-wise mean',
+    'rf_tile_sum': 'Compute the Tile-wise sum',
+    'rf_tile_min': 'Compute the Tile-wise minimum',
+    'rf_tile_max': 'Compute the Tile-wise maximum',
+    'rf_tile_stats': 'Compute the Tile-wise floating point statistics',
+    'rf_render_ascii': 'Render ASCII art of tile',
+    'rf_no_data_cells': 'Count of NODATA cells',
+    'rf_data_cells': 'Count of cells with valid data',
+    'rf_local_add': 'Add two Tiles',
+    'rf_local_subtract': 'Subtract two Tiles',
+    'rf_local_multiply': 'Multiply two Tiles',
+    'rf_local_divide': 'Divide two Tiles',
+    'rf_normalized_difference': 'Compute the normalized difference of two tiles',
     'local_agg_stats': 'Compute cell-local aggregate descriptive statistics for a column of Tiles.',
-    'agg_local_max': 'Compute the cell-wise/local max operation between Tiles in a column.',
-    'agg_local_min': 'Compute the cellwise/local min operation between Tiles in a column.',
-    'agg_local_mean': 'Compute the cellwise/local mean operation between Tiles in a column.',
-    'agg_local_data_cells': 'Compute the cellwise/local count of non-NoData cells for all Tiles in a column.',
-    'agg_local_no_data_cells': 'Compute the cellwise/local count of NoData cells for all Tiles in a column.',
-    'mask': 'Where the mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA.',
-    'inverse_mask': 'Where the mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA.',
-    'local_less': 'Cellwise less than comparison between two tiles',
-    'local_less_equal': 'Cellwise less than or equal to comparison between two tiles',
-    'local_greater': 'Cellwise greater than comparison between two tiles',
-    'local_greater_equal': 'Cellwise greater than or equal to comparison between two tiles',
-    'local_equal': 'Cellwise equality comparison between two tiles',
-    'local_unequal': 'Cellwise inequality comparison between two tiles',
-    'round': 'Round cell values to the nearest integer without changing the cell type',
-    'log': 'Performs cell-wise natural logarithm',
-    'log10': 'Performs cell-wise logartithm with base 10',
-    'log2': 'Performs cell-wise logartithm with base 2',
-    'log1p': 'Performs natural logarithm of cell values plus one',
-    'exp': 'Performs cell-wise exponential',
-    'exp2': 'Compute 2 to the power of cell values',
-    'exp10': 'Compute 10 to the power of cell values',
-    'expm1': 'Performs cell-wise exponential, then subtract one',
-    'resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
-    'st_extent': 'Compute the extent/bbox of a Geometry',
-    'extent_geometry': 'Convert the given extent/bbox to a polygon',
+    'rf_agg_local_max': 'Compute the cell-wise/local max operation between Tiles in a column.',
+    'rf_agg_local_min': 'Compute the cellwise/local min operation between Tiles in a column.',
+    'rf_agg_local_mean': 'Compute the cellwise/local mean operation between Tiles in a column.',
+    'rf_agg_local_data_cells': 'Compute the cellwise/local count of non-NoData cells for all Tiles in a column.',
+    'rf_agg_local_no_data_cells': 'Compute the cellwise/local count of NoData cells for all Tiles in a column.',
+    'rf_mask': 'Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA.',
+    'rf_inverse_mask': 'Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA.',
+    'rf_local_less': 'Cellwise less than comparison between two tiles',
+    'rf_local_less_equal': 'Cellwise less than or equal to comparison between two tiles',
+    'rf_local_greater': 'Cellwise greater than comparison between two tiles',
+    'rf_local_greater_equal': 'Cellwise greater than or equal to comparison between two tiles',
+    'rf_local_equal': 'Cellwise equality comparison between two tiles',
+    'rf_local_unequal': 'Cellwise inequality comparison between two tiles',
+    'rf_round': 'Round cell values to the nearest integer without changing the cell type',
+    'rf_log': 'Performs cell-wise natural logarithm',
+    'rf_log10': 'Performs cell-wise logartithm with base 10',
+    'rf_log2': 'Performs cell-wise logartithm with base 2',
+    'rf_log1p': 'Performs natural logarithm of cell values plus one',
+    'rf_exp': 'Performs cell-wise exponential',
+    'rf_exp2': 'Compute 2 to the power of cell values',
+    'rf_exp10': 'Compute 10 to the power of cell values',
+    'rf_expm1': 'Performs cell-wise exponential, then subtract one',
+    'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
+    'rf_extent': 'Compute the extent/bbox of a Geometry',
+    'rf_extent_geometry': 'Convert the given extent/bbox to a polygon',
 
     # ------- JTS functions -------
     # spatial constructors
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index d0c531533..2dbeb566f 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -52,7 +52,7 @@ def setUpClass(cls):
 
         # convert the tile cell type to provide for other operations
         cls.tileCol = 'tile'
-        cls.rf = rf.withColumn('tile2', convert_cell_type(cls.tileCol, 'float32')) \
+        cls.rf = rf.withColumn('tile2', rf_convert_cell_type(cls.tileCol, 'float32')) \
             .drop(cls.tileCol) \
             .withColumnRenamed('tile2', cls.tileCol).asRF()
         #cls.rf.show()
@@ -74,71 +74,71 @@ def test_tile_operations(self):
         df1 = self.rf.withColumnRenamed(self.tileCol, 't1').asRF()
         df2 = self.rf.withColumnRenamed(self.tileCol, 't2').asRF()
         df3 = df1.spatialJoin(df2).asRF()
-        df3 = df3.withColumn('norm_diff', normalized_difference('t1', 't2'))
+        df3 = df3.withColumn('norm_diff', rf_normalized_difference('t1', 't2'))
         df3.printSchema()
 
         aggs = df3.agg(
-            agg_mean('norm_diff'),
+            rf_agg_mean('norm_diff'),
         )
         aggs.show()
         row = aggs.first()
 
-        self.assertTrue(_rounded_compare(row['agg_mean(norm_diff)'], 0))
+        self.assertTrue(_rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
 
 
     def test_general(self):
         meta = self.rf.tileLayerMetadata()
         self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims',  tile_dimensions(self.tileCol)) \
-            .withColumn('type', cell_type(self.tileCol)) \
-            .withColumn('dCells', data_cells(self.tileCol)) \
-            .withColumn('ndCells', no_data_cells(self.tileCol)) \
-            .withColumn('min', tile_min(self.tileCol)) \
-            .withColumn('max', tile_max(self.tileCol)) \
-            .withColumn('mean', tile_mean(self.tileCol)) \
-            .withColumn('sum', tile_sum(self.tileCol)) \
-            .withColumn('stats', tile_stats(self.tileCol)) \
-            .withColumn('envelope', envelope('geometry')) \
-            .withColumn('ascii', render_ascii(self.tileCol)) \
-            .withColumn('log', log(self.tileCol)) \
-            .withColumn('exp', exp(self.tileCol)) \
-            .withColumn('expm1', expm1(self.tileCol)) \
-            .withColumn('round', round(self.tileCol))
+        df = self.rf.withColumn('dims',  rf_tile_dimensions(self.tileCol)) \
+            .withColumn('type', rf_cell_type(self.tileCol)) \
+            .withColumn('dCells', rf_data_cells(self.tileCol)) \
+            .withColumn('ndCells', rf_no_data_cells(self.tileCol)) \
+            .withColumn('min', rf_tile_min(self.tileCol)) \
+            .withColumn('max', rf_tile_max(self.tileCol)) \
+            .withColumn('mean', rf_tile_mean(self.tileCol)) \
+            .withColumn('sum', rf_tile_sum(self.tileCol)) \
+            .withColumn('stats', rf_tile_stats(self.tileCol)) \
+            .withColumn('extent', rf_extent('geometry')) \
+            .withColumn('ascii', rf_render_ascii(self.tileCol)) \
+            .withColumn('log', rf_log(self.tileCol)) \
+            .withColumn('exp', rf_exp(self.tileCol)) \
+            .withColumn('expm1', rf_expm1(self.tileCol)) \
+            .withColumn('round', rf_round(self.tileCol))
 
         df.show()
 
     def test_rasterize(self):
-        # NB: This test just makes sure rasterize runs, not that the results are correct.
-        withRaster = self.rf.withColumn('rasterize', rasterize('geometry', 'geometry', lit(42), 10, 10))
+        # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
+        withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
         withRaster.show()
 
     def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', reproject_geometry('center', 'EPSG:4326', 'EPSG:3857'))
+        reprojected = self.rf.withColumn('reprojected', rf_reproject_geometry('center', 'EPSG:4326', 'EPSG:3857'))
         reprojected.show()
 
     def test_aggregations(self):
         aggs = self.rf.agg(
-            agg_mean(self.tileCol),
-            agg_data_cells(self.tileCol),
-            agg_no_data_cells(self.tileCol),
-            agg_stats(self.tileCol),
-            agg_approx_histogram(self.tileCol)
+            rf_agg_mean(self.tileCol),
+            rf_agg_data_cells(self.tileCol),
+            rf_agg_no_data_cells(self.tileCol),
+            rf_agg_stats(self.tileCol),
+            rf_agg_approx_histogram(self.tileCol)
         )
         aggs.show()
         row = aggs.first()
 
-        self.assertTrue(_rounded_compare(row['agg_mean(tile)'], 10160))
-        print(row['agg_data_cells(tile)'])
-        self.assertEqual(row['agg_data_cells(tile)'], 387000)
-        self.assertEqual(row['agg_no_data_cells(tile)'], 1000)
-        self.assertEqual(row['agg_stats(tile)'].data_cells, row['agg_data_cells(tile)'])
+        self.assertTrue(_rounded_compare(row['rf_agg_mean(tile)'], 10160))
+        print(row['rf_agg_data_cells(tile)'])
+        self.assertEqual(row['rf_agg_data_cells(tile)'], 387000)
+        self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
+        self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
 
     def test_sql(self):
 
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims',  tile_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims',  rf_tile_dimensions(self.tileCol)).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
@@ -152,11 +152,11 @@ def test_sql(self):
                                 FROM r3""")
 
         ops.printSchema
-        statsRow = ops.select(tile_mean(self.tileCol).alias('base'),
-                           tile_mean("AndOne").alias('plus_one'),
-                           tile_mean("LessOne").alias('minus_one'),
-                           tile_mean("TimesTwo").alias('double'),
-                           tile_mean("OverTwo").alias('half')) \
+        statsRow = ops.select(rf_tile_mean(self.tileCol).alias('base'),
+                           rf_tile_mean("AndOne").alias('plus_one'),
+                           rf_tile_mean("LessOne").alias('minus_one'),
+                           rf_tile_mean("TimesTwo").alias('double'),
+                           rf_tile_mean("OverTwo").alias('half')) \
                         .first()
 
         self.assertTrue(_rounded_compare(statsRow.base, statsRow.plus_one - 1))
@@ -166,12 +166,12 @@ def test_sql(self):
 
     def test_explode(self):
         import pyspark.sql.functions as F
-        self.rf.select('spatial_key', explode_tiles(self.tileCol)).show()
+        self.rf.select('spatial_key', rf_explode_tiles(self.tileCol)).show()
         # +-----------+------------+---------+-------+
         # |spatial_key|column_index|row_index|tile   |
         # +-----------+------------+---------+-------+
         # |[2,1]      |4           |0        |10150.0|
-        cell = self.rf.select(self.rf.spatialKeyColumn(), explode_tiles(self.rf.tile)) \
+        cell = self.rf.select(self.rf.spatialKeyColumn(), rf_explode_tiles(self.rf.tile)) \
             .where(F.col("spatial_key.col")==2) \
             .where(F.col("spatial_key.row")==1) \
             .where(F.col("column_index")==4) \
@@ -182,7 +182,7 @@ def test_explode(self):
 
         # Test the sample version
         frac = 0.01
-        sample_count = self.rf.select(explode_tiles_sample(frac, 1872, self.tileCol)).count()
+        sample_count = self.rf.select(rf_explode_tiles_sample(frac, 1872, self.tileCol)).count()
         print('Sample count is {}'.format(sample_count))
         self.assertTrue(sample_count > 0)
         self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
@@ -195,13 +195,13 @@ def test_maskByValue(self):
         mask_value = 4
 
         rf1 = self.rf.select(self.rf.tile,
-                             local_multiply(
-                                 convert_cell_type(
-                                     local_greater_scalar_int(self.rf.tile, 25000),
+                             rf_local_multiply(
+                                 rf_convert_cell_type(
+                                     rf_local_greater_int(self.rf.tile, 25000),
                                      "uint8"),
                                   lit(mask_value)).alias('mask'))
-        rf2 = rf1.select(rf1.tile, mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
-        result = rf2.agg(agg_no_data_cells(rf2.tile) < agg_no_data_cells(rf2.masked)) \
+        rf2 = rf1.select(rf1.tile, rf_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
+        result = rf2.agg(rf_agg_no_data_cells(rf2.tile) < rf_agg_no_data_cells(rf2.masked)) \
             .collect()[0][0]
         self.assertTrue(result)
 
@@ -209,8 +209,8 @@ def test_maskByValue(self):
     def test_resample(self):
         from pyspark.sql.functions import lit
         result = self.rf.select(
-            tile_min(local_equal(
-                resample(resample(self.rf.tile, lit(2)), lit(0.5)),
+            rf_tile_min(rf_local_equal(
+                rf_resample(rf_resample(self.rf.tile, lit(2)), lit(0.5)),
                 self.rf.tile))
         ).collect()[0][0]
 
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 3ea7ab4a0..a649cd8a7 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -20,14 +20,11 @@
  */
 package org.locationtech.rasterframes.py
 
-import org.locationtech.rasterframes._
-import org.locationtech.jts.geom.Geometry
-import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
+import geotrellis.raster.{CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
-import org.locationtech.geomesa.spark.jts.util.WKBUtils
-import org.locationtech.rasterframes.RasterFunctions
+import org.locationtech.rasterframes.{RasterFunctions, _}
 import org.locationtech.rasterframes.model.LazyCRS
 import spray.json._
 
@@ -95,27 +92,18 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   /**
     * Convenience functions for use in Python
     */
-  def cell_type(name: String): CellType = CellType.fromName(name)
+  def rf_cell_type(name: String): CellType = CellType.fromName(name)
 
   /**
     * Convenience list of valid cell type strings
     * @return Java List of String, which py4j can interpret as a python `list`
     */
-  def cell_types = {
+  def rf_cell_types = {
     org.locationtech.rasterframes.functions.cellTypes().asJava
   }
 
-  /** DESERIALIZATION **/
-
-  def generate_tile(cellType: String, cols: Int, rows: Int, bytes: Array[Byte]): ArrayTile = {
-    ArrayTile.fromBytes(bytes, this.cell_type(cellType), cols, rows)
-  }
-
-  def generate_geometry(obj: Array[Byte]): Geometry =  WKBUtils.read(obj)
-
-  def explode_tiles_sample(sampleFraction: Double, seed: Long, cols: Column*): Column =
-    explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
-
+  def rf_explode_tiles_sample(sampleFraction: Double, seed: Long, cols: Column*): Column =
+    rf_explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
 
   def tileColumns(df: DataFrame): Array[Column] =
     df.asRF.tileColumns.toArray
@@ -126,51 +114,53 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   def temporalKeyColumn(df: DataFrame): Column =
     df.asRF.temporalKeyColumn.orNull
 
-  def tile_to_int_array(col: Column): Column = tile_to_array_int(col)
-
-  def tile_to_double_array(col: Column): Column = tile_to_array_double(col)
-
   // All the scalar tile arithmetic functions
 
-  def local_add_scalar(col: Column, scalar: Double): Column = local_add[Double](col, scalar)
+  def rf_local_add_double(col: Column, scalar: Double): Column = rf_local_add[Double](col, scalar)
 
-  def local_add_scalar_int(col: Column, scalar: Int): Column = local_add[Int](col, scalar)
+  def rf_local_add_int(col: Column, scalar: Int): Column = rf_local_add[Int](col, scalar)
 
-  def local_subtract_scalar(col: Column, scalar: Double): Column = local_subtract[Double](col, scalar)
+  def rf_local_subtract_double(col: Column, scalar: Double): Column = rf_local_subtract[Double](col, scalar)
 
-  def local_subtract_scalar_int(col: Column, scalar: Int): Column = local_subtract[Int](col, scalar)
+  def rf_local_subtract_int(col: Column, scalar: Int): Column = rf_local_subtract[Int](col, scalar)
 
-  def local_divide_scalar(col: Column, scalar: Double): Column = local_divide[Double](col, scalar)
+  def rf_local_divide_double(col: Column, scalar: Double): Column = rf_local_divide[Double](col, scalar)
 
-  def local_divide_scalar_int(col: Column, scalar: Int): Column = local_divide[Int](col, scalar)
+  def rf_local_divide_int(col: Column, scalar: Int): Column = rf_local_divide[Int](col, scalar)
 
-  def local_multiply_scalar(col: Column, scalar: Double): Column = local_multiply[Double](col, scalar)
+  def rf_local_multiply_double(col: Column, scalar: Double): Column = rf_local_multiply[Double](col, scalar)
 
-  def local_multiply_scalar_int(col: Column, scalar: Int): Column = local_multiply[Int](col, scalar)
+  def rf_local_multiply_int(col: Column, scalar: Int): Column = rf_local_multiply[Int](col, scalar)
 
-  def local_less_scalar(col: Column, scalar: Double): Column = local_less[Double](col, scalar)
+  def rf_local_less_double(col: Column, scalar: Double): Column = rf_local_less[Double](col, scalar)
 
-  def local_less_scalar_int(col: Column, scalar: Int): Column = local_less[Int](col, scalar)
+  def rf_local_less_int(col: Column, scalar: Int): Column = rf_local_less[Int](col, scalar)
 
-  def local_less_equal_scalar(col: Column, scalar: Double): Column = local_less_equal[Double](col, scalar)
+  def rf_local_less_equal_double(col: Column, scalar: Double): Column = rf_local_less_equal[Double](col, scalar)
 
-  def local_less_equal_scalar_int(col: Column, scalar: Int): Column = local_less_equal[Int](col, scalar)
+  def rf_local_less_equal_int(col: Column, scalar: Int): Column = rf_local_less_equal[Int](col, scalar)
 
-  def local_greater_scalar(col: Column, scalar: Double): Column = local_greater[Double](col, scalar)
+  def rf_local_greater_double(col: Column, scalar: Double): Column = rf_local_greater[Double](col, scalar)
 
-  def local_greater_scalar_int(col: Column, scalar: Int): Column = local_greater[Int](col, scalar)
+  def rf_local_greater_int(col: Column, scalar: Int): Column = rf_local_greater[Int](col, scalar)
 
-  def local_greater_equal_scalar(col: Column, scalar: Double): Column = local_greater_equal[Double](col, scalar)
+  def rf_local_greater_equal_double(col: Column, scalar: Double): Column = rf_local_greater_equal[Double](col, scalar)
 
-  def local_greater_equal_scalar_int(col: Column, scalar: Int): Column = local_greater_equal[Int](col, scalar)
+  def rf_local_greater_equal_int(col: Column, scalar: Int): Column = rf_local_greater_equal[Int](col, scalar)
 
-  def local_equal_scalar(col: Column, scalar: Double): Column = local_equal[Double](col, scalar)
+  def rf_local_equal_double(col: Column, scalar: Double): Column = rf_local_equal[Double](col, scalar)
 
-  def local_equal_scalar_int(col: Column, scalar: Int): Column = local_equal[Int](col, scalar)
+  def rf_local_equal_int(col: Column, scalar: Int): Column = rf_local_equal[Int](col, scalar)
 
-  def local_unequal_scalar(col: Column, scalar: Double): Column = local_unequal[Double](col, scalar)
+  def rf_local_unequal_double(col: Column, scalar: Double): Column = rf_local_unequal[Double](col, scalar)
 
-  def local_unequal_scalar_int(col: Column, scalar: Int): Column = local_unequal[Int](col, scalar)
+  def rf_local_unequal_int(col: Column, scalar: Int): Column = rf_local_unequal[Int](col, scalar)
+
+  def rf_reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
+    val src = LazyCRS(srcName)
+    val dst = LazyCRS(dstName)
+    rf_reproject_geometry(geometryCol, src, dst)
+  }
 
   // return toRaster, get just the tile, and make an array out of it
   def toIntRaster(df: DataFrame, colname: String, cols: Int, rows: Int): Array[Int] = {
@@ -195,11 +185,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def withSpatialIndex(df: DataFrame): RasterFrame = df.asRF.withSpatialIndex()
 
-  def reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
-    val src = LazyCRS(srcName)
-    val dst = LazyCRS(dstName)
-    reproject_geometry(geometryCol, src, dst)
-  }
+  //----------------------------Support Routines-----------------------------------------
 
-  def listToSeq(cols: java.util.ArrayList[AnyRef]): Seq[AnyRef] = cols.asScala
+  def _listToSeq(cols: java.util.ArrayList[AnyRef]): Seq[AnyRef] = cols.asScala
 }

From 3582b614eb080432f588905dd2fb7e686abb2ad8 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Apr 2019 17:26:24 -0400
Subject: [PATCH 051/380] Fixed integration test.

---
 .../experimental/datasource/ReadTilesExpression.scala           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
index 875d30c1a..37e32ba46 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -30,6 +30,7 @@ import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.{ProjectedRaster, Tile}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
+import geotrellis.spark.io.http.util.HttpRangeReader
 import geotrellis.spark.io.s3.S3Client
 import geotrellis.spark.io.s3.util.S3RangeReader
 import geotrellis.util.{ByteReader, FileRangeReader, RangeReader, StreamingByteReader}
@@ -41,7 +42,6 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
 import org.apache.spark.sql.types._
-import org.locationtech.rasterframes.ref.HttpRangeReader
 
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal

From d29cda26e855388dd971f0e07b22430c81c2e690 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Apr 2019 12:21:00 -0400
Subject: [PATCH 052/380] Changed constant tile injection via literal rather
 than UDF. Normalization of naming of tile creation functions.

---
 .../apache/spark/sql/rf/RasterSourceUDT.scala |  2 +-
 .../rasterframes/RasterFunctions.scala        | 67 ++++++++++++-------
 .../rasterframes/encoders/package.scala       |  8 ++-
 .../transformers/URIToRasterSource.scala      |  4 +-
 .../rasterframes/functions/package.scala      |  4 +-
 .../rasterframes/model/Cells.scala            | 19 ++++--
 .../rasterframes/rasterframes.scala           |  6 +-
 .../rasterframes/tiles/InternalRowTile.scala  | 36 +++-------
 .../rasterframes/util/package.scala           | 11 +--
 .../rasterframes/RasterFunctionsSpec.scala    | 37 ++++++++--
 .../rasterframes/TileStatsSpec.scala          |  2 +-
 docs/src/main/tut/reference.md                |  2 +-
 docs/src/main/tut/release-notes.md            |  6 +-
 .../datasource/ReadTilesExpression.scala      |  2 +-
 pyrasterframes/python/coverage-report.sh      |  9 +++
 .../python/pyrasterframes/rasterfunctions.py  | 14 ++--
 .../python/tests/PyRasterFramesTests.py       |  5 ++
 17 files changed, 146 insertions(+), 88 deletions(-)
 create mode 100755 pyrasterframes/python/coverage-report.sh

diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index fba25bf83..bbcafec2a 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -65,7 +65,7 @@ class RasterSourceUDT extends UserDefinedType[RasterSource] {
   }
 }
 
-object RasterSourceUDT extends RasterSourceUDT {
+object RasterSourceUDT {
   UDTRegistration.register(classOf[RasterSource].getName, classOf[RasterSourceUDT].getName)
 
   implicit val rasterSourceSerializer: CatalystSerializer[RasterSource] = new CatalystSerializer[RasterSource] {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 976be8330..95adea29f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -97,6 +97,14 @@ trait RasterFunctions {
   def rf_convert_cell_type(col: Column, cellTypeName: String): TypedColumn[Any, Tile] =
     SetCellType(col, cellTypeName)
 
+  /** Resample tile to different size based on scalar factor or tile whose dimension to match. Scalar less
+    * than one will downsample tile; greater than one will upsample. Uses nearest-neighbor. */
+  def rf_resample[T: Numeric](tileCol: Column, factorValue: T) = Resample(tileCol, factorValue)
+
+  /** Resample tile to different size based on scalar factor or tile whose dimension to match. Scalar less
+    * than one will downsample tile; greater than one will upsample. Uses nearest-neighbor. */
+  def rf_resample(tileCol: Column, factorCol: Column) = Resample(tileCol, factorCol)
+
   /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
   def rf_extent_geometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
 
@@ -201,27 +209,45 @@ trait RasterFunctions {
   def rf_local_divide[T: Numeric](tileCol: Column, value: T): TypedColumn[Any, Tile] = Divide(tileCol, value)
 
   /** Perform an arbitrary GeoTrellis `LocalTileBinaryOp` between two Tile columns. */
-  def rf_local_algebra(op: LocalTileBinaryOp, left: Column, right: Column):
-  TypedColumn[Any, Tile] =
-    withAlias(opName(op), left, right)(
-      udf[Tile, Tile, Tile](op.apply).apply(left, right)
-    ).as[Tile]
+  def rf_local_algebra(op: LocalTileBinaryOp, left: Column, right: Column): TypedColumn[Any, Tile] =
+    withTypedAlias(opName(op), left, right)(udf[Tile, Tile, Tile](op.apply).apply(left, right))
 
   /** Compute the normalized difference of two tile columns */
   def rf_normalized_difference(left: Column, right: Column) =
     NormalizedDifference(left, right)
 
-  /** Constructor for constant tile column */
-  def rf_make_constant_tile(value: Number, cols: Int, rows: Int, cellType: String): TypedColumn[Any, Tile] =
-    udf(() => F.makeConstantTile(value, cols, rows, cellType)).apply().as(s"constant_$cellType").as[Tile]
-
-  /** Alias for column of constant tiles of zero */
-  def rf_tile_zeros(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
-    udf(() => F.tileZeros(cols, rows, cellType)).apply().as(s"zeros_$cellType").as[Tile]
-
-  /** Alias for column of constant tiles of one */
-  def rf_tile_ones(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
-    udf(() => F.tileOnes(cols, rows, cellType)).apply().as(s"ones_$cellType").as[Tile]
+  /** Constructor for tile column with a single cell value. */
+  def rf_make_constant_tile(value: Number, cols: Int, rows: Int, cellType: CellType): TypedColumn[Any, Tile] =
+    rf_make_constant_tile(value, cols, rows, cellType.name)
+
+  /** Constructor for tile column with a single cell value. */
+  def rf_make_constant_tile(value: Number, cols: Int, rows: Int, cellTypeName: String): TypedColumn[Any, Tile] = {
+    import org.apache.spark.sql.rf.TileUDT.tileSerializer
+    val constTile = encoders.serialized_literal(F.makeConstantTile(value, cols, rows, cellTypeName))
+    withTypedAlias(s"rf_make_constant_tile($value, $cols, $rows, $cellTypeName)")(constTile)
+  }
+
+  /** Create a column constant tiles of zero */
+  def rf_make_zeros_tile(cols: Int, rows: Int, cellType: CellType): TypedColumn[Any, Tile] =
+    rf_make_zeros_tile(cols, rows, cellType.name)
+
+  /** Create a column constant tiles of zero */
+  def rf_make_zeros_tile(cols: Int, rows: Int, cellTypeName: String): TypedColumn[Any, Tile] = {
+    import org.apache.spark.sql.rf.TileUDT.tileSerializer
+    val constTile = encoders.serialized_literal(F.tileZeros(cols, rows, cellTypeName))
+    withTypedAlias(s"rf_make_zeros_tile($cols, $rows, $cellTypeName)")(constTile)
+  }
+
+  /** Creates a column of tiles containing all ones */
+  def rf_make_ones_tile(cols: Int, rows: Int, cellType: CellType): TypedColumn[Any, Tile] =
+    rf_make_ones_tile(cols, rows, cellType.name)
+
+  /** Creates a column of tiles containing all ones */
+  def rf_make_ones_tile(cols: Int, rows: Int, cellTypeName: String): TypedColumn[Any, Tile] = {
+    import org.apache.spark.sql.rf.TileUDT.tileSerializer
+    val constTile = encoders.serialized_literal(F.tileOnes(cols, rows, cellTypeName))
+    withTypedAlias(s"rf_make_ones_tile($cols, $rows, $cellTypeName)")(constTile)
+  }
 
   /** Where the rf_mask tile contains NODATA, replace values in the source tile with NODATA */
   def rf_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
@@ -237,9 +263,9 @@ trait RasterFunctions {
 
   /** Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value. */
   def rf_rasterize(geometry: Column, bounds: Column, value: Column, cols: Int, rows: Int): TypedColumn[Any, Tile] =
-    withAlias("rf_rasterize", geometry)(
+    withTypedAlias("rf_rasterize", geometry)(
       udf(F.rasterize(_: Geometry, _: Geometry, _: Int, cols, rows)).apply(geometry, bounds, value)
-    ).as[Tile]
+    )
 
   /** Reproject a column of geometry from one CRS to another. */
   def rf_reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
@@ -345,10 +371,5 @@ trait RasterFunctions {
   def rf_expm1(tileCol: Column): TypedColumn[Any, Tile] =
     ExpM1(tileCol)
 
-  /** Resample tile using nearest-neighbor */
-  def rf_resample[T: Numeric](tileCol: Column, value: T) = Resample(tileCol, value)
-
-  /** Resample tile using nearest-neighbor */
-  def rf_resample(tileCol: Column, column2: Column) = Resample(tileCol, column2)
 
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
index d2ebf0c84..8cb5a6f85 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/package.scala
@@ -21,6 +21,7 @@
 
 package org.locationtech.rasterframes
 
+import org.apache.spark.sql.rf._
 import org.apache.spark.sql.Column
 import org.apache.spark.sql.catalyst.expressions.Literal
 
@@ -43,7 +44,12 @@ package object encoders {
   /** Constructs a catalyst literal expression from anything with a serializer. */
   def SerializedLiteral[T >: Null: CatalystSerializer](t: T): Literal = {
     val ser = CatalystSerializer[T]
-    Literal.create(ser.toInternalRow(t), ser.schema)
+    val schema = ser.schema match {
+      case s if s.conformsTo(TileType.sqlType) => TileType
+      case s if s.conformsTo(RasterSourceType.sqlType) => RasterSourceType
+      case s => s
+    }
+    Literal.create(ser.toInternalRow(t), schema)
   }
 
   /** Constructs a Dataframe literal column from anything with a serializer. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index e5bd7904b..bd5792aee 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -23,7 +23,7 @@ package org.locationtech.rasterframes.expressions.transformers
 
 import java.net.URI
 
-import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.RasterSourceType
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
@@ -53,7 +53,7 @@ case class URIToRasterSource(override val child: Expression)
     val uriString = input.asInstanceOf[UTF8String].toString
     val uri = URI.create(uriString)
     val ref = RasterSource(uri)
-    RasterSourceUDT.serialize(ref)
+    RasterSourceType.serialize(ref)
   }
 }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index e404657c4..62933029a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -138,8 +138,8 @@ package object functions {
 
   def register(sqlContext: SQLContext): Unit = {
     sqlContext.udf.register("rf_make_constant_tile", makeConstantTile)
-    sqlContext.udf.register("rf_tile_zeros", tileZeros)
-    sqlContext.udf.register("rf_tile_ones", tileOnes)
+    sqlContext.udf.register("rf_make_zeros_tile", tileZeros)
+    sqlContext.udf.register("rf_make_ones_tile", tileOnes)
 
     sqlContext.udf.register("rf_cell_types", cellTypes)
     sqlContext.udf.register("rf_rasterize", rasterize)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
index 1c7840511..4566e7630 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
@@ -21,7 +21,7 @@
 
 package org.locationtech.rasterframes.model
 
-import geotrellis.raster.{ArrayTile, Tile}
+import geotrellis.raster.{ArrayTile, ConstantTile, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
@@ -32,6 +32,7 @@ import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 /** Represents the union of binary cell datas or a reference to the data.*/
 case class Cells(data: Either[Array[Byte], RasterRef]) {
   def isRef: Boolean = data.isRight
+
   /** Convert cells into either a RasterRefTile or an ArrayTile. */
   def toTile(ctx: TileDataContext): Tile = {
     data.fold(
@@ -42,21 +43,27 @@ case class Cells(data: Either[Array[Byte], RasterRef]) {
 }
 
 object Cells {
+
   /** Extracts the Cells from a Tile. */
   def apply(t: Tile): Cells = {
     t match {
       case ref: RasterRefTile =>
         Cells(Right(ref.rr))
-      case o                  =>
+      case const: ConstantTile =>
+        // TODO: Create mechanism whereby constant tiles aren't expanded.
+        Cells(Left(const.toArrayTile().toBytes))
+      case o =>
         Cells(Left(o.toBytes))
     }
   }
 
   implicit def cellsSerializer: CatalystSerializer[Cells] = new CatalystSerializer[Cells] {
-    override def schema: StructType = StructType(Seq(
-      StructField("cells", BinaryType, true),
-      StructField("ref", schemaOf[RasterRef], true)
-    ))
+    override def schema: StructType =
+      StructType(
+        Seq(
+          StructField("cells", BinaryType, true),
+          StructField("ref", schemaOf[RasterRef], true)
+        ))
     override protected def to[R](t: Cells, io: CatalystSerializer.CatalystIO[R]): R = io.create(
       t.data.left.getOrElse(null),
       t.data.right.map(rr => io.to(rr)).right.getOrElse(null)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index 8a7ac0fc2..03eb1a40f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -25,7 +25,7 @@ import com.typesafe.scalalogging.LazyLogging
 import geotrellis.raster.{Tile, TileFeature}
 import geotrellis.spark.{ContextRDD, Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.rf.TileUDT
+import org.apache.spark.sql.rf.{RasterSourceUDT, TileUDT}
 import org.apache.spark.sql.{DataFrame, SQLContext, rf}
 import org.locationtech.geomesa.spark.jts.DataFrameFunctions
 import org.locationtech.rasterframes.encoders.StandardEncoders
@@ -33,6 +33,7 @@ import org.locationtech.rasterframes.extensions.Implicits
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.util.ZeroSevenCompatibilityKit
 import shapeless.tag.@@
+
 import scala.reflect.runtime.universe._
 
 package object rasterframes extends StandardColumns
@@ -86,6 +87,9 @@ package object rasterframes extends StandardColumns
   /** TileUDT type reference. */
   def TileType = new TileUDT()
 
+  /** RasterSourceUDT type reference. */
+  def RasterSourceType = new RasterSourceUDT()
+
   /**
    * A RasterFrame is just a DataFrame with certain invariants, enforced via the methods that create and transform them:
    *   1. One column is a [[geotrellis.spark.SpatialKey]] or [[geotrellis.spark.SpaceTimeKey]]
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
index 9d18eb586..2785888d7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
@@ -24,7 +24,6 @@ package org.locationtech.rasterframes.tiles
 import java.nio.ByteBuffer
 
 import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
-import org.locationtech.rasterframes.model.TileDataContext
 import geotrellis.raster._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.locationtech.rasterframes.model.{Cells, TileDataContext}
@@ -33,36 +32,22 @@ import org.locationtech.rasterframes.model.{Cells, TileDataContext}
  * Wrapper around a `Tile` encoded in a Catalyst `InternalRow`, for the purpose
  * of providing compatible semantics over common operations.
  *
- * @groupname COPIES Memory Copying
- * @groupdesc COPIES Requires creating an intermediate copy of
- *           the complete `Tile` contents, and should be avoided.
- *
  * @since 11/29/17
  */
 class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
   import InternalRowTile._
 
-  /** @group COPIES */
-  override def toArrayTile(): ArrayTile = realizedTile
+  override def toArrayTile(): ArrayTile = realizedTile.toArrayTile()
 
-  // TODO: We want to reimpliement the delegated methods so that they read directly from tungsten storage
-  protected lazy val realizedTile: ArrayTile = {
-    val data = toBytes
-    if(data.length < cols * rows && cellType.name != "bool") {
-      val ctile = ConstantTile.fromBytes(data, cellType, cols, rows)
-      val atile = ctile.toArrayTile()
-      atile
-    }
-    else
-      ArrayTile.fromBytes(data, cellType, cols, rows)
-  }
+  // TODO: We want to reimplement relevant delegated methods so that they read directly from tungsten storage
+  protected lazy val realizedTile: Tile = cells.toTile(cellContext)
 
-  /** @group COPIES */
   protected override def delegate: Tile = realizedTile
 
-  private lazy val cellContext: TileDataContext =
+  private def cellContext: TileDataContext =
     CatalystIO[InternalRow].get[TileDataContext](mem, 0)
 
+  private def cells: Cells = CatalystIO[InternalRow].get[Cells](mem, 1)
 
   /** Retrieve the cell type from the internal encoding. */
   override def cellType: CellType = cellContext.cellType
@@ -75,13 +60,9 @@ class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
 
   /** Get the internally encoded tile data cells. */
   override lazy val toBytes: Array[Byte] = {
-    val cellData = CatalystIO[InternalRow]
-      .get[Cells](mem, 1)
-      .data
-
-    cellData.left
+    cells.data.left
       .getOrElse(throw new IllegalStateException(
-        "Expected tile cell bytes, but received RasterRef instead: " + cellData.right.get)
+        "Expected tile cell bytes, but received RasterRef instead: " + cells.data.right.get)
       )
   }
 
@@ -99,12 +80,11 @@ class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
   }
 
   /** Reads the cell value at the given index as an Int. */
-  def apply(i: Int): Int = cellReader(i)
+  def apply(i: Int): Int = cellReader.apply(i)
 
   /** Reads the cell value at the given index as a Double. */
   def applyDouble(i: Int): Double = cellReader.applyDouble(i)
 
-  /** @group COPIES */
   def copy = new InternalRowTile(mem.copy)
 
   private lazy val cellReader: CellReader = {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index f723224f9..2f1ee9ca4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.StringType
-import org.apache.spark.sql.{Column, DataFrame, SQLContext}
+import org.apache.spark.sql._
 import org.slf4j.LoggerFactory
 import spire.syntax.cfor._
 
@@ -83,18 +83,21 @@ package object util {
 
   /** Tags output column with a nicer name. */
   private[rasterframes]
-  def withAlias(name: String, inputs: Column*)(output: Column) = {
+  def withAlias(name: String, inputs: Column*)(output: Column): Column = {
     val paramNames = inputs.map(_.columnName).mkString(",")
     output.as(s"$name($paramNames)")
   }
 
+  /** Tags output column with a nicer name, yet strongly typed. */
+  private[rasterframes]
+  def withTypedAlias[T: Encoder](name: String, inputs: Column*)(output: Column): TypedColumn[Any, T] =
+    withAlias(name, inputs: _*)(output).as[T]
+
   /** Derives and operator name from the implementing object name. */
   private[rasterframes]
   def opName(op: LocalTileBinaryOp) =
     op.getClass.getSimpleName.replace("$", "").toLowerCase
 
-
-
   implicit class WithCombine[T](left: Option[T]) {
     def combine[A, R >: A](a: A)(f: (T, A) ⇒ R): R = left.map(f(_, a)).getOrElse(a)
     def tupleWith[R](right: Option[R]): Option[(T, R)] = left.flatMap(l ⇒ right.map((l, _)))
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index a7e94c2d2..a72be9347 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -24,7 +24,7 @@ package org.locationtech.rasterframes
 import geotrellis.proj4.LatLng
 import geotrellis.raster
 import geotrellis.raster.testkit.RasterMatchers
-import geotrellis.raster.{ArrayTile, ByteUserDefinedNoDataCellType, DoubleConstantNoDataCellType, ShortConstantNoDataCellType, Tile, UByteConstantNoDataCellType}
+import geotrellis.raster._
 import geotrellis.vector.Extent
 import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
@@ -67,6 +67,33 @@ class RasterFunctionsSpec extends FunSpec
   implicit val pairEnc = Encoders.tuple(ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder)
   implicit val tripEnc = Encoders.tuple(ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder)
 
+  describe("constant tile generation operations") {
+    val dim = 2
+    val rows = 2
+
+    it("should create a ones tile") {
+      val df = (0 until rows).toDF("id")
+        .withColumn("const", rf_make_ones_tile(dim, dim, IntConstantNoDataCellType))
+      val result = df.select(rf_tile_sum($"const") as "ts").agg(sum("ts")).as[Double].first()
+      result should be (dim * dim * rows)
+    }
+
+    it("should create a zeros tile") {
+      val df = (0 until rows).toDF("id")
+        .withColumn("const", rf_make_zeros_tile(dim, dim, FloatConstantNoDataCellType))
+      val result = df.select(rf_tile_sum($"const") as "ts").agg(sum("ts")).as[Double].first()
+      result should be (0)
+    }
+
+    it("should create an arbitrary constant tile") {
+      val value = 4
+      val df = (0 until rows).toDF("id")
+        .withColumn("const", rf_make_constant_tile(value, dim, dim, ByteConstantNoDataCellType))
+      val result = df.select(rf_tile_sum($"const") as "ts").agg(sum("ts")).as[Double].first()
+      result should be (dim * dim * rows * value)
+    }
+  }
+
   describe("arithmetic tile operations") {
     it("should local_add") {
       val df = Seq((one, two)).toDF("one", "two")
@@ -329,12 +356,12 @@ class RasterFunctionsSpec extends FunSpec
         .select($"stats.mean").as[Double]
         .first() should be(mean +- 0.00001)
       df.selectExpr("rf_tile_stats(rand) as stats")
-        .select($"stats.rf_no_data_cells").as[Long]
+        .select($"stats.no_data_cells").as[Long]
         .first() should be <= (cols * rows - numND).toLong
 
       val df2 = randNDTilesWithNull.toDF("tile")
       df2
-        .select(rf_tile_stats($"tile")("rf_data_cells") as "cells")
+        .select(rf_tile_stats($"tile")("data_cells") as "cells")
         .agg(sum("cells"))
         .as[Long]
         .first() should be (expectedRandData)
@@ -376,11 +403,11 @@ class RasterFunctionsSpec extends FunSpec
 
       df
         .select(rf_agg_stats($"tile") as "stats")
-        .select("stats.rf_data_cells", "stats.rf_no_data_cells")
+        .select("stats.data_cells", "stats.no_data_cells")
         .as[(Long, Long)]
         .first() should be ((expectedRandData, expectedRandNoData))
       df.selectExpr("rf_agg_stats(tile) as stats")
-        .select("stats.rf_data_cells")
+        .select("stats.data_cells")
         .as[Long]
         .first() should be (expectedRandData)
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index 3ddbc52ec..6309e3eda 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -221,7 +221,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
 
       ds.createOrReplaceTempView("tmp")
       val agg2 = sql("select stats.* from (select rf_agg_stats(tiles) as stats from tmp)")
-      assert(agg2.first().getAs[Long]("rf_data_cells") === 250L)
+      assert(agg2.first().getAs[Long]("data_cells") === 250L)
 
       val agg3 = ds.agg(rf_agg_stats($"tiles") as "stats").select($"stats.mean".as[Double])
       assert(mean === agg3.first())
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index fe9b91851..860a750d8 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -556,7 +556,7 @@ The following functions compute a statistical summary per row of a `tile` column
 ```python
 import pyspark.functions as F
 spark.sql("""
- SELECT 1 as id, rf_tile_ones(5, 5, 'float32') as t 
+ SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
  UNION
  SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
  """).select(F.col('id'), tile_sum(F.col('t'))).show()
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 9e5698c36..c831a15d7 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -55,8 +55,8 @@
 * Added [experimental DataSource implementations](https://github.com/locationtech/rasterframes/tree/develop/experimental/src/main/scala/astraea/spark/rasterframes/experimental/datasource/awspds) for [MODIS](https://registry.opendata.aws/modis/) and [Landsat 8](https://registry.opendata.aws/landsat-8/) catalogs on AWS PDS.   
 * _Change_: Default interpoation for `toRaster` and `toMultibandRaster` has been changed from `Bilinear` to `NearestNeighbor`.
 * _Breaking_: Renamed/moved `astraea.spark.rasterframes.functions.CellStatsAggregateFunction.Statistics` to
-`org.locationtech.rasterframes.stats.CellStatistics`.
-* _Breaking_: `HistogramAggregateFunction` now generates the new type `org.locationtech.rasterframes.stats.CellHistogram`.
+`astraea.spark.rasterframes.stats.CellStatistics`.
+* _Breaking_: `HistogramAggregateFunction` now generates the new type `astraea.spark.rasterframes.stats.CellHistogram`.
 * _Breaking_: `box2D` renamed `envelope`.
 
 ## 0.6.x
@@ -115,7 +115,7 @@
 ### 0.5.9
 
 * Ported to sbt 1.0.3
-* Added sbt-generated `org.locationtech.rasterframes.RFBuildInfo`
+* Added sbt-generated `astraea.spark.rasterframes.RFBuildInfo`
 * Fixed bug in computing `aggMean` when one or more tiles are `null` 
 * Deprecated `rfIinit` in favor of `SparkSession.withRasterFrames` or `SQLContext.withRasterFrames` extension methods
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
index 875d30c1a..37e32ba46 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
@@ -30,6 +30,7 @@ import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.reader.GeoTiffReader
 import geotrellis.raster.{ProjectedRaster, Tile}
 import geotrellis.spark.io.hadoop.HdfsRangeReader
+import geotrellis.spark.io.http.util.HttpRangeReader
 import geotrellis.spark.io.s3.S3Client
 import geotrellis.spark.io.s3.util.S3RangeReader
 import geotrellis.util.{ByteReader, FileRangeReader, RangeReader, StreamingByteReader}
@@ -41,7 +42,6 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
 import org.apache.spark.sql.types._
-import org.locationtech.rasterframes.ref.HttpRangeReader
 
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal
diff --git a/pyrasterframes/python/coverage-report.sh b/pyrasterframes/python/coverage-report.sh
new file mode 100755
index 000000000..8cde233a1
--- /dev/null
+++ b/pyrasterframes/python/coverage-report.sh
@@ -0,0 +1,9 @@
+#!/usr/bin/env bash -e
+
+# If `coverage` tool isn't installed: `{pip|conda} install coverage`
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+
+cd "$( dirname "${BASH_SOURCE[0]}" )"
+
+coverage run setup.py test && coverage html --omit='.eggs/*,setup.py' && open htmlcov/index.html
\ No newline at end of file
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index d23213a2a..9bf10af38 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -71,9 +71,9 @@ def _(value, cols, rows, cellType):
 def _create_tileZeros():
     """ Create a function mapping to the Scala implementation."""
     def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('rf_tile_zeros')
+        jfcn = RFContext.active().lookup('rf_make_zeros_tile')
         return Column(jfcn(cols, rows, cellType))
-    _.__name__ = 'rf_tile_zeros'
+    _.__name__ = 'rf_make_zeros_tile'
     _.__doc__ = "Create column of constant tiles of zero"
     _.__module__ = THIS_MODULE
     return _
@@ -82,7 +82,7 @@ def _(cols, rows, cellType = 'float64'):
 def _create_tileOnes():
     """ Create a function mapping to the Scala implementation."""
     def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('rf_tile_ones')
+        jfcn = RFContext.active().lookup('rf_make_ones_tile')
         return Column(jfcn(cols, rows, cellType))
     _.__name__ = 'rf_tile_ones'
     _.__doc__ = "Create column of constant tiles of one"
@@ -162,8 +162,8 @@ def _(data_tile, mask_tile, mask_value):
     'rf_mask_by_value': _create_maskByValue(),
     'rf_rasterize': _create_rasterize(),
     'rf_reproject_geometry': _create_reproject_geometry(),
-    'rf_tile_ones': _create_tileOnes(),
-    'rf_tile_zeros': _create_tileZeros(),
+    'rf_make_ones_tile': _create_tileOnes(),
+    'rf_make_zeros_tile': _create_tileZeros(),
 }
 
 
@@ -195,9 +195,6 @@ def _(data_tile, mask_tile, mask_value):
 _rf_column_functions = {
     # ------- RasterFrames functions -------
     'rf_tile_dimensions': 'Query the number of (cols, rows) in a Tile.',
-    'envelope': 'Extracts the bounding box (envelope) of the geometry.',
-    'tile_to_int_array': 'Flattens Tile into an array of integers. Deprecated in favor of `rf_tile_to_array_int`.',
-    'tile_to_double_array': 'Flattens Tile into an array of doubles. Deprecated in favor of `rf_tile_to_array_double`',
     'rf_tile_to_array_int': 'Flattens Tile into an array of integers.',
     'rf_tile_to_array_double': 'Flattens Tile into an array of doubles.',
     'rf_cell_type': 'Extract the Tile\'s cell type',
@@ -221,7 +218,6 @@ def _(data_tile, mask_tile, mask_value):
     'rf_local_multiply': 'Multiply two Tiles',
     'rf_local_divide': 'Divide two Tiles',
     'rf_normalized_difference': 'Compute the normalized difference of two tiles',
-    'local_agg_stats': 'Compute cell-local aggregate descriptive statistics for a column of Tiles.',
     'rf_agg_local_max': 'Compute the cell-wise/local max operation between Tiles in a column.',
     'rf_agg_local_min': 'Compute the cellwise/local min operation between Tiles in a column.',
     'rf_agg_local_mean': 'Compute the cellwise/local mean operation between Tiles in a column.',
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 2dbeb566f..feaab8cd6 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -69,6 +69,11 @@ def test_identify_columns(self):
         self.assertIsNone(col, '`temporalKeyColumn` should be `None`')
         print("Temporal key column: ", col)
 
+    def test_tile_creation(self):
+        base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
+        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"), rf_make_ones_tile(3, 3, "int32"))
+        tiles.show()
+        self.assertEqual(tiles.count(), 4)
 
     def test_tile_operations(self):
         df1 = self.rf.withColumnRenamed(self.tileCol, 't1').asRF()

From 7e6b33e81670331604ad4aecf16025a6a84f9ee0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Apr 2019 22:11:34 -0400
Subject: [PATCH 053/380] Updated reference.md to reflect function name
 changes.

---
 docs/src/main/tut/reference.md | 421 ++++++++++++++++-----------------
 1 file changed, 204 insertions(+), 217 deletions(-)

diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 860a750d8..208eef647 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -38,13 +38,13 @@ _SQL_: `rf_reproject_geometry`
 Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems. 
 
 
-#### envelope
+#### rf_extent
 
 _Python_:
 
-    Struct[Double minX, Double maxX, Double minY, Double maxY] envelope(Geometry geom)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] envelope(Geometry geom)
 
-Python only. Extracts the bounding box (envelope) of the geometry.
+Extracts the bounding box (extent/envelope) of the geometry.
 
 See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#st-envelope) which returns a Geometry type.
 
@@ -52,12 +52,12 @@ See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/
 
 Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and nodata](reference.md#masking-and-nodata) for additional discussion of cell types.
 
-#### cell_types
+#### rf_cell_types
 
 
 _Python_:
 
-    Array[String] cell_types()
+    Array[String] rf_cell_types()
     
 _SQL_: `rf_cell_types`
 
@@ -82,43 +82,43 @@ Print an array of possible cell type names, as below. These names are used in ot
 |float64   |
 
 
-#### tile_dimensions
+#### rf_tile_dimensions
 
 _Python_:
 
-    Struct[Int, Int] tile_dimensions(Tile tile)
+    Struct[Int, Int] rf_tile_dimensions(Tile tile)
    
 _SQL_: `rf_tile_dimensions`
 
 Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
-#### cell_type
+#### rf_cell_type
 
 _Python_:
 
-    Struct[String] cell_type(Tile tile)
+    Struct[String] rf_cell_type(Tile tile)
     
 _SQL_: `rf_cell_type`
 
-Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[cell_types](reference.md#cell-types) function.
+Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf_cell-types) function.
 
-#### convert_cell_type
+#### rf_convert_cell_type
 
 _Python_:
 
-    Tile convert_cell_type(Tile tileCol, String cellType)
+    Tile rf_convert_cell_type(Tile tileCol, String cellType)
     
 _SQL_: `rf_convert_cell_type`
 
 Convert `tileCol` to a different cell type.
 
-#### resample
+#### rf_resample
 
 _Python_: 
 
-    Tile resample(Tile tile, Double factor)
-    Tile resample(Tile tile, Int factor)
-    Tile resample(Tile tile, Tile shape_tile)
+    Tile rf_resample(Tile tile, Double factor)
+    Tile rf_resample(Tile tile, Int factor)
+    Tile rf_resample(Tile tile, Tile shape_tile)
     
 _SQL_: `rf_resample`
 
@@ -128,46 +128,46 @@ Change the tile dimension. Passing a numeric `factor` will scale the number of c
 
 Functions to create a new Tile column, either from scratch or from existing data not yet in a `tile`.
 
-#### tile_zeros
+#### rf_make_zeros_tile
 
 _Python_:
 
 ```
-Tile tile_zeros(Int tile_columns, Int tile_rows, String cell_type_name)
+Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 ```
 
-_SQL_: `rf_tile_zeros`
+_SQL_: `rf_make_zeros_tile`
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`cell_types`](reference.md#cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
-#### tile_ones
+#### rf_make_ones_tile
 
 _Python_:
 
 ```
-Tile tile_ones(Int tile_columns, Int tile_rows, String cell_type_name)
+Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 ```
 
-_SQL_: `rf_tile_ones`
+_SQL_: `rf_make_ones_tile`
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the specified cell type. See function @ref:[`cell_types`](reference.md#cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
-#### make_constant_tile
+#### rf_make_constant_tile
 
 _Python_: 
 
-    Tile make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
+    Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
     
 _SQL_: `rf_make_constant_tile`
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`cell_types`](reference.md#cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 
-#### rasterize
+#### rf_rasterize
 
 _Python_:
 
-    Tile rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
+    Tile rf_rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
     
 _SQL_: `rf_rasterize`
 
@@ -205,27 +205,27 @@ FROM
 ```
 
 
-#### array_to_tile
+#### rf_array_to_tile
 
 _Python_:
 
-    Tile array_to_tile(Array arrayCol, Int numCols, Int numRows)
+    Tile rf_array_to_tile(Array arrayCol, Int numCols, Int numRows)
     
 Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), filling values in row-major order.
 
-#### assemble_tile
+#### rf_assemble_tile
 
 _Python_:
 
-    Tile assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
+    Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
     
-Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`explode_tiles`](reference.md#explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`cell_types`](reference.md#cell-types).
+Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
 
 ### Masking and Nodata
 
 In raster operations, the preservation and correct processing of missing operations is very important. The idea of missing data is often expressed as a null or NaN. In raster data, missing observations are often termed NODATA; we will style them as nodata in this document.  RasterFrames provides a variety of functions to manage and inspect nodata within `tile`s. 
 
-See also statistical summaries to get the count of data and nodata values per `tile` and aggregate in a `tile` column: @ref:[`data_cells`](reference.md#data-cells), @ref:[`no_data_cells`](reference.md#no-data-cells), @ref:[`agg_data_cells`](reference.md#agg-data-cells), @ref:[`agg_no_data_cells`](reference.md#agg-no-data-cells).
+See also statistical summaries to get the count of data and nodata values per `tile` and aggregate in a `tile` column: @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells).
 
 It is important to note that not all cell types support the nodata representation: these are `bool` and when the cell type string ends in `raw`.
 
@@ -235,11 +235,11 @@ For float cell types, the nodata can either be NaN or a user-defined value; for
 
 For more reading about cell types and ndodata, see the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values).
 
-#### mask
+#### rf_mask
 
 _Python_:
 
-    Tile mask(Tile tile, Tile mask)
+    Tile rf_mask(Tile tile, Tile mask)
     
 _SQL_: `rf_mask`
 
@@ -248,42 +248,42 @@ Where the `mask` contains nodata, replace values in the `tile` with nodata.
 Returned `tile` cell type will be coerced to one supporting nodata if it does not already.
  
 
-#### inverse_mask
+#### rf_inverse_mask
 
 _Python_:
 
-    Tile inverse_mask(Tile tile, Tile mask)
+    Tile rf_inverse_mask(Tile tile, Tile mask)
     
 _SQL_: `rf_inverse_mask`
 
 Where the `mask` _does not_ contain nodata, replace values in `tile` with nodata. 
 
-#### mask_by_value
+#### rf_mask_by_value
 
 _Python_:
 
-    Tile mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
+    Tile rf_mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
     
 _SQL_: `rf_mask_by_value`
 
 Generate a `tile` with the values from `data_tile`, with nodata in cells where the `mask_tile` is equal to `mask_value`. 
 
 
-#### is_no_data_tile
+#### rf_is_no_data_tile
 
 _Python_:
 
-    Boolean is_no_data_tile(tile)
+    Boolean rf_is_no_data_tile(Tile)
 
 _SQL_: `rf_is_no_data_tile`
  
 Returns true if `tile` contains only nodata. By definition returns false if cell type does not support nodata.
 
-#### with_no_data
+#### rf_with_no_data
 
 _Python_:
 
-    Tile with_no_data(Tile tile, Double no_data_value)
+    Tile rf_with_no_data(Tile tile, Double no_data_value)
     
 Python only. Return a `tile` column marking as nodata all cells equal to `no_data_value`.
 
@@ -297,257 +297,236 @@ If input `tile` had a nodata value already, the behaviour depends on if its cell
 
 Some of these functions have similar variations in the Python API:
 
- - `local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
- - `local_op_scalar`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
+ - `rf_local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
+ - `rf_local_op_double`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
  - `local_op_scalar_int`: applies `op` to a `tile` and a literal scalar, without coercing the `tile` to a floating point type
  
-We will provide all these variations for `local_add` and then suppress the rest in this document.
+We will provide all these variations for `rf_local_add` and then suppress the rest in this document.
 
-The SQL API does not require the `local_op_scalar` or `local_op_scalar_int` forms.
+The SQL API does not require the `rf_local_op_double` or `rf_local_op_scalar_int` forms.
 
-#### local_add
+#### rf_local_add
 
 _Python_: 
     
-    Tile local_add(Tile tile1, Tile rhs)
-    Tile local_add(Tile tile1, Int rhs)
-    Tile local_add(Tile tile1, Double rhs)
+    Tile rf_local_add(Tile tile1, Tile rhs)
+    Tile rf_local_add_int(Tile tile1, Int rhs)
+    Tile rf_local_add_double(Tile tile1, Double rhs)
     
 _SQL_: `rf_local_add`
 
 Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
 
-#### local_add_scalar
-
-_Python_:
-
-    Tile local_add_scalar(Tile tile, Double scalar)
-    
-_SQL_: `rf_local_add_scalar`
-
-Returns a `tile` column containing the element-wise sum of `tile` and `scalar`. If `tile` is integral type, it will be coerced to floating before addition; returns float valued `tile`.
-
-
-#### local_add_scalar_int
-
-_Python_:
-
-    Tile local_add_scalar_int(Tile tile, Int scalar)
-    
-_SQL_: `rf_local_add_scalar_int`
- 
-Returns a `tile` column containing the element-wise sum of `tile` and `scalar`. If `tile` is integral type, returns integral type `tile`.
-
-#### local_subtract
+#### rf_local_subtract
 
 _Python_: 
     
-    Tile local_subtract(Tile tile1, Tile rhs)
-    Tile local_subtract(Tile tile1, Int rhs)
-    Tile local_subtract(Tile tile1, Double rhs)
+    Tile rf_local_subtract(Tile tile1, Tile rhs)
+    Tile rf_local_subtract_int(Tile tile1, Int rhs)
+    Tile rf_local_subtract_double(Tile tile1, Double rhs)
     
 _SQL_: `rf_local_subtract`
 
 Returns a `tile` column containing the element-wise difference of `tile1` and `rhs`.
 
 
-#### local_multiply
+#### rf_local_multiply
 
 _Python_: 
     
-    Tile local_multiply(Tile tile1, Tile rhs)
-    Tile local_multiply(Tile tile1, Int rhs)
-    Tile local_multiply(Tile tile1, Double rhs)
+    Tile rf_local_multiply(Tile tile1, Tile rhs)
+    Tile rf_local_multiply_int(Tile tile1, Int rhs)
+    Tile rf_local_multiply_double(Tile tile1, Double rhs)
     
 _SQL_: `rf_local_multiply`
 
 Returns a `tile` column containing the element-wise product of `tile1` and `rhs`. This is **not** the matrix multiplication of `tile1` and `rhs`.
 
 
-#### local_divide
+#### rf_local_divide
 
 _Python_: 
     
-    Tile local_divide(Tile tile1, Tile rhs)
-    Tile local_divide(Tile tile1, Int rhs)
-    Tile local_divide(Tile tile1, Double rhs)
+    Tile rf_local_divide(Tile tile1, Tile rhs)
+    Tile rf_local_divide_int(Tile tile1, Int rhs)
+    Tile rf_local_divide_double(Tile tile1, Double rhs)
     
 _SQL_: `rf_local_divide`
 
 Returns a `tile` column containing the element-wise quotient of `tile1` and `rhs`. 
 
 
-#### normalized_difference 
+#### rf_normalized_difference 
 
 _Python_:
 
-    Tile normalized_difference(Tile tile1, Tile tile2)
+    Tile rf_normalized_difference(Tile tile1, Tile tile2)
     
 _SQL_: `rf_normalized_difference`
 
 Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (tile1 + tile2)`. Result is always floating point cell type. This function has no scalar variant. 
 
-#### local_less
+#### rf_local_less
 
 _Python_: 
     
-    Tile local_less(Tile tile1, Tile rhs)
-    Tile local_less(Tile tile1, Int rhs)
-    Tile local_less(Tile tile1, Double rhs)
+    Tile rf_local_less(Tile tile1, Tile rhs)
+    Tile rf_local_less_int(Tile tile1, Int rhs)
+    Tile rf_local_less_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_less`
+_SQL_: `rf_local_less`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than `rhs`. 
 
-#### local_less_equal
+#### rf_local_less_equal
 
 _Python_: 
     
-    Tile local_less_equal(Tile tile1, Tile rhs)
-    Tile local_less_equal(Tile tile1, Int rhs)
-    Tile local_less_equal(Tile tile1, Double rhs)
+    Tile rf_local_less_equal(Tile tile1, Tile rhs)
+    Tile rf_local_less_equal_int(Tile tile1, Int rhs)
+    Tile rf_local_less_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_less_equal`
+_SQL_: `rf_local_less_equal`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than or equal to `rhs`. 
 
-#### local_greater
+#### rf_local_greater
 
 _Python_: 
     
-    Tile local_greater(Tile tile1, Tile rhs)
-    Tile local_greater(Tile tile1, Int rhs)
-    Tile local_greater(Tile tile1, Double rhs)
+    Tile rf_local_greater(Tile tile1, Tile rhs)
+    Tile rf_local_greater_int(Tile tile1, Int rhs)
+    Tile rf_local_greater_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_greater`
+_SQL_: `rf_local_greater`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than `rhs`. 
 
-#### local_greater_equal
+#### rf_local_greater_equal
 
 _Python_: 
     
-    Tile local_greater_equal(Tile tile1, Tile rhs)
-    Tile local_greater_equal(Tile tile1, Int rhs)
-    Tile local_greater_equal(Tile tile1, Double rhs)
+    Tile rf_local_greater_equal(Tile tile1, Tile rhs)
+    Tile rf_local_greater_equal_int(Tile tile1, Int rhs)
+    Tile rf_local_greater_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_greater_equal`
+_SQL_: `rf_local_greater_equal`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than or equal to `rhs`. 
 
-#### local_equal
+#### rf_local_equal
 
 _Python_: 
     
-    Tile local_equal(Tile tile1, Tile rhs)
-    Tile local_equal(Tile tile1, Int rhs)
-    Tile local_equal(Tile tile1, Double rhs)
+    Tile rf_local_equal(Tile tile1, Tile rhs)
+    Tile rf_local_equal_int(Tile tile1, Int rhs)
+    Tile rf_local_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_equal`
+_SQL_: `rf_local_equal`
 
 Returns a `tile` column containing the element-wise equality of `tile1` and `rhs`. 
 
-#### local_unequal
+#### rf_local_unequal
 
 _Python_: 
     
-    Tile local_unequal(Tile tile1, Tile rhs)
-    Tile local_unequal(Tile tile1, Int rhs)
-    Tile local_unequal(Tile tile1, Double rhs)
+    Tile rf_local_unequal(Tile tile1, Tile rhs)
+    Tile rf_local_unequal_int(Tile tile1, Int rhs)
+    Tile rf_local_unequal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_unequal`
+_SQL_: `rf_local_unequal`
 
 Returns a `tile` column containing the element-wise inequality of `tile1` and `rhs`. 
 
-#### round
+#### rf_round
 
 _Python_:
 
-    Tile round(Tile tile)
+    Tile rf_round(Tile tile)
 
 _SQL_: `rf_round`
 
 Round cell values to the nearest integer without changing the cell type.
 
-#### exp
+#### rf_exp
 
 _Python_:
 
-    Tile exp(Tile tile)
+    Tile rf_exp(Tile tile)
 
 _SQL_: `rf_exp`
 
 Performs cell-wise exponential.
 
-#### exp10
+#### rf_exp10
 
 _Python_:
 
-    Tile exp10(Tile tile)
+    Tile rf_exp10(Tile tile)
 
 _SQL_: `rf_exp10`
 
 Compute 10 to the power of cell values.
 
-#### exp2
+#### rf_exp2
 
 _Python_:
 
-    Tile exp2(Tile tile)
+    Tile rf_exp2(Tile tile)
 
 _SQL_: `rf_exp2`
 
 Compute 2 to the power of cell values.
 
-#### expm1
+#### rf_expm1
 
 _Python_:
 
-    Tile expm1(Tile tile)
+    Tile rf_expm1(Tile tile)
 
 _SQL_: `rf_expm1`
 
 Performs cell-wise exponential, then subtract one. Inverse of @ref:[`log1p`](reference.md#log1p).
 
-#### log
+#### rf_log
 
 _Python_:
 
-    Tile log(Tile tile)
+    Tile rf_log(Tile tile)
 
 _SQL_: `rf_log`
 
 Performs cell-wise natural logarithm.
 
-#### log10
+#### rf_log10
 
 _Python_:
 
-    Tile log10(Tile tile)
+    Tile rf_log10(Tile tile)
 
 _SQL_: `rf_log10`
 
 Performs cell-wise logarithm with base 10.
 
-#### log2
+#### rf_log2
 
 _Python_: 
 
-    Tile log2(Tile tile)
+    Tile rf_log2(Tile tile)
 
 _SQL_: `rf_log2`
 
 Performs cell-wise logarithm with base 2.
 
-#### log1p
+#### rf_log1p
 
 _Python_: 
 
-    Tile log1p(Tile tile)
+    Tile rf_log1p(Tile tile)
 
 _SQL_: `rf_log1p`
 
-Performs natural logarithm of cell values plus one. Inverse of @ref:[`expm1`](reference.md#expm1).
+Performs natural logarithm of cell values plus one. Inverse of @ref:[`rf_expm1`](reference.md#rf-expm1).
 
 ### Tile Statistics
 
@@ -559,11 +538,11 @@ spark.sql("""
  SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
  UNION
  SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
- """).select(F.col('id'), tile_sum(F.col('t'))).show()
+ """).select(F.col('id'), rf_tile_sum(F.col('t'))).show()
 
 
 +---+-----------+
-| id|tile_sum(t)|
+| id|rf_tile_sum(t)|
 +---+-----------+
 |  2|       75.0|
 |  1|       25.0|
@@ -571,89 +550,89 @@ spark.sql("""
 ```
 
 
-#### tile_sum
+#### rf_tile_sum
 
 _Python_:
 
-    Double tile_sum(Tile tile)
+    Double rf_tile_sum(Tile tile)
     
 _SQL_:  `rf_tile_sum`
 
 Computes the sum of cells in each row of column `tile`, ignoring nodata values.
 
-#### tile_mean
+#### rf_tile_mean
 
 _Python_:
 
-    Double tile_mean(Tile tile)
+    Double rf_tile_mean(Tile tile)
     
 _SQL_:  `rf_tile_mean`
 
 Computes the mean of cells in each row of column `tile`, ignoring nodata values.
 
 
-#### tile_min
+#### rf_tile_min
 
 _Python_:
 
-    Double tile_min(Tile tile)
+    Double rf_tile_min(Tile tile)
     
 _SQL_:  `rf_tile_min`
 
 Computes the min of cells in each row of column `tile`, ignoring nodata values.
 
 
-#### tile_max
+#### rf_tile_max
 
 _Python_:
 
-    Double tile_max(Tile tile)
+    Double rf_tile_max(Tile tile)
     
 _SQL_:  `rf_tile_max`
 
 Computes the max of cells in each row of column `tile`, ignoring nodata values.
 
 
-#### no_data_cells
+#### rf_no_data_cells
 
 _Python_:
 
-    Long no_data_cells(Tile tile)
+    Long rf_no_data_cells(Tile tile)
     
 _SQL_: `rf_no_data_cells`
 
 Return the count of nodata cells in the `tile`.
 
-#### data_cells
+#### rf_data_cells
 
 _Python_:
 
-    Long data_cells(Tile tile)
+    Long rf_data_cells(Tile tile)
     
 _SQL_: `rf_data_cells`
 
 Return the count of data cells in the `tile`.
 
-#### tile_stats
+#### rf_tile_stats
 
 _Python_:
 
-    Struct[Long, Long, Double, Double, Double, Double] tile_stats(Tile tile)
+    Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
     
-_SQL_: `tile_stats`
+_SQL_: `rf_tile_stats`
 
 Computes the following statistics of cells in each row of column `tile`: data cell count, nodata cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring nodata values. 
 
 
-#### tile_histogram
+#### rf_tile_histogram
 
 _Python_:
 
-    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] tile_histogram(Tile tile)
+    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_tile_histogram(Tile tile)
     
-_SQL_:  `rf_tile_histogram`
+_SQL_:  `rf_rf_tile_histogram`
 
-Computes a statistical summary of cell values within each row of `tile`. Resulting column has the below schema. Note that several of the other `tile` statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`agg_approx_histogram`](reference.md#agg-approx-histogram) which computes the statistics across all rows in a group.
+Computes a statistical summary of cell values within each row of `tile`. Resulting column has the below schema. Note that several of the other `tile` statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) which computes the statistics across all rows in a group.
 
 ```
  |-- tile_histogram: struct (nullable = true)
@@ -677,8 +656,8 @@ These functions compute statistical summaries over all of the cell values *and*
 
 ```python
 from pyspark.functions import month
-from pyrasterframes.functions import agg_mean
-rf.groupby(month(rf.datetime)).agg(agg_mean(rf.red_band).alias('red_mean_monthly'))
+from pyrasterframes.functions import rf_agg_mean
+rf.groupby(month(rf.datetime)).agg(rf_agg_mean(rf.red_band).alias('red_mean_monthly'))
 ```
 
 Continuing our example from the @ref:[Tile Statistics](reference.md#tile-statistics) section, consider the following. Note that only a single row is returned. It is averaging 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows.
@@ -688,73 +667,73 @@ spark.sql("""
 SELECT 1 as id, rf_tile_ones(5, 5, 'float32') as t 
 UNION
 SELECT 2 as id, rf_local_multiply_scalar(rf_tile_ones(5, 5, 'float32'), 3) as t 
-""").agg(agg_mean(F.col('t'))).show(10, False)
+""").agg(rf_agg_mean(F.col('t'))).show(10, False)
 
-+-----------+
-|agg_mean(t)|
-+-----------+
-|2.0        |
-+-----------+
++--------------+
+|rf_agg_mean(t)|
++--------------+
+|2.0           |
++--------------+
 ```
 
-#### agg_mean
+#### rf_agg_mean
 
 _Python_:
 
-    Double agg_mean(Tile tile)
+    Double rf_agg_mean(Tile tile)
     
-_SQL_: @ref:[`rf_agg_stats`](reference.md#agg-stats)`(tile).mean`
+_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).mean`
 
-Aggregates over the `tile` and return the mean of cell values, ignoring nodata. Equivalent to @ref:[`agg_stats`](reference.md#agg-stats)`.mean`.
+Aggregates over the `tile` and return the mean of cell values, ignoring nodata. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.mean`.
 
 
-#### agg_data_cells
+#### rf_agg_data_cells
 
 _Python_:
 
-    Long agg_data_cells(Tile tile)
+    Long rf_agg_data_cells(Tile tile)
     
-_SQL_: @ref:[`rf_agg_stats`](reference.md#agg-stats)`(tile).dataCells`
+_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
-Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`agg_stats`](reference.md#agg-stats)`.dataCells`. C.F. `data_cells`; equivalent code:
+Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.dataCells`. C.F. `data_cells`; equivalent code:
 
 ```python
-rf.select(agg_data_cells(rf.tile).alias('agg_data_cell')).show()
+rf.select(rf_agg_data_cells(rf.tile).alias('agg_data_cell')).show()
 # Equivalent to
-rf.agg(F.sum(data_cells(rf.tile)).alias('agg_data_cell')).show()
+rf.agg(F.sum(rf_data_cells(rf.tile)).alias('agg_data_cell')).show()
 ```
 
-#### agg_no_data_cells
+#### rf_agg_no_data_cells
 
 _Python_:
 
-    Long agg_no_data_cells(Tile tile)
+    Long rf_agg_no_data_cells(Tile tile)
     
-_SQL_: @ref:[`rf_agg_stats`](reference.md#agg-stats)`(tile).noDataCells`
+_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).noDataCells`
 
-Aggregates over the `tile` and return the count of nodata cells. Equivalent to @ref:[`agg_stats`](reference.md#agg-stats)`.noDataCells`. C.F. @ref:[`no_data_cells`](reference.md#no-data-cells) a row-wise count of no data cells.
+Aggregates over the `tile` and return the count of nodata cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
 
-#### agg_stats
+#### rf_agg_stats
 
 _Python_: 
 
-    Struct[Long, Long, Double, Double, Double, Double] agg_stats(Tile tile)
+    Struct[Long, Long, Double, Double, Double, Double] rf_agg_stats(Tile tile)
     
 _SQL_:  `rf_agg_stats`
 
 Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of nodata cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of nodata. 
 
-#### agg_approx_histogram
+#### rf_agg_approx_histogram
 
 _Python_:
 
-    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] agg_approx_histogram(Tile tile)
+    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
     
 _SQL_: `rf_agg_approx_histogram`
 
 Aggregates over the `tile` return statistical summaries of the cell values, including a histogram, in the below schema. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis. 
 
-Note that several of the other cell value statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`tile_histogram`](reference.md#tile-histogram) function which operates on a single row at a time.
+Note that several of the other cell value statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
 
 ```
  |-- agg_approx_histogram: struct (nullable = true)
@@ -775,7 +754,7 @@ Note that several of the other cell value statistics functions are convenience m
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension. 
 
-Consider again our example for Tile Statistics and Aggregate Tile Statistics, this time apply @ref:[`agg_local_mean`](reference.md#agg-local-mean). We see that it is computing the element-wise mean across the two rows. In this case it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+Consider again our example for Tile Statistics and Aggregate Tile Statistics, this time apply @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean). We see that it is computing the element-wise mean across the two rows. In this case it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
 
 
 ```python
@@ -784,10 +763,10 @@ lam = spark.sql("""
 SELECT 1 as id, rf_tile_ones(5, 5, 'float32') as t 
 UNION
 SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
-""").agg(local_agg_mean(F.col('t')).alias('l')) \
+""").agg(rf_local_agg_mean(F.col('t')).alias('l')) \
 
 ## local_agg_mean returns a tile
-lam.select(tile_dimensions(lam.l)).show()
+lam.select(rf_tile_dimensions(lam.l)).show()
 ## 
 +------------------+
 |tile_dimensions(l)|
@@ -796,7 +775,7 @@ lam.select(tile_dimensions(lam.l)).show()
 +------------------+ 
 ##
 
-lam.select(explode_tiles(lam.l)).show(10, False)
+lam.select(rf_explode_tiles(lam.l)).show(10, False)
 ##
 +------------+---------+---+
 |column_index|row_index|l  |
@@ -816,61 +795,61 @@ only showing top 10 rows
 ```
 
 
-#### agg_local_max 
+#### rf_agg_local_max 
 
 _Python_:
 
-    Tile agg_local_max(Tile tile)
+    Tile rf_agg_local_max(Tile tile)
     
 _SQL_: `rf_agg_local_max`
 
 Compute the cell-local maximum operation over Tiles in a column. 
 
-#### agg_local_min 
+#### rf_agg_local_min 
 
 _Python_:
 
-    Tile agg_local_min(Tile tile)
+    Tile rf_agg_local_min(Tile tile)
     
 _SQL_: `rf_agg_local_min`
 
 Compute the cell-local minimum operation over Tiles in a column. 
 
-#### agg_local_mean 
+#### rf_agg_local_mean 
 
 _Python_:
 
-    Tile agg_local_mean(Tile tile)
+    Tile rf_agg_local_mean(Tile tile)
     
 _SQL_: `rf_agg_local_mean`
 
 Compute the cell-local mean operation over Tiles in a column. 
 
-#### agg_local_data_cells 
+#### rf_agg_local_data_cells 
 
 _Python_:
 
-    Tile agg_local_data_cells(Tile tile)
+    Tile rf_agg_local_data_cells(Tile tile)
     
 _SQL_: `rf_agg_local_data_cells`
 
 Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
-#### agg_local_no_data_cells
+#### rf_agg_local_no_data_cells
 
 _Python_:
 
-    Tile agg_local_no_data_cells(Tile tile)
+    Tile rf_agg_local_no_data_cells(Tile tile)
     
 _SQL_: `rf_agg_local_no_data_cells`
 
 Compute the cell-local count of nodata cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
-#### agg_local_stats 
+#### rf_agg_local_stats 
 
 _Python_:
 
-    Struct[Tile, Tile, Tile, Tile, Tile] agg_local_stats(Tile tile)
+    Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
     
 _SQL_: `rf_agg_local_stats`
 
@@ -881,65 +860,73 @@ Compute cell-local aggregate count, minimum, maximum, mean, and variance for a c
 
 RasterFrames provides several ways to convert a `tile` into other data structures. See also functions for @ref:[creating tiles](reference.md#tile-creation).
 
-#### explode_tiles
+#### rf_xplode_tiles
 
 _Python_:
 
-    Int, Int, Numeric* explode_tiles(Tile* tile)
+    Int, Int, Numeric* rf_explode_tiles(Tile* tile)
     
 _SQL_: `rf_explode_tiles`
 
-Create a row for each cell in `tile` columns. Many `tile` columns can be passed in, and the returned DataFrame will have one numeric column per input.  There will also be columns for `column_index` and `row_index`. Inverse of @ref:[`assemble_tile`](reference.md#assemble-tile). When using this function, be sure to have a unique identifier for rows in order to successfully invert the operation.
+Create a row for each cell in `tile` columns. Many `tile` columns can be passed in, and the returned DataFrame will have one numeric column per input.  There will also be columns for `column_index` and `row_index`. Inverse of @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile). When using this function, be sure to have a unique identifier for rows in order to successfully invert the operation.
 
-#### explode_tiles_sample
+#### rf_explode_tiles_sample
 
 _Python_:
 
-    Int, Int, Numeric* explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
+    Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
     
-Python only. As with @ref:[`explode_tiles`](reference.md#explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0. 
+Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0. 
 
 ```python
-df.select(df.id, explode_tiles(df.tile1, df.tile2, df.tile3)) \
+df.select(df.id, rf_explode_tiles(df.tile1, df.tile2, df.tile3)) \
     .sample(False, 0.05, 8675309)
 # Equivalent result, faster
-df.select(df.id, explode_tiles_sample(0.05, 8675309, df.tile1, df.tile2, df.tile3)) \
+df.select(df.id, rf_explode_tiles_sample(0.05, 8675309, df.tile1, df.tile2, df.tile3)) \
 ```
 
-#### tile_to_int_array
+#### rf_tile_to_array_int
 
 _Python_:
 
-    Array tile_to_int_array(Tile tile)
+    Array rf_tile_to_array_int(Tile tile)
     
-_SQL_: `rf_tile_to_int_array`
+_SQL_: `rf_tile_to_array_int`
 
 
 Convert Tile column to Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Float cell types will be coerced to integral type by flooring.
 
 
-#### tile_to_double_array
+#### rf_tile_to_array_double
 
 _Python_:
 
-    Array tile_to_double_arry(Tile tile)
+    Array rf_tile_to_arry_double(Tile tile)
     
-_SQL_: `rf_tile_to_double_array`
+_SQL_: `rf_tile_to_array_double`
 
 Convert tile column to Spark [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Integral cell types will be coerced to floats.
 
 
-#### render_ascii
+#### rf_ender_ascii
 
 _Python_:
 
-    String render_ascii(Tile tile)
+    String rf_render_ascii(Tile tile)
 
 _SQL_:   `rf_render_ascii`
 
 Pretty print the tile values as plain text.
 
+#### rf_render_matrix
+
+_Python_:
+
+    String rf_render_matrix(Tile tile)
+
+_SQL_:   `rf_render_matrix`
 
+Render Tile cell values as numeric values, for debugging purposes.
 
 [RasterFunctions]: org.locationtech.rasterframes.RasterFunctions
 [scaladoc]: latest/api/index.html

From 1c2e71c0170ef65f6ec118050a5915792e8f581f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 28 Apr 2019 10:31:37 -0400
Subject: [PATCH 054/380] Updates from code review.

---
 .../rasterframes/RasterFunctions.scala        |  4 ++--
 .../expressions/localops/Round.scala          |  2 +-
 .../rasterframes/expressions/package.scala    | 15 +++++++-------
 .../transformers/GeometryToExtent.scala       |  2 +-
 .../util/ZeroSevenCompatibilityKit.scala      |  4 ++--
 .../rasterframes/RasterFunctionsSpec.scala    | 20 +++++++++----------
 .../rasterframes/TileAssemblerSpec.scala      |  6 +++---
 docs/src/main/tut/reference.md                | 12 +++++------
 .../python/pyrasterframes/rasterfunctions.py  |  1 +
 9 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 95adea29f..91686b3be 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -253,11 +253,11 @@ trait RasterFunctions {
   def rf_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
     Mask.MaskByDefined(sourceTile, maskTile)
 
-  /** Where the rf_mask tile equals the rf_mask value, replace values in the source tile with NODATA */
+  /** Where the `maskTile` equals `maskValue`, replace values in the source tile with `NoData` */
   def rf_mask_by_value(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
     Mask.MaskByValue(sourceTile, maskTile, maskValue)
 
-  /** Where the rf_mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
+  /** Where the `maskTile` does **not** contain `NoData`, replace values in the source tile with `NoData` */
   def rf_inverse_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
     Mask.InverseMaskByDefined(sourceTile, maskTile)
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
index 0ca22cb8f..92f1c2f89 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Round.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
   usage = "_FUNC_(tile) - Round cell values to the nearest integer without changing the cell type.",
   arguments = """
   Arguments:
-    * tile - tile column to rf_round""",
+    * tile - tile column to round""",
   examples = """
   Examples:
     > SELECT  _FUNC_(tile);
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index d426af4fd..e4d48c9e9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -21,23 +21,22 @@
 
 package org.locationtech.rasterframes
 
-import org.locationtech.rasterframes.expressions.accessors._
-import org.locationtech.rasterframes.expressions.aggstats._
-import org.locationtech.rasterframes.expressions.generators._
-import org.locationtech.rasterframes.expressions.localops._
-import org.locationtech.rasterframes.expressions.tilestats._
-import org.locationtech.rasterframes.expressions.transformers._
 import geotrellis.raster.{DoubleConstantNoDataCellType, Tile}
-import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection}
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
+import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection}
 import org.apache.spark.sql.rf.VersionShims._
 import org.apache.spark.sql.{SQLContext, rf}
+import org.locationtech.rasterframes.expressions.accessors._
 import org.locationtech.rasterframes.expressions.aggstats.CellCountAggregate.DataCells
+import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.generators._
 import org.locationtech.rasterframes.expressions.localops._
+import org.locationtech.rasterframes.expressions.tilestats._
+import org.locationtech.rasterframes.expressions.transformers._
 
-import scala.util.Try
 import scala.reflect.runtime.universe._
+import scala.util.Try
 /**
  * Module of Catalyst expressions for efficiently working with tiles.
  *
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
index cac726a8f..e9c259df4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  * @since 8/24/18
  */
 case class GeometryToExtent(child: Expression) extends UnaryExpression with CodegenFallback {
-  override def nodeName: String = "geometry_bounds"
+  override def nodeName: String = "rf_extent"
 
   override def dataType: DataType = schemaOf[Extent]
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index 083a58944..ecd122adb 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -246,12 +246,12 @@ object ZeroSevenCompatibilityKit {
     def tileOnes(cols: Int, rows: Int, cellType: String = "float64"): TypedColumn[Any, Tile] =
       udf(() => F.tileOnes(cols, rows, cellType)).apply().as(s"ones_$cellType").as[Tile]
 
-    /** Where the rf_mask tile equals the rf_mask value, replace values in the source tile with NODATA */
+    /** Where the mask tile equals the mask value, replace values in the source tile with NODATA */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def maskByValue(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
       delegate.rf_mask_by_value(sourceTile, maskTile, maskValue)
 
-    /** Where the rf_mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
+    /** Where the mask tile DOES NOT contain NODATA, replace values in the source tile with NODATA */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def inverseMask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
       delegate.rf_inverse_mask(sourceTile, maskTile)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index a72be9347..c89e66c26 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -485,17 +485,17 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_normalized_difference")
     }
 
-    it("should rf_mask one tile against another") {
+    it("should mask one tile against another") {
       val df = Seq[Tile](randTile).toDF("tile")
 
-      val withMask = df.withColumn("rf_mask",
+      val withMask = df.withColumn("mask",
         rf_convert_cell_type(
           rf_local_greater($"tile", 50),
           "uint8")
       )
 
       val withMasked = withMask.withColumn("masked",
-        rf_mask($"tile", $"rf_mask"))
+        rf_mask($"tile", $"mask"))
 
       val result = withMasked.agg(rf_agg_no_data_cells($"tile") < rf_agg_no_data_cells($"masked")).as[Boolean]
 
@@ -504,12 +504,12 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_mask")
     }
 
-    it("should inverse rf_mask one tile against another") {
+    it("should inverse mask one tile against another") {
       val df = Seq[Tile](randTile).toDF("tile")
 
       val baseND = df.select(rf_agg_no_data_cells($"tile")).first()
 
-      val withMask = df.withColumn("rf_mask",
+      val withMask = df.withColumn("mask",
         rf_convert_cell_type(
           rf_local_greater($"tile", 50),
           "uint8"
@@ -517,8 +517,8 @@ class RasterFunctionsSpec extends FunSpec
       )
 
       val withMasked = withMask
-        .withColumn("masked", rf_mask($"tile", $"rf_mask"))
-        .withColumn("inv_masked", rf_inverse_mask($"tile", $"rf_mask"))
+        .withColumn("masked", rf_mask($"tile", $"mask"))
+        .withColumn("inv_masked", rf_inverse_mask($"tile", $"mask"))
 
       val result = withMasked.agg(rf_agg_no_data_cells($"masked") + rf_agg_no_data_cells($"inv_masked")).as[Long]
 
@@ -527,11 +527,11 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_inverse_mask")
     }
 
-    it("should rf_mask tile by another identified by specified value") {
+    it("should mask tile by another identified by specified value") {
       val df = Seq[Tile](randTile).toDF("tile")
       val mask_value = 4
 
-      val withMask = df.withColumn("rf_mask",
+      val withMask = df.withColumn("mask",
         rf_local_multiply(rf_convert_cell_type(
           rf_local_greater($"tile", 50),
           "uint8"),
@@ -540,7 +540,7 @@ class RasterFunctionsSpec extends FunSpec
       )
 
       val withMasked = withMask.withColumn("masked",
-        rf_mask_by_value($"tile", $"rf_mask", lit(mask_value)))
+        rf_mask_by_value($"tile", $"mask", lit(mask_value)))
 
       val result = withMasked.agg(rf_agg_no_data_cells($"tile") < rf_agg_no_data_cells($"masked")).as[Boolean]
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index d484ff915..8fc11f878 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -65,14 +65,14 @@ class TileAssemblerSpec extends TestEnvironment {
       val sceneSize = (260, 257)
       val rs = InMemoryRasterSource(TestData.randomTile(sceneSize._1, sceneSize._2, ByteConstantNoDataCellType), Extent(10, 20, 30, 40), LatLng)
       val df = rs.toDF
-      val exploded = df.select($"spatial_index", $"extent", rf_tile_dimensions($"tile") as "rf_tile_dimensions", rf_explode_tiles($"tile"))
+      val exploded = df.select($"spatial_index", $"extent", rf_tile_dimensions($"tile") as "tile_dimensions", rf_explode_tiles($"tile"))
 
       val assembled = exploded
-        .groupBy($"spatial_index", $"extent", $"rf_tile_dimensions")
+        .groupBy($"spatial_index", $"extent", $"tile_dimensions")
         .agg(
           rf_convert_cell_type(
             rf_assemble_tile(COLUMN_INDEX_COLUMN, ROW_INDEX_COLUMN,
-            $"tile", $"rf_tile_dimensions.cols", $"rf_tile_dimensions.rows"), rs.cellType) as "tile"
+            $"tile", $"tile_dimensions.cols", $"tile_dimensions.rows"), rs.cellType) as "tile"
         )
 
       assert(
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 208eef647..1c44dbb3f 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -42,7 +42,7 @@ Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs`
 
 _Python_:
 
-    Struct[Double xmin, Double xmax, Double ymin, Double ymax] envelope(Geometry geom)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(Geometry geom)
 
 Extracts the bounding box (extent/envelope) of the geometry.
 
@@ -100,7 +100,7 @@ _Python_:
     
 _SQL_: `rf_cell_type`
 
-Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf_cell-types) function.
+Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function.
 
 #### rf_convert_cell_type
 
@@ -299,11 +299,11 @@ Some of these functions have similar variations in the Python API:
 
  - `rf_local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
  - `rf_local_op_double`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
- - `local_op_scalar_int`: applies `op` to a `tile` and a literal scalar, without coercing the `tile` to a floating point type
+ - `rf_local_op_int`: applies `op` to a `tile` and a literal scalar, without coercing the `tile` to a floating point type
  
 We will provide all these variations for `rf_local_add` and then suppress the rest in this document.
 
-The SQL API does not require the `rf_local_op_double` or `rf_local_op_scalar_int` forms.
+The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
 #### rf_local_add
 
@@ -860,7 +860,7 @@ Compute cell-local aggregate count, minimum, maximum, mean, and variance for a c
 
 RasterFrames provides several ways to convert a `tile` into other data structures. See also functions for @ref:[creating tiles](reference.md#tile-creation).
 
-#### rf_xplode_tiles
+#### rf_explode_tiles
 
 _Python_:
 
@@ -908,7 +908,7 @@ _SQL_: `rf_tile_to_array_double`
 Convert tile column to Spark [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Integral cell types will be coerced to floats.
 
 
-#### rf_ender_ascii
+#### rf_render_ascii
 
 _Python_:
 
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 9bf10af38..01ff4c282 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -223,6 +223,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_agg_local_mean': 'Compute the cellwise/local mean operation between Tiles in a column.',
     'rf_agg_local_data_cells': 'Compute the cellwise/local count of non-NoData cells for all Tiles in a column.',
     'rf_agg_local_no_data_cells': 'Compute the cellwise/local count of NoData cells for all Tiles in a column.',
+    'rf_agg_local_stats': 'Compute cell-local aggregate descriptive statistics for a column of Tiles.',
     'rf_mask': 'Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA.',
     'rf_inverse_mask': 'Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA.',
     'rf_local_less': 'Cellwise less than comparison between two tiles',

From 02d47a2963e6c8e552827aab2501296f5e37444b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 22 Apr 2019 21:15:23 -0400
Subject: [PATCH 055/380] Added ability to use a RasterSourceRelation against a
 table/view of paths.

---
 .../rastersource/RasterSourceDataSource.scala | 20 +++++++++++---
 .../rastersource/RasterSourceRelation.scala   | 26 +++++++++++++++++--
 .../datasource/rastersource/package.scala     |  6 +++++
 .../RasterSourceDataSourceSpec.scala          | 15 +++++++++++
 4 files changed, 62 insertions(+), 5 deletions(-)

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index 41b1ef76f..d8723f25f 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -29,10 +29,13 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   import RasterSourceDataSource._
   override def shortName(): String = SHORT_NAME
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
-    val inexes = parameters.bandIndexes
-    val files = parameters.filePaths
+    val bands = parameters.bandIndexes
     val tiling = parameters.tileDims
-    RasterSourceRelation(sqlContext, files, inexes, tiling)
+    val pathTable = parameters.pathTable
+    val files = parameters.filePaths
+    require(!(pathTable.nonEmpty && files.nonEmpty),
+      "Only one of a set of file paths OR a paths table column may be provided.")
+    RasterSourceRelation(sqlContext, files, pathTable, bands, tiling)
   }
 }
 object RasterSourceDataSource {
@@ -41,6 +44,11 @@ object RasterSourceDataSource {
   final val PATHS_PARAM = "paths"
   final val BAND_INDEXES_PARAM = "bandIndexes"
   final val TILE_DIMS_PARAM = "tileDimensions"
+  final val PATH_TABLE_PARAM = "pathTable"
+  final val PATH_TABLE_COL_PARAM = "pathTableColumn"
+
+  /** Container for specifying where to select raster paths from. */
+  case class PathColumn(tableName: String, columnName: String)
 
   private[rastersource]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
@@ -64,5 +72,11 @@ object RasterSourceDataSource {
       .get(BAND_INDEXES_PARAM)
       .map(_.split(',').map(_.trim.toInt).toSeq)
       .getOrElse(Seq(0))
+
+    def pathTable: Option[PathColumn] = parameters
+      .get(PATH_TABLE_PARAM)
+      .zip(parameters.get(PATH_TABLE_COL_PARAM))
+      .map(p => PathColumn(p._1, p._2))
+      .headOption
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index a063443dd..c4eaec291 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -21,6 +21,7 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 
+<<<<<<< HEAD:datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
@@ -32,6 +33,21 @@ import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{Row, SQLContext}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+=======
+import astraea.spark.rasterframes._
+import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource.PathColumn
+import astraea.spark.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
+import astraea.spark.rasterframes.encoders.CatalystSerializer._
+import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
+import astraea.spark.rasterframes.model.TileDimensions
+import astraea.spark.rasterframes.tiles.ProjectedRasterTile
+import astraea.spark.rasterframes.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.sources.{BaseRelation, TableScan}
+import org.apache.spark.sql.types.{StringType, StructField, StructType}
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.functions.col
+>>>>>>> Added ability to use a RasterSourceRelation against a table/view of paths.:datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
 
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
@@ -40,7 +56,7 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
   * @param bandIndexes band indexes to fetch
   * @param subtileDims how big to tile/subdivide rasters info
   */
-case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
+case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], pathTable: Option[PathColumn], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
 
   override def schema: StructType = StructType(Seq(
     StructField(PATH_COLUMN.columnName, StringType, false)
@@ -57,7 +73,13 @@ case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], band
     val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, URIToRasterSource($"path"))
     val refsToTiles = names.map(n => RasterRefToTile($"$n") as n)
 
-    val df = paths.toDF("path")
+    val pathsTable: DataFrame = pathTable match {
+      case Some(spec) =>
+        sqlContext.table(spec.tableName).select(col(spec.columnName) as "path")
+      case _ => paths.toDF("path")
+    }
+
+    val df = pathsTable
       .select(PATH_COLUMN, refs as names)
       .select(PATH_COLUMN +: refsToTiles: _*)
     df.rdd
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
index 69720d6d9..cddace678 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
@@ -50,6 +50,12 @@ package object rastersource {
         reader.option(RasterSourceDataSource.TILE_DIMS_PARAM, s"$cols,$rows")
       )
 
+    def fromTable(tableName: String, columnName: String): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.PATH_TABLE_PARAM, tableName)
+          .option(RasterSourceDataSource.PATH_TABLE_COL_PARAM, columnName)
+      )
+
     def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.PATHS_PARAM, newlineDelimPaths)
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 42765f6e6..bfb5c95dc 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -53,6 +53,11 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       val p = Map(TILE_DIMS_PARAM -> "4, 5")
       p.tileDims should be (Some(TileDimensions(4, 5)))
     }
+
+    it("should parse path table specificatino") {
+      val p = Map(PATH_TABLE_PARAM -> "pathTable", PATH_TABLE_COL_PARAM -> "path")
+      p.pathTable should be (Some(PathColumn("pathTable", "path")))
+    }
   }
 
   describe("RasterSource as relation reading") {
@@ -108,6 +113,16 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.schema.size should be(2)
     }
 
+    it("should read a multiple files from a table") {
+      Seq(cogPath, l8samplePath, nonCogPath).toDF("path").createOrReplaceTempView("pathsTable")
+      val df = spark.read.rastersource
+        .fromTable("pathsTable", "path")
+        .withTileDimensions(128, 128)
+        .load()
+      df.select("path").distinct().count() should be(3)
+      df.schema.size should be(2)
+    }
+
     it("should read a multiple files with heterogeneous bands") {
       val df = spark.read.rastersource
         .from(Seq(cogPath, l8samplePath, nonCogPath))

From 3a00ef572c506215dccd2ab8858c7565e21bd68e Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 23 Apr 2019 15:58:04 -0400
Subject: [PATCH 056/380] Switched to GDAL base image for CircleCI.

---
 .circleci/config.yml                          |  4 ++--
 .../rasterframes/bench/RasterRefBench.scala   |  9 ++++-----
 .../rasterframes/bench/package.scala          |  2 +-
 .../rastersource/RasterSourceRelation.scala   | 20 +++----------------
 4 files changed, 10 insertions(+), 25 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index fe3a6f3fc..787af5316 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -5,8 +5,8 @@ _defaults: &defaults
   environment:
     TERM: dumb
   docker:
-    - image: circleci/openjdk:8-jdk
-
+    - image: s22s/rasterframes-circleci:latest
+      
 _setenv: &setenv
   name: set CloudRepo credentials
   command: |-
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index a2a9104bf..dddabd09a 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -19,14 +19,13 @@
  *
  */
 
-package astraea.spark.rasterframes.bench
-
+package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.expressions.transformers.RasterSourceToTiles
-import astraea.spark.rasterframes.ref.RasterSource
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.transformers.RasterSourceToTiles
+import org.locationtech.rasterframes.ref.RasterSource
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
 import org.openjdk.jmh.annotations._
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
index f047336e3..65d8ab88f 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/package.scala
@@ -29,7 +29,7 @@ import geotrellis.raster.{ArrayTile, CellType, NODATA, Tile, isNoData}
  * @author sfitch 
  * @since 10/4/17
  */
-package object rasterframes {
+package object bench {
   val rnd = new scala.util.Random(42)
 
   /** Construct a tile of given size and cell type populated with random values. */
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index c4eaec291..efc4e663a 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -21,7 +21,6 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 
-<<<<<<< HEAD:datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
@@ -30,24 +29,11 @@ import org.locationtech.rasterframes.util._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.PathColumn
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
-=======
-import astraea.spark.rasterframes._
-import astraea.spark.rasterframes.datasource.rastersource.RasterSourceDataSource.PathColumn
-import astraea.spark.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
-import astraea.spark.rasterframes.encoders.CatalystSerializer._
-import astraea.spark.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
-import astraea.spark.rasterframes.model.TileDimensions
-import astraea.spark.rasterframes.tiles.ProjectedRasterTile
-import astraea.spark.rasterframes.util._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.sources.{BaseRelation, TableScan}
-import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.apache.spark.sql.functions.col
->>>>>>> Added ability to use a RasterSourceRelation against a table/view of paths.:datasource/src/main/scala/astraea/spark/rasterframes/datasource/rastersource/RasterSourceRelation.scala
 
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.

From 24ced8f735880572582821b25f38ae20491ec29b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Apr 2019 11:42:09 -0400
Subject: [PATCH 057/380] Incremental progress toward multi-file multiband
 RasterFrames.

---
 .../transformers/URIToRasterSource.scala      |  7 +-
 .../rasterframes/util/package.scala           |  7 +-
 .../locationtech/rasterframes/TestData.scala  |  8 +-
 .../rastersource/RasterSourceDataSource.scala |  8 +-
 .../rastersource/RasterSourceRelation.scala   | 99 +++++++++++++------
 .../datasource/rastersource/package.scala     |  4 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       |  2 +-
 .../RasterSourceDataSourceSpec.scala          | 43 ++++----
 8 files changed, 117 insertions(+), 61 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index e5bd7904b..84126441b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -23,7 +23,6 @@ package org.locationtech.rasterframes.expressions.transformers
 
 import java.net.URI
 
-import org.locationtech.rasterframes.ref.RasterRef
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
@@ -31,7 +30,7 @@ import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StringType}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.unsafe.types.UTF8String
-import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.ref.RasterSource
 
 
 /**
@@ -58,6 +57,6 @@ case class URIToRasterSource(override val child: Expression)
 }
 
 object URIToRasterSource {
-  def apply(rasterURI: Column): TypedColumn[Any, RasterRef] =
-    new Column(new URIToRasterSource(rasterURI.expr)).as[RasterRef]
+  def apply(rasterURI: Column): TypedColumn[Any, RasterSource] =
+    new Column(new URIToRasterSource(rasterURI.expr)).as[RasterSource]
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index f723224f9..e27af9156 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -32,7 +32,8 @@ import geotrellis.raster.{CellGrid, Tile, isNoData}
 import geotrellis.spark.Bounds
 import geotrellis.spark.tiling.TilerKeyMethods
 import geotrellis.util.{ByteReader, GetComponent}
-import org.apache.spark.sql.catalyst.expressions.{Expression, NamedExpression}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpression}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.rf._
@@ -103,7 +104,9 @@ package object util {
   implicit class ExpressionWithName(val expr: Expression) extends AnyVal {
     import org.apache.spark.sql.catalyst.expressions.Literal
     def name: String = expr match {
-      case n: NamedExpression ⇒ n.name
+      case n: NamedExpression if n.resolved ⇒ n.name
+      case UnresolvedAttribute(parts) => parts.mkString("_")
+      case Alias(_, name) => name
       case l: Literal if l.dataType == StringType ⇒ String.valueOf(l.value)
       case o ⇒ o.toString
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index 13f93cd6c..5840fe566 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -110,6 +110,12 @@ trait TestData {
     require((1 to 11).contains(band), "Invalid band number")
     readSingleband(s"L8-B$band-Elkton-VA.tiff")
   }
+
+  def l8SamplePath(band: Int) = {
+    require((1 to 11).contains(band), "Invalid band number")
+    getClass.getResource(s"/L8-B$band-Elkton-VA.tiff").toURI
+  }
+
   def l8Labels = readSingleband("L8-Labels-Elkton-VA.tiff")
 
   def naipSample(band: Int) = {
@@ -135,7 +141,7 @@ trait TestData {
   lazy val localSentinel: URL = getClass.getResource("/B01.jp2")
   lazy val cogPath: URI = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
   lazy val nonCogPath: URI = getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI
-  lazy val l8samplePath: URI = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI
+  lazy val l8B1SamplePath: URI = l8SamplePath(1)
 
   object JTS {
     val fact = new GeometryFactory()
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index d8723f25f..a09072f63 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -45,10 +45,10 @@ object RasterSourceDataSource {
   final val BAND_INDEXES_PARAM = "bandIndexes"
   final val TILE_DIMS_PARAM = "tileDimensions"
   final val PATH_TABLE_PARAM = "pathTable"
-  final val PATH_TABLE_COL_PARAM = "pathTableColumn"
+  final val PATH_TABLE_COL_PARAM = "pathTableColumns"
 
   /** Container for specifying where to select raster paths from. */
-  case class PathColumn(tableName: String, columnName: String)
+  case class RasterSourceTable(tableName: String, columnNames: String*)
 
   private[rastersource]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
@@ -73,10 +73,10 @@ object RasterSourceDataSource {
       .map(_.split(',').map(_.trim.toInt).toSeq)
       .getOrElse(Seq(0))
 
-    def pathTable: Option[PathColumn] = parameters
+    def pathTable: Option[RasterSourceTable] = parameters
       .get(PATH_TABLE_PARAM)
       .zip(parameters.get(PATH_TABLE_COL_PARAM))
-      .map(p => PathColumn(p._1, p._2))
+      .map(p => RasterSourceTable(p._1, p._2.split(','): _*))
       .headOption
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index efc4e663a..b56146f66 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -22,7 +22,7 @@
 package org.locationtech.rasterframes.datasource.rastersource
 
 import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.bandNames
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.{bandNames}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
 import org.locationtech.rasterframes.util._
@@ -31,52 +31,95 @@ import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.PathColumn
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.RasterSourceTable
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
-  * @param sqlContext
-  * @param paths list of URIs to fetch rastefrom.
+  * @param sqlContext Query context
+  * @param discretePaths list of URIs to fetch rastefrom.
   * @param bandIndexes band indexes to fetch
   * @param subtileDims how big to tile/subdivide rasters info
   */
-case class RasterSourceRelation(sqlContext: SQLContext, paths: Seq[String], pathTable: Option[PathColumn], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions]) extends BaseRelation with TableScan {
+case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[String],
+  pathTable: Option[RasterSourceTable], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
+  extends BaseRelation with TableScan {
 
-  override def schema: StructType = StructType(Seq(
-    StructField(PATH_COLUMN.columnName, StringType, false)
-  ) ++ {
+  lazy val inputColNames = pathTable
+    .map(_.columnNames)
+    .getOrElse(Seq(TILE_COLUMN.columnName))
+
+  def pathColNames = inputColNames
+    .map(_ + "_path")
+
+  def srcColNames = inputColNames
+    .map(_ + "_src")
+
+  def refColNames = inputColNames
+    .flatMap(bandNames(_, bandIndexes))
+    .map(_ + "_ref")
+
+  def tileColNames = inputColNames
+    .flatMap(bandNames(_, bandIndexes))
+
+  override def schema: StructType = {
     val tileSchema = schemaOf[ProjectedRasterTile]
-    for {
-      name <- bandNames(bandIndexes)
-    } yield StructField(name, tileSchema, true)
-  })
+    val paths = for {
+      pathCol <- pathColNames
+    } yield StructField(pathCol, StringType, false)
+    val tiles = for {
+      tileColName <- tileColNames
+    } yield StructField(tileColName, tileSchema, true)
+
+    StructType(paths ++ tiles)
+  }
 
   override def buildScan(): RDD[Row] = {
     import sqlContext.implicits._
-    val names = bandNames(bandIndexes)
-    val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, URIToRasterSource($"path"))
-    val refsToTiles = names.map(n => RasterRefToTile($"$n") as n)
-
-    val pathsTable: DataFrame = pathTable match {
-      case Some(spec) =>
-        sqlContext.table(spec.tableName).select(col(spec.columnName) as "path")
-      case _ => paths.toDF("path")
+
+    // The general transformaion is:
+    // input -> path -> src -> ref -> tile
+    // Each step is broken down for readability
+    val inputs: DataFrame = pathTable match {
+      case Some(spec) => sqlContext.table(spec.tableName)
+      case _ => discretePaths.toDF(inputColNames.head)
     }
 
-    val df = pathsTable
-      .select(PATH_COLUMN, refs as names)
-      .select(PATH_COLUMN +: refsToTiles: _*)
+    // Basically renames the input columns to have the '_path' suffix
+    val pathsAliasing = for {
+      (input, path) <- inputColNames.zip(pathColNames)
+    } yield col(input).as(path)
+
+    // Wraps paths in a RasterSource
+    val srcs = for {
+      (pathColName, srcColName) <- pathColNames.zip(srcColNames)
+    } yield URIToRasterSource(col(pathColName)) as srcColName
+
+    // Combines RasterSource + bandIndex into a RasterRef
+    val refs = for {
+      (inputColName, srcColName) <- inputColNames.zip(srcColNames)
+    } yield RasterSourceToRasterRefs(subtileDims, bandIndexes, col(srcColName)) as bandNames(inputColName, bandIndexes)
+
+    val refsToTiles = for {
+      (tileColName, refColName) <- refColNames.zip(tileColNames)
+    } yield RasterRefToTile(col(refColName)) as tileColName
+
+    val paths = pathColNames.map(col)
+
+    val df = inputs
+      .select(pathsAliasing: _*)
+      .select(paths ++ srcs: _*)
+      .select(paths ++ refs: _*)
+      .select(paths ++ refsToTiles: _*)
+
     df.rdd
   }
 }
 object RasterSourceRelation {
-  private def bandNames(bandIndexes: Seq[Int]): Seq[String] = bandIndexes match {
+  private def bandNames(basename: String, bandIndexes: Seq[Int]): Seq[String] = bandIndexes match {
     case Seq() => Seq.empty
-    case Seq(0) => Seq(TILE_COLUMN.columnName)
-    case s =>
-      val basename = TILE_COLUMN.columnName
-      s.map(n => basename + "_b" + n)
+    case Seq(0) => Seq(basename)
+    case s => s.map(n => basename + "_b" + n)
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
index cddace678..cae9e7fcd 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
@@ -50,10 +50,10 @@ package object rastersource {
         reader.option(RasterSourceDataSource.TILE_DIMS_PARAM, s"$cols,$rows")
       )
 
-    def fromTable(tableName: String, columnName: String): RasterSourceDataFrameReader =
+    def fromTable(tableName: String, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.PATH_TABLE_PARAM, tableName)
-          .option(RasterSourceDataSource.PATH_TABLE_COL_PARAM, columnName)
+          .option(RasterSourceDataSource.PATH_TABLE_COL_PARAM, bandColumnNames.mkString(","))
       )
 
     def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index d221acab9..4ece87c35 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -83,7 +83,7 @@ class GeoTiffDataSourceSpec
 
     it("should read in correctly check-summed contents") {
       // c.f. TileStatsSpec -> computing statistics over tiles -> should compute tile statistics -> sum
-      val rf = spark.read.geotiff.loadRF(l8samplePath)
+      val rf = spark.read.geotiff.loadRF(l8B1SamplePath)
       val expected = 309149454 // computed with rasterio
       val result = rf.agg(
         sum(tile_sum(rf("tile")))
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index bfb5c95dc..3f690fb3b 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -54,15 +54,15 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       p.tileDims should be (Some(TileDimensions(4, 5)))
     }
 
-    it("should parse path table specificatino") {
+    it("should parse path table specification") {
       val p = Map(PATH_TABLE_PARAM -> "pathTable", PATH_TABLE_COL_PARAM -> "path")
-      p.pathTable should be (Some(PathColumn("pathTable", "path")))
+      p.pathTable should be (Some(RasterSourceTable("pathTable", "path")))
     }
   }
 
   describe("RasterSource as relation reading") {
     it("should default to a single band schema") {
-      val df = spark.read.rastersource.load(l8samplePath.toASCIIString)
+      val df = spark.read.rastersource.load(l8B1SamplePath.toASCIIString)
       val tcols = df.tileColumns
       tcols.length should be(1)
       tcols.map(_.columnName) should contain("tile")
@@ -102,35 +102,24 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       dims should contain allElementsOf
         Seq(TileDimensions(4,128), TileDimensions(128,128), TileDimensions(128,93), TileDimensions(4,93))
 
-      df.select("path").distinct().count() should be(1)
+      df.select("tile_path").distinct().count() should be(1)
     }
     it("should read a multiple files with one band") {
       val df = spark.read.rastersource
-        .from(Seq(cogPath, l8samplePath, nonCogPath))
+        .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .load()
-      df.select("path").distinct().count() should be(3)
-      df.schema.size should be(2)
-    }
-
-    it("should read a multiple files from a table") {
-      Seq(cogPath, l8samplePath, nonCogPath).toDF("path").createOrReplaceTempView("pathsTable")
-      val df = spark.read.rastersource
-        .fromTable("pathsTable", "path")
-        .withTileDimensions(128, 128)
-        .load()
-      df.select("path").distinct().count() should be(3)
+      df.select("tile_path").distinct().count() should be(3)
       df.schema.size should be(2)
     }
-
     it("should read a multiple files with heterogeneous bands") {
       val df = spark.read.rastersource
-        .from(Seq(cogPath, l8samplePath, nonCogPath))
+        .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .withBandIndexes(0, 1, 2, 3)
         .load()
         .cache()
-      df.select("path").distinct().count() should be(3)
+      df.select("tile_path").distinct().count() should be(3)
       df.schema.size should be(5)
 
       df.select($"tile_b0").count() should be (df.select($"tile_b0").na.drop.count())
@@ -138,5 +127,21 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.select($"tile_b1").na.drop.count() should be (df.select($"tile_b2").na.drop.count())
       df.select($"tile_b3").na.drop.count() should be (0)
     }
+
+
+    it("should read a extent coherent bands from multiple files") {
+      val bandPaths = Seq((l8SamplePath(1).toASCIIString, l8SamplePath(2).toASCIIString, l8SamplePath(3).toASCIIString))
+        .toDF("B1", "B2", "B3")
+
+      bandPaths.createOrReplaceTempView("pathsTable")
+
+      val df = spark.read.rastersource
+        .fromTable("pathsTable", "B1", "B2", "B3")
+        .withTileDimensions(128, 128)
+        .load()
+      df.show(false)
+      df.select("path").distinct().count() should be(3)
+      df.schema.size should be(2)
+    }
   }
 }

From 6fc0b4d6871250e140cf2a88da55e2db580adbd9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Apr 2019 13:08:14 -0400
Subject: [PATCH 058/380] Updated RasterSourceRelation to allow for multiple
 extent-coherent files to be used as separate bands.

---
 .../RasterSourceToRasterRefs.scala            | 11 +++++-
 .../transformers/RasterSourceToTiles.scala    |  3 ++
 .../rastersource/RasterSourceRelation.scala   | 37 +++++++++----------
 .../RasterSourceDataSourceSpec.scala          |  6 ++-
 4 files changed, 34 insertions(+), 23 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 0bdfa1ee3..663f090ff 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
@@ -54,8 +55,8 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
 
   override def elementSchema: StructType = StructType(for {
     child <- children
-    band <- bandIndexes
-    name = child.name + (if (bandIndexes.length > 1) "_b" + band else "")
+    basename = child.name + "_ref"
+    name <- bandNames(basename, bandIndexes)
   } yield StructField(name, rasterRefSchema, true))
 
   private def band2ref(src: RasterSource, e: Option[Extent])(b: Int): RasterRef =
@@ -86,4 +87,10 @@ object RasterSourceToRasterRefs {
   def apply(rrs: Column*): TypedColumn[Any, RasterRef] = apply(None, Seq(0), rrs: _*)
   def apply(subtileDims: Option[TileDimensions], bandIndexes: Seq[Int], rrs: Column*): TypedColumn[Any, RasterRef] =
     new Column(new RasterSourceToRasterRefs(rrs.map(_.expr), bandIndexes, subtileDims)).as[RasterRef]
+
+  private[rasterframes] def bandNames(basename: String, bandIndexes: Seq[Int]): Seq[String] = bandIndexes match {
+    case Seq() => Seq.empty
+    case Seq(0) => Seq(basename)
+    case s => s.map(n => basename + "_b" + n)
+  }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
index 351c25783..5302640b4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
@@ -40,6 +40,7 @@ import org.locationtech.rasterframes.TileType
  *
  * @since 9/6/18
  */
+@deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
 case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean) extends Expression
   with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
 
@@ -74,7 +75,9 @@ case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean)
 
 
 object RasterSourceToTiles {
+  @deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
   def apply(rrs: Column*): Column = apply(true, rrs: _*)
+  @deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
   def apply(applyTiling: Boolean, rrs: Column*): Column =
     new Column(new RasterSourceToTiles(rrs.map(_.expr), applyTiling))
 }
\ No newline at end of file
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index b56146f66..c33722520 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -22,7 +22,6 @@
 package org.locationtech.rasterframes.datasource.rastersource
 
 import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceRelation.{bandNames}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
 import org.locationtech.rasterframes.util._
@@ -32,6 +31,7 @@ import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.RasterSourceTable
+import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
@@ -56,7 +56,7 @@ case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[Strin
   def srcColNames = inputColNames
     .map(_ + "_src")
 
-  def refColNames = inputColNames
+  def refColNames = srcColNames
     .flatMap(bandNames(_, bandIndexes))
     .map(_ + "_ref")
 
@@ -96,30 +96,29 @@ case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[Strin
       (pathColName, srcColName) <- pathColNames.zip(srcColNames)
     } yield URIToRasterSource(col(pathColName)) as srcColName
 
-    // Combines RasterSource + bandIndex into a RasterRef
-    val refs = for {
-      (inputColName, srcColName) <- inputColNames.zip(srcColNames)
-    } yield RasterSourceToRasterRefs(subtileDims, bandIndexes, col(srcColName)) as bandNames(inputColName, bandIndexes)
+    // Expand RasterSource into multiple columns per band, and multiple rows per tile
+    // There's some unintentional fragililty here in that the structure of the expression
+    // is expected to line up with our column structure here.
+    val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, srcs: _*) as refColNames
 
+    // RasterSourceToRasterRef is a generator, which means you have to do the Tile conversion
+    // in a separate select statement (Query planner doesn't know how many columns ahead of time).
     val refsToTiles = for {
-      (tileColName, refColName) <- refColNames.zip(tileColNames)
+      (refColName, tileColName) <- refColNames.zip(tileColNames)
     } yield RasterRefToTile(col(refColName)) as tileColName
 
-    val paths = pathColNames.map(col)
-
-    val df = inputs
+    // Add path columns
+    val withPaths = inputs
       .select(pathsAliasing: _*)
-      .select(paths ++ srcs: _*)
-      .select(paths ++ refs: _*)
+
+    // Path columns have to be manually pulled along through each step. Resolve columns once
+    // and reused with each select.
+    val paths = pathColNames.map(withPaths.apply)
+
+    val df = withPaths
+      .select(paths :+ refs: _*)
       .select(paths ++ refsToTiles: _*)
 
     df.rdd
   }
 }
-object RasterSourceRelation {
-  private def bandNames(basename: String, bandIndexes: Seq[Int]): Seq[String] = bandIndexes match {
-    case Seq() => Seq.empty
-    case Seq(0) => Seq(basename)
-    case s => s.map(n => basename + "_b" + n)
-  }
-}
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 3f690fb3b..4d9ee9445 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -133,15 +133,17 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       val bandPaths = Seq((l8SamplePath(1).toASCIIString, l8SamplePath(2).toASCIIString, l8SamplePath(3).toASCIIString))
         .toDF("B1", "B2", "B3")
 
+      bandPaths.show()
+
       bandPaths.createOrReplaceTempView("pathsTable")
 
       val df = spark.read.rastersource
         .fromTable("pathsTable", "B1", "B2", "B3")
         .withTileDimensions(128, 128)
         .load()
+
       df.show(false)
-      df.select("path").distinct().count() should be(3)
-      df.schema.size should be(2)
+      df.schema.size should be(6)
     }
   }
 }

From 4673e8c3342dd15f70009113e0012cff7a2450da Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Apr 2019 13:16:13 -0400
Subject: [PATCH 059/380] Revised unit test for multi-file bands in
 RasterSourceRelation.

---
 .../rastersource/RasterSourceDataSourceSpec.scala      | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 4d9ee9445..7cb0c34a7 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -130,11 +130,12 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
 
     it("should read a extent coherent bands from multiple files") {
-      val bandPaths = Seq((l8SamplePath(1).toASCIIString, l8SamplePath(2).toASCIIString, l8SamplePath(3).toASCIIString))
+      val bandPaths = Seq((
+        l8SamplePath(1).toASCIIString,
+        l8SamplePath(2).toASCIIString,
+        l8SamplePath(3).toASCIIString))
         .toDF("B1", "B2", "B3")
 
-      bandPaths.show()
-
       bandPaths.createOrReplaceTempView("pathsTable")
 
       val df = spark.read.rastersource
@@ -142,8 +143,9 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .withTileDimensions(128, 128)
         .load()
 
-      df.show(false)
       df.schema.size should be(6)
+      df.tileColumns.size should be (3)
+      df.select($"B1_path").distinct().count() should be (1)
     }
   }
 }

From adf229a5517a59b5248c4f39386253ab5e5ab759 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Apr 2019 15:37:29 -0400
Subject: [PATCH 060/380] Expressed clearer distinction between getting the
 extent from a Geometry vs ProjectedRasterTile.

---
 .../org/locationtech/rasterframes/RasterFunctions.scala     | 5 ++++-
 .../org/locationtech/rasterframes/expressions/package.scala | 3 ++-
 .../expressions/transformers/GeometryToExtent.scala         | 2 +-
 .../test/scala/org/locationtech/rasterframes/JTSSpec.scala  | 2 +-
 .../org/locationtech/rasterframes/ref/RasterRefSpec.scala   | 6 +++---
 docs/src/main/tut/reference.md                              | 4 ++--
 docs/src/main/tut/release-notes.md                          | 3 ++-
 pyrasterframes/python/pyrasterframes/rasterfunctions.py     | 3 ++-
 pyrasterframes/python/tests/PyRasterFramesTests.py          | 4 ++--
 9 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 91686b3be..279bc172d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -62,7 +62,10 @@ trait RasterFunctions {
   def rf_tile_dimensions(col: Column): Column = GetDimensions(col)
 
   /** Extracts the bounding box of a geometry as an Extent */
-  def rf_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
+  def st_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
+
+  /** Extracts the bounding box from a RasterSource or ProjectedRasterTile */
+  def rf_extent(col: Column): TypedColumn[Any, Extent] = GetExtent(col)
 
   /** Flattens Tile into a double array. */
   def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index e4d48c9e9..078736655 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -69,7 +69,8 @@ package object expressions {
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
     registry.registerExpression[GetDimensions]("rf_tile_dimensions")
     registry.registerExpression[ExtentToGeometry]("rf_extent_geometry")
-    registry.registerExpression[GeometryToExtent]("rf_extent")
+    registry.registerExpression[GeometryToExtent]("st_extent")
+    registry.registerExpression[GetExtent]("rf_extent")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
index e9c259df4..adb52468b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/GeometryToExtent.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
  * @since 8/24/18
  */
 case class GeometryToExtent(child: Expression) extends UnaryExpression with CodegenFallback {
-  override def nodeName: String = "rf_extent"
+  override def nodeName: String = "st_extent"
 
   override def dataType: DataType = schemaOf[Extent]
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
index a9da6cbcb..24cbe8f0f 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
@@ -82,7 +82,7 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
     it("should provide a means of getting a bounding box") {
       import spark.implicits._
-      val boxed = rf.select(GEOMETRY_COLUMN, rf_extent(GEOMETRY_COLUMN) as "extent")
+      val boxed = rf.select(GEOMETRY_COLUMN, st_extent(GEOMETRY_COLUMN) as "extent")
       assert(boxed.select($"extent".as[Extent]).first.area > 0)
       assert(boxed.toDF("bounds", "bbox").select("bbox.*").schema.length === 4)
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index ca14400a1..606b949f0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -110,12 +110,12 @@ class RasterRefSpec extends TestEnvironment with TestData {
     }
   }
 
-  describe("GetExtent Expression") {
+  describe("rf_extent") {
     it("should read from RasterRef") {
       import spark.implicits._
       new Fixture {
         val ds = Seq((1, fullRaster)).toDF("index", "ref")
-        val extent = ds.select(GetExtent($"ref"))
+        val extent = ds.select(rf_extent($"ref"))
         assert(extent.count() === 1)
         assert(extent.first() !== null)
       }
@@ -124,7 +124,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
       import spark.implicits._
       new Fixture {
         val ds = Seq((1, subRaster)).toDF("index", "ref")
-        val extent = ds.select(GetExtent($"ref"))
+        val extent = ds.select(rf_extent($"ref"))
         assert(extent.count() === 1)
         assert(extent.first() !== null)
       }
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 1c44dbb3f..a36df2cec 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -38,11 +38,11 @@ _SQL_: `rf_reproject_geometry`
 Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems. 
 
 
-#### rf_extent
+#### st_extent
 
 _Python_:
 
-    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(Geometry geom)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] st_extent(Geometry geom)
 
 Extracts the bounding box (extent/envelope) of the geometry.
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index c831a15d7..996697dcb 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -12,7 +12,8 @@
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * Added `rf_render_matrix` debugging function.
 * Added `RasterFrame.withExtent` extension method.
-* Added `rf_extent` and removed `envelope`. 
+* Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` types).
+* _Breaking_ : Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
 * _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, with an `rf_` prefix, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 01ff4c282..9ef89df00 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -242,7 +242,8 @@ def _(data_tile, mask_tile, mask_value):
     'rf_exp10': 'Compute 10 to the power of cell values',
     'rf_expm1': 'Performs cell-wise exponential, then subtract one',
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
-    'rf_extent': 'Compute the extent/bbox of a Geometry',
+    'st_extent': 'Compute the extent/bbox of a Geometry',
+    'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
     'rf_extent_geometry': 'Convert the given extent/bbox to a polygon',
 
     # ------- JTS functions -------
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index feaab8cd6..4c24fd828 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -103,13 +103,13 @@ def test_general(self):
             .withColumn('mean', rf_tile_mean(self.tileCol)) \
             .withColumn('sum', rf_tile_sum(self.tileCol)) \
             .withColumn('stats', rf_tile_stats(self.tileCol)) \
-            .withColumn('extent', rf_extent('geometry')) \
+            .withColumn('extent', st_extent('geometry')) \
             .withColumn('ascii', rf_render_ascii(self.tileCol)) \
             .withColumn('log', rf_log(self.tileCol)) \
             .withColumn('exp', rf_exp(self.tileCol)) \
             .withColumn('expm1', rf_expm1(self.tileCol)) \
             .withColumn('round', rf_round(self.tileCol))
-
+        # TODO: add test for rf_extent once rastersource connector is integrated.
         df.show()
 
     def test_rasterize(self):

From 51c7686d7ec201faa7b75497db0b3404fa3c8954 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Apr 2019 10:33:31 -0400
Subject: [PATCH 061/380] Added `rf_geometry`. Changed `rf_tile_dimensions` to
 `rf_dimensions`. Removed `RasterSourceToTiles`

---
 .../rasterframes/bench/RasterRefBench.scala   | 18 ++--
 .../rasterframes/RasterFunctions.scala        | 13 +--
 .../expressions/accessors/GetDimensions.scala | 15 +++-
 .../expressions/accessors/GetExtent.scala     | 11 ++-
 .../expressions/accessors/GetGeometry.scala   | 57 +++++++++++++
 .../rasterframes/expressions/package.scala    |  5 +-
 .../transformers/ExtentToGeometry.scala       |  2 +-
 .../transformers/RasterSourceToTiles.scala    | 83 -------------------
 .../transformers/URIToRasterSource.scala      |  2 -
 core/src/test/resources/log4j.properties      |  2 +-
 core/src/test/scala/examples/Tour.scala       |  2 +-
 .../rasterframes/ExplodeSpec.scala            |  2 +-
 .../rasterframes/GeometryOperationsSpec.scala |  2 +-
 .../rasterframes/RasterFrameSpec.scala        |  7 +-
 .../rasterframes/RasterFunctionsSpec.scala    | 20 +++++
 .../rasterframes/TileAssemblerSpec.scala      |  2 +-
 .../rasterframes/TileStatsSpec.scala          |  6 +-
 .../geotiff/GeoTiffDataSource.scala           |  4 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala | 10 +--
 .../RasterSourceDataSourceSpec.scala          |  2 +-
 docs/src/main/tut/reference.md                |  6 +-
 docs/src/main/tut/release-notes.md            |  7 +-
 .../datasource/awspds/L8Relation.scala        |  2 +-
 .../python/pyrasterframes/rasterfunctions.py  |  5 +-
 .../python/tests/PyRasterFramesTests.py       |  8 +-
 25 files changed, 153 insertions(+), 140 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetGeometry.scala
 delete mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala

diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index 0553daec2..a3450e1d7 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -23,17 +23,14 @@ package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.expressions.transformers.RasterSourceToTiles
-import org.locationtech.rasterframes.ref.RasterSource
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs}
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.ref.RasterSource
 import org.openjdk.jmh.annotations._
-/**
- *
- *
- * @since 11/1/18
- */
+
 @BenchmarkMode(Array(Mode.AverageTime))
 @State(Scope.Benchmark)
 @OutputTimeUnit(TimeUnit.MILLISECONDS)
@@ -47,11 +44,12 @@ class RasterRefBench  extends SparkEnv with LazyLogging {
   def setupData(): Unit = {
     val r1 = RasterSource(remoteCOGSingleband1)
     val r2 = RasterSource(remoteCOGSingleband2)
+
     singleDF = Seq((r1, r2)).toDF("B1", "B2")
-      .select(RasterSourceToTiles(false, $"B1", $"B2"))
+      .select(RasterRefToTile(RasterSourceToRasterRefs(Some(TileDimensions(r1.dimensions)), Seq(0), $"B1", $"B2")))
 
     expandedDF = Seq((r1, r2)).toDF("B1", "B2")
-      .select(RasterSourceToTiles(true, $"B1", $"B2"))
+      .select(RasterRefToTile(RasterSourceToRasterRefs($"B1", $"B2")))
   }
 
   @Benchmark
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 279bc172d..cae2e65d2 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -35,6 +35,7 @@ import org.locationtech.rasterframes.expressions.generators._
 import org.locationtech.rasterframes.expressions.localops._
 import org.locationtech.rasterframes.expressions.tilestats._
 import org.locationtech.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.{functions => F}
 
@@ -59,7 +60,7 @@ trait RasterFunctions {
     ExplodeTiles(sampleFraction, None, cols)
 
   /** Query the number of (cols, rows) in a Tile. */
-  def rf_tile_dimensions(col: Column): Column = GetDimensions(col)
+  def rf_dimensions(col: Column): TypedColumn[Any, TileDimensions] = GetDimensions(col)
 
   /** Extracts the bounding box of a geometry as an Extent */
   def st_extent(col: Column): TypedColumn[Any, Extent] = GeometryToExtent(col)
@@ -109,12 +110,15 @@ trait RasterFunctions {
   def rf_resample(tileCol: Column, factorCol: Column) = Resample(tileCol, factorCol)
 
   /** Convert a bounding box structure to a Geometry type. Intented to support multiple schemas. */
-  def rf_extent_geometry(bounds: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(bounds)
+  def st_geometry(extent: Column): TypedColumn[Any, Geometry] = ExtentToGeometry(extent)
+
+  /** Extract the extent of a RasterSource or ProjectedRasterTile as a Geometry type. */
+  def rf_geometry(raster: Column): TypedColumn[Any, Geometry] = GetGeometry(raster)
 
   /** Assign a `NoData` value to the Tiles. */
-  def rf_with_no_data(col: Column, nodata: Double): TypedColumn[Any, Tile] = withAlias("rf_with_no_data", col)(
+  def rf_with_no_data(col: Column, nodata: Double): TypedColumn[Any, Tile] = withTypedAlias("rf_with_no_data", col)(
     udf[Tile, Tile](F.withNoData(nodata)).apply(col)
-  ).as[Tile]
+  )
 
   /**  Compute the full column aggregate floating point histogram. */
   def rf_agg_approx_histogram(col: Column): TypedColumn[Any, CellHistogram] =
@@ -374,5 +378,4 @@ trait RasterFunctions {
   def rf_expm1(tileCol: Column): TypedColumn[Any, Tile] =
     ExpM1(tileCol)
 
-
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
index f12bbd77d..dffdfdecb 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetDimensions.scala
@@ -25,16 +25,23 @@ import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.OnCellGridExpression
 import geotrellis.raster.CellGrid
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.locationtech.rasterframes.model.TileDimensions
 
 /**
- * Extract a Tile's dimensions
+ * Extract a raster's dimensions
  * @since 12/21/17
  */
+@ExpressionDescription(
+  usage = "_FUNC_(raster) - Fetches the dimensions (columns & rows) of a Tile, ProjectedRasterTile or RasterSource.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(raster);
+         ....
+  """)
 case class GetDimensions(child: Expression) extends OnCellGridExpression with CodegenFallback {
-  override def nodeName: String = "rf_tile_dimensions"
+  override def nodeName: String = "rf_dimensions"
 
   def dataType = schemaOf[TileDimensions]
 
@@ -42,6 +49,6 @@ case class GetDimensions(child: Expression) extends OnCellGridExpression with Co
 }
 
 object GetDimensions {
-  def apply(col: Column): Column =
+  def apply(col: Column): TypedColumn[Any, TileDimensions] =
     new Column(new GetDimensions(col.expr)).as[TileDimensions]
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
index 9cfdea016..2266c69b5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetExtent.scala
@@ -33,13 +33,20 @@ import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes.model.TileContext
 
 /**
- * Expression to extract the Extent out of a RasterRef or ProjectedRasterTile column.
+ * Expression to extract the Extent out of a RasterSource or ProjectedRasterTile column.
  *
  * @since 9/10/18
  */
+@ExpressionDescription(
+  usage = "_FUNC_(raster) - Fetches the extent (bounding box or envelope) of a ProjectedRasterTile or RasterSource.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(raster);
+         ....
+  """)
 case class GetExtent(child: Expression) extends OnTileContextExpression with CodegenFallback {
   override def dataType: DataType = schemaOf[Extent]
-  override def nodeName: String = "extent"
+  override def nodeName: String = "rf_extent"
   override def eval(ctx: TileContext): InternalRow = ctx.extent.toInternalRow
 }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetGeometry.scala
new file mode 100644
index 000000000..7ff3bcfc7
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetGeometry.scala
@@ -0,0 +1,57 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.accessors
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.jts.JTSTypes
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.jts.geom.Geometry
+import org.locationtech.rasterframes.encoders.StandardEncoders.jtsGeometryEncoder
+import org.locationtech.rasterframes.expressions.OnTileContextExpression
+import org.locationtech.rasterframes.model.TileContext
+
+/**
+  * Expression to extract the Extent out of a RasterSource or ProjectedRasterTile column.
+  *
+  * @since 9/10/18
+  */
+@ExpressionDescription(
+  usage = "_FUNC_(raster) - Fetches the extent (bounding box or envelope) of a ProjectedRasterTile or RasterSource.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(raster);
+         ....
+  """)
+case class GetGeometry(child: Expression) extends OnTileContextExpression with CodegenFallback {
+  override def dataType: DataType = JTSTypes.GeometryTypeInstance
+  override def nodeName: String = "rf_geometry"
+  override def eval(ctx: TileContext): InternalRow =
+    JTSTypes.GeometryTypeInstance.serialize(ctx.extent.jtsGeom)
+}
+
+object GetGeometry {
+  def apply(col: Column): TypedColumn[Any, Geometry] =
+    new Column(GetGeometry(col.expr)).as[Geometry]
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 078736655..0905dad39 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -67,8 +67,9 @@ package object expressions {
     registry.registerExpression[ExplodeTiles]("rf_explode_tiles")
     registry.registerExpression[GetCellType]("rf_cell_type")
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
-    registry.registerExpression[GetDimensions]("rf_tile_dimensions")
-    registry.registerExpression[ExtentToGeometry]("rf_extent_geometry")
+    registry.registerExpression[GetDimensions]("rf_dimensions")
+    registry.registerExpression[ExtentToGeometry]("st_geometry")
+    registry.registerExpression[GetGeometry]("rf_geometry")
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[GetExtent]("rf_extent")
     registry.registerExpression[Subtract]("rf_local_subtract")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
index 7303baa29..9d2d12d2f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ExtentToGeometry.scala
@@ -40,7 +40,7 @@ import org.locationtech.geomesa.spark.jts.encoders.SpatialEncoders
  * @since 8/24/18
  */
 case class ExtentToGeometry(child: Expression) extends UnaryExpression with CodegenFallback {
-    override def nodeName: String = "rf_extent_geometry"
+    override def nodeName: String = "st_geometry"
 
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
deleted file mode 100644
index 5302640b4..000000000
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToTiles.scala
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2019 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes.expressions.transformers
-
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.util._
-import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.sql.Column
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.rf._
-import org.apache.spark.sql.types.{DataType, StructField, StructType}
-
-import scala.util.control.NonFatal
-import org.locationtech.rasterframes.TileType
-
-/**
- * Accepts RasterRef and generates one or more RasterRef instances representing the
- * native internal sub-tiling, if any (and requested).
- *
- * @since 9/6/18
- */
-@deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
-case class RasterSourceToTiles(children: Seq[Expression], applyTiling: Boolean) extends Expression
-  with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
-
-  private val RasterSourceType = new RasterSourceUDT()
-  override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
-  override def nodeName: String = "raster_source_to_tile"
-
-  override def elementSchema: StructType = StructType(
-    children.map(e ⇒ StructField(e.name, TileType, true))
-  )
-
-  override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
-    implicit val ser = TileUDT.tileSerializer
-
-    try {
-      val refs = children.map { child ⇒
-        val src = RasterSourceType.deserialize(child.eval(input))
-        val tiles = if (applyTiling) src.readAll() else {
-          Seq(src.read(src.extent))
-        }
-        tiles.map(_.mapTile(_.band(0)))
-      }
-      refs.transpose.map(ts ⇒ InternalRow(ts.map(r ⇒ r.tile.toInternalRow): _*))
-    }
-    catch {
-      case NonFatal(ex) ⇒
-        logger.error("Error fetching data for " + sql, ex)
-        Traversable.empty
-    }
-  }
-}
-
-
-object RasterSourceToTiles {
-  @deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
-  def apply(rrs: Column*): Column = apply(true, rrs: _*)
-  @deprecated("Use RasterSourceToRasterRefs and RasterRef to Tile instread", "4/28/19")
-  def apply(applyTiling: Boolean, rrs: Column*): Column =
-    new Column(new RasterSourceToTiles(rrs.map(_.expr), applyTiling))
-}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index 1c11ce8c7..4a9c2659c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -27,13 +27,11 @@ import org.locationtech.rasterframes.RasterSourceType
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, UnaryExpression}
-import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StringType}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.unsafe.types.UTF8String
 import org.locationtech.rasterframes.ref.RasterSource
 
-
 /**
  * Catalyst generator to convert a geotiff download URL into a series of rows
  * containing references to the internal tiles and associated extents.
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index db5aa310a..38dc57f1e 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -30,7 +30,7 @@ log4j.logger.org.apache.spark.repl.Main=WARN
 
 log4j.logger.org.apache=ERROR
 log4j.logger.com.amazonaws=WARN
-log4j.logger.geotrellis=INFO
+log4j.logger.geotrellis=WARN
 
 # Settings to quiet third party logs that are too verbose
 log4j.logger.org.spark_project.jetty=WARN
diff --git a/core/src/test/scala/examples/Tour.scala b/core/src/test/scala/examples/Tour.scala
index 8729c7d51..8dac067b8 100644
--- a/core/src/test/scala/examples/Tour.scala
+++ b/core/src/test/scala/examples/Tour.scala
@@ -57,7 +57,7 @@ object Tour extends App {
   rf.show(8, false)
 
   // Confirm we have equally sized tiles
-  rf.select(rf_tile_dimensions($"tile")).distinct().show()
+  rf.select(rf_dimensions($"tile")).distinct().show()
 
   // Count the number of no-data cells
   rf.select(rf_agg_no_data_cells($"tile")).show(false)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index 15701b166..2a44bf3cf 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -45,7 +45,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
       write(query)
       assert(query.select("cell_0", "cell_1").as[(Double, Double)].collect().forall(_ == ((1.0, 2.0))))
       val query2 = sql(
-        """|select rf_tile_dimensions(tiles) as dims, rf_explode_tiles(tiles) from (
+        """|select rf_dimensions(tiles) as dims, rf_explode_tiles(tiles) from (
            |select rf_make_constant_tile(1, 10, 10, 'int8raw') as tiles)
            |""".stripMargin)
       write(query2)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
index ea26594f7..0da6035a0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
@@ -62,7 +62,7 @@ class GeometryOperationsSpec extends TestEnvironment with TestData {
       val rasterized = toRasterize.withColumn("rasterized", rf_rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
 
       assert(rasterized.count() === df.count() * rf.count())
-      assert(rasterized.select(rf_tile_dimensions($"rasterized")).distinct().count() === 1)
+      assert(rasterized.select(rf_dimensions($"rasterized")).distinct().count() === 1)
       val pixelCount = rasterized.select(rf_agg_data_cells($"rasterized")).first()
       assert(pixelCount < cols * rows)
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
index a23cfb771..98ecd55fa 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
@@ -35,6 +35,7 @@ import geotrellis.spark.tiling._
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.{SQLContext, SparkSession}
+import org.locationtech.rasterframes.model.TileDimensions
 
 import scala.util.control.NonFatal
 
@@ -87,11 +88,9 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       assert(rf.schema.head.metadata.json.contains("tileLayout"))
 
       assert(
-        rf.select(rf_tile_dimensions($"tile"))
-          .as[Tuple1[(Int, Int)]]
-          .map(_._1)
+        rf.select(rf_dimensions($"tile"))
           .collect()
-          .forall(_ == (10, 10))
+          .forall(_ == TileDimensions(10, 10))
       )
 
       assert(rf.count() === 4)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index c89e66c26..c28f3c422 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -29,6 +29,7 @@ import geotrellis.vector.Extent
 import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
+import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.scalatest.{FunSpec, Matchers}
@@ -284,6 +285,25 @@ class RasterFunctionsSpec extends FunSpec
     }
   }
 
+  describe("raster metadata") {
+    it("should get the TileDimensions of a Tile") {
+      val t = Seq(randTile).toDF("tile").select(rf_dimensions($"tile")).first()
+      t should be (TileDimensions(randTile.dimensions))
+      checkDocs("rf_dimensions")
+    }
+    it("should get the Extent of a ProjectedRasterTile") {
+      val e = Seq(randTile).toDF("tile").select(rf_extent($"tile")).first()
+      e should be (extent)
+      checkDocs("rf_extent")
+    }
+
+    it("should get the Geometry of a ProjectedRasterTile") {
+      val g = Seq(randTile).toDF("tile").select(rf_geometry($"tile")).first()
+      g should be (extent.jtsGeom)
+      checkDocs("rf_geometry")
+    }
+  }
+
   describe("per-tile stats") {
     it("should compute data cell counts") {
       val df = Seq(TestData.injectND(numND)(two)).toDF("two")
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index 8fc11f878..7dbccc92a 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -65,7 +65,7 @@ class TileAssemblerSpec extends TestEnvironment {
       val sceneSize = (260, 257)
       val rs = InMemoryRasterSource(TestData.randomTile(sceneSize._1, sceneSize._2, ByteConstantNoDataCellType), Extent(10, 20, 30, 40), LatLng)
       val df = rs.toDF
-      val exploded = df.select($"spatial_index", $"extent", rf_tile_dimensions($"tile") as "tile_dimensions", rf_explode_tiles($"tile"))
+      val exploded = df.select($"spatial_index", $"extent", rf_dimensions($"tile") as "tile_dimensions", rf_explode_tiles($"tile"))
 
       val assembled = exploded
         .groupBy($"spatial_index", $"extent", $"tile_dimensions")
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index 6309e3eda..512a205fe 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -43,13 +43,13 @@ class TileStatsSpec extends TestEnvironment with TestData {
     it("should report dimensions") {
       val df = Seq[(Tile, Tile)]((byteArrayTile, byteArrayTile)).toDF("tile1", "tile2")
 
-      val dims = df.select(rf_tile_dimensions($"tile1") as "dims").select("dims.*")
+      val dims = df.select(rf_dimensions($"tile1") as "dims").select("dims.*")
 
       assert(dims.as[(Int, Int)].first() === (3, 3))
       assert(dims.schema.head.name === "cols")
 
       val query = sql("""|select dims.* from (
-           |select rf_tile_dimensions(tiles) as dims from (
+           |select rf_dimensions(tiles) as dims from (
            |select rf_make_constant_tile(1, 10, 10, 'int8raw') as tiles))
            |""".stripMargin)
       write(query)
@@ -57,7 +57,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
 
       df.repartition(4).createOrReplaceTempView("tmp")
       assert(
-          sql("select dims.* from (select rf_tile_dimensions(tile2) as dims from tmp)")
+          sql("select dims.* from (select rf_dimensions(tile2) as dims from tmp)")
             .as[(Int, Int)]
             .first() === (3, 3))
     }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index a9c1bdaca..4b306df1e 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -78,14 +78,14 @@ class GeoTiffDataSource extends DataSourceRegister
       val c = rf
         .where(SPATIAL_KEY_COLUMN("row") === sk.row)
         .agg(
-          F.sum(rf_tile_dimensions(tc)("cols") cast(LongType))
+          F.sum(rf_dimensions(tc)("cols") cast(LongType))
         ).first()
         .getLong(0)
 
       val r = rf
         .where(SPATIAL_KEY_COLUMN("col") === sk.col)
         .agg(
-          F.sum(rf_tile_dimensions(tc)("rows") cast(LongType))
+          F.sum(rf_dimensions(tc)("rows") cast(LongType))
         ).first()
         .getLong(0)
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index d8f8d8684..23e9283be 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -188,7 +188,7 @@ class GeoTrellisDataSourceSpec
         .withTileSubdivisions(param)
         .loadRF(layer)
 
-      val dims = df.select(rf_tile_dimensions(df.tileColumns.head)("cols"), rf_tile_dimensions(df.tileColumns.head)("rows")).first()
+      val dims = df.select(rf_dimensions(df.tileColumns.head)("cols"), rf_dimensions(df.tileColumns.head)("rows")).first()
       assert(dims.getAs[Int](0) === tileSize / param)
       assert(dims.getAs[Int](1) === tileSize / param)
 
@@ -201,7 +201,7 @@ class GeoTrellisDataSourceSpec
         .withTileSubdivisions(param)
         .loadRF(tfLayer)
 
-      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_dimensions(rf.tileColumns.head)("cols"), rf_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / param)
       assert(dims.getAs[Int](1) === tileSize / param)
@@ -223,7 +223,7 @@ class GeoTrellisDataSourceSpec
 
       // is it subdivided?
       assert(rf.count === testRdd.count * subParam * subParam)
-      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_dimensions(rf.tileColumns.head)("cols"), rf_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
@@ -451,7 +451,7 @@ class GeoTrellisDataSourceSpec
 
       assert(rf.count === testRdd.count * subParam * subParam)
 
-      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_dimensions(rf.tileColumns.head)("cols"), rf_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
@@ -466,7 +466,7 @@ class GeoTrellisDataSourceSpec
 
       // is it subdivided?
       assert(rf.count === testRdd.count * subParam * subParam)
-      val dims = rf.select(rf_tile_dimensions(rf.tileColumns.head)("cols"), rf_tile_dimensions(rf.tileColumns.head)("rows"))
+      val dims = rf.select(rf_dimensions(rf.tileColumns.head)("cols"), rf_dimensions(rf.tileColumns.head)("rows"))
         .first()
       assert(dims.getAs[Int](0) === tileSize / subParam)
       assert(dims.getAs[Int](1) === tileSize / subParam)
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index fa9cd4c02..25180b1ea 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -98,7 +98,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       df.count() should be(math.ceil(1028.0 / 128).toInt * math.ceil(989.0 / 128).toInt)
 
-      val dims = df.select(rf_tile_dimensions($"tile").as[TileDimensions]).distinct().collect()
+      val dims = df.select(rf_dimensions($"tile").as[TileDimensions]).distinct().collect()
       dims should contain allElementsOf
         Seq(TileDimensions(4,128), TileDimensions(128,128), TileDimensions(128,93), TileDimensions(4,93))
 
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index a36df2cec..75cd1f63f 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -82,13 +82,13 @@ Print an array of possible cell type names, as below. These names are used in ot
 |float64   |
 
 
-#### rf_tile_dimensions
+#### rf_dimensions
 
 _Python_:
 
-    Struct[Int, Int] rf_tile_dimensions(Tile tile)
+    Struct[Int, Int] rf_dimensions(Tile tile)
    
-_SQL_: `rf_tile_dimensions`
+_SQL_: `rf_dimensions`
 
 Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 996697dcb..b9609e9c0 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -12,8 +12,11 @@
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * Added `rf_render_matrix` debugging function.
 * Added `RasterFrame.withExtent` extension method.
-* Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` types).
-* _Breaking_ : Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
+* Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
+* Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
+* _Breaking_: Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
+* _Breaking_: Renamed `rf_extent_geometry` to `st_geometry`
+* _Breaking_: Renamed `rf_tile_dimensions` to `rf_dimensions`
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
 * _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, with an `rf_` prefix, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
index 3c734be2c..bfb48558e 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -89,7 +89,7 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
       .format(L8CatalogDataSource.SHORT_NAME)
       .load()
       .withColumnRenamed(PDSFields.ACQUISITION_DATE.name, PDSFields.TIMESTAMP.name)
-      .withColumn(PDSFields.BOUNDS.name, rf_extent_geometry(col(PDSFields.BOUNDS_WGS84.name)))
+      .withColumn(PDSFields.BOUNDS.name, st_geometry(col(PDSFields.BOUNDS_WGS84.name)))
       .drop(PDSFields.BOUNDS_WGS84.name)
 
     val filtered = aggFilters
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 9ef89df00..2481b8bc1 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -194,7 +194,7 @@ def _(data_tile, mask_tile, mask_value):
 
 _rf_column_functions = {
     # ------- RasterFrames functions -------
-    'rf_tile_dimensions': 'Query the number of (cols, rows) in a Tile.',
+    'rf_dimensions': 'Query the number of (cols, rows) in a Tile.',
     'rf_tile_to_array_int': 'Flattens Tile into an array of integers.',
     'rf_tile_to_array_double': 'Flattens Tile into an array of doubles.',
     'rf_cell_type': 'Extract the Tile\'s cell type',
@@ -244,7 +244,8 @@ def _(data_tile, mask_tile, mask_value):
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
     'st_extent': 'Compute the extent/bbox of a Geometry',
     'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
-    'rf_extent_geometry': 'Convert the given extent/bbox to a polygon',
+    'st_geometry': 'Convert the given extent/bbox to a polygon',
+    'rf_geometry': 'Get the extent of a RasterSource or ProjectdRasterTile as a Geometry',
 
     # ------- JTS functions -------
     # spatial constructors
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 4c24fd828..1a7174691 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -94,7 +94,7 @@ def test_tile_operations(self):
     def test_general(self):
         meta = self.rf.tileLayerMetadata()
         self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims',  rf_tile_dimensions(self.tileCol)) \
+        df = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)) \
             .withColumn('type', rf_cell_type(self.tileCol)) \
             .withColumn('dCells', rf_data_cells(self.tileCol)) \
             .withColumn('ndCells', rf_no_data_cells(self.tileCol)) \
@@ -104,12 +104,14 @@ def test_general(self):
             .withColumn('sum', rf_tile_sum(self.tileCol)) \
             .withColumn('stats', rf_tile_stats(self.tileCol)) \
             .withColumn('extent', st_extent('geometry')) \
+            .withColumn('extent_geom1', st_geometry('extent')) \
             .withColumn('ascii', rf_render_ascii(self.tileCol)) \
             .withColumn('log', rf_log(self.tileCol)) \
             .withColumn('exp', rf_exp(self.tileCol)) \
             .withColumn('expm1', rf_expm1(self.tileCol)) \
             .withColumn('round', rf_round(self.tileCol))
-        # TODO: add test for rf_extent once rastersource connector is integrated.
+        # TODO: add test for rf_extent and rf_geometry once rastersource connector is integrated and we have
+        #  a source of ProjectedRasterTiles.
         df.show()
 
     def test_rasterize(self):
@@ -143,7 +145,7 @@ def test_sql(self):
 
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims',  rf_tile_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 

From 7dccb8eb09f41b2ebc6407f5f0d3e03a767394b1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Apr 2019 15:39:57 -0400
Subject: [PATCH 062/380] Renamed `rf_reproject_geometry` to `st_reproject`
 Fixed L8 integration test.

---
 core/src/main/resources/reference.conf        | 18 +++++------
 .../rasterframes/RasterFunctions.scala        |  6 ++--
 .../transformers/ReprojectGeometry.scala      |  2 +-
 .../rasterframes/functions/package.scala      |  2 +-
 .../rasterframes/model/Cells.scala            |  3 ++
 .../rasterframes/ref/RasterRef.scala          |  3 +-
 .../util/ZeroSevenCompatibilityKit.scala      |  2 +-
 .../locationtech/rasterframes/JTSSpec.scala   |  8 ++---
 .../rasterframes/ReprojectGeometrySpec.scala  | 12 +++----
 docs/src/main/tut/reference.md                |  6 ++--
 docs/src/main/tut/release-notes.md            |  3 +-
 .../awspds/L8CatalogRelationTest.scala        |  0
 .../datasource/awspds/L8RelationTest.scala    | 32 +++++++------------
 .../awspds/MODISCatalogRelationTest.scala     |  0
 .../datasource/awspds/L8DataSource.scala      |  4 +--
 .../datasource/awspds/L8Relation.scala        | 18 ++++++-----
 .../src/test/resources/application.conf       |  8 +++++
 .../python/pyrasterframes/rasterfunctions.py  |  8 ++---
 .../rasterframes/py/PyRFContext.scala         |  2 +-
 19 files changed, 70 insertions(+), 67 deletions(-)
 rename experimental/src/it/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala (100%)
 rename experimental/src/it/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/L8RelationTest.scala (67%)
 rename experimental/src/it/scala/{astraea/spark => org/locationtech}/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala (100%)
 create mode 100644 experimental/src/test/resources/application.conf

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 00473ae15..b4ce78b06 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -4,15 +4,13 @@ rasterframes {
 }
 
 vlm.gdal {
-  settings {
-    options {
-      // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
-      //CPL_DEBUG = "OFF"
-      AWS_REQUEST_PAYER = "requester"
-      GDAL_DISABLE_READDIR_ON_OPEN = "YES"
-      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2"
-    }
-    // set this to `false` if CPL_DEBUG is `ON`
-    useExceptions = true
+  options {
+    // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
+    //CPL_DEBUG = "ON"
+    AWS_REQUEST_PAYER = "requester"
+    GDAL_DISABLE_READDIR_ON_OPEN = "YES"
+    CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2"
   }
+  // set this to `false` if CPL_DEBUG is `ON`
+  //useExceptions = false
 }
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index cae2e65d2..abf4d7c31 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -275,15 +275,15 @@ trait RasterFunctions {
     )
 
   /** Reproject a column of geometry from one CRS to another. */
-  def rf_reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
+  def st_reproject(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRSCol)
 
   /** Reproject a column of geometry from one CRS to another. */
-  def rf_reproject_geometry(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
+  def st_reproject(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRSCol, dstCRS)
 
   /** Reproject a column of geometry from one CRS to another. */
-  def rf_reproject_geometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
+  def st_reproject(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRS)
 
   /** Render Tile as ASCII string, for debugging purposes. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 83a5d876a..1b85ffc59 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -43,7 +43,7 @@ import org.locationtech.rasterframes.jts.ReprojectionTransformer
 case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: Expression) extends Expression
   with CodegenFallback with ExpectsInputTypes {
 
-  override def nodeName: String = "rf_reproject_geometry"
+  override def nodeName: String = "st_reproject"
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
   override def nullable: Boolean = geometry.nullable || srcCRS.nullable || dstCRS.nullable
   override def children: Seq[Expression] = Seq(geometry, srcCRS, dstCRS)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 62933029a..17b553336 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -144,6 +144,6 @@ package object functions {
     sqlContext.udf.register("rf_cell_types", cellTypes)
     sqlContext.udf.register("rf_rasterize", rasterize)
 
-    sqlContext.udf.register("rf_reproject_geometry", reprojectGeometryCRSName)
+    sqlContext.udf.register("st_reproject", reprojectGeometryCRSName)
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
index 4566e7630..975109f0c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
@@ -28,6 +28,7 @@ import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import org.locationtech.rasterframes.ref.RasterRef
 import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile.ConcreteProjectedRasterTile
 
 /** Represents the union of binary cell datas or a reference to the data.*/
 case class Cells(data: Either[Array[Byte], RasterRef]) {
@@ -47,6 +48,8 @@ object Cells {
   /** Extracts the Cells from a Tile. */
   def apply(t: Tile): Cells = {
     t match {
+      case prt: ConcreteProjectedRasterTile =>
+        apply(prt.t)
       case ref: RasterRefTile =>
         Cells(Right(ref.rr))
       case const: ConstantTile =>
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
index 634695595..ad2291c77 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.rf.RasterSourceUDT
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.locationtech.rasterframes.encoders.CatalystSerializer.{CatalystIO, _}
 import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
@@ -45,7 +46,7 @@ case class RasterRef(source: RasterSource, bandIndex: Int, subextent: Option[Ext
   def cols: Int = grid.width
   def rows: Int = grid.height
   def cellType: CellType = source.cellType
-  def tile: ProjectedRasterTile = ProjectedRasterTile(realizedTile, extent, crs)
+  def tile: ProjectedRasterTile = ProjectedRasterTile(RasterRefTile(this), extent, crs)
 
   protected lazy val grid: GridBounds = source.rasterExtent.gridBoundsFor(extent)
   protected def srcExtent: Extent = extent
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index ecd122adb..b5e62b42d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -259,7 +259,7 @@ object ZeroSevenCompatibilityKit {
     /** Reproject a column of geometry from one CRS to another. */
     @deprecated("Part of 0.7.x compatibility kit, to be removed after 0.8.x. Please use \"snake_case\" variant instead.", "0.8.0")
     def reprojectGeometry(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
-      delegate.rf_reproject_geometry(sourceGeom, srcCRS, dstCRS)
+      delegate.st_reproject(sourceGeom, srcCRS, dstCRS)
 
     /** Render Tile as ASCII string for debugging purposes. */
     @Experimental
diff --git a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
index 24cbe8f0f..92bd42b4d 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
@@ -110,9 +110,9 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
       val df = Seq((latLng, webMercator)).toDF("ll", "wm")
 
       val rp = df.select(
-        rf_reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
-        rf_reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
-        rf_reproject_geometry(rf_reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+        st_reproject($"ll", LatLng, WebMercator) as "wm2",
+        st_reproject($"wm", WebMercator, LatLng) as "ll2",
+        st_reproject(st_reproject($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
       ).as[(Geometry, Geometry, Geometry)]
 
 
@@ -125,7 +125,7 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
 
       df.createOrReplaceTempView("geom")
 
-      val wm4 = sql("SELECT rf_reproject_geometry(ll, '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs', 'EPSG:3857') AS wm4 from geom")
+      val wm4 = sql("SELECT st_reproject(ll, '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs', 'EPSG:3857') AS wm4 from geom")
         .as[Geometry].first()
       wm4 should matchGeom(webMercator, 0.00001)
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index c227944e0..46805a441 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -60,9 +60,9 @@ class ReprojectGeometrySpec extends FunSpec
         val df = Seq((latLng, webMercator)).toDF("ll", "wm")
 
         val rp = df.select(
-          rf_reproject_geometry($"ll", LatLng, WebMercator) as "wm2",
-          rf_reproject_geometry($"wm", WebMercator, LatLng) as "ll2",
-          rf_reproject_geometry(rf_reproject_geometry($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+          st_reproject($"ll", LatLng, WebMercator) as "wm2",
+          st_reproject($"wm", WebMercator, LatLng) as "ll2",
+          st_reproject(st_reproject($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
         ).as[(Geometry, Geometry, Geometry)]
 
 
@@ -79,9 +79,9 @@ class ReprojectGeometrySpec extends FunSpec
         val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
 
         val rp = df.select(
-          rf_reproject_geometry($"ll", $"llCRS", WebMercator) as "wm2",
-          rf_reproject_geometry($"wm", WebMercator, $"llCRS") as "ll2",
-          rf_reproject_geometry(rf_reproject_geometry($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+          st_reproject($"ll", $"llCRS", WebMercator) as "wm2",
+          st_reproject($"wm", WebMercator, $"llCRS") as "ll2",
+          st_reproject(st_reproject($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
         ).as[(Geometry, Geometry, Geometry)]
 
 
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 75cd1f63f..df28c11ab 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -28,12 +28,12 @@ Various LocationTech GeoMesa UDFs to deal with `geomtery` type columns are also
 
 RasterFrames provides two additional functions for vector geometry.
 
-#### reproject_geometry
+#### st_reproject
 
 _Python_:
-    Geometry reproject_geometry(Geometry geom, String origin_crs, String destination_crs)
+    Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
-_SQL_: `rf_reproject_geometry`
+_SQL_: `st_reproject`
 
 Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems. 
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index b9609e9c0..f04f6cf9a 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -14,10 +14,11 @@
 * Added `RasterFrame.withExtent` extension method.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
+* _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * _Breaking_: Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
 * _Breaking_: Renamed `rf_extent_geometry` to `st_geometry`
 * _Breaking_: Renamed `rf_tile_dimensions` to `rf_dimensions`
-* _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
+* _Breaking_: Renamed `rf_reproject_geometry` to `st_reproject`
 * _Breaking_: With the upgrade to JTS 1.16.0, all imports of `com.vividsolutions.jts` need to be changed to `org.locationtech.jts`.
 * _Deprecation_: Tile column functions (in `RasterFunctions`) and SQL registered names have all been renamed to follow `snake_case` conventions, with an `rf_` prefix, matching SQL and Python. A temporary compatibility shim is included so that code built against 0.7.1 and earlier still work. These will be marked as deprecated.
 * _Breaking_: In Scala and SQL, `..._scalar` functions (e.g. `local_add_scalar`) have been removed. Non-scalar forms now dynamically detect type of right hand side.
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
similarity index 100%
rename from experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
rename to experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
similarity index 67%
rename from experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
rename to experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
index 780ea9195..f53246c09 100644
--- a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
@@ -21,8 +21,6 @@
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import org.locationtech.rasterframes._
-import org.apache.spark.sql.DataFrame
-import org.locationtech.rasterframes.TestEnvironment
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
 /**
@@ -30,8 +28,6 @@ import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
  */
 class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeAndAfter {
 
-  private var scenes: DataFrame = _
-
   val query =  """
             |SELECT geometry, timestamp, B1, B2
             |FROM l8
@@ -46,28 +42,24 @@ class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeA
       .format(L8DataSource.SHORT_NAME)
       .load()
     l8.createOrReplaceTempView("l8")
-    scenes = sql(query).cache()
+    sql(query).createOrReplaceTempView("subscenes")
   }
 
   describe("Read L8 on PDS as a DataSource") {
+    import spark.implicits._
     it("should count scenes") {
-      assert(scenes.schema.size === 4)
-      assert(scenes.count() === 7)
+      val scenes = sql("SELECT entity_id FROM l8 DISTINCT")
+      scenes.count() shouldBe >(300400L)
+
+      val subscenes = sqlContext.table("subscenes")
+      subscenes.schema.size should be (4)
+      subscenes.count() should be(7)
     }
 
-    it("should count tiles") {
-      val l8 = spark.read
-        .format(L8DataSource.SHORT_NAME)
-        .option(L8DataSource.USE_TILING, true)
-        .load()
-      l8.createOrReplaceTempView("l82")
-      val scenes2 = sql(query.replaceAll("l8", "l82")).cache()
-      val scenesCount = scenes.count()
-      val scenes2Count = scenes2.count()
-      println(scenesCount, scenes2Count)
-      // Most L8 geotiffs are 16x16 tiles, but some are smaller.
-      // Test against the lower bound.
-      assert(scenes2Count > scenesCount * 15 * 15)
+    it("should compute statistics") {
+      val subscenes = sqlContext.table("subscenes")
+      val stats = subscenes.select(rf_agg_stats($"B1")).first()
+      stats.data_cells shouldBe >(420024000L)
     }
   }
 }
diff --git a/experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
similarity index 100%
rename from experimental/src/it/scala/astraea/spark/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
rename to experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
index 5a6c25b6e..95e70ff4f 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
@@ -33,12 +33,10 @@ class L8DataSource extends DataSourceRegister with RelationProvider {
   override def shortName(): String = L8DataSource.SHORT_NAME
 
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
-    val useTiling = parameters.get(L8DataSource.USE_TILING).exists(_.toBoolean)
-    L8Relation(sqlContext, useTiling)
+    L8Relation(sqlContext)
   }
 }
 
 object L8DataSource {
   final val SHORT_NAME = "awsl8"
-  final val USE_TILING = "useTiling"
 }
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
index bfb48558e..43c180cac 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
@@ -34,14 +34,14 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, Row, SQLContext}
-import org.locationtech.rasterframes.ref.RasterRef
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
  * Spark relation over AWS PDS Landsat 8 collection.
  *
  * @since 8/21/18
  */
-case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[Filter] = Seq.empty)
+case class L8Relation(sqlContext: SQLContext, filters: Seq[Filter] = Seq.empty)
   extends BaseRelation with PrunedFilteredScan with SpatialRelationReceiver[L8Relation] with LazyLogging {
   override def schema: StructType = L8Relation.schema
 
@@ -99,13 +99,15 @@ case class L8Relation(sqlContext: SQLContext, useTiling: Boolean, filters: Seq[F
 
     val nonTile = other.map(col)
 
-    val df = {
-      val dims = if (useTiling) Some(NOMINAL_TILE_DIMS) else None
+    val df = if (bands.nonEmpty) {
       val sources = bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b))
       // NB: We assume that `nativeTiling` preserves the band names.
-      val expanded = RasterSourceToRasterRefs(dims, Seq(0), sources: _*)
-      filtered.select(nonTile :+ expanded: _*)
-    }
+      val expanded = RasterSourceToRasterRefs(None, Seq(0), sources: _*)
+
+      filtered
+        .select(nonTile :+ expanded: _*)
+        .select(nonTile ++ bands.map(b => RasterRefToTile(col(b + "_ref")) as b): _*)
+    } else filtered
 
     // Make sure shape of resulting rows conforms to what was requested
     val selected = requiredColumns.headOption
@@ -129,7 +131,7 @@ object L8Relation extends PDSFields {
         case ACQUISITION_DATE ⇒ ACQUISITION_DATE.copy(name = StandardColumns.TIMESTAMP_COLUMN.columnName)
         case s if s.name == BOUNDS_WGS84.name ⇒ BOUNDS
         case s if s != DOWNLOAD_URL ⇒ s
-      } ++ L8Relation.Bands.values.toSeq.map(b ⇒ StructField(b.toString, schemaOf[RasterRef], true))
+      } ++ L8Relation.Bands.values.toSeq.map(b ⇒ StructField(b.toString, schemaOf[ProjectedRasterTile], true))
     )
   }
 }
diff --git a/experimental/src/test/resources/application.conf b/experimental/src/test/resources/application.conf
new file mode 100644
index 000000000..36c16db32
--- /dev/null
+++ b/experimental/src/test/resources/application.conf
@@ -0,0 +1,8 @@
+vlm.gdal {
+  options {
+    // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
+    //CPL_DEBUG = "ON"
+  }
+  // set this to `false` if CPL_DEBUG is `ON`
+  //useExceptions = false
+}
\ No newline at end of file
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 2481b8bc1..c59d1416b 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -102,11 +102,11 @@ def _(geometryCol, boundsCol, valueCol, numCols, numRows):
 
 
 def _create_reproject_geometry():
-    """ Create a function mapping to the Scala rf_reproject_geometry function. """
+    """ Create a function mapping to the Scala st_reproject function. """
     def _(geometryCol, srcCRSName, dstCRSName):
-        jfcn = RFContext.active().lookup('rf_reproject_geometry')
+        jfcn = RFContext.active().lookup('st_reproject')
         return Column(jfcn(_to_java_column(geometryCol), srcCRSName, dstCRSName))
-    _.__name__ = 'rf_reproject_geometry'
+    _.__name__ = 'st_reproject'
     _.__doc__ = """Reproject a column of geometry given the CRS names of the source and destination.
 Currently supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
 An example of a valid CRS name is EPSG:3005.
@@ -161,7 +161,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_make_constant_tile': _create_makeConstantTile(),
     'rf_mask_by_value': _create_maskByValue(),
     'rf_rasterize': _create_rasterize(),
-    'rf_reproject_geometry': _create_reproject_geometry(),
+    'st_reproject': _create_reproject_geometry(),
     'rf_make_ones_tile': _create_tileOnes(),
     'rf_make_zeros_tile': _create_tileZeros(),
 }
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index a649cd8a7..b1aae5d84 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -159,7 +159,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   def rf_reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
     val src = LazyCRS(srcName)
     val dst = LazyCRS(dstName)
-    rf_reproject_geometry(geometryCol, src, dst)
+    st_reproject(geometryCol, src, dst)
   }
 
   // return toRaster, get just the tile, and make an array out of it

From 52dadaf70413b79f474397263f6cc827fe622085 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 30 Apr 2019 20:07:38 -0400
Subject: [PATCH 063/380] python rastersource

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../RasterSourceDataSourceSpec.scala          |  2 +-
 .../python/pyrasterframes/__init__.py         |  1 +
 .../python/tests/PyRasterFramesTests.py       | 35 +++++++++++++++++++
 3 files changed, 37 insertions(+), 1 deletion(-)

diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 7cb0c34a7..f9df99fad 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -140,7 +140,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       val df = spark.read.rastersource
         .fromTable("pathsTable", "B1", "B2", "B3")
-        .withTileDimensions(128, 128)
+//        .withTileDimensions(128, 128)
         .load()
 
       df.schema.size should be(6)
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index b886bfabd..fe3f1b9ba 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -52,6 +52,7 @@ def _convertDF(df, sp_key = None, metadata = None):
 # TODO: make sure this supports **options
 DataFrameReader.geotiff = lambda df_reader, path: _reader(df_reader, "geotiff", path)
 DataFrameReader.geotrellis = lambda df_reader, path: _reader(df_reader, "geotrellis", path)
+DataFrameReader.rastersource = lambda df_reader, path: _reader(df_reader, "rastersource", path)
 
 
 
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index d0c531533..c7dce17dc 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -285,6 +285,41 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
+    def test_raster_source_reader(self):
+        # much the same as RasterSourceDataSourceSpec here
+        def l8path(b):
+            assert b in range(1, 12)
+            #ok
+            base = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/199/026/LC08_L1TP_199026_20180919_20180928_01_T1/LC08_L1TP_199026_20180919_20180928_01_T1_B{}.TIF"
+            base='s3://s22s-demeter-data-assets-prod/MOD11A1.006/18/06/2018153/MOD11A1.A2018153.h18v06.006.2018154084835_LSTD_B01.TIF'
+            #ok
+            # base = 'file:///Users/jbrown/src/rasterframes/core/target/scala-2.11/test-classes/L8-B{}-Elkton-VA.tiff'
+
+            # not ok base = '/Users/jbrown/src/rasterframes/core/target/scala-2.11/test-classes/L8-B{}-Elkton-VA.tiff'
+            return base.format(b)
+
+        # band_paths = self.spark.createDataFrame([{'B1': l8path(1), 'B2': l8path(2), 'B3': l8path(3)}])
+        # band_paths.createOrReplaceTempView('pathsTable')
+        df = self.spark.read.format('rastersource') \
+                    .options(tileDimensions='32, 32',  #Elkton files are 186 by 169
+                             path=l8path(1),
+                             ) \
+                    .load()
+        #[l8path(b) for b in [1,2,3]]) \
+
+        df.printSchema()
+
+        self.assertTrue(len(df.columns) == 2)  # tile_path and tile
+        tile_size_df = df.select(tile_dimensions(df.tile).rows.alias('r'), tile_dimensions(df.tile).cols.alias('c'))\
+            .groupby(['r','c']).count().toPandas()
+
+        print(tile_size_df)
+        # the most common should be as given in options above.
+        self.assertTrue(tile_size_df.r.value_counts().idxmax() == 32)
+        self.assertTrue(tile_size_df.c.value_counts().idxmax() == 32)  # columns
+        # all rows are from a single source URI
+        self.assertTrue(df.select(df.tile_path).distinct().count() == 1)
+
 
 def suite():
     function_tests = unittest.TestSuite()

From b8367e359142504448e7f1a75df53c3fcef937c7 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 30 Apr 2019 20:59:18 -0400
Subject: [PATCH 064/380] Tweaking the docs to reflect new function names;
 remove SQL repeat of function name where necessary; add extent functions

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/tut/reference.md | 271 ++++-----------------------------
 1 file changed, 32 insertions(+), 239 deletions(-)

diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index df28c11ab..6d009a3e6 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -14,53 +14,44 @@ ReturnDataType function_name(InputDataType argument1, InputDataType argument2)
 
 ## List of Available SQL and Python Functions
 
-The convention in this document will be to define the function signature as below, with its return type, the function name, and named arguments with their types.
-
-```
-ReturnDataType function_name(InputDataType argument1, InputDataType argument2)
-```
-
 @@toc { depth=3 }
 
 ### Vector Operations
 
 Various LocationTech GeoMesa UDFs to deal with `geomtery` type columns are also provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
 
-RasterFrames provides two additional functions for vector geometry.
+RasterFrames provides some additional functions for vector geometry operations.
 
 #### st_reproject
 
-_Python_:
     Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
-_SQL_: `st_reproject`
 
 Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems. 
 
 
 #### st_extent
 
-_Python_:
-
     Struct[Double xmin, Double xmax, Double ymin, Double ymax] st_extent(Geometry geom)
 
 Extracts the bounding box (extent/envelope) of the geometry.
 
 See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#st-envelope) which returns a Geometry type.
 
+#### st_geometry 
+
+    Geometry st_extent(Struct[Double xmin, Double xmax, Double ymin, Double ymax] extent)
+    
+Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`](reference.md#st-extent) or @ref:[`rf_extent`](reference.md#rf-extent). 
+
 ### Tile Metadata and Mutation
 
 Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and nodata](reference.md#masking-and-nodata) for additional discussion of cell types.
 
 #### rf_cell_types
 
-
-_Python_:
-
     Array[String] rf_cell_types()
     
-_SQL_: `rf_cell_types`
-
 Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on nodata](reference.md#masking-and-nodata) for additional details.
  
 |cell_types |
@@ -84,43 +75,36 @@ Print an array of possible cell type names, as below. These names are used in ot
 
 #### rf_dimensions
 
-_Python_:
-
     Struct[Int, Int] rf_dimensions(Tile tile)
    
-_SQL_: `rf_dimensions`
-
 Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
 #### rf_cell_type
 
-_Python_:
-
     Struct[String] rf_cell_type(Tile tile)
     
-_SQL_: `rf_cell_type`
-
 Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function.
 
 #### rf_convert_cell_type
 
-_Python_:
-
     Tile rf_convert_cell_type(Tile tileCol, String cellType)
-    
-_SQL_: `rf_convert_cell_type`
 
 Convert `tileCol` to a different cell type.
 
-#### rf_resample
 
-_Python_: 
+#### rf_extent
+
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(ProjectedRasterTile raster)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(RasterSource raster)
+
+Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `RasterSource` type tile columns.
+
+#### rf_resample
 
     Tile rf_resample(Tile tile, Double factor)
     Tile rf_resample(Tile tile, Int factor)
     Tile rf_resample(Tile tile, Tile shape_tile)
     
-_SQL_: `rf_resample`
 
 Change the tile dimension. Passing a numeric `factor` will scale the number of columns and rows in the tile: 1.0 is the same number of columns and row; less than one downsamples the tile; and greater than one upsamples the tile. Passing a `shape_tile` as the second argument outputs `tile` having the same number of columns and rows as `shape_tile`. All resampling is by nearest neighbor method. 
 
@@ -130,46 +114,34 @@ Functions to create a new Tile column, either from scratch or from existing data
 
 #### rf_make_zeros_tile
 
-_Python_:
-
 ```
 Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 ```
 
-_SQL_: `rf_make_zeros_tile`
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 #### rf_make_ones_tile
 
-_Python_:
-
 ```
 Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 ```
 
-_SQL_: `rf_make_ones_tile`
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 #### rf_make_constant_tile
 
-_Python_: 
-
     Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
     
-_SQL_: `rf_make_constant_tile`
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 
 #### rf_rasterize
 
-_Python_:
-
     Tile rf_rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
     
-_SQL_: `rf_rasterize`
 
 Convert a vector Geometry `geom` into a Tile representation. The `value` will be "burned-in" to the returned `tile` where the `geom` intersects the `tile_bounds`. Returned `tile` will have shape `tile_columns` by `tile_rows`. Values outside the `geom` will be assigned a nodata value. Returned `tile` has cell type `int32`, note that `value` is of type Int.
 
@@ -207,16 +179,12 @@ FROM
 
 #### rf_array_to_tile
 
-_Python_:
-
     Tile rf_array_to_tile(Array arrayCol, Int numCols, Int numRows)
     
 Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), filling values in row-major order.
 
 #### rf_assemble_tile
 
-_Python_:
-
     Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
     
 Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
@@ -237,12 +205,8 @@ For more reading about cell types and ndodata, see the [GeoTrellis documentation
 
 #### rf_mask
 
-_Python_:
-
     Tile rf_mask(Tile tile, Tile mask)
     
-_SQL_: `rf_mask`
-
 Where the `mask` contains nodata, replace values in the `tile` with nodata.
 
 Returned `tile` cell type will be coerced to one supporting nodata if it does not already.
@@ -250,39 +214,25 @@ Returned `tile` cell type will be coerced to one supporting nodata if it does no
 
 #### rf_inverse_mask
 
-_Python_:
-
     Tile rf_inverse_mask(Tile tile, Tile mask)
     
-_SQL_: `rf_inverse_mask`
-
 Where the `mask` _does not_ contain nodata, replace values in `tile` with nodata. 
 
 #### rf_mask_by_value
 
-_Python_:
-
     Tile rf_mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
     
-_SQL_: `rf_mask_by_value`
-
 Generate a `tile` with the values from `data_tile`, with nodata in cells where the `mask_tile` is equal to `mask_value`. 
 
 
 #### rf_is_no_data_tile
 
-_Python_:
-
     Boolean rf_is_no_data_tile(Tile)
-
-_SQL_: `rf_is_no_data_tile`
  
 Returns true if `tile` contains only nodata. By definition returns false if cell type does not support nodata.
 
 #### rf_with_no_data
 
-_Python_:
-
     Tile rf_with_no_data(Tile tile, Double no_data_value)
     
 Python only. Return a `tile` column marking as nodata all cells equal to `no_data_value`.
@@ -301,231 +251,160 @@ Some of these functions have similar variations in the Python API:
  - `rf_local_op_double`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
  - `rf_local_op_int`: applies `op` to a `tile` and a literal scalar, without coercing the `tile` to a floating point type
  
-We will provide all these variations for `rf_local_add` and then suppress the rest in this document.
-
 The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
 #### rf_local_add
 
-_Python_: 
-    
     Tile rf_local_add(Tile tile1, Tile rhs)
     Tile rf_local_add_int(Tile tile1, Int rhs)
     Tile rf_local_add_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_add`
 
 Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
 
 #### rf_local_subtract
 
-_Python_: 
-    
     Tile rf_local_subtract(Tile tile1, Tile rhs)
     Tile rf_local_subtract_int(Tile tile1, Int rhs)
     Tile rf_local_subtract_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_subtract`
 
 Returns a `tile` column containing the element-wise difference of `tile1` and `rhs`.
 
 
 #### rf_local_multiply
 
-_Python_: 
-    
     Tile rf_local_multiply(Tile tile1, Tile rhs)
     Tile rf_local_multiply_int(Tile tile1, Int rhs)
     Tile rf_local_multiply_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_multiply`
 
 Returns a `tile` column containing the element-wise product of `tile1` and `rhs`. This is **not** the matrix multiplication of `tile1` and `rhs`.
 
 
 #### rf_local_divide
 
-_Python_: 
-    
     Tile rf_local_divide(Tile tile1, Tile rhs)
     Tile rf_local_divide_int(Tile tile1, Int rhs)
     Tile rf_local_divide_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_divide`
 
 Returns a `tile` column containing the element-wise quotient of `tile1` and `rhs`. 
 
 
 #### rf_normalized_difference 
 
-_Python_:
-
     Tile rf_normalized_difference(Tile tile1, Tile tile2)
     
-_SQL_: `rf_normalized_difference`
 
 Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (tile1 + tile2)`. Result is always floating point cell type. This function has no scalar variant. 
 
 #### rf_local_less
 
-_Python_: 
-    
     Tile rf_local_less(Tile tile1, Tile rhs)
     Tile rf_local_less_int(Tile tile1, Int rhs)
     Tile rf_local_less_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_less`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than `rhs`. 
 
 #### rf_local_less_equal
 
-_Python_: 
-    
     Tile rf_local_less_equal(Tile tile1, Tile rhs)
     Tile rf_local_less_equal_int(Tile tile1, Int rhs)
     Tile rf_local_less_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_less_equal`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than or equal to `rhs`. 
 
 #### rf_local_greater
 
-_Python_: 
-    
     Tile rf_local_greater(Tile tile1, Tile rhs)
     Tile rf_local_greater_int(Tile tile1, Int rhs)
     Tile rf_local_greater_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_greater`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than `rhs`. 
 
 #### rf_local_greater_equal
 
-_Python_: 
-    
     Tile rf_local_greater_equal(Tile tile1, Tile rhs)
     Tile rf_local_greater_equal_int(Tile tile1, Int rhs)
     Tile rf_local_greater_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_greater_equal`
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than or equal to `rhs`. 
 
 #### rf_local_equal
 
-_Python_: 
-    
     Tile rf_local_equal(Tile tile1, Tile rhs)
     Tile rf_local_equal_int(Tile tile1, Int rhs)
     Tile rf_local_equal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_equal`
 
 Returns a `tile` column containing the element-wise equality of `tile1` and `rhs`. 
 
 #### rf_local_unequal
 
-_Python_: 
-    
     Tile rf_local_unequal(Tile tile1, Tile rhs)
     Tile rf_local_unequal_int(Tile tile1, Int rhs)
     Tile rf_local_unequal_double(Tile tile1, Double rhs)
     
-_SQL_: `rf_local_unequal`
 
 Returns a `tile` column containing the element-wise inequality of `tile1` and `rhs`. 
 
 #### rf_round
 
-_Python_:
-
     Tile rf_round(Tile tile)
 
-_SQL_: `rf_round`
-
 Round cell values to the nearest integer without changing the cell type.
 
 #### rf_exp
 
-_Python_:
-
     Tile rf_exp(Tile tile)
 
-_SQL_: `rf_exp`
-
 Performs cell-wise exponential.
 
 #### rf_exp10
 
-_Python_:
-
     Tile rf_exp10(Tile tile)
 
-_SQL_: `rf_exp10`
-
 Compute 10 to the power of cell values.
 
 #### rf_exp2
 
-_Python_:
-
     Tile rf_exp2(Tile tile)
 
-_SQL_: `rf_exp2`
-
 Compute 2 to the power of cell values.
 
 #### rf_expm1
 
-_Python_:
-
     Tile rf_expm1(Tile tile)
 
-_SQL_: `rf_expm1`
-
 Performs cell-wise exponential, then subtract one. Inverse of @ref:[`log1p`](reference.md#log1p).
 
 #### rf_log
 
-_Python_:
-
     Tile rf_log(Tile tile)
 
-_SQL_: `rf_log`
-
 Performs cell-wise natural logarithm.
 
 #### rf_log10
 
-_Python_:
-
     Tile rf_log10(Tile tile)
 
-_SQL_: `rf_log10`
-
 Performs cell-wise logarithm with base 10.
 
 #### rf_log2
 
-_Python_: 
-
     Tile rf_log2(Tile tile)
 
-_SQL_: `rf_log2`
-
 Performs cell-wise logarithm with base 2.
 
 #### rf_log1p
 
-_Python_: 
-
     Tile rf_log1p(Tile tile)
 
-_SQL_: `rf_log1p`
-
 Performs natural logarithm of cell values plus one. Inverse of @ref:[`rf_expm1`](reference.md#rf-expm1).
 
 ### Tile Statistics
@@ -552,86 +431,60 @@ spark.sql("""
 
 #### rf_tile_sum
 
-_Python_:
-
     Double rf_tile_sum(Tile tile)
     
-_SQL_:  `rf_tile_sum`
 
 Computes the sum of cells in each row of column `tile`, ignoring nodata values.
 
 #### rf_tile_mean
 
-_Python_:
-
     Double rf_tile_mean(Tile tile)
     
-_SQL_:  `rf_tile_mean`
 
 Computes the mean of cells in each row of column `tile`, ignoring nodata values.
 
 
 #### rf_tile_min
 
-_Python_:
-
     Double rf_tile_min(Tile tile)
     
-_SQL_:  `rf_tile_min`
 
 Computes the min of cells in each row of column `tile`, ignoring nodata values.
 
 
 #### rf_tile_max
 
-_Python_:
-
     Double rf_tile_max(Tile tile)
     
-_SQL_:  `rf_tile_max`
 
 Computes the max of cells in each row of column `tile`, ignoring nodata values.
 
 
 #### rf_no_data_cells
 
-_Python_:
-
     Long rf_no_data_cells(Tile tile)
     
-_SQL_: `rf_no_data_cells`
 
 Return the count of nodata cells in the `tile`.
 
 #### rf_data_cells
 
-_Python_:
-
     Long rf_data_cells(Tile tile)
     
-_SQL_: `rf_data_cells`
 
 Return the count of data cells in the `tile`.
 
 #### rf_tile_stats
 
-_Python_:
-
     Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
     
-_SQL_: `rf_tile_stats`
-
 Computes the following statistics of cells in each row of column `tile`: data cell count, nodata cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring nodata values. 
 
 
 #### rf_tile_histogram
 
-_Python_:
-
     Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_tile_histogram(Tile tile)
     
-_SQL_:  `rf_rf_tile_histogram`
-
 Computes a statistical summary of cell values within each row of `tile`. Resulting column has the below schema. Note that several of the other `tile` statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) which computes the statistics across all rows in a group.
 
 ```
@@ -664,9 +517,9 @@ Continuing our example from the @ref:[Tile Statistics](reference.md#tile-statist
 
 ```python 
 spark.sql("""
-SELECT 1 as id, rf_tile_ones(5, 5, 'float32') as t 
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
 UNION
-SELECT 2 as id, rf_local_multiply_scalar(rf_tile_ones(5, 5, 'float32'), 3) as t 
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t 
 """).agg(rf_agg_mean(F.col('t'))).show(10, False)
 
 +--------------+
@@ -678,8 +531,6 @@ SELECT 2 as id, rf_local_multiply_scalar(rf_tile_ones(5, 5, 'float32'), 3) as t
 
 #### rf_agg_mean
 
-_Python_:
-
     Double rf_agg_mean(Tile tile)
     
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).mean`
@@ -689,8 +540,6 @@ Aggregates over the `tile` and return the mean of cell values, ignoring nodata.
 
 #### rf_agg_data_cells
 
-_Python_:
-
     Long rf_agg_data_cells(Tile tile)
     
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
@@ -698,38 +547,31 @@ _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.dataCells`. C.F. `data_cells`; equivalent code:
 
 ```python
-rf.select(rf_agg_data_cells(rf.tile).alias('agg_data_cell')).show()
+rf.select(rf_agg_data_cells(rf.tile).alias('agg_data_cell'))
 # Equivalent to
-rf.agg(F.sum(rf_data_cells(rf.tile)).alias('agg_data_cell')).show()
+import pyspark.sql.functions as F
+rf.agg(F.sum(rf_data_cells(rf.tile)).alias('agg_data_cell'))
 ```
 
 #### rf_agg_no_data_cells
 
-_Python_:
-
     Long rf_agg_no_data_cells(Tile tile)
     
-_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).noDataCells`
+_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
 Aggregates over the `tile` and return the count of nodata cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
 
 #### rf_agg_stats
 
-_Python_: 
-
     Struct[Long, Long, Double, Double, Double, Double] rf_agg_stats(Tile tile)
     
-_SQL_:  `rf_agg_stats`
 
 Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of nodata cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of nodata. 
 
 #### rf_agg_approx_histogram
 
-_Python_:
-
     Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
     
-_SQL_: `rf_agg_approx_histogram`
 
 Aggregates over the `tile` return statistical summaries of the cell values, including a histogram, in the below schema. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis. 
 
@@ -759,23 +601,23 @@ Consider again our example for Tile Statistics and Aggregate Tile Statistics, th
 
 ```python
 import pyspark.functions as F
-lam = spark.sql("""
-SELECT 1 as id, rf_tile_ones(5, 5, 'float32') as t 
+alm = spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
 UNION
 SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
-""").agg(rf_local_agg_mean(F.col('t')).alias('l')) \
+""").agg(rf_agg_local_mean(F.col('t')).alias('l')) \
 
 ## local_agg_mean returns a tile
-lam.select(rf_tile_dimensions(lam.l)).show()
+alm.select(rf_dimensions(alm.l)).show()
 ## 
-+------------------+
-|tile_dimensions(l)|
-+------------------+
-|            [5, 5]|
-+------------------+ 
++----------------+
+|rf_dimensions(l)|
++----------------+
+|          [5, 5]|
++----------------+ 
 ##
 
-lam.select(rf_explode_tiles(lam.l)).show(10, False)
+alm.select(rf_explode_tiles(alm.l)).show(10, False)
 ##
 +------------+---------+---+
 |column_index|row_index|l  |
@@ -797,62 +639,38 @@ only showing top 10 rows
 
 #### rf_agg_local_max 
 
-_Python_:
-
     Tile rf_agg_local_max(Tile tile)
     
-_SQL_: `rf_agg_local_max`
-
 Compute the cell-local maximum operation over Tiles in a column. 
 
 #### rf_agg_local_min 
 
-_Python_:
-
     Tile rf_agg_local_min(Tile tile)
-    
-_SQL_: `rf_agg_local_min`
 
 Compute the cell-local minimum operation over Tiles in a column. 
 
 #### rf_agg_local_mean 
 
-_Python_:
-
     Tile rf_agg_local_mean(Tile tile)
     
-_SQL_: `rf_agg_local_mean`
-
 Compute the cell-local mean operation over Tiles in a column. 
 
 #### rf_agg_local_data_cells 
 
-_Python_:
-
     Tile rf_agg_local_data_cells(Tile tile)
     
-_SQL_: `rf_agg_local_data_cells`
-
 Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
 #### rf_agg_local_no_data_cells
 
-_Python_:
-
     Tile rf_agg_local_no_data_cells(Tile tile)
-    
-_SQL_: `rf_agg_local_no_data_cells`
 
 Compute the cell-local count of nodata cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
 #### rf_agg_local_stats 
 
-_Python_:
-
     Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
     
-_SQL_: `rf_agg_local_stats`
-
 Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of Tiles. Returns a struct of five `tile`s.
 
 
@@ -862,18 +680,12 @@ RasterFrames provides several ways to convert a `tile` into other data structure
 
 #### rf_explode_tiles
 
-_Python_:
-
     Int, Int, Numeric* rf_explode_tiles(Tile* tile)
-    
-_SQL_: `rf_explode_tiles`
 
 Create a row for each cell in `tile` columns. Many `tile` columns can be passed in, and the returned DataFrame will have one numeric column per input.  There will also be columns for `column_index` and `row_index`. Inverse of @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile). When using this function, be sure to have a unique identifier for rows in order to successfully invert the operation.
 
 #### rf_explode_tiles_sample
 
-_Python_:
-
     Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
     
 Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0. 
@@ -882,50 +694,31 @@ Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), b
 df.select(df.id, rf_explode_tiles(df.tile1, df.tile2, df.tile3)) \
     .sample(False, 0.05, 8675309)
 # Equivalent result, faster
-df.select(df.id, rf_explode_tiles_sample(0.05, 8675309, df.tile1, df.tile2, df.tile3)) \
+df.select(df.id, rf_explode_tiles_sample(0.05, 8675309, df.tile1, df.tile2, df.tile3)) 
 ```
 
 #### rf_tile_to_array_int
 
-_Python_:
-
     Array rf_tile_to_array_int(Tile tile)
-    
-_SQL_: `rf_tile_to_array_int`
-
 
 Convert Tile column to Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Float cell types will be coerced to integral type by flooring.
 
-
 #### rf_tile_to_array_double
 
-_Python_:
-
     Array rf_tile_to_arry_double(Tile tile)
     
-_SQL_: `rf_tile_to_array_double`
-
 Convert tile column to Spark [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Integral cell types will be coerced to floats.
 
-
 #### rf_render_ascii
 
-_Python_:
-
     String rf_render_ascii(Tile tile)
 
-_SQL_:   `rf_render_ascii`
-
 Pretty print the tile values as plain text.
 
 #### rf_render_matrix
 
-_Python_:
-
     String rf_render_matrix(Tile tile)
 
-_SQL_:   `rf_render_matrix`
-
 Render Tile cell values as numeric values, for debugging purposes.
 
 [RasterFunctions]: org.locationtech.rasterframes.RasterFunctions

From cb9991388c8db35baf8b0df88cdc6a0c15351655 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 23 Apr 2019 16:04:24 -0400
Subject: [PATCH 065/380] Add scala, python and SQL tile functions rf_for_all,
 rf_exists

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/RasterFunctions.scala        |  7 +++
 .../rasterframes/expressions/package.scala    |  2 +
 .../expressions/tilestats/Exists.scala        | 54 +++++++++++++++++++
 .../expressions/tilestats/ForAll.scala        | 54 +++++++++++++++++++
 .../expressions/tilestats/Sum.scala           |  2 +-
 .../rasterframes/RasterFunctionsSpec.scala    | 16 ++++++
 docs/src/main/tut/reference.md                | 13 ++++-
 .../python/pyrasterframes/rasterfunctions.py  |  2 +
 .../python/tests/PyRasterFramesTests.py       | 17 ++++++
 9 files changed, 165 insertions(+), 2 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index abf4d7c31..f12949709 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -169,9 +169,16 @@ trait RasterFunctions {
   def rf_no_data_cells(tile: Column): TypedColumn[Any, Long] =
     NoDataCells(tile)
 
+  /** Returns true if all cells in the tile are NoData.*/
   def rf_is_no_data_tile(tile: Column): TypedColumn[Any, Boolean] =
     IsNoDataTile(tile)
 
+  /** Returns true if any cells in the tile are true (non-zero and not NoData). */
+  def rf_exists(tile: Column): TypedColumn[Any, Boolean] = Exists(tile)
+
+  /** Returns true if all cells in the tile are true (non-zero and not NoData). */
+  def rf_for_all(tile: Column): TypedColumn[Any, Boolean] = ForAll(tile)
+
   /** Compute cell-local aggregate descriptive statistics for a column of Tiles. */
   def rf_agg_local_stats(col: Column) =
     LocalStatsAggregate(col)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 0905dad39..d38a21aa4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -98,6 +98,8 @@ package object expressions {
     registry.registerExpression[DataCells]("rf_data_cells")
     registry.registerExpression[NoDataCells]("rf_no_data_cells")
     registry.registerExpression[IsNoDataTile]("rf_is_no_data_tile")
+    registry.registerExpression[Exists]("rf_exists")
+    registry.registerExpression[ForAll]("rf_for_all")
     registry.registerExpression[TileMin]("rf_tile_min")
     registry.registerExpression[TileMax]("rf_tile_max")
     registry.registerExpression[TileMean]("rf_tile_mean")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
new file mode 100644
index 000000000..333c7b762
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
@@ -0,0 +1,54 @@
+package org.locationtech.rasterframes.expressions.tilestats
+
+import geotrellis.raster.{Tile, isData}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
+
+@ExpressionDescription(
+  usage = "_FUNC_(tile) - Returns true if any cells in the tile are true (non-zero and not nodata).",
+  arguments =
+    """
+    Arguments:
+       * tile - tile to check
+    """,
+  examples =
+    """
+    > SELECT _FUNC_(tile);
+       true
+    """
+)
+case class Exists(child: Expression) extends UnaryRasterOp with CodegenFallback {
+  override def nodeName: String = "exists"
+  override def dataType: DataType = BooleanType
+  override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = Exists.op(tile)
+
+}
+
+object Exists{
+
+  def apply(tile: Column): TypedColumn[Any, Boolean] = new Column(Exists(tile.expr)).as[Boolean]
+
+  def op(tile: Tile): Boolean = {
+
+    def doubleValueIs(d: Double): Boolean =  isData(d) & d != 0.0
+    def intValueIs(i: Int): Boolean =  isData(i) & i != 0
+
+    var (c, r) = (0, 0)
+    while (r < tile.rows) {
+      while(c < tile.cols) {
+        if(tile.cellType.isFloatingPoint) { if(doubleValueIs(tile.getDouble(c, r))) return true }
+        else { if(intValueIs(tile.get(c, r))) return true }
+        c += 1
+      }
+      c = 0; r += 1
+    }
+
+    false
+  }
+
+
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala
new file mode 100644
index 000000000..1a5846611
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala
@@ -0,0 +1,54 @@
+package org.locationtech.rasterframes.expressions.tilestats
+
+import geotrellis.raster.{Tile, isData}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
+
+@ExpressionDescription(
+  usage = "_FUNC_(tile) - Returns true if all cells in the tile are true (non-zero and not nodata).",
+  arguments =
+    """
+    Arguments:
+       * tile - tile to check
+    """,
+  examples =
+    """
+    > SELECT _FUNC_(tile);
+       true
+    """
+)
+case class ForAll(child: Expression) extends UnaryRasterOp with CodegenFallback {
+  override def nodeName: String = "for_all"
+  override def dataType: DataType = BooleanType
+  override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = ForAll.op(tile)
+
+}
+
+object ForAll{
+
+  def apply(tile: Column): TypedColumn[Any, Boolean] = new Column(ForAll(tile.expr)).as[Boolean]
+
+  def op(tile: Tile): Boolean = {
+
+    def doubleValueIs(d: Double): Boolean =  isData(d) & d != 0.0
+    def intValueIs(i: Int): Boolean =  isData(i) & i != 0
+
+    var (c, r) = (0, 0)
+    while (r < tile.rows) {
+      while(c < tile.cols) {
+        if(tile.cellType.isFloatingPoint) { if(! doubleValueIs(tile.getDouble(c, r))) return false }
+        else { if(! intValueIs(tile.get(c, r))) return false }
+        c += 1
+      }
+      c = 0; r += 1
+    }
+
+    true
+  }
+
+
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
index 825f88d1d..096acdab6 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Sum.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes.model.TileContext
 
 @ExpressionDescription(
-  usage = "_FUNC_(tile) - Computes the sum of all the cells in a tile..",
+  usage = "_FUNC_(tile) - Computes the sum of all the cells in a tile.",
   arguments = """
   Arguments:
     * tile - tile to sum up""",
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index c28f3c422..7fad416a0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -336,6 +336,22 @@ class RasterFunctionsSpec extends FunSpec
       df2.select(rf_is_no_data_tile($"not_nd")).first() should be(false)
       checkDocs("rf_is_no_data_tile")
     }
+
+    it("should evaluate exists and forall") {
+      val df0 = Seq(zero).toDF("tile")
+      df0.select(rf_exists($"tile")).first() should be(false)
+      df0.select(rf_for_all($"tile")).first() should be(false)
+
+      Seq(one).toDF("tile").select(rf_exists($"tile")).first() should be(true)
+      Seq(one).toDF("tile").select(rf_for_all($"tile")).first() should be(true)
+
+      val dfNd = Seq(TestData.injectND(1)(one)).toDF("tile")
+      dfNd.select(rf_exists($"tile")).first() should be(true)
+      dfNd.select(rf_for_all($"tile")).first() should be(false)
+
+      checkDocs("rf_exists")
+      checkDocs("rf_for_all")
+    }
     it("should find the minimum cell value") {
       val min = randNDTile.toArray().filter(c => raster.isData(c)).min.toDouble
       val df = Seq(randNDTile).toDF("rand")
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 6d009a3e6..f30660f14 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -471,9 +471,20 @@ Return the count of nodata cells in the `tile`.
 
     Long rf_data_cells(Tile tile)
     
-
 Return the count of data cells in the `tile`.
 
+#### rf_exists
+
+    Boolean rf_exists(Tile tile)
+    
+Returns true if any cells in the tile are true (non-zero and not NoData). 
+
+#### rf_for_all
+
+    Boolean rf_for_all(Tile tile)
+
+Returns true if all cells in the tile are true (non-zero and not NoData). 
+
 #### rf_tile_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index c59d1416b..f836171b1 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -199,6 +199,8 @@ def _(data_tile, mask_tile, mask_value):
     'rf_tile_to_array_double': 'Flattens Tile into an array of doubles.',
     'rf_cell_type': 'Extract the Tile\'s cell type',
     'rf_is_no_data_tile': 'Report if the Tile is entirely NODDATA cells',
+    'rf_exists': 'Returns true if any cells in the tile are true (non-zero and not NoData)',
+    'rf_for_all': 'Returns true if all cells in the tile are true (non-zero and not NoData).',
     'rf_agg_approx_histogram': 'Compute the full column aggregate floating point histogram',
     'rf_agg_stats': 'Compute the full column aggregate floating point statistics',
     'rf_agg_mean': 'Computes the column aggregate mean',
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 1a7174691..ee0e4358c 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -223,6 +223,23 @@ def test_resample(self):
 
         self.assertTrue(result == 1)  # short hand for all values are true
 
+    def test_exists_for_all(self):
+        df = self.rf.withColumn('should_exist', tile_ones(5, 5, 'int8')) \
+            .withColumn('should_not_exist', tile_zeros(5, 5, 'int8'))
+
+        should_exist = df.select(exists(df.should_exist).alias('se')).take(1)[0].se
+        should_any = df.select(any(df.should_exist).alias('se')).take(1)[0].se
+        self.assertTrue(should_exist)
+        self.assertTrue(should_any)
+
+        should_not_exist = df.select(exists(df.should_not_exist).alias('se')).take(1)[0].se
+        should_not_any = df.select(any(df.should_not_exist).alias('se')).take(1)[0].se
+        self.assertTrue(not should_not_exist)
+        self.assertTrue(not should_not_any)
+
+        self.assertTrue(df.select(for_all(df.should_exist).alias('se')).take(1)[0].se)
+        self.assertTrue(not df.select(for_all(df.should_not_exist).alias('se')).take(1)[0].se)
+
 
     def test_geomesa_pyspark(self):
         from pyspark.sql.functions import lit, udf, sum

From 3ac087a4e7ec49f3582aadaadf9d76badb07b3e4 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 1 May 2019 17:15:27 -0400
Subject: [PATCH 066/380] rf_for_all and rf_exists refinement; python tests
 passing.

---
 .../expressions/tilestats/Exists.scala        | 23 +++++---------
 .../expressions/tilestats/ForAll.scala        | 31 +++++++++----------
 .../rasterframes/rasterframes.scala           |  6 ++++
 .../rasterframes/RasterFunctionsSpec.scala    |  2 +-
 docs/src/main/tut/reference.md                |  6 ++--
 .../python/tests/PyRasterFramesTests.py       | 18 +++++------
 .../rasterframes/py/PyRFContext.scala         |  2 +-
 7 files changed, 40 insertions(+), 48 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
index 333c7b762..cd04b1467 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/Exists.scala
@@ -1,12 +1,14 @@
 package org.locationtech.rasterframes.expressions.tilestats
 
-import geotrellis.raster.{Tile, isData}
+import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.isCellTrue
 import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import org.locationtech.rasterframes.model.TileContext
+import spire.syntax.cfor.cfor
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Returns true if any cells in the tile are true (non-zero and not nodata).",
@@ -29,26 +31,17 @@ case class Exists(child: Expression) extends UnaryRasterOp with CodegenFallback
 }
 
 object Exists{
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.boolEnc
 
   def apply(tile: Column): TypedColumn[Any, Boolean] = new Column(Exists(tile.expr)).as[Boolean]
 
   def op(tile: Tile): Boolean = {
-
-    def doubleValueIs(d: Double): Boolean =  isData(d) & d != 0.0
-    def intValueIs(i: Int): Boolean =  isData(i) & i != 0
-
-    var (c, r) = (0, 0)
-    while (r < tile.rows) {
-      while(c < tile.cols) {
-        if(tile.cellType.isFloatingPoint) { if(doubleValueIs(tile.getDouble(c, r))) return true }
-        else { if(intValueIs(tile.get(c, r))) return true }
-        c += 1
+    cfor(0)(_ < tile.rows, _ + 1) { r ⇒
+      cfor(0)(_ < tile.cols, _ + 1) { c ⇒
+        if(tile.cellType.isFloatingPoint) { if(isCellTrue(tile.getDouble(c, r))) return true }
+        else { if(isCellTrue(tile.get(c, r))) return true }
       }
-      c = 0; r += 1
     }
-
     false
   }
-
-
 }
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala
index 1a5846611..a912a8a0b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/tilestats/ForAll.scala
@@ -1,12 +1,14 @@
 package org.locationtech.rasterframes.expressions.tilestats
 
-import geotrellis.raster.{Tile, isData}
+import geotrellis.raster.Tile
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.isCellTrue
 import org.locationtech.rasterframes.expressions.UnaryRasterOp
 import org.locationtech.rasterframes.model.TileContext
+import spire.syntax.cfor.cfor
 
 @ExpressionDescription(
   usage = "_FUNC_(tile) - Returns true if all cells in the tile are true (non-zero and not nodata).",
@@ -28,27 +30,22 @@ case class ForAll(child: Expression) extends UnaryRasterOp with CodegenFallback
 
 }
 
-object ForAll{
+object ForAll {
+  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.boolEnc
 
   def apply(tile: Column): TypedColumn[Any, Boolean] = new Column(ForAll(tile.expr)).as[Boolean]
 
   def op(tile: Tile): Boolean = {
-
-    def doubleValueIs(d: Double): Boolean =  isData(d) & d != 0.0
-    def intValueIs(i: Int): Boolean =  isData(i) & i != 0
-
-    var (c, r) = (0, 0)
-    while (r < tile.rows) {
-      while(c < tile.cols) {
-        if(tile.cellType.isFloatingPoint) { if(! doubleValueIs(tile.getDouble(c, r))) return false }
-        else { if(! intValueIs(tile.get(c, r))) return false }
-        c += 1
+    cfor(0)(_ < tile.rows, _ + 1) { r ⇒
+      cfor(0)(_ < tile.cols, _ + 1) { c ⇒
+        if (tile.cellType.isFloatingPoint) {
+          if (!isCellTrue(tile.getDouble(c, r))) return false
+        }
+        else {
+          if (!isCellTrue(tile.get(c, r))) return false
+        }
       }
-      c = 0; r += 1
     }
-
     true
   }
-
-
-}
\ No newline at end of file
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index 03eb1a40f..4224ef622 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -22,6 +22,7 @@
 package org.locationtech
 import com.typesafe.config.ConfigFactory
 import com.typesafe.scalalogging.LazyLogging
+import geotrellis.raster.isData
 import geotrellis.raster.{Tile, TileFeature}
 import geotrellis.spark.{ContextRDD, Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.rdd.RDD
@@ -134,4 +135,9 @@ package object rasterframes extends StandardColumns
       override val selfType: TypeTag[SpaceTimeKey] = implicitly
     }
   }
+
+  /** Test if a cell value evaluates to true: it is not NoData and it is non-zero */
+  def isCellTrue(v: Double): Boolean =  isData(v) & v != 0.0
+  /** Test if a cell value evaluates to true: it is not NoData and it is non-zero */
+  def isCellTrue(v: Int): Boolean =  isData(v) & v != 0
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 7fad416a0..d82eaec9a 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -337,7 +337,7 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_is_no_data_tile")
     }
 
-    it("should evaluate exists and forall") {
+    it("should evaluate exists and for_all") {
       val df0 = Seq(zero).toDF("tile")
       df0.select(rf_exists($"tile")).first() should be(false)
       df0.select(rf_for_all($"tile")).first() should be(false)
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index f30660f14..8fbb5f524 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -229,7 +229,7 @@ Generate a `tile` with the values from `data_tile`, with nodata in cells where t
 
     Boolean rf_is_no_data_tile(Tile)
  
-Returns true if `tile` contains only nodata. By definition returns false if cell type does not support nodata.
+Returns true if `tile` contains only nodata. By definition returns false if cell type does not support nodata. To count nodata cells or data cells, see @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_local_no_data_cells`](reference.md#rf-agg-local-no-data-cells), and @ref:[`rf_agg_local_data_cells`](reference.md#rf-agg-local-data-cells). This function is distinguished from @ref:[`rf_for_all`](reference.md#rf-for-all), which tests that values are not nodata and nonzero.
 
 #### rf_with_no_data
 
@@ -477,13 +477,13 @@ Return the count of data cells in the `tile`.
 
     Boolean rf_exists(Tile tile)
     
-Returns true if any cells in the tile are true (non-zero and not NoData). 
+Returns true if any cells in the tile are true (non-zero and not nodata). 
 
 #### rf_for_all
 
     Boolean rf_for_all(Tile tile)
 
-Returns true if all cells in the tile are true (non-zero and not NoData). 
+Returns true if all cells in the tile are true (non-zero and not nodata). See also @ref:[`rf_is_no_data_tile](reference.md#rf-is-no-data-tile), which tests that all cells are nodata.
 
 #### rf_tile_stats
 
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index ee0e4358c..4ef33a7cf 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -120,7 +120,7 @@ def test_rasterize(self):
         withRaster.show()
 
     def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', rf_reproject_geometry('center', 'EPSG:4326', 'EPSG:3857'))
+        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
         reprojected.show()
 
     def test_aggregations(self):
@@ -224,21 +224,17 @@ def test_resample(self):
         self.assertTrue(result == 1)  # short hand for all values are true
 
     def test_exists_for_all(self):
-        df = self.rf.withColumn('should_exist', tile_ones(5, 5, 'int8')) \
-            .withColumn('should_not_exist', tile_zeros(5, 5, 'int8'))
+        df = self.rf.withColumn('should_exist', rf_make_ones_tile(5, 5, 'int8')) \
+            .withColumn('should_not_exist', rf_make_zeros_tile(5, 5, 'int8'))
 
-        should_exist = df.select(exists(df.should_exist).alias('se')).take(1)[0].se
-        should_any = df.select(any(df.should_exist).alias('se')).take(1)[0].se
+        should_exist = df.select(rf_exists(df.should_exist).alias('se')).take(1)[0].se
         self.assertTrue(should_exist)
-        self.assertTrue(should_any)
 
-        should_not_exist = df.select(exists(df.should_not_exist).alias('se')).take(1)[0].se
-        should_not_any = df.select(any(df.should_not_exist).alias('se')).take(1)[0].se
+        should_not_exist = df.select(rf_exists(df.should_not_exist).alias('se')).take(1)[0].se
         self.assertTrue(not should_not_exist)
-        self.assertTrue(not should_not_any)
 
-        self.assertTrue(df.select(for_all(df.should_exist).alias('se')).take(1)[0].se)
-        self.assertTrue(not df.select(for_all(df.should_not_exist).alias('se')).take(1)[0].se)
+        self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
+        self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
 
 
     def test_geomesa_pyspark(self):
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index b1aae5d84..981cbac32 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -156,7 +156,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def rf_local_unequal_int(col: Column, scalar: Int): Column = rf_local_unequal[Int](col, scalar)
 
-  def rf_reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
+  def st_reproject(geometryCol: Column, srcName: String, dstName: String): Column = {
     val src = LazyCRS(srcName)
     val dst = LazyCRS(dstName)
     st_reproject(geometryCol, src, dst)

From c97af4ad35beb8a08c6725388b7673791bc8ef72 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 1 May 2019 16:20:21 -0400
Subject: [PATCH 067/380] Misc branch sync items.

---
 .circleci/config.yml                          |  2 -
 build/circleci/Dockerfile                     | 67 +++++++++++++++++++
 build/circleci/README.md                      |  6 ++
 .../rasterframes/RasterFunctions.scala        | 20 ++++--
 .../expressions/TileAssembler.scala           | 28 +++++++-
 .../rasterframes/expressions/package.scala    |  1 +
 .../rasterframes/functions/package.scala      |  9 ++-
 .../rasterframes/ExplodeSpec.scala            | 49 ++++++++++++--
 .../python/tests/PyRasterFramesTests.py       |  2 +-
 .../rasterframes/py/PyRFContext.scala         |  2 +-
 10 files changed, 169 insertions(+), 17 deletions(-)
 create mode 100644 build/circleci/Dockerfile
 create mode 100644 build/circleci/README.md

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 787af5316..da9f0e538 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -69,7 +69,6 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
 
       - run: *unsetenv
@@ -85,7 +84,6 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
       - run: cat /dev/null | sbt publish
 
diff --git a/build/circleci/Dockerfile b/build/circleci/Dockerfile
new file mode 100644
index 000000000..da964c32a
--- /dev/null
+++ b/build/circleci/Dockerfile
@@ -0,0 +1,67 @@
+FROM circleci/openjdk:8-jdk
+
+ENV OPENJPEG_VERSION 2.3.0
+ENV GDAL_VERSION 2.3.3
+ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/
+
+# most of these libraries required for
+# python-pip pandoc && pip install setuptools => required for pyrasterframes testing
+RUN sudo apt-get update && \
+    sudo apt-get install -y \
+    python-pip pandoc \
+      wget \
+      gcc g++ build-essential \
+            libcurl4-gnutls-dev \
+            libproj-dev \
+            libgeos-dev \
+            libhdf4-alt-dev \
+            libhdf5-serial-dev \
+            bash-completion \
+            cmake \
+            imagemagick \
+            libpng-dev \
+            swig \
+            ant \
+        && sudo apt-get clean all \
+        && pip install setuptools
+
+# install OpenJPEG
+RUN cd /tmp && \
+    wget https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz && \
+    tar -xf v${OPENJPEG_VERSION}.tar.gz && \
+    cd openjpeg-${OPENJPEG_VERSION}/ && \
+    mkdir build && \
+    cd build && \
+    cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local/ && \
+    make -j && \
+    sudo make install && \
+    cd /tmp && rm -Rf v${OPENJPEG_VERSION}.tar.gz openjpeg*
+
+# Compile and install GDAL with Java bindings
+RUN cd /tmp && \
+    wget http://download.osgeo.org/gdal/${GDAL_VERSION}/gdal-${GDAL_VERSION}.tar.gz && \
+    tar -xf gdal-${GDAL_VERSION}.tar.gz && \
+    cd gdal-${GDAL_VERSION} && \
+    ./configure \
+        --with-curl \
+        --with-hdf4 \
+        --with-hdf5 \
+        --with-geos \
+        --with-geotiff=internal \
+        --with-hide-internal-symbols \
+        --with-java=$JAVA_HOME \
+        --with-libtiff=internal \
+        --with-libz=internal \
+        --with-mrf \
+        --with-openjpeg \
+        --with-threads \
+        --without-jp2mrsid \
+        --without-netcdf \
+        --without-ecw \
+    && \
+    make -j 8 && \
+    sudo make install && \
+    cd swig/java && \
+    sudo make install && \
+    sudo ldconfig && \
+    cd /tmp && rm -Rf gdal*
diff --git a/build/circleci/README.md b/build/circleci/README.md
new file mode 100644
index 000000000..69b9cdff3
--- /dev/null
+++ b/build/circleci/README.md
@@ -0,0 +1,6 @@
+# CircleCI Dockerfile Build file
+
+```bash
+docker build -t s22s/rasterframes-circleci:latest .
+docker push s22s/rasterframes-circleci:latest
+```
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index abf4d7c31..08e1f35b8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -274,15 +274,27 @@ trait RasterFunctions {
       udf(F.rasterize(_: Geometry, _: Geometry, _: Int, cols, rows)).apply(geometry, bounds, value)
     )
 
-  /** Reproject a column of geometry from one CRS to another. */
+  /** Reproject a column of geometry from one CRS to another.
+    * @param sourceGeom Geometry column to reproject
+    * @param srcCRS Native CRS of `sourceGeom` as a literal
+    * @param dstCRSCol Destination CRS as a column
+    */
   def st_reproject(sourceGeom: Column, srcCRS: CRS, dstCRSCol: Column): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRSCol)
 
-  /** Reproject a column of geometry from one CRS to another. */
-  def st_reproject(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
+  /** Reproject a column of geometry from one CRS to another.
+    * @param sourceGeom Geometry column to reproject
+    * @param srcCRSCol Native CRS of `sourceGeom` as a column
+    * @param dstCRS Destination CRS as a literal
+    */
+    def st_reproject(sourceGeom: Column, srcCRSCol: Column, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRSCol, dstCRS)
 
-  /** Reproject a column of geometry from one CRS to another. */
+  /** Reproject a column of geometry from one CRS to another.
+    * @param sourceGeom Geometry column to reproject
+    * @param srcCRS Native CRS of `sourceGeom` as a literal
+    * @param dstCRS Destination CRS as a literal
+    */
   def st_reproject(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRS)
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
index fe284546a..c3fe0e17b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/TileAssembler.scala
@@ -28,7 +28,7 @@ import org.locationtech.rasterframes.util._
 import geotrellis.raster.{DataType => _, _}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.aggregate.{ImperativeAggregate, TypedImperativeAggregate}
-import org.apache.spark.sql.catalyst.expressions.{Expression, ImplicitCastInputTypes}
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription, ImplicitCastInputTypes}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
 import spire.syntax.cfor._
@@ -39,6 +39,24 @@ import org.locationtech.rasterframes.TileType
  *
  * @since 9/24/17
  */
+@ExpressionDescription(
+  usage = "_FUNC_(colIndex, rowIndex, cellValue, tileCols, tileRows) - Assemble tiles from set of column and row indices and cell values.",
+  arguments = """
+  Arguments:
+    * colIndex - column to place the cellValue in the generated tile
+    * rowIndex - row to place the cellValue in the generated tile
+    * cellValue - numeric value to place in the generated tile at colIndex and rowIndex
+    * tileCols - number of columns in the generated tile
+    * tileRows - number of rows in the generated tile""",
+  examples = """
+  Examples:
+    > SELECT _FUNC_(column_index, row_index, cell0, 10, 10) as tile;
+       ...
+    > SELECT _FUNC_(column_index, row_index, tile, 10, 10) as tile2
+      FROM (SELECT rf_explode_tiles(rf_make_constant_tile(4, 10, 10, 'int8raw')) as tile)
+      ...
+             """
+)
 case class TileAssembler(
   colIndex: Expression,
   rowIndex: Expression,
@@ -49,11 +67,17 @@ case class TileAssembler(
   inputAggBufferOffset: Int = 0)
     extends TypedImperativeAggregate[TileBuffer] with ImplicitCastInputTypes {
 
+  def this(colIndex: Expression,
+           rowIndex: Expression,
+           cellValue: Expression,
+           tileCols: Expression,
+           tileRows: Expression) = this(colIndex, rowIndex, cellValue, tileCols, tileRows, 0, 0)
+
   override def children: Seq[Expression] = Seq(colIndex, rowIndex, cellValue, tileCols, tileRows)
 
   override def inputTypes = Seq(ShortType, ShortType, DoubleType, ShortType, ShortType)
 
-  override def prettyName: String = "assemble_tiles"
+  override def prettyName: String = "rf_assemble_tiles"
 
   override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): ImperativeAggregate =
     copy(mutableAggBufferOffset = newMutableAggBufferOffset)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 0905dad39..110b2ab25 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -64,6 +64,7 @@ package object expressions {
 
     registry.registerExpression[Add]("rf_local_add")
     registry.registerExpression[Subtract]("rf_local_subtract")
+    registry.registerExpression[TileAssembler]("rf_assemble_tile")
     registry.registerExpression[ExplodeTiles]("rf_explode_tiles")
     registry.registerExpression[GetCellType]("rf_cell_type")
     registry.registerExpression[SetCellType]("rf_convert_cell_type")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 17b553336..c72efb52d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -68,6 +68,10 @@ package object functions {
     }
   }
 
+  private[rasterframes] val arrayToTile: (Array[_], Int, Int) ⇒ Tile = (a, cols, rows) ⇒ {
+    arrayToTile(cols, rows).apply(a)
+  }
+
   /** Set the tile's no-data value. */
   private[rasterframes] def withNoData(nodata: Double) = safeEval[Tile, Tile](_.withNoData(Some(nodata)))
 
@@ -86,6 +90,8 @@ package object functions {
     }
   }
 
+
+
   /** Alias for constant tiles of zero */
   private[rasterframes] val tileZeros: (Int, Int, String) ⇒ Tile = (cols, rows, cellTypeName) ⇒
     makeConstantTile(0, cols, rows, cellTypeName)
@@ -143,7 +149,8 @@ package object functions {
 
     sqlContext.udf.register("rf_cell_types", cellTypes)
     sqlContext.udf.register("rf_rasterize", rasterize)
-
     sqlContext.udf.register("st_reproject", reprojectGeometryCRSName)
+    sqlContext.udf.register("rf_array_to_tile", arrayToTile)
+
   }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index 2a44bf3cf..021b528d1 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -110,7 +110,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
     }
 
     it("should convert an array into a tile") {
-      val tile = FloatConstantTile(1.1f, 10, 10, FloatCellType)
+      val tile = TestData.randomTile(10, 10, FloatCellType)
       val df = Seq[Tile](tile, null).toDF("tile")
       val arrayDF = df.withColumn("tileArray", rf_tile_to_array_double($"tile"))
 
@@ -120,6 +120,11 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
       assert(result.toArrayDouble() === tile.toArrayDouble())
 
+      // Same round trip, but with SQL expression for rf_array_to_tile
+      val resultSql = arrayDF.selectExpr("rf_array_to_tile(tileArray, 10, 10) as backToTile").as[Tile].first
+
+      assert(resultSql.toArrayDouble() === tile.toArrayDouble())
+
       val hasNoData = back.withColumn("withNoData", rf_with_no_data($"backToTile", 0))
 
       val result2 = hasNoData.select($"withNoData".as[Tile]).first
@@ -128,7 +133,8 @@ class ExplodeSpec extends TestEnvironment with TestData {
     }
 
     it("should reassemble single exploded tile") {
-      val df = Seq[Tile](byteArrayTile).toDF("tile")
+      val tile = TestData.randomTile(10, 10, FloatCellType)
+      val df = Seq[Tile](tile).toDF("tile")
         .select(rf_explode_tiles($"tile"))
 
       val assembled = df.agg(
@@ -136,11 +142,44 @@ class ExplodeSpec extends TestEnvironment with TestData {
         COLUMN_INDEX_COLUMN,
         ROW_INDEX_COLUMN,
         TILE_COLUMN,
-        3, 3, byteArrayTile.cellType
+          10, 10, tile.cellType
       )).as[Tile]
 
       val result = assembled.first()
-      assert(result === byteArrayTile)
+      assert(result === tile)
+
+      val assembledSqlExpr = df.selectExpr("rf_assemble_tile(column_index, row_index, tile, 10, 10)")
+
+      val resultSql = assembledSqlExpr.as[Tile].first()
+      assert(resultSql === tile)
+
+      checkDocs("rf_assemble_tile")
+    }
+
+    it("should reassemble single exploded tile with user-defined nodata") {
+      val ct = FloatUserDefinedNoDataCellType(-99)
+      val tile = TestData.injectND(3)(TestData.randomTile(5, 5, ct))
+      val df = Seq[Tile](tile).toDF("tile")
+        .select(rf_explode_tiles($"tile"))
+
+      val assembled = df.agg(rf_assemble_tile(
+        COLUMN_INDEX_COLUMN,
+        ROW_INDEX_COLUMN,
+        TILE_COLUMN,
+        5, 5, ct
+      )).as[Tile]
+
+      val result = assembled.first()
+      assert(result === tile)
+
+      // and with SQL API
+      logger.info(df.schema.treeString)
+
+      val assembledSqlExpr = df.selectExpr(s"rf_convert_cell_type(rf_assemble_tile(column_index, row_index, tile, 5, 5), '${ct.toString()}') as tile")
+
+      val resultSql = assembledSqlExpr.as[Tile].first()
+      assert(resultSql === tile)
+      assert(resultSql.cellType === ct)
     }
 
     it("should reassemble multiple exploded tiles") {
@@ -149,8 +188,6 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
       val exploded = tinyTiles.select(tinyTiles.spatialKeyColumn, rf_explode_tiles(tinyTiles.tileColumns.head))
 
-      //exploded.printSchema()
-
       val assembled = exploded.groupBy(tinyTiles.spatialKeyColumn)
         .agg(
           rf_assemble_tile(
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 1a7174691..f12c639e3 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -120,7 +120,7 @@ def test_rasterize(self):
         withRaster.show()
 
     def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', rf_reproject_geometry('center', 'EPSG:4326', 'EPSG:3857'))
+        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
         reprojected.show()
 
     def test_aggregations(self):
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index b1aae5d84..981cbac32 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -156,7 +156,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def rf_local_unequal_int(col: Column, scalar: Int): Column = rf_local_unequal[Int](col, scalar)
 
-  def rf_reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
+  def st_reproject(geometryCol: Column, srcName: String, dstName: String): Column = {
     val src = LazyCRS(srcName)
     val dst = LazyCRS(dstName)
     st_reproject(geometryCol, src, dst)

From cc77f127c687f82223de0a5d5e445af17cf3c474 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 2 May 2019 10:04:04 -0400
Subject: [PATCH 068/380] Disabled use of GDAL-enabled image in CircleCI until
 this bug is fixed: https://github.com/geotrellis/gdal-warp-bindings/issues/37

---
 .circleci/config.yml               | 2 +-
 project/RFDependenciesPlugin.scala | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index da9f0e538..acf469090 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -5,7 +5,7 @@ _defaults: &defaults
   environment:
     TERM: dumb
   docker:
-    - image: s22s/rasterframes-circleci:latest
+    - image: circleci/openjdk:8-jdk
       
 _setenv: &setenv
   name: set CloudRepo credentials
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index 8beeafa2d..3c1fbd113 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -20,7 +20,7 @@
  *
  */
 
-import sbt.Keys.resolvers
+import sbt.Keys._
 import sbt._
 
 object RFDependenciesPlugin extends AutoPlugin {
@@ -59,6 +59,7 @@ object RFDependenciesPlugin extends AutoPlugin {
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
     rfSparkVersion := "2.3.2",
     rfGeoTrellisVersion := "2.2.0",
-    rfGeoMesaVersion := "2.2.1"
+    rfGeoMesaVersion := "2.2.1",
+    //dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.bf51177"
   )
 }

From 4398a05365979c3f75da9cc2ceecef2421f02fcd Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 2 May 2019 10:42:58 -0400
Subject: [PATCH 069/380] python rastersource reader tests

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../RasterSourceDataSourceSpec.scala          |  2 +-
 .../python/tests/PyRasterFramesTests.py       | 80 +++++++++++++------
 .../rasterframes/py/PyRFContext.scala         |  2 +-
 3 files changed, 58 insertions(+), 26 deletions(-)

diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index e21a2472a..25180b1ea 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -140,7 +140,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       val df = spark.read.rastersource
         .fromTable("pathsTable", "B1", "B2", "B3")
-//        .withTileDimensions(128, 128)
+        .withTileDimensions(128, 128)
         .load()
 
       df.schema.size should be(6)
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index fc4972bf3..88c336f2a 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -23,7 +23,6 @@ def _rounded_compare(val1, val2):
 
 class RasterFunctionsTest(unittest.TestCase):
 
-
     @classmethod
     def setUpClass(cls):
 
@@ -120,7 +119,7 @@ def test_rasterize(self):
         withRaster.show()
 
     def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', rf_reproject_geometry('center', 'EPSG:4326', 'EPSG:3857'))
+        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
         reprojected.show()
 
     def test_aggregations(self):
@@ -292,40 +291,73 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
+
     def test_raster_source_reader(self):
-        # much the same as RasterSourceDataSourceSpec here
+        import pandas as pd
+        # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
+
         def l8path(b):
             assert b in range(1, 12)
-            #ok
             base = "https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/199/026/LC08_L1TP_199026_20180919_20180928_01_T1/LC08_L1TP_199026_20180919_20180928_01_T1_B{}.TIF"
-            base='s3://s22s-demeter-data-assets-prod/MOD11A1.006/18/06/2018153/MOD11A1.A2018153.h18v06.006.2018154084835_LSTD_B01.TIF'
-            #ok
-            # base = 'file:///Users/jbrown/src/rasterframes/core/target/scala-2.11/test-classes/L8-B{}-Elkton-VA.tiff'
-
-            # not ok base = '/Users/jbrown/src/rasterframes/core/target/scala-2.11/test-classes/L8-B{}-Elkton-VA.tiff'
             return base.format(b)
 
-        # band_paths = self.spark.createDataFrame([{'B1': l8path(1), 'B2': l8path(2), 'B3': l8path(3)}])
-        # band_paths.createOrReplaceTempView('pathsTable')
+        path_param = '\n'.join([l8path(b) for b in [1, 2, 3]])  # "http://foo.com/file1.tif,http://foo.com/file2.tif"
+        tile_size = 512
         df = self.spark.read.format('rastersource') \
-                    .options(tileDimensions='32, 32',  #Elkton files are 186 by 169
-                             path=l8path(1),
-                             ) \
-                    .load()
-        #[l8path(b) for b in [1,2,3]]) \
+                            .options(paths=path_param, tileDimensions='{},{}'.format(tile_size, tile_size)) \
+                            .load()
 
+        # schema is tile_path and tile
         df.printSchema()
+        self.assertTrue(len(df.columns) == 2 and 'tile_path' in df.columns and 'tile' in df.columns)
 
-        self.assertTrue(len(df.columns) == 2)  # tile_path and tile
-        tile_size_df = df.select(tile_dimensions(df.tile).rows.alias('r'), tile_dimensions(df.tile).cols.alias('c'))\
-            .groupby(['r','c']).count().toPandas()
+        # the most common tile dimensions should be as passed to `options`, showing that options are correctly applied
+        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c'))\
+            .groupby(['r', 'c']).count().toPandas()
+        most_common_size = tile_size_df.loc[tile_size_df['count'].idxmax()]
+        self.assertTrue(most_common_size.r == tile_size and most_common_size.c == tile_size)
 
-        print(tile_size_df)
-        # the most common should be as given in options above.
-        self.assertTrue(tile_size_df.r.value_counts().idxmax() == 32)
-        self.assertTrue(tile_size_df.c.value_counts().idxmax() == 32)  # columns
         # all rows are from a single source URI
-        self.assertTrue(df.select(df.tile_path).distinct().count() == 1)
+        path_count = df.groupby(df.tile_path).count()
+        print(path_count.toPandas())
+        self.assertTrue(path_count.count() == 3)
+
+        ###  Similar to the scala side's `fromTable`, read from a table with columns giving URI paths
+
+        scene_dict = {
+            1: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/LC08_L1TP_015041_20190305_20190309_01_T1/LC08_L1TP_015041_20190305_20190309_01_T1_B{}.TIF',
+            2: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/042/LC08_L1TP_015042_20190305_20190309_01_T1/LC08_L1TP_015042_20190305_20190309_01_T1_B{}.TIF',
+            3: 'http://landsat-pds.s3.amazonaws.com/c1/L8/016/041/LC08_L1TP_016041_20190224_20190309_01_T1/LC08_L1TP_016041_20190224_20190309_01_T1_B{}.TIF',
+        }
+
+        def path(scene, band):
+            assert band in range(1, 12)
+            p = scene_dict[scene]
+            return p.format(band)
+
+        path_table_hive_name = 'path_table'
+        # Create a pandas dataframe (makes it easy to create spark df)
+        path_pandas = pd.DataFrame([
+            {'b1': path(1, 1), 'b2': path(1, 2), 'b3': path(1, 3)},
+            {'b1': path(2, 1), 'b2': path(2, 2), 'b3': path(2, 3)},
+            {'b1': path(3, 1), 'b2': path(3, 2), 'b3': path(3, 3)},
+        ])
+        # comma separated list of column names containing URI's to read.
+        csv_columns = ','.join(path_pandas.columns.tolist())  # 'b1,b2,b3'
+        path_table = self.spark.createDataFrame(path_pandas)
+        path_table.createOrReplaceTempView(path_table_hive_name)
+
+        path_df = self.spark.read.format('rastersource') \
+                            .options(pathTable=path_table_hive_name, pathTableColumns=csv_columns,
+                                     tileDimensions='512,512') \
+                            .load()
+
+        path_df.printSchema()
+        self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}
+        self.assertTrue(path_df.select('b1_path').distinct().count() == 3)  # as per scene_dict
+        b1_paths_maybe = path_df.select('b1_path').distinct().collect()
+        b1_paths = [s.format('1') for s in scene_dict.values()]
+        self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
 
 def suite():
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index b1aae5d84..981cbac32 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -156,7 +156,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def rf_local_unequal_int(col: Column, scalar: Int): Column = rf_local_unequal[Int](col, scalar)
 
-  def rf_reproject_geometry(geometryCol: Column, srcName: String, dstName: String): Column = {
+  def st_reproject(geometryCol: Column, srcName: String, dstName: String): Column = {
     val src = LazyCRS(srcName)
     val dst = LazyCRS(dstName)
     st_reproject(geometryCol, src, dst)

From 550e43fdfcb120a3579d2ca900ca7d6d3d6dcdf1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 2 May 2019 10:55:32 -0400
Subject: [PATCH 070/380] When GDAL docker image is not used, need to install
 python setuptools.

---
 .circleci/config.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index acf469090..1fffd77f6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -69,6 +69,7 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
+      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
 
       - run: *unsetenv
@@ -84,6 +85,7 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
+      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
       - run: cat /dev/null | sbt publish
 

From 7d268a2f0b30fdab00a789e4516a2bbabebcf688 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 2 May 2019 14:29:01 -0400
Subject: [PATCH 071/380] Testing noise elimination.

---
 core/src/it/resources/log4j.properties                        | 4 ++--
 core/src/test/resources/log4j.properties                      | 4 ++--
 .../scala/org/locationtech/rasterframes/RasterFrameSpec.scala | 1 -
 .../datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala  | 2 +-
 .../datasource/geotiff/GeoTiffDataSourceSpec.scala            | 4 ++--
 .../datasource/geotrellis/GeoTrellisDataSourceSpec.scala      | 2 +-
 experimental/src/it/resources/log4j.properties                | 2 +-
 .../datasource/geojson/GeoJsonDataSourceTest.scala            | 3 +--
 8 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/core/src/it/resources/log4j.properties b/core/src/it/resources/log4j.properties
index 8a20b7b7e..1135e4b34 100644
--- a/core/src/it/resources/log4j.properties
+++ b/core/src/it/resources/log4j.properties
@@ -37,8 +37,8 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.org.locationtech.rasterframes=DEBUG
-log4j.logger.org.locationtech.rasterframes.ref=INFO
+log4j.logger.org.locationtech.rasterframes=WARN
+log4j.logger.org.locationtech.rasterframes.ref=WARN
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties
index 38dc57f1e..39e791fa3 100644
--- a/core/src/test/resources/log4j.properties
+++ b/core/src/test/resources/log4j.properties
@@ -37,8 +37,8 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.org.locationtech.rasterframes=DEBUG
-log4j.logger.org.locationtech.rasterframes.ref=DEBUG
+log4j.logger.org.locationtech.rasterframes=WARN
+log4j.logger.org.locationtech.rasterframes.ref=WARN
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
index 98ecd55fa..f8a3c0ac9 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
@@ -113,7 +113,6 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       }
       catch {
         case NonFatal(ex) ⇒
-          rf.printSchema()
           println(rf.schema.prettyJson)
           throw ex
       }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
index 0ed2deefa..d50ef2b62 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
@@ -43,7 +43,7 @@ class GeoTiffCollectionDataSourceSpec
 
       assert(df.select("path").distinct().count() === expected)
 
-      df.show(false)
+      // df.show(false)
     }
   }
 }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index c24a0765f..04a532503 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -59,8 +59,8 @@ class GeoTiffDataSourceSpec
         .geotiff
         .loadRF(nonCogPath)
 
-      println(rf.count())
-      rf.show(false)
+      //println(rf.count())
+      //rf.show(false)
 
       assert(rf.count() > 1)
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 23e9283be..6333138aa 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -425,7 +425,7 @@ class GeoTrellisDataSourceSpec
         .withColumnRenamed(GEOMETRY_COLUMN.columnName, "foobar")
 
       assert(numFilters(df) === 1)
-      assert(df.count > 0, df.printSchema)
+      assert(df.count > 0, df.schema.treeString)
     }
 
     it("should handle dropped spatial filter columns") {
diff --git a/experimental/src/it/resources/log4j.properties b/experimental/src/it/resources/log4j.properties
index 5cc16f4db..85cd816dd 100644
--- a/experimental/src/it/resources/log4j.properties
+++ b/experimental/src/it/resources/log4j.properties
@@ -35,7 +35,7 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.org.locationtech.rasterframes=DEBUG
+log4j.logger.org.locationtech.rasterframes=WARN
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
index 8edab293f..000193085 100644
--- a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
+++ b/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
@@ -60,8 +60,7 @@ class GeoJsonDataSourceTest extends TestEnvironment {
         .option(GeoJsonDataSource.INFER_SCHEMA, true)
         .load(example2)
 
-      results.show()
+      results.count() should be (8)
     }
   }
-
 }

From b1ebac00475b603777ed87d5c9ca37286d429f37 Mon Sep 17 00:00:00 2001
From: Phil Varner <pvarner@astrea.io>
Date: Tue, 30 Apr 2019 16:32:04 -0400
Subject: [PATCH 072/380] Reapplication of MRF test change set

---
 .../rasterframes/ref/RasterSourceIT.scala     |  40 +++++---
 core/src/main/resources/reference.conf        |  18 ++--
 ...4.A2019111.h30v06.006.2019120033434_01.idx | Bin 0 -> 400 bytes
 ...4.A2019111.h30v06.006.2019120033434_01.lrc | Bin 0 -> 17514 bytes
 ...4.A2019111.h30v06.006.2019120033434_01.mrf |  12 +++
 ...11.h30v06.006.2019120033434_01.mrf.aux.xml |  92 ++++++++++++++++++
 .../locationtech/rasterframes/TestData.scala  |  12 ++-
 7 files changed, 151 insertions(+), 23 deletions(-)
 create mode 100644 core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.idx
 create mode 100644 core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.lrc
 create mode 100644 core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf
 create mode 100644 core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf.aux.xml

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 591332a5f..33a951853 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -21,13 +21,12 @@
 
 package org.locationtech.rasterframes.ref
 
+import java.lang.Math.ceil
 import java.net.URI
 
-import org.locationtech.rasterframes.util.time
-import org.locationtech.rasterframes.TestData
-import geotrellis.vector.Extent
 import org.locationtech.rasterframes
-import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.util.time
+import org.locationtech.rasterframes.{NOMINAL_TILE_SIZE, TestData, TestEnvironment}
 
 /**
  *
@@ -35,12 +34,6 @@ import org.locationtech.rasterframes.TestEnvironment
  * @since 8/22/18
  */
 class RasterSourceIT extends TestEnvironment with TestData {
-  def sub(e: Extent) = {
-    val c = e.center
-    val w = e.width
-    val h = e.height
-    Extent(c.x, c.y, c.x + w * 0.1, c.y + h * 0.1)
-  }
 
   describe("RasterSource.readAll") {
     it("should return consistently ordered tiles across bands for a given scene") {
@@ -70,9 +63,34 @@ class RasterSourceIT extends TestEnvironment with TestData {
   }
 
   describe("GDAL support") {
+
     it("should read JPEG2000 scene") {
-      val src = RasterSource(localSentinel.toURI)
+      val src = RasterSource(localSentinel)
       src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
     }
+
+    it("should read small MRF scene with one band converted from MODIS HDF") {
+      val (expectedTileCount, _) = expectedTileCountAndBands(2400, 2400)
+      RasterSource(modisConvertedMrfPath).readAll().flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+    }
+
+    it("should read remote HTTP MRF scene") {
+      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+      RasterSource(remoteHttpMrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+    }
+
+    it("should read remote S3 MRF scene") {
+      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+      RasterSource(remoteS3MrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+    }
+
+  }
+
+  private def expectedTileCountAndBands(x:Int, y:Int, bandCount:Int = 1) = {
+    val imageDimensions = Seq(x.toDouble, y.toDouble)
+    val tilesPerBand = imageDimensions.map(x ⇒ ceil(x / NOMINAL_TILE_SIZE)).product
+    val bands = Range(0, bandCount)
+    val expectedTileCount = tilesPerBand * bands.length
+    (expectedTileCount, bands)
   }
 }
diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index b4ce78b06..77597ab89 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -4,13 +4,13 @@ rasterframes {
 }
 
 vlm.gdal {
-  options {
-    // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
-    //CPL_DEBUG = "ON"
-    AWS_REQUEST_PAYER = "requester"
-    GDAL_DISABLE_READDIR_ON_OPEN = "YES"
-    CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2"
-  }
-  // set this to `false` if CPL_DEBUG is `ON`
-  //useExceptions = false
+    options {
+      // See https://trac.osgeo.org/gdal/wiki/ConfigOptions for options
+      //CPL_DEBUG = "OFF"
+      AWS_REQUEST_PAYER = "requester"
+      GDAL_DISABLE_READDIR_ON_OPEN = "YES"
+      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2,.mrf,.idx,.lrc,.mrf.aux.xml"
+    }
+    // set this to `false` if CPL_DEBUG is `ON`
+    useExceptions = true
 }
\ No newline at end of file
diff --git a/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.idx b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.idx
new file mode 100644
index 0000000000000000000000000000000000000000..f86df2587247a79736c43c4d5bb2a2c9e554f92a
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.lrc b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.lrc
new file mode 100644
index 0000000000000000000000000000000000000000..75163d4a2e243304fadf5ba87be254d616713531
GIT binary patch
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HcmV?d00001

diff --git a/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf
new file mode 100644
index 000000000..8245c4a7e
--- /dev/null
+++ b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf
@@ -0,0 +1,12 @@
+<MRF_META>
+  <Raster>
+    <Size x="2400" y="2400" c="1" />
+    <PageSize x="512" y="512" c="1" />
+    <Compression>LERC</Compression>
+    <DataValues NoData="255" />
+  </Raster>
+  <GeoTags>
+    <BoundingBox minx="13343406.23600000" miny="2223901.03933300" maxx="14455356.75566700" maxy="3335851.55900000" />
+    <Projection>PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not specified (based on custom spheroid)",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["Meter",1]]</Projection>
+  </GeoTags>
+</MRF_META>
diff --git a/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf.aux.xml b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf.aux.xml
new file mode 100644
index 000000000..5a18f6944
--- /dev/null
+++ b/core/src/test/resources/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf.aux.xml
@@ -0,0 +1,92 @@
+<PAMDataset>
+  <Metadata domain="IMAGE_STRUCTURE">
+    <MDI key="COMPRESSION">LERC</MDI>
+    <MDI key="INTERLEAVE">PIXEL</MDI>
+  </Metadata>
+  <Metadata>
+    <MDI key="ALBEDOFILEID">06121997</MDI>
+    <MDI key="ASSOCIATEDINSTRUMENTSHORTNAME.1">MODIS</MDI>
+    <MDI key="ASSOCIATEDINSTRUMENTSHORTNAME.2">MODIS</MDI>
+    <MDI key="ASSOCIATEDPLATFORMSHORTNAME.1">Terra</MDI>
+    <MDI key="ASSOCIATEDPLATFORMSHORTNAME.2">Aqua</MDI>
+    <MDI key="ASSOCIATEDSENSORSHORTNAME.1">MODIS</MDI>
+    <MDI key="ASSOCIATEDSENSORSHORTNAME.2">MODIS</MDI>
+    <MDI key="AUTOMATICQUALITYFLAG.1">Passed</MDI>
+    <MDI key="AUTOMATICQUALITYFLAGEXPLANATION.1">Passed was set as a default value. More algorithm will be developed</MDI>
+    <MDI key="AVERAGENUMBEROBS">0</MDI>
+    <MDI key="BRDFCODEID">AMBRALS_V4.0R1</MDI>
+    <MDI key="BRDFDATABASEVERSION">v1.0500m</MDI>
+    <MDI key="CHARACTERISTICBINANGULARSIZE">15.0</MDI>
+    <MDI key="CHARACTERISTICBINSIZE">463.312716527778</MDI>
+    <MDI key="COVERAGECALCULATIONMETHOD">volume</MDI>
+    <MDI key="DATACOLUMNS">2400</MDI>
+    <MDI key="DATAROWS">2400</MDI>
+    <MDI key="DAYNIGHTFLAG">Day</MDI>
+    <MDI key="Description">Mandatory QA:
+  0 = processed, good quality (full BRDF inversions)
+  1 = processed, see other QA (magnitude BRDF inversions)
+</MDI>
+    <MDI key="DESCRREVISION">6.1</MDI>
+    <MDI key="EASTBOUNDINGCOORDINATE">150.120692476232</MDI>
+    <MDI key="EXCLUSIONGRINGFLAG.1">N</MDI>
+    <MDI key="GEOANYABNORMAL">False</MDI>
+    <MDI key="GEOESTMAXRMSERROR">75.0</MDI>
+    <MDI key="GLOBALGRIDCOLUMNS">86400</MDI>
+    <MDI key="GLOBALGRIDROWS">43200</MDI>
+    <MDI key="GRINGPOINTLATITUDE.1">19.9448109058663, 30.0666177912155, 29.9990071837477, 19.8789125843729</MDI>
+    <MDI key="GRINGPOINTLONGITUDE.1">127.31379517564, 138.161359988435, 150.130532080915, 138.321766284772</MDI>
+    <MDI key="GRINGPOINTSEQUENCENO.1">1, 2, 3, 4</MDI>
+    <MDI key="HDFEOSVersion">HDFEOS_V2.19</MDI>
+    <MDI key="HORIZONTALTILENUMBER">30</MDI>
+    <MDI key="identifier_product_doi">10.5067/MODIS/MCD43A4.006</MDI>
+    <MDI key="identifier_product_doi">10.5067/MODIS/MCD43A4.006</MDI>
+    <MDI key="identifier_product_doi_authority">http://dx.doi.org</MDI>
+    <MDI key="identifier_product_doi_authority">http://dx.doi.org</MDI>
+    <MDI key="INPUTPOINTER">MYD09GA.A2019113.h30v06.006.2019115025936.hdf, MYD09GA.A2019114.h30v06.006.2019117021858.hdf, MYD09GA.A2019115.h30v06.006.2019117044251.hdf, MYD09GA.A2019116.h30v06.006.2019118031111.hdf, MYD09GA.A2019117.h30v06.006.2019119025916.hdf, MYD09GA.A2019118.h30v06.006.2019120030848.hdf, MOD09GA.A2019113.h30v06.006.2019115032521.hdf, MOD09GA.A2019114.h30v06.006.2019116030646.hdf, MOD09GA.A2019115.h30v06.006.2019117050730.hdf, MOD09GA.A2019116.h30v06.006.2019118032616.hdf, MOD09GA.A2019117.h30v06.006.2019119032020.hdf, MOD09GA.A2019118.h30v06.006.2019120032257.hdf, MCD43DB.A2019110.6.h30v06.hdf</MDI>
+    <MDI key="LOCALGRANULEID">MCD43A4.A2019111.h30v06.006.2019120033434.hdf</MDI>
+    <MDI key="LOCALVERSIONID">6.1.34</MDI>
+    <MDI key="LONGNAME">MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m</MDI>
+    <MDI key="long_name">BRDF_Albedo_Band_Mandatory_Quality_Band1</MDI>
+    <MDI key="MAXIMUMOBSERVATIONS">0</MDI>
+    <MDI key="NADIRDATARESOLUTION">500m</MDI>
+    <MDI key="NORTHBOUNDINGCOORDINATE">29.9999999973059</MDI>
+    <MDI key="NUMBEROFGRANULES">1</MDI>
+    <MDI key="PARAMETERNAME.1">NOT SET</MDI>
+    <MDI key="PERCENTLANDINTILE">0</MDI>
+    <MDI key="PERCENTNEWBRDFS">0</MDI>
+    <MDI key="PERCENTPROCESSEDINTILE">0</MDI>
+    <MDI key="PERCENTSHAPEFIXEDBRDFS">100</MDI>
+    <MDI key="PERCENTSUBSTITUTEBRDFS">0</MDI>
+    <MDI key="PGEVERSION">6.0.42</MDI>
+    <MDI key="PROCESSINGCENTER">MODAPS</MDI>
+    <MDI key="PROCESSINGENVIRONMENT">Linux minion7043 3.10.0-957.5.1.el7.x86_64 #1 SMP Fri Feb 1 14:54:57 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux</MDI>
+    <MDI key="PRODUCTIONDATETIME">2019-04-30T03:34:48.000Z</MDI>
+    <MDI key="QAPERCENTGOODQUALITY">0</MDI>
+    <MDI key="QAPERCENTNOTPRODUCEDCLOUD">0</MDI>
+    <MDI key="QAPERCENTNOTPRODUCEDOTHER">99</MDI>
+    <MDI key="QAPERCENTOTHERQUALITY">0</MDI>
+    <MDI key="RANGEBEGINNINGDATE">2019-04-13</MDI>
+    <MDI key="RANGEBEGINNINGTIME">00:00:00.000000</MDI>
+    <MDI key="RANGEENDINGDATE">2019-04-28</MDI>
+    <MDI key="RANGEENDINGTIME">23:59:59.999999</MDI>
+    <MDI key="REPROCESSINGACTUAL">processed once</MDI>
+    <MDI key="REPROCESSINGPLANNED">further update is anticipated</MDI>
+    <MDI key="SCIENCEQUALITYFLAG.1">Not Investigated</MDI>
+    <MDI key="SCIENCEQUALITYFLAGEXPLANATION.1">See http://landweb.nascom/nasa.gov/cgi-bin/QA_WWW/qaFlagPage.cgi?sat=aqua the product Science Quality status.</MDI>
+    <MDI key="SETUPFILEID">06121997</MDI>
+    <MDI key="SHORTNAME">MCD43A4</MDI>
+    <MDI key="SOUTHBOUNDINGCOORDINATE">19.9999999982039</MDI>
+    <MDI key="SPSOPARAMETERS">2015</MDI>
+    <MDI key="TileID">51030006</MDI>
+    <MDI key="units">concatenated flags</MDI>
+    <MDI key="valid_range">0, 254</MDI>
+    <MDI key="VERSIONID">6</MDI>
+    <MDI key="VERTICALTILENUMBER">6</MDI>
+    <MDI key="WESTBOUNDINGCOORDINATE">127.701332684185</MDI>
+    <MDI key="_FillValue">255</MDI>
+  </Metadata>
+  <PAMRasterBand band="1">
+    <Description>BRDF_Albedo_Band_Mandatory_Quality_Band1</Description>
+    <UnitType>concatenated flags</UnitType>
+  </PAMRasterBand>
+</PAMDataset>
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index 5840fe566..7acaea7f1 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -21,11 +21,10 @@
 
 package org.locationtech.rasterframes
 
-import java.net.{URI, URL}
+import java.net.URI
 import java.nio.file.Paths
 import java.time.ZonedDateTime
 
-import org.locationtech.rasterframes.expressions.tilestats.NoDataCells
 import geotrellis.proj4.{CRS, LatLng}
 import geotrellis.raster
 import geotrellis.raster._
@@ -38,6 +37,7 @@ import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SparkSession
 import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
+import org.locationtech.rasterframes.expressions.tilestats.NoDataCells
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 import scala.reflect.ClassTag
@@ -138,10 +138,16 @@ trait TestData {
 
   lazy val remoteMODIS: URI = URI.create("https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF")
   lazy val remoteL8: URI = URI.create("https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/017/033/LC08_L1TP_017033_20181010_20181030_01_T1/LC08_L1TP_017033_20181010_20181030_01_T1_B4.TIF")
-  lazy val localSentinel: URL = getClass.getResource("/B01.jp2")
+  lazy val remoteHttpMrfPath: URI = URI.create("https://s3.amazonaws.com/s22s-rasterframes-integration-tests/m_3607526_sw_18_1_20160708.mrf")
+  lazy val remoteS3MrfPath: URI = URI.create("s3://naip-analytic/va/2016/100cm/rgbir/37077/m_3707764_sw_18_1_20160708.mrf")
+
+  lazy val localSentinel: URI = getClass.getResource("/B01.jp2").toURI
   lazy val cogPath: URI = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
   lazy val nonCogPath: URI = getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI
+
   lazy val l8B1SamplePath: URI = l8SamplePath(1)
+  lazy val l8samplePath: URI = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI
+  lazy val modisConvertedMrfPath: URI = getClass.getResource("/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf").toURI
 
   object JTS {
     val fact = new GeometryFactory()

From d17f8a487b98204bb0bd63ad7b29925e8e18fa09 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 2 May 2019 20:47:16 -0400
Subject: [PATCH 073/380] Refactor python type Tile for TileUDT

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../apache/spark/sql/rf/RasterSourceUDT.scala |   6 +-
 .../python/pyrasterframes/rasterfunctions.py  |   1 +
 pyrasterframes/python/pyrasterframes/types.py | 105 +++++++++++++-----
 .../python/tests/PyRasterFramesTests.py       |  27 +++--
 .../rasterframes/py/PyRFContext.scala         |  14 ++-
 5 files changed, 112 insertions(+), 41 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index bbcafec2a..679da36e9 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -68,6 +68,9 @@ class RasterSourceUDT extends UserDefinedType[RasterSource] {
 object RasterSourceUDT {
   UDTRegistration.register(classOf[RasterSource].getName, classOf[RasterSourceUDT].getName)
 
+  /** Deserialize a byte array, also used inside the Python API */
+  def from(byteArray: Array[Byte]): RasterSource = KryoSupport.deserialize[RasterSource](ByteBuffer.wrap(byteArray))
+
   implicit val rasterSourceSerializer: CatalystSerializer[RasterSource] = new CatalystSerializer[RasterSource] {
 
     override def schema: StructType = StructType(Seq(
@@ -80,7 +83,8 @@ object RasterSourceUDT {
     }
 
     override def from[R](row: R, io: CatalystIO[R]): RasterSource = {
-      KryoSupport.deserialize[RasterSource](ByteBuffer.wrap(io.getByteArray(row, 0)))
+      RasterSourceUDT.from(io.getByteArray(row, 0))
     }
+
   }
 }
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index c59d1416b..106a9a4a5 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -211,6 +211,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_tile_max': 'Compute the Tile-wise maximum',
     'rf_tile_stats': 'Compute the Tile-wise floating point statistics',
     'rf_render_ascii': 'Render ASCII art of tile',
+    'rf_render_matrix': 'Pretty print a sample of cell values',
     'rf_no_data_cells': 'Count of NODATA cells',
     'rf_data_cells': 'Count of cells with valid data',
     'rf_local_add': 'Add two Tiles',
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index 960f18f62..2f51755a0 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -138,6 +138,59 @@ def deserialize(self, datum):
         bytes(datum[0])
 
 
+class Tile:
+
+    def __init__(self, array, crs=None, extent=None, band_index=None, ):
+        # this could be more flexible to allow more ways to access TileUDT
+        # do some checking on dtype and shape
+        self.array = array
+        self.crs = crs  # what is a CRS? is it a rasterio.crs.CRS? A pyproj CRS? https://pyproj4.github.io/pyproj/html/api/crs.html#pyproj.crs.CRS.from_string
+        self.extent = extent
+        self.band_index = band_index
+        # here may be okay to carry around the kryo serialized bytes so we can at least go back into the JVM world?:
+
+    def __str__(self):
+        return self.array.__str__()
+
+    @classmethod
+    def numpy_dtype_to_celltype(cls, dtype):
+        from pyrasterframes.rasterfunctions import _celltype
+        # TODO implement something that will work generally
+        # Also here we should convert to a string representation of the celltype
+        return _celltype(str(dtype))
+
+    def get_cell_type(self):
+        return self.numpy_dtype_to_celltype(self.array.dtype)
+
+    def dimensions(self):
+        # list of cols, rows as is conventional in GeoTrellis and RasterFrames
+        return [self.array.shape[1], self.array.shape[0]]
+
+    def to_tile_udt(self):
+        row = [
+            # cell_context
+            [
+                ['float64'],  #TODO !
+                self.dimensions()
+            ],
+            # cell_data
+            [
+                # cells -- still too many copies
+                bytearray(RFContext.call('list_to_bytearray', self.array.flatten().tolist(), *self.dimensions())),
+                # ref
+                [
+                    # cell_data.ref.source
+                    None,
+                    # cell_data.ref.bandIndex
+                    self.band_index,
+                    # cell_data.ref.subextent
+                    self.extent
+                ]
+            ]
+        ]
+        return row
+
+
 class TileUDT(UserDefinedType):
     @classmethod
     def sqlType(cls):
@@ -148,8 +201,7 @@ def sqlType(cls):
             StructField("cell_context", StructType([
                 StructField("cell_type", StructType([
                     StructField("cellTypeName", StringType(), False)
-                    ]), False),
-                # ], False),  # life wood be ez if string dough
+                ]), False),
                 StructField("dimensions", StructType([
                     StructField("cols", ShortType(), False),
                     StructField("rows", ShortType(), False)
@@ -159,11 +211,12 @@ def sqlType(cls):
                 StructField("cells", BinaryType(), True),
                 StructField("ref", StructType([
                     StructField("source", RasterSourceUDT(), False),
+                    StructField("bandIndex", IntegerType(), False),
                     StructField("subextent", StructType([
-                      StructField("xmin", DoubleType(), False),
-                      StructField("ymin", DoubleType(), False),
-                      StructField("xmax", DoubleType(), False),
-                      StructField("ymax", DoubleType(), False)
+                        StructField("xmin", DoubleType(), False),
+                        StructField("ymin", DoubleType(), False),
+                        StructField("xmax", DoubleType(), False),
+                        StructField("ymax", DoubleType(), False)
 
                     ]), True)
                 ]), True)
@@ -178,22 +231,8 @@ def module(cls):
     def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
-    def serialize(self, masked_array):
-        return_val = [
-            # cell_context
-            [
-                [masked_array.dtype.name],
-                [masked_array.shape[1], masked_array.shape[0]]
-            ],
-            # cell_data
-            [
-                    # cells
-                    bytearray(RFContext.call('list_to_bytearray', masked_array.flatten().tolist(), masked_array.shape[1], masked_array.shape[0])),
-                    # ref -- TODO implement
-                    None
-            ]
-        ]
-        return return_val
+    def serialize(self, tile):
+        return tile.to_tile_udt()
 
     def deserialize(self, datum):
         """
@@ -201,23 +240,31 @@ def deserialize(self, datum):
         :param datum:
         :return: A Tile object from row data.
         """
-        cell_type = datum.cell_context.cell_type.cellTypeName
+        cell_type = datum.cell_context.cellType.cellTypeName
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
         cell_value_list = list(RFContext.call('bytearray_to_list', cell_data_bytes, cell_type, cols, rows))
+        extent = datum.cell_data.ref.subextent
+
+        crs = None
+        band_index = None
+        if 'ref' in datum.cell_data:
+            band_index = datum.cell_data.ref.bandIndex
+            if 'source' in datum.cell_data.ref and datum.cell_data.ref.source is not None:
+                crs = RFContext.call('rastersource_bytearray_to_proj4', datum.cell_data.ref.source)
 
-        ma = MaskedArray(
+        t = Tile(
             numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type),
-            numpy.zeros((rows, cols))
+            crs,
+            extent,
+            band_index,
         )
-        return ma
+        return t
 
     deserialize.__safe_for_unpickling__ = True
 
-
-from numpy.ma import MaskedArray
-MaskedArray.__UDT__ = TileUDT()
+Tile.__UDT__ = TileUDT()
 
 class TileExploder(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 387fbb34d..627cdd243 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -266,7 +266,6 @@ def some_point(g):
         ).alias('s')).collect()[0].s
         self.assertTrue(intersect_total == df.count())
 
-
         # Collect to python driver in shapely UDT
         pandas_df_out = df.toPandas()
 
@@ -297,44 +296,52 @@ def test_tile_udt(self):
         import numpy as np
         from numpy.ma import MaskedArray
         self.assertIsInstance(self.rf.sql_ctx, SQLContext)
+        from pyrasterframes.types import Tile
+
+        # Try to create a tile from numpy.
+        a_tile = Tile(np.random.randn(10, 10))  # no extent and crs provided
 
-        # Try to create a tile from numpy.ma.MaskedArray
         to_spark = pd.DataFrame({
-            't': [MaskedArray(np.random.randn(10, 10), np.zeros((10, 10))) for _ in range(3)],
+            't': [Tile(np.random.randn(10,12)) for _ in range(3)],
             'b': ['a', 'b', 'c'],
             'c': [1, 2, 4],
         })
         rf_maybe = self.spark.createDataFrame(to_spark)
         print("Type of dataframe: ", type(rf_maybe))
         rf_maybe.printSchema()
-        print(rf_maybe.toPandas())
 
         # Try to do something with it.
-        sums = to_spark.t.apply(lambda a: a.sum()).tolist()
-        maybe_sums = rf_maybe.select(tile_sum(rf_maybe.t).alias('tsum'))
+        sums = to_spark.t.apply(lambda a: a.array.sum()).tolist()
+        maybe_sums = rf_maybe.select(rf_tile_sum(rf_maybe.t).alias('tsum'))
         print("Schema of tile sum")
         maybe_sums.printSchema()
 
         maybe_sums = [r.tsum for r in maybe_sums.collect()]
         np.testing.assert_almost_equal(maybe_sums, sums, 12)
 
+        # Back to python side.
+        print(rf_maybe.toPandas())
 
         # Test round trip for an array
-        simple_array = MaskedArray(np.array([[1, 2], [3, 4]]).astype('float64'), np.zeros((2, 2)))
+        simple_array = Tile(np.array([[1, 2], [3, 4]]).astype('float64'), )
         to_spark_2 = pd.DataFrame({
             't': [simple_array]
         })
 
         rf_maybe_2 = self.spark.createDataFrame(to_spark_2)
         print("RasterFrame `show`:")
-        rf_maybe_2.select(render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
+        rf_maybe_2.select(rf_render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
 
         pd_2 = rf_maybe_2.toPandas()
         array_back_2 = pd_2.iloc[0].t
         print("Array collected from toPandas output\n", array_back_2)
 
-        self.assertIsInstance(array_back_2, MaskedArray)
-        np.testing.assert_equal(array_back_2, simple_array)
+        self.assertIsInstance(array_back_2, Tile)
+        np.testing.assert_equal(array_back_2.array, simple_array.array)
+
+        # test CRS and extent correctly make round trips
+
+        # test raster source?
 
 
 def suite():
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 41dc46b4a..c06f13415 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -20,10 +20,11 @@
  */
 package org.locationtech.rasterframes.py
 
-import geotrellis.raster.{CellType, MultibandTile}
+import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
+import org.apache.spark.sql.rf.RasterSourceUDT
 import org.locationtech.rasterframes.{RasterFunctions, _}
 import org.locationtech.rasterframes.model.LazyCRS
 import spray.json._
@@ -104,6 +105,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def rf_explode_tiles_sample(sampleFraction: Double, seed: Long, cols: Column*): Column =
     rf_explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
+
   def list_to_bytearray(l: java.util.ArrayList[Double], c: Int, r: Int): Array[Byte] = {
     geotrellis.raster.ArrayTile(l.asScala.toArray, c, r).toBytes()
   }
@@ -117,6 +119,16 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     ).toListDouble.asJava
   }
 
+  def rastersource_bytearray_to_proj4(bytes: Array[Byte]): String = {
+    import org.apache.spark.sql.rf.RasterSourceUDT._
+    val rasterSource = from(bytes)
+    return rasterSource.crs.toProj4String
+
+  }
+
+  def generate_tile(cellType: String, cols: Int, rows: Int, bytes: Array[Byte]): ArrayTile = {
+    ArrayTile.fromBytes(bytes, this.rf_cell_type(cellType), cols, rows)
+  }
 
 
   def tileColumns(df: DataFrame): Array[Column] =

From f332bda8421f64076d6597033fda9febf6d49b76 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 2 May 2019 21:01:01 -0400
Subject: [PATCH 074/380] Add rf_render_matrix back to
 pyrasterframes.rasterfunctions

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/pyrasterframes/rasterfunctions.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index f836171b1..559ef690e 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -213,6 +213,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_tile_max': 'Compute the Tile-wise maximum',
     'rf_tile_stats': 'Compute the Tile-wise floating point statistics',
     'rf_render_ascii': 'Render ASCII art of tile',
+    'rf_render_matrix': 'Render Tile cell values as numeric values, for debugging purposes',
     'rf_no_data_cells': 'Count of NODATA cells',
     'rf_data_cells': 'Count of cells with valid data',
     'rf_local_add': 'Add two Tiles',

From 4137f94c2fda2636a13a972c56afaf8501c2a13b Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 3 May 2019 11:26:25 -0400
Subject: [PATCH 075/380] Fix python TileUDT deserialize for nullables in
 cell_data.ref

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/pyrasterframes/types.py      | 9 +++++----
 pyrasterframes/python/tests/PyRasterFramesTests.py | 9 +++++++--
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index 2f51755a0..1252e603a 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -217,7 +217,6 @@ def sqlType(cls):
                         StructField("ymin", DoubleType(), False),
                         StructField("xmax", DoubleType(), False),
                         StructField("ymax", DoubleType(), False)
-
                     ]), True)
                 ]), True)
             ]), False)
@@ -245,14 +244,16 @@ def deserialize(self, datum):
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
         cell_value_list = list(RFContext.call('bytearray_to_list', cell_data_bytes, cell_type, cols, rows))
-        extent = datum.cell_data.ref.subextent
 
         crs = None
         band_index = None
-        if 'ref' in datum.cell_data:
+        extent = None
+        if 'ref' in datum.cell_data and datum.cell_data.ref is not None:
             band_index = datum.cell_data.ref.bandIndex
-            if 'source' in datum.cell_data.ref and datum.cell_data.ref.source is not None:
+            if datum.cell_data.ref.source is not None:
                 crs = RFContext.call('rastersource_bytearray_to_proj4', datum.cell_data.ref.source)
+            if datum.cell_data.ref.subextent is not None:
+                extent = datum.cell_data.ref.subextent
 
         t = Tile(
             numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type),
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 247c0e3a9..73c34651c 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -306,10 +306,15 @@ def some_point(g):
     def test_tile_udt(self):
         import pandas as pd
         import numpy as np
-        from numpy.ma import MaskedArray
-        self.assertIsInstance(self.rf.sql_ctx, SQLContext)
         from pyrasterframes.types import Tile
 
+        self.assertIsInstance(self.rf.sql_ctx, SQLContext)
+
+        # Try to collect self.rf which is read from a geotiff
+        rf_collect = self.rf.take(2)
+        self.assertTrue(
+            all([isinstance(row.tile.array, np.ndarray) for row in rf_collect]))
+
         # Try to create a tile from numpy.
         a_tile = Tile(np.random.randn(10, 10))  # no extent and crs provided
 

From c0973acdd3aff20bd9a53cf8142bcf6375db9ad4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 6 May 2019 09:27:48 -0400
Subject: [PATCH 076/380] Added `withKryoSerialization` extension method to
 PyRasterFrames.

---
 project/RFDependenciesPlugin.scala                 |  2 +-
 pyrasterframes/python/pyrasterframes/__init__.py   | 10 ++++++++++
 pyrasterframes/python/tests/PyRasterFramesTests.py |  8 ++++----
 3 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index 3c1fbd113..93e92e79e 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -60,6 +60,6 @@ object RFDependenciesPlugin extends AutoPlugin {
     rfSparkVersion := "2.3.2",
     rfGeoTrellisVersion := "2.2.0",
     rfGeoMesaVersion := "2.2.1",
-    //dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.bf51177"
+    dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.3f7a866"
   )
 }
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index fe3f1b9ba..9f4822318 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -24,6 +24,15 @@ def _rf_init(spark_session):
 
     return spark_session
 
+def _kryo_init(builder):
+    """Registers Kryo Serializers for better performance."""
+    # NB: These methods need to be kept up-to-date wit those in `org.locationtech.rasterframes.extensions.KryoMethods`
+    builder \
+        .config("spark.serializer",  "org.apache.spark.serializer.KryoSerializer") \
+        .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator") \
+        .config("spark.kryoserializer.buffer.max", "500m")
+    return builder
+
 
 def _reader(df_reader, format_key, path, **options):
     """ Loads the file of the given type at the given path."""
@@ -44,6 +53,7 @@ def _convertDF(df, sp_key = None, metadata = None):
 
 # Patch new method on SparkSession to mirror Scala approach
 SparkSession.withRasterFrames = _rf_init
+SparkSession.Builder.withKryoSerialization = _kryo_init
 
 # Add the 'asRF' method to pyspark DataFrame
 DataFrame.asRF = _convertDF
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index bbac60981..6ef010301 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -36,9 +36,7 @@ def setUpClass(cls):
         cls.spark = (SparkSession.builder
             .config('spark.driver.extraClassPath', jarpath)
             .config('spark.executor.extraClassPath', jarpath)
-            .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-            .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator")
-            .config("spark.kryoserializer.buffer.max", "500m")
+            .withKryoSerialization()
             .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
         print(cls.spark.version)
@@ -56,6 +54,8 @@ def setUpClass(cls):
             .withColumnRenamed('tile2', cls.tileCol).asRF()
         #cls.rf.show()
 
+    def test_setup(self):
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"), "org.apache.spark.serializer.KryoSerializer")
 
     def test_identify_columns(self):
         cols = self.rf.tileColumns()
@@ -194,7 +194,7 @@ def test_explode(self):
         self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
 
 
-    def test_maskByValue(self):
+    def test_mask_by_value(self):
         from pyspark.sql.functions import lit
 
         # create an artificial mask for values > 25000; masking value will be 4

From a153d8da37d5fbb8de6dd91fa31de32d7ecc9840 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 6 May 2019 14:32:19 -0400
Subject: [PATCH 077/380] Updated `pysparkCmd` construction.

---
 pyrasterframes/build.sbt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index f2bacfaad..a8023a406 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -143,11 +143,11 @@ Python / packageBin := {
 }
 
 pysparkCmd := {
-  val _ = spPublishLocal.value
-  val id = (projectID in spPublishLocal).value
-  val args = "pyspark" ::  "--packages" :: s"${id.organization}:${id.name}:${id.revision}" :: Nil
+  val pyBin = (Python / packageBin).value
+  val jarBin = (spPackage / assembly).value
+
+  val args = "pyspark" :: "--jars" :: jarBin :: "--py-files" :: pyBin :: Nil
   streams.value.log.info("PySpark Command:\n" + args.mkString(" "))
-  // --conf spark.jars.ivy=(ivyPaths in pysparkCmd).value....
 }
 
 ivyPaths in pysparkCmd := ivyPaths.value.withIvyHome(target.value / "ivy")

From af77f4140c1de0c250e7935c83bc29d0d5f4dd19 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 7 May 2019 09:30:30 -0400
Subject: [PATCH 078/380] Use kryo sugar in python examples

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/examples/__init__.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/pyrasterframes/python/examples/__init__.py b/pyrasterframes/python/examples/__init__.py
index 3a0b73092..8bbea6d4f 100644
--- a/pyrasterframes/python/examples/__init__.py
+++ b/pyrasterframes/python/examples/__init__.py
@@ -1,4 +1,4 @@
-#examples_setup
+# examples_setup
 from pathlib import Path
 from pyspark.sql import SparkSession
 
@@ -7,19 +7,18 @@
 jarpath = list(Path('../target').resolve().glob('**/pyrasterframes*.jar'))
 if len(jarpath) > 0:
     pyJar = jarpath[0].as_uri()
+
     def example_session():
         return (SparkSession.builder
-        .master("local[*]")
-        .appName("RasterFrames")
-        .config('spark.driver.extraClassPath', pyJar)
-        .config('spark.executor.extraClassPath', pyJar)
-        .config("spark.ui.enabled", "false")
-        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
-        .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator")
-        .config("spark.kryoserializer.buffer.max", "500m")
-        .getOrCreate())
+            .master("local[*]")
+            .appName("RasterFrames")
+            .config('spark.driver.extraClassPath', pyJar)
+            .config('spark.executor.extraClassPath', pyJar)
+            .config("spark.ui.enabled", "false")
+            .withKryoSerialization()
+            .getOrCreate())
 
 
 # hard-coded relative path for resources
 resource_dir = Path('./static').resolve()
-#examples_setup
+# examples_setup

From 15ded729db7c5efae89f9919aa7af050ce06a96a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 8 May 2019 13:17:14 -0400
Subject: [PATCH 079/380] Reworked RasterFrames Jupyter Notebook build for
 future extensibility. Added `rf_crs`.

---
 build.sbt                                     |  11 +-
 .../rasterframes/RasterFunctions.scala        |   3 +
 deployment/README.md                          |  66 --
 deployment/build.sbt                          |  56 -
 deployment/docker/jupyter/Dockerfile          |  69 --
 deployment/docker/jupyter/README.md           |   3 -
 .../jupyter/samples/L8-B1-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B10-Elkton-VA.tiff     | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B11-Elkton-VA.tiff     | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B2-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B3-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B4-Elkton-VA.tiff      | Bin 63376 -> 0 bytes
 .../jupyter/samples/L8-B5-Elkton-VA.tiff      | Bin 63376 -> 0 bytes
 .../jupyter/samples/L8-B6-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B7-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 .../jupyter/samples/L8-B8-Elkton-VA.tiff      | Bin 250662 -> 0 bytes
 .../jupyter/samples/L8-B8-Robinson-IL.tiff    | Bin 775172 -> 0 bytes
 .../jupyter/samples/L8-B9-Elkton-VA.tiff      | Bin 63292 -> 0 bytes
 docs/src/main/tut/release-notes.md            |   2 +
 project/RFProjectPlugin.scala                 |   7 +
 project/plugins.sbt                           |   1 +
 pyrasterframes/build.sbt                      |   2 -
 .../python/pyrasterframes/__init__.py         |  46 +-
 .../python/pyrasterframes/context.py          |   3 +
 .../python/pyrasterframes/rasterfunctions.py  |   3 +-
 .../pyrasterframes/{types.py => rf_types.py}  |   0
 .../python/tests/PyRasterFramesTests.py       |  80 +-
 .../rasterframes/py/PyRFContext.scala         |   8 +
 rf-notebook/README.md                         |  32 +
 rf-notebook/build.sbt                         |  55 +
 rf-notebook/src/main/docker/Dockerfile        |  45 +
 .../src/main/docker}/docker-compose.yml       |   9 +-
 .../main/docker/jupyter_notebook_config.py    | 766 ++++++++++++++
 .../src/main/notebooks/Getting Started.ipynb  | 964 ++++++++++++++++++
 34 files changed, 1970 insertions(+), 261 deletions(-)
 delete mode 100644 deployment/README.md
 delete mode 100644 deployment/build.sbt
 delete mode 100644 deployment/docker/jupyter/Dockerfile
 delete mode 100644 deployment/docker/jupyter/README.md
 delete mode 100644 deployment/docker/jupyter/samples/L8-B1-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B10-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B11-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B2-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B3-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B4-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B5-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B6-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B7-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B8-Elkton-VA.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B8-Robinson-IL.tiff
 delete mode 100644 deployment/docker/jupyter/samples/L8-B9-Elkton-VA.tiff
 rename pyrasterframes/python/pyrasterframes/{types.py => rf_types.py} (100%)
 create mode 100644 rf-notebook/README.md
 create mode 100644 rf-notebook/build.sbt
 create mode 100644 rf-notebook/src/main/docker/Dockerfile
 rename {deployment/docker/jupyter => rf-notebook/src/main/docker}/docker-compose.yml (67%)
 create mode 100644 rf-notebook/src/main/docker/jupyter_notebook_config.py
 create mode 100644 rf-notebook/src/main/notebooks/Getting Started.ipynb

diff --git a/build.sbt b/build.sbt
index 0e21c7f37..81a39a236 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,3 +1,4 @@
+
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
@@ -8,8 +9,9 @@ lazy val root = project
   .enablePlugins(RFReleasePlugin)
   .settings(publish / skip := true)
 
-lazy val deployment = project
-  .dependsOn(root)
+lazy val `rf-notebook` = project
+  .dependsOn(pyrasterframes)
+  .enablePlugins(RFAssemblyPlugin, DockerPlugin)
   .disablePlugins(SparkPackagePlugin)
 
 lazy val IntegrationTest = config("it") extend Test
@@ -42,13 +44,14 @@ lazy val core = project
       scalatest
     ),
     buildInfoKeys ++= Seq[BuildInfoKey](
-      name, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
+      moduleName, version, scalaVersion, sbtVersion, rfGeoTrellisVersion, rfGeoMesaVersion, rfSparkVersion
     ),
     buildInfoPackage := "org.locationtech.rasterframes",
     buildInfoObject := "RFBuildInfo",
     buildInfoOptions := Seq(
       BuildInfoOption.ToMap,
-      BuildInfoOption.BuildTime
+      BuildInfoOption.BuildTime,
+      BuildInfoOption.ToJson
     )
   )
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 769c58bf7..c5caa1c5a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -68,6 +68,9 @@ trait RasterFunctions {
   /** Extracts the bounding box from a RasterSource or ProjectedRasterTile */
   def rf_extent(col: Column): TypedColumn[Any, Extent] = GetExtent(col)
 
+  /** Extracts the CRS from a RasterSource or ProjectedRasterTile */
+  def rf_crs(col: Column): TypedColumn[Any, CRS] = GetCRS(col)
+
   /** Flattens Tile into a double array. */
   def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
diff --git a/deployment/README.md b/deployment/README.md
deleted file mode 100644
index 5e008b8a1..000000000
--- a/deployment/README.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# RasterFrames Jupyter Notebook Docker Container
-
-RasterFrames provides a Docker image with a Jupyter Notebook pre-configured with RasterFrames support for Python 3 and Scala Spylon kernels.
-
-## Quick start
-
-This will use the [latest image](https://hub.docker.com/r/s22s/rasterframes-notebooks/) published to Docker Hub. 
-
-```bash
-# Optionally pull the latest image.
-$ docker pull s22s/rasterframes-notebooks
-
-# from root of the git repo
-$ cd deployment/docker/jupyter
-$ docker-compose up
-```
-
-## Custom run
-
-The `docker-compose` incantation automatically exposes port 8888 for the Jupyter Notebook and ports ports 4040-4044 for the Spark UI.
-
-The image can equivalently be run with: 
-
-    $ docker run -it --rm -p 8888:8888 -p 4040-4044:4040-4044 s22s/rasterframes-notebooks
-
-The `docker run` command can be changed to quickly customize the container.
-
-To mount a directory on the host machine (to load or save local files directly from Jupyter) add
- 
-    -v /some/host/folder/for/work:/home/jovyan/work
-    
-to the command.    
-
-Attach the notebook server to a different host port with 
- 
-    -p 8630:8888
-    
-if you already have a notebook server running on port 8888.
-
-If you want to use a known password, use
-
-```bash
-docker run -it --rm -p 8888:8888 -p 4040-4044:4040-4044 \
-    s22s/rasterframes-notebooks \
-    start-notebook.sh --NotebookApp.password='sha1:1c360e8dd3e1:946d17ef9e6b8cbb28c7bb0152329786918cc424'
-```
-    
-Where the password sha is generated with [`notebook.auth.passwd`](https://jupyter-notebook.readthedocs.io/en/stable/public_server.html#preparing-a-hashed-password).
-
-Please see the `Dockerfile` and the `docker-compose.yml` file on GitHub ([here](https://github.com/locationtech/rasterframes/tree/develop/deployment/docker/jupyter)) as a starting point to customize your image and container. 
-
-
-## For Development
-
-To build the Docker image based on local development changes:
-
-```bash
-# from the root of the repo
-sbt deployment/rfDocker
-```
-
-## Base images
-
-This image is based on [jupyter/pyspark-notebook](https://hub.docker.com/r/jupyter/pyspark-notebook), with some 
-portions from [jupyter/all-spark-notebook](https://hub.docker.com/r/jupyter/all-spark-notebook). 
-Much more extensive instructions can be found at those locations.
\ No newline at end of file
diff --git a/deployment/build.sbt b/deployment/build.sbt
deleted file mode 100644
index c76ef554b..000000000
--- a/deployment/build.sbt
+++ /dev/null
@@ -1,56 +0,0 @@
-import sbt.{IO, _}
-
-import scala.sys.process.Process
-
-moduleName := "rasterframes-deployment"
-
-val Docker = config("docker")
-val Python = config("python")
-
-
-lazy val rfDockerImageName = settingKey[String]("Name to tag Docker image with.")
-rfDockerImageName := "s22s/rasterframes-notebooks"
-
-lazy val rfDocker = taskKey[Unit]("Build Jupyter Notebook Docker image with RasterFrames support.")
-rfDocker := (Docker / packageBin).value
-
-lazy val runRFNotebook = taskKey[String]("Run RasterFrames Jupyter Notebook image")
-runRFNotebook := {
-  val imageName = rfDockerImageName.value
-  val _ = rfDocker.value
-  Process(s"docker run -p 8888:8888 -p 4040:4040 $imageName").run()
-  imageName
-}
-
-Docker / resourceDirectory := baseDirectory.value / "docker"/ "jupyter"
-
-Docker / target := target.value / "docker"
-
-Docker / mappings := {
-  val rezDir = (Docker / resourceDirectory).value
-  val files = (rezDir ** "*") pair Path.relativeTo(rezDir)
-
-  val jar = (assembly in LocalProject("pyrasterframes")).value
-  val py = (packageBin in (LocalProject("pyrasterframes"), Python)).value
-
-  files ++ Seq(jar -> jar.getName, py -> py.getName)
-}
-
-def rfFiles = Def.task {
-  val destDir = (Docker / target).value
-  val filePairs = (Docker / mappings).value
-  IO.copy(filePairs.map { case (src, dst) ⇒ (src, destDir / dst) })
-}
-
-Docker / packageBin := {
-  val _ = rfFiles.value
-  val logger = streams.value.log
-  val staging = (Docker / target).value
-  val ver = (version in LocalRootProject).value
-
-  logger.info(s"Running docker build in $staging")
-  val imageName = rfDockerImageName.value
-  Process("docker-compose build", staging).!
-  Process(s"docker tag $imageName:latest $imageName:$ver", staging).!
-  staging
-}
diff --git a/deployment/docker/jupyter/Dockerfile b/deployment/docker/jupyter/Dockerfile
deleted file mode 100644
index ebf52fdac..000000000
--- a/deployment/docker/jupyter/Dockerfile
+++ /dev/null
@@ -1,69 +0,0 @@
-FROM jupyter/pyspark-notebook:92fe05d1e7e5
-
-MAINTAINER Astraea, Inc.
-
-ENV RF_LIB_LOC /usr/lib
-ENV RF_JAR $RF_LIB_LOC/rasterframes.jar
-ENV PY_RF_ZIP $RF_LIB_LOC/pyrasterframes.zip
-
-USER root
-
-RUN echo "spark.driver.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf  && \
-    echo "spark.executor.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf
-
-EXPOSE 4040 4041 4042 4043 4044
-
-ENV SPARK_OPTS $SPARK_OPTS \
-    --py-files $PY_RF_ZIP \
-    --jars $RF_JAR \
-    --driver-class-path $RF_JAR \
-    --conf spark.executor.extraClassPath=$RF_JAR
-
-ENV PYTHONPATH $PYTHONPATH:$PY_RF_ZIP
-
-
-#================================
-# Copied from all-spark-notebook
-#================================
-
-# TODO:     resolve the issue that toree has with --py-files, above (it does not like .zips and
-# TODO:     the kernel will not start)
-# Apache Toree kernel
-#RUN pip install --no-cache-dir \
-#    https://dist.apache.org/repos/dist/dev/incubator/toree/0.2.0-incubating-rc5/toree-pip/toree-0.2.0.tar.gz \
-#    && \
-#    jupyter toree install --sys-prefix && \
-#    rm -rf /home/$NB_USER/.local && \
-#    fix-permissions $CONDA_DIR && \
-#    fix-permissions /home/$NB_USER
-
-# Spylon-kernel
-RUN conda install --quiet --yes 'spylon-kernel=0.4*' && \
-    conda clean -tipsy && \
-    python -m spylon_kernel install --sys-prefix 
-
-# Sphinx (for Notebook->html)
-RUN conda install --quiet --yes \
-    sphinx nbsphinx 
-
-# Cleanup pip residuals
-RUN rm -rf /home/$NB_USER/.local && \
-    fix-permissions $CONDA_DIR && \
-    fix-permissions /home/$NB_USER
-
-# Do these after the standard environment setup
-# since these change more regularly.
-COPY *.zip $PY_RF_ZIP
-COPY *.jar $RF_JAR
-
-RUN chown -R $NB_UID:$NB_GID $HOME
-
-USER $NB_UID
-
-# RUN pip install guzzle_sphinx_theme
-
-# TODO: This repo can change regularly without docker knowing that the
-# TODO: Layer this command is written in has become stale. Need to either
-# TODO: clone a specific revision that we manually update, or keep this
-# TODO: last, assuming the prior commends will be detected as stale.
-RUN git clone http://github.com/s22s/rasterframes-book && ln -s rasterframes-book/Python/samples
diff --git a/deployment/docker/jupyter/README.md b/deployment/docker/jupyter/README.md
deleted file mode 100644
index 815b78d8e..000000000
--- a/deployment/docker/jupyter/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# RasterFrames Jupyter Notebook
-
-Please visit `rasterframe-book` directory for example notebooks.
\ No newline at end of file
diff --git a/deployment/docker/jupyter/samples/L8-B1-Elkton-VA.tiff b/deployment/docker/jupyter/samples/L8-B1-Elkton-VA.tiff
deleted file mode 100644
index 0d5a644eafe7c238c7a1ff991b9e4ae1b2c2f002..0000000000000000000000000000000000000000
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diff --git a/deployment/docker/jupyter/samples/L8-B6-Elkton-VA.tiff b/deployment/docker/jupyter/samples/L8-B6-Elkton-VA.tiff
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diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index f04f6cf9a..5f40f0f43 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -12,6 +12,7 @@
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * Added `rf_render_matrix` debugging function.
 * Added `RasterFrame.withExtent` extension method.
+* Added `rf_crs` for `ProjectedRasterTile` columns.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
@@ -29,6 +30,7 @@
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `org.locationtech.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
+* Reworked build scripts for RasterFrames Jupyter Notebook. 
    
 ## 0.7.x
 
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index d41451017..5c641e6d4 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -11,6 +11,13 @@ object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = GitPlugin
 
+  object autoImport {
+    val Python = config("python")
+  }
+  import autoImport._
+
+  override def projectConfigurations: Seq[Configuration] = Seq(Python)
+
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
     organizationName := "LocationTech RasterFrames",
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 88fade4bd..783f50a06 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -16,5 +16,6 @@ addSbtPlugin("org.spark-packages" % "sbt-spark-package" % "0.2.7-astraea.1")
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.4.1")
 addSbtPlugin("net.vonbuchholtz" % "sbt-dependency-check" % "0.2.10")
 addSbtPlugin("com.github.gseitz" %% "sbt-release" % "1.0.9")
+addSbtPlugin("com.typesafe.sbt" % "sbt-native-packager" % "1.3.19")
 
 
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index a8023a406..9648b7a22 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -83,8 +83,6 @@ cleanFiles ++=
     "pyrasterframes.egg-info"
   ).map(f => pythonSource.value / f)
 
-val Python = config("python")
-
 Python / target := target.value / "python-dist"
 
 lazy val pyZip = taskKey[File]("Build pyrasterframes zip.")
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index 9f4822318..a05ab5246 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -9,7 +9,7 @@
 from pyspark.sql.column import _to_java_column
 
 # Import RasterFrame types and functions
-from .types import *
+from .rf_types import *
 from . import rasterfunctions
 from .context import RFContext
 
@@ -24,23 +24,18 @@ def _rf_init(spark_session):
 
     return spark_session
 
+
 def _kryo_init(builder):
     """Registers Kryo Serializers for better performance."""
     # NB: These methods need to be kept up-to-date wit those in `org.locationtech.rasterframes.extensions.KryoMethods`
     builder \
-        .config("spark.serializer",  "org.apache.spark.serializer.KryoSerializer") \
+        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
         .config("spark.kryo.registrator", "org.locationtech.rasterframes.util.RFKryoRegistrator") \
         .config("spark.kryoserializer.buffer.max", "500m")
     return builder
 
 
-def _reader(df_reader, format_key, path, **options):
-    """ Loads the file of the given type at the given path."""
-    df = df_reader.format(format_key).load(path, **options)
-    return _convertDF(df)
-
-
-def _convertDF(df, sp_key = None, metadata = None):
+def _convert_df(df, sp_key=None, metadata=None):
     ctx = SparkContext._active_spark_context._rf_context
 
     if sp_key is None:
@@ -51,18 +46,37 @@ def _convertDF(df, sp_key = None, metadata = None):
             df._jdf, _to_java_column(sp_key), json.dumps(metadata)), ctx._spark_session)
 
 
+def _layer_reader(df_reader, format_key, path, **options):
+    """ Loads the file of the given type at the given path."""
+    df = df_reader.format(format_key).load(path, **options)
+    return _convert_df(df)
+
+
+def _rastersource_reader(df_reader, path=None, band_indexes=None, tile_dimensions=(256, 256), **options):
+    if band_indexes is None:
+        band_indexes = [0]
+
+    def to_csv(comp): return ','.join(str(v) for v in comp)
+    # If path is list, convert to newline delimited string and pass as "paths" (plural)
+    options.update({
+        "bandIndexes": to_csv(band_indexes),
+        "tileDimensions": to_csv(tile_dimensions)
+    })
+    return df_reader \
+        .format("rastersource") \
+        .load(path, **options)
+
+
 # Patch new method on SparkSession to mirror Scala approach
 SparkSession.withRasterFrames = _rf_init
 SparkSession.Builder.withKryoSerialization = _kryo_init
 
 # Add the 'asRF' method to pyspark DataFrame
-DataFrame.asRF = _convertDF
+DataFrame.asRF = _convert_df
 
 # Add DataSource convenience methods to the DataFrameReader
-# TODO: make sure this supports **options
-DataFrameReader.geotiff = lambda df_reader, path: _reader(df_reader, "geotiff", path)
-DataFrameReader.geotrellis = lambda df_reader, path: _reader(df_reader, "geotrellis", path)
-DataFrameReader.rastersource = lambda df_reader, path: _reader(df_reader, "rastersource", path)
-
-
+DataFrameReader.rastersource = _rastersource_reader
 
+# Legacy readers
+DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
+DataFrameReader.geotrellis = lambda df_reader, path: _layer_reader(df_reader, "geotrellis", path)
diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/python/pyrasterframes/context.py
index f6d307475..ba28a6bb9 100644
--- a/pyrasterframes/python/pyrasterframes/context.py
+++ b/pyrasterframes/python/pyrasterframes/context.py
@@ -25,6 +25,9 @@ def list_to_seq(self, py_list):
     def lookup(self, function_name):
         return getattr(self._jrfctx, function_name)
 
+    def build_info(self):
+        return self._jrfctx.buildInfo()
+
     @staticmethod
     def active():
         """
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 559ef690e..102f7e247 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -245,7 +245,8 @@ def _(data_tile, mask_tile, mask_value):
     'rf_exp10': 'Compute 10 to the power of cell values',
     'rf_expm1': 'Performs cell-wise exponential, then subtract one',
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
-    'st_extent': 'Compute the extent/bbox of a Geometry',
+    'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
+    'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
     'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
     'st_geometry': 'Convert the given extent/bbox to a polygon',
     'rf_geometry': 'Get the extent of a RasterSource or ProjectdRasterTile as a Geometry',
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
similarity index 100%
rename from pyrasterframes/python/pyrasterframes/types.py
rename to pyrasterframes/python/pyrasterframes/rf_types.py
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 6ef010301..b0fc2aecc 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,4 +1,3 @@
-
 from pyspark.sql import SparkSession, Column
 from pyspark.sql.functions import *
 from pyrasterframes import *
@@ -10,6 +9,7 @@
 
 # version-conditional imports
 import sys
+
 if sys.version_info[0] > 2:
     import builtins
 else:
@@ -25,7 +25,6 @@ class RasterFunctionsTest(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-
         # gather Scala requirements
         jarpath = list(Path('../target/scala-2.11').resolve().glob('pyrasterframes-assembly*.jar'))[0]
 
@@ -34,16 +33,18 @@ def setUpClass(cls):
 
         # spark session with RF
         cls.spark = (SparkSession.builder
-            .config('spark.driver.extraClassPath', jarpath)
-            .config('spark.executor.extraClassPath', jarpath)
-            .withKryoSerialization()
-            .getOrCreate())
+                     .config('spark.driver.extraClassPath', jarpath)
+                     .config('spark.executor.extraClassPath', jarpath)
+                     .withKryoSerialization()
+                     .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
         print(cls.spark.version)
         cls.spark.withRasterFrames()
 
+        cls.img_uri = cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()
+
         # load something into a rasterframe
-        rf = cls.spark.read.geotiff(cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()) \
+        rf = cls.spark.read.geotiff(cls.img_uri) \
             .withBounds() \
             .withCenter()
 
@@ -52,10 +53,11 @@ def setUpClass(cls):
         cls.rf = rf.withColumn('tile2', rf_convert_cell_type(cls.tileCol, 'float32')) \
             .drop(cls.tileCol) \
             .withColumnRenamed('tile2', cls.tileCol).asRF()
-        #cls.rf.show()
+        # cls.rf.show()
 
     def test_setup(self):
-        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"), "org.apache.spark.serializer.KryoSerializer")
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
+                         "org.apache.spark.serializer.KryoSerializer")
 
     def test_identify_columns(self):
         cols = self.rf.tileColumns()
@@ -70,7 +72,8 @@ def test_identify_columns(self):
 
     def test_tile_creation(self):
         base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
-        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"), rf_make_ones_tile(3, 3, "int32"))
+        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"),
+                            rf_make_ones_tile(3, 3, "int32"))
         tiles.show()
         self.assertEqual(tiles.count(), 4)
 
@@ -89,11 +92,10 @@ def test_tile_operations(self):
 
         self.assertTrue(_rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
 
-
-    def test_general(self):
+    def test_tile_functions(self):
         meta = self.rf.tileLayerMetadata()
         self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)) \
+        df = self.rf.withColumn('dims', rf_dimensions(self.tileCol)) \
             .withColumn('type', rf_cell_type(self.tileCol)) \
             .withColumn('dCells', rf_data_cells(self.tileCol)) \
             .withColumn('ndCells', rf_no_data_cells(self.tileCol)) \
@@ -113,6 +115,11 @@ def test_general(self):
         #  a source of ProjectedRasterTiles.
         df.show()
 
+    def test_prt_functions(self):
+        df = self.spark.read.rastersource(self.img_uri)
+        df.printSchema
+        df.show()
+
     def test_rasterize(self):
         # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
         withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
@@ -139,12 +146,10 @@ def test_aggregations(self):
         self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
-
     def test_sql(self):
-
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims', rf_dimensions(self.tileCol)).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
@@ -159,11 +164,11 @@ def test_sql(self):
 
         ops.printSchema
         statsRow = ops.select(rf_tile_mean(self.tileCol).alias('base'),
-                           rf_tile_mean("AndOne").alias('plus_one'),
-                           rf_tile_mean("LessOne").alias('minus_one'),
-                           rf_tile_mean("TimesTwo").alias('double'),
-                           rf_tile_mean("OverTwo").alias('half')) \
-                        .first()
+                              rf_tile_mean("AndOne").alias('plus_one'),
+                              rf_tile_mean("LessOne").alias('minus_one'),
+                              rf_tile_mean("TimesTwo").alias('double'),
+                              rf_tile_mean("OverTwo").alias('half')) \
+            .first()
 
         self.assertTrue(_rounded_compare(statsRow.base, statsRow.plus_one - 1))
         self.assertTrue(_rounded_compare(statsRow.base, statsRow.minus_one + 1))
@@ -178,10 +183,10 @@ def test_explode(self):
         # +-----------+------------+---------+-------+
         # |[2,1]      |4           |0        |10150.0|
         cell = self.rf.select(self.rf.spatialKeyColumn(), rf_explode_tiles(self.rf.tile)) \
-            .where(F.col("spatial_key.col")==2) \
-            .where(F.col("spatial_key.row")==1) \
-            .where(F.col("column_index")==4) \
-            .where(F.col("row_index")==0) \
+            .where(F.col("spatial_key.col") == 2) \
+            .where(F.col("spatial_key.row") == 1) \
+            .where(F.col("column_index") == 4) \
+            .where(F.col("row_index") == 0) \
             .select(F.col("tile")) \
             .collect()[0][0]
         self.assertEqual(cell, 10150.0)
@@ -193,7 +198,6 @@ def test_explode(self):
         self.assertTrue(sample_count > 0)
         self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
 
-
     def test_mask_by_value(self):
         from pyspark.sql.functions import lit
 
@@ -205,13 +209,12 @@ def test_mask_by_value(self):
                                  rf_convert_cell_type(
                                      rf_local_greater_int(self.rf.tile, 25000),
                                      "uint8"),
-                                  lit(mask_value)).alias('mask'))
+                                 lit(mask_value)).alias('mask'))
         rf2 = rf1.select(rf1.tile, rf_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
         result = rf2.agg(rf_agg_no_data_cells(rf2.tile) < rf_agg_no_data_cells(rf2.masked)) \
             .collect()[0][0]
         self.assertTrue(result)
 
-
     def test_resample(self):
         from pyspark.sql.functions import lit
         result = self.rf.select(
@@ -235,7 +238,6 @@ def test_exists_for_all(self):
         self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
         self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
 
-
     def test_geomesa_pyspark(self):
         from pyspark.sql.functions import lit, udf, sum
         import shapely
@@ -278,7 +280,6 @@ def some_point(g):
         ).alias('s')).collect()[0].s
         self.assertTrue(intersect_total == df.count())
 
-
         # Collect to python driver in shapely UDT
         pandas_df_out = df.toPandas()
 
@@ -291,7 +292,7 @@ def some_point(g):
         self.assertTrue(all(xs_correct))
 
         centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
-                                                      g.centroid.coords[0][1]).tolist()
+                                                    g.centroid.coords[0][1]).tolist()
         numpy.testing.assert_almost_equal(centroid_ys, pandas_df.y.tolist())
 
         # Including from UDF's
@@ -304,7 +305,6 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
-
     def test_raster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
@@ -317,15 +317,15 @@ def l8path(b):
         path_param = '\n'.join([l8path(b) for b in [1, 2, 3]])  # "http://foo.com/file1.tif,http://foo.com/file2.tif"
         tile_size = 512
         df = self.spark.read.format('rastersource') \
-                            .options(paths=path_param, tileDimensions='{},{}'.format(tile_size, tile_size)) \
-                            .load()
+            .options(paths=path_param, tileDimensions='{},{}'.format(tile_size, tile_size)) \
+            .load()
 
         # schema is tile_path and tile
         df.printSchema()
         self.assertTrue(len(df.columns) == 2 and 'tile_path' in df.columns and 'tile' in df.columns)
 
         # the most common tile dimensions should be as passed to `options`, showing that options are correctly applied
-        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c'))\
+        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c')) \
             .groupby(['r', 'c']).count().toPandas()
         most_common_size = tile_size_df.loc[tile_size_df['count'].idxmax()]
         self.assertTrue(most_common_size.r == tile_size and most_common_size.c == tile_size)
@@ -360,10 +360,11 @@ def path(scene, band):
         path_table = self.spark.createDataFrame(path_pandas)
         path_table.createOrReplaceTempView(path_table_hive_name)
 
-        path_df = self.spark.read.format('rastersource') \
-                            .options(pathTable=path_table_hive_name, pathTableColumns=csv_columns,
-                                     tileDimensions='512,512') \
-                            .load()
+        path_df = self.spark.read.rastersource(
+            tile_dimensions=(512, 512),
+            pathTable=path_table_hive_name,
+            pathTableColumns=csv_columns,
+        )
 
         path_df.printSchema()
         self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}
@@ -377,4 +378,5 @@ def suite():
     function_tests = unittest.TestSuite()
     return function_tests
 
+
 unittest.TextTestRunner().run(suite())
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 981cbac32..660c30e60 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -40,6 +40,14 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   sparkSession.withRasterFrames
 
+  def buildInfo(): java.util.HashMap[String, String] = {
+    val retval = new java.util.HashMap[String, String]()
+    RFBuildInfo.toMap.foreach {
+      case (k, v) => retval.put(k, String.valueOf(v))
+    }
+    retval
+  }
+
   def toSpatialMultibandTileLayerRDD(rf: RasterFrame): MultibandTileLayerRDD[SpatialKey] =
     rf.toMultibandTileLayerRDD match {
       case Left(spatial) => spatial
diff --git a/rf-notebook/README.md b/rf-notebook/README.md
new file mode 100644
index 000000000..5295b9cc5
--- /dev/null
+++ b/rf-notebook/README.md
@@ -0,0 +1,32 @@
+# RasterFrames Jupyter Notebook Docker Image
+
+RasterFrames provides a Docker image with a Jupyter Notebook pre-configured with RasterFrames in the PySpark kernel.
+
+## Launching RasterFrames Notebook from Source
+
+First, have Docker installed.
+
+Then, from the source root directory, run
+
+```bash
+sbt rf-notebook/startRFNotebook
+```
+
+This will build the project, create a Docker image, and start an instance.
+
+To stop it:
+
+```bash
+sbt rf-notebook/stopRFNotebook
+```
+
+The above commands are just conveniences. The real work is done by `docker-compose` via the [`docker-compose.yml`](src/main/docker/docker-compose.yml) file in `rf-notebook/src/main/docker/docker-compose.yml`. By default it exposes and binds to ports `8888` and `4040-4044`.
+
+
+## Docker Image Build
+
+If you just want to build the image:
+
+```bash
+sbt rf-notebook/publishLocal
+``` 
diff --git a/rf-notebook/build.sbt b/rf-notebook/build.sbt
new file mode 100644
index 000000000..7c94d8271
--- /dev/null
+++ b/rf-notebook/build.sbt
@@ -0,0 +1,55 @@
+import sbt.{IO, _}
+
+import scala.sys.process.Process
+
+Docker / packageName := "rasterframes-notebook"
+
+Docker / version := version.value
+
+Docker / maintainer := organization.value
+
+Docker / sourceDirectory := baseDirectory.value / "src"/ "main" / "docker"
+
+Docker / target := target.value / "docker"
+
+dockerUpdateLatest := true
+
+Docker / mappings := {
+  val dockerSrc = (Docker / sourceDirectory).value
+  val dockerAssets = (dockerSrc ** "*") pair Path.relativeTo(dockerSrc)
+
+  val jar = assembly.value
+
+  val py = (LocalProject("pyrasterframes") / Python / packageBin).value
+
+  dockerAssets ++ Seq(jar -> jar.getName, py -> py.getName)
+}
+
+// This bypasses the standard DockerPlugin DSL-based Dockerfile construction
+// and just copies the separate, external one.
+Docker / dockerGenerateConfig := (Docker / sourceDirectory).value / "Dockerfile"
+
+// Save a bit of typing...
+publishLocal := (Docker / publishLocal).value
+
+// -----== Conveniences ==-----
+
+lazy val startRFNotebook = Def.taskKey[Unit]("Build and run a RasterFrames Notebook Docker container")
+lazy val quickStartRFNotebook = Def.taskKey[Unit]("Run RasterFrames Notebook Docker container without building it first")
+lazy val stopRFNotebook = Def.taskKey[Unit]("Stop a running RasterFrames Notebook instance")
+
+startRFNotebook := {
+  val _ = (Docker / publishLocal).value
+  val staging = (Docker / stagingDirectory).value
+  Process("docker-compose up --force-recreate -d", staging).!
+}
+
+quickStartRFNotebook := {
+  val staging = (Docker / stagingDirectory).value
+  Process("docker-compose up --force-recreate -d", staging).!
+}
+
+stopRFNotebook := {
+  val staging = (Docker / stagingDirectory).value
+  Process("docker-compose down", staging).!
+}
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
new file mode 100644
index 000000000..be5a2adeb
--- /dev/null
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -0,0 +1,45 @@
+FROM s22s/pyspark-notebook:spark-2.3.3-hadoop-2.7
+
+MAINTAINER Astraea, Inc.
+
+ENV RF_LIB_LOC /usr/local/rasterframes
+ENV RF_JAR $RF_LIB_LOC/rasterframes.jar
+ENV PY_RF_ZIP $RF_LIB_LOC/pyrasterframes.zip
+
+USER root
+
+RUN mkdir $RF_LIB_LOC
+
+RUN echo "spark.driver.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf  && \
+    echo "spark.executor.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf
+
+EXPOSE 4040 4041 4042 4043 4044
+
+ENV SPARK_OPTS $SPARK_OPTS \
+    --py-files $PY_RF_ZIP \
+    --jars $RF_JAR \
+    --driver-class-path $RF_JAR \
+    --conf spark.executor.extraClassPath=$RF_JAR
+
+ENV PYTHONPATH $PYTHONPATH:$PY_RF_ZIP
+
+# Sphinx (for Notebook->html)
+RUN conda install --quiet --yes \
+    anaconda sphinx nbsphinx shapely numpy
+
+# Cleanup pip residuals
+RUN rm -rf /home/$NB_USER/.local && \
+    fix-permissions $CONDA_DIR && \
+    fix-permissions /home/$NB_USER
+
+# Do these after the standard environment setup
+# since these change more regularly.
+COPY *.zip $PY_RF_ZIP
+COPY *.jar $RF_JAR
+
+RUN chown -R $NB_UID:$NB_GID $HOME
+
+USER $NB_UID
+
+COPY jupyter_notebook_config.py $HOME/.jupyter
+
diff --git a/deployment/docker/jupyter/docker-compose.yml b/rf-notebook/src/main/docker/docker-compose.yml
similarity index 67%
rename from deployment/docker/jupyter/docker-compose.yml
rename to rf-notebook/src/main/docker/docker-compose.yml
index 29f311c0f..a093ca7fd 100644
--- a/deployment/docker/jupyter/docker-compose.yml
+++ b/rf-notebook/src/main/docker/docker-compose.yml
@@ -1,9 +1,8 @@
 version: '3'
 
 services:
-  rasterframes-notebooks:
-    build: .
-    image: s22s/rasterframes-notebooks
+  rasterframes-notebook:
+    image: rasterframes-notebook
     ports:
       # jupyter notebook port
       - "8888:8888"
@@ -14,5 +13,5 @@ services:
       - "4043:4043"
       - "4044:4044"
 # To save locally at './work' from the container:
-#    volumes:
-#      - ./work:/home/jovyan/work
\ No newline at end of file
+    volumes:
+      - ../../../src/main/notebooks:/home/jovyan/work
\ No newline at end of file
diff --git a/rf-notebook/src/main/docker/jupyter_notebook_config.py b/rf-notebook/src/main/docker/jupyter_notebook_config.py
new file mode 100644
index 000000000..ceb183c50
--- /dev/null
+++ b/rf-notebook/src/main/docker/jupyter_notebook_config.py
@@ -0,0 +1,766 @@
+# Configuration file for PyRasterFrames-enabled jupyter-notebook.
+
+#------------------------------------------------------------------------------
+# Application(SingletonConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## This is an application.
+
+## The date format used by logging formatters for %(asctime)s
+#c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S'
+
+## The Logging format template
+#c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s'
+
+## Set the log level by value or name.
+#c.Application.log_level = 30
+
+#------------------------------------------------------------------------------
+# JupyterApp(Application) configuration
+#------------------------------------------------------------------------------
+
+## Base class for Jupyter applications
+
+## Answer yes to any prompts.
+#c.JupyterApp.answer_yes = False
+
+## Full path of a config file.
+#c.JupyterApp.config_file = ''
+
+## Specify a config file to load.
+#c.JupyterApp.config_file_name = ''
+
+## Generate default config file.
+#c.JupyterApp.generate_config = False
+
+#------------------------------------------------------------------------------
+# NotebookApp(JupyterApp) configuration
+#------------------------------------------------------------------------------
+
+## Set the Access-Control-Allow-Credentials: true header
+#c.NotebookApp.allow_credentials = False
+
+## Set the Access-Control-Allow-Origin header
+#  
+#  Use '*' to allow any origin to access your server.
+#  
+#  Takes precedence over allow_origin_pat.
+#c.NotebookApp.allow_origin = ''
+
+## Use a regular expression for the Access-Control-Allow-Origin header
+#  
+#  Requests from an origin matching the expression will get replies with:
+#  
+#      Access-Control-Allow-Origin: origin
+#  
+#  where `origin` is the origin of the request.
+#  
+#  Ignored if allow_origin is set.
+#c.NotebookApp.allow_origin_pat = ''
+
+## Allow password to be changed at login for the notebook server.
+#  
+#  While loggin in with a token, the notebook server UI will give the opportunity
+#  to the user to enter a new password at the same time that will replace the
+#  token login mechanism.
+#  
+#  This can be set to false to prevent changing password from the UI/API.
+#c.NotebookApp.allow_password_change = True
+
+## Allow requests where the Host header doesn't point to a local server
+#  
+#  By default, requests get a 403 forbidden response if the 'Host' header shows
+#  that the browser thinks it's on a non-local domain. Setting this option to
+#  True disables this check.
+#  
+#  This protects against 'DNS rebinding' attacks, where a remote web server
+#  serves you a page and then changes its DNS to send later requests to a local
+#  IP, bypassing same-origin checks.
+#  
+#  Local IP addresses (such as 127.0.0.1 and ::1) are allowed as local, along
+#  with hostnames configured in local_hostnames.
+#c.NotebookApp.allow_remote_access = False
+
+## Whether to allow the user to run the notebook as root.
+#c.NotebookApp.allow_root = False
+
+## DEPRECATED use base_url
+#c.NotebookApp.base_project_url = '/'
+
+## The base URL for the notebook server.
+#  
+#  Leading and trailing slashes can be omitted, and will automatically be added.
+#c.NotebookApp.base_url = '/'
+
+## Specify what command to use to invoke a web browser when opening the notebook.
+#  If not specified, the default browser will be determined by the `webbrowser`
+#  standard library module, which allows setting of the BROWSER environment
+#  variable to override it.
+#c.NotebookApp.browser = ''
+
+## The full path to an SSL/TLS certificate file.
+#c.NotebookApp.certfile = ''
+
+## The full path to a certificate authority certificate for SSL/TLS client
+#  authentication.
+#c.NotebookApp.client_ca = ''
+
+## The config manager class to use
+#c.NotebookApp.config_manager_class = 'notebook.services.config.manager.ConfigManager'
+
+## The notebook manager class to use.
+#c.NotebookApp.contents_manager_class = 'notebook.services.contents.largefilemanager.LargeFileManager'
+
+## Extra keyword arguments to pass to `set_secure_cookie`. See tornado's
+#  set_secure_cookie docs for details.
+#c.NotebookApp.cookie_options = {}
+
+## The random bytes used to secure cookies. By default this is a new random
+#  number every time you start the Notebook. Set it to a value in a config file
+#  to enable logins to persist across server sessions.
+#  
+#  Note: Cookie secrets should be kept private, do not share config files with
+#  cookie_secret stored in plaintext (you can read the value from a file).
+#c.NotebookApp.cookie_secret = b''
+
+## The file where the cookie secret is stored.
+#c.NotebookApp.cookie_secret_file = ''
+
+## Override URL shown to users.
+#  
+#  Replace actual URL, including protocol, address, port and base URL, with the
+#  given value when displaying URL to the users. Do not change the actual
+#  connection URL. If authentication token is enabled, the token is added to the
+#  custom URL automatically.
+#  
+#  This option is intended to be used when the URL to display to the user cannot
+#  be determined reliably by the Jupyter notebook server (proxified or
+#  containerized setups for example).
+#c.NotebookApp.custom_display_url = ''
+
+## The default URL to redirect to from `/`
+#c.NotebookApp.default_url = '/tree'
+
+## Disable cross-site-request-forgery protection
+#  
+#  Jupyter notebook 4.3.1 introduces protection from cross-site request
+#  forgeries, requiring API requests to either:
+#  
+#  - originate from pages served by this server (validated with XSRF cookie and
+#  token), or - authenticate with a token
+#  
+#  Some anonymous compute resources still desire the ability to run code,
+#  completely without authentication. These services can disable all
+#  authentication and security checks, with the full knowledge of what that
+#  implies.
+#c.NotebookApp.disable_check_xsrf = False
+
+## Whether to enable MathJax for typesetting math/TeX
+#  
+#  MathJax is the javascript library Jupyter uses to render math/LaTeX. It is
+#  very large, so you may want to disable it if you have a slow internet
+#  connection, or for offline use of the notebook.
+#  
+#  When disabled, equations etc. will appear as their untransformed TeX source.
+#c.NotebookApp.enable_mathjax = True
+
+## extra paths to look for Javascript notebook extensions
+#c.NotebookApp.extra_nbextensions_path = []
+
+## handlers that should be loaded at higher priority than the default services
+#c.NotebookApp.extra_services = []
+
+## Extra paths to search for serving static files.
+#  
+#  This allows adding javascript/css to be available from the notebook server
+#  machine, or overriding individual files in the IPython
+#c.NotebookApp.extra_static_paths = []
+
+## Extra paths to search for serving jinja templates.
+#  
+#  Can be used to override templates from notebook.templates.
+#c.NotebookApp.extra_template_paths = []
+
+## 
+#c.NotebookApp.file_to_run = ''
+
+## Extra keyword arguments to pass to `get_secure_cookie`. See tornado's
+#  get_secure_cookie docs for details.
+#c.NotebookApp.get_secure_cookie_kwargs = {}
+
+## Deprecated: Use minified JS file or not, mainly use during dev to avoid JS
+#  recompilation
+#c.NotebookApp.ignore_minified_js = False
+
+## (bytes/sec) Maximum rate at which stream output can be sent on iopub before
+#  they are limited.
+#c.NotebookApp.iopub_data_rate_limit = 1000000
+
+## (msgs/sec) Maximum rate at which messages can be sent on iopub before they are
+#  limited.
+#c.NotebookApp.iopub_msg_rate_limit = 1000
+
+## The IP address the notebook server will listen on.
+# c.NotebookApp.ip = 'localhost'
+
+## Supply extra arguments that will be passed to Jinja environment.
+#c.NotebookApp.jinja_environment_options = {}
+
+## Extra variables to supply to jinja templates when rendering.
+#c.NotebookApp.jinja_template_vars = {}
+
+## The kernel manager class to use.
+#c.NotebookApp.kernel_manager_class = 'notebook.services.kernels.kernelmanager.MappingKernelManager'
+
+## The kernel spec manager class to use. Should be a subclass of
+#  `jupyter_client.kernelspec.KernelSpecManager`.
+#  
+#  The Api of KernelSpecManager is provisional and might change without warning
+#  between this version of Jupyter and the next stable one.
+#c.NotebookApp.kernel_spec_manager_class = 'jupyter_client.kernelspec.KernelSpecManager'
+
+## The full path to a private key file for usage with SSL/TLS.
+#c.NotebookApp.keyfile = ''
+
+## Hostnames to allow as local when allow_remote_access is False.
+#  
+#  Local IP addresses (such as 127.0.0.1 and ::1) are automatically accepted as
+#  local as well.
+#c.NotebookApp.local_hostnames = ['localhost']
+
+## The login handler class to use.
+#c.NotebookApp.login_handler_class = 'notebook.auth.login.LoginHandler'
+
+## The logout handler class to use.
+#c.NotebookApp.logout_handler_class = 'notebook.auth.logout.LogoutHandler'
+
+## The MathJax.js configuration file that is to be used.
+#c.NotebookApp.mathjax_config = 'TeX-AMS-MML_HTMLorMML-full,Safe'
+
+## A custom url for MathJax.js. Should be in the form of a case-sensitive url to
+#  MathJax, for example:  /static/components/MathJax/MathJax.js
+#c.NotebookApp.mathjax_url = ''
+
+## Sets the maximum allowed size of the client request body, specified in  the
+#  Content-Length request header field. If the size in a request  exceeds the
+#  configured value, a malformed HTTP message is returned to the client.
+#  
+#  Note: max_body_size is applied even in streaming mode.
+#c.NotebookApp.max_body_size = 536870912
+
+## Gets or sets the maximum amount of memory, in bytes, that is allocated  for
+#  use by the buffer manager.
+#c.NotebookApp.max_buffer_size = 536870912
+
+## Dict of Python modules to load as notebook server extensions.Entry values can
+#  be used to enable and disable the loading ofthe extensions. The extensions
+#  will be loaded in alphabetical order.
+#c.NotebookApp.nbserver_extensions = {}
+
+## The directory to use for notebooks and kernels.
+#c.NotebookApp.notebook_dir = ''
+
+## Whether to open in a browser after starting. The specific browser used is
+#  platform dependent and determined by the python standard library `webbrowser`
+#  module, unless it is overridden using the --browser (NotebookApp.browser)
+#  configuration option.
+#c.NotebookApp.open_browser = True
+
+## Hashed password to use for web authentication.
+#  
+#  To generate, type in a python/IPython shell:
+#  
+#    from notebook.auth import passwd; passwd()
+#  
+#  The string should be of the form type:salt:hashed-password.
+#c.NotebookApp.password = ''
+
+## Forces users to use a password for the Notebook server. This is useful in a
+#  multi user environment, for instance when everybody in the LAN can access each
+#  other's machine through ssh.
+#  
+#  In such a case, server the notebook server on localhost is not secure since
+#  any user can connect to the notebook server via ssh.
+#c.NotebookApp.password_required = False
+
+## The port the notebook server will listen on.
+#c.NotebookApp.port = 8888
+
+## The number of additional ports to try if the specified port is not available.
+#c.NotebookApp.port_retries = 50
+
+## DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib.
+#c.NotebookApp.pylab = 'disabled'
+
+## If True, display a button in the dashboard to quit (shutdown the notebook
+#  server).
+#c.NotebookApp.quit_button = True
+
+## (sec) Time window used to  check the message and data rate limits.
+#c.NotebookApp.rate_limit_window = 3
+
+## Reraise exceptions encountered loading server extensions?
+#c.NotebookApp.reraise_server_extension_failures = False
+
+## DEPRECATED use the nbserver_extensions dict instead
+#c.NotebookApp.server_extensions = []
+
+## The session manager class to use.
+#c.NotebookApp.session_manager_class = 'notebook.services.sessions.sessionmanager.SessionManager'
+
+## Shut down the server after N seconds with no kernels or terminals running and
+#  no activity. This can be used together with culling idle kernels
+#  (MappingKernelManager.cull_idle_timeout) to shutdown the notebook server when
+#  it's not in use. This is not precisely timed: it may shut down up to a minute
+#  later. 0 (the default) disables this automatic shutdown.
+#c.NotebookApp.shutdown_no_activity_timeout = 0
+
+## Supply SSL options for the tornado HTTPServer. See the tornado docs for
+#  details.
+#c.NotebookApp.ssl_options = {}
+
+## Supply overrides for terminado. Currently only supports "shell_command".
+#c.NotebookApp.terminado_settings = {}
+
+## Set to False to disable terminals.
+#  
+#  This does *not* make the notebook server more secure by itself. Anything the
+#  user can in a terminal, they can also do in a notebook.
+#  
+#  Terminals may also be automatically disabled if the terminado package is not
+#  available.
+#c.NotebookApp.terminals_enabled = True
+
+## Token used for authenticating first-time connections to the server.
+#  
+#  When no password is enabled, the default is to generate a new, random token.
+#  
+#  Setting to an empty string disables authentication altogether, which is NOT
+#  RECOMMENDED.
+#c.NotebookApp.token = '<generated>'
+c.NotebookApp.token = ''
+
+## Supply overrides for the tornado.web.Application that the Jupyter notebook
+#  uses.
+#c.NotebookApp.tornado_settings = {}
+
+## Whether to trust or not X-Scheme/X-Forwarded-Proto and X-Real-Ip/X-Forwarded-
+#  For headerssent by the upstream reverse proxy. Necessary if the proxy handles
+#  SSL
+#c.NotebookApp.trust_xheaders = False
+
+## DEPRECATED, use tornado_settings
+#c.NotebookApp.webapp_settings = {}
+
+## Specify Where to open the notebook on startup. This is the `new` argument
+#  passed to the standard library method `webbrowser.open`. The behaviour is not
+#  guaranteed, but depends on browser support. Valid values are:
+#  
+#   - 2 opens a new tab,
+#   - 1 opens a new window,
+#   - 0 opens in an existing window.
+#  
+#  See the `webbrowser.open` documentation for details.
+#c.NotebookApp.webbrowser_open_new = 2
+
+## Set the tornado compression options for websocket connections.
+#  
+#  This value will be returned from
+#  :meth:`WebSocketHandler.get_compression_options`. None (default) will disable
+#  compression. A dict (even an empty one) will enable compression.
+#  
+#  See the tornado docs for WebSocketHandler.get_compression_options for details.
+#c.NotebookApp.websocket_compression_options = None
+
+## The base URL for websockets, if it differs from the HTTP server (hint: it
+#  almost certainly doesn't).
+#  
+#  Should be in the form of an HTTP origin: ws[s]://hostname[:port]
+#c.NotebookApp.websocket_url = ''
+
+#------------------------------------------------------------------------------
+# ConnectionFileMixin(LoggingConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## Mixin for configurable classes that work with connection files
+
+## JSON file in which to store connection info [default: kernel-<pid>.json]
+#  
+#  This file will contain the IP, ports, and authentication key needed to connect
+#  clients to this kernel. By default, this file will be created in the security
+#  dir of the current profile, but can be specified by absolute path.
+#c.ConnectionFileMixin.connection_file = ''
+
+## set the control (ROUTER) port [default: random]
+#c.ConnectionFileMixin.control_port = 0
+
+## set the heartbeat port [default: random]
+#c.ConnectionFileMixin.hb_port = 0
+
+## set the iopub (PUB) port [default: random]
+#c.ConnectionFileMixin.iopub_port = 0
+
+## Set the kernel's IP address [default localhost]. If the IP address is
+#  something other than localhost, then Consoles on other machines will be able
+#  to connect to the Kernel, so be careful!
+#c.ConnectionFileMixin.ip = ''
+
+## set the shell (ROUTER) port [default: random]
+#c.ConnectionFileMixin.shell_port = 0
+
+## set the stdin (ROUTER) port [default: random]
+#c.ConnectionFileMixin.stdin_port = 0
+
+## 
+#c.ConnectionFileMixin.transport = 'tcp'
+
+#------------------------------------------------------------------------------
+# KernelManager(ConnectionFileMixin) configuration
+#------------------------------------------------------------------------------
+
+## Manages a single kernel in a subprocess on this host.
+#  
+#  This version starts kernels with Popen.
+
+## Should we autorestart the kernel if it dies.
+#c.KernelManager.autorestart = True
+
+## DEPRECATED: Use kernel_name instead.
+#  
+#  The Popen Command to launch the kernel. Override this if you have a custom
+#  kernel. If kernel_cmd is specified in a configuration file, Jupyter does not
+#  pass any arguments to the kernel, because it cannot make any assumptions about
+#  the arguments that the kernel understands. In particular, this means that the
+#  kernel does not receive the option --debug if it given on the Jupyter command
+#  line.
+#c.KernelManager.kernel_cmd = []
+
+## Time to wait for a kernel to terminate before killing it, in seconds.
+#c.KernelManager.shutdown_wait_time = 5.0
+
+#------------------------------------------------------------------------------
+# Session(Configurable) configuration
+#------------------------------------------------------------------------------
+
+## Object for handling serialization and sending of messages.
+#  
+#  The Session object handles building messages and sending them with ZMQ sockets
+#  or ZMQStream objects.  Objects can communicate with each other over the
+#  network via Session objects, and only need to work with the dict-based IPython
+#  message spec. The Session will handle serialization/deserialization, security,
+#  and metadata.
+#  
+#  Sessions support configurable serialization via packer/unpacker traits, and
+#  signing with HMAC digests via the key/keyfile traits.
+#  
+#  Parameters ----------
+#  
+#  debug : bool
+#      whether to trigger extra debugging statements
+#  packer/unpacker : str : 'json', 'pickle' or import_string
+#      importstrings for methods to serialize message parts.  If just
+#      'json' or 'pickle', predefined JSON and pickle packers will be used.
+#      Otherwise, the entire importstring must be used.
+#  
+#      The functions must accept at least valid JSON input, and output *bytes*.
+#  
+#      For example, to use msgpack:
+#      packer = 'msgpack.packb', unpacker='msgpack.unpackb'
+#  pack/unpack : callables
+#      You can also set the pack/unpack callables for serialization directly.
+#  session : bytes
+#      the ID of this Session object.  The default is to generate a new UUID.
+#  username : unicode
+#      username added to message headers.  The default is to ask the OS.
+#  key : bytes
+#      The key used to initialize an HMAC signature.  If unset, messages
+#      will not be signed or checked.
+#  keyfile : filepath
+#      The file containing a key.  If this is set, `key` will be initialized
+#      to the contents of the file.
+
+## Threshold (in bytes) beyond which an object's buffer should be extracted to
+#  avoid pickling.
+#c.Session.buffer_threshold = 1024
+
+## Whether to check PID to protect against calls after fork.
+#  
+#  This check can be disabled if fork-safety is handled elsewhere.
+#c.Session.check_pid = True
+
+## Threshold (in bytes) beyond which a buffer should be sent without copying.
+#c.Session.copy_threshold = 65536
+
+## Debug output in the Session
+#c.Session.debug = False
+
+## The maximum number of digests to remember.
+#  
+#  The digest history will be culled when it exceeds this value.
+#c.Session.digest_history_size = 65536
+
+## The maximum number of items for a container to be introspected for custom
+#  serialization. Containers larger than this are pickled outright.
+#c.Session.item_threshold = 64
+
+## execution key, for signing messages.
+#c.Session.key = b''
+
+## path to file containing execution key.
+#c.Session.keyfile = ''
+
+## Metadata dictionary, which serves as the default top-level metadata dict for
+#  each message.
+#c.Session.metadata = {}
+
+## The name of the packer for serializing messages. Should be one of 'json',
+#  'pickle', or an import name for a custom callable serializer.
+#c.Session.packer = 'json'
+
+## The UUID identifying this session.
+#c.Session.session = ''
+
+## The digest scheme used to construct the message signatures. Must have the form
+#  'hmac-HASH'.
+#c.Session.signature_scheme = 'hmac-sha256'
+
+## The name of the unpacker for unserializing messages. Only used with custom
+#  functions for `packer`.
+#c.Session.unpacker = 'json'
+
+## Username for the Session. Default is your system username.
+#c.Session.username = 'username'
+
+#------------------------------------------------------------------------------
+# MultiKernelManager(LoggingConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## A class for managing multiple kernels.
+
+## The name of the default kernel to start
+#c.MultiKernelManager.default_kernel_name = 'python3'
+
+## The kernel manager class.  This is configurable to allow subclassing of the
+#  KernelManager for customized behavior.
+#c.MultiKernelManager.kernel_manager_class = 'jupyter_client.ioloop.IOLoopKernelManager'
+
+#------------------------------------------------------------------------------
+# MappingKernelManager(MultiKernelManager) configuration
+#------------------------------------------------------------------------------
+
+## A KernelManager that handles notebook mapping and HTTP error handling
+
+## Whether messages from kernels whose frontends have disconnected should be
+#  buffered in-memory.
+#  
+#  When True (default), messages are buffered and replayed on reconnect, avoiding
+#  lost messages due to interrupted connectivity.
+#  
+#  Disable if long-running kernels will produce too much output while no
+#  frontends are connected.
+#c.MappingKernelManager.buffer_offline_messages = True
+
+## Whether to consider culling kernels which are busy. Only effective if
+#  cull_idle_timeout > 0.
+#c.MappingKernelManager.cull_busy = False
+
+## Whether to consider culling kernels which have one or more connections. Only
+#  effective if cull_idle_timeout > 0.
+#c.MappingKernelManager.cull_connected = False
+
+## Timeout (in seconds) after which a kernel is considered idle and ready to be
+#  culled. Values of 0 or lower disable culling. Very short timeouts may result
+#  in kernels being culled for users with poor network connections.
+#c.MappingKernelManager.cull_idle_timeout = 0
+
+## The interval (in seconds) on which to check for idle kernels exceeding the
+#  cull timeout value.
+#c.MappingKernelManager.cull_interval = 300
+
+## Timeout for giving up on a kernel (in seconds).
+#  
+#  On starting and restarting kernels, we check whether the kernel is running and
+#  responsive by sending kernel_info_requests. This sets the timeout in seconds
+#  for how long the kernel can take before being presumed dead.  This affects the
+#  MappingKernelManager (which handles kernel restarts)  and the
+#  ZMQChannelsHandler (which handles the startup).
+#c.MappingKernelManager.kernel_info_timeout = 60
+
+## 
+#c.MappingKernelManager.root_dir = ''
+
+#------------------------------------------------------------------------------
+# ContentsManager(LoggingConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## Base class for serving files and directories.
+#  
+#  This serves any text or binary file, as well as directories, with special
+#  handling for JSON notebook documents.
+#  
+#  Most APIs take a path argument, which is always an API-style unicode path, and
+#  always refers to a directory.
+#  
+#  - unicode, not url-escaped
+#  - '/'-separated
+#  - leading and trailing '/' will be stripped
+#  - if unspecified, path defaults to '',
+#    indicating the root path.
+
+## Allow access to hidden files
+#c.ContentsManager.allow_hidden = False
+
+## 
+#c.ContentsManager.checkpoints = None
+
+## 
+#c.ContentsManager.checkpoints_class = 'notebook.services.contents.checkpoints.Checkpoints'
+
+## 
+#c.ContentsManager.checkpoints_kwargs = {}
+
+## handler class to use when serving raw file requests.
+#  
+#  Default is a fallback that talks to the ContentsManager API, which may be
+#  inefficient, especially for large files.
+#  
+#  Local files-based ContentsManagers can use a StaticFileHandler subclass, which
+#  will be much more efficient.
+#  
+#  Access to these files should be Authenticated.
+#c.ContentsManager.files_handler_class = 'notebook.files.handlers.FilesHandler'
+
+## Extra parameters to pass to files_handler_class.
+#  
+#  For example, StaticFileHandlers generally expect a `path` argument specifying
+#  the root directory from which to serve files.
+#c.ContentsManager.files_handler_params = {}
+
+## Glob patterns to hide in file and directory listings.
+#c.ContentsManager.hide_globs = ['__pycache__', '*.pyc', '*.pyo', '.DS_Store', '*.so', '*.dylib', '*~']
+
+## Python callable or importstring thereof
+#  
+#  To be called on a contents model prior to save.
+#  
+#  This can be used to process the structure, such as removing notebook outputs
+#  or other side effects that should not be saved.
+#  
+#  It will be called as (all arguments passed by keyword)::
+#  
+#      hook(path=path, model=model, contents_manager=self)
+#  
+#  - model: the model to be saved. Includes file contents.
+#    Modifying this dict will affect the file that is stored.
+#  - path: the API path of the save destination
+#  - contents_manager: this ContentsManager instance
+#c.ContentsManager.pre_save_hook = None
+
+## 
+#c.ContentsManager.root_dir = '/'
+
+## The base name used when creating untitled directories.
+#c.ContentsManager.untitled_directory = 'Untitled Folder'
+
+## The base name used when creating untitled files.
+#c.ContentsManager.untitled_file = 'untitled'
+
+## The base name used when creating untitled notebooks.
+#c.ContentsManager.untitled_notebook = 'Untitled'
+
+#------------------------------------------------------------------------------
+# FileManagerMixin(Configurable) configuration
+#------------------------------------------------------------------------------
+
+## Mixin for ContentsAPI classes that interact with the filesystem.
+#  
+#  Provides facilities for reading, writing, and copying both notebooks and
+#  generic files.
+#  
+#  Shared by FileContentsManager and FileCheckpoints.
+#  
+#  Note ---- Classes using this mixin must provide the following attributes:
+#  
+#  root_dir : unicode
+#      A directory against against which API-style paths are to be resolved.
+#  
+#  log : logging.Logger
+
+## By default notebooks are saved on disk on a temporary file and then if
+#  succefully written, it replaces the old ones. This procedure, namely
+#  'atomic_writing', causes some bugs on file system whitout operation order
+#  enforcement (like some networked fs). If set to False, the new notebook is
+#  written directly on the old one which could fail (eg: full filesystem or quota
+#  )
+#c.FileManagerMixin.use_atomic_writing = True
+
+#------------------------------------------------------------------------------
+# FileContentsManager(FileManagerMixin,ContentsManager) configuration
+#------------------------------------------------------------------------------
+
+## If True (default), deleting files will send them to the platform's
+#  trash/recycle bin, where they can be recovered. If False, deleting files
+#  really deletes them.
+#c.FileContentsManager.delete_to_trash = True
+
+## Python callable or importstring thereof
+#  
+#  to be called on the path of a file just saved.
+#  
+#  This can be used to process the file on disk, such as converting the notebook
+#  to a script or HTML via nbconvert.
+#  
+#  It will be called as (all arguments passed by keyword)::
+#  
+#      hook(os_path=os_path, model=model, contents_manager=instance)
+#  
+#  - path: the filesystem path to the file just written - model: the model
+#  representing the file - contents_manager: this ContentsManager instance
+#c.FileContentsManager.post_save_hook = None
+
+## 
+#c.FileContentsManager.root_dir = ''
+
+## DEPRECATED, use post_save_hook. Will be removed in Notebook 5.0
+#c.FileContentsManager.save_script = False
+
+#------------------------------------------------------------------------------
+# NotebookNotary(LoggingConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## A class for computing and verifying notebook signatures.
+
+## The hashing algorithm used to sign notebooks.
+#c.NotebookNotary.algorithm = 'sha256'
+
+## The sqlite file in which to store notebook signatures. By default, this will
+#  be in your Jupyter data directory. You can set it to ':memory:' to disable
+#  sqlite writing to the filesystem.
+#c.NotebookNotary.db_file = ''
+
+## The secret key with which notebooks are signed.
+#c.NotebookNotary.secret = b''
+
+## The file where the secret key is stored.
+#c.NotebookNotary.secret_file = ''
+
+## A callable returning the storage backend for notebook signatures. The default
+#  uses an SQLite database.
+#c.NotebookNotary.store_factory = traitlets.Undefined
+
+#------------------------------------------------------------------------------
+# KernelSpecManager(LoggingConfigurable) configuration
+#------------------------------------------------------------------------------
+
+## If there is no Python kernelspec registered and the IPython kernel is
+#  available, ensure it is added to the spec list.
+#c.KernelSpecManager.ensure_native_kernel = True
+
+## The kernel spec class.  This is configurable to allow subclassing of the
+#  KernelSpecManager for customized behavior.
+#c.KernelSpecManager.kernel_spec_class = 'jupyter_client.kernelspec.KernelSpec'
+
+## Whitelist of allowed kernel names.
+#  
+#  By default, all installed kernels are allowed.
+#c.KernelSpecManager.whitelist = set()
diff --git a/rf-notebook/src/main/notebooks/Getting Started.ipynb b/rf-notebook/src/main/notebooks/Getting Started.ipynb
new file mode 100644
index 000000000..d5d615664
--- /dev/null
+++ b/rf-notebook/src/main/notebooks/Getting Started.ipynb	
@@ -0,0 +1,964 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting Started with RasterFrames Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Spark Environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "from pyrasterframes import *\n",
+    "from pyrasterframes.rasterfunctions import *\n",
+    "\n",
+    "spark = SparkSession.builder \\\n",
+    "    .withKryoSerialization() \\\n",
+    "    .getOrCreate() \\\n",
+    "    .withRasterFrames()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'{\"builtAtMillis\":\"1557330142297\", \"name\":\"core\", \"rfSparkVersion\":\"2.3.2\", \"moduleName\":\"rasterframes\", \"scalaVersion\":\"2.11.12\", \"rfGeoTrellisVersion\":\"2.2.0\", \"version\":\"0.8.0-SNAPSHOT\", \"sbtVersion\":\"1.2.8\", \"builtAtString\":\"2019-05-08 15:42:22.297\", \"rfGeoMesaVersion\":\"2.2.1\"}'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyrasterframes import RFContext\n",
+    "RFContext.active().build_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read an image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "href = \"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\"\n",
+    "df = spark.read.rastersource(href)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- tile_path: string (nullable = false)\n",
+      " |-- tile: struct (nullable = true)\n",
+      " |    |-- tileContext: struct (nullable = false)\n",
+      " |    |    |-- extent: struct (nullable = false)\n",
+      " |    |    |    |-- xmin: double (nullable = false)\n",
+      " |    |    |    |-- ymin: double (nullable = false)\n",
+      " |    |    |    |-- xmax: double (nullable = false)\n",
+      " |    |    |    |-- ymax: double (nullable = false)\n",
+      " |    |    |-- crs: struct (nullable = false)\n",
+      " |    |    |    |-- crsProj4: string (nullable = false)\n",
+      " |    |-- tile: tile (nullable = false)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rf_extent(tile)</th>\n",
+       "      <th>crs(tile)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(14455356.755667, -2342509.0947641465, 1457396...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(14573964.81109804, -2342509.0947641465, 14692...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(14692572.86652908, -2342509.0947641465, 14811...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(14811180.92196012, -2342509.0947641465, 14929...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(14929788.97739116, -2342509.0947641465, 15048...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(15048397.0328222, -2342509.0947641465, 151670...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(15167005.08825324, -2342509.0947641465, 15285...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(15285613.14368428, -2342509.0947641465, 15404...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(15404221.199115321, -2342509.0947641465, 1552...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(15522829.254546361, -2342509.0947641465, 1556...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>(14455356.755667, -2461117.150195293, 14573964...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>(14573964.81109804, -2461117.150195293, 146925...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>(14692572.86652908, -2461117.150195293, 148111...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>(14811180.92196012, -2461117.150195293, 149297...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>(14929788.97739116, -2461117.150195293, 150483...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>(15048397.0328222, -2461117.150195293, 1516700...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>(15167005.08825324, -2461117.150195293, 152856...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>(15285613.14368428, -2461117.150195293, 154042...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>(15404221.199115321, -2461117.150195293, 15522...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>(15522829.254546361, -2461117.150195293, 15567...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(14455356.755667, -2579725.20562644, 14573964....</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(14573964.81109804, -2579725.20562644, 1469257...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(14692572.86652908, -2579725.20562644, 1481118...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>(14811180.92196012, -2579725.20562644, 1492978...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>(14929788.97739116, -2579725.20562644, 1504839...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>(15048397.0328222, -2579725.20562644, 15167005...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>(15167005.08825324, -2579725.20562644, 1528561...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>(15285613.14368428, -2579725.20562644, 1540422...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>(15404221.199115321, -2579725.20562644, 155228...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>(15522829.254546361, -2579725.20562644, 155673...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>(14455356.755667, -3172765.482782173, 14573964...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>(14573964.81109804, -3172765.482782173, 146925...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>(14692572.86652908, -3172765.482782173, 148111...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>(14811180.92196012, -3172765.482782173, 149297...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>(14929788.97739116, -3172765.482782173, 150483...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>(15048397.0328222, -3172765.482782173, 1516700...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>(15167005.08825324, -3172765.482782173, 152856...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>(15285613.14368428, -3172765.482782173, 154042...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>(15404221.199115321, -3172765.482782173, 15522...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>(15522829.254546361, -3172765.482782173, 15567...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>(14455356.755667, -3291373.53821332, 14573964....</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>(14573964.81109804, -3291373.53821332, 1469257...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>(14692572.86652908, -3291373.53821332, 1481118...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>(14811180.92196012, -3291373.53821332, 1492978...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>(14929788.97739116, -3291373.53821332, 1504839...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>(15048397.0328222, -3291373.53821332, 15167005...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>(15167005.08825324, -3291373.53821332, 1528561...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>(15285613.14368428, -3291373.53821332, 1540422...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>(15404221.199115321, -3291373.53821332, 155228...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>(15522829.254546361, -3291373.53821332, 155673...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>(14455356.755667, -3335851.559, 14573964.81109...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>(14573964.81109804, -3335851.559, 14692572.866...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>(14692572.86652908, -3335851.559, 14811180.921...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>(14811180.92196012, -3335851.559, 14929788.977...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>(14929788.97739116, -3335851.559, 15048397.032...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>(15048397.0328222, -3335851.559, 15167005.0882...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>(15167005.08825324, -3335851.559, 15285613.143...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>(15285613.14368428, -3335851.559, 15404221.199...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>(15404221.199115321, -3335851.559, 15522829.25...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>(15522829.254546361, -3335851.559, 15567307.27...</td>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      rf_extent(tile)  \\\n",
+       "0   (14455356.755667, -2342509.0947641465, 1457396...   \n",
+       "1   (14573964.81109804, -2342509.0947641465, 14692...   \n",
+       "2   (14692572.86652908, -2342509.0947641465, 14811...   \n",
+       "3   (14811180.92196012, -2342509.0947641465, 14929...   \n",
+       "4   (14929788.97739116, -2342509.0947641465, 15048...   \n",
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+       "6   (15167005.08825324, -2342509.0947641465, 15285...   \n",
+       "7   (15285613.14368428, -2342509.0947641465, 15404...   \n",
+       "8   (15404221.199115321, -2342509.0947641465, 1552...   \n",
+       "9   (15522829.254546361, -2342509.0947641465, 1556...   \n",
+       "10  (14455356.755667, -2461117.150195293, 14573964...   \n",
+       "11  (14573964.81109804, -2461117.150195293, 146925...   \n",
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+       "13  (14811180.92196012, -2461117.150195293, 149297...   \n",
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+       "18  (15404221.199115321, -2461117.150195293, 15522...   \n",
+       "19  (15522829.254546361, -2461117.150195293, 15567...   \n",
+       "20  (14455356.755667, -2579725.20562644, 14573964....   \n",
+       "21  (14573964.81109804, -2579725.20562644, 1469257...   \n",
+       "22  (14692572.86652908, -2579725.20562644, 1481118...   \n",
+       "23  (14811180.92196012, -2579725.20562644, 1492978...   \n",
+       "24  (14929788.97739116, -2579725.20562644, 1504839...   \n",
+       "25  (15048397.0328222, -2579725.20562644, 15167005...   \n",
+       "26  (15167005.08825324, -2579725.20562644, 1528561...   \n",
+       "27  (15285613.14368428, -2579725.20562644, 1540422...   \n",
+       "28  (15404221.199115321, -2579725.20562644, 155228...   \n",
+       "29  (15522829.254546361, -2579725.20562644, 155673...   \n",
+       "..                                                ...   \n",
+       "70  (14455356.755667, -3172765.482782173, 14573964...   \n",
+       "71  (14573964.81109804, -3172765.482782173, 146925...   \n",
+       "72  (14692572.86652908, -3172765.482782173, 148111...   \n",
+       "73  (14811180.92196012, -3172765.482782173, 149297...   \n",
+       "74  (14929788.97739116, -3172765.482782173, 150483...   \n",
+       "75  (15048397.0328222, -3172765.482782173, 1516700...   \n",
+       "76  (15167005.08825324, -3172765.482782173, 152856...   \n",
+       "77  (15285613.14368428, -3172765.482782173, 154042...   \n",
+       "78  (15404221.199115321, -3172765.482782173, 15522...   \n",
+       "79  (15522829.254546361, -3172765.482782173, 15567...   \n",
+       "80  (14455356.755667, -3291373.53821332, 14573964....   \n",
+       "81  (14573964.81109804, -3291373.53821332, 1469257...   \n",
+       "82  (14692572.86652908, -3291373.53821332, 1481118...   \n",
+       "83  (14811180.92196012, -3291373.53821332, 1492978...   \n",
+       "84  (14929788.97739116, -3291373.53821332, 1504839...   \n",
+       "85  (15048397.0328222, -3291373.53821332, 15167005...   \n",
+       "86  (15167005.08825324, -3291373.53821332, 1528561...   \n",
+       "87  (15285613.14368428, -3291373.53821332, 1540422...   \n",
+       "88  (15404221.199115321, -3291373.53821332, 155228...   \n",
+       "89  (15522829.254546361, -3291373.53821332, 155673...   \n",
+       "90  (14455356.755667, -3335851.559, 14573964.81109...   \n",
+       "91  (14573964.81109804, -3335851.559, 14692572.866...   \n",
+       "92  (14692572.86652908, -3335851.559, 14811180.921...   \n",
+       "93  (14811180.92196012, -3335851.559, 14929788.977...   \n",
+       "94  (14929788.97739116, -3335851.559, 15048397.032...   \n",
+       "95  (15048397.0328222, -3335851.559, 15167005.0882...   \n",
+       "96  (15167005.08825324, -3335851.559, 15285613.143...   \n",
+       "97  (15285613.14368428, -3335851.559, 15404221.199...   \n",
+       "98  (15404221.199115321, -3335851.559, 15522829.25...   \n",
+       "99  (15522829.254546361, -3335851.559, 15567307.27...   \n",
+       "\n",
+       "                                            crs(tile)  \n",
+       "0   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "1   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "2   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "3   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "4   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "5   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "6   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "7   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "8   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "9   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "10  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "11  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "12  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "13  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "14  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "15  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "16  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "17  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "18  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "19  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "20  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "21  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "22  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "23  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "24  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "25  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "26  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "27  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "28  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "29  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "..                                                ...  \n",
+       "70  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "71  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "72  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "73  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "74  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "75  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "76  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "77  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "78  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "79  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "80  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "81  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "82  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "83  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "84  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "85  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "86  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "87  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "88  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "89  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "90  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "91  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "92  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "93  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "94  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "95  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "96  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "97  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "98  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "99  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.select(rf_extent(\"tile\"), rf_crs(\"tile\")).toPandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(((14455356.755667, -2342509.0947641465, 14573...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(((14573964.81109804, -2342509.0947641465, 146...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(((14692572.86652908, -2342509.0947641465, 148...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(((14811180.92196012, -2342509.0947641465, 149...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(((14929788.97739116, -2342509.0947641465, 150...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(((15048397.0328222, -2342509.0947641465, 1516...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(((15167005.08825324, -2342509.0947641465, 152...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(((15285613.14368428, -2342509.0947641465, 154...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(((15404221.199115321, -2342509.0947641465, 15...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(((15522829.254546361, -2342509.0947641465, 15...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>(((14455356.755667, -2461117.150195293, 145739...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>(((14573964.81109804, -2461117.150195293, 1469...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>(((14692572.86652908, -2461117.150195293, 1481...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>(((14811180.92196012, -2461117.150195293, 1492...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>(((14929788.97739116, -2461117.150195293, 1504...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>(((15048397.0328222, -2461117.150195293, 15167...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>(((15167005.08825324, -2461117.150195293, 1528...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>(((15285613.14368428, -2461117.150195293, 1540...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>(((15404221.199115321, -2461117.150195293, 155...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>(((15522829.254546361, -2461117.150195293, 155...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(((14455356.755667, -2579725.20562644, 1457396...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(((14573964.81109804, -2579725.20562644, 14692...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(((14692572.86652908, -2579725.20562644, 14811...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>(((14811180.92196012, -2579725.20562644, 14929...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>(((14929788.97739116, -2579725.20562644, 15048...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>(((15048397.0328222, -2579725.20562644, 151670...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>(((15167005.08825324, -2579725.20562644, 15285...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>(((15285613.14368428, -2579725.20562644, 15404...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>(((15404221.199115321, -2579725.20562644, 1552...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>(((15522829.254546361, -2579725.20562644, 1556...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>(((14455356.755667, -3172765.482782173, 145739...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>(((14573964.81109804, -3172765.482782173, 1469...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>(((14692572.86652908, -3172765.482782173, 1481...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>(((14811180.92196012, -3172765.482782173, 1492...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>(((14929788.97739116, -3172765.482782173, 1504...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>(((15048397.0328222, -3172765.482782173, 15167...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>(((15167005.08825324, -3172765.482782173, 1528...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>(((15285613.14368428, -3172765.482782173, 1540...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>(((15404221.199115321, -3172765.482782173, 155...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>(((15522829.254546361, -3172765.482782173, 155...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>(((14455356.755667, -3291373.53821332, 1457396...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>(((14573964.81109804, -3291373.53821332, 14692...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>(((14692572.86652908, -3291373.53821332, 14811...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>(((14811180.92196012, -3291373.53821332, 14929...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>(((14929788.97739116, -3291373.53821332, 15048...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>(((15048397.0328222, -3291373.53821332, 151670...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>(((15167005.08825324, -3291373.53821332, 15285...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>(((15285613.14368428, -3291373.53821332, 15404...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>(((15404221.199115321, -3291373.53821332, 1552...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>(((15522829.254546361, -3291373.53821332, 1556...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>(((14455356.755667, -3335851.559, 14573964.811...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>(((14573964.81109804, -3335851.559, 14692572.8...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>(((14692572.86652908, -3335851.559, 14811180.9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>(((14811180.92196012, -3335851.559, 14929788.9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>(((14929788.97739116, -3335851.559, 15048397.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>(((15048397.0328222, -3335851.559, 15167005.08...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>(((15167005.08825324, -3335851.559, 15285613.1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>(((15285613.14368428, -3335851.559, 15404221.1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>(((15404221.199115321, -3335851.559, 15522829....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>(((15522829.254546361, -3335851.559, 15567307....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 tile\n",
+       "0   (((14455356.755667, -2342509.0947641465, 14573...\n",
+       "1   (((14573964.81109804, -2342509.0947641465, 146...\n",
+       "2   (((14692572.86652908, -2342509.0947641465, 148...\n",
+       "3   (((14811180.92196012, -2342509.0947641465, 149...\n",
+       "4   (((14929788.97739116, -2342509.0947641465, 150...\n",
+       "5   (((15048397.0328222, -2342509.0947641465, 1516...\n",
+       "6   (((15167005.08825324, -2342509.0947641465, 152...\n",
+       "7   (((15285613.14368428, -2342509.0947641465, 154...\n",
+       "8   (((15404221.199115321, -2342509.0947641465, 15...\n",
+       "9   (((15522829.254546361, -2342509.0947641465, 15...\n",
+       "10  (((14455356.755667, -2461117.150195293, 145739...\n",
+       "11  (((14573964.81109804, -2461117.150195293, 1469...\n",
+       "12  (((14692572.86652908, -2461117.150195293, 1481...\n",
+       "13  (((14811180.92196012, -2461117.150195293, 1492...\n",
+       "14  (((14929788.97739116, -2461117.150195293, 1504...\n",
+       "15  (((15048397.0328222, -2461117.150195293, 15167...\n",
+       "16  (((15167005.08825324, -2461117.150195293, 1528...\n",
+       "17  (((15285613.14368428, -2461117.150195293, 1540...\n",
+       "18  (((15404221.199115321, -2461117.150195293, 155...\n",
+       "19  (((15522829.254546361, -2461117.150195293, 155...\n",
+       "20  (((14455356.755667, -2579725.20562644, 1457396...\n",
+       "21  (((14573964.81109804, -2579725.20562644, 14692...\n",
+       "22  (((14692572.86652908, -2579725.20562644, 14811...\n",
+       "23  (((14811180.92196012, -2579725.20562644, 14929...\n",
+       "24  (((14929788.97739116, -2579725.20562644, 15048...\n",
+       "25  (((15048397.0328222, -2579725.20562644, 151670...\n",
+       "26  (((15167005.08825324, -2579725.20562644, 15285...\n",
+       "27  (((15285613.14368428, -2579725.20562644, 15404...\n",
+       "28  (((15404221.199115321, -2579725.20562644, 1552...\n",
+       "29  (((15522829.254546361, -2579725.20562644, 1556...\n",
+       "..                                                ...\n",
+       "70  (((14455356.755667, -3172765.482782173, 145739...\n",
+       "71  (((14573964.81109804, -3172765.482782173, 1469...\n",
+       "72  (((14692572.86652908, -3172765.482782173, 1481...\n",
+       "73  (((14811180.92196012, -3172765.482782173, 1492...\n",
+       "74  (((14929788.97739116, -3172765.482782173, 1504...\n",
+       "75  (((15048397.0328222, -3172765.482782173, 15167...\n",
+       "76  (((15167005.08825324, -3172765.482782173, 1528...\n",
+       "77  (((15285613.14368428, -3172765.482782173, 1540...\n",
+       "78  (((15404221.199115321, -3172765.482782173, 155...\n",
+       "79  (((15522829.254546361, -3172765.482782173, 155...\n",
+       "80  (((14455356.755667, -3291373.53821332, 1457396...\n",
+       "81  (((14573964.81109804, -3291373.53821332, 14692...\n",
+       "82  (((14692572.86652908, -3291373.53821332, 14811...\n",
+       "83  (((14811180.92196012, -3291373.53821332, 14929...\n",
+       "84  (((14929788.97739116, -3291373.53821332, 15048...\n",
+       "85  (((15048397.0328222, -3291373.53821332, 151670...\n",
+       "86  (((15167005.08825324, -3291373.53821332, 15285...\n",
+       "87  (((15285613.14368428, -3291373.53821332, 15404...\n",
+       "88  (((15404221.199115321, -3291373.53821332, 1552...\n",
+       "89  (((15522829.254546361, -3291373.53821332, 1556...\n",
+       "90  (((14455356.755667, -3335851.559, 14573964.811...\n",
+       "91  (((14573964.81109804, -3335851.559, 14692572.8...\n",
+       "92  (((14692572.86652908, -3335851.559, 14811180.9...\n",
+       "93  (((14811180.92196012, -3335851.559, 14929788.9...\n",
+       "94  (((14929788.97739116, -3335851.559, 15048397.0...\n",
+       "95  (((15048397.0328222, -3335851.559, 15167005.08...\n",
+       "96  (((15167005.08825324, -3335851.559, 15285613.1...\n",
+       "97  (((15285613.14368428, -3335851.559, 15404221.1...\n",
+       "98  (((15404221.199115321, -3335851.559, 15522829....\n",
+       "99  (((15522829.254546361, -3335851.559, 15567307....\n",
+       "\n",
+       "[100 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.select(\"tile\").toPandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From b84ec181c506dc88c1bc614023d5c47b1688aed2 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 8 May 2019 20:33:13 -0400
Subject: [PATCH 080/380] Removed non-core Tile attributes from Python-side
 Tile class. Wrapped CellType into a Python-side convenience class for numpy
 interop.

---
 .circleci/config.yml                          |   4 +-
 .../apache/spark/sql/rf/RasterSourceUDT.scala |   5 +-
 pyrasterframes/python/pyrasterframes/types.py | 120 ++++++++----------
 .../python/tests/PyRasterFramesTests.py       |  20 ++-
 .../rasterframes/py/PyRFContext.scala         |  13 +-
 5 files changed, 77 insertions(+), 85 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 1fffd77f6..125be0bb5 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -69,7 +69,7 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
+      - run: sudo apt-get update && sudo apt-get install -y python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
 
       - run: *unsetenv
@@ -85,7 +85,7 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get install python-pip pandoc && pip install setuptools # required for pyrasterframes testing
+      - run: sudo apt-get update && sudo apt-get install -y python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
       - run: cat /dev/null | sbt publish
 
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index 679da36e9..a5d878798 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -69,7 +69,7 @@ object RasterSourceUDT {
   UDTRegistration.register(classOf[RasterSource].getName, classOf[RasterSourceUDT].getName)
 
   /** Deserialize a byte array, also used inside the Python API */
-  def from(byteArray: Array[Byte]): RasterSource = KryoSupport.deserialize[RasterSource](ByteBuffer.wrap(byteArray))
+  def from(byteArray: Array[Byte]): RasterSource = CatalystSerializer.CatalystIO.rowIO.create(byteArray).to[RasterSource]
 
   implicit val rasterSourceSerializer: CatalystSerializer[RasterSource] = new CatalystSerializer[RasterSource] {
 
@@ -83,8 +83,7 @@ object RasterSourceUDT {
     }
 
     override def from[R](row: R, io: CatalystIO[R]): RasterSource = {
-      RasterSourceUDT.from(io.getByteArray(row, 0))
+      KryoSupport.deserialize[RasterSource](ByteBuffer.wrap(io.getByteArray(row, 0)))
     }
-
   }
 }
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index 1252e603a..40699da33 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -138,57 +138,45 @@ def deserialize(self, datum):
         bytes(datum[0])
 
 
-class Tile:
+class GTCellType:
+    def __init__(self, cell_type_name):
+        self.cell_type_name = cell_type_name
 
-    def __init__(self, array, crs=None, extent=None, band_index=None, ):
-        # this could be more flexible to allow more ways to access TileUDT
-        # do some checking on dtype and shape
-        self.array = array
-        self.crs = crs  # what is a CRS? is it a rasterio.crs.CRS? A pyproj CRS? https://pyproj4.github.io/pyproj/html/api/crs.html#pyproj.crs.CRS.from_string
-        self.extent = extent
-        self.band_index = band_index
-        # here may be okay to carry around the kryo serialized bytes so we can at least go back into the JVM world?:
+    @classmethod
+    def from_numpy_dtype(cls, np_dtype):
+        return GTCellType(str(np_dtype))
+
+    def to_numpy_dtype(self):
+        import numpy as np
+        if self.cell_type_name.endswith("raw"):
+            return GTCellType(self.cell_type_name[:-3]).to_numpy_dtype()
+        elif "ud" in self.cell_type_name:
+            raise Exception("Cell types with user-defined NoData values are not yet implemented.")
+        else:
+            # The remaining cell types should be compatible with numpy
+            return np.dtype(self.cell_type_name)
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self.cell_type_name == other.cell_type_name
+        else:
+            return False
 
     def __str__(self):
-        return self.array.__str__()
+        return self.cell_type_name
 
-    @classmethod
-    def numpy_dtype_to_celltype(cls, dtype):
-        from pyrasterframes.rasterfunctions import _celltype
-        # TODO implement something that will work generally
-        # Also here we should convert to a string representation of the celltype
-        return _celltype(str(dtype))
 
-    def get_cell_type(self):
-        return self.numpy_dtype_to_celltype(self.array.dtype)
+class Tile:
+    def __init__(self, cells):
+        self.cells = cells
+        self.cell_type = GTCellType.from_numpy_dtype(cells.dtype)
+
+    def __str__(self):
+        return self.cells.__str__()
 
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
-        return [self.array.shape[1], self.array.shape[0]]
-
-    def to_tile_udt(self):
-        row = [
-            # cell_context
-            [
-                ['float64'],  #TODO !
-                self.dimensions()
-            ],
-            # cell_data
-            [
-                # cells -- still too many copies
-                bytearray(RFContext.call('list_to_bytearray', self.array.flatten().tolist(), *self.dimensions())),
-                # ref
-                [
-                    # cell_data.ref.source
-                    None,
-                    # cell_data.ref.bandIndex
-                    self.band_index,
-                    # cell_data.ref.subextent
-                    self.extent
-                ]
-            ]
-        ]
-        return row
+        return [self.cells.shape[1], self.cells.shape[0]]
 
 
 class TileUDT(UserDefinedType):
@@ -231,42 +219,44 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
     def serialize(self, tile):
-        return tile.to_tile_udt()
+        row = [
+            # cell_context
+            [
+                [tile.cell_type.cell_type_name],
+                tile.dimensions()
+            ],
+            # cell_data
+            [
+                # cells
+                bytearray(RFContext.call('_list_to_bytearray', tile.cells.flatten().tolist(), *tile.dimensions())),
+                None
+            ]
+        ]
+        return row
 
     def deserialize(self, datum):
         """
-
+        Convert catalyst representation of Tile to Python version. NB: This is expensive.
         :param datum:
         :return: A Tile object from row data.
         """
-        cell_type = datum.cell_context.cellType.cellTypeName
+        cell_type = GTCellType(datum.cell_context.cellType.cellTypeName)
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
-        cell_value_list = list(RFContext.call('bytearray_to_list', cell_data_bytes, cell_type, cols, rows))
-
-        crs = None
-        band_index = None
-        extent = None
-        if 'ref' in datum.cell_data and datum.cell_data.ref is not None:
-            band_index = datum.cell_data.ref.bandIndex
-            if datum.cell_data.ref.source is not None:
-                crs = RFContext.call('rastersource_bytearray_to_proj4', datum.cell_data.ref.source)
-            if datum.cell_data.ref.subextent is not None:
-                extent = datum.cell_data.ref.subextent
-
-        t = Tile(
-            numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type),
-            crs,
-            extent,
-            band_index,
-        )
+
+        # This is incurring a back-and-forth of the data across the gateway... need to fix.
+        cell_value_list = list(RFContext.call('_bytearray_to_list', cell_data_bytes, cell_type.cell_type_name, cols, rows))
+        as_numpy = numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type.to_numpy_dtype())
+        t = Tile(as_numpy)
         return t
 
     deserialize.__safe_for_unpickling__ = True
 
+
 Tile.__UDT__ = TileUDT()
 
+
 class TileExploder(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     Python wrapper for TileExploder.scala
@@ -275,6 +265,7 @@ def __init__(self):
         super(TileExploder, self).__init__()
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.TileExploder", self.uid)
 
+
 class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     Python wrapper for NoDataFilter.scala
@@ -282,5 +273,6 @@ class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
     def __init__(self):
         super(NoDataFilter, self).__init__()
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.NoDataFilter", self.uid)
+
     def setInputCols(self, values):
         self._java_obj.setInputCols(values)
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index e142ba6b2..15a113be4 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -139,7 +139,6 @@ def test_aggregations(self):
         self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
-
     def test_sql(self):
 
         self.rf.createOrReplaceTempView("rf")
@@ -211,7 +210,6 @@ def test_mask_by_value(self):
             .collect()[0][0]
         self.assertTrue(result)
 
-
     def test_resample(self):
         from pyspark.sql.functions import lit
         result = self.rf.select(
@@ -303,6 +301,18 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
+    def test_cell_type(self):
+        from pyrasterframes.types import GTCellType
+        for ct in rf_cell_types():
+            wrapped = GTCellType(ct)
+            self.assertEqual(wrapped.to_numpy_dtype(),
+                             GTCellType.from_numpy_dtype(wrapped.to_numpy_dtype()).to_numpy_dtype(),
+                             "dtype comparison for " + ct)
+            if "raw" not in ct:
+                self.assertEqual(wrapped,
+                                 GTCellType.from_numpy_dtype(wrapped.to_numpy_dtype()),
+                                 "GTCellType comparison for " + ct)
+
     def test_tile_udt(self):
         import pandas as pd
         import numpy as np
@@ -313,7 +323,7 @@ def test_tile_udt(self):
         # Try to collect self.rf which is read from a geotiff
         rf_collect = self.rf.take(2)
         self.assertTrue(
-            all([isinstance(row.tile.array, np.ndarray) for row in rf_collect]))
+            all([isinstance(row.tile.cells, np.ndarray) for row in rf_collect]))
 
         # Try to create a tile from numpy.
         a_tile = Tile(np.random.randn(10, 10))  # no extent and crs provided
@@ -328,7 +338,7 @@ def test_tile_udt(self):
         rf_maybe.printSchema()
 
         # Try to do something with it.
-        sums = to_spark.t.apply(lambda a: a.array.sum()).tolist()
+        sums = to_spark.t.apply(lambda a: a.cells.sum()).tolist()
         maybe_sums = rf_maybe.select(rf_tile_sum(rf_maybe.t).alias('tsum'))
         print("Schema of tile sum")
         maybe_sums.printSchema()
@@ -354,7 +364,7 @@ def test_tile_udt(self):
         print("Array collected from toPandas output\n", array_back_2)
 
         self.assertIsInstance(array_back_2, Tile)
-        np.testing.assert_equal(array_back_2.array, simple_array.array)
+        np.testing.assert_equal(array_back_2.cells, simple_array.cells)
 
         # test CRS and extent correctly make round trips
 
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index c06f13415..e03f8f801 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -24,7 +24,6 @@ import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
-import org.apache.spark.sql.rf.RasterSourceUDT
 import org.locationtech.rasterframes.{RasterFunctions, _}
 import org.locationtech.rasterframes.model.LazyCRS
 import spray.json._
@@ -106,11 +105,11 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   def rf_explode_tiles_sample(sampleFraction: Double, seed: Long, cols: Column*): Column =
     rf_explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
 
-  def list_to_bytearray(l: java.util.ArrayList[Double], c: Int, r: Int): Array[Byte] = {
+  def _list_to_bytearray(l: java.util.ArrayList[Double], c: Int, r: Int): Array[Byte] = {
     geotrellis.raster.ArrayTile(l.asScala.toArray, c, r).toBytes()
   }
 
-  def bytearray_to_list(bytes: Array[Byte], cell_type_name: String, cols: Int, rows: Int): java.util.List[Double] = {
+  def _bytearray_to_list(bytes: Array[Byte], cell_type_name: String, cols: Int, rows: Int): java.util.List[Double] = {
     geotrellis.raster.ArrayTile.fromBytes(
       bytes,
       geotrellis.raster.CellType.fromName(cell_type_name),
@@ -119,18 +118,10 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     ).toListDouble.asJava
   }
 
-  def rastersource_bytearray_to_proj4(bytes: Array[Byte]): String = {
-    import org.apache.spark.sql.rf.RasterSourceUDT._
-    val rasterSource = from(bytes)
-    return rasterSource.crs.toProj4String
-
-  }
-
   def generate_tile(cellType: String, cols: Int, rows: Int, bytes: Array[Byte]): ArrayTile = {
     ArrayTile.fromBytes(bytes, this.rf_cell_type(cellType), cols, rows)
   }
 
-
   def tileColumns(df: DataFrame): Array[Column] =
     df.asRF.tileColumns.toArray
 

From 6d8727d015e7b22e230bd3593ac865610297e7e1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 8 May 2019 21:38:33 -0400
Subject: [PATCH 081/380] One-level reduction in byte array copying, relying on
 numpy.frombuffer for byte array interpretation. NoData handling not yet
 implemented.

---
 .../python/pyrasterframes/rasterfunctions.py  |  4 +-
 pyrasterframes/python/pyrasterframes/types.py | 29 +++++++------
 .../python/tests/PyRasterFramesTests.py       | 43 ++++++++++++-------
 .../rasterframes/py/PyRFContext.scala         | 19 +-------
 4 files changed, 46 insertions(+), 49 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 559ef690e..fb7271254 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -9,7 +9,7 @@
 from pyspark.sql.types import *
 from pyspark.sql.column import Column, _to_java_column
 from .context import RFContext
-
+from .types import CellType
 
 THIS_MODULE = 'pyrasterframes'
 
@@ -154,7 +154,7 @@ def _(data_tile, mask_tile, mask_value):
 _rf_unique_functions = {
     'rf_array_to_tile': _create_arrayToTile(),
     'rf_assemble_tile': _create_assembleTile(),
-    'rf_cell_types': lambda: _context_call('rf_cell_types'),
+    'rf_cell_types': lambda: [CellType(ct) for ct in _context_call('rf_cell_types')],
     'rf_convert_cell_type': _create_convertCellType(),
     'rf_explode_tiles': _create_explode_tiles(),
     'rf_explode_tiles_sample': _create_explode_tiles_sample(),
diff --git a/pyrasterframes/python/pyrasterframes/types.py b/pyrasterframes/python/pyrasterframes/types.py
index 40699da33..749c22df0 100644
--- a/pyrasterframes/python/pyrasterframes/types.py
+++ b/pyrasterframes/python/pyrasterframes/types.py
@@ -12,7 +12,7 @@ class here provides the PyRasterFrames entry point.
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
 from .context import RFContext
-import numpy
+import numpy as np
 
 __all__ = ['RasterFrame', 'TileUDT', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
 
@@ -138,18 +138,17 @@ def deserialize(self, datum):
         bytes(datum[0])
 
 
-class GTCellType:
+class CellType:
     def __init__(self, cell_type_name):
         self.cell_type_name = cell_type_name
 
     @classmethod
     def from_numpy_dtype(cls, np_dtype):
-        return GTCellType(str(np_dtype))
+        return CellType(str(np_dtype))
 
     def to_numpy_dtype(self):
-        import numpy as np
         if self.cell_type_name.endswith("raw"):
-            return GTCellType(self.cell_type_name[:-3]).to_numpy_dtype()
+            return CellType(self.cell_type_name[:-3]).to_numpy_dtype()
         elif "ud" in self.cell_type_name:
             raise Exception("Cell types with user-defined NoData values are not yet implemented.")
         else:
@@ -169,10 +168,16 @@ def __str__(self):
 class Tile:
     def __init__(self, cells):
         self.cells = cells
-        self.cell_type = GTCellType.from_numpy_dtype(cells.dtype)
+        self.cell_type = CellType.from_numpy_dtype(cells.dtype)
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return np.array_equal(self.cells, other.cells)
+        else:
+            return False
 
     def __str__(self):
-        return self.cells.__str__()
+        return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)".format(self.dimensions(), self.cell_type, self.cells)
 
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
@@ -187,7 +192,7 @@ def sqlType(cls):
         """
         return StructType([
             StructField("cell_context", StructType([
-                StructField("cell_type", StructType([
+                StructField("cellType", StructType([
                     StructField("cellTypeName", StringType(), False)
                 ]), False),
                 StructField("dimensions", StructType([
@@ -228,7 +233,7 @@ def serialize(self, tile):
             # cell_data
             [
                 # cells
-                bytearray(RFContext.call('_list_to_bytearray', tile.cells.flatten().tolist(), *tile.dimensions())),
+                bytearray(tile.cells.flatten().tobytes()),
                 None
             ]
         ]
@@ -240,14 +245,12 @@ def deserialize(self, datum):
         :param datum:
         :return: A Tile object from row data.
         """
-        cell_type = GTCellType(datum.cell_context.cellType.cellTypeName)
+        cell_type = CellType(datum.cell_context.cellType.cellTypeName)
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
 
-        # This is incurring a back-and-forth of the data across the gateway... need to fix.
-        cell_value_list = list(RFContext.call('_bytearray_to_list', cell_data_bytes, cell_type.cell_type_name, cols, rows))
-        as_numpy = numpy.reshape(cell_value_list, (rows, cols), order='C').astype(cell_type.to_numpy_dtype())
+        as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype()).reshape((rows, cols))
         t = Tile(as_numpy)
         return t
 
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 15a113be4..c175940d8 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -7,6 +7,8 @@
 from pathlib import Path
 import os
 import unittest
+from pyrasterframes.types import Tile
+import numpy as np
 
 # version-conditional imports
 import sys
@@ -302,21 +304,30 @@ def some_point(g):
         )
 
     def test_cell_type(self):
-        from pyrasterframes.types import GTCellType
+        from pyrasterframes.types import CellType
         for ct in rf_cell_types():
-            wrapped = GTCellType(ct)
-            self.assertEqual(wrapped.to_numpy_dtype(),
-                             GTCellType.from_numpy_dtype(wrapped.to_numpy_dtype()).to_numpy_dtype(),
-                             "dtype comparison for " + ct)
-            if "raw" not in ct:
-                self.assertEqual(wrapped,
-                                 GTCellType.from_numpy_dtype(wrapped.to_numpy_dtype()),
-                                 "GTCellType comparison for " + ct)
-
-    def test_tile_udt(self):
+            self.assertEqual(ct.to_numpy_dtype(),
+                             CellType.from_numpy_dtype(ct.to_numpy_dtype()).to_numpy_dtype(),
+                             "dtype comparison for " + str(ct))
+            if "raw" not in ct.cell_type_name:
+                self.assertEqual(ct,
+                                 CellType.from_numpy_dtype(ct.to_numpy_dtype()),
+                                 "GTCellType comparison for " + str(ct))
+
+
+    def test_tile_udt_serialization(self):
+        from pyrasterframes.types import TileUDT
+        udt = TileUDT()
+
+        cell_types = (ct for ct in rf_cell_types() if not ct.cell_type_name.endswith("raw"))
+        for ct in cell_types:
+            a_tile = Tile(np.random.randn(3, 3).astype(ct.to_numpy_dtype()))
+            round_trip = udt.fromInternal(udt.toInternal(a_tile))
+            self.assertEquals(a_tile, round_trip, "round-trip serialization for " + str(ct))
+
+
+    def test_tile_udt_general(self):
         import pandas as pd
-        import numpy as np
-        from pyrasterframes.types import Tile
 
         self.assertIsInstance(self.rf.sql_ctx, SQLContext)
 
@@ -326,10 +337,10 @@ def test_tile_udt(self):
             all([isinstance(row.tile.cells, np.ndarray) for row in rf_collect]))
 
         # Try to create a tile from numpy.
-        a_tile = Tile(np.random.randn(10, 10))  # no extent and crs provided
+        a_tile = Tile(np.random.randn(10, 10))
 
         to_spark = pd.DataFrame({
-            't': [Tile(np.random.randn(10,12)) for _ in range(3)],
+            't': [Tile(np.random.randn(10, 12)) for _ in range(3)],
             'b': ['a', 'b', 'c'],
             'c': [1, 2, 4],
         })
@@ -371,7 +382,7 @@ def test_tile_udt(self):
         # test raster source?
 
 
-    def test_raster_source_reader(self):
+    def test_zraster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
 
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index e03f8f801..981cbac32 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -20,7 +20,7 @@
  */
 package org.locationtech.rasterframes.py
 
-import geotrellis.raster.{ArrayTile, CellType, MultibandTile}
+import geotrellis.raster.{CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
@@ -105,23 +105,6 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   def rf_explode_tiles_sample(sampleFraction: Double, seed: Long, cols: Column*): Column =
     rf_explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
 
-  def _list_to_bytearray(l: java.util.ArrayList[Double], c: Int, r: Int): Array[Byte] = {
-    geotrellis.raster.ArrayTile(l.asScala.toArray, c, r).toBytes()
-  }
-
-  def _bytearray_to_list(bytes: Array[Byte], cell_type_name: String, cols: Int, rows: Int): java.util.List[Double] = {
-    geotrellis.raster.ArrayTile.fromBytes(
-      bytes,
-      geotrellis.raster.CellType.fromName(cell_type_name),
-      cols,
-      rows
-    ).toListDouble.asJava
-  }
-
-  def generate_tile(cellType: String, cols: Int, rows: Int, bytes: Array[Byte]): ArrayTile = {
-    ArrayTile.fromBytes(bytes, this.rf_cell_type(cellType), cols, rows)
-  }
-
   def tileColumns(df: DataFrame): Array[Column] =
     df.asRF.tileColumns.toArray
 

From 315d747e19ae1f8d6f41a575730f264678d52c3c Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 9 May 2019 16:46:42 -0400
Subject: [PATCH 082/380] Update docs for notebook location and rf_crs function

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/expressions/package.scala    |  1 +
 docs/src/main/tut/pyrasterframes.md           |  2 +-
 docs/src/main/tut/reference.md                | 22 ++++++++++++-------
 .../python/tests/PyRasterFramesTests.py       | 10 +++++----
 4 files changed, 22 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index bfd1b14e1..c574114d8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -73,6 +73,7 @@ package object expressions {
     registry.registerExpression[GetGeometry]("rf_geometry")
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[GetExtent]("rf_extent")
+    registry.registerExpression[GetCRS]("rf_crs")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/docs/src/main/tut/pyrasterframes.md b/docs/src/main/tut/pyrasterframes.md
index 35a5169d5..0d7fc8280 100644
--- a/docs/src/main/tut/pyrasterframes.md
+++ b/docs/src/main/tut/pyrasterframes.md
@@ -6,7 +6,7 @@ in the meantime:
 
 * [PyRasterFrames README](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/python/README.rst)
 * [PyRasterFrames Examples](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/python/examples)
-* [RasterFrames Jupyter Notebook](https://github.com/locationtech/rasterframes/blob/develop/deployment/README.md)
+* [RasterFrames Jupyter Notebook](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md)
 * @ref:[PyRasterFrames Functions](reference.md)
 
 Most features available in the Scala API are exposed in the Python API, refer to the @ref:[function reference](reference.md). Defining a [udf](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.udf) using a `Tile` column through the Python API is not yet supported. 
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index 8fbb5f524..c24f5d680 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -83,14 +83,7 @@ Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
     Struct[String] rf_cell_type(Tile tile)
     
-Get the cell type of the `tile`. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function.
-
-#### rf_convert_cell_type
-
-    Tile rf_convert_cell_type(Tile tileCol, String cellType)
-
-Convert `tileCol` to a different cell type.
-
+Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_convert_cell_type](reference.md#rf-convert-cell-type). 
 
 #### rf_extent
 
@@ -99,6 +92,19 @@ Convert `tileCol` to a different cell type.
 
 Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
+#### rf_crs
+
+    Struct[String] rf_crs(ProjectedRasterTile raster)
+    Struct[String] rf_crs(RasterSource raster)
+    
+Fetch the [proj4](https://proj4.org/) string representation of the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
+
+#### rf_convert_cell_type
+
+    Tile rf_convert_cell_type(Tile tileCol, String cellType)
+
+Convert `tileCol` to a different cell type. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function. 
+
 #### rf_resample
 
     Tile rf_resample(Tile tile, Double factor)
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index b0fc2aecc..45874be15 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -38,7 +38,7 @@ def setUpClass(cls):
                      .withKryoSerialization()
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
-        print(cls.spark.version)
+        print("Spark version:", cls.spark.version)
         cls.spark.withRasterFrames()
 
         cls.img_uri = cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()
@@ -111,12 +111,14 @@ def test_tile_functions(self):
             .withColumn('exp', rf_exp(self.tileCol)) \
             .withColumn('expm1', rf_expm1(self.tileCol)) \
             .withColumn('round', rf_round(self.tileCol))
-        # TODO: add test for rf_extent and rf_geometry once rastersource connector is integrated and we have
-        #  a source of ProjectedRasterTiles.
+
         df.show()
 
     def test_prt_functions(self):
-        df = self.spark.read.rastersource(self.img_uri)
+        df = self.spark.read.rastersource(self.img_uri) \
+            .withColumn('crs', rf_crs(self.tileCol)) \
+            .withColumn('ext', rf_extent(self.tileCol)) \
+            .withColumn('geom', rf_geometry(self.tileCol))
         df.printSchema
         df.show()
 

From d79a545ad331ce41c0ffbad8295a8b5abbdc1715 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 13 May 2019 10:15:21 -0400
Subject: [PATCH 083/380] Started breaking up unit tests for better
 organization.

---
 .../python/tests/PyRasterFramesTests.py       | 250 +++++++++---------
 1 file changed, 130 insertions(+), 120 deletions(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index c175940d8..f7773bab0 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,4 +1,3 @@
-
 from pyspark.sql import SparkSession, Column, SQLContext
 from pyspark.sql.functions import *
 from pyrasterframes import *
@@ -12,22 +11,24 @@
 
 # version-conditional imports
 import sys
+
 if sys.version_info[0] > 2:
     import builtins
 else:
     import __builtin__ as builtins
 
 
-def _rounded_compare(val1, val2):
-    print('Comparing {} and {} using round()'.format(val1, val2))
-    return builtins.round(val1) == builtins.round(val2)
-
+class TestEnvironment(unittest.TestCase):
+    """
+    Base class for tests.
+    """
 
-class RasterFunctionsTest(unittest.TestCase):
+    def rounded_compare(self, val1, val2):
+        print('Comparing {} and {} using round()'.format(val1, val2))
+        return builtins.round(val1) == builtins.round(val2)
 
     @classmethod
     def setUpClass(cls):
-
         # gather Scala requirements
         jarpath = list(Path('../target/scala-2.11').resolve().glob('pyrasterframes-assembly*.jar'))[0]
 
@@ -36,10 +37,10 @@ def setUpClass(cls):
 
         # spark session with RF
         cls.spark = (SparkSession.builder
-            .config('spark.driver.extraClassPath', jarpath)
-            .config('spark.executor.extraClassPath', jarpath)
-            .withKryoSerialization()
-            .getOrCreate())
+                     .config('spark.driver.extraClassPath', jarpath)
+                     .config('spark.executor.extraClassPath', jarpath)
+                     .withKryoSerialization()
+                     .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
         print(cls.spark.version)
         cls.spark.withRasterFrames()
@@ -54,10 +55,94 @@ def setUpClass(cls):
         cls.rf = rf.withColumn('tile2', rf_convert_cell_type(cls.tileCol, 'float32')) \
             .drop(cls.tileCol) \
             .withColumnRenamed('tile2', cls.tileCol).asRF()
-        #cls.rf.show()
+        # cls.rf.show()
 
     def test_setup(self):
-        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"), "org.apache.spark.serializer.KryoSerializer")
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
+                         "org.apache.spark.serializer.KryoSerializer")
+
+
+class VectorTypes(TestEnvironment):
+
+    def setUp(self):
+        import pandas as pd
+        self.pandas_df = pd.DataFrame({
+            'eye': ['a', 'b', 'c', 'd'],
+            'x': [0.0, 1.0, 2.0, 3.0],
+            'y': [-4.0, -3.0, -2.0, -1.0],
+        })
+        df = self.spark.createDataFrame(self.pandas_df)
+        df = df.withColumn("point_geom",
+                           st_point(df.x, df.y)
+                           )
+        self.df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
+
+    def test_spatial_relations(self):
+        from pyspark.sql.functions import lit, udf, sum
+        import shapely
+        import numpy.testing
+
+        # Use python shapely UDT in a UDF
+        @udf("double")
+        def area_fn(g):
+            return g.area
+
+        @udf("double")
+        def length_fn(g):
+            return g.length
+
+        df = self.df.withColumn("poly_area", area_fn(self.df.poly_geom))
+        df = df.withColumn("poly_len", length_fn(df.poly_geom))
+
+        # Return UDT in a UDF!
+        def some_point(g):
+            return g.representative_point()
+
+        some_point_udf = udf(some_point, PointUDT())
+
+        df = df.withColumn("any_point", some_point_udf(df.poly_geom))
+        # spark-side UDF/UDT are correct
+        intersect_total = df.agg(sum(
+            st_intersects(df.poly_geom, df.any_point).astype('double')
+        ).alias('s')).collect()[0].s
+        self.assertTrue(intersect_total == df.count())
+
+        # Collect to python driver in shapely UDT
+        pandas_df_out = df.toPandas()
+
+        # Confirm we get a shapely type back from st_* function and UDF
+        self.assertIsInstance(pandas_df_out.poly_geom.iloc[0], shapely.geometry.Polygon)
+        self.assertIsInstance(pandas_df_out.any_point.iloc[0], shapely.geometry.Point)
+
+        # And our spark-side manipulations were correct
+        xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == self.pandas_df.x
+        self.assertTrue(all(xs_correct))
+
+        centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
+                                                    g.centroid.coords[0][1]).tolist()
+        numpy.testing.assert_almost_equal(centroid_ys, self.pandas_df.y.tolist())
+
+        # Including from UDF's
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.area).values,
+            pandas_df_out.poly_area.values
+        )
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.length).values,
+            pandas_df_out.poly_len.values
+        )
+
+    def test_rasterize(self):
+        # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
+        withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
+        withRaster.show()
+
+    def test_reproject(self):
+        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
+        reprojected.show()
+
+
+class RasterFunctions(TestEnvironment):
 
     def test_identify_columns(self):
         cols = self.rf.tileColumns()
@@ -72,11 +157,12 @@ def test_identify_columns(self):
 
     def test_tile_creation(self):
         base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
-        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"), rf_make_ones_tile(3, 3, "int32"))
+        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"),
+                            rf_make_ones_tile(3, 3, "int32"))
         tiles.show()
         self.assertEqual(tiles.count(), 4)
 
-    def test_tile_operations(self):
+    def test_multi_column_operations(self):
         df1 = self.rf.withColumnRenamed(self.tileCol, 't1').asRF()
         df2 = self.rf.withColumnRenamed(self.tileCol, 't2').asRF()
         df3 = df1.spatialJoin(df2).asRF()
@@ -89,13 +175,12 @@ def test_tile_operations(self):
         aggs.show()
         row = aggs.first()
 
-        self.assertTrue(_rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
-
+        self.assertTrue(self.rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
 
     def test_general(self):
         meta = self.rf.tileLayerMetadata()
         self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)) \
+        df = self.rf.withColumn('dims', rf_dimensions(self.tileCol)) \
             .withColumn('type', rf_cell_type(self.tileCol)) \
             .withColumn('dCells', rf_data_cells(self.tileCol)) \
             .withColumn('ndCells', rf_no_data_cells(self.tileCol)) \
@@ -115,15 +200,6 @@ def test_general(self):
         #  a source of ProjectedRasterTiles.
         df.show()
 
-    def test_rasterize(self):
-        # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
-        withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
-        withRaster.show()
-
-    def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
-        reprojected.show()
-
     def test_aggregations(self):
         aggs = self.rf.agg(
             rf_agg_mean(self.tileCol),
@@ -135,17 +211,16 @@ def test_aggregations(self):
         aggs.show()
         row = aggs.first()
 
-        self.assertTrue(_rounded_compare(row['rf_agg_mean(tile)'], 10160))
+        self.assertTrue(self.rounded_compare(row['rf_agg_mean(tile)'], 10160))
         print(row['rf_agg_data_cells(tile)'])
         self.assertEqual(row['rf_agg_data_cells(tile)'], 387000)
         self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
     def test_sql(self):
-
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims',  rf_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims', rf_dimensions(self.tileCol)).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
@@ -160,16 +235,16 @@ def test_sql(self):
 
         ops.printSchema
         statsRow = ops.select(rf_tile_mean(self.tileCol).alias('base'),
-                           rf_tile_mean("AndOne").alias('plus_one'),
-                           rf_tile_mean("LessOne").alias('minus_one'),
-                           rf_tile_mean("TimesTwo").alias('double'),
-                           rf_tile_mean("OverTwo").alias('half')) \
-                        .first()
+                              rf_tile_mean("AndOne").alias('plus_one'),
+                              rf_tile_mean("LessOne").alias('minus_one'),
+                              rf_tile_mean("TimesTwo").alias('double'),
+                              rf_tile_mean("OverTwo").alias('half')) \
+            .first()
 
-        self.assertTrue(_rounded_compare(statsRow.base, statsRow.plus_one - 1))
-        self.assertTrue(_rounded_compare(statsRow.base, statsRow.minus_one + 1))
-        self.assertTrue(_rounded_compare(statsRow.base, statsRow.double / 2))
-        self.assertTrue(_rounded_compare(statsRow.base, statsRow.half * 2))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.plus_one - 1))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.minus_one + 1))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.double / 2))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.half * 2))
 
     def test_explode(self):
         import pyspark.sql.functions as F
@@ -179,10 +254,10 @@ def test_explode(self):
         # +-----------+------------+---------+-------+
         # |[2,1]      |4           |0        |10150.0|
         cell = self.rf.select(self.rf.spatialKeyColumn(), rf_explode_tiles(self.rf.tile)) \
-            .where(F.col("spatial_key.col")==2) \
-            .where(F.col("spatial_key.row")==1) \
-            .where(F.col("column_index")==4) \
-            .where(F.col("row_index")==0) \
+            .where(F.col("spatial_key.col") == 2) \
+            .where(F.col("spatial_key.row") == 1) \
+            .where(F.col("column_index") == 4) \
+            .where(F.col("row_index") == 0) \
             .select(F.col("tile")) \
             .collect()[0][0]
         self.assertEqual(cell, 10150.0)
@@ -194,7 +269,6 @@ def test_explode(self):
         self.assertTrue(sample_count > 0)
         self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
 
-
     def test_mask_by_value(self):
         from pyspark.sql.functions import lit
 
@@ -206,7 +280,7 @@ def test_mask_by_value(self):
                                  rf_convert_cell_type(
                                      rf_local_greater_int(self.rf.tile, 25000),
                                      "uint8"),
-                                  lit(mask_value)).alias('mask'))
+                                 lit(mask_value)).alias('mask'))
         rf2 = rf1.select(rf1.tile, rf_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
         result = rf2.agg(rf_agg_no_data_cells(rf2.tile) < rf_agg_no_data_cells(rf2.masked)) \
             .collect()[0][0]
@@ -236,72 +310,7 @@ def test_exists_for_all(self):
         self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
 
 
-    def test_geomesa_pyspark(self):
-        from pyspark.sql.functions import lit, udf, sum
-        import shapely
-        import pandas as pd
-        import numpy.testing
-
-        pandas_df = pd.DataFrame({
-            'eye': ['a', 'b', 'c', 'd'],
-            'x': [0.0, 1.0, 2.0, 3.0],
-            'y': [-4.0, -3.0, -2.0, -1.0],
-        })
-        df = self.spark.createDataFrame(pandas_df)
-        df = df.withColumn("point_geom",
-                           st_point(df.x, df.y)
-                           )
-        df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
-
-        # Use python shapely UDT in a UDF
-        @udf("double")
-        def area_fn(g):
-            return g.area
-
-        @udf("double")
-        def length_fn(g):
-            return g.length
-
-        df = df.withColumn("poly_area", area_fn(df.poly_geom))
-        df = df.withColumn("poly_len", length_fn(df.poly_geom))
-
-        # Return UDT in a UDF!
-        def some_point(g):
-            return g.representative_point()
-
-        some_point_udf = udf(some_point, PointUDT())
-
-        df = df.withColumn("any_point", some_point_udf(df.poly_geom))
-        # spark-side UDF/UDT are correct
-        intersect_total = df.agg(sum(
-            st_intersects(df.poly_geom, df.any_point).astype('double')
-        ).alias('s')).collect()[0].s
-        self.assertTrue(intersect_total == df.count())
-
-        # Collect to python driver in shapely UDT
-        pandas_df_out = df.toPandas()
-
-        # Confirm we get a shapely type back from st_* function and UDF
-        self.assertIsInstance(pandas_df_out.poly_geom.iloc[0], shapely.geometry.Polygon)
-        self.assertIsInstance(pandas_df_out.any_point.iloc[0], shapely.geometry.Point)
-
-        # And our spark-side manipulations were correct
-        xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == pandas_df.x
-        self.assertTrue(all(xs_correct))
-
-        centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
-                                                      g.centroid.coords[0][1]).tolist()
-        numpy.testing.assert_almost_equal(centroid_ys, pandas_df.y.tolist())
-
-        # Including from UDF's
-        numpy.testing.assert_almost_equal(
-            pandas_df_out.poly_geom.apply(lambda g: g.area).values,
-            pandas_df_out.poly_area.values
-        )
-        numpy.testing.assert_almost_equal(
-            pandas_df_out.poly_geom.apply(lambda g: g.length).values,
-            pandas_df_out.poly_len.values
-        )
+class UDT(TestEnvironment):
 
     def test_cell_type(self):
         from pyrasterframes.types import CellType
@@ -314,7 +323,6 @@ def test_cell_type(self):
                                  CellType.from_numpy_dtype(ct.to_numpy_dtype()),
                                  "GTCellType comparison for " + str(ct))
 
-
     def test_tile_udt_serialization(self):
         from pyrasterframes.types import TileUDT
         udt = TileUDT()
@@ -325,7 +333,6 @@ def test_tile_udt_serialization(self):
             round_trip = udt.fromInternal(udt.toInternal(a_tile))
             self.assertEquals(a_tile, round_trip, "round-trip serialization for " + str(ct))
 
-
     def test_tile_udt_general(self):
         import pandas as pd
 
@@ -382,7 +389,9 @@ def test_tile_udt_general(self):
         # test raster source?
 
 
-    def test_zraster_source_reader(self):
+class RasterSource(TestEnvironment):
+
+    def ignore_test_raster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
 
@@ -394,15 +403,15 @@ def l8path(b):
         path_param = '\n'.join([l8path(b) for b in [1, 2, 3]])  # "http://foo.com/file1.tif,http://foo.com/file2.tif"
         tile_size = 512
         df = self.spark.read.format('rastersource') \
-                            .options(paths=path_param, tileDimensions='{},{}'.format(tile_size, tile_size)) \
-                            .load()
+            .options(paths=path_param, tileDimensions='{},{}'.format(tile_size, tile_size)) \
+            .load()
 
         # schema is tile_path and tile
         df.printSchema()
         self.assertTrue(len(df.columns) == 2 and 'tile_path' in df.columns and 'tile' in df.columns)
 
         # the most common tile dimensions should be as passed to `options`, showing that options are correctly applied
-        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c'))\
+        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c')) \
             .groupby(['r', 'c']).count().toPandas()
         most_common_size = tile_size_df.loc[tile_size_df['count'].idxmax()]
         self.assertTrue(most_common_size.r == tile_size and most_common_size.c == tile_size)
@@ -438,9 +447,9 @@ def path(scene, band):
         path_table.createOrReplaceTempView(path_table_hive_name)
 
         path_df = self.spark.read.format('rastersource') \
-                            .options(pathTable=path_table_hive_name, pathTableColumns=csv_columns,
-                                     tileDimensions='512,512') \
-                            .load()
+            .options(pathTable=path_table_hive_name, pathTableColumns=csv_columns,
+                     tileDimensions='512,512') \
+            .load()
 
         path_df.printSchema()
         self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}
@@ -454,4 +463,5 @@ def suite():
     function_tests = unittest.TestSuite()
     return function_tests
 
+
 unittest.TextTestRunner().run(suite())

From 9ea577a8beb3c09928871a223b691b1cb0b99265 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 13 May 2019 14:30:26 -0400
Subject: [PATCH 084/380] Fleshed out CellType definition in Python.

---
 .../org/apache/spark/sql/rf/TileUDT.scala     |  3 +-
 project/RFProjectPlugin.scala                 |  1 +
 .../python/pyrasterframes/rf_types.py         | 62 ++++++++++++++-----
 .../python/tests/PyRasterFramesTests.py       | 31 +++++++---
 4 files changed, 71 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index 64507d578..c83d1fbba 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -20,12 +20,11 @@
  */
 
 package org.apache.spark.sql.rf
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.model.TileDataContext
 import geotrellis.raster._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{DataType, _}
 import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.model.{Cells, TileDataContext}
 import org.locationtech.rasterframes.tiles.InternalRowTile
 
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 5c641e6d4..288332730 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -35,6 +35,7 @@ object RFProjectPlugin extends AutoPlugin {
       "-Ywarn-unused-import"
     ),
     scalacOptions in (Compile, doc) ++= Seq("-no-link-warnings"),
+    console / scalacOptions := Seq("-feature"),
     javacOptions ++= Seq("-source", "1.8", "-target", "1.8"),
     cancelable in Global := true,
     publishTo in ThisBuild := sonatypePublishTo.value,
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 09e525aa6..5928c761e 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -7,14 +7,13 @@ class here provides the PyRasterFrames entry point.
 
 from pyspark.sql.types import UserDefinedType
 from pyspark import SparkContext
-from pyspark.sql import SparkSession, DataFrame, Column, Row
+from pyspark.sql import DataFrame, Column
 from pyspark.sql.types import *
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
-from .context import RFContext
 import numpy as np
 
-__all__ = ['RasterFrame', 'TileUDT', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
+__all__ = ['RasterFrame', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
 
 
 class RasterFrame(DataFrame):
@@ -138,7 +137,7 @@ def deserialize(self, datum):
         bytes(datum[0])
 
 
-class CellType:
+class CellType(object):
     def __init__(self, cell_type_name):
         self.cell_type_name = cell_type_name
 
@@ -146,17 +145,51 @@ def __init__(self, cell_type_name):
     def from_numpy_dtype(cls, np_dtype):
         return CellType(str(np_dtype))
 
-    def to_numpy_dtype(self):
-        if self.cell_type_name.endswith("raw"):
-            return CellType(self.cell_type_name[:-3]).to_numpy_dtype()
-        elif "ud" in self.cell_type_name:
-            raise Exception("Cell types with user-defined NoData values are not yet implemented.")
+    def is_raw(self):
+        return self.cell_type_name.endswith("raw")
+
+    def is_user_defined_no_data(self):
+        return "ud" in self.cell_type_name
+
+    def is_default_no_data(self):
+        return not (self.is_raw() or self.is_user_defined_no_data())
+
+    def is_floating_point(self):
+        return self.cell_type_name.startswith("float")
+
+    def base_cell_type_name(self):
+        if self.is_raw():
+            return self.cell_type_name[:-3]
+        elif self.is_user_defined_no_data():
+            return self.cell_type_name.split("ud")[0]
         else:
-            # The remaining cell types should be compatible with numpy
-            return np.dtype(self.cell_type_name)
+            return self.cell_type_name
+
+    def to_numpy_dtype(self):
+        return np.dtype(self.base_cell_type_name())
 
     def no_data_value(self):
-        pass
+        if self.is_raw():
+            return None
+        elif self.is_user_defined_no_data():
+            num_str = self.cell_type_name.split("ud")[1]
+            if self.is_floating_point():
+                return float(num_str)
+            else:
+                return int(num_str)
+        else:
+            if self.is_floating_point():
+                return  float('nan')
+            else:
+                n = self.base_cell_type_name()
+                if n is "uint8" or n is "uint16":
+                    return 0
+                elif n is "int8":
+                    return -128
+                elif n is "int16":
+                    return -32768
+                elif n is "int32":
+                    return -2147483648
 
     def __eq__(self, other):
         if type(other) is type(self):
@@ -168,7 +201,7 @@ def __str__(self):
         return self.cell_type_name
 
 
-class Tile:
+class Tile(object):
     def __init__(self, cells):
         self.cells = cells
         self.cell_type = CellType.from_numpy_dtype(cells.dtype)
@@ -180,7 +213,8 @@ def __eq__(self, other):
             return False
 
     def __str__(self):
-        return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)".format(self.dimensions(), self.cell_type, self.cells)
+        return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)" \
+            .format(self.dimensions(), self.cell_type, self.cells)
 
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 1c803af06..87fe90ed6 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -315,14 +315,32 @@ def test_cell_type_conversion(self):
             self.assertEqual(ct.to_numpy_dtype(),
                              CellType.from_numpy_dtype(ct.to_numpy_dtype()).to_numpy_dtype(),
                              "dtype comparison for " + str(ct))
-            if "raw" not in ct.cell_type_name:
+            if not ct.is_raw():
                 self.assertEqual(ct,
                                  CellType.from_numpy_dtype(ct.to_numpy_dtype()),
                                  "GTCellType comparison for " + str(ct))
 
+    def test_is_raw(self):
+        self.assertTrue(CellType("float32raw").is_raw())
+        self.assertFalse(CellType("float64ud1234").is_raw())
+        self.assertFalse(CellType("float32").is_raw())
+        self.assertTrue(CellType("int8raw").is_raw())
+        self.assertFalse(CellType("uint16d12").is_raw())
+        self.assertFalse(CellType("int32").is_raw())
+
+    def test_is_floating_point(self):
+        self.assertTrue(CellType("float32raw").is_floating_point())
+        self.assertTrue(CellType("float64ud1234").is_floating_point())
+        self.assertTrue(CellType("float32").is_floating_point())
+        self.assertFalse(CellType("int8raw").is_floating_point())
+        self.assertFalse(CellType("uint16d12").is_floating_point())
+        self.assertFalse(CellType("int32").is_floating_point())
+
     def test_cell_type_no_data(self):
-        # self.assertEqual(CellType("float32ud-98").no_data,
-        pass
+        import math
+        self.assertEqual(CellType("float32ud-98").no_data_value(), -98.0)
+        self.assertTrue(math.isnan(CellType("float64").no_data_value()))
+        self.assertEqual(CellType("uint8").no_data_value(), 0)
 
     def test_tile_udt_serialization(self):
         udt = TileUDT()
@@ -352,13 +370,10 @@ def test_tile_udt_general(self):
             'c': [1, 2, 4],
         })
         rf_maybe = self.spark.createDataFrame(to_spark)
-        print("Type of dataframe: ", type(rf_maybe))
 
         # Try to do something with it.
         sums = to_spark.t.apply(lambda a: a.cells.sum()).tolist()
         maybe_sums = rf_maybe.select(rf_tile_sum(rf_maybe.t).alias('tsum'))
-        print("Schema of tile sum")
-        #maybe_sums.printSchema()
 
         maybe_sums = [r.tsum for r in maybe_sums.collect()]
         np.testing.assert_almost_equal(maybe_sums, sums, 12)
@@ -383,10 +398,6 @@ def test_tile_udt_general(self):
         self.assertIsInstance(array_back_2, Tile)
         np.testing.assert_equal(array_back_2.cells, simple_array.cells)
 
-        # test CRS and extent correctly make round trips
-
-        # test raster source?
-
 
 class RasterSource(TestEnvironment):
 

From 82e2dd37530ebf6b696215f5f56a190222a279ce Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 14 May 2019 12:57:25 -0400
Subject: [PATCH 085/380] Initial encoding of CellType NoData in NumPy array.

---
 .../python/pyrasterframes/rf_types.py         | 31 ++++++++++++++-----
 .../python/tests/PyRasterFramesTests.py       | 12 +++++++
 2 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 5928c761e..d651bdd16 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -165,8 +165,8 @@ def base_cell_type_name(self):
         else:
             return self.cell_type_name
 
-    def to_numpy_dtype(self):
-        return np.dtype(self.base_cell_type_name())
+    def has_no_data(self):
+        return not self.is_raw()
 
     def no_data_value(self):
         if self.is_raw():
@@ -179,7 +179,7 @@ def no_data_value(self):
                 return int(num_str)
         else:
             if self.is_floating_point():
-                return  float('nan')
+                return float('nan')
             else:
                 n = self.base_cell_type_name()
                 if n is "uint8" or n is "uint16":
@@ -191,6 +191,9 @@ def no_data_value(self):
                 elif n is "int32":
                     return -2147483648
 
+    def to_numpy_dtype(self):
+        return np.dtype(self.base_cell_type_name())
+
     def __eq__(self, other):
         if type(other) is type(self):
             return self.cell_type_name == other.cell_type_name
@@ -198,13 +201,25 @@ def __eq__(self, other):
             return False
 
     def __str__(self):
-        return self.cell_type_name
+        return "CellType({}, {})".format(self.cell_type_name, self.no_data_value())
 
 
 class Tile(object):
-    def __init__(self, cells):
-        self.cells = cells
-        self.cell_type = CellType.from_numpy_dtype(cells.dtype)
+    def __init__(self, cells, cell_type=None):
+        if cell_type is None:
+            self.cell_type = CellType.from_numpy_dtype(cells.dtype)
+        else:
+            self.cell_type = cell_type
+
+        if self.cell_type.has_no_data():
+            if self.cell_type.is_floating_point():
+                self.cells = np.ma.masked_invalid(cells)
+            else:
+                nd_value = self.cell_type.no_data_value()
+                # if the value in the array is `nd_value`, it is masked as nodata
+                self.cells = np.ma.masked_equal(cells, nd_value)
+        else:
+            self.cells = cells
 
     def __eq__(self, other):
         if type(other) is type(self):
@@ -288,7 +303,7 @@ def deserialize(self, datum):
         cell_data_bytes = datum.cell_data.cells
 
         as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype()).reshape((rows, cols))
-        t = Tile(as_numpy)
+        t = Tile(as_numpy, cell_type)
         return t
 
     deserialize.__safe_for_unpickling__ = True
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 87fe90ed6..8ca1141dc 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -342,6 +342,18 @@ def test_cell_type_no_data(self):
         self.assertTrue(math.isnan(CellType("float64").no_data_value()))
         self.assertEqual(CellType("uint8").no_data_value(), 0)
 
+    def test_mask_no_data(self):
+        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
+        self.assertTrue(t1.cells.mask[1][0])
+        self.assertIsNotNone(t1.cells[1][1])
+        self.assertEqual(len(t1.cells.compressed()), 3)
+        t2 = Tile(np.array([[1.0, 2.0], [float('nan'), 4.0]]), CellType("float32"))
+        print(t2)
+        self.assertEqual(len(t2.cells.compressed()), 3)
+        self.assertTrue(t2.cells.mask[1][0])
+        self.assertIsNotNone(t2.cells[1][1])
+
+
     def test_tile_udt_serialization(self):
         udt = TileUDT()
 

From a47b5de237ef667e10bf1c2d258bf1038c84c84b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 15 May 2019 09:09:12 -0400
Subject: [PATCH 086/380] Fleshing out NoData handling in Python Tiles.

---
 .../org/apache/spark/sql/rf/TileUDT.scala     |  2 +-
 .../python/pyrasterframes/rf_types.py         | 29 ++++++++--
 .../python/tests/PyRasterFramesTests.py       | 54 ++++++++++++++++---
 3 files changed, 73 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index c83d1fbba..3a6168d6f 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -39,7 +39,7 @@ class TileUDT extends UserDefinedType[Tile] {
   import TileUDT._
   override def typeName = TileUDT.typeName
 
-  override def pyUDT: String = "pyrasterframes.TileUDT"
+  override def pyUDT: String = "pyrasterframes.rf_types.TileUDT"
 
   def userClass: Class[Tile] = classOf[Tile]
 
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index d651bdd16..df3744ffa 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -179,7 +179,7 @@ def no_data_value(self):
                 return int(num_str)
         else:
             if self.is_floating_point():
-                return float('nan')
+                return np.nan
             else:
                 n = self.base_cell_type_name()
                 if n is "uint8" or n is "uint16":
@@ -194,6 +194,9 @@ def no_data_value(self):
     def to_numpy_dtype(self):
         return np.dtype(self.base_cell_type_name())
 
+    def with_no_data_value(self, no_data):
+        return CellType(self.base_cell_type_name() + "ud" + str(no_data))
+
     def __eq__(self, other):
         if type(other) is type(self):
             return self.cell_type_name == other.cell_type_name
@@ -212,10 +215,10 @@ def __init__(self, cells, cell_type=None):
             self.cell_type = cell_type
 
         if self.cell_type.has_no_data():
-            if self.cell_type.is_floating_point():
+            nd_value = self.cell_type.no_data_value()
+            if np.isnan(nd_value):
                 self.cells = np.ma.masked_invalid(cells)
             else:
-                nd_value = self.cell_type.no_data_value()
                 # if the value in the array is `nd_value`, it is masked as nodata
                 self.cells = np.ma.masked_equal(cells, nd_value)
         else:
@@ -231,6 +234,24 @@ def __str__(self):
         return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)" \
             .format(self.dimensions(), self.cell_type, self.cells)
 
+    def __add__(self, right):
+        if isinstance(right, Tile):
+            other = right.cells
+        else:
+            other = right
+        result = np.add(self.cells, other)
+        ct = CellType.from_numpy_dtype(result.dtype)
+        if isinstance(result, np.ma.masked_array):
+            ct = ct.with_no_data_value(result.fill_value)
+        return Tile(np.add(self.cells, other), ct)
+
+    def __sub__(self, right):
+        if isinstance(right, Tile):
+            other = right.cells
+        else:
+            other = right
+        return Tile(np.subtract(self.cells, other), self.cell_type)
+
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
         return [self.cells.shape[1], self.cells.shape[0]]
@@ -269,7 +290,7 @@ def sqlType(cls):
 
     @classmethod
     def module(cls):
-        return 'pyrasterframes.types'
+        return 'pyrasterframes.rf_types'
 
     @classmethod
     def scalaUDT(cls):
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 8ca1141dc..fa39c4456 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -308,6 +308,7 @@ def test_exists_for_all(self):
         self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
         self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
 
+
 class UDT(TestEnvironment):
 
     def test_cell_type_conversion(self):
@@ -342,28 +343,68 @@ def test_cell_type_no_data(self):
         self.assertTrue(math.isnan(CellType("float64").no_data_value()))
         self.assertEqual(CellType("uint8").no_data_value(), 0)
 
+        ct = CellType.from_numpy_dtype("int8")
+        print(ct)
+        print(ct.to_numpy_dtype())
+        print(ct.no_data_value())
+        print(ct.base_cell_type_name())
+
+        a_tile = Tile(np.random.randn(3, 3).astype(ct.to_numpy_dtype()), ct)
+        print(a_tile)
+
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
         self.assertTrue(t1.cells.mask[1][0])
         self.assertIsNotNone(t1.cells[1][1])
         self.assertEqual(len(t1.cells.compressed()), 3)
         t2 = Tile(np.array([[1.0, 2.0], [float('nan'), 4.0]]), CellType("float32"))
-        print(t2)
         self.assertEqual(len(t2.cells.compressed()), 3)
         self.assertTrue(t2.cells.mask[1][0])
         self.assertIsNotNone(t2.cells[1][1])
 
-
     def test_tile_udt_serialization(self):
         udt = TileUDT()
-
-        cell_types = (ct for ct in rf_cell_types() if not ct.cell_type_name.endswith("raw"))
+        cell_types = (ct for ct in rf_cell_types() if not ct.is_raw())
         for ct in cell_types:
-            a_tile = Tile(np.random.randn(3, 3).astype(ct.to_numpy_dtype()))
+            cells = (100 + np.random.randn(3, 3) * 100).astype(ct.to_numpy_dtype())
+
+            if ct.is_floating_point():
+                nd = 33.0
+            else:
+                nd = 33
+
+            cells[1][1] = nd
+            a_tile = Tile(cells, ct.with_no_data_value(nd))
+            print(repr(a_tile.cells))
             round_trip = udt.fromInternal(udt.toInternal(a_tile))
             self.assertEquals(a_tile, round_trip, "round-trip serialization for " + str(ct))
 
-    def test_tile_udt_general(self):
+    def test_no_data_udf_handling(self):
+        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
+        print(t1)
+        schema = StructType([StructField("tile", TileUDT(), False)])
+        df = self.spark.createDataFrame([{"tile": t1}], schema)
+
+        @udf("double")
+        def increment(t: Tile):
+            return t + 1
+
+        print(df.select(increment("tile")).collect())
+
+
+class TileOps(TestEnvironment):
+
+    def test_addition(self):
+        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
+        e1 = np.ma.masked_equal(np.array([[5, 6], [7, 8]]), 7)
+        self.assertTrue(np.array_equal((t1 + 4).cells, e1))
+
+        t2 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud1"))
+        e2 = np.ma.masked_equal(np.array([[3, 4], [3, 8]]), 3)
+        r2 = (t1 + t2).cells
+        self.assertTrue(np.ma.allequal(r2, e2))
+
+    def test_tile_ops_general(self):
         import pandas as pd
 
         self.assertIsInstance(self.rf.sql_ctx, SQLContext)
@@ -386,7 +427,6 @@ def test_tile_udt_general(self):
         # Try to do something with it.
         sums = to_spark.t.apply(lambda a: a.cells.sum()).tolist()
         maybe_sums = rf_maybe.select(rf_tile_sum(rf_maybe.t).alias('tsum'))
-
         maybe_sums = [r.tsum for r in maybe_sums.collect()]
         np.testing.assert_almost_equal(maybe_sums, sums, 12)
 

From 24ae2aa0952eda56666ecfb7baa1b980d0f930b8 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 22 May 2019 12:52:45 -0400
Subject: [PATCH 087/380] Incremental backup commit during CellType debugging.

---
 .../python/pyrasterframes/rasterfunctions.py  |   2 +-
 .../python/pyrasterframes/rf_types.py         |  66 ++++++++++--
 .../python/tests/PyRasterFramesTests.py       | 100 ++++++++++++------
 3 files changed, 121 insertions(+), 47 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index e2102aee3..110a9f25c 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -154,7 +154,7 @@ def _(data_tile, mask_tile, mask_value):
 _rf_unique_functions = {
     'rf_array_to_tile': _create_arrayToTile(),
     'rf_assemble_tile': _create_assembleTile(),
-    'rf_cell_types': lambda: [CellType(ct) for ct in _context_call('rf_cell_types')],
+    'rf_cell_types': lambda: [CellType(str(ct)) for ct in _context_call('rf_cell_types')],
     'rf_convert_cell_type': _create_convertCellType(),
     'rf_explode_tiles': _create_explode_tiles(),
     'rf_explode_tiles_sample': _create_explode_tiles_sample(),
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index df3744ffa..722a6d4f1 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -117,7 +117,7 @@ def withSpatialIndex(self):
 
 class RasterSourceUDT(UserDefinedType):
     @classmethod
-    def sqlType(self):
+    def sqlType(cls):
         return StructType([
             StructField("raster_source_kryo", BinaryType(), False)])
 
@@ -138,11 +138,11 @@ def deserialize(self, datum):
 
 
 class CellType(object):
-    def __init__(self, cell_type_name):
+    def __init__(self, cell_type_name: str):
         self.cell_type_name = cell_type_name
 
     @classmethod
-    def from_numpy_dtype(cls, np_dtype):
+    def from_numpy_dtype(cls, np_dtype: np.dtype):
         return CellType(str(np_dtype))
 
     def is_raw(self):
@@ -182,17 +182,37 @@ def no_data_value(self):
                 return np.nan
             else:
                 n = self.base_cell_type_name()
-                if n is "uint8" or n is "uint16":
+                if n == "uint8" or n == "uint16":
                     return 0
-                elif n is "int8":
+                elif n == "int8":
                     return -128
-                elif n is "int16":
+                elif n == "int16":
                     return -32768
-                elif n is "int32":
+                elif n == "int32":
                     return -2147483648
+                elif n == "bool":
+                    return None
+        raise Exception("Unable to determine no_data_value from '{}'".format(n))
 
     def to_numpy_dtype(self):
-        return np.dtype(self.base_cell_type_name())
+        n = self.base_cell_type_name()
+        if n == "uint8":
+            return np.uint
+        elif n == "int8":
+            return np.int
+        elif n == "uint16":
+            return np.ushort
+        elif n == "int16":
+            return np.short
+        elif n == "int32":
+            return np.int
+        elif n == "float32":
+            return np.float
+        elif n == "float64":
+            return np.double
+        else:
+            # Shouldn't happen
+            return np.dtype(n)
 
     def with_no_data_value(self, no_data):
         return CellType(self.base_cell_type_name() + "ud" + str(no_data))
@@ -206,6 +226,9 @@ def __eq__(self, other):
     def __str__(self):
         return "CellType({}, {})".format(self.cell_type_name, self.no_data_value())
 
+    def __repr__(self):
+        return self.cell_type_name
+
 
 class Tile(object):
     def __init__(self, cells, cell_type=None):
@@ -234,6 +257,10 @@ def __str__(self):
         return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)" \
             .format(self.dimensions(), self.cell_type, self.cells)
 
+    def __repr__(self):
+        return "Tile({}, {})" \
+            .format(repr(self.cell_type), str(self.cells))
+
     def __add__(self, right):
         if isinstance(right, Tile):
             other = right.cells
@@ -297,6 +324,7 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
     def serialize(self, tile):
+        #print("in: ", repr(tile.cells.flatten().tobytes()))
         row = [
             # cell_context
             [
@@ -322,9 +350,23 @@ def deserialize(self, datum):
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
-
-        as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype()).reshape((rows, cols))
-        t = Tile(as_numpy, cell_type)
+        #print("out: ", repr(cell_data_bytes))
+        try:
+            as_numpy = np.frombuffer(cell_data_bytes)
+                #.astype(dtype=cell_type.to_numpy_dtype())
+                #.astype(dtype=np.dtype('B')) \
+            reshaped = as_numpy.reshape((rows, cols))
+            t = Tile(reshaped, cell_type)
+        except ValueError as e:
+            raise ValueError({
+                "cell_type": cell_type,
+                "cols": cols,
+                "rows": rows,
+                "cell_data.length": len(cell_data_bytes),
+                "cell_data.type": type(cell_data_bytes),
+                "cell_data.values": repr(cell_data_bytes),
+                "as_numpy.values": repr(as_numpy)
+            }, e)
         return t
 
     deserialize.__safe_for_unpickling__ = True
@@ -337,6 +379,7 @@ class TileExploder(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     Python wrapper for TileExploder.scala
     """
+
     def __init__(self):
         super(TileExploder, self).__init__()
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.TileExploder", self.uid)
@@ -346,6 +389,7 @@ class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
     """
     Python wrapper for NoDataFilter.scala
     """
+
     def __init__(self):
         super(NoDataFilter, self).__init__()
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.NoDataFilter", self.uid)
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index fa39c4456..dd5fa2f58 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,5 +1,7 @@
 from pyspark.sql import SparkSession, Column, SQLContext
 from pyspark.sql.functions import *
+from pyspark.sql.types import *
+
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyrasterframes.rf_types import *
@@ -56,11 +58,6 @@ def setUpClass(cls):
             .withColumnRenamed('tile2', cls.tileCol).asRF()
         # cls.rf.show()
 
-    def test_setup(self):
-        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
-                         "org.apache.spark.serializer.KryoSerializer")
-
-
 class VectorTypes(TestEnvironment):
 
     def setUp(self):
@@ -133,8 +130,8 @@ def some_point(g):
 
     def test_rasterize(self):
         # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
-        withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
-        withRaster.show()
+        with_raster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
+        with_raster.show()
 
     def test_reproject(self):
         reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
@@ -199,19 +196,20 @@ def test_general(self):
         #  a source of ProjectedRasterTiles.
         df.show()
 
+    def test_agg_mean(self):
+        mean = self.rf.agg(rf_agg_mean(self.tileCol)).first()['rf_agg_mean(tile)']
+        self.assertTrue(self.rounded_compare(mean, 10160))
+
     def test_aggregations(self):
         aggs = self.rf.agg(
-            rf_agg_mean(self.tileCol),
             rf_agg_data_cells(self.tileCol),
             rf_agg_no_data_cells(self.tileCol),
             rf_agg_stats(self.tileCol),
             rf_agg_approx_histogram(self.tileCol)
         )
-        aggs.show()
         row = aggs.first()
 
-        self.assertTrue(self.rounded_compare(row['rf_agg_mean(tile)'], 10160))
-        print(row['rf_agg_data_cells(tile)'])
+        # print(row['rf_agg_data_cells(tile)'])
         self.assertEqual(row['rf_agg_data_cells(tile)'], 387000)
         self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
@@ -309,17 +307,7 @@ def test_exists_for_all(self):
         self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
 
 
-class UDT(TestEnvironment):
-
-    def test_cell_type_conversion(self):
-        for ct in rf_cell_types():
-            self.assertEqual(ct.to_numpy_dtype(),
-                             CellType.from_numpy_dtype(ct.to_numpy_dtype()).to_numpy_dtype(),
-                             "dtype comparison for " + str(ct))
-            if not ct.is_raw():
-                self.assertEqual(ct,
-                                 CellType.from_numpy_dtype(ct.to_numpy_dtype()),
-                                 "GTCellType comparison for " + str(ct))
+class CellTypeHandling(unittest.TestCase):
 
     def test_is_raw(self):
         self.assertTrue(CellType("float32raw").is_raw())
@@ -339,18 +327,48 @@ def test_is_floating_point(self):
 
     def test_cell_type_no_data(self):
         import math
+        self.assertIsNone(CellType("bool").no_data_value())
+
+        self.assertTrue(CellType("int8").has_no_data())
+        self.assertEqual(CellType("int8").no_data_value(), -128)
+
+        self.assertTrue(CellType("uint8").has_no_data())
+        self.assertEqual(CellType("uint8").no_data_value(), 0)
+
+        self.assertTrue(CellType("int16").has_no_data())
+        self.assertEqual(CellType("int16").no_data_value(), -32768)
+
+        self.assertTrue(CellType("uint16").has_no_data())
+        self.assertEqual(CellType("uint16").no_data_value(), 0)
+
+        self.assertTrue(CellType("float32").has_no_data())
+        self.assertTrue(np.isnan(CellType("float32").no_data_value()))
+
         self.assertEqual(CellType("float32ud-98").no_data_value(), -98.0)
         self.assertTrue(math.isnan(CellType("float64").no_data_value()))
         self.assertEqual(CellType("uint8").no_data_value(), 0)
 
-        ct = CellType.from_numpy_dtype("int8")
-        print(ct)
-        print(ct.to_numpy_dtype())
-        print(ct.no_data_value())
-        print(ct.base_cell_type_name())
 
-        a_tile = Tile(np.random.randn(3, 3).astype(ct.to_numpy_dtype()), ct)
-        print(a_tile)
+class UDT(TestEnvironment):
+    def test_cell_type_conversion(self):
+        for ct in rf_cell_types():
+            self.assertEqual(ct.to_numpy_dtype(),
+                             CellType.from_numpy_dtype(ct.to_numpy_dtype()).to_numpy_dtype(),
+                             "dtype comparison for " + str(ct))
+            if not ct.is_raw():
+                self.assertEqual(ct,
+                                 CellType.from_numpy_dtype(ct.to_numpy_dtype()),
+                                 "GTCellType comparison for " + str(ct))
+
+    def test_array_flattening(self):
+        #a = np.frombuffer(bytearray([1, 0, 0, 0, 0, 0, 0, 0]))
+
+        a = np.array([[1, 2], [0, 4]], dtype=np.dtype("uint8"))
+        b = np.ma.masked_equal(a, 2)
+        c = builtins.bytearray(b.flatten().tobytes())
+        d = np.frombuffer(c, b.dtype).reshape(2, 2)
+        e = np.ma.masked_equal(d, 2)
+        print(repr(e))
 
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
@@ -364,7 +382,8 @@ def test_mask_no_data(self):
 
     def test_tile_udt_serialization(self):
         udt = TileUDT()
-        cell_types = (ct for ct in rf_cell_types() if not ct.is_raw())
+        cell_types = (ct for ct in rf_cell_types() if not (ct.is_raw() or ("bool" in ct.base_cell_type_name())))
+
         for ct in cell_types:
             cells = (100 + np.random.randn(3, 3) * 100).astype(ct.to_numpy_dtype())
 
@@ -375,21 +394,27 @@ def test_tile_udt_serialization(self):
 
             cells[1][1] = nd
             a_tile = Tile(cells, ct.with_no_data_value(nd))
-            print(repr(a_tile.cells))
             round_trip = udt.fromInternal(udt.toInternal(a_tile))
             self.assertEquals(a_tile, round_trip, "round-trip serialization for " + str(ct))
 
+            schema = StructType([StructField("tile", TileUDT(), False)])
+            df = self.spark.createDataFrame([{"tile": a_tile}], schema)
+
+            long_trip = df.first()["tile"]
+            self.assertEqual(long_trip, a_tile)
+
     def test_no_data_udf_handling(self):
-        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
-        print(t1)
+        t1 = Tile(np.array([[1, 2], [0, 4]]), CellType("uint8"))
+        e1 = Tile(np.array([[2, 3], [0, 5]]), CellType("uint8"))
         schema = StructType([StructField("tile", TileUDT(), False)])
         df = self.spark.createDataFrame([{"tile": t1}], schema)
 
-        @udf("double")
+        @udf(TileUDT())
         def increment(t: Tile):
             return t + 1
 
-        print(df.select(increment("tile")).collect())
+        r1 = df.select(increment(df.tile).alias("inc")).first()["inc"]
+        self.assertEqual(r1, e1)
 
 
 class TileOps(TestEnvironment):
@@ -453,6 +478,11 @@ def test_tile_ops_general(self):
 
 class RasterSource(TestEnvironment):
 
+    # Putting this here for convenience
+    def test_setup(self):
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
+                         "org.apache.spark.serializer.KryoSerializer")
+
     def test_raster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run

From 33e1e32fd20237d75b48796b82f704b4cf8aa911 Mon Sep 17 00:00:00 2001
From: Phil Varner <pvarner@astrea.io>
Date: Wed, 15 May 2019 09:40:21 -0400
Subject: [PATCH 088/380] Cherry pick 420eb2e6faabb299d1baff1b8a5852820a21d7d6

---
 .circleci/config.yml                          | 32 +++++++++++++++++--
 .../rasterframes/ref/RasterSourceIT.scala     | 19 ++++++++---
 2 files changed, 44 insertions(+), 7 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 1fffd77f6..5a3de91ae 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -5,8 +5,8 @@ _defaults: &defaults
   environment:
     TERM: dumb
   docker:
-    - image: circleci/openjdk:8-jdk
-      
+    - image: s22s/rasterframes-circleci:latest
+
 _setenv: &setenv
   name: set CloudRepo credentials
   command: |-
@@ -111,6 +111,28 @@ jobs:
       - save_cache:
           <<: *save_cache
 
+  itWithoutGdal:
+    working_directory: ~/repo
+    environment:
+      TERM: dumb
+    docker:
+      - image: circleci/openjdk:8-jdk
+    resource_class: xlarge
+    steps:
+      - checkout
+      - run: *setenv
+
+      - restore_cache:
+          <<: *restore_cache
+
+      - run:
+          command: cat /dev/null | sbt it:test
+          no_output_timeout: 30m
+      - run: *unsetenv
+
+      - save_cache:
+          <<: *save_cache
+
 workflows:
   version: 2
   all:
@@ -121,6 +143,11 @@ workflows:
             branches:
               only:
                 - /astraea\/feature\/.*-its/
+      - itWithoutGdal:
+          filters:
+            branches:
+              only:
+                - /astraea\/feature\/.*-its/
       - publish:
           filters:
             branches:
@@ -136,4 +163,5 @@ workflows:
                 - astraea/develop
     jobs:
       - it
+      - itWithoutGdal
       - staticAnalysis
diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 33a951853..3cfe59371 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -62,11 +62,19 @@ class RasterSourceIT extends TestEnvironment with TestData {
     }
   }
 
-  describe("GDAL support") {
-
-    it("should read JPEG2000 scene") {
-      val src = RasterSource(localSentinel)
-      src.readAll().flatMap(_.tile.statisticsDouble).size should be (64)
+  if (RasterSource.IsGDAL.hasGDAL) {
+    describe("GDAL support") {
+      it("should read JPEG2000 scene") {
+        RasterSource(localSentinel.toURI).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
+      }
+    }
+  } else {
+    describe("GDAL missing error support") {
+      it("should throw exception reading JPEG2000 scene") {
+          intercept[IllegalArgumentException] {
+            RasterSource(localSentinel.toURI)
+          }
+      }
     }
 
     it("should read small MRF scene with one band converted from MODIS HDF") {
@@ -93,4 +101,5 @@ class RasterSourceIT extends TestEnvironment with TestData {
     val expectedTileCount = tilesPerBand * bands.length
     (expectedTileCount, bands)
   }
+
 }

From b6dbad14d1fa80288d9918d11cff46f6707fe5c3 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 22 May 2019 13:57:52 -0400
Subject: [PATCH 089/380] Integration test fix.

---
 .../org/locationtech/rasterframes/ref/RasterSourceIT.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 3cfe59371..7027258ee 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -65,14 +65,14 @@ class RasterSourceIT extends TestEnvironment with TestData {
   if (RasterSource.IsGDAL.hasGDAL) {
     describe("GDAL support") {
       it("should read JPEG2000 scene") {
-        RasterSource(localSentinel.toURI).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
+        RasterSource(localSentinel).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
       }
     }
   } else {
     describe("GDAL missing error support") {
       it("should throw exception reading JPEG2000 scene") {
           intercept[IllegalArgumentException] {
-            RasterSource(localSentinel.toURI)
+            RasterSource(localSentinel)
           }
       }
     }

From cf2cbed7c3c53d61804dadf05c92e3b31820dc5c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 23 May 2019 14:59:37 -0400
Subject: [PATCH 090/380] Draft implementation of RasterJoin.

---
 .../rasterframes/RasterFunctions.scala        |  10 +-
 .../encoders/StandardEncoders.scala           |   3 +-
 .../encoders/StandardSerializers.scala        |  21 +++
 .../CellCountAggregate.scala                  |   2 +-
 .../CellMeanAggregate.scala                   |   2 +-
 .../CellStatsAggregate.scala                  |   2 +-
 .../HistogramAggregate.scala                  |   2 +-
 .../LocalCountAggregate.scala                 |   2 +-
 .../LocalMeanAggregate.scala                  |   2 +-
 .../LocalStatsAggregate.scala                 |   2 +-
 .../LocalTileOpAggregate.scala                |   2 +-
 .../aggregates/TileRasterizerAggregate.scala  | 109 +++++++++++++++
 .../rasterframes/expressions/package.scala    |   4 +-
 .../extensions/DataFrameMethods.scala         |   3 +
 .../rasterframes/extensions/RasterJoin.scala  | 125 ++++++++++++++++++
 .../util/ZeroSevenCompatibilityKit.scala      |   2 +-
 .../test/resources/L8-B4-Elkton-VA-4326.tiff  | Bin 0 -> 63934 bytes
 .../rasterframes/RasterJoinSpec.scala         |  57 ++++++++
 .../rasterframes/SpatialEncodingSpec.scala    |  43 ------
 .../rasterframes/encoders/EncodingSpec.scala  |   8 ++
 20 files changed, 345 insertions(+), 56 deletions(-)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/CellCountAggregate.scala (98%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/CellMeanAggregate.scala (97%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/CellStatsAggregate.scala (99%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/HistogramAggregate.scala (98%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/LocalCountAggregate.scala (98%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/LocalMeanAggregate.scala (97%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/LocalStatsAggregate.scala (99%)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{aggstats => aggregates}/LocalTileOpAggregate.scala (98%)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
 create mode 100644 core/src/test/resources/L8-B4-Elkton-VA-4326.tiff
 create mode 100644 core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
 delete mode 100644 core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index c5caa1c5a..6684cf0a5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.jts.geom.Geometry
 import org.locationtech.rasterframes.expressions.TileAssembler
 import org.locationtech.rasterframes.expressions.accessors._
-import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.aggregates._
 import org.locationtech.rasterframes.expressions.generators._
 import org.locationtech.rasterframes.expressions.localops._
 import org.locationtech.rasterframes.expressions.tilestats._
@@ -308,6 +308,14 @@ trait RasterFunctions {
   def st_reproject(sourceGeom: Column, srcCRS: CRS, dstCRS: CRS): TypedColumn[Any, Geometry] =
     ReprojectGeometry(sourceGeom, srcCRS, dstCRS)
 
+  /** Reproject a column of geometry from one CRS to another.
+    * @param sourceGeom Geometry column to reproject
+    * @param srcCRSCol Native CRS of `sourceGeom` as a column
+    * @param dstCRSCol Destination CRS as a column
+    */
+  def st_reproject(sourceGeom: Column, srcCRSCol: Column, dstCRSCol: Column): TypedColumn[Any, Geometry] =
+    ReprojectGeometry(sourceGeom, srcCRSCol, dstCRSCol)
+
   /** Render Tile as ASCII string, for debugging purposes. */
   def rf_render_ascii(col: Column): TypedColumn[Any, String] =
     DebugRender.RenderAscii(col)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
index f9e8d428f..8066265d3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
@@ -28,7 +28,7 @@ import org.locationtech.rasterframes.model._
 import org.locationtech.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
 import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
-import geotrellis.raster.{CellSize, CellType, Tile, TileLayout}
+import geotrellis.raster.{CellSize, CellType, Raster, Tile, TileLayout}
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TemporalKey, TemporalProjectedExtent, TileLayerMetadata}
 import geotrellis.vector.{Extent, ProjectedExtent}
@@ -51,6 +51,7 @@ trait StandardEncoders extends SpatialEncoders {
   implicit def stkBoundsEncoder: ExpressionEncoder[KeyBounds[SpaceTimeKey]] = ExpressionEncoder()
   implicit def extentEncoder: ExpressionEncoder[Extent] = ExpressionEncoder[Extent]()
   implicit def singlebandTileEncoder: ExpressionEncoder[Tile] = ExpressionEncoder()
+  implicit def rasterEncoder: ExpressionEncoder[Raster[Tile]] = ExpressionEncoder()
   implicit def tileLayerMetadataEncoder[K: TypeTag]: ExpressionEncoder[TileLayerMetadata[K]] = TileLayerMetadataEncoder()
   implicit def crsEncoder: ExpressionEncoder[CRS] = CRSEncoder()
   implicit def projectedExtentEncoder: ExpressionEncoder[ProjectedExtent] = ProjectedExtentEncoder()
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
index 36830c4c0..96815f90b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
@@ -29,6 +29,8 @@ import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.vector._
 import org.apache.spark.sql.types._
 import org.locationtech.jts.geom.Envelope
+import org.locationtech.rasterframes
+import org.locationtech.rasterframes.TileType
 import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
 import org.locationtech.rasterframes.model.LazyCRS
 
@@ -247,6 +249,25 @@ trait StandardSerializers {
     )
   }
 
+  implicit def rasterSerializer: CatalystSerializer[Raster[Tile]] = new CatalystSerializer[Raster[Tile]] {
+    import org.apache.spark.sql.rf.TileUDT.tileSerializer
+
+    override def schema: StructType = StructType(Seq(
+      StructField("tile", TileType, false),
+      StructField("extent", schemaOf[Extent], false)
+    ))
+
+    override protected def to[R](t: Raster[Tile], io: CatalystIO[R]): R = io.create(
+      io.to(t.tile),
+      io.to(t.extent)
+    )
+
+    override protected def from[R](t: R, io: CatalystIO[R]): Raster[Tile] = Raster(
+      io.get[Tile](t, 0),
+      io.get[Extent](t, 1)
+    )
+  }
+
   implicit val spatialKeyTLMSerializer = tileLayerMetadataSerializer[SpatialKey]
   implicit val spaceTimeKeyTLMSerializer = tileLayerMetadataSerializer[SpaceTimeKey]
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellCountAggregate.scala
similarity index 98%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellCountAggregate.scala
index 1e9e7e5e1..82c2d3f93 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellCountAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
 import org.locationtech.rasterframes.expressions.tilestats.{DataCells, NoDataCells}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellMeanAggregate.scala
similarity index 97%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellMeanAggregate.scala
index a77c3ee21..009a46cf3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellMeanAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
 import org.locationtech.rasterframes.expressions.tilestats.{DataCells, Sum}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellStatsAggregate.scala
similarity index 99%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellStatsAggregate.scala
index 84ea9632b..95c0bd837 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/CellStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/CellStatsAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
 import org.locationtech.rasterframes.stats.CellStatistics
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/HistogramAggregate.scala
similarity index 98%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/HistogramAggregate.scala
index cab065e58..44cc1324b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/HistogramAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/HistogramAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import java.nio.ByteBuffer
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalCountAggregate.scala
similarity index 98%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalCountAggregate.scala
index 8300cfd04..256cd63dd 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalCountAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalCountAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
 import org.locationtech.rasterframes.functions.safeBinaryOp
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
similarity index 97%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
index face1b8a8..e978d21f9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
 import org.locationtech.rasterframes.expressions.localops.{Add => AddTiles, Divide => DivideTiles}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalStatsAggregate.scala
similarity index 99%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalStatsAggregate.scala
index 65267ebc6..86b360dea 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalStatsAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalStatsAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
 import org.locationtech.rasterframes.functions.safeBinaryOp
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalTileOpAggregate.scala
similarity index 98%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalTileOpAggregate.scala
index 8aacffa9f..b739961c1 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggstats/LocalTileOpAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalTileOpAggregate.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.aggstats
+package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.TileType
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
new file mode 100644
index 000000000..1f99d0261
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -0,0 +1,109 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.aggregates
+
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import geotrellis.proj4.CRS
+import geotrellis.raster.resample.ResampleMethod
+import geotrellis.raster.{ArrayTile, CellType, MutableArrayTile, Raster, Tile}
+import geotrellis.vector.Extent
+import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
+import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.sql.{Column, Row, TypedColumn}
+import geotrellis.raster.reproject.Reproject
+import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+
+/**
+  * Aggregation function for creating a single raster from tiles.
+  * @param prd settings
+  */
+class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefinedAggregateFunction {
+
+  val projOpts = Reproject.Options.DEFAULT.copy(method = prd.sampler)
+
+  override def deterministic: Boolean = true
+
+  override def inputSchema: StructType = StructType(Seq(
+    StructField("crs", schemaOf[CRS], false),
+    StructField("extent", schemaOf[Extent], false),
+    StructField("tile", TileType)
+  ))
+
+  override def bufferSchema: StructType = StructType(Seq(
+    StructField("tile_buffer", TileType),
+    StructField("extent_buffer", schemaOf[Extent])
+  ))
+
+  override def dataType: DataType = schemaOf[Raster[Tile]]
+
+  override def initialize(buffer: MutableAggregationBuffer): Unit = {
+    buffer(0) = ArrayTile.empty(prd.cellType, prd.cols, prd.rows)
+  }
+
+  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
+    val crs = input.getAs[Row](0).to[CRS]
+    val extent = input.getAs[Row](1).to[Extent]
+
+    val localExtent = extent.reproject(crs, prd.crs)
+
+    if (prd.extent.intersects(localExtent)) {
+      val localTile = input.getAs[Tile](2).reproject(extent, crs, prd.crs, projOpts)
+      val bt = buffer.getAs[MutableArrayTile](0)
+      val merged = bt.merge(prd.extent, localExtent, localTile.tile, prd.sampler)
+      buffer(0) = merged
+      if (buffer.isNullAt(1))
+        buffer(1) = localExtent.toRow
+      else {
+        val curr = buffer.getAs[Row](1).to[Extent]
+        buffer(1) = curr.combine(localExtent).toRow
+      }
+    }
+  }
+
+  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
+    val leftTile = buffer1.getAs[MutableArrayTile](0)
+    val rightTile = buffer2.getAs[MutableArrayTile](0)
+    val leftExtent = buffer1.getAs[Row](1).to[Extent]
+    val rightExtent = buffer2.getAs[Row](1).to[Extent]
+    buffer1(0) = leftTile.merge(rightTile)
+    buffer1(1) =
+      if (leftExtent == null) rightExtent
+      else if (rightExtent == null) leftExtent
+      else leftExtent.combine(rightExtent)
+  }
+
+  override def evaluate(buffer: Row): Raster[Tile] = {
+    val t = buffer.getAs[Tile](0)
+    val e = buffer.getAs[Row](1).to[Extent]
+    Raster(t, e)
+  }
+}
+
+object TileRasterizerAggregate {
+  val nodeName = "tile_rasterizer_aggregate"
+  /**  Convenience grouping of  parameters needed for running aggregate. */
+  case class ProjectedRasterDefinition(cols: Int, rows: Int, cellType: CellType, crs: CRS, extent: Extent, sampler: ResampleMethod = ResampleMethod.DEFAULT)
+
+  def apply(prd: ProjectedRasterDefinition, crsCol: Column, extentCol: Column, tileCol: Column): TypedColumn[Any, Raster[Tile]] =
+    new TileRasterizerAggregate(prd)(crsCol, extentCol, tileCol).as(nodeName).as[Raster[Tile]]
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index c574114d8..a836bed76 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -28,8 +28,8 @@ import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection}
 import org.apache.spark.sql.rf.VersionShims._
 import org.apache.spark.sql.{SQLContext, rf}
 import org.locationtech.rasterframes.expressions.accessors._
-import org.locationtech.rasterframes.expressions.aggstats.CellCountAggregate.DataCells
-import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.aggregates.CellCountAggregate.DataCells
+import org.locationtech.rasterframes.expressions.aggregates._
 import org.locationtech.rasterframes.expressions.generators._
 import org.locationtech.rasterframes.expressions.localops._
 import org.locationtech.rasterframes.expressions.tilestats._
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 58b1e7e56..373a7f1db 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -168,6 +168,9 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     potentialRF
   }
 
+  /** Performs a jeft join on the dataframe `right` to this one, reprojecting tiles. */
+  def rasterJoin(right: DataFrame): DataFrame = RasterJoin(self, right)
+
   /**
    * Convert DataFrame into a RasterFrame
    *
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
new file mode 100644
index 000000000..327b51ce6
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -0,0 +1,125 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.extensions
+import geotrellis.proj4.CRS
+import geotrellis.raster._
+import geotrellis.raster.reproject.Reproject
+import geotrellis.raster.resample.ResampleMethod
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.util._
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.model.TileDimensions
+
+import scala.util.Random
+/*
+    val crs = input.getAs[Row](0).to[CRS]
+    val extent = input.getAs[Row](1).to[Extent]
+
+    val localExtent = extent.reproject(crs, prd.crs)
+
+    if (prd.extent.intersects(localExtent)) {
+      val localTile = input.getAs[Tile](2).reproject(extent, crs, prd.crs, projOpts)
+      val bt = buffer.getAs[MutableArrayTile](0)
+      val merged = bt.merge(prd.extent, localExtent, localTile.tile, prd.sampler)
+      buffer(0) = merged
+    }
+
+      // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
+  private def apply_mask = udf((t: Tile, extentStruct: Row, crsStruct: Row, geom: Seq[Geometry]) ⇒ {
+    val e = extentStruct.to[Extent]
+    val c = crsStruct.to[CRS]
+    val reprojectedGeoms = geom.map(g ⇒ GTGeometry(g).reproject(LatLng, c))
+    t.mask(e, reprojectedGeoms)
+  })
+ */
+
+object RasterJoin {
+  private val projOpts = Reproject.Options.DEFAULT
+
+  // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
+  def reproject_and_merge = udf((leftExtentEnc: Row, leftCRSEnc: Row, tiles: Seq[Tile], rightExtentEnc: Seq[Row], rightCRSEnc: Seq[Row], leftDimsEnc: Row) => {
+    require(tiles.length == rightExtentEnc.length && tiles.length == rightCRSEnc.length, "size mismatch")
+
+    val leftExtent = leftExtentEnc.to[Extent]
+    val leftDims = leftDimsEnc.to[TileDimensions]
+    val leftCRS = leftCRSEnc.to[CRS]
+    val rightExtents = rightExtentEnc.map(_.to[Extent])
+    val rightCRSs = rightCRSEnc.map(_.to[CRS])
+
+    //is there a GT function to do all this?
+    val cellType = tiles.map(_.cellType).reduce(_ union _)
+
+    val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
+    tiles.zip(rightExtents).zip(rightCRSs).map {
+      case ((tile, extent), crs) =>
+        tile.reproject(extent, crs, leftCRS, projOpts)
+    }.foldLeft(dest)((d, t) =>
+      d.merge(leftExtent, t.extent, t.tile, projOpts.method)
+    )
+  }).withName("reproject_and_merge")
+
+  def apply(left: DataFrame, right: DataFrame): DataFrame = {
+    val df = apply(left, right, left("extent"), left("crs"), right("extent"), right("crs"))
+    df.drop(right("extent")).drop(right("crs"))
+  }
+
+  def apply(left: DataFrame, right: DataFrame, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame = {
+    apply(left, right, st_intersects(
+      st_geometry(leftExtent),
+      st_reproject(st_geometry(rightExtent), rightCRS, leftCRS)
+    ), leftExtent, leftCRS, rightExtent, rightCRS)
+  }
+  def apply(left: DataFrame, right: DataFrame, joinExprs: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame = {
+    // Unique id for temporary columns
+    val id = Random.alphanumeric.take(5).mkString("_", "", "_")
+
+    // Post aggregation left extent. We preserve the original name.
+    val leftExtent2 = leftExtent.columnName
+    // Post aggregation left crs. We preserve the original name.
+    val leftCRS2 = leftCRS.columnName
+    // Post aggregation right extent. We create a new name.
+    val rightExtent2 = id + "extent"
+    // Post aggregation right crs. We create a new name.
+    val rightCRS2 = id + "crs"
+
+    // A representative tile from the left
+    val leftTile = left.tileColumns.headOption.getOrElse(throw new IllegalArgumentException("Need at least one target tile on LHS"))
+
+
+    val leftAggCols = left.columns.map(s => first(left(s), true) as s)
+    val rightAggCtx = Seq(collect_list(rightExtent) as rightExtent2, collect_list(rightCRS) as rightCRS2)
+    val rightAggTiles = right.tileColumns.map(c => collect_list(c) as c.columnName)
+    val aggCols = leftAggCols ++ rightAggTiles ++ rightAggCtx
+    val reprojCols = rightAggTiles.map(t => reproject_and_merge(col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(leftTile)) as t.columnName)
+    val finalCols = leftAggCols.map(c => col(c.columnName)) ++ reprojCols
+
+    left
+      .withColumn(id, monotonically_increasing_id())
+      .join(right, joinExprs, joinType = "left")
+      .groupBy(col(id))
+      .agg(aggCols.head, aggCols.tail: _*)
+      .select(finalCols: _*)
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index b5e62b42d..d937dd290 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -23,7 +23,7 @@ package org.locationtech.rasterframes.util
 
 import org.locationtech.rasterframes.expressions.TileAssembler
 import org.locationtech.rasterframes.expressions.accessors._
-import org.locationtech.rasterframes.expressions.aggstats._
+import org.locationtech.rasterframes.expressions.aggregates._
 import org.locationtech.rasterframes.expressions.generators._
 import org.locationtech.rasterframes.expressions.localops._
 import org.locationtech.rasterframes.expressions.tilestats._
diff --git a/core/src/test/resources/L8-B4-Elkton-VA-4326.tiff b/core/src/test/resources/L8-B4-Elkton-VA-4326.tiff
new file mode 100644
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diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
new file mode 100644
index 000000000..b260d50f8
--- /dev/null
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -0,0 +1,57 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes
+
+import geotrellis.raster.io.geotiff.GeoTiff
+import geotrellis.raster.{Raster, Tile}
+import geotrellis.raster.render.ColorRamps
+import org.locationtech.rasterframes.encoders.serialized_literal
+import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate
+import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+
+
+class RasterJoinSpec extends TestEnvironment with TestData {
+  describe("Raster join between two DataFrames") {
+    it("should join same scene in two projections, same tile size") {
+      val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
+      val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
+
+      val r1 = s1.projectedRaster.toRF(10, 10).withExtent().withColumn("crs", serialized_literal(s1.crs))
+      val r2 = s2.projectedRaster.toRF(10, 10)
+        .withExtent()
+        .withColumn("crs", serialized_literal(s2.crs))
+        .withColumnRenamed("tile", "tile2")
+
+      val joined = r1.rasterJoin(r2)
+      val result = joined.agg(TileRasterizerAggregate(
+        ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent),
+        col("crs"), col("extent"), col("tile2")) as "raster"
+      ).select(col("raster").as[Raster[Tile]]).first()
+
+      GeoTiff(result, s1.crs).write("target/out.tiff")
+      result.extent shouldBe s1.extent
+      //result.tile.renderPng(ColorRamps.greyscale(256)).write("target/out.png")
+    }
+  }
+
+}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
deleted file mode 100644
index fe48aa4b2..000000000
--- a/core/src/test/scala/org/locationtech/rasterframes/SpatialEncodingSpec.scala
+++ /dev/null
@@ -1,43 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2017 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes
-
-/**
- * Tests type encoding for spatial types.
- *
- * @since 12/17/17
- */
-class SpatialEncodingSpec extends TestEnvironment with TestData  {
-
-  import sqlContext.implicits._
-
-  describe("Dataframe encoding ops on spatial types") {
-
-    it("should code RDD[Point]") {
-      val points = Seq(null, extent.center.jtsGeom, null)
-      val ds = points.toDS
-      write(ds)
-      //ds.show()
-      assert(ds.collect().toSeq === points)
-    }
-  }
-}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
index 3139df995..b27d5cccd 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/encoders/EncodingSpec.scala
@@ -152,5 +152,13 @@ class EncodingSpec extends TestEnvironment with TestData {
       assert(ds.first === env)
     }
   }
+  describe("Dataframe encoding ops on spatial types") {
 
+    it("should code RDD[Point]") {
+      val points = Seq(null, extent.center.jtsGeom, null)
+      val ds = points.toDS
+      write(ds)
+      assert(ds.collect().toSeq === points)
+    }
+  }
 }

From 31e922f2e5ec57f0d623db7ded4387b089c4bca6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 24 May 2019 10:17:03 -0400
Subject: [PATCH 091/380] Added `rf_abs`.

---
 .../rasterframes/RasterFunctions.scala        |  4 ++
 .../expressions/localops/Abs.scala            | 50 +++++++++++++++++++
 .../rasterframes/expressions/package.scala    |  1 +
 .../tiles/ProjectedRasterTile.scala           |  1 +
 .../rasterframes/RasterFunctionsSpec.scala    |  9 ++++
 .../rasterframes/TestEnvironment.scala        |  7 ++-
 docs/src/main/tut/reference.md                |  8 ++-
 docs/src/main/tut/release-notes.md            |  2 +-
 .../python/pyrasterframes/rasterfunctions.py  |  1 +
 pyrasterframes/python/requirements.txt        |  5 ++
 pyrasterframes/python/setup.py                | 34 ++++---------
 .../python/tests/PyRasterFramesTests.py       |  5 +-
 12 files changed, 97 insertions(+), 30 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
 create mode 100644 pyrasterframes/python/requirements.txt

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 6684cf0a5..dc0eca688 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -376,6 +376,10 @@ trait RasterFunctions {
   def rf_round(tileCol: Column): TypedColumn[Any, Tile] =
     Round(tileCol)
 
+  /** Compute the absolute value of each cell. */
+  def rf_abs(tileCol: Column): TypedColumn[Any, Tile] =
+    Abs(tileCol)
+
   /** Take natural logarithm of cell values. */
   def rf_log(tileCol: Column): TypedColumn[Any, Tile] =
     Log(tileCol)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
new file mode 100644
index 000000000..196bf30b7
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
@@ -0,0 +1,50 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.localops
+
+import geotrellis.raster.Tile
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryLocalRasterOp}
+
+@ExpressionDescription(
+  usage = "_FUNC_(tile) - Compute the absolute value of each cell.",
+  arguments = """
+  Arguments:
+    * tile - tile column to apply abs""",
+  examples = """
+  Examples:
+    > SELECT  _FUNC_(tile);
+       ..."""
+)
+case class Abs(child: Expression) extends UnaryLocalRasterOp with NullToValue with CodegenFallback {
+  override def nodeName: String = "rf_abs"
+  override def na: Any = null
+  override protected def op(child: Tile): Tile = child.localAbs()
+}
+
+object Abs {
+  def apply(tile: Column): TypedColumn[Any, Tile] =
+    new Column(Abs(tile.expr)).as[Tile]
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index a836bed76..6ab3a1d10 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -86,6 +86,7 @@ package object expressions {
     registry.registerExpression[Unequal]("rf_local_unequal")
     registry.registerExpression[Sum]("rf_tile_sum")
     registry.registerExpression[Round]("rf_round")
+    registry.registerExpression[Abs]("rf_abs")
     registry.registerExpression[Log]("rf_log")
     registry.registerExpression[Log10]("rf_log10")
     registry.registerExpression[Log2]("rf_log2")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
index 59a9f24cb..12e9e201c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
@@ -45,6 +45,7 @@ trait ProjectedRasterTile extends FixedDelegatingTile with ProjectedRasterLike {
   def crs: CRS
   def projectedExtent: ProjectedExtent = ProjectedExtent(extent, crs)
   def projectedRaster: ProjectedRaster[Tile] = ProjectedRaster[Tile](this, extent, crs)
+  def mapTile(f: Tile => Tile): ProjectedRasterTile = ProjectedRasterTile(f(this), extent, crs)
 }
 
 object ProjectedRasterTile {
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index d82eaec9a..160e01ec8 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -619,6 +619,15 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_round")
     }
 
+    it("should abs cell values") {
+      val minus = one.mapTile(t => t.convert(IntConstantNoDataCellType) * -1)
+      val df = Seq((minus, one)).toDF("minus", "one")
+
+      assertEqual(df.select(rf_abs($"minus").as[ProjectedRasterTile]).first(), one)
+
+      checkDocs("rf_abs")
+    }
+
     it("should take logarithms positive cell values"){
       // rf_log10 1000 == 3
       val thousand = TestData.projectedRasterTile(cols, rows, 1000, extent, crs, ShortConstantNoDataCellType)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
index 78ead237e..0600b4745 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
@@ -43,8 +43,11 @@ trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
   //p.setProperty(“spark.driver.allowMultipleContexts”, “true”)
 
   lazy val sqlContext: SQLContext = {
-    val session = SparkSession.builder.config(_sc.getConf).getOrCreate()
-    org.locationtech.rasterframes.WithSQLContextMethods(session.sqlContext).withRasterFrames
+    val session = SparkSession.builder
+      .config(_sc.getConf)
+      .config("spark.sql.crossJoin.enabled", true)
+      .getOrCreate()
+    session.sqlContext.withRasterFrames
   }
 
   lazy val sql: String ⇒ DataFrame = sqlContext.sql
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/tut/reference.md
index c24f5d680..492773a7d 100644
--- a/docs/src/main/tut/reference.md
+++ b/docs/src/main/tut/reference.md
@@ -365,7 +365,13 @@ Returns a `tile` column containing the element-wise inequality of `tile1` and `r
 
 Round cell values to the nearest integer without changing the cell type.
 
-#### rf_exp
+#### rf_abs
+
+    Tile rf_abs(Tile tile)
+
+ Compute the absolute value of cell value.
+ 
+ #### rf_exp
 
     Tile rf_exp(Tile tile)
 
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 5f40f0f43..2a7cd08fb 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -5,7 +5,7 @@
 ### 0.8.0
 
 * Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
-* Added new tile functions `rf_round`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.
+* Added new tile functions `rf_round`, `rf_abs`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.
 * Support python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
 * SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 102f7e247..95dff69cb 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -236,6 +236,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_local_equal': 'Cellwise equality comparison between two tiles',
     'rf_local_unequal': 'Cellwise inequality comparison between two tiles',
     'rf_round': 'Round cell values to the nearest integer without changing the cell type',
+    'rf_abs': 'Compute the absolute value of each cell',
     'rf_log': 'Performs cell-wise natural logarithm',
     'rf_log10': 'Performs cell-wise logartithm with base 10',
     'rf_log2': 'Performs cell-wise logartithm with base 2',
diff --git a/pyrasterframes/python/requirements.txt b/pyrasterframes/python/requirements.txt
new file mode 100644
index 000000000..c4bb2804e
--- /dev/null
+++ b/pyrasterframes/python/requirements.txt
@@ -0,0 +1,5 @@
+pytz
+shapely
+pyspark==2.3.3
+numpy>=1.7
+pandas
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index a0f318008..e3f5fd107 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -2,10 +2,10 @@
 The operations in this file are designed for development and testing only.
 """
 
-
 from setuptools import setup, find_packages
 import distutils.log
 import importlib
+from os import environ
 
 
 def _extract_module(mod):
@@ -17,7 +17,6 @@ def _extract_module(mod):
         globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
 
 
-
 class ExampleCommand(distutils.cmd.Command):
     """A custom command to run pyrasterframes examples."""
 
@@ -52,7 +51,6 @@ def finalize_options(self):
         self.examples = map(lambda x: 'examples.' + x.stem,
                             map(self._check_ex_path, self.examples))
 
-
     def run(self):
         """Run the examples."""
         import traceback
@@ -98,42 +96,32 @@ def run(self):
             przip.write('LICENSE.md', 'pyrasterframes/LICENSE.md')
 
 
-
-
-
 with open('README.rst') as f:
     readme = f.read()
 
-#pyspark_ver = 'pyspark>=2.2.0,<2.3'
-pyspark_ver = 'pyspark==2.3.2'
+with open('requirements.txt') as f:
+    requirements = f.read().splitlines()
 
 setup_args = dict(
     name='pyrasterframes',
     description='Python bindings for RasterFrames',
     long_description=readme,
-    version='0.0.1',
+    version=environ.get('RASTERFRAMES_VERSION', 'SNAPSHOT'),
     url='http://rasterframes.io',
-    author='D. Benjamin Guseman',
-    author_email='guseman@astraea.io',
+    author='Astraea, Inc.',
+    author_email='info@astraea.io',
     license='Apache 2',
-    setup_requires=['pytest-runner', pyspark_ver, 'pathlib'],
-    install_requires=[
-        'pytz', 'shapely',
-        # pyspark_ver,
-        # 'pathlib'
-    ],
+    setup_requires=['pytest-runner', 'pathlib'],
+    install_requires=requirements,
     tests_require=[
-        pyspark_ver,
         'pytest==3.4.2',
-        'pypandoc',
-        'numpy>=1.7',
-        'pandas',
+        'pypandoc'
     ],
     test_suite="pytest-runner",
     packages=find_packages(exclude=['tests', 'examples']),
     include_package_data=True,
-    package_data={'.':['LICENSE.md'], 'pyrasterframes':['*.jar']},
-    exclude_package_data={'.':['setup.*', 'README.*']},
+    package_data={'.': ['LICENSE.md'], 'pyrasterframes': ['*.jar']},
+    exclude_package_data={'.': ['setup.*', 'README.*']},
     classifiers=[
         'Development Status :: 3 - Alpha',
         'Environment :: Other Environment',
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 45874be15..faa337010 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -4,7 +4,6 @@
 from pyrasterframes.rasterfunctions import *
 from geomesa_pyspark.types import *
 from pathlib import Path
-import os
 import unittest
 
 # version-conditional imports
@@ -110,7 +109,8 @@ def test_tile_functions(self):
             .withColumn('log', rf_log(self.tileCol)) \
             .withColumn('exp', rf_exp(self.tileCol)) \
             .withColumn('expm1', rf_expm1(self.tileCol)) \
-            .withColumn('round', rf_round(self.tileCol))
+            .withColumn('round', rf_round(self.tileCol)) \
+            .withColumn('abs', rf_abs(self.tileCol))
 
         df.show()
 
@@ -119,7 +119,6 @@ def test_prt_functions(self):
             .withColumn('crs', rf_crs(self.tileCol)) \
             .withColumn('ext', rf_extent(self.tileCol)) \
             .withColumn('geom', rf_geometry(self.tileCol))
-        df.printSchema
         df.show()
 
     def test_rasterize(self):

From d6234cde1f5a44a282a1d7fc9cd9558fcc8d597f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 24 May 2019 12:39:10 -0400
Subject: [PATCH 092/380] Incremental progress commit.

---
 .../encoders/StandardSerializers.scala        |  4 +-
 .../aggregates/TileRasterizerAggregate.scala  | 18 +----
 .../rasterframes/extensions/RasterJoin.scala  | 28 +-------
 .../rasterframes/RasterJoinSpec.scala         | 67 ++++++++++++++-----
 4 files changed, 56 insertions(+), 61 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
index 96815f90b..affe545b8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardSerializers.scala
@@ -21,7 +21,6 @@
 
 package org.locationtech.rasterframes.encoders
 
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.spark._
@@ -29,9 +28,8 @@ import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.vector._
 import org.apache.spark.sql.types._
 import org.locationtech.jts.geom.Envelope
-import org.locationtech.rasterframes
 import org.locationtech.rasterframes.TileType
-import org.locationtech.rasterframes.encoders.CatalystSerializer.CatalystIO
+import org.locationtech.rasterframes.encoders.CatalystSerializer.{CatalystIO, _}
 import org.locationtech.rasterframes.model.LazyCRS
 
 /** Collection of CatalystSerializers for third-party types. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
index 1f99d0261..f8f53a34e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -50,8 +50,7 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
   ))
 
   override def bufferSchema: StructType = StructType(Seq(
-    StructField("tile_buffer", TileType),
-    StructField("extent_buffer", schemaOf[Extent])
+    StructField("tile_buffer", TileType)
   ))
 
   override def dataType: DataType = schemaOf[Raster[Tile]]
@@ -71,31 +70,18 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
       val bt = buffer.getAs[MutableArrayTile](0)
       val merged = bt.merge(prd.extent, localExtent, localTile.tile, prd.sampler)
       buffer(0) = merged
-      if (buffer.isNullAt(1))
-        buffer(1) = localExtent.toRow
-      else {
-        val curr = buffer.getAs[Row](1).to[Extent]
-        buffer(1) = curr.combine(localExtent).toRow
-      }
     }
   }
 
   override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
     val leftTile = buffer1.getAs[MutableArrayTile](0)
     val rightTile = buffer2.getAs[MutableArrayTile](0)
-    val leftExtent = buffer1.getAs[Row](1).to[Extent]
-    val rightExtent = buffer2.getAs[Row](1).to[Extent]
     buffer1(0) = leftTile.merge(rightTile)
-    buffer1(1) =
-      if (leftExtent == null) rightExtent
-      else if (rightExtent == null) leftExtent
-      else leftExtent.combine(rightExtent)
   }
 
   override def evaluate(buffer: Row): Raster[Tile] = {
     val t = buffer.getAs[Tile](0)
-    val e = buffer.getAs[Row](1).to[Extent]
-    Raster(t, e)
+    Raster(t, prd.extent)
   }
 }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index 327b51ce6..117676e36 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -23,37 +23,15 @@ package org.locationtech.rasterframes.extensions
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.raster.reproject.Reproject
-import geotrellis.raster.resample.ResampleMethod
 import geotrellis.vector.Extent
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.util._
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.util._
 
 import scala.util.Random
-/*
-    val crs = input.getAs[Row](0).to[CRS]
-    val extent = input.getAs[Row](1).to[Extent]
-
-    val localExtent = extent.reproject(crs, prd.crs)
-
-    if (prd.extent.intersects(localExtent)) {
-      val localTile = input.getAs[Tile](2).reproject(extent, crs, prd.crs, projOpts)
-      val bt = buffer.getAs[MutableArrayTile](0)
-      val merged = bt.merge(prd.extent, localExtent, localTile.tile, prd.sampler)
-      buffer(0) = merged
-    }
-
-      // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
-  private def apply_mask = udf((t: Tile, extentStruct: Row, crsStruct: Row, geom: Seq[Geometry]) ⇒ {
-    val e = extentStruct.to[Extent]
-    val c = crsStruct.to[CRS]
-    val reprojectedGeoms = geom.map(g ⇒ GTGeometry(g).reproject(LatLng, c))
-    t.mask(e, reprojectedGeoms)
-  })
- */
 
 object RasterJoin {
   private val projOpts = Reproject.Options.DEFAULT
@@ -68,10 +46,10 @@ object RasterJoin {
     val rightExtents = rightExtentEnc.map(_.to[Extent])
     val rightCRSs = rightCRSEnc.map(_.to[CRS])
 
-    //is there a GT function to do all this?
     val cellType = tiles.map(_.cellType).reduce(_ union _)
 
     val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
+    //is there a GT function to do all this?
     tiles.zip(rightExtents).zip(rightCRSs).map {
       case ((tile, extent), crs) =>
         tile.reproject(extent, crs, leftCRS, projOpts)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index b260d50f8..72138c478 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -22,36 +22,69 @@
 package org.locationtech.rasterframes
 
 import geotrellis.raster.io.geotiff.GeoTiff
-import geotrellis.raster.{Raster, Tile}
-import geotrellis.raster.render.ColorRamps
+import geotrellis.raster.resample.Bilinear
+import geotrellis.raster.testkit.RasterMatchers
+import geotrellis.raster.{IntConstantNoDataCellType, Raster, Tile}
+import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.encoders.serialized_literal
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate
-import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
 
 
-class RasterJoinSpec extends TestEnvironment with TestData {
+class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
+  import spark.implicits._
   describe("Raster join between two DataFrames") {
-    it("should join same scene in two projections, same tile size") {
-      val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
-      val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
+    val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
+    val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
+
+    val r1 = s1.projectedRaster.mapTile(_.convert(IntConstantNoDataCellType)).toRF(10, 10)
+      .withExtent()
+      .withColumn("crs", serialized_literal(s1.crs))
+    val r2 = s2.projectedRaster.mapTile(_.convert(IntConstantNoDataCellType)).toRF(10, 10)
+      .withExtent()
+      .withColumn("crs", serialized_literal(s2.crs))
+      .withColumnRenamed("tile", "tile2")
+
+    it("should join the same scene correctly") {
+      val joined = r1.rasterJoin(r1.withColumnRenamed("tile", "tile2"))
+      joined.count() should be (joined.count())
 
-      val r1 = s1.projectedRaster.toRF(10, 10).withExtent().withColumn("crs", serialized_literal(s1.crs))
-      val r2 = s2.projectedRaster.toRF(10, 10)
-        .withExtent()
-        .withColumn("crs", serialized_literal(s2.crs))
-        .withColumnRenamed("tile", "tile2")
+      val measure = joined.select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")))
+
+    }
+
+    it("should join same scene in two projections, same tile size") {
 
       val joined = r1.rasterJoin(r2)
+
+
       val result = joined.agg(TileRasterizerAggregate(
-        ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent),
-        col("crs"), col("extent"), col("tile2")) as "raster"
+        ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent, Bilinear),
+        $"crs", $"extent", $"tile2") as "raster"
       ).select(col("raster").as[Raster[Tile]]).first()
 
-      GeoTiff(result, s1.crs).write("target/out.tiff")
+      // GeoTiff(result, s1.crs).write("target/out.tiff")
       result.extent shouldBe s1.extent
-      //result.tile.renderPng(ColorRamps.greyscale(256)).write("target/out.png")
-    }
+
+      // Not sure what the right test is... here's... something?
+      import geotrellis.raster.mapalgebra.local._
+      val sub = s1.extent.buffer(-s1.extent.width * 0.01)
+      val diff = Abs(
+        Subtract(
+          result.crop(sub).tile.convert(IntConstantNoDataCellType),
+          s1.raster.crop(sub).tile.convert(IntConstantNoDataCellType)
+        )
+      )
+
+      diff.statisticsDouble.get.mean should be (0.0 +- 200)
+
+
+      GeoTiff(diff, s1.extent, s1.crs).write("target/diff.tiff")
+
+
+      //assertEqual(result.crop(subRegion).tile, s1.raster.crop(subRegion).tile, 500)
+
+     }
   }
 
 }

From d4df1847c6762d7d554b42cf2d83314040df06e3 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 27 May 2019 09:51:56 -0400
Subject: [PATCH 093/380] Added overloaded rasterJoin method for specifying
 geolocation columns.

---
 .../extensions/DataFrameMethods.scala         | 22 ++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 373a7f1db..50d0c65dc 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -168,9 +168,29 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     potentialRF
   }
 
-  /** Performs a jeft join on the dataframe `right` to this one, reprojecting tiles. */
+  /**
+    * Performs a jeft join on the dataframe `right` to this one, reprojecting and merging tiles as necessary.
+    * The operation is logically a "left outer" join, with the left side also determining the target CRS and extents.
+    * Right side may have multiple Tile columns. Assumes both dataframes use the column names `extent` and `crs` for
+    * the Extent and CRS details for each row.
+    *
+    * @param right Right side of the join.
+    * @return joined dataframe
+    */
   def rasterJoin(right: DataFrame): DataFrame = RasterJoin(self, right)
 
+  /**
+    *
+    * @param leftExtent this (left) dataframe's Extent column
+    * @param leftCRS this (left) datafrasme's CRS column
+    * @param right right dataframe
+    * @param rightExtent right dataframe's CRS extent
+    * @param rightCRS right dataframe's CRS column
+    * @return joined dataframe
+    */
+  def rasterJoin(leftExtent: Column, leftCRS: Column, right: DataFrame, rightExtent: Column, rightCRS: Column): DataFrame =
+    RasterJoin(self, right, leftExtent, leftCRS, rightExtent, rightCRS)
+
   /**
    * Convert DataFrame into a RasterFrame
    *

From 85510c13e4a7c6a0604980bfcda1c99f93385087 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 10:33:51 -0400
Subject: [PATCH 094/380] Added CRS resolution benchmark

---
 bench/archive/jmh-results-20190528095237.json | 163 ++++++++++++++++++
 bench/archive/jmh-results-20190528102317.json | 163 ++++++++++++++++++
 .../rasterframes/bench/CRSBench.scala         |  56 ++++++
 .../bench/CatalystSerializerBench.scala       |   1 -
 .../rasterframes/model/LazyCRS.scala          |   9 +-
 5 files changed, 390 insertions(+), 2 deletions(-)
 create mode 100644 bench/archive/jmh-results-20190528095237.json
 create mode 100644 bench/archive/jmh-results-20190528102317.json
 create mode 100644 bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala

diff --git a/bench/archive/jmh-results-20190528095237.json b/bench/archive/jmh-results-20190528095237.json
new file mode 100644
index 000000000..11d8f15f0
--- /dev/null
+++ b/bench/archive/jmh-results-20190528095237.json
@@ -0,0 +1,163 @@
+[
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEquals",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 421.72586734818816,
+            "scoreError" : 21.639640737136855,
+            "scoreConfidence" : [
+                400.0862266110513,
+                443.365508085325
+            ],
+            "scorePercentiles" : {
+                "0.0" : 416.0271304058273,
+                "50.0" : 420.27062086802925,
+                "90.0" : 430.09766576285773,
+                "95.0" : 430.09766576285773,
+                "99.0" : 430.09766576285773,
+                "99.9" : 430.09766576285773,
+                "99.99" : 430.09766576285773,
+                "99.999" : 430.09766576285773,
+                "99.9999" : 430.09766576285773,
+                "100.0" : 430.09766576285773
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    417.8839821936131,
+                    416.0271304058273,
+                    430.09766576285773,
+                    424.349937510613,
+                    420.27062086802925
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.resolveCRS",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 358.41316103726996,
+            "scoreError" : 21.21533668234991,
+            "scoreConfidence" : [
+                337.19782435492004,
+                379.6284977196199
+            ],
+            "scorePercentiles" : {
+                "0.0" : 351.9024799408263,
+                "50.0" : 356.47836323413975,
+                "90.0" : 364.61463628732025,
+                "95.0" : 364.61463628732025,
+                "99.0" : 364.61463628732025,
+                "99.9" : 364.61463628732025,
+                "99.99" : 364.61463628732025,
+                "99.999" : 364.61463628732025,
+                "99.9999" : 364.61463628732025,
+                "100.0" : 364.61463628732025
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    356.47836323413975,
+                    364.61463628732025,
+                    355.393162879192,
+                    363.67716284487153,
+                    351.9024799408263
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.selfEquals",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 431.7566794354569,
+            "scoreError" : 27.353383248644004,
+            "scoreConfidence" : [
+                404.4032961868129,
+                459.11006268410085
+            ],
+            "scorePercentiles" : {
+                "0.0" : 426.33479529843424,
+                "50.0" : 428.0558107251467,
+                "90.0" : 443.6223317651236,
+                "95.0" : 443.6223317651236,
+                "99.0" : 443.6223317651236,
+                "99.9" : 443.6223317651236,
+                "99.99" : 443.6223317651236,
+                "99.999" : 443.6223317651236,
+                "99.9999" : 443.6223317651236,
+                "100.0" : 443.6223317651236
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    443.6223317651236,
+                    433.04934952339687,
+                    426.33479529843424,
+                    427.72110986518294,
+                    428.0558107251467
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    }
+]
+
+
diff --git a/bench/archive/jmh-results-20190528102317.json b/bench/archive/jmh-results-20190528102317.json
new file mode 100644
index 000000000..20e5712a1
--- /dev/null
+++ b/bench/archive/jmh-results-20190528102317.json
@@ -0,0 +1,163 @@
+[
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEquals",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 7.084784138200969,
+            "scoreError" : 0.17086123184222066,
+            "scoreConfidence" : [
+                6.913922906358748,
+                7.255645370043189
+            ],
+            "scorePercentiles" : {
+                "0.0" : 7.044718220088579,
+                "50.0" : 7.07291741895685,
+                "90.0" : 7.153508778664247,
+                "95.0" : 7.153508778664247,
+                "99.0" : 7.153508778664247,
+                "99.9" : 7.153508778664247,
+                "99.99" : 7.153508778664247,
+                "99.999" : 7.153508778664247,
+                "99.9999" : 7.153508778664247,
+                "100.0" : 7.153508778664247
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    7.07291741895685,
+                    7.044718220088579,
+                    7.101541478172196,
+                    7.153508778664247,
+                    7.051234795122972
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.resolveCRS",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 0.07204597602344914,
+            "scoreError" : 0.014595696186190624,
+            "scoreConfidence" : [
+                0.05745027983725852,
+                0.08664167220963977
+            ],
+            "scorePercentiles" : {
+                "0.0" : 0.067949003349235,
+                "50.0" : 0.07168162461398803,
+                "90.0" : 0.07812749538776566,
+                "95.0" : 0.07812749538776566,
+                "99.0" : 0.07812749538776566,
+                "99.9" : 0.07812749538776566,
+                "99.99" : 0.07812749538776566,
+                "99.999" : 0.07812749538776566,
+                "99.9999" : 0.07812749538776566,
+                "100.0" : 0.07812749538776566
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    0.0701740653091496,
+                    0.07812749538776566,
+                    0.07229769145710743,
+                    0.07168162461398803,
+                    0.067949003349235
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.selfEquals",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 0.04160068241214439,
+            "scoreError" : 8.000367242705733E-4,
+            "scoreConfidence" : [
+                0.04080064568787382,
+                0.04240071913641496
+            ],
+            "scorePercentiles" : {
+                "0.0" : 0.0413462799613575,
+                "50.0" : 0.04153631854728124,
+                "90.0" : 0.04183927734451199,
+                "95.0" : 0.04183927734451199,
+                "99.0" : 0.04183927734451199,
+                "99.9" : 0.04183927734451199,
+                "99.99" : 0.04183927734451199,
+                "99.999" : 0.04183927734451199,
+                "99.9999" : 0.04183927734451199,
+                "100.0" : 0.04183927734451199
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    0.0413462799613575,
+                    0.04183927734451199,
+                    0.04153631854728124,
+                    0.0417885245223439,
+                    0.0414930116852273
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    }
+]
+
+
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
new file mode 100644
index 000000000..3ccce8246
--- /dev/null
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
@@ -0,0 +1,56 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.bench
+
+import java.util.concurrent.TimeUnit
+
+import geotrellis.proj4.{CRS, LatLng}
+import org.locationtech.proj4j.CoordinateReferenceSystem
+import org.locationtech.rasterframes.model.LazyCRS
+import org.openjdk.jmh.annotations._
+
+@BenchmarkMode(Array(Mode.AverageTime))
+@State(Scope.Benchmark)
+@OutputTimeUnit(TimeUnit.MICROSECONDS)
+class CRSEncodingBench extends SparkEnv {
+
+  var crs: CRS = _
+
+  @Setup(Level.Invocation)
+  def setupData(): Unit = {
+    crs = LazyCRS("epsg:4326")
+  }
+  @Benchmark
+  def resolveCRS(): CoordinateReferenceSystem = {
+    crs.proj4jCrs
+  }
+
+  @Benchmark
+  def selfEquals(): Boolean = {
+    crs == crs
+  }
+
+  @Benchmark
+  def logicalEquals(): Boolean = {
+    crs == LatLng
+  }
+}
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
index aff447ac4..12a6b0486 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/CatalystSerializerBench.scala
@@ -23,7 +23,6 @@ package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import org.locationtech.rasterframes.encoders.CatalystSerializer
 import geotrellis.proj4.{CRS, LatLng, Sinusoidal}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
index d01d49317..611428b22 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
@@ -25,12 +25,19 @@ import LazyCRS.EncodedCRS
 import geotrellis.proj4.CRS
 import org.locationtech.proj4j.CoordinateReferenceSystem
 
-class LazyCRS(encoded: EncodedCRS) extends CRS {
+class LazyCRS(val encoded: EncodedCRS) extends CRS {
   private lazy val delegate = LazyCRS.mapper(encoded)
   override def proj4jCrs: CoordinateReferenceSystem = delegate.proj4jCrs
   override def toProj4String: String =
     if (encoded.startsWith("+proj")) encoded
     else delegate.toProj4String
+
+  override def hashCode(): Int = super.hashCode()
+
+  override def equals(o: Any): Boolean = o match {
+    case l: LazyCRS => encoded == l.encoded || super.equals(o)
+    case o => super.equals(o)
+  }
 }
 
 object LazyCRS {

From ff8293c43f596171048171f3d48da77ae7926b6d Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 11:33:47 -0400
Subject: [PATCH 095/380] Added performance enhancements to CRS parsing. Added
 `toDF` extention methods to SinglebandGeoTiff.

---
 .../rasterframes/bench/CRSBench.scala         |  2 +-
 build.sbt                                     |  1 +
 .../rasterframes/extensions/Implicits.scala   |  3 ++
 .../extensions/ProjectedRasterMethods.scala   |  3 +-
 .../rasterframes/extensions/RasterJoin.scala  | 41 ++++++++-------
 .../extensions/SinglebandGeoTiffMethods.scala | 52 +++++++++++++++++++
 .../rasterframes/model/LazyCRS.scala          |  7 +--
 .../rasterframes/RasterJoinSpec.scala         |  4 +-
 project/RFDependenciesPlugin.scala            |  2 +
 9 files changed, 91 insertions(+), 24 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala

diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
index 3ccce8246..04abc0b2b 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
@@ -31,7 +31,7 @@ import org.openjdk.jmh.annotations._
 @BenchmarkMode(Array(Mode.AverageTime))
 @State(Scope.Benchmark)
 @OutputTimeUnit(TimeUnit.MICROSECONDS)
-class CRSEncodingBench extends SparkEnv {
+class CRSBench extends SparkEnv {
 
   var crs: CRS = _
 
diff --git a/build.sbt b/build.sbt
index 81a39a236..90cf92780 100644
--- a/build.sbt
+++ b/build.sbt
@@ -41,6 +41,7 @@ lazy val core = project
         ExclusionRule(organization = "org.scalastic"),
         ExclusionRule(organization = "org.scalatest")
       ),
+      scaffeine,
       scalatest
     ),
     buildInfoKeys ++= Seq[BuildInfoKey](
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
index f97a5dae0..95f24507d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
@@ -24,6 +24,7 @@ package org.locationtech.rasterframes.extensions
 import org.locationtech.rasterframes.RasterFrame
 import org.locationtech.rasterframes.util.{WithMergeMethods, WithPrototypeMethods}
 import geotrellis.raster._
+import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.spark.{Metadata, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
 import org.apache.spark.SparkConf
@@ -51,6 +52,8 @@ trait Implicits {
   implicit class WithProjectedRasterMethods[T <: CellGrid: WithMergeMethods: WithPrototypeMethods: TypeTag](
     val self: ProjectedRaster[T]) extends ProjectedRasterMethods[T]
 
+  implicit class WithSinglebandGeoTiffMethods(val self: SinglebandGeoTiff) extends SinglebandGeoTiffMethods
+
   implicit class WithDataFrameMethods[D <: DataFrame](val self: D) extends DataFrameMethods[D]
 
   implicit class WithRasterFrameMethods(val self: RasterFrame) extends RasterFrameMethods
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
index 8edf30b14..917f329d4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
@@ -51,7 +51,8 @@ abstract class ProjectedRasterMethods[T <: CellGrid: WithMergeMethods: WithProto
    *
    * @param spark [[SparkSession]] in which to create [[RasterFrame]]
    */
-  def toRF(implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame = toRF(TILE_COLUMN.columnName)
+  def toRF(implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame =
+    toRF(TILE_COLUMN.columnName)
 
   /**
    * Convert the wrapped [[ProjectedRaster]] into a [[RasterFrame]] with a
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index 117676e36..a8d821a98 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -36,27 +36,32 @@ import scala.util.Random
 object RasterJoin {
   private val projOpts = Reproject.Options.DEFAULT
 
-  // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
-  def reproject_and_merge = udf((leftExtentEnc: Row, leftCRSEnc: Row, tiles: Seq[Tile], rightExtentEnc: Seq[Row], rightCRSEnc: Seq[Row], leftDimsEnc: Row) => {
-    require(tiles.length == rightExtentEnc.length && tiles.length == rightCRSEnc.length, "size mismatch")
+  val reproject_and_merge_f: (Row, Row, Seq[Tile], Seq[Row], Seq[Row], Row) => Tile = (leftExtentEnc: Row, leftCRSEnc: Row, tiles: Seq[Tile], rightExtentEnc: Seq[Row], rightCRSEnc: Seq[Row], leftDimsEnc: Row) => {
+    if (tiles.isEmpty) null
+    else {
+      require(tiles.length == rightExtentEnc.length && tiles.length == rightCRSEnc.length, "size mismatch")
+
+      val leftExtent = leftExtentEnc.to[Extent]
+      val leftDims = leftDimsEnc.to[TileDimensions]
+      val leftCRS = leftCRSEnc.to[CRS]
+      val rightExtents = rightExtentEnc.map(_.to[Extent])
+      val rightCRSs = rightCRSEnc.map(_.to[CRS])
 
-    val leftExtent = leftExtentEnc.to[Extent]
-    val leftDims = leftDimsEnc.to[TileDimensions]
-    val leftCRS = leftCRSEnc.to[CRS]
-    val rightExtents = rightExtentEnc.map(_.to[Extent])
-    val rightCRSs = rightCRSEnc.map(_.to[CRS])
+      val cellType = tiles.map(_.cellType).reduceOption(_ union _).getOrElse(tiles.head.cellType)
 
-    val cellType = tiles.map(_.cellType).reduce(_ union _)
+      val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
+      //is there a GT function to do all this?
+      tiles.zip(rightExtents).zip(rightCRSs).map {
+        case ((tile, extent), crs) =>
+          tile.reproject(extent, crs, leftCRS, projOpts)
+      }.foldLeft(dest)((d, t) =>
+        d.merge(leftExtent, t.extent, t.tile, projOpts.method)
+      )
+    }
+  }
 
-    val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
-    //is there a GT function to do all this?
-    tiles.zip(rightExtents).zip(rightCRSs).map {
-      case ((tile, extent), crs) =>
-        tile.reproject(extent, crs, leftCRS, projOpts)
-    }.foldLeft(dest)((d, t) =>
-      d.merge(leftExtent, t.extent, t.tile, projOpts.method)
-    )
-  }).withName("reproject_and_merge")
+  // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
+  def reproject_and_merge = udf(reproject_and_merge_f).withName("reproject_and_merge")
 
   def apply(left: DataFrame, right: DataFrame): DataFrame = {
     val df = apply(left, right, left("extent"), left("crs"), right("extent"), right("crs"))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
new file mode 100644
index 000000000..eaf09eac0
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
@@ -0,0 +1,52 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.extensions
+
+import geotrellis.raster.io.geotiff.SinglebandGeoTiff
+import geotrellis.util.MethodExtensions
+import org.apache.spark.sql.{DataFrame, SparkSession}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+
+trait SinglebandGeoTiffMethods extends MethodExtensions[SinglebandGeoTiff] {
+  def toDF(dims: TileDimensions = NOMINAL_TILE_DIMS)(implicit spark: SparkSession): DataFrame = {
+
+    val segmentLayout = self.imageData.segmentLayout
+    val re = self.rasterExtent
+    val crs = self.crs
+
+    val windows = segmentLayout.listWindows(256)
+    val subtiles = self.crop(windows)
+
+    val rows = for {
+      (gridbounds, tile) ← subtiles.toSeq
+    } yield {
+      val extent = re.extentFor(gridbounds, false)
+      ProjectedRasterTile(tile, extent, crs).toRow
+    }
+
+    spark.createDataFrame(spark.sparkContext.makeRDD(rows, 1), schemaOf[ProjectedRasterTile])
+      .select("tileContext.extent", "tileContext.crs", "tile")
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
index 611428b22..ce95b20ce 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
@@ -22,18 +22,17 @@
 package org.locationtech.rasterframes.model
 
 import LazyCRS.EncodedCRS
+import com.github.blemale.scaffeine.Scaffeine
 import geotrellis.proj4.CRS
 import org.locationtech.proj4j.CoordinateReferenceSystem
 
 class LazyCRS(val encoded: EncodedCRS) extends CRS {
-  private lazy val delegate = LazyCRS.mapper(encoded)
+  private lazy val delegate = LazyCRS.cache.get(encoded)
   override def proj4jCrs: CoordinateReferenceSystem = delegate.proj4jCrs
   override def toProj4String: String =
     if (encoded.startsWith("+proj")) encoded
     else delegate.toProj4String
 
-  override def hashCode(): Int = super.hashCode()
-
   override def equals(o: Any): Boolean = o match {
     case l: LazyCRS => encoded == l.encoded || super.equals(o)
     case o => super.equals(o)
@@ -50,6 +49,8 @@ object LazyCRS {
     case w if w.toUpperCase().startsWith("GEOGCS") => CRS.fromWKT(w) //only case-sensitive inside double quotes
   }
 
+  private val cache = Scaffeine().build[String, CRS](mapper)
+
   def apply(value: String): CRS = {
     if (mapper.isDefinedAt(value)) {
       new LazyCRS(value.asInstanceOf[EncodedCRS])
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 72138c478..608c0e19b 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -49,7 +49,9 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       val joined = r1.rasterJoin(r1.withColumnRenamed("tile", "tile2"))
       joined.count() should be (joined.count())
 
-      val measure = joined.select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")))
+      //val measure = joined.select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")))
+
+      // TODO: test tile comparison
 
     }
 
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index 93e92e79e..b9620e9dc 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -45,6 +45,8 @@ object RFDependenciesPlugin extends AutoPlugin {
     val `jts-core` = "org.locationtech.jts" % "jts-core" % "1.16.0"
     val `geotrellis-contrib-vlm` = "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0"
     val `geotrellis-contrib-gdal` = "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0"
+
+    val scaffeine = "com.github.blemale" %% "scaffeine" % "2.5.0"
   }
   import autoImport._
 

From 17beae18f9dcb44439e8cff519c4d2b3bc5e56b5 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 11:59:35 -0400
Subject: [PATCH 096/380] Fixed singleband extension method for Dataframe
 creation.

---
 .../extensions/SinglebandGeoTiffMethods.scala   | 17 ++++++++++++-----
 .../rasterframes/model/LazyCRS.scala            |  8 +++++---
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
index eaf09eac0..536470c9e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
@@ -21,13 +21,15 @@
 
 package org.locationtech.rasterframes.extensions
 
+import geotrellis.proj4.CRS
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
 import geotrellis.util.MethodExtensions
-import org.apache.spark.sql.{DataFrame, SparkSession}
+import geotrellis.vector.Extent
+import org.apache.spark.sql.types.{StructField, StructType}
+import org.apache.spark.sql.{DataFrame, Row, SparkSession}
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.model.TileDimensions
-import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 trait SinglebandGeoTiffMethods extends MethodExtensions[SinglebandGeoTiff] {
   def toDF(dims: TileDimensions = NOMINAL_TILE_DIMS)(implicit spark: SparkSession): DataFrame = {
@@ -43,10 +45,15 @@ trait SinglebandGeoTiffMethods extends MethodExtensions[SinglebandGeoTiff] {
       (gridbounds, tile) ← subtiles.toSeq
     } yield {
       val extent = re.extentFor(gridbounds, false)
-      ProjectedRasterTile(tile, extent, crs).toRow
+      Row(extent.toRow, crs.toRow, tile)
     }
 
-    spark.createDataFrame(spark.sparkContext.makeRDD(rows, 1), schemaOf[ProjectedRasterTile])
-      .select("tileContext.extent", "tileContext.crs", "tile")
+    val schema = StructType(Seq(
+      StructField("extent", schemaOf[Extent], false),
+      StructField("crs", schemaOf[CRS], false),
+      StructField("tile", TileType, false)
+    ))
+
+    spark.createDataFrame(spark.sparkContext.makeRDD(rows, 1), schema)
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
index ce95b20ce..a63c54ab8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
@@ -35,7 +35,7 @@ class LazyCRS(val encoded: EncodedCRS) extends CRS {
 
   override def equals(o: Any): Boolean = o match {
     case l: LazyCRS => encoded == l.encoded || super.equals(o)
-    case o => super.equals(o)
+    case c => delegate.equals(c)
   }
 }
 
@@ -43,13 +43,15 @@ object LazyCRS {
   trait ValidatedCRS
   type EncodedCRS = String with ValidatedCRS
 
-  private val mapper: PartialFunction[String, CRS] = {
+  @transient
+  private lazy val mapper: PartialFunction[String, CRS] = {
     case e if e.toUpperCase().startsWith("EPSG")   => CRS.fromName(e) //not case-sensitive
     case p if p.startsWith("+proj")                => CRS.fromString(p) // case sensitive
     case w if w.toUpperCase().startsWith("GEOGCS") => CRS.fromWKT(w) //only case-sensitive inside double quotes
   }
 
-  private val cache = Scaffeine().build[String, CRS](mapper)
+  @transient
+  private lazy val cache = Scaffeine().build[String, CRS](mapper)
 
   def apply(value: String): CRS = {
     if (mapper.isDefinedAt(value)) {

From cdbc227d08bfa84cee10b2840f86662b81b887c9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 12:06:02 -0400
Subject: [PATCH 097/380] Spelling fix.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f9572feb9..c074e0a0e 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ The documentation may be built with
 sbt makeSite
 ```
 
-The `pyrasteframes` build instructions are located [pyrasterframes/python/README.rst](pyrasterframes/python/README.rst)
+The `pyrasterframes` build instructions are located [pyrasterframes/python/README.rst](pyrasterframes/python/README.rst)
 
 ## Copyright and License
 

From 0a29b8845d2b32d9f6e7349f504d2d0874ded569 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 14:17:24 -0400
Subject: [PATCH 098/380] Incorporated PR feedback.

---
 .../aggregates/TileRasterizerAggregate.scala  |  5 ++--
 .../rasterframes/extensions/RasterJoin.scala  | 28 +++++++++++++++----
 .../rasterframes/RasterJoinSpec.scala         |  2 +-
 3 files changed, 26 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
index f8f53a34e..a4e048496 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -34,8 +34,9 @@ import geotrellis.raster.reproject.Reproject
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
 
 /**
-  * Aggregation function for creating a single raster from tiles.
-  * @param prd settings
+  * Aggregation function for creating a single `geotrellis.raster.Raster[Tile]` from
+  * `Tile`, `CRS` and `Extent` columns.
+  * @param prd aggregation settings
   */
 class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefinedAggregateFunction {
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index a8d821a98..32d98d621 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -69,11 +69,12 @@ object RasterJoin {
   }
 
   def apply(left: DataFrame, right: DataFrame, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame = {
-    apply(left, right, st_intersects(
-      st_geometry(leftExtent),
-      st_reproject(st_geometry(rightExtent), rightCRS, leftCRS)
-    ), leftExtent, leftCRS, rightExtent, rightCRS)
+    val leftGeom = st_geometry(leftExtent)
+    val rightGeomReproj = st_reproject(st_geometry(rightExtent), rightCRS, leftCRS)
+    val joinExpr = st_intersects(leftGeom, rightGeomReproj)
+    apply(left, right, joinExpr, leftExtent, leftCRS, rightExtent, rightCRS)
   }
+
   def apply(left: DataFrame, right: DataFrame, joinExprs: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame = {
     // Unique id for temporary columns
     val id = Random.alphanumeric.take(5).mkString("_", "", "_")
@@ -90,19 +91,34 @@ object RasterJoin {
     // A representative tile from the left
     val leftTile = left.tileColumns.headOption.getOrElse(throw new IllegalArgumentException("Need at least one target tile on LHS"))
 
-
+    // Gathering up various expressions we'll use to construct the result.
+    // After joining We will be doing a groupBy the LHS. We have to define the aggregations to perform after the groupBy.
+    // On the LHS we just want the first thing (subsequent ones should be identical.
     val leftAggCols = left.columns.map(s => first(left(s), true) as s)
+    // On the RHS we collect result as a list.
     val rightAggCtx = Seq(collect_list(rightExtent) as rightExtent2, collect_list(rightCRS) as rightCRS2)
     val rightAggTiles = right.tileColumns.map(c => collect_list(c) as c.columnName)
     val aggCols = leftAggCols ++ rightAggTiles ++ rightAggCtx
-    val reprojCols = rightAggTiles.map(t => reproject_and_merge(col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(leftTile)) as t.columnName)
+
+    // After the aggregation we take all the tiles we've collected and resample + merge into LHS extent/CRS.
+    val reprojCols = rightAggTiles.map(t => reproject_and_merge(
+      col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(leftTile)
+    ) as t.columnName)
     val finalCols = leftAggCols.map(c => col(c.columnName)) ++ reprojCols
 
+    // Here's the meat:
     left
+      // 1. Add a unique ID to each LHS row for subequent grouping.
       .withColumn(id, monotonically_increasing_id())
+      // 2. Perform the left-outer join
       .join(right, joinExprs, joinType = "left")
+      // 3. Group by the unique ID, reestablishing the LHS count
       .groupBy(col(id))
+      // 4. Apply aggregation to left and right columns:
+      //    a. LHS just take the first entity
+      //    b. RHS collect all results in a list
       .agg(aggCols.head, aggCols.tail: _*)
+      // 5. Perform merge on RHC tile column collections, pass everything else through.
       .select(finalCols: _*)
   }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 608c0e19b..5698ae253 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -47,7 +47,7 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
 
     it("should join the same scene correctly") {
       val joined = r1.rasterJoin(r1.withColumnRenamed("tile", "tile2"))
-      joined.count() should be (joined.count())
+      joined.count() should be (r1.count())
 
       //val measure = joined.select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")))
 

From bdbcb76ca66d1977d300fb3fdc5dec8fdde4acda Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 14:47:57 -0400
Subject: [PATCH 099/380] RasterJoin test fixes.

---
 .../extensions/SinglebandGeoTiffMethods.scala |  2 +-
 .../rasterframes/RasterJoinSpec.scala         | 38 +++++++------------
 .../encoders/CatalystSerializerSpec.scala     |  7 +---
 3 files changed, 17 insertions(+), 30 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
index 536470c9e..833ba80e3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/SinglebandGeoTiffMethods.scala
@@ -38,7 +38,7 @@ trait SinglebandGeoTiffMethods extends MethodExtensions[SinglebandGeoTiff] {
     val re = self.rasterExtent
     val crs = self.crs
 
-    val windows = segmentLayout.listWindows(256)
+    val windows = segmentLayout.listWindows(dims.cols, dims.rows)
     val subtiles = self.crop(windows)
 
     val rows = for {
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 5698ae253..43e027225 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -26,9 +26,9 @@ import geotrellis.raster.resample.Bilinear
 import geotrellis.raster.testkit.RasterMatchers
 import geotrellis.raster.{IntConstantNoDataCellType, Raster, Tile}
 import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.encoders.serialized_literal
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+import org.locationtech.rasterframes.model.TileDimensions
 
 
 class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
@@ -37,35 +37,33 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
     val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
     val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
 
-    val r1 = s1.projectedRaster.mapTile(_.convert(IntConstantNoDataCellType)).toRF(10, 10)
-      .withExtent()
-      .withColumn("crs", serialized_literal(s1.crs))
-    val r2 = s2.projectedRaster.mapTile(_.convert(IntConstantNoDataCellType)).toRF(10, 10)
-      .withExtent()
-      .withColumn("crs", serialized_literal(s2.crs))
+    val r1 = s1.toDF(TileDimensions(10, 10))
+    val r2 = s2.toDF(TileDimensions(10, 10))
       .withColumnRenamed("tile", "tile2")
 
     it("should join the same scene correctly") {
-      val joined = r1.rasterJoin(r1.withColumnRenamed("tile", "tile2"))
+      val r1prime = s1.toDF(TileDimensions(10, 10))
+        .withColumnRenamed("tile", "tile2")
+      val joined = r1.rasterJoin(r1prime)
       joined.count() should be (r1.count())
 
-      //val measure = joined.select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")))
-
-      // TODO: test tile comparison
-
+      val measure = joined
+        .select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "mean")
+        .agg(sum($"mean"))
+        .as[Double]
+        .first()
+      measure should be(0.0)
     }
 
     it("should join same scene in two projections, same tile size") {
-
       val joined = r1.rasterJoin(r2)
 
-
       val result = joined.agg(TileRasterizerAggregate(
         ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent, Bilinear),
         $"crs", $"extent", $"tile2") as "raster"
       ).select(col("raster").as[Raster[Tile]]).first()
 
-      // GeoTiff(result, s1.crs).write("target/out.tiff")
+      //GeoTiff(result, s1.crs).write("target/out.tiff")
       result.extent shouldBe s1.extent
 
       // Not sure what the right test is... here's... something?
@@ -77,16 +75,8 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
           s1.raster.crop(sub).tile.convert(IntConstantNoDataCellType)
         )
       )
-
       diff.statisticsDouble.get.mean should be (0.0 +- 200)
-
-
-      GeoTiff(diff, s1.extent, s1.crs).write("target/diff.tiff")
-
-
-      //assertEqual(result.crop(subRegion).tile, s1.raster.crop(subRegion).tile, 500)
-
+      //GeoTiff(diff, s1.extent, s1.crs).write("target/diff.tiff")
      }
   }
-
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
index 39b998201..2b02a5523 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -23,17 +23,14 @@ package org.locationtech.rasterframes.encoders
 
 import java.time.ZonedDateTime
 
-import org.locationtech.rasterframes.encoders.StandardEncoders._
-import org.locationtech.rasterframes.model.{CellContext, TileDataContext, TileDimensions}
-import org.locationtech.rasterframes.ref.RasterRef
-import org.locationtech.rasterframes.TestData
 import geotrellis.proj4._
 import geotrellis.raster.{CellSize, CellType, TileLayout, UShortUserDefinedNoDataCellType}
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
-import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.{TestData, TestEnvironment}
+import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.model.{CellContext, TileContext, TileDataContext, TileDimensions}
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.scalatest.Assertion

From 146273c85781bb0ddce3d53cbb200829328ad282 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 28 May 2019 15:23:49 -0400
Subject: [PATCH 100/380] Updated release notes. Cruft removal.

---
 .../rasterframes/expressions/DynamicExtractors.scala           | 3 +--
 .../locationtech/rasterframes/tiles/ProjectedRasterTile.scala  | 2 +-
 .../scala/org/locationtech/rasterframes/RasterJoinSpec.scala   | 1 -
 .../scala/org/locationtech/rasterframes/TileStatsSpec.scala    | 2 +-
 docs/src/main/tut/release-notes.md                             | 2 ++
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
index 1f263dcea..ecc9698f2 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
@@ -21,13 +21,12 @@
 
 package org.locationtech.rasterframes.expressions
 
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.ref.{ProjectedRasterLike, RasterRef}
 import geotrellis.raster.{CellGrid, Tile}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.rf.{TileUDT, _}
 import org.apache.spark.sql.types._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.model.TileContext
 import org.locationtech.rasterframes.ref.{ProjectedRasterLike, RasterRef, RasterSource}
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
index 12e9e201c..fd61cb88c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
@@ -65,7 +65,7 @@ object ProjectedRasterTile {
 
   implicit val serializer: CatalystSerializer[ProjectedRasterTile] = new CatalystSerializer[ProjectedRasterTile] {
     override def schema: StructType = StructType(Seq(
-      StructField("tileContext", schemaOf[TileContext], false),
+      StructField("tile_context", schemaOf[TileContext], false),
       StructField("tile", TileType, false))
     )
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 43e027225..4f8d132a4 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -21,7 +21,6 @@
 
 package org.locationtech.rasterframes
 
-import geotrellis.raster.io.geotiff.GeoTiff
 import geotrellis.raster.resample.Bilinear
 import geotrellis.raster.testkit.RasterMatchers
 import geotrellis.raster.{IntConstantNoDataCellType, Raster, Tile}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
index 512a205fe..50920ab1c 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileStatsSpec.scala
@@ -137,7 +137,7 @@ class TileStatsSpec extends TestEnvironment with TestData {
         forAll(means.zip(means2)) { case (l, r) => assert(l === r +- 1e-6) }
       }
       withClue("sum") {
-        val rf = l8Sample(1).projectedRaster.toRF
+        val rf = l8Sample(1).toDF()
         val expected = 309149454 // computed with rasterio
         val result = rf.agg(sum(rf_tile_sum($"tile"))).collect().head.getDouble(0)
         logger.info(s"L8 sample band 1 grand total: ${result}")
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 2a7cd08fb..f7fc7360d 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -12,6 +12,8 @@
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * Added `rf_render_matrix` debugging function.
 * Added `RasterFrame.withExtent` extension method.
+* Added `SinglebandGeoTiff.toDF` extension method.
+* Added `DataFrame.rasterJoin` extension method for merging two dataframes with tiles in disparate CRSs.
 * Added `rf_crs` for `ProjectedRasterTile` columns.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).

From 9cf18fb29b757d888e891a8de717db84ef53b515 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 28 May 2019 16:31:41 -0400
Subject: [PATCH 101/380] Update raster join test

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../test/resources/L8-B4-Elkton-VA-4326.tiff  | Bin 63934 -> 63946 bytes
 .../rasterframes/RasterJoinSpec.scala         |  37 +++++++++++++-----
 2 files changed, 27 insertions(+), 10 deletions(-)

diff --git a/core/src/test/resources/L8-B4-Elkton-VA-4326.tiff b/core/src/test/resources/L8-B4-Elkton-VA-4326.tiff
index df3fe975304cf9072ed2b0f2c89036194e57e456..2bc57e255d792613fbe3fd3c4c47c6ce356fb0f4 100644
GIT binary patch
delta 150
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z6oBk)K=y`q9tI{L`x}tGv7HI5pA9I`u!M=h9Vo5_6mJBwL23+un1Ny91F?zkcx3s2
kB36nF3~3Gw43pv+81~gMFua<}z#z7lfx&6BIOFmk016Bqp8x;=

delta 131
zcmX^0nR(x5W=>B}Ed~w-1_r^2oUTm27$ycNG2WV3tZbLW$iUFl!o$D@l&b==*S9k>
zC;-{3fb0$JJPb@g_A4NJV>=UA|1TiBVF?q1J5XGTapD6tStg(oJw*nFC<g|Hws;1H
XO?3<m_vSJ%aP4JaFx#xmxcmnIsstSb

diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 4f8d132a4..9d9a2fe7c 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -34,6 +34,7 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
   import spark.implicits._
   describe("Raster join between two DataFrames") {
     val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
+    // Same data, reprojected to EPSG:4326
     val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
 
     val r1 = s1.toDF(TileDimensions(10, 10))
@@ -44,28 +45,30 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       val r1prime = s1.toDF(TileDimensions(10, 10))
         .withColumnRenamed("tile", "tile2")
       val joined = r1.rasterJoin(r1prime)
+
       joined.count() should be (r1.count())
 
-      val measure = joined
-        .select(rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "mean")
-        .agg(sum($"mean"))
-        .as[Double]
-        .first()
-      measure should be(0.0)
+      val measure = joined.select(
+            rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "diff_mean",
+            rf_tile_stats(rf_local_subtract($"tile", $"tile2")).getField("variance") as "diff_var")
+          .collect()
+      measure.forall(r ⇒ r.getDouble(0) == 0.0) should be (true)
+      measure.forall(r ⇒ r.getDouble(1) == 0.0) should be (true)
     }
 
     it("should join same scene in two projections, same tile size") {
-      val joined = r1.rasterJoin(r2)
 
+      // r2 source data is gdal warped r1 data; join them together.
+      val joined = r1.rasterJoin(r2)
+      // create a Raster from tile2 which should be almost equal to s1
       val result = joined.agg(TileRasterizerAggregate(
         ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent, Bilinear),
         $"crs", $"extent", $"tile2") as "raster"
       ).select(col("raster").as[Raster[Tile]]).first()
 
-      //GeoTiff(result, s1.crs).write("target/out.tiff")
       result.extent shouldBe s1.extent
 
-      // Not sure what the right test is... here's... something?
+      // Test the overall local difference of the `result` versus the original
       import geotrellis.raster.mapalgebra.local._
       val sub = s1.extent.buffer(-s1.extent.width * 0.01)
       val diff = Abs(
@@ -74,8 +77,22 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
           s1.raster.crop(sub).tile.convert(IntConstantNoDataCellType)
         )
       )
+      // DN's within arbitrary threshold. N.B. the range of values in the source raster is (6396, 27835)
       diff.statisticsDouble.get.mean should be (0.0 +- 200)
-      //GeoTiff(diff, s1.extent, s1.crs).write("target/diff.tiff")
+      // Overall signal is preserved
+      val s1_stddev = s1.tile.statisticsDouble.get.stddev
+      val rel_diff = diff.statisticsDouble.get.mean / s1_stddev
+      rel_diff should be (0.0 +- 0.15)
+
+      // Use the tile structure of the `joined` dataframe to argue that the structure of the image is similar between `s1` and `joined.tile2`
+      val tile_diffs = joined.select((abs(rf_tile_mean($"tile") - rf_tile_mean($"tile2")) / lit(s1_stddev)).alias("z"))
+
+      // Check the 90%-ile z score; recognize there will be some localized areas of larger error
+      tile_diffs.selectExpr("percentile(z, 0.90)").as[Double].first() should be < 0.10
+      // Check the median z score; it is pretty close to zero
+      tile_diffs.selectExpr("percentile(z, 0.50)").as[Double].first() should be < 0.025
      }
+
+
   }
 }

From 7fa04d86c0d020e9782730b2285f24396aff0890 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 28 May 2019 16:48:01 -0400
Subject: [PATCH 102/380] Add raster join test on different tile  sizes / same
 gridding

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/RasterJoinSpec.scala         | 20 +++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 9d9a2fe7c..c4c5563b4 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -51,9 +51,25 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       val measure = joined.select(
             rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "diff_mean",
             rf_tile_stats(rf_local_subtract($"tile", $"tile2")).getField("variance") as "diff_var")
+          .as[(Double, Double)]
           .collect()
-      measure.forall(r ⇒ r.getDouble(0) == 0.0) should be (true)
-      measure.forall(r ⇒ r.getDouble(1) == 0.0) should be (true)
+      all (measure) should be ((0.0, 0.0))
+    }
+
+    it("should join same scene in different tile sizes"){
+      val r1prime = s1.toDF(TileDimensions(25, 25)).withColumnRenamed("tile", "tile2")
+      r1prime.select(rf_dimensions($"tile2").getField("rows")).as[Int].first() should be (25)
+      val joined = r1.rasterJoin(r1prime)
+
+      joined.count() should be (r1.count())
+
+      val measure = joined.select(
+        rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "diff_mean",
+        rf_tile_stats(rf_local_subtract($"tile", $"tile2")).getField("variance") as "diff_var")
+        .as[(Double, Double)]
+        .collect()
+      all (measure) should be ((0.0, 0.0))
+
     }
 
     it("should join same scene in two projections, same tile size") {

From 279c96fcc17f26f4f684e991371ff6bc7b8ebf8b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 08:20:39 -0400
Subject: [PATCH 103/380] Testing possible TravisCI config with GDAL support.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index fbe2823fa..3c6a81ca6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,7 +12,7 @@ scala:
   - 2.11.11
 
 jdk:
-  - oraclejdk8
+  - daunnc/openjdk-gdal:2.4.0
 
 addons:
   apt:

From a8d339b5b3ac252ff33864798ed75b6b5a8fb9d0 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 29 May 2019 09:18:49 -0400
Subject: [PATCH 104/380] RasterJoinSpec add test case, expanded naming for
 more bands etc

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/RasterJoinSpec.scala         | 57 ++++++++++++-------
 1 file changed, 36 insertions(+), 21 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index c4c5563b4..b457ca148 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -33,20 +33,21 @@ import org.locationtech.rasterframes.model.TileDimensions
 class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
   import spark.implicits._
   describe("Raster join between two DataFrames") {
-    val s1 = readSingleband("L8-B4-Elkton-VA.tiff")
+    val b4nativeTif = readSingleband("L8-B4-Elkton-VA.tiff")
     // Same data, reprojected to EPSG:4326
-    val s2 = readSingleband("L8-B4-Elkton-VA-4326.tiff")
+    val b4warpedTif = readSingleband("L8-B4-Elkton-VA-4326.tiff")
 
-    val r1 = s1.toDF(TileDimensions(10, 10))
-    val r2 = s2.toDF(TileDimensions(10, 10))
+    val b4nativeRf = b4nativeTif.toDF(TileDimensions(10, 10))
+    val b4warpedRf = b4warpedTif.toDF(TileDimensions(10, 10))
       .withColumnRenamed("tile", "tile2")
 
     it("should join the same scene correctly") {
-      val r1prime = s1.toDF(TileDimensions(10, 10))
+
+      val b4nativeRfPrime = b4nativeTif.toDF(TileDimensions(10, 10))
         .withColumnRenamed("tile", "tile2")
-      val joined = r1.rasterJoin(r1prime)
+      val joined = b4nativeRf.rasterJoin(b4nativeRfPrime)
 
-      joined.count() should be (r1.count())
+      joined.count() should be (b4nativeRf.count())
 
       val measure = joined.select(
             rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "diff_mean",
@@ -57,11 +58,11 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
     }
 
     it("should join same scene in different tile sizes"){
-      val r1prime = s1.toDF(TileDimensions(25, 25)).withColumnRenamed("tile", "tile2")
+      val r1prime = b4nativeTif.toDF(TileDimensions(25, 25)).withColumnRenamed("tile", "tile2")
       r1prime.select(rf_dimensions($"tile2").getField("rows")).as[Int].first() should be (25)
-      val joined = r1.rasterJoin(r1prime)
+      val joined = b4nativeRf.rasterJoin(r1prime)
 
-      joined.count() should be (r1.count())
+      joined.count() should be (b4nativeRf.count())
 
       val measure = joined.select(
         rf_tile_mean(rf_local_subtract($"tile", $"tile2")) as "diff_mean",
@@ -74,34 +75,34 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
 
     it("should join same scene in two projections, same tile size") {
 
-      // r2 source data is gdal warped r1 data; join them together.
-      val joined = r1.rasterJoin(r2)
-      // create a Raster from tile2 which should be almost equal to s1
+      // b4warpedRf source data is gdal warped b4nativeRf data; join them together.
+      val joined = b4nativeRf.rasterJoin(b4warpedRf)
+      // create a Raster from tile2 which should be almost equal to b4nativeTif
       val result = joined.agg(TileRasterizerAggregate(
-        ProjectedRasterDefinition(s1.cols, s1.rows, s1.cellType, s1.crs, s1.extent, Bilinear),
+        ProjectedRasterDefinition(b4nativeTif.cols, b4nativeTif.rows, b4nativeTif.cellType, b4nativeTif.crs, b4nativeTif.extent, Bilinear),
         $"crs", $"extent", $"tile2") as "raster"
       ).select(col("raster").as[Raster[Tile]]).first()
 
-      result.extent shouldBe s1.extent
+      result.extent shouldBe b4nativeTif.extent
 
       // Test the overall local difference of the `result` versus the original
       import geotrellis.raster.mapalgebra.local._
-      val sub = s1.extent.buffer(-s1.extent.width * 0.01)
+      val sub = b4nativeTif.extent.buffer(-b4nativeTif.extent.width * 0.01)
       val diff = Abs(
         Subtract(
           result.crop(sub).tile.convert(IntConstantNoDataCellType),
-          s1.raster.crop(sub).tile.convert(IntConstantNoDataCellType)
+          b4nativeTif.raster.crop(sub).tile.convert(IntConstantNoDataCellType)
         )
       )
       // DN's within arbitrary threshold. N.B. the range of values in the source raster is (6396, 27835)
       diff.statisticsDouble.get.mean should be (0.0 +- 200)
       // Overall signal is preserved
-      val s1_stddev = s1.tile.statisticsDouble.get.stddev
-      val rel_diff = diff.statisticsDouble.get.mean / s1_stddev
+      val b4nativeStddev = b4nativeTif.tile.statisticsDouble.get.stddev
+      val rel_diff = diff.statisticsDouble.get.mean /  b4nativeStddev
       rel_diff should be (0.0 +- 0.15)
 
-      // Use the tile structure of the `joined` dataframe to argue that the structure of the image is similar between `s1` and `joined.tile2`
-      val tile_diffs = joined.select((abs(rf_tile_mean($"tile") - rf_tile_mean($"tile2")) / lit(s1_stddev)).alias("z"))
+      // Use the tile structure of the `joined` dataframe to argue that the structure of the image is similar between `b4nativeTif` and `joined.tile2`
+      val tile_diffs = joined.select((abs(rf_tile_mean($"tile") - rf_tile_mean($"tile2")) / lit( b4nativeStddev)).alias("z"))
 
       // Check the 90%-ile z score; recognize there will be some localized areas of larger error
       tile_diffs.selectExpr("percentile(z, 0.90)").as[Double].first() should be < 0.10
@@ -109,6 +110,20 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       tile_diffs.selectExpr("percentile(z, 0.50)").as[Double].first() should be < 0.025
      }
 
+    it("should join multiple RHS tile columns"){
+      // join multiple native CRS bands to the EPSG 4326 RF
+
+      val multibandRf = b4nativeRf
+        .withColumn("t_plus", rf_local_add($"tile", $"tile"))
+        .withColumn("t_mult", rf_local_multiply($"tile", $"tile"))
+      multibandRf.tileColumns.length should be (3)
+
+      val multibandJoin = multibandRf.rasterJoin(b4warpedRf)
+      multibandJoin.printSchema()
+
+      multibandJoin.tileColumns.length should be (4)
+      multibandJoin.count() should be (multibandRf.count())
+    }
 
   }
 }

From 29b84f50a892a13979b83358d300f640c6daf1c4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 09:46:25 -0400
Subject: [PATCH 105/380] Revert "Testing possible TravisCI config with GDAL
 support."

This reverts commit 279c96fcc17f26f4f684e991371ff6bc7b8ebf8b.
---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 3c6a81ca6..fbe2823fa 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,7 +12,7 @@ scala:
   - 2.11.11
 
 jdk:
-  - daunnc/openjdk-gdal:2.4.0
+  - oraclejdk8
 
 addons:
   apt:

From ee572907b38927a7937b38b9a8c9d7e74ce0f57e Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 10:42:17 -0400
Subject: [PATCH 106/380] Moved GDAL required tests inside environment
 conditional block, which should help it:test in TravisCI to pass.

---
 .../rasterframes/ref/RasterSourceIT.scala     | 31 +++++++++----------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 7027258ee..afa4bfa01 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -67,6 +67,21 @@ class RasterSourceIT extends TestEnvironment with TestData {
       it("should read JPEG2000 scene") {
         RasterSource(localSentinel).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
       }
+
+      it("should read small MRF scene with one band converted from MODIS HDF") {
+        val (expectedTileCount, _) = expectedTileCountAndBands(2400, 2400)
+        RasterSource(modisConvertedMrfPath).readAll().flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
+
+      it("should read remote HTTP MRF scene") {
+        val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+        RasterSource(remoteHttpMrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
+
+      it("should read remote S3 MRF scene") {
+        val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+        RasterSource(remoteS3MrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
     }
   } else {
     describe("GDAL missing error support") {
@@ -76,22 +91,6 @@ class RasterSourceIT extends TestEnvironment with TestData {
           }
       }
     }
-
-    it("should read small MRF scene with one band converted from MODIS HDF") {
-      val (expectedTileCount, _) = expectedTileCountAndBands(2400, 2400)
-      RasterSource(modisConvertedMrfPath).readAll().flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
-    }
-
-    it("should read remote HTTP MRF scene") {
-      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
-      RasterSource(remoteHttpMrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
-    }
-
-    it("should read remote S3 MRF scene") {
-      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
-      RasterSource(remoteS3MrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
-    }
-
   }
 
   private def expectedTileCountAndBands(x:Int, y:Int, bandCount:Int = 1) = {

From 44b1c2e669d12416644f5b2621089f06c43a54a6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 11:12:41 -0400
Subject: [PATCH 107/380] Added form of rasterJoin where join expression can be
 provided.

---
 .../extensions/DataFrameMethods.scala         | 34 +++++++++++++++++--
 1 file changed, 31 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 50d0c65dc..e38ecee25 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -172,7 +172,11 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     * Performs a jeft join on the dataframe `right` to this one, reprojecting and merging tiles as necessary.
     * The operation is logically a "left outer" join, with the left side also determining the target CRS and extents.
     * Right side may have multiple Tile columns. Assumes both dataframes use the column names `extent` and `crs` for
-    * the Extent and CRS details for each row.
+    * the Extent and CRS details for each row. The join expression used is:
+    *
+    * {{{
+    *   st_intersects(st_geometry(leftExtent), st_reproject(st_geometry(rightExtent), rightCRS, leftCRS))
+    * }}}
     *
     * @param right Right side of the join.
     * @return joined dataframe
@@ -180,17 +184,41 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def rasterJoin(right: DataFrame): DataFrame = RasterJoin(self, right)
 
   /**
+    * Performs a jeft join on the dataframe `right` to this one, reprojecting and merging tiles as necessary.
+    * The operation is logically a "left outer" join, with the left side also determining the target CRS and extents.
+    * Right side may have multiple Tile columns. This variant allows for the specific geospatial columns to be
+    * specified. The join expression used is:
+    * {{{
+    *   st_intersects(st_geometry(leftExtent), st_reproject(st_geometry(rightExtent), rightCRS, leftCRS))
+    * }}}
     *
+    * @param right right dataframe
     * @param leftExtent this (left) dataframe's Extent column
     * @param leftCRS this (left) datafrasme's CRS column
-    * @param right right dataframe
     * @param rightExtent right dataframe's CRS extent
     * @param rightCRS right dataframe's CRS column
     * @return joined dataframe
     */
-  def rasterJoin(leftExtent: Column, leftCRS: Column, right: DataFrame, rightExtent: Column, rightCRS: Column): DataFrame =
+  def rasterJoin(right: DataFrame, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame =
     RasterJoin(self, right, leftExtent, leftCRS, rightExtent, rightCRS)
 
+  /**
+    * Performs a jeft join on the dataframe `right` to this one, reprojecting and merging tiles as necessary.
+    * The operation is logically a "left outer" join, with the left side also determining the target CRS and extents.
+    * Right side may have multiple Tile columns. This variant allows for the specific geospatial columns and join
+    * expression to be specified.
+    *
+    * @param right right dataframe
+    * @param leftExtent this (left) dataframe's Extent column
+    * @param joinExpr join expression
+    * @param leftCRS this (left) datafrasme's CRS column
+    * @param rightExtent right dataframe's CRS extent
+    * @param rightCRS right dataframe's CRS column
+    * @return joined dataframe
+    */
+  def rasterJoin(right: DataFrame, joinExpr: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame =
+    RasterJoin(self, right, joinExpr, leftExtent, leftCRS, rightExtent, rightCRS)
+
   /**
    * Convert DataFrame into a RasterFrame
    *

From ef80f38030e25a679b1fe3e3cf262e45fdb73df3 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 11:10:09 -0400
Subject: [PATCH 108/380] Added rf_inverse_mask_by_value.

---
 .../rasterframes/RasterFunctions.scala        |  4 ++++
 .../rasterframes/expressions/package.scala    |  1 +
 .../expressions/transformers/Mask.scala       | 20 ++++++++++++++++++
 .../rasterframes/RasterFunctionsSpec.scala    | 21 +++++++++++++++++++
 .../python/pyrasterframes/rasterfunctions.py  | 10 +++++++++
 .../python/tests/PyRasterFramesTests.py       |  6 ++++++
 6 files changed, 62 insertions(+)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index dc0eca688..3af47dfaf 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -278,6 +278,10 @@ trait RasterFunctions {
   def rf_inverse_mask(sourceTile: Column, maskTile: Column): TypedColumn[Any, Tile] =
     Mask.InverseMaskByDefined(sourceTile, maskTile)
 
+  /** Where the `maskTile` does **not** equal `maskValue`, replace values in the source tile with `NoData` */
+  def rf_inverse_mask_by_value(sourceTile: Column, maskTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
+    Mask.InverseMaskByValue(sourceTile, maskTile, maskValue)
+
   /** Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value. */
   def rf_rasterize(geometry: Column, bounds: Column, value: Column, cols: Int, rows: Int): TypedColumn[Any, Tile] =
     withTypedAlias("rf_rasterize", geometry)(
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 6ab3a1d10..97d87fc5b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -121,6 +121,7 @@ package object expressions {
 
     registry.registerExpression[Mask.MaskByDefined]("rf_mask")
     registry.registerExpression[Mask.MaskByValue]("rf_mask_by_value")
+    registry.registerExpression[Mask.InverseMaskByValue]("rf_inverse_mask_by_value")
     registry.registerExpression[Mask.InverseMaskByDefined]("rf_inverse_mask")
 
     registry.registerExpression[DebugRender.RenderAscii]("rf_render_ascii")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
index ac755cb0b..0f0754d97 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
@@ -143,4 +143,24 @@ object Mask {
     def apply(srcTile: Column, maskingTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
       new Column(MaskByValue(srcTile.expr, maskingTile.expr, maskValue.expr)).as[Tile]
   }
+
+  @ExpressionDescription(
+    usage = "_FUNC_(target, mask, maskValue) - Generate a tile with the values from the data tile, but where cells in the masking tile DO NOT contain the masking value, replace the data value with NODATA.",
+    arguments = """
+  Arguments:
+    * target - tile to mask
+    * mask - masking definition""",
+    examples = """
+  Examples:
+    > SELECT _FUNC_(target, mask, maskValue);
+       ..."""
+  )
+  case class InverseMaskByValue(leftTile: Expression, rightTile: Expression, maskValue: Expression)
+    extends Mask(leftTile, rightTile, maskValue, true) {
+    override def nodeName: String = "rf_inverse_mask_by_value"
+  }
+  object InverseMaskByValue {
+    def apply(srcTile: Column, maskingTile: Column, maskValue: Column): TypedColumn[Any, Tile] =
+      new Column(InverseMaskByValue(srcTile.expr, maskingTile.expr, maskValue.expr)).as[Tile]
+  }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 160e01ec8..f000efbfd 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -584,6 +584,27 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_mask_by_value")
     }
 
+    it("should inverse mask tile by another identified by specified value") {
+      val df = Seq[Tile](randTile).toDF("tile")
+      val mask_value = 4
+
+      val withMask = df.withColumn("mask",
+        rf_local_multiply(rf_convert_cell_type(
+          rf_local_greater($"tile", 50),
+          "uint8"),
+          lit(mask_value)
+        )
+      )
+
+      val withMasked = withMask.withColumn("masked",
+        rf_inverse_mask_by_value($"tile", $"mask", lit(mask_value)))
+
+      val result = withMasked.agg(rf_agg_no_data_cells($"tile") < rf_agg_no_data_cells($"masked")).as[Boolean]
+
+      result.first() should be(true)
+      checkDocs("rf_inverse_mask_by_value")
+    }
+
     it("should render ascii art") {
       val df = Seq[Tile](ProjectedRasterTile(TestData.l8Labels)).toDF("tile")
       val r1 = df.select(rf_render_ascii($"tile"))
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 95dff69cb..17ec25a3f 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -150,6 +150,15 @@ def _(data_tile, mask_tile, mask_value):
     _.__module__ = THIS_MODULE
     return _
 
+def _create_inverseMaskByValue():
+    """ Create a function mapping to Scala rf_inverse_mask_by_value function """
+    def _(data_tile, mask_tile, mask_value):
+        jfcn = RFContext.active().lookup('rf_inverse_mask_by_value')
+        return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
+    _.__name__ = 'rf_inverse_mask_by_value'
+    _.__doc__ = 'Generate a tile with the values from the data tile, but where cells in the masking tile do not contain the masking value, replace the data value with NODATA.'
+    _.__module__ = THIS_MODULE
+    return _
 
 _rf_unique_functions = {
     'rf_array_to_tile': _create_arrayToTile(),
@@ -160,6 +169,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_explode_tiles_sample': _create_explode_tiles_sample(),
     'rf_make_constant_tile': _create_makeConstantTile(),
     'rf_mask_by_value': _create_maskByValue(),
+    'rf_inverse_mask_by_value': _create_inverseMaskByValue(),
     'rf_rasterize': _create_rasterize(),
     'st_reproject': _create_reproject_geometry(),
     'rf_make_ones_tile': _create_tileOnes(),
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index faa337010..4dac724c0 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -216,6 +216,12 @@ def test_mask_by_value(self):
             .collect()[0][0]
         self.assertTrue(result)
 
+        rf3 = rf1.select(rf1.tile, rf_inverse_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
+        result = rf3.agg(rf_agg_no_data_cells(rf3.tile) < rf_agg_no_data_cells(rf3.masked)) \
+            .collect()[0][0]
+        self.assertTrue(result)
+
+
     def test_resample(self):
         from pyspark.sql.functions import lit
         result = self.rf.select(

From 1ec443760d504220b1d4955a642fa5c2dd6252ab Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 29 May 2019 11:05:51 -0400
Subject: [PATCH 109/380] Expose raster_join extension method in pyrasterframes

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../python/pyrasterframes/__init__.py         | 22 ++++++++++++++++++
 .../python/tests/PyRasterFramesTests.py       | 23 +++++++++++++++++++
 .../rasterframes/py/PyRFContext.scala         | 19 +++++++++++++++
 3 files changed, 64 insertions(+)

diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index a05ab5246..cd994a89c 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -46,6 +46,25 @@ def _convert_df(df, sp_key=None, metadata=None):
             df._jdf, _to_java_column(sp_key), json.dumps(metadata)), ctx._spark_session)
 
 
+def _raster_join(df, other, left_extent=None, left_crs=None, right_extent=None, right_crs=None, join_exprs=None):
+    ctx = SparkContext._active_spark_context._rf_context
+    if join_exprs is not None:
+        assert left_extent is not None and left_crs is not None and right_extent is not None and right_crs is not None
+        # Note the order of arguments here.
+        cols = [join_exprs, left_extent, left_crs, right_extent, right_crs]
+        jdf = ctx._jrfctx.rasterJoin(df._jdf, other._jdf, *[_to_java_column(c) for c in cols])
+
+    elif left_extent is not None:
+        assert left_crs is not None and right_extent is not None and right_crs is not None
+        cols = [left_extent, left_crs, right_extent, right_crs]
+        jdf = ctx._jrfctx.rasterJoin(df._jdf, other._jdf, *[_to_java_column(c) for c in cols])
+
+    else:
+        jdf = ctx._jrfctx.rasterJoin(df._jdf, other._jdf)
+
+    return RasterFrame(jdf, ctx._spark_session)
+
+
 def _layer_reader(df_reader, format_key, path, **options):
     """ Loads the file of the given type at the given path."""
     df = df_reader.format(format_key).load(path, **options)
@@ -74,6 +93,9 @@ def to_csv(comp): return ','.join(str(v) for v in comp)
 # Add the 'asRF' method to pyspark DataFrame
 DataFrame.asRF = _convert_df
 
+# Add `raster_join` method to pyspark DataFrame
+DataFrame.raster_join = _raster_join
+
 # Add DataSource convenience methods to the DataFrameReader
 DataFrameReader.rastersource = _rastersource_reader
 
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index faa337010..3f0f13ab4 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -34,6 +34,7 @@ def setUpClass(cls):
         cls.spark = (SparkSession.builder
                      .config('spark.driver.extraClassPath', jarpath)
                      .config('spark.executor.extraClassPath', jarpath)
+                     .config('spark.sql.crossJoin.enabled', 'true')
                      .withKryoSerialization()
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
@@ -374,6 +375,28 @@ def path(scene, band):
         b1_paths = [s.format('1') for s in scene_dict.values()]
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
+    def test_raster_join(self):
+        rf_prime = self.rf.withColumnRenamed('tile', 'tile2')
+        rf_joined = self.rf.raster_join(rf_prime)
+
+        self.assertTrue(rf_joined.count(), self.rf.count())
+        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + 1)
+
+        rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
+        self.assertTrue(rf_joined_2.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + 1)
+
+        # this will bring arbitrary additional data into join; garbage result
+        join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
+        rf_joined_3 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs,
+                                          rf_prime.extent, rf_prime.crs,
+                                          join_expression)
+        self.assertTrue(rf_joined_3.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + 1)
+
+        # throws if you don't  pass  in all expected columns
+        with self.assertRaises(AssertionError):
+            self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
 
 def suite():
     function_tests = unittest.TestSuite()
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 660c30e60..1e93f6347 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -24,6 +24,7 @@ import geotrellis.raster.{CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import org.apache.spark.sql._
+import org.locationtech.rasterframes.extensions.RasterJoin
 import org.locationtech.rasterframes.{RasterFunctions, _}
 import org.locationtech.rasterframes.model.LazyCRS
 import spray.json._
@@ -97,6 +98,24 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     df.asRF(spatialKey, jtlm)
   }
 
+  /**
+    * Left spatial join managing reprojection and merging of `other`
+    */
+  def rasterJoin(df: DataFrame, other: DataFrame): DataFrame = RasterJoin(df, other)
+
+  /**
+    * Left spatial join managing reprojection and merging of `other`; uses extent and CRS columns to determine if rows intersect
+    */
+  def rasterJoin(df: DataFrame, other: DataFrame, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame =
+    RasterJoin(df, other, leftExtent, leftCRS, rightExtent, rightCRS)
+
+  /**
+    * Left spatial join managing reprojection and merging of `other`; uses joinExprs to conduct initial join then extent and CRS columns to determine if rows intersect
+    */
+  def rasterJoin(df: DataFrame, other: DataFrame, joinExprs: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame =
+    RasterJoin(df, other, joinExprs, leftExtent, leftCRS, rightExtent, rightCRS)
+
+
   /**
     * Convenience functions for use in Python
     */

From 8c2d826402dc2a79ec15b6d51e47b3c94ad5bc47 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 29 May 2019 11:47:10 -0400
Subject: [PATCH 110/380] Pyrasterframes test setup does not need crossjoin
 config

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/tests/PyRasterFramesTests.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 3f0f13ab4..6e6705c82 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -34,7 +34,6 @@ def setUpClass(cls):
         cls.spark = (SparkSession.builder
                      .config('spark.driver.extraClassPath', jarpath)
                      .config('spark.executor.extraClassPath', jarpath)
-                     .config('spark.sql.crossJoin.enabled', 'true')
                      .withKryoSerialization()
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
@@ -376,7 +375,9 @@ def path(scene, band):
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
     def test_raster_join(self):
-        rf_prime = self.rf.withColumnRenamed('tile', 'tile2')
+        # re-read the same source
+        rf_prime = self.spark.read.geotiff(self.img_uri) \
+            .withColumnRenamed('tile', 'tile2').alias('rf_prime')
         rf_joined = self.rf.raster_join(rf_prime)
 
         self.assertTrue(rf_joined.count(), self.rf.count())

From 7a4e771ad10b44fff96f0b44ad3caf898bb2cd4a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 11:54:44 -0400
Subject: [PATCH 111/380] Update fixed documentation.

Co-Authored-By: Jason T Brown <brown27@gmail.com>
---
 .../rasterframes/expressions/transformers/Mask.scala          | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
index 0f0754d97..106a52a7b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/Mask.scala
@@ -149,7 +149,9 @@ object Mask {
     arguments = """
   Arguments:
     * target - tile to mask
-    * mask - masking definition""",
+    * mask - masking definition
+    * maskValue - value in the `mask` for which to mark `target` as data cells
+    """,
     examples = """
   Examples:
     > SELECT _FUNC_(target, mask, maskValue);

From 4d14cc25ac8aa06914d8e6d35568d41d745e53d6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 29 May 2019 14:06:20 -0400
Subject: [PATCH 112/380] Raster join test with heterogeneous LHS

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 core/src/test/resources/README.md             |   8 +++++
 .../m_3607717_sw_18_1_20160620_subset.tif     | Bin 0 -> 360982 bytes
 .../m_3607824_se_17_1_20160620_subset.tif     | Bin 0 -> 360980 bytes
 core/src/test/resources/m_3607_box.tif        | Bin 0 -> 3178 bytes
 .../rasterframes/RasterJoinSpec.scala         |  34 ++++++++++++++++++
 5 files changed, 42 insertions(+)
 create mode 100644 core/src/test/resources/README.md
 create mode 100644 core/src/test/resources/m_3607717_sw_18_1_20160620_subset.tif
 create mode 100644 core/src/test/resources/m_3607824_se_17_1_20160620_subset.tif
 create mode 100644 core/src/test/resources/m_3607_box.tif

diff --git a/core/src/test/resources/README.md b/core/src/test/resources/README.md
new file mode 100644
index 000000000..70aa76cd6
--- /dev/null
+++ b/core/src/test/resources/README.md
@@ -0,0 +1,8 @@
+# Test resources
+
+## NAIP Virginia UTM overlaps
+
+ 1. `m_3607717_sw_18_1_20160620_subset.tif` the southwest corner of NAIP m_3607717_sw_18_1. It is in its native CRS EPSG:26918
+ 2. `m_3607824_se_17_1_20160620_subset.tif` the southeast corner of NAIP m_3607824_se_17_1. Overlaps number 1; It is to the east. It is in its native CRS EPSG:26917.
+ 3. `m_3607_box.tif` - an aribtrary burned in polygon in EPSG:4326 partially overlapping both of the above NAIP subsets 1 and 2.
+ 
diff --git a/core/src/test/resources/m_3607717_sw_18_1_20160620_subset.tif b/core/src/test/resources/m_3607717_sw_18_1_20160620_subset.tif
new file mode 100644
index 0000000000000000000000000000000000000000..862c236452121137503544c6b9d08ed716f0573d
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diff --git a/core/src/test/resources/m_3607_box.tif b/core/src/test/resources/m_3607_box.tif
new file mode 100644
index 0000000000000000000000000000000000000000..50570db26b724b34904f714f0e6c4db161f6cb08
GIT binary patch
literal 3178
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literal 0
HcmV?d00001

diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index b457ca148..f00d70393 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -125,5 +125,39 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       multibandJoin.count() should be (multibandRf.count())
     }
 
+    it("should join with heterogeneous LHS CRS and coverages"){
+
+      val df17 = readSingleband("m_3607824_se_17_1_20160620_subset.tif")
+        .toDF(TileDimensions(50, 50))
+        .withColumn("utm", lit(17))
+      // neighboring and slightly overlapping NAIP scene
+      val df18 = readSingleband("m_3607717_sw_18_1_20160620_subset.tif")
+        .toDF(TileDimensions(60, 60))
+        .withColumn("utm", lit(18))
+
+      df17.count() should be (6 * 6) // file is 300 x 300
+      df18.count() should be (5 * 5) // file is 300 x 300
+
+      val df = df17.union(df18)
+      df.count() should be (6 * 6 + 5 * 5)
+      val expectCrs = Array("+proj=utm +zone=17 +datum=NAD83 +units=m +no_defs ", "+proj=utm +zone=18 +datum=NAD83 +units=m +no_defs ")
+      df.select($"crs".getField("crsProj4")).distinct().as[String].collect() should contain theSameElementsAs expectCrs
+
+      // read a third source to join. burned in box that intersects both above subsets; but more so on the df17
+      val box = readSingleband("m_3607_box.tif").toDF(TileDimensions(4,4)).withColumnRenamed("tile", "burned")
+      val joined = df.rasterJoin(box)
+
+      joined.count() should be (df.count)
+
+      val totals = joined.groupBy($"utm").agg(sum(rf_tile_sum($"burned")).alias("burned_total"))
+      val total18 = totals.where($"utm" === 18).select($"burned_total").as[Double].first()
+      val total17 = totals.where($"utm" === 17).select($"burned_total").as[Double].first()
+
+      total18 should be > 0.0
+      total18 should be < total17
+
+
+    }
+
   }
 }

From deb70f3b6c4a4f2f82ad87a0d37e8fb379432e0d Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 15:43:04 -0400
Subject: [PATCH 113/380] Fixed bug in maintaining RHS ancillary columns. Misc
 cleanup.

---
 .../rasterframes/expressions/accessors/GetCRS.scala    |  2 +-
 .../rasterframes/extensions/RasterJoin.scala           | 10 ++++++++--
 .../org/locationtech/rasterframes/RasterJoinSpec.scala |  6 ++++++
 .../datasource/awspds/MODISCatalogRelationTest.scala   |  3 ---
 4 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
index 1f1a9d926..00f8f5db4 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
@@ -39,7 +39,7 @@ import org.locationtech.rasterframes.model.TileContext
  */
 case class GetCRS(child: Expression) extends OnTileContextExpression with CodegenFallback {
   override def dataType: DataType = schemaOf[CRS]
-  override def nodeName: String = "crs"
+  override def nodeName: String = "rf_crs"
   override def eval(ctx: TileContext): InternalRow = ctx.crs.toInternalRow
 }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index 32d98d621..5fba843f9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -98,13 +98,19 @@ object RasterJoin {
     // On the RHS we collect result as a list.
     val rightAggCtx = Seq(collect_list(rightExtent) as rightExtent2, collect_list(rightCRS) as rightCRS2)
     val rightAggTiles = right.tileColumns.map(c => collect_list(c) as c.columnName)
-    val aggCols = leftAggCols ++ rightAggTiles ++ rightAggCtx
+    val rightAggOther = right.notTileColumns
+      .filter(n => n.columnName != rightExtent.columnName && n.columnName != rightCRS.columnName)
+      .map(c => collect_list(c) as c.columnName)
+    val aggCols = leftAggCols ++ rightAggTiles ++ rightAggCtx ++ rightAggOther
 
     // After the aggregation we take all the tiles we've collected and resample + merge into LHS extent/CRS.
     val reprojCols = rightAggTiles.map(t => reproject_and_merge(
       col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(leftTile)
     ) as t.columnName)
-    val finalCols = leftAggCols.map(c => col(c.columnName)) ++ reprojCols
+
+
+    def unresolved(c: Column): Column = col(c.columnName)
+    val finalCols = leftAggCols.map(unresolved) ++ reprojCols ++ rightAggOther.map(unresolved)
 
     // Here's the meat:
     left
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index f00d70393..8dde83515 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -159,5 +159,11 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
 
     }
 
+    it("should pass through ancillary columns") {
+      val left = b4nativeRf.withColumn("left_id", monotonically_increasing_id())
+      val right = b4warpedRf.withColumn("right_id", monotonically_increasing_id())
+      val joined = left.rasterJoin(right)
+      joined.columns should contain allElementsOf Seq("left_id", "right_id")
+    }
   }
 }
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
index e882e4edf..a71ec0218 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
@@ -41,13 +41,11 @@ class MODISCatalogRelationTest extends TestEnvironment {
       .cache()
 
     it("should provide a non-empty catalog") {
-      scenes.show(false)
       assert(scenes.count() === 1)
     }
 
     it("should construct band specific download URLs") {
       val b01 = scenes.select($"assets"("B01").as[String])
-      b01.show(false)
       noException shouldBe thrownBy {
         new URL(b01.first())
       }
@@ -65,7 +63,6 @@ class MODISCatalogRelationTest extends TestEnvironment {
     it("should download geotiff as tiles") {
       val b01 = scenes
         .select(read_tiles($"assets"("B01") as "B01", $"assets"("B02") as "B02"))
-      b01.show(false)
       assert(b01.count() === 100)
 
 //      val kv = b01.select($"B01_extent", $"B01_tile").as[(Extent, Tile)]

From 22ca7d03e510161fc527aa8e04b5effb989358c4 Mon Sep 17 00:00:00 2001
From: Phil Varner <pvarner@astraea.io>
Date: Wed, 29 May 2019 16:25:36 -0400
Subject: [PATCH 114/380] fix circleci paths and fix ITs broken by mis-merge

Signed-off-by: Phil Varner <pvarner@astraea.io>
---
 .circleci/config.yml                          |  8 +--
 .../rasterframes/ref/RasterSourceIT.scala     | 50 +++++++++++++------
 2 files changed, 38 insertions(+), 20 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 5a3de91ae..55d18b941 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -142,17 +142,17 @@ workflows:
           filters:
             branches:
               only:
-                - /astraea\/feature\/.*-its/
+                - /feature\/.*-its/
       - itWithoutGdal:
           filters:
             branches:
               only:
-                - /astraea\/feature\/.*-its/
+                - /feature\/.*-its/
       - publish:
           filters:
             branches:
               only:
-                - astraea/develop
+                - develop
   nightlyReleaseAstraea:
     triggers:
       - schedule:
@@ -160,7 +160,7 @@ workflows:
           filters:
             branches:
               only:
-                - astraea/develop
+                - develop
     jobs:
       - it
       - itWithoutGdal
diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 7027258ee..9372e29ba 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -64,34 +64,52 @@ class RasterSourceIT extends TestEnvironment with TestData {
 
   if (RasterSource.IsGDAL.hasGDAL) {
     describe("GDAL support") {
+
       it("should read JPEG2000 scene") {
         RasterSource(localSentinel).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
       }
+
+      it("should read small MRF scene with one band converted from MODIS HDF") {
+        val (expectedTileCount, _) = expectedTileCountAndBands(2400, 2400)
+        RasterSource(modisConvertedMrfPath).readAll().flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
+
+      it("should read remote HTTP MRF scene") {
+        val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+        RasterSource(remoteHttpMrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
+
+      it("should read remote S3 MRF scene") {
+        val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
+        RasterSource(remoteS3MrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      }
     }
   } else {
     describe("GDAL missing error support") {
       it("should throw exception reading JPEG2000 scene") {
-          intercept[IllegalArgumentException] {
-            RasterSource(localSentinel)
-          }
+        intercept[IllegalArgumentException] {
+          RasterSource(localSentinel)
+        }
       }
-    }
 
-    it("should read small MRF scene with one band converted from MODIS HDF") {
-      val (expectedTileCount, _) = expectedTileCountAndBands(2400, 2400)
-      RasterSource(modisConvertedMrfPath).readAll().flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
-    }
+      it("should throw exception reading MRF scene with one band converted from MODIS HDF") {
+        intercept[IllegalArgumentException] {
+          RasterSource(modisConvertedMrfPath)
+        }
+      }
 
-    it("should read remote HTTP MRF scene") {
-      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
-      RasterSource(remoteHttpMrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
-    }
+      it("should throw exception reading remote HTTP MRF scene") {
+        intercept[IllegalArgumentException] {
+          RasterSource(remoteHttpMrfPath)
+        }
+      }
 
-    it("should read remote S3 MRF scene") {
-      val (expectedTileCount, bands) = expectedTileCountAndBands(6257, 7584, 4)
-      RasterSource(remoteS3MrfPath).readAll(bands = bands).flatMap(_.tile.statisticsDouble).size should be (expectedTileCount)
+      it("should throw exception reading remote S3 MRF scene") {
+        intercept[IllegalArgumentException] {
+          RasterSource(remoteS3MrfPath)
+        }
+      }
     }
-
   }
 
   private def expectedTileCountAndBands(x:Int, y:Int, bandCount:Int = 1) = {

From 11ec028ae08fb8629b0d4a43193ad45d82b0aa18 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 17:23:37 -0400
Subject: [PATCH 115/380] Fixes to RasterJoin to ensure gathered RHS column
 names are resolvable/unique.

---
 .../extensions/DataFrameMethods.scala            |  8 ++++----
 .../rasterframes/extensions/RasterJoin.scala     | 16 +++++++++-------
 .../python/tests/PyRasterFramesTests.py          |  8 ++++----
 3 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index e38ecee25..d7f4af39e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -92,26 +92,26 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def tileColumns: Seq[Column] =
     self.schema.fields
       .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
-      .map(f ⇒ col(f.name).as[Tile])
+      .map(f ⇒ self.col(f.name).as[Tile])
 
   /** Get the columns that are not of type `Tile` */
   def notTileColumns: Seq[Column] =
     self.schema.fields
       .filter(f => !DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
-      .map(f ⇒ col(f.name))
+      .map(f ⇒ self.col(f.name))
 
   /** Get the spatial column. */
   def spatialKeyColumn: Option[TypedColumn[Any, SpatialKey]] = {
     val key = findSpatialKeyField
     key
       .map(_.name)
-      .map(col(_).as[SpatialKey])
+      .map(self.col(_).as[SpatialKey])
   }
 
   /** Get the temporal column, if any. */
   def temporalKeyColumn: Option[TypedColumn[Any, TemporalKey]] = {
     val key = findTemporalKeyField
-    key.map(_.name).map(col(_).as[TemporalKey])
+    key.map(_.name).map(self.col(_).as[TemporalKey])
   }
 
   /** Find the field tagged with the requested `role` */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index 5fba843f9..83f85fdd0 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -76,6 +76,9 @@ object RasterJoin {
   }
 
   def apply(left: DataFrame, right: DataFrame, joinExprs: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame = {
+    // Convert resolved column into a symbolic one.
+    def unresolved(c: Column): Column = col(c.columnName)
+
     // Unique id for temporary columns
     val id = Random.alphanumeric.take(5).mkString("_", "", "_")
 
@@ -88,8 +91,6 @@ object RasterJoin {
     // Post aggregation right crs. We create a new name.
     val rightCRS2 = id + "crs"
 
-    // A representative tile from the left
-    val leftTile = left.tileColumns.headOption.getOrElse(throw new IllegalArgumentException("Need at least one target tile on LHS"))
 
     // Gathering up various expressions we'll use to construct the result.
     // After joining We will be doing a groupBy the LHS. We have to define the aggregations to perform after the groupBy.
@@ -100,16 +101,17 @@ object RasterJoin {
     val rightAggTiles = right.tileColumns.map(c => collect_list(c) as c.columnName)
     val rightAggOther = right.notTileColumns
       .filter(n => n.columnName != rightExtent.columnName && n.columnName != rightCRS.columnName)
-      .map(c => collect_list(c) as c.columnName)
+      .map(c => collect_list(c) as (c.columnName + "_agg"))
     val aggCols = leftAggCols ++ rightAggTiles ++ rightAggCtx ++ rightAggOther
 
-    // After the aggregation we take all the tiles we've collected and resample + merge into LHS extent/CRS.
+    // After the aggregation we take all the tiles we've collected and resample + merge
+    // into LHS extent/CRS.
+    // Use a representative tile from the left for the tile dimensions
+    val leftTile = left.tileColumns.headOption.getOrElse(throw new IllegalArgumentException("Need at least one target tile on LHS"))
     val reprojCols = rightAggTiles.map(t => reproject_and_merge(
-      col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(leftTile)
+      col(leftExtent2), col(leftCRS2), col(t.columnName), col(rightExtent2), col(rightCRS2), rf_dimensions(unresolved(leftTile))
     ) as t.columnName)
 
-
-    def unresolved(c: Column): Column = col(c.columnName)
     val finalCols = leftAggCols.map(unresolved) ++ reprojCols ++ rightAggOther.map(unresolved)
 
     // Here's the meat:
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 879632ab5..113faf173 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -373,7 +373,6 @@ def path(scene, band):
             pathTableColumns=csv_columns,
         )
 
-        path_df.printSchema()
         self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}
         self.assertTrue(path_df.select('b1_path').distinct().count() == 3)  # as per scene_dict
         b1_paths_maybe = path_df.select('b1_path').distinct().collect()
@@ -384,14 +383,15 @@ def test_raster_join(self):
         # re-read the same source
         rf_prime = self.spark.read.geotiff(self.img_uri) \
             .withColumnRenamed('tile', 'tile2').alias('rf_prime')
+
         rf_joined = self.rf.raster_join(rf_prime)
 
         self.assertTrue(rf_joined.count(), self.rf.count())
-        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + 1)
+        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
 
         rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
         self.assertTrue(rf_joined_2.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + 1)
+        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
 
         # this will bring arbitrary additional data into join; garbage result
         join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
@@ -399,7 +399,7 @@ def test_raster_join(self):
                                           rf_prime.extent, rf_prime.crs,
                                           join_expression)
         self.assertTrue(rf_joined_3.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + 1)
+        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
 
         # throws if you don't  pass  in all expected columns
         with self.assertRaises(AssertionError):

From 9053de3cc23351592f9011a9997257de3a8823e6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 29 May 2019 17:42:33 -0400
Subject: [PATCH 116/380] Regression fix.

---
 .../scala/org/locationtech/rasterframes/RasterJoinSpec.scala   | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
index 8dde83515..b2cd5d8ce 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterJoinSpec.scala
@@ -119,7 +119,6 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       multibandRf.tileColumns.length should be (3)
 
       val multibandJoin = multibandRf.rasterJoin(b4warpedRf)
-      multibandJoin.printSchema()
 
       multibandJoin.tileColumns.length should be (4)
       multibandJoin.count() should be (multibandRf.count())
@@ -163,7 +162,7 @@ class RasterJoinSpec extends TestEnvironment with TestData with RasterMatchers {
       val left = b4nativeRf.withColumn("left_id", monotonically_increasing_id())
       val right = b4warpedRf.withColumn("right_id", monotonically_increasing_id())
       val joined = left.rasterJoin(right)
-      joined.columns should contain allElementsOf Seq("left_id", "right_id")
+      joined.columns should contain allElementsOf Seq("left_id", "right_id_agg")
     }
   }
 }

From 461c847712a9bb218625caccda9f48e79fbb57bb Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 30 May 2019 13:02:53 -0400
Subject: [PATCH 117/380] Fixes to CellType <-> dtype conversion.

---
 .../python/pyrasterframes/rf_types.py         | 39 ++++--------------
 .../python/tests/PyRasterFramesTests.py       | 41 ++++++++++++-------
 2 files changed, 36 insertions(+), 44 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 722a6d4f1..37d4a56e5 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -196,23 +196,7 @@ def no_data_value(self):
 
     def to_numpy_dtype(self):
         n = self.base_cell_type_name()
-        if n == "uint8":
-            return np.uint
-        elif n == "int8":
-            return np.int
-        elif n == "uint16":
-            return np.ushort
-        elif n == "int16":
-            return np.short
-        elif n == "int32":
-            return np.int
-        elif n == "float32":
-            return np.float
-        elif n == "float64":
-            return np.double
-        else:
-            # Shouldn't happen
-            return np.dtype(n)
+        return np.dtype(n)
 
     def with_no_data_value(self, no_data):
         return CellType(self.base_cell_type_name() + "ud" + str(no_data))
@@ -234,18 +218,18 @@ class Tile(object):
     def __init__(self, cells, cell_type=None):
         if cell_type is None:
             self.cell_type = CellType.from_numpy_dtype(cells.dtype)
+            self.cells = cells
         else:
             self.cell_type = cell_type
+            self.cells = cells.astype(cell_type.to_numpy_dtype())
 
         if self.cell_type.has_no_data():
             nd_value = self.cell_type.no_data_value()
             if np.isnan(nd_value):
-                self.cells = np.ma.masked_invalid(cells)
+                self.cells = np.ma.masked_invalid(self.cells)
             else:
                 # if the value in the array is `nd_value`, it is masked as nodata
-                self.cells = np.ma.masked_equal(cells, nd_value)
-        else:
-            self.cells = cells
+                self.cells = np.ma.masked_equal(self.cells, nd_value)
 
     def __eq__(self, other):
         if type(other) is type(self):
@@ -254,12 +238,12 @@ def __eq__(self, other):
             return False
 
     def __str__(self):
-        return "Tile(\n  dimensions={}\n  cell_type={}\n  cells={}\n)" \
+        return "Tile(dimensions={}, cell_type={}, cells={})" \
             .format(self.dimensions(), self.cell_type, self.cells)
 
     def __repr__(self):
         return "Tile({}, {})" \
-            .format(repr(self.cell_type), str(self.cells))
+            .format(repr(self.cells), repr(self.cell_type))
 
     def __add__(self, right):
         if isinstance(right, Tile):
@@ -324,7 +308,6 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
     def serialize(self, tile):
-        #print("in: ", repr(tile.cells.flatten().tobytes()))
         row = [
             # cell_context
             [
@@ -350,11 +333,8 @@ def deserialize(self, datum):
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
-        #print("out: ", repr(cell_data_bytes))
         try:
-            as_numpy = np.frombuffer(cell_data_bytes)
-                #.astype(dtype=cell_type.to_numpy_dtype())
-                #.astype(dtype=np.dtype('B')) \
+            as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype())
             reshaped = as_numpy.reshape((rows, cols))
             t = Tile(reshaped, cell_type)
         except ValueError as e:
@@ -364,8 +344,7 @@ def deserialize(self, datum):
                 "rows": rows,
                 "cell_data.length": len(cell_data_bytes),
                 "cell_data.type": type(cell_data_bytes),
-                "cell_data.values": repr(cell_data_bytes),
-                "as_numpy.values": repr(as_numpy)
+                "cell_data.values": repr(cell_data_bytes)
             }, e)
         return t
 
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index fde57ed9c..8e3ef5d66 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -46,8 +46,11 @@ def setUpClass(cls):
         print("Spark Version: " + cls.spark.version)
         cls.spark.withRasterFrames()
 
-        # load something into a rasterframe
-        rf = cls.spark.read.geotiff(cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()) \
+        cls.img_uri = cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()
+
+
+    # load something into a rasterframe
+        rf = cls.spark.read.geotiff(cls.img_uri) \
             .withBounds() \
             .withCenter()
 
@@ -384,16 +387,6 @@ def test_cell_type_conversion(self):
                                  CellType.from_numpy_dtype(ct.to_numpy_dtype()),
                                  "GTCellType comparison for " + str(ct))
 
-    def test_array_flattening(self):
-        #a = np.frombuffer(bytearray([1, 0, 0, 0, 0, 0, 0, 0]))
-
-        a = np.array([[1, 2], [0, 4]], dtype=np.dtype("uint8"))
-        b = np.ma.masked_equal(a, 2)
-        c = builtins.bytearray(b.flatten().tobytes())
-        d = np.frombuffer(c, b.dtype).reshape(2, 2)
-        e = np.ma.masked_equal(d, 2)
-        print(repr(e))
-
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
         self.assertTrue(t1.cells.mask[1][0])
@@ -429,6 +422,7 @@ def test_tile_udt_serialization(self):
 
     def test_no_data_udf_handling(self):
         t1 = Tile(np.array([[1, 2], [0, 4]]), CellType("uint8"))
+        self.assertEqual(t1.cell_type.to_numpy_dtype(), np.dtype("uint8"))
         e1 = Tile(np.array([[2, 3], [0, 5]]), CellType("uint8"))
         schema = StructType([StructField("tile", TileUDT(), False)])
         df = self.spark.createDataFrame([{"tile": t1}], schema)
@@ -453,7 +447,23 @@ def test_addition(self):
         r2 = (t1 + t2).cells
         self.assertTrue(np.ma.allequal(r2, e2))
 
-    def test_tile_ops_general(self):
+
+class PandasInterop(TestEnvironment):
+
+    def test_pandas_conversion(self):
+        import pandas as pd
+        pd.options.display.max_colwidth = 256
+        cell_types = (ct for ct in rf_cell_types() if not (ct.is_raw() or ("bool" in ct.base_cell_type_name())))
+        tiles = [Tile(np.random.randn(10, 12) * 100, ct) for ct in cell_types]
+        in_pandas = pd.DataFrame({
+            'tile': tiles
+        })
+
+        in_spark = self.spark.createDataFrame(in_pandas)
+        out_pandas = in_spark.select('tile').toPandas()
+        self.assertTrue(out_pandas.equals(in_pandas))
+
+    def test_extended_pandas_ops(self):
         import pandas as pd
 
         self.assertIsInstance(self.rf.sql_ctx, SQLContext)
@@ -466,13 +476,16 @@ def test_tile_ops_general(self):
         # Try to create a tile from numpy.
         self.assertEqual(Tile(np.random.randn(10, 10)).dimensions(), [10, 10])
 
+        tiles = [Tile(np.random.randn(10, 12), CellType("float32")) for _ in range(3)]
         to_spark = pd.DataFrame({
-            't': [Tile(np.random.randn(10, 12)) for _ in range(3)],
+            't': tiles,
             'b': ['a', 'b', 'c'],
             'c': [1, 2, 4],
         })
         rf_maybe = self.spark.createDataFrame(to_spark)
 
+        rf_maybe.select(rf_render_matrix(rf_maybe.t)).show(truncate=False)
+
         # Try to do something with it.
         sums = to_spark.t.apply(lambda a: a.cells.sum()).tolist()
         maybe_sums = rf_maybe.select(rf_tile_sum(rf_maybe.t).alias('tsum'))

From 4c797f5690ffa0828a276e1198bac1c3897d4804 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 30 May 2019 14:55:35 -0400
Subject: [PATCH 118/380] Fixed serialization issue with handling byte order.
 Added rf_identity Added class methods to get standard cell types.

---
 .../rasterframes/RasterFunctions.scala        |  3 +
 .../expressions/localops/Abs.scala            |  2 +-
 .../expressions/localops/Identity.scala       | 50 ++++++++++
 .../python/pyrasterframes/rasterfunctions.py  |  1 +
 .../python/pyrasterframes/rf_types.py         | 92 +++++++++++++------
 .../python/tests/PyRasterFramesTests.py       | 63 ++++++-------
 6 files changed, 151 insertions(+), 60 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Identity.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 3af47dfaf..bcb582632 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -416,4 +416,7 @@ trait RasterFunctions {
   def rf_expm1(tileCol: Column): TypedColumn[Any, Tile] =
     ExpM1(tileCol)
 
+  def rf_identity(tileCol: Column): TypedColumn[Any, Tile] =
+    Identity(tileCol)
+
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
index 196bf30b7..0fe6cac87 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Abs.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.expressions.{NullToValue, UnaryLocalRasterO
 case class Abs(child: Expression) extends UnaryLocalRasterOp with NullToValue with CodegenFallback {
   override def nodeName: String = "rf_abs"
   override def na: Any = null
-  override protected def op(child: Tile): Tile = child.localAbs()
+  override protected def op(t: Tile): Tile = t.localAbs()
 }
 
 object Abs {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Identity.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Identity.scala
new file mode 100644
index 000000000..60e607f8b
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Identity.scala
@@ -0,0 +1,50 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.localops
+
+import geotrellis.raster.Tile
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.{NullToValue, UnaryLocalRasterOp}
+
+@ExpressionDescription(
+  usage = "_FUNC_(tile) - Return the given tile or projected raster unchanged. Useful in debugging round-trip serialization across various language and memory boundaries.",
+  arguments = """
+  Arguments:
+    * tile - tile column to pass through""",
+  examples = """
+  Examples:
+    > SELECT  _FUNC_(tile);
+       ..."""
+)
+case class Identity(child: Expression) extends UnaryLocalRasterOp with NullToValue with CodegenFallback {
+  override def nodeName: String = "rf_identity"
+  override def na: Any = null
+  override protected def op(t: Tile): Tile = t
+}
+
+object Identity {
+  def apply(tile: Column): TypedColumn[Any, Tile] =
+    new Column(Identity(tile.expr)).as[Tile]
+}
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index 0b3c9cdf5..ffcc6c26f 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -255,6 +255,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_exp2': 'Compute 2 to the power of cell values',
     'rf_exp10': 'Compute 10 to the power of cell values',
     'rf_expm1': 'Performs cell-wise exponential, then subtract one',
+    'rf_identity': 'Pass tile through unchanged',
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
     'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
     'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 37d4a56e5..91fdb66dd 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -143,10 +143,42 @@ def __init__(self, cell_type_name: str):
 
     @classmethod
     def from_numpy_dtype(cls, np_dtype: np.dtype):
-        return CellType(str(np_dtype))
+        return CellType(str(np_dtype.name))
+
+    @classmethod
+    def bool(cls):
+        return CellType('bool')
+
+    @classmethod
+    def int8(cls):
+        return CellType('int8')
+
+    @classmethod
+    def uint8(cls):
+        return CellType('uint8')
+
+    @classmethod
+    def int16(cls):
+        return CellType('int16')
+
+    @classmethod
+    def uint16(cls):
+        return CellType('uint16')
+
+    @classmethod
+    def int32(cls):
+        return CellType('int32')
+
+    @classmethod
+    def float32(cls):
+        return CellType('float32')
+
+    @classmethod
+    def float64(cls):
+        return CellType('float64')
 
     def is_raw(self):
-        return self.cell_type_name.endswith("raw")
+        return self.cell_type_name.endswith('raw')
 
     def is_user_defined_no_data(self):
         return "ud" in self.cell_type_name
@@ -155,13 +187,13 @@ def is_default_no_data(self):
         return not (self.is_raw() or self.is_user_defined_no_data())
 
     def is_floating_point(self):
-        return self.cell_type_name.startswith("float")
+        return self.cell_type_name.startswith('float')
 
     def base_cell_type_name(self):
         if self.is_raw():
             return self.cell_type_name[:-3]
         elif self.is_user_defined_no_data():
-            return self.cell_type_name.split("ud")[0]
+            return self.cell_type_name.split('ud')[0]
         else:
             return self.cell_type_name
 
@@ -172,7 +204,7 @@ def no_data_value(self):
         if self.is_raw():
             return None
         elif self.is_user_defined_no_data():
-            num_str = self.cell_type_name.split("ud")[1]
+            num_str = self.cell_type_name.split('ud')[1]
             if self.is_floating_point():
                 return float(num_str)
             else:
@@ -182,24 +214,24 @@ def no_data_value(self):
                 return np.nan
             else:
                 n = self.base_cell_type_name()
-                if n == "uint8" or n == "uint16":
+                if n == 'uint8' or n == 'uint16':
                     return 0
-                elif n == "int8":
+                elif n == 'int8':
                     return -128
-                elif n == "int16":
+                elif n == 'int16':
                     return -32768
-                elif n == "int32":
+                elif n == 'int32':
                     return -2147483648
-                elif n == "bool":
+                elif n == 'bool':
                     return None
         raise Exception("Unable to determine no_data_value from '{}'".format(n))
 
     def to_numpy_dtype(self):
         n = self.base_cell_type_name()
-        return np.dtype(n)
+        return np.dtype(n).newbyteorder('>')
 
     def with_no_data_value(self, no_data):
-        return CellType(self.base_cell_type_name() + "ud" + str(no_data))
+        return CellType(self.base_cell_type_name() + 'ud' + str(no_data))
 
     def __eq__(self, other):
         if type(other) is type(self):
@@ -215,13 +247,9 @@ def __repr__(self):
 
 
 class Tile(object):
-    def __init__(self, cells, cell_type=None):
-        if cell_type is None:
-            self.cell_type = CellType.from_numpy_dtype(cells.dtype)
-            self.cells = cells
-        else:
-            self.cell_type = cell_type
-            self.cells = cells.astype(cell_type.to_numpy_dtype())
+    def __init__(self, cells, cell_type):
+        self.cell_type = cell_type
+        self.cells = cells.astype(cell_type.to_numpy_dtype())
 
         if self.cell_type.has_no_data():
             nd_value = self.cell_type.no_data_value()
@@ -233,7 +261,7 @@ def __init__(self, cells, cell_type=None):
 
     def __eq__(self, other):
         if type(other) is type(self):
-            return np.array_equal(self.cells, other.cells)
+            return self.cell_type == other.cell_type and np.array_equal(self.cells, other.cells)
         else:
             return False
 
@@ -250,11 +278,7 @@ def __add__(self, right):
             other = right.cells
         else:
             other = right
-        result = np.add(self.cells, other)
-        ct = CellType.from_numpy_dtype(result.dtype)
-        if isinstance(result, np.ma.masked_array):
-            ct = ct.with_no_data_value(result.fill_value)
-        return Tile(np.add(self.cells, other), ct)
+        return Tile(np.add(self.cells, other), self.cell_type)
 
     def __sub__(self, right):
         if isinstance(right, Tile):
@@ -263,6 +287,21 @@ def __sub__(self, right):
             other = right
         return Tile(np.subtract(self.cells, other), self.cell_type)
 
+    def __mul__(self, right):
+        if isinstance(right, Tile):
+            other = right.cells
+        else:
+            other = right
+        return Tile(np.multiply(self.cells, other), self.cell_type)
+
+    def __truediv__(self, right):
+        if isinstance(right, Tile):
+            other = right.cells
+        else:
+            other = right
+        return Tile(np.true_divide(self.cells, other), self.cell_type)
+
+
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
         return [self.cells.shape[1], self.cells.shape[0]]
@@ -308,6 +347,7 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.TileUDT'
 
     def serialize(self, tile):
+        cells = bytearray(tile.cells.flatten().tobytes())
         row = [
             # cell_context
             [
@@ -317,7 +357,7 @@ def serialize(self, tile):
             # cell_data
             [
                 # cells
-                bytearray(tile.cells.flatten().tobytes()),
+                cells,
                 None
             ]
         ]
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 8e3ef5d66..e875636a4 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -354,26 +354,26 @@ def test_is_floating_point(self):
 
     def test_cell_type_no_data(self):
         import math
-        self.assertIsNone(CellType("bool").no_data_value())
+        self.assertIsNone(CellType.bool().no_data_value())
 
-        self.assertTrue(CellType("int8").has_no_data())
-        self.assertEqual(CellType("int8").no_data_value(), -128)
+        self.assertTrue(CellType.int8().has_no_data())
+        self.assertEqual(CellType.int8().no_data_value(), -128)
 
-        self.assertTrue(CellType("uint8").has_no_data())
-        self.assertEqual(CellType("uint8").no_data_value(), 0)
+        self.assertTrue(CellType.uint8().has_no_data())
+        self.assertEqual(CellType.uint8().no_data_value(), 0)
 
-        self.assertTrue(CellType("int16").has_no_data())
-        self.assertEqual(CellType("int16").no_data_value(), -32768)
+        self.assertTrue(CellType.int16().has_no_data())
+        self.assertEqual(CellType.int16().no_data_value(), -32768)
 
-        self.assertTrue(CellType("uint16").has_no_data())
-        self.assertEqual(CellType("uint16").no_data_value(), 0)
+        self.assertTrue(CellType.uint16().has_no_data())
+        self.assertEqual(CellType.uint16().no_data_value(), 0)
 
-        self.assertTrue(CellType("float32").has_no_data())
-        self.assertTrue(np.isnan(CellType("float32").no_data_value()))
+        self.assertTrue(CellType.float32().has_no_data())
+        self.assertTrue(np.isnan(CellType.float32().no_data_value()))
 
         self.assertEqual(CellType("float32ud-98").no_data_value(), -98.0)
-        self.assertTrue(math.isnan(CellType("float64").no_data_value()))
-        self.assertEqual(CellType("uint8").no_data_value(), 0)
+        self.assertTrue(math.isnan(CellType.float64().no_data_value()))
+        self.assertEqual(CellType.uint8().no_data_value(), 0)
 
 
 class UDT(TestEnvironment):
@@ -392,7 +392,7 @@ def test_mask_no_data(self):
         self.assertTrue(t1.cells.mask[1][0])
         self.assertIsNotNone(t1.cells[1][1])
         self.assertEqual(len(t1.cells.compressed()), 3)
-        t2 = Tile(np.array([[1.0, 2.0], [float('nan'), 4.0]]), CellType("float32"))
+        t2 = Tile(np.array([[1.0, 2.0], [float('nan'), 4.0]]), CellType.float32())
         self.assertEqual(len(t2.cells.compressed()), 3)
         self.assertTrue(t2.cells.mask[1][0])
         self.assertIsNotNone(t2.cells[1][1])
@@ -421,9 +421,9 @@ def test_tile_udt_serialization(self):
             self.assertEqual(long_trip, a_tile)
 
     def test_no_data_udf_handling(self):
-        t1 = Tile(np.array([[1, 2], [0, 4]]), CellType("uint8"))
+        t1 = Tile(np.array([[1, 2], [0, 4]]), CellType.uint8())
         self.assertEqual(t1.cell_type.to_numpy_dtype(), np.dtype("uint8"))
-        e1 = Tile(np.array([[2, 3], [0, 5]]), CellType("uint8"))
+        e1 = Tile(np.array([[2, 3], [0, 5]]), CellType.uint8())
         schema = StructType([StructField("tile", TileUDT(), False)])
         df = self.spark.createDataFrame([{"tile": t1}], schema)
 
@@ -438,11 +438,11 @@ def increment(t: Tile):
 class TileOps(TestEnvironment):
 
     def test_addition(self):
-        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
+        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType.int8().with_no_data_value(3))
         e1 = np.ma.masked_equal(np.array([[5, 6], [7, 8]]), 7)
         self.assertTrue(np.array_equal((t1 + 4).cells, e1))
 
-        t2 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud1"))
+        t2 = Tile(np.array([[1, 2], [3, 4]]), CellType.int8().with_no_data_value(1))
         e2 = np.ma.masked_equal(np.array([[3, 4], [3, 8]]), 3)
         r2 = (t1 + t2).cells
         self.assertTrue(np.ma.allequal(r2, e2))
@@ -452,16 +452,16 @@ class PandasInterop(TestEnvironment):
 
     def test_pandas_conversion(self):
         import pandas as pd
-        pd.options.display.max_colwidth = 256
+        #pd.options.display.max_colwidth = 256
         cell_types = (ct for ct in rf_cell_types() if not (ct.is_raw() or ("bool" in ct.base_cell_type_name())))
-        tiles = [Tile(np.random.randn(10, 12) * 100, ct) for ct in cell_types]
+        tiles = [Tile(np.random.randn(5, 5) * 100, ct) for ct in cell_types]
         in_pandas = pd.DataFrame({
             'tile': tiles
         })
 
         in_spark = self.spark.createDataFrame(in_pandas)
-        out_pandas = in_spark.select('tile').toPandas()
-        self.assertTrue(out_pandas.equals(in_pandas))
+        out_pandas = in_spark.select(rf_identity('tile').alias('tile')).toPandas()
+        self.assertTrue(out_pandas.equals(in_pandas), str(in_pandas) + "\n\n" + str(out_pandas))
 
     def test_extended_pandas_ops(self):
         import pandas as pd
@@ -474,9 +474,9 @@ def test_extended_pandas_ops(self):
             all([isinstance(row.tile.cells, np.ndarray) for row in rf_collect]))
 
         # Try to create a tile from numpy.
-        self.assertEqual(Tile(np.random.randn(10, 10)).dimensions(), [10, 10])
+        self.assertEqual(Tile(np.random.randn(10, 10), CellType.int8()).dimensions(), [10, 10])
 
-        tiles = [Tile(np.random.randn(10, 12), CellType("float32")) for _ in range(3)]
+        tiles = [Tile(np.random.randn(10, 12), CellType.float64()) for _ in range(3)]
         to_spark = pd.DataFrame({
             't': tiles,
             'b': ['a', 'b', 'c'],
@@ -484,7 +484,7 @@ def test_extended_pandas_ops(self):
         })
         rf_maybe = self.spark.createDataFrame(to_spark)
 
-        rf_maybe.select(rf_render_matrix(rf_maybe.t)).show(truncate=False)
+        # rf_maybe.select(rf_render_matrix(rf_maybe.t)).show(truncate=False)
 
         # Try to do something with it.
         sums = to_spark.t.apply(lambda a: a.cells.sum()).tolist()
@@ -492,26 +492,23 @@ def test_extended_pandas_ops(self):
         maybe_sums = [r.tsum for r in maybe_sums.collect()]
         np.testing.assert_almost_equal(maybe_sums, sums, 12)
 
-        # Back to python side.
-        print(rf_maybe.toPandas())
-
         # Test round trip for an array
-        simple_array = Tile(np.array([[1, 2], [3, 4]]).astype('float64'), )
+        simple_array = Tile(np.array([[1, 2], [3, 4]]), CellType.float64())
         to_spark_2 = pd.DataFrame({
             't': [simple_array]
         })
 
         rf_maybe_2 = self.spark.createDataFrame(to_spark_2)
-        print("RasterFrame `show`:")
-        rf_maybe_2.select(rf_render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
+        #print("RasterFrame `show`:")
+        #rf_maybe_2.select(rf_render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
 
         pd_2 = rf_maybe_2.toPandas()
         array_back_2 = pd_2.iloc[0].t
-        print("Array collected from toPandas output\n", array_back_2)
+        #print("Array collected from toPandas output\n", array_back_2)
 
         self.assertIsInstance(array_back_2, Tile)
         np.testing.assert_equal(array_back_2.cells, simple_array.cells)
-
+        np.int8
 
 class RasterSource(TestEnvironment):
 

From 6343ea03d79bef709bdc7a0fe1eb280f815e10a4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 30 May 2019 17:58:39 -0400
Subject: [PATCH 119/380] Experiment in wrapping GT tiles in wrapper with more
 useful toString rendering.

---
 core/src/main/resources/reference.conf        |  2 +
 .../org/apache/spark/sql/rf/TileUDT.scala     |  2 +-
 .../aggregates/TileRasterizerAggregate.scala  |  8 +--
 .../extensions/DataFrameMethods.scala         |  1 -
 .../rasterframes/model/Cells.scala            | 11 +++-
 .../rasterframes/tiles/InternalRowTile.scala  |  4 +-
 .../tiles/ProjectedRasterTile.scala           |  8 ++-
 .../rasterframes/tiles/ShowableTile.scala     | 53 +++++++++++++++++++
 .../rasterframes/TileUDTSpec.scala            | 12 ++++-
 9 files changed, 90 insertions(+), 11 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 77597ab89..270f122fa 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -1,6 +1,8 @@
 rasterframes {
   nominal-tile-size = 256
   prefer-gdal = true
+  showable-tiles = true
+  showable-max-cells = 20
 }
 
 vlm.gdal {
diff --git a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
index 3a6168d6f..66c0d98a1 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/TileUDT.scala
@@ -56,7 +56,7 @@ class TileUDT extends UserDefinedType[Tile] {
         case ir: InternalRow ⇒ ir.to[Tile]
       }
       .map {
-        case realIRT: InternalRowTile ⇒ realIRT.toArrayTile()
+        case realIRT: InternalRowTile ⇒ realIRT.realizedTile
         case other ⇒ other
       }
       .orNull
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
index a4e048496..c68b8aecb 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -25,7 +25,7 @@ import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.proj4.CRS
 import geotrellis.raster.resample.ResampleMethod
-import geotrellis.raster.{ArrayTile, CellType, MutableArrayTile, Raster, Tile}
+import geotrellis.raster.{ArrayTile, CellType, Raster, Tile}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
@@ -68,15 +68,15 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
 
     if (prd.extent.intersects(localExtent)) {
       val localTile = input.getAs[Tile](2).reproject(extent, crs, prd.crs, projOpts)
-      val bt = buffer.getAs[MutableArrayTile](0)
+      val bt = buffer.getAs[Tile](0)
       val merged = bt.merge(prd.extent, localExtent, localTile.tile, prd.sampler)
       buffer(0) = merged
     }
   }
 
   override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
-    val leftTile = buffer1.getAs[MutableArrayTile](0)
-    val rightTile = buffer2.getAs[MutableArrayTile](0)
+    val leftTile = buffer1.getAs[Tile](0)
+    val rightTile = buffer2.getAs[Tile](0)
     buffer1(0) = leftTile.merge(rightTile)
   }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index d7f4af39e..599beb637 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -29,7 +29,6 @@ import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
 import spray.json.JsonFormat
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
index 975109f0c..e91bf38ef 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
@@ -24,10 +24,12 @@ package org.locationtech.rasterframes.model
 import geotrellis.raster.{ArrayTile, ConstantTile, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
+import org.locationtech.rasterframes
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.{CatalystSerializer, CatalystSerializerEncoder}
 import org.locationtech.rasterframes.ref.RasterRef
 import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.tiles.ShowableTile
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile.ConcreteProjectedRasterTile
 
 /** Represents the union of binary cell datas or a reference to the data.*/
@@ -37,14 +39,18 @@ case class Cells(data: Either[Array[Byte], RasterRef]) {
   /** Convert cells into either a RasterRefTile or an ArrayTile. */
   def toTile(ctx: TileDataContext): Tile = {
     data.fold(
-      bytes => ArrayTile.fromBytes(bytes, ctx.cellType, ctx.dimensions.cols, ctx.dimensions.rows),
+      bytes => {
+        val t = ArrayTile.fromBytes(bytes, ctx.cellType, ctx.dimensions.cols, ctx.dimensions.rows)
+        if (Cells.showableTiles) new ShowableTile(t)
+        else t
+      },
       ref => RasterRefTile(ref)
     )
   }
 }
 
 object Cells {
-
+  private val showableTiles = rasterframes.rfConfig.getBoolean("showable-tiles")
   /** Extracts the Cells from a Tile. */
   def apply(t: Tile): Cells = {
     t match {
@@ -54,6 +60,7 @@ object Cells {
         Cells(Right(ref.rr))
       case const: ConstantTile =>
         // TODO: Create mechanism whereby constant tiles aren't expanded.
+        // If we don't, the serialization breaks.
         Cells(Left(const.toArrayTile().toBytes))
       case o =>
         Cells(Left(o.toBytes))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
index 2785888d7..98be22446 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/InternalRowTile.scala
@@ -40,7 +40,7 @@ class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
   override def toArrayTile(): ArrayTile = realizedTile.toArrayTile()
 
   // TODO: We want to reimplement relevant delegated methods so that they read directly from tungsten storage
-  protected lazy val realizedTile: Tile = cells.toTile(cellContext)
+  lazy val realizedTile: Tile = cells.toTile(cellContext)
 
   protected override def delegate: Tile = realizedTile
 
@@ -113,6 +113,8 @@ class InternalRowTile(val mem: InternalRow) extends FixedDelegatingTile {
       case _: DoubleCells ⇒ DoubleCellReader(this)
     }
   }
+
+  override def toString: String = ShowableTile.show(this)
 }
 
 object InternalRowTile {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
index fd61cb88c..92e2d285d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ProjectedRasterTile.scala
@@ -59,10 +59,16 @@ object ProjectedRasterTile {
   case class ConcreteProjectedRasterTile(t: Tile, extent: Extent, crs: CRS)
       extends ProjectedRasterTile {
     def delegate: Tile = t
+
     override def convert(cellType: CellType): Tile =
       ConcreteProjectedRasterTile(t.convert(cellType), extent, crs)
-  }
 
+    override def toString: String = {
+      val e = s"(${extent.xmin}, ${extent.ymin}, ${extent.xmax}, ${extent.ymax})"
+      val c = crs.toProj4String
+      s"[${ShowableTile.show(t)}, $e, $c]"
+    }
+  }
   implicit val serializer: CatalystSerializer[ProjectedRasterTile] = new CatalystSerializer[ProjectedRasterTile] {
     override def schema: StructType = StructType(Seq(
       StructField("tile_context", schemaOf[TileContext], false),
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala
new file mode 100644
index 000000000..a021febe8
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala
@@ -0,0 +1,53 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.tiles
+import org.locationtech.rasterframes._
+import geotrellis.raster.Tile
+
+class ShowableTile(val delegate: Tile) extends FixedDelegatingTile {
+  override def equals(obj: Any): Boolean = obj match {
+    case ppt: ShowableTile => delegate.equals(ppt.delegate)
+    case o => delegate.equals(o)
+  }
+  override def hashCode(): Int = delegate.hashCode()
+  override def toString: String = ShowableTile.show(delegate)
+}
+
+object ShowableTile {
+  private val maxCells = rfConfig.getInt("showable-max-cells")
+  def show(tile: Tile): String = {
+    val ct = tile.cellType
+    val dims = tile.dimensions
+    val data = if (tile.cellType.isFloatingPoint)
+      tile.toArrayDouble()
+    else tile.toArray()
+
+    val cells = if (data.length <= maxCells)
+      data.mkString("[", ",", "]")
+    else {
+      val front = data.take(maxCells/2).mkString("[", ",", "")
+      val back = data.takeRight(maxCells/2).mkString("", ",", "]")
+      front + ",...," + back
+    }
+    s"[${ct.name}, $dims, $cells]"
+  }
+}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
index ceac41f64..88d1e754f 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
@@ -23,7 +23,9 @@ package org.locationtech.rasterframes
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf._
+import org.apache.spark.sql.types.StringType
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.tiles.ShowableTile
 import org.scalatest.Inspectors
 
 /**
@@ -39,7 +41,7 @@ class TileUDTSpec extends TestEnvironment with TestData with Inspectors {
   implicit val ser = TileUDT.tileSerializer
 
   describe("TileUDT") {
-    val tileSizes = Seq(2, 64, 128, 222, 511)
+    val tileSizes = Seq(2, 7, 64, 128, 511)
     val ct = functions.cellTypes().filter(_ != "bool")
 
     def forEveryConfig(test: Tile ⇒ Unit): Unit = {
@@ -90,5 +92,13 @@ class TileUDTSpec extends TestEnvironment with TestData with Inspectors {
         }
       }
     }
+
+    it("should provide a pretty-print tile") {
+      import spark.implicits._
+      forEveryConfig { tile =>
+        val stringified = Seq(tile).toDF("tile").select($"tile".cast(StringType)).as[String].first()
+        stringified should be(ShowableTile.show(tile))
+      }
+    }
   }
 }

From 2f63d6cee32d837242cf5948e67ef1b92c3596b8 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 30 May 2019 17:59:30 -0400
Subject: [PATCH 120/380] Changing TravisCI distribution in attempt to get
 Python 3.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index fbe2823fa..668aec8ef 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,5 +1,5 @@
 sudo: false
-
+dist: xenial
 language: scala
 
 cache:

From 9cc8df20b173f12d545c3d62ba5acb017496bff5 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 31 May 2019 10:12:39 -0400
Subject: [PATCH 121/380] Small tweaks to pyrasterframes tests

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../python/tests/PyRasterFramesTests.py       | 51 ++++++++++++-------
 1 file changed, 32 insertions(+), 19 deletions(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index e875636a4..03b29c9bf 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -48,8 +48,7 @@ def setUpClass(cls):
 
         cls.img_uri = cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()
 
-
-    # load something into a rasterframe
+        # load something into a rasterframe
         rf = cls.spark.read.geotiff(cls.img_uri) \
             .withBounds() \
             .withCenter()
@@ -61,6 +60,7 @@ def setUpClass(cls):
             .withColumnRenamed('tile2', cls.tileCol).asRF()
         # cls.rf.show()
 
+
 class VectorTypes(TestEnvironment):
 
     def setUp(self):
@@ -132,9 +132,33 @@ def some_point(g):
         )
 
     def test_rasterize(self):
-        # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
-        with_raster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
-        with_raster.show()
+        # simple test that raster contents are not invalid
+
+        # create a udf to buffer (the bounds) polygon
+        def _buffer(g, d):
+            return g.buffer(d)
+
+        @udf("double")
+        def area(g):
+            return g.area
+
+        buffer_udf = udf(_buffer, PolygonUDT())
+
+        buf_cells = 10
+        with_poly = self.rf.withColumn('poly', buffer_udf(self.rf.geometry, lit(-15 * buf_cells)))  # cell res is 15x15
+        area = with_poly.select(area('poly') < area('geometry'))
+        area_result = area.collect()
+        self.assertTrue(all([r[0] for r in area_result]))
+
+        cols = 194
+        rows = 250
+        with_raster = with_poly.withColumn('rasterized', rf_rasterize('poly', 'geometry', lit(16), cols, rows))
+        # expect a 4 by 4 cell
+        result = with_raster.select(rf_tile_sum(rf_local_equal_int(with_raster.rasterized, 16)),
+                                    rf_tile_sum(with_raster.rasterized))
+        expected_burned_in_cells = (cols - 2 * buf_cells) * (rows - 2 * buf_cells)
+        self.assertEqual(result.first()[0], float(expected_burned_in_cells))
+        self.assertEqual(result.first()[1], 16. * expected_burned_in_cells)
 
     def test_reproject(self):
         reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
@@ -196,8 +220,7 @@ def test_general(self):
             .withColumn('expm1', rf_expm1(self.tileCol)) \
             .withColumn('round', rf_round(self.tileCol)) \
             .withColumn('abs', rf_abs(self.tileCol))
-        # TODO: add test for rf_extent and rf_geometry once rastersource connector is integrated and we have
-        #  a source of ProjectedRasterTiles.
+
         df.first()
 
     def test_agg_mean(self):
@@ -211,16 +234,6 @@ def test_prt_functions(self):
             .withColumn('geom', rf_geometry(self.tileCol))
         df.show()
 
-    def test_rasterize(self):
-        # NB: This test just makes sure rf_rasterize runs, not that the results are correct.
-        withRaster = self.rf.withColumn('rasterized', rf_rasterize('geometry', 'geometry', lit(42), 10, 10))
-        withRaster.first()
-
-    def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
-        reprojected.first()
-
-
     def test_aggregations(self):
         aggs = self.rf.agg(
             rf_agg_data_cells(self.tileCol),
@@ -309,7 +322,6 @@ def test_mask_by_value(self):
             .collect()[0][0]
         self.assertTrue(result)
 
-
     def test_resample(self):
         from pyspark.sql.functions import lit
         result = self.rf.select(
@@ -452,7 +464,7 @@ class PandasInterop(TestEnvironment):
 
     def test_pandas_conversion(self):
         import pandas as pd
-        #pd.options.display.max_colwidth = 256
+        # pd.options.display.max_colwidth = 256
         cell_types = (ct for ct in rf_cell_types() if not (ct.is_raw() or ("bool" in ct.base_cell_type_name())))
         tiles = [Tile(np.random.randn(5, 5) * 100, ct) for ct in cell_types]
         in_pandas = pd.DataFrame({
@@ -612,6 +624,7 @@ def test_raster_join(self):
         with self.assertRaises(AssertionError):
             self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
 
+
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests

From 9f7cf89e566908ed7f930d7027e628ee5d943c0b Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 31 May 2019 13:26:21 -0400
Subject: [PATCH 122/380] Add unit test for TileUDT implicit numpy type
 conversions

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../python/tests/PyRasterFramesTests.py       | 25 ++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 03b29c9bf..4e0e0136e 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -398,6 +398,12 @@ def test_cell_type_conversion(self):
                 self.assertEqual(ct,
                                  CellType.from_numpy_dtype(ct.to_numpy_dtype()),
                                  "GTCellType comparison for " + str(ct))
+            else:
+                ct_ud = ct.with_no_data_value(99)
+                self.assertEqual(ct_ud.base_cell_type_name(),
+                                 repr(CellType.from_numpy_dtype(ct_ud.to_numpy_dtype())),
+                                 "GTCellType comparison for " + str(ct_ud)
+                                 )
 
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
@@ -440,12 +446,29 @@ def test_no_data_udf_handling(self):
         df = self.spark.createDataFrame([{"tile": t1}], schema)
 
         @udf(TileUDT())
-        def increment(t: Tile):
+        def increment(t):
             return t + 1
 
         r1 = df.select(increment(df.tile).alias("inc")).first()["inc"]
         self.assertEqual(r1, e1)
 
+    def test_udf_np_implicit_type_conversion(self):
+        import math
+        import pandas
+
+        a1 = np.array([[1, 2], [0, 4]])
+        t1 = Tile(a1, CellType.uint8())
+        exp_array = a1 * math.pi
+
+        @udf(TileUDT())
+        def times_pi(t):
+            return t * math.pi
+
+        df = self.spark.createDataFrame(pandas.DataFrame([{"tile": t1}]))
+        r1 = df.select(times_pi(df.tile)).first()[0]
+        self.assertTrue(np.all(r1.cells, exp_array))
+        self.assertEqual(r1.cells.dtype, exp_array.dtype)
+
 
 class TileOps(TestEnvironment):
 

From 4fc6360d9cfe95123af31b9b55a563e3022d5bd5 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 31 May 2019 13:33:50 -0400
Subject: [PATCH 123/380] Regression fixes.

---
 .../extensions/RasterFrameMethods.scala       | 24 +++++++++++++++++--
 .../python/tests/PyRasterFramesTests.py       |  2 +-
 2 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
index d37404e85..fd4b4f11d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
@@ -42,6 +42,7 @@ import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
 import com.typesafe.scalalogging.LazyLogging
 import org.locationtech.rasterframes.MetadataKeys
+import org.locationtech.rasterframes.tiles.ShowableTile
 
 import scala.reflect.runtime.universe._
 
@@ -226,12 +227,31 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
    */
   def toTileLayerRDD(tileCol: Column): Either[TileLayerRDD[SpatialKey], TileLayerRDD[SpaceTimeKey]] =
     tileLayerMetadata.fold(
-      tlm ⇒ Left(ContextRDD(self.select(self.spatialKeyColumn, tileCol.as[Tile]).rdd, tlm)),
+      tlm ⇒ {
+        val rdd = self.select(self.spatialKeyColumn, tileCol.as[Tile])
+          .rdd
+          .map {
+            // Wrapped tiles can break GeoTrellis Avro code.
+            case (sk, wrapped: ShowableTile) => (sk, wrapped.delegate)
+            case o => o
+          }
+
+        Left(ContextRDD(rdd, tlm))
+      },
       tlm ⇒ {
         val rdd = self
           .select(self.spatialKeyColumn, self.temporalKeyColumn.get, tileCol.as[Tile])
           .rdd
-          .map { case (sk, tk, v) ⇒ (SpaceTimeKey(sk, tk), v) }
+          .map {
+            case (sk, tk, v) ⇒
+              val tile = v match {
+                // Wrapped tiles can break GeoTrellis Avro code.
+                case wrapped: ShowableTile => wrapped.delegate
+                case o => o
+              }
+
+            (SpaceTimeKey(sk, tk), tile)
+          }
         Right(ContextRDD(rdd, tlm))
       }
     )
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index e875636a4..e1634a0c5 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -428,7 +428,7 @@ def test_no_data_udf_handling(self):
         df = self.spark.createDataFrame([{"tile": t1}], schema)
 
         @udf(TileUDT())
-        def increment(t: Tile):
+        def increment(t):
             return t + 1
 
         r1 = df.select(increment(df.tile).alias("inc")).first()["inc"]

From ffd4b4f600ec7bc85f61d906feb7dc16bf1db26f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 31 May 2019 14:21:51 -0400
Subject: [PATCH 124/380] Reconstituting python build rework.

---
 build.sbt                         |  16 ++-
 project/PIPBuildPlugin.scala      | 101 +++++++++++++
 project/RFProjectPlugin.scala     |   9 +-
 project/plugins.sbt               |   2 -
 pyrasterframes/build.sbt          | 231 +-----------------------------
 pyrasterframes/python/MANIFEST.in |   4 +
 pyrasterframes/python/setup.cfg   |   9 +-
 pyrasterframes/python/setup.py    | 117 ++++++---------
 8 files changed, 170 insertions(+), 319 deletions(-)
 create mode 100644 project/PIPBuildPlugin.scala
 create mode 100644 pyrasterframes/python/MANIFEST.in

diff --git a/build.sbt b/build.sbt
index 90cf92780..a544d6f93 100644
--- a/build.sbt
+++ b/build.sbt
@@ -12,7 +12,6 @@ lazy val root = project
 lazy val `rf-notebook` = project
   .dependsOn(pyrasterframes)
   .enablePlugins(RFAssemblyPlugin, DockerPlugin)
-  .disablePlugins(SparkPackagePlugin)
 
 lazy val IntegrationTest = config("it") extend Test
 
@@ -21,7 +20,6 @@ lazy val core = project
   .configs(IntegrationTest)
   .settings(inConfig(IntegrationTest)(Defaults.testSettings))
   .settings(Defaults.itSettings)
-  .disablePlugins(SparkPackagePlugin)
   .settings(
     moduleName := "rasterframes",
     libraryDependencies ++= Seq(
@@ -58,11 +56,18 @@ lazy val core = project
 
 lazy val pyrasterframes = project
   .dependsOn(core, datasource, experimental)
-  .enablePlugins(RFAssemblyPlugin)
+  .enablePlugins(RFAssemblyPlugin, PIPBuildPlugin)
+  .settings(
+    libraryDependencies ++= Seq(
+      geotrellis("s3").value,
+      spark("core").value ,
+      spark("mllib").value,
+      spark("sql").value
+    )
+  )
 
 lazy val datasource = project
   .dependsOn(core % "test->test;compile->compile")
-  .disablePlugins(SparkPackagePlugin)
   .settings(
     moduleName := "rasterframes-datasource",
     libraryDependencies ++= Seq(
@@ -83,7 +88,6 @@ lazy val experimental = project
   .settings(Defaults.itSettings)
   .dependsOn(core % "test->test;it->test;compile->compile")
   .dependsOn(datasource % "test->test;it->test;compile->compile")
-  .disablePlugins(SparkPackagePlugin)
   .settings(
     moduleName := "rasterframes-experimental",
     libraryDependencies ++= Seq(
@@ -99,10 +103,8 @@ lazy val experimental = project
 
 lazy val docs = project
   .dependsOn(core, datasource)
-  .disablePlugins(SparkPackagePlugin)
 
 lazy val bench = project
   .dependsOn(core % "compile->test")
-  .disablePlugins(SparkPackagePlugin)
   .settings(publish / skip := true)
 
diff --git a/project/PIPBuildPlugin.scala b/project/PIPBuildPlugin.scala
new file mode 100644
index 000000000..c75693b52
--- /dev/null
+++ b/project/PIPBuildPlugin.scala
@@ -0,0 +1,101 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+import sbt.KeyRanks.ASetting
+import sbt.Keys.{`package`, _}
+import sbt._
+import complete.DefaultParsers._
+import scala.sys.process.Process
+import sbtassembly.AssemblyPlugin.autoImport.assembly
+
+object PIPBuildPlugin extends AutoPlugin {
+  override def trigger: PluginTrigger = allRequirements
+  override def requires = RFAssemblyPlugin
+
+  object autoImport {
+    val Python = config("python")
+    val pythonSource = settingKey[File]("Default Python source directory.").withRank(ASetting)
+    val pythonCommand = settingKey[String]("Python command. Defaults to 'python'")
+    val pySetup = inputKey[Int]("Run 'python setup.py <args>'. Returns exit code.")
+  }
+  import autoImport._
+
+  val copyPySources = Def.task {
+    val s = streams.value
+    val src = (Compile / pythonSource).value
+    val dest = (Python / target).value
+    if (!dest.exists()) dest.mkdirs()
+    s.log(s"Copying '$src' to '$dest'")
+    IO.copyDirectory(src, dest)
+    dest
+  }
+
+  val buildPyDist= Def.task {
+    val buildDir = (Python / target).value
+    val pyDist = (packageBin / artifactPath).value
+    val retcode = pySetup.toTask(" build sdist --formats=zip").value
+    if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
+    val art = (Python / packageBin / artifact).value
+    val ver = version.value
+    IO.move(buildDir / "dist" / s"${art.name}-$ver.zip", pyDist)
+    pyDist
+  }
+
+  override def projectConfigurations: Seq[Configuration] = Seq(Python)
+
+  override def projectSettings = Seq(
+    assembly / test := {},
+    pythonCommand := "python",
+    pySetup := {
+      val s = streams.value
+      val wd = copyPySources.value
+      val args = spaceDelimited("<args>").parsed
+      val cmd = Seq(pythonCommand.value, "setup.py") ++ args
+      val ver = version.value
+      s.log.info(s"Running '${cmd.mkString(" ")}' in $wd")
+      Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
+    },
+    Compile / pythonSource := (Compile / sourceDirectory).value / "python",
+    Test / pythonSource := (Test / sourceDirectory).value / "python",
+    Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value
+  ) ++
+    inConfig(Python)(Seq(
+      target := target.value / "python",
+      packageBin := Def.sequential(
+        Compile / packageBin,
+        buildPyDist
+      ).value,
+      packageBin / artifact := {
+        val java = (Compile / packageBin / artifact).value
+        java.withType("zip").withClassifier(Some("python")).withExtension("zip")
+      },
+      packageBin / artifactPath := {
+        val dest = (Compile / packageBin / artifactPath).value.getParentFile
+        val art = (Python / packageBin / artifact).value
+        val ver = version.value
+        dest / s"${art.name}-python-$ver.zip"
+      },
+      test := {
+        pySetup.toTask(" test").value
+      }
+    )) ++
+    addArtifact(Python / packageBin / artifact, Python / packageBin)
+}
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 288332730..b7c904416 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -11,13 +11,6 @@ object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = GitPlugin
 
-  object autoImport {
-    val Python = config("python")
-  }
-  import autoImport._
-
-  override def projectConfigurations: Seq[Configuration] = Seq(Python)
-
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
     organizationName := "LocationTech RasterFrames",
@@ -25,7 +18,7 @@ object RFProjectPlugin extends AutoPlugin {
     homepage := Some(url("http://rasterframes.io")),
     git.remoteRepo := "git@github.com:locationtech/rasterframes.git",
     scmInfo := Some(ScmInfo(url("https://github.com/locationtech/rasterframes"), "git@github.com:locationtech/rasterframes.git")),
-    description := "RasterFrames brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis",
+    description := "RasterFrames brings the power of Spark DataFrames to geospatial raster data.",
     licenses += ("Apache-2.0", url("https://www.apache.org/licenses/LICENSE-2.0.html")),
     scalaVersion := "2.11.12",
     scalacOptions ++= Seq(
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 783f50a06..fa474439a 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -1,6 +1,5 @@
 logLevel := sbt.Level.Error
 
-resolvers += Resolver.bintrayIvyRepo("s22s", "sbt-plugins")
 addSbtCoursier
 addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.6")
 addSbtPlugin("com.eed3si9n" % "sbt-buildinfo" % "0.7.0")
@@ -12,7 +11,6 @@ addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.0")
 addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.4")
 addSbtPlugin("org.xerial.sbt" % "sbt-sonatype" % "2.1")
 addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")
-addSbtPlugin("org.spark-packages" % "sbt-spark-package" % "0.2.7-astraea.1")
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.4.1")
 addSbtPlugin("net.vonbuchholtz" % "sbt-dependency-check" % "0.2.10")
 addSbtPlugin("com.github.gseitz" %% "sbt-release" % "1.0.9")
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 9648b7a22..4d78e61fc 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,229 +1,2 @@
-import scala.sys.process.Process
-import sbt.Keys.`package`
-import sbt.Tests.Summary
-
-enablePlugins(SparkPackagePlugin, AssemblyPlugin)
-
-lazy val assemblyHasGeoTrellis = settingKey[Boolean]("If false, GeoTrellis libraries are excluded from assembly.")
-assemblyHasGeoTrellis := true
-
-assembly / assemblyExcludedJars ++= {
-  val cp = (fullClasspath in assembly).value
-  val gt = assemblyHasGeoTrellis.value
-  if(gt) Seq.empty
-  else cp.filter(_.data.getName.contains("geotrellis"))
-}
-
-assembly / test := {}
-
-//--------------------------------------------------------------------
-// Spark Packages Plugin
-//--------------------------------------------------------------------
-
-spName := "io.astraea/pyrasterframes"
-sparkVersion := rfSparkVersion.value
-sparkComponents ++= Seq("sql", "mllib")
-spAppendScalaVersion := false
-spIncludeMaven := false
-spIgnoreProvided := true
-spShade := true
-spPackage := {
-  val dist = spDist.value
-  val extracted = IO.unzip(dist, (target in spPackage).value, GlobFilter("*.jar"))
-  if(extracted.size != 1) sys.error("Didn't expect to find multiple .jar files in distribution.")
-  extracted.head
-}
-spShortDescription := description.value
-spHomepage := homepage.value.get.toString
-spDescription := """
-                   |RasterFrames brings the power of Spark DataFrames to geospatial raster data,
-                   |empowered by the map algebra and tile layer operations of GeoTrellis.
-                   |
-                   |The underlying purpose of RasterFrames is to allow data scientists and software
-                   |developers to process and analyze geospatial-temporal raster data with the
-                   |same flexibility and ease as any other Spark Catalyst data type. At its core
-                   |is a user-defined type (UDT) called TileUDT, which encodes a GeoTrellis Tile
-                   |in a form the Spark Catalyst engine can process. Furthermore, we extend the
-                   |definition of a DataFrame to encompass some additional invariants, allowing
-                   |for geospatial operations within and between RasterFrames to occur, while
-                   |still maintaining necessary geo-referencing constructs.
-                 """.stripMargin
-
-spPublishLocal := {
-  // This unfortunate override is necessary because
-  // the ivy resolver in pyspark defaults to the cache more
-  // frequently than we'd like.
-  val id = (projectID in spPublishLocal).value
-  val home = ivyPaths.value.ivyHome
-    .getOrElse(io.Path.userHome / ".ivy2")
-  val cacheDir = home / "cache" / id.organization / id.name
-  IO.delete(cacheDir)
-  spPublishLocal.value
-}
-
-//--------------------------------------------------------------------
-// Python Build
-//--------------------------------------------------------------------
-
-lazy val pythonSource = settingKey[File]("Default Python source directory.")
-pythonSource := baseDirectory.value / "python"
-
-def sparkFiles(dir: File):Seq[File] = Seq("metastore_db",
-                                          "spark-warehouse",
-                                          "derby.log").map(f => dir / f)
-
-cleanFiles ++=
-  sparkFiles(baseDirectory.value) ++
-  sparkFiles(pythonSource.value) ++
-  Seq(
-    ".eggs",
-    ".pytest_cache",
-    "build",
-    "dist",
-    "pyrasterframes.egg-info"
-  ).map(f => pythonSource.value / f)
-
-Python / target := target.value / "python-dist"
-
-lazy val pyZip = taskKey[File]("Build pyrasterframes zip.")
-
-// Alias
-pyZip := (Python / packageBin).value
-
-Python / packageBin / artifact := {
-  val java = (Compile / packageBin / artifact).value
-  java.withType("zip").withClassifier(Some("python")).withExtension("zip")
-}
-
-Python / packageBin / artifactPath := {
-  val dir = (Python / target).value
-  val art = (Python / packageBin / artifact).value
-  val ver = version.value
-  dir / s"${art.name}-python-$ver.zip"
-}
-
-addArtifact(Python / packageBin / artifact, Python / packageBin)
-
-val pysparkCmd = taskKey[Unit]("Builds pyspark package and emits command string for running pyspark with package")
-
-lazy val pyTest = taskKey[Int]("Run pyrasterframes tests. Return result code.")
-
-lazy val pyTestQuick = taskKey[Int]("Run pyrasterframes tests, skipping build and assembly. Return result code.")
-
-lazy val pyExamples = taskKey[Unit]("Run pyrasterframes examples.")
-
-lazy val pyWheel = taskKey[Unit]("Creates a Python .whl file")
-
-lazy val spJarFile = Def.taskDyn {
-  if (spShade.value) {
-    Def.task((assembly in spPackage).value)
-  } else {
-    Def.task(spPackage.value)
-  }
-}
-
-def gatherFromDir(root: sbt.File, dirName: String) = {
-  val pyDir = root / dirName
-  IO.listFiles(pyDir, GlobFilter("*.py") | GlobFilter("*.rst"))
-    .map(f => (f, s"$dirName/${f.getName}"))
-}
-
-Python / packageBin := {
-  val jar = (`package` in Compile).value
-  val root = pythonSource.value
-  val license = root / "LICENSE.md"
-  val files = gatherFromDir(root, "pyrasterframes") ++
-    gatherFromDir(root, "geomesa_pyspark") ++
-    Seq(jar, license).map(f => (f, "pyrasterframes/" + f.getName))
-  val zipFile = (Python / packageBin / artifactPath).value
-  IO.zip(files, zipFile)
-  zipFile
-}
-
-pysparkCmd := {
-  val pyBin = (Python / packageBin).value
-  val jarBin = (spPackage / assembly).value
-
-  val args = "pyspark" :: "--jars" :: jarBin :: "--py-files" :: pyBin :: Nil
-  streams.value.log.info("PySpark Command:\n" + args.mkString(" "))
-}
-
-ivyPaths in pysparkCmd := ivyPaths.value.withIvyHome(target.value / "ivy")
-
-pyTest := {
-  val _ = assembly.value
-  val s = streams.value
-  s.log.info("Running python tests...")
-  val wd = pythonSource.value
-  Process("python setup.py test", wd).!
-}
-
-pyTestQuick := {
-    val s = streams.value
-  s.log.info("Running python tests...")
-  val wd = pythonSource.value
-  Process("python setup.py test", wd).!
-}
-
-Test / executeTests := {
-  val standard = (Test / executeTests).value
-  standard.overall match {
-    case TestResult.Passed ⇒
-      val resultCode = pyTest.value
-      val msg = resultCode match {
-        case 1 ⇒ "There are Python test failures."
-        case 2 ⇒ "Python test execution was interrupted."
-        case 3 ⇒ "Internal error during Python test execution."
-        case 4 ⇒ "PyTest usage error."
-        case 5 ⇒ "No Python tests found."
-        case x if (x != 0) ⇒ "Unknown error while running Python tests."
-        case _ ⇒ "PyRasterFrames tests successfully completed."
-      }
-      val pySummary = Summary("pyrasterframes", msg)
-      val summaries = standard.summaries ++ Iterable(pySummary)
-      // Would be cool to derive this from the python output...
-      val result = if(resultCode == 0) {
-        new SuiteResult(
-          TestResult.Passed,
-          passedCount = 1,
-          failureCount = 0,
-          errorCount = 0,
-          skippedCount = 0,
-          ignoredCount = 0,
-          canceledCount = 0,
-          pendingCount = 0
-        )
-      }
-      else {
-        new SuiteResult(
-          TestResult.Failed,
-          passedCount = 0,
-          failureCount = 1,
-          errorCount = 0,
-          skippedCount = 0,
-          ignoredCount = 0,
-          canceledCount = 0,
-          pendingCount = 0
-        )
-      }
-      standard.copy(overall = result.result, summaries = summaries, events = standard.events + ("PyRasterFramesTests" -> result))
-    case _ ⇒
-      val pySummary = Summary("pyrasterframes", "tests skipped due to scalatest failures")
-      standard.copy(summaries = standard.summaries ++ Iterable(pySummary))
-  }
-}
-
-pyWheel := {
-  val s = streams.value
-  val wd = pythonSource.value
-  Process("python setup.py bdist_wheel", wd) ! s.log
-  val whl = IO.listFiles(pythonSource.value / "dist", GlobFilter("*.whl"))(0)
-  IO.move(whl, (Python / target).value / whl.getName)
-}
-
-pyExamples := {
-  val _ = spPublishLocal.value
-  val s = streams.value
-  val wd = pythonSource.value
-  Process("python setup.py examples", wd) ! s.log
-}
+Compile / pythonSource := baseDirectory.value / "python"
+Test / pythonSource := baseDirectory.value / "python" / "tests"
\ No newline at end of file
diff --git a/pyrasterframes/python/MANIFEST.in b/pyrasterframes/python/MANIFEST.in
new file mode 100644
index 000000000..cf48af02c
--- /dev/null
+++ b/pyrasterframes/python/MANIFEST.in
@@ -0,0 +1,4 @@
+include LICENSE.txt
+include README.rst
+recursive-include pyrasterframes *.jar
+exclude *.in
diff --git a/pyrasterframes/python/setup.cfg b/pyrasterframes/python/setup.cfg
index d8fb59953..ffdc900ea 100644
--- a/pyrasterframes/python/setup.cfg
+++ b/pyrasterframes/python/setup.cfg
@@ -1,8 +1,13 @@
+[metadata]
+license_files = LICENSE.txt
+
+[bdist_wheel]
+universal=1
 
 [aliases]
 test=pytest
 
 [tool:pytest]
 addopts = --verbose
-testpaths = tests
-python_files = *.py
\ No newline at end of file
+testpaths = src/test/python
+python_files = *.py
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index e3f5fd107..faf6743df 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -1,23 +1,22 @@
-"""
-The operations in this file are designed for development and testing only.
-"""
-
+# Always prefer setuptools over distutils
 from setuptools import setup, find_packages
-import distutils.log
+from os import path, environ
+from io import open
+from pathlib import Path
+import distutils.cmd
 import importlib
-from os import environ
 
+here = path.abspath(path.dirname(__file__))
 
-def _extract_module(mod):
-    module = importlib.import_module(mod)
+# Get the long description from the README file
+with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
+    readme = f.read()
 
-    if hasattr(module, '__all__'):
-        globals().update({n: getattr(module, n) for n in module.__all__})
-    else:
-        globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
+with open(path.join(here, 'requirements.txt')) as f:
+    requirements = f.read().splitlines()
 
 
-class ExampleCommand(distutils.cmd.Command):
+class RunExamples(distutils.cmd.Command):
     """A custom command to run pyrasterframes examples."""
 
     description = 'run pyrasterframes examples'
@@ -26,15 +25,22 @@ class ExampleCommand(distutils.cmd.Command):
         ('examples=', 'e', 'examples to run'),
     ]
 
+    @staticmethod
+    def _extract_module(mod):
+        module = importlib.import_module(mod)
+
+        if hasattr(module, '__all__'):
+            globals().update({n: getattr(module, n) for n in module.__all__})
+        else:
+            globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
+
     def initialize_options(self):
-        from pathlib import Path
         """Set default values for options."""
         # Each user option must be listed here with their default value.
         self.examples = filter(lambda x: not x.name.startswith('_'),
                                list(Path('./examples').resolve().glob('*.py')))
 
     def _check_ex_path(self, ex):
-        from pathlib import Path
         file = Path(ex)
         if not file.suffix:
             file = file.with_suffix('.py')
@@ -57,73 +63,45 @@ def run(self):
         for ex in self.examples:
             print(('-' * 50) + '\nRunning %s' % ex + '\n' + ('-' * 50))
             try:
-                _extract_module(ex)
+                self._extract_module(ex)
             except Exception:
                 print(('-' * 50) + '\n%s Failed:' % ex + '\n' + ('-' * 50))
                 print(traceback.format_exc())
 
 
-class ZipCommand(distutils.cmd.Command):
-    """A custom command to create a minimal zip distribution."""
-
-    description = 'create a minimal pyrasterframes source zip file'
-    user_options = []
-
-    def initialize_options(self):
-        pass
-
-    def finalize_options(self):
-        pass
-
-    def run(self):
-        """Create the zip."""
-        import zipfile
-        from pathlib import Path
-        import os
-        zfile = 'pyrasterframes.zip'
-
-        if os.path.isfile(zfile):
-            os.remove(zfile)
-        with zipfile.ZipFile(zfile, 'w') as przip:
-            przip.write('pyrasterframes')
-            # Bring in source files and readme
-            patterns = ['*.py', '*.rst', '*.jar']
-            root = Path('.').resolve()
-            for pattern in patterns:
-                for file in list(root.glob('pyrasterframes/' + pattern)):
-                    przip.write(str(file.relative_to(root)))
-            # Put a copy of the license in the zip
-            przip.write('LICENSE.md', 'pyrasterframes/LICENSE.md')
-
-
-with open('README.rst') as f:
-    readme = f.read()
-
-with open('requirements.txt') as f:
-    requirements = f.read().splitlines()
-
-setup_args = dict(
+setup(
     name='pyrasterframes',
-    description='Python bindings for RasterFrames',
+    description='RasterFrames for PySpark',
     long_description=readme,
+    long_description_content_type='text/x-rst',
     version=environ.get('RASTERFRAMES_VERSION', 'SNAPSHOT'),
-    url='http://rasterframes.io',
     author='Astraea, Inc.',
-    author_email='info@astraea.io',
+    author_email='info@astraea.earth',
     license='Apache 2',
-    setup_requires=['pytest-runner', 'pathlib'],
+    url='https://rasterframes.io',
+    project_urls={
+        'Bug Reports': 'https://github.com/locationtech/rasterframes/issues',
+        'Source': 'https://github.com/locationtech/rasterframes',
+    },
+    setup_requires=[
+        'pytest-runner',
+        'setuptools >= 0.8',
+        'pathlib2'
+    ],
     install_requires=requirements,
     tests_require=[
         'pytest==3.4.2',
-        'pypandoc'
+        'pypandoc',
+        'numpy>=1.7',
+        'pandas',
+    ],
+    packages=[
+        'pyrasterframes',
+        'geomesa_pyspark'
     ],
-    test_suite="pytest-runner",
-    packages=find_packages(exclude=['tests', 'examples']),
     include_package_data=True,
-    package_data={'.': ['LICENSE.md'], 'pyrasterframes': ['*.jar']},
-    exclude_package_data={'.': ['setup.*', 'README.*']},
     classifiers=[
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 4 - Beta',
         'Environment :: Other Environment',
         'License :: OSI Approved :: Apache Software License',
         'Natural Language :: English',
@@ -132,14 +110,11 @@ def run(self):
         'Topic :: Software Development :: Libraries'
     ],
     zip_safe=False,
+    test_suite="pytest-runner",
     cmdclass={
-        'examples': ExampleCommand,
-        'minzip': ZipCommand
+        'examples': RunExamples
     }
     # entry_points={
     #     "console_scripts": ['pyrasterframes=pyrasterframes:console']
     # }
 )
-
-if __name__ == "__main__":
-    setup(**setup_args)

From 1212c3f6ea6164864774b9e62af0013580e610d3 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 31 May 2019 15:48:07 -0400
Subject: [PATCH 125/380] `pyrasterframes/test` now works with new python build
 configuration.

---
 build.sbt                                     |  6 ++---
 project/PIPBuildPlugin.scala                  | 13 +++++++---
 pyrasterframes/python/setup.cfg               |  2 +-
 .../python/tests/PyRasterFramesTests.py       | 25 +++++++++++--------
 4 files changed, 28 insertions(+), 18 deletions(-)

diff --git a/build.sbt b/build.sbt
index a544d6f93..3015085b0 100644
--- a/build.sbt
+++ b/build.sbt
@@ -60,9 +60,9 @@ lazy val pyrasterframes = project
   .settings(
     libraryDependencies ++= Seq(
       geotrellis("s3").value,
-      spark("core").value ,
-      spark("mllib").value,
-      spark("sql").value
+      spark("core").value % Provided,
+      spark("mllib").value % Provided,
+      spark("sql").value % Provided
     )
   )
 
diff --git a/project/PIPBuildPlugin.scala b/project/PIPBuildPlugin.scala
index c75693b52..84a2db3d2 100644
--- a/project/PIPBuildPlugin.scala
+++ b/project/PIPBuildPlugin.scala
@@ -75,7 +75,11 @@ object PIPBuildPlugin extends AutoPlugin {
     },
     Compile / pythonSource := (Compile / sourceDirectory).value / "python",
     Test / pythonSource := (Test / sourceDirectory).value / "python",
-    Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value
+    Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value,
+    Test / test := Def.sequential(
+      Test / test,
+      Python / test
+    ).value
   ) ++
     inConfig(Python)(Seq(
       target := target.value / "python",
@@ -93,9 +97,10 @@ object PIPBuildPlugin extends AutoPlugin {
         val ver = version.value
         dest / s"${art.name}-python-$ver.zip"
       },
-      test := {
-        pySetup.toTask(" test").value
-      }
+      test := Def.sequential(
+        assembly,
+        pySetup.toTask(" test")
+      ).value
     )) ++
     addArtifact(Python / packageBin / artifact, Python / packageBin)
 }
diff --git a/pyrasterframes/python/setup.cfg b/pyrasterframes/python/setup.cfg
index ffdc900ea..8088676c6 100644
--- a/pyrasterframes/python/setup.cfg
+++ b/pyrasterframes/python/setup.cfg
@@ -9,5 +9,5 @@ test=pytest
 
 [tool:pytest]
 addopts = --verbose
-testpaths = src/test/python
+testpaths = tests
 python_files = *.py
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index e1634a0c5..8d0a13322 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,14 +1,12 @@
 from pyspark.sql import SparkSession, Column, SQLContext
 from pyspark.sql.functions import *
-from pyspark.sql.types import *
-
-from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyrasterframes.rf_types import *
 from geomesa_pyspark.types import *
-from pathlib import Path
 import unittest
 import numpy as np
+import os
+import glob
 
 # version-conditional imports
 import sys
@@ -18,6 +16,7 @@
 else:
     import __builtin__ as builtins
 
+HERE = os.path.dirname(os.path.realpath(__file__))
 
 class TestEnvironment(unittest.TestCase):
     """
@@ -31,23 +30,29 @@ def rounded_compare(self, val1, val2):
     @classmethod
     def setUpClass(cls):
         # gather Scala requirements
-        jarpath = list(Path('../target/scala-2.11').resolve().glob('pyrasterframes-assembly*.jar'))[0]
+
+        jarpath = glob.glob(os.path.join(HERE, '..', '..', 'scala-2.11', 'pyrasterframes-assembly*.jar'))
+
+        if not len(jarpath) == 1:
+            raise RuntimeError("Expected to find exactly one assembly. Found: ", jarpath)
 
         # hard-coded relative path for resources
-        cls.resource_dir = Path('./static').resolve()
+        cls.resource_dir = os.path.realpath(os.path.join(HERE, '..', 'static'))
 
         # spark session with RF
         cls.spark = (SparkSession.builder
-                     .config('spark.driver.extraClassPath', jarpath)
-                     .config('spark.executor.extraClassPath', jarpath)
+                     .config('spark.driver.extraClassPath', jarpath[0])
+                     .config('spark.executor.extraClassPath', jarpath[0])
                      .withKryoSerialization()
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
+
+        print(cls.spark.sparkContext._conf.getAll())
+
         print("Spark Version: " + cls.spark.version)
         cls.spark.withRasterFrames()
 
-        cls.img_uri = cls.resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri()
-
+        cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
 
     # load something into a rasterframe
         rf = cls.spark.read.geotiff(cls.img_uri) \

From 0048ffe4a9fb386df7381eeb929c3325f72131fc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 31 May 2019 16:22:30 -0400
Subject: [PATCH 126/380] Moved test resources to canonical sbt/maven location.
 Worked (but failed) trying to get `examples` to work again.

---
 build.sbt                                     |   2 +-
 ...ldPlugin.scala => PythonBuildPlugin.scala} |   2 +-
 pyrasterframes/build.sbt                      |   4 +-
 pyrasterframes/python/examples/__init__.py    |  25 +++++---
 pyrasterframes/python/setup.py                |  33 +++++-----
 .../python/tests/PyRasterFramesTests.py       |  60 +++++++++---------
 .../test/resources}/L8-B1-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B10-Elkton-VA.tiff     | Bin
 .../test/resources}/L8-B11-Elkton-VA.tiff     | Bin
 .../test/resources}/L8-B2-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B3-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B4-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B5-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B6-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B7-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B8-Elkton-VA.tiff      | Bin
 .../test/resources}/L8-B8-Robinson-IL.tiff    | Bin
 .../test/resources}/L8-B9-Elkton-VA.tiff      | Bin
 18 files changed, 68 insertions(+), 58 deletions(-)
 rename project/{PIPBuildPlugin.scala => PythonBuildPlugin.scala} (98%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B1-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B10-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B11-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B2-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B3-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B4-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B5-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B6-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B7-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B8-Elkton-VA.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B8-Robinson-IL.tiff (100%)
 rename pyrasterframes/{python/static => src/test/resources}/L8-B9-Elkton-VA.tiff (100%)

diff --git a/build.sbt b/build.sbt
index 3015085b0..e322a6591 100644
--- a/build.sbt
+++ b/build.sbt
@@ -56,7 +56,7 @@ lazy val core = project
 
 lazy val pyrasterframes = project
   .dependsOn(core, datasource, experimental)
-  .enablePlugins(RFAssemblyPlugin, PIPBuildPlugin)
+  .enablePlugins(RFAssemblyPlugin, PythonBuildPlugin)
   .settings(
     libraryDependencies ++= Seq(
       geotrellis("s3").value,
diff --git a/project/PIPBuildPlugin.scala b/project/PythonBuildPlugin.scala
similarity index 98%
rename from project/PIPBuildPlugin.scala
rename to project/PythonBuildPlugin.scala
index 84a2db3d2..7a409b233 100644
--- a/project/PIPBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -26,7 +26,7 @@ import complete.DefaultParsers._
 import scala.sys.process.Process
 import sbtassembly.AssemblyPlugin.autoImport.assembly
 
-object PIPBuildPlugin extends AutoPlugin {
+object PythonBuildPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = RFAssemblyPlugin
 
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 4d78e61fc..6810e01fa 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,2 +1,4 @@
 Compile / pythonSource := baseDirectory.value / "python"
-Test / pythonSource := baseDirectory.value / "python" / "tests"
\ No newline at end of file
+Test / pythonSource := baseDirectory.value / "python" / "tests"
+
+addCommandAlias("pyExamples", "pySetup examples")
\ No newline at end of file
diff --git a/pyrasterframes/python/examples/__init__.py b/pyrasterframes/python/examples/__init__.py
index 8bbea6d4f..c7d2a9fa8 100644
--- a/pyrasterframes/python/examples/__init__.py
+++ b/pyrasterframes/python/examples/__init__.py
@@ -1,15 +1,24 @@
 # examples_setup
-from pathlib import Path
+import os
+import glob
 from pyspark.sql import SparkSession
 
 __all__ = ["resource_dir", "example_session"]
 
-jarpath = list(Path('../target').resolve().glob('**/pyrasterframes*.jar'))
-if len(jarpath) > 0:
-    pyJar = jarpath[0].as_uri()
+HERE = os.path.dirname(os.path.realpath(__file__))
 
-    def example_session():
-        return (SparkSession.builder
+scala_target = os.path.realpath(os.path.join(HERE, '..', '..', 'scala-2.11'))
+resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
+jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
+
+if not len(jarpath) == 1:
+    raise RuntimeError("Expected to find exactly one assembly. Found: ", jarpath)
+
+pyJar = jarpath[0]
+
+
+def example_session():
+    return (SparkSession.builder
             .master("local[*]")
             .appName("RasterFrames")
             .config('spark.driver.extraClassPath', pyJar)
@@ -18,7 +27,3 @@ def example_session():
             .withKryoSerialization()
             .getOrCreate())
 
-
-# hard-coded relative path for resources
-resource_dir = Path('./static').resolve()
-# examples_setup
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index faf6743df..0c4905b8a 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -16,6 +16,15 @@
     requirements = f.read().splitlines()
 
 
+def _extract_module(mod):
+    module = importlib.import_module(mod)
+
+    if hasattr(module, '__all__'):
+        globals().update({n: getattr(module, n) for n in module.__all__})
+    else:
+        globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
+
+
 class RunExamples(distutils.cmd.Command):
     """A custom command to run pyrasterframes examples."""
 
@@ -26,21 +35,7 @@ class RunExamples(distutils.cmd.Command):
     ]
 
     @staticmethod
-    def _extract_module(mod):
-        module = importlib.import_module(mod)
-
-        if hasattr(module, '__all__'):
-            globals().update({n: getattr(module, n) for n in module.__all__})
-        else:
-            globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
-
-    def initialize_options(self):
-        """Set default values for options."""
-        # Each user option must be listed here with their default value.
-        self.examples = filter(lambda x: not x.name.startswith('_'),
-                               list(Path('./examples').resolve().glob('*.py')))
-
-    def _check_ex_path(self, ex):
+    def _check_ex_path(ex):
         file = Path(ex)
         if not file.suffix:
             file = file.with_suffix('.py')
@@ -49,6 +44,12 @@ def _check_ex_path(self, ex):
         assert file.is_file(), ('Invalid example %s' % file)
         return file
 
+    def initialize_options(self):
+        """Set default values for options."""
+        # Each user option must be listed here with their default value.
+        self.examples = filter(lambda x: not x.name.startswith('_'),
+                               list(Path('./examples').resolve().glob('*.py')))
+
     def finalize_options(self):
         """Post-process options."""
         import re
@@ -63,7 +64,7 @@ def run(self):
         for ex in self.examples:
             print(('-' * 50) + '\nRunning %s' % ex + '\n' + ('-' * 50))
             try:
-                self._extract_module(ex)
+                _extract_module(ex)
             except Exception:
                 print(('-' * 50) + '\n%s Failed:' % ex + '\n' + ('-' * 50))
                 print(traceback.format_exc())
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 8d0a13322..6fd2c0f3e 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -31,13 +31,15 @@ def rounded_compare(self, val1, val2):
     def setUpClass(cls):
         # gather Scala requirements
 
-        jarpath = glob.glob(os.path.join(HERE, '..', '..', 'scala-2.11', 'pyrasterframes-assembly*.jar'))
+        scala_target = os.path.realpath(os.path.join(HERE, '..', '..', 'scala-2.11'))
+
+        jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
 
         if not len(jarpath) == 1:
             raise RuntimeError("Expected to find exactly one assembly. Found: ", jarpath)
 
         # hard-coded relative path for resources
-        cls.resource_dir = os.path.realpath(os.path.join(HERE, '..', 'static'))
+        cls.resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
 
         # spark session with RF
         cls.spark = (SparkSession.builder
@@ -47,9 +49,9 @@ def setUpClass(cls):
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')
 
+        print("Spark Version: " + cls.spark.version)
         print(cls.spark.sparkContext._conf.getAll())
 
-        print("Spark Version: " + cls.spark.version)
         cls.spark.withRasterFrames()
 
         cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
@@ -522,6 +524,32 @@ def test_setup(self):
         self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
                          "org.apache.spark.serializer.KryoSerializer")
 
+    def test_raster_join(self):
+        # re-read the same source
+        rf_prime = self.spark.read.geotiff(self.img_uri) \
+            .withColumnRenamed('tile', 'tile2').alias('rf_prime')
+
+        rf_joined = self.rf.raster_join(rf_prime)
+
+        self.assertTrue(rf_joined.count(), self.rf.count())
+        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
+        self.assertTrue(rf_joined_2.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        # this will bring arbitrary additional data into join; garbage result
+        join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
+        rf_joined_3 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs,
+                                          rf_prime.extent, rf_prime.crs,
+                                          join_expression)
+        self.assertTrue(rf_joined_3.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        # throws if you don't  pass  in all expected columns
+        with self.assertRaises(AssertionError):
+            self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
+
     def test_raster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
@@ -591,35 +619,9 @@ def path(scene, band):
         b1_paths = [s.format('1') for s in scene_dict.values()]
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
-    def test_raster_join(self):
-        # re-read the same source
-        rf_prime = self.spark.read.geotiff(self.img_uri) \
-            .withColumnRenamed('tile', 'tile2').alias('rf_prime')
-
-        rf_joined = self.rf.raster_join(rf_prime)
-
-        self.assertTrue(rf_joined.count(), self.rf.count())
-        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
-        self.assertTrue(rf_joined_2.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        # this will bring arbitrary additional data into join; garbage result
-        join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
-        rf_joined_3 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs,
-                                          rf_prime.extent, rf_prime.crs,
-                                          join_expression)
-        self.assertTrue(rf_joined_3.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        # throws if you don't  pass  in all expected columns
-        with self.assertRaises(AssertionError):
-            self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
 
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests
 
-
 unittest.TextTestRunner().run(suite())
diff --git a/pyrasterframes/python/static/L8-B1-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B1-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B1-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B1-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B10-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B10-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B10-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B10-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B11-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B11-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B11-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B11-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B2-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B2-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B2-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B2-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B3-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B3-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B3-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B3-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B4-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B4-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B4-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B4-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B5-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B5-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B5-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B5-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B6-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B6-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B6-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B6-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B7-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B7-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B7-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B7-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B8-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B8-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B8-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B8-Elkton-VA.tiff
diff --git a/pyrasterframes/python/static/L8-B8-Robinson-IL.tiff b/pyrasterframes/src/test/resources/L8-B8-Robinson-IL.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B8-Robinson-IL.tiff
rename to pyrasterframes/src/test/resources/L8-B8-Robinson-IL.tiff
diff --git a/pyrasterframes/python/static/L8-B9-Elkton-VA.tiff b/pyrasterframes/src/test/resources/L8-B9-Elkton-VA.tiff
similarity index 100%
rename from pyrasterframes/python/static/L8-B9-Elkton-VA.tiff
rename to pyrasterframes/src/test/resources/L8-B9-Elkton-VA.tiff

From 785cc0dddf6e6d324fc47ce06fc9da2650497560 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 1 Jun 2019 20:57:48 -0400
Subject: [PATCH 127/380] Misc PR feedback tweaks.

---
 .../extensions/DataFrameMethods.scala         |  1 -
 .../python/pyrasterframes/rf_types.py         |  4 +-
 .../python/tests/PyRasterFramesTests.py       | 71 ++++++++++---------
 3 files changed, 39 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index d7f4af39e..599beb637 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -29,7 +29,6 @@ import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
 import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
 import spray.json.JsonFormat
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 91fdb66dd..98fd10023 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -130,7 +130,9 @@ def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.RasterSourceUDT'
 
     def serialize(self, obj):
-        # Not yet implemented. Kryo serialized bytes?
+        # RasterSource is opaque in the Python context.
+        # Any thing passed in by a UDF return value couldn't be validated.
+        # Therefore obj is dropped None is passed to Catalyst.
         return None
 
     def deserialize(self, datum):
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 4e0e0136e..3febaca46 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -227,13 +227,6 @@ def test_agg_mean(self):
         mean = self.rf.agg(rf_agg_mean(self.tileCol)).first()['rf_agg_mean(tile)']
         self.assertTrue(self.rounded_compare(mean, 10160))
 
-    def test_prt_functions(self):
-        df = self.spark.read.rastersource(self.img_uri) \
-            .withColumn('crs', rf_crs(self.tileCol)) \
-            .withColumn('ext', rf_extent(self.tileCol)) \
-            .withColumn('geom', rf_geometry(self.tileCol))
-        df.show()
-
     def test_aggregations(self):
         aggs = self.rf.agg(
             rf_agg_data_cells(self.tileCol),
@@ -543,7 +536,35 @@ def test_extended_pandas_ops(self):
 
         self.assertIsInstance(array_back_2, Tile)
         np.testing.assert_equal(array_back_2.cells, simple_array.cells)
-        np.int8
+
+class RasterJoin(TestEnvironment):
+
+    def test_raster_join(self):
+        # re-read the same source
+        rf_prime = self.spark.read.geotiff(self.img_uri) \
+            .withColumnRenamed('tile', 'tile2').alias('rf_prime')
+
+        rf_joined = self.rf.raster_join(rf_prime)
+
+        self.assertTrue(rf_joined.count(), self.rf.count())
+        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
+        self.assertTrue(rf_joined_2.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        # this will bring arbitrary additional data into join; garbage result
+        join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
+        rf_joined_3 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs,
+                                          rf_prime.extent, rf_prime.crs,
+                                          join_expression)
+        self.assertTrue(rf_joined_3.count(), self.rf.count())
+        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
+
+        # throws if you don't  pass  in all expected columns
+        with self.assertRaises(AssertionError):
+            self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
+
 
 class RasterSource(TestEnvironment):
 
@@ -552,6 +573,13 @@ def test_setup(self):
         self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
                          "org.apache.spark.serializer.KryoSerializer")
 
+    def test_prt_functions(self):
+        df = self.spark.read.rastersource(self.img_uri) \
+            .withColumn('crs', rf_crs(self.tileCol)) \
+            .withColumn('ext', rf_extent(self.tileCol)) \
+            .withColumn('geom', rf_geometry(self.tileCol))
+        df.first()
+
     def test_raster_source_reader(self):
         import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
@@ -621,33 +649,6 @@ def path(scene, band):
         b1_paths = [s.format('1') for s in scene_dict.values()]
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
-    def test_raster_join(self):
-        # re-read the same source
-        rf_prime = self.spark.read.geotiff(self.img_uri) \
-            .withColumnRenamed('tile', 'tile2').alias('rf_prime')
-
-        rf_joined = self.rf.raster_join(rf_prime)
-
-        self.assertTrue(rf_joined.count(), self.rf.count())
-        self.assertTrue(len(rf_joined.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        rf_joined_2 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs, rf_prime.extent, rf_prime.crs)
-        self.assertTrue(rf_joined_2.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_2.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        # this will bring arbitrary additional data into join; garbage result
-        join_expression = self.rf.extent.xmin == rf_prime.extent.xmin
-        rf_joined_3 = self.rf.raster_join(rf_prime, self.rf.extent, self.rf.crs,
-                                          rf_prime.extent, rf_prime.crs,
-                                          join_expression)
-        self.assertTrue(rf_joined_3.count(), self.rf.count())
-        self.assertTrue(len(rf_joined_3.columns) == len(self.rf.columns) + len(rf_prime.columns) - 2)
-
-        # throws if you don't  pass  in all expected columns
-        with self.assertRaises(AssertionError):
-            self.rf.raster_join(rf_prime, join_exprs=self.rf.extent)
-
-
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests

From 61ea1f06b8021ab0a90c03dd0aa17e11b77662e7 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 1 Jun 2019 21:10:25 -0400
Subject: [PATCH 128/380] Test tweak due to RasterRefs not getting resolved
 before collect.

---
 pyrasterframes/python/tests/PyRasterFramesTests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 3febaca46..8c6b903f6 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -578,7 +578,7 @@ def test_prt_functions(self):
             .withColumn('crs', rf_crs(self.tileCol)) \
             .withColumn('ext', rf_extent(self.tileCol)) \
             .withColumn('geom', rf_geometry(self.tileCol))
-        df.first()
+        df.select('crs', 'ext', 'geom').first()
 
     def test_raster_source_reader(self):
         import pandas as pd

From 28625516de369315a139b4b23460d9aef88837e4 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 3 Jun 2019 10:24:50 -0400
Subject: [PATCH 129/380] Built in Tile __add__ etc follows numpy dtype
 conversions

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../python/pyrasterframes/__init__.py         |  1 +
 .../python/pyrasterframes/rf_types.py         | 26 ++++++++++++++++---
 .../python/tests/PyRasterFramesTests.py       | 21 ++++++++++++---
 3 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index cd994a89c..bc3d12b33 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -13,6 +13,7 @@
 from . import rasterfunctions
 from .context import RFContext
 
+
 __all__ = ['RasterFrame', 'TileExploder']
 
 
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 98fd10023..f0f946a01 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -280,20 +280,36 @@ def __add__(self, right):
             other = right.cells
         else:
             other = right
-        return Tile(np.add(self.cells, other), self.cell_type)
+
+        _sum = np.add(self.cells, other)
+        ct = CellType.from_numpy_dtype(_sum.dtype)
+        if isinstance(_sum, np.ma.MaskedArray):
+            ct = ct.with_no_data_value(_sum.fill_value)
+
+        return Tile(_sum, ct)
 
     def __sub__(self, right):
         if isinstance(right, Tile):
             other = right.cells
         else:
             other = right
-        return Tile(np.subtract(self.cells, other), self.cell_type)
+        _diff = np.subtract(self.cells, other)
+        ct = CellType.from_numpy_dtype(_diff.dtype)
+        if isinstance(_diff, np.ma.MaskedArray):
+            ct = ct.with_no_data_value(_diff.fill_value)
+
+        return Tile(_diff, ct)
 
     def __mul__(self, right):
         if isinstance(right, Tile):
             other = right.cells
         else:
             other = right
+        prod = np.multiply(self.cells, other)
+        ct = CellType.from_numpy_dtype(prod.dtype)
+        if isinstance(prod, np.ma.MaskedArray):
+            ct = ct.with_no_data_value(prod.fill_value)
+
         return Tile(np.multiply(self.cells, other), self.cell_type)
 
     def __truediv__(self, right):
@@ -301,7 +317,11 @@ def __truediv__(self, right):
             other = right.cells
         else:
             other = right
-        return Tile(np.true_divide(self.cells, other), self.cell_type)
+        quot = np.true_divide(self.cells, other)
+        ct = CellType.from_numpy_dtype(quot.dtype)
+        if isinstance(quot, np.ma.MaskedArray):
+            ct = ct.with_no_data_value(quot.fill_value)
+        return Tile(quot, ct)
 
 
     def dimensions(self):
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 8c6b903f6..fcea16612 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -451,15 +451,30 @@ def test_udf_np_implicit_type_conversion(self):
 
         a1 = np.array([[1, 2], [0, 4]])
         t1 = Tile(a1, CellType.uint8())
-        exp_array = a1 * math.pi
+        exp_array = a1.astype('>f8')
 
         @udf(TileUDT())
         def times_pi(t):
             return t * math.pi
 
+        @udf(TileUDT())
+        def divide_pi(t):
+            return t / math.pi
+
+        @udf(TileUDT())
+        def plus_pi(t):
+            return t + math.pi
+
+        @udf(TileUDT())
+        def less_pi(t):
+            return t - math.pi
+
         df = self.spark.createDataFrame(pandas.DataFrame([{"tile": t1}]))
-        r1 = df.select(times_pi(df.tile)).first()[0]
-        self.assertTrue(np.all(r1.cells, exp_array))
+        r1 = df.select(
+            less_pi(divide_pi(times_pi(plus_pi(df.tile))))
+        ).first()[0]
+
+        self.assertTrue(np.all(r1.cells == exp_array))
         self.assertEqual(r1.cells.dtype, exp_array.dtype)
 
 

From e9371ef166518e66c4118595656e611ae3625051 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 3 Jun 2019 12:42:08 -0400
Subject: [PATCH 130/380] PR feedback.

---
 .../scala/org/locationtech/rasterframes/model/Cells.scala   | 2 +-
 .../org/locationtech/rasterframes/tiles/ShowableTile.scala  | 6 ++++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
index e91bf38ef..1f7ae4d75 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/Cells.scala
@@ -59,7 +59,7 @@ object Cells {
       case ref: RasterRefTile =>
         Cells(Right(ref.rr))
       case const: ConstantTile =>
-        // TODO: Create mechanism whereby constant tiles aren't expanded.
+        // Need to expand constant tiles so they can be interpreted properly in catalyst and Python.
         // If we don't, the serialization breaks.
         Cells(Left(const.toArrayTile().toBytes))
       case o =>
diff --git a/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala b/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala
index a021febe8..00872ff6c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/tiles/ShowableTile.scala
@@ -25,7 +25,7 @@ import geotrellis.raster.Tile
 
 class ShowableTile(val delegate: Tile) extends FixedDelegatingTile {
   override def equals(obj: Any): Boolean = obj match {
-    case ppt: ShowableTile => delegate.equals(ppt.delegate)
+    case st: ShowableTile => delegate.equals(st.delegate)
     case o => delegate.equals(o)
   }
   override def hashCode(): Int = delegate.hashCode()
@@ -37,12 +37,14 @@ object ShowableTile {
   def show(tile: Tile): String = {
     val ct = tile.cellType
     val dims = tile.dimensions
+
     val data = if (tile.cellType.isFloatingPoint)
       tile.toArrayDouble()
     else tile.toArray()
 
-    val cells = if (data.length <= maxCells)
+    val cells = if(tile.size <= maxCells) {
       data.mkString("[", ",", "]")
+    }
     else {
       val front = data.take(maxCells/2).mkString("[", ",", "")
       val back = data.takeRight(maxCells/2).mkString("", ",", "]")

From 6b36b7ca0e4d1e23900bef4c8d56c7b8f2b31c2a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 3 Jun 2019 14:52:48 -0400
Subject: [PATCH 131/380] Experiment with read retries to handle
 BufferUnderflowExceptions.

---
 .../rasterframes/ref/RasterSource.scala       | 47 ++++++++++++++++---
 1 file changed, 40 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 7cba2a590..4e7e10ac5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -226,16 +226,49 @@ object RasterSource extends LazyLogging {
       case _                       => EMPTY_TAGS
     }
     override def tags: Tags = info.tags
+
+    private def readWithRetry[R >: Null](f: () => R): R = {
+      val retryLimit = 10
+      var result: R = null
+      var cnt = 0
+      while(result == null && cnt < retryLimit) {
+        cnt = cnt + 1
+        try {
+          result = f()
+        }
+        catch {
+          case be: java.nio.BufferUnderflowException =>
+            if (cnt == 1)
+              logger.warn("Retrying read to " + source)
+            if (cnt == retryLimit) {
+              logger.warn(s"Failed to read '$source' after $cnt tries")
+              throw be
+            }
+            else Thread.sleep(100)
+            ()
+        }
+      }
+      result
+    }
+
     override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
-      delegate.readBounds(bounds, bands)
+      readWithRetry(() =>
+        delegate.readBounds(bounds, bands)
+      )
+
     override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
-      delegate
-        .read(bounds, bands)
-        .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
+      readWithRetry(() =>
+        delegate
+          .read(bounds, bands)
+          .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
+      )
+
     override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
-      delegate
-        .read(extent, bands)
-        .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
+      readWithRetry(() =>
+        delegate
+          .read(extent, bands)
+          .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
+      )
   }
 
   case class JVMGeoTiffRasterSource(source: URI) extends DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))

From 7618c0b7b32224a97d8db22da998891987619451 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 3 Jun 2019 15:57:20 -0400
Subject: [PATCH 132/380] python CellType ignore user defined nodata if same as
 default nodata.

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/python/pyrasterframes/rf_types.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index f0f946a01..6e3db164f 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -233,6 +233,8 @@ def to_numpy_dtype(self):
         return np.dtype(n).newbyteorder('>')
 
     def with_no_data_value(self, no_data):
+        if self.has_no_data() and self.no_data_value() == no_data:
+            return self
         return CellType(self.base_cell_type_name() + 'ud' + str(no_data))
 
     def __eq__(self, other):

From b55ff3dfe5a54a69f5ce50e800cf05ecae990c3b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 4 Jun 2019 10:59:14 -0400
Subject: [PATCH 133/380] Fixed `pyExamples`. Incorporated PR feedback

---
 project/PythonBuildPlugin.scala               |  11 +-
 pyrasterframes/build.sbt                      |  11 +-
 pyrasterframes/python/README.rst              |   6 +-
 pyrasterframes/python/examples/Clustering.py  |  12 +-
 .../python/examples/CreatingRasterFrames.py   |  19 +-
 pyrasterframes/python/examples/MeanValue.py   |  19 +-
 pyrasterframes/python/examples/NDVI.py        |  20 +--
 pyrasterframes/python/examples/__init__.py    |   4 +-
 .../python/pyrasterframes/README.md           |   0
 .../python/pyrasterframes/README.rst          |  15 --
 .../python/pyrasterframes/__init__.py         |  20 +++
 .../python/pyrasterframes/context.py          |  20 +++
 .../python/pyrasterframes/rasterfunctions.py  |  22 ++-
 .../python/pyrasterframes/rf_types.py         |  20 +++
 pyrasterframes/python/setup.py                |   6 +-
 .../python/tests/PyRasterFramesTests.py       | 165 +++++++-----------
 pyrasterframes/python/tests/__init__.py       |  88 ++++++++++
 17 files changed, 272 insertions(+), 186 deletions(-)
 delete mode 100644 pyrasterframes/python/pyrasterframes/README.md
 delete mode 100644 pyrasterframes/python/pyrasterframes/README.rst
 create mode 100644 pyrasterframes/python/tests/__init__.py

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 7a409b233..c6a669413 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -69,7 +69,10 @@ object PythonBuildPlugin extends AutoPlugin {
       val wd = copyPySources.value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
-      val ver = version.value
+      val ver = version.value match {
+        case "SNAPSHOT" => "dev"
+        case o => o
+      }
       s.log.info(s"Running '${cmd.mkString(" ")}' in $wd")
       Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
     },
@@ -79,7 +82,8 @@ object PythonBuildPlugin extends AutoPlugin {
     Test / test := Def.sequential(
       Test / test,
       Python / test
-    ).value
+    ).value,
+    Test / testQuick := (Python / testQuick).evaluated
   ) ++
     inConfig(Python)(Seq(
       target := target.value / "python",
@@ -100,7 +104,8 @@ object PythonBuildPlugin extends AutoPlugin {
       test := Def.sequential(
         assembly,
         pySetup.toTask(" test")
-      ).value
+      ).value,
+      testQuick := pySetup.toTask(" test").value
     )) ++
     addArtifact(Python / packageBin / artifact, Python / packageBin)
 }
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 6810e01fa..8e3732269 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,4 +1,13 @@
+import PythonBuildPlugin.autoImport.pySetup
+
 Compile / pythonSource := baseDirectory.value / "python"
 Test / pythonSource := baseDirectory.value / "python" / "tests"
 
-addCommandAlias("pyExamples", "pySetup examples")
\ No newline at end of file
+lazy val pyExamples = taskKey[Unit]("Run examples")
+
+pyExamples := Def.sequential(
+  assembly,
+  pySetup.toTask(" examples")
+).value
+
+addCommandAlias("pyTest", "pyrasterframes/test")
\ No newline at end of file
diff --git a/pyrasterframes/python/README.rst b/pyrasterframes/python/README.rst
index 8c80fb496..20e557cc8 100644
--- a/pyrasterframes/python/README.rst
+++ b/pyrasterframes/python/README.rst
@@ -10,7 +10,7 @@ SBT Commands
 
 Build the shaded JAR:
 
-    ``$ sbt pyrasterframes/spPublishLocal``
+    ``$ sbt pyrasterframes/assembly``
 
 Run tests:
 
@@ -52,7 +52,9 @@ Run examples:
 Spark Usage
 -----------
 
-Get a Spark REPL (after running ``$ sbt pyrasterframes/spPublishLocal``):
+*TODO: The following may be out of date.*
+
+Get a Spark REPL (after running ``$ sbt pyrasterframes/assembly``):
 
     ``$  pyspark --packages io.astraea:pyrasterframes:$VERSION --master local[2]``
 
diff --git a/pyrasterframes/python/examples/Clustering.py b/pyrasterframes/python/examples/Clustering.py
index 84e30c027..2fceeb5c9 100644
--- a/pyrasterframes/python/examples/Clustering.py
+++ b/pyrasterframes/python/examples/Clustering.py
@@ -1,20 +1,13 @@
-
-#examples_setup
 from . import example_session, resource_dir
-#examples_setup
-
-#py_cl_imports
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
 from pyspark.ml.feature import VectorAssembler
 from pyspark.ml.clustering import KMeans
 from pyspark.ml import Pipeline
-#py_cl_imports
 
-#py_cl_create_session
+
 spark = example_session().withRasterFrames()
-#py_cl_create_session
 
 # The first step is to load multiple bands of imagery and construct
 # a single RasterFrame from them.
@@ -32,13 +25,11 @@
 # We should see a single spatial_key column along with columns of tiles.
 joinedRF.printSchema()
 
-
 # SparkML requires that each observation be in its own row, and those
 # observations be packed into a single `Vector`. The first step is to
 # "explode" the tiles into a single row per cell/pixel
 exploder = TileExploder()
 
-
 # To "vectorize" the the band columns we use the SparkML `VectorAssembler`
 assembler = VectorAssembler() \
     .setInputCols(bandColNames) \
@@ -51,7 +42,6 @@
 # Combine the two stages
 pipeline = Pipeline().setStages([exploder, assembler, kmeans])
 
-
 # Compute clusters
 model = pipeline.fit(joinedRF)
 
diff --git a/pyrasterframes/python/examples/CreatingRasterFrames.py b/pyrasterframes/python/examples/CreatingRasterFrames.py
index 429bb0f48..5c018fee8 100644
--- a/pyrasterframes/python/examples/CreatingRasterFrames.py
+++ b/pyrasterframes/python/examples/CreatingRasterFrames.py
@@ -1,27 +1,13 @@
-
-#examples_setup
 from . import resource_dir, example_session
-#examples_setup
-
-#py_crf_imports
 from pyrasterframes import *
 from pyspark.sql import *
-#py_crf_imports
+import os
 
-#py_crf_create_session
 spark = example_session().withRasterFrames()
-#py_crf_create_session
-
-#py_crf_more_imports
-# No additional imports needed for Python.
-#py_crf_more_imports
 
-#py_crf_create_rasterframe
-rf = spark.read.geotiff(resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri())
+rf = spark.read.geotiff(os.path.join(resource_dir, 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
-#py_crf_create_rasterframe
 
-#py_crf_metadata
 rf.tileColumns()
 
 rf.spatialKeyColumn()
@@ -29,4 +15,3 @@
 rf.temporalKeyColumn()
 
 rf.tileLayerMetadata()
-#py_crf_metadata
diff --git a/pyrasterframes/python/examples/MeanValue.py b/pyrasterframes/python/examples/MeanValue.py
index 2f02f0bac..1bec83c64 100644
--- a/pyrasterframes/python/examples/MeanValue.py
+++ b/pyrasterframes/python/examples/MeanValue.py
@@ -1,25 +1,12 @@
-
-#examples_setup
 from . import resource_dir, example_session
-#examples_setup
-
-
-#py_mv_imports
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
-#py_mv_imports
+import os
 
-#py_mv_create_session
 spark = example_session().withRasterFrames()
-#py_mv_create_session
 
-#py_mv_create_rasterframe
-rf = spark.read.geotiff(resource_dir.joinpath('L8-B8-Robinson-IL.tiff').as_uri())
+rf = spark.read.geotiff(os.path.join(resource_dir, 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
-#py_mv_create_rasterframe
 
-#py_mv_find_mean
-tileCol = 'tile'
-rf.agg(rf_agg_no_data_cells(tileCol), rf_agg_data_cells(tileCol), rf_agg_mean(tileCol)).show(5, False)
-#py_mv_find_mean
\ No newline at end of file
+rf.agg(rf_agg_no_data_cells('tile'), rf_agg_data_cells('tile'), rf_agg_mean('tile')).show(5, False)
diff --git a/pyrasterframes/python/examples/NDVI.py b/pyrasterframes/python/examples/NDVI.py
index fc6594bb6..7fb4d28d2 100644
--- a/pyrasterframes/python/examples/NDVI.py
+++ b/pyrasterframes/python/examples/NDVI.py
@@ -1,30 +1,16 @@
-
-
-#examples_setup
 from . import resource_dir, example_session
-#examples_setup
-
-#py_nvdi_imports
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
-from pyspark.sql.functions import udf
-#py_nvdi_imports
+import os
 
-#py_nvdi_create_session
 spark = example_session().withRasterFrames()
-#py_nvdi_create_session
 
-#py_ndvi_create_rasterframe
-redBand = spark.read.geotiff(resource_dir.joinpath('L8-B4-Elkton-VA.tiff').as_uri()).withColumnRenamed('tile', 'red_band')
-nirBand = spark.read.geotiff(resource_dir.joinpath('L8-B5-Elkton-VA.tiff').as_uri()).withColumnRenamed('tile', 'nir_band')
-#py_ndvi_create_rasterframe
+redBand = spark.read.geotiff(os.path.join(resource_dir, 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
+nirBand = spark.read.geotiff(os.path.join(resource_dir, 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
 
-#py_ndvi_column
 rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
     .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
 rf.printSchema()
 rf.show(20)
-#py_ndvi_column
 
 spark.stop()
\ No newline at end of file
diff --git a/pyrasterframes/python/examples/__init__.py b/pyrasterframes/python/examples/__init__.py
index c7d2a9fa8..8bd561d0a 100644
--- a/pyrasterframes/python/examples/__init__.py
+++ b/pyrasterframes/python/examples/__init__.py
@@ -12,7 +12,9 @@
 jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
 
 if not len(jarpath) == 1:
-    raise RuntimeError("Expected to find exactly one assembly. Found: ", jarpath)
+    raise RuntimeError("""
+Expected to find exactly one assembly. Found '{}' instead. 
+Try running 'sbt pyrasterframes/clean' first. """.format(jarpath))
 
 pyJar = jarpath[0]
 
diff --git a/pyrasterframes/python/pyrasterframes/README.md b/pyrasterframes/python/pyrasterframes/README.md
deleted file mode 100644
index e69de29bb..000000000
diff --git a/pyrasterframes/python/pyrasterframes/README.rst b/pyrasterframes/python/pyrasterframes/README.rst
deleted file mode 100644
index cf9330217..000000000
--- a/pyrasterframes/python/pyrasterframes/README.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-PyRasterFrames
---------------
-
-PyRasterFrames provides a Python API for RasterFrames!
-
-To initialize PyRasterFrames:
-
-    >>> from pyrasterframes import *
-    >>> spark = SparkSession.builder \
-    ...     .master("local[*]") \
-    ...     .appName("Using RasterFrames") \
-    ...     .config("spark.some.config.option", "some-value") \
-    ...     .getOrCreate() \
-    ...     .withRasterFrames()
-
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/python/pyrasterframes/__init__.py
index cd994a89c..a1ec30b83 100644
--- a/pyrasterframes/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/python/pyrasterframes/__init__.py
@@ -1,3 +1,23 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 """
 Module initialization for PyRasterFrames. This is where much of the cool stuff is
 appended to PySpark classes.
diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/python/pyrasterframes/context.py
index 08cd81701..18940aaab 100644
--- a/pyrasterframes/python/pyrasterframes/context.py
+++ b/pyrasterframes/python/pyrasterframes/context.py
@@ -1,3 +1,23 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 """
 This module contains access to the jvm SparkContext with RasterFrame support.
 """
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
index ffcc6c26f..9d8ceff7a 100644
--- a/pyrasterframes/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/python/pyrasterframes/rasterfunctions.py
@@ -1,10 +1,28 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 """
 This module creates explicit Python functions that map back to the existing Scala
 implementations. Most functions are standard Column functions, but those with unique
 signatures are handled here as well.
 """
-
-
 from __future__ import absolute_import
 from pyspark.sql.types import *
 from pyspark.sql.column import Column, _to_java_column
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/python/pyrasterframes/rf_types.py
index 98fd10023..850809bce 100644
--- a/pyrasterframes/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/python/pyrasterframes/rf_types.py
@@ -1,3 +1,23 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 """
 This module contains all types relevant to PyRasterFrames. Classes in this module are
 meant to provide smoother pathways between the jvm and Python, and whenever possible,
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index 0c4905b8a..a7ced91c4 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -75,7 +75,7 @@ def run(self):
     description='RasterFrames for PySpark',
     long_description=readme,
     long_description_content_type='text/x-rst',
-    version=environ.get('RASTERFRAMES_VERSION', 'SNAPSHOT'),
+    version=environ.get('RASTERFRAMES_VERSION', 'dev'),
     author='Astraea, Inc.',
     author_email='info@astraea.earth',
     license='Apache 2',
@@ -84,12 +84,12 @@ def run(self):
         'Bug Reports': 'https://github.com/locationtech/rasterframes/issues',
         'Source': 'https://github.com/locationtech/rasterframes',
     },
+    install_requires=requirements,
     setup_requires=[
         'pytest-runner',
         'setuptools >= 0.8',
         'pathlib2'
-    ],
-    install_requires=requirements,
+    ] + requirements,
     tests_require=[
         'pytest==3.4.2',
         'pypandoc',
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/python/tests/PyRasterFramesTests.py
index 931c228a8..e49eedca2 100644
--- a/pyrasterframes/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/python/tests/PyRasterFramesTests.py
@@ -1,77 +1,38 @@
-from pyspark.sql import SparkSession, Column, SQLContext
-from pyspark.sql.functions import *
-from pyrasterframes.rasterfunctions import *
-from pyrasterframes.rf_types import *
-from geomesa_pyspark.types import *
-import unittest
-import numpy as np
-import os
-import glob
-
-# version-conditional imports
-import sys
-
-if sys.version_info[0] > 2:
-    import builtins
-else:
-    import __builtin__ as builtins
-
-HERE = os.path.dirname(os.path.realpath(__file__))
-
-class TestEnvironment(unittest.TestCase):
-    """
-    Base class for tests.
-    """
-
-    def rounded_compare(self, val1, val2):
-        print('Comparing {} and {} using round()'.format(val1, val2))
-        return builtins.round(val1) == builtins.round(val2)
-
-    @classmethod
-    def setUpClass(cls):
-        # gather Scala requirements
-
-        scala_target = os.path.realpath(os.path.join(HERE, '..', '..', 'scala-2.11'))
-
-        jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
-
-        if not len(jarpath) == 1:
-            raise RuntimeError("Expected to find exactly one assembly. Found: ", jarpath)
-
-        # hard-coded relative path for resources
-        cls.resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
 
-        # spark session with RF
-        cls.spark = (SparkSession.builder
-                     .config('spark.driver.extraClassPath', jarpath[0])
-                     .config('spark.executor.extraClassPath', jarpath[0])
-                     .withKryoSerialization()
-                     .getOrCreate())
-        cls.spark.sparkContext.setLogLevel('ERROR')
-
-        print("Spark Version: " + cls.spark.version)
-        print(cls.spark.sparkContext._conf.getAll())
-
-        cls.spark.withRasterFrames()
-
-        cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
-
-    # load something into a rasterframe
-        rf = cls.spark.read.geotiff(cls.img_uri) \
-            .withBounds() \
-            .withCenter()
-
-        # convert the tile cell type to provide for other operations
-        cls.tileCol = 'tile'
-        cls.rf = rf.withColumn('tile2', rf_convert_cell_type(cls.tileCol, 'float32')) \
-            .drop(cls.tileCol) \
-            .withColumnRenamed('tile2', cls.tileCol).asRF()
-        # cls.rf.show()
+import unittest
 
+import numpy as np
+from geomesa_pyspark.types import *
+from pyrasterframes.rasterfunctions import *
+from pyrasterframes.rf_types import *
+from pyspark.sql import SQLContext, Column
+from pyspark.sql.functions import *
+from pyspark.sql.types import *
+from . import TestEnvironment
 
 class VectorTypes(TestEnvironment):
 
     def setUp(self):
+        self.create_rasterframe()
         import pandas as pd
         self.pandas_df = pd.DataFrame({
             'eye': ['a', 'b', 'c', 'd'],
@@ -85,7 +46,7 @@ def setUp(self):
         self.df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
 
     def test_spatial_relations(self):
-        from pyspark.sql.functions import lit, udf, sum
+        from pyspark.sql.functions import udf, sum
         import shapely
         import numpy.testing
 
@@ -175,6 +136,9 @@ def test_reproject(self):
 
 class RasterFunctions(TestEnvironment):
 
+    def setUp(self):
+        self.create_rasterframe()
+
     def test_identify_columns(self):
         cols = self.rf.tileColumns()
         self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
@@ -194,8 +158,8 @@ def test_tile_creation(self):
         self.assertEqual(tiles.count(), 4)
 
     def test_multi_column_operations(self):
-        df1 = self.rf.withColumnRenamed(self.tileCol, 't1').asRF()
-        df2 = self.rf.withColumnRenamed(self.tileCol, 't2').asRF()
+        df1 = self.rf.withColumnRenamed('tile', 't1').asRF()
+        df2 = self.rf.withColumnRenamed('tile', 't2').asRF()
         df3 = df1.spatialJoin(df2).asRF()
         df3 = df3.withColumn('norm_diff', rf_normalized_difference('t1', 't2'))
         # df3.printSchema()
@@ -211,36 +175,36 @@ def test_multi_column_operations(self):
     def test_general(self):
         meta = self.rf.tileLayerMetadata()
         self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims', rf_dimensions(self.tileCol)) \
-            .withColumn('type', rf_cell_type(self.tileCol)) \
-            .withColumn('dCells', rf_data_cells(self.tileCol)) \
-            .withColumn('ndCells', rf_no_data_cells(self.tileCol)) \
-            .withColumn('min', rf_tile_min(self.tileCol)) \
-            .withColumn('max', rf_tile_max(self.tileCol)) \
-            .withColumn('mean', rf_tile_mean(self.tileCol)) \
-            .withColumn('sum', rf_tile_sum(self.tileCol)) \
-            .withColumn('stats', rf_tile_stats(self.tileCol)) \
+        df = self.rf.withColumn('dims', rf_dimensions('tile')) \
+            .withColumn('type', rf_cell_type('tile')) \
+            .withColumn('dCells', rf_data_cells('tile')) \
+            .withColumn('ndCells', rf_no_data_cells('tile')) \
+            .withColumn('min', rf_tile_min('tile')) \
+            .withColumn('max', rf_tile_max('tile')) \
+            .withColumn('mean', rf_tile_mean('tile')) \
+            .withColumn('sum', rf_tile_sum('tile')) \
+            .withColumn('stats', rf_tile_stats('tile')) \
             .withColumn('extent', st_extent('geometry')) \
             .withColumn('extent_geom1', st_geometry('extent')) \
-            .withColumn('ascii', rf_render_ascii(self.tileCol)) \
-            .withColumn('log', rf_log(self.tileCol)) \
-            .withColumn('exp', rf_exp(self.tileCol)) \
-            .withColumn('expm1', rf_expm1(self.tileCol)) \
-            .withColumn('round', rf_round(self.tileCol)) \
-            .withColumn('abs', rf_abs(self.tileCol))
+            .withColumn('ascii', rf_render_ascii('tile')) \
+            .withColumn('log', rf_log('tile')) \
+            .withColumn('exp', rf_exp('tile')) \
+            .withColumn('expm1', rf_expm1('tile')) \
+            .withColumn('round', rf_round('tile')) \
+            .withColumn('abs', rf_abs('tile'))
 
         df.first()
 
     def test_agg_mean(self):
-        mean = self.rf.agg(rf_agg_mean(self.tileCol)).first()['rf_agg_mean(tile)']
+        mean = self.rf.agg(rf_agg_mean('tile')).first()['rf_agg_mean(tile)']
         self.assertTrue(self.rounded_compare(mean, 10160))
 
     def test_aggregations(self):
         aggs = self.rf.agg(
-            rf_agg_data_cells(self.tileCol),
-            rf_agg_no_data_cells(self.tileCol),
-            rf_agg_stats(self.tileCol),
-            rf_agg_approx_histogram(self.tileCol)
+            rf_agg_data_cells('tile'),
+            rf_agg_no_data_cells('tile'),
+            rf_agg_stats('tile'),
+            rf_agg_approx_histogram('tile')
         )
         row = aggs.first()
 
@@ -252,7 +216,7 @@ def test_aggregations(self):
     def test_sql(self):
         self.rf.createOrReplaceTempView("rf")
 
-        dims = self.rf.withColumn('dims', rf_dimensions(self.tileCol)).first().dims
+        dims = self.rf.withColumn('dims', rf_dimensions('tile')).first().dims
         dims_str = """{}, {}""".format(dims.cols, dims.rows)
 
         self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
@@ -266,7 +230,7 @@ def test_sql(self):
                                 FROM r3""")
 
         # ops.printSchema
-        statsRow = ops.select(rf_tile_mean(self.tileCol).alias('base'),
+        statsRow = ops.select(rf_tile_mean('tile').alias('base'),
                               rf_tile_mean("AndOne").alias('plus_one'),
                               rf_tile_mean("LessOne").alias('minus_one'),
                               rf_tile_mean("TimesTwo").alias('double'),
@@ -280,7 +244,7 @@ def test_sql(self):
 
     def test_explode(self):
         import pyspark.sql.functions as F
-        self.rf.select('spatial_key', rf_explode_tiles(self.tileCol)).show()
+        self.rf.select('spatial_key', rf_explode_tiles('tile')).show()
         # +-----------+------------+---------+-------+
         # |spatial_key|column_index|row_index|tile   |
         # +-----------+------------+---------+-------+
@@ -296,7 +260,7 @@ def test_explode(self):
 
         # Test the sample version
         frac = 0.01
-        sample_count = self.rf.select(rf_explode_tiles_sample(frac, 1872, self.tileCol)).count()
+        sample_count = self.rf.select(rf_explode_tiles_sample(frac, 1872, 'tile')).count()
         print('Sample count is {}'.format(sample_count))
         self.assertTrue(sample_count > 0)
         self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
@@ -485,6 +449,8 @@ def test_addition(self):
 
 
 class PandasInterop(TestEnvironment):
+    def setUp(self):
+        self.create_rasterframe()
 
     def test_pandas_conversion(self):
         import pandas as pd
@@ -547,6 +513,9 @@ def test_extended_pandas_ops(self):
 
 class RasterJoin(TestEnvironment):
 
+    def setUp(self):
+        self.create_rasterframe()
+
     def test_raster_join(self):
         # re-read the same source
         rf_prime = self.spark.read.geotiff(self.img_uri) \
@@ -583,9 +552,9 @@ def test_setup(self):
 
     def test_prt_functions(self):
         df = self.spark.read.rastersource(self.img_uri) \
-            .withColumn('crs', rf_crs(self.tileCol)) \
-            .withColumn('ext', rf_extent(self.tileCol)) \
-            .withColumn('geom', rf_geometry(self.tileCol))
+            .withColumn('crs', rf_crs('tile')) \
+            .withColumn('ext', rf_extent('tile')) \
+            .withColumn('geom', rf_geometry('tile'))
         df.select('crs', 'ext', 'geom').first()                         
 
     def test_raster_source_reader(self):
diff --git a/pyrasterframes/python/tests/__init__.py b/pyrasterframes/python/tests/__init__.py
new file mode 100644
index 000000000..275612087
--- /dev/null
+++ b/pyrasterframes/python/tests/__init__.py
@@ -0,0 +1,88 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import glob
+import os
+import unittest
+
+from pyspark.sql import SparkSession
+from pyrasterframes.rasterfunctions import *
+
+import sys
+
+if sys.version_info[0] > 2:
+    import builtins
+else:
+    import __builtin__ as builtins
+
+
+class TestEnvironment(unittest.TestCase):
+    """
+    Base class for tests.
+    """
+
+    def rounded_compare(self, val1, val2):
+        print('Comparing {} and {} using round()'.format(val1, val2))
+        return builtins.round(val1) == builtins.round(val2)
+
+    @classmethod
+    def setUpClass(cls):
+        # gather Scala requirements
+        here = os.path.dirname(os.path.realpath(__file__))
+        target_dir = os.path.dirname(os.path.dirname(here))
+
+        scala_target = os.path.realpath(os.path.join(target_dir, 'scala-2.11'))
+
+        jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
+
+        if not len(jarpath) == 1:
+            raise RuntimeError("""
+Expected to find exactly one assembly. Found '{}' instead. 
+Try running 'sbt pyrasterframes/clean' first.""".format(jarpath))
+
+        # hard-coded relative path for resources
+        cls.resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
+
+        # spark session with RF
+        cls.spark = (SparkSession.builder
+                     .config('spark.driver.extraClassPath', jarpath[0])
+                     .config('spark.executor.extraClassPath', jarpath[0])
+                     .withKryoSerialization()
+                     .getOrCreate())
+        cls.spark.sparkContext.setLogLevel('ERROR')
+
+        print("Spark Version: " + cls.spark.version)
+        print(cls.spark.sparkContext._conf.getAll())
+
+        cls.spark.withRasterFrames()
+
+        cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
+
+    def create_rasterframe(self):
+        # load something into a rasterframe
+        rf = self.spark.read.geotiff(self.img_uri) \
+            .withBounds() \
+            .withCenter()
+
+        # convert the tile cell type to provide for other operations
+        self.rf = rf.withColumn('tile2', rf_convert_cell_type('tile', 'float32')) \
+            .drop('tile') \
+            .withColumnRenamed('tile2', 'tile').asRF()
+        # cls.rf.show()
\ No newline at end of file

From 635cf66ab22ee4b229ff31784f901cf12b672166 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 4 Jun 2019 14:18:02 -0400
Subject: [PATCH 134/380] Step toward building an assembly with python source
 included.

---
 project/PythonBuildPlugin.scala |  2 +-
 project/RFAssemblyPlugin.scala  |  2 +-
 pyrasterframes/build.sbt        | 22 +++++++++++++++++++++-
 3 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index c6a669413..692970348 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -48,7 +48,7 @@ object PythonBuildPlugin extends AutoPlugin {
     dest
   }
 
-  val buildPyDist= Def.task {
+  val buildPyDist = Def.task {
     val buildDir = (Python / target).value
     val pyDist = (packageBin / artifactPath).value
     val retcode = pySetup.toTask(" build sdist --formats=zip").value
diff --git a/project/RFAssemblyPlugin.scala b/project/RFAssemblyPlugin.scala
index 90f344b78..8f69776f6 100644
--- a/project/RFAssemblyPlugin.scala
+++ b/project/RFAssemblyPlugin.scala
@@ -55,7 +55,7 @@ object RFAssemblyPlugin extends AutoPlugin {
         "org.apache.http",
         "com.google.guava"
       )
-      shadePrefixes.map(p ⇒ ShadeRule.rename(s"$p.**" -> s"aee.shaded.$p.@1").inAll)
+      shadePrefixes.map(p ⇒ ShadeRule.rename(s"$p.**" -> s"rf.shaded.$p.@1").inAll)
     },
     assemblyOption in assembly :=
       (assemblyOption in assembly).value.copy(includeScala = false),
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 8e3732269..1c66443d0 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -3,7 +3,27 @@ import PythonBuildPlugin.autoImport.pySetup
 Compile / pythonSource := baseDirectory.value / "python"
 Test / pythonSource := baseDirectory.value / "python" / "tests"
 
-lazy val pyExamples = taskKey[Unit]("Run examples")
+// This setting adds relevant python source to the JVM artifact
+// where pyspark can pick it up from the `--packages` setting.
+
+Python / sourceDirectories := Seq(
+  (Compile / pythonSource).value / "pyrasterframes",
+  (Compile / pythonSource).value / "geomesa_pyspark"
+)
+
+Compile / packageBin / mappings ++= {
+  val pybase = (Compile / pythonSource).value
+  val dirs = (Python / sourceDirectories).value
+  for {
+    d <- dirs
+    p <- d ** "*.py" --- d pair (f => IO.relativize(pybase, f))
+  } yield p
+}
+
+// This is needed for the above to get included properly in the assembly.
+exportJars := true
+
+lazy val pyExamples = taskKey[Unit]("Run python examples")
 
 pyExamples := Def.sequential(
   assembly,

From 30254704d600363d3e5eedb0c601908cbacf07fd Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 4 Jun 2019 16:17:44 -0400
Subject: [PATCH 135/380] Created `pySpark` sbt command. Updated PyRasterFrames
 README.

---
 project/PythonBuildPlugin.scala  |  26 ++++----
 pyrasterframes/build.sbt         |  31 +++++-----
 pyrasterframes/python/README.md  | 101 +++++++++++++++++++++++++++++++
 pyrasterframes/python/README.rst |  77 -----------------------
 pyrasterframes/python/setup.py   |   4 +-
 5 files changed, 131 insertions(+), 108 deletions(-)
 create mode 100644 pyrasterframes/python/README.md
 delete mode 100644 pyrasterframes/python/README.rst

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 692970348..07584005d 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -48,15 +48,20 @@ object PythonBuildPlugin extends AutoPlugin {
     dest
   }
 
-  val buildPyDist = Def.task {
+  val buildWhl = Def.task {
     val buildDir = (Python / target).value
-    val pyDist = (packageBin / artifactPath).value
-    val retcode = pySetup.toTask(" build sdist --formats=zip").value
+    val retcode = pySetup.toTask(" build bdist_wheel").value
     if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
-    val art = (Python / packageBin / artifact).value
-    val ver = version.value
-    IO.move(buildDir / "dist" / s"${art.name}-$ver.zip", pyDist)
-    pyDist
+    val whls = (buildDir / "dist" ** "pyrasterframes*.whl").get()
+    require(whls.length == 1, "Running setup.py should have produced a single .whl file. Try running `clean` first.")
+    whls.head
+  }
+
+  val pyDistAsZip = Def.task {
+    val pyDest = (packageBin / artifactPath).value
+    val whl = buildWhl.value
+    IO.copyFile(whl, pyDest)
+    pyDest
   }
 
   override def projectConfigurations: Seq[Configuration] = Seq(Python)
@@ -69,10 +74,7 @@ object PythonBuildPlugin extends AutoPlugin {
       val wd = copyPySources.value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
-      val ver = version.value match {
-        case "SNAPSHOT" => "dev"
-        case o => o
-      }
+      val ver = version.value
       s.log.info(s"Running '${cmd.mkString(" ")}' in $wd")
       Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
     },
@@ -89,7 +91,7 @@ object PythonBuildPlugin extends AutoPlugin {
       target := target.value / "python",
       packageBin := Def.sequential(
         Compile / packageBin,
-        buildPyDist
+        pyDistAsZip,
       ).value,
       packageBin / artifact := {
         val java = (Compile / packageBin / artifact).value
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 1c66443d0..bf08614de 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -3,26 +3,23 @@ import PythonBuildPlugin.autoImport.pySetup
 Compile / pythonSource := baseDirectory.value / "python"
 Test / pythonSource := baseDirectory.value / "python" / "tests"
 
-// This setting adds relevant python source to the JVM artifact
-// where pyspark can pick it up from the `--packages` setting.
-
-Python / sourceDirectories := Seq(
-  (Compile / pythonSource).value / "pyrasterframes",
-  (Compile / pythonSource).value / "geomesa_pyspark"
-)
+exportJars := true
 
-Compile / packageBin / mappings ++= {
-  val pybase = (Compile / pythonSource).value
-  val dirs = (Python / sourceDirectories).value
-  for {
-    d <- dirs
-    p <- d ** "*.py" --- d pair (f => IO.relativize(pybase, f))
-  } yield p
+lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
+pySparkCmd := {
+  val s = streams.value
+  val jvm = assembly.value
+  val py = (Python / packageBin).value
+  val script = IO.createTemporaryDirectory / "pyrf_init.py"
+  IO.write(script, """
+from pyrasterframes import *
+from pyrasterframes.rasterfunctions import *
+""")
+  val msg = s"PYTHONSTARTUP=$script pyspark --jars $jvm --py-files $py"
+  s.log.debug(msg)
+  println(msg)
 }
 
-// This is needed for the above to get included properly in the assembly.
-exportJars := true
-
 lazy val pyExamples = taskKey[Unit]("Run python examples")
 
 pyExamples := Def.sequential(
diff --git a/pyrasterframes/python/README.md b/pyrasterframes/python/README.md
new file mode 100644
index 000000000..9fb8b16e2
--- /dev/null
+++ b/pyrasterframes/python/README.md
@@ -0,0 +1,101 @@
+# PyRasterFrames
+
+PyRasterFrames is the Python API for Spark RasterFrames. 
+
+Building RasterFrames requires [`sbt`](https://www.scala-sbt.org/), and all `sbt` commands referenced below must be 
+run from the root source directory, i.e. the parent of the `pyrasterframes` directory. Know that `sbt` is much 
+faster if run in "interactive" mode, where you launch `sbt` with no arguments, and subsequent commands are invoked
+via an interactive shell. But for context clarity, we'll prefix each command example below with `sbt`. 
+
+## Quickstart
+
+The quickest way to run a `pyspark` shell with the latest RasterFrames enabled is to run:
+
+```bash
+sbt pySparkCmd
+```
+
+This will: 
+
+1. Compile all the Scala/JVM code.
+1. Merge all JVM code and dependencies into a single "assembly" JAR file.
+1. Create the PyRasterFrames `.whl` package
+1. Construct a temporary initialization script
+1. Emit a `bash` command with requiesite arguments to load RasterFrames and import PyRasterFrames 
+
+You then copy/paste the emitted command into your `bash` shell to start up a spark shell. It assumes you have
+`pyspark` >= 2.3.2 installed in your environment.
+
+## Running Tests and Examples
+
+The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
+
+```bash
+sbt pyTests
+```
+
+Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
+
+```bash
+sbt pyExamples
+```
+
+## Creating and Using a Build
+
+Assuming that `$SCALA_VER` is the major verison of Scala in use, and `$VER` is the version of RasterFrames, 
+the primary build artifacts are:
+
+* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
+* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
+
+You build them with"
+
+```bash
+sbt pyrasterframes/package
+```
+
+Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy "GAV" coordinates
+`org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
+
+Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
+The Python package is published under the `python` classifier, `zip` extension.
+
+
+## Build Internals
+
+### Running `setup.py`
+
+Before a build is initiated, the Python sources are copied to `pyrasterframes/target/python`. This ensures the 
+version controlled source directories are not polluted by `setuptools` residuals, better ensuring repeatable builds. To
+simplify the process of working with `setup.py` in this context, a `pySetup` sbt interactive command is available. To
+illustrate its usage, suppose we want to run a subset of the Python unit test. Instead of running `sbt pyTests` you can:
+
+```bash
+sbt pySetup test --addopts "-k test_tile_creation"
+```
+
+Or to run a specific example:
+
+```bash
+sbt pySetup examples "--e NDVI.py"
+```
+
+*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
+
+
+### `SparkSession` Setup
+
+To initialize PyRasterFrames in a generic Python shell:
+
+```python
+# pyspark imports here...
+from pyrasterframes import *
+from pyrasterframes.rasterfunctions import *
+spark = SparkSession.builder \
+     .master("local[*]") \
+     .appName("Using RasterFrames") \
+     .config("spark.some.config.option", "some-value") \
+     .getOrCreate() \
+     .withRasterFrames()
+```
+
diff --git a/pyrasterframes/python/README.rst b/pyrasterframes/python/README.rst
deleted file mode 100644
index 20e557cc8..000000000
--- a/pyrasterframes/python/README.rst
+++ /dev/null
@@ -1,77 +0,0 @@
-==============
-PyRasterFrames
-==============
-
-PyRasterFrames provides a Python API for RasterFrames!
-
-------------
-SBT Commands
-------------
-
-Build the shaded JAR:
-
-    ``$ sbt pyrasterframes/assembly``
-
-Run tests:
-
-    ``$ sbt pyrasterframes/pyTest``
-
-Run examples:
-
-    ``$ sbt pyrasterframes/pyExamples``
-
-
----------------
-Python Commands
----------------
-
-Install the python package (for development / local use):
-
-    ``$ pip install -e python``
-
-
-To run tests and examples, ``$ cd python``, then:
-
-Run tests (if no Scala code has changed):
-
-    ``$ python setup.py test``
-
-    To run verbosely:
-    ``$ python setup.py test --addopts -s``
-
-    To run a specific test:
-    ``$ python setup.py test --addopts "-k my_test_name"``
-
-
-Run examples:
-
-    ``$ python setup.py examples [--e EXAMPLENAME,EXAMPLENAME]``
-
-
------------
-Spark Usage
------------
-
-*TODO: The following may be out of date.*
-
-Get a Spark REPL (after running ``$ sbt pyrasterframes/assembly``):
-
-    ``$  pyspark --packages io.astraea:pyrasterframes:$VERSION --master local[2]``
-
-You can then try some of the commands in ``python/tests/PyRasterFramesTests.py``.
-
-Submit a script (from the ``python`` directory):
-
-    ``$ spark-submit --packages io.astraea:pyrasterframes:$VERSION --master local[2] \``
-        ``examples/CreatingRasterFrames.py``
-
-To initialize PyRasterFrames:
-
-    >>> from pyrasterframes import *
-    >>> spark = SparkSession.builder \
-    ...     .master("local[*]") \
-    ...     .appName("Using RasterFrames") \
-    ...     .config("spark.some.config.option", "some-value") \
-    ...     .getOrCreate() \
-    ...     .withRasterFrames()
-
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/python/setup.py
index a7ced91c4..2ab4b7ebb 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/python/setup.py
@@ -9,7 +9,7 @@
 here = path.abspath(path.dirname(__file__))
 
 # Get the long description from the README file
-with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
+with open(path.join(here, 'README.md'), encoding='utf-8') as f:
     readme = f.read()
 
 with open(path.join(here, 'requirements.txt')) as f:
@@ -74,7 +74,7 @@ def run(self):
     name='pyrasterframes',
     description='RasterFrames for PySpark',
     long_description=readme,
-    long_description_content_type='text/x-rst',
+    long_description_content_type='text/markdown',
     version=environ.get('RASTERFRAMES_VERSION', 'dev'),
     author='Astraea, Inc.',
     author_email='info@astraea.earth',

From 9beb22afa6277ef55048d1325ee692be716856c5 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 4 Jun 2019 11:34:40 -0400
Subject: [PATCH 136/380] Reorganized Python sources to match sbt conventions.

---
 project/PythonBuildPlugin.scala               | 11 +++
 .../{ => src/main}/python/.gitignore          |  0
 .../main/python/LICENSE.txt}                  |  5 +-
 .../{ => src/main}/python/MANIFEST.in         |  0
 pyrasterframes/src/main/python/README.rst     | 77 +++++++++++++++++++
 .../{ => src/main}/python/coverage-report.sh  |  0
 .../main}/python/examples/Clustering.py       |  0
 .../python/examples/CreatingRasterFrames.py   |  0
 .../main}/python/examples/MeanValue.py        |  0
 .../{ => src/main}/python/examples/NDVI.py    |  0
 .../main}/python/examples/__init__.py         |  0
 .../main}/python/geomesa_pyspark/__init__.py  |  0
 .../main}/python/geomesa_pyspark/types.py     |  0
 .../main}/python/pyrasterframes/__init__.py   |  0
 .../main}/python/pyrasterframes/context.py    |  0
 .../python/pyrasterframes/rasterfunctions.py  |  0
 .../main}/python/pyrasterframes/rf_types.py   |  0
 .../{ => src/main}/python/requirements.txt    |  0
 .../{ => src/main}/python/setup.cfg           |  0
 pyrasterframes/{ => src/main}/python/setup.py |  3 +-
 .../test/python}/PyRasterFramesTests.py       |  0
 .../tests => src/test/python}/__init__.py     |  0
 22 files changed, 93 insertions(+), 3 deletions(-)
 rename pyrasterframes/{ => src/main}/python/.gitignore (100%)
 rename pyrasterframes/{python/LICENSE.md => src/main/python/LICENSE.txt} (82%)
 rename pyrasterframes/{ => src/main}/python/MANIFEST.in (100%)
 create mode 100644 pyrasterframes/src/main/python/README.rst
 rename pyrasterframes/{ => src/main}/python/coverage-report.sh (100%)
 rename pyrasterframes/{ => src/main}/python/examples/Clustering.py (100%)
 rename pyrasterframes/{ => src/main}/python/examples/CreatingRasterFrames.py (100%)
 rename pyrasterframes/{ => src/main}/python/examples/MeanValue.py (100%)
 rename pyrasterframes/{ => src/main}/python/examples/NDVI.py (100%)
 rename pyrasterframes/{ => src/main}/python/examples/__init__.py (100%)
 rename pyrasterframes/{ => src/main}/python/geomesa_pyspark/__init__.py (100%)
 rename pyrasterframes/{ => src/main}/python/geomesa_pyspark/types.py (100%)
 rename pyrasterframes/{ => src/main}/python/pyrasterframes/__init__.py (100%)
 rename pyrasterframes/{ => src/main}/python/pyrasterframes/context.py (100%)
 rename pyrasterframes/{ => src/main}/python/pyrasterframes/rasterfunctions.py (100%)
 rename pyrasterframes/{ => src/main}/python/pyrasterframes/rf_types.py (100%)
 rename pyrasterframes/{ => src/main}/python/requirements.txt (100%)
 rename pyrasterframes/{ => src/main}/python/setup.cfg (100%)
 rename pyrasterframes/{ => src/main}/python/setup.py (98%)
 rename pyrasterframes/{python/tests => src/test/python}/PyRasterFramesTests.py (100%)
 rename pyrasterframes/{python/tests => src/test/python}/__init__.py (100%)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 07584005d..387c523e3 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -48,6 +48,16 @@ object PythonBuildPlugin extends AutoPlugin {
     dest
   }
 
+  val copyPyTestSources = Def.task {
+    val s = streams.value
+    val src = (Test / pythonSource).value
+    val dest = (Python / target).value / "tests"
+    if (!dest.exists()) dest.mkdirs()
+    s.log(s"Copying '$src' to '$dest'")
+    IO.copyDirectory(src, dest)
+    dest
+  }
+
   val buildWhl = Def.task {
     val buildDir = (Python / target).value
     val retcode = pySetup.toTask(" build bdist_wheel").value
@@ -105,6 +115,7 @@ object PythonBuildPlugin extends AutoPlugin {
       },
       test := Def.sequential(
         assembly,
+        copyPyTestSources,
         pySetup.toTask(" test")
       ).value,
       testQuick := pySetup.toTask(" test").value
diff --git a/pyrasterframes/python/.gitignore b/pyrasterframes/src/main/python/.gitignore
similarity index 100%
rename from pyrasterframes/python/.gitignore
rename to pyrasterframes/src/main/python/.gitignore
diff --git a/pyrasterframes/python/LICENSE.md b/pyrasterframes/src/main/python/LICENSE.txt
similarity index 82%
rename from pyrasterframes/python/LICENSE.md
rename to pyrasterframes/src/main/python/LICENSE.txt
index 0dd8f3f8c..e7ca21e70 100644
--- a/pyrasterframes/python/LICENSE.md
+++ b/pyrasterframes/src/main/python/LICENSE.txt
@@ -1,12 +1,12 @@
 This software is licensed under the Apache 2 license, quoted below.
 
-Copyright 2017-2018 Astraea. Inc.
+Copyright 2017-2019 Astraea, Inc.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not
 use this file except in compliance with the License. You may obtain a copy of
 the License at
 
-    [http://www.apache.org/licenses/LICENSE-2.0]
+[http://www.apache.org/licenses/LICENSE-2.0]
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
@@ -14,3 +14,4 @@ WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 License for the specific language governing permissions and limitations under
 the License.
 
+SPDX-License-Identifier: Apache-2.0
diff --git a/pyrasterframes/python/MANIFEST.in b/pyrasterframes/src/main/python/MANIFEST.in
similarity index 100%
rename from pyrasterframes/python/MANIFEST.in
rename to pyrasterframes/src/main/python/MANIFEST.in
diff --git a/pyrasterframes/src/main/python/README.rst b/pyrasterframes/src/main/python/README.rst
new file mode 100644
index 000000000..20e557cc8
--- /dev/null
+++ b/pyrasterframes/src/main/python/README.rst
@@ -0,0 +1,77 @@
+==============
+PyRasterFrames
+==============
+
+PyRasterFrames provides a Python API for RasterFrames!
+
+------------
+SBT Commands
+------------
+
+Build the shaded JAR:
+
+    ``$ sbt pyrasterframes/assembly``
+
+Run tests:
+
+    ``$ sbt pyrasterframes/pyTest``
+
+Run examples:
+
+    ``$ sbt pyrasterframes/pyExamples``
+
+
+---------------
+Python Commands
+---------------
+
+Install the python package (for development / local use):
+
+    ``$ pip install -e python``
+
+
+To run tests and examples, ``$ cd python``, then:
+
+Run tests (if no Scala code has changed):
+
+    ``$ python setup.py test``
+
+    To run verbosely:
+    ``$ python setup.py test --addopts -s``
+
+    To run a specific test:
+    ``$ python setup.py test --addopts "-k my_test_name"``
+
+
+Run examples:
+
+    ``$ python setup.py examples [--e EXAMPLENAME,EXAMPLENAME]``
+
+
+-----------
+Spark Usage
+-----------
+
+*TODO: The following may be out of date.*
+
+Get a Spark REPL (after running ``$ sbt pyrasterframes/assembly``):
+
+    ``$  pyspark --packages io.astraea:pyrasterframes:$VERSION --master local[2]``
+
+You can then try some of the commands in ``python/tests/PyRasterFramesTests.py``.
+
+Submit a script (from the ``python`` directory):
+
+    ``$ spark-submit --packages io.astraea:pyrasterframes:$VERSION --master local[2] \``
+        ``examples/CreatingRasterFrames.py``
+
+To initialize PyRasterFrames:
+
+    >>> from pyrasterframes import *
+    >>> spark = SparkSession.builder \
+    ...     .master("local[*]") \
+    ...     .appName("Using RasterFrames") \
+    ...     .config("spark.some.config.option", "some-value") \
+    ...     .getOrCreate() \
+    ...     .withRasterFrames()
+
diff --git a/pyrasterframes/python/coverage-report.sh b/pyrasterframes/src/main/python/coverage-report.sh
similarity index 100%
rename from pyrasterframes/python/coverage-report.sh
rename to pyrasterframes/src/main/python/coverage-report.sh
diff --git a/pyrasterframes/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
similarity index 100%
rename from pyrasterframes/python/examples/Clustering.py
rename to pyrasterframes/src/main/python/examples/Clustering.py
diff --git a/pyrasterframes/python/examples/CreatingRasterFrames.py b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
similarity index 100%
rename from pyrasterframes/python/examples/CreatingRasterFrames.py
rename to pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
diff --git a/pyrasterframes/python/examples/MeanValue.py b/pyrasterframes/src/main/python/examples/MeanValue.py
similarity index 100%
rename from pyrasterframes/python/examples/MeanValue.py
rename to pyrasterframes/src/main/python/examples/MeanValue.py
diff --git a/pyrasterframes/python/examples/NDVI.py b/pyrasterframes/src/main/python/examples/NDVI.py
similarity index 100%
rename from pyrasterframes/python/examples/NDVI.py
rename to pyrasterframes/src/main/python/examples/NDVI.py
diff --git a/pyrasterframes/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
similarity index 100%
rename from pyrasterframes/python/examples/__init__.py
rename to pyrasterframes/src/main/python/examples/__init__.py
diff --git a/pyrasterframes/python/geomesa_pyspark/__init__.py b/pyrasterframes/src/main/python/geomesa_pyspark/__init__.py
similarity index 100%
rename from pyrasterframes/python/geomesa_pyspark/__init__.py
rename to pyrasterframes/src/main/python/geomesa_pyspark/__init__.py
diff --git a/pyrasterframes/python/geomesa_pyspark/types.py b/pyrasterframes/src/main/python/geomesa_pyspark/types.py
similarity index 100%
rename from pyrasterframes/python/geomesa_pyspark/types.py
rename to pyrasterframes/src/main/python/geomesa_pyspark/types.py
diff --git a/pyrasterframes/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
similarity index 100%
rename from pyrasterframes/python/pyrasterframes/__init__.py
rename to pyrasterframes/src/main/python/pyrasterframes/__init__.py
diff --git a/pyrasterframes/python/pyrasterframes/context.py b/pyrasterframes/src/main/python/pyrasterframes/context.py
similarity index 100%
rename from pyrasterframes/python/pyrasterframes/context.py
rename to pyrasterframes/src/main/python/pyrasterframes/context.py
diff --git a/pyrasterframes/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
similarity index 100%
rename from pyrasterframes/python/pyrasterframes/rasterfunctions.py
rename to pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
diff --git a/pyrasterframes/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
similarity index 100%
rename from pyrasterframes/python/pyrasterframes/rf_types.py
rename to pyrasterframes/src/main/python/pyrasterframes/rf_types.py
diff --git a/pyrasterframes/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
similarity index 100%
rename from pyrasterframes/python/requirements.txt
rename to pyrasterframes/src/main/python/requirements.txt
diff --git a/pyrasterframes/python/setup.cfg b/pyrasterframes/src/main/python/setup.cfg
similarity index 100%
rename from pyrasterframes/python/setup.cfg
rename to pyrasterframes/src/main/python/setup.cfg
diff --git a/pyrasterframes/python/setup.py b/pyrasterframes/src/main/python/setup.py
similarity index 98%
rename from pyrasterframes/python/setup.py
rename to pyrasterframes/src/main/python/setup.py
index 2ab4b7ebb..038675a18 100644
--- a/pyrasterframes/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -88,7 +88,8 @@ def run(self):
     setup_requires=[
         'pytest-runner',
         'setuptools >= 0.8',
-        'pathlib2'
+        'pathlib2',
+        'jupytext'
     ] + requirements,
     tests_require=[
         'pytest==3.4.2',
diff --git a/pyrasterframes/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/test/python/PyRasterFramesTests.py
similarity index 100%
rename from pyrasterframes/python/tests/PyRasterFramesTests.py
rename to pyrasterframes/src/test/python/PyRasterFramesTests.py
diff --git a/pyrasterframes/python/tests/__init__.py b/pyrasterframes/src/test/python/__init__.py
similarity index 100%
rename from pyrasterframes/python/tests/__init__.py
rename to pyrasterframes/src/test/python/__init__.py

From 222779e025c179988d3b71708253cfd741daae69 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 4 Jun 2019 11:57:13 -0400
Subject: [PATCH 137/380] Defined separate setting for Python tests target
 directory.

---
 project/PythonBuildPlugin.scala | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 387c523e3..6a24dc0f3 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -51,7 +51,7 @@ object PythonBuildPlugin extends AutoPlugin {
   val copyPyTestSources = Def.task {
     val s = streams.value
     val src = (Test / pythonSource).value
-    val dest = (Python / target).value / "tests"
+    val dest = (Python / test / target).value
     if (!dest.exists()) dest.mkdirs()
     s.log(s"Copying '$src' to '$dest'")
     IO.copyDirectory(src, dest)
@@ -98,7 +98,8 @@ object PythonBuildPlugin extends AutoPlugin {
     Test / testQuick := (Python / testQuick).evaluated
   ) ++
     inConfig(Python)(Seq(
-      target := target.value / "python",
+      target := (Compile / target).value / "python",
+      test / target := (Compile / target).value / "python" / "tests",
       packageBin := Def.sequential(
         Compile / packageBin,
         pyDistAsZip,

From b98b42f2aa507850e2b001037aff58947f131d55 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 5 Jun 2019 09:26:36 -0400
Subject: [PATCH 138/380] Yet another Travis Tweak (YATT).

---
 .travis.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 668aec8ef..81febf1a2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,7 +12,10 @@ scala:
   - 2.11.11
 
 jdk:
-  - oraclejdk8
+  - openjdk8
+
+python:
+  - "3.7"
 
 addons:
   apt:

From 2d6f60c51bbf5e1f1e1ecc6c212b42a6cf19433e Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 5 Jun 2019 10:18:36 -0400
Subject: [PATCH 139/380] Fixes to running python examples.

---
 project/PythonBuildPlugin.scala            | 23 ++---
 pyrasterframes/{python => }/README.md      |  0
 pyrasterframes/build.sbt                   |  3 -
 pyrasterframes/src/main/python/MANIFEST.in |  4 -
 pyrasterframes/src/main/python/README.md   | 99 ++++++++++++++++++++++
 pyrasterframes/src/main/python/README.rst  | 77 -----------------
 pyrasterframes/src/main/python/setup.py    |  6 +-
 7 files changed, 111 insertions(+), 101 deletions(-)
 rename pyrasterframes/{python => }/README.md (100%)
 delete mode 100644 pyrasterframes/src/main/python/MANIFEST.in
 create mode 100644 pyrasterframes/src/main/python/README.md
 delete mode 100644 pyrasterframes/src/main/python/README.rst

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 6a24dc0f3..beb7af513 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -38,25 +38,20 @@ object PythonBuildPlugin extends AutoPlugin {
   }
   import autoImport._
 
-  val copyPySources = Def.task {
+  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File]) = Def.task {
     val s = streams.value
-    val src = (Compile / pythonSource).value
-    val dest = (Python / target).value
-    if (!dest.exists()) dest.mkdirs()
-    s.log(s"Copying '$src' to '$dest'")
+    val src = srcDir.value
+    val dest = destDir.value
+    IO.delete(dest)
+    dest.mkdirs()
+    s.log.info(s"Copying '$src' to '$dest'")
     IO.copyDirectory(src, dest)
     dest
   }
 
-  val copyPyTestSources = Def.task {
-    val s = streams.value
-    val src = (Test / pythonSource).value
-    val dest = (Python / test / target).value
-    if (!dest.exists()) dest.mkdirs()
-    s.log(s"Copying '$src' to '$dest'")
-    IO.copyDirectory(src, dest)
-    dest
-  }
+  val copyPySources = copySources(Compile / pythonSource, Python / target)
+
+  val copyPyTestSources = copySources(Test / pythonSource, Python / test / target)
 
   val buildWhl = Def.task {
     val buildDir = (Python / target).value
diff --git a/pyrasterframes/python/README.md b/pyrasterframes/README.md
similarity index 100%
rename from pyrasterframes/python/README.md
rename to pyrasterframes/README.md
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index bf08614de..570dc1385 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,8 +1,5 @@
 import PythonBuildPlugin.autoImport.pySetup
 
-Compile / pythonSource := baseDirectory.value / "python"
-Test / pythonSource := baseDirectory.value / "python" / "tests"
-
 exportJars := true
 
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
diff --git a/pyrasterframes/src/main/python/MANIFEST.in b/pyrasterframes/src/main/python/MANIFEST.in
deleted file mode 100644
index cf48af02c..000000000
--- a/pyrasterframes/src/main/python/MANIFEST.in
+++ /dev/null
@@ -1,4 +0,0 @@
-include LICENSE.txt
-include README.rst
-recursive-include pyrasterframes *.jar
-exclude *.in
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
new file mode 100644
index 000000000..7daf8ed64
--- /dev/null
+++ b/pyrasterframes/src/main/python/README.md
@@ -0,0 +1,99 @@
+# PyRasterFrames
+
+PyRasterFrames is the Python API for Spark RasterFrames. 
+
+Building RasterFrames requires [`sbt`](https://www.scala-sbt.org/), and all `sbt` commands referenced below must be 
+run from the root source directory, i.e. the parent of the `pyrasterframes` directory. Know that `sbt` is much 
+faster if run in "interactive" mode, where you launch `sbt` with no arguments, and subsequent commands are invoked
+via an interactive shell. But for context clarity, we'll prefix each command example below with `sbt`. 
+
+## Quickstart
+
+The quickest way to run a `pyspark` shell with the latest RasterFrames enabled is to run:
+
+```bash
+sbt pySparkCmd
+```
+
+This will: 
+
+1. Compile all the Scala/JVM code.
+1. Merge all JVM code and dependencies into a single "assembly" JAR file.
+1. Create the PyRasterFrames `.whl` package
+1. Construct a temporary initialization script
+1. Emit a `bash` command with requiesite arguments to load RasterFrames and import PyRasterFrames 
+
+You then copy/paste the emitted command into your `bash` shell to start up a spark shell. It assumes you have
+`pyspark` >= 2.3.2 installed in your environment.
+
+## Running Tests and Examples
+
+The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
+
+```bash
+sbt pyTests
+```
+
+Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
+
+```bash
+sbt pyExamples
+```
+
+## Creating and Using a Build
+
+Assuming that `$SCALA_VER` is the major verison of Scala in use, and `$VER` is the version of RasterFrames, 
+the primary build artifacts are:
+
+* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
+* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
+
+You build them with"
+
+```bash
+sbt pyrasterframes/package
+```
+
+Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy "GAV" coordinates
+`org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
+
+Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
+The Python package is published under the `python` classifier, `zip` extension.
+
+## Build Internals
+
+### Running `setup.py`
+
+Before a build is initiated, the Python sources are copied to `pyrasterframes/target/python`. This ensures the 
+version controlled source directories are not polluted by `setuptools` residuals, better ensuring repeatable builds. To
+simplify the process of working with `setup.py` in this context, a `pySetup` sbt interactive command is available. To
+illustrate its usage, suppose we want to run a subset of the Python unit test. Instead of running `sbt pyTests` you can:
+
+```bash
+sbt 'pySetup test --addopts "-k test_tile_creation"'
+```
+
+Or to run a specific example:
+
+```bash
+sbt 'pySetup examples -e NDVI'
+```
+
+*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
+
+### `SparkSession` Setup
+
+To initialize PyRasterFrames in a generic Python shell:
+
+```python
+from pyspark.sql import SparkSession
+from pyrasterframes import *
+from pyrasterframes.rasterfunctions import *
+spark = SparkSession.builder \
+     .master("local[*]") \
+     .appName("Using RasterFrames") \
+     .config("spark.some.config.option", "some-value") \
+     .getOrCreate() \
+     .withRasterFrames()
+```
+
diff --git a/pyrasterframes/src/main/python/README.rst b/pyrasterframes/src/main/python/README.rst
deleted file mode 100644
index 20e557cc8..000000000
--- a/pyrasterframes/src/main/python/README.rst
+++ /dev/null
@@ -1,77 +0,0 @@
-==============
-PyRasterFrames
-==============
-
-PyRasterFrames provides a Python API for RasterFrames!
-
-------------
-SBT Commands
-------------
-
-Build the shaded JAR:
-
-    ``$ sbt pyrasterframes/assembly``
-
-Run tests:
-
-    ``$ sbt pyrasterframes/pyTest``
-
-Run examples:
-
-    ``$ sbt pyrasterframes/pyExamples``
-
-
----------------
-Python Commands
----------------
-
-Install the python package (for development / local use):
-
-    ``$ pip install -e python``
-
-
-To run tests and examples, ``$ cd python``, then:
-
-Run tests (if no Scala code has changed):
-
-    ``$ python setup.py test``
-
-    To run verbosely:
-    ``$ python setup.py test --addopts -s``
-
-    To run a specific test:
-    ``$ python setup.py test --addopts "-k my_test_name"``
-
-
-Run examples:
-
-    ``$ python setup.py examples [--e EXAMPLENAME,EXAMPLENAME]``
-
-
------------
-Spark Usage
------------
-
-*TODO: The following may be out of date.*
-
-Get a Spark REPL (after running ``$ sbt pyrasterframes/assembly``):
-
-    ``$  pyspark --packages io.astraea:pyrasterframes:$VERSION --master local[2]``
-
-You can then try some of the commands in ``python/tests/PyRasterFramesTests.py``.
-
-Submit a script (from the ``python`` directory):
-
-    ``$ spark-submit --packages io.astraea:pyrasterframes:$VERSION --master local[2] \``
-        ``examples/CreatingRasterFrames.py``
-
-To initialize PyRasterFrames:
-
-    >>> from pyrasterframes import *
-    >>> spark = SparkSession.builder \
-    ...     .master("local[*]") \
-    ...     .appName("Using RasterFrames") \
-    ...     .config("spark.some.config.option", "some-value") \
-    ...     .getOrCreate() \
-    ...     .withRasterFrames()
-
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 038675a18..959fd8d7b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -39,7 +39,7 @@ def _check_ex_path(ex):
         file = Path(ex)
         if not file.suffix:
             file = file.with_suffix('.py')
-        file = (Path('./examples') / file).resolve()
+        file = (Path(here) / 'examples' / file).resolve()
 
         assert file.is_file(), ('Invalid example %s' % file)
         return file
@@ -48,13 +48,13 @@ def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
         self.examples = filter(lambda x: not x.name.startswith('_'),
-                               list(Path('./examples').resolve().glob('*.py')))
+                               list((Path(here) / 'examples').resolve().glob('*.py')))
 
     def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.examples, str):
-            self.examples = re.split('\W+', self.examples)
+            self.examples = filter(lambda s: len(s) > 0, re.split('\W+', self.examples))
         self.examples = map(lambda x: 'examples.' + x.stem,
                             map(self._check_ex_path, self.examples))
 

From 36b963a82fd768a101f4eebef7dd58e207d78bf7 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 5 Jun 2019 11:48:11 -0400
Subject: [PATCH 140/380] Straggling PR feedback from #125.

---
 pyrasterframes/README.md                      | 100 +-----------------
 pyrasterframes/src/main/python/README.md      |  27 +++--
 .../src/main/python/examples/Clustering.py    |   6 +-
 3 files changed, 23 insertions(+), 110 deletions(-)

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 9fb8b16e2..70b448567 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -1,101 +1,3 @@
 # PyRasterFrames
 
-PyRasterFrames is the Python API for Spark RasterFrames. 
-
-Building RasterFrames requires [`sbt`](https://www.scala-sbt.org/), and all `sbt` commands referenced below must be 
-run from the root source directory, i.e. the parent of the `pyrasterframes` directory. Know that `sbt` is much 
-faster if run in "interactive" mode, where you launch `sbt` with no arguments, and subsequent commands are invoked
-via an interactive shell. But for context clarity, we'll prefix each command example below with `sbt`. 
-
-## Quickstart
-
-The quickest way to run a `pyspark` shell with the latest RasterFrames enabled is to run:
-
-```bash
-sbt pySparkCmd
-```
-
-This will: 
-
-1. Compile all the Scala/JVM code.
-1. Merge all JVM code and dependencies into a single "assembly" JAR file.
-1. Create the PyRasterFrames `.whl` package
-1. Construct a temporary initialization script
-1. Emit a `bash` command with requiesite arguments to load RasterFrames and import PyRasterFrames 
-
-You then copy/paste the emitted command into your `bash` shell to start up a spark shell. It assumes you have
-`pyspark` >= 2.3.2 installed in your environment.
-
-## Running Tests and Examples
-
-The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
-
-```bash
-sbt pyTests
-```
-
-Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
-
-```bash
-sbt pyExamples
-```
-
-## Creating and Using a Build
-
-Assuming that `$SCALA_VER` is the major verison of Scala in use, and `$VER` is the version of RasterFrames, 
-the primary build artifacts are:
-
-* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
-* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
-
-You build them with"
-
-```bash
-sbt pyrasterframes/package
-```
-
-Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy "GAV" coordinates
-`org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
-
-Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
-The Python package is published under the `python` classifier, `zip` extension.
-
-
-## Build Internals
-
-### Running `setup.py`
-
-Before a build is initiated, the Python sources are copied to `pyrasterframes/target/python`. This ensures the 
-version controlled source directories are not polluted by `setuptools` residuals, better ensuring repeatable builds. To
-simplify the process of working with `setup.py` in this context, a `pySetup` sbt interactive command is available. To
-illustrate its usage, suppose we want to run a subset of the Python unit test. Instead of running `sbt pyTests` you can:
-
-```bash
-sbt pySetup test --addopts "-k test_tile_creation"
-```
-
-Or to run a specific example:
-
-```bash
-sbt pySetup examples "--e NDVI.py"
-```
-
-*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
-
-
-### `SparkSession` Setup
-
-To initialize PyRasterFrames in a generic Python shell:
-
-```python
-# pyspark imports here...
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-spark = SparkSession.builder \
-     .master("local[*]") \
-     .appName("Using RasterFrames") \
-     .config("spark.some.config.option", "some-value") \
-     .getOrCreate() \
-     .withRasterFrames()
-```
-
+See [here](src/main/python/README.md) for details.
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 7daf8ed64..333910afc 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -1,11 +1,20 @@
 # PyRasterFrames
 
-PyRasterFrames is the Python API for Spark RasterFrames. 
+PyRasterFrames is the Python API for Spark RasterFrames.
 
-Building RasterFrames requires [`sbt`](https://www.scala-sbt.org/), and all `sbt` commands referenced below must be 
-run from the root source directory, i.e. the parent of the `pyrasterframes` directory. Know that `sbt` is much 
-faster if run in "interactive" mode, where you launch `sbt` with no arguments, and subsequent commands are invoked
-via an interactive shell. But for context clarity, we'll prefix each command example below with `sbt`. 
+
+## Prerequisites
+
+1. [`sbt`](https://www.scala-sbt.org/)
+2. ['pyspark`](https://pypi.org/project/pyspark/) > 2.3.2 
+
+RasterFrames is primarily implmented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
+All `sbt` commands referenced below must be run from the root source directory, i.e. the parent of the `pyrasterframes` 
+directory, including Python-related build steps. 
+
+As a tip, know that `sbt` is much faster if run in "interactive" mode, where you launch `sbt` with no arguments, 
+and subsequent commands are invoked via an interactive shell. But for context clarity, we'll prefix each command 
+example below with `sbt`. 
 
 ## Quickstart
 
@@ -21,7 +30,7 @@ This will:
 1. Merge all JVM code and dependencies into a single "assembly" JAR file.
 1. Create the PyRasterFrames `.whl` package
 1. Construct a temporary initialization script
-1. Emit a `bash` command with requiesite arguments to load RasterFrames and import PyRasterFrames 
+1. Emit a `bash` command with requisite arguments to start a pyspark interpreter with RasterFrames imports.
 
 You then copy/paste the emitted command into your `bash` shell to start up a spark shell. It assumes you have
 `pyspark` >= 2.3.2 installed in your environment.
@@ -34,6 +43,8 @@ The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tes
 sbt pyTests
 ```
 
+*See also the below discussion of running `setup.py` for more options to run unit tests.* 
+
 Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
 
 ```bash
@@ -42,13 +53,13 @@ sbt pyExamples
 
 ## Creating and Using a Build
 
-Assuming that `$SCALA_VER` is the major verison of Scala in use, and `$VER` is the version of RasterFrames, 
+Assuming that `$SCALA_VER` is the major verison of Scala in use (e.g. 2.11) , and `$VER` is the version of RasterFrames, 
 the primary build artifacts are:
 
 * JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
 * Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
 
-You build them with"
+You build them with:
 
 ```bash
 sbt pyrasterframes/package
diff --git a/pyrasterframes/src/main/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
index 2fceeb5c9..2b6a99218 100644
--- a/pyrasterframes/src/main/python/examples/Clustering.py
+++ b/pyrasterframes/src/main/python/examples/Clustering.py
@@ -5,7 +5,7 @@
 from pyspark.ml.feature import VectorAssembler
 from pyspark.ml.clustering import KMeans
 from pyspark.ml import Pipeline
-
+import os
 
 spark = example_session().withRasterFrames()
 
@@ -18,9 +18,9 @@
 # For each identified band, load the associated image file
 from functools import reduce
 joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
-                  map(lambda bf: spark.read.geotiff(bf[1]) \
+                  map(lambda bf: spark.read.geotiff(bf[1])
                       .withColumnRenamed('tile', 'band_{}'.format(bf[0])),
-                  map(lambda b: (b, resource_dir.joinpath(filenamePattern.format(b)).as_uri()), bandNumbers)))
+                  map(lambda b: (b, os.path.join(resource_dir, filenamePattern.format(b))), bandNumbers)))
 
 # We should see a single spatial_key column along with columns of tiles.
 joinedRF.printSchema()

From 72dc48d00c889f4f196825437278d2b632f341a6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 5 Jun 2019 21:43:23 -0400
Subject: [PATCH 141/380] Another attempt at reattempting reads after
 BufferUnderflowException.

---
 .../rasterframes/ref/RasterSource.scala       | 101 ++++++++++--------
 1 file changed, 57 insertions(+), 44 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 4e7e10ac5..1d5844b28 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -191,7 +191,25 @@ object RasterSource extends LazyLogging {
   /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
   abstract class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
     @transient
-    lazy val delegate = delegateBuilder()
+    @volatile
+    private var _delRef: GTRasterSource = _
+
+    private def retryableRead[R >: Null](f: GTRasterSource => R): R = synchronized {
+      try {
+        if (_delRef == null)
+          _delRef = delegateBuilder()
+        f(_delRef)
+      }
+      catch {
+        // On this exeception we attempt to recreate the delegate and read again.
+        case be: java.nio.BufferUnderflowException =>
+          _delRef = null
+          val newDel = delegateBuilder()
+          val result = f(newDel)
+          _delRef = newDel
+          result
+      }
+    }
 
     // Bad, bad, bad?
     override def equals(obj: Any): Boolean = obj match {
@@ -201,16 +219,16 @@ object RasterSource extends LazyLogging {
     override def hashCode(): Int = source.hashCode()
 
     // This helps reduce header reads between serializations
-    lazy val info: SimpleGeoTiffInfo = {
+    lazy val info: SimpleGeoTiffInfo = retryableRead { rs =>
       SimpleGeoTiffInfo(
-        delegate.cols,
-        delegate.rows,
-        delegate.cellType,
-        delegate.extent,
-        delegate.rasterExtent,
-        delegate.crs,
+        rs.cols,
+        rs.rows,
+        rs.cellType,
+        rs.extent,
+        rs.rasterExtent,
+        rs.crs,
         fetchTags,
-        delegate.bandCount,
+        rs.bandCount,
         None
       )
     }
@@ -221,53 +239,48 @@ object RasterSource extends LazyLogging {
     override def extent: Extent = info.extent
     override def cellType: CellType = info.cellType
     override def bandCount: Int = info.bandCount
-    private def fetchTags: Tags = delegate match {
+    private def fetchTags: Tags = retryableRead {
       case rs: GeoTiffRasterSource => rs.tiff.tags
       case _                       => EMPTY_TAGS
     }
     override def tags: Tags = info.tags
 
-    private def readWithRetry[R >: Null](f: () => R): R = {
-      val retryLimit = 10
-      var result: R = null
-      var cnt = 0
-      while(result == null && cnt < retryLimit) {
-        cnt = cnt + 1
-        try {
-          result = f()
-        }
-        catch {
-          case be: java.nio.BufferUnderflowException =>
-            if (cnt == 1)
-              logger.warn("Retrying read to " + source)
-            if (cnt == retryLimit) {
-              logger.warn(s"Failed to read '$source' after $cnt tries")
-              throw be
-            }
-            else Thread.sleep(100)
-            ()
-        }
-      }
-      result
-    }
+//    private def readWithRetry[R >: Null](f: () => R): R = {
+//      val retryLimit = 10
+//      var result: R = null
+//      var cnt = 0
+//      while(result == null && cnt < retryLimit) {
+//        cnt = cnt + 1
+//        try {
+//          result = f()
+//        }
+//        catch {
+//          case be: java.nio.BufferUnderflowException =>
+//            if (cnt == 1)
+//              logger.warn("Retrying read to " + source)
+//            if (cnt == retryLimit) {
+//              logger.warn(s"Failed to read '$source' after $cnt tries")
+//              throw be
+//            }
+//            else Thread.sleep(100)
+//            ()
+//        }
+//      }
+//      result
+//    }
+
 
     override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
-      readWithRetry(() =>
-        delegate.readBounds(bounds, bands)
-      )
+      retryableRead(_.readBounds(bounds, bands))
 
     override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
-      readWithRetry(() =>
-        delegate
-          .read(bounds, bands)
-          .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
+      retryableRead(_.read(bounds, bands)
+        .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
       )
 
     override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
-      readWithRetry(() =>
-        delegate
-          .read(extent, bands)
-          .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
+      retryableRead(_.read(extent, bands)
+        .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
       )
   }
 

From dc561dc9ad49eca043a6a0465f11d18d09566db1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 6 Jun 2019 09:54:07 -0400
Subject: [PATCH 142/380] Another round of CRS comparision improvements.

---
 bench/archive/jmh-results-20190606082738.json | 269 ++++++++++++++++++
 bench/archive/jmh-results-20190606094001.json | 269 ++++++++++++++++++
 .../rasterframes/bench/CRSBench.scala         |  29 +-
 .../rasterframes/expressions/package.scala    |   4 +
 .../transformers/ReprojectGeometry.scala      |  30 +-
 .../rasterframes/functions/package.scala      |   2 +-
 .../rasterframes/model/LazyCRS.scala          |  14 +-
 .../rasterframes/ref/RasterSource.scala       |  25 --
 ...Spec.scala => GeometryFunctionsSpec.scala} |  41 ++-
 .../rasterframes/GeometryOperationsSpec.scala |  77 -----
 .../locationtech/rasterframes/TestData.scala  |  19 +-
 .../rasterframes/model/LazyCRSSpec.scala      |  43 +++
 .../geotrellis/GeoTrellisDataSourceSpec.scala |   2 +-
 project/BenchmarkPlugin.scala                 |   4 +-
 project/plugins.sbt                           |   2 +-
 15 files changed, 700 insertions(+), 130 deletions(-)
 create mode 100644 bench/archive/jmh-results-20190606082738.json
 create mode 100644 bench/archive/jmh-results-20190606094001.json
 rename core/src/test/scala/org/locationtech/rasterframes/{JTSSpec.scala => GeometryFunctionsSpec.scala} (77%)
 delete mode 100644 core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
 create mode 100644 core/src/test/scala/org/locationtech/rasterframes/model/LazyCRSSpec.scala

diff --git a/bench/archive/jmh-results-20190606082738.json b/bench/archive/jmh-results-20190606082738.json
new file mode 100644
index 000000000..b8e7467b1
--- /dev/null
+++ b/bench/archive/jmh-results-20190606082738.json
@@ -0,0 +1,269 @@
+[
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEqualsFalse",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 13.115296907959536,
+            "scoreError" : 2.5400077963191556,
+            "scoreConfidence" : [
+                10.57528911164038,
+                15.655304704278691
+            ],
+            "scorePercentiles" : {
+                "0.0" : 12.590614558661818,
+                "50.0" : 12.853830352008682,
+                "90.0" : 14.25982363939229,
+                "95.0" : 14.25982363939229,
+                "99.0" : 14.25982363939229,
+                "99.9" : 14.25982363939229,
+                "99.99" : 14.25982363939229,
+                "99.999" : 14.25982363939229,
+                "99.9999" : 14.25982363939229,
+                "100.0" : 14.25982363939229
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    12.853830352008682,
+                    12.590614558661818,
+                    12.829707376038487,
+                    14.25982363939229,
+                    13.042508613696407
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEqualsTrue",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 6.5973550106567345,
+            "scoreError" : 0.1946737881542353,
+            "scoreConfidence" : [
+                6.402681222502499,
+                6.7920287988109695
+            ],
+            "scorePercentiles" : {
+                "0.0" : 6.523477357639692,
+                "50.0" : 6.6063669572343695,
+                "90.0" : 6.648688182671118,
+                "95.0" : 6.648688182671118,
+                "99.0" : 6.648688182671118,
+                "99.9" : 6.648688182671118,
+                "99.99" : 6.648688182671118,
+                "99.999" : 6.648688182671118,
+                "99.9999" : 6.648688182671118,
+                "100.0" : 6.648688182671118
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    6.635409622463296,
+                    6.523477357639692,
+                    6.648688182671118,
+                    6.572832933275196,
+                    6.6063669572343695
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalLazyEqualsFalse",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 13.265730662256157,
+            "scoreError" : 1.915211216125259,
+            "scoreConfidence" : [
+                11.350519446130898,
+                15.180941878381416
+            ],
+            "scorePercentiles" : {
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+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
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+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
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+        "primaryMetric" : {
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+            "scorePercentiles" : {
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+                "99.9999" : 0.06612649264562556,
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diff --git a/bench/archive/jmh-results-20190606094001.json b/bench/archive/jmh-results-20190606094001.json
new file mode 100644
index 000000000..02cdd3194
--- /dev/null
+++ b/bench/archive/jmh-results-20190606094001.json
@@ -0,0 +1,269 @@
+[
+    {
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+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEqualsFalse",
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+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
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+        ],
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+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
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+            "scoreError" : 1.6593497203225103,
+            "scoreConfidence" : [
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+                14.736207094173995
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+        }
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+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalEqualsTrue",
+        "mode" : "avgt",
+        "threads" : 1,
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+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
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+        "measurementIterations" : 5,
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+        "primaryMetric" : {
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+            "scoreError" : 0.015896893581796353,
+            "scoreConfidence" : [
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+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalLazyEqualsFalse",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
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+            "scoreConfidence" : [
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+                "99.0" : 13.341295384511856,
+                "99.9" : 13.341295384511856,
+                "99.99" : 13.341295384511856,
+                "99.999" : 13.341295384511856,
+                "99.9999" : 13.341295384511856,
+                "100.0" : 13.341295384511856
+            },
+            "scoreUnit" : "us/op",
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+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.logicalLazyEqualsTrue",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 0.03659740987415034,
+            "scoreError" : 0.0011385555881718446,
+            "scoreConfidence" : [
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+                0.037735965462322184
+            ],
+            "scorePercentiles" : {
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+                "99.999" : 0.03691511396572689,
+                "99.9999" : 0.03691511396572689,
+                "100.0" : 0.03691511396572689
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
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+                    0.0364980924818408,
+                    0.03691511396572689,
+                    0.03690962770339316
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    },
+    {
+        "jmhVersion" : "1.21",
+        "benchmark" : "org.locationtech.rasterframes.bench.CRSBench.resolveCRS",
+        "mode" : "avgt",
+        "threads" : 1,
+        "forks" : 1,
+        "jvm" : "/Library/Java/JavaVirtualMachines/jdk1.8.0_171.jdk/Contents/Home/jre/bin/java",
+        "jvmArgs" : [
+            "-Xmx2048M",
+            "-Xmx4g"
+        ],
+        "jdkVersion" : "1.8.0_171",
+        "vmName" : "Java HotSpot(TM) 64-Bit Server VM",
+        "vmVersion" : "25.171-b11",
+        "warmupIterations" : 8,
+        "warmupTime" : "10 s",
+        "warmupBatchSize" : 1,
+        "measurementIterations" : 5,
+        "measurementTime" : "10 s",
+        "measurementBatchSize" : 1,
+        "primaryMetric" : {
+            "score" : 0.06517305542168148,
+            "scoreError" : 0.005310236878903678,
+            "scoreConfidence" : [
+                0.05986281854277779,
+                0.07048329230058516
+            ],
+            "scorePercentiles" : {
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+                "50.0" : 0.06512777854120488,
+                "90.0" : 0.06700762802360496,
+                "95.0" : 0.06700762802360496,
+                "99.0" : 0.06700762802360496,
+                "99.9" : 0.06700762802360496,
+                "99.99" : 0.06700762802360496,
+                "99.999" : 0.06700762802360496,
+                "99.9999" : 0.06700762802360496,
+                "100.0" : 0.06700762802360496
+            },
+            "scoreUnit" : "us/op",
+            "rawData" : [
+                [
+                    0.06700762802360496,
+                    0.06512777854120488,
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+                    0.06355938701044708
+                ]
+            ]
+        },
+        "secondaryMetrics" : {
+        }
+    }
+]
+
+
diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
index 04abc0b2b..424533b58 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/CRSBench.scala
@@ -23,7 +23,7 @@ package org.locationtech.rasterframes.bench
 
 import java.util.concurrent.TimeUnit
 
-import geotrellis.proj4.{CRS, LatLng}
+import geotrellis.proj4.{CRS, LatLng, WebMercator}
 import org.locationtech.proj4j.CoordinateReferenceSystem
 import org.locationtech.rasterframes.model.LazyCRS
 import org.openjdk.jmh.annotations._
@@ -33,24 +33,37 @@ import org.openjdk.jmh.annotations._
 @OutputTimeUnit(TimeUnit.MICROSECONDS)
 class CRSBench extends SparkEnv {
 
-  var crs: CRS = _
+  var crs1: CRS = _
+  var crs2: CRS = _
 
   @Setup(Level.Invocation)
   def setupData(): Unit = {
-    crs = LazyCRS("epsg:4326")
+    crs1 = LazyCRS("epsg:4326")
+    crs2 = LazyCRS(WebMercator.toProj4String)
   }
+
   @Benchmark
   def resolveCRS(): CoordinateReferenceSystem = {
-    crs.proj4jCrs
+    crs1.proj4jCrs
+  }
+
+  @Benchmark
+  def logicalEqualsTrue(): Boolean = {
+    crs1 == LatLng
+  }
+
+  @Benchmark
+  def logicalEqualsFalse(): Boolean = {
+    crs1 == WebMercator
   }
 
   @Benchmark
-  def selfEquals(): Boolean = {
-    crs == crs
+  def logicalLazyEqualsTrue(): Boolean = {
+    crs1 == crs1
   }
 
   @Benchmark
-  def logicalEquals(): Boolean = {
-    crs == LatLng
+  def logicalLazyEqualsFalse(): Boolean = {
+    crs1 == crs2
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 97d87fc5b..432c66888 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -126,5 +126,9 @@ package object expressions {
 
     registry.registerExpression[DebugRender.RenderAscii]("rf_render_ascii")
     registry.registerExpression[DebugRender.RenderMatrix]("rf_render_matrix")
+
+    // TODO: Replace registration in `org.locationtech.rasterframes.functions.register`
+    // TODO: with this once String columns are supported as well.
+    // registry.registerExpression[transformers.ReprojectGeometry]("st_reproject")
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 1b85ffc59..89ad88fae 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -34,15 +34,26 @@ import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes.encoders.CatalystSerializer
 import org.locationtech.rasterframes.jts.ReprojectionTransformer
+import org.locationtech.rasterframes.model.LazyCRS
 
-/**
-  *
-  *
-  * @since 11/29/18
-  */
+@ExpressionDescription(
+  usage = "_FUNC_(geom, srcCRS, dstCRS) - Reprojects the given `geom` from `srcCRS` to `dstCRS",
+  arguments = """
+  Arguments:
+    * geom - the geometry column to reproject
+    * srcCRS - the CRS of the `geom` column
+    * dstCRS - the CRS to project geometry into""",
+  examples = """
+  Examples:
+    > SELECT _FUNC_(geom, srcCRS, dstCRS);
+       ..."""
+)
 case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: Expression) extends Expression
   with CodegenFallback with ExpectsInputTypes {
 
+  // TODO: Replace registration in `org.locationtech.rasterframes.functions.register`
+  // TODO: with proper Expression supporting String columns as well.
+
   override def nodeName: String = "st_reproject"
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
   override def nullable: Boolean = geometry.nullable || srcCRS.nullable || dstCRS.nullable
@@ -60,10 +71,15 @@ case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: E
     }
 
   override def eval(input: InternalRow): Any = {
-    val geom = JTSTypes.GeometryTypeInstance.deserialize(geometry.eval(input))
     val src = srcCRS.eval(input).asInstanceOf[InternalRow].to[CRS]
     val dst = dstCRS.eval(input).asInstanceOf[InternalRow].to[CRS]
-    JTSTypes.GeometryTypeInstance.serialize(reproject(geom, src, dst))
+    (src, dst) match {
+      // Optimized pass-through case.
+      case (s: LazyCRS, r: LazyCRS) if s.encoded == r.encoded => geometry.eval(input)
+      case _ =>
+        val geom = JTSTypes.GeometryTypeInstance.deserialize(geometry.eval(input))
+        JTSTypes.GeometryTypeInstance.serialize(reproject(geom, src, dst))
+    }
   }
 }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index c72efb52d..427dc504c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -149,8 +149,8 @@ package object functions {
 
     sqlContext.udf.register("rf_cell_types", cellTypes)
     sqlContext.udf.register("rf_rasterize", rasterize)
+    // TODO: replace this with full ReprojectGeometry with String handling.
     sqlContext.udf.register("st_reproject", reprojectGeometryCRSName)
     sqlContext.udf.register("rf_array_to_tile", arrayToTile)
-
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
index a63c54ab8..66352e258 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/LazyCRS.scala
@@ -34,8 +34,14 @@ class LazyCRS(val encoded: EncodedCRS) extends CRS {
     else delegate.toProj4String
 
   override def equals(o: Any): Boolean = o match {
-    case l: LazyCRS => encoded == l.encoded || super.equals(o)
-    case c => delegate.equals(c)
+    case l: LazyCRS =>
+      encoded == l.encoded ||
+        toProj4String == l.toProj4String ||
+        super.equals(o)
+    case c: CRS =>
+      toProj4String == c.toProj4String ||
+        delegate.equals(c)
+    case _ => false
   }
 }
 
@@ -53,7 +59,9 @@ object LazyCRS {
   @transient
   private lazy val cache = Scaffeine().build[String, CRS](mapper)
 
-  def apply(value: String): CRS = {
+  def apply(crs: CRS): LazyCRS = apply(crs.toProj4String)
+
+  def apply(value: String): LazyCRS = {
     if (mapper.isDefinedAt(value)) {
       new LazyCRS(value.asInstanceOf[EncodedCRS])
     }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 1d5844b28..2d07bb129 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -245,31 +245,6 @@ object RasterSource extends LazyLogging {
     }
     override def tags: Tags = info.tags
 
-//    private def readWithRetry[R >: Null](f: () => R): R = {
-//      val retryLimit = 10
-//      var result: R = null
-//      var cnt = 0
-//      while(result == null && cnt < retryLimit) {
-//        cnt = cnt + 1
-//        try {
-//          result = f()
-//        }
-//        catch {
-//          case be: java.nio.BufferUnderflowException =>
-//            if (cnt == 1)
-//              logger.warn("Retrying read to " + source)
-//            if (cnt == retryLimit) {
-//              logger.warn(s"Failed to read '$source' after $cnt tries")
-//              throw be
-//            }
-//            else Thread.sleep(100)
-//            ()
-//        }
-//      }
-//      result
-//    }
-
-
     override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
       retryableRead(_.readBounds(bounds, bands))
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
similarity index 77%
rename from core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
rename to core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
index 92bd42b4d..5a7c0b813 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/JTSSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
@@ -24,14 +24,17 @@ package org.locationtech.rasterframes
 import geotrellis.proj4.{LatLng, Sinusoidal, WebMercator}
 import geotrellis.vector.{Extent, Point => GTPoint}
 import org.locationtech.jts.geom._
+import spray.json.JsNumber
 
 /**
  * Test rig for operations providing interop with JTS types.
  *
  * @since 12/16/17
  */
-class JTSSpec extends TestEnvironment with TestData with StandardColumns {
-  describe("JTS interop") {
+class GeometryFunctionsSpec extends TestEnvironment with TestData with StandardColumns {
+  import spark.implicits._
+
+  describe("Vector geometry operations") {
     val rf = l8Sample(1).projectedRaster.toRF(10, 10).withGeometry()
     it("should allow joining and filtering of tiles based on points") {
       import spark.implicits._
@@ -70,7 +73,7 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
     }
 
     it("should allow construction of geometry literals") {
-      import JTS._
+      import GeomData._
       assert(dfBlank.select(geomLit(point)).first === point)
       assert(dfBlank.select(geomLit(line)).first === line)
       assert(dfBlank.select(geomLit(poly)).first === poly)
@@ -128,6 +131,38 @@ class JTSSpec extends TestEnvironment with TestData with StandardColumns {
       val wm4 = sql("SELECT st_reproject(ll, '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs', 'EPSG:3857') AS wm4 from geom")
         .as[Geometry].first()
       wm4 should matchGeom(webMercator, 0.00001)
+
+      // TODO: See comment in `org.locationtech.rasterframes.expressions.register` for
+      // TODO: what needs to happen to support this.
+      //checkDocs("st_reproject")
     }
   }
+
+  it("should rasterize geometry") {
+    val rf = l8Sample(1).projectedRaster.toRF.withGeometry()
+    val df = GeomData.features.map(f ⇒ (
+      f.geom.reproject(LatLng, rf.crs).jtsGeom,
+      f.data.fields("id").asInstanceOf[JsNumber].value.intValue()
+    )).toDF("geom", "__fid__")
+
+    val toRasterize = rf.crossJoin(df)
+
+    val tlm = rf.tileLayerMetadata.merge
+
+    val (cols, rows) = tlm.layout.tileLayout.tileDimensions
+
+    val rasterized = toRasterize.withColumn("rasterized", rf_rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
+
+    assert(rasterized.count() === df.count() * rf.count())
+    assert(rasterized.select(rf_dimensions($"rasterized")).distinct().count() === 1)
+    val pixelCount = rasterized.select(rf_agg_data_cells($"rasterized")).first()
+    assert(pixelCount < cols * rows)
+
+
+    toRasterize.createOrReplaceTempView("stuff")
+    val viaSQL = sql(s"select rf_rasterize(geom, geometry, __fid__, $cols, $rows) as rasterized from stuff")
+    assert(viaSQL.select(rf_agg_data_cells($"rasterized")).first === pixelCount)
+
+    //rasterized.select($"rasterized".as[Tile]).foreach(t ⇒ t.renderPng(ColorMaps.IGBP).write("target/" + t.hashCode() + ".png"))
+  }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
deleted file mode 100644
index 0da6035a0..000000000
--- a/core/src/test/scala/org/locationtech/rasterframes/GeometryOperationsSpec.scala
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes
-
-import java.nio.file.{Files, Paths}
-
-import geotrellis.proj4.LatLng
-import geotrellis.vector.io._
-import geotrellis.vector.io.json.JsonFeatureCollection
-import spray.json.DefaultJsonProtocol._
-import spray.json._
-
-import scala.collection.JavaConversions._
-
-/**
- *
- *
- * @since 5/30/18
- */
-class GeometryOperationsSpec extends TestEnvironment with TestData {
-  val geoJson = {
-    val p = Paths.get(getClass.getResource("/L8-Labels-Elkton-VA.geojson").toURI)
-    Files.readAllLines(p).mkString("\n")
-  }
-
-  describe("Geometery operations") {
-    import spark.implicits._
-    it("should rasterize geometry") {
-      val rf = l8Sample(1).projectedRaster.toRF.withGeometry()
-
-      val features = geoJson.parseGeoJson[JsonFeatureCollection].getAllPolygonFeatures[JsObject]()
-      val df = features.map(f ⇒ (
-        f.geom.reproject(LatLng, rf.crs).jtsGeom,
-        f.data.fields("id").asInstanceOf[JsNumber].value.intValue()
-      )).toDF("geom", "__fid__")
-
-      val toRasterize = rf.crossJoin(df)
-
-      val tlm = rf.tileLayerMetadata.merge
-
-      val (cols, rows) = tlm.layout.tileLayout.tileDimensions
-
-      val rasterized = toRasterize.withColumn("rasterized", rf_rasterize($"geom", GEOMETRY_COLUMN, $"__fid__", cols, rows))
-
-      assert(rasterized.count() === df.count() * rf.count())
-      assert(rasterized.select(rf_dimensions($"rasterized")).distinct().count() === 1)
-      val pixelCount = rasterized.select(rf_agg_data_cells($"rasterized")).first()
-      assert(pixelCount < cols * rows)
-
-
-      toRasterize.createOrReplaceTempView("stuff")
-      val viaSQL = sql(s"select rf_rasterize(geom, geometry, __fid__, $cols, $rows) as rasterized from stuff")
-      assert(viaSQL.select(rf_agg_data_cells($"rasterized")).first === pixelCount)
-
-      //rasterized.select($"rasterized".as[Tile]).foreach(t ⇒ t.renderPng(ColorMaps.IGBP).write("target/" + t.hashCode() + ".png"))
-    }
-  }
-}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index 7acaea7f1..a149f6b2a 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -22,7 +22,7 @@
 package org.locationtech.rasterframes
 
 import java.net.URI
-import java.nio.file.Paths
+import java.nio.file.{Files, Paths}
 import java.time.ZonedDateTime
 
 import geotrellis.proj4.{CRS, LatLng}
@@ -32,6 +32,7 @@ import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
 import geotrellis.spark._
 import geotrellis.spark.testkit.TileLayerRDDBuilders
 import geotrellis.spark.tiling.LayoutDefinition
+import geotrellis.vector.io.json.JsonFeatureCollection
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkContext
@@ -39,6 +40,7 @@ import org.apache.spark.sql.SparkSession
 import org.locationtech.jts.geom.{Coordinate, GeometryFactory}
 import org.locationtech.rasterframes.expressions.tilestats.NoDataCells
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+import spray.json.JsObject
 
 import scala.reflect.ClassTag
 
@@ -149,7 +151,7 @@ trait TestData {
   lazy val l8samplePath: URI = getClass.getResource("/L8-B1-Elkton-VA.tiff").toURI
   lazy val modisConvertedMrfPath: URI = getClass.getResource("/MCD43A4.A2019111.h30v06.006.2019120033434_01.mrf").toURI
 
-  object JTS {
+  object GeomData {
     val fact = new GeometryFactory()
     val c1 = new Coordinate(1, 2)
     val c2 = new Coordinate(3, 4)
@@ -162,6 +164,19 @@ trait TestData {
     val mpoly = fact.createMultiPolygon(Array(poly, poly, poly))
     val coll = fact.createGeometryCollection(Array(point, line, poly, mpoint, mline, mpoly))
     val all = Seq(point, line, poly, mpoint, mline, mpoly, coll)
+    lazy val geoJson = {
+      import scala.collection.JavaConversions._
+      val p = Paths.get(TestData.getClass
+        .getResource("/L8-Labels-Elkton-VA.geojson").toURI)
+      Files.readAllLines(p).mkString("\n")
+    }
+    lazy val features = {
+      import geotrellis.vector.io._
+      import geotrellis.vector.io.json.JsonFeatureCollection
+      import spray.json.DefaultJsonProtocol._
+      import spray.json._
+      GeomData.geoJson.parseGeoJson[JsonFeatureCollection].getAllPolygonFeatures[JsObject]()
+    }
   }
 }
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/model/LazyCRSSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/model/LazyCRSSpec.scala
new file mode 100644
index 000000000..1762c402e
--- /dev/null
+++ b/core/src/test/scala/org/locationtech/rasterframes/model/LazyCRSSpec.scala
@@ -0,0 +1,43 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.model
+
+import geotrellis.proj4.{CRS, LatLng, Sinusoidal, WebMercator}
+import org.scalatest._
+
+class LazyCRSSpec extends FunSpec with Matchers {
+  val sinPrj = "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs"
+  val llPrj = "epsg:4326"
+  describe("LazyCRS") {
+    it("should implement equals") {
+      LazyCRS(WebMercator) should be(LazyCRS(WebMercator))
+      LazyCRS(WebMercator) should be(WebMercator)
+      WebMercator should be(LazyCRS(WebMercator))
+      LazyCRS(sinPrj) should be (Sinusoidal)
+      CRS.fromString(sinPrj) should be (LazyCRS(Sinusoidal))
+      LazyCRS(llPrj) should be(LatLng)
+      LazyCRS(LatLng) should be(LatLng)
+      LatLng should be(LazyCRS(llPrj))
+      LatLng should be(LazyCRS(LatLng))
+    }
+  }
+}
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index 6333138aa..aefe01f07 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -298,7 +298,7 @@ class GeoTrellisDataSourceSpec
 
     it("should support query with multiple geometry types") {
       // Mostly just testing that these evaluate without catalyst type errors.
-      forEvery(JTS.all) { g ⇒
+      forEvery(GeomData.all) { g ⇒
         val query = layerReader.loadRF(layer).where(GEOMETRY_COLUMN.intersects(g))
           .persist(StorageLevel.OFF_HEAP)
         assert(query.count() === 0)
diff --git a/project/BenchmarkPlugin.scala b/project/BenchmarkPlugin.scala
index f795fe536..e4f1d1acc 100644
--- a/project/BenchmarkPlugin.scala
+++ b/project/BenchmarkPlugin.scala
@@ -70,7 +70,7 @@ object BenchmarkPlugin extends AutoPlugin {
       val pat = (".*" + file + ".*").r
       jmhRun(pat)
     }.evaluated,
-    libraryDependencies += "org.openjdk.jmh" % "jmh-generator-annprocess" % "1.19"
+    libraryDependencies += "org.openjdk.jmh" % "jmh-generator-annprocess" % "1.21"
   )
 
   def jmhRun(filePattern: Regex) = Def.taskDyn {
@@ -94,7 +94,7 @@ object BenchmarkPlugin extends AutoPlugin {
     (run in Jmh).toTask(args)
   }
 
-  val benchFilesParser: Def.Initialize[State => Parser[File]] = Def.setting { (state: State) =>
+  val benchFilesParser: Def.Initialize[State => Parser[File]] = Def.setting { state: State =>
     val extracted = Project.extract(state)
     val pat = new PatternFilter(
       extracted.getOpt(jmhFileRegex).getOrElse(".*".r).pattern
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 783f50a06..2aeb87262 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -9,7 +9,7 @@ addSbtPlugin("org.tpolecat" % "tut-plugin" % "0.6.10")
 addSbtPlugin("com.typesafe.sbt" % "sbt-ghpages" % "0.6.2")
 addSbtPlugin("com.typesafe.sbt" % "sbt-site" % "1.3.2")
 addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.0")
-addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.4")
+addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.6")
 addSbtPlugin("org.xerial.sbt" % "sbt-sonatype" % "2.1")
 addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")
 addSbtPlugin("org.spark-packages" % "sbt-spark-package" % "0.2.7-astraea.1")

From 7e71fcd36df73c6079cf10b517a4ed880bc911bb Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 6 Jun 2019 13:30:30 -0400
Subject: [PATCH 143/380] Initial pweave framework.

---
 project/PythonBuildPlugin.scala               | 16 ++---
 .../src/main/python/examples/Clustering.py    |  2 +-
 .../python/examples/CreatingRasterFrames.py   |  2 +-
 .../src/main/python/examples/MeanValue.py     |  2 +-
 .../src/main/python/examples/NDVI.py          |  4 +-
 .../src/main/python/examples/__init__.py      |  6 +-
 pyrasterframes/src/main/python/setup.py       | 62 +++++++++++++++++--
 7 files changed, 74 insertions(+), 20 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index beb7af513..63febc115 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -38,20 +38,22 @@ object PythonBuildPlugin extends AutoPlugin {
   }
   import autoImport._
 
-  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File]) = Def.task {
+  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
     val s = streams.value
     val src = srcDir.value
     val dest = destDir.value
-    IO.delete(dest)
+    if (deleteFirst)
+      IO.delete(dest)
     dest.mkdirs()
     s.log.info(s"Copying '$src' to '$dest'")
     IO.copyDirectory(src, dest)
     dest
   }
 
-  val copyPySources = copySources(Compile / pythonSource, Python / target)
-
-  val copyPyTestSources = copySources(Test / pythonSource, Python / test / target)
+  val copyPySources = Def.sequential(
+    copySources(Compile / pythonSource, Python / target, true),
+    copySources(Test / pythonSource, Python / test / target, false)
+  )
 
   val buildWhl = Def.task {
     val buildDir = (Python / target).value
@@ -76,7 +78,8 @@ object PythonBuildPlugin extends AutoPlugin {
     pythonCommand := "python",
     pySetup := {
       val s = streams.value
-      val wd = copyPySources.value
+      val _ = copyPySources.value
+      val wd = (Python / target).value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
       val ver = version.value
@@ -111,7 +114,6 @@ object PythonBuildPlugin extends AutoPlugin {
       },
       test := Def.sequential(
         assembly,
-        copyPyTestSources,
         pySetup.toTask(" test")
       ).value,
       testQuick := pySetup.toTask(" test").value
diff --git a/pyrasterframes/src/main/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
index 2b6a99218..be66b7b38 100644
--- a/pyrasterframes/src/main/python/examples/Clustering.py
+++ b/pyrasterframes/src/main/python/examples/Clustering.py
@@ -1,4 +1,4 @@
-from . import example_session, resource_dir
+from examples import example_session, resource_dir
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
diff --git a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
index 5c018fee8..a0a0d7fa5 100644
--- a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
+++ b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
@@ -1,4 +1,4 @@
-from . import resource_dir, example_session
+from examples import resource_dir, example_session
 from pyrasterframes import *
 from pyspark.sql import *
 import os
diff --git a/pyrasterframes/src/main/python/examples/MeanValue.py b/pyrasterframes/src/main/python/examples/MeanValue.py
index 1bec83c64..976adc3fe 100644
--- a/pyrasterframes/src/main/python/examples/MeanValue.py
+++ b/pyrasterframes/src/main/python/examples/MeanValue.py
@@ -1,4 +1,4 @@
-from . import resource_dir, example_session
+from examples import resource_dir, example_session
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
diff --git a/pyrasterframes/src/main/python/examples/NDVI.py b/pyrasterframes/src/main/python/examples/NDVI.py
index 7fb4d28d2..a3549cc84 100644
--- a/pyrasterframes/src/main/python/examples/NDVI.py
+++ b/pyrasterframes/src/main/python/examples/NDVI.py
@@ -1,4 +1,4 @@
-from . import resource_dir, example_session
+from examples import resource_dir, example_session
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 import os
@@ -13,4 +13,4 @@
 rf.printSchema()
 rf.show(20)
 
-spark.stop()
\ No newline at end of file
+spark.stop()
diff --git a/pyrasterframes/src/main/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
index 8bd561d0a..190f8712b 100644
--- a/pyrasterframes/src/main/python/examples/__init__.py
+++ b/pyrasterframes/src/main/python/examples/__init__.py
@@ -14,13 +14,13 @@
 if not len(jarpath) == 1:
     raise RuntimeError("""
 Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/clean' first. """.format(jarpath))
+Try running 'sbt pyrasterframes/pyTest' first. """.format(jarpath))
 
 pyJar = jarpath[0]
 
 
 def example_session():
-    return (SparkSession.builder
+    spark = (SparkSession.builder
             .master("local[*]")
             .appName("RasterFrames")
             .config('spark.driver.extraClassPath', pyJar)
@@ -28,4 +28,6 @@ def example_session():
             .config("spark.ui.enabled", "false")
             .withKryoSerialization()
             .getOrCreate())
+    spark.sparkContext.setLogLevel("ERROR")
+    return spark
 
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 959fd8d7b..84c9a319a 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -1,5 +1,6 @@
 # Always prefer setuptools over distutils
 from setuptools import setup, find_packages
+import os
 from os import path, environ
 from io import open
 from pathlib import Path
@@ -25,10 +26,15 @@ def _extract_module(mod):
         globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
 
 
+def _divided(msg):
+    divider = ('-' * 50)
+    return divider + '\n' + msg + '\n' + divider
+
+
 class RunExamples(distutils.cmd.Command):
     """A custom command to run pyrasterframes examples."""
 
-    description = 'run pyrasterframes examples'
+    description = 'Run PyRasterFrames examples'
     user_options = [
         # The format is (long option, short option, description).
         ('examples=', 'e', 'examples to run'),
@@ -61,17 +67,60 @@ def finalize_options(self):
     def run(self):
         """Run the examples."""
         import traceback
+
         for ex in self.examples:
-            print(('-' * 50) + '\nRunning %s' % ex + '\n' + ('-' * 50))
+            print(_divided('Running %s' % ex))
             try:
                 _extract_module(ex)
             except Exception:
-                print(('-' * 50) + '\n%s Failed:' % ex + '\n' + ('-' * 50))
+                print(_divided('%s Failed:' % ex))
                 print(traceback.format_exc())
 
 
+class PweaveDocs(distutils.cmd.Command):
+    """A custom command to run documentation scripts through pweave."""
+
+    description = 'Pweave PyRasterFrames examples'
+    user_options = [
+        # The format is (long option, short option, description).
+        ('files=', 'f', 'Specific files to pweave. Defaults to all in `examples` directory.'),
+    ]
+
+    def initialize_options(self):
+        """Set default values for options."""
+        # Each user option must be listed here with their default value.
+        self.files = filter(lambda x: not x.name.startswith('_'),
+                               list((Path(here) / 'examples').resolve().glob('*.py')))
+
+    def finalize_options(self):
+        """Post-process options."""
+        import re
+        if isinstance(self.files, str):
+            self.files = filter(lambda s: len(s) > 0, re.split('\W+', self.files))
+
+    def run(self):
+        """Run pweave."""
+        import traceback
+        import pweave
+        dest = path.join(here, 'docs-md')
+        os.mkdir(dest)
+
+        for ex in self.files:
+            name = path.splitext(path.basename(ex))[0]
+            print(_divided('Running %s' % ex))
+            try:
+                pweave.weave(
+                    file=str(ex),
+                    doctype='markdown',
+                    output=path.join(dest, name + ".md"),
+                    figdir=path.join(dest, "figures")
+                )
+            except Exception:
+                print(_divided('%s Failed:' % ex))
+                print(traceback.format_exc())
+
 setup(
-    name='pyrasterframes',
+    name='PyRasterFrames',
     description='RasterFrames for PySpark',
     long_description=readme,
     long_description_content_type='text/markdown',
@@ -89,7 +138,7 @@ def run(self):
         'pytest-runner',
         'setuptools >= 0.8',
         'pathlib2',
-        'jupytext'
+        'Pweave'
     ] + requirements,
     tests_require=[
         'pytest==3.4.2',
@@ -114,7 +163,8 @@ def run(self):
     zip_safe=False,
     test_suite="pytest-runner",
     cmdclass={
-        'examples': RunExamples
+        'examples': RunExamples,
+        'pweave': PweaveDocs
     }
     # entry_points={
     #     "console_scripts": ['pyrasterframes=pyrasterframes:console']

From 4b453bbdac32ee7b35e06e0ee2287d00d13d1350 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 6 Jun 2019 14:26:25 -0400
Subject: [PATCH 144/380] Example pweave-able document.

---
 pyrasterframes/build.sbt                      | 11 +++++
 .../src/main/python/docs/Background.md        |  8 ++++
 .../src/main/python/docs/GettingStarted.md    | 10 ++++
 .../python/docs/InitializingEnvironment.py    | 46 +++++++++++++++++++
 .../src/main/python/docs/__init__.py          | 30 ++++++++++++
 pyrasterframes/src/main/python/docs/index.md  |  5 ++
 pyrasterframes/src/main/python/setup.py       | 13 ++----
 7 files changed, 114 insertions(+), 9 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/Background.md
 create mode 100644 pyrasterframes/src/main/python/docs/GettingStarted.md
 create mode 100644 pyrasterframes/src/main/python/docs/InitializingEnvironment.py
 create mode 100644 pyrasterframes/src/main/python/docs/__init__.py
 create mode 100644 pyrasterframes/src/main/python/docs/index.md

diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 570dc1385..d6050a4d8 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -2,6 +2,17 @@ import PythonBuildPlugin.autoImport.pySetup
 
 exportJars := true
 
+Python / doc / target := (Python / target).value / "docs"
+Python / doc := {
+  val _ = Def.sequential(
+    assembly,
+    pySetup.toTask(" pweave")
+  ).value
+  (Python / doc / target).value
+}
+
+doc := (Python / doc).value
+
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
 pySparkCmd := {
   val s = streams.value
diff --git a/pyrasterframes/src/main/python/docs/Background.md b/pyrasterframes/src/main/python/docs/Background.md
new file mode 100644
index 000000000..36ec0b32d
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/Background.md
@@ -0,0 +1,8 @@
+# What is a RasterFrame?
+
+_This is a work in progress_
+
+
+1. Conceptual model
+2. Data model
+3. Compute model
diff --git a/pyrasterframes/src/main/python/docs/GettingStarted.md b/pyrasterframes/src/main/python/docs/GettingStarted.md
new file mode 100644
index 000000000..33a1a6716
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/GettingStarted.md
@@ -0,0 +1,10 @@
+# Getting Started
+
+_This is a work in progress_
+
+Outline: 
+
+1. Introduction
+1. Accessing the published artifacts (TODO)
+1. Running from a build (See README.md)
+
diff --git a/pyrasterframes/src/main/python/docs/InitializingEnvironment.py b/pyrasterframes/src/main/python/docs/InitializingEnvironment.py
new file mode 100644
index 000000000..0832b5143
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/InitializingEnvironment.py
@@ -0,0 +1,46 @@
+#' # Initializing PySpark
+
+#' There are a number of ways to use PyRasterFrames:
+#'
+#' 1. Standalone Script
+#' 2. `pyspark` shell
+#' 3. Notebook Environment
+#'
+
+#' ## Initializing a Python Script
+
+#+ echo=False
+from docs import *
+
+#' Most of RasterFrames is implemented in Scala, and compiled to a Java Virtual Machine (JVM)
+#' library file called a '.jar' file. The RasterFrames build provides a special `.jar` file,
+#' called an "assembly", containing all of RasterFrames as well as the libraries that RasterFrames
+#' rquires outside of Spark
+#' Let's assume we can locate the PyRasterFrames "assembly" `.jar` file with a function named
+#' `find_pyrasterframes_assembly`.
+
+#' The first step is to set up a `SparkSession`:
+
+from pyspark.sql import SparkSession
+from pyrasterframes import *
+jar = find_pyrasterframes_assembly()
+spark = (SparkSession.builder
+         .master("local[*]")
+         .appName("RasterFrames")
+         .config('spark.driver.extraClassPath', jar)
+         .config('spark.executor.extraClassPath', jar)
+         .withKryoSerialization()
+         .getOrCreate())
+spark.withRasterFrames()
+
+#' Now we have a standard Spark session with RasterFrames enabled in it.
+#' To import RasterFrames functions into the environment, use:
+from pyrasterframes.rasterfunctions import *
+
+#' Functions starting with `rf_` (raster-oriented) or `st_` (vector geometry-oriented) are
+#' become available for use with DataFrames.
+
+list(filter(lambda x: x.startswith("rf_") or x.startswith("st_"), dir()))
+
+#+ echo=False
+spark.stop()
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
new file mode 100644
index 000000000..d08030799
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -0,0 +1,30 @@
+import os
+import glob
+from pyspark.sql import SparkSession
+
+__all__ = ["create_spark_session", "find_pyrasterframes_assembly"]
+
+def find_pyrasterframes_assembly():
+    here = os.path.dirname(os.path.realpath(__file__))
+    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
+    jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
+    if not len(jarpath) == 1:
+        raise RuntimeError("""
+Expected to find exactly one assembly. Found '{}' instead. 
+Try running 'sbt pyrasterframes/package' first. """.format(jarpath))
+    return jarpath[0]
+
+def create_spark_session():
+    pyJar = find_pyrasterframes_assembly()
+    spark = (SparkSession.builder
+             .master("local[*]")
+             .appName("RasterFrames")
+             .config('spark.driver.extraClassPath', pyJar)
+             .config('spark.executor.extraClassPath', pyJar)
+             .config("spark.ui.enabled", "false")
+             .withKryoSerialization()
+             .getOrCreate())
+    spark.withRasterFrames()
+    spark.sparkContext.setLogLevel("ERROR")
+    return spark
+
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
new file mode 100644
index 000000000..0ebe3f623
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -0,0 +1,5 @@
+# PyRasterFrames Users' Manual
+
+1. [What is a RasterFrame?](Background.md)
+2. [Getting Started](GettingStarted.md)
+3. [Initializing a RasterFrames Environment](InitializingEnvironment.md)
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 84c9a319a..60902fe76 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -79,18 +79,17 @@ def run(self):
 
 class PweaveDocs(distutils.cmd.Command):
     """A custom command to run documentation scripts through pweave."""
-
-    description = 'Pweave PyRasterFrames examples'
+    description = 'Pweave PyRasterFrames documentation scripts'
     user_options = [
         # The format is (long option, short option, description).
-        ('files=', 'f', 'Specific files to pweave. Defaults to all in `examples` directory.'),
+        ('files=', 'f', 'Specific files to pweave. Defaults to all in `docs` directory.'),
     ]
 
     def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
         self.files = filter(lambda x: not x.name.startswith('_'),
-                               list((Path(here) / 'examples').resolve().glob('*.py')))
+                               list((Path(here) / 'docs').resolve().glob('*.py')))
 
     def finalize_options(self):
         """Post-process options."""
@@ -102,8 +101,6 @@ def run(self):
         """Run pweave."""
         import traceback
         import pweave
-        dest = path.join(here, 'docs-md')
-        os.mkdir(dest)
 
         for ex in self.files:
             name = path.splitext(path.basename(ex))[0]
@@ -111,9 +108,7 @@ def run(self):
             try:
                 pweave.weave(
                     file=str(ex),
-                    doctype='markdown',
-                    output=path.join(dest, name + ".md"),
-                    figdir=path.join(dest, "figures")
+                    doctype='markdown'
                 )
             except Exception:
                 print(_divided('%s Failed:' % ex))

From 896e09eea2a7a2a70ee0e8af8ac924d6d9b8dfd7 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 6 Jun 2019 14:58:43 -0400
Subject: [PATCH 145/380] Disabled `tut` and started simplification of
 Scala-based docs.

---
 build.sbt                                     |  24 ++++++++++-
 docs/build.sbt                                |  40 ------------------
 docs/src/main/{tut => paradox}/CNAME          |   0
 .../{tut => paradox}/RasterFramePipeline.svg  |   0
 .../{tut => paradox}/apps/geotrellis-ops.md   |   0
 docs/src/main/{tut => paradox}/apps/index.md  |   0
 docs/src/main/{tut => paradox}/apps/ndvi.md   |   0
 .../{tut => paradox}/creating-rasterframes.md |   0
 .../exporting-rasterframes.md                 |   3 +-
 .../main/{tut => paradox}/getting-started.md  |   0
 docs/src/main/{tut => paradox}/index.md       |   0
 .../main/{tut => paradox}/ml/L8-RGB-VA.png    | Bin
 .../{tut => paradox}/ml/classification.md     |   0
 .../main/{tut => paradox}/ml/clustering.md    |   0
 docs/src/main/{tut => paradox}/ml/index.md    |   0
 .../main/{tut => paradox}/ml/statistics.md    |   0
 .../main/{tut => paradox}/pyrasterframes.md   |   0
 docs/src/main/{tut => paradox}/reference.md   |   0
 .../main/{tut => paradox}/release-notes.md    |   0
 .../main/{tut => paradox}/spatial-queries.md  |   0
 project/plugins.sbt                           |   3 +-
 21 files changed, 25 insertions(+), 45 deletions(-)
 delete mode 100644 docs/build.sbt
 rename docs/src/main/{tut => paradox}/CNAME (100%)
 rename docs/src/main/{tut => paradox}/RasterFramePipeline.svg (100%)
 rename docs/src/main/{tut => paradox}/apps/geotrellis-ops.md (100%)
 rename docs/src/main/{tut => paradox}/apps/index.md (100%)
 rename docs/src/main/{tut => paradox}/apps/ndvi.md (100%)
 rename docs/src/main/{tut => paradox}/creating-rasterframes.md (100%)
 rename docs/src/main/{tut => paradox}/exporting-rasterframes.md (97%)
 rename docs/src/main/{tut => paradox}/getting-started.md (100%)
 rename docs/src/main/{tut => paradox}/index.md (100%)
 rename docs/src/main/{tut => paradox}/ml/L8-RGB-VA.png (100%)
 rename docs/src/main/{tut => paradox}/ml/classification.md (100%)
 rename docs/src/main/{tut => paradox}/ml/clustering.md (100%)
 rename docs/src/main/{tut => paradox}/ml/index.md (100%)
 rename docs/src/main/{tut => paradox}/ml/statistics.md (100%)
 rename docs/src/main/{tut => paradox}/pyrasterframes.md (100%)
 rename docs/src/main/{tut => paradox}/reference.md (100%)
 rename docs/src/main/{tut => paradox}/release-notes.md (100%)
 rename docs/src/main/{tut => paradox}/spatial-queries.md (100%)

diff --git a/build.sbt b/build.sbt
index e322a6591..6158b5335 100644
--- a/build.sbt
+++ b/build.sbt
@@ -102,7 +102,29 @@ lazy val experimental = project
   )
 
 lazy val docs = project
-  .dependsOn(core, datasource)
+  .dependsOn(core, datasource, pyrasterframes)
+  .enablePlugins(SiteScaladocPlugin, ParadoxPlugin, GhpagesPlugin, ScalaUnidocPlugin)
+  .settings(
+    apiURL := Some(url("http://rasterframes.io/latest/api")),
+    autoAPIMappings := true,
+    ghpagesNoJekyll := true,
+    ScalaUnidoc / siteSubdirName := "latest/api",
+    paradox / siteSubdirName := ".",
+    paradoxProperties ++= Map(
+      "github.base_url" -> "https://github.com/locationtech/rasterframes",
+      "version" -> version.value,
+      "scaladoc.org.apache.spark.sql.rf" -> "http://rasterframes.io/latest"
+    ),
+    paradoxTheme := Some(builtinParadoxTheme("generic")),
+    makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value,
+    Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value
+  )
+  .settings(
+    addMappingsToSiteDir(ScalaUnidoc / packageDoc / mappings, ScalaUnidoc / siteSubdirName)
+  )
+  .settings(
+    addMappingsToSiteDir(Compile / paradox / mappings, paradox / siteSubdirName)
+  )
 
 lazy val bench = project
   .dependsOn(core % "compile->test")
diff --git a/docs/build.sbt b/docs/build.sbt
deleted file mode 100644
index d7bb66c61..000000000
--- a/docs/build.sbt
+++ /dev/null
@@ -1,40 +0,0 @@
-import com.typesafe.sbt.SbtGit.git
-
-enablePlugins(SiteScaladocPlugin, ParadoxPlugin, TutPlugin, GhpagesPlugin, ScalaUnidocPlugin)
-
-name := "rasterframes-docs"
-
-libraryDependencies ++= Seq(
-  spark("mllib").value % Tut,
-  spark("sql").value % Tut
-)
-
-apiURL := Some(url("http://rasterframes.io/latest/api"))
-autoAPIMappings := true
-ghpagesNoJekyll := true
-
-ScalaUnidoc / siteSubdirName := "latest/api"
-paradox / siteSubdirName := "."
-
-addMappingsToSiteDir(ScalaUnidoc / packageDoc / mappings, ScalaUnidoc / siteSubdirName)
-addMappingsToSiteDir(Compile / paradox / mappings, paradox / siteSubdirName)
-
-paradoxProperties ++= Map(
-  "github.base_url" -> "https://github.com/locationtech/rasterframes",
-  "version" -> version.value,
-  "scaladoc.org.apache.spark.sql.gt" -> "http://rasterframes.io/latest"
-  //"scaladoc.geotrellis.base_url" -> "https://geotrellis.github.io/scaladocs/latest",
-  // "snip.pyexamples.base_dir" -> (baseDirectory.value + "/../pyrasterframes/python/test/examples")
-)
-paradoxTheme := Some(builtinParadoxTheme("generic"))
-//paradoxTheme / sourceDirectory := sourceDirectory.value / "main" / "paradox" / "_template"
-
-Compile / doc / scalacOptions++= Seq( "-J-Xmx6G", "-no-link-warnings")
-
-Tut / run / fork := true
-
-Tut / run / javaOptions := Seq("-Xmx8G", "-Dspark.ui.enabled=false")
-
-Compile / paradox := (Compile / paradox).dependsOn(tutQuick).value
-Compile / paradox / sourceDirectory := tutTargetDirectory.value
-makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value
diff --git a/docs/src/main/tut/CNAME b/docs/src/main/paradox/CNAME
similarity index 100%
rename from docs/src/main/tut/CNAME
rename to docs/src/main/paradox/CNAME
diff --git a/docs/src/main/tut/RasterFramePipeline.svg b/docs/src/main/paradox/RasterFramePipeline.svg
similarity index 100%
rename from docs/src/main/tut/RasterFramePipeline.svg
rename to docs/src/main/paradox/RasterFramePipeline.svg
diff --git a/docs/src/main/tut/apps/geotrellis-ops.md b/docs/src/main/paradox/apps/geotrellis-ops.md
similarity index 100%
rename from docs/src/main/tut/apps/geotrellis-ops.md
rename to docs/src/main/paradox/apps/geotrellis-ops.md
diff --git a/docs/src/main/tut/apps/index.md b/docs/src/main/paradox/apps/index.md
similarity index 100%
rename from docs/src/main/tut/apps/index.md
rename to docs/src/main/paradox/apps/index.md
diff --git a/docs/src/main/tut/apps/ndvi.md b/docs/src/main/paradox/apps/ndvi.md
similarity index 100%
rename from docs/src/main/tut/apps/ndvi.md
rename to docs/src/main/paradox/apps/ndvi.md
diff --git a/docs/src/main/tut/creating-rasterframes.md b/docs/src/main/paradox/creating-rasterframes.md
similarity index 100%
rename from docs/src/main/tut/creating-rasterframes.md
rename to docs/src/main/paradox/creating-rasterframes.md
diff --git a/docs/src/main/tut/exporting-rasterframes.md b/docs/src/main/paradox/exporting-rasterframes.md
similarity index 97%
rename from docs/src/main/tut/exporting-rasterframes.md
rename to docs/src/main/paradox/exporting-rasterframes.md
index b2b806fbd..5a855db53 100644
--- a/docs/src/main/tut/exporting-rasterframes.md
+++ b/docs/src/main/paradox/exporting-rasterframes.md
@@ -27,8 +27,7 @@ The cell values within a `Tile` are encoded internally as an array. There may be
 where the additional context provided by the `Tile` construct is no longer needed and one would
 prefer to work with the underlying array data.
 
-The @scaladoc[`rf_tile_to_array_int`][rf_tile_to_array_int] and @scaladoc[`rf_tile_to_array_double`][rf_tile_to_array_double]
-provide this facility.
+The `rf_tile_to_array_int` and `rf_tile_to_array_double` functions provide this facility.
 
 ```tut
 val withArrays = rf.withColumn("tileData", rf_tile_to_array_int($"tile")).drop("tile")
diff --git a/docs/src/main/tut/getting-started.md b/docs/src/main/paradox/getting-started.md
similarity index 100%
rename from docs/src/main/tut/getting-started.md
rename to docs/src/main/paradox/getting-started.md
diff --git a/docs/src/main/tut/index.md b/docs/src/main/paradox/index.md
similarity index 100%
rename from docs/src/main/tut/index.md
rename to docs/src/main/paradox/index.md
diff --git a/docs/src/main/tut/ml/L8-RGB-VA.png b/docs/src/main/paradox/ml/L8-RGB-VA.png
similarity index 100%
rename from docs/src/main/tut/ml/L8-RGB-VA.png
rename to docs/src/main/paradox/ml/L8-RGB-VA.png
diff --git a/docs/src/main/tut/ml/classification.md b/docs/src/main/paradox/ml/classification.md
similarity index 100%
rename from docs/src/main/tut/ml/classification.md
rename to docs/src/main/paradox/ml/classification.md
diff --git a/docs/src/main/tut/ml/clustering.md b/docs/src/main/paradox/ml/clustering.md
similarity index 100%
rename from docs/src/main/tut/ml/clustering.md
rename to docs/src/main/paradox/ml/clustering.md
diff --git a/docs/src/main/tut/ml/index.md b/docs/src/main/paradox/ml/index.md
similarity index 100%
rename from docs/src/main/tut/ml/index.md
rename to docs/src/main/paradox/ml/index.md
diff --git a/docs/src/main/tut/ml/statistics.md b/docs/src/main/paradox/ml/statistics.md
similarity index 100%
rename from docs/src/main/tut/ml/statistics.md
rename to docs/src/main/paradox/ml/statistics.md
diff --git a/docs/src/main/tut/pyrasterframes.md b/docs/src/main/paradox/pyrasterframes.md
similarity index 100%
rename from docs/src/main/tut/pyrasterframes.md
rename to docs/src/main/paradox/pyrasterframes.md
diff --git a/docs/src/main/tut/reference.md b/docs/src/main/paradox/reference.md
similarity index 100%
rename from docs/src/main/tut/reference.md
rename to docs/src/main/paradox/reference.md
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/paradox/release-notes.md
similarity index 100%
rename from docs/src/main/tut/release-notes.md
rename to docs/src/main/paradox/release-notes.md
diff --git a/docs/src/main/tut/spatial-queries.md b/docs/src/main/paradox/spatial-queries.md
similarity index 100%
rename from docs/src/main/tut/spatial-queries.md
rename to docs/src/main/paradox/spatial-queries.md
diff --git a/project/plugins.sbt b/project/plugins.sbt
index fa474439a..5ccdcda08 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -4,10 +4,9 @@ addSbtCoursier
 addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.6")
 addSbtPlugin("com.eed3si9n" % "sbt-buildinfo" % "0.7.0")
 addSbtPlugin("de.heikoseeberger" % "sbt-header" % "3.0.2")
-addSbtPlugin("org.tpolecat" % "tut-plugin" % "0.6.10")
 addSbtPlugin("com.typesafe.sbt" % "sbt-ghpages" % "0.6.2")
 addSbtPlugin("com.typesafe.sbt" % "sbt-site" % "1.3.2")
-addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.0")
+addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.5")
 addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.4")
 addSbtPlugin("org.xerial.sbt" % "sbt-sonatype" % "2.1")
 addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")

From 01b7e6360865dc0d16300eda40cdd59488274f8c Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 6 Jun 2019 16:58:41 -0400
Subject: [PATCH 146/380] Ability to run unit tests on python 2

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/rf_types.py    |  4 ++--
 pyrasterframes/src/main/python/setup.py       | 20 ++++++++++---------
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index b3a930af0..22f842b35 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -160,11 +160,11 @@ def deserialize(self, datum):
 
 
 class CellType(object):
-    def __init__(self, cell_type_name: str):
+    def __init__(self, cell_type_name):
         self.cell_type_name = cell_type_name
 
     @classmethod
-    def from_numpy_dtype(cls, np_dtype: np.dtype):
+    def from_numpy_dtype(cls, np_dtype):
         return CellType(str(np_dtype.name))
 
     @classmethod
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 959fd8d7b..60b2894e1 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -1,8 +1,8 @@
 # Always prefer setuptools over distutils
 from setuptools import setup, find_packages
 from os import path, environ
+from glob import glob
 from io import open
-from pathlib import Path
 import distutils.cmd
 import importlib
 
@@ -36,26 +36,28 @@ class RunExamples(distutils.cmd.Command):
 
     @staticmethod
     def _check_ex_path(ex):
-        file = Path(ex)
-        if not file.suffix:
-            file = file.with_suffix('.py')
-        file = (Path(here) / 'examples' / file).resolve()
 
-        assert file.is_file(), ('Invalid example %s' % file)
+        file = ex
+        suffix = path.splitext(ex)[1]
+        if suffix == '':
+            file += '.py'
+        file = path.join(here, 'examples', file)
+
+        assert path.isfile(file), ('Invalid example %s' % file)
         return file
 
     def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
-        self.examples = filter(lambda x: not x.name.startswith('_'),
-                               list((Path(here) / 'examples').resolve().glob('*.py')))
+        self.examples = filter(lambda x: not x[:1] == '_',
+                               glob(path.join(here, 'examples', '*')))
 
     def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.examples, str):
             self.examples = filter(lambda s: len(s) > 0, re.split('\W+', self.examples))
-        self.examples = map(lambda x: 'examples.' + x.stem,
+        self.examples = map(lambda x: 'examples.' + path.basename(x),
                             map(self._check_ex_path, self.examples))
 
     def run(self):

From 3a03193241121631abeedf92c893b3abcfb66705 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 6 Jun 2019 17:43:49 -0400
Subject: [PATCH 147/380] pyrasterframes setup examples parsing fix

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/examples/__init__.py | 2 +-
 pyrasterframes/src/main/python/setup.py             | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
index 8bd561d0a..27f2b589f 100644
--- a/pyrasterframes/src/main/python/examples/__init__.py
+++ b/pyrasterframes/src/main/python/examples/__init__.py
@@ -14,7 +14,7 @@
 if not len(jarpath) == 1:
     raise RuntimeError("""
 Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/clean' first. """.format(jarpath))
+Try running 'sbt pyrasterframes/clean && sbt pyrasterframes/test' first. """.format(jarpath))
 
 pyJar = jarpath[0]
 
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 60b2894e1..5c892c913 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -49,7 +49,7 @@ def _check_ex_path(ex):
     def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
-        self.examples = filter(lambda x: not x[:1] == '_',
+        self.examples = filter(lambda x: not path.basename(x)[:1] == '_',
                                glob(path.join(here, 'examples', '*')))
 
     def finalize_options(self):
@@ -57,7 +57,7 @@ def finalize_options(self):
         import re
         if isinstance(self.examples, str):
             self.examples = filter(lambda s: len(s) > 0, re.split('\W+', self.examples))
-        self.examples = map(lambda x: 'examples.' + path.basename(x),
+        self.examples = map(lambda x: 'examples.' + path.splitext(path.basename(x))[0],
                             map(self._check_ex_path, self.examples))
 
     def run(self):

From f6366a50ab83b71cfedff55361fbd13c2a5c7dbf Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 7 Jun 2019 11:32:43 -0400
Subject: [PATCH 148/380] Prototype merging python and scala docs.

---
 build.sbt                                    |  4 ++-
 docs/src/main/paradox/index.md               |  2 +-
 docs/src/main/paradox/pyrasterframes.md      | 14 ----------
 project/PythonBuildPlugin.scala              | 23 ++++++++---------
 pyrasterframes/build.sbt                     | 17 ++++++++----
 pyrasterframes/src/main/python/docs/index.md | 10 +++++---
 pyrasterframes/src/main/python/setup.py      | 27 ++++++++++++--------
 7 files changed, 51 insertions(+), 46 deletions(-)
 delete mode 100644 docs/src/main/paradox/pyrasterframes.md

diff --git a/build.sbt b/build.sbt
index 6158b5335..1b0b224da 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,5 +1,6 @@
 
 addCommandAlias("makeSite", "docs/makeSite")
+addCommandAlias("previewSite", "docs/previewSite")
 addCommandAlias("console", "datasource/console")
 
 lazy val root = project
@@ -117,7 +118,8 @@ lazy val docs = project
     ),
     paradoxTheme := Some(builtinParadoxTheme("generic")),
     makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value,
-    Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value
+    Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value.getParentFile,
+    Compile / paradox := (Compile / paradox).dependsOn(pyrasterframes / doc).value
   )
   .settings(
     addMappingsToSiteDir(ScalaUnidoc / packageDoc / mappings, ScalaUnidoc / siteSubdirName)
diff --git a/docs/src/main/paradox/index.md b/docs/src/main/paradox/index.md
index 7351536be..84dadce2f 100644
--- a/docs/src/main/paradox/index.md
+++ b/docs/src/main/paradox/index.md
@@ -39,7 +39,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 @@@ index
 * [Getting Started](getting-started.md)
-* [PyRasterFrames](pyrasterframes.md)
+* [PyRasterFrames](pyrasterframes/index.md)
 * [Creating RasterFrames](creating-rasterframes.md)
 * [Spatial Queries](spatial-queries.md)
 * [Applications](apps/index.md)
diff --git a/docs/src/main/paradox/pyrasterframes.md b/docs/src/main/paradox/pyrasterframes.md
deleted file mode 100644
index 0d7fc8280..000000000
--- a/docs/src/main/paradox/pyrasterframes.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# RasterFrames&nbsp;in&nbsp;Python
-
-RasterFrames includes Python bindings for using the library from within [PySpark](https://spark.apache.org/docs/latest/api/python/pyspark.html).
-While creating Python documentation on parity with Scala is forthcoming, these resources should help 
-in the meantime:
-
-* [PyRasterFrames README](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/python/README.rst)
-* [PyRasterFrames Examples](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/python/examples)
-* [RasterFrames Jupyter Notebook](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md)
-* @ref:[PyRasterFrames Functions](reference.md)
-
-Most features available in the Scala API are exposed in the Python API, refer to the @ref:[function reference](reference.md). Defining a [udf](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.udf) using a `Tile` column through the Python API is not yet supported. 
-
-If there's a specific feature that appears to be missing in the Python version [please submit an issue](https://github.com/locationtech/rasterframes/issues) so that we might address it for you.
diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 63febc115..a21fb117e 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -35,21 +35,20 @@ object PythonBuildPlugin extends AutoPlugin {
     val pythonSource = settingKey[File]("Default Python source directory.").withRank(ASetting)
     val pythonCommand = settingKey[String]("Python command. Defaults to 'python'")
     val pySetup = inputKey[Int]("Run 'python setup.py <args>'. Returns exit code.")
+    def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
+      val s = streams.value
+      val src = srcDir.value
+      val dest = destDir.value
+      if (deleteFirst)
+        IO.delete(dest)
+      dest.mkdirs()
+      s.log.info(s"Copying '$src' to '$dest'")
+      IO.copyDirectory(src, dest)
+      dest
+    }
   }
   import autoImport._
 
-  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
-    val s = streams.value
-    val src = srcDir.value
-    val dest = destDir.value
-    if (deleteFirst)
-      IO.delete(dest)
-    dest.mkdirs()
-    s.log.info(s"Copying '$src' to '$dest'")
-    IO.copyDirectory(src, dest)
-    dest
-  }
-
   val copyPySources = Def.sequential(
     copySources(Compile / pythonSource, Python / target, true),
     copySources(Test / pythonSource, Python / test / target, false)
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index d6050a4d8..d91bf8ab7 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,12 +1,20 @@
-import PythonBuildPlugin.autoImport.pySetup
+import PythonBuildPlugin.autoImport._
 
-exportJars := true
+addCommandAlias("pyTest", "pyrasterframes/test")
+addCommandAlias("pyDocs", "pyrasterframes/doc")
 
-Python / doc / target := (Python / target).value / "docs"
+exportJars := true
+Python / doc / sourceDirectory := (Python / target).value / "docs"
+Python / doc / target := (Python / target).value / "markdown" / "pyrasterframes"
 Python / doc := {
   val _ = Def.sequential(
     assembly,
-    pySetup.toTask(" pweave")
+    pySetup.toTask(" pweave"),
+    copySources(
+      Python / doc / sourceDirectory,
+      Python / doc / target,
+      deleteFirst = true
+    )
   ).value
   (Python / doc / target).value
 }
@@ -35,4 +43,3 @@ pyExamples := Def.sequential(
   pySetup.toTask(" examples")
 ).value
 
-addCommandAlias("pyTest", "pyrasterframes/test")
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 0ebe3f623..c1e07ef97 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -1,5 +1,9 @@
 # PyRasterFrames Users' Manual
 
-1. [What is a RasterFrame?](Background.md)
-2. [Getting Started](GettingStarted.md)
-3. [Initializing a RasterFrames Environment](InitializingEnvironment.md)
+@@@ index
+
+* [What is a RasterFrame?](Background.md)
+* [Getting Started](GettingStarted.md)
+* [Initializing a RasterFrames Environment](InitializingEnvironment.html)
+
+@@@
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 60902fe76..4b27ac914 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -60,9 +60,14 @@ def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.examples, str):
-            self.examples = filter(lambda s: len(s) > 0, re.split('\W+', self.examples))
-        self.examples = map(lambda x: 'examples.' + x.stem,
-                            map(self._check_ex_path, self.examples))
+            self.examples = filter(
+                lambda s: len(s) > 0,
+                re.split('\W+', self.examples)
+            )
+        self.examples = map(
+            lambda x: 'examples.' + x.stem,
+            map(self._check_ex_path, self.examples)
+        )
 
     def run(self):
         """Run the examples."""
@@ -88,8 +93,10 @@ class PweaveDocs(distutils.cmd.Command):
     def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
-        self.files = filter(lambda x: not x.name.startswith('_'),
-                               list((Path(here) / 'docs').resolve().glob('*.py')))
+        self.files = filter(
+            lambda x: not x.name.startswith('_'),
+            list((Path(here) / 'docs').resolve().glob('*.py'))
+        )
 
     def finalize_options(self):
         """Post-process options."""
@@ -102,16 +109,16 @@ def run(self):
         import traceback
         import pweave
 
-        for ex in self.files:
-            name = path.splitext(path.basename(ex))[0]
-            print(_divided('Running %s' % ex))
+        for file in self.files:
+            name = path.splitext(path.basename(file))[0]
+            print(_divided('Running %s' % name))
             try:
                 pweave.weave(
-                    file=str(ex),
+                    file=str(file),
                     doctype='markdown'
                 )
             except Exception:
-                print(_divided('%s Failed:' % ex))
+                print(_divided('%s Failed:' % file))
                 print(traceback.format_exc())
 
 setup(

From 0acadda44c9ee75763ffbeb341ed258af0bafa48 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 7 Jun 2019 11:58:55 -0400
Subject: [PATCH 149/380] Fixed issue with copying Python test directory before
 run.

---
 project/PythonBuildPlugin.scala                  | 16 +++++++++-------
 .../src/main/python/examples/__init__.py         |  2 +-
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index beb7af513..63febc115 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -38,20 +38,22 @@ object PythonBuildPlugin extends AutoPlugin {
   }
   import autoImport._
 
-  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File]) = Def.task {
+  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
     val s = streams.value
     val src = srcDir.value
     val dest = destDir.value
-    IO.delete(dest)
+    if (deleteFirst)
+      IO.delete(dest)
     dest.mkdirs()
     s.log.info(s"Copying '$src' to '$dest'")
     IO.copyDirectory(src, dest)
     dest
   }
 
-  val copyPySources = copySources(Compile / pythonSource, Python / target)
-
-  val copyPyTestSources = copySources(Test / pythonSource, Python / test / target)
+  val copyPySources = Def.sequential(
+    copySources(Compile / pythonSource, Python / target, true),
+    copySources(Test / pythonSource, Python / test / target, false)
+  )
 
   val buildWhl = Def.task {
     val buildDir = (Python / target).value
@@ -76,7 +78,8 @@ object PythonBuildPlugin extends AutoPlugin {
     pythonCommand := "python",
     pySetup := {
       val s = streams.value
-      val wd = copyPySources.value
+      val _ = copyPySources.value
+      val wd = (Python / target).value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
       val ver = version.value
@@ -111,7 +114,6 @@ object PythonBuildPlugin extends AutoPlugin {
       },
       test := Def.sequential(
         assembly,
-        copyPyTestSources,
         pySetup.toTask(" test")
       ).value,
       testQuick := pySetup.toTask(" test").value
diff --git a/pyrasterframes/src/main/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
index 27f2b589f..8bd561d0a 100644
--- a/pyrasterframes/src/main/python/examples/__init__.py
+++ b/pyrasterframes/src/main/python/examples/__init__.py
@@ -14,7 +14,7 @@
 if not len(jarpath) == 1:
     raise RuntimeError("""
 Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/clean && sbt pyrasterframes/test' first. """.format(jarpath))
+Try running 'sbt pyrasterframes/clean' first. """.format(jarpath))
 
 pyJar = jarpath[0]
 

From a78fbc72864db4b1afe4d078ea0c74194870952b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 7 Jun 2019 13:45:04 -0400
Subject: [PATCH 150/380] Fixes #136 and #140.

---
 .circleci/config.yml                          | 10 +--
 project/PythonBuildPlugin.scala               | 66 ++++++++++++++++---
 .../main/python/pyrasterframes/rf_types.py    |  2 +
 3 files changed, 63 insertions(+), 15 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 36fa49821..c9ca203b0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -148,11 +148,11 @@ workflows:
             branches:
               only:
                 - /feature\/.*-its/
-      - publish:
-          filters:
-            branches:
-              only:
-                - develop
+#      - publish:
+#          filters:
+#            branches:
+#              only:
+#                - develop
   nightlyReleaseAstraea:
     triggers:
       - schedule:
diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 63febc115..b88b4cd70 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -23,6 +23,8 @@ import sbt.KeyRanks.ASetting
 import sbt.Keys.{`package`, _}
 import sbt._
 import complete.DefaultParsers._
+import sbt.Tests.Summary
+
 import scala.sys.process.Process
 import sbtassembly.AssemblyPlugin.autoImport.assembly
 
@@ -83,17 +85,23 @@ object PythonBuildPlugin extends AutoPlugin {
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
       val ver = version.value
-      s.log.info(s"Running '${cmd.mkString(" ")}' in $wd")
+      s.log.info(s"Running '${cmd.mkString(" ")}' in '$wd'")
       Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
     },
     Compile / pythonSource := (Compile / sourceDirectory).value / "python",
     Test / pythonSource := (Test / sourceDirectory).value / "python",
     Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value,
-    Test / test := Def.sequential(
-      Test / test,
-      Python / test
-    ).value,
-    Test / testQuick := (Python / testQuick).evaluated
+    Test / testQuick := (Python / testQuick).evaluated,
+    Test / executeTests := {
+      val standard = (Test / executeTests).value
+      standard.overall match {
+        case TestResult.Passed =>
+          (Python / executeTests).value
+        case _ ⇒
+          val pySummary = Summary("pyrasterframes", "tests skipped due to scalatest failures")
+          standard.copy(summaries = standard.summaries ++ Iterable(pySummary))
+      }
+    }
   ) ++
     inConfig(Python)(Seq(
       target := (Compile / target).value / "python",
@@ -112,11 +120,49 @@ object PythonBuildPlugin extends AutoPlugin {
         val ver = version.value
         dest / s"${art.name}-python-$ver.zip"
       },
-      test := Def.sequential(
+      testQuick := pySetup.toTask(" test").value,
+      executeTests := Def.sequential(
         assembly,
-        pySetup.toTask(" test")
-      ).value,
-      testQuick := pySetup.toTask(" test").value
+        Def.task {
+          val resultCode = pySetup.toTask(" test").value
+          val msg = resultCode match {
+            case 1 ⇒ "There are Python test failures."
+            case 2 ⇒ "Python test execution was interrupted."
+            case 3 ⇒ "Internal error during Python test execution."
+            case 4 ⇒ "PyTest usage error."
+            case 5 ⇒ "No Python tests found."
+            case x if x != 0 ⇒ "Unknown error while running Python tests."
+            case _ ⇒ "PyRasterFrames tests successfully completed."
+          }
+          val pySummary = Summary("pyrasterframes", msg)
+          // Would be cool to derive this from the python output...
+        val result = if (resultCode == 0) {
+          new SuiteResult(
+            TestResult.Passed,
+            passedCount = 1,
+            failureCount = 0,
+            errorCount = 0,
+            skippedCount = 0,
+            ignoredCount = 0,
+            canceledCount = 0,
+            pendingCount = 0
+          )
+        }
+        else {
+          new SuiteResult(
+            TestResult.Failed,
+            passedCount = 0,
+            failureCount = 1,
+            errorCount = 0,
+            skippedCount = 0,
+            ignoredCount = 0,
+            canceledCount = 0,
+            pendingCount = 0
+          )
+        }
+        result
+        Tests.Output(result.result, Map("PyRasterFramesTests" -> result), Iterable(pySummary))
+      }).value
     )) ++
     addArtifact(Python / packageBin / artifact, Python / packageBin)
 }
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 22f842b35..41b5c0efb 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -345,6 +345,8 @@ def __truediv__(self, right):
             ct = ct.with_no_data_value(quot.fill_value)
         return Tile(quot, ct)
 
+    def __div__(self, right):
+        return self.__truediv__(right)
 
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames

From 029c220c3db035cfe0804f5135ce0e95417b021b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 7 Jun 2019 20:55:17 -0400
Subject: [PATCH 151/380] Fix for #142.

---
 pyrasterframes/src/main/python/docs/GettingStarted.md | 10 ----------
 pyrasterframes/src/main/python/docs/index.md          |  4 +---
 pyrasterframes/src/main/python/setup.py               |  6 ++++--
 3 files changed, 5 insertions(+), 15 deletions(-)
 delete mode 100644 pyrasterframes/src/main/python/docs/GettingStarted.md

diff --git a/pyrasterframes/src/main/python/docs/GettingStarted.md b/pyrasterframes/src/main/python/docs/GettingStarted.md
deleted file mode 100644
index 33a1a6716..000000000
--- a/pyrasterframes/src/main/python/docs/GettingStarted.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Getting Started
-
-_This is a work in progress_
-
-Outline: 
-
-1. Introduction
-1. Accessing the published artifacts (TODO)
-1. Running from a build (See README.md)
-
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index c1e07ef97..ff5669d78 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -2,8 +2,6 @@
 
 @@@ index
 
-* [What is a RasterFrame?](Background.md)
-* [Getting Started](GettingStarted.md)
-* [Initializing a RasterFrames Environment](InitializingEnvironment.html)
+* [Initializing a RasterFrames Environment](InitializingEnvironment.md)
 
 @@@
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 4b27ac914..545cb14c0 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -138,9 +138,11 @@ def run(self):
     install_requires=requirements,
     setup_requires=[
         'pytest-runner',
-        'setuptools >= 0.8',
+        'setuptools>=0.8',
         'pathlib2',
-        'Pweave'
+        'ipython==6.2.1',
+        "ipykernel==4.8.0",
+        'Pweave==0.30.3'
     ] + requirements,
     tests_require=[
         'pytest==3.4.2',

From 57539e77679598dacf5ace0795a6f33e2703d419 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 10 Jun 2019 17:03:49 -0400
Subject: [PATCH 152/380] Partial implementation of pip enabled installation

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/MANIFEST.in    |  4 +
 pyrasterframes/src/main/python/README.md      | 78 +++++++++++++------
 .../src/main/python/deps/jars/README.md       |  4 +
 .../main/python/pyrasterframes/__init__.py    | 33 ++++++++
 pyrasterframes/src/main/python/setup.py       | 73 ++++++++++++++---
 5 files changed, 161 insertions(+), 31 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/MANIFEST.in
 create mode 100644 pyrasterframes/src/main/python/deps/jars/README.md

diff --git a/pyrasterframes/src/main/python/MANIFEST.in b/pyrasterframes/src/main/python/MANIFEST.in
new file mode 100644
index 000000000..a97911543
--- /dev/null
+++ b/pyrasterframes/src/main/python/MANIFEST.in
@@ -0,0 +1,4 @@
+
+global-exclude *.py[cod] __pycache__ .DS_Store
+recursive-include deps/jars *.jar
+
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 333910afc..15a0637dc 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -1,39 +1,49 @@
 # PyRasterFrames
 
-PyRasterFrames is the Python API for Spark RasterFrames.
+PyRasterFrames is a library for distributed processing of geospatial raster data with Spark.
 
 
 ## Prerequisites
 
-1. [`sbt`](https://www.scala-sbt.org/)
+1. [`pip`](https://pip.pypa.io/en/stable/installing/)
 2. ['pyspark`](https://pypi.org/project/pyspark/) > 2.3.2 
 
-RasterFrames is primarily implmented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
-All `sbt` commands referenced below must be run from the root source directory, i.e. the parent of the `pyrasterframes` 
-directory, including Python-related build steps. 
-
-As a tip, know that `sbt` is much faster if run in "interactive" mode, where you launch `sbt` with no arguments, 
-and subsequent commands are invoked via an interactive shell. But for context clarity, we'll prefix each command 
-example below with `sbt`. 
-
 ## Quickstart
 
-The quickest way to run a `pyspark` shell with the latest RasterFrames enabled is to run:
+The quickest way to get started is to `pip` install the pyrasterframes package.
 
 ```bash
-sbt pySparkCmd
+pip install pyrasterframes
+```
+
+You can then access a [`pyspark SparkSession`]() using the [`local[*]` master](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls) as follows.
+
+```python
+import pyrasterframes
+spark = pyrasterframes.get_spark_session()
+```
+
+Then you can read a raster and do some simple processing on it.
+
+```python
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql.functions import lit
+# Read a Landsat 8 L1TP PDS scene
+df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/038/037/LC08_L1TP_038037_20190322_20190403_01_T1/LC08_L1TP_038037_20190322_20190403_01_T1_B4.TIF')
+# Add 3 to every cell, show some rows of the dataframe
+df.select(rf_local_add(df.tile, lit(3))).show(6, False)
 ```
 
-This will: 
+## Development
 
-1. Compile all the Scala/JVM code.
-1. Merge all JVM code and dependencies into a single "assembly" JAR file.
-1. Create the PyRasterFrames `.whl` package
-1. Construct a temporary initialization script
-1. Emit a `bash` command with requisite arguments to start a pyspark interpreter with RasterFrames imports.
+RasterFrames is primarily implemented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
+All `sbt` commands referenced below must be run from the root source directory, i.e. the parent of the `pyrasterframes`
+directory, including Python-related build steps.
+
+As a tip, know that `sbt` is much faster if run in "interactive" mode, where you launch `sbt` with no arguments,
+and subsequent commands are invoked via an interactive shell. But for context clarity, we'll prefix each command
+example below with `sbt`.
 
-You then copy/paste the emitted command into your `bash` shell to start up a spark shell. It assumes you have
-`pyspark` >= 2.3.2 installed in your environment.
 
 ## Running Tests and Examples
 
@@ -65,7 +75,7 @@ You build them with:
 sbt pyrasterframes/package
 ```
 
-Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy "GAV" coordinates
+Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy groupId:artifactId:version (GAV) coordinates
 `org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
 
 Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
@@ -92,7 +102,9 @@ sbt 'pySetup examples -e NDVI'
 
 *Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
 
-### `SparkSession` Setup
+## `SparkSession` Setup
+
+### Python shell
 
 To initialize PyRasterFrames in a generic Python shell:
 
@@ -104,7 +116,29 @@ spark = SparkSession.builder \
      .master("local[*]") \
      .appName("Using RasterFrames") \
      .config("spark.some.config.option", "some-value") \
+     .withKryoSerialization() \
      .getOrCreate() \
      .withRasterFrames()
 ```
 
+### Pyspark shell
+
+To initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the pyton source zip.
+
+```bash
+   pyspark \
+    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
+    --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
+    --conf spark.kryoserializer.buffer.max=500m \
+    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
+    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+   
+```
+
+Then in the pyspark shell import the module and call `withRasterFrames`.
+
+```python
+import pyrasterframes
+spark = spark.withRasterFrames()
+df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+```
diff --git a/pyrasterframes/src/main/python/deps/jars/README.md b/pyrasterframes/src/main/python/deps/jars/README.md
new file mode 100644
index 000000000..693a02cb2
--- /dev/null
+++ b/pyrasterframes/src/main/python/deps/jars/README.md
@@ -0,0 +1,4 @@
+# pyrasterframes.jars
+
+Submodule containing JARs needed for pyrasterframe
+
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index cc56a659c..252f0e9e5 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -56,6 +56,39 @@ def _kryo_init(builder):
     return builder
 
 
+def get_spark_session():
+    from pyspark.sql import SparkSession
+    import os
+    import sys
+
+    if sys.version < "3":
+        import imp
+        try:
+            module_home = imp.find_module("pyrasterframes")[1]  # path
+            jar_path = os.path.join(module_home, 'jars')
+        except ImportError:
+            print("pyrasterframes was not pip installed. See documentation for initialization instructions.")
+            return None
+    else:
+        from importlib.util import find_spec
+        try:
+            module_home = find_spec("pyrasterframes").origin
+            jar_path = os.path.join(os.path.dirname(module_home), 'jars')
+        except ImportError:
+            print("pyrasterframes was not pip installed. See documentation for initialization instructions.")
+            return None
+
+    jars_cp = ','.join([f.path for f in os.scandir(jar_path) if f.name[-3:] == 'jar'])
+    spark = (SparkSession.builder
+             .master("local[*]")
+             .appName("RasterFrames")
+             .config('spark.jars', jars_cp)
+             .withKryoSerialization()
+             .getOrCreate()
+             ).withRasterFrames()
+    return spark
+
+
 def _convert_df(df, sp_key=None, metadata=None):
     ctx = SparkContext._active_spark_context._rf_context
 
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 5c892c913..e05985a2d 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -12,9 +12,6 @@
 with open(path.join(here, 'README.md'), encoding='utf-8') as f:
     readme = f.read()
 
-with open(path.join(here, 'requirements.txt')) as f:
-    requirements = f.read().splitlines()
-
 
 def _extract_module(mod):
     module = importlib.import_module(mod)
@@ -72,6 +69,60 @@ def run(self):
                 print(traceback.format_exc())
 
 
+class PweaveDocs(distutils.cmd.Command):
+    from pathlib import Path
+    """A custom command to run documentation scripts through pweave."""
+    description = 'Pweave PyRasterFrames documentation scripts'
+    user_options = [
+        # The format is (long option, short option, description).
+        ('files=', 'f', 'Specific files to pweave. Defaults to all in `docs` directory.'),
+    ]
+
+    def initialize_options(self):
+        """Set default values for options."""
+        # Each user option must be listed here with their default value.
+        self.files = filter(
+            lambda x: not x.name.startswith('_'),
+            list((Path(here) / 'docs').resolve().glob('*.py'))
+        )
+
+    def finalize_options(self):
+        """Post-process options."""
+        import re
+        if isinstance(self.files, str):
+            self.files = filter(lambda s: len(s) > 0, re.split('\W+', self.files))
+
+    def run(self):
+        """Run pweave."""
+        import traceback
+        import pweave
+
+        for file in self.files:
+            name = path.splitext(path.basename(file))[0]
+            print(_divided('Running %s' % name))
+            try:
+                pweave.weave(
+                    file=str(file),
+                    doctype='markdown'
+                )
+            except Exception:
+                print(_divided('%s Failed:' % file))
+                print(traceback.format_exc())
+
+
+try:
+    with open(path.join(here, 'requirements.txt')) as f:
+        requirements = f.read().splitlines()
+except:
+    print("couldn't open requirements.txt")
+    requirements = [
+        'pytz',
+        'shapely',
+        'pyspark>=2.3',
+        'numpy>=1.7',
+        'pandas',
+    ]
+
 setup(
     name='pyrasterframes',
     description='RasterFrames for PySpark',
@@ -89,9 +140,9 @@ def run(self):
     install_requires=requirements,
     setup_requires=[
         'pytest-runner',
-        'setuptools >= 0.8',
+        'setuptools>=0.8',
         'pathlib2',
-        'jupytext'
+        'jupytext',
     ] + requirements,
     tests_require=[
         'pytest==3.4.2',
@@ -101,8 +152,15 @@ def run(self):
     ],
     packages=[
         'pyrasterframes',
-        'geomesa_pyspark'
+        'geomesa_pyspark',
+        'pyrasterframes.jars',
     ],
+    package_dir={
+        'pyrasterframes.jars': 'deps/jars'
+    },
+    package_data={
+        'pyrasterframes.jars': ['*.jar']
+    },
     include_package_data=True,
     classifiers=[
         'Development Status :: 4 - Beta',
@@ -118,7 +176,4 @@ def run(self):
     cmdclass={
         'examples': RunExamples
     }
-    # entry_points={
-    #     "console_scripts": ['pyrasterframes=pyrasterframes:console']
-    # }
 )

From f6d965bdba17766f36f6170661beeae62382823d Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 10 Jun 2019 17:27:52 -0400
Subject: [PATCH 153/380] Tweak to pass CI & unit tests

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/setup.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index e05985a2d..0aecab504 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -70,7 +70,6 @@ def run(self):
 
 
 class PweaveDocs(distutils.cmd.Command):
-    from pathlib import Path
     """A custom command to run documentation scripts through pweave."""
     description = 'Pweave PyRasterFrames documentation scripts'
     user_options = [
@@ -83,7 +82,7 @@ def initialize_options(self):
         # Each user option must be listed here with their default value.
         self.files = filter(
             lambda x: not x.name.startswith('_'),
-            list((Path(here) / 'docs').resolve().glob('*.py'))
+            glob(path.join(here, 'docs', '*.py'))
         )
 
     def finalize_options(self):
@@ -141,7 +140,6 @@ def run(self):
     setup_requires=[
         'pytest-runner',
         'setuptools>=0.8',
-        'pathlib2',
         'jupytext',
     ] + requirements,
     tests_require=[

From 0d9f131e2e72e6d76f37598a1bc7f6a91d970718 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 12 Jun 2019 12:14:46 -0400
Subject: [PATCH 154/380] Add unit tests using python TileUDT in UDF's.

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/test/python/PyRasterFramesTests.py    | 68 ++++++++++++++++++-
 pyrasterframes/src/test/python/__init__.py    |  1 +
 2 files changed, 67 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/test/python/PyRasterFramesTests.py b/pyrasterframes/src/test/python/PyRasterFramesTests.py
index f98eb0b3d..42936268f 100644
--- a/pyrasterframes/src/test/python/PyRasterFramesTests.py
+++ b/pyrasterframes/src/test/python/PyRasterFramesTests.py
@@ -352,8 +352,6 @@ def test_cell_type_no_data(self):
         self.assertTrue(math.isnan(CellType.float64().no_data_value()))
         self.assertEqual(CellType.uint8().no_data_value(), 0)
 
-
-class UDT(TestEnvironment):
     def test_cell_type_conversion(self):
         for ct in rf_cell_types():
             self.assertEqual(ct.to_numpy_dtype(),
@@ -370,11 +368,19 @@ def test_cell_type_conversion(self):
                                  "GTCellType comparison for " + str(ct_ud)
                                  )
 
+
+class UDT(TestEnvironment):
+
+    def setUp(self):
+        self.create_rasterframe()
+
+
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
         self.assertTrue(t1.cells.mask[1][0])
         self.assertIsNotNone(t1.cells[1][1])
         self.assertEqual(len(t1.cells.compressed()), 3)
+
         t2 = Tile(np.array([[1.0, 2.0], [float('nan'), 4.0]]), CellType.float32())
         self.assertEqual(len(t2.cells.compressed()), 3)
         self.assertTrue(t2.cells.mask[1][0])
@@ -403,6 +409,64 @@ def test_tile_udt_serialization(self):
             long_trip = df.first()["tile"]
             self.assertEqual(long_trip, a_tile)
 
+    def test_udf_on_tile_type_input(self):
+        import numpy.testing
+        # rf_local_add(t, 0) is to force lazy eval; accessing tile.tile is to get at the actual Tile type vs PRT struct
+        df = self.spark.read.rastersource(self.img_uri).withColumn('tile2', rf_local_add_int(col('tile.tile'), 0))
+        rf = self.rf
+
+        # create trivial UDF that does something we already do with raster_Functions
+        @udf('integer')
+        def my_udf(t):
+            a = t.cells
+            return a.size  # same as rf_dimensions.cols * rf_dimensions.rows
+
+        rf_result = rf.select(
+            (rf_dimensions('tile').cols.cast('int') * rf_dimensions('tile').rows.cast('int')).alias('expected'),
+            my_udf('tile').alias('result')).toPandas()
+
+        numpy.testing.assert_array_equal(
+            rf_result.expected.tolist(),
+            rf_result.result.tolist()
+        )
+
+        df_result = df.select(
+            (rf_dimensions(df.tile).cols.cast('int') * rf_dimensions(df.tile).rows.cast('int') -
+                my_udf(df.tile2)).alias('result')
+        ).toPandas()
+
+        numpy.testing.assert_array_equal(
+            np.zeros(len(df_result)),
+            df_result.result.tolist()
+        )
+
+    def test_udf_on_tile_type_output(self):
+        import numpy.testing
+
+        rf = self.rf
+
+        # create a trivial UDF that does something we already do with a raster_functions
+        @udf(TileUDT())
+        def my_udf(t):
+            import numpy as np
+            a = np.log1p(t.cells)
+            return Tile(a, CellType.from_numpy_dtype(a.dtype))
+
+        rf_result = rf.select(
+            rf_tile_max(
+                rf_local_subtract(
+                    my_udf(rf.tile),
+                    rf_log1p(rf.tile)
+                )
+            ).alias('expect_zeros')
+        ).collect()
+
+        numpy.testing.assert_almost_equal(
+            [r['expect_zeros'] for r in rf_result],
+            [0.0 for _ in rf_result],
+            decimal=6
+        )
+
     def test_no_data_udf_handling(self):
         t1 = Tile(np.array([[1, 2], [0, 4]]), CellType.uint8())
         self.assertEqual(t1.cell_type.to_numpy_dtype(), np.dtype("uint8"))
diff --git a/pyrasterframes/src/test/python/__init__.py b/pyrasterframes/src/test/python/__init__.py
index 275612087..5dac30274 100644
--- a/pyrasterframes/src/test/python/__init__.py
+++ b/pyrasterframes/src/test/python/__init__.py
@@ -64,6 +64,7 @@ def setUpClass(cls):
         cls.spark = (SparkSession.builder
                      .config('spark.driver.extraClassPath', jarpath[0])
                      .config('spark.executor.extraClassPath', jarpath[0])
+                     .config('spark.ui.enabled', False)
                      .withKryoSerialization()
                      .getOrCreate())
         cls.spark.sparkContext.setLogLevel('ERROR')

From 0a99e29b66d9834815b4f6b67ded1c77c19b4d80 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 12 Jun 2019 15:51:07 -0400
Subject: [PATCH 155/380] Python Tile type improvements: optional cell_type on
 declaration; equality correctly handles no_data; add __matmul__ operator

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/rf_types.py    | 49 ++++++-------
 .../src/test/python/PyRasterFramesTests.py    | 68 ++++++++++++++++---
 2 files changed, 81 insertions(+), 36 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 41b5c0efb..2849642ac 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -271,9 +271,16 @@ def __repr__(self):
 
 
 class Tile(object):
-    def __init__(self, cells, cell_type):
-        self.cell_type = cell_type
-        self.cells = cells.astype(cell_type.to_numpy_dtype())
+    def __init__(self, cells, cell_type=None):
+        if cell_type is None:
+            # infer cell type from the cells dtype and whether or not it is masked
+            ct = CellType.from_numpy_dtype(cells.dtype)
+            if isinstance(cells, np.ma.MaskedArray):
+                ct = ct.with_no_data_value(cells.fill_value)
+            self.cell_type = ct
+        else:
+            self.cell_type = cell_type
+        self.cells = cells.astype(self.cell_type.to_numpy_dtype())
 
         if self.cell_type.has_no_data():
             nd_value = self.cell_type.no_data_value()
@@ -285,7 +292,7 @@ def __init__(self, cells, cell_type):
 
     def __eq__(self, other):
         if type(other) is type(self):
-            return self.cell_type == other.cell_type and np.array_equal(self.cells, other.cells)
+            return self.cell_type == other.cell_type and np.ma.allequal(self.cells, other.cells)
         else:
             return False
 
@@ -303,51 +310,39 @@ def __add__(self, right):
         else:
             other = right
 
-        _sum = np.add(self.cells, other)
-        ct = CellType.from_numpy_dtype(_sum.dtype)
-        if isinstance(_sum, np.ma.MaskedArray):
-            ct = ct.with_no_data_value(_sum.fill_value)
-
-        return Tile(_sum, ct)
+        return Tile(np.add(self.cells, other))
 
     def __sub__(self, right):
         if isinstance(right, Tile):
             other = right.cells
         else:
             other = right
-        _diff = np.subtract(self.cells, other)
-        ct = CellType.from_numpy_dtype(_diff.dtype)
-        if isinstance(_diff, np.ma.MaskedArray):
-            ct = ct.with_no_data_value(_diff.fill_value)
-
-        return Tile(_diff, ct)
+        return Tile(np.subtract(self.cells, other))
 
     def __mul__(self, right):
         if isinstance(right, Tile):
             other = right.cells
         else:
             other = right
-        prod = np.multiply(self.cells, other)
-        ct = CellType.from_numpy_dtype(prod.dtype)
-        if isinstance(prod, np.ma.MaskedArray):
-            ct = ct.with_no_data_value(prod.fill_value)
-
-        return Tile(np.multiply(self.cells, other), self.cell_type)
+        return Tile(np.multiply(self.cells, other))
 
     def __truediv__(self, right):
         if isinstance(right, Tile):
             other = right.cells
         else:
             other = right
-        quot = np.true_divide(self.cells, other)
-        ct = CellType.from_numpy_dtype(quot.dtype)
-        if isinstance(quot, np.ma.MaskedArray):
-            ct = ct.with_no_data_value(quot.fill_value)
-        return Tile(quot, ct)
+        return Tile(np.true_divide(self.cells, other))
 
     def __div__(self, right):
         return self.__truediv__(right)
 
+    def __matmul__(self, right):
+        if isinstance(right, Tile):
+            other = right.cells
+        else:
+            other = right
+        return Tile(np.matmul(self.cells, other))
+
     def dimensions(self):
         # list of cols, rows as is conventional in GeoTrellis and RasterFrames
         return [self.cells.shape[1], self.cells.shape[0]]
diff --git a/pyrasterframes/src/test/python/PyRasterFramesTests.py b/pyrasterframes/src/test/python/PyRasterFramesTests.py
index 42936268f..3729d313c 100644
--- a/pyrasterframes/src/test/python/PyRasterFramesTests.py
+++ b/pyrasterframes/src/test/python/PyRasterFramesTests.py
@@ -29,6 +29,7 @@
 from pyspark.sql.types import *
 from . import TestEnvironment
 
+
 class VectorTypes(TestEnvironment):
 
     def setUp(self):
@@ -374,7 +375,6 @@ class UDT(TestEnvironment):
     def setUp(self):
         self.create_rasterframe()
 
-
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
         self.assertTrue(t1.cells.mask[1][0])
@@ -449,8 +449,7 @@ def test_udf_on_tile_type_output(self):
         @udf(TileUDT())
         def my_udf(t):
             import numpy as np
-            a = np.log1p(t.cells)
-            return Tile(a, CellType.from_numpy_dtype(a.dtype))
+            return Tile(np.log1p(t.cells))
 
         rf_result = rf.select(
             rf_tile_max(
@@ -461,6 +460,7 @@ def my_udf(t):
             ).alias('expect_zeros')
         ).collect()
 
+        # almost equal because of different implemenations under the hoods: C (numpy) versus Java (rf_)
         numpy.testing.assert_almost_equal(
             [r['expect_zeros'] for r in rf_result],
             [0.0 for _ in rf_result],
@@ -516,16 +516,63 @@ def less_pi(t):
 
 class TileOps(TestEnvironment):
 
+    def setUp(self):
+        # convenience so we can assert around Tile() == Tile()
+        self.t1 = Tile(np.array([[1, 2],
+                                 [3, 4]]), CellType.int8().with_no_data_value(3))
+        self.t2 = Tile(np.array([[1, 2],
+                                 [3, 4]]), CellType.int8().with_no_data_value(1))
+
     def test_addition(self):
-        t1 = Tile(np.array([[1, 2], [3, 4]]), CellType.int8().with_no_data_value(3))
-        e1 = np.ma.masked_equal(np.array([[5, 6], [7, 8]]), 7)
-        self.assertTrue(np.array_equal((t1 + 4).cells, e1))
+        e1 = np.ma.masked_equal(np.array([[5, 6],
+                                          [7, 8]]), 7)
+        self.assertTrue(np.array_equal((self.t1 + 4).cells, e1))
+
+        e2 = np.ma.masked_equal(np.array([[3, 4],
+                                          [3, 8]]), 3)
+        r2 = (self.t1 + self.t2).cells
+        self.assertTrue(np.ma.allequal(r2, e2))
 
-        t2 = Tile(np.array([[1, 2], [3, 4]]), CellType.int8().with_no_data_value(1))
-        e2 = np.ma.masked_equal(np.array([[3, 4], [3, 8]]), 3)
-        r2 = (t1 + t2).cells
+    def test_multiplication(self):
+        e1 = np.ma.masked_equal(np.array([[4, 8],
+                                          [12, 16]]), 12)
+
+        self.assertTrue(np.array_equal((self.t1 * 4).cells, e1))
+
+        e2 = np.ma.masked_equal(np.array([[3, 4], [3, 16]]), 3)
+        r2 = (self.t1 * self.t2).cells
         self.assertTrue(np.ma.allequal(r2, e2))
 
+    def test_subtraction(self):
+        t3 = self.t1 * 4
+        r1 = t3 - self.t1
+        # note careful construction of mask value and dtype above
+        e1 = Tile(np.ma.masked_equal(np.array([[4 - 1, 8 - 2],
+                                               [3, 16 - 4]], dtype='int8'),
+                                     3, )
+                  )
+        self.assertTrue(r1 == e1,
+                        "{} does not equal {}".format(r1, e1))
+        # put another way
+        self.assertTrue(r1 == self.t1 * 3,
+                        "{} does not equal {}".format(r1, self.t1 * 3))
+
+    def test_division(self):
+        t3 = self.t1 * 9
+        r1 = t3 / 9
+        self.assertTrue(np.array_equal(r1.cells, self.t1.cells),
+                        "{} does not equal {}".format(r1, self.t1))
+
+        r2 = (self.t1 / self.t1).cells
+        self.assertTrue(np.array_equal(r2, np.array([[1,1], [1, 1]], dtype=r2.dtype)))
+
+    def test_matmul(self):
+        r1 = self.t1 @ self.t2
+        nd = r1.cell_type.no_data_value()
+        e1 = Tile(np.ma.masked_equal(np.array([[nd, 10],
+                                               [nd, nd]], dtype=r1.cell_type.to_numpy_dtype()), nd))
+        self.assertTrue(r1 == e1)
+
 
 class PandasInterop(TestEnvironment):
     def setUp(self):
@@ -590,6 +637,7 @@ def test_extended_pandas_ops(self):
         self.assertIsInstance(array_back_2, Tile)
         np.testing.assert_equal(array_back_2.cells, simple_array.cells)
 
+
 class RasterJoin(TestEnvironment):
 
     def setUp(self):
@@ -705,8 +753,10 @@ def path(scene, band):
         b1_paths = [s.format('1') for s in scene_dict.values()]
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
+
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests
 
+
 unittest.TextTestRunner().run(suite())

From 636252c99ada17bf5483a9096b3e0ec1b6ad0df6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 12 Jun 2019 16:27:32 -0400
Subject: [PATCH 156/380] matmul test python 2 compatibility.

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../test/scala/org/locationtech/rasterframes/TestData.scala  | 1 -
 pyrasterframes/src/test/python/PyRasterFramesTests.py        | 5 ++++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index a149f6b2a..7e1acda1d 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -32,7 +32,6 @@ import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
 import geotrellis.spark._
 import geotrellis.spark.testkit.TileLayerRDDBuilders
 import geotrellis.spark.tiling.LayoutDefinition
-import geotrellis.vector.io.json.JsonFeatureCollection
 import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.commons.io.IOUtils
 import org.apache.spark.SparkContext
diff --git a/pyrasterframes/src/test/python/PyRasterFramesTests.py b/pyrasterframes/src/test/python/PyRasterFramesTests.py
index 3729d313c..b424979d9 100644
--- a/pyrasterframes/src/test/python/PyRasterFramesTests.py
+++ b/pyrasterframes/src/test/python/PyRasterFramesTests.py
@@ -567,7 +567,10 @@ def test_division(self):
         self.assertTrue(np.array_equal(r2, np.array([[1,1], [1, 1]], dtype=r2.dtype)))
 
     def test_matmul(self):
-        r1 = self.t1 @ self.t2
+        # if sys.version >= '3.5':  # per https://docs.python.org/3.7/library/operator.html#operator.matmul new in 3.5
+        #     r1 = self.t1 @ self.t2
+        r1 = self.t1.__matmul__(self.t2)
+
         nd = r1.cell_type.no_data_value()
         e1 = Tile(np.ma.masked_equal(np.array([[nd, 10],
                                                [nd, nd]], dtype=r1.cell_type.to_numpy_dtype()), nd))

From 4d42d11346eb5dcb5a7c5e62bb18a10527314478 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 13 Jun 2019 17:13:35 -0400
Subject: [PATCH 157/380] Initial prototype of pretty IPython rendering
 rf_types.Tile

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../pretty_rendering_rf_types.tile.ipynb      | 870 ++++++++++++++++++
 1 file changed, 870 insertions(+)
 create mode 100644 rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb

diff --git a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
new file mode 100644
index 000000000..cf90008bd
--- /dev/null
+++ b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
@@ -0,0 +1,870 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pretty rendering of `rf_type.Tile` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup Spark Environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "from pyrasterframes import *\n",
+    "from pyrasterframes.rasterfunctions import *\n",
+    "\n",
+    "spark = SparkSession.builder \\\n",
+    "    .withKryoSerialization() \\\n",
+    "    .getOrCreate() \\\n",
+    "    .withRasterFrames()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'scalaVersion': '2.11.12', 'builtAtMillis': '1560432286838', 'sbtVersion': '1.2.8', 'name': 'core', 'rfSparkVersion': '2.3.2', 'moduleName': 'rasterframes', 'rfGeoMesaVersion': '2.2.1', 'rfGeoTrellisVersion': '2.2.0', 'version': '0.8.0-SNAPSHOT', 'builtAtString': '2019-06-13 13:24:46.838'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyrasterframes import RFContext\n",
+    "RFContext.active().build_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read an EO raster source  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "href1 = \"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\"\n",
+    "\n",
+    "from pyspark.sql.functions import col\n",
+    "# this here contortion is to force the RasterRef to evaluate; and to get the tile type \n",
+    "df = spark.read.rastersource(href1).withColumn('tile2', \n",
+    "                                               rf_local_add_int(col('tile').tile, 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- tile_path: string (nullable = false)\n",
+      " |-- tile: struct (nullable = true)\n",
+      " |    |-- tile_context: struct (nullable = false)\n",
+      " |    |    |-- extent: struct (nullable = false)\n",
+      " |    |    |    |-- xmin: double (nullable = false)\n",
+      " |    |    |    |-- ymin: double (nullable = false)\n",
+      " |    |    |    |-- xmax: double (nullable = false)\n",
+      " |    |    |    |-- ymax: double (nullable = false)\n",
+      " |    |    |-- crs: struct (nullable = false)\n",
+      " |    |    |    |-- crsProj4: string (nullable = false)\n",
+      " |    |-- tile: tile (nullable = false)\n",
+      " |-- tile2: tile (nullable = true)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boo!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|tile_path                                                                                                     |tile                                                                                                                                                                                                                                                                                                                                                                                                                                      |tile2                                                                                                                               |\n",
+      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.4455356755667E7, -2342509.0947641465, 1.4573964811098041E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.4455356755667E7, -2342509.0947641465, 1.4573964811098041E7, -2223901.039333))))]    |[int16ud32767, (256,256), [1225,1244,1247,1222,1189,1216,1206,1185,1132,1040,...,1575,1489,1281,1189,1202,1145,1171,1189,1297,1382]]|\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.457396481109804E7, -2342509.0947641465, 1.4692572866529081E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.457396481109804E7, -2342509.0947641465, 1.4692572866529081E7, -2223901.039333))))]|[int16ud32767, (257,256), [1206,1140,1227,1147,1106,1026,994,1047,1020,1174,...,1793,1743,1685,1688,1706,1727,1766,1689,1561,1515]] |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.469257286652908E7, -2342509.0947641465, 1.4811180921960121E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.469257286652908E7, -2342509.0947641465, 1.4811180921960121E7, -2223901.039333))))]|[int16ud32767, (257,256), [1558,1546,1445,1329,1539,1653,1576,1533,1603,1610,...,1399,1434,1330,1429,1470,1451,1422,1407,1369,1310]]|\n",
+      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "only showing top 3 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.show(3, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Make it pretty\n",
+    "\n",
+    "To add this to the Tile class we can override `_repr_png_` (or `_repr_svg_` or others) to accomplish this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tile_to_png(t):\n",
+    "    # https://gist.github.com/rduplain/1641344\n",
+    "    import io\n",
+    "    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
+    "    from matplotlib.figure import Figure\n",
+    "    from matplotlib.pyplot import imshow\n",
+    "\n",
+    "    fig=Figure()\n",
+    "    canvas=FigureCanvas(fig)\n",
+    "    \n",
+    "    axis =  fig.add_subplot(1,1,1)\n",
+    "    array = t.cells\n",
+    "\n",
+    "    axis.imshow(array)\n",
+    "    \n",
+    "    output = io.BytesIO()\n",
+    "    canvas.print_png(output)\n",
+    "    return output.getvalue()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://ipython.readthedocs.io/en/stable/config/integrating.html#integrating-rich-display\n",
+    "# https://ipython.readthedocs.io/en/stable/config/integrating.html#formatters-for-third-party-types\n",
+    "import pyrasterframes.rf_types\n",
+    "png_formatter = get_ipython().display_formatter.formatters['image/png']\n",
+    "png_formatter.for_type(pyrasterframes.rf_types.Tile, tile_to_png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get a tile, I'm from Missouri!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tile = df.select(df.tile2).first().tile2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "Tile(masked_array(\n",
+       "  data=[[1225, 1244, 1247, ..., 1305, 1245, 1206],\n",
+       "        [1166, 1188, 1190, ..., 1381, 1251, 1193],\n",
+       "        [1156, 1110, 1122, ..., 1248, 1245, 1270],\n",
+       "        ...,\n",
+       "        [1485, 1749, 1761, ..., 1034,  996,  998],\n",
+       "        [1780, 1777, 1663, ..., 1008, 1027, 1174],\n",
+       "        [1728, 1647, 1562, ..., 1189, 1297, 1382]],\n",
+       "  mask=False,\n",
+       "  fill_value=32767,\n",
+       "  dtype=int16), int16ud32767)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boom!\n",
+    "\n",
+    "# Now do it with Pandas DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_df = df.select(rf_crs(df.tile), \n",
+    "                      rf_extent(df.tile), \n",
+    "                      df.tile2).limit(10).toPandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tile_to_html(t):\n",
+    "    import base64\n",
+    "    b64_img_html = '<img src=\"data:image/png;base64,{}\" />'\n",
+    "    \n",
+    "    bitz = tile_to_png(t)\n",
+    "    b64_img = base64.b64encode(bitz).decode('utf-8').replace('\\n', '')\n",
+    "    return b64_img_html.format(b64_img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is less general as we have to construct this call each time... Also need to enforce this max col width option."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rf_crs(tile)</th>\n",
+       "      <th>rf_extent(tile)</th>\n",
+       "      <th>tile2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import pandas as pd\n",
+    "pd.options.display.max_colwidth = -1\n",
+    "HTML(pandas_df.to_html(escape=False, formatters=dict(tile2=tile_to_html)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rf_crs(tile)</th>\n",
+       "      <th>rf_extent(tile)</th>\n",
+       "      <th>tile2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
+       "      <td>Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                         rf_crs(tile)  \\\n",
+       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "\n",
+       "                                                                  rf_extent(tile)  \\\n",
+       "0  (14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)      \n",
+       "1  (14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)    \n",
+       "2  (14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)    \n",
+       "3  (14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)    \n",
+       "4  (14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)    \n",
+       "5  (15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)     \n",
+       "6  (15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)    \n",
+       "7  (15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)    \n",
+       "8  (15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)   \n",
+       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                   tile2  \n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])  \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])  \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])  \n",
+       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])  \n",
+       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])  \n",
+       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])  \n",
+       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])  \n",
+       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])  \n",
+       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                      \n",
+       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                       "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Default viz remains\n",
+    "pandas_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pd_tile_to_html(dataframe):\n",
+    "    from IPython.display import HTML\n",
+    "    import pandas as pd\n",
+    "    pd.options.display.max_colwidth = -1\n",
+    "    \n",
+    "    tile_cols = []\n",
+    "    for c in dataframe.columns:\n",
+    "        if isinstance(dataframe.iloc[0][c], pyrasterframes.rf_types.Tile):\n",
+    "            tile_cols.append(c)\n",
+    "    # dict keyed by column with custome rendering function\n",
+    "    formatter = {c: tile_to_html for c in tile_cols}\n",
+    "    return dataframe.to_html(escape=False, formatters=formatter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.pd_tile_to_html(dataframe)>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this would possibly be able to be in an import  scenario?\n",
+    "html_formatter = get_ipython().display_formatter.formatters['text/html']\n",
+    "html_formatter.for_type(pd.DataFrame, pd_tile_to_html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rf_crs(tile)</th>\n",
+       "      <th>rf_extent(tile)</th>\n",
+       "      <th>tile2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
+       "      <td><img 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qh17e70HJmISIPQ8cWuwtsQDxKqy19XYDTRvYqTWW5p75XqA2Y2GVhl2CzPtbC5+zd/aROI8kSqwxOWjLJB5hTQt4do2rmqJ/QyiVRX1dsbwrQUouCbQjgpi4U3cMgBPcdoSBhnqhNC3JA9fBaSxWKr8ZLnZnEwnYmBbmkU2yRudJR5Yp73rZGLzX4slTGaM7jywMN1nn4SqRpr7HP7ws4xuBxb7Dl8p+5+vOPzmEtdaCTi8F9j++FvEh8fEZYXf3rLXcN7s8qoQ3cY48ohhgyxbJ3VIUdDee4DfGtn1e2/Zi39AakgicZ5IApa4dKzSMjTz3ToVq2DaYUYzsvUqsfDMDwrqsTB6u6Xdz6m2Heph/EZjg9AiCIp6s9WrE1wb8dO6q7gyq9JyjzudbebBwM68QrC5sFWGYt0gZYHbHxIOj8z0AYTjY7LHb1FdHVJvb7P4nuvkc2W546h2hXoro97xPP3/ntls2+EZenRM7ObI/NbIMg21yzls226vmEVMoWqGDcnsY96d8YWALhb2+Cyzs7PBwNqJZ8mFmDj/JAFLXDqkNZt67OdIiDQ7JdELOOg9bFlcyTh5rqT3MJLPMYu9ggQY3mnJZq3NfqmZQfzC9oSpE9yyQWIkDAuy426x5GyJZt4yEZ2DtjHzhCrS62zszuOGObpcEiZTXL+HLpZIv8ft/+oj4CCbQjuCxVVl9wuCRIg5lBN4/BfvI/Ml2jTE6czSNrzHFfkmukrEzq+yzMSxLG1RZndfxG2up9tXppOpLbFctRLX1v8kYInzTxKwxKVDq66N1y16VAEXlJA5jt6bM7gfkRZOn/G4GkJuZ2LlqVVP7TAjn7a0fWujuVX11USzzTtbzYIIcjbbGDhELF7KufVt7ayO4p2dP5Uljs6y/q6noG7IZ8rsFiyuQj4Rrn88Ug8dzVDwFWx/pbF26INDJMvQtrVsQ432XuuFDUTTJdJrl3e4Sp9fLdxsarR1a/GTEOxxbbux2XtvIpjciIkLQBKwxOWjX9Ju9wi9jGbL4xeRmAnLXY9rIBTC8opQnCquhZgBDtq+oM6RLTpjRxXBQcgdLiiuapHK2n5RMtrtPtkqiV7VKrC2tVSNzuFHVSGjkTkDvUPntZk7nn+KOCi4++17VPtK+VC49skl0kamj/dAIJ8r+1+sKD79Cu3ZFFfkyGoAumMtNquzri5VXsrCshm7a5I8X+cu4r2tbelahpQlOp11bcbO15UqsMQFIAlY4lIiUcnPKkLZpxl5ikmg7XXfVOgd2tmWOkBAWhMMFNqBw9celc1ws1taNBXOoZnDLVtc1azPvCTqpvpqWhRMxMbjdUJ9PDvDbW0Rb10jjAqO3t8jFjB6E3oPA9JG6t0C1yq+tkT806dKrn5STbyKYrP7az7H7e5YqzDaoPKqCltHValuXI/Omc2+LKw6iwG/u0M8OUWcWErHiq/OcUwkzilJwBKXDxGkCcQiQxSK05Z26ClPFV9HQiG0fWHnS0vOnrJqJ+TWSjTxiEirCKAZllovtjpFe2bckNDaGVvucRPbC0Znm5etsbUVmxaaBg22qNLv7nDv33uG5VWhPFb6R8rWGy35acP8Ro/ltZLhGzPCMMctWtxnX2KoCuORVV2rdSze44YDE6dueaZsjW2L87C/af817WbvF2wGrr1Dm9pEtksJWa1QiWfTzeLNROKck+bAEpcPwcQrRFwTaYeeZuBwrdK/t6T/oEEC1Ds5LqiF4861W6cC2TyimdAOPNELrtlsNZZgt9ut3vp23BrYedeyMtdhCPZ5Z6HX2QxtGm7/+WdxLYzfiBRnyuj1GYPXz2xlC7DYdyweGxBKj+bezrpGw835Wp5Z+r23f7aqFsArvR7aNDbbtZo7W6yuY9VaFLRp0Kq2s66VE1GcWeu7ZZz+yp49TyJxAUgVWOJSIiEiSrc2BbLK2oOhn3H8XMHoTqAe2ffMYQgxN7GaXc/JFpFiYrmHmgl+3loVppFYZjYTVjVokVmgbwiW4F4U6Hy+WTo56Nvg89PXufL7CzOInNU045x6t2T57BCJNlSdTy1YOGZC/updAtjzhAgaLOk+RhuUVrWPnXVfnDPhErEE/VXltcpBDMHOwdrWRKsTNsmdzbB1CfUmgJuYrUTiPJMqsMTlI0IY5JackQnZPKBiJg2/aBk8iDRDZwKXmXj5unMLKmSLSLZQYi5d4oWu94HF3Js4NoHYK3AnU/uBv7KydwIjWbYeYpZeydnTAxYHBdm0Ibt/Rj5tmTyRk88jW1+eAOArqxizeUs4fGgGi9VsV5ahsXNB7ozttUIwu/5sRjybWMU3nVqafbY6B+vs8l3lJXkOMay/pyvHIiD9XtrInLhQpL+picuHWNqGrzyaQbWbkc0iofTU2wXqhVCYvT52gewuWAiwX0SbCXZCM3AU09BlIoKrQLpgXxXBn842gcGZ32xlrhuzsGcZMhxw/wdukc+Urd8/ghBorm/z8L19tl9vKB4sWNwaMnhjwvzxMcW9GfLGbSJY1QQWAxU7h2MIyEm0luFoaKtQRiNYLMxiXze2Bbrt9o3lue0maxrwbj2DxmoXWNua6UQVUWdVY56nM7DEhSBVYIlLx6ptqA7a0m6HviOfWVsxFFCe2e18phRTJfouvDcTljseFIppsAT6qPhlt5W5cwG6urVqxYmJV5dzqLOFmSC6dSpH3/8Uw7st1bZtPj7+8AEnz/fpP4xk0wZ3+wHSKmfv3jbzyN0HaF3jb163xPgQNvNc68WYbl3xxdMz4umZidZsRjg+Jjx4QPWBJ2wT83Rme8UOriLbW/gb17u1KWLXmWWoqp21rfISV9FXicQ5J1VgiUuHqKJdFSVRCbl9jKWjHlkrEaDtA33BNdAMhd2XG6ptT8yFduDWA9Dlw4hExS0a6GKq1nNSoXMfdnZ1KR3aK5GmZfJN1+gfBQafu03/jTHH37LPcsdx45feon39DeL3fJDFtzxBcdZQHi3xkyWT73kXMQPXQrY4YPDFu+jpmbUCtXvtIkePTyxFYzjk7HufodpyHH0w8p7/4XV0uST7wlvrhPp4NoF3PYHMKzg6QcZjOxfLMqvQOsu9np7Z9/JH3l8icY5JApa4fETIZmF9Btb2BV9DzGT9/di1EbOFrj9OH8sY3gu4OlLtZDbbxUoQhdjL8IsGWdTIfAmALhbIYABlYZXNMkDn4hv/zlfs9USYvWuL7Rcn7D04Qaua2//1Rxi/Edl6eYK8+LpFUu3tgEC17Ri/1eKXwcwVTVft5Tlua4weHZuVfjRERwMO/9KcKz834D3/09vocmmrUkYjWFY2xDwcIg9O0BiJ8zlSFLjhwFyJS5sf0xA37sMs/VhIXAzS39TEpUPU7PArd6FrlJjZeZevFIlC6Nlt12wMHCp0zkSLizI/vtIOPOoyhq+e2vNXti+MVdZhmSOLytIsVkPE84W14vIMruxRHtbIl1+HLTNgXPtkRTZvcC+9aS28K3scf/tjNH3hymcmhGFO/ulXCJWtRhbvbYPz0UPIc4iR6Z96gv69BeVvj9n+5NsmoIDb39vY572z8zN1Zumva/zVKyBCPD7B7Wzb9a6cimW5eWwicc5JApa4fIgQSre+Pb3lKE4VX0HbE1xrH7OlEnpmny9PFN/NgTUDoZgpvopms48weGsCbUCW1WaFSpEjdYMsO4dfUaxnsXS+6Fx/GTKdU8wWaL9H8/QBEtV+ffJFokbc7i4n33ad3sOWrYdL3KyCz7wI47E9Z5FDCLi9a+jZBJ3OIM8Z/YvbaF0zento52NdxqHWDVLk6Gy2Cezt9XDDwfq54nSGK0vbnVZVtjm6qt8xN5ZInHeSgCUuHSpQ7Xgkwsm7HL1Dq7TaPiDQDiy8txlZWG4+VUIJxURxjVKeWLZhtgw0g4ze3TkEGxrWIjcrey/HHZ5ahZVZMC6rQN3pHDqhWDkFdVnB1T38Z15Csgynigz7aNMy/a6nGdyvqcc5zVZJ794JGnWTaQjI3i5x0EMeHuOu7KOzOTroQb9kuSvo8amdjVW1JYh0K1xEBNndsdUpqnYdWbdWBaCxFqM2jbUSqwpdLNMZWOJCkAQscekQhf79huV+Tv++sjiw7cWDu0ooIJtbpdU7jrhGEbXB3dUeMYmKn7ddDFXXLsycVShqtnl3NjfLejfnpU2zDtJFIzIcIGVpgjEeold3kKC4qyY+4r1lE167wnLHM7mVsfNSzcm7Cg7eyMkeOzBL/mxuM2WLJfLweLMqJcvg4SmSea7/4zds7jhEO9sKcb2dWeeWVB9nc6vCxiOrIB9J29D5EpwnHB7h93bNmDJPApY4/yQBS1w6VCAWNsNVjzN2vxSZHThCAfmM9eqU4rQl9C3nEDHhUif4KuCrsBG0JuBWpo08M5FqWmvTxYDGaGdHndHCRE6thTjoo94jVYs0rc1tZRkaItPvepqYCdWOsPNKS/+TrzH4bE48PoF+39qR4jpxFIt/0k6cvLczt51te9POzuu0qtdRU4hYVRYiMuhbWof3FndVO3v+tkUyTzw+QURsZiwvUgsxcSFIApY4N4jI68AECECrqt8mInvA/wM8BbwO/JiqHv+hzxOVWNg8l0Ro+rYcsnestD2hmEbb8ZVZZSYaibn90PeLQPSOdr9HflpbnuJKvJxYW7DRTYRTv9+lzXtrJ66qNLpVJ8vKqrROPChy9PSMkx96D21fqMfCY785wb36NrpYorLE7WyvF1faa/S6pZNiFdn2GJ0tcFf2rD0Zo6VpSNzERK02NHdRUoiz629aa2+CrX1xjnh6hoyGJn5ZZk7GGP7N/wEnEv+GSYPMifPGn1bVb1HVb+s+/+vAr6nqc8CvdZ//oagIIRdQ2H6tAoH+YVxHRrlWkVaptj3abRFxTcRVEd9E/KIlmzYg4KoWLXPC3gjtlTZErGpCUBYmBqu8wjaYnT7EdTjuWkC61l/7lTe5/Z+8j8W+LaxsRuCmSxOmlbAsl9ZeXLX8QrfepMjtTEwVGQ3svm1Yh/ZKlln7sixMWMGqqs4iL72eZTN6b+drRUF4eIzb3uoSOZq1s1J8+r9t4vyTBCxx3vkR4Ge62z8D/Ogf+QgH+TSSzyPL/ZzeccDXmx1b6oTlvke9WIBva6KWn9XQ5R6KmnjJvCKMe8TC4yYzm/+qG2vrVXW3siRs2nxNaytN+r21wYNrVwDsXOyD76P/IDK8H9h+taE8VjT3uOvXIC9w45EJ4EqsRJDtsQll3djzihDvH1o70jurzlaII87m6GRiFv8QkZ4tQrMZsaUJ6iOiF6cze+hoaJWcE1Kab+IikAQscZ5Q4FdF5JMi8pPd1w5U9Q5A9/HaH+eJ2qHD19YyrEeOticsdxz5NNKWVp2tZsTy04qYO9Q7/LIlO12QPZwhTUDLAj+rye+edpWWVTzSK62SidFaeCFYBROCiVqM1j6MEZnZPFh7bYt6v0f/qEWdcOcjmUVXPTbi8Htv0rz3FnEyXa9D0X7ZuRktFZ+82768rGx+y3X/fJ1bn3URA5JllqgRu8qryDeuQm+ZjVrV1kLslesztfUCzBCAdAaWOP+kPkHiPPFdqnpbRK4B/1REXvzjPrATvJ8EKPo7uK5FKFHpHZsho3ds81fDuy3NMOPsycxWl5zZD3gtHLQRLXPa3T5+1uBmFTKZoz3bZCwUJiiPmBykKNC627PlPXpyagKTZWa02BoT9scWAuwt4b4eCdc/Hpje9Ay+/IDBlyEePgTnLGGjDXAygWEf0M0yS2xVinTrWuj3NsaQ5dJah6vll1mGLiviySlub+eRxHzFjcyer7M57tqVd1SSq1ZkInHeSQKWODeo6u3u430R+Xngw8A9EXlMVe+IyGPA/T/gsT8N/DTAeOeWhlzoH7bEXJjcyhi/1dL2HaOXTomfexEP5P/Ot+LnDRIiUJgL0Qv+dIE7081+rdU5l6otquwVyLIb+s1za8WtzpryHHWuW20yY/G978UFpffJV5l873PUY4evlPFbDcXRgvIoI94/NPGjE8M2WNVUFu9YNrm6htUw8tptmHlrNbrB2iFJ5qGqkczjdrbt+prGri+23Q4ZM5U8ajxBhHhy+m/xTzmR+DdHaiEmzgUiMhSR8eo28IPA54BfBH6iu9tPAL/wRz2XipDPI6HnaAaO4f1ANg+MXjr+p0ahAAAgAElEQVTl5b+yu76f/41PWSJG0E7EwC1bS37vAm4BNOv2Za1aed6j0+nayq5tMHFxQjybIAdXkF7J8Y9+gGbkmF/NuP+j72ax79Zbn10TkTbiPvHFtXi5wQC3v2u7vFaDxmBi1LabzcxFbsK52r5cN2hu1ZaennVtQNshhqqdpXm/MXb0ela5Zd7chyObHdP5gng6Wf2B/Mn+IBOJryOpAkucFw6Anxf7wZkBf09Vf1lEfg/4ORH5z4E3gL/4Rz2ROljsZ6iDeizMM8f2azC9uUM+sR/M2dNPUt/cJb8/IQ57oCBtQBb1pvJa/RDPvJ1jdcnuToT25JRsNAKwtSRqohdnM2R3xMnzQ8rTyGLfg8CVT51x9vwYF5Ttj7/N8vkDZF4hwz7S6xHPJsTpDL86W1ss0e0xnE3grIGDK0jddNuS9Z1JGUVu4joaQLOZ4dJlZXFSYHNg/X53/lVBvyTevmsuRzChGw5wXUwW9b/Wn2Ui8XUhCVjiXKCqrwLf/DW+fgT8wL/Kc7mghBzyuZIL7L44J3/riPqpqwwedDmG/ZJYeNqrY9yywdUtblqtXhTKwgJ6nSCTCu2XyGRme7PaluzJx63aiWopF94Rutbb/GafwYOW2UFOObGB6cWtIafPOBA4efYJymPl4PcO7eysMTeg29slHh7hDq6i/SFyNiXOF8jTj5v7MXYmi+jQqkKGnZW+LMywETaVow0v98yN2O+b2/DooW1dblrCK6/bxue6WdvqtWns/WdZ8nAkLgSphZi4fETYerMhn0cGh7aIMu6Ncf/80+S/+gni930Qmpbi4y/ipxWxzMxg0cvtXKnI7ayr2ycGdC1Cq7R0NLBKpuk2JGeZDRAD/uAavfsVfhHoH7WMXjnDLwOza57+A+Xapxp6D5WDjx0Rq4rFn/4AhED7/qdp33qbuFwSHxwh0znx9MxCdrvtytrvqrPVa3YZi9K0a3ciZYGqgkbC3fsmZKsW4nBoNv/xCL+zg+SZLbH0bmMS6ez1icRFQDSFdiYuGcMrj+v3XP0rzJ/ZZfDSIc1jO4BlHZYv30P7Je2VMbHnCYXDV5FsajNg/mS6ac91Z1va7dDCe8K9B/iDq5B54v1DZDgkHB6SPXELqhrd3+HBh/eIhQVjxAxcbfmMxVRp+sLgQcvkVoYE2P8/f3t93f7qVcQ7ax2enMH2GI7PbK6sadFhH71zH7e9ZfNc3lvCB6zPu2y5ZpeiEeP6vbR37+Hf9244PCYePUTbFtfroaqICG53xyqwukFD4HcW/5jT9kGqwxLnmtRCTFw6XK3Mn9nl+IWceuuAYhrof/RFa/+VBdK0VFds+Lc4a/Aza53509n6/EszjzSt2eeX3owabTCLe1Wjx3NkPCI86AJw24Du7xCLjNGdlre/L6MdKHglO3McfCLiF5HFXkYshNktePK/3YhX9tj1jTlkNIDxsHsz5jzULaue3P4uLCuzzVc1CrYOZa8zp3QGlDiZIoMBOpsh/R7+4Br62pv23roKKy6XSFmuxUvyHHo9a40mEheAJGCJS4dmAg4GdyO7H30dyoL6W99F8eqDjXNPoTyuyU4WhGGBP12gvkuTd27t9pOqIc7nxJUZ4oPvQ770mlnZo9q+r14PHfQ4fe8Okyc8w9uR4kQoHwrVrnL944F67Bi/eMpX/sNtuH7Gk3/uS+vrzZ5+0lawDPswuGLnWVFRImyNiIMSN1msl2Rq2yI3r9t5Vrsaqra0fG1bM2OUe2aj3xqvd4TF+XzzmrduWjLHYkk8m9h77qovKYqv9x9ZIvEnIglY4vKhsNjL2Pm7v83qNMe9/gYtkD1+C50vKE5q2mGGqwv8yXxdeUll1VjcHa03L+sLT5G9fUi8tou7e4T2e9ayqxvczjaTD93CVUo+jey8rOSTwPj1lslTfa78vrXzCiLN3oBb/8Qx+PkvvfN6ReDaHnIygRAIh0eWT7i3Y+7Ct+9bpTVfEJcV/tkn0UEJX7lNeP4J/JffsPUsg75dc4iEB4c2/zXoE27fRbwnu35AOD4x48np2do9qU1rt3sl4gtzKaajhcQFIJk4EpeOWED7Y0e8+Q/ej3/+Wdw3vQDA8j/4MDrqE46PyR/O6b98iDtbwNExsqjMpp7b/+nc0Rk6mTJ/73X88Yx4ZZd2u2+VSpZZ9fPYVe79yLMsdzzTG/a4bBbI5g3NVo5rlemNjNmBJX5Mn+gR869xrBQCMl8S7h+iTYPb3TXL+yoCSpz9ihHJM+pbO8iyQcZj/EtvWdZinhEfHNmOsrY1e3xUdDrHlaUJ2fGJbWF2YoIVbaZNihzp9+2xGs0gkkhcANLf1MSlw1fK1f+uwD845exbb1CPHO7938HW3/sdAhC/74Nkr9y3FuHpxPaBlYUlUoSIbo+4/5175HNlueMoD4e4V2+zePfzHH7TUwzuB/J5RJ3QO45kC6UZOvJ5S8gdD98zpN4R+vcjveNI2xMmj3uqXSiPYfTItWZPPm5DyIMSv78LeY4uFrRvvc2//yvH/C+/9YOUdzJGb8DZn5nx9I9/Fv/rn2K17CS7dRMWNsDsRrZZWVffrKq1SUMXSxO1ujER69yVcTrFDQZWdYFVZ6vbicQ5J7kQE5eO7cEN/bYP/RSLg4LTZzx+CduvtUhQskWwjMN5g5vOzd036KGDEplXIEJzbUw7zJhfy9n/nXvooOTkfds0A6GYKvkscvZkRv/QNjqrw5Lvdz39w8Diiqd3HJgdZBRTpTgL3P/WnO1XI3d+IOAmnv59xxO/8MBmzQY95OGpWeB7JeFLLwMw+UvfweBezVvfX7L7ofucfuyAnZcjRx8QbnyspTxa4m8fmfh081xSFjaXlmV2HiZi51pdZbnOUcwzs8yLswzEEBDv0W6T8+/M/xGn4TC5EBPnmlSBJS4dsXAcvb/H9qsNVz4baQeO4qwhO1kS+zn+4RSZzqHfI+5vIU1AZkt01EdmS4qX75CHSL+pIS84+sgB+TzSO7ZKqzhVeg8j+TQSCyGbReotTz0SfO0JuRAzIVsoKnD7uzMkKtObjvK24y/8yD/n1//7j3D6/j22/9mXbWY476Kj7h9x+7/5COWRki2V4vdfZ//a8+S/s8cT907RT36e4uxDDF68Rzw6JoK1AAd9VNUqriyzvV+zGeQF4mSdheiGQ9o79/Bbo64KC6z+E5sqr8RFIwlY4lKy/VpLNmtxH/sM5Yc+QL1bEvcHFA8XAOh4aPFJywa8I+yN8LMa7h3Snp2h3/nNaOYIpcM3yvzAg4JfKtWup/cw0Awd2dwEMuTC7ks1k8cL+scB15oALa44XCv0HkA7gP3PBX7pzndz5fYCP6vXO790WSGZJxwfU31wBp8csvWZGkJg53dv077+Bv7xW+jzzzL89Bvrsytg3Q4E2wKtnZV+vQesbdc7xnS+wF/dRyfTdZW2XqWyqshcKrwSF4MkYIlLR70D/Y+/jGyNCN/2ftyswpee/GiGLCpoWuLVHdzJlLA7sBipz72CXL+GYmaP4rim2crIpy1NX2iGNowMwtWf/Szz738fzcARCmG5ayJXb2Xks8j8qrUt1UM+U/zSNi9LgDvfLTz312z+S7a20Gv7yLJGipyv/M1d2s89zdavQTOE/hfvEGYLXCdQOuojJxNrA4K5JvOV0UPMfNG2SJGjbUs8ObVqbDQ0YVosoSzRydRahoM+AlaBeY8bDIhVZS3GdLSQuAAkF2Li0pGfCYc/8gKz911H2sj8yW18FeDwxFIq8gx3OkOLHGkC7VYPuXkdFkvic4+TzQMSlN7dOYsrBVtfqXni5+9x46NTBg8iR3/xmwATmcUVR70NIRfqsWP7CycmavtC27Mw4WpXKU6Vx357TnnkeOlvfjvx+z5IfP4JOD6FIufOn3uS0T8c078vnD0D1/+337IB5dHQrnc8tjfnPcRN22+140vbYOkcIjYS0LZmo+/3bD2Kxm51ip3zSdHtNevaiyKybj+uF2UmEuecVIElLh3qIHqY3MrIzwrK4+5s59oeGiOad+tRlg3StGRnapWZc/h7J9z9yJNU+yXP/J032TqdE/ZGaJFT7Zb4OpLPlLZnP+SzheIrCKVQ7Qh3vm+fag9cAxKF4kx59u9PmN8aQFT695UbH2s5/ECfg9+siU/foN4paQeCiiBBefbnTpH9PQgRGQ7QqkZ6Hg5PUI02ON22Nni8avnFsHn/MxtY1m7RpRsNTaw6g4Z4bxmIbbu+j65aiDGSjF2Ji0ISsMTlQ2H+mLD3ov3Abgc5rg5IJUgEIiZiBUgIxF6Ge3sKN6/D4TE3/skdZDq3iubmdTRzPPjOXUa3W4rTluV+zuyaZ+eVGs2Eo/fklt4uJpwAfgGj24HZdc8bP7TF8Lby4FuGPPMzbxOHfR779Ktw/QpxWJKf1dz6xWPqWzvkDxdWHUIXKtxa1dSdd0m/v27vyXBg+8cGJo5SFlY99XtQVbjR2KKpAlapBas+cR6a2sSrLNG6XoubdoPNScISF4HUK0hcOmIO+18I+EqJhSc/XpLfOwPnUO8hc7h5ZakVTYtbNHZGpIpOZzYUfHJKXCzh7gMefMuQfKa4KrLcywm5MDiM9O7NKU4arn26IlsACv1D5eB3aw4+MWe54/AL5al/cJ96S+jfU5obu7iTCVzb561/9yr+wSn87u8TXnoV/+ufwh2eot1aFlnlFvZ7dn3eFltq09jm5tkcd2XPXIirPWJggrdavbJKzV8s7bnEqrW46NqNGi0my3uz3XtvFVqqwhIXgFSBJS4dEqEaO/J5pDiJ4IW4MyRmDj+vrVXnnS2qnC8J/RzfK4lv3rFh37MZ9EqbEasb8rmy99E3efBnnsBXyviNJdnnX0P2dmm3S4p7Mw4eLon9jJPnB5RHS9ys4tpvnKK9EpnOufEbx8iiJnz5FWKvBw8dj//9JTRWBa3SL3QyJVYV/sr+ur1HWWz2dnmHBFuV4q9f26xamdlYgO0LU3Mchrg+79LajCKrVqJkGVo3lko/HtvsWJ5ZNbYSwkTinJMqsMSlw7UwutPgGyU/nCJ1S8wc7vc+j2YOsq7a8rZ12S1b2jt3TSzWrbaC+jte4P5ffj+948Bbf+FJYg7ZUsk+/xpkFt3kPvoZ5O17ACyu98jnir93Ag9P4fAh7mRig8p3j5BObOJyibt2ZZ2/6MoSt7drgrU1xu1sWyvQic2HtcFETMRuq+L2dtC5tTl1ZlUjiyUAsr1lQtVs1iq/o0LrbPJS5F1Kx2I96LwabF5vo04kzjGpAktcOvwyUBwtkLZHGPfQzOHqgHv2KTQocjYjbvVx8xotc8Lnu3DdIrdKRiNaNxS/+XmuzJ5ncb3H9d+aUO/1cHWEq/vom7dxB7a/SwZ9cDB8Y0YsM6uSBn0Y9C1lvqpNQOrGoqOKHHUOZgukV3atO4e/ed3irKCzw3ep8DGuhQswcWvb9bkV4pA8R8Fs9U1jQrwSsCzbmDxCsO3L3UoV8d5MHkBcLK0KCwEJScAS558kYIlLhzoh9M304KoWakEWtYXh1i1xq48/nqFlTuwVuG9+D/KV2+b4a8+Qog83r9Nu93CLluGvfRGahv54bGG3ZYkcXDXjxLCzuU+WSFXjVsPFK6NFka9FA8QGkEO0CmnQA+eQp27BcbeDq1cii6WJVNNYW9CJCdsqs7FpTahW6R2wHkbWlQA6MafhYrmJiVK13wNfrG3zGgK6CGjTbt7b6r6JxDkntRATlw7pBn9diGjmkEVNHPfQYY92p480wSzoucefzpC6JbzwJHHct1SMpiF+6RWyL7yOv3tkSyRXyRfS/ZPpZqh00LNzprrZJGJ0+8Q089baKwt0vkSL3HZ2OUGdtS81zyxAeGvUuQ6Dnb+VRXemFez5ysJmvWYLM6M0zbrNp8EqRkKwastbYv46zDfPbBdYCJ3IVfZ+vF+fvYn3II64XK7zEBOJ804SsMSlI+YOiYrUEQlKuz8k9HPc8YTswQT3cIIul8jrt9FeweKJbd74wRFf/qvbSJYRjh52CRYj+4HftfrMEJFv7O3OIU1rZo/Mo6WlYqh7RLxCNGHbHtmG54EJadwZmvjlGTJb2POEuH4OW7qpJkZtsJmwsjCrfIxWgYGJWLceRfLcPmaZuSnFWaoGmCh1Z248mrTRpdXb7W6VSgikBmLiIpBaiIlLh2tso7GfVMR+jnqHq1p02AcglgXxc2+j3/nNZKcL8rOG5U1h64s5cTLF7+wggz7t27dx/T5y4wBZ1iZaXVWk46GZMlQh82ivtNeeL63SqW0xpm5tlqdoJxQyW+LaYMLUCYquRGhRocO+Cdra+m6GDsnzdRUFoNHEUbwzx+Ejpg27X1cl1g1+e6tbXGlGDq3rrqWodr8OKQrifJ5aiIkLQRKwxKVDnSVaxH4OIuQP5yYeTWvVz2e+wMO/+p1c+eQxi8e3eP3HQGaOG79+jDx1i8XjW7g6Us4XJhyzhbXk8hyaFrxDywKkWb+mmy02MU5gVVpU5HSydhBqka9FT/MMUbX2oCoSIloWSKyQ+dIGkPMCGQ03FeDqsSHa59OZvV8sA/FRZDTchPU2bWf0yGzJZV3b666SObKMWDfr7cxuMEAWqTmTOP8kAUtcOiREpAlo7gm9DPfabWR/F1nWvPKfHpD/8Ee49Sun1FeHLP7aCf/xjS/xfO8Of+c938X9sxH1yxm7X4Tys87aek2DOEe4vou0EXUOCQFpg4ll066FCOjEzcRMnKyzBVc2ehbLTQswdgYPVRus9t3zlFbR6WJprshltTZeWASU2H3BHu8d+HxTtYXYDT23Jkx1vWlN5rl9vnos4IocbWWTcJ9IXACSgCUuHyLIdEHcG+GnNXrrgPiZL/DS//7tIJGnfmHOl/+zEZorvY9f41dfv8ov57C8Itz42JziK2/SvvkWAfAH12zXVuZxp3N77u5lNOvacWUBmTd3YIhWoeUeWTZWaVVNl1fYteVWAre0YF06c8f6bErkHdWRijNDRl507cIAVXe25sSqqyyzNmK3qFLKwhyJKzej9yBCrBur/FaPDcHs90Vh518rZ2JqISYuAKlPkLh8xIiO+rbfywv1Xp9X/ufvoDj2PPd/L+B3f58f+PDnuP7kETc/uqTaFY7fp1z5Fw3FVw5p33wLv7uL+6YXkOHAnlM2ldTaCCFiwrVqT/YLE7UQbc8YWEjw6jHebZIyqi7xIrO2Hnm+TpZHFen1rErLso0l3gnatTW1O2NbnYHFydSyG+mGljsDx+qsS7u5MVfkZlDpPiKu+/XIbFiXTp9InHeSgCUuH87RjkvUC/qJz/Hmny2QVnjmb71MzBwv/90P8usvP8/uX4OTZ0rm15X9fyH035xAjPiDa3DjmoX+9grUO6QNm1/deZFUNTJboGWGDnsWECwCeWbnXZPZ2iWIqp1FrVqNq3Ze23aiFtfnZ9q2m3ZkVZshZLG0X7mJjpSlGUrK0iz1YNVZZ6fXyaT7mjeRW7Ufu7MvXSXR55nFanWrVOJ8bjb9R4wdicR5JbUQE5ePaKaI6vqI/LPw9N+wBZIBcPfuoz/+7bzrpz4OV/a59o8m7D3zGNmDMzg5Q8XZOdCigtKyAyVENOsGkGM0Ecg8oRuIBuzMTTK0zCB2bsQuu9BefPNYbZpHrOu6iXha29wdOpuZWeORcy4NsTtvk03kUyeMK4MGMaIxIv0+WlXWblxFR3UVGVhrkiLbmEKcQ1Vxg67iTEb6xAUgCVji0hEGGX5akR3NWI3kuvGY2FUlz/3Ux/FXr6KTCfLETbIHZ+iwB8MeMq/WAbmyrInDvkVDLeuuSlLwlkloRpEMqayCck1rAcHLVYSTNwdh5k30ujR56VqRGru5q9XM14oYbNA46+bINNr5VuzOuLqqirCpksLZFL+9ta7stKvqHm0F6ur+4jZVXteCXN939dhE4gKQBCxx6ZCoaJEReznZzRsmJIMezt9Ee1Z1MK/RJ67ZqNSytrzEaWVVVmemUO/MHr9q5y27NSWDngXqThbWKjybdjb7zCo3J2sXoGTeHudkfd4l3qodWc2AtV2V9KhwRIXQfZ5nUDcbh2Dslk8WOdrlF7rhwIwb3VnXo+dZwCMuSW+VWGBtKllVeuu5MO8haVjiApAELHHpkMYqjeztI+LVHXMELhq0lyF1a+7AXo5bNGjWOQa7amp1DiWLCmk744Vzm0BeQMGEq6qR6bwzY/hNhdSluesqPWPQN1FZWeDVrbcgS2sZhLRdBbaumFaxVLLOXBTvzKjRDShTN+szLmIkTqb2SFXLM6xr6PfXm5bXqRxtu9nCDDYbpo+8XoqSSlwQkoAlLh8iyLIl7m4RBpbEoaMcaSJZF+YrTUCLDPnKHfSJ67jJojNZyLqFaHFRhc16rdqAqsiyQpaVDR5Tm4B0A86s5q6axlpzqyWSYIsmAdquTZdlqPdmyFhVTeuIp2ghvHhw3UxX1VVqnWipdm1C7zcCCetWIL6r/lYRUc7ZXJj8Ad6tR2z5icRFIAlY4tIhUdHco7nrcgkFFPzSxESaYCG/bURvHlhF5gR6hVVUqrR3bcdX9th1pG5sIeT2VucEzNEyNzdiiOs4KcAEa9Vy7CouYCOOYJFOnVlifR62eh7nTXB6PcjadWjwut3XtBZW7L3NdzlHmM42Jg4n6wpMYoTcNjLba3Rne97ZPFh3PY/uCRMRNOlX4oKQBCxx6VAnaO5oRwXtwJPPWrLTCs27ZZaAW7Zo7nHTWRfhpKiDeHxC7OapAHQ0gIcnSJ4T7963BZS7O5aq0XT29yzrAncDWtUWuPvoeVZXHUnERGK95yugdTDRil0uYre3S1f2+lWLUK2ikk7MtKrseZzgxObCxJtRxCzyNRpNyNCI6uoszObZ1gssQ9isVqlryOycLXkQExeB9H+txNcVEfnbInJfRD73yNf2ROSfishL3cfdR773N0TkZRH5koj80B/nNdSLrUqpAq5Vyi++DY61QcMtbF2KzCs0zwhfetninapmLV7t9/8p/HueM9v67ja6NbIFljevW6uwqm0tSZdMr8ulDRmvgnVXaRrOdyG6qw3LnXitqirpzBvOb8QqdpVblr0jleMdv4+ZLZ6MK5NJV3lpVa2NGOL9puW4IiqxM6No06JR1+5EKQq0tdSQlMORuAgkAUt8vfm/gB/+qq/9deDXVPU54Ne6zxGR9wI/Dryve8zfEhHPH4FEJZQ2t5Wf1rRPHeDmNdJGCDZE7B+/afdtWrKnn4R+j7i/hRsOyZ58nPLuxEJ1M29txc4xKIvKxCtGdL5AT8+6FqJVNFp1qfV1Y2dJTb2pdjob/cqAYeaNrlKL3Y6ytu0s8Ioull0c1MZ8QdT1fVbblLWroqSbIZM8s9sa10PM4lfuw9DlMwriZF2hrVqUq/O0ROIikAQs8XVFVT8KPPyqL/8I8DPd7Z8BfvSRr/+sqlaq+hrwMvDhP87r+CpA1P+vvTeNsS29rsPW/s5w763hTT13szk3LVMwRMkEEQ0/FBvWYAeh9EMB/UMgECE0EAmQA/+RjCAWAhAwkNj+FTugYdkK4ligYTtilCCKIhBJLGtqKxKlJtVSk032yB7fe1V1pzN8Oz/28J372GS3IvJ13Yu9gEJV3eGcc+uxz+Lee+21kLoBtB2R5+I/SOZ2cX4h1dW2k3iUeYt0e4l03z1SvWw65NdvybxpPisRKcMgpDKOSFdOxQljkOgTVJW0D+taKrMqqU1UW2yjqjRRGsKjUgBo9ZRLy7GqPCqFtV3JnXomalWVV6vp39a9En2XSxegzVLKgisxjup9mD3UElncODgzEDVYYA8QBBa4DHiAmV8EAP1+vz7+CIBnJ697Th/7xrA9rqYCMoS0EpBuXUga86hOFecXwGIOXszAdUJ+9XXwrdvg128iH81lr0qdMOhiBV6uRMwxa0GnJ0Jk640QwGwGtA1osQD6rlRiiSSnK2llY7MtW06+E6ZCHEdw1yGvN5ITNp8JobWtqCGzqBTTYi5vG4vS0Kup2Uxbheq7aK4cPLGs0pkZgLKf1tSIKVhgHxAEFrjMeKO76BuWBkT0CSJ6nIge77sLcJNAY0alpEWjOFfQauN+hRgG8M3bGP/4i+CqwvkPfBC4/17g/ntBWyEh6gdgGDG+dhNIhIvvez9e/JH3ipN8lWSZWJWCbtVU12WBuGml4skliNKvuW2KI7w50+vjtJgLmbSty9rdJd6qNItLAcr+Vs4u+LDzUdPqvE3/c7eZWKUKRWsjAnKMaCEG9gShQgxcBrxERA8x84tE9BCAl/Xx5wA8OnndOwC88EYHYOZPAfgUAFw5fYSpz6JGPJpLm9BiRVIrN/+zcyEXzqjf+27g5Zu4crGWSkvbjOn4CMOXn0F1/TqoSrj4vvfj5N89jZNEyBdLUfPVtSsbfY+rH4R8QKUKuyP5WBKUh+Icb21K90VkJM0EE1KRH6muweu1yu6ztPs4Czk1skztQZZExWEDKsE3NSJJpWaqxmklFk4cgX1BVGCBy4DPAPi4/vxxAL80efxjRDQjovcAeAzAb7+lIxIhzypPO063zsUdwyowALh6gvyeh7F91w30774fuHlb2oBEEgw5ZlT33ADuvQ5637tw8utfBKDzs7aVyBOTzG+3PhuDzq52FoJt50pzu4hIWnVWQVmFRCTtRqBEoXglVZfqyGT1Kp0HtEIbtfqqKuSu38n1SmZFZXM3I9R0x0wuKrDAniAqsMBdBRH9CwDfD+BeInoOwN8B8HcBfJqIfgLAMwB+DACY+Qki+jSAz0Nqgp9k5je/u2ZGtexQ3c6ynzWMQFNjfOkVUNsgnRzhpR/9AFIPcAJOn+uxudHg+NdfQwVJQabFHFRXWH3Hu7H4nS+63J37AXR6opleW+Rx9DYf1UnDKbdCBKbuGyFiil4Wk8lamGbtdLGU3DFvB2p+V9O4Gz33A3ilu19VBYJUfGymv8xFrm+qxEm6Mo8Zec9ZTkcAACAASURBVOhcWk9V5b1YUturUv0lhIgjsA8IAgvcVTDzX/86T/3lr/P6TwL45J/mHJQZ6fZSiKtKks91vvTZUj5e4OqXOgyLCut7Kmxu1OgXhGMIeaXTEwxffQlf/S++B+MMeOeXroNffBn08AOgV19Xl/hx9xZvAon1RpeFSYigggo5hl05vVZqvN7I/lXXF+k7hJS468R3cbNFOl4I6bAa+aq7fTIVZM7ShlRne7ZZllVbukDtfogaqwJK4rxhC9LjKCQXIo7AHiBaiIHDgwVGJvJF4Hx2hnT9GlBV6O87wvq+BturCWlknL0r4cY/lcywdOUUvFrjtf/su7G+j9HeYiGvowVoK2pC7npZWm4bd8OAytNl9mSeiCqoMHKzpeFxRF5vwJttEWtYW9PIjbO3DIWY1CnDhBq219V1QlZqLZXXxUUEVm0RSQqzVVrT57VK5H5wqyke+q8RnAQClxFBYIHDBIllEojAxwukRx/G8NzzwL3XcfMDM6zuTxjmhOVDCff9fu9v42un6P7i+3HlKz3mrxIe+uVnAAD5tdeRX7sps67zc5HNTyXv260sF7eNkIxJ2JUweLP1PS2oHJ4W8xKv0snCMne9OnDIDE0qosl/phaK2akwBVCBiMzK0nxWqi8jJOYdYvN2oXkq2lKzBV+27e6uWiBwSREtxMBBgnXGRJutLCDfOkP1gfehv3EEJuDan/SYv7ySDLDb5+DH3gsQYfPoVVTbEfPnzvDwZ7+MkTPS0RHo+nVX+1FTI9+6jXRy7EIIWsyFfCgBGMEb2Q+jcdQ4lCPwcgVUM1cHSrWlxzw+kte1Ijzh9VpmYHUtszMjS63QvI1pe1u5VFZEBJ6oGW2PDIDM4Jraqy1/vQpO3tznJBC4PIgKLHCQoK6XyJOjuVQq91yXfa+acP/j5zj+3POoXhO7KDQN+Pmvgs4uUC8HnD86A158BQCQrl4B3bi2UwWNr72OdOOazNcgFQ2vN0IEZgXV9yrEmCQopyTVVdcjL9cYX5JtAQ+h7LoSVqktQm+HWkXJDDQqztAZGHLebUNCLKFY5248DMibrZAWS7IzW7SLWlP53M5anoHAHiAILHB4yBm8XIuI49VboH7AcOMY1WPvRfsHX0H10q2y8wSAZw3G73gML//we/GVH17g5PkOePBebe314NvnGB+5F+Orr+GP/sv3gb/nO8D33ZA3mzkvmXAjSxtRqyebhZnrRb5121uN6fQUvF4L8ZiL/DAgXyxF2MHskngJrswu7pAZ324r0ZaczXqKEvl8LLWN20gxi2WUkbLNx+w63C0/ELjkCAILHCDIHeLzzZtAP6D63FPAqzeBYUB+TZSEvJiBrxzj1e97EMSMcQbkGnj5u+ZCfG0rs6thAPUj+h/4MB776d9E8+It0FdfkQTk7VYl623J9LLsrkEWmnnMhSxUBg9gl3xU/UiVLEfn83NpCyohZlMrsi44Wx6YEhwAt5KiSuNSrIrTViN33Q5pGXgYkLdbvc5K24sh4ghcfsQMLHCAYNDJMXi1QnXvPTJzms+AVImnYN+Dj+ZYvfsanv/+Gle+CORZhe4q4epTjFwzhvc/jOoPvyStQgDoR7Svb0Cnp+CbtwBKMgOzEMlOhCCWruwtR0s5hnoNAtJe1CoISWTuNJvJIjQgLUJVK6IS1SO1jZMQm/BjEpjpRGXRKHpuTihEVtdapXEJrUwEQCs8wK2pKGT0gT1AEFjgAKGtvKaRmJSXXxUj3NO5VD03rmI8nePZv1Kh2gBVB7z8XQv0p4wHf2sLToT0u38kwgxNO+7vP0HzxDPArBVyqVIRVmQuS8MmaTdvQqjqL2dvAwJwE16DxKMkN+WlY7F68r0vnW/t7H2pkAMplbTlpi7+iHY9AJikMjNCtRDL3d0vReSBBfYEQWCBA4QmFqtDRbp21ffBKCXwV1/F0z/+bTh+jrB+kHH6bIdq26A5I7RfeA68WoNOjsWUdzYDNQ3aJ18QpZ4mJjuJVeRtQ+40+4uzuG9QAlIGNTNJX95sd0jPhReJPNoE44jcdVLdYQT6Uaovc/AAiuJQd8R4sy3+hzl7uCW1Tfk7aKvS6iqv1Jq6WEkZuVUhRQzsB4LAAocHhszAuh5YrUE3roGPFxiPWzz9oyeo1oTjF4CH/oc/lNnPdosrAKrr14FKWoO8XgvpacgkjGDq2hOTebWW1p+qCAEUojFV3wiJU2naYtgLqGO92k2ZypAJdFSL+bC6aJAqGKlt5POMo1tMcT/IjM5mb4AQ1VRFqMvVNJvJ+82811zwx1EtqMyRXudpd+vfKhD4MyAILHB4sMDHWQ2az6SF+MiDuP3fjMifP8Zjn3oB4wtfxbjdShpzPyC/+hroaOFWTO4L2NRirmsLxsMgysJpSjLUJd4WlbMKOayQqSqpwHSWZeTEm42YAdvxmlbeb4RJSTLJtI1ocyxMKiQXiGhGGDR92ReR1dLKyIuIwNYgNEd6a3nmSWzLt/wfKRD4syMILHB4IIDmc3HhOJqD3vEQvvyfPIDqs8AH/tdXwbfOkBZz0I3rnq2VHrwfvFwjn52huv8+McvdbPQmrzMlzjvkNY0t8ciSUVp+7kSv7u422+Kud5Iic9yw2BTO4E0nZNP1QqgmFK4qYJbAqxXQdSL0SKI2NEWhJzFnRlrMi/WUzb8mQZhk7GrL2VUFxlg+UyCwBwgCCxwchlOxQvriJ96J7irj6h8nPPL/rFH95udB16+BoQSX9KY+n4ErqXaqhx+UsMqjBbDZAG1TXDem2V5VBaSpg7uKNNoGvN6UMEkVd+zI182jUZWLMAHIIJUbLeagptHASnmNqRxtmdmWpqGyd5tbUVUBNSGbvF9bh8wMsoVni2EBwEw+WzPyoroGtt/Kf6FA4JuDILDAwaG6p8MzP/IuPPibPTbXK1z//deBp5+Vm71VVYCQCJHMnLSS8u+rNdC04NVaRCAKXq58luVZW9MgSjPLNZLIkzwv6/z1qvxbzCX12WJXmhqYzUC1pimP4pjB44h0dFTEFn1JbpZzKTmm2udkJmAB4K1DALuKSSU1+1tYm5GHIWZggb1ALDIHDg7bVYvFK4yXv6vBPf/2edAzLwApIV29Im4Yq5UQ2TgKeZkkvWnAp8dlBpZHqdSs+jFxRFUBjThZuHuFZXxNTXTNvgkoZNYPulBc+Q4XAI87QSI3/sU4go4WSKcn0l40EYcHWGZ1kc9lD62uxSi40TiWYdBra1ze78nR0BleUqFHo++t6zInCwQuMaICCxwc5i91uPGH57j3l18ETo5x8z/6IK7/9kvIz70IWiyQ3vUO0DAWn8Exi2fiOIIuVtK2G7OQV85gaxs2rc7Gtu4k72jqEoTZ6iJyPZGoq+LPqi6fo1k1VNfgYRSZftuUXLExu/qRMPix0DZSJdFE9DFxn6cMUVP2RSHplZnBlqmnVdh6rWGc35J/mkDgm4ogsMDhgQjp2ZfBF0uAGadPr7F91w3U956ievUcZBXVMPoiMC3Xmrjcq4pwmJjcVpKyDAhxVJWQUBZnd6KqkISKPHjMRXGo7UHKxTmjOF6gqBszA1bVnZ5IVcUZYCXB2UxmX9rypPkcebkSif1yJa+pquKR2DQledmubyo2qSqp3uxnddOnKkWcSmAvEAQWODwktY5at+BH7kf1uadQnxzLc10PvudaqbSUcOj4WCqXucai5NGFFDRrgbqSRWQVTgDF8d3UfdKGlP+kCCiJyJV4HfIkBgVNo16H0q4kTVh20cZyKRVfXctjFmC52Wh4Zi3kVWtszGIhDvMpiUGwKhtNzAGo2a99XiLk1QppPi/VoDreT6NWAoHLjCCwwOFhHEVscXoCur0Erl8DL1c+E8Jq5a00WszlZp+SuHRst1KFqJGvxJz0nrrMXZZKSkFKWDxxx3BXjrb1XTBazCUbzAQknKUyahtdLIYQkV/XQlqKRk6bjVRqTQuqK3GsP1rIsSzCBRAiXSzE6NeqLZuntS1AuYhKdAnbdtiyZphN98wCgcuMEHEEDg9EEiA5DMivvCYVTNuAHrof67/yHUjvflSXiqXCQS3O7k5CeZRZkFUiRjJm0ms+h9oqFIKTfS4TbrjrPIvlFG+2HkYJQCqqo4XOocRRni+WntPFZgmVs7QMqwqoa+SzM3mPLTyvN75b5jJ4DbCkthEpfVV5ReYpzmki69fqjprWs8uigRjYBwSBBQ4PmYFtByLC9vs+iO6D78D40st49kcfwuZ6hYtvu4HqkQfd1YIvlsXZ3cxzAWkHNvVkbqSqv34QEQfgxEFVAvpOiGNeFpOt4rKMLcn4Sm5HhX4QshpHr/5cbGFzt2GQ1ue2Qzo6Aq/Wsvzcl6rLW5EWr0JCmnkrxGlyfBNscNf5Z9pJdjYhyLf0HygQ+OYgWoiBw4O2+zCOaP+vPwBvtxj+0l/EO//HL8nS8mIGvn2uN+7sRr8OrXawLVWTGe8ys1ROJroAgMXcyYb7AdhsC4mZrL6pMb5+U9zkAZ9d+WwrZ9DpiRxjtS47YW0rcylz128b8WCsa1FHAuKq0TYYX7+l3olF0FGdnoKHQYjPnOvPz70iM4cOUhsqb3EGhQX2AEFggcPDMAoJjCPSlSug+QzV7z8NNp/A1VragoMS3XarkveJ2rAfpO3YNMhn5+4XSEcLkbar0zyatuxzZa1idC42ldnzcoV0cqIKv1Qk8sBkgVrMfeloISIPQCqmttlRMNKslbblbKbvY6DrxT7qTpGJzuI8lmUcRbgxIWbYvE7bohjHyAML7AWCwAKHhyqBjo+KFLzvRdhgjhXNQkgOEJKZhFDy1MdQZ2HelmubsnNlkSl5LNZSlVo+5SxVjb6O+0FiTzqds5lqUedgnMWSim+fyets+VjVj+gHmaMNo5BX34NmLfLtMyE7I7PNRkhss/UZnJEXd52rDKmuRZ1Y10XGP3UWoRT1V2AvEDOwwOEhpeIv2NRCBFUCN9q2GySbSwQOsnflLT13lzf5YCpiDHPC4FzSk4nkORN5TBaFeb1WhaHOvExQYcTaNm6+i3FEuu9eEWSoYwjGDD6/0J0zFmf9m7eA7Rb59ZuFvKDGwzkjrzfyOzN4vRYhy3JVWpFaZVHbIqsAxNw4zCnEl6sDgUuOqMACh4mcveLitgH1A+hipYTDbmY7vPwqAKC6emWS9zWC+1yWkG2mZpWKLjGjriXrCxBSGzMAqazydotkcyYzy62qSYilJjrXtQo0JOzSW5SjOIXQfC5kpf6I1KjwpFtKK1Q/qx1bXOj7ItYYR6TFHPniAunoSOZtSnqpbeR6TL3YNiCud+eBgcAlRhBY4PBgFlGAVEVdXxaQtV1nbb76oQdkXlYlgCaEREmWmscsj/WaAzabicWUoarEQqqukc/PkU5Py2V0okpE5jKb6nt1w5955WVzL2oakc33vRCNWVqdnviCs7U309UrUj1pa5Ahe13gXCqotpXdM0DNgJPbR7lHoxF6XYtSMTMoEULEEdgHBIEFDhDqMj/mEjzJuttku1gqauBRKzUTVsxmJXHZXC+qSoQSKpX31GWVslPbALMZqrYRSX7TlEqIErhbiyij74W4KHl1OM0D45yBrSQs08mxqA712sEakqlVnxxbAyrHLEvYXef7XbzZFhf6JNZQNGvFEWQYSoWmi8y560FVhdRKy5TGmC4ELj/if6WBAwSrRD3JHKnvfQ/LyeuNkFlUiOaBqMnGdHQkz1eVxp3I/+/LZ2cYz8/lfZ1WS227s5fFWxFUuCO8YeLI4edWMQbNWnHhgO5pAWXnayrft0NpW9HPDTEJTrOZkJ9WWbztitO+thxZ416oqsTB3oj96/2NAoFLhCCwwAFChBroJduL2naHtFhv4tObNK9Wpc1oZrZ24+86IZrt1snDlH2VCS6mGEeMZ2cyU9I5ExLtSuinUSqjutDb7pk60Odbt32J2eTtbLZR03NaNbVey+fsxRnEZmK0mE/yybR6HEek2QzJAjfbpuykGWkGApccQWCBw4MS0/jSy+p6kYWIhkGk6CpkMBUiNAvL05YB3cniUpVRKgTDYtxbXbtW5luc5fjMQNOgunKlZHRVk//M7ByJvGrzY1sLs65F4m4GxHY9APL5eTEEBkQWrztcbl9lrUezrxoGsbjqB5HV63VmNf81QoPO2ciiWgKBS44gsMABQiTk1QP3g1VWjqYWx3egEM5mU3wGKZX4EiMeaztau280AYiSlknS1RqKqlTISh/nsbQTi8sFpLqbzZTQxp1laCNDEaPkUnkxIx0fg8exGBMTCRER7Rj6AvDkaFBCOhbj37zelCgXouI8n5IHW4I5WoiBvUCIOAIHCBKlILOQVs5iHWWehUhFFg+Iwi+JyMFbc8MgUnoLk1SpuQlDyLpslApxdIP7IvoyMVCEI+pziKaVSs7CLptWzjOZb91JRgCK9J5Iwip1L22nIjPk7ATnBr45u3UUmz2WKTJzLm70kQUW2BNEBRa4qyCinyeil4noDyeP/RwRPU9Ev6dff3Xy3M8S0VNE9CQR/eBbOwl2Z15d2dUCUKonGHkVMvOFZJPKG8ltNkU92LSyIG2u9MBOHhiPubi9W3vO3qsJyjJD67Wi0wpsQkLUNlrRVZPk6FGqJ2bk5VqMhafvM7K2kE6b82mlxaNEqexI6P18LdLxsTiWpAi0DOwHgsACdxv/DMAPvcHj/4CZP6Rf/xsAENEHAXwMwLfre/4hEb15WJX5/ln1kSp35pCAyhm470uFAhR1Yt/5TpQ8rhWQpSZbW09dK2wx2XPB1psiqrBWnFVa2qLkriupx3coCs2vkNUlI5uLvC1Dz2fyO2d3wndVocroqa7l51wWou08VFW+YA1ACI8ZebWSv0ff+ywsELjsCAIL3FUw8/8N4PW3+PKPAvhFZt4y89MAngLwkT/FyQAAdDT34EpqmlJhVJXvWgGQm3zTKrGl3appOr9SpwxqW51zSWXEnc7TmHfUfpPP7m0/mZkJF3PXe0wLD4MQlD6X1JGemlqUgtoyTCrtl+DLQUQpde2CFTEfTsXaKpH7Mrrd1NRCSj9PzL4C+4QgsMBlwU8R0ee0xXhdH3sEwLOT1zynj705bLZTV4D5FtbaKmQGLRZSqeQsLbwxlzYioPOo3QRlaPRKXq2EjIZB2oVGGI0QDJQ8bEkYgC8P02JeZmOJSqUH+HzLWoGw+BbLDzMxiErkaa7OGxNJvVlDAXC3EWsZppNjySszt/yqkpakeiOaP2QgsC8IAgtcBvwjAO8D8CEALwL4e/r4G91N37BEIKJPENHjRPR4l9deXfBqUxaIk1ZUtqjs6cRVcZiHpRRrZbLZ6HxK5O58sVQi2A20zNttUfXl6XFEfWjEYA4ZluZMde2pyX6NliGmx9/ZWVMFInIJ14RGvdjxfOY3hbY8/dqsOpysDkgUjFSoQWOBfUCoEANvO5j5JfuZiP4xgF/WX58D8Ojkpe8A8MLXOcanAHwKAK62D7BYK/XSdlOVnVlHUZXAy5Xnb+18z6OSH2ll0hQXjKaWqqcfwNwXUhpHpJNj5IvlxBi3KBp5GITokgg88nojzhdWnRk5WY7XbObzLB6zZIHpecw+St8o33MueV5AIWb5gwqxJhThilZppE4hoORKRh5HIdOoxAJ7gKjAAm87iOihya8/CsAUip8B8DEimhHRewA8BuC33/yAkBBIXWLmzUa+ehVR9H0hLXW9ADDJ9pq07CDHQlNL9QS4x6DNtET8MIh106RtaPtcO+4WatQ7NdR16HzLJfR9X6opnW25zdOEpHi7LRWZ+RxOHUhs/02trtJCqkdbtAbKPMzamzELC+wDogIL3FUQ0b8A8P0A7iWi5wD8HQDfT0QfgrQHvwzgbwAAMz9BRJ8G8HkAA4CfZOY3z/pgTPapRreEEnIiIFXg5VLc280Bw+TodS3+iUY8A5e222IuzvM6Q3IX+EnbLi3UwT5nD690MlDfQXehB0pSMlBEGpU4c/AwSCsvkYRnArvtPdv1kgOV83e9SO23W6+mqK7L7CyjVKdcBBzI2T9HzMIC+wCK/6cVODRcbR/g73ngY1J5qCQ8n1+IKW8u8yVfIAYm7bhdM1sXUAAumXfSMcGFeg/mzRbVyfGuGnG6SKyvK4vN5Oa64hpPpcVpbUt1maeqKu09i2XR11LbIq9W5Th6rZbOjKpyorNWo6VEo+/LjLAvDiS/cf5LuD2+GiwWuNSICixwgGCVlcuN26smC6xMVVEVTv8PnC0cm9PG1BsREzLLE9LLZWm5OjmW9zRNyR2DVmrDADSNKP2A4vhhooy69iqLUcIvPazSqiTzLExpZwXAZm7TGBWbtXHXi9N8P2kvNvVuNtpkfuaejoHAJUfMwAIHCbNMAiVV17XugrHjBA9VBmrW1o6Cz0x37Qtw9aI5zXslpUrAHYIDXBhhRJEW8yLEUMWiCSrMj9D9FC2V2aJRuq5UdEQ639t6dcVdJ/ti2i6UUMzBr52a2o/JXaftShWajNlbn77DFghcckQFFjg8WOsOkJu3uWgYCdnviqmCjxZzccxo6mLaq1WQz5QAn2+haXbc5k1g4Y+ptJ6qBIYqCXWHzF5P6tjh7T+dT1HbyDwrT563qk8DMmXmpkRX1/LeKpWML2tlWsWnjh1ie9WU1uJcQzU3W61WowILXH4EgQUOEOQkQUmIw2/IqSoVlfsYcvnddrfUFd4tn2YzEE+qqySGvabo8zMrcUnlk1w+j8xlT8xmYE0DmlRrU1WipT77jlldq43UrrmvKxJ78VU0IkrWtmTZ/yKdm8k1qkzeqkerEqFVWlRfgT1BEFjgcJEZnHVZ19p8hrouknlTBjYE9JMWojrNU5XEPd5EGJPUYxF0cCEubcO52MJ8E6cVlrvXq+IPAK/XanVVl4XkqfDCUp41XXrHx9FmWZNry8PgTvNSyXUlFdpIcyL4IP37eKRMkFhgDxAzsMDhwZSCQJHIT1tiJpmvJl6BdV2qL/t9KmnXoMppYCTuqJhcJGGqQ85eMZnK0IUdbSs+h3p+ms10NiUtQD/3VFwx/Qw6C7OFZ3fwsNeaQ4f+7vZV+rtfo8WxaDuR2sZ9FgOBy44gsMABgotNkrXJrOIaRyBVbvNUcrdUXt8PvguG2Uzeozthd4ozjBSoSkUUYa7044i8XIvcfRCjX+4HNeVt3QV+x0JqWiWaT2FVSRtycn7zYcQ4+uN5uRJxiFVdJtgAisrQWpcq/vBzT9KY7VoDgX1AEFjg8GDZXFktk2YzFUBkkZ5nvbGvN0XwMRV9dL24b3DW9uMktNJIw0xye3GS9xlVXwiAqlSqNW/xcSE6m0l50KYuRY+5JC7n7L9Pq0jSaza3jSlxWhbYtBo0oYfNvLz9aZWY7pC5BVaIOAJ7gCCwwOGBtN2mVk683RaLJxNoAIAJMMzB3eZWVSUkZgpAba2ZRN0XlQFVIVYTY2BpS1LbivO7mvEC2HG64DEXFaDK70V0UTK8zJrKYerDnJG7UjF5rpi1EVXQYe/Nq1U5hkn1jUDtmBM3jgi0DOwLgsACBwj21h8AzfWqJMRyvZaZVt+LMEOVh3m5Ap9flBYiUPan1MvQvAapbXdDLFXo4c4Y1r6jpDL4XHLAbIdMd8GmeV0mg5+KKyx8Mm+3Tl5UVaJ+NHm87op5fIotQM9mABHSYiHkZPtnuuTs0SxmHDwxBw4E9gGhQgwcIEj3rlq5sZs6T5OWqUqiTjQrqcygmZrYXixBVhF5pEnlEnhq21IxGdnowjA1Fp9iwpHsLUAkkbVzZlA/yP7WoDMudf0wBaLP7XIGa2WXjo7ce5GZ5XNM88smGWIuDHEHj8ZdOeS7iDxMlUiW5twPvkMWThyBfUAQWODwQATMZ6DpzpQSGB0fAcMIXq5AtcnoVRyx7WSP6g7yYsvnsjahJxg3xSex62TfrOtdeGEVDk3mTGkx3zXvVfunnV0xazmqAMQl7ibKUEGGeTJSrU75LHL6NJtJxTaOMpfTVigqIB0virUUMxIRxvNzT5cOBPYJQWCBwwNBVIRTayfWlqJWKunkGNz3cvPXxGbfvbI9rkQ7kSPT1pq1D7P5LDZNyQKbibDCjHSnO2K83kjel83kMjtpImVfYLY5W1IHENh+FiBp0kAx953kie1UirbcXKsxsTp7yHlLu1HIS0mTCDSOEWgZ2AsEgQUOD4wy/1Jz2ulN25wrqGnEEqqufYHZnTds/qVOGACk8tpuRVyx2cqNX1uU1DZCTmZTlScS+ayO8zP9z01nZW5tZbO12QxUFYEHoCRFBDSVS+8B7DjP28qAzebydqvnTz57o7ZRo9/kzh48DCBTbNpxAOSujxZiYC8QPYPAYUIXk/n8wmXv7ryusyILiHRZvSoQpzfvaXoxtU1pKTZNkabnUlkB2GlB2uwrXyyBrpdqj0XUweuNkIqJQbquKCTNUNdUg5O5lu+F6R7ZdFnaqjKbZbkisR98EZtq8TqcJjt7paltzjDzDewDgsAChwnzLZzNnLh4K8vG7hForcO2FcKj0nIUVw0huLzeyO+m+kuTxWW1fwJ0DjZxw/DKDbqH1dRIV68IibSFAFkVgZbuvEMetoAMuEBEnONHl/Sb/6F/biVim53li4vd404sp+xv4wIUm/cFgQX2AEFggcMDoZj5ztrSRrOqZBzBm437IPKglRSbgW9VQi2rCvWjDyMdL1z+jr4XCfzxEcbbZ05I1jI04konx6BrV5GuXkGaz7D87veDrxx7leXLxkoW1Ja24fS7kWheLv0j8jD40jJylramhWOaFZU6hbhllbUsp+QFFH/EyWcOBPYBQWCBw8PENZ43GyEGc9u4s6IBfGbFm618dV1xvxhHjM+/CJrPy1LzYqGpyAPS8RF4uSrKxKoqkvflChhG5NtnSFev4OiZM3BTAdVkP8ziV6buHvYZWI5pj6X53AnLlqXtufH83KunvFzLIdTQV6T/Kp3XnDQe4P1ANgAAIABJREFUs7dQ3U9R41esQg0ELjtCxBE4QBB4UDNcXeaFzX0AgBl5zCAlLtsFs/Ybta37Hxph5fMLOXLbyA5W20hY5jj6rhYt5u7ikdcbIZdXXwOdnoJvXEX+3B/5FdbvfifyK68peWZkF35MrJ/s00z3vcwRH+pgr8SX9HOaRN58EU2RSJzBOWO8fQaqG1U+Zp/xOWHZHlkXLcTA5UcQWOAAweDlEnTPDVcj8nIl1lFGFHUtsyfzBjw+cul4cXgnnwuV9p44XPB67RWTZ4KNWYQazNI+nLUYX7uJ/NrrqKuE6tpVr+zyazfdscODJxNJBbjdSpWnxrty4lLhAZDrbpqSMQapuNKVkyLISARKsrwNCPkmqx5zlvOYpN8EIxPD4EDgsiNaiIGDRLrnBni18lYcHS2KOKFtdKn5WMhjsUC+WEq7zmZnZrfU90WCnifKPRVUJBWJyI7X2tuS3HUYb94CgCKQAJSUSqWFlOQ8ScQZIs1vv4aYbM4lzh7mnN9P5mVldgeo2W9meXwxL8IUwzh6bIrMxyZejnr+QOCyIwgscJDgrgMtFlJJWD4YTcIrOYtg42gh34lAJ8dFzKC7Y16JNBIKmdcbad2Z0KPrhNxUWg91lqeqQjo5RjpeiMDi/ML3wQAAmX1W5SGWd1ZTKstPi7mmMY+l5deoRZZK6W1/jXsxHuZhcDcO9ENxnrdYlsmsyz9rL8bAU/VkIHCZEQQWODwwxL6JGaSSd0oJyGOxTKIEDEOR0i8WwHZbnCoA5OWy7Fmp+IPmM+SuR95uke69x4UPbpRrN39T/FECmlqIr+vcjT6vVm4l5fL1NBFyWKUFiIlwr4IMrZZoolxktYyiRx4UolsW93kjM1c8jqPL9T1Z2khLnfCNOAOBy44gsMDhYTrPYQZSJaIOc94AgL6TWVGViqmvzqHc9X02U5Pb3o+Zlyuk+UysqHS/Sm7+/DVL0F7pqXmvX141SUjOuQg3TO4/rfzMbV4FGdN4FWobqZpSwnjrNsY//qIsR+ueWJrNfB5mbUdzzpfHsn+f7ohFBRbYFwSBBQ4P5rBhzvCJPBuMtJ3IoxKWVWKbLfLZBdLREfKffzf4LzyGsx/5TjzzMx/x9ht3HaqrV0rciQoyTFjB6/Ud16HqxqZ2QYhJ2j1iZeKZOHUIcY9DdeFwh426LmSjBEXvfBgAUD/ysMzKjhe+xAzAF7TzduuBlVTXvoBt8zeQkn4OAUdgPxAEFjg8kJLLciUV1zgCw4B8flHk8LNW50NdIbtE+Gu//hT++D9dgCvC9d96Ae/+759EunYVVKlUnVUUYbtZWqWZvZSLMzKLdN+ywSx+xcQU6p9o8ygAuwvEannlx9Z5G2mVx12H8aWXwX2H8YknUV25gnzrtmaBiUGvWFNlj5BJJyelOrX5naVHN5IoTW0rjvWhQgzsAUJGHzhAkLTXzI2+qiQqRQmCUkJercW7kHUXqqpAR0f4zAe3+AB+GwCQj45Aizl4tXYFI281y2s6F1NxhBvkEglxUQK6bcn9anR+ZYbBJqyYYhK9gnFEahvxUaTkQo6pIwcApNPTokwEikMHxCZrx13DbLAm57CoFdt7kwozCCxw+REEFjg85CykUyXZzeIs1c6YxFEDEPVh14tqkNR1Y7VCdeUKYKTVTyyo1LcwLeZeQXnbz3wH1TmemWXBWWda7ig/tXCyZeTNtngZKrlZsjM0/iQdHRWZfVOjunZ11zDY5mYQcvZr0GrN9sysqiqxMSxKTSO0RIVYI1AlsAcIAgscHuzeO2YhMap9HpXPzkvbbrIc7BlaTe1KQs/pAjxx2U19J6Sx42gB9TScmOLuOGkA6v7eSbtuMde2Xy4V4kTUYblggBKPBmZ6NEzORcxh1zJJjqYMF65QlWRReswiKqkq8Hot16BiD3fbDwT2AEFggcOEtQ+BEpfS1EIuzCWDS2Xp6fgYyCPyxdIfmzrQW+6XO1XYUrQSFysZYhwBq2iaxtt6RfihLbtEO8vCPtu6WAGUNDl5ErQ5Ib283bosXr5zIVclL7kW7Mz3uOtlDeDsQo7dNPJaFZFw1xe/yJiBBfYAIeIIHB4YvldlESrwll52AjFBA1WiRITuWvEwuOegV1VNXfaorGrTedGOAS8kENLIy814jQgni84AhFQnwZTp6Egk+p063ttcKmefZ/k+GLMQm7UG5zOZk5nU3lSMSk6iZsyorl/dEZyY+weIXJkYThyBfUAQWOAwoW7xREUibnMqg8vIpyAS54vFAgC8vWeS9yJ8yH4Mt3CyVl9Tu82TkRCgEShdX4QUWhWZw4dFnvB6PWlpNpP3dmUeh0mrErrTlVlsqWy5WtcIJKE5y/e+d3Lk0dzwSfwSU3IFY+SBBfYBQWCBw4Pde7V1ZvlYgBKBVShURBO8Xks11GiSs5IFaWaYO2akJCGWbSuVkpGAEZUa7ALQSi8VCyfACYhmM2ndQclHk5t9FjUhHpujUduC+8Gzv6aRL3S0QD4/L5WdCUsgVlR5s/EW5A6Jq/0UAIli2W53lqUDgcuMmIEFDg864/I5mNk6JfKlYBipNbVUam3re1pkwg2bCWn+1p3iBp+XAVJVmRhjPtO8rU4Vi+TmurSYSUtvuy1O9+rMYa1CawlyX1KU2QI3AXnfIPOudHoKcEa+dVtk++Y0b44bo1xnde0qQKmc145hsOgYE4gEAnuAqMACdw1E9CgRfZaIvkBETxDRT+vjN4joV4noT/T79cl7fpaIniKiJ4noB9/iiYSY7rR2AnyGBCoydotO8VDJ6XuYiyjCHD5S8n0vk6h7e9JEGuY63zZCcioEyauVKxjdpd52ttSk10ixunLiQgtADYq1OjR7K1YXkbSYq9Iy78zu7FyA+i/qDM6XnFUowoP4LTq5BQJ7gCCwwN3EAOBvMfOfB/AfAPhJIvoggJ8B8GvM/BiAX9Pfoc99DMC3A/ghAP+QiN787mpEA5WkN60/RVXyeZLvP2W1UWob0EyiRFzU0DYYz87kveoK70a685mrD914l3UORSQyfBNS6M+Wv+VzrLoWZw/A253U1DvSdrluiX3hoSgNwYx8cSHKwuW6+Ctahel/Dt5tPdoem5I1b7egukZ1cqzXEbeFwH4g/pcauGtg5heZ+Xf153MAXwDwCICPAvgFfdkvAPgR/fmjAH6RmbfM/DSApwB85M3PpC1Dqyi2W8kAa1rN3GpAJ8egxUIISb0KedvJF6sJb0rgrkd17arI1y+W4M0W48XSY1SECPud1puR5HTWxJst8mbr1ZyIN3qfN2V1wmdPc86FZBN5oCbVNej0tPzctiIAmc+cHEHkpr5iTMz+Ga1qRN9r1lm7U+HthGgGApccQWCBtwVE9G4A3wngtwA8wMwvAkJyAO7Xlz0C4NnJ257Tx94UJoyACxQy0HfezrNlZZl7sSsWfYm4Fyn9TvtNW3ZJPQO94gGEdO5sVxoZ9D3yaoV0fCTVjsnVNccrXyzLsSYtSVqIKW++WLpyEACyVoRsrchJNpmbBQPFr1HdQ1hjYHgYJN9sLDttZK3M6cwtELjkCAIL3HUQ0QmAfwXgbzLz2Td66Rs89oZ3ViL6BBE9TkSPd3ldTHWrSuZhFpfCEzsnVsumRC70cCFHU4uUPhUlHwCkoyOpjPqhtCBZhBtOPGoN5aSgKcfo5djZMsf6XirCut5VG6qcHr24btBioanPstuWjo6kokRJa3aVos7JhGAbF3T4/poReM6e/+VOJEaGQV6BPUEQWOCugogaCHn9c2b+1/rwS0T0kD7/EICX9fHnADw6efs7ALzwRsdl5k8x84eZ+cNtWkh1odWTV1k2p1KPQlAqXxO7JktIzquVeA5utmV/S9070mK+k5psoorc9SVfayKG8MqoqiSSBWXHzHK6bD+Muw7j2cXXutMDyMu1VGTrjRy3qqSa0rleOjpyYrJrdvn/1Gx4onQ04Udeb2R+l3M4IQb2AkFggbsGkh7YPwHwBWb++5OnPgPg4/rzxwH80uTxjxHRjIjeA+AxQK3iv+GJNOxxvZHqaLKX5cIISiJ0SHfMy5palIl9rw71C69YvDqqKl8Y5skCMwARQthj1kK0aBQlKt5uPXAynZ64jyJvtkI8ppRUxaMTTkoee4JxFEn8xB3EiDCvN+6Az+u1+ym6WtL+TLOZ/J1cXSmVqO21BQKXHbEHFrib+F4APw7gD4jo9/Sxvw3g7wL4NBH9BIBnAPwYADDzE0T0aQCfhygYf5KZ34LTLInL/JESC2fJA1tvihJvGkQ5qYQAFCcLANz3pVphLi1CUx9qRbZzLMV0b4w0p8t20XD/Pbj14Qewui/hkX/5RfBqDVpUKuAYvbJC34ONQGczwBavLYDSjm+Vo/ohslWcE/sqW7oW4mN16W/9/UKyOhfjEHIELj+CwAJ3Dcz8b/H1czr+8td5zycBfPJPeSa/QYMZvFwB0DmWtgTTlZNi9qtKQNLKiqq0uw9lsnoIKbGa6U5d5p3M1EEjLRayiJxI1IEThwt65CGs3n8vjp/fot404OtXwLduy3F6aQO6MbB6KtJiUQi273dssKzC8uNbq7CWSs2JzfbDxhF51KVlIiF1yyxLCXcubAcClxVBYIHDA1ERa/SDiypMmk5V0nZi9zUVVDpeOLHl83NwZqS2KYvRql50tZ6Rhi0yLxZabUlkCb3rEXCdkL78gu+TYRzR3tqCtiPoalPOp/tYyNL25K4Tu6q+d3LcUQfeEeMCaAVo865O/RgnOWAmKkmT17rzhzpxSJsxWoiBy48gsMDhwWZKo1oqaQVk7TQTYyAlpJNjIIkikdcbpKMT8GYjFQslpLmp9hjcb71q8Vka5zIHI8nbSldPZcn4PQ8jJ8J43IDvewyvfGiGq18acfzsEmkzYPWOE3SnCce3zoCqEkFI07jKsTJXesslSwSMrLtd6rhhuWAKAty+CjAXfXmvOHE0Hg1jJMnqwu9RMzZ3CwQuOYLAAocHIpldZXG+AODmtl6/GOn0A9BqrldVIS9XLr6gttFolUHSjqvkFY87emQG1Y22K7XqGwbgvnuQlltwU2F73xzdSYVHfuV1LN9/RchtXuPoy2eYP/Ekctu6atESms0bUSoyjUzpJg4eanvlwZxd54vMvkLQ1tIS1cgWOjkpdlew2R8XkkMhv9gDC+wDgsAChwfLAwNESq+7Vtx1LpEHAJrPhWwogdcrl7F/jQTezHbtOZtNDTLjkliU0e2e8nqD6nwJzFrQmHE0Mo5fu4V8/3XUFyNyW6Na90iv3UJWYYgpG2VOp9VV24jaEfDFZmjb02JWqJLgTVosilei2U2lhDSfefCmO2zYygCgCsbihm/n+jrrdoHApUIQWODwwKKm8ygUqNAhldmXBELqTEwrJ1supvlsx4dQ0oo7b+Xl7RbYbHfiWDx/y/e1VjJnuucauCKR46eE+f/7NGg+x/D8C8ipQjqWNmGaz5Awl32s83Ok+RygtgRXjiNoWn0BvvwsHouty+ipbXVRmT2TzNuOppY0FWbTgNAXFw5m2X+LGVhgDxAEFjg8WAVWpZKvpUo8BpwUnNx0VmZzMl8SrmudE8kxeL3xWBRwLvO07Rbp5AT54kLOry3I8eYt0Nm5/KwejJx0TnZ0JMdUoQV3fYleUak/zCGeMzgzWIUZ1Iq8HgDG5RLp+Bj5/ELcNyYkam1FryynUSkqtSeoeGOxkOs6XoD6FjQEgQUuP2KROXB4IHIRh/w6Wd41z0Ga+iSObhXlir7Mk/c0oPlM5ObjpA2nVUuazYS8KPlSscjxq52qx87lbvRV5YnOrHJ3T3DOvGN9ldrSMvTzVxWq69ddUs9dh3xxIWS43bpjvbnrexWqrvjUNtJ+nM28wnRCjRlYYA8QFVjg8GCiBnN+T0nUe8miVKSKyXqTt1RiF3xoYjJVlVRsTQ2Qmu42daliJsa3tiBt86ydDC87Bszglz2TyxWARMjrdTlOlUSKr0pE7jonI1cQmhsH8AbWVWVvbLxYIrWN+zGaI79knTXu+kFEYCIxK95EBRa4/AgCCxwcmFnmVVUloodOFn+prjFeLEvuFcrSr4kcmBnp9FSITiXlMvdauWDCkp596TczGKwCEZ05WbTKTDPBiKTVaK1DnWnJXE1amOnk5Gt8CMezC7n2CblaVUdmewUUn0Yjqc3Wr7M6OVYjYBGGoCvuIj4na9s7gjy/Vf86gcA3D9FCDBwctg8f4Sv/+bfjlf/4zxUxh7bp0nzmzhUAwOt1cWpX+XperYQE1EeQh0FajBp/4nEoeWLF1DZSEZmn4hQWgDmzhWoVW9iCtcnlJ7ZVAKQNqG1FV1VaEOU4TtKkVYlo7ceq0gyxVkyHWcl1vd5NXdYv0uTqnfZkILAHCAILHBzmr49ozwCugOrhB+Qmr/ZLrHMvW2QmrcakJZdL4GNKqjKsfBeLrDXYNPJY03gFZM+lSQqzydJt98ryuFw8spksV5tjvLrc8zAgHR3JcY+PREVoe19mJZUKMfvumFZayOz5ZSaVp8ViR05vs0EP38xc2pSxyBzYAwSBBQ4O/XGFsQVuPLHC8OwLyOu1Sst1adck5TmDL5blpt+bUz1pW02EE7Ln1XnlBOwa+1r1tZNk3PfuIs/65dWUgkcJu2TmclxNbS5ikox8duE/WwaZE/F67WIRE2kQkVhiJWllZnO5X6+LUTCKDZavFahdlvwNvqn/JIHAtwRBYIGDQ8rAA7+zBv3m50rVpAvH1DZ+o+dhkHBJE0Qwe2KxkY95CgIo7T0lK3udV0PT9h9ziWGx1h6wk5ZMbePnJ1VO+vHN7Z4Z6XhR2oXmmKFqQZDO4XxepwGWFh1TVUjHMne7M0WaFnNpic5m8rltTaCJ0XhgPxAEFjg4VKsBpO2w9N53Ip0ca0yJRJBQ23pbj5rab/jTvCyJLNlNMeau26myzN1jKq03EjIJuz3ulY16KZoxMDW1k50rDdvGLaJ2CBQoPoYqv5/K8B2aPeZtwL7fsaDK2y2oElWl7cCl2axcpxFiIHDJEQQWODyMI5qvvAIAyF/8cllMnsuul8+A1IWe2lZ2oKYkYEGXSmIWNzI1zmWzlLIssZxlxqWiCzf59YXp0duUO7tptlhdVaDFHGja0uLcbkXq3jY+n5PF587TlL0taCTZNDuL2qgqbzkCk124zKVtatcxjhhv3Q4VYmAvEL2CwMEiHR25wW1qG6DvpLWmsSGm5rOZkMeqWEKxOb0bsWUGY/TXgpUApm7wRiJWqelzMttqdkiFdBF6GobJm61XhXY9tivm8v26LvySElJdeygnb7ci1jDz4nEUVSXJuVjdPWgx9+gY0uOaW0d15Qpw/i37ZwkEvmkIAgscHnJGfv2mV0KyN6U+iNoSNFPfHQGEwSqnqcrP/AVRbKmM4Hz3azLfkh20vsykEpWdK201mojCHOO5H2RGt9lO/Bq1pZh5x90jLebSWtSqz+ZbtnRtpEtNLftlk7mai05GlHYq4IITTCvRQOASI1qIgcMDTyokYOeGzMMgZNH1Ze9J4TfzplEikDZfmmmysS0Pd72KKCYmwE1TnDnGLOSipIUJAdqSs83TbB/MY0z09+r0VEmpkePZPMzUhzYX0/BKi3exHbDSTlQLKRWJJM1GM99E3m6l8rPdOJ2dhZVUYB8QBBY4SGQ15gUgRrVGNiqnZ92N8j2uut6ZjU0f91mW5nUhZ1lCVpm6ZIaJ/yB3HdLxQtKYdZ+K2lYWpo1UbJ/LKrZeyM7nYiTP54ulV1nTCsqd421BWsM0qUrF45GzqAlZqjVRKYoSM50cy4xukm1G81mxwZqIWQKBy4wgsMBBwiXpicQ5vkq7O1xaEdnN3Rab3UC3aXzuZJXQtLXIm60KKnKJKyGdd5l3oR2jqsRCKqVS2WilkyyE0udYEmfCqhS0WBT/XCZxt1afiTdYqr58+47hVSfBnq5I1JmaXUfebl1yL/6RjTwXBBbYAwSBBQ4PKlagquxNubs74NZMsr8lu2FpNiuCDEqgWevGv243NYlckcyxsbQhezEIlmpP25Az2T+jKilRSASLS+0tZ2yxkGPY3MpIqZmQycRf0X72hecJkYnMnkobU8FbyS/j5WpHaGKrALAZns0Mg78Ce4AgsMBBwkxz82ol5JWoWDoNgwsjeMxSkVgasQZdouuLgS7grTxvL2qrDVXlCcmyT5adoHjbIS/XkrO1mAthmaBjuheWs6c5u3wfKPZOwNfO8yZWVdLCLDJ5F5joc2bya7Mv7jr/W/jxTNzR9d5mDQQuO0KFGDhI2M09nZ5KsOXFUlt6qcyyoNWOWSgZwam3oFVf1LZONDwM0irMDJppJXV66hEsgKgKkUQ0QXOt7HJRIHqLcZJNlrdi92TxKRZt4m71vYg7krrGExHyaiVEbMvJ3jbVpOjNtrQslcStVelVnl23VX9tIxVnVGCBPUBUYIGDBBGJsMKiUppGb/ZJLJM0wNL2orwNpztY+WIpJGBOHHnitjGfgeYzcfdQBR8Pg8yupopHFXVMTXZNRehzNRNikIgrxC2Eyn6axaBodErWY/rcrOuF+Cy2xXK9+qHM5yZ7bHm7dVWlL1+vVjsei3mzuUv/SoHAnw1BYIHDAzMwm4GuXvE5mM2qXKiwWqsLR1NmQr3MvNLpiZBA1yMdHUkbkrnsjUEWkz3SZGI5xdttURRS8jgTO7e3D4nkvVPHDEquOCStCrkfMHzX+50YebsVFSER6PhIyDUl2RGrayHLobQTS2RKcjcPZhaC6ydtU0D+Tm270zoNBC4zgsAChwdmEVlcLIUU2gaYzUqiMlBUf/aWMZf5U9eXyBTAicRIxaqxpJVYiTKZeC2qRD4viwDECCYvV3Jc3R0zxWJerXzBGnWN8eZNrP7ah9B8/jmkk2Ok4wWq69dFXj8M3iI04YZDpfsim5frocVC26XisE9N7dctH0YUkj4zjBFYYA8QM7DA4YFI2oRtK2rCMQN9J5WLCTqy+ggCLlVnFVO4OMJsnpiBVuZmrmBUWb3viOVcJO6AVD36HTmLXF69ENN85s7xgO6paQtvPLuQBGX1b9yeVjgBgFSBlyvkrkd15cStn3gYwJBlayNCAMUpXyvQfH4un3/T+QzNJfjDsKNsBABcfKv/kQKBPzuCwAKHhySLuRhHsJnVVhVQ18B2C4yQNh/KDpUsDyfwtvOWGwC3b+Ku23HwyOuNVGBquuvLxZorZjtdlGWnzOye7Pr8PdYynEjrsZgjv/Qy0vEx7vlfPi+iEEjVmOapkHDTgIByPYu5kJWRlyY+I5GIWQBRSy5XSFevqPu95oVxRlosSghmILAHiBZi4PAwEVwgVSqBZyEvy8myHK1xdBEHbzu3eeLN1pWKsEwxtZJCVSGpe72REmviskSXmJEvuWzdDYOVVKipZXfs6MgjUsyRHusNqitX3K7KLKDKZ9L06Co50VJViWR/Yk6MzELaD9wnpPTgvaXSu3XL/RfTfIbq2jWffXmQZyBwyREEFjg87CjvdNmYJyrCI1kc9nmYfa+URCw0Ultt3HUihe9LsCX3MtPK262QSduqUKJ1AqC2leopc/EwVFIUr8QEbLcl6qSqXDkpqkVyd3oAnraMrM7zm20RY6g60txE6OoV8DBgfPU1jE8+ha9+/C9g/MKfeAZZOjpSuX8qrciuk5WCpg0njsBeIAgscHggSOUBvPGNeLobBUj7MJGTkhMBUEjD3toP7sDhrTbW7C51c6emdi9EqtJOEjQPg8/QXO1nrcS+3/FLtPdbuKaHbWr6Ms1noqR0FaUQLHcdMAzoP/JtAIBnfu57sL0KfOW//m45tDrZExHyxYVEzqzXLsMfX3klAi0De4EgsMBhgpKQmCr6oIa3lqnlFZlCVIipCBxyljyxejImTqVdN80AAyWR3M/K0rEtLqNpZDZl7/V0517TnMeSgKwCDKvErHUp8vtUlIbqam8u9TyOyOfn5X0AcOMa7v/k0/iVF34P7W3g0U/+O7zrv/oNpNPT4sRvHo08kc9nlnlZVGCBPUCIOAKHCbNsqmsJslSRBkwu37O0y8bRyUwyvbQSs30qUwrOZ+Axl/BIYOJdmEDHR6IctDRmTIx3p8nH5qIxmZ8BkNah+SJa0rM6xqfZTD0RE5AyKAkJkoZzghlJ06bp9BTb73gnZq+u8RtPvg9/7rPfhrZWRxKr0HTx2ZeuiZCXS1A/SMjlGNVXYD8QFVjgroGIHiWizxLRF4joCSL6aX3854joeSL6Pf36q5P3/CwRPUVETxLRD76lE5lT+zgW8rJASfUB5HEErzfanstOFpLLlUqwo7YF83It1ZVK4Xm9Li4XjXgIwiT36kJvbhisx95RG6obvgdpNmIEbNWfhHBWpe1oNlaAx8DYbpnL4QHkszMsnnwJz/+la6B1hff9s6/i0X/6pH7mrKbDbXHf10XqdHTkuWRTh5BA4DIjKrDA3cQA4G8x8+8S0SmAf09Ev6rP/QNm/m+nLyaiDwL4GIBvB/AwgP+TiD7AzG8SGTzJ1eIsX/aOlLxdSGrZ5Oq/cZQqaCazpTx1h6dCcu5snxLS0ZGQjGZrQdOWPcSSZdfMCWu6PG3SenPBB5DVesrk+Gkxd8Uh9wPY5Pf6fJ4kNwM631qv8ei/fAbIGeNLL2um2WRPbUKkPHGmxzi6xVZ4IQb2AUFggbsGZn4RwIv68zkRfQHAI9/gLR8F8IvMvAXwNBE9BeAjAH7jTU+m4Y0YobOhYSc0krsOrGnGJkuH2j9hHMVcdyLusPmTLACTpx+bEz3pc24bxQx4pIssStNiLsdeb5BmM9DRUUla1vf5zM2so9TP0IMtJ5Enon5sSnWVCOggllhbqeyoFUWhL2QTFYFGIqAbRMTRda7OlOXraCMGLj+ihRh4W0BE7wbwnQB+Sx/6KSL6HBH9PBFd18ceAfDs5G3P4RsTnh4cwGwm8y91jkdTl1ZiIml614QbAAAFyUlEQVTXLeYu6jBi8ypFqzcicg9BACXZua7VsqmRttxkAdiyyDwrLO0mLO+oCSffMZmLcdeVqtAyutRD0T+m7Wup2a/vmpmYI2cnLO76Qo52/n4oZsJtKzZYdS1WWtFCDOwBgsACdx1EdALgXwH4m8x8BuAfAXgfgA9BKrS/Zy99g7e/YWlARJ8goseJ6PEub4R47CY8yb7irveKyhaFTclnKcmAxoqYSGIywwJn5K5H7nppM643JUtMKyxTCPr+V87Iy1UJoKwqbUXmQnK5SPGprj3BGVPF4ySF2UnvDdKTraW589ikHSpJ0hIZ4y1DMzs28csdKs1A4DIiCCxwV0FEDYS8/jkz/2sAYOaXmHlk5gzgH0PahIBUXI9O3v4OAC+80XGZ+VPM/GFm/nBLMjfirVgl7RjdqpuGZ26pm7zL2K0qGgbZ5arrYsRrX7rnlVcr3wszZ3pfQFaJvKU6l2VkUfzpRe+ET6KqwOu1vEcJzpWD5ldoZrw295qIO9xoOE3JSFqYaT6TpWud9/numAlYtp0IXuxxiltD4PIj/lcauGsgKYn+CYAvMPPfnzz+0ORlPwrgD/XnzwD4GBHNiOg9AB4D8Ntv6WR9B6h4washU/NpG9CWjlFVIF08BjSryyqfplExhqgRXfkHMeGtTo5RnZ6WGZPOuADNA+u1nahJznm5ln2rvi8CkkmFaEnS6JX0lIAsNgWAz9QASCVI5JllRlD6d5Xr0fZhWsxdQYkkhse83QJNLe1ItbFyQg8ELjlCxBG4m/heAD8O4A+I6Pf0sb8N4K8T0Ycg7cEvA/gbAMDMTxDRpwF8HqJg/Mk3VyAqSGyazMkC0JnRxCaKu05UhFolucTc2nLmNG+HbFtpEap4w+dUShzJ23GVKxxJTlYyvkxEoaQk8zI1C65rITWNQ3Evximm12QO+E0j12yCkDuUiZXaSvF66xloyPIZ3fDXhCq2Gxd5KoE9AN3ZKw8E9h1X6/v4u698VH6ZqulUsOGSd2v3WXKxKQ4Bl6nvLDQ3jRDiZrtj+eS+iyqe8ARmoFQy1t6zJWWrovS4Uym9XPekSlTinMLID0QlQ8zIzUjQZmXqjk9TMjZBR1MLeQGSm6btyN+4/T/j9vhqKDkClxpBYIGDAxGdA3jy7b6Ot4h7Abz6dl/EG+BdzHzf230RgcA3QrQQA4eIJ5n5w2/3RbwVENHj+3KtgcBlQ4g4AoFAILCXCAILBAKBwF4iCCxwiPjU230Bfwrs07UGApcKIeIIBAKBwF4iKrBAIBAI7CWCwAIHAyL6Ic0Ne4qIfubtvp47QURfJqI/0Myzx/WxG0T0q0T0J/r9+psdJxAICILAAgcBIqoA/HcAfhjAByHuHh98e6/qDfEfMvOHJtL5nwHwa8z8GIBf098DgcBbQBBY4FDwEQBPMfOXmLkD8IuQPLHLjo8C+AX9+RcA/MjbeC2BwF4hCCxwKPj/lx12d8EA/g8i+vdE9Al97AEN+rTAz/vftqsLBPYM4cQROBS85eywtxHfy8wvENH9AH6ViP7o7b6gQGCfERVY4FDwlrPD3i4w8wv6/WUA/wbS9nzJ4mT0+8tv3xUGAvuFILDAoeB3ADxGRO8hohbAxyB5YpcCRHRMRKf2M4AfgOSefQbAx/VlHwfwS2/PFQYC+4doIQYOAsw8ENFPAfgVABWAn2fmJ97my5riAQD/RkMzawD/EzP/70T0OwA+TUQ/AeAZAD/2Nl5jILBXCCeOQCAQCOwlooUYCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAv8f8Bo+qwuHDki7gAAAAASUVORK5CYII=\" /></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "                                                                         rf_crs(tile)  \\\n",
+       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "\n",
+       "                                                                  rf_extent(tile)  \\\n",
+       "0  (14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)      \n",
+       "1  (14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)    \n",
+       "2  (14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)    \n",
+       "3  (14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)    \n",
+       "4  (14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)    \n",
+       "5  (15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)     \n",
+       "6  (15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)    \n",
+       "7  (15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)    \n",
+       "8  (15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)   \n",
+       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                   tile2  \n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])  \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])  \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])  \n",
+       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])  \n",
+       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])  \n",
+       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])  \n",
+       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])  \n",
+       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])  \n",
+       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                      \n",
+       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                       "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pandas_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rf_crs(tile)</th>\n",
+       "      <th>rf_extent(tile)</th>\n",
+       "      <th>tile2</th>\n",
+       "      <th>threex</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
+       "      <td><img 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X6R7VaAGNUSWdqekbuTAz4R4hMAyfibHEOVeZvp4h/kgsflqgWqNRDd6iMom8hywWe5lp0Wg0itiLau77jTZZfROUnAABDj9ESy+SHRnkf0jjW2G2pwGfyIRbf0mkwzYToihX0hdafF5lpTkVNGeKfxcyGL6taE9z4STgA4arTPXtzOMSezXFURF8dqhO4XbQTaDavayOKZZ7V6i2GKTRahzpkmFvDYWg0pySK/avZxHdIr2Qu7l/Ev5NxUzrv3Lf1dH/NVjJLC/BijKQIyaGBXGRoK3xNYwOWkoXcBHg98XUCRmy4ZF1WF0ou0LnI2UKrPrCoxJGJPoe8vTszWP6h0gKUOtMtNKFI7GSNQ2Lztq47nuJqSs2YcCHw1NcLTBSiQYJCqr5h2+lMK7iorbywWqiuTOYPYHpuFeDo/8mV0CDcWlFXXhVBbcbIbXJ1FT56gl6iqSRGNaZNnaJdSQ2tQqEzqLshJx2SKy7xwpatRpT+wqtr8WODvfsKha1l3Fctrw67//Fd/75kO8tzTWMZt3nJg9n06v6aNl1VSs9jVdYTmr9/zOyUu65Nh7h/5uw/VqRs6wDwUXkx3PZ3f88ecfiRhhb1FlIu8tJshi3y8yyUGsJGJMRT4KQ/TaYvaKZAAlm4L2ccLsNbZV2J2k67IG0ymSFdGGO2nZrytyY1DzQG4M9BrqQKrjMdVKGFoHDm0DJpPLRH3eoHVmdz3BK4PbKNwG3C7TnSi++I8caREgJfTWUNxpirVEbGki6c2sFX6h6E7Az4UUt9+JqF5TXJuj6MMvIRWZcBZQnWY+6eiDodlOhGQ12Ds5B7tjEMqA8ZlQSwrWtiLC0EGIc/eROUaWh4jLdGBuZYPg9pluodh+kgmLiF1JaJy1EN7td/7lfoIR7wNGAnvgUCrjTMToxM6XHBp3zh6v+fTkhqtmxi9fnrM425GBJ/MtpQms2prCRK43U1JSpKgpK8+8bpmd9tTWU5jA3Hbc6Yq3uykhaUrn6YNlXnVMnTR+VSawHyKv2np+eXOKUpm+c8RB7Zezovh0w+q8onjpUJ3GLjrqRcO2qvAKqVspIZ/qrcFPh8YeMv404e40uofkpGYEDL1BmTQdFpgyol2irDzttiR2BqUzygqxmUKiIaWlptJ6Q44ia0+PelztuXm94CYt+a3f+IbnkztC1vSdJd8V8Bi2vuS/++l/wj/84Ic8qdf88OVTtEmcLfd8NLvldbeg1IF/59lP2YWSy/l8uEeOH79+Svh8BlXGrhX9RUTdODCinkxOHQk8zuLAzgpz4zB7henv5fXZisCluDVCdsMtiJOMaRT+3GPrgI6KcFWLErFTcGcIdYalh62jvjRkzZEMuvNM/ZsrrIn8u89/yv/++W/TvJlgGs3sUpMsFKtMmCpRB9qE6hX2ynHyE6nbrX470n0ayEHhph6vMmUZqFxgbgPXm6nUsqKi+spR3ImIIxbST1j/3i2bXUWuFLfriexr6kA17fG9JRiHipb2HNCZ8kpTrqSWVvlB9GPue7xMJzXUrCFUMP8qUdxFslG0p4bXfz+jAri1YvqVRWXYP8nECoo1nPxorIG9jxgJ7IEjRU3MCu8tsbGkVnaOftrypy+foVSmmnVMyp5Z0fN7p19z1c/Y9iVtsBQuUBeewkTumorSRGrrmdgeqxJ3vmLrS8KQnnQmMS0aXsxWWB2pjadLlpA1KStuugmzuqMLhr632JlH6URoHWaSIEGxUhQrAy8CGbBFJH0xpVrJ4t2dJdonkewSujHHJtgwkZRiLhLJZNRu2CW7oUamM6ZIFKUnRo12iRSHvqXWkAdyUDqznHX4aOhah++cyPQngdC6YxP46+2MdVdxs51gbMQ93ZKz4k9+/iGLsx3/z+Y5KSv+w+/8gD5Zuii1wl9uTjmpGv5s9Zi7fU1diOLx5idnZAPqWUtZBdqqws49rGpMp5i8ylS3mf0Tzd3fCHKv3liKO6lFHZqSRaU4pL+CpFrfbUrXvcI/7TE3jtgaSbcqEYNkLVL77BJqayluBgFIFDFGmGQ++FuXPJ5s+Hpzwj/++e/QfzVFKzj9vsLtE6vvaNoLIQPtQe+0RIkus/lY43agW03CUtxpYungecN+W9JcLuW104AuI3lnSS7TPpIaX7aJPI18MN3TdI6AbDAyYF6XdK4gLQKqlfdMdaZ6aaiv5B7YTtg91ArTCyHGUqKofi71uOoa+pmiOXfHtOz5H2v6ucIvoHkirRd2rzBrKG/eacQe8V5hJLAHDqUzVicW8x0vdwXVE0n7ORNps0NreDTf8aje8muzK3w2fLE549XtgtmkxZpIHwy3mwlPTjZU1mNVZN1XNMGhVSYNNbLCirtE1LLVn9meJjq2vsRHw00jUvYQtRBqZ1B7K71Rk0jfWwia3SeR8o0hfX8pacFfb5h8R6T3nbfooDEK+usKgLgYIhGvRaVYRkhyTKIIRJQdUotZmltdGbAu0O0q+d0hlanKSFEGjM70QUl6tIjkqElBwc6iZh51U7D740fcXASK85ayDEzf6ZnrveVyt+DtZsoX1RmFiTgTeX03p+8cX7Xn6CKSekNbiwY7VdLYHVqLm7dUTze0vcN/2JK+qvALxd13M6kO6FZj9godFbHmXkQwRF/aSxos1BJp2L3UgpKF8tfXnBSeK7+kfOUIk4x5sSdGUWf2U6lzVZcieAnTLGnHaeDR0zXbruDy5jn1P5sSPsi4RvHkDwM6ZL7+ty3hJGDXBvXhHmcTKSkMEKOm+N4EuxPhA1tDdSXEqH6jY/NyTtFDPA+cPtpgTeL6+pz+RAQkqU7oqacoA6flnjduhnOR/bYktRLJ252ir6UWF12memWFfCtFeSe1rm4pThuxUO84hmRUFqGL2w5N9zNpAbA+s/5UCF53HEUeuoP6rRDj5HpksPcRI4H9NUBMmlnRcXq+ERWgC0ycZ172bLqC2nr6ZPnT1TNerRdYE5lNpCodomG3L6nrnl0vhNVFK0Q1fPtvtxMhnwEXpxv2oWDuWnzWJBRXeyEvZxLbpiQlRTHx6FlPt3fi2FH1uMcR7w390mC+qUhkuC7Z3ZTkWcBNPMYOuTAN+eS+GTUD1aIjJVFIpihRX+wM6taRzySlaYtICPepQTMJ4nYVNNZFrI0SIXaWFBUpaJTJFJWn21kmsw69aGn2JTqBMemYpr1ZzYjekPaWL9/WoKGpKmwZ5H56Q97LvTqqL/cWNLiVpOpUr9lczqnOG1FAqkz6qKH7JJFuKnHrGBqPQSKt5DLaq6PEPZbS9GtaicaKjUQOfhn5ZL7lZjcBl8i/uUUPFkpp60TEc+ukiTlC8zgRF5Fi2VEUgauXS9y1BYUoE3vF2Q8T7anh+vcyIM3C/ZMA24LgpAiZvcZeO+wu0y/FCkpHOc/d77ZMkkbNAubxng/nO15eL4nrQiy+ppEnz25Y7WqqwrNvSz6e3PD4xZYf3j5lWbe8fHNC8ZUTspt68k15rIHpwRZKxUw/15hexBvNI1EckqXXy23lNduPhNjcRmpku2dil6aGPrdUJuxOoz1UqyRk5kfDh/cRI4E9cGideDzf8mY7IyRNShofDXFI+bW9oykcd12Fj5pJ2XOznmJMoi5Fbde0jr63zKqOLhoqG+ijQanMvivoe4sCSaO5yL4reM2MjS9ZFg3XzYR9W6JUplUZrRN1GQhJs7megtc8+ehGeqaS5tZPMDYRX7SyqPYKskI1Bh8VPoPyGqqILiJl6dm/naKComsnqGVP6g3aRTlOY0iLgLGZnMB3Vnq8vELVkeS1kIrNFLNOzrOVdgOlua+Z7Qsok7zXJJC3luqiwffSnnDbGHEHGZqOs83kenB66I30N6XBL3JoGAeOKskwlz4oPfeUVeDj8xtumgltUxA3jtRLxJVdxm0U5YpBgJCO6ToVpa9LJUWYCMllJfUa/emOD082tMHSNAXaJvrbiuLKYAykJ57sNfVAXu15Jp4FTs63dN4S/+gEu8j4U7GdUnuDCoo3fw/iWY97XRDLjJ9nylf26BHp5wnTK+pLhdtnyrU0C/cLRXcu5La7nGJ2hnbiuPSGFCR1mauELiIvv3gEJrM3GVNG/rcf/01cETAmcTppmC0aNp9oiWq3jvpKS80OMI3c6EPT8eGeZC3RVj+XhmjxsFQoD1jozjL9KfhllM+gEuHL5EszOIbA7omhXKX7puwR7xVGAnvgiFHzi9fnKKCedOghvadVpukdWmeuNtNBpSiklpOiKAKdd4SYKMvA08UGTaZPhrtGUnfLuiUmjS8M1sYhCsl03nKznvKmNyiTj+9ZFAmjE723bLa1pMomnrLyLMuWNKgT5qWsBjf7mts0Q7UFaRqPAg566f9JMzl2sy2hjFBnia56A17BXSn+uAvP/HTPfl+SvIUgdS8zC8Q7h4oKNQ+YItK2jrhxoEHXgXrSs7ue0DQWrHgu1t9YSJbuUaK9qTAzCYWUEyk/pbiCYLKYA3dDfe1gbjwPWJuIQZN39ih5z9OI2hnslxXRwi9+PKNYKyp9b2wcq0wehBq755kwT+jTnhwVodeozmA3Gn8aYJmIUd73s4/ekLLii58/obq0+I96zi7WPP9oTaED3/vBZ9i3jjCP9L+7R6lMuCsBuFtN5DPxN7fUpWf71QK1t+inLfoi0W8KcXK58JKyVdBNM2pvyEVGN5riVpOceAyGCYOhbsZ/2Inqb2OxH0sNsd8VgwIyovYW+6YS1WUtBB87zdmLFf/FZ/+MuW750/0LPt8+4mffO6V+nQcj3kHwMfSdqQRhqghT2NUKu2e4h/fEHwtoPulxsx5/U1G9tugezN5gG5HS2xZ0SIRK+vFMLz1vzdm4VL6PGJ/KQ0dWVJVH60TbFGiTKItwbFIudEYVmdWmJidF8ppy2mMH0umDYV53NN7ReMvdeoo2karyFDrSapGhh2DoOodSYv6bB+l6ajTRZqp5Rwiapi9IO8vBCd7jcEVg3ZfkoZbmTKQ0QgraZGKRhHQGWTxJnDrKSc+k6tkpiMFQVpIi9JsCFUQ2n+cBVwWJ9NKg0HMJU0YhGRC13cYR7xy5SKigYB4wNtJ+Pkc5yIOU3K4NbiMLYvNhAickamwiKPFNzBlSL+rGnBW5iuLLOLj9Z68JjUF1Gh3VUQCgBmupWEray+7FgT7MhgU/ihVT+yyQnCaXmVxGYmMo5j3L84artwvsk46L2Z7XNwtSb8h7w+dfPJb3CIr5v/EW2ztS0nz/y2fotwWqzOQPGwwQNg6ztqjHHcpIL1jsDP22IP98isuQP9tL07eR/jM1ONlnAAXFtGf6uONuPUEvMnFbY+4U/WMx60UBNmFMJvaa4tmOxbTl7ZsFamPJVcSWkdAY/GJoA9hp0oct02nLv//iJ/xo9wF/9Fp8rW/eLKi0kFR5czDezeK5mGD3/L6he/lziVj3TzWmEassPTiVmFsHbx31VsyLVRZiK+4yxU7MkLMWuzMdBtl9yt92qh/x3mAksAcO5wJn0z0+iXDCezMQmMLojDWR12+WAORGOjy9tXTbEjI8f37DrOh4uV6wuZ1AVERlKIpIFy0hGqZVz9X1nLL2Uj/qLH5TSoouK7IWt4t+X8g4jFK+7bnTKJvo2oKdSUwKT+PFcNgng9HS54QZ5PLDGBNVyAiM+UTUjFLvErf93V2Nasz9eJCohNB6Leo8l9F26GdrCklFrqXhNctLRLXYGtKVE1VgkqgUoLhVuF1m/2Qgw/xOn13QkpoEUPkYhamNFdl2IV6Tam+OlkYqKXRQkDj2sLmtwm7FASJMEeIOch5xklFekSZDg9uQUvvu0zf84uYMVKZrHCtViyCxNVAmZmd7lnXLi/mK63bK229OxKi4TOjne05nLVffLCmuLPGJh2etCHSuS+mdywpVB9LHLY9ON/TB0AfLB8s1X3qDKwJdWxCDpFCLYlCvnm14c7UAN/gkBnmOug6kzogwJin6txPeXteimqwiqhpYvUqotUV78CeJk/meSeH5xe6cH7x+Sh78Nd1rx+RVJlaDi/zQWJwzdGeKZDLVtWL6MpHN0Bj9KOPnCbvXFGtpXhbneXG7P5CS3UN1l4iFOjqMxPJeKAOMBPaeYiSwvwZwJvJitmJd71m1NfteHDBiVOLOoZD6jMrgMkoj9Y1lz+2u5mY7od0Vx2a5CfILAAAgAElEQVRWXUaafcFXOymyK8Q38NBz1qYCVEZFPaTHDN1OdN6mjLJoKWSRylDVPaWN3NxNqWoxBU5ZsdtUZK9RQUsENJj3ljMRahyk+P2+wBSR7dX0KPlWGak3eXN03EiTKCSIpFaVTeQyYTbm6AOovER6utHEk4DqNcrLuJRUiHx6/1iRHJRvLN2LnrYpyGnwTszIyJcwOD/0IgBIk3wkPBZeLKN2FjoIM3Hdd1eiRlRBjq+9KAgPs6kOBrNyMYgt1qPAs/M7vrw9pess1VSi0LYpJAqsIqfnG57MtjTB8eX6dCCUhHnSo5LG7wpuv5riPISPW2iNpFGB+o34Q/nf3nN+suV2M8HoROkyL5Z3/Nbikk1X8vZmjrGRGAZFZVK8ujxF3TiMV7iNOs7fUp0mJdH5m2t5fRiUpNLWp8R15NRLTavQ9I8zs4sdn51e8+n0mn/889/BeyNJ569ryrWiuRgalK9F5RiLISX9ZcJPFTrIM+5nis1n4qN58iNN1pJaVFlcTuz+nYbmw8bBKaJT9+4cQSIwHfKxpjbi/cNIYA8cCpg7qSkVOrDa1yJQiAplJAUU24O3jvxObI1EDkDfOUJjoRMbJlUH6bsZXNzRoAqp3bS6pInVcTYXSaIOfdILaSHponLak6LI1ovSiwtFJ+a/SmVC0jKSZW9Rk4CuZYXISRSEOYsQIw1RDyoT7opBnadJk4Tq9LeGP2aXUIVsk5XJ4vHXWOzKkMr7xlYU5DKh9jIehZnHfFOSpexF+8KLxPyVpX6b8XNLGtJnuojHPrusszRoVxnlEmYgTpQ4fvjWonfSw2Y6TVAHX8Jh8YxCWKR70kpOXCHMThNn6djX9dXLM5SC2ckegO1qQu7l/LOCi+mOJjhe3izpN4W0FGRF+qamvNb4ZZb6VR1kw9AachWpvipQEfYvApWL7DppRr+6E/l6HwzrruL1N6fonSEFKFpF/4GIaszGUKxklEl/Jl6LcZbExFhLZBoWETWR0SnqVlKouleEZcSUYr6s557ZvOVvP/2aLlr+79ef0X89JWuo3mjsYJV1GGbZLaXe5nYZ22SpVzkgQ/tE058IUU6/uRdzZC2EpoOkBGHYQKhDGnGwMRvEG7bNRxVoMu9sLEa8VxgJ7IFDq0zImpOi4afrC4xOIqrQEL0hRBmloqtA8gZlkqSdBn/AsB/qVS6DV9gyEIMhR8BmTCUqPn3WExsDQaP3Q1RnwJ5LLidrTU4KbRPnix1mWMiVyqzbEqMzIWoKG2l2BdqIAay2Ca3EXLeoJVXXtw5tBwuoKApFszWyU64T9k58/HIhozMOUVXuBzspBpGFS8SpTPg1e0k9hZNI+crSn4ozR95ZUikz0rRLcFfcK9JaOP2B4vr3FbqKKJ3RVSS1yD3TmWLihWh7I5OXkyJtLdWtRGsqykgTe6ePU4YB8rDgHpxERCo/2Eed9LBxcN4xnbVYnbiY7lj3JbebCfOByJrWUVWez9+cU1dyHotHO9rO0e8d819oVr8bUHVAZUW6Ko9+j2alhayBctlysdjyYrYS15FoaaM0Zn/15oxy2RKmhtgZYiMWZSlp+naK6WVECjZjZp5hjBjGJLzJPL24w6jM5fWSfNFJWhEwdSDdltjzBrQiJs1PVhe8+vkFdq0pOoVpZFMRajmoDgOZWKiv0xAxKfYfDGm/AsoV1JeD+W7OAyFl3Fodpfbq4L2Zh03NIPdX+V2Suyct8UP81/s9HvGvhpHAHjiUysxcxw9vn3K5mosP4t6KOq6KKJuoTlrabSHKPQvFSUd/V8JKduu5GBzOTRbPxIxEX1ERo0JPAkXpafYyBiRNEqqK2DLgCiEdM6juUJnVTmZRTErPtOiZFh6fNKv1RFJ7WqK/ctoTB2f5nLknUwWmDKQvp7hD5ASQwd1p/EmSlOBgD5WdSPCzy5JW3IlYwzQySfc4MkXJdXWPIspr3JXFPwro0wajM75xMmDxWUO7KIiVY/q1mMb2F4PqEESwAeAS/XUldUAnQpTi2ojj/llEd3owilXHSb8gkVYeUoYHV43kMvnUM102dK0jLod6mYmsVlO2+5LlrBUxzW2Fu7WQYXMWcIue9e0E+7qgjTWmUZSFjF6pXlr8fBCfmHsnjv40Up82WBs5nwoh/mJ9JqNzTMDqxE++Pif3mnRTiAXVucc0mubrOaZVaGD3cURNA58+v+Kj2S1v2xkz1/HF3Rk3ecLrN0vMZQkmy+dmEmFricPnrfzejFiBLzN3myXPfhZpT6C9kHtVrIWA0iFCQtSCzbmWcTEMbiBhGL1TchzSWbwZ0smFuMuHmmMkfhgImtXQAxbuP2NiN3Uvvkn2fuMx4v3C+Fj+GqANjnVb3v/FMH5ED75/3ZsJau4lNdgY+n6Q05dDU2hvoNMyIVcPZrhpEFUoUdw1jYXBUUM7ca4IvaEoA5Oqx0cZm2JMYlLKzn5WdhQ60gTHriuGvqssggmd8d5gTCJ3+l7IkUFXAd9ZbJSFPTmwjQgf+tMkqsJDParXR0WiGq4hzQJEkUHrXsQS5bWTcSQTIetcJPxC0lhKZ/ptIaRdRiH3IHWdWEnRPxYWnrWcnWyJSXFzNYfOoNKhFiYpvTDL4ijR6GFcvYSEKiP+g3Csd6koEUU483z80RW/fHXG9nYi/W0u4rcFm++fi1p/krn1U4n6GiXu9EuPdel4X8OZWCyRNItfwOo7cr1qkL67O022cP5vXjIvum81rRudWBQdTyZr9qHgjz//iNwb1N4MEYnCvi6wraI7TYRlFIeVaWA6F1uuP7t9TEyaH908RX9TUd4qKg9+Dv47LXSG3BjKawMYmXg8iDHcWnrats/NUalZrCX68YvBvBiohvoXQ18cSDR1EFmYRlzltZd77idD8WqYaWY6cQaRadaiAj3UwdTwnMj3jcvJSuR+7Okb8V5hJLAHDgV8fntGsy8HiXvGVjL7KyUt9S6bUGsn4+snCfp7yXpqpMFXzz2LRUPbO9p1iZ55kYj7Q9gAaibHzUkdG4yVyvgoq4sxiarwnE92+GQoTeDVesF6U5N6g6kCsZPX2jJystjT9I7eSB0JpKk4NhY7FP+zERIwjaJfyoDDwzmp1sCg/lNDvS5rUSaSFaZTVG9lYewXQ+3JixFwOAlom0hBE1uLLiKqzvL/e1lE/UwGH05eSZ2l3VpumBF7jVJg1jJEMU7SUUGZFZhbK8Q5+DAefAvTMMIjXfTiBq/gs1+/pA2WL7+4wNxZUpEhWiJQHMaWKChuhRD3zxPxJIiQZFUQ1ZBGnYpz+/LPDNUq0TzShKVYUqmEXM8ic/LpLU8mG9rosDqhlee2rUVUEwvebGfcrSbYb0pJ2ZZC4FmLqjKWkvpFg5p5iiLQd5aNKWk6R/xixuJLmWzcn8D6t7xMHdg47MpQv1WDIfOhKXsY/1JwrGke0nmhhjzjmOpzW/AzIeNQ3RNO+Tof61cgzvIqga/VcbzMgfC6k3s3k6OyUMt7HHwRdcwko46z1Q61sBHvH0YCe+DooqFtZbEvKzGx9b3Ft8OjPcja64irPH0jBqbWRbRJxGCIXgQXu0aUhLqMYt3UGamdBU152h2tm2whjhw5c4y6jMpMS5niDJCy4tV6QeetRFlGSEW7hNYZbZJItXuDKtJxLlXcW8ytw7TveNJ5icTiTGotyiVyY9CNkERUCd2KP16aDanCwa0+TCS1JGIKJfOtqoyZBnHoiApTRbSRa2ovpYlbh4E8QiYbRXUFfmlIXotIIWhR1pVDnumg8uyl9wg1nPOhfuPB/dqGWd3hg6GbWRaTli9fn0vKN9zbGbmNkFWyoHqOC/5h6rGpIsWypbmrBrKG4ouK6TcZ7TPrjzX7jwN6r8lnnpTv+7ieLda83C6ZFj1dhFVTUbmAVpnaea5u5wD4RwGzNqgoPVHdWcafB8zU43TC7wqS13Rtid5YVpNBhapg85kwQ7romM46+Uy+LjGdwk8hncp16n6Yd6YH1V8vmw3thWzjaZb04PD3MoVA7oVtD/UqGYSZnDoKNJIBpe6Ji0EgmsqhbtvkIcqT1CL5nqgkKh5qaEOddxDHjngPMRLYg4ciDRFQs5c0YooiZ1dAHhbYoghonVETiZqmVU/r5fGH1qJMIkVD7LWY5tqhxqRgsmiPdS6dwdr75ubFtD1aT+17x753XMx2NN6JUa03Qlw2M5m2UvzvrPST9Ra/LbCTIJ6EO7GVMj33kmwv8uwwTTJQ0Usth6RIdcaeNeSdqC7ziZdr7rU0Eff3aSbyMJk3SyQUtgc1hSI1hjT3+FWFHZzbs5FoLywyppU+r+LmMBBRUnF+GSUS6kVWrztDOA3YvaQvURBsJj7tMLXndLZHAW+/POXRRyuWZcvr7Sl6qN+laURvzTESUQm680SqE5OLHY/rjkeTHTPX8baZ4ec72mBpvcX8ySndmWL/LJKnEX1n4aLDFTLc1BWBadXzxc2ZjLoJhqYrxHuwK1Aqs92XGBsxFvzaYfcKu1dsPwugM3YqUbnfFpJi7jXVKzsMI5WU7u43eqmrBnXcUAU/KDdNlnpgljEwfibRox7mj4VJJlWZVCbK8waCIb+spAZ6IBcnY1+yERXi7BvP7qm7VxdmyFqkhaaXZ5C1pGpVhPoqkZWkht9tUFbpnqRUksg7OY7R69gH9n5iJLAHjhgVKUqqz1Tx6B/Xd/a+3oRESgdPw5wV1kaafSnjRqIiZ00MyI7eJWkGtvff2qIQQ9xZJfWOu22NMYl52dEFy2pbk5K4guy9o+kdMWicixTDDl/rxLZzEvlFTb8vZJFvrJBmEpm89rLAocUZQSXpnVJBgQP2RqY124RflSLpz0hqMYPyGtsoquv72smhYfkgt6ZI0EiEkW1Gbd3w7/mYXspWGq37RZbm1z2kHsjSV2S39ljLai8i+pM9qrU0z7SIOqzMIctB091WXP1khv+w4/lnVzgT6ZP0sKWlRynQJpGA9tSjNhbzqJMNQuHZdQXboc75+f6clBQxGEJnKL4p6P9uPziDgF5JuvjkdMe2KSnLDmcid9sKsqKqe25fLkV2XhWU016erxLhXujEfNjPZCKzHogr9kZk+L2mfC01rHKVcftMe6LpzhGLqE4IpFtVqFaTC5kK7rYy5dmfDJ+rDNMvRfSSNTQfe8zMM62k9aK7qUWN2EoqUAfQeyEY3QiB+ZkhFRLBmaGfLlkwXl6XtXz+jRcFoo7QnnIkOpXzUeThBm/FUInPZFYyzdntM6EcQ7D3ESOBPXAoBaen26OrfLsSN/PjiJGhZqV0xriE0glnZepyXEs6UU0CRTUsUgfni8HBfDZrxfC3d1gT8dGgVWY2aSls5NVqQfCG2bTFmoQPhj4Ia8xnou47eB9+9fYU31qxeUpKyLUfUnKA7qVeEyqZNJzrKM7oSASWqnT0FWRomC2vDSpIyim3WmpPXlHcivMCDMQ1uDeoKIKQdFUclXmql56yg1JQDVZWYmGviPPE/lx6z3QrUQlZUV2DCiIq0CcK98cz6gY2nySyVVKfUxr31omx7EbRe40zkXnRselLnjy/JSbNSd1wvZsQ5pr9vsR8XcCTxK4p6LyV2Wo28vqXZ9KYbTK5lNpX+Tsr6Bzqx9I7FT9rmM8abm6npM7QZkmLqk4LWetMcSpR9WTSMa86VrsaayO+t5yfb9nPHJPSU9jA5Y8eU95p7Bb6pURMqcwEpUiFosnqSBzFtRFH9yrJxmgqUapp5HmoJE3kAIvPoT1DZr9NowhGbmo6VaMDOC3WWgcl4qH5+NBy0J6Ky8mhp847JY3OrURWyR5k+DIbLFmR3R+ax81wTO3zsV8sWdnsHEQc2kv0puOo4ngfMRLYA0dhA84kQjRHgcRBiq5MwhYRrfNR4l6WEg2tX0qtgypSTcTdISWO5GVM4mQx9Bv1jmIYQeIjWJ2OzvcpqSNR5ayYVx13TYU1kZQ006IjJs3lao5vnBDCkKJUG0suk9TpBuIBSFUmLzxqN4wlKbI0MGcZb6+iwq0MphH3cT+9TyvqTmF3Cj8T9WKxHuoZw4IWZrIQmV6iUmkmFl+8rMCfJZKS3jKRVyswiALTJaKDOFGDG7zGNhAmQsD759KYq846yjJQl57OWxqQOtWqoHjl+ObNM8I0s/xkxW9fXDK1HV/tTmm6gnZTovaG+CiivRGX/F2BeV3ibabcSt0oLQLlosO5yLTs2VzO4VHEnbWk3nL7enFUoxKBII4juRYzZGUyk0lHzorLqyXGJJl1lhU3qymTaceT2YZfXJ8xeaUxDXRnyCRsJeNejv1RCpKW5xQm8owAzp6v+PTkhle7BZfNY7KWiK5YK6qrjO4lRQqg70T4AsPomF7htmpIT4rcPZXD5zqC6e8VnW6fjw4ah3oYSuykpIYln4WshkhrLfXVrO/7u7KRa7hPI8ozi9VQE7NjBPY+YiSwB46ud9ysJ2IZ1YnDhmxTAQX9uhQVoslHovLBSArNa0whvoEAxkAIIm3XJrHeVRgjgyx3bUE7WEsZG8lJhB9nJ1uWVcvey8yvNljMYBRcFZ7bfS2WUZ04yOtWooBc5PsF1svCmeph177wKJ1xd5JWxAqZ5CFSI6jjjjlMMn6ZUKc9eefwJYS5vI+KEnnFIh+L8SQwvcLPkhCjV4OvoowFyeZe0SiutgrdKJKTFoPDBORsZPfenyXyJDI9aXAmEpKm2ZdM647NrqIoJGxwdaB9ZDn/7jVv3yzAa+ZVxz/9F9+BIlHMe6rS03Y1ZLDLnhQVXeMoviyp3kq042cQ5wFdRRbTlmnR03gnUWyv8SuJtnAijMleo7cG3SlxyXgnrXwwfyaL6CbeFag6im3Xqxk/fDnH3mkKDe0jOayKHEUph2bfWIldVrJgLlqquufZYk2fDH/y1QvxkKwTbmUoryRK6k7UcTOhgsIMDvyxyphOUd6qY++VCkJCB7NmSQ3Ke5t2qG8dWvQqjcoZP1HHPq/jFGszkJGSiOoQcUvkdS/oOLzeDPVTpSGPNbD3EiOB/TWAv6lk0S0yugrklTQjJw3KJbRLWBdISRG8KBZJYuFzutxR2MDV3Yx+V4jLeC1Rkw+GwkasSdzdlmJsoPOQZgRU5qPFLW/2c3w05KxoescHyzUhaXZ9wX4npsFkwGZJAw7bXImakDrU/ODbg6QVg5ZUUcVRnWe3hlhmTCt1EVSmP00iBNDglqJ4y1GRlMOuNf1JIuuMipL6052oEGX6rjpKr8M0SyO0EodzrCgNs03EM0m70WmIUqMjD2lGwL517HeS1nMrQ3wUSDPN2WIvY2SWa95sZsz+xoaJ8zz7bM0uFPhoePrpNa9/ckGeetZvZuhWU7/WTP6oHpSHivIW5l8H7j6xXPz9V7xdz/DeMHGexjuuVzNZ3KtBVFKk4zRoEqR5IE2k1klnMIteVKC3w+dm4TE6opY9MWjMTycYlQeCUPjpQSkoN0sH8MtMshLZPPr1azpvaTvHbNJSu8DL9YLtq5mkhYH/l703+Zmty/Kznt2dc6J729t8XbaVWXaVAWMoFSojLCNLTBnBlIEli78APGbkKUNqxgQBEwsGCBlZxqKRMAILm6xyVWVl5tff9u0i4rR778Vg7XPiSxpXuVxJ3lt69+S99703uhMRe+211m89P98b6ptSanSFDN9pkLGDliRTLTSvFRM1w3TdoMHL9Swea7N83g2Cm079KRVaFE5iPdM4inDHF+l+CYB2Yikxiy0jGBQHZwOulxKcy2ueM77H9U6txwD2vq9oCA+O7IW0i7ppBdEMC+X3WVvk8vPwrwFCJlRx6ZMwWqWar3uaELlvVaSRxDCNQftoPqsBpEtcnx85r3vedFv2fc0YHTlbLrYtGz9yjBXHviJ3HttEqPIy+yTIgmuSWgOH8VlFGIVyIT4TdxTwbikFRRZlHKIDslgVfvBVQ3w64utIzEphjx9P2DfFaIuyUQYhbk/DvSqnLmWvIjU3g4HkyoZpYAImV+TcRoMs+sOIBlh/1Oc9XSTCbtQNfQw82xxo3MSPf+9DvvXrd/zh6yc01cSvPXnJ615NSI1A/Q83BA/9k0x2cPMvCmmrWUv3YeLwl0ea1YFtNfDtj2/54eYVAL97+IBh8hyNlokzWjqee4xuG8GIwnu9UJ0rj3JsK1UWPulJ0eGc0N9WrL7wTGeqwPSdKUaaev2ms0zaFN7kOuJCZrMaubnb6nuWYS8Nd5MjDw7XWsRBNWO0rAakHEqvqcjn52zW9Xq4EHeSzn8zYInMQ8sqnXelT+XsKVilWhWgpRoKDtJcWS8lz1SbZdZsDnDz92JWG+ZgFmaiG4RwfAxg7+J6DGDv+7JqIz8HLAaLPZuwZa7KGOF82/NwbMhFzGFtZrqvGUq5aj5Zy2R52K954OS+7G0mgcryjfZNto2KMqwRshjG6Bj6it2246waOMaKl/utOhwXsC6T0jZMURuqMZXB3XvSeTGMdIKQNZj1Dttbcq1iCt+WVKn0JkA3Q3F6es8b5SrGySlncRV1SBvdyGxEhSEGqju7DBXHbcIUcgfZYB/KbYKciPPFPdiW8qHOFmkATE3GjhaJWv4yk2XqvQ6RD47fl6dMo8c/OA5jzfXuyH3X8Ae3T7m931A3I1c/uOH4SUX3VllJ3VnEdEV6/u2Os/XAw+stm4sDf/n6J2xdz0+6p/zo7kPeHDb0Q9DMM+v4BKAsSDOXk80iSJkGz+XlgSdnRz7YPPC23/D2qK7Q7uC0J2XKzNykAonpTCkf66uWOqjX3MPbDebLmq7SLC6tBM4nnT+cDHbUhlJ1a5ZB4NScMqFca/btW0OuiogiUDJlHXmws49a0qHm+n4WVhRCvFWV6bAzBfZboL4leIk7zXfZeTSj4LuW3lfpj6oxZhl/oIB+rcEkzf5m8v3jerfWYwB735cYwr1DjFO6xG7S07eBqp7I2fDmiwtWT1q2656H/Zr4aoVNRoODBXMxKsF9dMQHDTopOEYj6iEm4M7UWdnZzOu7Ld5nzbxGj7XC5fmRs6bnplvTT57zVc8wBJJTIK+pMnUz6u/uKxUUNIl8kQh1JI4eaYs1ylFtNEyG+sYtAQi0nzVtVeoez3WQ2PpMVev4QHes1YVZjIIVGkGMYHqFAVOI5tOTCduow7R6kWXS4BZWni0buB2MZggZxUZlFRZkB7kyYPT/pUrVeakWuAnkWmXj+VUgjJpRvHrYEnzi2e6ANerV5oywCWUmL6k4RET7f80bQ1x5Hr5TYdaRLIb/4fUPeLHfcfzDc8Je+3zWQPBaWp1ZfiYrdmm8MBw/zoRnHaGK7FYD98cV3VBxc1jT3ay0RzZb4GRKeRaG68TqowO5q6Dz9J/t6IG8yrjdRPxIkM4Rbh31jcV/Xi/YJVcGsJvbzMN3LPINHJMdS7a10vcxHDRw5KQZWXbgSt9JvF5nE2FaaVCbNvr/ZwHJDOBlksWiJtVKA6F8jk7cQxaF4pwB2kmH1eMaxgtRtWTSzLq6s6RGEVWP691bjwHsfV9GmC7UssJ5LR2RtcyXCq+QDN2rNUO/RerypU6aeclGuYZ50M1ZxR1mmfepbiy5ht3H99oPsxlnhSwwTEEpHC5ztWp5064RMRxebjlQbIbdbDeSVBxihFRnPW17YXPWc7hbqRvywZFWeVG5VfdaOsQVSnvZQ9JGxRZUuYhKlAfYt5X6luXS75kxS5OW6XKxU4lnuQzbWr1LK1gv2vMqwcsA5NJ3mS910s0SU7KDCDDPrKmgxBRbGXfU+07rjFxlFVncN3TA/mGFtB4yrJ61nF33XK47hsuwEFSmTxKbf+nA/rML7RNaoZ88P755Cm9qmjeW5kYYz8o1GnWTz0GFK5QeFlnl+2la0QXY/XAghLj4rPk7r8SQddb32muZdPh+T9VM5KzwZc3mUCXhZiIdFLlVPVjCUdFO47kOKoeDXi/xcP99u9A25mu4/BQW4kjclNKhBX8skvtwQkZV9+rGnIMKN3wn+F6RT/Ns3wx9tqOW/eYeVy4+X/OaPwfLezoBkzBcmgVpZSdovtbP/rRBvxuP651bjwHsPV82ZFZPWrr7BltFsjhMUKpGntTU0USDuRxxV7p7pOjgRY14UVuLaLWkB1qiy0ZhtG2B055FzldKXfU2U/vIED1TcrTHhtTqnNLYB3gIsFF/r4vrA/d3a5WWW+F4aIpPWUYmtXRpQuQA6rJcGHW2L1lN6YfMGYXrygl60mArgDSQfT4R+AcN2NIkledPWsKKu6zmk0FgG7U3OPMiR0sqGZA4FXHk0oeZT+xmKF5epYSZixZGirhBrChCCs3UTFLgrjQZu45stj19pzuqiCFvJyRavE/8+OUTpi4o7mnQ0mn9yYHLdYd8y3D3Ykd+CKTVqAKdILQ/HOkfPK7VHpXrtQRHyVbrG8NwqWVTk6F+a2m/O/Hq1bnyJu8Dtrd6YMiGcGsZzwVzNZAMNM3E+abDGaH2kbuuobuoCD7RdRVZdLB8+HgkriOp85jW4VtHqmG8/HkPtm+SLFy5llDKg2dy8knLOgLhBsN4kbV/VhSPcx9qnulKwSzqQzgpI3/OCkWAYqEipVdm5PTvs7Dj4fuQ66QO2p0hHFW237zWB0jNn8KX9XH9qa/HAPaeL2OK+/DRwdpQN2PxavLqT3VbEfaWmGqmTcL0Ft9a4ibjzhXGSoVmL2KwIakZpBOma7V+v7o6IGJop7Bgo7Ko23PaB3DCcKgxPmMvRy7Pj2zrgcNQ8+zpAz+4eMObfsOX9+cMfWBqdR6Mu4q3Zkt4URWJM5hReYX1nVrCm8RiOqjlJCmbEpisisM00+yTQYzALhGaqNnDlw3Z6yxUXiuElt6RjVBtRrq7oDNSo5bvFF2l2cbcp7HRLExG0E1PfCGpGxh3J7bfnCnGTbF8MfDs+oFtNfLFdEGKFmMz3kG2wv7FDv/g4CzpmIDTrLE/1Hw6XPOrH71kf1jx/Pqexkc+7SuqeqIOkfHcMeBNSYYAACAASURBVE2OsQ+4lxXhYAh7CAehe2rofq3HF8JGtgJ3DfZtwPWGvMuYD3rkrdI94vd6fuXDNwSrgb2LgTeHDX/h6QtWbuIfth+Ts+FwaMi9wwyWdDWxuz7StjWmL5R5A3FLyV4p829mCfjhoBliDpopDReyIJvGq4RJ+h7EneCP+nqUIl+ucTxl4nNpcMZ2iRSeYcnGFl+vUlKc10xPme932hrSruCybjz+eJpx659qabH/4JHo+y6uxwD2nq+UDNOLNbKN1M2kmdBDDcngDpbqXstiYQ/uleKS4kaQC8G6hPeJ9qj0DuMzvK4xdfEIcyofd1Z4c9gQnG5uAO1Q0e5PEnljBR8Sm7UKPO7aFbtmwNnM1+0Z1ghNiDp0W9X0XUXdTHQPjZbeSrnOtVZLSDWLzUWuIdbq2TTPitmSTUldkE2dQ+qEqRSBFScH9wFrSxZXCWZ2nY4GWaForVCQUlHvL++ilosK8cOMBqJmBTZqeStXcxlWf2+yZoWzci9VooF2NLgrhd/edSvWzcD9XjPSEBIpqftyvIy49QkuLHXm4w9uqX3krOr54Pqes7onZctHT+6ofeTtUWf/pkOFOZYyYK3y8+4DyN/quD4/krJhf1gxPgRMb0kXkSToeIURNt964HLdcVF3/PjtE+ow0YRIP3mebI9EsfyDr79N31XEQ9BrE7L6ra10U7dG8G8cYa9lRO1ZmsUqZUZ4mfzzmUxalXInRQAT9fBgo2HzpT3J7YdCzIjF+LM6lQSz1+DkBjnJ5f0pU04VyxDyPDs4KxvtKIw7zeL04GRZf2UYd/p/wl64+VcS66dHfvXins9+UV/ix/UnXkbksTn5Pq/6u5/ID/7jv86qmnj99TlkQ/XKLw19k7VfY0cls7ffmzB1UvfiaLG9xRffq7zK+GtFDEm0GKeyeefTIq/fVQNv2w1vXpzBaNl9tMfbzPWmpZ0Cbx82xOiQbPCFWB9C4nrbcnNUB8L2UOtGWEgbYgV3cJrhzAPKF6oMDA/qvgzobYyolNsLpkrYoD04yQbrMtPoyYegWU1GPbIGq+aWTgOLNJmwHbVsdyhGmFWm2o6atYrRDDNaeAhaspSikOs0AOZKTozGdOqbTFt09qzKrJ62rOqRu/uNKiJLX279pMVaYRg8ki1xcNDpELrbRhUv3FXYq5HzsyNj9BzerlXJaQW3d1R3djmMxJUgVxMcvQaWO0846qa9/lff8L2LG/Zjwx++eEraB/zZyIfX9yQxPHQNfacK0m09sq0G2qniou74ye0V+9dbrj64x1lZEFjtpCXEh14PItMx8PzveWyEu1+1izmkli+1NzdnprlW5aZvVdnpj0ZLn4AdNEObuZX1rcrl5xKy2qioseUJyPt/szspQXLhXsJpYNkX0UYsVi1OPcfCXuifaKn5+JG6c7tGVbfxvsJ2lrzKfPbv/wf/m4j8xi/0C/24/pnWYwb2nq+68OqOfYXpHX5v1QJjQLMjqxtDXEP/tJAuRqs9p6SCj/GTUbOaWvFPeVK/q5mhuF33BJepSgbWDuUkbuBi1dNHz8v9lq6t1ZolWWxITF3ANxPeqWLxfN3x8u059mW9sPwQqF87PSlbfb5xm1TK3VqlaARFO/m+DJaWapD1wm7Tk8TQ94EYHfmoknUxYCzKOSyWIyYa5GoiNFHLmKNF6ozbRnbbjjE62rsV1XbEipCiV1FLckWdp2XCXJVNNZlF9Ybophg3WTfbjTpV7w8rlbevI+H3V6RaaN2KzVVHVSW6n62pjobxOmHX6reWJo85m1itBw5tQ11P7J4cOf5UAby5EobrrPNZpbckAtWTDvN7G6p7Q/th5q/9lf+D2kaOsebr4xlVPWFXI3WYeP2wZWh1hw+NSuP3fc3nP3uC2018enyKPTpYJ+4fNpiSeb8UQ3oImHXEvq5Yf2W5epWZNobhqsxgzUKhDGbSUuzcl3K9DqTPfSg3gJSgYiJLv8wUurzOfCnPMFWG2Py8KGPmF85lxbxisUKx00k0op8nve9pW/qEr4XhwtD+UPFVaaMjFc5nQkg01cThsxXpg5Gzi/YX+TV+XH/C9RjAfknLGPMzYE8h1YnIbxhjroD/Avgu8DPg3xWR23/a/WQx/Lmr1/zvX35yIiVMWkpCNPvKtcqD81ksJb9SUgmCOR/xhWOIUcl7s1XkVIqWD64eWIeRPga6KfB6v1UE0Sry8bM7shh29cDb260ORAPm6BDxmCtl9Qlw7Cru79eYm2pxerZlUHZ4oruMHebOO5CNSuBrDbqutYvbMUF/l0bL4dhQN0pzT61OsMbrSd2VS09r3hjjWaJaT4u9h1mXEphL9GNg6ALNmZZAh043dzNpkLNHJXCkWtRXyqrQIK7LbFJSIYedDLHKyOCIRjSYV4k8WZ7/g4nuiae9qYj/mg4UN2+szqitLdWzie1q4HV/zmo74G2G4gJw2DfUbyzdtyJmVcqNXl23ZXRsf1Tj+lqxTr/1wL/xyadksXzRXvCm2/Lq5oxmNVL5yP7YkLPFV0n7nlY47Bua31nhnmQ4OIxHxyy8vj5Br2/1MpDPMlf/Y40bCoz30i6SdCP6vkLBeK0LxmtSEc4s5pjnsexEgR7r7+0odM8N53+YS/al5eQU9GcO5kTVKH2sOcNajCkpwVCKp1w6CTyy07JiDsJ4Xg4cpXxse4ukANeq1Lz/2QV8NHBxcdRxi8f1zq3HAPbLXf+miLz5xt//JvB3ReRvGWP+Zvn7f/hPu4O1n/i6PWO4WeF7dSGGUw8A1Agxr5SDZ0NG7irMxchqMzKOStDAiPa3SkluHDzWZQ5DxXEMrEKkHQMxWqwVtpueMTkaH3lxr2BgE7LKw6H0z6A71MioxpjuRf1z/lyxQHiNlPmlWbJeSCFmk5DW4462NOZVlj8bblqnfbdxdOTk9Pe9Q6TYqmSdaM2VIJuILz2bmdZvgzoyI4ZpLFb2SYn8Eq0GQVCu4mSYdgU3hW6Q2Z+ebw6yYIjsZGBypGiWAXNpPXGt5a/240w+VKx2w0KMEAdxctxNa+ovAp3PmO1A3wfy6JBk6J8n3WxHR9gNrJqJw6GBvcf10D0Txg8nPth0TNnx+w9PuW9XjINnt+0QMeyPDZvVSMyWw8utZravHXWC9gej9kHFEFYa6PMhsLs+kMWwHx3jk4i/9YxnGlBAxwfEg9+bJfMVx4J+iishFDWoXisKePdU5rNxHjbXQWaTIa7s0rdS7qUOLs/KRmawzBzEvIqabNJAFVfKM3SD/v85y89BmC7zIhhKjfZ87XbCWsijY/yDS+xaOL888LBf48Ocyj2ud2k9BrB3a/3bwF8tf/5Pgf+ePyKA3fcNn/3oQ1yhhGcn+KFQvAWGS812TFLZeT4ETDKcn7e0faVlvjrhQyJnw7oZWdcjqVH35fOmZyqqwxgdVRXxjW6AU7LcHXbF10sU2OsEWQn4TJ5JFr0lT4GqnSG8svQpTDrZ1X8T8GuskCe9vzzL6YtHmLFCPnpyk+jbRuekVkn7dvBzJcO8TbiNDnfnZEiT1+eJKvN8pRuTMYILgpQSKtFgj468ytheYcK5Eh03mBWRtSwEdTueSps6NFveoGzIo2P9mWfcqvw7PBiGlWOsPXajwo/mlWVq19R3hvpWePstg7WZy7OWuwfdQHurgg2MEPtA/4cbbNDrOVxD+n7Pd5/dUNnETx+u1JMtWZ5e7gF4eXMGwMPDivDThqZc+1wJ40cTu6sjTYisi6v26/2G7rbicGzUs23uD1bCcFVmzoIsZPpciyoMq0LyiIa4UjVh9VCysoAqO/OJjjE7JysfURTS0hR35jjboBSCfAmAc9/LTSUoleHjOZDN96eoqtP3Ja5h/CDSXPT0Nw32zoMV6msN8NOblYp2fqVjvRloQuSm98S76o/7HX5c/z+uxwD2y1sC/B2jzYX/RER+G3guIl8DiMjXxphnf+S9FNnxXCZRwoaWcoZL7dXUb6zy7c4yebLYJwOrauJhvyasJlbNRMoW51jsUGYDyq/uzphGz9X5EWvVhXlIgXUzMiWnM2VWiQymGErKWqGyMg9GBz2JD1ep9G1KYJsMWMhNXlSPtk7UzUTwieOxIcc5wGmACOuJqQ2YlTbZZVKHX0kZ10So0zITJkFoLnr1NsOeZsVGq8GmVvWFLSXU823H6xfnau0xCzXmAOt0GNcMhvrWUt3D/V8aSKPF7TV7c4P2pFIlUOttiQZzcNiCQwIVDoh32K/W+A7qG83kVq91k+6vjVrNXBiMEap6wrnMVDvsywpza3VOKWh/04ih+9YEg+PTr69ZbQa6Y40cPc2TjnZQT7HUemVN1onxOiM+Q5XxdYLB0f74nP0usbrumEZPequ9ynhXFQoJIApS1sCjJVNfDCfFgJ8oQgolgzQHQ1qdSnmzGGMeS3BHOc2ECfRX+pfN16LZVjCkxqiL9koDZn2njxcOsrANZyrHfHiY+202zs+nBE8LbjORf2+L92Uo3gvDfYPdO6i1NCxfN/Qfi87uzaXtx/XOrccA9stb/7qIfFWC1H9njPknf9wbGmP+BvA3ANz1hdbvk2H8aILRYjZR8UwPNaa3dNcJ10RkdEoqd4mvfvYEdzZS19rAX1UTxggvbs7Ios7KAfAuY5uJly8u2F62DJ3Oi93Ns1ygj1mCkngNVma0SoFvnc43zaaHQeXwS8YVTqVNY4W6nojRKUcRtJRnBbdN+BAZ+0DYjOTkSPug6CUvWJ9Jh6CP3agIxBihf9D5NBk0wIrRMmBz3S2mnaCw4zdvd9i9P9nLl3mzuRdkRt3ITITuuSj5fbKKYUI3dHGQ1yUYN1EFHF/WVPdKjYgbiBstM4rX7KJ/arAjpcejnmXmalSo8mFFig4fitP2dWT7B4H6RhZSulLTvZbyKqHbB8jgektvVgyrgHlbYSohPO30fkppM1SRoQtsf7cme+gddHmtAd6rilCcwHYq5WEhiRqHujJwLlaIa4i70svs9N+xQtzp82tel+fqNJjMik0OGnyGayBDfa9oJ0V16cerf6rvxeoVWiMsQTB7g8unYXd9ADT7mgo1pfTG+meylA/r31kvvThz62g/SdQvPb493c9wKaTXDc3HB84+aXX84XG9c+sxgP2Sloh8VX6+Msb8beA3gZfGmA9L9vUh8Or/47a/Dfw2qIzejgbXGSaBzfMj7aFmbHXAeO7ZWCO41UQIie5YsXrS4n3CAOsC570/rtSHy2aCS6ru6yrk0zXNrxxUyl0CihlUlm5HuzTR83lktkORuflfl2DSWmSVdF5rDl6AffAqjlhlZBfx64GpiCwkWtxKg8BsUY/A1DvNpJIaNBon5KMGL6pcRgAUNAxFTVknDUpJN1YRJZKkfSDVSVWXAgS1Wpl7aGYq9I65h29guCp9sOIAnZ3a2evAbMFJJUN2brmdWDWEFHsqndlvlHrn4JUatRWR3rFvi4WNES53LWN03Nw0jDsNhvPzmVFKrjeYg1kGr1Mt5JViwWStBwXQXts8GjFMATl4UlChzzf90KQoD81KD0Rj67GtvmbfG1ynj919lKBRCxd5W+NbzdBSpZmrndTHLAdZnK/doGVeG4Vpo30vO5bbGIPxYJIOGZuo8npVYLJQO2zU7GvacFIvllaVncpBIcJ4rocLkw31jZI95vJi/0wzZRNZGIjTuY5qXH/3lpu7Df3LzSJMeVzv1noMYL+EZYzZAFZE9uXP/xbwHwH/NfDvAX+r/Pyv/sj7Gg2rl3qCt1NF/LLCrnUjyuuM3Uz4KlFVKus+3q20PPVRZF1NVC4RXOL1YUPOhqkLSDTEtYJ9w61DvIobFp7daJc/iyvKQNGTsTl6ZBtxTSIdvZ7kS3/OlN7THLxmKnjcJqgz9XrEuwQErM+IEVLvtYQzq8AsZZ4KOJsUtxgyyah5I2VYWaKogm5m2Flh9j/x2wnvM8OxEDwmq1liyRqhSOSLIEbvh6Wv5kb9mbIGat8Wib7TjXm4ToqyAvwqMl1apjvP+uWJXZiLQhRYcFU2Ar2WLGOwXGw7hslrL7Lu6V3gJmRycEsWMweqOTAsAaIEAzMZxFoVyqw1izNeEVp39xvc5w1V1Os628eYDCICG/VS2571tMcaM+jsWVpnmhsNzv3TrDN2o0WMx3flvRElzdvRLAPGsgL3DZfluNHA5AYNTMMVei0LbzIHHVPwvQazuCr2LhF8r9L8aXNS3s6QXpP057TVAf5U63B0dWsWF2YxMDxVMHT9Vn3jTDbE84hpEtVqIiaL3Fb4vdW5ysf1zq3HAPbLWc+Bv22MAX0P/jMR+W+NMf8r8F8aY/468Bnw7/xx7qx7Xnh3N3qqnXaC/aDn209Vgf/yfkdwiaFX8Ju5GrjYtjgjHMcABNq2xtpM+KIq800V3mpGkJ8PpLsGt3ekUiYSK9jOanYSBHqjMNw6EzYjxoDZCsYWLmM08BAWcgZZsy81jxSIhmFfM/ZhmTnK0apPWcgauEpmU7/0jN/tFTILiBgtEZZehTjt7RCtkjbmVWWq9cST8wO3h7XipsSoAWbvl8BoxtOGJRZsE8mT0019tqz3CsCdg9yCLkog24h5CMg6kZPBHZxuqGvtW2Wn4oOZFTgr68ShHMBS+npzt2W9HjgrGbIxgm0SufLKsSzEfN+ZRUxhk2ZF0wbGZ1HLaU3UOSiXCT6xbQa+/vFT3FF7gc2NzldhQeqsyr4mUa9HduuB+4PiM2bRjeuLGtBCeNC/G4HxTNQ+ZSpKwzKAPAed+m0J/gXkW91rUJrRT2GvvwMF986yfM2w5oxN38rx3JysUqZTbw1YhCQmQf9ESCsVysyvIW5h2oj2H99a0lqobrVPPJefx7ZifL2mfu2YdkJqvvE5elzvzHoMYL+EJSI/Af7i/8vv3wJ/7Z/pzqyeiG1vaF5rf2G8NDSrkTeHDZt65NnZgbfHtQ7vAr5SGw8B9seGqQSnWGdWx1L+aYTpLFN/2JInh3lwOvu0dwtAVaz6cBENNJkZR6U9GxVjpGRJEzoPFQ0SjJLTkyE3GVYJc/CLO3JuRO3bLZhNhHXUzGrULMx2Vi1LyilfslmUi7OHlylIJpzovwOMjuqyZ7MaiqJSVZZpcNC7MvAM+SwiTki+CFKqjDVodhkLHDiVUlcQdbleKR199pMyrUdCxq2jWrTUGd+5U/krU8QeukFjVAmYGs2CUgP+bMSHRIyO41CRxPCt3R2f20stF3amYLH0OthBA0FcC9NOs+L6UllOdRU5tjWhinzn8pYfv36CbCIJjz9YFUd4cK0hr5QGkpPBGLjfr5i6AL3FJc2qqrvSq9vo85cy1+5GHY2YA4vJRlWx3clwUoeLBTGG7M2SMdpBM9K4Nssg88wszEE9yexQQMD2lGnOKsNZ0i9Oe4ipkFLUH0ztaYzodV+9FKqmzJV5CPeG9tuR1dOWgDo3bH/qieuS1VUC5/P8x+N6l9ZjAPuzsMrp8+EH+oWMT0eOn+8I95bjtwcVSIBmGCGrFN5mPv3iCeFFwAXdfNxg6Z9m8lYhvlUzaa/orqIaAKMeTrm4I9tYQLoGoAzVZkOoIqt6Yn9YLXy/WRUIaJ+qbNyIWSToZjL4wSybVsp+wTwRskrSVxm5isWw0ehjzmq/MkxsUlEm+qyoKC/sPlIpecqapeVsi2WKPvZ8srd1wlhIB/1qmCpjrZBn+f86kwu1HqelO9fr6zARXITBCWYVSZ3DHvw3pPYnEcN4rpnWPBy9lN2OBjcaukuPsZnz7cC//PRLNn7gRX9GOgR8KcnaSXtHqSmCh6gZ0tx4y9nw/WdvuenW7K4G/srzH/OT9okaZ1aqvktNseNJBn/dYQaPMcL11ZGHY6Mil9HiD47q1rB6LcWQUj8LqRYF7hY+Zf+0ZCqDoboHHzXg+3ZmJJ5ck3Ol124hxBslzae6OHAnGM+0opBWahz6TRuUma04z98pZFnRWlyO5IPX13XUz7UrakmsHjrmEnaulfrSvV1hNxHqzOGHExioz3seBfTv7noMYO/5MgnCg5IQtp/pl7q7q+mfZsYnCRcyZ9uO/WFF2Cmp/rhvaH96BrsE32sxYqjXg5YUuwoZPewDw8HjOovLhvFpOu0yAtRoWdALpphKSlL01DR4hmOlWVOhbmAV1KuGi8ofFKPzUzPAFU4nbN9DvBDcbiJ1s9GTiilksurnlQzprsIOmh2IB4JmMeHBwYMlfjRwdXWgCZF2qOiGwMObDabKsA8LRDidJb3/QtcnCJgi9ii2NDKdmIomWiRqsJGgPSNxqnExo4HGYI5ey2ARuqeyDO3mWkn1KRuqN07NHWuVnYcjqsbbDlxsOx7ahr//kx8sWRBGMz/bWqq9ltfmTMQkLUHmOnPx4QO/+eGn/P2f/YDpqw2b797zn/+j32C9G0q/0ugYQas8TLmY+NYTJat8+facmx9fqdhmlajeOKq7Eixrw3imgcO1p75S/0yH5Tc/8/r+dZTRgpIJeZXCpyJYmYeT7aj9rOGyBPmoNI64VsVm/zQr3aNXg8/UqJw+OZbP42w4Omee4aJnumuwZ0pombYq6nGriaEN5Je1/v8nk+LTJkdYj6S2Uq5mMEgsJelzzWAPh0c/lXdxPQaw93xlryUTgLs/B5svrfZBLidcnWjKjNfZruXmq3NM7xSN9HzQWa7RI2IYR49zmd2m5+ZwBusI0ZK8+mqRAWuKGMLopleMDk1BUOVo9f9Goxu8z9i9xx/McnKWWZhXRAwzfUOs4EompiaURl2SD16DoBWYUVWd099VeaF4pHXW7COBmwpD0Qur7UBMjrddTYqO2HsldtxVOug8W6zMirvCwkvRwn1Y/LwALasVBV0OkK0KBKD0dbwhr5XwwF04ETY2RXEZtecyXFpSE/U192pBEo5Kb6/vhIfvw9gFXh4rGFzpyyixRFzWgeFwIqmL19mztC6gYyMkMfydH/069Wc1ATB/75L6EuKvT5qh9l4FOOeRq2cPTMkxRK9jFK8bHQiPBv8yFOK7enyRtTRoR05SdIH1lxYjlmmnQSkH7WfNPTCxak45namcPW3VlDT8rte5twThqApECaVseK6fiXCvwXPaaWlbPeFmmb1Q3ZeMrk7gM/KzDWYlSKq0HB0E01sGo5YzYiFuMyYorBojTPc14UwPeGSD301cnLU8HBv2d2vMbfjFfYkf1594PQaw930ZyFtt0pMM45kO2dr7ADEwTisWA8ZdgosR64RdUbilZMmDI99VSJOIW4crGzZVKZfF4jeW9AHnfpU6/hryISwlMAyLjNyXDEQ85TZlvqcueCVrFuSVjSr/nnsXqZGlt0UhY5hYAqkXxAimUwxULhJxcYJtXSFwZNhOtA+NijnqxGz9kgeHuxrJyWhQjKUG5QVfRaa20iBZbFVSIfebIk9fSlYezSy7QmC34G6sbqxlHik1mm3ZTm1ihkvNEuaZtPbbEVfKnHY0jBeG4VmEzkOdsFuliFR1RAqE2HTqXZZrWUwgxSkJY+4B7r/esf7MlwFeoX9aAt2gxHrxlDJx5O5+gySjoxN3lb6VVyoVzE8F95OG+g4GtGxY3WvJWcn7OtfnSpnURO3Dhb3Cd2eEU640eJmoDtH56HCjStxdodDbSUuy4+pUivStOZlUzs7cQDrL2nftLSa7hUlpWke8SJBh+xNP3KqXV96qu0GKjvxkVJrL4EgJGKwKbQ6OVGb4Yut486CCJjP8vHHm43p31mMAe9+X0WFhkw22N6xew/pVpr41DJeqCLv/9biU+gAkKRPPuawA3jLTNc8sVZVS0a0VVtVEP3n2ww7bnXoQgGYuuWRl/nR7uy8g3FJ1ZCqCj8CJJViyOm3Gq4ouBym0d934yUaHpCezgH7TrpT6kg4CM/MNR7uoEHMQpE7Uq0ldop1SO3JSIYivE7H3uvlNZT6tTvg6EgevjtHeYEe9NpQemxvUoRlY5NgzLNiNJ2iweN2o47bMu1kl76d46gOa0YDTYWNxJSvZZNJGBRzxGGi2I/1BszC/GUjJ0nxeKTl9JYsgxIiQvZZoU5OVQtHOMw+lhGvAHwzVj1Z0/0JHPgTcy5rxzGsvr0rc3W1glfS17QPihOpLjxi4+wuR7c88zY2W+Pbf1d6Rbw1mKu9zURPOBpZzb8smvYR+ry4JOcxiDmjeFFxUMKTaLMQMW2xq7GB0BqwcmFxnisLQwF7NJ+MuY3tLuPUKhnZCuBjgp1v8EWxr8R/3jA81aadl9RASfeexDyfO1GymKaKfz/qVY3iSkCcj/tP6T+Xr+rj+dNdjAPuzsCzkkLn8x47QZtqnluFK/2k8kyXwSGeREhjynUUE/GiUIOGVA5iSpaoioWz6h65GxODPRyIVfq/WInGdtRTZRKwTUrSKkyqDzmSzmBTawqjLlaisuwQaVYkV9WAudIoZUTTqILAZDFLpTJsYLS3KTLqoE84nPVX3pa/mNWtz68g0elabgTpERAzHVl+LAGbvi4JOio0JGrysDkWbItQwyWA6s/TolMWnz8ftNYvwB32N00Y9sOyoyjyTlEox0x1UrDGX4QBjiGcqmJFekVymyoQq4kPSMmZRUQ59RboPhFpVfrNrtKCvwR8sZgLfutJn1IxrtnqZs2ME8gxczuAOlnSuvmreJyY8svdsPneEvTBtYfrNPc2Pdmw/z+y/bVWAMmgwWg4p5f00QxFWOA1w33xs17PMgGHL/FetgpAcTqzDWaVpR/VfA0rmLoR7gwxgR7s4dDevHN3Hkctfu+OD4gR+e7+heyasX6i4Z7xtcEfNguPGMVpw0SwB3oi+jyZaUqP8RrXyydB67QE/rnduPQaw930Z8AfL6mtDdcwcP7TERss4uVKqhN9b4jYjm4SttJSWvVO476TBTQd3FWTbUZELf89aISVDPIQFDzVLls1oydmrv1gyRdWHKsZ6W/h0ooovL8reAyxuoSnoAG/JwJye1t1oNECGjDQ6I4allBSztx7WFwAAIABJREFUzphZzRBztrohV1kDmsvkbNVteNezqUdujyu6faMik84pnHej/EXj9f5yMtiCvLKbSckeDkyrZbBYemxzzy7culNvZyvL65FSWnSD9m5yDdMukxth9YXDDbpRT2clAF1o8MhGkM4jg6W3lQKHk9ESYsjku4rqxuJGpeLnIEv2qxmq9tByYFHwzT5Zc9JsJxVXrD4PSqpYC+NGAypfN9SfWyoHroPQKgUDC2f/zZZpC+0HVl9bb4o7NQv5PZfg038gC5cwNfp5sEmpHeKgv2Ypl6Zae3d2MstneZ7p8q0+xnClYwEmwfZTy8NfHHF1Io0Ws1eKS/Zatn7zxQVvAHzGvwmkD0b4YUt6scNMRvuDWNJlxLSOvE5snx053K75+KMbzuqe+6Hh6xeXxKdqkkqyND8LNDc8OjK/g+sxgL3vKylGyohuJOGgJn2pKb2WUb/gADaU4DU5qt1IHJ36ISbD4hE2WTLQdivq3aAZi1AGfXS+BlhO82Y4GUbOgdDkubdlFsUd830YyE3W28mpVOSSkuqx6tGklI2yQVeCCwnndO4qJ4uxWgZqHxoIGeuVpeh8ZlMNJDGM0bE/nOtm5wTrMlmKY29hL7qQSJPDWMF7JfJPYzjhsoIqEYHFb23eoJlFmU5FGNOmZFicNuTsNQv192XIuNL/b6cSYI5epyBGQ2j1WozXFtaR+lxFNuZNRfNGDwTjuSBWCA86bK0UEH6OuC5zX3FSmgUluLpef9Y3Ra5+MPjWYZIGVjsKbjhJ2e0k+NbQX5klq5rvxx/0sXJVKBtFxu76b8y3leBlR5bnYEdIVjMnMRAOGqiyL7OHDUspMtclOI9aCu9+64AZ/KJKlXUit47pTAUh7mALokrNWvNNoH9xweZOP1vjpZZ289xzbB3TP7rA10L7JPD6fsu4r1hfdnT7Bv91xeqVIVWF2/i43rn1GMDe9yVaToprg9xp43y2undHLUGlswhBkNEttImcLCIGU6n8nd4tKCHpPITMsK9xTUIyp6xENPOaxRdSqXhCfNbyoWjfQvsfsvS6xJY/pxLkoln6JtnNvTBV9kmt6kLjNQO0TstbACl6qnoiZ8vQV/hSRszJUtWROkxMSTe4GPWOw0qHUHMu6Cjg4vLIqproxsAweYYhaCmyDPCKF8RpxpqLUWY2WSG2Q3Ee9kX1B0BhPFrIKymuzSfY7YkoIUtJcboquKl7t5RQNbOCqyd7rIE3h5qqM8umHrdZ+2ZliNn15WOQT8O8JhVM1RLEVOHnj1IODWCyHip0hk1vFzp9bv21PQVEUQsSNwAjC0dwFqrMVPlYel5xrdnVHJRsq3goLIw7FsI/IqSNIEej0v/i1WWi/qzulTDSPhfc9w84l2lfb/Q5t/q5Fq9ikeGZHszSNiMfTMhNhestvlOZvxt06Lp5VSghd47xXJh2WccXBsPh2JBerHDR0A2OcKuVCR2EPikuH9e7tR4D2Hu+DIDoYGZc6WZjkjbS41azGbuJGCtKhfAZ67VnBSyZkTTa/J4zMdNpQJMq/T8f0GpWIVZPwTYoDDd3HrG62S42JGUAWcUO31AvFuHCLHxg/rdvzo35RF1HmqBCjX7yhCoSoyMni3WalQ3HirCaqMPEoW3Iyap/lRXqzcg0eJX4RxVkmCaxqibaoeJhv1quQ85Gy0ai4hGMQEXhL7IE51w8uNI2qQ/ZwWvGEPT1mnVSQrupFsKEeojNIwP6kO5oC0NRN/DpslzrKhOT4+HNBnvwCvc1p2Frk1XMUN/N92cYL/i5suxCbK+01BcOUspzKr/HmOW2NmowG84McWNObsepzHuN0LzVQ5IdhWlXuI99ybxq7bcNT3XkwkZO2dWk/xYbzbB8p70lZTkaxquMnVQIIrUGuLAv9j9/6aAb1P+5o7tO+F5BvAsgOWrwm8cn7JNBzT9XGTo1w5zOhDQYfK/XQLP8wg8dHOOVDnTL16uldG37AkSOWk5tP/pGHfZxvVPrMYC972uuWgHtc/2WNW9YNjTZJLzLxMlpmc2p83Lqi/R9Vql5wTjB3PpFXIHXoIQV3fx9LhYiZjGYtFWiqhUSy3pkHDzTvjrZW8zEitZrtlZnVRDO7Lrm9DoQsLuJqtKMKo4e54TkEseuZuo1E6o3I5CJoyP2SvqQbLj54kKf6+zsLEYHqrM5KSS3E8YKb+63pC/WKnTYRVyd1CvtXjmDMxfQzOrMwgGcLpKWNjOK1brXr1CezTjnINU7rTJ6QZjnpopgppRKF1m+NeRVIlz0KqR5W9M2tYptmoyZzMm5eLC4TikX2ev7vQScb0i9TcnIfFfAtwlCl3GDkGpH/0SzSN/p4ys3kEVsMWdgRiDslVloR1lEISqX1+xr2qBlvCbj7h12VEbinP2NZ0WA8WCWfpYyG4V8PSkoeipjCqNeq+FZpv5fdsQNCkeuBHM4Pa/ZusZEg2l1lCLf1Jr9Gw2W88xX9aDXo78y2huuVTQUNxl7PZBva+pnLeOXG1YvHP2TTP8sUr/27H+z00PN/eMc2Lu4HgPY+76KKk6sztfMyq64EeJTLZ0Zq8PGxqlJpZlPk4I2JNCekBSxhJpjalDQ8qAoysmgRpLnI9Zm0uQW19p2COSiYnTbSQebO79sTIsNfCkfAkswm18HBiRZpk4VjRgYgKELi8JRQmb0HmPA+UxY6/Bp/1BjNhFbhCKpLQ7Cs/2LF3w9sVkPHNuaODp4NkBSpFTqnGZdpSRLrQIP5zLJCkasshtnVV0+kdFnQ0bxUg4CGQa/BKh5+DdVJ6sWoAB8BVknttctTYi8eb2DVWK1Htgfg27oJXvNtW7KvtPAoY95ypaAZXA4rrRsaEeoHoT6LhH2E+NlxXChAatqlZQBLEPRdtDbuxGqO6G5y3TXlupB3/9xawiHIvBAh4vHy6Tq1mhYvbBLWdFknfOaJf9DybZmpWnaZDUgm4fZqwzZcv1Xv+arf/ycw58f1U+u3Pf8kZ0DkBu0/5tWAr2qDdOq9FxDRp6PTG3FtFPFqZkKL/IiUp0PBNBD0W5ieLHGJmi/raaoTJa4ychDxfpzx3jxjdPB43pn1mMAe8+XmBPpobovvZJCkTdO2F601D6R12gWM/pFeYigm0cuGUM0SJ11Q5j9tgrxgljuW5RRGHYD1kbaQ80xN3r7Wj2h0uA0SBapN8no5jRv9LPnVDTQJEITF3k7QB6dUjZaB3dhyRAl5GXerFmPjKPDWqHvPE+eP3B/aE7ke1Ft9Oqq4/tP3vIwNLzZb3h4WGGsBkoRfW4zPQQn5LOIX0dW6wEDdF2lQ8zTqezG5agGmvcVeQ3+wSkbsreYnQ5FSdbNMjVaulsCV+ll2UGDWzyLGJ853Kw5DGoMKnVWJ+BokCaTvMG1lurWUt/o+zudsQTQpQw7B8YSgOykakI3Cu1zz/CrgbjWrMskLev1V/o8XK/3YycttfkixugvLdkb2uc6A5eKyjGt9L21g6F55fCto7kR2udadvTdPOitz8vGkmlWJ9iv7SxyNSK9wx0seSU0Hx358nefky/jUlK2B7eAeW0scnfR8uh4mcmFyCJGB8RVDWqIY5npaxIiBns9aen5JpBvN9orrnVWTzaJ5ssK/6klVV6H0CvgVq/V5rPHGuK7uB4D2Pu+CoXdHazSDEqPon5raS8NIoZNNTJly80XF7oR77SZYlxR2ElREho0WM1/Fi3RCPbnfu+aWCT2mbEL6posBumcZjJV1nJjye7YKAXBzExDo+VHjDoxx9HpHJQX3CoWWbnBJF/6cGXuq0ms1uNi8qjBq6KqI2/fbhf3ZgDbRDa7nk/O78liOI6Bvq2UbG/VwVn2AWqleFBr0G7OB0JQ2HHKlji5pX9l5iAOpMFhe0veJuI2Ud06ms8N+YsVhx9E3FiygeK6TBEBiBO9XZlts7UCkClqznnQuaoj+dyQDkHn5ozK8av7Qo8vsnUV0szCjBLU5p6bVQuXcacBQErJ0PUq5sjhG/YmhV949mkqt7OqSJyEw8eqxEsNS52yvinZvp+DpTCca2bn4/xaIRfgca4UAWVHg4sloDmIDwHXqyJ19cEe7zJTb8i1XeYFvxkEXW8WcYoaYOr1xICcJ5pnrcKKxSjXUoAm4dcTxmYICfkgEQen4p7oqOvI6u9uMSLERkufGMpICIWG/4v5+j6uf771GMD+DCwxWlI5fqLZ1OxeW30ZOB53hO8kxuhx56NmBqg3lLFKp4izoWNhGy4mkIaFd+ibWBiGhjw6umRo1mMRhKAB5ugUE5UNcvRLcJC+gFGNLLNo0pXeUa8EXJMMIkKi9Nwmq4FrDnSNCkXaQ82qUCnqKmLriYf9alFOArgqc7ZrcVYYkueuazi0zRK8qrWexOtnLSJGs9JkIEAV1CMsZcsweg2KpmSNZV5JxoKw2iZsEzF3DR/+T5Hu2tN+oOrPeV7sm8rLvNGh5YyHIKwuO+LkmA61vv5ySDCTZVVNjIMnD8UBufS97AghqUcosPSk5r6aWC0D2pmOYZV0IcaUQWJ9XtO6HEY6fZ/dIDp3dW4JrWCTkIJhODPqQD3P63md0Zop8ph5ANksg9N5/n2jWZoYluDvjwVc3BriRj3Mqu/t+fXnL/j6eMZXX1xhPhipvlQGfFwprxED3HjShejg+KSjIq43xE8GLi8PXK07Xu239KukmbvTMnXYjnif2a4G+snTtTWh0dpvnix911CtSuCy2rNLK1kOHW4w+OMv6Mv7uP651mMAe9+XaKnPDYbhSsG0udZGtp20vNN/GNg0I8El3eyzoVpHxtGpWs+rGAMgP4QlGzNRKRim0fmoWSyBGCQ7HSIuDfiZ9mGygdHAVp2g4+gQn5cAeXF15HBsyGVzxaL9CleEFgYNoF5w24kcLc16pG8rNYesssr/jdAPQe0+KDL/aHFVpm5GpuQYo2Hf1uRkSdFiq4Tzme26Z1ePfHVzRhw8LhRl5iHQ14G6ntQzbPKLGtEMhUI8E5o6i+wieXCsbwxf/5ZXmO5ZxN/6wnDUKDPLylP05FGl/JvrlnHwxJtmEXuQDXZUWsTdw1of7npA+pUKcszcNyvS9HK9smcpz5qC59L3T07sRltKf1WZ6bKnrM1GzRKzB2c1GoqF/olh2oJJsvSuwtEsfMvZiNNGzVJSVXpy5e+IBoXUZOT5gHFC/HxFfWNIK32O6SJyvun4w5sn3H19hu319YcHw3SmAUquMxeXR9KVZf/lGeINcSuM10kPFdlgDeyHGmsEW0rBMljCbiBny8eXt9Qu8rZba2m4DzTNxNX5kcolvnp4jj/ooP48mygW6luzjCo8rndvPQaw93wZr03r2Ig2uw1ILDNdowoHxtsVfVWxPe/wIRFCwtnM9LDWjc9pOS6WrEi8YIdCm7cgo5pZLqsMNedskN6dSjhBFp8sF7I2yCed2dHyWObuxQ7buVPpbtTypAmFfBANplZeXU5aluxigymZ0+xlduwqnRGzGYwhDp6wmnBOiNGRktI4AOpm4tnuwJQctY9MyfHpF08wRWa/bgbuHzbghbqeEDG0x7p4jhWBQV1QVo02+E1WF2gytN8p1sFiNIPKAMWssWCjdH4MhmBgGzm+2hBuHKYRFYc0CbP3VHdqbx98YrMauL3dUu81AMa1Kuq+GXwWdeJ4mvnSJ1z+3X1DwVjoHGI5DbcLjGtzwl1Fg1yUx5hKoDRaQjSTvkQ/nAayjWgvbV5zgNWMDfrnSbOgQvg33+oYvgXpbU11a2G0vPz9p8swu0na40orDZBplzG3FbdHjz2bcJcD7llieLGm+UrZjGIdb3/VY85H7aXO8Okqs10P/PnrV9Qu8j9/+j2MEZzLOKfl5/3XO777g5fkpyPDxlO9djRvYbg49bzSoyHYO7seA9h7vmSyp0HYVVQzRqMluFwLWIPdO/JOB3s3q4FVNfHV59dFHJFxVSYeggoZSiYgpbdGragjY4XUa1PcWEHELP2hXOWSTcmSzeVo1QpkJsiXzMqMhlxl7HYiFPl931bKH1xHmrORptIgcvdqt1i8hzoi2dCESFWaLMPkCUGzw7qZMEZo2xrJBluoHLt1z3nTsws9b7otL/db+j5oqXUzEUKkHwN1M0Jz0kJYK3pNlD4EdcJVWakehTc4iyYWsUsZM4ibjGutBpmkmWyudMA33Fvc7Ee11QyJOmND1tKjUVJJXUVuXp7RfFGdynD5JJmHUsIr54BwKD2m9I1ZMFDVjTWn/4++FzOTcIbuyswmDKesijIKJ8y0eC0XxjXLc5pxVbmSQhjRTFoHt7V/6TYTP/jgNTfdmpdfXGqG2gjjdwcoxqFuMKWvpfDe9kx7Z2Gm+2PJncesI/3bFTapd5pYOHxHSRzSe/xmKqYJGsAu1x0PU8NPX1xhjJCiCn8A0ssVxgmf/s6HXPwTPWCIO1E31GtPyJU5Xc/H9U6txwD2ni9fR9Q40JBfNKRtWtR/uc64QeXiwSdW9Ug3VNx9eqFCi6r0wd5W6ou0m+CgjW+pVTjhqowxovT2DDJ6/EXPdj3QDRXxq0ZLRJuECSozzw8BM1ll+jblm/9/sfcmMbamaX7X752+6QxxIuKOmVk5VFVXt7tsd4MbD2AjSzYSkhGsQLACyZI3SGxtVqwsecWKlRcIWNjgFWaHMBK28NgYt+0u2+2qrBxv5h1jOMM3vROL5z0nrpEHkKtamVa8UurmjYh74sSJiO/5nuf5/39/lcFl7JlQNFJQzIO0Bh+884aYNMtqYj/X3A6NdE82sViPPFjKAuIwV0zengpYzooYRbrf1JJ7tugmjJYxozWJznn2c4WPhjf7jhAMvq9ozibBRnnLPJV93N5BLbu2HMTwnL2WUeBkiKOBJsI6CLHDJfJgxLzcG9H86yLXLqO72B5HcCJrj5X8eeT9Hd4DgiIFi7LyOlWvDLu2RQ2G2ORCW1dUV7okG6s7EQcl7dj+k4UrG06xL2bMJzK8jP3UHbXj2CGWjs5MR7GE4KRSJblcKt2N0kSmn0+Wi1RJB6gijO/NstPsys+lFUXmx3/1A7kR+W7Pd777gqtDxzA5aKSrPo6C+5uWaWtPRRorr6U9KJ78JYXKlv1TI9iqKWPHTKw04yMF7w3StR8sdq+J2fLZb7wjESka3GZkveqZ//ID7ADVsVtV8n2JNac7mO5Fpn+s2H0EsY2w8T/bX9z78zM59wXsW35SFiWZGYWTp2YRKkj3lIiNxphEXQfmYJknJ/T34nPKNxW5TpilRKiEtmRfRBnlSR6YZGYpr3EPBs6Wggj3sxVKvDuOLvVp5IbJJJOLCboEXJooY8WSs6VmTfPkwGUjBep2bklZUbvA6C3vPr3G6sR+qglRE5JGqYzTd7fD82yJe0fzyHPWDTidmKIRcUbU3A4NczAiS0eeo9KZ4I2Q7EuKtDGJuM60rewKQ9IMfU2Yi0+uk6DLTJH5K+l+KarBI2UDKMbjfOq6QLH8QoQgZlAnA7AOiKLuYO4yrxQoDeq6KuDkjB7E75Qt+IWMJm2fcXsRZfhOYYufS8aDBXCbOBFCklWnrulYzI4etWzuBBkiAClostKZkdXJH/b2Pg0gdlLcwiKTHs4Yk1EqEL0WruaNFX/iL+2wNvJ4tSckTVfPHIYK5yLeW5rK04+ViHe0FCyVSgBmAlt2rGZIrL4MAgbeGPxCimx9pdAvulMn2b5ODA8M+w+keKkmwqcL+nFJt5OdX6zv+I3NzVvhmwbGS8V0kUirSH02UlXh5/Dbe3/+Zc99AfuWH60z0zse++MKHRQ5ZbJV0hHpTD6fsWXMNhxqMQgb4QPO1wLCVYV3qMrugig7rqMs3TSB/KIhtYn1YsSZSD+JqEJMzkdtuQIDVKlI6ynqw9LRlaOM3JWv3tvyvYvXNCbwYlhxO8oypbGBJ8sdb4aO7Viz3bciW7cJbTJfDRUxajFaR0W9GWkruUO+HRpSVmiVSVkxzZZ5dOhiSjaNJ0aNn6wUo6Axx/eZJJiqosI0NhIbjTa58BeVqDcbzzxLJ4dX6C5gF7N0g6NF35gyvoVj2vJ8JmO+2HDiEUqXXKDLUaEmyFUmLALtx7WkD+sSFlnGdGkWNWK1zfiFZGipJOKMI1D3WFzms6NUsfysFHn4ESYM0jXlIksPnbwtViVxuuzL3OFuZ/b2yYUmkupM+8GOflfzweM39N7x4sUGgqZ5V6i/86dL1Ec73hw6bm876tbzwcNrbseGmBTrRgDMvrXUzxuSycI4tIn6K8fy80z/wFBvpbBOZ5pYCS7t2DkeM9q6l/KzphK4W037QtO8lgQE1yf8QqFzlhTsScgg07ncCB7FKLER1aVeeObBMe3u88C+iee+gH3Lj1EJVSXMLHlTRzKEKhf7FGQflLOQK0K2mMXM3Jf5icsiRqgStpkJoWysdUY5eQytMr5JVOcjy3rC6iTFoZsZZn0yOVPJPi2ORtRhRx/ZhWe16WU/FUWGX5+NXCx6UtbsfY3TkXeWW8YoP5JzNIzecrvtZLRnRamYIuSs0Tbx8GJHyopdL0Vrmiq6embpZlrr2fmaT59fkr1Gu4jWWTq2QghJTeT8cleI+5IX9jZrMQxW6CVOjLBVFZgmx3wlyzIVFaw8y9XIMDritpLxayGjKK9OTEi/ypJQrGU3FC5EGq4GLSIY5IIZXUbfOuEfGtkJqVCQUL0oBmOlmDZagLxwCog8QmezgVirU+E6vl3GmQUfdbTkxbvidcRRHXdluRSw4zRPFS96OOa6lfiYXGW8N9ivaz6zl2JkH2TcWrvAzdUCLj3TWDFNjs3mwIOuZwiO169XPH50y6qaGLxjT0syme6FIrYa7Q3NK4VflGJr5MZIOsyM2yHdYuks66vMvFD4pcIOmeWXgtKSfaT4vNxBzN3ZKOalwi8Ufg1zUWCqKJ67eOnRUZGjFjHS/fnGnfsC9i0/Phq6HzXEuuwuegUNRK9JOZEHi29kPBi98Azr2tOPshDPpWtqV6NEd0RFLl6wPOsyTTSsnu6obaQ2Aa0yIYrS7xjFohe+cAMVajQyJlRi1rVVZBzdaQzTnPU8We3o7IzViYWZ2YWaV8OSwTv62dFVnqtXaymOVZIusMj4zzY9SmVuD614pXaOeWWxLuKjIbWKITieX61JB4tZSbsxj/ZElqeWq/X19RJtculAs5idoxavF4BLpw52ni0xlD1YUnSPBrRObF8vRE1ZBDBxGWWUm+/k2CopoUY4yK38e1zCDPqEgsolsdruNdW2SPCDjAtDJ0kDb1PiQczIUnDyaUyYnHyJR0PzUf6v57K/6u4eIxcF4XEPduzgYsWd+KaIRVS+2xOFhew7c5VRbSA86zAfHtBZEYPF7DV5I0oRtbPUT3sen+1orOfVYcEnry5YdhNnmx6nE5uq5+vdmpwU/iyxXYjXq7opBJAaqttSsKu7YusXUqhzEZNMF/L3407P9Vn4j5XCLzV2ythBKvGw1qfX49S9KkGnpUcyDpUvIGN2b+XV3J9vzLkvYN/yk4JmOs/oCNWNxgwwGSlWqo7Yzch8qFhsBoLNtOse7410FUfDshbpubGJsBIpeoqK3Is5WJcCdHExUJnIHA0halFzKcTHldTdhV9laEUE4hYzZCXZXbcti83AeTfQGM/LfoUzkdXqDSFp5mh4tNjz8GLPlCxXtwtROoKMBLUiBRFnbA+N5JlNUlCSl2J7lMFfHTr8aNGLcFIqokB3ombMoTzXoixESQFDy3hTd1JsjY04G6lsJETNzluqzks3N1lhKposohgQpeVoTpE22XAaBaY2ixS/5IyposBTJcRzuAyoWb4nyd6N+YQeD3eZa1LspNWWj01vMSWPRe44Gjx6yFJ1V9iO+62jefe4+zlme53YirmM58oF/sjdVOcz+aqSIrdzpHPPuhVG5vVk0R7m0TDVlrwKPD7bYXTixW7FODt+/wefcjN3PNuuOcyOX//yA6bBnRBm9qCwvRTnWFFSwEWp2LxJuD6zf2oKnFjGr66XvK/QKjFi1wqfYDor/MiQ0SEzrQ2hlSI3PJJx6fSOl5sxm4SfOWuiydiiwo2L+0Tmb+K5L2Df8tM1M7w/MI9WCBc3Faw8xiWaxnO4bTBNYOhlvDUOFeEgIzll8olQnzNYGxljhbKJzaanb6oSMaLZrAYuGjF9WmUYJkdKSnZcUxntgCzazzyxt+hbSzAZWweGr5b84Jef8c7iluup43Zu6dxMzJrfvHrCHAyreubr3ZpPr88xSrTYdSvyeD9bUJmHj7ZM3jIfKvRWkphVUHDm0SZy2DXscyt+n0rahpzldZqtwc8WbRJzXxW1Zj6NW5VO+MFhSncmAgPD7qYrqCtRWaSpIi8CzFrEAYCyGbYWe5DU4tQVafcRAHycQGVQo8ZtRTJvRkXsMr7K2L34ysIi0T43J6GFLvLuY2YWHMdplMe4UwOeAia1SOVjwTCBjCCPu63j9wruMsVCV/xkRa9wVCrGWv5NbEURmW1msxpQ657bn5zDwwlrZay8/fgCHcG/P2GMhISaKrGpB+ZkmLwlBs3f+uIDVt3Ebt8SgyaN9rR7VQm654r2TWI8l84z1WXXNWa2H2nGhwl7yFS3iuZ1Zj6TsWF1K3vG/WNwu7uvrX+kCEtV8FAynjWDYr6M5EVAmYyrRQ2ZgoyoF93EMFQ0rUd188/ht/f+/Mue+wL2LT8+aS43exobGINlWllC0lid8NFgrhz5sYzBgjeEm0okxcuANkKmNyZR2cDtbScS6FZCIY1JWAshZM6bgbNq5M204OVhSV0FKht5fVtzomeojPIikddtwK6n0/N87wcv+d76NZ8fzrkeW4zKWCXP8TBV5Ky4joZxctJZHRxmPZOiEsm6zlR1YD/UDK872YMsA3pvyS7LKMpGYlIMU4UrHZOfLW3tiUVyr3QWAcfx+SowLpKSRitKVAsoBdOhkm7MRaqLmeCtjBo7qJuZYV9jqyjoqW2F8TIyjOciPjBkcMltAAAgAElEQVRtJAV16nSVyeTenqgWWZeLaSkyWWeyk8DG5O5GgApRBuq3hHDZqLuE5DmTjHQnKoIu+VvHxzWSPnMa/x2L4BHqe1QWmqFI7a2M5I6y+1QJ0zA2mdxEzh/t+HBzxdeHNeksoG4rfBcJVw31tWZ8f4aDJQJufaBbjMzJcDs1LJqZXapZtBMfnF3xpd6w7RuGvlyKbCK2iv6J4vDuMWk8U9+IEKZ/KgBkuzUn1eDwWIQsbicd1byWZOnll1IMU5avNXQQlplkhbYxveM5Jgu4zjPvJXpHVZG2nU8/iwCXm/3P61f4/vxLnPsC9i0/Czvzw4vnfLq/4OUrIY7aOpBcZBwqGVVlRLV3U4HNuLOJzbpnP9SMh4o8WA5GRBtq1Hjr8INDFTHIYjly3vRUOvCw3lPpwN7X9L6SQMfjxSeLKlFlhav8qThqnbjuW/7K4XtUhYQxTRZrxWN2REPFrEsCc8ZdDMyjk87HJZbr4fQ1u43cVvvrhtQk3Gbkg801jZWZ23Zu2M81r7ZLYtRst60INWZdvFvl+TqxEvjBoXpLNBm3GXEu3r2OSa72076WPZfJVOsJYxLLs4FpcjJyQgQX84MovMXVTJiNGJRLHlvaO5HYH1Osl5HqaxHTSDGSLCzzdqIxnOTtGdl5qQiUbuTo/dLInicfR4DprpPKCvLRN6agvs5FUi+iEgVQkpCT4+Qpi7WMP1NV/mwj6wcHahf40ddPSVHLuPRsJg+W+qXBr9Lpddo83nF9tcQ1gU018LjdcT11vDBLJm/50fOnWCvj3dXjPUNfH+sJM6C8JteJ5rmleZ25+tVErhL6UADLRmT2iy8VZs7sPizm8AyqyPDFI5kZHimmBxG716TLgGoiOsnNS/Qi2iGDqiLLswFnIvvrjmox8+Bsz+vb5c/4N/f+/CzOfQH7lp8xOf7aFx/hZyuFREHIEiWhX1bUt5rpHOK2Qq09m7OeRT3z7MsL2TdMGnUES2glQoRZxBm5TphK9mNf7c/YVg1TtDhduIlZSaxIVS5awJEwD6B1IgSNUpq2nmXPFSwhSFhlSulkYj3K2EURCNYmpr1cwZvljFFZukKd2KwG+qni7Ds3tM7jTORxu+Wd+hanIn9v9y7Pbs+IQfP9p6/48uaMcajQnQhQ/CR31cpkIYYAuRYDuFLSfc2zQemMVhG/rWVvZaV4na96GhvwSeNMZLdvSaUwCfBXMd/WuNWMKtSHo0cuO8TiYBL6jeyQ4iJLQZoFUJucmJyzkj2WCsXb5e72YnD3/8mWjkzfFam3Tc3HHZI7wJHkLnT6ss8qHxda8VSpULiGBsiKWCeyzrg3luF6w77K5MsSg3I+EQeLey17r9RmwWLpzM2LFaoS68WrccnN0HIYK4bXHaoNvP/0ijFYeuWwOvH4YsvLmyW6SaS3ksD9QXPzOyCbTPWqGOqt7OOqG83wCKb3RESUvYZB8GXH2JbbX9DEJtM+N/hVxjQRVwRF45sW1QVSBFUlFquR2kZJ/+5mFu3Em+3iZ/kre39+hue+gH3Lzzg4ppcd9nIgVgbTROKtQyVFfSXR7LaKLDcHnEn4qHl1u0RNBjWVEU25yKq9FYWcQgzISeTl0+DYuZqYFZ3zvOkX1FaYhOr4RJxglHAZbeTiM02iPFx3I7uhFgJHUmJmRiJJhr2TTqj4zpRNaJvEd6OhfdCzbCd2fSMxJyZyGCvqgpQag+XD9Rt+9/JLpuT4ej7jH795SO0CZ+1IyJoQjMj3R0M8JksfvV51PDETY9CkqOkHJ4XjSNKfpXipReBsOfBoIeOkN0NHiAI1ziaTGrnI6oV0n0fafwwadeXg8q09yq3DjAp/LkZwd21xtwrjFNW20C38nTz+jpwho0TtOQkcjsXr7dh7EZHIGNDt80npqOPR8KyIphA00p360IyFLm+KYKOkHptJ4fYKvxRl6dGMnp83NDe6hFdmWHrUXoI41cOJxXLE6MSnLy4Jg0XXkfN3bukqz26qcCZx0Q08v1nJCBuK/UNM6yjZJ7obQ3VjiU3GbxKcebh1jI/i6WfXDoqwSlDyyl7/WvGDBVE0+hWkDwa6bmLoa1JSLEvnFydDdzaQkuJ21558hjez5emjGyoT+cnP6Hf2/vzszn0B+7afLF4k5yJ0njAbUXHtNbEGd6uYomaYKq5ed+hB4jlMERZkneVtXqgKuSzSdRvIUUu+lxLu4GGoyGWkZmzEmETbTYwqEyaL7SZJSXaBh8sDWmWeb1dcbTv8vogmkhKPmZLnbhZyJxz7cmcdDDFLNtjv+eFPGaOj0oGxc0zRkrLwEH+wecWFO3Duep5PZ/zlqx8AsLIT/853foupKBX+4t/9VVHKBS1FtkS16KVHaRlxHo+tIvO+Qu3F/5W17PQ4n9EqUzWBfqr41BeRCUI1j6ORQEWT0UsvSdRRMeOkq5s1aR3ka76uqK+k4EnXkKg+r6m2ivaVdETjxZELyMnrpWcpRqowCn2n5H1Hr7JFpO6l8zJTxvUSiZJKJmjWECoROxwBvKasKWPDKYDyKAzRk4wYyZn5QcQ/TCKcqSP15zXzRSRfzoxLg6qj0EMGC23EXs64MsaeJsflZs+urqls5HbbsbfxJFP3zURVhRMaLCVF3luqVxa/SthZ1Jbj90e5qTg4ONhTuKndieDFX4TTzUZyivrBgJ8sTTdjTWK3bcl7x27vcOuJpxd7Xt4siQeLWXq8N7x7ectXV2ekvUW3gbN1z26s2V3dd2HfxHNfwL7tp9BWx6HCfNFQT4L9iW0+7aPiYGWn0AVydOSyGzjSItQMSUv3oJJCLT1VE/DekLY1ucqyK0vqFFcSi0DCtLLrQeUTkkkBL3dLpsmSkibu5QqqVD7FxwOimkyQr2q5MB2j4zO4s4nXwxJnIo3xVGVs+bpfsKhmdr5miI7f2j1mP9c01hOz5s244MvDhpA1L3fLUxyKHjTZS5GhSRh7p1BMsYwUj+TajUj/1d7CmSdHha4EP5WSwhYaloSBKnQtYg3VRNLBYm8Ly+/4pWpgp0kljiSbzPzBhNaZ5h+02EHQR9rD8FAEHIIHKwbczDFgWsaECWzIJ6NxcnddlpnvzOzTRkgdyVKAuHL8KpOcBHSeEFb6LtlbB/ArcHslcSZlOmzaQBoMDIb4Cz3MhvrjhulhpD4fiVXkO5e3HOaKy+7A1dARKxEEvb5esViM3FwtUL3FdwHVec5XPf3sGEdHTprkNcYlmDTJZeprTfM6MzxWJJdOnsM0WNSoyctIcJnuogdvJIU5KbJNOFcy5K5bVG+EBlP+/fcfvyYm2bm69YS/aYgZPr1uMGvPxZNbQjQolaV4HcM178836twXsG/70ZwyuvSsqK4pGU5HEgYwaXQncvF49CfZEn3i8mmZj0uovdyxAsRdA006BUXaWpJs+6GFoDHruVx4pA3IGbw39FONsYlYLkauqBGP6CUUqLKHSEnhm8JfDEouPl0QRFY0NNYzRodVic+vzzE6MXrLV1dnACzaCaMzr3YLrEmYwknc9zX+tkYXo7A8QSXoIyMA2FOEjBfRgRjJ3jJArT3aptM4KyOv9TRWaC0gZKUzqS9sxKPXzN/J3I8kjVRn7CxhjvPjIGOyg0UXEgZZEE6hk2Jih0xsFCmKKMGMogg88gyP3q1k70jpWUF0Ct/JPiss5HFVMTNLIjKEddlp3d4xGKE85yqTDNi9Fm7jMuMfFjDvxy11rxieJMFqvayYfzBgbeR3PH7OR4s3DKliipbfunlEP1VC/geq2rO76XBNwK1HahdYNxPXfUtXeS66gVe7BWOsCKNFA8sv4PCOiDPI+YQzU15CWbPL4BXubGIaK+FkFil8ezZzuG3Jkz51qe5aunx9MTBFy+v9Qqgrs5BoqsWMMYmzxcB+rMlZsX2+Qs0ygrw/37xzX8C+7SeJ6CAnUaf1T8XUbEZRtCUHWOECxp3DlL3X0TAqKkVJQyYpchdlZ5Q5RZl0SylAh2shpOtBkzq5sGctXVkKiugNtopcXuyJSRGSPt1ZRy8xJNlkqBNVIwqEGOTiQdDgFbkIAGIwjLMjFqFEygrvDUFpIc3biDVJiPi+wHuNaMK1SfK45k6irpJcnE+S/wxhMujbu18BlSEewcRlBxaLwjIUZV0sFHqsFgFGCbo8jlxzuXHQXsGxkGkZx6W67G8ypIOV0WLpjua1IrQCmFVBxoi2L7ss/5Y5Wb+1FytdVejk358CQfNbBuQjdLeV4jWdl4iTG2FBYgFfRpDl39qDxm0Vw7tSzM1WHsTMks6sLiaS16QHXpBcXlOZyLNxw9/80fcEI7WZ0S6RvGZ1NtBWnuAN1kV+8eFLfm3zOU4H/s7t+7wal3z++pxUPFggSQq3/6Yn31aYvcbtFeNCKq1AkTV6J5FBXtdy8wEn+4LVYsBPKqOuKsygcDspyKtu4mZo6HtZLJ5f7nAm8eZmiR8cw3XLEUpdnY/M1w2xvc9T+Sae+wL2bT8lVFJ5xbxJsmDPsPnNo7QdepfkLjPdjYOykfEhp05NyUimysy9kxiVImnXOnHYN6jRlNTa8u9nzdnDPftDQ04a7RJdMxOixppEP1rZffmyf7JZOpxcolSK4lDtRT2ZF0E6Hi+kj5v9SmDCxU/lmkBdBXyQC6oPRqgiKqNNImdwVaCrPQedCSbhQeTzSYncOwq7MU0GNZoTuikriMtiOlYZ4zJx1ugmyriqiuSoT0o1ZyI31wtREx4jY0hkDLEV6bkZlCg8tSQa+2X+J8eKCiGe7zXWiHT+KKow0533S+UCma0LSqoUKj2V3ZXhLnk53+3KUpHD6wD1AOMDCGuR+ac64bYa2yumc/l7dhm7k4I8XSbql6ZI1RPaKxFMrD3ZG+rlxHQQ39SDx1u2c8PrfkG9Gcln4n0LkyVHRYhS4AB+6dEL/t2HPyJlxVVY0hrPy90SayNRZayLKJVpH85cvVijoqg75zMkL82UAjbKaDQuo9wYVXLj0ZxNOBfY7aUIZa/JbSJXCn+RMeuZYXZMpTP84NEVPhq+fH5Ons2JAapMomoCWifymex2788379wXsJ/jUUr9t8C/B7zMOf/O8rYL4H8CPgQ+Bf6jnPN1ed9/CfxxhJ/6X+Sc/9d/8SdBxjuDJjyULKb2M0fowO1kmW0K5iiORoj1CrmjVIhaLJT0YK9IQFaGiIwMlU6iHjyIATcdY0MS6LqwB6NCKVgtRlbNxJt9x/7QkKLCLST/K84FqFfYiTmYk6AjW+nKlELQUEGXkEiJwoi3DtWJaCQWJWMMljCK1LmqomCTdEapzPW2o+vK2FI76fhWk1Dpq0j0DtUb2RmNJbvLSaExbUSbKB1ckFRqVUlXqF1gtRjx0bDbt6edIEgHnIKMq5IFE+VCm0wZ44Ko5DruurSLibh3uJ2mfZ3xS1Uk8TJCFAiwOo0Oj2pEFSVg8ih5N5QonaCodpyKpB7kcfxSMW9g/nAs4hkwo3Tjw+NE7qSouVfFk6ZkhDi+E+geHkj/eE11q+h/h6dZzCiVOV8MTN3E65drbrYd20NzCowMNxLCqVceZeCDi2uG4PjPfvg3MCrx69uP+Hx/TsgaHw3WRLp6FkpH0sSkufpiAzZL1loUleOxMOckdgNRSGrsoPCLhFvK6Puwa+RmwwrOjCy7TtUJVmyeDdrIzdanX1+SRosahGWpjikOSQlnczbYJpwSCu7PN+vcF7Cf7/nvgP8G+B/eetufAv73nPOfUUr9qfL3P6mU+mXgPwZ+CLwD/CWl1A9yzv9cCJuKYPYGPYF7Udh0CJU7VbD7SMzC6XWNmRXhrGz4E6ckYe2V+JdUGbM1CVtH6sajdaLvazGV6gxGRAjuQgy//b5Gmwwk5mC4OnRMY4WxkZTMSTIvu6TC/pvlsfTSC5fweHFQGeXE2JzLKCgOBrOZy0JeusGcRUxhalGyeW8EUNzXGCPL+/1nZ3Lxshmz8syjg1sngNlcRns2k2pKZpd0oXE0xGjRvREB3lzo5yaDS+xoTixFpY/PRwkoeTQnjl+2Yv41RckXq8x8GU8S/tWjPfNsSZVhfBqIjaF9UczHvuChcj7hn6aNjIhNIcnHqoyHS8F0O4XbFTtEuhN+zGvFeJkJi4y6rtCjIjyeCZtAuCwdYSiJAh8eOFuOXG87poNDjZr54zXdS0X9R1/xuJ6ojVTjf/jxO6cbjXgw5ElG0uEs8v1f/JoPl1e809ywDzVfj2c0xvPnfvJraC2y+Y/Wb/h8f45PopDdHXRJTVA0tT9F9KioSOdeGI5vHPpo8laK2Eb5/O+N/L6PPuPXP/kAv7NU64n14sDVtQhG1CQ/59WnNdOFO/Emb+sWNYki121lNBEb6aBVLlOGKhOSvafRf0PPfQH7OZ6c819RSn34/3rzfwD84fL//z3wfwB/srz9f8w5T8AnSqmfAL8X+Ov/3M/h8inddz6Xu1IU+HLh0k9GtBFVl0rIXWYvF+Vc5VPcPcWEm+soggKvmZVw69LBSV5VkWjnS3+62CiViQcLJjPpqijzwA+FolEAuWm0EJRgkiyoc5GxozJVK1lbKWkZdWoRVMRZo+tIUwppCAbvrUB6bca6IIxE4PLi9hSLMo0O+7gnJ43SiTCLbFBfCJpKNZHcRDEXv3FSz98dsSbht4V4W16ffFRNJjG6Hon4eTSoJhKD7OTwGj1p7E4KVs6gyq4ta/mSlFcwG3IX2V130o2W8artLdVtZjpX1DcZN8g403eqpDjL8zjuyVCiVMxKxo71bT4pFUHePl4q5k2WbrvEu8Q2s9wMdwrRg5OvzWt+7/ufs3YjH7cP+PGP3kVPmu5rhf+3tvihZvJWfHhDLT7C+W7vZkahkPyhX/lHtMbTGs+XwznXc8vvOvuK//mT383j9Y5fPf+Sj/cP+fj2AYN3HMaqRNVEtE5Mk2N7tZCR7WCxDwfaxkud/ck5KAhdJpTk8VwnfvX9L/nx1QPSbGg2I082Wx62e95cLcltJDtF/bUTcU0RY7idRr/hTiBTGJF6VieRTCpWE4IhN/cw32/iuS9gv/3ncc75a4Cc89dKqUfl7e8Cf+Otj/uyvO2ff6Isp7OFZHNRFxr8QrhvTe0Z+wo7gz9L2DaQjkq7qMS3o7KkApeLn3Ky51Eqs/9iLYSIhaQRu7MJaxLWJpHZB134cWWUFt8atRT1Il6jZoXttTzHOmEKQNiYxHSoxPhb9liC9EnYOmBdJEYt45ys6LqJ9nwmZyVA4ahYrQdikkISjgZYwLrIPFm0ydhGujClMkpDelOhk3STcZFgNOSxwh7Eo5UtMsIsETHU0jml8FY2lMokb2CWTC89FxWlzidlYbaQqoyZdblByEWwQqlqoCYjRT1LocpG1ITH+JLk5DVNJcLtmOR83Gcec7lOaksFxsN0kYnrcFeQrUSf7F8sUW2g7jx6mfG3NfbRwLvtDf/ni+9ytVtgBk19regfZ0xSmN9Ysf1OOH2d1ahOny8byc96/P4VtY7sfENrPO+3VzzrP+JvvvmQP/bBjwD4a6++y26q2O66O7ENEE0kJ0forYhkkO4rjI4BEfuk94K8hk7M4sbJF/5brx7Rv1qgl57Nsqe1nk9vL8h7y/JTS3WTGS9hfJhIbaL9wmJm4SMekVTZljGyyRJsaQEr4pu4SHcw5vvzjTr3Beybc/5pQ/b8T3kbSqk/AfwJALs5J3QiyEhNQpfxXFiBedLTVJ7x85X8Qq49m3VPyjDOjv51JwrAI1YIIGgun2xRKvPqs3Nsr4ldQrWBdjlRWRnlzcHKPiFWksCcpGOiqPROY0GZLsooyIicXLWST2asXMC0FV9WjFIMMfnEKMxZYaxcKeNk2G1X8pwzKFuyxibHVDqxEAw5Kao6kDNUdWAeLXNfYSp57jFqMJDK47jNhB8tuYvkC0EjnZ67QjaSs5AZVCp5aQA7JytIm+WxkAJtB/nawyqjRyVy9LKzAYTgbzMc7Qw2MV/CYTLoGdpXWbBOTjqu45jwmDgMyPf7rcgVld4qZBn6x4rURlR9t6MD5IYlQZ4NkwJuHTgBJf+F//vXTjgmo4UrqD86sPzfltz8QIptahNZa6obIYaETgrluz98QW0Db6aOn1w9QPGER6s9//6Tv8eZOfC3dt/jq2FNSJrbXQngghMLM/oSjZMVeRJjtN7MmPLzFqGoSjOqKkkKWu52+ttWdrlfN7y4rni5mcnXFeufGuyQ6d9RJJfpnmtU0JgZVCGSxObu1y6ZfKL+xyaRmyS5bpUwM+/PN+/cF7Df/vNCKfW0dF9PgZfl7V8C33nr494DvvqnPUDO+c8Cfxagee87WWWIxbSclVx4uueawXTcvqNJi4h5MrNqZ1KGi27g48/OZZxYigODiCrc2UjKcPVqjQqKcOlRNvP40S2d81z3LUZnls3E5O1dh3K82GtE6Uf5u9dCs0AUY1RJol7aWRSEWvZtWmf6nZDtlc6SwuwSm3UvY6upEiRTlWR0WIjyOUvhTEGf4jjIilwFzlc9b26WGBexrYw9x9EJff/pXszY3uB3dynUOalTvIw8aSkgBBG7ALAsJujCVMQKC1LiU6QryxrsQbx52kNohYOoRk2qMnqniW0GfScNHx8lmhcav7pLGFYJSSPOlCyszHghakTt78aIR6uAykJkH54kWHkZk3pdpIzqxCkU/5qVEdpeMyxELapvpFO039vjXCD8+jm7P7pHZyAa4q2IP2Kb6Tvp6tXFzNWh43c//oq//eV3uFgf+CNP/zG/tviEH0+P+Xp4hx/dPOHZ6w1+X6FcollOwsS8qdBrMc5TxBLH/Dat8ylIVH6e5EZN2XRiZ4ZgUL2oY22v8FmTfI3xJQl7oVAemluFPYiqMzTqBD4GGcGmQibJWpLN5S+la9WZxWr8//jrfX9+O899AfvtP/8L8J8Cf6b8+RffevufU0r914iI4xeAv/UverCMyJyFMgA0mfrzStRpo8LWHttNtJVn0wxYnbgeW1h7TDHpcuuk8NQRPzhuoobJnGT2i8serTLPrs7IWfF4I+GE+7E+CUKkU1Fv+XFkjyXvy0L9UEjuUhFkOBeJWrK+wmTKxyvyJLJ600Ruth3Ra/LhmBYNU2NOasYwSHdHU9qPWcIoF+1EYwNVLVJoZ0Qx2XUTVif2fU0qCsrjqFMVczFlN0cB8Mo7y+tdZQkDjVo6sajk4+oy/vT6tJ9SQYIUQ1PGf0mVaPtSFAuZRNexBII6YgO7i3wH3DWy81EZUhdR/9CVx85ko9C+vK5ZhB2hVex+4NFdwBjpXDLyuVUTUFrMviHI9zfbTFICT9Y3hrhMfO9Xn/F6v+D2szPydwLsKogKs7UsX8hoc/8rI5vNgXfWW4bgeH95TSwCjD/4+KfsY81f3f0CV/OCQ6wYvHTTuhVT9LivRSDTRtargZRFbn+2mIR+MTT0u5qcFM1yZrxu5GvoAt1iYhwqxtmQbyvctSgR5zPZB1dbjT3A/ruS9UVWTDeWxZeaaid5YWYsOMfyvXlbuGFGyR/LJTXbNv4ku78/36xzX8B+jkcp9ecRwcYDpdSXwH+FFK6/oJT648DnwH8IkHP+kVLqLwD/AAjAf/4vUiAeT+6imDYng33lSDZzeD+jZ8X0yQr9vS2t81QmsrQTnZ356ssLYokXad8p3chkSb6QCepI82DC2YgPhpQV56ued5e3vBkXfPHqnLNVz9TIL3YMxcSr5A45DxY9SvJwrkTIoepI1XqW7cQUjABTjwo+kIt5E0lbJ93O85qkSy4WcjFP61A6JCWjPZVRa0+eDXbheXyxZeFm5iT+sMqKam4OFqsTk7dM0RG/FnBsXodSWJMUye6tDJIq8PjhLa+u1iSvyQ2o3pIODuooakpzd7d+NEirCHYvndeRmq6AWCe5MB6tCFWi3YxSwL0oGGObSU1xpyNCENsrQpexV5bDe+kUZGkHML1i+YXEhRw+KjEhNmFsxN8KELnZjGL2Ll9XCEbqcVQsP7VyD/Jiwfj9ibNfr/n8+j3mRwG1Dtgqoj9pqW7ExBx//5YQNH/ko4/54rDhxX7FHAwP2z03U0tXz3zWX9CHO3bV17sVt7tObiSscDCdC8yz5XJ9YOlmpmh51O3Y+5p/+OlTXOt5/+kVV30rghdAj5qEpVe13GzMhvpKOq/mdaa+ltBOVGb/bon22Vv5WVIwPM70T0GljD2o064rNnITMJ+XCUEX6NqZEAzBG7TO+JILdn++Wee+gP0cT875P/lnvOuP/DM+/k8Df/r/z+dQGbqfVDKOygp3kBFiaiPZinjiuGd4M3RsdcPr/QKCZvN0e9pnSTaXjHHGfYV2iUUzU9twmg6GpJmTeHW6bqIfa6oiEsmDOY0Ksy40heWxOGTyrDnbSJTLFCwhGKyNeC+KwhTK6PAgLDsVSjyIzWR9F9bIVIpGlcBCtx4JwVAtJ95Zb2mtJ2QRclQm8jotmGYrHjNdRo7BkFbhrmM0GXYObKbufJHri0R+nIUdmaNCHYyYrkqxyo3AgVVJas5edmRHuntsRRmovSJRRptNPHW2qkon5SSTJleJsJYI+1QIFLaXQmYm6T7NrUSeTI+iFLW9OakN5WeofK9mi2oitrpTSiqVRSRRRdg5qluNX0rHcfEHn/P8Nx8xn8F8GTErT9dN7F4uaXvF4b1E8+6eRTPzixcveTUumaJl3Yys3MSPXj5h6Gv+0Pd+Qms8g53RZD4/nHNzIyBcP1aovSEvIr6VInrZ9izthNWRZ4cNP/34MfVzx5M/8IY3h479q4XcFEyavPG4OpCyOmW2zR8kur9RFyWkdK2xEt4jR0FQ8TnGTtKk3Y0hNtKJpbrsvZpEczngXJBU7xdLqNLJsK3eYnjen2/OuS9g/wocv87YvTrBXacHBQF00FS3iu6XPEYnniy2fLHbUDtP30Ru3ixxradtCz3DCj0cRPxgdEKrTG0DX15tWHUjV0PHYarY33TYOuBPrNCJQ5sAACAASURBVKJSDHS+Q1TZLLuipLBLz6YTtWAo+V8hGGJUdIsRqxPbfYvuhEqevJa75+JPS1XxOB2N0Flh1jPT5FgtRn7xwUvejAuJrY+W2gSmILJv00ihmLwlRqF/HPd18gJqiZHRmWlwctE75nhlGWvqbflVCaCUgkGTVoLayknJyDUh0GAtj60LRT25fEJ6vT1azXuLN5m4L2R1LUUueS0RILOQOnIbUVVCv6ow01tEDwPTAyFo6FEgtd1ykuRplaldkI5zcqxXPUZntqYRzqSCudPoKtKtex51O54tHuCDQhVU1+7VkuqVZf5hzy+/8wKApZt4v73irx8+4jBX3OxawptWvjez4seXD7loe9Zu5EevnrDbt7g6MA8OvbUCjO4NXFual5ovl2d89/wNv3X1kA/Orvl9v/Nj3nx/wVc3a/rrFlzCXDmShXY1UtlILF3kajFy+8UZeoZqn0kW/FLSnMOjmfaTSl4rhYyySwo3Hx0Io5PssATVZiJ4w3hbM/UdeRVQbWBzfmDf1yglPsb788079wXsW36yFg+YSjAvZWyoRw36LhSxnx2bZuD3nH3Gyk78G+tP+I0n7zNFi9WRN9OCv/vFeyiVudzsudp2tPVM6zyrauLr3ZqcJVJlmB3DUIHOrJYDk3ccrlsZ72yCGGKDkg5Jy4XWVInLzZ7tWNOPdSG6xxPDEKS7S16yPHJSJ8DucdFO4oQLUqNGe03UDt0F3j275bPtOf1UEZPm6dkWn4yMn/aSxhwGy/KiJwRdwiWRzustlZnSWZRwRV6vXaTpZsavpIM4Qm/JiLQ6I0QPX8ZVXokSsOCjVC72hmI4zk2UbjQpMYYvAilIQVajPIY5E1VGrBP+XAqgXXriLOSQ8UEi1xk9yGgxFgxgXEYYLfvRgk20q4l9X2Nt4nzd8+7qlme7M8JsuLzYM+5q1hcHhtFx8+mGv1Ovi8cqiUfut5Ysd/L4ISn+0bMntN3Espn425+9T7yuUZOScaeRGxX3YOIwVdQ28NM3l4WOkphfdtIFBRmpEhVxmVj88mueLHZ8cnPBupmoTCSUuJ7+uj2lZ6c6Yx8MjH1FP9hTltuAdKb9U8VQdq/TZcLtFct/VDE8ToKaGsUiYh8NnK0GrrcdrvXkWmGd/BymYicRQLDE7bSV/HdzaBn7t3D+9+cbc+4L2Lf9lLtLfyZ38NormleS7zU8zuQnIzpp/u0HP6GPNSFrDAmrIvtcsTE9n86XpKhoOi/7jM2ej9ZXhKx5flgzehkxZhAxhI24tmB7boWRmDZBUoWXXgC4hWu36CZqF+hnx/62xTh5HIlg4ZSMe9g1cpGPSoj1SRbqKgolJBvJ5lJeFSm3UO5/6elLhiB7OKMTH51foVXiRb8iJaFl+L2jXkmIIfBPJEb7WMmFK0jcClGKr2kCKWjGFwvp+jgpv++KXyzxLMWkrKKgtrIGbaT4ZF18XSUqJgeF7s3pQsloBMbsMqoLpbAXwUqVMEtP2Ak3Mmwi9YWELoZXLWqrMciINVYKTJLuxmgGr3HrCWsj62bk7/70PdbnPe8+uuGrN2eonWV8tgH5cGKWwMr5ImJfi6hnfJBJVtSK3WVPSornn19gry22dLHZKnKVWTzsWTYTRie2Y3Py7uWbSgzPqnit6sT64Z4fPnwOwN9/+RRnBL772facFy826DcOzsJJ8q/PZ3LS5K3cOJ2+B1WifnigX3cnw4m9sqggys0PfuUrvni9oX3sebA88OHyioTix/Yh63rEJ8Pnr8/xt7W83k0El+jOB4xJQtOfHTHoQpu5P9+0c1/A/hU4KkF9JdlP1TZjRwgt7L8fudgc+NcfPeO79UvmbPiV7nP+Tv8Bv/HmPW4G0Q7vb9vTWO69s1seNztSVoy+kTyukn6sgO1YU1eBykZCFOl6dqLfVu7u4mKqhKvE7xWTpCIrk09CAqXBOoHzHg7NaVRHAhVktJOdFAkJViy7JQNYucj8woPXdHamszM+GpbVTGM912OHj0Z2druKej2ddkBKp7LvEzGBsYnxqrlTUVYJVQlFPU8G0+tTJ6mi7OXiIkqRLYGKxy5RKChCN8nmTo6tZ4k9AYEK60kKWVxEUY9qiblfr3pC0pJG3RYq/1YKwFEIo3WWcdysTmNV7SF5hZqs+M5mCK2G1cxh38jNQVQ8We14dnuG/rxl8UK4i9N5JjWZvPbY90YIhrmuCiVEirKaNP2LBe7G4MrPW9ZHWLDCrKUzO6tHpmjZess0OBl5FrJF/s5I202s25F/7fIZQ3T8+PYhi3omJs3z7Yr+0zU6KOI64JYz0RtUoZrEwYrS1GtUE+nWIzFq+qtSvKLC7M1Jjdj9wg1/4MEn/LEnB25jy7Xv2IWGF8OKZ58+4OtRk5qEmhXNlWH8YOI771wJpf7QoE0UQLVJJxrN/fnmnfsC9m0/hSSgJqCC4aEsrPv3A+2DnsYF/vIn3+Ov2o9kvwOnMMeHmz2ViQx9Td143tvckLKS/1CEZPDRSFe22HM1dCLC8JbDWDFPTkZwtYzfXBM4Ww5sbXNCTQ1ThffmbjSYRdSxWkgm1M2+PUWXpNGIybfUQVOSdiVhF4G7doHf9d1nbKeG54cVViesTqTSHn21P8MnzWGssCZx/mjHODvptpCLoZ/tKdtLKTBLGeVlJ3xFXbxZYetIlSjWUBAWSQpJ5i61OgnNJBfDMEmKTepkJ2SGImwxiPl4EUidQhVCfg7IOPHWcr11uM3Ek3euefFiQx7MSckpJFsYXneoLhCXkfQg4ppAGBzqypFdJq4yaisd+Go50LrAV8/PcQvPs9szKQbvjRxcTf1a435xy0ebW378m++RljPhWUd7Jc95ukzkc49+7aTLa+/2SfnCs9r0PFntTgrCkDQ/+fvvYfcKNon8aCJkhasD713e0FrP0k28nhf8xrN3mV916OGtHWeVyRcz7WLmbDHQT9XxR5zdvEBXkc2jLfu+OeWMYcQQbs9m6tJp+aQZveXP//1fw37WYA8Kf5YJnXyP1p/I3nZea8angfH9GdcEvvjqAtcEtImCH8tI96uL+fz+fOPOfQH7tp8sO5awAFWiN/p3RZAQguZqtyCMDl/MrLYLtO18on5//uoMbTKP1zu0ygzBcT23jNExBMebQ8flomdTDWiVmaI9CSJSUKhFoGo91iaWzcQwO7qmkMVLPLwuRHulAMVprLjtGyFnHCX4JY35iJPIRjh+ahGwlZDEL5c9bwaRVS8qGWPaspwagqNzM68OC4xJjJMjzIamm2WcVbqwDOjiwUpZEXuLOljxg9VR7vxf1JhQghOPmK66IIUyYnrOSkzbR6/YUSSgke4lFcp/BNUrMf3aRFYajr7YutD4B9nLaSNSf1sHkkvo83IxnUrURxVFVNIFXB0wJjGnitzdEXz9ecRuDd87f0PKiumBZQ6G7XUnyK4bJ7T5h4k/+p2f8vfevINKEH66pCoFxS8yqS2ILQ3pLNBtBvqblvZslMws4HpsqUzk4+tL+R4HiB+NdO1MzvBwdWCKciMEsJ9rvnpzhh/EqJ6SqEpNLwU9GMvics+2l5ugYahIr2vcQeN+MHB9tZTX3Ctwsqf88P03rNzEztc8v12JKvZWOlcVVeEc5lMoZf9E8GHVox6XRJXqe3f6niqQkaHKEsp6c3+Z/Kae++/Mt/3YEiGvwQ3QvyMXfjVqvKoJVcTWAbeSXZPWmWm2bJZD6X4U69VB8pmGjou2Z06WV4clczC0lceZSB8qfDQM3pGyoqk8ep0Z+prgLSlFtqmRXKdSrKxNp5TmHItysESiXN0u8IMT/1WhuKupRJsU71TuIs2Z7HFi1ILFCpbOiarSFYNYYwI7X1PbgNWpjDwjq2biq68u8N6IqjAhIGCXsE0QQUcUYHDKIqePe6Gw5yZj9mVk+GS683nNWi6clZiPT6QOAJXRiyA+urK705OkMKvyvZKojrf2ODqXwi1swemmYcoN7eVACBm/q+X9VnZm1Wqirb28FrOT3KtMcTKLgnD57pZ92/J//eRDNhd7rl+uWF72LDcDZ+3Is3yOuql594cveDGsef75BSYWtWSA3EDYRIlSSZp5YYv5HB4+uZUbnV1NTJrJW/ZDLV620bL47o55NoSgOV/1GJ04Lz6vT756QPYSc6JMgoPD7jSxy8R1xCwCi6KIncZKkgi6ABtPvMj4m1YEGQlYyd3ak4e39N7x7OqMeXASo9IbzOEuiTl2JdhUFbKGkV3cdKhglPwzs5nlpqZEwhgrdJjoK1IrCKv788079wXs236SIlbi5RkfZAksdAl9Fk+qvtVyOBl5AQ5Dw1ZnppsGVSXmYNlPcBgr3lvd8LIXc+rpU2TF7dzQWgHvhSLTHvoaVKaqAk3l6Sp5/03fMo0OV0mHoJTc0cZZy7oiasJkpaj8P+y9Sayu2Xrf9VvN23/dbk5/qrm97bjDNuTaiAQkGiGQAkQI5ogZMMgMJASIKRKTiBkIQaQgJCABBIFEIUKJjZM4jmzfJnXrVnPq9Lv72rdbHYPn3d+5YUDsga0qay/pqKp27ebsr1nPWs/z///+6t2vIq1CkZqrLJJVTnBG3pDZIFEbVnxaTSY+I60iPhkq6yiNI9eBy7YhN4FNV4JTx8iVMBhMEf4hBeRtq1MSlRXJGVIlfEh/HkQJN2opXFXAzDyLecv7yzU/eP0Ar9P0dfI5cZfJrAaI9wecThKIOXnk4mAwN0JGR4Mr4zuFopNYE7N0dNtSIMGtpF+jEqoKFLmnLsbjjE8eON4V0plj93qO7jXZ4wPrz1bo05HMBJlbRk3xrKB/7Fh3JeuupHqe0Z9PmXG58A7tYmTVdLy+XJK2OT6BfXhgvatwu4LqpGMcLG5dSBHOI9Wil8PCdHhph5zCBDDwxcUJqbXCzRwNemfEEnAS0HOH0YnlvEUruNk0hNZipvwuXTiUgkFnUIkvsSwds3Lg7fVCfv3BoNeZEEvmAV+LGCVUCT+XjoTqb0Ndk7QEB/FJpiwdfWVuMi5nWZAWuU6omWN+h5L6Uq67AvbHYPkmob0oyIrTjnktarDBWeblwK4X9d2q7hiD4ZAXdBf18QbQFCPX25o8DzzbntCNMl+wOjIvBvQkxNgMJRc3cyFoBElIXjQ9TT7iombwlsEbhon6XmSecDv7QmgXWRZwo0WZCWPVmePMK1URXXq0SUczcYxayB3OMjiLj5pm2sCrzLEfJY7jfrPn1WHB6C1V5iSqoy0wM0FJlWUk5l7amkFjbMQ7g+8spgySX6YMwSRBFg1iRUijxqxGUqEwmUS7fLi6xurIn/rwxzzbn/DpxSluKDAHI+3AmcPWUsy1TgzXhYQu3pONNJlEZFIwTvlgTHlrKinCwaJ6OXzEOkr8S1Ckg+WQFVgT2O5qfGvJGkcYM0yrCWcOm3v8TU5sAsNlJXOl1nLTzzm5vxNfk0nYmWP/fIHuNGr2DqMUltNmryMvX56KCnAxiiF6OniYxtFd1NiNQc0iSSeKZmRWyWsFYL2vGKPhtGnZjwX+ukTNnLT/gjrG/lBEYm+YnbVSlKfbj1kEZnXP4GR+qQDfW3CK+YM9AG8vFmIwvizQEbRXcoADGDV+FkSEg6QoxLlYNVIrlJhYiWUAkwhePq8s5XY/OoufCm5RuWMK+N36cq27AvYVXypOzMMWqlcWt59xVdfiKVqMrK8bsjc5ScPn5Rx7vxMau4KTxxueLjdoEtfbmnE09H1zjERJSbHdVZOakKMIpGoGaQVON7HrtmK/rY4zmaoWM+3uUJJlgbwQNeJhWzK8qY+GZ2ySuVN2ywMEpWUOBDCv+6OZOjeBMRh80Dyo99RWWj6tO2XdloSoMToSouL1zZzgDFkhbdNbT1FRO7yTjSgmhVYJW/kjnUMp0KW0ysxSEqCHPiPP3yXydm3Bb19/IDOYaaZ3zC9beVQRSIcMt83FhB2RING5pFGr1hCriKo9KWhsEQSl1YriUbuJkD4ZoGHCXCWNqmVz7seMuh4Ys8CsGrhJivy+l5lRm9O8v0Urme8Ng7T/2jcN2x+dYHqFnyXSVYHpNKGQVq3KxaOW57egZM3spD3eqPJMbj7jtiCuc/TpwOq9Ne2QEaO0d0vrqbORl9sFWieqwvF2O5PHfwov1Xkgnw/HA46PmswE+jFj6DKU5kgMuV4XqCLy9NE1m64kq0esjezezt7Bo50mLZ3wKEuPBvF0gQSJnnpM6SeySsBNIa7Fe4JPG29KVKug9JwuDyJQ6nJiMOhSVLwxgQ93BezLuO4K2B+Dle2kgNkOklJop3GrCC8qilaiJPwiQBHxg4WkyE966tzx+c0J4+TzKnPHzdu53EBuOYZFZNhL4VJlkJgTZ8htoKl7lkXPi3HJbNFhVMKaeLzBiXhDkWWRcbSo68kMqmRmd2sgzkpPXY60fS6JyjYwjDLr8lEzBoPzooa0JuKnIKw33ZyYFGezlv2Qs2sr3GgxRvLMmmLkzZvVdOJXBC8EkJgk1ysEhTpYmW9o0I2bYjqkON9y8ELQU3THu1sAGszaCuoqT6QqkDej3PDKQOoNSSX0oAnzCDMnYZYzKarJ3ZL1g+CdosLPpY2oghh0w6kjr53M/8pRlJTOiEghKspmZFV1zIrhnSG87shMoMlGPr8Rj5NTcrjJdrD9GSdm8FETKkEr3ebC5acdbrAYG5k10jLz3lDetoZfLbBrQ5hH8tJzvW4oSnc8aMzyge1Q0rUFdT0Aovi0tRwAzOS/yzPBQR0m9etuX5EXTtrMTpOiZnVvj4+aEDQ3bSVz1KhJKZHNR0n6dppUBIpmZNgXR5HOrVLSLB154fBexETukE8zzEi3KY+G6JQnHp9t8FHjdOTeas/VtkEpCFGx3Vf/cM7d3frSrLsC9hVfyk8AU5C4iIWQtPMrPWUbJdxKIKW32COdRR6dbDktDzKQtxmjl6KkBoPuREEXY4JJPhyrQPKK5A3RaELpuFcfAHiy3DAES+cyBmcxk6z9NmHZDZY0amn3TPtAmuTPKRpGLyfhW39XP2TEoHm7nTF0GSYL0wk6UeWOy7bhStUUJvDefI2Pmrebh4x9RtUMNOV43NCVFZ9VM++JUTEMUlxTKy/9NOGuyAQOmzWibIyj5IqpIkgo6JAx7vNju/OYoWaSUOlHjbMWNhnZ/Y6QRVKEmIthWyX5XrdcSiY80XDIZS5Th+PNNCnwReLkbI/RidHLzVOyzrTEw9iItQFNYgyG3ATWbUWReRZFz8HlLKqeQ1XS/G5JMrD7pR61yQSbNYlS7FaLsMErBlOiS7mR7g/iEdQm0vY54xQ5Ex4NKJMwJnL/bEthAi5q6szhomEM5mhOX7+Zs7i/p8g8m32FmW7WbVsIWcREuragmopdSgpbyG1/88WSZBPN/QPeG2IQT9ZwyKWYaLm92yIwtlKYkkoCQZ4UhNoEKVS3ylavjrdl1GRfMAkzd7ioUcC95sBuLFjNOoyOXKxnRK+Pt/m79eVadwXsK75UhPI6oUPi8EgfSei+EQJEAnnT6iRG4sEQe0PxyDNGefp3bUFdjtzsalFvgcw+ehlwx0KG9AwGMvF8jaPl7WHG49kGDEc/Vm4C14ca74WtGFXCrQvsVqI6CBzpGtqJ8i7UgmFKjxzdvoBNJuGSSREHQzxkqMqTlZ7NoULryEnT8bDZctHN+OzVmTAAvWEYMupCbgybg0i989LRtYUkQFtpEcbKwz47bm7qoIgnYgfof2KTTAn6IZMWYxZFrIF8TZyF46wMGyUfzCZi0NhMCoFnOjTYhH5W4paB+eMdw5CJkGRCZjFq8Yalid5Re4HgRnXEXJWlY4wKP1isHVlVPXuXsyx6LttGlKEq8Xo3J7eBt5+c0TzZkX51IDMB8w9OCGVE9YYPvvOa/VCw7wr6ywrdiWE7bXJCmHLMPLgqkVaOatXTH6SIZblnVsq8y01CEh81WiXqKfVg3VZ855svcdHwZjsnThlxrp/EO9ON9t6pFMFXNwvCJiO6Qnx3j0eahdwCs8xTl4HByfMQlcxf89zT7gs4WMzJQFnKbXXsLbG1xC8asiCG8lhEKEWcc2tzSGXAlIGqHlDA0/ma57sVN3t5/QYv6tWiklvm3fryrbsC9hVfAjCFmGn6ewL1VROPDyR+Xh8k90ofJmPw0463+5n4jayc5nf7SlpZt2F+NklciYbsXse4KaZvmNBWaBBX3vB4tiHXnr0r0Aj413vN2OYkr1CD/Ex/4mXeFZQUh6hIuUJ3WjaXRYRRgLjKIDOlzqD3lphLcRidJDY35y1v1zNeXy+kkLSWziSi09jCE6KiHzNiVKTrnGE+5ZvZSLIaWzrBFE0ndeIUU2+jtOf8pFBLiqgTfijQg0LriTmY5HHBC+6JCSdFAlUF8sLJ7TOCyYLMoNYVJgCT4TkGLdljSeZi6ETK4jtcVZDClTRHSbea5lq28Lx3suZBveVH63tctg37riCb6Cj35nIz1qdCvlgfKtZv5zCXv/vqyZbPPrmPmXlCa9HDZJY+ttQiYRaJU+H82fdf8fnNCYOWFO3VrKPJR5Z5h08GTSI30hZ8dVjw+mLJew9u2I0FV9tGCvlgINgj4NmYiDYRHwxvPjlHDYq8Fd/WeBKYr1p224qz0z2LsqfJRj69PhURxqgZR4Pr5ICiz6V4KZXot4U8D71BB2T22HhBmEVIQVM00o49m7WEybj/dL5mPVS8vZnj2gydy7yxKBzGxDsRx5d03RWwPwbLtsJ+S7eXp0JI6LaD6m3C15pxLi1FN0uEQ8ZBiSfp1mPlO/tOXMHU4tOIz0ynd9lXSUkIpJKTeEyancuPxSvXno8P99E2kvpMvkZJu0ftJajS7ic6hUI8OAtHisBlMeWHRezM4Q8ZsQnchkvq2rNctLR9jusyzGVOOHMCv50k8nke2E9oquA0ptf4RmEXIzGIkjAGI99z8laRCT5Km4TfZpi5k3j5iQxi94pQitIzeyWBl8NZEH6h06QwFTCbUCYyDNmxXRajpr0uqD/J8LNE1oy0h5IwSjFWTk3Q4nSUyqfOoIvA6cmBe82eTy/PCF7TTvOp1bzDJ812rGiHnK7LqaoRoyOnTcvL6yXp04awCrwcTuQ10jiUjjTVyO5QYvZyE6cKpJUjr0eZ+XjhR/pRRDBNNRCToutyHt3b8LDZssx6TvPDkX6ydjUXw4y3hxn7vuA7T9/w+fUJfZuTl15mj3kktJYUACV4qSwL7NqCVHtSDeNcUyx7KiO8zPmiIyZ4s51z2JTTa0q6AIwa22pCngiD4dDbo7BDtUYy8h715EhrMgaNtmIEHw7Cv3zR5hSV42TWSsbd1QrXW4q5tDStjZLc0BfsuzuU1Jdx3RWwr/hKBvpziUtPSqJVkhb00nieOHyQsBtzJNZnO4XtM/oPIrNVxzhahqsK8sjs/kGED89EPn07NxsOOSwcWsvJuaxGjBL5/bPNSoQWoyUGTVFK+y62csNRvSZZsEXAJzECu3OHqQLaBFbzDueF1GDmPVolMhvITWBbFzL3SYpv3b9glbc8251y6ArmJy3NwzWjN2x29ZG0Ow6WmOSWV9QO+y1h5g19hrGRFLUQ4CeKg2lGEWp4Q1UPHIIUQ8apyC4cbpVQNznBJPxsmiWahF2M+H12TAq2eZjiYzR1KYVCf1ZRX8mGPZ4EOORSNEGYkeeDjKNGQ+oMbESJF/sJ1+WXVMVIMfMsctlYP/r8IVd54N7pjvkE0L25mKNMonooN4bx/Q4Gw/yklWIeRBCx3VXCWJxJIKfqzDS/i9w73TE4S9vnpJscfwbrwTK4jHsnO6yO9EGM7Dej3H7etnN2fXGc0SmV+MFHT1CFtFt7r8lrx+niQHEv0LqMOhNz/OcXJyxnPR+eX3PVNmx2FWeLA/N84ONX99FaYnDCaKAzRDWR/3PQc4fLBV6sFMxODrRtQRwN5kwe05N5Sz9mWBO52TQolXBdRuoM5XlHmcutbdNWvNxIoZ+tOmblwCwf8VHz8nqJf1ljxjsRx5dx3RWwr/oyCTeH8lLiU2KejqGKYaK5xzLhtUBlbaukNTiYY/aXXY4s5u3R7HzvvRtSUlxezAERQtxK6ZVKMuuygc7Jy2e/qUiDQVeevsvR60yUeXlEnYwyCwsGnQeKRU+3LQnbjOqhRGjkZmrXWE9lHS4YIorLbUNdjnz77AIfNZf9DB8195Z7yYQKhn1bHhVieSmy8iYfqbORzme8uFriR0teynwrBE1wGp0FskLmKONoqeoBq6OQOfKAKsVEmxKMfTYFa4rYQ2WRvHKMu1yCFpswCVSkeC3mrcxr1jlZL9BcNdEeUlDEUeZeKo8oHcX7NUgGGEB23mFtoMwdKSneX67pvKC99kOOMpFf/vAZ/8nT/4Vf777OX73+GX7zYk4173n56gRbBOp6IFWKe7MDzfRYfPr6nHiwmLkoEW9BwtlslINE0GzWtdxy5l5EKE5eJ0XmcDqxG3J8MISo5bEZMowNDLsCXQTqZqB5vMHoRDvkHNYV4y7n9XUpN16TqJcdJ03He+dr5tnAZdcQE5yt9oSoebufyS0ffWwZ3n6tXv5EYrYX8cvJ6Z7RT+1YI2nUee4JUbM/lPhtjm4cJo/YbGCwGUObTZgzMbkD5LU7pnb7ybIx7nPqt5r2G+Mf0Rv6bv1B1l0B+6qvJNy63dfUdPN6hynSvcJ0woIzgwQk6imImEJEEkUpNxAfDPt1xVnTclq1fO/ZI8HsWMnIKh72Eg6pE/Ni4KatGJx9NzNSiABA3SL5fmLWoRPWumPKcb3sGIaMcbTieUKSeVd5xxgNm1CJUblwFJnn5X5JZR3zvKe2Ix9d3cM5K8XUClnDjRJW6YOmtI4QNdu+IDiDtvEogx+6DG0T5cTqc84eaRw+akwepDUaldw895bsRhOnEWAoI6ZxoppMwNLJXC8qgjOYLHBetzy7PiFbG2wraKz+NB3ndRgRMVTzXpiNfQZ5RM8dajoeHQAAIABJREFUZekoMiFTrLc1eeH5YrsEkI/dNJyd7flzj/9PrmPJdWiojMOUQVp2taMoHN4bHq3ED/Ziu+D6zQKzsbCainYjz70xETP57F5vxBj87W+9FA/gWOCDYdcWGJ0wOtK7HOcs/UZSkLNm5MPzax69v0WT+N71Q0GNWU9mRHU59BlVNXI4yMGlXYvdYbVoWXfl9L3FLH8+KVt3+0rGiqOWNm0WyWfj5NcTo3WwQvEHWNYd9+Z7DmPOh8trboaaT37nieS6nY6crqbv2xbiEzMitgleia9tOR5fI90gQpYYDLMf5uy/5tHTz7lbX651V8C+8ksRi8iQS56TGYTfd0tQz9fyWcmCF2U0WQtdVIRRMyDzgNDLDOyLtyfHDR0FqhOZdW5lM8pNoPcWayIP5zc8N0t2NzV6azGdwp0GKEBFBZlEUdyCUotmJMvEwNp3ORhRCj5abXlUbelCxkU3o/dyAp6XA2MQTFRh/YS0En5jSrezDfPO/4N4z7RKdMHK31lLxtjQZyidqBppw3mvj2xGawPOWcbekpee7rrCXluqrcCF8y34WsQyoEmvSlSRSI3nNr1ZT/6xLAt8fnXCcFVRtYqYC9U9njnS7pagPsnJNxX1siMGQ1Z65rOOeTEyywd++OIhxsqmmZJEoXz8+h7Jab5xcsn/vPnHWPuav/Xia+zW9UTPENWkAj48u+biMON6LWBjlUdiKf8zm55LY8SfV+SeV5dL4mjQWeCzy1PyXOJztEqMfcaQe4pMipL3hnI5UJcDv/rwc35l/ikf9w/4WxdfZ9cVxKhZT7fiLAtU1Ug/CFosWAuDxmnLPisEC1aO5CYwywdm2cDL/VJieJw5Ckvy5UBdjnSTIhSVMJUndJZ7Dw9kJnDTy6Hqh5f3xY83Cyzu7cltoHcW54wcSrQcrnxrJ7yUJJBXhdgvqkI8if5NQX+esDsD+zsRx5dx3RWwr/pSYqS1G83p9xLjXNqHtotoLzL7kIOrFb5UuAZMj8xhohg1SZBdZqgA/n2PzWSDc5kmKdlABm9YVf07mO4k1+77DFMEorUkO4kSJmgqURiBt6dXpeCsaY8ZYYdDybjL2VUD3/cPJFE5KQobhKoxneSHYPFJ44LhMGZ0bfEThPuJIK4j86ZnVfWEKJ97tWkIE/8uXwgdpNsX6CweRRa3huUQNFUz0m5LQQtVCTa35uDbVGURa8TzEW2TiBKiGLyP32tfSBuy0/jmlpEFqTWSBdZpktGSLZYlWiqUiRQzj1bgoubNfk4+3T5BCtjr3Ry3y/n619+gSfzF3/gulKIS5GBpHu9YTL97aT25DnRjJggqLcnDrhKQrlaJr51dUxrHb/3u1/GdJi69AH/LgdJ6lnnPm3bG5lCRl44EOG8oMs9qtWWZd7zf3PBT1Stejif8zbff4OXVEjMZ4n3UlJln8Ibd50vU6UB0BpUHdC2FtluXnNzfHWkrWiVe7JdHvmFeOuxMLACZDYSoJkC0kllWa9BziYnp+wxrwxGj9ub1inLVT687eR6CF8iyLgLRGTFCmwRlPJq1y9yx7wv44YzzH8PmWzDe8+j6zgf2ZVx3BeyrvqIivxCRRnemsK2oB12jiEahQ2L2MhCtwQyyEccC7NbgZ0HMnVOScKykyIy9ldN4HogTZ69fZlw4S0qwSRXjQUIPdSX0dZWm1OGEGGW1GHWzRvBSq6bjUbNlM1bsBunHPb13w3VbcfFmCYNs+nHh2XeGkw9v+BOnrzmEnDftnHVX4icih502dmn1aZwzFIXnvG7pfMYh5Fxcz0lB0Zx09H3GuC2kdzrlfbkuw5YOYxIRWDbC4tN5IBykmPf3IrZTU1gojKfC/cNpIhG9EyxU6g0BZE4zaPSg0dM8SwQfoAdN+VZQUTooxjkMZ+l4g7Mm0jvLbkqNjlEx9NURiNztSnTteX92w997/RQVFOWnOaaXm2G7kNuMAlzQxKRoX84wh4m4MZMQTlsFHsx3PFuv2H++FF9vEzG15xcevJSv8zk/vj5jf1WTz0dO5u2xtXeL4Hparym059lwxl998VNcvlyCV2QPDmgd8WNGGxXtuhLV4HRQSk6TdCCtc/Ktpl3kVLmwK3d9wW5fEaabann/wIPFjsJ4tEq8PcwYRyMRLKOGUpSjXZtjMyle1gQub+bYUm5URif8JGDRJrK6t8eFaf47GcuLUg4Lu66gyh37y4a6V2y+mXBPR4nauSr+CN/Ud+v3u+4K2Fd9aZF3Jw3bnwoy9+olxsN2CES20YRc5l/lNSif2H8IetTEPJJtDKZXDGeBuJd5DDoRDxm6lw0wXlQEPxlcR6hGha8S7lRuFSSIZUQvHE0zHE/fhQ1YE6gzxxgtlXU8bdaEpPj7b56wu6kp5wOxUbjBoi9z0HD9cskPM09pPYcxl1gPJ3Ou4Aw2l1bWrO7pdcZJLdLyXV/QDRlFKQihdl+8M85GYDD4vSVlCdcZ3CTld67CtBrjkRZokERoN4/4Rh25hiopmcvsrYB2nTp67lQv/9I8fxd+GC24hXy/mEEowc2jmMwXDgZDjIpdqqiaQcQHQTNsK1QecaOlXrSsHt6w6wv+9ov3cR8tmL8W4koowC0j5nnJbpmhkqJ6buiB8pe3aJ04rCuyeuRnHr2htiO/9X/9FONZ4MG3Lnk6X7N3BUOwPN+v+Mbikl9YPOdfefDbvHFLNr7i1bDkop/xZj9nUfY8adYcfMHHu3t8/PqeYJ2i3Lzbm4pWVQIMLiRDLLdBhDlR0R9ybB5wgK8SGXCxnh2VoDEodCNYqRgVLhjWndgi9m1xRKGpPGJLR1U6ui7HDZahMLRDhu8tpvRU060KkCigbcm4LVjc25Plnv66RA2aQ21ZPe0oMs+rN3Jz634qwEZSpSWb7o/m7Xy3/mDrroB91dd00neLRCoCQWuS0pR7hekhGtA+kbVgRrC9tFM2OcSlQ2cRHxVulY7ptnpriXWQuPhBoZ0h200y9WXEN3KLsAeFbS1uLgGBVIHlQqJGdq4gJsUy77E6sHcFu7E4nqR3XcE4ZNNczLOqeq4PNYfeYBoPnRGKfgm5maJUcsehLahnA1V+mwkWaXLHouh5tZvTDdmxCMTwDuibbMBfVYLJCuroaUOJatP04qOzrRTocSVFRkUlRBIjzLzblOhQC9Uh1UJvZ9SYnaK8mD5XS+vWNwk9CnA55uBnkVTcGp+lKOqZx1gRYURnsKVHFwGbC5HdB0M3ZpSZ5+JiRabg8H48Yq3yJwf0b89xpwnVaVSEw7dHMq9x6xIKsSu8aWe8fnEC9zw//e0XzLKBT9dnNPlIYTzfWlzwQXXJt4o3AKxDTW0Mn27PADitWvYuZztWnBQtzzdLstyznHf4oAXI+7ommcTs8Y57s4OkGHQlWkfGMZsMxQp73k8+xCjJCaNF6UgKBg4Z6XSgu6n4Yl1Sn3THeaea5ld1M9AUEsy6GwXXtX++QPcKZpHmdGBZ9OzHgi+enwkSLY+Uq558yqzLFiPBa8rKcb2vBSZsEv1e8F7YNAWSKuKRIXa3vkzrroB91VeWGE4jLJ1EXnhNxOJrha8hOyhCrkhKYTt5Q5oxkqyCpChKR1dYoZHrRPEPKkI1hfoNE5kBOS0nzTHczwV1JB3kV5ryytD94sCsGPFJM88GPpxJIvDzdsVmKCmtZ92VtH1BCPKzlRJY7qYrqXJH/nCL0Ymr9Qn7bUWdO6oJ6tsO4uVaVjLb0CqxH3Luz/Y8W69wE5S47/KJlC9zj+i0SMN1kt9bye8iqkyF8opQCdfQDALqFdK8YLSSVhL1cjCERuI3iAryiCkDYTDYG0t2kMckGDADDKeiCk1WCuO4FA8ZAVEv9gbVeMFlOY22otDsu1zEJwkOXUEMIjixWaD8IsPNE9FC9UbTfnOkNJH+LDJ/sOfxYkv70zndxQo+azBZIo6Kq/UZ+VqT28TJL18wBMtmKNl34uH6Jx9/ygfVJS+GE5amo9EDtR75qH2IUon9UKCKxM2+xgXDg2rL7rqhWvQsyp7LfUMIivKtoXvk6fuMV35BjIq6HHm62JKfBGo7sh4rOp9hdeT59YpxkPRukvgFVelx+1wIMjPPOGRS75P45ZSV+aWZ8u3Oz3dc3cyIWSTUiXI+8HP3X/FbL95jaDMRGD3saOqBMneM3lBkkie23ld4Z45w4KOZP8k/QxNJKh2TAO7Wl2vdFbCv+Moyz+PvvOXmUNF/PkdpxBu2jOhB+Ha79zXZDpJRaJfId5pso/AnI5kNdAD7DLOTeI2QI5JvI2007eQmFm0idYpkJacq5BHVSXvSP4iETc4Xu3PMzPPdr32Kj4brsaYPYl6trDvmRQ0uIzOBYfKS7dY1qTWcPN0AMHuy5fFiy7qvuNjN0DoeZ2mzfKD3GZuu5P5sT24Cw5BRFO5d8dIyD4pBkbzcdFAQ84ge9RHZlNSEG9JgOvm8WCRiFYXQ0RswyKzq3iAG5yhF0JYe31myi4x8rcj20iZUAdpHifi4JzpN9ibHzROcDxMpXVBat8KA25gRmweZzbwtCGUkNorZVNDqehDDd5OIdcTs9TthCfD4Z99gdMRFw+WuIe4z0kw2+PKNIZSJ4SxQPdlzs6vZmpIydwxtRvCazw6nuKSZmYFnwxnXrmE9VlTG8aTZcG1rHlQ7fvX8U06yA79x/XW+/v5bmmzkuquJSQn5/msjOBFZhJuMVCQGK89TbUchXtysaMqRbswYe0sK77xeVSNBmUSIM5GvZ7nHjTJ/VTZissnPFzW5Cdy0FcYG7CqwnHX83NkrfrS5x/i8kQNFHYnbnO3rinUZyU97UlRUlXi7mnrAzjuuLuewtWQPesrCMYyWMBnpo3vXFr5bX551V8C+4ssHw9WukRiRQYgPKEWoImEeCFlEeckDS0paYrfzmPxHFeWnJYUB203S4kJh+4T28mdYGEIB1XUg2ndKxu6eZrgvyCoVQY0K5Q3JJOYPd1z2Dc/3K1ZFx3m5ZztWRBTvz254UFm2riTXgqL6+OpcxBMJNp+cEMvIyeMNLhp2XYExkbOmJdcydN+PBQm4P9szRsPb9exoSM5yj00KO0nQ9+saANV40mCEDKJkHihqSbkd2YPMlIbzSFx6TBEIuww1TtDeXMghTKzIFBXhpsBOhwE5IMjMa1wl0pOeOGrUNiOaqW7dwoOzaR5XJgl4TPL9XJfB1qKDQtWBB/c3rPc1xaQCHJ7NiMsJIIy0c/UmI84H9oMEew7OSppzBLPXhDrSPwycfXDDz5+/JKJ4flgRk2Lbl5AUfjRolXjTLdhYx1XfsO4qmnzkG4tL4S/qyDLrKLTntzfvT1lsIrZ42GzJTODJcsOPXt5ndtqzfT1n9kxjhsTNL0ohfb5f8fzNicSmhHcK0hSkmCudOFxX2KsM/aQ/ClxuVaZlEWi3JfFtSa8Tw0l2ZE0WU+acVonffvtEcGJZIi5FqKT3hlhHsuXAuM+xlRdBkI1s1kJySYMmu9fz+HTDMu/53S8ew2VBnHma0+6P7D19t37/666AfcVX8orhbY3uhNeHOlKVZNaDEOpjLjcD5SU37OQHEMpE+0CRrBSi26+V+VaSXKpRpPg33zb4SiT41duE7SFda8bTiPYiGgHQTrH5Ykl8qpiXA6V1FDrwwzf3eXSyBeCsOHDPSKpurj3dMuOlWtCGgqQT2XLg1x59xt/44pv4Sbq96cqjNLu0ntOyZTOWrNuKfswYBzuR5oWCL14xJWgmlUiZRjmF6WVG5OuICaKM8/VELMkS6XSEoInrHOVF6n47/vB9hsklv4uoyLYa0ymyg4gzVIR8m9h/00NvMNeZbKL11IaKSv45Za3V84HDdQU2kUYprhgIS8/p2Y5dVxK8Js883zi95KZpefa9R+Rrja/SMShTbieKcbCSlF05QqbxGn7up5/xnfkbfv3t1wD4bHfK1+dXPD+s5PWT4OGDDZ3PmGc960FUooM3fG21o7Him7seal73Cw62YIyGm6HmMOTM8pHfe/WIIvec1B3v3b/BR82urBlODeNJpD5vuWqFtlHWI926JEQrrTqTyGrH/ZMdISn6JmM8MbRXtagXvRT3W0+cKQJhLjdYgsYdMpxOqDLw4N6G0nqu1jPJbcsi5ipDB5lJRpVwm4Lm/kHa1dbz8vkpOM3DD6+mKKDEZ1/ck+fcaZRNmCow9HcsxC/juitgfxxWmm4SB8X4eKScjbKZXedkG5FPmQ7yTaK8kZvVzbeNqNjyJH6xQkIxVZQip718X1+BmymGVcL2akoKlpuGr6afraC4mTbvp4rZ0y1V7ng6X/OkXBPQfHB2w97llMZxNTSUxvHN5oInxQ0zM/Cffv1/4kwP/Nid8BcufpWPd+fSMlKJts95uNrRe0uImt5bPl+f0E3ZXjGqo9w8TEy+GKeU5bNBNvU8YKYwzhjVLYRevn6f4ROEKkJnRVlYS9RGOFhUEaRt5TT2o5q4isQ8yoEgcizsMYPDE2SOEhVhLmxE5URan2xC6dvTReJwISd/5X/CP6cEMXV9OadoRrwzbPczKV5vTln9QFFsIvsnmu6B3OT8LsOHHHMQSkX1zQ3bixmr37M8+/7X+aT6OqGA7398n2Gp+Nv/fMOiktnV2QeSCZfrwHXfkJlAaT3tmPE7Lx/zffsQ5wwn85bW5YKyUontribLPftRnpvWZbQu4+Z759iv7fna0wt29wqurmcMQ8YwZGJfOORgEvlcqBphnEIrDxVns5btFIlDEhuGzeXWHYNmXg0MQyZt3bljthCLRIyasM9483ZJmtqzeIXuDDzucYMhb0ZuITW59fzTj3/Eebbn8v6MTAX+8ic/R3tTkV1mZFEOe24Vsec97pCRXdwVsC/juitgfwxWKqK07xSY64yhtehOYUb5kx0gX4uAI98GklHYHoYqYVuFGWQjNr3IskMByYoaL+aAguaFoj+H7klg+FnJT0pBoa4K8o0i28ttLpSJYV2z1xL98cHjayyRed4zRkNpHI+rDZkKuGT4UfeATAX+y4s/RaYiXcj4187/Hp+N5/wXb/40SidO5i2ZCRTGc3C5xNDbwOjNu8j73E8hlmJwrqcE49wGDoPI8Ps+I3T2mMqr8igYLMQLJfJCuXWZaiJRVB4UBKfJnhdCoS8jZmvItwrtwHQJN1dS7OeTmjMTZaJycvNLNklR3Nh3uC+T0Dsj7V2TZNaG3IpsERhuROZNntj2BavlgeoqF1GOBr/y6MqTWoseNV/7peecFC2frs/ILuWtHXLI9nK40E6e71ktc0QXDaXx/M7nT0iD4b33L9kNBVfrGUXpWM2kbbYJFSFquqjZ7iv01Pobns94syqwpWDCxj5j/u01763WfPz2nPFVI7zFPKLzgJ/mnSTFeMjJ6pHmpOc752+Z24G/f/GYdlOJYtAkyfQyiXryd23bkrDN0U4Re0N7sYQEYeUxM5HMax1xbY5uDbEJZCaQcrnFAdgsUOcOlwwhaV71Sz66uUe7kdalcnIQcaeBbDngL0rynUa7O5jvl3HdFbCv+lIIUNaKwq64mjbkUpR2ykO2S6got7SYi5CjvEwUN7zbTJP8UQFCpXA1WP3uRpamf69eGHhe037gyZYD1Ycb2vsF7T4ju7Y0Xyj2Jic+6UlJ8b3NI2bZwDwb2I0llXEcfMHNWB1P/n2w/Ivnv4ebzDa/3X7Af//RL6F04v3zGwZv2Y8CHk5TftPgrBhao0ZNSKS6HKXlM3H7tEoixUcUa6G1CNV4+p0nMQZW4jlu3w2q9oLWmgoaCvTG4lYyy9E7Q3GpMZ3MDseFon0UiXVEtxqzk/BONapjazY2E/mjiRPlXwpcMok0EwVomlRw2kb4omL+VlG9TVz+cuKX7r/g1//SL6Dn8vOG04QqAnGXUb62lL98zbcWF1yPNe2QUVwrTn440N3LGJaKUCiGhebqu55VX7Aoen7x5Dl/6Ye/gHld4E8ds3zgveaG7F5k7SredHM6JyKPEBVGJ04WLe2QMf5oQZxFTB4gKYZtTnaR0ReeH3zxEPWmgElkorOAmbK/8pnwF6tipMw83733GQB/7/o9bq7mZG8zfJXIHx1oqoE/+fAZv3f9iEXR83F3DnlELUbUaEjaEFeOcjYSgqKaZoV+sMQqonpNaGvK93Z0h4I0GPIzYX0+O5zwXK246huZhV1mQq2pE+n+gDGJ4DXVK8NwFvH1nYz+y7juCthXfcVJYYcUo5grtIwtxItUJYZTRXGTqC/iJOBQjHOZfSUNZpA5j55ah14jt7ZtQjtAwTgXoUK+le+ngjpGx8+antZEfO3ZPFYUH5fET0vWecnN04blQigO53XLzpXcL3cM0dCHjEJ7/tnT7/Mv1J8QgL87POR/ePVL9JuCn/7GS8KU+JvpiIuadhT59TDKS9dmQeTb0xAfBI/lg/AarY7iT+qySR4/MRPLQBzMsd1HFaSIaWCdoSaDs+k0fhFI+fQYDVKUzCiPRSjg8F4k1kFCKCffkNkZoWScRNRypKoc3a6AKJlh7DPsRk/xLIJWKpY9brTEbcbiC0V5HXnz3cS//k/9Jv/7f/trLF5HLn9BYYaEPSiyf1DKbWGemNvAb7z8kPZ3Tph9Ic9pLDT5PjLODbZPDCdyi7g/2/Mnlq940a0IFyW/8msfscx6bsaKMVp+sDlnP+Tcaw6EpDhdHrA6kpnA6/UC96whNpGUR/xgMJc56sHA6ucvufrRGbqXxyGZdEwKUDoxW3YYlShzx736wI8vz1ivJEvM6kjRjPRnmtV9mYcB/Hh7fswdG25KdONFbRpEXENQ5JlH54lDl+PaXLxiWSQZBXnAmkgz7zl50HG/3uGjofU5i7xn3UrYp7aJVMotmdGgekP9zNCfT3DJuwvYl3LdFbCv+lIShqiSIi0cfWko3k6be6fEZJxzPIGLaGEKvXSgXcJXaprnSMEqLxM6JJJSqDj5oDRkezn9hwKKNwa/XtA9GUm9IVv1NLOe/bZi/HZHiorUG+yLkv1nJfVLxef34aP3Bx4/ujlGqNg6cogFu6TYxYy/8Pq7dC7jg/cv2QwlZ1VLZZ0INrpyYt4JD8/awP3Fnkf1lh/d3CPeduZ0QqvpxhM14zQrUzaS0OAUcZehghT/lCdJbDYJnGCf8rUYj2Oe0L0WWb1J6L2luFJku8SwUnSP0kQnsfjFxHwMkG8UbpZgJjlh3Ubo9br28LbATJu8OhlpZgPLqmfTlQzXFXZrUCGx/bM7/p3v/AZ//q//c5wcEptvaFGHOplXjqvEuEykhePqozOUVxStoj8TUUl/nslNe5MwY8IMmphlZN8K/JXPf5qYFN/+uS/4dH1G7yxV7vBBY3Ti8WLL9z5+QrXqWTYdhyHn0BY0f7NBLyD+0g7nDGGf0XxrzTdOLzkrWv7aixUkUaOmRhSAcZcxFsKb/ObDC1ZFx/cvHhCC5v958QEAZ7MWraWYb9Y1+jIn3R+oMsFIfXp1yoP3bo4GeBSYRmwZzhu817hNId2ITAQzNJ7ViRyelsVIZR0v9ssj3eWjLx6gL3O0lnONGjSMMpPMDtNrfqeEr3ZXwL6US6V0dzX+Kq/i/ffSw//w35XWihEiubHhnRQRUB/XxAyKtWx8SUvxIkGoRP6db6TN2J/KGzdpxBs1yOeNy0QoEzGXgpltDQkoLxVujhDt8yn3qpeCqH8iukk+Lv+d7aW9ufs6KAezX7ziXnNglg2M0fJ+fcOvzD/lf734eZ7Wa763fsSmL+mdPWaYGRuZVQNn9YG3+xlGJ2K6LV6JbsyOIYtZJnDgrs8IzhC9Ru0EJyUPEEJzv938vOCxTC9/7+F+QPeabKMp1vLY+UqMyvYgcz/fJLmFKY7zF0AUh1VAF4G0ydGDkDpiHjFLR1mNHNYV+asM7RRuHvlX/5nf5H/83i9y8jdKil1inCnaB7KJjotEmEfMQVO9eYex8uV0IPEcxSV6BJVETWqn+Wa04gd0DRw+8KjGU9RiKD+ZtSyLnh8+ewjrHHOvJ4yGD/6iws0Ml/9Gy8PVjrfbmdg2bOAXn7zgPD9wPdb85icfEkdDVjsJFY1KSP06Hk3lpyvx7V1uG/FYAWEw6JuMWEYef/3yWGBGbyRgc1vCYNAzR/LSMkaJ+EYNWkzhW0txaabXpxjv7f2O06UUMBc063WDsRF/UZLtRMmpghQr4rvH7qjG9fJ4q0n089F/9Od+K6X0K3947+a79QdddzewP8SllPqvgH8ZeJtS+tnpY/8x8G8DF9On/Qcppf9t+n//PvBvIayGfy+l9H/8I3+IEdm5Uhyzt4DjDCgkxX5RYTsRHKgArgF/nt4JDhK4mSbm4FZeZjeDHEtDIzlV6SY/8u5QHFOV94uM+rnFHoT80T2MKA/Gy03tdnaQNKRHPXEw5C8zbKuoXsuGWmaez69OubfYsyh6fuf6MZdjw6+dfMLf3XyAUZF5MXDo86OSEK8ZvOHldoH3BmtlYK9VYnBWTuVOTKjhNmZEIcVrb4+CiVspN16jB4Vq9VFUEWppB9qtkWIQ3hWB7qFwEEORGM/DcV6moiJWQSTxaRLYZJHoxagbo5D69dwxn3Vs1jXVJznZAcZf25EBf/mvfJdyrxiX4BbqXXEK4uNLG4OfJZm7lQndK7KdJpTywlFJDgYmm3x6CXzzbo6pPPTnoo5MoyFVToQwJvCjv/MB+mmHvt/h9jmqNXz2ZyJ2NWCAZy/PSJ3hwQfX5Cbwcr/k77z9AK4KYhExC4frrTzGGmIVCMDJ+e7IJnzx2bnwM+8NR3FFXHr0zvLy2RknD7eSCp0U7nUtLcGlO97qUya4Mz19XfODQn7HGsazgDmIdcRfl7zZvIPwmoMmaKGvhCKRrzW25QhcDiUkLW1hdQve0ODLqUV8t750666A/eGu/xr488B/8//5+H+eUvrPfvIDSqmfAf5N4E8Aj4G/ppT6dkrp//+do8B1GSZ/FxFidGR0lnGwxMGgbCLwAAH6AAAgAElEQVQaRX+W8PN4TP5NZZDo90Ez3Je5gSkD85mATQdnWVY9Rkc+vXos87MioWqPzb3khs1hXBjJylITBNcyye3lthaayOzBnlk5sDlUhNfZtFlAvoN9X/BguePqUB9nL9ux5K9ffgcXDMui46qXXKthyCSE0USaYsRPeWDFpErc7Su0iVNYoSaORnh39SibZVKkMr5DBkWFGvSRd6g9RCMkDhVlEGb2En+Clk1uXEZpL/WyYVIEGRwmSJUXenlUpDLIzfg6h9OR/K2Rm9rSEXvL/uUJ9bUi3yZO/uwLPv3+I+qXhrBK0n5MUnyqi4SbyYFgWMljGvNE9dqIOEdNN2otN8PbG9i4SNNzMj3fOpFvFXmbqC6k1RhPPXU58v5yzfdePCI+6uFtKYjNOmDvd5TVSAia9rpGV57mYUeIGm09n784EwTTzMvrLEy3rjNRqganSZ1lVoysyo7dWGDmjlgpFPDo/pqrbYN70RDrwMnDrUT5jFYOTSuJrolBDM9EMGvZtmKWqD+WG3nIYDiJkmhQJtJEUamfG7ST16QZYFyKdUQFIaeYAcbFdNA6mv3l80MlFH17v8PeCn/u1pdq3RWwP8SVUvq/lVIf/j4//c8A/11KaQA+VUp9DPwTwG/8I34K9aLHT0m142hwXSmbyi0uqfYEbQhKTqHaSdtLlUGUb1nClA6S4qeevOb95gZN4nJs+GxzymnVyjxtFVC1Rxt5MzfVwE2bywxuMjMnA76JuJMk8vEskZ/0PFlu2I8FmQ30Twf0Rc7J54r9+0LweHWzwA2WvHRcqkSZiYy9HTO2Y8Gy6PnHH37Bt5s3/J31B+zGkg/nV3y0uc/lmwV/+ud/lyFKbtjzw4pPXp7znadv+PHbc8Z9TndRi8py5lGZkPZvf389FZyj4XXyb9EZdK9Rk8Al5Am3kpuX9lPxAilYMy+FezCkOpA3I340qNclYR5Q+4zx/VF+npEboZ8HTG/59r/0MZ9vTmi+MAxn0qot3+qj/D1MN7BsJ7NOPSiyVpiU7SOFm0XqVxrlQBvIN8jMa0TaxGWiO1fHW3h9EUlaE//k/8vem8Zqmqb3Xb97edZ3PWutXd0908usHsfOJI4TLIcEAgkmIIGUSCgRi4IIEUIC8ZUlAj5A+BIhJPiEECEJEkQhiiHgJA6JPWPsmfFMz7iX6aV6qaqzn3d59nvhw/WctywREgvIpBudWyr1qarT55x6l+d6ruv6/3//NbOiw+rAST2lLDvWz2Yw8djJgE1GmsmHc2L5PBxSKVF5fnK+RJlIUnYMnSXWViTzIx1jMWl28SuZdWz6jNP1dFfkQEj0MWj2X73cRaEoHQidIb9b4/1IKBk0qpMbCd0L2iw/RbqnkV1pW80wFduAyw3JFrnpSOVx6OfgR5JKSKA9irt0BRRy02BHSkoEu9UMy+f+wdvz6Tu3BewfzvlTSqk/Bvwq8G/FGK+AB8A3ftPnfDz+2d/zaC3JulniqFvxOyktFzdl5UJp0kDQEbbJc+KGibBKMIdi9GVMNX7n5Ig3naQB99uUo7tCaXA3PqnRXJznMg66d/eKy8mE7mkJE0iORWnWVSn0ckHzXvP4Yp/9WcXgDOZpxv73x25mHkiC2pmWu63Aayf7a7ZdykvLS16eXDBEw0v5BXVI+fryMUM0/MLJ67y2OOXeF9b8K4d/i3eHI/7C6dd5OLnm9S+cElC8MLmichnvrg44u5oBMkbM9hu6TYbeWKKCmAcJ4bTjKLDXpNcaFRQul3Grz+IuqJNRbYhTxJnb/Rk6olcWt7IiZLjTkWaONJV4j3awbD+ek15rskvFvT/0ISf1jOYbh7QveWLhUZVh8V5ABehmAmM2fSQkI7uxhG5f0R/I59Nr2gNJ5FYBun0YZvJ0zd6HbBVZvNtj6wHdDFz+2JLqAfguIUbFtOjYyxuefXAg/67Co7RctK31TB+tR4+dEOkP8orfOL/Dnf01nbOECOncs6oK2jolyRzLaY1RkZPL+UhIkcKRJY7ZYccwAoqvryakhbyWzi7n+NqiakN6t2borfx+HMeyHDCfiO9QOSlCepA8NDeNFM+kQN/sYruDSLJWu85rmAXSlRaLiZFiFlIZJfoiEnOPrg3JWnLbbA3mlUbo9P5WxfFpPLcF7Ed//gvgTyNvyT8N/BngX+LvrnP6u84tlFJ/AvgTAPZwQWY9IULwWnY8JqKtJ3glYZVOkU962qgYjNxJKqck6+ta8rfQkdnxls2zGXo6YK2nV+C85l65ZnjV8OSHR0SvcHlg01hQMN+vKLIe7gkRw9oRSNtrkjuNEMSDJkaou5Rmk5E3in4ud8TRBjHs3nSMJjIoy8n1TLK+fMLTdsHjzR5PJgtenlzw8x98ifrjKa9++RP+6ME3+dki8C9/+PsozMAXZ8/wUXPSzXnazLEq4KKAX2eTlkXRsswaKpeymuRcpFOCExK81uB7DYPBtCMsFxnD+SyK0GOQDiDebbGJJ3gj/jIVnyf9Rgh7A7/t8x+iVeTdy0O6wXK5mqA+Ktj/4Tjm85GfOnyf//aN34EpZJeVPk3Ze8uzflE6v5uRVnskHw/zQJw60mmPGgyxN5AE3ETjZoHFoxVaB+YmcLkuuZoUzN4zNAcZ+WXC5GlPfu0pn1m6rsQVker1yPWmIDto6NYZsbYMjcXOe/o2IUsdVZXxyr0zXl+csHE5WeJYtxllOpDpQN0LskupyHLacH41Q79foD5XMym6nZn8znzD4A0nH+2BBpV6ulXO8PEEWyniNBImnv5adleq8KjCEbYJyZOM7GKU6OtRZLGA+qUBe2Wp70eUQ17PQSwkroBQSvRMcq1lrG1uWJjquXAjQnJhSTaK2UeB098ZOf78BSfPljAodHXbgX0az60K8R/wGUeIf+VGxPF/93ejgIMY4388/t3/Avx7Mca/5wixeOV+/LH//I+xaXK6Ntl5s2JUglPyWhBLzhAju0U4VmjqSeqYly1WB54822O5v8UFjVGRLx6eYLXnw80+V3WBNZ6rixnRSaBgkjkOF1sWmUTZu6jpnGXdZhxNK15fnEpsx5Dza2+/xOGdNS8uLnlazVnVBW2bkI+RKiAFsD6ZkFwb3Fw6ouW9NS/vXXBSz3jy4QHFhwluEnHTgO4VdqvpHnXYsxT1Qo3SETcYQm3BRGYHFdtVgT7N8AcDr7/0lI+ulvxzr3yHJ63wAM/aqdAoGkNyZaUA2VFtOA8wG4it4etffo8vzp6hifz2yXv8G7/8R1ksaq7OZzAo7j66pHeGl5eXfOv9R8StpTiWTsK+VY7qxZGJaCPuxZZHdy7p/8u7lKc93TLh+vOWfjHmkRXjiLLXxImDqDC5+KCyRMgjLmi6zuLOCtJLg5sE/NLBoDHzQWJaLhKWbyoW7/WsH6UkdaTdV6w/H0lf2rI/rcms43w7oest3VbSq+1kIEbF0f6al+eX9MHw9sURVgdeOzjj8XqPukuZ5h1nVzNC0KKqvCogwt7xhixxJDrQj0UrPbf0+578sEGpSHNeYipNGG+lk7VCO0V37ImZqDqJQK9Z/sASjAhS2qOA7hTpWgDW0UqhvxmZSjyOFKdkpZ+LMpDPG+ZiGbGV7Bbbe25UkI5QgFR2n/qBKBmbPuGNP/ynb1WIn7Jz24H9iI9S6l6M8en4238WeGP8+C8Df04p9Z8hIo5XgV/5+329GBVnVzN8Z4Sd5/RurJinHq0D19cTwvA8R0mNicveaZbzHqsDVSdzl0eLawAal/CkWlAmPYfFlo/PlxL1noofSumI6w3XVUHTJxxPtzyaXHHRTYjAz937Lrka+HMf/Q6eXc557aVnHOQVJ82M3Dpeu/8hVgU2LkMTeefqiMt39tl7W7H+nHSIs4drtA58+42XSS8Mehpp73my41qQT0ETPizY+5WU7QuI+s2MRAsT0aknfGOP9IaicJHw9uYFYhL4peXn0EQWmSQ5axPxaRB8VjKKOTLpvmJt+Ud/2w94WFzxM9M3OXNz/tLFT/LPfOnX+Ss//AoEKA9rzr97zPxdeDc9JLkXmX/tgqvVhOVfz0mawNUXlEjlF4FwMKAinPziAyazSDfP2b4oe7ZoI2q/E8xVY8ju1Nxdrkm1592TQ4wJ9E5EOjEqAdyvNP2Bf26aPi/xrUHVhuKpZv64JySa4srjkzG1exTzuKDxQ8LmkznJpUY96lCpPGZF2bFpcp6YBU8uFoSoeOHoinevDrm4nEKE7UYSk6PTDNZIIOpyYLMtqEwgywa5iSgd/REQFMOYHKAnAyGXOJXpOwmmHTvzbDSGD5rZwzWb6xKfWrqDSL/nMZXB9OyKVFRSuPQgNhDTip8LJQg1n8ddZwYw7HmUUwxzBQcdbBL2ft1Q31P0h4KR2n8kBu66T0jtrQrx03huC9g/wKOU+u+AnwUOlVIfA/8u8LNKqR9H7is/AP5VgBjj95VSfxH4AeCAf/3vq0BEeIS+SsbdFBgjvpubaI2uTcY4ivHC7hSYiC09ZdnRDpbz0zk69ZSzjsfXe7y8dyHUhc2MZ/1sF3mBFbk5QGgM5IH6vKSbOoyOzNOWn9p7nxfvntOGhL9x9QWOyw1l0pMaz0kzY/CGP3T/DTI98K31I1qX8M75IfV5yeSZSNjzC/CpYnuQi3LQRHitYn/S4rzmJ+58zMR2PGkWfFff53pWEG1AX4uXiNxDUJiPcqpXexn96QjX6S748/R/eoF+Cf71isWsQRuPjwbdKVwRQUloZHKt+Znf/11+bv/bHJsNz/yCv3jy2/lovUfTJzJqjQr1Kwte+HaP8pEP/sXA4V/LKX5pQVZo1o8U11+M+L2B5Cxh8pFmk1hJgG4Ellzfi9J1qohpxnFqlFiVxaRhkvS8c3K0e26neUc9fjwMhu7+wGy/4v58zVuP74rC0imSa3m+rl5Lya6DqBWB/DqwdprmrGTxUsNe3nB9YuiO5SUXncaUPTEqqlVOdV2Qzzq+cucZV11J78xOzBMjKCKx1QybEvZ7jI1oEyQs8qQgTjzlXkOr2YGXQ1CkuWN4PGHykWaYibDCTSU0VI2klOYHS/JKsX1JUsLTK+F+Ri3vouaul3HkcNOujWPBXEa+IY0MSy8KxkZETKbSu/GsepJjPFz9mCc/ajieSHeYW8fTq7nsc2/Pp/LcjhA/4yd78YV499//U7vRkhhM5cIBI+IwjIF8/XhhtFG6sXVCTAK6FFqEMYHFpOGorPjgao+6zsRL5jXRaSkmTu6Ko4mSSJxJTlaWyUjrd95/zFk3JdWOZ9WczlmMDhxPtrxzesT+rOKPv/gNPuwO+OXzl6n6lMvVRIyvXhFaS3Ju8XkkfVihdSBPB6omI0bFneWGZd5QDSnrNidEWBYtZ3/1oVwA7w+o1pBdatoXevbvSMJzO1jaNmG4ytHT4bkfbDFQTDpiVDRXBfbS7szaatlTTHr++Gvf4If1Md+/vMfpKAQ5WG45OVugLlP2vytp11dfVBRfu2L6Xy9o9jTtoaJfjgbw5cBsr2aS9XSDZVvl+GcF+anGTcUIrRzEOx0H+1s2dU7fWZSCvOg5nFYoFTldT8kShzWBwRk2VY5fpbuRXNcmlJOOxHg22wKlIu4s5+hXJZsrGrBtlIyyAOdf05gvr/Fe013n6Mrs0osBri8nqNoSS0eSO0IQf+DDw2sy41h1ucSXrFKJPxmLGkFBr9GNxtaK4VEnoZUmkk16Xjy4Yt1nnLx9RHqt6e4OmKmTrrExxIkj+yjF5+wIJ8mlwdY3NH/5T3soO0HGblJPRntHVMSTjDB3JNOeoUolZ6wf94hWXr96LffwX/3J9wHog6Fxo7gl7fjB43sycveKD/+1f+d2hPgpO7cd2Gf+iOzXd4a6kf2Ns8LbUxopXDqibu5YTcRknniWEfKAymXPcFO8usHy5tNjAJLE07ci1rCZw3WWGKR4mdlAkjraTYYPBtdZylnHe5sDzrcTmjojHxNvjQ68dHzBv/C1X2apawKaT/o99rKaZ6sZDw6vWbcZV5dTsIH4YkNZCOy1c4ZZ1nNvtqFxEtnxvXcfwiC8weTFiurbB8S9iPaK8oOE7jDgv1AxyRxVI6rGGCVTav/hNXemW4Zg+PB8D/dJSe005aKRseRNVzHx/PiLH/P56Tn/1Ru/G3deoJwiTDy6cJx9/4j7vxxRIfLJHx6YLWvMN/fY+7MTovHEI70LuAxZQCdCyT+/muFqy/x7KdULge6rNWHQmCQwn9UAtIMlSRxJIvvMOzPJTjvfTshHM/DgDIfTihAVW69otynlvCUverrOElNFlg9UFyXFmcFnggwrzgO6j5AoQqLILhTtm3OUUxQ9tPc8wzpl/bQQr5SXvd1sr+bhYsVVW/Dso32yO46DvOKsmqBNoDiqaasU8zQTZeAiCNUkQr/vpd4oUcSWec/TzYzNkxnLdzTVg4iZOnkcLhPCcUf5Zk66EuTZ+jd5DH0hY0Llxbt1o9pUXvaKQVti6klyhzuS0aB/VpK0kjAeLDB3HB2tmaQ9Z5spRgeO8i0uat5dHQrAOCgu64LoNLo2ktZ9ez5157aAfdaPBjUWrOC0YNvG4pXlPTFTuMHg+hvmkCKuMrkD1VHeoNYz9JYVBV2ToEzciSvSmaMfrNzJdyKMuGHQdVWKzR3LeY3zmrZPeHK5oF9l6NIRgmJ/WnN/uuKl/IIvp894Zzji72xeZYiG03rG0azisioZnEEngUnZ7YgNs6zjpeklLmpOmhmbLmNT59izhOxKESykH87ILwPBKq6/EPGv18zKjsR6fFDEmOyQRcYE6jajXF7SB8vQJKQbTTdVIv9edLghh5njhXuX3C02fNTscX9/zYcnJWHm0KlHP8k5/E6kumtYvxKItcG9vUe6gqe/J8PW7H6FVOEnEBpLtUkonliyHtY/3lHMOpzTZFMhYTgvychWB/bKBq0il3XBk+v5LussSyKp9RzONizThm2f0jQpSkP7eEay0Qx3HLMH11xfT4SmMiYTTJ94ysdr2vszbONxpcE2iuxS0RyJuKR4YvC5ljBPK2PU+6+f8tL8khAVQzDowvHO9x/wQ68IpeyLujYRW8bntvRtIl1LEIGFng2YxDN0GeEypXmnQHewdxXxhQhl4iohuTYM+x4aS3Em3WJfjFSYCHG0EahxPznsO7kByzxxnaA7RYia6BV9L/s/42Sf6jN5DACiU2zqnFnWkSUDk3RgNeRcdSUhKopkoBkS6tYKRSUbwcC351N3bkeIn/GTfe5hfPAf/kligDSX3UKRDkzSHqUil1XJ+nQqRt1m3F8VATOXvKwYNNoEYoTZpKXpUvouIQZQRsQg07Kld7JPUzqymDZ89eApD4sr3q2OqF3CeTOl6iVc8KvHTzhtZnxwvo8bLD/+6CORw29maAUvLy94Vs0pkx6tIl/ff0yiPE+6JW+tjnk0vcKqwBuXd2W0N3INQ9C0V/kuiVK5sWPKPXfurOidwZqAD4pl0ZJoz0U9oXOGYbAMg+HO/prXlmd869lD/Df2qO978vsVzmlh9f1CjisV2xci2WtrjA5sVgWsE+xGE15q8GsRvKhCdm0xKJKy33njJInacVmVMuJbp5jZwMOjKzpvRLHpNVWT7Ua9RNAmkqSOw1lFmfS0LqF1lsFr0pE00g0JRgdCVOSJI7WOVV1QXRciUBjE7Dt5sKFa55iTDBXAbhTzDwLLt7ZEq/G5ZfVyRrcn3rFhJsrH8oneCSP6RSS8WpNmjqZOmUxblkVLmfQcFxsSFVgNOY9X+2ybjL6zHOxtqUYMVLvNoLLYlSbZKEImvr9QjpEyg2LxRoKtI6vXpChFBZNPRDUoNosohnETd1L2MBXF4E2wZewMqtPYtZaU6jGZPH1ljXOGadmyrXPBUu135OU4GTCBus4oy46jaUXrrLzWgjA0m02GquyO0vLBv/lv344QP2XntgP7zJ+Ibwz0mi4qbOLpe4MrNFWT4norJswo1PVYeCbLRriBOtB0KW0tKbvbOpd913gxTbOB149O+b0Hb/Nhtw/AEA2roaAwA5rIYbblcLpFLyOnw4xn7ZzzdsphXvHlzz/l3e0RV13Jh6f7QpBPHN+t7+MGgcH2Vcpr81OmpmNiOo6KLb/84Uu7rinLBowJtHVKaO2OnqFsIAaFzcQkO3i9K16DN1w3Oftlw35Rs5fXbIeMZdpwJ1vThJT+O3uEWSRmgeasJFl2cJ5x8bMdsbYoLxaEvjeUs450ryK1niIZOElnNKfC6JvMG2Z5xzTpebaZEcaCu27ynS8uO6oIQXO6nu7sDTFC8EZ2i06RTAaKomevbMiMo/OWxHiGMU6md2Z3cxGcwqayF+pGEntSDJJM7BV60UuK8v6Wbe4YWkuyzmmONNMnKaZx6CEwTEcVYjFCfjVUDwP5qSat5c/v7G1o+oTeGqpNTl1nzCYtPmqumwLnNcfTLdZ4yr2BTy4X9HWKfZZSrOV1p72AhUdRoKgDB0N2bugOYPXbBpQNKBWJ24R+LgpJ5aHfC+jJQJo7kiNHU4vEP7RWzPoRVBJQGyMJADMpXm7uUYNhaBIu1xkMCmXibtdbbXNA9ot3ZltCVNRdSp4O9E0ukwgd0Y3eoahuz6fv3D4tn/UT1fOOZEzKVcD16YzkwqLyiELo58lhw6ToGLwRpNM4GpzOGyZZz17e8HsO3uW7mwcskoavTJ7gUVy6CVPT8deefoHN6PG6V6457yec1DMy43hQrgB2Y6aTZsYPzu+wrTNcb4lekZYDfTOOl9JAcIr5fsX/9vh1lmWDj0q6jFWOajX5vQqtI22TCgxXRWJtmd3dkBhPaj11l+K8JjGBq01Jf5WDDSgrBfn+/oqJ6dFE8amFhPNugsuFB9jvKV74/BmZdVzPxOvW9gndYEmsp3FS3G/8TB+f7eE2CWoqHrphsFw5w9ZmWONx3lA1GVk2MMkHEVOMoZpdK52btWKARkViUJjCo0eO5arJabqEg3nF4I2IT/pkJ0xARdltRrXriPsqRY8XZjWTUEetI5ttISZyr+n2Iot3I8PEovvA+VcLhpkkbqtBPG9h6kXE0Y2y9IlnkbU0vczuYpDC6YLmfDtBqciiaLlqC3xQrKqC/qwkudakK4Uf42hCUISbNAMLeKGGxC9vOF5sudiWcoNSJehW0x969HRguaiYKrDGc70t2ZyKbF85URKGTIRIer/HLxxdnxATYW+q3OOflmgFYe7ABqZ7NVYHVquSGBTFtGNWdISouKxKfFQ4L0i2YtJTrXKR4JeR4qn+0b+3b8/f99wWsM/4UTpipgMv37lgL6s5zrZUPuWim/D+5T7ea/anNZ2zfP3Oh+wnFUM0/PzjLzIMlq5OWCxremdYdzn30ytePDxniIaNLwhR83G7xy+89TrRK3QSuDfbcJhtMQS+9dFDfvsLH3HVF5zUM7ZthtGBbZ3tyPjaSCpvP9LNyST6xSaezbpA28BaB/JEVG7Tw4phMISgcINkeZXzlq5LKIqetklpoqIsO6oqRxvPeTclXGWUHxuiheaBI100LNKWWdLy5ekTtAqc9nM+qPZRAeq7kexQdk0fXy6JEcpcRqv3lmvOthNsIpT7k7MFMMrLZ8KNXE5F+WdUpO4TBi+ZVzEojIrsFzX1kMo4ap2jdKSYSabM0FlC/5zuYK2MBdsu2T1uifEclxsu2wkXVYn3UkRM4nfpA32XoMffA7hW3tI6l6iWGBWxUFQRtg9S8kvF+lFJvxS/WzARHcU7hVfkJxrTgXYi53///EB2qFWCKZ0oWoPCoTmebXm2mjGM4aIxKmY/NCjHjt+ovIwo1Si8MK2Cn1wxKzrOr2YMQe9SAvRk4OjRhn7keoagqUbcVV9LBpjKPLFRYvL2ipgGfGswV1bMyxMnSdedxtxpca0lKQYOllt6Z+idRWkwiaPrEqwJpMaP3xO2jcUNBn+RieHcRIpnGv8can97PkXntoD9/+B8/s45f+DOD9AqcD7MOO8nPCqveGlyQaYdpen5qx99mV947zX6q5zf/WNv80c+9y3e2Nxn6zLmSStmXiL/wTf/KYiKf/5rv8bvmv6Qa1/y1vUxSeY4WmzZL2qOsi19sLy3OeCFw2t+7eMXiEGJGi0bhEzh1DgzQroto3cXHABtA95pjA2UZYfRMv6bZD0RaK0ltdLR1F0ydhKKuspQOhKcpqozsryXu/dtgmnkQuyziOqlQ/nZw7e4n1xx6ab8ravXeFrPhWDxoMMkAWsDl3VBMsKDXdBk1lMPcuFsNhmtSZ976BQURc/L+5dUQ0rrrARRdla6lKgoyo5l2bDqcjGZr8VHpGfjmM8J8guvIJHRWd9Z0swxLVuqJmPbZuxPalZ9gQsarSLGRCaFdKpdl9DWKdpEsU10Fu9F7p3MO+l6gtwETIqO2mb0C8gvpbjcCDtu2Ji+iORPRTyhB8hXnubY0kxzdGOg9ASnKBc9w2CxxvH+J4doG1nMK66upsx+LUeFEUDcCa/xJqbEvVpztLfBecP52YyuSwiDxnnDrGhZlg2bNqMbLL0zLCcNz84WhNqinIYkkMx6hnr0PDaGWHiyaYf7aEIoory2eo3uRSnrOulyQTBm7ZhkgIoMlcQDuWxgCJpmmxF7g0oCbCym0YQ0kp1rmjuBycurfzhv7tvz9zy3BewzfowOfGX5hKf9gv/hBz/OYl5TNRmHiy1P3j8cs5EEchpS0Gnkmx+8xOSVjlenp+xZ6cg+avf5n9/5EuYk4/Crp1QuowoZ36se8vHTfabLmpfmlzva+1k1oelSltOa1++e8vFqQdsnXF9OsOeJjKBmHpJAuWzo2hQfpWNMRrHJDebKGk9iAkUy0HtD3aVYE9i2Gc4ZvFcSRFlb9MTtQgp9UGyqHGUi2UGDPoqER4p+lYNXPDi8pvYZ3+lf5O3tMZs+56IqSYwnBoXrJGolzQey1DHNO85XU/reErxmuMpQE8dk1lKtc2IUH932suSHXhUzyaYAACAASURBVJOnAy5oqk0uxteyZ5L3JMZzXRcMo6qQeY9fpwQvpmPfWujleYmJmJGVisyKlt4ZjAki2vBmV1SneUejx5RpZxhaS/SaEIUsj47QSkfnLgqGTIzDy6l0fEnu6A89/bmhXwBRkZ+DbSKulEIz/UiKWrBQH0kGmllb8cS1mnxfdqd1neG94v6dazLreP/JIcWbOdpDeRrQLrKdChOy/2LDT7z4Id96/Ihnjw/kZ1l0DKsMe23ol4beGdouGVFnmgistRTmYCMx8eSzDjcY9MaKOTkL0Gr6zQR13IHXUBt0rwmLAZMGfGOYH1Y4r2malKFKpPCNwaOMIqXL9YRYyd6TVkvOWx7Qg6K9EwiLgc355Ef8zr49v5Vzq0L8jJ/Za3fjH/lz/zhXfcmfvPc3uA4l73R36EKCJvKt1Qt89++8ynDgsJOBrz78hN93+CY/N/0NcqX4X+tH/Def/BQ/fHaEMYGHB9cc5BWrrmDdZzhv2DYZX7v/Ces+583H9wAoZi1KQXVdoBMvHVAtd60EGRepkSkYJ45i2dJ3CWk24L3sE7SW/UiSeLwXAYIxkSLrMTqy2uYEbwjuuYRZ6UhWDBzOKnxUrJscqwNtnzAMRliPTgtG6nK0C2SefNqznNZsmpzqsoCo0IXbxXqAGL6FECE/n+8MxbyVuPrrnL37K9peRpp9b0lTRzIihvrBjiM+jRoLTZZJUewGy+ANfW+JQUkH1hlM4Sknrewoo9oVa6Vgb/SEbZps91glRvZPTZ1JhzVocJp03qFHBmSM8uex15jpgNbSZbZVSvpBjm2gOIlMThzb+xY9QD9XZKvI9WvP07RtC/0Mujse1SvyRxuOZhWrJhec1PKaD6+X1O8sSVeK9p4nv1Pxux5+wDefvEj3wzlu6cadmiLYuEN0+TKgJiOzc2tJ9lti0Phenj+dekJv0NcWfbcleEWoEshkVKrPUkwLw75wKlklEhyaBtiKuIPCQ2soPhFjuvt8K49LUBzdWbGpRagRvRiud5ipoOSGr5NuPtkohnkkpOHWyPwpPLcd2Gf8LNOGP3rwTWa65YPhkAOz5afLd/jLq5/gN9Z3+dz0nJ/4g3+dO8mKd9tjHmUXvJBe8IP+gP/+/Ov89e98CVUIiWNaCpT3tJ6RGM9RWfHq9BStIn/pza/hzzMZk82cdFS9xuRupLGDnwqUzlRCbA9JJE4dKgkCbZ0Lq1Epxh2HFIxhENCwtdKN+KCJMYpwIUSy0kl+ljMQFUXWU/UJ3ZDQ94aQ+OeRF0GiUPQ2xc89dmVwC1jercmM59n5mAuWe1HIqREbFfRYUMfi1RoYNENvcesUPRFfXJH19M5iTCCxfhxz6lFxGIgxMgxGipvxrOtcTORjWoAfRFSRTAayfBAwivH0zhJGMY7WkcOy4qIpCUHvDM1KReo628UWKAXJWLxCEAWqdxrfJKiJQ2spxs06RTWG7sgxf/GKsydL1s8s8/ehPZC4kaF8zhJUAZrjiHvQEVuD2RuwJnBVF0JVmVa8+cldzPs5kwtF+JlrjrOey9WEX/w7X5EgyKkn/zjZJR5vX3FgI16BPUuYvmFxE6i+0mJMpF0lIpPfFyuCuUgkRy0qotdgg4S2vp+jnaJfBJg4oldMH27Ynk5glYjIBUXsDDfp4fpLG+5Oa6ouZVk2bLvs//J6uRFCaSc3XX4SIECXIcXt9j7/U3luC9hn/Ex1y1I3eBQvJee8Oxzxn7z9B7i4mBK95nvhIclk4LV7p/zU/vsc2Q3XfsKffff3cn41w8x7skwuUACts6TG87nZOT+zeJsDs6WNCd+585CP7BJjAi/uX3HZlJx8vIcHcGJ8VU74cn7qYSZ3/3rcC4WJXJi0jjtFnVLysVJipPZeiohWkTwdaMaObRgM3hm0jmRFT2r9ruNSSr4mY16V6jUx98T9jjIbqPMCes3J6YLYiwlXGfkeIJ0PY9diM8dy2rKucvwoHvHnGeagZzJpZVw5Ft80dfigubzK0UZiRNou3Unw26l+XuBGX5GrZU+GlcJy8/O3XbKjhfhe43TktJoyOMNi0rDIWtZ9JnlZm4Rk0aGUYrqsqaocFyHLhRzvnQYbib3Ga0McwyNjJl6qdTWqNINChUh+KY/DUI5jZhsJ6Ujhj6ALh7tOWa9SZvc2WBO4bgp4muHKSPfaFr63YFsp2IvELKJb8WNFA91+xC1G35dXlO+mpGvJNKseevKyJwQlsSmzAU5ydK9wx+LV8v2IQIuKeCo3Su2xIz1o6bcpDJrqwznpWmJWQqKILzYkqaDN9HFgmndM04551vL2kzv4RlLKo9OocQeoDjpCZwgmiko2Isi1VIzq87duL5WfxnP7rHzGz7Uv+bMnv4/VkPP15WP+/Hs/yfXpjH/kK2/x9flj6iACgn9y9j1myvGeW/Buf4fT8/l4V6lQOUzzjtTIOOyF6RVz25Iox0w3lHQ8uZ6TJJ778zXrLmfTZKSLMYlXRdSgd4nMvpDOxo5Mxtm0kYLTS+ciirnnt7Rtk44ikMh8UWGNxwc9djSCtJqMWCqlonwtPxY14+k6izKBOHqDpgc1DxYrtIq8VaeEIOPBF188ox4S2sFSbXPyoqfvElyVkEx7HhysONtMSVNHeW/N9bMZ6d0GgKrOpBgYkcHnieP8ZC7J15OB4MzImhQzblYIraTpEiFTjBBkTBzNz9JRht5gUk+E3ajUptLVlVm/i6q5CZTUo18MoKpyglNoG3FOY0YMlkoCygTSzImaszeoNDCZSXK3PUspn0Z8CkaQh4QM0pUUsfZQBBEqKkKvyY8aUR46QxMV26dTVB5Fofc3Z3T7UL/gUYMiWWuijgzzCJMgXdB0gEEzeSclXUWqB9A/7MkmvZiFNxbdafzcYZwiPGjlcW1GRFThUYkn+oQ+lxut+O6EolaYHvpZpL/rZE93alBvlPTLiNuTzrVSBafpfMc0xERiLfvMOFL309SRTDqJplERnQR8Z1BXCQ//ZqD8ZL2Ljbg9n55zW8A+48eqwD+2933+xuoL/Pn3fpKvHj/htZdP8VHzYbfPzy2/zXv9Mf/RJ3+QjzdLimTg/ZMDiQ/ZWuxs4MFihdGBekg5Ljd8YXLCzLT8zdUX+SeW36WNCV+9+5QfnN3h2WZGljgOpjVPzpewSsSXNPEoZ4X7VzjxKTUJMSiuqhmM8nPnDHk24McOxDuzk+dn2YDzGh8k52pWtGTGE6IS2C/gg2Zd5ztaSGD0UzVW1Gc6Un0y462LElNKt3Vz8V63GatVSRgM2bSTMeg6QRWexaxhlnZ83O4RvBTT8rAmtZ7By24p6ptwTvGr6Y0lTGRfo0xEz4bdDm7oLG6wYjIfE69vuhCsoL58Y8Ep3E2aM2ALJ5lajYRFrrscFzSD1xjr2ZvVYiwerBS8KAR27wzBQ/QiIY+pFHu/TsEGyr127HgDk48VSRXx+ZhcvFQME0gqaO5EofE7RboQGX67yeTxahLUSUZWaYZFoHhi2L4YcQsvht9eibrRRkyj8BMpcjSW9MKQVNAvFMPCQ2fofMb0zVRsD3cCBEXIA3GbiCjlN4/uFOAhuzDc+yWHCgPDxLC9Z3ClYvp2QnEaKS4c/VzTHGpWU0lQ0JUljOrX3Sm8PGcmjmng0LUJrrOCC1MR3xle/HmHaTztcfEje0/fnt/6uS1gn/GTqx6P5u3VMXWb8kvvf45vmpdIU8dXj5/yF/3v5MN6j7dPjujWGao1xCxgJgNHD6+ZZR0HecUsafmZxdv8dP6Yx27OJ26Ph9kV/+l7f4Av7j3j//iV1whFYHZ3Q24dWkW+9PApz5az3QW937OkiSNLnJikeS5OUCPV3nvNdiujuBuJMyOJvutkLJhljjxxXK4neK9JU+EqDqMh2qaeLO85mlV03nB2OeeVV59SDwlPPtkXVWLZk6WOWd5xcjmXHYlmJ9zwzlBOWpiIv6zpE354dkhe9Lvx4s1/78w3bDrxt03Tnm2fsqoK1FHHC4fXGB24rguaLqFbj3vCm73KSO1XehxNJRG8wm8S2cMZ4VjeBDfGqKg2OeW0Y9Xk9L1lUnT4oJlPWk6eLbG5dBYmEStCjGATUVbm856mTomrlM6OsGYd6buELB/ou4TFSaCfCafQ5aJEnDwNdEuNyxVuTDDuqlSep14zfDxh+pGmOA1sHoGbsqNemI2RTK5kzOMC3EK+r+o0+Ykh3cD1lxx22aOjwtcWVVkZMU4F/JueC22+Owz4hUOXjjRzUjgvUl75CzXmsmK4MwcgvWiYfADKe1CK6qUZ6WZg8uY14afvoluNCs9z3VTpxA4RFSbzpDdjRh12RmqA5IOMo+84TBvZPEwINtlxFG/Pp+vcFrDP+HnSLfkzb/1+7s/X5JmMrRZFy8PpNefthK/MnlD5lDTdo1MZ9rDheLkVmbcz7M1rvjR7yleKj9EEEgWl7vhbq9f47sV9zi7nXFblLu22bROeNSlKR86s0NFvOpQkcQzOSNcyWNp1BooduqoJ4sNJC4fVATd2My0iMVfI2HEYBFvEmCDdNFZGSgHQkM1bQlB8cr4kBMXrD052lHSVBmbLmjLr2bYZT88XhG2CKpwQ4cdd180YLsIuhkQpUfIZG3ZKyKpNOVmLqEUnkXpIuFiJpLqctMyzlmpIKbOepktQSZCLpBc5dsy8fN/GYmYS4xJbI0isCDbzGOulWxvzr3Th8F7v+IyroQQQZV7iCV4RR29ZzDwq87jeUEx6yqwnSwbWOuJqi0oDWTGwHEUM2a+XYwgW5NcRPUQmH2wZ9nKuX8kIKeRnhv4LDcZ6gtfoT1ImH4u44eRnPRiHzZ2oLlepdFkRVBDxjhoUutVkl5riRLqwq59wMP5MOhszx7KA05LNlV0rfA7N6x3ZpKewnmqT017mEBXZtSYqRfPyHv3CYptA0Tr0tqF7uOD68ykhUeTnPbHM2D5QRBsIuUwEjIkoHXAxYbKodzdUifUyfXDyelu+YTl8o6E+Ttnctzt14uL94Uf8zr49v5VzW8A+40chKcp7WU2678iNIzOO/aTi1ckpQzSk2vFwsWJ6eEqqPS5qXl2eMbMtr5fPSJTn1+tHDNHwP178JBfdRKLqB8v+cktqPE/VDNVrhm1KsWw5nFXMs5Z1l9MMUgQGZ7DpwPnZTCCoi575vCFPB7pBvFXAjuSglIgpsrwnBL1DYYXwHI8FyNhNCzw3DppqpFqkucM7LYKSD/ZRg4Y00DQp202OSTyhsZL6OxiijhTTDmMCTZuQJCIeGdbpKECJmMWAqyxdk+y+vbEBXQYGr7nalLg2QZlACzy+2tvth1xvxAzbiixbeSWZazZS3KnwXhMGLcKA1kAmCKndjqyT/y+OxeumM1VjgaDXBDeqLDslSjkFoUpI91qKdCC3jqu6wI0kdaUjaeLEPnBVsKhh9l4FWqFcQNc99AOmTJg/Dpgu8vT3KEJr8IMQ4vNTKS7rLwyoJDBd1tR1RtgmJFtNslKEBPk3O1EzmpEgH6yieiQ+MoIizgchkDhF+aHF9CLo6JcRd79juay4ejZn8l6C2Yu4gwE84inbT9k8tASjKM8iqhlQ25rtgyNcodAeTNVz+lN7uKl0gwENhXS2eea4u7fhsiqlI/eK7LChW+UkZ5bFD2HvzYrLL5U0R/L10uvI3tsd2QfnP8J39e35rZ7bAvYZPzdjrotuImSIEQT7tjvCB83D2TUHWcUrszMAumBxwTBLWlww/OLla9QuZZk2fLxdUvXpjnY+zXrKpGfTZ3InqiM6k4Tgh9NrntZzmkFI8Yn1HE4rjJK4+ziHedmylzeytwJc0aPGnzlERT+IqAMgTweOp1uu2oKLyynKBNnTOY2yTgrQ6HtSmScMBm8Di1nD4CUoMeYObYMUEqd3I6HQSAyM7DsE0ktUsgPbWlRrBNhqFH6dPA9l1DeiCPGp1fXoyUrGfUpUO9PzTXAoXhFzCXaMRpMsOvJCzM1XV1MZJY4EDm0DwWvBgeUOO/MMI51+2Kay3xpk96ZKR7JsxdAdEryNKKdkv2NFhOC85trlVKtC4L5RURQ9zmvq6wJbOLq9FLPtaB/OsZUjJAblM9rDlH6m0JkiFJ70mZBNfC6qxPpBwM4GyrJjf1IT//YeIWGnKITR95fIr5hFTAftsRJDtBLRRLhOUE6Rn4tqMGro9gPqWCT7za8ekGuoXuulaHcauzEUp5H8tKVdTlAxYpuIn2dgl3RLRbRw9xevuf7SnG5P5rF6kDFpqKX7TYznYlvSvTcn8eDTiP9gik4ii3fg4HsbVq9NaQ9H/JWHpIqkZxXD/T14/x/gG/n2/D86twXsM35CVKz6nEnSc1UXdIPlYFrz08fv8xvru3z/2T3+6Ve+x6PsgkR5Toc5K1fwt08+x9WmZG9Ws5c3fLjZ42JbkiWO1AbW25K7RxtAAhbjfi/dAHC1KTk5WxC3lsldoWKUWS/hin1OlgxoBUdlRaod582Utk9o24TgDUnqRoOtx+rA8XRLYQdWfc7ltRDbbwzGSkXxAQXJPTNlv0MEhaC5Wk0ITjE5krHQjTBEJOtIVzCO8ZSKz31oOhJ6EUHEwuNnUVSEv2nZr3Qc8emR+lKu0ioJ6JEEb8fCFmFnkiUJmFxc3CoXeXsImsZJUcUIRR8te5kQ1I5j2FWixgRg0OhWy+5FRdR0xB+5UfAxepyChZgE2iZlNm3YrkpipxmiZf9wQ2qF46jXlvTdlGQL29eWbO5bok0pzgPTTzom76+5enWfbBU5+FVDcRkYCkVzrKkeBuJ+T5YPFOnAk2/cJzHQ70WheiieE9tv1pqJeL5owXSKqKSgoBWmBdvI54YU0muNPi2wNRICmohXTI1PRbJRmC5iztdkdwv6qabdMwyTgqEscQXc/98r3DSlOdD0yygeRCsMxuTK4nrN1RiDw9IRaoNplezHAuz/RsX6lSnb+xrTyL8nWwVmH3aodYW/O/3//s17e/5fn9sC9hk/gzM0Q4LVgbuzDT998B7nw5Sf/+BLNHWKTTxP2zmn3QyAk2aG1YF1nTOftEzTns5brupi5wU7vZyTFz3vX+5TpANGR2bzhqrK8b1mGF82qvT0ncU7LXlUg4gW7j644t5kTWkHzpopnTdUdbZDR2ktqKRJJoKJs2pC7yxtI+botBxk92ICfhB6hRqFEH7QmFR4dsFp1GUqku+5IJNiVAK0VUiXk4u4wXdGQLCjUdkmDm0jPhHBAsPoIxtjPcJIhTBZkJFhKqo1NXrOjPUSbTLu1G6KWppJcXbO7LxuAEM/QndNBBuxozm522YSQDpyfZUJRC+YKeUUOshFPq5S4SZm4/jRBvBGCmEeZBTprPydBgZN3WZM5lvSfKBbKtrDAD8oUE4uzipAugnY6xYCVC97tmng8JcsydrR7KcMpTSCsbJUtcU/XUAa6fdGhaEWeod2CpdGfDYmWicBcgiJdF43nErTQrp6bqHQA0SlcFOoF8JhtLVi7wfQHAvRXnlI6ojfn+IzxVDKc9XtaYYJFKfS7V6/mtMdPDci61YQW8PcY6YOpcSv53pRvro8YtaWo29FNi+WrF7WJBUs3nPkpw0hNejewzDgyufg5dvz6Tm3BewzfhLrOT2fc7qRcdlvJA/AjzicRhM9fDt9SGI8d2cbrA5ctwXDYLjsJvigOJpU3J1vuG4KqjalLDsicDCpuVNuWCQNhZEl9mbIGaLmui/Z9BknqxlF0dO0CUOTYDPPg+mK82bKW5uJwG2zngeH1zRDsrugX21KNtuCGGTHhIpo49GFCAdcZzCZKO1Iwg7PFPVv8kTZgF8MmMuE7bPp89HfKEtXNhKvUhFTlAE/erB0IrH2NeyCQG+6H22lwCaZI0nE79X0iSz8E8fmbIqZSEbZjToyRiWZXImnSAc2dSbZZ9aTjaipG9zVtOgIEbpB2H/FXOTty0nDts3Eb2YiphgEDeXHwmoiDFrGqEq6xWgiyUFLljnct5fURx4mI8PLabqnJU8eT7CPKl64f8lH7x6RX0aGybiLtIphoqlfkJube7+oCNYQLFy/KqKI4ixiW43LNSEb050VlM+E4OEzhSsEBnyDD4tjd4kSP5lPDcSIreR5iUYxTKFbRmwrY0rlJXrFF1KwbatItpHhrowakypgztfoFyZoJ9J/V0oBnJx4zr9WsP0/2XuzWFvX7Dzr+bq/md1qdn/2OaeOXbbjsl1BTqwIgSIhAYIgAVcgbhBIkXIBAiEhlMAdF5FyFYlbS1yABCKRQCIXiAiQIFgCRyRExi677CpX1anT7GZ1s/u7r+NifPNf20lsbJIq7WOtTyrVPmvv1cy55vzHP8Z43+f9sFBVWkFVJezsu+OV5IiFOmN7BQlWn8Kj3xhIlebNn6558mue9m/9Fmm/Ry+XmE8+ZHq6JP3UC66+aeFv/KjfzQ/nj3oeCthX/PjRCYg0iOflJH7IVt7I9EIytyaxGxu2vYRWRm/mUMSbfiFJtEHuMvfblhy0ZG7pyOPqyKthwxAcjZXdilWRw1izaoUkb3RiFw3Ra75781gMvF2FqSJN5VlXI4exZt/VTJ0UC2UTOSoRSRQ6RwxGGH8lfyojo7ycNFon1iXCBODm1RnmzkoXkpnHcqeLJ3tLNhJaOf+b0/MWhYJhitla2YRWFJm+qBDP24HaBH7zsw/BJoYgXiAF1E46rdPXEvq7ZlssAotmonZhNl4fu5rlQopXSvLcxpLd9nh15K5vGEbJ3VJakplRxd91cBDk3+ZWQLR6EXB1YNmO3G2X5KcRNh5jsmCwoiIvIuks8fx8z23Xsvkdy/ozjwoZMybGS0cyQq9IlrmzSZbSCWWOL4v/rSqd5yBPQLXL87gvVfI56TRC1EJE0R60F3O06ZVwEGsYzySoMrWZ2Kr7300ukSsjbL4f2X3NUO3AHWQcOH38mGQUZspM58JXbN8qdh9b9j+RUFE+luvit6sTyibMqxo9ibgEk1FR8eJXAtXdRPdBw/5jw5O/N9H+zls426A3a/L+gNoemH7qjC/+rCZeTj+6N/HD+f99HgrYV/1k+V9uI7oNEq9+vL+ox0e+GIAVN/2CEDQpaZI3mNbL2CkrjnctzXqkcoEhVaAzy8rz9c0VrZk4+EuOvmJKhu3YsB9qhsHx0eM7nI4slhO37cDNccFu12JcRFeRqvZsmpGQNE+WBzbNwPe6J/PPftp1aZ0JQQgV6lTQCivxJGs/pRW/Pqy4+eIMNWq5409K4K0n9WJGZPcgI66VMAdD2YEkwHtD8JYTy9rYKAKJyRBcpFpGnI58udtAlbBvHHGd0Bcjq+VASJqlCxxKWKVQ9RNDclxujhiV54BOrROXZ8fZOlCaKpzKnDUD10fZr+WS1Nw0ns1i4DDUjFmREqhJkV0WIY1NcybYdr8gdRaaYr4tDEeqhHYRXbrSx6sjP/jgnGQdj77liWjGtezYdITomHdZts8Mlxr/tBSmOpOMZITZXmE7GC4L5NZJSGVqE9lkqmtD1oqwScRVJAZVfpZEGo0IZgYRV2SbML0hm5NoQmF6oeRPay2F0QBLRag1dc5MK41fFdDuUXHx255P/wVDfiQ7WnPjBCemsnTggyE2CdsZ6MEdNB//jwfUGLn7uTXdM40O0PxwS3y84e0vrlAZHv/fe7JW/PDPQXXR8WRz5NMf8Vv54fzRz0MB+6ofVbqOqCRjymQ48/c8t0mzv11IPLqVHYC1kcXlSM6K7lALzqgpOxwbca3cyaesOISKnW/49O4cBZy1A+tKUmwXxQNWm8DR11RaLqJpsGgjvECtM292K4xJ/Muf/Dq/tn3J6rwT9V4RMOQsdPc03ec3Ja9xrce5yGYhRI6rw5JhdExHkb0D5HJHzWhkVGjzHJWRS0eabuviUxKhQU5KkqG9FmFIFfF3DWpSqI2EanZjxRQM+2PD4jsVyUJ4EnEu4mzkvOq5OixJSfF4faS2gZuupaoUw+QIZdfWuMCyEhWij4aQNFMwnLUDCzexHRuMlt/L2brnOIh5ePRWIlyOgllCQW5Euag0RG/ISWgRqpIOMw1Wxsdtgf9qEZocp4qfvnjLzc/csq3PePp3EsNjh5kyZiq5YKF0Pz4TaoXtMtNGPqa9orqF+k72XskpDt+YME3kyeUOgF3X0H+xQv3MgaYKVMVfdcKTvd2tGLxGd5psy/4sFQ5hklwyM0pxdMdMvYuExuDXiuYmY3ym+sEVj/Zr3v7SOe6oqK8zySo++FuZ0FZc/0lFWCdySeQGqN5YNt+F8+/0xFqjQ2b/yUKM3Brat0LvwAeuv7ni8BGg4PjBhlhn2sd7msrz+ref/Fjezg/nj3YeCthX/SQZlTx+uhNQK8juxMiupDkbC61BaOXATDcfeglErEoA4tmy5/lyT2UDPhqWbuJuWnDTL1g3Iy9XWxrjuRmXuCK1dybik1yYh2BRKrN5ciAVokTuDRj5Of7ab/xp+f6VfH9rE9NkZ7r66lwiRPquJiMMRKXgOFZ0lNHm0UIbyS5RX1tSVUZDGfIyYupITiLmyF1JCi7dqIqFTZQArdBFjh/uKmHuLcSX1bYTl8uO3VCTbmv6Z4lcifpw0Yw0NjAEi9WJj87v6IMjZcWzlTzu73z5lMVy4LwdSlCnZgyW86ZnU0O1DgxRPmcKhn6ssDaKGdxGahfYHRsRmVRixjVNwOk052VVxQjtqjAju5RLqDb9Htr/sK3x3vCb8Rm1C6w/3jFebFAps3wje83pzJKcYveJxh1kJDidyfNmO4VvMuMloBSHnxt5/vyOD1Rm8HaOl1m3I8uflDGb0YlniwMfLLZcjUu+OJzx4eUd3/OPSFUWgUcrr8VoxCNW3WrMIOPDepdw+0jdSDr0SY2YD0e4WDGdySumOmSqrUePgTe/tCZZIcgTFXkwnP+a5cX/dg3jRHgmidr7j2v8Ah7/Wof79Irc1qA1h194wuFDKYDqyYi/q1CTIk1WkhSmd3yJkErC1gAAIABJREFUD+e9OQ8F7Kt+XOKjl9d8srnmV65+WsZpKmOXntVymCM1lvU0M/UuFz1v9ivWq54pWOHj1XLx+d3bS56sjiycZ4yW7djQTY5n6wPbseWH0zm1kbBFgHGQojkW1d2qGenGisoGpjowHS0kxXisWJ71jKMlBoPSaTY2K51wVq5SzkRyOzGOlnF01LVcZGPSEpGBCBryqIrnKJPaKEbkhLAHYU6CFo27knFjAjVqmTJqwXroKpJURldC6ciFCt97J2PKCO55R/CGpp1oXcCXhORuqPju8BhrI8/PRCDTB8fzR1vOm57GeLaTBFs+X+542hxo9cQutPQx8PnxDB/NHIx5unEYvaWtPYOC+p1d2na/ICdFVQeck04xRi3PR1bzSLZQqch3FfaoiaNhebnjsu34PG54+6c07SvF/qVEjoQWhqeJbCJm0IQnk0j7j5bpXIC3qg2sv3ngk2akNoEvdpJsrVWeDdTOROlCTaaxnlZPLK3j4/Utf/fzDwUfdTGVnWeSrLfOYg9aOr0I2ougI9aa4VzMxPVdIjqFOltz8yfPpcCeZ6o7hfEViy8iZ9/3tDeG5ecTb3+xZbyAx7/Rw9UtqqlRMXH7JxbEBl7+zRvU62tyEoUhmxVf/lOGcOHBK+znDXkTWX6y5/B6hbst0SwP5707DwXsK36MSVw2HV2ohKmHZnXeUduIUpmF89x2LW+3K5GTR83Nmw2mDVgrPDir08wzdCZy1zfsj434jnRGKdhfLzGtpCG/2i/mIEpjEuY09kuKu8OCpvLsDy3GJszGE4tC8nC9ECNyVBJjAaRVRK9HpknoF2ky6Dry/PGW19dndIW6oV3CLCV7TF1X6KJaS1aKUrai0kMVAkZEqBZVgqAwBy2fY0AFQ6wTedLkoEAXH1dQNGeCZx+95dlHt1I4ju2cIH21E2WlMVKAF4uRykYeNUcqHbEq8p1vv+DNxchHT2550h54vDnyaljzm3fPeNIe0Crz2f6c26NYF2IZN3ZDzXoxsKwmSWMuZJJ+rJhG8bW5WpSPpy41R0WaDO3ZINllR5H8LzcD6kN5LM/WB86qnpAMrQuM39ihv1kCJbMiDBUqaioXGTsHfdmpXYzEbYVaBB5dHhiD4fOrc0kX6CuadsIaeZ19cbMhRUO7GJmCBc6ZomE7tfzw6lwEGlXi4uJASpraBW6Khy9VGb+GVCusU+waQ7XVnH9nwq8t00qjcsa/vGT7k5rpIpE1TBuN6xRf/NkV7iDGaT0GLn9rIlaaWBvu/qWv072Qm53Fq8yL/+GKT/+VJ/j1Bf48Sde9lRQDd2UJi8zjb75h9FYeR5XwzxLPnt/x3R/bu/rh/GHPQwH7ip+cFd+7vZSd1EouWClp9p10OluTmLp7ddtJlk1BNnlvMZXnOFbF6GukkPR23q3lpGgfy3ivGytsuYMG2DQjUzRMQyW4nspz92aNbkRFl0/jn1GjvJZojKWMy9RNhdkZgnPkUaN7A6uAUtJxaZ0wi4jWeQ69VKPGjEVpCaK8NKDXntRb1Fggulok3ESFHjXZQKhl35OrJCw/X7q6KF2GbuK9bysanI0yKk2KuhJI8Wl8uO8a1quei0XPECyvjhu0yrzerlHLwNm6Z+nE3P1pd8FNv6CbHEOwOJ2YomHdjtxsl2iTeHm5JSZNKoGeb242wocsWpQcy75IMT/3IrOXf3Cx6nj19gzdBhbLcf65GhsIJaS0tV7wX0M9/65i0jw+O3BWD3y+PcM7A27i+eWOkDTpTDF6KxlpP1wQl4ltVlyeiYF98JbdfiHeuwRuHVnVEz6JGjUmMY7HQhwZvWPdDnRjhb+txVAuWEfqG0V9m/ErRbNNVNcD9dvE+GyJnhKxNuJd22rcTvxh3TNNbCBrRbXLfPbPrUkO/CZRX8nlzR3g/Luexe/ekZ1husj4x35Wq2qviE2CT3pqmzgMNVoJpZ4Muoq8vVn/43/zPpx/5PNQwL7iJ02G3RflzaURg+vpzyA7ASgXZ4jJzIUlKY21gbEgnS4XPa/u1nJRzCKFJgPrQAias9Ug6cPlIutM5Hq/JAYxHVsrezFA0E9JSBdmM3G27sT3Vcy9xiaGYg5VxQaQqoSuI9pEru9WM3A1eIXWmTgZqBOpLz6mpkindSYfnIg4qgxa4uWVyqibChTEtexcRLaoBIgb9b3sHikOp5gW4yKLWi7Q474mrxQ+GMwyEYoh+3BsGCbH2bInZcWua/jgYsuLlztSVlwPS/rgMCoxBsujZcfVYckxK2obubpb8eRyx8J5hmDx0YgdoWuku8qK7IWHqKtI1YQ5wTr4YjdIipcvb3hzt2K5FjP3sp646xsBEBfepM+Kq26BK7/n41TN+6uLpp9/p1on6jpwHCvWjYh1rt5uaH+7xmYIg+LrP/uGlRu5HRcch42IcWwSIn5WXB8W0g16ERLN4iEne7vtsaXf17idEdm7AbfTtFeZ8Uzk9tVdYHixoLqb0FMitIbumRWvWSz4qkIkqe7kY/1TSQEXX5kiLDL1raK5SSy/9QZS4s0//yH+MoDJmCoRjxYV4MXPvMXpxOdX51gbmbxlGi2L816mE+M9G/PhvD/noYD9MThua4rcOZG1+MFORAlt8mx/SpMpSCZR4iVvGILG1TJO3PaNcP0m6ZZIwLlnvelZ1BNTkH3Nvm8YB0fnayGrt5GqEDYyFCge5FiQT0oTopk7QH9XE3tRAOY2YR/1hEmI8zkrUpSok8OuhSxm5pSUkNizIbssxW4UMy8ICT0vIroRuntOYobWsSCOANUGUWcmKbAi5sgoJ1lUcdIwCmRXaRkjjpOlWk3kLAXl7rDAe0NVBdarnk0zsq5GGuOpzgSU/PnxTCDFJlAX6fxl2/H59oxpMqyXA7e7BfFg6ZYVjQ0YlTEncYhJ9EELC9ILTSJHzXCo0E4sBiAFl8JlXJVRptGJfnKleIFPmuNUEZMUKB8N+77GmoRWmctlR2s9r45C3B+S47hvcLXc2AxdRfvtGh1huMy8+MVX/PzZl/zq20/kMZZdnJ8surwGnp91/PDqXCJlNGQTaRcTQ1/hRytq2UkTFgnbaZhkHLz/msjptYerf6KmustkXZKdN4ZYnfZk4FfyOXqUbDOA5Rfgl4rxQsaSL/93T3UzYD57S14tePUvfsDwSKGGgv26cZikCC9HrndLxoOYnQ+DBKRqJ0QVYyTc8uG8f+ehgH3Fj4pFHm4yeRFBg2slXn61HOQiXHZLeI1eeZargf3blYwHgyJXgRAMPiv8bU11a8S8+rWeDx5tGYLltuy9opeFdjo4EYusPcYK1d1aKTRqNGSVZ2+WdkJ/93eNRGccNMllwjqDFVQTSJhjSpLPdN4OGJXZHxviZGa/FgrSJkgRArENuIxuwgw2tjbhJ406WlJ7Mski40INVBEGIxd/hRTs3szxZDQinugHRxhcKRqJGGQMW1VhNnAfxpohWH7h8hXX44IuyCjVmcCmGhiCPLarTp6/n3hyw/evLokHB0nxdH3AF0GMM5HjJOpE6yK+d6jezOZgmljCPyMZTcpwftahVaYqxI99GQ9OkyC42nZi3Yy0zjMGizOR/VjjjBBTKh3pguOy7fj29TOMiyw3I8euxg+WZjkxPBOaycU3rvnGxSu+d3xEbSX14O1+WfLI5He3acaiRhVupHFp/nmUTmRvqZcTobJEZwhZYQaFv4iYgxGjdJZCFhtIlYhTFm8Di2+/4fbPvKB/pNG1IKjqrWCmQiOfY4bM818NVLcT7stbyJnppz/gzZ9q8QVn6Laa2BYW5bORZ0+2vL1do6s42zpOZ7qrMQdDfXxQcbyPR+V3f1sP5yt36q99mJ//p/8eqMxHH9ygVZacrfL3MWn6yVEVUcehr5kmSzw4GbPZNI+i0BkGgx4lXVdP4C8S9nIQufboSNfiqcouY84muTPNclEFSOVOVddR7rRPXh+dYetwB4VfJ/GqJVUUhcIpNKUAnp8dqWxk9Ja72yU56NmnBsj4b3rnaw9aTL7q73stn645qYhGDDLS6vVMmjeD3OGHVSlmdRSKQ1LolZ/3TqhM206smlFk/V0thXbd040VzkTO2oFUlHmbeqDSIpSotPzsr3sZ9Q7Bsu1aVs3IZdtxmGpJsA6WECVWZnyH1J+SptvX5KPFXQ5UVaR24sHrC7Yq3cqoNFvhJT56vOejzS3P2z3fP1yyGxtqKz/Ps3bP9bDE6MR51TNEy69/8YL1ckArOA5VCcCceLTquDku8N/a4PaK6TzTfOMOgO7YiPhlFHIKdcQ1EkIZgnRlOSnBXyHPPUHL76KIbdIqYBeBxWJkv2tR1xVmlNdec6VYfxZprieyVugpYW+O8PlrcBZS2Vf+/Nfon9UMF5r2JnH3dcO0yZx9V0ghsVYsX0XMkDm+MPilonuRic9HfuKlxKQcp0pQXq+X1G/lBq6+UZgJhscwPon84N/5j/5OzvmX/pHesA/nH+t56MC+4kfZLHeOOmN0Yukmjr4iJi2oIpXnsdIwOAlUDBqskC7y0crFp7D2VFSktdxNm0WgbSa8N0J5PzhZGSVFznkugqgsIznALEQpmEtkCSX2Xh9k3zQ9ipJQDFQLYQqO2skyvwhG7rZLqjLWRDHT3eNgBak0aFQWu4CKEkOv2pK2e6JxqHus1ly8EuhBYzspWiAG3qgzeiiZVq48F7oUYw14RftIEFZXt2tSEVScbQZut0usiyyKTSEDtQ2SRJ0MUzIEHemDoy0YrrOq51Eropjd2BCzEpRX0jQlzfp82WNU5u1uxTRa6R6dXLDHwbFqRrb7hYCDjxVq7VE6UzeBs2XPT51fce56bqYFY7TshhqjKp6t99QmzN61zlfshpqn5wdcsUcMxvLs0RanE/ux4vh2ga0kgdn81IFNM3K1k1DPHMSikOskv2eTmCYjI+GCh2ISsn6qZWdJFqG/6RXZGdZP92KKD4YhKeKtw+419phRMXN4WZcMsECsN1SVRQ0eUmL86IK3v1hz/DChAtwphdvDo1/PvP4nBammJyVij4WMns9+S375OSl+99OnqKNh8ZnBrzJ1BndQtK9ll3b7C4m8jDPs+eG8X+ehgH3FT04ybvqJJ9c0JjBEO2d6jd7SjxWxUCtSkP3WqSjE0Yh/ymQhOIyiEkQLsihOmq5vpZsqxSO7ou7L8u+ZioS9jtg6zinAOWjUsXRjgxbUkBYWnXaRZiEhlv2+hlD2bUb+Po+GMSm8lfFZziXTC0qnyCyTT1UqScflwljGhafujAwYBC2UxVc0XUaqW019U9SMRjE8hriMsvuLpUjrTK4y7nwQasmxIQ4GZTL1cmJ7aGgX4wzsnaLYEHrvGIPFJ40rnMhKRxrjCclIlxw1jfE01jOODdYkaicR91YnFs5z07WClmon9HKk72oRsyR4/eklVIkYFaoS4r52ifNVx7PFgdf9mpA1b/sV+7Gm62riKFirnz/7kt/OT7g9LFAqs25HahvYDQ0fbSRJ4NtXT0lJYU1i9fTIsW2wrXjyXl2fEbtyM+FVYU0ysxtP/MrkZadoOk1cJKjSnJlm9oZspGM89jVTsJwtJfz0Ti0xXzT0TxXdCzvzEfunjmqXUZ9UqCSWCDtkxotMdaeFsn+raG8S00YSmVGgX3YCjI6aeKjwa0P7RpGva4bLTFwlwjILCaREqRw+VkybQnOZ9L046uG8V+ehgH3VT4YPLrc0JnAMlVw8vRXk0r6SDqmotXKQpbouajDSiRyeBc2kS3JuMQOrCLmW2BJz0BJRsfTYKjLtKwlTrPI8OorxFFUS8Z0YmGVEl6ULqiGXPKxu18hozwpDDwOmLp1WVtgq4O/EJJ0zAmEtKb/oEtdRJUxbsrc0qCaIETkpKYhZzVw8jEjovY1QJ+JRl0DGjF8q2YmU75FNlq5uEaSRi4YUkYLvhFSvdcIYocyDKPkAdlNNYwM3xwXP13ta63ndrfDKcFb3bKqelOV5GqLE4IzesqonWuvpg6OxQbBZkxMyfzCkoOU5GS3p4KTAeoVeBBnV9oaIUEtu9IJNPfC2X7EbGg59TSy2iE0z8juHp1Qm0tYTl4seTWY7NjxZHgjZ8Pl2g/dmHmH6oiYcbhqxQiyiiIF8uZko6C5VgMwnCshpHJ0NmLNpNi/HSZO8xMUor5nuanwbcSYSkkbbzPHjOMOX62uDX2eSg+EJ+IuAGjX2qGnfaBl1rzMqQ2yheykIrOpZx6PNkbtjS7dtUXuLziXSZZfZ/SSkRoJBVUFpxRr6J7nsyPL9Y4wPIo738TwUsB/hUUp9BPyXwHPkkvrLOef/TCl1Cfw14BPg+8C/nnO+LZ/zHwN/Hukx/v2c89/8g75HXXtqE7gZFgzBMnjLYSc0+bkTMkJ9P0nqU9lbcfIWlWgOXeTzlGYmLeQTlC8jNyVd3HRwEtVSyExEMUhrLcUsjCUvLEE4i1D2Smo092OlKP/Lo0WtArYKZbSEIIFglrzLB6UQzsR9U+TwQSGRxTIS5Z3dGAlyIxdCNUgnQIJwkUl1ZngkJHYyuJ1iUoZUSRQHkxiJtZVU6By0QIZtYrUYcEb8QjkrWjdhdJq9Xvu+4cXZDqMTn27P8dHwE5c3LO3EMVRYlbA64SeRza/qievDAmMSj5YdUzTc3SxFUJAUeIVqI8t2Yudb+f24YhXQ0u1kK4Zz7y2pUYSk2Q0N20NDXQeq1cRmOfByuQVgDHaGLJ/y5HZjw3Gs5AbCJnKGYZQomezl5odVEMN4Lp1XleS/YxnTqvJ8jUYSErKAfhkNMVsZdVeJuAqz+rOqxR5w7GpJovZKxqU6Y/aG4VmAWr5PtNL95yoTdKJTukSvCIl+ehpYPO54tOq46xtevT2T3Li9dHIoGC+k69YeVJAbm7jITHUmLcrYei/vkWwgkx9IHO/peShgP9oTgP8w5/x3lVJr4O8opf4n4N8G/pec819RSv0l4C8Bf1Ep9XPAvwH8PPAB8D8rpX4m5xx/n6+Pj4a3xyU+Go4HWaqfiBQ5GLnwKzWPeFAUsUbxeqVCOaf4qqbipbIJVSTnquyS9KDJXmGPIoGewbllVDcbg0shY+mxReZubJRokKhIRyfdVFFOapuoKukK/ST7npCR8aZ+5+LhCtNwLKKAd4zSQqYvBmaT5XGpLJH0O4PtRVod6/uiGJeJ7jyje409KhEpXEL2Gt2GuQPIfbmYJcAmIWOYNKvVhmDFzB0M02R5eblFk7npFxidWdU9IWm+7DY0JrCsO763e8RxEgfv7XYpd/vA0QWub1cQdOmSFdXFwGY5EJMqe7k8P/50Ekgk4f95Z9kPNWOwpAxPzw+E4tn7aHPLwdd8sNhyN7WELPzKw1Bzuew4jBU+iuJT68xUihdZfIT5tL/yCmrx7KXOzobgPBkZu55ulorYhzqC11Rno1D7bWRyVn7XvWGa9EyOV1FGt1jprlOZBpgbR3zkMU2QsFH0vJeqb+DwUUZ/2PF43TMFw/VhQXfbSpHMatb3pDYxaTBDuWnzCv8okAfZ46k6kYP4yXIrFBcVJLX74bx/56GA/QhPzvlL4Mvy571S6jeBl8C/Cvwz5Z/9F8D/CvzF8vH/Juc8At9TSn0H+DPA//H7fY+UFLfXaxabYS5eZhlIoai/otzNKifopFNUfVqc4keKPyrIhSpb0DsLFCVioZGnUuSyPrl+AZOpzkesjQx9Nf9M1XrCuTgbbnNUs/AhDbaIKkphmjQ0MqbKSWTqyiWUyaRFEF/WqfBmZA+3LN1aAt2Z4h1SZSSq5nqXi44kVZmpyjMU1m4NsS0F2CbSMhODRNDIBVTIFzlo0ijCluxyGceKqTXFMub0hqaR3ZD3lhQ1nXe8WO5orcfqSGMCN+OChZ2YkuXXr16wPTSE0aJdKqGakm59fbOSnDYney0AayPWRMZQoVzCVpHVcsDoPIdjLp5MHIdK7AwF7/TResemkjDSn1q84ZvNZ5zrjlfxjG/VL/nBcAnA28WKb716jtYZrTMpaYwJohwNxVBdXjvzmTTZpTmqRqk8d8zGRVKtYS3UEH+oqM5G6tqL12xyjMfyejmJcUwiW0VOWrLENl5eMzpj90Z2aIOGm6Z0nWXE/XHP7rlBGWF+bvctMWqxeYBYJTKkNmLXHltG2P2ZvX9/JCXFKipRVGqE7rKX11ZcSbL3w3n/zkMB+zEdpdQnwC8Cvwo8K8WNnPOXSqmn5Z+9BP7Pdz7ts/KxP+DrwqPHe1b1WP5b6BVeWUJSMCGFaTBgM3rpSbUAYNk7zOVIGCx6b6UDKxlbehJfTVYQKyleOpSiYCh+qkRde7quluJ5EotEzVSUgMYkTC1iA987uWtW3MegpMIhLNikOMgI0NZJdj2TpClT7v6VF9GIGjQqKfRJku3l+hla3im2ed7BoMseLsv3Jiv0oGCULi/VJfF5GWdVZy4FIod72X0KaiaVhCQU+JTK3i2DrSIXTU/IhpQVjfZMSbqIITo+vb0QSXyUkWROimYxFQKI7I9iMDPSKgOL2mNUxnvLz378it3YoFUWXmJWPFkf2A01SkHdnODHCqsjrlTtMTk+9xe4KvC741O6VLGxA1pl/vaXXyNnhZ/k57Qu4r2QNHJS5N7OnXh2STrjNlG3fiaDnLo2YyJDV82Iq5gsaGba/u3titxZzNqjTcQXq0XeVWhfhDNNFF+Z1vhWEwoSTAWF+mCgaTxDXxH3DgO064EQ7tmRRKQDbBLaZpJKmDaxWIxYEzl0jRj9jWSUvSv2QcmNSo4KM4lfTHcadaz/v9/kD+fHfh4K2I/hKKVWwH8L/Ac5551Sv+9A/R/2F/+AUU8p9ReAvwDQPlvx9YsrdlOD0YlhdITJoDS4JsgFIpxGbkkW/l5jm4m4yYSDkztPI3fQeRFnVFNWIjOfs7SUFK+sM9kmXBM4bFv5i9GIUstA6CxmEbAmUVVeAjSTgUmKzr0vS+Tqp04jFT+RqiIxaNmhAPZa7qYliiPPezQ9ltFQlJ8zLGQXlLWMEKs7EWqYSQlAOMmeJCzkIsl4IkWUZb0C04hXavawgYg5VJaLXfm5cpWwCy8X+0m6t6bxmBIz0wdHbQJdqOiD44u7DSlpptGiTMaW5yVn5iTsHBU5a2wjakStE+vlQGUDvbd8fHnLGAvfz0RW1YhPRnLGSiq0KWT6kDRTshxjBREeuSOdqfnW8CFXfsWYLK3x/Or1J0xBClBZfxKDUO7TJMnOp1dmLrljucqYpcc5IdCfKCUSlioFoVlJRE8q6DGtM92xJncWvZLilZOMalNvxX+XhLJiVvL4w3QvnFCLSFNYn7koU1UTiYMlFag0QCivmdMI2diI1pL6HaLmcGhoF5MU53z/2O5ToYGtwx41qc7oIB9L9T/wNnw478F5KGA/4qOUckjx+q9yzv9d+fBrpdSL0n29AN6Uj38GfPTOp38IfPH3f82c8y8Dvwxw/rNP8+tuzZvdihg1i2bimOry7xRuPeJvGvFeFao7VZpTmVGAKwgqBXjparKRC3vSoAIimDjRC6qMaiOuCtJVqQxOTNFKiyLQ2ogtYgyjEzgIC7lY6YMphasIRJIYR1UTUSaSjk4k0DZjyoUkVWnmO6pJOqK4kMKjvRSmU6Ely1JfRTFKn3Z9J+6eemePlu198cpOuIHBm3mkejJk56IcVKGoKuuIdYX4vxhnrJLRCZ8MrpiX++B4vV/d45Z0Rpf9mdaJqg4S6Flgt7b2kvlV5PPWJAZvcSbxqDnShQqnpZs5cSeNTlgTZ5RUyoqLhcS5VDqwshO1DlyFFTFr7sKCSgf+r5uP6byTn9ukOWQ0TPbeUxfVfVBo2WfqTmMuEt5LlpvAljXGZJwLxBiLijFTN168iKqMJ+tIGgwpWcxKzOy6DQXqK0KNnBCVax1FtFOIHsCcl1a7wO2XG3kdZQheUFYpGnmNBRk/pihIsdBZ+b21gf5YibBDUYQwZZfoFfpo0JMiFmO76orN5J3p6cN5f85DAfsRHiWt1n8O/GbO+a++81d/A/i3gL9S/v+/f+fj/7VS6q8iIo6fBv72H/Q9+snxdr8qIYYwTO737ADM2ouqDrB1ICdNjGoWJlDEEkKhSCivRRpfvFNE6bpmKnpJ021a2SeIdBqUTVStJwTDYjHiCwwVlbE2MQ6O5I3I1Kt8v+CH39NjngQeuYQTxrYUrnJ3rE4+sCAjyuwyTIqwLp1XuQP3q4ztRV2YHHPqcLQZMyhSnWUxPxWKh01U64kwmTl3jHd2dyfxSLZSvNuVJFpbk6hsFMJJMAIErjyt8/ikud4uiwxf/HqmqPuUkk7nlABgnYRp+tGSo6Jdj6SkJZm5q9msRKbfWM/bfkk/OdrqPittXfLcVIH3AkzR0hiPT4YvxjP2vsGWwlprQ2MCnapYVJ4pGgkgzWou3uhiK3By4UcBd460lP2cj7qkahehjkk4ExlHR9N4ahtZVqLQvDoscZV0VnE0VGvZnY65ApUl962GcXTEXYVqBF586pROWLSUlQCWR4dde1mLelvEPBptIiwgavH9xVKIs8pQJVZnvWDUkrz+lL5Xr6rCbgznYhPQZZyuvHowMr+n56GA/WjPPw38m8D/o5T6e+Vj/wlSuP66UurPA58C/xpAzvk3lFJ/HfgWomD8d/8gBSLI3W9beW63S6EyTGYG1tIK2d26POd+jd7Sh1qKQJKhkZiXKXsOpLAFwJU3eSjKRIXsF4DhUDGERtSKOmNLTlVde4ZB4ltSVsSjI9ZRdiJBkVfh98joBagL2DRTHTipG6skd/1JCSXEq3vyRpSf0x5kjBiqJMWrFDszyohxfp7Kt60GkU9Pz2IpgtL56SoSJiOG7bJvyTbLn5OaI1dUEzGFmWeMUMt3V0sRnpQ9We2kIKSsRHlZPHGch3nHFaOmaSf6vpI9U5Ixl7FJhDQ6o3Vi8pam8SKvT4brYTnHffhoWNcSMHlCiPlkGIJlXY1c1B3HUBHKDi4W265vAAAgAElEQVRkjSVy8DUH4G2/ZApWusZQoll0xjRBXAip2BQoxuSgYSW2BN87XOuJ4bQ3k+7z9laAg3Ut49STsduaSD+I+KVejdRVkBqp01zYxsGRdk4651WxcOiEVhCSZrMY5g4zZ8XhZoFbSBHDMHe2KKHTpElu0pTNsArUrYhIiApqUb/OY9IotoCspMNXk3T29iCj6fjB+Id+0z+cH995KGA/wpNz/hV+fwfJP/v7fM5fBv7yH/57KA59zXI5sL9byOjHZVwJZlwvB/aHlr4rY0UQzl8T0F82UkseR9mTlYu24H7e+ckzmF6TbDH4FhVgzqC3jmwyYa1oViKT9pOdx1vKlt2Rydg7CQ6M5wFVRfkaUUkh8RpzlB1ZPGmep9L22ZNsXMgb1JGMQfdiQFZR7pZNLwVJe0gWwiqTbUEg9QozKcZNlg6zBGvmQoeIBzd3WbNtoL8fM4oHLaGrOF8oj/vmHjelijJOZba7JdqIbSBHdY/bigKvPWVp7XuhY7gmUC89beVLYdKM3jFNlkUz8QtPvsSpxPW4lMSArDhrB6ZohPgRpWjd7VvO1z3reuS6W/LlbiNPn5G7Dq2gG11RGor4JAbNcjGyPzaYKlI3vkTYiIBDLwNNK6nQi8rTe8uxF8GItfdxLXfXK8bQUJ2NxKgZRzcHnZqSCt40Hm9E2DNOlhAMbTtJ0GkRwUgIaaSuA00l6K1cXufbY0sMmnDTkFXGXYxUdaDvqtLRipAjFml/tZrmGwHvjdx03TVy0zQa0qRFNq8zWIms0ToTxxoVFGEdUR8PrFpJYng47995KGB/DM7pTj1PGtXIbiaWcdb+2MgyPAv1ggz1cmLY1uQmob1C3zlsp0imLKtzkaUXJWJycgGXUVueTb1mGUi9IZuMcYJCCkkXbI/sUpRNKHsf5ZIrZKQzm1/z3GXpoETxuAkYl2b9wDy+KRc4dbBF8n8qbLKXMpNGj5CqQsPqILYZt9PoIEVNB/n3qILAyoiYI4PKEsmSC/9QmTJ+KtR6lAhOqlY6hhzFLK6NjFFzgpTl8cdgSKNgp4QUkmepeV+I84vas3jiOfT1XAhSVhidqWzAGcXT1YEpWZJKNFZSBmLUHCeHM4lYLvAAbeM5DtUMF7YmYrR83cpErvZLKR6jFbm6kh3V/tAS9472cccwiBoyFx/aia4B0E2OY1+TkxThlCXscrtdQFC49SThqVGhFsKD7CfpvlKW8NSUFDEaYjCYk/3ihAYzGUzC1JHL9ZFtQWmdCm3wBu5kNK7Og2TK9RWmoLz8sRJ6f8GhnfaMOSsx13stpJmTOfkUxaMFbyYTDEtuonj+64TvHbtg/iFSqofzPpyHAvbH4IxDJXfymxGtM84JlkeVMdOjR1uhOxxbgfIGLeIEBfq1w4xSAHQG28vSOiwzcRVFMl3oByqoe1WaQ+50i/w8BbmY5awYj5VcPIqII/UWe2vvFYJDKR5NGRPtLWZUxCYLnbyK0gAWzw4GVC97OhKiYoz3487UJkyJaIlVEWmU0EMzyZgyVhkdxYysR9mJpToL+7Ds1/KJKqGQkWZbOtN3utHkNYe7di6+CiRHLMkd/GnPNWxrTCty8JOBOwaNsXDsaupa9mTHyUneVNl31U5GvZWJWJ3YVANWpXl8eF7CKEPULKsJpyPX3ZKxmKhT1POerakk82tdj9x0rRiTJ412SS76Wcney2v00tNvm/lxKp0xtXRYbT2hFWz3LSkrnlzsMSpz17WMg0ObjD0XKbt2CdMUYUTBSoUofMQYpevTKrNYjoyDIxSfn17KaLJqJKrmzY10j6HsI5USlSbr4k/LkkhtrCgQh65CDUZk/k5GsVUlRanvJLdOH819N438TqVI37+XjE2EEuSqeiNTCSXKy4fz/p2HAvYVPznLRVW7iHOi0ALmjKmn6wNWJe66FmsStlwsJyTqIiwL9+3U0ZxoFidfVJFOUzw6J0Vajkpk89GgJoXqK/rRUL22qLV8frJFyXcshtBFLuOa4rXJCHdRZ4kzqRJmId3NjJHKqnRBefZiqQlUFJN1smVH5zKheGN1ULK/03kuZipDMvfx9TMDLyrxgDUReoOeCv3DC8VEZTHBUp04XEXZWCWy1yKGKfu6nBXBS3CnqhKPL/YSYTPUpKQY+gptEpvlwHnb03tHN9T3UvoGrEmsi6fPKAnD/P72EqMTm2okKUVlIseh4s1+JaNhlbE24gvCK2WFMYpJZyorxasbZIRs6liM5SdUlozQ3o3UUaaoVUt3NwXLcduSvebxC6HUv92tZuk9CEGFTOk+xRcXS3K1c7FI7ItfzmTG0Ra7R/leSLe0Xgxy86UTvpNf1kloYVuJXQEZi5489Xe7hQhvlOwohVWZqZ2nH6t7E/0ioVeePBjcamK1GGe7wv4gdpB4sp0oUONppyrpCw/n/TsPBewrfoxJ1EtRoGktHVc67XDKBX83ycVLxjgGP1qaxYSrAt1gYNRiIgUpEkmhOyMX7gxELUSDE8JJC3cPRKmoksYMimrriFUmrcMcaaKP0nn5dVGxFfpHDkX5FRS5SbiNXJhOgZlawT36Ck7MRRUKIFhL8UpO/p3ptey+qnJRVVLkcvn8bGSECJLqe+q6UpXksU2CojqNSiWzSoqoGgyqK16t09hRISOouhT4XgIylcr40aJNppvcbLCdRkdVS3exqkc677g7tkyD7GtOQYqnzK6QNH10bMeGmBSPiyx+O7W8uVvNIaDZCwdy8XgsSlTZb+WsSEm64pC0eLacAJdj1DJi1glKBIwq+K+TolBr6aKMSRxuFxAVF892xKR4s12JECUII1GVx52Twh+dAKPLjrOpJSYlFaFKjmo2tisj0voM6KJWzUVlmKIRbFTpgG0TOd90xKR4tOzQZBKKq4PE2VSX4qs7X3XEpAlRMwXLNJaRYxOpGjFe25V0psPkuFh1pKyoGz+/b/LmXkByUpCe3ksP5/06DwXsK35yVrRFQj16S0qaVTuSMhidOU4VUzAMk2MaZMSnTcYaUZ6d/E/Riuz4NIoTqbvckVPFeWR26oRyRnKejoZcZfyTQKgS+aS4owg4VnJB1k1RhYGgrcrFKgPtZS/+o1DMpRm5OzcCgaVImU/iEtnJSYd1Km7ZcMo3JFuJyNCDxu7fkcEnGR3GJt/vz0qUDKV4qaVw+3KTBXKckcdbhBonzqKaivn7hB4CtMlUtSd4g+8dnaq5KBddpZgl54exFtn9YEmTwTQRYyMhGPZDzbZvSiHSsxVhmBwXy57LtuNrT275cruhP1byHFHqvE4C3s2Kqgps2kFMzkkLySOK+vFknFZIwVOqjENLAVQ6YW0SQUMUf5TbyGvs1OHfXq1FZOMSeRQGorIygrVnE6vlIDlng0MpZLc0GszSz9/zpD6cFZjlsQyTY7PuOPa13ECozGo58CcuxS55iol5e1gyTRZX/Hjnyx4FPFvt0Crx7bdPSUFiZpp6ku9VPHI3XStdazSygyyPtaoDtfNoBYO3PFruuD4u5tSBh/N+nYcC9hU/KWh2h5aqCnzy6IYf3Fxwu1vQttN8EeyPleRpFaVXXnl2N0uUSegiSFAqi/jiUYYgxAlGGSspVYIqb2sICu2N+KhWQYIUKSKNDO5sxA8WRkMOAmcFSMVjc6JaKJNZbnrO2oHrw4LxWM9cOhSoKmJeSfpzXCQpqIW8EZeFbl8KWnaZ5DOmcAyTE7Ot9opUyQhRl6bwtPeYPWWTdDDal6/Vl3DGUvS0BxU1KiGGbg2hzaQ24S4GctJs1h1tubA7E9kNNax6+rHi6u2aejnNd/c3dyu5o7eFJF8EIGEyM5FE68xwqFDFfLt6dgDg9c2GeKG4aHrBR7maYXKi1ktSuE4MysYFYoH4hqjZH9p5lKaUmKQlQSDOqsShr4q3TfZL2iTa2tO+mOSif1xwd5AQzdVFR2UDWiFxLVHhjxWbF3uskcIwDI5YPG62CjSbXtKlD/X8Grg8OxZTtnTdIWq6bcvUBLRJVLXnctXxk2eSnPy623DwFetq5MOzLZ2vGEKhk2jpBN/2SyojNwTGJZROhGJd0CqzHys2zSgKzmBoKs/h2JCSxrkRZyTD7esXV/xgd8E3nrwG4Nd+LO/oh/NHOQ8F7Kt+shQPtxhpjOeD8x2HqeJ6K4m5svwuM/1QSN/XNerckycjpHAQAGs0WCdx8OyFssA6YBvx++SmGH+TFo/V6MjPy52zS8TB4L0o81QvBUR7KSixTeRWlGPRaxarkYsiSOi2rRRLJ74vXUXSKAt37ZXwDIN0W9kK7JfCalU2SbHtNWGZhKbRa8wko0bKDixbULFI6E/ToNOYEunMspadn90Z8ZnxzirOFAUlkNYRXZBT2ohPKWZFbSLrauTqsGQcpRteng3yfJ6OylSNCDtCEVSQC7qqdCM+ahlJJjAXciNyKi7Xdyt2VVP2mZFlM+GDkPBDkCK2bMc50LQbKimOUYtMXGVcJV2GqTM+SIeWUknqBqbSKWsXaWsxS392fT7Dlo2BTTuwcCJzTxmOfU17NsiYOpqiONQzrWSzHIlJcXu1RPVGVps20zza4pN83+NYCZosKC7OjlRGxEhP2gNGZV73a6ZkeLHccVH1bH0zC0XW1YgmM0TLYazpxqr8vAljhK5yAg3nrOgmV8j7MmqtG0/jAlMwxKR5ujwwBMdH6ztSVg8d2Ht6HgrYV/0o2YP0fcXrbs3gLTdvNzBo1CLKjgRkDGYFTIqG3JlZEs+k8aNB1ZGkdRFoiNjiZNpNRZ2nRyleqZKCknZOvE5BYRIzgsqMohTMmrloKJ2li1uLofS2a2WMOZ0UhjKyTHsnvq5BESvpsLLN9zijoUSKmDwjnk6SaFM6L3tUJ9C5PE1eCtC8P2uQjqsoIU/RJRiJklcJYS1GKcAnUn0qUTNPHu15c7XBVoHBW/CW4AJfXJ8JYUODLhfgk5RbgiHFyByOjvaiF/DyKOZptNA5TunTuUrEwXLwEvJ4AjXrqKmddD8xqfL1LUrBejGwrGTcNwVTkF5xZhUqJR1eiAYfRTzyrsBCV5HcWZTX6CawrCfeblf43mEbT1N7FmVkfdO1jF7uJLTOhKCZJiuqyqixVoRFWmWhcbw9Qx0teirK0bUUQF2ELiJIkZHyT55fszQTL5otfXT8oLuk8xWbWorkmAxWJYbgWJXiddUv2dQikHmzX+EqucmoXaBde5xOsnvct1SV/G5OEThtOzEGIwEJpahbnbAqcTMtiPmdm5CH896chwL2VT8ZCKL4+uLTR5hlwDSBmCy5M0JvrxNqKDEqbUJ5hd0ZwibOqCAKM1CbhGkD0Uu7octY5lRcTqBcGd+V7qVKoPWsVNSDRnnQUZFMRg+K8CjCZFi/3HF+GhsOFSmUeHlbxB+FOK+iiC6yKYw8IwqzHOT7zMbXKEIQXT5mJiHpgxRTPUoHFValkiX5uvPz0ku3NZNGkiry/PKfVtRraiz4qSrx0Qc33HYtxsquyHsRKWiVCaN430B8WSmrGXaecxE5KLBLzzRZZpyXlVFiKobqk+pR9YbcAFaUeUaLcGdVishxcqzrSViLWjqnKUoXEZOmqcQ7NnqLMfdF0L8L2lWZOBpMHYljyZCrkvgJs2I6VnMr2g/iYXu8EjN24wJ3x5bgzRxqelLGRsA54TO+/uEFKojQJp9y4Eyc930KWK962spTmcjzZseF7bgNC67GFVplni933A4LehxHX/Nqv2ZRTyyyYh9qYqG/OB35+uUVr7s1MWl81JzVA5WWouWDoS4j1v2xIcd7xWRTyc1VHxxLN/F2WNF5N6t6H877dR4K2B+Ho+TCp7wiYu+ztqoSyDcp8jISi69JBUV8PqFtoi7KrHFw+KNjUq5EryTc0rNsJ7n4tYW+EaUrsQGSU8SWMtoTRdsczd5m8iTju1RnUJnlZc+mGbk+LOj3jXwfmIuXKiKQ3ESS0rJzOqkJTSYfrYRqGmRPldVMzVChkOnLrio7+bo5SMelpxLV4aQgnbxteRHlz5OWC2wnpufT8xqbE4VDurUnL+9orafeBL5z9ZzQW5RLLNcDx67GljgTYzIhnjqeREpKdoNGdj8pipgmdFbEIirPXSQKyW9LRSjiNVHBxf/b3ruFWJpl+X2/tff+bucSEZlZmVXd1TXqbnmGYYyhPRiBGSP05NvL2A+G0YMtsLD8oME22A8jvXhACGxjyfhJMMICGSwLgS08GCFZBoExyLJGQ8+l1RrNpdvdVV2VlZWREef23fbFD2t/34lqd9W0x12dEe3vD0FGnog4Z5/vnLPXXmv91///7EibtS4/eLXFOb3fpvBs6552LAhR8EE33M9f7IgIN21D15aISTTNkJ+cwY+G6EV7gzEbdiagCkq3T5xL0ZmFZ22izv2+EA2v9qu5dzeRMMa2mGfNxtGq0kYmyYRtxKw9hQvZQVrfAzEKTy6PbIqBN1c7huj42v5zdKHgzXrPT24+4G9/56cAuKg6QjS8td3TuJHnp4327vL83NPqQB8dpQ1UZc+Xty8B+KDdchhKqsLn+TTtiUVzJneMmfACzASYEA3H7ux3t+D+YAlgDx2WM63bgN3ZWcdvKuelWpXiEXKZ0CGScEWgcHoi9VkTrmpGYqUiqsEbbm/y5pOHdW2E4ZlXTcAPKrWtMDGryytLMW6UwRfrBNuRZtNTFZ5Hq5bTWGi56qReVkkSvNHDodA5rBxwJDMLJZf2yo9y6anQEl+cePw5wJlRWYahyv22fF1i9i4zI4yX8eP9r0zNx1udb8viwqHWA0CstbcnRaR4pJvezV7LnvtDk403I+lVSfdBpYH4LfXYKooRZyLdUNB1FeE2zzQ1WYPPG/xevbLSys+WN2Y1IkYdrMfOkcpIsRoIo+XlR1u2VyeqYuSq6ei9o3KefV+pukcUtk3PVd3Oyh4fHjfsjvXsV9aeKlbrDj9a1TlMGriks6STxT4elM3oDcOgLEdbe1IQ6mZgW/fEJHzr+eN5PmvqnWH0kEGCYq0yTuPgcJm4M/nBxc4izZnx+PaTW0oT6IPjOJa8d7zipq15ulaCxz9+9SZfffE2zgZ8sPzOzRuUZeDp9jBrNd4eG8rC89Z6x7bo+MnqFW9WO/pY0EfH11+9ya6rVNUjqKC1c3EO6Ju65zgUDN7RtSVXFyfaoeBy1XLKw+YL7h+WAPbQkdBZpATSOe3X1OcPm3pdGagCm4uWdaUZ1W6/IgTDftfo6Ts7N2+ant2xJnZZ4y/rEcqYezSPR2UAviqVll6HcwCdh54NBLBv9Dy6PLIuBz64uaAqPP3o6G6zOeBmpKiU0DAFlEmLMU1zN0E1DJPcoc9bst+TXgDxyjaMk1fYHbhWsB15gNkwblXlXo0x83VK6sg7qc3HSoeqSdoTskVgbAvEaVktJmG17jjsGlIwpHXAVxGp1QqGlc5bOZOp6K07q3tEUbX13uB6wb8x5oWqC3VZe8rC03aaxeTkhnql1ipT6W7Smny+21IVSk2visBl1c29myFaQlRB4eQ1y6s3g6pvBEPq1B3AevQ98FaHK7z25ARWFx0iidOhwpUBm2Ww+tERRzPPrkmWAkudvo72cuBi3dEOBd5EfO8oaq/9NaeHJD9aitLz9qNbChM45FnFTdnjTCTW+vx2Q0VhIk0xsu8y67IeeZqvwxgsx75k6BzGRJyJVMbzTw6fo7Ke62HFdw6X7LoKK4l107E71aRk5ixsVel9TyMG1oU547reawZ6uW7/gB/QBZ8llgD20JFyn0Ty5p11C6chYNN4YucoSqUVf3TaUlYjVT3Q90rAmMpnbFVLbzgVs9itOeXMwEOoEvZQKpli2llHowG0y0zHtYe99pcutidOfcmLDy55/GzHsS9V+y4LDpOy95RXpXmNSEmtLIKoduGQVeG92qJIYlbemL7HJEK+v3MZMctjTRWzqOuf+mX+MivwT06/bSa3GPQAkMCuRppm4LjL5U7vGNaanVb1SOot1WWX9QVVgT+MiTAYuljS20JFgvPw9lS+Na32+MI2qgafVeNF5yLbRstjh0NNHI3KORmd26sKPwem41ByfbtWOzcbWFcD3VDw4WEzMxSPfUl7qggnh5SBshlVaPmY+2yFqnCEiV6fhP5QYWtVvJju05VKBOmGQs0uMxkkBQ3wReUZukKJLnXg6aM9gJalDyWMwvrRkXKrpInDqcIVQenrQzl7m31+s9PnNpY8qY/4aLmOhlNfEqLhctXyeH2a5bNK5/nJqw95fvslUg4+v/qtd3j5bM2j6sRXX3yewkb2bUXpwqzduKpGKudnlRFQvUVQFX1Ag3VfZNeBuLAQ7ymWAPbQkXKWFYS4Cmf7EYBJCT4I403FaNTt1loVQU15RkuyE3LqLN3NWgeNQ3Y8zmK+MgiuVb3ClGeD/UXMahdJA9LkM1Yktm8e8MFy/HCN3Y74YDmdKsKuxLSGeOlnA8zQ2dyjgmTUuiUBxSvtd+F1HdOXXydiEzBtnkfLxBIki+pmLyjTiqrRNwm/jXN/Ltl0DlrkICh5WNklGIXmSYtzmnWk0Zyzwt5SbgbVDfSq+7i5almverq+gIZZiT7m0pr0WhNNZcrzaxA2qrWousIqt3TRKMPu5asN6baEWgMHaMYVomBNZHdacTjVxMHiKu0n+WA5tiXWpln1Yuz0423XqnI/dAVpUN3DyV2aXFomaClQjFrvFDZw6kuG0c3qHSno/U6za1Ofrt8re1Bc4o0newZvaftSHz8I7mrQflwSrtsVAEUuKT9tDgzBUtpAaTzX/RpnIidfctvXquKR5Z4uq44xWr796gqArzx9j7Xr6W5qpAyz+PDz/Ybn+w1WErddRVl4JSMldaq2JtB7x+2hBqCuR4yJVNkQtLSBQ19ibWRVjTxZHXnW7Pn7n/mHecH/WywB7KFDskrFKKSC8zBwqdYfsVW7e7zehiT2r1azxxZkBh7at1JiRFazcDmTc4mQtLcRmsyOyzYoae211NVrOcp4SG/2dF3BeFNjtyNN1q+LryodLl4HXOOVMj4azSAnS5KoM1vkf6c+nvozaYYkXrA7p87Qo2CiEFZxvh7mpIPHsUwMF2kW+51ggpYgQxPnA8DsuLsZubo6URWqpn7sNGOcSpqy8lyuWx5vj3znPe0DHfe1SnNlPcphsMSjkjNspx5bsQB7MBivqv8aDA3rx0cEpXqf+pKbD7aY1pIuPOVa1SMmGvzgHc8PG45tRQyGaq39qkk2SbX8VDB3GpY2d5iHaVSdP2lU5klWXp9/FEyj7tCgQ8y3w0qdqEejAWsScZ4MINFgEVunNjOF/v317VoNNSWLATfjzMacrGJisKTk2XU126JniFZ7dlhqq8H2914+obDKgkxJqAvPbV/z/PqCsvT85LPnNHbgw26r79sism4GrlYth76iKUYOfUldjmzrns47+lEp80O2cgEl2Bx2DatNT+l6aufpveOi7onVQOedBv+4bJX3Ecur8iMAycaMJNTfKNuDxM7qybR1mqGcnNKk0c00lnm2qlDnXRllDhYYnQVLFujz7NRGswEZDLY1OQAIfKT+UKmIRCcYgfGmZvPmgbrwHFoVsyVkYkQZ8TclpjNQJozPpcK8sZujwW8D46MwrzUZGC80+7Ot9uNM7t1Ids+dfuZOoj/L8deMFt/o35terWLGizypPAWukG1TWsfONuprdiyQ3mJbIax13eutmip2o6O57OiOJWkwDLYgjoaiGRn31TzXlSZzTnLQzDJWkkQtW+A8dHxSo9FYR+ptr3qBmalnsi7hqcteXEVQfUMbWJcjt9FgbFRfrC4rqBSJkANQHFRZfRpcN7WnqDxFcZ4Rm8qDKenAst6HSkVNDs14o+XQIs3zasZFmpUOXPengmI1ZhUYdUaYfM5arwQeY9TSZV0N7MeKynp8tAzR8cFhq7OB6HB4yD2qq6bl3esrrI28ebnnaX3gW8fH/NrvvKOs2Hrk8frEj21eETbC+6dL9l1FVXjGrFLibCAWqsAvRq1kLtctq2KciTDA7ARw29X0ox4a2HxmH98F/x+wBLAfAcRLf2bUHVU7TypVe08np9boAdxR+0yxTNpfGvM4lYHUBNI6IXunJbzJKynPV8U8S2ZbQ6ijyjtZzexSHpKmjEgV4IOKRz/+irYvebmrz0PKVyM2329IovNdSUje4rdRN/beYHsoX9rZYmX6KvYanPonmjmZQfTLQ/A5kGckATvCcJEYPjfq7NVH6nsWmgQXoyqUDEYzxxEY1Nss9RVxEKwkwmNPugi88+yGQ1+yPzT0X78krBJxHZAqIC6bKAZhyKMBkkkWsYmzELJ/lF8nm9T5uIwkVIFeJNGsejoT8V1BSrBuNMMyAn2WQrranCiMSh29PK44thVtr4QDP+rHebPtNBgCRa1sRlMGbKOO3WU1fkwTESAGw3hTZTbr1EcFOjtn22ZkztD9ZUBWqqzSrHraU4U/FKzfOAFQOo81iabQntJhqOiDxdnIZt0BcL1bsS8qurbUnlilpp6TIHHfVWzWHU0xctvVfOmNl3xxc42TwK9+9A7Pry8gCJsf2/GFy1uMJL65f0xKwnEo2R9r9jAr4k9yXq4IlJXn6fZAaQI+GRUQjgZn40yjXxXjHPhO40Kjv49YAthDh6jkjxgIvSVJLov1SsyQUSh2SlyIRVKmnmR5JckZQmQuJ85DwCbTynMWQ9BgwUTca3IG0xstQWaSQgyC1InDqZoV08VEcKo8HsazgjkWlbNC71+irksCM2U+Wih2huKgQ8XjRp9D/cJqSTEPKU/BKxYJgwY19fziYwPKsdQemtgESXttk68Y3KHuSyJcBi4eH9nUPe++/xj7vNT+1Sqpooc1SO2JQcuEs1faVJZMaI8pgWyUSp6SmnmKaK8sJe1rFTY7CQSrvUEhDx+rikd7Ummkty73nMaCzqswcwwGlxXsm1VPjMJhr/0xUwbC6NTexaTZZblvi7aXEaUAACAASURBVNklO46G1Oqhx7RG7WNKHdhmIneMgm1F5+ucvg72QoNrWQb6Pvt6ZU1FEX39m3rEGrXvgewnFs3skeZbxxgEqQPDUXtmu3Gth43Gz0aVhQ2sioF31q9Y255ff/U2z68v8K3O1X1uu2flBr61e6RCxs7TDsVMyJi0Hqfv15mgUllPaQNt18y2KpNqyBAsg3ezm/Uu29EsuF9YAthDh0Ac7dxzEEn4ozLf5gzJgi8TMuYAMWpmA2CP2vcKm6C9tCjErEEYJrkoF1VCKhMtZmQGnzICdR5MRkOqAylLE00DsGJ0aJcoyn7LIsFMZcsgmC730EQDiVqlZOJGrfNZYZVYv2txR/Br3VDNqJtraLIyiEnEQlQsN4J7UWiZcRRClQirTD7pz4PXmBwEnQa1WCeunu2pCs/7v/WM4iD4JpFWCXdUCxL3VMVpbR2I/uyMTBYZVp0vQao8+zUaUm9VK9Kp+29dZ5flJFQuUF8dssSR49QXqu4Rhc2moylHbtp6Vtbw3lJWfpZE8t4ynEqSF3UCyASelAWSsUpQSStdGpOKfJD5gDJpP8od5Ql3PAd3UDFlh5IfYjTKWs0BbFLjKAp1hG6HYrZTGU/KVAzHWj3hXCSNjnRwH5MFExeJQajqSOEC18cV62pgY3t+4+bzfOPDJxq8Mh7VJ3w0jN4Scsa0bXQeL0RDm+e7UgIRnfkykuiD46Zr6HPmmpKwO9XsqOe5r01mc05Z2YL7hSWAPXBYG2ZTwKL09NnSfaLRp0oJFiSwXjfhmWI/Sh7+TbidnbMxSVk9AzKlXTeWUCmTLpmEHJxmIRPZw0veHUZlex0Lbe5nAdmUqYtShbNNfZ5hM68cZgokqItytAm/SrMWoTsJ5iAURy1l+TWEAqzyQwjTAdnkAJhJHCn/HwO+jISrLLNxWyCWOaOUMA1/nzeq25sVqXUUp3PvKhVJPc9Wkdomde/NGoLlOrMTQfuKJs5My+gNKajQsSkiKar546rULKF2frZaOfUlLm+gbVfkIOg5ZRUOM10/VMHCi5a++k4Hj8XmQ81gtDdoE2nrqTeDWrgcCpX7CtNMnZZfTbiTgY6S+4f5+mZV/2TzDF0mV7SnMvf7lOyTUtZzNEKbe1h+tCqR5eI5S42QBpffb6KqZPn1T0eHuRwZR8tH+y1lM1IVnr/33o9zOOm8VrEa8YOlXg/EJPzu9Rsc25Krrfa0jkOp2R/6NivLs1tAmSWsRm8pnLp/n46Vfo4m8d+sUq8zYwI5G1xwv7AEsAeOlIT1pWrIjd6qDUatA6iT6SC3VpXZIWvR5U0q5QHfvBnOZJAoSEqkrHYxe2fBfGKfek/DNqtwVBGz1l5TPOlA65R9+dFS1iPeWIxLGKuzTcFb3ft8Lk/ZXPabWIE26ypGYVyrDJSMEK0SJMobCPUk93QeI5MIs4pvUlJE2npl7QVDeN4gXgPjJO6rpUstC6Yi4fZGyRAuK3/UmW6eGZq4qCW5IFTrARHoDtU8yC0unBmA3ih9vQozoWP6d99WPN0eGaMyCUUSdTnyar/6GFMuJejakroZGEfH0DtiZzF10OHqoIzOFOVsFHpnKLuofVbfMB/rFd7NuswoMKiIcSxStqERHf5G3ytxG3DrkarWPlocDeISRbZnCT4fljI5xFrtu4kX6B1pFVTTMgiS3QLmMnYU7bFe+lmJxJWqiB+i4XanslXNutcsr/R84eqWky95Y3NkXQ2si4HdUKmQcZ7dWpXKgrQmUjvP9XFF1xekKHSnMmfFWWezJGd/Ojjuo6p2TAeNBfcLSwB74IhR6HulBk/2EWoYqB/EdHJIguEqzqUgScwq8WbUk3co8//zhuY38Uw4sAmzd9heMzgJnEtNuYRYXKnEUJiGZJMqNDgX5s2tzOw0a9WfyfcW+1GJ22ehXJdp9FkGyp40e+ieBR2W9oJ4Q9oZbC8c39ZAN2VuKZtbxpJZvV5GUdFem+hvatwrR9HD8CjOz9VMWVyjz59OdAA6CUkE/8jroHcW/ZXHA9tNx+FQUzTj3M8xVTj39KJomfBQaHZaRx0gztJUJmd626Zn35eMwbLOpotTOXCaIyuawDg4ykq928ZBlTCwiWY1UNjAsS1JQ5F7iTKTalQoOM6ZG4POtNle5tcpVgnTZ2UTOWeukvLwd7xD1HFxdm3u24LVRceqGnm1WxH2RR5Qz5cgvy7RG9wha1gmi9xaYpODlmEuYUqEcBEo1iPDsUScVhV8NHOZz7pIjIZt07IuB764fcm3j49mmv51u6IdClbVwKtjk4kvLc/WB8Zgee/2kuOrBjna88ENdJYxx3WXtR67USn0Ty4OXFTdD/Jju+AHhCWA/QjAZ9mhlNB5rFZPvHHrkcYT1iNFqcO14eiQVlMwLZllFQxJ8/xXdJm8EVUp3gxKXBgvcgYxZgHYkjmz8d4i7zaYImHfailKj7PawJ8yieFQ6ubmDW5nKTwUB2HcJmwrpNyfgjvZ4NNeCSaT3XsuLcYiaeY2AlEp8oBqB171qpbRjKzqnsOpZnilpT3zxaOWMPcV8TISeoup/VzSvLw6cXO9RmrPeCwoPiqQbN+S8rhB8iW7k0N6g0/ZYsYlxBdIrZ5kqTf4rBJCE2dlk1RA6izBJYpm5Pn7V/rcXORgm5y96LqK2isBI+tWhmDoXtWQoLgYeOvxjlUx6HBwEqqrToNURt8WxCHLSHl9XMqoWSda8TUj2EHHLsZNttzp9Vr7lZZUbWtm81CSMO5KfOWwZaA7lfR9oXt/Jq5Inj8LgyVlludsSjoJJ48G1wo+3+4e51NEFMZDSXnRKy0/Z0rG5J5eFbjanHhUtzyuTgzRsetrvrC9wUnk3cMVm6rnsuz4J8dnOBczFX7L4K2KBwtqo5MVUsxg5v5l2BeEVCJ1wFWesXPsd80ctBfcLywB7KEjm17FIFoK6bJfV50yG9FiN6MKw/Zne5WJNaeqFPmuJoKGaPZj8/xXWCntXn/pnN1gdBMM3hI6teuIF55VVmSPSV1+Y1DmnfuoUALFqP0uO6hcVPOh0L6Z8E9Gvc/W6szYpUeinE0fTZ5vOpmcFWiGEKuoJ38X2T46qTNx6fHecv3hBWbvYBWorjqKItB3BdVGGXsx6xNOyviDt2yuWrq2xOwdfq3rTaiyh/GC3+rmZ7o8CJ79zybV+9CoTJXJZSn1GsvMzSBI1oz002xWFPBWN9EyqvxUqVmTRGXOOafrJoHUgbrRzOs0lupHJomq9FgTGYOl7wrtg405lSoScjEgL6u5LziVUZHca8y2MckyM1DtKetDFpBWYR7XSAn8rsReDJSlp91XOiOWCTo+jxUgnAfFi5jL01qCjk5tVezKa7CLMhubpqhlYvJrOZyUxr5a9xy6iqerI5XxfPPwmHUx8Lg8qRhw7n3dnBoljowWayKnvqDvCy2hGs32CTrTCOTHQpVYSIhV3cui9Gr7M/nqLbhXWALYjwCM0wCm7sNaQrInIQQh1QFj1QVZqqCBZ9S5HshzXpLloiaFhVY3vZirYbYT4mXMH+4MQTfGvcNXRj3JsrLFcVdjCqUkh0FnrezeqhlkZhiOT0dGwN44urcDUkWq1YAfHSmbaLrsHpxyk99bS7gp5/ZWLHLwqiLrRy3WxNnOHoSqGgnvrgibQHnZU5WeYXT43pEGg90oDz8eCh3kTmDeSBxuGmgtUugGm/KQd6yzIkUuvU0LubseiahliqDBK4gGkal8t9KsahJATrlEizdqe4OBtceWqupR5/6NDwbrIuZRx3bVs6myukkmShiT2NY9IcnsXSVO58zmMYuDw3UaoMzkOB3P5Ay8ZBJMfl7+Tjk5lz7ZFxqMO4usVDMxBIMto/Yu0WuTQAN1guT0PUACc9QeWVir7JltgmZv2XNO1h5XBB0i7yxp7YleLX7MeqRtS9arnjfrPS/7Fceh5MuXL7kZGr61f6SBqiuV4l95mlL1PSdHZjGJmN+TZrgTlIbJAkH09TPaXw7BUFYjoyxb5X3E8qo8dEQIg5mVE4i6KcY6ZYHfbEgI+gHNAWvqPaRS1S3mgOYzoSEpscJ4meeC5M4pVDde/d4clcEoVwPx5EjeEdFT9UQpDxulN5ZXPWWpau0AfmNI3lI3w1xqTJNGH9rvEUm6UR8KLWWRGZFrz+SdNTkfG4HTqdKsKlP1pbMMuwpfW+JgtSRoVSVesg4jAsWtYfjaJWadcCdh/EKvWcU0L+cSrMK80XOaNBJhvAjzCIKYNM9WzYE+CGY7KtEiaFap5pxKqrBHtZ+R7TDrEdaZOODzELMIvHW159lqj4+Gky85pYJxcFgXOWUliZQEYyP0OsCsepOO4pWb3zMJ8tp0FCGs0hxEECXr4MDXCVl5LQm2VucJs+bm5rJVuadgSBG13BENnK7SweF4dHkGT7C3hQ7Rb732AbPLtD8pjb7YDJAE3zvMwRFr7V0C4CLx5GietPz0W+9SmIBPltp5Hpcndr7i1bHBe0uz6imdOlbHpBksWd4qDnYmCKU7AVffVPq6ShlwhWfoHellxTiRTBbcOywB7KEjKess2fOHLLq8AU2zP5I31RxQpib/9AE2ncoDzVT2ieTRJGKEUOf5rmm4FWZKtJYjNXuIgwYGU6kCvorYTstMFI97qkqdiCcqeFGoCnvXan9MxWLjTEKZlIOt0www+US4yNlAOP/s2JbqN5UHdIlgTuovFmzSINuaWa3fbEYNtnmjNp3RvtoouJMOO5sX5WzbMplgunrEmIQfHeHyLAiM1eAmcue6T9detJwYWsfkvjw9Pxm0VBvWkeJZy6pWBXiRxClbelgbebQ90Q0FbzQH3qiOXA8rYvKYuoUrHXp2Ns5kB0BLr2hfxxztzNLUIessbHzH9XoaJSCX76KLyErZlGkaVK+VTPPord2sz5iSaLlyOngkIQaZTVYnNwF/GbAXA6tKr2EIhvamhiSUq4GUYOwcsiuIjWblYrT/540ecq7WLS/7NSunNgOV9XzQbXnvcEnpQnZUjqzLgVenhhANpdMDUwyirtM3pR7Gps9AFWZCB5JwZWDsHfa9WgfXSyE8WliI9xFLAHvoSCixoFTWWZQ7dX3JAr0nJSFI0t5Gsvq7pgiztcYkfmu7LOZLDmKFZlqpCcjRzu7IZLZdqiJmoqJ3luQi6brS2akmzf0PSUJZBu2JZcZY0wy4PLRbN0pFL2zAR5PV8jXrcKVKFgHEMs8/dWeqfhgsqcsEkUxiKF45fQ5OnxOi6vm4yPqx9rjILEp30EBrB2G4jMQqZvkt3eRjk2ZjyYkkEQaDtCrWGzIhQHtdmg1Lnr+iDnPwMuVUt0N7KoMyCUOdSBvPqh6oy5F+dAx9gXWBddPTFF6tUfqCP7z5CB8Nz6pAXzheDSuMJF5JM7Pmem/oX+mwMEGFj3W0IL9fcgYZayWmpLyBJ4POZRlILmK3I0WhDMhJ95AEstLXbBgsYbTE3mIOehhIVXYEyPqbqUza08rvOYBxcDTNQADsSt0Rxs6RRoM56GxY84b2MmMU/OgoSk/XlhTbIxPlYuUGLoqOb+0fUdnAZjNwGpVQ0ns3HwQSEIJoCfXGYUodi7AXqkJvXaRvC82Iozo3FDeamftNPM9ELrh3WALYjwAkCvbG4B+n85zRVB2xSfetlarGO6sGg9V6IAQhVobUm5mAkHJJSYdYhYjOWNm5/JQDSKn2FPZwnjGLg6gCRlDFi1hH3MWAP2gG0P3edhYQDk1g3JfntRYJW3uombXwTBHnoWcRFV8dBjtLVM3K6J3BHu3c10kCMRMPzBs9Za00/roIDIPj+GJF/Z2CMkL/OBKLRHlrGNca7GyvhIpkVTBYvGBvLeNYYy/HeUwBi1Lsi6jXOF9z+7zEjErLD1k+C5vXus9ZyR3TUftGT1lp4Nofa4yJ/Niza5xEdkPFaSioCs87Vzd84/iE2o40duRFt8FIYuUGdlJzbCvtISaQJiAvS6XLZxua0OhhJNY5aMHcr4NEstojsmXAZgX8cbTEa+07piZrXSY47SvM1BP1OjuXcql0ysQmuTFxEU6F9pSikMowz2FZG7E2MmSl/PR44PLyxKkrGfYl0ltkEPoLy89+5at88/CEiHBZtDR25KN+w/VxpVJbXnuNVTNq7zAK46CBKeXyd3irZ3vZEvNzG9qCcV9ibx12mESlE6GE4XGAy3HWTlxw/7AEsIeOpEQMf6V9CcnCvWBIRYCYFeW9odwMrOqevio4vlhhWqsBooyYVmWcsvrRx42Noz6GGfXnAcF6M89dzY7MmUSCaPCSlccfi5nWj8lstDJ+jBpPFfImqkHAe2WklblcN1lghJPTv0tkFfnMastMu1DlIeaUS2N1IEVoDxVF7YlRGF7W2KNFInRvBlIREW9oN57ixjI+Cpo91QH3yimrcKJcZzsakaTBcwoCku4MEGspMtRoCTHoDJi4qMSRnK1OShjhkddSomjgdi5Qlp59X3FoKz53tWNTDrzZ7Hmz2vFi2FCZwIXrqFaeo6940W84DoVqMsLMfBRB5/am1wiU9JLXJXXQXtwdualJ0SVGYdhVyOmcaZOzYDFJy4pAHFy28lEzSwDTjESvvmFKWBGKt05qnDlpTkbBFYG+LRjGUoOeSRinw8PDy1ozYC/UP7bnX3rn9/inu2f0wXFZtlTG80F3wddfvEkIRg83vWZcReEJSRi6Ald6fJQsoAwkYX+zInnJ/Vk1bTVjHuRGDy5Tdrq9aLlqOsJUYlxwr7AEsIcOUaUJqdXQD6O32V4I5PmljUesirkeYs3YOew+b0ylZnCzGkZ2PSZlokQ2s5QpKEiWb8q9Kb/RwIHoEPDENJSUh6hHITWBsMqlzkqVO8ibBTapJl8Rid4wUFDVI96qnt7xtlF7mKieZ7NFCeimN2eazF5hkiBF7UGlm5JUR4ZgGHKmZjsYfrIlHR321hGrSHljswag2qrYg1XTzrU+QCy1NxS9wRQ5s8jrTuFMxU5VgOzyS9Dryp0Sb1jFrBuZF57/TUlV6Tebnm3V83y/4dHmRGU9F2XH2ql+325oeFof2PmaITpCEjauZ39oVBmkzzIrg3qYpTIh3dkfbbKPkUadlI1JdH0JSftDKaEWMVH7eylT/ilUa9NapbgrcUMZlqnIZcLRUF6oSkZ0ohqJg8Gs1fuNTMYxJs49sLgvzqorjWY5p0OlwevRQLMa+Bff/iYb2/P8sOGq6YjJ8J32kpMvMTnoKxNTg28+S1FUnpTIBBQNYmk0+XqcTVLzJAqxUEJTLHIPuQoUNtB5x+2h+YF+bBf8YLAEsB8BpDJnCKPS5kmW5FVBPGySUrSdx3c6xGw6mUtLkOWEIpggRKMqDNFpEKSXeV4ouTT7dklQen2oOBNCpixqomKn8zyRjEZP/dOJvow5owGydxdZyXwcLcYGTodKmW0hZ1k26zD6aX4tMyeTBnGSykwlq8y5svYMY626foOZCQVhpcQGe9SNvbzRDa1/pAQFE7UMmmAWFib7epmsbZg6O9PnudNz1PRqOu0DOfjHO+ruE009S0BgnZbRnm0PrN3A7VBz1XRc1S1OAlvX09iR62FNaT2vhobaegoT6ELBt3aPlNU5rScreKRGLXAgZxSZYIHVDEqJDVDkQe4wGnVldpn9dzXmw4BmiNEbJaKQn1sOSoheA2mC0tVBe2YRTB4b8IPl8kqtVvrs8qwq/vm65RKrdQE/FGzf2fFse8CQWLue3z08pc4moxHBR8uur7OosUpYTeXmri8YTgU299xiJmzQm/P7PbNrY6Uq/NExj5PELHdV1p62LylcYLNalDjuI5YA9tBh0pl1t9YTrDSe9KH6cJleoLPEzmBGDVyT7l+osnSSQWWSRAMXKWdZogaRocpZVc6aJOjtqdDHV+ai6uelKmJaO2/WZGIDNmlPZBL+najJEyXdgC217xK9UauR64qiPdPmCYIJmRxR6N/M9GarGVIymg00m17VQVaedFT2X8wBs7i1uOdKbEhG+4TDZSSVmnkBc1lUIoQyQq0kDmsT3aE8U+vjVBpN89/Ns10JUqFlqnnAPGe4ccpsgrBZd3zh8pbjWPKiXSPAG82R2o0MwXI7qorIt46PKExg5Qa64Hjebnn/9oL2WM42Lef5PgMhzVJfyU2nFV1zGFTay9rIMDi95i5Rb5XgEnqrWbJB+15RSG2WX8rzXdN9gWaXqbOYrarum9pji6Du0IPl0ZM9b1/s2PU116lR0eMxHypqHRwGLXtW64G3L2/5iYsPiUn4teu3OfQVF3XHk/rI1vW8317go6EuRzoKXH5+3hvtkXpDiDKPlkhn59dbQp5ZNPr5mP3Nsn6k3Y44F+b+nLOBfix+oB/bBT8YLAHsM4SIvAP8t8Bb6Fb7Syml/1pEfhH494AX+Vf/bErpb+W/+TPAn0S1uf+DlNLf+fQHQWdoak9ZKVMLQJ51hNuS8jo7CteJ4XEgbhL21mHGpDJCkyaeOQ/iJqNfGC0RJlG9wIlmL1FlhmIJs9mlV3o+g1LyJbPa3CmzGo3gN0DHPDA9MRRTqfJCvispHnfq6Nw5bJs1CdHHSXk8wHjNNlWeSI0wbeM1Owi6afVdiclK/amKc0nMncwctJJTDcjwaESODnvQebZZuHfy9PKC7B3h1jFeeM0IJ8Fcm2Ab5uCbcvl10oH0oyN0lmSFaIz23FZezx0ucnVxoh0KfvejJ6yqkabQweWbvoFey1arYmA31GyLDmci7x0ueXVYqRBtzoLklJmGGanKQ8SN9kbnLDEIxaOOGKyW+LKSvis9403N8bZANp5ipazQlGDcVZl2ntfuos7QDUbvP+n9UqoNilgtq46HQg8MVx2Dd3zj+jGnQ0WcbHXKyJf+mQ95cVjjszL8o1XLV568y8b2fPXmC7y/3zIGy+e2e57UR9ZuwEjkqmxpfZEJNTnzGop5LGMYnNLlg5aeXXuuOIRVmqXKktW+oFmPrLI6i7XqEuBMZNdVjMFizDIIdh+xBLDPFh74j1NKvyoiW+AficjfzT/7r1JK/+XdXxaRnwJ+Dvhngc8D/6uI/ERK6ZO9HKJAawkuchqr2VsJo3T6qTw4XkY1VbwuZzFeDLMv2BS4/DoiUVT4d8zZgrsT6CT/zdSPF+2zkAV4p2FQIrhW5vsNTc4G7hA/JCnNWlrto5iLkTqrnI+pwGVR3WRzwEFPz2aEcZvOLLciUtcjx94iR0sqk5p7luhmmbOdVApj4zX4BKMq86swDzbHifZ/JzuSQUuuMooOY+c5uFRFktNNeLXtcDZyPNbEo6N+3OFcoG1Lwr7Q/qTRLKjMNiDWqTNxNzrG0VKWXlVEsnBtk4eYV4VupCunckm//vLzvNyt8/tFrwlJ+4yMcs6QvMy+Y2qnMxFm1IeMTPKgiKrSMVoNUEXMJU19XzgXiBtVnZ9KvCkfElIV5wMDVrOoolKzypSSen5lVZX2VKkLgU2q2OESF0+OxCRs657BO66alp9+/G1qM/Jbhzc5DBXdUHC11mDV2JEL1/L13VvUdmRbqhrJvq1pj+X8WptcApUkFDs9aMRC3QxiptBL9ssznWaB9WqYLWpcdrw+9uWscuKWAHYvsQSwzxAppfeB9/P3exH5OvD2p/zJzwJ/PaXUA98Qkd8B/gjw9z/5QVSMNJ70RFwcDX6jGdGUwfSP88Z8cOoBlrIr8cQshLkHYIZzf8wOmnG5iOolGs1Y4LwfTtqGd8tjxMxGzJ959ZDSUtsU0OIqzuXP8v2C4a2RuhlUVT+p2WSc1OTzvuxO+qB+pfdjXxb4i0CyifaoTLZUxbkXF09OM4fekJrILP2U7zOug/aIhJnWbhqv5cYhD+bm5+IvtMQkndHfNYmLpwd8nlXzwRCOynJJCZWjCjIPM5up7+QtZa0qG5On1pOLY5aLsgzeEqLh2eZAmWfihmDZ9Rc8P215uV8TvFWlDZgJGzi9llJkWxCB9WWrVHFvSCP5hUx6yDFo8JKkZpzTAPh07TJRY+g12M0GpJJ7Sl7OGapNGvxyr0vVLpIOmwuqBDMKac3Mdnz61i1fvnrJH1pd87Xbz3EcSwyJb58eERGen7ZcnxrqcmQMllWtPah/8OKLbEoltAze0o6F6j62DtNadWWBXP5M+j4P2WUBLfvKWkdKwrEg1hGz0VKqqtxrULzNWopV4SlcUL3JBfcOy6vyQ4KIfBH454F/APwM8PMi8u8Av4Jmaa/Q4PZ/3Pmzd/n0gKebS84WilttRtuTMtAm+xQTdNOZDAolQfUySzk1aXZBxqAMrXC+71CrRqI7nYPcx+w1JuLG1DrKJT4TcqCyE/tNVc3VFDNTsqPgXhSMjyLFZph7DikJYWPhI0csNWCZEUJ59v0yfaZuj0IyZs4eUtRMUU6WVE8u0yCtIzQxZ3AakOze6hrXGqXFanaRWndWXwdCzkpNbwjbPK5gE21b4rLz8PFUacZhIsOpVAfmYOdZteQNMUG51fm7/bEmesPnn93MdiE+q0aos7Ch98LT5sBurLntayobWNU9JypiVJo6rdEMLwegNBqVZAJlCqZc0rO5hNjaM3EiCCnqGicJKOP0+g/HUunyZZiFbE2h/a25vzk5BpRBtRBHMyvekxVMGFWNvnx2mrUFq3XPuhxwErke1rS+oB0LgjXcDA19cLzYb1hVA7VT4oYh8dUXn2cMlmNf8l681GsgWbFlGrB3Wk73gyUZOZN7qtwfbZScsVl1FFeRQ1exqgZOfUmVy7ftUFAVquhRZhbiuASwe4nlVfkhQEQ2wP8A/EcppZ2I/CXgz6Fb5J8D/gLw73IeQ76L9N03iMifAv4UgLt6pK67ed5rmtXStEIzreh0FkhGwXYyB7HJ6dj26mjs1wnrtRcWSmbRV9vmzGuiGtvMPCzz0sxdtqGcldkjhKmvhuA3YaZx698liqMQ3x60JzPo/QVvCfsCF87ZlqTcryh0sw2bO9JHmVafphKmi8rGSLTsrwAADGxJREFUzKVQk52opwAUa11nWEeVEQLdcPMs0jSSYJxmEOm6xPSG2GjmJTaxuToRo85b+ZBFe4OQsmxXHKyW1hJM5lp2q3Nt42gJuxKzGdl16jBclyOFJG53a4rSsy2ULt4FDWxPV5ql7ToNXmPnVG8xQpqietYTrCpP25b4URmOYtADQ6/zeKlMZ+64U7PLmIQUshyYpOzsnM5K/QZCq8FJSr0vQn7tu2ycOr1bp36by6XbKlJmJY2qGtk2PW+u9qxdz2/dvEnnHc5EYpLZzwtg9JbRW7Z1j0+Gti+py1EDfu9o6hEjiVMUpfEXUF/0GKNK/pN0FrXaAIkkqtXI4+2RwkT6YClsYPSqWO+DBQKfv9jROC1N3A7q3GynjHfBvcISwD5jiEiBBq//LqX0PwKklJ7f+flfBv7n/N93gXfu/PkXgO98932mlH4J+CWA6p13EkYtUWynp9DotOQ3XCXNvgotDZq84cQSJUfkORi/1kwr2jtZTg5eCc2gTJ9px0XuR+lnXWWmTMoWKZrdJHOer5kGd/0qB7s87yMC8bqk/4mWFAwpQDSGGA2hU8mqaQZLRqXuT4E45HJfyqru4iI2z2YxieQWkTRYkoOY0uxpBtoblIjOik0br9F+jXEpOyDL3BOJZSJUfu6vPHl8oHae41AweEfblsRDoZnZyWnw9HeIHkXKvT+hu6nnOaQUSvajYfvoRD8W2m+xgbLwWiKLhpu2wZqIkZHSeEIOmmnIsl6TgnqWBguj5dgVKhi8GlUiaRphyGuZB88F6s2gqidRA4aYNA8qh9FAfl5mr2XJaNDrWwWkt/NrqkLRZpapYjNmexRL9bjV8txGh4K/vH3Jq6HhvdMVnXdcVh0+GTrvOPYlhw/X2O1IG0rK2nPZdOy6mqrwszNy5cLs0SU2QUo02w5rz7qaFPp6bbYdPhicjbNi/8vjSgfHTcSaxKnXoOms3q+RyEfthn1X4aOhzXYuC+4XlgD2GUJEBPhvgK+nlP7inds/l/tjAP8m8Jv5+18G/pqI/EWUxPHjwP/5qY+R8vBuAHKfKAnEClLIxIeo9hXBQDjp7I1tNTswnlweE+Iq4bdJe0Mnq35NeXbInLI3VfavmoaJJaiS+pQnGp89ujLrWKIqHKRCqeh4QT6odJC0TKRdqcO/BhXjHVQY2J7M7G4RC+bSHaK9lRRUc9BVHvVDs4hNWuLauZxlwJjdlGUUpa4XUdcy9YHyDJIp4qzJF7zTslpCs7iLATGRph6xRgVzd8ea/rqZ7WgwCbxoeXTMKvW1qlxUj1uKInD6vy60Wue1tJVKVW0PwTDm3l9ZjVw2HV0oeH7YcrtvGA+lXrfJbSCrXpiVp6y0l9ZnqaY06hyXKSPjsZiVSpJNs+pIcpHisicGS38qVD6qyFR5tE8Vu9znyhT6WGtf1bRGWaetnZVUJBvJySgUbx8pS0+MOljc94VavTTaw3IS+c3rt+hH1Spc1wMf7LecThWgvTepA9EbvvLFb2Mk8dsvn9J2Bc+uDjy/viAMdiaMFIV6o9XlSFN4PrzZqF3OaKguO642LU0x8qQ+YiTxjZsnvLzesNpor6vtS4I3PLk6cFH2HMZSXZv3NWWtvbFxXDKw+4olgH22+Bng3wZ+Q0S+mm/7s8AfF5GvoFvkN4F/HyCl9DUR+RvAP0YZjH/6UxmI6D24kw4b2xF8c85+JrVxM2ifKNWBuAlIFQixzDJJYIY0Z1zjOmJ3WmoKTcIMVjfMdIdej2Z4qQF7MOeB3vy4s/hpzBlZLozal8Us2TNeRs0A89qmrMD0Oi+FaP8Mo+7LAMVmULWJrIZRVJrJ+Tz4HKNmDKm4k2llxuOkDkKnJpKpiLPHlboH67ySuEjKklpiE9urI8dThTWJtitmMd+hLWapIxIIQrxUAoh47UHaG0dsVFm/bUslEPg7iu95WPp0o3R5cRFvLZ/f3PJr33n7nK2ZKQvNPccyzg7c0+Bw2YxqR+NU0URswtSB2FuSz8GY3AeyifF0Fj+2RVQW4sQozEPoExtTXCKF7GScS7jiZR4kj2Wm628iVek5HmvCkD3CgkDlGbxlXY4MUYkqh2NNUXpuD/Xdt/LslfbP/dh3cCbyncMlx2PN08c73vvmG+f3yUXAmERTjpQ20HvHh6+2+F63NCm0bKnZVMJHy4t2jTWRejXM1PsYZdY5fHFcI5JUNLjysxZkimZxZL6nWALYZ4iU0v/O9+5r/a1P+Zs/D/z57/cxJEJ1rZvI4csB0wnltRIx+qcBk9UHJIA5WGKVSF4w4dwL840qWaQiYfcqtRQrZu+tWW0+nJ9KLJOWEFMeBk0QrbK+pk1wki+K9blfJZ0wPFFauRmM3nepmYUOlebHyJulDvwChWZHIlA0KrAa47QBKfXe9yp9JK0hrYPudSczq4eoRmL2NmtCVn6IVPWIXWsPJiVBtgnvLWXhlV2YbebNZgQXdEPLFiM6FyfqJh0Ec9CAbzpDLBNyOdAfS1XuuEMMSYX236bZNbtRZfS6GjEk+lMxExNMloKS0RA2QUkZmSkZo0VcoloP1LUKDQ+DI+U+mTk4fc5FVJKJTVk1f+rPifq2wVm7MWbaZ8z9RG/ODgc2Zd3MPOBu1ZetaEZEYP/tC9I6UDSjZkJeCNYSo5YIQ1S25XbTUrrA9W5NzGSTaYYuJWHlBoZoOQ0FlxdHrnfrzCLN70lJfO5qx9vrG3775imvdislkURRA0+bqFygsoF1MbAfK2ISBm+py1FLsZJ4tO2xJrLvKoZB31/NqldxkTziIKDu2QvuHSSl5WTxkCEiL4Aj8NHrXsv3gTd4GOuEh7PWh7JOeDhr/aR1/qGU0tMf9mIWfDKWAPYjABH5lZTSv/C61/H74aGsEx7OWh/KOuHhrPWhrHPBx0nNCxYsWLBgwYPBEsAWLFiwYMGDxBLAfjTwS697Ad8nHso64eGs9aGsEx7OWh/KOv9/j6UHtmDBggULHiSWDGzBggULFjxILAHsAUNE/lUR+S0R+R0R+YXXvZ7vhoh8U0R+Q0S+KiK/km97LCJ/V0R+O//76DWs66+IyIci8pt3bvvEdYnIn8nX+LdE5F+5B2v9RRF5L1/Xr4rIv/661yoi74jI3xORr4vI10TkP8y336vr+inrvHfXdMH3gZTS8vUAv1AHrt8FvgyUwK8BP/W61/Vda/wm8MZ33fZfAL+Qv/8F4D9/Dev6o8BPA7/5+60L+Kl8bSvgS/ma29e81l8E/pPv8buvba3A54Cfzt9vgX+a13OvruunrPPeXdPl6/f/WjKwh4s/AvxOSun3UkoD8NdRP7H7jp8F/mr+/q8C/8YPewEppf8NuP6umz9pXbNHW0rpG8Dk0fZDwSes9ZPw2taaUno/pfSr+fs9MHnf3avr+inr/CS81td/wadjCWAPF28D377z/9/fO+yHjwT8LyLyj7IFDMCbKQsZ53+fvbbVfRyftK77ep1/XkR+PZcYp7LcvVjrd3nf3dvr+l3rhHt8TRd8bywB7OHi+/IOe834mZTSTwP/GvCnReSPvu4F/QFwH6/zXwL+MPAV1PH7L+TbX/tav9v77tN+9Xvc9kNb6/dY5729pgs+GUsAe7j4vrzDXidSSt/J/34I/E209PJcRD4HaisDfPj6VvgxfNK67t11Tik9TymFlFIE/jLnktZrXev38r7jHl7XT/Lou4/XdMGnYwlgDxf/EPhxEfmSiJTAz6F+YvcCIrIWke30PfAvo75nvwz8ifxrfwL4n17PCv8f+KR1/TLwcyJSiciX+D482j5rTAEh47v95F7LWj/J+457dl0/zaPvzq/di2u64PfHYqfyQJFS8iLy88DfQRmJfyWl9LXXvKy7eBP4m7pf4IC/llL62yLyD4G/ISJ/EvgW8G/9sBcmIv898MeAN0TkXeA/Bf6z77Wu9AfxaPvs1/rHfmB+cj84fJL33X27rp+9R9+CHxoWJY4FCxYsWPAgsZQQFyxYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPEEsAWLFiwYMGDxBLAFixYsGDBg8QSwBYsWLBgwYPE/w30JyZO5/oSxQAAAABJRU5ErkJggg==\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
+       "      <td><img 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gYhPPyrGNatU9UAYcuuRyer2+w6xAdZf8/OnFduMsbXrdiB4okDhJ6tSb9USm8zIvxGYWlcSXgYtQIFk7b98HcJJ4K0yyoAFSSgUATGkYqqktAzLjDABY4u5mhTeW4lAfasIHVuPuYLViYhD3ylqa+WOq1zl8X/NOy0t2eR5vqHTkkhzdaciVhFqmb+5STGtXQG18HX29hM3afkepiBMeLAKXAB/SpjWvHfhLkHv7lhZGbpQNQG+BmH292H5gGw2wPH0i/myLvELjyWBPfbIC8Cg2H1qnCsohnOP9k0i9Lt2kJaQcrUVpXtSly96KrtowXDOmUpGdmXOzuqVojraAhSI4IMSnZdqV3bD6rmQcbH28H1CXDkMFy2Gc6uoWr731CrCkSXd4SM3f14EBuMsxTYDJNjhiy3QfThh9TJgeBoR1aG5YVIazriADzuP6sAFd/NqQmyctZN4ydrriMMHocxx3AAES47OEgNBJgHNbUJsHTcBdi0hrB76c1YjvlQdapUVwRJKDAGad4r+iaDao3Cx4ID6mrOkcWOwdUsCbgROT6W04PJ1nVaC+pbJsb3mDCh5XtPTB1aNjEB9zVZugZ9XvKaxsj8bsAVJCvRckm1KIhNlphKcngtWr3hepAow2axeW3XdEIzDjRJQ39jsSBz8CAxbQXVUNNeRbWcDw2QeXrRn0EU1cAvsmRRLqkyUUGD7km3saeUghrzMfML8XKoDwjGhutN5nlmL0UoS6m8OQFXBiUBjhNQ1nPdwL57j5l96weq5ZmV4euqwfhVxfO75PfuvfoHf2yV+IbEksEceqSKkOqPQmmvF6iohHCLUC4YtOVDqWCUAQFx5dJeucIQKp0u4uOXFPwMT3Ei0X3VIJUECgERLaDUM3swFqXKcN7hJMW45c5syuMDPbbP6VtC+ze/FWYYbuVDnhZYAFBT4uO+BzacB/aVi+9OA7ZeJ4IBqnvP0TxVxJdh+kQqhNdaC5jYiNg7j2mHcCdxAWHd1TPZzJoxxLVhdJbiJbcxU552/YGqdXW+UmU4KbBfGmlXl+mtWc+PGgBJrKRVIf8GKLCe4/oLn6ru5zTVueJ/CKUPu5muRr920IiAkNuRWFZJymFt3AOzcgfHsXnV8y8o6ozQBJqL61j4nMQGqIww+VayKwkk5j1LeZyZ5AkdgM8fYsuImMpXzOBd5PaaWVVSpxCom0ORZQaqggGYgbCFWR/K9gqFo81xLVCAZYGO8RK3lH/h+ZJJ5OClS7RB3Ddwvfw8yTOQzX90AdYXrX3+B7tLN4KMN0aPdBZNXpqQs8bBiSWCPPHLbrL4hQTbYjn04I68lnBKJnmeerZjMXaqsJae5KrB5wZTgDCrdnTu0b9iGCZ1yfiQA3PxlHncew8YZ/H6u5jgH4qI0rh3hz4aIuz8XmdZMYpLYmstgD3XAcJ5P0mZDR74HW3Lc8Q87mSsKsIJo7gTbl0xA6tkedZGLPpVBSHZtr5K1ADnjkoQCzBh2Dv05r1N9Q8rBsJMyj3LWToPgvWRV7ost6ONaDDhBHlZsLFlNrGpEwWsMVhuiChfJu8qJIs/UvM0dh61VzpZYVexY7D3uq564CVi9YaU77niszbWWuVYmEqsDqls+P+oEXSOFVAww2VVHRTjyeSrnbO3F9p2WDU136TFuqZSyep1Qv4vonoS5UsrnVM1tyFjN1WB34bB+G+Em+/eDzkTrfH1zKzTxcyFWreZHU7jBChlev+I91g8ahEOAKFC/OaL/tY9R3XToLxyvp+ezUZ3IPVTn4UcUROMSDyuWBPbIQx3bR+tvtKhbTCuDWsc5YU2tQHSGZIcjSmumPqQyE8lor9gI2utki4qh6hoHNxAUEht+4Yetg4qUYb4fuADEmjv0nIxiIwh7LpqxEWshkvMlkQtGb7vc1PDcUs3j8z0BGOE4q1iQT0TIPuHbGeGo2H02wJ8miCpOL9oCvnB2Pca1JatVRjhy4Q6nhONzj7EB+svc2oJVa0xk3TOBP3EBzQhCYK5a/UB0YaEC2Dwst8qqO7YJ40oM0MHjr06K5N9PQsmS7bTOq7Kiv6TqRJFdMmShG6wFaHJiROkZ+bfhn8PRNgKWCDXMqiHVnvMrdcBkFWIG/+y+iIaQNNqCtVDvQ9/DKRWKQZa8EgcjjAdC7w144UedFUyA0hnIc63124ipdYiVoN4TgegGgo0y780uh7VcFalm8s/cMTcmTK1HbAVT42z+l8tZQXKC/Y/O0J85JF+zytrkDgQ3bN2lx9SyEgvdTC1Z4uHEksAeebgRePJ7sbRR3MT2SgpMLsOWVUS1V2sZGV+q5eLFWRZI2PVzNeYGtt+cfenzvCy2vlQdJWQGFPiBiWXYsZqC8rPz4p4CF89xm9UgTNGiopxUbGFKIFxwM19LImHXk0klAeST5VZjtVc014r23UQuUHBAtOqzyoumkZOjQf7t+PIMa/9xIAJy4CLmDczBJE/04O5zRfeEChIZXp/boKkC+guqXcgEwM+twlRZdWqSWdWeiZxEY0oySSSCknNHsRkTSmU3buekqgp4wxb4XjFa5RqbGS7fvlYiC62NmiWnIDMKEeB19v3M24otgTb13uD1WyYTN2mprqvjTD+ojglu4KwTAKpDKq1HP2ipbt1kVdqgcIFgFjdxg5RMxzAnORW+r++slds4goy8FAkpdYJxS0g/lM+t2nv05+QA5OfLTYqQ5tcQmJIQq3lD440uQPCQw82PATcIwk8VfX3/gV/iocSSwP4cxO13vbX4bGFuCVtXz6rh4o/uJbg4z5yoDcjkoSIQKMIpvt8KU3LExLT+JHKW4ExyytdsO0rivKA/F4I4prwocuGdBXOZNNq3M8F03EkhI4cDtRuzLl99zfmFm4DuqYMbFJMTTBtT2GjYHtt9NsFFRX/uEWsjFPesLqt9wuGFR/9ULDlxwT99wF319jOH+lZRX7MyYrJPBczhB2tpbqUkS3/iIk74OudQrG4U7bXxkzomKW/3RpLi9MzBTzznDFop3KiRuozTihuA2N5LSpm2Z23B3IaUyM8+fDcZgEEQ9oLmrVKmySpr3xvSz+admR9XOG4be2Zk5qElLxDHStkPM9JRHeDUEH49K/ZxxwWeGyhKeo07KUjH9atEZKIAq9cTK/lWAEMdOiMYp8o2DhP/n/UkU+0Q27mHOLUzhD4colWdgunMoz/zqI7klp2eUv6qOiqJz5NSPqwWjBuH5l1E+05x+/0KzRWr1f4pr3s4MLkfX7gih7XEw4olgT3ycJPi8g9HxNrh9gdUiKCmHrD9klVL98QX9Fh1SKXiAriLzVBlmbQgzXIouCOWKWHa+Fnjb4Sh5AiAGDYO04YE3eqoOLwgQi8cuSD6ziDkWxSlCTewknATIJ1BunP7ydQZmlsmg/6Sn9s9Y2JSD4xnCWHPHfi0dgSBRGDytot3CojD6Yc8p3AgwGGE4PAx2cq7PyDIRSLgrUrMVUOep/RnYlUElS0SSKg+PnOojGe1/iZhXDMRd5f5Gooh/AhIGM9cUbWXxDZodUCZM7kJGDdWNWe5JeFruIijXJ9wYkU9rqmkIZMgdJbAOt6fVBsAw1rDGSAxrlyBzKdqlq3SYDD+nvcnX+v65l5ruZorVm9t12gtuqKGYRWnRFZsEnNVmYE9DsfnviQqda4om2TuFv+Nn5sah1i7Un3HGpgaQX2YPz+uDDziBO07btj6XcD5zyZutsCKbtj50qloryJ8H9FfVkACDt+ZKQuZqO2sFZz5dEs8rFgS2COPWAluvxugYd4dZy7RsBUEG/KHfobAZ35YBjTk5OUHKo8D8zxCIn8+rZm8Yi1FdkiFpNiptWG7AUhOz5yJ25JAjAEADDiigAz8f2wMaQcjP1syK7v2N8lmaTyW/Q8StFL4OwcNVK3PFVVsBOiJNsyoxNQQLQjw87rnxrsaAPesh/usLf+WAQG+n9FxGaF3+0ODZ58E9TXbpPuPPFZvEjldNaulnPgp0qulPUnVCf6bGky/uqPFSE5UoycNIOsxFvkozL+Xf1bdEYDClq1gXDnsfpbnmyRaQ4H6zqqvcebA5YoxgygItpjnouFk+pHG13MjNzsUNgaq48ydo9Ym56VFMNdayOOGidUPsDYmivSV2iYr1YLNV4n3Kgh8ypuqOflNK19UPVwE+p2p7ZvMFRXniar0XUI4RvQXFboLj/YmobqL9ygeBDVloImbgKkJSBVw/MjU/o+shsNJuQm4B0pa4uHFksAeeahZVLgpJwRg9SqhOil8l4oqQU4UOXlxDqLw3c+1Fm2X7kxrT1SNa2TzhzTPyTQTbcWg1JgH5TKx2slzhaz6kJOeG4DKVBimtZS5mu8A6VF29MNWEFeC4wu2LNuvPIbLhGStwzyPqfZsIx2fB/RPeCyZvxRrA3hc8X2HC8C5hLCXcqw8dqtIrFN1eiroLwSxUcSVYjwD3OjQ3CZsvuZ8rcyUIiHj6kw2yxvSrrXE0JmUFVCU+/N8676VCWAIRiobIdWc9WUe2+alormLBXJPdRQeC2SuHDIfzw9aLGvyfCj0hj7cGmBGc9VhFZq1/sqM0kA3VAnha2h1wopm2HLeSUoGE1dOXvU+mUiv4PChGPI1t0sN7GOKJJWhDXNbMquB5LmYi0pBYWXiAmZkoqgS5GHP+ObrhPbVEeod4qaiJJjwvPYfhzLTyzqh4cD7mAEukwimNJOt8/Ve4mHFksAee8i8U999lsjTsRmGOlNd72ZEXEGC9bOfEmBIMNOay4tpFrjNgBA3qQ3sDa4cBafnDtNKyhDdTcDhYxPxHTgEL2KuayadLIybJZuyiG1uIQGcnw025PcnRXMtGC6A/ldOSMeA5puA+lqw+4LzNxhcP8Odw8mqsDp/FiCHDAYRuP9rg+pOsboyjbzGEH+NYLhgZRJrRWoU208F/VPB+mtFc0NyePJS2l1ZZ4+zRGuNGq0gq4HEiu01qmlI8cmq7mjtIg5E0I2Zs2RoRJ3BLjKRhJ3bgZnky6oVBZAznFGFozlFxNaVTUaRpLLXJQOZzJWv8f6GP8XWZDAAhGc7dVwbuMMqNlqxAH3F+xK6hLCP0OBw/aMKwxkTMJM579OwpX4iUZtsId8nnUNM/9CEj8tmS412kEX57ZmfveoEcA7DZYNp41HtY7HHCUeFNzHqWPH5O1m7FInI2Nhi9hcblWosSzy4WBLYIw83AqvXWlp4zuDikr/klQBwhaPjhoRp5dly/PlNpaGzMow+KyVMrbzXgsxtRqrFW+ut44JA8IHNlDpBqhVTxTlQ+1aKwkfWBMzeTgBQ71G4aTk5iFJWqey6Q0JygD9RlSJWXDSn1mFqHY4vLCnaew7nXHR3n3KBH87YYtx9pkXVfWrn8+2eWQWW2DJsvlS01wnNzbygx62zhXSuRlM1bwyaW8VkrcGshzhuDLZesW2YF+lZFd9mVgDGLLabKzIDNPie7gGSrNpQwPWK4Wz2KMsbmtCxOsrtudz29Xas49n74rqZcsHnBMU2JTZi7bQZ4k/OnQAQ47dJaRnLRE5afReBBHTPKpyeOQxnVK6vDtlsck6ktDeRAkrJGxs+v7mlKuWaJ8zIVfqpAVBWbcNZgB8SXK/Yf2/N4++08MCcWeDJqKgOEUEEp2dE1spE09NcEWauWawFKosa/UOMJYE98nCjFoXy6kAx1Klx8xd+VNTvBs4UbOfc3vUYz1sO322RzMPr2DBB9WcOwzl3q+FkMlNGGgZsRnbhsHqbUB0T7j4OePWXgLSOqF9zQRguE+prWl74Dmy7NYK7Hwi6jycCK94FuBFo32SYvqH2OiIY82erAON5gvxsjc07KluEExPbuHa4+56guTaE4U4wGApz9UownAN3v8SKsL7mon38kNm6uVGqYQQgrrjFb64E1R13+jKxKlPbsY8rQWfK6utvuCuPNRfT5saEgqMl8+0sMCxq+oFW3WaU3XAmxUqlviFwJYsQ58Tue7biKqsCprX5XAnnSaFPqPdqSheWWC0h9RdMHkyugBvFroti+9WIceNxeuLglYTmrOCfASN+zLMoKa3W7E+mAojjXDBLUWXIfJbqUgFWbxJ2n5Nj119QYNiPpFRkkeBsRJlnZW5kVZ2VX1QE7T05qtApwlVEht5nhKIzB4HDhx610UboMm0SVqKob4n07C5g0OEEAAAgAElEQVQ9+jPeo83LVIxC3WjKKkYHgaJYxCzxsGJJYI89xBTWDVSBxIUt8238kCFuGZiQyLPKOoiVlF0wgFmNolesv7GWo+fi5HvMi2tl/k8RGLZcUNs3hE5PWy56q28cQRMjk8HpmcPNr0RsPrkDrleov6jhBy6sGUjR3CQCKYJAnUN/icKZqq+4gxblnCaj4catlFaheinCtammmWN2/d18gSKo2z8xoIuKVZE8p+ZKrJK1Kq4kdroTj1ueT/OO5OtMUE4BBb6/+WZie62ZDSLdaJVaRv45mEoITH2fc5bYwNymUa5Lbs0CBDXkmRarLS0crAz+YJIUHD4kEjTVM7IwtkBzpTj/aYdUORyfOwwXWYGCyT3zAWePtLlFl5PCsHOF+DtlHtUpt2Mx299cK9qrCbffrQh53+u9ud+cMIv/mMzJbGqpo8gWbcLhA09C9kln2aqJ1dWwIek5WjVNQWYt3DWq2wvqveL0zNvcls9M6BTjhnO8cOT8LosJp8BjSMtK+SBjuS2PPfJMQ4wz4znEzotECgI3CBBTgc7DC6a1n6HV9xaqYevKYDx7K00tv/hZy9CPpjBh85xcURDCT6PK9g0VDPLufTJX3rB36P/+ObZvBas3SkHfji2l9loB5eJIQq4CIjg9FwwXivqayWLcANvPtOgW3v5ywuZzh3BUHF/wNf0lW1X1raC5mqW2MhmYrSpFf8nKyw2C6pbnQkdlttqqvRbtPSQjLadMDUgAMrzbPm+vGDce48pmQ/dQnYSXm1jyyOSmgqKrmO8nPbbMYNJAKr5PuWtXWmG+J9FXIluKqTWkn/mzpZqfMcHsTKxlt3qbIFNC92GN4VzQP2Gl2V7Nm5cUhMk7g1ME5r+VK/xcrRMsksV/74u5u0hEandR2b21ahRADCg+ZMnn580oFYYAbTvOGqdW4IK1KY9sd8aayS8ZERlgG/n+fDAFVqWT2bY0d7H4uznjKcII/FmvMsuoZbK/7ylYXdQ/lnhQsSSwxx73vlfFqNB+nhMAEV0ero8QKOKmKtVK9DJL7NigP3O9MnS7OnJXHWsmpSyTNOwEza1i2Aj2n3DeVd84bD6n9E52E5bEOctwJggnirC6UdGf2+IeCNnudxTOzQRedXRudoOgfT1XatNa0V8Kxh0tZNpvHNq3fL+cjNZfy1zdGGyfivWmcl6pQe0V1a1D+wYYzqw9loEs4Mys2puSCVi9ZUCEGt8sJxU/MNG4QSHJleqMIBXBVNtsL3KBpnuzIo1SJIyKxiC4EFenZA7S1mJtHGeSp4R2P0FGblb0IszAEeOZrV7xPlV3nFONZ9xguAmIa+oB+g5oX/EcMv3ARZKaZZq5cXnOl88zCmeQ2QlBFOjPBe07cr5yy7GyZ6WAgqwh4E3xIhq1YmqzvBcopDxoQbau3kxEfK49fee8K7Jg6o0k/3PJnbqfrmwO3AQiacUAKzZvm1rKkbHqQkFk5vtWnRRXv1pRl/N/+QV/d5f4p44lgT3yyDt7KBMB4cGw3j9biKnmahxbtk5CFymkam0sQIzvY/OcDHePKDbxqbIvuS0cbiK4Ydg43PxzQPzuCVU9obtt8OR3QiHKDmezkkV9xx18qslLC1kzcS04fihz22wixH5aCzZfAlDu8AEueLEGXAvUrxW7TwGAiyZ9yaQYWErKRGououOO1ca0yRwsujm3b9iWqm/nVikTLNDeMIkkT/sUCgHPiE7u+KW0nTIAIVaz4WJseP6brxMVJ9au6D5WB/K2XPYwUyb7wlkaMxTURJgDP7vapzl51XnmqUbyZkU47Iyj1tj1mKhcsv/Iozp3xZcMsKrDyONuIuAiq8E7m4OpAOGUSjVSKBHTbPWSpct8P0PQMzE8ZW3IyPlgblfzvvGc/WAGqMbF8+MsEpydr50BTcaVlDbntDI4/FEhRVKKr89dBKjRGowEntU8Mmdv3HL+Wh+o6DKuQT+3laIQI5d4ULEksEcekgCke7OSPpsEZssKqhhkqDkSd6L3f79YSLTGmRq5mAFZPSEnH7Zz/JgReYLbXxKk752w3XQ4HBtc/J0agHlF1QQZwCDZwZQXmq+jOS47nJ5SkWFlJOTMqQIMqbYWrL9JZbHpngvUK9Zfc9E5fNfIzOa15U8AbA6UeUupmhfx4WmE9NyJh4Oh2JScNtfRXDOcEqbWYf2aFRf18hxWbyOa6xGx4uI77vy8mDuKA2dpKDGppayc4XvYoq0FvSiJQJRc9VIOjPSGaUVH4dxGTLaoh04LuVw99QGHc36NudjP8lAqUuZR7ZVSb9EU3scNE02e/+UqJfPxDt8RbL6ckYeFp3fiPKoLHiFLTpnOY3/OGeHmpZGnDf6e3QpYofJ+hE7N4iYVxCNkFoGu7sHWY0sPsPYml0aWrGwDlPUfs+RYBoEUcWMA45qJ3fdUihm2zlCodm92Up7R7txhOLvncqAzeGmJhxVLAvtzELlVKBHFLwlAIXaKGik5cvdLYvJsDQLMFUtzO7sWa5hnW+NG0D0xe5FbLgZ3vzKifi1of3sF9Cs8faMQ5YpBOD3fc/tlRDglXP+oQjgqmmt+ZnWY4LuI2x/UXFCbfCzCFl+t6AW4+8sDtPPwdx6+A+L5hLd/ReCODuuXbGsCmLlZNgtMnsoRU6vongrbkSeH+tqh2hsHCibcO+SBv6A/o+pIex2t5afYvOyBpEgtBzanDypL0iifXXzVHKCYCdH5GmcFiynw+GIr6C4dVoamo1uzKxypqeXCmduwxXG4kRka78UShJl/jiiO2Tc/ZvuQrUm2Qpt3pvhh3K77yas2F+jqSKWPbOeSEyIrJod4JhjXOWnMKi/NHdu400agJ0AqIAUm4qmVMg+FyOzKbCr22eom1lII1dPKsa0ZPFZXnPUNO1eARBlW39zw9aFLmBpWlupYFScv9HurKUc2rQX72iOuWHGFIxGZq7fUapxWUqrH5Dk/nTYoleoSDyuWBPbYIyO6wIU2Nzpy8koNVSFCl0qLq7vkLCFXKnmBzCTaWBti0VBYseaOfFoT3t5fCiCK1ecVqr0N8Y+WKEXQXVAcFgpsP0/oLh2O/7xHrBVP/x9DOxrwYFpxwRnvySgVfloCqpNgdMwSqeU/uKOHRKB97Uqllzk7MBh78gZgaIHTCx77tEmo33nUN5aYK7oi+964c4biEwVWV5TNGLfmLHxeFfmh/oJzsO1XESrU8Vu/mUyMVopyRba3yfw6Vq82L3pCXcTqyMo2yzGxGsmmj/PxzPYsqSTK2MyfdfgOK4j6hkCV7gOSkau9JaaKCXvaSJFIAizRW0LwvZZ2L5S6h+pmfpaKCRrbTC5vHPIcq76JcKMj/HxrrcE7tvNiY/qZNnOtjlSKiW1WmAfGRswME6zMB5qzxoq/M65d0YcE5lZufRchkxpp25Q0WoEk45MZhyvrGtZ7xX7nClVhWlEoetyhUAViq8UNwff8Lizx8GJJYI89HHf/Ym0VDTNwIlue+F5JgB0T7n7Qoj9zJdmIatlJZ9djdYSzjxsxDyjB8CxCvUK9Q/ta4CIJwfclgQiDVow7LjTNO8Xd9+m51b+Y8J2/Laj2CdV+ggbBtPL039rZueTqJSiqvRQR2W5wlFTacDH0HVDdkKsF5Q6Zrsgou/O8+J+eC8YtF9rN5x7NFWdr/YXJNd0HwVjVlttO/aWnEO4EnJ4GdM+ktMvqm9mo00XzwTLuXQYrZDv7rPJeHdnWG3cw1Y1cKTN5TRspROB8D/PMKPOb6AYgpaWYKkF/LhQFBonY3fPEtuFLh+ZaTdF+VgYJByaUfN1jqwgHKTw1P6g9U7ze/bnM3CxkdXr+m1NWhes3k0lkKWLlDAGqprfI2VSma2Tlktw1GDdSznVcz4ajzd1sdKmes9nmxvhipmIPkMaRW6LZtRmwWetBiz8dwHt1911yE+t3piDSCrpnCjeaIopSYmuCIpxk0UJ8wLEksD8PYfBlSQAmJSIrw+vzgqrA6UXDRcoDiFI06VZvE4atM3gxXXfrmxH6YWOKB4CMAr932H3Kgfu0otKGP3FwTw1AV5Bk1R5sOTYEDqCJePUv11h/5XH+U0FsHG6/z5lJahT+OC+w/jQrMUhkYqKDNDlm1a2gvptbav2lYvOllDmfBiYn9ZS0qu4MzfZKqbKgM4w6IxXHxt1TBFEcXgSEjmCJuJr9v9zI3bgGLob0VzOC8cphXLtSIdHRWaxVZtwm89vKyMK77wkqq2TC4V7VNWB2ybb2JlJCXHlLeNwYTCujDZwD3YcT3OCw+dyZGjyvW/suoTpyMzJuZuUMbhi0zAKzqG+WaJpaMZUSU8CoMsrRKlebuW1f0Q1huHAl+YcjyoakvqMLwrRyfJ9AfU43ctYHBXxk+zHb0oSjors0VRXblGTX6eym4EfOsqZVJmrz/kdDNLqO9/K+NY2buPkQJcpx2hCR6geZk5/jfDS3mAHMdjZLPKhYEthjDwXEjAIBGw1k7pEJyvou4vS8QveEnJis7wbMqgqhV6QJ6M49Nl+POH7UYP8dj+6pgQC+cbj4o4TqQLfc6kjzxv6plB297/n/zZe2uOyE7buLiN3lEXcnD3/yJvnEFlo4AofnEdOWCbK6k5JcqmPW62MVtHrNHXn3FOieMjnVN4LNS0uyyThpNsAf17OiQ/MWRdU+1lyAXeTinhF8zlQ3BlNSr44JoYvFhXj3GVVHjs88NMDsVBShY8uPZpkG/GiluA7nRJRnYm4kZF/D3KbNM673YdxS2r5MBpRDSoHtve4JE/q0Uqr0Hx3Of5+KILniaN9RvSKbOAKzfmBzNXu2+Z7XOFcv49YAOCXZz8emDth8MzHJrR1OTwOqI33XBqvuUxBoTmYmKp08Z2jhlOhcbfYyfjK1+zZb+8wdhKLKn+eazixdRootZ7sYP/C6Z9h8RpJ2Twil5ybCNl9rVq/Z6TsmqwSNgE8pLdrSZH3KPFde4mHFksAeeVA3cJYYypI55ALxNadnFfpzVxbKcFLsPhsRDhPG8wqxdiZqynbP279Qlwroye+keTfvgbtPAmIjOD0n12j9FVta4aiFgNo9JWBg3Ar6JxGoE+7ebvD8//RobhNOT5wRrtn++/iX3uCr1+fwV+0MKhhm9QMqIbCC6J4J9j8e4fYe4Sior+cEl2opLspZxT2cMn+M853czlq/TgUR58YEL/TJqvepkFllUoxbnu/mGwIthq1JElmrLf/5ZDOt4cJsTYb3+UZlzjgpujMpnmeAQbsPlrgsgeTX5j+nIKzwVgQiAFn6itSB5p1DfUNqAwA4c0wezpx5dAmqExfvcEJxHVBhhXS/wlCrxJPwXOpbLWoh3RMK9U4rx6rKqp3qjm9wesq+dei0eHxlUNCwM2BFlf3VzH9t5P0NRx5HFtEtor4yE4tjw3aomMN4MsX6aS1FgDcc54Qzq5yQIpDnXv7Ef5MJqMacKOfkHmuUNrhMKCafSzysWBLYI4+pEbz9C4RINTfJYNZ59kLu0variO1X1B70HRUdxp1Hf9kWLs19UVoNhB0DQKpdgdo3VwRx+J7SO/3TBAjddrNeYfdUiuJE+1axek2UyLgVnF4Apw88W2YJOHyiCL98h69/+wVWb6mEAFX0T4yk/KMe4asa9Y3DcKaYdgmuF5z9blWchlkWWDvvwMqrOxOMZ4pUKcYXI8KbCpvPjSfVaZGgyh5kNGRU1PuE7pKGh81dxHDOGdjqzQR1RAimijPB9kqLdYqoGHkZuPiDCcPOlYU9q0Lw8+ZkcfiE10uULVLAEH9bk85KMgv9es45s6FjOBKosXrFFm6957m60axPMFdK44aJQ4V6fs11wrjm/chzrFzdjGZIOlywqs78KICf6Udg9YYVXWwcxo0rPMTTBxXCKeHi94+YdhXGrYcktu0yaMh3TGZ5NjvsBHouBVFIe54su4UCeU8Zndixx5eTU24N5hZsRkpWR+P9bdkGrO84Sz09ZSu4fctELpEdCN8B/ROClTIPsL6bUZnqgX67DMEeYiwJ7JGHS3lRpFfSsHMlmXD3qWhfD5jWHuPOY9h5cx1OSBsgK8PnSiG2wLih7Ul14MLVX1APLs+3uqeC03ciXD8v5LFC8eGi0O68C55Wgu45kLyieccKIjZcG0OIGByH7IePTT1+M0KCwr2uUd3Oliu6ipBDVWYTsea8xJmiPBXFCU7I0O/q6wrNdd6dC9yJIAIY6p2q/AQtdB86qqUL0O98Qeb1F1xxx7Wgeyo4+xlNEU9PXElSvgfqQ8K4cUXyiHMfmauqXgrcvn1Ng01vsycAgOkJZtQeEYAELISemo7NjcLJrN8HU0QJJ56T2LxtaqXMO6uDkYqTYtg49BcUMU6eAL2cUDLgY/OSSMes/nHfqkRU4TpFOPCHKVRWaZGC0H3QFCdlgMc/tayaqmMq1zSDNYodigIy3EPAjlqkuLJJ69SKAT4UcS2IK1Zpyc98NyiKwguRjnxm+0tHi59pblVm6x4qs/B5kWjV86BlEwdhp2CJhxdLAnvkIVGxej0BAtz8oCqowt1nRPoNO4fbH7boLzjAr/Z5B88FOtXAtDeotNrsac+KYjKBU7H5wbg1lNuHEeoU1RuP7tKg1JZA/MkQjcIdfWzIO+qfRzTfcHbUXxDdNT2ZMPy9C7iaSvNI1COUmwrhyKQTV4pJuCNffVpTJd7md7PALc8tHIH+A2DaJro1HznHSJ7cteaGfmnj2r2nJgFDDzY3WlqpmWQ8rQTdhUNcsSW5+xmrkuSZPMctAAikIsy7uYmU7pIZCp5JsNkJuTopxjOiKMNBocFIxS0Tam6duQlI02wt014l20TMLbgCdlGUlmtubzqDvucZWDLLktUbVtp+MOsQU89nZe1M+ouzv2EnaA6stNVJMfHM1TqvveD80xGxcVb5E1XkB0LgV2+5scrXOydmSaa+72BoWibf3F5Wj2KLE81uhohAJm4xTcr7qEOkmZytQSDmOK0O2H5mYKA4X/NxZ/y0Nau7VCuGS0XzxhW7mVwVLvHwYklgjzxiI7j+cVV4PqtXitNzh7d/scJwpohrxeprGjSq06I1GI7cmWaF8tHUKtwINF1Gv81zntMzcrWGiwStEvyttdpu+drjc7HkZUiwipyjbIgYbhz8ABy+H6GriHESbP+gQjgRQRdbwPdmYzICdz/k5579dNYyzCCH3NaJNZNc/4RCv6mxhQhELOa5XXUwAduo1vriolpaV35uoUJnF2oobNjPxXr1jWImibMSzfOZYO7S/ZnH6u2E2JCg7IbEdpp9Tjgp9t/xpaqIKyMWK33NnM0FY8WEXFvrrvi01RlC/j6wwA+mjC8oJOcsT+WHeQ4XayILNQBOZzJ3VhzJppi5UqL11z3PtjtWX3Hl0O887n7AOWhsKGo8mjJ9sgRbZYPRzo7PRIBjw/ZdMHCIm5g8h50UJZlceed2LJ0JUCpUgNeJosOgajHMzLTmeWR7mswXjC3QXwCAltY553S83/7ETVROsNmPbOGBPcxYEtgjj0xEbq44DN9/Qv+ncZeQ1gkICYfGo3njMa0Vyb6svpt3r1llIIMwoqkmhKOWXeq4A7oPJsg6UhQ9BvgeaG4ihp1HOGoxhZRkicIg674DqoPg7i93qFcj+qsVtj8NCAegf2oJ6sR5RXultrhxYQwnFB2+LAoL2MK2zoN28qeGC+VC4+ZhfDgB7dtkbc5ZyxDgcapkZ2VKWw07h/ouFeJwrg6a69nuPptXAnyv5EnwjQ2rsuZWOGs01GFuVaWNw+0PHIZzuj2HAxNJtqnJm4n+kgm5eK8ZsVhGhVNuRN5rvxnII/PPWDEnTGuH5jqyEmqIFowtr6FEFF+zgny0CjEcAHWeTtGTSWMlfQ+0cvgg4PAxFd99D5wuHQVzV0JVF0MTcs7HijEL5kqk7qQ6wemZw/oVQSEkDfM1seF7kcMGE4omF2w41yLI7AYpNIlMGE8BxWmayhqceY0be0ZGbpZkyojDjDTEe7qiqaJjdnYoWOLhxZLAHnm4Adi+pDPy7fdNo+8sIW2p+SenADcKbUv2ziwkQOCDLeRZ7SEaUfTwsXCR9GBLZp0QzyLqrwM2v+3LTESdYv9RwHAmxU7+dGZtpfOE1CTIJKhuHfxJsP27LcKxwSoIhh1w+8tzkl3/tGYb9MfA9nNg98e2SBkU/b4sU6q5YJPwSxj16QPz9YLxyCIXobPPIpN0TRWMTKbNKE0N1PCTyMrAD0zEWW0joyJnhQdXEsvm64TjBw7hmOkIrD6nlUPaYvbTMpDJ4WPBuGEirG95jMenbAs2b8mJ6y+Tfaa5HZ9JkfPyAwELfjQvslqK8nu2YJFoyWvlMK6oiB96gkr6Sy7C4cDWqD/la6oFZp45g7HhJoc+Z0B94HmJJhw+qHH8iBXLs7+boGZ5QpkxRTjMYIhSGcK80AIQV64gFGMDdJecybmJFa96WuuMPVVfmmvO+Q7fTVDP57i54oZBTTQ6g07yXDQT8cedzCRn0O8tHFF8yO7fo+zC4EagectnpD9zGM/nNuUSDyuWBPbIw0VF+3YkX8g3uP5VIF2M8HVCDA44BrRfOkwbq3SSzRcEiFu2EdVTnT2cuFBwFy+Y1ok70U2Evw6ob83mxCDqsQVWr8G5zh7mDQV0zxO0UqBKwBAwbRSxVVR3zhQ4gOGSu2cNgvWnNcLB1BfeZcABEXWsLniuasaa44atsjFwxuUHzspCx8Upa/zVN6bBJ+QNZbMq9YLTU1Za0aD36qQQjQvJOXOidlK8qgDu5t1IjzA3crHvn9xrs92yHVntZ3X6YWVt0m5WUO8vlSR0AcYzLqh+YFUTWxJuAZSqKVcXjanXZyWOaS0FTVjvaWSaVmyFhk5L63D1hjOp40dspbmem45qz3Zm1nZ007zAezPipMguEYW+V/RPE85/j+fSnZN+4ftZG1KUPEB1Yn5mBnc3iTGA9/Ly9yOrMlOGyVy8+7D5cWccrr3AjdyEFQpCQ2J7Rt7mFva0ugcWEpvt3vFz8vcgJ2t6p7HL4EYAA4oZ6XDO+5mrzyUeViwJ7FsKEfkUwB2ACGBS1b8iIk8A/A8AfgDgUwD/vqq++4e9jzpBfxFweubw7i8lyPkA/6pBfDJy1n10ZT7mrGUytUD3gpB0Lpq2kB3UyL+GYJwE4xk1AVevpUCgu+dEsK1eUU4qGy26yVo/W3Km5BBQ3Qr8IFh/rTQYXHNnv35p85rKW4uRC2Z/Sbh0Vp/P1WHe4U/mApzFZyVSmzDsueDXezWUJb25xo1H8nlhnisVAAQWWLWRYdfVXouZZCZ7j+DCWHhaI+HdxxfOZovzLIXvC7RvSSKeVg6HFx7DBVGYfuIGIQUgrrWoTKgHqltBbBWwtmJp9+U5jRoAZE23aiq6o1SK+fhi40oCyLy1TPDOgrq+M6uZCUQ/DjMBe6i5gSGalE7auWVaHXgPtU4Ydw6n5Ga0ogGBiAg19GViVdWfA8UlwMw1q70a2V6BJMWx2o/A/kO7Zvc4iG6SQrbOFVV1a+aeBpaJ7ezXVqxxBpgV0HyNckwrVrlZpcMNfIYkKbpLh+Gc7YrQLSXYQ4wlgX278W+o6pt7f/9NAH9bVf+6iPym/f0//4e9wbgDXv5rArgEdzkAXzWQJAjf1AUKn3fV4chFbNwKNp8xseWKJe+4uVBTYgcJqK4C6ne2wF/MvKHtF0x+4w6oblHmPd1ThTt5hDtBfcdFsNonpCDoPia/KLfpYL+WJa9SBVqnrLijr29mt9zi72Wmj26AQeZ5rFSV0NlBN1BV3tnCPzYCL4S6Tw1V4F3kjG1azajCxuSKVEgtGNcO1Z0Wf7NYC9qrhNArti8TxjW1CEPHimP9SrH+ZuRnbjy6S2dq5rRs0YycNLCJ7w00MAJxxdcBrM78IPPGY6RocraxSd4ADJhRmbmyy8R2blgSnAnhnp66UhECNucB4MwvK1dyGY4+nPE+QIHNNwnDRsxyRbH+jCCeKWsXmq7kuDZVFAOIaACm2kAXAjTvzMjSCMl8DVuQ69epAFH6Jyjvk2o+B2FviTKxakyBmoV5fjWtqcShMrcVfWc0kTgDPvLccloJpi2KeWc48rxp1urQPwFCZ7zIBUb/IGNJYA8r/hqAf93+/N8A+D/wZyQwQG1GIsDLtiwckogY7D9Q1FcO1WtCw7unVtmE+cuN+4vWvbYLoeyK4YJqBPUdKx3fqy22wOobLQvNuBab0+Qdue2cbdaRKi7S4QhUo5raAaub2lQVhp0tmrAFaWOwaFN0SA2PO7d0qr2Rq21X76JiOOfiSkQde295ZjRsmLgyuXXcSLEtof8WZiNFAy5wfsZSGQ2Pua8E628mVHtg9ZagjrOfESzSPQ0Fep8rtFQDMs6ISj9ImcFxkVeKDttGn20/hes5q8pGkxkiH2spiD0Xea2zZFVsqTOYPbWyJJO31lhOilKS6Zy4cju1gBnsenSXYoLNTPr35ZpKZP6a8cqYqIzyYMAhVjym2tIL4kpwfEHl9+6C0lw0lswtP6IuM7BDA5/tcTeDcVxvXLB7lie56ioUBjNSzR2IVDPZTS2PVxzlvfKGJ79H9khbYPQPM5YE9u2FAvhbIqIA/ktV/QmAF6r6FQCo6lci8sGf9SYSBf4kOPsC3LmalNG4U2ij0DYiBe4m3WC2FRtF9yJh/bmnKkXZdZraesVdbwrAdBERbjw2X1OlwUVTJw+slpI3g8S1kWZvc8sPZRgv5qDbXJk2oSvjKCqTW1uIM65ZyDeDOCAoXmGZTA2YwjnmFmNuDap1rxQARAoPShJQmaOwOkV/QQsN3xnxONufqDk8V272S2tzBahFhT8rdWQUY/fEVNEtKQGsFGJB9xmBNifbSYhIbAxKPtxD0/lZBit0WrhMGAVaWxUaTSTYZ/diVl9uUIRTxLibv94SrQ1bE1G5fk2bmwxtB8DWpmko5tZdvg9QKZuGaUWEap4jJatM1TQT2UsAACAASURBVO5V1lAkl23mB6qziimavc1KaWGSj1EJxvEnAmSKVZCBcFIl8EcDtHRsJxeouwJhP187ifNcj9daMcJ4iecoHLMqq6DUPLbqLUxthdXw5os5iS3x8GJJYN9e/Kuq+tKS1P8uIn//H/UXReQ3APwGANTrC6xe2WznpGjfmivvFTBuPWLtC7cnVUS5pUax+0NvLUDBsKOVhHpg2iRIFNTXbD+mVwF+II8q83WqIzUBr3/sioGiiwYKOCnufol2J5uXqcxd3DTPuaY1d7ehM8UFQ9nFSiCTwiXjdLWzQgKs7VXdWZUQOO/wHaWaJGYfKVdmZFPLBIpoCS2xlTaujXitFPUNpos3WiUybRzqWyaq2BLt6AeYrT2ruTx7S94ZbYBgkPYd/zy1BHCMO5vZ1MCkKO7IktgWba7MHbmGmXhy81Dd0HSzMs2/TAqnWK3SpNLAGQDbi+pQ6Av9pS+IuuyP5Yf5XI/P3OwzZpy61KJoWvpuTh4ZoZlbhgAKryzVtqHIIJI7AEd+7rQCYDOv7qnObgP3qvRYK7QiqCe2NDJ1W9DPzhKfJFaFxWYmMOk277R42OV5ZTZqpWCyIJrbgAorbxcVT383ci53Rq83gJy+ZOCV4Ywk7/o2m4aitF2XeFixJLBvKVT1pf3/lYj8DQB/FcA3IvKRVV8fAXj1p/zuTwD8BABWH35X168S6ruIq1+t2N+vZjRYXljuzxL83pWd6rhl8qIqg0LibCORzfwqQ73lOdm0IiQ7Njk5CdwJRaIp7LPdCYxbIxhXlEPKpo0uzokvV41QG8JbCyrVM1Is78olAscXroA76GnGz8kUgex8HE6gSr/nnMqNTBa0e9G5uloTcZbBAb7nIpbNE2nlMoMfcuWRibESBas3rMIO33GorxXHD6kUn2otdIKgDpKMMD4onOexZMuZaa2AwcQBJmAofcKyM3M45DahtQDt3laHVGxWsnJErmDV27URFOuRTAqPDZNsrIH2am6fqQNgeor59dOGx14WczuODOLJJPPmnWI4N03MUo0Jmne81sDMeZs21inwirhikgx7h8MnqZC/swfc9nNuWLKsWZY/q46csWbyNV2qpXi7uYnUguxKPjV8frJIdD7X+saemwNKxZXdG7Kw8hIPK5YE9i2EiGwAOFW9sz//2wD+CwD/M4D/GMBft///T3/We1VHxebLHtMmFBhzVhDPJpPZ6K+6ZbuxiPWa71FcUd5JA+H0MH08sZlIqjOyjIv2aIuDM66VDKy+ptWMaqzuqLKQd8/tDcETyWYYuSVHmw3BfXfoaC2ycOTCu3rD2VIGGmTRVsgshbS+RUlekrigq6cAb/eEiSZzysKBwIFgiMX7x9XcMAkPYKvNjbl99f7sbTKDyJwk+wtDV36dcHzhCMZQ2tBkuaLqljB/ylixLTitgeEyljaejEQpqldoJZTRslmVG2GmkbzmoiRQ+17Rn3mozRDzBiATc/1Jy4Ym1RlNKvNcsmUlRHoAk4Sb5vPO3mA0kgRgCR3GE8wJPZPE1XHDUY/5+nGeStktm2FtFamxjVVL+KYGwN8ETLsEbRJkdHAnQX3nqApjx0XB3syX05K8yvcrKUKfqRPzRop8N77eD7bZ6k2xZO0LGjV0rFRjY/JWHRYe2AONJYF9O/ECwN8QEYD34L9T1f9VRH4LwP8oIv8JgM8A/Ht/1hupAHffa3D4mMZ+9TXlfuivhGJSiDQvVuqUSgTRZmUmfOs7gVZcaDOv6/SM39zQEWKeWzYwsm0WjwXYAqwOHMKnKvO4pIAB3KRobmB2Gihq6b5nNZQ8K6pgquXhkJXWgcOHrBrzDClzrsLJkmyw5AfyjoYtLVsOH7Mt2D+P0Eqx+jIw8dxaBXJPU9CZp9rUChqzJVHH6oOgj3kWE6v31crDkVYmedF3o0Cjmr0Hr+HqjXlu2bFOWyaqcLTsZZWNHwTTirQDFwE5yJyAjBIBB6QMwQ8Ahln2KK5m8AytZcQMNGGGkjOwgwTtOZnWNzDNQxSI/7SaW7k50bt7KiDZuNIPvGdUZNGC9pPE5BOOrKqPL+Ykqw5wdwH+yOcRDohnEYgC1wnaN6Q61LczStZFzv0c2BLOotV8hqQIOWdSM0WIs0aioDpwThhbZ4oeYm3dVJyuc0s+V2hFcHmJBxVLAvsWQlV/CuBf/BN+/hbAv/mP816xEdz+0CE2xsFZS4E2q8/tk6w1mHfxgrsfTSTiHo0Yqmxl1ddiu29bCCtFtadtR6yEkPhKMFyw9bT5ggt3tg0Ztg6bryOdiQFLOmqzBM4UoqJYzQMm/muvvY/2qkzY9fiBM9QaisKGM3mjXMXltlxlaDYNhGLX74DjdxTtRwcMf7wryMsyxN/PSQuR71mbfl4W/C2txhUtTQr5eMPk2F7x/KZGcPzQFaShAkWLcVq9b+8xtcD6K1Y9WS4JmFt/wZAoKWgxWmyvDFZfSLzZbTlhalwBKFDJXtG+mavp+0aZgM0IDUmKe+3XcZulpFBas9nFOgVWLWItZ3LnDNxyBHafU5kE95KaWtWWW8OpIrAFEIxnVNao7lwB60xblnPu5LH6xpVjoS4nzyWaVNjUmKHldK8KU1aXOUkn+3vUGfARnGDcufesbrIFUNaRzOjcYcvEGqZ/nG/lEv9/xZLAHnnk+YZEQX1j85tzLuh+4IJY3yhiD5PW4SIh2wl6DEi1LUo2+4qtwt3x96FAfcPd+90P88JDXtO0YXLYvqRw7emJR+gU9V16j4Sc5x+E2GsRZ+0vs+LF7FZMTpAJ6QrMX4yVlxtmm5Tm3Tz/AAAfteyQ+3MTLvaAjMBwAUzPRuinW7RvOBejQaNxtza5FZiPg0ASNXRl8oLQsbLqzwiNz7y6/4+9N4m1dcnSg74VEX+zm9Pe5t3X5suszHJ1si0GMAUxRWICCEYMLHliianNiJEljxgx8gABAxrPYIYEEjCx8YhCriqnqyorm9fd9jS7+5uIWAy+FfGfLJddsnCW7i2dkJ7uu/vsu7t/n1ixvvU1kggrppan/fXrjIufJNx/SbutbifW5dkG6YEQtc6lAOD8ZwnHZzSxJe1e60ypzN6qmHttRaEIoRtBa/M+FxVxzW6FFHISYYrHYjMsHadP9v6KfgtkO5bIkKLZKp6A6hWqxigsgmmlNkoi87VcBPafWByNkSjczA4pbnhQGM4EzZHwcdyy85e0wJQAEA4OenQIB2oIhydS9WSaYK/DvlsGJRdoFUpD4PL5hoGPWW3DDOIsULckyiVKF14E6X5UhCGbgFp+iSzzuN6v9VjAPvDFKAg7qV+U4bv5GApJGeM1ACXDq3QPuGsQTq7Ciy7aRuGNnmwEgdNHivhkRvOy4aZ1zg25uefz338RoE5w9nXkibYRpBKCqUB2gmizjtgzT6sQLshgI+xYKPzNTrF9lRBOjOBo9jT3LdqgdiRcVyyS5i2tkcanpBq2bzjoUgHh0DvB9f/dVL2Zmzn3IPlE0ZxQC66fCT3mB92hG4HpzJNo0S6HgtwA2tIc1p9IWhnPSaAIB0WzAw6fCHLRQU2C6YJEhe6tFUwlG7CGgIZCilC0Nw6uIbmjQKaxJ828f6tYvcuVvu9PGcOTgPOfR4MKGWSaH8SRAOZNOKqlECgaWWQTtBQDslfoCnzdygKjDtBGIRYv4kapcC5ABp+zHK0iIyiHosUdRNDdLbq49kYAsVDJPW9v7xgzU0goqeV7bc1Q1z9I6VbrnCRrJVjkIIzL2TjE8MvXq5BRos391Im5tRTNH+p3iibXgmTSkCKuflzv33osYB/4UgcMLyI7qKGYm3JGJVHMXeABmaMUqwMH9sE6iEKzL0QOUW7Oqc9wdwEQdgfhSHNViZzhuCjYflPCDSkULsGL2UuFmFSA6cIo6SOqeBjKmHrJJGs0R0U4JkhSDNdhKSQGnbmo9dQcV8DwjJqi9p0zWJCu5dQ9AZtvuOm1gyKcchUB929mWiCpRxgyVGhCPF6wEyrJ08Wa6JfiNQqEZ64ZheY/bxfHkLgBXR9Oy7ynORh1f8dgyNOzhfI/bvlY2rD4xjObgU0CSYJm4GdV0oqhwLx28LN5CCZuyqdnzphznJ+xI/rlfDM/SdXt+ekBIcYSidMmEyY8ceN2EcjJ7JSs4ykkHA2ot5WQSGmlmh/XTinRD7I+nonTuxulnCKSjQoYamAzr+JJ6R/Q2QtcWB1FvADK2ZiaSwm0GCxztjluFgNhwtFaIcnZnPyLJhIKaCc1GPbhfPRxvV/rsYB96Msps69Mx8XNVeDMoqj80qsxDotrQ9ygij29gqdkByASepo3Wp2/adLLghOO1p0ZYzCYN19/w8iO1LEQxJUJnyeLa+84C6qRFYDFWRgb7MjHau4jNDicngUcX0h1fSiaJBVguGaUfVyzQ2lvixbKNEED3dqbQ67QD4kEGbkzr8WNt45JKg07tSSArN4U6yGb2TXWqZh1EXVRajM7Pn5u+LNs7629A6A0gyUZgxurN4f1YhqMzH+T1iWzixqx3CjaW1ehrmJ5xQ1/IamI5WOVYMm4KsQFc6KIIOYIYN642smUrmW4crX7m88y40luHX0H6+MYI886ozAssy9YMc8N4CNqIGcV/xoMWYtvcdYfUGevxVYqt7xP/0ZrgZOstUPKYfE5fBitEy1hGWARD4Mi3NELkybNds07Fip94BtZZAilGPpZq/AZdQ6qaO8eh2Dv43osYB/84gm9uJhLlnoajmeEfVyUSsYotGYIN0UXyWYr3RkaWlBJMispc+UuQ/m4ArwjlFhO3P1Nqgy9ebtohdKKIZjJoLBmT8q8WmJue6/odgnzioSP1Ane/VaHeYNKo+ftvD+tj4zV2PB1NQcrXqPWzqSEJEpU9PuIuPLwQ4YfE3LjENce+0/YVUq0TqYYAE/LZiaq8AM3sLSyLme27iFbcrU5ZUgE9tfmhp85H4pbrSaxWRQOgqnnNVLHeHs/2gl/FKQ19WJulGr1VFz1NRictl9CNUs8jIt0AZHE+0peYLEShBlXssTjGFNz3tJxQgOvT/+aZBlJy/UuDvWpaPIMSiuGvGm1dNhpBeRoGrvM+6Ru6eKKoLssNylOT6VmvoUDamZYu2MnSpiPszTJCsHSYeVGKlMy91LjW/xcstzE5oeK7J11qbCfLwcb9aTlt7vEfyNWOE9ac8Ae88Dez/VYwD70lQE3MPqEUCGFydngNKeC5LkRkzlHQbOLZgHVFQYWHy6ulTT7kzNHDkIr85nCaP+YLkma6N4JuvuM1DucrlwNICxpuABqzHtzYJaVi4RjwqAIR6Yk3/4gEG6y4bubOWMrr0kSN7SSy7X/grOi5o5uIZIKKxHVOqi/iQiHCA0Ml1QHxE3AcMX8spozViHBRbD6MAdMTDcUjoQTAXYg0Vz7/aTVS7B/w842rkl88IPAec6uOFPiTBKSSZ5xQG6dxdkD3VtvhwLUROaSIOBOpavkJltE1i4pYu+s2Nl1ubMiJ3yf89rkDscSxklyzHjF65ob81wcjcDhCikClX5fwk5zoNu+n5brXOBCxsiQcVjhRAVQirHwscscK/WEsIs0gGbMLDDZCyFBJ5i2ZiM1LdclrqRClMVlJK5R9Wvle8AUA6lGvsiFgKM1YsclsMNSwKcEJEXYT4Aq0pohc/PZI4/+fVyPBexDX0K/QokC9RnZigxZhQI/CtTbfKVAd4nMwtSjCjSpk+GG09w5+Imdjp+5qcU1i0/Ys0NYveQmsPvM14F4OCj6d4r5XnD8iA/c3PM1tneK1bsE9Swo44XHzV/jplAG7e5k3U8G4MhuA4DTc8H+MyHFWjlXam/N83HDOZdkoL9N6G5n+P0EiCBtGpyetoSYHpAAivZHQyl+9t6GbBu11k6DmiATUKtZGyXg4icJ47nDYDq5uLLH7cyx3xE67N8IkE2QPBspwtOFvryW9tZVeUAx2G0KpHrga4kd51ap8ZYWzXnXdL4U7/aeUSDltR+fksFZXEdE+TqPLzg3zD07Pj8IwlHMY5Jr3pb4kSVGJ5nzux/4OcAIRP6AWphKwkFJoQ6H0i1aJ9eTLDRdAFgvjvEuapUHFKgv+cU5vziHTNsld41aNqlQaZlvAeaS0ixsVhcpywjHXP0e/UBJSHsf0bw+QoYR8fk5hmcdpqu22lLxuR47sPdxPRawD32FDHQJ6hz8jhVKZsJdzJ2iaLkMvelfp2h2/MXnzMpmGKNAjjZjEHMgMCZbcegAuBnMW1QxdHNQrN4mhENC7hzuvs/CVHLFwknR32VMW4NxxGG4dosu7cE8AjDo7kBK/HRBSDP3GegzVucDwj88p5nrurhqAFe/f4IbIySSkJFXHqnzlegAANPGLU7pBkv6ibBcYQWKOXkUyyx1sqQTN4QLm4OS+n5Bcfd4aTDZDKxfgoGRBpmSJcrnl2R0dSMzhCOLIpwVDJAc0ewFjcW6DNfOCq8Ao6K71yUKxPLCXATOfpG46Vs21+Fjel12t0ZucFYYErB6CRw/AdJ5tg7VE95bc56aG62FI3UFDmS31OwoSk69wXM265rOWEDWL7XCjnHDz0eFh5D5XGsYZW61hntWuYWJrFMraHeksheYsLBa/cQudmE6FhG2VD3aIt+w63viPMyNmV25F6TeQ72gGTPab+6hqxbzx5cYr/ndzaHMzjLGc1+L5uN6v9bjZfnQlwowesjo6mnRAYRKElljBZZTzx8yZBI1ar1EVECAtCpzlcWhQgUQR0r4bP5+zV6opTJixunaw517+hraKZzedPQw9FNGDh7TVjBduBouqAGQeSE/NAcWiPGCBTOc2C1Na+DyyR67Q4/OfP36N+w26P4R4JtFPM2uiX+fV1Lp0ZKAtOXG2ezVNkpFs4uAEyAr/JwRV9whc2NaoUbQ3XJudHxu7/FAw9iHQux5K+xkswnIbd6YO7IjS5RIOEpNKU69Iq0WdxR1nB+qs+4pAU552Lj/3FdiTjgCm1c2t/GCLNZJm0tI7G12ZW77wZHIcPhUMJ8p3MGju3FodnzOydtsbmbXni0s082o88KiTyuHDQ1AMnJHs18IFmlF9xCx7ne+IKGn2ZWu/yE7k9cge6ldJwub1M40B0KKi8s9G3WKvaUSPnIA1q+pRWx32aBHFq9Kt+987agkK/K2w+mTDcYLR7PlNzP8nIGk2H/OjJbS2T2u92s9FrAPfamZ85YlhAI9BFkMTsqFTYUKJc0bVJeEcgrOraJYRPlxgbPi1QJBcg4hGJ8nyCTo3njqeM7ZLbmRAufiqLF6m2tmVA4mIjUh7kPnjeZAR4/TE4fDx25Jd26B+SIDTnH8J1dYvxUE2/DaPTeqxdhVETeBjhRbx5O2iYjL6T5aYWjvFe29ojlmuIm+etmbKDo4xLV7IGwGupN5OVYYFij08BzwS5lnnP8Ixisa9NKGSVk0Zutc7fniWpF6s0FqFLpRqHfYfMNwzWkjWL1L5tfncPelRxgoOQDM2sqTSenssOJnqXMkANW1orihzBtg87Wr3UqJqBEFYAST8t0oomE3Ld6QuQXG7fL969/SJiqumW5QuhXJvG8OgD8uBKIqWjch9XgpmDLtnOaNXZ8HbiguKZC1yj1cIsGC1lIZx+cBkoDLH++hjcfpebfkuQ0KP3HWKqqAE+TGGenEYboIwNOGLMjA177/tF0g6fsEKLD/9HGrfB/X41X50FeSOvPgDCbDTbSWCkbxDkfOPEphau8U45UsdPhgNkF233K6b3fmyt6ya6MgVTE/i4AoFITkdj+wofiJ7LnmyI2o2asl9FLvRJGpILfGbhyA1WtqsyQDt7/mOVOx4MXjDydgdgi3Hs1rj/aWRITuPteYCz+rBTkqcksRclw5buztEq9R04ot1Xj9JllIZikeAogg9p4khkxXB0kKb4UsdWTv5c5miB0AI4PMKx4cIECzK/Eo5Tk5iwRgDEQjGGwo0i5uKOrJUCwQZvZAt2NxTZ2rqdFuMq1Yz0IVhszXdsiLNZJw7lRo6MOlQ2ppsbX5Wus1IgnDXFiutHZa5buBzCDT9m7pktShekNKBNoDO7vpXHB6ziLXvRMju/B6tjssWXNANShWb9ZaBRI1KLlYOBWrMEnGLgUPK+FIs+a4dli9TVh9d0JuPeImVO/DZscnya1Dbp0lNZvQXvkZ0rWDNHsVEmLCkNHsM+atw3jhbf73OAN7H9djAfvQV+mc1AbuSpJF/4bZUWWW0d2YLst0OM3BHDzMg06Up+T1yyXYsWz+zT0hp7TKnN28DWh21kV0gEyLDqeYqOZAiJLzL0X3bsbtjzoM1yxy7T1w+cczwn7G6aMOd9/3lSQQV9Z1zQ7NrUdzV9h/7NIKQaA8nzrBdBFIuDAha1xLTdgNByuwE9Dd8T34U65dm1i6ZvYOaeWgTjCe+ap9ao6cpVz8NEIy0N067D/xRsm2TmqjdSY0bxXhZLObWHRZBVpkkUgW+Jn7DF0lGzpm6OjRveEFKRBu6qTGujhzw8g260qNMKPsuGjeCjGnWCKNF74GTjIWx2JCHmi4Cizp4oONWg0C/poFslh8FT1Xc8zwg1H4syIcim7PhPPW+apj2nFNUJ4XEsj6ZSKL8kGmWhEgz8Y0dNEIKF1hFyoPKfa5qADDU6aRp56vc/Vmgh8i0jqY8z4PN7ST0gpfAvz+9G8nTOcB85p6uebIme18Zs/xuFO+l+vxsnzoS6khksy5hRsdNCjGJ7y9vaf4tSTrxvViqppMlNveoDpezFupjhKF+MF4d0FuPGNaehbHEsMRjot4trhVbL/J2P5iROoI09z8escuYccTNwDc/qBBGAKmM+rFih1Uc+PQ3DnIrUNzTxf3dpd5sk5AOCULnvSEfYwaXqJepnMWFj8xsJKOFLlGoRRCQJnjJEtezo3DcOkxPHFVg1Q8/VSA8ZJkh+NHzrRWy4xr9d3CRqwelPfsNKcLgwmt6ygO7RoYYYPoILMgHALCnlBad8+069gJjs9dpax3d2pCaphhLTf9UDo8IzVIsiKlDt19QnMUHJ9Zhwq6d/hZa2ROWgkufwwMTxextGRgPJfK9Cv2W4WqPq8sHLQBmhOwfp3QHBz8nCs0Xb6jOUh1UCkpBHRhcQZhPmQcsliVvK4a4yKwf8vr099ytpo6h3nLgwf1jRnZO+SzFhrYpZXOsegVIWBHa3Oxuy+7WqSyB979lcYQB9TZ8uN6/9ZjAfvQl5LdllYZfnCcowQytcLRZjTWZc1nS4xJWnFD8pleic0elVpfHBcA1CLWHLXORFJhjmW6e5QuRB3nJGdfJRyfetz+qK+zLAZM8vQfRsV4xr/nZtGKhb1D94bFMK7JaCuFt3YIM2dW45OG8ymD2mgtxcLav9Wa1tzt1DZ10tDrvCwKXFbkxiE3Dqcnvnoz+lGrL2TpIOJKON95YL+kDpAHTifJPB9ZPKzAd1o7JzeZM4ojxOUmgZtM+zVwQ+/fUVA9nrlKK28OCti8av06WWSIQzyXmp7sotKeC1ITlJsD4cdpSwcOmBYstQJRElO0R9XRpY4Fv+inSEQp9wfaPSHKkg3nIiFiPy+z1LgGcCyvS+vMsDAhH0brlO9vuc1PinDk/cJg/phhOXC0O612UO0+V0G3mxQO/LzDKcOfSN5wc4E8l+JVClxxzIcyvXo6J+TNmR3fX1xzRkkI+C/gd/lx/SuvxwL2oS8B0jpD2ww5Omir0DYjbhIkNZVNeProgXhXFqrxvLHMqWhzG0/Irb9RzBuDEQ0OdBHYf0GLqTIrobsGN/tmxwI2Xjga5hoatfk2k7whTMQdL+zUbenIpOIDm284i5suWXxrh7gvzEijw180UOGG/1C06qKivefm3d5bB3HGE7gkc8xI7BZS7xA3bunazgixdrfL6y7zp2m7wFXVEHe9WB+FE4twOJqQ1wgR6ssspnRHqAeCItr2w8JIDEcrHuZekR4Iu3NA9ZyMW86bNACIhM2GS3OiD8uBJa5chQyTuYeUqJ1mb92UBUK6pLXAj1dS2Y+pk5oBV6ycSsFWT23VeEEd2dRwlpbbxUkjdTw0rV8ThlQj1TBsdfEuLF6TRW9XkwaM7FHMfSUruvsEsdeeOgeBIq0chdAnSgPUCeLG1dfcHFm0JCvCkDCdNZDEf0fo2eBHswuLvclNLIT0cb2f67GA/WVYAqBRxJUVlyzovm3Qv+GmeHwh9RRbRLarV4LhCeGx7Vf8hVUHrL9TbF4S62p3DoePHGIQ7L/I0Fbhjw7I1oUkbk7NHjj7KgOqGK4cVm9twN4Jul1Cc4gIR8HxRYPD577qhAo0s32plcix+8wtBAItGWaKdpfgxwQVMe86FiY/s/PI9ngqslD/lT6MOZAwIkegPVhMRiM1iTm1hMgk8TkZxUGx8HAh6Ha2WdpsL63I5PNGL0+d5YoJqqt+gaNcmQ8KqusHCy6LWndLOUBx8i9QmZuNMbi213MCu0QjFBRnDLJHgdlLDcuUyJyw8t2YtmWGxvs01vmyoC/daVyz+yxZZ90d9XF+VjT7hPnMA7LMI0UV08Zh/SrXQpTXUmdY0xnvu36TMVzxYjP7jN1Ru2fxc8mCKm1uVj47P9n31bpwiUB3mxl3Y6Qa9QuJqdlFpN4jrlz9DP1khUvZvU8XAadngYcEWPF/yscuuWmlwFMK8s8nPj+u92c9FrAPfdkMTCdeSndw8KNUTU5cS4WiwBET/Mihev+GhWA6X4S981YwHTmUZ/6VYHhCY9kCK7qICrGV2cjuc0afnP0ion91wnzewSWKitUJcutw+yOHuOLmVsIZV6/576ftkstFd3WtZr/hpOZRV5iQjnBZmcWs2UFpYBxL9oLxycL2aw4WcXLUmiM2XBUqNcM6nUGtJQtLPTC3wrDOpEhbV0MSY2/MzYKCPTCjVa8oScfNXky0y84ynBYLKA0KPxmxJrFIlJiX7AnTheKYLrSDKqzKuLbnLbAY7EDhLRG5FwzXzObiwQpSoQAAIABJREFUwUSWojAY42/DDrimEDhu2M0e8M3C9gOAcKBdWDjyyeLKwU+5Forh0i1ki3IIEGDzklDy6drVuZ0zi6mSsRVOWh00CtyoAgSbV6olItPNJXN+Kw7+lJBbhlJOZw7hpDh+RKPFAi3SzzPT/3LloFt+b+JqkUOUzLXik+giGBsz8rlzY9+L/l/Lb+vj+te8HgvYX5LlIuMvcrPQ2SUD4xNAHUW0JU4+N4SuJHE2ATEIZaXY/oIbQOzJwIo95zPtjr/o7F74C9/cKnbfN0PbE+HD/uURbohwfUDqPeCkbnTqSYpoDgJkkgW6O25yfrSZysYYeAXyy0Bul9Td2ql4gdpGFU5ac8amC4Oxil7LWGokQdhMxGj8qac/I2CwWGvPZaa+TcyY1xQwN4fFi5HzFeD0MSNP2htnEJRR+0+C5sSOq0SuSKKUoejywomHjIs/sViXDEznfNH8fMkgnNe26a4XW6Pij1i65mKUG9fF+w81oqTkZvnRBNEzg0HjBhUSLX9uvkvWbYh1L5ayfeExbUg1h/C65IbROcWoN4wLQaMkZB8/CtTb7dU8Ms3eybGQdtWdxYyYrStqTtZV+4V5WbpNqMDNLEpQY2g6wXhp/otW+AobVi8C/Jjr3wFUo1+KoPn6H0KVAJbDyhrVq/FxvX/rsYB96Mtzc/AGTfmJNkapJ51ZZiBMCxW4u+UmO1wLDp+VX/aFIXf4VKDOI660/lKHA3D4NKPZOTR7dk2pJTx3/hMKitvbiHCcMV/0cOuMtCJDcDqjO8fwROBPFjNilPDpgs9VugpJPJGX+VAO3EgJ9Xn4icWFnQDnGyVAMRzxIILD0nh1mTWpF4yXhD9d5N+Lx59EatTaHbD/2Fc/xO52hj95HD4OxkaTxTD31mZ1mSnDAGdZ1NTxuREBeDqiqCOxxg9LUnBzrxgvQ014njbcVL0V4NOVN1LNsjmrA8W4vsCrNlOyDqFQzpO5ZTDAUioRo79N7GDXLCSpswJ0LHMpMhljz6LgItC/y1i/yfxsMmHa7dcTpq3jZ6pGEOoNdlPg7OeZMKZ9xXIoc0n+VyDAUhRLYSxdnwrQRNLlm0NebL4Sapp06lz1k3woOcies9XiwC9KW63qmt8vs8VmbxIKO5RkzxTv4kjTvxGEt/pYwN7T9VjA/jKsixlx8AjHADcB8RL2C0sspzqrV7YcN1pGdrC7chNhxbgC4jbXRGMo3edJhaYYmg4ZHPSvX84Ih8gNJmY0dwPmyx7ZC47PQk1TTj2Nbf1EO6aSD5V6myeZR958ZonNA5ODxVzIw0g4MvVldrXQqwsDkJ6Ny6yPeh8SR0pHCtjcyC8n+823EWFIOLxoEU6E8vyQrQASRmQAZ8bxGVmD81rQ3SyXoNorqb0WgxIhQBYLY0wUQUcQqkud4GQdRj6zApm0Mt6mC3YMbuZMazpnIfInRROX+5B4YdlfYEH1uhA2Cv28vScsPBtc+7Coxg0AOLR7Nfd3SwB4EIPV3StOTxhY6ucG46WrxcOf+F8peOoJxQGE8USdidrpcwhR+H22IrS4dxStl5+1xtwU3R5d+AkB8kLyv/5tMrhPMLWWXzdal2muH+GkdSbXvzXhuBhpRPCgu+Yhy701WYQdDE7PH4dg7+N6LGAf+nIK3ybE0WHeKNJHCW5wcBMJFiGaTVDHDiV1gIoFNrYWVmjsw3YHHNf0OmzfeUJiwZzeX7t6evUDN7d2nxkaGBzccYbEjLRpMVwHdg3cp9gpjILuhs4eAJleEOD0XNHeSw1A9INW773UCroDU5r9SDitBBTOWxa/QlwAygyP5IruRg12WkSrarMtyLI5N4eM4dojhwCXFJc/GZAat7g3BKC7mdGKIPUOK78k9ZIcQkZiiqaNK1T6mfls3Hy5Kbf3y/yHECDjYNSDkSOJ877h2lXYiknK9FzMQbB6mys7MDf2ntf8jJvdknLtZixzKXP2iL0gXvMalgyyzbdadVnqgeGJ1G5DhZ2c9GJzJP7bzbcZp2t2s2FUnJ6JGRkvrMYw0HrLFY32MUPFQjizCZFbdnTNnn6O0wPpwLxypjlTOxQ4s3syTZlBhe2e7vKT6cBcpM1UDoI4AM7zcxquOUP0J7tmK8KyFUIcyWAtsGXxYAwq1Y3mcb1/67GA/SVZMnhoo/B7dghpnc0SySGtFbnNgNlF+Z2HgpEkpStrdixwfhLIjWMH4Ut+FdC/prg2tWJRFagbi58y5vMWLlFXFXuH6cwymkz0HIYCV7ED8wPnaC4ycsXPSm0OUEXGm1eJVGsnOD0JFq5J93e64QNOARkBNeNaRr4YjGdan9RJPa3TckqwesMQzthTMKueGyUykK0rmLcebqZRcLOLaO5n9K9OOL1YYzpzEJX62HGDJbLePlOfpNLN213JNFtc7/3IPLXjU19ni8cLV4XOZQZYhbTCzbsU/3BUNHvF4WOHpFI70ZIw3BzUXDYIfbpUIDV2iP0bPj8AqHNMcx7tcPGAHMJrya6x2bOLcwbnjVd8/92tQW43rEC5kSX6RAA/JJor5zJHy7XjdFMGHGeN0VKju102AbksDhoWI0MPRH6Xy/fRJSCD37cCTUrigYZwNWHA9tbIP+ZkEk78XswbMj5/KePNoODC+Hxc7996LGAf+sqCNDtolxFuaW+Ue+Xs5eiqPyLaDDl4tLeuutGXuPfpnAnLKsDwUSK0NJtjfCThYPUuVQaXugehibb8nBF7j8OLhvOu0TaXctp/IAxu9tzAAQqh53NAjxQeFxureS1VhEqhMirVPRWXEGOOaUAVEIuaDisLYA7uNNDV2n01O606pP4mob0dMT7pEFcOcROATLcHoHQwiukyYPXNCeoE7e2E7p3i+EkPN5MFhxtzl4A9fyrvlcJuukEAzSlDIhDXDv1NwvFZqK8rN+yac2DB86NWzVhupcKi0pgOzC0dblyjunUE6+rG60VT1b9lBpcfgWllUHAPHF74Wvgfmu+6CVUOEE6o0C9nb4rTc6mQ7fYrdkrNMUNiRu7cL3kwzpuA1RvqrfykcCOh1OwBBzJUm11EPg98DUFAdy+FH5LF/njMW34nnWi1KfOT1o5YvdihSGuQZTgBKVM24mYW2tRYyoBBzHHD9+snI6esFgZr7h7MNB/Xe7ceC9hfgqVZrKvicoNAjmImsUYMuAkIB4fVa1hXwdnT+FGim70IYk8RNDLNZ9ffstPp3rFTcYnU6BwE47mDn8lYi2uP6cxj97nDvGXApB+B01PBdKl0Io+AGouxuyvu71LZcOGkZEbaBtbubENMxVSXJ/oyMwunMrexgbwVyOyNSGDsRXrrmXVWw6JVIKQwAPPWYTpfY7gUjFeC6x8rjubK4Sagu0vYvwiAA8azLVbvIvwpww+Rjg6FpNBwA/QjMGepr2n1biEurG4Twm6GBod2B4xXTYUhx0uH2d6Pt3Ti2fRbnC8CEgpRRysUBizwn0RUWny2olQeb94YAea0OIJAOOMp1PbSZcQVDxNxwz+ncxOWG4NwMrcTsc4lrklgcVGRO8eufCCxp3+rmDcB+099hVElC9yoCPuE44sG89rXcMkiTM/BwjuNKj+b6NxFS3nORsw5JyPVzUpUAKi+j36wWBZZOuHpYpEykETEz8znB79PspBCcjCI+sHPH9f7sx4L2Ae+2i7i7OqIXXSQycHv3DJPOgniRtF/G6pz+P5zrf6GkoDupcd0lTFu6ebRfRvqBhdXQPeWlO7bHzljzzmSODpFOHq09x7DNTfZ9s58F7EMvfvXPCmHE3B6xtumi8WBw48kBwA2izM39tQKxgtX2WMAMF2S1h+OqHMtb3Txot8CzAF9JMuvu1d6/XXUitXNuAX2n7laHNp7ns7vvwhMj77JuP/c4+57DVLPz2K6VACBEfS3HcIR6N9mbL6dcf4n9Gc8fdTVANH+XcLq6z2QFNoHDM9XOH7S05bKyANlFlML+ZEFRTILhiihL0lAuM2VfJB6WTRsVng0AG5cAjT9IHUuSMGzMBy0S2huPNo7zuDmjTEdc3EW0aoNi2sxgsxSTP0EnP9stoRnuqrcfy9gOm/w9P89MZfNrsXpWQtJlrtmsN944eFHxebrGc2R7vFx4y1w1Kj0hwxXCmTH53DmUuJnduqEFenXSEkIqvVUboD5wkTkJaOsZf5rua0eeswln/pDds0lfqjZ8b6P6/1cjwXsA1/THDCcWvjNjHzqTCNE2jZU0OwE81mGZHZkkoBw55BbhZqOqMx+/B0hyHmr6N8KNt9yTnF6xs3t8FmCH5y5RShcFExnIIvLmHNlZpI94ZeQzfVCtdKY45rPWfU3pcswijuNVtlZhsmiUhqps6H53LwMraNLPaoXI1DMhxX9DTut01OH6QKYz0hQOX2iaO4c2ls+V/dOq5+jmxXDU8H9rzmknsnV6vn+wlGwemkmsmPG4YW3+VDA2U8nnF6sqt+emmOFBgd3GiBzxPBXtkzAXi9C2mLKW8yHS7Ge1zT1BYwdF0y3tid5JoyKw3OH1PN5uhtUEolLUjvDZs/PZzq3otRwVhXPSb5Jls/monVVVgTKrEkDcP6zzG7T1njhWdAyMJ57K9b8+emjFnff92jvFFd/OKK9T9UxI7WWbj0R+oMX+DGj2Ue4U4QfOxxeNDb3JJEje85CY8/HaCw+RqJCg2C45AfJn5uuzRxZfCGjZACWwFDYkaXL8gMgqjWyBcrrB4fqipJaPJI43tP1WMA++KWIs4dODuHo4CLZb8wwysiXccHvs6B9FZAbXajegRlUYW8O6yM36RJEefyI0NZ8bqd/05117xiI2N6Zbscgs0LSAID21gqSCYynS63OB82OnZCfSN6o6c/mYgEBxIrXvHbYfc5E6EKUoNiZc5hyEvcnwpOFjRZ7wfGFYLpQzE8jXB/hQ8Z83+L694zJ2BGWPL4AN62ywVlhT53avE2QVixu4xNXfQzbOxbo4XkPf0roTgnp8x7zGjg+Cxgvz9DdrTGvHYZrVli1GRMEyAloRj5XcYOIvc21VovAVj1p8PNGyN5TxrxMZyxa/TtFdwscn5eOiUXx+IlivkxAUMjogCgIJ1etwB7qm4q7Sd3EwddRvBWhQPd2Qjh4jNcNoPQlbO8iRBXjZYObH4U6g4srj+7NAPUO97+2qnq29j7BjxnTeWMMyAA9bzBvHFavI1xSHJ8HuuxnMlDb24TpojG/QkF7TBi2TY1QKQJxUa0HmeL9KYb0qrfCFZWzN9MIIptpsCuCaSIEVZ5hj/O43r/1WMA+9KUCPQagzUjbjPgswfcJac/bNhcDUnIY7jqEd2GZ2awzkPjv83lE7h1WXwf0rxXb71IVNq9eZ+TgMHySafgbgPZODD6jywL1PdwwY1cyl1Cd8NPG0novMtzJoTkQunLzEg1SiB5xTWaaOhJBxnN2gNosLEM4IAu1TqXgtXeK9SvOURBZKOPqgUD32wAgwI2Cszd07og952hxpYAzQslGcfZTQTpJfV3jE2XRXzEs1A+CzVeK4uQwXggke/Qj9XNnPz1i9711pWM70zSV5OtS6LNp3NystdDnliSM1Bf2nVYB9XRmSdkGkTF4VNHuDEK124v7x/AEiFcR0mboTFgvHDjbrM7x+Z/foFkEOO/KAMYrV6nr07bHvBFc/HRG9mKWUh7zmUd7F3H+s7REpKwd5u+t0e5yhYPXb1hNpvOifaBl13TmLADUM3vtj46ImwbzloL4dNnU7244ZcybYCGY8ktkotSwmBXWYm54nQtVvtkbXJgUzgpd6ha9XLbi7WbO0OatVIH843r/1mMB+9CXPvjzbAaOAbgNwFlC00d4l3F4uUHY0Y1gPiNRQ2Y7tXrF6ictcqvVdXu48OjuM6Yzh+lcMF3CQisFq+8c2rvC/ipFZZkxlXBMwBwNOq3Ubr93aO+EQlHzJARQzXVL6GGJv5guCFGOTzJyp3CTwO9sIz7afT3nbn5kBzFvGHviT4yAwYmbGJSQXLtLyI1gPAdWO+DQCLyzRGLzaRyvWQTiWuE/PiK9XEMSsPrGo3+ncCauHZ5IZVby5N7AjwHn//QWF/90Ru4C/M0B6WqDs6jYfdGSMDDR9Lh0AOpIsMgN3xcLlNYIEUnWURsJxU+KcEut03hBfRZ1YvZVKPDkhoQcHXiDG6SyI11UeMciDQDIZJZ6S3s+PV18AItmrb2HpSvTkuvwIiCb07yfaOirD0yF2WklnJ5RIE59muLwcaiOK24G2n1CSSpYvZmQOg/11q06wekJvTld5MxweCCgLu7+pWt0WevPGAvETp3O+yzObhamQs/LAai740EsDGoekszFK1o6f/qV/PY+rv+f67GAfegrCmRmh4J9A3iFfDzAq2C+7ZC/XmF152wgr9SCdRnbP2zhIrB+mXF6ApxeAMdPbM6RBZIccq+QZwNUgfN/vIIfFfvPFfMZ52wy2ybe0Bi4vVecntLzb/rYTFoTuwY3mXOFcHPLEKCFbdrUM63eqp3OBaengtMLWgg1dw6rny7zM3XA8JT+js1uGdCLCtq9eRa2JI0cP85wETj7CbD5dgZMV7T7InDzvANiApCB1Rvg5q9mjEHQvXF4+ruKm1/fIl1nuFkwXyhEKciOvWD7NR87dlJTneeNw/HLcwCEqvRJbynPqLRvP2a0+2zu9mRXzuek0McNap5Y9XY0cXI4KXI0zVOzsDebg+LijyOOLxqcnrGVaPZA9wcO+8+kbvLjpWnQDGbzA3VW7Y5/338mcDOJP3GTreCQkSozafmEebnRr25KNpmgexfhB9pU9fPirh/XTc2Rc1GZTQYYC1QRRs6z/JThJkVcBagAtz9cWTdpUF8o3yMSgrQQM4y84yKIKGBhXzaDiblnrYJlFTFSS3E50apXFOXnPVxLNXdWD2B+7MDe1/VYwD705QHdJA7cRSGTIL/s4SZBG4H2hjTnuFGkFyOcV6SJ7t2rt/QZjBsjE3Q2zwqZLVNm14XbhplizwVxneEnQRaFCIkh4cDnOHwCqGMBCUez6DFnfD+gzlGKp18Wmule/XjA8LQl42zFudW8pXu7GwWr15z1zGeE+kpXAAHGKyBugas/sMJ2TVf8Zq8YrwQX/4wbn58Ub3+7RXMguaN7p3WOtXlJl4/dl4L+Ow8/ANuvMx3yA99LbgB/FFz+YcLuMw8XGXlfZiwSKQso4m7JinjGBOdk5sGrt9k6CYU/RMa1XDeMelFziOiMNGKWXe7B5lnmhN1NROodRID1twPUCfaf9+wgjoTJAGD3hc0IPbtl2olxA1+/JCTpEjvVZp8hyVuRMhcREHIs8TapRMTMwOEjh+5eq9tG6D3CKVnEise0FTz9vRNS45CuCf8lM/lNHeUNOVDrl849mkM24gjJGbPZPpUiwggarQ4j+oC0U+d2pt16CIeWrgt4IIb2lHvQPsqGffa9qtZkYYF7tVDpH9d7tx4vy4e+MiAnT3uoiVla4ciN3yUy9sanCaIC3DWQk8P2FSGracv50vHTBD2jpbkcPQcfDdBeDphuO6y/8zh8YhDXwX7hhQUvGbOx+M5x9iUQM5othrMAqvC2pDOf/2xGezNh+KizeQ8LmJuA9TecwUjkLEc9GYTqgfbW0S/QWHLhKNh9wcefz8ig7G7M6zCxSxyfsMDPRz7H+c8ShkvCb4dPPHJHmGg+J+QkGVi9TWh3gtf/BndKycD+U0+CxF4xn3NzL2La8nz33/M4fJopsJ5Z5MJRcP3jCc13Oxx+dI3UOoRToiOF94xsaXjfZq/Ip2LJpNVF3SV2qKdnAd0dk6lTH+ASCTbhhEp/Tz0/Pyjod2nmxs7YjsMTS77ugeFSsP80VKswZGD10lV4WgPnhLlTuNHmoM6y4I7U6+VGcHreYV6XGaji8HGHcMoYz82/UNhZcn4n6N4RzgOA4/NQbZxyK9ZR2+u3olSIL4WMoYIlKqj8LpRirzBWoprjCl1D/MTXT8gZTKQuJCKTdUDIXJRSzB4ZiO/teixgH/oyZiDAP89+Yqf+FTBes+tarSecbnusf9KivQedJs4Fx48V8TzCbWZ2ZvuGrMQ+oVnNmE8NVr9osHqtmM/LaZabTzaz2kp/n4W0dKDOhYDFyaIGUSZCQJd/eIKkjLhp4GZF2M9YJcX2K8F87jGvHd791jKjyX2GP9CJYfMNN/XpnDRxhjWyy3RGkQ7G0Ds+J/SXVoRH0xbQ4HDaOZJOHN0a5MBiSao6atCjesF8xR00rRzi2qG9J3OxFIDpQhC3iu6d4OKPE25/RC9KRM6y4nlCDh7zOuD4V5+gu0vwp4S4CWhvJqSur/OtxiyUpq2Ht0BGP0u1PnJRbcNV82skc3T93YzjR2TlFTNeidyE6TKBmjRd1nhB4k2BgcWg1EJFr0sLeWYRQA9PgSkK/CDwk0M40H6J2W2ohsl3XwbE9XKYYIF+UJQSSTDZEgMK4xIOyJaEUN/zw3mvEAZMFkIpJtPIJRKl6AeNaDNvxZIVCH0Olx5ubfNdXSDJbLB2KZxxY13/9Kc+vMf1XqzHAvaBL4lA+5odxMWPCQXGM2B4osjPJjRdxKafMN5v0d4SbuMpeEkg9iEjZwfpEnTw0JNHettg89Jh9Upx/4NlcyvFCzYob+4dmgO1VDnQTZ5d2OIELxkQUSBxWL75aoAbubv6weDPIEiezh73XwTc/1qGtkzglCRobh1WL9nxnJ5Zp2ebzPlXCcMVJQThyFyr9i5i+zWw/7TFu9+xIt9l+HcNpuuM/A1Zb3FLw9rckrgRTjz9z2s6QBw+dXzjBqdKJuOxu1PsP3M0u83sTLu3inDK6N9SqJt6we6HGf3lgItPT/hu9xzPfjdhuApovSCtHCQG+CHX5OnmdkTaNPBDRnM/ITcO0wXjpkVpw5R6h2QOFSV/K68curtkJrmWNmAWSC4ukJtEzr5yI/Al8+qMYulwWLwLSyxMbpWsRiVztbkcEIcG6aZB2Jvv4JEkDzjOL928UP7ZfVrxiiTiFJagOmC4kpp4XCJ/SHVfCl5uSvEocSr2nm0O6EdeIpgone+ZZJTYCYan1tFlg8q9VKeTYprc3dAjczoHw0ePxc2Dji2F4PS43q/1WMB+hUtE/msA/x6AV6r6O3bbNYD/CcCXAH4K4D9S1Rv72X8O4G+A4+j/TFX/1z/3ORJw9WNlIvIpYz4Xdl7XM5ouQkRx+/tPsPlOELdFrEtIa7ow4sGhWdwGHNB9F7D5ms4Juy8ZklmYZbFRSoQUaO65ebmJRRGwU2wDiOVtFXjGRWD7zQx/SvCHmUSChuy4/Wet5W8lzFuHw6cK7TLQZLhdQNjTeHi6AKbLjLM/cZBIi6hKhrA5SXNgwjEjXhTdvUc4BKSVQKYAfTFAo8Ph0xaSBeML4pvhLqC9FXz8DxnW1X51i/mTC9x/uYJEQfeGLE5CeCQDMFaGgu721twxOmeUbeDwiUJGwTQGvB7OoE8STtcsMHFljMKxeE5mtHcTZE60aDpvkRsH9aTfu0jTW1EAY0aziya2FczbYJ+xoruNmNctwsHE32Ex+IUCTs3cGOYJ2Qn6mYW5FAENNtNbKebnM/yKrAiJDnEKtC4bpW7uYhlkKgAaQqCF4VdmXX5Ug+a02l7RQWXRcGmzdInZCg5m1DiU1PI+sO+fWMI4APNBtBZNUG3HmCvH2wpzNLce7b1WaBoQHD8xiFKBdkez6oIk0LXmUQj2Pq7HAvarXf8NgP8KwH/34La/A+B/V9W/JyJ/x/7+t0XktwD8xwB+G8AnAP43Efl1Vf2X8p8kk7iweq2YV4L735jhz2a4LMjZYd63uPjKrKGsW4hrxXyV4U4Cf3BgOwUwwM/BD8DtbyryNsHfceDgQKiKDLnFsWHeADgDOxSz+5FxYYjFDZ0e2jvg7OfcBHIf6s4znTdoDhl3P/B4+9cE+PKAVT9j92aDi/+nxXhFB4Xzn9GNIZwU3e1I/8Wtw/4Tj/EKOH1vRv91g/YeuPt+wPGJx/pNwu0PA8anCdplblZDwOpnDU5fzEAWNO8CVi8Fl38c4ceM++91WL1N+PY/eIH53DwH7x3GZwm6jnBNRj4FSGowXWRS1YNifOYgs2D/Of0mT8+B6bOpWFFCXnb47P/McNPMkM615zXpnbERA8argPWrCc2bI1wfkFu6XEjMCBPgZgaF8hrSKd9NCf2rE/IqQOaMuA7obxOmMwf/mpZLx2du6VSwkCIKi7FAZs2J3fLb3xbMF4Rc3V1AcgrfJcLMhwBEweq14OInVpCtIIqUbk/qgQbWwIZBq1sGYPBfz24QWGZ2RY/VvdOFHu/IWpwuUAtsaumuj7zQ4V1kx+dsjjVvrJM095H2nt/fcFyKUTGDduPy/S1dtUww78jFWupxvV/rsYD9Cpeq/l8i8uWfuvnfB/Bv2///twD+DwB/227/H1V1BPAnIvJHAP5NAP/wz3sennKBm39rgm8z0ujh2wzNwOrnZIDFVSFdmD4oCrq3rhrdAmUGBOy/zMjrBDnRT0cbBczhw2VBOJFtV375oYC4BWZkhIm5xoPzl+bImY0f2BnJnIGkcOuAt7/TYPg4orkYEULG/nYNGTzGa8I565eKZpfQveMpfj7j5t3dJYsqEUCaOnfbfJPR3UbkxmHzTcbmG8HNbwZztCf1fjx4UvvfCi5+GqEOuPlhi/6WFlHJinR3C7p0KOCajK6fkX7WI24V+mJE10bE6NH+dI3NN4q7HwHtLa2upu8pNucDhlMLfDIg9h36IyFAAPBDRrwMdLEXVO1TXreQZBXGC5wC4TAjbhtIVAj4WUrOcFOCpAy/n5BWDTSQAdneZ37OCejuKPgtDLuHxUQmS0KeaQF2fCGYL5MFXTLdG6NHGj1kRVF085od3vGZM2eMBaYsXXeFCc2Gq6RC54BaqNQTcqw0e8uB8yM7s2KrlU1vWOBDfqekphGU728Wftdyy8dxCYjFof/IAkTjYZuJeX6fUstomdS4yRniAAAgAElEQVTbR26dWHHnn7dSmZ2P6/1ajwXsL359pKrfAoCqfisiz+32TwH8owf3+8pu+5cvB9z+hiKvMuS+Qb6Y4buEdGiw/pMGzc4G7lcJ2irNCaPDs39kOV4rzktSJzi+UESzjELizCevMnQT0d50zAfTQj/GQiBRgTNz4KLBEQXaG2444aTYfJeqY4IkRdy2lm3lkDqFzIL5rkM+nwCnWH3tMZ9R69Xf8EHjmoLW1DqM51Jp7v27jO3P+bxhUKxez1CjR/fvIu6+31Bj9SRCAwvj1e8LmoNi/6ngzW8HhIGn/ubA4MXznwi2X00Yrxvc/xpYsJNgHBrgiwF59nAA5ilAXnZYvVHsPxfEbUJaedz/ZgSSw/HQQZMA9w1SJyy+FsYY174Wr/ZesX41QaaM3LAIhbcHzC/O2Hn1/FX1Y7KDQILEXIkNadsidSb4nRUuGmmlo7NF7NjhtAdF9g+iXzqD30bOAyUB/UuPuNYqq0AGZHbA2LKD2VOtXqju/mTwsqUXl05P0kKwgVusm4qBbtwopusEf3AIR0F7Z6+p/WUiUCl4LmLx7lTUwllWKWhMOoDlpikPOODP2ntacI1Xy7/zg1SiSfWjVBJlhidSn/txvX/rsYC9P+vPwij+TOBdRP4mgL8JAP76EvlyBgYP/2xA00ac7nps/rjh4FmAuMnQVQZChrtrsPrW4fAJADXx5qUibi0HLFEYDSfI2wQ4hX/TVhgG+ZfZbBKl0uWLIzjURLJO4E6K7bcJ7c2E3PH4nPoA9YLjiwbDtcPZTxWnZx6nzyLSoUH7KiBuFE/+iXkhbryZr7LVO3xEHY8Gc3L4eYYbydgDgOkyLIN8x5iUeDVD2oT8jLvi7osOuaOcwJ8E7d2SF9bdZ8ResP+sxbzlfdJZhg+KdPKQwdNbsEu4ujjgLiSc3m0BB2x+bjKENkN8Rt416F8GdO8IAQKokgHmqmU0BxI2JCty55EDXdndfoI/zCxeqkZQULghWXelkDlBG4/UOBJnBHa7QYBOIJmEj3HjEAbF6bnlak2KqTi2N1hYlVf5l5wu2nfePk92r8znWlxISrbWekcrsEIYUSOJaAAw/TLLb94q8qcDPSYlwA8e4xU1hYVx+FDrBVg3l1CTTf405PfQN1I9MG9IxihoQWHMxlWBUaUya93M99UcjF5v73/9kmSUxzyw93M9FrC/+PVSRD627utjAK/s9q8AfP7gfp8B+ObPegBV/fsA/j4AdF9+pi5kyEWC8xmnmxXWP2lIVY40n9UXI5qQEN+s0L111H2tExCZs4XIWVhJcVav7NaSINwEipBhc4JKYUb1Lyw5XiWnqd3zlB+GjO4+obmPgDk26JrFK649Di+cDevZhay+DtVFo72DsQHZZa1fZXR3CeNlwOFTdoLdjWD9raK9i8heMF4GGr3uE9yYEQ4Rpxc9oakkdCoxXdT0IsLfe6TexLoJOPt6Rlw7uCmjNX1ScxSMlw5p8si3Hg2A+Zz0OPeLHm+OAUiC+fNIW66fNjh9zPBQqMfV7ztqpQ50dM+NVBq8m7I5UGTk1nPOItTJiQLT8w2a3VQZnVAeIkQpS9AgEC9Q7+DnDJkz3JwhExmeadVA5ozmfklJbu+zufgTPvOTM49DwekFkwsgdE4pAmF/4mvu32ntcpJ1W6WIEPYrhwypScjFLFgK3Ax7zEGAr/kgxc6pfyfYfE1SzvB0MWrmAwBi9PgCNVZmZV66sxzYeaXWBPUnkkqm8yUY1RurMPVASVymzECQGq2EFDUpHMXkjxXsfVyPBewvfv0vAP5TAH/P/vyfH9z+34vIfwmSOH4E4B//eQ8mXtH2ESk6zCd2LwAH1S4CYwK8z2h+dwucK+KGDvQAWLwyAKdIa4VuEmTvoY3CnRz8SarQtH/Lx5wuuDlBABjbrqTWuom/7OuXM8KRs5miAcu9x7z1WL2akFYe736DoZGbr7XGtcQNmXPdbWEUEk5rbhT9mxm5dTg+c8hdhhvFZhmkyqswJ4qnbcF0QVJEcVpYfRvQ3RiD0CnCywZpRYiyvVX0d9nEsVIzqdavJv5dWtz+ukPeKHJQNPcMD5UZkKNHOAn0ixNyFgwfO/TferQ74PKPZhrmolC4CQ2mzk70uvj98X/4B30ijSF61hLCSsZCTLnOytwUkfuGcGm2GdoYIcMMXbUIb3bQTY/hxYbQ30kZLJkIG84b00YdBc2RVPr21lXKePFCXH/HEFA/A6drqREuRVicWqO6K78j89aIPllqV5RbgxNbzlqbPbD5hnO3tAKaexahAm0CvH/qtFLr2zupjv2FiOLmBdKOK/pZkv0o1XIqrqSSjlIP6IFSiDQYBDpRw5Z6dpB+tLRmI5Tw+R5JHO/jeixgv8IlIv8DSNh4KiJfAfgvwML1D0TkbwD4OYD/EABU9fdE5B8A+H0AEcDf+vMYiACgWTDsOsjem3MFYz/e/vUMXM4ITUL4vQ00gIJcr3AHD0y2eQYWNL93CDfNg0E8/e/8CNoIdcB4KXY6X0TNZJIBYU+T1+4+ob0ZkVuP6aLFdO5Z3BK7qd0XK7R3iss/omYprmxzc9ygVq/YvTXGFJNo2qLUoL1PWL/OGC8pQi6n8fHCvAaPZD3mxtHlIXLjv/rDSOp67+DNraIERfopL753QSrsKKqIPXOvVq9nbL5hMZ3PfIUAX/910slVAHy1gjhKDNJaoUfBcE09mJ/UDH+ZIzY8bXB85hiMee8Qhoxml+rcDlmRNr4SCSQr3DEi9QE+K+AEw7MW/tRg9fM7QATaBmjjIXMCnAA5I19uoF4Q9jPiJsC1PJAU/VMO1jU74PCxdZx7mJiZLMDUE4Id8QDac4TtUofFXX8GwgCTB7CbAmwWFRgWWmZanH1SO+Ynm4etC+xo5IwA6g0fdPsAagCpFhxRAX9SSGP+ikczcj5YptiWcgdecwqdhycGmxrzNg/C79W5LLR+IZJQCmr/tjzh43qf1mMB+xUuVf1P/gU/+nf/Bff/uwD+7r/Sk2SgedXQOmoCTj8c4ZqMEBLmfQv5eYfpKnOG5ZSa3IsZiDYUUCFhI9qw2qjy2tgmYsNyAJWiXFwd1JsThp1UWdyo70q9R24Lu47eiW6m9kgSMF6a0/2FksShgLuhXmi1Y+d2+CiwIAXAvcqQmNHsAD8T25nPzIW9Afp3lti8Avo3LF6dQWfFP9BvPLugpHWjLUJrF6lXcjMLpx/5GqL3JqR11m0UcW5G3ApwMaNdzZher6Et2Z25E6g4uMnh+g9G+DHx8wiC4UmD0xNH4bZDjS6Zzz31VEnhskLiQrVXAcZLXoTVK8V0wRmfJEW83gAA3DCbDstDShfm+ZjN/QBgza5r7Sr0Gw6EJlPP4uPMad7NnAeOF47hlh4W+/LApcJ0Y650jcnIFyXrDFbcDF5sd3RPaQ6K6Rwsum5hqpa5VUkEQCbTUAUQ96AbKt+zAhk2wPBM6lzWGQmDc7aFyVjmeYX8EUp8jXVl+09ctfHq7rTqCsNpiVV5XO/feixgH/jyR8Hm54TP7v6dE2R2cN/0kBlYnViMpicJsonA4CE9N3V1irCKiLsG3StPjzsTj8atVp+5tCKy5ceFaVg2i+Kani3tlnTsjNsfrtCaOasK0L+jLun+B8XNnAWy2QGr7wS7H9Amat4C807Qv2M0R8m4Wr+kvZIoffLW3ynmNV0/csfNtbsl8YJRLXx9ceXQ7jj193NGnh3U0RHdJSNBlI6itZmP0fpSy4KVG4E4qbBrgSnHCwfdRlxfk189bVpgdIBTaEPN0eptZrGRwDyvrcN47pB61M2yFEx+oKgzsbgJjH25EEznAj/QDHi8bur8KrcO0Qm6V0d2X4m7ta5admIgO9FNEW7iqaTbJUxnAWEmlFmystzMItPuMk5PXPUupBWYkSROivXLjHnjcHpKViPnWXzt8AAisHqT61zKRXo8plbQ7rI912IRJonzsc1XLBq7H6B27JxjmRt+Jiw5b/ld7N6ZY38RaBvzUBK7uWzu9dR8LQGVuSFRJBu5pHyXmU9nQnjLZ4u9mGvNY/F6X9djAfvQlzUHN7+l8D9f4ekfUJA6ngmGp8Dh+xH+jHifbDPi6CmYESC/6nH2C1dPrC6RkZhaRbMX+BOjNeCXX/CH3VgJjSz2PeOZ4P57HUMeTxThhoNi/3nA8EQRn06Qg4cfbL42AeMTPl7qOevo7jMkkVG4euchOaPZk5ygnq87nDLCwIJDliAwnpNp198uBSEcM0kddxHzhuLp8YI5XO0NN8ziqyfKxy1r3jocn7KIdXfc2FNHT0JG0wiaVw3Gf/YUwxOFeAprw5HFrrkniSV7MVo3af9xyy6hOSj6d8YUjHzNFH8zPLK5nzBerDCdc/PkZ8KDRf92wnTZIpt/Ytq0CDdHQAQYJ6Bt7Bopws0JyBlujOjeDtDGI/YO4wW7zSZx7lcg2/HcoTmoafeyRcVYx2PddBhMRFxIHva64pqZa7F7SFM18fDAUM8yM6OejR2OZBrtjldqInl21WHgASiulJ2Z15pjV9iT4Yg63xL/EIoEYrCZ1mSvP6EiDQWK9EadB/j38dxhPDfY1nLq/MAZ2eN6/9ZjAfvAV+pITHjyu9wojx8JdivBfKZIFzOgQNfzN/R4u6obdnjT8ORszEE3LvBQ944bzfiEnPnuhs7h/x97bxIr25amB33/avaOHRGnv+c2r8nMysyqcuGUbSyBkSyEJSSYICGPDBMGIMwAxIQRTECyPKOREBKSLRBCohFDhJCQGCAPDLhBVuGqcmVVVj6/eve9+253muh2s9b6GXz/2nFe2ZSxUBX3orOk1Mt7Tpw4ETv2Wf/6/6+bHRcyxy7ENxTaAnCC4VKQF2aqaqfW/YvK+KIYWtsC7CmgVs+v80QtWH1DSvl4FtBsMrrXI1JnjhXRG/bCwtVfCDcnE+FyU4WdnpUkkqJIHceGADuycCA1/xjrIsdwTaVAmBR6j+KpjerecfPdP3Xmls5NuPsWGE9hRYvXpbkna29xW/javM6BndGMaf0AxAeMRLVCGg7EyFwq6K9bjGtBc0/6/PJtpu/gjkUv7Jm9VbwgdR4oHcLdAeVijRIc4AT+zuijRYHM0e60PqZyS7LOJTJ0tNlQHO4mxbT28EOhw715FdZuMTdu1mMdx8liQZBANdqFUfrDRPwTwJFZqCxes97L2I7E1AQ1iSCdKNIJhdVh6+Y0hNrhlQBoc8S4KruwOOuuHA9Zvn+g5bLXXfV4pZFZtFwTtLt3PLCkTtBfPQj+fFwf1HosYB/5ijvg4u8UTGuOmoYr/lG6CXBvA6ZzjpL6Q4O4GlGKQ75vsHgjM74hasWoAdp3HCX2T5j71b6XY3ETK14N0F8qNCr8XhA3DLGEbT6loQh2vMxY/zzgcK3zaM7tPLu7nlY/zZ3g6m9PM3FiWvuZYl67S4Y+sggt36R5A/W9OU1sat4VcRbfk1FYWs+i1ZfZj08C5vBJ9VUjJBT+CtN/D9fmIdgp2hvB/ppkk+mUKc35JMPtPYZninB/FCq5iSMnN7Hjmuw1V2GsS3x99OfjGCsORlZRFl6XOHotUWbRsUuGvRnxoLTeHCMKnzfbyHXdQoryue4GyDBCuwbIGeId3JAgq4D2jmPEmoFVY1ua+zIXW+bLCcI+A529j6Qo0aFE5o5VLZs2x9FwNdn1A8e6lfI+NiRIVHZl3PPzivuC7SfeHEIAUd5L/mD3FIB47+F7SiaKZ9J36pSklPG79+Yslg6WllADVR+MQtsb2mflKDMu5geF7zGnRIeDYvsJdYvfceZ/XB/UeixgH/kq4WgB1H8+wS0Syujh7gO1Ng4Yh4gyepRtRLzxiIOgv1Y0t9wAXI3RGIHdZ4q4Eaz/rrPYdxaJ6jKfG3Yl1SQ1P01IO1LvJQk0FnQv6eRw+puBnniOmE33uxHLbxXrryfDz9xMh04d0XkpNV/MI+ypISvR0bF9LDSvvaQg1xuL0B+46cfNhNJ4HK4jxnVAXghW35LtCDwc1bF4hIOFSw4Z+6cN04wLqfl5wXiUaQX0PyiMLHkpuPw1hR8d7n/gZucSXSZg8NB7kia6N0D3NqG01L7lRuhSf1PgRzfbKvnxOO4sXpAbZzEujjRuAXw5OvrTZokYGcAxF4yVKLlQfB4cwtstZJygXYu8bpGeko3Y3I3wh4wSHaq4100c24a+zCzJEsiyLI1g8iSfuKzor4Mx+o6YUnvzQDdlDhu+P96XfuAYrwS6jYj5Ik4dRcR3PwxYvOUBpLTWNSkNqeeC7Ui68AOAlvdtcyczi3L/nIcnmY4sSX8wDKwnDlaxrtwxA6+6xriJHfy0Mvf5g+FmQoxyuDLD4t0f0h/04/qHWo8F7CNfJQA3f1Sx+HSDJjtMPc1WJbETglekuwYyuhkrUK90kjdcK60xhxmGnWDxhhtrssRhhNpVANW1XqM5xgMoyyN2JJPg8Lxg+TW7luGSQZRuFDz9P6b5cdPaYVy5eYSknniHn9QYgOwANNLhPS8cSnRw0xETqyOt0jhuvI3H4WnEuCJVvJ66K3OtmJanYjqSyTwcTyKGc46JphM7pb9ndzGeKdobh8UbYPk6w0+KqXNYviqI94LDM6YYA7Zp9iwshyeBnZ41n7l1GNd879k6lmlJHKp2BuppL1WxMHV1rKiAdY3VIsofJkAVMmTAAdMlWYZhM5Lh10RjIZqlVOfQNwvia5uE5duM4dRDlNiamxTDhdlVjRauWZi6zRRjY/cFmYsfAzTlO4Lg5l4R9ryu9ZCTFsZuTNT2MaqGsox4rxYwSVyteOD0i4LbX3JIS6U7TOG9rNZl+SrCHjBH2UxnitJVVxXB4p3OcSvqjZ2ovL+YTM1Cmx/kf6kwmLU0gtx65FZmYshsa/W4Pqj1WMA+8qVRIZcDUnKYbheItx7NDTeP/Q8S1k922L5eUf81ODvhAnPmk7OuakmgfPn1kX1XVy0UVcxZzojLyOCAMx57NTuKifeMJWnuFcO5YP2lQIrH4n1BXvD3l0B2XQmCZqPwfQHEIezpnpFWgbHzvWI6CfNmKOZcHg6ZXUli0aqb9Pazhg7kXoy+zY3Y/R4XhcriS0s3v5bxVDCeUtDdfeswnPNU/+RvcSN0E8W8hyvqs+JBZ1ukuHUoQVEiv7Z4xw0+LcSo2Do7VKSFZVAlbqCL9xNy44wyLzNJBso4m+YuId4coE2Y/RAlFYiZIqfT6mZh4zknyGcdJJfZHxGohwGOzUIw4k7ATLJIrUezLUeHjsFGqyMPCvtnEbnlNa1kjarRo6u+sUHfW4dbbaQ8STCLmzLjYnFfMJ44dG85+kytzIxVdlTO0rcVZaHwWx7IWCWNHm9J2JKB9oaFB54kmtU3Be0dR5OVxg/AMNFjzA8xWArzq9as4nFQYPUt5Q+P68NdjwXsY18CxN/p0L1mhAdP+0D//RHryz2GIcBvPA17VwXIQNg5OmgEoxy3irQuiDduFq/WEyvAsQuMdQiw0xifUUsmdxFwzMRavOFoqr8uGK6Ip9E1XhEP3Aj7C8+NPQPL2wIoOy5Ud/KzaAbAJHP4vsAfuDFXn78SHUJK0OhmOvn2RTW7BQB2DHHHEaE8qF9u1CM9Xm0Tayu7UTA+ydSWtYrlS0f3/gUgLe2vqnN6brhRtzeY3R4A0z2NZuFlDEYAllkmR/utyTbgUrEv63IApNaRrm8rnS8wLQNcVsT7EW4/QqaE6XoNP2SoAHEz0rh3M8w/N1wvZ4KIS4rmlm4muTmCOrPjeyRGdbii7s1NiuXrQq9CL8e8L8cRW24EZWm2TZGjOpRjLlzc88AhBWjvMkrDjjZuM/bXAc2mYPF+wu5FQ4JIJntxWpK4UYJ9hrdMt27u7f44MW2h4WzDBTvtuJG5AO2fOkzLY6Fi4bUin2Ap1sRgq3P9Q9eNYu+njhfnQ9/j+uDWYwH7yJdM3ETb28JRWwv0T7krDX1EmjwQAW0KpAjCnhtC8bRuSktFWRb4rWNERToWwcqsI1GAG29pgP5Fpvh5oIt4WivCVpA6aqB8/8DQ1U7jqXUIStJBHccMp2Tm5ZbEADcomv1ktkgCdBT8plUldhiov5nYYXQBhyeRAL5txMcRkc4RH3U85IdjMQGMvGLOD2EH7D5VoC0YnyrabyJWr6gtq6JZUrq5meeRuizJZCiGPand45mieG7Wi/cKFCML4Mi4W73J8Ac6ZuTI0ag6jjjF2JF+KIi7hOId0sJDsqK5GZDWEW47Ip91pM0rtXf98xXHjLKcO1Ipx+iWEgTTKf/ca2cFkGnpPMfDm089SgS6t/RuZFElXjd1lVUIUuGNbVix03Cohwu+1/HUHFp6RW7J7CTm5rC4zfAmWciRhf7kq4T9dWDhSQC8IN6LabM4ws5NJWSw+0uW+NzeVG9IzPdvNRwGviuszi0e3B8CnY5MRkmYu0/Y2Jehn5id9x/Xh7UeC9hHvuKW4tLtpw6HZ4pyzdZJAEz7SDzsjOGN2AS6OjxNLEonE8ouonnrEXZm61Ppwvrd/w4XJDakdZkjVLTLKNcT9L5BaYHFG56C+2tibO0Ni2B/JYAKunfUYUkxu6ai6C+Igy1fJ/h9ggZHJ4vgELfZmIJldsrIC4e7X+iOGWMPHOS/s2y/cYk4FDxJCfVrbmRyc9xmrIpi90kDfCUYdg1OfvcYR1Lj7ZvDEReCdW6HK2euEiygcctxVv+E3n7qqcWruqP2RhF3iuHUwXeOo1mh9igc1IgU1rW82aO0ARqJ7wFMafaHhOmaBot+N0GDo2mvMTfdlOlQ7x38kIHCMWwdA6fOoXszIkeH0y9H9FfRinuBFJorV7KLJEVpBf2l+w7JAdV5RfkZOLP1opZO564ndfRN3H1ieq2kEKV58OEqmOEz5Q+bzwL6S445JVOo7DKvvZswx/J0bwtz1JYO+0g7rmnFol9M37h4R4nC4rZg86nHtOZzVQzNpWo5dnTiKFGRVmQvShJMJ5RpnH6ZUTyw/uaxBfsQ12MB+8hX6gS3v8iRYL6aENuEUhzK5CChAAHwsSDtIuAV+ZkVMwVKHxBuSVGuwuS6qlEqlH/guaPAWaPC9Q7lNMG3mUz39QS59Qg7oL9mXpRLwHRqz5UJ7ucoaO/Y1dQR3EMAvTQeLrNT9IeE4TzCTTzVq+cG2p8RXI97Omk4GBvNWHqkTmMmUAD8PdXFff63U2RP1t94GTCt+HrXL/kYP7DQwHHjrGLdigOqAuoUi3eGyeTj5wEcqd2AsTzBw4afdA5mJE5klkqtwE9Cjz9RlDYgd4GMQUfdF38xu4fiHfIqzsbFYZfhxgx/35svomBas3D5gZIESUqxdlakc2JufqCzBkAq/WisSXaRxOyGC7ExJDvcyhSs16R26OGgLEKGx9Vr0twpdp/Y53dwHDfaIcD3JIFU9w23NVG9OcnX9+tzLT4cCacFuzH1R1sptRyxwxOSRA7X3rw2qTWUBNO7Hck9xXRpLtFD0SW+HjeR0NOfPZBCPK4Pbj0WsI98Vf2MPwCy88gxI7YJkwYWMVdPpgpdMHIFcNAkcDs/53dV7zcAs24JBnSTnVU4hhwcSsNRYN4HSJsh7xv4vUXEDxwd+YEjvBonT7yH3cbsLmHaocO1Q+oiTr8Y0Z81mJbEY3LD8dTydcbtjynAndZ0R6/jrakTxIMaC5Ebr7PsrNrpVRCsFjI3Uejshgy/nxD3LQ6XHYo/GhYfrj1HTQlY7jLUfZf2LkqcJy0EZeT7pP8e5o6sbuBu4Oa8uM10djgUjKce44mbO4tmQ0Pf0jiE7YTxrKHANh+LV+o8miFjWgaUlrR7N5HoAQDqHUobmRU2cExXGodp7c1QN/M1eYq3x87NI9R51Go6ZwdzirfX4A/8HFUAn4HufZ47NQCYOofhXKAix/gXK+CpNTwq82vVBBiwCJaM2RwasIOT47UMe2MPml3VcMoObsatAJN7WKIy7HGXmGn0uXC8WGJlMh7xymKfUzhgNiVubxjWmRbA4ak5kYz/7/5OH9cfzHosYB/5mouNAO5yhA8ZYx+hI0/u4gqcU0iX4UNGzg5l5+EOxK/CVo7di9HNq+N8ifRCTEujzBeBLvhf8Yr2dMDwzRK+F8NBFGMjGM95svYH4kRViJsWLExzarP51uWWJ9/7X2iQLKojL/je+ivg/ofeXBcYUBhtTDWuyDYk8cTN5rEVK6sbaaXPQ2gvBbAY6Cogf7LA5jM/i2PVaOLVxHX5tsw4nhRz0PDWmZzTv7G6QlQ2m+/N+WHS+X1KoYOF702zti9IC4fmphhVnkXK95nZYFZkanpjWni4kbhfMUur2Xi4z+y2RgCeRsIyZSOXuPk6hQML8f5ZS7zKilZuzbPytiAMiv21w3hmnWRg59jcK5otcVaoztjVbA/V8rn8aJ1v5mNq0kC1bgLMEUNMdOxYaOK2jogx58qFkV1jxb1KFPgJc/xM7WxFgXF5LGjFsLCHTEgUwFVRs41poYIAwN3x9aSOBRx278atPhBmP44QP8T1WMA+8iUKtO8Vu08FISY4p5gUgFegCEpyUFWIKHLyyAcPtBn+xrN46XGDAcCRjTG1css8phJ54kVk5+XXE5om4bBp4YrM3olSaNAbN7UoAeNazDpJ58JT3c2LedXFLTeVmWFmHUxa1IwwQD1dMar91bjiZtZszTW+NYPeQtJAtT4q5l1IjKrMVO1p5TCcORyuBeMF3/zpz4D98yp+ZZzGbJk0HUdi8Hzdu08r5ZxuJdwk+d66N0YAMXcI0uYLwiFbnIhDGI50fvVAc8NjflpH+LFAHnQ4dNjAzCCsIuzmdjBT4gANgvG8RdgnaOtRWofDJa9FvC8YTwNS52b3/aq7qgSVElmY04JOG5XAEwZF9y4j7BLuf0D2RtwXjGtnn42RJ6pRczDWZc0AM3EzST2YA1Brh0bnfTJfw16/8z2A9+JkmWe8QaxQguSN6r0YN4owML+74xcAACAASURBVALlcG2YrnVPfsB8b80BrEER9kbuMGG1eqPkA2huMYu9H3VgH+Z6LGAf+1Jg/4lg+OGAVoBppMW2u4soJwm+KcgHP49W4npEyX7uvOqptWY9Ld5xMxlPaJ2kAuSTjPasR5oCtIAZZNsW4dsG+aRg8bWfqcp1s3cTZmPdms48rrlpx33BuHIYL44OGcM5MZL6nshyY8clxTYTf3wsDXHLrCGqmqAcqS9zyYI0Ld24vZ3IyLOOY/upx7QyYsWGJrvjObD6huy1+ny+p7FwbkmmyK1DbszUN9gmX1jwKiXbDRQph6GSVayDaARJ6DHIMWZB6o5BnON5M3+sUgAREjPUy8yck6IWaVPg+8z32DqgKDVfqkjryG5vLFh9m+H7PBf39j2wf0GmzuqbAeNJRGkE44qfxbQE3VvOKDKe1gLcAH7I8H1C6AtS52gLpd8dlaoD5QMO37m3NABhg1mSUCJmfVyJTJau9HU4mYMy673gB4qdYfdHs33Q2Qop/bUbS5259w8sXHHkvegS78M5Ry5XlqrF2iR6NuZGkP3xufxI7VgNRn1cH9Z6LGAf+crrgv7HPfQQ0E82f/EKuRrQNBklO/guQwtQBg/57RViMlGrAdgU4JJ91V8D6XKcOziJBaLAcLsgozHTkFd6h9AL0jnFuwmYNxLtBXFDu6CKd20798BuiF1AHSn1T4iRxA1fz3henTJk3nxcUriDabBaFovDlaMQejIPvnAsxHXDWb0aMa0Cbn/cmKGror902H3C9zydCRS1k6LGrb0j2aGGSfaXwajwFGrtnwnSkg4QtBwiG0/7SsZQwJFx6Xt2nn5S7J55ey+Y3S9mg1kBhlOP1NEUd/WyR9iOSCetESLUnEjYeQBgSOVUIBM7u9nmaUlReV54ei+eudn30aWaCC3YP204+uwE8VDQXzikFbFHhpWyEIgCtz9s4XJLzdlB0Z/bGG+yomANkpqnpgqweGudvJCZWbyak4vwsxZgODs60ld9lnqmGKQlu7iaCed7jvXYwSuKEAOdGYWRWN3l30k4XHpMKxaeh6nYKTFck7pG4WFo4nVJnYmajczEW0hms+jH9eGtxwL2kS/vC0KTobGQdzF6wClUBeO2QVxOyJOD7gPiLY+WdTRUT8Jukjk8kBR72+yazOcugmkf6IbggPguIOwFuWWSsyjFpwA3dVpICcWoZqqqTtHcHRldFZuo48I6tiyB3QzzmKpbBJl+swYoUxtUT+ap0tvxAG/JFgfSUUPWvaX7w3BGyrofZDYVdtZp5UawuOGYdFo6xtKce/MzZHGqr5FvhBvm8nWZU6FrUYaSeQnwPY4rN+Mp4aAz3ZyvWTDZ9/2gaLaZhr2Df/C+BL5PmE6bWV8naocMqexNFsQM2M/Jd/Rl6oFpIbPrvGTFeBqMxSez+DccjEhhzxP3OseqlMjxbcUKsx2AauL2dEJtYffKob/WI66qHLXWa0PGK8d8cfdgPGuPrSbTooDfV+NnfYB/upm56HtF3GBOJ9g99dbN8Z5KhqeGvX1m1vG6rOjesVOf1mSh5kbheyED8sGq2sHH9WGtxwL2ka9SHIoe/7h8k9EuuPuk5FCygxZB2LB4udE2ntY2Rs+NJXWKdGZ06yYTL7DnnQ6Rm0pUNO+8xaVYMGADTCfmim5C6HSq8ziwREXp+H3cOILkRkFXV0/bRtxouHG62bMRs/u92IZW9V/8+hEzm1lvtvm199zQ/VAQNiNaJxh/eUX7paVFm4wMX6TZLrE2ySyC7aZwHOllfl0lsmDAcJTcAhe/lbF/4mbht6vZU1Z4idewqEt5MM6KcuwkzJneJY5XK5OvRBMhRze7uBcLhyRh5chEdBOlBlXrVoLMprzzvWKShbQQ7J5FxAMJJCU4HK5okAzrXkVtBBsFfqTZ7+bTMHe5LimyiZMBme3I0lIRDjTjdYn3RNhbpzZWTEnnQ0ulylfrr+poP61kdsJgYYcROPj40BMTHE5IDJpO+P6Gc7p2tHeKN/9Yhusd2vf8Wg0wdclGu7leJ3abKEDcEpOtJJNsFllIeFwf4HosYB/5UuUOLqLIo4dvMrwraELGNrdIQ4B/F4kHbE3/1YDWTRZ/0j/P0C5Ddh7ubKLBQ2E3N+4WkEKX+fg2zPhG6oDxPNOLMR9ZfqNt4mFH6nJaAmUBhHvmekFIyKidVQXOazfjEn8mdTWNFxCjN4edjdzmEzxml/HSHP/tR2Bc09E9tw5SIsbT42sPB0sj7oDtpxWHY+ZZ6DkKK0HQ3NMCqeZhTSuP3Nl4MwHrl/TbG84o8g57QXjg/lG8pSifulm4XWLdxAUL08QVLwh9YVemarRy8w40BiUNjWtQJaAicJMdDISYWPGCuGMBHC7jEYMykoYkkl5KFLS3GeFA9uLhiiNGSey2cs08s86leGC48kbqMSLGJLOOqnZm9TASNzKLkP3Ia+FtBJdbFqcSLcXbSBuV4HHE1ayYhZoRZtctAc2mQFSxfR6QlpbVZe8RhqXtnwrjfu6NUTocTZPreJk2YnxNbgAWB7VCqTNrNq3YwT2uD3M9FrCPfRnYf3qyx/1midgkNCFjSB45CzQJneFXxw4HJuKkE73ZDm09tCvQObFZ+aBYoFng9t6KB8duqVNow+KlTiHKk6xTtVGbBVs2imB5TgDmTa/aEtWxW9jz7Uwrfs8lmR3Q66gtLwTo6U4+M9iUxIPQGwZiG2ZlIbZvD5hOW4wnDs22zKOy4YIFxw9knVUtVFrw5E93fodx7Waz2tw96BoE2H3iZqGsGPswbnX2S2y2ZTbvZRCmmfvuFa4/FhY/qY0RST5pbxNkykBwQFaUZTDcUJGWjo4g1mnGTYZLxMHifkJeNZjOjn/WlcziLJKljlelAMN5xP7aoQZPrl5lbtytZXfV625Fpzr1iwmHazcWdsB4ZmPb3dFH0x901lBxxMgE7tzSxsnd1qJ2pNaHg6J4xXjimRfW6+xTCLB4+bFg+yIe7co8ccijuTLvwXDrETcy32u1c2RxNANh6+jqqPahFVk94NXf87g+vPVYwD7yJV7RLibsDi18yFAVbA8tFs0E7xVTchguOdqRLOZZxw1wvDBbqKZAggJZ4Bo+h04OmAQyOTS3Ds0dx0QA0D8rc/4XsgBRCbygMskKR5VF4Pem+xKlSLZuJsrXoZ4bRm6PBIy5qE3HE74QfiOwH7kZ+9E6AnOU4KmZj6eTuGB40mH7InA8Jixe++cs0A83xvaWRI7cMPoDqPhPoTnsCTu2cDj6RE4r8/s7EOhfvOPj+3OH7IHdil6OuX2w2e9pbrx4n2fT3OoV6JKieZcQNiOvA4Cy8MiNmzvBh9dIhVEzKkBZBuhlS1JHxdY8Zi2W7AV+YpfW3jEjbfuJh0uK9TeZurKxYlG0lRKlmPnuRw655Wgw7Hh9Dk8s4sRGfqVRKwS8pq7S1sHrtb9kQGUJNkYdYCxRnTtx4IhtNRs+d2WnSqa3JO3HAg8VHW+K5p6vu4rQpxPBeMqRd9gLwo4aNvVkj/LzkPkQ50d94JHIz6pEEpCk8LMuR4Lo4/qA1mMB+8iXFsHu9QqLrwO9Ck8L5HxEv204OvTAeG3EDFGKZCOxAckcs0AATQLZBSxeNUhrBgz6gSOh+Y+6BcbTgtIWwFmH5nV2WICwqIUNT/VhdxQtV/0PwI2/GgW78QjsA0eCST3p+9+DPVTMSFTnCA9veEizVbTmdnG49EhLsir9AWhv+bxpRaLB9HSCvo1obwXtOxZGhjMq/RofUMPzQiyRuUCdQ/seWL0qHNd5QX9G9/i4U6SF0bh7c4jISp1bMo/DW/o7bl8ElAY4+9kI3xcs3qlhNIrptDGckWSJ3LhjvI0xEv0DZ4iK/6kHitBdY+qEG7yam0XRmSSy/STMETHtvaI/p1PH7S/6OUG5dpZQBns2tywW05IUew06O7UU0PvSmyOJs846t+x001qpxeoFcbDDSc/7yU0yR9W4rJiWvH65O3ZX9XPor4H9TUSJwPJbHgSmNa/19nNB/zzB7x2k0Fpt/ZXYtbKRsenFxjPen82totkdR5V0iqF/Y4ksvGEPTGs9Ence1we1Hj+Wj30p4C0eZXhagJMJZR8oYn46ollMKNkhTZ7uHIG5YC4JcleANiN2E6b7Fs2dzK4FcUeW4XhOJmH71mE8Lyhd3dmZ0SSJJ1l4/Q6Jovoowk7klfQA8N9SMEe9V6ZapdnXzbmC/oAB/3UMBDXnCJ1FwnFfZnbecOZxeEr69PA0oXnvZxp5f11QzhPcfUD3WrD6hu9nWvIXUd/Fjm82pV1wZBrvnDn/H9OCVYDFXcHi3QgZC7bf6zgGC4LxXGbsRwoxsGT2TaFXyB44XEeKpAsdRJrNRDxK6QqSFscRH0erVsBLpeEzUXo4jxQWhyOZxfeYU5LF/C9nH8ZI4kiNkklLkjAqHlUaIC8ZsZNbCsqniwK/c0Ah8Uc9Uwj8dBxNV4/BqvcqLa9THSvO42LhwSPYKHX/1NmYFTNOqN78JwPlDdNaMT5NaF4HdL9OQfjmex7DrxTIKGjfBJz+XDHaqJNkJY6jKymoOs3UVexaTCf8rCq+O52YZ2XNontk0X+Q67GAfewrC1Zf0kVcYwGyg+vS/Ec6jQHOKTQ5YHTQpkAXSlODiV9L+w6+F6SVoj0ImnuC11Jo4usPHP+oV7hlgjj6ICILMQLbFNwo88buDxxTpuXRyeDhyE6S+cu5YzeGasRrhaoWv+ZeOb7rOdJq7nmqLjWgcaBIuo7WxjU3rHAAhoZBmm6yuJerEc4pgABJQHuTMFySHFGCsQdL3TQV8RbYfh9YvhK0tw+i6RtB94behVCF347IXTSGG3VufqczJled59OCadEl0O0iHo5Uc44lA0eiY5ptp4rZNrnE8E9denNpV0hSTKuA/RNvI1AWPzdhTixm3hhfh59IE8+Req7QKw7GovQDXVXoTFGgjiO4w/MCeEXYOMQ7c9jwOnfYLh0PLGl1LIbqFKVVuEHgssz3QC0IeUHbsYoFajgWHsAK4VIteFJx8cP3uLlZQyXg7U8CDp8mxIsd/O9S6EWHFR4OsjEZ7ZlQE7/rYQrAbHFWbbDUC/pL3vNxS/bk4bmSsfug6D2uD2c9FrCPfNUIlP6KxcWHAh8KxiFAi0Cz0MzXvBEBAImkjOrQXSLMVYIY2bQGULhp0HqHxQ1FoCooIynHSGah5ABJbh4/lahHTzuYsLUWKWA2bk3Lymjja1i803nzSkvqlSqA7oxCHXdWuKxziFvmcUlRFHNBr2QDfwDitxHnPwUAxbs/pmiXE3IWlASsvylIKz+z34phb6OJW3MUDJfA4g1tioYzc2Zw3MSbXZgLWnPnkVYBJcocpBn3OidQV83btDbW4UFx+uWIae2ROtpg+V5n1p06Qer8rD9ySRE3GRpkzgqTRPcN9eyC2vuMceVmx3yXmU1WvCAZxjhUc97BBOjCx4b9sUNOZwUaCmR0OLxgDonfOsh0PNCEXmY8E0aEKFb8xbLjcksMVArQ3OFoKVULtuNnVBp2PdV/UwAmXDd83npPlOLsnuf1h1fIby+JjyZBc8v7o4rnGQ3DQlktxPxBZ8JIWhqj9IGbSHsH6Ibd+3RifwNOEbaPLI4PcT0WsI98lQjsnxeUriB4xXrVox8jREDnjdGhe77F/q6D7D3iW2MEKgtHbsgic4l/9DVRl/52AnGK4bKge0UmY9IGamPEKkx1g5i2CbNvomRiZNVqCXjQiQXMOV6101q8U8ARmB8uMTtzTGv+jvZOSYtvjk4Rkvn9uKWQdlwDhxduHk+O58D5TzkC3L8QwCnGlytAgfXvOrS3I4uJkLQQd8S/YMV1PCX24UfB4amN2lbEdeCUBrsTO0Mp3eyU3r3Llp1F1/lpebST8j2wfp8Qdhlp5ZEWzsajateoQFXmIMqKnUk2j0fTUeXooGbpRK0WHfgp9KVV1/7aIe6PWJWa92QJx5He7JxRPQyXlT0ikPMRZfJwd8H0U8SbxLK1HgZ9jqfHe1IS4Mz+qv4eN5HAUrzMTheVkJKbB123AGmhsxRB8rGrH/7aJT75zYxxpbj/EdB9GbF4r9g/5+uKO2JodTV35uZRo21MsF1ixeTsXgwsiHOuWTVQ7hRuFBpT948t2Ie4HgvYx77aAn0yomkTlosRy2bClD3yLsBvPO2SxgDZe3QvzYnjwYmzRlnQugnIZ1W0C45VHEeTaSWYzgt0wR+UXZgjJvJCkR7GsRjd3g1Ha6Ca0SEFQLJD+wPniuFKZqC80q5Tx06lvSum25LjxmksNLpJVGo/C2lzyy5tkZhllRYw4bRR/wdS44czP2deifI1DxeCw7UidwqNBW5wGM5ZxDhm4nipuSexo0QAB9patfdHsXEJgtS6+T2VAMQDu6L+3APnZACGnrZVvie5Y443cdZBiVH5xNiWh4zcMYplPJF5w23v6dnY3GVAFdNyQe2THSyqd2H7XmfhbjVsnrVcniQcXWfqqUYPTA5+kBnrmkd9xhqcnegNv5w19dX5opJ3ylHTBRzvh+Hc7ovpiHtRksBCU+/REpgUvX/i0F8JVi8ZSlqTkhfviIc2G94PYc8Ort7rteusHVdaPJRpKMIOs1ZPQ5U7kF0Z9vJI4vhA1+PH8pGvJiSsTnoc9i3ubpe4f38ONwmaSkH3QPzVFRajnbbtj7a6RoQDsPtUkVcZ3dcU+/ZPC/GgLiOsJ4goRt/wVL4LMw2+BJI90oq+ggDY2anMI7/qi1cB/LrJzTqiOn6yzaa5wxxcWDfzce2O5ILIAhH2/H3tvc5sQJdkDl1kenLB7S96QIFmIxg/zQz5XAiktNhfG8VNyHDrP53gbwP8CLiNoESPsJf5NVHMfHSYP/+tjLgtGM79jL81Gxb4HGkDFQ/A4ibD9wWHp3HutqpLPpOQFXlxtI2CEkerVl91qQNd7PcZ/pDRvQbGs2DYoqC/CAhLh9Q6DGfE2qYTvu7FG8X6mwR1wOs/GZE7SivKOgOZHperL3g99H2cCSDqgPFC6Z1pguLKFE0dyRr6oEg5634lW3E3Ioyrh5gHFPvxjFhZ6NmV+T0glgzuBnaLtUNz3jq1AjT39rrOeaA5+5npyU4E41ntCGUemccdC3n/ROaOU2yMTcso3k/+YNT9E8zkIJfozPEw7PVxfTjrsYB95GvKHruXJ8QeEjdxFbOKUoGzMd/hGUkYksQCAmXO4/IDEPYBvgeGJxwhuQSU0SH1ARgcZLQiEo0+n0mzHj3m53UPxoWloZNGwfEUXB3D1VhmdJ0/WjiFncwgf+rkgXErT8nTgpidU511Uw91VDkC3U2BPxSyMs88xnOOi3KncLcBLgnWX1gy9NICG4ti9RLoXgX0T8h248iLTDvOOjGbwtbR2Lh26C8cmq2NN6PpslwlTijiNiO3DoentOMqkV1j6NV0V/rAPR8zW7L6GvJ6sDMb1w7NpgCNQ3szQIaM9nXG/vunGE/YNeSGeKAbj0QWN/LAMp562l51SklAYDeKArjBYf+ioLl3s9jcWRFyoxxJNeD3wggUo7rPnbce/1fZhLVo1bDMEoDG7MFgj1Vnr7dS5gXz2C83v+d3FEWzOT4ODjg8YdFLHYwwY9ouM5euhTwvFMkwPGdEktRVKzUeVGZxdADcgQSiamj8uD689VjAPvKlKsDJBN0H/sF7zJR2jRnSOwzOQYPOxSw3Om9SvjcWljmyTxeJ4mQTNqN3cAN/XpIAkxnEnmRIU4DbCNeLmfbKvOlWBw0/mOC4OZ5iS/VDDBXAV3N04FjLH3iSjztA1HCTIOjeFUwdGXPNlqJhlzg6oosFE3u7iXT1m19menReKHRR4Dce6y8Ey7cFu2c8nlcro7Ri4Yg7CpPrxtvcsVOohAMa4VIsvHvGoj6tWAibbUExkoUUUvOHU3ZWLsPo8jpLAqBGhe8zinfQxh1d5xOjVMJ+wnDVYveMf6pxZya+ttIpfY78qBhXNCCm0S+v/3TC7qiMgv0zfv7T2g4yk0AbhXYFxQHOsNHuLeauuNp7VZyM3o783b7agAGzx6Ub2b1U8fFcfExyEHccG5ao8wgPKjNDNW7BKJ8TXrOwEevIif25bCbPzbHgefuddTLgzCczrR507eb9Ge+PnRmUOsgUFcuvTeawOlpX1aKVW+Kcj+vDW48F7P8HSzP1XdI7O4Ur3HoCsqO/YQKQhbBDBtIqA8uE/L7lGKmjvmf4LEEOHrrIcG1G2UT4Hcdjcqix7AXaFVL1AZQFx3cyycxEc5nEDm9jmnqarhtO3HKDy2o6Ia8UVtsYLneGWYzEa5pdMQyjWJditO9REPYFeSVzSGGJwP33A3yvWLzjRpdbAL3D8iW7pXEl8+8YTSBcHdFnxmA86tSiOXM0W44rpeJzPR3Y/ajmz0eyBQWxzgq6Gr4GwxRZ7FjkBG4yvMnMfUuoUTPlO4a6dSQ7rh1/5xCQLlqkFR+/v6ZbSJ8Fi3eK/pJMuipCrx6GdUkGSqdwB4G/MZLGcAwX9b1lstmqY8EqUk4LEyFXbZUeDyzqeZCqacuV4HN0uVCklUJbhYx093CjIK0VwyXvR3dwJLPUjslMnqelzDR93/PQMppnoz8Q/0wd6fn9swTJgvaNR/uWhat43nPxnmL0/TPagcUtgzCZMXYci1bRfL13H9eHtR4L2Ee+VAHZe8hkI6RVgVtPKINHeBdt46e+pywVaZ3Qnfd4errFm+UK4xBR+gCcjUAfgPWE50/v8O5uBXfHHUyDonTgKXqVIbHAOUVODmgKdyupY0OQcl3sJN48OBlPRwJGPc1DAb93WH95JEVQ1GubXRA48zBUT2GvSxxjjieCaRlMOMufhxigb04TuaPYtrljdhhTno9EiRIqdsWTfFryOWq3OJwJVq+KFSFFWjjzX6TeqL3nbMsfKtUS6C/8TAiomztg188xGTrej4xGqS76RmevLE0SDTx0FTB1br5mMrEgbj9pkJYyx9FsPwcgiu6VZVxlsIAHFqLqP1gCo2T8QYB99bLk57J8pehuCrpXPUrjkVYe/YU/WlgV6to4vgXQm6egx5ymnDqxaw9U0XC1CZtOWQjyyizMugysFTm1mE7z7K05k4e8wo0O4cDP4+7HgAZeZzeyuCXr8OiHyGu5+WHBj//YV/jtb66h7xt+dnIkmJCwAeyeE2es92TYK9zGDiWTIuwVh2sHiDxiYB/oeixgH/saHfzeIS8LdJ0gdxHh3YLFI3BM09zJ0VFbgEUz4du7E4xDQNkHrK93uFge8Mn6Dn/ji+/j1csLIDngPPMk7ABtWRhjyIjR/BIR4L5qMV1k4GqCJoecBf5bOoOo2DinJdYghi2MZ4p0Qgwm3niEneDwzEaUtvwA9JcsVtuOXWQNFXQTqGtKNvoDx04AsS51xzwnSTpjOMOFmDUUvxe2hq8lc+Bw7Ph2P6A7PwaH9g31cnHH9xJ6hT8UxA3NfqtkIHcOqZW5g6uBlQB/JuwJANaIk/5py9iUVGb3CgCY1g7FA9vnHtOpfW42vmxvFOOJWUTBuqiWxaP71lh2mbT55buM3Dj05yz8MdNCK7ccQ9b4m9pxrb/JaG8nE48L3JAR9fi608JjWjukzjR3wZ53Y9E2C3aKcEfMTYWjx/FUkZ6NcLcRYSvQ3iGf04JFbqIVKoHfON4Ln2boIiN+HW28WzFdFj8yGu2QtChkTnpioYu3Dt23gt/9X74HfZ7hDw7ZwlrDngep9kbnzri/MMyxOpQ4zEa+tXCnjj6Mj+vDW48F7A9wich/DuCfA/BaVX9iX/v3APyrAN7Yw/4dVf0f7Xv/NoB/BbTG/TdV9X/6B/4SB6TzhHg6YHq/gMCAb88Ry/IbkiGmE0Jaq4sDNtsOaRPJKuwyPjm9xz/x5Of4jc1z6LctfO3khmPxkiTAmxbpYkIykTQGj3zJDU73vJVkoKC50q37c8ynX3WK6VSJ0SkjVgBiYKHnSE4shmMeu4GFo4L4cU/XjcUtTXP9gfox1x07uLTi+PDul0hsCb1hcC01RgCw+tqKgDk/pBW1TOPTCdJlYB9w+pvBZAbc0GRksVIvc6FRJ0d3/Rn3Iz4HAG5UarvMCYLmvs5GVIqwZ0hltsRqYkyK1WvF7YmnfdeyIN6wE3IPOkeOZ0nrzy0QXtN/cflqgiiw+5E31h4LX14Y2WY0bPIALN8WxG1GczvCDQnT2QK5pbDajQWuzxgvG1pgRWrwqJuSmZRSVvxsqizCj4oMQTph4SmtAgO79PEqA22Bfx/R3An6pxmiJB+hAPsfjoAA7VcNlq+OReP9H88zdiVKTZ9CZh9OSYLm3sGNxCnvfonjyeqgEXe8DiUCoS+AyMxqpLFvxSX5a2p4qpuAoHIMKn1cH9R6LGB/sOu/APCfAPgvf8/X/yNV/fcffkFE/hEA/wKAPwrgEwD/s4j8kqrm3/c3iAKxIPUR8bJHCAXDIUJeLeB7Js1CBGlZ4K6JRKd9gFsmnJ/v8E9+8js4Cwf89fffx2++fIYageJ37C7gAW0K3CaiLB9wugfTlA1uLi5uoEPDeHoc61QtDxSYzgrgARnkOwLV5s4wDotuDwegv7RYjnLcGCUTW5JCLAiobDeZ2WcuCbo3dlpfFHTfeIQ90F+i8lTgD4L7HzPLzB3cvHkKgHAbkJoCNAWbH2W07zy6tzrjN+OJQ26O3npVs1Z9AF1lGFr3JUWN4q+AF+RgprJi7+eqnc13yVAEuvcZ2xeBWWoXE8QpJgBQFiTf09ornWXIMmG8i1h/QXup9rbMEgQ/sOusGFRz4xj5suEocPUqYfGmBwo39PFiwYODA1xvLiVrIwc1QosuI0/wWus8ulVHcojL/Jw18KAig7Mkb6WPZiHmFvYsqLrKnCT3HjII4usINwna26qnAzY/ZicFHJmHDMqkPVjVjUGJn27PABSZQzTbG0H3tmB/7bC40dkYuXbMko/6MCkKB5g1ugAAIABJREFUjGR+0vkFyFEfWYgf6HosYH+AS1X/ioj84P/hw/95AP+tqg4Afi4ivw3gHwfwv/6+PyUAJge3Spi2DabJoX0dEHf8Y09LYP8980bcRezftwg7hz/3z/7v+PHiW9zmJf7jv/5PQ0JBOQS4UGnTxEvkaY/GF/ifN+gbWlNh8hQDxwJtCwW/m8Bu0IIda9RGJQ6U1k7/+2NEBd0+auGqTEVBv7ROzBKM405NAFythLgJpSULdHNPg93pRGa7omlFbM2PtMbyI0eH8Z6kAfxkT4H3ZkEK/1YwnSp++U99gSl7/PS3PsHJzzzOfp6QFkbTb48dW426V/eAdVcAX9N8oyAcClB0jlaracq54es8XB1ZFW6q7EHFtCadfzwrwOChThEt22o6ZRebLiecPtmhqGAnQG49wqAIh2x4pF2rwbrdnuPIsDe8601GvJ/M+d4E2A0PB24omNaBOWCdmz9DeisqYK7u04kYKYOp3JJYlKCAO7BwaaQZtNt5FjUHSBZ2YicTcAiI7/neqO3i4/GeHdLhuY2WTfrB9GZBc2NhqaeKaOzaKnlob0wPZvKOacVxdNzyfVbWqDreS77nf2vXTxy26gwzJjNgflwf3nosYP/frH9DRP4lAH8DwL+lqjcAPgXwvz14zFf2td9/CSBNQR48ZO+x/NrD90B/zQKSPh3gnEKc0hcxR8j3d1i6Ef/Vyz+F3/7NFxzxRYH0xCCIaQDlPCEAGN8vEJcKbQswHmn1MB3R8otmdkuoxqtuops9gHkUGLe05BkuC0rHjQ7WiaW1IGyP6bc+s6i19wWpFfQndLXwA10l0pKjUUaVEGebVsZC/CF98cIBR+F0oDi2dhjD6yXCxsFbqKU60vh/+uoa06bF6W8EXPzWhGIbXerw95zCx1OZtWHVyihbZEmNQ4ESK1LPrCtvbMLpRDCcs1A3GyBbtH3tBv1A8k1Zc/MPG0H3VpH+xA6LdkLODvdv1hzZms2RHxTxfkReBOTWYTzjNdo/k1lrN61ZxMI+Iy8DvKUyqxeEfZ7F0jV+hA4iimQJ1rllZzNcAodPeTCSiabO6ekEWEacPzjkxTE3bv2lm++Rw3NF92KLaQwIP12gvaHEgE4qgrhhZw0BmhuZCS714OBGfm+45H1WqfpVJkJKvuW9GVmov6I8ItRoG4u58ZPOkT85UhoAkKhCD0ULA9XHFuxDXI8F7A9//acA/gK4ff8FAP8BgH8Z87T+O+vv+1cjIn8ewJ8HgHh9hmY5IvzNEwxXiulESYh41mO1HFBUkLODc4rdtwQrnl9s8Jf/6j9FCvPeIV2Z9qswHqU0itwVyCZAbwLCJJjOMmQU+N7RkikA7auA9uaoxwk9cLimS7kau2vOiVJugIcXxDK0KfP4UQo3LWfuIRUzC73icOHMO5EmuRBuNM29ztjU/rnD4ZkiLzLixmH5FYtLtboCzCnC2GglAOufcRw3p++KYPeZYtq0WLyM6N6W2RKqv6QPZBXy9k8V6TQz8XdLAWzYH4XH1WVDvTtq3syJIkduwt2bgnhvycfKzXuWGnjLoPpejyZkyKsVFu+IY8WYsXm3gn8f0d0bKaWjuPf0p/fIywbTacD+qYMkYP/cZASiyB0/j+Xb41R6uIrozzxJJ0nnLnFcu5lYIpmeglVfBgHSWgGnCCcTUh/w+R8hpPvVmwukXSSpKCiadx7NneDq10ZsPovY/IBj5f3bJWRyCPLAheVe0b2hSH0yL8XmjrjVtBb01zpbn6mwyAN2bRMPTn40Iskgs7dhNZJmcTbiTiXkmGtLiYCKoD+vOWZG91+Q6FJtwh7Xh7UeC9gf8lLVb+v/F5G/DOB/sH9+BeDzBw/9DMDX/zfP8ZcA/CUA6F58ru1fPUGJwPf+5EtElzEVj5t9h5t3a2paFFj83RZyUYCzCS/fnAMF0EaRXUF8E+DHo88gNVLmejFxLNS9DCitzr53fu8QfnKPqQim3zlBvBNMjT1+SVbZeJExXpGssf6SLLgaYOm2ATIB02UxvY2Zq7bsonQB3HfOHDrMHmrBQhZ6bubVdVwDEO8ET/4Wqev9+ZFY4Xp2HDNFXyuBgTgQPRG56S1fCfRNRPeGgFjq3OxIP60Uec1NefFNQHMTZlfz6h5SjITRnwvcdByzjaff1S2Vhrhf2CnSmsLedGKY3J40/bwskCwoKUCvMyQFsghfryhAjorDZwkykYV6+kXC5senyI3g8MRhPDeWZqf8PCbB4o1g8VZxuPLAlZ8La+jVYmCA4cxBRb5DFKm447Q2Wn5HpmO4CYhfRjQFePObn0IUOHunZsHEShf6gvWXe2x+sMTq24Rm63H3QwcVZ4GW1rmb6a4s+RnOeWvmb8gsN5JCVCwB+y29MlPHMaEz3MuZZq25pxvHdIKZ3AHw/VIveYxcqePuEiiAhmFt3rq9Gob5uD6s9VjA/pCXiLxQ1W/sn38WwN+2///fA/ivReQ/BEkcvwjgr/0Dn8/A5/6JIrqMJ4sd7qcF9jEitBlNO6HfNwh//BZRFJs3a2BrwtWdm/Oq8oKbxlRFoXuxkR8TeA+fJyAWjoiagh989haqgq9+9fl8EnYjHRTSs5FTn9GheRPQ3AqGMxbHcHBo7jii7P/EHlIE/nc6FjFz63DJcKw1i01aA6OX2Rx2fyb04LNMKklA95qblQpfb9VyjSc4uq574nMwF/T+SmYCShVCL1/TB7KSNrafEXdZvBWMyWHxhs/tJhuzBm6MD7vH2qkBhkEJr2VasgjHDcMT/QjrMgRu8nM0SYmKvAJ0F4CmwJ1MGE8mlG0ks84DyCwg3jqNzecezcY27DULFxwwnWeEe0oVaHh8JMuo42Eg7gskKXbPA/0Ha8flSfhIJ0f8TxRobilqDzvMMoKw5yEj7gtyQ7F1Dcwc/+gaflBMKyY/L79R81E0sbV1R7nltaSPorEME/PdDs8E6XKE7D0w8npWkg/UErdhEgmj8vuBI+nq6zhcEP+Mu6MTTf2M0pIElbTkYaU6i6QOiIkSi8f14a3HAvYHuETkvwHwZwA8EZGvAPy7AP6MiPwJcKD1BYB/DQBU9ddE5L8D8OsAEoB//R/IQAT/MHefKeIPaRBXVLAMI1q/wIurOxQVnF+9x8u7M9x9ecYRkAfijZvxqtIq/N5YXg7IzwbgPsIlB5gxL5oy66K6P36DKXu8/PIK7c6hvTVG3SWQno14cr3B22/OsHgZ4SZg/2kh1tQL4j0fu/2FBHnbYvk1N7V8ys3HTZgd1EsExlDHfMZIhNkMJWJWNbU5twI5KMKg6F4Du0/kaNZqnZGz/DL1/H4NfQS4mYU9sZ5pxY3s8IIkDD6HzGy2Gpdy/70wjyHDoJCDjaMM8C82ulq8rY75FvuxZcEbTwX756ZtG62A28/EG4+0LnP0CN62aDbHaJDSKn0pB9N3dUDfOyuS1JblZWG3ZuSG3HKj795TI+amAj8WDGce+2cO0xJYv6QjRep4kOivxUS+7GxKS8lB+56MRwip+87iYOg0wlFoWrL7VA9c/2pGvM/wfcJ41qC/8kiZ1zXsFbNFleFcHJ5zZFwd52Hdph/kgcMLi7Dv+XN5tPHi/F+1JAPM9laHK8eDBfg4l+vvw+yBmS3BoJoSV2zscX1Y67GA/QEuVf0X/z5f/s9+n8f/RQB/8R/md5ROcfWTN9j2LbZji/P2gO3U4rTt8bO3V9jfL/C1O0f8skW3F7K8DAvIiwJdMLhwjkYxL0QRcwG3rmr52w3ils71IorXt2ugCKbTAlGH/Ms7nJ/uobsFbn7jCiEDwyWP7HWs2L4X9E8V06cjMDrEt+w6ejMQpobKTvsKsiAVKCIWcMjNurk5UqRpu6QzYSMtSKLwkyC7B0XRCs14RvymFsi0BLrXQqGqYrayGq4UzY1D2PN52/c6b2L9hae3oRU/EgH483EqmFYUNaelfAeDEfPl2/wASKeZNl32ujSaHu4gCDtLRl4UxNWEaRfhrgaMXYDbe+jFBAkF+RBQFgptCvxtwHhFNoJMAkwCv3dwg8y/o5IVqg9jiYLxJFAj1pAynha8RrlTjAs1vZpR1pcWaNkLFjdHAbY6QJWjvxzNWHdhpJKtYPm6sOPsE9xU0L7rAbfANHqkhRFQxoofYvY+VAegWK5XJ1h+GZCWdk94wFVvRiPhVA2hf8BghRxF/Lw/gMVOZ9sySiNk/l2SiOVWl3oAc/rA4/rw1mMB+8hXExOerrZ4d7vGPiTc9Etcd1v8n69f4HTZwznF9rajBss56Gc9VIFy1/B0nuhYXwJIcV5k4Lahma8j5pC7gjQ6HP7IgF/45C3u+wWmuxZ+43H9k9fIlpR7+6tPaA78+Qgkh+YtjfKmiwJkYPd5gZ5OcKFA9x4uCQ5PjWY9Mb23npKnJSM8tAC6UJSgGJ9wHOYSN7yw1+O4Lxqt2wNwgvFEkdZmYGxas/6KY0c3VeIFsHhf0GwyxhOH4gX9laB9r7j6tYT+0s+FNXV0u0e0EWIyosmgs+UV3Rwe5paZSwUEYgLr3AJ5leFOJmDvEMwpourYpquEvPD0klwmJMvkcsuExZM9gi/Y7RYo7xoW6dMMOKWzRbVhWhRop3DbgNIomjtiibkBsBbsxM92T9T9HUei05rs0WyC73r4OFQ3i56j3ByNnWeavMoEbCyNev3yKDwGgLCdUBoPjQ6S6GYynB6rwoy5ZSDuFVN1G4GlWEedpRcFipBkJmI8DEllsdZZagFUBxdBMu2ds4OIzL+XGCiMSKK+uuDL8T08QmAf5HosYB/5WoURzxYbPPsRR4jRZfz6zXP8yvW3OIsHfLG5wq7r8ao9xbgKkPeWqLzI0AW4+Tme2sP1AXpLNw8ZOXqikawg/UKPF0/u8PPfeIHuaw9/qdAXPf6ZT/4O/ubN9/DTV9e4/ke/xbZvMf78DIvXDmmtSN/r0S1HDF+cIF8kxEXC1AfIRKF0WRbIYPjMUo/5V1Kd8q0zamn463tBc88xFyndHDNtPydO4SbaSLlMyjRA+nQ92bc3tD+qQD4A7J5xI53WpNTHvaK/9EjtMYCxJvdyxMUOLu5IKqkkjvq6q89jFWcfrlm8phPD4PYO/t1ixuWkAON1oqnxPXGtvFCUDeeHbnAorxY4XHr4Vw1izw4tnRb4O498krF8sodziqGPNMFNzoIcKfatI8FyIK0fMLyodlBCyyegvn66fviDoBiRIexkNufdfo9d2bG7VchIluVcIKzTk6woLZ1ESnQoxnIcT20UGATNfaEwPJEwkS0uR0EsVCPg9oLYG/ZmHVeJoJGy4XP1vahjkS3+aAnV3NP9pHai7LZltsYKPUehKR4dPBgGyuvwuD689VjAPvK1TQ1+/eYZlnHCi+Ud3vRrLOOI7dTib3/7ArvXK8gyIcSMH/3gFbow4evtKXZ9g+AL+iFi/WSDX758g6+251g+fYc+Rby+X6N/ucbiveDwvYSTdY+vf/cKiIrln36LP/vZb+BZvMdf+umfRs4OJXu8envGDf5Zj8/+2Fv8yvkrnIUD/srrH+PrwynCLqK9aXB6r7j5iaL/fAQmBwRiWKXl2C6tmJ68/pLjNnVA+62fGXzTWo7A/Jri3rAFTr5UbD8X7D6nMWy89ZhOM6bJYfk1/RnDrrqVk8wRt2oYDqnS01IwnJkjvZM5lBPgxlhxMyY5qwUuHtN9i+cpPvQsstOK3nt+5GgQAMZzGtu2n+yQk8N0iGheRo7p1gr90Q6LmHG67PHq6wsUp6hhonEjSCfMOIt3DuMFO53xZ6dHarkHFvu/1/5ojhaxkVhe4BgbopjdMWY/Q8FsquwPMrM204KJAtV6CWa+vHyb4UaeCiQr4Ph+h4vwHfeL6l9ZAkkSzQbYfM/h8KKgLAr81qF9JxgvzNLsYPowE+dTRM6YnfGMUpB4YCQMROYC1l/KjH/Grc6xPiTfWFKAGS6XQMyuZpixIDILLIKEk8f14a3HAvaRLxHg2XKDdRzwW7fXaH2GiOLLby/JWksC/b/Ye7NQW7csz+s3m69Za+21m9Of20TcaDKyKiszLalSwUQQXxQRRLApH2wgoV4EEUqo0hcfLV8KfE3wQUHRAgWbl8IGoSisLsq0KpuozMiIuBFx4/S7W83XzTmHD2PO79s300zUqkjOSfaEwzln7b1X862155j/Mf5NsDTbnqt+xYtpS99XnJ30eBd5st3zenfCt3/8KR89uOE41ez6hpQMeGF4nPjs66/54t2ZxrWcj/zJx1/wvL7mr119k+7YEPce0yR8O1FVka89vOS87njZn/KD8JAfvnzAap/nCQMcPjakdci8c3CPBsLgaH/QzIir2lmGC/USTLWSFlKtiKJEi6iwVjdXG+D2a5buk4A/1YHe5CrsztFcWmJ2qg8rM2/YVUZosTbE3Bac5yHlJJ/ZaLC0n4rNECG3FnMLLtW6wYvLRsSTzuaqTnAvIu9+ztN9EhEnrB8f+NrDS379O5/Otl3Hr038U7/w90li+Wvf+SbDUNGeDjiXOLxbw2Czn2FGTA8jJhjcperRYq3tL783M+kFmLO2SjtsDpOMigxdFkLXN0rQMEFnkaSsmxoU1c5CbtEN38LsfVgQixEIa5UfVIekzL6VPnCh6psg7L6ujx8boftIZuspkiG1Mrc13Tur/x6K3Zh2BRSBK2qvb8BITmOoNFOuBHyK00OLOJ1VpmzlVa4h5CJe33mf76A5k8rs8J6F+D6u+wL2ga82Mwv+r1cfczi0+CqQkkGSwZ5MOJd4crEjJMvNYUW/ayAYxnbi7U/OsHu1+GmeH7ntG85XPZtm5PrVlurWkr7S869/8rf4T179s9jR8NGTax7UB77XPebvvX5O3FXqeecSkizPzq65HVp+88fPMFawRqi/uwJRZHL7dYjbqLvHBHgh3Na4vSXVQsqRGmGTLakyrV5P+2Z2kxCn9Oj6Vje643NhOku405E4WWR0EA1pnRhF25Gpym2wTq2nUqUFKhaboEy3n3VdOWrFTfr4ynSTnOKskfal7VbQVUFkeqrXkMthawhPvSLFGy1WXdvybr3m5Lue/TcC1Vc6fvbhJd+5fMrlzYavf+U11givdyfcvtxSXTpSI2rBVAmPP7rm3eUJvGkULVn1AzRBSQdF0zfrm3JwKCYXuOy+XoS//gDdcyGcaNWbDNhe28env62ttpAz1OY2KWTxc5ESCONWW4VuytqtMzd/73iiTvbdM4MJ2jqVhyPGCd4lNYnuncbxBLBkO7OgFzY2d9qfTU7ZDspiFKMFTc2cVcvlBiWAIHDyIuKGRF0ZDvhZEwh6Hea0cKMSARuU2VhSoe9nYO/nui9gH/g6hppf+8lzUrJIghAcaXTI0Wl3pxJes+XxxQ7voxYaLLevTvA3nupGXTMGt2bYVlQucXtU2lb8tOc/+lP/I7+0+gH/cf/Ps/najp85e8OL/pS/+p2fwew8nEQtFL3n7OktAD9+eYG5qomrSPVFxen31c7o5luZbRh0gxcjEAzVrSN5mX30SoJvbJXFVm6T/GlNtZ78bWCO7ohtPr1fNkgbKVlj2+96pq0yGl2vjh++kzngUexSvCAXrjvZTyahLbJJHyfUC4W7fyi5nbXMTE5+lLj9TOd/ZgKw+E7blKrZU7Tof7vh8HefcvhG5NGn1/ST5zjVPFgdAfjs5JLfuHrKft9ighZ0rLB6cmTVjIRoMa8b6iyH8Efm3DWxd/bbcu2M0urtoEXgbvikifrz/SO97rLS9zSuE+m2Iq4sqhXMuWJjRkNZa9VeJ0XKvhgS68XsH7hZJH3zNaetuxxb40b1emzWE+PgmXY1ZtQZ5+YLtcAaHqinYXILCoTl/Wmu9PtKCxCyELldXlesdbZ1fORYvwXXxRw8ml+r0zkmqLh9Frvn1mrMXo9y74X4Xq77AvaBrxDsEm8y2bktZ0K2fTJw8vSWd7cbhtsGu/PqaVgJ4SQS1/mXd28J1nPY1qyakdXzkcol/pn1D/grh2/i14FvPXrNDw8X/PDtBSSjMStWMHXkycNbYrJ87/MnYAU5m3BvazYvhKs/AWEd1bcu55QhYEZLtTc5lmSh0Rc6u+9MdhrPicKGeaZRTsaFXWeDDkeSB9NrdEf7E8fDXxsZLjzTWjfO4r5QNjg3Zk+8UegeqfVTaS2J1U3M5JZZaSfGSjdzN6DGx7kAVAehf2BzerEWieqg4mWT1OkDlLofVjCdgrsYuNmt+PrTtwzR07pA6wN/9QdfJ0VHGpx6Tm4nrBfGH5wwAojq0kwC05fWZt6Yc+tQfQOF4SIXrM7O7TF/XFqM41kuXoDbWdJgSKcB30TCicGOFTZAV8H4KNK+9PraJ5ltncZTm5Gyuu+nzNAEbbmW4uM7PTQcPknIKjJ9vtFe5EqfTCHbjOd3WoBdzi0bdX4ZWr0f1+uMatwuB4hxm/V0TmeQ63129aC0EwPtlZuTsUNrSFu9b2y5Zvr9sWJuu5Z0gfv1fq37AvahLzFI57G9GuxKlXKrTWcY8TxyfaUeiO7Ga3JztBoMWCfdpI9ODXh7yzQ5LtYd/9zz3+BfOv0/eRVr/ubua5xuj/xkf8YULcYItolqAGyEs9Mjt8eW/ljrRpIM7trjO7j8+WwC7BPRiWrMeqdu5QXdoBunOJnp/HbIp2p/BxVlg1abgy9TrTV72spSdOqEOTra15bqCP0Dz3RiZr1RscJCFImZqBtgqsz8OIVRWIpBaSeWrLFC5lBSibY8zUHZi+Lg4rcC08YynFoV4WY06Eahf2g4fhphE+DgaarIVx9e8oO3D7jYHvnB73xVv98KJlhYRarznrqOHC5X1IOZX2tsMorKiKQU2nqn7TYTZUYT4SS3xNBrKx5FiLLM7wpSs8HArSckM5sti4XhWQArhFbY/ChfjxWaHxaY2XygGjBlCeqM0He5ZWgMx4+WeVd9bRnPE+1Ln5mnej/jaU6PzmbLJDTKpVoEz6lSoTTZNcVOGjw5XJhZXhHyAa3aCXZY2JDlcCJWkZcisUVTNuvbbEZf8b6AvY/rvoB96CspBV4M+J3FTjbPBxLhNB+xr7NbfCvINrJ9smd3ucG3gdPtkW6o6W5aROBf/tav8m9e/HX+p90v8K/96i+z+8kWs44ao1J+h5OhOe+52B6JybLLLUfn1ZkhjY70eCR9HJBjDaPFHPyMVozkFmDMM46IukWYPIuY9GuF5WaiilPDOg/VB0UQqVFH8ljLnBtmd5qgDLq53n62OIyQN6RCv+4f3SlEOb16bsEFMLmQpWSYNmU+B6vXOXZkhPWrNEdvVMdEcyMggu8SvkvKZHSKQm4+cwzn2ja175Rg07sVv9U9RY6eV6/X2GAwk87uxAjuXYX5oiL1hnVuXaZaqK8z+op3/iSZDwXNtbr4l6KWKsCrjdXqjX7f4WOl2APctccCnen5N5qIfHyu1+jh3/Bz/tqUC0dZNiPh6iBzNElYGfpzm4NE80Ejo2/bKfoWz5xQQI4/SZVl84U+x+6JMjbtpIXM9fkQ4lE7rb3S4sNKi2No8+9CtxQ+0Ne//8TjO8fmReb5C9S3Ft8Frr7Zzi3Fei9LEXP6GS2Hhvv1fq37AvahL6Obvw3qtWcipNMAwWJWAesF6RrM04Gz7ZFVPbHrG7751VeEZLFGuN2tMVXCV5G/c/Up39k95Ye3F+xvVtAmZLKa/+V1szNtZN2OxGR5+26LdaJkjsTccpHREt/VGEuOrtfnB8xtp6KBKqintLRMpmbPYZjk2dGkrSJffBO9uqunC0hPByRYdaDoPVJBzKnUZWOKjVCc7lOtrhc2LiLZYmHljzmivi2zM92gbWdmZwu1poL9p5bmSli9U58r1yfC2qkTxJCTmMUwNXZ2mEhR51H1ZUai+5rUCmYyy/MdDOTWW32j4urhQkkjxU29udKWmngll4g1OUBTZz9VRphhZegf5XDHa+geZ63cCLJhLoSFbFFst+pdpsz3mfRSa5EoiHLWe4kiPkR9GCdKJI/KCNS8WNvDYcWsyTJJ2H2m//cHZQ/6g4UE3RMzR+HYbB0FuXXbZ8FxRkluUEq9H/Q9mDZK1Y81pDbPSidFpO1VxB8mSII4taXqnjSarnAttFeB2Ng5JLU/NzM6u1/v37ovYB/6igbbW9q3lu5ZJK21PePXI+1qZP/ihObjAxfbI84Il/s13a7hsNMT58lJz8mmJySLiOHF7SmbZuQ41BgnyGBmx/PkBXkwsTnTgUZMBucT8U2rRr8WaCLGgLms8qahw/bkgfXC9nLDMocK69zKjCqOLYhCnM4+SvS7zs+WzLBioBtPA6vfahkvEn5v1Qy3yaLhTIUPa9WW2WBmkojO3hQRJAfSLubA45mQNhF71LDFcspPtRaN9i0MF7mdlWBaG7yFVHlcnxaGmze5xSZzoU7nAQY7EyzspDE2vtP7PXwipLOA2fscNaOEhpCFx5DzydJCLRejsSyFPl8OCVhl1G0/158v165/LAtT0S1uHKkqiDibHQdYv1ZdW5rAZYbe3VBIovlSC7HkwkF2TOmWj6vv1ZYrbAz9A3X1sONC5xcn2Gjm99zmom+HrFvLjFQxC8V9/7FVFuRhafuZiBJ2epnZpGKhP3fEZoU/qgfktNbCuXqX8IdEWFlcn7AjxNYqsvTLdb9f79e6L2Af+DIJ6hvL8DAPMJKAGFIy7C/X2EGzwHZdS3esFaUESwLq9cQwerxPOJu4vVqzOu3px4r+sp2Lo0kQTiInz/fUPlD7SIiOm92KcF3DKupMowRndh43KeryB+aAzLJZ+33WG+WihPnyZjUbqOaYlVJwCkIoCccqStX/dJ8ETd+1SVuO3dLyFKsMQCuZIOIUtapVlSFuItQJc/CKXlbZKFcMrjOZGp9bmVmPVF6P3y/PWTdUmWds06mfKfdlNoUxVLe1mu6uFC1NjyfcOhBt4mbwbM86plcnYITVS6F/ZDh+EtSJZMhzoI6M6FQfGt2XAAAgAElEQVTUbKPMWWNq6ZQYN3b++nCRn3NkRsJ+rwzUZLSYjRcRO+QkZQvVLTQ3wupNyKxQi0lpNiwu70dYa/EzJpMpChliYN74bZA5jHT2IWyE8TzhD5k8kxFzme25Qe3C2ivRdOZHdnbG8DkN+/CRUTQXVQPme/2wqH6whIRmBN5m4sZK4KHNadpGdWz5s+IPMQd6Wqas8Yv1ffV6X9d9AfvAlzgYnkTECGayatGUDGmw1Jeqwen3jbIUBUyVMLXCoPG6wUyW6XzEuYRrIqtm5Ha3Vlujzunp/CTy8VfeMQSPd/qzQ3CIGFgp4pJkYMyeeKMhrhLiDeN5nr1ZoX3libUQHzLPrAr92SSDmZh1THrbUrTE5FyvTSYOrPQ+U6MkCmkjkiwGgxsM1cHcYS0W6mIhhOQi74E8tzODA6utKCvqclEKz3heKPDK7iz2Sc1VbtnWUHXZTikJYWVn0e64MbOLQ3UQmistWsfGkCLIUyVohBdr/f95IETL9ume/fWaq1904AS3d/i9kiVcpy20UijFoQSFTKDwnWQLJRgu7HKd8/fO/o4bma9zSaq2g16/5lpDN0+/16nryMbjOhUmizNzZlhY68+Gipmar0LoXECcPp/mRlHbcKHILtXC9HSC0RJsUnTcW80aQ2dYev+GY060LknMYrNLfmau6lxQi01YWyRT6bFatPxRGYvVXu+7JHsDmJgygUfjVMazao7l0WK5vM779f6t+wL2oS8vbD7asb9aI02i2oyE1yuMGKaPR1wdkaNXNlmwcHRKEtioINZsRiQapqHi4vGOj09v6YaacKhITcKdjTy+2FG5iDVCFMPNcYW1OjOLwSrBYzLYg9ON8NlA7B2py2a4o8EGO1sviUUFswK4/PWsu0k1auBrlEThBpS6XuswP9ZavKqdVic7ZrHzzs4BnCZkM1afh/H5Ux4dmd1naG+yz2LeiEuOV2nBJa8bfDqfMEePeH1S9VuH9EoQmAuswLA1iFUHiv6h6pfUyT1v7L0GZ67fJIbTIiQW5KZmilo04kniq5++5cl6x9/+7mfYtxUmZd1Up1ZKrl8KVHXUtp0birejtiVtlNmYODbKUgxrJaG4LousZ3q4fj3WQnXpqG8NzZWa8boxIc6C6MbfPfbaavVKoJlO9OcKUcQNqlezg1L8m+vEtNHncXxuGB5HZK0aPeMEVyViAruriJuINEpwKQQKn1O0fVfYlCqWdpP+HWtLaNUTczjT4Eojd8TcpZ06is7jKn1P/FH/b0dh8yoRG8vhmYZsFoF8SWoeLspz+EP9rb5f/y/XfQH7wJe1iZQMZucxDwemXQPriF8HjM2U+johg1OmX5sUi0SlSEtUGj5OGCbPF7enjKMHn6hOAn/6Kz+kj54Xh1NisgyTx9nEFJzaTQHm6HEHpeanVcRc11Q7q0awmSBBbgMiypAsejVYhKhaDBY3jmr3ZY0YosjO9cvp243aQiooQxxIDWUIdVcb5YLO1UpScaoAu7SPyHZEqRGl9w8G97ImVUJqLdWN1XnYVMS0WbSbySfDmbaoxlMlLfhOdWclHmY8g5ut2kFNF7qRS6VxNKkGthNP1jteHk4RUUJDfbtQwYtlVawNq3c6N1Q3dkV149ZQHYRhbRkuCvsRxtOEVOozKRamU/1c2ADJaoGURrDZ7ms6MXQPHZuXiqKPTxrGEy0UbhDaq4SdrKYZm2zCHA0hB4b6DrDqb3j8SoAm4puohu/BIsFifKJpR7q3WwCqa6fve3a8Ly4i1SGjJgdkZGQEppWlOiZ8n+3BkrYWbQCTZ2CFwVranbEpZJWSOG3oHrgvkYc0JFNm5qZGAsHwD/W39n79w1r3BewDXyKG480KtkHHD6tA005UPjJOnjBZqiYgdSAMy9ttaiEdKuTgMWIQIwx9zfG2xTWR7cWRTTPyqttynCpissRcdKbgmEZP6Hw2402kj0acFcKhon3hZsq5NQZ7zJqtjZDaXLxygTGDoigyktFNRDdGnZkAWZ8dTpLOgLIbRHWt+WCkRSA9Ez7scmoWA8YoOiM7SIS1YLwiETvq6xInpAbMpBsouQVpoqF9ocUk1Yu1ULXX+dIcmpitqeqd6o7SPDdTA1875nRga/A3lunZpIeISlFLCpa//b2vYqzA4JhOE7HVQl7lWVu1kxkhFqJGWFvcKNS3QmgN/WPDdKJU++lU0Yu/sTMalTZhD24u3KkCMXpNXQebVwk3CscnFbFWUTFo2/L0RxPuGOj/2EqNfXPxkjL+mlTaMD4O+O2EB6xLWCv0Vy3tRU9dBbq+0s9ayGxGWzwt85zS5IIjy6zNjgKtxtX4Xlu1khmmw4OFTVnt9P7CWlHinIeWWZPFnNmOWvhLAQQwIbvVZxF4fWsx09LWvl/v17ovYB/4kmSoNyMn656b3Zqm0d/Srq8Io8c6YdzX2CZivZCiio9dFZG1QXqHRDDBkq5rJTM0ke7YIGLYdaoP8y5hjRCipds10Dna157pZzri0ZMOHg4OkxHP6rWZmV/TVltNcRMxySjhg9JaNIgXvd0ubR/Xa4CiHbXwhfOAWWk71HaW1UttGU2PJ/x5TwyWMHotyFlLNT4Q6ks7JyHHJl+0TDRQF/XF8cEkg+QgxNgqKvG3luYqo5VqQXm+U4SlHnwZKXqdw7heaeC1JtzoRhzUPFgdPYTpudq9m9Gqjs+D8QmualKTFBlmur94RT7NjczO9/p6DNPaUB+0cIWVoXusacJVNvStr5VwMV5kZDsZnfWdKjGDyWA79Y0siC15ePfzluksUV9ZmistnKt3CTdETBKqo6AINLdDvbY1pxMIpwnTJMLgNPtNHOOuon3YEaNhd1irNKNTVJsqwfZG5RF+Kc52yu3SStFSETDrHEz1WTEb+7ZvyEzP/LnJhTG55f32uyw7yK1dUDRrg87r7hZ0/duw/oksj3u/3rt1X8A+8GWscHbS0fpAVweaKjBMnrqOOCd4H5kqbfel6JT2Hqy2Gs2XfytlFfFtoG4mptEzDB5JlpQM7Wqk72rSvlLj3QqGBxHZVZghOzFMmQBwpZtvaA1xpQVIvOhpurAVR6vIyiu1faZFTzpLcT2zg7y47NywqzQN+LWleyqEJyNVGxj6ijQ6GC04QdqI8Yn6+y0nn0Mxgp0Frxnt2fhlUgfZow+yMe5RmZTtpV6nWBdrK5lbU4WCP0dxAOOFMh8HbyBpbpgRTbMWC/H5AJ3+6rmLAWMTbTvRVoFLvyHuKkxvqa8WwXRsDYPo3Cu0uoFXB4HGMG40/Xk4V4Slbcal5Tg8TJq0PVmowdQJY4QUrJJXEpCJDSbCzTcs0ycD5rpi84WKoqe1oXtgmTYN9S5pu7TMNKssRbiYOLk44l3k6t0WVyeqOrBqRm7SmvGLjWoJTSbJRJb7KCbKGXlj8ywvO2/YkFvCuR1tg5JWSsjl7GtYjHdF3+uwllkUP21VkiCusFHNTGixY25VZuKJDTorW1w/fnq/w/fr//+6L2Af+GqriWcnO358c8bpusfbxJOTPdYIXajYVCNf3JwBkMTQdTUkg99MGJuIkyP1HjGJajVxtu242beESVtMErR/092caGsNMr1cEUtxcDBJN4lqr8ggrHO7rdWTf9nkZlZguuO0Udo3xTWhzkGa2ahXHNhbjw3KXhvPhfRxz2Y94myi6yv1DKwSBAODo/68wR9g8zqoNdHWMW50XpYqODR6gtfH143MDprZZfPzqnbC6jIxnmTUMyo6u3siV4G0mendKRMiVNOkm/txq8JlaROmTnz09JoxOqwRPjq54SubKy7HNb/25jneR6KtFBkGGB6lWUC+euHwXUZmFfQXOaMro9xUL8hCvLI2x1O9now2k2YEiYbmZML7yD6tISOw5MGhBb39nYaz7yaqY6Q/d3OBrG8NyRmGc60S4pSCn04jX//0Dbd9y9sfnevjuMQ0OfqbLe46O8DnJOVUQZ3Fy8VcV23CCgKXuS2pVlTMwuYi0LYooqpvZNZ+1Tt1Pxm3Jpscm7k4qiGxma3Aii5PfCYCjcshyojMWrbSlr5f79+6L2Af+BIxfP/yASkZTtuBt/sN3eT5xccv8CZxCDVfcMb1uxOKg4KpFYXJ5BUN1UUZKtzsVjTtRNMEQnD0b1f6QEnbYCYa2jdm3mjGs/yjOdb9+Ew3NX1yhrRaNmDbWdzBZhSzaGtiLSoXyifwWKuT+3QmpJOgDMpVJARL+3mN+0duWDcjU3Dc3q4w+fH820qFyJJzpp4JL08d7dvFUilmp/Jqx0w7L84f1V5UDBzg5GUg1kpUsBM0l5HmapoJG3HluP20ykVaW2fjRcJ1hs1P1IGie56W0EfQWV0yvL7aEoOl+fsrLk8e8+t55iJei4fNG+d4LmqL1ZtZ1xbWqicbzi3TCQwXkl37tTgUHVv5XpOYCTUlR208E+KrmmChPSy+gNWhzLnSfNjwh0j4yNE/0ud2OIW9E+L5RHs2cNKMOofta3709pzpttEDQTBwbLT9PCgxQ/0M9TWVlurwIN8WlexSHWD9SlmGJaHZjVrMukeWmNOaSxEnqQjZ5nyv6SS/p1FRtY3gdxmRNsshw3VL4dRrJPP1cdnM1yRtzU6nyxzufr1f676AfeBrSo4QHG09cdO1mo7sLL/+7hkPVkf6UHF7u4LRcv78lspHjkPN4XqlLbNmDkJSV/tgSWKoKg3GrM4HMEKKjjhazG1F/0SLmXgVq9qgZAFN8827tUVF1eisC9SHz2QnjNmcNxe/QmNPXuNJpAJ5MCpgGy3cVLCOmADdoab2gUNXs9qMjKPDvahy7IXQvNW5yHSeaN5qmGW1y4RDnwmY+eSfsv+hnbTF5A9QDZIdG7QgtJeJahdJzhLXltQYYqVtu2Jp5QY4+y0t7LHJcoBCAklqEeWujOrXqLBJCwkowUSsorVUZ0eTvFIw+J2mVNsAzVUmhnTZEeSdobmC628BTdIW28MBiZb6h/VsXlwcR2aRcSaAaPaZkKwWlNPPR8LaKXlknwgbNxd6jDI03bMj8bah39cMx0rnsOuJODns0eUWX/ZqDCwemEn1XclD/zgR/uSR8JM17RtFh+u3Mh+y/KAzNZsFyoU1CIt2sBReNwj7jxy7b0Sd2V3rZ0kcdA+SFnafiT+3ZmkfZ9Tmj5l1mGN6iq1ZWCur1PWZ2HG/3rt1X8A+8CUCziWSGMbJE6Oehus68OPhnNoHPn16RUgWbxONC3RjpY4ZArHz2s4bLHYwxIug7hTRMB0bXBOp6kAYtTWnOi4hnmVCgBjGR2EmHcz0+Cystp3uxsoQM/PMqTioF6qzWNVd1Tfa4pnONZOK3unPNQl76+m+MtG2gTF4YnAc361wO4vN/oTVXmnsw8Okzg6DogrdoMysYxJDDkoEwtK+Sg2Mztzx+dOvxdbOERyzhRKa9mvjsvFBpnOP0L62WXCdZ2oRzNEwnqtwt8TdpEpyuzFhB2ULcjYhnVOxdp0PBaPS5wHiyuIPyzVMbcI+HHj2QGNtxuC4ffPgS4a/4oCkbcAi1E2NXpsSIXL71Xq2hRpPPMOFoX8oTOcJ92Ag7SuYHG4TSMFkyGyY3qzmFqwJqlmbW3KhZIXll3EupIuJ6dWK1WurdlIT6rNo9AWF1mSLLJ2FERQhTxujCQX5+icP09rSP5Ic16OsyFSh19mpEbQ6wBjNaYsQoqIzOzInFZgkuHytYq3Xyh+z3OKexPFervsC9kdg1T4gokUnZYXqMHicE/pU8Wy702RkG3hxOCUlg28nqioyDhWh9/OJlSmn3npot4MWuWDZbHsO0pKS5/xrtxz7mvGmUQgjYCY7O8rjRGnaoIUtlbnHos2aHy/PJkoBKaGJWMG/qQmnEbYTHD1ppZqcftdg9o72tZuNbbsn+tjDhfogmmCobhzrV0njVDb5FF+iR+6gPljsoIqtVQm6dDmMUozJ0SBmdrOob1VU67ILxrTJsS25XZlyTlnJowobTTwWL9jO4o+GaZuQWll4Ina+DvZFrffTCPHBhGsi8oMVVz+r7ijF8aK+VYKKO1rS1PL6Rytiq206GxTxDBfqzWgmQ71TEXa5DuIVAfqjycJk5lTpVMv83kijnpc2gb30hMcqATCjxe/V4kq8UN0qm9BnV3o/qMnwcAYYNRUWB9WrOouK8yEhW4aZIFqw8vJHdfsPa6fi4gjdhb43vtd2qrjMenWZLZrZlM3VQhTxx0wUynNUOwI5pNTIEmY5rc3s6q9uJSZLMu4r2Pu47gvYB76MEUSUGl9VkSkjB+cEY4RNOzIlx8pPvD5uOQy1WkABQ18TRwu9RWpBRJT9ValuxxihriLbduDFmzNkcPjzEe8S421D9darINeAuGyfUSXM3mvLKjPD7Mi8uRSaNlbmVpAP+WvZ3kmaBNEQzoOyD28q3GTwe+0ZmWSw2QmjsM1mKnyE9Uv11qt2OpuaNjpzSXmcpzO6pbDobZkBN5YLy7yRpSygLVR1cSqwVfcLmQujxrHkmPse6LkjwoZpk+87O2sA89/AHDFTRLxxLaR1ZLUdcC5xXLfqlt8pxbvqMoHjRF35Mfk+e9XKte+U1l/tM1liUpKHHTKPpjAy0bywaatIUd8vRZLhyQCdw+4caZVwNw6DzhvL61JXFC36vlPUUuX8r+IjmJyGTSrS0+vZvmVmDOo8SqiO+tz8oEnO4g3j2oGB8UQNgG3QWVlpK47bRS84Ez0OeZaaFCVO2SzYFX9Gk+d+e/0cxqagP77s6t9kVmezvE/36/1Z9wXsA18pWfaHlnY1Mo0eEUPTTnNhO/Q1p23Pdb+aHefD5Jh6Dzm5GL8gJ9dE2vzzxgjj5Pni1QklECleNVy+aql6o20+g7L/AFNFbSE6oUSrmEEZh6VFWISnxQGjvnIzchMLaa0EAttb0iphOpf1THaOiymZYvPmF7NIdcpEBqMbdXUQTeuFnGOVPRQLVTvTt22AmAuNSWDc0lKriptGdtRIPheBKdO8vSE0JgucM7kg+w4WNOcP2sqctgbfO2Jts3OEOnL4G7cgQlhQTw597HYNjJb2yv4eg+Pp1Pye+Ux9q0UuZmak2FK8DP4ohLXGmuibpu01G5RyPlwoISJuEqaNuteParHkd/p5kTynQjQGpQjHlYav7+W0XpBqacfC0l71k74/Ya1uJm4oKFhwk869TNTPiTg9PExbw+aFzFq86cQgTbENU0mGVGpSPJ1kYs+xOHQs16noAv1RdWRYRX6FVq/6O2XS+mNBhf9Av6b366e07gvYH4HVtLpDnG27wpvg0Klq17nE1XFFN9TEYLXFaATjhK984zV98Ly92ipRI1iaduS4b5BgsbXOvx5/cs2bH16AF6RWe6F0AoWWjUFFuMko4WIdYcrei5bZDb5ovUz2/itstPFcNxW8UF1poUqbiNtpGzJlD7v2jW68qVGavQmom3qthcsI+EPW8HQyWwsN2+wYkSnSiJmTimeqvlkQnO9LoKHh8MzMbbVi4FvvEsOpZbhQ+DLTtFmIBcpcFOqD4IaE6xO7z2oluuTXVB0M7oVXEkW95JXZXiuZHdTqi86yemnxHRw/0sgXO2oBiKNqoU6+0MLf3ERia7j+hmfaLIQEMeolGbboG5fF4n6fhb1TNqy16kZSXTud+zk9aMz6uEGvdbXXa9xeqSase2KIVluRqZJM2BCqgzIKU21wY75eBi0aSVt385Js+5SRT39hEa8kmnqfI118sY3SYoxALZCcIWb3+PJ+utEwnsmMtMVq4dp8kYugV7o+LJZcycPxqSVsWIqvM4T7nfK9XPdvyx+BJQKVU3Fq7RL9WM2Bk0kMw+SJWc9lbaLJotnDWLPPhS6NDukcaTVhfQKf+NrTdzxsD/yt731VWzGjUVPbPLfCCW6lpA8JVk/mNmuOICOcZahebKJcZxa9TT4Va7SIapQATO+obpUEUd/oidoNhrhW26USPileh/fAfJtG3S+5X8C8iZm0ULjtiIqpC+LJyC6szIzuyv2BFsdpbRguXNZ7LbMzExdmW0l0VgSiG+TVz9Yq5s6P4frSdlvIH3bIuq7MXEwVEMDvtLU6ncDqpdG5V2Y/1ntRT8CDHiyGM8fuK0qxT7XOfspc0QYtfKVtWZBTdYThHOInPXKoqC4dqVG3f6wKy/2upCfnjK+jursX82LQIljlogKLj6GyAfUzoZ6X6iyCQH3IbVoHxydasKZNPgBcC36n3x8rmVmhsTKz60aqMgVeBLuHSnRuZZtsM4a+x8VxZQ5QtQtyL0jaGEXsYZMPNJUwZlRW7f9BfkPv109r3RewD3wZIzRVWOZak76lY/B0xxprBV9FrEt4n2gqLV4fn9xgjfCb4Sn9u5UWo1MVt469p1lNdFPFbx0eYyykJs6zg7k95pMWr2h0n47aLlRXd2Wn3TVK9Qcz06vdoC0unCKrYo6b6ryhB9VViReqb+0JNyv8scblkErVc90pXpFZ1Fvc5QsaUiE1ShxJzOyzuFpIC8p203laaRGW4jeeKkKaTpQQUIgZzbtlrleKQWmbFZ2SOMPuY5c3ZTXOLV6PYrJmK+UWX1go3tOpbvC+U/G1vkhFI8O5JaxUL1UdE81lwB8nwrri7S/4rHXSzTvWWU81mRn9GtEC67NH5XgO44OIedeoXsvohm8nRVPVTT5w9IpAm5tErA39Q8vxmeAPOXG5L+QHtZqyoVhcOYZzdQ5JlWrkfGfYvNAPhzrKL76FbtDiFVqTU7EVmc3Pu4dQWn9355jFYSUj69iYTJNntgKz4zKXMyLL56aC8dwyXHz58yNGlkPN/Xrv1n0B+8CXzXZQUQwVEJNVH8TBY73OwVIy1HVQJqKPtD4wRs/1sOJwbGgfdjiXqH1gf2g1iVkMl3sNQUrRaI7YHYRUZmJpXynqsgJBff3saLLnHjMFvMxKyH8KgcPOSCoLjRvRFpSgpIHtxPHNhvqtmwtobLI90LSgmPo6D/LzobvYEk1nMttV+W4pqMOF/u0GfexqrxuoWKjHYpibM8PMUghBSRCuNzNxY04/zo9d5lmxMRyf6CasFwsMZg6ULLZPKkBWBFoer8zPNIBS2YZiDLtP1bnejjCeGlZvkhavTcXrP9UwO5sU5JkMTJJfq7bQyv1PWy3iYsDv7Hw4sVmwjlFNHkB9o3Oq9dtIWFmuf1YRVbVT1FiQtJjs+m9g2lhCo+1FE+H4nPn5Va/1OYWVRqGkJqMcU4gpZmYplhnadLIcNrBfnq1hmMXMmIyo4nKwcf3iqVgYj5KUOdk9tnPrsTiZlLa06vb43a5r9+s9WfcF7ANfgqKtGC0hh0zG4LCVDj4kgfea5eVs4jDUrKuJPnqMET55dM1N17JplLVw6BoqmzjbdEzREqJjMEKaHDIYLVRO1FcvqeZH4Qaa5is68xIvcw5XiT8RC5JdN+yoqKCk70avs63ia5eye0e6rhUJ+Hy/q1wM74Requi0sBvzhUl5Puaz80PKpAizWAmVdlK1z6nCUckcJZssZNeOVKGMSC9Ur6psaZWRWmB28rABDaXMLvewzMeUqajzJSgzGm2dpY06dmgLUYM9++cRmw2Nq71hPFsy00Afa/U2UR0Dtpt4+0+cKLrY6PWbW5gmu3mY5fUWpArqSFFMfE0sLMZ8bTNRo9rpz2xeBcQZDk8t9dXyc2risoR7jpXCS3GKltYvhcNHJrvN6313Twz9Iw2bBBZNW75mbtC5aWGFasvWzO/HXLxEWaYuHzruxqfMZJ98zcI6J01nR/+5dTtmsk++RuV+lSiiFMnx4r6CvY/rvoB94CtGyzg6jIFpqJREkY1p29NBvfWi5fZyw+q05+OLG85qzRnxNtGFiraeeHl5ysXpkT/+7BWHUDNFx77fcHi9wQR1kkh1JmwU9GPv/FIn/X+shfb5gTA5dfYYLGL1dC9V2X2VXWd7i+319K1oJrMUs2uC6awKm+1CcJjp8rLMvOJKiQ2lLaemrkrEsKNZCBqZbWZyxEvRUIV1tl2K6nQxnShqKiJmG5h1bWEjhK22Nlc/9rPDhOv1vgrbUUrHKbcVw1pmdICYWbytLEVtHZpotOW3Anew1NdmdmUvhUssVCN35k81hz/ezg7sVWbdGb/MrEp7Uw8N+euFxXiibdhqt2SszYxIUQS0/SJgJyGsLf2Zpk1Pmzut2z5nluXUYn1MUUPmjK7doMUtbNTcudob1i81AsZm4fDxqSIh3+t7U8g0qcrtT9HibgO5TZ3Zs1lgjgjxRJFxcfGwE/SPtOXsOmVn2pDRfs5u08Tlgur1uboARY4mbZoTFO7X+7XuC9gHvoxRzdc0OaRXU1alxifqKihCGyrWZx3nm479WHPTt/ST5+FGj7/7vsFlEsj3rx7grB5Dj3ttSdkhs+KmjLjKDGwyC/nCJ40j2QQVSlcRX0UGV5Gsx64C56dH+rHCuaRxLX1DaoX0UB0e2pd+vu+7Dh2FAJIyu8xPQNJNKTU6K1Lxs7Z9UiMz269ke2k7SecoVt2xGM/M7I0Y1rqpzkiO5TGBuUBq603wt46qzFbcIniF5bbibViE2zh9fuS7QpiZmGSEVhBktTOzVKD8fKq1IFRHZkPcw1M7+//NqdLFGGVY0OTcvrV3yCZGWYP+oF8vLbrlOQjVAXyXtCW4yiSLk8U/0UQtSsXUuTwPf9RU49DofLS+LddB56TVviAqYaq1zRg2+T0vhxB/57lnaUNJEyhty1lXiM4by+uCfB1yQWquMpEo+04aYTacnme7ef5m8mdrno8Gswjz79d7te4L2Ae+RFAHjmA1ooQ8U1kH+qHi6fmO81XPYay4PqxIybJqRpwRtvXAm+OGaXKEyfHm5Rlf/fQtAFOyXN9U1Ld2bn+BoqQSc5/q0sIzJDFIG6lWEy4HGI45j6x9cMS5hHeJtp7ohpoUDA+/ecmqmlj5ie99+9N5UwZmc+D58VBroaKxSo0+PgUxkIvdSggnUU/rg5lP4f7yTDAAACAASURBVHbSFlzxKow1ivxyq9Mfy6a6zFlSrY9lioYoFxR/cIuXYN64xeUx4CqTU/I1i4WUYoX6WtOYC9lA2YgLK47yELIU1KL7Uu9ILV5A1k8pCpkd3Afm/CuxWjSA2capWGeVDb7MjAptvbAnld6uYuDNixE3JfYfNXpgyGhIZ1O5KDXFqxDat+rA4XKkS4msqY7CuMmZXvlQoo+p6DxV+j6WJOsSdyLl4OJUL2azYbF+DhbXjKLbEwcxXwM3qBt/CTZVobh+rvoHOoss87BS3MUDYSmWBRnfJYvcr/dn3RewD3wZw7wjSZ3mX/yqDlgrnDY9h6mmHyvGoeLh+R5vEyf1QG0Dp/XAm7Al3VaYk8BuqLm6PEE6D3cSjtNKadUmGWaT3qhoLLUC24m6DbTNRNfVpGTxVaBdjWyakeNYUbmIs4mmCjRnkSSGxgd++8dPYJ19AP3SwirDen1cZtFxqnQgf9d9o/j6IeA69XUsLDVl1GUEk9tn1Q5S0M29OijaCOuFtCHZ/24WN8c7G3xBRpmtVgxyw6pstsvzLyjFHayixARuMvrvuDjGq98ixFXCRDWdLc95RqN5nhZzonC1h5QdIuy4UP7FaBtOZ1ILk69s5F8iaySWwpBjYopBbnMbSZXl8LxWB5CjtvzG0qbzhbFIbi1rS68UzJKrZSJMKzMnVJuoh4ewFlJjdMaWs8gKQpo26ppRGJ6gQvCZ8TnP3/T7yqwr1cyBlcOFPq8i2i4HjbAyhE3RgqntWDxNKp53UO/UVmt4EtTL0+bP1v1679Z9AfspLmPMp8B/ATxDz3i/IiL/qTHmAfDfAJ8BPwD+VRG5yj/zHwC/DETg3xWRv/IHPYYkw9RVeu95Y6rOB07WA2ernv3YsBt0x1uvBw5Dzaqe+Hh9w5vhhMYHfvGTL7h6vOZHby64fHmG6Sw2ahRKatSrr5A3JLdaEKMWVBcT1gtNm2M1jg3GJk63R1b1xNP1jlfHLW0VuO1aKhfxLjEArQ8kMfg6EkOtFlEdYLMNUDn1miWTyYDGzptMMz9oG1MRiFnIIXU2gs0mumGTi25QuyPN9VJmn++zbVC7ICETmE1jC3OvWCfNaIxlI1WBtN7mu9LWMiR0tiJGb5t1XsV3MRfDsEnYT46crweuv38xHw6KCNlOYMaMWvJvbUEX6ve3kBJM1PdIo0fIomLmoluQiOvU8ql4N44lNiSpsDfWhtBabNRgRzepKLk6wOG5Q6TM9ZYCXxBsuQ5ukFxUDXYPbkzYKHQPLLffzAi7ONfHBX2BFmPfL2xFYM7xMjEzSDOaTG55P+wd8sbdeV5s9f/jmcxaRptb4tWTTg2Jg+XwcULWEZyoN7WAnQo76H69T+u+gP10VwD+nIj8HWPMFvi2MeZ/Bv5t4H8Vkb9ojPkLwF8A/rwx5ueAPwP8CeAj4H8xxnxLRH7/BoaAsaJ2UECzmlg1I6ftAMBxqtgdWoyBlCxtPeFs4ocH5ZH/Yw8+58f9OT+6vSBNllnDBXoi3USqre4UYXLq0HHrkVrwB0s4g7rR4UflI84l2iqwqUcerRTtJTE4qzR9Z4UpOC0OYnh7WDMdK2Xi5yKgRUNnWfMGKYuWqVhP1beGWKkwN64EvzfzKdwIs8v8eK5ZT4UUEVvwqaTtaixJQSlzqGZGYHZcZjFF+Az6nPLl1wLS3CFHkIkfARBFHtOpLBEeSeaNs5BNTDJMtw1Xtw31bUbUefZVEKbJsyRuyYGSy3O525Y06Q4SGZdrWlBh1Qm+1wJe2qIhp1/boHT50OZW4SDaHs6PUYgemxcRkwTfRY5Pa0VlPrf5pqXIj1s9QDz8u7ek2nP4pEVz5AxxFXEHRd02k00K29JOatQLEBolaaxfqrvKeKaBqvN8EH1fUkHvovZdJX6lPO/y3seTNCN1KTM/AWkjcVK2ZLRWEwFyEGha3/cQ38d1X8B+iktEXgAv8r93xpjfBD4G/kXgn87f9p8D/zvw5/Pt/7WIDMD3jTHfBf5x4P/4fR/ECtVqoq4DbRUI0dINNSFZxtEz9spMfPrRNd4mdn3Dxyc3/NqL54zHmu9+/pTVqfKmm/VEX4pYNMQz5TyHvoJDNuitE+k0YLxQPe/Z+MimGRmjw9nEGByrSgvaF/szKps4DjUxWR6dHLg6rhAgTp5urOiODfVPKqXaZ9p7bHU24Y65FVXrzmxro+4cmYo+btVpwuVMMmUAFq9DZU2Gk4Q/GOprtaHqH2k6tA2WsNYNTZ3lmTVXs7bLLi1KYG5bpjzbKTR7m8kCft6EVfCsTh6K7ppLm2c3kpFe+rKgObc/7dFho87jlJwCkpl5rmd2oygFYo5JQb9eZjpuQKUEeVN3o8wxIYX6HppF3mAE2ncKce0k8/cXrVqhttsxYYNgh8h4XnN4VjOttRi7UcXHRrT17EZor7Si33xrixE4PrZ0TzUxwB0s61eG5lJ1f8O5tu7GUzXeTV6vg87chGmj3o+Frg8LQoWlPdlcLzZipaVbULSM2jZURKav10yGdNmqDCRBeBAwdUKGnLSQDGILv/5+vU/rvoD9IS1jzGfAPwr8DeBpLm6IyAtjzJP8bR8Df/3Oj/043/YH3C8YI8RoOaaK/ljjKm3JYQQZLbaNvHp5DtGweXjk85sHpGg5uzgwBt39pskxHWvMoK7jNElTdZ0gg4U2YpuIBIvxiaoODH2N3/QMwROi5RBqnp3teHmznZ9ft2+oVxMPTw8cp4pHJweSGL54d0boK8xlpXOJzaIbkztmuKnO6DLvH2WzVVq9IQVFN6kWUiuY0xHrhGl0mgb8usYE3RjFK+HB7ex8/6VNV5hshZFWaOlfety0oKypBYy6ytu4bKLTSb6bYmvVyEzlT0WonAXNZaaVmoR5MKrbf9a4iVMixfzcftcIpmzcMTvx25ALa8jtz4IER1kYnI0+j+LAXuZ7qmVT54xiWqsaKk3GFgumlxl1Akzbiv7CzYcJRFmFvpMZkaYK+gc2FzctUGKVEVjMlkFmSn9zLTqbrKF7lAtmvrbzLPIO87C4pdisa/OjXqRpo0U03GmrTqcQWj28KNvGqCYxGKQRbEFajebYiZBNjAVOAvXqrmr6fr0v676A/SEsY8wJ8N8C/56I3Bpjft9v/X+47fdMj40xfxb4swD+0RkA/aEGAw8f7Gl84OqwYpoczenAsG8wVlifdzTVRDdWPHtwizXCbqjZ7VeEty1uVNRlm0g6etzeEc8CpknU65Gx0z5WVQfGvsJkcXRM6rcYguXzVw9xPhJGh0Q1BAa43G346OIGbxJXwwrnhOgSqU2YeMeR3otqxCYzu8+TBbZ2yI4VNrcTI0xbPUXHjbp2mPz8Qu9htPjOzLqx4sFYfO2OH2UN1K1+vXjwJQFTCoioYwVJ401io0QWf1Qj3Dldmjz/CcscLbbqA+mOZiZ2UDRtWexdaPVyXVNfW7XGyu94WLO042BxBclp2KV4uPHOBh+zq3r58Fijbuoj+D6pf2CRCaTlQDA/Zs4z81IQjMENmsllJ8GIkJwhbDSypjipFORWHfXrhQiSvIqwp62iqPZquR8T8+MZZZj253a+dtWhIFuZTZMLzV1sdhCx2ios4mUTtXjZCMmoDi7WKh6XrFk0k2aYiRfwCXeqsg+5rOfdUIwoo9eC3U6ImGJpeb/es3VfwH7KyxhTocXrvxSR/y7f/MoY8zyjr+fA63z7j4FP7/z4J8BPfvd9isivAL8C0Hz9YxkuV5jB8i/80rdJYvnx8ZyQLIP37A8t5w/3jEERybFvqKvAg/ZIFyou44rwrtU2SitsHx1IyXIYHPE8YKqEBMv4eg2nE+16pD/WSDS02xFnhXdvt6xPe7xPtOuBMXimwePbiRQdw00LTohnlpe7LdPoCZODXYU72tnkNq2UmYeRmbFWWIiIbkgCs0FwcZgv2rR0VWMmw5jAkVtDTuYwx7usu/1nGWXkUMPEogEKq4XVJy4TSoRZW1aEx3fp33c1a/rGZ5ulPJ+LjZ7+U70QCEzQjLPZWDYTTsQvM5tUswRQ5pZhQWWFLJGq4sIvueUpM1qZiT1HPSDEeiF0KIlBqA8Jv48MD6o5jbn8cRnVxFbZHSaCPwSOzyp9/YU4EqDZq09jYUFCRqxRkDqTZ4IWLzEmi9XMnKqcanLx08csszglUYhGxLRK6LCTXotiAVYdhSmzI4s9VtEKSiXz+yNekFYJGq5OWpySQdYRU2fYuvfYIbeve49tAzHc68Dex3VfwH6KyyjU+s+A3xSRv3TnS/8D8G8BfzH//d/fuf2/Msb8JZTE8TPA3/wDH2S0VNeOk5+7JIjj1y6f0wedL6VkONn0c5vw4cmRlZ/Y1j1JDK/3J+xvVkiVlHp/PrC/XanYFLSF2FUao9IkGBy91NgqEYeK7s2aPmzgdGKaHNbKnE1WNYG6DlRuwJwKjzcHfuflY3XFv660fSOQ8hwitbm1k/U9IEiexSki00K20Ofz7m10g3KHRcEqPm/gktuRcZlrqSeewY46Zynst+K6MZ7p36HWOUpqE6sXjv6xzqx8p4jJRObk37Ripssza5J0NhfWoizJqAcEMxlMjiRRoscdtqFlYccVxqFFo2vIouSM+Oy4MA9N1MKQvJmJGT6jn+IkYkSp7CYXrdioI7wbBdclYutITn/OxDsMUCCslIloJ8ENkeFhg5jFa7Dc57i1M+uvPI/qUBxRoDok7Fi0V5Jd5rOLB1nYnAktbtBC5wedNxY3/mInVVqox6dmdvKQzPJMkmd8J2TEri3Cea45OKROxGAxdcRVCVMrzJ32Ne5olREbhAikcEcdfb/eq3VfwH6665eAfwP4e8aYX823/Ydo4frLxphfBn4I/CsAIvLrxpi/DPwGymD8d/5ABiLoSfIbez47v+R/+/7P4L2eIsfB46tIN6jzxem6p3KRTTXgbeLbn3+FcKh085wsUifqJhBfrEibZagj2VORYCCjsTg4SAa/c4SLAL1j7DymjdmZXnVobRVovLqBvNptMTZhPcSTqDOIKs9VnKhzfTkBG0GChUFnEAaQzCiz4Y4mJ5oZjRWXDUHbi8XCSe8PLRoRxKn/YnVrZlRT/sQ6swIdhG3CTHr/488fkWBJb2tizNlYoinGsxdgUuakKSzIkF3sMyITA7Y3+IOZ9WGgCBAW8oWJ2Qy3eBXmTTf5O7f12p5zA3OSsMai5BRjqyGbpWibJEwrsxTGWlt29U4JGbG1WnwyOnW9HmjsqIXN9wl/iJgoHD5uCa0WyljnMMpRGM4s01aTsgGmlGn3WQdWHbWQTRsthrNQ2d6x+kqZBZnRWyFhpKL1W5lZ21dmlXbSQweoDdjs3JL1guSCODfikx6EzKDIX4IljI71gyPH6xX+siKu9PRjxvxGBcuXbNPu13uzjNw3dz/odfKtZ/JP/sqf4fX+hNv9iqqKVFWg8ZEHqyPOJn7n9SNStDgfETFYK9nOqSb1DuMFSQbXRNK7eqaK294sjtzbgDk45CSw2g4MfUUaXC5swuq0xxhhuxoYg2O3X6kT/mRZn/acrTv6ydOPFd4lah85DhUx2mXGIIYYLWl0MCmBZC6eJTizViIJgm4sRsAJ1ZtqdseYzpMixmRoXi+tnxJ1kmrmFlRxip/1RG3K3ysYMVRXlvVPDIdP7kS3JN0cxwcxa9eskkN6MyPD8SLpDOWoKcplDld0WAX93WU5ls1+XoWqn4vknLwsmeQxMLvAF9RTaP+gaAq0eAxbtZyqd8L5d/ak2tE/aTg+tiSn5Iv1W6XGa6hkpsUnwfXCtLEM52Y22HWTFqTQmlk/5npYZSZjrM2XfBDttOR32YnZaqto60r0TAmY7B5n95M88yv5ceX9KllesYXDx0kzy/YmW0NpCzqs5I5DvR5s7ATTmXpZ2s7ORtPh0YTpHWIEfz4SOo/Ze/zBzIeQ7//7f+7bIvKn/7//lt6vn9a6R2Af+IrJ8mq35djXWJtYtwNPT/Z0oaILFdddq0GXzcTpuqdxkZAsx7HiGBttF5pEux0Zuiqf5jO5AIjbpJvyaJFVxFaJ7rbFeI1XoUpsTzusEWymGl+9PMWudGd1daSpJiqbeL1bc356xLtIP1aE4HRWVgdSdKTJalxLyNxyI5gqUp8pTElJb4851kUsMFhNbi4swItcvMopu7T5qhzTEjSTLGXrJ7/X2UysFVElC2wCq5OBcfTEvuXmj6k7RlwLqVENke1tdiVR1KaO+arvGi+0eNr+jog6U/vVtX4hdcxmwCML47HMlVKeMaXfBQDM0k40IrO9U3JAdu6wk9Lkm50wbqw6W/RCvU/EdUVsLCYJ9U7mzKtpbekeKTlj9TYxbZSsUd2qSwkGXD7wqmjZaFFLGrfS3CpyW/K2wB21OA3ndtamlTZoIaNQCChC/jyqVVW1l/+bvTeJ1W3J8rt+KyJ2833f6W7z7uvyZaazOperDBgDQkJCYoLEACHPYMAEhBmAEBIjPEKyLE/AFhOQjGCABEKWQLSWLDAjkAHbYOx0ucqVWZlZL1++d7vTfe3eOyIWgxWx97k2LsmDtO61TkhX971zv2Z/zYl/rLX+TSGrGMBO6wev00MsFlsaFLd1uARj+Yz8yc2avOqyTxWFD4K7d7OBMUDaeCN2dIl43+J3jrC3DoAWacbjev/WI4B94CtnMTCYPM+e7Gh84uXujMZnhikwTYGmSfzSs9cEl/lmf8F+aMkq9JuR1dORmDz335wbpbixikbbTFoVMBHlyaf3DFNgOLX4PhpYqdD1E06U/bGl6yK3dx1ubzMGCTYkv7k5YzgbCE0iJsdhaBhO5ofogpprfXS2eVV6O4prE75JOKe4soPnLPiQiZMn37S016UV5B/kgRUQ9AeHP1qas0QhjFZBTBfVZ1BmANHGAKb7bMuzzYE+TPzwm4+QSWjujJE2Xhh40Sg5JOTo8acyyxqrPknJbZ7bmksul/3tprJBV1unAjb1NlXjNVcNcanAwvGBLZJCjXC5+PHE8XkohAlmckl3Z09qdlnmuuFPucy4zM2+kkZyEE4fL7qw3RfmDN+/he4+40qi92yVVaqi9ctc/CZ1BifJpgcD+0yMJWkfbLXBqkbM5i4vPAwknT+TUqE1u8XSLBW3/6q9SytwgyMc4fB5QtcJtzVWTgUed2ROZHaTzTHfcfFowO+czc8aT24sgsZNMFw8sFB7XO/degSwD3yJ2LzL+8zu2DGNNvtqmsg42sf7S89eA/Cbb17QN5Ffff6Sl8dzsgpfvn6C9xm6hIYluDIXZ3t/cKR15n67Jh0CYTMRmjS71zc+kbIjhMzu7dpCLR3o0cMK+vMTbUgkFcbRs933VmmdvFHKJ51p8vaCgCi4wZFVYAM5efrVaMauWcjJEQcPbSZupCQu1wGYPYwbnLnSR3vI5tZaoXFtm1Nz/+D03ZZO5SbzbHXi9XbD4e2a7uuGcITTR3l+DhkcbEZ0Moq+tbLs+nOrTGeKvxpJ2wb2bgawHJhp9+GwVCGz319aNu9ZTCGLu4TLhc6vy0beHc2bsL0b2X0WmJ0ppAp3hWkts+ny6IRpFQjnvrho2PBv2jjGFbjBiA/Vw3D90mjvfrTqdZrtnOww0d2lmTySOgeqZmQMsyM/xV4q9UKs7drAgyFg+asSQmamKLOAWb0Usfe7mrj58RpleGog5faNzb+q0LkkS9vj2eGh2S62YbMRcwnx1DJftQNR0aiphbQ+rvdvPQLYPwDr4vzI1frI2/3a2oUhMUZP20a+++SGL++fsD1YZMqT8yPXw5qswn5s2awHUnasn47sDx0pOUKTGEcPhSDRPz8y7FtWT46IKMHnuVBatxMxO7Z3K9xuiUOpuptxsFZhnLylN0shbqhgg6+yMXgblruDoYoGnftmvmSaAXifiVOw2w7OnC5aAy9JgooWBwqlGstW7RCY67wkmT31zFuwnvCFb77/An8UViUEctpYZaFdRrrE5acHnp/t+eFvfoZMvNNGVG8zNHnd44vINtdsM2zjnBOFYbZMqtXWw78rhb+SFdIDcbcorF5by27zu3v2X2yK3GDuvNpcpxOLPimPYfle1i5sdpm4tjdl9g1UZfVymRE2+4yLMJ47xjN7z9qdEUXiykArtWW2FcpMz1mV9TDipBr7tveVrWi398d3/Qwro3EO7RwLAxHFOZlfew7mOF9bt+21o93C/nP7HtTgTMnOPiOFmqAgo8wV7UNjZEsEMGcXWPLa3FYKi/ERwN7H9QhgH/hSBecyb/drPj7fMSXP3bFn3Zmo95vdOff7nueXOz7d3LObOlSF+1NHzo51N+JE2Z062i6iCjEWC52DJ30yzL6KIkrfTrSFDNL6ROMTr95coftAONkcqNLBXZsQl5mGANtmIS2ooKEOfFjCAp0aAGTB7xyxUOwrzyiERM5SWJCFcBEKg3Gy9qcWX0iVjBw97V0RuBbGIIC2WjRHJY8rCYJFx7tollaxUdwgMxgQBVmbTODup5f4faFaj8VrMUL8fMBdt0X0nKFRqyajJ9zL7DhRiQnV6UObhYVXQaiKg1Wsyq5kh/FS6d8Iq7cJP2SG5yuOT52FaRYjWzfYa53OhfGcxSrqjfk/9m8mpgs/52eN5wbylz8eObxoyI3M+q/Yizm+j8UZIxpg1S+fZGi32UgbnRBLG1e0EDk6+y5UZuFwVaqgnQFWXJXvSokvES1RLYXduDivmNBcS1VHXogwkuH01DR2/RuHRIhNfY+XNqkbZWnPwhxtU8Xj1SDY7MDKwWMCn2SOa3lc79d6BLAPfalwe7fhe5++4e7U8+b1BWwDZEEvJpo+8snTe/Zjw9vTBifKlDxOQEVRFS77I1PyvL05QwScT3b/s8hHz7aoCiLKODSIwHk3IqLcHXtufnZp2iaF6WlJUsyC23l058kZXJZ5cwbIq4xEZ6JlWOx9HDZ/azJ6NdL4QudXIU6OcQjGfCzaMGMiCgRFNhN539gJ2yk4QdvM8KxaNlW6/TLU94Md6ePGTvNVTFxXpes3WyGtHEkaZO9w2CY3fDZRnfnzKPifdYQyc3ODw98Zbd6XSuuhO/rseq9qziCFFJEDFt5YtE/aQHamWQPY/FQ4/yrSXY+Mlw27z0OJHlF8hNxY1eUKKSScbCMPJ6W7zfRvI6l3pMbai6mz2Vo4Kde/2s3SgrgSa0/uM80hE3trh1atli/VERjI5SDE3p47lTldzdmSMiOrbhmzfdUD8srD2BTUtGGpN1p+uzN2pR+Ko3xcWq85wOmZSQraGz9rvdxQKvLqZzmV1mu/CNLdaMDtB/uMXTRK/vgkk1uLV+nemtC8zh4f1/u1HgHsQ1+ifPL8jsv2yA9+8jHhtWm74mVifXHiVz56xY9untGExJQ8jU9M2dGGyMfrLVnNw3B76M1pvk3mWn91Yt0PTNGTVRjHQN9PrLuRmB33p85Ez97A4/LyQPCZ69sz8ptuPoVLkjlDK7UGXrWd9c7fhekok0PXySrBYfb2mWn2c8XW5OX2QcmDx2896TzRXIz4kJkmb+O1bHKAqrlKvRLbbI9RhMMkQY4e98BNo1K3l1mUOeQ3d2YoLAePdhm/9fNGnIO5sWfEqrvKmtO/zQZJDFzCaNVB0W7Pm3j1Eqyv3ypG5fKHSrNLTGeB7RfB3N4z5mzhDSikRLakzoDMnyxgUjJMF97yuorouZJJTk+KPq0wAy03q6Q/F2JGaq16yl4IQ2Y8s9aiZEVrBlgRWbtYwEaXatNMlpkr0Tk9+aHEoLItdflu5AcZYPUzqXMytByIkmP9mpl1+s5jYAeB5NVy3iqAFiLM7Mi/huFZgouI7IMRfaYlBfpxvX/rEcA+8OWckrLjm/0FjI54mZA+8cWn1zxf7cjqSCq4LAwaOIwNp7Hh209viOr5envB7f0acZlnz7ecdSZ0BvjZ7QWH+57N5Ymum8hZOAwtbw82OHAh45pEvxq5XJ243q/J1+286ZOXFg9iG42WXDEEq6QKyPm7htVLc4w/fp7JdYjycOPwigTl+Uf33G5XTLEzWyAH/iYYOHolZ0cencXDNBkmh/bW5iILBANRCZlQvBrjENAspkGD2RQ3HIThaXFHL+0x9aVqBMKtkVGyB/EFC9fFab4Y9laaeFyX+0tp9Y02o3PRqs/cLK/XJZCSxkyzzMe624gKbL9ozHapAF7Nzqop0fAADB4YSaTGWn3qTISsjbUQVYypONPFZ4aeoKV6SiUcE4XxwqQLccU8U3RlnlVTkf2J2UzYZn0KyQBwbjPWFuJUgK60DdWV6lBAizj6YZBlzenKAVZfeXJj1VN14vdjeb8LUMZeS+vSwkRzcQMRB2nNrCHTPsPJE7aO7qYcAI6L7+Tjer/WI4B94Ctnx8ufXRn7bxU5uzzy2cU9rU/cDGvujr05Y/hMyiYaPlsNnDUDp9SwP7Ws1gN9E2l8YhUmzpqB18czToeWdm35YafByoFxNEG090pKQtdFNt3I7WHF/c/ODUBW2OxnWkgaqS3EjkaRpoiFo9h1l6F7XJsWq7nz1uYRI1hoY8AkPuOCFv86O65LnYd1im4i4nUxXnUKpyL48va84tRYlpMrtxVyMj+8elqvdHiKPVGzE3JjllD+5Eh9mdUNbg7TlGTCb3yhbEdrf7nJwGi8xE7+00IBN1Nb24Tjqtg+FTYezfzWzTqwcCfEjed06eY5Y80JCwddKpZG5jmbvfdCOGZOT9zio5hAvMwhj81J5xkaGJFjOpPinuFJjSxu9yV5WaW0EgcTNtfWHzzUqTGnLItCdhRKPbOwubbomq2Z+Ma1zLZQ3Y09bm7tO+QL6SWujWAjCvGjke7L1sgbxT9Svc3SpnPm75IZNivpzA5SMpnMIpzMjmrqFTkZOWj1Smhviz7ubDkoPK73az0C2Ae/dKaeffvTa/ow8fawIWXhOLSkJFye2QR9e+i42JzoQyS4zP2h58WFaccApuR52h24HiydJD1KMgAAIABJREFUGYGmMZq8ZiFnh8iiyWoau9/tbs1waJYIlihGyFhB1rLZdWVn9Dbv0iQwuVl7Nl0oqXVF/1MiSIrzPNkqJ40OdzmwP7XEbYNEVzLKFNfH+TZa7X/K8xmZhBmoVMX88LKZ/c5tzMrga5S0yogawcMPph3ToIVtWITelZ2WyyzlJOXELzMhIK6s0LRQSiXL4r9ojhfMVYIr7hK1rSXZCByVBLF6oxyeO5MO1FaZMBMM1EFui1vGYOa26g0Ijs9KcORYmIjZNvpwYGZGGngW8sbaAFjUkporzd1N5jZfdVlVw1Yrr5o4DZSWopQWoi5auMRM78+dvS9hL7PTSDgYy9Efq+7LqjFfHDxyB6dPIrTWRl7/9lIezaGiarcbrzLhIMUbcWET2udqmjA3lscNNidtb60ajatiLFxnl4/rvVuPAPaBLxH4xe99w3lz4hBbVIWYHPfbFT4YCeLmbkO/Grk6Mxr8uhlZ+YlfuXrFkD3344rt1PH1zQU/e3tJ00a8zyScWUblhxWKEAdP00c75frI+fpEio7pGEqLTQ3M7OaIV6t8CmlktoLqss0kJmdEkFTGYmrglTYZOquatAhJ4+QJTaK9Gua5WC47ZtNFcnI4n2eQjcWINZfemgtKThapAeVasyCjzGJViVZBcTkRXUC9Iz2xoUm49oS9WUeFPUsbrQCOPymbl3lu+U0rs2AyN5BCrpAKNvYWzRt+mM8iuAjHj9WIMUF59hcbxjNhvCr3KyC4JC5XYoqWOZsBQi0cXATiouGq1k8u2nzMRRM2V7utZmc6qLiSd+yuYi9zblslYqiHVGZgsV+Arf5Rb+zDqkerDv9+NJAMe3NDkbSkY4ejVb+xzN1cBC2VqTo4+2Ggvde5SnzoSZm7omlbFZr9rTBelcRuAbl/4NJRQHO6UNo7Z16RKzg+lwKYdj2zjdfjeq/WI4B94EtEebU94xVnPN0ceLtfs7tdgyjqnNkvYWLn/myPd5mzZuDbq2tOueF63BDV8aOvniPXLevv3gMs3oSxgFd1InCK79JMkojRs8s9+RisuvKKa4xmr0mQWcuVDQjj0qojlsd1NovJvdqO65jzwYhWMVWiR3XhqMxIRHGA87YjhSYhUuZgWYwIooJvk4FuFkSEWFmPxXNR1xnNQnPXmC5MwL80Mkra2Em//zqYlqlUPC6WSrRshu295WFVUgPA5uXE+hWMl4Htt9xCGnELYUNYdFOVzLD/IiMvTrhXPc2dY/sdYyT4UjGgSyzKQ1JELvEkc9jj7PJh5JIwGJW+mgM/9Ff0kyKDMq3c7HgxJx4LxV/QrLHaO4FCEKnsPomLMW+VAdj8y5wsqv+kKHRvrcU8FX0ZPHCajwvBw2aROruQuCizC/94YUGllRhSzX/9yQ4Xx09tFjldPAA5zxIa6pb325+sUkw1O6wRYqlY3ci7s9jH9d6sRwD7wFfOggLBZd7sNrMbhtvYrqBJaPpI0ySG5Pn1y9d83N1zM6355nTBN/sLvrm+QI+B8Il5Go5TIGe3zJKS2J9G6TYjItA0ERHleL2aQajquQysLLkZQFxGszOQSzJT7RcmYmG3rQptvhr/KeCMuAHMThz9apqvr2sjq3Zie+xmo2Kr9BTNNudarQdW7cSqmTjFwPbQm8YsCpIc/nzE+cy4a8mdEs8yYedYvbTW1f5pZvWThu62DPPF2lw1k0smI1CEQWe2Xm7s9Q1XwdpTZd5TN/D59VXCRW17ldvwfCC/6az9VWJk/EnmGZAfLV4krpVmK7T3tXLTUlktlaE/GTg1+0w4ZsZzT1qVHVlNb+WixaWgyunKk4oVlLoCEAnGZ1aVVdusmhydG1BZ3O9TCbps73UWarf3lgFWnTtcKoSRMstLhTlYq7oalinRDjLTmUkFxs0yM4trLQcdc1ap80O7Xp3bwuqU8VmCPsHgCbt6Wliek7iwFxe5hR0SqgHx43r/1iOAfeDL+0zfRJt5HVtLU/ZKnjz5ZGDw2dM7fvnyFY1kXDn2//RwxZf3T3jz8gKicPHJlqebAwCvtxZANU02J6oEiHY10bWR52d79mPLNrmlmiqtOJy1C9cri2/J2TFGzziU427ZVKhC5lrdZZBkFYqCPU6X8UUMnZMFcvarkb613aQNibN25Pqwwjmlbya6YGSU49QwFPf7q/WRF+sth9jiRFlfTvxk1+LW5rOoKkzHBgZ78v6lZ/VSZ6Pc1U8a2jtmUbBKoZKfzPy32T+YBTlhuHTm0O6h2Sqra50rngp45GX2hLPWW6XTp16Rr3vD+c5EyWEnhMPy/NPGNtj2zn7ebs3fUJ3MkSJuguaoNIdMexdJrWM898TegKQ5WAxK6oThCvobo8bvPxXixoyJ5wyyYrobDjJrsvyos9Ysl8OLS8B1YfiV70bsymHF24wOIBaiCdXfsJoQRyNzVPCaZ2pOGS/Njur0rcm+b5PDnQoR41Aeo1Raw1MLqUyNI12opRjsA+Hel3w25tcH5bMobdn5cypavJmy/7jeu/UIYB/4cqLc7Vbk5Fit7LfxsO/IuwZZRzbnJ3758hVXzZGvTxe8Op5z0Z54czzjzTcXoMJH37rlsj8Rs2M/tjQ+4Vy2OVhyxMlblpczm6rD1HC3r66yYKVDLSXgyfmBVTMZiGSH95mmje/M0nQqO7GCDG6elWirC5iNjrgSmvVEaCKb1TC/7jYkOp+4Pa4YYyC4zLP1nrOSd7abOr66u5zB7utiYjyOgWkMbC5PjENgOgXTk40ON8nMZPNjoZeXCmQ6W2juqfwse9NSmcGtzdjUw3gp5luYIRyF1KoRIUo1ZLo4a70dX9i/z9ql8p5qo1AyrfzRWQo0iyaqu7EqQ70RDoAZvOocq9sqfsz4k1U/42XgdCXFtNaqmtSa8Diu4fCJJ66UeJHQoIRbT3+QWccWTszty+5GcSX4cgnVfLj5l9ZiW2ZmUq63VGoVIFwsKdEFgKQkQgOzxRWeOYZmujRSRXjdGBDVOVzRrtX06vEqs7o8Mf7ovJgEB6bLRHwScQdPe+foXzG3ex/aStWDhZQWpunGlMf1/q1HAPvAV0wWSeJDpvGJw6nF+0z3/EATEr/vyTXPmj23cU3nEr2PxOx4eXuO6xOXFwdOU+A4nuPKkdm7TB/MOf5U1LQ1KPMwNDgX+OTqntvDihO9bTJlEBQ6m0HdHlZkFVwlbqhprMQVPrVXmz9RhakPaOwqhIMwPi2OICqse3PNT9mxaid8cQI5jQ0pCZ98dEPrE7upI6ljN7bmOOIyd8eeGD3DsUELeeOQOnQfEBUoDhxuWnRrw+XiKFHJAlCYgg/EuDmAltj7CijThvl1xRWcnhlAdze2gVtmVWVuWEBmKvZWLkIKFcSFZmvVaSV8NDvorpXNq8jheSgxMSV6xINi1zK3MxshtQ7wDJcmYB6eLJt0nQulTumu7X3fr8zFxJWO7juzMJjTkrMXnBbdlkhxw7dWJRjLstpOhWOpHr2S6+yvEl+OJrqupBJgFnabgFuIa7N3am+E7m1TiB6F9VlE15Ktkk0rLN4mC+kywr0nXSSa85Fp29LeOdrbAvTuAfmkfP9yzTCrs87J/v9xvX/rEcA+8FXZeb44bXifeXG5owuRVZjow8SPD8/40f1TbndrUhLaNvELL97wozfP2O5WhCYxHBr0GHBnE6v1yJSUGL1VTSVoUhXS5MnHwO7VxnaZJpd5liA+E0+BNzfniChx8nO15fqEL1R65zJZhTQ6JLo5TRlsU/LHMs/I3rz6XsDt5Aid2TmM41KueK88u9wzJc9ubNkee1JyTGMgHezr7TfTLKkCjPW4DegqIweh2bqZzKAB22ALk04ddHdmVXR8LkUoqw8AzDbX4Ul1LreKDAf9Xrj8nWQtugtheCJzFZNDEdpilUU7lADIoiPLjZZKS/BHJRSyxMXvHJkuGk5P/WylZBu3sQ7DaWndhUMmFkJGdV5HFbYyEzLMNNjIIVoIJuuvHdk729jX2M6ehHBXQCiIgWFDIWeUyipY+29X3D6M4q+Ekz1uDkUYXapImWwOFi+symt2y8wsd/a4LlmXwRWbLMnWsm12VhVXNqcUGUBcK8MnkfMXO3ZfXbD+qTdbrmuH5Aa3VsK+aMWk0O7VQLQSbHy1OCvzvbg2X8nH9f6tRwD7B2TFaMy8T5/cc4qBxifeHteMrWdIgftDz1ismYbseHPYmLN8k5gmT/i6w40wOGU/Wo/HtxnNGPniTEml3TYvsTkETo0tGM0iIR2KLqcSNvpsyc9tJjQ2F0vHwnuOhcAR9B0WW3IYC7K4axj93ZOT0K2mdwgcjcvsx5bj2BCjI0VPOvk5Nh6wKnBatGF5lfF7R9g5E8Ou7Xabn1pqsWRluLLNP3Y2ewknY9SNF1ZtrL9RQmHSmfjZQCIcxPwNgbtf8DavytC/towu9cLpWWHTBdNAZQ/awHSWUQftrZtbcuoEP2bCUTl91HF86mZWXHVvd0VrZSy7TLNNdt+Nmz0Wm6PO7UMK4MSNmoNGqyV/a6mEc3H0l1yYgSV2JHVL9SZq7dNq6xQ35qaCGCjUlOlps4BXDfIEFmPgomFzqRr4KqkTEtbmy21xQgGGp0o8T/ijo7l385xuulDWv37DP//Fb/E//ODXZ4/O7JeKyvR7VkXj7PWrCnSlXVg627USt89T4RG/3sv1CGAf+BKnNF0kRU/T2Hxqe+i5269oGysrrrcbxhoaKUrTT2wPPadth+w8/ujIHqbnqRAqFGmUpo0M+xZ/NpGjzaQeMufIYmLSagk1OfzBEQ5CXBXhbwNub5VYvohMozORsTKLSbXEtkA57Hd2v9wpeZOQJs+MSB8yIRjopmy7+Nvd2mJbDgEJOqc6yyRGiS+VmJy8UaQFmjtvuVHN4nhu5q7K8SO7ru4mlxmNCZXdwSqGLhnTcP06cboyH8SpgLA/Cd0NjFemdUpr0yKFXbFqctZijCurfJqt0N3Y4+2eZTQUmndp8aV2mTv5IXN4Ya8lHGyDryJbKa08PyphnxBV9h+3xN6qlvXbRHsXmc4CKo4ROHy86LSgAFK2g8QcKJkMkKuFViotxxpHMuvoy1diFjiP5VpK5ShaXhMGHi7qfP8a6inlvX6Yg1Zd7eMKhufJQKkYOKeLRDpPPPl/As0Obi/gT//6n8VJ5rc/fsH3f/K9Wf+VOuZKVBtw3oTNcWWHlUYdKdj3Aey7UGdwcWOi6sf1/q1HAPvAlyqMh4aLJweCTxzHxtp30TGJ5/XtmTHsSovv049vOW8H/tZvfAsXhdxnci50ZK8QFN8nNpsT+0OHZptjpRo6WW8HlrhccrpkdIStgVfqrOWWVPBJSJtsThy1Wis2Pq44rFeWmYvFEDbY/+eVZXCFNiLCnFU2nBprEw6wv1lZSzAVPddom6U7Cbm3/CfbdbGNcnDzPEdbzFnDKe2Np72HwyfFRaPEf+Rg8yTKDMRF5XTlcNGCINUZmNR4en8UDp8VzdVR8KNt/v5kpIkcYP/tjPvsQJw83V/tjRa+tk2zfWNV6PhEaW9lFv7GlRB7C60Mgy6bfGllglVqptdyyJBnpw8jMQjDVWMOGEGYzq1KrMzH3OhsumwawiXBuAJNLp6MroSE1gqravfmSifqbLg7f77lAGLWWjrT5cfzWunUYMyl0rN2qv3RM3ue9s4xXmX8vae9cQzPMsOVOYNM3zrxH339z/D9bz6F//eCvoiSK8jiDLSyBzb2HTMHkCVNe2Ymlsrt+HmivXbI5rEEex/XI4B96EuFs6sjTzcHfnZ9SUrG+gvBEpPbNjHtW3DK5dWBPkR+59Uz2+CfT9b6c1YF0Si+j4SQ2G17iy7JQhw9rilGvMUqIrSJ1Wrk/noDUfB7Z5u12KaXejXG3qqAl5rbhTYUZ/g6gNd5JpI9pLNktP3RQWtmu84pOTlCSOa/2Bdj4bvV3NKUUXBpGepb7H05RVd/wfJ+aVDyOhdgcYSDWVIdPrEWoumijFgQTsbky75s/Cv7dz/avMlFSIVC391UQbKlP1dBcXtnt73/BRifR777vVfsho63b8+4+Ge/4fXdGeOuhckRDoHUW3xIbhfCSLuzxzs9ccS1GfFWf0H1ZtLrR6PS+1Mmt87mPM40YNkXobIKuy+E8Ym1KsO+Mi8t2dqu2T7jmpNlb3ABzAhVIC0JA68He3vc2ByspkvnkjmWW5md6mdgcwsAV7uoXNiEaWWP7RvzL4znSnftCQcD83AQultw0ZFWMLbQ/3bP3/jNX5kd9P2gxLFE1IiRQNRZdEo4lblh8U6Ma/ss6/tdM8jU6fxvj+v9W48A9oGvpol8crHly7dXALRt5OnZgSEGhujZvjqDLPiLkdu3Z2x/cGUn7osEo8MdvM2JuozvjD6fk1vcLwQ0Os4v9+z3PSIZcRnvlWG0mVi498aWkzpUN+NbbYoRb7aWnhavuepHpw6qw4aqkNcRt7adQnvhyZWRM6bJ07TRKrCQURVOhxZONguSwmas+V1zhIsYWM4gWU7Z2tn8q9k6XMmYSm3JhdqXOdLBCASr1xPTuTchcpnfoDZPit1ivdRf57ndpb68MDHw0gDb7wjjxyPN2ch5a+XcXbtiP7TWHnVqadTlAFDzyVZvlPMvI8dnfjb8VQdOTculBUzCSWmOmbBLBQzqeyxMKwgDDBeO6UKsCmkVbTMpBpptkQ0EA0azu5KZ+DELi6tjfHH5qJ9fnX9ZG9aqJz8wg17srW3qiqnvXNGFpR1JeSzzT1RcEqZzZVhldJXAKYMLpM4Vj0zh9BFIOSBUXVyzZ9ZyGfNRSVeyEFackjuso+AsjDT1SvfWzSnd9UA1XWTCzpst1eaRRv8+rkcA+8DXNAZ+8Nuf4s8tgdn7TCp85+AyFy923P/snHzT2cbYG6hImxGfYR1xYszA87MjYwwcDx0uZLJCt55YdSP32zW5EjUmxwS2kd9Zey6e2ylfy+A/t9k2pSRznDvFnDdV147SrlytRgOpJtE1lvi8O3UchxZXol1yNvBKybG/62yO5q3Ko08065Fh18HJiBmVyTd9MsEkuEIakayEG2/C4KO5xMeNxavEHqaNY3xilcnqtTJchdklo+ZHuWJq245aKOiO01NHLO4V1epIMhw/sRN87pXwpmEaHd9/9R2aO8fmayGtNrQNtGVDT73ZLoWd0G6t9bj7LCyehNFYkZK1zKWsSjr78oSLGfWO48cdx2eOcLRW3rSWoo0qUoBB6E4esic3yvjEHqu5F7zabVOnpM7AqrsWfH5QOXlYvVZ7naX6ksTsZF/ZfeZ5aI/XX2sRWAsZsxhTKcCWChPzVgmNdQSOL5R0nnCbyTLaJoc/OfwgrL82QPIPjInbW2MK5sZYhOZmr7R7JXXe2IYRcNbiHj+ejFl5G+hfW8JAlU34QQzUB0t39iPo9WML8X1cjwD2gS/xivSJdPKcPT3QN5HOJ2LyxOzY3a+QdYJtsGiS4pjRrCaj4Iuy6ic+Od9yjA0xWbUjonQbq4YOp9ZYg6WScqMjtxnJUnRICqUSma9LBS2C1MoEnEXP5bTdXwxcbo6M0dOGSBtMQ3azXZs7xmlxt60RKPUaCGrU/MZajOOpMXAZHephOs9GANma35Mbymxr52h2pu06PRViYf3V6Jb7X5vov2q4+kFi9Y0Nwk4vutk/MLVimVZBiCth/6kjrm0DrXObWpXkpuiYXoxw11i1GI22v3plLMDqOZjbwnTLpWroBe7tOccLm0eFQ3WQWLRSkpTVm4iLmeFJR01NDgclrmWJRGkWB3l/smuMqwJCjenOGkqVSZk/JZvjPQQmVwyBrcJZQChurFUX9taqdBNkBY/OhsM5LCCg3kCo2dnrqUnVLurcIsx3Ht1bYsH6Wkp1Ze4o1bW/atHwdj15pETUCNNG4GDV6eAhrYt9lVfC24b+jcwHk1gsqlyJhzEXlVKRl6r3cb1/6xHAPvQlih49tJlnmwOdjxxjw912tSQaO0XXCb+KCHB+djTBc5NZdSNfXNwxlp3rrB+4V2EYAs6ZDmw8NQV0FDl6Yw2Wo3e6jMYSrM4a0SJIiIIE0BIeCZidT4k58SsDx+PY0DURJ0pW4XDqTIAaPXryC0GhshSziWylyThvfofj4Oz5J4ckqx60T+X2BmrhWPztIrR3trmPlwYEeZ0gmBTg8vsNVz+YaLYRDY7pLBB7Rzhlmr0a8cFLIXHYe9ZsS8sxlgytMocbnxiI+letBWN+GpHB0V1LqdzsteWmuHkEnedSq9cGfkNn+i5/smpvdV3IGWW25QclnBJx08wt33DKRbDs3olnyQ3zIcNE04K/X0TA1QxXhRLJIrOAdzHWXVqKzaHMB4NnujAwXr3WWSeXG4illVoJLOoXMKihk+EoxfVCZwaja2DzVUnSVp3bpakz3Z08qAirXi+uDOjbu0w4WCU8ra2d3Ozs8zb92cKqbLbW/o0rI/dUwTUUsCwHkrh6rMDex/UIYB/40izQJ86ujjQ+cTf0XN+vSYUyf355nONGnChdE5liETxf7DhvB6I6dmOHd5ntYcVh33F2dkKBw25loJNB9uGBg4PSXA5s1rbD7fY98RhMR9NkM+AVxVcdVsYYi05xTca5zOm2Jzzfs24mvnz1xDYxlxdfwi4b07FWXqU6AejXZpMwB1ImKfMue1/MnkoIOyNoSNEj+aFsvlulLYy8UQG1zfrZ9wf8kJCUSatAWpmOyk8CJYDSjdncLdTEt36A/jYR+yU6ZTpTtFX81tPcO+JGjVH5OjBcGUHCqg8YnivpLCHR0b7xdDfMLDhfSQ9im2v/diQ1rhANFIlmhTSdefwpo0EYN0b08EX3lXojUZBrBVX9C63V1o4yz6cc9lZXQo56++wesgpzEHwyGr9L0G0zqXO4XuZKKq6smjOChKVaq7eZlZtkNht2sYCjLuGeGqB/o7NTfG6ADPHcqrY59LNcX1yb6LlWvcOle4cJqd7aw34orfW9SR3WrzKxE7bfduZnOdhz58KCzK2wfpMYzxx+eJyBvY/rEcA+8BV85rvfekPKjphdAa8SN+KU46mh65YKZ39qEYGL9YlTDEtic0hs2pF1N/L55R1f3V2y3a2WyqZqgxob/ktvIp2YHSmV2BanEMB1iaaNc+BlSs5o/aPH98lagT7zh3//j/jO+pr/6Ye/Ri7i6TQ0M/GiPx9ISUhilSCNGmlkMzGcGgunnCsvq/zU2QyJVNlnC4HDRehudZ7RuKlSv41YcvEjJRwmy6XaNMTekz1097WHVv4qlUBzVNp9Nhul1plpbWW7Ncr6S48kOH2kpI8H/DedAUK5rrgpFlKNsS793tHel9acY6bgm6u7mfKCVYC5FfwpIyi5cYznjvTUMzytlaqBdOqZnd3bLTPpI26WiszV2VY1HM7La51fcyFeSAGNVOyzUmOt1OpFOG2sfVzbhamzP+2dzJVezeCqz6MBKKGSNUlaA1CsrKomLZd2KxjQqgOK7dPUG4txvDDg626Ep78RGS5tPjk8T7hJ6N7Ye9zsleMzZx6XwR5Xeootl31PmoMSjvmdtu3jer/WI4B94CurMCZPHyI3hxVpWmyWms4MdKfJ28ajlqKsKtxs10ylxdj2NvP61auXnIcT/+tXv8x2tyJtG6pwWaKQ12l2iNcspMkzACl68uTM3q/JrDdmqCuiHId2NgNeXZ6Ik6e/GPnes7fE7Pk/Xn/XyBdRIBfq/OXE5bkpR2NynHzD5CCVOVK8b60NVATIGkzvVQkkNucr19zYibvZW5vPTFsLmeNgot2zr+D8d2vlpaSit4prh3ohHBNusqpLvbEMXdJ5o9WigarEBYD1V9a+O3xq5rhyZ++lkTQAbH64/kbwo7f5kYUBMG0ekBSORVcVlfHccXjRE47K6rWhwHjZMFw5o/m3i1iX4uAxk0/mL4xR1Csg+XEBjNpirZXNfJcAUv+/CKfdZKGZ1R0fDGjGy4W2Ho4lyuXErDd7OB+s7vMqdr/ZAUOBwyIVqPq2VNwy5sqsZH/FFZxeZNKTiH/b0L0RLn6cOT3xDFdGCJEo+IPMtlbDE2G8sM+jplqrNx1fbu0z6O4zblLiCo4XD92WH9f7sh4B7ANfqsLX3zxBk9BfDLR9ZBoD+eCZnM6VkLnKB3JyRo7IMs+hrs4O/NOf/JBGEv/dj/4gh11HPgWqW4ZMQr6MM83eeaPaU4IjfUi0nenHVA0wJ6wK9N7so+q/vXiyBeDL26vZiLc7G3BOOd4XR1VRA66xmSNd8t5Axe+sGqx09vq3i2YlVGn17bUjHKzF199k00hlAwGU2ag2HK31dPyoodl7c6JISuqN0m6O6o7UOtyUzWW9/JnOA6k1X8DxSkokioU9Tue2aXY3wtmXgfHCSBPhWKyPZpcJA4PmvryGM9tIu1sK+JR8sZIM3N0WU+UXjYGVZ2H3NQsAaJlBSXVTn+qcq5AkpvraFuq4iw9aaNPCKsyBOQ5m9jGszMz8gISi1kIMJ3vO3FhFVkFbkhn2VuurmTBSH7sC4VSqRha5hYoJp/1o+q7UCe29zdtcgoufwLRuCi1fOT41okd/nbn8cebwkef0VGaHj9M/ubPcuN85ww/C2e8qm1cJ9XB45jl+LLz+2HH+I2f2WOu/D7/Mj+vveT0C2Ae+NFkbTZpMSpZArKPH9ZHQJLrWQCqXY/i0bSEJzZMT55sT3im/cPmWtRv5K7ffttZjrWDK3CmfR1xru5mqMI3FlsrZCZkgeG86LSi+jNnCJVNyaLKW1+XZkS5E9mNLVnMLd2WmlZKjPx8Y9i3OKftjS4q2w+VDQKIj3Bt77/hCF+ZguX9s7f8pm25aKXFDcQaxE/W0kdk26vJH9r5MazenIedW6G4ice2X6iVZ9eOHZHOvoo9KKzd7D7poeVQWLVIjWGT2Moxr28SbnTncz6m/znK+mp0x5QDYydwmk+KflT34bGDb3if8kDk9a0idK9Xegp15AAAXw0lEQVSSiZjVCa7qsx7oqpCFITl1MoNUJSu4ouNSt/gcin0F3l3uwUxprTPjEijuFTbbi4WObq0/XUI8pXgaFnsscwgp95/KfG4sIFtalLBcTzjaexCOxqZNrXB64mi3aszHCP5goJZaYVoLzUGZ1iZxmNOXB8X/tTPLHsvWLgwn5e2vBaZz04WlTaJ7GWiOuXzPHkkc7+N6BLAPfPkmETYTPiSrioDu8sT5ekAKaA1TMCumySF9wofMn/xH/1tex3N6N/G3jp/wF775Fb788hn95TAz/tQrchbxTSZHR05idlDV8LUpRrvewEBEGYaGdN/SPT2yWQ30TeRQ9FyX/YmswnFsGEarBp3P83WqgmuyVYrXLVwUa/KgUNwudt/OtDdu9qyzlp4BghtN5+ZOMhvqarBcr+ncqpHuRml3tsGJQhgy4SbjB0O+8bwhdQZCzcFaSORiraSgKG5UUhdwyaQEzV7pbzOpETvlNzK3uGrrzI1ixA5PYUpAey/veBG6Cdpt5vDCPscqCu5v07yJdm9P5pzyorWKrZc5e6uC4OmpENc6kxbae2aAErX3ubIOEWZfw5q5RRUXF7f7uaVYaP5VqO5GKQed8jrtEzFrpry8LsmLr2ButETXLDlrDyu6WtX5UWej31pVzi1Fb+9FakvFGIo2r/grxpXlnNVIGReNMSkKw5Vw/Y9P9L/bsv7KwM5Pyqt/zPH5H/6KVZj4wcvnpEOLH2Dc2OP468cZ2Pu4HgHsH4B1dXFgd+y4PD8SfKJxmSk7YvJkhXEKFpdSaOnts4Hf377kD3Zf8+d3f4C/dP0d3u6sR5KiwxXgAhOQptvW2mmTLH2eYGSI0CW6MmsbThZa1T878snVPb948YaswtfHC46x4cu3V4hAnAopA3Ce2fnDedup0q6xDatmh73tymzCmH3jlble+IPNwdTXozqEu8I+3JfKpVQX5z/JpgsCczcvJAM/UcTKVnX5wR477PI8RwqHCN6YjuqEuF7mIZKUbp+IG8/xuZvJIZWsIQlyD6cXaZYf1GusrTowtwrdwCBWga1eKd29JSmrwPAkFMCxGZzKErgpYn3U1IqREjrFjxZcWa2RzIWiiqUfasEMtGq1CMw2ULXVl/qF6p97AxE/FCq6AA5zrgfcyc2Pk72ZMlej4aoHDHuh2Vk1VtuD4kBbcAEocygpgvHKfqxAV7PHaoCnzR4La9ItM73pzLH7zM0zrtzB4TuRf/iXvuSv959xPK7o39p7310LP/nNT2juHPEqI2eRwy+M5NDSboXNz/6OevRxvQfrEcA+8OWdsj+15Cx0IdJ4mzU1LjNMgWEKnO47O90W+5xpDPzJn/1z/Nb1C/anlnFoSEeLO5kOpX+VjMknk5mdAvMcA4W0MU2YbEZUhWGw+6WThUi+3a85Tp+atmtobS52Ckb7H80CKmwsFkVVUAzI8uQgCdplNDpkF2ZAqFZRrmzOWvFUF0f7asRbiQOpNY318bkzd4aT4kdrUamHYWVg4FKxToqCG21zNV2SktbLr0kuG6VkxQ+Z4bLh8MIzrWUhQugCBrM10iioCO29M4HszrRoVRvlHlgvuRG6+0yzSwxXgdTZDCwMdi3TOliL7UFX6/TU/CeNMm+sxc1P3ewxWcErt9bOrP8N1fbJZAQ1H0tDibQRA6e00tkJRLLMydBIKchDOVCUM8ds31VBrlShNVm65mvNKc6pVlF2DeFollY19BIMzMBo/JIgr+tczuyyKtuyVmmxt7lo7uywpauEtJmojj/0nS/pvxf5resXvPnmgqd/qSHs7WCy9454rjTryOm74H675fjRo5L5fVyPAPZzXCLyBfCfA59gI4U/o6r/oYj8e8C/BrwuN/1jqvrnyn3+XeBfxaY5/5aq/vnf6zlyFi7WJ7wzC6ntfs0wBoa9mcO6g0eK0wLZ2nHTXcf//pu/uBjh9olmM6EqFkkyLWa71aECASZLuPXrOEeoTIeWOGRcTWReReIQ2G5btqHMyLIgJ4c/miYrr2yGEbfF3rzJdi2tPYYkAzTZBiQJ6TwZhV5Bjt4iWEosyhInUt4QV+YzwSjqUrqPbhBWb5RwhLvfF+aMp1qJuUm5+sGIP0XclJku2uK2sWiAVDDhbhZOTwO3vxhmckO1Q8qtRan4o4lkjTkn9K9lMag92mN118rhk7qhG7BYnpdw/21Ps3d0d/bckm0TfvurPePVQkaY/QY3OnsXdtfmRnL82NqIxvozcHMT5nKvVn3FVWFmdmrXsMqG+Bl88WbMobY+zXnD3DaM2WlvDOWLUlp6DcXzkrn9WKs8N/HOz+3zXuZcksDH+phLa9Gl+oPylIVQMl6ZVZc/GTim3l6LP8k8U1MnNEfQbWD8OPI3fvg557/RcvbTjGuFqwthuFqq4bAT4m1LvJzozwdOHwXC6RHA3sf1CGA/3xWBf0dV/28ROQf+ioj8z+Xf/rSq/vsPbywifwD4F4FfAz4D/hcR+WVVTfzdlsCUnFVahdU33fa2sU9GfQfb+AlqzhhBkZ23k/Qm0W1GVA0MH7qPW8QKaMhm+lvsolJ1yMjgV2b/VFuCOZorhnRpBhgdvLWZWssIU1Gr7EZrNxVWOXLyy8Y1Cf7grOoqlG9GR3NrP8vFtaKmObvBNrCHhqwWz6JzxTaeFzZZpZYrczW2eptp3x7QNjBdtMZCBPwpm9PFKuCScnxuTLfjMyN/iL4bvTKdWbpxFRCrfyACzqY/8qelNVevt7pEWAyLVSAUx4vULDTyuCl082pV1ehcpdjMrDjhNzBdJtLJ4U8yW0jV90gUKGSL6TJbdlqpcP3OzYa4sFRAaHGuj0b5r3quKjY2d5AKGg+qr2ysynZrlaEZHjPHpcyGvpWAIkt1l9ryOGWuVu2mavswriCdZfzRz4GXOYBuTMg8Pkv4y4kxCXr0+M1E9/01q1fKeCbzvLO7tbZyDuYmsv5G2H2nxR86rm70Ucj8nq5HAPs5LlX9Gvi6/PdWRP4m8PnvcZd/AfivVHUAfiQiPwD+CeAv/t3ukJIzrVV0pMmT96Y3ktGhXREc35a2YAaS4PaevM5oSLg+LWGXYKfsVoHlxCnRlWrMcrxojZWYdk15nUJokgmooRyly3uQrCUoY7VyEiQX1HKghe4mKu/Q3vzRWbzHKkPIFjVy581odpPtVH4qDuKtuYmDsQ9TaY1Vdl2d5VSCgEVtMLeaugeb0/5b6+K8USqfAhBx7Tg+LRt7qX78sGy+qbNqADU/wCoYlhMl3dk24Li20Mzq0p5bGJ7oQi8vxIvhqngYnoT+jVVP44Vpl6AcBsqmP6cm6zKLqt6KFShmmvw8M7Lbmrmz/emeHhnerOboGVdAzA+yPJfaZ2gzLWYCSG1HyhETF58odlTGQmx3OldRFdiqJZVmmd9H97eBIjBnt0m0a8rBPB7Tyh6rufE0OwPm1EOThLix74FMjrRtkJV1DfzvrNj8rASmNgVUo9p8NMDmZabZZ+6+G8yt45tsMomLRxbi+7ge6+K/T0tEvgv8IeD/LD/6N0Xkr4nIfyYiT8rPPge+fHC3n/J7Ax4AMTo0O9NulaV9QtaxpDCXNlsS/NGc2gkZ15kRriYxSryoMf4EM3gtbDmA3BnjToP9IO2bOdjS+YUB6ZuMX1mysyaxOVl+MF+aFsHq+mfC5iuHGy3c0p2M4egGYfNVESY7NTJHk0mbbFlZgxC2S/zFbCEllflX0qBDzbtatExuVFavF4FqJXas3oykTWstvMFeY+yMtHF4ERg3bq6CyJUkYHOW3BpBgNp1LXM4SUahPz2zCJPp3Np0cS2mLdoYeM1OE2WGM898ish3OoPhUmZg1kKgya21YnOrFityK6y/Waqb/ptg2VcHmxHGlXL6KDNeKtOFMn6UyOeRyigdbnvat758V0r11+n82UmS2WKqVuC1dTvPHUtV5cZFYxcGA4l6gIAKpObikbpFE2bpzMXj8bBUlhXgY2/zvuGJXV97s8zVEHPRiGsldUp8ZqpniYIeA/5oM8Hxwg5TZ18n+ps0W1itX5tecPstj5b4mMPHzqq0R/x6L9djBfb3YYnIGfBfA/+2qt6LyH8M/HFs6/jjwH8A/Cu8M5af19/RuxCRPwr8UYDw/JKcC/lhktns1p9NpFOAwRkzLBi93EX7BZc2o1mI0UBDquluSUwmOsvw8mbN5HdlY5uMDTc9SUibzIdQTfclTnFO8SExpgb2wdqFxfXeDY7UWWunu7Vr2X5rmXuh5oTQbG2zDgdHikIuQKltRkaHRDez2lwspJJSeeUuz2JmS9tlbkF2tzqb4e4+ddbKm5R2l0mdx4lwunJzKyv7Es8xmDt8OCjtVueI++piMV4ZGCJGJLEgT2aad7Wy0sqG84tg2QTABhDtjdBf2/uz/7S4u5+M8JBqNRKU6TKbviqWjTUKZ1+COmV4YgDTvzR/v5py7MaSMZaFeGFtNaegb7u5RdfcL6/dALX4EzaUsgmm6oYRZSZ8VGAyoFsqstQb21BHm1mm1q5t2shsSGz5Z/pOxljNPXOTkV1mY+B2+fVwE7O04qFn4/FFIZxcTTRftwZuWp/H/qxeZ9ptxkVl+3kgboSrH064Sdl92hjAFbAMx+U5Htf7tx4B7Oe8RKTBwOu/UNX/BkBVXz749/8E+B/L//4U+OLB3b8F/Oxvf0xV/TPAnwHovve5pl1jG3th70mfzFtwdMjoyOuMDML6pXD4VMl9NoAqv9hUQkQ2V3Ccwr23tpTIbB0kGZI3sEAUHT3SLMfwPDm6fuK47SxMch1t/jUJ2ijxLEO2+dDwRDk9LxXUw02wbEbTeaFuF7d7spRWk5LOMiRwg3sng6zStP3OEQ4yb6r+ZK0iFTg8d+S2OEYcLS9qWgkqnuGimR3Na3qwkUIMvPxgeqLh0ujyuTO38tqGk8FAJ64XVqF9YCBZ6O4M8KbzhVGproD2XmjvrSo5PnhfahAjYtc/fjrh2mQekCnQvfSsv9HZj7AKca0NKHPacOofsCJWydpqg1vIDpMRbB6Kk7W16tynB5WtWLt3nouxtCVr1VZnZ83eXq96OHzk5xYkzkA8N4tZcW3Nps5ayS4pWtqss9tHKJlf1Ww3LEQQFfOc7H/pjrMQuf7yitVLWdqe2Rioq2sDrunMGTNVzGKsuZvYf95z/FjmeV4VV/tBH+NU3tP1CGA/xyUm0PlPgb+pqn/qwc8/LfMxgD8CfL/8938P/Jci8qcwEscvAf/X7/kkyRlBQxQ9jzOY6Mkj64icKWd/ecXu25n7X4k0lwNehXRfhxYKIuiIMf1SmUW12fAtC6hRtLWQMFCWMEmnaHImjvVqScmDN29DNUNaf7SZBNmAbFyVa/QFOJOQBLobA9XhWUa7bKzEDDo6A9zS4nRbm4+l84SM5jif1oo/uLmdNFPZxeYip7aQF5IRN/zRNsXDR+U5n1jFVHVc4YTFeJRI+uGpcOiV4YWCm3A7I7XoxYRvMxoF1QYZrNpxcSE2oEvlNT5VYwx6qyibG09aKWMwc99aqfmTVVc1PRkgnyX6H7czW84lSntU2H+RSVel55iEsYfpicAm0q5H+NEZ/VuzchrPPdpAKo8rheyS+vJcpWLWYO93vEhmmDxZq1d9SSgumGjtxQJAYSG1jOcQQskyKw4a9dBUtVzjRUmPLnPKWjFPqyW/rB5w6p+ZNFKE1uEP3tmvwn3P8JuXrP8mfHGfSE0CKYzNDOOZsP/EG/F1Z61kdXYNb/6h9TuC8tqePj2zz/MRwN7P9QhgP9/1TwH/MvDXReSvlp/9MeBfEpF/BPuV/DHwrwOo6t8QkT8L/AbGYPw3fk8GIswkhfmXr8nkYwCvxg4cPfe/PhowAdNdt9y3pjILJl4W+6MqdP3EOHojhpRKy4dMGh+Ke7CqzZcKqTjDu4Ml56bONsO41uJf+IB4gG2cYeeIF3lOUB6fFMq8PABTwaj2UZDBWcUTMjI5wsERL7M954nZocMPsvj/jZWMoHR3VnEdX9hFNLsHLa/CeKv5UrWiOL1Qxk8mm7GsJtousm96exHRkQZvrMqTm2dfDy2S6kxpOgdtMBGzU3Ph2leaezGWVebW5/HTBJcT3De4ozPQDMrpqd2uuXf4kiqdzmuPsrSAy0Yv24B+1dIcl5wysHajzcZgurCNPHfZqvjWmKqzkfPOvDPnCrm1VqkfZI6pqbT2HHQmXzT3RqWvDL5Yss0sqRnUW6uuPoabdP4+SyrGup218uDdNiVYtRl+actwauB3V1z9GFZvM9svPIdPAquXizNHbg1E/VHZvEz4U2J42jCeLa3MOotr9jBewPGzSHPrzYPy5rGH+D6uRwD7OS5V/d/4/++e/7nf4z5/AvgTf0/PU0ChWU1s1gP3rMjHgN60sElIMFBiEmv/TEI+j4S+WEC5TE7mjrE5G+hCsqDJLiL9RM4Wh+KKOXCMZp+QkrNo+CTGNpysmtFWiZ2xC7XMeLTGlkyKHF2pHmwu01w7uhvh8LlZUxnYygKUonOky0ypL0Lr6SIhJcvrXXA0qnd1dGh2y/wq9TJvgqnDTvxnhZAyWSuODLGREnooNPet6bh+bSBGR7OamO4tAdmqVQeD4pKRS2qMSDhYRTJeQjzLuEHQVsmrVK6/zHhaLaBibb3haZlJ3rQW+DgBIgwfWyhme+NZf212S3Gtc0pAteVKyRF+3BvppRIvYK5G/NFaZWn1oJI6uVJVgybs0KCgfbY2cP1YSio3pfUXjgXsW8r7W4gcycgtuXnwudVZ1Kizy0Ylq+TGbLEMaJeqp84Qa9U1XmbcJydePL0nZcf4Fz7iyW+NTOeem18JHF/kIg63tGw/wsd/+Ui4PXH81v/X3r2DyFWHYRh/XowJGFMYE0MKkSg2qSSIjRBSeWu0EdKlsFSwsVgrU6pgLSgE0qjYBFN5KQTbRImYINGoUbyQVQQJ4iXRz2LO6mayZ3YJu3POPz4/GOZwOCwv38B+zJndeW/mxl8u8fvOLfx2ayblpJeKyzdNbv2mJq8VBVu/3sQf22vy+l+86qNojUCqfGFaluRH4Ffgp6GzrMEO2sgJ7WRtJSe0k7Uv5x1VtXPeYdTPBXYdSHKyqu4dOsdqWskJ7WRtJSe0k7WVnPL/wCRJjXKBSZKa5AK7PrwydIA1aiUntJO1lZzQTtZWcv7v+RmYJKlJvgOTJDXJBdawJA8lOZvkXJKFofNMS3I+ySdJTiU52Z3bnuS9JJ93z7es9nM2INeRJItJTi8715srybPdjM8meXAEWQ8n+a6b66kkjwydNcntSd5P8mmSM0me7s6Paq4zco5uplqDqvLR4INJ7eAXwJ3AZuBjYO/QuaYyngd2TJ17EVjojheAFwbItR/YB5xeLRewt5vtFmBPN/MbBs56GHhmhWsHywrsBvZ1x9uAz7o8o5rrjJyjm6mP1R++A2vXfcC5qvqyqv4E3mDSJzZ2jwJHu+OjwGPzDlBVHwA/T53uy/VvR1tVfQUsdbTNRU/WPoNlraofquqj7vgisNR9N6q5zsjZZ9DXX7O5wNp1Td1hc1bAu0k+7CpgAHZV90XG3fNtg6W7Ul+usc553frk1ttU991o57qRHX2aDxdYu9bUHTaw+6tqH/Aw8GSS/UMHugZjnPPLwF3APUwav1/qzg+edbr7btalK5ybW9YVco52purnAmvXmrrDhlRV33fPi8AxJrdeLiTZDZNaGWBxuIRX6Ms1ujlX1YWq+quq/gZe5b9bWoNmXan7jhHOta+jb4wz1WwusHadAO5OsifJZuAgkz6xUUiyNcm2pWPgASa9Z8eBQ91lh4C3hkl4lb5cx4GDSbYk2cNaOto22NJC6Ez3yQ2Sta/7jpHNdVZH37LLRjFTrc46lUZV1eUkTwHvMPmLxCNVdWbgWMvtAo5Nfl+wCXitqt5OcgJ4M8kTwDfA4/MOluR14ACwI8m3wHPA8yvlqmvpaNv4rAfWrU9u/fR1341trhvf0ae58Zs4JElN8haiJKlJLjBJUpNcYJKkJrnAJElNcoFJkprkApMkNckFJklqkgtMktQkF5gkqUkuMElSk1xgkqQmucAkSU1ygUmSmuQCkyQ1yQUmSWqSC0yS1CQXmCSpSS4wSVKTXGCSpCa5wCRJTfoH+iG+PaIB2UgAAAAASUVORK5CYII=\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
+       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
+       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
+       "      <td><img 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qh17e70HJmISIPQ8cWuwtsQDxKqy19XYDTRvYqTWW5p75XqA2Y2GVhl2CzPtbC5+zd/aROI8kSqwxOWjLJB5hTQt4do2rmqJ/QyiVRX1dsbwrQUouCbQjgpi4U3cMgBPcdoSBhnqhNC3JA9fBaSxWKr8ZLnZnEwnYmBbmkU2yRudJR5Yp73rZGLzX4slTGaM7jywMN1nn4SqRpr7HP7ws4xuBxb7Dl8p+5+vOPzmEtdaCTi8F9j++FvEh8fEZYXf3rLXcN7s8qoQ3cY48ohhgyxbJ3VIUdDee4DfGtn1e2/Zi39AakgicZ5IApa4dKzSMjTz3ToVq2DaYUYzsvUqsfDMDwrqsTB6u6Xdz6m2Heph/EZjg9AiCIp6s9WrE1wb8dO6q7gyq9JyjzudbebBwM68QrC5sFWGYt0gZYHbHxIOj8z0AYTjY7LHb1FdHVJvb7P4nuvkc2W546h2hXoro97xPP3/ntls2+EZenRM7ObI/NbIMg21yzls226vmEVMoWqGDcnsY96d8YWALhb2+Cyzs7PBwNqJZ8mFmDj/JAFLXDqkNZt67OdIiDQ7JdELOOg9bFlcyTh5rqT3MJLPMYu9ggQY3mnJZq3NfqmZQfzC9oSpE9yyQWIkDAuy426x5GyJZt4yEZ2DtjHzhCrS62zszuOGObpcEiZTXL+HLpZIv8ft/+oj4CCbQjuCxVVl9wuCRIg5lBN4/BfvI/Ml2jTE6czSNrzHFfkmukrEzq+yzMSxLG1RZndfxG2up9tXppOpLbFctRLX1v8kYInzTxKwxKVDq66N1y16VAEXlJA5jt6bM7gfkRZOn/G4GkJuZ2LlqVVP7TAjn7a0fWujuVX11USzzTtbzYIIcjbbGDhELF7KufVt7ayO4p2dP5Uljs6y/q6noG7IZ8rsFiyuQj4Rrn88Ug8dzVDwFWx/pbF26INDJMvQtrVsQ432XuuFDUTTJdJrl3e4Sp9fLdxsarR1a/GTEOxxbbux2XtvIpjciIkLQBKwxOWjX9Ju9wi9jGbL4xeRmAnLXY9rIBTC8opQnCquhZgBDtq+oM6RLTpjRxXBQcgdLiiuapHK2n5RMtrtPtkqiV7VKrC2tVSNzuFHVSGjkTkDvUPntZk7nn+KOCi4++17VPtK+VC49skl0kamj/dAIJ8r+1+sKD79Cu3ZFFfkyGoAumMtNquzri5VXsrCshm7a5I8X+cu4r2tbelahpQlOp11bcbO15UqsMQFIAlY4lIiUcnPKkLZpxl5ikmg7XXfVOgd2tmWOkBAWhMMFNqBw9celc1ws1taNBXOoZnDLVtc1azPvCTqpvpqWhRMxMbjdUJ9PDvDbW0Rb10jjAqO3t8jFjB6E3oPA9JG6t0C1yq+tkT806dKrn5STbyKYrP7az7H7e5YqzDaoPKqCltHValuXI/Omc2+LKw6iwG/u0M8OUWcWErHiq/OcUwkzilJwBKXDxGkCcQiQxSK05Z26ClPFV9HQiG0fWHnS0vOnrJqJ+TWSjTxiEirCKAZllovtjpFe2bckNDaGVvucRPbC0Znm5etsbUVmxaaBg22qNLv7nDv33uG5VWhPFb6R8rWGy35acP8Ro/ltZLhGzPCMMctWtxnX2KoCuORVV2rdSze44YDE6dueaZsjW2L87C/af817WbvF2wGrr1Dm9pEtksJWa1QiWfTzeLNROKck+bAEpcPwcQrRFwTaYeeZuBwrdK/t6T/oEEC1Ds5LqiF4861W6cC2TyimdAOPNELrtlsNZZgt9ut3vp23BrYedeyMtdhCPZ5Z6HX2QxtGm7/+WdxLYzfiBRnyuj1GYPXz2xlC7DYdyweGxBKj+bezrpGw835Wp5Z+r23f7aqFsArvR7aNDbbtZo7W6yuY9VaFLRp0Kq2s66VE1GcWeu7ZZz+yp49TyJxAUgVWOJSIiEiSrc2BbLK2oOhn3H8XMHoTqAe2ffMYQgxN7GaXc/JFpFiYrmHmgl+3loVppFYZjYTVjVokVmgbwiW4F4U6Hy+WTo56Nvg89PXufL7CzOInNU045x6t2T57BCJNlSdTy1YOGZC/updAtjzhAgaLOk+RhuUVrWPnXVfnDPhErEE/VXltcpBDMHOwdrWRKsTNsmdzbB1CfUmgJuYrUTiPJMqsMTlI0IY5JackQnZPKBiJg2/aBk8iDRDZwKXmXj5unMLKmSLSLZQYi5d4oWu94HF3Js4NoHYK3AnU/uBv7KydwIjWbYeYpZeydnTAxYHBdm0Ibt/Rj5tmTyRk88jW1+eAOArqxizeUs4fGgGi9VsV5ahsXNB7ozttUIwu/5sRjybWMU3nVqafbY6B+vs8l3lJXkOMay/pyvHIiD9XtrInLhQpL+picuHWNqGrzyaQbWbkc0iofTU2wXqhVCYvT52gewuWAiwX0SbCXZCM3AU09BlIoKrQLpgXxXBn842gcGZ32xlrhuzsGcZMhxw/wdukc+Urd8/ghBorm/z8L19tl9vKB4sWNwaMnhjwvzxMcW9GfLGbSJY1QQWAxU7h2MIyEm0luFoaKtQRiNYLMxiXze2Bbrt9o3lue0maxrwbj2DxmoXWNua6UQVUWdVY56nM7DEhSBVYIlLx6ptqA7a0m6HviOfWVsxFFCe2e18phRTJfouvDcTljseFIppsAT6qPhlt5W5cwG6urVqxYmJV5dzqLOFmSC6dSpH3/8Uw7st1bZtPj7+8AEnz/fpP4xk0wZ3+wHSKmfv3jbzyN0HaF3jb163xPgQNvNc68WYbl3xxdMz4umZidZsRjg+Jjx4QPWBJ2wT83Rme8UOriLbW/gb17u1KWLXmWWoqp21rfISV9FXicQ5J1VgiUuHqKJdFSVRCbl9jKWjHlkrEaDtA33BNdAMhd2XG6ptT8yFduDWA9Dlw4hExS0a6GKq1nNSoXMfdnZ1KR3aK5GmZfJN1+gfBQafu03/jTHH37LPcsdx45feon39DeL3fJDFtzxBcdZQHi3xkyWT73kXMQPXQrY4YPDFu+jpmbUCtXvtIkePTyxFYzjk7HufodpyHH0w8p7/4XV0uST7wlvrhPp4NoF3PYHMKzg6QcZjOxfLMqvQOsu9np7Z9/JH3l8icY5JApa4fETIZmF9Btb2BV9DzGT9/di1EbOFrj9OH8sY3gu4OlLtZDbbxUoQhdjL8IsGWdTIfAmALhbIYABlYZXNMkDn4hv/zlfs9USYvWuL7Rcn7D04Qaua2//1Rxi/Edl6eYK8+LpFUu3tgEC17Ri/1eKXwcwVTVft5Tlua4weHZuVfjRERwMO/9KcKz834D3/09vocmmrUkYjWFY2xDwcIg9O0BiJ8zlSFLjhwFyJS5sf0xA37sMs/VhIXAzS39TEpUPU7PArd6FrlJjZeZevFIlC6Nlt12wMHCp0zkSLizI/vtIOPOoyhq+e2vNXti+MVdZhmSOLytIsVkPE84W14vIMruxRHtbIl1+HLTNgXPtkRTZvcC+9aS28K3scf/tjNH3hymcmhGFO/ulXCJWtRhbvbYPz0UPIc4iR6Z96gv69BeVvj9n+5NsmoIDb39vY572z8zN1Zumva/zVKyBCPD7B7Wzb9a6cimW5eWwicc5JApa4fIgQSre+Pb3lKE4VX0HbE1xrH7OlEnpmny9PFN/NgTUDoZgpvopms48weGsCbUCW1WaFSpEjdYMsO4dfUaxnsXS+6Fx/GTKdU8wWaL9H8/QBEtV+ffJFokbc7i4n33ad3sOWrYdL3KyCz7wI47E9Z5FDCLi9a+jZBJ3OIM8Z/YvbaF0zento52NdxqHWDVLk6Gy2Cezt9XDDwfq54nSGK0vbnVZVtjm6qt8xN5ZInHeSgCUuHSpQ7Xgkwsm7HL1Dq7TaPiDQDiy8txlZWG4+VUIJxURxjVKeWLZhtgw0g4ze3TkEGxrWIjcrey/HHZ5ahZVZMC6rQN3pHDqhWDkFdVnB1T38Z15Csgynigz7aNMy/a6nGdyvqcc5zVZJ794JGnWTaQjI3i5x0EMeHuOu7KOzOTroQb9kuSvo8amdjVW1JYh0K1xEBNndsdUpqnYdWbdWBaCxFqM2jbUSqwpdLNMZWOJCkAQscekQhf79huV+Tv++sjiw7cWDu0ooIJtbpdU7jrhGEbXB3dUeMYmKn7ddDFXXLsycVShqtnl3NjfLejfnpU2zDtJFIzIcIGVpgjEeold3kKC4qyY+4r1lE167wnLHM7mVsfNSzcm7Cg7eyMkeOzBL/mxuM2WLJfLweLMqJcvg4SmSea7/4zds7jhEO9sKcb2dWeeWVB9nc6vCxiOrIB9J29D5EpwnHB7h93bNmDJPApY4/yQBS1w6VCAWNsNVjzN2vxSZHThCAfmM9eqU4rQl9C3nEDHhUif4KuCrsBG0JuBWpo08M5FqWmvTxYDGaGdHndHCRE6thTjoo94jVYs0rc1tZRkaItPvepqYCdWOsPNKS/+TrzH4bE48PoF+39qR4jpxFIt/0k6cvLczt51te9POzuu0qtdRU4hYVRYiMuhbWof3FndVO3v+tkUyTzw+QURsZiwvUgsxcSFIApY4N4jI68AECECrqt8mInvA/wM8BbwO/JiqHv+hzxOVWNg8l0Ro+rYcsnestD2hmEbb8ZVZZSYaibn90PeLQPSOdr9HflpbnuJKvJxYW7DRTYRTv9+lzXtrJ66qNLpVJ8vKqrROPChy9PSMkx96D21fqMfCY785wb36NrpYorLE7WyvF1faa/S6pZNiFdn2GJ0tcFf2rD0Zo6VpSNzERK02NHdRUoiz629aa2+CrX1xjnh6hoyGJn5ZZk7GGP7N/wEnEv+GSYPMifPGn1bVb1HVb+s+/+vAr6nqc8CvdZ//oagIIRdQ2H6tAoH+YVxHRrlWkVaptj3abRFxTcRVEd9E/KIlmzYg4KoWLXPC3gjtlTZErGpCUBYmBqu8wjaYnT7EdTjuWkC61l/7lTe5/Z+8j8W+LaxsRuCmSxOmlbAsl9ZeXLX8QrfepMjtTEwVGQ3svm1Yh/ZKlln7sixMWMGqqs4iL72eZTN6b+drRUF4eIzb3uoSOZq1s1J8+r9t4vyTBCxx3vkR4Ge62z8D/Ogf+QgH+TSSzyPL/ZzeccDXmx1b6oTlvke9WIBva6KWn9XQ5R6KmnjJvCKMe8TC4yYzm/+qG2vrVXW3siRs2nxNaytN+r21wYNrVwDsXOyD76P/IDK8H9h+taE8VjT3uOvXIC9w45EJ4EqsRJDtsQll3djzihDvH1o70jurzlaII87m6GRiFv8QkZ4tQrMZsaUJ6iOiF6cze+hoaJWcE1Kab+IikAQscZ5Q4FdF5JMi8pPd1w5U9Q5A9/HaH+eJ2qHD19YyrEeOticsdxz5NNKWVp2tZsTy04qYO9Q7/LIlO12QPZwhTUDLAj+rye+edpWWVTzSK62SidFaeCFYBROCiVqM1j6MEZnZPFh7bYt6v0f/qEWdcOcjmUVXPTbi8Htv0rz3FnEyXa9D0X7ZuRktFZ+82768rGx+y3X/fJ1bn3URA5JllqgRu8qryDeuQm+ZjVrV1kLslesztfUCzBCAdAaWOP+kPkHiPPFdqnpbRK4B/1REXvzjPrATvJ8EKPo7uK5FKFHpHZsho3ds81fDuy3NMOPsycxWl5zZD3gtHLQRLXPa3T5+1uBmFTKZoz3bZCwUJiiPmBykKNC627PlPXpyagKTZWa02BoT9scWAuwt4b4eCdc/Hpje9Ay+/IDBlyEePgTnLGGjDXAygWEf0M0yS2xVinTrWuj3NsaQ5dJah6vll1mGLiviySlub+eRxHzFjcyer7M57tqVd1SSq1ZkInHeSQKWODeo6u3u430R+Xngw8A9EXlMVe+IyGPA/T/gsT8N/DTAeOeWhlzoH7bEXJjcyhi/1dL2HaOXTomfexEP5P/Ot+LnDRIiUJgL0Qv+dIE7081+rdU5l6otquwVyLIb+s1za8WtzpryHHWuW20yY/G978UFpffJV5l873PUY4evlPFbDcXRgvIoI94/NPGjE8M2WNVUFu9YNrm6htUw8tptmHlrNbrB2iFJ5qGqkczjdrbt+prGri+23Q4ZM5U8ajxBhHhy+m/xTzmR+DdHaiEmzgUiMhSR8eo28IPA54BfBH6iu9tPAL/wRz2XipDPI6HnaAaO4f1ANg+MXjr+p0ahAAAgAElEQVTl5b+yu76f/41PWSJG0E7EwC1bS37vAm4BNOv2Za1aed6j0+nayq5tMHFxQjybIAdXkF7J8Y9+gGbkmF/NuP+j72ax79Zbn10TkTbiPvHFtXi5wQC3v2u7vFaDxmBi1LabzcxFbsK52r5cN2hu1ZaennVtQNshhqqdpXm/MXb0ela5Zd7chyObHdP5gng6Wf2B/Mn+IBOJryOpAkucFw6Anxf7wZkBf09Vf1lEfg/4ORH5z4E3gL/4Rz2ROljsZ6iDeizMM8f2azC9uUM+sR/M2dNPUt/cJb8/IQ57oCBtQBb1pvJa/RDPvJ1jdcnuToT25JRsNAKwtSRqohdnM2R3xMnzQ8rTyGLfg8CVT51x9vwYF5Ttj7/N8vkDZF4hwz7S6xHPJsTpDL86W1ss0e0xnE3grIGDK0jddNuS9Z1JGUVu4joaQLOZ4dJlZXFSYHNg/X53/lVBvyTevmsuRzChGw5wXUwW9b/Wn2Ui8XUhCVjiXKCqrwLf/DW+fgT8wL/Kc7mghBzyuZIL7L44J3/riPqpqwwedDmG/ZJYeNqrY9yywdUtblqtXhTKwgJ6nSCTCu2XyGRme7PaluzJx63aiWopF94Rutbb/GafwYOW2UFOObGB6cWtIafPOBA4efYJymPl4PcO7eysMTeg29slHh7hDq6i/SFyNiXOF8jTj5v7MXYmi+jQqkKGnZW+LMywETaVow0v98yN2O+b2/DooW1dblrCK6/bxue6WdvqtWns/WdZ8nAkLgSphZi4fETYerMhn0cGh7aIMu6Ncf/80+S/+gni930Qmpbi4y/ipxWxzMxg0cvtXKnI7ayr2ycGdC1Cq7R0NLBKpuk2JGeZDRAD/uAavfsVfhHoH7WMXjnDLwOza57+A+Xapxp6D5WDjx0Rq4rFn/4AhED7/qdp33qbuFwSHxwh0znx9MxCdrvtytrvqrPVa3YZi9K0a3ciZYGqgkbC3fsmZKsW4nBoNv/xCL+zg+SZLbH0bmMS6ez1icRFQDSFdiYuGcMrj+v3XP0rzJ/ZZfDSIc1jO4BlHZYv30P7Je2VMbHnCYXDV5FsajNg/mS6ac91Z1va7dDCe8K9B/iDq5B54v1DZDgkHB6SPXELqhrd3+HBh/eIhQVjxAxcbfmMxVRp+sLgQcvkVoYE2P8/f3t93f7qVcQ7ax2enMH2GI7PbK6sadFhH71zH7e9ZfNc3lvCB6zPu2y5ZpeiEeP6vbR37+Hf9244PCYePUTbFtfroaqICG53xyqwukFD4HcW/5jT9kGqwxLnmtRCTFw6XK3Mn9nl+IWceuuAYhrof/RFa/+VBdK0VFds+Lc4a/Aza53509n6/EszjzSt2eeX3owabTCLe1Wjx3NkPCI86AJw24Du7xCLjNGdlre/L6MdKHglO3McfCLiF5HFXkYshNktePK/3YhX9tj1jTlkNIDxsHsz5jzULaue3P4uLCuzzVc1CrYOZa8zp3QGlDiZIoMBOpsh/R7+4Br62pv23roKKy6XSFmuxUvyHHo9a40mEheAJGCJS4dmAg4GdyO7H30dyoL6W99F8eqDjXNPoTyuyU4WhGGBP12gvkuTd27t9pOqIc7nxJUZ4oPvQ770mlnZo9q+r14PHfQ4fe8Okyc8w9uR4kQoHwrVrnL944F67Bi/eMpX/sNtuH7Gk3/uS+vrzZ5+0lawDPswuGLnWVFRImyNiIMSN1msl2Rq2yI3r9t5Vrsaqra0fG1bM2OUe2aj3xqvd4TF+XzzmrduWjLHYkk8m9h77qovKYqv9x9ZIvEnIglY4vKhsNjL2Pm7v83qNMe9/gYtkD1+C50vKE5q2mGGqwv8yXxdeUll1VjcHa03L+sLT5G9fUi8tou7e4T2e9ayqxvczjaTD93CVUo+jey8rOSTwPj1lslTfa78vrXzCiLN3oBb/8Qx+PkvvfN6ReDaHnIygRAIh0eWT7i3Y+7Ct+9bpTVfEJcV/tkn0UEJX7lNeP4J/JffsPUsg75dc4iEB4c2/zXoE27fRbwnu35AOD4x48np2do9qU1rt3sl4gtzKaajhcQFIJk4EpeOWED7Y0e8+Q/ej3/+Wdw3vQDA8j/4MDrqE46PyR/O6b98iDtbwNExsqjMpp7b/+nc0Rk6mTJ/73X88Yx4ZZd2u2+VSpZZ9fPYVe79yLMsdzzTG/a4bBbI5g3NVo5rlemNjNmBJX5Mn+gR869xrBQCMl8S7h+iTYPb3TXL+yoCSpz9ihHJM+pbO8iyQcZj/EtvWdZinhEfHNmOsrY1e3xUdDrHlaUJ2fGJbWF2YoIVbaZNihzp9+2xGs0gkkhcANLf1MSlw1fK1f+uwD845exbb1CPHO7938HW3/sdAhC/74Nkr9y3FuHpxPaBlYUlUoSIbo+4/5175HNlueMoD4e4V2+zePfzHH7TUwzuB/J5RJ3QO45kC6UZOvJ5S8gdD98zpN4R+vcjveNI2xMmj3uqXSiPYfTItWZPPm5DyIMSv78LeY4uFrRvvc2//yvH/C+/9YOUdzJGb8DZn5nx9I9/Fv/rn2K17CS7dRMWNsDsRrZZWVffrKq1SUMXSxO1ujER69yVcTrFDQZWdYFVZ6vbicQ5J7kQE5eO7cEN/bYP/RSLg4LTZzx+CduvtUhQskWwjMN5g5vOzd036KGDEplXIEJzbUw7zJhfy9n/nXvooOTkfds0A6GYKvkscvZkRv/QNjqrw5Lvdz39w8Diiqd3HJgdZBRTpTgL3P/WnO1XI3d+IOAmnv59xxO/8MBmzQY95OGpWeB7JeFLLwMw+UvfweBezVvfX7L7ofucfuyAnZcjRx8QbnyspTxa4m8fmfh081xSFjaXlmV2HiZi51pdZbnOUcwzs8yLswzEEBDv0W6T8+/M/xGn4TC5EBPnmlSBJS4dsXAcvb/H9qsNVz4baQeO4qwhO1kS+zn+4RSZzqHfI+5vIU1AZkt01EdmS4qX75CHSL+pIS84+sgB+TzSO7ZKqzhVeg8j+TQSCyGbReotTz0SfO0JuRAzIVsoKnD7uzMkKtObjvK24y/8yD/n1//7j3D6/j22/9mXbWY476Kj7h9x+7/5COWRki2V4vdfZ//a8+S/s8cT907RT36e4uxDDF68Rzw6JoK1AAd9VNUqriyzvV+zGeQF4mSdheiGQ9o79/Bbo64KC6z+E5sqr8RFIwlY4lKy/VpLNmtxH/sM5Yc+QL1bEvcHFA8XAOh4aPFJywa8I+yN8LMa7h3Snp2h3/nNaOYIpcM3yvzAg4JfKtWup/cw0Awd2dwEMuTC7ks1k8cL+scB15oALa44XCv0HkA7gP3PBX7pzndz5fYCP6vXO790WSGZJxwfU31wBp8csvWZGkJg53dv077+Bv7xW+jzzzL89Bvrsytg3Q4E2wKtnZV+vQesbdc7xnS+wF/dRyfTdZW2XqWyqshcKrwSF4MkYIlLR70D/Y+/jGyNCN/2ftyswpee/GiGLCpoWuLVHdzJlLA7sBipz72CXL+GYmaP4rim2crIpy1NX2iGNowMwtWf/Szz738fzcARCmG5ayJXb2Xks8j8qrUt1UM+U/zSNi9LgDvfLTz312z+S7a20Gv7yLJGipyv/M1d2s89zdavQTOE/hfvEGYLXCdQOuojJxNrA4K5JvOV0UPMfNG2SJGjbUs8ObVqbDQ0YVosoSzRydRahoM+AlaBeY8bDIhVZS3GdLSQuAAkF2Li0pGfCYc/8gKz911H2sj8yW18FeDwxFIq8gx3OkOLHGkC7VYPuXkdFkvic4+TzQMSlN7dOYsrBVtfqXni5+9x46NTBg8iR3/xmwATmcUVR70NIRfqsWP7CycmavtC27Mw4WpXKU6Vx357TnnkeOlvfjvx+z5IfP4JOD6FIufOn3uS0T8c078vnD0D1/+337IB5dHQrnc8tjfnPcRN22+140vbYOkcIjYS0LZmo+/3bD2Kxm51ip3zSdHtNevaiyKybj+uF2UmEuecVIElLh3qIHqY3MrIzwrK4+5s59oeGiOad+tRlg3StGRnapWZc/h7J9z9yJNU+yXP/J032TqdE/ZGaJFT7Zb4OpLPlLZnP+SzheIrCKVQ7Qh3vm+fag9cAxKF4kx59u9PmN8aQFT695UbH2s5/ECfg9+siU/foN4paQeCiiBBefbnTpH9PQgRGQ7QqkZ6Hg5PUI02ON22Nni8avnFsHn/MxtY1m7RpRsNTaw6g4Z4bxmIbbu+j65aiDGSjF2Ji0ISsMTlQ2H+mLD3ov3Abgc5rg5IJUgEIiZiBUgIxF6Ge3sKN6/D4TE3/skdZDq3iubmdTRzPPjOXUa3W4rTluV+zuyaZ+eVGs2Eo/fklt4uJpwAfgGj24HZdc8bP7TF8Lby4FuGPPMzbxOHfR779Ktw/QpxWJKf1dz6xWPqWzvkDxdWHUIXKtxa1dSdd0m/v27vyXBg+8cGJo5SFlY99XtQVbjR2KKpAlapBas+cR6a2sSrLNG6XoubdoPNScISF4HUK0hcOmIO+18I+EqJhSc/XpLfOwPnUO8hc7h5ZakVTYtbNHZGpIpOZzYUfHJKXCzh7gMefMuQfKa4KrLcywm5MDiM9O7NKU4arn26IlsACv1D5eB3aw4+MWe54/AL5al/cJ96S+jfU5obu7iTCVzb561/9yr+wSn87u8TXnoV/+ufwh2eot1aFlnlFvZ7dn3eFltq09jm5tkcd2XPXIirPWJggrdavbJKzV8s7bnEqrW46NqNGi0my3uz3XtvFVqqwhIXgFSBJS4dEqEaO/J5pDiJ4IW4MyRmDj+vrVXnnS2qnC8J/RzfK4lv3rFh37MZ9EqbEasb8rmy99E3efBnnsBXyviNJdnnX0P2dmm3S4p7Mw4eLon9jJPnB5RHS9ys4tpvnKK9EpnOufEbx8iiJnz5FWKvBw8dj//9JTRWBa3SL3QyJVYV/sr+ur1HWWz2dnmHBFuV4q9f26xamdlYgO0LU3Mchrg+79LajCKrVqJkGVo3lko/HtvsWJ5ZNbYSwkTinJMqsMSlw7UwutPgGyU/nCJ1S8wc7vc+j2YOsq7a8rZ12S1b2jt3TSzWrbaC+jte4P5ffj+948Bbf+FJYg7ZUsk+/xpkFt3kPvoZ5O17ACyu98jnir93Ag9P4fAh7mRig8p3j5BObOJyibt2ZZ2/6MoSt7drgrU1xu1sWyvQic2HtcFETMRuq+L2dtC5tTl1ZlUjiyUAsr1lQtVs1iq/o0LrbPJS5F1Kx2I96LwabF5vo04kzjGpAktcOvwyUBwtkLZHGPfQzOHqgHv2KTQocjYjbvVx8xotc8Lnu3DdIrdKRiNaNxS/+XmuzJ5ncb3H9d+aUO/1cHWEq/vom7dxB7a/SwZ9cDB8Y0YsM6uSBn0Y9C1lvqpNQOrGoqOKHHUOZgukV3atO4e/ed3irKCzw3ep8DGuhQswcWvb9bkV4pA8R8Fs9U1jQrwSsCzbmDxCsO3L3UoV8d5MHkBcLK0KCwEJScAS558kYIlLhzoh9M304KoWakEWtYXh1i1xq48/nqFlTuwVuG9+D/KV2+b4a8+Qog83r9Nu93CLluGvfRGahv54bGG3ZYkcXDXjxLCzuU+WSFXjVsPFK6NFka9FA8QGkEO0CmnQA+eQp27BcbeDq1cii6WJVNNYW9CJCdsqs7FpTahW6R2wHkbWlQA6MafhYrmJiVK13wNfrG3zGgK6CGjTbt7b6r6JxDkntRATlw7pBn9diGjmkEVNHPfQYY92p480wSzoucefzpC6JbzwJHHct1SMpiF+6RWyL7yOv3tkSyRXyRfS/ZPpZqh00LNzprrZJGJ0+8Q089baKwt0vkSL3HZ2OUGdtS81zyxAeGvUuQ6Dnb+VRXemFez5ysJmvWYLM6M0zbrNp8EqRkKwastbYv46zDfPbBdYCJ3IVfZ+vF+fvYn3II64XK7zEBOJ804SsMSlI+YOiYrUEQlKuz8k9HPc8YTswQT3cIIul8jrt9FeweKJbd74wRFf/qvbSJYRjh52CRYj+4HftfrMEJFv7O3OIU1rZo/Mo6WlYqh7RLxCNGHbHtmG54EJadwZmvjlGTJb2POEuH4OW7qpJkZtsJmwsjCrfIxWgYGJWLceRfLcPmaZuSnFWaoGmCh1Z248mrTRpdXb7W6VSgikBmLiIpBaiIlLh2tso7GfVMR+jnqHq1p02AcglgXxc2+j3/nNZKcL8rOG5U1h64s5cTLF7+wggz7t27dx/T5y4wBZ1iZaXVWk46GZMlQh82ivtNeeL63SqW0xpm5tlqdoJxQyW+LaYMLUCYquRGhRocO+Cdra+m6GDsnzdRUFoNHEUbwzx+Ejpg27X1cl1g1+e6tbXGlGDq3rrqWodr8OKQrifJ5aiIkLQRKwxKVDnSVaxH4OIuQP5yYeTWvVz2e+wMO/+p1c+eQxi8e3eP3HQGaOG79+jDx1i8XjW7g6Us4XJhyzhbXk8hyaFrxDywKkWb+mmy02MU5gVVpU5HSydhBqka9FT/MMUbX2oCoSIloWSKyQ+dIGkPMCGQ03FeDqsSHa59OZvV8sA/FRZDTchPU2bWf0yGzJZV3b666SObKMWDfr7cxuMEAWqTmTOP8kAUtcOiREpAlo7gm9DPfabWR/F1nWvPKfHpD/8Ee49Sun1FeHLP7aCf/xjS/xfO8Of+c938X9sxH1yxm7X4Tys87aek2DOEe4vou0EXUOCQFpg4ll066FCOjEzcRMnKyzBVc2ehbLTQswdgYPVRus9t3zlFbR6WJprshltTZeWASU2H3BHu8d+HxTtYXYDT23Jkx1vWlN5rl9vnos4IocbWWTcJ9IXACSgCUuHyLIdEHcG+GnNXrrgPiZL/DS//7tIJGnfmHOl/+zEZorvY9f41dfv8ov57C8Itz42JziK2/SvvkWAfAH12zXVuZxp3N77u5lNOvacWUBmTd3YIhWoeUeWTZWaVVNl1fYteVWAre0YF06c8f6bErkHdWRijNDRl507cIAVXe25sSqqyyzNmK3qFLKwhyJKzej9yBCrBur/FaPDcHs90Vh518rZ2JqISYuAKlPkLh8xIiO+rbfywv1Xp9X/ufvoDj2PPd/L+B3f58f+PDnuP7kETc/uqTaFY7fp1z5Fw3FVw5p33wLv7uL+6YXkOHAnlM2ldTaCCFiwrVqT/YLE7UQbc8YWEjw6jHebZIyqi7xIrO2Hnm+TpZHFen1rErLso0l3gnatTW1O2NbnYHFydSyG+mGljsDx+qsS7u5MVfkZlDpPiKu+/XIbFiXTp9InHeSgCUuH87RjkvUC/qJz/Hmny2QVnjmb71MzBwv/90P8usvP8/uX4OTZ0rm15X9fyH035xAjPiDa3DjmoX+9grUO6QNm1/deZFUNTJboGWGDnsWECwCeWbnXZPZ2iWIqp1FrVqNq3Ze23aiFtfnZ9q2m3ZkVZshZLG0X7mJjpSlGUrK0iz1YNVZZ6fXyaT7mjeRW7Ufu7MvXSXR55nFanWrVOJ8bjb9R4wdicR5JbUQE5ePaKaI6vqI/LPw9N+wBZIBcPfuoz/+7bzrpz4OV/a59o8m7D3zGNmDMzg5Q8XZOdCigtKyAyVENOsGkGM0Ecg8oRuIBuzMTTK0zCB2bsQuu9BefPNYbZpHrOu6iXha29wdOpuZWeORcy4NsTtvk03kUyeMK4MGMaIxIv0+WlXWblxFR3UVGVhrkiLbmEKcQ1Vxg67iTEb6xAUgCVji0hEGGX5akR3NWI3kuvGY2FUlz/3Ux/FXr6KTCfLETbIHZ+iwB8MeMq/WAbmyrInDvkVDLeuuSlLwlkloRpEMqayCck1rAcHLVYSTNwdh5k30ujR56VqRGru5q9XM14oYbNA46+bINNr5VuzOuLqqirCpksLZFL+9ta7stKvqHm0F6ur+4jZVXteCXN939dhE4gKQBCxx6ZCoaJEReznZzRsmJIMezt9Ee1Z1MK/RJ67ZqNSytrzEaWVVVmemUO/MHr9q5y27NSWDngXqThbWKjybdjb7zCo3J2sXoGTeHudkfd4l3qodWc2AtV2V9KhwRIXQfZ5nUDcbh2Dslk8WOdrlF7rhwIwb3VnXo+dZwCMuSW+VWGBtKllVeuu5MO8haVjiApAELHHpkMYqjeztI+LVHXMELhq0lyF1a+7AXo5bNGjWOQa7amp1DiWLCmk744Vzm0BeQMGEq6qR6bwzY/hNhdSluesqPWPQN1FZWeDVrbcgS2sZhLRdBbaumFaxVLLOXBTvzKjRDShTN+szLmIkTqb2SFXLM6xr6PfXm5bXqRxtu9nCDDYbpo+8XoqSSlwQkoAlLh8iyLIl7m4RBpbEoaMcaSJZF+YrTUCLDPnKHfSJ67jJojNZyLqFaHFRhc16rdqAqsiyQpaVDR5Tm4B0A86s5q6axlpzqyWSYIsmAdquTZdlqPdmyFhVTeuIp2ghvHhw3UxX1VVqnWipdm1C7zcCCetWIL6r/lYRUc7ZXJj8Ad6tR2z5icRFIAlY4tIhUdHco7nrcgkFFPzSxESaYCG/bURvHlhF5gR6hVVUqrR3bcdX9th1pG5sIeT2VucEzNEyNzdiiOs4KcAEa9Vy7CouYCOOYJFOnVlifR62eh7nTXB6PcjadWjwut3XtBZW7L3NdzlHmM42Jg4n6wpMYoTcNjLba3Rne97ZPFh3PY/uCRMRNOlX4oKQBCxx6VAnaO5oRwXtwJPPWrLTCs27ZZaAW7Zo7nHTWRfhpKiDeHxC7OapAHQ0gIcnSJ4T7963BZS7O5aq0XT29yzrAncDWtUWuPvoeVZXHUnERGK95yugdTDRil0uYre3S1f2+lWLUK2ikk7MtKrseZzgxObCxJtRxCzyNRpNyNCI6uoszObZ1gssQ9isVqlryOycLXkQExeB9H+txNcVEfnbInJfRD73yNf2ROSfishL3cfdR773N0TkZRH5koj80B/nNdSLrUqpAq5Vyi++DY61QcMtbF2KzCs0zwhfetninapmLV7t9/8p/HueM9v67ja6NbIFljevW6uwqm0tSZdMr8ulDRmvgnVXaRrOdyG6qw3LnXitqirpzBvOb8QqdpVblr0jleMdv4+ZLZ6MK5NJV3lpVa2NGOL9puW4IiqxM6No06JR1+5EKQq0tdSQlMORuAgkAUt8vfm/gB/+qq/9deDXVPU54Ne6zxGR9wI/Dryve8zfEhHPH4FEJZQ2t5Wf1rRPHeDmNdJGCDZE7B+/afdtWrKnn4R+j7i/hRsOyZ58nPLuxEJ1M29txc4xKIvKxCtGdL5AT8+6FqJVNFp1qfV1Y2dJTb2pdjob/cqAYeaNrlKL3Y6ytu0s8Ioull0c1MZ8QdT1fVbblLWroqSbIZM8s9sa10PM4lfuw9DlMwriZF2hrVqUq/O0ROIikAQs8XVFVT8KPPyqL/8I8DPd7Z8BfvSRr/+sqlaq+hrwMvDhP87r+CpA1P+vvTeNsS29rsPW/s5w763hTT13szk3LVMwRMkEEQ0/FBvWYAeh9EMB/UMgECE0EAmQA/+RjCAWAhAwkNj+FTugYdkK4ligYTtilCCKIhBJLGtqKxKlJtVSk032yB7fe1V1pzN8Oz/28J372GS3IvJ13Yu9gEJV3eGcc+uxz+Lee+21kLoBtB2R5+I/SOZ2cX4h1dW2k3iUeYt0e4l03z1SvWw65NdvybxpPisRKcMgpDKOSFdOxQljkOgTVJW0D+taKrMqqU1UW2yjqjRRGsKjUgBo9ZRLy7GqPCqFtV3JnXomalWVV6vp39a9En2XSxegzVLKgisxjup9mD3UElncODgzEDVYYA8QBBa4DHiAmV8EAP1+vz7+CIBnJ697Th/7xrA9rqYCMoS0EpBuXUga86hOFecXwGIOXszAdUJ+9XXwrdvg128iH81lr0qdMOhiBV6uRMwxa0GnJ0Jk640QwGwGtA1osQD6rlRiiSSnK2llY7MtW06+E6ZCHEdw1yGvN5ITNp8JobWtqCGzqBTTYi5vG4vS0Kup2Uxbheq7aK4cPLGs0pkZgLKf1tSIKVhgHxAEFrjMeKO76BuWBkT0CSJ6nIge77sLcJNAY0alpEWjOFfQauN+hRgG8M3bGP/4i+CqwvkPfBC4/17g/ntBWyEh6gdgGDG+dhNIhIvvez9e/JH3ipN8lWSZWJWCbtVU12WBuGml4skliNKvuW2KI7w50+vjtJgLmbSty9rdJd6qNItLAcr+Vs4u+LDzUdPqvE3/c7eZWKUKRWsjAnKMaCEG9gShQgxcBrxERA8x84tE9BCAl/Xx5wA8OnndOwC88EYHYOZPAfgUAFw5fYSpz6JGPJpLm9BiRVIrN/+zcyEXzqjf+27g5Zu4crGWSkvbjOn4CMOXn0F1/TqoSrj4vvfj5N89jZNEyBdLUfPVtSsbfY+rH4R8QKUKuyP5WBKUh+Icb21K90VkJM0EE1KRH6muweu1yu6ztPs4Czk1skztQZZExWEDKsE3NSJJpWaqxmklFk4cgX1BVGCBy4DPAPi4/vxxAL80efxjRDQjovcAeAzAb7+lIxIhzypPO063zsUdwyowALh6gvyeh7F91w30774fuHlb2oBEEgw5ZlT33ADuvQ5637tw8utfBKDzs7aVyBOTzG+3PhuDzq52FoJt50pzu4hIWnVWQVmFRCTtRqBEoXglVZfqyGT1Kp0HtEIbtfqqKuSu38n1SmZFZXM3I9R0x0wuKrDAniAqsMBdBRH9CwDfD+BeInoOwN8B8HcBfJqIfgLAMwB+DACY+Qki+jSAz0Nqgp9k5je/u2ZGtexQ3c6ynzWMQFNjfOkVUNsgnRzhpR/9AFIPcAJOn+uxudHg+NdfQwVJQabFHFRXWH3Hu7H4nS+63J37AXR6opleW+Rx9DYf1UnDKbdCBKbuGyFiil4Wk8lamGbtdLGU3DFvB2p+V9O4Gz33A3ilu19VBYJUfGymv8xFrm+qxEm6Mo8Zec9ZTkcAACAASURBVOhcWk9V5b1YUturUv0lhIgjsA8IAgvcVTDzX/86T/3lr/P6TwL45J/mHJQZ6fZSiKtKks91vvTZUj5e4OqXOgyLCut7Kmxu1OgXhGMIeaXTEwxffQlf/S++B+MMeOeXroNffBn08AOgV19Xl/hx9xZvAon1RpeFSYigggo5hl05vVZqvN7I/lXXF+k7hJS468R3cbNFOl4I6bAa+aq7fTIVZM7ShlRne7ZZllVbukDtfogaqwJK4rxhC9LjKCQXIo7AHiBaiIHDgwVGJvJF4Hx2hnT9GlBV6O87wvq+BturCWlknL0r4cY/lcywdOUUvFrjtf/su7G+j9HeYiGvowVoK2pC7npZWm4bd8OAytNl9mSeiCqoMHKzpeFxRF5vwJttEWtYW9PIjbO3DIWY1CnDhBq219V1QlZqLZXXxUUEVm0RSQqzVVrT57VK5H5wqyke+q8RnAQClxFBYIHDBIllEojAxwukRx/G8NzzwL3XcfMDM6zuTxjmhOVDCff9fu9v42un6P7i+3HlKz3mrxIe+uVnAAD5tdeRX7sps67zc5HNTyXv260sF7eNkIxJ2JUweLP1PS2oHJ4W8xKv0snCMne9OnDIDE0qosl/phaK2akwBVCBiMzK0nxWqi8jJOYdYvN2oXkq2lKzBV+27e6uWiBwSREtxMBBgnXGRJutLCDfOkP1gfehv3EEJuDan/SYv7ySDLDb5+DH3gsQYfPoVVTbEfPnzvDwZ7+MkTPS0RHo+nVX+1FTI9+6jXRy7EIIWsyFfCgBGMEb2Q+jcdQ4lCPwcgVUM1cHSrWlxzw+kte1Ijzh9VpmYHUtszMjS63QvI1pe1u5VFZEBJ6oGW2PDIDM4Jraqy1/vQpO3tznJBC4PIgKLHCQoK6XyJOjuVQq91yXfa+acP/j5zj+3POoXhO7KDQN+Pmvgs4uUC8HnD86A158BQCQrl4B3bi2UwWNr72OdOOazNcgFQ2vN0IEZgXV9yrEmCQopyTVVdcjL9cYX5JtAQ+h7LoSVqktQm+HWkXJDDQqztAZGHLebUNCLKFY5248DMibrZAWS7IzW7SLWlP53M5anoHAHiAILHB4yBm8XIuI49VboH7AcOMY1WPvRfsHX0H10q2y8wSAZw3G73gML//we/GVH17g5PkOePBebe314NvnGB+5F+Orr+GP/sv3gb/nO8D33ZA3mzkvmXAjSxtRqyebhZnrRb5121uN6fQUvF4L8ZiL/DAgXyxF2MHskngJrswu7pAZ324r0ZaczXqKEvl8LLWN20gxi2WUkbLNx+w63C0/ELjkCAILHCDIHeLzzZtAP6D63FPAqzeBYUB+TZSEvJiBrxzj1e97EMSMcQbkGnj5u+ZCfG0rs6thAPUj+h/4MB776d9E8+It0FdfkQTk7VYl623J9LLsrkEWmnnMhSxUBg9gl3xU/UiVLEfn83NpCyohZlMrsi44Wx6YEhwAt5KiSuNSrIrTViN33Q5pGXgYkLdbvc5K24sh4ghcfsQMLHCAYNDJMXi1QnXvPTJzms+AVImnYN+Dj+ZYvfsanv/+Gle+CORZhe4q4epTjFwzhvc/jOoPvyStQgDoR7Svb0Cnp+CbtwBKMgOzEMlOhCCWruwtR0s5hnoNAtJe1CoISWTuNJvJIjQgLUJVK6IS1SO1jZMQm/BjEpjpRGXRKHpuTihEVtdapXEJrUwEQCs8wK2pKGT0gT1AEFjgAKGtvKaRmJSXXxUj3NO5VD03rmI8nePZv1Kh2gBVB7z8XQv0p4wHf2sLToT0u38kwgxNO+7vP0HzxDPArBVyqVIRVmQuS8MmaTdvQqjqL2dvAwJwE16DxKMkN+WlY7F68r0vnW/t7H2pkAMplbTlpi7+iHY9AJikMjNCtRDL3d0vReSBBfYEQWCBA4QmFqtDRbp21ffBKCXwV1/F0z/+bTh+jrB+kHH6bIdq26A5I7RfeA68WoNOjsWUdzYDNQ3aJ18QpZ4mJjuJVeRtQ+40+4uzuG9QAlIGNTNJX95sd0jPhReJPNoE44jcdVLdYQT6Uaovc/AAiuJQd8R4sy3+hzl7uCW1Tfk7aKvS6iqv1Jq6WEkZuVUhRQzsB4LAAocHhszAuh5YrUE3roGPFxiPWzz9oyeo1oTjF4CH/oc/lNnPdosrAKrr14FKWoO8XgvpacgkjGDq2hOTebWW1p+qCAEUojFV3wiJU2naYtgLqGO92k2ZypAJdFSL+bC6aJAqGKlt5POMo1tMcT/IjM5mb4AQ1VRFqMvVNJvJ+82811zwx1EtqMyRXudpd+vfKhD4MyAILHB4sMDHWQ2az6SF+MiDuP3fjMifP8Zjn3oB4wtfxbjdShpzPyC/+hroaOFWTO4L2NRirmsLxsMgysJpSjLUJd4WlbMKOayQqSqpwHSWZeTEm42YAdvxmlbeb4RJSTLJtI1ocyxMKiQXiGhGGDR92ReR1dLKyIuIwNYgNEd6a3nmSWzLt/wfKRD4syMILHB4IIDmc3HhOJqD3vEQvvyfPIDqs8AH/tdXwbfOkBZz0I3rnq2VHrwfvFwjn52huv8+McvdbPQmrzMlzjvkNY0t8ciSUVp+7kSv7u422+Kud5Iic9yw2BTO4E0nZNP1QqgmFK4qYJbAqxXQdSL0SKI2NEWhJzFnRlrMi/WUzb8mQZhk7GrL2VUFxlg+UyCwBwgCCxwchlOxQvriJ96J7irj6h8nPPL/rFH95udB16+BoQSX9KY+n4ErqXaqhx+UsMqjBbDZAG1TXDem2V5VBaSpg7uKNNoGvN6UMEkVd+zI182jUZWLMAHIIJUbLeagptHASnmNqRxtmdmWpqGyd5tbUVUBNSGbvF9bh8wMsoVni2EBwEw+WzPyoroGtt/Kf6FA4JuDILDAwaG6p8MzP/IuPPibPTbXK1z//deBp5+Vm71VVYCQCJHMnLSS8u+rNdC04NVaRCAKXq58luVZW9MgSjPLNZLIkzwv6/z1qvxbzCX12WJXmhqYzUC1pimP4pjB44h0dFTEFn1JbpZzKTmm2udkJmAB4K1DALuKSSU1+1tYm5GHIWZggb1ALDIHDg7bVYvFK4yXv6vBPf/2edAzLwApIV29Im4Yq5UQ2TgKeZkkvWnAp8dlBpZHqdSs+jFxRFUBjThZuHuFZXxNTXTNvgkoZNYPulBc+Q4XAI87QSI3/sU4go4WSKcn0l40EYcHWGZ1kc9lD62uxSi40TiWYdBra1ze78nR0BleUqFHo++t6zInCwQuMaICCxwc5i91uPGH57j3l18ETo5x8z/6IK7/9kvIz70IWiyQ3vUO0DAWn8Exi2fiOIIuVtK2G7OQV85gaxs2rc7Gtu4k72jqEoTZ6iJyPZGoq+LPqi6fo1k1VNfgYRSZftuUXLExu/qRMPix0DZSJdFE9DFxn6cMUVP2RSHplZnBlqmnVdh6rWGc35J/mkDgm4ogsMDhgQjp2ZfBF0uAGadPr7F91w3U956ievUcZBXVMPoiMC3Xmrjcq4pwmJjcVpKyDAhxVJWQUBZnd6KqkISKPHjMRXGo7UHKxTmjOF6gqBszA1bVnZ5IVcUZYCXB2UxmX9rypPkcebkSif1yJa+pquKR2DQledmubyo2qSqp3uxnddOnKkWcSmAvEAQWODwktY5at+BH7kf1uadQnxzLc10PvudaqbSUcOj4WCqXucai5NGFFDRrgbqSRWQVTgDF8d3UfdKGlP+kCCiJyJV4HfIkBgVNo16H0q4kTVh20cZyKRVfXctjFmC52Wh4Zi3kVWtszGIhDvMpiUGwKhtNzAGo2a99XiLk1QppPi/VoDreT6NWAoHLjCCwwOFhHEVscXoCur0Erl8DL1c+E8Jq5a00WszlZp+SuHRst1KFqJGvxJz0nrrMXZZKSkFKWDxxx3BXjrb1XTBazCUbzAQknKUyahtdLIYQkV/XQlqKRk6bjVRqTQuqK3GsP1rIsSzCBRAiXSzE6NeqLZuntS1AuYhKdAnbdtiyZphN98wCgcuMEHEEDg9EEiA5DMivvCYVTNuAHrof67/yHUjvflSXiqXCQS3O7k5CeZRZkFUiRjJm0ms+h9oqFIKTfS4TbrjrPIvlFG+2HkYJQCqqo4XOocRRni+WntPFZgmVs7QMqwqoa+SzM3mPLTyvN75b5jJ4DbCkthEpfVV5ReYpzmki69fqjprWs8uigRjYBwSBBQ4PmYFtByLC9vs+iO6D78D40st49kcfwuZ6hYtvu4HqkQfd1YIvlsXZ3cxzAWkHNvVkbqSqv34QEQfgxEFVAvpOiGNeFpOt4rKMLcn4Sm5HhX4QshpHr/5cbGFzt2GQ1ue2Qzo6Aq/Wsvzcl6rLW5EWr0JCmnkrxGlyfBNscNf5Z9pJdjYhyLf0HygQ+OYgWoiBw4O2+zCOaP+vPwBvtxj+0l/EO//HL8nS8mIGvn2uN+7sRr8OrXawLVWTGe8ys1ROJroAgMXcyYb7AdhsC4mZrL6pMb5+U9zkAZ9d+WwrZ9DpiRxjtS47YW0rcylz128b8WCsa1FHAuKq0TYYX7+l3olF0FGdnoKHQYjPnOvPz70iM4cOUhsqb3EGhQX2AEFggcPDMAoJjCPSlSug+QzV7z8NNp/A1VragoMS3XarkveJ2rAfpO3YNMhn5+4XSEcLkbar0zyatuxzZa1idC42ldnzcoV0cqIKv1Qk8sBkgVrMfeloISIPQCqmttlRMNKslbblbKbvY6DrxT7qTpGJzuI8lmUcRbgxIWbYvE7bohjHyAML7AWCwAKHhyqBjo+KFLzvRdhgjhXNQkgOEJKZhFDy1MdQZ2HelmubsnNlkSl5LNZSlVo+5SxVjb6O+0FiTzqds5lqUedgnMWSim+fyets+VjVj+gHmaMNo5BX34NmLfLtMyE7I7PNRkhss/UZnJEXd52rDKmuRZ1Y10XGP3UWoRT1V2AvEDOwwOEhpeIv2NRCBFUCN9q2GySbSwQOsnflLT13lzf5YCpiDHPC4FzSk4nkORN5TBaFeb1WhaHOvExQYcTaNm6+i3FEuu9eEWSoYwjGDD6/0J0zFmf9m7eA7Rb59ZuFvKDGwzkjrzfyOzN4vRYhy3JVWpFaZVHbIqsAxNw4zCnEl6sDgUuOqMACh4mcveLitgH1A+hipYTDbmY7vPwqAKC6emWS9zWC+1yWkG2mZpWKLjGjriXrCxBSGzMAqazydotkcyYzy62qSYilJjrXtQo0JOzSW5SjOIXQfC5kpf6I1KjwpFtKK1Q/qx1bXOj7ItYYR6TFHPniAunoSOZtSnqpbeR6TL3YNiCud+eBgcAlRhBY4PBgFlGAVEVdXxaQtV1nbb76oQdkXlYlgCaEREmWmscsj/WaAzabicWUoarEQqqukc/PkU5Py2V0okpE5jKb6nt1w5955WVzL2oakc33vRCNWVqdnviCs7U309UrUj1pa5Ahe13gXCqotpXdM0DNgJPbR7lHoxF6XYtSMTMoEULEEdgHBIEFDhDqMj/mEjzJuttku1gqauBRKzUTVsxmJXHZXC+qSoQSKpX31GWVslPbALMZqrYRSX7TlEqIErhbiyij74W4KHl1OM0D45yBrSQs08mxqA712sEakqlVnxxbAyrHLEvYXef7XbzZFhf6JNZQNGvFEWQYSoWmi8y560FVhdRKy5TGmC4ELj/if6WBAwSrRD3JHKnvfQ/LyeuNkFlUiOaBqMnGdHQkz1eVxp3I/+/LZ2cYz8/lfZ1WS227s5fFWxFUuCO8YeLI4edWMQbNWnHhgO5pAWXnayrft0NpW9HPDTEJTrOZkJ9WWbztitO+thxZ416oqsTB3oj96/2NAoFLhCCwwAFChBroJduL2naHtFhv4tObNK9Wpc1oZrZ24+86IZrt1snDlH2VCS6mGEeMZ2cyU9I5ExLtSuinUSqjutDb7pk60Odbt32J2eTtbLZR03NaNbVey+fsxRnEZmK0mE/yybR6HEek2QzJAjfbpuykGWkGApccQWCBw4MS0/jSy+p6kYWIhkGk6CpkMBUiNAvL05YB3cniUpVRKgTDYtxbXbtW5luc5fjMQNOgunKlZHRVk//M7ByJvGrzY1sLs65F4m4GxHY9APL5eTEEBkQWrztcbl9lrUezrxoGsbjqB5HV63VmNf81QoPO2ciiWgKBS44gsMABQiTk1QP3g1VWjqYWx3egEM5mU3wGKZX4EiMeaztau280AYiSlknS1RqKqlTISh/nsbQTi8sFpLqbzZTQxp1laCNDEaPkUnkxIx0fg8exGBMTCRER7Rj6AvDkaFBCOhbj37zelCgXouI8n5IHW4I5WoiBvUCIOAIHCBKlILOQVs5iHWWehUhFFg+Iwi+JyMFbc8MgUnoLk1SpuQlDyLpslApxdIP7IvoyMVCEI+pziKaVSs7CLptWzjOZb91JRgCK9J5Iwip1L22nIjPk7ATnBr45u3UUmz2WKTJzLm70kQUW2BNEBRa4qyCinyeil4noDyeP/RwRPU9Ev6dff3Xy3M8S0VNE9CQR/eBbOwl2Z15d2dUCUKonGHkVMvOFZJPKG8ltNkU92LSyIG2u9MBOHhiPubi9W3vO3qsJyjJD67Wi0wpsQkLUNlrRVZPk6FGqJ2bk5VqMhafvM7K2kE6b82mlxaNEqexI6P18LdLxsTiWpAi0DOwHgsACdxv/DMAPvcHj/4CZP6Rf/xsAENEHAXwMwLfre/4hEb15WJX5/ln1kSp35pCAyhm470uFAhR1Yt/5TpQ8rhWQpSZbW09dK2wx2XPB1psiqrBWnFVa2qLkriupx3coCs2vkNUlI5uLvC1Dz2fyO2d3wndVocroqa7l51wWou08VFW+YA1ACI8ZebWSv0ff+ywsELjsCAIL3FUw8/8N4PW3+PKPAvhFZt4y89MAngLwkT/FyQAAdDT34EpqmlJhVJXvWgGQm3zTKrGl3appOr9SpwxqW51zSWXEnc7TmHfUfpPP7m0/mZkJF3PXe0wLD4MQlD6X1JGemlqUgtoyTCrtl+DLQUQpde2CFTEfTsXaKpH7Mrrd1NRCSj9PzL4C+4QgsMBlwU8R0ee0xXhdH3sEwLOT1zynj705bLZTV4D5FtbaKmQGLRZSqeQsLbwxlzYioPOo3QRlaPRKXq2EjIZB2oVGGI0QDJQ8bEkYgC8P02JeZmOJSqUH+HzLWoGw+BbLDzMxiErkaa7OGxNJvVlDAXC3EWsZppNjySszt/yqkpakeiOaP2QgsC8IAgtcBvwjAO8D8CEALwL4e/r4G91N37BEIKJPENHjRPR4l9deXfBqUxaIk1ZUtqjs6cRVcZiHpRRrZbLZ6HxK5O58sVQi2A20zNttUfXl6XFEfWjEYA4ZluZMde2pyX6NliGmx9/ZWVMFInIJ14RGvdjxfOY3hbY8/dqsOpysDkgUjFSoQWOBfUCoEANvO5j5JfuZiP4xgF/WX58D8Ojkpe8A8MLXOcanAHwKAK62D7BYK/XSdlOVnVlHUZXAy5Xnb+18z6OSH2ll0hQXjKaWqqcfwNwXUhpHpJNj5IvlxBi3KBp5GITokgg88nojzhdWnRk5WY7XbObzLB6zZIHpecw+St8o33MueV5AIWb5gwqxJhThilZppE4hoORKRh5HIdOoxAJ7gKjAAm87iOihya8/CsAUip8B8DEimhHRewA8BuC33/yAkBBIXWLmzUa+ehVR9H0hLXW9ADDJ9pq07CDHQlNL9QS4x6DNtET8MIh106RtaPtcO+4WatQ7NdR16HzLJfR9X6opnW25zdOEpHi7LRWZ+RxOHUhs/02trtJCqkdbtAbKPMzamzELC+wDogIL3FUQ0b8A8P0A7iWi5wD8HQDfT0QfgrQHvwzgbwAAMz9BRJ8G8HkAA4CfZOY3z/pgTPapRreEEnIiIFXg5VLc280Bw+TodS3+iUY8A5e222IuzvM6Q3IX+EnbLi3UwT5nD690MlDfQXehB0pSMlBEGpU4c/AwSCsvkYRnArvtPdv1kgOV83e9SO23W6+mqK7L7CyjVKdcBBzI2T9HzMIC+wCK/6cVODRcbR/g73ngY1J5qCQ8n1+IKW8u8yVfIAYm7bhdM1sXUAAumXfSMcGFeg/mzRbVyfGuGnG6SKyvK4vN5Oa64hpPpcVpbUt1maeqKu09i2XR11LbIq9W5Th6rZbOjKpyorNWo6VEo+/LjLAvDiS/cf5LuD2+GiwWuNSICixwgGCVlcuN26smC6xMVVEVTv8PnC0cm9PG1BsREzLLE9LLZWm5OjmW9zRNyR2DVmrDADSNKP2A4vhhooy69iqLUcIvPazSqiTzLExpZwXAZm7TGBWbtXHXi9N8P2kvNvVuNtpkfuaejoHAJUfMwAIHCbNMAiVV17XugrHjBA9VBmrW1o6Cz0x37Qtw9aI5zXslpUrAHYIDXBhhRJEW8yLEUMWiCSrMj9D9FC2V2aJRuq5UdEQ639t6dcVdJ/ti2i6UUMzBr52a2o/JXaftShWajNlbn77DFghcckQFFjg8WOsOkJu3uWgYCdnviqmCjxZzccxo6mLaq1WQz5QAn2+haXbc5k1g4Y+ptJ6qBIYqCXWHzF5P6tjh7T+dT1HbyDwrT563qk8DMmXmpkRX1/LeKpWML2tlWsWnjh1ie9WU1uJcQzU3W61WowILXH4EgQUOEOQkQUmIw2/IqSoVlfsYcvnddrfUFd4tn2YzEE+qqySGvabo8zMrcUnlk1w+j8xlT8xmYE0DmlRrU1WipT77jlldq43UrrmvKxJ78VU0IkrWtmTZ/yKdm8k1qkzeqkerEqFVWlRfgT1BEFjgcJEZnHVZ19p8hrouknlTBjYE9JMWojrNU5XEPd5EGJPUYxF0cCEubcO52MJ8E6cVlrvXq+IPAK/XanVVl4XkqfDCUp41XXrHx9FmWZNry8PgTvNSyXUlFdpIcyL4IP37eKRMkFhgDxAzsMDhwZSCQJHIT1tiJpmvJl6BdV2qL/t9KmnXoMppYCTuqJhcJGGqQ85eMZnK0IUdbSs+h3p+ms10NiUtQD/3VFwx/Qw6C7OFZ3fwsNeaQ4f+7vZV+rtfo8WxaDuR2sZ9FgOBy44gsMABgotNkrXJrOIaRyBVbvNUcrdUXt8PvguG2Uzeozthd4ozjBSoSkUUYa7044i8XIvcfRCjX+4HNeVt3QV+x0JqWiWaT2FVSRtycn7zYcQ4+uN5uRJxiFVdJtgAisrQWpcq/vBzT9KY7VoDgX1AEFjg8GDZXFktk2YzFUBkkZ5nvbGvN0XwMRV9dL24b3DW9uMktNJIw0xye3GS9xlVXwiAqlSqNW/xcSE6m0l50KYuRY+5JC7n7L9Pq0jSaza3jSlxWhbYtBo0oYfNvLz9aZWY7pC5BVaIOAJ7gCCwwOGBtN2mVk683RaLJxNoAIAJMMzB3eZWVSUkZgpAba2ZRN0XlQFVIVYTY2BpS1LbivO7mvEC2HG64DEXFaDK70V0UTK8zJrKYerDnJG7UjF5rpi1EVXQYe/Nq1U5hkn1jUDtmBM3jgi0DOwLgsACBwj21h8AzfWqJMRyvZaZVt+LMEOVh3m5Ap9flBYiUPan1MvQvAapbXdDLFXo4c4Y1r6jpDL4XHLAbIdMd8GmeV0mg5+KKyx8Mm+3Tl5UVaJ+NHm87op5fIotQM9mABHSYiHkZPtnuuTs0SxmHDwxBw4E9gGhQgwcIEj3rlq5sZs6T5OWqUqiTjQrqcygmZrYXixBVhF5pEnlEnhq21IxGdnowjA1Fp9iwpHsLUAkkbVzZlA/yP7WoDMudf0wBaLP7XIGa2WXjo7ce5GZ5XNM88smGWIuDHEHj8ZdOeS7iDxMlUiW5twPvkMWThyBfUAQWODwQATMZ6DpzpQSGB0fAcMIXq5AtcnoVRyx7WSP6g7yYsvnsjahJxg3xSex62TfrOtdeGEVDk3mTGkx3zXvVfunnV0xazmqAMQl7ibKUEGGeTJSrU75LHL6NJtJxTaOMpfTVigqIB0virUUMxIRxvNzT5cOBPYJQWCBwwNBVIRTayfWlqJWKunkGNz3cvPXxGbfvbI9rkQ7kSPT1pq1D7P5LDZNyQKbibDCjHSnO2K83kjel83kMjtpImVfYLY5W1IHENh+FiBp0kAx953kie1UirbcXKsxsTp7yHlLu1HIS0mTCDSOEWgZ2AsEgQUOD4wy/1Jz2ulN25wrqGnEEqqufYHZnTds/qVOGACk8tpuRVyx2cqNX1uU1DZCTmZTlScS+ayO8zP9z01nZW5tZbO12QxUFYEHoCRFBDSVS+8B7DjP28qAzebydqvnTz57o7ZRo9/kzh48DCBTbNpxAOSujxZiYC8QPYPAYUIXk/n8wmXv7ryusyILiHRZvSoQpzfvaXoxtU1pKTZNkabnUlkB2GlB2uwrXyyBrpdqj0XUweuNkIqJQbquKCTNUNdUg5O5lu+F6R7ZdFnaqjKbZbkisR98EZtq8TqcJjt7paltzjDzDewDgsAChwnzLZzNnLh4K8vG7hForcO2FcKj0nIUVw0huLzeyO+m+kuTxWW1fwJ0DjZxw/DKDbqH1dRIV68IibSFAFkVgZbuvEMetoAMuEBEnONHl/Sb/6F/biVim53li4vd404sp+xv4wIUm/cFgQX2AEFggcMDoZj5ztrSRrOqZBzBm437IPKglRSbgW9VQi2rCvWjDyMdL1z+jr4XCfzxEcbbZ05I1jI04konx6BrV5GuXkGaz7D87veDrxx7leXLxkoW1Ja24fS7kWheLv0j8jD40jJylramhWOaFZU6hbhllbUsp+QFFH/EyWcOBPYBQWCBw8PENZ43GyEGc9u4s6IBfGbFm618dV1xvxhHjM+/CJrPy1LzYqGpyAPS8RF4uSrKxKoqkvflChhG5NtnSFev4OiZM3BTAdVkP8ziV6buHvYZWI5pj6X53AnLlqXtufH83KunvFzLIdTQV6T/Kp3XnDQe4P1ANgAAIABJREFUs7dQ3U9R41esQg0ELjtCxBE4QBB4UDNcXeaFzX0AgBl5zCAlLtsFs/Ybta37Hxph5fMLOXLbyA5W20hY5jj6rhYt5u7ikdcbIZdXXwOdnoJvXEX+3B/5FdbvfifyK68peWZkF35MrJ/s00z3vcwRH+pgr8SX9HOaRN58EU2RSJzBOWO8fQaqG1U+Zp/xOWHZHlkXLcTA5UcQWOAAweDlEnTPDVcj8nIl1lFGFHUtsyfzBjw+cul4cXgnnwuV9p44XPB67RWTZ4KNWYQazNI+nLUYX7uJ/NrrqKuE6tpVr+zyazfdscODJxNJBbjdSpWnxrty4lLhAZDrbpqSMQapuNKVkyLISARKsrwNCPkmqx5zlvOYpN8EIxPD4EDgsiNaiIGDRLrnBni18lYcHS2KOKFtdKn5WMhjsUC+WEq7zmZnZrfU90WCnifKPRVUJBWJyI7X2tuS3HUYb94CgCKQAJSUSqWFlOQ8ScQZIs1vv4aYbM4lzh7mnN9P5mVldgeo2W9meXwxL8IUwzh6bIrMxyZejnr+QOCyIwgscJDgrgMtFlJJWD4YTcIrOYtg42gh34lAJ8dFzKC7Y16JNBIKmdcbad2Z0KPrhNxUWg91lqeqQjo5RjpeiMDi/ML3wQAAmX1W5SGWd1ZTKstPi7mmMY+l5deoRZZK6W1/jXsxHuZhcDcO9ENxnrdYlsmsyz9rL8bAU/VkIHCZEQQWODwwxL6JGaSSd0oJyGOxTKIEDEOR0i8WwHZbnCoA5OWy7Fmp+IPmM+SuR95uke69x4UPbpRrN39T/FECmlqIr+vcjT6vVm4l5fL1NBFyWKUFiIlwr4IMrZZoolxktYyiRx4UolsW93kjM1c8jqPL9T1Z2khLnfCNOAOBy44gsMDhYTrPYQZSJaIOc94AgL6TWVGViqmvzqHc9X02U5Pb3o+Zlyuk+UysqHS/Sm7+/DVL0F7pqXmvX141SUjOuQg3TO4/rfzMbV4FGdN4FWobqZpSwnjrNsY//qIsR+ueWJrNfB5mbUdzzpfHsn+f7ohFBRbYFwSBBQ4P5rBhzvCJPBuMtJ3IoxKWVWKbLfLZBdLREfKffzf4LzyGsx/5TjzzMx/x9ht3HaqrV0rciQoyTFjB6/Ud16HqxqZ2QYhJ2j1iZeKZOHUIcY9DdeFwh426LmSjBEXvfBgAUD/ysMzKjhe+xAzAF7TzduuBlVTXvoBt8zeQkn4OAUdgPxAEFjg8kJLLciUV1zgCw4B8flHk8LNW50NdIbtE+Gu//hT++D9dgCvC9d96Ae/+759EunYVVKlUnVUUYbtZWqWZvZSLMzKLdN+ywSx+xcQU6p9o8ygAuwvEannlx9Z5G2mVx12H8aWXwX2H8YknUV25gnzrtmaBiUGvWFNlj5BJJyelOrX5naVHN5IoTW0rjvWhQgzsAUJGHzhAkLTXzI2+qiQqRQmCUkJercW7kHUXqqpAR0f4zAe3+AB+GwCQj45Aizl4tXYFI281y2s6F1NxhBvkEglxUQK6bcn9anR+ZYbBJqyYYhK9gnFEahvxUaTkQo6pIwcApNPTokwEikMHxCZrx13DbLAm57CoFdt7kwozCCxw+REEFjg85CykUyXZzeIs1c6YxFEDEPVh14tqkNR1Y7VCdeUKYKTVTyyo1LcwLeZeQXnbz3wH1TmemWXBWWda7ig/tXCyZeTNtngZKrlZsjM0/iQdHRWZfVOjunZ11zDY5mYQcvZr0GrN9sysqiqxMSxKTSO0RIVYI1AlsAcIAgscHuzeO2YhMap9HpXPzkvbbrIc7BlaTe1KQs/pAjxx2U19J6Sx42gB9TScmOLuOGkA6v7eSbtuMde2Xy4V4kTUYblggBKPBmZ6NEzORcxh1zJJjqYMF65QlWRReswiKqkq8Hot16BiD3fbDwT2AEFggcOEtQ+BEpfS1EIuzCWDS2Xp6fgYyCPyxdIfmzrQW+6XO1XYUrQSFysZYhwBq2iaxtt6RfihLbtEO8vCPtu6WAGUNDl5ErQ5Ib283bosXr5zIVclL7kW7Mz3uOtlDeDsQo7dNPJaFZFw1xe/yJiBBfYAIeIIHB4YvldlESrwll52AjFBA1WiRITuWvEwuOegV1VNXfaorGrTedGOAS8kENLIy814jQgni84AhFQnwZTp6Egk+p063ttcKmefZ/k+GLMQm7UG5zOZk5nU3lSMSk6iZsyorl/dEZyY+weIXJkYThyBfUAQWOAwoW7xREUibnMqg8vIpyAS54vFAgC8vWeS9yJ8yH4Mt3CyVl9Tu82TkRCgEShdX4QUWhWZw4dFnvB6PWlpNpP3dmUeh0mrErrTlVlsqWy5WtcIJKE5y/e+d3Lk0dzwSfwSU3IFY+SBBfYBQWCBw4Pde7V1ZvlYgBKBVShURBO8Xks11GiSs5IFaWaYO2akJCGWbSuVkpGAEZUa7ALQSi8VCyfACYhmM2ndQclHk5t9FjUhHpujUduC+8Gzv6aRL3S0QD4/L5WdCUsgVlR5s/EW5A6Jq/0UAIli2W53lqUDgcuMmIEFDg864/I5mNk6JfKlYBipNbVUam3re1pkwg2bCWn+1p3iBp+XAVJVmRhjPtO8rU4Vi+TmurSYSUtvuy1O9+rMYa1CawlyX1KU2QI3AXnfIPOudHoKcEa+dVtk++Y0b44bo1xnde0qQKmc145hsOgYE4gEAnuAqMACdw1E9CgRfZaIvkBETxDRT+vjN4joV4noT/T79cl7fpaIniKiJ4noB9/iiYSY7rR2AnyGBCoydotO8VDJ6XuYiyjCHD5S8n0vk6h7e9JEGuY63zZCcioEyauVKxjdpd52ttSk10ixunLiQgtADYq1OjR7K1YXkbSYq9Iy78zu7FyA+i/qDM6XnFUowoP4LTq5BQJ7gCCwwN3EAOBvMfOfB/AfAPhJIvoggJ8B8GvM/BiAX9Pfoc99DMC3A/ghAP+QiN787mpEA5WkN60/RVXyeZLvP2W1UWob0EyiRFzU0DYYz87kveoK70a685mrD914l3UORSQyfBNS6M+Wv+VzrLoWZw/A253U1DvSdrluiX3hoSgNwYx8cSHKwuW6+Ctahel/Dt5tPdoem5I1b7egukZ1cqzXEbeFwH4g/pcauGtg5heZ+Xf153MAXwDwCICPAvgFfdkvAPgR/fmjAH6RmbfM/DSApwB85M3PpC1Dqyi2W8kAa1rN3GpAJ8egxUIISb0KedvJF6sJb0rgrkd17arI1y+W4M0W48XSY1SECPud1puR5HTWxJst8mbr1ZyIN3qfN2V1wmdPc86FZBN5oCbVNej0tPzctiIAmc+cHEHkpr5iTMz+Ga1qRN9r1lm7U+HthGgGApccQWCBtwVE9G4A3wngtwA8wMwvAkJyAO7Xlz0C4NnJ257Tx94UJoyACxQy0HfezrNlZZl7sSsWfYm4Fyn9TvtNW3ZJPQO94gGEdO5sVxoZ9D3yaoV0fCTVjsnVNccrXyzLsSYtSVqIKW++WLpyEACyVoRsrchJNpmbBQPFr1HdQ1hjYHgYJN9sLDttZK3M6cwtELjkCAIL3HUQ0QmAfwXgbzLz2Td66Rs89oZ3ViL6BBE9TkSPd3ldTHWrSuZhFpfCEzsnVsumRC70cCFHU4uUPhUlHwCkoyOpjPqhtCBZhBtOPGoN5aSgKcfo5djZMsf6XirCut5VG6qcHr24btBioanPstuWjo6kokRJa3aVos7JhGAbF3T4/poReM6e/+VOJEaGQV6BPUEQWOCugogaCHn9c2b+1/rwS0T0kD7/EICX9fHnADw6efs7ALzwRsdl5k8x84eZ+cNtWkh1odWTV1k2p1KPQlAqXxO7JktIzquVeA5utmV/S9070mK+k5psoorc9SVfayKG8MqoqiSSBWXHzHK6bD+Muw7j2cXXutMDyMu1VGTrjRy3qqSa0rleOjpyYrJrdvn/1Gx4onQ04Udeb2R+l3M4IQb2AkFggbsGkh7YPwHwBWb++5OnPgPg4/rzxwH80uTxjxHRjIjeA+AxQK3iv+GJNOxxvZHqaLKX5cIISiJ0SHfMy5palIl9rw71C69YvDqqKl8Y5skCMwARQthj1kK0aBQlKt5uPXAynZ64jyJvtkI8ppRUxaMTTkoee4JxFEn8xB3EiDCvN+6Az+u1+ym6WtL+TLOZ/J1cXSmVqO21BQKXHbEHFrib+F4APw7gD4jo9/Sxvw3g7wL4NBH9BIBnAPwYADDzE0T0aQCfhygYf5KZ34LTLInL/JESC2fJA1tvihJvGkQ5qYQAFCcLANz3pVphLi1CUx9qRbZzLMV0b4w0p8t20XD/Pbj14Qewui/hkX/5RfBqDVpUKuAYvbJC34ONQGczwBavLYDSjm+Vo/ohslWcE/sqW7oW4mN16W/9/UKyOhfjEHIELj+CwAJ3Dcz8b/H1czr+8td5zycBfPJPeSa/QYMZvFwB0DmWtgTTlZNi9qtKQNLKiqq0uw9lsnoIKbGa6U5d5p3M1EEjLRayiJxI1IEThwt65CGs3n8vjp/fot404OtXwLduy3F6aQO6MbB6KtJiUQi273dssKzC8uNbq7CWSs2JzfbDxhF51KVlIiF1yyxLCXcubAcClxVBYIHDA1ERa/SDiypMmk5V0nZi9zUVVDpeOLHl83NwZqS2KYvRql50tZ6Rhi0yLxZabUlkCb3rEXCdkL78gu+TYRzR3tqCtiPoalPOp/tYyNL25K4Tu6q+d3LcUQfeEeMCaAVo865O/RgnOWAmKkmT17rzhzpxSJsxWoiBy48gsMDhwWZKo1oqaQVk7TQTYyAlpJNjIIkikdcbpKMT8GYjFQslpLmp9hjcb71q8Vka5zIHI8nbSldPZcn4PQ8jJ8J43IDvewyvfGiGq18acfzsEmkzYPWOE3SnCce3zoCqEkFI07jKsTJXesslSwSMrLtd6rhhuWAKAty+CjAXfXmvOHE0Hg1jJMnqwu9RMzZ3CwQuOYLAAocHIpldZXG+AODmtl6/GOn0A9BqrldVIS9XLr6gttFolUHSjqvkFY87emQG1Y22K7XqGwbgvnuQlltwU2F73xzdSYVHfuV1LN9/RchtXuPoy2eYP/Ekctu6atESms0bUSoyjUzpJg4eanvlwZxd54vMvkLQ1tIS1cgWOjkpdlew2R8XkkMhv9gDC+wDgsAChwfLAwNESq+7Vtx1LpEHAJrPhWwogdcrl7F/jQTezHbtOZtNDTLjkliU0e2e8nqD6nwJzFrQmHE0Mo5fu4V8/3XUFyNyW6Na90iv3UJWYYgpG2VOp9VV24jaEfDFZmjb02JWqJLgTVosilei2U2lhDSfefCmO2zYygCgCsbihm/n+jrrdoHApUIQWODwwKKm8ygUqNAhldmXBELqTEwrJ1supvlsx4dQ0oo7b+Xl7RbYbHfiWDx/y/e1VjJnuucauCKR46eE+f/7NGg+x/D8C8ipQjqWNmGaz5Awl32s83Ok+RygtgRXjiNoWn0BvvwsHouty+ipbXVRmT2TzNuOppY0FWbTgNAXFw5m2X+LGVhgDxAEFjg8WAVWpZKvpUo8BpwUnNx0VmZzMl8SrmudE8kxeL3xWBRwLvO07Rbp5AT54kLOry3I8eYt0Nm5/KwejJx0TnZ0JMdUoQV3fYleUak/zCGeMzgzWIUZ1Iq8HgDG5RLp+Bj5/ELcNyYkam1FryynUSkqtSeoeGOxkOs6XoD6FjQEgQUuP2KROXB4IHIRh/w6Wd41z0Ga+iSObhXlir7Mk/c0oPlM5ObjpA2nVUuazYS8KPlSscjxq52qx87lbvRV5YnOrHJ3T3DOvGN9ldrSMvTzVxWq69ddUs9dh3xxIWS43bpjvbnrexWqrvjUNtJ+nM28wnRCjRlYYA8QFVjg8GCiBnN+T0nUe8miVKSKyXqTt1RiF3xoYjJVlVRsTQ2Qmu42daliJsa3tiBt86ydDC87Bszglz2TyxWARMjrdTlOlUSKr0pE7jonI1cQmhsH8AbWVWVvbLxYIrWN+zGaI79knTXu+kFEYCIxK95EBRa4/AgCCxwcmFnmVVUloodOFn+prjFeLEvuFcrSr4kcmBnp9FSITiXlMvdauWDCkp596TczGKwCEZ05WbTKTDPBiKTVaK1DnWnJXE1amOnk5Gt8CMezC7n2CblaVUdmewUUn0Yjqc3Wr7M6OVYjYBGGoCvuIj4na9s7gjy/Vf86gcA3D9FCDBwctg8f4Sv/+bfjlf/4zxUxh7bp0nzmzhUAwOt1cWpX+XperYQE1EeQh0FajBp/4nEoeWLF1DZSEZmn4hQWgDmzhWoVW9iCtcnlJ7ZVAKQNqG1FV1VaEOU4TtKkVYlo7ceq0gyxVkyHWcl1vd5NXdYv0uTqnfZkILAHCAILHBzmr49ozwCugOrhB+Qmr/ZLrHMvW2QmrcakJZdL4GNKqjKsfBeLrDXYNPJY03gFZM+lSQqzydJt98ryuFw8spksV5tjvLrc8zAgHR3JcY+PREVoe19mJZUKMfvumFZayOz5ZSaVp8ViR05vs0EP38xc2pSxyBzYAwSBBQ4O/XGFsQVuPLHC8OwLyOu1Sst1adck5TmDL5blpt+bUz1pW02EE7Ln1XnlBOwa+1r1tZNk3PfuIs/65dWUgkcJu2TmclxNbS5ikox8duE/WwaZE/F67WIRE2kQkVhiJWllZnO5X6+LUTCKDZavFahdlvwNvqn/JIHAtwRBYIGDQ8rAA7+zBv3m50rVpAvH1DZ+o+dhkHBJE0Qwe2KxkY95CgIo7T0lK3udV0PT9h9ziWGx1h6wk5ZMbePnJ1VO+vHN7Z4Z6XhR2oXmmKFqQZDO4XxepwGWFh1TVUjHMne7M0WaFnNpic5m8rltTaCJ0XhgPxAEFjg4VKsBpO2w9N53Ip0ca0yJRJBQ23pbj5rab/jTvCyJLNlNMeau26myzN1jKq03EjIJuz3ulY16KZoxMDW1k50rDdvGLaJ2CBQoPoYqv5/K8B2aPeZtwL7fsaDK2y2oElWl7cCl2axcpxFiIHDJEQQWODyMI5qvvAIAyF/8cllMnsuul8+A1IWe2lZ2oKYkYEGXSmIWNzI1zmWzlLIssZxlxqWiCzf59YXp0duUO7tptlhdVaDFHGja0uLcbkXq3jY+n5PF587TlL0taCTZNDuL2qgqbzkCk124zKVtatcxjhhv3Q4VYmAvEL2CwMEiHR25wW1qG6DvpLWmsSGm5rOZkMeqWEKxOb0bsWUGY/TXgpUApm7wRiJWqelzMttqdkiFdBF6GobJm61XhXY9tivm8v26LvySElJdeygnb7ci1jDz4nEUVSXJuVjdPWgx9+gY0uOaW0d15Qpw/i37ZwkEvmkIAgscHnJGfv2mV0KyN6U+iNoSNFPfHQGEwSqnqcrP/AVRbKmM4Hz3azLfkh20vsykEpWdK201mojCHOO5H2RGt9lO/Bq1pZh5x90jLebSWtSqz+ZbtnRtpEtNLftlk7mai05GlHYq4IITTCvRQOASI1qIgcMDTyokYOeGzMMgZNH1Ze9J4TfzplEikDZfmmmysS0Pd72KKCYmwE1TnDnGLOSipIUJAdqSs83TbB/MY0z09+r0VEmpkePZPMzUhzYX0/BKi3exHbDSTlQLKRWJJM1GM99E3m6l8rPdOJ2dhZVUYB8QBBY4SGQ15gUgRrVGNiqnZ92N8j2uut6ZjU0f91mW5nUhZ1lCVpm6ZIaJ/yB3HdLxQtKYdZ+K2lYWpo1UbJ/LKrZeyM7nYiTP54ulV1nTCsqd421BWsM0qUrF45GzqAlZqjVRKYoSM50cy4xukm1G81mxwZqIWQKBy4wgsMBBwiXpicQ5vkq7O1xaEdnN3Rab3UC3aXzuZJXQtLXIm60KKnKJKyGdd5l3oR2jqsRCKqVS2WilkyyE0udYEmfCqhS0WBT/XCZxt1afiTdYqr58+47hVSfBnq5I1JmaXUfebl1yL/6RjTwXBBbYAwSBBQ4PKlagquxNubs74NZMsr8lu2FpNiuCDEqgWevGv243NYlckcyxsbQhezEIlmpP25Az2T+jKilRSASLS+0tZ2yxkGPY3MpIqZmQycRf0X72hecJkYnMnkobU8FbyS/j5WpHaGKrALAZns0Mg78Ce4AgsMBBwkxz82ol5JWoWDoNgwsjeMxSkVgasQZdouuLgS7grTxvL2qrDVXlCcmyT5adoHjbIS/XkrO1mAthmaBjuheWs6c5u3wfKPZOwNfO8yZWVdLCLDJ5F5joc2bya7Mv7jr/W/jxTNzR9d5mDQQuO0KFGDhI2M09nZ5KsOXFUlt6qcyyoNWOWSgZwam3oFVf1LZONDwM0irMDJppJXV66hEsgKgKkUQ0QXOt7HJRIHqLcZJNlrdi92TxKRZt4m71vYg7krrGExHyaiVEbMvJ3jbVpOjNtrQslcStVelVnl23VX9tIxVnVGCBPUBUYIGDBBGJsMKiUppGb/ZJLJM0wNL2orwNpztY+WIpJGBOHHnitjGfgeYzcfdQBR8Pg8yupopHFXVMTXZNRehzNRNikIgrxC2Eyn6axaBodErWY/rcrOuF+Cy2xXK9+qHM5yZ7bHm7dVWlL1+vVjsei3mzuUv/SoHAnw1BYIHDAzMwm4GuXvE5mM2qXKiwWqsLR1NmQr3MvNLpiZBA1yMdHUkbkrnsjUEWkz3SZGI5xdttURRS8jgTO7e3D4nkvVPHDEquOCStCrkfMHzX+50YebsVFSER6PhIyDUl2RGrayHLobQTS2RKcjcPZhaC6ydtU0D+Tm270zoNBC4zgsAChwdmEVlcLIUU2gaYzUqiMlBUf/aWMZf5U9eXyBTAicRIxaqxpJVYiTKZeC2qRD4viwDECCYvV3Jc3R0zxWJerXzBGnWN8eZNrP7ah9B8/jmkk2Ok4wWq69dFXj8M3iI04YZDpfsim5frocVC26XisE9N7dctH0YUkj4zjBFYYA8QM7DA4YFI2oRtK2rCMQN9J5WLCTqy+ggCLlVnFVO4OMJsnpiBVuZmrmBUWb3viOVcJO6AVD36HTmLXF69ENN85s7xgO6paQtvPLuQBGX1b9yeVjgBgFSBlyvkrkd15cStn3gYwJBlayNCAMUpXyvQfH4un3/T+QzNJfjDsKNsBABcfKv/kQKBPzuCwAKHhySLuRhHsJnVVhVQ18B2C4yQNh/KDpUsDyfwtvOWGwC3b+Ku23HwyOuNVGBquuvLxZorZjtdlGWnzOye7Pr8PdYynEjrsZgjv/Qy0vEx7vlfPi+iEEjVmOapkHDTgIByPYu5kJWRlyY+I5GIWQBRSy5XSFevqPu95oVxRlosSghmILAHiBZi4PAwEVwgVSqBZyEvy8myHK1xdBEHbzu3eeLN1pWKsEwxtZJCVSGpe72REmviskSXmJEvuWzdDYOVVKipZXfs6MgjUsyRHusNqitX3K7KLKDKZ9L06Co50VJViWR/Yk6MzELaD9wnpPTgvaXSu3XL/RfTfIbq2jWffXmQZyBwyREEFjg87CjvdNmYJyrCI1kc9nmYfa+URCw0Ultt3HUihe9LsCX3MtPK262QSduqUKJ1AqC2leopc/EwVFIUr8QEbLcl6qSqXDkpqkVyd3oAnraMrM7zm20RY6g60txE6OoV8DBgfPU1jE8+ha9+/C9g/MKfeAZZOjpSuX8qrciuk5WCpg0njsBeIAgscHggSOUBvPGNeLobBUj7MJGTkhMBUEjD3toP7sDhrTbW7C51c6emdi9EqtJOEjQPg8/QXO1nrcS+3/FLtPdbuKaHbWr6Ms1noqR0FaUQLHcdMAzoP/JtAIBnfu57sL0KfOW//m45tDrZExHyxYVEzqzXLsMfX3klAi0De4EgsMBhgpKQmCr6oIa3lqnlFZlCVIipCBxyljyxejImTqVdN80AAyWR3M/K0rEtLqNpZDZl7/V0517TnMeSgKwCDKvErHUp8vtUlIbqam8u9TyOyOfn5X0AcOMa7v/k0/iVF34P7W3g0U/+O7zrv/oNpNPT4sRvHo08kc9nlnlZVGCBPUCIOAKHCbNsqmsJslSRBkwu37O0y8bRyUwyvbQSs30qUwrOZ+Axl/BIYOJdmEDHR6IctDRmTIx3p8nH5qIxmZ8BkNah+SJa0rM6xqfZTD0RE5AyKAkJkoZzghlJ06bp9BTb73gnZq+u8RtPvg9/7rPfhrZWRxKr0HTx2ZeuiZCXS1A/SMjlGNVXYD8QFVjgroGIHiWizxLRF4joCSL6aX3854joeSL6Pf36q5P3/CwRPUVETxLRD76lE5lT+zgW8rJASfUB5HEErzfanstOFpLLlUqwo7YF83It1ZVK4Xm9Li4XjXgIwiT36kJvbhisx95RG6obvgdpNmIEbNWfhHBWpe1oNlaAx8DYbpnL4QHkszMsnnwJz/+la6B1hff9s6/i0X/6pH7mrKbDbXHf10XqdHTkuWRTh5BA4DIjKrDA3cQA4G8x8+8S0SmAf09Ev6rP/QNm/m+nLyaiDwL4GIBvB/AwgP+TiD7AzG8SGTzJ1eIsX/aOlLxdSGrZ5Oq/cZQqaCazpTx1h6dCcu5snxLS0ZGQjGZrQdOWPcSSZdfMCWu6PG3SenPBB5DVesrk+Gkxd8Uh9wPY5Pf6fJ4kNwM631qv8ei/fAbIGeNLL2um2WRPbUKkPHGmxzi6xVZ4IQb2AUFggbsGZn4RwIv68zkRfQHAI9/gLR8F8IvMvAXwNBE9BeAjAH7jTU+m4Y0YobOhYSc0krsOrGnGJkuH2j9hHMVcdyLusPmTLACTpx+bEz3pc24bxQx4pIssStNiLsdeb5BmM9DRUUla1vf5zM2so9TP0IMtJ5Enon5sSnWVCOggllhbqeyoFUWhL2QTFYFGIqAbRMTRda7OlOXraCMGLj+ihRh4W0BE7wbwnQB+Sx/6KSL6HBH9PBFd18ceAfDs5G3P4RsTnh4cwGwm8y91jkdTl1ZiIml614QbAAAFyUlEQVTXLeYu6jBi8ypFqzcicg9BACXZua7VsqmRttxkAdiyyDwrLO0mLO+oCSffMZmLcdeVqtAyutRD0T+m7Wup2a/vmpmYI2cnLO76Qo52/n4oZsJtKzZYdS1WWtFCDOwBgsACdx1EdALgXwH4m8x8BuAfAXgfgA9BKrS/Zy99g7e/YWlARJ8goseJ6PEub4R47CY8yb7irveKyhaFTclnKcmAxoqYSGIywwJn5K5H7nppM643JUtMKyxTCPr+V87Iy1UJoKwqbUXmQnK5SPGprj3BGVPF4ySF2UnvDdKTraW589ikHSpJ0hIZ4y1DMzs28csdKs1A4DIiCCxwV0FEDYS8/jkz/2sAYOaXmHlk5gzgH0PahIBUXI9O3v4OAC+80XGZ+VPM/GFm/nBLMjfirVgl7RjdqpuGZ26pm7zL2K0qGgbZ5arrYsRrX7rnlVcr3wszZ3pfQFaJvKU6l2VkUfzpRe+ET6KqwOu1vEcJzpWD5ldoZrw295qIO9xoOE3JSFqYaT6TpWud9/numAlYtp0IXuxxiltD4PIj/lcauGsgKYn+CYAvMPPfnzz+0ORlPwrgD/XnzwD4GBHNiOg9AB4D8Ntv6WR9B6h4washU/NpG9CWjlFVIF08BjSryyqfplExhqgRXfkHMeGtTo5RnZ6WGZPOuADNA+u1nahJznm5ln2rvi8CkkmFaEnS6JX0lIAsNgWAz9QASCVI5JllRlD6d5Xr0fZhWsxdQYkkhse83QJNLe1ItbFyQg8ELjlCxBG4m/heAD8O4A+I6Pf0sb8N4K8T0Ycg7cEvA/gbAMDMTxDRpwF8HqJg/Mk3VyAqSGyazMkC0JnRxCaKu05UhFolucTc2nLmNG+HbFtpEap4w+dUShzJ23GVKxxJTlYyvkxEoaQk8zI1C65rITWNQ3Evximm12QO+E0j12yCkDuUiZXaSvF66xloyPIZ3fDXhCq2Gxd5KoE9AN3ZKw8E9h1X6/v4u698VH6ZqulUsOGSd2v3WXKxKQ4Bl6nvLDQ3jRDiZrtj+eS+iyqe8ARmoFQy1t6zJWWrovS4Uym9XPekSlTinMLID0QlQ8zIzUjQZmXqjk9TMjZBR1MLeQGSm6btyN+4/T/j9vhqKDkClxpBYIGDAxGdA3jy7b6Ot4h7Abz6dl/EG+BdzHzf230RgcA3QrQQA4eIJ5n5w2/3RbwVENHj+3KtgcBlQ4g4AoFAILCXCAILBAKBwF4iCCxwiPjU230Bfwrs07UGApcKIeIIBAKBwF4iKrBAIBAI7CWCwAIHAyL6Ic0Ne4qIfubtvp47QURfJqI/0Myzx/WxG0T0q0T0J/r9+psdJxAICILAAgcBIqoA/HcAfhjAByHuHh98e6/qDfEfMvOHJtL5nwHwa8z8GIBf098DgcBbQBBY4FDwEQBPMfOXmLkD8IuQPLHLjo8C+AX9+RcA/MjbeC2BwF4hCCxwKPj/lx12d8EA/g8i+vdE9Al97AEN+rTAz/vftqsLBPYM4cQROBS85eywtxHfy8wvENH9AH6ViP7o7b6gQGCfERVY4FDwlrPD3i4w8wv6/WUA/wbS9nzJ4mT0+8tv3xUGAvuFILDAoeB3ADxGRO8hohbAxyB5YpcCRHRMRKf2M4AfgOSefQbAx/VlHwfwS2/PFQYC+4doIQYOAsw8ENFPAfgVABWAn2fmJ97my5riAQD/RkMzawD/EzP/70T0OwA+TUQ/AeAZAD/2Nl5jILBXCCeOQCAQCOwlooUYCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAvEQQWCAQCgb1EEFggEAgE9hJBYIFAIBDYSwSBBQKBQGAv8f8Bo+qwuHDki7gAAAAASUVORK5CYII=\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "                                                                         rf_crs(tile)  \\\n",
+       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "\n",
+       "                                                                  rf_extent(tile)  \\\n",
+       "0  (14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)      \n",
+       "1  (14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)    \n",
+       "2  (14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)    \n",
+       "3  (14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)    \n",
+       "4  (14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)    \n",
+       "5  (15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)     \n",
+       "6  (15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)    \n",
+       "7  (15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)    \n",
+       "8  (15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)   \n",
+       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                   tile2  \\\n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])   \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])   \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])   \n",
+       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])   \n",
+       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])   \n",
+       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])   \n",
+       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])   \n",
+       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])   \n",
+       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                       \n",
+       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                        \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                  threex  \n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3675 3732 3741 ... 3915 3735 3618]\\n [3498 3564 3570 ... 4143 3753 3579]\\n [3468 3330 3366 ... 3744 3735 3810]\\n ...\\n [4455 5247 5283 ... 3102 2988 2994]\\n [5340 5331 4989 ... 3024 3081 3522]\\n [5184 4941 4686 ... 3567 3891 4146]])  \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3618 3420 3681 ... 5199 4830 4674]\\n [3579 3474 3345 ... 4806 5277 5472]\\n [3810 3462 3210 ... 5445 5259 5535]\\n ...\\n [2994 3255 3498 ... 5274 5265 5103]\\n [3522 3906 3876 ... 5268 5046 4806]\\n [4146 3864 3627 ... 5067 4683 4545]])  \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[4674 4638 4335 ... 5070 5073 5145]\\n [5472 5349 5286 ... 5445 5388 5337]\\n [5535 5319 4968 ... 5151 5235 5508]\\n ...\\n [5103 4869 4860 ... 4206 4056 4326]\\n [4806 4890 4881 ... 4122 4251 4260]\\n [4545 4569 4455 ... 4221 4107 3930]])  \n",
+       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[5145 5295 5025 ... 3303 3420 3447]\\n [5337 5400 5442 ... 3543 3636 3720]\\n [5508 5403 5337 ... 3186 3639 3690]\\n ...\\n [4326 4434 4338 ... 5367 5244 5220]\\n [4260 4152 4059 ... 5235 5190 5223]\\n [3930 3684 3828 ... 5184 5202 5052]])  \n",
+       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3447 3513 3816 ... 1839 1851 1866]\\n [3720 3822 3852 ... 2055 1917 1953]\\n [3690 3678 3519 ... 2007 1947 1902]\\n ...\\n [5220 5328 5058 ... 3648 3507 3348]\\n [5223 4998 4608 ... 3555 3198 3282]\\n [5052 4581 4731 ... 3255 3306 3273]])  \n",
+       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1866 1878 1881 ... 1092  849  744]\\n [1953 2019 2106 ...  792  738  744]\\n [1902 2325 2313 ...  738  780  795]\\n ...\\n [3348 3294 3303 ... 2913 2778 2817]\\n [3282 3279 3342 ... 3039 3063 2700]\\n [3273 3294 3381 ... 3138 2853 2442]])  \n",
+       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 744  684  753 ... 3168 3237 3138]\\n [ 744  741  810 ... 3372 3438 3564]\\n [ 795  837  840 ... 3138 3534 3870]\\n ...\\n [2817 2958 3291 ... 3246 3198 3243]\\n [2700 3045 3789 ... 3090 3027 2883]\\n [2442 2988 4287 ... 2964 2694 2400]])  \n",
+       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3138 3264 3720 ... 2238 2481 2628]\\n [3564 3846 3900 ... 2502 2298 1995]\\n [3870 3798 3672 ... 1989 2052 1929]\\n ...\\n [3243 3027 3000 ... 1881 1740 1584]\\n [2883 2823 2805 ... 1821 1722 1830]\\n [2400 2595 2514 ... 1359 1341 1572]])  \n",
+       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[2238 2250 2226 ...  432  351  336]\\n [1875 1899 2067 ...  312  552  429]\\n [1878 2022 1998 ...  483  441  513]\\n ...\\n [1758 1779 1779 ...  198  141  168]\\n [1755 1629 1548 ...  159  132  201]\\n [1545 1362 1347 ...  162  153  150]])  \n",
+       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[402 531 543 ... 138  99  75]\\n [438 372 462 ... 129 156 114]\\n [525 507 483 ... 141  75 114]\\n ...\\n [162 147 192 ... 132 141 138]\\n [159 156 129 ... 171 147 129]\\n [138 138 144 ... 150 147  90]])                                       "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pandas_df['threex'] = pandas_df.tile2.apply(lambda t: t * 3)\n",
+    "pandas_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9d1b2f4a9d7355ffd60d2c14fb04aaf69faf2ba6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 14 Jun 2019 14:27:13 -0400
Subject: [PATCH 158/380] Add Python Tile png and html display methods for
 IPython

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/rf_types.py    |   95 +-
 .../src/main/python/requirements.txt          |    1 +
 .../pretty_rendering_rf_types.tile.ipynb      | 1434 +++++++++++------
 3 files changed, 1034 insertions(+), 496 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 41b5c0efb..1637c551a 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -349,9 +349,102 @@ def __div__(self, right):
         return self.__truediv__(right)
 
     def dimensions(self):
-        # list of cols, rows as is conventional in GeoTrellis and RasterFrames
+        """ Return a list of cols, rows as is conventional in GeoTrellis and RasterFrames."""
         return [self.cells.shape[1], self.cells.shape[0]]
 
+    def to_png(self):
+        """ Provide image of Tile."""
+        if self.cells is None:
+            return None
+
+        import io
+        from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+        from matplotlib.figure import Figure
+
+        # Set up matplotlib objects
+        [width, height] = self.dimensions()
+        dpi = 300
+        nominal_size = 5.5  # approx full size for a 256x256 tile
+        fig = Figure(figsize=(nominal_size * width / dpi,   nominal_size * height / dpi))
+        canvas = FigureCanvas(fig)
+        axis = fig.add_subplot(1, 1, 1)
+
+        axis.imshow(self.cells)
+        axis.set_aspect('equal')
+        axis.xaxis.set_ticks([])
+        axis.yaxis.set_ticks([])
+
+        axis.set_title('{}, {})'.format(self.dimensions(), self.cell_type.__repr__()))  # compact metadata as title
+
+        with io.BytesIO() as output:
+            canvas.print_png(output)
+            return output.getvalue()
+
+    def to_html(self):
+        """ Provide HTML string representation of Tile image."""
+        import base64
+        b64_img_html = '<img src="data:image/png;base64,{}" />'
+        png_bits = self.to_png()
+        b64_png = base64.b64encode(png_bits).decode('utf-8').replace('\n', '')
+        return b64_img_html.format(b64_png)
+
+    def _repr_png_(self):
+            """Provide default PNG rendering in IPython and Jupyter"""
+            return self.to_png()
+
+    @classmethod
+    def pandas_df_to_html(cls, df):
+        """
+        Provide HTML formatting for pandas.DataFrame or pandas.Series of Tile
+        """
+        import pandas as pd
+        # honor the existing options on display
+        if not pd.get_option("display.notebook_repr_html"):
+            return None
+
+        if len(df) == 0:
+            return df._repr_html_()
+
+
+        tile_cols = []
+        for c in df.columns:
+            if isinstance(df.iloc[0][c], cls):  # if the first is a Tile try formatting
+                tile_cols.append(c)
+
+        def tile_to_html(t):
+            if isinstance(t, Tile):
+                return t.to_html()
+            else:
+                return t.__repr__()
+
+        # dict keyed by column with custom rendering function
+        formatter = {c: tile_to_html for c in tile_cols}
+
+        # This is needed to avoid our tile being rendered as `<img src="only up to fifty char...`
+        default_max_colwidth = pd.get_option('display.max_colwidth')  # we'll try to politely put it back
+        pd.set_option('display.max_colwidth', -1)
+        return_html = df.to_html(escape=False,  # means our `< img` does not get changed to `&lt; img`
+                          formatters=formatter,  # apply custom format to columns
+                          render_links=True,  # common in raster frames
+                          notebook=True,
+                          max_rows=pd.get_option("display.max_rows"),  # retain existing options
+                          max_cols=pd.get_option("display.max_columns"),
+                          show_dimensions=pd.get_option("display.show_dimensions"),
+                          )
+        pd.set_option('display.max_colwidth', default_max_colwidth)
+        return return_html
+
+
+try:
+    from IPython import get_ipython
+    # modifications to currently running ipython session, if we are in one; these enable nicer visualization for Pandas
+    if get_ipython() is not None:
+        import pandas
+        html_formatter = get_ipython().display_formatter.formatters['text/html']
+        html_formatter.for_type(pandas.DataFrame, Tile.pandas_df_to_html)
+except ImportError:
+    pass
+
 
 class TileUDT(UserDefinedType):
     @classmethod
diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index c4bb2804e..44ea6904b 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -3,3 +3,4 @@ shapely
 pyspark==2.3.3
 numpy>=1.7
 pandas
+matplotlib
diff --git a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
index cf90008bd..dd13f9af1 100644
--- a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
+++ b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
@@ -11,7 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setup Spark Environment"
+    "### Setup Spark Environment\n",
+    "\n",
+    "Minimal imports"
    ]
   },
   {
@@ -21,8 +23,7 @@
    "outputs": [],
    "source": [
     "from pyspark.sql import SparkSession\n",
-    "from pyrasterframes import *\n",
-    "from pyrasterframes.rasterfunctions import *\n",
+    "import pyrasterframes\n",
     "\n",
     "spark = SparkSession.builder \\\n",
     "    .withKryoSerialization() \\\n",
@@ -30,27 +31,6 @@
     "    .withRasterFrames()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'scalaVersion': '2.11.12', 'builtAtMillis': '1560432286838', 'sbtVersion': '1.2.8', 'name': 'core', 'rfSparkVersion': '2.3.2', 'moduleName': 'rasterframes', 'rfGeoMesaVersion': '2.2.1', 'rfGeoTrellisVersion': '2.2.0', 'version': '0.8.0-SNAPSHOT', 'builtAtString': '2019-06-13 13:24:46.838'}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pyrasterframes import RFContext\n",
-    "RFContext.active().build_info()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -60,21 +40,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "href1 = \"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\"\n",
     "\n",
     "from pyspark.sql.functions import col\n",
+    "from pyrasterframes.rasterfunctions import rf_local_add_int\n",
     "# this here contortion is to force the RasterRef to evaluate; and to get the tile type \n",
-    "df = spark.read.rastersource(href1).withColumn('tile2', \n",
-    "                                               rf_local_add_int(col('tile').tile, 0))"
+    "df = spark.read.rastersource(href1)\\\n",
+    "        .withColumn('tile2', rf_local_add_int(col('tile').tile, 0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -106,92 +87,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Boo!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
-      "|tile_path                                                                                                     |tile                                                                                                                                                                                                                                                                                                                                                                                                                                      |tile2                                                                                                                               |\n",
-      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
-      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.4455356755667E7, -2342509.0947641465, 1.4573964811098041E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.4455356755667E7, -2342509.0947641465, 1.4573964811098041E7, -2223901.039333))))]    |[int16ud32767, (256,256), [1225,1244,1247,1222,1189,1216,1206,1185,1132,1040,...,1575,1489,1281,1189,1202,1145,1171,1189,1297,1382]]|\n",
-      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.457396481109804E7, -2342509.0947641465, 1.4692572866529081E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.457396481109804E7, -2342509.0947641465, 1.4692572866529081E7, -2223901.039333))))]|[int16ud32767, (257,256), [1206,1140,1227,1147,1106,1026,994,1047,1020,1174,...,1793,1743,1685,1688,1706,1727,1766,1689,1561,1515]] |\n",
-      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF|[[[1.469257286652908E7, -2342509.0947641465, 1.4811180921960121E7, -2223901.039333], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], RasterRefTile(RasterRef(JVMGeoTiffRasterSource(https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF),0,Some(Extent(1.469257286652908E7, -2342509.0947641465, 1.4811180921960121E7, -2223901.039333))))]|[int16ud32767, (257,256), [1558,1546,1445,1329,1539,1653,1576,1533,1603,1610,...,1399,1434,1330,1429,1470,1451,1422,1407,1369,1310]]|\n",
-      "+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+\n",
-      "only showing top 3 rows\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.show(3, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Make it pretty\n",
+    "# Rendering of python object in Jupyter / IPython  \n",
     "\n",
-    "To add this to the Tile class we can override `_repr_png_` (or `_repr_svg_` or others) to accomplish this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tile_to_png(t):\n",
-    "    # https://gist.github.com/rduplain/1641344\n",
-    "    import io\n",
-    "    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
-    "    from matplotlib.figure import Figure\n",
-    "    from matplotlib.pyplot import imshow\n",
+    "A `pyrasterframes.rf_types.Tile` will automatically render nicely in Jupyter or IPython.\n",
     "\n",
-    "    fig=Figure()\n",
-    "    canvas=FigureCanvas(fig)\n",
-    "    \n",
-    "    axis =  fig.add_subplot(1,1,1)\n",
-    "    array = t.cells\n",
-    "\n",
-    "    axis.imshow(array)\n",
-    "    \n",
-    "    output = io.BytesIO()\n",
-    "    canvas.print_png(output)\n",
-    "    return output.getvalue()"
+    "A `pandas.DataFrame` containing a `Tile` column will automatically render nicely in Jupyter or IPython."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://ipython.readthedocs.io/en/stable/config/integrating.html#integrating-rich-display\n",
-    "# https://ipython.readthedocs.io/en/stable/config/integrating.html#formatters-for-third-party-types\n",
-    "import pyrasterframes.rf_types\n",
-    "png_formatter = get_ipython().display_formatter.formatters['image/png']\n",
-    "png_formatter.for_type(pyrasterframes.rf_types.Tile, tile_to_png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get a tile, I'm from Missouri!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,12 +105,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "Tile(masked_array(\n",
        "  data=[[1225, 1244, 1247, ..., 1305, 1245, 1206],\n",
@@ -220,7 +125,7 @@
        "  dtype=int16), int16ud32767)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,146 +138,60 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Boom!\n",
-    "\n",
-    "# Now do it with Pandas DF"
+    "You can also still access the string representation easily."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pandas_df = df.select(rf_crs(df.tile), \n",
-    "                      rf_extent(df.tile), \n",
-    "                      df.tile2).limit(10).toPandas()"
+    "str(tile)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def tile_to_html(t):\n",
-    "    import base64\n",
-    "    b64_img_html = '<img src=\"data:image/png;base64,{}\" />'\n",
-    "    \n",
-    "    bitz = tile_to_png(t)\n",
-    "    b64_img = base64.b64encode(bitz).decode('utf-8').replace('\\n', '')\n",
-    "    return b64_img_html.format(b64_img)"
+    "## `pandas.DataFrame` example"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "This is less general as we have to construct this call each time... Also need to enforce this max col width option."
+    "from pyrasterframes.rasterfunctions import rf_crs, rf_extent\n",
+    "pandas_df = df.select(df.tile_path,\n",
+    "                      rf_crs(df.tile), \n",
+    "                      rf_extent(df.tile), \n",
+    "                      df.tile2\n",
+    "                     ).limit(10).toPandas()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rf_crs(tile)</th>\n",
-       "      <th>rf_extent(tile)</th>\n",
-       "      <th>tile2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
-       "      <td><img 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\" 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-       "    </tr>\n",
-       "  </tbody>\n",
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-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from IPython.display import HTML\n",
-    "import pandas as pd\n",
-    "pd.options.display.max_colwidth = -1\n",
-    "HTML(pandas_df.to_html(escape=False, formatters=dict(tile2=tile_to_html)))"
+    "In this output you may like to double-click a cell in the `tile2` column to \"expand\" the rows to full size rendering of the tile image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -398,6 +217,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>tile_path</th>\n",
        "      <th>rf_crs(tile)</th>\n",
        "      <th>rf_extent(tile)</th>\n",
        "      <th>tile2</th>\n",
@@ -406,436 +226,1060 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
-       "      <td>Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                         rf_crs(tile)  \\\n",
-       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
+       "                                           tile_path  \\\n",
+       "0  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "1  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "2  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "3  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "4  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "5  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "6  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "7  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "8  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "9  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "\n",
+       "                                        rf_crs(tile)  \\\n",
+       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
+       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
        "\n",
-       "                                                                  rf_extent(tile)  \\\n",
-       "0  (14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)      \n",
-       "1  (14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)    \n",
-       "2  (14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)    \n",
-       "3  (14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)    \n",
-       "4  (14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)    \n",
-       "5  (15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)     \n",
-       "6  (15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)    \n",
-       "7  (15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)    \n",
-       "8  (15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)   \n",
-       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
+       "                                     rf_extent(tile)  \\\n",
+       "0  (14455356.755667, -2342509.0947641465, 1457396...   \n",
+       "1  (14573964.81109804, -2342509.0947641465, 14692...   \n",
+       "2  (14692572.86652908, -2342509.0947641465, 14811...   \n",
+       "3  (14811180.92196012, -2342509.0947641465, 14929...   \n",
+       "4  (14929788.97739116, -2342509.0947641465, 15048...   \n",
+       "5  (15048397.0328222, -2342509.0947641465, 151670...   \n",
+       "6  (15167005.08825324, -2342509.0947641465, 15285...   \n",
+       "7  (15285613.14368428, -2342509.0947641465, 15404...   \n",
+       "8  (15404221.199115321, -2342509.0947641465, 1552...   \n",
+       "9  (15522829.254546361, -2342509.0947641465, 1556...   \n",
        "\n",
-       "                                                                                                                                                                                                                                                                                                                   tile2  \n",
-       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])  \n",
-       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])  \n",
-       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])  \n",
-       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])  \n",
-       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])  \n",
-       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])  \n",
-       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])  \n",
-       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])  \n",
-       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                      \n",
-       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                       "
+       "                                               tile2  \n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "3  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "4  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "5  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "6  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "7  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "8  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "9  Tile(dimensions=[96, 256], cell_type=CellType(...  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Default viz remains\n",
     "pandas_df"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def pd_tile_to_html(dataframe):\n",
-    "    from IPython.display import HTML\n",
-    "    import pandas as pd\n",
-    "    pd.options.display.max_colwidth = -1\n",
-    "    \n",
-    "    tile_cols = []\n",
-    "    for c in dataframe.columns:\n",
-    "        if isinstance(dataframe.iloc[0][c], pyrasterframes.rf_types.Tile):\n",
-    "            tile_cols.append(c)\n",
-    "    # dict keyed by column with custome rendering function\n",
-    "    formatter = {c: tile_to_html for c in tile_cols}\n",
-    "    return dataframe.to_html(escape=False, formatters=formatter)"
+    "You still get the default string representatation of a `pandas.Series`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<function __main__.pd_tile_to_html(dataframe)>"
+       "tile_path          https://modis-pds.s3.amazonaws.com/MCD43A4.006...\n",
+       "rf_crs(tile)       (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...\n",
+       "rf_extent(tile)    (15404221.199115321, -2342509.0947641465, 1552...\n",
+       "tile2              Tile(dimensions=[256, 256], cell_type=CellType...\n",
+       "Name: 8, dtype: object"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# this would possibly be able to be in an import  scenario?\n",
-    "html_formatter = get_ipython().display_formatter.formatters['text/html']\n",
-    "html_formatter.for_type(pd.DataFrame, pd_tile_to_html)"
+    "pandas_df.iloc[8]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Tile(dimensions=[256, 256], cell_type=CellType...\n",
+       "1    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "2    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "3    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "4    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "5    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "6    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "7    Tile(dimensions=[257, 256], cell_type=CellType...\n",
+       "8    Tile(dimensions=[256, 256], cell_type=CellType...\n",
+       "9    Tile(dimensions=[96, 256], cell_type=CellType(...\n",
+       "Name: tile2, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pandas_df.tile2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And nothing different happens for a `pandas.DataFrame` that doesn't have a `Tile` in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>rf_crs(tile)</th>\n",
-       "      <th>rf_extent(tile)</th>\n",
-       "      <th>tile2</th>\n",
+       "      <th>iata</th>\n",
+       "      <th>airport</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>country</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>cnt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
-       "      <td><img src=\"data:image/png;base64,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\" /></td>\n",
+       "      <td>ORD</td>\n",
+       "      <td>Chicago O'Hare International</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>41.979595</td>\n",
+       "      <td>-87.904464</td>\n",
+       "      <td>25129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>ATL</td>\n",
+       "      <td>William B Hartsfield-Atlanta Intl</td>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.640444</td>\n",
+       "      <td>-84.426944</td>\n",
+       "      <td>21925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>DFW</td>\n",
+       "      <td>Dallas-Fort Worth International</td>\n",
+       "      <td>Dallas-Fort Worth</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>32.895951</td>\n",
+       "      <td>-97.037200</td>\n",
+       "      <td>20662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>PHX</td>\n",
+       "      <td>Phoenix Sky Harbor International</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.434167</td>\n",
+       "      <td>-112.008056</td>\n",
+       "      <td>17290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>DEN</td>\n",
+       "      <td>Denver Intl</td>\n",
+       "      <td>Denver</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>39.858408</td>\n",
+       "      <td>-104.667002</td>\n",
+       "      <td>13781</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>IAH</td>\n",
+       "      <td>George Bush Intercontinental</td>\n",
+       "      <td>Houston</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>29.980472</td>\n",
+       "      <td>-95.339722</td>\n",
+       "      <td>13223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>SFO</td>\n",
+       "      <td>San Francisco International</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.619002</td>\n",
+       "      <td>-122.374843</td>\n",
+       "      <td>12016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>LAX</td>\n",
+       "      <td>Los Angeles International</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.942536</td>\n",
+       "      <td>-118.408074</td>\n",
+       "      <td>11797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>MCO</td>\n",
+       "      <td>Orlando International</td>\n",
+       "      <td>Orlando</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>28.428889</td>\n",
+       "      <td>-81.316028</td>\n",
+       "      <td>10536</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>CLT</td>\n",
+       "      <td>Charlotte/Douglas International</td>\n",
+       "      <td>Charlotte</td>\n",
+       "      <td>NC</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>35.214011</td>\n",
+       "      <td>-80.943126</td>\n",
+       "      <td>10490</td>\n",
        "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "                                                                         rf_crs(tile)  \\\n",
-       "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)   \n",
-       "\n",
-       "                                                                  rf_extent(tile)  \\\n",
-       "0  (14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)      \n",
-       "1  (14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)    \n",
-       "2  (14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)    \n",
-       "3  (14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)    \n",
-       "4  (14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)    \n",
-       "5  (15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)     \n",
-       "6  (15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)    \n",
-       "7  (15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)    \n",
-       "8  (15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)   \n",
-       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
-       "\n",
-       "                                                                                                                                                                                                                                                                                                                   tile2  \n",
-       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])  \n",
-       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])  \n",
-       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])  \n",
-       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])  \n",
-       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])  \n",
-       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])  \n",
-       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])  \n",
-       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])  \n",
-       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                      \n",
-       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                       "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pandas_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rf_crs(tile)</th>\n",
-       "      <th>rf_extent(tile)</th>\n",
-       "      <th>tile2</th>\n",
-       "      <th>threex</th>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>SLC</td>\n",
+       "      <td>Salt Lake City Intl</td>\n",
+       "      <td>Salt Lake City</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.788388</td>\n",
+       "      <td>-111.977773</td>\n",
+       "      <td>9898</td>\n",
        "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>11</th>\n",
+       "      <td>TPA</td>\n",
+       "      <td>Tampa International</td>\n",
+       "      <td>Tampa</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>27.975472</td>\n",
+       "      <td>-82.533250</td>\n",
+       "      <td>9182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>12</th>\n",
+       "      <td>EWR</td>\n",
+       "      <td>Newark Intl</td>\n",
+       "      <td>Newark</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.692497</td>\n",
+       "      <td>-74.168661</td>\n",
+       "      <td>8678</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>13</th>\n",
+       "      <td>LAS</td>\n",
+       "      <td>McCarran International</td>\n",
+       "      <td>Las Vegas</td>\n",
+       "      <td>NV</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>36.080361</td>\n",
+       "      <td>-115.152333</td>\n",
+       "      <td>8523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>14</th>\n",
+       "      <td>PHL</td>\n",
+       "      <td>Philadelphia Intl</td>\n",
+       "      <td>Philadelphia</td>\n",
+       "      <td>PA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>39.871953</td>\n",
+       "      <td>-75.241141</td>\n",
+       "      <td>7965</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>15</th>\n",
+       "      <td>MSP</td>\n",
+       "      <td>Minneapolis-St Paul Intl</td>\n",
+       "      <td>Minneapolis</td>\n",
+       "      <td>MN</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.880547</td>\n",
+       "      <td>-93.216922</td>\n",
+       "      <td>7690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>16</th>\n",
+       "      <td>SEA</td>\n",
+       "      <td>Seattle-Tacoma Intl</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>47.448982</td>\n",
+       "      <td>-122.309313</td>\n",
+       "      <td>7541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>17</th>\n",
+       "      <td>LGA</td>\n",
+       "      <td>LaGuardia</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.777243</td>\n",
+       "      <td>-73.872609</td>\n",
+       "      <td>7392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>18</th>\n",
+       "      <td>MDW</td>\n",
+       "      <td>Chicago Midway</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>41.785983</td>\n",
+       "      <td>-87.752424</td>\n",
+       "      <td>6979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>19</th>\n",
+       "      <td>IAD</td>\n",
+       "      <td>Washington Dulles International</td>\n",
+       "      <td>Chantilly</td>\n",
+       "      <td>VA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>38.944532</td>\n",
+       "      <td>-77.455810</td>\n",
+       "      <td>6779</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <th>20</th>\n",
+       "      <td>SAN</td>\n",
+       "      <td>San Diego International-Lindbergh</td>\n",
+       "      <td>San Diego</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>32.733556</td>\n",
+       "      <td>-117.189657</td>\n",
+       "      <td>6233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>STL</td>\n",
+       "      <td>Lambert-St Louis International</td>\n",
+       "      <td>St Louis</td>\n",
+       "      <td>MO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>38.747687</td>\n",
+       "      <td>-90.359990</td>\n",
+       "      <td>6204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>DTW</td>\n",
+       "      <td>Detroit Metropolitan-Wayne County</td>\n",
+       "      <td>Detroit</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.212059</td>\n",
+       "      <td>-83.348836</td>\n",
+       "      <td>6044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>JFK</td>\n",
+       "      <td>John F Kennedy Intl</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.639751</td>\n",
+       "      <td>-73.778926</td>\n",
+       "      <td>5945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>MIA</td>\n",
+       "      <td>Miami International</td>\n",
+       "      <td>Miami</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>25.793250</td>\n",
+       "      <td>-80.290556</td>\n",
+       "      <td>5907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>BOS</td>\n",
+       "      <td>Gen Edw L Logan Intl</td>\n",
+       "      <td>Boston</td>\n",
+       "      <td>MA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.364348</td>\n",
+       "      <td>-71.005179</td>\n",
+       "      <td>5627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>SMF</td>\n",
+       "      <td>Sacramento International</td>\n",
+       "      <td>Sacramento</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>38.695422</td>\n",
+       "      <td>-121.590767</td>\n",
+       "      <td>4943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BWI</td>\n",
+       "      <td>Baltimore-Washington International</td>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>MD</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>39.175402</td>\n",
+       "      <td>-76.668198</td>\n",
+       "      <td>4749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>SNA</td>\n",
+       "      <td>John Wayne /Orange Co</td>\n",
+       "      <td>Santa Ana</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.675659</td>\n",
+       "      <td>-117.868223</td>\n",
+       "      <td>4616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>MSY</td>\n",
+       "      <td>New Orleans International</td>\n",
+       "      <td>New Orleans</td>\n",
+       "      <td>LA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>29.993389</td>\n",
+       "      <td>-90.258028</td>\n",
+       "      <td>4432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>GTF</td>\n",
+       "      <td>Great Falls Intl</td>\n",
+       "      <td>Great Falls</td>\n",
+       "      <td>MT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>47.482002</td>\n",
+       "      <td>-111.370685</td>\n",
+       "      <td>108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>MEI</td>\n",
+       "      <td>Key</td>\n",
+       "      <td>Meridian</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>32.333133</td>\n",
+       "      <td>-88.751206</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>MQT</td>\n",
+       "      <td>Marquette County Airport</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>46.353639</td>\n",
+       "      <td>-87.395361</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>TEX</td>\n",
+       "      <td>Telluride Regional</td>\n",
+       "      <td>Telluride</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.953759</td>\n",
+       "      <td>-107.908480</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>CHA</td>\n",
+       "      <td>Lovell</td>\n",
+       "      <td>Chattanooga</td>\n",
+       "      <td>TN</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>35.035268</td>\n",
+       "      <td>-85.203788</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>LWS</td>\n",
+       "      <td>Lewiston-Nez Perce County</td>\n",
+       "      <td>Lewiston</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>46.374498</td>\n",
+       "      <td>-117.015394</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>CDC</td>\n",
+       "      <td>Cedar City Muni</td>\n",
+       "      <td>Cedar City</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.700970</td>\n",
+       "      <td>-113.098575</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>ALB</td>\n",
+       "      <td>Albany Cty</td>\n",
+       "      <td>Albany</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.748119</td>\n",
+       "      <td>-73.802979</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>BTV</td>\n",
+       "      <td>Burlington International</td>\n",
+       "      <td>Burlington</td>\n",
+       "      <td>VT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.473004</td>\n",
+       "      <td>-73.150312</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>FCA</td>\n",
+       "      <td>Glacier Park Intl</td>\n",
+       "      <td>Kalispell</td>\n",
+       "      <td>MT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>48.311405</td>\n",
+       "      <td>-114.255069</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>MLB</td>\n",
+       "      <td>Melbourne International</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>28.102750</td>\n",
+       "      <td>-80.645806</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>DAB</td>\n",
+       "      <td>Daytona Beach International</td>\n",
+       "      <td>Daytona Beach</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>29.179917</td>\n",
+       "      <td>-81.058056</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>ABE</td>\n",
+       "      <td>Lehigh Valley International</td>\n",
+       "      <td>Allentown</td>\n",
+       "      <td>PA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>40.652363</td>\n",
+       "      <td>-75.440402</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>DLH</td>\n",
+       "      <td>Duluth International</td>\n",
+       "      <td>Duluth</td>\n",
+       "      <td>MN</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>46.842090</td>\n",
+       "      <td>-92.193649</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>CYS</td>\n",
+       "      <td>Cheyenne</td>\n",
+       "      <td>Cheyenne</td>\n",
+       "      <td>WY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>41.155722</td>\n",
+       "      <td>-104.811838</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>RKS</td>\n",
+       "      <td>Rock Springs-Sweetwater County</td>\n",
+       "      <td>Rock Springs</td>\n",
+       "      <td>WY</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>41.594217</td>\n",
+       "      <td>-109.065193</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>LWB</td>\n",
+       "      <td>Greenbrier Valley</td>\n",
+       "      <td>Lewisburg</td>\n",
+       "      <td>WV</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.858306</td>\n",
+       "      <td>-80.399472</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>CRW</td>\n",
+       "      <td>Yeager</td>\n",
+       "      <td>Charleston</td>\n",
+       "      <td>WV</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>38.373151</td>\n",
+       "      <td>-81.593190</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>BLI</td>\n",
+       "      <td>Bellingham Intl</td>\n",
+       "      <td>Bellingham</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>48.792750</td>\n",
+       "      <td>-122.537528</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>MMH</td>\n",
+       "      <td>Mammoth Yosemite</td>\n",
+       "      <td>Mammoth Lakes</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>37.624049</td>\n",
+       "      <td>-118.837772</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>ATW</td>\n",
+       "      <td>Outagamie County Regional</td>\n",
+       "      <td>Appleton</td>\n",
+       "      <td>WI</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.257408</td>\n",
+       "      <td>-88.519476</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>BKG</td>\n",
+       "      <td>Branson Airport</td>\n",
+       "      <td>Hollister</td>\n",
+       "      <td>MO</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>36.385913</td>\n",
+       "      <td>-92.548828</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>PIE</td>\n",
+       "      <td>St. Petersburg-Clearwater International</td>\n",
+       "      <td>St. Petersburg</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>27.910763</td>\n",
+       "      <td>-82.687439</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>SPS</td>\n",
+       "      <td>Sheppard AFB/Wichita Falls Municipal</td>\n",
+       "      <td>Wichita Falls</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>33.988796</td>\n",
+       "      <td>-98.491893</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>FAY</td>\n",
+       "      <td>Fayetteville Municipal</td>\n",
+       "      <td>Fayetteville</td>\n",
+       "      <td>NC</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.991472</td>\n",
+       "      <td>-78.880000</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>EAU</td>\n",
+       "      <td>Chippewa Valley Regional</td>\n",
+       "      <td>Eau Claire</td>\n",
+       "      <td>WI</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.865257</td>\n",
+       "      <td>-91.485072</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>DBQ</td>\n",
+       "      <td>Dubuque Municipal</td>\n",
+       "      <td>Dubuque</td>\n",
+       "      <td>IA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>42.402959</td>\n",
+       "      <td>-90.709167</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>RST</td>\n",
+       "      <td>Rochester International</td>\n",
+       "      <td>Rochester</td>\n",
+       "      <td>MN</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>43.908826</td>\n",
+       "      <td>-92.497987</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>UTM</td>\n",
+       "      <td>Tunica Municipal Airport</td>\n",
+       "      <td>Tunica</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>34.681499</td>\n",
+       "      <td>-90.348816</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>BIL</td>\n",
+       "      <td>Billings Logan Intl</td>\n",
+       "      <td>Billings</td>\n",
+       "      <td>MT</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>45.807662</td>\n",
+       "      <td>-108.542861</td>\n",
+       "      <td>23</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>"
+       "</table>\n",
+       "<p>221 rows × 8 columns</p>\n",
+       "</div>"
       ],
       "text/plain": [
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-       "\n",
-       "                                                                  rf_extent(tile)  \\\n",
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-       "9  (15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)   \n",
+       "    iata                                  airport               city state  \\\n",
+       "0    ORD             Chicago O'Hare International            Chicago    IL   \n",
+       "1    ATL        William B Hartsfield-Atlanta Intl            Atlanta    GA   \n",
+       "2    DFW          Dallas-Fort Worth International  Dallas-Fort Worth    TX   \n",
+       "3    PHX         Phoenix Sky Harbor International            Phoenix    AZ   \n",
+       "4    DEN                              Denver Intl             Denver    CO   \n",
+       "5    IAH             George Bush Intercontinental            Houston    TX   \n",
+       "6    SFO              San Francisco International      San Francisco    CA   \n",
+       "7    LAX                Los Angeles International        Los Angeles    CA   \n",
+       "8    MCO                    Orlando International            Orlando    FL   \n",
+       "9    CLT          Charlotte/Douglas International          Charlotte    NC   \n",
+       "10   SLC                      Salt Lake City Intl     Salt Lake City    UT   \n",
+       "11   TPA                     Tampa International               Tampa    FL   \n",
+       "12   EWR                              Newark Intl             Newark    NJ   \n",
+       "13   LAS                   McCarran International          Las Vegas    NV   \n",
+       "14   PHL                        Philadelphia Intl       Philadelphia    PA   \n",
+       "15   MSP                 Minneapolis-St Paul Intl        Minneapolis    MN   \n",
+       "16   SEA                      Seattle-Tacoma Intl            Seattle    WA   \n",
+       "17   LGA                                LaGuardia           New York    NY   \n",
+       "18   MDW                           Chicago Midway            Chicago    IL   \n",
+       "19   IAD          Washington Dulles International          Chantilly    VA   \n",
+       "20   SAN       San Diego International-Lindbergh           San Diego    CA   \n",
+       "21   STL           Lambert-St Louis International           St Louis    MO   \n",
+       "22   DTW        Detroit Metropolitan-Wayne County            Detroit    MI   \n",
+       "23   JFK                      John F Kennedy Intl           New York    NY   \n",
+       "24   MIA                      Miami International              Miami    FL   \n",
+       "25   BOS                     Gen Edw L Logan Intl             Boston    MA   \n",
+       "26   SMF                 Sacramento International         Sacramento    CA   \n",
+       "27   BWI       Baltimore-Washington International          Baltimore    MD   \n",
+       "28   SNA                    John Wayne /Orange Co          Santa Ana    CA   \n",
+       "29   MSY               New Orleans International         New Orleans    LA   \n",
+       "..   ...                                      ...                ...   ...   \n",
+       "191  GTF                         Great Falls Intl        Great Falls    MT   \n",
+       "192  MEI                                     Key            Meridian    MS   \n",
+       "193  MQT                 Marquette County Airport                NaN   NaN   \n",
+       "194  TEX                       Telluride Regional          Telluride    CO   \n",
+       "195  CHA                                  Lovell         Chattanooga    TN   \n",
+       "196  LWS                Lewiston-Nez Perce County           Lewiston    ID   \n",
+       "197  CDC                          Cedar City Muni         Cedar City    UT   \n",
+       "198  ALB                               Albany Cty             Albany    NY   \n",
+       "199  BTV                 Burlington International         Burlington    VT   \n",
+       "200  FCA                        Glacier Park Intl          Kalispell    MT   \n",
+       "201  MLB                 Melbourne International           Melbourne    FL   \n",
+       "202  DAB              Daytona Beach International      Daytona Beach    FL   \n",
+       "203  ABE              Lehigh Valley International          Allentown    PA   \n",
+       "204  DLH                     Duluth International             Duluth    MN   \n",
+       "205  CYS                                 Cheyenne           Cheyenne    WY   \n",
+       "206  RKS           Rock Springs-Sweetwater County       Rock Springs    WY   \n",
+       "207  LWB                        Greenbrier Valley          Lewisburg    WV   \n",
+       "208  CRW                                   Yeager         Charleston    WV   \n",
+       "209  BLI                          Bellingham Intl         Bellingham    WA   \n",
+       "210  MMH                         Mammoth Yosemite      Mammoth Lakes    CA   \n",
+       "211  ATW                Outagamie County Regional           Appleton    WI   \n",
+       "212  BKG                          Branson Airport          Hollister    MO   \n",
+       "213  PIE  St. Petersburg-Clearwater International     St. Petersburg    FL   \n",
+       "214  SPS     Sheppard AFB/Wichita Falls Municipal      Wichita Falls    TX   \n",
+       "215  FAY                   Fayetteville Municipal       Fayetteville    NC   \n",
+       "216  EAU                 Chippewa Valley Regional         Eau Claire    WI   \n",
+       "217  DBQ                        Dubuque Municipal            Dubuque    IA   \n",
+       "218  RST                  Rochester International          Rochester    MN   \n",
+       "219  UTM                 Tunica Municipal Airport             Tunica    MS   \n",
+       "220  BIL                      Billings Logan Intl           Billings    MT   \n",
        "\n",
-       "                                                                                                                                                                                                                                                                                                                   tile2  \\\n",
-       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1225 1244 1247 ... 1305 1245 1206]\\n [1166 1188 1190 ... 1381 1251 1193]\\n [1156 1110 1122 ... 1248 1245 1270]\\n ...\\n [1485 1749 1761 ... 1034  996  998]\\n [1780 1777 1663 ... 1008 1027 1174]\\n [1728 1647 1562 ... 1189 1297 1382]])   \n",
-       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1206 1140 1227 ... 1733 1610 1558]\\n [1193 1158 1115 ... 1602 1759 1824]\\n [1270 1154 1070 ... 1815 1753 1845]\\n ...\\n [ 998 1085 1166 ... 1758 1755 1701]\\n [1174 1302 1292 ... 1756 1682 1602]\\n [1382 1288 1209 ... 1689 1561 1515]])   \n",
-       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1558 1546 1445 ... 1690 1691 1715]\\n [1824 1783 1762 ... 1815 1796 1779]\\n [1845 1773 1656 ... 1717 1745 1836]\\n ...\\n [1701 1623 1620 ... 1402 1352 1442]\\n [1602 1630 1627 ... 1374 1417 1420]\\n [1515 1523 1485 ... 1407 1369 1310]])   \n",
-       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1715 1765 1675 ... 1101 1140 1149]\\n [1779 1800 1814 ... 1181 1212 1240]\\n [1836 1801 1779 ... 1062 1213 1230]\\n ...\\n [1442 1478 1446 ... 1789 1748 1740]\\n [1420 1384 1353 ... 1745 1730 1741]\\n [1310 1228 1276 ... 1728 1734 1684]])   \n",
-       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1149 1171 1272 ...  613  617  622]\\n [1240 1274 1284 ...  685  639  651]\\n [1230 1226 1173 ...  669  649  634]\\n ...\\n [1740 1776 1686 ... 1216 1169 1116]\\n [1741 1666 1536 ... 1185 1066 1094]\\n [1684 1527 1577 ... 1085 1102 1091]])   \n",
-       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 622  626  627 ...  364  283  248]\\n [ 651  673  702 ...  264  246  248]\\n [ 634  775  771 ...  246  260  265]\\n ...\\n [1116 1098 1101 ...  971  926  939]\\n [1094 1093 1114 ... 1013 1021  900]\\n [1091 1098 1127 ... 1046  951  814]])   \n",
-       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 248  228  251 ... 1056 1079 1046]\\n [ 248  247  270 ... 1124 1146 1188]\\n [ 265  279  280 ... 1046 1178 1290]\\n ...\\n [ 939  986 1097 ... 1082 1066 1081]\\n [ 900 1015 1263 ... 1030 1009  961]\\n [ 814  996 1429 ...  988  898  800]])   \n",
-       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1046 1088 1240 ...  746  827  876]\\n [1188 1282 1300 ...  834  766  665]\\n [1290 1266 1224 ...  663  684  643]\\n ...\\n [1081 1009 1000 ...  627  580  528]\\n [ 961  941  935 ...  607  574  610]\\n [ 800  865  838 ...  453  447  524]])   \n",
-       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[746 750 742 ... 144 117 112]\\n [625 633 689 ... 104 184 143]\\n [626 674 666 ... 161 147 171]\\n ...\\n [586 593 593 ...  66  47  56]\\n [585 543 516 ...  53  44  67]\\n [515 454 449 ...  54  51  50]])                                       \n",
-       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[134 177 181 ...  46  33  25]\\n [146 124 154 ...  43  52  38]\\n [175 169 161 ...  47  25  38]\\n ...\\n [ 54  49  64 ...  44  47  46]\\n [ 53  52  43 ...  57  49  43]\\n [ 46  46  48 ...  50  49  30]])                                        \n",
+       "    country        lat        long    cnt  \n",
+       "0       USA  41.979595  -87.904464  25129  \n",
+       "1       USA  33.640444  -84.426944  21925  \n",
+       "2       USA  32.895951  -97.037200  20662  \n",
+       "3       USA  33.434167 -112.008056  17290  \n",
+       "4       USA  39.858408 -104.667002  13781  \n",
+       "5       USA  29.980472  -95.339722  13223  \n",
+       "6       USA  37.619002 -122.374843  12016  \n",
+       "7       USA  33.942536 -118.408074  11797  \n",
+       "8       USA  28.428889  -81.316028  10536  \n",
+       "9       USA  35.214011  -80.943126  10490  \n",
+       "10      USA  40.788388 -111.977773   9898  \n",
+       "11      USA  27.975472  -82.533250   9182  \n",
+       "12      USA  40.692497  -74.168661   8678  \n",
+       "13      USA  36.080361 -115.152333   8523  \n",
+       "14      USA  39.871953  -75.241141   7965  \n",
+       "15      USA  44.880547  -93.216922   7690  \n",
+       "16      USA  47.448982 -122.309313   7541  \n",
+       "17      USA  40.777243  -73.872609   7392  \n",
+       "18      USA  41.785983  -87.752424   6979  \n",
+       "19      USA  38.944532  -77.455810   6779  \n",
+       "20      USA  32.733556 -117.189657   6233  \n",
+       "21      USA  38.747687  -90.359990   6204  \n",
+       "22      USA  42.212059  -83.348836   6044  \n",
+       "23      USA  40.639751  -73.778926   5945  \n",
+       "24      USA  25.793250  -80.290556   5907  \n",
+       "25      USA  42.364348  -71.005179   5627  \n",
+       "26      USA  38.695422 -121.590767   4943  \n",
+       "27      USA  39.175402  -76.668198   4749  \n",
+       "28      USA  33.675659 -117.868223   4616  \n",
+       "29      USA  29.993389  -90.258028   4432  \n",
+       "..      ...        ...         ...    ...  \n",
+       "191     USA  47.482002 -111.370685    108  \n",
+       "192     USA  32.333133  -88.751206    104  \n",
+       "193     USA  46.353639  -87.395361    104  \n",
+       "194     USA  37.953759 -107.908480    104  \n",
+       "195     USA  35.035268  -85.203788    104  \n",
+       "196     USA  46.374498 -117.015394    102  \n",
+       "197     USA  37.700970 -113.098575     96  \n",
+       "198     USA  42.748119  -73.802979     93  \n",
+       "199     USA  44.473004  -73.150312     91  \n",
+       "200     USA  48.311405 -114.255069     90  \n",
+       "201     USA  28.102750  -80.645806     74  \n",
+       "202     USA  29.179917  -81.058056     72  \n",
+       "203     USA  40.652363  -75.440402     60  \n",
+       "204     USA  46.842090  -92.193649     58  \n",
+       "205     USA  41.155722 -104.811838     56  \n",
+       "206     USA  41.594217 -109.065193     56  \n",
+       "207     USA  37.858306  -80.399472     56  \n",
+       "208     USA  38.373151  -81.593190     56  \n",
+       "209     USA  48.792750 -122.537528     56  \n",
+       "210     USA  37.624049 -118.837772     56  \n",
+       "211     USA  44.257408  -88.519476     56  \n",
+       "212     USA  36.385913  -92.548828     56  \n",
+       "213     USA  27.910763  -82.687439     52  \n",
+       "214     USA  33.988796  -98.491893     50  \n",
+       "215     USA  34.991472  -78.880000     50  \n",
+       "216     USA  44.865257  -91.485072     48  \n",
+       "217     USA  42.402959  -90.709167     48  \n",
+       "218     USA  43.908826  -92.497987     37  \n",
+       "219     USA  34.681499  -90.348816     32  \n",
+       "220     USA  45.807662 -108.542861     23  \n",
        "\n",
-       "                                                                                                                                                                                                                                                                                                                  threex  \n",
-       "0  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3675 3732 3741 ... 3915 3735 3618]\\n [3498 3564 3570 ... 4143 3753 3579]\\n [3468 3330 3366 ... 3744 3735 3810]\\n ...\\n [4455 5247 5283 ... 3102 2988 2994]\\n [5340 5331 4989 ... 3024 3081 3522]\\n [5184 4941 4686 ... 3567 3891 4146]])  \n",
-       "1  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3618 3420 3681 ... 5199 4830 4674]\\n [3579 3474 3345 ... 4806 5277 5472]\\n [3810 3462 3210 ... 5445 5259 5535]\\n ...\\n [2994 3255 3498 ... 5274 5265 5103]\\n [3522 3906 3876 ... 5268 5046 4806]\\n [4146 3864 3627 ... 5067 4683 4545]])  \n",
-       "2  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[4674 4638 4335 ... 5070 5073 5145]\\n [5472 5349 5286 ... 5445 5388 5337]\\n [5535 5319 4968 ... 5151 5235 5508]\\n ...\\n [5103 4869 4860 ... 4206 4056 4326]\\n [4806 4890 4881 ... 4122 4251 4260]\\n [4545 4569 4455 ... 4221 4107 3930]])  \n",
-       "3  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[5145 5295 5025 ... 3303 3420 3447]\\n [5337 5400 5442 ... 3543 3636 3720]\\n [5508 5403 5337 ... 3186 3639 3690]\\n ...\\n [4326 4434 4338 ... 5367 5244 5220]\\n [4260 4152 4059 ... 5235 5190 5223]\\n [3930 3684 3828 ... 5184 5202 5052]])  \n",
-       "4  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3447 3513 3816 ... 1839 1851 1866]\\n [3720 3822 3852 ... 2055 1917 1953]\\n [3690 3678 3519 ... 2007 1947 1902]\\n ...\\n [5220 5328 5058 ... 3648 3507 3348]\\n [5223 4998 4608 ... 3555 3198 3282]\\n [5052 4581 4731 ... 3255 3306 3273]])  \n",
-       "5  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[1866 1878 1881 ... 1092  849  744]\\n [1953 2019 2106 ...  792  738  744]\\n [1902 2325 2313 ...  738  780  795]\\n ...\\n [3348 3294 3303 ... 2913 2778 2817]\\n [3282 3279 3342 ... 3039 3063 2700]\\n [3273 3294 3381 ... 3138 2853 2442]])  \n",
-       "6  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[ 744  684  753 ... 3168 3237 3138]\\n [ 744  741  810 ... 3372 3438 3564]\\n [ 795  837  840 ... 3138 3534 3870]\\n ...\\n [2817 2958 3291 ... 3246 3198 3243]\\n [2700 3045 3789 ... 3090 3027 2883]\\n [2442 2988 4287 ... 2964 2694 2400]])  \n",
-       "7  Tile(dimensions=[257, 256], cell_type=CellType(int16ud32767, 32767), cells=[[3138 3264 3720 ... 2238 2481 2628]\\n [3564 3846 3900 ... 2502 2298 1995]\\n [3870 3798 3672 ... 1989 2052 1929]\\n ...\\n [3243 3027 3000 ... 1881 1740 1584]\\n [2883 2823 2805 ... 1821 1722 1830]\\n [2400 2595 2514 ... 1359 1341 1572]])  \n",
-       "8  Tile(dimensions=[256, 256], cell_type=CellType(int16ud32767, 32767), cells=[[2238 2250 2226 ...  432  351  336]\\n [1875 1899 2067 ...  312  552  429]\\n [1878 2022 1998 ...  483  441  513]\\n ...\\n [1758 1779 1779 ...  198  141  168]\\n [1755 1629 1548 ...  159  132  201]\\n [1545 1362 1347 ...  162  153  150]])  \n",
-       "9  Tile(dimensions=[96, 256], cell_type=CellType(int16ud32767, 32767), cells=[[402 531 543 ... 138  99  75]\\n [438 372 462 ... 129 156 114]\\n [525 507 483 ... 141  75 114]\\n ...\\n [162 147 192 ... 132 141 138]\\n [159 156 129 ... 171 147 129]\\n [138 138 144 ... 150 147  90]])                                       "
+       "[221 rows x 8 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pandas_df['threex'] = pandas_df.tile2.apply(lambda t: t * 3)\n",
-    "pandas_df"
+    "import pandas\n",
+    "pandas.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')"
    ]
   },
   {

From 6efd952faf90b884b90e503076095a3167164343 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 14 Jun 2019 14:56:17 -0400
Subject: [PATCH 159/380] matplotlib requirement to accomodate Python 2.7
 support

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index 44ea6904b..b715f2f7e 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -3,4 +3,4 @@ shapely
 pyspark==2.3.3
 numpy>=1.7
 pandas
-matplotlib
+matplotlib<3.0.0  # no python 2.7 support after v2.x.x

From 720635e0f8fbeb48746e776597bc418a4e1db583 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 17 Jun 2019 15:04:38 -0400
Subject: [PATCH 160/380] Fixed JMH plugin.

---
 project/plugins.sbt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/plugins.sbt b/project/plugins.sbt
index 5ccdcda08..5835c4f93 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -7,7 +7,7 @@ addSbtPlugin("de.heikoseeberger" % "sbt-header" % "3.0.2")
 addSbtPlugin("com.typesafe.sbt" % "sbt-ghpages" % "0.6.2")
 addSbtPlugin("com.typesafe.sbt" % "sbt-site" % "1.3.2")
 addSbtPlugin("com.lightbend.paradox" % "sbt-paradox" % "0.5.5")
-addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.4")
+addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.6")
 addSbtPlugin("org.xerial.sbt" % "sbt-sonatype" % "2.1")
 addSbtPlugin("com.jsuereth" % "sbt-pgp" % "1.1.1")
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.4.1")

From 3392a82632289d51a586973941102cb0ec4b0861 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 17 Jun 2019 19:37:32 -0400
Subject: [PATCH 161/380] Moved Python tests back into main Python source
 directory (keep it pythonic).

---
 pyrasterframes/build.sbt                                       | 3 +++
 .../{test/python => main/python/tests}/PyRasterFramesTests.py  | 0
 .../src/{test/python => main/python/tests}/__init__.py         | 0
 pyrasterframes/src/main/python/{ => tests}/coverage-report.sh  | 2 +-
 4 files changed, 4 insertions(+), 1 deletion(-)
 rename pyrasterframes/src/{test/python => main/python/tests}/PyRasterFramesTests.py (100%)
 rename pyrasterframes/src/{test/python => main/python/tests}/__init__.py (100%)
 rename pyrasterframes/src/main/python/{ => tests}/coverage-report.sh (86%)

diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 570dc1385..2e9e31f42 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,5 +1,8 @@
 import PythonBuildPlugin.autoImport.pySetup
 
+
+Test / pythonSource := (Compile / sourceDirectory).value / "tests"
+
 exportJars := true
 
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
diff --git a/pyrasterframes/src/test/python/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
similarity index 100%
rename from pyrasterframes/src/test/python/PyRasterFramesTests.py
rename to pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
diff --git a/pyrasterframes/src/test/python/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
similarity index 100%
rename from pyrasterframes/src/test/python/__init__.py
rename to pyrasterframes/src/main/python/tests/__init__.py
diff --git a/pyrasterframes/src/main/python/coverage-report.sh b/pyrasterframes/src/main/python/tests/coverage-report.sh
similarity index 86%
rename from pyrasterframes/src/main/python/coverage-report.sh
rename to pyrasterframes/src/main/python/tests/coverage-report.sh
index 8cde233a1..6b547e026 100755
--- a/pyrasterframes/src/main/python/coverage-report.sh
+++ b/pyrasterframes/src/main/python/tests/coverage-report.sh
@@ -4,6 +4,6 @@
 
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
-cd "$( dirname "${BASH_SOURCE[0]}" )"
+cd "$( dirname "${BASH_SOURCE[0]}" )"/..
 
 coverage run setup.py test && coverage html --omit='.eggs/*,setup.py' && open htmlcov/index.html
\ No newline at end of file

From f6aa775c217d1217ad8e75138b1641ce11944b21 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 17 Jun 2019 20:02:12 -0400
Subject: [PATCH 162/380] Reproduced lazy-read error condition in simple test.

---
 .../src/main/python/tests/PyRasterFramesTests.py   | 14 ++++++++++----
 pyrasterframes/src/main/python/tests/__init__.py   | 10 +++++++---
 2 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index f98eb0b3d..416b4815a 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -139,6 +139,10 @@ class RasterFunctions(TestEnvironment):
     def setUp(self):
         self.create_rasterframe()
 
+    def test_setup(self):
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
+                         "org.apache.spark.serializer.KryoSerializer")
+
     def test_identify_columns(self):
         cols = self.rf.tileColumns()
         self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
@@ -560,10 +564,12 @@ def test_raster_join(self):
 
 class RasterSource(TestEnvironment):
 
-    # Putting this here for convenience
-    def test_setup(self):
-        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
-                         "org.apache.spark.serializer.KryoSerializer")
+    def test_handle_lazy_eval(self):
+        import numpy.testing
+        # rf_local_add(t, 0) is to force lazy eval; accessing tile.tile is to get at the actual Tile type vs PRT struct
+        df = self.spark.read.rastersource(self.img_uri)
+        t = df.select('tile.tile').first()
+        print(t)
 
     def test_prt_functions(self):
         df = self.spark.read.rastersource(self.img_uri) \
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 275612087..1dcb13afe 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -44,10 +44,14 @@ def rounded_compare(self, val1, val2):
 
     @classmethod
     def setUpClass(cls):
+        def pdir(curr):
+            return os.path.dirname(curr)
         # gather Scala requirements
-        here = os.path.dirname(os.path.realpath(__file__))
-        target_dir = os.path.dirname(os.path.dirname(here))
-
+        here = pdir(os.path.realpath(__file__))
+        target_dir = pdir(pdir(here))
+        # See if we're running outside of sbt build and adjust
+        if os.path.basename(target_dir) != "target":
+            target_dir = os.path.join(pdir(pdir(target_dir)), 'target')
         scala_target = os.path.realpath(os.path.join(target_dir, 'scala-2.11'))
 
         jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))

From ec37b8344d27a4b768e14c9abfc8c9e99b8f55f7 Mon Sep 17 00:00:00 2001
From: Tom Schroeder <tschroeder@astraea.io>
Date: Tue, 18 Jun 2019 12:27:51 -0400
Subject: [PATCH 163/380] Upgrade to GDAL 2.4.1 Correct permissions on gdal
 installation delete

Signed-off-by: Tom Schroeder <tschroeder@astraea.io>
---
 build/circleci/Dockerfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/build/circleci/Dockerfile b/build/circleci/Dockerfile
index da964c32a..0eb8bb522 100644
--- a/build/circleci/Dockerfile
+++ b/build/circleci/Dockerfile
@@ -1,7 +1,7 @@
 FROM circleci/openjdk:8-jdk
 
 ENV OPENJPEG_VERSION 2.3.0
-ENV GDAL_VERSION 2.3.3
+ENV GDAL_VERSION 2.4.1
 ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/
 
 # most of these libraries required for
@@ -64,4 +64,4 @@ RUN cd /tmp && \
     cd swig/java && \
     sudo make install && \
     sudo ldconfig && \
-    cd /tmp && rm -Rf gdal*
+    cd /tmp && sudo rm -Rf gdal*

From b9568fafeae66c1c347cb2af5f7543fb1edce4bb Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 18 Jun 2019 12:34:42 -0400
Subject: [PATCH 164/380] Address PR comments

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/README.md                      | 118 ++++++++++++++++-
 pyrasterframes/src/main/python/MANIFEST.in    |   1 -
 pyrasterframes/src/main/python/README.md      | 123 ++----------------
 .../main/python/pyrasterframes/__init__.py    |   2 +-
 .../src/main/python/pyrasterframes/version.py |  23 ++++
 pyrasterframes/src/main/python/setup.py       | 107 +++++++--------
 6 files changed, 203 insertions(+), 171 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/pyrasterframes/version.py

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 70b448567..71622e8fb 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -1,3 +1,119 @@
 # PyRasterFrames
 
-See [here](src/main/python/README.md) for details.
\ No newline at end of file
+## Using PyRasterFrames
+
+See [here](src/main/python/README.md) for user facing details.
+
+## Deployment
+
+The pip installation focuses on local mode operation of spark. To deploy spark applications you will need to consider a few configurations.
+
+### `SparkSession` Setup
+
+#### Python shell
+
+To initialize PyRasterFrames in a generic Python shell follow the pattern below, substituting configs as needed.
+
+```python
+from pyspark.sql import SparkSession
+from pyrasterframes import *
+from pyrasterframes.rasterfunctions import *
+spark = SparkSession.builder \
+     .master("local[*]") \
+     .appName("Using RasterFrames") \
+     .config("spark.some.config.option", "some-value") \
+     .withKryoSerialization() \
+     .getOrCreate() \
+     .withRasterFrames()
+```
+
+#### Pyspark shell or app
+
+To initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. See below for how to build or download those artifacts.
+
+```bash
+   pyspark \
+    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
+    --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
+    --conf spark.kryoserializer.buffer.max=500m \
+    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
+    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+   
+```
+
+Then in the pyspark shell or app, import the module and call `withRasterFrames` on the SparkSession.
+
+```python
+import pyrasterframes
+spark = spark.withRasterFrames()
+df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+```
+
+
+## Development
+
+RasterFrames is primarily implemented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
+All `sbt` commands referenced below must be run from the root source directory, i.e. the parent of the `pyrasterframes`
+directory, including Python-related build steps.
+
+As a tip, know that `sbt` is much faster if run in "interactive" mode, where you launch `sbt` with no arguments,
+and subsequent commands are invoked via an interactive shell. But for context clarity, we'll prefix each command
+example below with `sbt`.
+
+
+## Running Tests and Examples
+
+The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
+
+```bash
+sbt pyTests
+```
+
+*See also the below discussion of running `setup.py` for more options to run unit tests.* 
+
+Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
+
+```bash
+sbt pyExamples
+```
+
+## Creating and Using a Build
+
+Assuming that `$SCALA_VER` is the major verison of Scala in use (e.g. 2.11) , and `$VER` is the version of RasterFrames, 
+the primary build artifacts are:
+
+* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
+* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
+
+You build them with:
+
+```bash
+sbt pyrasterframes/package
+```
+
+Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy groupId:artifactId:version (GAV) coordinates
+`org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
+
+Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
+The Python package is published under the `python` classifier, `zip` extension.
+
+## Build Internals
+
+### Running `setup.py`
+
+Before a build is initiated, the Python sources are copied to `pyrasterframes/target/python`. This ensures the 
+version controlled source directories are not polluted by `setuptools` residuals, better ensuring repeatable builds. To
+simplify the process of working with `setup.py` in this context, a `pySetup` sbt interactive command is available. To
+illustrate its usage, suppose we want to run a subset of the Python unit test. Instead of running `sbt pyTests` you can:
+
+```bash
+sbt 'pySetup test --addopts "-k test_tile_creation"'
+```
+
+Or to run a specific example:
+
+```bash
+sbt 'pySetup examples -e NDVI'
+```
+
+*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
diff --git a/pyrasterframes/src/main/python/MANIFEST.in b/pyrasterframes/src/main/python/MANIFEST.in
index a97911543..68903bdfe 100644
--- a/pyrasterframes/src/main/python/MANIFEST.in
+++ b/pyrasterframes/src/main/python/MANIFEST.in
@@ -1,4 +1,3 @@
 
 global-exclude *.py[cod] __pycache__ .DS_Store
 recursive-include deps/jars *.jar
-
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 15a0637dc..9b6688656 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -1,144 +1,49 @@
 # PyRasterFrames
 
-PyRasterFrames is a library for distributed processing of geospatial raster data with Spark.
+PyRasterFrames enables access and processing of geospatial raster data in PySpark DataFrames.
 
+## Getting started
 
-## Prerequisites
+### Prerequisites
 
 1. [`pip`](https://pip.pypa.io/en/stable/installing/)
 2. ['pyspark`](https://pypi.org/project/pyspark/) > 2.3.2 
 
-## Quickstart
-
 The quickest way to get started is to `pip` install the pyrasterframes package.
 
 ```bash
 pip install pyrasterframes
 ```
 
-You can then access a [`pyspark SparkSession`]() using the [`local[*]` master](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls) as follows.
+You can then access a [`pyspark SparkSession`](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession) using the [`local[*]` master](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls) in your python interpreter as follows.
 
 ```python
 import pyrasterframes
 spark = pyrasterframes.get_spark_session()
 ```
 
-Then you can read a raster and do some simple processing on it.
+Then you can read a raster and do some work with it.
 
 ```python
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql.functions import lit
-# Read a Landsat 8 L1TP PDS scene
-df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/038/037/LC08_L1TP_038037_20190322_20190403_01_T1/LC08_L1TP_038037_20190322_20190403_01_T1_B4.TIF')
-# Add 3 to every cell, show some rows of the dataframe
-df.select(rf_local_add(df.tile, lit(3))).show(6, False)
-```
-
-## Development
-
-RasterFrames is primarily implemented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
-All `sbt` commands referenced below must be run from the root source directory, i.e. the parent of the `pyrasterframes`
-directory, including Python-related build steps.
-
-As a tip, know that `sbt` is much faster if run in "interactive" mode, where you launch `sbt` with no arguments,
-and subsequent commands are invoked via an interactive shell. But for context clarity, we'll prefix each command
-example below with `sbt`.
-
-
-## Running Tests and Examples
-
-The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
-
-```bash
-sbt pyTests
-```
-
-*See also the below discussion of running `setup.py` for more options to run unit tests.* 
-
-Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
-
-```bash
-sbt pyExamples
-```
-
-## Creating and Using a Build
-
-Assuming that `$SCALA_VER` is the major verison of Scala in use (e.g. 2.11) , and `$VER` is the version of RasterFrames, 
-the primary build artifacts are:
-
-* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
-* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
-
-You build them with:
-
-```bash
-sbt pyrasterframes/package
+# Read a MODIS surface reflectance granule
+df = spark.read.rastersource('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
+# Add 3 element-wise, show some rows of the dataframe
+df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 ```
 
-Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy groupId:artifactId:version (GAV) coordinates
-`org.locationtech.rasterframes:pyrasterframes_$SCALA_VER:$VER`.
-
-Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
-The Python package is published under the `python` classifier, `zip` extension.
 
-## Build Internals
+## Support
 
-### Running `setup.py`
+Reach out to us on [gitter](https://gitter.im/s22s/raster-frames)!
 
-Before a build is initiated, the Python sources are copied to `pyrasterframes/target/python`. This ensures the 
-version controlled source directories are not polluted by `setuptools` residuals, better ensuring repeatable builds. To
-simplify the process of working with `setup.py` in this context, a `pySetup` sbt interactive command is available. To
-illustrate its usage, suppose we want to run a subset of the Python unit test. Instead of running `sbt pyTests` you can:
+Issue tracking is through [github](https://github.com/locationtech/rasterframes/issues). 
 
-```bash
-sbt 'pySetup test --addopts "-k test_tile_creation"'
-```
+## Contributing
 
-Or to run a specific example:
+Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/s22s/raster-frames) so the community can help you get started!
 
-```bash
-sbt 'pySetup examples -e NDVI'
-```
 
-*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
 
-## `SparkSession` Setup
 
-### Python shell
-
-To initialize PyRasterFrames in a generic Python shell:
-
-```python
-from pyspark.sql import SparkSession
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-spark = SparkSession.builder \
-     .master("local[*]") \
-     .appName("Using RasterFrames") \
-     .config("spark.some.config.option", "some-value") \
-     .withKryoSerialization() \
-     .getOrCreate() \
-     .withRasterFrames()
-```
-
-### Pyspark shell
-
-To initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the pyton source zip.
-
-```bash
-   pyspark \
-    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
-    --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
-    --conf spark.kryoserializer.buffer.max=500m \
-    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
-    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
-   
-```
-
-Then in the pyspark shell import the module and call `withRasterFrames`.
-
-```python
-import pyrasterframes
-spark = spark.withRasterFrames()
-df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
-```
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 252f0e9e5..fc5c14c1b 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -32,7 +32,7 @@
 from .rf_types import *
 from . import rasterfunctions
 from .context import RFContext
-
+from .version import __version__
 
 __all__ = ['RasterFrame', 'TileExploder']
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/version.py b/pyrasterframes/src/main/python/pyrasterframes/version.py
new file mode 100644
index 000000000..ce58048ae
--- /dev/null
+++ b/pyrasterframes/src/main/python/pyrasterframes/version.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# Translating Java version from version.sbt to PEP440 norms
+__version__ = '0.8.0.dev0'
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 0aecab504..16a189e94 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -1,11 +1,41 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 # Always prefer setuptools over distutils
 from setuptools import setup, find_packages
 from os import path, environ
+import sys
 from glob import glob
 from io import open
 import distutils.cmd
 import importlib
 
+try:
+    exec(open('pyrasterframes/version.py').read())  # executable python script contains __version__; credit pyspark
+except IOError:
+    print("Run setup via `sbt 'pySetup arg1 arg2'` to ensure correct access to all source files and binaries.")
+    sys.exit(-1)
+
+
+VERSION = __version__
+
 here = path.abspath(path.dirname(__file__))
 
 # Get the long description from the README file
@@ -69,65 +99,12 @@ def run(self):
                 print(traceback.format_exc())
 
 
-class PweaveDocs(distutils.cmd.Command):
-    """A custom command to run documentation scripts through pweave."""
-    description = 'Pweave PyRasterFrames documentation scripts'
-    user_options = [
-        # The format is (long option, short option, description).
-        ('files=', 'f', 'Specific files to pweave. Defaults to all in `docs` directory.'),
-    ]
-
-    def initialize_options(self):
-        """Set default values for options."""
-        # Each user option must be listed here with their default value.
-        self.files = filter(
-            lambda x: not x.name.startswith('_'),
-            glob(path.join(here, 'docs', '*.py'))
-        )
-
-    def finalize_options(self):
-        """Post-process options."""
-        import re
-        if isinstance(self.files, str):
-            self.files = filter(lambda s: len(s) > 0, re.split('\W+', self.files))
-
-    def run(self):
-        """Run pweave."""
-        import traceback
-        import pweave
-
-        for file in self.files:
-            name = path.splitext(path.basename(file))[0]
-            print(_divided('Running %s' % name))
-            try:
-                pweave.weave(
-                    file=str(file),
-                    doctype='markdown'
-                )
-            except Exception:
-                print(_divided('%s Failed:' % file))
-                print(traceback.format_exc())
-
-
-try:
-    with open(path.join(here, 'requirements.txt')) as f:
-        requirements = f.read().splitlines()
-except:
-    print("couldn't open requirements.txt")
-    requirements = [
-        'pytz',
-        'shapely',
-        'pyspark>=2.3',
-        'numpy>=1.7',
-        'pandas',
-    ]
-
 setup(
     name='pyrasterframes',
-    description='RasterFrames for PySpark',
+    description='Access and process geospatial raster data in PySpark DataFrames',
     long_description=readme,
     long_description_content_type='text/markdown',
-    version=environ.get('RASTERFRAMES_VERSION', 'dev'),
+    version=VERSION,
     author='Astraea, Inc.',
     author_email='info@astraea.earth',
     license='Apache 2',
@@ -136,12 +113,22 @@ def run(self):
         'Bug Reports': 'https://github.com/locationtech/rasterframes/issues',
         'Source': 'https://github.com/locationtech/rasterframes',
     },
-    install_requires=requirements,
+    install_requires=[
+        'pytz',
+        'shapely',
+        'pyspark',
+        'numpy',
+        'pandas',
+    ],
     setup_requires=[
+        'pytz',
+        'shapely',
+        'pyspark',
+        'numpy',
+        'pandas',
         'pytest-runner',
         'setuptools>=0.8',
-        'jupytext',
-    ] + requirements,
+    ],
     tests_require=[
         'pytest==3.4.2',
         'pypandoc',
@@ -167,7 +154,9 @@ def run(self):
         'Natural Language :: English',
         'Operating System :: Unix',
         'Programming Language :: Python',
-        'Topic :: Software Development :: Libraries'
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Scientific/Engineering :: GIS',
+        'Topic :: Multimedia :: Graphics :: Graphics Conversion',
     ],
     zip_safe=False,
     test_suite="pytest-runner",

From 3b8e7564aca80fafa6e97d1152c9cca61440a3a4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 18 Jun 2019 13:06:32 -0400
Subject: [PATCH 165/380] Added `rf_realize_tile`.

---
 .../rasterframes/RasterFunctions.scala        | 25 ++++----
 .../expressions/accessors/ExtractTile.scala   |  5 +-
 .../expressions/accessors/RealizeTile.scala   | 55 +++++++++++++++++
 .../rasterframes/expressions/package.scala    |  1 +
 .../transformers/URIToRasterSource.scala      |  2 +-
 .../rasterframes/RasterFunctionsSpec.scala    | 61 ++++++++++++-------
 docs/src/main/tut/release-notes.md            |  1 +
 .../python/pyrasterframes/rasterfunctions.py  |  1 +
 .../main/python/pyrasterframes/rf_types.py    | 17 ++++--
 .../main/python/tests/PyRasterFramesTests.py  |  7 +--
 10 files changed, 130 insertions(+), 45 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index bcb582632..e0a5cd60d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -48,17 +48,6 @@ trait RasterFunctions {
   import util._
 
   // format: off
-  /** Create a row for each cell in Tile. */
-  def rf_explode_tiles(cols: Column*): Column = rf_explode_tiles_sample(1.0, None, cols: _*)
-
-  /** Create a row for each cell in Tile with random sampling and optional seed. */
-  def rf_explode_tiles_sample(sampleFraction: Double, seed: Option[Long], cols: Column*): Column =
-    ExplodeTiles(sampleFraction, seed, cols)
-
-  /** Create a row for each cell in Tile with random sampling (no seed). */
-  def rf_explode_tiles_sample(sampleFraction: Double, cols: Column*): Column =
-    ExplodeTiles(sampleFraction, None, cols)
-
   /** Query the number of (cols, rows) in a Tile. */
   def rf_dimensions(col: Column): TypedColumn[Any, TileDimensions] = GetDimensions(col)
 
@@ -71,6 +60,9 @@ trait RasterFunctions {
   /** Extracts the CRS from a RasterSource or ProjectedRasterTile */
   def rf_crs(col: Column): TypedColumn[Any, CRS] = GetCRS(col)
 
+  /** Extracts the Tile component of a RasterSource, ProjectedRasterTile (or Tile) and ensures the cells are fully fetched. */
+  def rf_realize_tile(col: Column): TypedColumn[Any, Tile] = RealizeTile(col)
+
   /** Flattens Tile into a double array. */
   def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
@@ -416,7 +408,18 @@ trait RasterFunctions {
   def rf_expm1(tileCol: Column): TypedColumn[Any, Tile] =
     ExpM1(tileCol)
 
+  /** Return the incoming tile untouched. */
   def rf_identity(tileCol: Column): TypedColumn[Any, Tile] =
     Identity(tileCol)
 
+  /** Create a row for each cell in Tile. */
+  def rf_explode_tiles(cols: Column*): Column = rf_explode_tiles_sample(1.0, None, cols: _*)
+
+  /** Create a row for each cell in Tile with random sampling and optional seed. */
+  def rf_explode_tiles_sample(sampleFraction: Double, seed: Option[Long], cols: Column*): Column =
+    ExplodeTiles(sampleFraction, seed, cols)
+
+  /** Create a row for each cell in Tile with random sampling (no seed). */
+  def rf_explode_tiles_sample(sampleFraction: Double, cols: Column*): Column =
+    ExplodeTiles(sampleFraction, None, cols)
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
index cc01b1f8f..4fc0a0374 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/ExtractTile.scala
@@ -32,12 +32,13 @@ import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes.model.TileContext
 import org.locationtech.rasterframes.tiles.InternalRowTile
+import org.locationtech.rasterframes._
 
 /** Expression to extract at tile from several types that contain tiles.*/
 case class ExtractTile(child: Expression) extends UnaryRasterOp with CodegenFallback {
-  override def dataType: DataType = new TileUDT()
+  override def dataType: DataType = TileType
 
-  override def nodeName: String = "extract_tile"
+  override def nodeName: String = "rf_extract_tile"
   implicit val tileSer = TileUDT.tileSerializer
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any = tile match {
     case irt: InternalRowTile => irt.mem
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala
new file mode 100644
index 000000000..8e7cee24f
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala
@@ -0,0 +1,55 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.accessors
+
+import geotrellis.raster.Tile
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.rf.TileUDT
+import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.UnaryRasterOp
+import org.locationtech.rasterframes.model.TileContext
+
+@ExpressionDescription(
+  usage = "_FUNC_(raster) - Extracts the Tile component of a RasterSource, ProjectedRasterTile (or Tile) and ensures the cells are fully fetched.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(raster);
+         ....
+  """)
+case class RealizeTile(child: Expression) extends UnaryRasterOp with CodegenFallback {
+  override def dataType: DataType = TileType
+
+  override def nodeName: String = "rf_realize_tile"
+  implicit val tileSer = TileUDT.tileSerializer
+
+  override protected def eval(tile: Tile, ctx: Option[TileContext]): Any =
+    (tile.toArrayTile(): Tile).toInternalRow
+}
+
+object RealizeTile {
+  def apply(col: Column): TypedColumn[Any, Tile] =
+    new Column(new RealizeTile(col.expr)).as[Tile]
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 432c66888..b169e79d7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -74,6 +74,7 @@ package object expressions {
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[GetExtent]("rf_extent")
     registry.registerExpression[GetCRS]("rf_crs")
+    registry.registerExpression[RealizeTile]("rf_realize_tile")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index 4a9c2659c..ab138ec91 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.ref.RasterSource
 case class URIToRasterSource(override val child: Expression)
   extends UnaryExpression with ExpectsInputTypes with CodegenFallback with LazyLogging {
 
-  override def nodeName: String = "uri_to_raster_source"
+  override def nodeName: String = "rf_read_raster_source"
 
   override def dataType: DataType = RasterSourceType
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index f000efbfd..1aa1325cf 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -30,6 +30,8 @@ import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
 import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.scalatest.{FunSpec, Matchers}
@@ -52,8 +54,8 @@ class RasterFunctionsSpec extends FunSpec
   lazy val three = TestData.projectedRasterTile(cols, rows, 3, extent, crs, ct)
   lazy val six = ProjectedRasterTile(three * two, three.extent, three.crs)
   lazy val nd = TestData.projectedRasterTile(cols, rows, -2, extent, crs, ct)
-  lazy val randTile = TestData.projectedRasterTile(cols, rows, scala.util.Random.nextInt(), extent, crs, ct)
-  lazy val randNDTile  = TestData.injectND(numND)(randTile)
+  lazy val randPRT = TestData.projectedRasterTile(cols, rows, scala.util.Random.nextInt(), extent, crs, ct)
+  lazy val randNDPRT  = TestData.injectND(numND)(randPRT)
 
   lazy val randDoubleTile = TestData.projectedRasterTile(cols, rows, scala.util.Random.nextGaussian(), extent, crs, DoubleConstantNoDataCellType)
   lazy val randDoubleNDTile  = TestData.injectND(numND)(randDoubleTile)
@@ -65,6 +67,8 @@ class RasterFunctionsSpec extends FunSpec
     TestData.randomTile(cols, rows, UByteConstantNoDataCellType)
   )).map(ProjectedRasterTile(_, extent, crs)) :+ null
 
+  def lazyPRT = RasterRef(RasterSource(TestData.l8samplePath), 0, None).tile
+
   implicit val pairEnc = Encoders.tuple(ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder)
   implicit val tripEnc = Encoders.tuple(ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder, ProjectedRasterTile.prtEncoder)
 
@@ -287,21 +291,36 @@ class RasterFunctionsSpec extends FunSpec
 
   describe("raster metadata") {
     it("should get the TileDimensions of a Tile") {
-      val t = Seq(randTile).toDF("tile").select(rf_dimensions($"tile")).first()
-      t should be (TileDimensions(randTile.dimensions))
+      val t = Seq(randPRT).toDF("tile").select(rf_dimensions($"tile")).first()
+      t should be (TileDimensions(randPRT.dimensions))
       checkDocs("rf_dimensions")
     }
     it("should get the Extent of a ProjectedRasterTile") {
-      val e = Seq(randTile).toDF("tile").select(rf_extent($"tile")).first()
+      val e = Seq(randPRT).toDF("tile").select(rf_extent($"tile")).first()
       e should be (extent)
       checkDocs("rf_extent")
     }
 
+    it("should get the CRS of a ProjectedRasterTile") {
+      val e = Seq(randPRT).toDF("tile").select(rf_crs($"tile")).first()
+      e should be (crs)
+      checkDocs("rf_crs")
+    }
+
     it("should get the Geometry of a ProjectedRasterTile") {
-      val g = Seq(randTile).toDF("tile").select(rf_geometry($"tile")).first()
+      val g = Seq(randPRT).toDF("tile").select(rf_geometry($"tile")).first()
       g should be (extent.jtsGeom)
       checkDocs("rf_geometry")
     }
+
+    it("should realize a delayed load tile") {
+      val df = Seq(lazyPRT).toDF("prt").as[ProjectedRasterTile]
+      val lazyTile = df.select(col("prt.tile").as[Tile]).first()
+      lazyTile shouldBe a[RasterRefTile]
+
+      val realized = df.select(rf_realize_tile(col("prt"))).first()
+      realized should not be a [RasterRefTile]
+    }
   }
 
   describe("per-tile stats") {
@@ -353,33 +372,33 @@ class RasterFunctionsSpec extends FunSpec
       checkDocs("rf_for_all")
     }
     it("should find the minimum cell value") {
-      val min = randNDTile.toArray().filter(c => raster.isData(c)).min.toDouble
-      val df = Seq(randNDTile).toDF("rand")
+      val min = randNDPRT.toArray().filter(c => raster.isData(c)).min.toDouble
+      val df = Seq(randNDPRT).toDF("rand")
       df.select(rf_tile_min($"rand")).first() should be(min)
       df.selectExpr("rf_tile_min(rand)").as[Double].first() should be(min)
       checkDocs("rf_tile_min")
     }
 
     it("should find the maximum cell value") {
-      val max = randNDTile.toArray().filter(c => raster.isData(c)).max.toDouble
-      val df = Seq(randNDTile).toDF("rand")
+      val max = randNDPRT.toArray().filter(c => raster.isData(c)).max.toDouble
+      val df = Seq(randNDPRT).toDF("rand")
       df.select(rf_tile_max($"rand")).first() should be(max)
       df.selectExpr("rf_tile_max(rand)").as[Double].first() should be(max)
       checkDocs("rf_tile_max")
     }
     it("should compute the tile mean cell value") {
-      val values = randNDTile.toArray().filter(c => raster.isData(c))
+      val values = randNDPRT.toArray().filter(c => raster.isData(c))
       val mean = values.sum.toDouble / values.length
-      val df = Seq(randNDTile).toDF("rand")
+      val df = Seq(randNDPRT).toDF("rand")
       df.select(rf_tile_mean($"rand")).first() should be(mean)
       df.selectExpr("rf_tile_mean(rand)").as[Double].first() should be(mean)
       checkDocs("rf_tile_mean")
     }
 
     it("should compute the tile summary statistics") {
-      val values = randNDTile.toArray().filter(c => raster.isData(c))
+      val values = randNDPRT.toArray().filter(c => raster.isData(c))
       val mean = values.sum.toDouble / values.length
-      val df = Seq(randNDTile).toDF("rand")
+      val df = Seq(randNDPRT).toDF("rand")
       val stats = df.select(rf_tile_stats($"rand")).first()
       stats.mean should be (mean +- 0.00001)
 
@@ -406,7 +425,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should compute the tile histogram") {
-      val df = Seq(randNDTile).toDF("rand")
+      val df = Seq(randNDPRT).toDF("rand")
       val h1 = df.select(rf_tile_histogram($"rand")).first()
 
       val h2 = df.selectExpr("rf_tile_histogram(rand) as hist")
@@ -487,7 +506,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should compute local data cell counts") {
-      val df = Seq(two, randNDTile, nd).toDF("tile")
+      val df = Seq(two, randNDPRT, nd).toDF("tile")
       val t1 = df.select(rf_agg_local_data_cells($"tile")).first()
       val t2 = df.selectExpr("rf_agg_local_data_cells(tile) as cnt").select($"cnt".as[Tile]).first()
       t1 should be (t2)
@@ -495,7 +514,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should compute local no-data cell counts") {
-      val df = Seq(two, randNDTile, nd).toDF("tile")
+      val df = Seq(two, randNDPRT, nd).toDF("tile")
       val t1 = df.select(rf_agg_local_no_data_cells($"tile")).first()
       val t2 = df.selectExpr("rf_agg_local_no_data_cells(tile) as cnt").select($"cnt".as[Tile]).first()
       t1 should be (t2)
@@ -522,7 +541,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should mask one tile against another") {
-      val df = Seq[Tile](randTile).toDF("tile")
+      val df = Seq[Tile](randPRT).toDF("tile")
 
       val withMask = df.withColumn("mask",
         rf_convert_cell_type(
@@ -541,7 +560,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should inverse mask one tile against another") {
-      val df = Seq[Tile](randTile).toDF("tile")
+      val df = Seq[Tile](randPRT).toDF("tile")
 
       val baseND = df.select(rf_agg_no_data_cells($"tile")).first()
 
@@ -564,7 +583,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should mask tile by another identified by specified value") {
-      val df = Seq[Tile](randTile).toDF("tile")
+      val df = Seq[Tile](randPRT).toDF("tile")
       val mask_value = 4
 
       val withMask = df.withColumn("mask",
@@ -585,7 +604,7 @@ class RasterFunctionsSpec extends FunSpec
     }
 
     it("should inverse mask tile by another identified by specified value") {
-      val df = Seq[Tile](randTile).toDF("tile")
+      val df = Seq[Tile](randPRT).toDF("tile")
       val mask_value = 4
 
       val withMask = df.withColumn("mask",
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index f7fc7360d..39025f78e 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -15,6 +15,7 @@
 * Added `SinglebandGeoTiff.toDF` extension method.
 * Added `DataFrame.rasterJoin` extension method for merging two dataframes with tiles in disparate CRSs.
 * Added `rf_crs` for `ProjectedRasterTile` columns.
+* Added `rf_realize_tile` for cases where you want to ensure no lazy tile loading is happening.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 9d8ceff7a..5eb0c51b4 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -275,6 +275,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_expm1': 'Performs cell-wise exponential, then subtract one',
     'rf_identity': 'Pass tile through unchanged',
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
+    'rf_realize_tile': 'Extracts the Tile component of a RasterSource, ProjectedRasterTile (or Tile) and ensures the cells are fully fetched.',
     'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
     'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
     'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 41b5c0efb..1ce5d55d1 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -143,20 +143,22 @@ def sqlType(cls):
 
     @classmethod
     def module(cls):
-        return 'pyrasterframes.types'
+        return 'pyrasterframes.rf_types'
 
     @classmethod
     def scalaUDT(cls):
         return 'org.apache.spark.sql.rf.RasterSourceUDT'
 
+    def needConversion(self):
+        return False
+
+    # The contents of a RasterSource is opaque in the Python context.
+    # Just pass data through unmodified.
     def serialize(self, obj):
-        # RasterSource is opaque in the Python context.
-        # Any thing passed in by a UDF return value couldn't be validated.
-        # Therefore obj is dropped None is passed to Catalyst.
-        return None
+        return obj
 
     def deserialize(self, datum):
-        bytes(datum[0])
+        return datum
 
 
 class CellType(object):
@@ -419,6 +421,9 @@ def deserialize(self, datum):
         cols = datum.cell_context.dimensions.cols
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
+        if cell_data_bytes is None:
+            raise Exception("Null cell data: " + str(datum))
+
         try:
             as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype())
             reshaped = as_numpy.reshape((rows, cols))
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 416b4815a..4c1dbd493 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -565,11 +565,10 @@ def test_raster_join(self):
 class RasterSource(TestEnvironment):
 
     def test_handle_lazy_eval(self):
-        import numpy.testing
-        # rf_local_add(t, 0) is to force lazy eval; accessing tile.tile is to get at the actual Tile type vs PRT struct
         df = self.spark.read.rastersource(self.img_uri)
-        t = df.select('tile.tile').first()
-        print(t)
+        tdf = df.select(rf_realize_tile('tile'))
+        self.assertGreater(tdf.count(),  0)
+        self.assertIsNotNone(tdf.first())
 
     def test_prt_functions(self):
         df = self.spark.read.rastersource(self.img_uri) \

From d59055f068d36c4dc8109a85395c5490cf2c43dd Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 18 Jun 2019 16:02:14 -0400
Subject: [PATCH 166/380] Refactor python Tile viz to rf_ipython

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/rf_ipython.py  | 110 ++++++++++++++++++
 .../main/python/pyrasterframes/rf_types.py    |  93 +--------------
 2 files changed, 113 insertions(+), 90 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
new file mode 100644
index 000000000..771b8b19e
--- /dev/null
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -0,0 +1,110 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import pyrasterframes.rf_types
+
+
+def tile_to_png(tile):
+    """ Provide image of Tile."""
+    if tile.cells is None:
+        return None
+
+    import io
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib.figure import Figure
+
+    # Set up matplotlib objects
+    [width, height] = tile.dimensions()
+    dpi = 300
+    nominal_size = 5.5  # approx full size for a 256x256 tile
+    fig = Figure(figsize=(nominal_size * width / dpi,   nominal_size * height / dpi))
+    canvas = FigureCanvas(fig)
+    axis = fig.add_subplot(1, 1, 1)
+
+    axis.imshow(tile.cells)
+    axis.set_aspect('equal')
+    axis.xaxis.set_ticks([])
+    axis.yaxis.set_ticks([])
+
+    axis.set_title('{}, {})'.format(tile.dimensions(), tile.cell_type.__repr__()))  # compact metadata as title
+
+    with io.BytesIO() as output:
+        canvas.print_png(output)
+        return output.getvalue()
+
+
+def tile_to_html(tile):
+    """ Provide HTML string representation of Tile image."""
+    import base64
+    b64_img_html = '<img src="data:image/png;base64,{}" />'
+    png_bits = tile_to_png(tile)
+    b64_png = base64.b64encode(png_bits).decode('utf-8').replace('\n', '')
+    return b64_img_html.format(b64_png)
+
+
+def pandas_df_to_html(df):
+    """Provide HTML formatting for pandas.DataFrame with rf_types.Tile in the columns.  """
+    import pandas as pd
+    # honor the existing options on display
+    if not pd.get_option("display.notebook_repr_html"):
+        return None
+
+    if len(df) == 0:
+        return df._repr_html_()
+
+    tile_cols = []
+    for c in df.columns:
+        if isinstance(df.iloc[0][c], pyrasterframes.rf_types.Tile):  # if the first is a Tile try formatting
+            tile_cols.append(c)
+
+    def _safe_tile_to_html(t):
+        if isinstance(t, pyrasterframes.rf_types.Tile):
+            return tile_to_html(t)
+        else:
+            # handles case where objects in a column are not all Tile type
+            return t.__repr__()
+
+    # dict keyed by column with custom rendering function
+    formatter = {c: _safe_tile_to_html for c in tile_cols}
+
+    # This is needed to avoid our tile being rendered as `<img src="only up to fifty char...`
+    default_max_colwidth = pd.get_option('display.max_colwidth')  # we'll try to politely put it back
+    pd.set_option('display.max_colwidth', -1)
+    return_html = df.to_html(escape=False,  # means our `< img` does not get changed to `&lt; img`
+                             formatters=formatter,  # apply custom format to columns
+                             render_links=True,  # common in raster frames
+                             notebook=True,
+                             max_rows=pd.get_option("display.max_rows"),  # retain existing options
+                             max_cols=pd.get_option("display.max_columns"),
+                             show_dimensions=pd.get_option("display.show_dimensions"),
+                             )
+    pd.set_option('display.max_colwidth', default_max_colwidth)
+    return return_html
+
+
+try:
+    from IPython import get_ipython
+    # modifications to currently running ipython session, if we are in one; these enable nicer visualization for Pandas
+    if get_ipython() is not None:
+        import pandas
+        html_formatter = get_ipython().display_formatter.formatters['text/html']
+        html_formatter.for_type(pandas.DataFrame, pandas_df_to_html)
+except ImportError:
+    pass
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index ddcfe7654..c66eb414e 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -347,98 +347,11 @@ def dimensions(self):
         """ Return a list of cols, rows as is conventional in GeoTrellis and RasterFrames."""
         return [self.cells.shape[1], self.cells.shape[0]]
 
-    def to_png(self):
-        """ Provide image of Tile."""
-        if self.cells is None:
-            return None
-
-        import io
-        from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-        from matplotlib.figure import Figure
-
-        # Set up matplotlib objects
-        [width, height] = self.dimensions()
-        dpi = 300
-        nominal_size = 5.5  # approx full size for a 256x256 tile
-        fig = Figure(figsize=(nominal_size * width / dpi,   nominal_size * height / dpi))
-        canvas = FigureCanvas(fig)
-        axis = fig.add_subplot(1, 1, 1)
-
-        axis.imshow(self.cells)
-        axis.set_aspect('equal')
-        axis.xaxis.set_ticks([])
-        axis.yaxis.set_ticks([])
-
-        axis.set_title('{}, {})'.format(self.dimensions(), self.cell_type.__repr__()))  # compact metadata as title
-
-        with io.BytesIO() as output:
-            canvas.print_png(output)
-            return output.getvalue()
-
-    def to_html(self):
-        """ Provide HTML string representation of Tile image."""
-        import base64
-        b64_img_html = '<img src="data:image/png;base64,{}" />'
-        png_bits = self.to_png()
-        b64_png = base64.b64encode(png_bits).decode('utf-8').replace('\n', '')
-        return b64_img_html.format(b64_png)
 
     def _repr_png_(self):
-            """Provide default PNG rendering in IPython and Jupyter"""
-            return self.to_png()
-
-    @classmethod
-    def pandas_df_to_html(cls, df):
-        """
-        Provide HTML formatting for pandas.DataFrame or pandas.Series of Tile
-        """
-        import pandas as pd
-        # honor the existing options on display
-        if not pd.get_option("display.notebook_repr_html"):
-            return None
-
-        if len(df) == 0:
-            return df._repr_html_()
-
-
-        tile_cols = []
-        for c in df.columns:
-            if isinstance(df.iloc[0][c], cls):  # if the first is a Tile try formatting
-                tile_cols.append(c)
-
-        def tile_to_html(t):
-            if isinstance(t, Tile):
-                return t.to_html()
-            else:
-                return t.__repr__()
-
-        # dict keyed by column with custom rendering function
-        formatter = {c: tile_to_html for c in tile_cols}
-
-        # This is needed to avoid our tile being rendered as `<img src="only up to fifty char...`
-        default_max_colwidth = pd.get_option('display.max_colwidth')  # we'll try to politely put it back
-        pd.set_option('display.max_colwidth', -1)
-        return_html = df.to_html(escape=False,  # means our `< img` does not get changed to `&lt; img`
-                          formatters=formatter,  # apply custom format to columns
-                          render_links=True,  # common in raster frames
-                          notebook=True,
-                          max_rows=pd.get_option("display.max_rows"),  # retain existing options
-                          max_cols=pd.get_option("display.max_columns"),
-                          show_dimensions=pd.get_option("display.show_dimensions"),
-                          )
-        pd.set_option('display.max_colwidth', default_max_colwidth)
-        return return_html
-
-
-try:
-    from IPython import get_ipython
-    # modifications to currently running ipython session, if we are in one; these enable nicer visualization for Pandas
-    if get_ipython() is not None:
-        import pandas
-        html_formatter = get_ipython().display_formatter.formatters['text/html']
-        html_formatter.for_type(pandas.DataFrame, Tile.pandas_df_to_html)
-except ImportError:
-    pass
+        """Provide default PNG rendering in IPython and Jupyter"""
+        from pyrasterframes.rf_ipython import tile_to_png
+        return tile_to_png(self)
 
 
 class TileUDT(UserDefinedType):

From 840ed1a9ab50d4e248d2c8532154b08fa6a5f649 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 19 Jun 2019 14:50:51 -0400
Subject: [PATCH 167/380] Properly addressed resolving RasterRef in Python
 TileUDT.

---
 .../apache/spark/sql/rf/RasterSourceUDT.scala   |  2 +-
 .../rules/SpatialUDFSubstitutionRules.scala     |  4 ++--
 .../src/main/python/pyrasterframes/context.py   | 12 ++++++++++++
 .../src/main/python/pyrasterframes/rf_types.py  | 10 +++++++++-
 .../main/python/tests/PyRasterFramesTests.py    |  5 +++++
 .../rasterframes/py/PyRFContext.scala           | 17 ++++++++++++++++-
 6 files changed, 45 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
index a5d878798..772bde6fe 100644
--- a/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
+++ b/core/src/main/scala/org/apache/spark/sql/rf/RasterSourceUDT.scala
@@ -40,7 +40,7 @@ class RasterSourceUDT extends UserDefinedType[RasterSource] {
   import RasterSourceUDT._
   override def typeName = "rf_rastersource"
 
-  override def pyUDT: String = "pyrasterframes.RasterSourceUDT"
+  override def pyUDT: String = "pyrasterframes.rf_types.RasterSourceUDT"
 
   def userClass: Class[RasterSource] = classOf[RasterSource]
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
index e2ee23b1f..d6fea76b0 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rules/SpatialUDFSubstitutionRules.scala
@@ -34,8 +34,8 @@ import org.apache.spark.sql.catalyst.rules.Rule
 object SpatialUDFSubstitutionRules extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = {
     plan.transform {
-      case q: LogicalPlan ⇒ q.transformExpressions {
-        case s: ScalaUDF ⇒ SpatialRelation.fromUDF(s).getOrElse(s)
+      case q: LogicalPlan => q.transformExpressions {
+        case s: ScalaUDF => SpatialRelation.fromUDF(s).getOrElse(s)
       }
     }
   }
diff --git a/pyrasterframes/src/main/python/pyrasterframes/context.py b/pyrasterframes/src/main/python/pyrasterframes/context.py
index 18940aaab..ce7904474 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/context.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/context.py
@@ -48,6 +48,18 @@ def lookup(self, function_name):
     def build_info(self):
         return self._jrfctx.buildInfo()
 
+    # NB: Tightly coupled to `org.locationtech.rasterframes.py.PyRFContext._resolveRasterRef`
+    def _resolve_raster_ref(self, ref):
+        f = self.lookup("_resolveRasterRef")
+        return f(
+            ref.source.raster_source_kryo,
+            ref.bandIndex,
+            ref.subextent.xmin,
+            ref.subextent.ymin,
+            ref.subextent.xmax,
+            ref.subextent.ymax,
+        )
+
     @staticmethod
     def active():
         """
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 1ce5d55d1..5a30cb60d 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -31,6 +31,7 @@ class here provides the PyRasterFrames entry point.
 from pyspark.sql.types import *
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
+from pyrasterframes.context import RFContext
 import numpy as np
 
 __all__ = ['RasterFrame', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
@@ -422,7 +423,14 @@ def deserialize(self, datum):
         rows = datum.cell_context.dimensions.rows
         cell_data_bytes = datum.cell_data.cells
         if cell_data_bytes is None:
-            raise Exception("Null cell data: " + str(datum))
+            if datum.cell_data.ref is None:
+                raise Exception("Invalid Tile structure. Missing cells and reference")
+            else:
+                payload = datum.cell_data.ref
+                cell_data_bytes = RFContext.active().resolve_raster_ref(payload)
+
+        if cell_data_bytes is None:
+            raise Exception("Unable to fetch cell data from: " + repr(datum))
 
         try:
             as_numpy = np.frombuffer(cell_data_bytes, dtype=cell_type.to_numpy_dtype())
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 4c1dbd493..2588e4524 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -570,6 +570,11 @@ def test_handle_lazy_eval(self):
         self.assertGreater(tdf.count(),  0)
         self.assertIsNotNone(tdf.first())
 
+        ltdf = df.select('tile')
+        self.assertGreater(ltdf.count(), 0)
+        self.assertIsNotNone(ltdf.first())
+
+
     def test_prt_functions(self):
         df = self.spark.read.rastersource(self.img_uri) \
             .withColumn('crs', rf_crs('tile')) \
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 1e93f6347..31ee5107a 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -20,13 +20,18 @@
  */
 package org.locationtech.rasterframes.py
 
+import java.nio.ByteBuffer
+
 import geotrellis.raster.{CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
+import geotrellis.vector.Extent
 import org.apache.spark.sql._
 import org.locationtech.rasterframes.extensions.RasterJoin
-import org.locationtech.rasterframes.{RasterFunctions, _}
 import org.locationtech.rasterframes.model.LazyCRS
+import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.util.KryoSupport
+import org.locationtech.rasterframes.{RasterFunctions, _}
 import spray.json._
 
 import scala.collection.JavaConverters._
@@ -215,4 +220,14 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   //----------------------------Support Routines-----------------------------------------
 
   def _listToSeq(cols: java.util.ArrayList[AnyRef]): Seq[AnyRef] = cols.asScala
+
+  type jInt = java.lang.Integer
+  type jDouble = java.lang.Double
+  // NB: Tightly coupled to the `RFContext.resolve_raster_ref` method in `pyrasterframes.context`. */
+  def _resolveRasterRef(srcBin: Array[Byte], bandIndex: jInt, xmin: jDouble, ymin: jDouble, xmax: jDouble, ymax: jDouble): AnyRef = {
+    val src = KryoSupport.deserialize[RasterSource](ByteBuffer.wrap(srcBin))
+    val extent = Extent(xmin, ymin, xmax, ymax)
+    val ref = RasterRef(src, bandIndex, Some(extent))
+    ref.tile.toArrayTile().toBytes()
+  }
 }

From a05d6f227abb29c3ea90506a1016161ca6e6fdb2 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 19 Jun 2019 15:26:41 -0400
Subject: [PATCH 168/380] Removed `rf_realize_tile` until we're fully sure it's
 needed.

---
 .../locationtech/rasterframes/RasterFunctions.scala    |  3 ---
 .../rasterframes/expressions/accessors/GetCRS.scala    |  7 +++++++
 .../rasterframes/expressions/package.scala             |  1 -
 .../rasterframes/RasterFunctionsSpec.scala             | 10 ----------
 docs/src/main/tut/release-notes.md                     |  1 -
 .../src/main/python/pyrasterframes/rasterfunctions.py  |  1 -
 .../src/main/python/pyrasterframes/rf_types.py         |  2 +-
 .../src/main/python/tests/PyRasterFramesTests.py       |  4 ----
 8 files changed, 8 insertions(+), 21 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index e0a5cd60d..5ca8e0748 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -60,9 +60,6 @@ trait RasterFunctions {
   /** Extracts the CRS from a RasterSource or ProjectedRasterTile */
   def rf_crs(col: Column): TypedColumn[Any, CRS] = GetCRS(col)
 
-  /** Extracts the Tile component of a RasterSource, ProjectedRasterTile (or Tile) and ensures the cells are fully fetched. */
-  def rf_realize_tile(col: Column): TypedColumn[Any, Tile] = RealizeTile(col)
-
   /** Flattens Tile into a double array. */
   def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
index 00f8f5db4..10efc40b7 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/GetCRS.scala
@@ -37,6 +37,13 @@ import org.locationtech.rasterframes.model.TileContext
  *
  * @since 9/9/18
  */
+@ExpressionDescription(
+  usage = "_FUNC_(raster) - Fetches the CRS of a ProjectedRasterTile or RasterSource.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(raster);
+         ....
+  """)
 case class GetCRS(child: Expression) extends OnTileContextExpression with CodegenFallback {
   override def dataType: DataType = schemaOf[CRS]
   override def nodeName: String = "rf_crs"
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index b169e79d7..432c66888 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -74,7 +74,6 @@ package object expressions {
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[GetExtent]("rf_extent")
     registry.registerExpression[GetCRS]("rf_crs")
-    registry.registerExpression[RealizeTile]("rf_realize_tile")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 1aa1325cf..84f00d30b 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -30,7 +30,6 @@ import org.apache.spark.sql.Encoders
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.expressions.accessors.ExtractTile
 import org.locationtech.rasterframes.model.TileDimensions
-import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
@@ -312,15 +311,6 @@ class RasterFunctionsSpec extends FunSpec
       g should be (extent.jtsGeom)
       checkDocs("rf_geometry")
     }
-
-    it("should realize a delayed load tile") {
-      val df = Seq(lazyPRT).toDF("prt").as[ProjectedRasterTile]
-      val lazyTile = df.select(col("prt.tile").as[Tile]).first()
-      lazyTile shouldBe a[RasterRefTile]
-
-      val realized = df.select(rf_realize_tile(col("prt"))).first()
-      realized should not be a [RasterRefTile]
-    }
   }
 
   describe("per-tile stats") {
diff --git a/docs/src/main/tut/release-notes.md b/docs/src/main/tut/release-notes.md
index 39025f78e..f7fc7360d 100644
--- a/docs/src/main/tut/release-notes.md
+++ b/docs/src/main/tut/release-notes.md
@@ -15,7 +15,6 @@
 * Added `SinglebandGeoTiff.toDF` extension method.
 * Added `DataFrame.rasterJoin` extension method for merging two dataframes with tiles in disparate CRSs.
 * Added `rf_crs` for `ProjectedRasterTile` columns.
-* Added `rf_realize_tile` for cases where you want to ensure no lazy tile loading is happening.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 5eb0c51b4..9d8ceff7a 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -275,7 +275,6 @@ def _(data_tile, mask_tile, mask_value):
     'rf_expm1': 'Performs cell-wise exponential, then subtract one',
     'rf_identity': 'Pass tile through unchanged',
     'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
-    'rf_realize_tile': 'Extracts the Tile component of a RasterSource, ProjectedRasterTile (or Tile) and ensures the cells are fully fetched.',
     'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
     'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
     'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 5a30cb60d..d27bdcc7c 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -427,7 +427,7 @@ def deserialize(self, datum):
                 raise Exception("Invalid Tile structure. Missing cells and reference")
             else:
                 payload = datum.cell_data.ref
-                cell_data_bytes = RFContext.active().resolve_raster_ref(payload)
+                cell_data_bytes = RFContext.active()._resolve_raster_ref(payload)
 
         if cell_data_bytes is None:
             raise Exception("Unable to fetch cell data from: " + repr(datum))
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 2588e4524..569fe95e5 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -566,10 +566,6 @@ class RasterSource(TestEnvironment):
 
     def test_handle_lazy_eval(self):
         df = self.spark.read.rastersource(self.img_uri)
-        tdf = df.select(rf_realize_tile('tile'))
-        self.assertGreater(tdf.count(),  0)
-        self.assertIsNotNone(tdf.first())
-
         ltdf = df.select('tile')
         self.assertGreater(ltdf.count(), 0)
         self.assertIsNotNone(ltdf.first())

From d90f576f1a327d345c5243ba9fbe144976685c91 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 19 Jun 2019 16:55:11 -0400
Subject: [PATCH 169/380] Added copying of assembly jar to Python deps/jars
 directory.

---
 project/PythonBuildPlugin.scala | 26 ++++++++++++++++++--------
 pyrasterframes/README.md        |  3 ++-
 2 files changed, 20 insertions(+), 9 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index b88b4cd70..24a34c6e7 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -57,19 +57,29 @@ object PythonBuildPlugin extends AutoPlugin {
     copySources(Test / pythonSource, Python / test / target, false)
   )
 
-  val buildWhl = Def.task {
-    val buildDir = (Python / target).value
-    val retcode = pySetup.toTask(" build bdist_wheel").value
-    if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
-    val whls = (buildDir / "dist" ** "pyrasterframes*.whl").get()
-    require(whls.length == 1, "Running setup.py should have produced a single .whl file. Try running `clean` first.")
-    whls.head
-  }
+  val buildWhl = Def.sequential(
+    (Compile / assembly), // Needed because SBT will try to parallelize this step out of sequence.
+    Def.task {
+      val log = streams.value.log
+      val buildDir = (Python / target).value
+      val asmbl = (Compile / assembly).value
+      val dest = buildDir / "deps" / "jars" / asmbl.getName
+      IO.copyFile(asmbl, dest)
+      val retcode = pySetup.toTask(" build bdist_wheel").value
+      if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
+      val whls = (buildDir / "dist" ** "pyrasterframes*.whl").get()
+      require(whls.length == 1, "Running setup.py should have produced a single .whl file. Try running `clean` first.")
+      log.info(s"Python .whl file written to '${whls.head}'")
+      whls.head
+    }
+  )
 
   val pyDistAsZip = Def.task {
+    val log = streams.value.log
     val pyDest = (packageBin / artifactPath).value
     val whl = buildWhl.value
     IO.copyFile(whl, pyDest)
+    log.info(s"Maven Python .zip artifact written to '$pyDest'")
     pyDest
   }
 
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 71622e8fb..d81df2918 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -83,7 +83,8 @@ Assuming that `$SCALA_VER` is the major verison of Scala in use (e.g. 2.11) , an
 the primary build artifacts are:
 
 * JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
-* Python package: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
+* Python .whl package with assembly: `pyrasterframes/target/python/dist/pyrasterframes-$VER-py2.py3-none-any.whl`
+* Python package as Maven .zip artifact: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
 
 You build them with:
 

From a8464aa744434cd9c31d4ea3c42166ce123ce245 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 20 Jun 2019 10:00:26 -0400
Subject: [PATCH 170/380] Fixed issue with assembly not being available before
 whl is built.

---
 project/PythonBuildPlugin.scala | 43 ++++++++++++++++++---------------
 1 file changed, 24 insertions(+), 19 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 24a34c6e7..6fcfedf6d 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -56,28 +56,32 @@ object PythonBuildPlugin extends AutoPlugin {
     copySources(Compile / pythonSource, Python / target, true),
     copySources(Test / pythonSource, Python / test / target, false)
   )
+  
+  val pyWhlJar = Def.task {
+    val log = streams.value.log
+    val buildDir = (Python / target).value
+    val asmbl = (Compile / assembly).value
+    val dest = buildDir / "deps" / "jars" / asmbl.getName
+    IO.copyFile(asmbl, dest)
+    log.info(s"PyRasterFrames assembly written to '$dest'")
+    dest
+  }
 
-  val buildWhl = Def.sequential(
-    (Compile / assembly), // Needed because SBT will try to parallelize this step out of sequence.
-    Def.task {
-      val log = streams.value.log
-      val buildDir = (Python / target).value
-      val asmbl = (Compile / assembly).value
-      val dest = buildDir / "deps" / "jars" / asmbl.getName
-      IO.copyFile(asmbl, dest)
-      val retcode = pySetup.toTask(" build bdist_wheel").value
-      if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
-      val whls = (buildDir / "dist" ** "pyrasterframes*.whl").get()
-      require(whls.length == 1, "Running setup.py should have produced a single .whl file. Try running `clean` first.")
-      log.info(s"Python .whl file written to '${whls.head}'")
-      whls.head
-    }
-  )
+  val pyWhl = Def.task {
+    val log = streams.value.log
+    val buildDir = (Python / target).value
+    val retcode = pySetup.toTask(" build bdist_wheel").value
+    if(retcode != 0) throw new RuntimeException(s"'python setup.py' returned $retcode")
+    val whls = (buildDir / "dist" ** "pyrasterframes*.whl").get()
+    require(whls.length == 1, "Running setup.py should have produced a single .whl file. Try running `clean` first.")
+    log.info(s"Python .whl file written to '${whls.head}'")
+    whls.head
+  }.dependsOn(pyWhlJar)
 
-  val pyDistAsZip = Def.task {
+  val pyWhlAsZip = Def.task {
     val log = streams.value.log
     val pyDest = (packageBin / artifactPath).value
-    val whl = buildWhl.value
+    val whl = pyWhl.value
     IO.copyFile(whl, pyDest)
     log.info(s"Maven Python .zip artifact written to '$pyDest'")
     pyDest
@@ -118,7 +122,8 @@ object PythonBuildPlugin extends AutoPlugin {
       test / target := (Compile / target).value / "python" / "tests",
       packageBin := Def.sequential(
         Compile / packageBin,
-        pyDistAsZip,
+        pyWhl,
+        pyWhlAsZip,
       ).value,
       packageBin / artifact := {
         val java = (Compile / packageBin / artifact).value

From cf774a8d5b3b47eedcbb5501b914f7d2b32745bc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 20 Jun 2019 10:20:37 -0400
Subject: [PATCH 171/380] Version of pyspark must be anchored to same version
 in `RFDependenciesPlugin`.

---
 pyrasterframes/src/main/python/setup.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 16a189e94..5d0d8bc2c 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -116,15 +116,15 @@ def run(self):
     install_requires=[
         'pytz',
         'shapely',
-        'pyspark',
-        'numpy',
+        'pyspark==2.3.3',
+        'numpy>=1.7',
         'pandas',
     ],
     setup_requires=[
         'pytz',
         'shapely',
-        'pyspark',
-        'numpy',
+        'pyspark==2.3.3',
+        'numpy>=1.7',
         'pandas',
         'pytest-runner',
         'setuptools>=0.8',

From 6146f0890fc85a6e024843ecd84f284463ae0626 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 17 Jun 2019 19:37:32 -0400
Subject: [PATCH 172/380] Moved Python tests back into main Python source
 directory (keep it pythonic).

---
 pyrasterframes/build.sbt                                       | 3 +++
 .../{test/python => main/python/tests}/PyRasterFramesTests.py  | 0
 .../src/{test/python => main/python/tests}/__init__.py         | 0
 pyrasterframes/src/main/python/{ => tests}/coverage-report.sh  | 2 +-
 4 files changed, 4 insertions(+), 1 deletion(-)
 rename pyrasterframes/src/{test/python => main/python/tests}/PyRasterFramesTests.py (100%)
 rename pyrasterframes/src/{test/python => main/python/tests}/__init__.py (100%)
 rename pyrasterframes/src/main/python/{ => tests}/coverage-report.sh (86%)

diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 570dc1385..2e9e31f42 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,5 +1,8 @@
 import PythonBuildPlugin.autoImport.pySetup
 
+
+Test / pythonSource := (Compile / sourceDirectory).value / "tests"
+
 exportJars := true
 
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
diff --git a/pyrasterframes/src/test/python/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
similarity index 100%
rename from pyrasterframes/src/test/python/PyRasterFramesTests.py
rename to pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
diff --git a/pyrasterframes/src/test/python/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
similarity index 100%
rename from pyrasterframes/src/test/python/__init__.py
rename to pyrasterframes/src/main/python/tests/__init__.py
diff --git a/pyrasterframes/src/main/python/coverage-report.sh b/pyrasterframes/src/main/python/tests/coverage-report.sh
similarity index 86%
rename from pyrasterframes/src/main/python/coverage-report.sh
rename to pyrasterframes/src/main/python/tests/coverage-report.sh
index 8cde233a1..6b547e026 100755
--- a/pyrasterframes/src/main/python/coverage-report.sh
+++ b/pyrasterframes/src/main/python/tests/coverage-report.sh
@@ -4,6 +4,6 @@
 
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
-cd "$( dirname "${BASH_SOURCE[0]}" )"
+cd "$( dirname "${BASH_SOURCE[0]}" )"/..
 
 coverage run setup.py test && coverage html --omit='.eggs/*,setup.py' && open htmlcov/index.html
\ No newline at end of file

From 2c67c3aa07721b4ac403bcce116c8dea5a1deb6a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 20 Jun 2019 15:29:12 -0400
Subject: [PATCH 173/380] Efforts to unify creatation of RasterFrames-enabled
 SparkSession in Python.

---
 LICENSE                                       |  2 +-
 build.sbt                                     | 27 +++++-
 project/PythonBuildPlugin.scala               | 30 +++---
 project/RFProjectPlugin.scala                 |  4 +
 pyrasterframes/README.md                      | 18 +++-
 pyrasterframes/build.sbt                      | 14 +--
 .../src/main/python/examples/Clustering.py    |  7 +-
 .../python/examples/CreatingRasterFrames.py   |  7 +-
 .../src/main/python/examples/MeanValue.py     |  7 +-
 .../src/main/python/examples/NDVI.py          |  9 +-
 .../src/main/python/examples/__init__.py      | 31 +------
 .../main/python/pyrasterframes/__init__.py    | 30 +-----
 .../src/main/python/pyrasterframes/utils.py   | 91 +++++++++++++++++++
 .../main/python/tests/PyRasterFramesTests.py  |  2 +-
 .../src/main/python/tests/__init__.py         | 46 ++++------
 15 files changed, 200 insertions(+), 125 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/pyrasterframes/utils.py

diff --git a/LICENSE b/LICENSE
index 152d8c948..53d10c005 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 This software is licensed under the Apache 2 license, quoted below.
 
-Copyright 2017-2018 Astraea, Inc.
+Copyright 2017-2019 Astraea, Inc.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not
 use this file except in compliance with the License. You may obtain a copy of
diff --git a/build.sbt b/build.sbt
index e322a6591..ada00bf4b 100644
--- a/build.sbt
+++ b/build.sbt
@@ -1,7 +1,30 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2017-2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
 
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("console", "datasource/console")
 
+// Prefer our own IntegrationTest config definition, which inherits from Test.
+val IntegrationTest = RFProjectPlugin.IntegrationTest
+
 lazy val root = project
   .in(file("."))
   .withId("RasterFrames")
@@ -13,8 +36,6 @@ lazy val `rf-notebook` = project
   .dependsOn(pyrasterframes)
   .enablePlugins(RFAssemblyPlugin, DockerPlugin)
 
-lazy val IntegrationTest = config("it") extend Test
-
 lazy val core = project
   .enablePlugins(BuildInfoPlugin)
   .configs(IntegrationTest)
@@ -67,6 +88,8 @@ lazy val pyrasterframes = project
   )
 
 lazy val datasource = project
+  .configs(IntegrationTest)
+  .settings(Defaults.itSettings)
   .dependsOn(core % "test->test;compile->compile")
   .settings(
     moduleName := "rasterframes-datasource",
diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 6fcfedf6d..e9280a617 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -136,21 +136,19 @@ object PythonBuildPlugin extends AutoPlugin {
         dest / s"${art.name}-python-$ver.zip"
       },
       testQuick := pySetup.toTask(" test").value,
-      executeTests := Def.sequential(
-        assembly,
-        Def.task {
-          val resultCode = pySetup.toTask(" test").value
-          val msg = resultCode match {
-            case 1 ⇒ "There are Python test failures."
-            case 2 ⇒ "Python test execution was interrupted."
-            case 3 ⇒ "Internal error during Python test execution."
-            case 4 ⇒ "PyTest usage error."
-            case 5 ⇒ "No Python tests found."
-            case x if x != 0 ⇒ "Unknown error while running Python tests."
-            case _ ⇒ "PyRasterFrames tests successfully completed."
-          }
-          val pySummary = Summary("pyrasterframes", msg)
-          // Would be cool to derive this from the python output...
+      executeTests := Def.task {
+        val resultCode = pySetup.toTask(" test").value
+        val msg = resultCode match {
+          case 1 ⇒ "There are Python test failures."
+          case 2 ⇒ "Python test execution was interrupted."
+          case 3 ⇒ "Internal error during Python test execution."
+          case 4 ⇒ "PyTest usage error."
+          case 5 ⇒ "No Python tests found."
+          case x if x != 0 ⇒ "Unknown error while running Python tests."
+          case _ ⇒ "PyRasterFrames tests successfully completed."
+        }
+        val pySummary = Summary("pyrasterframes", msg)
+        // Would be cool to derive this from the python output...
         val result = if (resultCode == 0) {
           new SuiteResult(
             TestResult.Passed,
@@ -177,7 +175,7 @@ object PythonBuildPlugin extends AutoPlugin {
         }
         result
         Tests.Output(result.result, Map("PyRasterFramesTests" -> result), Iterable(pySummary))
-      }).value
+      }.dependsOn(assembly).value
     )) ++
     addArtifact(Python / packageBin / artifact, Python / packageBin)
 }
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index b7c904416..9f50f3fda 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -11,6 +11,10 @@ object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = GitPlugin
 
+  lazy val IntegrationTest = config("it") extend Test
+
+  override def projectConfigurations: Seq[Configuration] = Seq(IntegrationTest)
+
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
     organizationName := "LocationTech RasterFrames",
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index d81df2918..00f5b7069 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -29,7 +29,17 @@ spark = SparkSession.builder \
 
 #### Pyspark shell or app
 
-To initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. See below for how to build or download those artifacts.
+To quickly get the command to run a `pyspark` shell with PyRasterFrames enabled, run
+
+```bash
+sbt pySparkCmd
+
+# > PYTHONSTARTUP=/var/foo/pyrf_init.py pyspark --jars /somewhere/rasterframes/pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar --py-files /somewhere/rasterframes/pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+```
+
+The runtime dependencies will be created and the command to run printed to the console.
+
+To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. See below for how to build or download those artifacts.
 
 ```bash
    pyspark \
@@ -66,7 +76,7 @@ example below with `sbt`.
 The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
 
 ```bash
-sbt pyTests
+sbt pyrasterframes/test # alias 'pyTest'
 ```
 
 *See also the below discussion of running `setup.py` for more options to run unit tests.* 
@@ -74,7 +84,7 @@ sbt pyTests
 Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
 
 ```bash
-sbt pyExamples
+sbt pyrasterframes/run # alias 'pyExamples'
 ```
 
 ## Creating and Using a Build
@@ -89,7 +99,7 @@ the primary build artifacts are:
 You build them with:
 
 ```bash
-sbt pyrasterframes/package
+sbt pyrasterframes/package # alias 'pyBuild'
 ```
 
 Release versions of these artifacts are published to https://central.sonatype.org/ under the Maven/Ivy groupId:artifactId:version (GAV) coordinates
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 2e9e31f42..57af72f7e 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,8 +1,14 @@
-import PythonBuildPlugin.autoImport.pySetup
 
+addCommandAlias("pyTest", "pyrasterframes/test")
+addCommandAlias("pyBuild", "pyrasterframes/package")
+addCommandAlias("pyExamples", "pyrasterframes/run")
 
 Test / pythonSource := (Compile / sourceDirectory).value / "tests"
 
+Compile / run := pySetup.toTask(" examples").dependsOn(assembly).value
+
+//RFProjectPlugin.IntegrationTest / test := (Compile / run).inputTaskValue
+
 exportJars := true
 
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
@@ -20,11 +26,5 @@ from pyrasterframes.rasterfunctions import *
   println(msg)
 }
 
-lazy val pyExamples = taskKey[Unit]("Run python examples")
 
-pyExamples := Def.sequential(
-  assembly,
-  pySetup.toTask(" examples")
-).value
 
-addCommandAlias("pyTest", "pyrasterframes/test")
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
index 2b6a99218..3825ede28 100644
--- a/pyrasterframes/src/main/python/examples/Clustering.py
+++ b/pyrasterframes/src/main/python/examples/Clustering.py
@@ -1,4 +1,5 @@
-from . import example_session, resource_dir
+from . import test_resource_dir
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
@@ -7,7 +8,7 @@
 from pyspark.ml import Pipeline
 import os
 
-spark = example_session().withRasterFrames()
+spark = create_rf_spark_session()
 
 # The first step is to load multiple bands of imagery and construct
 # a single RasterFrame from them.
@@ -20,7 +21,7 @@
 joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
                   map(lambda bf: spark.read.geotiff(bf[1])
                       .withColumnRenamed('tile', 'band_{}'.format(bf[0])),
-                  map(lambda b: (b, os.path.join(resource_dir, filenamePattern.format(b))), bandNumbers)))
+                  map(lambda b: (b, os.path.join(test_resource_dir(), filenamePattern.format(b))), bandNumbers)))
 
 # We should see a single spatial_key column along with columns of tiles.
 joinedRF.printSchema()
diff --git a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
index 5c018fee8..99ae32b05 100644
--- a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
+++ b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
@@ -1,11 +1,12 @@
-from . import resource_dir, example_session
+from . import test_resource_dir
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes import *
 from pyspark.sql import *
 import os
 
-spark = example_session().withRasterFrames()
+spark = create_rf_spark_session().withRasterFrames()
 
-rf = spark.read.geotiff(os.path.join(resource_dir, 'L8-B8-Robinson-IL.tiff'))
+rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
 
 rf.tileColumns()
diff --git a/pyrasterframes/src/main/python/examples/MeanValue.py b/pyrasterframes/src/main/python/examples/MeanValue.py
index 1bec83c64..06960b7fc 100644
--- a/pyrasterframes/src/main/python/examples/MeanValue.py
+++ b/pyrasterframes/src/main/python/examples/MeanValue.py
@@ -1,12 +1,13 @@
-from . import resource_dir, example_session
+from . import test_resource_dir
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql import *
 import os
 
-spark = example_session().withRasterFrames()
+spark = create_rf_spark_session().withRasterFrames()
 
-rf = spark.read.geotiff(os.path.join(resource_dir, 'L8-B8-Robinson-IL.tiff'))
+rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
 
 rf.agg(rf_agg_no_data_cells('tile'), rf_agg_data_cells('tile'), rf_agg_mean('tile')).show(5, False)
diff --git a/pyrasterframes/src/main/python/examples/NDVI.py b/pyrasterframes/src/main/python/examples/NDVI.py
index 7fb4d28d2..94e7fe031 100644
--- a/pyrasterframes/src/main/python/examples/NDVI.py
+++ b/pyrasterframes/src/main/python/examples/NDVI.py
@@ -1,12 +1,13 @@
-from . import resource_dir, example_session
+from . import test_resource_dir
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes import *
 from pyrasterframes.rasterfunctions import *
 import os
 
-spark = example_session().withRasterFrames()
+spark = create_rf_spark_session().withRasterFrames()
 
-redBand = spark.read.geotiff(os.path.join(resource_dir, 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
-nirBand = spark.read.geotiff(os.path.join(resource_dir, 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
+redBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
+nirBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
 
 rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
     .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
diff --git a/pyrasterframes/src/main/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
index 8bd561d0a..795a3bd62 100644
--- a/pyrasterframes/src/main/python/examples/__init__.py
+++ b/pyrasterframes/src/main/python/examples/__init__.py
@@ -1,31 +1,10 @@
 # examples_setup
 import os
-import glob
-from pyspark.sql import SparkSession
 
-__all__ = ["resource_dir", "example_session"]
+__all__ = ["test_resource_dir"]
 
-HERE = os.path.dirname(os.path.realpath(__file__))
-
-scala_target = os.path.realpath(os.path.join(HERE, '..', '..', 'scala-2.11'))
-resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
-jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
-
-if not len(jarpath) == 1:
-    raise RuntimeError("""
-Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/clean' first. """.format(jarpath))
-
-pyJar = jarpath[0]
-
-
-def example_session():
-    return (SparkSession.builder
-            .master("local[*]")
-            .appName("RasterFrames")
-            .config('spark.driver.extraClassPath', pyJar)
-            .config('spark.executor.extraClassPath', pyJar)
-            .config("spark.ui.enabled", "false")
-            .withKryoSerialization()
-            .getOrCreate())
 
+def test_resource_dir():
+    here = os.path.dirname(os.path.realpath(__file__))
+    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
+    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index fc5c14c1b..884538630 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -59,35 +59,9 @@ def _kryo_init(builder):
 def get_spark_session():
     from pyspark.sql import SparkSession
     import os
-    import sys
-
-    if sys.version < "3":
-        import imp
-        try:
-            module_home = imp.find_module("pyrasterframes")[1]  # path
-            jar_path = os.path.join(module_home, 'jars')
-        except ImportError:
-            print("pyrasterframes was not pip installed. See documentation for initialization instructions.")
-            return None
-    else:
-        from importlib.util import find_spec
-        try:
-            module_home = find_spec("pyrasterframes").origin
-            jar_path = os.path.join(os.path.dirname(module_home), 'jars')
-        except ImportError:
-            print("pyrasterframes was not pip installed. See documentation for initialization instructions.")
-            return None
-
-    jars_cp = ','.join([f.path for f in os.scandir(jar_path) if f.name[-3:] == 'jar'])
-    spark = (SparkSession.builder
-             .master("local[*]")
-             .appName("RasterFrames")
-             .config('spark.jars', jars_cp)
-             .withKryoSerialization()
-             .getOrCreate()
-             ).withRasterFrames()
-    return spark
+    from pyrasterframes.utils import create_rf_spark_session
 
+    return create_rf_spark_session()
 
 def _convert_df(df, sp_key=None, metadata=None):
     ctx = SparkContext._active_spark_context._rf_context
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
new file mode 100644
index 000000000..155ce0f46
--- /dev/null
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -0,0 +1,91 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import glob
+from pyspark.sql import SparkSession
+import os
+import sys
+
+__all__ = ["create_rf_spark_session", "find_pyrasterframes_jar_dir", "find_pyrasterframes_assembly"]
+
+
+def find_pyrasterframes_jar_dir():
+    """
+    Locates the directory where JVM libraries for Spark are stored.
+    :return: path to jar director as a string
+    """
+    jar_dir = None
+
+    if sys.version < "3":
+        import imp
+        try:
+            module_home = imp.find_module("pyrasterframes")[1]  # path
+            jar_dir = os.path.join(module_home, 'jars')
+        except ImportError:
+            pass
+
+    else:
+        from importlib.util import find_spec
+        try:
+            module_home = find_spec("pyrasterframes").origin
+            jar_dir = os.path.join(os.path.dirname(module_home), 'jars')
+        except ImportError:
+            pass
+
+    # Case for when we're running from source build
+    if jar_dir is None or not os.path.exists(jar_dir):
+        def pdir(curr):
+            return os.path.dirname(curr)
+
+        here = pdir(os.path.realpath(__file__))
+        target_dir = pdir(pdir(here))
+        # See if we're running outside of sbt build and adjust
+        if os.path.basename(target_dir) != "target":
+            target_dir = os.path.join(pdir(pdir(target_dir)), 'target')
+        jar_dir = os.path.join(target_dir, 'scala-2.11')
+
+    return os.path.realpath(jar_dir)
+
+
+def find_pyrasterframes_assembly():
+    jar_dir = find_pyrasterframes_jar_dir()
+    jarpath = glob.glob(os.path.join(jar_dir, 'pyrasterframes-assembly*.jar'))
+
+    if not len(jarpath) == 1:
+        raise RuntimeError("""
+Expected to find exactly one assembly. Found '{}' instead. 
+Try running 'sbt pyrasterframes/package' first. """.format(jarpath))
+    return jarpath[0]
+
+
+def create_rf_spark_session():
+    jar_path = find_pyrasterframes_jar_dir()
+
+    jars_cp = ','.join([f.path for f in os.scandir(jar_path) if f.name[-3:] == 'jar'])
+
+    spark = (SparkSession.builder
+             .master("local[*]")
+             .appName("RasterFrames")
+             .config('spark.jars', jars_cp)
+             .config("spark.ui.enabled", "false")
+             .withKryoSerialization()
+             .getOrCreate())
+    spark.withRasterFrames()
+    return spark
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index b424979d9..35ab325f8 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -574,7 +574,7 @@ def test_matmul(self):
         nd = r1.cell_type.no_data_value()
         e1 = Tile(np.ma.masked_equal(np.array([[nd, 10],
                                                [nd, nd]], dtype=r1.cell_type.to_numpy_dtype()), nd))
-        self.assertTrue(r1 == e1)
+        self.assertEqual(r1, e1)
 
 
 class PandasInterop(TestEnvironment):
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 5dac30274..267701f34 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -24,6 +24,7 @@
 
 from pyspark.sql import SparkSession
 from pyrasterframes.rasterfunctions import *
+from pyrasterframes.utils import create_rf_spark_session
 
 import sys
 
@@ -33,6 +34,22 @@
     import __builtin__ as builtins
 
 
+def test_resource_dir():
+    here = os.path.dirname(os.path.realpath(__file__))
+    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
+    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
+
+
+def testing_spark_session():
+    spark = create_rf_spark_session()
+    spark.sparkContext.setLogLevel('ERROR')
+
+    print("Spark Version: " + spark.version)
+    print("Spark Config: " + str(spark.sparkContext._conf.getAll()))
+
+    return spark
+
+
 class TestEnvironment(unittest.TestCase):
     """
     Base class for tests.
@@ -44,35 +61,10 @@ def rounded_compare(self, val1, val2):
 
     @classmethod
     def setUpClass(cls):
-        # gather Scala requirements
-        here = os.path.dirname(os.path.realpath(__file__))
-        target_dir = os.path.dirname(os.path.dirname(here))
-
-        scala_target = os.path.realpath(os.path.join(target_dir, 'scala-2.11'))
-
-        jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
-
-        if not len(jarpath) == 1:
-            raise RuntimeError("""
-Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/clean' first.""".format(jarpath))
-
         # hard-coded relative path for resources
-        cls.resource_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
-
-        # spark session with RF
-        cls.spark = (SparkSession.builder
-                     .config('spark.driver.extraClassPath', jarpath[0])
-                     .config('spark.executor.extraClassPath', jarpath[0])
-                     .config('spark.ui.enabled', False)
-                     .withKryoSerialization()
-                     .getOrCreate())
-        cls.spark.sparkContext.setLogLevel('ERROR')
-
-        print("Spark Version: " + cls.spark.version)
-        print(cls.spark.sparkContext._conf.getAll())
+        cls.resource_dir = test_resource_dir()
 
-        cls.spark.withRasterFrames()
+        cls.spark = testing_spark_session()
 
         cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
 

From ac0e0d9c9fdb9dc28b08ce554fd75bd881649a90 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 20 Jun 2019 19:38:19 -0400
Subject: [PATCH 174/380] Switched from .py to .pymd as the source for
 IPython-evaluated docs.

---
 build.sbt                                     |   2 +-
 docs/src/main/paradox/apps/geotrellis-ops.md  |  44 ----
 docs/src/main/paradox/apps/index.md           |  12 -
 docs/src/main/paradox/apps/ndvi.md            |  61 -----
 .../src/main/paradox/creating-rasterframes.md | 212 ----------------
 .../main/paradox/exporting-rasterframes.md    | 189 ---------------
 docs/src/main/paradox/getting-started.md      |  56 -----
 docs/src/main/paradox/index.md                |  51 ----
 docs/src/main/paradox/ml/L8-RGB-VA.png        | Bin 75390 -> 0 bytes
 docs/src/main/paradox/ml/classification.md    | 229 ------------------
 docs/src/main/paradox/ml/clustering.md        | 152 ------------
 docs/src/main/paradox/ml/index.md             |  18 --
 docs/src/main/paradox/ml/statistics.md        |  95 --------
 docs/src/main/paradox/spatial-queries.md      |   7 -
 .../src/main/python/docs/Background.md        |   8 -
 .../python/docs/InitializingEnvironment.py    |  46 ----
 .../src/main/python/docs/__init__.py          |  29 +--
 .../src/main/python/docs/getting-started.pymd |  41 ++++
 pyrasterframes/src/main/python/docs/index.md  |  46 +++-
 .../src/main/python/pyrasterframes/utils.py   |  91 +++++++
 pyrasterframes/src/main/python/setup.py       |   4 +-
 21 files changed, 178 insertions(+), 1215 deletions(-)
 delete mode 100644 docs/src/main/paradox/apps/geotrellis-ops.md
 delete mode 100644 docs/src/main/paradox/apps/index.md
 delete mode 100644 docs/src/main/paradox/apps/ndvi.md
 delete mode 100644 docs/src/main/paradox/creating-rasterframes.md
 delete mode 100644 docs/src/main/paradox/exporting-rasterframes.md
 delete mode 100644 docs/src/main/paradox/getting-started.md
 delete mode 100644 docs/src/main/paradox/index.md
 delete mode 100644 docs/src/main/paradox/ml/L8-RGB-VA.png
 delete mode 100644 docs/src/main/paradox/ml/classification.md
 delete mode 100644 docs/src/main/paradox/ml/clustering.md
 delete mode 100644 docs/src/main/paradox/ml/index.md
 delete mode 100644 docs/src/main/paradox/ml/statistics.md
 delete mode 100644 docs/src/main/paradox/spatial-queries.md
 delete mode 100644 pyrasterframes/src/main/python/docs/Background.md
 delete mode 100644 pyrasterframes/src/main/python/docs/InitializingEnvironment.py
 create mode 100644 pyrasterframes/src/main/python/docs/getting-started.pymd
 create mode 100644 pyrasterframes/src/main/python/pyrasterframes/utils.py

diff --git a/build.sbt b/build.sbt
index 1b0b224da..34a99934e 100644
--- a/build.sbt
+++ b/build.sbt
@@ -118,7 +118,7 @@ lazy val docs = project
     ),
     paradoxTheme := Some(builtinParadoxTheme("generic")),
     makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value,
-    Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value.getParentFile,
+    Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value,
     Compile / paradox := (Compile / paradox).dependsOn(pyrasterframes / doc).value
   )
   .settings(
diff --git a/docs/src/main/paradox/apps/geotrellis-ops.md b/docs/src/main/paradox/apps/geotrellis-ops.md
deleted file mode 100644
index 56ccbbabb..000000000
--- a/docs/src/main/paradox/apps/geotrellis-ops.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# GeoTrellis Operations
-
-```tut:invisible
-import org.locationtech.rasterframes._
-import geotrellis.raster._
-import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-
-implicit val spark = SparkSession.builder().
-  withKryoSerialization.
-  master("local[*]").appName("RasterFrames").getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-import spark.implicits._
-val scene = SinglebandGeoTiff("../core/src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128)
-```
-
-
-GeoTrellis provides a [rich set of Map Algebra operations](https://docs.geotrellis.io/en/latest/guide/core-concepts.html#map-algebra) and other tile processing features that can be used with RasterFrames via Spark's UDF support.
-
-Here's an example creating a UDFs to invoke the `equalize` transformation on each tile in a RasterFrame, and then compute the resulting per-tile mean of it.
-
-```tut
-import geotrellis.raster.equalization._
-val equalizer = udf((t: Tile) => t.equalize())
-val equalized = rf.select(equalizer($"tile") as "equalized")
-equalized.select(rf_tile_mean($"equalized") as "equalizedMean").show(5, false)
-```
-
-Here's an example downsampling a tile and rendering each tile as a matrix of numerical values.
-
-```tut  
-val downsample = udf((t: Tile) => t.resample(4, 4))
-val downsampled = rf.where(rf_no_data_cells($"tile") === 0).select(downsample($"tile") as "minime")
-downsampled.select(rf_tile_to_array_double($"minime") as "cell_values").limit(2).show(false)
-```
-
-
-```tut:invisible
-spark.stop()
-```
-
diff --git a/docs/src/main/paradox/apps/index.md b/docs/src/main/paradox/apps/index.md
deleted file mode 100644
index c0f7bc28e..000000000
--- a/docs/src/main/paradox/apps/index.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Applications
-
-In this section we outline a few use cases for using *RasterFrames* in your own applications.  
-
-@@ toc { depth=2 }
-
-@@@ index
-
-* [GeoTrellis Operations](geotrellis-ops.md)
-* [Calculating NDVI](ndvi.md)
-
-@@@
diff --git a/docs/src/main/paradox/apps/ndvi.md b/docs/src/main/paradox/apps/ndvi.md
deleted file mode 100644
index 85c59f2b2..000000000
--- a/docs/src/main/paradox/apps/ndvi.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Computing NDVI
-
-```tut:invisible
-import org.locationtech.rasterframes._
-import geotrellis.raster._
-import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.spark._
-import geotrellis.spark.io._
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-
-implicit val spark = SparkSession.builder().
-  withKryoSerialization.
-  master("local[*]").appName("RasterFrames").getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-import spark.implicits._
-val scene = SinglebandGeoTiff("../core/src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128).cache()
-```
-
-Here's an example of computing the Normalized Differential Vegetation Index (NDVI) is a 
-standardized vegetation index which allows us to generate an image highlighting differences in
-relative biomass. 
-
-> “An NDVI is often used worldwide to monitor drought, monitor and predict agricultural production, assist in predicting hazardous fire zones, and map desert encroachment. The NDVI is preferred for global vegetation monitoring because it helps to compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors” (Lillesand. *Remote sensing and image interpretation*. 2004).
-
-```tut:silent
-def redBand = SinglebandGeoTiff("../core/src/test/resources/L8-B4-Elkton-VA.tiff").projectedRaster.toRF("red_band")
-def nirBand = SinglebandGeoTiff("../core/src/test/resources/L8-B5-Elkton-VA.tiff").projectedRaster.toRF("nir_band")
-
-// We use `asRF` to indicate we know the structure still conforms to RasterFrame constraints
-val rf = redBand.spatialJoin(nirBand).withColumn("ndvi", rf_normalized_difference($"red_band", $"nir_band")).asRF
-
-val pr = rf.toRaster($"ndvi", 466, 428)
-
-val brownToGreen = ColorRamp(
-  RGBA(166,97,26,255),
-  RGBA(223,194,125,255),
-  RGBA(245,245,245,255),
-  RGBA(128,205,193,255),
-  RGBA(1,133,113,255)
-).stops(128)
-
-val colors = ColorMap.fromQuantileBreaks(pr.tile.histogramDouble(), brownToGreen)
-pr.tile.color(colors).renderPng().write("target/scala-2.11/tut/apps/rf-ndvi.png")
-```
-
-![](rf-ndvi.png)
-
-For a georefrenced singleband greyscale image, we could have done this instead: 
-
-```scala
-GeoTiff(pr).write("ndvi.tiff")
-```
-
-
-```tut:invisible
-spark.stop()
-```
-
diff --git a/docs/src/main/paradox/creating-rasterframes.md b/docs/src/main/paradox/creating-rasterframes.md
deleted file mode 100644
index 75b65f869..000000000
--- a/docs/src/main/paradox/creating-rasterframes.md
+++ /dev/null
@@ -1,212 +0,0 @@
-# Creating&nbsp;RasterFrames
-
-## Initialization
-
-There are a couple of setup steps necessary anytime you want to work with RasterFrames. the first is to import the API symbols into scope:
-
-
-
-```tut:silent
-import org.locationtech.rasterframes._
-import org.apache.spark.sql._
-```
-
-
-Next, initialize the `SparkSession`, and call the `withRasterFrames` method on it:
-
-
-```tut:silent
-implicit val spark = SparkSession.builder().
-  master("local").appName("RasterFrames").
-  config("spark.ui.enabled", "false").
-  withKryoSerialization.
-  getOrCreate().
-  withRasterFrames
-```
-
-And, as is standard Spark SQL practice, we import additional DataFrame support:
-
-```tut:silent
-import spark.implicits._
-```
-
-```tut:invisible
-spark.sparkContext.setLogLevel("ERROR")
-```
-
-Now we are ready to create a RasterFrame.
-
-## Reading a GeoTIFF
-
-The most straightforward way to create a `RasterFrame` is to read a [GeoTIFF](https://en.wikipedia.org/wiki/GeoTIFF)
-file using a RasterFrame [`DataSource`](https://spark.apache.org/docs/latest/sql-programming-guide.html#data-sources)
-designed for this purpose. [Cloud Optimized GeoTIFF](http://www.cogeo.org/) files are preferred, as they don't require re-tiling and can be read in a more memory-efficient way.
-
-First add the following import:
-
-
-```tut:silent
-import org.locationtech.rasterframes.datasource.geotiff._
-import java.io.File
-```
-
-(This is what adds the `.geotiff` method to `spark.read` below.)
-
-Then we use the `DataFrameReader` provided by `spark.read` to read the GeoTIFF:
-
-
-```tut:book
-val samplePath = new File("../core/src/test/resources/LC08_RGB_Norfolk_COG.tiff")
-val tiffRF = spark.read.
-  geotiff.
-  loadRF(samplePath.toURI)
-```
-
-
-Let's inspect the structure of what we get back:
-
-
-```tut
-tiffRF.printSchema()
-```
-
-As reported by Spark, RasterFrames extracts 6 columns from the GeoTIFF we selected. Some of these columns are dependent
-on the contents of the source data, and some are are always available. Let's take a look at these in more detail.
-
-* `spatial_key`: GeoTrellis assigns a `SpatialKey` or a `SpaceTimeKey` to each tile, mapping it to the layer grid from
-which it came. If it has a `SpaceTimeKey`, RasterFrames will split it into a `SpatialKey` and a `TemporalKey` (the
-latter with column name `temporal_key`).
-* `extent`: The bounding box of the tile in the tile's native CRS.
-* `metadata`: The TIFF format header tags found in the file.
-* `tile` or `tile_n` (where `n` is a band number): For singleband GeoTIFF files, the `tile` column contains the cell
-data split into tiles. For multiband tiles, each column with `tile_` prefix contains each of the sources bands,
-in the order they were stored.
-
-See the section [Inspecting a `RasterFrame`](#inspecting-a--code-rasterframe--code-) (below) for more details on accessing the RasterFrame's metadata.
-
-## Reading a GeoTrellis Layer
-
-If your imagery is already ingested into a [GeoTrellis layer](https://docs.geotrellis.io/en/latest/guide/spark.html#writing-layers),
-you can use the RasterFrames GeoTrellis DataSource. There are two parts to this GeoTrellis Layer support. The first
-is the GeoTrellis Catalog DataSource, which lists the GeoTrellis layers available at a URI. The second part is the actual
-RasterFrame reader for pulling a layer into a RasterFrame.
-
-Before we show how all of this works we need to have a GeoTrellis layer to work with. We can create one with the RasterFrame we constructed above.
-
-
-```tut:silent
-import org.locationtech.rasterframes.datasource.geotrellis._
-import java.nio.file.Files
-
-val base = Files.createTempDirectory("rf-").toUri
-val layer = Layer(base, "sample", 0)
-tiffRF.write.geotrellis.asLayer(layer).save()
-```
-
-Now we can point our catalog reader at the base directory and see what was saved:
-
-```tut
-val cat = spark.read.geotrellisCatalog(base)
-cat.printSchema
-cat.show()
-```
-
-As you can see, there's a lot of information stored in each row of the catalog. Most of this is associated with how the
-layer is discretized. However, there may be other application specific metadata serialized with a layer that can be use
-to filter the catalog entries or select a specific one. But for now, we're just going to load a RasterFrame in from the
-catalog using a convenience function.
-
-```tut
-val firstLayer = cat.select(geotrellis_layer).first
-val rfAgain = spark.read.geotrellis.loadRF(firstLayer)
-rfAgain.show()
-```
-
-If you already know the `LayerId` of what you're wanting to read, you can bypass working with the catalog:
-
-```tut
-val anotherRF = spark.read.geotrellis.loadRF(layer)
-```
-
-## Writing a GeoTrellis Layer
-
-See @ref:[Exporting to a GeoTrellis Layer](creating-rasterframes.md#exporting-to-a-geotrellis-layer).
-
-## Using GeoTrellis APIs
-
-If you are used to working directly with the GeoTrellis APIs, there are a number of additional ways to create a `RasterFrame`, as enumerated in the sections below.
-
-First, some more `import`s:
-
-```tut:silent
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.spark.io._
-```
-
-
-### From `ProjectedExtent`
-
-The simplest mechanism for getting a RasterFrame is to use the `toRF(tileCols, tileRows)` extension method on `ProjectedRaster`.
-
-
-```tut
-val scene = SinglebandGeoTiff("../core/src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128)
-rf.show(5, false)
-```
-
-
-### From `TileLayerRDD`
-
-Another option is to use a GeoTrellis [`LayerReader`](https://docs.geotrellis.io/en/latest/guide/tile-backends.html),
-to get a `TileLayerRDD` for which there's also a `toRF` extension method.
-
-
-```scala
-import geotrellis.spark._
-val tiledLayer: TileLayerRDD[SpatialKey] = ???
-val rf = tiledLayer.toRF
-```
-
-
-## Inspecting a `RasterFrame`
-
-`RasterFrame` has a number of methods providing access to metadata about the contents of the RasterFrame.
-
-### Tile Column Names
-
-
-```tut:book
-rf.tileColumns.map(_.toString)
-```
-
-
-### Spatial Key Column Name
-
-
-```tut:book
-rf.spatialKeyColumn.toString
-```
-
-### Temporal Key Column
-
-Returns an `Option[Column]` since not all RasterFrames have an explicit temporal dimension.
-
-```tut:book
-rf.temporalKeyColumn.map(_.toString)
-```
-
-### Tile Layer Metadata
-
-The Tile Layer Metadata defines how the spatial/spatiotemporal domain is discretized into tiles, and what the key bounds are.
-
-```tut
-import spray.json._
-// NB: The `fold` is required because an `Either` is returned, depending on the key type.
-rf.tileLayerMetadata.fold(_.toJson, _.toJson).prettyPrint
-```
-
-```tut:invisible
-spark.stop()
-```
-
diff --git a/docs/src/main/paradox/exporting-rasterframes.md b/docs/src/main/paradox/exporting-rasterframes.md
deleted file mode 100644
index 5a855db53..000000000
--- a/docs/src/main/paradox/exporting-rasterframes.md
+++ /dev/null
@@ -1,189 +0,0 @@
-# Exporting&nbsp;RasterFrames
-
-```tut:invisible
-import org.locationtech.rasterframes._
-import geotrellis.spark._
-import geotrellis.raster._
-import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-
-implicit val spark = SparkSession.builder().config("spark.ui.enabled", "false").
-  withKryoSerialization.
-  master("local[*]").appName("RasterFrames").getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-import spark.implicits._
-val scene = SinglebandGeoTiff("../core/src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128).cache()
-```
-
-While the goal of RasterFrames is to make it as easy as possible to do your geospatial analysis with a single 
-construct, it is helpful to be able to transform it into other representations for various use cases.
-
-## Converting to Array
-
-The cell values within a `Tile` are encoded internally as an array. There may be use cases 
-where the additional context provided by the `Tile` construct is no longer needed and one would
-prefer to work with the underlying array data.
-
-The `rf_tile_to_array_int` and `rf_tile_to_array_double` functions provide this facility.
-
-```tut
-val withArrays = rf.withColumn("tileData", rf_tile_to_array_int($"tile")).drop("tile")
-withArrays.show(5, 40)
-```
-
-You can convert the data back to an array, but you have to specify the target tile dimensions. 
-
-```tut
-val tileBack = withArrays.withColumn("tileAgain", arrayToTile($"tileData", 128, 128))
-tileBack.drop("tileData").show(5, 40)
-``` 
-
-Note that the created tile will not have a `NoData` value associated with it. Here's how you can do that:
-
-```tut
-val tileBackAgain = withArrays.withColumn("tileAgain", rf_with_no_data(rf_array_to_tile($"tileData", 128, 128), 3))
-tileBackAgain.drop("tileData").show(5, 50)
-```
-
-## Writing to Parquet
-
-It is often useful to write Spark results in a form that is easily reloaded for subsequent analysis. 
-The [Parquet](https://parquet.apache.org/)columnar storage format, native to Spark, is ideal for this. RasterFrames
-work just like any other DataFrame in this scenario as long as @scaladoc[`rfInit`][rfInit] is called to register
-the imagery types.
-
-
-Let's assume we have a RasterFrame we've done some fancy processing on: 
-
-```tut:silent
-import geotrellis.raster.equalization._
-val equalizer = udf((t: Tile) => t.equalize())
-val equalized = rf.withColumn("equalized", equalizer($"tile")).asRF
-```
-
-```tut
-equalized.printSchema
-equalized.select(aggStats($"tile")).show(false)
-equalized.select(aggStats($"equalized")).show(false)
-```
-
-We write it out just like any other DataFrame, including the ability to specify partitioning:
-
-```tut:silent
-val filePath = "/tmp/equalized.parquet"
-equalized.select("*", "spatial_key.*").write.partitionBy("col", "row").mode(SaveMode.Overwrite).parquet(filePath)
-```
-
-Let's confirm partitioning happened as expected:
-
-```tut
-import java.io.File
-new File(filePath).list.filter(f => !f.contains("_"))
-```
-
-Now we can load the data back in and check it out:
-
-```tut:silent
-val rf2 = spark.read.parquet(filePath)
-```
-
-```tut
-rf2.printSchema
-equalized.select(aggStats($"tile")).show(false)
-equalized.select(aggStats($"equalized")).show(false)
-```
-
-
-## Exporting a Raster
-
-For the purposes of debugging, the RasterFrame tiles can be reassembled back into a raster for viewing. However, 
-keep in mind that this will download all the data to the driver, and reassemble it in-memory. So it's not appropriate 
-for very large coverages.
-
-Here's how one might render the image to a georeferenced GeoTIFF file: 
-
-```tut:silent
-import geotrellis.raster.io.geotiff.GeoTiff
-val image = equalized.toRaster($"equalized", 774, 500)
-GeoTiff(image).write("target/scala-2.11/tut/rf-raster.tiff")
-```
-
-[*Download GeoTIFF*](rf-raster.tiff)
-
-Here's how one might render a raster frame to a false color PNG file:
-
-```tut:silent
-val colors = ColorMap.fromQuantileBreaks(image.tile.histogram, ColorRamps.BlueToOrange)
-image.tile.color(colors).renderPng().write("target/scala-2.11/tut/rf-raster.png")
-```
-
-![](rf-raster.png)
-
-## Exporting to a GeoTrellis Layer
-
-For future analysis it is helpful to persist a RasterFrame as a [GeoTrellis layer](http://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html).
-
-First, convert the RasterFrame into a TileLayerRDD. The return type is an Either;
-the `left` side is for spatial-only keyed data
-
-```tut:book
-val tlRDD = equalized.toTileLayerRDD($"equalized").left.get
-```
-
-Then create a GeoTrellis layer writer:
-
-```tut:silent
-import java.nio.file.Files
-import spray.json._
-import DefaultJsonProtocol._
-import geotrellis.spark.io._
-val p = Files.createTempDirectory("gt-store")
-val writer: LayerWriter[LayerId] = LayerWriter(p.toUri)
-
-val layerId = LayerId("equalized", 0)
-writer.write(layerId, tlRDD, index.ZCurveKeyIndexMethod)
-```
-
-Take a look at the metadata in JSON format:
-```tut
-AttributeStore(p.toUri).readMetadata[JsValue](layerId).prettyPrint
-```
-
-## Converting to `RDD` and `TileLayerRDD`
-
-Since a `RasterFrame` is just a `DataFrame` with extra metadata, the method 
-@scaladoc[`DataFrame.rdd`][rdd] is available for simple conversion back to `RDD` space. The type returned 
-by `.rdd` is dependent upon how you select it.
-
-
-```tut
-import scala.reflect.runtime.universe._
-def showType[T: TypeTag](t: T) = println(implicitly[TypeTag[T]].tpe.toString)
-
-showType(rf.rdd)
-
-showType(rf.select(rf.spatialKeyColumn, $"tile".as[Tile]).rdd) 
-
-showType(rf.select(rf.spatialKeyColumn, $"tile").as[(SpatialKey, Tile)].rdd) 
-```
-
-If your goal convert a single tile column with its spatial key back to a `TileLayerRDD[K]`, then there's an additional
-extension method on `RasterFrame` called [`toTileLayerRDD`][toTileLayerRDD], which preserves the tile layer metadata,
-enhancing interoperation with GeoTrellis RDD extension methods.
-
-```tut
-showType(rf.toTileLayerRDD($"tile".as[Tile]))
-```
-
-```tut:invisible
-spark.stop()
-```
-
-[rfInit]: org.locationtech.rasterframes.package#rfInit%28SQLContext%29:Unit
-[rdd]: org.apache.spark.sql.Dataset#frdd:org.apache.spark.rdd.RDD[T]
-[toTileLayerRDD]: org.locationtech.rasterframes.RasterFrameMethods#toTileLayerRDD%28tileCol:RasterFrameMethods.this.TileColumn%29:Either[geotrellis.spark.TileLayerRDD[geotrellis.spark.SpatialKey],geotrellis.spark.TileLayerRDD[geotrellis.spark.SpaceTimeKey]]
-[tile_to_array_int]: org.locationtech.rasterframes.ColumnFunctions#tile_to_array_int
-
diff --git a/docs/src/main/paradox/getting-started.md b/docs/src/main/paradox/getting-started.md
deleted file mode 100644
index b16dac563..000000000
--- a/docs/src/main/paradox/getting-started.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# Getting&nbsp;Started
-
-## Quick Start
-
-RasterFrames is available in in a Jupyter Notebook Docker container for quick experimentation.
-
-1. Install [Docker](https://www.docker.com/get-docker) for your OS flavor.
-2. Run  
-   `docker run -it --rm -p 8888:8888 -p 4040-4044:4040-4044 s22s/rasterframes-notebooks`
-
-Additional instructions can be found [here](https://github.com/locationtech/rasterframes/blob/develop/deployment/README.md).
-
-## General Setup
-
-*RasterFrames* is published via [Maven Central](https://search.maven.org/search?q=g:org.locationtech.rasterframes) (click link to see latest versions).
-
-To use RasterFrames, add the following library dependencies:
-
-@@dependency[sbt,Maven,Gradle] {
-  group="org.locationtech.rasterframes"
-  artifact="rasterframes_2.11"
-  version="$version$"
-}
-
-@@dependency[sbt,Maven,Gradle] {
-  group="org.locationtech.rasterframes"
-  artifact="rasterframes-datasource_2.11"
-  version="$version$"
-}
-
-Optional:
-
-@@dependency[sbt,Maven,Gradle] {
-  group="org.locationtech.rasterframes"
-  artifact="rasterframes-experimental_2.11"
-  version="$version$"
-}
-
-It assumes that SparkSQL 2.3.x is available in the runtime classpath. Here's how to add it explicitly:
-
-@@dependency[sbt,Maven,Gradle] {
-  group="org.apache.spark"
-  artifact="spark-sql"
-  version="2.3.2"
-}
-
-
-Until GeoTrellis 3.0 is released, the following artifact resolver needs to be added to your build.
-
-```scala
-resolvers += "Azavea Public Builds" at "https://dl.bintray.com/azavea/geotrellis"
-```
-  
-@@@ note
-Most of the following examples are shown using the Spark DataFrames API. However, many could also be rewritten to use the Spark SQL API instead. We hope to add more examples in that form in the future.
-@@@
diff --git a/docs/src/main/paradox/index.md b/docs/src/main/paradox/index.md
deleted file mode 100644
index 84dadce2f..000000000
--- a/docs/src/main/paradox/index.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# RasterFrames
-
-RasterFrames® brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
-
-RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
-
-To learn more, please see the @ref:[Getting Started](getting-started.md) section of this manual.
-
-The source code can be found on GitHub at [locationtech/rasterframes](https://github.com/locationtech/rasterframes).
-
-<img src="RasterFramePipeline.svg" width="600px"/>
-
-<hr/>
-
-@@@ div { .md-left}
-
-## Detailed Contents
-
-@@ toc { depth=2 }
-
-@@@
-
-@@@ div { .md-right }
-
-## Related Links
-
-* [Gitter Channel](https://gitter.im/s22s/raster-frames)&nbsp;&nbsp;[![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-* [API Documentation](latest/api/index.html)
-* [GitHub Repository](https://github.com/locationtech/rasterframes)
-* [Astraea, Inc.](http://www.astraea.earth/) (the company behind RasterFrames)
-
-@@@
-
-@@@ div { .md-clear }
-
-&nbsp;
-
-@@@
-
-@@@ index
-* [Getting Started](getting-started.md)
-* [PyRasterFrames](pyrasterframes/index.md)
-* [Creating RasterFrames](creating-rasterframes.md)
-* [Spatial Queries](spatial-queries.md)
-* [Applications](apps/index.md)
-* [Machine Learning](ml/index.md)
-* [Exporting Rasterframes](exporting-rasterframes.md)
-* [UDF Reference](reference.md)
-* [Release Notes](release-notes.md)
-@@@
-
diff --git a/docs/src/main/paradox/ml/L8-RGB-VA.png b/docs/src/main/paradox/ml/L8-RGB-VA.png
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diff --git a/docs/src/main/paradox/ml/classification.md b/docs/src/main/paradox/ml/classification.md
deleted file mode 100644
index fb50e5664..000000000
--- a/docs/src/main/paradox/ml/classification.md
+++ /dev/null
@@ -1,229 +0,0 @@
-# Classification
-
-In this example we will do some simple cell classification based on multiband imagery and a
-target/label raster. As a part of the process we'll explore the cross-validation support in
-SparkML.
-
-## Setup
-
-First some setup:
-
-```tut:silent
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.ml.{NoDataFilter, TileExploder}
-import geotrellis.raster._
-import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.classification.DecisionTreeClassifier
-import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
-import org.apache.spark.sql._
-
-implicit val spark = SparkSession.builder().
-  withKryoSerialization.
-  master("local[*]").appName(getClass.getName).getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-
-import spark.implicits._
-
-// Utility for reading imagery from our test data set
-def readTiff(name: String): SinglebandGeoTiff = SinglebandGeoTiff(s"../core/src/test/resources/$name")
-```
-
-## Loading Data
-
-The first step is to load multiple bands of imagery and construct a single RasterFrame from them.
-To do this we:
-
-1. Identify the GeoTIFF filename. 
-2. Read the TIFF raster
-3. Convert to a raster frame of `tileSize` sized tiles, with an appropriate column name
-4. Use the RasterFrames `spatialJoin` function to create a new RasterFrame with a column for each band
- 
-
-```tut:silent
-val filenamePattern = "L8-%s-Elkton-VA.tiff"
-val bandNumbers = 2 to 7
-val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
-val tileSize = 10
-
-// For each identified band, load the associated image file
-val joinedRF = bandNumbers.
-  map { b ⇒ (b, filenamePattern.format("B" + b)) }.
-  map { case (b, f) ⇒ (b, readTiff(f)) }.
-  map { case (b, t) ⇒ t.projectedRaster.toRF(tileSize, tileSize, s"band_$b") }.
-  reduce(_ spatialJoin _)
-```
-
-We should see a single `spatial_key` column along with 6 columns of tiles.
-
-```tut
-joinedRF.printSchema()
-```
-
-Similarly pull we pull in the target label data. When load the target label raster we have 
-to convert the cell type to `Double` to meet expectations of SparkML. 
-
-```tut:silent
-val targetCol = "target"
-
-val target = readTiff(filenamePattern.format("Labels")).
-  mapTile(_.convert(DoubleConstantNoDataCellType)).
-  projectedRaster.
-  toRF(tileSize, tileSize, targetCol)
-```
-
-Take a peek at what kind of label data we have to work with.
-
-```tut
-target.select(rf_agg_stats(target(targetCol))).show
-```
-
-Join the target label RasterFrame with the band tiles to create our analytics base table
-
-```tut:silent
-val abt = joinedRF.spatialJoin(target)
-
-```
-
-## ML Pipeline
-
-The data preparation modeling pipeline is next. SparkML requires that each observation be in 
-its own row, and those observations be packed into a single `Vector` type. The first step is 
-to "explode" the tiles into a single row per cell/pixel. Then we filter out any rows that
-have `NoData` values (which will cause an error during training). Finally we use the
-SparkML `VectorAssembler` to create that `Vector`. 
-
-```tut:silent
-val exploder = new TileExploder()
-
-val noDataFilter = new NoDataFilter().
-  setInputCols(bandColNames :+ targetCol)
-
-val assembler = new VectorAssembler().
-  setInputCols(bandColNames).
-  setOutputCol("features")
-```
-
-We are going to use a decision tree for classification. You can swap out one of the other multi-class
-classification algorithms if you like. With the algorithm selected we can assemble our modeling pipeline.
-
-```tut:silent
-val classifier = new DecisionTreeClassifier().
-  setLabelCol(targetCol).
-  setFeaturesCol(assembler.getOutputCol)
-
-val pipeline = new Pipeline().
-  setStages(Array(exploder, noDataFilter, assembler, classifier))
-```
-
-## Cross Validation
-
-To extend the sophistication of the example we are going to use the SparkML support for 
-cross-validation and hyper-parameter tuning. The first step is to configure how we're 
-going to evaluate our model's performance. Then we define the hyperparmeter(s) we're going to 
-vary and evaluate. Finally we configure the cross validator. 
-
-```tut:silent
-val evaluator = new MulticlassClassificationEvaluator().
-  setLabelCol(targetCol).
-  setPredictionCol("prediction").
-  setMetricName("accuracy")
-
-val paramGrid = new ParamGridBuilder().
-  addGrid(classifier.maxDepth, Array(2, 3, 4)).
-  build()
-
-val trainer = new CrossValidator().
-  setEstimator(pipeline).
-  setEvaluator(evaluator).
-  setEstimatorParamMaps(paramGrid).
-  setNumFolds(4)
-```
-
-Push the "go button":
-
-```tut
-val model = trainer.fit(abt)
-```
-
-## Model Evaluation
-
-To view the model's performance we format the `paramGrid` settings used for each model and 
-render the parameter/performance association.
-
-```tut:silent
-val metrics = model.getEstimatorParamMaps.
-  map(_.toSeq.map(p ⇒ s"${p.param.name} = ${p.value}")).
-  map(_.mkString(", ")).
-  zip(model.avgMetrics)
-```
-```tut
-metrics.toSeq.toDF("params", "metric").show(false)
-```
-
-Finally, we score the original data set (including the cells without target values) and 
-add up class membership results.
-
-```tut
-val scored = model.bestModel.transform(joinedRF)
-
-scored.groupBy($"prediction" as "class").count().show
-```
-
-## Visualizing Results
-
-The predictions are in a DataFrame with each row representing a separate pixel. 
-To assemble a raster to visualize the class assignments, we have to go through a
-multi-stage process to get the data back in tile form, and from there to combined
-raster form.
-
-First, we get the DataFrame back into RasterFrame form:
-
-```tut:silent
-val tlm = joinedRF.tileLayerMetadata.left.get
-
-val retiled = scored.groupBy($"spatial_key").agg(
-  rf_assemble_tile(
-    $"column_index", $"row_index", $"prediction",
-    tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType
-  )
-)
-
-val rf = retiled.asRF($"spatial_key", tlm)
-```
-
-To render our visualization, we convert to a raster first, and then use an
-`IndexedColorMap` to assign each discrete class a different color, and finally
-rendering to a PNG file.
-
-```tut:silent
-val raster = rf.toRaster($"prediction", 186, 169)
-
-val clusterColors = IndexedColorMap.fromColorMap(
-  ColorRamps.Viridis.toColorMap((0 until 3).toArray)
-)
-
-raster.tile.renderPng(clusterColors).write("target/scala-2.11/tut/ml/classified.png")
-```
-
-| Color Composite    | Target Labels          | Class Assignments   |
-| ------------------ | ---------------------- | ------------------- |
-| ![](L8-RGB-VA.png) | ![](target-labels.png) | ![](classified.png) |
-
-
-```tut:invisible
-val raster = SinglebandGeoTiff("../core/src/test/resources/L8-Labels-Elkton-VA.tiff").raster
-
-val k = raster.tile.findMinMax._2
-
-val clusterColors = IndexedColorMap.fromColorMap(
-  ColorRamps.Viridis.toColorMap((0 to k).toArray)
-)
-
-raster.tile.renderPng(clusterColors).write("target/scala-2.11/tut/ml/target-labels.png")
-
-spark.stop()
-```
diff --git a/docs/src/main/paradox/ml/clustering.md b/docs/src/main/paradox/ml/clustering.md
deleted file mode 100644
index 36de19652..000000000
--- a/docs/src/main/paradox/ml/clustering.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# Clustering
-
-In this example we will do some simple cell clustering based on multiband imagery.
-
-## Setup 
-
-First some setup:
-
-```tut:silent
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.ml.TileExploder
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.raster._
-import geotrellis.raster.render._
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.clustering.{KMeans, KMeansModel}
-import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.sql._
-
-// Utility for reading imagery from our test data set
-def readTiff(name: String): SinglebandGeoTiff = SinglebandGeoTiff(s"../core/src/test/resources/$name")
-
-implicit val spark = SparkSession.builder().
-  withKryoSerialization.
-  master("local[*]").appName(getClass.getName).getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-
-import spark.implicits._
-```
-
-## Loading Data
-
-The first step is to load multiple bands of imagery and construct a single RasterFrame from them.
-
-```tut:silent
-val filenamePattern = "L8-B%d-Elkton-VA.tiff"
-val bandNumbers = 1 to 4
-val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
-
-// For each identified band, load the associated image file, convert to a RasterFrame, and join
-val joinedRF = bandNumbers.
-  map { b ⇒ (b, filenamePattern.format(b)) }.
-  map { case (b, f) ⇒ (b, readTiff(f)) }.
-  map { case (b, t) ⇒ t.projectedRaster.toRF(s"band_$b") }.
-  reduce(_ spatialJoin _)
-```
-
-We should see a single `spatial_key` column along with 4 columns of tiles.
-
-```tut
-joinedRF.printSchema()
-```
-
-## ML Pipeline 
-
-SparkML requires that each observation be in its own row, and those
-observations be packed into a single `Vector`. The first step is to
-"explode" the tiles into a single row per cell/pixel.
-
-```tut:silent
-val exploder = new TileExploder()
-```
-
-To "vectorize" the the band columns, as required by SparkML, we use the SparkML 
-`VectorAssembler`. We then configure our algorithm, create the transformation pipeline,
-and train our model. (Note: the selected value of *K* below is arbitrary.) 
-
-```tut:silent
-val assembler = new VectorAssembler().
-  setInputCols(bandColNames).
-  setOutputCol("features")
-
-// Configure our clustering algorithm
-val k = 5
-val kmeans = new KMeans().setK(k)
-
-// Combine the two stages
-val pipeline = new Pipeline().setStages(Array(exploder, assembler, kmeans))
-
-// Compute clusters
-val model = pipeline.fit(joinedRF)
-```
-
-## Model Evaluation
-
-At this point the model can be saved off for later use, or used immediately on the same
-data we used to compute the model. First we run the data through the model to assign 
-cluster IDs to each cell.
-
-```tut
-val clustered = model.transform(joinedRF)
-clustered.show(8)
-```
-
-If we want to inspect the model statistics, the SparkML API requires us to go
-through this unfortunate contortion:
-
-```tut:silent
-val clusterResults = model.stages.collect{ case km: KMeansModel ⇒ km}.head
-```
-
-Compute sum of squared distances of points to their nearest center:
-
-```tut
-val metric = clusterResults.computeCost(clustered)
-println("Within set sum of squared errors: " + metric)
-```
-
-## Visualizing Results
-
-The predictions are in a DataFrame with each row representing a separate pixel. 
-To assemble a raster to visualize the cluster assignments, we have to go through a
-multi-stage process to get the data back in tile form, and from there to combined
-raster form.
-
-First, we get the DataFrame back into RasterFrame form:
-
-```tut:silent
-val tlm = joinedRF.tileLayerMetadata.left.get
-
-val retiled = clustered.groupBy($"spatial_key").agg(
-  rf_assemble_tile(
-    $"column_index", $"row_index", $"prediction",
-    tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType
-  )
-)
-
-val rf = retiled.asRF($"spatial_key", tlm)
-```
-
-To render our visualization, we convert to a raster first, and then use an
-`IndexedColorMap` to assign each discrete cluster a different color, and finally
-rendering to a PNG file.
-
-```tut:silent
-val raster = rf.toRaster($"prediction", 186, 169)
-
-val clusterColors = IndexedColorMap.fromColorMap(
-  ColorRamps.Viridis.toColorMap((0 until k).toArray)
-)
-
-raster.tile.renderPng(clusterColors).write("target/scala-2.11/tut/ml/clustered.png")
-```
-
-| Color Composite    | Cluster Assignments |
-| ------------------ | ------------------- |
-| ![](L8-RGB-VA.png) | ![](clustered.png)  |
-
-
-```tut:invisible
-spark.stop()
-```
diff --git a/docs/src/main/paradox/ml/index.md b/docs/src/main/paradox/ml/index.md
deleted file mode 100644
index 5798ff3b7..000000000
--- a/docs/src/main/paradox/ml/index.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Machine Learning
-
-@@@ note
-
-This examples here only scratch the surface of the type of machine learning one can perform 
-with RasterFrames. More examples are forthcoming. 
-
-@@@
-
-@@ toc { depth=2 }
-
-@@@ index
-
-* [Statistics](statistics.md)
-* [Clustering](clustering.md)
-* [Classification](classification.md)
-
-@@@
diff --git a/docs/src/main/paradox/ml/statistics.md b/docs/src/main/paradox/ml/statistics.md
deleted file mode 100644
index 94611be0b..000000000
--- a/docs/src/main/paradox/ml/statistics.md
+++ /dev/null
@@ -1,95 +0,0 @@
-# Raster Statistics
-
-```tut:invisible
-import org.locationtech.rasterframes._
-import geotrellis.raster._
-import geotrellis.raster.render._
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.spark._
-import geotrellis.spark.io._
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-
-implicit val spark = SparkSession.builder().
-  withKryoSerialization.
-   master("local[*]").appName("RasterFrames").getOrCreate().withRasterFrames
-spark.sparkContext.setLogLevel("ERROR")
-import spark.implicits._
-val scene = SinglebandGeoTiff("../core/src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128).cache()
-```
-
-RasterFrames has a number of extension methods and columnar functions for performing analysis on tiles.
-
-## Tile Statistics 
-
-### Tile Dimensions
-
-Get the nominal tile dimensions. Depending on the tiling there may be some tiles with different sizes on the edges.
-
-```tut
-rf.select(rf.spatialKeyColumn, rf_tile_dimensions($"tile")).show(3)
-```
-
-### Descriptive Statistics
-
-#### NoData Counts
-
-Count the numer of `NoData` and non-`NoData` cells in each tile.
-
-```tut
-rf.select(rf.spatialKeyColumn, rf_no_data_cells($"tile"), rf_data_cells($"tile")).show(3)
-```
-
-#### Tile Mean
-
-Compute the mean value in each tile.
- 
-```tut
-rf.select(rf.spatialKeyColumn, rf_tile_mean($"tile")).show(3)
-```
-
-#### Tile Summary Statistics
-
-Compute a suite of summary statistics for each tile. Use `tile_stats` for integral cells types, and `tile_stats_double`
-for floating point cell types.
-
-```tut
-rf.withColumn("stats", rf_tile_stats($"tile")).select(rf.spatialKeyColumn, $"stats.*").show(3)
-```
-
-### Histogram
-
-The `tile_histogram` function computes a histogram over the data in each tile. 
-
-In this example we compute quantile breaks.
-
-```tut
-rf.select(rf_tile_histogram($"tile")).map(_.quantileBreaks(5)).show(5, false)
-```
-
-## Aggregate Statistics
-
-The `rf_agg_stats` function computes the same summary statistics as `rf_tile_stats`, but aggregates them over the whole 
-RasterFrame.
-
-```tut
-rf.select(rf_agg_stats($"tile")).show()
-```
-
-A more involved example: extract bin counts from a computed `Histogram`.
-
-```tut
-rf.select(rf_agg_approx_histogram($"tile")).
-  map(h => for(v <- h.labels) yield(v, h.itemCount(v))).
-  select(explode($"value") as "counts").
-  select("counts._1", "counts._2").
-  toDF("value", "count").
-  orderBy(desc("count")).
-  show(10)
-```
-
-```tut:invisible
-spark.stop()
-```
-
diff --git a/docs/src/main/paradox/spatial-queries.md b/docs/src/main/paradox/spatial-queries.md
deleted file mode 100644
index c492b114a..000000000
--- a/docs/src/main/paradox/spatial-queries.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Spatial&nbsp;Queries
-
-An important aspect of working with big geospatial data is the ability to filter based on spatio-temporal predicates. RasterFrames uses [GeoMesa Spark JTS](http://www.geomesa.org/documentation/current/user/spark/spark_jts.html) for specifying and executing spatial queries against RasterFrame data sources.
-
-@@@ note
-Until this section is written, please see [the `GeoTrellisDataSource` tests](https://github.com/s22s/raster-frames/blob/develop/datasource/src/test/scala/astraea/spark/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) for examples on how to use the spatial query support.
-@@@
diff --git a/pyrasterframes/src/main/python/docs/Background.md b/pyrasterframes/src/main/python/docs/Background.md
deleted file mode 100644
index 36ec0b32d..000000000
--- a/pyrasterframes/src/main/python/docs/Background.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# What is a RasterFrame?
-
-_This is a work in progress_
-
-
-1. Conceptual model
-2. Data model
-3. Compute model
diff --git a/pyrasterframes/src/main/python/docs/InitializingEnvironment.py b/pyrasterframes/src/main/python/docs/InitializingEnvironment.py
deleted file mode 100644
index 0832b5143..000000000
--- a/pyrasterframes/src/main/python/docs/InitializingEnvironment.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#' # Initializing PySpark
-
-#' There are a number of ways to use PyRasterFrames:
-#'
-#' 1. Standalone Script
-#' 2. `pyspark` shell
-#' 3. Notebook Environment
-#'
-
-#' ## Initializing a Python Script
-
-#+ echo=False
-from docs import *
-
-#' Most of RasterFrames is implemented in Scala, and compiled to a Java Virtual Machine (JVM)
-#' library file called a '.jar' file. The RasterFrames build provides a special `.jar` file,
-#' called an "assembly", containing all of RasterFrames as well as the libraries that RasterFrames
-#' rquires outside of Spark
-#' Let's assume we can locate the PyRasterFrames "assembly" `.jar` file with a function named
-#' `find_pyrasterframes_assembly`.
-
-#' The first step is to set up a `SparkSession`:
-
-from pyspark.sql import SparkSession
-from pyrasterframes import *
-jar = find_pyrasterframes_assembly()
-spark = (SparkSession.builder
-         .master("local[*]")
-         .appName("RasterFrames")
-         .config('spark.driver.extraClassPath', jar)
-         .config('spark.executor.extraClassPath', jar)
-         .withKryoSerialization()
-         .getOrCreate())
-spark.withRasterFrames()
-
-#' Now we have a standard Spark session with RasterFrames enabled in it.
-#' To import RasterFrames functions into the environment, use:
-from pyrasterframes.rasterfunctions import *
-
-#' Functions starting with `rf_` (raster-oriented) or `st_` (vector geometry-oriented) are
-#' become available for use with DataFrames.
-
-list(filter(lambda x: x.startswith("rf_") or x.startswith("st_"), dir()))
-
-#+ echo=False
-spark.stop()
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index d08030799..4a1d1d5bc 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -1,30 +1,3 @@
-import os
-import glob
-from pyspark.sql import SparkSession
 
-__all__ = ["create_spark_session", "find_pyrasterframes_assembly"]
-
-def find_pyrasterframes_assembly():
-    here = os.path.dirname(os.path.realpath(__file__))
-    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
-    jarpath = glob.glob(os.path.join(scala_target, 'pyrasterframes-assembly*.jar'))
-    if not len(jarpath) == 1:
-        raise RuntimeError("""
-Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/package' first. """.format(jarpath))
-    return jarpath[0]
-
-def create_spark_session():
-    pyJar = find_pyrasterframes_assembly()
-    spark = (SparkSession.builder
-             .master("local[*]")
-             .appName("RasterFrames")
-             .config('spark.driver.extraClassPath', pyJar)
-             .config('spark.executor.extraClassPath', pyJar)
-             .config("spark.ui.enabled", "false")
-             .withKryoSerialization()
-             .getOrCreate())
-    spark.withRasterFrames()
-    spark.sparkContext.setLogLevel("ERROR")
-    return spark
+__all__ = []
 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
new file mode 100644
index 000000000..a5436b030
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -0,0 +1,41 @@
+# Initializing PySpark
+
+There are a number of ways to use PyRasterFrames:
+
+1. Standalone Script
+2. `pyspark` shell
+3. Notebook Environment
+
+## Initializing a Python Script
+
+```python, echo=False
+from docs import *
+```
+
+RasterFrames requires some special configuration to Spark to be fully functional. As a convenience you can use a provided utility function to get a preconfigured Spark session: 
+
+The first step is to set up a `SparkSession`:
+
+```python
+from pyspark.sql import SparkSession
+from pyrasterframes.utils import create_rf_spark_session 
+spark = create_rf_spark_session()
+```
+
+Now we have a standard Spark session with RasterFrames enabled in it.
+To import RasterFrames functions into the environment, use:
+
+```python
+from pyrasterframes.rasterfunctions import *
+```
+
+Functions starting with `rf_` (raster-oriented) or `st_` (vector geometry-oriented) are
+become available for use with DataFrames.
+
+```python
+list(filter(lambda x: x.startswith("rf_") or x.startswith("st_"), dir()))
+```
+
+```python, echo=False 
+spark.stop()
+```
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index ff5669d78..92d71730c 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -1,7 +1,45 @@
-# PyRasterFrames Users' Manual
+# RasterFrames
 
-@@@ index
+RasterFrames® brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
+
+RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
+
+To learn more, please see the @ref:[Getting Started](getting-started.md) section of this manual.
+
+The source code can be found on GitHub at [locationtech/rasterframes](https://github.com/locationtech/rasterframes).
+
+<img src="RasterFramePipeline.svg" width="600px"/>
+
+<hr/>
+
+@@@ div { .md-left}
+
+## Detailed Contents
+
+@@ toc { depth=2 }
 
-* [Initializing a RasterFrames Environment](InitializingEnvironment.md)
+@@@
+
+@@@ div { .md-right }
+
+## Related Links
+
+* [Gitter Channel](https://gitter.im/s22s/raster-frames)&nbsp;&nbsp;[![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+* [API Documentation](latest/api/index.html)
+* [GitHub Repository](https://github.com/locationtech/rasterframes)
+* [Astraea, Inc.](http://www.astraea.earth/) (the company behind RasterFrames)
+
+@@@
+
+@@@ div { .md-clear }
+
+&nbsp;
+
+@@@
+
+@@@ index
+* [Getting Started](getting-started.md)
+* [UDF Reference](reference.md)
+* [Release Notes](release-notes.md)
+@@@
 
-@@@
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
new file mode 100644
index 000000000..155ce0f46
--- /dev/null
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -0,0 +1,91 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import glob
+from pyspark.sql import SparkSession
+import os
+import sys
+
+__all__ = ["create_rf_spark_session", "find_pyrasterframes_jar_dir", "find_pyrasterframes_assembly"]
+
+
+def find_pyrasterframes_jar_dir():
+    """
+    Locates the directory where JVM libraries for Spark are stored.
+    :return: path to jar director as a string
+    """
+    jar_dir = None
+
+    if sys.version < "3":
+        import imp
+        try:
+            module_home = imp.find_module("pyrasterframes")[1]  # path
+            jar_dir = os.path.join(module_home, 'jars')
+        except ImportError:
+            pass
+
+    else:
+        from importlib.util import find_spec
+        try:
+            module_home = find_spec("pyrasterframes").origin
+            jar_dir = os.path.join(os.path.dirname(module_home), 'jars')
+        except ImportError:
+            pass
+
+    # Case for when we're running from source build
+    if jar_dir is None or not os.path.exists(jar_dir):
+        def pdir(curr):
+            return os.path.dirname(curr)
+
+        here = pdir(os.path.realpath(__file__))
+        target_dir = pdir(pdir(here))
+        # See if we're running outside of sbt build and adjust
+        if os.path.basename(target_dir) != "target":
+            target_dir = os.path.join(pdir(pdir(target_dir)), 'target')
+        jar_dir = os.path.join(target_dir, 'scala-2.11')
+
+    return os.path.realpath(jar_dir)
+
+
+def find_pyrasterframes_assembly():
+    jar_dir = find_pyrasterframes_jar_dir()
+    jarpath = glob.glob(os.path.join(jar_dir, 'pyrasterframes-assembly*.jar'))
+
+    if not len(jarpath) == 1:
+        raise RuntimeError("""
+Expected to find exactly one assembly. Found '{}' instead. 
+Try running 'sbt pyrasterframes/package' first. """.format(jarpath))
+    return jarpath[0]
+
+
+def create_rf_spark_session():
+    jar_path = find_pyrasterframes_jar_dir()
+
+    jars_cp = ','.join([f.path for f in os.scandir(jar_path) if f.name[-3:] == 'jar'])
+
+    spark = (SparkSession.builder
+             .master("local[*]")
+             .appName("RasterFrames")
+             .config('spark.jars', jars_cp)
+             .config("spark.ui.enabled", "false")
+             .withKryoSerialization()
+             .getOrCreate())
+    spark.withRasterFrames()
+    return spark
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 33477fda0..da72ee90d 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -96,8 +96,8 @@ def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
         self.files = filter(
-            lambda x: not x.name.startswith('_'),
-            list((Path(here) / 'docs').resolve().glob('*.py'))
+            lambda x: not path.basename(x)[:1] == '_',
+            glob(path.join(here, 'docs', '*.pymd'))
         )
 
     def finalize_options(self):

From ddc5207f3114031bcef4dfb95c3480000642cf83 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 20 Jun 2019 20:31:14 -0400
Subject: [PATCH 175/380] Scaffolding for new documentation.

---
 project/PythonBuildPlugin.scala               |   2 +
 pyrasterframes/build.sbt                      |  12 +++---
 .../src/main/python/docs/aggregation.md       |   2 +
 .../src/main/python/docs/description.md       |  37 +++++++++++++++++
 .../src/main/python/docs/getting-started.pymd |  38 +++++++++++++++---
 pyrasterframes/src/main/python/docs/index.md  |   9 +++++
 .../src/main/python/docs/local-algebra.pymd   |   1 +
 .../src/main/python/docs/nodata-handling.pymd |   2 +
 .../src/main/python/docs/pandas-numpy.md      |   2 +
 .../src/main/python/docs/raster-io.pymd       |   7 ++++
 .../src/main/python/docs/raster-processing.md |  12 ++++++
 .../main/python/docs/rasterframe-anatomy.png  | Bin 0 -> 216018 bytes
 .../python/docs/rasterframes-data-sources.png | Bin 0 -> 498181 bytes
 .../docs/rasterframes-locationtech-stack.png  | Bin 0 -> 630758 bytes
 .../src/main/python/docs/spark-ml.pymd        |   2 +
 .../main/python/docs/spatial-relations.pymd   |   1 +
 .../src/main/python/docs/time-series.pymd     |   1 +
 17 files changed, 117 insertions(+), 11 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/aggregation.md
 create mode 100644 pyrasterframes/src/main/python/docs/description.md
 create mode 100644 pyrasterframes/src/main/python/docs/local-algebra.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/nodata-handling.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/pandas-numpy.md
 create mode 100644 pyrasterframes/src/main/python/docs/raster-io.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/raster-processing.md
 create mode 100644 pyrasterframes/src/main/python/docs/rasterframe-anatomy.png
 create mode 100644 pyrasterframes/src/main/python/docs/rasterframes-data-sources.png
 create mode 100644 pyrasterframes/src/main/python/docs/rasterframes-locationtech-stack.png
 create mode 100644 pyrasterframes/src/main/python/docs/spark-ml.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/spatial-relations.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/time-series.pymd

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index a0d5971ea..789150997 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -37,6 +37,8 @@ object PythonBuildPlugin extends AutoPlugin {
     val pythonSource = settingKey[File]("Default Python source directory.").withRank(ASetting)
     val pythonCommand = settingKey[String]("Python command. Defaults to 'python'")
     val pySetup = inputKey[Int]("Run 'python setup.py <args>'. Returns exit code.")
+
+    // TODO: figure out how to rewrite this using the standard `mappings` facility.
     def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
       val s = streams.value
       val src = srcDir.value
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 942fbf9c5..9b488f804 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -8,9 +8,9 @@ Test / pythonSource := (Compile / sourceDirectory).value / "tests"
 
 exportJars := true
 Python / doc / sourceDirectory := (Python / target).value / "docs"
-Python / doc / target := (Python / target).value / "markdown" / "pyrasterframes"
-Python / doc := {
-  val _ = Def.sequential(
+Python / doc / target := (Compile / target).value / "py-markdown"
+Python / doc := (Python / doc / target).toTask.dependsOn(
+  Def.sequential(
     assembly,
     pySetup.toTask(" pweave"),
     copySources(
@@ -18,9 +18,9 @@ Python / doc := {
       Python / doc / target,
       deleteFirst = true
     )
-  ).value
-  (Python / doc / target).value
-}
+  )
+).value
+
 
 doc := (Python / doc).value
 
diff --git a/pyrasterframes/src/main/python/docs/aggregation.md b/pyrasterframes/src/main/python/docs/aggregation.md
new file mode 100644
index 000000000..140aec39d
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/aggregation.md
@@ -0,0 +1,2 @@
+# Aggregation
+
diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
new file mode 100644
index 000000000..8a27c66ad
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -0,0 +1,37 @@
+# Description
+
+Human beings have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis. 
+
+Today, DataFrames are the _lingua franca_ of data science. The evolution of the tabular form has continued with Apache Spark SQL, which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables interactive and batch-oriented cluster computing without having to be versed in the highly specialized skills typically required for high-performance computing. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
+
+RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. According to a World Bank document on assets for post-disaster situation awareness[^1]:
+
+> Of the 1,738 operational satellites currently orbiting the earth (as of 9/[20]17), 596 are earth observation satellites and 477 of these are non-military assets (ie available to civil society including commercial entities and governments for earth observation, according to the Union of Concerned Scientists). This number is expected to increase significantly over the next ten years. The 200 or so planned remote sensing satellites have a value of over 27 billion USD (Forecast International). This estimate does not include the burgeoning fleets of smallsats as well as micro, nano and even smaller satellites... All this enthusiasm has, not unexpectedly, led to a veritable fire-hose of remotely sensed data which is becoming difficult to navigate even for seasoned experts.
+
+RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
+
+## Architecture
+
+RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
+[GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
+[JTS](https://github.com/locationtech/jts), and
+[SFCurve](https://github.com/locationtech/sfcurve) (see Figure 1).
+
+![LocationTech Stack](rasterframes-locationtech-stack.png)
+
+RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As showin in Figure 2, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
+
+![RasterFrame Anatomy](rasterframe-anatomy.png)
+
+Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. 
+
+![RasterFrame Data Sources](rasterframes-data-sources.png)
+
+[R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
+[Pandas]:https://pandas.pydata.org/
+[GeoJSON]:https://en.wikipedia.org/wiki/GeoJSON
+[GTLayer]:https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html#layouts-and-tile-layers
+[PDS]:https://registry.opendata.aws/modis/
+
+[^1]: [_Demystifying Satellite Assets for Post-Disaster Situation Awareness_](https://docs.google.com/document/d/11bIw5HcEiZy8SKli6ZFQC2chVEiiIJ-f0o6btA4LU48).
+World Bank via [OpenDRI.org](https://opendri.org/resource/demystifying-satellite-assets-for-post-disaster-situation-awareness/). Accessed November 28, 2018. 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index a5436b030..623eb2fcc 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -1,12 +1,40 @@
-# Initializing PySpark
+# Getting Started
+
 
 There are a number of ways to use PyRasterFrames:
 
-1. Standalone Script
-2. `pyspark` shell
-3. Notebook Environment
+1. `pyspark` shell
+2. Jupyter Notebook
+3. Standalone Python Script
+
+## `pyspark` shell
+
+
+To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. See below for how to build or download those artifacts.
+
+```bash
+   pyspark \
+    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
+    --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
+    --conf spark.kryoserializer.buffer.max=500m \
+    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
+    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+   
+```
+
+Then in the pyspark shell or app, import the module and call `withRasterFrames` on the SparkSession.
+
+```python, evaluate=False
+import pyrasterframes
+spark = spark.withRasterFrames()
+df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+```
+
+## Jupyter Notebook
+
+See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook)/README.md
 
-## Initializing a Python Script
+## Standalone Python Script
 
 ```python, echo=False
 from docs import *
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 92d71730c..f92aae9ed 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -38,8 +38,17 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 @@@
 
 @@@ index
+* [Description](description.md)
 * [Getting Started](getting-started.md)
+* [Raster Data I/O](raster-io.md)
+* [Spatial Relations](spatial-relations.md)
+* [Raster Processing](raster-processing.md)
+* [Pandas & NumPy Interoperabiliyt](pandas-numpy.md)
 * [UDF Reference](reference.md)
 * [Release Notes](release-notes.md)
 @@@
 
+
+
+Raster processing
+Installation 
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
new file mode 100644
index 000000000..d8d257d14
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -0,0 +1 @@
+# Local Algebra
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
new file mode 100644
index 000000000..dde8433a2
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -0,0 +1,2 @@
+# "NoData" Handling
+
diff --git a/pyrasterframes/src/main/python/docs/pandas-numpy.md b/pyrasterframes/src/main/python/docs/pandas-numpy.md
new file mode 100644
index 000000000..35c124f61
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/pandas-numpy.md
@@ -0,0 +1,2 @@
+# Pandas and NumPy Interoperabilty
+
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
new file mode 100644
index 000000000..375f9c4f8
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -0,0 +1,7 @@
+# Raster Data I/O
+
+## Reading Raster Data
+
+## Writing Raster Data
+
+
diff --git a/pyrasterframes/src/main/python/docs/raster-processing.md b/pyrasterframes/src/main/python/docs/raster-processing.md
new file mode 100644
index 000000000..0d3189c66
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/raster-processing.md
@@ -0,0 +1,12 @@
+# Raster Processing
+
+@@@ index
+
+* [Local Algebra](local-algebra.md)
+* ["NoData" Handling](nodata-handling.md)
+* [Aggregation](aggregation.md)
+* [Time Series](time-series.md)
+* [Spark ML](spark-ml.md)
+
+@@@
+
diff --git a/pyrasterframes/src/main/python/docs/rasterframe-anatomy.png b/pyrasterframes/src/main/python/docs/rasterframe-anatomy.png
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zt&Ak--`+V-fK4<1$-+YT*Df<w0jWwlm;;1=5^y_{>jG7?WPy*>T+_Gn;RP=_^!|#D
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HcmV?d00001

diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/spark-ml.pymd
new file mode 100644
index 000000000..f68de2f29
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/spark-ml.pymd
@@ -0,0 +1,2 @@
+# Spark ML Support
+
diff --git a/pyrasterframes/src/main/python/docs/spatial-relations.pymd b/pyrasterframes/src/main/python/docs/spatial-relations.pymd
new file mode 100644
index 000000000..764d6e620
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/spatial-relations.pymd
@@ -0,0 +1 @@
+# Spatial Relations
diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
new file mode 100644
index 000000000..90587a9a6
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -0,0 +1 @@
+# Time Series
\ No newline at end of file

From 8097fc8c80da705bc2a5bc1f5a119f967c76f8e6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 21 Jun 2019 11:17:15 -0400
Subject: [PATCH 176/380] Moved examples into pymd files. Updated Python
 README.

---
 pyrasterframes/README.md                      |  66 ++++++----
 pyrasterframes/build.sbt                      |  16 +--
 .../src/main/python/docs/__init__.py          |   9 +-
 .../src/main/python/docs/aggregation.md       |   2 -
 .../src/main/python/docs/aggregation.pymd     |  22 ++++
 .../src/main/python/docs/local-algebra.pymd   |  28 +++++
 .../src/main/python/docs/spark-ml.pymd        | 113 ++++++++++++++++++
 .../src/main/python/examples/Clustering.py    |  74 ------------
 .../python/examples/CreatingRasterFrames.py   |  18 ---
 .../src/main/python/examples/MeanValue.py     |  13 --
 .../src/main/python/examples/NDVI.py          |  17 ---
 .../src/main/python/examples/__init__.py      |  10 --
 pyrasterframes/src/main/python/setup.py       |  68 +----------
 .../src/main/python/tests/__init__.py         |   8 +-
 14 files changed, 225 insertions(+), 239 deletions(-)
 delete mode 100644 pyrasterframes/src/main/python/docs/aggregation.md
 create mode 100644 pyrasterframes/src/main/python/docs/aggregation.pymd
 delete mode 100644 pyrasterframes/src/main/python/examples/Clustering.py
 delete mode 100644 pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
 delete mode 100644 pyrasterframes/src/main/python/examples/MeanValue.py
 delete mode 100644 pyrasterframes/src/main/python/examples/NDVI.py
 delete mode 100644 pyrasterframes/src/main/python/examples/__init__.py

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 00f5b7069..062b6b6db 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -12,19 +12,30 @@ The pip installation focuses on local mode operation of spark. To deploy spark a
 
 #### Python shell
 
-To initialize PyRasterFrames in a generic Python shell follow the pattern below, substituting configs as needed.
+
+To initialize RasterFrames in a generic Python shell, use the provided convenience function as follows:
+
+```python
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes.rasterfunctions import *
+spark = create_rf_spark_session()
+```
+
+If you require further customization you can follow the general pattern below:
 
 ```python
 from pyspark.sql import SparkSession
-from pyrasterframes import *
+from pyrasterframes.utils import find_pyrasterframes_assembly
 from pyrasterframes.rasterfunctions import *
-spark = SparkSession.builder \
-     .master("local[*]") \
-     .appName("Using RasterFrames") \
-     .config("spark.some.config.option", "some-value") \
-     .withKryoSerialization() \
-     .getOrCreate() \
-     .withRasterFrames()
+rf_jar = find_pyrasterframes_assembly()
+spark = (SparkSession.builder
+    .master("local[*]")
+    .appName("RasterFrames")
+    .config('spark.jars', rf_jar)
+    .config("some.spark.setting", "some.setting.value")
+    .withKryoSerialization()
+    .getOrCreate())
+spark.withRasterFrames()
 ```
 
 #### Pyspark shell or app
@@ -34,7 +45,7 @@ To quickly get the command to run a `pyspark` shell with PyRasterFrames enabled,
 ```bash
 sbt pySparkCmd
 
-# > PYTHONSTARTUP=/var/foo/pyrf_init.py pyspark --jars /somewhere/rasterframes/pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar --py-files /somewhere/rasterframes/pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+# > PYTHONSTARTUP=/var/somewhere/pyrf_init.py pyspark --jars <src-root>/pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar --py-files <src-root>/pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
 ```
 
 The runtime dependencies will be created and the command to run printed to the console.
@@ -54,12 +65,11 @@ To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pysp
 Then in the pyspark shell or app, import the module and call `withRasterFrames` on the SparkSession.
 
 ```python
-import pyrasterframes
-spark = spark.withRasterFrames()
+from pyrasterframes.utils import create_rf_spark_session
+spark = create_rf_spark_session()
 df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
 ```
 
-
 ## Development
 
 RasterFrames is primarily implemented in Scala, and as such uses the Scala build tool [`sbt`](https://www.scala-sbt.org/).
@@ -71,7 +81,7 @@ and subsequent commands are invoked via an interactive shell. But for context cl
 example below with `sbt`.
 
 
-## Running Tests and Examples
+## Running Tests
 
 The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tests`. To run them:
 
@@ -79,22 +89,32 @@ The PyRasterFrames unit tests can found in `<src-root>/pyrasterframes/python/tes
 sbt pyrasterframes/test # alias 'pyTest'
 ```
 
-*See also the below discussion of running `setup.py` for more options to run unit tests.* 
+*See also the below discussion of running `setup.py` for more options to run unit tests.*
+
+## Running Python Markdown Sources
 
-Similarly, to run the examples in `<src-root>pyrasterframes/python/examples`:
+The markdown documentation in `<src-root>/pyrasterframes/src/main/python/docs` contains code blocks that are evaluated by the build to show results alongside examples. The processed markdown source can be found in `<src-root>/pyrasterframes/target/python/docs`.  
 
 ```bash
-sbt pyrasterframes/run # alias 'pyExamples'
+sbt pyrasterframes/doc # alias 'pyDoc'
 ```
 
+To build the full complement of documentation and generate the HTML website, run:
+
+```bash
+sbt makeSite
+``` 
+
+Results will be found in `<src-root>/docs/target/site`.
+
 ## Creating and Using a Build
 
 Assuming that `$SCALA_VER` is the major verison of Scala in use (e.g. 2.11) , and `$VER` is the version of RasterFrames, 
 the primary build artifacts are:
 
-* JVM library: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
-* Python .whl package with assembly: `pyrasterframes/target/python/dist/pyrasterframes-$VER-py2.py3-none-any.whl`
-* Python package as Maven .zip artifact: `pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
+* JVM library: `<src-root>/pyrasterframes/target/scala-$SCALA_VER/pyrasterframes_$SCALA_VER-$VER.jar`
+* Python .whl package with assembly: `<src-root>/pyrasterframes/target/python/dist/pyrasterframes-$VER-py2.py3-none-any.whl`
+* Python package as Maven .zip artifact: `<src-root>/pyrasterframes/target/scala-$SCALA_VER/pyrasterframes-python-$VER.zip`
 
 You build them with:
 
@@ -121,10 +141,10 @@ illustrate its usage, suppose we want to run a subset of the Python unit test. I
 sbt 'pySetup test --addopts "-k test_tile_creation"'
 ```
 
-Or to run a specific example:
+Or to build a specific document:
 
 ```bash
-sbt 'pySetup examples -e NDVI'
+sbt 'pySetup pweave -f docs/raster-io.pymd'
 ```
 
-*Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
+*Note: You may need to run `sbt pyrasterframes/package` at least once for certain `pySetup` commands to work.*
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 9a305de7c..e8f3f00e7 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -1,26 +1,18 @@
 addCommandAlias("pyDocs", "pyrasterframes/doc")
 addCommandAlias("pyTest", "pyrasterframes/test")
 addCommandAlias("pyBuild", "pyrasterframes/package")
-addCommandAlias("pyExamples", "pyrasterframes/run")
 
 Test / pythonSource := (Compile / sourceDirectory).value / "tests"
 
-Compile / run := pySetup.toTask(" examples").dependsOn(assembly).value
-
-//RFProjectPlugin.IntegrationTest / test := (Compile / run).inputTaskValue
-
 exportJars := true
 Python / doc / sourceDirectory := (Python / target).value / "docs"
-Python / doc / target := (Compile / target).value / "py-markdown"
+Python / doc / target := (Python / target).value / "docs"
+  //(Compile / target).value / "py-markdown"
 Python / doc := (Python / doc / target).toTask.dependsOn(
   Def.sequential(
     assembly,
-    pySetup.toTask(" pweave"),
-    copySources(
-      Python / doc / sourceDirectory,
-      Python / doc / target,
-      deleteFirst = true
-    )
+    Test / compile,
+    pySetup.toTask(" pweave")
   )
 ).value
 
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index 4a1d1d5bc..3172f8410 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -1,3 +1,10 @@
+import os
 
-__all__ = []
+__all__ = ["resource_dir"]
 
+
+# This is temporary until we port to run on web assets.
+def resource_dir():
+    here = os.path.dirname(os.path.realpath(__file__))
+    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
+    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
diff --git a/pyrasterframes/src/main/python/docs/aggregation.md b/pyrasterframes/src/main/python/docs/aggregation.md
deleted file mode 100644
index 140aec39d..000000000
--- a/pyrasterframes/src/main/python/docs/aggregation.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# Aggregation
-
diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
new file mode 100644
index 000000000..21dd89998
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -0,0 +1,22 @@
+# Aggregation
+
+```python, echo=False
+from docs import *
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql import *
+import os
+
+spark = create_rf_spark_session()
+```
+
+## Cell Counts & Tile Mean
+
+```python
+rf = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B8-Robinson-IL.tiff'))
+rf.show(5, False)
+
+stats = rf.agg(rf_agg_no_data_cells('tile'), rf_agg_data_cells('tile'), rf_agg_mean('tile'))
+stats.show(5, False)
+```
+
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index d8d257d14..124ab9342 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -1 +1,29 @@
 # Local Algebra
+
+```python, echo=False
+from docs import *
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql import *
+import os
+
+spark = create_rf_spark_session()
+```
+
+## Computing NDVI
+
+```python, term=True
+redBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
+nirBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
+
+rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
+    .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
+rf.printSchema()
+rf.show(20, truncate=False)
+
+```
+
+
+```python, echo=False
+spark.stop()
+```
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/spark-ml.pymd
index f68de2f29..3dda84182 100644
--- a/pyrasterframes/src/main/python/docs/spark-ml.pymd
+++ b/pyrasterframes/src/main/python/docs/spark-ml.pymd
@@ -1,2 +1,115 @@
 # Spark ML Support
 
+## Clustering
+
+```python, echo=False
+from docs import *
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes import *
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql import *
+from pyspark.ml.feature import VectorAssembler
+from pyspark.ml.clustering import KMeans
+from pyspark.ml import Pipeline
+import os
+
+spark = create_rf_spark_session()
+```
+
+The first step is to load multiple bands of imagery and construct
+a single RasterFrame from them.
+
+```python
+filenamePattern = "L8-B{}-Elkton-VA.tiff"
+bandNumbers = range(1, 8)
+bandColNames = list(map(lambda n: 'band_{}'.format(n), bandNumbers))
+```
+
+For each identified band, load the associated image file:
+
+```python, term=True
+from functools import reduce
+joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
+                  map(lambda bf: spark.read.geotiff(bf[1])
+                      .withColumnRenamed('tile', 'band_{}'.format(bf[0])),
+                  map(lambda b: (b, os.path.join(resource_dir(), filenamePattern.format(b))), bandNumbers)))
+```
+
+We should see a single spatial_key column along with columns of tiles.
+
+```python
+joinedRF.printSchema()
+```
+
+SparkML requires that each observation be in its own row, and those
+observations be packed into a single `Vector`. The first step is to
+"explode" the tiles into a single row per cell/pixel
+
+```python
+exploder = TileExploder()
+```
+
+To "vectorize" the the band columns we use the SparkML `VectorAssembler`
+
+```python
+assembler = VectorAssembler() \
+    .setInputCols(bandColNames) \
+    .setOutputCol("features")
+```
+
+Configure our clustering algorithm
+
+```python
+k = 5
+kmeans = KMeans().setK(k)
+```
+
+Combine the two stages
+
+```python
+pipeline = Pipeline().setStages([exploder, assembler, kmeans])
+```
+
+Compute clusters
+
+```python
+model = pipeline.fit(joinedRF)
+```
+
+Run the data through the model to assign cluster IDs to each
+
+```python
+clustered = model.transform(joinedRF)
+clustered.show(8)
+```
+
+If we want to inspect the model statistics, the SparkML API requires us to go
+through this unfortunate contortion:
+
+```python
+clusterResults = list(filter(lambda x: str(x).startswith('KMeans'), model.stages))[0]
+```
+
+Compute sum of squared distances of points to their nearest center
+
+```python
+metric = clusterResults.computeCost(clustered)
+print("Within set sum of squared errors: %s" % metric)
+
+tlm = joinedRF.tileLayerMetadata()
+layout = tlm['layoutDefinition']['tileLayout']
+
+retiled = clustered.groupBy('spatial_key').agg(
+    rf_assemble_tile('column_index', 'row_index', 'prediction',
+        layout['tileCols'], layout['tileRows'], 'int8')
+)
+
+rf = retiled.asRF('spatial_key', tlm)
+rf.printSchema()
+rf.show()
+```
+
+
+```python, echo=False
+spark.stop()
+```
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
deleted file mode 100644
index 3825ede28..000000000
--- a/pyrasterframes/src/main/python/examples/Clustering.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
-from pyspark.ml.feature import VectorAssembler
-from pyspark.ml.clustering import KMeans
-from pyspark.ml import Pipeline
-import os
-
-spark = create_rf_spark_session()
-
-# The first step is to load multiple bands of imagery and construct
-# a single RasterFrame from them.
-filenamePattern = "L8-B{}-Elkton-VA.tiff"
-bandNumbers = range(1, 8)
-bandColNames = list(map(lambda n: 'band_{}'.format(n), bandNumbers))
-
-# For each identified band, load the associated image file
-from functools import reduce
-joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
-                  map(lambda bf: spark.read.geotiff(bf[1])
-                      .withColumnRenamed('tile', 'band_{}'.format(bf[0])),
-                  map(lambda b: (b, os.path.join(test_resource_dir(), filenamePattern.format(b))), bandNumbers)))
-
-# We should see a single spatial_key column along with columns of tiles.
-joinedRF.printSchema()
-
-# SparkML requires that each observation be in its own row, and those
-# observations be packed into a single `Vector`. The first step is to
-# "explode" the tiles into a single row per cell/pixel
-exploder = TileExploder()
-
-# To "vectorize" the the band columns we use the SparkML `VectorAssembler`
-assembler = VectorAssembler() \
-    .setInputCols(bandColNames) \
-    .setOutputCol("features")
-
-# Configure our clustering algorithm
-k = 5
-kmeans = KMeans().setK(k)
-
-# Combine the two stages
-pipeline = Pipeline().setStages([exploder, assembler, kmeans])
-
-# Compute clusters
-model = pipeline.fit(joinedRF)
-
-# Run the data through the model to assign cluster IDs to each
-clustered = model.transform(joinedRF)
-clustered.show(8)
-
-
-# If we want to inspect the model statistics, the SparkML API requires us to go
-# through this unfortunate contortion:
-clusterResults = list(filter(lambda x: str(x).startswith('KMeans'), model.stages))[0]
-
-# Compute sum of squared distances of points to their nearest center
-metric = clusterResults.computeCost(clustered)
-print("Within set sum of squared errors: %s" % metric)
-
-tlm = joinedRF.tileLayerMetadata()
-layout = tlm['layoutDefinition']['tileLayout']
-
-retiled = clustered.groupBy('spatial_key').agg(
-    rf_assemble_tile('column_index', 'row_index', 'prediction',
-        layout['tileCols'], layout['tileRows'], 'int8')
-)
-
-rf = retiled.asRF('spatial_key', tlm)
-rf.printSchema()
-rf.show()
-
-spark.stop()
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
deleted file mode 100644
index 99ae32b05..000000000
--- a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyspark.sql import *
-import os
-
-spark = create_rf_spark_session().withRasterFrames()
-
-rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
-rf.show(5, False)
-
-rf.tileColumns()
-
-rf.spatialKeyColumn()
-
-rf.temporalKeyColumn()
-
-rf.tileLayerMetadata()
diff --git a/pyrasterframes/src/main/python/examples/MeanValue.py b/pyrasterframes/src/main/python/examples/MeanValue.py
deleted file mode 100644
index 06960b7fc..000000000
--- a/pyrasterframes/src/main/python/examples/MeanValue.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
-import os
-
-spark = create_rf_spark_session().withRasterFrames()
-
-rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
-rf.show(5, False)
-
-rf.agg(rf_agg_no_data_cells('tile'), rf_agg_data_cells('tile'), rf_agg_mean('tile')).show(5, False)
diff --git a/pyrasterframes/src/main/python/examples/NDVI.py b/pyrasterframes/src/main/python/examples/NDVI.py
deleted file mode 100644
index 94e7fe031..000000000
--- a/pyrasterframes/src/main/python/examples/NDVI.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-import os
-
-spark = create_rf_spark_session().withRasterFrames()
-
-redBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
-nirBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
-
-rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
-    .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
-rf.printSchema()
-rf.show(20)
-
-spark.stop()
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/examples/__init__.py b/pyrasterframes/src/main/python/examples/__init__.py
deleted file mode 100644
index 795a3bd62..000000000
--- a/pyrasterframes/src/main/python/examples/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# examples_setup
-import os
-
-__all__ = ["test_resource_dir"]
-
-
-def test_resource_dir():
-    here = os.path.dirname(os.path.realpath(__file__))
-    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
-    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 818f1bdbc..a86cbc62d 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -19,13 +19,12 @@
 #
 
 # Always prefer setuptools over distutils
-from setuptools import setup, find_packages
-from os import path, environ
+from setuptools import setup
+from os import path
 import sys
 from glob import glob
 from io import open
 import distutils.cmd
-import importlib
 
 try:
     exec(open('pyrasterframes/version.py').read())  # executable python script contains __version__; credit pyspark
@@ -43,71 +42,11 @@
     readme = f.read()
 
 
-def _extract_module(mod):
-    module = importlib.import_module(mod)
-
-    if hasattr(module, '__all__'):
-        globals().update({n: getattr(module, n) for n in module.__all__})
-    else:
-        globals().update({k: v for (k, v) in module.__dict__.items() if not k.startswith('_')})
-
-
 def _divided(msg):
     divider = ('-' * 50)
     return divider + '\n' + msg + '\n' + divider
 
 
-class RunExamples(distutils.cmd.Command):
-    """A custom command to run pyrasterframes examples."""
-
-    description = 'Run PyRasterFrames examples'
-    user_options = [
-        # The format is (long option, short option, description).
-        ('examples=', 'e', 'examples to run'),
-    ]
-
-    @staticmethod
-    def _check_ex_path(ex):
-        file = ex
-        suffix = path.splitext(ex)[1]
-        if suffix == '':
-            file += '.py'
-        file = path.join(here, 'examples', file)
-
-        assert path.isfile(file), ('Invalid example %s' % file)
-        return file
-
-    def initialize_options(self):
-        """Set default values for options."""
-        # Each user option must be listed here with their default value.
-        self.examples = filter(lambda x: not path.basename(x)[:1] == '_',
-                               glob(path.join(here, 'examples', '*.py')))
-
-    def finalize_options(self):
-        """Post-process options."""
-        import re
-        if isinstance(self.examples, str):
-            self.examples = filter(
-                lambda s: len(s) > 0,
-                re.split('\W+', self.examples)
-            )
-            self.examples = map(
-                lambda x: 'examples.' + path.splitext(path.basename(x))[0],
-                map(self._check_ex_path, self.examples)
-            )
-
-    def run(self):
-        """Run the examples."""
-        import traceback
-        for ex in self.examples:
-            print(_divided('Running %s' % ex))
-            try:
-                _extract_module(ex)
-            except Exception:
-                print(_divided('%s Failed:' % ex))
-                print(traceback.format_exc())
-
-
 class PweaveDocs(distutils.cmd.Command):
     """A custom command to run documentation scripts through pweave."""
     description = 'Pweave PyRasterFrames documentation scripts'
@@ -128,7 +67,7 @@ def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.files, str):
-            self.files = filter(lambda s: len(s) > 0, re.split('\W+', self.files))
+            self.files = filter(lambda s: len(s) > 0, re.split(',', self.files))
 
     def run(self):
         """Run pweave."""
@@ -212,7 +151,6 @@ def run(self):
     zip_safe=False,
     test_suite="pytest-runner",
     cmdclass={
-        'examples': RunExamples,
         'pweave': PweaveDocs
     }
 )
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 267701f34..1858116ea 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -34,13 +34,13 @@
     import __builtin__ as builtins
 
 
-def test_resource_dir():
+def resource_dir():
     here = os.path.dirname(os.path.realpath(__file__))
     scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
     return os.path.realpath(os.path.join(scala_target, 'test-classes'))
 
 
-def testing_spark_session():
+def spark_test_session():
     spark = create_rf_spark_session()
     spark.sparkContext.setLogLevel('ERROR')
 
@@ -62,9 +62,9 @@ def rounded_compare(self, val1, val2):
     @classmethod
     def setUpClass(cls):
         # hard-coded relative path for resources
-        cls.resource_dir = test_resource_dir()
+        cls.resource_dir = resource_dir()
 
-        cls.spark = testing_spark_session()
+        cls.spark = spark_test_session()
 
         cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
 

From ed4cebf62cc343dd0e3966a3880e26723c04d7da Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 21 Jun 2019 12:56:35 -0400
Subject: [PATCH 177/380] Changed default name of raster column in
 RasterSourceRelation schema to `proj_raster`. Added `rf_tile`.

---
 core/src/main/resources/reference.conf        |  2 +-
 .../rasterframes/RasterFunctions.scala        |  3 +++
 .../rasterframes/StandardColumns.scala        |  4 ++++
 .../rasterframes/expressions/package.scala    |  1 +
 .../encoders/CatalystSerializerSpec.scala     |  6 ++---
 .../rasterframes/ref/RasterRefSpec.scala      |  2 +-
 .../rastersource/RasterSourceRelation.scala   |  2 +-
 docs/README.md                                |  4 ++++
 .../src/main/python/docs/getting-started.pymd |  4 ----
 .../python/pyrasterframes/rasterfunctions.py  |  1 +
 .../main/python/tests/PyRasterFramesTests.py  | 23 +++++++++++--------
 11 files changed, 32 insertions(+), 20 deletions(-)
 create mode 100644 docs/README.md

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 270f122fa..0052f5f5a 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -11,7 +11,7 @@ vlm.gdal {
       //CPL_DEBUG = "OFF"
       AWS_REQUEST_PAYER = "requester"
       GDAL_DISABLE_READDIR_ON_OPEN = "YES"
-      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.jp2,.mrf,.idx,.lrc,.mrf.aux.xml"
+      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.tiff,.jp2,.mrf,.idx,.lrc,.mrf.aux.xml"
     }
     // set this to `false` if CPL_DEBUG is `ON`
     useExceptions = true
diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 5ca8e0748..0ea7f785e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -60,6 +60,9 @@ trait RasterFunctions {
   /** Extracts the CRS from a RasterSource or ProjectedRasterTile */
   def rf_crs(col: Column): TypedColumn[Any, CRS] = GetCRS(col)
 
+  /** Extracts the tile from a ProjectedRasterTile, or passes through a Tile. */
+  def rf_tile(col: Column): TypedColumn[Any, Tile] = RealizeTile(col)
+
   /** Flattens Tile into a double array. */
   def rf_tile_to_array_double(col: Column): TypedColumn[Any, Array[Double]] =
     TileToArrayDouble(col)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
index a53415f2a..128729209 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
@@ -30,6 +30,7 @@ import geotrellis.vector.{Extent, ProjectedExtent}
 import org.apache.spark.sql.functions.col
 import org.locationtech.jts.geom.{Point => jtsPoint, Polygon => jtsPolygon}
 import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
  * Constants identifying column in most RasterFrames.
@@ -70,6 +71,9 @@ trait StandardColumns {
   // This is a `def` because `TileUDT` needs to be initialized first.
   def TILE_COLUMN = col("tile").as[Tile]
 
+  /** Default column name for a tile with its CRS and Extent. */
+  def PROJECTED_RASTER_COLUMN = col("proj_raster").as[ProjectedRasterTile]
+
   /** Default RasterFrame `TileFeature.data` column name. */
   val TILE_FEATURE_DATA_COLUMN = col("tile_data")
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 432c66888..776412e1c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -74,6 +74,7 @@ package object expressions {
     registry.registerExpression[GeometryToExtent]("st_extent")
     registry.registerExpression[GetExtent]("rf_extent")
     registry.registerExpression[GetCRS]("rf_crs")
+    registry.registerExpression[RealizeTile]("rf_tile")
     registry.registerExpression[Subtract]("rf_local_subtract")
     registry.registerExpression[Multiply]("rf_local_multiply")
     registry.registerExpression[Divide]("rf_local_divide")
diff --git a/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
index 2b02a5523..4e8bfdfcc 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/encoders/CatalystSerializerSpec.scala
@@ -118,17 +118,17 @@ class CatalystSerializerSpec extends TestEnvironment with TestData {
       assertContract(ext)
     }
 
-    it("should eserialize ProjectedExtent") {
+    it("should serialize ProjectedExtent") {
       val pe = ProjectedExtent(ext, ConusAlbers)
       assertContract(pe)
     }
 
-    it("should eserialize SpatialKey") {
+    it("should serialize SpatialKey") {
       val v = SpatialKey(2, 3)
       assertContract(v)
     }
 
-    it("should eserialize SpaceTimeKey") {
+    it("should serialize SpaceTimeKey") {
       val v = SpaceTimeKey(2, 3, ZonedDateTime.now())
       assertContract(v)
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index 606b949f0..8bdb9cad0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -110,7 +110,7 @@ class RasterRefSpec extends TestEnvironment with TestData {
     }
   }
 
-  describe("rf_extent") {
+  describe("GetExtent") {
     it("should read from RasterRef") {
       import spark.implicits._
       new Fixture {
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index c33722520..14fcd01d1 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -48,7 +48,7 @@ case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[Strin
 
   lazy val inputColNames = pathTable
     .map(_.columnNames)
-    .getOrElse(Seq(TILE_COLUMN.columnName))
+    .getOrElse(Seq(PROJECTED_RASTER_COLUMN.columnName))
 
   def pathColNames = inputColNames
     .map(_ + "_path")
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 000000000..4ea8233a2
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,4 @@
+# RasterFrames Documentation Build
+
+* Global documentation assets and Scala specific content: `<src-root>/docs/src/main/paradox`
+* Technical content and Python examples: `<src-root>/pyrasterframes/src/main/python/docs`
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 623eb2fcc..b6620a0d4 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -36,10 +36,6 @@ See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/
 
 ## Standalone Python Script
 
-```python, echo=False
-from docs import *
-```
-
 RasterFrames requires some special configuration to Spark to be fully functional. As a convenience you can use a provided utility function to get a preconfigured Spark session: 
 
 The first step is to set up a `SparkSession`:
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 9d8ceff7a..7bcf0d782 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -278,6 +278,7 @@ def _(data_tile, mask_tile, mask_value):
     'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
     'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
     'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
+    'rf_tile': 'Extracts the Tile component of a ProjectedRasterTile (or Tile).',
     'st_geometry': 'Convert the given extent/bbox to a polygon',
     'rf_geometry': 'Get the extent of a RasterSource or ProjectdRasterTile as a Geometry',
 
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index f26eff74d..22472da28 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -416,7 +416,7 @@ def test_tile_udt_serialization(self):
     def test_udf_on_tile_type_input(self):
         import numpy.testing
         # rf_local_add(t, 0) is to force lazy eval; accessing tile.tile is to get at the actual Tile type vs PRT struct
-        df = self.spark.read.rastersource(self.img_uri).withColumn('tile2', rf_local_add_int(col('tile.tile'), 0))
+        df = self.spark.read.rastersource(self.img_uri)
         rf = self.rf
 
         # create trivial UDF that does something we already do with raster_Functions
@@ -435,8 +435,8 @@ def my_udf(t):
         )
 
         df_result = df.select(
-            (rf_dimensions(df.tile).cols.cast('int') * rf_dimensions(df.tile).rows.cast('int') -
-                my_udf(df.tile2)).alias('result')
+            (rf_dimensions(df.proj_raster).cols.cast('int') * rf_dimensions(df.proj_raster).rows.cast('int') -
+                my_udf(rf_tile(df.proj_raster))).alias('result')
         ).toPandas()
 
         numpy.testing.assert_array_equal(
@@ -681,16 +681,19 @@ class RasterSource(TestEnvironment):
 
     def test_handle_lazy_eval(self):
         df = self.spark.read.rastersource(self.img_uri)
-        ltdf = df.select('tile')
+        ltdf = df.select('proj_raster')
         self.assertGreater(ltdf.count(), 0)
         self.assertIsNotNone(ltdf.first())
 
+        tdf = df.select(rf_tile('proj_raster'))
+        self.assertGreater(tdf.count(),  0)
+        self.assertIsNotNone(tdf.first())
 
     def test_prt_functions(self):
         df = self.spark.read.rastersource(self.img_uri) \
-            .withColumn('crs', rf_crs('tile')) \
-            .withColumn('ext', rf_extent('tile')) \
-            .withColumn('geom', rf_geometry('tile'))
+            .withColumn('crs', rf_crs('proj_raster')) \
+            .withColumn('ext', rf_extent('proj_raster')) \
+            .withColumn('geom', rf_geometry('proj_raster'))
         df.select('crs', 'ext', 'geom').first()                         
 
     def test_raster_source_reader(self):
@@ -712,16 +715,16 @@ def l8path(b):
 
         # schema is tile_path and tile
         # df.printSchema()
-        self.assertTrue(len(df.columns) == 2 and 'tile_path' in df.columns and 'tile' in df.columns)
+        self.assertTrue(len(df.columns) == 2 and 'proj_raster_path' in df.columns and 'proj_raster' in df.columns)
 
         # the most common tile dimensions should be as passed to `options`, showing that options are correctly applied
-        tile_size_df = df.select(rf_dimensions(df.tile).rows.alias('r'), rf_dimensions(df.tile).cols.alias('c')) \
+        tile_size_df = df.select(rf_dimensions(df.proj_raster).rows.alias('r'), rf_dimensions(df.proj_raster).cols.alias('c')) \
             .groupby(['r', 'c']).count().toPandas()
         most_common_size = tile_size_df.loc[tile_size_df['count'].idxmax()]
         self.assertTrue(most_common_size.r == tile_size and most_common_size.c == tile_size)
 
         # all rows are from a single source URI
-        path_count = df.groupby(df.tile_path).count()
+        path_count = df.groupby(df.proj_raster_path).count()
         print(path_count.toPandas())
         self.assertTrue(path_count.count() == 3)
 

From 2985d1c2237c2ae65ba000d03ebc3f9c42831283 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 21 Jun 2019 14:40:41 -0400
Subject: [PATCH 178/380] Refactored path specification code in
 RasterSourceRelation.

---
 build.sbt                                     |  2 +-
 .../rastersource/RasterSourceDataSource.scala | 64 +++++++++++++------
 .../rastersource/RasterSourceRelation.scala   | 29 +++++----
 .../RasterSourceDataSourceSpec.scala          | 39 ++++++-----
 project/RFProjectPlugin.scala                 |  4 --
 5 files changed, 83 insertions(+), 55 deletions(-)

diff --git a/build.sbt b/build.sbt
index bad42231b..6ffcffa4e 100644
--- a/build.sbt
+++ b/build.sbt
@@ -24,7 +24,7 @@ addCommandAlias("previewSite", "docs/previewSite")
 addCommandAlias("console", "datasource/console")
 
 // Prefer our own IntegrationTest config definition, which inherits from Test.
-val IntegrationTest = RFProjectPlugin.IntegrationTest
+lazy val IntegrationTest = config("it") extend Test
 
 lazy val root = project
   .in(file("."))
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index a09072f63..651d488db 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -21,6 +21,8 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.util._
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
 import org.locationtech.rasterframes.model.TileDimensions
@@ -31,11 +33,8 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
     val bands = parameters.bandIndexes
     val tiling = parameters.tileDims
-    val pathTable = parameters.pathTable
-    val files = parameters.filePaths
-    require(!(pathTable.nonEmpty && files.nonEmpty),
-      "Only one of a set of file paths OR a paths table column may be provided.")
-    RasterSourceRelation(sqlContext, files, pathTable, bands, tiling)
+    val spec = parameters.pathSpec
+    RasterSourceRelation(sqlContext, spec, bands, tiling)
   }
 }
 object RasterSourceDataSource {
@@ -47,36 +46,61 @@ object RasterSourceDataSource {
   final val PATH_TABLE_PARAM = "pathTable"
   final val PATH_TABLE_COL_PARAM = "pathTableColumns"
 
+  final val DEFAULT_COLUMN_NAME = PROJECTED_RASTER_COLUMN.columnName
+
+  trait WithBandColumns {
+    def bandColumnNames: Seq[String]
+  }
+  /** Container for specifying raster paths. */
+  case class BandSet(bandPaths: String*)
+  case class RasterSourcePathTable(sceneRows: Seq[BandSet], bandColumnNames: String*) extends WithBandColumns {
+    require(sceneRows.forall(_.bandPaths.length == bandColumnNames.length),
+      "Each scene row must have the same number of entries as band column names")
+  }
   /** Container for specifying where to select raster paths from. */
-  case class RasterSourceTable(tableName: String, columnNames: String*)
+  case class RasterSourcePathTableRef(tableName: String, bandColumnNames: String*) extends WithBandColumns
 
   private[rastersource]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
-    def filePaths: Seq[String] = (
-      parameters
-        .get(PATHS_PARAM)
-        .toSeq
-        .flatMap(_.split(Array('\n','\r'))) ++
-        parameters
-          .get(RasterSourceDataSource.PATH_PARAM)
-          .toSeq
-      )
-      .filter(_.nonEmpty)
 
     def tileDims: Option[TileDimensions] =
       parameters.get(TILE_DIMS_PARAM)
-      .map(_.split(',').map(_.trim.toInt))
-      .map { case Array(cols, rows) => TileDimensions(cols, rows)}
+        .map(_.split(',').map(_.trim.toInt))
+        .map { case Array(cols, rows) => TileDimensions(cols, rows)}
 
     def bandIndexes: Seq[Int] = parameters
       .get(BAND_INDEXES_PARAM)
       .map(_.split(',').map(_.trim.toInt).toSeq)
       .getOrElse(Seq(0))
 
-    def pathTable: Option[RasterSourceTable] = parameters
+    def filePaths: Option[RasterSourcePathTable] = {
+      val paths = (
+        parameters
+          .get(PATHS_PARAM)
+          .toSeq
+          .flatMap(_.split(Array('\n','\r'))) ++
+          parameters
+            .get(RasterSourceDataSource.PATH_PARAM)
+            .toSeq
+        ).filter(_.nonEmpty)
+
+      if (paths.isEmpty) None
+      else
+        Some(RasterSourcePathTable(paths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
+    }
+
+    def pathTable: Option[RasterSourcePathTableRef] = parameters
       .get(PATH_TABLE_PARAM)
       .zip(parameters.get(PATH_TABLE_COL_PARAM))
-      .map(p => RasterSourceTable(p._1, p._2.split(','): _*))
+      .map(p => RasterSourcePathTableRef(p._1, p._2.split(','): _*))
       .headOption
+
+    def pathSpec: Either[RasterSourcePathTable, RasterSourcePathTableRef] = {
+      (filePaths, pathTable) match {
+        case (Some(f), None) => Left(f)
+        case (None, Some(p)) => Right(p)
+        case _ => throw new IllegalArgumentException("Only one of a set of file paths OR a paths table column may be provided.")
+      }
+    }
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index 14fcd01d1..f3f337310 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -21,34 +21,32 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
-import org.locationtech.rasterframes.util._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.RasterSourceTable
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourcePathTable, RasterSourcePathTableRef}
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
+import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
   * @param sqlContext Query context
-  * @param discretePaths list of URIs to fetch rastefrom.
+  * @param pathTable Specifiction of raster path sources
   * @param bandIndexes band indexes to fetch
   * @param subtileDims how big to tile/subdivide rasters info
   */
-case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[String],
-  pathTable: Option[RasterSourceTable], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
+case class RasterSourceRelation(
+  sqlContext: SQLContext,
+  pathTable: Either[RasterSourcePathTable, RasterSourcePathTableRef],
+  bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
   extends BaseRelation with TableScan {
 
-  lazy val inputColNames = pathTable
-    .map(_.columnNames)
-    .getOrElse(Seq(PROJECTED_RASTER_COLUMN.columnName))
+  lazy val inputColNames = pathTable.merge.bandColumnNames
 
   def pathColNames = inputColNames
     .map(_ + "_path")
@@ -82,8 +80,11 @@ case class RasterSourceRelation(sqlContext: SQLContext, discretePaths: Seq[Strin
     // input -> path -> src -> ref -> tile
     // Each step is broken down for readability
     val inputs: DataFrame = pathTable match {
-      case Some(spec) => sqlContext.table(spec.tableName)
-      case _ => discretePaths.toDF(inputColNames.head)
+      case Right(spec) => sqlContext.table(spec.tableName)
+      case Left(spec) => spec.bandColumnNames match {
+        case Seq(single) => spec.sceneRows.map(_.bandPaths.headOption.orNull).toDF(single)
+        case bands => spec.sceneRows.map(_.bandPaths).toDF(bands: _*)
+      }
     }
 
     // Basically renames the input columns to have the '_path' suffix
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 25180b1ea..59ccf331b 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -29,25 +29,30 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
   import spark.implicits._
 
   describe("DataSource parameter processing") {
+    def singleCol(paths: Iterable[String]) = {
+      val rows: Seq[BandSet] = paths.map(BandSet(_)).toSeq
+      RasterSourcePathTable(rows, DEFAULT_COLUMN_NAME)
+    }
+
     it("should handle single `path`") {
       val p = Map(PATH_PARAM -> "/usr/local/foo/bar.tif")
-      p.filePaths should be (p.values.toSeq)
+      p.filePaths should be (Some(singleCol(p.values)))
     }
 
     it("should handle single `paths`") {
       val p = Map(PATHS_PARAM -> "/usr/local/foo/bar.tif")
-      p.filePaths should be (p.values.toSeq)
+      p.filePaths should be (Some(singleCol(p.values)))
     }
     it("should handle multiple `paths`") {
       val expected = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val p = Map(PATHS_PARAM -> expected.mkString("\n\r", "\n\n", "\r"))
-      p.filePaths should be (expected)
+      p.filePaths should be (Some(singleCol(expected)))
     }
     it("should handle both `path` and `paths`") {
       val expected1 = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val expected2 = "/usr/local/barf/baz.tif"
       val p = Map(PATHS_PARAM -> expected1.mkString("\n"), PATH_PARAM -> expected2)
-      p.filePaths should be (expected1 :+ expected2)
+      p.filePaths should be (Some(singleCol(expected1 :+ expected2)))
     }
     it("should parse tile dimensions") {
       val p = Map(TILE_DIMS_PARAM -> "4, 5")
@@ -56,16 +61,18 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
     it("should parse path table specification") {
       val p = Map(PATH_TABLE_PARAM -> "pathTable", PATH_TABLE_COL_PARAM -> "path")
-      p.pathTable should be (Some(RasterSourceTable("pathTable", "path")))
+      p.pathTable should be (Some(RasterSourcePathTableRef("pathTable", "path")))
     }
   }
 
   describe("RasterSource as relation reading") {
+    val b = DEFAULT_COLUMN_NAME
+
     it("should default to a single band schema") {
       val df = spark.read.rastersource.load(l8B1SamplePath.toASCIIString)
       val tcols = df.tileColumns
       tcols.length should be(1)
-      tcols.map(_.columnName) should contain("tile")
+      tcols.map(_.columnName) should contain(DEFAULT_COLUMN_NAME)
     }
     it("should support a multiband schema") {
       val df = spark.read
@@ -74,7 +81,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .load(cogPath.toASCIIString)
       val tcols = df.tileColumns
       tcols.length should be(3)
-      tcols.map(_.columnName) should contain allElementsOf Seq("tile_b0", "tile_b1", "tile_b2")
+      tcols.map(_.columnName) should contain allElementsOf Seq("_b0", "_b1", "_b2").map(s => DEFAULT_COLUMN_NAME + s)
     }
     it("should read a multiband file") {
       val df = spark.read
@@ -84,7 +91,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .cache()
       df.schema.size should be (4)
       // Test (roughly) we have three distinct but compabible bands
-      val stats = df.agg(rf_agg_stats($"tile_b0") as "s0", rf_agg_stats($"tile_b1") as "s1", rf_agg_stats($"tile_b2") as "s2")
+      val stats = df.agg(rf_agg_stats($"${b}_b0") as "s0", rf_agg_stats($"${b}_b1") as "s1", rf_agg_stats($"${b}_b2") as "s2")
       stats.select($"s0.data_cells" === $"s1.data_cells").as[Boolean].first() should be(true)
       stats.select($"s0.data_cells" === $"s2.data_cells").as[Boolean].first() should be(true)
       stats.select($"s0.mean" =!= $"s1.mean").as[Boolean].first() should be(true)
@@ -98,18 +105,18 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       df.count() should be(math.ceil(1028.0 / 128).toInt * math.ceil(989.0 / 128).toInt)
 
-      val dims = df.select(rf_dimensions($"tile").as[TileDimensions]).distinct().collect()
+      val dims = df.select(rf_dimensions($"$b").as[TileDimensions]).distinct().collect()
       dims should contain allElementsOf
         Seq(TileDimensions(4,128), TileDimensions(128,128), TileDimensions(128,93), TileDimensions(4,93))
 
-      df.select("tile_path").distinct().count() should be(1)
+      df.select($"${b}_path").distinct().count() should be(1)
     }
     it("should read a multiple files with one band") {
       val df = spark.read.rastersource
         .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .load()
-      df.select("tile_path").distinct().count() should be(3)
+      df.select($"${b}_path").distinct().count() should be(3)
       df.schema.size should be(2)
     }
     it("should read a multiple files with heterogeneous bands") {
@@ -119,13 +126,13 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         .withBandIndexes(0, 1, 2, 3)
         .load()
         .cache()
-      df.select("tile_path").distinct().count() should be(3)
+      df.select($"${b}_path").distinct().count() should be(3)
       df.schema.size should be(5)
 
-      df.select($"tile_b0").count() should be (df.select($"tile_b0").na.drop.count())
-      df.select($"tile_b1").na.drop.count() shouldBe <(df.count())
-      df.select($"tile_b1").na.drop.count() should be (df.select($"tile_b2").na.drop.count())
-      df.select($"tile_b3").na.drop.count() should be (0)
+      df.select($"${b}_b0").count() should be (df.select($"${b}_b0").na.drop.count())
+      df.select($"${b}_b1").na.drop.count() shouldBe <(df.count())
+      df.select($"${b}_b1").na.drop.count() should be (df.select($"${b}_b2").na.drop.count())
+      df.select($"${b}_b3").na.drop.count() should be (0)
     }
 
 
diff --git a/project/RFProjectPlugin.scala b/project/RFProjectPlugin.scala
index 9f50f3fda..b7c904416 100644
--- a/project/RFProjectPlugin.scala
+++ b/project/RFProjectPlugin.scala
@@ -11,10 +11,6 @@ object RFProjectPlugin extends AutoPlugin {
   override def trigger: PluginTrigger = allRequirements
   override def requires = GitPlugin
 
-  lazy val IntegrationTest = config("it") extend Test
-
-  override def projectConfigurations: Seq[Configuration] = Seq(IntegrationTest)
-
   override def projectSettings = Seq(
     organization := "org.locationtech.rasterframes",
     organizationName := "LocationTech RasterFrames",

From 11be7b25169e74dee300edd0fdd78eef7f43cbcd Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 21 Jun 2019 15:41:08 -0400
Subject: [PATCH 179/380] Added ability to initialize a `rastersource`
 datasource with a CSV of paths.

---
 .../rastersource/RasterSourceDataSource.scala | 28 ++++++-
 .../rastersource/RasterSourceRelation.scala   |  7 +-
 .../datasource/rastersource/package.scala     |  5 ++
 .../RasterSourceDataSourceSpec.scala          | 41 ++++++++-
 .../src/main/python/docs/raster-io.pymd       | 83 +++++++++++++++++++
 .../main/python/pyrasterframes/__init__.py    |  8 +-
 6 files changed, 164 insertions(+), 8 deletions(-)

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index 651d488db..956dc89c3 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -27,6 +27,8 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
 import org.locationtech.rasterframes.model.TileDimensions
 
+import scala.util.Try
+
 class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   import RasterSourceDataSource._
   override def shortName(): String = SHORT_NAME
@@ -44,6 +46,7 @@ object RasterSourceDataSource {
   final val BAND_INDEXES_PARAM = "bandIndexes"
   final val TILE_DIMS_PARAM = "tileDimensions"
   final val PATH_TABLE_PARAM = "pathTable"
+  final val PATH_CSV_PARAM = "pathCSVTable"
   final val PATH_TABLE_COL_PARAM = "pathTableColumns"
 
   final val DEFAULT_COLUMN_NAME = PROJECTED_RASTER_COLUMN.columnName
@@ -57,6 +60,24 @@ object RasterSourceDataSource {
     require(sceneRows.forall(_.bandPaths.length == bandColumnNames.length),
       "Each scene row must have the same number of entries as band column names")
   }
+
+  object RasterSourcePathTable {
+    def apply(csv: String): Option[RasterSourcePathTable] = Try {
+      val lines = csv
+        .split(Array('\n','\r'))
+        .map(_.trim)
+        .filter(_.nonEmpty)
+
+      val header = lines.head.split(',').map(_.trim)
+      val rows = lines.tail.map(_.split(',').map(_.trim)).map(BandSet(_: _*))
+      RasterSourcePathTable(rows, header: _*)
+    }.toOption
+
+    def apply(singlebandPaths: Seq[String]): Option[RasterSourcePathTable]  =
+      if (singlebandPaths.isEmpty) None
+      else Some(RasterSourcePathTable(singlebandPaths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
+  }
+
   /** Container for specifying where to select raster paths from. */
   case class RasterSourcePathTableRef(tableName: String, bandColumnNames: String*) extends WithBandColumns
 
@@ -84,9 +105,10 @@ object RasterSourceDataSource {
             .toSeq
         ).filter(_.nonEmpty)
 
-      if (paths.isEmpty) None
-      else
-        Some(RasterSourcePathTable(paths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
+      RasterSourcePathTable(paths)
+        .orElse(parameters
+          .get(PATH_CSV_PARAM)
+          .flatMap(RasterSourcePathTable(_)))
     }
 
     def pathTable: Option[RasterSourcePathTableRef] = parameters
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index f3f337310..7fab2fbbd 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -83,7 +83,12 @@ case class RasterSourceRelation(
       case Right(spec) => sqlContext.table(spec.tableName)
       case Left(spec) => spec.bandColumnNames match {
         case Seq(single) => spec.sceneRows.map(_.bandPaths.headOption.orNull).toDF(single)
-        case bands => spec.sceneRows.map(_.bandPaths).toDF(bands: _*)
+        case bands =>
+          val bsel = bands.zipWithIndex.map { case (b, i) => col("value")(i).as(b) }
+
+          spec.sceneRows.map(_.bandPaths)
+            .toDF("value")
+            .select(bsel: _*)
       }
     }
 
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
index cae9e7fcd..2e284043f 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
@@ -56,6 +56,11 @@ package object rastersource {
           .option(RasterSourceDataSource.PATH_TABLE_COL_PARAM, bandColumnNames.mkString(","))
       )
 
+    def fromCSV(pathTableCSV: String): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.PATH_CSV_PARAM, pathTableCSV)
+      )
+
     def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.PATHS_PARAM, newlineDelimPaths)
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 59ccf331b..ff1c315e7 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -21,7 +21,7 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 import org.locationtech.rasterframes.{TestEnvironment, _}
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource._
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourcePathTable, _}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.util._
 
@@ -61,7 +61,19 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
     it("should parse path table specification") {
       val p = Map(PATH_TABLE_PARAM -> "pathTable", PATH_TABLE_COL_PARAM -> "path")
-      p.pathTable should be (Some(RasterSourcePathTableRef("pathTable", "path")))
+      p.pathSpec should be (Right(RasterSourcePathTableRef("pathTable", "path")))
+    }
+
+    it("should parse path table from CSV") {
+      val bands = Seq("B1", "B2", "B3")
+      val paths = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif", "/usr/local/barf/baz.tif")
+      val csv =
+        s"""
+          |${bands.mkString(",")}
+          |${paths.mkString(",")}
+        """.stripMargin.trim
+      val p = Map(PATH_CSV_PARAM -> csv)
+      p.pathSpec should be (Left(RasterSourcePathTable(Seq(BandSet(paths:_*)), bands:_*)))
     }
   }
 
@@ -135,8 +147,31 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.select($"${b}_b3").na.drop.count() should be (0)
     }
 
+    it("should read a set of coherent bands from multiple files from a CSV") {
+      val bands = Seq("B1", "B2", "B3")
+      val paths = Seq(
+        l8SamplePath(1).toASCIIString,
+        l8SamplePath(2).toASCIIString,
+        l8SamplePath(3).toASCIIString
+      )
+
+      val csv =
+        s"""
+           |${bands.mkString(",")}
+           |${paths.mkString(",")}
+        """.stripMargin.trim
+
+      val df = spark.read.rastersource
+        .fromCSV(csv)
+        .withTileDimensions(128, 128)
+        .load()
+
+      df.schema.size should be(6)
+      df.tileColumns.size should be (3)
+      df.select($"B1_path").distinct().count() should be (1)
+    }
 
-    it("should read a extent coherent bands from multiple files") {
+    it("should read a set of coherent bands from multiple files in a table") {
       val bandPaths = Seq((
         l8SamplePath(1).toASCIIString,
         l8SamplePath(2).toASCIIString,
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 375f9c4f8..135017c6a 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -1,7 +1,90 @@
 # Raster Data I/O
 
+The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames are compatible with existing generalized DataSources, such as Parquet, but provide specialized ones oriented around geospatial raster data.
+
 ## Reading Raster Data
 
+```python, echo=False
+from IPython.display import display
+from pyrasterframes.utils import create_rf_spark_session 
+from pyrasterframes.rasterfunctions import *
+spark = create_rf_spark_session()
+```
+
+RasterFrames registers a DataSource named `rastersource` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URLs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
+
+```python
+data_root = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp'
+def s2_band(idx):
+    return f"{data_root}/B0{idx}.tif"
+    
+```
+
+### Single Raster
+
+The simplest form is reading a single raster from a single URL:
+
+```python
+rf = spark.read.rastersource(s2_band(2))
+rf.printSchema()
+```
+
+[[Discuss the schema]]
+
+Let's unpack the `proj_raster` column and look at the contents in more detail. 
+
+```python
+crs = rf.select(rf_crs("proj_raster").alias("value")).first()
+
+parts = rf.select(
+    rf_extent("proj_raster").alias("extent"), 
+    rf_tile("proj_raster").alias("tile")
+)
+
+print("CRS", crs.value.crsProj4)
+parts.show(5)
+
+```
+
+Let's elect a single tile and view it.
+
+```python
+tile = rf.select(rf_tile("proj_raster")).first()
+# TODO: IPython rendering
+print(tile)
+```
+
+
+### Single Scene, Multiple Bands
+
+Generally we'll want to work with multiple bands. If you have several singleband image files of the same scene, `rastersource` can make use of a CSV (or other DataFrame) specifying the band columns and scene rows.
+
+```python
+csv = f"""
+red,green,blue
+{s2_band(4)},{s2_band(3)},{s2_band(2)}
+"""
+
+print(csv)
+
+rf2 = spark.read.rastersource(csv=csv)
+rf2.printSchema()
+
+rf2.show(5)
+```
+
+
 ## Writing Raster Data
 
+### Overviews
+
+### GeoTIFFs
+
+### Tile Samples
+
+### GeoTrellis Layers
+
+
+
 
+[DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 884538630..5ab872df3 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -99,7 +99,7 @@ def _layer_reader(df_reader, format_key, path, **options):
     return _convert_df(df)
 
 
-def _rastersource_reader(df_reader, path=None, band_indexes=None, tile_dimensions=(256, 256), **options):
+def _rastersource_reader(df_reader, path=None, csv=None, band_indexes=None, tile_dimensions=(256, 256), **options):
     if band_indexes is None:
         band_indexes = [0]
 
@@ -109,6 +109,12 @@ def to_csv(comp): return ','.join(str(v) for v in comp)
         "bandIndexes": to_csv(band_indexes),
         "tileDimensions": to_csv(tile_dimensions)
     })
+
+    if csv is not None:
+        options.update({
+            "pathCSVTable": csv
+        })
+
     return df_reader \
         .format("rastersource") \
         .load(path, **options)

From 290858153f90e4a026599ad6b3bc7a3c1dd9a631 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 23 Jun 2019 13:47:18 -0400
Subject: [PATCH 180/380] Incremental progress commit.

---
 .../src/main/python/docs/raster-io.pymd       | 60 ++++++++++++++++++-
 .../main/python/pyrasterframes/__init__.py    |  7 ++-
 2 files changed, 64 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 135017c6a..138829ca9 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -11,7 +11,7 @@ from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
 ```
 
-RasterFrames registers a DataSource named `rastersource` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URLs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
+RasterFrames registers a DataSource named `rastersource` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
 
 ```python
 data_root = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp'
@@ -22,7 +22,7 @@ def s2_band(idx):
 
 ### Single Raster
 
-The simplest form is reading a single raster from a single URL:
+The simplest form is reading a single raster from a single URI:
 
 ```python
 rf = spark.read.rastersource(s2_band(2))
@@ -73,6 +73,62 @@ rf2.printSchema()
 rf2.show(5)
 ```
 
+### Multiple Scenes, Multiple Bands
+
+The example above will work with a reasonable number of rows in the CSV string. However, in many cases it's preferable to have that information organized in another DataFrame. For the purposes of this example use one of the AWS PDS daily scene listing files for MODIS surface reflectance as our catalog. 
+
+```python
+from pyspark import SparkFiles
+cat_filename = "2018-07-04_scenes.txt"
+spark.sparkContext.addFile(f"https://modis-pds.s3.amazonaws.com/MCD43A4.006/{cat_filename}")
+modis_catalog = spark.read \
+    .format("csv") \
+    .option("header", "true") \
+    .load(SparkFiles.get(cat_filename))
+modis_catalog.printSchema()
+print("Available scenes: ", modis_catalog.count())
+modis_catalog.show(5, truncate=False)
+```
+
+MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using `(h,v)` grid coordinates. 
+
+![MODIS Grid](https://modis-land.gsfc.nasa.gov/images/MODIS_sinusoidal_grid1.gif)
+
+For sake of example, lets say we wanted all the MODIS data right above the equator. The grid position is encoded in the GUID, with `h07` being the strip just above the equator . To go from `download_url` to band URL we strip off `index.html`, and append the GUID, band specifier, and file extension.
+
+```python
+from pyspark.sql import functions as F
+    
+# Index URIs take the form:
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html    
+# Image URIs take the form:
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
+
+#    .filter(modis_catalog.download_url.contains("h07")) \
+
+equator = modis_catalog \
+    .withColumn('base_url', 
+        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',) 
+    ) \
+.select(
+    F.concat('base_url', F.lit("_B1.TIF")).alias('red'),
+    F.concat('base_url', F.lit("_B2.TIF")).alias('nir')
+) \
+.where(F.col('gid').like('%h07%'))
+equator.show(5)
+```
+
+Now that we have a catalog of multiple scenes and mutiple bands withit each scene, we can pass it along to `rastersource` load the imagery. To complete the example, we compute some aggregate statistics over NDVI.
+
+```python
+equator.createOrReplaceTempView("equator")
+spark.read.rastersource()
+```
+
+### Multiband Rasters
+
+[[Need to know _a priori_ how many bands there are, specified with `band_indexes`]]
+ 
 
 ## Writing Raster Data
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 5ab872df3..d3b8128ba 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -99,7 +99,12 @@ def _layer_reader(df_reader, format_key, path, **options):
     return _convert_df(df)
 
 
-def _rastersource_reader(df_reader, path=None, csv=None, band_indexes=None, tile_dimensions=(256, 256), **options):
+def _rastersource_reader(
+        df_reader, path=None,
+        csv=None,
+        band_indexes=None, tile_dimensions=(256, 256),
+        **options):
+
     if band_indexes is None:
         band_indexes = [0]
 

From 4722fba4313da05eb991f2b58bfc1a87ab6a4e44 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 23 Jun 2019 20:55:09 -0400
Subject: [PATCH 181/380] Added ability to pass catalog DataFrame into
 `rastersource` read.

---
 .../RasterSourceToRasterRefs.scala            |  6 ++-
 .../rastersource/RasterSourceDataSource.scala | 52 +++++++++++--------
 .../rastersource/RasterSourceRelation.scala   | 10 ++--
 .../datasource/rastersource/package.scala     |  8 +--
 .../RasterSourceDataSourceSpec.scala          | 20 +++----
 .../src/main/python/docs/local-algebra.pymd   |  1 +
 .../src/main/python/docs/raster-io.pymd       | 33 +++++++-----
 .../src/main/python/docs/spark-ml.pymd        |  1 +
 .../main/python/pyrasterframes/__init__.py    | 35 ++++++++++---
 .../src/main/python/pyrasterframes/utils.py   |  1 -
 .../main/python/tests/PyRasterFramesTests.py  | 10 ++--
 11 files changed, 106 insertions(+), 71 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 663f090ff..7ba2bb13d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -63,9 +63,10 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
     if (b < src.bandCount) RasterRef(src, b, e) else null
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
+    var src: RasterSource = null
     try {
       val refs = children.map { child ⇒
-        val src = RasterSourceType.deserialize(child.eval(input))
+        src = RasterSourceType.deserialize(child.eval(input))
         subtileDims.map(dims =>
           src
             .layoutExtents(dims)
@@ -77,7 +78,8 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
     }
     catch {
       case NonFatal(ex) ⇒
-        logger.error("Error fetching data for " + input, ex)
+        val payload = if (src == null) input else src
+        logger.error("Error fetching data for " + payload, ex)
         Traversable.empty
     }
   }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
index 956dc89c3..73f5e2597 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
@@ -45,9 +45,9 @@ object RasterSourceDataSource {
   final val PATHS_PARAM = "paths"
   final val BAND_INDEXES_PARAM = "bandIndexes"
   final val TILE_DIMS_PARAM = "tileDimensions"
-  final val PATH_TABLE_PARAM = "pathTable"
-  final val PATH_CSV_PARAM = "pathCSVTable"
-  final val PATH_TABLE_COL_PARAM = "pathTableColumns"
+  final val CATALOG_TABLE_PARAM = "catalogTable"
+  final val CATALOG_TABLE_COLS_PARAM = "catalogColumns"
+  final val CATALOG_CSV_PARAM = "catalogCSV"
 
   final val DEFAULT_COLUMN_NAME = PROJECTED_RASTER_COLUMN.columnName
 
@@ -56,30 +56,32 @@ object RasterSourceDataSource {
   }
   /** Container for specifying raster paths. */
   case class BandSet(bandPaths: String*)
-  case class RasterSourcePathTable(sceneRows: Seq[BandSet], bandColumnNames: String*) extends WithBandColumns {
+  case class RasterSourceCatalog(sceneRows: Seq[BandSet], bandColumnNames: String*) extends WithBandColumns {
     require(sceneRows.forall(_.bandPaths.length == bandColumnNames.length),
       "Each scene row must have the same number of entries as band column names")
   }
 
-  object RasterSourcePathTable {
-    def apply(csv: String): Option[RasterSourcePathTable] = Try {
+  object RasterSourceCatalog {
+    def apply(csv: String, bandColumnNames: String*): Option[RasterSourceCatalog] = Try {
       val lines = csv
         .split(Array('\n','\r'))
         .map(_.trim)
         .filter(_.nonEmpty)
 
-      val header = lines.head.split(',').map(_.trim)
+      val header = lines.head.split(',').map(_.trim).toSeq
       val rows = lines.tail.map(_.split(',').map(_.trim)).map(BandSet(_: _*))
-      RasterSourcePathTable(rows, header: _*)
+      val bands = if (bandColumnNames.isEmpty) header
+      else bandColumnNames
+      RasterSourceCatalog(rows, bands: _*)
     }.toOption
 
-    def apply(singlebandPaths: Seq[String]): Option[RasterSourcePathTable]  =
+    def apply(singlebandPaths: Seq[String]): Option[RasterSourceCatalog]  =
       if (singlebandPaths.isEmpty) None
-      else Some(RasterSourcePathTable(singlebandPaths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
+      else Some(RasterSourceCatalog(singlebandPaths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
   }
 
   /** Container for specifying where to select raster paths from. */
-  case class RasterSourcePathTableRef(tableName: String, bandColumnNames: String*) extends WithBandColumns
+  case class RasterSourceCatalogRef(tableName: String, bandColumnNames: String*) extends WithBandColumns
 
   private[rastersource]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
@@ -94,7 +96,7 @@ object RasterSourceDataSource {
       .map(_.split(',').map(_.trim.toInt).toSeq)
       .getOrElse(Seq(0))
 
-    def filePaths: Option[RasterSourcePathTable] = {
+    def catalog: Option[RasterSourceCatalog] = {
       val paths = (
         parameters
           .get(PATHS_PARAM)
@@ -105,20 +107,26 @@ object RasterSourceDataSource {
             .toSeq
         ).filter(_.nonEmpty)
 
-      RasterSourcePathTable(paths)
+      RasterSourceCatalog(paths)
         .orElse(parameters
-          .get(PATH_CSV_PARAM)
-          .flatMap(RasterSourcePathTable(_)))
+          .get(CATALOG_CSV_PARAM)
+          .flatMap(RasterSourceCatalog(_, catalogTableCols: _*))
+        )
     }
 
-    def pathTable: Option[RasterSourcePathTableRef] = parameters
-      .get(PATH_TABLE_PARAM)
-      .zip(parameters.get(PATH_TABLE_COL_PARAM))
-      .map(p => RasterSourcePathTableRef(p._1, p._2.split(','): _*))
-      .headOption
+    def catalogTableCols: Seq[String] = parameters
+      .get(CATALOG_TABLE_COLS_PARAM)
+      .map(_.split(',').toSeq)
+      .getOrElse(Seq.empty)
 
-    def pathSpec: Either[RasterSourcePathTable, RasterSourcePathTableRef] = {
-      (filePaths, pathTable) match {
+    def catalogTable: Option[RasterSourceCatalogRef] = parameters
+      .get(CATALOG_TABLE_PARAM)
+      .map(p => RasterSourceCatalogRef(p, catalogTableCols: _*))
+
+    def pathSpec: Either[RasterSourceCatalog, RasterSourceCatalogRef] = {
+      val a = catalogTable
+      val b = catalogTableCols
+      (catalog, catalogTable) match {
         case (Some(f), None) => Left(f)
         case (None, Some(p)) => Right(p)
         case _ => throw new IllegalArgumentException("Only one of a set of file paths OR a paths table column may be provided.")
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index 7fab2fbbd..a939db026 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourcePathTable, RasterSourcePathTableRef}
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourceCatalog, RasterSourceCatalogRef}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
@@ -36,17 +36,17 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
   * @param sqlContext Query context
-  * @param pathTable Specifiction of raster path sources
+  * @param catalogTable Specifiction of raster path sources
   * @param bandIndexes band indexes to fetch
   * @param subtileDims how big to tile/subdivide rasters info
   */
 case class RasterSourceRelation(
   sqlContext: SQLContext,
-  pathTable: Either[RasterSourcePathTable, RasterSourcePathTableRef],
+  catalogTable: Either[RasterSourceCatalog, RasterSourceCatalogRef],
   bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
   extends BaseRelation with TableScan {
 
-  lazy val inputColNames = pathTable.merge.bandColumnNames
+  lazy val inputColNames = catalogTable.merge.bandColumnNames
 
   def pathColNames = inputColNames
     .map(_ + "_path")
@@ -79,7 +79,7 @@ case class RasterSourceRelation(
     // The general transformaion is:
     // input -> path -> src -> ref -> tile
     // Each step is broken down for readability
-    val inputs: DataFrame = pathTable match {
+    val inputs: DataFrame = catalogTable match {
       case Right(spec) => sqlContext.table(spec.tableName)
       case Left(spec) => spec.bandColumnNames match {
         case Seq(single) => spec.sceneRows.map(_.bandPaths.headOption.orNull).toDF(single)
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
index 2e284043f..bbe0b7774 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
@@ -52,13 +52,13 @@ package object rastersource {
 
     def fromTable(tableName: String, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
-        reader.option(RasterSourceDataSource.PATH_TABLE_PARAM, tableName)
-          .option(RasterSourceDataSource.PATH_TABLE_COL_PARAM, bandColumnNames.mkString(","))
+        reader.option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tableName)
+          .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
       )
 
-    def fromCSV(pathTableCSV: String): RasterSourceDataFrameReader =
+    def fromCSV(catalogCSV: String): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
-        reader.option(RasterSourceDataSource.PATH_CSV_PARAM, pathTableCSV)
+        reader.option(RasterSourceDataSource.CATALOG_CSV_PARAM, catalogCSV)
       )
 
     def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index ff1c315e7..7b2360f31 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -21,7 +21,7 @@
 
 package org.locationtech.rasterframes.datasource.rastersource
 import org.locationtech.rasterframes.{TestEnvironment, _}
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourcePathTable, _}
+import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourceCatalog, _}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.util._
 
@@ -31,28 +31,28 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
   describe("DataSource parameter processing") {
     def singleCol(paths: Iterable[String]) = {
       val rows: Seq[BandSet] = paths.map(BandSet(_)).toSeq
-      RasterSourcePathTable(rows, DEFAULT_COLUMN_NAME)
+      RasterSourceCatalog(rows, DEFAULT_COLUMN_NAME)
     }
 
     it("should handle single `path`") {
       val p = Map(PATH_PARAM -> "/usr/local/foo/bar.tif")
-      p.filePaths should be (Some(singleCol(p.values)))
+      p.catalog should be (Some(singleCol(p.values)))
     }
 
     it("should handle single `paths`") {
       val p = Map(PATHS_PARAM -> "/usr/local/foo/bar.tif")
-      p.filePaths should be (Some(singleCol(p.values)))
+      p.catalog should be (Some(singleCol(p.values)))
     }
     it("should handle multiple `paths`") {
       val expected = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val p = Map(PATHS_PARAM -> expected.mkString("\n\r", "\n\n", "\r"))
-      p.filePaths should be (Some(singleCol(expected)))
+      p.catalog should be (Some(singleCol(expected)))
     }
     it("should handle both `path` and `paths`") {
       val expected1 = Seq("/usr/local/foo/bar.tif", "/usr/local/bar/foo.tif")
       val expected2 = "/usr/local/barf/baz.tif"
       val p = Map(PATHS_PARAM -> expected1.mkString("\n"), PATH_PARAM -> expected2)
-      p.filePaths should be (Some(singleCol(expected1 :+ expected2)))
+      p.catalog should be (Some(singleCol(expected1 :+ expected2)))
     }
     it("should parse tile dimensions") {
       val p = Map(TILE_DIMS_PARAM -> "4, 5")
@@ -60,8 +60,8 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     }
 
     it("should parse path table specification") {
-      val p = Map(PATH_TABLE_PARAM -> "pathTable", PATH_TABLE_COL_PARAM -> "path")
-      p.pathSpec should be (Right(RasterSourcePathTableRef("pathTable", "path")))
+      val p = Map(CATALOG_TABLE_PARAM -> "catalogTable", CATALOG_TABLE_COLS_PARAM -> "path")
+      p.pathSpec should be (Right(RasterSourceCatalogRef("catalogTable", "path")))
     }
 
     it("should parse path table from CSV") {
@@ -72,8 +72,8 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
           |${bands.mkString(",")}
           |${paths.mkString(",")}
         """.stripMargin.trim
-      val p = Map(PATH_CSV_PARAM -> csv)
-      p.pathSpec should be (Left(RasterSourcePathTable(Seq(BandSet(paths:_*)), bands:_*)))
+      val p = Map(CATALOG_CSV_PARAM -> csv)
+      p.pathSpec should be (Left(RasterSourceCatalog(Seq(BandSet(paths:_*)), bands:_*)))
     }
   }
 
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index 124ab9342..a281ce1a2 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -13,6 +13,7 @@ spark = create_rf_spark_session()
 ## Computing NDVI
 
 ```python, term=True
+# TODO: rewrite to use `rastersource`
 redBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
 nirBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
 
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 138829ca9..c9179540a 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -67,7 +67,7 @@ red,green,blue
 
 print(csv)
 
-rf2 = spark.read.rastersource(csv=csv)
+rf2 = spark.read.rastersource(catalog=csv, catalog_col_names=['red', 'green', 'blue'])
 rf2.printSchema()
 
 rf2.show(5)
@@ -104,25 +104,34 @@ from pyspark.sql import functions as F
 # Image URIs take the form:
 #    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
 
-#    .filter(modis_catalog.download_url.contains("h07")) \
-
-equator = modis_catalog \
+equator = (modis_catalog 
     .withColumn('base_url', 
         F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',) 
-    ) \
+    )
 .select(
-    F.concat('base_url', F.lit("_B1.TIF")).alias('red'),
-    F.concat('base_url', F.lit("_B2.TIF")).alias('nir')
-) \
-.where(F.col('gid').like('%h07%'))
-equator.show(5)
+    F.concat('base_url', F.lit("_B01.TIF")).alias('red'),
+    F.concat('base_url', F.lit("_B02.TIF")).alias('nir')
+)
+.where(F.col('gid').like('%v07%'))
+)
+equator.show(3)
 ```
 
 Now that we have a catalog of multiple scenes and mutiple bands withit each scene, we can pass it along to `rastersource` load the imagery. To complete the example, we compute some aggregate statistics over NDVI.
 
 ```python
-equator.createOrReplaceTempView("equator")
-spark.read.rastersource()
+rf = spark.read.rastersource(
+    catalog=equator, 
+    catalog_col_names=['red', 'nir']
+).repartition()
+rf.show(2, truncate=False)
+(rf 
+    .select(rf_normalized_difference('nir', 'red').alias('ndvi')) 
+    .agg(rf_agg_stats('ndvi').alias('stats')) 
+    .select('stats.*') 
+    .show(truncate=False)
+)
+    
 ```
 
 ### Multiband Rasters
diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/spark-ml.pymd
index 3dda84182..34314e659 100644
--- a/pyrasterframes/src/main/python/docs/spark-ml.pymd
+++ b/pyrasterframes/src/main/python/docs/spark-ml.pymd
@@ -28,6 +28,7 @@ bandColNames = list(map(lambda n: 'band_{}'.format(n), bandNumbers))
 For each identified band, load the associated image file:
 
 ```python, term=True
+# TODO: rewrite using `rastersource`.
 from functools import reduce
 joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
                   map(lambda bf: spark.read.geotiff(bf[1])
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index d3b8128ba..d8433b09d 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -101,24 +101,43 @@ def _layer_reader(df_reader, format_key, path, **options):
 
 def _rastersource_reader(
         df_reader, path=None,
-        csv=None,
-        band_indexes=None, tile_dimensions=(256, 256),
+        band_indexes=None,
+        tile_dimensions=(256, 256),
+        catalog=None,
+        catalog_col_names=None,
         **options):
 
+    def to_csv(comp):
+        if isinstance(comp, str):
+            return comp
+        else:
+            return ','.join(str(v) for v in comp)
+
     if band_indexes is None:
         band_indexes = [0]
 
-    def to_csv(comp): return ','.join(str(v) for v in comp)
-    # If path is list, convert to newline delimited string and pass as "paths" (plural)
     options.update({
         "bandIndexes": to_csv(band_indexes),
         "tileDimensions": to_csv(tile_dimensions)
     })
 
-    if csv is not None:
-        options.update({
-            "pathCSVTable": csv
-        })
+    if catalog is not None:
+        if catalog_col_names is None:
+            raise Exception("'catalog_col_names' required when DataFrame 'catalog' specified")
+        if isinstance(catalog, str):
+            options.update({
+                "catalogCSV": catalog,
+                "catalogColumns": to_csv(catalog_col_names)
+            })
+        elif isinstance(catalog, DataFrame):
+            import uuid
+            # Create a random view name
+            name = str(uuid.uuid4()).replace('-', '')
+            catalog.createOrReplaceTempView(name)
+            options.update({
+                "catalogTable": name,
+                "catalogColumns": to_csv(catalog_col_names)
+            })
 
     return df_reader \
         .format("rastersource") \
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index 155ce0f46..f09eb672e 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -84,7 +84,6 @@ def create_rf_spark_session():
              .master("local[*]")
              .appName("RasterFrames")
              .config('spark.jars', jars_cp)
-             .config("spark.ui.enabled", "false")
              .withKryoSerialization()
              .getOrCreate())
     spark.withRasterFrames()
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 22472da28..acf4ade06 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -728,8 +728,6 @@ def l8path(b):
         print(path_count.toPandas())
         self.assertTrue(path_count.count() == 3)
 
-        ###  Similar to the scala side's `fromTable`, read from a table with columns giving URI paths
-
         scene_dict = {
             1: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/LC08_L1TP_015041_20190305_20190309_01_T1/LC08_L1TP_015041_20190305_20190309_01_T1_B{}.TIF',
             2: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/042/LC08_L1TP_015042_20190305_20190309_01_T1/LC08_L1TP_015042_20190305_20190309_01_T1_B{}.TIF',
@@ -741,7 +739,6 @@ def path(scene, band):
             p = scene_dict[scene]
             return p.format(band)
 
-        path_table_hive_name = 'path_table'
         # Create a pandas dataframe (makes it easy to create spark df)
         path_pandas = pd.DataFrame([
             {'b1': path(1, 1), 'b2': path(1, 2), 'b3': path(1, 3)},
@@ -749,14 +746,13 @@ def path(scene, band):
             {'b1': path(3, 1), 'b2': path(3, 2), 'b3': path(3, 3)},
         ])
         # comma separated list of column names containing URI's to read.
-        csv_columns = ','.join(path_pandas.columns.tolist())  # 'b1,b2,b3'
+        catalog_columns = ','.join(path_pandas.columns.tolist())  # 'b1,b2,b3'
         path_table = self.spark.createDataFrame(path_pandas)
-        path_table.createOrReplaceTempView(path_table_hive_name)
 
         path_df = self.spark.read.rastersource(
             tile_dimensions=(512, 512),
-            pathTable=path_table_hive_name,
-            pathTableColumns=csv_columns,
+            catalog=path_table,
+            catalog_col_names=catalog_columns
         )
 
         self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}

From 103b7669d41c1ac51a57dd6489a2aa2371b6a7e9 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 24 Jun 2019 11:36:28 -0400
Subject: [PATCH 182/380] Tweak to create_rf_spark_session so python 2 test run
 / pass

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/__init__.py    |  3 +--
 .../src/main/python/pyrasterframes/utils.py   | 19 +++++++++++--------
 .../src/main/python/tests/__init__.py         |  3 +--
 3 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 884538630..45962a34d 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -57,8 +57,7 @@ def _kryo_init(builder):
 
 
 def get_spark_session():
-    from pyspark.sql import SparkSession
-    import os
+    """ Create a SparkSession with pyrasterframes enabled and configured. """
     from pyrasterframes.utils import create_rf_spark_session
 
     return create_rf_spark_session()
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index 155ce0f46..09f64bb16 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -76,16 +76,19 @@ def find_pyrasterframes_assembly():
 
 
 def create_rf_spark_session():
-    jar_path = find_pyrasterframes_jar_dir()
-
-    jars_cp = ','.join([f.path for f in os.scandir(jar_path) if f.name[-3:] == 'jar'])
+    """ Create a SparkSession with pyrasterframes enabled and configured. """
+    jar_path = find_pyrasterframes_assembly()
 
     spark = (SparkSession.builder
              .master("local[*]")
-             .appName("RasterFrames")
-             .config('spark.jars', jars_cp)
-             .config("spark.ui.enabled", "false")
+             .appName("PyRasterFrames")
+             .config('spark.jars', jar_path)
              .withKryoSerialization()
              .getOrCreate())
-    spark.withRasterFrames()
-    return spark
+
+    try:
+        spark.withRasterFrames()
+        return spark
+    except TypeError as te:
+        print("Error setting up SparkSession; cannot find the pyrasterframes assembly jar\n", te)
+        return None
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 267701f34..36f3fadfd 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -22,7 +22,6 @@
 import os
 import unittest
 
-from pyspark.sql import SparkSession
 from pyrasterframes.rasterfunctions import *
 from pyrasterframes.utils import create_rf_spark_session
 
@@ -78,4 +77,4 @@ def create_rasterframe(self):
         self.rf = rf.withColumn('tile2', rf_convert_cell_type('tile', 'float32')) \
             .drop('tile') \
             .withColumnRenamed('tile2', 'tile').asRF()
-        # cls.rf.show()
\ No newline at end of file
+        # cls.rf.show()

From 8f773adfe6ab69b87d481e5685a93d22bc8f6bfb Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 24 Jun 2019 11:53:13 -0400
Subject: [PATCH 183/380] python examples use get_spark_session, clean up
 imports

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/examples/Clustering.py     |  8 +++++---
 .../python/examples/CreatingRasterFrames.py    |  6 ++----
 .../src/main/python/examples/MeanValue.py      | 10 +++++-----
 .../src/main/python/examples/NDVI.py           | 18 +++++++++++-------
 4 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/pyrasterframes/src/main/python/examples/Clustering.py b/pyrasterframes/src/main/python/examples/Clustering.py
index 3825ede28..fc1b9dfe9 100644
--- a/pyrasterframes/src/main/python/examples/Clustering.py
+++ b/pyrasterframes/src/main/python/examples/Clustering.py
@@ -1,11 +1,13 @@
 from . import test_resource_dir
+
+from pyrasterframes import TileExploder
 from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
+from pyrasterframes.rasterfunctions import rf_assemble_tile
+
 from pyspark.ml.feature import VectorAssembler
 from pyspark.ml.clustering import KMeans
 from pyspark.ml import Pipeline
+
 import os
 
 spark = create_rf_spark_session()
diff --git a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
index 99ae32b05..2a7499e5e 100644
--- a/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
+++ b/pyrasterframes/src/main/python/examples/CreatingRasterFrames.py
@@ -1,10 +1,8 @@
 from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyspark.sql import *
+import pyrasterframes
 import os
 
-spark = create_rf_spark_session().withRasterFrames()
+spark = pyrasterframes.get_spark_session()
 
 rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
diff --git a/pyrasterframes/src/main/python/examples/MeanValue.py b/pyrasterframes/src/main/python/examples/MeanValue.py
index 06960b7fc..7409b10b5 100644
--- a/pyrasterframes/src/main/python/examples/MeanValue.py
+++ b/pyrasterframes/src/main/python/examples/MeanValue.py
@@ -1,11 +1,11 @@
 from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
+
+import pyrasterframes
+from pyrasterframes.rasterfunctions import rf_agg_no_data_cells, rf_agg_data_cells, rf_agg_mean
+
 import os
 
-spark = create_rf_spark_session().withRasterFrames()
+spark = pyrasterframes.get_spark_session()
 
 rf = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
diff --git a/pyrasterframes/src/main/python/examples/NDVI.py b/pyrasterframes/src/main/python/examples/NDVI.py
index 94e7fe031..cb9b69854 100644
--- a/pyrasterframes/src/main/python/examples/NDVI.py
+++ b/pyrasterframes/src/main/python/examples/NDVI.py
@@ -1,15 +1,19 @@
 from . import test_resource_dir
-from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
+
+import pyrasterframes
+from pyrasterframes.rasterfunctions import rf_normalized_difference
+
 import os
 
-spark = create_rf_spark_session().withRasterFrames()
+spark = pyrasterframes.get_spark_session()
+
+red_path = os.path.join(test_resource_dir(), 'L8-B4-Elkton-VA.tiff')
+nir_path = os.path.join(test_resource_dir(), 'L8-B5-Elkton-VA.tiff')
 
-redBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
-nirBand = spark.read.geotiff(os.path.join(test_resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
+red_band = spark.read.geotiff(red_path).withColumnRenamed('tile', 'red_band')
+nir_band = spark.read.geotiff(nir_path).withColumnRenamed('tile', 'nir_band')
 
-rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
+rf = red_band.asRF().spatialJoin(nir_band.asRF()) \
     .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
 rf.printSchema()
 rf.show(20)

From 318a1164c4692cb814f0cfffd58b07ba1ddb9573 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 24 Jun 2019 13:18:47 -0400
Subject: [PATCH 184/380] Clean up python wildcard imports

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/README.md                       | 18 ++++++++++++++++--
 pyrasterframes/build.sbt                       |  2 +-
 .../src/main/python/pyrasterframes/__init__.py |  4 ++--
 .../python/pyrasterframes/rasterfunctions.py   |  1 -
 .../src/main/python/pyrasterframes/rf_types.py |  2 +-
 .../main/python/tests/PyRasterFramesTests.py   | 12 ++++++++++--
 .../src/main/python/tests/__init__.py          |  2 +-
 7 files changed, 31 insertions(+), 10 deletions(-)

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 00f5b7069..afc7fe25c 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -16,8 +16,7 @@ To initialize PyRasterFrames in a generic Python shell follow the pattern below,
 
 ```python
 from pyspark.sql import SparkSession
-from pyrasterframes import *
-from pyrasterframes.rasterfunctions import *
+import pyrasterframes 
 spark = SparkSession.builder \
      .master("local[*]") \
      .appName("Using RasterFrames") \
@@ -108,6 +107,20 @@ Release versions of these artifacts are published to https://central.sonatype.or
 Latest version can be found [here](https://search.maven.org/search?q=g:org.locationtech.rasterframes). 
 The Python package is published under the `python` classifier, `zip` extension.
 
+### Building and publishing for pypi
+
+To create a source and binary distribution appropriate for pypi:
+
+```bash
+sbt pyrasterframes/package # alias 'pyBuild'
+```
+
+Observe the output messages such as:
+
+    [info] Python .whl file written to '/Users/monty/rasterframes/pyrasterframes/target/python/dist/pyrasterframes-0.8.0.dev0-py2.py3-none-any.whl'
+    
+This wheel is suitable for publishing to a package index. See [https://packaging.python.org/tutorials/packaging-projects/#uploading-the-distribution-archives](packaging.python.org).
+
 ## Build Internals
 
 ### Running `setup.py`
@@ -128,3 +141,4 @@ sbt 'pySetup examples -e NDVI'
 ```
 
 *Note: You may need to run `sbt pyrasterframes/assembly` at least once for certain `pySetup` commands to work.*
+
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 57af72f7e..578d364c0 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -18,7 +18,7 @@ pySparkCmd := {
   val py = (Python / packageBin).value
   val script = IO.createTemporaryDirectory / "pyrf_init.py"
   IO.write(script, """
-from pyrasterframes import *
+import pyrasterframes
 from pyrasterframes.rasterfunctions import *
 """)
   val msg = s"PYTHONSTARTUP=$script pyspark --jars $jvm --py-files $py"
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 45962a34d..f6b2557bf 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -29,10 +29,9 @@
 from pyspark.sql.column import _to_java_column
 
 # Import RasterFrame types and functions
-from .rf_types import *
-from . import rasterfunctions
 from .context import RFContext
 from .version import __version__
+from .rf_types import RasterFrame, TileExploder, TileUDT, RasterSourceUDT
 
 __all__ = ['RasterFrame', 'TileExploder']
 
@@ -62,6 +61,7 @@ def get_spark_session():
 
     return create_rf_spark_session()
 
+
 def _convert_df(df, sp_key=None, metadata=None):
     ctx = SparkContext._active_spark_context._rf_context
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 9d8ceff7a..4b37022c3 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -24,7 +24,6 @@
 signatures are handled here as well.
 """
 from __future__ import absolute_import
-from pyspark.sql.types import *
 from pyspark.sql.column import Column, _to_java_column
 from .context import RFContext
 from .rf_types import CellType
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 2849642ac..1a2a2c391 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -28,7 +28,7 @@ class here provides the PyRasterFrames entry point.
 from pyspark.sql.types import UserDefinedType
 from pyspark import SparkContext
 from pyspark.sql import DataFrame, Column
-from pyspark.sql.types import *
+from pyspark.sql.types import (StructType, StructField, BinaryType, DoubleType, ShortType, IntegerType, StringType)
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
 import numpy as np
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 35ab325f8..0795ad3d8 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -21,12 +21,11 @@
 import unittest
 
 import numpy as np
-from geomesa_pyspark.types import *
+import geomesa_pyspark.types
 from pyrasterframes.rasterfunctions import *
 from pyrasterframes.rf_types import *
 from pyspark.sql import SQLContext, Column
 from pyspark.sql.functions import *
-from pyspark.sql.types import *
 from . import TestEnvironment
 
 
@@ -48,6 +47,7 @@ def setUp(self):
 
     def test_spatial_relations(self):
         from pyspark.sql.functions import udf, sum
+        from geomesa_pyspark.types import PointUDT
         import shapely
         import numpy.testing
 
@@ -102,6 +102,7 @@ def some_point(g):
         )
 
     def test_rasterize(self):
+        from geomesa_pyspark.types import PolygonUDT
         # simple test that raster contents are not invalid
 
         # create a udf to buffer (the bounds) polygon
@@ -387,6 +388,8 @@ def test_mask_no_data(self):
         self.assertIsNotNone(t2.cells[1][1])
 
     def test_tile_udt_serialization(self):
+        from pyspark.sql.types import StructType, StructField
+
         udt = TileUDT()
         cell_types = (ct for ct in rf_cell_types() if not (ct.is_raw() or ("bool" in ct.base_cell_type_name())))
 
@@ -468,6 +471,8 @@ def my_udf(t):
         )
 
     def test_no_data_udf_handling(self):
+        from pyspark.sql.types import StructType, StructField
+
         t1 = Tile(np.array([[1, 2], [0, 4]]), CellType.uint8())
         self.assertEqual(t1.cell_type.to_numpy_dtype(), np.dtype("uint8"))
         e1 = Tile(np.array([[2, 3], [0, 5]]), CellType.uint8())
@@ -516,6 +521,8 @@ def less_pi(t):
 
 class TileOps(TestEnvironment):
 
+    from pyrasterframes.rf_types import Tile
+
     def setUp(self):
         # convenience so we can assert around Tile() == Tile()
         self.t1 = Tile(np.array([[1, 2],
@@ -578,6 +585,7 @@ def test_matmul(self):
 
 
 class PandasInterop(TestEnvironment):
+
     def setUp(self):
         self.create_rasterframe()
 
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 36f3fadfd..8dc6a1126 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -22,7 +22,6 @@
 import os
 import unittest
 
-from pyrasterframes.rasterfunctions import *
 from pyrasterframes.utils import create_rf_spark_session
 
 import sys
@@ -68,6 +67,7 @@ def setUpClass(cls):
         cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
 
     def create_rasterframe(self):
+        from pyrasterframes.rasterfunctions import rf_convert_cell_type
         # load something into a rasterframe
         rf = self.spark.read.geotiff(self.img_uri) \
             .withBounds() \

From c52cba5d786ff9448c52379dbe98e75e1ff5f6ce Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 24 Jun 2019 15:32:14 -0400
Subject: [PATCH 185/380] Unpin pyspark version

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index b715f2f7e..278f275c7 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -1,6 +1,6 @@
 pytz
 shapely
-pyspark==2.3.3
+pyspark<2.4
 numpy>=1.7
 pandas
 matplotlib<3.0.0  # no python 2.7 support after v2.x.x

From 4c6b93cace2439e58c04ef92bba310bf7f922ce2 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 24 Jun 2019 15:48:51 -0400
Subject: [PATCH 186/380] Unpin spark version, try 2

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/requirements.txt | 2 +-
 pyrasterframes/src/main/python/setup.py         | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index 278f275c7..9ce9fb620 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -1,6 +1,6 @@
 pytz
 shapely
-pyspark<2.4
+pyspark==2.3.3  # See issue # 154
 numpy>=1.7
 pandas
 matplotlib<3.0.0  # no python 2.7 support after v2.x.x
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 5d0d8bc2c..e48ef6973 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -116,14 +116,14 @@ def run(self):
     install_requires=[
         'pytz',
         'shapely',
-        'pyspark==2.3.3',
+        'pyspark<2.4',
         'numpy>=1.7',
         'pandas',
     ],
     setup_requires=[
         'pytz',
         'shapely',
-        'pyspark==2.3.3',
+        'pyspark<2.4',
         'numpy>=1.7',
         'pandas',
         'pytest-runner',

From 9a71c22023e8f31d757068b36998c134ef642aa9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 24 Jun 2019 17:11:47 -0400
Subject: [PATCH 187/380] Converted installation of pyrasterframes in notebook
 to use whl file.

---
 project/PythonBuildPlugin.scala        | 9 +++++----
 rf-notebook/build.sbt                  | 9 +++------
 rf-notebook/src/main/docker/Dockerfile | 8 ++------
 3 files changed, 10 insertions(+), 16 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index e9280a617..22a67dcf4 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -37,6 +37,7 @@ object PythonBuildPlugin extends AutoPlugin {
     val pythonSource = settingKey[File]("Default Python source directory.").withRank(ASetting)
     val pythonCommand = settingKey[String]("Python command. Defaults to 'python'")
     val pySetup = inputKey[Int]("Run 'python setup.py <args>'. Returns exit code.")
+    val pyWhl = taskKey[File]("Builds the Python wheel distribution")
   }
   import autoImport._
 
@@ -65,9 +66,9 @@ object PythonBuildPlugin extends AutoPlugin {
     IO.copyFile(asmbl, dest)
     log.info(s"PyRasterFrames assembly written to '$dest'")
     dest
-  }
+  }.dependsOn(copyPySources)
 
-  val pyWhl = Def.task {
+  val pyWhlImp = Def.task {
     val log = streams.value.log
     val buildDir = (Python / target).value
     val retcode = pySetup.toTask(" build bdist_wheel").value
@@ -85,7 +86,7 @@ object PythonBuildPlugin extends AutoPlugin {
     IO.copyFile(whl, pyDest)
     log.info(s"Maven Python .zip artifact written to '$pyDest'")
     pyDest
-  }
+  }.dependsOn(pyWhl)
 
   override def projectConfigurations: Seq[Configuration] = Seq(Python)
 
@@ -94,7 +95,6 @@ object PythonBuildPlugin extends AutoPlugin {
     pythonCommand := "python",
     pySetup := {
       val s = streams.value
-      val _ = copyPySources.value
       val wd = (Python / target).value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
@@ -102,6 +102,7 @@ object PythonBuildPlugin extends AutoPlugin {
       s.log.info(s"Running '${cmd.mkString(" ")}' in '$wd'")
       Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
     },
+    pyWhl := pyWhlImp.value,
     Compile / pythonSource := (Compile / sourceDirectory).value / "python",
     Test / pythonSource := (Test / sourceDirectory).value / "python",
     Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value,
diff --git a/rf-notebook/build.sbt b/rf-notebook/build.sbt
index 7c94d8271..8a1d44b2a 100644
--- a/rf-notebook/build.sbt
+++ b/rf-notebook/build.sbt
@@ -1,6 +1,5 @@
-import sbt.{IO, _}
-
 import scala.sys.process.Process
+import PythonBuildPlugin.autoImport.pyWhl
 
 Docker / packageName := "rasterframes-notebook"
 
@@ -18,11 +17,9 @@ Docker / mappings := {
   val dockerSrc = (Docker / sourceDirectory).value
   val dockerAssets = (dockerSrc ** "*") pair Path.relativeTo(dockerSrc)
 
-  val jar = assembly.value
-
-  val py = (LocalProject("pyrasterframes") / Python / packageBin).value
+  val py = (LocalProject("pyrasterframes") / pyWhl).value
 
-  dockerAssets ++ Seq(jar -> jar.getName, py -> py.getName)
+  dockerAssets ++ Seq(py -> py.getName)
 }
 
 // This bypasses the standard DockerPlugin DSL-based Dockerfile construction
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index be5a2adeb..db7e4570a 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -3,8 +3,6 @@ FROM s22s/pyspark-notebook:spark-2.3.3-hadoop-2.7
 MAINTAINER Astraea, Inc.
 
 ENV RF_LIB_LOC /usr/local/rasterframes
-ENV RF_JAR $RF_LIB_LOC/rasterframes.jar
-ENV PY_RF_ZIP $RF_LIB_LOC/pyrasterframes.zip
 
 USER root
 
@@ -16,8 +14,6 @@ RUN echo "spark.driver.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-de
 EXPOSE 4040 4041 4042 4043 4044
 
 ENV SPARK_OPTS $SPARK_OPTS \
-    --py-files $PY_RF_ZIP \
-    --jars $RF_JAR \
     --driver-class-path $RF_JAR \
     --conf spark.executor.extraClassPath=$RF_JAR
 
@@ -34,8 +30,8 @@ RUN rm -rf /home/$NB_USER/.local && \
 
 # Do these after the standard environment setup
 # since these change more regularly.
-COPY *.zip $PY_RF_ZIP
-COPY *.jar $RF_JAR
+COPY *.whl $RF_LIB_LOC
+RUN ls -1 $RF_LIB_LOC/*.whl | xargs pip install
 
 RUN chown -R $NB_UID:$NB_GID $HOME
 

From 14303b287d2d114d8a04b5c0dd0fd92218b3fec6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 25 Jun 2019 11:44:32 -0400
Subject: [PATCH 188/380] Update pattern for Getting Started python notebook

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/notebooks/Getting Started.ipynb  | 872 ++++--------------
 1 file changed, 173 insertions(+), 699 deletions(-)

diff --git a/rf-notebook/src/main/notebooks/Getting Started.ipynb b/rf-notebook/src/main/notebooks/Getting Started.ipynb
index d5d615664..9b164ad0e 100644
--- a/rf-notebook/src/main/notebooks/Getting Started.ipynb	
+++ b/rf-notebook/src/main/notebooks/Getting Started.ipynb	
@@ -17,60 +17,93 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pyspark.sql import SparkSession\n",
-    "from pyrasterframes import *\n",
-    "from pyrasterframes.rasterfunctions import *\n",
-    "\n",
-    "spark = SparkSession.builder \\\n",
-    "    .withKryoSerialization() \\\n",
-    "    .getOrCreate() \\\n",
-    "    .withRasterFrames()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "                <p><b>SparkSession - in-memory</b></p>\n",
+       "                \n",
+       "        <div>\n",
+       "            <p><b>SparkContext</b></p>\n",
+       "\n",
+       "            <p><a href=\"http://dc4d2a69c533:4040\">Spark UI</a></p>\n",
+       "\n",
+       "            <dl>\n",
+       "              <dt>Version</dt>\n",
+       "                <dd><code>v2.3.3</code></dd>\n",
+       "              <dt>Master</dt>\n",
+       "                <dd><code>local[*]</code></dd>\n",
+       "              <dt>AppName</dt>\n",
+       "                <dd><code>PyRasterFrames</code></dd>\n",
+       "            </dl>\n",
+       "        </div>\n",
+       "        \n",
+       "            </div>\n",
+       "        "
+      ],
       "text/plain": [
-       "'{\"builtAtMillis\":\"1557330142297\", \"name\":\"core\", \"rfSparkVersion\":\"2.3.2\", \"moduleName\":\"rasterframes\", \"scalaVersion\":\"2.11.12\", \"rfGeoTrellisVersion\":\"2.2.0\", \"version\":\"0.8.0-SNAPSHOT\", \"sbtVersion\":\"1.2.8\", \"builtAtString\":\"2019-05-08 15:42:22.297\", \"rfGeoMesaVersion\":\"2.2.1\"}'"
+       "<pyspark.sql.session.SparkSession at 0x7f133e7f0c18>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from pyrasterframes import RFContext\n",
-    "RFContext.active().build_info()"
+    "import pyrasterframes\n",
+    "import pyrasterframes.rf_ipython  # enables nicer visualizations\n",
+    "spark = pyrasterframes.get_spark_session()\n",
+    "spark"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Read an image"
+    "### Get a PySpark DataFrame from [open data](https://landsatonaws.com/)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "href = \"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\"\n",
-    "df = spark.read.rastersource(href)"
+    "uri = 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/'\\\n",
+    "      'LC08_L1TP_015041_20190305_20190309_01_T1/'\\\n",
+    "      'LC08_L1TP_015041_20190305_20190309_01_T1_B2.TIF'\n",
+    "df = spark.read.rastersource(uri)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "930"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +113,7 @@
       "root\n",
       " |-- tile_path: string (nullable = false)\n",
       " |-- tile: struct (nullable = true)\n",
-      " |    |-- tileContext: struct (nullable = false)\n",
+      " |    |-- tile_context: struct (nullable = false)\n",
       " |    |    |-- extent: struct (nullable = false)\n",
       " |    |    |    |-- xmin: double (nullable = false)\n",
       " |    |    |    |-- ymin: double (nullable = false)\n",
@@ -99,488 +132,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rf_extent(tile)</th>\n",
-       "      <th>crs(tile)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(14455356.755667, -2342509.0947641465, 1457396...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(14573964.81109804, -2342509.0947641465, 14692...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(14692572.86652908, -2342509.0947641465, 14811...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(14811180.92196012, -2342509.0947641465, 14929...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(14929788.97739116, -2342509.0947641465, 15048...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(15048397.0328222, -2342509.0947641465, 151670...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(15167005.08825324, -2342509.0947641465, 15285...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>(15285613.14368428, -2342509.0947641465, 15404...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(15404221.199115321, -2342509.0947641465, 1552...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(15522829.254546361, -2342509.0947641465, 1556...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>(14455356.755667, -2461117.150195293, 14573964...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>(14573964.81109804, -2461117.150195293, 146925...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>(14692572.86652908, -2461117.150195293, 148111...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>(14811180.92196012, -2461117.150195293, 149297...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>(14929788.97739116, -2461117.150195293, 150483...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>(15048397.0328222, -2461117.150195293, 1516700...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>(15167005.08825324, -2461117.150195293, 152856...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>(15285613.14368428, -2461117.150195293, 154042...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>(15404221.199115321, -2461117.150195293, 15522...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>(15522829.254546361, -2461117.150195293, 15567...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(14455356.755667, -2579725.20562644, 14573964....</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(14573964.81109804, -2579725.20562644, 1469257...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(14692572.86652908, -2579725.20562644, 1481118...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(14811180.92196012, -2579725.20562644, 1492978...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>(14929788.97739116, -2579725.20562644, 1504839...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>(15048397.0328222, -2579725.20562644, 15167005...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>(15167005.08825324, -2579725.20562644, 1528561...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>(15285613.14368428, -2579725.20562644, 1540422...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>(15404221.199115321, -2579725.20562644, 155228...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>(15522829.254546361, -2579725.20562644, 155673...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>(14455356.755667, -3172765.482782173, 14573964...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>(14573964.81109804, -3172765.482782173, 146925...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>(14692572.86652908, -3172765.482782173, 148111...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>(14811180.92196012, -3172765.482782173, 149297...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>(14929788.97739116, -3172765.482782173, 150483...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>(15048397.0328222, -3172765.482782173, 1516700...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>(15167005.08825324, -3172765.482782173, 152856...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>(15285613.14368428, -3172765.482782173, 154042...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>(15404221.199115321, -3172765.482782173, 15522...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>(15522829.254546361, -3172765.482782173, 15567...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>(14455356.755667, -3291373.53821332, 14573964....</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>(14573964.81109804, -3291373.53821332, 1469257...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>(14692572.86652908, -3291373.53821332, 1481118...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>(14811180.92196012, -3291373.53821332, 1492978...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>(14929788.97739116, -3291373.53821332, 1504839...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>(15048397.0328222, -3291373.53821332, 15167005...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>(15167005.08825324, -3291373.53821332, 1528561...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>(15285613.14368428, -3291373.53821332, 1540422...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>(15404221.199115321, -3291373.53821332, 155228...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>(15522829.254546361, -3291373.53821332, 155673...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>(14455356.755667, -3335851.559, 14573964.81109...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>(14573964.81109804, -3335851.559, 14692572.866...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>(14692572.86652908, -3335851.559, 14811180.921...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>(14811180.92196012, -3335851.559, 14929788.977...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>(14929788.97739116, -3335851.559, 15048397.032...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>(15048397.0328222, -3335851.559, 15167005.0882...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>(15167005.08825324, -3335851.559, 15285613.143...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>(15285613.14368428, -3335851.559, 15404221.199...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>(15404221.199115321, -3335851.559, 15522829.25...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>(15522829.254546361, -3335851.559, 15567307.27...</td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      rf_extent(tile)  \\\n",
-       "0   (14455356.755667, -2342509.0947641465, 1457396...   \n",
-       "1   (14573964.81109804, -2342509.0947641465, 14692...   \n",
-       "2   (14692572.86652908, -2342509.0947641465, 14811...   \n",
-       "3   (14811180.92196012, -2342509.0947641465, 14929...   \n",
-       "4   (14929788.97739116, -2342509.0947641465, 15048...   \n",
-       "5   (15048397.0328222, -2342509.0947641465, 151670...   \n",
-       "6   (15167005.08825324, -2342509.0947641465, 15285...   \n",
-       "7   (15285613.14368428, -2342509.0947641465, 15404...   \n",
-       "8   (15404221.199115321, -2342509.0947641465, 1552...   \n",
-       "9   (15522829.254546361, -2342509.0947641465, 1556...   \n",
-       "10  (14455356.755667, -2461117.150195293, 14573964...   \n",
-       "11  (14573964.81109804, -2461117.150195293, 146925...   \n",
-       "12  (14692572.86652908, -2461117.150195293, 148111...   \n",
-       "13  (14811180.92196012, -2461117.150195293, 149297...   \n",
-       "14  (14929788.97739116, -2461117.150195293, 150483...   \n",
-       "15  (15048397.0328222, -2461117.150195293, 1516700...   \n",
-       "16  (15167005.08825324, -2461117.150195293, 152856...   \n",
-       "17  (15285613.14368428, -2461117.150195293, 154042...   \n",
-       "18  (15404221.199115321, -2461117.150195293, 15522...   \n",
-       "19  (15522829.254546361, -2461117.150195293, 15567...   \n",
-       "20  (14455356.755667, -2579725.20562644, 14573964....   \n",
-       "21  (14573964.81109804, -2579725.20562644, 1469257...   \n",
-       "22  (14692572.86652908, -2579725.20562644, 1481118...   \n",
-       "23  (14811180.92196012, -2579725.20562644, 1492978...   \n",
-       "24  (14929788.97739116, -2579725.20562644, 1504839...   \n",
-       "25  (15048397.0328222, -2579725.20562644, 15167005...   \n",
-       "26  (15167005.08825324, -2579725.20562644, 1528561...   \n",
-       "27  (15285613.14368428, -2579725.20562644, 1540422...   \n",
-       "28  (15404221.199115321, -2579725.20562644, 155228...   \n",
-       "29  (15522829.254546361, -2579725.20562644, 155673...   \n",
-       "..                                                ...   \n",
-       "70  (14455356.755667, -3172765.482782173, 14573964...   \n",
-       "71  (14573964.81109804, -3172765.482782173, 146925...   \n",
-       "72  (14692572.86652908, -3172765.482782173, 148111...   \n",
-       "73  (14811180.92196012, -3172765.482782173, 149297...   \n",
-       "74  (14929788.97739116, -3172765.482782173, 150483...   \n",
-       "75  (15048397.0328222, -3172765.482782173, 1516700...   \n",
-       "76  (15167005.08825324, -3172765.482782173, 152856...   \n",
-       "77  (15285613.14368428, -3172765.482782173, 154042...   \n",
-       "78  (15404221.199115321, -3172765.482782173, 15522...   \n",
-       "79  (15522829.254546361, -3172765.482782173, 15567...   \n",
-       "80  (14455356.755667, -3291373.53821332, 14573964....   \n",
-       "81  (14573964.81109804, -3291373.53821332, 1469257...   \n",
-       "82  (14692572.86652908, -3291373.53821332, 1481118...   \n",
-       "83  (14811180.92196012, -3291373.53821332, 1492978...   \n",
-       "84  (14929788.97739116, -3291373.53821332, 1504839...   \n",
-       "85  (15048397.0328222, -3291373.53821332, 15167005...   \n",
-       "86  (15167005.08825324, -3291373.53821332, 1528561...   \n",
-       "87  (15285613.14368428, -3291373.53821332, 1540422...   \n",
-       "88  (15404221.199115321, -3291373.53821332, 155228...   \n",
-       "89  (15522829.254546361, -3291373.53821332, 155673...   \n",
-       "90  (14455356.755667, -3335851.559, 14573964.81109...   \n",
-       "91  (14573964.81109804, -3335851.559, 14692572.866...   \n",
-       "92  (14692572.86652908, -3335851.559, 14811180.921...   \n",
-       "93  (14811180.92196012, -3335851.559, 14929788.977...   \n",
-       "94  (14929788.97739116, -3335851.559, 15048397.032...   \n",
-       "95  (15048397.0328222, -3335851.559, 15167005.0882...   \n",
-       "96  (15167005.08825324, -3335851.559, 15285613.143...   \n",
-       "97  (15285613.14368428, -3335851.559, 15404221.199...   \n",
-       "98  (15404221.199115321, -3335851.559, 15522829.25...   \n",
-       "99  (15522829.254546361, -3335851.559, 15567307.27...   \n",
-       "\n",
-       "                                            crs(tile)  \n",
-       "0   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "1   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "2   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "3   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "4   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "5   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "6   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "7   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "8   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "9   (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "10  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "11  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "12  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "13  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "14  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "15  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "16  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "17  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "18  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "19  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "20  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "21  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "22  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "23  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "24  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "25  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "26  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "27  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "28  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "29  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "..                                                ...  \n",
-       "70  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "71  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "72  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "73  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "74  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "75  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "76  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "77  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "78  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "79  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "80  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "81  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "82  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "83  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "84  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "85  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "86  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "87  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "88  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "89  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "90  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "91  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "92  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "93  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "94  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "95  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "96  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "97  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "98  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "99  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...  \n",
-       "\n",
-       "[100 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.select(rf_extent(\"tile\"), rf_crs(\"tile\")).toPandas()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
@@ -609,327 +164,246 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>(((14455356.755667, -2342509.0947641465, 14573...</td>\n",
+       "      <td>(((504825.0, 3142635.0, 512505.0, 3150315.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>(((14573964.81109804, -2342509.0947641465, 146...</td>\n",
+       "      <td>(((497145.0, 3127275.0, 504825.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>(((14692572.86652908, -2342509.0947641465, 148...</td>\n",
+       "      <td>(((643065.0, 3127275.0, 650745.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>(((14811180.92196012, -2342509.0947641465, 149...</td>\n",
+       "      <td>(((650745.0, 3127275.0, 658425.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>(((14929788.97739116, -2342509.0947641465, 150...</td>\n",
+       "      <td>(((489465.0, 3119595.0, 497145.0, 3127275.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>(((15048397.0328222, -2342509.0947641465, 1516...</td>\n",
+       "      <td>(((512505.0, 3119595.0, 520185.0, 3127275.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[    0     0     0 ... 34835 35040 34929]\\n [    0     0     0 ... 34438 33838 34300]\\n [    0     0     0 ... 34169 34047 35019]\\n ...\\n [    0     0     0 ... 28880 28858 28860]\\n [    0     0     0 ... 28868 28882 28825]\\n [    0     0     0 ... 28892 28830 28751]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>(((15167005.08825324, -2342509.0947641465, 152...</td>\n",
+       "      <td>(((566265.0, 3088875.0, 573945.0, 3096555.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[25072 24691 24206 ... 14174 14055 13722]\\n [24987 24851 24668 ... 13488 13960 14160]\\n [25287 25327 25125 ... 14932 14992 15673]\\n ...\\n [18261 18773 18356 ... 28507 29489 29209]\\n [18898 19475 18998 ... 27742 28559 28831]\\n [20193 20706 19194 ... 27191 27340 27728]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>(((15285613.14368428, -2342509.0947641465, 154...</td>\n",
+       "      <td>(((650745.0, 3081195.0, 658425.0, 3088875.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[37009 36664 36718 ... 30211 31565 32545]\\n [37220 36770 36653 ... 26997 30338 32433]\\n [37509 36985 36639 ... 26815 29523 31915]\\n ...\\n [21496 22198 22629 ... 50496 49952 49069]\\n [21319 21920 22650 ... 44582 47035 48996]\\n [21015 21923 23135 ... 40391 41619 46114]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>(((15404221.199115321, -2342509.0947641465, 15...</td>\n",
+       "      <td>(((481785.0, 3073515.0, 489465.0, 3081195.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>(((15522829.254546361, -2342509.0947641465, 15...</td>\n",
+       "      <td>(((643065.0, 3073515.0, 650745.0, 3081195.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[41963 41787 41288 ... 19905 19831 20434]\\n [41612 41503 41209 ... 20569 20559 20786]\\n [41520 41262 41159 ... 20901 21337 21310]\\n ...\\n [35591 34598 33741 ... 28094 28141 28347]\\n [35111 34607 34556 ... 28344 28428 28404]\\n [35020 35115 35496 ... 28928 28943 28659]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>(((14455356.755667, -2461117.150195293, 145739...</td>\n",
+       "      <td>(((550905.0, 3065835.0, 558585.0, 3073515.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[29634 29074 27867 ... 34042 35793 36101]\\n [28292 28290 27806 ... 35532 36229 36043]\\n [26764 27215 27229 ... 35614 36731 37212]\\n ...\\n [12799 11281 11560 ... 22493 22390 24250]\\n [12380 12208 12146 ... 21462 24578 26095]\\n [11854 12212 12253 ... 21606 23951 26103]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>(((14573964.81109804, -2461117.150195293, 1469...</td>\n",
+       "      <td>(((581625.0, 3065835.0, 589305.0, 3073515.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[29596 29158 29310 ... 31524 32105 32622]\\n [29449 30052 29751 ... 31945 32458 32848]\\n [30147 30365 29524 ... 31432 32140 32678]\\n ...\\n [34789 34764 34617 ... 38622 38460 38426]\\n [34196 34286 34005 ... 38503 38249 38346]\\n [33628 33690 33329 ... 38487 38527 38578]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>(((14692572.86652908, -2461117.150195293, 1481...</td>\n",
+       "      <td>(((527865.0, 3050475.0, 535545.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23166 23360 22026 ... 17733 17719 17761]\\n [20816 22672 20776 ... 18288 17669 17715]\\n [21089 20806 19823 ... 17316 17232 16957]\\n ...\\n [13103 12781 12562 ... 18723 19452 19702]\\n [12532 12190 12241 ... 18387 19321 19802]\\n [11338 11578 11682 ... 18910 19432 20186]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>(((14811180.92196012, -2461117.150195293, 1492...</td>\n",
+       "      <td>(((550905.0, 3050475.0, 558585.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16526 16306 15638 ... 15329 14712 14664]\\n [16903 16944 16832 ... 15041 15341 16860]\\n [15429 16114 16805 ... 16944 17966 20436]\\n ...\\n [16725 15873 14367 ... 14874 16061 16183]\\n [17029 15729 14388 ... 14999 15990 16224]\\n [17588 16339 15071 ... 16020 16677 16706]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>(((14929788.97739116, -2461117.150195293, 1504...</td>\n",
+       "      <td>(((566265.0, 3050475.0, 573945.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[33341 32022 31451 ... 26398 27977 28861]\\n [32632 31144 30286 ... 25217 26510 27947]\\n [32467 31358 30482 ... 24477 24832 26911]\\n ...\\n [24057 21928 19082 ... 27809 28046 29053]\\n [24680 23947 22570 ... 27101 28215 29453]\\n [25789 24841 23734 ... 26425 27868 29834]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>(((15048397.0328222, -2461117.150195293, 15167...</td>\n",
+       "      <td>(((627705.0, 3050475.0, 635385.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[38204 38705 39130 ... 26587 26447 26839]\\n [38392 38655 38823 ... 26448 25693 25283]\\n [38544 38747 38786 ... 26832 24899 23747]\\n ...\\n [26022 26438 26731 ... 25005 25354 25707]\\n [26400 26930 27321 ... 24669 25009 25260]\\n [27408 27625 28089 ... 25586 24755 24192]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>(((15167005.08825324, -2461117.150195293, 1528...</td>\n",
+       "      <td>(((589305.0, 3042795.0, 596985.0, 3050475.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[32575 32134 30849 ... 30972 31822 32286]\\n [31885 31061 30993 ... 30657 32024 32868]\\n [31407 31448 31981 ... 29817 30815 32045]\\n ...\\n [35508 35433 35177 ... 33603 33305 33672]\\n [35484 35003 34429 ... 33247 33058 33417]\\n [35343 34631 34415 ... 33020 33076 33331]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>(((15285613.14368428, -2461117.150195293, 1540...</td>\n",
+       "      <td>(((512505.0, 3035115.0, 520185.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[13629 13641 13654 ... 15836 15926 15930]\\n [13589 13639 13708 ... 15108 15343 15375]\\n [13691 13537 13669 ... 14280 14724 14503]\\n ...\\n [24557 24697 24844 ... 18465 18186 18502]\\n [25020 25233 25155 ... 17952 17631 18332]\\n [25061 25075 25074 ... 18569 19170 19982]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>(((15404221.199115321, -2461117.150195293, 155...</td>\n",
+       "      <td>(((573945.0, 3035115.0, 581625.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24336 24964 25037 ... 33327 33051 33358]\\n [22415 23612 24149 ... 33521 33440 33479]\\n [21209 23243 24114 ... 33880 33893 33683]\\n ...\\n [33638 32723 31054 ... 33057 32382 30685]\\n [32698 31960 31017 ... 32298 31310 30263]\\n [31792 31185 30859 ... 31983 31021 30331]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>(((15522829.254546361, -2461117.150195293, 155...</td>\n",
+       "      <td>(((612345.0, 3035115.0, 620025.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[40280 41293 42603 ... 32490 31581 31512]\\n [41099 41587 42092 ... 32941 32131 32091]\\n [41203 41661 42110 ... 33382 32299 32086]\\n ...\\n [24846 25986 26169 ... 27485 27374 27173]\\n [25559 27182 27638 ... 27592 27364 27008]\\n [26692 28129 28436 ... 27810 27397 27121]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>(((14455356.755667, -2579725.20562644, 1457396...</td>\n",
+       "      <td>(((635385.0, 3035115.0, 643065.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24378 24438 23453 ... 29819 31058 33367]\\n [24208 23636 23144 ... 31847 33205 34472]\\n [23688 22993 22775 ... 33441 34319 34732]\\n ...\\n [33234 32463 33299 ... 11817 11144 10977]\\n [32762 33111 33942 ... 12565 11693 11084]\\n [32264 33099 33582 ... 12812 12458 11561]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>(((14573964.81109804, -2579725.20562644, 14692...</td>\n",
+       "      <td>(((512505.0, 3027435.0, 520185.0, 3035115.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24858 24940 24606 ... 19891 21038 20948]\\n [25196 25118 24577 ... 20240 21793 21361]\\n [25752 25520 25170 ... 19794 20813 20261]\\n ...\\n [18146 17475 16777 ... 13993 13820 13694]\\n [17344 17460 17196 ... 13948 13775 13770]\\n [17364 17248 16749 ... 14023 13933 13923]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>(((14692572.86652908, -2579725.20562644, 14811...</td>\n",
+       "      <td>(((481785.0, 3019755.0, 489465.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>(((14811180.92196012, -2579725.20562644, 14929...</td>\n",
+       "      <td>(((543225.0, 3019755.0, 550905.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[26791 27865 28601 ... 22455 23951 25011]\\n [26513 27266 28239 ... 24150 25929 26738]\\n [25921 26332 27075 ... 24996 26887 28051]\\n ...\\n [28977 28830 28319 ... 27381 27470 26778]\\n [26999 26694 27336 ... 27126 26994 26973]\\n [25323 26363 27737 ... 27885 27729 29269]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>(((14929788.97739116, -2579725.20562644, 15048...</td>\n",
+       "      <td>(((620025.0, 3019755.0, 627705.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[27887 29274 29420 ... 28594 28067 26775]\\n [27511 27157 27926 ... 28321 27372 26632]\\n [26124 26258 28093 ... 28248 27692 27230]\\n ...\\n [25102 23556 20447 ... 13549 12968 12387]\\n [25162 22693 18945 ... 12300 11879 12002]\\n [24361 20969 19145 ... 11435 11760 12192]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>(((15048397.0328222, -2579725.20562644, 151670...</td>\n",
+       "      <td>(((527865.0, 3012075.0, 535545.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23505 21881 21270 ... 28139 28527 28544]\\n [22847 23503 24292 ... 28372 28332 28020]\\n [25271 26722 26837 ... 28584 28075 27104]\\n ...\\n [26081 26353 25850 ... 23742 24155 24609]\\n [26054 25953 25177 ... 24958 25520 24955]\\n [25083 24297 23914 ... 25547 24948 24104]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>(((15167005.08825324, -2579725.20562644, 15285...</td>\n",
+       "      <td>(((558585.0, 3012075.0, 566265.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[26250 26128 25784 ... 31877 31438 31180]\\n [26943 27098 26642 ... 31622 31352 30721]\\n [27795 27798 26474 ... 31461 30972 29782]\\n ...\\n [30301 30720 30815 ... 33041 32485 32388]\\n [30441 30736 30864 ... 32788 32812 33148]\\n [30632 30872 30957 ... 33857 34225 34098]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>(((15285613.14368428, -2579725.20562644, 15404...</td>\n",
+       "      <td>(((604665.0, 3012075.0, 612345.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16956 16754 16345 ... 21434 24875 28081]\\n [14837 14482 14216 ... 20008 21061 26474]\\n [12628 12588 12772 ... 17531 17956 24291]\\n ...\\n [12289 11931 12192 ... 16177 15880 15761]\\n [11916 12498 12292 ... 16069 15753 15868]\\n [12397 12705 11561 ... 16069 15868 15818]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>(((15404221.199115321, -2579725.20562644, 1552...</td>\n",
+       "      <td>(((566265.0, 3004395.0, 573945.0, 3012075.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[33574 33483 33158 ... 24957 24142 22776]\\n [34258 34099 34122 ... 26301 25233 24223]\\n [34544 34011 34466 ... 26305 25216 24065]\\n ...\\n [19789 19914 18905 ...  9703  9691  9696]\\n [18684 18597 18354 ...  9718  9699  9705]\\n [18358 16960 17366 ...  9672  9694  9752]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>(((15522829.254546361, -2579725.20562644, 1556...</td>\n",
+       "      <td>(((689145.0, 2996715.0, 696825.0, 3004395.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
+       "      <th>30</th>\n",
+       "      <td>(((497145.0, 2981355.0, 504825.0, 2989035.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[12994 12892 13184 ... 13493 13857 13523]\\n [12845 13003 13482 ... 13903 14242 13732]\\n [13498 13647 13934 ... 14212 14245 13795]\\n ...\\n [15265 14704 14405 ... 14367 14409 14607]\\n [15671 15221 14812 ... 14792 14543 14227]\\n [15696 15458 15443 ... 15313 14650 13765]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>(((14455356.755667, -3172765.482782173, 145739...</td>\n",
+       "      <th>31</th>\n",
+       "      <td>(((512505.0, 2973675.0, 520185.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16204 16261 15205 ... 20715 21030 21177]\\n [16838 16326 15415 ... 20730 21139 21452]\\n [17289 16132 15167 ... 20711 21213 21590]\\n ...\\n [29109 30660 32185 ... 17231 14617 12620]\\n [30143 31752 32885 ... 15871 14035 12636]\\n [30663 32379 33617 ... 15469 14269 12847]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>(((14573964.81109804, -3172765.482782173, 1469...</td>\n",
+       "      <th>32</th>\n",
+       "      <td>(((535545.0, 2973675.0, 543225.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23036 24603 24745 ... 23201 23318 21658]\\n [19719 22610 23585 ... 25232 24894 23378]\\n [16386 18718 20205 ... 27731 27839 26234]\\n ...\\n [16662 17750 19966 ... 13344 16500 20875]\\n [20821 19012 19282 ... 12865 16932 20117]\\n [21951 20509 20907 ... 14476 18089 18942]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>(((14692572.86652908, -3172765.482782173, 1481...</td>\n",
+       "      <th>33</th>\n",
+       "      <td>(((566265.0, 2973675.0, 573945.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[11473 11996 11959 ...  9030  9031  9030]\\n [11654 11890 12173 ...  9014  8991  8969]\\n [11921 12394 13089 ...  8959  8989  8919]\\n ...\\n [ 9002  8979  8991 ...  9451  9251  9239]\\n [ 8960  8976  9001 ...  9237  9314  9308]\\n [ 9123  9089  8963 ...  9778  9518  9274]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>(((14811180.92196012, -3172765.482782173, 1492...</td>\n",
+       "      <th>34</th>\n",
+       "      <td>(((604665.0, 2973675.0, 612345.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8788 8805 8800 ... 8783 8788 8792]\\n [8792 8790 8793 ... 8789 8796 8785]\\n [8794 8804 8794 ... 8792 8802 8811]\\n ...\\n [8792 8770 8771 ... 8785 8769 8769]\\n [8785 8769 8787 ... 8769 8772 8777]\\n [8780 8763 8790 ... 8762 8770 8774]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>(((14929788.97739116, -3172765.482782173, 1504...</td>\n",
+       "      <th>35</th>\n",
+       "      <td>(((612345.0, 2973675.0, 620025.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8796 8790 8797 ... 8802 8807 8807]\\n [8801 8806 8787 ... 8811 8818 8850]\\n [8795 8801 8806 ... 8797 8814 8815]\\n ...\\n [8771 8769 8787 ... 8802 8809 8816]\\n [8774 8777 8781 ... 8798 8819 8810]\\n [8777 8775 8766 ... 8782 8805 8786]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>(((15048397.0328222, -3172765.482782173, 15167...</td>\n",
+       "      <th>36</th>\n",
+       "      <td>(((658425.0, 2973675.0, 666105.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8962 8957 8922 ... 9001 8995 9002]\\n [8938 8941 8936 ... 9001 9001 9012]\\n [8949 8939 8944 ... 8991 8986 9015]\\n ...\\n [8951 8962 8946 ... 9000 9019 9000]\\n [8960 8941 8930 ... 8996 8977 8971]\\n [8945 8924 8964 ... 8993 8976 9017]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>(((15167005.08825324, -3172765.482782173, 1528...</td>\n",
+       "      <th>37</th>\n",
+       "      <td>(((543225.0, 2958315.0, 550905.0, 2965995.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[12803 12598 11798 ...  9106  9120  9118]\\n [14612 13071 11067 ...  9091  9080  9075]\\n [13822 11982 11517 ...  9057  9078  9065]\\n ...\\n [13038 13592 14077 ...  9273  9263  9250]\\n [12724 13054 13805 ...  9275  9309  9292]\\n [12043 11960 13863 ...  9384  9389  9388]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>(((15285613.14368428, -3172765.482782173, 1540...</td>\n",
+       "      <th>38</th>\n",
+       "      <td>(((704505.0, 2958315.0, 709815.0, 2965995.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[177, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>(((15404221.199115321, -3172765.482782173, 155...</td>\n",
+       "      <th>39</th>\n",
+       "      <td>(((527865.0, 2950635.0, 535545.0, 2958315.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[20582 20739 20425 ...  9685  9588  9523]\\n [21068 20891 20466 ...  9582  9398  9422]\\n [20643 20751 20623 ...  9626  9413  9405]\\n ...\\n [10269  9976  9906 ...  9966  9957  9938]\\n [10262 10023  9695 ...  9949  9944  9936]\\n [10215  9936  9576 ...  9903  9921  9945]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>(((15522829.254546361, -3172765.482782173, 155...</td>\n",
+       "      <th>40</th>\n",
+       "      <td>(((620025.0, 2942955.0, 627705.0, 2950635.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8818 8819 8814 ... 8844 8815 8810]\\n [8804 8817 8821 ... 8797 8785 8783]\\n [8805 8804 8807 ... 8809 8821 8781]\\n ...\\n [8817 8826 8844 ... 8815 8824 8852]\\n [8841 8822 8792 ... 8804 8801 8821]\\n [8804 8811 8807 ... 8794 8796 8823]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>(((14455356.755667, -3291373.53821332, 1457396...</td>\n",
+       "      <th>41</th>\n",
+       "      <td>(((481785.0, 2917785.0, 489465.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>(((14573964.81109804, -3291373.53821332, 14692...</td>\n",
+       "      <th>42</th>\n",
+       "      <td>(((520185.0, 2917785.0, 527865.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>(((14692572.86652908, -3291373.53821332, 14811...</td>\n",
+       "      <th>43</th>\n",
+       "      <td>(((658425.0, 2917785.0, 666105.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[   0    0    0 ... 9231 9227 9247]\\n [   0    0    0 ... 9245 9242 9246]\\n [   0    0    0 ... 9262 9252 9265]\\n ...\\n [   0    0    0 ...    0    0    0]\\n [   0    0    0 ...    0    0    0]\\n [   0    0    0 ...    0    0    0]]))</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>(((14811180.92196012, -3291373.53821332, 14929...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>(((14929788.97739116, -3291373.53821332, 15048...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>(((15048397.0328222, -3291373.53821332, 151670...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>(((15167005.08825324, -3291373.53821332, 15285...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>(((15285613.14368428, -3291373.53821332, 15404...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>(((15404221.199115321, -3291373.53821332, 1552...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>(((15522829.254546361, -3291373.53821332, 1556...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>(((14455356.755667, -3335851.559, 14573964.811...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>(((14573964.81109804, -3335851.559, 14692572.8...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>(((14692572.86652908, -3335851.559, 14811180.9...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>(((14811180.92196012, -3335851.559, 14929788.9...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>(((14929788.97739116, -3335851.559, 15048397.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>(((15048397.0328222, -3335851.559, 15167005.08...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>(((15167005.08825324, -3335851.559, 15285613.1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>(((15285613.14368428, -3335851.559, 15404221.1...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>(((15404221.199115321, -3335851.559, 15522829....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>(((15522829.254546361, -3335851.559, 15567307....</td>\n",
+       "      <th>44</th>\n",
+       "      <td>(((673785.0, 2917785.0, 681465.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                                 tile\n",
-       "0   (((14455356.755667, -2342509.0947641465, 14573...\n",
-       "1   (((14573964.81109804, -2342509.0947641465, 146...\n",
-       "2   (((14692572.86652908, -2342509.0947641465, 148...\n",
-       "3   (((14811180.92196012, -2342509.0947641465, 149...\n",
-       "4   (((14929788.97739116, -2342509.0947641465, 150...\n",
-       "5   (((15048397.0328222, -2342509.0947641465, 1516...\n",
-       "6   (((15167005.08825324, -2342509.0947641465, 152...\n",
-       "7   (((15285613.14368428, -2342509.0947641465, 154...\n",
-       "8   (((15404221.199115321, -2342509.0947641465, 15...\n",
-       "9   (((15522829.254546361, -2342509.0947641465, 15...\n",
-       "10  (((14455356.755667, -2461117.150195293, 145739...\n",
-       "11  (((14573964.81109804, -2461117.150195293, 1469...\n",
-       "12  (((14692572.86652908, -2461117.150195293, 1481...\n",
-       "13  (((14811180.92196012, -2461117.150195293, 1492...\n",
-       "14  (((14929788.97739116, -2461117.150195293, 1504...\n",
-       "15  (((15048397.0328222, -2461117.150195293, 15167...\n",
-       "16  (((15167005.08825324, -2461117.150195293, 1528...\n",
-       "17  (((15285613.14368428, -2461117.150195293, 1540...\n",
-       "18  (((15404221.199115321, -2461117.150195293, 155...\n",
-       "19  (((15522829.254546361, -2461117.150195293, 155...\n",
-       "20  (((14455356.755667, -2579725.20562644, 1457396...\n",
-       "21  (((14573964.81109804, -2579725.20562644, 14692...\n",
-       "22  (((14692572.86652908, -2579725.20562644, 14811...\n",
-       "23  (((14811180.92196012, -2579725.20562644, 14929...\n",
-       "24  (((14929788.97739116, -2579725.20562644, 15048...\n",
-       "25  (((15048397.0328222, -2579725.20562644, 151670...\n",
-       "26  (((15167005.08825324, -2579725.20562644, 15285...\n",
-       "27  (((15285613.14368428, -2579725.20562644, 15404...\n",
-       "28  (((15404221.199115321, -2579725.20562644, 1552...\n",
-       "29  (((15522829.254546361, -2579725.20562644, 1556...\n",
-       "..                                                ...\n",
-       "70  (((14455356.755667, -3172765.482782173, 145739...\n",
-       "71  (((14573964.81109804, -3172765.482782173, 1469...\n",
-       "72  (((14692572.86652908, -3172765.482782173, 1481...\n",
-       "73  (((14811180.92196012, -3172765.482782173, 1492...\n",
-       "74  (((14929788.97739116, -3172765.482782173, 1504...\n",
-       "75  (((15048397.0328222, -3172765.482782173, 15167...\n",
-       "76  (((15167005.08825324, -3172765.482782173, 1528...\n",
-       "77  (((15285613.14368428, -3172765.482782173, 1540...\n",
-       "78  (((15404221.199115321, -3172765.482782173, 155...\n",
-       "79  (((15522829.254546361, -3172765.482782173, 155...\n",
-       "80  (((14455356.755667, -3291373.53821332, 1457396...\n",
-       "81  (((14573964.81109804, -3291373.53821332, 14692...\n",
-       "82  (((14692572.86652908, -3291373.53821332, 14811...\n",
-       "83  (((14811180.92196012, -3291373.53821332, 14929...\n",
-       "84  (((14929788.97739116, -3291373.53821332, 15048...\n",
-       "85  (((15048397.0328222, -3291373.53821332, 151670...\n",
-       "86  (((15167005.08825324, -3291373.53821332, 15285...\n",
-       "87  (((15285613.14368428, -3291373.53821332, 15404...\n",
-       "88  (((15404221.199115321, -3291373.53821332, 1552...\n",
-       "89  (((15522829.254546361, -3291373.53821332, 1556...\n",
-       "90  (((14455356.755667, -3335851.559, 14573964.811...\n",
-       "91  (((14573964.81109804, -3335851.559, 14692572.8...\n",
-       "92  (((14692572.86652908, -3335851.559, 14811180.9...\n",
-       "93  (((14811180.92196012, -3335851.559, 14929788.9...\n",
-       "94  (((14929788.97739116, -3335851.559, 15048397.0...\n",
-       "95  (((15048397.0328222, -3335851.559, 15167005.08...\n",
-       "96  (((15167005.08825324, -3335851.559, 15285613.1...\n",
-       "97  (((15285613.14368428, -3335851.559, 15404221.1...\n",
-       "98  (((15404221.199115321, -3335851.559, 15522829....\n",
-       "99  (((15522829.254546361, -3335851.559, 15567307....\n",
-       "\n",
-       "[100 rows x 1 columns]"
+       "0   (((504825.0, 3142635.0, 512505.0, 3150315.0), ...\n",
+       "1   (((497145.0, 3127275.0, 504825.0, 3134955.0), ...\n",
+       "2   (((643065.0, 3127275.0, 650745.0, 3134955.0), ...\n",
+       "3   (((650745.0, 3127275.0, 658425.0, 3134955.0), ...\n",
+       "4   (((489465.0, 3119595.0, 497145.0, 3127275.0), ...\n",
+       "5   (((512505.0, 3119595.0, 520185.0, 3127275.0), ...\n",
+       "6   (((566265.0, 3088875.0, 573945.0, 3096555.0), ...\n",
+       "7   (((650745.0, 3081195.0, 658425.0, 3088875.0), ...\n",
+       "8   (((481785.0, 3073515.0, 489465.0, 3081195.0), ...\n",
+       "9   (((643065.0, 3073515.0, 650745.0, 3081195.0), ...\n",
+       "10  (((550905.0, 3065835.0, 558585.0, 3073515.0), ...\n",
+       "11  (((581625.0, 3065835.0, 589305.0, 3073515.0), ...\n",
+       "12  (((527865.0, 3050475.0, 535545.0, 3058155.0), ...\n",
+       "13  (((550905.0, 3050475.0, 558585.0, 3058155.0), ...\n",
+       "14  (((566265.0, 3050475.0, 573945.0, 3058155.0), ...\n",
+       "15  (((627705.0, 3050475.0, 635385.0, 3058155.0), ...\n",
+       "16  (((589305.0, 3042795.0, 596985.0, 3050475.0), ...\n",
+       "17  (((512505.0, 3035115.0, 520185.0, 3042795.0), ...\n",
+       "18  (((573945.0, 3035115.0, 581625.0, 3042795.0), ...\n",
+       "19  (((612345.0, 3035115.0, 620025.0, 3042795.0), ...\n",
+       "20  (((635385.0, 3035115.0, 643065.0, 3042795.0), ...\n",
+       "21  (((512505.0, 3027435.0, 520185.0, 3035115.0), ...\n",
+       "22  (((481785.0, 3019755.0, 489465.0, 3027435.0), ...\n",
+       "23  (((543225.0, 3019755.0, 550905.0, 3027435.0), ...\n",
+       "24  (((620025.0, 3019755.0, 627705.0, 3027435.0), ...\n",
+       "25  (((527865.0, 3012075.0, 535545.0, 3019755.0), ...\n",
+       "26  (((558585.0, 3012075.0, 566265.0, 3019755.0), ...\n",
+       "27  (((604665.0, 3012075.0, 612345.0, 3019755.0), ...\n",
+       "28  (((566265.0, 3004395.0, 573945.0, 3012075.0), ...\n",
+       "29  (((689145.0, 2996715.0, 696825.0, 3004395.0), ...\n",
+       "30  (((497145.0, 2981355.0, 504825.0, 2989035.0), ...\n",
+       "31  (((512505.0, 2973675.0, 520185.0, 2981355.0), ...\n",
+       "32  (((535545.0, 2973675.0, 543225.0, 2981355.0), ...\n",
+       "33  (((566265.0, 2973675.0, 573945.0, 2981355.0), ...\n",
+       "34  (((604665.0, 2973675.0, 612345.0, 2981355.0), ...\n",
+       "35  (((612345.0, 2973675.0, 620025.0, 2981355.0), ...\n",
+       "36  (((658425.0, 2973675.0, 666105.0, 2981355.0), ...\n",
+       "37  (((543225.0, 2958315.0, 550905.0, 2965995.0), ...\n",
+       "38  (((704505.0, 2958315.0, 709815.0, 2965995.0), ...\n",
+       "39  (((527865.0, 2950635.0, 535545.0, 2958315.0), ...\n",
+       "40  (((620025.0, 2942955.0, 627705.0, 2950635.0), ...\n",
+       "41  (((481785.0, 2917785.0, 489465.0, 2919915.0), ...\n",
+       "42  (((520185.0, 2917785.0, 527865.0, 2919915.0), ...\n",
+       "43  (((658425.0, 2917785.0, 666105.0, 2919915.0), ...\n",
+       "44  (((673785.0, 2917785.0, 681465.0, 2919915.0), ..."
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.select(\"tile\").toPandas()"
+    "#Look at a sample\n",
+    "pandas_df = df.select(df.tile).sample(0.05).toPandas()\n",
+    "pandas_df"
    ]
   },
   {

From c2ed8003caeb65f89b8110ac6d78a7e891ac5a56 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 26 Jun 2019 11:23:44 -0400
Subject: [PATCH 189/380] PR feedback fixes.

---
 .../datasource/rastersource/RasterSourceRelation.scala |  2 +-
 pyrasterframes/src/main/python/docs/raster-io.pymd     | 10 +++++-----
 .../src/main/python/tests/PyRasterFramesTests.py       |  1 -
 3 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
index a939db026..d49f3d911 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
@@ -36,7 +36,7 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 /**
   * Constructs a Spark Relation over one or more RasterSource paths.
   * @param sqlContext Query context
-  * @param catalogTable Specifiction of raster path sources
+  * @param catalogTable Specification of raster path sources
   * @param bandIndexes band indexes to fetch
   * @param subtileDims how big to tile/subdivide rasters info
   */
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index c9179540a..39ede7d21 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -16,7 +16,7 @@ RasterFrames registers a DataSource named `rastersource` that enables reading of
 ```python
 data_root = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp'
 def s2_band(idx):
-    return f"{data_root}/B0{idx}.tif"
+    return "{}/B0{}.tif".format(data_root, idx)
     
 ```
 
@@ -60,10 +60,10 @@ print(tile)
 Generally we'll want to work with multiple bands. If you have several singleband image files of the same scene, `rastersource` can make use of a CSV (or other DataFrame) specifying the band columns and scene rows.
 
 ```python
-csv = f"""
+csv = """
 red,green,blue
-{s2_band(4)},{s2_band(3)},{s2_band(2)}
-"""
+{},{},{}
+""".format(s2_band(4), s2_band(3), s2_band(2))
 
 print(csv)
 
@@ -80,7 +80,7 @@ The example above will work with a reasonable number of rows in the CSV string.
 ```python
 from pyspark import SparkFiles
 cat_filename = "2018-07-04_scenes.txt"
-spark.sparkContext.addFile(f"https://modis-pds.s3.amazonaws.com/MCD43A4.006/{cat_filename}")
+spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/{}".format(cat_filename))
 modis_catalog = spark.read \
     .format("csv") \
     .option("header", "true") \
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 4e594d1b5..917f0f844 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -418,7 +418,6 @@ def test_tile_udt_serialization(self):
 
     def test_udf_on_tile_type_input(self):
         import numpy.testing
-        # rf_local_add(t, 0) is to force lazy eval; accessing tile.tile is to get at the actual Tile type vs PRT struct
         df = self.spark.read.rastersource(self.img_uri)
         rf = self.rf
 

From ad0d6fea825f2cb57c38ca9365ec0891a9559cfc Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 26 Jun 2019 16:55:00 -0400
Subject: [PATCH 190/380] Refactor raster-ml doc to use rastersource

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/paradox/reference.md            |  14 +-
 .../src/main/python/docs/__init__.py          |  11 +-
 .../src/main/python/docs/spark-ml.pymd        | 126 ++++++++++--------
 3 files changed, 89 insertions(+), 62 deletions(-)

diff --git a/docs/src/main/paradox/reference.md b/docs/src/main/paradox/reference.md
index 492773a7d..a8874f7e0 100644
--- a/docs/src/main/paradox/reference.md
+++ b/docs/src/main/paradox/reference.md
@@ -85,17 +85,23 @@ Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
     
 Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_convert_cell_type](reference.md#rf-convert-cell-type). 
 
+#### rf_tile
+
+    Tile rf_tile(ProjectedRasterTile proj_raster)
+    
+Get the `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
+
 #### rf_extent
 
-    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(ProjectedRasterTile raster)
-    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(RasterSource raster)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(ProjectedRasterTile proj_raster)
+    Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(RasterSource proj_raster)
 
 Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
 #### rf_crs
 
-    Struct[String] rf_crs(ProjectedRasterTile raster)
-    Struct[String] rf_crs(RasterSource raster)
+    Struct[String] rf_crs(ProjectedRasterTile proj_raster)
+    Struct[String] rf_crs(RasterSource proj_raster)
     
 Fetch the [proj4](https://proj4.org/) string representation of the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index 3172f8410..01478ecd5 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -1,10 +1,13 @@
 import os
 
-__all__ = ["resource_dir"]
-
 
 # This is temporary until we port to run on web assets.
 def resource_dir():
     here = os.path.dirname(os.path.realpath(__file__))
-    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
-    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
+    test_resource = os.path.realpath(os.path.join(here, '..', '..', '..', 'src', 'test', 'resources'))
+
+    return test_resource
+
+
+def resource_dir_uri():
+    return 'file://' + resource_dir()
diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/spark-ml.pymd
index ed36dc317..d6456436c 100644
--- a/pyrasterframes/src/main/python/docs/spark-ml.pymd
+++ b/pyrasterframes/src/main/python/docs/spark-ml.pymd
@@ -1,87 +1,100 @@
-# Spark ML Support
+# Machine Learning with RasterFrames
 
-## Clustering
+RasterFrames provides access to train and predict with a wide variety of machine learning models through [Spark ML Pipelines](https://spark.apache.org/docs/latest/ml-guide.html). This library provides a variety of pipeline components for supervised learning, unsupervised learning, and data preparation that can be used to repeatably represent a variety of tasks in machine learning.
+
+In this example we will demonstrate how to fit and score an unsupervised learning model with a [sample of Landsat 8 data](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/src/test/resources).
+
+## Imports and Data Preparation
 
 ```python, echo=False
-from docs import *
-from pyrasterframes import TileExploder
+from IPython.core.display import display
+from docs import resource_dir_uri
 from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes.rasterfunctions import rf_assemble_tile
-
-from pyspark.ml.feature import VectorAssembler
-from pyspark.ml.clustering import KMeans
-from pyspark.ml import Pipeline
 
 import os
 
 spark = create_rf_spark_session()
+
+import pandas as pd
 ```
 
-The first step is to load multiple bands of imagery and construct
-a single RasterFrame from them.
+We import various Spark components that we need to construct our Pipeline.
 
-```python
-filenamePattern = "L8-B{}-Elkton-VA.tiff"
-bandNumbers = range(1, 8)
-bandColNames = list(map(lambda n: 'band_{}'.format(n), bandNumbers))
-```
+```python, echo=True
+from pyrasterframes import TileExploder
+from pyrasterframes.rasterfunctions import rf_assemble_tile, rf_crs, rf_extent, rf_tile, rf_dimensions
 
-For each identified band, load the associated image file:
+from pyspark.ml.feature import VectorAssembler
+from pyspark.ml.clustering import KMeans
+from pyspark.ml import Pipeline
 
-```python, term=True
-# TODO: rewrite using `rastersource`.
-from functools import reduce
-joinedRF = reduce(lambda rf1, rf2: rf1.asRF().spatialJoin(rf2.drop('bounds').drop('metadata').drop('extent').drop('crs')),
-                  map(lambda bf: spark.read.geotiff(bf[1])
-                      .withColumnRenamed('tile', 'band_{}'.format(bf[0])),
-                  map(lambda b: (b, os.path.join(resource_dir(), filenamePattern.format(b))), bandNumbers)))
 ```
 
-We should see a single spatial_key column along with columns of tiles.
+The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from [the I/O page](raster-io.html#Single-Scene--Multiple-Bands). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. The function `resource_dir` gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
-```python
-joinedRF.printSchema()
+
+```python, term=True
+filenamePattern = "L8-B{}-Elkton-VA.tiff"
+catalog_df = pd.DataFrame([
+    {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
+])
+df = spark.read.rastersource(catalog=catalog_df.to_csv(index=None), catalog_col_names=catalog_df.columns)
+df = df.select(
+    rf_crs(df.b1).alias('crs'),
+    rf_extent(df.b1).alias('extent'),
+    rf_tile(df.b1).alias('b1'),
+    rf_tile(df.b2).alias('b2'),
+    rf_tile(df.b3).alias('b3'),
+    rf_tile(df.b4).alias('b4'),
+    rf_tile(df.b5).alias('b5'),
+    rf_tile(df.b6).alias('b6'),
+    rf_tile(df.b7).alias('b7'),
+)
+df.printSchema()
 ```
 
-SparkML requires that each observation be in its own row, and those
-observations be packed into a single `Vector`. The first step is to
-"explode" the tiles into a single row per cell/pixel
+## Create ML Pipeline
+
+SparkML requires that each observation be in its own row, and features for each
+observation be packed into a single `Vector`. For this unsupervised learning problem, we will treat each _pixel_ as an observation and use one feature per band. The first step is to
+"explode" the tiles into a single row per pixel. In the scala API a pixel is called a `cell`.
 
 ```python
 exploder = TileExploder()
 ```
 
-To "vectorize" the the band columns we use the SparkML `VectorAssembler`
+To "vectorize" the the band columns we use the SparkML `VectorAssembler`. We use each of the seven bands for a different feature.
 
 ```python
 assembler = VectorAssembler() \
-    .setInputCols(bandColNames) \
+    .setInputCols(list(catalog_df.columns)) \
     .setOutputCol("features")
 ```
 
-Configure our clustering algorithm
+Configure our clustering algorithm.
 
 ```python
-k = 5
-kmeans = KMeans().setK(k)
+kmeans = KMeans().setK(5).setFeaturesCol('features')
 ```
 
-Combine the two stages
+Combine the stages into a single pipeline.
 
 ```python
 pipeline = Pipeline().setStages([exploder, assembler, kmeans])
 ```
 
-Compute clusters
+## Fit the Model and Score
+
+Fit the pipeline; this actually executes exploding the tiles, assembling the `Vector`s, and fitting the k-means clustering.
 
 ```python
-model = pipeline.fit(joinedRF)
+model = pipeline.fit(df)
 ```
 
-Run the data through the model to assign cluster IDs to each
+Score the training data in the fitted pipeline model. This will add a column called `prediction` with the closest cluster identifier.
 
 ```python
-clustered = model.transform(joinedRF)
+clustered = model.transform(df)
 clustered.show(8)
 ```
 
@@ -89,29 +102,34 @@ If we want to inspect the model statistics, the SparkML API requires us to go
 through this unfortunate contortion:
 
 ```python
-clusterResults = list(filter(lambda x: str(x).startswith('KMeans'), model.stages))[0]
+cluster_stage = model.stages[2]
 ```
 
-Compute sum of squared distances of points to their nearest center
+Compute sum of squared distances of points to their nearest center. This is elemental to most of the cluster quality metrics.
 
 ```python
-metric = clusterResults.computeCost(clustered)
+metric = cluster_stage.computeCost(clustered)
 print("Within set sum of squared errors: %s" % metric)
+```
 
-tlm = joinedRF.tileLayerMetadata()
-layout = tlm['layoutDefinition']['tileLayout']
+## Visualize Prediction
 
-retiled = clustered.groupBy('spatial_key').agg(
-    rf_assemble_tile('column_index', 'row_index', 'prediction',
-        layout['tileCols'], layout['tileRows'], 'int8')
+We can recreate the tiled data structure using the metadata added by the `TileExploder` pipeline stage.
+
+```python
+tile_dims = df.select(rf_dimensions(df.b1).alias('dims')).first()['dims']
+retiled = clustered.groupBy('extent', 'crs') \
+    .agg(
+        rf_assemble_tile('column_index', 'row_index', 'prediction',
+            tile_dims['cols'], tile_dims['rows'], 'int8').alias('prediction')
 )
 
-rf = retiled.asRF('spatial_key', tlm)
-rf.printSchema()
-rf.show()
+retiled.printSchema()
+retiled.show()
 ```
 
+Take a look at the resulting output.
 
-```python, echo=False
-spark.stop()
-```
\ No newline at end of file
+```python
+display(retiled.select('prediction').first()['prediction'])
+```

From 41ca73bbfbc50a96d7c0ad95221714e356a29ebb Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 26 Jun 2019 19:54:06 -0400
Subject: [PATCH 191/380] Attempted CircleCI fix.

---
 project/PythonBuildPlugin.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 22a67dcf4..71d66b076 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -95,6 +95,7 @@ object PythonBuildPlugin extends AutoPlugin {
     pythonCommand := "python",
     pySetup := {
       val s = streams.value
+      val _ = copyPySources.value
       val wd = (Python / target).value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args

From 67d86a9cbe34315038086960fb2f8356781b7842 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 26 Jun 2019 20:40:09 -0400
Subject: [PATCH 192/380] More CircleCI hacking for Python build.

---
 .circleci/config.yml                            | 4 +++-
 pyrasterframes/src/main/python/requirements.txt | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index c9ca203b0..96a1461dd 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -69,7 +69,9 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get update && sudo apt-get install -y python-pip pandoc && pip install setuptools # required for pyrasterframes testing
+      - run: sudo apt-get update && sudo apt-get install -y python3 python3-pip
+      - run: sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 1
+      - run: pip3 install ipython==6.2.1
       - run: cat /dev/null | sbt test
 
       - run: *unsetenv
diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index 9ce9fb620..84a79d5c8 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -4,3 +4,4 @@ pyspark==2.3.3  # See issue # 154
 numpy>=1.7
 pandas
 matplotlib<3.0.0  # no python 2.7 support after v2.x.x
+ipython==6.2.1

From 40bcbd76167f298dab267617138aca261f23b5b8 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 27 Jun 2019 09:02:14 -0400
Subject: [PATCH 193/380] Fixed image link.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c074e0a0e..4f96ade94 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 _RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
 
-<img src="docs/src/main/tut/RasterFramePipeline.svg" width="600px"/>
+<img src="docs/src/main/paradox/RasterFramePipeline.svg" width="600px"/>
 
 Please see the [Getting Started](http://rasterframes.io/getting-started.html) section of the Users' Manual to start using RasterFrames.
 

From 80cd7408f394719ddd61ebd5d3d4ccb21b382304 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 27 Jun 2019 09:04:09 -0400
Subject: [PATCH 194/380] Updated pyrasterframes README link.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4f96ade94..fa878e5b1 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ The documentation may be built with
 sbt makeSite
 ```
 
-The `pyrasterframes` build instructions are located [pyrasterframes/python/README.rst](pyrasterframes/python/README.rst)
+The `pyrasterframes` build instructions are located at [pyrasterframes/src/main/python/README.md](pyrasterframes/src/main/python/README.md)
 
 ## Copyright and License
 

From bf7ca2f90e103d3d9c3e4ff9360731062313dbfd Mon Sep 17 00:00:00 2001
From: Phil Varner <pvarner@astraea.io>
Date: Thu, 27 Jun 2019 11:27:13 -0400
Subject: [PATCH 195/380] Update build image to only python 3 exists

Signed-off-by: Phil Varner <pvarner@astraea.io>
---
 .circleci/config.yml      |  4 ----
 build/circleci/Dockerfile | 15 +++++++++++----
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 96a1461dd..431dcb2ac 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -69,9 +69,6 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get update && sudo apt-get install -y python3 python3-pip
-      - run: sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 1
-      - run: pip3 install ipython==6.2.1
       - run: cat /dev/null | sbt test
 
       - run: *unsetenv
@@ -87,7 +84,6 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
-      - run: sudo apt-get update && sudo apt-get install -y python-pip pandoc && pip install setuptools # required for pyrasterframes testing
       - run: cat /dev/null | sbt test
       - run: cat /dev/null | sbt publish
 
diff --git a/build/circleci/Dockerfile b/build/circleci/Dockerfile
index 0eb8bb522..334c1b15f 100644
--- a/build/circleci/Dockerfile
+++ b/build/circleci/Dockerfile
@@ -7,8 +7,13 @@ ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/
 # most of these libraries required for
 # python-pip pandoc && pip install setuptools => required for pyrasterframes testing
 RUN sudo apt-get update && \
-    sudo apt-get install -y \
-    python-pip pandoc \
+    sudo apt remove \
+      python python-minimal python2.7 python2.7-minimal \
+      libpython-stdlib libpython2.7 libpython2.7-minimal libpython2.7-stdlib \
+    && sudo apt-get install -y \
+      python3 \
+      python3-pip \
+      pandoc \
       wget \
       gcc g++ build-essential \
             libcurl4-gnutls-dev \
@@ -22,8 +27,10 @@ RUN sudo apt-get update && \
             libpng-dev \
             swig \
             ant \
-        && sudo apt-get clean all \
-        && pip install setuptools
+    && sudo apt autoremove \
+    && sudo apt-get clean all \
+    && pip3 install setuptools ipython==6.2.1 \
+    && sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 1
 
 # install OpenJPEG
 RUN cd /tmp && \

From 9f69cc6ba059b8f7d1d3b8d413d41be7f2977cfc Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 27 Jun 2019 16:51:01 -0400
Subject: [PATCH 196/380] Docs for local algebra demo

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/local-algebra.pymd   | 66 +++++++++++++++----
 1 file changed, 55 insertions(+), 11 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index a281ce1a2..ce494f84e 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -1,27 +1,71 @@
-# Local Algebra
 
 ```python, echo=False
-from docs import *
+from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes.rasterfunctions import *
-from pyspark.sql import *
+from pyrasterframes.rasterfunctions import rf_normalized_difference, rf_tile, rf_dimensions, rf_extent, rf_tile_mean
+
+import pandas as pd
 import os
 
+
 spark = create_rf_spark_session()
 ```
 
+# Local Map Algebra
+
+[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single `tile`,  between a `tile` and a scalar, between two `tile`s, or among many `tile`s. These operations are common in processing of earth observation and other image data.
+
+
 ## Computing NDVI
 
+Here’s an example of computing the Normalized Differential Vegetation Index (NDVI). NDVI is a vegetation index which emphasizes differences in relative biomass and vegetation health. The term _index_ in Earth observation means a combination of many raster bands into a single band that highlights a phenomenon of interest. Various indexes have proven useful visual tools and frequently appear as features in machine learning models using Earth observation data.
+
+> NDVI is often used worldwide to monitor drought, monitor and predict agricultural production, assist in predicting hazardous fire zones, and map desert encroachment. The NDVI is preferred for global vegetation monitoring because it helps to compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors (Lillesand. _Remote sensing and image interpretation_. 2004)
+
+### Create DataFrame
+
+We will use the @ref:[catalog pattern](raster-io.md) for data access. To compute NDVI we need to compute local algebra on the *red* and *near infrared* bands: `(n - r) / (n + r)`. This form of `(x - y) / (x + y)` is common in remote sensing and is called a normalized difference. It is used with other band pairs to highlight water, snow, and other phenomena.
+
 ```python, term=True
-# TODO: rewrite to use `rastersource`
-redBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B4-Elkton-VA.tiff')).withColumnRenamed('tile', 'red_band')
-nirBand = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B5-Elkton-VA.tiff')).withColumnRenamed('tile', 'nir_band')
+bands = {4: 'red', 8: 'nir'}
+uri_pattern = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0{}.tif'
+catalog_df = pd.DataFrame([
+    {bands[b_num]: uri_pattern.format(b_num) for b_num in bands.keys()}
+])
+df = spark.read.rastersource(catalog=catalog_df.to_csv(index=None),
+                             catalog_col_names=list(catalog_df.columns))
+df.printSchema()
+```
+
+Observe how the bands we need to use to compute the index are arranged as columns of our resulting DataFrame. The rows or observations are various spatial extents within the entire coverage area.
+
+### Local Map Algebra
+
+RasterFrames provides a wide variety of local map algebra functions. There are several different broad categories, based on how many tiles the function takes as input:
+
+ * A function on a single Tile is a unary operation; example: @ref:[rf_log](reference.md#rf-log);
+ * A function on two Tiles is a binary operation; example: @ref:[rf_local_multiply](reference.md#rf-local-multiply);
+ * A function on a Tile and a scalar is a binary operation; example: @ref:[rf_local_less](reference.md#rf-local-less); or
+ * A function on many Tiles is a n-ary operation; example: @ref:[rf_agg_local_min](reference.md#rf-agg-local-min)
+
+We can express the normalized difference with a combination of `rf_local_divide`, `rf_local_subtract`, and `rf_local_add`. Since the normalized difference is so common there is a convenience method `rf_normalized_difference` which we use in this example. We will append a new column to the DataFrame, which will apply the map alegbra function to each row.
+
+```python
+df = df.withColumn('ndvi', rf_normalized_difference(df.nir, df.red))
+df.printSchema()
+```
 
-rf = redBand.asRF().spatialJoin(nirBand.asRF()) \
-    .withColumn("ndvi", rf_normalized_difference('red_band', 'nir_band'))
-rf.printSchema()
-rf.show(20, truncate=False)
+We can inspect a small sample of the data. The yellow values are areas of healthy vegetation and the purple areas are bare soil or impervious surfaces.
 
+```python
+ndvi_tiles = df.sort(-rf_tile_mean('ndvi')) \
+               .select(rf_tile('ndvi').alias('ndvi')) \
+               .take(5)
+for r in ndvi_tiles:
+    display(r['ndvi'])
+#display(
+#    df.select(rf_extent('ndvi'), rf_tile('ndvi')).toPandas()
+#)
 ```
 
 

From d7ac696c09b61986e1686bbb375145ba351a52c6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 28 Jun 2019 09:15:31 -0400
Subject: [PATCH 197/380] Extended unit test to trigger #166.

---
 .../datasource/rastersource/RasterSourceDataSourceSpec.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
index 7b2360f31..b145fe129 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
@@ -188,6 +188,9 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.schema.size should be(6)
       df.tileColumns.size should be (3)
       df.select($"B1_path").distinct().count() should be (1)
+
+      val diffStats = df.select(rf_tile_stats($"B1") =!= rf_tile_stats($"B2")).as[Boolean].collect()
+      diffStats.forall(identity) should be(true)
     }
   }
 }

From 24e0ae7d52f964d2e2fa93e46e490842f1b8c43e Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 28 Jun 2019 11:31:51 -0400
Subject: [PATCH 198/380] Thou shall not use `map` with side effects.

---
 .../transformers/RasterSourceToRasterRefs.scala           | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index 7ba2bb13d..dd8ac7ca8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -36,6 +36,7 @@ import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRast
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
+import scala.util.Try
 import scala.util.control.NonFatal
 
 /**
@@ -63,10 +64,9 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
     if (b < src.bandCount) RasterRef(src, b, e) else null
 
   override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
-    var src: RasterSource = null
     try {
       val refs = children.map { child ⇒
-        src = RasterSourceType.deserialize(child.eval(input))
+        val src = RasterSourceType.deserialize(child.eval(input))
         subtileDims.map(dims =>
           src
             .layoutExtents(dims)
@@ -78,8 +78,8 @@ case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[
     }
     catch {
       case NonFatal(ex) ⇒
-        val payload = if (src == null) input else src
-        logger.error("Error fetching data for " + payload, ex)
+        val payload = Try(children.map(c => RasterSourceType.deserialize(c.eval(input)))).toOption.toSeq.flatten
+        logger.error("Error fetching data for one of: " + payload.mkString(", "), ex)
         Traversable.empty
     }
   }

From ffd4da6b9b26f5f2cd1f7bd57240251ef6f4ccac Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 28 Jun 2019 12:50:48 -0400
Subject: [PATCH 199/380] Update getting started docs and related

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/paradox/reference.md            |  18 +-
 pyrasterframes/src/main/python/README.md      |   8 +-
 .../src/main/python/docs/__init__.py          |   2 +
 .../src/main/python/docs/getting-started.pymd |  97 ++--
 pyrasterframes/src/main/python/docs/index.md  |  10 +-
 .../src/main/python/docs/local-algebra.pymd   |  19 +-
 .../src/main/python/docs/raster-io.pymd       |  10 +-
 .../src/main/python/docs/raster-processing.md |  12 +-
 .../src/main/python/docs/time-series.pymd     |   5 +-
 rf-notebook/src/main/docker/Dockerfile        |   2 +-
 .../src/main/notebooks/Getting Started.ipynb  | 504 ++++++++----------
 11 files changed, 335 insertions(+), 352 deletions(-)

diff --git a/docs/src/main/paradox/reference.md b/docs/src/main/paradox/reference.md
index a8874f7e0..3d4d41cbf 100644
--- a/docs/src/main/paradox/reference.md
+++ b/docs/src/main/paradox/reference.md
@@ -1,10 +1,6 @@
 # Function Reference
 
-For the most up to date list of User Defined Functions using Tiles, look at API documentation for @scaladoc[`RasterFunctions`][RasterFunctions]. 
-
-The full Scala API documentation can be found [here][scaladoc].
-
-RasterFrames also provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module. 
+RasterFrames provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module. 
 
 The convention in this document will be to define the function signature as below, with its return type, the function name, and named arguments with their types.
 
@@ -12,6 +8,8 @@ The convention in this document will be to define the function signature as belo
 ReturnDataType function_name(InputDataType argument1, InputDataType argument2)
 ```
 
+For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`][RasterFunctions]. The full Scala API documentation can be found [here][scaladoc].
+
 ## List of Available SQL and Python Functions
 
 @@toc { depth=3 }
@@ -40,7 +38,7 @@ See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/
 
 #### st_geometry 
 
-    Geometry st_extent(Struct[Double xmin, Double xmax, Double ymin, Double ymax] extent)
+    Geometry st_geometry(Struct[Double xmin, Double xmax, Double ymin, Double ymax] extent)
     
 Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`](reference.md#st-extent) or @ref:[`rf_extent`](reference.md#rf-extent). 
 
@@ -253,11 +251,11 @@ The `no_data_value` argument is a literal Double, not a Column expression.
 
 If input `tile` had a nodata value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, nodata values on the input `tile` remain nodata values on the output. For integral cell type `tile`s, the previous nodata values become literal values. 
 
-### Map Algebra
+### Local Map Algebra
 
-[Map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations between a `tile` and a scalar, between two `tile`s, or among many `tile`s. 
+[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single tile (unary), between a `tile` and a scalar, between two `tile`s, or across many `tile`s. 
 
-Some of these functions have similar variations in the Python API:
+The binary local map algebra functions have similar variations in the Python API depending on the left hand side type:
 
  - `rf_local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
  - `rf_local_op_double`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
@@ -265,6 +263,8 @@ Some of these functions have similar variations in the Python API:
  
 The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
+Local map algebra operations for more than two tiles are implemented to work across rows in the data frame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
+
 #### rf_local_add
 
     Tile rf_local_add(Tile tile1, Tile rhs)
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 9b6688656..1ccfcb0d3 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -4,12 +4,7 @@ PyRasterFrames enables access and processing of geospatial raster data in PySpar
 
 ## Getting started
 
-### Prerequisites
-
-1. [`pip`](https://pip.pypa.io/en/stable/installing/)
-2. ['pyspark`](https://pypi.org/project/pyspark/) > 2.3.2 
-
-The quickest way to get started is to `pip` install the pyrasterframes package.
+The quickest way to get started is to [`pip`](https://pip.pypa.io/en/stable/installing/) install the pyrasterframes package.
 
 ```bash
 pip install pyrasterframes
@@ -33,7 +28,6 @@ df = spark.read.rastersource('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/
 df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 ```
 
-
 ## Support
 
 Reach out to us on [gitter](https://gitter.im/s22s/raster-frames)!
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index 01478ecd5..a1949f49c 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -3,6 +3,8 @@
 
 # This is temporary until we port to run on web assets.
 def resource_dir():
+    # we may consider using gitpython which I think would be appropriate in the context of building docs
+    # see https://stackoverflow.com/a/41920796/2787937
     here = os.path.dirname(os.path.realpath(__file__))
     test_resource = os.path.realpath(os.path.join(here, '..', '..', '..', 'src', 'test', 'resources'))
 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index b6620a0d4..c720a7f16 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -1,16 +1,69 @@
 # Getting Started
 
+There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames. Let's start with the simplest. Python 3.6 or greater is recommended.
 
-There are a number of ways to use PyRasterFrames:
+```bash
+$ python3 -m pip install pyrasterframes
+```
+
+Then in a python interpreter of your choice, you can get a [`pyspark` `SparkSession`](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession) using the [`local[*]` master](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls).
+
+```python
+import pyrasterframes
+spark = pyrasterframes.get_spark_session()
+```
+
+Then you can read a raster and work with it in a Spark DataFrame.
+
+```python
+from pyrasterframes.rasterfunctions import rf_local_add
+from pyspark.sql.functions import lit
+
+# Read a MODIS surface reflectance granule
+df = spark.read.rastersource('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
+
+# Add 3 element-wise, show some rows of the dataframe
+df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
+
+```
+
+This example is extended in the [getting started Jupyter notebook](https://nbviewer.jupyter.org/github/locationtech/rasterframes/blob/develop/rf-notebook/src/main/notebooks/Getting%20Started.ipynb).
+
+To understand more about how and why RasterFrames represents Earth observation in DataFrames, read the project @ref:[description](description.md). For more hands-on examples, see the chapters about @ref:[reading](raster-io.md) and @ref:[processing](raster-processing.md) with RasterFrames.
+
+## Raster Functions
+
+To import RasterFrames functions into the environment, import from `pyrasterframes.rasterfunctions`.
+
+```python
+from pyrasterframes.rasterfunctions import *
+```
+
+Functions starting with `rf_`, which are for raster, and `st_`, which are for vector geometry,
+become available for use with DataFrames. See the complete @ref:[function reference](reference.md).
+
+```python
+[fn for fn in dir() if fn.startswith('rf_') or fn.startswith('st_')]
+```
+
+## Other Options
 
+You can also use RasterFrames in the following environments:
+
+1. Jupyter Notebook
 1. `pyspark` shell
-2. Jupyter Notebook
-3. Standalone Python Script
 
-## `pyspark` shell
+### Jupyter Notebook
+
+**TODO** User facing quick instructions e.g. how to pull and run docker hub hosted container
+
+See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on running a Jupyter notebook server within a Docker container that has a fully set up environment.
 
+### `pyspark` shell or app
 
-To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. See below for how to build or download those artifacts.
+To initialize RasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip.
+
+**TODO** how to build or download those artifacts.
 
 ```bash
    pyspark \
@@ -19,10 +72,9 @@ To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pysp
     --conf spark.kryoserializer.buffer.max=500m \
     --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
     --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
-   
 ```
 
-Then in the pyspark shell or app, import the module and call `withRasterFrames` on the SparkSession.
+Then in the pyspark shell, import the module and call `withRasterFrames` on the SparkSession.
 
 ```python, evaluate=False
 import pyrasterframes
@@ -30,36 +82,11 @@ spark = spark.withRasterFrames()
 df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
 ```
 
-## Jupyter Notebook
-
-See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook)/README.md
-
-## Standalone Python Script
-
-RasterFrames requires some special configuration to Spark to be fully functional. As a convenience you can use a provided utility function to get a preconfigured Spark session: 
-
-The first step is to set up a `SparkSession`:
-
-```python
-from pyspark.sql import SparkSession
-from pyrasterframes.utils import create_rf_spark_session 
-spark = create_rf_spark_session()
-```
-
-Now we have a standard Spark session with RasterFrames enabled in it.
-To import RasterFrames functions into the environment, use:
-
-```python
-from pyrasterframes.rasterfunctions import *
-```
-
-Functions starting with `rf_` (raster-oriented) or `st_` (vector geometry-oriented) are
-become available for use with DataFrames.
+Now you have the configured SparkSession with RasterFrames enabled.
 
-```python
-list(filter(lambda x: x.startswith("rf_") or x.startswith("st_"), dir()))
-```
 
 ```python, echo=False 
 spark.stop()
 ```
+
+
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index f92aae9ed..abd890ea6 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -25,9 +25,9 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 ## Related Links
 
 * [Gitter Channel](https://gitter.im/s22s/raster-frames)&nbsp;&nbsp;[![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-* [API Documentation](latest/api/index.html)
+* [Scala API Documentation](latest/api/index.html)
 * [GitHub Repository](https://github.com/locationtech/rasterframes)
-* [Astraea, Inc.](http://www.astraea.earth/) (the company behind RasterFrames)
+* [Astraea, Inc.](http://www.astraea.earth/), the company behind RasterFrames
 
 @@@
 
@@ -43,12 +43,8 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 * [Raster Data I/O](raster-io.md)
 * [Spatial Relations](spatial-relations.md)
 * [Raster Processing](raster-processing.md)
-* [Pandas & NumPy Interoperabiliyt](pandas-numpy.md)
+* [Pandas, NumPy & RasterFrames](pandas-numpy.md)
 * [UDF Reference](reference.md)
 * [Release Notes](release-notes.md)
 @@@
 
-
-
-Raster processing
-Installation 
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index ce494f84e..a03a7f4ea 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -1,13 +1,13 @@
 
 ```python, echo=False
-from IPython.core.display import display
+from IPython.core.display import display, HTML
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import rf_normalized_difference, rf_tile, rf_dimensions, rf_extent, rf_tile_mean
+import pyrasterframes.rf_ipython
 
 import pandas as pd
 import os
 
-
 spark = create_rf_spark_session()
 ```
 
@@ -55,19 +55,16 @@ df = df.withColumn('ndvi', rf_normalized_difference(df.nir, df.red))
 df.printSchema()
 ```
 
-We can inspect a small sample of the data. The yellow values are areas of healthy vegetation and the purple areas are bare soil or impervious surfaces.
+We can inspect a sample of the data. Yellow indicates very healthy vegetation, and purple represents bare soil or impervious surfaces.
 
 ```python
-ndvi_tiles = df.sort(-rf_tile_mean('ndvi')) \
-               .select(rf_tile('ndvi').alias('ndvi')) \
-               .take(5)
-for r in ndvi_tiles:
-    display(r['ndvi'])
-#display(
-#    df.select(rf_extent('ndvi'), rf_tile('ndvi')).toPandas()
-#)
+df.select(rf_tile('ndvi').alias('ndvi')).limit(5).toPandas()
 ```
 
+**TODO** fix the display once issue #166 fix.
+
+We continue examining NDVI in the @ref:[time series](time-series.md) section.
+
 
 ```python, echo=False
 spark.stop()
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 39ede7d21..4a8c2ae66 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -29,7 +29,7 @@ rf = spark.read.rastersource(s2_band(2))
 rf.printSchema()
 ```
 
-[[Discuss the schema]]
+\[Discuss the schema\]
 
 Let's unpack the `proj_raster` column and look at the contents in more detail. 
 
@@ -46,12 +46,12 @@ parts.show(5)
 
 ```
 
-Let's elect a single tile and view it.
+Let's select a single tile and view it.
 
 ```python
-tile = rf.select(rf_tile("proj_raster")).first()
-# TODO: IPython rendering
-print(tile)
+tile = rf.select(rf_tile("proj_raster")).first()[0]
+display(tile)
+# todo verify its chilly
 ```
 
 
diff --git a/pyrasterframes/src/main/python/docs/raster-processing.md b/pyrasterframes/src/main/python/docs/raster-processing.md
index 0d3189c66..3ac4c8ac4 100644
--- a/pyrasterframes/src/main/python/docs/raster-processing.md
+++ b/pyrasterframes/src/main/python/docs/raster-processing.md
@@ -2,11 +2,13 @@
 
 @@@ index
 
-* [Local Algebra](local-algebra.md)
-* ["NoData" Handling](nodata-handling.md)
-* [Aggregation](aggregation.md)
-* [Time Series](time-series.md)
-* [Spark ML](spark-ml.md)
+* @ref:[Local Algebra](local-algebra.md)
+* @ref:["NoData" Handling](nodata-handling.md)
+* @ref:[Aggregation](aggregation.md)
+* @ref:[Time Series](time-series.md)
+* @ref:[Machine Learning](spark-ml.md)
 
 @@@
 
+
+Explore what you can do with RasterFrames to work with, analyze, manipulate, and summarize Earth observation data.
diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
index 90587a9a6..304f05a9d 100644
--- a/pyrasterframes/src/main/python/docs/time-series.pymd
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -1 +1,4 @@
-# Time Series
\ No newline at end of file
+# Time Series
+
+/[ Example of generating time series of NDVI as visual phenology indicators. /]
+
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index db7e4570a..cf9b54ba7 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -21,7 +21,7 @@ ENV PYTHONPATH $PYTHONPATH:$PY_RF_ZIP
 
 # Sphinx (for Notebook->html)
 RUN conda install --quiet --yes \
-    anaconda sphinx nbsphinx shapely numpy
+    anaconda sphinx nbsphinx shapely numpy folium geopandas geojsonio
 
 # Cleanup pip residuals
 RUN rm -rf /home/$NB_USER/.local && \
diff --git a/rf-notebook/src/main/notebooks/Getting Started.ipynb b/rf-notebook/src/main/notebooks/Getting Started.ipynb
index 9b164ad0e..b2d49d5df 100644
--- a/rf-notebook/src/main/notebooks/Getting Started.ipynb	
+++ b/rf-notebook/src/main/notebooks/Getting Started.ipynb	
@@ -17,82 +17,131 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyrasterframes\n",
+    "import pyrasterframes.rf_ipython  # enables nicer visualizations of pandas DF\n",
+    "from pyrasterframes.rasterfunctions import (rf_local_add, rf_dimensions, rf_extent, rf_crs, \n",
+    "                                            st_geometry, st_reproject, rf_tile)\n",
+    "import pyspark.sql.functions as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
+   "outputs": [],
+   "source": [
+    "spark = pyrasterframes.get_spark_session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a PySpark DataFrame from [open data](https://docs.opendata.aws/modis-pds/readme.html)\n",
+    "\n",
+    "Read a single \"granule\" of MODIS surface reflectance data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uri = 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059' \\\n",
+    "      '/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF'\n",
+    "df = spark.read.rastersource(uri)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <div>\n",
-       "                <p><b>SparkSession - in-memory</b></p>\n",
-       "                \n",
-       "        <div>\n",
-       "            <p><b>SparkContext</b></p>\n",
-       "\n",
-       "            <p><a href=\"http://dc4d2a69c533:4040\">Spark UI</a></p>\n",
-       "\n",
-       "            <dl>\n",
-       "              <dt>Version</dt>\n",
-       "                <dd><code>v2.3.3</code></dd>\n",
-       "              <dt>Master</dt>\n",
-       "                <dd><code>local[*]</code></dd>\n",
-       "              <dt>AppName</dt>\n",
-       "                <dd><code>PyRasterFrames</code></dd>\n",
-       "            </dl>\n",
-       "        </div>\n",
-       "        \n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<pyspark.sql.session.SparkSession at 0x7f133e7f0c18>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- proj_raster_path: string (nullable = false)\n",
+      " |-- proj_raster: struct (nullable = true)\n",
+      " |    |-- tile_context: struct (nullable = false)\n",
+      " |    |    |-- extent: struct (nullable = false)\n",
+      " |    |    |    |-- xmin: double (nullable = false)\n",
+      " |    |    |    |-- ymin: double (nullable = false)\n",
+      " |    |    |    |-- xmax: double (nullable = false)\n",
+      " |    |    |    |-- ymax: double (nullable = false)\n",
+      " |    |    |-- crs: struct (nullable = false)\n",
+      " |    |    |    |-- crsProj4: string (nullable = false)\n",
+      " |    |-- tile: tile (nullable = false)\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "import pyrasterframes\n",
-    "import pyrasterframes.rf_ipython  # enables nicer visualizations\n",
-    "spark = pyrasterframes.get_spark_session()\n",
-    "spark"
+    "df.printSchema()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Get a PySpark DataFrame from [open data](https://landsatonaws.com/)"
+    "Do some work with the raster data; add 3 element-wise to the pixel/cell values and show some rows of the DataFrame."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|rf_local_add(proj_raster, 3)                                                                                                                                                                                                                                                                            |\n",
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|[[[-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3408,3471,3110,2875,2798,2973,3255,3169,3,3217,...,3,3,3,3,3,3,3,2519,3036,3]]]                              |\n",
+      "|[[[-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [2337,2346,2581,2751,2575,2364,2223,2384,2618,2296,...,3,3,2688,2702,2967,3200,3257,3052,2914,2534]]]      |\n",
+      "|[[[-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [2602,2728,2784,2781,2567,2539,2254,2327,2436,2888,...,2726,2695,2788,2898,3139,3121,2939,2778,2859,2728]]]|\n",
+      "|[[[-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3058,3163,3036,3228,2877,3310,2885,2932,2931,2940,...,2299,2190,2180,2441,2563,2431,2347,2414,2525,2778]]] |\n",
+      "|[[[-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [3238,3355,3502,3055,3343,3334,3,3,3,3,...,2629,2841,2983,2839,3107,2762,2524,3175,3190,3181]]]            |\n",
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "uri = 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/'\\\n",
-    "      'LC08_L1TP_015041_20190305_20190309_01_T1/'\\\n",
-    "      'LC08_L1TP_015041_20190305_20190309_01_T1_B2.TIF'\n",
-    "df = spark.read.rastersource(uri)"
+    "df.select(rf_local_add(df.proj_raster, F.lit(3))).show(5, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The extent struct tells us where in the [CRS](https://spatialreference.org/ref/sr-org/6842/) the tile data covers. The granule is split into arbitrary sized chunks. Each row is a different chunk. Let's see how many.\n",
+    "\n",
+    "Side note: you can configure the default size of these chunks, which are called Tiles, by passing a tuple of desired columns and rows as: `rastersource(uri, tile_dimensions=(96, 96))`. The default is `(256, 256)`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "930"
+       "100"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,40 +150,114 @@
     "df.count()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What area does the DataFrame cover?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "coverage_area = df.groupby(df.proj_raster_path).agg(\n",
+    "    F.first(rf_crs(df.proj_raster).crsProj4).alias('crs'),\n",
+    "    F.min(rf_extent(df.proj_raster).xmin).alias('xmin'),\n",
+    "    F.min(rf_extent(df.proj_raster).ymin).alias('ymin'),\n",
+    "    F.min(rf_extent(df.proj_raster).xmax).alias('xmax'),\n",
+    "    F.min(rf_extent(df.proj_raster).ymax).alias('ymax'),\n",
+    ").withColumn('footprint_4326', st_geometry(F.struct('xmin', 'ymin', 'xmax', 'ymax')))\n",
+    "coverage_area = cov_area()\n",
+    "coverage_area"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "root\n",
-      " |-- tile_path: string (nullable = false)\n",
-      " |-- tile: struct (nullable = true)\n",
-      " |    |-- tile_context: struct (nullable = false)\n",
-      " |    |    |-- extent: struct (nullable = false)\n",
-      " |    |    |    |-- xmin: double (nullable = false)\n",
-      " |    |    |    |-- ymin: double (nullable = false)\n",
-      " |    |    |    |-- xmax: double (nullable = false)\n",
-      " |    |    |    |-- ymax: double (nullable = false)\n",
-      " |    |    |-- crs: struct (nullable = false)\n",
-      " |    |    |    |-- crsProj4: string (nullable = false)\n",
-      " |    |-- tile: tile (nullable = false)\n",
+      "+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m \n",
+      "+--------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|proj_raster_path                                                                                              |footprint                                                                                                                                                                                                |\n",
+      "+--------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-70.85954815687087 8.933333332533772, -71.07986282542622 9.999999999104968, -69.99674110618135 9.999999999104968, -69.7797836135278 8.933333332533772, -70.85954815687087 8.933333332533772))  |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-69.77978361352781 8.933333332533772, -69.99674110618135 9.999999999104968, -68.91361938693649 9.999999999104968, -68.70001907018472 8.933333332533772, -69.77978361352781 8.933333332533772)) |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-68.70001907018474 8.933333332533772, -68.9136193869365 9.999999999104968, -67.8304976676916 9.999999999104968, -67.62025452684163 8.933333332533772, -68.70001907018474 8.933333332533772))   |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-67.62025452684165 8.933333332533772, -67.83049766769162 9.999999999104968, -66.74737594844675 9.999999999104968, -66.54048998349857 8.933333332533772, -67.62025452684165 8.933333332533772)) |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-66.54048998349859 8.933333332533772, -66.74737594844676 9.999999999104968, -65.66425422920187 9.999999999104968, -65.4607254401555 8.933333332533772, -66.54048998349859 8.933333332533772))  |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-65.4607254401555 8.933333332533772, -65.66425422920187 9.999999999104968, -64.58113250995702 9.999999999104968, -64.38096089681244 8.933333332533772, -65.4607254401555 8.933333332533772))   |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-64.38096089681244 8.933333332533772, -64.58113250995702 9.999999999104968, -63.498010790712144 9.999999999104968, -63.30119635346936 8.933333332533772, -64.38096089681244 8.933333332533772))|\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-63.30119635346937 8.933333332533772, -63.49801079071215 9.999999999104968, -62.41488907146726 9.999999999104968, -62.221431810126276 8.933333332533772, -63.30119635346937 8.933333332533772))|\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-62.22143181012629 8.933333332533772, -62.41488907146727 9.999999999104968, -61.33176735222239 9.999999999104968, -61.14166726678321 8.933333332533772, -62.22143181012629 8.933333332533772)) |\n",
+      "|https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF|POLYGON ((-61.14166726678322 8.933333332533772, -61.3317673522224 9.999999999104968, -60.92559670750556 9.999999999104968, -60.736755563029554 8.933333332533772, -61.14166726678322 8.933333332533772)) |\n",
+      "+--------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "only showing top 10 rows\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "df.printSchema()"
+    "crs = df.agg(F.first(rf_crs(df.proj_raster)).crsProj4.alias('crs')).first()['crs']\n",
+    "print(crs)\n",
+    "coverage_area = df.select(\n",
+    "                           df.proj_raster_path,\n",
+    "                           st_reproject(st_geometry(rf_extent(df.proj_raster)), crs, 'EPSG:4326').alias('footprint')\n",
+    "                         )\n",
+    "coverage_area.show(10, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So where in the world is that? We'll generate a little visualization on http://geojson.io."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       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+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import geopandas\n",
+    "import geojsonio\n",
+    "\n",
+    "geojsonio.display(\n",
+    "    geopandas.GeoDataFrame(\n",
+    "        coverage_area.select('footprint').toPandas(), geometry='footprint', crs='EPSG:4326').to_json()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at a sample of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
    },
    "outputs": [
     {
@@ -158,251 +281,90 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>proj_raster_path</th>\n",
+       "      <th>extent</th>\n",
        "      <th>tile</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>(((504825.0, 3142635.0, 512505.0, 3150315.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>(((497145.0, 3127275.0, 504825.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>\n",
+       "      <td><img 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sX37dnz0ox/F/vvvP6dqz9Nhw4YNOP/883HnnXfOKfAwX2i1WhgZGcE3v/lNvOpVr8KCBQuwaNEiO2a33HIL2u02ZmZmAAD//u//jhtvvBEAcNxxx1mL9cILL8Qtt9yCY489Fhs2bMDAwACuvfZa3Hvvvbjhhhvs9f78z/8cBx98MM4++2yUZYnh4WHcdNNN+NGPftTXr+HhYZx99tm46KKL8Pa3vx2vf/3rsXXrVmzYsGGOW/jqq6/G3XffjaOPPhqrVq1Cu93G3Xffjc985jN4yUte0ufy/bu/+zts3LgRp512GtatW9f3vJMkweGHHz5njDZu3IhVq1bNcfsarF27Fu9+97vx2c9+Fq1WC695zWvw4IMP4iMf+QgOP/xwvOENb9hlm8Du5zAA/PSnP8XChQuxbt263Z7nsZfjuQr2evxx4ukITYac8nT/qqSYjRs30pFHHkkLFiygMAxpeHiYjjnmGLrtttt2ec1Op0ODg4P08pe/fLd9++u//muq1Wo0MTGx2/Ne8YpX7LavBuPj43TiiSfS6tWrqVarURzH9LznPY/OOeccmpyc3GXbX/jCF+gFL3gBxXFMCxcupDe/+c20devWXZ67K0LT+9//fmKM0W9/+9vd3sPTEZp2VfDCXKeK22+/nQ4//HBKkoQA0CmnnGK/GxkZedqxeSop6d/+7d/o+OOPp1arRWma0lFHHUXf/va35/ThwQcfpKOPPpoGBgZo8eLFdOaZZ9K//Mu/zLkHKSV94hOfoFWrVlEcx/TCF76Qvv3tb88pIvHjH/+YTjjhBFqxYgXFcUz1ep1e9KIX0YUXXjiHqbu7+9nVXDZkrfPOO2/uwFdQliV98pOfpIMOOoiiKKLly5fTO9/5zl3Ovz2dw1JKGhkZoTPPPHO353ns/WBEOoDi4QFlHd5111146KGHwBjbpZX5XGHZsmV4y1vegssuu+y57spuQUQQQuCCCy7AhRdeiJ07d1oX6pFHHomRkRF84xvfeI576fFc4Pvf/z6OPvpo3H///U9bv9rjTwPeLewxB5s3b0YURTj00ENx3333PdfdAQDcf//96HQ6+OAHP/hcd+UZcdVVV+F973vfnOPT09O49957/6AiDB57Ny666CK7qYTHnza85erRh02bNlmWZ61We8YKNx5zsWPHDmzZssX+fdhhh80LSctj78bExASuuuoqvOtd79olQ9njTwteuHp4eHh4eMwzfCqOh4eHh4fHPMMLVw8PDw8Pj3mGF64eHh4eHh7zjH2KZSGlxLZt29BqteZ1OzMPDw8Pj/kDEWFmZgYrVqzYa0uL7lPCddu2bVi1atVz3Q0PDw8Pjz3A1q1bn1X98j8m7FPCtdVqAQDW/eO7EdQTdPMIUSAAAGsX7sBYr46b/+KSXf720P95BQCAT0dgAvjNu89Ux794FeJxVWiBFQyiRhA1qf4ezpHUCvU7TmhP1IBMa2EBgeX6MwEgZUlTKnD/qe/Fus99BgAgawKQ6rugyxFOc/zyH95t+/XC/0+fFxIYMUBdGskoB7RxXgwQRF3aa1FdIGlltl+9WVWLljoBwpkAPNM/ZATSM0RGgExVG3w4Q5yUtg9pXCAv1Ym1uMDkVB1SqDb+7Y1nzxnLdZ9VfWaLM4iO+l0wEyDdqcajqAMycST2ZIKhs596TmGbo6wRwNX39/2Pv7PnHX7TJcinEkQD6t6iWCLr6RsgBjETAQCay2YhfzaE3lpVe7c10EM3U+f96q8+NKe/Bmf/+nQ8mbWwPJ0GAFz6oi/a71787U/g5//tH+zfL7jm/wUFqo/3n/73/ff/9U+B6+/itITQY0WSgSSDKNU4hLHAr050eb2H/bOam0EoIUoOpseg2ucXfP1yUC+wc4b3uH2e5XBpj4dTAcpBAaavzQZyRPqZNms58jJAofsxUMsw1VHF+ZcPTWPb+CDydgwAqA32kETqdwNpD51CHQ+YRCPO8fjEoO0b1/1tpDmIGAKm5xOXyPT8SUPdh1g9w5k8MdMYvTJEu6vaT2KBJCpRlOrdS6MCd/zX8wEAx975ETy+Ywikf5nUcpS5ap8xQpmHYIG6tuyG4LXSfg7q6jMREKcFRKHal8Qgx9V7wgQDBQSqqTkZN3OEkbT3SQRIPc5FFmJ4qKPGnAt7n0ubM+iWEcY7qtRknoXggWuDMaAs9PvQjYFSP89uoN7hyL0fwaw6Lxln6C1Wx4MuULbIrgF8UQ9lV49BSCACIJz3LtBjEIQCogjtZ5IMYazuM4lKhJU+ZkUIzsiOfyPOAQAT3RriQGB0Qq23UjLUGrn9nbmvfDpB1MogpfqbBAMzRmreweZ3XW7X7L0R+5RwNa7gqd4CcJ6CB4Rcz5VfjQ0gDCUGBgZ2+Vue1gAA8cIcQnB7XhDUUR6gJma0MwISAquryciSAAXUoiRzDt4A+JB+qQsOlE7QLl02qY4Tw7qbPosg1tuKtUoEE+oxySGCTFhfH2mREhhGsLKm6ks+ECGa0JN2gGDeibDNQAKgWTVpy5AQ6YWASQApQHozFGIAF2bsYNuQszVgoI0kVtfqZnUMDKotz5KQIGPg3v92wdM+h6FD1bnT0zXwQvWfFhOyplrI4kmOfFAi6Kp+iUVAY1IvLiVQElAMqs688PprwHvqOxkPg6cEwdQCvGjBFEYn1HPjW1KsOPxJAMATo4OQfxagvlCNDwtS1NRpOPanF4MzgtDKzsRsHfsNTwEAOsUaLK61sQ1qgE677xz84tH91SOM6va5rPvWeagdECLrqHs78vYr8R//XW0ecMD1H0c0GNkFBZFAxI2QIXQfGgRL9EBPcKz72tUAgHRhF0FDzYk4LtHtJBB6kVr31asRtNXngA8ADOBL1RhLwUBCL74pQzGtBUQZgtedgvT8A8fA9EJJxCAh0dHPJmAh8kjNwRk2hKARY3BY/bbXqyNuqmuxiGHVsBIk29st/ODYK/FnN20AoARqpCdQwAIEXIJIPe9SRhgbU0K4LQgPn3Su7dfafz4fC5qqzagMUUvUbxqJxPj0EBYMtQEA/+uYK+1vxsVCQNTBG1pg1DnChrq3sgwgJlKrhPIECBpKAWYJA+PqPikPUDIBCVehjDfUZwolgkYJQ1kp8xRJXSlq7R0NNJfOItTPtJQMmWwCAHIGJPpak2wBChZAhGqMy+kaggElgEQvRJCW4KHqcxhxYEwrwC1CNM4htHDlBYMYUp97w4B+hCgXSbCSgcxaVEsR9BLbf1YwINI3FkmYhEzBJBDrz8RAjEHouZUlAkGs7ku0QwSNEvW6evaTvRbaTF8rAqK4QGORVjCKAFIvKmUeIDBKuowggwhkQnTEwVK9lqb6mezF4bt9SrgayE4EUAQ+FVhKF0kgi58+5TfcoWZifHAHvW6MA792MQCALWRWI5f7l3i0sjBUMbLxUgSNQglVoE9rBCNMtdXqHoYCLz34YTzeVovNoUPb8dn/6ytPfzOmnViCJwJSa6c8Z8hXqAmKjFvLmM0wyAgoB7QlOOUWD9KnVbViaVVfMk2ACUA82sRMQ70k4cIeZjrqxZ1lCZq1DCfd8z8AAJN5Dbe+4qq+Lk8+MWAaB9fWe9Bz4yFSAkUEqRfAR973fvvdoR+6EvE0IFJ9PyUD6UWIFQw8Y9D6AEYnm+DaM7HwsB34L0seBQBcfrQqOn/oN5XAE5Ijz9U4DKQZAi4xMdsAAEShszbyMsTmyWGksRrXgBH2XzaOpyKJShCAB95yHgBg5NpLsearH1djTAzFbIy4pRbSB/76PLzgW+q8+157AQ749OXY9B5l7R/+L+daj0CehyhG1Rz53TvPxup/+gQwoy0RqEUWAMoBCTRKJHpxak/UENXV52I6QfykVtRiYPMZ59g+H/sDZ11P5wmacYZCqDEZa6fWEgOAVqOHUn+XJCVKLbwnihoCLVTGpxo44PqPY/FCZYHGgcBj24dVAzMRXnzYQ9g6M6TGmEvr6YjTEkfe+g/438d+AoASykbREZJhqKYW8ygQGF4yitlczbt13zoPM5NqAQ+TAAgIXFuT3anUWmYkGUBAOK0F+3AJ0dOep16gPEUA+FQICgOwQTV2tWYGs85nvQhJWqA9pq7HYjPjAN4s0J5OMbxgVt0Pl6hpy76UHK1EjcfDW5eACOATWjmOCVyPndASstRzkgoOFrp3kgsGqfUi4gDTn5n+2/4xUCDQYyCmYnBt/VICIJHWGmadAKQVOuqGQGAlLVitBJlFMgsgjLdN9yfLtAIWEHL9uZyJkLUKhJHQ30nr3ejy2CqdYa1EvZ6h3VZKo+gGkFyvRzH2euydkWIPDw8PD48/YuxTFZqmp6cxODiINeddDJ6myu1pNP6GBC+ZjU9RSBCptkhTQjSp4wQLBcDJutRoPAYZN0etxKNv7t+Ue+RLKk7GAgkWkLUsAafxppV4RLOW4dCF2/HDh9R+lfVGhvtfd779/v++7UPWauhkEcjEaokh60YIQu2O6rm4EnVDFdeFihfxjEHqbgRdZlUsGaoYjbEEec6ctSqdVkwJIXkysK5ZYi4eCwCsLjCkNfdfn3BR/3hsvBThWGjHlRm3c8FsnJUJgEJYd+/D55yFw995he6j6nuuQ3lFiyC1x2HTme/HAVddbtuUNbJtiOESTe2yzLIQi4dmMdWp2X6Zt6CR5ijKANPT6rukVqA7rTTrMC0RRgLLhlTM9fHRIaxZqkpFFjLAZFedNz1Tx0Crg18df7Ftf/U/flJ9mA2x6V0uDv38Gy9AZly13QBMMLAlyjpbuWgSM5lS4Xt5hO4Tyr1Y/f1Tsfqzn0K0uItct4lQ2tgsZYGNVQMAAsKCRWrbuT9buAM7u6r9LePD6I2n1tsRtAprWUIy1Fo9ZD3VL5FzBIka8JEl42jn6nim43bG6kyjEkJbv424wIrmFDqlOveB7UtQPq6sQDlUIqrnkHqOS8FQb+oYeiBszG9qpoahgY69TpaHEKX7DUYT1Fer51QUAfKONoU4gQSzFqMcKm3/rVUGqDFjKu4KqLjhoHZ/A8DozhZgLF7BwAZz/Ts1l4y7evx3CxAsUy7jRj3DAcNjAIDfPrkMnEsIbfVnk6nlChSzMfh0CDmgTdKcI5rU95YQIJ23hgKyaxgFgNRuW9YogZnIvvdolSoWr587BLOhBArcO0+xcXmr+DQBoB06tBVLG8NFQEAkrXcAlWklNR+gMdCzY1eYmDcnG36oJQXSuMDEjI47dyNrabfCCdz7+ssxNTX1tKG6P3bsk25hkRAoIYQdhkC/L0GPgxEg9JpUVtwSLHdCt7Y1hEwIFBiiDJDrxV2W/Y6A1Z/7FKBp5MQZkAoXFBHMxlSIGBa21Ms42anhy0d+EThybr8P/5dzIWWErna/SMkQGGJMVKp/oVooOklkf9eRzMZ3WcZBDEhH1d+9xQJBV3+nSQ48Z+5vK5QrHWkrQcgKJ7iqBBrkHJPTyuW35qsfh2jrsWJAujWC1G5nJpkV2Kz6csYESiVkUx0cueYyQG/JyTIOnjMnzAnWjQpoZckIyk3cPk++M8bsAbqP7QA7H2mgWK4WRB5K++yCQC94ehEpS27jc2UvRDkdY3NXje3zVz5pCTwAwPVvliyYxsRs3R5f/blPwWgzZswMim0NMD0eVBeK1LFTLWZb8oVgWlkKAolw5pkdTclogLLdwOYPnAUAWHPl5agfqGLGM9tagJ6rq1aNoVtEeOlytXH3aNbE9hkVh+9NJWAld32dTZDq+H1viUQn56gtUAIjowiBFniPbluEIS1UJkebiOoF0poa4ygQqOuFs53HeGB0iXWDhqFAsUidd/DKJ0HEsG3aLai5XpiF4Aj1/C6mY/RqOUotUOtpjulZpRBFSQm5XGD2SaUsgJF1YwaxgOhFkE3n/hWpmYRAbZFSwLrTqeI6rFLCtcxCzP672tkoyIDNn3AbM6z+3KcsGSzvRliyeNqSe37x9+/HCXcr8uNYt45ts0orLPJQCa8nNLeiLm14KWoUKGMJJo3QFBCZWUcAXhKkduMGbacwRVMcMtL8g46O8egXS0QBghktoFMlJKUecyp5H9HSxl8zpaBTQ7/8goEZo0IrW+bZEzEw/TzjtERZBPa5MUZo1JXiUEpuQwySGITklrsRBNK2NzO59/uF90nL9fnv+ziCJEXZIEQzxjQDRAQzN4+HAAAgAElEQVQUg1pQxmQXcJYzxBOBPR70GAI1V0AMyBY5q00mBOImBshtLFGkBKoLx0aUsFZmEEo88Ncq7nb0Xe/FA5uWI9Is48XDM7jnaGX9/pfvfhBP7BhCraEu3plOEWqtmwBwRmg2lLYw8ZhjabKCW+FFIWkr0TCcCCxziwsEQ9gx8UxAauFEAUHoGGs8FkAkBLHABHsI6KrxCdoBGMEyZUVDgmsShyw5IBm4jhVSQE6Ql8wKXdkQ4I0SclYrCBJOY4YS4Kb/6Y4Q2bAex4wh3cmQDc+d0jICZM0wsvT1jICOHPs4aWWQgkNqiyuKhNW65VisLHu92ER1x0IF6XgeAD6pLAaq7NYXa89H0ZKghKzVAOnOk6lE0OU2Hs6bBaRWTIJmiUdO7veKrP7sp9SHVmFJS/XBLjo7GlYwUirBzByRDEw/l/2XjSMTAUYGJgAAvxtfZNv91fEXY+Say2wbrGCIZvU8TtQCHx+gLN4iD21sLY5KzM5q0lWiWNCx9s4wRhjWpJ+pbopaXEBoUtfUbIoFA0qopWEJxgjjbaWcTD/ZBLQ1tvmUfib3yNWXWUUqGs4s21Y80kS5uLB8BN4J3HiEhNYjHFOHurlriE+Pvql/fOcLlp/BJQqtmNV/l6g1RPd/Zl1mz4/rBYTgEIbpLgE+7bgUDFCMebh5BSijQdSM4qqOmfUtXyAd8S8lxGMByrq+OCfnlYrJjekkR75IAIkTrmb9kqMJEADMePoW5UpIA4gbORYNOs/QUL1rDduZXoKBVN1rLgJMt1PUU+e5M56IfLrAI6d+fK+2XH3M1cPDw8PDY56xT7qFywZBpiqm0l5ttDKVR8q1Aqlik8qkiGadq5FCgOcAN0TcIUKgLT1RI7CcgbT2iIBQLjK0Pv2fcbMGzrpijDDyRZ3DWF8MCGZjMSOtiYrm28TgYMf+TjZz9MaUdsgyDlETmBxVlgMDEC5SVmyZhZDasjTWKTPB1JyBGdagsehMPFbCWuEyddadjFSsVphbKLmy/qCtrw53eaqJcPeaKfe0YWSykttxkTHZuA+IgcYSoGaCp8y5yEIJXjAEOn6UD0pwY4SU6LMYgx6zGjyTBKtLcoKMyLaJnNn7VM+IWfdUWQQ23kitEjyU4NvVGJczoU0XopDsOAYZg4zJXhukYvqA6hvvMXCtrIsaudzTGQ4mgNayGdsXUdPpHuXcTetNmkUQSUC7XIs8BG8VwA4dx62VkJp1GtZKRNoFJ4lhbKph04Ampxqo1VWn1lx5OVjsXAWyKZCH2i3ZEOCxsLH+shNChJp5GweQ+rlIbc1mvcgMAdpTmhGfFpDEkOnc4qITY0xbsaLg2PSWf8A6zaCGZHZ8qhj5wqWoL+mgs0Oxuovp2DHil2dAwbH57YoNvfa8K5Efot4b2QsxtY7Z7/5PwMysOBIod6ox+O1F78PIly6x8XA2E9q82WJ7HRRJsMK5aqXOU6eEKVa8Xn9kTH1hlrDtPGW8YM46BWzOetDmyIckgszkzrr1DVxlE6gGFata1FzIh4+qL7nUzHSDmQispVn0gcTEbN2uYe0sxtSEek48FmhXvBtJXNoYLGewv6FdPPO9DfukcCWViaPcg9otGU9yO2EBlapQakJTWXOkAREDQcXeD3Jm45EUaXeNdlmyWFjXVFIvlCAc1fGjukAwqCR5rxNb+rvMAyCU1sVyz4NrbApAmEpMT9fsxKu3MrBUu/xiCfS4LQogmyWKKe3TjaWLnRYMlEgrCECOzMAyZt25AMALF2+kEIBe4I3b0sRpeMYh9UJPdYEyrAiubgCROeIHcXLpTwFBpmbspHVPB7McvGAoGsZtxUD6BngnQNhhKFou5mpcY6wEZg9yuZui7sha0QTHgvvV585Sjnyw0g8GOz5ZosYs0SSaej3DzGM6p7nH7PiqAwSh48LhNLfudBmpEKsRriIl9Xz0b6JtkU2f4GVlAeSADICZcbUQsUAqUgoASOdeFCXHpr/9B3AtwKjSp3IqwaZ3na3i1ADQjoDQxcV6htjTaqPoxCgG9TPkhPaojhM39LPQix6rEH1ASkAFhoTHYJUuUXLUh5Xrt7OzAUhg8//zAfvTkatVn0oAxXiqSDcANp/qCmUYGLd8c9ks0sg905HrlBLKuyF+e+IGq5RuPs21ccD1HwevOZJAPkj2nUkqKTV/CEY+f5l9Z3m9tDF71g7Bh/M+F76JE0vJseJ5OwFA5f/mDavURYu7NvdZaIXCpMcwwVzoBor/IA3ZMnJpbMQBYd4LrosylE6AGiXUhGKFUYAlQzKhziuaZNdBCtQcDjqOkyErZEeWM8hhdXKUlhDbleKQB4q0ZJTS6bxmuQOiF9o1sZcHiBaUCPQYTLdT5F2Tt19ZjPdSeLewh4eHh4fHPGOfJDSNXKRScYIeILTlFLVZlVwH4mTdo0y4lB1eAGWTHFmF4Ig4MSmXZkuz39LSasyyFyDeHqFYpSwiEhwrVqgCBPccfQmef6OqaDTc6mCqXbPMwc50isagtgYeHYAcLmwaARhAC5wFQQV3RSUkQzjpSFjGqioGpWKUGmuJWW8aKFTEBqO5Kver/l1LQjSdJk2hVG5dABAAdNoAdQPFAjZafc+xTkVD9Fk6LOOKQAFlUVvWslDWniEtsUap7g0AnwlVn3T3y5UZgu3K2pQrepBZYJPjQU5zp4BswQyKFCnNPEMmXMUnCgkIpSNuaDcloAlEEqhvUz8sGoRigU7D6nB7z0wwla5lyFoCKPZXz52Nxn3uauKwqUTpKEdZg52EZQ1oblFtZAvg7nltBw+fdC4OuuFCde2KS1tKVdQkm9ITu2DWQqReYJ8ZhRK8G0DqudpX1KRUFrpN1QgrZTBjqTwhxlKrlWATyhqWAyWgrdxoQs8xY/1KBqmJYPFAhnw2tuGCTW/9kL2Xh97wUVSx5msX45FdFGYZufbSZ3TtmupQrVpmKyZt2zEEKjhquqKYqZz1bDBy7aVg3cC+G+VQ2WdZUkiIFqj2a2mBmceV54NqAnxKk+MGSrDZ0M4R0RAuVGNY++Y9LBnq2zQxqaXWH2t1cljvkqxL51lpFqBOCG68QRXXb9nQc5O7d69qzBvrNOzoVDx9a8WAS8WhSKoSrjbjAS7lSzLw0J3LGCBm9QSKpVtwBEPUypBUSqkWutxk90nCY+/ZsFcTmvZp4Qq4/MzQlI8zL0xKNm6YjHIEJsYRKEaxcQVXJ5yZ5AY8c6zWoMch6hKk3bgHr3kCvVIJyS2/W4pQv4ytZheznRT5hOrf0PJprBpSpREfvnUNikGCtDFF2ImfLy0ADusqlL3QugORBX1Chnc5wq5hEQpXkUUyxGOBnftBjyHW4b/eAkLZdExEGZEVjAjcIhpMRCo+ZGYVQ5/QqcY2AedyDLuOLUyBcmuHWsCltRyzkzontRuo2LgW2MWSAujpQUgkkHOEOuVApNL1MREIRpUQiGYZigFplScwsq7lfEiCQkI4W6lcZcaHlDA0Fax4r8LCDgjxuFYASiVQw64e1hhor9RusZpENB2A6fkkUldiEqQErWFlM8FsfrUMgXhKC9qFEq2DJm0FpV43tq73cDQCccLCe9W5O17q0r/i0dDmbgcrO/jd650gG/nCpVbwhtM63ck8jxAom5VcLA4b40XBXYpHXYJ3XG6VaAn3XeJYqBQql7xJ6+ABQZq4PycEkbCs2s2nfEhVowKskgMoF3mZBVbIbz7dCdo1V1yOgedP2OpW9SS3NcS3PbYAkAxBUz2AR046FyNf1jnIJbftvPiWDyMrQnR1zLiYSawA5RlTLHh9r0t+Bjx5lGoi3clRNsnG4plQwgwwyjfseETT3Cm2AVwGQqCet5l3FLp1ScSKK2IrMQUuJY5JuDz9QM01c4Hayhl0H1OpVrzQnBKtl8vILWEE9Nct7rGK8q3eKdWIMkAsF4KTVQ4oFQgmQlvPfODBELmWj72Vhb1Y0ChQr2eWPTxRMSpmdxK2nHHhXi1cvVvYw8PDw8NjnrFPEpp4AfBAWUukNetiUEJGBJG4vEtjHWULpXMDc8XuDDJnCRpXDNUFop2RdflVrUJpqqpoSy0NSjy6c6H6XSoU2xHAxHgKYoTaNvVoJuMmJjXBxZQlMBpu1b2YL2Lg0wFI59yyLu9nXmltkWcc4IqYBWgNdtZZHhTAat1Fi5AtdC4iAxlqq5wbxg6zO3MwCWWFGK2bkyNFBaQ0bW31sEBat3nJI6fqkXL/Sp0Pmy1zjNp0RwhWqLw9AKouqiFZJAKIBaRxhXU4mCZXlzUOYSre8EBbH9oaCyXyIWXJUC8Anw0s4ahskLUaeK5cdNkizXYmWEvEjBmgSSYCyIbd38YarT0eIlsoMbBV9bG7lOyzCDOGsuVceyxnNp+RCUd8YgVDuxujmNU/LLgjqAEQLYEdf6E+s1gi1eSsXtnApne6ghvv+eWb8K8PvECdl8V2joSzTLmBjVuv1MVBoPIgWY+Bm91iImWhPhVMMERjofXwJGPcvkPmf5EYDwM5yykhlBKWFLj6s5+yk3Vk46UublPGiCYDPHTOWXOuLVPC9GzNWvO9OEZNFzGIWzmKnlv2Rq6+DCzXjOZU4KCvq4pijNcUU1wzrZNtrviJqKt33jBzo65Q5D/ojIGiEiqqO6sznGXWimVSWYR964gJIYUEXgKFfvY8c8RCXjCIeoWQKJitGcxExcKtq/ZMxabe1pYdYyZZH3kz7DHbfj4kbXs8U3nuoc5xlglBaAeSreiESijEeKpnQoABrYfVODcfF5gJ9LMgWBa07CWYmYnQaSov3YKhWbvLTjRUYAv2buyTwpW4+lc0yLLriKtUClNogHe5jT2AA2RiR8TUZDOek4iAwgmxYlg4d2wlkEFDhTpPLyz//sRSu0MJQrKJ8kQM0VAPbKl2Hw9P4vEpVRAiP6yAFNw2m89G4B3NxDUVXDomtuHaBCMwHfOQiVq8E12hKQtcMQXeYygb0lZ5sdvUAbZIBACVGpCSFagUEspBXVhhIlAuRSO8K15gYoRgOoBM9MtVE64cX0RuV5AsUKX6TPGDLERzgZKSswyIt0UuXWiWq4IBAGg8RvpkYBUOGcGlBIWEUMe7wlkGRkBhrp1zy84OJwOImoup88LFoINMpSlEU85Va9NtWCUFKINSYFIX1zbCqbs6Bzhh6hB9AwKIZlysOZzl1u3PpGNmV5P+mQRoawO6CJBKBTPh75jAO4Gbx5Hsiysefdd7AQBPTA+g2z0EUsdLecZt7LcYoP6iHYVixQOqtCBF1OfC5zpWznNmixYYBdCkdRQD/a5SMFjhFHaYG6uOEuylGbqS2fEpC+cOlXWJYmiuUAfURhKDrS5Gd2iWd+hSh6Tgals/I2A5WQFXJswquRAq7mzftSFp46MyleBdbvkaxB1xwSjkRlgx6UJIfe5cqcIHln1bqdzFhAolmHgpqxQaqbJ3q+erRoBYFeNCV1evNMVveMYQ6Pli3Lw2bBEBpdHcK8x5ChWjuGy5TT5s1S6mDRDDVI7cO2PS0sz2d9kwt8+KZS6kI1MCBLMhgU4WI9ZbDgrp4rB7K/ZJ4VouLMBrOl5jYkelJrgEbkF0+XUEJt2ElqHT3FAtZ1dyRdIxMjnjNn4RTXDkCwTihTr3dFsdzW2qzfsvcaXUDrl5A7qbWpZf8mA7sbVVAUDMRFZI1GacNVPW9YunLXEKyIWAI+niMiVDuoNbggoYLOWft0NlYRmSVBZYogsTQKyFSm+5iu+KyjuebndTqdyvBzauF+027xNUxGFTISjkfdJXtM0OIUK939ryQMnQ1lvkBZla9LhefctBYavXEFfWdjVmaRYe3nGpMvmwzpWdMow1l2olQ3WvZhEUQyWkVmCIKwEkjUXRcTFXEQFF0ykjqrSmI6iQSZHIubLKdMyPCg6mF8B8SEKmpPIOoeL0gRYCFBLiaW0dJWqHIitoEke+K811jFNhOsIB16sdeWQ7AuLlqu+xAHYmrv9N4YRkqRZjyytouWfEJCA5wYgdXsBZvG1WUUTUIm6EK8+Zs4SFEhZWGckB2G0DCSiBSN9rPOVKZD74Ubc70gFXXe6qjGmMfOFS1V6TQ0iGuiYCFkXgdrDpBCqv0oSGS2bjiMoarFiShesz6a0UAa1MEKzF/tjRACt0TL1UVdmqYxbkLmZvpzvpVJdy7jgC2iNlA/qu7rZIlTIQaiUm7AKF3vK0rDtPEy/VHDTzOshgU3GCrvKqmPrc8ZSLC4PBxmKLQQkMFoBW2EVLOqWhUEaGeYbhDLdeFiqUl04YwlToYsEyJZXvDoDlAAODZKr9dsnRNrpJb+8XTT7m6uHh4eHhMc/Y+9WD3wclV/8YOcus5IBwLq6g51xVMtbUc+hYSeRcK7zHQZYWT6BEItCVSuRoYoutF4MSjJjdo1EmEvdf4jRxg/yBAchFpXM7dwOUqUv0BweSMaPlV1yPAOSS3BUUEMy5qwl9KS+dkdL2kT+e2mTvYrgEKzjkpNtBxLhtyxpZb3cwHap4pfG4znLkAy6VhbIAZOKbCG3MRqQq1cfW2RXMav9Bq7C7klDBEY0HVtMuGwQxqFNjWABRulqo1V18AEC0yKbiECf7oIKsP8VAFc/QTQiGYsiFB8w4AQCfDW38Vca6CpN2TchEItbpTvmwqxSldichPPRBFw9cc8Xlqn+xiruTvoGg7ax3CgBIQGgXu4yctRFNOSuZlUCYO6tBPGV3IQqcERRNc5jKreFMYPfBDWcilc5hdq15InRuPU6qPrQZH+k+i7pmx+t+hR3WZ31Za1pbrMYiCrquHrdxp9viK8y1L0P9G+1BKpvMjv+Bl11hXde1Jzl6i/WmCNDxfOP6LRkmJxtu8/fxBKG2lkRDqvfcehUYmHlNOFzcUDhXPQC1ObnhEYABjOyesDJ2vAKQyiAwYyIjsilmFAKi6hkjN99E6qx+Jky4oeJGNxZ1xW0LqHQtE5qo7WA2fk+h3sFLe5tE7OYSa6hrMVfSt8JaJhQLHF+CVdLZwG03lJu5xyC056zqtSGu3klbfEJWwidw/SCpPAPGK8hmAvt8qec2HtlbsU+m4qy67ELwWqomSmVSVYPy0LmK5juXg8l03qs6rRggyKaaLVGjQNELXbm/6cilcejrmLgiZ2QLnnenU0t0WrF6FE88tNi6ahEQ4sqWdGlS2J1v8jJAoasmmSowHb1peRQJRGaT5jJAb5suP5YzRCNt67rOd9TtPbPhXL3wJu4kHS2fd7mNORF3cTM1JpVYEpSssOPTIlu9iZVcVXUxBfSTSsys5+I5YZtrYkjF5a4RzSjhYSo0yZq0sSmeMZV+YOJTmSN7iJq0rimQJpeZZ9Kr7M4j1EJjqyvVnCAPu8ym0KiT4XKh4dxpIkXfxg8AXM4oA2RD2BSkdHtgi63LULnNTD9F6nJluXCutSBT35WGXBKRjV8CSrCZRbsaywPBFXYXSuBx4a5l3Nis1IIgrbgiOy6mWNadMI+nXHxd1AjRtOsjMVUdyYxJ1SXKBPr6bNyoMlFlMG2edCW0ErQ5knE3B3uLHfmLIifgollVwtPcK4VkQzxmoafq1NLPl+esQhyCVtzMmFZy2wFAwl676tJlJZTyXY2tGiWCOwKT1P01Y1LdzcnGWM14MVdNiUKAFbDz2lwTUNexCkygzjVKjgzdtWSoXMs2XJMB+ZAe77yiSKVKmbQlRAmVWLJTIlUf3DZ4MqI+4Vq9NwB9Smg1r179Vv/f6+HRDef6VBwPDw8PDw8Ph33SLUwRKU2XkS04D8AyggFYchKgrVVjAXDNoDPsuqHcJk+XuhC52UpONEsEutA1k0p7S5dWfDEam0/9INbourHbdw4qrU9v0yY6oS0oEQ31MDNds5seR63cbrMVconZTmJJQQNLuzaJvjud2tQGUQNQOBU8XNhzRdinYmVRGdeoYFb9kom0xfKVVVEhe1R4SaZgeKbZgUGH9zGhqeZczci5teB4j6NRqUJTLC0QjOl7edhZkmUNyIdht0DL6q4QSNDT1aEq/bdFKwiuwlShGK+RJhIFXWY1cAAgVwNezROzd2WPQQaO2CKTyraEBUNgixWrSkZVy9u2L5lyRervugdW5kOpx1TThXnOsPIupeZvfVXgUpo47FaA6j4rjFFiulKOcyna9IyaY8aClIVVJs5iMVa5YYka1rJMJJIxt7Ve1dKoWlgiJpTLVSejKY6wzSyjVs0fVzCEmCM0lU23yQEFhHCW97n9DWmQArLkHSaVS9oVAoGrZ61h7zVzBTGYYP3pcxFZFzEj9LG/q5XAipazEBmp8TJWFnHYoixgAElYy5u4fZyI2gyRLsqSDzLI0PWDCTeOXDxlP2lRsVRLbV1WKsRVeE8olIMK6RghH3DjoQpH6PYK9V5bpnLGnFUb91vTTLr+s4rbn0IA0nkfeMbss6GWUBkQxnLNXUggmnV9LJvOe2Su58Iy2OuxTwpX6BJu9cGuKzPYTlQMwFQcqZSDYzr9AFBuO2JkUyswFtsdKyABhARh0mEaJWipmlUiCxBMhpjVrMU1q3egnev8vS9dAsa14CI4xjKAoCbsW13moSrkrivbFJ3I5owWeQhZcLsQTexs2X1TWUDOzUyALDhCXXKsmI3BdZoOiwgYLMB1m2ImcvcZ9rsURUM6V5twLrIgY4p2b25hec8Wn3e5eY6VWo31lHr3jd4KVUav8bj6e+F9HYwfUtPnKbeTYSYilDa2LAP1kvMZ43qDjQmlOwP7m3JA+ePMQlE2nJszbKuFwLCw47GgL9UBcIKSZ0ylYeh7MyzloKPyTsPFihledCMsW672Td15/2LEo6HdlEASEOiqXSJT423SXsqWxOYT9Zi33WIeCOX2NeznZIK7GGcdAANiXYi9L19VODd2kOmF09xL6dy7xNU+x0FP328UorOfTotKJFgqbDnKInc5ttQUYLNukwaZVOLhVR8ZqWdvBFnQg7t2U6BouA0vwonQCXPGrJu8GJC6rKdRzpyyJ+L++C8rAWYp6rovRumSTlmounOVQINz1epzAcV5YBIoBsz1KqlJXN1XPKnnYOz2XpUVNy0TAEI3/jJyzwlayJu+BCWzu3XJxHEXDGwJxZTsOPfAkEwA2ZC+XlUI674UhifBXR4t4+6dZFLds43fzzplRtQJIJf/TlwpD6pDIcoFhWM4ly7kQMyVnAUBySiruKGdS9oy7fdi7JPCNUpVKk49cepRhxJUN7wGYElMkFztogMXm7WxsBIodOEJiiXACVzHSOuNHma26xSSVqFKH2pC0yObllghytqBincCgGAIWxkC/d2CVhvjM3pbLQAilK4ObFkpn5ZxBB1ucwLVJK6+uBVyEyeUk0rI80rYk+oleCAhtIWqyDvqu6DNVTwT0IUmuC2JRwkQjesdc5pS3ZeJWWaBTR2SIYESsuPK2455UwwLdNeqMYiTEmUeoDZqNF+B4Qe1kpIGyAcCjK7TAoiHVns2KVLGEk8mGYpKvpCJPyajAcoat9p0WXcWRNkk1J5kNlbIpHvR80FVEtMSeEqAzzjB/vB7FUFtzRWXY9N7zsaay68AABx0xGPYvGOB6n9TotsS1mPCQmmJN0idcFLP0FnNFEuwzBRuUHFpI5yKprOqqvVjbTt6COJpRw5iAkDFqmLCWeTEgF5TusIYvKIYUaCs/qZ6VmUWWkELAsIlKv1Fthsq7qyJaOhxmy9JTFvmZkfBkKxXgcdC5Ry3tVfhKYusEWI8V/F0c5dBpVwjl1CxZN1+WXfCz5YHrMZZjTAp3OJu0rqs4sDdtXtL1L2anN4oYygGXfvRLBAaxaSieMq4IjBzAGCuRjD6FZ/qloUydqlWBlXr0igwouYswXiC2Zg8YJRSd28iJaQ73Vwz9wZilTiqvo7Zyk84HUnqsSqa7nlYcqWEmre6bnWZclCkrzXm5q3xPtl7I8dbqCoPeyv+BG7Bw8PDw8Pjjwv7pOVazMTgZYwJycEDE4hQ7lNMm3wEgLQmRqkE165YygMQI5R682iWSKe5lwy1xyNEbfVnPlBDWDOuEVUiUC5S1jJrh5Y1BwLkmA6iMUJR50gXqUa2PbbAFicHoKzCwlDcma2cZJiU1u0EoKjEk20llRA2Dgz0s1DLMoZIK+XPSmat1XKwBGu7XWWwsAv0jNtZoliiY48hKeavsa4Lbt2l4TQHVS0FCeSmlGCttG7mfCZGUBMuVSPgCDp63IjwxEvctI2m3V6UKjGfobNMWxiLpbXkZKDLvAGQAVm2rGkjW+SYjtMHiz6LpVqsgeeOWVwMSTuuMiFbYJ5aAQ74nx8HQvVMH/nNfo4hHUrwmRDhClVxKgyF3Zh9Zqap3KV6XsRjgY2tyuU9lNqbkWwPrcsWUBaR8XKGHWWhmNQiVjJ7n9mQ24wibLM5JS2tq32wAOUcFKkbjaa4LWCRLRRAyWxh/SCWCFJdWasMcPSBDwAAbikOUTsUGVR20gk7KrZmq3hVmK+yE4K3XUnLoiX7GM/W4tSbI9h0JO2mBNAXV1btO8cNoNnUkfODWtZsWdkFqqnSaVg1RGOYvoF6B/JhvT4wuJBBqF3VpnkBWzIw6DmPQjrKICLXpkjJzs8yJeVitZarc10r9rqzqCErLtdBx9YG1LtuaSQl7GpPHH3nMVKuZ0CNp+GQiIQgGcCkXsMS1heTZrLCRmd4Sjw/gExNBRfnxs4HnOtaJKovwVwaiqusthdjnxSurCbA9GbKRadSp0+yPoq7LR8IFae0KDiCptnRQ6DQPuNkMAc90bLuGOY2JAHXW5wF1kUKK7jSHWHFpcJQEsNMqdzJ0XigXK0AQEy5B02sM2e2kk22pAQvAutm4iXrX2SMrKsLyEFhqySJmRBkNpbOucpr07+rElLYbACmKzfFcYlsNnEEGxE4AlY7UouSeVkjsiSFckDqPEz9sgIud7DgYF3jLmQQAdmdRqYOatjFJR+k/pSPLkPRtMODeKbibtjRnmMAACAASURBVOQuHgWmBQO0+54YwA1jxwk0WdPpH6YmdF2CAkfMMDl8BsLsFiMY4i1KEpYNvVG0qV0ckI2rRnGJHgeKJ9UkKYYKtAaUKzVsFRDtAAGrCHbTjyxwNVkjIJlw9aEBl4IBqAXPpvPUpd2Q3uxCAwDx4ylqo66Wdra8QNRSz1dKDsnJzv+ChQhMuCEksFiCNBFNlBxStxGnBf73jv3VfSYlstkIXOdTk3S7q2TLCgTToUvdqKk4rjkvbDOU5plGZPkPslKOU5YMCF2IAQSEk7qSkN45xgpXQqVernKjSu5igNzU9A0A6PeQF8rNbMMulWWBkVII7G5PBASm7GOPQSROoPLMEYmKhhMsneUEVjpXbTU8Ayglry9XNneKoEhh3+cgrwg87mLQYVfFW+3OXuMMZB5/AoC7OcNKJegApbSYSEq1wpjBU921hhTFpNuMXUZKmBriJYiBLdWV6bqhNQ7i0UCnRjlhbtOKdpGGt7fBu4U9PDw8PDzmGfuk5YqZCCgjiFiCxVpjzgOVFmK8LalzKQajjhFMkQTrcUAX3S8aEslCXcM0C8GbrratouXpNroqvcBqwqlwm42TSxK3lWsKQ7BxBcNBDCRcUr2ySA35QBW3iGZNRRZyhKbKXpws58B0pGqGAqC6cBZjpghMpmqSaMEW5xeDwrEGI4FwqAuhVdysG9lC6CznoLqw1ka8I7TM26DHlFsrNi7LStpPOwBpS49KBkhm977NFgDp/iqHISSGYiZBlhomDiHZrnc1CQmlrnkKAN0DCghDUCuZs9y6IVjGK25n5xI1m0jL1I1rqolV0QwwfXBp02goFW5j9oiQLzXFW5WlaipOJY3cegqyboRg3L12oh1iWui6t+1QFcWqFFjPF+uxiqS1LMusjqLu0ifSMZfewASQjgJMP5teCLAFykQhyUBtde3eYkIyBcTTul+LGWLNIM+zELzPjSohSJnJ4XQA6jgiHThshS9WK9DuqfOymUQxhjWBrzHURa5T1cpRVVHCWn4lQ3NIucm7nQRlI3SFRypFJKiyATfqUtWeNvMnC/oKGgAVFnDFCCobUlWZMkUq2kH/fr3MncxLZglfvFpliJR71tajjghly1mxrHRWuozRl25S3b+1+r+Ee+680OQ8S0gky9TnhSKlpaPGfQ10D9KFafRuNOo3jilvrmPTByvFKwBltdqxCtAXruJFxaqWLgWsCBQZy2ZMVVz0MlL9jJ508zzP1Q/ZUOGqb40UkO3IpjzGk9z2o/wTMPv2SeFKjGxOK2WVXWUqTGFWMJCeLTKV/ZWWeGXHmJBQPKZWtrCtKt6wysyQFaFZtlwsj9UESO9KkWn2IQAQKUFsHWAMdqEHade1YSrnjtUadNQuHRS4PENbxL1kLtc0JLBmqQQYAATObUucQQ6WdsP1MBYQA+peanGJPFNCbHZHA7wbqJ1+APBIQpJuv05A17kwyxq5+NmQcmkHsyZfFkif0AX+S6CzQt9mqljXrHBCvzOh/GxBvUQwHrkxEQzZCrOTPUA8VDsTafCaKQcDd6zNVS5lNc/PhN4FUNaYK2JfuEo2xIH0yRDZAr3w9wKrKFRTgkCAyLmN1+W9yM2t2VCtsGZz+dkA1HXzhZhz97JKv1lAtgpXsSiHaCc2Ztrez91cMqE2NXB5nBJDg1pwZTF62lVX28Ew/gIJSk2AV6IW6+fJJfI8hCidAmNd4Qy2WpM6GTa22mMpmksUV4C1A8Vef1J9132+AI2rBTYZ4ygbrqoUBUBPl7tLaznaC93OTxQSyLwMlappKLljpQIqDmlKBJrqWybuT841yzgD73GncDLHOZCRi9tSoGPzlZi9EabElbvauE8ZOfe9SBl4Tv2bNpgNCiobSchIX08rBPEEt6zZ0OwMpIUjl8wKOF6o/NjZEeciNyl38SS3Md18CIimnMu4t4Asuxma+Wz5GWV/LNVARko5sO5jWWU0Q6dbmfuu8DtygErYalo8hy0TWjQD5JoPIOsSyYIusraa8Gw8sfFXsfeHXL1b2MPDw8PDY76xT1quFgRrYfGeKjNkmJUUurwt4nA+IaEIEfFOl3No9oQtYgLLmbUawo7TCONJBlGruLh6gduLtWRu+6wAeuNk2PZtJaRUKPatYZCmrqJJGQHhbAC5SKl+BWJXn3WggNQWC28UkCW3rpkoLcG2Kc2xGJII0xJlz7jvEiRj2vW7tmtdvazgym2u+y+Yy1dlqaqba6wSEcMyZaMpRc4yVmfQ5qpKC9Qeq6F2QRchAcQt0UoMFG4/2W2pyjU1W+gtzqxbkgqOYrBSwCLnwIzZjq6iCgfKKrD7W1bYjKpyUKViEOvPbxSxq/erno1higV2TIeXzKhqWdryk+3QWuvhrCrAb2rpqm3aNKlooVSbkWfOM5Hs0BvGxxKki+63BrqYPYAwODyrxjUQGP3ZUjVWCZANuS3PWF0gK1QbeebchrzQhLUFJvEVSEJHeMqyyObflt3IWtoyJFX72XhkqOJilAzdrppLpDf8tvml21J7Xj4kVW1b48IMyVYNE6FEEEqICgPfut6r1ihTRBgy9Zt1SEPdGwOYc8HyCjOcF5q41A3tM7T1ofty3El5QoxbuFepnRtrIlWo52CP22IrPAdqTzJLaBKV91cGsCuujBRjPZ7QJLUYtv9lkxDOurxdMKDUpLTmZgZiTG0Hp78Lp1xuu/EqBNyMkTrNFA1RF1PEPUOSAhwBS/XTkSLDTuV46PooI2Wt2kL7HG4DEwDRuCtsIkP0MavNxh18J0cnTqynLFsskJta4D1nme+t2CeFq2GzssxVl1FuSIZ8salILm0xfd7ldhcZkQegXqXY+qICXKe2iKZE85EAM8/XO7gsLcF3asEl1MbCZsoE7bDigiJbbB6mMouJ92YM0sZ6AnXc/C7+/9l7s1/btvQ+6Dea2axmr92ffbp7z723yuVbVbYKEyxLSJAH/gh4QIhGhISIgJuUDSYohMRyOXYVMQZBojREQoiHQIQAgZQ3BDwAQnaoVLluU/fc5nS7X93sRsPD941vzFXlvNWDD2ePp33OXnuu2Y/x/b5fMwosN4C/38GWLNw+hfTQgtcISQhuIjAoKIaP3XkN7GcLP7fMFFRzv0EzpX/XH0zQPqHZSIEmUc39aj/6G/uixLDv0df4sTGcDNCVp8kdgNMG9XPu1Yax24uBuzfAMmRprEeXjC0CSUvCw5EWhR2gUHmoOLI81BGhzBO7MIILcpgKidHZjCBitkZMLEiz0RIeH2xkqQJ/r8qMTrvRGHiiavsCQ1NA3xZpN2QxUF3xiyzlAytk5merYK907pVXUaD9yX6LLiWF1MC94yV+/vRTOnxX4+p9ak24D+YwWmE4o/t1Mu8EUk893TTKW4WmrtKhoNln+04dURQeWjObfdJj9YzY62Sggl1GfIIsWw1vCznfxCUAX1NA8yWr1hrtaUBkkwH0Wia2ri1kIQiAJtPUktHYgXoxfv/+iJVgtLvM1nROTaMAO+p96vx3Y2vH8lbDTaMEG7g6Zsg00uQtJv997vOXK8DN8vbHcKmKeSIPJS/okrRoyI5YfkJuRYnh7Oa51zxMqU+eFqIq5IVDdZWPd1gQCzhZEk7O8z61p8RaDiPW+xiSTuxpndjMo3sm9Vx9SdvQI25Iusfrl7RvzRnDv5OIkhfp1U02t4gawKAQWbERaw8wJ0DsJF/j8UZOrlSeqB1JRbQRxiuUFxwhdhZhb3Kk1GRKjYdVN8X0qUV3wj3G1mQHnEuDzeMgDXq9CLnf0tEEaK94YpnGkUdrls0MewGhCrBiIwdEvqHtiioznSb9tcqr7QctwsZi4ApverKVoqp1BvUBPbnWeqwbi9ikpylK9Z4eouqImsFaRwz8cmnv5xi82acGm7cAc48XHAH5xeaA4rCVJ7me9EJ8Opg1uN3mJXLYU2gUsyxsgOKXql1qqLWBS5VTa6E4QUd5i+pSYb1gneVGIyYCmdMwhx1CsrB0GmASTXWZ+1bDFFDIhKZgIdUi0sJm/GyPi94uW9GpYlTpKEhftSoc9H7EJllC3mRNc7RAfRHRHeYXmOhobe7pAVQFFVzZtH6GyXPuW+3V2HzN43/47s8CAPYPtmhv6SZZfO0GTVvgbEElx7YvYJjI5XstMWn9Pu2zVOFVxNVn7JVnIlBEzA7oPiity0jHhlNx0nFzTB5AvcskywkFPU8yqbVqx22pfrTGfEInshuspDm5dUGVano2I/KEakL2Ah+YfMj3iJ05+BRqb0C9VPEkRvaq5WpO+plOiVUhkP+/OyL/ccXNP+VzpZ367NPP8usz/a5fYMf7V4+StoLNPVzTqR2CUNSRNKBpH0fWoNW5wfRFQg6A9nTkvrbvxINbhdzTpb5wXvj0i7xPiAqmkV2na5XQkj/C0zdVoBgRMk1Px5UqV7vJYekkewuZ2KkifJkXkyX7KzenEWZl8PEvkbPZ+3/hO2hZLz/W4b6u467nejfuxt24G3fjbvyExxtZuSa5C/U9csWiOwilHty3Aag31f/+IQBg1gCmAYqntC65/Rmfe6xrWp03b/Hy71kt0M6wiHCzIMuZ4kbLz6ZVaB5SdVEdN+iu68xGVhBm83AWYC6LbJJQRPj7tPq3xiNOgMD90vbTvZ3c1DAQ9NKmcIKUpdoakeXYyQClIva4Su+dgT+mirdflcJK3D4MUIe9uPRA5dW8e9LDgIwIAGCzrqFe0vK8/+kBfW/kd1U14OjRDQDg+mouLljO1yhWGoHhIv+wk6Qh97DDcKplX+xGoeCfuyMP77QYHFRflLCjFbqI+ffIyzaxRJVXO8kx6rCHTuYNEw19Qfth+t08V+1HK/lWIXCo/c3lHOg1zIor8SbLQ6IBtmc5JaQ9yTmhitNQ0jaHee7vTl6YnAtqgc2qRuSAiBs/x/w4N8fuH64wLQjCP6gbtFO6Tp+9ui/QeHcUOM+Vv7tV0Cyb8ZOAqLPn8c3NTNjmLpnDpyJllOvrJ1E+5wFEm6VWAqWCqq5CR9Tc4236AuEVJz81VH2N+Q5jtrYYuxQBEVqgd1cZgRKLlcKwiPIM6Tazj/uzAbARBcu3QpmTdkIZd3NSR0k1ykOkMRTkEbIBxDCiZBQRvhpB5UOuJnUcMc8NGUMIB0Mh/1xEmAuL8ob3+TCiWKcdiegOMtvZvipQ3o4QgSv63PYeMY4TOhe9kspY+13Gt/JZ1hVHfdUUciChBKOWiOKkIZEqOQqQAOj5Mq2C3WQTidSj37wVpG1jGg1/6PCV/+g7dCwOuP9/0J9cPMFrP97MyZVJGClkGyDCQn/sBY5SXbbtK2+MGHErhvEEKnFKXHo6GNgGKC6YEDSLAE9wn/yZXwEAPPkv/ioA7Bh2D4sAs6C39vtnr/CBOZE+WbmXYdWHx7f43BxCcy9VA5jV9HfbbQVjg2g5Q69JHgIQBN5l6FdNvBB9dKuTvzn6oFDvdXBJI9kV6C/ppWc2+eWuBgDrWiz2YKNAd6G1iLdljluL+eG8/v4RGbQn4s/REl88p0WLKQNqPpbNpEDxvEBBfB00fYXhXboAcdAonxc7MVsJyrJbjdCXEl5QXWWI1U2RjekNEKcees090U6JPAAAZvMODcOU2kZJNho6A3tZwC3oRFQvLQxPVr4ayy8sEbT4BdYfRPBch2Eed0z1gwWmz2kn+31qFSQzdBWyNVx/MOoD9wrqRY16kyH79QX1XHXt0A8Wx3skiZmXHa4Zilf3WgwFHdf0wRqb8ynq5+kc5HPl7w84PVlh29EJqyYDyoIOtJv16JaVJCnZTdaCxonf6Y9GlXuDuh9Zc3qF7bM5irfoPE7KAWsmr9m13Q0dMFH0z3BKeBDKKVqgprXxVf47t0dkIcv3wXDiRMuNoFDOOgxzjm28HYF3inuJSAvwEeGnzvthao9wU6I/zLK3dK/+mKRlBKWOF/PRkBVgeqYSKTIdW3vmYViiFaqI7X12XtpE+GmUd4zdKjlu0yg0p7z9gmRwaRHXH2SSHsH3WZqje4ysKEfPiWbXJJFCjUhfiqDtZPVKlpD5HCQ9btqOPK9mpG9+soHq7I48afU2X99RK+Z1HXew8N24G3fjbtyNu/ETHm9k5SqMW5X9M3VHWYVRZzi2YrmNGrEew4xWnclNRXklK1qvI/xedgJSOhKpBsCTv/FbUBMPzbCzV4WEdfuFx9nxLQDg09sDHM+32GOyh9UBz14S0eRyPcXx8RrLDVWTWkeRPigdELwhz1WAXFi2CbPBKD5PIToFfcBm64lpC0DfWnQmoNvS/9lnFUySUoyyLaMBhmMnBKTqSueItpYqsQTHtkdAd4+rko0mqIrj9Z5f7AsJSKmIquAqvPYY5jlKDshwt94YFBsl18MMuXItXhJRzTYsnTnKWZHRZHZkqAPqvQ49s53DiyoTQYqIze0Es306gL63KJmBHSYK5ckaPUtstphltrPP0okw8widRfM+VdvF55VUF9EAft9h9gFdNzfN57W6VjuZn+MwcLuh1gLAphc2onuH7hFTejGY6JsCfWtxralafbx3gw839+i7I1DdI/h482qG+oUVcpZtiegFEDrQDVaIYW4wKLhyHXoLVQYhnKigaMMAiitLWb5p2GzOXyxH0KOm2L8bQ4GoauoEMRr2A+xmZKqhFVwKmi+COJdhb8CgCqpeQYEFqaoakjl8kqGsTCbXlAHea8Qyf18qLWMVRgxgDd1khCnMPSwbkrhlCeUUKnbukuxXUCVPmbojQlaqrt04tB1QJhtY6EKNzGIIWUnHrXqFloMl9D6dPzM2vkiPfDmC0y3JCiXr9dQJWTOUkWL4Uoj7eoQq/IgChgLMIX+nYq7sdaeyv7UeQeGpuk0ghgNCykteayGN+cEgDlrOXb8f0aXzc/v6l65v5OQaqgjUBE+M3ZTIFJsnvLmXB8F0+WUOgIOSc/8l6T1jFTA53MpLqbutodejCc6PdHkmiiG/njicX1Pjx53XWJ+2IqPZ3ExEB9ZsKnRtKWHs5aKTh1gpwLcGiqHgOHdZCtIqYR+7aYQKBsN9Ppgq5Ek4ktuSZ7g02JEV4shDbvK5xXCoUJ+PYHWXoSnbZIgx2cEBrIurAvV5AcDlbEffGNxw361cKvRv9/ATDkS40JhxT7G93WPYCbLPYrvGP6fQ9VBCJFO+jvIigwb6L2YIvCiyQZFuFyTLCcoiLvhF3RSIH3Ge7oFHvLeVvFg1cej3+eXe5AQhvTZwh04cm4Z5NphXUQFeYfMuXd+9H1iB7kIJdIcjjS1rngGgP8494lBFuPs9igmdhP66hrrl/u7bWxwfrLFf0cR+r1rhydklAODjj8/Q8j7ZW0MWmfxyDIWSRVAxG7Be1Yi3bHl43MLxgsIWHv22yLpgk4PmVZ8lR9HS8xWO6Ti3MyO/0z1pe5MLljnwCK+yJGhspYcA6G26z/L9Zi8r6K+sMbQzvr65t4lAUiuRs7UaKmk660hSsHSOywjw86XWdoelHMZB852Gi+l50uIyBdA939Xj85EnJN3lCW/8LNDfIT+/XkGNnJD8DJkX4SwcB5urnqVoo3xa2b6KcPs5LCJUGvoVncj6pRWYdigjYhWofw6gWBc72bHZiYqDCxL3pFWigaVjxY52NrGFZZJNjllq3JKJMpn6TQG91eiOuU8/CTmRZ/Yjs/xrON7IydWuNbTTHEjMF7OMCEde7PLUdSkrrOEgwPBquj5X2LztEZP3aZu9Se2lhfnBAp6rOJs1+0RYcBqeCRnlooPhnk1zWyOyVjSymUTLFakuPXSyJ4wgwwS++YeLiQjsYxFQv7AkmQFQnVuZWOxaoWP9bqy559km/VAQATwA4HkNlVbrZURxzUkjdUTgJKDuiOO4fo6q7a4rMP2/qexxU6q8cmWfX7ii5+SJvrrQQr0vbzVK2hy29yNiUGKI0T8ImDKiYN7aYqun4vdL145/qEA95TRhVxF4QpPyfNKjZzOF2FtKp2H5ijsZpGfspx56adF8QoudehTu7GYazmlYXsCYIqAcLTBSek6wEfba5km/jFlCUjnAKyi+pu09g5Ij81IVG97lwPHPJtnz1UZ4nqB9UDCXBYaTVBpEOgYAe5Me87LHvKA39XGxwemEGoKfTI+hP695X5OxSZZPJNMO9dEUVgH6K/R39w+WeHFDVWZ/XWN+tsaGe/Zmq1Gu04QKeU7cgq0+PVf2TslEEkrqT6eqyvUG8TiZyiqoxki/Wo2uJ1n/yemG1mFHTyoolAfUSmdrvmnIhDIVEVP/FWDSDp+Dg558t/l8jzW8iAo6GbE4ktGI6cM09051z8YLaZKYQNYE3mYtaiJVRiNvCMRJWvwxhyGRkaog/IlYK0BZ0dX6SSZuKY61TNdCt0p6waYHeiZu+WmgybwfaeTTBD2a0+LoNAF0X4t0q6Tzk96RKuRiPZRUrabfRZMlO8pjxxs9Gsikr0ZVbBwtQl7XcddzvRt3427cjbtxN37C442sXH0VEZkFJ0y1A49yv4NjG7ZYj+DSuYOb8ip7Q2xUlzIg6yCwVbFSqG4yI7A7jhl+qgD1oBWZS104ND2blS86tOxvpnoN92qSDeHLQMxfUG/KVg4DC7Kj0zD83UEpdGeZrdmdOVntuhPAMMTkt5b2KS0Mfc6l1D1XMtIjAlySBBmIGUFoDRCo/wYA8WWdq7tJRLHOgeV+GqXa0ANlOJZJIB4h9oqmB0xLf7P3CeBelVh9ib5v/miJ/QnBnGq2xWfOoDEVn9e81DYrg3Daw1a55y390qhSaxB+WQDrGuoeXYtxILbSEd7nKst+UaI9zqtzPxjszWhf2nWFbUokWVrpg5HRhJKl66BiNiJfFrBHHaZ8HzSVw3ZCVX8sSKo14X3ubQS4UlC9QsHSHl/S+fJsBGLWGvZtom1OywEffvBA7oP1lyo8X1LVWXw0EXanaShRJSEaZp0TZoa3OyAoTNnFq/dGzl1x0KJpSuEZ6JXOjFc7guvrAF07ce9Sfpedj4gsKbsuEVM+8sQhBCUuXsorSXpSPoeNu0nEsKol5NtNR71HS1wKabs0OW0l+JLY/nV2JYsqtSmydaka6B5IFbQesqVksBG+zvaBwWb7zFBE9IuR29jIZAMxM8H1QHaEY9VAYoaHQgEmyn1Z7zXSimgvJ5xJm441ClpgmwyNp0zbBMf6Gmg5YSnaCN1q2cdgM7Q/ZkxDc2E/SskJP1p1pp3XgE9ITR2gRyxsymzlY+wUXGK9bzVV0GJ8EYWDgde/cH0zJ1fdK2itEEaxbHprMHQT0YWZrRYPzKY0mJ7RW2n7NqA2FmBSDjqTvTI9kWj6nyMsJnw2g35MsORH//y/DwD4uf/x1wEA66ZCu822gWdvXQMAXn5+COiI6SFBg+22zN65GwsXFRSnkFQv893dTbg/t5OvxS+lQQHs4oK5p5e+zg9QOKQ3T+i1BE4DTKFPD9ZRB8/fm0gOw5r3f+qxecx/Y4B+T+3IXpIOj/SkQLnkPuJCZdhT07/Tz80DD8UvXBc0tgPt/+2aHZ7S+R90tmGMQFEPAtsW1qNhy8AQdN7/VqNYKTi2x3FHeSGCQUOv8zV1Ewixqlgr6OcTtIb2odZA8zin7sgLCvSyT/CXckomu8mDNbY3E6xYU1pOe5QP6N7qX06BCLE5DAuPwAup8lpLnx8g1HD+UTLFBTYndMLXNkDPBnz97ecAgBfrPZRMdnJtjqZzU8D/9AZF6vldzRCSD3MZYRe97Mfzz+dQfL6VptZEevFHk7c51k/oWws9GeD5/+pzi47bICoQHCq92kGh/ow1zZMIrUY9+wAhXUULmahUAFQRBG4E8kSlnNqZbMsbJfFr/sBBbYy0QpRTmHzOE9dJ7nebhlzT+gM+TjWSpESFWEDSesY2hiR/UfLdvsw9xlhE8hoG4FTc0U3rITvG+T0iVtlnHGupK/h7veyvWCeCiU0J2R8gky40fbZlJzm/52Uf1dbAdEq0ruWKwtuBtDDJ14XOe8zHycO01OsNI+g4HWdxbohQlRYLZXbL8lV+LqInMpxnpzfyW+fr0uG1H3ew8N24G3fjbtyNu/ETHm9k5eqOB+gJV33JB7jVUCc9cMMr6GkAnlAFqralVJlm6uCjgnmVPyd+pPcC7Ekjnr5v/RPPsO5o9fkL/8uvYVoMmJW01Ftv6+w61Fi8fM6+roHYpPH/2ad/PhkygzEoxK1BsWQW6iKMpCZxh4mIACEf7MBgjlbdCUpSI3lArMi5KLlKlDc6p1msCpRXLLxfEaTVnmQ2YGIVq4FW1mOphuyjJnhq85Dh0zZnRQJ55etrqp6mM/rl/qSVxJbLqznUi1rMOfR+dlOKXmG4qVGcbvK1vuAc2I1OISawG3LKScxKPXEwDIHunza4up7BPk/O9HnFHgxQrCKaeyOpQfqxiAKFKQ/KtU0kqUkQuGt7O6HwAoZ0u4uJwKVKAWaj4GaCJ0tqCpATVEwLNPczGzZqoP6I7rPtfYtoIr772QM6Nh0ljGEWge4+VQnFfoe6HsTTt1or6JRtHIBhYXIAQgHJYg0zh2I2QCfIeG5hkfYrtxiiiXCdRbHP8LeNIt2KnhjPEnqwzkYIbuFhFz08P4f61mQmq8mwcJx4qOsyk51c/m5oOoYk03HzzMw315ZM+ZnIVV0rdEepLYIdj2yV7bR3snWLtcIwzz7BypMnOPgzlK2cCTxJaVAsNWwyXSioykwtEz+Jsn271ihe5OP2NeDSfu0NiDclinV+1hO5aUxAUk6hP/FCUoOKUKvks60x7AdpY2wfZG9hFaLA3cpBUqvSPguEm6N++d8ZNfN/hIuXbEWNKmINUg8wway8NBJC0Mzx2o83cnIt5z30VKO/rkWiEh60mM9buAlhGcYEbNbMrBxZ6gEkY0hBwJsn2aA62sj9UrqVPlmXePSI6IGvbuY4/3yK2Wf8wP98g7BMaS755QsdYZYmO0B5BZUgLLZkTAzPaOIuSvKpKgAAIABJREFUEzeMqP4a0qsaZtnWLepIPa004c2dyJF0Q3o3YWoGJQ42bprDlZNDVYLh9ACBmZWnqCkzCq1pT5PpvkJ5q4VJrF1+SNvTKOfUdABUFJtEFzTg2JGm9HAPWkz4OgEkUUrnoDhoYfnFv7yeykKEXmT8Ap/yxJrWLFsrbbGmcgirgvrlPJIExm6Bm6+PmKdF3JFLpP6lXZPUQxyVGi39KHvcIH46E1ReeWS4ztHkEVOAwcrIAmmYR5ieYcNaYf5U58BslV+w5YVBqCKGtF82SDyicpAJf9iUUB/NMH+Rmb59gvIt4PayrSdFvfGPG4shKhR7vCpaDBimdAB+aXcC6H2nMTTs8LXfIyTGa1AoPy9HCxOM3r6A2xQC2wab+4YIox67U1BD1s4qlyfGqOlZHKajRWnqFXa0sEp9/2TyD/D3xHyP6CH3QfWgUL2kn/sF94t5V6bnCk1IoQrUN0/uR+1JFIlWtHmlph39fVpcRpPPgWkpJECCJvaCOEyZIwc38RgsbWfyhZHFPdTuedSNgtqwfKgfQe0cwSfnLmR2P5CtClHubs+0+VylSMEUeNEfRllEmxY7em014njoXsEn2VKVQ07onGT4HsevPy78Rk6uSkdoHWEWPQ64J1pah9vtBF2XTRXS5GcPeoSSX/zWAwdAU7LiXgORe372kvR/gZ86c1vgfE5LMP9iimKj8Q+/84sAgJ/6jW/jg3/vlwAAT/7Ot2QCJRJCRC+mD8Anf5asE9/9a7+TXxIAxrKTyWcW/UEU8wbllfSjirVCz7FyoQ7kz5om8xGhyU8j6W91sqzL0Xr1eSaFaA8097L0IZQRqmZDBq9gV6NIPguRO6lA3rRVmydRSQ1q8wMeNaA6LZZ7Xz96gbNqCQD4n/uvYoMKPV8nt7Uozjkhp1Po+yk2/FKav9RSlfhpEJlCsVGINttbqiabplbFgHBvi56XzsWtxuQVb6NSqF8aNA+YfNNrKL5OYepF3hQKeqGmBJpYZMu3oSmAhcfsh9zfHBmS9Ie76IPpIOlLoQqIjDZUlwrtaZZFpGMHgOoauP1Kvr5HhxtcpBest1j8IOXbAusnARUn+1Q3WQfcnnDUXnrJxjypFVcW2Fg4nrxne614EK/cHGaZShqguMxyJBdLQWDS4jFJSGIRcx5qw1nB6facefQqHfco+nFNOrc0oeoh3+/Jj1hs9oKSRWixosVdIhaFcrSYdNhJ8Yma0KF0LbYPINtXjs41QPe49BtXGlHlSD5EjGRAmfDlJ6QxTn8n8h6QoYcvIcYsft/BJjLhi3qnl9cfxOzVO+r1KqcweanyM2WB7QNGypAncIA5Bano7yH3TijjjlQmHU/aXtRASNpWlxeTqiRLTHmv7O3K/dJ9qzpDqFla95x4gHkWYTMqw1/TcddzvRt3427cjbtxN37C442sXLubGrqroSqPy0uqUFK+aarAbOUQeZUcQl7ZubbAJ//Sr8m23vuvfwPaMBvTRsQhVzP6bEC/4d7R2FUIkKoVAJ7+y78qP7/7u7+DUAcYNh2XSgDAD//tX8aTv/lbUOxsY9daoJf+iHpwvhrBv8kNKmYZxOQLi9mznMRy+2VQnxVsYh4g0Ex36lBeMjP5JmKYMQS0v1sZRA2BzZMwfZztmALLlVOwI3Zjc5ZN7HW725uNNmamLxTOWQG/aZjhm65XlyF6PVCfuDvkazXL59ysNerL3OttT/OKGTZSUDOAGBWUysJ8BEVGAAAmr8hsXbJ2HeC4N4iJh9lku0xo7Mg4+hRsYDWhHUm2MILP+sPctwaouko9ruLWyO4OewTJ+dQPUxGGr+/6LQALh5Ldm+ZVB8VM9NVxjSUbZ8yPtyh6C3zKza0I1NdcSRoNKA3vMkoyjNjBykP63F1bwL/kE1QFYccPqwqh18L+tCtDrGzQ/VZeGoHKlVfUewNzH5wiJAAAyiD3VncUxZ1MOU09UZNhXEmBAhCnHipdjwAxwY8mwk8yq1b3ueLSMRtbQDFEz5/rp16QCNMR673fh3w2VXdhGmHX2TVJO4xaMhhBsQoqjFJ4Yr4nXE2fS7+zl8UOk1u5XHUGFXf6oOmK1bd0b5VsI+hmSvqZoaLnIlXGtsn2h7YduSml2SFBum7XGYoY2fkeSShCMPT8GnFE05nz0WmY0XswVhD3Mrs0cJZPQmp3vcbjjZxczcpADwZ+H6g/p7uqPXOwo4DuwRciN1BXBcIBQ4GFx7v/1W/ItpSJiCHpTklyERlW6dclnv4rNHG+85/+Nr7/l37xH7tPT/72t2h7R4HuV9Y0xh+5Qk//tW9Kss6D/93j6qv0gWGuMHypEetC8mFlGPSgxXBNjY7Jh4aJRQzNxCxZKFZqR8njp5Aw5/Y46/yiifD7HpPDHKq+OWet5tSjnyCTGZzOvbtIaRybL/O5XBnUL0d6uPSCqmgx4F7RS/sPZg9E5zd0FtErGJYMFUudfZ8t94ZTuPw6S4Kq65HsxzI0NXqAA7/Zbq5nsKXHnGFbPRAEDgCbhxxwr/J2EmFErywqdlrqjthli2ME9WxA4OtS3BoieCSSVBnhnlCDuqocuotJht7KKHIPPag84TNZROwQi/xyDFXE2dmN+B9XxqHkhcPJdAOc0Oe+uN3H0BTQPLfaLaT1Uaxp8u73E0EFEkdHMLaC+iHdT34SUbBUyU01BtYfq8oj+pwYBQUU5xkKd7MMZyqvUCQIXVPbM7Le3FxlaNnf7+SeNhuNqCM8JxTp2iHyOTYbjRDMDmQsZKEtTQrj4HbpvReZVyA+0WmSNxGmy/dEv58lJX4aYPl5NS1P5t2P93SHvYD2mL9TRdhN5h+EAiNZEU3e8gzFfL8Ut2y7mM5dr0cuUkCR5DW3NOku3wP/O++TdkB7L+TF91UmEkWV2xTBANpny0M1AOofg3VGkyVTAHMcRIoGiV+MJoqVIyKAoGRRSiQt1nK7O1j4btyNu3E37sbduBs/Mt7IypUgGQV4hWJF/zd9YbB5BISC2cOTADSj1WdCCZ0mo4GUfIMMUcIrKBvEzUbZDJl88md/Be/83m8LueGH/84v7+4Ur8jV0iJMgkBcH/+bv/Jj+5/MA179U0D7mJaZT96+wMV6BsXylc2qFp9k1xayer55P2L+qZZVsa9ilixMI6prhZ5VQSgCQsGQ3PstKs5b7doCZeHR3LCrVGuAGX2XqTxBdykQYeYy09pzisZIMiGQ05Dh0VAC8XCA5e+7uZmJFCTOyRBfcjhV3oaKBGmnimVYBCFTmR470oBimdM53IHH7B5pJJzT6JsCKqUX1cgMWHawSmzwWHthcepuVD2eDHjn0QVmHOJamwEXTJBathXWmxrtRS3bDAx1dluL6sIIm1KN/GuJTcr342wAXK7YVavh36Lr/ife+RQPJ7c4tATPfrC5h5b9fa3OsOmyrrEpcxpFe6LEmWf6kggpvAk090OG9r3C5JnJqSw2w5l6UFDJi3qioaISIpRZ9BgsV34ri2KpEBgC9KNK0lcRCEoMeoqlQvOISrO9RYPVDSEkQkZjMmH0WoIA0n4m4wLETKLxHEYvrmQjItSYbasHBT8P0KmqWik5H74maDu5qKkh+08XayIRmY42tH4rk6TCPOdFE5EQ0Jej+5h/VV0TBJ4Y/W4exIktKqA612gfsnHKns9EqKXJPr2eiVZDIifl7dsNoIIW17nkFgUwtJwMKrhVI8jBNL/P3DRk6RMIaRKEWFELaCyNSiiF3Si4IzaN6DTstd1BlFIrxRd3sPBrOZIdl/IK63f54bQsa0lohInSvyhWGkPFbMZqQDSRWMMgSDRyy6ksHbqmQOSXZWI2plEs9U6vNY0nf/O38PRPfRMA8OXf/DZMY3aju0bjq7/+HfT38k2dLAk/vziA/XAik3elIXCOjZAXdnlN1oSSnlEG0bICQHemhS1YzHvIEXQGbcg3/NAUKPdo8ogzhYEZqXheA2wMDgDqJk/sMBHYGxDTQmJk7q0HlaOoznocn6xwOiOd8cv1HDfnDDeuLOqXeWKEzg5Kyu32ghHGdm0KxTazX/0kW92FWmNzyyeo1yhHoQf68RbDqkqbg2o19CHbJgKIVwz3rrLjkD4vcbE/w62hbd4upxK+4FoLdBr1fU75WVY5yaixDM9llmvqXbuFFz6AKQJ8r2WyVUHJ/fhqu4c922HjOOxdBWh+67WuwAcXhAs7ZxAuK2HNjhcf7QlQXwDrt3hSqAK1GQDSEu9rFOy65Q6d9NmhKEqO9knTy50tRJ2vgNTXLiJ8qWCbDFOKW1MgaDtJqJpHHprdoQZnRKak5gNiZ+S8hrWFkYmE+uny0lZZNhYLYkWPQ80l2lDlfmN7z0GFnPykQp50SF8eJTRDDQoDP0/DaV7AAICyQZyW7NZiOMiyPeIW0Od0B+EOJC5Cgm3NRss+RkNh6aIVNbmtY7cKNrkrBWBYKJH6VFdR+BhuyhaH6TEZGfJHPWpZFBRsIhPeSIubTqwwf8POfwtsnbYpFooKwDIFvWtMzjOjefsw92mTTv51Hnew8N24G3fjbtyNu/ETHm9k5erqCD2JgEcWpQeqWlPuo11afPirucp88jd+iz62KfD0T/15+f93/u5vys/f/xd+HU/+1rfw9F//pvzfV3/9OwAIsvzgN3ar1p/97/8DAICZ1njnP/ttAMAnv/bjMPB4fO+v/CKe/Jf0nbbK1W3oDZkYMOxkLy1mzxgS7TLT1025CkqsSG+EKVvOe/hKi2m66yzMLcN8Bw4xOfhUHjFm0oFSUcwJos6G3ADg65AzbItA7lUmV5Njpm+CVR89vMRRvcEHl6e0X9YhJDKPiuiOFNQjIlP5QWNgxq5pyTA/QYzDfpBowO1DJcYWwyKivFKSIwkboVLUWO3hZlnz6c4nGVqrA1UMz5nMYwEl4ddqBxps/3Afw3tUee8vtsJyRgBQBtGGLo43WL4gJrRuNZoHHjGRgJxC+ZLZsb2Capg9fdzD7A1CnKtPe7x7TGYlWkUEKAxMsgtRY9nX8rtmxT9fFlAGmJzz+e8z+zUUEcM8VxQqKKmalQLUQYeG90Wt7Q4TN1XaugdQRExe0e9ar2G5ik05qZJ5OmRIOjA02LMPcZw5YQg3t4Uwdp2JUJ2GZqKestnDNxHaUhSh8tmQAY70q2I4YSIck7qGRcjvA0OORtnDN8OjyilivXZ5G+I2dTBgaK0EZSBmRKM/8SiuRjrgH6nO0neVt3QfpwhDX8cMXQfOneZjM4OR+9rXEevDjARNn2eSka84xJ3PT7nK/0ZEbhPVuRKOGgg65jZFzKRD0/L9nl6fhQApVP0qyHMYDb1z6HToDNFPItbf6KCuWFHRZwMLtcVrP97IyfX//dP/FhaLBZk3sJuPflXBzzxinV78uaj/qb/ybRQFnaowNnEAEFuzM9mWLwph806eWaQ2V3MWaAJdEFb1yb/472J5wY7nUQmM8s7v0SRrucfy4Td3J+Qv/fa3AQ7odgDKL+jGDI976nVwD0oPFs3pCHKdj+DFPkOiUeWHqb+uoWcD1EWJHxsK4spTFH7HbMN1RjCQMEkORgzD2QjFMLBeGXLuSQHmC4cVmzyo1kDt09t8OxQIcS4B3c2H+wD3acy1hd0qDAmiXhc52aiMCFMPc0hvG+M1zFN6g5itQj+C5JKcBQBJm1JbGAbFSpGgHSApSDIE0LvSqFAGQclCmV88tmHLuk/p+t7et5QkBEDXHmXlsJjSPt5uJnLtdavgZxluRFDoHzG22WskjN7YgOm0k6SgdVfiuqWdnBc9nrl9vD1n+Y2z2A50PQvjcXxCJIPlF8ckeUnyj0WG06OmSS5Z7PX3QobydUQ98QieT9i2lBe49lqej2E/wLRazBTqCyU/u3mkMPkEJerRfXtRwsds+zjcC7j/gI7l1fUewn5yK9NAm40uyH6Prye7NaX9p8mUT2nBcHG6cDovIhA1eu5l2vOC5DE6/914oi1W2eIwSbUAYNgWUI2RkAiYCH8/t4fcPEP5ymFkO0jOT0CGjNMY/1zeUm9//ikv/mbAlvcZCwesmZEdgO0ZhMGeXNUAwK4Jjk6hAbbBiAGvdrJdTTcKSB+tBcYpOrSNHBC/9zSi39dYv8U96QjhPsQqouA+8/Bei7AqJOvY64hilVtGr/t4IyfXr/+d/xh6UkP1BorJHh//Mrsl/XWaGMulxpP/nH5++ut//o/eEIC33j3H+//tfwgAGD7ag9LA03+DPv+NP/cd/P7vZvnN1/7+X8TmFb1wn/ztb0m1p3otEV6xJzFcure+/qvfwXe/9Yvy94df77Fp2dfYa3jLE6GOmJ+tsb6hJz0WuZfUL2K2I4zUd0okHTVo8VcGgNDaTMSKOalDmSB2hCFEInK1SQSoZeWuW0oTSatYN8+kovSSk6irztJLFsDs4QoDyy/2ql3rM+2B4lM6zu4oIL6/hvqcpT+nPQz3RJN/bP+CfqePe5EmuFnW9kYb4edeCDC2UxjYwYqIKwqBJ9HhMEJVqSdq4MvsvUp9Pa6S94IEuPd7cSdKLGwsDDvPnByu4ING4BPkBoPpMS3Tt3YCe15IPF2sg2g1sT/k6mLQ6HoL8Ev93nyNkrXW3/30AeKyxPU79Mvzzw6hZ6zXXhfkzgXAFoBxu9prbtNSWkvIlVr9vED3Lt1A0Ws02xK2oO/rjwaYa3aAWgH9MU+SrYZpxhMXJR0BoEVglwlIoQpCeIma9ivJvupJj0VF331VTNFccO99UDtEmfLCiJtSkt4kQtDYilOD7sG0wFNB5ftCQ1y2EGnCSGHjw16WyvgSwDRkEtY0UGQfAPSK+QO0L7O31rJIbFfVjo52WESYi0w4SvcMuRapUZ8yyvlQjo4zEQ1VAGq2t3SnLfSUTni3qmCuLaJWaROZmMRSuXRsFIVHP7oRaYmONf7YRApQPaDGH1WZ+Hf7U4RYCdGwVbn/GEfuWANFZopLW5M5BvFot3f9Oo67nuvduBt3427cjbvxEx5vZOVKeaYkj3nyt8i84Z2/+5vQpYdmo/Hu2OPpn/6jK9Z3/pPfQWR498mjKIk5Zavwg7/wi/i5P/NtAMDkJkrPtXnoUZ5pKJbHqOtSXGlMo3OYNrP1Up9y/b7Hl/+bv0x/ow18odA2DAWvCpiUaDNobD5dwKS+k43iwBLKCJ2g2VYjLBzBjACgsnTFrDXGaRZ+HkRiE4OC50o1OI2icjuJQuP4EF9m9qBda6koo43wQUs/0+8FadTMqh77CyoxzjczvL1/g7ePCA78wU2NgStjs99jaK0kD9kvspzELjXcAlTlAdDGo32csNSIekEV8eOjGzy7WWD4gHC4UESBIX0V4Stgyv1qd11g84TOQXnFSUp8bOWNFoN25SHZn1CEDqRepLm0cAwLXwC4f3wLz8e9N29QsYey9xrxiwKWq9XuWElubewMFPcscdjjnZMrqVY3Q4lXayo99v+3GtEonE+ogXr29hU8Iw4X/UJ8mCNXGgNDkabJaEPkfmjyqe6OAmKSVnmF0BcYhtTn1tI3706CBBnETsPNCBoGqGoSOYmlykyNYGHDz0XYGgx7EJlL31l87+OHAIDpQSPbV22BYINUv8NeQP0qOznp+OPQJV1rclEa54smU4OoozCftad7OAU4RJ0h3Poc2D7M1bVZG+np6iZXwgCwuZ3k1tONlW34OqJY5cq4OR1BwVHR96dtKpUla31iAjPSshy1NLzGvUOC/btphwu/jyi5xHn7UWf0CEiwLXh7u33gYS+3jcZITbCR3lWpg2Gi/Dzse6CI4iQXLSQ7FjojCvf/J4vnf3Ik79nLHtyqwWs/3sjJdXrUwCSCTOolrTXKxx3uff0FAODZ1QI//ff+EgCCcxJBwbcGClZikl4tczaSdsDXv/kdYI9upM2jDPXYtUZXTFDccLzSjUJku5PuJEgYdTUZ0CxrcQ/SS4shOc1sDK72yzyRFQGe27bmxsJslVimdcc+y4pi7reoex3MsxrTF1m+svwZmoDUSQOtIzqWpagiABsm1MRRT25Q6JvstaY7JS8XMuofkT9ifqmGiqDZBK+ZdXaoae4XeHtBk+nZdIlXzR4+enkixy1uMmBrSsYwQxF3YL1YBHkX7JCR9rzoj2/bGtuLKSYMmw/ziO6el30i6Jo+XKyA9pRfuL3CsAgC7RW3Co7Pv+6zWbufsVVeepkZiOxkWBZ4qRaYTjmKrSlxzeSgyQ9LhCKiO6N9OXvvAvsMib5c7eH2KU2Y+lWJ1b0KNcfwXf13j+VamCHCW7pvAODi+gSGLQlN7eHYvD1uLIql2bHES20ECfdO70OnENl+j/S8WetrWmDYT31PuUTQA12P9LtxOwAgbWl6ySIg9yhtBGIU6Y97NYHixdL2ZiLOXIiAUpk4RwsF3qeOepNJBzyOBlQB1OON+Xci3SqDEI7sWrFpfToJEQVlR2CY07mSc7cja4HoqOk85+d1rFtWjngPiUQWTYZVU1B7cvpSgZOWALQPiDORJDxh1H7QOsqi7WY5xeRplsFFNZY7KdgmX+d+bwSTj4h56fMidQtZ+zzMFfr9cRB87pOqqBB9PuekJaaffR3lPnj+Jwnel/D0kW583Gd+Xcf/Dw7hbtyNu3E37sbd+OM13sjKtWsttC7wtb//FwFL5JfqdI3CetSWWakmwjMUZiovHq+qIXcaxyux5moibFhfRgniBohtaVIEXASq69HpDnlVH8oosFvfFfThJE3wSohPALE1xfFoZOpQvL2BGwzcy1r+L0Fr1XOb4Z5Xk52A8u2DKC40R4+2ePn0SKprPWTJgq8yscGzsXiCR/XIK1f3ADTgODzaXGnYROLYKrSPRuHvvRay008d3GA9UKnxTx9+hH+oHuMVx/WtdcA+V3q9s9hcT1A1uQpKJAzjAXNdwNej8yxwu0HHtM6LV1OokJ1zfB2BOV13Zyz5TrP3cr+XIbP+IBAUJjmbUaqXYR6lHEokGQmS1szKBkGIvq+xnGdXr3T+TQcEO4oADBpXDd2f7x5e4lVFZf7zi320g8X5DZ0f9c+uEZ5SCT19ThXb/fdfAQCefXGE0wW5T92sp2iXdA7syqBYZnmSCiOhP5+35DIEZMOHUALtMTCk4IFilC888SJpUkHtbE8Po39HwM0hOaf6uJMyWU8GeGdQfET76WYB8Yah7DLAs8e3ag1iERD5YdO92qkQ+0WGfscVIzGFVWYZW4gsLUILezpBvqmlUaw0tg9zAEV9lUPWx37cROzZrfrS/eNmcSd71U8CJi/42mtAr7IZh91AkC3t8v7bpYaf5GeRzPP5WnQGyy3LxC4q6GmWCEULOKlyIwCNYjO6PnwOEuIA0LUt1koybYFMfIoasK2CSwEFfnSOt4T+JBctIF9rGGA4Tb0rBd2ZjNK1VvyzkyPY6zzeyMnVWA9jPfreoJgzu64t0GuLH3bkrO19Lup9a0QeoQcF3QG6ZKizDph8nrVr7UmUl2p5mwOJTcPQT7KK88DsGf3crw3ab9Dk4W9LVMcN3MD60saILjTskfVfyn6Ne05sBr3TUJ9OoFMvcqvh2VXKjBJnDL/Htg9pm4uvXcJxPMnLT492EjdMo0ZsUpWzLbcK9WXWyXWHUR5AuyW4KMFYxSpLMADA3ljod+ip7m8qFEf0dn8wWeKfO/hHAID/c/0ujsoNlmv6w3Be4+aAXa+KAHtRZBazU2Icr0YPOEALgvQ5aMh5jACKG5Ph5JmD4oOefmox7MVssI4Md2koNnfPEFqCIkORX3KqV/CHg7QcdKvlRW+3Cr2NeV+G3Lc1De1cx9em6Qs0W1pwGB2kdxp6g9s/PJIXkYoKNkF8+0B36jHnz1Z7HV5dUGP1vYcXeJpcqTZTlMssn2iPM+RXLiOiUeLoY3piywJs5t6rvOLQ+R6PIWuMJalF+nxR7peoaRHQHXJbpCill+q9oVbImPnL6Ty2M7Avkr1iBGKWw4xZz1ER41lsBwuCLekc0zGnSS5Lx8hcPh1L0num3/k6T1R6AHyVpSwYazwVAJ110kD+Ll/nFobdKrYI5P1qs30m5crmxVkokOFdTZB1Os+2oXcO/c6i0cymTgx90ZTnAHdEWtD3Nn2f2mVUJ3g30IIg3Qe6hyTaeEt9Vkn58dndSkZSDEyj7E8og6SGRa8R6oiK2c6+ilRMAMIReZ3HGzm5WhthbMDRLCuVn1/uI3iFnslC9qKQ1ZZaZH/cYqVI1pEmsaXB4hMmHz3WcPsBOr1grhSKJT/8vPpcv51kKTGnqBxGxFRxzrxISQAAUy/kI90aVFejmLmooE/pqXCDQTwdYC844H0LTF6mjdDLEqCXZH8QER7R322aCh1LWepnlvppvH1fQ6rcsUWa9gqbx0FW/8orSgQCPWDlbZ50Nm956akk4odnGQ1mQSL5/ten7+EffPerAIB33jrHZ6+OUH6PJlcVgIb9jl1rUTf5ZWC3eaFgWgWE/GKnxJyMDqQuiBoUgo3wLAPCoKFvExkmIJRRKtcw87CMOBQrqlbSeTDdKIR+32Ubw0EDJkolosJu3ypMAjSTXDCJIgcb9jiqL72wVJQ4w3Vboee34S/89Mf4g+cP4bh8dL1B3NA1bO/1uP/oGi8/oH51LCLOnpDBxLovUVV0zMOWJhnRVuqY+39a7dj9AflFrz3gC0jP1VkvCxPls2GCijQRVzcJ+ciGBtC0CEg9V3trRFoSbUR9ruW+0wNZVQIUxZYmKhUAqEwyAv9btn+QLThNoxBj/rtQxp14usQJoMjGXFkql6s2PSiR/aS+qpCibMxrDQcEkyfWaPN+KafAwBgtxsaTpskLBMUe3GliHE+uuqPnLz2jTuf9MFsNV1rZD8SMPGmV76tY0O9Sla5Uvqd9nfejWBFXIOnlo1FIHLFggeEg8w90r+X6xjrCHTuZRLGyYniCImDKevnNbQ3sDWj5udFNbtqP1siv7Xj9lwd3427cjbtxN+7GH7PymFjaAAAgAElEQVTxRlauIRBsMgQtPQp8PoEdsNNXTC5JxYsqJ6jMIkIdYVOYeaNw+2Xuz+0Hsqy7SezS/J3doUJ7EklcDfqeFBQ+ZlmqxuDg+woDM47txkAPUbZhtxmRc3PAfMzQ6X7YYfT6SsGXacmcIb9gqTdsPqXjDgCmyclGAXaUkGH6XG2ooOB5JeqrCNzrxDTd35QSIO5rYoEK0/Skg2d7QhXp//c+ZnnATKNj+cqDnz/He29fAgAmZsDTL07QMmvWbjTUZZkOBbrLUO3mUYZjfUVoQBznVp7yfjgF1eXzkSDDdBKEuWoj2WImJy6v4PbSet1QqgeL4PsqQp+1sl9JqoSB3JTCyMYwweShiKifWwwL/vehE6ay6hRwr0MqfNa3E4B7mEMZcPr4BgAZ8ANUsQKAeVnBvEOajl94/Bn+r3/wVXAADcr3l3i8R3/38fUxtmsOS7+i6kjM7Y2Ct6NKCfk+C2WWa/lJlIoH4KovVa6jU+omlHdafsLHWSpw3j0xxoscRO7vd7lfd16iuR+kx+smMTtYjZzFVMrVlT6rkh6ir6jCzZVgZipHTduxt7mVk7gDFASf2cfa53MRTcz3TOQKNe1/lUPPdaPI/EOM8Eds3mF0Tg2xZAVKjRlWNS0/Y6kF6zFKIYqwm2yM0R9AjjsUkHaD3/dQ3kInpzQFUQ9Ezb3VZLIW8n7twMMeOy2dJBME6Pinn5vMyE7nEtxr9bkVVd5rMCS7TB3RsHSxqB0Z0/C7Qzkl8HE3Uge8ruONnFyH3sJbi5frg5yyca+H8ypDGYCQjD785q/K/737u78Ds9awqX+BUW+kpABkeVg1BFZqZoDb9znNQgGadaLljcLsOW1kmCsgRsyeMdxoM9S2fUjewJNz+nd1oUcTC0FpPZMF2joKEco0mQofLFBd6fwQqdHEknqtI5KLnLNFjphShz3CoGFf8kMy+lwoI8JeBNiRSAUlQdLlDaV2pMWC3So03+Ce63SJ656e5JdhD8VkwMDyj+pCof0nmRn2GX0maXhVyBZvwQLtUcT0eZ5cU18slnlCLZYa7n6Pg0N6K23/4FD8aF3toaLOmkwTBf5O/scS6l5H2O8TxN2914nuVw2KXxT83Tov2qorhfZe7r2Nw8Bto4CntfjGxsMB02eseT1RuFnSd11c7OHkZIXhw718zhkyfr5d4PDnX+HlS8LRv3x4jRV7C998cpAdpWb0ck2Lp/oSO/7TY69nX2XNq+4B2yt0RSLbKHkWfJ19aAHSBWuXZr+IdIO5mnp1afHpbwvRBCMARfujcC/fx63aWZCqkJ8922SY080iT8S8IxqycFCBdNgpFUp1OWy8WOceqN5o9Pv5nic5CcOXvcYwz4QpIBN2TKuJ45AWl05lOHy8GKi5d5/g2GlAdcmuYHucWsXfbVZmBL8qDPPxtYniwhSqKO8vWzkMNsCes29vlydT5fi8jt5byV8ZcfTcL3P7I/0uTZjJIjM9G8Fmjaxdk8YZfC94Z2SBpFREvKV9cryITTwR2AjNiyDVvf6g6ut/BHfjbtyNu3E37sYfs/FGVq6LPTKRuLmdCd09Dhp6lRmk+qSDYgrmk7/1LTz9V6l6/eGf+2U8+et/FYo/6B52AlnGmYe+0RIqrJ1CW2Q4Rzc5VDkUWUzdPhpA7AbAtgQZ2bEXMC+Byhuiwg/ZtwLbL6Xlv0KosxQi2gh9QL8rJz2aF/RHdql3qhJgBFUVxAaUissCDWfHhrnPjiu3JaAJHgcA3SqUt5mw05/0MMkE4LbEgk3G21NgcxJEQlRdgXJJAbioUbO91EUzR10N+Po3ngMApn9iwPcu7wEAlmqCYS+KKfuYmEEQYmZP6iFDU9FmEktfRESnsGEmrjsKMBuuOnuuWhOC7BUQMtsTUYm5OFYjUf15mQX1mqCx+lliQUK2t30U4eYeiiv7eF1KG8G0tL3uKO2zRfOYyqripMHQsSl7Z3D+ch/gzN/ioMX+nG4YHzSeLK5huKT7w99/G2HCBhmdlusUDUOnfB9MLqjtAJDbkRwjn2PxfNVAOPLZZauM8CcZLh27+QDA5iFXM22ueooNkwIF9sxkIdMpQEGIYuN83urK7Ow/HQNto7kf8j5N6XrqMUo0Ck73hz6Te6ogMjvfZ6elYR52Ar/9NLtBjZEqGSP/7JhvH2Hnp79LMGrUkSDkVNH5HBYRNbNrE4u5ydIh3VOVmM6dr+h6AfTsGQ5AsIWH9xqOWxPlYLKJWkLyR7ufiFzFSpP7EoDNW3SNJO8WI9SmBGDysRUrJe89IhZqeE83l68Dub8BCKf9CA3QiKUXp7r6k1JaJGhff0rTGzm5Lj84hK5rhEMnT0H1jNJV5AZ/VYmjjzLAu3/tdwAAYeqhvBYdp7oqsxH9UFBAdOoHvuugGV6sLyjgW2QuW43ujD5ollYens0JPdSKn57qOgpMWC6pt7N+m2/ODlDpRq1Jizt7TDYyzbbCZJpDvdNEOxQWgBV9ZrRx9MADUAr8TCCUo/5br1Gxe02/CNBej5jE2IEDo9PSDxTLQP67o69coWWZ0faTBf6Z9z8AABwUDRo+0GnR42I1w0VDC4L7syWuviCYkw2LBOZWLi8G7FbBXgEtW9a5aYQdSUM890qrswbtskL8jN7MdqTTVQPBicKUHb2Yq0uN/jCS+xUI1hetYxHEEjMGhThosi8EdmUZRwNUEVDW9NnBl+gPaXsH/4gWXz2/qKdfaDnO4UhhOqcTvnfawnmDq2vStrqLCS6YdX1pA15O99DzRDz9QqO5n69N6hfblSLmO78Qt2dK2gMq8st/JB9KcCxUJBcrjLY5zVZ8khzT08SR7p9uMnqBr9XOolE5IEzyJKaGbKFkGp0DEfYj+gepkQqg05h+Sjs9eaEx8D4GCQVPEHKWT9m1RnmtJdJOjaDdWER4bngXKw03y71mkuDldsM4Hk0PatS7pvuxvccLuUOfI/kUZCEVuG2TFok+ERIAmF7vuGANixx+UdxqxHlm9BO8nmdvlyDXylJbSKdrNNbGBqAK2U5TQRYO/UGQfVQdQ/2TdC0ybO6riOpGCWQ8zOMIlg+oLkg5AQDlucla6MbC7PFzMgMwGMy/x/s8o8U/AMT16w+qvv5HcDfuxt24G3fjbvwxG29k5eoPHeLEoZz16G+obOvOHOzSiFA8TAPMgqq9wub8ynBTkben+MbmFWc0EQg5yFhvc6ByexoQqkAm9yBT/PqYWCJ9MxuJpynLMsFdbppju5qzgHjSCdFK31pEXn16bWAWPSwHyBrrxYAgRiXbLy4swaXjmC0eSZ+azAnglZAq7CpndRZrBbtREh01znLs9yPU1khIeX/PoWSHHdMrXH5yiJN3SXd5+LWXOCqz1njC7K/SeMzqXmLZXm33hCQ2Ft4D7G6V0IBOYZghM08dBOIyrYLmc1WeOQwTA3+W0QcR5TMElio1w166adh1NlcYx9bFWRD2tOsNVBEybN7nqnBy0OKtwxvcdlTqX9w3CA39sj8wKFakLwTIgF7IJSoKknJ5O0P8ZCZB7dOXGpsn9LtYKrSNFZctMszPLGnR6LIBfEIthnnMMtGBjCGSAYcvR1Ao6zGTIUGwQHlt0i5K9ULOTRnFiSZrY6Pmipk3GsuYAy1uC8SZE712NMDmMUPEE4/Jx/RwNU8GQEf0h/napOsUqgCEfGOHaRBCzVClynh0rKktMnJWUoEe6/Q+GJOnoqK4uATpTs7zPelqOs9i3qDpPkl/mMzVqnND7YNJ3v/0N+6ENNOJbIkyIPLz2/eUMys+zTFX6MRY5vuRCUGizZ17lBxcYUDoSqjZuKMzogRQ/YgkyUYa6dmAGqFErUJ/kB3K+lMvQfYIwOQXLlIiIq6meyifpUB0A/H7iHRs/SJV6KP7fWQK8rqON3JytZWDrh0WsxbXadJ0Gr7TiIn6WnkxV/dei5l9cWN2bjg3yVmL2u0QAsnh5BFt4+37V3h3cYmGMdfeG2zZQ/EHL6YwDIc4Qw9cgpzIUWYEXW2t0PzLWy0mAJRGY6F4X8rSYXXNWG1UYkgOTQw/cdJxue/jK5qo8oshw2Jump1sqiue3JPbj80wVX2hMexFHH+JZDWvzhfY/Cw9xO8+vMCqq/D14xdyPD9YUi91anv88Iaajd1QYPNyhsNHtwCA62f7IvUJVSTpgMv7n9QOyXknQXS6z70qN4uIh/QLazyqasB2w5mwBwMMM59DRczPBOWpMAoeKPj7E2S51cB9ht6vSkQ2ldcamL93iydPyILrD1/cg2fYtrmp8cPhGO6Sro1uNEqGuuvLCN0TK5y2r+Te8m0By2L7YVlhslGYssOXr4DqJbOK79GkOJxwhqu1Au9++Ku/hK/8ZUpsas54UZhOXsisVu0UB47zv7sMw1eXaqdfH4rMENYuT9bKMzucWcabx0Em6/7MoXpWZJekRQ+Veo8nHcrvTnMrYWQOH7oMkxdXlqwRU1ukV4m2ANQeceIBDi8gk4o8yUQbYRKD/TYbkrjZuJe5y2gONk9iacFsuU/vppAJQjs+Vwzjqh6IKXy8V9L/9ltqP9Sv0rM9Oq/K7Jh4RMWJQ3xex8+or5FZv8VuH1h3GvGU7oNyMkiB4DoDdAawyfkCklmMQYuloYp0rCInimpHbkUwce4ZyzmeBGgFHEzo4l+FhagY6AN8bgYF1VoxGunv5aStcWrP6zreyMnVtRZaWSxNDfU5ra+qNZMsuCqMrcamoLdGvC4lkFh5oFxhxyIwVTKhJKJNqlKaJw76it4GT9tTPDUn8iBMDxs8PKD+aCwDTJIEefJgtakXM3p4TKdQrE3u8Ra5hwMF9CcebU9P6PZ2InR2PYy8YS0RZ1I8mtvP5A6YSLqaFC8Wcy/GbJXIVTTbs401fGJ3ZwD19iYHnp8uxbYPAL5x8gxHJUlgNq7CvKDPlYlJAnJuMWuD1fdpsjVmFNgc2aGGN1lfjPxOk4Ql9aNaSAi6GhTMK/rAVXOAd778EliQNvTz80P49FJiHeS4WhX3mklEKKNUasMiIF6zo9dKiyRomANNW6BhPaoxAV9/7ws630Hj1XqOG3ZX8qVBLPjnQqNcBkxearlW7U/T+akmA+YTPqdHgLuwcCydMW3WjJq1xkff/CXZ93d+77fx8S/9MgDgS7/1bQlK0oOCn2RnHtJu5v606anfT/s1QjoMk2oSOlMqIE1+Rb5O2hGZp1jzYu9GYxDSD01cKaquLQr5u1BEuHmUCUyF3BdVTkkYe0JGFOsnTacwHCYyjCHP5i5ri9NQvYbZaJT8Qp++GMmiWoXZK9rG9VcMfMg9YxXzglr3Cv1BEOlJfxCx90P63OodInmlBYEeNLDR8nfdvdRbjrArhe4knZ/ct7VslZqey+2jKEiBr7iHnOwEOfoNgBCD6BoqhDqgmNBGp3WP5Ypryc4QgS+m6haymIkm7lauyPvhJ2FHIhWquOMfrF3q1WpcfH4Az2S2o3tLLFe0kHU32SMxFmRPuvqZUZnKtofd/bHR9es57nqud+Nu3I27cTfuxk94vJGVazEZoKcG/tOZmDz0BwGxjEIZj1WEfcq5pi6zKqsbheZehkuhMquyPlcolxHNGVcUNzZDiCwETz2F7sbio4JMAGw3Mhww1C4a5kmwrhBTxcgsVqH2e8ByMTPsAfGmTKgK1NqIw5RykFBmXwLbR14CxbG1OaUmkl4/ppSKTku/zm4VkGQnsyiQMH1w5MkKYG/WomJZzWANDPeB39u7xCfrI3xvOAMANIPFWwuCfreuxPXTQ9qAJWhXpBQaOR/WM9M0Gd/b8bmjno300KooMH+sIwyHNCzqAS9v97LD1HUFM+SqLWq6JgA72Ugfka57SkrRnYJl5GD+WRSIXkVgWJe43eOEEq/x4asTOT99U5D/MGiVn/pWnvt1qdob5gqxSVWtx+UVlejxuiSA4f9j7816LU2y67AVwzec8Z4751SZWWNP1WSTkEWKlmQYgmAB/gOG4FcbsCVb6FarCdKibVJqNptUVZGCDEEGLA8yDMOAXwjoxZZgg6YI23RPbDa7u6asISuHO98zfVMMftgRsb/TJN/6Qck8ARTq3rxn+Ib4IvZee+21Ql2+PujB5JnHq7/6Zpqfk0uFV79GUDAy8LzNqTQQa8yq6Yk6BJh3fcTZY063CcW1RzdkcRRdsYFDq/laZVcEe8bMT6+R6qMu8+jGDi4akRcOahRak9YarhVsltBxHbw5cKn2PniQQ3akyw3QnIwZrqwlpNnUIY5Zp5rLMH/CPc2RlMy6MbC6G58ZH7SS+zVd+r8ZehTnMkGwogOWd5G+K1uKxEYuzmXKSJs9DxHO0wkNXym8G1CGT/3KWygu+JicZta7K5jXYQcOes364j7zzEZWAsUZI2xm7GEe0825loOEUuQ1mRoktSuLnhGGZLMLR899MkQvfI8NDkCKlJW7/Q4+OEv5ZQY9V7gU4YHIHWSAoNVOB7uKZrShLBE1hftIcA9+flbHc7m5Ft8aQRUl9NpjfSPAK2MDfaUT7FFe8J1evcCQZeuDnVLJRBkd5MeKqwh3hdrSlGj1QCBHOCQSUDT2BgDnZOptJENxdtXow0PChhpjhOE0u5WIjuAzG0kQ2mP4iD+jC6beZugh9tpEkID2yM6CcPy+gbjMUFz/cUCjnfX7EmmzTpJsOYvnt7MOZWZQBvuMTilkQXz+mye3cXEyTYbXO69dIFqbv/3kEAgUfXWWE9EkXPbmpkER3FD6mydAAvCxRpRfC6DkhaI5silwkIXFaEA7gpQObadhomRdLRgW00B5JtIiXh8w7CZbUL03RTd8LKtbgtsNBJA/znAKUsJXAwu7Zos5em9czEQqAXhJC72uuX5XPqZVVB6s0a2DkYEkuLs96AVuYRNwQQwnQqHVEdDthdah3qLMlmh8LSO87rJNeb+3f+mL+MJ/TBt0MxPQVY+EYz10rKFZFnk3Q0BkgHF8rZJt2lKSgXwsfRgBtRP4DVoCXienIzNAKkcIK6DC9etGBF/GZ8EWPtXyXOHgckGuUeFrVZOFc6Nnq7hE+Hw2LI+cAwCQwQCiL4fI5RkqEfQ3nWTtloXXhb+t75rk+ENfEja4M7KufP3Lb9G5jXqByMrDzgQTCyXQRRORRqYgDwCyK5muD0G94byG1H4UoWI9VynAaGcuBK98WOi1XcXgSljaWJM9n+Dzao6onCRCDdkbAd9yjVtYcsACALNn4JqIvft0DYQRgBQog+1eN+45TrXPPqj67J/BdmzHdmzHdmzHv2bjucxc26mHKj2qQ1bYgfRJIQcgCMcm2IcJHe/+/Jc2Pusn/5O3EnQUBST6jLqYIXQ7Dm7A0BU8kF2zj2GEvlwLuExwG4dBgsiiXi1nGEy40BWxh5s8iLmvJFYhmzR7ZgP6laBm7njeMdotHxHNP5GHRB+O5X8fPhGQncfyHv1ubjYM6XQSmXTUPgPgyeUE48C6vroYIzvNUtZ/eTnG4YhSp6Iw6IKgtx26ZP4OAFjrxJotzgXqQ5cILTAisXnXLxoc37nEybvBjDVzGO0SY9FamRSZhPTo6p67QOmTPm5+xZAkQNF9jOTN2JMF3XnwFJ042IAIeO1T65atNPInWWrSlycZXBAd8Rm1nfggsqHXIpHLZAfUR0B+GaC2Iqp3UeYlKr63AOvZStubI5KM6iOTFRJA0MT1rUikk+KCREmcihkut1b5jEg+Ea7+7C++he5OvCI0V/N5+K2n0tM3AoAj0k6fMBWRDhLPZ+IZBNiSLAhI9P1jE5+2x6AVnpmzQMiC4nmOLWRm4cP8EZVKGtPSMEoBhDkdyYnNphgEEDx2AXRTJPKORcjqIqveiQ2fUxLI6BHiAiQqlIddRuhgs/XHDNgW0uZkbahqJmRFYqErHGSvXcbl/BnCMRSOUEqJLVnZQrAedzhn31v9E0vaYqP9iNj39PvgKWuU10OB4lTBvEZz3mkB+SgQQJUnkZ14r2qZft4kQQo47VAfhhOYdYyonT37uPBzubl2u44WpkkHG+QCi0/yDeNhl28aAcdJ8crX36SHMNScVInUKqNXm3R+rz2qF+ipEz14GKBFNfmhelbOoUUI0OHBKs57bFgJFJcMBXdTALfp6ffKobkuUi9nhJcBEAM4mg8LwC2z9NC5jKUEVQM0Mw/p+eGKD+7oEwETNhLZeuq/DfXe0bRGFXxwbx1fYpw3+N57t+kzVgrrG2HzyBxszgGGW2s0wcv0YLxCFxZDPXMosg510ObzXiSZxG5KsLgNC1Y+7eB2ggpWZtFZmfop1cBgdUY1J1HLtCHrocFoWmM1DzX1TiTmp6rpHthArLRDhsNd6SCXCjbUXDHuMJpSH0emLFZV6CvOLdpdmWphZuSBaTTyFPArTQsk6PqWZ3y9V6/XGH2OGl2Nlejepjq0ONfAjdB3/bgg5nLst809dILawnGHAKa4s2S47qMc0/c4EKwOeGNvDwyXHzoBVcs0V7sJs1WbXQ/V8hx32eb8TF7Ayod+6l6rRuwGs0DfYcXvsn2U6CSE6wnoW+7TjZKH9L2b7jyQYIPw+E8BllS1gGr5Ge1D1Kr1KM655h1bh8yAnuU4D1QloPK4wdGcyC96TlCxh1fFWm24eEOT+pjznQZ+SDfIFgrFUmB536VrFyHubkprQnTQ6nM1fpQB3TeIgGOuAJVGegpKU17PsiVvkgAxjvscEtdrp9KVSNeqG3uWOFyQIpadB/OOS8UtcJ1EC/TkUSUzmQufAn1XOMi5hgv8D+GRFOdif/qzPJ7LzVWtJKSVsCJLC303cWiOHLuhjAyLNdQyReBeMrkDCBtveMibfQCuZyUnQjYIIii5ns1ZthBok8MECzeITmD0CaP1tqRaLkDar2bUa6MZuVRHrNYF2TzFqFwxOcu6LLXseOXRzVyavP0aLjz65iVQDTB8yrW8mK3U++TMERfwdlFiPKVVaVZWuDO8wuMjigAuL8ZorsImVljSIw215mKvwiC4R4+zBrUJfaLCwzoJn9GBVQ8nGJzxomILAXUebNeuMjaLfmWOdV1AhwzSOZHakfIriS5E5LZWKO+vIXfomOe1QgytVeNhBj0pQCPgC14kvOplES7DMiycorSp7SHIVacMtLrXJfKU1B625cVGNjqRg0xJ9nFNOK7lokQWMtB21yYSlNd07WNA4PJeu1DpkV1zbbW6KlMt0o8d2p1em8+Bh70Rv5wzOh02O/uIAhMnOCgEgOqQF+oo90fnItK80msBrzkwkfBMlAmaz9HKT2cOKgRLBvR5MXjVa5FkNptdrnPSRt63wnPJJlBeaUDo1LKm1z2RhyGSQATdrN7G1aszx002ITcOmD6gn5d36PhjwE3yivEaEJmqCy1D6ixLrVXVkcboVbL/Wx5IrIeKBTgMB+zwSKS5eK6xdShmvE7z35LpuQJkQN5MHlqmwjUffiJSENTsO+qR3+GNPSF4vpeRg4OkOOKmriz1vObByYeycBE/AtlCwrYxWkOSc7U9rWhZS3IaivXjYQcbenGjDd2zPLY11+3Yju3Yju3Yjh/zeC4z10h6k5WAOwzhohfAmn0Ti90K1SMK9VzO9QM4oK10gkpkyzU5osz3ZNgyoIlwiAREK1IU6AokNm83ZgPubteg2ZOp1iM7oN4LzMkJKfhEiM4LifZ7RHW0B3ajdcZlLBwhwJG4cAKyVSyZ1iGdi1fEuI0tAN3Uo9mLcKnYrHcZhrHUgxLrcVCbekHjg3wXhQ4sQidIbg4ACgs/7TCe0cGMihaP50TX/8LxJ3ghmHq/c36Izio0b9PflOBWH70WGD30qA9Cs/kuN7LXDyawM4PPvETSRVo6vFtSC0w9KZIa0fHhNaZ5g8MB9Sf93vwliJB6REGQWI8SDok57HOPDWk7ICn9+ErCXAYYe7eDnHZoA9xVZCap43TznFsQ0Ks7gkQbugmwuA7CJsMO9Y1Qcx0b6HcH6ZiKC85OVc3H5DRlzjHTGX+Ub9QYOWMjdxoXjlkILgHY3GI4brA+opTRdRI+uK+rdWjViEzrhuYlAHR7JmUhXtNcjJlf12vVQhAtiLU9M89h0vMkwlzz4X6ABfkzrumKqHAYNCH9yEKokInZDGohU3bXTj3Kc8782h0PpfoXJXy3AfJrrksqIbC6Tb+PHwp+Dsck9JHqm71rJ4OxuY2ypCOH5UvB2OBKoQmsZZVbmEYmlKs44wy92wl+tD0Gb6yXJnGKKNoi+WdhsaEW5wXzP+pDz8frAws4ckqcwO4P6Oezn8AGKmFzj3ippEEyXCeDeDLjAIgh3G99chlLI5oRl54iRwGgMgs8IEKHQ5drRmeKZ1//8LncXOHpP68AHynikhbOyesk27eqigQzCSOSWwM8UdmLU1Y3iQ+8y0CL1pxrMy7NTIR+07Dh2V6NZcKwsAyklbiRmWFvoe+o9hMhHNmKBCmqRqPZcxvuJfqUTcrjMEP67nzeg3oixKSofy31BLYM8znteZF2QLbmzZvMwAN8qUZoJgb1MECkAvChFUE/LeC0xzLUG5dilJzW/1DdxPGYao3rOkd7XqJouBYcoXgvyb4sStapFsgDMUz81DVybSHD6vDu6QHKnI6j0Rnu36B7+8r0FN88eQEf/5/UnDhZ8j2M8n2xnuwUkukzBCAawa05nejNEXAt1gODQQsVFvvF+QhiqeNHwA9takcyA4/6Rlgd8yBgG8lmww6TYwoAMmVxuUsBzO73JL71jzeJda/+KrXK6ApoDi2GoXWpG3NPqst6dU4RJCF75YEEH18UqB+VCIg9zL06tWpEfeK0EQ97cK9hVaTYH9m3VItQeDfx1NsabPfsSkMGGLA8JfnM+GykDQEBCkY8F7IQjFrdaCVU4E8IS/eAdZn5vkYLuLgJudwzoe9EJsN4W9B1i3N8ddtvlAqqG45auIAN1yO9IsnHZjf+g0+liW7XQjylAMlrT/XlXkkm9gFnc5JCjJttX4DQUk8AACAASURBVAJS2EA0Cs+56PqlIIaxTUYKWNG6cnnX0WYGCpCEFcmcvdlzuPgMb+Sx3ENqXHxuNgcHVSa0IIV5YaY2BUuypmb8mHSohmvQshZMtgPQ7rKsqr/OUm189/cNPsazPbaw8HZsx3Zsx3Zsx495PJeZq7tTA0Ngf7bEwZBaQT4434MQPrWGTMsG9YRSxCePd4FADopqL5E114+sXeEA7eECVKjWAmUwd673PLnHhMiynfoU3ZodbjJXawkz8FBRCaUWsDF7zIPRt4rQL6BPeifmAX1Mx2waDRsy1+LCozriiBOmHxX3BMlrgdVdy0IH6LVP9IQtXE5R7fBJIDRNmEksDCC0I/gToGws0vxHpLATYWI/tBgGtq0QPjGM5UIn1iBA0fryXg/GFky+6Wflq8djrLXHckUhs874uu7NVrgMIgz/+6PPQD0qMI5CAkNABWEE5QVswaQxgLO97LLH/g3/7gIDWY1M0u21jUK1KNO5oZPJXUi0AmKpknG76vlWDqY1EZDCqE8H6FYBXtzrkvavGZD4QBcEcGQD+CCE4B0x02OW1c9W9Zohv25M8GbMOv3AplYfXzjkd9Zo3qcvyEuDJqP76a0gwlaCIgWimTx6whAQ9HvKlHPHGsHBjUdKbm2J16O6QZB3nJN6xfdaWEKNgJBxKk8lG5BIQsxUhaGyi43lmZL9UL2iTDK1r5geSjTmLBYesEPOBOF77jaCzlusuWQSj8uWHl0n2NijBBAydLHQnNlfYYMlZgfMyN79ocXlaypBwYSGhee3IyQrwcSOASXRgn0YdEDSwhQcfyDRTWT6LjvkTFwa7lYYPhKpGyFbUVacnHsczRMAsJ5KIhFV0wvZE/Sg57KbMsTeBfj4xu95nP0kw8Vwm4zyIhAXLz/bg+2f0fFcbq46N1C5welHu7g+o5qcLQE7seh2CJqsugxnl5P0nmgIrWqJbCFSD6xs+eF3Ow7DnQpVS7VaYQXasJrl89CjOom1Nu5tFRcK3T7NTLNjoVYSNjx47Y6DLxhuFE7ABtZfdi1R7zN0WlxKWNqfMJrWqEdF+AyRFg2ngdGjXnuJZiqoHXiolUwtQkCvvthjEZdn1Idnhr2FNByiqgTkJwW64wALKw8fYZ8AvUeGoa0kRseE967qHIMPaAHvRh5mx/XqbgJmjz5v8EGOdoLUV7i+4TYs6NRSwqhQP12XME28Fwx3j5d0PlG1Ktaj47kI1wt4HDA4ooClXuVk9hzrrNpDB8m32wdXie18crIDzDXWouCPbWM5gFiWdj8wcqe84Qnhqf404XpTJE+ikQmirA/o/vX7RpMd4JzmZ2SG9pV4et4I1JLiABmOPy8M6ni8jYRzEvaAjtHXmmu6AwdnBZSM9UebFKsSRAtaYO3EpnnRh4Uh6b9uFWDcWqZNR7WhDS4qSK2B9a34IX2Y0odSTnz4kL7LlvQ8xPttO7Vh0daHgrsdBx/gUiN5IglLcK/vB4ZxE6gl9FpiHHDLZhcJzux2DXxPXUgULgVZKzOADcGA8BpZzwDEFQxdP/6L1Eee2rBPuf+2m8QyTDiusYUI81G4HqQrCLaNKmT9JMCWNH/i/RBWILtiDkMsdXQjAIKNDXzmIYJNXTlsUakS5UehN33gMX2PXnf9WlDVipdcIQVW56/LtJa6zJOU4126Psd7c5y0JI3qazzz47ncXE2ryYqr6snBOUA0Eg9+cBMAkB1WsKFOJtcqTTgztrClSA3eccMAgN2DBZT0qGLPoeOoz2mKBGMEWp7yprw+9hDRiSZkxrEOka0EbBk22gMDDGzy9WwLCRHaQrK5hC2AO7vUL/PofAf2FqWnqzxHHiQN9Qrohhyd9sku0Q2m7/jDYTEv4PVeWNjThu2hog9uERbzeA1yz+F0K5FfSWRBcm95aODDlzV1DsSsRLPNG0B1vXgc9U0L2UMA+kNWJECQn+j0OTGb6WYWiKIdA6pf56m3lReebghURw4i9COWwxbVIohPrDRkK9JCLTuBsBbAeYE8yDzmHxYwYwfxlN5ndkzqvcWsgdQWMjgFCQDuKWWr1VUOnztydQkjbuSqBZPhMspy4qYsOyB2PdgBbThxkc0WpHcNUMYbs90f/PIX8dP/wZs4/9lwrTILoSMhSKB5xPVwNc/hpoGgpj3EpEt/E5dFymJd4YBA5FENILxiOUFLrWMAgKEh0YjkwiPSxm+jKELKJkF+rAAgPXSsXXuJbmzSptwPHLIl1YTjs0dCIL2AsUf0ofNjpCmiEiTQIVMdGhZQQcLSZx7CkBQkEEh1O5Hd5yEqkeqbqBTW8xDgCWwEyk5j40Did5enhDzEzK868lwvreSGFnBfBGZDVMPThmqiJKf2XOt0wfouvtaIRKwzQ06FfUaetTo8N06QXWccsrApYxdW4OzPcV9uu2sTQqXXorfR8mYtGwmbe7gQZD2aHwDBKhFPezfoGR3bmut2bMd2bMd2bMePeTyXmauzFDUL2VMRaQUJDYTaQHcy2IDd9CIw/mYWvnBwMVMbOOgdgs+aTmN9OoLuGan3swthkbI2hMAYoAyq0VxHNUMPHV1sCqDd47Dct5IzwR4708sAa4eM6JUbp5g3lBE9UTupZqbXCu0MKaIFetR7SZleecYxV/910Jzmes2G6y5n5nN+KbB+rYEIUb5YFOnzXEYQkQmqN9MbC1wHj0n5UclGAJmn+k5kMQtwLWzSYXCrTmYAl2cTiFCX9GpTUQaCoVMzNOgirNdJ5GcqqXHZglnX3djDzrilpH48SqIQfmRw/PIVWkPfd/lgFzrUDa+rEosrEl3QBbVSdMGoWmiP0VFPIR9A0wQJxU5BHIe6sxdwlzlkNGcPaAcA6GWPyeyJMR4zgNXdHoS+a+Ausj8RCq4PuRQBAOtbnD1ao1AEhnddq8D2jQIHXCcXDvBNDhMz1948lo1MptiiUhC+V1Zo+b74ViJ/qtnZRfE8ky2JmsQszpRA+TggSG2v/meA7Gm+wbZNRuqLcB+jCpH0SUQlSjymeS2pvAIA7Z7jTLslc4GUaTrOFr32MOOeqpoD9r5Bx3j9b1WQww6ujqbO3N4njUA35vKPsIpr0gq9B1Ek4X26mEhqX7Z0kJ1Ivzu3WfKJw0uC1vvG7tHQ3ZUkoehGAY3IPVwPvo8onVeEOpldOmY5NGgvy3RNRe6Sm5QdO2LSIyJGgtvNFAt1tDOX2nniEAEFnLynMP9ceGYcnvnxXG6u+aCDGirY3GIQFI7qJkNzWaaFTbYC2T1aEOtFAXkS6kPBySHWv9ohMAzaucsPdjB+xBtTdYheq0OAO+sI9Wy62yT5scIRYeE2wy9R+ae7LGiRCw+hL5leb0NQcLWizUoJn1pSDvfmOJcETTWLIS164evMuC9XAwyeyKQIZQYiLVB2wG/yhUd/7vdt8MzQQ6w19DUvxinAMEDz6TXsgq7l4aDG+u0Z3ZOF2NjIpRFwccMYeDZwXyss1xqDA6qD6tJAjen6t0+GMENujTD7HcpXA8GrymEirFdaeCkTVFXdNshm9Bm39q8xzhu886/u02sF0IUN44VbF/gLRw/w3pLq9OVrBlXLjaqxR88pgtQipCi0RVPT68pBi1HRJjh8dVUmCNq3Ehha+KhU1XLblWoJIqXr7dHuCG5P0h4+WJmhibAs1yJdRj/n11zzB4D6wCGLxu8eUOe0cBaOvjducLSYh/k+8/BD12utkCkA6EZ8zj5KbkZY1QmCvNEjwsSNJfNsEzhwgOyRmDySfZteibR556cao4+5vFHvc/mhPiCXmtRbWTIsLE1smYukNI92L0KbDEHLlUR+LdP7XK/NRbj4LIt0jFefptcN/mBIEpGxJ7ynXWxLvj7IHOyA1woqwfA1hidiFwA0u/y06R6xEgDMzKYgSLV8XjK4LZVBo3d1h1um1Jp6moSJxVSu6brCJ31fIYggF6U6neDaqWwkvGQSmb6WpCeM6NQjUv+5bBji9plPtWsYtQFrr2576MtQprBbs/Tt2I7t2I7t2I7t+JHxXGauWWahMoN7+3O8/5RNrCE9XNA7Fa1EfR4gy7VM5JdqRNFuVAwSrcBiTq/LlsQubEIbh6qB9ihAL42GvuoJNggWKihXQDCEga8lRe8BrlMjJv3ISQfXqiTCL5eKM+gbFuO9NUyIRp9eTZCFVpRqncMFN47cUwYQjyNbMOU/QTE9gkSE7uyox/n3dH1Sw/rIwURf2Z2WQtzrIn1mzH5d4XEwW2LvJqVjD693YI+bcF2zBL17APkaaAIMqhcCiC0ppYcdWVQXQa2osDDX9AXC0/VPaMFaYVXTgXntEoSOhrJWs0/h+t7RHK/unQEAJrrBu/ODdH/9jQbHe8QgH2Yt3lkc4uGCsu29wRrjjJCPJ4sJsgmdSyc9xFolMpJ1AmpG0G+ZGdRthnUwDYB2ScMayhOZKdyH8oyRg27s0RxTNK8mHdT7A3TjMEdWEi5k0F77JB4fRzJxd4yQvPhbb8BPDLqi19AfHHjEUqHbEUkoxQ56cHKE+iJiknmIpMjvmUmd+Q3dbfnCKkgqAW4o4c9yPkaPNOdkJTecoFzB7T0uR8p6bOHRTskhCSBoM7m5dARDRgWi/Jodf5wGQbyxraZhgwK3a4AAUQpDpLnUfjb0KSu0AwdknoUcGgUfWLTVoYVbaVYlsyJBy/m5gg2ohF4TGhCZ6s0uE76k6ZU2ELL5cFzSi42SjGjlps5zJEJeCwyferQTLrVEreskQhHVtCQ/5/BI5yLXCtJIDE7odcsXXcqciYXeM1hoAcgonkOQckQBy/MeAbFS6KZRzoqy6VTaUnwPo+PTszyey801jqFu8fptksqrbYYfmmP4K1rNSMWEXpctBU8+gCZgvHIKLDp+v0bbSeigSNSusrQYqUqESR0m/5LrlGbA9TRi0zFUYleaa6xe0APRf5hiy8J5hnasMR7SIu6cxPVDan4sn6ikaAQPZKseDKdEclepjwyypUazy8zfaBq/3EE6DtEQNB3r02qnQx7kypo6I9m+PR2+W/MidHedapQAsLgYQSzoddlcoL0RNo9rhW4s0uLYZyZ7CWSjNtnT+VpBB7an8CTXxmxEvm+yUyy0Xjp0imvX1/MRPg4mAbtlhVxalC/RhvrS/jmuatrIny4mOBivoMI5LNsCn3xE9nZqZFLfJhwp0qR6/riDCxvL6ZMdiJVKNbN+S4QfWchxBxV7Jq9GqcZoeq0gttLw9+rUS9xXjfJji+xRxkbze0BzmwKA4pM8LcRuaiAXmoXwJwb5iF7XdgXESrH5uyZJvnhuXjjoiyCH2ATjbGBjQ1ArudF+I6XHoKTgY34xgm7YsaU+YmapaskyLy78qncPzY5JUnnwgpjpsc2rX6PXHlbx39qZRxb6zatblqDNuFk1En4vmhfINJf6Vovx99grK1sJBwcR7qkvXFLcciXVmqN6k8t4w2tvdMmGUJ9JMl2PdfQWEOE5dIr6V+t4XdWm6UG/bq5WEibU9julEsu+m3i0lcDi5bBR1nID5tcrDkxUwyUqrwTkKtrUSeRzYHkvHEfp0EXW8oVGthToIhS8kGzR6WhzjOcN8FwC2GrzR4N52fHnRfegZ3lsYeHt2I7t2I7t2I4f83guM9d6nUOiwPCow6fHTwAAJ+0EP8Rx8mj00nPv14Bs2gCC1fRSbvgpmjJcxo6i9UjcyMZtMvxuZyHSjWILDTZ6NZOnpxeojgEf4FIhfTKSdsuMRAii/Z32aPfoOPR+jcOdJe5PLwAA335yO7H32qlHEXU/e5rGANAcsvi2CpBc3ydzfbMnYBFh1cBetDeCsLsVaCo2gbZGYvBJ7J/kqPv2/jX+vTu/j9+5fI2OJbdwRWQSKxTBnk/4wBj2HMmnMFAAXcVG5LC9xvncQU27RJjqGyVs9MUagfLmKtlbmVZtEJOsk/jsEc2LnYy72V/eOcOiK5JN3nfffoFs9ABkuUl+tCJzcD3ikPBgE2gBYKeDBV+vSBLx0sNbCXcSfGHHPYu1oYUIDF3fKPIIjcSQvNf3CGKXm1GAAC31I8YDiRDr9A9zLF6xSbCCcyEAmYfPPbrUS8ls+fJMYD0UKVtyc50s+PRKoLvbhNuiIVqRPHSHZYt1nafr73IurciWNXaFCVBsRCpu15AfRWcMkRStVC1gSp+0r1UtIMKt6htM0D8geZQWp4r8aUOGam41kNECLe/QHYb3VgqdFBg9DNmpZgi3G5MfayTswDBK4UWAOSP5r+w9bJ1EeRKywjnB3H/0tS8CAF75tTfTy7yiZz5B0r0cyAvKoPsKU+I0zCUHFqZxwPqW7yEkHvEud1OH5tjx8wx+n+wI7QBIbKa48jBluFZLJgGqRqAbM7Gtvt0lNAOestP4zK1veCZn7bvEnnaFR7dj0/yHAvKzsP4ue4azz+h4LjdXe1bCD0r8wfAmvv2EJI3aRsPVOtU9iBVMr/cFQzs+9/CNgBvEtg6B4iFNBDN2sCMHFyCuctqiDGzkxVLDDJgFOXoEFIvexhUWgvUBqdWsQKuBHbmkipLNanTzAiIs4n7AXePdMsdqmKfzEcJD3KDVxq4yVGVcACXMQPQk/BybKM8lVA3o2NBfsktMfqWTyosdOdo0IzN1xIpC3grId4ZcMxsxk/izu0/wXn2E3//wHh3j4xJJodADbfDALE4ULeI3eHGP3p/Iid0cNxO500KFD+kWBbwHs2+dRhbUoPQazA4+djBGwtlw/JlFG2ph3guUeYfTiqi5T1bT1N70ZDnBp/ee4pvffJk+qLBQYcNrqiwp86grTfctLDzOCchBrAEIeuqiQbT0mM2Ild4ajdWiTDJ7cmiSDKQC0oW0nYfIuFbrhaDrAlA9vrdTeolU6mh3HdoguemHlmDx8BmqNOyliVD/snFjceh2AxfBKFLBioITey3yB8Gv1wPZhzRvXU7tJrN96imzTiY+gKhU8mOlD+EN0WVR9SzMTwfgfqBMzxnW7iY0b3feCWWLF/icXUbye/G1tvSob7DIg7rSHEBe5on5bgeWa8aSoM7osTr+2CfRCL0KzPYeqzYGA3qh4ErfCxZkCrYhXXL4ya8EuhHw+S++Rd93CxsMaa89KVwB6LsoOQEYK1mxaYe5BEKCmc+hbtuXFkzQrCA4OTGXBcuhOs216uUdgeVdkUpKJF4RXqc8ho9lgrXNrHcPCwe9kgn27VULoNYilZO8RPJIBoD8XKK4ioe0Ee49k+O53Fyz4zXk0OELx59g2dHs+Na796CudGorMCXXJWMtgf4gyJg51u9yXoSyuYTdNZjt0YKilUuqPcVdC3tH4PIxsUtsqWGio0TJNQ8zEqgOueaaXSrYkN11u4DIXXKZEaDFGQC8lajbLGXKANXm6AdWfIqqS4nKv2ORBfq7Vx7Nbs/6asLZRZ8oFOvRPsjFWU81RwAQJwWaY66NCQ8M7lL98l+8+ykc7S5gQmapPRNXbAGUT9l42Ra9DbWnM6wHBuYqB2IvnhfoYtZsBVyjktIQSof6bpv+Fmn+duSQa4cmLPam1TDBuX44bqCtw3UXXGucTHViITx+78FLaTHQQwN3UqZ7EcllKZMOOqxCO7hV6PutFfS0wWs3TgEAH1/NMA21yIuVQl52iJ7ZeWnQheOyrUwyeg1yuKt8w/Ul9ZM6ur9Rq1cGBxQApMoTg5K5hpualA3bSieinFoGCcwZHcnt46t0DT4q94EVZ8152cGMaPOWnSAlLABqqTC9sUh16PnVCC60QumG2jRE0q3GxnADlzJerTjyVAuVaqduL0gx6rCxeL9R12t3farp2hzId+nataucNYIBiJHhZ2iZbfSK9glZ7WTTbByKHWHQ0xKGJ/5AbANaZwLl42Az17Am8OJlB2EBM47XgI89SqqKkAnasWOOh6dnt71Ju6tc6nQ+opfZQyD0uop0Ln2Fr35A7wVrabf7nEl6JeAKn9qfsjOdrrEwQGcEmn0mgMb1QS+phYm1nkUKKoTblO0UFiTBCgrm4/Xf//azbzm3rblux3Zsx3Zsx3b8mMdzmbm26xzS5/jdd15haKQm9mgUcxcOSTMVope1VQSnJFhm6lno3gHqUmNRhhYe5VJWJZSDtxL5WYhGS4FGcOQe1WXaHU/C2gVDsFHo3iCDKx3r7gqkuo8oLawVMEGnEzF7CyMef338Iyw8gdT87YXf8Hnspuya4nYctbD0r0Gs/ToFF9iX+UqguL+GDNlAmRlcf5cYteWZwLUcYRBKaPWhhSVRI5SPVYKEmhnptabWimmXWonsZUHM61uUiZhGoS+CgU5CB8EPl3vYoC6T7zToAjw3nNQoMoM6Mo4rBRFYrfV7U9RgZSMz8KzSozzkUqEMAhnqgU7RuWy5rieswOI1CxmY0CJ4XwI0r0aDFi4c89FkiUXoOVqvCwjhcXxI6hDWSZyeE44rGonmjI7XDRzVM6NikPLpu9TNNbrLkkUejIAJrQ+yUkmVx806qMJyfdowH0B2AnbscOuYbsi0qJPaF4SHmLYYBmbxsGhxdhCOa64hAoIhd2sYK5PxuzwpIIJAgy3JIYcF4R0LFYws8mmT5k/zdJiyseJaoLgM87EQqEce809FuLrva0p1fpf1YO6I6LQSwrEZu7cCPqAKeqE2jCrMwKds1RUsaA8Aei65dpj5xGguLojFXB3x8xez1cEpl2PcxBBvIAqNGLGhUCZbwVrMnstSkISMiIbVs/KVSq9LghU5weMqKqfNe6zrIb0ulaS1h407QeYS18SHem2Eys2052FrBJoDm+rC/c4FAButYF5xZi4N14vzS4Fm36VnQzYCDS0VePwXNfC/4Zkez+XmKpaKpMcGAiI81AKA2+/gA5yp5yrddC9Zfiy9NkqtLUValAanAu1EoAowGXqQlm80IIH2iGZ7N5W8iY0YQpGGeuBcVELpGM4BPNR602wgfb4HtaeETXWyu8Z6Heq2UkEEI29fK4KJY29Z/8Io2kgiz0Hv1almnCmLgyHVvk5XI3gvknPP4skkPYDNrQ6yyjHboTriYl0kaNllAk71LNsKbi+p7ju4SGhyAVoNa4arFdQ0uMisMlgNjKOyFvIEMxcnCs2hJdUpgFpiwsLT1iUQNsn15QDtRQbExUt6IGxO9qClVqrYVtPotNDLxyXyK1aSqj/VAWH+ZFcqkdxULZBdaHTBWUfsmVQflaXB0XiJPOzeP3x4nOqqAtTWNcwYEjsfx97TgntMMweved5hYFOMOMgtzNAAcyZo9YkrUWrODz2twyrOH5+gdiMBMTA4HNA9LHWH67C5jncqFNqmdqSnT3cgwubkc488CLv/lZfexvevjvH4D6kdrNtxDKevFPJLkWr45n7o3w7DPRjFllgUS5bOKy98coGyuScXnl5cFZ1pojpTE+qPrvTwl/RMZitSJ0r3Zq3TddRrkTa7bkqLfpIn1J5Up4DUgx5rk67X/tXsewjHhuCuYFJaddizbWxoc46ym7LlNhQImkNJZctzW1o0SxdRSa4Dbv4uXfNmlxWTrl+UaHe5rl3v8wZXXJAh/cF36Pf1ocT6Vpi7pYV4WIZjImg4tSUp3jSd9htG6sIhGVr4ICJnSyaBxuOwiu3zzMgT/J1MPzxsCMD0QzzzYwsLb8d2bMd2bMd2/JjHc5m5ShNaNLqecPZ+i6wwaEOLBxF26G+qYRg4vxKo9z0LHNRsEhwt5mJ0p3oGzt2ugRyYlAmurwdoQpScLTk6HDwhlaH1TYYRWRdVwEwsR4+5xzBo7Bqj0F6UQGAWLy5GG+fsIsGoUQnaASgCjhCoyyTMjsHoFhGQpoMa18FgXGqLyoQMMTPYLSt8/8EtuibnipVVRhI+c1gs6X3dKoOIEHfsqIiR8MBChkzbrjXaXRYSB5iE4j1QlnQDFo1CNugS89QsMqglMzz1tYK9SQQhX+kEKY4+VgSHxeMQHu4m3QtxXqSMQl7nZCYdjsFrgoIBmhPNnuMWDOExPAz606McLmTQ2VxDV4AK4vyNFUzAygUe/D8vkI1e+ExxM6g3DVrc3rnGCyOCY79zdgs+qE9JI5KAOjIP1IKhVOWTeYS1EvmgQxPbjLRPJCY9adAFM3ax1DBtr7ViKcmwAICctfAe+OHJEQDgM8dPMK/ofV2nMC0bmGiZt9bJC3T0Exe4MaG50zmFx5dT1uYtXLIu02tirkbh++m0wtUJKWnlJxrFheipeiG126xuI0EtfVQJoMwvwo3lGTGFm9gOo9gs3ebUZpQHTWVb+HQ/9QrJzCF2C4jw3ItKwARD+kj4iZmaa3vM5OBFG9cHW/qUMao1eyUPHmmQITufR3yus7mAGTNBy/fIU7IjW8h4rrITOH+d5l11zOdJrT4efhaQjwtGMn7wK9T+c++/+fVw0GyxiLMCZTBtr29YKjn0fHr76AzZG4bjaER6vuGxYRsIi8SsRjA9oGMk1nNEz1zv2no+3Gd2PJ+bayMghYDX/OACwQknTiTPCi+qFgnaXN929P7kdMEPeL1HUEecIMSEC99ZKaidJn2XKixsVIlpdfqM6oh6asUuLZamk1Cx1lY4ZFeK2aiZw63ZPH3me2e3WEpPO65FOiALdcjYApH6PwGGd5yHvtaoJvR900GNNmwQXafw8h2SCGytwrfevZfgNGEYtsr2akxGNUY5PdQfzw+QhR63dkoSill4eJu1hot1XIMECclRB7fKcDyjhfpsMULT0PHvHSxSywodMwcK7bFBNm4xG9FmdYURxLxI9ykO2QHNAS8Mbmgx/ISvT7tnUx+k6JAWiW7sIfYb5KH1p13kWMea4pVG0E9HfbujmnRoUfKLjP1PnxYwOy71KtuBSw9hXeWwU4kfXtGmdvrRLoaf0IfWB9yX6HMDMeY2nTI3yILClLWSasmRYbviBd2PJERoCRJjT9J8Ieiy+136PCkdumUOBIj3rBoj1/Tz/miNQhl8cEbNkHouU4/kqipwHe57qTo0FwOIwFpWXjSaxgAAIABJREFUS5V6uZ0G1vdsCp6E8KzIVIuNHnDn2UVJeEC4cIx95itoIxEJ0iUDiagOpecqyel57Sng6lESYmnIljxPnKLPjEGXagRsuGf5pYSu+LknJi79bIJJQISo1VrA9HqeI4vbjAXKU96QXM6ShkCopcbe0MMeu9lR+9H1q4HdO+PNKrbf0C/A7ncFzn8uPOc77PR077/+DdrIwzqiRl3qw3aFQ3U31OhXikoQEaI/l+lZcBmxsSNfw4F5HaIVUK1IUom2ZEid/Gjpu9pdC+HY+1bWMl1vPPsCTc/n5ko9LEQGiu0SMJKsxUJdq9t1KWLru74UZxLZgh9+W5KUIQAsXqS+0yQtmHMW6x3QXZWwoYa2QT7o1Xbs0MFnHjo0m7o1twe5WlLzfIh2P/PaJ/jCjIoTf3B9G3K3gbugzSR/rNN3d1O3ISXowA3q2ZIXr/bQAtpBhPrX6eUEJmyuMnN4/4rYBk2nkY9adE8oFWxfrFOPp+wUlPQ4jQbRLT+QXnv4gUv1zQ3Sw3GDQchOhfAwucWipnPR2uJgEjJEo7FeFfBrbliPRtWDWY1MW6yqsKGe5civwjUYU80OoLYKrz18aFVSC5WugexI1CCJGGS8iEJ56AclzIthU9Ce+w9LJuXoMw13v0quRMMHOpFavA7HayMhRcCGe4Zph7ujS5w2dO3Oj1boZnSMuXYYhpadQWZwcjlBF2zNJuMOXfg87wW8F2mjtFMLUYUsv9LpvMSlBrRn+cBGYuceEakWqxLQLrV1PfzhEbJjUlAoM4OPnhxBPQ7a0R6oA3nHXRVoQ2DzzvkhkZYmQdJynieugMtAggdhjrdGJ66ALTzWx0AR7lt+xRtpN/EbmWtqhQFtyrEnNZL2UhuKB8pTzlThOTNyuU8Pd31kk8iDNkDXaxvRK0BXLMxhi54Bu+dNTa8F6l0DNwjBd8PowPhjgWYeCY3hmQ9/E64XKBSkp91vLUqBYO4xfxlwsf2mkel1qpJprraZRzujliuAziOuYXbo8MHf+HL66Pv/w69Re1W4VjHYgATUnOVX80VwHwrDZZ4N7w2SAI8Z0zVOmgFWpOP3guvAsqXatIl99SOHWGyXfwYKln8GTmE7tmM7tmM7tuNfr/FcZq6iA6QCfGkhVICL1jo03IfXtLInnccRs5cCZoTNOmuoD5EsIlPSfa9LRBoBrCVcFguOSPXRbt8nyE8YMkK2a8oKVS+izRYCza6HPKTs4HiwwP2SoNrfefoK7Fpj77she6qA+f14vhzh59citfvQ8XMmDADqmsXcncvgY/tEITBfEYbVXJfQo45bVKxAMaWsqpkXODufwC9DJCw4wleVhCl7gt5GwE8pWx2NmlRH/Ymbj3BZD/FoToIbVZUn5jNA7RMRZoVAkkKsFgWqVkKu2Zkl+khm1wLLO+FrRw4+d8jDMbeuYDUc6aHPsg3mY8r6A7s71laRO3KxAQCJBG+JThGrOdbhbju4qNKTEQQaIUyXOzYiFx7fPr0FGS5P+9401euy+4v078ZJdNdFuq/XYsTyih4QuWN4sOfKImrFtbtWwM66pCamRh3uzS4BABflEJ+czmCCMYDcbeFCDffJ6Q7k04Kh/U9X6bskgDqIb6wWJSsTAejuNrAhO5XCQz4doHuH7m+3z5BleS5QHXk0u4y0qCA7mC1EUgTSFc3bCK+nGmC4BsiYpWsGPZZuQCLi/cWY66qyksxsHxMXIqJStgC3skQhhYgCGLB6WfA9Tg5MjuHSxYsOg8ehreiSWmNS3TYH2h3H51L6JBwhG5EQBqc97E5PClN7iMAJKE97hujBszZCzcUl17HhJV7+n7+a3La8UWmtE51I0LXoSNzGh5bB6pCPSbjw93iLpScjdNC6p2oWrumLhLiR5fJGBnTSJSN7HxDF/jV+lsdzubm2N4hclA072FijdIJILrEoLxjSdT1osDkiBZNoHq1a0jgFiPKvKv5bXwnJC5AlWliwuomDOyAcq69+hKmBaSXyp3xr4gPSjT3MYYfD0ObyYL6P718cAwCePp4BncTFnwuQWF83dM6BwvqWgxta3Lp7DgC4XA5hFmHFahT0Cyu0j4kM5QsPFVR6BGhTBWgzE+M2fb7UDjYaLyuPybTCwhPO6tca+gUiXXWNBtY6EVkwa/FTL34MAPh4votVWMBbq1BoAxeNt61IzkNSORgv4MKCIisBG6zjhtMa69NRWkjtgFsuuiOXTOf9eYnh/jodf9dIYMqG5a7wKB6GzWPGwZPQHnbsMLlJteDVooQ8jeo7AqNHYSNcely/km24xcTrqEIwtxuuydPHM+6tvChwscqS3KW6U6VOk6bJsDuiXebkm8fIJPMF5CJP97edOXhnE5SHwqV2IbWS6T3CAiq3sD2Znkhgah6N4AuXasau0uleIHMQxw3qYTjvhyVMUGWS447qpwD295Y4P5vAx0BKsUa2l0B+e4X6kuZTNm6pxgtgec/BTQwf81JB9rh5Km6KjnpKmz0m/XT7Ue5LQBQ2lSrQ9iT3lEd+JVPABAHc+Zf0tyc/qzhQaEmTO8L50vRKKQsiHJWnvHl0wdrNCo/sSsGEoLQ8VZh8i/5mhiB2Hkit6f/7b7+UzuuVr7/JinClp003HqJjyNtLATVXkKFcY8Yutf20M58UskRD8oMi0DzWN1jm1EvALTOo6xAAF45sHcPIr5kgaHptgugFJTb3SU8ZCFCvixs5zbMYEJDbDfi1MeHoSAGq2Y+EMpEIXn8WxhYW3o7t2I7t2I7t+DGP5zJzVcMOcqgoCwrkHS+IYRSjKtF3W8l6DOBgohzhkXbguJE9cCOSSIJmOBkA8muZHGh8xpE8jMAktL8s5wOIRia1n/ySlVW6mYOQHrfGxBD+6zf/b/z22U8BAE7fPiDoMRKolGcB+z2H4ZiyHiU9GqMwySmkXeoCdSAHeelhjII+pqxKa5ego3peMIyqM1gjE9tzZ8zknacf76JpNWTIkLKDKhFXlHJYdwr+Nr3v5v41vvuQjAb2Z0sUgfE6zhqMdYsnJYW71cMJukA8K3dr2Eohu+Ym/aiJa4yCKC18VOaR3KJi1jqpInnt0dQ5eeWChAVcQ5lTcSlQnntkKw7XI2FHtgJeeSwDQ9i3Cj4IVqiG266EB4ZPfHKmwZ0Kn7v9GADw23/pH+FT/+uvoLN8jIg/aw8Inxi8plYo9wORKO/w5Jx6QQZXAstXu3SvXScS+U5VAlZI+KDEJJaKzagFCy34exV0ZlnR61Sh1TTpBk8lzEjABmGQ8VN2hFne86T5HDJjc8DH4Z3A4gnds+XAwFeKCTuFhQrz4KWjc3gv8F53AADo1r2+C0/XJD5vdmxZKK1lNml909BrIlkud8lAwtaavjt+ZOaB0JLiO4mmcEn7unyk8PjnwrmMWBjBq8Amj9B+4VPWrBrg+1/9Ev7yv0utLL/zz7+C179MAvy3/q8Oj3+2SHq5ALC4G9YOB3z/q9QG8+pX38Qrv/YmqxMJkc5ZWQCuBzs7Fq2xBWW1EanQK5mQLTPskby0h5Me7/ytv5OO48X/6VcBIKmdxXsoG5kYzV56tBERqSVUI/Duz1OG/eJvvsFr4m4L38kET2OeJUGSZt8CEsgued5FtrawIiGCduSIRRzZ+CWTqXz+7Gewz+XmapcZvM3gDNfxokh3qrn2qPo+45vtAehryfWQltnBXhE0HB9QO3QYHROEW61y+MUgyc+JVkJeBZm+qUk1xaODOebDEu2HxBi1A2zIxI2mNeqgVfYP3/8racEFQLU7wUw819ITqW+tsD+iDfPu6BLvzQ+w7gIMtyxDYEGw3nDYoK7ob/U6T3Zc5Yc52leDmHphMRg2WDylYyxnC9wY0YZ/ORvCWRnRL2htE6RYLQuItYINi/1FPoI9p/M+uSxQHNExfuPRC3BOJgu37AZDuNZIiBW3LuFGA/1x6MHMHdTTnGveAsnE3Xv22VALhfIDnepT2VKguIjwH7foAMDwxEGGzXv+ImjzizXStUrXO1sCNqM3rl6l1iR1nwp23ScjfKehgu+rT/7eRtCicgd/GdqACr8hgA6PJNy/P1mlXtD6C2tIJ/Dgr/8iAOClN99Aec4119WBZY5A4TB6n67j6t+oIDSbPtSnA2ahZjzn1y+3xJ7vGOaLczCbCzgt4UJPLBqZams7Nxa4qhj/EwPLAeR5AR+ei/cfEM7ropKW8snVxw0c4JhljEolc21VC2h6nNDuCYi9Nik7ZcOWDRyio1XcnPqKVVHMPlzjds+xXZxmIwBZk5pYqolq5lnoNb3+d/75V9K5/uE/+CL64zP/GW22+ZyPo90BXv1asJYLj2oqLzU86WyvrQig10WTcmlCvTN8pqoFfvhf0He//Otvcr92Tz0pjmgeoa/UhmuTsGBFrHOVngufeXSHPQF9yfyN4fdK1Hse7g4Fyg/+xpdx75/8RrrGcq1SfdwNLUSY73opNyznhJFo98Kc9Bxg9Hvxn9WxhYW3Yzu2Yzu2Yzt+zOO5zFzlWkE6BTfsR28kDJE8CXO/wXZMtmfaw3iwaXMj0N6n6O3VOyd4cLoH93CY3la/T5G8zwh2ivZutmQhB9dJ2HArTt0E/iKHiIjx0MMdEbR578YFMmVhw0Gu2wz2OqgC3Vhjb7rG0yczeuOlTpZhALAMWexop0GhTNKKdWvNCiwTwDkJF8JY/bBgC7HCI3s/qC69VJES0Ixg4vPlEA+fksC8zix0ZhNRbP3hNLFos1mN2cECF9eUudTnA2QHwXPWyCRY4RoFWdikZiWlR1OHLN9JqErAHIWIep5BRfUjE9Rr7tBxKWXRBvGJwbDB+gNipw7OBGbvWZz+JPcx29iXuCDfzvlL9PHCgPuRRwHCiuIBDlBNFAIBvvOPGPJzGWAugsDEYQMVIDitLfy3dlC+Q9fk8t92EHsxuwbkdbbBbJUf0n06URazIyodXJ2OASvw4m++AQB48KW/jf746f/wTZz/TFBbWit89006rtd/+z9HGaD30493N7IjP7QJwRDaAyuZmNwXf9lj+g06DtUA4rgBIuwq/QYbdHxEqWWmLIZ5l3qVF+spgv4DhiekzNONg+7znoOPPeXaA1YlFrBs2c7NDJj8IjpBFnbh+TWrQWKdugxEsorWjJc5+6s6SV7EKrJqe72alol/73+Rrulrf58yzdkPgHxFn/d7/wv3iP5pIxoMeCnSvACAz32FMtpuQhBvZF2rDpxpW7GBJPiepq/ZMRBWI15MaQU+9V/SZ8oMsIqzQv8jyV8W9LmNkfjgP/oyXnrrjfR9kYxkCxbfUJWEKzj/evCfbs6zl956A+IJ3d9Xvv4mPvx5gqDv/7OvAWvFpDrTy8LdxkcQzBxLB63Aj9oPPsvjudxcfc6uM2pBkydbCHQ7nhl1JXtfopMs8L8ScEMLG/wUb964xF+7+UcAgIfNLt55eAQdayfnKjHm9JLqJnFxoAkcNuiFSl3TdiCBCQu9l7Man7/5CADwucljfOPqLq5qWrQvTyZAqEX+m/cfQAuXNq7OIwnO55nBqzPyDx2oDpmyaAO7V65UmtA6sxDCwwbHDTdi6bNu7JOPbJ5brOclEF7XDVgtqKs1IDyyoO7T7gleiAEMsw5dEBqwwwY/c/MjAMC3T2/j7BOCuMdHK4zLBsuwMGfKovYUHNhKAbsmOd9AsBReudPAjlvkOR1ntcrhwnVcXo+hjul71UdDXL6qEpNYP1ZJSevydQ83MlCxXn1SpAV38p7C4kWRghafe3RRcWck8MqvB8hPA2ZqIYO4upMauzcINr8xWeD7LwzRHAQRibLHavVBjCIGO9oDLxEkPhq0WAW4XhaWDNDDy+7/s6/Bt1HeUsL/nCWXJwBu1uHeP/16eF+JKqowVXKDlQuP5AwkhKeNNta/lEd1FN7XCcgPS9jIzFU+vW9vtMZuEeQ4nYIULlEO5tL32rqCyXY8zaVMovXCCciKHX8AbkUTHskRSgBQ55oX5i6IoIRjghHp+IVBknbMzjK4gUv3xiukTUCteCN56c03IDuBSRCQzyq/UUftj1e/9mYKyvVa4Ae//MW0jqSIIoziKrTsFIFLEYP5nqGFLSiISX60vU24PKN5L6Pn8rTXtiLY9NwrCjRf/K03wusFvKZ1I19vBhA/Ou7/439A51IJCKdSgJFfiQ34u19Ge/fnv4TX/l7Y5Mc5iZeEwFPPucXRZYAbMe9ELSX0ggU+4utiS9SzPJ7LzVUtJaSRUC3r3dbHlvsvAcALZCOaqabOUj+sVB439+aJwPPTBx/jTk5tLf/v5X34RrHBeE8+zeZio+8VmUu9iWotUnuK8AL6SY5ujxaDu3uX+Ju3/iUAIIfFR9Ve2lw/8/KjdLgTTRtHJIcUkwajAR3/tGzw/jWRR77x6AVI6bE8o01YSKT6ljkZoBv1eugKl2qnyBxk2FxNq6ByBxlaWyajGtfz0C9hJPLcppaM9skQCPXqQdnBeYGdAR3rvC7wf7z3KgBgNGxSMDMqWijhsYrEoU6yNqknooUN9TrRCuCAzlMIj71phUXFtfS0wLYytcHMX28xfDdPSjHt59cp0y6HLQZFSypFALqpITUj0II1eSABT38zQzLlBgAzM+hSmwu5DonetVvXtDG+9517GH1ujvWSjvGDf/8XcO+/+zofb8f9ssgdXFBeunoySZNneLhCozz3tl4z0qHWEkZ7iHHY/K6yRG6SykOGayBuVHDrLG1O2YWGuRW0na9zqFrCxd7K0m20gwmHRAjyQ5tqqU/nEwz2uEY3r4ekvgTQJhKQoOpuh+yC+6n1mh1g7MBvaOn27dBkK1Ld04yILJj6PweeN0fPvaJAaKMJLVPdAfXUxnvjckavINmxpbgSGJ44jD4mJtf554eojnl9+OwvvpV6cX3pkc1Zvemzv/AWRGjNMWPgM383bDotgL1evdGxPKqySJmraikQjyQgVQuMP46ZNq0lcd3qRh7vfYVbeuKItd2YKb73lS/hpTfo397+u3/89XG8+FtvILITok5yfP2rX30zfYbwwPBM4nu/xpvt279EP3/+S29hdVuwslzmU+2622FimB3S/WXZR+avmD8Dm+u25rod27Ed27Ed2/FjHs9l5ioN/edyqmHwH3oZEoDxMLSreJE0dq0FHj2dQQQoadVm+GiH6o0fXc+QTVp0Plp6iEShh6NoOdVZvYAPxtJdwVq2+lKTCHh0n5AOv/nwrwIAdvMKf3R5nPw+bw+vcackVZ2lLZAJi70jgh87q1I2eXE6TRlcvluTZmwvMJQBRnVjAEZgHNo/hgcdnn5EAu1C+8RwxTyDd6yZf2VUcm/JFhIrP0TxJEytHQ9RhvqfIM3hmEEeTxep/ebkfAodROWdF/jkg4OUidgB0/VlQ9cqaUJ7leqZr994jCerKfLAiB3tLXHmAnu1k2gXdIyiUgSfhUzKA0mkYndMsGYTrsl4uMByRFnmSg2hlwKDIB6ga8AGtqc9tqmFRjQS+aVMMJ/UDn/h7gcAgH9lX8LqagD0VLNeuE3Ix+/+1V8n5Ry+NcjzcO0GLVbnVMtfX4Safo8HEIfZ74itGbV6xzbNVdtJFoMAUE6bxMjuMp3QmPx4jbbRiY0MIGXXcq6JWR/5CA5J9GJ1PsRJQZD5qs6xvhxABqUu9NrE4AnFibrP7YyPXxjKzlQUzGoFGw8IcL3bYsMwwme92p0hJafkg+z5fRH5iJNXLVXyVLWFTwzX7/zDPz27+8m/+RYyiYR0CderFQrORgEgW7AIiS1ozgCs9pTg0hyMXhkBO7K9c1W4foV+1mtB1yteMgG8/BuUTb73d/iY3/kF+vlTv0xZ86tfexO6j8z9KePB3/rb+PQv0XuaQ7rHEVouWpm8kmVDSm+f/UV6raqAlpZBtPcI6Rg8DTyDNTB/OWTeCmltI+Upvo7KcIknwv/P8nguN9du5iAHDr6wmB6EVpk6g2k1jg5oc9LSJTJGhNIAwFcKopWp6H+xmqFug+XTqoDObJo8k70lFifUruJ82BhifWeuElmgOF6jDSLstpHAC00yA//hJ8ep/uo9tcHE9pJJ1qTN9SifoxQG/86dHwAAPljv49uGekhrKzA7IDLMfDnAdFJBBqm1xXf3YWn/RDlp0LUaWVjZYs0TAORpnqAdMyYjgCir55dZ2qi6PQd9oXmBUbxwruscUnqq1wJ47AQ5uADAdYaDV0jKcaeocap34EMdOj9XqQ9PHDZwlzn0OV3z2WfP8TPHH9I5r/awarMEwVrLD+jo9gLVe0RoKi5I4FyH+pXZkZDBUOFqNUBd5XBB1H+lHY6PSdD+/KMR2hsGg1DvNSXQhNq78NzeoRcESeoXqRVnNl7jv//z/5QO5M/3XElA7RP2Jq1K9//Hr8H3DO/hBepw73Vu0r/LqwxuxyRB/tQICgADB+8EpIx1LW6HGYybRPAaDhtYJ9HE9hXlk3pYvcoA7XhDzS0Q5vjwocTqHve5yrXiDbtwOH+HzB1kLSBv1/w3K9JGm19KmCFLHJZnAoO/RPf+4nIEv8qS3KL0glWZJMsM6pUABODjHq88+gYREjK1l3gBuF26T+oshxuyPKSqRbI6hPIowrx67e+/ifHHwDf/yR/fZJf3PPSKjc7FBhkJyCrugwe4hQcCaGkKojwV6MbcZmfKno2coQAzP+He5dgf+yeNSGj6kwabCwBmx/2pr4vjxd96AzJA2pHAdP+/ohpsN+IApptZiFbAFiHAMNzqls2pnWz5Qjht2wsmHAv604s91BVdrN3vA6tbIflQ21ac7diO7diO7diO7fiR8VxmrnDhPyNRh0jeWQU4gc/vk5LON09uw4To2RoFeUpRvWooek56sNolmE5dK2LuhQxjcTGCiJ6t/z97bxprW3Kdh31Vtaezz3jHN7/73utuNtlNiqQkJnFkw85gWzYQR85g2EgUC0pkK5QEiaQ4yDJla+DQJLtbkWVGVpRQgxXLBmwEQhwhkIEICS1FE6kWh577zcOdz7zHqsqPtarqXIqS84OA9fRu/Xn3vnvOPnvX3qdqrW+t7/sagWQq4EK/Nre+C7I8zvzroKhr10Gww8ESA7YaG6Ql8qhGxFX/1kr87vgyANLj/faLn8XbMtLq/fjxX0Kxz/ChAC7vkAE3hmPMmhSXe5TxvvBWgYgz8+NJF3HSYswdx6YMIgm6p6GdPkDMmLCD+Vx3JvzleZ9HCCDtUogfRXRdLpOqihjWWV0BmC4po5XCEgytHOQnfGZgjhKoQkLuMOJQx9hji7Y8qiEF0OxyI1QS7kUWt1gwhNjmFrZ/UjUmZbu7po4gpEXETUyyAfaWlI3ZgYaaKFQMf5WXmpBlCgvXVdReKSHvZb5J6sE9fgMPgiXp5+SJqc/koQVkEbg+pqf9/DSN9PZhpt/i5rd98MQxH/ulj9DftICKje8UbxaxV3mS0sLs0WdN0wRq0HhBDFQSlt8jGgErhH8mjQ4KUMUZC5sZj0hEC4GaAQ4qLzAC0zeQyuICa1jf2x3BcDpXXmqhxpFHQopzBo/3CR1Q0kCvSRzzMyivZyslAQHJGZCNqEvWNcBEM4WWu7/lUsLkBskBN10pBCMJAyorOAWgQnjvXhNbVJtMG5tILM4Bb33/8/w3+Exb5wbCSH+/hYHvFk6OLZpuMBgwSejghQ0NRq7r9jHuMF9tSnrLDz6P0UsSxXY4/h81nG7yY58M+sQQBMFal7kKnOjA/sPG9e8NHcSPfeo59G4K9NlspNyy0O4ZqSQLWnCmuWKvaXoGVmBF7WrlGgygFsxUaCmLdQIodR9eyxmLf+Op/rEfwlr7b57xPyFjOp1iOBzi4o//CGQng+w2yHvs5lJFUMriu5/+NQDAcdvFP7/+DgBAntYeIp5POrArcKNQBhjTxmtzTTWoJnD2PF+vkujsCQ+XmjS00Lf9oGBihYUcNIi51vbWc/exnRG8qK3ATnaE15dbAIDfuHPFn0eWNLg8HOO/PPs7AIBfOXwbfuP1qwCAtNNgkFOxZ5QVKNsYl/tHAIBSx/jig3M0B0UMU0RQ3nNW+I5R9SBFy3xbGPILtew+I6wg6hJAUoTKYvMcLZZp1OLufdpcVKph9zKvUpUOS9QOlpzFwCB0mnq/VuAEtKy6DfQs9nzKtg2wZJo2yJMGuzfW/ZzbDabN1BKdmwxBW6BeCx630VaB9QEL6T8YoftKEhxVutYvXtFWiaxTe7h5OekEgfk+nZcfsYHgjdcuApf4+vedpD/s/NQn/fXFgxr2Vu7Nx+s1g4RlHl/+oQAL7vzMJ6C6LbRz5LFAn8sbG90lFnWC8ZQCK91KGK6r3vy2D+KJjz7nj6NXmqrNVg21y3B6x8DmGmIRvEBb5x/abSFj42vBdR1Bc729czdCyWYFNrZINwp840UK9q5P13HvJgUpSAziBwmiOU/yO6e+9u7+rdhdZ3JnCMl1bdmE74+qaVNZVRlykphRCSzPGb9512cadF+lc8wfWEyeABqGSGW5IuIP+GAyGUvUG8YHHGpOvHiAFNpMYn2tMD0W+PLHwv158w89T96zoDXAmYbLJjgsmZiF6l09OV6l1FjEs7DGmBh4/fu/eg346Q8+j+yQ15GOwOIiH08yhWkFXV1VgXrtKzqMXXexq9W68R/8+Y+jWqNJLzakr/16LqyrjQqEhAO06bprU2XgKpvYnqg3C6xcd2QRM1e5Spe4/b4PYzKZYDAYfNVr/+M+HsnMVfVryFwizyv/f6N+gWFW4l/uvg0A8B9vvYT//OrvAQA+N76Eb9ikReK308uomghv2iDe6G9++TEi3QPobS7QtgolO3ygUt6xIp6SXZxzfTCJQcIWZiLXwT1EgjInXpj3ln2vibuRLqGEwdcPqMZ4Z22Et46IjrOdzPBnuy+hZBfoV463MBwG2UDXmDTfXiCJWtxXxCltjcQ6N/E8qIbobi5R14GXmjpbvMsaI56vRZGgPegbRABcAAAgAElEQVQgWgYBBX2N/mYTgSjR2Mhpsb8/HUAytUeXEbm08LVViwS9EWVVi0nIYiFAr3F2aBOF7BrVjN965j4S2eLWjK5nd9JHc0CZqh4prOdFqL2t1+gwnao86vt6nblcwjTSn5dSFgdjyn7lNKKGkSt0/rpRfn3qdGokkca8ccLSMjRdycjb4KX9CnURo8OBm+403k7vyj/+GGylfOYqjPSUINzIIQROuIQ4dOPEaAV0qTxlSkiLiGuse9MeymUCFfMmt9LAtPPTnwTWw2FEE4QiUIbMDwlpWHurujMlBMswyshAVwoF16TFQmFwna6ts2ew9QKd7+2/KDDqFfjcPZJ9fOvZ+7ifscDJJIZJrHdKsXWEwmXvALbOTDA+4PtRiRMbktsgZCWQHqoTC7irWbYdqlPWZ+ja5DyCpkcER19Hohf5XZaEvNJ6VCQ+Vj7INQkgKgFwAKJqwPAx2h7bJjoqV1Ag9ecpeVqbTrB3pA2IfpQNawG756ABJNcZs0MJVQHrX6bAcO/rE3/sa88/i86u9Blee85icZmPL1fEbUqB19//Xi8UYTILY9xnC+z81Cdx8ztJ9OHac89C8XU/+cPP+zltBwY3fvVD/rPf8e7ncOH/pkm+9ZdoY/WUpshiVbVCGISgWAgftFi5Io7h9NU5gLGp9jXcZP/hV5M4rbmejtNxOk7H6TgdX+PxSGau33TlDSS9BC+Pt31H+zAtIYXFu9YoK5TCIGcTxT+9/jqupXv8/xa5rDFuCXZLhyXqPfp5fphTuMJZaHykEM8daZwiXpcNxMcRrINHVFBrshaQ0iBluo2SBnlEP7dW4rXlNr557QsAgHdt3ESh6SBLk+D/LR7DkrG+y4NjvLRPXq91rULNbJHA5kDR0PsiaTDKKHts1yS2u3MvUjEpMhSlO0nrhRVgyYDbmYG3fQvh3IWMQJTX2J1RWpJEGtsb1IFdtwrHx70gfgB4Kojtaiim4shbHQgd6EK6Y7FgKcEv2HNYHnf8+0ViAKY06VmMo24HvTMEoxNkzOT+rQqN666dxehuh6KOMcJH3VZZGCVgOdN0Ck8AdZSXIka9CJmE3Wb0YxGfoDfd+NYfwOP/9Mfol5s59BmG1CuF+FgFA/alQMOZT9uljCiZOBJ/ON7Ozz4Tjh8LyER7d5N4vfSv62Y1ulntTcsbZVHf5hp6ZrxDjjpbIOvUWPI9NfMo1OQaASuFz8SNVtQFDxAlbaXzV9YC+QNGAGrrM4/BKwqT/W3UT9CzNak6iJje1EQRdMf4uvbar3bQdul902sGB1EQ/5eNQOtMAloByxl0OgaKbetrnU3feLjRxgZqKX03uNQIfqiKztllucMvRagYrdaZhXRiB32LqBDQnGXV52uvNCZbyWpRfJ+yIBTRDCyiWqDlS0gPlVefIj9X4c/JSuA1hnuvPfsc3nhfgGSf/sDzmF+g83/xI+/B295Hx7eXybTcvXbnpz8Zngsb1Kzc9eqBkzwSXp3fCpI2dBSb1b7cpmd9JhnNJa58+lO48W6SexzeaHHnz7H4/xQnMnFhBBQ/hvXQwgh7QvjFQehSWY/aaAXKaBldEYvoxPfwYR+P5OYaSY1IarRG+iYaYwWWVYJBTE9IRzW4mhM9YKYzzFgS5Xf2L+O/uPQ5/OrdN9P7TGi2QSu5IYV/7Rs066GOIlID5SDSvPVP9Wi4wPERwWCwAtujOQYpncfdyRANq/QUTYRLgwk+9TrxXpdVgrWcFq/dSR/lLMVogzaWqomCHu8sxugqNTSd6c9wIZ/4Tfn6dB3HY4Lrpsc5Jr0OhmzKXZSxdxoR8whynb6xulSI96MTTUuO4wllobXEjBvFcC/DzjsIus7jBt2kwdzZu8Wt51bem6Xemk5Lqsc0o1DXcXKEZZEgylsM+gRlK2lxyHM3Oj/HfJmiYfP3uFeT8hOomWpeBs3nYpF4iUZrBSKu9dVDgehMg4qhZtlvPC1HRwqmVkGdSFqAXX2EtD5YktLibb/8Q2gmvMKut4DbpIWFqgQ2f4950mcFmkuBa22lhW7omLpjvIk7lsG+LZoqmEQBrKyltYTmhVNJi6ujQ7x2RIpc9Qq32qbCu7DoljSkt9Yp8Fl2EyxfouegHWpABVjeKAtt2KLtZoJ6LdQiYQUe/AUK/rIbqQ8a0iOgvNCgm9Mzc1Tk/llSS1LYatnkvh4m6N+me985EDh+MqNFHsTrNEzVWLVaW1wgSNXBr/XQeEhUFApSBzlBsQzKP7BAehh+L85YT5tZrUNe/YlnIZYSxskyTmNfN7TKMrzJQfQifJaJLMqzoQNJtME1iOB/bopKaGO6+hO0weErUNC2CxxepuM8/aHn8aVnWbf6Y8/BRFyrBwCBEDAVKzXcCLjy6U/h7K87S03g4J2BHCu08HrLsgkNWaqEh8ZVKWBi4aFl+xeVv++On+q4yiaCL3nRPAl/b+LZCt/fUrAMMD1HrWzCMtwL0334N9dTWPh0nI7TcTpOx+n4Go9Hslv42s/9AFSeodupPGzYTWvcvb+GTp8yncvrxxilDJca6RtGzmZTL0oOAP/7l97mRR7EUlH3oktWz5QechGCdF0dxaY+yjz8kq4Fsr3RCknaIOYsbrHIvDuMEBbDTonxkrKqTtLg8JCyNrWbIpqHaNGk8HCU7mukI8qEv+78PQzjEvslve+F1y5Bzrgr9EyBJG2xvMtZdL/1Ha/ifgZ7jo4hpUU7jz1sKMcxEjZGFoYbchwzpxHk0Qkg2izQY/ECACirGCkLYhgjUXKmLaUBrne9Iw8SA1QutLbobQd/2kWdYMZeuHGs0TQKFev2Zv3QsGYtUHFGC25GUawcpZRB63xB0xbWCp9lWQufwbnPF8vQpXvjuwky2/mZTwR4qyIDa0cxaDtARowU1H2g2jTQThlMC0jOysVuimQiffbRrJkAj1kE/E5aysDYrH7QLXFw4LqDBJ2z88/MWu+ZCwnkt7iRZ6dFZ3uJgudERsZD4FmvhpQGBc+jjA01o4EamIRe8d3UIeuxykLngd5kExPUowy8YEurJf7s5dfx8mQbADD5pxcCvNsVKFfgXhj4hi/RhjmVNTC7FvTAo4VAfSbMqTACYkjfG1MqT2OCoEzKve/lv/+HizN83fc9jzlnj7JeaazKCNp0esKqDNrgzYBbYPleJfvBvMPENrAFnIes+55YYP336ZflGYHyTNBzjhYiQKkZNUV6T9QIXswCCP6wOiUTkq+mI/ymH6Ps12XvJgkw7Or65ebKNWQ1IwO1Rt8p8yDjzDYcNz3i571rYZKg09zm4X7KJmSu6aH03sH0R+sNUsy8wu0P/t3TbuGHbTR7OXQnQ5VmSIb0sKRxiyd3HuCVF6m78eWDHDvXqM762OAAfYaLF22K39u/gGOmOthGQjoayiyiuhZTWawWiBh6NIa6JwtWuml7ynd01kUMccQ82lKgOKMghrSxCxEUjUbrc8zKFNc2aKX+8p1z6Lzs6qAn+Xar9T9RSmiuIf7+vfPIsxpn+9R9qzLta6fdTo26jiA3uMu1jAIlZqNBh7mgxTijWucK77LhhU1PEqil9IuGVRbRJl2LbhQ2uku8fmeL5yuGXueOY2mg3WflLezFEqmbOy3RMK4kYgOtJY45wDBWoMMbdN0q1MvEb8SlSX1HbZo3GGzQ4l7VEZKk9fZrx5OuN4WPY422VV4SUq+4zyAyVBfm+/3G3wjygKuFK5tpvP49H8Cf+U8Jurv1l4HsMPAsTWLDMaWFupv5W1acD+UCUUnfbd6uCOKLUiHaKNFnyPvgsA/L0DUsLZDOMKLtB+UcGxssrvHml2kUkwzZbZrXZAy/kVSzCHaFt6hTvbIAAmh88yea9aAUZTONzi16vuunCvR7BbKEznu6zHwJ49bLZ/BTf/UX8C2ffTcAoP9fv4EHCwoOjh6MIJbK1/lWx9UffxaLi0xviixRwOAgRoHObfrsas0QR9g9T62A4WBGJhpVFnsnlj9qNF0EKFiG+iIMACv8JqFWjL1FK7huHoJNnYSNy8k6QnB91HdCGxz8KZ7imUI8kV7JSGdBacndW9c1bfqG6uOg82u4xprthY70rxxORtJv9FFwAxMrwZKs6HMdVUZUAvYec9E1X78LIo6lh35tdHJTbrt2hasrPEXKRvDqTACZL7i6/7+J2/swjFNY+HScjtNxOk7H6fgaj0cyc+3cV1CpQjKRqAcU7U7fxRExa+SqvMX9Y4Ij9qY9r8m6tT5FErXYWqPM78F0A7Z1PDOLfGsRBO4RSPEAoI303bdCwkOPqBSisxTV72wf4fXb20GcXQYY8rgaoH9mji+8RKKd0SQ6QSDXKYlkA8DsrPHGAFAWLXuv6pay2G++9CIAYNkkuM1G5+6stcuCpEXKhuhxrL3mcLRJ2V3h/Brz1lvM6dRQfwJD3vFBhMfPUGPYhXyCl8fb4YMsfOewscpnR/ZBBtPTqBY8P4kJHYwpfZaDlutaYc7Qpigl4rE84evpROurZQyd0Hv6vQJKWt9Rm6SN1ziu6wj1cYZ0ne6HybVXmOrnJfK4wb2jrwJTiZCFyGlEDSd/mf6U7isYJwOcEcyZMzKhtUTlOM6RxdrmDHNndzdJYVwasaKIZZWFvt/BAdsLitXOSkHwezOmY4jEeN1nGRlYRkisEVBzhZoFMgavC0zfxvB0bJBmDaqx8zWTvnlHlpQ5eYH1lQzQtDI0pDCU3nIz3sXRBIZvfHZugbf+8g/h8ohefFx2vI61nND/Pf4MiRqY2KLDGrvXP/4+XPnJT/F0Cy45MMIwbLyimjqOkOxFqLf5ekxQBTOczTr4enU89Xee913EsEA7snj17xCseuXTnwpNXKDMyvmVWhUg0Hii0AyD8IhcMQCXjT0h8C8bAZ4eJOModN5qgnXd780waCELDUgIGBMyWfc6nVoPoRcXWkRj5cUhgCAQEbHFpesoFrHwzYmGs073unotZLWr16JKAVnD28oJA5QbvBZtNdDTYCkoNLx/trGA5mYl29GAEVB8z+Uy+Pj+CUhcH83NVZWs2NcRKN/OZtSRRqMlLlwiyPXp9fv413euAQDRQJxkXz/CfJH5Wi2EhWSxb5NZSGmRMY2mm9RQXKs9XnYwW6ZhQwXQeYO7QlOLpqGF7EHWp4WAn2gRG0hnTH0/g31tDWvzsKFGRejDbzsC9chdo4DmblvbSsQ5nVOaNb5DFyBYNWVf1qJI0Cxj3w0rhrUXUO/2Ci9mIYTFYp4hZkg9SbSvl4rIQGatr9816wKvPWAY+CxQtgFqjucSug2dsd7DUwGy03plIbTCz38zThFtaRQs1GHrINFoc41zT+76TuvDIvfKWsVLIzRr3HGctHhyaw8ld0zfmQxx7TwFAAfzLtqe8lDzm87se2rSuMgwmXfQTOmYOz/7jF8FVN7i6hl6dl579Rxkr/FUmbZrgxtSbIHEoGJzgeFggYbpJVmnxuXhGGWPfn+lOQOjmP7RaaGiFTpMkfhasG3gqVYmMyTXyV3r0VqJms/XNhJiBY7W6w0i9jnd+1M61LXnEeo1MpegN4Z6o2xIbeekqhH/UwuUV7jOXSoSguAa2oEYefecc2ePcak/9s/TQiVIIvYUvrBAc6+LljcnUQsUZ+jnqz/+LARLDlrBvq+8udpaer9h3RN44z0f8Ke38z99Auu/TXO6PCsAGRSvvv5vP4fZFT7/TvB5tVzXdnQVm1qvvAZFJt+OMqVqIJ5xXbIvoGuiuvj7zfPT9EKtFILqkDe+hxS7HvvEc14C0XXnemi0DTVuOhmckBp0il46s75jWraC5CG5BgsDr86l10mS0R0jLsh3FgDye0EYIj2mznYT0+uKswZmRN8LvYwQTaXf9JdnjcdBb37bB3HtuWe99KJJrX8+IUINnQ60cl0y1GbtVxNPecjGI7m5dg4MVGJQrUloXgCRVyiqxEfav373KuoqGD2vszNKEmm0nRqzY84sEwPJGZBea9G2CpozvIN5Fx2uOSWRRpu0K1J9Laqn2HJunng1k/luD0ILbF4hecLZMvPOJUqT/JvOeKNfsyioJwTD11jyLAnRY5+zo2KZ+mAgkgaLIsFvHl4BQJudU8dRKUWSbsOwrUSrw4ba5aDhcJHD1Ao5qx8ZI3ymknVrCGFR1Jw5aYH2gH5+1W5jNAiqUW3PQKxx+DyLQ+PKqssLQE0xXHKUS4VykZxQHlK8wHa7JS72xtgvaKW4d289FAeHGpI1TS8+PsFGusStOWXs/azyvN/JcRej9Tm2e0RpMlagZMPv2XEO1BKSkQSTGfTO0usW0wx7M1YVKiRRZZzVW25QOCcQXrTzLtfwi9Rn18U8xW7e81m5kDa4vkxjSLZm6/QqJKMlaq6jFw96vsYKK2FUBHATmbneRXSZs+RaId5j/msfQBXqiGqhoJn2I0ogeylDue0kAuGvxcSA6QcaChA2AZ1agBES0QigVUEWUwtfx87jBsdljpZX0iv9I1y3RDY93usDWchb5MriazrG00RELWghnrrvr4Y95r6FWuDqjz/rsyVhJMZPucYqCzNqfWasnw4bnpBBTcl0DEQtVuT9gh60yQ3MqEZjOLOfC5iIGxXXqAosG+tuh9+A3EYKgDLwryjKPc46w91dCdkAy3OWj6kBJxGo6ZxcfVPWQRISItRLTWyhCuFbL1QjvF0c2CKvds+kBdZfYKWxJDjYLM4JREt4dSu7UXsN7lf+5odw5ec/joZ7Jm5+RwhmnvjYc0AiYOOVhsQyXKxTNZO1IpUql5U3bLUHwJqHf3M9rbmejtNxOk7H6TgdX+PxSGauVpICzeK8RXaXwrRFL0WeVz7TnC4zNGyunW8sEbNzzJvX9vBU7x5emFLd89dfeeyEP2HbKDQRZzZGwAVgqTTIk8aXG3tZhZrrsUdaIuHO2Govh800Gs5KmjryVJ94Ri36yRHXs/rWQ0LHT1MNxXtHmuBnaozAaEidspv5EtMkxbigqLuoEsQMC0exhsorLCbsKtNIxD3KLCeLDmo2wr44nADDiYeXX9vb9N2qsl/BmNAlrSPjMzOjBQ6PelB9+rzHzu5j3rDJ+rbBjMUlju6MYJcRVI+PUSlIrh+LXgtTKA8TA5SxAqSe9fn7FzyFRGUt+vy36awDm9N7DpY5bvzGJcgn537OncEC1hoIYTGvg6q9q+XBihM0Cyjr69CwAtP71PGqNNVdnR/mY7/0EV+TfvzsPpZNgoaz04mRqBlSt0Zg/2iA4YDuVSevUbjbOY29y87W5gLGCuxPKVO2eQvBz2q0ELBlFLpJIwv5EqEs4i0LGMWvW0pYABHDhtWmhuDjm0ELM058NhwvBKQO6j46t6QlC+qOdXVPUUvEx1zbzyxMv/X3KR5Wvvb+767fwK/tPeHRglvzNX/vISnz97W3jg2QaGQBl2UaFphgPV5TC2QHdI7JmNSQvFdq13pdad0zZFR/hRCUSFro27m/Ft9tHxlyBloEuN2nWJGBKSLEjYNLtRdy8CUK3y4f6DZXPv0pn8We+3+AtiNxtSYIOCmlP0dZAy/8g/fgHe9+js8rQuXqx5boL67GaxV8Rmrj4CQVTUkT291D2QYxETpm+Fu72WBxnuZ/Fe43CVCe1VBDZj+UCs0hqX1d/cWPwjaRv787P/VJn6qpTFJGzSUBi5Mwr+Xvv5UWdhp75StVChieb/v/w9j9j/t4JDfXpkcPTnoksLjEG2NLPMv5XWpW2dg5xjuevgsA2C36GCYMseoYt8t1vHJMdURrBIQTP28ldK1gWAJOSovZnCkjeYU40n4BbrelpykA8GL/8XqJrbUZ6taZpysI5qHWA2pVL684bTOBJnI4ENCu6bDpGIFLa6TK9Opy26s8bWQLTOsU0u0lvSUS3iC0kdjszPH5ox0AtFhipT7rFoynh/exGc/xG0fX/P+7DXOUF6i1QqG4Dm2Etz8rigRmHvkyy83DNW9QoKTxgY3sNTBlBD1hfCq2sOy8otZL2Ay+/qiURc6b/tG0i2a/g3iL7tVaf4lvOvMGAODX7j2O40Ome+wNoK6WeOd5ur/JxdY3Dn3+/gVMZzlG2wTLHy87mL1IaveRpcXMMGwplwqLL7AS/sUK3TO0KTrq1LV/8lGa10WMeEAQei+usJ4u8dqYFZRq5WvejnFeMl1rOe5AMJQdLaVnw9w/HqCeJ8hu0ZzIoUE8CYtRMgHGb+dnK7KhrjVJkfPr8gcWTU9gcYk/dNDgxrf+AADgyi98DPUoOPLoLDTXOFqJq+1FpUDt6pTKohkGTF91tOdor3eXuHVMMPxLv3+ZAhM+/l7PIj1yzXEWsgHqcys8YKf+JcPmYVjhyG8edeB4v/CT78G15571m63OAHsmyFSma6U3UtCLGMLBxzrUXKP9GM1Qh8A5NkGZywpk9yIPhyfjAACaRJyg7az0NpKVH5///jslGY7nPF/T1DvfvP27SepQd7imW8LzP4UBkokMxuoNYJ2xuFEBYrXMdXXKkSl8QGQjwPaCY5coghynDQqEaAcaSAzsPjcMKgvD7kioFKAFIq7Lq4r43O6zraDGUQAot8N1246GYPco228gTGiOq/ohScEcD/04hYVPx+k4HafjdJyOr/F4JDNXVVH/UDWiDlMAQC3RlKmnLTjLNAC4vb+GN5bUOXT+whEudY693+Tm9hRbXQqzjoocdat8hqetgGQ4eZQXuH88wOg8abkWVega3t6YYnef1NujWOPe3XXETP+QsYZi0Xf9ICfhfxfgKeupQ9laSbDhSqZ5f0pZuIoMUs70bs3WkCqNoynBO4NuiX7CUb2ihqfuGmV+1sJTVJSySPgYv32wg6uDQ3z53lkAwNXtQ7x2nzL5oomQxS3yLqURHu4DIHKLUljfXFXv56j7zjsW2LxAc75bKbKc46dza3uK8z2at4Oii/Gy43WTy0WEfe4q1uME6fbSd/pmUYv7Jc1rrAwbJABpt8XOxhGWLZ9bBNybswVfqzDoL31TUdWEDEU2JxVvrv7Esz7KVw9SlJwZ5IMSda3QHHPDi7TocgY3rTO8sbfhRT1MqaAZmTC5huy2qFgtSo0jD+X13wDanJW0/sMK3U6FcdT1n83S13jlw+/BzmdWRP4tgnJUK/DiR04qEl17jmDJG9/6A9T9DCDOW5gzFlXK82NChuj8aB//OEGW7apaUBz8PbMjgeU5ifgaPUt3Hqx532NhGIJ1zVSVWBFJAOrzK4IZiYHZZNhZwKM4ZtAi7tWonb1jI7FM6bOv/g/PAhJYPEnPtUoMlRJA9Jn6QR7Sio4+UWKQznc0oiYvs0LZWRWTN6n1cyzboOlrYgvdNZ6mA6x8JVcSs7ZrIXqt17euR2E9WJ6zeMvffR4vsp7w27/nebgTbrsW1ZZGypZsWq0Yxi9D5q1KwAqBZuCYBcFCUBYSyaFCzWyCeCL9PWyTAPnHY4U2D3NjYxuuQVLjY6AuCWgu3chaIp5ILC/S79FMod1kNbRxFLLkVpK9oTPvSAyEa3x6+B3nHk35w3f+9Y9AJRnGTwLtDtXkDLuadLaoFlMVMeyU6R6J8dCayFuoyKDlGuPa1gx/4xoZlE/aHP/itbejwzDlbJEh4+66skhgjPQC8XGs0XU1zP4YNbf87S162L217r+c2YU5in2uCRkBWQR1lnpD+2+u7LZQyngYrlimMHzO3X6JhoOBi+tjxFLj1fsULKwNF5gXBPsM8hKJ0r47tpvUuL1PUN7maI4Fb5RSWBRF4kXxJ+MgiA+QXN5TFx4AAO7PBl6esBpnUL3G03SSrPGwcFnGGLIY/2SWQ5cRuuz1enE0xq0jOo80bjE57sLWrg4aPlfUEmKt9lxGSIsLF4/83x8c0gba7ZYovzTy3NPkiak3kz+cdHGWOcwA8OB3z/pNwKTW11Hd2PnpT/JnC9heCNQuXDnAXTYHF6WC7QUOqV1GHpKzPY14NyyszUj7GqMqpK8BRktB9xsABg3S1zO/CNZb4T03vuv7sfOzz3jVJLRBpN6kxlNEzj69h6N57s0SZgdd5G/Q/S2fKmC18P66ohV+Yb75HR+gTlwna6hObjqOcmGlRXymQHufno1VX9Z4JlBtan8uq8MKwKw1YMYN8kGJsuB6YCPDfTeC5Dddbc6IE1KLopVI+LuslCFje4CkEQsJxefZDrT3F44O4tCzIJy4Pm+aXR14rbFFNFMehn71B96LnX/0SX8Naim9vCF9Jh/SrMCjkYXIW9+P4NYaAJCFQHIssf4yvbEcSk8XgsAJ6UJ6A8J1O7hYCzJc4C5jq+Ch96ZL0LsLyGRz8hir1B5hAtwuDNWXAXo2hYWHzW1koVx5TFjoWYyYXY9MZKnWDXo+3LOq+xrRWCHfpd/LDetlJF/4tu/AcDh8qOUPH8nN9anv/ChUmmF2xQS+WGYgK+F1QJuhJmNvAEIZSFevsBRBS+avqsj4zXR83IVdRkjYAswagcYJISguRPDioPoNJNc6+90Sf3Xn9wEAv3O8gy/fPwP1IjWrrFppQVqoQnryt4moGQGg+pY18DzGzbUZZrxp9jsVNjkTL9oYtw9GaCb0t+7WEv0One83bN7B5w8v4GjGFmVGeM3dvFfh7WfI3WbaZJjVKTYyOubesu+bUw4Pe8i6NS6vHwMAbh6uI+aAYjHP0O2FxbJZxH4+kBmIOX1WeqRQXmg853hZx75mvJhmsK1ExjXMcr8TXEciC0igs0mLajHO/KIxWF9gyNe5+9tnIZuVReMbJ55nWXAAUc6C242jkLzx138QAPEtAVD9ye0x08hrq5ZnDEyuIZh3aYtgpC4iA8xjWMdj7raeTiJr4vM6O7Frzz4XGnsyA8Vauer1Dl79wZBBX/nJT0FusI6uFohvp4GaIwGzUqd0lmQ2trjxXSclBh0VpNluoFIN7TbXUvpCnGwAWHEiG9NcK7PKesoOAIhUQ+5zU+A9ieWFkCnVa8bfN1XDz6OVQLOmgwjJsFKAmSsAACAASURBVCKqGkBiKi6RMoJEUtzGXih/r4OuMc/5MgJcE00rIWZRkOATK4ISImTeztTbGcjLUvpau5opr7MNAKZWgWqiLJKDyNsF+iAEFKQ4qonQ1Bzn5f5q4elNJjUQtcTGC/T74TuN1+52G6Kny0XWb+SyEivShSJIbQLY+ZUGgrsrdSJx+y9E/hmxEdC9zc1Z29bTn3RCtXZviB5b//yIViBaCLR9bqA6t8QaB8eH4x6MFhB7KV+PJcEIAGikp+KsPutfOdxa/TBvrqc119NxOk7H6Tgdp+NrPB7JmqvOAKTU5eelvQ4VdEImyQAoimXXFKuF9w9dywt04xqZokh42Sb40usXAADdVxIsnyo97eL85gS3NHeaxpqoMUcUemrEuPrEgT+nVxYE086aFN1OjZmTI1txnXDD/Z/QCJ6bsUGaN6jZvWT/qO+FHYp5inGHYLFRr8CFjQnKIb1uXqaeUnNQdzEvU29g3u+WEAwrbXSXiCVFn+8c3cYL44uYs+/oerbEvZaiy06vQlXFePkVmpNkrfT13u7GFHWrMONsXqxkGzJrobjOrBdddF+Psf+AzN71Y4XPnGylIFfkFoUW/hjxWoVet/SmBA+SPsa3SLJqkaVYvkw/xxUR79W/Q9n1fNpBh4Xx47hFVQXj8yhr8Npf+/CJ+Xd1xys//3Ev3yhbnKhnPv6J52BiJzYRvF7FcYJoJlBfrP31RGyUYO9lJyJ5s1X7rHmQV6gZ2i8dPMwjO7/wnefLMoGoMtRrgZ7hMoV2qHHzb70ff9hwfqY7n3kGnRc6KLY40+lrJIdBZEBo+Dl//QPvxc7PfZxPWPheAVgBrQOs3fTg63rl+RaiFV5mr+0E+LV3bYLESO/M1Iwz35GqU+sz1WgmYMsYbZ8zwRXqhtACNrNQ3I9gVOPpbLAnhRekDmpKNgp11Kjgjt/EqZwJ/9lmqya1K4c4rEC0NmI1LgYE22ilLimDypMwJz8PCjBRgE6juYCq6I/ZbihA1hvaox4AICvpIXvbKFz/3pNlC4eyXP8rMc59lt734N8XhNS52n4hvGCFzi3Sw4B62Mji9ffw8/7pTyGau9qvQbNTe1hbSuu9sfO8wnKZQjuUpBOQCFEL6F6gbv1JHo/k5urkD60ItQsrqTlDb9LisHPhEBPmgpZ17BdzYwWOijyYfB+MvPZvMrGoHqQYvY2abyZF5r889WEG0W1hufbwn3zj57EW04b9m4dXMK6pNrU77WN5vwfFi3Z2LFBcdK7GFliGBQsgLVMA0JVEuYj8AiC7Ld5+7Q6dv4483cZYAWMFHoxpMywPO6iGtEjsHg6RZrXXFo4GxjdrbWZzJFyQ2q/7WEuXOKy4KSopkLDryAvzCzAHKcDwV1PEOGAIWnVbJGkTIMYVjdo41kG96piUYSwX3vT9LFiZtQLGxKj5yzq8PPFz0c8qLOrY2wMOsgpjrnWawxQids0dVE+q9lnzrRU4BP2cdho0VeSt9to6wrVf+ggA0n2u2gj3ufnsxn+z4orzFcNEdkX9SHrnoXjKxtF8rWoSeQ1ok1rs/KNP4ubfpg1QjGPoDt3saaXQX+N+ACtw9See9TBlPFRY8vHaVkHm1ju1tBLQ3BCnEo23/vIPAQC++Fd+5A899+4rCaIFEDMVxHRCw5FzZHENPE/+yPMQ6/S8XH7zrufvHkx66HZKVDfoO9R2bbjXx1SvdNQNkxpYNn4fdkqSyDxPz11xrxdMvSWIowr6vupegJZhQ23ZJkQ/csElWfDxxTUUUHtrxpXNz0rha4NYSKgKiPfou9GMVmD+eUSf6wK8VgYFq9SgPGshvbJTCCra7go8WlITkd+AotUdmhMAEX4vz/D7WgFpxQmKT7TiuOS0hK2k91uXLBjg/p/heaxpY/NqTmlQprLSotwM57KqKoUVhx/b1Z6bDwBRpFFwb4W1Au0iDjVwLbz8alPLICP5VaisrofBTqo/+MeHbPzJDh1Ox+k4HafjdJyOfwvjkcxcZ49pyI5GPJU+8jWphT5b+wx1d9L3P58dTfH2dRIcOKh6ePFwGzN2LjHjxMNKUQl09gSOJpTRtY3ysAkyUiravERQZGUi/B+3n6bzWaaoDiiMF90W6DVopXPvECsNOxyxc1Yo6yCcLRoBDDUev0wetFvZHPM2RJL7SzqntaxAY5QXrVgdKtIolwl5lgKoWoX9BWV0RRvjTUM69p3lCHlU452j2wCAq+k+arZDMVbgcw8eh2JN2UF/GYQ0jMCwW3gj+N6lCspB0rMuYm76Kdctoo7wGUtyJFFyZL392CEaLT0tSBuJN63tAwCWbQyg5wXhe0nl1afWtic+o90b94BWeR1cCOHvdT8vUU5SH1VHBzHabcr8bh+MYG92Yc9SVH31Fz+K/At0bdECqKihGbIFzPkA3eluoDC4RqqGNCQoa5uFpjdZSFx7lpV5ICAEn2Nq/Pl3z89Qf3lIjTQAdE96/WYIytycAIRJLDQjK9oCM9Y/3vnMM0AjfXdvtBDeNaUZWFgpEDORv7rW+AxaHMRoetY3ykRL4Q3jr/zCx5DwfDdlBK0lBF9aVAiAm4hMQgLtesOlj/CZfGMksqhFIVlPe6OCqfi71rGhOzgxBI86D3QLn9USDC69B246qFCxeQGERTtsvSAEZBCzEBqe2iZmEvXIUBYMAJHxz4gFPGQKALa1odmpS+pWTpFIltI/SzbXiDnLdDrgXhnMytCcxf91/CQjN7kJ6krO1MOJVEQWcGpccTgn2YgTx7dJgKB11wCChCUAIN2NfPYu2qCOZSVRrrRTjmoDU6FFhHYa+dfWkcXlf0nHuPPXaL4Ur1OmlV4tDonxVEV3blf+R3I6spmGU7dZ1a5+WMcj2S18+ac/DJlnMLMY6T5vCqlFM9Le6DzvVjg7oNpdo5Wnp0yXGYSw3slEP+j4Tr56ZAgKHNFiHCWhNiakRVNG6LGY/nKZQtzu+L+7xUoYgqqc4Hk0iXxdKZoqwALtNqshbcwx3iXVobc8cReXusfoKVr4f+3e41iyI0y1SHztbthfYjzu+nMzWno41moBzGL0LxKsvd5dYphQV+S46mCzQ6vtrekatBF4ywZttvMmxYtcH22KGHGnwZkRzd0oK/DlO+f4swQ21ue4OqIu4H9vdB2/fO/rABAftuKO44ujMV5++YLn8gkjULL8mxo2uLh17M/lwWKAjnMhimos2sR3Md+bD7GeEZR6/Xgd8zkt0lGsYS2Cs5EVXvFJa0mQv7PQe5D7Rcl0DORSwvCiMXgpRtPlG2iB4ird95vf/kG86UefR0oCWZhfNrBrLDF5L0FUCG9A/dqH3uvrYl/ZwXvl059CfpvnQAPL81xD7BMtJGZloK1v3MWSVZ2O7w6pDs1lhfRQ+sXRXCo9PQuT+ARu5WFM0KXHU0HG7SDJSeXqga93IBvhKRNOVciNqz/BndROxcrVM+UK/LpBtTrxgPsPNhrvyCMLmt9VtxtPK8IKL90IZHdjVFx/jmbSQ+H1Gs23g/altP7+1kUMGVnYXba4q4PpuatdAtQRrNcbH2j2u6GXYlGkMJrU2ADALlXofC4lWQJyGUMtpQ8ckqlAcS7IGK5SmE7UY2vqJFaF22gIUgYoGIOAp7OIVnjY2SobNnJFG63vRm6F38xMVxMszH+LpipsqCLIJAoDNOuhI1uNoxNQrmwEmlFY49wzZFN78tpS7SUOZSV9wGKlBVKD+EGA3l2wp3WB29//4Ye6W/iRzFy9PqwKi6jja8kVKsHBnFbORivU/EVqqwi2Uv6LYHONxvEIexoi05Ar9RMnImE0RbDzKev2lgryPG1c0c0MmherZF/BRBYtkzCttFDzsLioSkBwHWjZTZBv0OYxSEp88egclkwlGd8d+HqsUkH6bHq7A3mp8BQb0yjEOW0K25tzTAYZehlt0NpIHJVUC1bS+AamLGpxZXCIl45oQ42VxhNnKHs8LjvoxTWmrM375d+66qP4zqUZLvQnXrzhc9PLXps3iTSeXKdjvLV/D5WOcLNLPFFbS/piA4j3OliO5jgUdG/u3lv3gYNpJLrDEpe7hA7UWuHVA0oRyyKB4nuhW4V+r/AZtRAWcaz9z1Jaz/+cRxbJId97plJETOtYvquBPgiLtJyGr9MrH34P6a2CF1KXmW21MFOF9CBsZq5Bygk6uHHj3SepMk98hDLaV7/vA7j23LPea3TnZ5/xcwD7B9939X8lGUYBqhcCwM3v+n4SW3AJUWK9jF+bW1TrBhE/d6aSaNllR1yuYBuJ6ICewcc+8dyJTdrZjDm5QE9FkfA1V2iJ/u8l6N6nY97/j4SvM7tx5R9SNpNMJfQV5qJrAcxdqgqU5xtPcWqHFvrI6eMSSuQ21KaI0bKABQQgJsozhkwE2Hil3sl/UJcX0NMUmvmn42WEzojOo60VVGSgeM7bViDd48+OLQWrK/QVVwsuzxgSrQAHso30VECr4P1ihaD3O7qQ0PDIR2spiHANWSY3UNNQw/TUwsRS3bYMvysnd6gjmNz47LDta/+zWkp/n1yA5LP8lRFPJKqt0KgELfxzDJDWcH7bJS6RDyZtZD01TM0UMFfhuVDWB67i6OHPXE9rrqfjdJyO03E6TsfXeDySmatpFFArYKXrTlYCOjPosYuKtsILF1Rl7H0Mh70Sx5Ouh5WsFNDcKSsKhahf4/wGdbCalZa+ewcjyMj4SFg0AobNuvW1Ar3f5QyxAqq1IGZhIgvNtRFXp/FR4CJBzRHtF8U5LG/3PZQUIUDNnV2JivXlcWWBS5tjXL9DcoXdUYHHNwIlqGoj7B8T1NzrltjqEsR6pX+IBRdpllGNg7Lns+S6inDmAsHAb12/j56q8CvXn6JzjCzi83SMt529jzuzETTPy5lshknJ7ftxA8Np1G8dX8G5fIrpGhudVwmqOdUKy7Mt2kkXh5p+T7q1zzJLJDgzmKEbUeZ9ZXiE39q7SscflF74o20Vxrt9JGz2HscaKUPLvcxgusyw5Jq67Wi88uGQCV77Jx/1aEQUaTRMVxGv5ai36Dye/Oc/gqaOEDMdJs0aL9eoWwl8Bbzmxhvvfd8f+D9n1i1aAc2ygDv/8yeAPvDYJymTvfn+D+LKP6DX3fyeP3gM9+wWR53g3gLg+ve+z3dndm8E4YP0SKA4Z7wqYDwTMFyfbkDZUcwqPuW2Dh2wrfSG6PI4JhjdUcVS7btHxUJBp8Deu+htN//b4AUKUMdr74hFQy4ZKGcsMcuCAlRKkKdcyTqdOw8Sqo86iUnUEooNEIQl+o37Dp2AOQvp4cwajEi4z4sFipn7IgpaQxj1Uh2Naj24I9lM+4741eOLWsBGTu4LVLd15y/Ca8WS5rgeuKzfeqRDZ8BrH3yvv9/pvkI9WJkDB5sLC7lQISMtQg+DyQ1dp3ubgC8jWBFOPZoo6M5K3XnFnLba1LDKeo9k/7k8bn7n+z36EE8kKWGB4WyX7PZJHUs4dMMi+AGfuuI8nCPejSEzkjpztQwYAbQC33T+OgDg1nINr+zSBtTJK8yOuLg2Ar5+5xaiK/Rk/s6ty2iPQjOJ3u3gHn9O2yhEDDfGCdunuaaIGJ5DpyuFtZd5gzYWbUei7tOXaX5JeLjIQT1inTaFrNOgmzHNQhos8hxiEQze4x028t5MEGdBdvH2wQjg1zUdhQE7/rxyvI2yiYKqVLfEPdYnfvWlC+iwMXg3qzFbZp4Pm6YN7i/odZE0aCPl67iyFr5Z6wvqHKwVeN/T/woAMDMZKsa3dos+ag5mWitxVOVYFEE20VF7oAXkGx0PuTj5SoAg/et3tvDG6wRXy27r9YSX0wz90dK/brA99/rBadx4J5pKkMRisceyfaUkY2sAGDa48a0n6TdXfp44nuqxAgOWg5TCwlogSYIylZnT8bMHEWCAF38scGKv/iLDtspCTxN07tKcvPTD7/G8xZ2f/iQirv/poxTRVOK1D4V65wkj7n/4qVB7yzT6m2HuvpIH6Xivb/57zyNifzudAt3bCuUG152TQFkTjUA0l36htr3Wb3BmugKEbVVApWAdFJkYWIYX44lEPbAe2nz8mec8FURVAgLA/Ir2x18FJT2MCkvcaL6/baMguGaqEg0hLNpD/l5mGmaLtboPY6iZRDMMsn0ewk2DGTssEI3D8qiV9bVTUSrc+O9XauP/+GOhAccSvUq7mnMlg/zkiu2aaEgH3Bm6wwh/bfFUoh4aT+USFkhYkbPie+I4wqINrjz1mgnBgLKARNhQExtqqS3toMLxq42AFQzhSoGY72PTN/Q6J/UsreeoIraQ46C7LVrptanphgh0WNazeWoJ8PMvKgk4Bo8EoBXgWDeROSEx+bCPU1j4dJyO03E6Tsfp+BqPRzJzHb4KqARo+gIFCSMRFCIlfu324wCAxV4X/bMULlZfHKE3pqhveT7Cb08yOHNNUUvvpKHYlcJwE5BUQMOdvg1AkJmLyISBYAgq71U4fHrI/w8sdlps/zr/2sJH9SYzQGaQcwPSIC8xYEebSpOAhM/Ehw1GHY5MrUDOTUqzRYa2jjC6RK2s33zpRZ89fq68BGvhDdLrNvI6wOg3yNltZl6kKKcpEn6dtQIxQ6735kOsd5a+QajabDyB3BiJJG7x6xOa41cnW1Ar8ja7S4KjE6VxuMhRsyPP+rkJ/tz51wAA/+fNt0CeNd6PE1qGDHGakZKO02hdRMHLFMC0ISh5tD1DGre+w/bo/hBqwhSJsyVsXntdaasFLGchTg1rdSR8L6pFgsX9YfiDEaj43iMyPjsqt9s/IFjvumZNaiCGDcpN+vvOz3wCcZ+O39nUpJUMokSYOBDub/6t9+Pa8wQT6r6GsEEcXRYxipzhTGmx879QdnHz2z944hyKK7Wft+xODJ2c1NV1WU+0kFBF6LCN9pJgsN3T3pPWLiLIQgWo1ojgFCOogceL5K8kKaoQMLH1Gr8oErQdujeyEUHpZ1DDNMojApAWXRbq11qiaZSHePO1wqMsWsVou9Z3WpuEqSkAC9/zd5ndd9x1jnbGHumYjXMyC3diKLtpmANlIUrhz19gJWOVK5l3TFnyaoewm+NqQ5+gztjEYPz1YY52PvMMxIC7b5vgxQq7ogZlaC3yAhw2CIGocwXipMVyn9A4WcrQhQ3hywPCUCfyKkRr2HlIzr7Ct1ZZyEkwetCbNYpLnPKumhJUwQ1JNixG4hTWxpFfv3TnJMz8MI5HcnOdXxBQKT181XagudjtCnXFD0jeoma6TX2+gY2Yd1cJmEKdgIEcvNWZCjQ9oBkxbaTfBC5lKwgWYwhNJsZ3Ji/2uhCXGQaTFogs9r6J4eSx8g8jYouz545R88YyL1OkXI9azxYwF8KXQFuBXXaB0bXEuy7epNedX+Jf3XwSA95s31huYj2hRWkxyYBKId+iGmlZxtAM5clEo2Wu2rBboKkjtI3rRjZIWfi+0QqzOvWOP0UnQVMwPGSBKorxf03fBIDcehy9QQmLlAX+bx8NoFYs4i4Mpri5pKKxkgb9rPJdxgfzrhfdt/0Ki3HHQ6I3v+MDvmMXWTjeZm+BM50Z7sxJDnFeDXz97Pp/dRL2ffyf/SgS7grN0xqP/7MfPSGHWB0R5N0/O8PMsAVcpyV1ILdwVso7o0AAb7znJDTrJA93PvMMoKXnRkfjCA3/3CBGzDZ+9jBGO9TIb4avrxdvXyrYtdrXSOVCkWIWgM6e8pvF4x9/Dq996L146n/7+3RaKkNvg+57PYxQVxGevEBUq91ZH2NWs4oPYlQbxtf2274Oi28jiD8MCjrVUsAq5tEer0DJkqeb3xfPwoLb9KiW6tSDVCU81JmMhZcn1TOiErlNTPe1N6mXsaH6OhsdFIs08MFdLOfqfqn16kiiCTVX3bXMUaXfl2WCluUn404Du2JqoXvab5qiFRAaaNfChu2DMy1O1oyF9TVwCpj4vi8lmrT1wQ4a6dcNZ9nmPk+nljjEYMW5lS5oYRHKAzIEEXmnxuw4R3zM59+xJ4JQ0wmQuclC3VlWIWhoB/qE+TuwUnfVAmIenaTj8By0fXhqj5hEJ7BTnxgAJxgSD+s4hYVPx+k4HafjdJyOr/F4JDNXpxHQDFZEzQcaaKVXvYnzxsOSMPDen1ID8VShGbiuPEA5/8MEqDcDnGFa4fmH4jiFrAWs5O66NILhhgJExuvoQlmIyEJOVojVnLGkWYv9owG0I9VLeOUTISxqrbB/SNCqNQJvurQLALi+v4Ev7pOQg4OyvChGneHFfWoA6vQrtJnyGYAQCLy/yPimn2GnRK9besP3qoxx+5CywDyrsShS1EtuYJhH3sLKKgsMDc5tEyR9OO0iZUg3Sxrk3LG710roRYzeJmVSbxk8wL/evUbnXcWIlEY3DpC0O6+6itFfX2C+4i8bzWiuXv/OkzzKb/nsu3Fn30kqwbdI7vzMJ3Dzvwvdq9GKMk/ZRHjHxbv4ls++GwDwwq2LiNkQfTlIceYcXdf53gSff/GKb1g7/9g+qUIBwUD9q42KdFedmEGbN96ST/abIHpxsUT2cgf1kM7rsV/6CKwKz6pdRB56lpVAfp9+njzVehF8/5HcxSwi4znYWV4jTls0/NCvd4PKlmhioG/BglxQSxmUlrQIgg/ColkzXpxdlaEzX2iChB1MmR1avPiRkxZ6biRj6eHjL308NIE9+cPP00emTvxAAVus0dxSxry9QWIoB8d9tA5B2qzRjmPfBNS7JdGy+lSx0wTepgV0HBSO6r08COQvUmCD+L4AgMgGODy1qBOE7GxFRQ1gEQg3RFBUalkcAgDMwODmt3/QN7qZSkG4prHEnoBpVSmCupKBz/gdpO9fq4UvFSxfHQHDFs2G63YKzVRqKT3MLEsqPzjEQVj464RgMwp3ObEJ9pGCdcNdn2ctIPm5a7eaYBeJjEoF7nIsfCafPnj4875HU6Hp4z8GmWX0cMfhy6QWK18EEagsqhRIj11LO3VTLi/yF2091KqsFkh6QUKxmgYZPbFU5MXK61Cbr3xJRDAT7p+dYX43KJKohUTLi1dvfYkkajGd0eZhrcCbztMGOq0yFE3kVZmaOsImm34fTXMY7sQ14wRyrcIW/y1VGsdLWjiVNOiltVc1emV/C8U0bAYuUHCKNy4eGPWWOOZz0q2ihcwRz2MDyfSMt1++g0RptLxJvHq4he0+dSAvmxj37qz7uYcMriadvMJywQIWnRp52mCQUZfw8bKD43tc63QwlBPxqNQf6gLzp3/1A3jAlCN9L/dwmh5oJIPKQ35Ckrk6QEIXo07hKVbX727643V6lQ9c4kijqiMfOMwmHWSvsgnEOQ0Y+I5MnQZBextbWtxXpN8ctQUALC882b0YwgDRN7BYRh2FQHARAd0W4phrkZYUndz83Py2k7VWR5fAoEHWDbV8AGg4cBtkFW7cZOrWqwmW5w06vPi1XQv7GD0vzTwJ1Ay3WfC9iPfildqgQHokTrgIfeVwghpvvPd9wT83Nz4Ytiw/6DYMCMAMV4TksxYtO0TJSeTrmbpjfCctHRQQbO4QJa2n75hFRP0RDhKtJYyTRsw1oqwBrrPIzDCIMIiWuvtPdLuuskqc2H8jke6Fkk+9HryB5UZNa8JKrdI/2y1By25DjWcSLStwWRk2b9kIljnkz2vDZmVjg3RUhmvdywKEa8OGfP373ofHn3nOb9hWwgcfVgBm0IZ5tBRIA4DttujcTFCt83yt1LL12QoJU8OqI/Jidmp0cTcEkM2uxp33/NCpQtPDNvpXJlB5iekbo6DtKagm4R6saC6QsqzhqgNFsW3pi5w5rqPxUXHcrSGl9bXIuFd7o2cbWXJK4R1JVsJHl4gN1eUAzMc55DJY4QmDE/WQySyHXgaVmpfvUtbZ7ZUQoHooAIw2CsxZJamTNZge0nkkY4nkfO0bkG7e2vRfkCuX9nGpd+yVl7ReCTZaAcmZRhRr9PMSaxl9loHAXk1fANtKyNj4mt+brj7AmZwyiBvTDeRxjWt9kj887uVYOpP1aReSXUeihBpjuh1a7GfzDno9WvA7bK22qOl6emmNiPWajyZdSGHRjB0fEb7Rx0SAHdB7b/7ND+Gzf/4TePO/+GEAQLPWIL5Px4vuxdDHEewZR1MAFsx5ndYSZpNqwAAwWltg6lWegK5rGltmZMH1gDZvkWkkbN5TbQjYtQZ2Hu7HSz8SNpmdn/s4FM+DniawnAmqeVA7qrY15FqFjO/hfC+HXKO5EnOJVilgpcnFPz+SXHcAotSIVkB4IdsEBS/AxTQjaU2usw529v3zWa9Z2NRg+ThtSEmvxpCDj4NxGhZpAQqs+FpMGvkgQtRAvYY/cuj1sFEalhKMj5XPopohywA6JxwFiJU6nezWSDhYaGZBOzdaSDSbjc8EbanCYr9IILiRSVUSerP2CIDZaE5koLDBGUjYUIt0/+c31MT42rJoZGiE1PQaF2yLRkDwM2e0gC0iKLZ3073QEBePFZo1jXisTn4OaI1RTj6wYwADKKbcrUohQlk0VRSawTLjgxQb24DifOYZqCQKjVUyzHdUCrQiCipOtfQ85ng/RnGtgjpiS74mZNd6FiPuc/DWbSCVDdMqLOye4xcHit3DOh7+3Pt0nI7TcTpOx+n4YzYeycy1ahRUE6F3deINtHVuKMJ38GC2EqZa+DCkGRpShuEIEdMOYidQc95CSwvLcJq1KqS9iYFtQru9ja2ne6AKXYpyHCOeh85BG5HTBkCuMrqMfG1D5NpndGUVI89qD1nO6tRnp0oa71IjnqBO29uvE8wHCd91erDWxe39NV93jrIgqA4B6H3K4Do7E8RK42BJUHBZx0EsI6+xnKW+3thaiZRN1kdZgWmV4daC0pbjZQfHu5Txqm6DHke0UliMj7rORAVSEcwKAPNZhk639hSeSdPxdSClDKwRUJyh6kVEETyff3L3pBPQS//Z3/M/X/n/2HvTGMmy7Dzsu8t7JssDeAAAIABJREFUL17sGZlZe3VVrzM9w6HIoUTKGGiBAAmkYBmWIAgyIMMQIAGCRdMgKZOUh5YtkJRIztKyLAs0ZFgwDRskbBkQDFuALZuCKJIakjPD2bp7eq2q7qrKrNwiMpa33cU/zr3nRg5t/zDmB4uVFxhMZUfky4i33HvPd77lHwY3mTOC3KLhQX6cfFExNVgs+wz/3hifQ0cTAyuZwWw6hbzoiC0OYDZdYf4pqnDf/wv/Ce7+g8/y/SVcMpHwpwXE1HMxIkzqk7m+A0KoRO/tHpqJwNkRVcayFcCjIOfJPRmlR5ZoJyGDgYj3gqRKAGSjLuaaZoCL95UTOH93iuxGgHutYujOfAvbVmvL7PVsp0YXXYy0A5zgkHLVCuSnW/KXwuPFz36eP/N25eQFEOJ1cfcXfxaySvwDhoG9oAZgTFixKZBezRrycr5P96fYepZtz0NsFLxO/dJoeIJK8XNuRxaoFVWsAHrDhtn91krUpz2IiA6YVMX6viXWdJQMKQdvt1yZ4nO+3WsECA2L6VdGQm6SxEZ0gr+bud6SoVIezqX2WzKgrY6CE9xXBoB21/EzCQB+nrMxSzFsmPUOB2SrwAR3xEyOLQwvUk83Wwp0Q4/yw6ComCVHL6cBsdbJ/zh3aX5rJVbz8LeMhLOp32t7FohM5fbp71Y+k4vrT378n6E/UviJ3/5zDL+mZAnw/7sooevS74pW0EOyRxCO6STf0VKRNs2HSaq3U6NZhwl9paHXEib0Vr3yaZFsJTuu2JLsDWVcsK+13LsrMoON8JjeJFiybjOsQ0Bxlll0VrG1YN1pbMJrALlAAYA7yWH2FIrjlDgT3V+W70wgDVAs47ko0IUUj/xYMYllsylQVzm6QFpSpWFdq/fAZLrBtB8gYy/w3pIM+JdNgeOTEXZ2qM/adBqvvkRRfn3d4v059VyNVcj7KVnn0ckEg5IW3rbOsD4rMdih41+bnqMK0LLzAlJ43mCcYsgQ+rda7G2P5//+59hwvps6ZAsJGeHYOun+5LmG0w7fsf8YADDWDUPvjVRoA0nMNAq2k3ju+ikA4GA+Qhd6oi/80s9Adj3ec9nCIwuh02bfw4UJByB9o48aTC+AEGJvex79r5R8r9qcEpkA4gBkueV2BKyAi22E3G1pTT1kkzaTwgF5sBx0GeBvVbgW+vJVlzG8i3MdJvtAjrF9yLj5axXkMuovNYe0A8Qx6D8W/LeaieDFxfaSnlS2YaGKm4DSwA7SBi8+Mz5zROjrp4dThGi9st/QAhj14ZMOXgV4NGxo2QlIe5h2axqMEHruIPuGNwdtk3H/0s7zi2k0MiXJyKWGl0C3E56HdTq2zzwfwyvaOMXnPD+TaPIw4QTIWgdYuJtZ2jDF43jAB7lfdphxGlA33YJ3c9K8yy3yU4T2RSegKoEubOBNl6BfVX2Le1Xm0IX5rTiT6IJcptnxKA8k983/+Pf/HO7/6RTaLquUuiMbmTYf2jP0DgnApXPnfIL13aVD0+W4HJfjclyOy3E5vnU8k5XrQDboS4Vb+2d4cBgM2jURlSIxQTbgSo1gWvq32giYkUfXoxd7jzLezXmt4SYGKsB3UjpMZ1SlnbVjdDueYpYA2KmBiHKJVlwQUPvCc8TXdGfNwez12RivvvwQixAevalzlIH0k2sDJT2b6XsPDhB4eDRlsofPPGylgcAw3P+Sx/p6hAMDCzAUPd/8Wz/MsgjbA2wIn762s8Tx2WiLPexwa0YylEfzMTqrOPTgZDlgd6jTozGEclABMvvemw9QKjrme8tdmPA71kko5ZjFPB5WXBVK5TGarLAOzlGLqsfmE0p4HAcGMEDh75NrJOf5nv/t0zgLIfb2PLsQcfb+D/0oXvp5giiLE4Fmx2PwgM5JM/OJSTywQCfxq6+TCcbsyjln2h5VQzxe0t+eztbYG64hQzkwHtQ4Dl7F1gPqWgN3HiQwA4M2yGGwyOALl4gnTjABhr5QqDQGHt3Ic3C1HTgMblCVuVkVVB1FY4TCsymFW2UoHwdi0sTDZVsuSUhIDRzF961ifOF8wBWY9BSF1t6k66a2oEY0kmU/q5c6iKVOEWilRbVP/y6PBGzfczg7AA6ITx8mfLdGMVEGrUxG95JMWnrh/u86BZOHyttJ1Jsc+nqAtWsNETKW4QT8llkEgEQaqxIKBaNgjUC2E0hGXrCPMaYt7DxPvvdGwOzR+RAbhexcckyh7Xk2+PAywdjxNRF6H82+ZQ9xOAGfeYhVhLwFRJDmeekhJCCCQYYtE6kIuQNifm7fwiMpZSA9Z6p66WFutFCR1GUFEGB/K8BEKi8F3DBV282O4/Ofnyk0u+naP/hTiTQWWcVMYuo5lti4HBxIH9ttPnoqL9K9brfCCJ7W8Uwurve7XZStph7ULOgla0UBwkkdwwbTLkswhTCC9GFxontlQ24xoGBzJzzcaUyPySGu0OQuagnfc8noupJb7izpmY5ZjV7Rf5gfjPiB7O+v8XAxQRucYrwT7CKVKYtcGcyDw5TSDgen1M90y4xlM6K0KAYtmiCZWPy5NaqzoG98mNHDuwWD3/tBMih//j//HLKQI3uoppjsrHlRm5Ub1kTemJ7j8WKMawOCrh+9tQ+7oQZa/+VzFJnBCyEsfaBa3FsTFPxkNeTjWSewqQsKLQewWvfQRVlF5tB0mmUE58s+yi3DfOcEyn6acOPGpKsy3P/3fgLb49VPk1byjZ/5YWah1nse7/5HadL/xI+8hs11+hzFY02TQ5gDTs0ED8PGYdUUqAIM3+832Ck2eBLsHI+PxhcWzPf+nf+YNy3ysEhw44D6lGjizeBZJyo6QTAuqEfpCwcXPojaabA6pHMsBx1MqzlTWEhwGhAGHbpgm/fuj/0IXvz5z3NfDFvzvus7qMzxBkmc5On+8WDpBxAWvwg9Zg6rV8Mi1glkVyq00TxfebQh8L6byGRvGI8Tk6UKS1Bs1JA6kZyutjgRYtxiZ7LG6RltmNxG80O0NhLwAm24Z0Qj4cMpKK5vYDoFgy1oMl6anocMJFWnAWQ+aYsB4B36W3klUN3pWG6jTzSK92kjYsvQGoob83MJ49L15d5jIzivFaCFpZumlpEwglOW4LGlFadCoP8Nembr/a1FqE2bBlFYmtPiQiY8Q/kIWbI2nFfVNxCnWw50ca4LSoWooPAqyXTaKwboBO7+d3+XjtnLeOFVSwqP58/iROoLa8+yJa88ZJWCDWxORQcACPP0w8LP5OL6fr2PQmcYFzULwW/fPUamLA7PaULsXh/zrhsOrHOVDeBVhu5F6vmZ8zzp01pAH+Xwt+g1O89Rvx8qqUiqiJOUJa0fQDetCaE7ei2ga2D5YviwyiN7EhIrdiWGgwqbMBnfvjLHvC75e63b1BdqG52qI58mZrHO0T1nEaNG6nXOhJd2oqDXAm/+1O/WH+ZnEtk6fKSHBc7/sOOd/OEHO5gGH+a94Rr1+yN8KRBbvPbopoEM82iI8UvHHJb+ocl4As+1xaykSuOsLnHzyhHGWaLjv35M8qDOKlgruY8o+4Z9hp0TkNIToQrAvX/3b174DtGLV7QCvnAQN+naP//3PgclBf6fhqo9k6KyFZFMmGhiJBZVIHnlHV64StF9pe6wV6xxUg/4OHFSErWkuLAwZ0sLXhRUo2AGPhkNKA8v4oSbRPlQHpCed/xmmbE0oyvIDCUuJqKR/L7i9gpv/niydxQA4tohHMUdAoDrA2aRo7lH95ZU4P6payREK1F8EKITrUAd+vJynfx8hRXo2pIXZVQq3fuGJtuosb331/8GE4Ck9nBwTFITmUskrFomK9DjAicnBU/oYkuuBukhtSMLSpCcKnp6C+FhllkyeVCO+6peebio4uolDgFA+uompwuQndN1jD1XW3iYUdCX1mTlWBzF3jBSleyQbFNT65t+7ARXbmbgeYEFgMHNJW+i7SaDWGlUH6c5RkgAYXMvrGDrQik8UEtkR4EH0Pfwo+g/Keiei+jAeQ6E10wv9cJFF6R4WxsrXngLC9n3XFgIl7S0LgOUIc04AOizZIVoC8+VsctJ0pgH/oe0Aq4XXvMXN19P43j6v8HluByX43Jcjsvxe2w8kw5Nr/z3PwHVL7A+K7nq/PhLDzHNK/zGb34UAIn7dajUbA9sApCtPNqJwPJuVFNv9W6URzatUQZR+nJRpn5XoyAaxf0GuVbM8jOlhw4Wir0TAeGA6mpgr24xLrOeQVdrlCMqMSaDii0DT9d9jHpNch3qFFwUrK/0RUnHtgObE1AhH9Y0CtlhzrBwMU+9Zi+Adhqg8L0OqmehA4y7M0oOTe1hH3ol4Z6jqvPa7gKPj8lBafzrJZYvOBTPU5V7ZbxCHuiStwZz/kwv9o/QeYWvnd8AADxcTXB6TlXgbEwXZR36gVo67ARm8uP5GE2dsYHF/b+c3Ii2M04hAC98ql46Cb3c2mcK8DlSjcA3/9Ot7NW/9zm4AF1dvTbHpAhOUXXJ8qBx3mBSVPhgSTKvg4MpV3qm7ym5Jl6bRnD1AgTIN1Y6yvNnlqVhqdJmU8DUmk0SlHKoAxvZW0FhEQEWVqXFjT06t4fzEdrAXhcbRZBfrHCNQHlEf6zap88YpRtCely9SsdoOo3mX++y9aIrfHIuMoKrI3GekaSJ3acc5GbLvWmr5ykaeRHyHlou6YRI7HuYBFEKI4KjFfj4fMzYu4zQsvTMdlaZhdYOdTgPvtoC7zKH4WzDP25WBcuufKP4+P23c6gWWN0JsK1M1WmEQ/Ngiu9UYkJvD2GJyxEDELxO7YZu5IixHZyjVGmY7Y9GQS8UzE5QEEyTIqF3v0AX1QiSKtkoq7GFTz11kDm/3qX7yXvBELGvNJ/77IT6qBHidjqZ55DZBNgxC15w60M4AE6w+Qe9nCDi/gf0XZqZhxlb7k97BUa5/KLFBz/+k5cOTU/buD09gx4UuH7jPr5xeg0AcLwZ4P7ZzlZkU5Lg2CL1o3RFN+n+bwcpwrU0KW9uOnTzHjAJD4wT9FACFBbtkh2ZGxt4FW8qD5cFAkROcIkMa2pWdmxd2J0VUJVEEybOuRc4Cm5Q3glk2mI2DrFbTuLkNPThapl0d1bAZw4qeH3akU3JNLmDB1CeJMlE7EXaEjDBNWe6t4IQHq/sHgEAvnZwnc+BsBRHFjWTUnjMprQgnj3fgx065JpO8vX+Of7A+EMAwJ5e4sOW+q8bl6OyGd4Mnsfrkz7LLFa5QVXlKEL83fXREjcGtPNpjMbD5Q5rc1/8zOfTQpUJ3tgIKyBcmgy8TCks2TktOCzJ+hZsx00Mb5g2bYZVsJsUwmMS5EKTosIbR1exXgRbyeOc01yKE4nOCPKyBuBLzwkgrgxaa4YRt2QUKqUGrVHQwsgLkMdkkjSpTZ2x9Z87y/HBimwaszMNFfXTcd1p0rXe+0okyUiYnsT8JTpG97ENDp8Ei8nzDH2X4MH3f+hHGW6XrYALMgthw0YuLnRb0hU38ECXFkq1kWmRzB1ZDsbz3jfJAnKesWOSLQPxK75mZHJdqtTFXmHfku4VtOm0nbq4qMaNyJbVZF3lcOuMN6VCgv+dL4Hr//sh7v95uj91lZ6T85ctIIE2+ByrpbrgVxyH10A7AaLyWjaCJTC27yA6yYRBAJyuJYKtoQibhfbxgKU7tkjSm6h/bWZhoTwXAPswB+7IOjgo9SzruuWgg19EVzkES0X6vXwh0UTbbuUhapk2O442igDQjYOjVOzFaw+9SnNddTVcp70WwgNt3JhsaX9TBN7TOy5h4ctxOS7H5bgcl+PbPJ7JylUIqqgKZfDcmHxpv3T/Oaj3e5Hng2bPor4bd8UC+IB2eXYhUM8EisjwLMBViSstZXCCqpnh1RXWwfjed5JgrZhiY0WChUcuVcyCwpIxpcpMe4E8VGltVcCWKSzde8EVHAB0RqGXp5+jWcOZB5fe6jAHRAoQEE6jCxXc9Ms5vvL3/9/N1OO489/+LL7npftYdcEHVHpwdyGadIed9um6j09e/wAAcP69C5zWfcwjCUh1kAGXnKoN9oMH8aNuB8dmhI9dOQAAfHH9HBBIV3WoWmMa0G5vjcrStXl0sAO5yDi83meJjCR8YprKWmD6TYH5K6EK2u3gQ0XUjYNjUPS6rwVe/AzJRPRGAHc9dDB92B+uOV2o7jTGASI+WI9RbQqoUHnkL5zjjT/7nwGgatrlHjKygC1gd6JNUgrPBgLEGKpt7wQjEXF3zwYlRqIJyUk6oALjWYDPTydwYQ8dIUiAqhxhBPIF/berv9Wgd49ML9av7kMaz/fI9V/OsbpO17PeE9jctMyUvfMLn2HWu3Bbnzc6Y0VHJQmY/cgkJh/aeAzfCb5OctDBW8mVpt9ohuilS5IXmVt6LQ6PxCQuHHyl4IMDkVAeMqbKGEkEpi3IO5IaXSdRVVsuXiK5PvV2K9SndN/2Dz3E+Qp3/hHdr6JXoH6Fqth8mcMpoNmJcCzQBEbvdv6p1x7CJdmb3wrvQGHhSwtfRw9xn6pfIQjdCL7AvrTJEEOlcxB9iyMCY4ap3aBqCgOZfjHI2b6vZnTANQrFSfSRttArmbJ7AxOa3kgM53j88lBgdSe1B9zEXEASovpBtls+w9qhKDpUYb5ELflaR5ni0zyeycX1Tv8M+SDDv7XzJXyzob7ewXqMD0+uJHvCPOkDvZFox/Tv4086qErCBpIu9TICbHKksfOmx+gvk4OPhMfjsAhXmwJGajx3ixilw7zBwwVBbfOHY+TnaeLzmYcKUJXpFIeNSwlMb56z3Vwv79APi+ms3ODDxYR1nlluyFUGQH/UoK6iVMAFXS39LVkLhlGdBl79ydfwxk//fy+wQnp8uJziNOhGzUkPPrj06JYkBjacr6vjJc5bOllvHe1zbxAAprc3eCEnaHlfn+Ofnn0SAHB/M8Mrwyf4eoCb/TxHeSO4OtX0+9d3aGKbtyWfR/0ox7alnOgERIDNzSSljuRnkiazIP8oxzWqcEGzE836UToI+PeaPYurVxd4dUZJRMuugAlwfn/Uorb0OG1Ejtl0hTpIpqpvTvHy36UF2vcCEzTqP3N/IarLy9Rn9SL927WK36fWElIAMjBIzV7H/XUX5EjDIBFaX2+QvU+Lgu15hoFVLaDXwP6X6X29e6cQdYCFCwEvBfoH9MVPPq5RXQswdt8CXqSJ0wMuTNrOi5RIZCT0ueL2hi3AG5hoIi/KoK0cCG6XeCuR99vEMgaYlV7XGTCn6y9Lck8S/RgzlxZkNEEKorfaM/FzAbRZjkjt9vpsBGzYLJHXn4Aa0xfIMoM6POdP/pDE+fMvYvfr9Oz1f/M9FPdpY6KXQ4jO4cM/RfdkfcUlLfHW9YSkc2cD89xNHZ87qR3KQYv1E3q+RJtiCGOvObKZ0Uq+l7xAanUokvhtJ29lgVeQLwQql6G6FuDYs/wCJ6MbJ4embsdCxvCCgU39MQm0u5YD100/udxJA7SlZLhaDgwztwEkWc5Jgf7zG2xC+4Sg96ixxVM/fh98hctxOS7H5bgcl+P31ngmK9c4vlI/h4WhDn2hDaZ351i8G7KwdNLaQQJ2HFiQrSSTf6RdeL5Iu63zuwLPFUQu+WOzt/Ar+UcAAF9rbkAVFv0Q8v3q+ADvPCGiifCJlduNHUQ/efXWjYJQyTe2yAw2oXorMoM/uP8AAPBgvYPW6JQQt6V1LLIOk8CoPckGMB8MGH6xW4y+boALGtdXP/0as0k3tyyGt6la3JlZrJsc8m06dzoDRKgoCOryDFV9eDJl4o29N0SxEZCfIALS43qCg5J2+LezE3xjQZXqqi3w1Q9vssPUsmhx+iQwBhsJmxk8PqOfrVEw4W/3WqrGbNCDvvm3L7J8OXxh5NGNwG5Z7XIIEUgbZkBkjLgLF2JLVD9tUbUZFi39gZ4yOK4Iqq1Mxt9z3ebYH6yw6ehzNd/Z4vAoEYJEl1oCQGLs2p6/EI2XlR18EKI6K+Di+0aeWgzhNVEp9Gd0fTNl4bzA0ZcIpkSWQiikEcgIAIApgeu/voHc0N/yh8dA8LYd3lvj4Z8Yo4ns8JGFj9Fi2sNvJNAm4lJ8DZlDOSZo3FqJriuZFa1qwAUTAysB5Jb1t8ImchmER1tnzBCWPcuhELbSHPZuRA4x7DgkQw86mEDQUSsJt9clQlgrk5a1VkBhkQdP4va8gD4JGtKh45YCvIAdWuAxXeuV7rEPsB04VFLg0R+l39Of/AhrwJ2m/7U74Zx3uGia0KZzIDyYUSt6lp/zwajG9dES9wLq0h2l8wiXmLdxMLyu/YWYOpt5huy98njrR34UAHDnv/pMMG9I7ZPIAs6PFLqAOqEMmc1VZLbLpJVtFIQV7JXusuSPbvr0d330/M59iqO7ukEddOjFsMHJ8QjqNGjWW5Ge0S0y39M6nsnF9f/6F38AsteDl8D3/ZE3AAAH5yNid+4GHCu4vABkOiBCX8zlEvpJMo4QVqC+mfqcxbTG60+IgfzgfIclI6ZTEIIM6gHgjfNrnESh6tSHcIWHNxL1ScCdpYce0e/ojCbOCH8ZJ/Grj14AQJmn3XmOLMBYg7JFrejm3tRFcipaFNBW8AMpnEB2mgTvd//Lz3IKhpJkaAEAo/cUqn063vX9Jd56+wbyiHv4EPYMYn7mZxJe0ott3mODg/ycbB5dgLXnbYkm7Co++/D70QRY1TiJKztL/NU7vwoA+KdH34VeYMou6wKZshwKb496UDHjc0B5uXEB+cSPvoavfY4WWNkKuFv0ZYS2GJQtFotAfTzLE/TVc/BDCxfYjXqTUjucF5gNNni8Dtm1XqSQgE3JiN/t6RxvH+7z5qzsdWyxJ0AbEMuWeKkX6hUA4TGcBoMA4QkKBaAk9Q4BwNYaomfhQgtWPyoY9n/uxiHeenwFvdBm2NywPMHOvu6RrZNUQ61biPeIrY08Q/cddwAAjz5FoQBRQuJV2ix5G0wGwjkXRiRHPytQBY4BOgHkDi4LE7NKhgk+D2zSKHPZsgX0RkLUSUrklhm6YDqi6mQGD0W9bxth/yaFBggrOFieDgrIIzqGyz28B6LATWQJthXnMgXXB9N6V0RcHsCVwHVYZbBDDxtd2q5bRLsTmTnYVcZMWb1S6dnW4EUmn0vaSEfYdqXhIqQ7bHBa9dmVTExaTqoCFBtiAACyZIIhjODNsl5R/zKe827icfcXfzZ8SInBQ4mvf4aejTv/6OeRhQ2GK8BpOt3MQci0AVdrCXkYmMQSMDsm3RdZMuAQCOzp0Gf18Jybbbp0IzTnBfLD7AKLOrp/Ofn0s4WfycW1m1jIkmK5vvaEqiUhPPRBji7ox/Sog3hAC5ztpeQSq0JUV7Qq1eCbQw0Nitzg4/tExHmw3GGd3GS84f4oAPRUxySFfJ4swLL9imK8Qr/UVBomPFjOCyys5DD2yheog7bSWnnBrq01it/nveA4K1giVcTFFeMO4gZ9rmG/gWozrEv63vokgw4Tv+yA7owmzrc210jfFtsvnWBdXz4n+8bYu+491ixTcJqiqtr3qZf0xqLAq2M6V1J4PDgkKc54VOHPvvBFfDx/BAAYXa3wv2TfDQB4e7GPzkmcRXLP1vDKw72ygQ52iF/5Mz+Fl/8O9TqVA3yoQrpZh2K8wW7wfT45m7Erkt8KtwaIbBZ9e6+MVvjgaAeDcPwiM1iEsHT2nQVw/4zQDxOsKPWgTouH9vBdcsGhxTucx6WAywTfM5myHHMGgNNy0BIqUYQIPeGB4iv0Oe69cRfTQ48rv0a9/ff//B7anbigCvzaP6Hq5Qdu/CD8poKYkRa3u76DJ5+kY7gcaMcu3SN6i7AmqN8ZJ3svPZPXZC3TpjO4MMVj2F7SocqeAY4KuEjkkgIi2HF6SIhJyxsVV+mUkNMBJpAHs1EL52RadDpJPUEA4lxDVluLWhZ0uwDgADVpaQEEXZZoOygbkewgtafKd5zIZnyNBx1cl3rgMktuUK5VKGcVqgXda0aCN5eudNDLqPX1cEMLHYhoHuBjNI1GtSkgw2J7a/8MTfTqng/RzQsmRsJhqx+bzr/NPWQn2IFLVhI2bHSgPC+sfBLCCVeVQHOLJjfhBcTWyke2rIm8KRrJm1JIjzYkAREnxW2R9rZkWAc9iPC9ekcK7dizFldvgHYvXKct8t3TOi57rpfjclyOy3E5Lse3eTyTlatoJYSianH5kCC+4soG5UfnEKFi7A5LTO/T+9c3BZrjwGgzAr21YAp6t99BlsGpaLLGq7sH+J3DmwCA1aJkyGZy8xS3RnO8OqRK7bAdo79LvdmqGjE05VuFW7tzDEJvtrUKT1ZUpdVthvpwgOIoOpxY5NfoGLrXwSiHPFTYs8EG69BDm88HDCULQyJ0zoesFNrgQXwcvFPZ67ZO39Pk4F336OYC3Y7C6iAY8j/Q3L9xGdCOPcNrshHQdeytAfncMwza++gKv/7keTof7+zhzv9KO/CHf2wHvyw+iS/OngMAvDo6wNIQ5nSy7qNtNXrBnUiOGphwjrV2qDY5Z9y+9POfTyHTY4til+DWq+M1lPAJGozh2gBQaejFVq7k9Ro6oAMfnkwhpGc4WSrPzjm9UXNBBrWuNYrQ13MumXjACqqSY2tPCuhoYu8Jio0QL0pwkHe3KJKJvwdErZO4/0xg+nbwb+4JTF9fwJUhTKICdl+n9w3vr/En9V+kz94rIHR6/Ne3S6702omDG6VwCliBLFRY1kj4sxxZdOYRCcXpxlvyGkmVJueJeskMUGcEMDEp8Uf55O7TCQryDkOa5GLU7ViGUU2QxURGKhwQKcbkyxuCEMJ5ZaOIvoGtt6Y96bnyswPHCIM+ylC8vGLJV9eplJErPWRuoYIkbjqqOGRisS7RVBlEaAPIRrDvsBp3YD967SFXCrZJ1z6eO+MFvE+JQ4uqhyrMS3wvtalijCYkuNYwuiGOc9ihgy+jHMmlHnSjcOcXPsPPrGokM3jN0HGmbf+tHtqJB9uHvyvwAAAgAElEQVRu+yTt8Yq8lSMioMctcE7npzyQWN9Nzl162kKEBJ2iMFid0PPTjUJ/ONzH3diney5LSNDTOp7JxdUPDHxpIJbJUHp/vMLxcsBpK7ITTMDY+6rD4oXQ/zCRpBCOJTXkx2jSrjuN33j/BZigvxO1YjjkkZhB3ABqQw9JZTKMSoIXm/0Me9MVf76j1QAv3SCJyv3VDPsDYku88+4t6EqgPIjNKwWzFxba8xwoLGYTeu+HhztMGIETbP8mGgnbM5yaUowaho+dk7DLDHoe+43pezoN1iK2RmPYa7CKcgxNNm90PmiidEGn6xoF9UTx57CFYMLRrfE5bJhxj3YbtCNaFK/8tsNxu4sn/wadn518g48MSf5y/3wHSy+Q66A1HayxCsShVV3QZuaD8AcEYK/RMcaTCn/w2gd8jn/13gt8rd1Gc+A3WkERWaH3XhQdp8pMhhU2TZ7OlxEMWXoPDqrPlIXvJOsia1eyObkZ0QLEdnAmkUKAsLjEe8sjBXlHs3WE+88KJqVd+XKL8o0DPobbHUNYOv7gscPki/TaP3v3s/yeH7j5H+D4Tz7P1219Q6QIMQFAgqVokB4yQKKm0RTEHUl2Iml2Xe6SHagT8F5yX5Js9MKGq5bwRiQiVH0xJccVacHzOgWzQySXJDe0gPZQ4b62aw0ZFhnVhBSW6ErWJUKTXWcQucWd25TM1FmFZezfO4kqSMXMPpA5gef36H1vPrjGvUKhPMbjivvty00P0yFtck2nkBUGTS/aFWq21jTIkQd+Qzclh6noRKQKCxuD4GsFtZb8vC2s4Oc1Doa8c08bIVBrxW+yeMngjeBF3udJ7iQrid5RakU1syQnlI2ADxC0NLTZ2974xHYPPOB6qRdf9DqY4OxkBuE+jgv2MoMMkqnVJkdvEua9nPrFLkoeN1u2nU+e/qXpEha+HJfjclyOy3E5vs3j6d8e/P8ZllxO3MhgtEeVnvPkzcuG3lND1SCA2esdxpJ2hOurEtKSKB4gAkAVfmc0qSCV4wxF30qoSHTwBO9EiHGYN2wG4a3A6TlBJUIAz++f4N5yFwDwwdkUVQjazlcE0/7OP0x5oy/98k8DAOSwQ14YPn45aFicjU7CRaJJzyWvVhDZKctpV9k0FFkXXXuET2Ju0/ccS9U2Gk+WBeSWj2xk+ZmRgy8c+kGSsTnpk69pOJ5wYNLItKiwl9P5H+gW7gcDHKVbVKd7+NgOVVyVzfA/vfNd/HnrTY4/9MoD/g7L4BT15uYq5HGGLnggX719hjIEG+TSQgcG4v3VDEVhUEeZiwSfg25G1yDyd+pVwQzY4c4Ge8M1ZnsUkH6jPMc35sQMP5iPORbMaYl81KINVQQaxXCaqgSM9kw8cT13gf3pex46uvh4kcxEMrdljiMhRYo502uDqNPxTQM5l3BPiNA0/XoL99GXAAQS04wkQeb5q3Aa2FwNkOsoGRwIA4ilRn6dro0xiklWQjtigkc27DpB6KJTFCgPqsjVRrBJg9ntGD1xrQJqyVWPrBILmM7H1jO02pqiCgsR2PCFtiiLDusAobueAIL/dPSG5vu8SR64XpAj0/1H9HxNd9aw4T6oq5x9dYURqGWBN45u0WesJbtOeeuxeDBhJCG/vkahwvcWHqbTyWwBqcoUrYAZhmsb2frh+tqNxqsvPQQAHK2HWHx1F1l4vmokMpuYtpADw5U4tEce4GlrFJHKQH9eVYKdkZzfNpjw2DzfIY/VoU+MdTN0jBosP2qBTkCFoHm70cB5dI0i8p8KIRabVcH3T32N/lv/fjBVuW2Y6T4cbrBe9vhcQXr2NYYAxDo8Q1l03nh6x7O5uOYOyB3ESrPU4TuvPMZp0cebcUFyAk0IK77/pzPMvpagqeWdZCXm8yQjOD8YYXRtCRf6NL6SzITztyzKvEMXHuSHiwmaLt2oKlqTKYd3Dvah3qfPkS8E8pBAMnkH+O1/nBbWVz/9GjIQ89buO7ibFiYcf9BrsQmLgmgFEKBMMW0xna5x+gGxRNXXS2xuh77JXgW7lYspO6DZiYumZ1ipqzLIc438LMKBW+dWAPm4Ybaz3Cio0JNTjYfLBKoX6D98+cNb+Ddf+joA4I/vfRPvVfsAgLUp8BfufhmnwUbq3mqGrkuT+GBUYxaEhZXLcb0gPaxxCm85wb3PT119D983ehcAcNhN8c0NLYS5srg5WaAe0Gc80COGiHVmKS92GeQfS8WT0qodQlzzuDWkhJhStTwxA0AbnLSKSQVkQB4mntW8JC1KGL5v2ZTdbTRE+Le3EkJ6nkirZcGTUjZoWXbicgXxJENvQffP6rbE5KvEaBY3rsKfnEHcIJ2rbDpgGUSYQnAvdnOjh/UNwXIbV3iGCWVHutMusJ2zwvC/XaWp9xvbscVWn0wQw5QOElyCwrUXlUqTqPJA7rht4QDOZZ1eW6JqMjRBCym8IEYyALHRDL07JdE1mvJMQRN1fF83IViZW4Wlg6hjb1MASO5rmzpHcx52yu2WLeKAdCy9Q/re5YHH+D79TrWvsbolUH2ENpBl0TGb13sBu0kQtR04dgnTG8H9RdGE3mngO/SubrBo6HlV0qGbWdgIlQeYHgD8IqdzJdNxxgP6HEcPpyhjjqwiy8O42dFLleRfhbvA7oXyUHHRVICIecLnGr5w3BZxuWNZmuwEfC05oCM7lWivhgUxbGSqW+H4W8doGp02lpsM2ZliNYHXqf+q6qcfVH0mF1c5zyDrDNIIlq4AVN3Em12vJVwv7TJPvjvdmGrUIQserl2r4bYTJbwAPgwh05aCjwFAaYvOKKyDAYT3KdVkMKyZOJEpC5cL1JNIr1d4+2/SgvqJH3kNr376NWhq8eKNz2+ZJPwPfwdKOSwrmigGvRa9Ic1stRN8Q5f9FqcfTrnH5SVw79//G3ycj/6t13gxlC24ijUvVdxDMBvNwc4AeY7GMO3pbHXhu53VGqvb4ZxuBFQNZEf08871OVYBAnjSjlHZQIhQHSZqg4miPtY35VX0Qoyf9wJ/5NZ7/HuH1Rjfc+UeAGCiK7x3NmPC0ZfK2ygCU+x35rd48nJeoJ91GObU+/nUc8d4LyAFk7xGX7cs0VrpPtQ8ebxGZAAgrfLZhq51fVImaZW2KHSDhw9JWoRGcpXptUfWb9FFG0gj4KOjtQeZRgwSkShpmjP2KvbWY/w+sLpF7zv5DgFpPgYAGP/zN4GigH2XzoksS7hNIL3dvsV92rOXKfg9JrHQcSPpLVQ5UT7kBWTcTFYKskPywYVP3r+tTClKAwdbOpiwMURhLyS7eC85rkyaZMe5OBuQRjXuZcdtqtKWeqvHLeFlWiQhQFF1oIUcViT9ZyW5/4ewMMUKtUWeLE+Vh57RQqWUR9cpNEG/3X8kYfqRgBMQnfAZl+sezpdBxtTJdL9gK5IN9JxE8xCvPUSTzETqskA+JZOWh/Mh5LCDK9KGgGP3guaYN/elhYl+5bljH2O1EUQoi9amhWfNqz5V8Hdb+F7YqMzz1E9uBXzQm8tOQK4120PDCjbSMH0H30ubFtv3fG3IejIVIHKpgUju84J9nq11EFZh/D4d8+R7HBCOz3rmp3g8/duDy3E5LsfluByX4/fYeCYrV595YjyODVTYnR9VQ/yx/bfR/2RwONItfuWdV+j985whJ+QEceShR1fkBl1gBlorsZqXKKokit7cDGxAK2lXHvs0gxbDPu2SnZPYBBi1qXMobZEFyFU1BP8CgMyBZs9BP/jdeyLXKqhBAxPgTWMlbs0IvnQ7AqdrqubqL81w5X0PEZqwqkn914/87degW3D1YUrKqAVokx7hSrVSMAOXDLi9wHBK1VGuLWblBrNgAfmvVyVcESqWlUJ9xcEFw/yT+RD3CqoY1djjRo8+b+0y/PriRVzv0U6+dQqDIoXGz9sS05zK9/3eCl1oaO7oNW6Mz3EYduFPzof4grgLAHjvnWvcj3J9i3JWYScwPN95soebM4KWe7rDmydXuEItxg0alb7nuNfgrbP98F6D1fGAP1dvh65naxRWVYJ0vcSF+8dZ6jkCQLZQW/IGn84pgmwkVnvaMQtdzTXmH/WU/wpiDi9v0/sGrzwH/eAJRMgKFmUP+ip9XjgHdUzfc/TBCMvnUoXolWf5g3MBCg/VUtvlkAE2zDYCNqceP0AwsaxS5c3GIo0kGHG7vx//v3BQww42D7aDRiSWcTR4jxXqec6G8wDQxV5lpShMnK8NmDnsc0t5rdHQvm/53zAColHJ+m+rQvK5R7cgRKTTHro0UHuEbtS7fbg8st6Jpcv98GWWeqzawc46vvZY6RRos2UXKJwgZCyaN5xpfFDu8GeBRzqG9/AB6BCVQnGs+M81N1vM53QP+lolBjYkRCfY1lAvFUys7DsF1yrmFaha8OeXTWrzTN8UOP1Ol4xBti0JFajnHK93K7Dttm+zrXCKgYUL51WUhitXoRy6qcPx94Wf28Qa734fVK7P5uIqQ3/GSOQhJmy/XKGvGvzQ9X8OADhxAzzcEPnjm/4qfICLs54hmCbcjOOy5knjeDGE1I6jurwEw1HOCoLdZPw53YiLRR/ZA7r52pnF9f8jhwmQtC0SAQgC8CL93nf99c9j9UdpgZjuks6SE3Myg8Ml6VDbVqP3q5SW0289pEk2eLaQ+PiP0eLd3nJo9pNVmd4InsALbdHFlA4LSCs4Paa93vH5cF6gMhne2tCE7lqF/sMgF9qhKLcI5Slt2fLwVnGGJx19xrUpMMs3eH9NC+8PXPkG/udHRGj6S7e+gLfqa+xDLIXH25r6i1+f34CDwCgkwpxtSnxwlCYsloL0Ddr7QxyCzo8dONwLZKTzaQ9l3uHknL7rqN/wRspaiSv9Jb72mJKUmsyQ2xBIctSEnqvpFOxRj7XLsk16YTsQ8HONwRPJ54T7lwNHk0+YtL0TKIKet8gNlsEH2/Yd9ErBBcURHPlCA8CD7x9h8GgIYV8Ixxe4+X9SrOLZJyYM+Z9+TKCbGb7WaqPgrgTnqd0K16fnOFjQ9WjqHDYseuJcQ8Gji4uV9BeSV3iSNoBXvxvOBIDBc+foZQb710l+9u6TPbSnkR9A+sm4KHjt2RaQdauge1Dklhcgd1LA5tHmzEMUls9jr9exdtgfFfAC0MEHt913nOgEL/jelJmD0hZt4GRs7hggEnTuGEB4iNMQuWhSXxhtSOQp0uIQfcP1GuwHLQ3QDZO0CAIQ90r+Nzy4lSB3G5aoYuhgF2lThFYCoS2lagHHPXQHISQnD5nMMU5pCwd9kPPzYEcp7UbWkls+q9u0QMZn2wuPbj9Mbh313eWMbigjc4aBIT1k37CjlWlV8nbuJEuYVG6RXVvzeao3OUzYiKuDpx9Uffq/weW4HJfjclyOy/F7bDyTlSsKBxQOQnn8iTtvAwA+0j+AhYANW8LX65t4416ojjJ6PwBMRhWuDpc4q2mXuWkz1G2oWFoF/aDH1YEtUvKKnDTQuWWiz83ZAjrQMxfnA0ZU8hOFX/sniRH8reOTf/XzKJZ0jPVVMrgHgHWT4+D+LlcHYrfB/oxeK7RF3aMqZHXXY/SuhAlkhtVtkbxnBYnIYyXS7lpkQfDtnGDvVlVpMnaPLkx5gv6qNoOSDlU4J71hg+pqYA4bgpFi2LjWDkVw3XcQmASmVmUzVDbDnT5lZB6bIf7SrS8AAP7V4mV88eAWVueBNKY87g+oOj0/HAajgWgm7qBCVSL6hmG8vOigXqyxfETnRJ+r6EOPTZlhWlbYv0pym2VX4ElAADbHfXzx/ssJSr25xs0rBGUfnI5hw+58b2eJJ06g+AZ9xmaWmLjFoaK8y5g4s5UzC+mBVcayqfL6ipnP66rYIhxRUk/0dfWZR30teefWz1neNusnGT74fjo/XtJnAQB/rUaWWWYgy8yiDNelfnuCgxc9uigtOi7Qi+YHkYFaBRlWm0IgZE1ZvgDQzig3OLKR0UkmzayXPYhxhcfnMQABSSqTOTiZwhJ84WCn0eBDAhH2HHXkOxzPXZZYy9GQQYZKdrMq+LnzUwpRaCOT1UiGk4uiQx6IivPHYxT77dbxHTZ3Q9WmPdAlec92ipIdBPet6Be9FSDgVSKCWSMuIBoQnpnWwgbTjRjUsMoSOzhzsP0tstC4A44C5GoFByroWtK9VW2RyAKKJjfhtTDUSlECEAgpiND14IGklJ+IkGQe0z1CGxZnA3gPyCBBcsMORZkcyto6gwvWTjJzkfMGX2n48DseIT0pOGZ5I9ntbkvJ9NSOZ3JxFWsF4RR85jHNCFY9NQN84fQu3hjQguq8hIg6zt2Ww6FP35phNd9D91H6PXgBFR5IZyTpxaKrUeYZGnReoMw7XBnRzTnJK+yEvmF20+L1jqBGbwTu/uLPQpzRgpQvJES4Z9/8qR9GsfT4jV8i8/W7/+CziPb1k7JGd0WxZGVvsmL4ZVpWePSHqdfm74/gCqApw+Ql00MMUA+QJ0Tp0Z0F6KuR3DtyGfWK2NjdSNZ4CuFxcLiLIkSgmU5zj9WvNPzIsFXc1fESuz2ChSQ8DhqabE+aATYmZ5lLqTuAlEN4sNohhne0W3QCq/cIvi+CDtjxhCVJUwpgurtiFvGmKtGfVrj9IrlgVV2GedAZb+YlutEaS0/fuzaa7dqyUw1VCb6m5oZgxrHcMu4HALfKUO+nnmgc3cgT27aX2JnJpV5AVoL7rtW6SL/Xaoig+aRUGcGLiVc+9ZPHJtjzhMm+77AJN4mXnjWL3khiLMd+mvIM/5mxhX04TPKPkUEd0228IEg6aia1hxvGCXEraD70JaNu0Rcu9TdbieWTYfrbpWWnomjgj9DrxFrz9xQ+uVe5TVhYffp7KkiTMNdA36GLC8vQoD+k4wnhsUIfYk7Pl+gE5DD182NbRQ46krI1Wzre4DIktYP1msPYY8wbAL4O7GSEtAn1meO+pWwF6b7j/FAgRRtCQPqtTaJwiYm70nBjgzy0C5yVePc//FH+Wy98joIqXEbtnWitafMUd+cyIJ/V3HMVxwM4neRCccH/2ms/jE/88GuwgSXdv3POhQTFISa1hW8U2mhbOW1JdhXkbF4DKm4+Cs8B9EpbdI3m2EB5rgER+vCz5Fj3tA7hOe7i9/84Pz/HZDLBnZ/5acheD7jeQAexcll0qJoM16ZU7S2qHuZPqLKBFakCtbQguevUq51O1+iCxm2zKeAPexh8EG7oXppEmz2L6c1z6DAJL1Y9TIZ0jJPjEYuz82sb2C3RvpSO7fbUQUHpIsFa8Or+ApPQM26shpYO84qqpVVVcG6iWKvUAHBANpccD6VqoJ2kW6A4laivh2qvE5BNJH7gQsoI5BYZRHjWxmVlh17RYRj6nnWncfqIFj9RWuzOVmjCBFZoixd3yOzg1eFBktc0I7RO46ymRU1Jh3sns3A+PJo6g9nyh415kNlKwmnPE1s3TNZtrqR+JgAmlU1ntLAv5n0g5tHuNuiVLa5N6D4YZQ2+eo+8on2lSfd6jc55r9+iCv7KXqXeabTp01GGsfWEOU0FXKzwbJ42YM1eqjgBkBY73C9CeqrUQLGGWWaxOguReVs2jNmsRrfJefJ98coxDpZ0H89PhyknNSTb9MZ0nXZHa45HXFc5TKs5Jg8CEJEsZCSRiaIlXs+iFxau6rRMi4APm8z4fUYd9zNdq4AmaUpVz3JVaxc59WcDEco3KpGRMod+iOOrNzncOksbF5s2Ma7nyPRhy7bPDVOlJhrJ783OBcSrNJE7J9AGQxi50MiWghfDZs+m3mwnyZ+8S9yEuKE2Q7K3jK+J7c+Vp9zUmEsbF1QI8GtqIyEbwXMHZFqEhSPUQt+gzX27zulcglCvKP0TV2u89xc/jRc/Q4utmRm+hpPJBlWToQ1yMP045w21qgVv5rupu7DxRuHY9AIAVOZggtGO6AR8QLZkZuGdSLpmCajFVhzg7VBUZBbNOudjKm3RxTkLK9z/Kz+FxWKB8XiMp3Fc9lwvx+W4HJfjclyOb/N4JmHhvY8eQw0K3B7N8eCc+lGdlcgyi0entEuyJplIi1bD7oZUECsw3Fvje6+T/d6753s4XgWXpI3G7A2BL/8CmTt8/MdfQyDAQo47bOpkZfSpu+/jGyfX+GcxiyHnVAXMF3RMpR1b8/kXWignOCll0+RYBdPxveEa95/MGAp2J0UMCaHdZ9xGCaCbOK7uXCZSZmsL1HcbYkQDwL0+o27CIsGXmb8g3YD0bHe4fjKAXfWxvh5M9ydrlCGNRkoPITwfZtNkXJ1+2dzGoxWd+7uTU7wyfIJ5Tq994fAOm2xYSztcl4fP/Ki3lRcbKqM8sSAZgtr6+DqzmI3WeBKuta80VIztDFDXug27euGAkP2Z7VZ45aNHGGf0XX/r/h12FlILzSxx1Qq0E5eUCY4LMwgH6LVAfSOgA6WBDxIbvVQXek2uL+BVgN10sgRs6wx5blCEirFrNH85axREpdC2IXvXXGVD+Lzf4eMvPAYAvH+2i+WqZEZnY5L7UbfJoXsGJppbOMHZotaR2bzMQmXpyI6SvgxYUuM1VTnxu6GTyWlJOaDvWJKhlOOKEZLuq9i/a4WHq9MxqhXd7+qggG6Bdj82NxMzXw47+FLCR4Zzp1AcBLixoACKeM+0z7XwIfEKAPJT+s7dhAwZIswrjAAiLF9aYNTBL+m+aPcss3L9WpOcJLZQCo+Iv3qdpDf2egOdWYhHMTPaIQvVnS1IkuVCSLloJLOPvaaebnQUgwPkKJwr5blnaRuFO//1z0OUkSIsuJXy4uwYX3znDkRQQLjc4/0fCq2m/+JzEHGakiD0ZEsWo7LEW6g3OYd8mLGFCvOG3eiUeARA1IKfBTu0QDDw8KMOvWHDLHvnJKJjxfB30jV5Wsczubj+27e+it5Q4zcXd7EJPYTroyUenY/RVckPlgkLDhcinrwXeLCmRfnh8RQm9GWKhznqXVpUASBfeKxeDj6bZYuX945xsKbV9osHt3jBAMALoRRAoQ1OQwh0vdYY7AeHHWUJeg6TYN0QCQOgdA/TqjSZac9axP6gxTr0DUUnoTeS4ahuZjC6SrCYMQp94blv6xXp3gDAZwRhAgHm8oIhI7FW2KjUH3S5Z5h1XXS4vUOkn9YpWCdho8YQlA4UX5sHl5vnb5zgO/sP8EZFcKzbQqYGZYNcWxyd0HmkmLB4EkM/MiJXykPHfrgTfG2dE6h7miGt7FTxgvzyzSd45/E+nhySDOi4kuB26nKA15+UrCWUKwWECdD1HHQgGJnSQzUC2SL1uOLiCkeuXREWNABH3PnMw04NZOgdyo2E3aPNQd7vODi9LFr08w7DMemAa5PxpupwPoKZtOxoZToFF6LS2lZC3ghEKifhOolGhPPfbkWqGQljs7Rg2NTrLEcNtLbognWne2vIGwJzo022jkYC8yzZE240LxZ6WpMDUujTt+ucdb+ibyG043vQGcl8B+8l9+dcsB2NpC5XOu6JwgP5g5wizMJ5jQQyisgTyX2tkyg/DJ9j5jg4HY56rL4L88H2rseElJ0ImWqfEqhCu1tFWNhs/S3t0u/MM7guT3aCjWSuAOuXo/ey2bIY1R537hzhg8MgMVMebpnxa7yDDPOXHxn+ObYEvvj2XQiV4uje+ys/hjv/zc/R+waAOg0EzdJBlZZbZz5E4QFAtSogTvMtfgYwGdE85QYSq2/u8DOl6mT7CO3YY9obiabOUs/1cY4yPDOyS+Sop3VcwsKX43JcjstxOS7Ht3k8k5XryhbobIabvTm+8IgSQ6rVFPZaAx12VaaVEAHacEPDuy0Ioo+fBMcjs8hRPgqEmnMyu189R7u0dgK8+jIlXbw0OoLzEj1NO7LaZLg7pKzIK9kS//KYPscHZ1Os65xhNwcFFdyCcm3RaMsGFHuTFbLwmhAeujBw0bdznjOctq40iilBmXagYGTB6SKjUY1e2JlmvQaLTckZoiLzLIDfFvbLRqK4sUY9Dxx9L9iTWe208EcFVCDsZDdtYgQLj1VX4GS1R+d8XbDhQyEdPnn7Q75GR2aMvqLXbo8XeBSqtp42sF5gMgmEjkHLqUSuU1S1RivgSqELkL0rLWe29soWZWZw9TmS+hxixvDxuwf78CcF8vkWAzSQSV3h0Z8r2OjIlXvkwUe22bdoZ+FadOIC6xJIxDY4QFcCXUhHiZUXkIgvkezUTR2zVeWwZWLbedtHlRscdgRr9/sNVqvgm1xp+huBLNfUGVco/Z0Ko4zO6Z+5+3X8ysHLON/Q7zVNxrAhHCCsTMxd6ZkQ5HuGEI5w3U5nJUQ4rzqzKGLFbBTqjWYioPDEhAeoPaC15coVDsh2Gj5G/NwAVVExFF54QGwS29ZrDxdYxuVuhTpAxn6RozgRRBADUF7ZoIr3qqGEqOgEpE4UfLjHXebhY+7xRhH6EiFRAz4/2akk4lD0490i1wlDnr7xudlmBKuFZgQjIkdsjGHBci21VBT4HottK9j0ohsaHC0HXKEKEeQ4ICjYBcQrsvUdh70nqFqsFHyepFF3/vHPpc+/xY7WCwVrBcpAQHJeoIlzg/Qobq9QBQISjGRiJwCYvY7RCNmoVFELMlyJv+Nd+p3escDwAzoJ68HTr8V5JhdXKRyUcPi1wxcgwsPpGglvBdxxSKbw4IcayuPKXrDiC0ze8xVBmMWRQkFrJLoxcP6qwV/71K8AAO7Xu5ze8rieYKAbnIde2PFmgFt9gktfX13H0TpYmHmBps4IVgMAD1TBXSYfVXBW8iS7rAteGE/OhugPGlTv0oQrJSDCgroz3jAUm2uLubbMip711jBhFThYjWCMhI/yiYlhmE9mFkUMGigbjHsNHkeo89EI7Z2ghz3N0bu14j7cbLDB14+ot/wd+wfYLdZYT+m195tdltsUynD83ERVGMgGu4rg6r5uOUHo9PU95M8vGe7tFy3aNkb3Sfg2xW65YYJYRSMxuErn4+7OGSqTYVGHKLmrq9XfI0IAACAASURBVAS11xnErIHZA48IiYon5O4jo97UJ8a0XieoXVUCei14suxGiUnsM8C5xLyWzZZOtBNAJ9FeD5CY9NyXbD8ccJvC5R6tLVhasSqiEBHAlFi5ywOCzUUrgADP5dri7Tk5Z32lvXGBA6C1hUXoLS+CveU46Usj07etNcpBiyBhRP9KcthRynGcmFtlgCc2OkCaaWz1jJt1zhuHfKdGd0Cb1S4j96ko+/ISkOEYLkvyL+cDuzbArNVZCRVgYTs0WL6cLA7LooWfBP1to6E+7DEEa8vkACVcanWojQwm9UGe1E9SFmGDZEWm6xF7isoC2UqgiranK8n8BuGSTEx2xPqNfejiRG0FCADNLPXsvd5qfXQS9Sbn+xXnOrVrtqQ3jQP2rp3j9CxwN4YdbGCbe+/pWkRmt/Lcn/bSM3egGztg3GF+Qox4mVkOcHBGpl47wE5lALBe9cjkv02wto23aJvg9OF7Gu3E8yZUNuBggGzz9ItYLmHhy3E5LsfluByX49s8nsnK9RvLG8hcjs5e3FvkD3OG7/Raoo1Vm3YsLj89GkP1DGyAgkQBVNfCbi4DZjfnWBiqav/a/r/AL519LwDgtw5uY9xrWEvovMB7KyLN3D/dSYb7wVSb2ZhWYjah6sA6CaUdTKjUVg/HmL18CADo7c/x6GTC38XPOszGwXe4rLhKs07iO64/hglb6PfPdvl3hPBQyjGRJe+3mA4JElrVBcOvZ1WGbqJSfqkFsg/pNXO3xrBs8F3XCQ5/Z77H0K+WFo3TuFpS1bzaybFY07k6OhnhLX0FALA7XeHmcMGf66QeYBUqIlyvYTrFphW1zMgIHyB2agHcfJ7MIZZ1gfUgwFZOYtav+JjvPty/4HEc83TteU4M3lCdDiYVhwYcGglV5anSseBqUiIZlUcIjEkoPv030VG1K7YqM2ZS9jyyaZ2IRU5QBQgA5UWTCljBKK5sExtTLDWEFYjJeLIR6HapqjBO4vv27wEAvnjy3IXK1XsBGSo/9j8In3FwZY36/VE6/0aikYFV26YppPWCoPlwDtQqGZIIL8gQAoA3LjHNAXQHffgAbcIKZAtN1T5CVdhPGlV9Eo4R9aGRaNhzcJEdvFFQawkzpt87Oxmx5jI7U4DHBSa923KAkgs63157iq2LEtulgOnHzyRgdgzl2oKcqbbzbZUWEE26F+K1cXnyYbYlef1GBycvE2HQjML74vRkxQWNrdskdvgF8FRe1M2eLQbQYQ7rGs0wv+9byIVm4pTfgowhgG7k+HjsDQ0AJwVM0LLCCshRx8oFt86w3GIIb2u7Tenhg25ZNDIZknXAjX/ZsRmHFwKD9wjNW35qiKd9PJOL68PlBMoVeH56iuWaJm2jNEw/Oau0z9fc11uuSn5fb9SgPi6ZmWgmFupJ6MGVHrm2+J35LfoZEv/qkAzUr4+W+K7ZhxgFnOx/fO+7cf+UGH+beckG5OWoxmY1gDihh3z48hy3RiEtxmZYVQUJ6wHs3J6zZOT4YAx5ruFCD0esNJrpVg8kbCTOVyUKbVBmNJm9vHuEN49pUTOdhlLugp3h2ZLgutl4jf3gLuW9wKbLsI5Q4dQlyEz83+y9SaxtS3om9EWzut2e9ravfy+dadKZbsrIdtFIlFDBgE4lFUICMWdQA2CIhIQYUAJVMUNiBgNGMECyEDOsAmM7nXY5+5cvM19z7323O/1uVxMdgz/ij3VKDHPA5Z6QUu/cPGevvVezI+L//q+h99paWtR+++QFLns6xlk7x1cXR2yE/3C+wemENg6bocKLK9ocSBHgg8DOVPx+DEc5AWsU29mZbWYpCyMhlwOGuNiODThwXULcG5llNAZDn1ylkMPYRSCzjjjx7DZzJC8I4eLzMZInmWk+ZoK0yJ0o99Cky2zJoCKbNLKwXRUgjuI/BAXRq7i5ESIAl1GSEPLE6ecOmFhuHcjaMuQHS9AykiNOHbjvWTx0+GxDIQe9U7BW5dQdAbbYgxPQi4Gdx4xR3BvUoFD4Pm60pssORbSzW60meWWWZKaQ+vSQgQLIAaBTEFsNedrxtXNxE6E26ta1gwgcPB50gD2MEiYbjSDS5W8V90vROIQ+w5LBSBT70WKnkKUyTWATgzAynlD7aP1X5OfOzUfh4hMLF9nmvhC5/+ppEU5Qp0fOcAWyMYQcBBmKJGMWPcrITeYb6evrR73QAIi5yZuuIIBoWxmkZBcvDBK+VZAxUUhq8P0NGw3h88YQTtAGAYCvA2R0x3KdpmtzHaUzRciGHh5wY/esXuZ+tQwoDzr0Rdy8dYpbAqEIkJGnICzgGoliHYsY42GX9Lwvv3jz2cJv5eL69979AeqZxt5VFJAO4M/Xn0BssySjrA12+zxxV/EB3u8qNF9riBhV093zaH6bUkf68xnsqKn/w+vHvLO82E/wfw4f43v/2j8EAPznvzUiEvSSJCQA2lczCAfybwXNAR/PyMXo0/UDctKJfrNCBFz9gpyLtKGJxkeijGglNlfUb9m8mkNFLZzbazxfn2CaemW52KWFVWRZgfEa336XdJGtLbDp8/WYFAZHj8l/9zPzEPUv6YvUTkpcrwrY2Et97/E1yjj5vt5X8E5CJfvDyRrfmlLl/fn+FOuo2T2/nlOocvy7bVdhMun5vXe7Gi5NIkHkZBFJi8RN7IcLkXfe0gNPPqXer/DRUSe+bJjlSLLFgw328wrijDZT02eSKw87IXJTmviDAnSclIaFh0x6YUcLcFp41eg1vggIGrCJKGYz6USOUl7o8weYuJjIrcobmJWCOww8Wdp1mTXZvaQKJspeoD2Rc0CbxJ9+Hd2yJg7lZGC3Kt8r7sFBBujCoYkVezcULOuyvQKMhI7/NkahKpJIeFRHqQC/NOzbi+cN5Du0WTUd+Sun924mA/ZDWo00+ns2e/KKUZU60mqyDCxekzCyfEwSFu5zTxxMBD5kq6JtIvj1Pla1cuS6BEl/kzTgACBGIeh+rRlNkIMg32zQBnvcbPMHFiHaqMqTPkfqnZcQPkcMhoWD3OUFNBQhx+6NvM1hJC12KTVoYTg5yfQa9iTw34XSs05aWqoMAdIDu4nPZa8MCIZ3A6RTBQAvECAzyqACkzxD5Uj/HDkNei8wNEmTHdBvK0YVZCdRnNHfLb4I6A9jH3sKnP+2xuxpLBY+28M19HeTnz/Fmz7ueq53427cjbtxN+7Gr3m8lZXrDzfvoPAlXu8X7LsqWjLyT/2donDw0T2g3VbYvqYegNpJ2FngjNXphyt2thGlx9XNlCHLy8+OoaIH6GzSc8WWho5uKjhv0JzTATcfWxSnPU5j2s39yQZnPX3G+/UGm6Fif+LBqlvOP0HkFB5fB+jI4JNTTy4+II9Pt3DMsJ3qAR8dkiTlqptgUXX4WcxKvXe6xvk+s5hT0s3RdI/7kw1Xp7PDPab/EkHXCxHwcLrGd5YvAAAvugP87JygSGM0vJec9NK5Aj/akFHEk/VhzoT1Epc3M3am+vjkkpnVn63uYbNquN+FgBwQrQPqZsAkVly90bCTLE344IjO88V6gevnyyyDMYIrgX/9/U/xvD3AXxfv0jE2M2ad+jJA+Oy9GmSW2IQiwKdKQ1AKToKJfQGuEvROktE+mwwAxsR+9akhH+uYRGSH7KsrbDb0F576isnLVc0N9xuxsNTbjNVGMTEwsdpwFxUQTQWq6YAQsixCFD57waoA5wRuItM0tDp75Uoy6khBBUIEtJFHILRnpEBoj+Akf65wMsDtYrleOYi1Al5SObmbFTm8PPryjkPMkymINAASM7wg2DlJakIRspuYB30ZOA08Q79eAAjg/nLVGDZzGXwNlZzAVPxOxRsnDFDeZGi5feCzG5UKXIUndCFBq7qwMJE/4bY6hyHogFAGyMPk0iJYRhci8pEqP9HYLHXbSeC9lr2161HogNKee95iQs5fqcK3c5+ThxL0HdGsMPJdgRUQ6TmImc3c0pD8V3QedpRepJCRDwHAiOw4VYcMr5fA8qvYUqskhrnA7hG97vDnQHHdxfe6k+K8kePPfvQbkE0NtVUMOVU3Et17A/eM2n3Fk6WQOeKp/nCDk9kOzy8opsV7if0zWvxC5dGc7mAiOSkcGRTxi/Voscai6BgKFp1CcUIP6/I7FyyVqbsKzgl0SXrSTfGTF7TYVZXFtBrY+D4EAT+yZWQtLqjP5OJEdHxvi9dXBB/jUQdxXWE+jXaFtkQRV4FKW1zsp/jwMcHQ667m95qUBsuGXlNKh5u+wUdz0iC9894Nzjq6Bi/3C0gR8Dc3tDidVluczgiC7qzGxWrGYQProcaLGDtWaceL9aOTG7ggcL2lXu1ED2hknkSOj7e43FC/WlgBPaffvXt6jUpZDmBX0jO0PK0GdoM6nu5hHiiWjQgAv/PBM3ovOWBvC3zjHpGifnzRAJGMoV/H3lPCe0LumRU3kk3TdU+WkpbJPCTPAWjCtpN8DOnBSTLWSEB7+CcxlF4FyIQUFvkZFJ1EsRaMMLqRvIE+jM/9UxGyc9EE/Dx225L6rGlR6xX3Nv3SUnjEaLFNR1dbCacEk+pU4fiYvlMMyQYVCJpUacIN7LQEUK+ZSS6D5IAId2QBIwi+BW0+1AiaTdcYHhnCjfeC3RrLAGHAvU55UWSSWUGSGhvbMH1bZFmLjHApSFuKQWTpTACHXbg6IMxcXvRtjtozM7pPqbViheLABagAmdoZAoAV3LeVU8P3rKgs7FmTF28jKVIPgD1wwK7A5Ig27dYqdu4y2zL3Vb2g84n6ZGEkL+zCU7h7kphBAOUyW2lO57kXvls1HCoihkyyEntFUHwKXGjAkXnBAJAUigDEMID4PekPgYNfRr258ehOS6w/oYv81b89weM/ofnsj//kv8Jy+d/hTR53sPDduBt3427cjbvxax5vZeUKQf9zBxYiQoNmGiKslXNJg0vmBEAxj7md0qPWBp//e/8ZHy75chZTg0llWLZz716OhNsOFb68PLpFUkhet+erCioSRMrSwnYlbuIxbjBF3URT/2qAD4LZmdfXM0a+dGMhpEcTHXK6vsBwleU3VTTPbyqDnfRsPvHtg5dQcQv+rD28RSTqjGaIux0KXLVUUS3nLaZlriQv+hmzg7X0sF7hJobJH1c7dmjqXIHz6zleRyi+Lg02EW7XD9eoIjv13mSD42qHdlnw6/poebPpK9ysJ1meoT0eHJHBx3uza7zYLbkSr7VgByKg5PN6MF1DLgL8gqD3z78+xc/PCLr2QWJjKg6yl3ODMpLZhlVBCPJ5rEIDWEbgixyIHjShknaW4DQyjAeIWKLaDHu6BtxiEFaQB2+C0KajIHUVGCZ0Mwd1qZjhGWRmMKtOwJwYhgsPZnuWO7WrGjYyP1F5hACo5O7TKja+hwwQAhwjGLyAWifGLlV1yf1Ll46qG4DINmm7biQdJzF2C89GBQCFG7iR9wVjuk4ApYeKkhvXBJY0+TJwFasGAT9yMbLFiORkycCDfW99VgGEMiDc6zGN36ndVYMiXitZG/RXTTzPQC2HeE28B8MNdu7pcyZC2cLBRncuudH02oQIjK6dcFkypVuqIFOwR5iCGfH2qia/YnbSyBW62ii4pcX+IsYNesHG/VCBIwqDITQjQbxBBW6DEFNYcvX+7juXuNjQd3sYJLbn9LNsLFXAI0/xkCLnSg+hAlwRz20/siOTFKmX2iJqAzamKLYBPn5nyrM9Zlc7vBMDO24+KaEGOv6/83f+Id708VYurnqjII1irRlAE1ZVG4ANzzVEhKZC6VFGecr9+RZfnB3/vx7XP29wcVigiguxdZIN1UMQ2F9OMpW99gypqJlhSUS7LoHK5YnICbSWZqF2U0Moj8WCFkpdWtjYS/VeQAkBHRfeo8WA1/F36x8cszRg/9EOReHwbpT3XA0TfGdOmtQrM8UlpnhvSuznqR6ws1mX+yxa0lWaFpt1pGB2TmPdRxs9p3DTNjhqCLb66eUDhsmn1YD5rIVKk5JTOHhAC5xxCoeT7Mj05y8+YH3spqvwV+v36QSuS4Spxd/73X8KAPjb819h4+m9/+/VN/BbBy/w09gzbrThMOfVdY13P6Rzvldt8Wq3wDb2CuvpgHsLeu+rmNLzxXOSJ8EIdFH2IANJaPqjEdybJB5lnsDlIOBL3OobFlHPw4HY/0z/CyBoWfiR7tUruKTBFPTcAhSA7poAt0xG9QLFhc7HN5KTRrqyQHuRQhsEQmKdxhZCVdPCYrXiUGwWjqTEgm3Ji5ovAaeyJGPYF0CCcCcOiDrR+lyhe2SANPH3ipNSUv84hYFD5P60uNRwcw87y9KNUOSFN8QZS26B4V5AeZ2dhVKLRxjSp6Z/BxUQ0uIqArwT2F3HDYEEhxx0XUE9TUQd6ghpF17wYi0PeyAIyKd1fG/ATXNvPEhARPN77v/Gn1Pftj8KgPB5Y4URS7egxZv70LnbQ1m1jYWKULztNX9OVWd99uSoJV5F/FzzWYt1lNWFFzWCAorIMh6cYrtJBMFMcCowJEKSGQ0y90KdQDASi0/T64D1N5PPY4CrAvqYLTD9WmD5FR2z2FrsHtBOs1xVkNdb1D+mlszDz5u4iwFSXsKbPN7KxfX3/+gzFNMSP3z9CP2nJE0ICnDrGcT7VGXdP17hpaW+qlQB92OVs+krSBnwjf/5vwQA1JWBmtDD4gCoxnJsnHUKF+cx1swJwApUJ7QwTpuetbPLWcdkIe8FqsKii//u1hVXp0IFNNOBe5/35xtctTlQvDcafXwqB6vZxtAXwOSbtLAEUD/500gyerxc4VlB/dijYofLYopNrEJbW6BS9IXpnWbv32XRQomAbVx4359csVzoLy/ex1VfYojNqmXd4esruo7LpkNTWNahVtriIEaLndZbdLFXetVPUSiHV5/SAic8IH2eoHzQeN3Tdf2seIh9LG1Wpsaz3QEmOibJKIfjOd3Pg9ML3K/pHk51DxcEtvH6Hx9sWS+860q065qrxOJG8Rzr60C2d3F+GROavM6VkxxE9JTNFUd6jS8AV3meOGVPBgUA7e59ebunm1YTt3REeAKR79zE51gvGWCTvtkKCgqn4gPdUGSfWS+4P1qXBkeTFr2N13w3Yf1x25aoa8OoRacCXJ8XEmFHiUhbParSPMQyLlQ1GRPoeEyz09kwQRA/gbWhjYNROXZPbeSIvCVyBScC7CI1ChVUKzCkFBsB3jAEKzgKML0hL7y9pA1AkvcEYHM2MixI8ppeUtIOI00BiIuOEiH2x8G/q86z1v2Lf/Cf4qN//I/4vVmzG4DFE/rHqw8dxcOllB8rUS3pe216TWHjQ274ytj39JWndm1E2EKr2Rfbtpqr6d3ZFKJ2ODymZ743BcvZNqdkZ5kKhvOredbNisBEwmTUkh5eOQj41PMONJ8V28RZ8egPo8/2Pcs9aABoLjwmf/kVvWy/x+ERrbru7ByYTZEcPfy0gVvGDXthgF/ijR53Pde7cTfuxt24G3fj1zzeysr1+0/eg5zUKD6boIzEOLMI8CqzKY1TmER49/5igz1XhArDyynDa0NZZsp87aC1w2BHfduRZOT9T85gknylGBiy3PcF/6yUR10arlyFzmLvqqFqIoWzhwDsr2LvRQWoyjEDMATB/S67cAwXT0uDQjmucK/aCWYHdJ5r2+C9yTVetFQVnjZbfHZF1WNTGByVMVdWOPggGU5WCDjSBKvef7jGX9x8iHcbgpaf7I+ASFQupMPL9QIyUvqtyHu7edFhHjfPQ8TKVp5eKIds4QcZ0Bx03Ff9Zv2SYeGv9se46RpsJFXendHYR2MKFwRD11IEtEOBd0/pMy7Llo0uXu0WeNYWCJE5a6eeWamypzSbZBgge2QMNWDkxAPYOmOKQQJ9MuN3gliju8SGFezWJBzICCNBpC6zUDsJdjsKKrI0E6raZXP4IOj3ydpRiMDB2nZQXK1MSwMfBKMIfVfAJYOATsFqx0HqdlVC3mJICyg/grlT49Nk2BaaTBA4Yclks4+QGLmJjTxiO7uaqnpuM6rcexResNOPXUbTiFRB7xWHf1dXEt3DABHZyNhpNmkRRz3EqxrBje5PYuVW2azBN44yUNm2TzESZKI0id2n5hZdnR2+6Jjx2EU2F/EVcPlbWbbne8UMalUE9DG0XR8M0LVFf5MD5DnooZMIs9sylZRLPIaxZS/hPXCNef7/4r3XpSOlQTL9CBhl0/psaTgohDYn2thFNluBClCt5M9VXVsc/jxC3i8Vym3A/Bk92MVPngA6LzWhJSRFzmcQ0wme/v0oezsKcNFisvpcAn+CN3q8lYtrOKsR6hrCZQguSKD51g17/BqbnWe+eHaanXNWBfRewB3R73yn+QtfTQco5bE9i1IKI4FoZQcrqB8Z5SVmonBvRgvSVxdHPAE66XHeLhiakTpwWLHWDt6LTGYQGEViCaipYz1r2Gkg+ZEC3OdsTYHT6Y5TeHwQ+KfX9HCf1Dt8uT5iCUz/9QwPvkUuTH9070u8U17z8b63+hAvWrJ2lCLgUUNewJW0eLe5xoOK/v2yW7Kv8eX1DEo73kjcP1pzf/egaPGbDWljd77CH++/C/l4z+93Oqcv5KQwmBQDpoqgsC+GUyY7Pd0cYj8U6GL/yHYFQiRavN4XqCZ0z47mO/RG42xDcGCxdPjqhn6+fkH6V93nxaochZ73p45lI+XIk1UNyBFzIgAiL5Lh0PDfzU5bGKMQRlCkjRCuNJH4kY7Z5+dTDWLUe4xEmzSRq8AyEbWVgBGwcaEZBJgQBxEwjy0FHwQ6q9lf+2Cxx8XzaNcVBISgHj/dVH/LfGlMVIIHVEwhwl6yTWLQ0R83Ldg6wMdNlT5ugSBgViPryggZq17A6wCVHI/qwBIbkvfExalTCJW/5WqUYvHMIFAse+Y7uEHyouA7DdF47q3aewNvRLDX2XLQkL2iTsHhM59nywBAjPSipQPiwuVWJd7/7/8bFPG5s4usaQ4euZfcKuoNlzLd0sz/GGIr4oDuW7ASPnotY6cRtpphbdnK7GA1NxTiDlCPuczQeDPviPMBCqsXAhj6uIGXYDhZqMCOYcTYy7+TneJzThu6/X06t2qtsfg8ur5JAbXpgT49dwLhAfFUxLAEHH2uzbdP0B1K7D6Mi/5RBx+5ArvfzI5sb+q4g4Xvxt24G3fjbtyNX/N4KytXaQSkEvBFhJeQd6HvHhLx55vL1/gnX1OA+eygxe4pQaXljaSGfSKvTA0HPEtJkIq+pstqjyxXvMVswKwccGmpYlnvaqyiB66zCmVFVdVi0uHiZoZ+n3aVgaHqvtewXcEEA32TDdVDlFbwLnxqUUQJSVE4bNsoldEOLkgcxAqm0QaLgn7e2wICJNkAaOeVqtif1g/xvzz7XQBA9VkD3QLtady5f7QFiKCLb81f40jnjM+nm0OsdylUHVAq4MNTIj/95sErri4K4XAVr82VnUKKwEk1QgQOHrBKorUFFpoq2ft6BRMxuH/x3uf4Xz//LkKsjIXyCCKSpyaGYU8lAk7nO5Y03XQNNvFe6LWC14ENIFSbzdb7hxbVskO/pmtpTAHPKUpZKO8LOldGRRL8GcfQFpDxddVl3t8ykSmhdSL/LOxIamIkhJWUywnyxGWSlQ63iFS6sCgju/v+YsMthYeLNcmmYuX02a8esWOP1g7WqOy2VDnImO3qWg1VO3iTzdxd/BzFVsAmNnIAxIEBzmP4QhOARXIEknQvEunISG6z+ED+vik4gYhP8WQ8sn9xCnKYjzJnU47v/R5KBsj4uXyZTTWU9rC2ZLMIVXrI+BwYK3OLxwkIK1Bsk8GE5GciNA5yYZjdK6WHjhKVfi7g9zkUPajs5kYHTvdTQHUS6iZeglkOORBXBdwhOO9W1I6Z275xHEIOkOEHkpdyq7PX8tSRtDA+8+224ms2dAWk9pkoKTJkHJzM7QEvIKYWIeXAFiGW2EQsNEcWJoZhDAcF7sko1fvJFWAdQp3C6zdQM/rd+ncf8md//neA0BjolMa0Kfn+ijFS8oaOt3JxdVOP0Hi4h4YXo7KyqAuL66jPvKhneLwkaHM7VPjwd8g678c/fQ/VmYaLsgh7YOGb+EDICEHdo0lEFo7dTh7ON1j1NS8m3mjuPerCskl6qRzF2cVvp67y5Ljb1Lcmart0bNgOLzCfdih15u2fXdCGwDsJn6QTSuOFk3jngM5tUXR4uae/a02B168OoCIUpgagf0oL3i9/PsckwqPFJsA1Av4xndvjgw2uY/LNn7Yf46TZ4mxPvZ7BKY6tO+sWOJ7v8GhK733Rz3hh/9n6ASY60vWlw6e/eHzLdcgv6L2XTYf7kw3ulaRtXbkp5oqO/+3Jc5iPFP7s7EMAwKuLJcfnHc132Mb+601bw1qF5ZRet+tL2MiWTJcz9U99GWDv0+dq5j28F5CxD+fmilmpRkoUq5Elo85SEDop+rvt6xnkXnLCSg+g2EaD8x7oDwOzh0XIGtjhQZ6EbJRryesoETJ5A+CrAFSBr12/rtBv6Ly304o3aqVyWPc1rvdRkuIE3v0f6c2e/PuS+vsRijRtkWFhSWzS+h26di4IbqUMgyZolS4IfZ4I1aqZ4RQfNyiIm4JtK9OCkK6dm7rMLM6peFBzw+usO68hjYDY5YUmWfF5V2JYCiwOqa1QzVuWg/VGA5sGyfDL+AouaqbLRc/acIKZLdqD+D18WY50zAHeSIayja0RlnRA3ylABY5tk62E5w1H1iPLTkAacLSeagVSY1sE8MIKEIScNolyZkiVPmIgs6m/Ffl6SbJkzPrTUTKNV3BG5gUVERoG4PeStduyor67SWxwKyHia/yqALxAERnIdqqwP6H3Orhe4/zvfojJGT2vjfoYwzw+g48VbyLL+xtMm545KnsvuCXidqPvzhs67mDhu3E37sbduBt349c83srK9ei9G6hJBecFfvsekWh+fn0Pr8+WDONeraYMvy4Xelv4rgAAIABJREFUu5wRujDwa5WZeSqwWw2swOzBNmeNGs1m/VftBOtdnQ3WBeAioUCWDl2Efa4uYpBAjKkyncJVCs9OusZUXjnBbEMhA4xTuFlFJrEXDAkVVQ8XfXWDEVATj28uKert09UDmHhuJ5Mdrpop5D76nQ7gHbIvI9wJIGiBH/+j//jWNf03/q9/AIAg1tXQ4GxFFa/pNcoYIFDWBlfbCUCyVxTS4VVL57u3JQ5Lqoa+3h0AOrCJhzACfprzHa2XTGKayAF/vv4YANC6An/x1YdwL6kakyZX16t9g66NhhhWQMiA1zGTFKuCXWhCEcisPFWCC5sNEwZNgQ79iBnKjEsBs8jwqPDZxN0DEPEZ8aWnOLjx61I0XQGYhYcv6L7pvcBwGCugynHrYP6ohfMS2yJmEe+zkYCMARRlZIB7J0n/CGo/uGjs/uzmgCIVk5HJcsDlf0SvqYzG8XyHlv2tNSMmcqfQy8CQ4gcnV/j6hm6o24yU/4HIMYmx661kgwNdOdhGMilKeAGR/DAUUKxUDv12IhN9dgU7nBU7MnVIlb3qR9pbI4BOYRsJWfpgh128984oysONl3+cW6u1h41aVqkCqtrwee67/J0XWwVIZBazEXQP4v2Ey20mfa+Fid8n9JkAB0F5rsw4RiYmpVYDa2xVyN95kFlEMp0pJyaTIbtRAIIkhyyO3StDJj8Guq4pbCCM4GQhwO2GsNWUixtf5yceMs43i59JnPxoj+6UrvHs55cwD6LH+nyK/kBg/VGc34ZDDBEBcHObr8GupJZVJFqp64KJosG9+XXfW7m4Xt9MIIcay8Uev7w5BQC8Pl/SBJImg30OAv6dj19gHuHL/33zzwEWMCeRor9XORmiCGh/teQvll9Ydr05PtiSOLzNl5z7IwcefZeDhYUVPDGjl/CxLxZmliaXNCkZyZ93Ou9QFxZ+QvBUuyvZgeWj00ssoubo9X6OdVfhB5cU6P7icsn2gU+uD2E6DdyP7OSVwuRlahLl6+dGUB0AfPxf/2PID0my887xDQ6rPeQxXYNf/PIR+mS5FwTkaYcXO2KlKukZJgfA9oTzsgMGScxXkASgjuzpzmqshgbfu/4AAMGb2xiqLkWA95LhO70T8BEGfbxc4VzTxqPUDu1QYH0ek46MyGudJCg4QXnVvGdJyrzpUWuLs/g5h0FDPIt2eSrw5sOVxPpNhvzOZHcfNA6y8PBxIRo7NFlNC3JaJLwGdISIw75GO6fr2B0M8IOCvoxuSDfZVq87JWehOi7EWjn0Nf0du/AAWK8bQACzGT0X7x9e470JMbd/cPkY78xvcB3dqvyRxOqrGFSxsIAX3Hf+bPtwBFGKHBgA6s+m8O4wcbwRdFYCpc/rzF5xULjcSxx+Ctz8RlpYRmxYQwYZ9Iex3xo3kDYTj+nXU8Mb5eurGYq4wfO74lbIul2XvHEzA3gjSOYtgto8oL5nEQ1PhssaRezNA2TwIRIzd1MALrt1ic+nEPPcP055qL4MNPvGz+Frn6HwwpNcKz0jRXaYEo0jS8VRSEeOxgLbgqZ+qziiOUCP2kVmU+V+dzy3EHFh0SnoVXap8iWgU995r1B+Ts/E8isDfbHF/MV1/IwaxQtq9wjrsHsnQL5L3It+X/K1kyLwBm+xaNH2BW9e3WgjK4vR+b2h461cXJV2kNphtZpgnYrBFxW8DuBbGgRXTn/yV9++pWfUoy+FcJnIorcSs6eAndJB19+JTXoAm6qG1B4ufglVbZkQoc5LuEU6COn6XJRdCCsQkqTGSqit4p1tmFmeDHabGptNwdFpcAJDfIiv2gm7FjXaIFQCz87IJUUXDgc1VYy91egbQ0HIAJytIJNGss5SE7MI+OZ/8d/mRBgI4Ee0UH15v0H/G+eYFvR+YmJRntHM1z8yODnYYhX1pvemW+h4kIt2hsvYt/386gRiYiFiZBWKgIPYHy2VQyEdnqzo82/3NSbRwm+wCov5HouoX930JRO3bJAc1bftKuxfznhOcpMcHyYnFko79jnu2hKhyE42ADiFxK1KiEi80XvJCSRu7uFEgIi6SGFHi06v4FvFVQkFbdOvgozewHEy9jnMBV6BST/lFw2aM0BEraZrgCGF3qtMHErXJw27K3gh1JG89GBODj6/PDvFF+qYz/n5y8NcXRuZpWjJs7ZNPdFAkWgg4lNy+hFBIPSZqESVbFxAn9dwUw91HPt1pWcCi7pSuPlG7kXicGDP3eDBZBsvA8XPpUVm1K9TjYPrsj5T1RZDQn8KT0k9ifSlPfw4Ci9+t4qJgR0U5E3kHzzcYxGTpK6MgrWC4v3ie4eEZpRkXeiS9aKUXE3q64wwuDoAggh0AGAf9byJRk9VvYoafKuAKsrSrFGYTHtso5+zW5eMDswebLFbx/u9KqB6gSKet/lGy5Is46lCTXwTb7NeOEwt7CTOPXtywaovsh5WDfGZqwQQAmDj9d9sgSPagD35dx/DzS1SIBKMgOlpDhCNI+kSaMN72U6QBH79Li/CS7XFU7zZ482vve/G3bgbd+Nu3I3/j423snKVimQe9qLMKTUScHPHonRfe3Zngc8m2pzbmcTU+9xbCxqwU8EJEOpaI9yn3XnfFcQCjtCPbyuGjnwdGNqplz2GXiNcxb6nEfCxsSScgN4JhJQduQSGFLcSPWUTTd/XHuqGXne2P8bZnBjBunSACNyP9TJwjioA6g/FYqM+U7CRTOo1sYcBgomkGZmtj8w41FOF9kPNEG9RWZTfparzwaTFpi/ZQPzieo6TGApfKodfXBC03LUlgpEwsZov5j2cTwbt0RkrMgxnkw73oxnHqq/x4vkRbrZU1R5/fMWewaVyuLyexWuvySA/QbIjqYY3EhCBq1PfKQzbDKcKI2ATTDlOqqlC7lu1mq5Hag/U2aNWWKqw1EhOwUb9TYDeCX4u6AX0H+kAcZPN+e0kP5PDMqA/jXBg49BMe2zWEa52IledAcwjENLjw9MrqHg9u20JdLnHrY5z6pGzGXZWO0k/p484MZCxH+hGLY8gKcg8Gf4PjwwzUu2Rhb7R3AtWjeXPZWeerk00UEDALW9tNubYaaDy/N5jL1631eT+FH2OpfRcXdt1CbmXlDgEoFr06G9irq8A2piKI3cKetR7N/sS15Gb4HsFsbDZfe2WwwZiYkySFmnI+HdeB4xN/IMEzEFkKlcWJk7H8lqTmUb8avulhYlOV26vsbUSWXuFnB1rR5KvQHNH9yBW15sCu1FfOHiBEA0b1MKwaU23LZlhLAIdJ6EiQQVUV/F+ziS++A8eoKZIZ9RXHoc/Il3R/JkH/iDL8UyTg0iKxjAC8NXVEdpdyb1fVTmGjM8ul3jTx1u5uAZP1l9h4jhgXGoHXNdEdgCgppn2b9clJ0PYWYDqBJpXowl3RIvfPfaYfkK9h5PSsK1eawpctItbvcvsCJ+t4IZKk4tMCn5emBGZRMCVYFNz+oM0+0ZHvDjBS6Oy+fkgICI06x918E6QrSKA77zzHL93QKkU/9vzb6Oa9XBf0iIUdEAXA4CKreCFVnhAd0C5itekAcdIuQrYf/8EV+/SxFYtOxxNaHF9Z3aDp+EQ1/GLFlqN1/GbS9aN8UumHUI9auX5nC4kRMBB2aI8itfVFqgVvderYY7D0w1WZXSYsgoxEQu7vsxQfuHJJjK1k7UH4gIaZIDrJVw0kpcbna3nrIDaC5hoHu9rWgzpWuXPCxC8m6LSQhVuwcAYSTcxktuoViCoERGqyotJdZVhz/4ooD/O8Wv9Y8P9zOAETcSbDHXq2DpwIpPvnFX44vUJbPw7tcl9Tw4dH0XEpVgz10iIvc5xdzdZpiMkMHlIGx1jFIxrMB5JDoYA+IcdECdSfz1yahpFtQGkMTfx7+wowF1YgaAFB51LHeAijKqvNclcouWe+WcWv6DofAFgsBMghclvdA4oL6lFxI5EWwWXPr8ICFaMvnuBY+V8GRCqcItbcUuLO4q+Ey7Dsd7JkVWqoDD5uDHHrgCidh4Tj+A0bxYoZi5CtU7wohsqj/KdLeyL7ASWLCDhBDldpWD18wpdFeF8l+MQYWlzYZOsaBCYf0m/6g8kXBOw/v0Ysv5pje07ZFfaHwaUAcxVKGsDX9LnqiuDPrWarESK/wTivJzg9e7N77newcJ3427cjbtxN+7Gr3m8tZVrYtwl317vyDGGjbSV592znBn4dWZ3BiFueRJX0WXFa3LcsR/SLy9+esqs0/IBERLYPWdEhBIui8vxqoaoPPS9KNI3CohRbEECUIBvUoURmPUnNppE9SmBy42qAEWG6ABQaAe3q1Ae0DZ/Zyr86QVJWV69OERxVjDcK41Afy9en1JycLRsKdfRR8ZCsQ+8TfMawHc2+FsPXgKgOLrkM/ysO8KP2ocZ0tIe5SRBdwGPDunvtPDYmhJnVwu+ZzdrqkbdTCCEDBn5ILCsou9waWC9ZOOOzfWE2arl/T3v8Jtph3ZfZqlSbTGkSkN7HN1f51CFeQkTDdX9fIBdFRCH0fN1p1n4H0S+t27m4Jee4TW9VSw1kTbDwABBwalSVa2ArwLsJMO49XlkcVrAjJLRzJGFjlFyB4Ul9i8ACAGzLfOzpQNsrPxCryA4lk3CbTSky+8dVDwXFRDOq/xMVh4qIh1Ke3IySiHcE3crkHsfDSuCkQwrAsT+DOwsponUlbx5dzLLaLygZzWSCXei4ZZJaDVLUoIQEL3MofNddkbzVUBQOdhbKA8d3cpCTQ5EiQWsbjR8nAZD5SFjYEN1JdHdz9VT0IF9jYuVhjmybCIBFRglCo0n0lf6XqpReIcAVCSCWe3onFPl6vMc4GYOajlgGg0adgCc1PxesnJkVgGQcUREjVjyA2Bxf4vNuskeyscGKjGmtxWEkxm5MQKI4EGQgc9LGkJn2D1LSKy+SS8qr+n3LhK+hoPArlo4MGi3FcP09UGH4yXBxFIE7Pv8OZV2sOf07IpBZN8Yk33R39TxVi6u1lJYui4chth3CHtNNPc44Q77OuvMSj9KIIkTYDRbNwce/XGEsJ5Ioq5/L0KdywA3C/k917cvd5pkRcjU/QCaYExyipHIk0sVIIXMzkUB7OSiWglXBmbAJnNxAARfxZ+HmwqQYIZtow2+vD7ivxOOzgkA/FGPR1Gmc3+SXZi+/ptHEC4bzvtCQEeP/fY9g0+ObtjI/xvNaxwo+uVfr95H1xUEqYEsIU+WEUZ0CjctnfNmV6MsHeuF57MWJxM6RqEceqdxFZ2FTqc7doNyQWC9r7PswEheuIZXE+jTuKG4aSBkYGg8eMEw2/JgT9Z5cZeitcOQWgcyoHm85dSQ/rzKOa2NzxCiE8Au97+EBXSEcM000AKbQrKtYBa2L8nZh2VYI+2sWeT2g50GFPOB+8KrmwmHEnQXDfRqBPHKwGxbv1d87YEYBjBu76aNmaVFK02qxUFHtpsAsNMUdp7yUXea2c7eA0hpTksDudLkGAVAXFb52RRx88ebEX9LMwoBdhOSpeOeYgCyDG1Gtn8iLobCCRQRou/rAH+/5wU6OMn2hO1VRRrqeHxfBcbzRenhox1quwQdP35GvVeUCgPALB3qlwXrk93cZ5epUWoM3ZwiCw0ax5se1UpajJKOvFcsnfONhFI+91AFGIKWE4uysujWo7ZU3MQFHTB/QByGti2BVZH1wlbCxfQcgZjNWo04ASY/c2n4MkDtJGxi0rcyS77uebiph0yscQmWErmtRnHYQ0bmb7epEOsPHM93/L3u1xVEr/J7+xFUPZJ0vanjrVxcw14jBA39vIZ5FGUERkCvShRrutHtQ5f7IzI/cMIJBATuuwUdeFK1E4nZ04DuJE7oxw7NCS0K3fMZQuUxeRatvj4eUC0i2WlTIawiacnTZ5HJH9flatSXgXa7aYIsMtvDTTz9Li3Yg+CH3Vce+pSqHK09uldTnE4z4eBkRj87LxGOABW/YP/qB58xMWkiB9w0tLg+f2+J/WGVJxKAd+rFxEAJj794/QEA4LP6Pv72yRcAgPVQw1mFYkaTyB++/xV8/Lb+8PUj7KLo//CQFtyU4LKsOu5dWy+x6mqOkvvVeorFnK7xdlcjhNyHK5c9/CzuwoPIvstWIIya3w65x7fZNnhwvMJNXLzbbcUTjm81umgrB9C9d6lHVzue+NWWzAKSZKq6EbAxyEiaiCTESqfYCiROWiI6Jb9o+MCIA5zgKsSXHuGihkkTeOmZ8AJNG6xUCR4s92yg4A8HlLGCW85arLYN3NOY4DQKJZc2PlbJ2nFTcTUTdECxknBx76cG6g/SuYn82S1NxNy/G1X2dkl6xuImLx4uhXAvDcKgUM6jbKQtuPKrj1u+Z2bQdG1S33MA2ncMXw95UY4lt+jauBMU1B9Nmw8hwClHrlOsrXZLR0YRcXFK/skAIKaWIu36hCrkzatwUaecfjdqxAcvYE5yrxQ22xPKXsLEHreYWPirCrzeNQ5yZvgY3UXD184XgDuOtqGTAbst3Ri/o+ScdM3VRjEiFQRVssmiUi1M7q/vRqQ0FYCOtMd0bsgIVR0wOd2xvG39Ys7zDSRtiOfR0vVZr9kr/bVZQMbXiL2iSh9Jxz+wYYXuOrzp467nejfuxt24G3fjbvyax1tZuZLxg4CZB94R+l0BU1vY6KgUGodQxV3fWuXqJaV1jJh35WWmru8fZChPTCzaKOou9gJ2KmC+S1Xio8MNzq8JzizOR/BNEFBDZt+OcyMhQGbn6d+dHPVVA8QkM25hFVcvoQw4iHmol9cznHx4xdeiVBan0TD/tNnixXbJVo9//OPv4tsfkj3kQdlibehclrMO06M1KmX5dWm83C/wejPH/WhOcFTt8en2AQBAS+pjzyMV/wevH2fT9y/nQEwqGeYa7x7cwHjFr5OxebcdKlxczElSBGA67di+UciAWdPxbtpYhfUmSlKuSiTFkRAg9nds/fhRGEIz6dEazYk52BZZ/jG30HqUmasCOwYJozPrUVL1lyC/3XsOms35Cfoq17lXK01qTEaJRur7rxVX9qEMfIz03yEypoMkKC4d0BxZzO7RczaMLA/L2jCUbJ1EUTgMkR3vG5HdcSwxo43OcCO7ickAcz+bVOhXBaoL+jxmma+HLz3CYWbci6sCzRn9rj8Q8GUOxvaVh4iM3QQBp2sMmWVvADCk//+iAhYWIt6noCW7NZXPS9hJgFvGKlEgM8V1IJlPDCIPE8dVs9hqTstJI1VjwmSEIVgJNbMov6ZnqzvxCCm3WQWqppMMa2zjOQiEWNmra31LchUUGCmTrysUG4H+OFubMovWSIiJRVjlOSe1N5xR+fvvBfRG5bZFGeCitFBOLWAkiikhZ87KzMKeG5bNiMJD3KhsywiyQKSL7OG9xOYlEQHEwkAdxflgscc78xtcdoQW/MFHX/Fccd7N8NNfkDuc9IJMQFKPVwe4CHcXT6Z408dbubgGGV2Q7vc52igA8ALVQ4IYjVHA8+iCMh9BxIWnGTFJHfdR9wpKLkEQTBqpJwPqkiaidTVBVWa9mhKBSSLd0uUF2gPDwUir4QX3XEX80qYvU3E8YIhwCzoFXRuY1ENWge+uXgyYV/RFurALnL84QH9KvyyUwyfzcwDAH84+x59Vn+C4oIn5tz75Gv/Tqz+gc/MK6+is9P7yCj4ITrEBgI+ndIyDosUvxSkeT6jn+rBesYzmr6/eAzYFrvbk5KJ2kiPX1DQg3Yztyxk+PZ9ifo8W7b/14Gv84PVjAATb1tOBaPwA+kGjj9pEFLR434va2XYosp/tXjKpaDi1qA479mSdHXb4zRPyWr4ZGvROo4862tWoN4itxrDJk6IKmTTmRrIZ1v0mvst2BBCJQPazEUq189x3E8llKMl2rEB1FQkpk8C9TdVFa8QEE/ciO3yBSDK76OBTTgbWlwoB9PH5GOJzyJIOAYiorVatuEW0Cl6wxacw5B6UJm1X3W6RpOuRXiejNlfa7KEMGaUoaTFpJUL0voYOlPKSeooeQNzQduuKNLGIHrWdZNu+6rjlXrixDX2W1CssPE/awQWCTJOz0yDzNZhbdtVCaYkAlrTok3yf0Es4kwlPei2hz6PF5JGHuN+zz7KcGr4GbhpyWlBJJChevEd+xEETIYt71NeZZOjmDlgX2WL1xKBKvc1VBZVkRTtJz1hqHQiwl7MXGmKQcHEe8d0oqi5oiOQIty4o3D19DgkmlE2WLdrnM+CA3ns67/j6T8sBe1vi6WvicnzyjQsU8QTWfU22rek8t5ptQsWzGnXU8FuV55Y3ddzBwnfjbtyNu3E37savebyVlatqJWSQcJWEilKcYAVCETBrqMK7fHGEMB1R8csMhwgBZl0GkUkP5csCIgB9zAM2r+bQv0E8ucV8D608Wk072lfXc9jL6AYz5Eo4HVTts4wmmRFMP1jh3/rgJ/j55j4A4MubI4T4eUMQaPuCDb2dlQzz2XWJr7+kyk/FqNj5u/S6D6ZXOIxU31d2iUI4/IcHfwkA+LPuffwrx78AAHx//T6evSbno81BiXcXK5y3BAn94cmXKGLZtnMlvrl8jQcxb/Xnu/s4i8k3v/jyAYp1lgDIAeyAA4zMMRqHcmLwwSG5jpbSZlKOFWj7mpnXVgWIKNIPnYJ71eB59HOWE8vpGq4MqGIF2Twt0DqBWTQ8eP/gGgcxkeeqn+D1eo7ddaz8Vvnz2mkknI2IXMywDeDsUuwVoSNJwlAK3q2Lkqq4sR91ugKqF1SVVnnPm+79+D3NPMDMR25FGlkWIgGxF/xZtPawlt6hu67Z7CAoqpZkMucYyWaCBmyTw7vVXjJ6Ik0k7SQp2iiz9haxR3uoVxX747oys9BD5SG6HLBATNNETBJwI/KTn7pcfY1MEuAE1MHABhT9ruRnXywH1I3hlox8WrMpSxAEQ3NZUXpuMQBAiD/bCMP6aOQQnOBEGL8pIBpLUiwQezuFNvilhQI4eF6IkIlirUC6QL4OJMdL3LUqcC6rrx0GlRESr0fEqEiySkS3b773ikmHn149hnzS8DUNM8ckOLe0mTwVn8+EaKipgUOstBuLcJ0hc7FTHAYQBFiqCNDxuZUgAqbRu/ij+SV+ePEIB1F+84Pzx3h/Sa0oJT0+/ha1mp5eHML0Gi46vcjGwj6ne+ayQdgbO97KxdUdGoRG4eBoR6kMGDHoox1c+XDH9H0pPbsM7YYSVzdThChN8EvL9PFBKUAHZsO6rcLmIvYOgoCaWGblBSNvR0pxw05A+CypsUuLRYRH//5Hf4O/O/8xfn9Ki9z/4P8F/OwlLbSnB1tUhcXFTeyBDJJp8moAYvuSWJJzg28enNG5CY+fbmg38Mn0HL83e4Lvde8CAD5tH+H7l+/TeZsS337vJV9DD8G91okceHH9oLnE2tboQ360nlxGqY+RMIeOJ2q1USxDKdaSFxBVOUybHmXEQb/YnKCuabLqBWBeTW5BVRwgX3iEYbSQvaxRb/ImJfXCfRFQHHXYvqZr9aNNjZ9qugZKO/TrKi/e08B9JtHL6LyUj8MbsAAOUfcB0CsNFxeC5LwE0OJU3Qi+H2YRkBAwkmSB7TPT79PnDyPIVe8Ehkcx+aa2DMmF65J77XyvIoSurzX39lVHyUvMdG8y0zz0uMXuJeZsPOZAm43qLLYxtpIhbl/kDZK4KhFENuD3jc9JLI6sGzlNSgb4mBoUCjK+DzHNBaM4veJKZ4a0FXCrkjZrAJwp4OaZp+BKx/C6OXTQiV078Qi1Rz2nRbO7qhFq+rvFfD8K7qbkmCTNa+Y92rjhElOLsCc2LgAUx3sOBtCNpTD7NERg9j1ZqubdmFxr2HkSlYPZ5vQgZOcuAPz8IADlYYfDyJDvncZXn9McIAYBs4wbjIkDuvz90pcF7IlJh0AxHXh+63Ylh6A3kwH72B4QbWTzxmsgZwY6hlhYq6Bry5uQzbpBX9MH/ifrj2G2JWbH9BlLbaHjvHpY7dE5mjvL0sI9nfIGxneKox+9ut37fhPHW7m4pkF6ytxzOn33mnuTR80es4J+nuqBNZ4AsC5q6FP68leFRRe1fa2roS8LjrAq39kxAcN3ijIYRZ4cchqKwJAi7KyAPBqg4uf6T779f+CvNx8AAFyQ+HH3Li4sVYKd03h8TL3NbV/h8nLG6RwigA0sgs5+taFy+M77L3A90ERx2U+xs/Rl+pl7iEszxaNo+nA+zPB8RZpdIUKW5RQDNkPFSTsbV2MeSxQPgS93xyydWQ81ZNqwHHUUu5cmkUcdfCSW9A8cx3GFeF3T+x1We3x9FTNDn04hRcgTswPCg3gvagN5GDhaLbR1rvgEIN+nnfS86bHd1SiWkdDxuoGPCS2LeQ/vJJM/gpVYHESpz6aG2NYsN5EzA5UIQnsN/YR23WZB/bkk6yp2AmaWFzw7iYsZYvXIki/A1bhlBJIsDu008GukiRVt8jXeVlxBqkFECUwkdRmVfaSrAJ/0o1ZBBGBI/dJJlhLZiYSfZZ9tsVG39NPCCY4ddBM3WjByBR1clD6ly28EQmKUlR5+YSmNBYC+0rCxd1fPBvrMHOPooeK9MCqgiJGKxlYolz30vaRpVrzhxarAMEjWyqLLm4gwtygagy5G5hXLnjduhfK4jmhSaBX0SsHei/3MkUEDAi1kImWPBmB+TM/W5noSTTHoT33pmQQpO5ElR54WW0QilFwVeVM0dxBTCx+NTeRWwU9SQx+wg8JVNFWxg0Z5kbW+/cMs2QHAPANfBt4gNTFGMdlKChnQRI3t7z18hr/y79ElHTRFv6YqflsgLONztS6pEEgb4p1EP0v9ao9iatDu6RoPWuNyQkXGVA9oY3/93nyL9W9aXF2PMqijp7Vo3/zS9a7nejfuxt24G3fjbvyax1tZuT64fwM9rfDy7ADdQLur2eM1rlZTXF7FAG3t8W7MBb3EFIcVVS/3JhucrWc4ntG/K23xZE8wbb3s4ecDp3h0lw2zEoupQd0HUoM7AAAgAElEQVQMmCT28L5Gm9I4+pw9iaXByeEGv3FIsO2f3nwDNja4Pt+f4EW/xEzFiitItIZ2gRdPD6DXI9q8Bu9UhUOW+hiJRdGhSmUzwOfmgsBPrh7iT26+AQA4mLfsptKUhmFO4xVmxcAh5Wtbo45Y5g9v3sGL7YIRgJu25h1y8PKWY43XAfNHxOzdXE0RIh1zMunxz58+xY2hKuIvn73P4dw6EMSdzidUHkWEkIZBo6oMw3LlyDxfWmCIrlfimJjaCVVo3tvgIML+666CLhwbTjy6f4WzdQwyWJVUlcYKQynPhhUYJPePi1V0hopbVzMNuZIpAoIh8wggV7D0BlS1JrcrADDzePhjBx3lF7oVsMgmBmHiUL2IVZuICTmx2ggvS5z8Mh5jIbD+VqxUZYCZgrNfJ7Me+5QRGqhaYlZ0m3uDwwFVYkl6IrRnK0S5U1DR0N8Xgc4nVU6N50qyaAyEAEwKEWgUm0F06wpypXP1Xo0ylo1kW0o1M5jUA+Y1PWfX+wZmFTNDFTkL8euKkB2rOgW3yrnH5rqGqeghaS8KhAjTChcdjOI10BOL9I0JTkAZAbyMSMWB5feGJ+TAjzgaqSLVVyVEhERtNKlIkKuvPWe7il0Jc2K4V+UX9hYsL1Vgpjs2BYbHVOWVX5fQUVpob0oIK+CiPObk3hrrHX3eujTohoLd6aQO2K3od2cHcxzGue3l56cIpYeMtonQnvN0Wd6U3m8ioOI5nxxucLNtctJOW+LzZ5R4Vc96VlA08b9Hh1T1X8tpDnf4/8F4KxfXzhRQQ4F7J2uYSHjpTYG6NjidUx/xyYtjnG1oUi21xdWOYJh7sy3+6J2vYOKC99OLB/yFt4NEM+3zhHvQsa6wv2xgOg23IPhUyoAiwsfGC56wq8bg9esl9hFqPpq0rLl8Z3qDhe7wsiOo9osXJyii447sJXwdsoetATtLBZmTOYqpweAVnu/oGMuyg440+Ze7Ba63EyYmHNYtDmOQ+lU7wXns51aVxclsx/DO99v38G8+/gkA4F8+/iV+NbmHRtIxvmc/wCZed7suoXpBPTwAaBU20bpQrxVsjN8KQeBXm1O83tL7DfuSiRTufk8L36ivtYwaXuskbl7Pcfz9kW0cCxyByYsYHv97E4jlwF9+IQJf44Omw8VmyvaQL66W3E8rVxJm4TE5Sr0kh9UNPRcpSg4AzKEnHW2yQ2xHVoguyi5G6WBJoiIAhF6guqZ/t/dz5JleDrAqWd6RpladZhcbM4+LaUna7QQLFhuNYp+IVfk9gwJ84zCZ0eKklUcVf+5RwUuQ/ywo6ShtBoIE5GEPH31jy68q9kIOcrTpUXGBSZCxERCbEQltBBmHuR21S6j3zjC0RyZE6BGZxgvcnM2xmUZHIiM5NJychbLcRlWOrf9QeISpzZKhjUYI6TwDdEzL0XuB7n72DzbXNd9PPTWwR4A+j73JUXygPBogDzxUIoDJgD4uXK4E3IOYdDMoSrRJdn9O5UfVgiRCKeFG5tjAIAG7KrPt4IFlm8fhgWWvaNnRdQzRB7u3ihfk1XpCcqR4fFd6vv6rvsar51QsQAXIxvIcprRDv0v9AAE5M2ytabcFQvQZPnMCunB4eEikxld+gT62noa+YDmiaRQWTcd97roZ4OIzZ8yb33O9g4Xvxt24G3fjbtyNX/N4KyvXm6spZFtDFh4qmrwfLfbwQeDlNSWxSB1YaN+2JVP+b+QCT08OOatwPHThIARwb0G12vlmxjsxCACtgmkiOcBnI225zwSILgCqcaijRKizmhnMEgGfb08wRBrhN995jZ8/ibqf4wGhVShiMohtAjv9oHFoIjvyk9ML7G3JLkyv9zOuTmfFgI2usItEhObgGr84P6VrsMmZm2ZVYXs9QRlJECfLLVYxu/P96gK/3TzBH9/8LgDgg8UVLtaRMa09hFe5EhHA4WMiT908O+DjN6XBZ8/vZ5ZoYzCNkqOb6ylwXgFVhlNX0YVJKo/Zr4rse9uNGLACuP5u3P0Lytu0cUdelRmZUJJ8ehMc7r+coklVmwLKK4V9TX87TA3LMXQ72qe6AEz9Lccjlq5UAUaBQ6dFAGxiI8fghGGRoVUbyVOh1VyZ2UNAtoqzWcvpAHcSk3q2GnVjMEQWfHfqsf94lDDC5CYPaI92l2yHwBmkIkLa7iBW9q2Ci4k8QQe4dclOTGYWEOJ7wwv4RFZzApCBs2RtV3K16K0ElEcRfxcKAXMTIV1DDmVmnmU7/LzIAJeC670ACg8Xr4GqHCbR+UuJgFU7Sk4aRlVhq2755wqHLDWRgIvOaHYRWMYCAHIn4d+Lx9cOQnrYB/F52mmuJF2r4IRiswgxCJSb0bVKhMNBIpSeSV5yL1la5SoQC95HRKPPph2ilQhF4LB3sVdM1moOW05Acqc9vBe5BeMlE5N0bSHnBv5VrPpLDxGD1F89O4KMkh2/sAhecKi939RI017QgFgGhonVzKA8Sv7lDvtdjcv4nTqY7aHmBP3Oyh5f39B3vd1XzNinF3qoJkkJ71Jx3sghthrCaYSlQROZgoNV0MozBFJWhl2AmmbA+4/IwednTx6iO2/Q3CNosKmG3FMMZDY/nqhLTQ+JmWu4LlvReTPCBQWYOVw+KdGdeOwiLFlqx/KaL35FNoLTZG03KNy/Tzrabiiw1TUQGbDLZmDrv4PlDjYupme7GQar0JsIzQwa+4YW+Uk1oO8L2Aj5fX51nK/HxGBIPa1BQuwEhrgAvTJLdnx6t7jElZvhG81rPj0b3wtDTAJJmJ8X+OjwEgBwUffYx/7xvOpxoxuYCMcG7dniUBUergi3UjPcazpPqwL8UUAf1+nqRkBHZ0azANNwhRcIDsALmlz2vmFJRJKftMs46RUBk1d0jN3jmP4T+5uu0cBBiuQLzM6GDjg83WCzjc5R1zqfciDodoisSz/xDF/KqwK2CczELW8EZLwXQaoMuWpaeNn5p7YIyf6w8tA6x7t1hzKb2wdkaFUFqNLfsk1MIxSBXJMiXC2Pe/gLuvf1Sw0zuy0Vq6ND0NDrLItKcGFkg8sux8PBCWBf5Jg/n52upBEYHg38eYQM2ZpPhxGVmv6T2iJlaXkja4NAca/FcF3zaafFT3h6bTrdUAa+PmonOWxBNBa+L1GuIs9gHtihSSmPqvQwKWlnr+EPUnMZmB62aJ9Su6M+k+hO40Zhbm+FXaiVZh2wtIKdroQV2RUsDZGf9+q4zWle4fbfytGm32xLIEpsCuW4f+xXNT2raVO3VznVBxI6yteGUiI0ITvOHe1vbcbgBW8unQB0hLz7/rYLmPeSQzi09NzfRa8otWmfCAk5CnB6sMKbPu5g4btxN+7G3bgbd+P/Ye9Nmi1brjShz5vdnf620Tev05P0lMqsSiVWYBQYUIZVGdNiAGN+RY3g58AABmCAJVBYZZKWlY2kVCrVPb0m4r3obn/a3XrDwJcvPyFjqAFRcd1Mphvv3HvOPnv73u7rW1/zex7vZeXqKxvYjoPEOpo8WIHiIBFEtHZsMP/RwRWbUOvcQo0GZrwNVjHhBR54U+dMUtCFYV9d2yhklxmGGcEoix4+o93iBJgSKWe1GiH7poD5VYCnmwMHT36h+UGLfpMzy/VoscW6LvmY7xyu2VvBOokRGXOvNyPe0ebaom5zJhW4XmEgyHtdOpzcWWHpQuXdtRlHlI2rjjXBu02JrExQba4tfn5zHwDwpv1P8enkDP/28gMAwNPpFfus6rWCrfxbrNEDckY6yBv8dhUgaOdFqPCp+hl2GVclZdWjrnQS3Eufii4fSCkZ7bzhgDa8ZfBrnpOesQxVjiWGT7ZOHrKhkgHDerIXWAXyNPQuIAyxiLB70LQbW97hZ6WBkp6hZTNJjFFRWvhacTg1ACakRC/fSGJq7ziuSoQD+wwP0+Cuk8UwgIsRmvsR2vTouow/e3ZQYyAiizESjhAAKR2GOoPaxsoYb1VAwgPqLJyfYWbZg1vY8KI5CHNBVRZtnP+tCm5OdC1ckfTIbmbYXGXY5QHujVCtlXs/C+Sv8hRK4MFmDb5PVbgkeLSnoA2tLVpy8apGPbQeYMb03a4L1prqjYJwiXjltGfXKrePiAwSKBwcuQdla4nBh4tWz1UIQY/HXFp2b7KNCoHxVKXXH6YqHFawdthrDzuxgCSY3oaQ9PBhPuTYRt23E8l5zQoo5ZjgVB7XXCG2uxzyPByjGzmUpzXm43B/CeHZNQrTAeK6gIjM7omFj2Q3mxj2+Y3C4AVXtUOWCHB9rzCsC24PyF6gnhV8vN5I9AORmDYauyw8P9+owxSZGRnHDDV7qEWYI22T8mrf1fFeLq4wlKAhAxsOACbTFtOywyQnE4miRkXWOQ+rG/zP1z8AEMTTprTcazCDgqI+pxwo7YNuksFlaRHIHPTHG5wSzT2ygYFgmBBhPN9LNpQA6IFHD/q+znD/4TXWFCouhcf3TgP8elTsMFEdzrqwKH+9PuQH7GjU8aK7o4XVRxcWgZToYQSsE+zC0rUZJiR1+Gf3f8Nym4d5StUBgJ9sn+AvXofF9Kvf3sVfqY8wIneWm7ZiGYGdOPi9B1GWWTzbBvemTKZw9MvtGFlhYBX1j4zEZEL9LunQZI77j4eLHcunfKMgN4rTS4b7LjhoAfjwyTkeT0ha1Y3x/OYAa4Ilh2OXoFMT+o0xuNqMFbsASQN0B56RSWkARw8XfdrsOXp5rDYVbwjErIeKi6vwMBsNSaYVSrkEXxI6lt/Q9T5IBgQAUEc5CRs6EJyZASA2+JQML6LkCwDLVdZtgc0NOYspEfqP8b0EeNcgrIDq0mIPKO79tvcsfJHmp91pNoRXW5lScj05NtGGRiif+ouFhWsVB5ajlYyh2dJBOMnGCP2DAYujLZ+7m9dhfruJBbRDOUlmA7GF0fcK3mXwK4KkDdJnd8H1yu/Z+MXeMgC+1yAAaAeXJ6P9yJjW5zl2H/fQo/R3LFHxAnk1YHwYjvnmZsLzOtoKAmET9Ifff45v16E3nO3BuZfLCew2Q0Z5z6bXfG2yeTCAiN+1uRzxAudHNqV2qdAPFXtwcjRlGVYFsrUMpvwIdojR6rK4UDDUzTBjDzHvA7MYgKszlKQk0FpAzDv0Gcl5LvLEyJY+uHHFFsGejazYaO5xw5PdYvw9AZ7TZr9t9o6O93JxFaUNFYRPPZvtpkTT5Pjso2Dxp4TniLWfLh+h6WJ1Shc/aiaH1MAZDsgHNd4nreTUi/yNRrvVeCXCDu47P3iB//r+XwEA/oc3f4LLOvz3yVENYxTviovcYLOMSdsezgsckCZTSccV9ZvdDM2g8WgWehXOC5aXLJdj5JFYMii4bQYZvVtHjm37pPTojeZec6x8AeBlu2CXqp+IR9gNBT6ZhSScV82M6fTCCXgP1kwOveaHhHMyOO/EHb8Iji1ASN15eRmapeayhDjo8cn9oPUtlWHZz7ovoAuD79wNn31Y7PAPLpC6bvpp+HydFon4WQdFjX9x+PcAgP/x4o9DNRc30LVib1thBcxpz73xkPQSfs+MPWzh37Kii6kg//TpV3ixC8f/25enoQdKmxZdWF7DzDankPvweUUxYKDz6lV4b0McD9UJ5CuKtxNgyYtdGJQvM0i6PMIBwzz83saMw/Wk95iWHZPXui5jEpE1Ep76j3FEC0JZi7Aw0nm0uU+V9yAhO/1WggtX/donre9JAzEofnB6K2CINAMV+nhMGst96jsvs0D+or1nMem43953GiL2taWH32m0dI+WBy2fY2fCpkG28bjSz8PMw+cp4k5lDnkRrmGzK1Ife+4Avxf+njlYqpJxoQAZ5CfhAnuM5uGeHHIF5wR2DfETpOeF0B30/LwBgL9//oDn5707S8yLtIE883MM5AolpGdp1Qkt2tF3++vqkN9vezFOLk+5g+kVI1veC16wZCsDAhMJZiPLm8v+cdLpCxHIWiLO48yy61I1oskX/csXhpEbv9Ph+sW5tXcfTp+ssD4Pk1OOTCCU3qEkspsSxT+E50a5evf1rrc919txO27H7bgdt+P3PN7LypWHkcxkFRI4OdjgL1895ZcXxHBbNiXaKEXRod8hmuReY6NPbGnDTp12aSFNJPzsdICVotn6y9Uc/93P/iUAYPzBiqvMKKq2tJNcLseQVwS9PKhhncSdUdi9rvoSyzrs9DJtkWuLDbkmtX2G5RXtEFcabRXeQ4wMkDuUJ4FxXOYDOmLptk2O7c2Iw4uz0x0agq9//OYhatq12lpDFhYXO/IE9QL1swDXCQ/40rMxuhkULPWIi3EPkXu0W9rVCw9HJ+jlas5yDNVKCOmxbMN3643CpOj5ezmr8Mtnocc7mrVcAWWTHnajE5RaWEwPwq5YCh/M2AFo4ZBpiyZeaA+uJL3ygJHwm+iT6pNJggowvasSrBWdkL7aHGGeU8/eCeitYlmKKRTGd4jhLTxMpyHperdtltx3nKT3JjjZhuQgIBgQxGpRrVXIUZ1GaVGSk6jSwFvBbYaL5QSGzn9eDdyrM5clVCvf6hvH6q64ESFsnKpQn3uoKDXy4Tz4PFVInCfgwaYRpldBAhNfo5BvAJDaoSyHxKSfKO4bDlsNsweF46ZEH6tV4UO/GlRB++TfPKwmDG36wqF8lXH/1OV7qTsScIcDIznWCtRkyK8qy2xhDBLIXAgWByCVh6EeYndqIRqFkq6p9wJdSxC0smQqQ8fiBPuEKw02J7EuPHvi3H19vsDkQUBq2kEH32VCl7J5j8mo5etZlQMuV+Qkt+cSpvbNQ3Lzllww0xa2iKhFjn7u4WcxuSfcR+Hn5M2ttIMtJEPS03GL5Src801dBPI5XdOy6hlRe/H6EOo8Z0MbM/YhoB7A4bjGOsIqnkw2LsP5H32r0R1T73r27ptIvJeLqzciRMYpD0160ukowE+RRm4GBUNEkExbDij3nYJ8VaK4okm8AzYf0A250SGSjh64dmFYtwVt0a8Lfhhv30zw8Q9fAgCenR1htAgL5iTrcWMV4lPp6HCLyy2Z1jcZ8sUWy44WVGUxpkXnsKpxvpuwnGX5eobsOnzWcGSYzOCdgB/SA6VWZUq9qAzJNdIDd7slLVyvIIj8ojoJVyosr4nAkDnkFJEXJLgS7U0UmyapQ7suIIuUsLK9HONXREKJeuN43rDJ4OYEVRmNV58H1xiGfenhLheOHbLsEOwfOX5tEGhLeugJj79cfwQA+Pn5PazfTJmoobeSrf7kINAWnhcFtZMJQvchqFzRNRxmDoLOa2s0pAjn45NHZ8ieWnz+Oli+uVWB3Rm1A77zCuu+YGnU9c0YfkIPvUyFxYrOg3ACckkLrQHb77nShxZElIbsVDLxHzJAeWzJXUw4AdD7Oyu5D+wzDyscLzpegMlH/cxTOIDncxDhRrsw4dzEc2wEw4s+dxD0QHfbDKqWKcQ9c/DUz7S1xm6do4m9wsOeJR3SCPixhaS+v3ciaUNLy1D78ZMbXF1P0JW0EGwU9x7RB2crs9+jps6Kzz2ElejbtKkuuG0hoE/ICtQo2F5x39bkDpLIWsID4l7a1Bkjef5aozCdNExkXO9yCOphtuuCF5LDR0v804++QGfDcWxNgXUX7hklPbLSsA5ba4uKnlO18li/nmJ6L2jpM2U5OGQ+7XijXGQDDkcNYqDhy9WcCYnbgyAJykdh7k5GLUPvbZMImTFpyUTyYyW559rWOdwugyT7w4NRg+8dBM3aomzwc/sQiiB2CI8xLd65tBgfh3O8W5XohoIJa8PMJ6KZfPcX11tY+HbcjttxO27H7fg9j/eycoXygVSx0zAF0dgHi2FQ6CloW+QOLfloNk7sySUkdC1Y/O0Fkll54YHMw/u064q7W2tl+NxoBL5W+PKXAdoszxUujsKu9WxmUE47nMxCJZspi0c/+Dq8Vk/x5mqOMUmEnBfscLK7t8a92RovKCIO2sFMiKRTOIaIitGATT1hv1uvPBuG23E4xuI4wDv3F2uOkXp1uYCNsgovIFuZ4FeVIMR8KQEvIM/Dvq09dkyUCVlyicjireA8yKd3rjC9G3a+r7ZzPJwusenJ5KHLuXqUnYA9dhhR0PkoHzgr0hiJbqSgL0hgrwH3IpQsP/aPGT6zmwyylsyGlSaZeKgeKC4VOyrZMu2mhRGQVrAXMGSIuQOAUTagI1LXctDojWaJk94qGGKQb4ccpTZoYsZnraFX6Ta040Tk0GsJG/MtczC5prq3hVIOmyuC5VuZAsU1MZ/j28wGnB4Hj9e6zzBQG6RtFVA54JAqzUaz4YD3AiI3EITcWCcwn4dqwziJ3ap6yxBCHlCQxC5j+YgYGdhMJtMEK9nxyEsAAilGbdhrnyyGYE4QI9OkT+SpTrGvcztoSOXhYkV9kNjR6iYLuaaREbxnkAEP+FZBE4vcdBodkY+gPEuOfKtCC4iqfrHTwUAfwPiwQVPn7IIlhIenCvGr/+pf4Xv/03+LPCIxAiydy0YDjKLz32f4qxdP0FDEHbTH4jixovt1wYb5bZ3jFbVSynEPUdk0563CmKDmUht2dnv1zRGWozHf96ZXeHI/GLYcjBq8eBOCygFg40sUBBkPrYYmYlIMwIiIxnI7S57MItwPfhT+/e03xzgdhWraeRFybRMAxs+pL7YFFKESWTVw3B8A7MoSIGRlmm/xro/3cnFVVzlkmcNOLMM59a4I8gCCpzwMG+s7JzkRxikP0wr0TwkyawX35GQn4OcWh6fhYaakw9kZLXaWpA/x5lXgHMx+Ltjdp4xZiwR1HpY1rokRvGkLSOkYctouK4bCtrsSddVityGZziZdWr/K4Onm8V5gdrplZmXbZnBzgnuFx4PjJe6PA+N4rHv87DJsAGbTGltFD6FpeCgpeniZTZbgRUWWcvEeNCLhmQhwIedsKs/Q1Dxv8AezVwCAkR7wdHSFz7cBVl1XBVYUNFBeevTHQP2SNhXjiqFIbyTkWsNG5xmbMnPxooKlDF7RBRjYUGKILSxDj8XrLMChrLsEXDSlkR595qGbqD2VaF+Fa/P1mzEQofdeQZYmwLUAbOVYvzdYhfPrGVvKiVZB13HjAPROcoABPqjZ/UtqFwK6AYyKIVg+xs3OyO31bUWAiPeg/dUuPMCroscmJjHVCr5wsDGBRyCltxQGWWZxNA89RWMVNtRvd/Z3wK7CcfaqyFxiB3fBOD5uYFzuGXo3Ixd0rnFDsOe2pa4z2MoxY9ULcJ94crhjidHZ60X4rkNcONOGwmtPEjaa1w05gyG1FQbaRMvKQEdbyV4nSVZh33KHQmV5ntXbAt4KOIK5o+wMAH70v/0rzEaOYX94pA1HZvl71Tfh+ikKCsD9FssLCgqZ9lDjgWFc5wS663ANWwAfPTrnNCwtHZ4/O+HPyhdho1MetKG1dUntmfmAB3Rf//T1g8ACpuMfZoL1z0J62D3rRngRrhX928cNtRWhJx2Tt0qDL6+Pw2fnQ5Am0aksZ13KtR4kxtNQHLRthu03M1QPwkKqMsvzfftyind9vJeLqzvpgCr4bsaHhVIOzmQ8kWRhuZGvM8NRSB4S8oMdDJFvXCVS+sZowH/56U/xsqUeqRfYkYRnezMK/UD21fXQV0T7v9uzJswMCqOyg6XFNfZXgUCwsmWHmt5TbDUc7awzbXG1GwXfVgDKCGhi7PTzlL5hrYTxgie7Ug6H1O8dZQMKZXBchH9f92McVhQU3ifXAyU91qJkE4mbdc7VO0CJPPFruiApAajyyh37zXokPdtVO4YjT91CGpx1M7zchgX18uensajC6pMgC9HbGM8lMVBfT4wsXOl4wyFMSlfxAknDSMcSpQM6txB0TJ0RyK8U/N6dEb+LtHgrKFxaQFMfbphbiGj15wBnBQQ9iMo3Cg31Bi/WB+HBH9NQpgZ2RxVQ9CKuo4a6TBWdAHJCFC7PAnmMKzMrIOKiVlgIKziezowkBkpvaWclpifh2m6uxhDRdg4Ifc54koVHmQ+4uA6fY3sJQTaJYhDIaoFhQRsa7+CbpN/kWWAE/GyAbVKQd+yBinkPrR0bm3SbghdaO7OByBOvlU867M31GHWU7BQ2VLddlj6YTkckNsVzbCYW+at0jvvDVNV6K7n69b18e0NdpwXIOw8vkmZdZI4jEoUgYhrCxsd7gYLIWsW042p4GNLmXTYS9riHPYqWh5JfsyMDZyQaCkTPD1qoGRGhGo1lU+HyIiw+KndsTAEB+Ouw2esedPgPPv4Kf1F/HF6zAn/74lF4jRA5QVprP0g2egkvULFgBOTdZKxjlvlbCUVyq4OxB8AbSwDoBo3xtMVuHRb2LDOpP60lNnSMAHDn46uUTNbmzB2Iz+F3edz2XG/H7bgdt+N23I7f83gvK9esMAG2Uw4NsWbhAOQp03IxaXCzIRvAbcHsSOEEisMBntDeYVWwS0yeGcx1jYx22v/P5UcssVGFhdMOPmZ73h8Yms2UY9h2uClw0yscHMUUVKBQ4bXeKWzqineB8rDnpIvZuIX3AttoeO6CQw5A1VHsn9nQE3VrcuqRwGWEHpXHdx+8wa9WISBgmrUo6bNftbO3mNRSetRtcmTpqZKRg4AZhaoOCMHesaIwUw9VGnaz8bVGtgyf/Wx3im0XquN70zXWXYlJRlDqZ5e4OA9VVDnpcTjdMbvx8nKKR3eDoL63CterMfc6syb1Ts3cJMhPe2BsoPd6jEPcucsAbUeoNtgC0nnMEMrtWJXbZIGYXyo+3+ZOD7HOuDJop4JZ4nIywF/nqO5S73BbQH4cqknz5STIuqiSyq8Uw9rDxKPPSKo0GYJ9IQXIwwgUF1QhGoXuyPExj14qluyoVmPjiUU8BPvA+FlCOXbY8Y3E0o9QPA7H1eyqZLLhBLwKgfAAYMZ7ph1DQi9UK2CUZqjZZ8lsIvu6RHdqkVHIhKhTWDqED1VL3PYPwQ4RCNfFVNS2KW2oosqIGhmGp/0mC6ziWGWRfAgIFpYnfyNpZBQAACAASURBVC1x/u/T9Vj0/HdQHkNNF7GTgT9R7fWFoyJoq8N/p+/WNjm3l3ZtjnZbpFaFE5w2JGvJCHh5JdDed7ARcWgUV9OuV8hHPTQ9i6yVcHQYsrC4fD3nY7GDZPmTqpO0ajJr8HI3T5aT25yhZXggP2rRUwWbj3v0VKFn057vz0cfXKA1mivLxek12pi9qg2++u1d7qPbkcSOEIyiMJhVLZ6Q0cU3ywVaMsTAOgOo115OO0yLFNhxs4fo8MPxHR7v5eJqjYI3Cv0uZ19ROAF32rEOzXmBg2l4AK6kR9uHiemVR10XDH8VBy068sHMM4O/vP4Q352e8XsUWZRBCGCTcQ/Kjwxr7fLMoKGJKSYGfqeZUj8tel5Me6swWIURyW+qUYdWhL+7up7A1ZofWOZe8m7NS4OBpAdY55CdQEYEoeHQoKBUE6UcXq7muDMNC/s8b/Gz89BzresiPbycgNlmMLFHNxlg+MEgIXqJ8nl0OIryHEDNBhzOd7i6IZ2bFUwWko3CJwfBdam1Gg8nS9wrQ4+ocxr/dx8MfrV0KLXBEcHVp+MtL7TOC+SFgZ0SVKuSpAYe/DD0RPqJDxFnEvFGNjIEUkcSlgRUlBmJ0DfUoRWJbOfh1f8H+PNtAXhg/R2SKuUJqnYeEPOBJTHHJ2tcPCeZ0Tg9JIFot7iXnkMLkN1lEOMBJXlh+19Okd+Az3erPDxFp9kC0Du61nPPwd6+pD5tlNEYyckxbmogS8sxg2KQvNlgr+PIZ9oPYJfg2DTuW9M5V5MBlmQt/ZGD2knYSTp35KyJ/EZBGoXd03BvzH6j0R0RqUsn4pnfKbiJhYjzzkjuSQMImxl6TV9lGBYxSF3h/IllmDXLLKzYmwdxCHC8G/87zv/SQlYGjj7PbjJo6t/3XQZsNM9r5A7FOS3kg+B51dxxgAM02a/iqkoylDJoV1NwuNo30kIxbxlq7uoMvk/PsNhrb9sMW13wphom6Yx9p0LCVbwf9ghwzkk8OA1JW6OsD6TJlrTzmcHFTYCjnQ8bG0mbY9xoGJIrTkYdJlnPdovGKC4qVCtgSRaVZQYSHjfb8B+E2rOlTG3sd3bcwsK343bcjttxO27H73m8n5Vrr+CVClBYFLmD3EJIqrDe5MljU3rI8fDWe0xIDpNpiysSWfeDRm1y/J+vPgUALNcjDFTViq0KzLsoK2g0QJWr29ul5uWAg5MVfngUmLNfbY6xInH55c0UQjrsyIMXHlBrgpwcUH2yRpGF4+zyDPPoQSw8LtahWuyvcvi7HfwLYo02ioX4BwdbPJqt8NEkVJCNy3EyJmchoyGJ/j/KB3SDxnYX3mMxq9ltp+5yLMlcHQiVjflu2IYezGocVjVK+t0XV3fgjqKxu2O3plxZGC9x2ZPDFDweLcJu+rIe46jcQZPh771yhY7w2L86e4IqH/h7v3p2DBGdeeYDszZjGgdn6vYJ8nOVg5cyxWe65LFbXkYT9/BSlwlkpBjwEjC0IzdjD6+DOUI4CalC1+MB1ahjM/174zWGB+E4lheTEFJO0L6uBW9/RS+gT8KckypIq5rX5NJTetiSjDpOHeRRD0MJLrbwfBzFaICJZBbhIS9ydiTyOkleYCT8SjG7d/87Sx8kZy5yoaSHJkN7YQXDkq7w4dgjACDdfpQpvCL5D8iUgSpiM/HIVyKx4D9wyRFr7/0gfXDkMjEfOVXeAODmBupa03taZqjbu4byfMPndUOW0I1WAZFIkzt4vycfUuC0pX4uQuUWCY9rha4IJ2iyaNAS6x4AbuoKa0qaV2vFzxtZGjy+e82oy8t1zoYMGCTMoLg6dVc5V9BiZNA3GQpi2evCMGzripSiZF+NcH2q2PShE8moZSA5UiSN6czi8MENn7uHk3Dsy77CxXrChhvNLudWit1lQCdZKQGdmNWbbYVtXbC8p8gMcBTuySYvWNa4eT3FblNCxgraCii6TgzHv8PjvVxcg91dgMKyS7JdOx3gNhn6vfSSaFw/LTu8uQxNVtcrKGX4pqjbnPuXrdL4Vi7QkCZN7SVv5EsZHroqUgclWnJrKY8bzGcpzeR7B2f4ahNo7d9cHqAgY3HTaqCVqF5T30kmOK07dKg3BUwVbpgf3HuN0zI8+f/m7DFH5B1+/xxNn+FmTpaKjeS0jhtM8MHiGs/rYAauZVrwplWLjBbXQhvMi+RQs6jYSBDLXQVoh+6YXFcWFiA48HKb4aqY8E2Y7QTr/oq7O7zYhE3Dy+dHUNMBj07CDX9SbfFoHH6e5S1Oiw0MNdG0dPiSHKwiszm63oxOdhzuLJWHjQutDTBYfBirLi0KXnuoRnCPzo4tP3x7E5x/GPosPGp6GBRvNC9AZuRCMHaUYEgPTXaQ80mLQhs+1qNih/MyQG32WOCHp6+Z1TkMY7YkdAXYHhKZg7rJUJL1X3vHYjdLspZMWXjq947GHW/c2l3OiUQQHnZu2c1KDIL7qlYTI3tDCwvAvesY3RcXJ3WdMRvcK8BOCdom7WucI0p5DJGhqwFMHMSK3JWalIrjHdCcOtbAypEB4iLcSYy+Je7AjzZhwY6wZ+QyxGEFLLGTs3nH8jWlXICCI0N1lwedMH1RSZtVtlKMWfLawxHUnq0F+vsOGempqzOBmjLbtlZAbjRefhPCJFzuIaJVpAdLh47ub+G9wJogV1EZ1vaO5y2cExwLWT3YcpxefCbNyJr1/HoGtWflGF3U3MTiew/O8OEkaFt/fPmQJVnlvMEGFY4obeiTgwssqe1VDzn++vkTAMCT02tMqg7XpCzwyxyONhv6UQNfAjYy5DuJjDYzvRFQ8wEdzd1O+ES5NxKSGMFuHBjf8SnpO4XxPBzT8s27Hzl3CwvfjttxO27H7bgdv+fxflauZIotG8WVHwBkh4koIKVnF5RtW/B/z6oB00mDQ4Iev3x5krRwOomgAcBeFpBuD1pLsYxQBx3v6rsmhVuP8gGFNOwJejjboafducws5GWGfp521ZGs4jUAETySAcA4hT/7Nnjp9p3mjNa2z4JnaIRiVDomV2v8+Ncf4OnTYCA+znpse2JqeoEV5cg2TY7ppIEiZl+lB1xQZF7zZgIIj+GIDM9LA08sVLVWEFZzhJstPROJlHK4XIf3EEbCrnM82wUTicvjMe7OAsnqTw6fQwqP121AEv7NN5+hIeOMyaLGn9z9ls/3omjQHoQp/ptn9/i/q11gTEeozUswtGnLUNXGY4RE0kve7UN1QTtvUVpoChR3GgyVChv8cKPW1N7pcbLnHX2xG+P1RTie5WnFDMxZ2eGL5THnwvaFR0HVoxvA111Ij+I6kVBgRdJPSw9nFcaUf1vXRSLqrDMIqtbNxEIv+mDMDkCeFTwR8mv1dhB86eEpWs/boA9njFckNMaMfCCLgVis2wySzDP8IBIjGEFfqqJDVr9n1KFoTtI51uclJH3WMHXYfUJQ+zZHVg0MLcteppZLFuZVZCe7lxXD3/ldanMQ1OltOi7ZJw9ltVHBR5l8h7HKMRxGyAJAJ3mOj7+3QU73qH82xeLXApundDl2Avs8qRhBeXkxg9AuOUL1io36d42CmiYC2MHjHRMjYyvm2qTnjCa413UFV/wit/jVV/fx9Iche3ldlynvdtA4PVnzc+U31yeQdBxHox1+9OQbfu9NV2BBZiJt1bMrlTUUHxkrfCcwLPbmjBGwNvzu7HDHyMGwKt7SIXsjUS3Cs9RXA+v7/10o+97LxVU2CpJwP/NReAjNxi2k8Az3Kul4Ig9Nxg+eihxQ4u9J5eGiNKZWMG0yfZB7UFu/cPBjmzIPveAF9c7JCiPqlR6VO3xTH3B+aaEszs4C7JlVA4Y7HUBQjJ8YqIfh+M2LCeRZgTX1OWZlyzKgui+g6KE39Bq21nwjQ3tIeoDLVsGOHc6pP6uVg6INRjdoNsg3nUZfpqnzcj1j2zVf2OA6dBFN/jM2gB9mDtlacpi5v9vxDX863eJsHeDR2dNrrLZVYDQC2K0qPCNYzDiJB+MVrikDtT4bJ5hpJvH3l/dR0bmc5B1erSmtZ6u4HyWNeAv6jdcKAIT3sCXesjyMv+cHCWjHi6arJUPEwqUF2U7CdY5sajjB7ObGZPBe8Ibj2/6Ie19q1sOuc2hiYBaN4FQc2QsYgiz1TkA4oD3ecziKD6Oxge0ltkM4PzK3bIo/+zJtJlffFzCNTk5eIuXUOgVka4numOaq9hDxs1sZjEHIpUrvgIHMdGQHNrMQjYJqBAYVM0+TaQduckD6ZPi/hwC63AMipfAID3S0qPmxDeYuAHBZwF/kwJQWlmIvo/UyB/b6uC738JRuM/Qa9qJMzl1jxwu5MEjsae0hcsuL3+LxDZZLspu8LuBHFop4GKOix4jm9MUDhe12Ah+ZsocOmvqN/psxX89hIjFd1OiJ42HXWeonWwH1dcUGVpfV7C3+RzXpGDL+7MFrfH0T2jhd7rC4EzahQnisNyP8xcsP+HtH+8+8GHBY1fhsHrKrB69w3oaLuMgbPCkDlPxFfYp/7/QbDNQT+OvXT8LmGcEIxBvJOba1r9I83ij46QBJzwTrJB4SZ+LLJg/tLSDInaTngiRuQAGgniTTmnd1vJeLaxx+P9gcaVcIkHRmrzJQdHMao7BdVrim7ai+0kCMnLOhD5etkvVZ3J2bmcGHDy9wVIZd4E03wtdnR+Fz24I1tWd6iknZ8QLRGh0SfAD0dQY0CjqGQPcZmsgnaAXMgYGmm/r5+SEshTmrcYqfanuSnUSpQBP+LvyihywsDsbhhjm7nuGzB+EGzJXBF2Rv1tUZtq8nLBnZThyTMVRl4dca+TrpbfsoIW0DASL26NybgiUez+wR26LVXR42NFSJFOOeK43r3QgHRY3n10G+ohZ92CwgPOTuTja4buhcbifYrklCpT3bTWKn3rZotEnLqkzov+73H/V8T/rUZlAxeq9KyTFyENyXPLizhpIej2ahT3xeT7GjuLKvviWruqjn6yVrNd1lAeQe9j6hD51C+YIqhcIjv0mLgBmBqyzZSj7f3aHgOQeE+ZhvYlUOdAfxWgSrTh/dfaTgjUK2ETBjz+5T4fyk6wmfev1+D43p547D412j4OapMitJswkA3VUO2aaNiZdpMyMNYMchsSe8tseCGmSSdPTUJ6eedzVvWTrk8hCILul33XzgkHjThw2G2GNXVQ/DgjQbtZww05uUigUEiQoTsER4bkRf3OWu4sVOSI9h5pJOWnj2t5Y2zRcMAk2bceLMW2lUpCWORDqsNQSdV6UdPjm+ZOe263bEG+DDeyv0hILs1mWQV01oY7vNWbaEYsBNW2E5Csd11Y2wo6jK2uT4ZhcmyW7ICb0Kr3XDnm55mwEOGEbEnxgNLPdznUQ16hlRmh+ucViQrvt4yX3m5c0Y8ryAiaX9YouP5pfhOu2meI53e/w7UHzfjttxO27H7bgd//8a72Xl6gWxH73gvMbtRQE32aN/q9QPFK2Ei72pIqR2RJalHbkEb0mE3ErQjrnwIZ0DwXh93ZZ4eR16hafzLcaxP2plSq3JBjifwq6X2xFEpKdvNVQtGdJyKfYV4kmNadXj4TwYL/zqVw/Z/ejk8RXvCP/GPoGtBMwFwdcCqAi2UsqhaXLuNT8+vcbOhPOzMzl2lB7inYDsZfIMnnncOw6fe7Or0GQ5mrvRPD9BiNICVoMF/ZAeliDF8bhPYezr/C0DAjMoONoVr3dTfAEw21MKH9x6EJJSXq5nuL6kN+1UyAAFkB10cK/Cd9Y7gWwj0M9JNpIB7QkZDtQCuk5VqHvQs5H55noMdZVB7vfQojF9Bk4Junk1R7ZooVWApK+WE/59IT3KcQ9PiUXdqzF7CcMHx6yMoH016mGIZeysAr4JlUC+ElA7wBbUr+4EIzCqFcG0I/ZPbyRXmf0iVeQu9/C15n6msAIZ9e91C3i1Z/BhBVf5LvdsqgGEatjEgHGJt+VORjLq0rSjVOJWDnqnOOmI/x7hflLTgXOWh05Dvwzf2zchyB0I91pzx0PR9e17zf7Z8rAPMGs85xsNG5nKtYY9SqHxB4sdy83mRYNnqwCxxh5hbIUs30wZ8sZ8gNQOPZk3CAEM5A9dLVq4Oy3cizDXZr/R6Akt6E4sfPQhX2qYYcTVqrBg56J81KNT5VsOU4K+jz/ocd2O8IbMHIZNgcO74d67M9miVOFi9wcajcnwZhV+TxSWTTb+48++xG9Wp1gN4bw+HC1R0EPlf/36+6gvQ0WrpgOqqsf2nLyAbZKGQYfghSi36XvNTnWtE6ivR2zQ83p9gukPwjze9Tk223BuZOaoPx7e8tWLQ9wbh9CTp7Mb/C3e7fF+Lq5FSOUQZs+ZZLRngI0Ag0UIx+cp/WE2atE2OeQV6VfN3mKRO6jKovyUJCFthoKIRMZI9EbhaEbkAKOhqSfqvGCdqPcCF98uwuQF8BYbAmmhAgBzZDmQePABMnqzCTfT+O4OJ59s+bMeVwGivPfxGv/LV59hiJCTBy/kw6DgBomLTbiZnJPcF26WJcNKWTXAZg75k3DDfHp4jV+9DJaJ9qYIhvkRbnfpZ+8AN7KY3gnHNS56dn/xXsDSAlq+0WjvGtx5GI657jO0tOBXZSA9xMAFMyjWydV1EXZOMfy9lXCx11wXELRY9Ece3WmK2tO1gI+S1DycFBuh/jcldweynYApPQw5Ke0nvdi5Sf1GK2CuKuwINiyrnjcDSjsMvU6pOPOBLfBkLYFZSkM5GDVMItOqxeoJfc9ewXtwNFh/VQDzBBvaRkNf0sNYBt1tOC6gO6WFe96H70VwvlnmvNGxuYJuBPJV1J4i6RlFcF6K11QOaVEulgrtEX3PnuL6UkuXyTZ6pdDfMUyuUrVM84U2kn2drJ/iRkc4oH5A/dfME3mLFttGQ5EMJQa6uyl916Xm1gFEsCKNJKCYsgMAX94c8c9VPuBqOeFIRDjB2ksh/VvXNMssyuPwPus3U+iVwumPw/ma/naNz/8b2lxNBii671zuIKQPKVEIZKoYnCClRzFvORJO5xb9TZgHtlV4cz3jjXkjPS9Wi6rFSIeJ/O1ygbbLWKoktYMjN6jLbox/fvcXmMrQhnk9LPB1Hb67MYqvwXTcom5z7klDAprIR3HTUe/ShvuTB4EI6Q4Fvvj6DicRQQK/+c2DcO4qC6H3UqxO20R0MxK/uQwkxt3VrXH/7bgdt+N23I7bcTt+Z7yXlWscXiczceFSpioAKJf2HcII4IJ2aIcBSo5h6cIIiOuwO+wPgfKw4Sp015dcFXoA202JHZGmhPAsj3l8eIMfLIIj04+vHuNGeWbeCSeY2eszB+FT9Zp/qdmUHV7DjgcWs//o4bd4tg4Q139273P8i9nPAAB/1z7Bf/L4C/yZ+jAc46ZEu0xErnLRskG/XecQ0fvUC4b8vv/0DJOsw1kTSp0XqznEN2H3nPd4K+rNaXD03TDx0CuNDZVIG5nM3uePV1AxTuwPVvhwvsGH08BaLKTBb9ZhRyvhcVmP+Bxv6wJDzCitDPJqgI3l0lEHdV7Eww/+vCBWbq05DHyYBhYzAKgO8DJFA8o+kV+EDRVT3JHLTiJ/EqrwvtfwxKjNiH3dkLmFf9TAEYR4dLxBqzW2FBQgVhqe2hHuYMB40jEsf1Mn5nndFizYR+kwXjTsMX3ZK55Lwy4LcGwknsgU4GALzw5Ezkjk5cCkneygQ0YVcy1HqH6ZwaSPD2YaQKhqlhrZNkHZsbLMtilesDkN53B8Fv5d73ldmwMTIP+Ieiqk6sUHR6JIoIJIZCc3N4lRC/qbJeWa7rVL5JDBFp65iu5oSM5CRkJKx5XxqyYLLHB6v5MHgdX6dH6N3iisyAhXHyY/6Om4RW8UoydWeoxHRMab9Zj+XQXVh09vHibY300BS9F9kMC9R1c4E2RO0xUwVMUaH8h8iu69vs6QLcL7D9scEB6rFfnxSs9z69XNHN+agEE7L2AbjZygWmsUG4j84uwufvL8MccBwgooiq5U2uLDR8Gh7Xo3wtBqlqzJyYCM5lnXSThCUMJBSnzx+oSPSY0MUBGqU2tGKbxQKbrPi4DaUCV7eGeN5SrG0SVjmnd1vJ+Lq3bhf04g3uF6J4GnOwx009m9B7/sBCxR/le7Cj53cDlNkP1gbSewu67QEDQrjYChv4Pyb4VCA4BZx1zFa5zmgbF4tRtBjwe4M+ozZZ77eqJREE5AEfGyX3jWuXb3g73fdBIm5b88+Vt8/0EIEPhJ+xDfDgH2cV5ibQp+qOrcsiqnGvU4HNd48XlYyPK1hBmF73nnuxfs0HRQ1DgtNvhyFd5z1xTMbFR16PEZYina0qM/iBuAIIHRFBAdFwAgOF19dDfc1C+Wc2y6Al/4Y349moB3VmO8B+XFhwwAiOscxhQMEzsnAIJwZSvZUs83JarrtHny7V57IPOUKJT6sZoUAsM0QI7VPGVcHk8DzH+1HaHzJX9nXScZTb/MUZ2GVoEQnjdcQOj/OmKMygGoRbC0A4LNYYQGvRespxadRFvk4fsB8EZgIHidzfcjrO0E91zNIgV+S4Kn44ZJVWmhlVuF5jQtfsPCMmPa9gqyF1D07JMmmfebCgwDewHAAe0hncexTfN/CBIMTwu2LR23HIT0kEvN7krIHcOIwoN7pa5T1JcPv2anDjaaTw0CrnIoiUuwmNT4Lx78AgDw1zdP8fX1IWzUowvgmPgCx6MdbkhKp4WDdZLnkpQe9w7Cwqukw6ubOQxdm6IccPYmbKQmv8whB4+zP6YN97GFbBMLmO0sG4nlrkJBQSH1THJyUrwecRweb9BQhrPJHIZNwQuSEB6ged36Im3KS4vxQcNzxLwaMSegznXon9J3mx1vMSfHJyk8dqRtX15NoG407CFZlAoknWujMfl1ju1HYa6KXsDT37mCQiHM3gZs36Uqfk0LPgYg6Lx/dCfo1P/8t/fxro/3cnEVtYLwCsIKnnBeAKbJIGln5psUg+U1WLehVEiziCJ74fZkCka8ZcPmRo77F+gl9JWGWaTFdnY3LKilMjigqJV/cv85/vXn3+HJKAbBJA74QCDZfCdM6I8/eY0Tsjj8ZnOA15dzTtP5bXcH/7h4AwD4x+ULTGlx+jfNI3w2eY0vJ2Hh6o3GvWkgETy/OcDgJPRxuNFsV3HfapZ3/OB5tjnE1+sjtlPLc4OOEYDQnxtm4XuX93a4Mw/f881yBvfrSbIWvDOwRKNZlbioUojyclPh4jqmxRh+qArpUZYDvnsSNg79kcK5j1rWHK5ybNvnJmnjA5di5GQv0B24tGn5nfaOyxI6IBy4/+pOemS5RUv2lsW4R0Pne1+2IaJUxad50VxQfGE3CdIQsoqTRkBTAsgw85C55SoUAA4Pwrxo+gx1/C7Sww0S7VU4X2pPBiTvtDCrnIkyrtvT8zrBpB/Xq1CxRRlNXzDnQPUCw4nhuDIxKNgY47eUyJepjy77JMuxBThqUDjAzF1KOQEgutRbtlMAhFSIWkNFGUfuw/mn1JrxqMOwF5c4kGUom6BEglBl2Tv3g5MrvFzNGQHYdTm+bcNcWrYVpPA4oUjH6CUOBD1m/PdPXj1Eu8vZw1dKx/3vus1hBsWbIO8F90tHZx7NiUD5B4Ev8MFsjRfLsPA2e31kuVJoXk5QPQj378npGrs2vd7sCr42u6ZIAee1TnwMALgsgNhbrgyspE1zOSBTFiuS+PnFwIu3UA4i85iQ9O3JwQ160u0/mVzjT3/xfQBA+SyH/MMVWy9KZQOxDkA27lH/0HJfsTge0NIxlqMBXZOxRaaeDUwK9M3e8VsV/JZp8T5bTXkTHXXz7/K47bnejttxO27H7bgdv+fxXlaufmThKxsKC9pRmUMTGHXEKtxnvLoyVaDOCajpwEkdrtacnKEaAS8TyxgAVGQV27DDj+47+SdrFn9/fXOI/118BiCYSwjl4aNapUsZkNIKdCcGdx4HS7N/dvprZKTE/2Ryjv8Ln8JQr/hmGOPPmycAgLt6ibEIlcCH+TlKOeCfnATo+lfru/j8LPRKul2O4tTgkOzOpqfX+OpVqHCfXR5iIPnOpQ+Ma0k9IVdrziKVvaJAbZLRGMk7/vaiglg46JMA131yco1Kh13r7iDHy5s5n7fpuMWOqqyh08yqzOYdvAd+8mX4biGLkqoYDaitZDhTbhVb4qlWcD9wGIcEGBv9E/pkoMAG8rHaE+AK1Nca9iqHvBOq7T9+8C3+8qvggONXOVR0BNLB5SlWv+W5Ysau3gmYNmdpi608O1bZykMADOV1qxJdlfH7R8mCo1AAERGAicGYWJzWStiRgTqLRheOQ9sVwCEQzkl0dQb1OvyebgTyAGBg89Sx+xAQGMjZK6p6XDgnDP/u9XQBsrREgIt9vud+1CW7STu1gAOyN1Sp+fR3OOkgPJjduzESk1n4bm2ds+RIQMDnju9fawWsot5jMcOk7HD2eZjXqhP4t3gKADiehLl9fhnQDqk8Hp2G++myHiWOhBfwvUK5CLCC8wIb4kvYWkONDKMAzS7nhKXz/zD0k4l4jc5q7ku6nYYwexI+LzCi61Fqg4xg8nVTQueG+5vGqISI0ffXFwTBZomfobWFjdWpAJouQ07w8vBizJwDP8gQLk/HWKqBTU7+9Gc/4P5o+7jHQlt08T5cF8lMp9PANkN5J5xPpRyqiEJtynBfRvTECSByDDwAk5Ydca/lRch7gWevibW86fGuj/dycQ1CVwGR29SQNxKu1uzqgqMOH98PPcB1X/DNeDip0e1Njst+BkkT2pYe0gj2zpTTAc6Eh1d5KTGMPSwtQrm2LHMxVuKnv34KANBLjeJGMBS5156D0x7CC4a7/vzqY9yrwhPxqhvBAzh7GeCv//7qj/HgNHgL/9HRC/zhOPQyTvQa/9A8xNe7MIl/+/o0yQ16iW1TYEbkjHne4LPHwaHp5796zLITV4V+tYs3jBPALNx0XfG2pMm+HmFtCO6dWohZ5egH7QAAIABJREFUjw9PA1HJeok3O5IO5T0/2EZlBynAkhRnJbJDWjyMgpQ+0flrzXCjzzy8ATSRbVzmkd3QZma1twiMA0lp37qQ9cImui0RDO33pFYC8IcDyzj+7s0DOLIW1Juk++0eDJAmg6TeuBml/mW/CBKwSL6xBWDJIas4aEPsYZssA6MOO1tK2KfhupRVj3pT8DFCeNQEVWOTQfRpY6h2EqYguUetUE/C8aoqWFTF+WgnQPsw/Dw6rOG9QHOe+tl+kdoguhFo79IxnyuepC5P/ACvPOROsa7W72Nk8wHiMsdw8Du6cgDCSAjlWEpkDgz8NPaWJTuLiUFA1Ap+z0s3js2rKbaDhG6iM5XHZhm+y5Ye/JHgVJQDW5nem26w7hL0++EHZ0wqa+pEKMsOW5TlgD6m1ox7tvCT0sMMCgPBy8++uMMkMlmrtGEvgPmjFa6ugkzHO4FiTD7MXgQ5DkGj9dUoweAI9q1xkfWZx+RugJYzZdFTxOXQaRTVwMflSsccEjULC+vmOtyXf3P+IY7vr9J1YOsyYHk+hWiTFaW8LuNL4fNPBX/2jpycIDz0RZ6kjNnvJBbFZ64IvIJxFW6U5fUEkjYNqn+bn/IujltY+HbcjttxO27H7fg9j/eyci0XDdTIh53nWYTFAtvR3w3VwdN7V+wD3DuFEUEe1gs4DyYKwCemqfBEdIr8IyuAA9qN3pSozgU2nxDEYhUGIjAMreZqT1i8FR4dobTwD0BtJF5+E6rO1ckOn78J0Jd3EmaVo3oZs14zvCT5ED4D/vSr74bv0maQyjP5w/YKFbkAlfkAJT0nU/z26gQVMXMX99ZY0e5fXuVvySL0eGAZx1Aq+KkMbE4A+maviiX50fk27NatkwyNP51vUyIGgE2XY3NB4nvtIKlSzUuD3aZkCQZGFn5PQmWFSDIOJ0JoNwCXCQzEYC6WAbnwexBwrGpd5kmOQxDyJHkLZ7nB0GveaRsn8eknQUL1+S8fwhxQhVJY2FzDHqdqjx2OFgboU7C3Kx0H3vf9CH5scXAcyDa90SjInOBaLUCnCifTLbb5gKtrqnqsYHN+1QrIPjkqSQuMvqYqcOQxiEgccmF+RjMR5RkK7rsMZpVD0Zy0BwNIdYLR85BbGw04svWeqclEoH1EJKhWoXyl0DyMbFKZEpxeF4ENHKf2nhva5KSGALCZRshYYHsWvmd1XLN/MCofknViluk2g75JRhH7rRnZCrhlMn0RIlmK+w8MXl+EdsTsYZucmayE84K9hoFQsQIhyEMIoCRIdzAKJro1STA0C7xtmuAqCTUlghSA5fWYw8GLsmPEaF2XqFcVG9eI0nIGMjIPVxi4EVWMsw5jkmRZJ1MLo1EYlGeDkuygY2RIKod+mzOcjCKhaMWkwxBJnbssGLGQTAcCsLH1MQutCUOVbFdl8H26f83CcEtAtiKhG6cdE8EAwPsgpwu/6JNc7ibNq3d1vJeLaxzOSI5M6kdAdXfLziNAMLEGQlJNhGJfLWdwTr4VEswYiRNQbbKAk3PLaTTtqYbLFP/u7mrED4O8STCktCLAiHvQzL7OUhrA0UNvV5YsFbDrDNkqwU628vBH4aa7P1nxQ+NGVei6LBjjA8BaoyXtnT1pYK1kQ/IiG3C9Dgvq4axmnZw/7SBWOTKSswyH4UYGAGQkcaKbVVgkvWTmw0OI/m1sOtcSnm/wZV2hNwr3HwX4uOkz5LQZOHt5AFjB1nlhgYzwKJCfa36IDzPPhvzdoedzI0z43djrFBaJsTsNbxdhW9UD3TrKIDRkbhlG/PT4HD99HoLNvfKMA7leQTmkhX2UZCjFuEeWWYYUTa+AGcW5rUP6UoQDny6u0VJs12o6hqfj/+YX9+CmBhkxWZ2SsHtOXsIAiGuCB4e4ew1eTL0N+unyOMDto7LD8SichN++PIXYi1/LxgPb+3XHDqoWsPQQbO4JWNLpTu9s0ZNZO1Ya0oAThADAPAybEr/VgATbc5bnAtsnkdMQdKg+suwHwVBw12a4fy+wcC9XE3gPlsPInUpJNwg9YcsOa3t6dgpRH98PG5imyXnjvO5KjLNwz/zhoxfIpWUWrXWS76ErowL0S7ImMyi2rOzWBeRWQ9OGdTJOsq3R8Yo3kE2fYb2dsLx3UIoj4HJtYcY9997FVc7w9/i4hhApSWboNLucjYoeFTGATaVgrWSt7GTc8j1kncQaYKN9oTyWm3BTDnWObERM7bsthkGjq2ljIj30g/CaeTVCvpTcvhq8eEu6KIxgZYQtouwRUJljuSNIWpXT54nLHJ6Yw/3JXsvgHR3v5eJqBwU/KPhG8booZn3YjVKl1hnNZJvL3Zj7rEIE8gT3fhpw704NACTQx0m1zWFidBaCHjCSn+JEBIj8QfEh/aELi2gKEUl6UBH6R9n9UFGPq46lN7tWAVAYZrTYFh5TioPqreIiwRiF+bTGzdeUKtNK4GF8wPZYXk5QX4QH5G6vImzKAScH4YHU9hmWtUY02ZW1ZFMNM5FwpUP5hno/s/QecqnhJgIg3pK1koX5P3tznxeVzbbCaNziO4vQ87ZeYN0TmWo0hb8okm5OeOjLPP4IXScDi2wj2GTDThyKM+o/UYIgJWthmISEGwAYph4uT+df1wLjZ5Q08tRgdpxisX59cSdIIQAI7fkc51c6kNtogWszz6WSKULP+NO7wSquMxpfEvpQHDVQymFDBLBu0GzPlxcDS4CyjYC1GgN94OigAU+XpQ4PvFi5QsDQgmorz4tWVhrIsUNLsX79yzGu56H/XU07dMJzv3eoM8yOw5zrZhqjsscB5RkPVuGf3/slAODPLj/Gt3Q9h64kj2PQOfdhUQ0HBSgPQ1rWzdyHTRnCphMyVbL7javZtIGKucdlH1Ks9kgJTKYaBHzuIQ/DWRFewFJlL6yCLyy2V2TCkDt0tEDvZPLVdn2FL789ZYmNdwLzWdh8eCOhcouMFqu+y7gqFJkDjjs8Obrm43p+Fcxcdk2S2tw92EDft1gTAuac5I39YINBRbR2dFUihvWdxunBBm+awAFxuww1ffbuasQ+w05bFJnhhdc6iafzyCEpoZXFlQuIgPOCjVhkIzHQxnW4LiGnAzxd0+rXOXrqvSsbZFiR2Fa+1ugX1L+fWqi1Zv91X1pkc4rqXBapGBEeYqPRx3lx1DPxSe3e/cX1tud6O27H7bgdt+N2/J7He1m5qsxCZRamsskOrleofYE8mq3nAwdtF5lhk/0iA3Zn45TBmcsEOUkEU3eqalVh4eJuVb7tSKQspdogQJvtvT1MywM2EjVLm5xbPAABdFcE4bRjiDuhZMzOM3gVeh0A8J//0T8g23NHmOfh9643Y4zzAf2jUIXWuwKjKsAyWrkQrk29Q6UdRmQQ/mixRE642+fNKZA5uA9C9VIUQ2A00jEKIzFMIy0yVRZCeohecjX2j05f4st1kPq8WU7RxlJPeGwuJvhFFsIAvnNwgefLUGlL6TDMB+5V2a1mxmtxpfh8AlSF0rXIrpM93jANEK7LSOS+C/moQJDPZLvEJHYZWM4D7TBYxWkow6rg+SOsSBCoCH3cuJMfneygCUIcFT2sk3hJc+vTwwt8JcjQo83wwf1LTk6q1xMsqR/rc89WlHbkYacWJVUDbZMH6QMAlMRGjixdJJMHlwfzgnDeKgyLgV10XOFCtiyC5MXXGpJ6zfNZw73HcdWhNxrP3oS+/+Fih6shsE4Pixr6KHzPz59qNK3mPFeZOSCa5xsRmNCxp9goSJJaucIFNCVC+B4clLC8HkNTepHzAkeLLecgD6ss2fQNAqJPQRC6NFwZ2yOHrBq4n5nnBnemW37PJaEGVWYwmrX4+CikSX2zWmBFLRJvBHwm0NC97YxALMdkFu6ZUoVr9euzU2hqDWnt+W++/eokQO8kbcnGw1t50rZXyU1rMsBfE4rQZFhXBRv3q0nDxv1eJRP/6aTBalehpZ4ovMByFpQF59sJNtsKhlpbYmKgybTD+Jz5H/6oR14auOfhPabfOCz+dEMXRkA+P4OYhHOy/kd3cfFHhMqtMvQzl/r5SClWENizg7RwMwN9Qdd+4YEF9Y9VkoK9q+O9XFzL3EDlA4Zec59v2IaYs0hlv7IT7hvOj1qcrwOEIgQA7TmRRI17Tp4YOhXgrAjnGMEWbwAgDxJxoG81FPVchz3YUEwMsiJp3EZFj8uL8CD2nYTcKZQXpM0de/TUC5Mi9Nqq5+Hf/0f7Q3z3D4L85rCocacIN9Z/9OQLAMDNmG6KeYklOS91g8bJ0YaD2pshY5js0+kZLvpwDqR0yEcDE3usk8hmHZ+fflUwxCVbwZBxf+KgNgo7snL7+dU9HJQp7u4tT2MrcPEmLDIXr+dBOgLg5HCNWd7h8ST03v71599BTlFyHHLOOgFwwHh4Pfy/e9LC1tQDB2CqtJjaykPatEDvJ99kkx59p7mPNhQG29fhnOitREYpMrby0A3Qca8wRxNhzhmweT6HJ1ednxJsBwDjaYvr3Yg9a70HZOy7tQJzagdsMgcFcEC3dwJYhXNXXKlgRUnPNTP1sKP0kIv+2aKyQC+hyIpSDsBwSr3fRkHtJFy0wWuTq07fangrmWBTVxn+/E2QfI2yATW1KfLCQGmHuwuSiu1G2OzoHhoHMlz0kraZgyTI25YO/qhnD1690sjvBjh2OupQ0xzJtcXF1TTwJgDo9v9l791iLTvS87CvqtZ93/c+1z7dzWY32SSH5FAzoxnJuiKBpCRAEiCQgBhInAQB4iRvtgDHucCAnMQBHDjWW4w4SJCHIDAcAzYkR1IUSdZIGlkzoxkNZ8gh2c1m30+f677vda+qPPz/qnVag7wE85BOn3phE+ecvdeqVavq/7//+79PuNqyUSy3x3Neb3z3LkeDAvkidOup38kdcfE06zqYdrx7jis7C8wKPlBtq26FWkIvpbOxE4FxNVdryC7yuw/IBUb5ppUgLD1n+4ZIA53a9a8qzyDgssi7O8+wHy3wOKOA8hv3brSKbZ7BzdEUkgPnVRW5wEdriZprsWXtQcqLTDqg4mi+0gpJUmDZXIuFIzjafgUdMDlrFgD3Iux/kx2XcoO6zwTQpg+15nr7R1P0PmGnnFf6OPwZDxUH+kIZR3AE6FAFiORmI91KxK59p1oXhGs8xos9LmHhy3E5LsfluByX44c8XsrMtRcW8CKCQX2Ge0/Rg+drBxcNkgx/6frXAQD/9OTzLhOzFoCwqJ0Wagt7+lGNuvBgbZspXBQkV552kSUq0XpdGjgFKCEtylmEsskUOh7ElNWJMoFyv0LKTfUyqhGzAkseBAg/i5yyUHim8NFdip57O2v8hSuUWd6Mz/Bny2soNWcG0mAcU2YwEzFC1TIkhbAui9WQeLNDer6vvnoOX2iEjDeudYTfeEwKU+sshOpWQKeh/WtH9LkxWeDJ6chBV6YWONWUnYq0bYxHZCA6lYOUk16Bz+2QTvJrHSJlfGd+lT6jEXAHtaEAzwtvlGNmOEt2VQEgKol4lCET3BB/7rvsLpxJVF3jslVZCWdKgCcJ9EADnLl2owLeAWcQwwglOwPJiqBny89QSoMovgD7j0oEnOls99foBvRsjlY9ZEXgyDFxr0DesIyTCpMOPaftzgZ3Huw5tyT41pG4dGihQ9vOxQXnJxMal6lqLSBsq6RlBMGzAKAmBWqB9ndDQHIriw34vhoP3XmMqsPZb98iZVRCSoOyaLeXXlTA26P5WKch6txH3WROlYQ+YHhj7UPnCjKh+Qm2Uvzs9XsAgK89fdWxZDtRie3JyiFBRxhCznx3z+hd0NKtJARD3ElEmWvTonJ60sfpMa1BPykxGRJEfLUzx6P1CG8MiHi2nyzwwek+AGBeS9Jl5laW0e7SaeFOVx0UheeUkXxfO8KUvaAL7OByhoWNEXh3hwRbfmp4F2d1D3+aXm//jsftq8cIVO28Z6enfZdRWyMctF8og2E/xfZNzspXHTw5J43juiJSnVMjS1t2c7FSCJ6x8fsxtas1e1wwr+Hdpdaz4vPXEQU+4NO1Tb84QXzGhhO1hb8UqJMmW1VA94KrEl+j2kgyW+C1KwuJpSJ0I+lcYHS+oOOlPFyVNFDSoNYSEVuXjQcbRF6NKSuyvNqf4vspOTNsqgAx95JtshAy0jD8cprUg+D6qxeR6HrTi2dzBY/72uKkcO0XAMjtI2sgOeHgLbOW8ObKue7YMx/glo5yu0bczzHq0iarjcTxI2Iidj/zAAOkvNmr/RQdZjq+s32EmiGhby2ukzk714Q+1znFHxy/BgCotXresUUaDAKCbXf8FWZcmLy/meAXtr7vfu/95TW8NaHD7yTrodDtfc7TGDtd2rB6QY7BQY7TjGp0h4fjC0ozxpmNwzeIOyU2U3oWee7jT1nu8Pu9PWz31nh0RPctUuVqilZyCw2/rCYQTl1JDUt0ubacZwGy08S1OJnQuvpfHZPKVjVg+7uyrambyEBGNWbnbYlA8RwPeimmQ1aX2UhYAQSnDPvHGjF/txQWSbdwdnHGCmdw3Q1LFJUHwT22/SR3h8eV4dIx1mdpjN39Oc5mxO7VK99tlHXXQOYS1Vi7a2n6flXRssb9hULdNa6ebALramH2aQwl7PPONI0lmaIA1LgamsWgSwdjLywwZcuw7dEKAsCSYW9jpKs7N6M7Sd2/m1qkqARE5kGwAlS2ivA7927TPM4iV+Pe2TvFONzgo3Oqy6NQF6QrLbywhmnqrMvA9QFPswGtma2mjGFhDynIqnKJUy7x/PTePXg9gzmz1Odl7NjsQtJ66ezQwbXJQvgX7q2aR5DcM6xrBdnUTjsV7JTZ5eOC+nEbudVhjW8fUsBYGoW9aOlavrq7a2wWdB13Hu9C+caZO3hnPvQe3wsAyd0IlQqwCWqcPhyh+WGzjm1AdnyuY0ECKe9ZcuWhT7EMkrMa4VkB75wZ8sZAdOg6ok+OYAddnH2ZDnlZW+RjXu+d9nsAcqSyRdsl0ZRE6m39vBPQ7gaGk5b1rFUHe1HHJSx8OS7H5bgcl+Ny/JDHS5m5NuPqYIEV62G+1pthXYUYM7mhthKrmiJaTxrX/F0VHmwlXZZlPeuEHJQyqCoFvxHLziPUGQuQr32IUDu4qD/K0blKGcvjk7Hr75KpbNmpYL3cEf3N1tYK/Sh3Gczx45FT+tEhkB3U6O5SlPmFvSc4TokI9dHZLr64+wQAcLge4C/f+AMMFWUNHVngzpr8Wz94to+6aoXGO90c45izpTrBfkA9dN+dHeCDzQF+bkAemdfjqWOMXuvMcV4kuDclBuwbWydYljSPkapxq/8MfwYSXjgUI6BqlKmEY05K36AsVasZvGpZp5vMQ11LJ+SvCgnTcIIM9d01EGk1aOFLKc1zCjUQgL/guQsA9piH7hqCSl32awBulB9fWSArAmQLuh916jsloJWwkF0mBHUJoqs5G761f4Y1k4OsFUjCEjlrzy7KliE6XSeoa4maEY5eWOD2iGDJZ+nAGSBsdTd48GAHPkN5RgfuXmAJkm4UlOq+xoh1Y8vaw+aEdZ4D41iaAFAHGrYRvRAGwVTB+JzJKguPdW9fmcxw/2TSzqW0jklfG4mbu8Su7foF8sjHWUrfd3zcd6G8F9UQXttfCrRC/ejVSAZtxp7q0PVgeoMSMbNkt6M1Pp7tYMYMXlGKVklIC3ieQX7BhEOlvJZ4Hbm+10hDXqHMW0xD18v69dMbmG4SjBmK34rXOJ9zP3JUwYYCm2YdBMYpjUVBhaqv0OPSQRRUjnDkdzV2rhHCU2gP948n6O3Q+7qeJ7CMMHwabCHtB85+rR/nWJ+yvWCnRhC2xu11V6PDIhibkw7MhNbgtf0pVnmILGn7dK3im68FgplyOtBVzyA4YkbwQ4vR94lUJJcZbOTDPHyCPz9kHOHs565jfZU+IzoHdET/TvctZGEde94EbR+zf+6jjC/kdI3oDKjkZi8gIi/6eCkP10orWK1wc3SGz9Z0CMzLGH0/R8000bQOcJ7Tgj5ZdZ2akFQWnf7GqZ1M5x0ELINWlh6xXjdN+411CilKGWgtkbA58ruTZ/jOKcHOZh44BxVZCejIuvogKuGk/87Pu1gEsavXJVspqrt0gGYHNcJxhldGxKL93skVZMys/OlXPnP3Powy3Ct28RcH36T/lwZ7Eb1MdwPaPD63RxvA4+UIfZ82iS91HiBi/PVGbxdfe/YqYqbLz6sYhufto9ku0guKSlJYDEOClsfBBqdlzzn3eKFGzbudF1d4lQX9S6OQVT5OGu/OC++Z0AJl5rdQZw4H6VYDi/BMohxwDWpU4q3rdC9pFWDF9cBsE1CrRiMU49tWQtGjl10wk9XmCt6CIUU9hMok/Kpp4RHQ/JjKzHdBFQBU69B5jT5b9BH6rRvNRXg0DirMuRQhJSlzNcHaqghRaYK/ayOdFKUnDd67/Qjfe0zrR/RLVEyVtaGBCDUMz8nuZOUY36Mkw+OmjSjzoXdLeNx6Jo+SVnRfsNl9E+QJ60zb79zfI4iRN8u6UDguqGbZ62fuoI36Ne6fj50XqPCsOzzqtQ8o+3ytsBmFgpQGIa8fdAF/QAfcah0jZam8757uY77oONnNUsHVQGW3cu1E9Jxkq1zWM7AX/IFtJaEeMVN5orHJ6N/9qIDWEle7ZJBeGuVUsDpJgdcnpzjNqDzw5HSEwwe0j8RbKXxf460t4ifEqsKDNT3Dk1XXyRYucjIQaAT+7QUj9cUiwToNXReCfBoB2xyU91IM4twFWpObG7w5pADsQX+MnAPvxCev4Q4LyRS5TwIOoNqmVUAx4TXe1RDn/JyMxfQdKjcYr4fRxxk874KLjWrceSxkbR1De/6mdSpnVlmUW4YY6aDWpYbB768Fqmu8N3gWeu05gYy6jFuP5c0Fq6UXdLyUh6u2ROb49tk1txgbYlMjcaaNRMh1yWvDuXPLWHkaxgpHAupHuauNaCtwOu+6Nh2ELZnHaIkwrLDXo4Ps+7NdTLnFJjyX7nA1PqnZiDVndKPSZW1hp4TW0kX1m1kCyao0vb0VboxmeDijGssro5nrS32aDvCAVWK2ehtoK/FWQBH/353exGdrqpu8OpriL+3/cwwVZe//OP5RfDinmtY/897ELwwpUx36GfZ7K3y4IIKHFNZZVsV+hX6YY8HzdW+2hU7Atc7aR20l7p3QRlRnHrpjmsd/7cYHuB3TQfgPn/0oGSdzr6aoWhs/OSqgc88FtuW2RnjMEngFUI4MPK471ZXEM+4nfWU4w/mG7ln52mmk/sCoJL3svFH7q7bFJph50FEbVOvgz0XXjctR4UHENXzWva0qhYBr+1IaaCNcr+8ii9r2rNJDHJeuttcJSrc+V3mIYUyBziKPoGKDmGXjtrobnCYUCApBdcT8Dh14x7mHLqsriQv10Y1nIKV1pCPrWehGGrFD194cXNkqgmEERgSGVLG45icj4wJGISx6XEsujYJS5rmDUzBpzxYKMAKaFaBUv3IpZcNf2O3SezION06G9KEcu1acxbIDz9dQHKioUeEMuU0tgVoiGNG9lkFAhDkAYlii0ymQpU0t2KLcbTPeBrXxlcZPXf8MQ5/W5731ljOu7wQljBWud/xB6jlz953+GqGqcRDTofwsH7j2pMiv8XRBz2Wvt8JoP0NW08/WYYmMg7/ySQcagL9sWu6M01AO/Rqhqh2SlvhtQLcdr907f2+xhbPTnqtpykzCK9oaa90xjswm1sopma2vSoRTVsE6NfD+7C5gfzCLFJ0E8WmNfMz9tz3rXI78mYLZrjHgoOhi9p73feCQ1qrcTYEuYM5broJm9KTpXX+Rx2XN9XJcjstxOS7H5fghj5cycx2EObzQ4mb3HFkjjF5FeLVzjhFjGw/zsYM9V1WER3OisVsr8N7uIW4kBGGell08WLHBryXR+0YM4eIopxFMEeHTO5RJqUKAy0fkmsJoS35QQdQSllsRwqjG7oCieCEs1kWIguGieJBDdyk+2u5uUBuJV0ekafp0NXAM3ierIcbMMH5n/AxrHeKX7v2c+9l7W08BAFfCBSqrnLH6L42/ic/W/zIA4Hc+eROPr1JWvC5DGCtwo0/flWsPcxbnLbXC6bTndGnRrTBl+C8dkwbr/oiEBc78jmthAICcrWkqo5BNY3jM5tWJcWHg3niJrPQxE8zYVRZVn2uD4xJBXDkFK9kgCAA+OdmhpnpwM39s4K3atqhg3aorVV3btqhcsKK0io0Iuq1+8+5tag1SwrpauE4Ee3K23r3NSPwKRe1htmGVrdLDa3v0GQ18+4Szm7z2sGK2rScNMs6AFiyy/pNXCe6/Fs1wNKK/qazEb3/nHaDP3yktNk8J5gt3U5f9ArSetlmd6MFi1zm29HspQr/G8Ql9pq2kg8nDpIJSBt2obZWYc2tVXStsdzbuevtRgZy9aatp1IbyykItlFvzWnmQTWa5UyH0NLo+15OtxLpqhTYcpB4RitO05oRRhVIy5L0KEQ5yvLlLcOm8iHE4pfcuZhi8wzXR5QVWqkhq1Hy9r/dPcZJ3EXImuKlCnD2mPWDyxlMEUuP+nNCg/tYGBdfJHx1OIH3jMtK3RkeYMMr1cDbCL1z/GADwRnKEe/kOpiVlcZ/Md1w2WoxLakniWr9ODMAwadlTCBKNa0PKjANZozDtNv5oRde0SGMqbzTqcXWL/liPyiDNnKtMtPvPxKJD2wHCeQURBLAlC0ZI6doMhZTIxx5K1gkPZhI1Q7rGA7DysbA0t/PMg+D9zG4813aI+x2ES4H0Bj2Tulc7lA7rFz9zfSkP11DV8JVEpn13gFZWYllHSBQtpPOigzvnVIOsa+Xaa/6dL/4JfrJzB+/n1IO21iF+fOs+fYZR+J3iDbdJLtexa7GBZ+GdX7A5E60KkLBwKkYyUzCxxt7enK9Vu7pPWpNikmSw+WTdRcUbyjKPMEk2mOa0oDtBifePqc+11hJv79JybOHxAAAgAElEQVRBeyWcY1YleLpmeKqzws2YSChSGBxWIzdPR/XAweQQcBDr65NTdLwSK970FmXsYPVVHsKsfNdnqYXn7vO07uNgf4YnZ7RJhWHl4PVH2dj1rk6iDT71LGomZyTDzBFojk4HUL5G0qcJs1YADI++uXOMK/ESHwwJrk4r36lbqeMAwZxritdqqFS6Wq0JLHSvkUlUiI8kdNTIAlpo5hwZnwTxm7+zocEx25VJz2DEZJg4IAPuGwMKPk7SHjZMaJr05vCEwU7CJgjax7UO1clDWcNY6Uh2gdS0SYIUdJpgSW1Z/MnjG/i93/8Rer497erS/jin/leecxloGIZLi9THmaDNfNDJ4EuDQLLbys7GkXI8ZXA67cM2UHAlyEUHQF5LdEYZMob5jBWuNmhOI3xqiRz307c+desBAOSWdYpeq3WMqg6dK44sPNSsDuULi8Cr3bu3XCWImhaq3Hcws8kVUEsHI8MK5/ICLWCMwNMVPZtXh+eu7l8ahWURuV5uP64QMoch3YQ42KVnkWkfe9EKXzt8FQAwP+uif4frzuU12NBg6yq9l7fGZ7jPfadl7kOnHh4/pv+fZ5FrwbNWYMrEv2flEJn2sappfkKvxoADn73eCssydEpmq7MOQlZA2+5scO9s4vrx10dd52wkpUHGSle2koAW8HfoZ90kx2zaGrMLAaccJQv1HAzbENmCJ3PYooBoaq6BD9Tclzubo3u4C+PTy6F9YPk6T39k4C0kKlZ6kt3KtRwhMFBzdoTqG1Rj0/Y0R7WD+Vfnf85g/QUcl7Dw5bgcl+NyXI7L8UMeL2XmehAvECQ+VnXkIBUlLLaDNXwWmW1IFABpdv7Se98GAPzR6S1URuEZMyS3gjX2fYpgpbC42tvFkxVlZj9+4wHePyZG57pMnAcpQJBi00LiL0Rr1h0aDPdWjgBQCA/fz3cBkNVbxFkRAKznMfwjus50v0Q+8Bw7s9vJseLG804/R8QZ+pa3wsfrPUfzf2dwiFcCylx/d/457IZLfJpR9nF3ue1IQKaSWCzo33exjSQssczaNpKtLsGBi3kCUcpWOF5J1/5ghcXJvOukf/tJjl5DClmOnRqUJwxeuXqGszVF+b7SWC5Z1WnjwygPmgke+9sL3BzQ9fvCYF7FrV50TbaCAKkmNXBuMFUod2vHeIVo26l0LqHK9jmptIXT6p6B2kiXlctSwTCRRRcKc24LGXIG28zxXmeJJ4bWRFoHqLTCKKLfqa3CecGs9LSHblC0c36BIfzFnccoea3OigTGCOgdhutYcQoAsmVEik1N0/5JCHBWonzjxFDKWiHVATaqLWE0sGRzHxdZ2mrNmXwgYAYCxrRKSQ2qkKrQadQuygifnm7R9YA0cRvGsck8sk9k2BMWzlA8jktUWkE16ICFa4dZyAjLMyZuFQqiX8JjEY8y9SEXrUJTHFWOSBdI7TSCK7ZfLJil2+vk7l2T0mLD//7nj27A3OsiYDJbP2uJbMFcouoDZyeEiizWkfOVxTyANK1Jx+azgWMqm8jiqzmld9XrClIYvJIQuuEJ49jH2/EahfYgGXr3dzU6vA6mWYK6Vs6P2Vsp1Cv6O90xkCx8b6WGKRXKFROOwgrKbzxzBQmBcJZoV63KGWQrAoHlGraqIXpsTXd9F+rxSbMk4L//GSZsl5jdGKIxEd4cKJQjA9HY9dUSAZtM1KWCHvOX5QqwAoah/cIIyrjpieJFHy/l4TqrEvhlgGUVubYQABj4GQZcc300GzmGZ26B783pkDzozPGd+VUHlx5uBvjJ63cBAIko8O/tfw3f6JOQubECIbvd/P6Dd1BH1r2sOoAz8hYW7nAVlcDiwdC9yeF+inzGh1gtYLYyt0mJpe88Ma0W2OmtsQroxD59PHJ9Zqmy2A0JhvzjxS2c5R38Bzf+CABwzT/HytDB9fOjD/Dd9DqWFX3ffrLA1Q4FDneDbZwtaWOLggqzdeL6PZNhhgeP6CVDJaEquA1F5sJtomrpoTIC3T2q870+PMV+RPXXtA5cna22xNTe7dM1z9IYEUO/2SKAyCXA9dJVL8Q0ouvq+zle755gHNBB/5unbztFn3ILTg2qu7+GzgIHlwpp25YOP3DuQ3QDrayiDqmFQfsto7HHhXO/p1FfkI2sauVaubLKx5ANCoZBhtOsi5xrcqVWmGa08XvS4HTTRcruKFFcutYWAHjGfcuPpiOUszawUf0Sgw59/qCT4XTWg5hxHXRcocdsYa2lO0iUMhAAVvxd1TpAZ0yfcWWywDIPMSuoVisKD3rSWOsId0ADwDDO3CF2fmAdtPnxyS6KzHfOLgCJ0wOALQmybEwEoq3MCfwv8xB55Tm50X4vc4zpLPcd41jGGsrTqI65vq4FTGOS4VkYK5zZxumq41ybvJmHelJha4++b75MXC86jMB0QUFQ9z6VM5rAsO6Qehdwwcg75/XTgVsvgxtzzI972D6g9+bsrOcgUX+uYDf0fn4tvQ2EBsMJvQtJWGK/Q9fU8WhNNdB74GnnLe0rjbQIHBRf97Trr+50C+zxO5NVPk5mPXfo17VyPcK+0ihrz/UZG2kRTOnzrv92Cv+QoHGz3kAOekCf5vHwZ3pQGa2J/a+eA0enwBkFB7G10BFB4cXEQ1ULqEO617prUFbtPtsEtY0iWLPCbQCMduj61SDFD3bXvljjpTxcz/MEngqxKCKEvHlVRuKj+R6+o6nut1lEyHgR2Fo6R4mTrIdFEWEQ0ibyxuAYkndtJQw+yg7w2YZaTY6zHo6XvEEZts9qsqeFcAbd3oYMuwEgnFIU2dQp67wLwfR0YYDqScdtIv5O5rSKx6M18trDkjfLeCtFzmQJ/36EX3vw43SfQwNZCPyXH9J9dq8tHcklrz2ssxDXx/RyjcMU/YAzLCOdU8/R4cjpgwJAepa01nIC0AMNzdZjca9AkxtlZ8lz2dCD5QRvd0lP9Vpnhm+fkLhEE9Q0GfpFQlDWrWHXHiQfeOknQ3x0QPf8b7/7DSzqGL/+/nv0y5aMxAE6TMZs8L0Vr7EoYzxb0GGVhCVmLNtnfYOqf8GyTbUZi9DkvtLsuLqn3Wc+Phm5emARkaVZk4GWtecCk63+Bjf6U5S8nkqj2ho91ydvMcHp//oXfhU/+pv/OQDg4XoMzYtCKYNke+OIc/1uayJ+MuvBnEauJidXHtKY5sfUAmCbMdsn274mqKiMQMr1uqe5D+Vp9MbswmM6iFmwIpvG2GQh6qqtIzbZdRxUOGbiUL326cBhtEYYgRqNPrFBPM4QXfi7o3nf3RsA7A/ooFmXgZN5NFo4sZVwb408DSAndLDvThY45zmW0qIsPQRNK1EWPGd9CAmcHdP3yUCTOTsANfMQsBhH1bWoutZJZAZzgfIKXW/YK1CcxU6gJAwrh5bMn/YhexUqPvw6/Ryp5LafTXtaq1TCdi/83SZ21z/pb5wsKwCkpe9QnUor1qqm+/a7GToc7DTrCKC1pGvp7q2upUMYtJYoCh+Ge57DlcTkA26tSiuYE+ZgbI1RvLqNZz9B66fuWOcedf6lMcLFAN2vfkLPt9bwskbLFDCJgeZgyh8Uz9W1m/q98a3rVweAZJDh+oBbmM5e/KPpsuZ6OS7H5bgcl+Ny/JDHix8e/L8Yt/unCLo+vvrkNczLpr4lsAzqtsXAChfZ3X7lCK/3KZsojIf3RhvMK0o7r0VT/OHqDQDA28lTpCbAfkRyc0/WQ2QPKOqOziT8DSBLriVJOEUTWVuoolFoIuPui8bq3qYVmPBXAnbF7E8rcOUGRZmVVtBG4ivXHgKgGrK8Sp/xz+7chjzljCXS0FI6GKg+HuGcA05/TRDXnQOKoDsHK7zNbjRC2NatZZCTTB/DSp1+js0h3adVFvAMXjmgViVjhWOM5ppcWJpa5LoI8GtP3wUALLPIwVSTwQaTOHWCGN2ocNlr0s+Rrrut3+pWiS4LI3z1+HWCAptsKWoz3nQToc/PdhJucOd820nIrdIINWdEopLw1m3MWYxq+DOeq0kNzD0HAapOjYdPWbh85kPu0ueXuY8wrlzdsCw8p0bkSYPSKOTcApZ4pUNFrvSXOF738M6QnEd+7P/8T1GzCcLj2dBlo0JYCCGcYbYQFkecMUphYXs1NM+xUK0yks08+CmvpS3KfhrlKIyA4oTehdo30JFEyW0pIjAosqZuwdkqf3dZe88hCztjyjiP9ABStWvG1MKxj2VSIwoqXB3Qe1LUHs4M1w0ZrWjKNYMwh+Qa3SoPIXrcQnPYgywkNEPL8yhG0UDlyqK3tXHIvs5aTUwdWYiNQvcaXacx0rFms0I5HXkTG6h+Cd0Yit/c4N0tWtPDIMVn/S3sMYw78HN8+7gR3U9gNj5y5gR4nnb1fLNbtDXiRYhOp3ROR5VWeH1Ae0xtJb51dBVf2iNg9PiCGYYFUBQ+IlaFq43E00Nqv5G+bkU7rKB7bgkPyLn+6kU1zDTE7h/Tz4YfTIFPH/HzbTPJ37z/dwEAX/zL9N/5W9a9d8ubErvftMA+8TOWb41x9BWGea/k8HyN8YDLEUa6Or6dBrCNQ06iAd9A8Bxnqwjvn1MXhlJLvOjjpTxcH6dD+CJAVvgE7wCAZ1F6AbKQ2yA6bV1JSeOUWiJZIZElEkk/fy96hH+SfQkA8KQcY9+f47slwZuzNHZ6rcKAesJMo8VJptzNaOAWVdIhWtFZBX8loPi8r/rWqasA5LLRHFyTToplHuLxmg6kd0eHyFjf78dv3Ud9kxb+B0f7KB923QHhZXCuMlUHyHcNLMPhm2WExzHVoF4fnmLdoe96vBzBWMDvcx+n0hi9xvDogy1I36DgTXI3WeOUa1+wgCgkVo/pIAh20+cgxebQPToc4dgbQB1zG8ftuZOfjIIK5SR3cLjNFdZMdlo96xGMx3qqcbdw0KmUBtM1veBfS29ic9QBoh/spbOeRTk0TufY71SoWV9ZKoNkd+1aVpIL0oUbAFjyYdSpYYxw/ajVKkDEakHHix5maexccQZhDsUF6nUZIvBqfMD1/VorNz+DOG/bkeY9DLuZI+XUWuGdA4LXn64GWHsGOff6Iqnb+rFvoFnX1dYCgyRz8GPs13jaaPh2KihPu8NKblQLjfsWVeihZIlPFWon4NOLC2c5F8YVsvPY9TR2BjmyxmzcEjmvaSnr+AW+cJUPkrSHrPJdi1CzJgA81z8uOFBrYMX0LHF9m3Zc4s2tEzxYsHOSb1xpRfVLGCNdXdvza5QNOahfYnSNDoT5MoExEqJpYyp8POVWtLKj0A0KFyBNwo1T1fK2MlRpgHxKn+8PCueK8/bBseMVvB9cQVUpVzoYJZnrqZVWYr+3cm2CkaocxyOOKggAWdUoyxmkzLOwupUZlJ6BNYDh8o0xEv4J9xwPJYQAFrdYhjQboPegPQp+a/4/uX//hX/z76Deo3ml8hUHjEOD9Z6HYMraztoiOueyiA1RXykclL3MQxeMrXrKqUapfolOUkAOW3nLxgqwWrW9zS/quISFL8fluByX43Jcjh/yeCkz16erIZQOUR4nzhTbKoZMQZFTr5s5hZ3Xeqf40ynBFedpB7FfIfEp87i72YHPUfadzQ5WUeSEuqWwkKwZLOzzWrT5pM1IIeAiTltTi0KTTcq6ZRJbQQxjn2HLykaoGH7SRqKsPcyZgfcgmDgW6la8weGaBf6XERAbyMZxRgCLH2GN2p0ldBqi36jXbCLXnvGtw2uOwVk97MBsVXj1KsFYlVZOPai/u0bo1+gyg/TxcuDgRitJDL5hN0ppMV0RBK1rifqUXUZKAX8l23aGu0PXDpP2aoJ9m6m0LclFOn9K1kYNK2z3KBM5W3ccYUecBxC+RZcJO+ndIThpIHGP65nLmOqzyLkUKfZ4baC8ovZQculAegbhNj1Qz9MYxrkT1nhox47Na6xA5NWO/TkMM3z/lFqtspRIKA2rti4U3rtJGd1uvMSdBUFwo16KflBgKZgZPuvBjOje3xif4KPzHfSutSIb5yweIDwDO2bT8G4BXxrHxN3vLOHf4Ot9ukVQaUO+KQQJaIA8OuV54NBD3RHIGQDIs8CZSpSFB1FLp8yzmcVOLB/CQimDh3NCWYZJKxJyvkmwWUVYJkyiqSUChlK7SY7ZOUE6clxC5woeG2Poadjqc3sWd8+3HXPZauHmVNcSVkvYvDGCl46clwwyfGGb5ru7V+DrpzdwIrnc8TjB3ONruqmw11vheE0/a3S1AcBoRcIdDPUbIx2x6v5s7K4pS0NIpR2hyRMGnyzp+Q6CDLFXYc3tgMZKBBdY4/JCiUYb2RogLALHiDcg1aTmGRYHJQwjXqP3FcqBwPhjuq7uV+8CLMj/W+d/HxfH/DWFgsmJohaOOV8PDFQpoFJeT081Sm7ZybcFlSMueENv1gzZZwpo1tJxhPE7M0w5e086OSqeqzT7f9D+foHGS3m4elJDKQ30amDaMhhlUruWj0orXO8T7r8XLHEScu2UxfxzroFMiwRvDwiSux5O8X8cv4N5zm0QWjrD6XolYXyg5g3aWyiIxrar1S2HAGACwFu1i+uikpPQrVSiHtQwDGmF4xqeNNjna/7ex9fQZTNnX2kHq0ILyFy6wyRYWYy/zm0hyRaKN2rUv0cvSSwF5rt038VujfhxA0UBlfAxutWaXTd9wZ406Ps5vvYJtSOhlM7pQmqB+NbSQZ1KGixTeumKk457cWGpdlsnF+TfGsWhhefk29zvNoLwUkCUklo9AJw9GuKM/06tFJquEMXtQZsHrK5kgWpCExKNcuTnsWsPCBbSiZrXKx/LvK0x1lq6gKPby7HDUoJZ5WMrXjsI8EffeuTsC3eDJSqrsOT/P8r72Gczh4elB+VpB1NK3+Bo03NzfKNHbQ935tuYZgm6IX3+j9944NqPzoou/uKr30JP0ib++7M3cHZEgRUE3AHnK+0gSQCYxKkTon/l4AzTTYKM79P0FHDEMJ1nEFzN8PoOBVbLIsLxgtdI5juIOOkUWKcebKOaZOBqv1D2OTPsovIuWNgZ7G4vMGcIv859RCxZuGA2LQBIpeH3a+dUIwYlqkS557lctf3OQVK5vlBrBB2mTXCmBTyWfcxyH8cZzdU35iPMpx33YgYVEJ3Qd1374hwHyRyfH5FO4LxK8J1TUkPTmYIIjLNLxNxD1ahbdQ3WA2ZulwpBv3KtUOs0codwGvuYxClOuO3lk4d7rpy0vbeAtQLrxnhAK2j+LmHQlgAMMP7QIj6j/Wx1LUByzGv8aI18P0Hnfbp+63mwG1o//9KP/A3c/XdZ6lUCyVeW0HMuMVw4GEUpUcdANaaf+ecpRh/Q3lMMh8jTGMccaFVle8x09jbIUuZ/+AaLLMKIGfePPt6F5ZKOqFpDghd1XMLCl+NyXI7LcTkuxw95vJSZ60FvAb8TQAmLo4Bt3zwNzzOOOTjqpNiKKBP5g7PXXI/hl8cP8SCduM8qjcLjjOCthkCUli3xQrH9mZdSximZpWtlm4H605bcVHUEvLVwPonlWMOfc39jKlBsGcgDyhjf2Dl3vW3b0Rq/uP9tfHdDZKqPvCtIHzAU/EaJn79G/WjqmsGv3X8X6RPKTmWpYJtVYIDomYfv/Pd/1V3/u3/1VwEAOlIotlnhJTTo7a+eE1R/rUes5ViV+KPjmw5q82aeC+H8pUDx0QBpo8yzXThIS1j6OQCoiszfG9hcaAUdc3bqkb5vA/3CCAeFQZDer2QRflWQNy4AmMhActO/lRb+QkBz8lR1rcsMitSHiLRTiqk7FmDVG+VZbI1WeGtMXp0fnu+5rKH0Pdc3+7mdI3S8Eq8npGZTWeVgyZ7K8Vm2DckkpkBq1/PaiQtkReCyyzgpnZDAx+sdB9dLQQbljadnVvkYx3Qz7w4PoWDxpKTShLHC9WOiks5fWEqD/gWVrZNN10GPN/rnuD04cYYUJ+suilcok7g+niFSNe5N6WdCWHTjlrWcndF1FAAZtvOzqfu6DeU1oJIK5pzWT+H76PZaXd1RlOJ8zujJnRAaTGz7/BoJz4ESFsUFsQkAzlYu6BWwtu2TNhdgYIDIUI3ilO4I1Jz9+oMCHx0RRO/7mqBNVrrSN3LUt2nuPj94iu8uDvCQv/vh+dgR7AAAc78tUaAlDNbKuj5jKItsHjlzCVMolCzeskYX+dU5lismC0nr1mNW+ljPEmdJCSscAxxAK84vgfltCX9Dv7f1tWNguebnJJA8rmE1P5zAh7hBbOe6E6L3GX3X5qc2SDchhlw+qY3EmglM/lxicdtiwz3me19XCM9aUwhZwGXso8GG7O8AbE4Tl/2ilFhvIsxOWAtAAOBnYTsX2J4v6HgpD9faSAgjcTLrYWtIC24cp7hzuOvqrLFfoevRplqHKQ43BCF+f7kPT2q3uWx1N/i3rnwdAPB6cIT/VfwE+gyvffOjm+g+YwPnipi/jQi8v6YFCBA0azyu/fYJ3pEXUJG6wweETxJqI2492YlXjrH4enKCm8EJ/skRibkLZZ0Qwsm9Ce52qe0hUBpKGtccbyVQ9tuD62e/8n3c+gd/i/7/WQTzJW5QD2oYZpN2djb43PaxqxVedDGRwpIwR/MCtaU75Psa0e4GlmGiIKyQPeVDXrdyeNn1Gmql2tpyTzsYWFQM6V2o5zQsURMaIDBgwSnIWrk5MJaY0XRjggIKvkQvFU6tyWYBOYhs84G6X0Dx4VRXCtNlgq+nr9D/163vqzECgw7NwV60RChrfGdJG5YnDAzjdX+8uUluN2zKba1wwgndqMAwXuKE5ey2uhvHml1frOtZgbJWDvacLhP37/dnB3hz4EGhPbzdXPkGI26PuNJdYponzlXmLOsi5PaOblAgUhVO1nQdwyTDJGJBiTLCw/nItTF5nnY13VsHp/iUWcoi1tBawDYtQZ26NQIIDUypEO5RkBj4tZPP/NntuzipeqjYBaY7s4jP6F6mXhfF2/S+dpMcQlhoVj/qTFIMtmmND8IcD6cjBzeeVAp1E4BFBsiUK8/ANwi6LBlo4e4rDCvs78yxHdN1GQjc7J65Z/BwNnKBeJlf2EZrCX8p4aXt++XTR0BoD+WwkS6zpFJVXDiUL5QZp0+GLjC34xqSgyIhLJJBhiLkeuwsgGoCgKB9501oIWqg7LHBu+8BJT1fqxvHJGZaf/EVmJD+7a800n16ZlFcQqCVihTCuja3crtGd3uD4hPaF4UBjn+MywMjIL9aOfGYxTq64G3c1oXhGVSpD8GcBqGFY3+b8ILazAs6XsrDNVAaviLT82aTG8cprIXr3bzemeEXx98EAPzG/D2XXdRGIfFKtxk8Ohrjv8vIvi30axw9HUExiUNk0vWyBivKuDi5xQWXKOQT6aQQdUgHa/Pz+JlqpQQrwMwVwlv0y19/dAPlmj7w3s4E/8vmx1AtmLQTa9Rb9DIJzzq94/MpteE0ZCFZEUkFAH7xJ76Bg3CGzXX6zPv9iWsnsUbCH9KhPkgyvNU9cpnrLI/x5SH11/7Z8tpztTxZAtWAD8ZBCaUMag5gqtJzh7wshLtPCMB6aEk0sXFauTYwQC0gMul+15HSJBudN9nSsEbnM3rF61i4XkehKcJvaqlEGmvmg1s8ONN8dXvq7kUIi1UZ4uiMNhTP17j2Cm24V7tzHHON7M5yB9aK52zHal4/42gDY9tsKyt9R56arRJEo6XbtOdpjLe3qc/4dLPrNrlJN4VVwv0/0G6A436Kk7yHeUnP7XjdBXRb0L89olqpLzVqKzHzWbPZCFLaAjCNCwQXyDbTTeJcfabzLnShnHl3kfkuK//00Q4EB0HKMzCD0vXmJkmB1Kf1ogsFkXrIWfe5SGpSUQLwPz78aXzp9gPsXiGlntmzbVgm2+jYojqnAG+6eL5VY3+wdKbkd57tIAhqpxNcZj6CCa3dIKjhTUyrVb3yUbHrS9IrsJmyE03mIU8CzLj2209yfHZOAXUSlsjSVkcZAG5eoXXw6d191F3rkKfoRMDb8LqrBaJzevbpPtm8BTN6NlXPoj6g4MwaAe8kcAG2WXmwHst9rnxyPuJno8YF8oDN2A99RPR4MbhfQ5YV4g+IoGXzHPC5Z3c0gA19VFsUFK2veJi+x++o8aE4CNVa4upwgRlzSOarVpUKkvgV8llLCF28ze42oxzDqETJKlVF4UMveUKEdQGpzCRMYiCYg2GVdQHvhXbbF3Zc1lwvx+W4HJfjclyOH/J4KTPXR4sRVB1CAPjKASmTfHi+hzCqnIvHONjgqGbfUVk7V43pJsFO18d+Qsy4d986xCcLgkefzCmjaaAqf9PGLjpo2nGYyt6hLBVgCLhpbQifr8daZZE8a/4tkBwbZ7RtHnbQmdIfrp9NUI011ICzVWFxsWox5wwUp/SlnUOu45ZAye07/+hPvozxtTk+v01fuFhHTnt22MkcatXxS3xrfh2fnpCGcrEM8bWY2MHLIoLWEmpGc+ClAqqpS5Yh1hPpmshF2UbylE3zfKQKVlqUW3wHWrh2GBEYWCc/AMi89WWVJWed/EN/6rlIuE6sE4q3gYU/KODxLxoj0YBzQhocTBaOmQu0gvlvD55hWnVwtqCIf9DN8OUtytj7Xo5hQCH/YTrAp+db6HHbxWerLafwVVuFo3UPCzYY17VCv0fwxk9evw9Pajx8RhmS18/wvRPypl2ed9CfEFoihUWgtKvHRlHlWqbuL8e4NTjHu6zydLq5DclZpikVvn1IUHWR+xgONg46Pcr7DsqfrRJsD9Yuc621xIoZwZPJGt2wcC08s1XimL7/4lufuja0x2dDmGUEn91QPGnQa1q8joeIrq8c6zorfNSc5YS9AnfPt51hgf/2EtkbDHuWHjmpgIQhbKFcrdxYgZyzd516yDY+yoTmx49qxxYuVwH8TgXFOtliaKEXzJXAhVFJDHoZNH/+dNFxer6nh0PsXZ3icyOqvZ8XHRynXN7IJOykRM2cg3oTwOO2kgemsVMAACAASURBVM6RhvEZqSkFdChcdhqsBDbcWtWgKA16Zf2WLe8tFSoZQbEbkwXgsT/q1vsavQ8pgxZFCbveABG7EsUxLAvwH/3sNjonGkWP5jI505gxHC52CtRcF67mMe5uQmxNiM0eBBqGUbl+N0NtJNLr3OIU+4Cp3RxrLR0fQc5a6NcmF3xjRxWCpGoFYeK2Lm/b8u0LO17Kw3Wns4bfKfHF7SfoefQUb107hYTFw5w2hz88uoVfW5E0n66VO2S6cYHQq/Gvb33Hfd4bCUF3f2/xMy1sAiLo+JtGdo1qrMI2CidA1buw2TPRKZwKeClcbVZY4SQTIS3qWCD+LVY48lviUzECVCbRO6DNcrWOHVxntHBwcbRsoWqA6ridxwzteB7Etyf4kyt0aFZXK6BHL/FCtKpCpY5JMvACLPm9T2nTvnIwRfGki2DJJJENyTnSFwjIme9+5qXtBmL8C7Vf35JBd1O3reHm1VYSIpeuNiPsBWLYCpCldH3BAJDtcu3x2gb5BSjxYGuOUUgTcW+6hVdGZFawLCLc7J2jYug2174jazXksWYzyEof84puTgqLtzvU2nA1nOH9T68hZZWZ7tVTd+jM8hibPHC1vYvQohQWp3m3lUpUGgVDnWGvcCpArw9OcV4k0I0z05Oxq6uu4xqzVYL3/SvuM5uDxJQKOff6xr0c1/oLV+7QWsLjQ3jc32BTBPC4Fpxngbums6cDnMcadsMPLmqN68+LDnpcezdGAp5ByG00250NHs8oWO3fnGN+1sVbtx8AoDauh1MiBValh9U8cbB8ENYOWg6jCuUFmFkJC5/nb1WE+LmrdwAA050O3j+74tx6NmWAs6xdFFXqw2cFNpKLrNznN/NjpcHZUR/XrpHkYT9qA4royhzWCmy4xtP1C3wwpfm2gSWlpAuchpo7iLKJdPVXLwP81Lr1v7lyoXdbAOZKDrNpfCjhyHjGJ+i0kWZNPgqx9T12xfn+MZpeKD3uY/3lK0i36e8m38vw+OdprerIYvpFQOYMLecKpkvPPvRrlB0+4QoJKIFNTvdZFG1b1O3JKZ6shkj36dkXVezUrOSwhKkkFLfI6cgAfLjKUDvCXhhWSBexU8OrjHDrzPz/oM/1Eha+HJfjclyOy3E5fsjjpcxc+0EGP9CQsE6AP5IVUh3gt7/3NgBAZAqCiTKmV7sWAG0EzrMEf7Aksf6jrIePTwgWLjIfyBQkR8KqAM5/hDMuz6J7X6FgEXIdAN42feZkuMZpn221VgmRBVL+vVi4Nh0jgKoroAqOOEsLq5jltxbI94zz6+x0cgetZUWA8oQhJ2WhAwHFsIu3sQ6qChacFRw33+05eKtKNDbsTyo6NZJeAdyhuYvTFuI+nu/gxnuHOPxDymR1BEeQ8DcC5cA6pao6utA6EFjIEUWwJleQqQ/vjLV6azjYvBppQKFloeayRQsst940vzsw7jPjsETBxI+ruzN8Zeshxl4Ls342a1tLnmyGuNGjjCXXPs7Yuu+s7ODxauiyscivUXDqUVnlGLr/6AExtiOOyIvaw3aXWK7GClgrXOZalR7ee4Ug3Ez7eLgYweNM8+2tI3zzCTGTL6rdfO98H1npt4mOb2C5FGHWPvQyQhm3qjqm0VAWAJiBnWytsKkDp6wVxSX6CS2KTRFgvYiBNQsvKNsSaHoVPn/1Ke5NCd0oK8/dy/ef7To1pTCskJWtnd66CnBtRCSlp4sBtvcWrtTy2eNt9EeEIhjPwFrtPFZt0GauH/0bv+Is+BKf7NoaolWgtBPqaP6/Uf5ZTjvo8udHfo2s9FEycVF5rX1bWXrAmrfE0EBGrZLW6abjYGxrBbY6a1eciFUFzeQslQuY2nda4bJqRWBUCZQ9VlaKiezXkIAAIDhvakH8rLhNRy58UlsCEE6BcigRctllcL9C5yNq+bJhgNOfIF/lzVUBfwn0ntBnHP5MgmK7UY4xSEYZUi6BmVABzV5nBLyosbejuX19i5CbR4uhU5h6vBpimUUY9Wlez7uhe9fCsIKXGFeq2KxaH14AznIOADrDDAUbRNhaQvgvfgtOM17KwzXXPrT2ETJjEgBOyx48ofH2LYL2PvzkKgRDL2rmI5sSpCU0MO8Z5+c6DlMH12XnMUQtYKfMioyAhFtxNq9orG9px9Lt7q7dhnl8PASWLA6vgc0Vi4B7Pje3qpbl6ln4TwN4a67bJhaK/QWqroWNtIM+q0S5hZ9vgrbFMCJoKX+ND51eDnyL2a858O3/4Zfxhf+YXDD69wDjMxzlKXeoL28qpADsHt335FsK3/77vwwAeOu/+FUc/d5VfPJft72yn/8r1CtbRxbWaw9zqeGMDayw5NcJQMU1dJ/UlgCqTzl2dWBgSwmP+/x0ZJ3alI4srLJOFUvEtWut2umukTQmAbxhbnlUS/qp8ad4s0f1s19/8A7O0ra1JVAar3dp87q/maCsPSdlGCrt6rFb4RrHFc1jPyowuJ47j9InxyOsWdD+ne1nzigdINjzO8ek7vML1z/Go2DUdEjg0WoMzXPieQZJ4+lpJLpR4Wrg6SaCiXkj9g3EoIDf9AHf6SB6SJO3vF1jcotqyUJYnK07bu1+Yf+pE47//buvw2rpRPfhGQc7X92a43pnhsM13Wt5QV3pYLJwB1pZeZhsrZxvajHIsZAUjPXiHKezHvwxXfO1K1PH7JXCYrEJ0B3T2o2DCn/6r/w3AIDX//f/CtbyZ2wV6Pile5Zdv8BrMT2nT9JdjOPUmVoEndKxketaYdLboAhoTuYX2piMlpDjRpMU2BqunYRlbSQ8S/Nxfk61y6VPh9MqDx0MbAJSD1NcB9WhdWz2fCzcWq0TIHujQm9n7Z6hOG4OdkuwbyO639HI+fkWrxokH4cu+O589xnAakbFzS3M3ubAuKvx8d/8a/iZf/W/pXk8lTA+f/47K4w6mQum5uvEGT0YI5GwyL6uFLq9HI+X9KyXq8TV6I0ViPwaa+62SK6sWylQabFJQ1dzhW+ccpcAEHPQqaRBVSvUaXsMWfPiw8HNeCkP12frPjwb4vbeCZ7ltHDmZYw3uscoEpqSD9WBIyJ4m5Z4YCUQvLF25JXSKNesLnJy1m7k+PIdtGSbYQldSogVff76qAvVZwp6IV3UN3lzhienI9SfNm49Bn/9x34LAPD15U18dfE2au4HNcMawaHvrise5K5mRCQXuv64WyDl5mwvrhGElaO63xhP8eEN2hC7LG/4Z3/vl39gzj7/V34Vy5v072AhkG5Jd/jN3wDe+Jt0gEZLoP+oxi986VfolyWQ/hLboWnAlsIRi3SiXdO4qAQsZw22/7z0mSzav4EFSel5bR26SVWNbwFJpBIAsJFwRJa7hzuIWdpyPEmxH8yhOeQYqzXO2IZolBBR48EZ1UiVMvjwMZGKzNKHGpYYMAFpUwQuM/vd1W2XwWXLCCJTaBhlKpWYcWaTj88wDDNMA+4HBbCYUWb8D4+/jGSSIueDYCY6GPQpuw487ZxREr/CyarrDsZeN8NiTs8wCCvc3j51G+JsHEM0qZOEWxPdsEJReVgwOe6BHKNsRBcyDzACHpNmdKkgWcquNhJHeb+VrdwEuLJH9epeUJDlHwjF0VpgvMOSeJXvDiopLDpJ4e4n8mr43G4z3USwtXAEJ+vXeOsf/wqvBIUub/zvjg6xqGI82QzdZzbCHLVRmOcxXh1RIPHp+ZZz8bFG4NR03YFqAWRcGxdLH+GVjXvugzDHsqD73O+tXH1abi9xNuvhnA/UpFtAcc3SaAFUEjXbqomOQN1tNZpF40DVtxClxGbVGNlLoHG7KgV0JUiEA1SzHH1Iz3D6BQ1vA2Q79JlPfuk6moQ9faWGqDkQZ+LRH/zT/wTNeO/X/wYAYPF4gKeLsCUZFVQfB4gMtmoOu1piufGddaMXVXhyTvMdhRXioELMPIy89F0AlmUB9Mp3iJKKNTTX6K20TrRDC4m6Uhjv0hpZrJLWJrB88SuWL/4dXI7LcTkux+W4HP8fGy9l5ppVPlTp47uLA/R9gkY8qbGsI3w4pSxluL3GImD5sQehMzOvE6BYhehd5drDZuTgEHgWcljC44xCKeOyi/m0CxUYV2+U3crBJnvXp1AcSS8yan9xNcWVh984JdbyadoBvNbDUoUa4jZF8r2wwmLWcSxLYwTWLAr+5pVj3GRTdQWD33rwlpuLDz++hsm36DqWrwq89rf/Lj796z+YuS6+ULgWGr2nIVaeyzDN0n+u63t228PJF7gtKbJOHMIobjPqNWozxMoEAJQKgkW7TeYRJNm0zqQBBIv/wyj28qT/FVXr1FFu1+jtrJGyqHkY1MgaRx7PYMxuNvM8xvura9gP2dQ+H2LJogv9MMcHD6441mKx8V1Uf3DzDKFX4/7TLf5yqhM1z0I37jzdCqYWANfDjbZQDGN/88ObkEmNyXjt5ivlOqWMK5SF7747DCtX54u82olzrKYdeFGN5ZJF/S0gmpaUuMJuvETEcOk3HoxQ7LPkYVRjyS1Ay1XynBjE6aLrhOP9fgFdtQpQkBYeX8dsE+Nk1nN151vXTvBjkwcAqGZ855hqfkm3QJ4FSBmaldI44ZW08jHupG09tgycR+7nDw4xKxKHBs3S2EkcjnopfnyXvuus6OI46znJSQD43x58me7l8QjRJMMVViUD4GqKVkqUSx/hFsObZdvOY/uVU6yyFnh4PoLmDHprtMJu0j4za+DYvek6dKgLlHUIFgBaI/zOm93clWp2uyt8erKFYt1C1yWjGyYQkJkiyUgACAy++O9/AAB4sB7jd9m8HABe+Z//tssYo2GOCbtAFbWHm//gb7k2o4f/4V9z+4G7rqYM063d2oWhrgMA0B2D7u4ae30qn/T8HI+XI/58hcUmduviYLTAIZdB8sc9eKVA3eMadaidwpSpJYoGKchIHtMwn0UArivjz6NXL+J4KQ/Xnzv4BGHXR2WVg4VPsh6ebobYYrmznWSF7zBcF8yFIyWYwCLqlK4+FakKdWNM3eFF2udWCs+gbrRPhYV8FMHPGwhToRrRhrLKIlcbLAqfdE3fpY3BB/B4TlDMchUj2t24XtxRL4XijXjKB2tzxuVpK5f3eD5EwNe7KiPkj3rwuR1GjDTOv8Kb71rBKosv/Ef08taJQEHvEsZfXGG+oPnwfI3aMwC/uMkziXJI15FeAe7+Z7+MW3+n3QAMX6O/ltAGsIrl1GrhNESt36qzbO3TvTd6pCZoSUqyJD1FkzRyiG09VmQKeRY42NZagfCAIKes8NHnOnmlFd4/uYJvGyJdbe4NoBtILpWwHY1wTL97Zf8cAy4BeNLgJ0b38HRMk/I0G+Lr3yBiG2ofrHqJUlhE49zJ4pmgPcR8aRCGba0qW0Ruc+9MUpRGwPKayVaRI52Mt1YtqSmXMH4rFWeVhbfg+txE4BvPXsFikbj5gWjq5tIRbWwtSDGMSUtaAHVEm3uUlOgmpHMMAHFctryC0kcctzXMmwetJGAoa+yPaL4fHY1hlwFwSu9G0TU4fJV+zxiJQSdDj119lmmEvQFt4LVR2I1XWLLObqOfDABvj4/w0fz/Zu9Nfm3L0vyg31pr96c/t3t9vGgyI6KyLbLKiUqGEsK4agBiYEaA+BtATBADI4QNwpJlEBJTRmAMBqmYAbZLVGV1WZnVZGVGRkRG89r7bn/afXazOgbr298+UUMrJ0/vrkm+yHvvObtda32/79fcAQB8b/4MyzZnLfG2TVjn+s57Z5DC41fHzwEAp9sJypvOE9NDTVtoIjR5JyCJ7+DnLVtRtnUMbwXLRo6KEhExC2/KAiq2sJ2f8FUKP6b7W2iYTDHBDB699hzA9dWIr6MQYVMGBG0oiHjmawmfWAzm4bnLE42PFoE02Wmsu5GcxTBv1XRdBc5vwgJnaCM8OAnz2df+9/8KUdz1Yw2SXCMju0vrJMoOhtUSIHlNkWr86p0XOErCpuKyHXIA+mqTQyqHrHsudIxBRpKaSiDeCLiI2jM26ds4dE2AXr+7JG/heNSgWtB9un79l6ZbWPh23I7bcTtux+34JY/Xf3vwLzE+3R4j9gkWTe8uo53EapejzMidxEoI2sUKC2jaMLZHBoXwOK1CxXtTD+CjLi1aQbSC4d6T4xtmM368S6FnFuog7EYHacsi/SwymGcEF+VrvJXdoKGomt89+zq+PQ9SjbVJ8YNP3+Pd7mpV8M56Oi1xczqBpBSMqBZs+L8sI3xK5xlJB5c6NHf3KO90/NmjHfQnY9QHZOj9tkZxEI5r8eWMoUcDqjSpcmqmPWvZZsDb//N/jYgqD3gg6gzIJYAUPaxdC7iMZEAHNQaUrqKkwyBpcROHStknvpdFpT54B3eom+mlDhAe9izH4qCXNDy6G0gt9ycrJqF9ujhCuxd0boeWXW5c4iEyiwfzIBv5/sETvKwDcqCdwo9Wj/HBMJiG/M6zbyO96arCYGIBALZQqH3OfsjIe/N8axSGkxI3lE0qStUbYhx6Mk0guHRZMEScxwYbgnSROEynJebkZ7vTMS6psvFPB9higLhjkU8dZ2RGqe2ZmQKAlnxdhRFwXUjKuEKkHJod+TLHCrUih6Mywexww1Dtn549YrhxPikxz0lSU0WI5zXqnORUlULbhH87J7CLY3aY8l5gXYfPeHkzweG4xLruDT+6avKPXjzG8ThUUUtToNQJxkl4Zj4/P4Ql04VB2uKoKPGyCfettar3V6bRsVIFoTUA4BuFlq6P0AJiqpHm4f398mbOHtBVHUMvM0RLgpAV2C/XtCoYnXTf54K5PgD4zDLJp1xl4Zi6Z3ePAd/BwV1VXjYJrp6Hc0muFT74L/4RNL3bPvO4Q4EFR3nJTlEXnxwhe7jhVojzAg2ZYNSrFKZVmM/D33V5vAAQDTUeHAWC2kmxwVU9RK7CNWhdhA9n4dmvdIyyThiBMYlkdEPPLOJ1BJd3bHMPkXTPYC+tatYpROIgu2CMNmL4OH24wes+3sjF9fl6CmVSOCe5r5Qnod/y3p0wYW11it2PD/lvujSLZNLgNx48QWX3bIDoRVVj0ruRRZsUHq824cFVykGMW5ZxPBgtcbEb8e91qTJKePx0cw8/fhGi45LE4IfmEf+ebxVwRb2ZqQkuKgDWz3JklUBN8hiXAzG9/CgVNoJinXIDoSWiZTjmdCE4LcaLFLsPDe58L1j1HUuHF5cBAo22kqFZdlUiGEutFAevmAMN7CJocluKSsmLXzsN0XpdD9YWHtEmHKPWBVa0KOZFi7JJ2IVGeMAVe2Jf+D4+TgHC9ab7AOBogkxmNb45C1aON22Bj67CArRcDkIEWdxpQSW3yHzsIa4TPDkLkPGv/+2niOiAL9shXq4m+IOP3qOTlRBJ53LTBw9klxK67lsJ1ipgYPjc3hoveKI+3cUcn1dXCbwXvLh6H8zkAeDsegK7pmfOAzfPp1g2FCs3sADJWoQRiLayt5Kct5CdnOQqZzamVz70tTuG8NBCDfo+V1kn/N3egzeCg7zBN49e4dGjMAE/2R3gE3kcztNJfHEZ9ML5vILRCsUkPFx1ksAuaMF0wE///f8c3/q//i6A4Aq0XFNv3AtcLIe9VeKmgKDrEceWGcfaKVgn8ZIShPxFxrDq5WIE7wXeJ3nVo/EC5z5cK28FvBSYUc97k+TQFH6RTWs0xCqWEwspewkJXfbwe5mGG0sY0emw+7AI3wbNqNr1jkodfwJWQJGkxhmBKDO99Z/oP0MUBm/dvcZzeveclvy8myK4OnVtjA+++Zx711fVgCWC8oMLZJHBkmD1k+EWZ22YA5JRi8mw4g3MfLjDjmRq3zx6xcESOxPDOokfnYe5yDmJk1FY9NLIhMKikyA5gYo2T/G0Rl0VKEh9YL+7QUz6Va1V395QHkL6nrm9iyHoGbw3WeETvN7jFha+HbfjdtyO23E7fsnjjaxc6zaGigKhoNsJn19PkKSaTQFuygKOmvAuEYhKMj6QHos2Z3eZxS7vK7qrEAbgqSL67MsTpC9JvP5OBaE8Li4o/1B4GKoaItVrGBsb4bwaoV2EHacdaWyvu1RvSTArQYwjAdn0+6Nm3rugiIGBHVIlcp1yOLSvE0w+FmjmxLCdeDSU/W4Tj7feu+ivk4nwtbvhvz/WdyAojktqARd5xIseFuPN6DLiqgkIIvruZy7xgN6LiDN9hGWyEmgpFLtcx0DsOfQ8jK5SDcxbS0xEYQUkwc5mZIHMQlDlmqUan20C+iCF79mSTkAkDr/x9c8BAD968RCNCpBrPK2hdwkcVQr/+M/+Rn8IToTdNumYvRVwVIyptvdQNoWHV+gzQ6Xvo/Wkw84kDInKyAWNI4LPcJ5qdtlCrtGQr6uzgiFcqPCMxYekgU16l5+6jqGTFIoQgSTVHGGHkQ5m93RM4STC/4jUcnUBANU2RdRBebHFlvSY81mJDwbneC8L8OCj9Bq1Ddf7i8UBE+qqdQaxVdCEOKjCoLgXqp5YWbz1P/23AGmL5U5i+E6AKJUMLmOLZ6FqixcS+mGooPNM45wq1U7nu9t26Re+D0BPIlxcjfFPX/xa+Ixxg9Gd8N3bVY446SF2IT3GR6GKreoYU6poj4dbfHZ2xMYmzgn21VXCI44tZBelKIIBBRCIRHIT95Vs0rdPRCvhySktLgMRLys7WB6oj8MfFcMGkXTw5+Gap4seNWonDmZi+b2/LIfMtNZWMiKyaxK8uphiNA7IwcfP7nCL4f0H54ikY13zZ6+OMadc64tqhIbu5yhpkEcaU4KnXy3HeEYe0MZIZHnLeuQ4tgHqRnCU8olHfUiIA9DfJwCOiH7xqIGzClL181YXLPLZywd43ccbubg6JyCcxDBv2Ny7SFu0JmKHmdVigIReLD3y3L/89XsvcZxu2C6v/GyCgpJdpAVMHuj+QFgIOpacXybwIwNFE9blYsQv6zBzzI4cRC3nUgKAWyYQtEhGVzHUrk+IURsFl9EGACGXVHRymcIgpxDo3Srh1U9VAnEJ/MX/GByUvv0f/yP85L/r3ZS+/3//Z9gSXNS2Ec4vyb0pNXCUEmCmBtmsRk0LUpfLGH7RAVZw79cVjhegeK0gLHoTjML1EpshuDcIhElPUI/IR77/PRUMJTqjiPRGsrxJZAKiiln6sFnnqDuoKrZsKPG3vvFzGC/xo1cB7mqWGYvuw8XrNy1yrdgt6+A3X+HD2Rn+9CzA9KvrGW/A4ARq6mOnJzu0dYTRiCwzrcRuSazZJsZHL+7yV0nl4Zp+Al+vc+7zZZnBjuBFIYMNJwAkhUYUWYZq/9V7T/D//OxXwufFDiK1GJ2ExaTREUOdoowYuhZWwA0sbwTj2GJIPe/GKCS5ZkatLnv7uuW6wA+u38WDu8Ee8kqPsGnDs1u3MTKCF20uoa3AmDYAxko09HmVTVF8nvCx6F/doiT5lF0mEEPDC6VwgKQFad0qzkqW0qGqEkQvqec3cLC0mYlzjSi2qMiTUy8y6Cjle+tSwxN6s0l7WNJJlgB99uoYtlbcdrGJYlnOdFJiMqigaUFdLIahzYBgxyn8nsmJxVdlOV2rIKX/m35U3bXwJD0r0hbPr6ccuJ6sAE2Ko6gUQKmg3wr3alulzA94uZigpXfmYLDD8XDLxhebdc6bJe0UnlzPmZkbjxrunc7zHRJiRSfSQAqPJRmSNHWM6Alt+gsPnTm2eu02F0DgqAjbs/h1G/Wh8Ht9fn8zACTgO7OVyMPSPOKxt+C+puONXFzfPbpGPEjwajNGQcyPaVJhZxJ8uQi9GZlYNPfDz8ROQczDv4/TDZyX2NJk4BX6xeMmLK5dZJSeW97BxisFsUo4Rm10Z4Mj0v2drUY4LMK/f3z+INj0ddZzVSBIAGF99FG/0Lu0J/rAA+m1RHU/fP7f+dafY0bxNx/duYsf/l7QtsYbgc1j4Fv/SXBU+qu9hRUA1r9/AlPQ8U8cfOc887KAopc92saw1xHUXu9V5P3i5CG5HysayTpU2YaT6FJ5dCp6iU0jYIm6L6yAjx1Xp8mV4mDzjhAiaEGqDx07YoncQiaWk2bGgxpZRDrO9ZAnyr+8ugcpPN6dhwXiY614B65XKUQjeXI0hYV8m5JevEBpUr73wvR2ds2BY+u8B/MlzjdD1tvuV4T6Kg/9TiKRpeMGguwa5+MdrJNc1dq9CcsbyeQ174FR3nAY9e/+s+8CJ2GSm09LtEbh/iRUgh89uQd1RdVv4nkxhaJKinqAQnr+3rZVIYaPjttbyRWvBfDzT+/jHyx+C0DgKnSVUxobXpykcoAHS3byvEVVklXesMHwb25YiiPhsW7Dz67lAGlq4AbhZ9U2hSefYbFTsPS825j8jo/I0nJaM2HKGokkNT2cIkI/FQCOxlu0VqEkmdH4zgq6C/WWnp8D2yiMD0oMs94O8fw6LDKr9SDomGmos5TXTzjSdtN/Si14AyxrwYfkYw9VCvbWFk7w83n5dAYkDuQSivKBR1x2L0ro7+NpuF71fYmXIhxXdZ0zcevO+xs4CExIH3Z4sOF0m9PlGNUmZYc1vcyQnoTK9YvzQxxQFTvLdni6nmGxoL52pWCGNPeMDNBKqJdhsdUHhp9pqQPfQLDnN9jKURjBKI45boFawVf9e88jef0X19ue6+24HbfjdtyO2/FLHm9k5frRxw8g8wyyltg9Crvd3SjB5WYA3VI/ILaICWaa3Kvwbz8IDil/o/gc/+Vn/w4MieOFD7ANEHpuNvXQx9TrFODdKAR41wcAu12KF7TTbpcpfnYRpBnFcYmDwQ6DaeiV+AmY4r7OC9jnGeyUyiUtEZEZRLoIrkWdrOCvlveYRfjpq+PeTeahweBphPWvkMPO//r3e8b0Dwdo73gkS953Q5MkyCtAz3vjBrXrWcbWSRgqY+N5jeOTLS5J7K4vc3TlaVd5d9v65FoyC1tqweHuXnlks5qPq6mGDJnBf3WHKzVYBmETh5P5Go9Ggcn6a5OnCT9koQAAIABJREFUSMkU+p9ffYCPRWAL36wCy7Gr/EZF0wdtJ4HB2UHIolTc/z6zMrB2yUknsoChqjM6qjAZhirhJN9gWeXYUG/Jbnp/VjUzKPZ8dcd5zf8umwTFXiXYtBHLS2AE5g8C/Geswq6NubK992uv8Lfv/Dyci6qxsjk+2gToeTIrsercgxzfCnjlSAoSrnG7TaAJyleHDWwZQ476qq2DY50NaEPHVchjjZTQgURZzjxFCly3Ecx1uHbrNEFBZvxaK2yRYrGhLFzpmTVrO7czuiZpoeHznsXckGFL/ixG9UAjJv/jukxC2ABdq7JVEFSleyP4vU4jgzQyXLkaK9k5apbucFYG/PVKeGyWBfQwHM98uENOvsaDtMViU8A+C++s1GRugpDTanLBSIvLHDxJ58Qm7vw8kF6H6m5HDmv37y7YAMIjcAQgw71X/W0IrHQDJHXnV5ygPqH71oggVUMw1bBO4orCZMtdCtv5eCsfPL07ToaWXLG/fXyNGckCRxGxxfeM9dVJeOmTyKFeZjCUSS0zA7ft21k29ZBHfeJ5NCWf6qsU6m74/EGmoXUE82WYK4QD8wNEuc+3eD3HG7m4qkpCegmXeZ6gzpYj6DZiTR1bvyGEq/9q8QQAUPsYJ8UGl0fhgbCnBRtnV3cc3v3mS47xOruesLzOtjJoQzv5B4D2Jvyh2tPaAQEm+1tvBSL6H569jUnWOwvVImNHmWQlEQdDnNDnsej1AgBOstB308cKTzoCy02G8u1+UoLw0HQc0RAoTgWHO7dzi9mDsHOYFRUut/SibjLosYK66Z2WOg2sdxKbOu0dcAoLQ9CjWkZhEuoITntqJpv1ELdPHOpV2ssWMsc9UV9G8KmDpcWqM/4HQhDzcbHBzoSJ82fbe/hiE3rjj0c3mA3DS325HCJOLbtbbasUNS2gQsseOkWvyQUAd5MG/SNDeeBjzjON7x6FRCXnBQ6KEuU4HAe7ziA8V1WVcM+vjSOcDMN9WmwKbK4HfH2EcshnYTKLI4vDgjTHdY5YWYa8nRf43cuvh2ssHBob4b1xkJRlicaSIG6hBT9/UB4ycn0KiQA86RJNHSEetsi76LHIcSh5VcdQuWa50MVqiGYZrl08avHenSDjypTG1Zfz/rwbid0lOZ7Nauw2KcOGWd4ip/tbnk/gTMIbyPSwDFFwCBu1ZLUnwWolDC3G3gkO3ZbSo15mvSUhwJGRmyaF830wQBIZJkkZJ/HyIuhJXR1hfmeFmO7TO5MrPDgJm5vLdoRP1DFevuysNQUcaXacFmiOLQQtJp00BwDMgWHY1swdRBnhW++EZ2YY9Svopk5Dr3NCbYWFQkTOi8IBqg6RdQCQrIE67BnhUh/mGIQN2Mlwg2fk7qarmHX7k8kOKyvgaEOjVhEa6hn/v3/n7+K9f/L3sD+6AAeziXkjOMkbZIlm+U1zkzPMbPfbDwgQe0b8D3lS8dxqrYS1Apaeu2gjucXjRd9KeV3HLSx8O27H7bgdt+N2/JLHG1m5RmsJ1UgY7WFnnXFBg2aZIZr0O8guGumb41PcUaFErH2E3z76GTbE7v28jhG9EyqK40TjuiywIP9QUSkWk6uWmMSKWKhDx017e+AQkfdspBwWdY7fff41AIE5eEUVo/cC9qTl7NfsoidTmTyQez784EX4u6hlZ5WjfIvTJEBObRTINB0MN5nsoO9QhXgsUGmF+4dhh/7bdz7CX64DJf60nOBg0AdOGytRviQTgxh8nmKTYz1LOOgZWkAQbKtqARcDk1/Qfdh5XHw//NsL8K4bVgBWIKZ7ISae4SIvPbJpjUfzAP2ersdMmrG7CF8uDvDhYTAP+GJzwIL1xkZICMr87oOXuK4HDGHWiwnfcx85RIuIWZ3Ckg8vANyrUQxqbM7C/dUHhl2YPIBp3D0HazQuQj0Ou/qnVwXLmHRhISsFR8j+jRzgBnQdRxYitRiSfEIAHDg9KypsNXk5xxqNVfji2THd/B4NmN9ZoW5jLHd9tTw4DrBnucwxOwxVcpFoWC+w3IbqSymH8or+nRnoOoImWPvxgytcUnUnpUeRNVz1N8uMKxZzmWE1I4JLrCDnTS8zepWy8YGuo6+wkWeDCtMsnPPPiiGEkQwhC+HZuWs9lwzTxrnGrKhZzlbumdJzEHoHRW5jdpSKpENjIn6elqsB7h4GdOb+YAV5N5zX+XqEWDl86yCYkIyjCh+te5a3dhKPvhV+tqoyrAni1tcJxKTFgFoEdZVAE1lLlhKOCIJyHcFHHj//42C47GIPNyF/4kEL00SM1thGou3I+GUwKynOyMBFA+t3e8cze5euVZlhtclZfgPTIy7rbY7BqMZmQdqxPdbye//k70F3rQgngkSL5iZ4AUvPxMIPoCLLz4jILBRVuM4KoOqXFqF6o4g0NXwvBEmadFdtDzwigqrbBV778UYuroEqHmCaLhB92xZQQ413jwOc9puHv8D/8ey7AIBvFC/xs+YeAOAffvxvAQhh0ADga4X1dVj81loCsYO6IZu3oxaSzO2dIoZgB7FoBUkLUJa3bHo9Shsc5VsMyE/wzy/uc29wdzmA2krkF3u0d3rupQFUK/DZWUgl+e2vfYTKhu/unIkA4P13T7FqMuyo51Q1MWsThfCYjne4W4SNxI9Xj7DVxOIsC2zPwwQ7vrMJQccUPBBtJUzeWUV2Vk30AjUyuDsB0KNgxXf9PZro1gqSWL92rrk/rXYSwgnUoL72wAR4DYAaaSjleKEBwG4+pUxQpC0eF4EFXNsILzYBFtuaXmd3up3gZltwAAKcYFY36EXv2JnNrGcj/7sf/BViYfH7ybvhZzpCQXmW2ir8ZHGfruM9XGyHbHwfkkY6xnRYuNWePSE3m7WAyDzmFEhdxC1ersLC//TFIUs6xrMdJnmNmHqRRitEpHVtTYRB1vKmYl1mPAHK2LJUo9ERksjiW3eDteaz9QwffHhO1y1GYyPe1J3eTHq5ihfYlBlM06fAcCvCC1wuwzMyH+8wGe1wQ7Z9LvEYPgjPldYRRkWN6zb8bhoZbGkBUhsFd9zupdMIeEoXOpmv8fVpgJ1zpfHR4g6aPa14o/dkV3sMbSvAfdU7gzU+uTomLgAgZy3uDMJxTeMKKS1+5+sRltscf7ALi993775EEYWbdlpO0OgIR8Twf3E5g13RgjSwkMoz3wHCQ9KC4VyMmOBRDSA9jblX6xKgjanPOAiSKncZns/8leQ8aWGBdOWh2nDRF19XiKpucXLM6jbCI3qWMTsZc8uOZ7ACm0bxwuvyvV6S8HyMo0GN1kTcOhNGAB3zWVlEkYWn1yosmp2kTELkmqVdVRsztG+04kQl5wSqzV5Sj/Lcc0+oBfI6jzdycbUflvCFxShv8d48LKZFpFHbCB+QZdrb6QX+mw/+TwChz/rTKmgiO90k96o8et/S2AWv2I5740mOA0C0Aj5zcFTFyb2UiKZO8O5hWBDeGV7hMN6ipobkx8kxJ5zEkwbyfADbrSsFWNbiBeAij5Qm2SflAa6r8HfWSSagrJosyD26as9KOAoyhhUwc4lXVOXmkYYmnVxdJbwxWJ8PIRrJWlCnwAuoGToIK1ib62MJ4+maJUSk6DbTUwO5I+nGJuIFCCJoYbsIuiixAGn0BkWDk9EGY5IYdNUoAJyMNvj+wRP86c1bAIDz7ZB3yZe7Aa4WvYeqVA4xhacbGQMUYi9aCUFyCgDwQ8Mxfk+2B3g8vOZe53qXYUMVk7tOsSBJgaoEvPKs84sEmGhiB0Gj6/ckVNxjNQLeSjw/D0L9+8dLGJrYulQdALgz2uDeYMX39GIz5HSS1ijsmgQTWqCVchiSMcLJcItni/DZnWzlGUWIrcqcI9q+PXmJL3aHuNirVjsyjLUS3gl+/uU6gqNEmCg3TARLZhaRdBztZnTEC85OK1w+n/GG5snFPdYm+6GDWCTQQ0IEcgOR9oSmzrzlZ9sxZlmFwzyc25998YjfKSkdrJF91TzQrB1/sppjW2asqU6zFhOKncxVy6Yj5XUBUUkm0g0etvjxeUBxksjiO8en+MllqGStkYxCzY/XWG8KLCjpRewUFBmSSACmCMcxOiyxccPeRcWBbVT1KkX2KsbwKV0T6Zmf4BKB8q5AM6d377hmHbCwgvc5UWIRfbBm+ZNwAjnprqsyhTcyxOYBEEPN/W9Txyz9M5mG94I3VsnxDiMidSWRQdXGHIm4v0nvULEuoE+3ET8/+aBBQu/P4mYYkodog5G/UtAjulYPb6U4t+N23I7bcTtux+34a+ONrFydF4AXEAC+JKel4+EWv3XyEb6TPwUAvNQzgFCUn1YP8fvXwaw9TTWbAwAIO9aOPh45+FygUwQI5aFWnWOSg8wMFFVB80nJO/n3p73lYGVj6Ejh023opw3jFvePQw/09HoCtZcIYwaApuzY5siiOC4ZXi510rNJU4GzlxTM6kVIqOhYf7MSbkjQrJNc8QBAIi1XrqNhxdm0pRPwJmGGs9R9eHm8kvAR4AIaCJVpGOoRR+tg8N8J531uuc8q697RRdWASAHXVbVFi+kwHNedQRDH/8YsWBd2iSAAEAuLH5eP8XgYknCKqMXHlwESN7XEkODj1iiMi5rdp5KiZbYkxho6i9g+MC40vv3gJX/HZ5ujvle7Z+kGgN2apOqE9HSjJOBOusQfDxtHwGHXEtCoKGs0O41RDy18HcqUF+czJAShzSclGmpFfHk5xy+enzBMPJtvcTQIdcLz5RRNHeOMXJlU5CAJedvphPNhfStRFA025MxTX+f4i1fvAAD+cv4AUnq2/lORY5YoENoHUcfWvqvhO8ivivhdcF7gaj1As6Zr1EosyFoUiljznbGAA1zeBzP4wiIhaDKOLR/zuKhxug2fESsL4yV+8jK0a8Y/ylDep+rrbgOxSEIaEYJM5PomVOEqtr0dJH3mPAnw7qt6ws87hIfPHQShEf/sL36FGfYPjxZIpMFySbR6DwwpPWqUtqjbGCN6XtebApYqQUiPnMwxstjAzKu+NbHPeq8UvPSojveuOT1K5X0HHzsICoLAKuYfetnPN24xQCM9Zu+Hd2GcNTzf3CQ5FjdDKGL02lZxdeqc4ACHLlChaytM5yVXnaOkQaIs5+0WWYsl5V/7KoIa6Z5hvkkBgqt3ZYS6Q7Wq8J51LZLqrmXGumhe/6Xp9T+Df4lhyxjexVDDCnco5SFTGn+1vY8ndVhs/+jsbVx/ERak7F7JD4pA8HLtJDsmtvxgtm0U4EDRpdFETDPvMIKuF/be9Arfn3wJAPhaeoa1Cw/p0/YQv/PiO+wR+u7kCiuyl/v2107x5N4cP38eAqOjpxkvtLKSaJsYD6YUsq4sw8LjpMEVkSPMJga8YLu29Tpn7d1gUGNTp9wfuYoHDDcuXk763lrioCrB9mw27RcWm/kAbxKBx8YeimAfYanvTBo9sdvzIXY9dKrn5MdKx9WsU+wI7n6qZzBO4nfa7wAAUmVwQ+d5WJR4PLzBn5x1KUIBzgaA2V4c2oPBEj989QjdSBMD05ExtIQaaYBs3Y5nG/z8IizQWgcT5Q72AvbITmovqScC3J6Uy6nQV+/Gro4wJIiuaSMmfNnEB5iVbA79LkLTOSPlDSbk8brbpVCJxXjU96XOKH2p2qVw1wnDrMdvXfMz9+JqyvB3vJXYZDlDgFAevgvd2cbAQMMRKcVFHpJgeaclYCRrN32tEI/DgmFczFIfj+C5m5MzUlPHvQUeEWi6IO9ylfWLiwfgBE/oaapZi3txNebEI5Fb5IMG9hVpZY2Hpd7hdFZiEztGXI1W/Lx7J4K1ZtQlDwmUZJM4iBo8GgYmjX0U4HmfEvwtAx8BACLh8IPn7/D5yMyyteOLagqlHF9zW6m9TYTA4CDMN9YJxJGFob65FhEiuqZmE8NvI3b/ssmewk54qEpCbMj2cWRhOze0zMJ1pki1ApzgHnsebbFuyGI10dCTCjVt4iAkL9Aicrzxvl4MoZRDUoRjLKsEO3J5usIQUnokND94hD4xAFjtYMsI65YIU1bsRQrt9X5TC2d6f24xbzmOLkX/vryu4xYWvh2343bcjttxO37J442sXEUtIYTEalXgeydBuvJecYFYWPzZOlQ0y3WB/H6A2rRWqCjxIR01kNJzZamUw452kSp2wey7Y8oWhkkWSaqRJRrfPAr0/Xmyw8aGz3ymD5CInrBiveDEivNqxN/12eYQT6/nkOTnKdvekcUnHnie4zMX2MJxYtAQ3HhR9ySiqCWyTVelmISrzt3zDPFWsBNTm3qU3Y5TeUjagbsO9u7gcOuhuiB1FVyY2O+3lZBdddeRdzrFDVW54cB6AbzQAjhsOO2lyFqGRDeLHGglmnn473vzNZN+AOBZOeNUGSGAr98LBLX7xQofDsK1/8HNu7gz3uAFsbDXNwMm16TjBofjkj/vw9k5nsUBwfji/BC27b2exR60qSoJRYY0Pg7kJFt0JCaLiiBkSX/Tpcx4Jzg1xStif+9VkwlVhR8eXGAaB6jxeltgt85wU076e0PsWFdHQOIhCdpblTnqHTHgljFiMmEQPnjimkMiC+0ZYkABrlUYHIZKzRiJho4/Pothhns5p8ozkWU43aFpemeQ6bCvrG+sREvEufHJFu8fXmDVhOfzhe99lKfDCpF02NE9TCKLNgt/11Yxw85Z0SKJLAi1xfIDD5D0pjURhoMaliqw7fMxozNyowJaQsesZ5Kh4HlSoiRWuXGSq1wgmPV3BLIXy0kIYqDPd61iQ3vlAf2oQdvdw324d97wZ1xdTQOJ6Cp8X7qSfR5wCZhhn5sMgKtyP7CIRg07TgnTV53eCiYmidQBopew7XTC+a3VLoXVsn+OKwUckOxNeiREINNtFNoKBCe3Tcx+084JRJHdS+RRsOuewY/I9TIgAXh6YIT0jER4S/NS51Y2qph9n7UbfIzXe7yRi6tPPHziEScWb+Uk23AxvmgO8cEwRGn9pLjLkojGKtx0845ycE7i4ZSs6JxEMwqX8cnzo6+wYX2rYB3BT4mAcRLvFoGd/GHe9/H+ZPMuLIEIp9UEk7TGPCWLsKjFnxCE6b1AvUk7tQj0xMHOaFFuJVQDSHrJm7u6l5d4wBHr0ccCatubwMMJJNfUp4k9VAskS4IpCx/M9kHQL31xtFL8/wNkXdi9/IkDIs+9KmEFbNr3ZvcdpGQr2GqxnQAdpuXnLeLYMvSeRJbhez1dY1VnOKYe41G2RTMI1/+mKXCSb7AYhEm7amN8f/4k3DfhcEPWUwfpDp/fHCImSKspC4hZOKH78xUeDRccu/VkO0eqeus/v1NAl3RUqZ4xnfSSlHgjIWxISQIAK8ETjZQONrUMKYpGcRiBmdoQdNBFpxUOjw4DTGm8xJMy6GHfPrjBepihpAVouRzwpAcRoFu3IWtNCQ60Ruqgu154KWEmPRMasmeaBga3QkmbM7WIUSxoIYkAPduT3xiBlvq73gl2nto1CQ6HJbYt/cwHKBcImt1B1OJ026WtJGyEX8YWWWwYflw1ES+MUWowmJKFolVhAaTeoypMH7e2IbP/bqEZGWY3O6sgrIAiCdgg0diYHha+aqlvCABeoCC9qvcCr86It7COoA5aWNpYQUvoMYUX3CgkT1O2P0xWklOgxMzjZkN92mWCeCn5PVLNnjxrEEIuYqLb6hGgvhHkQkPpUW4yfp686xdUGAnfdPZnACRQ0++dWYmou9cAvJFIzsIzYgYedkkLY+pweBTmtmWVYbvKEaX982/Q21Tem69Yh73Y5Xy9geCW1rG1u35ruAhg2Zhpo0BQoPu2vBpiSwEg5q/xGV7H8UYursmsgio8psMKX+4C9X4U15jGFf74hkTdTn4l+m1MZJhh2mAQt/jGJFRB72YX+Bc3HwAArmYD7MoUvntQiz6ZQwiPItGIydZrY3M8a0N/dxg1vHu+k60RCYd5EiaR02oMRQ/falmEjMx5eACzeY2CFoi6SoDzAbqSQi2ifuFtZN/zhUdUC9RTkrmsVS87iTxUJWDIfyA/F7BdXmkN2LQ3cnAxODnGZp4Xb6EF0lcRirPws807gB5Tfy4SEKZPxRF75gcQ4D4hjIRe5rC0cJkWuO4sdg81YATMnfCzL64OkNNu97DY4TjdALSAzOMS3HnzEs+qMDleVCMcDktMkrB5+uCtn+F3z77G9/qyHrKmUQnHGsxHd26wmmQ88TdlArmmXp4FV+hm4CFM6DEDYSGTdL2V8tCNgqIMWjuyLOWKVqFn3/WhhZb48jw8I8txznrnPNHQVnKFnhUtS46A0NPqEAHvwT0zEyn4nI4xlxCxg6QJ11YRFBHPXBKsKH0k+Bi75Bh9HK6/oImz672G7xKcALRe55DSYUFEoiixbEf49OkR1icpR+Y5LXnxK09H2EYOBVXNLnIs73GRwIbOUwqSB9FxJEmvs4xSj9F8yzpj5wTaS+rNtgJ2YkF7Xry6nnD1JeFxXYfFb9ckmM63OB6GFc56GXgHABB5zKdb1oqXlwWTH5sjC7mTvHibzMOS/7RfJzBEBMuuJIozj3bU3Y+gdQWA6l6wTyymAUHZrgvEXZXsAK8lostwTSLTV7VwAnZAz9lEwzaK+5siM2jp31neojR9Tq63EqrzPxY+JEgBmI9KeC/4uupNiuic/LKnIQ+2I7pprXB4t8/kXe8yRvsQOb5P3gq+n2jlVzbcoo0g6Ly6+eR1Hrc919txO27H7bgdt+OXPN7IyjVNLFRiIITHeR1YlifpGp9uj9noGgAnpRijUBP0dVVNIXcSn5jHAID/8Lf+P3xvEuQ7Z+UYu20KDHu2ZzwLFW+WaDwYLXHWBimBEg6/ILnNw2KBL8tQocySHdY6w7IN5eOq6Y0KfK2AvN/RWSNhyEHJXqfIWmD2afj5+fcFO0C5rIeqhZZwkUe0JHhn4BAdhmN0RmIjU4w/69iNHrrojR26SsxHe/1BhDSODruTtYDNPBbf6XquAoogYtkEyQ670sSAiXtzBQ5EbyIkK8GsYhcDihRCyTJBO/VYROG+feOdl5hSBfrJzTH+tx/9Ovcwv/Hhc3xv9ixcj3bMkPxVPURtIgzIEefT7TEejihwejtB2SZoTbhPi4sR7j8IcoYuSaXrBdsnQ5YjeRlYwQCFKCiw85LLPAYkqdmscsRLxRC7WkV8Xbt+dMcWhkAPdTZ9EPnF1RhYx8xoLkYNB6LLxEJlBrZrTZQRWkEOTYUBllQ1xB6oFEAVlsg95+dC+IAndzCf9Awny00UmN0Uag0n2ALSRg7labgvXnmsVcEMUr2Le1OQTJNDFMmTEssZoutdhqaOe+eebcSSmv3+HLd86Rq0TS9/GWUNWqtgiOG/26VsJG+jEA7OdoixxdOPghnE86MZV97fufcSD/MF/vmLEIgQKYd42vclb5ZDzg0WjWQJyeBgFypZOi6c9ox+eMHJPe3YQ1jB2cnmrbpP9ZEeQnpcPesuOiAoPWpYNMiO11jsAgqjStlL22LHDmnuKgVSx9cniQ22L0Ll3e4EMPR90pTyfd/WC9QUxJDNVkiGJZ6dUwCD6dN+hBGoddTbYJYJzPNQ9bvU4+TdK+jPwrNgjvVXzP87MCk9jyC1wPQX4Tj++H/5T9GNt/7B38frPt7IxbV8OobMMuxOalxch0n0o6d3UYwahoiSyLCX7oubKdLPiUSkg59v805YkD7ZnuDZJjzoZ9cT+HUC39HaByb0FQDYTOPpao6rKkAun8VHfDw7kyCiBl1lYyzqHNfr8KAq5WBXe0QB2fc1rJEc85Rdq2CBqMPPD/9cAP9B6FNO71VY1rRYlznMl0MmskSZ6SeltMXT9hDNvxHOzf58hDSsOTAZuK9kxzZYle3ZFXYQsUsBeHAI98Fba96kvH94gatqiEvqOzXLnOEtYUQPLVvAxYIXHeHQh6V7wGYOh8fUg4obnFfhJV6sAjFJ0mL+5GaO/+jeHwIABoMGv7cJ8P04qfHh+IwXrh+cv8NWdq2JYKzE8jLcJ2jJ5BopHTbbnN1mlAFEx+ah4wTCQmuzvi/phWfSldcSZuAQHYVrnmUam1fh+FUpg/9uZ/hVaIbd6irpw9MXCeKNgCPi2SivURFpaVA0aNoIjoLnvZBIh2FRaF8NWN4RX4Ukpg6KtMr1YsrIw8c9XC0qyf17QRJpJqIJAUm+0lb2fU4kDlL1xKdo1HASjXMSQlisSrK3lA4Xl+E9HIzrrxBb1lXGMhe5H2ZuJPJBg+NxWJQnSY2fnwXJVBxZlHXCchipHBT1Zm2toMuEpUUafT8wyzQezUKPuzYxzpsRS1nKSiGihbepgqzIJbTQjwxbUVa7BCJ1/GyZ1PO7IFPLC7KRHrv5XltEq94xrI1gtzETBuODijd0s6LC04s524ZKDdgJLeS7iDd00gSeRPd9TRtBnpDOu4rCxq1rA+SWD8MuEyQLen/KexBHDdsVVrXifYKoJVabAifz8B5++PgVbk4C9H55M0KjI4jHJR2LYltDNdLc3y1OgfFTjcvvhvfr8f/wD3njjdlezt5rOm5h4dtxO27H7bgdt+OXPN7IyjW9lFCpBC4LNDOCQ45alIucnWGKrMWqDtVqvU6RdgxRYr92AcJ/8sVjJg3IdQTVCphjynLU/d7FOgnnLbscvVxNeHe7LTM2otCbBO+8cw5LO/TmJu9hpcj3Mg0EIkiXSCKbsFM9/174u8FL4G8eB7h6HNV4kARo81/cfIAfnb/Hu+R/7fEXOEzD7v+qGeJyOuBw9sVdDUkG+fEWqI+7kxGQO9Xnr0Z7VVoSduqdF25XtQLBEP5qO8C9adjtfnZTcMUbvoSCABZ7Qa8IVaB/EHbdUln4ZRaqVAB/tsmhN+EYpycbrC+mgSSBUGl+VgfDjUxq/N5ZMNxvjcJlPsSvHQTI+KTYYkPwemsU6ibmLFmfWfZPXW5z6FWKmCB1sUeaBdAbYvgAgXcyKTE0aIgEJWKHZNxN6RscAAAgAElEQVQwc3OQtqgJbnTbIsDve1mY/qyXXTVHoYIQ0sO+U7NEom5jJgStT0cY3CkxID9hYxXfz8s0B+hZNYUPCAFd/3ihWPrhihAm0LHG9firJyo8OKM02vbwfX1kMZiEivxgsINxEsMknNv5ZoRdZ6xgFMqLQZ9trHqTiu3VIARvE9HncFhC5+GYX11NeiMKEaDa7h36cjFnA4Kb1aAPmUeQ33T+x2KnQnV0N3yfrSLOXk1jjYQQpNPtGH/54hHyg3A+3uMrkiakvTMbaoV4HJ5PIYB2HUN3jl9Dg8OjgCDVOkJJjkxCOcQd3A0Kq696CZnMDRtYdKlAAPDiegopHcTDHR+X6EIOAJaz2ShAve2KJFTjpr+FJiRO5fNQWd6frHC1C+/TlZYwE0KhGgWhJWIiSbVFz7rOjoMU6oCMWa6rgpGIODHIE431Nvy30xKC5ktbKUTbcLzVHYH11xQSSsCZ/EJyNnZZ7xn6v6bjjVxck20fAdcNbwXUTYy2i8+q+4k/Wck+DDwOPUbTWcPFFjFR1dvIwV8mzIxThWGj/lgFiUG3uGqjeDIYj3YcWQUPfPH5SQ+vebDVmVA+LOQdXBc7gKjrdpVCWIH2fpiA3//XX+BPL4OEZ5ZV2I7DS7ZowgTuSEf7IF+gID3A090cQoQ4MgBYKM9IoXBAet0JXCW8BPcbTdzDwBDBZL6DsrfrhJ2Lfv50DB97fLakCy96ZqxXPujtEGBVCKA5oAWusABNVi4SgPSwJDWxHgAxMCd5DfHuAluypxxnDX5wHRbUTGnMKNbspgqSnZ+tQq/tfDvCYk0hB2UMWIHp3bABaHSELekDm2UGtZXQHQvb90xHWUuOqcOshbxIgKMwKUoBthLs4ru6OMPWKOTk3rQepYD07CYkUtMzsg/b0H8EEJUSWiWwdM2X6xQF2e9Nj5coYs0bw5PhikMOLs8miOga2wTQM8uwpHXgSS9eBhi4nfV91W7DIixCD7BLFJw5dLb6InIoV+HeWiuRpy0vfnfHa14wt22K62QAa/vF1naLphbwiQjaYwDrqwFS0vpmeYvaE/w9qnE82uKGovXWl0OMj8KGor7MEU1bGGqZuNxB0jPiWgknQy8aANRQs+TLe4EvFqG/KADIgUF1EZ4LMTS8oUbiIXcK6rpr+fRwtWnD4tH11F3isKRnKy8aTndCJYGTit2+4kGLIZniD9MWzgukncH9Lmfms3cCKnIosvC7i+sRUgqJb/YCFaAlRGZ7rSnAmzGLAD0Xk7C4XpZD7EifrGKHwR+F4918v4IQfViJLWPMTsJ7USQajYmYVd8mim1ULxcjnH5xyHNYdhpj/GX4d35t4GkDI6yHHkiswisKmwAxCevTTj72Go9bWPh23I7bcTtux+34JY83snI1GeBToDrpfTSTsxg29Yg64+vUIyVzBTgwEcTLoPk0BZE4xgqadt1qGcGOLCJyxzHbmIkU5S6FtopzKvVVDtDP2kwx+UJoCZ/1xvpCC94xewGI3AB7OrEOmnVJ8Bntdqof/egxV1L+/SuAPNNfrcaQ0xYpGfz/449+DXpBDlNlIEW9nFNo9tBwZekiIFnT56kAQWv6TB/5wCJF0Ke5KmVigtSCq194ILqWcHFvMNGxJa3yPdMx83BDw9AsAAhypXKxB2R/P1zSO77EyoYdNUHRpxdT5PdD/mdlYvx79/8MAPBPX/4r+OHTtxBFvdtMN1Rh4BYJlq/o5IRH/jxc7+JXVzh4a4eXl4HFmeUtxkWoCs9ezoIhBEJVVSnPn0+fHD4jaxEph7LTyuqYq1g1bmEbxdpT3URf0f52CICe2oBoUCU7ebTCgAhA83yHZZ2jiMMzeL9Y4QfPgiE/tERz0lG8fUBYOvKdl4xERAvJgRAAII9qDskWm4jY0J22EkjvhqrZWgF0nttaQWbga/X+/XNYeiAvV0M4K2GvSUQ9MpyBrHYCdtry57syYqcxeKA47t2zrsuCIXuZGZSEWMTzOrhG7bUcuopueHeL7dkQGbH4h3mDnK7VYpd/JUfW1Yqj5O4eL3FGQQ/xFzm87L2MferZC9k7ATfXEPR3g2HDn1ntUgwIYSgvBjBtr0ONE9O3H6oM1koU5JTkvcCInjNtFDbbHAvyVIYEmg7ROKyQph0yonBvtmIDFO0UG3pcWoU46U03ImXRUpRfkmqsfz18VxxbtJsE1hBcnffH6AFsqxQ/vH4cPiOx7J89KBroxATDfgD1iYHswh3yCC4O92LyuYa0QPEqHEd9IJgUODjdb7i8nuPNXFzzsLiqqncWcpFAvJZsWbcPS+anihcIFwPlY9tPLtuYXXqkEbACsGRpJhTgyMDejTVMq+A7a0TTm+dr18OL0U7CaYGMAtHN0PNC1U4dnOwDxl3mEG/7ZBGfWw4cF1ZAEF53/mKG36FUHFkqCAO01NwwE9tLZQwwegLoq/AyNdOI4a36CNxbkyYstqbo3KfIDgbBElG0ghdKL8EMRtUGBrDr5srU8wZAWMEMVDvXmB5uuVe4KnPYbnOTh+uRnxOsOvEw1DfXVmG56+3+pPI4yUO/a60zHEVhd/D1yQUi6bCswoFcXYyRjQLM9mi+wJNoDn1W8HXFd8PfDbIWV5sBOx7tFnmwwQPl0dL12bVjuNRBEqw6He/YcGD5agzsGS/ACu6xysgjHzUcnAALlpDAg/uGsAKilfDUC6vbmNsNp+dTZIMWXz+65K/oGLuykr04XwByJ1lSk54rDuQ2hYdqBDOO7ToJmzqEZ8xHHqCFPp7XaF+QBGOqUez1HlfbjBm2Unh8/jIw5NNco8hqVDRRt1UcWNIA3JFBJHwwHgDgMxESbgCIo4Yh3LujDRobcfattxKuYy0nDipxzHCe313h3Rk5sdkYP13lvPGpdYSK2ODNL8YMd5uBR3q37E0xqowhdBeHfnVnDgHp0UVhCRXY3t27XdZFn5qVWbTdZiY3YWGlZ77Zpmhpw7td5cA6xi7p7r3or38VAcJDdJvvtpdF5Znmd6Z0AhebIW/a6yrhxS+KDZpdjNNtuHbpoA2JRgDwPIc/oN5+5sM5q35O7Jjzo6zBfFRCF9RmcAIlJSyNigblLuXNX7yS2D0On7l7rw9NWH0tQXYlkV2Fzy8uHMPCTXS7uL6WozkxkLkJzjm0cNncw4heTiEbwQ+wHnt2XBEGiDYSekJvYezh6I9cglCpDknDqBzaG2rMdy9Ft7haQJJLj9sLDPbSQzhg95AmUom+ukgdVGJhacK9d3eB04w0aFYgGbT8faIvfiF3qt8MICyAHdkmWu8lvEigmQm2ZCvOBapj6pssxVdSOmzq+QtkbuAi2oHnGrqOEF3QAmd6j9Tu77v+nc4cHOkDo7XqZQnSI0s0OxLFsUF9h2b+RgK1RDOnfm/huGdZxC2+fljiRRqqpfemV4ipv3NdFfjvv/g3AQCnTw8AKxDTuYvCoaZojpMHa1wVBcq7vX6vq2yvFyEJpNMjDg920D8J39VOXehhAsgOqiDX6ILaX00guvg8K5CcRjADqtgzH5AKAH5gUJdJT4QzkjWkPnFsH4g4ON50k1dTK9T0TKhVhBYp/ooST1RkkZGUolWez9lLSruhjVU7d/wuqEpAj9yeU5SAJBmWVx7qWYbJZ3SIxRAVqcrsieXJfDrYYacSnuxfria8saw3Kdygt00EwAthlBjkmWbpUhxb7DqXISN443G6HmO7zCEIMcHYIBr1iJFtFCMai8UQP6XNzcPZEumgxXZBlV8tIcfh75QD9Jz6gZQi1f2eUA4Rka6ir29Q3eRBNwwgL1pUdC6uCc/x+Dj0f7fbjElYfqfQOYiH1Bqg8zKNMtP3VZuwmZedteOzDIb61cIDwvR+3WZskc4Dl8A6iTVp4o2Ogs9517s0Ajt6rrwRyCYNJoPwd0o6nNLxm5FFTKTOtoq/6u62i1DH4fdGWYOTYgNHu+PaRnzvyzqBu0lRvKANxmPD54lWQqyoyldhnurmXBsLgNwhb9697bnejttxO27H7bgdt+OvjTeycpUDDVkoWBHDtf0OySZ9movcKiSUIOIiDz0i2YAhqU1nSm0F77rVRENFFoZ22u02ZblBxxz2JVWo+R7sIQB0DNRFjKgU8OR45ArH1YvcKrg0QjQn6YYXGM5CD2dXpmgXWW8qL3vvX9n2CS0ut4DvoWVzYEJ4OgLk5JVEdkWQdIH+94qeIcr95w5itF/do8nIMcynbiSiLm3FBbitq2SFA8PHNvPwVEH8+tee4Djd4pN10P7c7HK89SC4Kwnh8eTFITw5DanD3kjgneE1/uD0bTZeeLUbc0B0rSNsd4QieIFoo2C7fuaer/hWpzgZbvGLnwfHrPjhDgeUkqOtgnUCCx0MJqoqgXtMxu5GYnxAXrCbDOIq4ZzNaNsHHag6tADYcCJ1XAF1cHPXu4URfI3lQENckBvXxGB8f81GC0WscbUldq0bhWva+RVnDtuXoX/sEw/X0vOhQluB0YLYwXVVspJwsed+b7RWwAuCvx/uoO9orG24/npm2SfZnWeoCLo2WkFvEiTn/RQjh/2zqSvFX+1F32/X2wSmiTAhg/5JXqMDuHcXA7RrCiQAoOYtq2GEcszIHh9vQ09wTdKQpynqcai4nlgJITzi85ivA0fJjhxiyp897hyjkt67W2+pukttQGvoPlU+2XNXCqzpju0stgoRoQMu9ZDEyDYzE+DirqXeKJhOPuQE/Mgw4lDdUYiIZW6NhNtF7I0M6eE6SDq2/G9ng0Nbp0hwVvSmGtsYTeLgqI/bNAnD+U2S4O2jINs7XY/RJDHwZajeXeIxeRCq3WHcwnjF5jQA2K3JWgkMDbwkDsLTCDHxNTZve9gpqR8aCZsChvyuE+175LB+/SvXN3JxTQsNVUhUXkCRDsx0MG836SWeyTayCX1AgCLDtOQ+mY/6kGCpLKyVcN1L4gFB/TW7jSG0/Eq/tJtdhBHAqncq8hLw9H2dBAXoLPUELME7613G5ufTSYmb7ZR1r7ZwjEuIpnfUkbWCj/bOVwsI0onGVe+KBFAfd+8Z76DMeB2C0s2uN+Pu+ojOC6jYQhPUaQoBRTI96YOkw3cJMVr0favc4s6dYAf1+eIAf7F9AE33Jp1XLFVyXdh1t64vUlTTcO1e7KaIlGNZTZ5o7qdt1jkTheRQw0jPkWrWSoZ+L3Yj5LHmzYF8mSM5DMd1d7CG8wIxXfObVa/V/M33P8U0Dp/3O598OyQM7fZ0zml3DR1UI6BP+lihLsEGEoFAQ9dOlKoPH/cCOKEL2SrcHa+xIw3y6WLCx3/0cIHL02lw6wFQOdG7+TjBN9R1EHO3mUwsFC04URWIZpY8FM3QQnY2epGDriX0pNfispOWBcupzDYChgbt0R4pLd+zdYwcuw45J9Bc0yStPGTksSGN5Hqd88LFrlAAhg/XKBKN3YDu76Lg+L+Nz+FbiewFtSYaQJN9YLugaLhO05s7CLre+ajGW/MgulzUOc4vJvxuZ5lGwmQhCaMjpNSn103EfW0hPXyjoGjD5KSHGdFBewFP10eWCjhsGCofTXccQ+jqCKKMUFMrZzjbsQ6+rmOI1KClhV6uIug44u/m4UPfu+t5G6hwT0D67FrhZhk2ibZSHGp/ONvgpurfnzzRWL1L8pjIMlz/4fQMn22OcL0Nv9uRtgAgihz0KmZS0vahgO32tXdqKHotXJVAWCBZUQtmLjD4OFy3+1/s8Dle73ELC9+O23E7bsftuB2/5PFGVq5tE0GqGL5SXMHJgYbbxoi6ZvseW6190LJ3qC4TyHXETihIHMemul0EUUuAjNfFOoYk5qoXFEnW7XCt6r9DgP08TU4U/26XaQUEBRmryEIvMgxox2yMxO5y0J+Y8L1rjO+ZpmqmGTKz2xjxSrGrC2TvvqMnDmonuRIJYeZ0bnGQyABAsgrZn8mapCEjB+xIaG4k1Kxm0b6XUW+uQIWTGe1VPZ05wdDi8ibAl3Ydf+W4osgxvCWEDz67FGOXvopwqUNs4EV0AJ9ZlgVdXmR9uIATMPQ3s3srvPf4it14/vDTdwHyWR0fX+F8O0RChhnNicGqCtvu67KAtZLlCPss4F+sjnB6HaQarlWIyh6laO5pJteIUsHLPoPTG/mVLa7Yd76KPb739ScAgGfrGZNVDg5XqE3Mea5xbPCI8oWf3MwB4SE7xKORTCDzUZ/Pi20gxRgS/jst4IiQZR9qyNMM0Y5Y6iMXQtgB1FRld5CxsAKOCm9hRfAkRpD2wAlk5HB0MCqxJMOHNNbwXrBvb1PFkMPwfsVpiI4z5Cec5Jqrcp9Y9qjdXAyx1ZKZ6Eg8V+FZ0cKmEqagwPVGMIpgKK6xCykQjeR7czDcYRiHd+uz80NgE8PSc9zK/hn0XoT2DxHuVOQwGIXKzxiFapPCXRNZa6Ih98iEGTlYtWTMEJPvs5IO7937/9s7sx27kis9/xGxpzOfPDmRSbJUVapSqdTqthqS3Q3YDdgw3Jd+O7+CX8AwYBjdhgDLsG9asrtUkko1kSwyyWRmnjzjHmNH+CLWXjvrzjYO3Ka5PkCAsjLzcOeeVsQa/j8kwP/4/Dxct6uQ7thverF7Pa+hlOddc3sbA6RoVjca8YTGYaYl21wCwO1qzCnpqFCoR0HUAgAKpPy35XXMpZRhYnEy2GNHXcBlkSCdhuxM7SJsqgz5Fb1/dG+iUOwTHP1B8XNvf7pDSY1o2ngeAXIaGL728BSF5l9blEfhGFefDIH/ireadzK4ahUucLwoubW/LGOoQQt7L6gmpAyDPEZD6aTBywjV4l5wKAzia5rxjDrBdnoZpI7TgbrUIb1GIwym7juEXfR9UfxkWrFyi6sN3jsLqaqz4RafmQuutW3bjF9eVRFDG48fXVwBCPXHhu7u3989wJzUiSob4a4csD/nJKuwKnqpse3VGJY8oWE8zLKbX/X80m+TIBjerSqCKwelekdNMHDuYnyfiYRXQHVmOe0ZDog6Kbcxv+TGD3aYDkr2sf2L06co2vCy+u3dQ+zTBss3oUu6mXjuhFa1QrSLuZbqNXBPVx/JTfi3xh/UWFcDbMinNcoa7uJ8tjxC9J9naH4RzpdqFRtCL4YFHo9XWCShtjo2Fb7ah1bZy92M76W2jWCHDobq5ipt4TtjbR06MjW9fNVJhYcXocZlncbtZ6f8Uvrwz17iiBx/nuOIZfDyKsF8WGBAnbhZbDkonIz32N8M+Zzoul/QmVyx0piLw3+OaJTLGR1UoECzmqlHS2UAeNWPY9AiTdMstzIebkLlgdL0i8dGAdCoaCb75WaB4Tz8LUnUIq8S5DQH6Z3CgIKF1h6TQYmrq9CFPUgbPJoFn9Bvbo5hu3o01St91o/DdONUxTbF7DcpTElpyfc82oedWpaH28X8HLphy5Ki2zLFYypTpKkFthqqW/ROom5qBn7aYDwvOOhnSYMJPZO3+yE+fHKNZ3R/nsx33/PhvaZUrC0jJKO6Xzh4BUsXXik6z92UQNObWngHeCi0S+p2zhwvHJKXMcoH4fP+6s+/Rmos/sPvfgIgGM0ffxLGkX50dI1fXz7G7pbm2VcRyod0WhsdxokAFJnFpuxNy+PEsofwL599hPJqhJjeP00Ro6UNAbyCNwrLf0lWVq+G8HO6Xy4HaI7paevUx2ju9eZPIhTdlIQq8LbzTgbXo2kOM2qxK1O4X9Nu4zwEvm5H6iaWB8PRaMQkLqFaILvWXH+M9gquu/9cWIV1oWN0tsdsGG6SvEqwXg/hqU4GB67p+tizRdVgVKNtNaYTaq/3im/wi9EaH5zc4osXwf3D1YZnxpTxeHx6hw/H4QH6aHiF/7YK4gGrIsMNOdF0833d6tRox40OQJjF03TTPz69Q34eHqbN353AFP3PuRg8zgOnWP5QITSzdLUlFXne2duFhSo1vKLvNQrJeVgJV+uMRwBi0yLWDiXtbH55+THLs0XG4YOjJaoPqc1/l/FMYXqbBON22pWYUcMyjNHG8Pn+7uUx/uKTbzCK+xfuLA07jy9fnaE9AgeJi/MVRnFfHx1FFbY0I+y8DnKS9Bk/f/wdAOCL5SmWlzPYzuXnLkHUnTsNtAPHtnLGeNbfzZskzFDSCXt2fYTvbkOQsdZwg0tO+rQn4xDkjXLY1KSDbaPQ+JZ1L+O+ju4N2J6sHbehvt8FJ9eLUnjjgcjz4s/sIs6yqDYEtbarKRaGZ6ujnWG3JW+CWxKOacFxb8H15mYKE/fSi6gMCoR7/OcfPUNmLM6HoaFoXWe42pF1mdXI6B4pfQoPzT0NOvIsCWiMQ/4gQbyjnfdFjSMyHt/uBnDG83OurIanZ+FkvGerx93VGCb2aKlWi8T1ut6twu52yH0XbqixvKHCaq1xPMrxsycvwpcuwi3VMGPtMOqkLhuD1hqWR50MK3z7+xDhdKVCXZ7q2uPHG5RFuI/HoxKt09h2QXkf8XPo2qARDQC/+ps/xfgf3XJj2C8efIdXRcgMvcqnOBrnbKPZHjeYjML9v3014bGrAS0YnKaxqKjFmM7xcjuCH/auX0r70CMAwB012HzYp2PcxPIKu524kN0DAA2sPwaicIiIin4EKu2Grt9ipOYqCIIgCAfmndy5ZnGDKNZ489Ux/BPqAh5aQPedm0enW+7QW76c90pCHn0NEUDxwH1PZN8UmldwZdp7cNZVBGzjXuWo6XcUzRQsJF6UBmZrsJqQytOgRUYONp9fP0BRxtyNfPJ4hQW5UqSRxevdBL+5fQQA+Pe//jMo2tFp23c7wytkkwp1N9NtIzychT75VTGAmhV4nzwt3+zHnD5uxp53rqYmuUXaCZpSoaV5FmcVBpcRyvM+9cuqQJ7Gd6g25lOw+49Ke8NsrTys03z+z8db3EVhR1HUMco24pU8AJaaq39YYD7fI6ed/vFkj1dtyEy0Ta8apdcR/v7VBafkWmswPKeaX2JhY995v+NmM8I2CZ/3ZL7C3qZ4ull0fw4aMkComgjOh3z6aj2Cqu91hqeesxs+8fDaI6HaWFNF+PLvn4SfG7ZQiwauS606ja6i/z0pReWx3WfISULxYrHGIKKegFaH4XzaEZlc9VmWopeXgwIwbZANw++VtwNOL7q039kB4Xr7TmYzDZ3o3bXxVf8weON5FwvtoX6Q8/cGkwYlCVv4uwTuuOZ7HpHnTu7PLi/Cjq67VsZjSLuqLGvQNL0MY5Qr2M64ojBY+ZCdGY4q2Is6KJ8BQKtwdznrT9/Qcse6qgw0jbks8wGnPXWu4QaeO5S9vpca73aN9HxVmxi6E2twwPV2hHkaMk8vN1MWE1HKc4rYRA7tqwFAu86bco5o36utuSiIigBBjaozPcibGGUTfU/prRNicY3h8S+feFRNjP3rcE7+dv0JfvQ4lIycVyjqGOdnId2emha3+05Osb/uq+UYvjAsXjKYlFwnHw8qDLMKd7dhx+5zw88MLCl/0fXNFiXLN16/nnGGBD6UdLpOYjvkfxr59b0v3lLeyeC6zIcwSKFPKnaKaPYJVOS4sUIpj4xqWuPzHYpdSKm4KLys7KOQHvGN5uaCJLHYvZwiokYftclQdco2gza8cLp7V917eVUKlpqF9NCiVUDU1Tpzg4JelHVGZsuUCrt5PcV22kuO1dbg9i7UdFSj+WXg741goFEodwnXuPbLAb55Hh4QN2jx5P0bbKpwt++rBMW9IFY87HQMAdj+Rc3jNACgSLKxq2ml/UgQnAJixy9Sv0n6Gpr2eP3dov8c7TloLtMRPjgN6e4ssvjjZ0+4XpOe5TwGMBmV2GyHbP5+MV7jYhxeIL8dPERJzRfJac66pwAwX+zxehPOQZknSCsF9ZSUriqFPV3Db943iKMWhu6ZzXbI989PH11iWYbP15cZvPbcSBSawcLPJYsSR5McG5q5ra3mhjVUBufnK2ypBr5/M+LxlXTY9GbpCKbW3Vzwy9sZPjoPc8AnwxyLnxYs+7iqB/jd5++F44jQzxgmDuNp0ZcEtOc0rclDzawboVJO8fe8dvDGw9MCz8zuNcupiBcwmDZo8oSDbQP0Wr+Jh/fA6DykaqsyZmm+3d0wNGTRHLOqFPL3KO1ZG3gKdvF5jqaIWUVtMC9RXobz3yQWUWrZ1H5xtsd6R8pFdQRsYtb1hgeG1FQUG4cNlR/cUZBy5EXcKuFgNz3KYVuN4gUFlmnDKeI4sRhlNU6zsFB8tjricbm8itFQGtU5DXORoyWZTZ842K4WaXyYayVXn+fLI1SUwvUklxmPOhk1BXcZFp72tEZCiyVbm1DTvtfkVZJG8F0eei66XoLb8p4939AiIU3jysfwGdh1p65ilhYdT0psV30AVCMLWtvAvEkp4JNa1zjn/ol4VLOLksla+OsUlqwU0SqobXfd771T3lIkLSwIgiAIB+ad3LnWtYGJIkT32umH8wJNY2BpFXf3/AhqSibKg4abBuzIo009To7DziCLLKbUDJOZBs+iFqvfBnWfbnQFAHTWQg16cYXoOuGOTm/AqZcu5ctesnca6iasFsuLFjiqYOjrdtRyh2pexTDG4fgorJiXasyD7QDw8x+EZptn6wVuXsxR0Q53cKdRHXcdl0DdGtyRYENQfKK2/B9sUewpVa099GXG3cI2c+w5ix2N3nRpw4Hr9U1V+F9CaTgzqlHR+bZFxE1E89MdWq+46WqaVfjrs98DAP7TzSd479PX2JPDx/JuxLsv6zTSe+LliW5RtuEWP59tcfrgVTiPbYxvlwvelVdNxM0dWMdBuP6DcB4noxJrEjSoqxjNV5N+SWo8GtoJLssRrqlpzMVh19pp9aqB58SBrQ2WmyGaVd+h3SkjqZGFUZ53w6OzPe+SAbDrizIeatLg7Dik81unsaZswziu8f54iYHpSwnRghxV8pizLF1KPDbhvhud5KhoFKprvurKFi4G36vtrIWKHadEs0Hdlz60h+t8Uq/DuNT0j+HregK0n4adams1vBSE0J4AABVpSURBVNXYL3vhiF1F/7/SYbyn6y5Ne23kwilu/3atARrN42bFcgAMeyMAIHiTAqGU0KVmQ4dxy5mbdLHH2XjH5yIjD9XSRnBe4XQSjrlcRHw/auXx3YtjoBPicAqjcdlfpzLBL7/+OBy/8qwYNhxWaOi50NphkDWY/PgNAOCfnn+DL7eh8/yuGqJoYjTUhV3bCBWNiqlp8JVt7mV8FKXwk2GDo0lIxd9tw9hYly2IoxZbGhurmyiMtNEu3VYRBrR7Hw1qTo1HcYvRNOcdblNHLOixz9OQEeue7VZBU1lh+PEKi2HBpYPWaby+m/B165ow23WM6KzsPW4jx0IgnQrc28w7GVx/cHyHaJQiMS1qqpktiyEqE2HXjcAYHxwoAJStxg9/cgkAKJoYo7jGxSikG388eo1nZQimr4sJams4EMcb3VuqlQbptIKmzrtmca+G02i+SeM7g2bW8tyrcuB6ndlquCYLAQshJcfyZk5hezdkJ42//OBb/GL2FADwfnKDf/P8X4Sf88D0wRY5vaiLScz/9nCRo7kvZag8VNR9voanB1oNbJBv68Y9khaOUljRJoiOt12ae9ebBvhxsMTrFgRp3Hyvdtqp2uRlEgwIurrwgz1+tQyOyn91/BVS3eDfPv3H4Zs3KZ+P3X6MeG0w/EVY+CyrIb/YHg43GJjwkn6+OUJVRZjRi2i1HnGwQORhZy3mNAO42g7QdBZ/pYGJ+5Ek/V4OR2m+lzdztEuy7qtDGpVVjHw/z7s4CgFzRdetLmMW6jeRQ2UjbqK1ViMv+oDq6H7M5iUujtY4HYSgcLmb8exgGllcl2PcFCHQ74sEzaq7gTzfL0+OV2haw+eniS32c1ocZBp+3sCQLGZbm/78gDqQu9lZDzY9jxOLiv6W9tjh8b+L8OJfhWsaH5Vsh1YUCbzzMHTd2l3EM5JtrYFa49FDUkraD7hDVSmP/bZflJhxw5ZtSFuuQwIhsHbpTecUB2g18Ggaw4upSseYU0d/YWPuqh8PKjTWQCVhkXIxXuOmCAvSq9UkLBS7mq5TLLU4GFWIjIMn9am6jjCke+l4lKMdUMBsDRaDHI0Lx/9frj7ka9g4jfpe2hYAzj4KZRHnFRLT4jXNVGeDGtUg3J+fPrjCF2+CZGjzZoDopISl61FWMZKkv2Zx3PK438nJlue1u+8DQJ3HqPO4H2fTvq+xxw46buEoQOtJg4jmpPM8RRZb7tfY5ynfZ9q0aHc0Ajevg0xjJ7/aqu6RZ4W0txlJCwuCIAjCgXknd67ngy2SYYVX+ZS7+iLtcJOPUGe0W80jRNTcpI3DtiLRdKfx6dEVPrsNM2mtV/i7y9DtWZUJ3F3CdlC6ATeFuNLAjTSn0NQ+6k0CCo2YOuiasYeuNGryAo13mpudFEKqrtsZm6jl9F7TBJNtQ12l5+kGT+IgTvA3qz9B3pDQemswTBps3lDjU2EQb6kh5WqG1cxx8w0SxyvVssm4q9JELbJFjaLTQt3FMLvvaxp3aW0Pj+So8/j0mI5KbKmZZ7maQq8o3WVCtgCgubrUsWfofp3hN6sfAAC+XpzgLy+ecjoTABvct0MH96TEw2FIl14XY8zo+i6rIaf1ijpGs0+w7FLBTgWtVwQVLTuzrEkM3wsclCYB8oQ9gHE7YLu4dh33Zu9J6ILsOm6TGwNPg/J5FSONLe8Gkqxh8/gmj3GzmffenWXfqcnqXwCqYojX2uGjaVD02aUpLjchpZs3MVqn+q5X3Yvih10mNbGQ0XhC59Eoj/nDcN7yMsGfP36B3Ibz883tMfYNzfPuTBDnWPSiDx3VNu27Ta3Gy39tMZp2ykWa7eLadYJo1gsowCu0tCs/fbxCbFqcDEI69no9RkUdwpF2mNO86r5I4VrNZgfjRcGf71oNdW9+u64jpPeMx6u7DPEsHP/ZfMcKXKejPXdlb/MMdRnxjmu5H2JPClltZYKGcvenewDUgFUZj2LZl3yiec2lp3WRsWJS02rclQNu9LFO42wUMhH7JsG+SrgzOo4tJmk43klcIjMWZ8OQnSnbGN9ch8zZZ88vED0Lxxh7wLweoXlEO+giQt1dG+3hRhoj2lHbVvNutaxj1NS0NJyWYb6azt12PYCm+fXsNMc4q/DGT/k+OJmF4y+bCFUTccZh9WrK92D6YIfBefi5xhpYq7nZ0ra9vy03xr3FvJPB9TKfIVIpRlGNr++CdJ7RDq3TYWQGQUwhpVTS+XTLNT7bany1OcGK6nC/+vInvbhC7OFHfXDStwa26zpPHNrXvYMENFhk3ycebZeyOa6hY8cvmyjvO/ls91KnoKyUZxk1pTyOFzsM43DM1/UYr21IHX2zO+a0T6Qdssj2w/dbw4sBuCCK0b2A27SFpjrWeFLyy8WWMZptyg8CTO90odMWbh/zMLi3mtNPSnusdwN2DYJH7xnqwgIEIIH3WrP3KDaGZQy32xn+4/VP2R8V91r5Bxc7JFGL59swEnO7G+LbJnQg2zri9KveREj2isXcTa7hOrWYUagRd+pBUWq5dqSUDyNN9x58Q4uD+y5E8UqjWXhOm9ePGla9WQwq7MoUJQmvd6IIAKXXAfYN1YOGVZkGscWb/x7EQ5KVwn6S4TfXjwEAjycrrjPvyhRVFbF8INDXSwenOWIKHnevp4juIrRkIBCl9nu/83S94MCyvxqx7683weC+S8c2jWEhATOw7K3rveJ6OhAkLLuxEOUVP1sAsN/G3G0LAOfDLWqyTnr/ZMn15KbVHDC9D3V6TffxfpvxaI8uNNSDEiWVKo6OtzxC0qkSdcdc2YjdXDZ1yoHQUYd0dw52NyOWD9QnFbCOgroTgHhas0B+XcVIznNEVFd0TvG1eTDZcif+ejtEklheEEzHBTRF60g5GOWRd5KBOpQLAGAQGeyswbfLcF/v7oa9cf3GsO/04vctlp8aZG/o7/yg5PtqMKiDkhe9025XY0ypRFLbCDbp/5azxQaXr8LzZBKHdkbdyFbj/dmSFz73iY1DbXsTegCIl+EYczXqDRimFt73hgO+NIjpXWqtBNe3kuvdGMalGKY1zxRWdYwsttwk8sF0ic+vH9DPj/ihq4oYq+WIV1tRoXiHohsV1JnovugMvQFAlRrJSvP4Sj1z8ORy4hqDiKT5oq8zuNQjKfrdb6cH205caCKim9Faw84i3iu0TmGRhZX9PC7w+T7MvH57c8wr8iz+vqSZrhQqMoj2WbC361R7TOLw40evwzF6ha+7QP4yg51b/jtV1nIAcrsYMJ4VmszYwtGD4koDlziuVancsH6tS/pgpIxHdpJDnYavi10KfR1eBKZQsLHiSSZvAH0UzmNkHOKo/Z7Gb1crTI5K1JR9iHYKUaFgWEoPqI/o+FVwszG35JY0TuDOupkUIM4svwzqu6yff3bgAN08qXF0vGUN4l2Zosj7zEcSWRiyCqybiBcfcArQnmu8SnvMaazo4/k1xv8kHMcXXzyC2kes+fpKT7GnxVNZkLn7ikY3spaddd5b3OH5MrwoE5Ls7CTrkmkJTQuA6ajE1dUMJu08Bj3rMsMD2Wkv/eec4jnsuoqx3/S2flAee5qfVIXuZ0EB5Jdj+GE32uV5BwoE5av3RyHrUrkIf/u7nwIABq8M6iktgh5WQK257IlGcfOOG7XQreLP3OUZG7Or3CA6Lfmc78uElYj2VYIxLWaqJkKlPHKqD5p1b0HZNBmUAyJyHrIjy3Z0qtIwD2v+vdGk5Aa1n81f4I+7UBNNI4vr3QiLcT8L3NXJ08gGe0NafDc6wpv1mH9uVyXYkaZ4tIq48awLrACw/NSgGXnYOT33w4Y3DtvVELbVbNU4HpXQ3bOsPOyGFn7HBV5fz3gx2daa7wnvFazTHFy97xcRN6tx2BzcGzusj/tr3X2eWsXwY9tf+7RF+4jq5Pt+0fm2IjVXQRAEQTgw7+TOtRPu3+QZD3U/WGzwz86+xlVFYhFQ2NHgubd9TSKYo/er5HbgecXYTNuwWhuTaoyPkL2hVFITDKi7FKyLAfMqfH6SK057KgfU896cXTcK8bbTNAX8sEVEq8c4sbzS1sojMS3e5KHl/aYY43oTVrvlzQCTh6FGM0pq3OUD7qzUNhiOA8D5YoOL8ZprbS/XM9ZFvV2PYMlzMykUojxGQ8o/rfa97mrXQTzqlXPYjTpt4SvTp8ON565iKPDYj6sNagXu9jw73eDaUCv/bYp4UXL3Z1kkmJEO8zitsK8TriXtkbKucft8xBoG3fVylHFoJh6q251uYyirQscvgNYC6gXV2mYtMG7w6cOwm/98/4hrrjCOTQg6BaVOR9Y5xTv7zT6D1h5l518au3uOORHUtMGAFImc08gptfb57QO0tE2bPNyGOuK9VHA3VqSA0J1MwhS+Mrh4FHaBkXYoaUelRh4uc4ho3OyfP/kKoyj8zmerC2zHGe90hic5MqoVxsZhXyWczlQK/LelWc3CCjAePvJIrinNmvS6xi51QOzZ+9Xf281cv57h2s3xP9QTunCKyyDVohdD8VYDiYOhPgAXaXjahceLEkoBNe1O67uMxWGwaNHkfYe8mTisVmEX6J1CQbv8o9kerdNwX4Vrn94ppKtw/FWpUM96DWjvFOtiu6FGGltE0z7NvaBu5OfFAq/3U/7vCv3mznnF3cLP3izQbpLg2APA5hEyEtyoWxOOl+4FU/RiHy5Gv7NfNNBJi9NF2J3eXE97ve9GodAplpSpGGcVjsjY48urU850lLcDIHFIyOc3WSnsPiHXnZM9Pru8CF39AJC4XuiC/K59l6GKe71y74Dsabj3yzNSt+vKQbni7I+3b/8szjsZXJ1XUF5Ba4+GUiCP31uhchEqqvX85tUjFs/Pkga36/AA2iqCiR2PXagWLDkGDSB2rCKjdF9TdDFgpy2iVT/ykVxp/ox6TqneqUN6lqPckGNI0qIc9ZJsKmvx3ll4WabG4g/PQ+o6Si3s9YCfVh97DmLagdPaWdTAtiNENzRPqwBLKcTpwxKzuMQ1jRysbvtUlMoNC7m3Aw+X+L7x6f5bInaA7+unQKiBAYDehLGcbv5XuX6eUa9jTjm1mwROe9QgG7usZgWf6gQ4ne94XGUxLPDDaVAn+npzgmHcBBNzAO7eoijKFVgLXIXrwtdm1PIDbrYGuulfUi51XEs10xptEaEgpRudtCy8Dq8Q3dLxjhxa7bHa0fmzGoaCWJJYxKbF5Ak1+rQalkYuzCKYhHdNQmXep4xtQs0fHZSWA8LCqmvYaeoIfp2y9CIGjuvwr7YTvHdBIx0PFQZRw+M8o6jCF5tQ010WQ1R5jJgC13RY4uN5aJ6axwWe7xd4vg6GAndXUzRUo1Pa4+jDcG8ubyYhgGnD578kUX92tKFmLR23bGmnSg048Ixw1yAHAO6kr12jNlCxY2MGKGD8MPwtVRkjTixicuG5b+Sttcf8eMPpzLxM8Ogs2PW1JAsIBKOBfJMh6tL+bS9yphxQn1qk1KjnNyksBZJ0VCMvExxPO1MFj5d31PtwecIBJ84s7Ksh9k2vkOW6/oysJcWsbg7eszJSHIUSjKb7qRk1qOld5FPHv3N0usXxKMeu7kdsuqDpvQZ2EcqU1Ki8wo5KRVnacAPT5noMcxfxSJkpDdfGG2vCs9U9940G9vTe8wo+A8uvIvIAlW5M7FCe0yI6cxjPi1A3BjB5uMdsEM7p8vbtr7lKWlgQBEEQDsw7tXP1tI3ZXgN6ADw+W8JQa/96DVT7ISIVVoQ/m32FWRxWUV/vTrDq0rnWoG0UXNx5XxqYbnTijlrwaUencgPbdd5WgFcV6iHtIL+N4SvS8DzywFlobHhwtMW6zDAc9CvftR3xv6XyGt8+DSu94bBGuwvHoZ7GUKj7BhvteTzGTh3snqyioLB/GiG9plySBrrR7a+ej7AcK0zTsANQbgDX7dCrFnZMKdAIYcVKvRi61jxG42tqTKLdl95GcLrrHKadiKXvFQauodV0a2G+ooNPangAlj6z0g28IyUhs0ezs3hzTULs6xgvH4QV+DBrUDURVBvOnW77TuUqaZHQ+WAT5y5NaWvoJTUR7ULHru3MBVTL+rgDv8fpyQ67Ne2yqhyuG+dpFByZryMHVFL3whrTGinCdTozW+ybBL+YPQcAfFccwZKQwOfPH0LdeexU+Ew3bjCYlXSOHeo1pZK3EfzYAuSp+qcPX3FX6xc3Z6i0RUu2fiiAp9+F1N1HZzcY0nEkxuKb1TG+/DZkPh4shnhDXqPNLsXoeI2Wdr+vnqVoKZ2p1AS7KkVdd2LxOVgstjIoDY1r+S2aIkFJzU4Ye2BDTYBbA7togH2f0dDkqOsjD1316lbNqWUlJPUm4s7n0YMNJlmJayrjtNsY9Tb8W8N0jyqP0dSdYH6DAZURYt0iqStktMMzKoUqw+9N4wZNHZ6tq1dzqNLCb8LfaRvFu7Qi83CVhd1RQ+K698nN7xTOnywxbmnETzm8JBEPu9UsvlF5hSiveiMFB9BlD533CqyAZoyFtuEcrP8wxPQbhfKIzs/c4eiHQXksixq+D6LWYbmMsd5SQ1+8RkkNcNjEUB4wM3rHwGKzDfdWW0WhOSx8EbJw9HfnY8CvyCSjrdFs+nPnU8c7Y1UYeOvDOB0QPm8frm+UNbB0386mOVZXI2Abvk6nW1Q7KjHYruu8bwp921D+bT76/01evHiBJ0+e/EMfhiAIgvC/wHfffYfHjx//Qx/G/xHvVHB1zuHy8hKTyQRKvf05fUEQhP8f8d5ju93i4uKCJWPfNt6p4CoIgiAI/zd4O5cEgiAIgvD/MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MBJcBUEQBOHASHAVBEEQhAMjwVUQBEEQDowEV0EQBEE4MP8T3ebx+jgkHW8AAAAASUVORK5CYII=\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>(((643065.0, 3127275.0, 650745.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-6953397.249648973, 756126.3533735601, -6834789.194217825, 874734.4088047068)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>(((650745.0, 3127275.0, 658425.0, 3134955.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-7783653.637667, 281694.1316489733, -7665045.582235852, 400302.18708011997)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>(((489465.0, 3119595.0, 497145.0, 3127275.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-7783653.637667, 163086.07621782675, -7665045.582235852, 281694.1316489733)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>(((512505.0, 3119595.0, 520185.0, 3127275.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[    0     0     0 ... 34835 35040 34929]\\n [    0     0     0 ... 34438 33838 34300]\\n [    0     0     0 ... 34169 34047 35019]\\n ...\\n [    0     0     0 ... 28880 28858 28860]\\n [    0     0     0 ... 28868 28882 28825]\\n [    0     0     0 ... 28892 28830 28751]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(((566265.0, 3088875.0, 573945.0, 3096555.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[25072 24691 24206 ... 14174 14055 13722]\\n [24987 24851 24668 ... 13488 13960 14160]\\n [25287 25327 25125 ... 14932 14992 15673]\\n ...\\n [18261 18773 18356 ... 28507 29489 29209]\\n [18898 19475 18998 ... 27742 28559 28831]\\n [20193 20706 19194 ... 27191 27340 27728]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>(((650745.0, 3081195.0, 658425.0, 3088875.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[37009 36664 36718 ... 30211 31565 32545]\\n [37220 36770 36653 ... 26997 30338 32433]\\n [37509 36985 36639 ... 26815 29523 31915]\\n ...\\n [21496 22198 22629 ... 50496 49952 49069]\\n [21319 21920 22650 ... 44582 47035 48996]\\n [21015 21923 23135 ... 40391 41619 46114]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(((481785.0, 3073515.0, 489465.0, 3081195.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(((643065.0, 3073515.0, 650745.0, 3081195.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[41963 41787 41288 ... 19905 19831 20434]\\n [41612 41503 41209 ... 20569 20559 20786]\\n [41520 41262 41159 ... 20901 21337 21310]\\n ...\\n [35591 34598 33741 ... 28094 28141 28347]\\n [35111 34607 34556 ... 28344 28428 28404]\\n [35020 35115 35496 ... 28928 28943 28659]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>(((550905.0, 3065835.0, 558585.0, 3073515.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[29634 29074 27867 ... 34042 35793 36101]\\n [28292 28290 27806 ... 35532 36229 36043]\\n [26764 27215 27229 ... 35614 36731 37212]\\n ...\\n [12799 11281 11560 ... 22493 22390 24250]\\n [12380 12208 12146 ... 21462 24578 26095]\\n [11854 12212 12253 ... 21606 23951 26103]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>(((581625.0, 3065835.0, 589305.0, 3073515.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[29596 29158 29310 ... 31524 32105 32622]\\n [29449 30052 29751 ... 31945 32458 32848]\\n [30147 30365 29524 ... 31432 32140 32678]\\n ...\\n [34789 34764 34617 ... 38622 38460 38426]\\n [34196 34286 34005 ... 38503 38249 38346]\\n [33628 33690 33329 ... 38487 38527 38578]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>(((527865.0, 3050475.0, 535545.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23166 23360 22026 ... 17733 17719 17761]\\n [20816 22672 20776 ... 18288 17669 17715]\\n [21089 20806 19823 ... 17316 17232 16957]\\n ...\\n [13103 12781 12562 ... 18723 19452 19702]\\n [12532 12190 12241 ... 18387 19321 19802]\\n [11338 11578 11682 ... 18910 19432 20186]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>(((550905.0, 3050475.0, 558585.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16526 16306 15638 ... 15329 14712 14664]\\n [16903 16944 16832 ... 15041 15341 16860]\\n [15429 16114 16805 ... 16944 17966 20436]\\n ...\\n [16725 15873 14367 ... 14874 16061 16183]\\n [17029 15729 14388 ... 14999 15990 16224]\\n [17588 16339 15071 ... 16020 16677 16706]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>(((566265.0, 3050475.0, 573945.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[33341 32022 31451 ... 26398 27977 28861]\\n [32632 31144 30286 ... 25217 26510 27947]\\n [32467 31358 30482 ... 24477 24832 26911]\\n ...\\n [24057 21928 19082 ... 27809 28046 29053]\\n [24680 23947 22570 ... 27101 28215 29453]\\n [25789 24841 23734 ... 26425 27868 29834]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>(((627705.0, 3050475.0, 635385.0, 3058155.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[38204 38705 39130 ... 26587 26447 26839]\\n [38392 38655 38823 ... 26448 25693 25283]\\n [38544 38747 38786 ... 26832 24899 23747]\\n ...\\n [26022 26438 26731 ... 25005 25354 25707]\\n [26400 26930 27321 ... 24669 25009 25260]\\n [27408 27625 28089 ... 25586 24755 24192]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>(((589305.0, 3042795.0, 596985.0, 3050475.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[32575 32134 30849 ... 30972 31822 32286]\\n [31885 31061 30993 ... 30657 32024 32868]\\n [31407 31448 31981 ... 29817 30815 32045]\\n ...\\n [35508 35433 35177 ... 33603 33305 33672]\\n [35484 35003 34429 ... 33247 33058 33417]\\n [35343 34631 34415 ... 33020 33076 33331]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>(((512505.0, 3035115.0, 520185.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[13629 13641 13654 ... 15836 15926 15930]\\n [13589 13639 13708 ... 15108 15343 15375]\\n [13691 13537 13669 ... 14280 14724 14503]\\n ...\\n [24557 24697 24844 ... 18465 18186 18502]\\n [25020 25233 25155 ... 17952 17631 18332]\\n [25061 25075 25074 ... 18569 19170 19982]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>(((573945.0, 3035115.0, 581625.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24336 24964 25037 ... 33327 33051 33358]\\n [22415 23612 24149 ... 33521 33440 33479]\\n [21209 23243 24114 ... 33880 33893 33683]\\n ...\\n [33638 32723 31054 ... 33057 32382 30685]\\n [32698 31960 31017 ... 32298 31310 30263]\\n [31792 31185 30859 ... 31983 31021 30331]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>(((612345.0, 3035115.0, 620025.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[40280 41293 42603 ... 32490 31581 31512]\\n [41099 41587 42092 ... 32941 32131 32091]\\n [41203 41661 42110 ... 33382 32299 32086]\\n ...\\n [24846 25986 26169 ... 27485 27374 27173]\\n [25559 27182 27638 ... 27592 27364 27008]\\n [26692 28129 28436 ... 27810 27397 27121]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(((635385.0, 3035115.0, 643065.0, 3042795.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24378 24438 23453 ... 29819 31058 33367]\\n [24208 23636 23144 ... 31847 33205 34472]\\n [23688 22993 22775 ... 33441 34319 34732]\\n ...\\n [33234 32463 33299 ... 11817 11144 10977]\\n [32762 33111 33942 ... 12565 11693 11084]\\n [32264 33099 33582 ... 12812 12458 11561]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(((512505.0, 3027435.0, 520185.0, 3035115.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[24858 24940 24606 ... 19891 21038 20948]\\n [25196 25118 24577 ... 20240 21793 21361]\\n [25752 25520 25170 ... 19794 20813 20261]\\n ...\\n [18146 17475 16777 ... 13993 13820 13694]\\n [17344 17460 17196 ... 13948 13775 13770]\\n [17364 17248 16749 ... 14023 13933 13923]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(((481785.0, 3019755.0, 489465.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(((543225.0, 3019755.0, 550905.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[26791 27865 28601 ... 22455 23951 25011]\\n [26513 27266 28239 ... 24150 25929 26738]\\n [25921 26332 27075 ... 24996 26887 28051]\\n ...\\n [28977 28830 28319 ... 27381 27470 26778]\\n [26999 26694 27336 ... 27126 26994 26973]\\n [25323 26363 27737 ... 27885 27729 29269]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>(((620025.0, 3019755.0, 627705.0, 3027435.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[27887 29274 29420 ... 28594 28067 26775]\\n [27511 27157 27926 ... 28321 27372 26632]\\n [26124 26258 28093 ... 28248 27692 27230]\\n ...\\n [25102 23556 20447 ... 13549 12968 12387]\\n [25162 22693 18945 ... 12300 11879 12002]\\n [24361 20969 19145 ... 11435 11760 12192]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>(((527865.0, 3012075.0, 535545.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23505 21881 21270 ... 28139 28527 28544]\\n [22847 23503 24292 ... 28372 28332 28020]\\n [25271 26722 26837 ... 28584 28075 27104]\\n ...\\n [26081 26353 25850 ... 23742 24155 24609]\\n [26054 25953 25177 ... 24958 25520 24955]\\n [25083 24297 23914 ... 25547 24948 24104]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>(((558585.0, 3012075.0, 566265.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[26250 26128 25784 ... 31877 31438 31180]\\n [26943 27098 26642 ... 31622 31352 30721]\\n [27795 27798 26474 ... 31461 30972 29782]\\n ...\\n [30301 30720 30815 ... 33041 32485 32388]\\n [30441 30736 30864 ... 32788 32812 33148]\\n [30632 30872 30957 ... 33857 34225 34098]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>(((604665.0, 3012075.0, 612345.0, 3019755.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16956 16754 16345 ... 21434 24875 28081]\\n [14837 14482 14216 ... 20008 21061 26474]\\n [12628 12588 12772 ... 17531 17956 24291]\\n ...\\n [12289 11931 12192 ... 16177 15880 15761]\\n [11916 12498 12292 ... 16069 15753 15868]\\n [12397 12705 11561 ... 16069 15868 15818]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>(((566265.0, 3004395.0, 573945.0, 3012075.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[33574 33483 33158 ... 24957 24142 22776]\\n [34258 34099 34122 ... 26301 25233 24223]\\n [34544 34011 34466 ... 26305 25216 24065]\\n ...\\n [19789 19914 18905 ...  9703  9691  9696]\\n [18684 18597 18354 ...  9718  9699  9705]\\n [18358 16960 17366 ...  9672  9694  9752]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>(((689145.0, 2996715.0, 696825.0, 3004395.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>(((497145.0, 2981355.0, 504825.0, 2989035.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[12994 12892 13184 ... 13493 13857 13523]\\n [12845 13003 13482 ... 13903 14242 13732]\\n [13498 13647 13934 ... 14212 14245 13795]\\n ...\\n [15265 14704 14405 ... 14367 14409 14607]\\n [15671 15221 14812 ... 14792 14543 14227]\\n [15696 15458 15443 ... 15313 14650 13765]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>(((512505.0, 2973675.0, 520185.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[16204 16261 15205 ... 20715 21030 21177]\\n [16838 16326 15415 ... 20730 21139 21452]\\n [17289 16132 15167 ... 20711 21213 21590]\\n ...\\n [29109 30660 32185 ... 17231 14617 12620]\\n [30143 31752 32885 ... 15871 14035 12636]\\n [30663 32379 33617 ... 15469 14269 12847]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>(((535545.0, 2973675.0, 543225.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[23036 24603 24745 ... 23201 23318 21658]\\n [19719 22610 23585 ... 25232 24894 23378]\\n [16386 18718 20205 ... 27731 27839 26234]\\n ...\\n [16662 17750 19966 ... 13344 16500 20875]\\n [20821 19012 19282 ... 12865 16932 20117]\\n [21951 20509 20907 ... 14476 18089 18942]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>(((566265.0, 2973675.0, 573945.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[11473 11996 11959 ...  9030  9031  9030]\\n [11654 11890 12173 ...  9014  8991  8969]\\n [11921 12394 13089 ...  8959  8989  8919]\\n ...\\n [ 9002  8979  8991 ...  9451  9251  9239]\\n [ 8960  8976  9001 ...  9237  9314  9308]\\n [ 9123  9089  8963 ...  9778  9518  9274]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>(((604665.0, 2973675.0, 612345.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8788 8805 8800 ... 8783 8788 8792]\\n [8792 8790 8793 ... 8789 8796 8785]\\n [8794 8804 8794 ... 8792 8802 8811]\\n ...\\n [8792 8770 8771 ... 8785 8769 8769]\\n [8785 8769 8787 ... 8769 8772 8777]\\n [8780 8763 8790 ... 8762 8770 8774]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>(((612345.0, 2973675.0, 620025.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8796 8790 8797 ... 8802 8807 8807]\\n [8801 8806 8787 ... 8811 8818 8850]\\n [8795 8801 8806 ... 8797 8814 8815]\\n ...\\n [8771 8769 8787 ... 8802 8809 8816]\\n [8774 8777 8781 ... 8798 8819 8810]\\n [8777 8775 8766 ... 8782 8805 8786]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>(((658425.0, 2973675.0, 666105.0, 2981355.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8962 8957 8922 ... 9001 8995 9002]\\n [8938 8941 8936 ... 9001 9001 9012]\\n [8949 8939 8944 ... 8991 8986 9015]\\n ...\\n [8951 8962 8946 ... 9000 9019 9000]\\n [8960 8941 8930 ... 8996 8977 8971]\\n [8945 8924 8964 ... 8993 8976 9017]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>(((543225.0, 2958315.0, 550905.0, 2965995.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[12803 12598 11798 ...  9106  9120  9118]\\n [14612 13071 11067 ...  9091  9080  9075]\\n [13822 11982 11517 ...  9057  9078  9065]\\n ...\\n [13038 13592 14077 ...  9273  9263  9250]\\n [12724 13054 13805 ...  9275  9309  9292]\\n [12043 11960 13863 ...  9384  9389  9388]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>(((704505.0, 2958315.0, 709815.0, 2965995.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[177, 256], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>(((527865.0, 2950635.0, 535545.0, 2958315.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[20582 20739 20425 ...  9685  9588  9523]\\n [21068 20891 20466 ...  9582  9398  9422]\\n [20643 20751 20623 ...  9626  9413  9405]\\n ...\\n [10269  9976  9906 ...  9966  9957  9938]\\n [10262 10023  9695 ...  9949  9944  9936]\\n [10215  9936  9576 ...  9903  9921  9945]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>(((620025.0, 2942955.0, 627705.0, 2950635.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 256], cell_type=CellType(uint16raw, None), cells=[[8818 8819 8814 ... 8844 8815 8810]\\n [8804 8817 8821 ... 8797 8785 8783]\\n [8805 8804 8807 ... 8809 8821 8781]\\n ...\\n [8817 8826 8844 ... 8815 8824 8852]\\n [8841 8822 8792 ... 8804 8801 8821]\\n [8804 8811 8807 ... 8794 8796 8823]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>(((481785.0, 2917785.0, 489465.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>(((520185.0, 2917785.0, 527865.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>(((658425.0, 2917785.0, 666105.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[   0    0    0 ... 9231 9227 9247]\\n [   0    0    0 ... 9245 9242 9246]\\n [   0    0    0 ... 9262 9252 9265]\\n ...\\n [   0    0    0 ...    0    0    0]\\n [   0    0    0 ...    0    0    0]\\n [   0    0    0 ...    0    0    0]]))</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>(((673785.0, 2917785.0, 681465.0, 2919915.0), (+proj=utm +zone=17 +datum=WGS84 +units=m +no_defs ,)), Tile(dimensions=[256, 71], cell_type=CellType(uint16raw, None), cells=[[0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n ...\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]\\n [0 0 0 ... 0 0 0]]))</td>\n",
+       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
+       "      <td>(-6834789.194217826, 163086.07621782675, -6716181.138786679, 281694.1316489733)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                 tile\n",
-       "0   (((504825.0, 3142635.0, 512505.0, 3150315.0), ...\n",
-       "1   (((497145.0, 3127275.0, 504825.0, 3134955.0), ...\n",
-       "2   (((643065.0, 3127275.0, 650745.0, 3134955.0), ...\n",
-       "3   (((650745.0, 3127275.0, 658425.0, 3134955.0), ...\n",
-       "4   (((489465.0, 3119595.0, 497145.0, 3127275.0), ...\n",
-       "5   (((512505.0, 3119595.0, 520185.0, 3127275.0), ...\n",
-       "6   (((566265.0, 3088875.0, 573945.0, 3096555.0), ...\n",
-       "7   (((650745.0, 3081195.0, 658425.0, 3088875.0), ...\n",
-       "8   (((481785.0, 3073515.0, 489465.0, 3081195.0), ...\n",
-       "9   (((643065.0, 3073515.0, 650745.0, 3081195.0), ...\n",
-       "10  (((550905.0, 3065835.0, 558585.0, 3073515.0), ...\n",
-       "11  (((581625.0, 3065835.0, 589305.0, 3073515.0), ...\n",
-       "12  (((527865.0, 3050475.0, 535545.0, 3058155.0), ...\n",
-       "13  (((550905.0, 3050475.0, 558585.0, 3058155.0), ...\n",
-       "14  (((566265.0, 3050475.0, 573945.0, 3058155.0), ...\n",
-       "15  (((627705.0, 3050475.0, 635385.0, 3058155.0), ...\n",
-       "16  (((589305.0, 3042795.0, 596985.0, 3050475.0), ...\n",
-       "17  (((512505.0, 3035115.0, 520185.0, 3042795.0), ...\n",
-       "18  (((573945.0, 3035115.0, 581625.0, 3042795.0), ...\n",
-       "19  (((612345.0, 3035115.0, 620025.0, 3042795.0), ...\n",
-       "20  (((635385.0, 3035115.0, 643065.0, 3042795.0), ...\n",
-       "21  (((512505.0, 3027435.0, 520185.0, 3035115.0), ...\n",
-       "22  (((481785.0, 3019755.0, 489465.0, 3027435.0), ...\n",
-       "23  (((543225.0, 3019755.0, 550905.0, 3027435.0), ...\n",
-       "24  (((620025.0, 3019755.0, 627705.0, 3027435.0), ...\n",
-       "25  (((527865.0, 3012075.0, 535545.0, 3019755.0), ...\n",
-       "26  (((558585.0, 3012075.0, 566265.0, 3019755.0), ...\n",
-       "27  (((604665.0, 3012075.0, 612345.0, 3019755.0), ...\n",
-       "28  (((566265.0, 3004395.0, 573945.0, 3012075.0), ...\n",
-       "29  (((689145.0, 2996715.0, 696825.0, 3004395.0), ...\n",
-       "30  (((497145.0, 2981355.0, 504825.0, 2989035.0), ...\n",
-       "31  (((512505.0, 2973675.0, 520185.0, 2981355.0), ...\n",
-       "32  (((535545.0, 2973675.0, 543225.0, 2981355.0), ...\n",
-       "33  (((566265.0, 2973675.0, 573945.0, 2981355.0), ...\n",
-       "34  (((604665.0, 2973675.0, 612345.0, 2981355.0), ...\n",
-       "35  (((612345.0, 2973675.0, 620025.0, 2981355.0), ...\n",
-       "36  (((658425.0, 2973675.0, 666105.0, 2981355.0), ...\n",
-       "37  (((543225.0, 2958315.0, 550905.0, 2965995.0), ...\n",
-       "38  (((704505.0, 2958315.0, 709815.0, 2965995.0), ...\n",
-       "39  (((527865.0, 2950635.0, 535545.0, 2958315.0), ...\n",
-       "40  (((620025.0, 2942955.0, 627705.0, 2950635.0), ...\n",
-       "41  (((481785.0, 2917785.0, 489465.0, 2919915.0), ...\n",
-       "42  (((520185.0, 2917785.0, 527865.0, 2919915.0), ...\n",
-       "43  (((658425.0, 2917785.0, 666105.0, 2919915.0), ...\n",
-       "44  (((673785.0, 2917785.0, 681465.0, 2919915.0), ..."
+       "                                    proj_raster_path  \\\n",
+       "0  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "1  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "2  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "3  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "4  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "5  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
+       "\n",
+       "                                              extent  \\\n",
+       "0  (-7427829.47137356, 993342.4642358534, -730922...   \n",
+       "1  (-7309221.415942414, 993342.4642358534, -71906...   \n",
+       "2  (-6953397.249648973, 756126.3533735601, -68347...   \n",
+       "3  (-7783653.637667, 281694.1316489733, -7665045....   \n",
+       "4  (-7783653.637667, 163086.07621782675, -7665045...   \n",
+       "5  (-6834789.194217826, 163086.07621782675, -6716...   \n",
+       "\n",
+       "                                                tile  \n",
+       "0  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "1  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
+       "2  Tile(dimensions=[257, 257], cell_type=CellType...  \n",
+       "3  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "4  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "5  Tile(dimensions=[256, 256], cell_type=CellType...  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#Look at a sample\n",
-    "pandas_df = df.select(df.tile).sample(0.05).toPandas()\n",
+    "pandas_df = df.select(\n",
+    "    df.proj_raster_path,\n",
+    "    rf_extent(df.proj_raster).alias('extent'),\n",
+    "    rf_tile(df.proj_raster).alias('tile'),\n",
+    ").sample(0.05).toPandas()\n",
     "pandas_df"
    ]
   },

From 7da526746b49a0cb8183d50edb1780e0b6fc6522 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 28 Jun 2019 15:08:24 -0400
Subject: [PATCH 200/380] Testing CI build with deletion of Python sources
 removed.

---
 project/PythonBuildPlugin.scala | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 71d66b076..32c01538c 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -41,21 +41,20 @@ object PythonBuildPlugin extends AutoPlugin {
   }
   import autoImport._
 
-  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File], deleteFirst: Boolean) = Def.task {
+  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File]) = Def.task {
     val s = streams.value
     val src = srcDir.value
     val dest = destDir.value
-    if (deleteFirst)
-      IO.delete(dest)
-    dest.mkdirs()
+    if (!dest.exists())
+      dest.mkdirs()
     s.log.info(s"Copying '$src' to '$dest'")
     IO.copyDirectory(src, dest)
     dest
   }
 
   val copyPySources = Def.sequential(
-    copySources(Compile / pythonSource, Python / target, true),
-    copySources(Test / pythonSource, Python / test / target, false)
+    copySources(Compile / pythonSource, Python / target),
+    copySources(Test / pythonSource, Python / test / target)
   )
   
   val pyWhlJar = Def.task {
@@ -122,6 +121,7 @@ object PythonBuildPlugin extends AutoPlugin {
     inConfig(Python)(Seq(
       target := (Compile / target).value / "python",
       test / target := (Compile / target).value / "python" / "tests",
+      includeFilter := "*",
       packageBin := Def.sequential(
         Compile / packageBin,
         pyWhl,

From 6a7bc0cf9dcde2439a80695dd05a255c8244a7be Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 28 Jun 2019 15:55:18 -0400
Subject: [PATCH 201/380] "Modernized" handling of Python sources in build
 plugin.

---
 project/PythonBuildPlugin.scala | 33 +++++++++++++++++----------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 32c01538c..d3dd3ea1b 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -24,6 +24,7 @@ import sbt.Keys.{`package`, _}
 import sbt._
 import complete.DefaultParsers._
 import sbt.Tests.Summary
+import sbt.util.CacheStore
 
 import scala.sys.process.Process
 import sbtassembly.AssemblyPlugin.autoImport.assembly
@@ -41,22 +42,22 @@ object PythonBuildPlugin extends AutoPlugin {
   }
   import autoImport._
 
-  def copySources(srcDir: SettingKey[File], destDir: SettingKey[File]) = Def.task {
-    val s = streams.value
-    val src = srcDir.value
-    val dest = destDir.value
-    if (!dest.exists())
-      dest.mkdirs()
-    s.log.info(s"Copying '$src' to '$dest'")
-    IO.copyDirectory(src, dest)
-    dest
+  val copyPySources = Def.task {
+    val log = streams.value.log
+
+    val destDir = (Python / target).value
+    val cacheDir = streams.value.cacheDirectory
+    val maps =  (Python / mappings).value
+
+    val resolved = maps map { case (file, d) => (file, destDir / d) }
+
+    log.info(s"Synchronizing ${maps.size} files to '${destDir}'")
+    resolved.foreach(println)
+
+    Sync.sync(CacheStore(cacheDir / "python"))(resolved)
+    destDir
   }
 
-  val copyPySources = Def.sequential(
-    copySources(Compile / pythonSource, Python / target),
-    copySources(Test / pythonSource, Python / test / target)
-  )
-  
   val pyWhlJar = Def.task {
     val log = streams.value.log
     val buildDir = (Python / target).value
@@ -94,8 +95,7 @@ object PythonBuildPlugin extends AutoPlugin {
     pythonCommand := "python",
     pySetup := {
       val s = streams.value
-      val _ = copyPySources.value
-      val wd = (Python / target).value
+      val wd = copyPySources.value
       val args = spaceDelimited("<args>").parsed
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
       val ver = version.value
@@ -122,6 +122,7 @@ object PythonBuildPlugin extends AutoPlugin {
       target := (Compile / target).value / "python",
       test / target := (Compile / target).value / "python" / "tests",
       includeFilter := "*",
+      mappings := Path.selectSubpaths((Compile / pythonSource).value, (Python / includeFilter).value).toSeq,
       packageBin := Def.sequential(
         Compile / packageBin,
         pyWhl,

From 27b47441c6ad7ace370b37b62039f0c1e40554e9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 29 Jun 2019 11:04:18 -0400
Subject: [PATCH 202/380] Further refinement of python source file management.

---
 project/PythonBuildPlugin.scala | 16 +++++-----------
 pyrasterframes/build.sbt        |  2 --
 2 files changed, 5 insertions(+), 13 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index d3dd3ea1b..859fdec1f 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -19,7 +19,6 @@
  *
  */
 
-import sbt.KeyRanks.ASetting
 import sbt.Keys.{`package`, _}
 import sbt._
 import complete.DefaultParsers._
@@ -35,7 +34,6 @@ object PythonBuildPlugin extends AutoPlugin {
 
   object autoImport {
     val Python = config("python")
-    val pythonSource = settingKey[File]("Default Python source directory.").withRank(ASetting)
     val pythonCommand = settingKey[String]("Python command. Defaults to 'python'")
     val pySetup = inputKey[Int]("Run 'python setup.py <args>'. Returns exit code.")
     val pyWhl = taskKey[File]("Builds the Python wheel distribution")
@@ -44,16 +42,11 @@ object PythonBuildPlugin extends AutoPlugin {
 
   val copyPySources = Def.task {
     val log = streams.value.log
-
     val destDir = (Python / target).value
     val cacheDir = streams.value.cacheDirectory
     val maps =  (Python / mappings).value
-
     val resolved = maps map { case (file, d) => (file, destDir / d) }
-
     log.info(s"Synchronizing ${maps.size} files to '${destDir}'")
-    resolved.foreach(println)
-
     Sync.sync(CacheStore(cacheDir / "python"))(resolved)
     destDir
   }
@@ -103,8 +96,6 @@ object PythonBuildPlugin extends AutoPlugin {
       Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
     },
     pyWhl := pyWhlImp.value,
-    Compile / pythonSource := (Compile / sourceDirectory).value / "python",
-    Test / pythonSource := (Test / sourceDirectory).value / "python",
     Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value,
     Test / testQuick := (Python / testQuick).evaluated,
     Test / executeTests := {
@@ -119,10 +110,13 @@ object PythonBuildPlugin extends AutoPlugin {
     }
   ) ++
     inConfig(Python)(Seq(
+      sourceDirectory := (Compile / sourceDirectory).value / "python",
+      sourceDirectories := Seq((Python / sourceDirectory).value),
       target := (Compile / target).value / "python",
-      test / target := (Compile / target).value / "python" / "tests",
       includeFilter := "*",
-      mappings := Path.selectSubpaths((Compile / pythonSource).value, (Python / includeFilter).value).toSeq,
+      excludeFilter := HiddenFileFilter || "__pycache__" || "*.egg-info",
+      sources := Defaults.collectFiles(Python / sourceDirectories, Python / includeFilter, Python / excludeFilter).value,
+      mappings := Defaults.relativeMappings(Python / sources, Python / sourceDirectories).value,
       packageBin := Def.sequential(
         Compile / packageBin,
         pyWhl,
diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 896454e36..0b6575e7e 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -2,8 +2,6 @@ addCommandAlias("pyDocs", "pyrasterframes/doc")
 addCommandAlias("pyTest", "pyrasterframes/test")
 addCommandAlias("pyBuild", "pyrasterframes/package")
 
-Test / pythonSource := (Compile / sourceDirectory).value / "tests"
-
 exportJars := true
 Python / doc / sourceDirectory := (Python / target).value / "docs"
 Python / doc / target := (Python / target).value / "docs"

From 493e10d0e8bd055a12f062b8ec0c59d02b0ff024 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 29 Jun 2019 14:41:16 -0400
Subject: [PATCH 203/380] Replaced all "_rf_unique_functions" with explicit
 forms.

---
 .../python/pyrasterframes/rasterfunctions.py  | 198 ++++++------------
 .../rasterframes/py/PyRFContext.scala         |   2 +-
 2 files changed, 61 insertions(+), 139 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 4876247f3..cb589ddc7 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -36,162 +36,92 @@ def _context_call(name, *args):
     return f(*args)
 
 
-def _celltype(cellTypeStr):
+def _parse_cell_type(cell_type_str):
     """ Convert the string cell type to the expected CellType object."""
-    return _context_call('rf_cell_type', cellTypeStr)
+    return _context_call('_parse_cell_type', cell_type_str)
 
 
-def _create_assembleTile():
-    """ Create a function mapping to the Scala implementation."""
-    def _(colIndex, rowIndex, cellData, numCols, numRows, cellType):
-        jfcn = RFContext.active().lookup('rf_assemble_tile')
-        return Column(jfcn(_to_java_column(colIndex), _to_java_column(rowIndex), _to_java_column(cellData), numCols, numRows, _celltype(cellType)))
-    _.__name__ = 'rf_assemble_tile'
-    _.__doc__ = "Create a Tile from  a column of cell data with location indices"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_cell_types():
+    """Return a list of standard cell types"""
+    return [CellType(str(ct)) for ct in _context_call('rf_cell_types')]
 
 
-def _create_arrayToTile():
-    """ Create a function mapping to the Scala implementation."""
-    def _(arrayCol, numCols, numRows):
-        jfcn = RFContext.active().lookup('rf_array_to_tile')
-        return Column(jfcn(_to_java_column(arrayCol), numCols, numRows))
-    _.__name__ = 'rf_array_to_tile'
-    _.__doc__ = "Convert array in `arrayCol` into a Tile of dimensions `numCols` and `numRows'"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_assemble_tile(col_index, row_index, cell_data_col, num_cols, num_rows, cell_type_str):
+    """Create a Tile from  a column of cell data with location indices"""
+    jfcn = RFContext.active().lookup('rf_assemble_tile')
+    return Column(jfcn(_to_java_column(col_index), _to_java_column(row_index), _to_java_column(cell_data_col), num_cols, num_rows, _parse_cell_type(cell_type_str)))
 
 
-def _create_convertCellType():
-    """ Create a function mapping to the Scala implementation."""
-    def _(tileCol, cellType):
-        jfcn = RFContext.active().lookup('rf_convert_cell_type')
-        return Column(jfcn(_to_java_column(tileCol), _celltype(cellType)))
-    _.__name__ = 'rf_convert_cell_type'
-    _.__doc__ = "Convert the numeric type of the Tiles in `tileCol`"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_array_to_tile(array_col, num_cols, num_rows):
+    """Convert array in `array_col` into a Tile of dimensions `num_cols` and `num_rows'"""
+    jfcn = RFContext.active().lookup('rf_array_to_tile')
+    return Column(jfcn(_to_java_column(array_col), num_cols, num_rows))
 
 
-def _create_makeConstantTile():
-    """ Create a function mapping to the Scala implementation."""
-    def _(value, cols, rows, cellType):
-        jfcn = RFContext.active().lookup('rf_make_constant_tile')
-        return Column(jfcn(value, cols, rows, cellType))
-    _.__name__ = 'rf_make_constant_tile'
-    _.__doc__ = "Constructor for constant tile column"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_convert_cell_type(tile_col, cell_type):
+    """Convert the numeric type of the Tiles in `tileCol`"""
+    jfcn = RFContext.active().lookup('rf_convert_cell_type')
+    return Column(jfcn(_to_java_column(tile_col), _parse_cell_type(cell_type)))
 
 
-def _create_tileZeros():
-    """ Create a function mapping to the Scala implementation."""
-    def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('rf_make_zeros_tile')
-        return Column(jfcn(cols, rows, cellType))
-    _.__name__ = 'rf_make_zeros_tile'
-    _.__doc__ = "Create column of constant tiles of zero"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_make_constant_tile(value, cols, rows, cell_type):
+    """Constructor for constant tile column"""
+    jfcn = RFContext.active().lookup('rf_make_constant_tile')
+    return Column(jfcn(value, cols, rows, cell_type))
 
 
-def _create_tileOnes():
-    """ Create a function mapping to the Scala implementation."""
-    def _(cols, rows, cellType = 'float64'):
-        jfcn = RFContext.active().lookup('rf_make_ones_tile')
-        return Column(jfcn(cols, rows, cellType))
-    _.__name__ = 'rf_tile_ones'
-    _.__doc__ = "Create column of constant tiles of one"
-    _.__module__ = THIS_MODULE
-    return _
+def rf_make_zeros_tile(cols, rows, cell_type='float64'):
+    """Create column of constant tiles of zero"""
+    jfcn = RFContext.active().lookup('rf_make_zeros_tile')
+    return Column(jfcn(cols, rows, cell_type))
 
 
-def _create_rasterize():
-    """ Create a function mapping to the Scala rf_rasterize function. """
-    def _(geometryCol, boundsCol, valueCol, numCols, numRows):
-        jfcn = RFContext.active().lookup('rf_rasterize')
-        return Column(jfcn(_to_java_column(geometryCol), _to_java_column(boundsCol), _to_java_column(valueCol), numCols, numRows))
-    _.__name__ = 'rf_rasterize'
-    _.__doc__ = 'Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value.'
-    _.__module__ = THIS_MODULE
-    return _
+def rf_make_ones_tile(cols, rows, cell_type='float64'):
+    """Create column of constant tiles of one"""
+    jfcn = RFContext.active().lookup('rf_make_ones_tile')
+    return Column(jfcn(cols, rows, cell_type))
 
 
-def _create_reproject_geometry():
-    """ Create a function mapping to the Scala st_reproject function. """
-    def _(geometryCol, srcCRSName, dstCRSName):
-        jfcn = RFContext.active().lookup('st_reproject')
-        return Column(jfcn(_to_java_column(geometryCol), srcCRSName, dstCRSName))
-    _.__name__ = 'st_reproject'
-    _.__doc__ = """Reproject a column of geometry given the CRS names of the source and destination.
-Currently supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
-An example of a valid CRS name is EPSG:3005.
-"""
-    _.__module__ = THIS_MODULE
-    return _
+def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
+    """Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value."""
+    jfcn = RFContext.active().lookup('rf_rasterize')
+    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(bounds_col), _to_java_column(value_col), num_cols, num_rows))
 
 
-def _create_explode_tiles():
-    """ Create a function mapping to Scala rf_explode_tiles function """
-    def _(*args):
-        jfcn = RFContext.active().lookup('rf_explode_tiles')
-        jcols = [_to_java_column(arg) for arg in args]
-        return Column(jfcn(RFContext.active().list_to_seq(jcols)))
-    _.__name__ = 'rf_explode_tiles'
-    _.__doc__ = 'Create a row for each cell in Tile.'
-    _.__module__ = THIS_MODULE
-    return _
+def st_reproject(geometry_col, src_crs_name, dst_crs_name):
+    """Reproject a column of geometry given the CRS names of the source and destination.
+    Currently supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
+    An example of a valid CRS name is EPSG:3005."""
+    jfcn = RFContext.active().lookup('st_reproject')
+    return Column(jfcn(_to_java_column(geometry_col), src_crs_name, dst_crs_name))
 
 
-def _create_explode_tiles_sample():
-    """ Create a function mapping to Scala rf_explode_tiles_sample function"""
-    def _(sample_frac, seed, *tile_cols):
-        jfcn = RFContext.active().lookup('rf_explode_tiles_sample')
-        jcols = [_to_java_column(arg) for arg in tile_cols]
-        return Column(jfcn(sample_frac, seed, RFContext.active().list_to_seq(jcols)))
+def rf_explode_tiles(*args):
+    """Create a row for each cell in Tile."""
+    jfcn = RFContext.active().lookup('rf_explode_tiles')
+    jcols = [_to_java_column(arg) for arg in args]
+    return Column(jfcn(RFContext.active().list_to_seq(jcols)))
 
-    _.__name__ = 'rf_explode_tiles_sample'
-    _.__doc__ = 'Create a row for a sample of cells in Tile columns.'
-    _.__module__ = THIS_MODULE
-    return _
 
+def rf_explode_tiles_sample(sample_frac, seed, *tile_cols):
+    """Create a row for a sample of cells in Tile columns."""
+    jfcn = RFContext.active().lookup('rf_explode_tiles_sample')
+    jcols = [_to_java_column(arg) for arg in tile_cols]
+    return Column(jfcn(sample_frac, seed, RFContext.active().list_to_seq(jcols)))
 
-def _create_maskByValue():
-    """ Create a function mapping to Scala rf_mask_by_value function """
-    def _(data_tile, mask_tile, mask_value):
-        jfcn = RFContext.active().lookup('rf_mask_by_value')
-        return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
-    _.__name__ = 'rf_mask_by_value'
-    _.__doc__ = 'Generate a tile with the values from the data tile, but where cells in the masking tile contain the masking value, replace the data value with NODATA.'
-    _.__module__ = THIS_MODULE
-    return _
 
-def _create_inverseMaskByValue():
-    """ Create a function mapping to Scala rf_inverse_mask_by_value function """
-    def _(data_tile, mask_tile, mask_value):
-        jfcn = RFContext.active().lookup('rf_inverse_mask_by_value')
-        return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
-    _.__name__ = 'rf_inverse_mask_by_value'
-    _.__doc__ = 'Generate a tile with the values from the data tile, but where cells in the masking tile do not contain the masking value, replace the data value with NODATA.'
-    _.__module__ = THIS_MODULE
-    return _
+def rf_mask_by_value(data_tile, mask_tile, mask_value):
+    """Generate a tile with the values from the data tile, but where cells in the masking tile contain the masking
+    value, replace the data value with NODATA. """
+    jfcn = RFContext.active().lookup('rf_mask_by_value')
+    return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
 
-_rf_unique_functions = {
-    'rf_array_to_tile': _create_arrayToTile(),
-    'rf_assemble_tile': _create_assembleTile(),
-    'rf_cell_types': lambda: [CellType(str(ct)) for ct in _context_call('rf_cell_types')],
-    'rf_convert_cell_type': _create_convertCellType(),
-    'rf_explode_tiles': _create_explode_tiles(),
-    'rf_explode_tiles_sample': _create_explode_tiles_sample(),
-    'rf_make_constant_tile': _create_makeConstantTile(),
-    'rf_mask_by_value': _create_maskByValue(),
-    'rf_inverse_mask_by_value': _create_inverseMaskByValue(),
-    'rf_rasterize': _create_rasterize(),
-    'st_reproject': _create_reproject_geometry(),
-    'rf_make_ones_tile': _create_tileOnes(),
-    'rf_make_zeros_tile': _create_tileZeros(),
-}
+
+def rf_inverse_mask_by_value(data_tile, mask_tile, mask_value):
+    """Generate a tile with the values from the data tile, but where cells in the masking tile do not contain the
+    masking value, replace the data value with NODATA. """
+    jfcn = RFContext.active().lookup('rf_inverse_mask_by_value')
+    return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
 
 
 _rf_column_scalar_functions = {
@@ -360,11 +290,6 @@ def _(data_tile, mask_tile, mask_value):
 }
 
 
-__all__ = list(_rf_column_functions.keys()) + \
-          list(_rf_column_scalar_functions.keys()) + \
-          list(_rf_unique_functions.keys())
-
-
 def _create_column_function(name, doc=""):
     """ Create a mapping to Scala UDF for a column function by name"""
     def _(*args):
@@ -396,8 +321,5 @@ def _register_functions():
     for name, doc in _rf_column_scalar_functions.items():
         globals()[name] = _create_columnScalarFunction(name, doc)
 
-    for name, func in _rf_unique_functions.items():
-        globals()[name] = func
-
 
 _register_functions()
\ No newline at end of file
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 31ee5107a..4590eccc4 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -124,7 +124,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
   /**
     * Convenience functions for use in Python
     */
-  def rf_cell_type(name: String): CellType = CellType.fromName(name)
+  def _parse_cell_type(name: String): CellType = CellType.fromName(name)
 
   /**
     * Convenience list of valid cell type strings

From 84a8f1a2237bda0879fab10eb3c95db087a65292 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 29 Jun 2019 16:11:46 -0400
Subject: [PATCH 204/380] Replaced all `_rf_column_scalar_functions` with
 explicit forms.

---
 .../python/pyrasterframes/rasterfunctions.py  | 142 +++++++++++++-----
 1 file changed, 106 insertions(+), 36 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index cb589ddc7..d7177a068 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -124,29 +124,112 @@ def rf_inverse_mask_by_value(data_tile, mask_tile, mask_value):
     return Column(jfcn(_to_java_column(data_tile), _to_java_column(mask_tile), _to_java_column(mask_value)))
 
 
-_rf_column_scalar_functions = {
-    'rf_with_no_data': 'Assign a `NoData` value to the Tiles in the given Column.',
-    'rf_local_add_double': 'Add a scalar to a Tile',
-    'rf_local_add_int': 'Add a scalar to a Tile',
-    'rf_local_subtract_double': 'Subtract a scalar from a Tile',
-    'rf_local_subtract_int': 'Subtract a scalar from a Tile',
-    'rf_local_multiply_double': 'Multiply a Tile by a scalar',
-    'rf_local_multiply_int': 'Multiply a Tile by a scalar',
-    'rf_local_divide_double': 'Divide a Tile by a scalar',
-    'rf_local_divide_int': 'Divide a Tile by a scalar',
-    'rf_local_less_double': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
-    'rf_local_less_int': 'Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0',
-    'rf_local_less_equal_double': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
-    'rf_local_less_equal_int': 'Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0',
-    'rf_local_greater_double': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
-    'rf_local_greater_int': 'Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0',
-    'rf_local_greater_equal_double': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
-    'rf_local_greater_equal_int': 'Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0',
-    'rf_local_equal_double': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
-    'rf_local_equal_int': 'Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0',
-    'rf_local_unequal_double': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
-    'rf_local_unequal_int': 'Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0',
-}
+def _apply_scalar_to_tile(name, tile_col, scalar):
+    jfcn = RFContext.active().lookup(name)
+    return Column(jfcn(_to_java_column(tile_col), scalar))
+
+def rf_with_no_data(tile_col, scalar):
+    """Assign a `NoData` value to the Tiles in the given Column."""
+    return _apply_scalar_to_tile('rf_with_no_data', tile_col, scalar)
+
+def rf_local_add_double(tile_col, scalar):
+    """Add a floating point scalar to a Tile"""
+    return _apply_scalar_to_tile('rf_local_add_double', tile_col, scalar)
+
+
+def rf_local_add_int(tile_col, scalar):
+    """Add an integral scalar to a Tile"""
+    return _apply_scalar_to_tile('rf_local_add_int', tile_col, scalar)
+
+
+def rf_local_subtract_double(tile_col, scalar):
+    """Subtract a floating point scalar from a Tile"""
+    return _apply_scalar_to_tile('rf_local_subtract_double', tile_col, scalar)
+
+
+def rf_local_subtract_int(tile_col, scalar):
+    """Subtract an integral scalar from a Tile"""
+    return _apply_scalar_to_tile('rf_local_subtract_int', tile_col, scalar)
+
+
+def rf_local_multiply_double(tile_col, scalar):
+    """Multiply a Tile by a float point scalar"""
+    return _apply_scalar_to_tile('rf_local_multiply_double', tile_col, scalar)
+
+
+def rf_local_multiply_int(tile_col, scalar):
+    """Multiply a Tile by an integral scalar"""
+    return _apply_scalar_to_tile('rf_local_multiply_int', tile_col, scalar)
+
+
+def rf_local_divide_double(tile_col, scalar):
+    """Divide a Tile by a floating point scalar"""
+    return _apply_scalar_to_tile('rf_local_divide_double', tile_col, scalar)
+
+
+def rf_local_divide_int(tile_col, scalar):
+    """Divide a Tile by an integral scalar"""
+    return _apply_scalar_to_tile('rf_local_divide_int', tile_col, scalar)
+
+
+def rf_local_less_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('foo', tile_col, scalar)
+
+
+def rf_local_less_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is less than a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_less_double', tile_col, scalar)
+
+
+def rf_local_less_equal_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_less_equal_double', tile_col, scalar)
+
+
+def rf_local_less_equal_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is less than or equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_less_equal_int', tile_col, scalar)
+
+
+def rf_local_greater_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_greater_double', tile_col, scalar)
+
+
+def rf_local_greater_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is greater than a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_greater_int', tile_col, scalar)
+
+
+def rf_local_greater_equal_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_greater_equal_double', tile_col, scalar)
+
+
+def rf_local_greater_equal_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is greater than or equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_greater_equal_int', tile_col, scalar)
+
+
+def rf_local_equal_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_equal_double', tile_col, scalar)
+
+
+def rf_local_equal_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_equal_int', tile_col, scalar)
+
+
+def rf_local_unequal_double(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_unequal_double', tile_col, scalar)
+
+
+def rf_local_unequal_int(tile_col, scalar):
+    """Return a Tile with values equal 1 if the cell is not equal to a scalar, otherwise 0"""
+    return _apply_scalar_to_tile('rf_local_unequal_int', tile_col, scalar)
 
 
 _rf_column_functions = {
@@ -302,24 +385,11 @@ def _(*args):
     return _
 
 
-def _create_columnScalarFunction(name, doc=""):
-    """ Create a mapping to Scala UDF for a (column, scalar) -> column function by name"""
-    def _(col, scalar):
-        jfcn = RFContext.active().lookup(name)
-        return Column(jfcn(_to_java_column(col), scalar))
-    _.__name__ = name
-    _.__doc__ = doc
-    _.__module__ = THIS_MODULE
-    return _
-
 
 def _register_functions():
     """ Register each function in the scope"""
     for name, doc in _rf_column_functions.items():
         globals()[name] = _create_column_function(name, doc)
 
-    for name, doc in _rf_column_scalar_functions.items():
-        globals()[name] = _create_columnScalarFunction(name, doc)
-
 
 _register_functions()
\ No newline at end of file

From 83fbba59624311fa9f951629a4743b850ce6d01f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sat, 29 Jun 2019 16:25:58 -0400
Subject: [PATCH 205/380] Finished conversions of `_register_functions` to
 explicit forms.

---
 .../python/pyrasterframes/rasterfunctions.py  | 814 ++++++++++++++----
 1 file changed, 651 insertions(+), 163 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index d7177a068..f4fa2e9f4 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -49,7 +49,9 @@ def rf_cell_types():
 def rf_assemble_tile(col_index, row_index, cell_data_col, num_cols, num_rows, cell_type_str):
     """Create a Tile from  a column of cell data with location indices"""
     jfcn = RFContext.active().lookup('rf_assemble_tile')
-    return Column(jfcn(_to_java_column(col_index), _to_java_column(row_index), _to_java_column(cell_data_col), num_cols, num_rows, _parse_cell_type(cell_type_str)))
+    return Column(
+        jfcn(_to_java_column(col_index), _to_java_column(row_index), _to_java_column(cell_data_col), num_cols, num_rows,
+             _parse_cell_type(cell_type_str)))
 
 
 def rf_array_to_tile(array_col, num_cols, num_rows):
@@ -85,7 +87,8 @@ def rf_make_ones_tile(cols, rows, cell_type='float64'):
 def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
     """Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value."""
     jfcn = RFContext.active().lookup('rf_rasterize')
-    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(bounds_col), _to_java_column(value_col), num_cols, num_rows))
+    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(bounds_col), _to_java_column(value_col), num_cols,
+                       num_rows))
 
 
 def st_reproject(geometry_col, src_crs_name, dst_crs_name):
@@ -128,10 +131,12 @@ def _apply_scalar_to_tile(name, tile_col, scalar):
     jfcn = RFContext.active().lookup(name)
     return Column(jfcn(_to_java_column(tile_col), scalar))
 
+
 def rf_with_no_data(tile_col, scalar):
     """Assign a `NoData` value to the Tiles in the given Column."""
     return _apply_scalar_to_tile('rf_with_no_data', tile_col, scalar)
 
+
 def rf_local_add_double(tile_col, scalar):
     """Add a floating point scalar to a Tile"""
     return _apply_scalar_to_tile('rf_local_add_double', tile_col, scalar)
@@ -232,164 +237,647 @@ def rf_local_unequal_int(tile_col, scalar):
     return _apply_scalar_to_tile('rf_local_unequal_int', tile_col, scalar)
 
 
-_rf_column_functions = {
-    # ------- RasterFrames functions -------
-    'rf_dimensions': 'Query the number of (cols, rows) in a Tile.',
-    'rf_tile_to_array_int': 'Flattens Tile into an array of integers.',
-    'rf_tile_to_array_double': 'Flattens Tile into an array of doubles.',
-    'rf_cell_type': 'Extract the Tile\'s cell type',
-    'rf_is_no_data_tile': 'Report if the Tile is entirely NODDATA cells',
-    'rf_exists': 'Returns true if any cells in the tile are true (non-zero and not NoData)',
-    'rf_for_all': 'Returns true if all cells in the tile are true (non-zero and not NoData).',
-    'rf_agg_approx_histogram': 'Compute the full column aggregate floating point histogram',
-    'rf_agg_stats': 'Compute the full column aggregate floating point statistics',
-    'rf_agg_mean': 'Computes the column aggregate mean',
-    'rf_agg_data_cells': 'Computes the number of non-NoData cells in a column',
-    'rf_agg_no_data_cells': 'Computes the number of NoData cells in a column',
-    'rf_tile_histogram': 'Compute the Tile-wise histogram',
-    'rf_tile_mean': 'Compute the Tile-wise mean',
-    'rf_tile_sum': 'Compute the Tile-wise sum',
-    'rf_tile_min': 'Compute the Tile-wise minimum',
-    'rf_tile_max': 'Compute the Tile-wise maximum',
-    'rf_tile_stats': 'Compute the Tile-wise floating point statistics',
-    'rf_render_ascii': 'Render ASCII art of tile',
-    'rf_render_matrix': 'Render Tile cell values as numeric values, for debugging purposes',
-    'rf_no_data_cells': 'Count of NODATA cells',
-    'rf_data_cells': 'Count of cells with valid data',
-    'rf_local_add': 'Add two Tiles',
-    'rf_local_subtract': 'Subtract two Tiles',
-    'rf_local_multiply': 'Multiply two Tiles',
-    'rf_local_divide': 'Divide two Tiles',
-    'rf_normalized_difference': 'Compute the normalized difference of two tiles',
-    'rf_agg_local_max': 'Compute the cell-wise/local max operation between Tiles in a column.',
-    'rf_agg_local_min': 'Compute the cellwise/local min operation between Tiles in a column.',
-    'rf_agg_local_mean': 'Compute the cellwise/local mean operation between Tiles in a column.',
-    'rf_agg_local_data_cells': 'Compute the cellwise/local count of non-NoData cells for all Tiles in a column.',
-    'rf_agg_local_no_data_cells': 'Compute the cellwise/local count of NoData cells for all Tiles in a column.',
-    'rf_agg_local_stats': 'Compute cell-local aggregate descriptive statistics for a column of Tiles.',
-    'rf_mask': 'Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA.',
-    'rf_inverse_mask': 'Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA.',
-    'rf_local_less': 'Cellwise less than comparison between two tiles',
-    'rf_local_less_equal': 'Cellwise less than or equal to comparison between two tiles',
-    'rf_local_greater': 'Cellwise greater than comparison between two tiles',
-    'rf_local_greater_equal': 'Cellwise greater than or equal to comparison between two tiles',
-    'rf_local_equal': 'Cellwise equality comparison between two tiles',
-    'rf_local_unequal': 'Cellwise inequality comparison between two tiles',
-    'rf_round': 'Round cell values to the nearest integer without changing the cell type',
-    'rf_abs': 'Compute the absolute value of each cell',
-    'rf_log': 'Performs cell-wise natural logarithm',
-    'rf_log10': 'Performs cell-wise logartithm with base 10',
-    'rf_log2': 'Performs cell-wise logartithm with base 2',
-    'rf_log1p': 'Performs natural logarithm of cell values plus one',
-    'rf_exp': 'Performs cell-wise exponential',
-    'rf_exp2': 'Compute 2 to the power of cell values',
-    'rf_exp10': 'Compute 10 to the power of cell values',
-    'rf_expm1': 'Performs cell-wise exponential, then subtract one',
-    'rf_identity': 'Pass tile through unchanged',
-    'rf_resample': 'Resample tile to different size based on scalar factor or tile whose dimension to match',
-    'rf_crs': 'Get the CRS of a RasterSource or ProjectedRasterTile',
-    'st_extent': 'Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)',
-    'rf_extent': 'Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)',
-    'rf_tile': 'Extracts the Tile component of a ProjectedRasterTile (or Tile).',
-    'st_geometry': 'Convert the given extent/bbox to a polygon',
-    'rf_geometry': 'Get the extent of a RasterSource or ProjectdRasterTile as a Geometry',
-
-    # ------- JTS functions -------
-    # spatial constructors
-    'st_geomFromGeoHash': '',
-    'st_geomFromWKT': '',
-    'st_geomFromWKB': '',
-    'st_lineFromText': '',
-    'st_makeBox2D': '',
-    'st_makeBBox': '',
-    'st_makePolygon': '',
-    'st_makePoint': '',
-    'st_makeLine': '',
-    'st_makePointM': '',
-    'st_mLineFromText': '',
-    'st_mPointFromText': '',
-    'st_mPolyFromText': '',
-    'st_point': '',
-    'st_pointFromGeoHash': '',
-    'st_pointFromText': '',
-    'st_pointFromWKB': '',
-    'st_polygon': '',
-    'st_polygonFromText': '',
-    # spatial converters
-    'st_castToPoint': '',
-    'st_castToPolygon': '',
-    'st_castToLineString': '',
-    'st_byteArray': '',
-    # spatial accessors
-    'st_boundary': '',
-    'st_coordDim': '',
-    'st_dimension': '',
-    'st_envelope': '',
-    'st_exteriorRing': '',
-    'st_geometryN': '',
-    'st_geometryType': '',
-    'st_interiorRingN': '',
-    'st_isClosed': '',
-    'st_isCollection': '',
-    'st_isEmpty': '',
-    'st_isRing': '',
-    'st_isSimple': '',
-    'st_isValid': '',
-    'st_numGeometries': '',
-    'st_numPoints': '',
-    'st_pointN': '',
-    'st_x': '',
-    'st_y': '',
-    # spatial outputs
-    'st_asBinary': '',
-    'st_asGeoJSON': '',
-    'st_asLatLonText': '',
-    'st_asText': '',
-    'st_geoHash': '',
-    # spatial processors
-    'st_bufferPoint': '',
-    'st_antimeridianSafeGeom': '',
-    # spatial relations
-    'st_translate': '',
-    'st_contains': '',
-    'st_covers': '',
-    'st_crosses': '',
-    'st_disjoint': '',
-    'st_equals': '',
-    'st_intersects': '',
-    'st_overlaps': '',
-    'st_touches': '',
-    'st_within': '',
-    'st_relate': '',
-    'st_relateBool': '',
-    'st_area': '',
-    'st_closestPoint': '',
-    'st_centroid': '',
-    'st_distance': '',
-    'st_distanceSphere': '',
-    'st_length': '',
-    'st_aggregateDistanceSphere': '',
-    'st_lengthSphere': '',
-}
-
-
-def _create_column_function(name, doc=""):
-    """ Create a mapping to Scala UDF for a column function by name"""
-    def _(*args):
-        jfcn = RFContext.active().lookup(name)
-        jcols = [_to_java_column(arg) for arg in args]
-        return Column(jfcn(*jcols))
-    _.__name__ = name
-    _.__doc__ = doc
-    _.__module__ = THIS_MODULE
-    return _
-
-
-
-def _register_functions():
-    """ Register each function in the scope"""
-    for name, doc in _rf_column_functions.items():
-        globals()[name] = _create_column_function(name, doc)
-
-
-_register_functions()
\ No newline at end of file
+def _apply_column_function(name, *args):
+    jfcn = RFContext.active().lookup(name)
+    jcols = [_to_java_column(arg) for arg in args]
+    return Column(jfcn(*jcols))
+
+
+def rf_dimensions(*args):
+    """Query the number of (cols, rows) in a Tile."""
+    return _apply_column_function('rf_dimensions', *args)
+
+
+def rf_tile_to_array_int(*args):
+    """Flattens Tile into an array of integers."""
+    return _apply_column_function('rf_tile_to_array_int', *args)
+
+
+def rf_tile_to_array_double(*args):
+    """Flattens Tile into an array of doubles."""
+    return _apply_column_function('rf_tile_to_array_double', *args)
+
+
+def rf_cell_type(*args):
+    """Extract the Tile's cell type"""
+    return _apply_column_function('rf_cell_type', *args)
+
+
+def rf_is_no_data_tile(*args):
+    """Report if the Tile is entirely NODDATA cells"""
+    return _apply_column_function('rf_is_no_data_tile', *args)
+
+
+def rf_exists(*args):
+    """Returns true if any cells in the tile are true (non-zero and not NoData)"""
+    return _apply_column_function('rf_exists', *args)
+
+
+def rf_for_all(*args):
+    """Returns true if all cells in the tile are true (non-zero and not NoData)."""
+    return _apply_column_function('rf_for_all', *args)
+
+
+def rf_agg_approx_histogram(*args):
+    """Compute the full column aggregate floating point histogram"""
+    return _apply_column_function('rf_agg_approx_histogram', *args)
+
+
+def rf_agg_stats(*args):
+    """Compute the full column aggregate floating point statistics"""
+    return _apply_column_function('rf_agg_stats', *args)
+
+
+def rf_agg_mean(*args):
+    """Computes the column aggregate mean"""
+    return _apply_column_function('rf_agg_mean', *args)
+
+
+def rf_agg_data_cells(*args):
+    """Computes the number of non-NoData cells in a column"""
+    return _apply_column_function('rf_agg_data_cells', *args)
+
+
+def rf_agg_no_data_cells(*args):
+    """Computes the number of NoData cells in a column"""
+    return _apply_column_function('rf_agg_no_data_cells', *args)
+
+
+def rf_tile_histogram(*args):
+    """Compute the Tile-wise histogram"""
+    return _apply_column_function('rf_tile_histogram', *args)
+
+
+def rf_tile_mean(*args):
+    """Compute the Tile-wise mean"""
+    return _apply_column_function('rf_tile_mean', *args)
+
+
+def rf_tile_sum(*args):
+    """Compute the Tile-wise sum"""
+    return _apply_column_function('rf_tile_sum', *args)
+
+
+def rf_tile_min(*args):
+    """Compute the Tile-wise minimum"""
+    return _apply_column_function('rf_tile_min', *args)
+
+
+def rf_tile_max(*args):
+    """Compute the Tile-wise maximum"""
+    return _apply_column_function('rf_tile_max', *args)
+
+
+def rf_tile_stats(*args):
+    """Compute the Tile-wise floating point statistics"""
+    return _apply_column_function('rf_tile_stats', *args)
+
+
+def rf_render_ascii(*args):
+    """Render ASCII art of tile"""
+    return _apply_column_function('rf_render_ascii', *args)
+
+
+def rf_render_matrix(*args):
+    """Render Tile cell values as numeric values, for debugging purposes"""
+    return _apply_column_function('rf_render_matrix', *args)
+
+
+def rf_no_data_cells(*args):
+    """Count of NODATA cells"""
+    return _apply_column_function('rf_no_data_cells', *args)
+
+
+def rf_data_cells(*args):
+    """Count of cells with valid data"""
+    return _apply_column_function('rf_data_cells', *args)
+
+
+def rf_local_add(*args):
+    """Add two Tiles"""
+    return _apply_column_function('rf_local_add', *args)
+
+
+def rf_local_subtract(*args):
+    """Subtract two Tiles"""
+    return _apply_column_function('rf_local_subtract', *args)
+
+
+def rf_local_multiply(*args):
+    """Multiply two Tiles"""
+    return _apply_column_function('rf_local_multiply', *args)
+
+
+def rf_local_divide(*args):
+    """Divide two Tiles"""
+    return _apply_column_function('rf_local_divide', *args)
+
+
+def rf_normalized_difference(*args):
+    """Compute the normalized difference of two tiles"""
+    return _apply_column_function('rf_normalized_difference', *args)
+
+
+def rf_agg_local_max(*args):
+    """Compute the cell-wise/local max operation between Tiles in a column."""
+    return _apply_column_function('rf_agg_local_max', *args)
+
+
+def rf_agg_local_min(*args):
+    """Compute the cellwise/local min operation between Tiles in a column."""
+    return _apply_column_function('rf_agg_local_min', *args)
+
+
+def rf_agg_local_mean(*args):
+    """Compute the cellwise/local mean operation between Tiles in a column."""
+    return _apply_column_function('rf_agg_local_mean', *args)
+
+
+def rf_agg_local_data_cells(*args):
+    """Compute the cellwise/local count of non-NoData cells for all Tiles in a column."""
+    return _apply_column_function('rf_agg_local_data_cells', *args)
+
+
+def rf_agg_local_no_data_cells(*args):
+    """Compute the cellwise/local count of NoData cells for all Tiles in a column."""
+    return _apply_column_function('rf_agg_local_no_data_cells', *args)
+
+
+def rf_agg_local_stats(*args):
+    """Compute cell-local aggregate descriptive statistics for a column of Tiles."""
+    return _apply_column_function('rf_agg_local_stats', *args)
+
+
+def rf_mask(*args):
+    """Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA."""
+    return _apply_column_function('rf_mask', *args)
+
+
+def rf_inverse_mask(*args):
+    """Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA."""
+    return _apply_column_function('rf_inverse_mask', *args)
+
+
+def rf_local_less(*args):
+    """Cellwise less than comparison between two tiles"""
+    return _apply_column_function('rf_local_less', *args)
+
+
+def rf_local_less_equal(*args):
+    """Cellwise less than or equal to comparison between two tiles"""
+    return _apply_column_function('rf_local_less_equal', *args)
+
+
+def rf_local_greater(*args):
+    """Cellwise greater than comparison between two tiles"""
+    return _apply_column_function('rf_local_greater', *args)
+
+
+def rf_local_greater_equal(*args):
+    """Cellwise greater than or equal to comparison between two tiles"""
+    return _apply_column_function('rf_local_greater_equal', *args)
+
+
+def rf_local_equal(*args):
+    """Cellwise equality comparison between two tiles"""
+    return _apply_column_function('rf_local_equal', *args)
+
+
+def rf_local_unequal(*args):
+    """Cellwise inequality comparison between two tiles"""
+    return _apply_column_function('rf_local_unequal', *args)
+
+
+def rf_round(*args):
+    """Round cell values to the nearest integer without changing the cell type"""
+    return _apply_column_function('rf_round', *args)
+
+
+def rf_abs(*args):
+    """Compute the absolute value of each cell"""
+    return _apply_column_function('rf_abs', *args)
+
+
+def rf_log(*args):
+    """Performs cell-wise natural logarithm"""
+    return _apply_column_function('rf_log', *args)
+
+
+def rf_log10(*args):
+    """Performs cell-wise logartithm with base 10"""
+    return _apply_column_function('rf_log10', *args)
+
+
+def rf_log2(*args):
+    """Performs cell-wise logartithm with base 2"""
+    return _apply_column_function('rf_log2', *args)
+
+
+def rf_log1p(*args):
+    """Performs natural logarithm of cell values plus one"""
+    return _apply_column_function('rf_log1p', *args)
+
+
+def rf_exp(*args):
+    """Performs cell-wise exponential"""
+    return _apply_column_function('rf_exp', *args)
+
+
+def rf_exp2(*args):
+    """Compute 2 to the power of cell values"""
+    return _apply_column_function('rf_exp2', *args)
+
+
+def rf_exp10(*args):
+    """Compute 10 to the power of cell values"""
+    return _apply_column_function('rf_exp10', *args)
+
+
+def rf_expm1(*args):
+    """Performs cell-wise exponential, then subtract one"""
+    return _apply_column_function('rf_expm1', *args)
+
+
+def rf_identity(*args):
+    """Pass tile through unchanged"""
+    return _apply_column_function('rf_identity', *args)
+
+
+def rf_resample(*args):
+    """Resample tile to different size based on scalar factor or tile whose dimension to match"""
+    return _apply_column_function('rf_resample', *args)
+
+
+def rf_crs(*args):
+    """Get the CRS of a RasterSource or ProjectedRasterTile"""
+    return _apply_column_function('rf_crs', *args)
+
+
+def st_extent(*args):
+    """Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)"""
+    return _apply_column_function('st_extent', *args)
+
+
+def rf_extent(*args):
+    """Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)"""
+    return _apply_column_function('rf_extent', *args)
+
+
+def rf_tile(*args):
+    """Extracts the Tile component of a ProjectedRasterTile (or Tile)."""
+    return _apply_column_function('rf_tile', *args)
+
+
+def st_geometry(*args):
+    """Convert the given extent/bbox to a polygon"""
+    return _apply_column_function('st_geometry', *args)
+
+
+def rf_geometry(*args):
+    """Get the extent of a RasterSource or ProjectdRasterTile as a Geometry"""
+    return _apply_column_function('rf_geometry', *args)
+
+
+def st_geomFromGeoHash(*args):
+    """"""
+    return _apply_column_function('st_geomFromGeoHash', *args)
+
+
+def st_geomFromWKT(*args):
+    """"""
+    return _apply_column_function('st_geomFromWKT', *args)
+
+
+def st_geomFromWKB(*args):
+    """"""
+    return _apply_column_function('st_geomFromWKB', *args)
+
+
+def st_lineFromText(*args):
+    """"""
+    return _apply_column_function('st_lineFromText', *args)
+
+
+def st_makeBox2D(*args):
+    """"""
+    return _apply_column_function('st_makeBox2D', *args)
+
+
+def st_makeBBox(*args):
+    """"""
+    return _apply_column_function('st_makeBBox', *args)
+
+
+def st_makePolygon(*args):
+    """"""
+    return _apply_column_function('st_makePolygon', *args)
+
+
+def st_makePoint(*args):
+    """"""
+    return _apply_column_function('st_makePoint', *args)
+
+
+def st_makeLine(*args):
+    """"""
+    return _apply_column_function('st_makeLine', *args)
+
+
+def st_makePointM(*args):
+    """"""
+    return _apply_column_function('st_makePointM', *args)
+
+
+def st_mLineFromText(*args):
+    """"""
+    return _apply_column_function('st_mLineFromText', *args)
+
+
+def st_mPointFromText(*args):
+    """"""
+    return _apply_column_function('st_mPointFromText', *args)
+
+
+def st_mPolyFromText(*args):
+    """"""
+    return _apply_column_function('st_mPolyFromText', *args)
+
+
+def st_point(*args):
+    """"""
+    return _apply_column_function('st_point', *args)
+
+
+def st_pointFromGeoHash(*args):
+    """"""
+    return _apply_column_function('st_pointFromGeoHash', *args)
+
+
+def st_pointFromText(*args):
+    """"""
+    return _apply_column_function('st_pointFromText', *args)
+
+
+def st_pointFromWKB(*args):
+    """"""
+    return _apply_column_function('st_pointFromWKB', *args)
+
+
+def st_polygon(*args):
+    """"""
+    return _apply_column_function('st_polygon', *args)
+
+
+def st_polygonFromText(*args):
+    """"""
+    return _apply_column_function('st_polygonFromText', *args)
+
+
+def st_castToPoint(*args):
+    """"""
+    return _apply_column_function('st_castToPoint', *args)
+
+
+def st_castToPolygon(*args):
+    """"""
+    return _apply_column_function('st_castToPolygon', *args)
+
+
+def st_castToLineString(*args):
+    """"""
+    return _apply_column_function('st_castToLineString', *args)
+
+
+def st_byteArray(*args):
+    """"""
+    return _apply_column_function('st_byteArray', *args)
+
+
+def st_boundary(*args):
+    """"""
+    return _apply_column_function('st_boundary', *args)
+
+
+def st_coordDim(*args):
+    """"""
+    return _apply_column_function('st_coordDim', *args)
+
+
+def st_dimension(*args):
+    """"""
+    return _apply_column_function('st_dimension', *args)
+
+
+def st_envelope(*args):
+    """"""
+    return _apply_column_function('st_envelope', *args)
+
+
+def st_exteriorRing(*args):
+    """"""
+    return _apply_column_function('st_exteriorRing', *args)
+
+
+def st_geometryN(*args):
+    """"""
+    return _apply_column_function('st_geometryN', *args)
+
+
+def st_geometryType(*args):
+    """"""
+    return _apply_column_function('st_geometryType', *args)
+
+
+def st_interiorRingN(*args):
+    """"""
+    return _apply_column_function('st_interiorRingN', *args)
+
+
+def st_isClosed(*args):
+    """"""
+    return _apply_column_function('st_isClosed', *args)
+
+
+def st_isCollection(*args):
+    """"""
+    return _apply_column_function('st_isCollection', *args)
+
+
+def st_isEmpty(*args):
+    """"""
+    return _apply_column_function('st_isEmpty', *args)
+
+
+def st_isRing(*args):
+    """"""
+    return _apply_column_function('st_isRing', *args)
+
+
+def st_isSimple(*args):
+    """"""
+    return _apply_column_function('st_isSimple', *args)
+
+
+def st_isValid(*args):
+    """"""
+    return _apply_column_function('st_isValid', *args)
+
+
+def st_numGeometries(*args):
+    """"""
+    return _apply_column_function('st_numGeometries', *args)
+
+
+def st_numPoints(*args):
+    """"""
+    return _apply_column_function('st_numPoints', *args)
+
+
+def st_pointN(*args):
+    """"""
+    return _apply_column_function('st_pointN', *args)
+
+
+def st_x(*args):
+    """"""
+    return _apply_column_function('st_x', *args)
+
+
+def st_y(*args):
+    """"""
+    return _apply_column_function('st_y', *args)
+
+
+def st_asBinary(*args):
+    """"""
+    return _apply_column_function('st_asBinary', *args)
+
+
+def st_asGeoJSON(*args):
+    """"""
+    return _apply_column_function('st_asGeoJSON', *args)
+
+
+def st_asLatLonText(*args):
+    """"""
+    return _apply_column_function('st_asLatLonText', *args)
+
+
+def st_asText(*args):
+    """"""
+    return _apply_column_function('st_asText', *args)
+
+
+def st_geoHash(*args):
+    """"""
+    return _apply_column_function('st_geoHash', *args)
+
+
+def st_bufferPoint(*args):
+    """"""
+    return _apply_column_function('st_bufferPoint', *args)
+
+
+def st_antimeridianSafeGeom(*args):
+    """"""
+    return _apply_column_function('st_antimeridianSafeGeom', *args)
+
+
+def st_translate(*args):
+    """"""
+    return _apply_column_function('st_translate', *args)
+
+
+def st_contains(*args):
+    """"""
+    return _apply_column_function('st_contains', *args)
+
+
+def st_covers(*args):
+    """"""
+    return _apply_column_function('st_covers', *args)
+
+
+def st_crosses(*args):
+    """"""
+    return _apply_column_function('st_crosses', *args)
+
+
+def st_disjoint(*args):
+    """"""
+    return _apply_column_function('st_disjoint', *args)
+
+
+def st_equals(*args):
+    """"""
+    return _apply_column_function('st_equals', *args)
+
+
+def st_intersects(*args):
+    """"""
+    return _apply_column_function('st_intersects', *args)
+
+
+def st_overlaps(*args):
+    """"""
+    return _apply_column_function('st_overlaps', *args)
+
+
+def st_touches(*args):
+    """"""
+    return _apply_column_function('st_touches', *args)
+
+
+def st_within(*args):
+    """"""
+    return _apply_column_function('st_within', *args)
+
+
+def st_relate(*args):
+    """"""
+    return _apply_column_function('st_relate', *args)
+
+
+def st_relateBool(*args):
+    """"""
+    return _apply_column_function('st_relateBool', *args)
+
+
+def st_area(*args):
+    """"""
+    return _apply_column_function('st_area', *args)
+
+
+def st_closestPoint(*args):
+    """"""
+    return _apply_column_function('st_closestPoint', *args)
+
+
+def st_centroid(*args):
+    """"""
+    return _apply_column_function('st_centroid', *args)
+
+
+def st_distance(*args):
+    """"""
+    return _apply_column_function('st_distance', *args)
+
+
+def st_distanceSphere(*args):
+    """"""
+    return _apply_column_function('st_distanceSphere', *args)
+
+
+def st_length(*args):
+    """"""
+    return _apply_column_function('st_length', *args)
+
+
+def st_aggregateDistanceSphere(*args):
+    """"""
+    return _apply_column_function('st_aggregateDistanceSphere', *args)
+
+
+def st_lengthSphere(*args):
+    """"""
+    return _apply_column_function('st_lengthSphere', *args)

From 371a321ccf0827548b043bb298cc236e42d7c440 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 30 Jun 2019 11:10:33 -0400
Subject: [PATCH 206/380] Renamed DataSource name 'rastersource' to just
 'raster'. Fixed #159

---
 ...pache.spark.sql.sources.DataSourceRegister |  2 +-
 .../RasterSourceDataSource.scala              |  6 +--
 .../RasterSourceRelation.scala                |  4 +-
 .../{rastersource => raster}/package.scala    | 19 +++++---
 .../RasterSourceDataSourceSpec.scala          | 43 ++++++++++++++-----
 pyrasterframes/README.md                      |  2 +-
 pyrasterframes/src/main/python/README.md      |  2 +-
 .../src/main/python/docs/getting-started.pymd |  4 +-
 .../src/main/python/docs/local-algebra.pymd   |  4 +-
 .../src/main/python/docs/raster-io.pymd       | 12 +++---
 .../src/main/python/docs/spark-ml.pymd        |  2 +-
 .../main/python/pyrasterframes/__init__.py    | 14 +++---
 .../python/pyrasterframes/rasterfunctions.py  |  2 +-
 .../{context.py => rf_context.py}             |  0
 .../main/python/pyrasterframes/rf_types.py    |  2 +-
 .../main/python/tests/PyRasterFramesTests.py  | 10 ++---
 .../src/main/notebooks/Getting Started.ipynb  |  4 +-
 .../pretty_rendering_rf_types.tile.ipynb      |  2 +-
 18 files changed, 82 insertions(+), 52 deletions(-)
 rename datasource/src/main/scala/org/locationtech/rasterframes/datasource/{rastersource => raster}/RasterSourceDataSource.scala (97%)
 rename datasource/src/main/scala/org/locationtech/rasterframes/datasource/{rastersource => raster}/RasterSourceRelation.scala (96%)
 rename datasource/src/main/scala/org/locationtech/rasterframes/datasource/{rastersource => raster}/package.scala (78%)
 rename datasource/src/test/scala/org/locationtech/rasterframes/datasource/{rastersource => raster}/RasterSourceDataSourceSpec.scala (85%)
 rename pyrasterframes/src/main/python/pyrasterframes/{context.py => rf_context.py} (100%)

diff --git a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index d60fa152d..c3148d7ce 100644
--- a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,4 +1,4 @@
 org.locationtech.rasterframes.datasource.geotiff.GeoTiffDataSource
 org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource
 org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisCatalog
-org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource
+org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
similarity index 97%
rename from datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
index 73f5e2597..6626bd6db 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.datasource.rastersource
+package org.locationtech.rasterframes.datasource.raster
 
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.util._
@@ -40,7 +40,7 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   }
 }
 object RasterSourceDataSource {
-  final val SHORT_NAME = "rastersource"
+  final val SHORT_NAME = "raster"
   final val PATH_PARAM = "path"
   final val PATHS_PARAM = "paths"
   final val BAND_INDEXES_PARAM = "bandIndexes"
@@ -83,7 +83,7 @@ object RasterSourceDataSource {
   /** Container for specifying where to select raster paths from. */
   case class RasterSourceCatalogRef(tableName: String, bandColumnNames: String*) extends WithBandColumns
 
-  private[rastersource]
+  private[raster]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
 
     def tileDims: Option[TileDimensions] =
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
similarity index 96%
rename from datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
index d49f3d911..c5169b8c9 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
@@ -19,14 +19,14 @@
  *
  */
 
-package org.locationtech.rasterframes.datasource.rastersource
+package org.locationtech.rasterframes.datasource.raster
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourceCatalog, RasterSourceCatalogRef}
+import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, RasterSourceCatalogRef}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
similarity index 78%
rename from datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
index bbe0b7774..1376ea5c9 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/rastersource/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
@@ -22,18 +22,19 @@
 package org.locationtech.rasterframes.datasource
 
 import java.net.URI
+import java.util.UUID
 
-import org.apache.spark.sql.DataFrameReader
+import org.apache.spark.sql.{DataFrame, DataFrameReader}
 import shapeless.tag
 import shapeless.tag.@@
-package object rastersource {
+package object raster {
 
   trait RasterSourceDataFrameReaderTag
   type RasterSourceDataFrameReader = DataFrameReader @@ RasterSourceDataFrameReaderTag
 
-  /** Adds `rastersource` format specifier to `DataFrameReader`. */
+  /** Adds `raster` format specifier to `DataFrameReader`. */
   implicit class DataFrameReaderHasRasterSourceFormat(val reader: DataFrameReader) {
-    def rastersource: RasterSourceDataFrameReader =
+    def raster: RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.format(RasterSourceDataSource.SHORT_NAME))
   }
@@ -50,7 +51,15 @@ package object rastersource {
         reader.option(RasterSourceDataSource.TILE_DIMS_PARAM, s"$cols,$rows")
       )
 
-    def fromTable(tableName: String, bandColumnNames: String*): RasterSourceDataFrameReader =
+    def fromCatalog(catalog: DataFrame, bandColumnNames: String*): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader] {
+        val tmpName = UUID.randomUUID().toString.replace("-", "")
+        catalog.createOrReplaceTempView(tmpName)
+        reader.option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tmpName)
+          .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
+      }
+
+    def fromCatalog(tableName: String, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tableName)
           .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
similarity index 85%
rename from datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
index b145fe129..48c524980 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/rastersource/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
@@ -19,9 +19,9 @@
  *
  */
 
-package org.locationtech.rasterframes.datasource.rastersource
+package org.locationtech.rasterframes.datasource.raster
 import org.locationtech.rasterframes.{TestEnvironment, _}
-import org.locationtech.rasterframes.datasource.rastersource.RasterSourceDataSource.{RasterSourceCatalog, _}
+import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, _}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.util._
 
@@ -81,14 +81,14 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     val b = DEFAULT_COLUMN_NAME
 
     it("should default to a single band schema") {
-      val df = spark.read.rastersource.load(l8B1SamplePath.toASCIIString)
+      val df = spark.read.raster.load(l8B1SamplePath.toASCIIString)
       val tcols = df.tileColumns
       tcols.length should be(1)
       tcols.map(_.columnName) should contain(DEFAULT_COLUMN_NAME)
     }
     it("should support a multiband schema") {
       val df = spark.read
-        .rastersource
+        .raster
         .withBandIndexes(0, 1, 2)
         .load(cogPath.toASCIIString)
       val tcols = df.tileColumns
@@ -97,7 +97,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     }
     it("should read a multiband file") {
       val df = spark.read
-        .rastersource
+        .raster
         .withBandIndexes(0, 1, 2)
         .load(cogPath.toASCIIString)
         .cache()
@@ -111,7 +111,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     }
     it("should read a single file") {
       // Image is 1028 x 989 -> 9 x 8 tiles
-      val df = spark.read.rastersource
+      val df = spark.read.raster
         .withTileDimensions(128, 128)
         .load(cogPath.toASCIIString)
 
@@ -124,7 +124,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.select($"${b}_path").distinct().count() should be(1)
     }
     it("should read a multiple files with one band") {
-      val df = spark.read.rastersource
+      val df = spark.read.raster
         .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .load()
@@ -132,7 +132,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.schema.size should be(2)
     }
     it("should read a multiple files with heterogeneous bands") {
-      val df = spark.read.rastersource
+      val df = spark.read.raster
         .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
         .withTileDimensions(128, 128)
         .withBandIndexes(0, 1, 2, 3)
@@ -161,7 +161,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
            |${paths.mkString(",")}
         """.stripMargin.trim
 
-      val df = spark.read.rastersource
+      val df = spark.read.raster
         .fromCSV(csv)
         .withTileDimensions(128, 128)
         .load()
@@ -171,6 +171,27 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       df.select($"B1_path").distinct().count() should be (1)
     }
 
+    it("should read a set of coherent bands from multiple files in a dataframe") {
+      val bandPaths = Seq((
+        l8SamplePath(1).toASCIIString,
+        l8SamplePath(2).toASCIIString,
+        l8SamplePath(3).toASCIIString))
+        .toDF("B1", "B2", "B3")
+
+      val df = spark.read.raster
+        .fromCatalog(bandPaths, "B1", "B2", "B3")
+        .withTileDimensions(128, 128)
+        .load()
+
+      df.schema.size should be(6)
+      df.tileColumns.size should be (3)
+      df.select($"B1_path").distinct().count() should be (1)
+
+      val diffStats = df.select(rf_tile_stats($"B1") =!= rf_tile_stats($"B2")).as[Boolean].collect()
+      diffStats.forall(identity) should be(true)
+    }
+
+
     it("should read a set of coherent bands from multiple files in a table") {
       val bandPaths = Seq((
         l8SamplePath(1).toASCIIString,
@@ -180,8 +201,8 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
       bandPaths.createOrReplaceTempView("pathsTable")
 
-      val df = spark.read.rastersource
-        .fromTable("pathsTable", "B1", "B2", "B3")
+      val df = spark.read.raster
+        .fromCatalog("pathsTable", "B1", "B2", "B3")
         .withTileDimensions(128, 128)
         .load()
 
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index eed711957..6f82e8ebf 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -67,7 +67,7 @@ Then in the pyspark shell or app, import the module and call `withRasterFrames`
 ```python
 from pyrasterframes.utils import create_rf_spark_session
 spark = create_rf_spark_session()
-df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+df = spark.read.raster('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
 ```
 
 ## Development
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 1ccfcb0d3..16d6163ff 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -23,7 +23,7 @@ Then you can read a raster and do some work with it.
 from pyrasterframes.rasterfunctions import *
 from pyspark.sql.functions import lit
 # Read a MODIS surface reflectance granule
-df = spark.read.rastersource('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
+df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
 # Add 3 element-wise, show some rows of the dataframe
 df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 ```
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index c720a7f16..147c1a1c6 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -20,7 +20,7 @@ from pyrasterframes.rasterfunctions import rf_local_add
 from pyspark.sql.functions import lit
 
 # Read a MODIS surface reflectance granule
-df = spark.read.rastersource('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
+df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
 
 # Add 3 element-wise, show some rows of the dataframe
 df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
@@ -79,7 +79,7 @@ Then in the pyspark shell, import the module and call `withRasterFrames` on the
 ```python, evaluate=False
 import pyrasterframes
 spark = spark.withRasterFrames()
-df = spark.read.rastersource('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+df = spark.read.raster('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
 ```
 
 Now you have the configured SparkSession with RasterFrames enabled.
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index a03a7f4ea..6f9d03b69 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -32,8 +32,8 @@ uri_pattern = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0{}.tif'
 catalog_df = pd.DataFrame([
     {bands[b_num]: uri_pattern.format(b_num) for b_num in bands.keys()}
 ])
-df = spark.read.rastersource(catalog=catalog_df.to_csv(index=None),
-                             catalog_col_names=list(catalog_df.columns))
+df = spark.read.raster(catalog=catalog_df.to_csv(index=None),
+                       catalog_col_names=list(catalog_df.columns))
 df.printSchema()
 ```
 
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 4a8c2ae66..5b66afeaf 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -11,7 +11,7 @@ from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
 ```
 
-RasterFrames registers a DataSource named `rastersource` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
+RasterFrames registers a DataSource named `rasters` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
 
 ```python
 data_root = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp'
@@ -25,7 +25,7 @@ def s2_band(idx):
 The simplest form is reading a single raster from a single URI:
 
 ```python
-rf = spark.read.rastersource(s2_band(2))
+rf = spark.read.raster(s2_band(2))
 rf.printSchema()
 ```
 
@@ -57,7 +57,7 @@ display(tile)
 
 ### Single Scene, Multiple Bands
 
-Generally we'll want to work with multiple bands. If you have several singleband image files of the same scene, `rastersource` can make use of a CSV (or other DataFrame) specifying the band columns and scene rows.
+Generally we'll want to work with multiple bands. If you have several singleband image files of the same scene, `raster` can make use of a CSV (or other DataFrame) specifying the band columns and scene rows.
 
 ```python
 csv = """
@@ -67,7 +67,7 @@ red,green,blue
 
 print(csv)
 
-rf2 = spark.read.rastersource(catalog=csv, catalog_col_names=['red', 'green', 'blue'])
+rf2 = spark.read.raster(catalog=csv, catalog_col_names=['red', 'green', 'blue'])
 rf2.printSchema()
 
 rf2.show(5)
@@ -117,10 +117,10 @@ equator = (modis_catalog
 equator.show(3)
 ```
 
-Now that we have a catalog of multiple scenes and mutiple bands withit each scene, we can pass it along to `rastersource` load the imagery. To complete the example, we compute some aggregate statistics over NDVI.
+Now that we have a catalog of multiple scenes and mutiple bands withit each scene, we can pass it along to `raster` load the imagery. To complete the example, we compute some aggregate statistics over NDVI.
 
 ```python
-rf = spark.read.rastersource(
+rf = spark.read.raster(
     catalog=equator, 
     catalog_col_names=['red', 'nir']
 ).repartition()
diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/spark-ml.pymd
index d6456436c..d871a8831 100644
--- a/pyrasterframes/src/main/python/docs/spark-ml.pymd
+++ b/pyrasterframes/src/main/python/docs/spark-ml.pymd
@@ -38,7 +38,7 @@ filenamePattern = "L8-B{}-Elkton-VA.tiff"
 catalog_df = pd.DataFrame([
     {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
 ])
-df = spark.read.rastersource(catalog=catalog_df.to_csv(index=None), catalog_col_names=catalog_df.columns)
+df = spark.read.raster(catalog=catalog_df.to_csv(index=None), catalog_col_names=catalog_df.columns)
 df = df.select(
     rf_crs(df.b1).alias('crs'),
     rf_extent(df.b1).alias('extent'),
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 9c7379dac..772a6b107 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -29,7 +29,7 @@
 from pyspark.sql.column import _to_java_column
 
 # Import RasterFrame types and functions
-from .context import RFContext
+from .rf_context import RFContext
 from .version import __version__
 from .rf_types import RasterFrame, TileExploder, TileUDT, RasterSourceUDT
 
@@ -98,7 +98,7 @@ def _layer_reader(df_reader, format_key, path, **options):
     return _convert_df(df)
 
 
-def _rastersource_reader(
+def _raster_reader(
         df_reader, path=None,
         band_indexes=None,
         tile_dimensions=(256, 256),
@@ -131,15 +131,15 @@ def to_csv(comp):
         elif isinstance(catalog, DataFrame):
             import uuid
             # Create a random view name
-            name = str(uuid.uuid4()).replace('-', '')
-            catalog.createOrReplaceTempView(name)
+            tmp_name = str(uuid.uuid4()).replace('-', '')
+            catalog.createOrReplaceTempView(tmp_name)
             options.update({
-                "catalogTable": name,
+                "catalogTable": tmp_name,
                 "catalogColumns": to_csv(catalog_col_names)
             })
 
     return df_reader \
-        .format("rastersource") \
+        .format("raster") \
         .load(path, **options)
 
 
@@ -154,7 +154,7 @@ def to_csv(comp):
 DataFrame.raster_join = _raster_join
 
 # Add DataSource convenience methods to the DataFrameReader
-DataFrameReader.rastersource = _rastersource_reader
+DataFrameReader.raster = _raster_reader
 
 # Legacy readers
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index f4fa2e9f4..6b6620a84 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -25,7 +25,7 @@
 """
 from __future__ import absolute_import
 from pyspark.sql.column import Column, _to_java_column
-from .context import RFContext
+from .rf_context import RFContext
 from .rf_types import CellType
 
 THIS_MODULE = 'pyrasterframes'
diff --git a/pyrasterframes/src/main/python/pyrasterframes/context.py b/pyrasterframes/src/main/python/pyrasterframes/rf_context.py
similarity index 100%
rename from pyrasterframes/src/main/python/pyrasterframes/context.py
rename to pyrasterframes/src/main/python/pyrasterframes/rf_context.py
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 12b0ec14f..ddf334d7a 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -31,7 +31,7 @@ class here provides the PyRasterFrames entry point.
 from pyspark.sql.types import (StructType, StructField, BinaryType, DoubleType, ShortType, IntegerType, StringType)
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
-from pyrasterframes.context import RFContext
+from pyrasterframes.rf_context import RFContext
 import numpy as np
 
 __all__ = ['RasterFrame', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 917f0f844..a5a3a8002 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -418,7 +418,7 @@ def test_tile_udt_serialization(self):
 
     def test_udf_on_tile_type_input(self):
         import numpy.testing
-        df = self.spark.read.rastersource(self.img_uri)
+        df = self.spark.read.raster(self.img_uri)
         rf = self.rf
 
         # create trivial UDF that does something we already do with raster_Functions
@@ -687,7 +687,7 @@ def test_raster_join(self):
 class RasterSource(TestEnvironment):
 
     def test_handle_lazy_eval(self):
-        df = self.spark.read.rastersource(self.img_uri)
+        df = self.spark.read.raster(self.img_uri)
         ltdf = df.select('proj_raster')
         self.assertGreater(ltdf.count(), 0)
         self.assertIsNotNone(ltdf.first())
@@ -697,7 +697,7 @@ def test_handle_lazy_eval(self):
         self.assertIsNotNone(tdf.first())
 
     def test_prt_functions(self):
-        df = self.spark.read.rastersource(self.img_uri) \
+        df = self.spark.read.raster(self.img_uri) \
             .withColumn('crs', rf_crs('proj_raster')) \
             .withColumn('ext', rf_extent('proj_raster')) \
             .withColumn('geom', rf_geometry('proj_raster'))
@@ -715,7 +715,7 @@ def l8path(b):
         path_param = '\n'.join([l8path(b) for b in [1, 2, 3]])  # "http://foo.com/file1.tif,http://foo.com/file2.tif"
         tile_size = 512
 
-        df = self.spark.read.rastersource(
+        df = self.spark.read.raster(
             tile_dimensions=(tile_size, tile_size),
             paths=path_param
         )
@@ -756,7 +756,7 @@ def path(scene, band):
         catalog_columns = ','.join(path_pandas.columns.tolist())  # 'b1,b2,b3'
         path_table = self.spark.createDataFrame(path_pandas)
 
-        path_df = self.spark.read.rastersource(
+        path_df = self.spark.read.raster(
             tile_dimensions=(512, 512),
             catalog=path_table,
             catalog_col_names=catalog_columns
diff --git a/rf-notebook/src/main/notebooks/Getting Started.ipynb b/rf-notebook/src/main/notebooks/Getting Started.ipynb
index b2d49d5df..33e3c6b6a 100644
--- a/rf-notebook/src/main/notebooks/Getting Started.ipynb	
+++ b/rf-notebook/src/main/notebooks/Getting Started.ipynb	
@@ -55,7 +55,7 @@
    "source": [
     "uri = 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059' \\\n",
     "      '/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF'\n",
-    "df = spark.read.rastersource(uri)"
+    "df = spark.read.raster(uri)"
    ]
   },
   {
@@ -127,7 +127,7 @@
    "source": [
     "The extent struct tells us where in the [CRS](https://spatialreference.org/ref/sr-org/6842/) the tile data covers. The granule is split into arbitrary sized chunks. Each row is a different chunk. Let's see how many.\n",
     "\n",
-    "Side note: you can configure the default size of these chunks, which are called Tiles, by passing a tuple of desired columns and rows as: `rastersource(uri, tile_dimensions=(96, 96))`. The default is `(256, 256)`"
+    "Side note: you can configure the default size of these chunks, which are called Tiles, by passing a tuple of desired columns and rows as: `raster(uri, tile_dimensions=(96, 96))`. The default is `(256, 256)`"
    ]
   },
   {
diff --git a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
index 45167345d..d0541a821 100644
--- a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
+++ b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
@@ -46,7 +46,7 @@
     "from pyspark.sql.functions import col\n",
     "from pyrasterframes.rasterfunctions import rf_crs, rf_extent\n",
     "# here we flatten the structure to get the tile type \n",
-    "df = spark.read.rastersource(uri) \\\n",
+    "df = spark.read.raster(uri) \\\n",
     "        .withColumn('tile2', col('tile').tile) \\\n",
     "        .withColumn('crs', rf_crs(col('tile'))) \\\n",
     "        .withColumn('ext', rf_extent(col('tile'))) \\\n",

From dbe18a597d40a209a82ed0044c7f304221ed0972 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 30 Jun 2019 11:27:19 -0400
Subject: [PATCH 207/380] Promoted `geojson` DataSource from experimental.
 Added to Python API.

---
 ...pache.spark.sql.sources.DataSourceRegister |   1 +
 .../datasource/geojson/DOM.scala              |   8 +-
 .../geojson/GeoJsonDataSource.scala           |   8 +-
 .../datasource/geojson/package.scala          |   2 +-
 .../src/test/resources/buildings.geojson      |   0
 .../src/test/resources/example.geojson        |   0
 .../geojson/GeoJsonDataSourceTest.scala       |   4 +-
 .../src/it/resources/larger-example.geojson   |  91 --
 ...pache.spark.sql.sources.DataSourceRegister |   1 -
 pyrasterframes/src/main/python/docs/index.md  |   2 +-
 .../main/python/docs/spatial-relations.pymd   |   1 -
 .../src/main/python/docs/vector-data.pymd     |  17 +
 .../main/python/pyrasterframes/__init__.py    |   6 +
 .../main/python/tests/PyRasterFramesTests.py  |   8 +
 .../src/main/python/tests/__init__.py         |  11 +-
 .../src/test/resources/buildings.geojson      | 899 ++++++++++++++++++
 16 files changed, 952 insertions(+), 107 deletions(-)
 rename {experimental/src/main/scala/org/locationtech/rasterframes/experimental => datasource/src/main/scala/org/locationtech/rasterframes}/datasource/geojson/DOM.scala (97%)
 rename {experimental/src/main/scala/org/locationtech/rasterframes/experimental => datasource/src/main/scala/org/locationtech/rasterframes}/datasource/geojson/GeoJsonDataSource.scala (95%)
 rename {experimental/src/main/scala/org/locationtech/rasterframes/experimental => datasource/src/main/scala/org/locationtech/rasterframes}/datasource/geojson/package.scala (94%)
 rename {experimental => datasource}/src/test/resources/buildings.geojson (100%)
 rename {experimental => datasource}/src/test/resources/example.geojson (100%)
 rename {experimental/src/test/scala/org/locationtech/rasterframes/experimental => datasource/src/test/scala/org/locationtech/rasterframes}/datasource/geojson/GeoJsonDataSourceTest.scala (95%)
 delete mode 100644 experimental/src/it/resources/larger-example.geojson
 delete mode 100644 pyrasterframes/src/main/python/docs/spatial-relations.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/vector-data.pymd
 create mode 100644 pyrasterframes/src/test/resources/buildings.geojson

diff --git a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index c3148d7ce..a44f6fccd 100644
--- a/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/datasource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -2,3 +2,4 @@ org.locationtech.rasterframes.datasource.geotiff.GeoTiffDataSource
 org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisLayerDataSource
 org.locationtech.rasterframes.datasource.geotrellis.GeoTrellisCatalog
 org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource
+org.locationtech.rasterframes.datasource.geojson.GeoJsonDataSource
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/DOM.scala
similarity index 97%
rename from experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/DOM.scala
index 1122f2bc7..dfbbb92f3 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/DOM.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/DOM.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,13 +19,13 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource.geojson
+package org.locationtech.rasterframes.datasource.geojson
 
+import geotrellis.vector.Extent
 import org.locationtech.jts.geom.{Envelope, Geometry}
 import org.locationtech.jts.io.geojson.{GeoJsonReader, GeoJsonWriter}
-import geotrellis.vector.Extent
-import spray.json._
 import spray.json.DefaultJsonProtocol._
+import spray.json._
 
 /**
  * Lightweight DOM for parsing GeoJSON feature sets.
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSource.scala
similarity index 95%
rename from experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSource.scala
index e2cb93a91..1bda41cd7 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSource.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,10 +19,8 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource.geojson
+package org.locationtech.rasterframes.datasource.geojson
 
-import org.locationtech.rasterframes.experimental.datasource.geojson.DOM._
-import org.locationtech.jts.geom.Geometry
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.jts.JTSTypes
@@ -30,6 +28,8 @@ import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationP
 import org.apache.spark.sql.types.{DataTypes, StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.locationtech.geomesa.spark.jts._
+import org.locationtech.jts.geom.Geometry
+import org.locationtech.rasterframes.datasource.geojson.DOM._
 import spray.json.DefaultJsonProtocol._
 import spray.json._
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/package.scala
similarity index 94%
rename from experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala
rename to datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/package.scala
index 0ffda39a0..6c49d75bc 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/geojson/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geojson/package.scala
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource
+package org.locationtech.rasterframes.datasource
 
 import org.apache.spark.sql.DataFrameReader
 
diff --git a/experimental/src/test/resources/buildings.geojson b/datasource/src/test/resources/buildings.geojson
similarity index 100%
rename from experimental/src/test/resources/buildings.geojson
rename to datasource/src/test/resources/buildings.geojson
diff --git a/experimental/src/test/resources/example.geojson b/datasource/src/test/resources/example.geojson
similarity index 100%
rename from experimental/src/test/resources/example.geojson
rename to datasource/src/test/resources/example.geojson
diff --git a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSourceTest.scala
similarity index 95%
rename from experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
rename to datasource/src/test/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSourceTest.scala
index 000193085..3d8ec9db3 100644
--- a/experimental/src/test/scala/org/locationtech/rasterframes/experimental/datasource/geojson/GeoJsonDataSourceTest.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geojson/GeoJsonDataSourceTest.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,7 +19,7 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource.geojson
+package org.locationtech.rasterframes.datasource.geojson
 import org.apache.spark.sql.types.{LongType, MapType}
 import org.locationtech.rasterframes.TestEnvironment
 
diff --git a/experimental/src/it/resources/larger-example.geojson b/experimental/src/it/resources/larger-example.geojson
deleted file mode 100644
index d0a3b1b82..000000000
--- a/experimental/src/it/resources/larger-example.geojson
+++ /dev/null
@@ -1,91 +0,0 @@
-{
-  "type": "FeatureCollection",
-  "crs": {
-    "type": "name",
-    "properties": {
-      "name": "EPSG:3857"
-    }
-  },
-  "features": [{
-    "type": "Feature",
-    "geometry": {
-      "type": "Point",
-      "coordinates": [0, 0]
-    },
-    "properties": {
-      "ID": 1
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "LineString",
-      "coordinates": [[4e6, -2e6], [8e6, 2e6]]
-    },
-    "properties": {
-      "ID": 2
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "LineString",
-      "coordinates": [[4e6, 2e6], [8e6, -2e6]]
-    },
-    "properties": {
-      "ID": 3
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "Polygon",
-      "coordinates": [[[-5e6, -1e6], [-4e6, 1e6], [-3e6, -1e6],[-5e6, -1e6]]]
-    },
-    "properties": {
-      "ID": 4
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "MultiLineString",
-      "coordinates": [
-        [[-1e6, -7.5e5], [-1e6, 7.5e5]],
-        [[1e6, -7.5e5], [1e6, 7.5e5]],
-        [[-7.5e5, -1e6], [7.5e5, -1e6]],
-        [[-7.5e5, 1e6], [7.5e5, 1e6]]
-      ]
-    },
-    "properties": {
-      "ID": 5
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "MultiPolygon",
-      "coordinates": [
-        [[[-5e6, 6e6], [-5e6, 8e6], [-3e6, 8e6], [-3e6, 6e6]]],
-        [[[-2e6, 6e6], [-2e6, 8e6], [0, 8e6], [0, 6e6]]],
-        [[[1e6, 6e6], [1e6, 8e6], [3e6, 8e6], [3e6, 6e6]]]
-      ]
-    },
-    "properties": {
-      "ID": 6
-    }
-  }, {
-    "type": "Feature",
-    "geometry": {
-      "type": "GeometryCollection",
-      "geometries": [{
-        "type": "LineString",
-        "coordinates": [[-5e6, -5e6], [0, -5e6]]
-      }, {
-        "type": "Point",
-        "coordinates": [4e6, -5e6]
-      }, {
-        "type": "Polygon",
-        "coordinates": [[[1e6, -6e6], [2e6, -4e6], [3e6, -6e6]]]
-      }]
-    },
-    "properties": {
-      "ID": 7
-    }
-  }]
-}
\ No newline at end of file
diff --git a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index 3db32938c..0f44a610b 100644
--- a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,4 +1,3 @@
-org.locationtech.rasterframes.experimental.datasource.geojson.GeoJsonDataSource
 org.locationtech.rasterframes.experimental.datasource.awspds.L8CatalogDataSource
 org.locationtech.rasterframes.experimental.datasource.awspds.MODISCatalogDataSource
 org.locationtech.rasterframes.experimental.datasource.awspds.L8DataSource
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index abd890ea6..63a0d57a0 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -41,7 +41,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 * [Description](description.md)
 * [Getting Started](getting-started.md)
 * [Raster Data I/O](raster-io.md)
-* [Spatial Relations](spatial-relations.md)
+* [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)
 * [Pandas, NumPy & RasterFrames](pandas-numpy.md)
 * [UDF Reference](reference.md)
diff --git a/pyrasterframes/src/main/python/docs/spatial-relations.pymd b/pyrasterframes/src/main/python/docs/spatial-relations.pymd
deleted file mode 100644
index 764d6e620..000000000
--- a/pyrasterframes/src/main/python/docs/spatial-relations.pymd
+++ /dev/null
@@ -1 +0,0 @@
-# Spatial Relations
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
new file mode 100644
index 000000000..241ca6974
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -0,0 +1,17 @@
+# Vector Data
+
+* `geojson` DataSource for going right into Spark
+* Shapely or Python-side processing, getting into Spark via Pandas  
+
+## GeoJSON DataSource
+
+Show `spark.read.geojson(...)` example.
+
+## Shapely
+
+Show Shapely interop
+
+## Spatial Relations
+
+Using `st_` functions, particularly boolean relations.
+
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 772a6b107..09805643f 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -98,6 +98,10 @@ def _layer_reader(df_reader, format_key, path, **options):
     return _convert_df(df)
 
 
+def _aliased_reader(df_reader, format_key, path, **options):
+    """ Loads the file of the given type at the given path."""
+    return df_reader.format(format_key).load(path, **options)
+
 def _raster_reader(
         df_reader, path=None,
         band_indexes=None,
@@ -155,6 +159,8 @@ def to_csv(comp):
 
 # Add DataSource convenience methods to the DataFrameReader
 DataFrameReader.raster = _raster_reader
+DataFrameReader.geojson = lambda df_reader, path: _aliased_reader(df_reader, "geojson", path)
+
 
 # Legacy readers
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index a5a3a8002..3ba625e9d 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -134,6 +134,14 @@ def area(g):
     def test_reproject(self):
         reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
         reprojected.show()
+        self.assertEqual(reprojected.count(), 8)
+
+    def test_geojson(self):
+        import os
+        sample = 'file://' + os.path.join(self.resource_dir, 'buildings.geojson')
+        geo = self.spark.read.geojson(sample)
+        geo.show()
+        self.assertEqual(geo.select('geometry').count(), 8)
 
 
 class RasterFunctions(TestEnvironment):
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 3d09a4d75..3ed3e2edf 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -33,9 +33,16 @@
 
 
 def resource_dir():
+    def pdir(curr):
+        return os.path.dirname(curr)
+
     here = os.path.dirname(os.path.realpath(__file__))
-    scala_target = os.path.realpath(os.path.join(here, '..', '..', 'scala-2.11'))
-    return os.path.realpath(os.path.join(scala_target, 'test-classes'))
+    scala_target = os.path.realpath(os.path.join(pdir(pdir(here)), 'scala-2.11'))
+    rez_dir = os.path.realpath(os.path.join(scala_target, 'test-classes'))
+    # If not running in build mode, try source dirs.
+    if not os.path.exists(rez_dir):
+        rez_dir = os.path.realpath(os.path.join(pdir(pdir(pdir(here))), 'test', 'resources'))
+    return rez_dir
 
 
 def spark_test_session():
diff --git a/pyrasterframes/src/test/resources/buildings.geojson b/pyrasterframes/src/test/resources/buildings.geojson
new file mode 100644
index 000000000..91aea5924
--- /dev/null
+++ b/pyrasterframes/src/test/resources/buildings.geojson
@@ -0,0 +1,899 @@
+{
+  "type": "FeatureCollection",
+  "crs": {
+    "type": "name",
+    "properties": {
+      "name": "urn:ogc:def:crs:EPSG::3968"
+    }
+  },
+  "bbox": [
+    10453.2340000011,
+    137465.4443,
+    0,
+    203572.226800002,
+    261914.089200001,
+    518
+  ],
+  "features": [
+    {
+      "type": "Feature",
+      "properties": {
+        "OBJECTID": 2560367,
+        "BLDGHEIGHT": 0,
+        "NUMSTORIES": 0,
+        "FEATURECOD": null,
+        "LASTUPDATE": "2017\/08\/15",
+        "LASTEDITOR": "VGIN",
+        "RuleID": 0,
+        "BUILDINGCL": 0,
+        "DATASOURCE": "Jurisdiction",
+        "EDITCOMMEN": null,
+        "SOURCEFEAT": "23712149512",
+        "RuleID_1": 1,
+        "SFIDdupes": 0,
+        "FIPS": "51680",
+        "MUNICIPALI": "Lynchburg City",
+        "SHAPE_Leng": 112.01513810500001,
+        "SHAPE_Area": 612.03868604499996,
+        "AREA_SQFT": 6587.9
+      },
+      "bbox": [
+        23695.017599999904633,
+        149496.648900002241135,
+        0.0,
+        23732.747999999672174,
+        149527.1985,
+        0.0
+      ],
+      "geometry": {
+        "type": "MultiPolygon",
+        "coordinates": [
+          [
+            [
+              [
+                23730.124099999666214,
+                149514.097699999809265,
+                0.0
+              ],
+              [
+                23728.569000001996756,
+                149511.027800001204014,
+                0.0
+              ],
+              [
+                23732.747999999672174,
+                149508.911100000143051,
+                0.0
+              ],
+              [
+                23731.134500000625849,
+                149505.72520000115037,
+                0.0
+              ],
+              [
+                23727.252300001680851,
+                149507.691500000655651,
+                0.0
+              ],
+              [
+                23725.473999999463558,
+                149504.180500000715256,
+                0.0
+              ],
+              [
+                23722.397100001573563,
+                149505.523900002241135,
+                0.0
+              ],
+              [
+                23717.634100001305342,
+                149496.648900002241135,
+                0.0
+              ],
+              [
+                23696.2179000005126,
+                149507.229299999773502,
+                0.0
+              ],
+              [
+                23698.083399999886751,
+                149510.912300001829863,
+                0.0
+              ],
+              [
+                23696.541700001806021,
+                149511.693300001323223,
+                0.0
+              ],
+              [
+                23695.017599999904633,
+                149512.464999999850988,
+                0.0
+              ],
+              [
+                23702.387800000607967,
+                149527.1985,
+                0.0
+              ],
+              [
+                23730.124099999666214,
+                149514.097699999809265,
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+}
\ No newline at end of file

From 867e9205932d6b3c5920aa02a9b3d1f18759a27c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Sun, 30 Jun 2019 17:02:45 -0400
Subject: [PATCH 208/380] Initial rework of PDS L8 catalog DataSource.

---
 .../encoders/SparkBasicEncoders.scala         |   1 +
 .../src/it/resources/log4j.properties         |   2 +-
 .../awspds/L8CatalogRelationTest.scala        |  89 ++++++++--
 .../datasource/awspds/L8RelationTest.scala    |  65 -------
 .../awspds/MODISCatalogRelationTest.scala     |  21 ---
 ...pache.spark.sql.sources.DataSourceRegister |   1 -
 experimental/src/main/resources/slippy.html   |  83 ---------
 .../{awspds => }/CachedDatasetRelation.scala  |   8 +-
 .../datasource/DownloadExpression.scala       |  73 --------
 .../datasource/ReadTilesExpression.scala      | 165 ------------------
 .../{awspds => }/ResourceCacheSupport.scala   |   9 +-
 .../awspds/L8CatalogDataSource.scala          |   3 +-
 .../datasource/awspds/L8CatalogRelation.scala |  47 +++--
 .../datasource/awspds/L8DataSource.scala      |  42 -----
 .../datasource/awspds/L8Relation.scala        | 137 ---------------
 .../awspds/MODISCatalogDataSource.scala       |   5 +-
 .../awspds/MODISCatalogRelation.scala         |   3 +-
 .../datasource/awspds/package.scala           |  23 ++-
 .../experimental/datasource/package.scala     |  13 --
 19 files changed, 133 insertions(+), 657 deletions(-)
 delete mode 100644 experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
 delete mode 100644 experimental/src/main/resources/slippy.html
 rename experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/{awspds => }/CachedDatasetRelation.scala (90%)
 delete mode 100644 experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
 delete mode 100644 experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
 rename experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/{awspds => }/ResourceCacheSupport.scala (92%)
 delete mode 100644 experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
 delete mode 100644 experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
index 7ec8121b5..e2830f7f1 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/SparkBasicEncoders.scala
@@ -40,3 +40,4 @@ private[rasterframes] trait SparkBasicEncoders {
   implicit val boolEnc: Encoder[Boolean] = Encoders.scalaBoolean
 }
 
+object SparkBasicEncoders extends SparkBasicEncoders
\ No newline at end of file
diff --git a/experimental/src/it/resources/log4j.properties b/experimental/src/it/resources/log4j.properties
index 85cd816dd..5cc16f4db 100644
--- a/experimental/src/it/resources/log4j.properties
+++ b/experimental/src/it/resources/log4j.properties
@@ -35,7 +35,7 @@ log4j.logger.org.spark_project.jetty=WARN
 log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
-log4j.logger.org.locationtech.rasterframes=WARN
+log4j.logger.org.locationtech.rasterframes=DEBUG
 log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index 448bacca8..85639ff41 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -20,11 +20,11 @@
 
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import java.net.URL
+import java.net.{HttpURLConnection, URL, URLConnection}
 
 import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.experimental.datasource._
-import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes.{TestEnvironment, rf_agg_stats}
+import org.locationtech.rasterframes.datasource.raster._
 
 /**
  * Test rig for L8 catalog stuff.
@@ -32,38 +32,89 @@ import org.locationtech.rasterframes.TestEnvironment
  * @since 5/4/18
  */
 class L8CatalogRelationTest extends TestEnvironment {
+
+
   describe("Representing L8 scenes as a Spark data source") {
     import spark.implicits._
-    val catalog = spark.read.format(L8CatalogDataSource.SHORT_NAME).load()
+    val catalog = spark.read.l8Catalog.load()
 
     val scenes = catalog
       .where($"acquisition_date" === to_timestamp(lit("2017-04-04 15:12:55.394")))
       .where($"path" === 11 && $"row" === 12)
 
     it("should provide a non-empty catalog") {
-      assert(scenes.count() === 1)
+      scenes.count() shouldBe 1
+    }
+
+    it("should provide 11 band + 1 QA urls") {
+      scenes.schema.count(_.name.startsWith("B")) shouldBe 12
     }
 
-    it("should construct band specific download URLs") {
-      val b01 = scenes.select(l8_band_url("B1"))
-      noException shouldBe thrownBy {
-        new URL(b01.first())
+    it("should construct valid URLs") {
+
+      def urlResponse(urlStr: String): Int = {
+        val conn = new URL(urlStr).openConnection().asInstanceOf[HttpURLConnection]
+        try {
+          conn.setRequestMethod("GET")
+          conn.connect()
+          conn.getResponseCode
+        }
+        finally {
+          conn.disconnect()
+        }
       }
+
+      val urlStr = scenes.select("B11").as[String].first
+      val code = urlResponse(urlStr)
+      code shouldBe 200
     }
 
-    it("should download geotiff as blob") {
-      import org.apache.spark.sql.functions.{length ⇒ alength}
-      val b01 = scenes.limit(1)
-        .select(download(l8_band_url("B1")) as "data")
+    it("should work with SQL and spatial predicates") {
+      catalog.createOrReplaceTempView("l8_catalog")
+      val scenes = spark.sql("""
+        SELECT st_geometry(bounds_wgs84) as geometry, acquisition_date, B1, B2
+        FROM l8_catalog
+        WHERE
+         st_intersects(st_geometry(bounds_wgs84), st_geomFromText('LINESTRING (-39.551 -7.1881, -72.2461 -45.7062)')) AND
+         acquisition_date > to_timestamp('2017-11-01') AND
+         acquisition_date <= to_timestamp('2017-11-03')
+        """)
 
-      val len = b01.select(alength($"data").as[Long])
-      assert(len.first() >= 4000000)
+      scenes.count() shouldBe > (200L)
     }
+  }
+
+  describe("Read L8 scenes from PDS") {
+    import spark.implicits._
+    val catalog = spark.read.l8Catalog.load().repartition(8)
 
-    it("should download geotiff as tiles") {
-      val b01 = scenes
-        .select($"*", read_tiles(l8_band_url("B1")))
-      assert(b01.count() === 1089)
+    it("should be compatible with raster DataSource") {
+      val df = spark.read.raster
+        .fromCatalog(catalog, "B1", "B8")
+        .withTileDimensions(512, 512)
+        .load()
+
+      df.printSchema()
+      df.show(false)
     }
+
+//    it("should count scenes") {
+//
+//
+//
+//
+//      val scenes = sql("SELECT entity_id FROM l8 DISTINCT")
+//      scenes.count() shouldBe > (300400L)
+//
+//      val subscenes = sqlContext.table("subscenes")
+//      subscenes.schema.size should be (4)
+//      subscenes.count() should be (7)
+//    }
+//
+//    it("should compute statistics") {
+//      val subscenes = sqlContext.table("subscenes")
+//      val stats = subscenes.select(rf_agg_stats($"B1")).first()
+//      stats.data_cells shouldBe > (420024000L)
+//    }
   }
 }
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
deleted file mode 100644
index f53246c09..000000000
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8RelationTest.scala
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea. Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- *
- */
-
-package org.locationtech.rasterframes.experimental.datasource.awspds
-
-import org.locationtech.rasterframes._
-import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
-
-/**
- * @since 8/21/18
- */
-class L8RelationTest extends TestEnvironment with BeforeAndAfterAll with BeforeAndAfter {
-
-  val query =  """
-            |SELECT geometry, timestamp, B1, B2
-            |FROM l8
-            |WHERE
-            |  st_intersects(geometry, st_geomFromText('LINESTRING (-39.551 -7.1881, -72.2461 -45.7062)')) AND
-            |  timestamp > to_timestamp('2017-11-01') AND
-            |  timestamp <= to_timestamp('2017-11-03')
-          """.stripMargin
-
-  override protected def beforeAll(): Unit = {
-    val l8 = spark.read
-      .format(L8DataSource.SHORT_NAME)
-      .load()
-    l8.createOrReplaceTempView("l8")
-    sql(query).createOrReplaceTempView("subscenes")
-  }
-
-  describe("Read L8 on PDS as a DataSource") {
-    import spark.implicits._
-    it("should count scenes") {
-      val scenes = sql("SELECT entity_id FROM l8 DISTINCT")
-      scenes.count() shouldBe >(300400L)
-
-      val subscenes = sqlContext.table("subscenes")
-      subscenes.schema.size should be (4)
-      subscenes.count() should be(7)
-    }
-
-    it("should compute statistics") {
-      val subscenes = sqlContext.table("subscenes")
-      val stats = subscenes.select(rf_agg_stats($"B1")).first()
-      stats.data_cells shouldBe >(420024000L)
-    }
-  }
-}
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
index a71ec0218..c7d11c917 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
@@ -22,7 +22,6 @@ package org.locationtech.rasterframes.experimental.datasource.awspds
 import java.net.URL
 import java.sql.Timestamp
 
-import org.locationtech.rasterframes.experimental.datasource._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.TestEnvironment
 
@@ -50,25 +49,5 @@ class MODISCatalogRelationTest extends TestEnvironment {
         new URL(b01.first())
       }
     }
-
-    it("should download geotiff as blob") {
-      import org.apache.spark.sql.functions.{length ⇒ alength}
-      val b01 = scenes.limit(1)
-        .select(download($"assets"("B01")) as "data")
-
-      val len = b01.select(alength($"data").as[Long])
-      assert(len.first() >= 4000000)
-    }
-
-    it("should download geotiff as tiles") {
-      val b01 = scenes
-        .select(read_tiles($"assets"("B01") as "B01", $"assets"("B02") as "B02"))
-      assert(b01.count() === 100)
-
-//      val kv = b01.select($"B01_extent", $"B01_tile").as[(Extent, Tile)]
-//      kv.collect.zipWithIndex.foreach { case ((extent, tile), idx) ⇒
-//        GeoTiff(tile, extent, Sinusoidal).write(s"target/b01-tile-$idx.tiff")
-//      }
-    }
   }
 }
diff --git a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index 0f44a610b..97275f043 100644
--- a/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/experimental/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,3 +1,2 @@
 org.locationtech.rasterframes.experimental.datasource.awspds.L8CatalogDataSource
 org.locationtech.rasterframes.experimental.datasource.awspds.MODISCatalogDataSource
-org.locationtech.rasterframes.experimental.datasource.awspds.L8DataSource
diff --git a/experimental/src/main/resources/slippy.html b/experimental/src/main/resources/slippy.html
deleted file mode 100644
index 4f616928f..000000000
--- a/experimental/src/main/resources/slippy.html
+++ /dev/null
@@ -1,83 +0,0 @@
-<!DOCTYPE html>
-<!--
-  ~ This software is licensed under the Apache 2 license, quoted below.
-  ~
-  ~ Copyright 2018 Astraea. Inc.
-  ~
-  ~ Licensed under the Apache License, Version 2.0 (the "License"); you may not
-  ~ use this file except in compliance with the License. You may obtain a copy of
-  ~ the License at
-  ~
-  ~     [http://www.apache.org/licenses/LICENSE-2.0]
-  ~
-  ~ Unless required by applicable law or agreed to in writing, software
-  ~ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-  ~ WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-  ~ License for the specific language governing permissions and limitations under
-  ~ the License.
-  ~
-  ~
-  -->
-
-<html lang="en">
-<head>
-    <title>RasterFrames</title>
-    <meta charset="utf-8" />
-   	<meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.3.1/dist/leaflet.css" integrity="sha512-Rksm5RenBEKSKFjgI3a41vrjkw4EVPlJ3+OiI65vTjIdo9brlAacEuKOiQ5OFh7cOI1bkDwLqdLw3Zg0cRJAAQ==" crossorigin=""/>
-    <script src="https://unpkg.com/leaflet@1.3.1/dist/leaflet.js" integrity="sha512-/Nsx9X4HebavoBvEBuyp3I7od5tA0UzAxs+j83KgC8PU0kgB4XiK4Lfe4y4cgBtaRJQEIFCW+oC506aPT2L1zw==" crossorigin=""></script>
-    <link rel="stylesheet" href="https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.css" />
-    <script src="https://unpkg.com/leaflet-control-geocoder/dist/Control.Geocoder.js"></script>
-    <style>
-        #mapid {
-            position: absolute;
-            top: 10px;
-            bottom: 10px;
-            left: 10px;
-            right: 10px;
-        }
-    </style>
-</head>
-<body>
-
-<div id="mapid"></div>
-
-<script>
-
-    var map = L.map('mapid')
-        .setView([${viewLat}, ${viewLon}], ${maxZoom});
-
-   	L.tileLayer(
-   	    'https://api.tiles.mapbox.com/v4/{id}/{z}/{x}/{y}.png?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw', {
-   		maxZoom: 18,
-   		attribution: 'Map data &copy; <a href="http://openstreetmap.org">OpenStreetMap</a> contributors, ' +
-   			'<a href="http://creativecommons.org/licenses/by-sa/2.0/">CC-BY-SA</a>, ' +
-   			'Imagery © <a href="http://mapbox.com">Mapbox</a>',
-   		id: 'mapbox.streets'
-   	}).addTo(map);
-
-   	L.tileLayer(
-   	    '{id}/{z}/{x}/{y}.png', {
-   	        maxZoom: ${maxZoom},
-            id: '${id}'
-        }
-    ).addTo(map);
-
-    L.control.scale().addTo(map);
-
-    L.Control.geocoder().addTo(map);
-
-   	var popup = L.popup();
-
-   	function showPos(e) {
-   		popup
-   			.setLatLng(e.latlng)
-   			.setContent(e.latlng.toString())
-   			.openOn(map);
-   	}
-
-   	map.on('click', showPos);
-
-</script>
-</body>
-</html>
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/CachedDatasetRelation.scala
similarity index 90%
rename from experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/CachedDatasetRelation.scala
index 37be5335e..231b2411e 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/CachedDatasetRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/CachedDatasetRelation.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2089 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,13 +19,13 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource
 
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
+import org.apache.hadoop.fs.{FileSystem, Path => HadoopPath}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.sources.BaseRelation
+import org.apache.spark.sql.{Dataset, Row}
 import org.locationtech.rasterframes.util._
 
 /**
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
deleted file mode 100644
index 0a39071ff..000000000
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/DownloadExpression.scala
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes.experimental.datasource
-
-import java.net.URI
-
-import org.locationtech.rasterframes.util.GeoTiffInfoSupport
-import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.sql.{Column, TypedColumn}
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure, TypeCheckSuccess}
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.catalyst.expressions.{Expression, Generator, GenericInternalRow, UnaryExpression}
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-import org.locationtech.rasterframes.util._
-
-/**
- * Downloads data from URL and stores it in a column.
- *
- * @since 5/4/18
- */
-case class DownloadExpression(override val child: Expression, colPrefix: String) extends UnaryExpression
-  with Generator with CodegenFallback with GeoTiffInfoSupport with DownloadSupport with LazyLogging {
-
-  override def nodeName: String = "download"
-
-  override def checkInputDataTypes(): TypeCheckResult = {
-    if(child.dataType == StringType) TypeCheckSuccess
-    else TypeCheckFailure(
-      s"Expected '${StringType.typeName}' but received '${child.dataType.simpleString}'"
-    )
-  }
-
-  override def elementSchema: StructType = StructType(Seq(
-    StructField(colPrefix + "_data", BinaryType, true)
-  ))
-
-  override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
-    val urlString = child.eval(input).asInstanceOf[UTF8String]
-    val bytes = getBytes(URI.create(urlString.toString))
-    Traversable(new GenericInternalRow(Array[Any](bytes)))
-  }
-}
-
-object DownloadExpression {
-  import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders.arrayEnc
-
-  def apply(urlColumn: Column): TypedColumn[Any, Array[Byte]] =
-    new Column(
-      new DownloadExpression(urlColumn.expr, urlColumn.columnName)
-    ).as[Array[Byte]]
-}
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
deleted file mode 100644
index 37e32ba46..000000000
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ReadTilesExpression.scala
+++ /dev/null
@@ -1,165 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes.experimental.datasource
-
-import java.net.URI
-
-import org.locationtech.rasterframes.TileType
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import com.typesafe.scalalogging.LazyLogging
-import geotrellis.proj4.CRS
-import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.{ProjectedRaster, Tile}
-import geotrellis.spark.io.hadoop.HdfsRangeReader
-import geotrellis.spark.io.http.util.HttpRangeReader
-import geotrellis.spark.io.s3.S3Client
-import geotrellis.spark.io.s3.util.S3RangeReader
-import geotrellis.util.{ByteReader, FileRangeReader, RangeReader, StreamingByteReader}
-import geotrellis.vector.Extent
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.Path
-import org.apache.spark.sql.Column
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, Generator, Literal}
-import org.apache.spark.sql.types._
-
-/**
- * Catalyst generator to convert a geotiff download URL into a series of rows containing the internal
- * tiles and associated extents.
- *
- * @since 5/4/18
- */
-case class ReadTilesExpression(children: Seq[Expression]) extends Expression
-  with Generator with CodegenFallback with DownloadSupport with LazyLogging {
-
-  override def nodeName: String = "download_tiles"
-
-  def inputTypes = Seq.fill(children.length)(StringType)
-
-  override def elementSchema: StructType = StructType(Seq(
-    StructField("crs", schemaOf[CRS], true),
-    StructField("extent", schemaOf[Extent], true)
-  ) ++
-    children
-      .zipWithIndex
-      .map {
-        case (l: Literal, _) ⇒ String.valueOf(l.value)
-        case (a: Alias, _) ⇒ a.name
-        case (_, i) ⇒ s"_${i + 1}"
-      }
-      .map(name ⇒ {
-        StructField(name, TileType, true)
-      })
-  )
-
-  private def reader(uri: URI): ByteReader = {
-    val rr: RangeReader = uri.getScheme match {
-      case "http" | "https" ⇒ HttpRangeReader(uri)
-      case "file" ⇒ FileRangeReader(uri.getPath)
-      case "hdfs" | "s3n" | "s3a" | "wasb" | "wasbs" ⇒
-        HdfsRangeReader(new Path(uri), new Configuration())
-      case "s3" ⇒
-        S3RangeReader(uri, S3Client.DEFAULT)
-    }
-    StreamingByteReader(rr)
-  }
-
-  def checkDimensions(tiles: Array[Array[ProjectedRaster[Tile]]], uris: Seq[URI]) = {
-    val rowDims = tiles.map(row ⇒ {
-      row.map {
-        case null ⇒ null
-        case t ⇒ t.tile.dimensions
-      }
-    })
-
-    val notSameDim = rowDims
-      .map(_.distinct.count(_ != null))
-      .indexWhere(_ > 1)
-
-    if (notSameDim >= 0) {
-      throw new IllegalArgumentException("Detecuris: Seq[Option[String]] ted rows with different sized tiles: " +
-        rowDims(notSameDim).mkString(", ") + "\nfrom: " + uris.mkString(", "))
-    }
-  }
-
-
-  private def safeTranspose(tiles: Seq[Seq[ProjectedRaster[Tile]]]) = {
-    // We need to transpose the nested sequence of from column major to row major order
-    // However, some columns have no rows, the default transpose needs a regular grid...
-    // so we do it manually.
-    val numRows = tiles.map(_.size).max
-    val numCols = tiles.length
-    val tileGrid = Array.fill(numRows)(Array.ofDim[ProjectedRaster[Tile]](numCols))
-
-    for {
-      (rows, col) ← tiles.zipWithIndex
-      (tile, row) ← rows.zipWithIndex
-    } {
-      tileGrid(row)(col) = tile
-    }
-    tileGrid
-  }
-
-  override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
-    val uris = children.map(_.eval(input).toString).map(URI.create)
-    val colMajor = uris.map { uri ⇒
-      val tiff = GeoTiffReader.readSingleband(reader(uri))
-      val segmentLayout = tiff.imageData.segmentLayout
-      val windows = segmentLayout.listWindows(256)
-      val re = tiff.rasterExtent
-
-      val subtiles = tiff.crop(windows)
-      for {
-        (gridbounds, tile) ← subtiles.toSeq
-      } yield {
-        val extent = re.extentFor(gridbounds, false)
-        ProjectedRaster(tile, extent, tiff.crs)
-      }
-    }
-
-    val rowMajor = safeTranspose(colMajor)
-
-    checkDimensions(rowMajor, uris)
-
-    rowMajor.map(row ⇒ {
-      val serializedTiles = row.map {
-        case null ⇒ null
-        case t ⇒ TileType.serialize(t.tile)
-      }
-
-      val firstBandExt = row
-        .find(_ != null)
-        .map(p ⇒ Seq(p.crs.toInternalRow, p.extent.toInternalRow))
-        .getOrElse(Seq(null, null))
-
-      InternalRow(firstBandExt ++ serializedTiles: _*)
-    })
-  }
-
-}
-
-object ReadTilesExpression {
-  def apply(urls: Seq[Column]): Column =  new Column(
-    new ReadTilesExpression(urls.map(_.expr))
-  )
-}
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ResourceCacheSupport.scala
similarity index 92%
rename from experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
rename to experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ResourceCacheSupport.scala
index f203a187f..45173572f 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/ResourceCacheSupport.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/ResourceCacheSupport.scala
@@ -1,7 +1,7 @@
 /*
  * This software is licensed under the Apache 2 license, quoted below.
  *
- * Copyright 2018 Astraea, Inc.
+ * Copyright 2019 Astraea, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License"); you may not
  * use this file except in compliance with the License. You may obtain a copy of
@@ -19,17 +19,16 @@
  *
  */
 
-package org.locationtech.rasterframes.experimental.datasource.awspds
+package org.locationtech.rasterframes.experimental.datasource
 
 import java.net.URI
 import java.time.{Duration, Instant}
 
-import org.locationtech.rasterframes.experimental.datasource.DownloadSupport
-import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.commons.io.FilenameUtils
-import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
+import org.apache.hadoop.fs.{FileSystem, Path => HadoopPath}
 import org.apache.hadoop.io.MD5Hash
+import org.locationtech.rasterframes.util._
 
 import scala.util.Try
 import scala.util.control.NonFatal
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
index 47ff0afab..aad1cd2fc 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogDataSource.scala
@@ -28,6 +28,7 @@ import com.typesafe.scalalogging.LazyLogging
 import org.apache.hadoop.fs.FileSystem
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+import org.locationtech.rasterframes.experimental.datasource.ResourceCacheSupport
 
 /**
  * Data source for querying AWS PDS catalog of L8 imagery.
@@ -48,7 +49,7 @@ class L8CatalogDataSource extends DataSourceRegister with RelationProvider {
 }
 
 object L8CatalogDataSource extends LazyLogging with ResourceCacheSupport {
-  final val SHORT_NAME: String = "awsl8-catalog"
+  final val SHORT_NAME: String = "aws-pds-l8-catalog"
   private val remoteSource = URI.create("http://landsat-pds.s3.amazonaws.com/c1/L8/scene_list.gz")
   private def sceneListFile(implicit fs: FileSystem) =
     cachedURI(remoteSource).getOrElse(throw new FileNotFoundException(remoteSource.toString))
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
index 3f5c9a142..a7b426176 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
@@ -22,12 +22,14 @@
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
-import org.locationtech.jts.geom.Envelope
-import org.apache.hadoop.fs.{Path ⇒ HadoopPath}
+import geotrellis.vector.Extent
+import org.apache.hadoop.fs.{Path => HadoopPath}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Dataset, Row, SQLContext}
+import org.apache.spark.sql.{Dataset, Row, SQLContext, TypedColumn}
+import org.locationtech.rasterframes.encoders.SparkBasicEncoders.stringEnc
+import org.locationtech.rasterframes.experimental.datasource.CachedDatasetRelation
 /**
  * Schema definition and parser for AWS PDS L8 scene data.
  *
@@ -43,9 +45,12 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
   protected def cacheFile: HadoopPath = sceneListPath.suffix(".parquet")
 
   protected def constructDataset: Dataset[Row] = {
-    import org.locationtech.rasterframes.encoders.StandardEncoders.envelopeEncoder
+    import org.locationtech.rasterframes.encoders.StandardEncoders.extentEncoder
     import sqlContext.implicits._
     logger.debug("Parsing " + sceneListPath)
+
+    val bandCols = Bands.values.toSeq.map(b => l8_band_url(b) as (b.toString))
+
     sqlContext.read
       .schema(inputSchema)
       .option("header", "true")
@@ -54,12 +59,13 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
       .where(not($"${PRODUCT_ID.name}".endsWith("RT")))
       .drop("download_url")
       .withColumn(BOUNDS_WGS84.name, struct(
-        $"min_lon" as "minX",
-        $"max_lon" as "maxX",
-        $"min_lat" as "minY",
-        $"max_lat" as "maxY"
-      ).as[Envelope])
+        $"min_lon" as "xmin",
+        $"min_lat" as "ymin",
+        $"max_lon" as "xmax",
+        $"max_lat" as "ymax"
+      ).as[Extent])
       .withColumnRenamed("__url", DOWNLOAD_URL.name)
+      .select(col("*") +: bandCols: _*)
       .select(schema.map(f ⇒ col(f.name)): _*)
       .orderBy(ACQUISITION_DATE.name, PATH.name, ROW.name)
       .distinct() // The scene file contains duplicates.
@@ -69,6 +75,17 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
 
 object L8CatalogRelation extends PDSFields {
 
+
+  /**
+    * Constructs link with the form:
+    * `https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_{bandId].TIF`
+    * @param band Band identifier
+    * @return
+    */
+  def l8_band_url(band: Bands.Band): TypedColumn[Any, String] = {
+    concat(col("download_url"), concat(col("product_id"), lit(s"_$band.TIF")))
+  }.as(band.toString).as[String]
+
   private def inputSchema = StructType(Seq(
     PRODUCT_ID,
     ENTITY_ID,
@@ -84,6 +101,13 @@ object L8CatalogRelation extends PDSFields {
     DOWNLOAD_URL
   ))
 
+  object Bands extends Enumeration {
+    type Band = Value
+    val B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, BQA = Value
+    val names: Seq[String] = values.toSeq.map(_.toString)
+  }
+
+
   def schema = StructType(Seq(
     PRODUCT_ID,
     ENTITY_ID,
@@ -92,9 +116,8 @@ object L8CatalogRelation extends PDSFields {
     PROC_LEVEL,
     PATH,
     ROW,
-    BOUNDS_WGS84,
-    DOWNLOAD_URL
-  ))
+    BOUNDS_WGS84
+  ) ++ Bands.names.map(n => StructField(n, StringType, true)))
 }
 
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
deleted file mode 100644
index 95e70ff4f..000000000
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8DataSource.scala
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes.experimental.datasource.awspds
-
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
-
-/**
- * Experiment in providing RasterFrames over whole PDS catalog.
- *
- * @since 8/21/18
- */
-class L8DataSource extends DataSourceRegister with RelationProvider {
-  override def shortName(): String = L8DataSource.SHORT_NAME
-
-  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
-    L8Relation(sqlContext)
-  }
-}
-
-object L8DataSource {
-  final val SHORT_NAME = "awsl8"
-}
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
deleted file mode 100644
index 43c180cac..000000000
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8Relation.scala
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- */
-
-package org.locationtech.rasterframes.experimental.datasource.awspds
-
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.experimental.datasource.awspds.L8Relation.Bands
-import org.locationtech.rasterframes.expressions.transformers._
-import org.locationtech.rasterframes.rules.SpatialFilters.{Contains, Intersects}
-import org.locationtech.rasterframes.rules._
-import org.locationtech.rasterframes.util._
-import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, Row, SQLContext}
-import org.locationtech.rasterframes.tiles.ProjectedRasterTile
-
-/**
- * Spark relation over AWS PDS Landsat 8 collection.
- *
- * @since 8/21/18
- */
-case class L8Relation(sqlContext: SQLContext, filters: Seq[Filter] = Seq.empty)
-  extends BaseRelation with PrunedFilteredScan with SpatialRelationReceiver[L8Relation] with LazyLogging {
-  override def schema: StructType = L8Relation.schema
-
-  /** Create new relation with the give filter added. */
-  override def withFilter(value: Filter): L8Relation = copy(filters = filters :+ value)
-
-  /** Check to see if relation already exists in this. */
-  override def hasFilter(filter: Filter): Boolean = filters.contains(filter)
-
-  // Most implementations don't bother with this... the hassle vs. benefit may be questionable.
-  override def unhandledFilters(filters: Array[Filter]): Array[Filter] = {
-    val TS = PDSFields.TIMESTAMP.name
-    super.unhandledFilters(filters).filterNot {
-      case GreaterThan(TS, _) ⇒ true
-      case GreaterThanOrEqual(TS, _) ⇒ true
-      case LessThan(TS, _) ⇒ true
-      case LessThanOrEqual(TS, _) ⇒ true
-      case _ ⇒ false
-    }
-  }
-
-  // TODO: Is there a more clean, direct way of delegating filtering other
-  // TODO: having to reconstitute the predicates like this?
-  private def colExpr(filter: Filter): Column = filter match {
-    case GreaterThan(name, value) ⇒ col(name) > value
-    case GreaterThanOrEqual(name, value) ⇒ col(name) >= value
-    case LessThan(name, value) ⇒ col(name) < value
-    case LessThanOrEqual(name, value) ⇒ col(name) <= value
-    case EqualTo(name, value) ⇒ col(name) === value
-    case Intersects(name, value) ⇒ st_intersects(col(name), geomLit(value))
-    case Contains(name, value) ⇒ st_contains(col(name), geomLit(value))
-  }
-
-  override def buildScan(requiredColumns: Array[String], sparkFilters: Array[Filter]): RDD[Row] = {
-    logger.debug(s"Required columns: ${requiredColumns.mkString(", ")}")
-    val aggFilters = (sparkFilters ++ splitFilters(filters)).distinct
-    if(aggFilters.isEmpty) {
-      logger.warn("No filters provided. Full catalog scan invoked.")
-    }
-    else {
-      logger.debug(s"Filters: $aggFilters")
-    }
-
-    val catalog = sqlContext.read
-      .format(L8CatalogDataSource.SHORT_NAME)
-      .load()
-      .withColumnRenamed(PDSFields.ACQUISITION_DATE.name, PDSFields.TIMESTAMP.name)
-      .withColumn(PDSFields.BOUNDS.name, st_geometry(col(PDSFields.BOUNDS_WGS84.name)))
-      .drop(PDSFields.BOUNDS_WGS84.name)
-
-    val filtered = aggFilters
-      .foldLeft(catalog)((d, filter) ⇒ d.where(colExpr(filter)))
-
-    val (bands, other) = requiredColumns.partition(Bands.names.contains)
-
-    val nonTile = other.map(col)
-
-    val df = if (bands.nonEmpty) {
-      val sources = bands.map(b ⇒ URIToRasterSource(l8_band_url(b)).as(b))
-      // NB: We assume that `nativeTiling` preserves the band names.
-      val expanded = RasterSourceToRasterRefs(None, Seq(0), sources: _*)
-
-      filtered
-        .select(nonTile :+ expanded: _*)
-        .select(nonTile ++ bands.map(b => RasterRefToTile(col(b + "_ref")) as b): _*)
-    } else filtered
-
-    // Make sure shape of resulting rows conforms to what was requested
-    val selected = requiredColumns.headOption
-      .map(head ⇒ df.select(head, requiredColumns.tail: _*))
-      .getOrElse(df)
-
-    selected.rdd
-  }
-}
-
-object L8Relation extends PDSFields {
-  object Bands extends Enumeration {
-    type Bands = Value
-    val B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, BQA = Value
-    val names: Seq[String] = values.toSeq.map(_.toString)
-  }
-
-  lazy val schema: StructType = {
-    StructType(
-      L8CatalogRelation.schema.collect {
-        case ACQUISITION_DATE ⇒ ACQUISITION_DATE.copy(name = StandardColumns.TIMESTAMP_COLUMN.columnName)
-        case s if s.name == BOUNDS_WGS84.name ⇒ BOUNDS
-        case s if s != DOWNLOAD_URL ⇒ s
-      } ++ L8Relation.Bands.values.toSeq.map(b ⇒ StructField(b.toString, schemaOf[ProjectedRasterTile], true))
-    )
-  }
-}
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
index d1c886755..c4498c63a 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
@@ -28,10 +28,11 @@ import java.time.temporal.ChronoUnit
 import org.locationtech.rasterframes.util.withResource
 import org.locationtech.rasterframes._
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.hadoop.fs.{FileSystem, Path ⇒ HadoopPath}
+import org.apache.hadoop.fs.{FileSystem, Path => HadoopPath}
 import org.apache.hadoop.io.IOUtils
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+import org.locationtech.rasterframes.experimental.datasource.ResourceCacheSupport
 
 
 /**
@@ -70,7 +71,7 @@ class MODISCatalogDataSource extends DataSourceRegister with RelationProvider wi
 }
 
 object MODISCatalogDataSource extends LazyLogging with ResourceCacheSupport {
-  final val SHORT_NAME = "aws-pds-modis"
+  final val SHORT_NAME = "aws-pds-modis-catalog"
   final val MCD43A4_BASE = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/"
   override def maxCacheFileAgeHours: Int = Int.MaxValue
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
index 32ea9ce87..322bd5698 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
@@ -22,11 +22,12 @@
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.hadoop.fs.{Path ⇒ HadoopPath}
+import org.apache.hadoop.fs.{Path => HadoopPath}
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
+import org.locationtech.rasterframes.experimental.datasource.CachedDatasetRelation
 
 /**
  * Constructs a dataframe from the available scenes
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
index ccf1331ff..6a91c3d04 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
@@ -21,9 +21,9 @@
 
 package org.locationtech.rasterframes.experimental.datasource
 
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders._
+import org.apache.spark.sql.DataFrameReader
+import shapeless.tag
+import shapeless.tag.@@
 
 /**
  * Module support.
@@ -31,14 +31,13 @@ import org.locationtech.rasterframes.encoders.StandardEncoders.PrimitiveEncoders
  * @since 5/4/18
  */
 package object awspds {
-  /**
-   * Constructs link with the form:
-   * `https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_{bandId].TIF`
-   * @param bandID Band ID suffix, e.g. "B4"
-   * @return
-   */
-  def l8_band_url(bandID: String): TypedColumn[Any, String] = {
-    concat(col("download_url"), concat(col("product_id"), lit(s"_$bandID.TIF")))
-  }.as(bandID).as[String]
+  trait CatalogDataFrameReaderTag
+  type CatalogDataFrameReader = DataFrameReader @@ CatalogDataFrameReaderTag
 
+  /** Adds `raster` format specifier to `DataFrameReader`. */
+  implicit class DataFrameReaderHasL8CatalogFormat(val reader: DataFrameReader) {
+    def l8Catalog: CatalogDataFrameReader =
+      tag[CatalogDataFrameReaderTag][DataFrameReader](
+        reader.format(L8CatalogDataSource.SHORT_NAME))
+  }
 }
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
index 57e8e8f53..6c8c08aac 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/package.scala
@@ -22,8 +22,6 @@
 package org.locationtech.rasterframes.experimental
 
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
-import org.apache.spark.sql.rf.VersionShims._
 
 
 /**
@@ -32,17 +30,6 @@ import org.apache.spark.sql.rf.VersionShims._
  * @since 9/3/18
  */
 package object datasource {
-  /** Downloads the referenced URL into an uninterpreted binary data array. */
-  def download(urlColumn: Column): TypedColumn[Any, Array[Byte]] =  DownloadExpression(urlColumn)
-
-  /** Downloads the contents at each of the referenced URLs, interpreting
-   * them as equally sized and  */
-  def read_tiles(urls: Column*): Column = ReadTilesExpression(urls)
-
   def register(sqlContext: SQLContext): Unit = {
-    // Expression-oriented functions have a different registration scheme
-    // Currently have to register with the `builtin` registry due to Spark data hiding.
-    val registry: FunctionRegistry = rf.registry(sqlContext)
-    registry.registerExpression[ReadTilesExpression]("rf_read_tiles")
   }
 }

From 7f83abfc7becfa0de906a3e38caaed85041abb8f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 1 Jul 2019 11:08:01 -0400
Subject: [PATCH 209/380] Separated out various implementations of RasterSource
 into their own files.

---
 core/src/main/resources/reference.conf        |   1 +
 .../RasterSourceToRasterRefs.scala            |   5 +-
 .../rasterframes/model/TileDataContext.scala  |   1 -
 .../ref/DelegatingRasterSource.scala          |  89 +++++++
 .../rasterframes/ref/GDALRasterSource.scala   |  70 +++++
 .../ref/HadoopGeoTiffRasterSource.scala       |  35 +++
 .../ref/InMemoryRasterSource.scala            |  52 ++++
 .../ref/JVMGeoTiffRasterSource.scala          |  28 ++
 .../ref/RangeReaderRasterSource.scala         |  64 +++++
 .../rasterframes/ref/RasterRef.scala          |   2 +-
 .../rasterframes/ref/RasterSource.scala       | 245 ++----------------
 .../rasterframes/ref/SimpleRasterInfo.scala   |  84 ++++++
 .../rasterframes/util/RFKryoRegistrator.scala |   8 +-
 .../rasterframes/TileAssemblerSpec.scala      |   3 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |   1 -
 .../awspds/L8CatalogRelationTest.scala        |  33 +--
 16 files changed, 465 insertions(+), 256 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/HadoopGeoTiffRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/InMemoryRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/JVMGeoTiffRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/RangeReaderRasterSource.scala
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index 0052f5f5a..ef635bb61 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -3,6 +3,7 @@ rasterframes {
   prefer-gdal = true
   showable-tiles = true
   showable-max-cells = 20
+  raster-source-cache-timeout = 120 seconds
 }
 
 vlm.gdal {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
index dd8ac7ca8..967ba52a5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
@@ -21,9 +21,6 @@
 
 package org.locationtech.rasterframes.expressions.transformers
 
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.ref.RasterRef
-import org.locationtech.rasterframes.util._
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
@@ -32,9 +29,11 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.util._
 
 import scala.util.Try
 import scala.util.control.NonFatal
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala b/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
index bbf49705d..9f6bd358f 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/TileDataContext.scala
@@ -21,7 +21,6 @@
 
 package org.locationtech.rasterframes.model
 
-import org.locationtech.rasterframes.encoders.CatalystSerializerEncoder
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.raster.{CellType, Tile}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
new file mode 100644
index 000000000..b0224d5cd
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
@@ -0,0 +1,89 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import java.net.URI
+
+import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
+import geotrellis.proj4.CRS
+import geotrellis.raster.io.geotiff.Tags
+import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster}
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes.ref.RasterSource.URIRasterSource
+
+/** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
+abstract class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
+  @transient
+  @volatile
+  private var _delRef: GTRasterSource = _
+
+  private def retryableRead[R >: Null](f: GTRasterSource => R): R = synchronized {
+    try {
+      if (_delRef == null)
+        _delRef = delegateBuilder()
+      f(_delRef)
+    }
+    catch {
+      // On this exeception we attempt to recreate the delegate and read again.
+      case _: java.nio.BufferUnderflowException =>
+        _delRef = null
+        val newDel = delegateBuilder()
+        val result = f(newDel)
+        _delRef = newDel
+        result
+    }
+  }
+
+  // Bad?
+  override def equals(obj: Any): Boolean = obj match {
+    case drs: DelegatingRasterSource => drs.source == source
+    case _                           => false
+  }
+
+  override def hashCode(): Int = source.hashCode()
+
+  // This helps reduce header reads between serializations
+  lazy val info: SimpleRasterInfo = SimpleRasterInfo.cache.get(source, _ =>
+    retryableRead(rs => SimpleRasterInfo(rs))
+  )
+
+  override def cols: Int = info.cols
+  override def rows: Int = info.rows
+  override def crs: CRS = info.crs
+  override def extent: Extent = info.extent
+  override def cellType: CellType = info.cellType
+  override def bandCount: Int = info.bandCount
+  override def tags: Tags = info.tags
+
+  override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
+    retryableRead(_.readBounds(bounds, bands))
+
+  override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
+    retryableRead(_.read(bounds, bands)
+      .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
+    )
+
+  override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
+    retryableRead(_.read(extent, bands)
+      .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
+    )
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
new file mode 100644
index 000000000..0b764766d
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
@@ -0,0 +1,70 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import java.net.URI
+
+import geotrellis.proj4.CRS
+import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster}
+import geotrellis.raster.io.geotiff.Tags
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes.ref.RasterSource.URIRasterSource
+import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
+
+case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSource {
+
+  @transient
+  private lazy val gdal: VLMRasterSource = {
+    val cleaned = source.toASCIIString.replace("gdal+", "")
+    // VSIPath doesn't like single slash "file:/path..."
+    val tweaked =
+      if (cleaned.matches("^file:/[^/].*"))
+        cleaned.replace("file:", "")
+      else cleaned
+
+    VLMRasterSource(tweaked)
+  }
+
+  protected lazy val tiffInfo = SimpleRasterInfo.cache.get(source, _ => SimpleRasterInfo(gdal))
+
+  override def crs: CRS = tiffInfo.crs
+
+  override def extent: Extent = tiffInfo.extent
+
+  private def metadata = Map.empty[String, String]
+
+  override def cellType: CellType = {
+    println(SimpleRasterInfo.cache.stats().toString)
+    tiffInfo.cellType
+  }
+
+  override def bandCount: Int = tiffInfo.bandCount
+
+  override def cols: Int = tiffInfo.cols
+
+  override def rows: Int = tiffInfo.rows
+
+  override def tags: Tags = Tags(metadata, List.empty)
+
+  override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
+    gdal.readBounds(bounds, bands)
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/HadoopGeoTiffRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/HadoopGeoTiffRasterSource.scala
new file mode 100644
index 000000000..3249f1bce
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/HadoopGeoTiffRasterSource.scala
@@ -0,0 +1,35 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import java.net.URI
+
+import geotrellis.spark.io.hadoop.HdfsRangeReader
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.locationtech.rasterframes.ref.RasterSource.{URIRasterSource, URIRasterSourceDebugString}
+
+case class HadoopGeoTiffRasterSource(source: URI, config: () => Configuration)
+    extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
+  @transient
+  protected lazy val rangeReader = HdfsRangeReader(new Path(source.getPath), config())
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/InMemoryRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/InMemoryRasterSource.scala
new file mode 100644
index 000000000..3a6a2f5e1
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/InMemoryRasterSource.scala
@@ -0,0 +1,52 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import geotrellis.proj4.CRS
+import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster, Tile}
+import geotrellis.raster.io.geotiff.Tags
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes.ref.RasterSource.EMPTY_TAGS
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+
+case class InMemoryRasterSource(tile: Tile, extent: Extent, crs: CRS) extends RasterSource {
+  def this(prt: ProjectedRasterTile) = this(prt, prt.extent, prt.crs)
+
+  override def rows: Int = tile.rows
+
+  override def cols: Int = tile.cols
+
+  override def cellType: CellType = tile.cellType
+
+  override def bandCount: Int = 1
+
+  override def tags: Tags = EMPTY_TAGS
+
+  override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+    bounds
+      .map(b => {
+        val subext = rasterExtent.extentFor(b)
+        Raster(MultibandTile(tile.crop(b)), subext)
+      })
+      .toIterator
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/JVMGeoTiffRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/JVMGeoTiffRasterSource.scala
new file mode 100644
index 000000000..cedb81c61
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/JVMGeoTiffRasterSource.scala
@@ -0,0 +1,28 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import java.net.URI
+
+import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
+
+case class JVMGeoTiffRasterSource(source: URI) extends DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RangeReaderRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RangeReaderRasterSource.scala
new file mode 100644
index 000000000..90df001bd
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RangeReaderRasterSource.scala
@@ -0,0 +1,64 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import com.typesafe.scalalogging.LazyLogging
+import geotrellis.proj4.CRS
+import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster}
+import geotrellis.raster.io.geotiff.Tags
+import geotrellis.raster.io.geotiff.reader.GeoTiffReader
+import geotrellis.util.RangeReader
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes.util.GeoTiffInfoSupport
+
+trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
+  protected def rangeReader: RangeReader
+
+  private def realInfo =
+    GeoTiffReader.readGeoTiffInfo(rangeReader, streaming = true, withOverviews = false)
+
+  protected lazy val tiffInfo = SimpleRasterInfo(realInfo)
+
+  def crs: CRS = tiffInfo.crs
+
+  def extent: Extent = tiffInfo.extent
+
+  override def cols: Int = tiffInfo.rasterExtent.cols
+
+  override def rows: Int = tiffInfo.rasterExtent.rows
+
+  def cellType: CellType = tiffInfo.cellType
+
+  def bandCount: Int = tiffInfo.bandCount
+
+  override def tags: Tags = tiffInfo.tags
+
+  override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
+    val info = realInfo
+    val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
+    val intersectingBounds = bounds.flatMap(_.intersection(this)).toSeq
+    geoTiffTile.crop(intersectingBounds, bands.toArray).map {
+      case (gb, tile) =>
+        Raster(tile, rasterExtent.extentFor(gb, clamp = true))
+    }
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
index ad2291c77..f191f4188 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
@@ -63,7 +63,7 @@ object RasterRef extends LazyLogging {
   case class RasterRefTile(rr: RasterRef) extends ProjectedRasterTile {
     val extent: Extent = rr.extent
     val crs: CRS = rr.crs
-    override val cellType = rr.cellType
+    override def cellType = rr.cellType
 
     override val cols: Int = rr.cols
     override val rows: Int = rr.rows
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 2d07bb129..e39f32508 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -23,29 +23,21 @@ package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
-import org.locationtech.rasterframes.NOMINAL_TILE_DIMS
-import org.locationtech.rasterframes.model.TileDimensions
-import RasterSource.SINGLEBAND
 import com.azavea.gdal.GDALWarp
+import com.github.blemale.scaffeine.Scaffeine
 import com.typesafe.scalalogging.LazyLogging
-import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
-import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
-import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
 import geotrellis.proj4.CRS
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.Tags
-import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.spark.io.hadoop.HdfsRangeReader
-import geotrellis.util.RangeReader
 import geotrellis.vector.Extent
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
 import org.locationtech.rasterframes.model.{TileContext, TileDimensions}
-import org.locationtech.rasterframes.tiles.ProjectedRasterTile
-import org.locationtech.rasterframes.util.GeoTiffInfoSupport
+import org.locationtech.rasterframes.{NOMINAL_TILE_DIMS, rfConfig}
+
+import scala.concurrent.duration.Duration
 
 /**
  * Abstraction over fetching geospatial raster data.
@@ -53,7 +45,9 @@ import org.locationtech.rasterframes.util.GeoTiffInfoSupport
  * @since 8/21/18
  */
 @Experimental
-sealed trait RasterSource extends ProjectedRasterLike with Serializable {
+trait RasterSource extends ProjectedRasterLike with Serializable {
+  import RasterSource._
+
   def crs: CRS
 
   def extent: Extent
@@ -96,22 +90,29 @@ sealed trait RasterSource extends ProjectedRasterLike with Serializable {
 object RasterSource extends LazyLogging {
   final val SINGLEBAND = Seq(0)
   final val EMPTY_TAGS = Tags(Map.empty, List.empty)
+  val cacheTimeout: Duration = Duration.fromNanos(rfConfig.getDuration("raster-source-cache-timeout").toNanos)
+  private val rsCache = Scaffeine()
+    .expireAfterAccess(RasterSource.cacheTimeout)
+    .build[URI, RasterSource]
 
   implicit def rsEncoder: ExpressionEncoder[RasterSource] = {
     RasterSourceUDT // Makes sure UDT is registered first
     ExpressionEncoder()
   }
 
-  def apply(source: URI): RasterSource = source match {
-    case IsGDAL()          => GDALRasterSource(source)
-    case IsHadoopGeoTiff() =>
-      // TODO: How can we get the active hadoop configuration
-      // TODO: without having to pass it through?
-      val config = () => new Configuration()
-      HadoopGeoTiffRasterSource(source, config)
-    case IsDefaultGeoTiff() => JVMGeoTiffRasterSource(source)
-    case s                  => throw new UnsupportedOperationException(s"Reading '$s' not supported")
-  }
+  def apply(source: URI): RasterSource =
+    rsCache.get(
+      source, {
+        case IsGDAL()          => GDALRasterSource(source)
+        case IsHadoopGeoTiff() =>
+          // TODO: How can we get the active hadoop configuration
+          // TODO: without having to pass it through?
+          val config = () => new Configuration()
+          HadoopGeoTiffRasterSource(source, config)
+        case IsDefaultGeoTiff() => JVMGeoTiffRasterSource(source)
+        case s                  => throw new UnsupportedOperationException(s"Reading '$s' not supported")
+      }
+    )
 
   object IsGDAL {
 
@@ -128,6 +129,7 @@ object RasterSource extends LazyLogging {
     }
 
     val gdalOnlyExtensions = Seq(".jp2", ".mrf", ".hdf")
+
     def gdalOnly(source: URI): Boolean =
       if (gdalOnlyExtensions.exists(source.getPath.toLowerCase.endsWith)) {
         require(hasGDAL, s"Can only read $source if GDAL is available")
@@ -153,33 +155,6 @@ object RasterSource extends LazyLogging {
     }
   }
 
-  case class SimpleGeoTiffInfo(
-    cols: Int,
-    rows: Int,
-    cellType: CellType,
-    extent: Extent,
-    rasterExtent: RasterExtent,
-    crs: CRS,
-    tags: Tags,
-    bandCount: Int,
-    noDataValue: Option[Double]
-  )
-
-  object SimpleGeoTiffInfo {
-    def apply(info: GeoTiffReader.GeoTiffInfo): SimpleGeoTiffInfo =
-      SimpleGeoTiffInfo(
-        info.segmentLayout.totalCols,
-        info.segmentLayout.totalRows,
-        info.cellType,
-        info.extent,
-        info.rasterExtent,
-        info.crs,
-        info.tags,
-        info.bandCount,
-        info.noDataValue
-      )
-  }
-
   trait URIRasterSource { _: RasterSource =>
     def source: URI
 
@@ -187,176 +162,6 @@ object RasterSource extends LazyLogging {
       s"${getClass.getSimpleName}(${source})"
     }
   }
-
-  /** A RasterFrames RasterSource which delegates most operations to a geotrellis-contrib RasterSource */
-  abstract class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRasterSource) extends RasterSource with URIRasterSource {
-    @transient
-    @volatile
-    private var _delRef: GTRasterSource = _
-
-    private def retryableRead[R >: Null](f: GTRasterSource => R): R = synchronized {
-      try {
-        if (_delRef == null)
-          _delRef = delegateBuilder()
-        f(_delRef)
-      }
-      catch {
-        // On this exeception we attempt to recreate the delegate and read again.
-        case be: java.nio.BufferUnderflowException =>
-          _delRef = null
-          val newDel = delegateBuilder()
-          val result = f(newDel)
-          _delRef = newDel
-          result
-      }
-    }
-
-    // Bad, bad, bad?
-    override def equals(obj: Any): Boolean = obj match {
-      case drs: DelegatingRasterSource => drs.source == source
-      case _                           => false
-    }
-    override def hashCode(): Int = source.hashCode()
-
-    // This helps reduce header reads between serializations
-    lazy val info: SimpleGeoTiffInfo = retryableRead { rs =>
-      SimpleGeoTiffInfo(
-        rs.cols,
-        rs.rows,
-        rs.cellType,
-        rs.extent,
-        rs.rasterExtent,
-        rs.crs,
-        fetchTags,
-        rs.bandCount,
-        None
-      )
-    }
-
-    override def cols: Int = info.cols
-    override def rows: Int = info.rows
-    override def crs: CRS = info.crs
-    override def extent: Extent = info.extent
-    override def cellType: CellType = info.cellType
-    override def bandCount: Int = info.bandCount
-    private def fetchTags: Tags = retryableRead {
-      case rs: GeoTiffRasterSource => rs.tiff.tags
-      case _                       => EMPTY_TAGS
-    }
-    override def tags: Tags = info.tags
-
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
-      retryableRead(_.readBounds(bounds, bands))
-
-    override def read(bounds: GridBounds, bands: Seq[Int]): Raster[MultibandTile] =
-      retryableRead(_.read(bounds, bands)
-        .getOrElse(throw new IllegalArgumentException(s"Bounds '$bounds' outside of source"))
-      )
-
-    override def read(extent: Extent, bands: Seq[Int]): Raster[MultibandTile] =
-      retryableRead(_.read(extent, bands)
-        .getOrElse(throw new IllegalArgumentException(s"Extent '$extent' outside of source"))
-      )
-  }
-
-  case class JVMGeoTiffRasterSource(source: URI) extends DelegatingRasterSource(source, () => GeoTiffRasterSource(source.toASCIIString))
-
-  case class InMemoryRasterSource(tile: Tile, extent: Extent, crs: CRS) extends RasterSource {
-    def this(prt: ProjectedRasterTile) = this(prt, prt.extent, prt.crs)
-
-    override def rows: Int = tile.rows
-
-    override def cols: Int = tile.cols
-
-    override def cellType: CellType = tile.cellType
-
-    override def bandCount: Int = 1
-
-    override def tags: Tags = EMPTY_TAGS
-
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
-      bounds
-        .map(b => {
-          val subext = rasterExtent.extentFor(b)
-          Raster(MultibandTile(tile.crop(b)), subext)
-        })
-        .toIterator
-    }
-  }
-
-  case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSource {
-
-    @transient
-    private lazy val gdal = {
-      val cleaned = source.toASCIIString.replace("gdal+", "")
-      // VSIPath doesn't like single slash "file:/path..."
-      val tweaked =
-        if (cleaned.matches("^file:/[^/].*"))
-          cleaned.replace("file:", "")
-        else cleaned
-
-      VLMRasterSource(tweaked)
-    }
-
-    override def crs: CRS = gdal.crs
-
-    override def extent: Extent = gdal.extent
-
-    private def metadata = Map.empty[String, String]
-
-    override def cellType: CellType = gdal.cellType
-
-    override def bandCount: Int = gdal.bandCount
-
-    override def cols: Int = gdal.cols
-
-    override def rows: Int = gdal.rows
-
-    override def tags: Tags = Tags(metadata, List.empty)
-
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
-      gdal.readBounds(bounds, bands)
-  }
-
-  trait RangeReaderRasterSource extends RasterSource with GeoTiffInfoSupport with LazyLogging {
-    protected def rangeReader: RangeReader
-
-    private def realInfo =
-      GeoTiffReader.readGeoTiffInfo(rangeReader, streaming = true, withOverviews = false)
-
-    protected lazy val tiffInfo = SimpleGeoTiffInfo(realInfo)
-
-    def crs: CRS = tiffInfo.crs
-
-    def extent: Extent = tiffInfo.extent
-
-    override def cols: Int = tiffInfo.rasterExtent.cols
-
-    override def rows: Int = tiffInfo.rasterExtent.rows
-
-    def cellType: CellType = tiffInfo.cellType
-
-    def bandCount: Int = tiffInfo.bandCount
-
-    override def tags: Tags = tiffInfo.tags
-
-    override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] = {
-      val info = realInfo
-      val geoTiffTile = GeoTiffReader.geoTiffMultibandTile(info)
-      val intersectingBounds = bounds.flatMap(_.intersection(this)).toSeq
-      geoTiffTile.crop(intersectingBounds, bands.toArray).map {
-        case (gb, tile) =>
-          Raster(tile, rasterExtent.extentFor(gb, clamp = true))
-      }
-    }
-  }
-
-  case class HadoopGeoTiffRasterSource(source: URI, config: () => Configuration)
-      extends RangeReaderRasterSource with URIRasterSource with URIRasterSourceDebugString { self =>
-    @transient
-    protected lazy val rangeReader = HdfsRangeReader(new Path(source.getPath), config())
-  }
-
   trait URIRasterSourceDebugString { _: RangeReaderRasterSource with URIRasterSource with Product =>
     def toDebugString: String = {
       val buf = new StringBuilder()
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala
new file mode 100644
index 000000000..4799607e6
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala
@@ -0,0 +1,84 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.ref
+
+import java.net.URI
+
+import com.github.blemale.scaffeine.Scaffeine
+import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
+import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
+import geotrellis.proj4.CRS
+import geotrellis.raster.io.geotiff.Tags
+import geotrellis.raster.io.geotiff.reader.GeoTiffReader
+import geotrellis.raster.{CellType, RasterExtent}
+import geotrellis.vector.Extent
+import org.locationtech.rasterframes.ref.RasterSource.EMPTY_TAGS
+
+case class SimpleRasterInfo(
+  cols: Int,
+  rows: Int,
+  cellType: CellType,
+  extent: Extent,
+  rasterExtent: RasterExtent,
+  crs: CRS,
+  tags: Tags,
+  bandCount: Int,
+  noDataValue: Option[Double]
+)
+
+object SimpleRasterInfo {
+  def apply(info: GeoTiffReader.GeoTiffInfo): SimpleRasterInfo =
+    SimpleRasterInfo(
+      info.segmentLayout.totalCols,
+      info.segmentLayout.totalRows,
+      info.cellType,
+      info.extent,
+      info.rasterExtent,
+      info.crs,
+      info.tags,
+      info.bandCount,
+      info.noDataValue
+    )
+
+  def apply(rs: GTRasterSource): SimpleRasterInfo = {
+    def fetchTags: Tags = rs match {
+      case gt: GeoTiffRasterSource => gt.tiff.tags
+      case _                       => EMPTY_TAGS
+    }
+
+    SimpleRasterInfo(
+      rs.cols,
+      rs.rows,
+      rs.cellType,
+      rs.extent,
+      rs.rasterExtent,
+      rs.crs,
+      fetchTags,
+      rs.bandCount,
+      None
+    )
+  }
+
+  lazy val cache = Scaffeine()
+    .recordStats()
+    .build[URI, SimpleRasterInfo]
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
index 7fea0a93e..8275c6402 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/RFKryoRegistrator.scala
@@ -22,10 +22,9 @@
 package org.locationtech.rasterframes.util
 
 import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
-import org.locationtech.rasterframes.ref.RasterRef
-import org.locationtech.rasterframes.ref.RasterSource._
+import org.locationtech.rasterframes.ref.{DelegatingRasterSource, RasterRef, RasterSource}
+import org.locationtech.rasterframes.ref._
 import com.esotericsoftware.kryo.Kryo
-import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 
 
 /**
@@ -41,10 +40,11 @@ class RFKryoRegistrator extends geotrellis.spark.io.kryo.KryoRegistrator {
     kryo.register(classOf[RasterRef])
     kryo.register(classOf[RasterRefTile])
     kryo.register(classOf[DelegatingRasterSource])
+    kryo.register(classOf[JVMGeoTiffRasterSource])
     kryo.register(classOf[InMemoryRasterSource])
     kryo.register(classOf[HadoopGeoTiffRasterSource])
     kryo.register(classOf[GDALRasterSource])
-    kryo.register(classOf[SimpleGeoTiffInfo])
+    kryo.register(classOf[SimpleRasterInfo])
     kryo.register(classOf[geotrellis.raster.io.geotiff.reader.GeoTiffReader.GeoTiffInfo])
   }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index 7dbccc92a..0c3478d8f 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -27,8 +27,7 @@ import geotrellis.raster._
 import geotrellis.raster.render.ColorRamps
 import geotrellis.vector.Extent
 import org.apache.spark.sql.{functions => F, _}
-import org.locationtech.rasterframes.ref.RasterSource
-import org.locationtech.rasterframes.ref.RasterSource.InMemoryRasterSource
+import org.locationtech.rasterframes.ref.{InMemoryRasterSource, RasterSource}
 
 /**
  *
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 7fbc274f4..439934f38 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -21,7 +21,6 @@
 
 package org.locationtech.rasterframes.ref
 
-import RasterSource.{GDALRasterSource, JVMGeoTiffRasterSource}
 import org.locationtech.rasterframes.TestData
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index 85639ff41..e4c2c8eac 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -20,10 +20,10 @@
 
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import java.net.{HttpURLConnection, URL, URLConnection}
+import java.net.{HttpURLConnection, URL}
 
 import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.{TestEnvironment, rf_agg_stats}
+import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource.raster._
 
 /**
@@ -90,31 +90,16 @@ class L8CatalogRelationTest extends TestEnvironment {
 
     it("should be compatible with raster DataSource") {
       val df = spark.read.raster
-        .fromCatalog(catalog, "B1", "B8")
+        .fromCatalog(catalog, "B1", "B3")
         .withTileDimensions(512, 512)
         .load()
 
-      df.printSchema()
-      df.show(false)
-    }
+      // Read just a single tile.
+      val sub = df.select($"B1", $"B3").where(
+        st_contains(st_geometry(rf_extent($"B1")), st_makePoint(602205, 7843335))
+      )
 
-//    it("should count scenes") {
-//
-//
-//
-//
-//      val scenes = sql("SELECT entity_id FROM l8 DISTINCT")
-//      scenes.count() shouldBe > (300400L)
-//
-//      val subscenes = sqlContext.table("subscenes")
-//      subscenes.schema.size should be (4)
-//      subscenes.count() should be (7)
-//    }
-//
-//    it("should compute statistics") {
-//      val subscenes = sqlContext.table("subscenes")
-//      val stats = subscenes.select(rf_agg_stats($"B1")).first()
-//      stats.data_cells shouldBe > (420024000L)
-//    }
+      sub.show(false)
+    }
   }
 }

From 9e9c0acaba0ca97fa23aec18d116697e14a8913c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 1 Jul 2019 12:42:58 -0400
Subject: [PATCH 210/380] Rewrote L8 catalog to provide band URLs, for
 compatibility with RasterSourceDataSource. Added additional caching
 optimization uncovered during testing.

---
 .../ref/DelegatingRasterSource.scala            |  2 +-
 .../rasterframes/ref/GDALRasterSource.scala     |  7 ++-----
 .../rasterframes/ref/RasterSource.scala         |  8 +++++---
 .../rasterframes/ref/SimpleRasterInfo.scala     |  6 ++----
 .../awspds/L8CatalogRelationTest.scala          | 17 +++++++++++------
 5 files changed, 21 insertions(+), 19 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
index b0224d5cd..c460911a0 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/DelegatingRasterSource.scala
@@ -62,7 +62,7 @@ abstract class DelegatingRasterSource(source: URI, delegateBuilder: () => GTRast
   override def hashCode(): Int = source.hashCode()
 
   // This helps reduce header reads between serializations
-  lazy val info: SimpleRasterInfo = SimpleRasterInfo.cache.get(source, _ =>
+  def info: SimpleRasterInfo = SimpleRasterInfo.cache.get(source.toASCIIString, _ =>
     retryableRead(rs => SimpleRasterInfo(rs))
   )
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
index 0b764766d..eabbac002 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
@@ -44,7 +44,7 @@ case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSour
     VLMRasterSource(tweaked)
   }
 
-  protected lazy val tiffInfo = SimpleRasterInfo.cache.get(source, _ => SimpleRasterInfo(gdal))
+  protected def tiffInfo = SimpleRasterInfo.cache.get(source.toASCIIString, _ => SimpleRasterInfo(gdal))
 
   override def crs: CRS = tiffInfo.crs
 
@@ -52,10 +52,7 @@ case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSour
 
   private def metadata = Map.empty[String, String]
 
-  override def cellType: CellType = {
-    println(SimpleRasterInfo.cache.stats().toString)
-    tiffInfo.cellType
-  }
+  override def cellType: CellType = tiffInfo.cellType
 
   override def bandCount: Int = tiffInfo.bandCount
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index e39f32508..287bb8064 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -90,10 +90,12 @@ trait RasterSource extends ProjectedRasterLike with Serializable {
 object RasterSource extends LazyLogging {
   final val SINGLEBAND = Seq(0)
   final val EMPTY_TAGS = Tags(Map.empty, List.empty)
+
   val cacheTimeout: Duration = Duration.fromNanos(rfConfig.getDuration("raster-source-cache-timeout").toNanos)
+
   private val rsCache = Scaffeine()
     .expireAfterAccess(RasterSource.cacheTimeout)
-    .build[URI, RasterSource]
+    .build[String, RasterSource]
 
   implicit def rsEncoder: ExpressionEncoder[RasterSource] = {
     RasterSourceUDT // Makes sure UDT is registered first
@@ -102,7 +104,7 @@ object RasterSource extends LazyLogging {
 
   def apply(source: URI): RasterSource =
     rsCache.get(
-      source, {
+      source.toASCIIString, _ => source match {
         case IsGDAL()          => GDALRasterSource(source)
         case IsHadoopGeoTiff() =>
           // TODO: How can we get the active hadoop configuration
@@ -162,7 +164,7 @@ object RasterSource extends LazyLogging {
       s"${getClass.getSimpleName}(${source})"
     }
   }
-  trait URIRasterSourceDebugString { _: RangeReaderRasterSource with URIRasterSource with Product =>
+  trait URIRasterSourceDebugString { _: RasterSource with URIRasterSource with Product =>
     def toDebugString: String = {
       val buf = new StringBuilder()
       buf.append(productPrefix)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala
index 4799607e6..1a67822e5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/SimpleRasterInfo.scala
@@ -21,8 +21,6 @@
 
 package org.locationtech.rasterframes.ref
 
-import java.net.URI
-
 import com.github.blemale.scaffeine.Scaffeine
 import geotrellis.contrib.vlm.geotiff.GeoTiffRasterSource
 import geotrellis.contrib.vlm.{RasterSource => GTRasterSource}
@@ -79,6 +77,6 @@ object SimpleRasterInfo {
   }
 
   lazy val cache = Scaffeine()
-    .recordStats()
-    .build[URI, SimpleRasterInfo]
+    //.recordStats()
+    .build[String, SimpleRasterInfo]
 }
\ No newline at end of file
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index e4c2c8eac..cbe01dc63 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -87,19 +87,24 @@ class L8CatalogRelationTest extends TestEnvironment {
   describe("Read L8 scenes from PDS") {
     import spark.implicits._
     val catalog = spark.read.l8Catalog.load().repartition(8)
+    val scenes = catalog
+      .where($"acquisition_date" === to_timestamp(lit("2017-04-04 15:12:55.394")))
+      .where($"path" === 11 && $"row" === 12)
 
     it("should be compatible with raster DataSource") {
       val df = spark.read.raster
-        .fromCatalog(catalog, "B1", "B3")
+        .fromCatalog(scenes, "B1", "B3")
         .withTileDimensions(512, 512)
         .load()
 
-      // Read just a single tile.
-      val sub = df.select($"B1", $"B3").where(
-        st_contains(st_geometry(rf_extent($"B1")), st_makePoint(602205, 7843335))
-      )
+      // Further refine down to a tile
+      val sub = df.select($"B3")
+        .where(st_contains(st_geometry(rf_extent($"B1")), st_makePoint(574965, 7679175)))
+
+      val stats = sub.select(rf_agg_stats($"B3")).first
 
-      sub.show(false)
+      stats.data_cells should be (512*512)
+      stats.mean shouldBe > (10000.0)
     }
   }
 }

From 1621fb247af0de28b6f95f272b3e47e41fd8190f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 1 Jul 2019 14:48:30 -0400
Subject: [PATCH 211/380] Normalized schemas of L8 and MODIS catalogs, making
 them compatible with RasterSourceRelation. Deprecated separate `geotiff`
 reader.

---
 .../datasource/geotiff/GeoTiffRelation.scala  |  2 +-
 .../datasource/geotiff/package.scala          |  3 +
 .../GeoTiffCollectionDataSourceSpec.scala     |  2 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       | 27 ++------
 .../awspds/L8CatalogRelationTest.scala        | 41 +++---------
 .../awspds/MODISCatalogRelationTest.scala     | 54 +++++++++++----
 .../datasource/awspds/TestSupport.scala       | 38 +++++++++++
 .../datasource/awspds/L8CatalogRelation.scala |  5 +-
 .../awspds/MODISCatalogRelation.scala         | 66 ++++++++-----------
 .../datasource/awspds/PDSFields.scala         |  1 -
 .../datasource/awspds/package.scala           |  5 +-
 11 files changed, 133 insertions(+), 111 deletions(-)
 create mode 100644 experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/TestSupport.scala

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
index d16d69fb1..b08ebc830 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffRelation.scala
@@ -109,7 +109,7 @@ case class GeoTiffRelation(sqlContext: SQLContext, uri: URI) extends BaseRelatio
         }
     }
     else {
-      logger.warn("GeoTIFF is not already tiled. In-memory read required: " + uri)
+      //logger.warn("GeoTIFF is not already tiled. In-memory read required: " + uri)
       val geotiff = HadoopGeoTiffReader.readMultiband(new Path(uri))
       val rdd = sqlContext.sparkContext.makeRDD(Seq((geotiff.projectedExtent, Shims.toArrayTile(geotiff.tile))))
 
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index 39fb16ca1..2178c7ee6 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -35,16 +35,19 @@ package object geotiff {
 
   /** Adds `geotiff` format specifier to `DataFrameReader`. */
   implicit class DataFrameReaderHasGeoTiffFormat(val reader: DataFrameReader) {
+    @deprecated("Use `raster` instead.", "7/1/2019")
     def geotiff: GeoTiffRasterFrameReader =
       tag[GeoTiffRasterFrameReaderTag][DataFrameReader](reader.format(GeoTiffDataSource.SHORT_NAME))
   }
 
   implicit class DataFrameWriterHasGeoTiffFormat[T](val writer: DataFrameWriter[T]) {
+    @deprecated("Use `raster` instead.", "7/1/2019")
     def geotiff: DataFrameWriter[T] = writer.format(GeoTiffDataSource.SHORT_NAME)
   }
 
   /** Adds `loadRF` to appropriately tagged `DataFrameReader` */
   implicit class GeoTiffReaderWithRF(val reader: GeoTiffRasterFrameReader) {
+    @deprecated("Use `raster` instead.", "7/1/2019")
     def loadRF(path: URI): RasterFrame = reader.load(path.toASCIIString).asRF
   }
 }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
index d50ef2b62..9b69fd89e 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionDataSourceSpec.scala
@@ -35,7 +35,7 @@ class GeoTiffCollectionDataSourceSpec
     it("shiould read a directory of files") {
 
       val df = spark.read
-        .geotiff
+        .format("geotiff")
         .load(geotiffDir.resolve("*.tiff").toString)
       val expected = geotiffDir.toFile.list(new FilenameFilter {
         override def accept(dir: File, name: String): Boolean = name.endsWith("tiff")
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 04a532503..704c7db53 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -35,18 +35,14 @@ class GeoTiffDataSourceSpec
   describe("GeoTiff reading") {
 
     it("should read sample GeoTiff") {
-      val rf = spark.read
-        .geotiff
-        .loadRF(cogPath)
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
 
       assert(rf.count() > 10)
     }
 
     it("should lay out tiles correctly"){
 
-      val rf = spark.read
-        .geotiff
-        .loadRF(cogPath)
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
 
       val tlm = rf.tileLayerMetadata.left.get
       val gb = tlm.gridBounds
@@ -55,12 +51,7 @@ class GeoTiffDataSourceSpec
     }
 
     it("should lay out tiles correctly for non-tiled tif") {
-      val rf = spark.read
-        .geotiff
-        .loadRF(nonCogPath)
-
-      //println(rf.count())
-      //rf.show(false)
+      val rf = spark.read.format("geotiff").load(nonCogPath.toASCIIString).asRF
 
       assert(rf.count() > 1)
 
@@ -83,7 +74,7 @@ class GeoTiffDataSourceSpec
 
     it("should read in correctly check-summed contents") {
       // c.f. TileStatsSpec -> computing statistics over tiles -> should compute tile statistics -> sum
-      val rf = spark.read.geotiff.loadRF(l8B1SamplePath)
+      val rf = spark.read.format("geotiff").load(l8B1SamplePath.toASCIIString).asRF
       val expected = 309149454 // computed with rasterio
       val result = rf.agg(
         sum(rf_tile_sum(rf("tile")))
@@ -93,23 +84,19 @@ class GeoTiffDataSourceSpec
     }
 
     it("should write GeoTIFF RF to parquet") {
-      val rf = spark.read
-        .geotiff
-        .loadRF(cogPath)
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
       assert(write(rf))
     }
 
     it("should write GeoTIFF") {
-      val rf = spark.read
-        .geotiff
-        .loadRF(cogPath)
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
 
       logger.info(s"Read extent: ${rf.tileLayerMetadata.merge.extent}")
 
       val out = Paths.get("target", "example-geotiff.tiff")
       logger.info(s"Writing to $out")
       noException shouldBe thrownBy {
-        rf.write.geotiff.save(out.toString)
+        rf.write.format("geotiff").save(out.toString)
       }
     }
   }
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index cbe01dc63..cfc901b9a 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -20,8 +20,6 @@
 
 package org.locationtech.rasterframes.experimental.datasource.awspds
 
-import java.net.{HttpURLConnection, URL}
-
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource.raster._
@@ -32,16 +30,16 @@ import org.locationtech.rasterframes.datasource.raster._
  * @since 5/4/18
  */
 class L8CatalogRelationTest extends TestEnvironment {
+  import spark.implicits._
 
+  val catalog = spark.read.l8Catalog.load()
 
-  describe("Representing L8 scenes as a Spark data source") {
-    import spark.implicits._
-    val catalog = spark.read.l8Catalog.load()
-
-    val scenes = catalog
-      .where($"acquisition_date" === to_timestamp(lit("2017-04-04 15:12:55.394")))
-      .where($"path" === 11 && $"row" === 12)
+  val scenes = catalog
+    .where($"acquisition_date" === to_timestamp(lit("2017-04-04 15:12:55.394")))
+    .where($"path" === 11 && $"row" === 12)
+    .cache()
 
+  describe("Representing L8 scenes as a Spark data source") {
     it("should provide a non-empty catalog") {
       scenes.count() shouldBe 1
     }
@@ -51,22 +49,9 @@ class L8CatalogRelationTest extends TestEnvironment {
     }
 
     it("should construct valid URLs") {
-
-      def urlResponse(urlStr: String): Int = {
-        val conn = new URL(urlStr).openConnection().asInstanceOf[HttpURLConnection]
-        try {
-          conn.setRequestMethod("GET")
-          conn.connect()
-          conn.getResponseCode
-        }
-        finally {
-          conn.disconnect()
-        }
-      }
-
       val urlStr = scenes.select("B11").as[String].first
-      val code = urlResponse(urlStr)
-      code shouldBe 200
+      val code = TestSupport.urlResponse(urlStr)
+      code should be (200)
     }
 
     it("should work with SQL and spatial predicates") {
@@ -85,12 +70,6 @@ class L8CatalogRelationTest extends TestEnvironment {
   }
 
   describe("Read L8 scenes from PDS") {
-    import spark.implicits._
-    val catalog = spark.read.l8Catalog.load().repartition(8)
-    val scenes = catalog
-      .where($"acquisition_date" === to_timestamp(lit("2017-04-04 15:12:55.394")))
-      .where($"path" === 11 && $"row" === 12)
-
     it("should be compatible with raster DataSource") {
       val df = spark.read.raster
         .fromCatalog(scenes, "B1", "B3")
@@ -103,7 +82,7 @@ class L8CatalogRelationTest extends TestEnvironment {
 
       val stats = sub.select(rf_agg_stats($"B3")).first
 
-      stats.data_cells should be (512*512)
+      stats.data_cells should be (512L * 512L)
       stats.mean shouldBe > (10000.0)
     }
   }
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
index c7d11c917..3f777d322 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelationTest.scala
@@ -19,11 +19,12 @@
  */
 
 package org.locationtech.rasterframes.experimental.datasource.awspds
-import java.net.URL
 import java.sql.Timestamp
 
 import org.apache.spark.sql.functions._
-import org.locationtech.rasterframes.TestEnvironment
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.raster._
+import org.locationtech.rasterframes.stats.CellStatistics
 
 /**
  * Test rig for MODIS catalog stuff.
@@ -31,23 +32,48 @@ import org.locationtech.rasterframes.TestEnvironment
  * @since 5/4/18
  */
 class MODISCatalogRelationTest extends TestEnvironment {
-  describe("Representing MODIS scenes as a Spark data source") {
-    import spark.implicits._
-    val catalog = spark.read.format(MODISCatalogDataSource.SHORT_NAME).load()
-    val scenes = catalog
-      .where($"acquisition_date".as[Timestamp] === to_timestamp(lit("2018-1-1")))
-      .where($"granule_id".contains("h24v03"))
-      .cache()
+  import spark.implicits._
+  val catalog = spark.read.modisCatalog.load()
+  val scenes = catalog
+    .where($"acquisition_date".as[Timestamp] === to_timestamp(lit("2018-1-1")))
+    .where($"granule_id".contains("h24v03"))
+    .cache()
 
+  describe("Representing MODIS scenes as a Spark data source") {
     it("should provide a non-empty catalog") {
-      assert(scenes.count() === 1)
+      scenes.count() should be (1)
     }
 
-    it("should construct band specific download URLs") {
-      val b01 = scenes.select($"assets"("B01").as[String])
-      noException shouldBe thrownBy {
-        new URL(b01.first())
+    it("should provide 7 band and 7 qa urls") {
+      scenes.schema.count(_.name.startsWith("B")) should be (14)
+    }
+
+    it("should construct valid URLs") {
+      val urlStr = scenes.select("B03").as[String].first
+      val code = TestSupport.urlResponse(urlStr)
+      withClue(urlStr) {
+        code should be(200)
       }
     }
   }
+
+  describe("Read MODIS scenes from PDS") {
+    it("should be compatible with raster DataSource") {
+      val df = spark.read.raster
+        .fromCatalog(scenes, "B03")
+        .withTileDimensions(128, 128)
+        .load()
+
+      // Further refine down to a tile
+      val sub = df.select($"B03", st_centroid(st_geometry(rf_extent($"B03"))))
+        .where(st_contains(st_geometry(rf_extent($"B03")), st_makePoint(7175787.353582373, 6345530.965564346)))
+        .withColumn("stats", rf_tile_stats(rf_tile($"B03")))
+
+      val stats = sub.select($"stats".as[CellStatistics]).first()
+
+      stats.data_cells shouldBe < (128L * 128L)
+      stats.data_cells shouldBe > (128L)
+      stats.mean shouldBe > (1000.0)
+    }
+  }
 }
diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/TestSupport.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/TestSupport.scala
new file mode 100644
index 000000000..a8d33f77e
--- /dev/null
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/TestSupport.scala
@@ -0,0 +1,38 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.experimental.datasource.awspds
+
+import java.net.{HttpURLConnection, URL}
+
+object TestSupport {
+  def urlResponse(urlStr: String): Int = {
+    val conn = new URL(urlStr).openConnection().asInstanceOf[HttpURLConnection]
+    try {
+      conn.setRequestMethod("GET")
+      conn.connect()
+      conn.getResponseCode
+    }
+    finally {
+      conn.disconnect()
+    }
+  }
+}
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
index a7b426176..f87597362 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelation.scala
@@ -49,7 +49,7 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
     import sqlContext.implicits._
     logger.debug("Parsing " + sceneListPath)
 
-    val bandCols = Bands.values.toSeq.map(b => l8_band_url(b) as (b.toString))
+    val bandCols = Bands.values.toSeq.map(b => l8_band_url(b) as b.toString)
 
     sqlContext.read
       .schema(inputSchema)
@@ -69,13 +69,12 @@ case class L8CatalogRelation(sqlContext: SQLContext, sceneListPath: HadoopPath)
       .select(schema.map(f ⇒ col(f.name)): _*)
       .orderBy(ACQUISITION_DATE.name, PATH.name, ROW.name)
       .distinct() // The scene file contains duplicates.
-      .repartition(8)
+      .repartition(8, col(PATH.name), col(ROW.name))
   }
 }
 
 object L8CatalogRelation extends PDSFields {
 
-
   /**
     * Constructs link with the form:
     * `https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/149/039/LC08_L1TP_149039_20170411_20170415_01_T1/LC08_L1TP_149039_20170411_20170415_01_T1_{bandId].TIF`
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
index 322bd5698..8fa3422e3 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogRelation.scala
@@ -28,6 +28,7 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.locationtech.rasterframes.experimental.datasource.CachedDatasetRelation
+import org.locationtech.rasterframes.experimental.datasource.awspds.MODISCatalogRelation.Bands
 
 /**
  * Constructs a dataframe from the available scenes
@@ -41,19 +42,7 @@ case class MODISCatalogRelation(sqlContext: SQLContext, sceneList: HadoopPath)
 
   protected def cacheFile: HadoopPath = sceneList.suffix(".parquet")
 
-  private val inputSchema = StructType(Seq(
-    StructField("date", TimestampType, false),
-    DOWNLOAD_URL,
-    GID
-  ))
-
-  def schema = StructType(Seq(
-    PRODUCT_ID,
-    ACQUISITION_DATE,
-    GRANULE_ID,
-    GID,
-    ASSETS
-  ))
+  override def schema: StructType = MODISCatalogRelation.schema
 
   protected def constructDataset: Dataset[Row] = {
     import sqlContext.implicits._
@@ -63,47 +52,46 @@ case class MODISCatalogRelation(sqlContext: SQLContext, sceneList: HadoopPath)
       .option("header", "true")
       .option("mode", "DROPMALFORMED") // <--- mainly for the fact that we have internal headers from the concat
       .option("timestampFormat", "yyyy-MM-dd HH:mm:ss")
-      .schema(inputSchema)
+      .schema(MODISCatalogRelation.inputSchema)
       .csv(sceneList.toString)
 
+    val bandCols = Bands.values.toSeq.map(b => MCD43A4_band_url(b) as b.toString)
+
     catalog
       .withColumn("__split_gid", split($"gid", "\\."))
       .withColumn(DOWNLOAD_URL.name, regexp_replace(col(DOWNLOAD_URL.name), "index.html", ""))
-      .select(
+      .select(Seq(
         $"__split_gid" (0) as PRODUCT_ID.name,
         $"date" as ACQUISITION_DATE.name,
         $"__split_gid" (2) as GRANULE_ID.name,
-        $"${GID.name}",
-        MCD43A4_BAND_MAP as ASSETS.name
+        $"${GID.name}") ++ bandCols: _*
       )
       .orderBy(ACQUISITION_DATE.name, GID.name)
-      .repartition(col(GRANULE_ID.name))
+      .repartition(8, col(GRANULE_ID.name))
   }
 }
 
 object MODISCatalogRelation extends PDSFields {
 
-  def MCD43A4_LINK(suffix: String) =
-    concat(col(DOWNLOAD_URL.name), concat(col(GID.name), lit(suffix)))
+  def MCD43A4_band_url(suffix: Bands.Band) =
+    concat(col(DOWNLOAD_URL.name), concat(col(GID.name), lit(s"_${suffix}.TIF")))
 
-  val MCD43A4_BAND_MAP = map(
-    lit("B01"), MCD43A4_LINK("_B01.TIF"),
-    lit("B01qa"), MCD43A4_LINK("_B01qa.TIF"),
-    lit("B02"), MCD43A4_LINK("_B02.TIF"),
-    lit("B02qa"), MCD43A4_LINK("_B02qa.TIF"),
-    lit("B03"), MCD43A4_LINK("_B03.TIF"),
-    lit("B03qa"), MCD43A4_LINK("_B03qa.TIF"),
-    lit("B04"), MCD43A4_LINK("_B04.TIF"),
-    lit("B04qa"), MCD43A4_LINK("_B04qa.TIF"),
-    lit("B05"), MCD43A4_LINK("_B05.TIF"),
-    lit("B05qa"), MCD43A4_LINK("_B05qa.TIF"),
-    lit("B06"), MCD43A4_LINK("_B06.TIF"),
-    lit("B06qa"), MCD43A4_LINK("_B06qa.TIF"),
-    lit("B07"), MCD43A4_LINK("_B07.TIF"),
-    lit("B07qa"), MCD43A4_LINK("_B07qa.TIF"),
-    lit("metadata.json"), MCD43A4_LINK("_meta.json"),
-    lit("metadata.xml"), MCD43A4_LINK(".hdf.xml"),
-    lit("index.html"), concat(col(DOWNLOAD_URL.name), lit("index.html"))
-  )
+  object Bands extends Enumeration {
+    type Band = Value
+    val B01, B01qa, B02, B02qa, B03, B03aq, B04, B04qa, B05, B05qa, B06, B06qa, B07, B07qa = Value
+    val names: Seq[String] = values.toSeq.map(_.toString)
+  }
+
+  def schema = StructType(Seq(
+    PRODUCT_ID,
+    ACQUISITION_DATE,
+    GRANULE_ID,
+    GID
+  ) ++ Bands.names.map(n => StructField(n, StringType, true)))
 
+  private val inputSchema = StructType(Seq(
+    StructField("date", TimestampType, false),
+    DOWNLOAD_URL,
+    GID
+  ))
 }
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
index f615bbbe2..a0af40148 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
@@ -45,7 +45,6 @@ trait PDSFields {
   final val PATH = StructField("path", ShortType, false)
   final val ROW = StructField("row", ShortType, false)
   final val BOUNDS = StructField(GEOMETRY_COLUMN.columnName, JTSTypes.GeometryTypeInstance, false)
-  final val ASSETS = StructField("assets", DataTypes.createMapType(StringType, StringType))
   final def BOUNDS_WGS84 = StructField(
     "bounds_wgs84", StandardEncoders.envelopeEncoder.schema, false
   )
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
index 6a91c3d04..bbd476528 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/package.scala
@@ -34,10 +34,13 @@ package object awspds {
   trait CatalogDataFrameReaderTag
   type CatalogDataFrameReader = DataFrameReader @@ CatalogDataFrameReaderTag
 
-  /** Adds `raster` format specifier to `DataFrameReader`. */
   implicit class DataFrameReaderHasL8CatalogFormat(val reader: DataFrameReader) {
     def l8Catalog: CatalogDataFrameReader =
       tag[CatalogDataFrameReaderTag][DataFrameReader](
         reader.format(L8CatalogDataSource.SHORT_NAME))
+
+    def modisCatalog: CatalogDataFrameReader =
+      tag[CatalogDataFrameReaderTag][DataFrameReader](
+        reader.format(MODISCatalogDataSource.SHORT_NAME))
   }
 }

From b77df639050741714bdd1d47829c7bb1bbe0c309 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 1 Jul 2019 18:54:04 -0400
Subject: [PATCH 212/380] Added joining of extra columns from catalog into
 RasterFrame. Normalized handling of CSV tables into DataFrame input path.

---
 .../raster/RasterSourceDataSource.scala       | 62 +++++++++++--------
 .../raster/RasterSourceRelation.scala         | 34 +++++-----
 .../datasource/raster/package.scala           | 10 ++-
 .../raster/RasterSourceDataSourceSpec.scala   | 34 ++++++----
 4 files changed, 81 insertions(+), 59 deletions(-)

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
index 6626bd6db..b0b4185d8 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
 import org.locationtech.rasterframes.model.TileDimensions
 
-import scala.util.Try
-
 class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   import RasterSourceDataSource._
   override def shortName(): String = SHORT_NAME
@@ -36,9 +34,11 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
     val bands = parameters.bandIndexes
     val tiling = parameters.tileDims
     val spec = parameters.pathSpec
-    RasterSourceRelation(sqlContext, spec, bands, tiling)
+    val catRef = spec.fold(_.registerAsTable(sqlContext), identity)
+    RasterSourceRelation(sqlContext, catRef, bands, tiling)
   }
 }
+
 object RasterSourceDataSource {
   final val SHORT_NAME = "raster"
   final val PATH_PARAM = "path"
@@ -55,29 +55,39 @@ object RasterSourceDataSource {
     def bandColumnNames: Seq[String]
   }
   /** Container for specifying raster paths. */
-  case class BandSet(bandPaths: String*)
-  case class RasterSourceCatalog(sceneRows: Seq[BandSet], bandColumnNames: String*) extends WithBandColumns {
-    require(sceneRows.forall(_.bandPaths.length == bandColumnNames.length),
-      "Each scene row must have the same number of entries as band column names")
-  }
-
-  object RasterSourceCatalog {
-    def apply(csv: String, bandColumnNames: String*): Option[RasterSourceCatalog] = Try {
+  case class RasterSourceCatalog(csv: String, bandColumnNames: String*) extends WithBandColumns {
+    def registerAsTable(sqlContext: SQLContext): RasterSourceCatalogRef = {
+      import sqlContext.implicits._
       val lines = csv
         .split(Array('\n','\r'))
         .map(_.trim)
         .filter(_.nonEmpty)
 
-      val header = lines.head.split(',').map(_.trim).toSeq
-      val rows = lines.tail.map(_.split(',').map(_.trim)).map(BandSet(_: _*))
-      val bands = if (bandColumnNames.isEmpty) header
+      val dsLines = sqlContext.createDataset(lines)
+      val catalog = sqlContext.read
+        .option("header", "true")
+        .option("ignoreTrailingWhiteSpace", true)
+        .option("ignoreLeadingWhiteSpace", true)
+        .csv(dsLines)
+
+      val tmpName = tmpTableName()
+      catalog.createOrReplaceTempView(tmpName)
+
+      val cols = if (bandColumnNames.isEmpty) catalog.columns.toSeq
       else bandColumnNames
-      RasterSourceCatalog(rows, bands: _*)
-    }.toOption
 
+      RasterSourceCatalogRef(tmpName, cols: _*)
+    }
+  }
+
+  object RasterSourceCatalog {
     def apply(singlebandPaths: Seq[String]): Option[RasterSourceCatalog]  =
       if (singlebandPaths.isEmpty) None
-      else Some(RasterSourceCatalog(singlebandPaths.map(BandSet(_)), DEFAULT_COLUMN_NAME))
+      else {
+        val header = DEFAULT_COLUMN_NAME
+        val csv = header + "\n" + singlebandPaths.mkString("\n")
+        Some(new RasterSourceCatalog(csv, header))
+      }
   }
 
   /** Container for specifying where to select raster paths from. */
@@ -85,15 +95,16 @@ object RasterSourceDataSource {
 
   private[raster]
   implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
+    def tokenize(csv: String): Seq[String] = csv.split(',').map(_.trim)
 
     def tileDims: Option[TileDimensions] =
       parameters.get(TILE_DIMS_PARAM)
-        .map(_.split(',').map(_.trim.toInt))
-        .map { case Array(cols, rows) => TileDimensions(cols, rows)}
+        .map(tokenize(_).map(_.toInt))
+        .map { case Seq(cols, rows) => TileDimensions(cols, rows)}
 
     def bandIndexes: Seq[Int] = parameters
       .get(BAND_INDEXES_PARAM)
-      .map(_.split(',').map(_.trim.toInt).toSeq)
+      .map(tokenize(_).map(_.toInt))
       .getOrElse(Seq(0))
 
     def catalog: Option[RasterSourceCatalog] = {
@@ -110,13 +121,13 @@ object RasterSourceDataSource {
       RasterSourceCatalog(paths)
         .orElse(parameters
           .get(CATALOG_CSV_PARAM)
-          .flatMap(RasterSourceCatalog(_, catalogTableCols: _*))
+          .map(RasterSourceCatalog(_, catalogTableCols: _*))
         )
     }
 
     def catalogTableCols: Seq[String] = parameters
       .get(CATALOG_TABLE_COLS_PARAM)
-      .map(_.split(',').toSeq)
+      .map(tokenize(_).filter(_.nonEmpty).toSeq)
       .getOrElse(Seq.empty)
 
     def catalogTable: Option[RasterSourceCatalogRef] = parameters
@@ -124,12 +135,13 @@ object RasterSourceDataSource {
       .map(p => RasterSourceCatalogRef(p, catalogTableCols: _*))
 
     def pathSpec: Either[RasterSourceCatalog, RasterSourceCatalogRef] = {
-      val a = catalogTable
-      val b = catalogTableCols
       (catalog, catalogTable) match {
         case (Some(f), None) => Left(f)
         case (None, Some(p)) => Right(p)
-        case _ => throw new IllegalArgumentException("Only one of a set of file paths OR a paths table column may be provided.")
+        case (None, None) => throw new IllegalArgumentException(
+          s"Unable to interpret paths from: ${parameters.mkString("\n", "\n", "\n")}")
+        case _ => throw new IllegalArgumentException(
+          "Only one of a set of file paths OR a paths table column may be provided.")
       }
     }
   }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
index c5169b8c9..dc0cfac97 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.sources.{BaseRelation, TableScan}
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, RasterSourceCatalogRef}
+import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.RasterSourceCatalogRef
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
@@ -42,11 +42,11 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
   */
 case class RasterSourceRelation(
   sqlContext: SQLContext,
-  catalogTable: Either[RasterSourceCatalog, RasterSourceCatalogRef],
+  catalogTable: RasterSourceCatalogRef,
   bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
   extends BaseRelation with TableScan {
 
-  lazy val inputColNames = catalogTable.merge.bandColumnNames
+  lazy val inputColNames = catalogTable.bandColumnNames
 
   def pathColNames = inputColNames
     .map(_ + "_path")
@@ -61,6 +61,11 @@ case class RasterSourceRelation(
   def tileColNames = inputColNames
     .flatMap(bandNames(_, bandIndexes))
 
+  def extraCols: Seq[StructField] = {
+    val catalog = sqlContext.table(catalogTable.tableName)
+    catalog.schema.fields.filter(f => !catalogTable.bandColumnNames.contains(f.name))
+  }
+
   override def schema: StructType = {
     val tileSchema = schemaOf[ProjectedRasterTile]
     val paths = for {
@@ -70,7 +75,7 @@ case class RasterSourceRelation(
       tileColName <- tileColNames
     } yield StructField(tileColName, tileSchema, true)
 
-    StructType(paths ++ tiles)
+    StructType(paths ++ tiles ++ extraCols)
   }
 
   override def buildScan(): RDD[Row] = {
@@ -79,18 +84,7 @@ case class RasterSourceRelation(
     // The general transformaion is:
     // input -> path -> src -> ref -> tile
     // Each step is broken down for readability
-    val inputs: DataFrame = catalogTable match {
-      case Right(spec) => sqlContext.table(spec.tableName)
-      case Left(spec) => spec.bandColumnNames match {
-        case Seq(single) => spec.sceneRows.map(_.bandPaths.headOption.orNull).toDF(single)
-        case bands =>
-          val bsel = bands.zipWithIndex.map { case (b, i) => col("value")(i).as(b) }
-
-          spec.sceneRows.map(_.bandPaths)
-            .toDF("value")
-            .select(bsel: _*)
-      }
-    }
+    val inputs: DataFrame = sqlContext.table(catalogTable.tableName)
 
     // Basically renames the input columns to have the '_path' suffix
     val pathsAliasing = for {
@@ -115,15 +109,17 @@ case class RasterSourceRelation(
 
     // Add path columns
     val withPaths = inputs
-      .select(pathsAliasing: _*)
+      .select($"*" +: pathsAliasing: _*)
 
     // Path columns have to be manually pulled along through each step. Resolve columns once
     // and reused with each select.
     val paths = pathColNames.map(withPaths.apply)
 
+    val extras = extraCols.map(f => inputs(f.name))
+
     val df = withPaths
-      .select(paths :+ refs: _*)
-      .select(paths ++ refsToTiles: _*)
+      .select(extras ++ paths :+ refs: _*)
+      .select(paths ++ refsToTiles ++ extras: _*)
 
     df.rdd
   }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
index 1376ea5c9..3ac06946a 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
@@ -29,6 +29,8 @@ import shapeless.tag
 import shapeless.tag.@@
 package object raster {
 
+  private[raster] def tmpTableName() = UUID.randomUUID().toString.replace("-", "")
+
   trait RasterSourceDataFrameReaderTag
   type RasterSourceDataFrameReader = DataFrameReader @@ RasterSourceDataFrameReaderTag
 
@@ -53,9 +55,10 @@ package object raster {
 
     def fromCatalog(catalog: DataFrame, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader] {
-        val tmpName = UUID.randomUUID().toString.replace("-", "")
+        val tmpName = tmpTableName()
         catalog.createOrReplaceTempView(tmpName)
-        reader.option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tmpName)
+        reader
+          .option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tmpName)
           .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
       }
 
@@ -65,9 +68,10 @@ package object raster {
           .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
       )
 
-    def fromCSV(catalogCSV: String): RasterSourceDataFrameReader =
+    def fromCSV(catalogCSV: String, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader](
         reader.option(RasterSourceDataSource.CATALOG_CSV_PARAM, catalogCSV)
+          .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
       )
 
     def from(newlineDelimPaths: String): RasterSourceDataFrameReader =
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
index 48c524980..58aa44dfd 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
@@ -20,6 +20,7 @@
  */
 
 package org.locationtech.rasterframes.datasource.raster
+import org.apache.spark.sql.functions.lit
 import org.locationtech.rasterframes.{TestEnvironment, _}
 import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, _}
 import org.locationtech.rasterframes.model.TileDimensions
@@ -30,7 +31,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
   describe("DataSource parameter processing") {
     def singleCol(paths: Iterable[String]) = {
-      val rows: Seq[BandSet] = paths.map(BandSet(_)).toSeq
+      val rows = paths.mkString(DEFAULT_COLUMN_NAME + "\n", "\n", "")
       RasterSourceCatalog(rows, DEFAULT_COLUMN_NAME)
     }
 
@@ -73,7 +74,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
           |${paths.mkString(",")}
         """.stripMargin.trim
       val p = Map(CATALOG_CSV_PARAM -> csv)
-      p.pathSpec should be (Left(RasterSourceCatalog(Seq(BandSet(paths:_*)), bands:_*)))
+      p.pathSpec should be (Left(RasterSourceCatalog(csv)))
     }
   }
 
@@ -177,39 +178,48 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
         l8SamplePath(2).toASCIIString,
         l8SamplePath(3).toASCIIString))
         .toDF("B1", "B2", "B3")
+        .withColumn("foo", lit("something"))
 
       val df = spark.read.raster
         .fromCatalog(bandPaths, "B1", "B2", "B3")
         .withTileDimensions(128, 128)
         .load()
 
-      df.schema.size should be(6)
+      df.schema.size should be(7)
       df.tileColumns.size should be (3)
       df.select($"B1_path").distinct().count() should be (1)
 
+      df.columns.contains("foo") should be (true)
+      df.select($"foo").distinct().count() should be (1)
+      df.select($"foo".as[String]).first() should be ("something")
+
       val diffStats = df.select(rf_tile_stats($"B1") =!= rf_tile_stats($"B2")).as[Boolean].collect()
       diffStats.forall(identity) should be(true)
     }
 
 
-    it("should read a set of coherent bands from multiple files in a table") {
-      val bandPaths = Seq((
-        l8SamplePath(1).toASCIIString,
-        l8SamplePath(2).toASCIIString,
-        l8SamplePath(3).toASCIIString))
-        .toDF("B1", "B2", "B3")
+    it("should read a set of coherent bands from multiple files in a csv") {
+      def b(i: Int) = l8SamplePath(i).toASCIIString
 
-      bandPaths.createOrReplaceTempView("pathsTable")
+      val csv =
+        s"""
+          |B1, B2, B3, foo
+          |${b(1)}, ${b(2)}, ${b(3)}, something
+        """.stripMargin
 
       val df = spark.read.raster
-        .fromCatalog("pathsTable", "B1", "B2", "B3")
+        .fromCSV(csv, "B1", "B2", "B3")
         .withTileDimensions(128, 128)
         .load()
 
-      df.schema.size should be(6)
+      df.schema.size should be(7)
       df.tileColumns.size should be (3)
       df.select($"B1_path").distinct().count() should be (1)
 
+      df.columns.contains("foo") should be (true)
+      df.select($"foo").distinct().count() should be (1)
+      df.select($"foo".as[String]).first() should be ("something")
+
       val diffStats = df.select(rf_tile_stats($"B1") =!= rf_tile_stats($"B2")).as[Boolean].collect()
       diffStats.forall(identity) should be(true)
     }

From 29142313e28a8958a893bb2c5156fc7ddea87bcc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Jul 2019 08:53:51 -0400
Subject: [PATCH 213/380] Documentation tweaks.

---
 .../src/main/python/docs/raster-io.pymd       | 28 +++++++++++++++++--
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 5b66afeaf..318fec5ee 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -2,6 +2,26 @@
 
 The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames are compatible with existing generalized DataSources, such as Parquet, but provide specialized ones oriented around geospatial raster data.
 
+Three types of DataSources will be introduced below: 
+
+* Catalog Readers
+  - `aws-pds-l8-catalog`: experimental
+  - `aws-pds-modis-catalog`: experimental
+  - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
+* Raster Readers
+  - `raster`: the standard reader for most operations
+  - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
+  - `geotiff`: a deprecated reader for reading single GeoTIFF files
+* Raster Writers 
+  - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
+  - `geotiff`: beta
+  - `parquet`: for writing raster data in an analytics functional form  
+
+There's also some support for vector data (for masking and data labeling):
+
+* Vector Readers
+  - `geojson`: read GeoJson files with `geometry` column aside attribute values
+
 ## Reading Raster Data
 
 ```python, echo=False
@@ -73,7 +93,7 @@ rf2.printSchema()
 rf2.show(5)
 ```
 
-### Multiple Scenes, Multiple Bands
+### Raster Catalogs
 
 The example above will work with a reasonable number of rows in the CSV string. However, in many cases it's preferable to have that information organized in another DataFrame. For the purposes of this example use one of the AWS PDS daily scene listing files for MODIS surface reflectance as our catalog. 
 
@@ -136,7 +156,8 @@ rf.show(2, truncate=False)
 
 ### Multiband Rasters
 
-[[Need to know _a priori_ how many bands there are, specified with `band_indexes`]]
+> Need to know _a priori_ how many bands there are, specified with `band_indexes`.
+> Must be homogenous, or tolerant of `null` Tiles.
  
 
 ## Writing Raster Data
@@ -152,4 +173,5 @@ rf.show(2, truncate=False)
 
 
 
-[DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
\ No newline at end of file
+[DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
+[GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
\ No newline at end of file

From d4f0ca87f658f3098b5b78f1b1dce403ad22af42 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Jul 2019 11:04:42 -0400
Subject: [PATCH 214/380] `RasterFrame` renamed to `RasterFrameLayer` `asRF`
 now `asLayer` Fixed snake caseing of Python `RasterFrameLayer` methods.

---
 .../rasterframes/MetadataKeys.scala           |   2 +-
 .../rasterframes/PairRDDConverter.scala       |   2 +-
 .../rasterframes/StandardColumns.scala        |  16 +-
 .../encoders/StandardEncoders.scala           |   2 +-
 .../extensions/ContextRDDMethods.scala        |  14 +-
 .../extensions/DataFrameMethods.scala         |  48 ++--
 .../rasterframes/extensions/Implicits.scala   |   4 +-
 .../extensions/ProjectedRasterMethods.scala   |  55 ++--
 .../extensions/RFSpatialColumnMethods.scala   |  28 +--
 ...ds.scala => RasterFrameLayerMethods.scala} |  43 ++--
 .../rasterframes/rasterframes.scala           |   8 +-
 .../rasterframes/util/debug/package.scala     |   4 +-
 .../rasterframes/util/package.scala           |   4 +-
 .../test/scala/examples/Classification.scala  |   8 +-
 core/src/test/scala/examples/Clustering.scala |   6 +-
 .../scala/examples/CreatingRasterFrames.scala |  10 +-
 core/src/test/scala/examples/Exporting.scala  |  14 +-
 .../test/scala/examples/LocalArithmetic.scala |   6 +-
 core/src/test/scala/examples/Masking.scala    |   6 +-
 core/src/test/scala/examples/MeanValue.scala  |   2 +-
 core/src/test/scala/examples/NDVI.scala       |   6 +-
 core/src/test/scala/examples/Tour.scala       |  10 +-
 .../rasterframes/ExplodeSpec.scala            |   4 +-
 .../rasterframes/ExtensionMethodSpec.scala    |   6 +-
 .../rasterframes/GeometryFunctionsSpec.scala  |   4 +-
 .../rasterframes/MetadataSpec.scala           |   2 +-
 .../rasterframes/RasterFrameSpec.scala        |  66 ++---
 .../rasterframes/SpatialKeySpec.scala         |   2 +-
 .../locationtech/rasterframes/TestData.scala  |   2 +-
 .../rasterframes/TileAssemblerSpec.scala      |   2 +-
 .../rasterframes/TileUDTSpec.scala            |   2 +-
 .../geotiff/GeoTiffDataSource.scala           |   2 +-
 .../datasource/geotiff/package.scala          |   6 +-
 .../GeoTrellisLayerDataSource.scala           |   4 +-
 .../datasource/geotrellis/package.scala       |  12 +-
 .../src/test/scala/examples/Creating.scala    | 235 ------------------
 .../geotiff/GeoTiffDataSourceSpec.scala       |  12 +-
 .../geotrellis/GeoTrellisCatalogSpec.scala    |   2 +-
 .../geotrellis/GeoTrellisDataSourceSpec.scala |  74 +++---
 docs/src/main/paradox/release-notes.md        |   7 +-
 .../src/main/python/docs/architecture.md      |  39 +++
 .../src/main/python/docs/description.md       |  17 --
 .../main/python/pyrasterframes/__init__.py    |  16 +-
 .../main/python/pyrasterframes/rf_context.py  |   2 +-
 .../main/python/pyrasterframes/rf_types.py    |  50 ++--
 .../main/python/tests/PyRasterFramesTests.py  |  28 +--
 .../src/main/python/tests/__init__.py         |   8 +-
 .../rasterframes/py/PyRFContext.scala         |  56 ++---
 48 files changed, 374 insertions(+), 584 deletions(-)
 rename core/src/main/scala/org/locationtech/rasterframes/extensions/{RasterFrameMethods.scala => RasterFrameLayerMethods.scala} (90%)
 delete mode 100644 datasource/src/test/scala/examples/Creating.scala
 create mode 100644 pyrasterframes/src/main/python/docs/architecture.md

diff --git a/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
index d3f90a502..9eb4000d9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/MetadataKeys.scala
@@ -29,6 +29,6 @@ trait MetadataKeys {
   /** Key under which ContextRDD metadata is stored. */
   private[rasterframes] val CONTEXT_METADATA_KEY = "_context"
 
-  /** Key under which RasterFrame role a column plays. */
+  /** Key under which RasterFrameLayer role a column plays. */
   private[rasterframes] val SPATIAL_ROLE_KEY = "_stRole"
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala b/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
index 0b3caf09e..658c0d65d 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/PairRDDConverter.scala
@@ -36,7 +36,7 @@ import scala.annotation.implicitNotFound
  *
  * @since 4/8/18
  */
-@implicitNotFound("An RDD converter is required create a RasterFrame. " +
+@implicitNotFound("An RDD converter is required create a RasterFrameLayer. " +
   "Please provide an implementation of PairRDDConverter[${K}, ${V}].")
 trait PairRDDConverter[K, V] extends Serializable {
   val schema: StructType
diff --git a/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
index 128729209..2e82ab356 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/StandardColumns.scala
@@ -38,20 +38,20 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
  * @since 2/19/18
  */
 trait StandardColumns {
-  /** Default RasterFrame spatial column name. */
+  /** Default RasterFrameLayer spatial column name. */
   val SPATIAL_KEY_COLUMN = col("spatial_key").as[SpatialKey]
 
-  /** Default RasterFrame temporal column name. */
+  /** Default RasterFrameLayer temporal column name. */
   val TEMPORAL_KEY_COLUMN = col("temporal_key").as[TemporalKey]
 
-  /** Default RasterFrame timestamp column name */
+  /** Default RasterFrameLayer timestamp column name */
   val TIMESTAMP_COLUMN = col("timestamp").as[Timestamp]
 
-  /** Default RasterFrame column name for an tile extent as geometry value. */
+  /** Default RasterFrameLayer column name for an tile extent as geometry value. */
   // This is a `def` because `PolygonUDT` needs to be initialized first.
   def GEOMETRY_COLUMN = col("geometry").as[jtsPolygon]
 
-  /** Default RasterFrame column name for the center coordinates of the tile's bounds. */
+  /** Default RasterFrameLayer column name for the center coordinates of the tile's bounds. */
   // This is a `def` because `PointUDT` needs to be initialized first.
   def CENTER_COLUMN = col("center").as[jtsPoint]
 
@@ -64,17 +64,17 @@ trait StandardColumns {
   /** Default CRS column name. */
   def CRS_COLUMN = col("crs").as[CRS]
 
-  /** Default RasterFrame column name for an added spatial index. */
+  /** Default RasterFrameLayer column name for an added spatial index. */
   val SPATIAL_INDEX_COLUMN = col("spatial_index").as[Long]
 
-  /** Default RasterFrame tile column name. */
+  /** Default RasterFrameLayer tile column name. */
   // This is a `def` because `TileUDT` needs to be initialized first.
   def TILE_COLUMN = col("tile").as[Tile]
 
   /** Default column name for a tile with its CRS and Extent. */
   def PROJECTED_RASTER_COLUMN = col("proj_raster").as[ProjectedRasterTile]
 
-  /** Default RasterFrame `TileFeature.data` column name. */
+  /** Default RasterFrameLayer `TileFeature.data` column name. */
   val TILE_FEATURE_DATA_COLUMN = col("tile_data")
 
   /** Default GeoTiff tags column. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
index 8066265d3..badce6283 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
@@ -39,7 +39,7 @@ import org.locationtech.rasterframes.model.{CellContext, Cells, TileContext, Til
 import scala.reflect.runtime.universe._
 
 /**
- * Implicit encoder definitions for RasterFrame types.
+ * Implicit encoder definitions for RasterFrameLayer types.
  */
 trait StandardEncoders extends SpatialEncoders {
   object PrimitiveEncoders extends SparkBasicEncoders
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
index d7aa0a665..7bf3230b3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ContextRDDMethods.scala
@@ -25,7 +25,7 @@ import org.locationtech.rasterframes.PairRDDConverter._
 import org.locationtech.rasterframes.StandardColumns._
 import Implicits._
 import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.{PairRDDConverter, RasterFrame}
+import org.locationtech.rasterframes.RasterFrameLayer
 import geotrellis.raster.CellGrid
 import geotrellis.spark._
 import geotrellis.spark.io._
@@ -35,16 +35,16 @@ import org.apache.spark.sql.SparkSession
 import org.locationtech.rasterframes.PairRDDConverter
 
 /**
- * Extension method on `ContextRDD`-shaped RDDs with appropriate context bounds to create a RasterFrame.
+ * Extension method on `ContextRDD`-shaped RDDs with appropriate context bounds to create a RasterFrameLayer.
  * @since 7/18/17
  */
 abstract class SpatialContextRDDMethods[T <: CellGrid](implicit spark: SparkSession)
     extends MethodExtensions[RDD[(SpatialKey, T)] with Metadata[TileLayerMetadata[SpatialKey]]] {
   import PairRDDConverter._
 
-  def toRF(implicit converter: PairRDDConverter[SpatialKey, T]): RasterFrame = toRF(TILE_COLUMN.columnName)
+  def toLayer(implicit converter: PairRDDConverter[SpatialKey, T]): RasterFrameLayer = toLayer(TILE_COLUMN.columnName)
 
-  def toRF(tileColumnName: String)(implicit converter: PairRDDConverter[SpatialKey, T]): RasterFrame = {
+  def toLayer(tileColumnName: String)(implicit converter: PairRDDConverter[SpatialKey, T]): RasterFrameLayer = {
     val df = self.toDataFrame.setSpatialColumnRole(SPATIAL_KEY_COLUMN, self.metadata)
     val defName = TILE_COLUMN.columnName
     df.mapWhen(_ ⇒ tileColumnName != defName, _.withColumnRenamed(defName, tileColumnName))
@@ -53,16 +53,16 @@ abstract class SpatialContextRDDMethods[T <: CellGrid](implicit spark: SparkSess
 }
 
 /**
- * Extension method on `ContextRDD`-shaped `Tile` RDDs keyed with [[SpaceTimeKey]], with appropriate context bounds to create a RasterFrame.
+ * Extension method on `ContextRDD`-shaped `Tile` RDDs keyed with [[SpaceTimeKey]], with appropriate context bounds to create a RasterFrameLayer.
  * @since 9/11/17
  */
 abstract class SpatioTemporalContextRDDMethods[T <: CellGrid](
   implicit spark: SparkSession)
   extends MethodExtensions[RDD[(SpaceTimeKey, T)] with Metadata[TileLayerMetadata[SpaceTimeKey]]] {
 
-  def toRF(implicit converter: PairRDDConverter[SpaceTimeKey, T]): RasterFrame = toRF(TILE_COLUMN.columnName)
+  def toLayer(implicit converter: PairRDDConverter[SpaceTimeKey, T]): RasterFrameLayer = toLayer(TILE_COLUMN.columnName)
 
-  def toRF(tileColumnName: String)(implicit converter: PairRDDConverter[SpaceTimeKey, T]): RasterFrame = {
+  def toLayer(tileColumnName: String)(implicit converter: PairRDDConverter[SpaceTimeKey, T]): RasterFrameLayer = {
     val df = self.toDataFrame
       .setSpatialColumnRole(SPATIAL_KEY_COLUMN, self.metadata)
       .setTemporalColumnRole(TEMPORAL_KEY_COLUMN)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 599beb637..43177117b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -23,7 +23,7 @@ package org.locationtech.rasterframes.extensions
 
 import org.locationtech.rasterframes.StandardColumns._
 import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.RasterFrame
+import org.locationtech.rasterframes.RasterFrameLayer
 import geotrellis.raster.Tile
 import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
@@ -44,7 +44,7 @@ import scala.util.Try
  * @since 7/18/17
  */
 trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with MetadataKeys {
-  import Implicits.{WithDataFrameMethods, WithMetadataBuilderMethods, WithMetadataMethods, WithRasterFrameMethods}
+  import Implicits.{WithDataFrameMethods, WithMetadataBuilderMethods, WithMetadataMethods, WithRasterFrameLayerMethods}
 
   private def selector(column: Column) = (attr: Attribute) ⇒
     attr.name == column.columnName || attr.semanticEquals(column.expr)
@@ -82,7 +82,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def setTemporalColumnRole(column: Column): DF =
     addColumnMetadata(column, _.tagTemporalKey)
 
-  /** Get the role tag the column plays in the RasterFrame, if any. */
+  /** Get the role tag the column plays in the RasterFrameLayer, if any. */
   private[rasterframes]
   def getColumnRole(column: Column): Option[String] =
     fetchMetadataValue(column, _.metadata.getString(SPATIAL_ROLE_KEY))
@@ -135,7 +135,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def withPrefixedColumnNames(prefix: String): DF =
     self.columns.foldLeft(self)((df, c) ⇒ df.withColumnRenamed(c, s"$prefix$c").asInstanceOf[DF])
 
-  /** Converts this DataFrame to a RasterFrame after ensuring it has:
+  /** Converts this DataFrame to a RasterFrameLayer after ensuring it has:
    *
    * <ol type="a">
    * <li>a space or space-time key column
@@ -145,23 +145,23 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    *
    * If any of the above are violated, and [[IllegalArgumentException]] is thrown.
    *
-   * @return validated RasterFrame
+   * @return validated RasterFrameLayer
    * @throws IllegalArgumentException when constraints are not met.
    */
   @throws[IllegalArgumentException]
-  def asRF: RasterFrame = {
+  def asLayer: RasterFrameLayer = {
     val potentialRF = certifyRasterframe(self)
 
     require(
       potentialRF.findSpatialKeyField.nonEmpty,
-      "A RasterFrame requires a column identified as a spatial key"
+      "A RasterFrameLayer requires a column identified as a spatial key"
     )
 
-    require(potentialRF.tileColumns.nonEmpty, "A RasterFrame requires at least one tile column")
+    require(potentialRF.tileColumns.nonEmpty, "A RasterFrameLayer requires at least one tile column")
 
     require(
       Try(potentialRF.tileLayerMetadata).isSuccess,
-      "A RasterFrame requires embedded TileLayerMetadata"
+      "A RasterFrameLayer requires embedded TileLayerMetadata"
     )
 
     potentialRF
@@ -219,36 +219,36 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     RasterJoin(self, right, joinExpr, leftExtent, leftCRS, rightExtent, rightCRS)
 
   /**
-   * Convert DataFrame into a RasterFrame
+   * Convert DataFrame into a RasterFrameLayer
    *
    * @param spatialKey The column where the spatial key is stored
    * @param tlm Metadata describing layout under which tiles were created. Note: no checking is
    *            performed to ensure metadata, key-space, and tiles are coherent.
-   * @throws IllegalArgumentException when constraints outlined in `asRF` are not met.
-   * @return Encoded RasterFrame
+   * @throws IllegalArgumentException when constraints outlined in `asLayer` are not met.
+   * @return Encoded RasterFrameLayer
    */
   @throws[IllegalArgumentException]
-  def asRF(spatialKey: Column, tlm: TileLayerMetadata[SpatialKey]): RasterFrame =
-    setSpatialColumnRole(spatialKey, tlm).asRF
+  def asLayer(spatialKey: Column, tlm: TileLayerMetadata[SpatialKey]): RasterFrameLayer =
+    setSpatialColumnRole(spatialKey, tlm).asLayer
 
   /**
-   * Convert DataFrame into a RasterFrame
+   * Convert DataFrame into a RasterFrameLayer
    *
    * @param spatialKey The column where the spatial key is stored
    * @param temporalKey The column tagged under the temporal role
    * @param tlm Metadata describing layout under which tiles were created. Note: no checking is
    *            performed to ensure metadata, key-space, and tiles are coherent.
-   * @throws IllegalArgumentException when constraints outlined in `asRF` are not met.
-   * @return Encoded RasterFrame
+   * @throws IllegalArgumentException when constraints outlined in `asLayer` are not met.
+   * @return Encoded RasterFrameLayer
    */
   @throws[IllegalArgumentException]
-  def asRF(spatialKey: Column, temporalKey: Column, tlm: TileLayerMetadata[SpaceTimeKey]): RasterFrame =
+  def asLayer(spatialKey: Column, temporalKey: Column, tlm: TileLayerMetadata[SpaceTimeKey]): RasterFrameLayer =
     setSpatialColumnRole(spatialKey, tlm)
       .setTemporalColumnRole(temporalKey)
-      .asRF
+      .asLayer
 
   /**
-   * Converts [[DataFrame]] to a RasterFrame if the following constraints are fulfilled:
+   * Converts [[DataFrame]] to a RasterFrameLayer if the following constraints are fulfilled:
    *
    * <ol type="a">
    * <li>a space or space-time key column
@@ -256,9 +256,9 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    * <li>tile layout metadata
    * <ol>
    *
-   * @return Some[RasterFrame] if constraints fulfilled, [[None]] otherwise.
+   * @return Some[RasterFrameLayer] if constraints fulfilled, [[None]] otherwise.
    */
-  def asRFSafely: Option[RasterFrame] = Try(asRF).toOption
+  def asLayerSafely: Option[RasterFrameLayer] = Try(asLayer).toOption
 
   /**
    * Tests for the following conditions on the [[DataFrame]]:
@@ -271,9 +271,9 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    *
    * @return true if all constraints are fulfilled, false otherwise.
    */
-  def isRF: Boolean = Try(asRF).isSuccess
+  def isLayer: Boolean = Try(asLayer).isSuccess
 
-  /** Internal method for slapping the RasterFreame seal of approval on a DataFrame.
+  /** Internal method for slapping the RasterFreameLayer seal of approval on a DataFrame.
    * Only call if if you are sure it has a spatial key and tile columns and TileLayerMetadata. */
   private[rasterframes] def certify = certifyRasterframe(self)
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
index 95f24507d..563e03e87 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/Implicits.scala
@@ -21,7 +21,7 @@
 
 package org.locationtech.rasterframes.extensions
 
-import org.locationtech.rasterframes.RasterFrame
+import org.locationtech.rasterframes.RasterFrameLayer
 import org.locationtech.rasterframes.util.{WithMergeMethods, WithPrototypeMethods}
 import geotrellis.raster._
 import geotrellis.raster.io.geotiff.SinglebandGeoTiff
@@ -56,7 +56,7 @@ trait Implicits {
 
   implicit class WithDataFrameMethods[D <: DataFrame](val self: D) extends DataFrameMethods[D]
 
-  implicit class WithRasterFrameMethods(val self: RasterFrame) extends RasterFrameMethods
+  implicit class WithRasterFrameLayerMethods(val self: RasterFrameLayer) extends RasterFrameLayerMethods
 
   implicit class WithSpatialContextRDDMethods[T <: CellGrid](
     val self: RDD[(SpatialKey, T)] with Metadata[TileLayerMetadata[SpatialKey]]
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
index 917f329d4..81f5054f9 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ProjectedRasterMethods.scala
@@ -23,20 +23,19 @@ package org.locationtech.rasterframes.extensions
 
 import java.time.ZonedDateTime
 
-import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.{RasterFrame, StandardColumns}
 import geotrellis.raster.{CellGrid, ProjectedRaster}
 import geotrellis.spark._
 import geotrellis.spark.tiling._
 import geotrellis.util.MethodExtensions
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SparkSession
-import org.locationtech.rasterframes.{PairRDDConverter, StandardColumns}
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{PairRDDConverter, RasterFrameLayer, StandardColumns}
 
 import scala.reflect.runtime.universe._
 
 /**
- * Extension methods on [[ProjectedRaster]] for creating [[RasterFrame]]s.
+ * Extension methods on [[ProjectedRaster]] for creating [[RasterFrameLayer]]s.
  *
  * @since 8/10/17
  */
@@ -46,64 +45,64 @@ abstract class ProjectedRasterMethods[T <: CellGrid: WithMergeMethods: WithProto
   type XTileLayerRDD[K] = RDD[(K, T)] with Metadata[TileLayerMetadata[K]]
 
   /**
-   * Convert the wrapped [[ProjectedRaster]] into a [[RasterFrame]] with a
+   * Convert the wrapped [[ProjectedRaster]] into a [[RasterFrameLayer]] with a
    * single row.
    *
-   * @param spark [[SparkSession]] in which to create [[RasterFrame]]
+   * @param spark [[SparkSession]] in which to create [[RasterFrameLayer]]
    */
-  def toRF(implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame =
-    toRF(TILE_COLUMN.columnName)
+  def toLayer(implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrameLayer =
+    toLayer(TILE_COLUMN.columnName)
 
   /**
-   * Convert the wrapped [[ProjectedRaster]] into a [[RasterFrame]] with a
+   * Convert the wrapped [[ProjectedRaster]] into a [[RasterFrameLayer]] with a
    * single row.
    *
-   * @param spark [[SparkSession]] in which to create [[RasterFrame]]
+   * @param spark [[SparkSession]] in which to create [[RasterFrameLayer]]
    */
-  def toRF(tileColName: String)
-    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame = {
+  def toLayer(tileColName: String)
+    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrameLayer = {
     val (cols, rows) = self.raster.dimensions
-    toRF(cols, rows, tileColName)
+    toLayer(cols, rows, tileColName)
   }
 
   /**
-   * Convert the [[ProjectedRaster]] into a [[RasterFrame]] using the
+   * Convert the [[ProjectedRaster]] into a [[RasterFrameLayer]] using the
    * given dimensions as the target per-row tile size.
    *
    * @param tileCols Max number of horizontal cells per tile
    * @param tileRows Max number of vertical cells per tile
-   * @param spark [[SparkSession]] in which to create [[RasterFrame]]
+   * @param spark [[SparkSession]] in which to create [[RasterFrameLayer]]
    */
-  def toRF(tileCols: Int, tileRows: Int)
-    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame =
-    toRF(tileCols, tileRows, TILE_COLUMN.columnName)
+  def toLayer(tileCols: Int, tileRows: Int)
+    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrameLayer =
+    toLayer(tileCols, tileRows, TILE_COLUMN.columnName)
 
   /**
-   * Convert the [[ProjectedRaster]] into a [[RasterFrame]] using the
+   * Convert the [[ProjectedRaster]] into a [[RasterFrameLayer]] using the
    * given dimensions as the target per-row tile size.
    *
    * @param tileCols Max number of horizontal cells per tile
    * @param tileRows Max number of vertical cells per tile
    * @param tileColName Name to give the created tile column
-   * @param spark [[SparkSession]] in which to create [[RasterFrame]]
+   * @param spark [[SparkSession]] in which to create [[RasterFrameLayer]]
    */
-  def toRF(tileCols: Int, tileRows: Int, tileColName: String)
-    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrame = {
-    toTileLayerRDD(tileCols, tileRows).toRF(tileColName)
+  def toLayer(tileCols: Int, tileRows: Int, tileColName: String)
+    (implicit spark: SparkSession, schema: PairRDDConverter[SpatialKey, T]): RasterFrameLayer = {
+    toTileLayerRDD(tileCols, tileRows).toLayer(tileColName)
   }
 
   /**
-   * Convert the [[ProjectedRaster]] into a [[RasterFrame]] using the
+   * Convert the [[ProjectedRaster]] into a [[RasterFrameLayer]] using the
    * given dimensions as the target per-row tile size and singular timestamp as the temporal component.
    *
    * @param tileCols Max number of horizontal cells per tile
    * @param tileRows Max number of vertical cells per tile.
    * @param timestamp Temporal key value to assign to tiles.
-   * @param spark [[SparkSession]] in which to create [[RasterFrame]]
+   * @param spark [[SparkSession]] in which to create [[RasterFrameLayer]]
    */
-  def toRF(tileCols: Int, tileRows: Int, timestamp: ZonedDateTime)
-    (implicit spark: SparkSession, schema: PairRDDConverter[SpaceTimeKey, T]): RasterFrame =
-    toTileLayerRDD(tileCols, tileRows, timestamp).toRF
+  def toLayer(tileCols: Int, tileRows: Int, timestamp: ZonedDateTime)
+    (implicit spark: SparkSession, schema: PairRDDConverter[SpaceTimeKey, T]): RasterFrameLayer =
+    toTileLayerRDD(tileCols, tileRows, timestamp).toLayer
 
   /**
    * Convert the [[ProjectedRaster]] into a [[TileLayerRDD[SpatialKey]] using the
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
index c7c46920b..4eade42ad 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RFSpatialColumnMethods.scala
@@ -22,7 +22,7 @@
 package org.locationtech.rasterframes.extensions
 
 import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.RasterFrame
+import org.locationtech.rasterframes.RasterFrameLayer
 import org.locationtech.jts.geom.Point
 import geotrellis.proj4.LatLng
 import geotrellis.spark.SpatialKey
@@ -36,12 +36,12 @@ import org.locationtech.geomesa.curve.Z2SFC
 import org.locationtech.rasterframes.StandardColumns
 
 /**
- * RasterFrame extension methods associated with adding spatially descriptive columns.
+ * RasterFrameLayer extension methods associated with adding spatially descriptive columns.
  *
  * @since 12/15/17
  */
-trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with StandardColumns {
-  import Implicits.{WithDataFrameMethods, WithRasterFrameMethods}
+trait RFSpatialColumnMethods extends MethodExtensions[RasterFrameLayer] with StandardColumns {
+  import Implicits.{WithDataFrameMethods, WithRasterFrameLayerMethods}
   import org.locationtech.geomesa.spark.jts._
 
   /** Returns the key-space to map-space coordinate transform. */
@@ -65,9 +65,9 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
     * Append a column containing the extent of the row's spatial key.
     * Coordinates are in native CRS.
     * @param colName name of column to append. Defaults to "extent"
-    * @return updated RasterFrame
+    * @return updated RasterFrameLayer
     */
-  def withExtent(colName: String = EXTENT_COLUMN.columnName): RasterFrame = {
+  def withExtent(colName: String = EXTENT_COLUMN.columnName): RasterFrameLayer = {
     val key2Extent = sparkUdf(keyCol2Extent)
     self.withColumn(colName, key2Extent(self.spatialKeyColumn)).certify
   }
@@ -76,9 +76,9 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
    * Append a column containing the bounds of the row's spatial key.
    * Coordinates are in native CRS.
    * @param colName name of column to append. Defaults to "geometry"
-   * @return updated RasterFrame
+   * @return updated RasterFrameLayer
    */
-  def withGeometry(colName: String = GEOMETRY_COLUMN.columnName): RasterFrame = {
+  def withGeometry(colName: String = GEOMETRY_COLUMN.columnName): RasterFrameLayer = {
     val key2Bounds = sparkUdf(keyCol2Extent andThen (_.jtsGeom))
     self.withColumn(colName, key2Bounds(self.spatialKeyColumn)).certify
   }
@@ -87,9 +87,9 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
    * Append a column containing the center of the row's spatial key.
    * Coordinate is in native CRS.
    * @param colName name of column to append. Defaults to "center"
-   * @return updated RasterFrame
+   * @return updated RasterFrameLayer
    */
-  def withCenter(colName: String = CENTER_COLUMN.columnName): RasterFrame = {
+  def withCenter(colName: String = CENTER_COLUMN.columnName): RasterFrameLayer = {
     val key2Center = sparkUdf(keyCol2Extent andThen (_.center.jtsGeom))
     self.withColumn(colName, key2Center(self.spatialKeyColumn).as[Point]).certify
   }
@@ -98,9 +98,9 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
    * Append a column containing the center of the row's spatial key.
    * Coordinate is in (longitude, latitude) (EPSG:4326).
    * @param colName name of column to append. Defaults to "center"
-   * @return updated RasterFrame
+   * @return updated RasterFrameLayer
    */
-  def withCenterLatLng(colName: String = "center"): RasterFrame = {
+  def withCenterLatLng(colName: String = "center"): RasterFrameLayer = {
     val key2Center = sparkUdf(keyCol2LatLng)
     self.withColumn(colName, key2Center(self.spatialKeyColumn).cast(RFSpatialColumnMethods.LngLatStructType)).certify
   }
@@ -109,9 +109,9 @@ trait RFSpatialColumnMethods extends MethodExtensions[RasterFrame] with Standard
    * Appends a spatial index column
    * @param colName name of new column to create. Defaults to `index`
    * @param applyOrdering if true, adds `.orderBy(asc(colName))` to result. Defaults to `true`
-   * @return RasterFrame with index column.
+   * @return RasterFrameLayer with index column.
    */
-  def withSpatialIndex(colName: String = SPATIAL_INDEX_COLUMN.columnName, applyOrdering: Boolean = true): RasterFrame = {
+  def withSpatialIndex(colName: String = SPATIAL_INDEX_COLUMN.columnName, applyOrdering: Boolean = true): RasterFrameLayer = {
     val zindex = sparkUdf(keyCol2LatLng andThen (p ⇒ Z2SFC.index(p._1, p._2).z))
     self.withColumn(colName, zindex(self.spatialKeyColumn)) match {
       case rf if applyOrdering ⇒ rf.orderBy(asc(colName)).certify
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameLayerMethods.scala
similarity index 90%
rename from core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
rename to core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameLayerMethods.scala
index fd4b4f11d..28f2839ed 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterFrameLayerMethods.scala
@@ -24,7 +24,7 @@ package org.locationtech.rasterframes.extensions
 import java.time.ZonedDateTime
 
 import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.RasterFrame
+import org.locationtech.rasterframes.RasterFrameLayer
 import geotrellis.proj4.CRS
 import geotrellis.raster.resample.{NearestNeighbor, ResampleMethod}
 import geotrellis.raster.{MultibandTile, ProjectedRaster, Tile, TileLayout}
@@ -47,17 +47,18 @@ import org.locationtech.rasterframes.tiles.ShowableTile
 import scala.reflect.runtime.universe._
 
 /**
- * Extension methods on [[RasterFrame]] type.
- * @since 7/18/17
+ * Extension methods on [[RasterFrameLayer]] type.
+  *
+  * @since 7/18/17
  */
-trait RasterFrameMethods extends MethodExtensions[RasterFrame]
+trait RasterFrameLayerMethods extends MethodExtensions[RasterFrameLayer]
   with RFSpatialColumnMethods with MetadataKeys with LazyLogging {
-  import Implicits.{WithDataFrameMethods, WithRasterFrameMethods}
+  import Implicits.{WithDataFrameMethods, WithRasterFrameLayerMethods}
 
   /**
-   * A convenience over `DataFrame.withColumnRenamed` whereby the `RasterFrame` type is maintained.
+   * A convenience over `DataFrame.withColumnRenamed` whereby the `RasterFrameLayer` type is maintained.
    */
-  def withRFColumnRenamed(existingName: String, newName: String): RasterFrame =
+  def withRFColumnRenamed(existingName: String, newName: String): RasterFrameLayer =
     (self: DataFrame).withColumnRenamed(existingName, newName).certify
 
   /** Get the spatial column. */
@@ -75,7 +76,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
   def tileLayerMetadata: Either[TileLayerMetadata[SpatialKey], TileLayerMetadata[SpaceTimeKey]] = {
     val spatialMD = self.findSpatialKeyField
       .map(_.metadata)
-      .getOrElse(throw new IllegalArgumentException(s"RasterFrame operation requsted on non-RasterFrame: $self"))
+      .getOrElse(throw new IllegalArgumentException(s"RasterFrameLayer operation requsted on non-RasterFrameLayer: $self"))
 
     if (self.findTemporalKeyField.nonEmpty)
       Right(extract[TileLayerMetadata[SpaceTimeKey]](CONTEXT_METADATA_KEY)(spatialMD))
@@ -83,12 +84,12 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
       Left(extract[TileLayerMetadata[SpatialKey]](CONTEXT_METADATA_KEY)(spatialMD))
   }
 
-  /** Get the CRS covering the RasterFrame. */
+  /** Get the CRS covering the RasterFrameLayer. */
   def crs: CRS = tileLayerMetadata.fold(_.crs, _.crs)
 
-  /** Add a temporal key to the RasterFrame, assigning the same temporal key to all rows. */
-  def addTemporalComponent(value: TemporalKey): RasterFrame = {
-    require(self.temporalKeyColumn.isEmpty, "RasterFrame already has a temporal component")
+  /** Add a temporal key to the RasterFrameLayer, assigning the same temporal key to all rows. */
+  def addTemporalComponent(value: TemporalKey): RasterFrameLayer = {
+    require(self.temporalKeyColumn.isEmpty, "RasterFrameLayer already has a temporal component")
     val tlm = tileLayerMetadata.left.get
     val newBounds: Bounds[SpaceTimeKey] =
       tlm.bounds.flatMap[SpaceTimeKey] {
@@ -110,14 +111,14 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
   }
 
   /** Create a temporal key from the given time and assign it as thea temporal key for all rows. */
-  def addTemporalComponent(value: ZonedDateTime): RasterFrame = addTemporalComponent(TemporalKey(value))
+  def addTemporalComponent(value: ZonedDateTime): RasterFrameLayer = addTemporalComponent(TemporalKey(value))
 
   /**
    * Append a column containing the temporal key rendered as a TimeStamp.
    * @param colName name of column to add
-   * @return updated RasterFrame
+   * @return updated RasterFrameLayer
    */
-  def withTimestamp(colName: String = TIMESTAMP_COLUMN.columnName): RasterFrame = {
+  def withTimestamp(colName: String = TIMESTAMP_COLUMN.columnName): RasterFrameLayer = {
     self.withColumn(colName, (TEMPORAL_KEY_COLUMN.getField("instant").as[Long] / 1000).cast(TimestampType))
       .certify
   }
@@ -133,7 +134,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
    * @param joinType One of: `inner`, `outer`, `left_outer`, `right_outer`, `leftsemi`.
    */
   @Experimental
-  def spatialJoin(right: RasterFrame, joinType: String = "inner"): RasterFrame = {
+  def spatialJoin(right: RasterFrameLayer, joinType: String = "inner"): RasterFrameLayer = {
     val left = self
 
     val leftMetadata = left.tileLayerMetadata.merge
@@ -146,7 +147,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
       )
     }
 
-    def updateNames(rf: RasterFrame,
+    def updateNames(rf: RasterFrameLayer,
                     prefix: String,
                     sk: TypedColumn[Any, SpatialKey],
                     tk: Option[TypedColumn[Any, TemporalKey]]) = {
@@ -188,7 +189,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
   /**
    * Performs a full RDD scans of the key column for the data extent, and updates the [[TileLayerMetadata]] data extent to match.
    */
-  def clipLayerExtent: RasterFrame = {
+  def clipLayerExtent: RasterFrameLayer = {
     val metadata = tileLayerMetadata
     val extent = metadata.merge.extent
     val layout = metadata.merge.layout
@@ -222,7 +223,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
   }
 
   /**
-   * Convert a single tile column from RasterFrame to a GeoTrellis [[TileLayerRDD]]
+   * Convert a single tile column from RasterFrameLayer to a GeoTrellis [[TileLayerRDD]]
    * @param tileCol column with tiles to be the
    */
   def toTileLayerRDD(tileCol: Column): Either[TileLayerRDD[SpatialKey], TileLayerRDD[SpaceTimeKey]] =
@@ -287,7 +288,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
   private[rasterframes] def extract[M: JsonFormat](metadataKey: String)(md: Metadata) =
     md.getMetadata(metadataKey).json.parseJson.convertTo[M]
 
-  /** Convert the tiles in the RasterFrame into a single raster. For RasterFrames keyed with temporal keys, they
+  /** Convert the tiles in the RasterFrameLayer into a single raster. For RasterFrames keyed with temporal keys, they
     * will be merge undeterministically. */
   def toRaster(tileCol: Column,
                rasterCols: Int,
@@ -322,7 +323,7 @@ trait RasterFrameMethods extends MethodExtensions[RasterFrame]
     ProjectedRaster(croppedTile.tile, md.extent, md.crs)
   }
 
-  /** Convert the Red, Green & Blue assigned tiles in the RasterFrame into a single color composite raster.
+  /** Convert the Red, Green & Blue assigned tiles in the RasterFrameLayer into a single color composite raster.
    * For RasterFrames keyed with temporal keys, they will be merged underterministically. */
   def toMultibandRaster(
     tileCols: Seq[Column],
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index 4224ef622..a58f851a0 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -56,7 +56,7 @@ package object rasterframes extends StandardColumns
   final val NOMINAL_TILE_DIMS: TileDimensions = TileDimensions(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE)
 
   /**
-   * Initialization injection point. Must be called before any RasterFrame
+   * Initialization injection point. Must be called before any RasterFrameLayer
    * types are used.
    */
   def initRF(sqlContext: SQLContext): Unit = {
@@ -92,14 +92,14 @@ package object rasterframes extends StandardColumns
   def RasterSourceType = new RasterSourceUDT()
 
   /**
-   * A RasterFrame is just a DataFrame with certain invariants, enforced via the methods that create and transform them:
+   * A RasterFrameLayer is just a DataFrame with certain invariants, enforced via the methods that create and transform them:
    *   1. One column is a [[geotrellis.spark.SpatialKey]] or [[geotrellis.spark.SpaceTimeKey]]
    *   2. One or more columns is a [[Tile]] UDT.
    *   3. The `TileLayerMetadata` is encoded and attached to the key column.
    */
-  type RasterFrame = DataFrame @@ RasterFrameTag
+  type RasterFrameLayer = DataFrame @@ RasterFrameTag
 
-  /** Tagged type for allowing compiler to help keep track of what has RasterFrame assurances applied to it. */
+  /** Tagged type for allowing compiler to help keep track of what has RasterFrameLayer assurances applied to it. */
   trait RasterFrameTag
 
   type TileFeatureLayerRDD[K, D] =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
index 2d0c8c780..e33529b02 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/debug/package.scala
@@ -33,14 +33,14 @@ import spray.json.JsValue
  * @since 4/6/18
  */
 package object debug {
-  implicit class RasterFrameWithDebug(val self: RasterFrame)  {
+  implicit class RasterFrameWithDebug(val self: RasterFrameLayer)  {
 
     /** Renders the whole schema with metadata as a JSON string. */
     def describeFullSchema: String = {
       self.schema.prettyJson
     }
 
-    /** Renders all the extents in this RasterFrame as GeoJSON in EPSG:4326. This does a full
+    /** Renders all the extents in this RasterFrameLayer as GeoJSON in EPSG:4326. This does a full
      * table scan and collects **all** the geometry into the driver, and then converts it into a
      * Spray JSON data structure. Not performant, and for debugging only. */
     def geoJsonExtents: JsValue = {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index 7f54ce540..99bf80d96 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -77,8 +77,8 @@ package object util {
 
   type KeyMethodsProvider[K1, K2] = K1 ⇒ TilerKeyMethods[K1, K2]
 
-  /** Internal method for slapping the RasterFrame seal of approval on a DataFrame. */
-  private[rasterframes] def certifyRasterframe(df: DataFrame): RasterFrame =
+  /** Internal method for slapping the RasterFrameLayer seal of approval on a DataFrame. */
+  private[rasterframes] def certifyRasterframe(df: DataFrame): RasterFrameLayer =
     shapeless.tag[RasterFrameTag][DataFrame](df)
 
 
diff --git a/core/src/test/scala/examples/Classification.scala b/core/src/test/scala/examples/Classification.scala
index ae87aa6cf..6f234e45c 100644
--- a/core/src/test/scala/examples/Classification.scala
+++ b/core/src/test/scala/examples/Classification.scala
@@ -46,7 +46,7 @@ object Classification extends App {
   import spark.implicits._
 
   // The first step is to load multiple bands of imagery and construct
-  // a single RasterFrame from them.
+  // a single RasterFrameLayer from them.
   val filenamePattern = "L8-%s-Elkton-VA.tiff"
   val bandNumbers = 2 to 7
   val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
@@ -56,7 +56,7 @@ object Classification extends App {
   val joinedRF = bandNumbers
     .map { b ⇒ (b, filenamePattern.format("B" + b)) }
     .map { case (b, f) ⇒ (b, readTiff(f)) }
-    .map { case (b, t) ⇒ t.projectedRaster.toRF(tileSize, tileSize, s"band_$b") }
+    .map { case (b, t) ⇒ t.projectedRaster.toLayer(tileSize, tileSize, s"band_$b") }
     .reduce(_ spatialJoin _)
 
   // We should see a single spatial_key column along with 4 columns of tiles.
@@ -70,7 +70,7 @@ object Classification extends App {
   val target = readTiff(filenamePattern.format("Labels"))
     .mapTile(_.convert(DoubleConstantNoDataCellType))
     .projectedRaster
-    .toRF(tileSize, tileSize, targetCol)
+    .toLayer(tileSize, tileSize, targetCol)
 
   // Take a peek at what kind of label data we have to work with.
   target.select(rf_agg_stats(target(targetCol))).show
@@ -147,7 +147,7 @@ object Classification extends App {
     )
   )
 
-  val rf = retiled.asRF($"spatial_key", tlm)
+  val rf = retiled.asLayer($"spatial_key", tlm)
 
   val raster = rf.toRaster($"prediction", 186, 169)
 
diff --git a/core/src/test/scala/examples/Clustering.scala b/core/src/test/scala/examples/Clustering.scala
index c5e08ca1b..66e7e58b1 100644
--- a/core/src/test/scala/examples/Clustering.scala
+++ b/core/src/test/scala/examples/Clustering.scala
@@ -39,7 +39,7 @@ object Clustering extends App {
   import spark.implicits._
 
   // The first step is to load multiple bands of imagery and construct
-  // a single RasterFrame from them.
+  // a single RasterFrameLayer from them.
   val filenamePattern = "L8-B%d-Elkton-VA.tiff"
   val bandNumbers = 1 to 7
   val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
@@ -48,7 +48,7 @@ object Clustering extends App {
   val joinedRF = bandNumbers
     .map { b ⇒ (b, filenamePattern.format(b)) }
     .map { case (b,f) ⇒ (b, readTiff(f)) }
-    .map { case (b, t) ⇒ t.projectedRaster.toRF(s"band_$b") }
+    .map { case (b, t) ⇒ t.projectedRaster.toLayer(s"band_$b") }
     .reduce(_ spatialJoin _)
 
   // We should see a single spatial_key column along with 4 columns of tiles.
@@ -94,7 +94,7 @@ object Clustering extends App {
       tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
   )
 
-  val rf = retiled.asRF($"spatial_key", tlm)
+  val rf = retiled.asLayer($"spatial_key", tlm)
 
   val raster = rf.toRaster($"prediction", 186, 169)
 
diff --git a/core/src/test/scala/examples/CreatingRasterFrames.scala b/core/src/test/scala/examples/CreatingRasterFrames.scala
index a1e7e0536..8b5c00c72 100644
--- a/core/src/test/scala/examples/CreatingRasterFrames.scala
+++ b/core/src/test/scala/examples/CreatingRasterFrames.scala
@@ -27,7 +27,7 @@ package examples
 object CreatingRasterFrames extends App {
 //  # Creating RasterFrames
 //
-//  There are a number of ways to create a `RasterFrame`, as enumerated in the sections below.
+//  There are a number of ways to create a `RasterFrameLayer`, as enumerated in the sections below.
 //
 //  ## Initialization
 //
@@ -49,21 +49,21 @@ object CreatingRasterFrames extends App {
 
 //  ## From `ProjectedExtent`
 //
-//  The simplest mechanism for getting a RasterFrame is to use the `toRF(tileCols, tileRows)` extension method on `ProjectedRaster`.
+//  The simplest mechanism for getting a RasterFrameLayer is to use the `toLayer(tileCols, tileRows)` extension method on `ProjectedRaster`.
 
   val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
-  val rf = scene.projectedRaster.toRF(128, 128)
+  val rf = scene.projectedRaster.toLayer(128, 128)
   rf.show(5, false)
 
 
 //  ## From `TileLayerRDD`
 //
-//  Another option is to use a GeoTrellis [`LayerReader`](https://docs.geotrellis.io/en/latest/guide/tile-backends.html), to get a `TileLayerRDD` for which there's also a `toRF` extension method.
+//  Another option is to use a GeoTrellis [`LayerReader`](https://docs.geotrellis.io/en/latest/guide/tile-backends.html), to get a `TileLayerRDD` for which there's also a `toLayer` extension method.
 
 
 //  ## Inspecting Structure
 //
-//  `RasterFrame` has a number of methods providing access to metadata about the contents of the RasterFrame.
+//  `RasterFrameLayer` has a number of methods providing access to metadata about the contents of the RasterFrameLayer.
 //
 //  ### Tile Column Names
 
diff --git a/core/src/test/scala/examples/Exporting.scala b/core/src/test/scala/examples/Exporting.scala
index 71ebb7faf..25fa321c1 100644
--- a/core/src/test/scala/examples/Exporting.scala
+++ b/core/src/test/scala/examples/Exporting.scala
@@ -38,7 +38,7 @@ object Exporting extends App {
 
   import spark.implicits._
   val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
-  val rf = scene.projectedRaster.toRF(128, 128).cache()
+  val rf = scene.projectedRaster.toLayer(128, 128).cache()
 
   //  While the goal of RasterFrames is to make it as easy as possible to do your geospatial analysis with a single
   //    construct, it is helpful to be able to transform it into other representations for various use cases.
@@ -73,9 +73,9 @@ object Exporting extends App {
   //  the imagery types.
   //
   //
-  //    Let's assume we have a RasterFrame we've done some fancy processing on:
+  //    Let's assume we have a RasterFrameLayer we've done some fancy processing on:
   val equalizer = udf((t: Tile) => t.equalize())
-  val equalized = rf.withColumn("equalized", equalizer($"tile")).asRF
+  val equalized = rf.withColumn("equalized", equalizer($"tile")).asLayer
 
   equalized.printSchema
   equalized.select(rf_agg_stats($"tile")).show(false)
@@ -103,7 +103,7 @@ object Exporting extends App {
 
   //  ## Converting to `RDD` and `TileLayerRDD`
   //
-  //  Since a `RasterFrame` is just a `DataFrame` with extra metadata, the method
+  //  Since a `RasterFrameLayer` is just a `DataFrame` with extra metadata, the method
   //  @scaladoc[`DataFrame.rdd`][rdd] is available for simple conversion back to `RDD` space. The type returned
   //  by `.rdd` is dependent upon how you select it.
 
@@ -118,14 +118,14 @@ object Exporting extends App {
   showType(rf.select(rf.spatialKeyColumn, $"tile").as[(SpatialKey, Tile)].rdd)
 
   //  If your goal convert a single tile column with its spatial key back to a `TileLayerRDD[K]`, then there's an additional
-  //  extension method on `RasterFrame` called [`toTileLayerRDD`][toTileLayerRDD], which preserves the tile layer metadata,
+  //  extension method on `RasterFrameLayer` called [`toTileLayerRDD`][toTileLayerRDD], which preserves the tile layer metadata,
   //  enhancing interoperation with GeoTrellis RDD extension methods.
 
   showType(rf.toTileLayerRDD($"tile".as[Tile]))
 
   //  ## Exporting a Raster
   //
-  //  For the purposes of debugging, the RasterFrame tiles can be reassembled back into a raster for viewing. However,
+  //  For the purposes of debugging, the RasterFrameLayer tiles can be reassembled back into a raster for viewing. However,
   //  keep in mind that this will download all the data to the driver, and reassemble it in-memory. So it's not appropriate
   //  for very large coverages.
   //
@@ -147,7 +147,7 @@ object Exporting extends App {
   //  [*Download GeoTIFF*](rf-raster.tiff)
 
   //  # Exporting to a GeoTrellis Layer
-  // First, convert the RasterFrame into a TileLayerRDD. The return type is an Either;
+  // First, convert the RasterFrameLayer into a TileLayerRDD. The return type is an Either;
   // the `left` side is for spatial-only keyed data
   val tlRDD = equalized.toTileLayerRDD($"equalized").left.get
 
diff --git a/core/src/test/scala/examples/LocalArithmetic.scala b/core/src/test/scala/examples/LocalArithmetic.scala
index 26c9bf6d4..428fcc64a 100644
--- a/core/src/test/scala/examples/LocalArithmetic.scala
+++ b/core/src/test/scala/examples/LocalArithmetic.scala
@@ -49,11 +49,11 @@ object LocalArithmetic extends App {
   val joinedRF = bandNumbers.
     map { b ⇒ (b, filenamePattern.format(b)) }.
     map { case (b, f) ⇒ (b, readTiff(f)) }.
-    map { case (b, t) ⇒ t.projectedRaster.toRF(s"band_$b") }.
+    map { case (b, t) ⇒ t.projectedRaster.toLayer(s"band_$b") }.
     reduce(_ spatialJoin _)
 
-  val addRF = joinedRF.withColumn("1+2", rf_local_add(joinedRF("band_1"), joinedRF("band_2"))).asRF
-  val divideRF = joinedRF.withColumn("1/2", rf_local_divide(joinedRF("band_1"), joinedRF("band_2"))).asRF
+  val addRF = joinedRF.withColumn("1+2", rf_local_add(joinedRF("band_1"), joinedRF("band_2"))).asLayer
+  val divideRF = joinedRF.withColumn("1/2", rf_local_divide(joinedRF("band_1"), joinedRF("band_2"))).asLayer
 
   addRF.select("1+2").collect().apply(0) .getClass
 
diff --git a/core/src/test/scala/examples/Masking.scala b/core/src/test/scala/examples/Masking.scala
index 4470e8871..6270bcef1 100644
--- a/core/src/test/scala/examples/Masking.scala
+++ b/core/src/test/scala/examples/Masking.scala
@@ -24,18 +24,18 @@ object Masking extends App {
   val joinedRF = bandNumbers.
     map { b ⇒ (b, filenamePattern.format(b)) }.
     map { case (b, f) ⇒ (b, readTiff(f)) }.
-    map { case (b, t) ⇒ t.projectedRaster.toRF(s"band_$b") }.
+    map { case (b, t) ⇒ t.projectedRaster.toLayer(s"band_$b") }.
     reduce(_ spatialJoin _)
 
   val threshold = udf((t: Tile) => {
     t.convert(IntConstantNoDataCellType).map(x => if (x > 10500) x else NODATA)
   } )
 
-  val withMaskedTile = joinedRF.withColumn("maskTile", threshold(joinedRF("band_1"))).asRF
+  val withMaskedTile = joinedRF.withColumn("maskTile", threshold(joinedRF("band_1"))).asLayer
 
   withMaskedTile.select(rf_no_data_cells(withMaskedTile("maskTile"))).show()
 
-  val masked = withMaskedTile.withColumn("masked", rf_mask(joinedRF("band_2"), joinedRF("maskTile"))).asRF
+  val masked = withMaskedTile.withColumn("masked", rf_mask(joinedRF("band_2"), joinedRF("maskTile"))).asLayer
 
   val maskRF = masked.toRaster(masked("masked"), 466, 428)
   val b2 = masked.toRaster(masked("band_2"), 466, 428)
diff --git a/core/src/test/scala/examples/MeanValue.scala b/core/src/test/scala/examples/MeanValue.scala
index 5a2571404..2ee264469 100644
--- a/core/src/test/scala/examples/MeanValue.scala
+++ b/core/src/test/scala/examples/MeanValue.scala
@@ -39,7 +39,7 @@ object MeanValue extends App {
 
   val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
 
-  val rf = scene.projectedRaster.toRF(128, 128) // <-- tile size
+  val rf = scene.projectedRaster.toLayer(128, 128) // <-- tile size
 
   rf.printSchema
 
diff --git a/core/src/test/scala/examples/NDVI.scala b/core/src/test/scala/examples/NDVI.scala
index 0173c7807..48a6f6e51 100644
--- a/core/src/test/scala/examples/NDVI.scala
+++ b/core/src/test/scala/examples/NDVI.scala
@@ -46,8 +46,8 @@ object NDVI extends App {
 
   import spark.implicits._
 
-  def redBand = readTiff("L8-B4-Elkton-VA.tiff").projectedRaster.toRF("red_band")
-  def nirBand = readTiff("L8-B5-Elkton-VA.tiff").projectedRaster.toRF("nir_band")
+  def redBand = readTiff("L8-B4-Elkton-VA.tiff").projectedRaster.toLayer("red_band")
+  def nirBand = readTiff("L8-B5-Elkton-VA.tiff").projectedRaster.toLayer("nir_band")
 
   val ndvi = udf((red: Tile, nir: Tile) => {
     val redd = red.convert(DoubleConstantNoDataCellType)
@@ -55,7 +55,7 @@ object NDVI extends App {
     (nird - redd) / (nird + redd)
   })
 
-  val rf = redBand.spatialJoin(nirBand).withColumn("ndvi", ndvi($"red_band", $"nir_band")).asRF
+  val rf = redBand.spatialJoin(nirBand).withColumn("ndvi", ndvi($"red_band", $"nir_band")).asLayer
 
   rf.printSchema()
 
diff --git a/core/src/test/scala/examples/Tour.scala b/core/src/test/scala/examples/Tour.scala
index 8dac067b8..0c2896949 100644
--- a/core/src/test/scala/examples/Tour.scala
+++ b/core/src/test/scala/examples/Tour.scala
@@ -48,7 +48,7 @@ object Tour extends App {
   val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
 
   // Convert it to a raster frame, discretizing it into the given tile size.
-  val rf = scene.projectedRaster.toRF(64, 64)
+  val rf = scene.projectedRaster.toLayer(64, 64)
 
   // See how many tiles we have after discretization
   println("Tile count: " + rf.count())
@@ -72,7 +72,7 @@ object Tour extends App {
   val contrast = udf((t: Tile) ⇒ t.sigmoidal(0.2, 10))
 
   // Let's contrast adjust the tile column
-  val withAdjusted = rf.withColumn("adjusted", contrast($"tile")).asRF
+  val withAdjusted = rf.withColumn("adjusted", contrast($"tile")).asLayer
 
   // Show the stats for the adjusted version
   withAdjusted.select(rf_agg_stats($"adjusted")).show(false)
@@ -82,7 +82,7 @@ object Tour extends App {
   GeoTiff(raster).write("contrast-adjusted.tiff")
 
   // Perform some arbitrary local ops between columns and render
-  val withOp = withAdjusted.withColumn("op", rf_local_subtract($"tile", $"adjusted")).asRF
+  val withOp = withAdjusted.withColumn("op", rf_local_subtract($"tile", $"adjusted")).asLayer
   val raster2 = withOp.toRaster($"op", 774, 500)
   GeoTiff(raster2).write("with-op.tiff")
 
@@ -91,7 +91,7 @@ object Tour extends App {
   val k = 4
 
   // SparkML doesn't like NoData/NaN values, so we set the no-data value to something less offensive
-  val forML = rf.select(rf.spatialKeyColumn, rf_with_no_data($"tile", 99999) as "tile").asRF
+  val forML = rf.select(rf.spatialKeyColumn, rf_with_no_data($"tile", 99999) as "tile").asLayer
 
   // First we instantiate the transformer that converts tile rows into cell rows.
   val exploder = new TileExploder()
@@ -126,7 +126,7 @@ object Tour extends App {
     rf_assemble_tile($"column_index", $"row_index", $"prediction", tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
   )
 
-  val clusteredRF = retiled.asRF($"spatial_key", tlm)
+  val clusteredRF = retiled.asLayer($"spatial_key", tlm)
 
   val raster3 = clusteredRF.toRaster($"prediction", 774, 500)
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
index 021b528d1..da0af2397 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExplodeSpec.scala
@@ -184,7 +184,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
     it("should reassemble multiple exploded tiles") {
       val image = sampleSmallGeoTiff
-      val tinyTiles = image.projectedRaster.toRF(10, 10)
+      val tinyTiles = image.projectedRaster.toLayer(10, 10)
 
       val exploded = tinyTiles.select(tinyTiles.spatialKeyColumn, rf_explode_tiles(tinyTiles.tileColumns.head))
 
@@ -199,7 +199,7 @@ class ExplodeSpec extends TestEnvironment with TestData {
 
       val tlm = tinyTiles.tileLayerMetadata.left.get
 
-      val rf = assembled.asRF(SPATIAL_KEY_COLUMN, tlm)
+      val rf = assembled.asLayer(SPATIAL_KEY_COLUMN, tlm)
 
       val (cols, rows) = image.tile.dimensions
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index dd497d0fa..3b30a38ae 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -34,19 +34,19 @@ import org.locationtech.rasterframes.util.SubdivideSupport
  */
 //noinspection ScalaUnusedSymbol
 class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSupport {
-  lazy val rf = sampleTileLayerRDD.toRF
+  lazy val rf = sampleTileLayerRDD.toLayer
 
   describe("DataFrame exention methods") {
     it("should maintain original type") {
       val df = rf.withPrefixedColumnNames("_foo_")
-      "val rf2: RasterFrame = df" should compile
+      "val rf2: RasterFrameLayer = df" should compile
     }
     it("should provide tagged column access") {
       val df = rf.drop("tile")
       "val Some(col) = df.spatialKeyColumn" should compile
     }
   }
-  describe("RasterFrame exention methods") {
+  describe("RasterFrameLayer exention methods") {
     it("should provide spatial key column") {
       noException should be thrownBy {
         rf.spatialKeyColumn
diff --git a/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
index 5a7c0b813..54321d0dc 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/GeometryFunctionsSpec.scala
@@ -35,7 +35,7 @@ class GeometryFunctionsSpec extends TestEnvironment with TestData with StandardC
   import spark.implicits._
 
   describe("Vector geometry operations") {
-    val rf = l8Sample(1).projectedRaster.toRF(10, 10).withGeometry()
+    val rf = l8Sample(1).projectedRaster.toLayer(10, 10).withGeometry()
     it("should allow joining and filtering of tiles based on points") {
       import spark.implicits._
 
@@ -139,7 +139,7 @@ class GeometryFunctionsSpec extends TestEnvironment with TestData with StandardC
   }
 
   it("should rasterize geometry") {
-    val rf = l8Sample(1).projectedRaster.toRF.withGeometry()
+    val rf = l8Sample(1).projectedRaster.toLayer.withGeometry()
     val df = GeomData.features.map(f ⇒ (
       f.geom.reproject(LatLng, rf.crs).jtsGeom,
       f.data.fields("id").asInstanceOf[JsNumber].value.intValue()
diff --git a/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
index 244b9ab3d..0f179937a 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/MetadataSpec.scala
@@ -35,7 +35,7 @@ class MetadataSpec extends TestEnvironment with TestData  {
 
   describe("Metadata storage") {
     it("should serialize and attach metadata") {
-      //val rf = sampleGeoTiff.projectedRaster.toRF(128, 128)
+      //val rf = sampleGeoTiff.projectedRaster.toLayer(128, 128)
       val df = spark.createDataset(Seq((1, "one"), (2, "two"), (3, "three"))).toDF("num", "str")
       val withmeta = df.mapColumnAttribute($"num", attr ⇒ {
         attr.withMetadata(sampleMetadata)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
index f8a3c0ac9..e77a0fecc 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFrameSpec.scala
@@ -40,7 +40,7 @@ import org.locationtech.rasterframes.model.TileDimensions
 import scala.util.control.NonFatal
 
 /**
- * RasterFrame test rig.
+ * RasterFrameLayer test rig.
  *
  * @since 7/10/17
  */
@@ -74,12 +74,12 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
   }
 
-  describe("RasterFrame") {
+  describe("RasterFrameLayer") {
     it("should implicitly convert from spatial layer type") {
 
       val tileLayerRDD = TestData.randomSpatialTileLayerRDD(20, 20, 2, 2)
 
-      val rf = tileLayerRDD.toRF
+      val rf = tileLayerRDD.toLayer
 
       assert(rf.tileColumns.nonEmpty)
       assert(rf.spatialKeyColumn.columnName == "spatial_key")
@@ -95,7 +95,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
       assert(rf.count() === 4)
 
-      val cols = tileLayerRDD.toRF("foo").columns
+      val cols = tileLayerRDD.toLayer("foo").columns
       assert(!cols.contains("tile"))
       assert(cols.contains("foo"))
     }
@@ -104,7 +104,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
       val tileLayerRDD = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2)
 
-      val rf = tileLayerRDD.toRF
+      val rf = tileLayerRDD.toLayer
 
       try {
         assert(rf.tileColumns.nonEmpty)
@@ -116,7 +116,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
           println(rf.schema.prettyJson)
           throw ex
       }
-      val cols = tileLayerRDD.toRF("foo").columns
+      val cols = tileLayerRDD.toLayer("foo").columns
        assert(!cols.contains("tile"))
        assert(cols.contains("foo"))
     }
@@ -135,7 +135,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
       val tileLayerRDD = TileFeatureLayerRDD(tileRDD, metadata)
 
-      val rf = tileLayerRDD.toRF
+      val rf = tileLayerRDD.toLayer
 
       assert(rf.columns.toSet === Set(SPATIAL_KEY_COLUMN, TILE_COLUMN, TILE_FEATURE_DATA_COLUMN).map(_.columnName))
     }
@@ -154,14 +154,14 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
       val tileLayerRDD = TileFeatureLayerRDD(tileRDD, metadata)
 
-      val rf = tileLayerRDD.toRF
+      val rf = tileLayerRDD.toLayer
 
       assert(rf.columns.toSet === Set(SPATIAL_KEY_COLUMN, TEMPORAL_KEY_COLUMN, TILE_COLUMN, TILE_FEATURE_DATA_COLUMN).map(_.columnName))
     }
 
     it("should support adding a timestamp column") {
       val now = ZonedDateTime.now()
-      val rf = sampleGeoTiff.projectedRaster.toRF(256, 256)
+      val rf = sampleGeoTiff.projectedRaster.toLayer(256, 256)
       val wt = rf.addTemporalComponent(now)
       val goodie = wt.withTimestamp()
       assert(goodie.columns.contains("timestamp"))
@@ -172,7 +172,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
 
     it("should support spatial joins") {
-      val rf = sampleGeoTiff.projectedRaster.toRF(256, 256)
+      val rf = sampleGeoTiff.projectedRaster.toLayer(256, 256)
 
       val wt = rf.addTemporalComponent(TemporalKey(34))
 
@@ -186,11 +186,11 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
 
     it("should have correct schema on inner spatial joins") {
-      val left = sampleGeoTiff.projectedRaster.toRF(256, 256)
+      val left = sampleGeoTiff.projectedRaster.toLayer(256, 256)
         .addTemporalComponent(TemporalKey(34))
 
       val right = left.withColumnRenamed(left.tileColumns.head.columnName, "rightTile")
-        .asRF
+        .asLayer
 
       val joined = left.spatialJoin(right)
       // since right is a copy of left, should not drop any rows with inner join
@@ -205,19 +205,19 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       assert(joined.tileColumns.toSet !== joined.notTileColumns.toSet)
     }
 
-    it("should convert a GeoTiff to RasterFrame") {
+    it("should convert a GeoTiff to RasterFrameLayer") {
       val praster: ProjectedRaster[Tile] = sampleGeoTiff.projectedRaster
       val (cols, rows) = praster.raster.dimensions
 
       val layoutCols = math.ceil(cols / 128.0).toInt
       val layoutRows = math.ceil(rows / 128.0).toInt
 
-      assert(praster.toRF.count() === 1)
-      assert(praster.toRF(128, 128).count() === (layoutCols * layoutRows))
+      assert(praster.toLayer.count() === 1)
+      assert(praster.toLayer(128, 128).count() === (layoutCols * layoutRows))
     }
 
     it("should provide TileLayerMetadata[SpatialKey]") {
-      val rf = sampleGeoTiff.projectedRaster.toRF(256, 256)
+      val rf = sampleGeoTiff.projectedRaster.toLayer(256, 256)
       val tlm = rf.tileLayerMetadata.merge
       val bounds = tlm.bounds.get
       assert(bounds === KeyBounds(SpatialKey(0, 0), SpatialKey(3, 1)))
@@ -225,7 +225,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
     it("should provide TileLayerMetadata[SpaceTimeKey]") {
       val now = ZonedDateTime.now()
-      val rf = sampleGeoTiff.projectedRaster.toRF(256, 256, now)
+      val rf = sampleGeoTiff.projectedRaster.toLayer(256, 256, now)
       val tlm = rf.tileLayerMetadata.merge
       val bounds = tlm.bounds.get
       assert(bounds._1 === SpaceTimeKey(0, 0, now))
@@ -235,7 +235,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 //    it("should clip TileLayerMetadata extent") {
 //      val tiled = sampleTileLayerRDD
 //
-//      val rf = tiled.reproject(LatLng, tiled.metadata.layout)._2.toRF
+//      val rf = tiled.reproject(LatLng, tiled.metadata.layout)._2.toLayer
 //
 //      val worldish = Extent(-179, -89, 179, 89)
 //      val areaish = Extent(-90, 30, -81, 40)
@@ -262,14 +262,14 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
 
     it("shouldn't clip already clipped extents") {
-      val rf = TestData.randomSpatialTileLayerRDD(1024, 1024, 8, 8).toRF
+      val rf = TestData.randomSpatialTileLayerRDD(1024, 1024, 8, 8).toLayer
 
       val expected = rf.tileLayerMetadata.merge.extent
       val computed = rf.clipLayerExtent.tileLayerMetadata.merge.extent
       basicallySame(expected, computed)
 
       val pr = sampleGeoTiff.projectedRaster
-      val rf2 = pr.toRF(256, 256)
+      val rf2 = pr.toLayer(256, 256)
       val expected2 = rf2.tileLayerMetadata.merge.extent
       val computed2 = rf2.clipLayerExtent.tileLayerMetadata.merge.extent
       basicallySame(expected2, computed2)
@@ -294,7 +294,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
 
     it("should rasterize with a spatiotemporal key") {
-      val rf = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toRF
+      val rf = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toLayer
 
       noException shouldBe thrownBy {
         rf.toRaster($"tile", 128, 128)
@@ -302,8 +302,8 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
     }
 
     it("should maintain metadata after all spatial join operations") {
-      val rf1 = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toRF
-      val rf2 = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toRF
+      val rf1 = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toLayer
+      val rf2 = TestData.randomSpatioTemporalTileLayerRDD(20, 20, 2, 2).toLayer
 
       val joinTypes = Seq("inner", "outer", "fullouter", "left_outer", "right_outer", "leftsemi")
       forEvery(joinTypes) { jt ⇒
@@ -315,9 +315,9 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
     it("should rasterize multiband") {
       withClue("Landsat") {
-        val blue = TestData.l8Sample(1).projectedRaster.toRF.withRFColumnRenamed("tile", "blue")
-        val green = TestData.l8Sample(2).projectedRaster.toRF.withRFColumnRenamed("tile", "green")
-        val red = TestData.l8Sample(3).projectedRaster.toRF.withRFColumnRenamed("tile", "red")
+        val blue = TestData.l8Sample(1).projectedRaster.toLayer.withRFColumnRenamed("tile", "blue")
+        val green = TestData.l8Sample(2).projectedRaster.toLayer.withRFColumnRenamed("tile", "green")
+        val red = TestData.l8Sample(3).projectedRaster.toLayer.withRFColumnRenamed("tile", "red")
 
         val joined = blue.spatialJoin(green).spatialJoin(red)
 
@@ -328,9 +328,9 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
         }
       }
       withClue("NAIP") {
-        val red = TestData.naipSample(1).projectedRaster.toRF.withRFColumnRenamed("tile", "red")
-        val green = TestData.naipSample(2).projectedRaster.toRF.withRFColumnRenamed("tile", "green")
-        val blue = TestData.naipSample(3).projectedRaster.toRF.withRFColumnRenamed("tile", "blue")
+        val red = TestData.naipSample(1).projectedRaster.toLayer.withRFColumnRenamed("tile", "red")
+        val green = TestData.naipSample(2).projectedRaster.toLayer.withRFColumnRenamed("tile", "green")
+        val blue = TestData.naipSample(3).projectedRaster.toLayer.withRFColumnRenamed("tile", "blue")
         val joined = blue.spatialJoin(green).spatialJoin(red)
 
         noException shouldBe thrownBy {
@@ -345,7 +345,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
       // 774 × 500
       val praster: ProjectedRaster[Tile] = sampleGeoTiff.projectedRaster
       val (cols, rows) = praster.raster.dimensions
-      val rf = praster.toRF(64, 64)
+      val rf = praster.toLayer(64, 64)
       val raster = rf.toRaster($"tile", cols, rows)
 
       render(raster.tile, "normal")
@@ -366,7 +366,7 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
     it("shouldn't restitch raster that's has derived tiles") {
       val praster: ProjectedRaster[Tile] = sampleGeoTiff.projectedRaster
-      val rf = praster.toRF(64, 64)
+      val rf = praster.toLayer(64, 64)
 
       val equalizer = udf((t: Tile) => t.equalize())
 
@@ -374,13 +374,13 @@ class RasterFrameSpec extends TestEnvironment with MetadataKeys
 
       intercept[IllegalArgumentException] {
         // spatial_key is lost
-        equalized.asRF.toRaster($"equalized", 128, 128)
+        equalized.asLayer.toRaster($"equalized", 128, 128)
       }
     }
 
     it("should fetch CRS") {
       val praster: ProjectedRaster[Tile] = sampleGeoTiff.projectedRaster
-      val rf = praster.toRF
+      val rf = praster.toLayer
 
       assert(rf.crs === praster.crs)
     }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
index 773c28877..b99b5c48e 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/SpatialKeySpec.scala
@@ -38,7 +38,7 @@ class SpatialKeySpec extends TestEnvironment with TestData {
   describe("Spatial key conversions") {
     val raster = sampleGeoTiff.projectedRaster
     // Create a raster frame with a single row
-    val rf = raster.toRF(raster.tile.cols, raster.tile.rows)
+    val rf = raster.toLayer(raster.tile.cols, raster.tile.rows)
 
     it("should add an extent column") {
       val expected = raster.extent.jtsGeom
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index 7e1acda1d..d47fafbc1 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -127,7 +127,7 @@ trait TestData {
   def rgbCogSample = readMultiband("LC08_RGB_Norfolk_COG.tiff")
 
   def sampleTileLayerRDD(implicit spark: SparkSession): TileLayerRDD[SpatialKey] = {
-    val rf = sampleGeoTiff.projectedRaster.toRF(128, 128)
+    val rf = sampleGeoTiff.projectedRaster.toLayer(128, 128)
     rf.toTileLayerRDD(rf.tileColumns.head).left.get
   }
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
index 0c3478d8f..757231595 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileAssemblerSpec.scala
@@ -41,7 +41,7 @@ class TileAssemblerSpec extends TestEnvironment {
 
     it("should reassemble a small scene") {
       val raster = TestData.l8Sample(8).projectedRaster
-      val rf = raster.toRF(16, 16)
+      val rf = raster.toLayer(16, 16)
       val ct = rf.tileLayerMetadata.merge.cellType
       val (tileCols, tileRows) = rf.tileLayerMetadata.merge.tileLayout.tileDimensions
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
index 88d1e754f..3081b2f64 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TileUDTSpec.scala
@@ -29,7 +29,7 @@ import org.locationtech.rasterframes.tiles.ShowableTile
 import org.scalatest.Inspectors
 
 /**
- * RasterFrame test rig.
+ * RasterFrameLayer test rig.
  *
  * @since 7/10/17
  */
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 4b306df1e..c62d1a7e3 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -64,7 +64,7 @@ class GeoTiffDataSource extends DataSourceRegister
     require(pathO.get.getScheme == "file" || pathO.get.getScheme == null, "Currently only 'file://' destinations are supported")
     sqlContext.withRasterFrames
 
-    require(data.isRF, "GeoTIFF can only be constructed from a RasterFrame")
+    require(data.isLayer, "GeoTIFF can only be constructed from a RasterFrameLayer")
     val rf = data.certify
 
     // If no desired image size is given, write at full size.
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index 2178c7ee6..f89ebc2d6 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -29,7 +29,7 @@ import shapeless.tag
 
 package object geotiff {
   /** Tagged type construction for enabling type-safe extension methods for loading
-   * a RasterFrame in expected form. */
+   * a RasterFrameLayer in expected form. */
   type GeoTiffRasterFrameReader = DataFrameReader @@ GeoTiffRasterFrameReaderTag
   trait GeoTiffRasterFrameReaderTag
 
@@ -45,9 +45,9 @@ package object geotiff {
     def geotiff: DataFrameWriter[T] = writer.format(GeoTiffDataSource.SHORT_NAME)
   }
 
-  /** Adds `loadRF` to appropriately tagged `DataFrameReader` */
+  /** Adds `loadLayer` to appropriately tagged `DataFrameReader` */
   implicit class GeoTiffReaderWithRF(val reader: GeoTiffRasterFrameReader) {
     @deprecated("Use `raster` instead.", "7/1/2019")
-    def loadRF(path: URI): RasterFrame = reader.load(path.toASCIIString).asRF
+    def loadRF(path: URI): RasterFrameLayer = reader.load(path.toASCIIString).asLayer
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
index 8662fdc64..d12ea1e17 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisLayerDataSource.scala
@@ -81,8 +81,8 @@ class GeoTrellisLayerDataSource extends DataSourceRegister
     require(layerName.isDefined, s"'$LAYER_PARAM' parameter for raster layer name required.")
     require(zoom.isDefined, s"Integer '$ZOOM_PARAM' parameter for raster layer zoom level required.")
 
-    val rf = data.asRFSafely
-      .getOrElse(throw new IllegalArgumentException("Only a valid RasterFrame can be saved as a GeoTrellis layer"))
+    val rf = data.asLayerSafely
+      .getOrElse(throw new IllegalArgumentException("Only a valid RasterFrameLayer can be saved as a GeoTrellis layer"))
 
     val tileColumn = parameters.get(TILE_COLUMN_PARAM).map(c ⇒ rf(c))
 
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
index bb4cc4840..c4a7dc425 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotrellis/package.scala
@@ -36,11 +36,11 @@ package object geotrellis extends DataSourceOptions {
   def geotrellis_layer = col("layer").as[Layer]
 
   /** Tagged type construction for enabling type-safe extension methods for loading
-   * a RasterFrame from a GeoTrellis layer. */
+   * a RasterFrameLayer from a GeoTrellis layer. */
   type GeoTrellisRasterFrameReader = DataFrameReader @@ GeoTrellisRasterFrameReaderTag
   trait GeoTrellisRasterFrameReaderTag
   /** Tagged type construction for enabling type-safe extension methods for writing
-   * a RasterFrame to a GeoTrellis layer. */
+   * a RasterFrameLayer to a GeoTrellis layer. */
   type GeoTrellisRasterFrameWriter[T] = DataFrameWriter[T] @@ GeoTrellisRasterFrameWriterTag
   trait GeoTrellisRasterFrameWriterTag
 
@@ -71,7 +71,7 @@ package object geotrellis extends DataSourceOptions {
         .option("path", layer.base.toASCIIString)
   }
 
-  /** Extension methods for loading a RasterFrame from a tagged `DataFrameReader`. */
+  /** Extension methods for loading a RasterFrameLayer from a tagged `DataFrameReader`. */
   implicit class GeoTrellisReaderWithRF(val reader: GeoTrellisRasterFrameReader) {
     def withTileSubdivisions(divs: Int): GeoTrellisRasterFrameReader =
       tag[GeoTrellisRasterFrameReaderTag][DataFrameReader](
@@ -83,13 +83,13 @@ package object geotrellis extends DataSourceOptions {
         reader.option(NUM_PARTITIONS_PARAM, partitions.toLong)
       )
 
-    def loadRF(uri: URI, id: LayerId): RasterFrame =
+    def loadLayer(uri: URI, id: LayerId): RasterFrameLayer =
       reader
         .option(LAYER_PARAM, id.name)
         .option(ZOOM_PARAM, id.zoom.toString)
         .load(uri.toASCIIString)
-        .asRF
+        .asLayer
 
-    def loadRF(layer: Layer): RasterFrame = loadRF(layer.base, layer.id)
+    def loadLayer(layer: Layer): RasterFrameLayer = loadLayer(layer.base, layer.id)
   }
 }
diff --git a/datasource/src/test/scala/examples/Creating.scala b/datasource/src/test/scala/examples/Creating.scala
deleted file mode 100644
index fed1fac4a..000000000
--- a/datasource/src/test/scala/examples/Creating.scala
+++ /dev/null
@@ -1,235 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2018 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- */
-
-package examples
-
-import java.io.File
-import java.nio.file.Files
-
-/**
- * Examples of creating RasterFrames
- *
- * @since 1/16/18
- */
-object Creating extends App {
-/*
-# Creating RasterFrames
-
-## Initialization
-
-There are a couple of setup steps necessary anytime you want to work with RasterFrames. the first is to import the API symbols into scope:
-
-*/
-// tut:silent
-import org.locationtech.rasterframes._
-import org.apache.spark.sql._
-
-
-/*
-Next, initialize the `SparkSession`, and call the `withRasterFrames` method on it:
-*/
-
-// tut:silent
-implicit val spark = SparkSession.builder().
-  master("local").appName("RasterFrames").
-  getOrCreate().
-  withRasterFrames
-
-/*
-And, ss is standard Spark SQL practice, we import additional DataFrame support:
-*/
-
-// tut:silent
-// tut:invisible
-spark.sparkContext.setLogLevel("ERROR")
-
-
-/*
-Now we are ready to create a RasterFrame.
-
-## Reading a GeoTIFF
-
-The most straightforward way to create a `RasterFrame` is to read a [GeoTIFF](https://en.wikipedia.org/wiki/GeoTIFF)
-file using a RasterFrame [`DataSource`](https://spark.apache.org/docs/latest/sql-programming-guide.html#data-sources)
-designed for this purpose.
-
-First add the following import:
-*/
-
-import org.locationtech.rasterframes.datasource.geotiff._
-/*
-(This is what adds the `.geotiff` method to `spark.read` below.)
-
-Then we use the `DataFrameReader` provided by `spark.read` to read the GeoTIFF:
- */
-
-// tut:book
-val samplePath = new File("src/test/resources/LC08_RGB_Norfolk_COG.tiff")
-val tiffRF = spark.read
-  .geotiff
-  .loadRF(samplePath.toURI)
-
-/*
-Let's inspect the structure of what we get back:
- */
-
-// tut
-tiffRF.printSchema()
-
-/*
-As reported by Spark, RasterFrames extracts 6 columns from the GeoTIFF we selected. Some of these columns are dependent
-on the contents of the source data, and some are are always available. Let's take a look at these in more detail.
-
-* `spatial_key`: GeoTrellis assigns a `SpatialKey` or a `SpaceTimeKey` to each tile, mapping it to the layer grid from
-  which it came. If it has a `SpaceTimeKey`, RasterFrames will split it into a `SpatialKey` and a `TemporalKey` (the
-  latter with column name `temporal_key`).
-* `extent`: The bounding box of the tile in the tile's native CRS.
-* `metadata`: The TIFF format header tags found in the file.
-* `tile` or `tile_n` (where `n` is a band number): For singleband GeoTIFF files, the `tile` column contains the cell
-  data split into tiles. For multiband tiles, each column with `tile_` prefix contains each of the sources bands,
-  in the order they were stored.
-
-See the section [Inspecting a `RasterFrame`](#inspecting-a--code-rasterframe--code-) (below) for more details on accessing the RasterFrame's metadata.
- */
-
-
-/*
-## Reading a GeoTrellis Layer
-
-If your imagery is already ingested into a [GeoTrellis layer](https://docs.geotrellis.io/en/latest/guide/spark.html#writing-layers),
-you can use the RasterFrames GeoTrellis DataSource. There are two parts to this GeoTrellis Layer support. The first
-is the GeoTrellis Catalog DataSource, which lists the GeoTrellis layers available at a URI. The second part is the actual
-RasterFrame reader for pulling a layer into a RasterFrame.
-
-Before we show how all of this works we need to have a GeoTrellis layer to work with. We can create one with the RasterFrame we
-constructed above.
-
- */
-import org.locationtech.rasterframes.datasource.geotrellis._
-
-val base = Files.createTempDirectory("rf-").toUri
-val layer = Layer(base, "sample", 0)
-tiffRF.write.geotrellis.asLayer(layer).save()
-
-/*
-Now we can point our catalog reader at the base directory and see what was saved:
-*/
-
-val cat = spark.read.geotrellisCatalog(base)
-cat.printSchema
-cat.show()
-
-/*
-As you can see, there's a lot of information stored in each row of the catalog. Most of this is associated with how the
-layer is discretized. However, there may be other application specific metadata serialized with a layer that can be use
-to filter the catalog entries or select a specific one. But for now, we're just going to load a RasterFrame in from the
-catalog using a convenience function.
- */
-
-val firstLayer = cat.select(geotrellis_layer).first
-val rfAgain = spark.read.geotrellis.loadRF(firstLayer)
-rfAgain.show()
-
-/*
-If you already know the `LayerId` of what you're wanting to read, you can bypass working with the catalog:
- */
-
-val anotherRF = spark.read.geotrellis.loadRF(layer)
-
-/*
-## Using GeoTrellis APIs
-
-If you are used to working directly with the GeoTrellis APIs, there are a number of additional ways to create a `RasterFrame`, as enumerated in the sections below.
-
-First, some standard `import`s:
-*/
-
-// tut:silent
-import geotrellis.raster.io.geotiff.SinglebandGeoTiff
-import geotrellis.spark.io._
-
-/*
-### From `ProjectedExtent`
-
-The simplest mechanism for getting a RasterFrame is to use the `toRF(tileCols, tileRows)` extension method on `ProjectedRaster`.
-*/
-
-val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
-val rf = scene.projectedRaster.toRF(128, 128)
-rf.show(5, false)
-
-/*
-### From `TileLayerRDD`
-
-Another option is to use a GeoTrellis [`LayerReader`](https://docs.geotrellis.io/en/latest/guide/tile-backends.html),
-to get a `TileLayerRDD` for which there's also a `toRF` extension method.
-
-*/
-
-/*
-```scala
-import geotrellis.spark._
-val tiledLayer: TileLayerRDD[SpatialKey] = ???
-val rf = tiledLayer.toRF
-```
-*/
-
-/*
-## Inspecting a `RasterFrame`
-
-`RasterFrame` has a number of methods providing access to metadata about the contents of the RasterFrame.
-
-### Tile Column Names
-
-*/
-
-//```tut:book
-rf.tileColumns.map(_.toString)
-
-/*
-### Spatial Key Column Name
-*/
-
-//```tut:book
-rf.spatialKeyColumn.toString
-
-/*
-### Temporal Key Column
-
-Returns an `Option[Column]` since not all RasterFrames have an explicit temporal dimension.
-*/
-
-//```tut:book
-rf.temporalKeyColumn.map(_.toString)
-
-/*
-### Tile Layer Metadata
-
-The Tile Layer Metadata defines how the spatial/spatiotemporal domain is discretized into tiles, and what the key bounds are.
-*/
-
-import spray.json._
-// NB: The `fold` is required because an `Either` is returned, depending on the key type.
-rf.tileLayerMetadata.fold(_.toJson, _.toJson).prettyPrint
-
-
-//tut:invisible
-spark.stop()
-
-}
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 704c7db53..aecf2d848 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -35,14 +35,14 @@ class GeoTiffDataSourceSpec
   describe("GeoTiff reading") {
 
     it("should read sample GeoTiff") {
-      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
 
       assert(rf.count() > 10)
     }
 
     it("should lay out tiles correctly"){
 
-      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
 
       val tlm = rf.tileLayerMetadata.left.get
       val gb = tlm.gridBounds
@@ -51,7 +51,7 @@ class GeoTiffDataSourceSpec
     }
 
     it("should lay out tiles correctly for non-tiled tif") {
-      val rf = spark.read.format("geotiff").load(nonCogPath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(nonCogPath.toASCIIString).asLayer
 
       assert(rf.count() > 1)
 
@@ -74,7 +74,7 @@ class GeoTiffDataSourceSpec
 
     it("should read in correctly check-summed contents") {
       // c.f. TileStatsSpec -> computing statistics over tiles -> should compute tile statistics -> sum
-      val rf = spark.read.format("geotiff").load(l8B1SamplePath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(l8B1SamplePath.toASCIIString).asLayer
       val expected = 309149454 // computed with rasterio
       val result = rf.agg(
         sum(rf_tile_sum(rf("tile")))
@@ -84,12 +84,12 @@ class GeoTiffDataSourceSpec
     }
 
     it("should write GeoTIFF RF to parquet") {
-      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
       assert(write(rf))
     }
 
     it("should write GeoTIFF") {
-      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asRF
+      val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
 
       logger.info(s"Read extent: ${rf.tileLayerMetadata.merge.extent}")
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
index 042fa4c3b..c409eb216 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisCatalogSpec.scala
@@ -70,7 +70,7 @@ class GeoTrellisCatalogSpec
         .collect
       assert(layer.length === 2)
 
-      val lots = layer.map(sqlContext.read.geotrellis.loadRF).map(_.toDF).reduce(_ union _)
+      val lots = layer.map(sqlContext.read.geotrellis.loadLayer).map(_.toDF).reduce(_ union _)
       assert(lots.count === 60)
     }
   }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
index aefe01f07..ecd3351df 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala
@@ -91,7 +91,7 @@ class GeoTrellisDataSourceSpec
     outputDir.deleteOnExit()
 
     // Test layer writing via RF
-    testRdd.toRF.write.geotrellis.asLayer(layer).save()
+    testRdd.toLayer.write.geotrellis.asLayer(layer).save()
 
     val tfRdd = testRdd.map { case (k, tile) ⇒
         val md = Map("col" -> k.col,"row" -> k.row)
@@ -117,7 +117,7 @@ class GeoTrellisDataSourceSpec
     val tlfRdd = ContextRDD(tfRdd, testRdd.metadata)
     writer.write(tfLayer.id, tlfRdd, ZCurveKeyIndexMethod.byDay())
 
-    //TestData.sampleTileLayerRDD.toRF.write.geotrellis.asLayer(sampleImageLayer).save()
+    //TestData.sampleTileLayerRDD.toLayer.write.geotrellis.asLayer(sampleImageLayer).save()
     val writer2 = LayerWriter(sampleImageLayer.base)
     val imgRDD = TestData.sampleTileLayerRDD
     writer2.write(sampleImageLayer.id, imgRDD, ZCurveKeyIndexMethod)
@@ -126,12 +126,12 @@ class GeoTrellisDataSourceSpec
   describe("DataSource reading") {
     def layerReader = spark.read.geotrellis
     it("should read tiles") {
-      val df = layerReader.loadRF(layer)
+      val df = layerReader.loadLayer(layer)
       assert(df.count === tileCoordRange.length * tileCoordRange.length)
     }
 
     it("used produce tile UDT that we can manipulate") {
-      val df = layerReader.loadRF(layer)
+      val df = layerReader.loadLayer(layer)
         .select(SPATIAL_KEY_COLUMN, rf_tile_stats(TILE_COLUMN))
       assert(df.count() > 0)
     }
@@ -141,7 +141,7 @@ class GeoTrellisDataSourceSpec
       val  bbox = testRdd.metadata.layout
         .mapTransform(boundKeys.toGridBounds())
         .jtsGeom
-      val wc = layerReader.loadRF(layer).withCenter()
+      val wc = layerReader.loadLayer(layer).withCenter()
 
       withClue("literate API") {
         val df = wc.where(CENTER_COLUMN intersects bbox)
@@ -154,7 +154,7 @@ class GeoTrellisDataSourceSpec
     }
 
     it("should invoke Encoder[Extent]") {
-      val df = layerReader.loadRF(layer).withGeometry()
+      val df = layerReader.loadLayer(layer).withGeometry()
       assert(df.count > 0)
       assert(df.first.length === 5)
       assert(df.first.getAs[Extent](2) !== null)
@@ -162,7 +162,7 @@ class GeoTrellisDataSourceSpec
 
     it("should write to parquet") {
       //just should not throw
-      val df = layerReader.loadRF(layer)
+      val df = layerReader.loadLayer(layer)
       assert(write(df))
     }
   }
@@ -172,21 +172,21 @@ class GeoTrellisDataSourceSpec
       val expected = 2
       val df = spark.read.geotrellis
         .withNumPartitions(expected)
-        .loadRF(layer)
+        .loadLayer(layer)
       assert(df.rdd.partitions.length === expected)
     }
     it("should respect partitions 20") {
       val expected = 20
       val df = spark.read.geotrellis
         .withNumPartitions(expected)
-        .loadRF(layer)
+        .loadLayer(layer)
       assert(df.rdd.partitions.length === expected)
     }
     it("should respect subdivide 2") {
       val param = 2
-      val df: RasterFrame = spark.read.geotrellis
+      val df: RasterFrameLayer = spark.read.geotrellis
         .withTileSubdivisions(param)
-        .loadRF(layer)
+        .loadLayer(layer)
 
       val dims = df.select(rf_dimensions(df.tileColumns.head)("cols"), rf_dimensions(df.tileColumns.head)("rows")).first()
       assert(dims.getAs[Int](0) === tileSize / param)
@@ -197,9 +197,9 @@ class GeoTrellisDataSourceSpec
     }
     it("should respect subdivide with TileFeature"){
       val param = 2
-      val rf: RasterFrame = spark.read.geotrellis
+      val rf: RasterFrameLayer = spark.read.geotrellis
         .withTileSubdivisions(param)
-        .loadRF(tfLayer)
+        .loadLayer(tfLayer)
 
       val dims = rf.select(rf_dimensions(rf.tileColumns.head)("cols"), rf_dimensions(rf.tileColumns.head)("rows"))
         .first()
@@ -216,7 +216,7 @@ class GeoTrellisDataSourceSpec
         .geotrellis
         .withNumPartitions(7)
         .withTileSubdivisions(subParam)
-        .loadRF(layer)
+        .loadLayer(layer)
 
       // is it partitioned correctly?
       assert(rf.rdd.partitions.length === 7)
@@ -233,7 +233,7 @@ class GeoTrellisDataSourceSpec
       val subs = 4
       val rf = spark.read.geotrellis
         .withTileSubdivisions(subs)
-        .loadRF(sampleImageLayer)
+        .loadLayer(sampleImageLayer)
 
 
       assert(rf.count === (TestData.sampleTileLayerRDD.count * subs * subs))
@@ -250,13 +250,13 @@ class GeoTrellisDataSourceSpec
 
     it("should throw on subdivide 5") {
       // only throws when an action is taken...
-      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(5).loadRF(layer).cache)
+      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(5).loadLayer(layer).cache)
     }
     it("should throw on subdivide 13") {
-      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(13).loadRF(layer).cache)
+      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(13).loadLayer(layer).cache)
     }
     it("should throw on subdivide -3") {
-      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(-3).loadRF(layer).count)
+      assertThrows[IllegalArgumentException](spark.read.geotrellis.withTileSubdivisions(-3).loadLayer(layer).count)
     }
   }
 
@@ -287,7 +287,7 @@ class GeoTrellisDataSourceSpec
 
     it("should support extent against a geometry literal") {
       val df: DataFrame = layerReader
-        .loadRF(layer)
+        .loadLayer(layer)
         .where(GEOMETRY_COLUMN intersects pt1)
 
       assert(numFilters(df) === 1)
@@ -299,7 +299,7 @@ class GeoTrellisDataSourceSpec
     it("should support query with multiple geometry types") {
       // Mostly just testing that these evaluate without catalyst type errors.
       forEvery(GeomData.all) { g ⇒
-        val query = layerReader.loadRF(layer).where(GEOMETRY_COLUMN.intersects(g))
+        val query = layerReader.loadLayer(layer).where(GEOMETRY_COLUMN.intersects(g))
           .persist(StorageLevel.OFF_HEAP)
         assert(query.count() === 0)
       }
@@ -311,7 +311,7 @@ class GeoTrellisDataSourceSpec
       val mkPtFcn = sparkUdf((_: Row) ⇒ { Point(-88, 60).jtsGeom })
 
       val df = layerReader
-        .loadRF(layer)
+        .loadLayer(layer)
         .where(st_intersects(GEOMETRY_COLUMN, mkPtFcn(SPATIAL_KEY_COLUMN)))
 
       assert(numFilters(df) === 0)
@@ -323,7 +323,7 @@ class GeoTrellisDataSourceSpec
     it("should support temporal predicates") {
       withClue("at now") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(TIMESTAMP_COLUMN === Timestamp.valueOf(now.toLocalDateTime))
 
         assert(numFilters(df) == 1)
@@ -332,7 +332,7 @@ class GeoTrellisDataSourceSpec
 
       withClue("at earlier") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(TIMESTAMP_COLUMN === Timestamp.valueOf(now.minusDays(1).toLocalDateTime))
 
         assert(numFilters(df) === 1)
@@ -341,7 +341,7 @@ class GeoTrellisDataSourceSpec
 
       withClue("between now") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(TIMESTAMP_COLUMN betweenTimes (now.minusDays(1), now.plusDays(1)))
 
         assert(numFilters(df) === 1)
@@ -350,7 +350,7 @@ class GeoTrellisDataSourceSpec
 
       withClue("between later") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(TIMESTAMP_COLUMN betweenTimes (now.plusDays(1), now.plusDays(2)))
 
         assert(numFilters(df) === 1)
@@ -361,7 +361,7 @@ class GeoTrellisDataSourceSpec
     it("should support nested predicates") {
       withClue("fully nested") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(
             ((GEOMETRY_COLUMN intersects pt1) ||
               (GEOMETRY_COLUMN intersects pt2)) &&
@@ -376,7 +376,7 @@ class GeoTrellisDataSourceSpec
 
       withClue("partially nested") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where((GEOMETRY_COLUMN intersects pt1) || (GEOMETRY_COLUMN intersects pt2))
           .where(TIMESTAMP_COLUMN === Timestamp.valueOf(now.toLocalDateTime))
 
@@ -390,7 +390,7 @@ class GeoTrellisDataSourceSpec
     it("should support intersects with between times") {
       withClue("intersects first") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(GEOMETRY_COLUMN intersects pt1)
           .where(TIMESTAMP_COLUMN betweenTimes(now.minusDays(1), now.plusDays(1)))
 
@@ -398,7 +398,7 @@ class GeoTrellisDataSourceSpec
       }
       withClue("intersects last") {
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where(TIMESTAMP_COLUMN betweenTimes(now.minusDays(1), now.plusDays(1)))
           .where(GEOMETRY_COLUMN intersects pt1)
 
@@ -408,7 +408,7 @@ class GeoTrellisDataSourceSpec
       withClue("untyped columns") {
         import spark.implicits._
         val df = layerReader
-          .loadRF(layer)
+          .loadLayer(layer)
           .where($"timestamp" >= Timestamp.valueOf(now.minusDays(1).toLocalDateTime))
           .where($"timestamp" <= Timestamp.valueOf(now.plusDays(1).toLocalDateTime))
           .where(st_intersects(GEOMETRY_COLUMN, geomLit(pt1.jtsGeom)))
@@ -420,7 +420,7 @@ class GeoTrellisDataSourceSpec
 
     it("should handle renamed spatial filter columns") {
       val df = layerReader
-        .loadRF(layer)
+        .loadLayer(layer)
         .where(GEOMETRY_COLUMN intersects region.jtsGeom)
         .withColumnRenamed(GEOMETRY_COLUMN.columnName, "foobar")
 
@@ -430,7 +430,7 @@ class GeoTrellisDataSourceSpec
 
     it("should handle dropped spatial filter columns") {
       val df = layerReader
-        .loadRF(layer)
+        .loadLayer(layer)
         .where(GEOMETRY_COLUMN intersects region.jtsGeom)
         .drop(GEOMETRY_COLUMN)
 
@@ -440,14 +440,14 @@ class GeoTrellisDataSourceSpec
 
   describe("TileFeature support") {
     def layerReader = spark.read.geotrellis
-    it("should resolve TileFeature-based RasterFrame") {
-      val rf = layerReader.loadRF(tfLayer)
+    it("should resolve TileFeature-based RasterFrameLayer") {
+      val rf = layerReader.loadLayer(tfLayer)
       //rf.show(false)
       assert(rf.collect().length === testRdd.count())
     }
-    it("should respect subdivideTile option on TileFeature RasterFrame") {
+    it("should respect subdivideTile option on TileFeature RasterFrameLayer") {
       val subParam = 4
-      val rf = spark.read.option(TILE_SUBDIVISIONS_PARAM, subParam).geotrellis.loadRF(tfLayer)
+      val rf = spark.read.option(TILE_SUBDIVISIONS_PARAM, subParam).geotrellis.loadLayer(tfLayer)
 
       assert(rf.count === testRdd.count * subParam * subParam)
 
@@ -462,7 +462,7 @@ class GeoTrellisDataSourceSpec
       val rf = spark.read
         .option(TILE_SUBDIVISIONS_PARAM, subParam)
         .option(NUM_PARTITIONS_PARAM, 10)
-        .geotrellis.loadRF(tfLayer)
+        .geotrellis.loadLayer(tfLayer)
 
       // is it subdivided?
       assert(rf.count === testRdd.count * subParam * subParam)
diff --git a/docs/src/main/paradox/release-notes.md b/docs/src/main/paradox/release-notes.md
index 2a4cb1a7e..96db8e579 100644
--- a/docs/src/main/paradox/release-notes.md
+++ b/docs/src/main/paradox/release-notes.md
@@ -6,9 +6,10 @@
 
 * Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
 * Build `pyrasterframes` binary distribution for pip installation.
+* Added support for rendering RasterFrame types in IPython/Jupyter.
 * Added new tile functions `rf_round`, `rf_abs`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.
-* Support python-side Tile User-Defined Type backed by [numpy](https://www.numpy.org/) `ndarray` or `ma.MaskedArray`.
-* Support python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
+* Support Python-side Tile User-Defined Type backed by [numpy](https://www.numpy.org/) `ndarray` or `ma.MaskedArray`.
+* Support Python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
 * SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
@@ -19,6 +20,8 @@
 * Added `rf_crs` for `ProjectedRasterTile` columns.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
+* _Breaking_: The type `RasterFrame` renamed `RasterFrameLayer` to be reflect its purpose.
+* _Breaking_: All `asRF` methods renamed to `asLayer`.
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * _Breaking_: Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
 * _Breaking_: Renamed `rf_extent_geometry` to `st_geometry`
diff --git a/pyrasterframes/src/main/python/docs/architecture.md b/pyrasterframes/src/main/python/docs/architecture.md
new file mode 100644
index 000000000..5dc5735a5
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/architecture.md
@@ -0,0 +1,39 @@
+# Architecture
+
+RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
+[GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
+[JTS](https://github.com/locationtech/jts), and
+[SFCurve](https://github.com/locationtech/sfcurve) (see below).
+
+![LocationTech Stack](rasterframes-locationtech-stack.png)
+
+
+## Concepts
+
+> * Cell
+> * Cell Type
+> * Tile
+> * Extent
+> * Coordinate Reference System (CRS)
+> * Projected Extent 
+> * Projected Raster
+> * Layer 
+
+RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As showin in Figure 2, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
+
+![RasterFrame Anatomy](rasterframe-anatomy.png)
+
+Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. 
+
+> Update to match components in raster-io.md
+
+![RasterFrame Data Sources](rasterframes-data-sources.png)
+
+
+
+
+[R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
+[Pandas]:https://pandas.pydata.org/
+[GeoJSON]:https://en.wikipedia.org/wiki/GeoJSON
+[GTLayer]:https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html#layouts-and-tile-layers
+[PDS]:https://registry.opendata.aws/modis/
diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 8a27c66ad..c9665ad8d 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -10,23 +10,6 @@ RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings to
 
 RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
 
-## Architecture
-
-RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
-[GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
-[JTS](https://github.com/locationtech/jts), and
-[SFCurve](https://github.com/locationtech/sfcurve) (see Figure 1).
-
-![LocationTech Stack](rasterframes-locationtech-stack.png)
-
-RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As showin in Figure 2, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
-
-![RasterFrame Anatomy](rasterframe-anatomy.png)
-
-Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. 
-
-![RasterFrame Data Sources](rasterframes-data-sources.png)
-
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
 [Pandas]:https://pandas.pydata.org/
 [GeoJSON]:https://en.wikipedia.org/wiki/GeoJSON
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 09805643f..6d8c69052 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -28,12 +28,12 @@
 from pyspark.sql import SparkSession, DataFrame, DataFrameReader
 from pyspark.sql.column import _to_java_column
 
-# Import RasterFrame types and functions
+# Import RasterFrameLayer types and functions
 from .rf_context import RFContext
 from .version import __version__
-from .rf_types import RasterFrame, TileExploder, TileUDT, RasterSourceUDT
+from .rf_types import RasterFrameLayer, TileExploder, TileUDT, RasterSourceUDT
 
-__all__ = ['RasterFrame', 'TileExploder']
+__all__ = ['RasterFrameLayer', 'TileExploder']
 
 
 def _rf_init(spark_session):
@@ -66,10 +66,10 @@ def _convert_df(df, sp_key=None, metadata=None):
     ctx = SparkContext._active_spark_context._rf_context
 
     if sp_key is None:
-        return RasterFrame(ctx._jrfctx.asRF(df._jdf), ctx._spark_session)
+        return RasterFrameLayer(ctx._jrfctx.asLayer(df._jdf), ctx._spark_session)
     else:
         import json
-        return RasterFrame(ctx._jrfctx.asRF(
+        return RasterFrameLayer(ctx._jrfctx.asLayer(
             df._jdf, _to_java_column(sp_key), json.dumps(metadata)), ctx._spark_session)
 
 
@@ -89,7 +89,7 @@ def _raster_join(df, other, left_extent=None, left_crs=None, right_extent=None,
     else:
         jdf = ctx._jrfctx.rasterJoin(df._jdf, other._jdf)
 
-    return RasterFrame(jdf, ctx._spark_session)
+    return RasterFrameLayer(jdf, ctx._spark_session)
 
 
 def _layer_reader(df_reader, format_key, path, **options):
@@ -151,8 +151,8 @@ def to_csv(comp):
 SparkSession.withRasterFrames = _rf_init
 SparkSession.Builder.withKryoSerialization = _kryo_init
 
-# Add the 'asRF' method to pyspark DataFrame
-DataFrame.asRF = _convert_df
+# Add the 'asLayer' method to pyspark DataFrame
+DataFrame.as_layer = _convert_df
 
 # Add `raster_join` method to pyspark DataFrame
 DataFrame.raster_join = _raster_join
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_context.py b/pyrasterframes/src/main/python/pyrasterframes/rf_context.py
index ce7904474..0b96a6348 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_context.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_context.py
@@ -19,7 +19,7 @@
 #
 
 """
-This module contains access to the jvm SparkContext with RasterFrame support.
+This module contains access to the jvm SparkContext with RasterFrameLayer support.
 """
 
 from pyspark import SparkContext
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index ddf334d7a..ffe2aa925 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -21,7 +21,7 @@
 """
 This module contains all types relevant to PyRasterFrames. Classes in this module are
 meant to provide smoother pathways between the jvm and Python, and whenever possible,
-the implementations take advantage of the existing Scala functionality. The RasterFrame
+the implementations take advantage of the existing Scala functionality. The RasterFrameLayer
 class here provides the PyRasterFrames entry point.
 """
 
@@ -34,15 +34,15 @@ class here provides the PyRasterFrames entry point.
 from pyrasterframes.rf_context import RFContext
 import numpy as np
 
-__all__ = ['RasterFrame', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
+__all__ = ['RasterFrameLayer', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
 
 
-class RasterFrame(DataFrame):
+class RasterFrameLayer(DataFrame):
     def __init__(self, jdf, spark_session):
         DataFrame.__init__(self, jdf, spark_session._wrapped)
         self._jrfctx = spark_session.rasterframes._jrfctx
 
-    def tileColumns(self):
+    def tile_columns(self):
         """
         Fetches columns of type Tile.
         :return: One or more Column instances associated with Tiles.
@@ -50,7 +50,7 @@ def tileColumns(self):
         cols = self._jrfctx.tileColumns(self._jdf)
         return [Column(c) for c in cols]
 
-    def spatialKeyColumn(self):
+    def spatial_key_column(self):
         """
         Fetch the tagged spatial key column.
         :return: Spatial key column
@@ -58,7 +58,7 @@ def spatialKeyColumn(self):
         col = self._jrfctx.spatialKeyColumn(self._jdf)
         return Column(col)
 
-    def temporalKeyColumn(self):
+    def temporal_key_column(self):
         """
         Fetch the temporal key column, if any.
         :return: Temporal key column, or None.
@@ -66,7 +66,7 @@ def temporalKeyColumn(self):
         col = self._jrfctx.temporalKeyColumn(self._jdf)
         return col and Column(col)
 
-    def tileLayerMetadata(self):
+    def tile_layer_metadata(self):
         """
         Fetch the tile layer metadata.
         :return: A dictionary of metadata.
@@ -74,16 +74,16 @@ def tileLayerMetadata(self):
         import json
         return json.loads(str(self._jrfctx.tileLayerMetadata(self._jdf)))
 
-    def spatialJoin(self, other_df):
+    def spatial_join(self, other_df):
         """
-        Spatially join this RasterFrame to the given RasterFrame.
-        :return: Joined RasterFrame.
+        Spatially join this RasterFrameLayer to the given RasterFrameLayer.
+        :return: Joined RasterFrameLayer.
         """
         ctx = SparkContext._active_spark_context._rf_context
         df = ctx._jrfctx.spatialJoin(self._jdf, other_df._jdf)
-        return RasterFrame(df, ctx._spark_session)
+        return RasterFrameLayer(df, ctx._spark_session)
 
-    def toIntRaster(self, colname, cols, rows):
+    def to_int_raster(self, colname, cols, rows):
         """
         Convert a tile to an Int raster
         :return: array containing values of the tile's cells
@@ -91,7 +91,7 @@ def toIntRaster(self, colname, cols, rows):
         resArr = self._jrfctx.toIntRaster(self._jdf, colname, cols, rows)
         return resArr
 
-    def toDoubleRaster(self, colname, cols, rows):
+    def to_double_raster(self, colname, cols, rows):
         """
         Convert a tile to an Double raster
         :return: array containing values of the tile's cells
@@ -99,41 +99,41 @@ def toDoubleRaster(self, colname, cols, rows):
         resArr = self._jrfctx.toDoubleRaster(self._jdf, colname, cols, rows)
         return resArr
 
-    def withBounds(self):
+    def with_bounds(self):
         """
         Add a column called "bounds" containing the extent of each row.
-        :return: RasterFrame with "bounds" column.
+        :return: RasterFrameLayer with "bounds" column.
         """
         ctx = SparkContext._active_spark_context._rf_context
         df = ctx._jrfctx.withBounds(self._jdf)
-        return RasterFrame(df, ctx._spark_session)
+        return RasterFrameLayer(df, ctx._spark_session)
 
-    def withCenter(self):
+    def with_center(self):
         """
         Add a column called "center" containing the center of the extent of each row.
-        :return: RasterFrame with "center" column.
+        :return: RasterFrameLayer with "center" column.
         """
         ctx = SparkContext._active_spark_context._rf_context
         df = ctx._jrfctx.withCenter(self._jdf)
-        return RasterFrame(df, ctx._spark_session)
+        return RasterFrameLayer(df, ctx._spark_session)
 
-    def withCenterLatLng(self):
+    def with_center_lat_lng(self):
         """
         Add a column called "center" containing the center of the extent of each row in Lat Long form.
-        :return: RasterFrame with "center" column.
+        :return: RasterFrameLayer with "center" column.
         """
         ctx = SparkContext._active_spark_context._rf_context
         df = ctx._jrfctx.withCenterLatLng(self._jdf)
-        return RasterFrame(df, ctx._spark_session)
+        return RasterFrameLayer(df, ctx._spark_session)
 
-    def withSpatialIndex(self):
+    def with_spatial_index(self):
         """
         Add a column containing the spatial index of each row.
-        :return: RasterFrame with "center" column.
+        :return: RasterFrameLayer with "center" column.
         """
         ctx = SparkContext._active_spark_context._rf_context
         df = ctx._jrfctx.withSpatialIndex(self._jdf)
-        return RasterFrame(df, ctx._spark_session)
+        return RasterFrameLayer(df, ctx._spark_session)
 
 
 class RasterSourceUDT(UserDefinedType):
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 3ba625e9d..94b158c2d 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -32,7 +32,7 @@
 class VectorTypes(TestEnvironment):
 
     def setUp(self):
-        self.create_rasterframe()
+        self.create_layer()
         import pandas as pd
         self.pandas_df = pd.DataFrame({
             'eye': ['a', 'b', 'c', 'd'],
@@ -147,20 +147,20 @@ def test_geojson(self):
 class RasterFunctions(TestEnvironment):
 
     def setUp(self):
-        self.create_rasterframe()
+        self.create_layer()
 
     def test_setup(self):
         self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
                          "org.apache.spark.serializer.KryoSerializer")
 
     def test_identify_columns(self):
-        cols = self.rf.tileColumns()
+        cols = self.rf.tile_columns()
         self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
         print("Tile columns: ", cols)
-        col = self.rf.spatialKeyColumn()
+        col = self.rf.spatial_key_column()
         self.assertIsInstance(col, Column, '`spatialKeyColumn` was not found')
         print("Spatial key column: ", col)
-        col = self.rf.temporalKeyColumn()
+        col = self.rf.temporal_key_column()
         self.assertIsNone(col, '`temporalKeyColumn` should be `None`')
         print("Temporal key column: ", col)
 
@@ -172,9 +172,9 @@ def test_tile_creation(self):
         self.assertEqual(tiles.count(), 4)
 
     def test_multi_column_operations(self):
-        df1 = self.rf.withColumnRenamed('tile', 't1').asRF()
-        df2 = self.rf.withColumnRenamed('tile', 't2').asRF()
-        df3 = df1.spatialJoin(df2).asRF()
+        df1 = self.rf.withColumnRenamed('tile', 't1').as_layer()
+        df2 = self.rf.withColumnRenamed('tile', 't2').as_layer()
+        df3 = df1.spatial_join(df2).as_layer()
         df3 = df3.withColumn('norm_diff', rf_normalized_difference('t1', 't2'))
         # df3.printSchema()
 
@@ -187,7 +187,7 @@ def test_multi_column_operations(self):
         self.assertTrue(self.rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
 
     def test_general(self):
-        meta = self.rf.tileLayerMetadata()
+        meta = self.rf.tile_layer_metadata()
         self.assertIsNotNone(meta['bounds'])
         df = self.rf.withColumn('dims', rf_dimensions('tile')) \
             .withColumn('type', rf_cell_type('tile')) \
@@ -263,7 +263,7 @@ def test_explode(self):
         # |spatial_key|column_index|row_index|tile   |
         # +-----------+------------+---------+-------+
         # |[2,1]      |4           |0        |10150.0|
-        cell = self.rf.select(self.rf.spatialKeyColumn(), rf_explode_tiles(self.rf.tile)) \
+        cell = self.rf.select(self.rf.spatial_key_column(), rf_explode_tiles(self.rf.tile)) \
             .where(F.col("spatial_key.col") == 2) \
             .where(F.col("spatial_key.row") == 1) \
             .where(F.col("column_index") == 4) \
@@ -386,7 +386,7 @@ def test_cell_type_conversion(self):
 class UDT(TestEnvironment):
 
     def setUp(self):
-        self.create_rasterframe()
+        self.create_layer()
 
     def test_mask_no_data(self):
         t1 = Tile(np.array([[1, 2], [3, 4]]), CellType("int8ud3"))
@@ -598,7 +598,7 @@ def test_matmul(self):
 class PandasInterop(TestEnvironment):
 
     def setUp(self):
-        self.create_rasterframe()
+        self.create_layer()
 
     def test_pandas_conversion(self):
         import pandas as pd
@@ -649,7 +649,7 @@ def test_extended_pandas_ops(self):
         })
 
         rf_maybe_2 = self.spark.createDataFrame(to_spark_2)
-        #print("RasterFrame `show`:")
+        #print("RasterFrameLayer `show`:")
         #rf_maybe_2.select(rf_render_matrix(rf_maybe_2.t).alias('t')).show(truncate=False)
 
         pd_2 = rf_maybe_2.toPandas()
@@ -663,7 +663,7 @@ def test_extended_pandas_ops(self):
 class RasterJoin(TestEnvironment):
 
     def setUp(self):
-        self.create_rasterframe()
+        self.create_layer()
 
     def test_raster_join(self):
         # re-read the same source
diff --git a/pyrasterframes/src/main/python/tests/__init__.py b/pyrasterframes/src/main/python/tests/__init__.py
index 3ed3e2edf..177c9a8c7 100644
--- a/pyrasterframes/src/main/python/tests/__init__.py
+++ b/pyrasterframes/src/main/python/tests/__init__.py
@@ -73,15 +73,15 @@ def setUpClass(cls):
 
         cls.img_uri = 'file://' + os.path.join(cls.resource_dir, 'L8-B8-Robinson-IL.tiff')
 
-    def create_rasterframe(self):
+    def create_layer(self):
         from pyrasterframes.rasterfunctions import rf_convert_cell_type
         # load something into a rasterframe
         rf = self.spark.read.geotiff(self.img_uri) \
-            .withBounds() \
-            .withCenter()
+            .with_bounds() \
+            .with_center()
 
         # convert the tile cell type to provide for other operations
         self.rf = rf.withColumn('tile2', rf_convert_cell_type('tile', 'float32')) \
             .drop('tile') \
-            .withColumnRenamed('tile2', 'tile').asRF()
+            .withColumnRenamed('tile2', 'tile').as_layer()
         # cls.rf.show()
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 4590eccc4..10c83fef8 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -37,7 +37,7 @@ import spray.json._
 import scala.collection.JavaConverters._
 
 /**
- * py4j access wrapper to RasterFrame entry points.
+ * py4j access wrapper to RasterFrameLayer entry points.
  *
  * @since 11/6/17
  */
@@ -54,53 +54,53 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     retval
   }
 
-  def toSpatialMultibandTileLayerRDD(rf: RasterFrame): MultibandTileLayerRDD[SpatialKey] =
+  def toSpatialMultibandTileLayerRDD(rf: RasterFrameLayer): MultibandTileLayerRDD[SpatialKey] =
     rf.toMultibandTileLayerRDD match {
       case Left(spatial) => spatial
       case Right(other) => throw new Exception(s"Expected a MultibandTileLayerRDD[SpatailKey] but got $other instead")
     }
 
-  def toSpaceTimeMultibandTileLayerRDD(rf: RasterFrame): MultibandTileLayerRDD[SpaceTimeKey] =
+  def toSpaceTimeMultibandTileLayerRDD(rf: RasterFrameLayer): MultibandTileLayerRDD[SpaceTimeKey] =
     rf.toMultibandTileLayerRDD match {
       case Right(temporal) => temporal
       case Left(other) => throw new Exception(s"Expected a MultibandTileLayerRDD[SpaceTimeKey] but got $other instead")
     }
 
   /**
-   * Converts a `ContextRDD[Spatialkey, MultibandTile, TileLayerMedadata[Spatialkey]]` to a RasterFrame
+   * Converts a `ContextRDD[Spatialkey, MultibandTile, TileLayerMedadata[Spatialkey]]` to a RasterFrameLayer
    */
-  def asRF(
+  def asLayer(
     layer: ContextRDD[SpatialKey, MultibandTile, TileLayerMetadata[SpatialKey]],
     bandCount: java.lang.Integer
-  ): RasterFrame = {
+  ): RasterFrameLayer = {
     implicit val pr = PairRDDConverter.forSpatialMultiband(bandCount.toInt)
-    layer.toRF
+    layer.toLayer
   }
 
   /**
-   * Converts a `ContextRDD[SpaceTimeKey, MultibandTile, TileLayerMedadata[SpaceTimeKey]]` to a RasterFrame
+   * Converts a `ContextRDD[SpaceTimeKey, MultibandTile, TileLayerMedadata[SpaceTimeKey]]` to a RasterFrameLayer
    */
-  def asRF(
+  def asLayer(
     layer: ContextRDD[SpaceTimeKey, MultibandTile, TileLayerMetadata[SpaceTimeKey]],
     bandCount: java.lang.Integer
-  )(implicit d: DummyImplicit): RasterFrame = {
+  )(implicit d: DummyImplicit): RasterFrameLayer = {
     implicit val pr = PairRDDConverter.forSpaceTimeMultiband(bandCount.toInt)
-    layer.toRF
+    layer.toLayer
   }
 
   /**
-    * Base conversion to RasterFrame
+    * Base conversion to RasterFrameLayer
     */
-  def asRF(df: DataFrame): RasterFrame = {
-    df.asRF
+  def asLayer(df: DataFrame): RasterFrameLayer = {
+    df.asLayer
   }
 
   /**
-    * Conversion to RasterFrame with spatial key column and TileLayerMetadata specified.
+    * Conversion to RasterFrameLayer with spatial key column and TileLayerMetadata specified.
     */
-  def asRF(df: DataFrame, spatialKey: Column, tlm: String): RasterFrame = {
+  def asLayer(df: DataFrame, spatialKey: Column, tlm: String): RasterFrameLayer = {
     val jtlm = tlm.parseJson.convertTo[TileLayerMetadata[SpatialKey]]
-    df.asRF(spatialKey, jtlm)
+    df.asLayer(spatialKey, jtlm)
   }
 
   /**
@@ -138,13 +138,13 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     rf_explode_tiles_sample(sampleFraction, Some(seed), cols: _*)
 
   def tileColumns(df: DataFrame): Array[Column] =
-    df.asRF.tileColumns.toArray
+    df.asLayer.tileColumns.toArray
 
   def spatialKeyColumn(df: DataFrame): Column =
-    df.asRF.spatialKeyColumn
+    df.asLayer.spatialKeyColumn
 
   def temporalKeyColumn(df: DataFrame): Column =
-    df.asRF.temporalKeyColumn.orNull
+    df.asLayer.temporalKeyColumn.orNull
 
   // All the scalar tile arithmetic functions
 
@@ -196,26 +196,26 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   // return toRaster, get just the tile, and make an array out of it
   def toIntRaster(df: DataFrame, colname: String, cols: Int, rows: Int): Array[Int] = {
-    df.asRF.toRaster(df.col(colname), cols, rows).tile.toArray()
+    df.asLayer.toRaster(df.col(colname), cols, rows).tile.toArray()
   }
 
   def toDoubleRaster(df: DataFrame, colname: String, cols: Int, rows: Int): Array[Double] = {
-    df.asRF.toRaster(df.col(colname), cols, rows).tile.toArrayDouble()
+    df.asLayer.toRaster(df.col(colname), cols, rows).tile.toArrayDouble()
   }
 
   def tileLayerMetadata(df: DataFrame): String =
     // The `fold` is required because an `Either` is retured, depending on the key type.
-    df.asRF.tileLayerMetadata.fold(_.toJson, _.toJson).prettyPrint
+    df.asLayer.tileLayerMetadata.fold(_.toJson, _.toJson).prettyPrint
 
-  def spatialJoin(df: DataFrame, right: DataFrame): RasterFrame = df.asRF.spatialJoin(right.asRF)
+  def spatialJoin(df: DataFrame, right: DataFrame): RasterFrameLayer = df.asLayer.spatialJoin(right.asLayer)
 
-  def withBounds(df: DataFrame): RasterFrame = df.asRF.withGeometry()
+  def withBounds(df: DataFrame): RasterFrameLayer = df.asLayer.withGeometry()
 
-  def withCenter(df: DataFrame): RasterFrame = df.asRF.withCenter()
+  def withCenter(df: DataFrame): RasterFrameLayer = df.asLayer.withCenter()
 
-  def withCenterLatLng(df: DataFrame): RasterFrame = df.asRF.withCenterLatLng()
+  def withCenterLatLng(df: DataFrame): RasterFrameLayer = df.asLayer.withCenterLatLng()
 
-  def withSpatialIndex(df: DataFrame): RasterFrame = df.asRF.withSpatialIndex()
+  def withSpatialIndex(df: DataFrame): RasterFrameLayer = df.asLayer.withSpatialIndex()
 
   //----------------------------Support Routines-----------------------------------------
 

From 1e4952962006c17755c78cf99ea2986b98d0cc97 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Jul 2019 12:00:09 -0400
Subject: [PATCH 215/380] Upgraded to gdal-warp-bindings "33.58d4965"

---
 .../org/locationtech/rasterframes/ref/RasterSourceIT.scala    | 4 ++++
 project/RFDependenciesPlugin.scala                            | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 9372e29ba..1123c6a08 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -24,6 +24,7 @@ package org.locationtech.rasterframes.ref
 import java.lang.Math.ceil
 import java.net.URI
 
+import com.azavea.gdal.GDALWarp
 import org.locationtech.rasterframes
 import org.locationtech.rasterframes.util.time
 import org.locationtech.rasterframes.{NOMINAL_TILE_SIZE, TestData, TestEnvironment}
@@ -63,8 +64,11 @@ class RasterSourceIT extends TestEnvironment with TestData {
   }
 
   if (RasterSource.IsGDAL.hasGDAL) {
+    println("GDAL version: " + GDALWarp.get_version_info("--version"))
+
     describe("GDAL support") {
 
+
       it("should read JPEG2000 scene") {
         RasterSource(localSentinel).readAll().flatMap(_.tile.statisticsDouble).size should be(64)
       }
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index b9620e9dc..a4caa89c0 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -62,6 +62,6 @@ object RFDependenciesPlugin extends AutoPlugin {
     rfSparkVersion := "2.3.2",
     rfGeoTrellisVersion := "2.2.0",
     rfGeoMesaVersion := "2.2.1",
-    dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.3f7a866"
+    dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.58d4965"
   )
 }

From 7d75cb7afe7e08b34c69ebd084399887e04eed2f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Jul 2019 13:52:17 -0400
Subject: [PATCH 216/380] Implemented switch to RasterSourceRelation to control
 lazy or strict reading of tiles.

---
 .../rasterframes/bench/RasterRefBench.scala   |  3 +-
 .../RasterSourceToRasterRefs.scala            | 16 ++--
 .../generators/RasterSourceToTiles.scala      | 88 +++++++++++++++++++
 .../transformers/URIToRasterSource.scala      |  2 +-
 .../rasterframes/ref/RasterRefSpec.scala      |  2 +-
 .../raster/RasterSourceDataSource.scala       |  8 +-
 .../raster/RasterSourceRelation.scala         | 50 ++++++-----
 .../datasource/raster/package.scala           |  7 +-
 .../raster/RasterSourceDataSourceSpec.scala   | 25 +++++-
 .../src/main/python/docs/raster-io.pymd       |  5 ++
 .../main/python/pyrasterframes/__init__.py    | 11 ++-
 .../main/python/tests/PyRasterFramesTests.py  | 12 ++-
 12 files changed, 184 insertions(+), 45 deletions(-)
 rename core/src/main/scala/org/locationtech/rasterframes/expressions/{transformers => generators}/RasterSourceToRasterRefs.scala (85%)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToTiles.scala

diff --git a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
index a3450e1d7..448fab9c3 100644
--- a/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
+++ b/bench/src/main/scala/org/locationtech/rasterframes/bench/RasterRefBench.scala
@@ -26,7 +26,8 @@ import java.util.concurrent.TimeUnit
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
 import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs}
+import org.locationtech.rasterframes.expressions.generators.RasterSourceToRasterRefs
+import org.locationtech.rasterframes.expressions.transformers.RasterRefToTile
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.RasterSource
 import org.openjdk.jmh.annotations._
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToRasterRefs.scala
similarity index 85%
rename from core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
rename to core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToRasterRefs.scala
index 967ba52a5..68c7209e5 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/RasterSourceToRasterRefs.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToRasterRefs.scala
@@ -19,45 +19,41 @@
  *
  */
 
-package org.locationtech.rasterframes.expressions.transformers
+package org.locationtech.rasterframes.expressions.generators
 
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
+import org.locationtech.rasterframes.expressions.generators.RasterSourceToRasterRefs.bandNames
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.RasterSourceType
 
 import scala.util.Try
 import scala.util.control.NonFatal
 
 /**
- * Accepts RasterRef and generates one or more RasterRef instances representing the
- * native internal sub-tiling, if any (and requested).
+ * Accepts RasterSource and generates one or more RasterRef instances representing
  *
  * @since 9/6/18
  */
 case class RasterSourceToRasterRefs(children: Seq[Expression], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions] = None) extends Expression
   with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
 
-  private val RasterSourceType = new RasterSourceUDT()
-  private val rasterRefSchema = schemaOf[RasterRef]
-
   override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
-  override def nodeName: String = "raster_source_to_raster_ref"
+  override def nodeName: String = "rf_raster_source_to_raster_ref"
 
   override def elementSchema: StructType = StructType(for {
     child <- children
     basename = child.name + "_ref"
     name <- bandNames(basename, bandIndexes)
-  } yield StructField(name, rasterRefSchema, true))
+  } yield StructField(name, schemaOf[RasterRef], true))
 
   private def band2ref(src: RasterSource, e: Option[Extent])(b: Int): RasterRef =
     if (b < src.bandCount) RasterRef(src, b, e) else null
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToTiles.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToTiles.scala
new file mode 100644
index 000000000..32b3f4b11
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/generators/RasterSourceToTiles.scala
@@ -0,0 +1,88 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.generators
+
+import com.typesafe.scalalogging.LazyLogging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.expressions.generators.RasterSourceToRasterRefs.bandNames
+import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
+import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.RasterSourceType
+
+import scala.util.Try
+import scala.util.control.NonFatal
+
+/**
+ * Accepts RasterRef and generates one or more RasterRef instances representing the
+ * native internal sub-tiling, if any (and requested).
+ *
+ * @since 9/6/18
+ */
+case class RasterSourceToTiles(children: Seq[Expression], bandIndexes: Seq[Int], subtileDims: Option[TileDimensions] = None) extends Expression
+  with Generator with CodegenFallback with ExpectsInputTypes with LazyLogging {
+
+  override def inputTypes: Seq[DataType] = Seq.fill(children.size)(RasterSourceType)
+  override def nodeName: String = "rf_raster_source_to_tiles"
+
+  override def elementSchema: StructType = StructType(for {
+    child <- children
+    basename = child.name
+    name <- bandNames(basename, bandIndexes)
+  } yield StructField(name, schemaOf[ProjectedRasterTile], true))
+
+  override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
+    try {
+      val tiles = children.map { child ⇒
+        val src = RasterSourceType.deserialize(child.eval(input))
+        val maxBands = src.bandCount
+        val allowedBands = bandIndexes.filter(_ < maxBands)
+        src.readAll(subtileDims.getOrElse(rasterframes.NOMINAL_TILE_DIMS), allowedBands)
+          .map(r => bandIndexes.map {
+            case i if i < maxBands => ProjectedRasterTile(r.tile.band(i), r.extent, src.crs)
+            case _ => null
+          })
+      }
+      tiles.transpose.map(ts ⇒ InternalRow(ts.flatMap(_.map(_.toInternalRow)): _*))
+    }
+    catch {
+      case NonFatal(ex) ⇒
+        val payload = Try(children.map(c => RasterSourceType.deserialize(c.eval(input)))).toOption.toSeq.flatten
+        logger.error("Error fetching data for one of: " + payload.mkString(", "), ex)
+        Traversable.empty
+    }
+  }
+}
+
+object RasterSourceToTiles {
+  def apply(rrs: Column*): TypedColumn[Any, ProjectedRasterTile] = apply(None, Seq(0), rrs: _*)
+  def apply(subtileDims: Option[TileDimensions], bandIndexes: Seq[Int], rrs: Column*): TypedColumn[Any, ProjectedRasterTile] =
+    new Column(new RasterSourceToTiles(rrs.map(_.expr), bandIndexes, subtileDims)).as[ProjectedRasterTile]
+}
+
+
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
index ab138ec91..903e62dde 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/URIToRasterSource.scala
@@ -41,7 +41,7 @@ import org.locationtech.rasterframes.ref.RasterSource
 case class URIToRasterSource(override val child: Expression)
   extends UnaryExpression with ExpectsInputTypes with CodegenFallback with LazyLogging {
 
-  override def nodeName: String = "rf_read_raster_source"
+  override def nodeName: String = "rf_uri_to_raster_source"
 
   override def dataType: DataType = RasterSourceType
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index 8bdb9cad0..aeffdcd66 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -23,7 +23,7 @@ package org.locationtech.rasterframes.ref
 
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.expressions.accessors._
-import org.locationtech.rasterframes.expressions.transformers._
+import org.locationtech.rasterframes.expressions.generators._
 import RasterRef.RasterRefTile
 import geotrellis.raster.Tile
 import geotrellis.vector.Extent
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
index b0b4185d8..6cea717ec 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSource.scala
@@ -33,9 +33,10 @@ class RasterSourceDataSource extends DataSourceRegister with RelationProvider {
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
     val bands = parameters.bandIndexes
     val tiling = parameters.tileDims
+    val lazyTiles = parameters.lazyTiles
     val spec = parameters.pathSpec
     val catRef = spec.fold(_.registerAsTable(sqlContext), identity)
-    RasterSourceRelation(sqlContext, catRef, bands, tiling)
+    RasterSourceRelation(sqlContext, catRef, bands, tiling, lazyTiles)
   }
 }
 
@@ -48,6 +49,7 @@ object RasterSourceDataSource {
   final val CATALOG_TABLE_PARAM = "catalogTable"
   final val CATALOG_TABLE_COLS_PARAM = "catalogColumns"
   final val CATALOG_CSV_PARAM = "catalogCSV"
+  final val LAZY_TILES_PARAM = "lazyTiles"
 
   final val DEFAULT_COLUMN_NAME = PROJECTED_RASTER_COLUMN.columnName
 
@@ -107,6 +109,10 @@ object RasterSourceDataSource {
       .map(tokenize(_).map(_.toInt))
       .getOrElse(Seq(0))
 
+
+    def lazyTiles: Boolean = parameters
+      .get(LAZY_TILES_PARAM).forall(_.toBoolean)
+
     def catalog: Option[RasterSourceCatalog] = {
       val paths = (
         parameters
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
index dc0cfac97..6af519f56 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceRelation.scala
@@ -28,8 +28,9 @@ import org.apache.spark.sql.types.{StringType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.RasterSourceCatalogRef
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.expressions.transformers.RasterSourceToRasterRefs.bandNames
-import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, RasterSourceToRasterRefs, URIToRasterSource}
+import org.locationtech.rasterframes.expressions.generators.{RasterSourceToRasterRefs, RasterSourceToTiles}
+import org.locationtech.rasterframes.expressions.generators.RasterSourceToRasterRefs.bandNames
+import org.locationtech.rasterframes.expressions.transformers.{RasterRefToTile, URIToRasterSource}
 import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
@@ -43,8 +44,10 @@ import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 case class RasterSourceRelation(
   sqlContext: SQLContext,
   catalogTable: RasterSourceCatalogRef,
-  bandIndexes: Seq[Int], subtileDims: Option[TileDimensions])
-  extends BaseRelation with TableScan {
+  bandIndexes: Seq[Int],
+  subtileDims: Option[TileDimensions],
+  lazyTiles: Boolean
+) extends BaseRelation with TableScan {
 
   lazy val inputColNames = catalogTable.bandColumnNames
 
@@ -61,7 +64,7 @@ case class RasterSourceRelation(
   def tileColNames = inputColNames
     .flatMap(bandNames(_, bandIndexes))
 
-  def extraCols: Seq[StructField] = {
+  lazy val extraCols: Seq[StructField] = {
     val catalog = sqlContext.table(catalogTable.tableName)
     catalog.schema.fields.filter(f => !catalogTable.bandColumnNames.contains(f.name))
   }
@@ -96,17 +99,6 @@ case class RasterSourceRelation(
       (pathColName, srcColName) <- pathColNames.zip(srcColNames)
     } yield URIToRasterSource(col(pathColName)) as srcColName
 
-    // Expand RasterSource into multiple columns per band, and multiple rows per tile
-    // There's some unintentional fragililty here in that the structure of the expression
-    // is expected to line up with our column structure here.
-    val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, srcs: _*) as refColNames
-
-    // RasterSourceToRasterRef is a generator, which means you have to do the Tile conversion
-    // in a separate select statement (Query planner doesn't know how many columns ahead of time).
-    val refsToTiles = for {
-      (refColName, tileColName) <- refColNames.zip(tileColNames)
-    } yield RasterRefToTile(col(refColName)) as tileColName
-
     // Add path columns
     val withPaths = inputs
       .select($"*" +: pathsAliasing: _*)
@@ -115,12 +107,30 @@ case class RasterSourceRelation(
     // and reused with each select.
     val paths = pathColNames.map(withPaths.apply)
 
+    // Input columns along for the ride.
     val extras = extraCols.map(f => inputs(f.name))
 
-    val df = withPaths
-      .select(extras ++ paths :+ refs: _*)
-      .select(paths ++ refsToTiles ++ extras: _*)
-
+    val df = if (lazyTiles) {
+      // Expand RasterSource into multiple columns per band, and multiple rows per tile
+      // There's some unintentional fragililty here in that the structure of the expression
+      // is expected to line up with our column structure here.
+      val refs = RasterSourceToRasterRefs(subtileDims, bandIndexes, srcs: _*) as refColNames
+
+      // RasterSourceToRasterRef is a generator, which means you have to do the Tile conversion
+      // in a separate select statement (Query planner doesn't know how many columns ahead of time).
+      val refsToTiles = for {
+        (refColName, tileColName) <- refColNames.zip(tileColNames)
+      } yield RasterRefToTile(col(refColName)) as tileColName
+
+      withPaths
+        .select(extras ++ paths :+ refs: _*)
+        .select(paths ++ refsToTiles ++ extras: _*)
+    }
+    else {
+      val tiles = RasterSourceToTiles(subtileDims, bandIndexes, srcs: _*) as tileColNames
+      withPaths
+        .select((paths :+ tiles) ++ extras: _*)
+    }
     df.rdd
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
index 3ac06946a..d85f435d2 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/raster/package.scala
@@ -53,13 +53,18 @@ package object raster {
         reader.option(RasterSourceDataSource.TILE_DIMS_PARAM, s"$cols,$rows")
       )
 
+    /** Indicate if tile reading should be delayed until cells are fetched. Defaults to `true`. */
+    def withLazyTiles(state: Boolean): RasterSourceDataFrameReader =
+      tag[RasterSourceDataFrameReaderTag][DataFrameReader](
+        reader.option(RasterSourceDataSource.LAZY_TILES_PARAM, state))
+
     def fromCatalog(catalog: DataFrame, bandColumnNames: String*): RasterSourceDataFrameReader =
       tag[RasterSourceDataFrameReaderTag][DataFrameReader] {
         val tmpName = tmpTableName()
         catalog.createOrReplaceTempView(tmpName)
         reader
           .option(RasterSourceDataSource.CATALOG_TABLE_PARAM, tmpName)
-          .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(","))
+          .option(RasterSourceDataSource.CATALOG_TABLE_COLS_PARAM, bandColumnNames.mkString(",")): DataFrameReader
       }
 
     def fromCatalog(tableName: String, bandColumnNames: String*): RasterSourceDataFrameReader =
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
index 58aa44dfd..a8ca3bef8 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
@@ -20,10 +20,12 @@
  */
 
 package org.locationtech.rasterframes.datasource.raster
-import org.apache.spark.sql.functions.lit
+import geotrellis.raster.Tile
+import org.apache.spark.sql.functions.{lit, udf}
 import org.locationtech.rasterframes.{TestEnvironment, _}
 import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, _}
 import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.ref.RasterRef.RasterRefTile
 import org.locationtech.rasterframes.util._
 
 class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
@@ -135,6 +137,7 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     it("should read a multiple files with heterogeneous bands") {
       val df = spark.read.raster
         .from(Seq(cogPath, l8B1SamplePath, nonCogPath))
+        .withLazyTiles(false)
         .withTileDimensions(128, 128)
         .withBandIndexes(0, 1, 2, 3)
         .load()
@@ -197,7 +200,6 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       diffStats.forall(identity) should be(true)
     }
 
-
     it("should read a set of coherent bands from multiple files in a csv") {
       def b(i: Int) = l8SamplePath(i).toASCIIString
 
@@ -223,5 +225,24 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       val diffStats = df.select(rf_tile_stats($"B1") =!= rf_tile_stats($"B2")).as[Boolean].collect()
       diffStats.forall(identity) should be(true)
     }
+
+    it("should support lazy and strict reading of tiles") {
+      val is_lazy = udf((t: Tile) => {
+        t.isInstanceOf[RasterRefTile]
+      })
+
+      val df1 = spark.read.raster
+        .withLazyTiles(true)
+        .load(l8SamplePath(1).toASCIIString)
+
+      df1.select(is_lazy($"proj_raster.tile").as[Boolean]).first() should be (true)
+
+      val df2 = spark.read.raster
+        .withLazyTiles(false)
+        .load(l8SamplePath(1).toASCIIString)
+
+      df2.select(is_lazy($"proj_raster.tile").as[Boolean]).first() should be (false)
+
+    }
   }
 }
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 318fec5ee..6bc2d3a37 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -154,6 +154,11 @@ rf.show(2, truncate=False)
     
 ```
 
+### Lazy vs. Strict Raster Reads
+
+> Discuss the `spark.read.raster(lazy_tiles=False)` behavior
+
+
 ### Multiband Rasters
 
 > Need to know _a priori_ how many bands there are, specified with `band_indexes`.
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 6d8c69052..2af1677a1 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -103,11 +103,13 @@ def _aliased_reader(df_reader, format_key, path, **options):
     return df_reader.format(format_key).load(path, **options)
 
 def _raster_reader(
-        df_reader, path=None,
-        band_indexes=None,
-        tile_dimensions=(256, 256),
+        df_reader,
+        path=None,
         catalog=None,
         catalog_col_names=None,
+        band_indexes=None,
+        tile_dimensions=(256, 256),
+        lazy_tiles=True,
         **options):
 
     def to_csv(comp):
@@ -121,7 +123,8 @@ def to_csv(comp):
 
     options.update({
         "bandIndexes": to_csv(band_indexes),
-        "tileDimensions": to_csv(tile_dimensions)
+        "tileDimensions": to_csv(tile_dimensions),
+        "lazyTiles": lazy_tiles
     })
 
     if catalog is not None:
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 94b158c2d..6fd925459 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -712,7 +712,6 @@ def test_prt_functions(self):
         df.select('crs', 'ext', 'geom').first()                         
 
     def test_raster_source_reader(self):
-        import pandas as pd
         # much the same as RasterSourceDataSourceSpec here; but using https PDS. Takes about 30s to run
 
         def l8path(b):
@@ -725,8 +724,9 @@ def l8path(b):
 
         df = self.spark.read.raster(
             tile_dimensions=(tile_size, tile_size),
-            paths=path_param
-        )
+            paths=path_param,
+            lazy_tiles=False
+        ).cache()
 
         # schema is tile_path and tile
         # df.printSchema()
@@ -743,6 +743,9 @@ def l8path(b):
         print(path_count.toPandas())
         self.assertTrue(path_count.count() == 3)
 
+    def test_raster_source_catalog_reader(self):
+        import pandas as pd
+
         scene_dict = {
             1: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/LC08_L1TP_015041_20190305_20190309_01_T1/LC08_L1TP_015041_20190305_20190309_01_T1_B{}.TIF',
             2: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/042/LC08_L1TP_015042_20190305_20190309_01_T1/LC08_L1TP_015042_20190305_20190309_01_T1_B{}.TIF',
@@ -767,7 +770,8 @@ def path(scene, band):
         path_df = self.spark.read.raster(
             tile_dimensions=(512, 512),
             catalog=path_table,
-            catalog_col_names=catalog_columns
+            catalog_col_names=catalog_columns,
+            lazy_tiles=True # We'll get an OOM error if we try to read 9 scenes all at once!
         )
 
         self.assertTrue(len(path_df.columns) == 6)  # three bands times {path, tile}

From 98fab79ac4aebe8fc2392ac1d18ae557a111ab90 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 2 Jul 2019 21:02:21 -0400
Subject: [PATCH 217/380] Added conversion of .pymd files not Jupyter
 Notebooks, and include in rf-notebook.

---
 pyrasterframes/build.sbt                |  9 ++++++++-
 pyrasterframes/src/main/python/setup.py | 13 +++++++++++--
 rf-notebook/build.sbt                   | 19 +++++++++++++------
 rf-notebook/src/main/docker/Dockerfile  |  1 +
 4 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/pyrasterframes/build.sbt b/pyrasterframes/build.sbt
index 0b6575e7e..707f6ce14 100644
--- a/pyrasterframes/build.sbt
+++ b/pyrasterframes/build.sbt
@@ -5,7 +5,6 @@ addCommandAlias("pyBuild", "pyrasterframes/package")
 exportJars := true
 Python / doc / sourceDirectory := (Python / target).value / "docs"
 Python / doc / target := (Python / target).value / "docs"
-  //(Compile / target).value / "py-markdown"
 Python / doc := (Python / doc / target).toTask.dependsOn(
   Def.sequential(
     assembly,
@@ -16,6 +15,14 @@ Python / doc := (Python / doc / target).toTask.dependsOn(
 
 doc := (Python / doc).value
 
+val nbInclude = Def.setting[FileFilter](GlobFilter("*.ipynb"))
+
+lazy val pyNotebooks = taskKey[Seq[File]]("Convert relevant scripts into notebooks")
+pyNotebooks := {
+  val _ = pySetup.toTask(" notebooks").value
+  ((Python / doc / target).value ** "*.ipynb").get()
+}
+
 lazy val pySparkCmd = taskKey[Unit]("Create build and emit command to run in pyspark")
 pySparkCmd := {
   val s = streams.value
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 0a2b9aac5..459383bc5 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -69,6 +69,9 @@ def finalize_options(self):
         if isinstance(self.files, str):
             self.files = filter(lambda s: len(s) > 0, re.split(',', self.files))
 
+    def doctype(self):
+        return "markdown"
+
     def run(self):
         """Run pweave."""
         import traceback
@@ -80,11 +83,16 @@ def run(self):
             try:
                 pweave.weave(
                     file=str(file),
-                    doctype='markdown'
+                    doctype=self.doctype()
                 )
             except Exception:
                 print(_divided('%s Failed:' % file))
                 print(traceback.format_exc())
+                exit(1)
+
+class PweaveNotebooks(PweaveDocs):
+    def doctype(self):
+        return "notebook"
 
 setup(
     name='pyrasterframes',
@@ -151,6 +159,7 @@ def run(self):
     zip_safe=False,
     test_suite="pytest-runner",
     cmdclass={
-        'pweave': PweaveDocs
+        'pweave': PweaveDocs,
+        'notebooks': PweaveNotebooks
     }
 )
diff --git a/rf-notebook/build.sbt b/rf-notebook/build.sbt
index 8a1d44b2a..753f13cef 100644
--- a/rf-notebook/build.sbt
+++ b/rf-notebook/build.sbt
@@ -13,14 +13,21 @@ Docker / target := target.value / "docker"
 
 dockerUpdateLatest := true
 
-Docker / mappings := {
-  val dockerSrc = (Docker / sourceDirectory).value
-  val dockerAssets = (dockerSrc ** "*") pair Path.relativeTo(dockerSrc)
+Docker / mappings := Def.sequential(
+  LocalProject("pyrasterframes") / pyWhl,
+  Def.task  {
+    val dockerSrc = (Docker / sourceDirectory).value
+    val dockerAssets = (dockerSrc ** "*") pair Path.relativeTo(dockerSrc)
 
-  val py = (LocalProject("pyrasterframes") / pyWhl).value
+    val py = (LocalProject("pyrasterframes") / pyWhl).value
+    val _ = (LocalProject("pyrasterframes") / pySetup).toTask(" notebooks").value
 
-  dockerAssets ++ Seq(py -> py.getName)
-}
+    val nbFiles = ((LocalProject("pyrasterframes") / Python / doc / target).value ** "*.ipynb").get()
+
+    val examples = nbFiles.map(f => (f, "examples/" + f.getName))
+    dockerAssets ++ Seq(py -> py.getName) ++ examples
+  }
+).value
 
 // This bypasses the standard DockerPlugin DSL-based Dockerfile construction
 // and just copies the separate, external one.
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index cf9b54ba7..023e1ee17 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -39,3 +39,4 @@ USER $NB_UID
 
 COPY jupyter_notebook_config.py $HOME/.jupyter
 
+COPY examples $HOME/examples

From 8a9a53ea6338375c87ac403984f4d0ae436c311c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 8 Jul 2019 09:24:38 -0400
Subject: [PATCH 218/380] Updating documentation on the documentation process,
 with the hope of making it easier for others to contribute.

---
 docs/README.md                                | 100 +++++++++++++++++-
 docs/documentation-principles.md              |  98 +++++++++++++++++
 pyrasterframes/README.md                      |   7 +-
 .../src/main/python/docs/description.md       |   2 +-
 .../src/main/python/docs/local-algebra.pymd   |   3 +-
 5 files changed, 199 insertions(+), 11 deletions(-)
 create mode 100644 docs/documentation-principles.md

diff --git a/docs/README.md b/docs/README.md
index 4ea8233a2..e8b3071f8 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,4 +1,100 @@
-# RasterFrames Documentation Build
+# RasterFrames Documentation
 
+The conceptual principles to consider when writing RasterFrames users' documentation are covered in [Documentation Principles](documentation-principles.md). This document covers the mechanics of writing, evaluating, and building the documentation during the writing process.
+
+## Organization
+
+The documentation build is a two step process, whereby two sources (three if API docs are included) are merged together and converted into a static HTML website. They are:
+
+* Technical content and Python examples: `<src-root>/pyrasterframes/src/main/python/docs`
 * Global documentation assets and Scala specific content: `<src-root>/docs/src/main/paradox`
-* Technical content and Python examples: `<src-root>/pyrasterframes/src/main/python/docs`
\ No newline at end of file
+
+The build constructs in `<src-root>/docs` are (due to legacy reasons) the top-level mechanisms of bringing it all together, but the meat of the content is in  `<src-root>/pyrasterframes/...`, and will be the focus of most of this document.
+
+## Prerequisites
+
+* [`sbt`](https://www.scala-sbt.org/)
+* Python 3
+* Markdown editor. [Visual Studio Code](https://code.visualstudio.com/) with [`language-weave` extension](https://marketplace.visualstudio.com/items?itemName=jameselderfield.language-weave) is one option. [Atom](https://atom.io/) is another which might actually have better support for evaluating code in Markdown, but I've not tried it.
+
+> Note: If you're using Visual Studio Code, you can associate the `.pymd` with the `language-weave` plugin by adding this to your `settings.json` file.
+      
+```json
+"files.associations": {
+ "*.pymd": "pweave_md"
+}
+```
+
+## Content Development Process
+
+Start with one of the existing files in `<src-root>/pyrasterframes/src/main/python/docs` as a template. [`local-algebra.pymd`](../pyrasterframes/src/main/python/docs/local-algebra.pymd) is a good example. If the content will have code blocks you want evaluated an results injected into the output, use the file extension `.pymd`. If the content doesn't use evaluatable code blocks, use `.md`.
+
+All `.pymd` files are processed with a tool called [Pweave](http://mpastell.com/pweave), which produces a regular Markdown file where identified code blocks are evaluated and their results (optionally) included in the text. Matplot lib is supported! It is much like `knitr` in the R community. If we run into issues with Pweave, we can also consider [`knitpy`](https://github.com/jankatins/knitpy) or [`codebraid`](https://github.com/gpoore/codebraid). Codebraid looks particularly powerful, so we may think to transition to it.
+
+Pweave has a number of [code chunk options](http://mpastell.com/pweave/chunks.html) for controlling the output. Refer to those documents on details, and experiment a little to see what conveys your intent best.
+
+To set up an environment whereby you can easily test/evaluate your code blocks during writing:
+
+1. Run `sbt` from the `<src-root>` directory. You should get output that looks something like:  
+    ```
+    $ sbt
+    ...
+    [info] Loading settings for project pyrasterframes from build.sbt ...
+    [info] Loading settings for project rf-notebook from build.sbt ...
+    [info] Set current project to RasterFrames (in build file:<src-root>/)
+    sbt:RasterFrames>    
+    ```  
+2. The first time you check out the code, or whenever RasterFrames code is updated, you need to build the project artifacts so they are available for Pweave.
+    ```
+    sbt:RasterFrames> pyrasterframes/package
+    [info] Compiling 4 Scala sources to <src-root>/core/target/scala-2.11/classes ...
+   ... lots of noise ...
+   [info] PyRasterFrames assembly written to '<src-root>/pyrasterframes/target/python/deps/jars/pyrasterframes-assembly-0.8.0-SNAPSHOT.jar'
+   [info] Synchronizing 44 files to '<src-root>/pyrasterframes/target/python'
+   [info] Running 'python setup.py build bdist_wheel' in '<src-root>/pyrasterframes/target/python'
+   ... more noise ...
+   [info] Python .whl file written to '<src-root>/pyrasterframes/target/python/dist/pyrasterframes-0.8.0.dev0-py2.py3-none-any.whl'
+   [info] Maven Python .zip artifact written to '<src-root>/pyrasterframes/target/scala-2.11/pyrasterframes-python-0.8.0-SNAPSHOT.zip' 
+    [success] Total time: 83 s, completed Jul 5, 2019 12:25:48 PM
+    sbt:RasterFrames>
+    ```
+3. To evaluate all the `.pymd` files, run:
+    ```
+    sbt:RasterFrames> pyrasterframes/pySetup pweave
+    ```
+    To build the artifact (step 1) and evaluate all the `.pymd` files, you can run:
+    ```
+    sbt:RasterFrames> pyrasterframes/doc
+    ```
+    There's a command alias for this last step: `pyDocs`.
+4. To evaluate a single `.pymd` file, you pass the `-f` option and the  filename relative to the `python` direoctry:
+    ```
+    sbt:RasterFrames> pyrasterframes/pySetup pweave -f docs/getting-started.pymd
+    [info] Synchronizing 44 files to '<src-root>/pyrasterframes/target/python'
+    [info] Running 'python setup.py pweave -f docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
+    running pweave
+    --------------------------------------------------
+    Running getting-started
+    --------------------------------------------------
+    status
+    status
+    Processing chunk 1 named None from line 14
+    ...
+    Weaved docs/getting-started.pymd to docs/getting-started.md
+    [success] Total time: 21 s, completed Jul 5, 2019 12:31:09 PM
+    sbt:RasterFrames>
+    ```
+5. The _output_ Markdown files are written to `<src-root>/pyrasterframes/target/python/docs`. _Note_: don't edit any files in the `pyrasterframes/target` directory... they will get overwritten each time `sbt` runs a command.
+6. To build all the documentation and convert to a static html site, run:
+    ```bash
+    sbt makeSite
+    ``` 
+    Results will be found in `<src-root>/docs/target/site`.
+
+## Notebooks
+
+The `rf-notebooks` sub-project creates a Docker image with Jupyter Notebooks pre-configured with RasterFrames. Any `.pymd` file under `.../python/docs/` is converted to an evaluated Jupyter Notebook and included as a part of the build (an additional bonus of the Pweave tool). 
+
+## Submission Process
+
+Submit new and updated documentation as a PR against locationtech/rasterframes. Make sure you've signed the Eclipse Foundation ECA and you ["Signed-off-by:"](https://stackoverflow.com/questions/1962094/what-is-the-sign-off-feature-in-git-for) each commit in the PR. The "Signed-off-by" address needs to be the exact same one as registered with the [Eclipse Foundation](https://wiki.eclipse.org/Development_Resources/Contributing_via_Git).
diff --git a/docs/documentation-principles.md b/docs/documentation-principles.md
new file mode 100644
index 000000000..3f626ddb8
--- /dev/null
+++ b/docs/documentation-principles.md
@@ -0,0 +1,98 @@
+# Documentation Principles
+
+This document outlines some concrete considerations for the planned rewrite of the _RasterFrames Users' Manual_. 
+See [`docs/README.md`](https://github.com/locationtech/rasterframes/blob/develop/docs/README.md) for technical details on the mechanics of building the documentation.
+
+## Title
+
+The project is "RasterFrames". Documentation shall use the name "RasterFrames". The RasterFrames runtime is deployed in two forms: `rasterframes` (for Scala/Java/SQL-only), and `pyrasterframes` (Python). But the user should know and think of the project as one thing: RasterFrames.
+
+## Format
+
+The documentation shall be rendered in Markdown (`.md`) and Python Markdown (`.pymd`). The source of this documentation is committed in the same project as the code, in `pyrasterframes/src/main/python/docs`. Additional details on processing the docs can be found [here](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes#running-python-markdown-sources).
+
+Filenames shall be in skewer case; lower case with dashes ('-') separating words. For example, `foo-in-bar-with-baz.md`.
+
+## Target Audience
+
+The target audience for the _RasterFrames User's Manual_ is the intermediate data scientist or developer, already adept at either Python or Scala. Eventually this should be expanded to include SQL adepts. This user may or may not be an expert at EO data usage, so attention to jargon, undefined terms, new concepts etc. should be kept in mind, making use of authoritative external resources to fill in knowledge. 
+
+> Enumerate concepts they are aware of, including:
+> * Scene discretization
+> * Temporal revisit rate
+> * Spatial resolution
+> * Spatial extent
+
+The user may or may not be familiar with Apache Spark, so they should also be guided in filling in minimum requisite knowledge. At a minimum we have to explain what a `SparkSession` is, and that we have to configure it; note the difference between an "action" and "transformation"; what a "collect" action is, and the consequences if the data is large; awareness of partitioning.
+
+There's also an opportunity to emphasize the scalability benefits over, say, a rasterio/Pandas-only solution (but that we interop with them too).
+
+The users' goals with EO data are could be from a number of different perspectives:
+
+* Creating map layers
+* Statistical analysis
+* Machine learning
+* Change detection
+* Chip preparation
+
+While the emphasis of the documentation should be on the core capabilities (and flexibility therein) of RasterFrames, a nod toward these various needs in the examples shown can go a long way in helping the user understand appropriateness of the library to their problem domain.
+
+## Pedagogical Technique
+
+The documentation shall emphasize the use of executable code examples, with interspersed prose to explain them. The RasterFrames tooling supports the `.pymd` (Python Markdown) format whereby delimited code blocks are evaluated at build time in order to include output/results. The project currently uses ['Pweave'](http://mpastell.com/pweave/chunks.html) to do this (this may change, but `.pymd` will remain the source format). `Pweave` also has the ability to convert `.pymd` to Jupyter Notebooks, which may serve useful. Through this process we can be assured that any examples shown are code the user can copy into their own projects.
+
+Visuals are always helpful, but even more so when there's a lot of code involved that needs continual contextualization and explanation. `Pweave` supports rendering of `matplotlib` charts/images, a capability we should make use of. Furthermore, where beneficial, we should create diagrams or other visuals to help express concepts and relationships.
+
+This "code-first" focus is admittedly in tension with the competing need to explain some of the more abstract aspects of distributed computing necessary for advanced monitoring, profiling, optimization, and deployment. We should evolve the documentation over the long term to address some of these needs, but in the near term the focus should be on the core conceptual model necessary for understanding tile processing. Diagrams can be helpful here.
+ 
+## Polyglot Considerations
+
+In terms of implementation, RasterFrames is a Scala project first. All algorithmic, data modeling, and heavy lifting, etc. are implemented in Scala.
+
+However, due to user base preferences, RasterFrames is primarily _deployed_ through Python. As such, documentation, examples, etc. should first be implemented in Python. 
+
+Secondarily to that, SQL should be used to highlight the analyst-friendly expression of the functionality in SQL. At least a handful of examples in a dedicated SQL page would go far in showing the cross-language support.
+
+Thirdly, Scala developers should be encouraged to use the platform, clearly stating that the APIs are on equal footing, using consistent naming conventions, etc. and that most examples should translate almost one-to-one.
+
+In the long term I'd love to see Python, Scala, and SQL all treated in equal footing, with examples expressed in all languages, but that's a tall order this early in the project development.
+
+## User Journey
+
+As noted in the _Target Audience_ section, the documentation needs to guide the user through process from curiosity around EO data to scalable processing of it. Within the first section or so the user should see an example that reads an image and does something somewhat compelling with it, noting that the same code will work on a laptop with a small amount of imagery, as well as on 100s of computers with TB (or more) of imagery. Problems "solved in the small" can be grown to "global scale".
+
+With a "journey" focus, concepts and capabilities are introduced incrementally, building upon previous examples and concepts, adding more complexity as it develops. Once the fundamentals are covered, we then move into the examples that are closer to use-cases or cookbook entries.
+
+The preliminary outline is a follows, but is open for refinement, rethinking, etc.
+
+1. Description
+2. Architecture
+3. Getting Started
+    * `pyspark` shell
+    * Jupyter Notebook
+    * Standalone Python Script
+4. Raster Data I/O
+    * Reading Raster Data
+    * Writing Raster Data
+5. Spatial Relations
+6. Raster Processing
+    * Local Algebra
+    * “NoData” Handling
+    * Aggregation
+    * Time Series
+    * Spark ML Support
+    * Pandas and NumPy Interoperability
+7. Cookbook Examples
+8. Extended Examples / Case Studies
+9. Function Reference
+
+## Hiccups
+
+During the documentation process we are likely to run into problems whereby the goal of the writer is inhibited by a bug or capability gap in the code. We should use this opportunity to improve the library to provide the optimal user experience before resorting to workarounds or hacks or addition of what might seem to be arbitrary complexity.
+
+## Testing
+
+To "be all that we can be", testing the documentation against a new user is a boon. It may be hard to capture volunteers to do this, but we should consider enlisting interns and friends of the company to go through the documentation and give feedback on where gaps exist.
+
+
+
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 6f82e8ebf..a7c3b3235 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -99,13 +99,8 @@ The markdown documentation in `<src-root>/pyrasterframes/src/main/python/docs` c
 sbt pyrasterframes/doc # alias 'pyDoc'
 ```
 
-To build the full complement of documentation and generate the HTML website, run:
+See [`docs/README.md`](../docs/README.md) for details.
 
-```bash
-sbt makeSite
-``` 
-
-Results will be found in `<src-root>/docs/target/site`.
 
 ## Creating and Using a Build
 
diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index c9665ad8d..49df72744 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -1,6 +1,6 @@
 # Description
 
-Human beings have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis. 
+We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis. 
 
 Today, DataFrames are the _lingua franca_ of data science. The evolution of the tabular form has continued with Apache Spark SQL, which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables interactive and batch-oriented cluster computing without having to be versed in the highly specialized skills typically required for high-performance computing. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
 
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index 6f9d03b69..c90f24322 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -1,3 +1,4 @@
+# Local Map Algebra
 
 ```python, echo=False
 from IPython.core.display import display, HTML
@@ -11,8 +12,6 @@ import os
 spark = create_rf_spark_session()
 ```
 
-# Local Map Algebra
-
 [Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single `tile`,  between a `tile` and a scalar, between two `tile`s, or among many `tile`s. These operations are common in processing of earth observation and other image data.
 
 

From ffc5e7d20cb7d36596bb9785c6735ac0b4429d5d Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 8 Jul 2019 11:03:34 -0400
Subject: [PATCH 219/380] Enableing core dumps in CircleCI

---
 .circleci/config.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 431dcb2ac..262f3ce13 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -30,6 +30,9 @@ _save_cache: &save_cache
     - ~/.sbt
     - ~/.rf_cache
 
+before_script:
+  - ulimit -c unlimited -S
+
 jobs:
   staticAnalysis:
     <<: *defaults

From e2eacb2b7703e433c160c1fb064f64c83dd18323 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 8 Jul 2019 12:41:36 -0400
Subject: [PATCH 220/380] CircleCI tweaks and fixes. (#183)

* Enableing core dumps in CircleCI

* Adding CircleCI job for building docs.
Adding core file archiving.

* Fixed core dump copy to ignore errors.

* Added matplotlib to setup_requires.

* Fixed list indentation.

* Experimenting with invoking subproject tests individually.

* Changed docs archive destination in CircleCI
---
 .circleci/config.yml                          | 118 ++++++++++++------
 .../src/main/python/docs/raster-io.pymd       |  20 +--
 pyrasterframes/src/main/python/setup.py       |   1 +
 3 files changed, 90 insertions(+), 49 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 431dcb2ac..4e28ab89c 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -31,63 +31,60 @@ _save_cache: &save_cache
     - ~/.rf_cache
 
 jobs:
-  staticAnalysis:
+  test:
     <<: *defaults
-
+    resource_class: large
     steps:
       - checkout
       - run: *setenv
       - restore_cache:
           <<: *restore_cache
 
-      - run: cat /dev/null | sbt dependencyCheck
-      - run: cat /dev/null | sbt --debug dumpLicenseReport
+      - run: ulimit -c unlimited -S
+      - run: cat /dev/null | sbt -batch core/test datasource/test experimental/test pyrasterframes/test
+      - run:
+          command: |
+            mkdir -p /tmp/core_dumps
+            cp core.* *.hs /tmp/core_dumps 2> /dev/null || true
+          when: on_fail
 
-      - run: *unsetenv
+      - store_artifacts:
+          path: /tmp/core_dumps
 
+      - run: *unsetenv
       - save_cache:
           <<: *save_cache
-      - store_artifacts:
-          path: datasource/target/scala-2.11/dependency-check-report.html
-          destination: dependency-check-report-datasource.html
-      - store_artifacts:
-          path: experimental/target/scala-2.11/dependency-check-report.html
-          destination: dependency-check-report-experimental.html
-      - store_artifacts:
-          path: core/target/scala-2.11/dependency-check-report.html
-          destination: dependency-check-report-core.html
-      - store_artifacts:
-          path: pyrasterframes/target/scala-2.11/dependency-check-report.html
-          destination: dependency-check-report-pyrasterframes.html
 
-  test:
+  docs:
     <<: *defaults
-    resource_class: large
+    resource_class: xlarge
     steps:
       - checkout
       - run: *setenv
+
       - restore_cache:
           <<: *restore_cache
 
-      - run: cat /dev/null | sbt test
+      - run: ulimit -c unlimited -S
+      - run:
+          command: cat /dev/null | sbt makeSite
+          no_output_timeout: 30m
 
-      - run: *unsetenv
-      - save_cache:
-          <<: *save_cache
+      - run:
+          command: |
+            mkdir -p /tmp/core_dumps
+            cp core.* *.hs /tmp/core_dumps 2> /dev/null || true
+          when: on_fail
 
-  publish:
-    <<: *defaults
-    resource_class: large
-    steps:
-      - checkout
-      - run: *setenv
-      - restore_cache:
-          <<: *restore_cache
+      - store_artifacts:
+          path: /tmp/core_dumps
 
-      - run: cat /dev/null | sbt test
-      - run: cat /dev/null | sbt publish
+      - store_artifacts:
+          path: docs/target/site
+          destination: rf-site
 
       - run: *unsetenv
+
       - save_cache:
           <<: *save_cache
 
@@ -101,9 +98,20 @@ jobs:
       - restore_cache:
           <<: *restore_cache
 
+      - run: ulimit -c unlimited -S
       - run:
           command: cat /dev/null | sbt it:test
           no_output_timeout: 30m
+
+      - run:
+          command: |
+            mkdir -p /tmp/core_dumps
+            cp core.* *.hs /tmp/core_dumps 2> /dev/null || true
+          when: on_fail
+
+      - store_artifacts:
+          path: /tmp/core_dumps
+
       - run: *unsetenv
 
       - save_cache:
@@ -131,6 +139,36 @@ jobs:
       - save_cache:
           <<: *save_cache
 
+  staticAnalysis:
+    <<: *defaults
+
+    steps:
+      - checkout
+      - run: *setenv
+      - restore_cache:
+          <<: *restore_cache
+
+      - run: cat /dev/null | sbt dependencyCheck
+      - run: cat /dev/null | sbt --debug dumpLicenseReport
+
+      - run: *unsetenv
+
+      - save_cache:
+          <<: *save_cache
+      - store_artifacts:
+          path: datasource/target/scala-2.11/dependency-check-report.html
+          destination: dependency-check-report-datasource.html
+      - store_artifacts:
+          path: experimental/target/scala-2.11/dependency-check-report.html
+          destination: dependency-check-report-experimental.html
+      - store_artifacts:
+          path: core/target/scala-2.11/dependency-check-report.html
+          destination: dependency-check-report-core.html
+      - store_artifacts:
+          path: pyrasterframes/target/scala-2.11/dependency-check-report.html
+          destination: dependency-check-report-pyrasterframes.html
+
+
 workflows:
   version: 2
   all:
@@ -146,12 +184,13 @@ workflows:
             branches:
               only:
                 - /feature\/.*-its/
-#      - publish:
-#          filters:
-#            branches:
-#              only:
-#                - develop
-  nightlyReleaseAstraea:
+      - docs:
+          filters:
+            branches:
+              only:
+                - /feature\/.*docs.*/
+
+  nightly:
     triggers:
       - schedule:
           cron: "0 8 * * *"
@@ -162,4 +201,5 @@ workflows:
     jobs:
       - it
       - itWithoutGdal
+      - docs
       - staticAnalysis
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-io.pymd
index 6bc2d3a37..423de7d82 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-io.pymd
@@ -5,22 +5,22 @@ The standard mechanism by which any data is brought in and out of a Spark Datafr
 Three types of DataSources will be introduced below: 
 
 * Catalog Readers
-  - `aws-pds-l8-catalog`: experimental
-  - `aws-pds-modis-catalog`: experimental
-  - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
+    - `aws-pds-l8-catalog`: experimental
+    - `aws-pds-modis-catalog`: experimental
+    - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
 * Raster Readers
-  - `raster`: the standard reader for most operations
-  - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
-  - `geotiff`: a deprecated reader for reading single GeoTIFF files
+    - `raster`: the standard reader for most operations
+    - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
+    - `geotiff`: a deprecated reader for reading single GeoTIFF files
 * Raster Writers 
-  - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
-  - `geotiff`: beta
-  - `parquet`: for writing raster data in an analytics functional form  
+    - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
+    - `geotiff`: beta
+    - `parquet`: for writing raster data in an analytics functional form  
 
 There's also some support for vector data (for masking and data labeling):
 
 * Vector Readers
-  - `geojson`: read GeoJson files with `geometry` column aside attribute values
+    - `geojson`: read GeoJson files with `geometry` column aside attribute values
 
 ## Reading Raster Data
 
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 459383bc5..35ae8a8d2 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -120,6 +120,7 @@ def doctype(self):
         'shapely',
         'pyspark<2.4',
         'numpy>=1.7',
+        'matplotlib<3.0.0',
         'pandas',
         'pytest-runner',
         'setuptools>=0.8',

From d4bb4f140547b797741ec380d810477b807eb219 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Jul 2019 11:14:24 -0400
Subject: [PATCH 221/380] Converted generic varargs to named args for various
 Python rf_ functions to improve readability and discoverability.

---
 .../python/pyrasterframes/rasterfunctions.py  | 244 +++++++++---------
 .../src/main/python/pyrasterframes/utils.py   |   2 +-
 2 files changed, 124 insertions(+), 122 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 6b6620a84..587891614 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -99,10 +99,10 @@ def st_reproject(geometry_col, src_crs_name, dst_crs_name):
     return Column(jfcn(_to_java_column(geometry_col), src_crs_name, dst_crs_name))
 
 
-def rf_explode_tiles(*args):
+def rf_explode_tiles(*tile_cols):
     """Create a row for each cell in Tile."""
     jfcn = RFContext.active().lookup('rf_explode_tiles')
-    jcols = [_to_java_column(arg) for arg in args]
+    jcols = [_to_java_column(arg) for arg in tile_cols]
     return Column(jfcn(RFContext.active().list_to_seq(jcols)))
 
 
@@ -243,300 +243,302 @@ def _apply_column_function(name, *args):
     return Column(jfcn(*jcols))
 
 
-def rf_dimensions(*args):
+def rf_dimensions(tile_col):
     """Query the number of (cols, rows) in a Tile."""
-    return _apply_column_function('rf_dimensions', *args)
+    return _apply_column_function('rf_dimensions', tile_col)
 
 
-def rf_tile_to_array_int(*args):
+def rf_tile_to_array_int(tile_col):
     """Flattens Tile into an array of integers."""
-    return _apply_column_function('rf_tile_to_array_int', *args)
+    return _apply_column_function('rf_tile_to_array_int', tile_col)
 
 
-def rf_tile_to_array_double(*args):
+def rf_tile_to_array_double(tile_col):
     """Flattens Tile into an array of doubles."""
-    return _apply_column_function('rf_tile_to_array_double', *args)
+    return _apply_column_function('rf_tile_to_array_double', tile_col)
 
 
-def rf_cell_type(*args):
+def rf_cell_type(tile_col):
     """Extract the Tile's cell type"""
-    return _apply_column_function('rf_cell_type', *args)
+    return _apply_column_function('rf_cell_type', tile_col)
 
 
-def rf_is_no_data_tile(*args):
+def rf_is_no_data_tile(tile_col):
     """Report if the Tile is entirely NODDATA cells"""
-    return _apply_column_function('rf_is_no_data_tile', *args)
+    return _apply_column_function('rf_is_no_data_tile', tile_col)
 
 
-def rf_exists(*args):
+def rf_exists(tile_col):
     """Returns true if any cells in the tile are true (non-zero and not NoData)"""
-    return _apply_column_function('rf_exists', *args)
+    return _apply_column_function('rf_exists', tile_col)
 
 
-def rf_for_all(*args):
+def rf_for_all(tile_col):
     """Returns true if all cells in the tile are true (non-zero and not NoData)."""
-    return _apply_column_function('rf_for_all', *args)
+    return _apply_column_function('rf_for_all', tile_col)
 
 
-def rf_agg_approx_histogram(*args):
+def rf_agg_approx_histogram(tile_col):
     """Compute the full column aggregate floating point histogram"""
-    return _apply_column_function('rf_agg_approx_histogram', *args)
+    return _apply_column_function('rf_agg_approx_histogram', tile_col)
 
 
-def rf_agg_stats(*args):
+def rf_agg_stats(tile_col):
     """Compute the full column aggregate floating point statistics"""
-    return _apply_column_function('rf_agg_stats', *args)
+    return _apply_column_function('rf_agg_stats', tile_col)
 
 
-def rf_agg_mean(*args):
+def rf_agg_mean(tile_col):
     """Computes the column aggregate mean"""
-    return _apply_column_function('rf_agg_mean', *args)
+    return _apply_column_function('rf_agg_mean', tile_col)
 
 
-def rf_agg_data_cells(*args):
+def rf_agg_data_cells(tile_col):
     """Computes the number of non-NoData cells in a column"""
-    return _apply_column_function('rf_agg_data_cells', *args)
+    return _apply_column_function('rf_agg_data_cells', tile_col)
 
 
-def rf_agg_no_data_cells(*args):
+def rf_agg_no_data_cells(tile_col):
     """Computes the number of NoData cells in a column"""
-    return _apply_column_function('rf_agg_no_data_cells', *args)
+    return _apply_column_function('rf_agg_no_data_cells', tile_col)
 
 
-def rf_tile_histogram(*args):
+def rf_tile_histogram(tile_col):
     """Compute the Tile-wise histogram"""
-    return _apply_column_function('rf_tile_histogram', *args)
+    return _apply_column_function('rf_tile_histogram', tile_col)
 
 
-def rf_tile_mean(*args):
+def rf_tile_mean(tile_col):
     """Compute the Tile-wise mean"""
-    return _apply_column_function('rf_tile_mean', *args)
+    return _apply_column_function('rf_tile_mean', tile_col)
 
 
-def rf_tile_sum(*args):
+def rf_tile_sum(tile_col):
     """Compute the Tile-wise sum"""
-    return _apply_column_function('rf_tile_sum', *args)
+    return _apply_column_function('rf_tile_sum', tile_col)
 
 
-def rf_tile_min(*args):
+def rf_tile_min(tile_col):
     """Compute the Tile-wise minimum"""
-    return _apply_column_function('rf_tile_min', *args)
+    return _apply_column_function('rf_tile_min', tile_col)
 
 
-def rf_tile_max(*args):
+def rf_tile_max(tile_col):
     """Compute the Tile-wise maximum"""
-    return _apply_column_function('rf_tile_max', *args)
+    return _apply_column_function('rf_tile_max', tile_col)
 
 
-def rf_tile_stats(*args):
+def rf_tile_stats(tile_col):
     """Compute the Tile-wise floating point statistics"""
-    return _apply_column_function('rf_tile_stats', *args)
+    return _apply_column_function('rf_tile_stats', tile_col)
 
 
-def rf_render_ascii(*args):
+def rf_render_ascii(tile_col):
     """Render ASCII art of tile"""
-    return _apply_column_function('rf_render_ascii', *args)
+    return _apply_column_function('rf_render_ascii', tile_col)
 
 
-def rf_render_matrix(*args):
+def rf_render_matrix(tile_col):
     """Render Tile cell values as numeric values, for debugging purposes"""
-    return _apply_column_function('rf_render_matrix', *args)
+    return _apply_column_function('rf_render_matrix', tile_col)
 
 
-def rf_no_data_cells(*args):
+def rf_no_data_cells(tile_col):
     """Count of NODATA cells"""
-    return _apply_column_function('rf_no_data_cells', *args)
+    return _apply_column_function('rf_no_data_cells', tile_col)
 
 
-def rf_data_cells(*args):
+def rf_data_cells(tile_col):
     """Count of cells with valid data"""
-    return _apply_column_function('rf_data_cells', *args)
+    return _apply_column_function('rf_data_cells', tile_col)
 
 
-def rf_local_add(*args):
+def rf_local_add(tile_col):
     """Add two Tiles"""
-    return _apply_column_function('rf_local_add', *args)
+    return _apply_column_function('rf_local_add', tile_col)
 
 
-def rf_local_subtract(*args):
+def rf_local_subtract(tile_col):
     """Subtract two Tiles"""
-    return _apply_column_function('rf_local_subtract', *args)
+    return _apply_column_function('rf_local_subtract', tile_col)
 
 
-def rf_local_multiply(*args):
+def rf_local_multiply(tile_col):
     """Multiply two Tiles"""
-    return _apply_column_function('rf_local_multiply', *args)
+    return _apply_column_function('rf_local_multiply', tile_col)
 
 
-def rf_local_divide(*args):
+def rf_local_divide(tile_col):
     """Divide two Tiles"""
-    return _apply_column_function('rf_local_divide', *args)
+    return _apply_column_function('rf_local_divide', tile_col)
 
 
-def rf_normalized_difference(*args):
+def rf_normalized_difference(tile_col):
     """Compute the normalized difference of two tiles"""
-    return _apply_column_function('rf_normalized_difference', *args)
+    return _apply_column_function('rf_normalized_difference', tile_col)
 
 
-def rf_agg_local_max(*args):
+def rf_agg_local_max(tile_col):
     """Compute the cell-wise/local max operation between Tiles in a column."""
-    return _apply_column_function('rf_agg_local_max', *args)
+    return _apply_column_function('rf_agg_local_max', tile_col)
 
 
-def rf_agg_local_min(*args):
+def rf_agg_local_min(tile_col):
     """Compute the cellwise/local min operation between Tiles in a column."""
-    return _apply_column_function('rf_agg_local_min', *args)
+    return _apply_column_function('rf_agg_local_min', tile_col)
 
 
-def rf_agg_local_mean(*args):
+def rf_agg_local_mean(tile_col):
     """Compute the cellwise/local mean operation between Tiles in a column."""
-    return _apply_column_function('rf_agg_local_mean', *args)
+    return _apply_column_function('rf_agg_local_mean', tile_col)
 
 
-def rf_agg_local_data_cells(*args):
+def rf_agg_local_data_cells(tile_col):
     """Compute the cellwise/local count of non-NoData cells for all Tiles in a column."""
-    return _apply_column_function('rf_agg_local_data_cells', *args)
+    return _apply_column_function('rf_agg_local_data_cells', tile_col)
 
 
-def rf_agg_local_no_data_cells(*args):
+def rf_agg_local_no_data_cells(tile_col):
     """Compute the cellwise/local count of NoData cells for all Tiles in a column."""
-    return _apply_column_function('rf_agg_local_no_data_cells', *args)
+    return _apply_column_function('rf_agg_local_no_data_cells', tile_col)
 
 
-def rf_agg_local_stats(*args):
+def rf_agg_local_stats(tile_col):
     """Compute cell-local aggregate descriptive statistics for a column of Tiles."""
-    return _apply_column_function('rf_agg_local_stats', *args)
+    return _apply_column_function('rf_agg_local_stats', tile_col)
 
 
-def rf_mask(*args):
+def rf_mask(tile_col):
     """Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA."""
-    return _apply_column_function('rf_mask', *args)
+    return _apply_column_function('rf_mask', tile_col)
 
 
-def rf_inverse_mask(*args):
+def rf_inverse_mask(tile_col):
     """Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA."""
-    return _apply_column_function('rf_inverse_mask', *args)
+    return _apply_column_function('rf_inverse_mask', tile_col)
 
 
-def rf_local_less(*args):
+def rf_local_less(tile_col):
     """Cellwise less than comparison between two tiles"""
-    return _apply_column_function('rf_local_less', *args)
+    return _apply_column_function('rf_local_less', tile_col)
 
 
-def rf_local_less_equal(*args):
+def rf_local_less_equal(tile_col):
     """Cellwise less than or equal to comparison between two tiles"""
-    return _apply_column_function('rf_local_less_equal', *args)
+    return _apply_column_function('rf_local_less_equal', tile_col)
 
 
-def rf_local_greater(*args):
+def rf_local_greater(tile_col):
     """Cellwise greater than comparison between two tiles"""
-    return _apply_column_function('rf_local_greater', *args)
+    return _apply_column_function('rf_local_greater', tile_col)
 
 
-def rf_local_greater_equal(*args):
+def rf_local_greater_equal(tile_col):
     """Cellwise greater than or equal to comparison between two tiles"""
-    return _apply_column_function('rf_local_greater_equal', *args)
+    return _apply_column_function('rf_local_greater_equal', tile_col)
 
 
-def rf_local_equal(*args):
+def rf_local_equal(tile_col):
     """Cellwise equality comparison between two tiles"""
-    return _apply_column_function('rf_local_equal', *args)
+    return _apply_column_function('rf_local_equal', tile_col)
 
 
-def rf_local_unequal(*args):
+def rf_local_unequal(tile_col):
     """Cellwise inequality comparison between two tiles"""
-    return _apply_column_function('rf_local_unequal', *args)
+    return _apply_column_function('rf_local_unequal', tile_col)
 
 
-def rf_round(*args):
+def rf_round(tile_col):
     """Round cell values to the nearest integer without changing the cell type"""
-    return _apply_column_function('rf_round', *args)
+    return _apply_column_function('rf_round', tile_col)
 
 
-def rf_abs(*args):
+def rf_abs(tile_col):
     """Compute the absolute value of each cell"""
-    return _apply_column_function('rf_abs', *args)
+    return _apply_column_function('rf_abs', tile_col)
 
 
-def rf_log(*args):
+def rf_log(tile_col):
     """Performs cell-wise natural logarithm"""
-    return _apply_column_function('rf_log', *args)
+    return _apply_column_function('rf_log', tile_col)
 
 
-def rf_log10(*args):
+def rf_log10(tile_col):
     """Performs cell-wise logartithm with base 10"""
-    return _apply_column_function('rf_log10', *args)
+    return _apply_column_function('rf_log10', tile_col)
 
 
-def rf_log2(*args):
+def rf_log2(tile_col):
     """Performs cell-wise logartithm with base 2"""
-    return _apply_column_function('rf_log2', *args)
+    return _apply_column_function('rf_log2', tile_col)
 
 
-def rf_log1p(*args):
+def rf_log1p(tile_col):
     """Performs natural logarithm of cell values plus one"""
-    return _apply_column_function('rf_log1p', *args)
+    return _apply_column_function('rf_log1p', tile_col)
 
 
-def rf_exp(*args):
+def rf_exp(tile_col):
     """Performs cell-wise exponential"""
-    return _apply_column_function('rf_exp', *args)
+    return _apply_column_function('rf_exp', tile_col)
 
 
-def rf_exp2(*args):
+def rf_exp2(tile_col):
     """Compute 2 to the power of cell values"""
-    return _apply_column_function('rf_exp2', *args)
+    return _apply_column_function('rf_exp2', tile_col)
 
 
-def rf_exp10(*args):
+def rf_exp10(tile_col):
     """Compute 10 to the power of cell values"""
-    return _apply_column_function('rf_exp10', *args)
+    return _apply_column_function('rf_exp10', tile_col)
 
 
-def rf_expm1(*args):
+def rf_expm1(tile_col):
     """Performs cell-wise exponential, then subtract one"""
-    return _apply_column_function('rf_expm1', *args)
+    return _apply_column_function('rf_expm1', tile_col)
 
 
-def rf_identity(*args):
+def rf_identity(tile_col):
     """Pass tile through unchanged"""
-    return _apply_column_function('rf_identity', *args)
+    return _apply_column_function('rf_identity', tile_col)
 
 
-def rf_resample(*args):
-    """Resample tile to different size based on scalar factor or tile whose dimension to match"""
-    return _apply_column_function('rf_resample', *args)
+def rf_resample(tile_col, scale_factor_col):
+    """Resample tile to different size based on scalar factor or tile whose dimension to match
+    Scalar less than one will downsample tile; greater than one will upsample. Uses nearest-neighbor."""
+    return _apply_column_function('rf_resample', tile_col, scale_factor_col)
 
 
-def rf_crs(*args):
+def rf_crs(tile_col):
     """Get the CRS of a RasterSource or ProjectedRasterTile"""
-    return _apply_column_function('rf_crs', *args)
+    return _apply_column_function('rf_crs', tile_col)
 
 
-def st_extent(*args):
+def st_extent(geom_col):
     """Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)"""
-    return _apply_column_function('st_extent', *args)
+    return _apply_column_function('st_extent', geom_col)
 
 
-def rf_extent(*args):
+def rf_extent(proj_raster_col):
     """Get the extent of a RasterSource or ProjectedRasterTile (a tile with embedded extent and CRS)"""
-    return _apply_column_function('rf_extent', *args)
+    return _apply_column_function('rf_extent', proj_raster_col)
 
 
-def rf_tile(*args):
+def rf_tile(proj_raster_col):
     """Extracts the Tile component of a ProjectedRasterTile (or Tile)."""
-    return _apply_column_function('rf_tile', *args)
+    return _apply_column_function('rf_tile', proj_raster_col)
 
 
-def st_geometry(*args):
+def st_geometry(geom_col):
     """Convert the given extent/bbox to a polygon"""
-    return _apply_column_function('st_geometry', *args)
+    return _apply_column_function('st_geometry', geom_col)
 
 
-def rf_geometry(*args):
+def rf_geometry(proj_raster_col):
     """Get the extent of a RasterSource or ProjectdRasterTile as a Geometry"""
-    return _apply_column_function('rf_geometry', *args)
+    return _apply_column_function('rf_geometry', proj_raster_col)
 
+# ------ GeoMesa Functions ------
 
 def st_geomFromGeoHash(*args):
     """"""
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index 09f64bb16..aef478831 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -71,7 +71,7 @@ def find_pyrasterframes_assembly():
     if not len(jarpath) == 1:
         raise RuntimeError("""
 Expected to find exactly one assembly. Found '{}' instead. 
-Try running 'sbt pyrasterframes/package' first. """.format(jarpath))
+Try running 'sbt pyrasterframes/clean pyrasterframes/package' first. """.format(jarpath))
     return jarpath[0]
 
 

From 37208eff68f196eace10dba61e7e6ee187f77744 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Jul 2019 11:30:42 -0400
Subject: [PATCH 222/380] Fixed bad explicit parameter definitions in
 rasterfunctions.py.

---
 .../python/pyrasterframes/rasterfunctions.py  | 64 +++++++++----------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 587891614..e342842d9 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -66,22 +66,22 @@ def rf_convert_cell_type(tile_col, cell_type):
     return Column(jfcn(_to_java_column(tile_col), _parse_cell_type(cell_type)))
 
 
-def rf_make_constant_tile(value, cols, rows, cell_type):
+def rf_make_constant_tile(scalar_value, num_cols, num_rows, cell_type):
     """Constructor for constant tile column"""
     jfcn = RFContext.active().lookup('rf_make_constant_tile')
-    return Column(jfcn(value, cols, rows, cell_type))
+    return Column(jfcn(scalar_value, num_cols, num_rows, cell_type))
 
 
-def rf_make_zeros_tile(cols, rows, cell_type='float64'):
+def rf_make_zeros_tile(num_cols, num_rows, cell_type='float64'):
     """Create column of constant tiles of zero"""
     jfcn = RFContext.active().lookup('rf_make_zeros_tile')
-    return Column(jfcn(cols, rows, cell_type))
+    return Column(jfcn(num_cols, num_rows, cell_type))
 
 
-def rf_make_ones_tile(cols, rows, cell_type='float64'):
+def rf_make_ones_tile(num_cols, num_rows, cell_type='float64'):
     """Create column of constant tiles of one"""
     jfcn = RFContext.active().lookup('rf_make_ones_tile')
-    return Column(jfcn(cols, rows, cell_type))
+    return Column(jfcn(num_cols, num_rows, cell_type))
 
 
 def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
@@ -353,29 +353,29 @@ def rf_data_cells(tile_col):
     return _apply_column_function('rf_data_cells', tile_col)
 
 
-def rf_local_add(tile_col):
+def rf_local_add(left_tile_col, right_tile_col):
     """Add two Tiles"""
-    return _apply_column_function('rf_local_add', tile_col)
+    return _apply_column_function('rf_local_add', left_tile_col, right_tile_col)
 
 
-def rf_local_subtract(tile_col):
+def rf_local_subtract(left_tile_col, right_tile_col):
     """Subtract two Tiles"""
-    return _apply_column_function('rf_local_subtract', tile_col)
+    return _apply_column_function('rf_local_subtract', left_tile_col, right_tile_col)
 
 
-def rf_local_multiply(tile_col):
+def rf_local_multiply(left_tile_col, right_tile_col):
     """Multiply two Tiles"""
-    return _apply_column_function('rf_local_multiply', tile_col)
+    return _apply_column_function('rf_local_multiply', left_tile_col, right_tile_col)
 
 
-def rf_local_divide(tile_col):
+def rf_local_divide(left_tile_col, right_tile_col):
     """Divide two Tiles"""
-    return _apply_column_function('rf_local_divide', tile_col)
+    return _apply_column_function('rf_local_divide', left_tile_col, right_tile_col)
 
 
-def rf_normalized_difference(tile_col):
+def rf_normalized_difference(left_tile_col, right_tile_col):
     """Compute the normalized difference of two tiles"""
-    return _apply_column_function('rf_normalized_difference', tile_col)
+    return _apply_column_function('rf_normalized_difference', left_tile_col, right_tile_col)
 
 
 def rf_agg_local_max(tile_col):
@@ -408,44 +408,44 @@ def rf_agg_local_stats(tile_col):
     return _apply_column_function('rf_agg_local_stats', tile_col)
 
 
-def rf_mask(tile_col):
+def rf_mask(src_tile_col, mask_tile_col):
     """Where the rf_mask (second) tile contains NODATA, replace values in the source (first) tile with NODATA."""
-    return _apply_column_function('rf_mask', tile_col)
+    return _apply_column_function('rf_mask', src_tile_col, mask_tile_col)
 
 
-def rf_inverse_mask(tile_col):
+def rf_inverse_mask(src_tile_col, mask_tile_col):
     """Where the rf_mask (second) tile DOES NOT contain NODATA, replace values in the source (first) tile with NODATA."""
-    return _apply_column_function('rf_inverse_mask', tile_col)
+    return _apply_column_function('rf_inverse_mask', src_tile_col, mask_tile_col)
 
 
-def rf_local_less(tile_col):
+def rf_local_less(left_tile_col, right_tile_col):
     """Cellwise less than comparison between two tiles"""
-    return _apply_column_function('rf_local_less', tile_col)
+    return _apply_column_function('rf_local_less', left_tile_col, right_tile_col)
 
 
-def rf_local_less_equal(tile_col):
+def rf_local_less_equal(left_tile_col, right_tile_col):
     """Cellwise less than or equal to comparison between two tiles"""
-    return _apply_column_function('rf_local_less_equal', tile_col)
+    return _apply_column_function('rf_local_less_equal', left_tile_col, right_tile_col)
 
 
-def rf_local_greater(tile_col):
+def rf_local_greater(left_tile_col, right_tile_col):
     """Cellwise greater than comparison between two tiles"""
-    return _apply_column_function('rf_local_greater', tile_col)
+    return _apply_column_function('rf_local_greater', left_tile_col, right_tile_col)
 
 
-def rf_local_greater_equal(tile_col):
+def rf_local_greater_equal(left_tile_col, right_tile_col):
     """Cellwise greater than or equal to comparison between two tiles"""
-    return _apply_column_function('rf_local_greater_equal', tile_col)
+    return _apply_column_function('rf_local_greater_equal', left_tile_col, right_tile_col)
 
 
-def rf_local_equal(tile_col):
+def rf_local_equal(left_tile_col, right_tile_col):
     """Cellwise equality comparison between two tiles"""
-    return _apply_column_function('rf_local_equal', tile_col)
+    return _apply_column_function('rf_local_equal', left_tile_col, right_tile_col)
 
 
-def rf_local_unequal(tile_col):
+def rf_local_unequal(left_tile_col, right_tile_col):
     """Cellwise inequality comparison between two tiles"""
-    return _apply_column_function('rf_local_unequal', tile_col)
+    return _apply_column_function('rf_local_unequal', left_tile_col, right_tile_col)
 
 
 def rf_round(tile_col):

From c2b52173613e561c433e92de5f6b976ca61c38dc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Jul 2019 17:15:03 -0400
Subject: [PATCH 223/380] Restructured IO-related docs.

---
 .../src/main/python/docs/raster-catalogs.pymd | 164 ++++++++++++++++++
 .../src/main/python/docs/raster-io.md         |  34 ++++
 .../docs/{raster-io.pymd => raster-read.pymd} |  40 -----
 .../src/main/python/docs/raster-write.pymd    |  11 ++
 .../src/main/python/pyrasterframes/utils.py   |   2 +-
 5 files changed, 210 insertions(+), 41 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/raster-catalogs.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/raster-io.md
 rename pyrasterframes/src/main/python/docs/{raster-io.pymd => raster-read.pymd} (77%)
 create mode 100644 pyrasterframes/src/main/python/docs/raster-write.pymd

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
new file mode 100644
index 000000000..33d459ef8
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -0,0 +1,164 @@
+# Raster Catalogs
+
+While much interesting processing can be done on single raster files, RasterFrames shines when _Catalogs_ of raster data are to be processed. In its simplest form, a _Catalog_ is a listing of URLs referencing raster files. This listing can be manifested as CSV in a character string or an external file, or as a Spark or Pandas DataFrame. This _Catalog_ then serves as the input into the `raster` DataSource, described in the next page.
+
+A _Catalog_ can be zero, one or two dimensions:
+
+* Zero-D: A single URL to a single raster file
+* One-D: Multiple URLs in a single column, where all referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different spatiotemporal locations (scenes).
+* Two-D: One or more columns of multiple URLs, where each column references the same content type, and each row represents the same spatiotemporal location. For example, red-, green-, and blue-band columns for scenes covering Europe. Each spatiotemporal location (scenes), and each raster cell within it must be of the same dimensions, extent, crs, etc.
+
+## Creating a Catalog
+
+This section will provide some examples of creating your own _Catalogs_, as well as introduce some experimental _Catalogs_ built into RasterFrames. The consuption of _Catalogs_ is covered in more detail in the next page.
+
+```python, echo=False
+from IPython.display import display
+from pyrasterframes.utils import create_rf_spark_session 
+from pyrasterframes.rasterfunctions import *
+spark = create_rf_spark_session()
+```
+
+```python
+from pyspark.sql import Row
+```
+
+### Zero-D
+
+A single URL is this simplest form of a catalog.
+
+```python
+my_cat = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
+# or
+my_cat_df = spark.createDataFrame([Row(B01=my_cat)]) 
+```
+
+### One-D
+
+Example of a single column representing the same content type:
+
+```python
+scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
+scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
+
+# As CSV string
+my_cat = '\n'.join(['B01', scene1_B01, scene2_B01]) 
+# or
+my_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
+my_cat_df.printSchema()
+```
+
+### Two-D
+
+Example of a multiple columns representing multiple content types (bands) across multiple scenes.
+
+```python
+scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
+scene1_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B02.TIF"
+scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
+scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B02.TIF"
+
+
+# As CSV string
+my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02]) 
+# or
+my_cat_df = spark.createDataFrame([Row(B01=scene1_B01, B02=scene1_B02), Row(B01=scene2_B01, B02=scene2_B02)])
+my_cat_df.printSchema()
+```
+
+## Using External Catalogs
+
+The simplest example of an external _Catalog_ is a CSV file (or a transformation of) in one of the formats above. Here's an extended example of reading an external CSV file of MODIS scenes and transforming it into a _Catalog_
+
+```python
+from pyspark import SparkFiles
+from pyspark.sql import functions as F
+
+spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/2018-07-04_scenes.txt")
+
+# The scenes list file has index URIs take the form:
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html    
+# Image URIs take the form:
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
+
+modis_catalog = spark.read \
+    .format("csv") \
+    .option("header", "true") \
+    .load(SparkFiles.get("2018-07-04_scenes.txt")) \
+    .withColumn('base_url', 
+        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
+    ) \
+    .withColumn('B01' , F.concat('base_url', F.lit("_B01.TIF"))) \
+    .withColumn('B02' , F.concat('base_url', F.lit("_B02.TIF"))) \
+    .withColumn('B03' , F.concat('base_url', F.lit("_B03.TIF"))) 
+# ... and so on.  
+modis_catalog.printSchema()
+```
+
+
+## Using Built-in Experimental Catalogs
+
+RasterFrames comes with two experimental catalogs over the AWS PDS Landsat 8 and MODIS repositories. They are created by downloading the latest scene lists and transforming as in the prior example.
+
+> Note: The first time you run these may take some time, as the catalogs are large. However, they are cached and subsequent invocations should be faster.
+
+### MODIS
+
+```python, evaluate=False
+modis_catalog2 = spark.read.format('aws-pds-modis-catalog').load()
+modis_catalog2.printSchema()
+```
+```
+root
+ |-- product_id: string (nullable = false)
+ |-- acquisition_date: timestamp (nullable = false)
+ |-- granule_id: string (nullable = false)
+ |-- gid: string (nullable = false)
+ |-- B01: string (nullable = true)
+ |-- B01qa: string (nullable = true)
+ |-- B02: string (nullable = true)
+ |-- B02qa: string (nullable = true)
+ |-- B03: string (nullable = true)
+ |-- B03aq: string (nullable = true)
+ |-- B04: string (nullable = true)
+ |-- B04qa: string (nullable = true)
+ |-- B05: string (nullable = true)
+ |-- B05qa: string (nullable = true)
+ |-- B06: string (nullable = true)
+ |-- B06qa: string (nullable = true)
+ |-- B07: string (nullable = true)
+ |-- B07qa: string (nullable = true)
+```
+
+### Landsat 8
+
+```python, evaluate=False
+l8 = spark.read.format('aws-pds-l8-catalog').load()
+l8.printSchema()
+root
+ |-- product_id: string (nullable = false)
+ |-- entity_id: string (nullable = false)
+ |-- acquisition_date: timestamp (nullable = false)
+ |-- cloud_cover_pct: float (nullable = false)
+ |-- processing_level: string (nullable = false)
+ |-- path: short (nullable = false)
+ |-- row: short (nullable = false)
+ |-- bounds_wgs84: struct (nullable = false)
+ |    |-- minX: double (nullable = false)
+ |    |-- maxX: double (nullable = false)
+ |    |-- minY: double (nullable = false)
+ |    |-- maxY: double (nullable = false)
+ |-- B1: string (nullable = true)
+ |-- B2: string (nullable = true)
+ |-- B3: string (nullable = true)
+ |-- B4: string (nullable = true)
+ |-- B5: string (nullable = true)
+ |-- B6: string (nullable = true)
+ |-- B7: string (nullable = true)
+ |-- B8: string (nullable = true)
+ |-- B9: string (nullable = true)
+ |-- B10: string (nullable = true)
+ |-- B11: string (nullable = true)
+ |-- BQA: string (nullable = true) 
+```
+
diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
new file mode 100644
index 000000000..60fe0e913
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -0,0 +1,34 @@
+# Raster Data I/O
+
+The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames are compatible with existing generalized DataSources, such as Parquet, but provide specialized ones oriented around geospatial raster data.
+
+Three types of DataSources will be introduced: 
+
+* Catalog Readers
+    - `aws-pds-l8-catalog`: experimental
+    - `aws-pds-modis-catalog`: experimental
+    - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
+* Raster Readers
+    - `raster`: the standard reader for most operations
+    - `geotiff`: a simplified reader for reading single GeoTIFF files
+    - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
+* Raster Writers 
+    - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
+    - `geotiff`: beta
+    - `parquet`: for writing raster data in an analytics functional form  
+
+There's also some support for vector data (for masking and data labeling):
+
+* Vector Readers
+    - `geojson`: read GeoJson files with `geometry` column aside attribute values
+
+
+@@@ index
+* [Raster Catalogs](raster-catalogs.md)
+* [Raster Readers](raster-read.md)
+* [Raster Writers](raster-write.md)
+@@@
+
+
+[DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
+[GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-io.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
similarity index 77%
rename from pyrasterframes/src/main/python/docs/raster-io.pymd
rename to pyrasterframes/src/main/python/docs/raster-read.pymd
index 423de7d82..b791cbadc 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -1,27 +1,3 @@
-# Raster Data I/O
-
-The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames are compatible with existing generalized DataSources, such as Parquet, but provide specialized ones oriented around geospatial raster data.
-
-Three types of DataSources will be introduced below: 
-
-* Catalog Readers
-    - `aws-pds-l8-catalog`: experimental
-    - `aws-pds-modis-catalog`: experimental
-    - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
-* Raster Readers
-    - `raster`: the standard reader for most operations
-    - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
-    - `geotiff`: a deprecated reader for reading single GeoTIFF files
-* Raster Writers 
-    - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
-    - `geotiff`: beta
-    - `parquet`: for writing raster data in an analytics functional form  
-
-There's also some support for vector data (for masking and data labeling):
-
-* Vector Readers
-    - `geojson`: read GeoJson files with `geometry` column aside attribute values
-
 ## Reading Raster Data
 
 ```python, echo=False
@@ -164,19 +140,3 @@ rf.show(2, truncate=False)
 > Need to know _a priori_ how many bands there are, specified with `band_indexes`.
 > Must be homogenous, or tolerant of `null` Tiles.
  
-
-## Writing Raster Data
-
-### Overviews
-
-### GeoTIFFs
-
-### Tile Samples
-
-### GeoTrellis Layers
-
-
-
-
-[DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
-[GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
new file mode 100644
index 000000000..1c4e4edb5
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -0,0 +1,11 @@
+## Writing Raster Data
+
+### Overviews
+
+### GeoTIFFs
+
+### Tile Samples
+
+### GeoTrellis Layers
+
+
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index aef478831..5faa62a22 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -81,7 +81,7 @@ def create_rf_spark_session():
 
     spark = (SparkSession.builder
              .master("local[*]")
-             .appName("PyRasterFrames")
+             .appName("RasterFrames")
              .config('spark.jars', jar_path)
              .withKryoSerialization()
              .getOrCreate())

From e7098a140fcc7ce76bf9fbfc9452863ee2b7bd14 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 10 Jul 2019 17:15:20 -0400
Subject: [PATCH 224/380] Misc build tweaks.

---
 project/PythonBuildPlugin.scala        | 4 ++--
 rf-notebook/src/main/docker/Dockerfile | 9 ---------
 2 files changed, 2 insertions(+), 11 deletions(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 859fdec1f..e4383a7b9 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -130,7 +130,7 @@ object PythonBuildPlugin extends AutoPlugin {
         val dest = (Compile / packageBin / artifactPath).value.getParentFile
         val art = (Python / packageBin / artifact).value
         val ver = version.value
-        dest / s"${art.name}-python-$ver.zip"
+        dest / s"${art.name}-$ver-py2.py3-none-any.whl"
       },
       testQuick := pySetup.toTask(" test").value,
       executeTests := Def.task {
@@ -171,7 +171,7 @@ object PythonBuildPlugin extends AutoPlugin {
           )
         }
         result
-        Tests.Output(result.result, Map("PyRasterFramesTests" -> result), Iterable(pySummary))
+        Tests.Output(result.result, Map("Python Tests" -> result), Iterable(pySummary))
       }.dependsOn(assembly).value
     )) ++
     addArtifact(Python / packageBin / artifact, Python / packageBin)
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index 023e1ee17..0a22c820a 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -8,17 +8,8 @@ USER root
 
 RUN mkdir $RF_LIB_LOC
 
-RUN echo "spark.driver.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf  && \
-    echo "spark.executor.extraClassPath $RF_JAR" >> /usr/local/spark/conf/spark-defaults.conf
-
 EXPOSE 4040 4041 4042 4043 4044
 
-ENV SPARK_OPTS $SPARK_OPTS \
-    --driver-class-path $RF_JAR \
-    --conf spark.executor.extraClassPath=$RF_JAR
-
-ENV PYTHONPATH $PYTHONPATH:$PY_RF_ZIP
-
 # Sphinx (for Notebook->html)
 RUN conda install --quiet --yes \
     anaconda sphinx nbsphinx shapely numpy folium geopandas geojsonio

From 3b7bfa8d5594f73386591e5f8304e5215833f07c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 16 Jul 2019 10:03:54 -0400
Subject: [PATCH 225/380] Removed CircleCI caching of ~/.rf_cache so code is
 tested with each IT.

---
 .circleci/config.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 6fec4977e..8697bb425 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -19,16 +19,15 @@ _delenv: &unsetenv
 
 _restore_cache: &restore_cache
   keys:
-    - v2-dependencies-{{ checksum "build.sbt" }}
-    - v2-dependencies-
+    - v3-dependencies-{{ checksum "build.sbt" }}
+    - v3-dependencies-
 
 _save_cache: &save_cache
-  key: v2-dependencies--{{ checksum "build.sbt" }}
+  key: v3-dependencies--{{ checksum "build.sbt" }}
   paths:
     - ~/.cache/coursier
     - ~/.ivy2/cache
     - ~/.sbt
-    - ~/.rf_cache
 
 before_script:
   - ulimit -c unlimited -S

From f02a2804c9524680fed0110af017ba4208afd406 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 16 Jul 2019 11:06:07 -0400
Subject: [PATCH 226/380] Flesh out vector data support; move function ref to
 pymd file; clean up of getting started

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/getting-started.pymd |  22 ++--
 .../src/main/python/docs/raster-io.md         |  35 +++---
 .../src/main/python/docs/reference.pymd       |  51 +++-----
 .../src/main/python/docs/vector-data.pymd     | 109 ++++++++++++++++--
 pyrasterframes/src/main/python/setup.py       |   1 +
 5 files changed, 145 insertions(+), 73 deletions(-)
 rename docs/src/main/paradox/reference.md => pyrasterframes/src/main/python/docs/reference.pymd (96%)

diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 147c1a1c6..957eba2d8 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -2,6 +2,8 @@
 
 There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames. Let's start with the simplest. Python 3.6 or greater is recommended.
 
+## pip install pyrasterframes
+
 ```bash
 $ python3 -m pip install pyrasterframes
 ```
@@ -31,20 +33,6 @@ This example is extended in the [getting started Jupyter notebook](https://nbvie
 
 To understand more about how and why RasterFrames represents Earth observation in DataFrames, read the project @ref:[description](description.md). For more hands-on examples, see the chapters about @ref:[reading](raster-io.md) and @ref:[processing](raster-processing.md) with RasterFrames.
 
-## Raster Functions
-
-To import RasterFrames functions into the environment, import from `pyrasterframes.rasterfunctions`.
-
-```python
-from pyrasterframes.rasterfunctions import *
-```
-
-Functions starting with `rf_`, which are for raster, and `st_`, which are for vector geometry,
-become available for use with DataFrames. See the complete @ref:[function reference](reference.md).
-
-```python
-[fn for fn in dir() if fn.startswith('rf_') or fn.startswith('st_')]
-```
 
 ## Other Options
 
@@ -89,4 +77,10 @@ Now you have the configured SparkSession with RasterFrames enabled.
 spark.stop()
 ```
 
+## Installing GDAL 
+
+GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below.
+
+__TODO__ Verify notebook env has GDAL? 
 
+__TODO__ How to install it.
diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index 60fe0e913..22fc1c898 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -1,34 +1,33 @@
 # Raster Data I/O
 
-The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames are compatible with existing generalized DataSources, such as Parquet, but provide specialized ones oriented around geospatial raster data.
+The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames provides specialized DataSources for geospatial raster data and maintains compatibility with existing general purpose DataSources, such as Parquet.
 
 Three types of DataSources will be introduced: 
 
 * Catalog Readers
-    - `aws-pds-l8-catalog`: experimental
-    - `aws-pds-modis-catalog`: experimental
+    - `aws-pds-l8-catalog`: built-in catalog over [Landsat on AWS][Landsat]
+    - `aws-pds-modis-catalog`: built-in catalog over [MODIS on AWS][MODIS]
     - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
 * Raster Readers
-    - `raster`: the standard reader for most operations
-    - `geotiff`: a simplified reader for reading single GeoTIFF files
-    - `geotrellis` (for reading [GeoTrellis layers][GTLayer])
+    - `raster`: the standard reader for most raster data
+    - `geotiff`: a simplified reader for reading a single GeoTIFF file
+    - `geotrellis`: for reading a [GeoTrellis layer][GTLayer])
 * Raster Writers 
-    - `geotrellis`: for creating [GeoTrellis layers][GTLayer]
-    - `geotiff`: beta
-    - `parquet`: for writing raster data in an analytics functional form  
-
-There's also some support for vector data (for masking and data labeling):
-
-* Vector Readers
-    - `geojson`: read GeoJson files with `geometry` column aside attribute values
+    - `geotrellis`: for creating a [GeoTrellis layer][GTLayer]
+    - `geotiff`: beta writer to GeoTiff file
+    - [`parquet`][Parquet]: general purpose writer 
 
+There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.
 
 @@@ index
-* [Raster Catalogs](raster-catalogs.md)
-* [Raster Readers](raster-read.md)
-* [Raster Writers](raster-write.md)
+* @ref:[Raster Catalogs](raster-catalogs.md)
+* @ref:[Raster Readers](raster-read.md)
+* @ref:[Raster Writers](raster-write.md)
 @@@
 
 
 [DS]: https://spark.apache.org/docs/latest/sql-data-sources.html
-[GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
\ No newline at end of file
+[GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
+[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html
+[MODIS]: https://docs.opendata.aws/modis-pds/readme.html
+[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
\ No newline at end of file
diff --git a/docs/src/main/paradox/reference.md b/pyrasterframes/src/main/python/docs/reference.pymd
similarity index 96%
rename from docs/src/main/paradox/reference.md
rename to pyrasterframes/src/main/python/docs/reference.pymd
index 3d4d41cbf..8bf336964 100644
--- a/docs/src/main/paradox/reference.md
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -12,11 +12,22 @@ For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`]
 
 ## List of Available SQL and Python Functions
 
-@@toc { depth=3 }
+To import RasterFrames functions into the environment, import from `pyrasterframes.rasterfunctions`.
+
+```python
+from pyrasterframes.rasterfunctions import *
+```
+
+Functions starting with `rf_`, which are for raster, and `st_`, which are for vector geometry,
+become available for use with DataFrames. You can view all of the available functions with the following.
+
+```python, evaluate=False
+[fn for fn in dir() if fn.startswith('rf_') or fn.startswith('st_')]
+```
 
 ### Vector Operations
 
-Various LocationTech GeoMesa UDFs to deal with `geomtery` type columns are also provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
+Various LocationTech GeoMesa user-defined functions (UDFs) to deal with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
 
 RasterFrames provides some additional functions for vector geometry operations.
 
@@ -51,25 +62,10 @@ Functions to access and change the particulars of a `tile`: its shape and the da
     Array[String] rf_cell_types()
     
 Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on nodata](reference.md#masking-and-nodata) for additional details.
- 
-|cell_types |
-|----------|
-|bool      |
-|int8raw   |
-|int8      |
-|uint8raw  |
-|uint8     |
-|int16raw  |
-|int16     |
-|uint16raw |
-|uint16    |
-|int32raw  |
-|int32     |
-|float32raw|
-|float32   |
-|float64raw|
-|float64   |
 
+```python
+rf_cell_types() 
+``` 
 
 #### rf_dimensions
 
@@ -171,19 +167,6 @@ FROM
            st_geomFromWKT('POLYGON((0.0 0.0, 2.0 0.0, 2.0 2.0, 0.0 2.0, 0.0 0.0))') AS b
    ) r
 """).show(1, False)
-
------------
-|∘∘∘∘∘∘∘∘∘∘
-∘∘∘∘∘∘∘∘∘∘
-∘∘∘∘∘∘∘∘∘∘
-∘∘∘∘∘∘∘ ∘∘
-∘∘∘∘∘∘  ∘∘
-∘∘∘∘∘   ∘∘
-∘∘∘∘    ∘∘
-∘∘∘     ∘∘
-∘∘∘∘∘∘∘∘∘∘
-∘∘∘∘∘∘∘∘∘∘|
------------
 ```
 
 
@@ -377,7 +360,7 @@ Round cell values to the nearest integer without changing the cell type.
 
  Compute the absolute value of cell value.
  
- #### rf_exp
+#### rf_exp
 
     Tile rf_exp(Tile tile)
 
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
index 241ca6974..2fb2faa4e 100644
--- a/pyrasterframes/src/main/python/docs/vector-data.pymd
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -1,17 +1,112 @@
 # Vector Data
 
-* `geojson` DataSource for going right into Spark
-* Shapely or Python-side processing, getting into Spark via Pandas  
+RasterFrames provides a variety of ways to work with spatial vector (points, lines, and polygons) data alongside raster data. There is a convenience DataSource for geojson format, as well as the ability to convert from [GeoPandas][GeoPandas] to Spark. Representation of vector geometries in pyspark is through [Shapely][Shapely] which provides a great deal of flexibility. RasterFrames also provides access to Spark functions for working with geometries.
 
 ## GeoJSON DataSource
 
-Show `spark.read.geojson(...)` example.
+```python, echo=False
+import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
+spark = create_rf_spark_session()
+```
 
-## Shapely
+```python
+from pyspark import SparkFiles
+spark.sparkContext.addFile('https://raw.githubusercontent.com/datasets/geo-admin1-us/master/data/admin1-us.geojson')
 
-Show Shapely interop
+df = spark.read.geojson(SparkFiles.get('admin1-us.geojson'))
+df.printSchema()
+```
 
-## Spatial Relations
+The properties of each feature are available as columns of the dataframe, along with the geometry. 
 
-Using `st_` functions, particularly boolean relations.
+## GeoPandas and RasterFrames
 
+You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as `df` defined above by the geojson reader. Note that in a geopandas dataframe there can be heterogeneous geometry types in the column, but this may fail Spark's schema inference. 
+
+```python
+import geopandas
+from shapely.geometry import MultiPolygon
+
+def poly_or_mp_to_mp(g):
+    """ Normalize polygons or multipolygons to all be multipolygons. """
+    if isinstance(g, MultiPolygon):
+        return g
+    else:
+        return MultiPolygon([g])
+
+gdf = geopandas.read_file('https://raw.githubusercontent.com/datasets/geo-admin1-us/master/data/admin1-us.geojson')
+gdf.geometry = gdf.geometry.apply(poly_or_mp_to_mp)
+df2 = spark.createDataFrame(gdf)
+df2.printSchema()
+```
+
+## Shapely Geometry Support
+
+The geometry column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working on the Python side. This means that when the data is collected to the driver, it will be a Shapely geometry object. 
+
+```python
+the_first = df.first()
+print(type(the_first['geometry']))
+```
+
+Since it is a geometry we can do things like this.
+
+```python
+the_first['geometry'].wkt
+```
+
+You can also write user-defined functions that input geometries, output geometries, or both, via user defined types in the [geomesa_pyspark.types](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/geomesa_pyspark/types.py) module. Here is a simple example using both a geometry input and output. The example is repeated below with a built-in function.
+
+```python
+from pyspark.sql.functions import udf
+from geomesa_pyspark.types import PointUDT
+
+@udf(PointUDT())
+def get_centroid(g):
+    return g.centroid
+
+df = df.withColumn('naive_centroid', get_centroid(df.geometry))
+df.printSchema()
+```
+
+We can take a look at a sample of the data. Notice the geometry columns print as well known text.
+
+```python
+df.show(4)
+```
+
+
+## GeoMesa Functions and Spatial Relations
+
+As documented in the @ref:[function reference](reference.md), the various user-defined functions already available through GeoMesa are available for use. The example above is logically equivalent but less efficient than this.
+
+
+```python
+from pyrasterframes.rasterfunctions import st_centroid 
+df = df.withColumn('centroid', st_centroid(df.geometry))
+df.select('name', 'geometry', 'naive_centroid', 'centroid').show(4)
+```
+
+The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example we use the @ref:[built-in MODIS catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter only those within approximately 500 km of a selected point.
+
+```python, evaluate=True
+from pyrasterframes.rasterfunctions import st_geometry, st_bufferPoint, st_intersects, st_point
+from pyspark.sql.functions import lit
+l8 = spark.read.format('aws-pds-l8-catalog').load()
+
+l8 = l8.withColumn('geom', st_geometry(l8.bounds_wgs84))
+l8 = l8.withColumn('paducah', st_point(lit(-88.6275), lit(37.072222)))
+
+l8_filtered = l8.filter(st_intersects(l8.geom, st_bufferPoint(l8.paducah, lit(500000.0))))
+```
+
+```python, evaluate=False, echo=False
+# suppressed due to run time. 
+l8_filtered.count()
+```
+
+
+[GeoPandas]: http://geopandas.org
+[OGR]: https://gdal.org/drivers/vector/index.html
+[Shapely]: https://shapely.readthedocs.io/en/latest/manual.html
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 35ae8a8d2..2c1c9e753 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -122,6 +122,7 @@ def doctype(self):
         'numpy>=1.7',
         'matplotlib<3.0.0',
         'pandas',
+        'geopandas',
         'pytest-runner',
         'setuptools>=0.8',
         'ipython==6.2.1',

From 4d5568b30e5d2a10e712a5d65159863b501fdd6f Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 16 Jul 2019 17:17:58 -0400
Subject: [PATCH 227/380] Raster catalog and read content and smoothing

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-catalogs.pymd |  45 ++---
 .../src/main/python/docs/raster-io.md         |   6 +-
 .../src/main/python/docs/raster-read.pymd     | 162 ++++++++++--------
 3 files changed, 116 insertions(+), 97 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 33d459ef8..1f89a7343 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,41 +1,36 @@
 # Raster Catalogs
 
-While much interesting processing can be done on single raster files, RasterFrames shines when _Catalogs_ of raster data are to be processed. In its simplest form, a _Catalog_ is a listing of URLs referencing raster files. This listing can be manifested as CSV in a character string or an external file, or as a Spark or Pandas DataFrame. This _Catalog_ then serves as the input into the `raster` DataSource, described in the next page.
+While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _Catalogs_ of raster data are to be processed. In its simplest form, a _Catalog_ is a listing of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This listing can be manifested as a DataFrame, CSV file or CSV string. The _Catalog_ is input into the `raster` DataSource, described in the next page.
 
-A _Catalog_ can be zero, one or two dimensions:
+A _Catalog_ can have one or two dimensions:
 
-* Zero-D: A single URL to a single raster file
-* One-D: Multiple URLs in a single column, where all referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different spatiotemporal locations (scenes).
-* Two-D: One or more columns of multiple URLs, where each column references the same content type, and each row represents the same spatiotemporal location. For example, red-, green-, and blue-band columns for scenes covering Europe. Each spatiotemporal location (scenes), and each raster cell within it must be of the same dimensions, extent, crs, etc.
+* One-D: A single column containing one or many URLs across the rows. All referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different places and times.
+* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row reperesents a single spatiotemporal location (or scene) with the same dimensions, extent, CRS, etc across the row.
 
 ## Creating a Catalog
 
-This section will provide some examples of creating your own _Catalogs_, as well as introduce some experimental _Catalogs_ built into RasterFrames. The consuption of _Catalogs_ is covered in more detail in the next page.
+This section will provide some examples of creating your own _Catalogs_, as well as introduce some experimental _Catalogs_ built into RasterFrames. Reading raster data represented by a _Catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
 
 ```python, echo=False
 from IPython.display import display
 from pyrasterframes.utils import create_rf_spark_session 
 from pyrasterframes.rasterfunctions import *
+import pandas as pd
 spark = create_rf_spark_session()
 ```
 
-```python
-from pyspark.sql import Row
-```
-
-### Zero-D
+### One-D
 
 A single URL is this simplest form of a catalog.
 
 ```python
+from pyspark.sql import Row
 my_cat = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
-# or
+# or 
 my_cat_df = spark.createDataFrame([Row(B01=my_cat)]) 
 ```
 
-### One-D
-
-Example of a single column representing the same content type:
+A single column represents the same content type with different observations along the rows. In this example it is band 1 of MODIS, which is visible red. In the example the location of the images is the same (h04v09) but the dates differ.
 
 ```python
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
@@ -46,11 +41,14 @@ my_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
 # or
 my_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
 my_cat_df.printSchema()
+# or
+pandas_cat = pd.DataFrame([{'B01': scene1_B01}, {'B01': scene2_B01}])
+my_cat = spark.createDataFrame(pandas_cat)
 ```
 
 ### Two-D
 
-Example of a multiple columns representing multiple content types (bands) across multiple scenes.
+Example of a multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same. The first column is band 1 and the second is band 2, near infrared.
 
 ```python
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
@@ -62,13 +60,15 @@ scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43
 # As CSV string
 my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02]) 
 # or
-my_cat_df = spark.createDataFrame([Row(B01=scene1_B01, B02=scene1_B02), Row(B01=scene2_B01, B02=scene2_B02)])
+my_cat_df = spark.createDataFrame([
+    Row(B01=scene1_B01, B02=scene1_B02), 
+    Row(B01=scene2_B01, B02=scene2_B02)])
 my_cat_df.printSchema()
 ```
 
 ## Using External Catalogs
 
-The simplest example of an external _Catalog_ is a CSV file (or a transformation of) in one of the formats above. Here's an extended example of reading an external CSV file of MODIS scenes and transforming it into a _Catalog_
+The simplest example of an external _Catalog_ is a DataFrame (or a transformation of) in one of the formats above. Here's an extended example of reading an external CSV file of MODIS scenes and transforming it into a _Catalog_. The metadata describing the content of each URL is an important aspect of processing raster data. This example includes some minimal metadata.
 
 ```python
 from pyspark import SparkFiles
@@ -76,7 +76,7 @@ from pyspark.sql import functions as F
 
 spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/2018-07-04_scenes.txt")
 
-# The scenes list file has index URIs take the form:
+# The scenes list file has index URIs in the download_url column, for example:
 #    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html    
 # Image URIs take the form:
 #    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
@@ -95,10 +95,9 @@ modis_catalog = spark.read \
 modis_catalog.printSchema()
 ```
 
-
 ## Using Built-in Experimental Catalogs
 
-RasterFrames comes with two experimental catalogs over the AWS PDS Landsat 8 and MODIS repositories. They are created by downloading the latest scene lists and transforming as in the prior example.
+RasterFrames comes with two experimental catalogs over the AWS PDS [Landsat 8][Landsat] and [MODIS][MODIS] repositories. They are created by downloading the latest scene lists and transforming as in the prior example.
 
 > Note: The first time you run these may take some time, as the catalogs are large. However, they are cached and subsequent invocations should be faster.
 
@@ -132,6 +131,8 @@ root
 
 ### Landsat 8
 
+The Landsat 8 catalog includes a richer set of metadata describing the contents of each scene.
+
 ```python, evaluate=False
 l8 = spark.read.format('aws-pds-l8-catalog').load()
 l8.printSchema()
@@ -162,3 +163,5 @@ root
  |-- BQA: string (nullable = true) 
 ```
 
+[MODIS]: https://docs.opendata.aws/modis-pds/readme.html
+[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index 22fc1c898..d10bab8f1 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -4,15 +4,15 @@ The standard mechanism by which any data is brought in and out of a Spark Datafr
 
 Three types of DataSources will be introduced: 
 
-* Catalog Readers
+* @ref:[Catalog Readers](raster-catalogs.md)
     - `aws-pds-l8-catalog`: built-in catalog over [Landsat on AWS][Landsat]
     - `aws-pds-modis-catalog`: built-in catalog over [MODIS on AWS][MODIS]
     - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
-* Raster Readers
+* @ref:[Raster Readers](raster-read.md)
     - `raster`: the standard reader for most raster data
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
     - `geotrellis`: for reading a [GeoTrellis layer][GTLayer])
-* Raster Writers 
+* @ref:[Raster Writers](raster-write.md)
     - `geotrellis`: for creating a [GeoTrellis layer][GTLayer]
     - `geotiff`: beta writer to GeoTiff file
     - [`parquet`][Parquet]: general purpose writer 
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index b791cbadc..74b749a61 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -1,4 +1,4 @@
-## Reading Raster Data
+# Reading Raster Data
 
 ```python, echo=False
 from IPython.display import display
@@ -7,29 +7,20 @@ from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
 ```
 
-RasterFrames registers a DataSource named `rasters` that enables reading of GeoTIFFs (and other formats when GDAL is installed) from arbitrary (sets of) URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket:
+RasterFrames registers a DataSource named `raster` that enables reading of GeoTIFFs (and other formats when @ref:[GDAL is installed](getting-started.md#installing-gdal)) from arbitrary URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket.
 
-```python
-data_root = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp'
-def s2_band(idx):
-    return "{}/B0{}.tif".format(data_root, idx)
-    
-```
-
-### Single Raster
+## Single Raster
 
 The simplest form is reading a single raster from a single URI:
 
-```python
-rf = spark.read.raster(s2_band(2))
+```python read_one_uri
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 rf.printSchema()
 ```
 
-\[Discuss the schema\]
-
-Let's unpack the `proj_raster` column and look at the contents in more detail. 
+Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_. 
 
-```python
+```python unpack_schema
 crs = rf.select(rf_crs("proj_raster").alias("value")).first()
 
 parts = rf.select(
@@ -38,51 +29,65 @@ parts = rf.select(
 )
 
 print("CRS", crs.value.crsProj4)
-parts.show(5)
+parts.show(5, False)
 
 ```
 
-Let's select a single tile and view it.
+You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on managable amounts of data per task. 
 
-```python
-tile = rf.select(rf_tile("proj_raster")).first()[0]
-display(tile)
-# todo verify its chilly
+```python count_by_uri
+rf.groupby(rf.proj_raster_path).count().show()
 ```
 
+Let's select a single tile and view it. The tile preview image as well as the string representation provide some basic information about the tile: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the tile. For more about cell types, refer to @ref:[the NoData page](noData-handling.md#cell-types).
 
-### Single Scene, Multiple Bands
+```python show_tile_sample
+tile = rf.select(rf_tile("proj_raster")).first()[0]
+display(tile)
+```
 
-Generally we'll want to work with multiple bands. If you have several singleband image files of the same scene, `raster` can make use of a CSV (or other DataFrame) specifying the band columns and scene rows.
+## URI Formats
 
-```python
-csv = """
-red,green,blue
-{},{},{}
-""".format(s2_band(4), s2_band(3), s2_band(2))
+/[ TODO Populate this with valid URI's we can use including how to use gdal /]
 
-print(csv)
+https://
+http:// ?
+s3a://
+s3n:// ?
+hdfs:// ?
+ftp:// ?
+azure ?
+google cloud ?
 
-rf2 = spark.read.raster(catalog=csv, catalog_col_names=['red', 'green', 'blue'])
-rf2.printSchema()
+gdal vsi? https://gdal.org/user/virtual_file_systems.html 
 
-rf2.show(5)
-```
 
-### Raster Catalogs
+## Raster Catalogs
 
-The example above will work with a reasonable number of rows in the CSV string. However, in many cases it's preferable to have that information organized in another DataFrame. For the purposes of this example use one of the AWS PDS daily scene listing files for MODIS surface reflectance as our catalog. 
+Considering the definition of a @ref:[_Catalog_](raster-catalogs.md) previously discussed, let's read the raster data contained in the catalog URIs. We will start with the @ref:[external catalog](raster-catalogs.md#using-external-catalogs) of MODIS surface reflectance that we previously demonstrated.
 
-```python
+```python catalog_prep
 from pyspark import SparkFiles
+from pyspark.sql import functions as F
+
 cat_filename = "2018-07-04_scenes.txt"
 spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/{}".format(cat_filename))
+
 modis_catalog = spark.read \
     .format("csv") \
     .option("header", "true") \
-    .load(SparkFiles.get(cat_filename))
+    .load(SparkFiles.get(cat_filename)) \
+    .withColumn('base_url', 
+        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',) 
+    ) \
+    .drop('download_url') \
+    .withColumn('red' , F.concat('base_url', F.lit("_B01.TIF"))) \
+    .withColumn('nir' , F.concat('base_url', F.lit("_B02.TIF")))
+
 modis_catalog.printSchema()
+
 print("Available scenes: ", modis_catalog.count())
+
 modis_catalog.show(5, truncate=False)
 ```
 
@@ -90,53 +95,64 @@ MODIS data products are delivered on a regular, consistent grid, making identifi
 
 ![MODIS Grid](https://modis-land.gsfc.nasa.gov/images/MODIS_sinusoidal_grid1.gif)
 
-For sake of example, lets say we wanted all the MODIS data right above the equator. The grid position is encoded in the GUID, with `h07` being the strip just above the equator . To go from `download_url` to band URL we strip off `index.html`, and append the GUID, band specifier, and file extension.
+For example, MODIS data right above the equator is all grid coordinates with `v07`.  
 
-```python
-from pyspark.sql import functions as F
-    
-# Index URIs take the form:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html    
-# Image URIs take the form:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
-
-equator = (modis_catalog 
-    .withColumn('base_url', 
-        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',) 
-    )
-.select(
-    F.concat('base_url', F.lit("_B01.TIF")).alias('red'),
-    F.concat('base_url', F.lit("_B02.TIF")).alias('nir')
-)
-.where(F.col('gid').like('%v07%'))
-)
-equator.show(3)
+```python catalog_filtering
+equator = modis_catalog.where(F.col('gid').like('%v07%'))
+equator.select('date', 'gid').show(10, False)
 ```
 
-Now that we have a catalog of multiple scenes and mutiple bands withit each scene, we can pass it along to `raster` load the imagery. To complete the example, we compute some aggregate statistics over NDVI.
+Now that we have prepared our catalog, we simply pass the DataFrame or CSV string to the `raster` DataSource to load the imagery. The `catalog_col_names` parameter gives the columns that contain the URI's to be read.
 
-```python
+```python read_catalog
 rf = spark.read.raster(
     catalog=equator, 
-    catalog_col_names=['red', 'nir']
-).repartition()
-rf.show(2, truncate=False)
-(rf 
-    .select(rf_normalized_difference('nir', 'red').alias('ndvi')) 
-    .agg(rf_agg_stats('ndvi').alias('stats')) 
-    .select('stats.*') 
-    .show(truncate=False)
+    catalog_col_names=['red', 'nir'],
 )
-    
+rf.printSchema()
+```
+
+Observe schema of the resulting dataframe has a projected raster struct for each column passed in `catalog_col_names`. For reference the URI is now in a column appended with `_path`. We can take a quick look at the representation of the data. We see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent from within that coverage.
+
+```python cat_read_sample
+rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')).show(3, False)
 ```
 
-### Lazy vs. Strict Raster Reads
+### Lazy Raster Reads
+
+By default the raster reads are delayed as long as possible. The DataFrame will contain metadata and pointers to the appropriate portion of the data until
+
+Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the realized tile is queried right away.
+
+```python lazy_demo
+uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
+spark.read.raster(uri) \
+    .select('proj_raster.tile').show(1, False)
 
-> Discuss the `spark.read.raster(lazy_tiles=False)` behavior
+spark.read.raster(uri, lazy_tiles=False) \
+    .select('proj_raster.tile').show(1, False)
+```
 
+In the initial examples on this page, we used `rf_tile` to explicitly request the realized tile from the lazy representation.
 
-### Multiband Rasters
+## Multiband Rasters
 
 > Need to know _a priori_ how many bands there are, specified with `band_indexes`.
-> Must be homogenous, or tolerant of `null` Tiles.
+
+band indexes start at 0
+
+
  
+> Must be homogenous, or tolerant of `null` Tiles.
+Not sure what that means?
+
+For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting dataframe represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
+
+```python Multiband
+mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif', 
+                       band_indexes=[0, 1, 2, 3],
+                      )
+mb.printSchema()
+```
+
+As with reading a singleband image we can use the `band_indexes` parameter to operate over a catalog of multiband rasters.

From e96a64c3eaeb01fd4457b881081f0222be2505aa Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 17 Jul 2019 09:30:47 -0400
Subject: [PATCH 228/380] Add multiband raster discussion

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-read.pymd     | 37 ++++++++++++++-----
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 74b749a61..a95942fb0 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -2,6 +2,7 @@
 
 ```python, echo=False
 from IPython.display import display
+import pandas as pd
 from pyrasterframes.utils import create_rf_spark_session 
 from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
@@ -48,8 +49,9 @@ display(tile)
 
 ## URI Formats
 
-/[ TODO Populate this with valid URI's we can use including how to use gdal /]
+/[ TODO Populate this with valid URI's schemes we can use with an example? including how to use gdal /]
 
+file://  (is this only valid for spark local? or if file is )
 https://
 http:// ?
 s3a://
@@ -137,16 +139,11 @@ In the initial examples on this page, we used `rf_tile` to explicitly request th
 
 ## Multiband Rasters
 
-> Need to know _a priori_ how many bands there are, specified with `band_indexes`.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands may represent intensity of different wavelengths of light (or other electromagnetic radiation). The different bands may represent other phenomena such as measurement time, quality indications, or additional measurements.
 
-band indexes start at 0
+When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader. 
 
-
- 
-> Must be homogenous, or tolerant of `null` Tiles.
-Not sure what that means?
-
-For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting dataframe represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
+For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting dataframe still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
 
 ```python Multiband
 mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif', 
@@ -155,4 +152,24 @@ mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.
 mb.printSchema()
 ```
 
-As with reading a singleband image we can use the `band_indexes` parameter to operate over a catalog of multiband rasters.
+If a band is passed into `band_index` that exceeds the number of bands in the raster, a projected raster column will still be generated in the schema but it will be full of `null` values.
+
+You can also pass a `catalog` and `band_indexes` together into the `raster` reader. This will create a projected raster column for the combination of all items passed into `catalog_col_names` and `band_indexes`. Again if a band in `band_indexes` exceeds the number of bands in a raster, it will have a `null` value for the corresponding column.
+
+Here is a trivial example with a _Catalog_ over multiband rasters. We specify two columns containing URIs and two bands, resulting in four projected raster columns.
+
+```python multiband_catalog
+import pandas as pd
+mb_cat = pd.DataFrame([
+    {'foo': 's3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
+     'bar': 's3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
+    },
+])
+mb2 = spark.read.raster(catalog=spark.createDataFrame(mb_cat),
+                       catalog_col_names=['foo', 'bar'],
+                       band_indexes=[0, 1],
+                       tile_dimensions=(64,64)
+                      )
+mb2.printSchema()
+```
+ 
\ No newline at end of file

From e02b6a7fce51b269e8c9f72329cfa810f0ddcc40 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 17 Jul 2019 16:57:28 -0400
Subject: [PATCH 229/380] NoData chapter, cell types, masking extended example
 and restyle as NoData

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/nodata-handling.pymd | 156 ++++++++++++++++++
 .../src/main/python/docs/reference.pymd       |  65 ++++----
 2 files changed, 188 insertions(+), 33 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index dde8433a2..795771577 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -1,2 +1,158 @@
 # "NoData" Handling
 
+## What is NoData?
+
+In raster operations, the preservation and correct processing of missing observations is very important. In [most dataframes and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a null or NaN value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the NoData value. 
+
+RasterFrames provides a variety of functions to inspect and manage NoData within `tile`s. 
+
+## Cell Types
+
+To understand how NoData is handled in RasterFrames, we first need to understand the different underlying types of data called cell types. The cell types are GeoTrellis `CellType`s, so the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values) is a valuable resource on how these are defined.
+
+Use the function `rf_cell_type` to find the cell type of a specific set of raster data.
+
+```python setup, echo=False
+import pyrasterframes
+from pyrasterframes.rasterfunctions import *
+import pyrasterframes.rf_ipython
+from IPython.display import display
+
+spark = pyrasterframes.get_spark_session()
+```
+
+```python ct_from_sen
+spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif') \
+    .select(rf_cell_type('proj_raster')).distinct().show()
+```
+
+The function `rf_cell_types` provides a convenient list of basic cell types. Note that this list is not exhaustive. 
+
+```python
+rf_cell_types()
+```
+
+### Understanding Cell Types and NoData
+
+Use the `CellType` class to learn more about a specific cell type.
+
+```python
+from pyrasterframes.rf_types import CellType
+ct = CellType('uint16raw')
+ct, ct.is_floating_point(), ct.has_no_data()
+```
+
+We can see that for the above data source, there is no defined NoData value. This means that each value is interpreted as a valid observation. Often such data is meant to be combined with another band indicating the quality of observations at each location. The lack of NoData is indicated by the `raw` at the end of the type name. Consider also the `uint16` cell type.
+
+```python
+from pyrasterframes.rf_types import CellType
+ct = CellType('uint16')
+ct, ct.is_floating_point(), ct.has_no_data(), ct.no_data_value()
+```
+
+In this case, the minimum value of 0 is designated as the NoData value. For integral valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with a `ud`. 
+
+```python
+CellType.uint16().with_no_data_value(99).cell_type_name
+```
+
+Floating point types by default have `NaN` as the NoData value. However a user-defined NoData can be set. 
+
+```python float_ud
+print(CellType.float32().no_data_value())
+print(CellType.float32().with_no_data_value(-99.9).no_data_value())
+```
+
+## Masking
+
+Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no defined NoData. The quality information is in a separate file called the scene classification (SCL), which delineates areas of missing data and probable clouds. For much more information on that, see the [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm). Figure 3 tells us how to interpret the scene classification. For this example, we will exclude NoData, defective pixels, probable clouds, and cirrus clouds: values 0, 1, 8, 9, and 10.
+
+The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog and now the blue band and SCL tiles are aligned across rows.
+
+```python blue_scl_cat
+from pyspark.sql import Row
+
+blue_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
+scl_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/SCL.tif'
+cat = spark.createDataFrame([Row(blue=blue_uri, scl=scl_uri),])
+unmasked = spark.read.raster(catalog=cat, catalog_col_names=['blue', 'scl'])
+unmasked.printSchema()
+unmasked.select(rf_cell_type('blue'), rf_cell_type('scl')).distinct().show()
+```
+
+Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create a new tile column containing our indicator of unwanted pixels, as defined above.
+
+```python def_mask
+from pyspark.sql.functions import lit
+
+unmasked = unmasked.withColumn('nodata', rf_local_equal('scl', lit(0)))
+unmasked = unmasked.withColumn('defect', rf_local_equal('scl', lit(1)))
+unmasked = unmasked.withColumn('cloud8', rf_local_equal('scl', lit(8)))
+unmasked = unmasked.withColumn('cloud9', rf_local_equal('scl', lit(9)))
+unmasked = unmasked.withColumn('cirrus', rf_local_equal('scl', lit(10)))
+
+unmasked = unmasked.withColumn('mask', rf_local_add('nodata', 'defect'))
+unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cloud8'))
+unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cloud9'))
+unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cirrus'))
+
+unmasked.select(rf_cell_type('mask')).distinct().show()
+```
+
+Now we will use the @ref:[`rf_mask_by_value`](reference.md#rf-mask-by-value) to designate the cloudy and other unwanted pixels as NoData in the blue column. Because there is not a NoData already defined, we will choose one. Note that in this particular example the minimum value is greater than zero, so we can use 0 as the NoData value.
+
+```python pick_nd
+unmasked.agg(rf_agg_stats('blue').min.alias('blue_min')).show()
+```
+
+We can now construct the cell type string for our blue band's cell type, but designating 0 as NoData.
+
+```python get_ct_string
+blue_ct = unmasked.select(rf_cell_type('blue')).distinct().first()[0][0]
+masked_blue_ct = CellType(blue_ct).with_no_data_value(0)
+masked_blue_ct.cell_type_name
+```
+
+Convert the cell type and apply the mask. Since the mask column is bit type, the addition done above was equivalent to a logical or. So the true values are 1.
+
+```python mask_blu 
+with_nd = rf_convert_cell_type('blue', masked_blue_ct.cell_type_name)
+masked = unmasked.withColumn('blue_masked', 
+            rf_mask_by_value(with_nd, 'mask', lit(1))) \
+            .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
+```
+
+We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` tile.
+
+```python
+masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
+```
+
+It's also nice to view a sample.
+
+```python show_masked
+sample = masked.orderBy(-rf_no_data_cells('blue_masked')).select(rf_tile('blue_masked'), rf_tile('scl')).first()
+display(sample[0])
+```
+
+And the original SCL data.
+
+```python show_scl
+display(sample[1])
+```
+
+## NoData in Arithmetic Operations
+
+local algebra example; same celltype what happens to nodata 
+Possibly use st_geomFromWkt and rf_rasterize to create something to work from
+
+agg
+
+
+## Dealing with Multiple Cell Types
+
+Quick demo of one ND tile one raw tile
+
+Quick demo of ND in two different cell types
+
+
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 8bf336964..f2866fbd3 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -12,6 +12,9 @@ For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`]
 
 ## List of Available SQL and Python Functions
 
+@@toc { depth=3 }
+
+
 To import RasterFrames functions into the environment, import from `pyrasterframes.rasterfunctions`.
 
 ```python
@@ -55,13 +58,13 @@ Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`]
 
 ### Tile Metadata and Mutation
 
-Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and nodata](reference.md#masking-and-nodata) for additional discussion of cell types.
+Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and NoData](reference.md#masking-and-NoData) for additional discussion of cell types.
 
 #### rf_cell_types
 
     Array[String] rf_cell_types()
     
-Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on nodata](reference.md#masking-and-nodata) for additional details.
+Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on NoData](reference.md#masking-and-NoData) for additional details.
 
 ```python
 rf_cell_types() 
@@ -149,12 +152,12 @@ Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with
     Tile rf_rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
     
 
-Convert a vector Geometry `geom` into a Tile representation. The `value` will be "burned-in" to the returned `tile` where the `geom` intersects the `tile_bounds`. Returned `tile` will have shape `tile_columns` by `tile_rows`. Values outside the `geom` will be assigned a nodata value. Returned `tile` has cell type `int32`, note that `value` is of type Int.
+Convert a vector Geometry `geom` into a Tile representation. The `value` will be "burned-in" to the returned `tile` where the `geom` intersects the `tile_bounds`. Returned `tile` will have shape `tile_columns` by `tile_rows`. Values outside the `geom` will be assigned a NoData value. Returned `tile` has cell type `int32`, note that `value` is of type Int.
 
 Parameters `tile_columns` and `tile_rows` are literals, not column expressions. The others are column expressions.
 
 
-Example use. In the code snip below, you can visualize the `tri` and `b` geometries with tools like [Wicket](https://arthur-e.github.io/Wicket/sandbox-gmaps3.html). The result is a right triangle burned into the `tile`, with nodata values shown as ∘.
+Example use. In the code snip below, you can visualize the `tri` and `b` geometries with tools like [Wicket](https://arthur-e.github.io/Wicket/sandbox-gmaps3.html). The result is a right triangle burned into the `tile`, with NoData values shown as ∘.
 
 
 ```python
@@ -182,57 +185,53 @@ Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/do
     
 Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
 
-### Masking and Nodata
-
-In raster operations, the preservation and correct processing of missing operations is very important. The idea of missing data is often expressed as a null or NaN. In raster data, missing observations are often termed NODATA; we will style them as nodata in this document.  RasterFrames provides a variety of functions to manage and inspect nodata within `tile`s. 
+### Masking and NoData
 
-See also statistical summaries to get the count of data and nodata values per `tile` and aggregate in a `tile` column: @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells).
+See @ref:[NoData handling](nodata-handling.md) for conceptual discussion of cell types and NoData.
 
-It is important to note that not all cell types support the nodata representation: these are `bool` and when the cell type string ends in `raw`.
+There are statistical functions of the count of data and NoData values per `tile` and aggregate over a `tile` column: @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells).
 
-For integral valued cell types, the nodata is marked by a special sentinel value. This can be a default, typically zero or the minimum value for the underlying data type. The nodata value can also be a user-defined value. For example if the value 4 is to be interpreted as nodata, the cell type will read 'int32ud4'. 
-
-For float cell types, the nodata can either be NaN or a user-defined value; for example `'float32ud-999.9'` would mean the value -999.9 is interpreted as a nodata.
-
-For more reading about cell types and ndodata, see the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values).
+Masking is a raster operation that sets specific cells to NoData based on the values in another raster.
 
 #### rf_mask
 
     Tile rf_mask(Tile tile, Tile mask)
     
-Where the `mask` contains nodata, replace values in the `tile` with nodata.
+Where the `mask` contains NoData, replace values in the `tile` with NoData.
+
+Returned `tile` cell type will be coerced to one supporting NoData if it does not already.
 
-Returned `tile` cell type will be coerced to one supporting nodata if it does not already.
+See also @[`rf_rasterize`](reference.md#rf-rasterize).
  
 
 #### rf_inverse_mask
 
     Tile rf_inverse_mask(Tile tile, Tile mask)
     
-Where the `mask` _does not_ contain nodata, replace values in `tile` with nodata. 
+Where the `mask` _does not_ contain NoData, replace values in `tile` with NoData. 
 
 #### rf_mask_by_value
 
     Tile rf_mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
     
-Generate a `tile` with the values from `data_tile`, with nodata in cells where the `mask_tile` is equal to `mask_value`. 
+Generate a `tile` with the values from `data_tile`, with NoData in cells where the `mask_tile` is equal to `mask_value`. 
 
 
 #### rf_is_no_data_tile
 
     Boolean rf_is_no_data_tile(Tile)
  
-Returns true if `tile` contains only nodata. By definition returns false if cell type does not support nodata. To count nodata cells or data cells, see @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_local_no_data_cells`](reference.md#rf-agg-local-no-data-cells), and @ref:[`rf_agg_local_data_cells`](reference.md#rf-agg-local-data-cells). This function is distinguished from @ref:[`rf_for_all`](reference.md#rf-for-all), which tests that values are not nodata and nonzero.
+Returns true if `tile` contains only NoData. By definition returns false if cell type does not support NoData. To count NoData cells or data cells, see @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_local_no_data_cells`](reference.md#rf-agg-local-no-data-cells), and @ref:[`rf_agg_local_data_cells`](reference.md#rf-agg-local-data-cells). This function is distinguished from @ref:[`rf_for_all`](reference.md#rf-for-all), which tests that values are not NoData and nonzero.
 
 #### rf_with_no_data
 
     Tile rf_with_no_data(Tile tile, Double no_data_value)
     
-Python only. Return a `tile` column marking as nodata all cells equal to `no_data_value`.
+Python only. Return a `tile` column marking as NoData all cells equal to `no_data_value`.
 
 The `no_data_value` argument is a literal Double, not a Column expression.
 
-If input `tile` had a nodata value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, nodata values on the input `tile` remain nodata values on the output. For integral cell type `tile`s, the previous nodata values become literal values. 
+If input `tile` had a NoData value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, NoData values on the input `tile` remain NoData values on the output. For integral cell type `tile`s, the previous NoData values become literal values. 
 
 ### Local Map Algebra
 
@@ -435,14 +434,14 @@ spark.sql("""
     Double rf_tile_sum(Tile tile)
     
 
-Computes the sum of cells in each row of column `tile`, ignoring nodata values.
+Computes the sum of cells in each row of column `tile`, ignoring NoData values.
 
 #### rf_tile_mean
 
     Double rf_tile_mean(Tile tile)
     
 
-Computes the mean of cells in each row of column `tile`, ignoring nodata values.
+Computes the mean of cells in each row of column `tile`, ignoring NoData values.
 
 
 #### rf_tile_min
@@ -450,7 +449,7 @@ Computes the mean of cells in each row of column `tile`, ignoring nodata values.
     Double rf_tile_min(Tile tile)
     
 
-Computes the min of cells in each row of column `tile`, ignoring nodata values.
+Computes the min of cells in each row of column `tile`, ignoring NoData values.
 
 
 #### rf_tile_max
@@ -458,7 +457,7 @@ Computes the min of cells in each row of column `tile`, ignoring nodata values.
     Double rf_tile_max(Tile tile)
     
 
-Computes the max of cells in each row of column `tile`, ignoring nodata values.
+Computes the max of cells in each row of column `tile`, ignoring NoData values.
 
 
 #### rf_no_data_cells
@@ -466,7 +465,7 @@ Computes the max of cells in each row of column `tile`, ignoring nodata values.
     Long rf_no_data_cells(Tile tile)
     
 
-Return the count of nodata cells in the `tile`.
+Return the count of NoData cells in the `tile`.
 
 #### rf_data_cells
 
@@ -478,19 +477,19 @@ Return the count of data cells in the `tile`.
 
     Boolean rf_exists(Tile tile)
     
-Returns true if any cells in the tile are true (non-zero and not nodata). 
+Returns true if any cells in the tile are true (non-zero and not NoData). 
 
 #### rf_for_all
 
     Boolean rf_for_all(Tile tile)
 
-Returns true if all cells in the tile are true (non-zero and not nodata). See also @ref:[`rf_is_no_data_tile](reference.md#rf-is-no-data-tile), which tests that all cells are nodata.
+Returns true if all cells in the tile are true (non-zero and not NoData). See also @ref:[`rf_is_no_data_tile](reference.md#rf-is-no-data-tile), which tests that all cells are NoData.
 
 #### rf_tile_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
     
-Computes the following statistics of cells in each row of column `tile`: data cell count, nodata cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring nodata values. 
+Computes the following statistics of cells in each row of column `tile`: data cell count, NoData cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring NoData values. 
 
 
 #### rf_tile_histogram
@@ -547,7 +546,7 @@ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
     
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).mean`
 
-Aggregates over the `tile` and return the mean of cell values, ignoring nodata. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.mean`.
+Aggregates over the `tile` and return the mean of cell values, ignoring NoData. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.mean`.
 
 
 #### rf_agg_data_cells
@@ -571,14 +570,14 @@ rf.agg(F.sum(rf_data_cells(rf.tile)).alias('agg_data_cell'))
     
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
-Aggregates over the `tile` and return the count of nodata cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
+Aggregates over the `tile` and return the count of NoData cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
 
 #### rf_agg_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_agg_stats(Tile tile)
     
 
-Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of nodata cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of nodata. 
+Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of NoData. 
 
 #### rf_agg_approx_histogram
 
@@ -677,7 +676,7 @@ Compute the cell-local count of data cells over Tiles in a column. Returned `til
 
     Tile rf_agg_local_no_data_cells(Tile tile)
 
-Compute the cell-local count of nodata cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
+Compute the cell-local count of NoData cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
 #### rf_agg_local_stats 
 

From 76ad2fe4965fe8169d266aa541aa7616fcbf5a40 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 17 Jul 2019 17:17:43 -0400
Subject: [PATCH 230/380] Fix some docs links

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-catalogs.pymd | 5 +++--
 pyrasterframes/src/main/python/docs/raster-read.pymd     | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 1f89a7343..55d5ec68f 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -5,7 +5,7 @@ While much interesting processing can be done on a @ref:[single raster file](ras
 A _Catalog_ can have one or two dimensions:
 
 * One-D: A single column containing one or many URLs across the rows. All referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different places and times.
-* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row reperesents a single spatiotemporal location (or scene) with the same dimensions, extent, CRS, etc across the row.
+* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row reperesents a single spatiotemporal location (or scene) with the same dimensions, extent, [_CRS_][CRS], etc across the row.
 
 ## Creating a Catalog
 
@@ -164,4 +164,5 @@ root
 ```
 
 [MODIS]: https://docs.opendata.aws/modis-pds/readme.html
-[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
\ No newline at end of file
+[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index a95942fb0..29cb55c14 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -40,7 +40,7 @@ You can also see that the single raster has been broken out into many arbitrary
 rf.groupby(rf.proj_raster_path).count().show()
 ```
 
-Let's select a single tile and view it. The tile preview image as well as the string representation provide some basic information about the tile: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the tile. For more about cell types, refer to @ref:[the NoData page](noData-handling.md#cell-types).
+Let's select a single tile and view it. The tile preview image as well as the string representation provide some basic information about the tile: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the tile. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
 
 ```python show_tile_sample
 tile = rf.select(rf_tile("proj_raster")).first()[0]
@@ -172,4 +172,5 @@ mb2 = spark.read.raster(catalog=spark.createDataFrame(mb_cat),
                       )
 mb2.printSchema()
 ```
- 
\ No newline at end of file
+
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file

From e5deda712d95fdab3f8121934eb21eb072b5c2c8 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 22 Jul 2019 13:10:28 -0400
Subject: [PATCH 231/380] time series docs

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/time-series.pymd     | 117 +++++++++++++++++-
 pyrasterframes/src/main/python/setup.py       |   1 +
 2 files changed, 117 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
index 304f05a9d..6edd61ed1 100644
--- a/pyrasterframes/src/main/python/docs/time-series.pymd
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -1,4 +1,119 @@
 # Time Series
 
-/[ Example of generating time series of NDVI as visual phenology indicators. /]
+## Analysis Plan
 
+```python setup, echo=False
+from IPython.display import display
+import pyrasterframes
+from pyrasterframes.rasterfunctions import *
+import pyrasterframes.rf_ipython
+
+spark = pyrasterframes.get_spark_session()
+```
+
+In this example we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will start with our @ref:[built-in MODIS data catalog](raster-catalogs.md#using-built-in-experimental-catalogs).
+
+```python catalog
+cat = spark.read.format('aws-pds-modis-catalog').load().repartition(200)
+cat.printSchema()
+```
+
+We will summarize the change in NDVI over 2018 in the Cuyahoga Valley National Park in Ohio USA. First, we will retrieve open vector data delineating the park boundary from the US National Park Service's LandsNet.
+
+```python get_park_boundary
+import requests
+import geopandas
+nps_data_query_url = 'https://services1.arcgis.com/fBc8EJBxQRMcHlei/arcgis/rest/services/NPS_Park_Boundaries/FeatureServer/0/query?where=1%3D1&outFields=*&geometry=-82.451%2C41.075%2C-80.682%2C41.436&geometryType=esriGeometryEnvelope&inSR=4326&spatialRel=esriSpatialRelIntersects&outSR=4326&f=json'
+r = requests.get(nps_data_query_url)
+with open('/tmp/parks.geojson', 'wb') as f:
+    for chunk in r.iter_content(chunk_size=128):
+        f.write(chunk)
+
+park_df = geopandas.read_file('/tmp/parks.geojson')
+park_geo = park_df[park_df.UNIT_CODE=='CUVA'].geometry[0]
+
+park_geo.wkt[:100]
+```
+
+The entire park boundary is contained in MODIS granule h11 v4. We will simply filter on this granule, rather than using a proper @ref:[spatial relation](vector-data.md#geomesa-functions-and-spatial-relations). The time period selected should show the change in plant vigor as leaves emerge over the spring into early summer.
+
+```python query_catalog
+from pyspark.sql.functions import lit
+park_cat = cat.filter(
+                    (cat.granule_id == 'h11v04') &
+                    (cat.acquisition_date > lit('2018-02-19')) &
+                    (cat.acquisition_date < lit('2018-07-01')) 
+            )
+park_cat.printSchema()
+```
+
+## Catalog Read
+
+Now we have a catalog with several months of MODIS data for a single granule. However the granule is larger than our park boundary. We will combine the park geometry with the catalog, and read only the bands of interest to compute NDVI, which we discussed @ref:[earlier](local-algebra.md#computing-ndvi).
+
+```python read_catalog
+raster_cols = ['B01', 'B02',] # red and near-infrared respectively
+park_rf = spark.read.raster(catalog=park_cat.select(['acquisition_date', 'granule_id'] + raster_cols), 
+                            catalog_col_names=raster_cols,
+                           ) \
+                    .withColumn('park', st_geomFromWKT(lit(park_geo.wkt))) 
+park_rf.createOrReplaceTempView('catalog_read')
+park_rf.printSchema()
+```
+
+## Vector and Raster Data Interaction
+
+Now we have the vector representation of the park boundary alongside the tiles of red and near infrared bands. Next we need to create a tile representation of the park to allow us to limit the time series analysis to pixels within the park. This is similar to the masking operation demonstrated in @ref:[NoData handling](nodata-handling.md#masking).
+
+We do this using two SQL transformation. This demonstrates that we can use any `rf_` or `st_` function in SQL. The first one will reproject the park boundary from coordinates to the MODIS sinusoidal projection. The second one will create a new tile aligned with the imagery containing value 1 where the pixels are contained within the park and NoData elsewhere.
+
+```python burn_in
+cr_1 = spark.sql("""
+SELECT *, 
+    st_reproject(park, 'EPSG:4326', rf_crs(B01).crsProj4) as park_native
+FROM catalog_read
+""")
+cr_1.createOrReplaceTempView('cr_1')
+
+cr_2 = spark.sql("""
+SELECT *, 
+    rf_rasterize(park_native, rf_geometry(B01), 1, rf_dimensions(B01).cols, rf_dimensions(B01).rows) as park_tile
+FROM cr_1
+""").where(rf_tile_sum('park_tile') > 0)
+cr_2.printSchema()
+```
+
+## Create Time Series
+
+Next we will compute NDVI as the normalized difference of near infrared (band 2) and red (band 1). The tiles are masked by the `park_tile`. We will then aggregate across the remaining values to arrive at an average NDVI for each week of the year. Note that the computation is creating a weighted average, weighting by the number of valid observations per week.
+
+```python ndvi_time_series
+from pyspark.sql.functions import col, year, weekofyear, month
+from pyspark.sql.functions import sum as sql_sum
+
+rf_ndvi = cr_2.withColumn('ndvi', rf_normalized_difference('B02', 'B01')) \
+              .withColumn('ndvi_masked', rf_mask('ndvi', 'park_tile'))
+                  
+time_series = rf_ndvi \
+        .withColumn('ndvi_wt', rf_tile_sum('ndvi_masked')) \
+        .withColumn('wt', rf_data_cells('ndvi_masked')) \
+        .groupby(year('acquisition_date').alias('year'), weekofyear('acquisition_date').alias('week')) \
+        .agg(sql_sum('ndvi_wt').alias('ndvi_wt'), sql_sum('wt').alias('wt')) \
+        .withColumn('ndvi', col('ndvi_wt') / col('wt'))
+
+```
+
+Finally we will take a look at the NDVI over time.
+
+```python time_series_display
+import matplotlib.pyplot as plt
+
+time_series_pdf = time_series.toPandas()
+time_series_pdf = time_series_pdf.sort_values('week')
+plt.plot(time_series_pdf['week'], time_series_pdf['ndvi'], 'go-')
+plt.xlabel('Week of year, 2018')
+plt.ylabel('NDVI')
+plt.title('Cuyahoga Valley NP Green-up')
+```
+
+We can see two fairly clear elbows in the curve at week 17 and week 21, indicating the start and end of the green up period. Estimation of such parameters is one technique [phenology](https://en.wikipedia.org/wiki/Phenology) researchers use to monitor changes in climate and environment!
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 2c1c9e753..91d5cd188 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -123,6 +123,7 @@ def doctype(self):
         'matplotlib<3.0.0',
         'pandas',
         'geopandas',
+        'requests',
         'pytest-runner',
         'setuptools>=0.8',
         'ipython==6.2.1',

From 6eb934bd9c82a3314caca3aecf29b7c6c152add3 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 22 Jul 2019 13:22:35 -0400
Subject: [PATCH 232/380] Add numpy interop outline to populate; tweaks of
 opportunity

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../expressions/localops/NormalizedDifference.scala       | 2 +-
 pyrasterframes/README.md                                  | 2 +-
 pyrasterframes/src/main/python/docs/pandas-numpy.md       | 8 ++++++++
 3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
index 079fd7273..e62ccfc37 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/NormalizedDifference.scala
@@ -38,7 +38,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
     * right - second tile argument""",
   examples = """
   Examples:
-    > SELECT _FUNC_(nir, red);
+    > SELECT _FUNC_(nir, red) as ndvi;
        ..."""
 )
 case class NormalizedDifference(left: Expression, right: Expression) extends BinaryRasterOp with CodegenFallback {
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index a7c3b3235..b857762f5 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -135,7 +135,7 @@ Observe the output messages such as:
 
     [info] Python .whl file written to '/Users/monty/rasterframes/pyrasterframes/target/python/dist/pyrasterframes-0.8.0.dev0-py2.py3-none-any.whl'
     
-This wheel is suitable for publishing to a package index. See [https://packaging.python.org/tutorials/packaging-projects/#uploading-the-distribution-archives](packaging.python.org).
+This wheel is suitable for publishing to a package index. See [packaging.python.org](https://packaging.python.org/tutorials/packaging-projects/#uploading-the-distribution-archives).
 
 ## Build Internals
 
diff --git a/pyrasterframes/src/main/python/docs/pandas-numpy.md b/pyrasterframes/src/main/python/docs/pandas-numpy.md
index 35c124f61..812825170 100644
--- a/pyrasterframes/src/main/python/docs/pandas-numpy.md
+++ b/pyrasterframes/src/main/python/docs/pandas-numpy.md
@@ -1,2 +1,10 @@
 # Pandas and NumPy Interoperabilty
 
+\[ be sure to discuss the performance implications; basically do as much on spark as possible and expect sluggish collect and udt applications \]
+
+* Small/realistic DF toPandas, 
+    * inspect contents of what you get; t.cells is np ndarray
+    * reference to writing page section on previews.
+* UDT on tile; log1p from test_udf_on_tile_type_output
+* Bonus : pandas 
+* Array to spark dataframe; using the Tile class

From 0642a97b22cd559c4e728ff723817742fdfae23a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 18 Jul 2019 16:50:16 -0400
Subject: [PATCH 233/380] Fix for #190; L8 catalog use Extent schema not
 envelope

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../awspds/L8CatalogRelationTest.scala        | 23 +++++++++++++++++--
 .../datasource/awspds/PDSFields.scala         |  2 +-
 2 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
index cfc901b9a..9b6d8480f 100644
--- a/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
+++ b/experimental/src/it/scala/org/locationtech/rasterframes/experimental/datasource/awspds/L8CatalogRelationTest.scala
@@ -62,10 +62,29 @@ class L8CatalogRelationTest extends TestEnvironment {
         WHERE
          st_intersects(st_geometry(bounds_wgs84), st_geomFromText('LINESTRING (-39.551 -7.1881, -72.2461 -45.7062)')) AND
          acquisition_date > to_timestamp('2017-11-01') AND
-         acquisition_date <= to_timestamp('2017-11-03')
+         acquisition_date <= to_timestamp('2017-12-13')
         """)
 
-      scenes.count() shouldBe > (200L)
+      scenes.count() shouldBe > (100L)
+    }
+
+    it("should construct expected extents") {
+      catalog.createOrReplaceTempView("l8_catalog")
+
+      catalog.filter($"bounds_wgs84.xmin" > $"bounds_wgs84.xmax").count() shouldBe (0)
+      catalog.filter($"bounds_wgs84.ymin" > $"bounds_wgs84.ymax").count() shouldBe (0)
+
+      val geo_area_row = spark.sql(
+        """
+           SELECT min(st_area(st_geometry(bounds_wgs84))) AS area
+           FROM l8_catalog
+           WHERE st_intersects(st_geometry(bounds_wgs84), st_geomFromText('LINESTRING(-78.035 39.004,-80.166 37.241)')) AND
+          acquisition_date > to_timestamp('2017-11-01') AND
+          acquisition_date <= to_timestamp('2017-11-16')
+        """).first()
+      val geo_area = geo_area_row.getDouble(0)
+      geo_area shouldBe < (6.5)
+      geo_area shouldBe > (4.5)
     }
   }
 
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
index a0af40148..40f8949f7 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/PDSFields.scala
@@ -46,7 +46,7 @@ trait PDSFields {
   final val ROW = StructField("row", ShortType, false)
   final val BOUNDS = StructField(GEOMETRY_COLUMN.columnName, JTSTypes.GeometryTypeInstance, false)
   final def BOUNDS_WGS84 = StructField(
-    "bounds_wgs84", StandardEncoders.envelopeEncoder.schema, false
+    "bounds_wgs84", StandardEncoders.extentEncoder.schema, false
   )
 }
 

From 4b2ecf7987efa0e73386bbfbbf51328dda02035e Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 23 Jul 2019 11:29:09 -0400
Subject: [PATCH 234/380] convert function ref code chunks to running code

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/reference.pymd       | 252 +++++++-----------
 1 file changed, 95 insertions(+), 157 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index f2866fbd3..da268f32f 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -1,6 +1,6 @@
 # Function Reference
 
-RasterFrames provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module. 
+RasterFrames provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module.
 
 The convention in this document will be to define the function signature as below, with its return type, the function name, and named arguments with their types.
 
@@ -39,7 +39,7 @@ RasterFrames provides some additional functions for vector geometry operations.
     Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
 
-Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems. 
+Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
 
 
 #### st_extent
@@ -50,11 +50,11 @@ Extracts the bounding box (extent/envelope) of the geometry.
 
 See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#st-envelope) which returns a Geometry type.
 
-#### st_geometry 
+#### st_geometry
 
     Geometry st_geometry(Struct[Double xmin, Double xmax, Double ymin, Double ymax] extent)
-    
-Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`](reference.md#st-extent) or @ref:[`rf_extent`](reference.md#rf-extent). 
+
+Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`](reference.md#st-extent) or @ref:[`rf_extent`](reference.md#rf-extent).
 
 ### Tile Metadata and Mutation
 
@@ -63,29 +63,26 @@ Functions to access and change the particulars of a `tile`: its shape and the da
 #### rf_cell_types
 
     Array[String] rf_cell_types()
-    
+
 Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on NoData](reference.md#masking-and-NoData) for additional details.
 
-```python
-rf_cell_types() 
-``` 
 
 #### rf_dimensions
 
     Struct[Int, Int] rf_dimensions(Tile tile)
-   
+
 Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
 #### rf_cell_type
 
     Struct[String] rf_cell_type(Tile tile)
-    
-Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_convert_cell_type](reference.md#rf-convert-cell-type). 
+
+Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_convert_cell_type](reference.md#rf-convert-cell-type).
 
 #### rf_tile
 
     Tile rf_tile(ProjectedRasterTile proj_raster)
-    
+
 Get the `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
 
 #### rf_extent
@@ -99,23 +96,23 @@ Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `Ras
 
     Struct[String] rf_crs(ProjectedRasterTile proj_raster)
     Struct[String] rf_crs(RasterSource proj_raster)
-    
+
 Fetch the [proj4](https://proj4.org/) string representation of the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
 #### rf_convert_cell_type
 
     Tile rf_convert_cell_type(Tile tileCol, String cellType)
 
-Convert `tileCol` to a different cell type. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function. 
+Convert `tileCol` to a different cell type. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function.
 
 #### rf_resample
 
     Tile rf_resample(Tile tile, Double factor)
     Tile rf_resample(Tile tile, Int factor)
     Tile rf_resample(Tile tile, Tile shape_tile)
-    
 
-Change the tile dimension. Passing a numeric `factor` will scale the number of columns and rows in the tile: 1.0 is the same number of columns and row; less than one downsamples the tile; and greater than one upsamples the tile. Passing a `shape_tile` as the second argument outputs `tile` having the same number of columns and rows as `shape_tile`. All resampling is by nearest neighbor method. 
+
+Change the tile dimension. Passing a numeric `factor` will scale the number of columns and rows in the tile: 1.0 is the same number of columns and row; less than one downsamples the tile; and greater than one upsamples the tile. Passing a `shape_tile` as the second argument outputs `tile` having the same number of columns and rows as `shape_tile`. All resampling is by nearest neighbor method.
 
 ### Tile Creation
 
@@ -142,7 +139,7 @@ Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the sp
 #### rf_make_constant_tile
 
     Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
-    
+
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
@@ -150,7 +147,7 @@ Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with
 #### rf_rasterize
 
     Tile rf_rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
-    
+
 
 Convert a vector Geometry `geom` into a Tile representation. The `value` will be "burned-in" to the returned `tile` where the `geom` intersects the `tile_bounds`. Returned `tile` will have shape `tile_columns` by `tile_rows`. Values outside the `geom` will be assigned a NoData value. Returned `tile` has cell type `int32`, note that `value` is of type Int.
 
@@ -165,7 +162,7 @@ spark.sql("""
 SELECT rf_render_ascii(
         rf_rasterize(tri, b, 8, 10, 10))
 
-FROM 
+FROM
   ( SELECT st_geomFromWKT('POLYGON((1.5 0.5, 1.5 1.5, 0.5 0.5, 1.5 0.5))') AS tri,
            st_geomFromWKT('POLYGON((0.0 0.0, 2.0 0.0, 2.0 2.0, 0.0 2.0, 0.0 0.0))') AS b
    ) r
@@ -176,13 +173,13 @@ FROM
 #### rf_array_to_tile
 
     Tile rf_array_to_tile(Array arrayCol, Int numCols, Int numRows)
-    
+
 Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), filling values in row-major order.
 
 #### rf_assemble_tile
 
     Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
-    
+
 Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
 
 ### Masking and NoData
@@ -196,53 +193,53 @@ Masking is a raster operation that sets specific cells to NoData based on the va
 #### rf_mask
 
     Tile rf_mask(Tile tile, Tile mask)
-    
+
 Where the `mask` contains NoData, replace values in the `tile` with NoData.
 
 Returned `tile` cell type will be coerced to one supporting NoData if it does not already.
 
 See also @[`rf_rasterize`](reference.md#rf-rasterize).
- 
+
 
 #### rf_inverse_mask
 
     Tile rf_inverse_mask(Tile tile, Tile mask)
-    
-Where the `mask` _does not_ contain NoData, replace values in `tile` with NoData. 
+
+Where the `mask` _does not_ contain NoData, replace values in `tile` with NoData.
 
 #### rf_mask_by_value
 
     Tile rf_mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
-    
-Generate a `tile` with the values from `data_tile`, with NoData in cells where the `mask_tile` is equal to `mask_value`. 
+
+Generate a `tile` with the values from `data_tile`, with NoData in cells where the `mask_tile` is equal to `mask_value`.
 
 
 #### rf_is_no_data_tile
 
     Boolean rf_is_no_data_tile(Tile)
- 
+
 Returns true if `tile` contains only NoData. By definition returns false if cell type does not support NoData. To count NoData cells or data cells, see @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_local_no_data_cells`](reference.md#rf-agg-local-no-data-cells), and @ref:[`rf_agg_local_data_cells`](reference.md#rf-agg-local-data-cells). This function is distinguished from @ref:[`rf_for_all`](reference.md#rf-for-all), which tests that values are not NoData and nonzero.
 
 #### rf_with_no_data
 
     Tile rf_with_no_data(Tile tile, Double no_data_value)
-    
+
 Python only. Return a `tile` column marking as NoData all cells equal to `no_data_value`.
 
 The `no_data_value` argument is a literal Double, not a Column expression.
 
-If input `tile` had a NoData value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, NoData values on the input `tile` remain NoData values on the output. For integral cell type `tile`s, the previous NoData values become literal values. 
+If input `tile` had a NoData value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, NoData values on the input `tile` remain NoData values on the output. For integral cell type `tile`s, the previous NoData values become literal values.
 
 ### Local Map Algebra
 
-[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single tile (unary), between a `tile` and a scalar, between two `tile`s, or across many `tile`s. 
+[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single tile (unary), between a `tile` and a scalar, between two `tile`s, or across many `tile`s.
 
 The binary local map algebra functions have similar variations in the Python API depending on the left hand side type:
 
  - `rf_local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
  - `rf_local_op_double`: applies `op` to a `tile` and a literal scalar, coercing the `tile` to a floating point type
  - `rf_local_op_int`: applies `op` to a `tile` and a literal scalar, without coercing the `tile` to a floating point type
- 
+
 The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
 Local map algebra operations for more than two tiles are implemented to work across rows in the data frame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
@@ -252,7 +249,7 @@ Local map algebra operations for more than two tiles are implemented to work acr
     Tile rf_local_add(Tile tile1, Tile rhs)
     Tile rf_local_add_int(Tile tile1, Int rhs)
     Tile rf_local_add_double(Tile tile1, Double rhs)
-    
+
 
 Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
 
@@ -261,7 +258,7 @@ Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
     Tile rf_local_subtract(Tile tile1, Tile rhs)
     Tile rf_local_subtract_int(Tile tile1, Int rhs)
     Tile rf_local_subtract_double(Tile tile1, Double rhs)
-    
+
 
 Returns a `tile` column containing the element-wise difference of `tile1` and `rhs`.
 
@@ -271,7 +268,7 @@ Returns a `tile` column containing the element-wise difference of `tile1` and `r
     Tile rf_local_multiply(Tile tile1, Tile rhs)
     Tile rf_local_multiply_int(Tile tile1, Int rhs)
     Tile rf_local_multiply_double(Tile tile1, Double rhs)
-    
+
 
 Returns a `tile` column containing the element-wise product of `tile1` and `rhs`. This is **not** the matrix multiplication of `tile1` and `rhs`.
 
@@ -281,71 +278,71 @@ Returns a `tile` column containing the element-wise product of `tile1` and `rhs`
     Tile rf_local_divide(Tile tile1, Tile rhs)
     Tile rf_local_divide_int(Tile tile1, Int rhs)
     Tile rf_local_divide_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise quotient of `tile1` and `rhs`. 
 
+Returns a `tile` column containing the element-wise quotient of `tile1` and `rhs`.
 
-#### rf_normalized_difference 
+
+#### rf_normalized_difference
 
     Tile rf_normalized_difference(Tile tile1, Tile tile2)
-    
 
-Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (tile1 + tile2)`. Result is always floating point cell type. This function has no scalar variant. 
+
+Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (tile1 + tile2)`. Result is always floating point cell type. This function has no scalar variant.
 
 #### rf_local_less
 
     Tile rf_local_less(Tile tile1, Tile rhs)
     Tile rf_local_less_int(Tile tile1, Int rhs)
     Tile rf_local_less_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise evaluation of `tile1` is less than `rhs`. 
+
+Returns a `tile` column containing the element-wise evaluation of `tile1` is less than `rhs`.
 
 #### rf_local_less_equal
 
     Tile rf_local_less_equal(Tile tile1, Tile rhs)
     Tile rf_local_less_equal_int(Tile tile1, Int rhs)
     Tile rf_local_less_equal_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise evaluation of `tile1` is less than or equal to `rhs`. 
+
+Returns a `tile` column containing the element-wise evaluation of `tile1` is less than or equal to `rhs`.
 
 #### rf_local_greater
 
     Tile rf_local_greater(Tile tile1, Tile rhs)
     Tile rf_local_greater_int(Tile tile1, Int rhs)
     Tile rf_local_greater_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than `rhs`. 
+
+Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than `rhs`.
 
 #### rf_local_greater_equal
 
     Tile rf_local_greater_equal(Tile tile1, Tile rhs)
     Tile rf_local_greater_equal_int(Tile tile1, Int rhs)
     Tile rf_local_greater_equal_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than or equal to `rhs`. 
+
+Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than or equal to `rhs`.
 
 #### rf_local_equal
 
     Tile rf_local_equal(Tile tile1, Tile rhs)
     Tile rf_local_equal_int(Tile tile1, Int rhs)
     Tile rf_local_equal_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise equality of `tile1` and `rhs`. 
+
+Returns a `tile` column containing the element-wise equality of `tile1` and `rhs`.
 
 #### rf_local_unequal
 
     Tile rf_local_unequal(Tile tile1, Tile rhs)
     Tile rf_local_unequal_int(Tile tile1, Int rhs)
     Tile rf_local_unequal_double(Tile tile1, Double rhs)
-    
 
-Returns a `tile` column containing the element-wise inequality of `tile1` and `rhs`. 
+
+Returns a `tile` column containing the element-wise inequality of `tile1` and `rhs`.
 
 #### rf_round
 
@@ -358,7 +355,7 @@ Round cell values to the nearest integer without changing the cell type.
     Tile rf_abs(Tile tile)
 
  Compute the absolute value of cell value.
- 
+
 #### rf_exp
 
     Tile rf_exp(Tile tile)
@@ -412,34 +409,26 @@ Performs natural logarithm of cell values plus one. Inverse of @ref:[`rf_expm1`]
 The following functions compute a statistical summary per row of a `tile` column. The statistics are computed across the cells of a single `tile`, within each DataFrame Row.  Consider the following example.
 
 ```python
-import pyspark.functions as F
+import pyspark.sql.functions as F
 spark.sql("""
- SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
+ SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
  UNION
- SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
+ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
  """).select(F.col('id'), rf_tile_sum(F.col('t'))).show()
-
-
-+---+-----------+
-| id|rf_tile_sum(t)|
-+---+-----------+
-|  2|       75.0|
-|  1|       25.0|
-+---+-----------+
 ```
 
 
 #### rf_tile_sum
 
     Double rf_tile_sum(Tile tile)
-    
+
 
 Computes the sum of cells in each row of column `tile`, ignoring NoData values.
 
 #### rf_tile_mean
 
     Double rf_tile_mean(Tile tile)
-    
+
 
 Computes the mean of cells in each row of column `tile`, ignoring NoData values.
 
@@ -447,7 +436,7 @@ Computes the mean of cells in each row of column `tile`, ignoring NoData values.
 #### rf_tile_min
 
     Double rf_tile_min(Tile tile)
-    
+
 
 Computes the min of cells in each row of column `tile`, ignoring NoData values.
 
@@ -455,7 +444,7 @@ Computes the min of cells in each row of column `tile`, ignoring NoData values.
 #### rf_tile_max
 
     Double rf_tile_max(Tile tile)
-    
+
 
 Computes the max of cells in each row of column `tile`, ignoring NoData values.
 
@@ -463,21 +452,21 @@ Computes the max of cells in each row of column `tile`, ignoring NoData values.
 #### rf_no_data_cells
 
     Long rf_no_data_cells(Tile tile)
-    
+
 
 Return the count of NoData cells in the `tile`.
 
 #### rf_data_cells
 
     Long rf_data_cells(Tile tile)
-    
+
 Return the count of data cells in the `tile`.
 
 #### rf_exists
 
     Boolean rf_exists(Tile tile)
-    
-Returns true if any cells in the tile are true (non-zero and not NoData). 
+
+Returns true if any cells in the tile are true (non-zero and not NoData).
 
 #### rf_for_all
 
@@ -488,14 +477,14 @@ Returns true if all cells in the tile are true (non-zero and not NoData). See al
 #### rf_tile_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
-    
-Computes the following statistics of cells in each row of column `tile`: data cell count, NoData cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring NoData values. 
+
+Computes the following statistics of cells in each row of column `tile`: data cell count, NoData cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring NoData values.
 
 
 #### rf_tile_histogram
 
     Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_tile_histogram(Tile tile)
-    
+
 Computes a statistical summary of cell values within each row of `tile`. Resulting column has the below schema. Note that several of the other `tile` statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) which computes the statistics across all rows in a group.
 
 ```
@@ -517,33 +506,20 @@ Computes a statistical summary of cell values within each row of `tile`. Resulti
 
 These functions compute statistical summaries over all of the cell values *and* across all the rows in the DataFrame or group. Example use below computes a single double-valued mean per month, across all data cells in the `red_band` `tile` type column. This would return at most twelve rows.
 
-
-```python
-from pyspark.functions import month
-from pyrasterframes.functions import rf_agg_mean
-rf.groupby(month(rf.datetime)).agg(rf_agg_mean(rf.red_band).alias('red_mean_monthly'))
-```
-
 Continuing our example from the @ref:[Tile Statistics](reference.md#tile-statistics) section, consider the following. Note that only a single row is returned. It is averaging 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows.
 
-```python 
+```python
 spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
 UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t 
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
 """).agg(rf_agg_mean(F.col('t'))).show(10, False)
-
-+--------------+
-|rf_agg_mean(t)|
-+--------------+
-|2.0           |
-+--------------+
 ```
 
 #### rf_agg_mean
 
     Double rf_agg_mean(Tile tile)
-    
+
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).mean`
 
 Aggregates over the `tile` and return the mean of cell values, ignoring NoData. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.mean`.
@@ -552,22 +528,15 @@ Aggregates over the `tile` and return the mean of cell values, ignoring NoData.
 #### rf_agg_data_cells
 
     Long rf_agg_data_cells(Tile tile)
-    
-_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
-Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.dataCells`. C.F. `data_cells`; equivalent code:
+_SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
-```python
-rf.select(rf_agg_data_cells(rf.tile).alias('agg_data_cell'))
-# Equivalent to
-import pyspark.sql.functions as F
-rf.agg(F.sum(rf_data_cells(rf.tile)).alias('agg_data_cell'))
-```
+Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.dataCells`.
 
 #### rf_agg_no_data_cells
 
     Long rf_agg_no_data_cells(Tile tile)
-    
+
 _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
 Aggregates over the `tile` and return the count of NoData cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
@@ -575,16 +544,16 @@ Aggregates over the `tile` and return the count of NoData cells. Equivalent to @
 #### rf_agg_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_agg_stats(Tile tile)
-    
 
-Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of NoData. 
+
+Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of NoData.
 
 #### rf_agg_approx_histogram
 
     Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
-    
 
-Aggregates over the `tile` return statistical summaries of the cell values, including a histogram, in the below schema. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis. 
+
+Aggregates over the `tile` return statistical summaries of the cell values, including a histogram, in the below schema. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis.
 
 Note that several of the other cell value statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
 
@@ -605,71 +574,48 @@ Note that several of the other cell value statistics functions are convenience m
 
 ### Tile Local Aggregate Statistics
 
-Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension. 
+Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
 Consider again our example for Tile Statistics and Aggregate Tile Statistics, this time apply @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean). We see that it is computing the element-wise mean across the two rows. In this case it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
 
 
 ```python
-import pyspark.functions as F
+import pyspark.sql.functions as F
 alm = spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t 
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
 UNION
-SELECT 2 as id, rf_local_multiply(rf_tile_ones(5, 5, 'float32'), 3) as t 
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
 """).agg(rf_agg_local_mean(F.col('t')).alias('l')) \
 
 ## local_agg_mean returns a tile
 alm.select(rf_dimensions(alm.l)).show()
-## 
-+----------------+
-|rf_dimensions(l)|
-+----------------+
-|          [5, 5]|
-+----------------+ 
-##
 
 alm.select(rf_explode_tiles(alm.l)).show(10, False)
-##
-+------------+---------+---+
-|column_index|row_index|l  |
-+------------+---------+---+
-|0           |0        |2.0|
-|1           |0        |2.0|
-|2           |0        |2.0|
-|3           |0        |2.0|
-|4           |0        |2.0|
-|0           |1        |2.0|
-|1           |1        |2.0|
-|2           |1        |2.0|
-|3           |1        |2.0|
-|4           |1        |2.0|
-+------------+---------+---+
-only showing top 10 rows
 ```
 
 
-#### rf_agg_local_max 
+#### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)
-    
-Compute the cell-local maximum operation over Tiles in a column. 
 
-#### rf_agg_local_min 
+Compute the cell-local maximum operation over Tiles in a column.
+
+#### rf_agg_local_min
 
     Tile rf_agg_local_min(Tile tile)
 
-Compute the cell-local minimum operation over Tiles in a column. 
+Compute the cell-local minimum operation over Tiles in a column.
 
-#### rf_agg_local_mean 
+#### rf_agg_local_mean
 
     Tile rf_agg_local_mean(Tile tile)
-    
-Compute the cell-local mean operation over Tiles in a column. 
 
-#### rf_agg_local_data_cells 
+Compute the cell-local mean operation over Tiles in a column.
+
+#### rf_agg_local_data_cells
 
     Tile rf_agg_local_data_cells(Tile tile)
-    
+
 Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
 #### rf_agg_local_no_data_cells
@@ -678,14 +624,14 @@ Compute the cell-local count of data cells over Tiles in a column. Returned `til
 
 Compute the cell-local count of NoData cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
-#### rf_agg_local_stats 
+#### rf_agg_local_stats
 
     Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
-    
+
 Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of Tiles. Returns a struct of five `tile`s.
 
 
-### Converting Tiles 
+### Converting Tiles
 
 RasterFrames provides several ways to convert a `tile` into other data structures. See also functions for @ref:[creating tiles](reference.md#tile-creation).
 
@@ -698,15 +644,8 @@ Create a row for each cell in `tile` columns. Many `tile` columns can be passed
 #### rf_explode_tiles_sample
 
     Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
-    
-Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0. 
 
-```python
-df.select(df.id, rf_explode_tiles(df.tile1, df.tile2, df.tile3)) \
-    .sample(False, 0.05, 8675309)
-# Equivalent result, faster
-df.select(df.id, rf_explode_tiles_sample(0.05, 8675309, df.tile1, df.tile2, df.tile3)) 
-```
+Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0.
 
 #### rf_tile_to_array_int
 
@@ -717,7 +656,7 @@ Convert Tile column to Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/
 #### rf_tile_to_array_double
 
     Array rf_tile_to_arry_double(Tile tile)
-    
+
 Convert tile column to Spark [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Integral cell types will be coerced to floats.
 
 #### rf_render_ascii
@@ -734,4 +673,3 @@ Render Tile cell values as numeric values, for debugging purposes.
 
 [RasterFunctions]: org.locationtech.rasterframes.RasterFunctions
 [scaladoc]: latest/api/index.html
-

From 51ef846fc442d941537129d474394f2fc43b6b69 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 23 Jul 2019 14:43:01 -0400
Subject: [PATCH 235/380] fix tile stats and histogram functions; promote
 headings up 1 level; remove rf_cell_types

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/reference.pymd       | 211 ++++++++----------
 1 file changed, 90 insertions(+), 121 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index da268f32f..60ae7285e 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -10,7 +10,7 @@ ReturnDataType function_name(InputDataType argument1, InputDataType argument2)
 
 For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`][RasterFunctions]. The full Scala API documentation can be found [here][scaladoc].
 
-## List of Available SQL and Python Functions
+# List of Available SQL and Python Functions
 
 @@toc { depth=3 }
 
@@ -28,13 +28,13 @@ become available for use with DataFrames. You can view all of the available func
 [fn for fn in dir() if fn.startswith('rf_') or fn.startswith('st_')]
 ```
 
-### Vector Operations
+## Vector Operations
 
 Various LocationTech GeoMesa user-defined functions (UDFs) to deal with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
 
 RasterFrames provides some additional functions for vector geometry operations.
 
-#### st_reproject
+### st_reproject
 
     Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
@@ -42,7 +42,7 @@ RasterFrames provides some additional functions for vector geometry operations.
 Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
 
 
-#### st_extent
+### st_extent
 
     Struct[Double xmin, Double xmax, Double ymin, Double ymax] st_extent(Geometry geom)
 
@@ -50,62 +50,55 @@ Extracts the bounding box (extent/envelope) of the geometry.
 
 See also GeoMesa [st_envelope](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#st-envelope) which returns a Geometry type.
 
-#### st_geometry
+### st_geometry
 
     Geometry st_geometry(Struct[Double xmin, Double xmax, Double ymin, Double ymax] extent)
 
 Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`](reference.md#st-extent) or @ref:[`rf_extent`](reference.md#rf-extent).
 
-### Tile Metadata and Mutation
+## Tile Metadata and Mutation
 
-Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and NoData](reference.md#masking-and-NoData) for additional discussion of cell types.
+Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and NoData](nodata-handling.md#masking-and-NoData) for additional discussion of cell types.
 
-#### rf_cell_types
-
-    Array[String] rf_cell_types()
-
-Print an array of possible cell type names, as below. These names are used in other functions. See @ref:[discussion on NoData](reference.md#masking-and-NoData) for additional details.
-
-
-#### rf_dimensions
+### rf_dimensions
 
     Struct[Int, Int] rf_dimensions(Tile tile)
 
 Get number of columns and rows in the `tile`, as a Struct of `cols` and `rows`.
 
-#### rf_cell_type
+### rf_cell_type
 
     Struct[String] rf_cell_type(Tile tile)
 
 Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_convert_cell_type](reference.md#rf-convert-cell-type).
 
-#### rf_tile
+### rf_tile
 
     Tile rf_tile(ProjectedRasterTile proj_raster)
 
 Get the `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
 
-#### rf_extent
+### rf_extent
 
     Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(ProjectedRasterTile proj_raster)
     Struct[Double xmin, Double xmax, Double ymin, Double ymax] rf_extent(RasterSource proj_raster)
 
 Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
-#### rf_crs
+### rf_crs
 
     Struct[String] rf_crs(ProjectedRasterTile proj_raster)
     Struct[String] rf_crs(RasterSource proj_raster)
 
 Fetch the [proj4](https://proj4.org/) string representation of the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
 
-#### rf_convert_cell_type
+### rf_convert_cell_type
 
     Tile rf_convert_cell_type(Tile tileCol, String cellType)
 
 Convert `tileCol` to a different cell type. Available cell types can be retrieved with the @ref:[rf_cell_types](reference.md#rf-cell-types) function.
 
-#### rf_resample
+### rf_resample
 
     Tile rf_resample(Tile tile, Double factor)
     Tile rf_resample(Tile tile, Int factor)
@@ -114,11 +107,11 @@ Convert `tileCol` to a different cell type. Available cell types can be retrieve
 
 Change the tile dimension. Passing a numeric `factor` will scale the number of columns and rows in the tile: 1.0 is the same number of columns and row; less than one downsamples the tile; and greater than one upsamples the tile. Passing a `shape_tile` as the second argument outputs `tile` having the same number of columns and rows as `shape_tile`. All resampling is by nearest neighbor method.
 
-### Tile Creation
+## Tile Creation
 
 Functions to create a new Tile column, either from scratch or from existing data not yet in a `tile`.
 
-#### rf_make_zeros_tile
+### rf_make_zeros_tile
 
 ```
 Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
@@ -127,7 +120,7 @@ Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
-#### rf_make_ones_tile
+### rf_make_ones_tile
 
 ```
 Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, String cell_type_name)
@@ -136,7 +129,7 @@ Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 
 Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
-#### rf_make_constant_tile
+### rf_make_constant_tile
 
     Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
 
@@ -144,7 +137,7 @@ Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the sp
 Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 
-#### rf_rasterize
+### rf_rasterize
 
     Tile rf_rasterize(Geometry geom, Geometry tile_bounds, Int value, Int tile_columns, Int tile_rows)
 
@@ -170,19 +163,19 @@ FROM
 ```
 
 
-#### rf_array_to_tile
+### rf_array_to_tile
 
     Tile rf_array_to_tile(Array arrayCol, Int numCols, Int numRows)
 
 Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), filling values in row-major order.
 
-#### rf_assemble_tile
+### rf_assemble_tile
 
     Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
 
 Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
 
-### Masking and NoData
+## Masking and NoData
 
 See @ref:[NoData handling](nodata-handling.md) for conceptual discussion of cell types and NoData.
 
@@ -190,7 +183,7 @@ There are statistical functions of the count of data and NoData values per `tile
 
 Masking is a raster operation that sets specific cells to NoData based on the values in another raster.
 
-#### rf_mask
+### rf_mask
 
     Tile rf_mask(Tile tile, Tile mask)
 
@@ -201,26 +194,26 @@ Returned `tile` cell type will be coerced to one supporting NoData if it does no
 See also @[`rf_rasterize`](reference.md#rf-rasterize).
 
 
-#### rf_inverse_mask
+### rf_inverse_mask
 
     Tile rf_inverse_mask(Tile tile, Tile mask)
 
 Where the `mask` _does not_ contain NoData, replace values in `tile` with NoData.
 
-#### rf_mask_by_value
+### rf_mask_by_value
 
     Tile rf_mask_by_value(Tile data_tile, Tile mask_tile, Int mask_value)
 
 Generate a `tile` with the values from `data_tile`, with NoData in cells where the `mask_tile` is equal to `mask_value`.
 
 
-#### rf_is_no_data_tile
+### rf_is_no_data_tile
 
     Boolean rf_is_no_data_tile(Tile)
 
 Returns true if `tile` contains only NoData. By definition returns false if cell type does not support NoData. To count NoData cells or data cells, see @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells), @ref:[`rf_data_cells`](reference.md#rf-data-cells), @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells), @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells), @ref:[`rf_agg_local_no_data_cells`](reference.md#rf-agg-local-no-data-cells), and @ref:[`rf_agg_local_data_cells`](reference.md#rf-agg-local-data-cells). This function is distinguished from @ref:[`rf_for_all`](reference.md#rf-for-all), which tests that values are not NoData and nonzero.
 
-#### rf_with_no_data
+### rf_with_no_data
 
     Tile rf_with_no_data(Tile tile, Double no_data_value)
 
@@ -230,7 +223,7 @@ The `no_data_value` argument is a literal Double, not a Column expression.
 
 If input `tile` had a NoData value already, the behaviour depends on if its cell type is floating point or not. For floating point cell type `tile`, NoData values on the input `tile` remain NoData values on the output. For integral cell type `tile`s, the previous NoData values become literal values.
 
-### Local Map Algebra
+## Local Map Algebra
 
 [Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single tile (unary), between a `tile` and a scalar, between two `tile`s, or across many `tile`s.
 
@@ -244,7 +237,7 @@ The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms
 
 Local map algebra operations for more than two tiles are implemented to work across rows in the data frame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
 
-#### rf_local_add
+### rf_local_add
 
     Tile rf_local_add(Tile tile1, Tile rhs)
     Tile rf_local_add_int(Tile tile1, Int rhs)
@@ -253,7 +246,7 @@ Local map algebra operations for more than two tiles are implemented to work acr
 
 Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
 
-#### rf_local_subtract
+### rf_local_subtract
 
     Tile rf_local_subtract(Tile tile1, Tile rhs)
     Tile rf_local_subtract_int(Tile tile1, Int rhs)
@@ -263,7 +256,7 @@ Returns a `tile` column containing the element-wise sum of `tile1` and `rhs`.
 Returns a `tile` column containing the element-wise difference of `tile1` and `rhs`.
 
 
-#### rf_local_multiply
+### rf_local_multiply
 
     Tile rf_local_multiply(Tile tile1, Tile rhs)
     Tile rf_local_multiply_int(Tile tile1, Int rhs)
@@ -273,7 +266,7 @@ Returns a `tile` column containing the element-wise difference of `tile1` and `r
 Returns a `tile` column containing the element-wise product of `tile1` and `rhs`. This is **not** the matrix multiplication of `tile1` and `rhs`.
 
 
-#### rf_local_divide
+### rf_local_divide
 
     Tile rf_local_divide(Tile tile1, Tile rhs)
     Tile rf_local_divide_int(Tile tile1, Int rhs)
@@ -283,14 +276,14 @@ Returns a `tile` column containing the element-wise product of `tile1` and `rhs`
 Returns a `tile` column containing the element-wise quotient of `tile1` and `rhs`.
 
 
-#### rf_normalized_difference
+### rf_normalized_difference
 
     Tile rf_normalized_difference(Tile tile1, Tile tile2)
 
 
 Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (tile1 + tile2)`. Result is always floating point cell type. This function has no scalar variant.
 
-#### rf_local_less
+### rf_local_less
 
     Tile rf_local_less(Tile tile1, Tile rhs)
     Tile rf_local_less_int(Tile tile1, Int rhs)
@@ -299,7 +292,7 @@ Compute the normalized difference of the the two `tile`s: `(tile1 - tile2) / (ti
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than `rhs`.
 
-#### rf_local_less_equal
+### rf_local_less_equal
 
     Tile rf_local_less_equal(Tile tile1, Tile rhs)
     Tile rf_local_less_equal_int(Tile tile1, Int rhs)
@@ -308,7 +301,7 @@ Returns a `tile` column containing the element-wise evaluation of `tile1` is les
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is less than or equal to `rhs`.
 
-#### rf_local_greater
+### rf_local_greater
 
     Tile rf_local_greater(Tile tile1, Tile rhs)
     Tile rf_local_greater_int(Tile tile1, Int rhs)
@@ -317,7 +310,7 @@ Returns a `tile` column containing the element-wise evaluation of `tile1` is les
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than `rhs`.
 
-#### rf_local_greater_equal
+### rf_local_greater_equal
 
     Tile rf_local_greater_equal(Tile tile1, Tile rhs)
     Tile rf_local_greater_equal_int(Tile tile1, Int rhs)
@@ -326,7 +319,7 @@ Returns a `tile` column containing the element-wise evaluation of `tile1` is gre
 
 Returns a `tile` column containing the element-wise evaluation of `tile1` is greater than or equal to `rhs`.
 
-#### rf_local_equal
+### rf_local_equal
 
     Tile rf_local_equal(Tile tile1, Tile rhs)
     Tile rf_local_equal_int(Tile tile1, Int rhs)
@@ -335,7 +328,7 @@ Returns a `tile` column containing the element-wise evaluation of `tile1` is gre
 
 Returns a `tile` column containing the element-wise equality of `tile1` and `rhs`.
 
-#### rf_local_unequal
+### rf_local_unequal
 
     Tile rf_local_unequal(Tile tile1, Tile rhs)
     Tile rf_local_unequal_int(Tile tile1, Int rhs)
@@ -344,67 +337,67 @@ Returns a `tile` column containing the element-wise equality of `tile1` and `rhs
 
 Returns a `tile` column containing the element-wise inequality of `tile1` and `rhs`.
 
-#### rf_round
+### rf_round
 
     Tile rf_round(Tile tile)
 
 Round cell values to the nearest integer without changing the cell type.
 
-#### rf_abs
+### rf_abs
 
     Tile rf_abs(Tile tile)
 
  Compute the absolute value of cell value.
 
-#### rf_exp
+### rf_exp
 
     Tile rf_exp(Tile tile)
 
 Performs cell-wise exponential.
 
-#### rf_exp10
+### rf_exp10
 
     Tile rf_exp10(Tile tile)
 
 Compute 10 to the power of cell values.
 
-#### rf_exp2
+### rf_exp2
 
     Tile rf_exp2(Tile tile)
 
 Compute 2 to the power of cell values.
 
-#### rf_expm1
+### rf_expm1
 
     Tile rf_expm1(Tile tile)
 
 Performs cell-wise exponential, then subtract one. Inverse of @ref:[`log1p`](reference.md#log1p).
 
-#### rf_log
+### rf_log
 
     Tile rf_log(Tile tile)
 
 Performs cell-wise natural logarithm.
 
-#### rf_log10
+### rf_log10
 
     Tile rf_log10(Tile tile)
 
 Performs cell-wise logarithm with base 10.
 
-#### rf_log2
+### rf_log2
 
     Tile rf_log2(Tile tile)
 
 Performs cell-wise logarithm with base 2.
 
-#### rf_log1p
+### rf_log1p
 
     Tile rf_log1p(Tile tile)
 
 Performs natural logarithm of cell values plus one. Inverse of @ref:[`rf_expm1`](reference.md#rf-expm1).
 
-### Tile Statistics
+## Tile Statistics
 
 The following functions compute a statistical summary per row of a `tile` column. The statistics are computed across the cells of a single `tile`, within each DataFrame Row.  Consider the following example.
 
@@ -418,14 +411,14 @@ spark.sql("""
 ```
 
 
-#### rf_tile_sum
+### rf_tile_sum
 
     Double rf_tile_sum(Tile tile)
 
 
 Computes the sum of cells in each row of column `tile`, ignoring NoData values.
 
-#### rf_tile_mean
+### rf_tile_mean
 
     Double rf_tile_mean(Tile tile)
 
@@ -433,7 +426,7 @@ Computes the sum of cells in each row of column `tile`, ignoring NoData values.
 Computes the mean of cells in each row of column `tile`, ignoring NoData values.
 
 
-#### rf_tile_min
+### rf_tile_min
 
     Double rf_tile_min(Tile tile)
 
@@ -441,7 +434,7 @@ Computes the mean of cells in each row of column `tile`, ignoring NoData values.
 Computes the min of cells in each row of column `tile`, ignoring NoData values.
 
 
-#### rf_tile_max
+### rf_tile_max
 
     Double rf_tile_max(Tile tile)
 
@@ -449,60 +442,52 @@ Computes the min of cells in each row of column `tile`, ignoring NoData values.
 Computes the max of cells in each row of column `tile`, ignoring NoData values.
 
 
-#### rf_no_data_cells
+### rf_no_data_cells
 
     Long rf_no_data_cells(Tile tile)
 
 
 Return the count of NoData cells in the `tile`.
 
-#### rf_data_cells
+### rf_data_cells
 
     Long rf_data_cells(Tile tile)
 
 Return the count of data cells in the `tile`.
 
-#### rf_exists
+### rf_exists
 
     Boolean rf_exists(Tile tile)
 
 Returns true if any cells in the tile are true (non-zero and not NoData).
 
-#### rf_for_all
+### rf_for_all
 
     Boolean rf_for_all(Tile tile)
 
 Returns true if all cells in the tile are true (non-zero and not NoData). See also @ref:[`rf_is_no_data_tile](reference.md#rf-is-no-data-tile), which tests that all cells are NoData.
 
-#### rf_tile_stats
+### rf_tile_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_tile_stats(Tile tile)
 
-Computes the following statistics of cells in each row of column `tile`: data cell count, NoData cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring NoData values.
+Computes the following statistics of cells in each row of column `tile`: data cell count, NoData cell count, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance are computed ignoring NoData values. Resulting column has the below schema.
 
+```python echo=False
+spark.sql("SELECT rf_tile_stats(rf_make_ones_tile(5, 5, 'float32')) as tile_stats").printSchema()
+```
 
-#### rf_tile_histogram
+### rf_tile_histogram
 
-    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_tile_histogram(Tile tile)
+    Struct[Array[Struct[Double, Long]]] rf_tile_histogram(Tile tile)
 
-Computes a statistical summary of cell values within each row of `tile`. Resulting column has the below schema. Note that several of the other `tile` statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) which computes the statistics across all rows in a group.
+Computes a count of cell values within each row of `tile`. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis. Resulting column has the below schema. Related is the @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) which computes the statistics across all rows in a group.
 
-```
- |-- tile_histogram: struct (nullable = true)
- |    |-- stats: struct (nullable = true)
- |    |    |-- dataCells: long (nullable = false)
- |    |    |-- noDataCells: long (nullable = false)
- |    |    |-- min: double (nullable = false)
- |    |    |-- max: double (nullable = false)
- |    |    |-- mean: double (nullable = false)
- |    |    |-- variance: double (nullable = false)
- |    |-- bins: array (nullable = true)
- |    |    |-- element: struct (containsNull = true)
- |    |    |    |-- value: double (nullable = false)
- |    |    |    |-- count: long (nullable = false)
+```python echo=False
+spark.sql("SELECT rf_tile_histogram(rf_make_ones_tile(5, 5, 'float32')) as tile_histogram").printSchema()
 ```
 
-### Aggregate Tile Statistics
+## Aggregate Tile Statistics
 
 These functions compute statistical summaries over all of the cell values *and* across all the rows in the DataFrame or group. Example use below computes a single double-valued mean per month, across all data cells in the `red_band` `tile` type column. This would return at most twelve rows.
 
@@ -516,7 +501,7 @@ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
 """).agg(rf_agg_mean(F.col('t'))).show(10, False)
 ```
 
-#### rf_agg_mean
+### rf_agg_mean
 
     Double rf_agg_mean(Tile tile)
 
@@ -525,7 +510,7 @@ _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).mean`
 Aggregates over the `tile` and return the mean of cell values, ignoring NoData. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.mean`.
 
 
-#### rf_agg_data_cells
+### rf_agg_data_cells
 
     Long rf_agg_data_cells(Tile tile)
 
@@ -533,7 +518,7 @@ _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
 Aggregates over the `tile` and return the count of data cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.dataCells`.
 
-#### rf_agg_no_data_cells
+### rf_agg_no_data_cells
 
     Long rf_agg_no_data_cells(Tile tile)
 
@@ -541,38 +526,22 @@ _SQL_: @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`(tile).dataCells`
 
 Aggregates over the `tile` and return the count of NoData cells. Equivalent to @ref:[`rf_agg_stats`](reference.md#rf-agg-stats)`.noDataCells`. C.F. @ref:[`rf_no_data_cells`](reference.md#rf-no-data-cells) a row-wise count of no data cells.
 
-#### rf_agg_stats
+### rf_agg_stats
 
     Struct[Long, Long, Double, Double, Double, Double] rf_agg_stats(Tile tile)
 
 
 Aggregates over the `tile` and returns statistical summaries of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. The minimum, maximum, mean, and variance ignore the presence of NoData.
 
-#### rf_agg_approx_histogram
-
-    Struct[Struct[Long, Long, Double, Double, Double, Double], Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
+### rf_agg_approx_histogram
 
+    Struct[Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
 
-Aggregates over the `tile` return statistical summaries of the cell values, including a histogram, in the below schema. The `bins` array is of tuples of histogram values and counts. Typically values are plotted on the x-axis and counts on the y-axis.
 
-Note that several of the other cell value statistics functions are convenience methods to extract parts of this result. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
+Aggregates over the `tile` return a count of each cell value to create a histogram with values are plotted on the x-axis and counts on the y-axis. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
 
-```
- |-- agg_approx_histogram: struct (nullable = true)
- |    |-- stats: struct (nullable = true)
- |    |    |-- dataCells: long (nullable = false)
- |    |    |-- noDataCells: long (nullable = false)
- |    |    |-- min: double (nullable = false)
- |    |    |-- max: double (nullable = false)
- |    |    |-- mean: double (nullable = false)
- |    |    |-- variance: double (nullable = false)
- |    |-- bins: array (nullable = true)
- |    |    |-- element: struct (containsNull = true)
- |    |    |    |-- value: double (nullable = false)
- |    |    |    |-- count: long (nullable = false)
-```
 
-### Tile Local Aggregate Statistics
+## Tile Local Aggregate Statistics
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
@@ -594,78 +563,78 @@ alm.select(rf_explode_tiles(alm.l)).show(10, False)
 ```
 
 
-#### rf_agg_local_max
+### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)
 
 Compute the cell-local maximum operation over Tiles in a column.
 
-#### rf_agg_local_min
+### rf_agg_local_min
 
     Tile rf_agg_local_min(Tile tile)
 
 Compute the cell-local minimum operation over Tiles in a column.
 
-#### rf_agg_local_mean
+### rf_agg_local_mean
 
     Tile rf_agg_local_mean(Tile tile)
 
 Compute the cell-local mean operation over Tiles in a column.
 
-#### rf_agg_local_data_cells
+### rf_agg_local_data_cells
 
     Tile rf_agg_local_data_cells(Tile tile)
 
 Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
-#### rf_agg_local_no_data_cells
+### rf_agg_local_no_data_cells
 
     Tile rf_agg_local_no_data_cells(Tile tile)
 
 Compute the cell-local count of NoData cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
 
-#### rf_agg_local_stats
+### rf_agg_local_stats
 
     Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
 
 Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of Tiles. Returns a struct of five `tile`s.
 
 
-### Converting Tiles
+## Converting Tiles
 
 RasterFrames provides several ways to convert a `tile` into other data structures. See also functions for @ref:[creating tiles](reference.md#tile-creation).
 
-#### rf_explode_tiles
+### rf_explode_tiles
 
     Int, Int, Numeric* rf_explode_tiles(Tile* tile)
 
 Create a row for each cell in `tile` columns. Many `tile` columns can be passed in, and the returned DataFrame will have one numeric column per input.  There will also be columns for `column_index` and `row_index`. Inverse of @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile). When using this function, be sure to have a unique identifier for rows in order to successfully invert the operation.
 
-#### rf_explode_tiles_sample
+### rf_explode_tiles_sample
 
     Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
 
 Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0.
 
-#### rf_tile_to_array_int
+### rf_tile_to_array_int
 
     Array rf_tile_to_array_int(Tile tile)
 
 Convert Tile column to Spark SQL [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Float cell types will be coerced to integral type by flooring.
 
-#### rf_tile_to_array_double
+### rf_tile_to_array_double
 
     Array rf_tile_to_arry_double(Tile tile)
 
 Convert tile column to Spark [Array](http://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.types.ArrayType), in row-major order. Integral cell types will be coerced to floats.
 
-#### rf_render_ascii
+### rf_render_ascii
 
     String rf_render_ascii(Tile tile)
 
 Pretty print the tile values as plain text.
 
-#### rf_render_matrix
+### rf_render_matrix
 
     String rf_render_matrix(Tile tile)
 

From 1a59195c920c0abc975c12ecfb12a159df841b4e Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 23 Jul 2019 15:09:32 -0400
Subject: [PATCH 236/380] change link on line 61 to go to nodata-handling.md

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/reference.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 60ae7285e..b4749aeef 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -58,7 +58,7 @@ Convert an extent to a Geometry. The extent likely comes from @ref:[`st_extent`]
 
 ## Tile Metadata and Mutation
 
-Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See below section on @ref:[masking and NoData](nodata-handling.md#masking-and-NoData) for additional discussion of cell types.
+Functions to access and change the particulars of a `tile`: its shape and the data type of its cells. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion of cell types.
 
 ### rf_dimensions
 

From b8435bfe552fa3056dbeebfeaef685db7073b9ea Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 24 Jul 2019 10:10:13 -0400
Subject: [PATCH 237/380] raster reader can take a Pandas DataFrame or
 geopandas GeoDF

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/pyrasterframes/__init__.py    | 21 ++++++-
 .../main/python/tests/PyRasterFramesTests.py  | 58 ++++++++++++++++++-
 2 files changed, 76 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 2af1677a1..4018a57a3 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -112,12 +112,19 @@ def _raster_reader(
         lazy_tiles=True,
         **options):
 
+    from pandas import DataFrame as PdDataFrame
+
     def to_csv(comp):
         if isinstance(comp, str):
             return comp
         else:
             return ','.join(str(v) for v in comp)
 
+    def temp_name():
+        """ Create a random name for a temporary view """
+        import uuid
+        return str(uuid.uuid4()).replace('-', '')
+
     if band_indexes is None:
         band_indexes = [0]
 
@@ -136,14 +143,24 @@ def to_csv(comp):
                 "catalogColumns": to_csv(catalog_col_names)
             })
         elif isinstance(catalog, DataFrame):
-            import uuid
             # Create a random view name
-            tmp_name = str(uuid.uuid4()).replace('-', '')
+            tmp_name = temp_name()
             catalog.createOrReplaceTempView(tmp_name)
             options.update({
                 "catalogTable": tmp_name,
                 "catalogColumns": to_csv(catalog_col_names)
             })
+        elif isinstance(catalog, PdDataFrame):
+            # Handle to active spark session
+            session = SparkContext._active_spark_context._rf_context._spark_session
+            # Create a random view name
+            tmp_name = temp_name()
+            spark_catalog = session.createDataFrame(catalog)
+            spark_catalog.createOrReplaceTempView(tmp_name)
+            options.update({
+                "catalogTable": tmp_name,
+                "catalogColumns": to_csv(catalog_col_names)
+            })
 
     return df_reader \
         .format("raster") \
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 6fd925459..a3080b6a2 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -704,6 +704,19 @@ def test_handle_lazy_eval(self):
         self.assertGreater(tdf.count(),  0)
         self.assertIsNotNone(tdf.first())
 
+    def test_strict_eval(self):
+        df_lazy = self.spark.read.raster(self.img_uri, lazy_tiles=True)
+        # when doing Show on a lazy tile we will see something like RasterRefTile(RasterRef(JVMGeoTiffRasterSource(...
+        # use this trick to get the `show` string
+        show_str_lazy = df_lazy.select('proj_raster')._jdf.showString(1, -1, False)
+        self.assertTrue('RasterRef' in show_str_lazy)
+
+        # again for strict
+        df_strict = self.spark.read.raster(self.img_uri, lazy_tiles=False)
+        show_str_strict = df_strict.select('proj_raster')._jdf.showString(1, -1, False)
+        self.assertTrue('RasterRef' not in show_str_lazy)
+
+
     def test_prt_functions(self):
         df = self.spark.read.raster(self.img_uri) \
             .withColumn('crs', rf_crs('proj_raster')) \
@@ -725,7 +738,7 @@ def l8path(b):
         df = self.spark.read.raster(
             tile_dimensions=(tile_size, tile_size),
             paths=path_param,
-            lazy_tiles=False
+            lazy_tiles=True,
         ).cache()
 
         # schema is tile_path and tile
@@ -780,6 +793,49 @@ def path(scene, band):
         b1_paths = [s.format('1') for s in scene_dict.values()]
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
+    def test_raster_source_catalog_reader_with_pandas(self):
+        import pandas as pd
+        import geopandas
+        from shapely.geometry import Point
+
+        scene_dict = {
+            1: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/041/LC08_L1TP_015041_20190305_20190309_01_T1/LC08_L1TP_015041_20190305_20190309_01_T1_B{}.TIF',
+            2: 'http://landsat-pds.s3.amazonaws.com/c1/L8/015/042/LC08_L1TP_015042_20190305_20190309_01_T1/LC08_L1TP_015042_20190305_20190309_01_T1_B{}.TIF',
+            3: 'http://landsat-pds.s3.amazonaws.com/c1/L8/016/041/LC08_L1TP_016041_20190224_20190309_01_T1/LC08_L1TP_016041_20190224_20190309_01_T1_B{}.TIF',
+        }
+
+        def path(scene, band):
+            assert band in range(1, 12)
+            p = scene_dict[scene]
+            return p.format(band)
+
+        # Create a pandas dataframe (makes it easy to create spark df)
+        path_pandas = pd.DataFrame([
+            {'b1': path(1, 1), 'b2': path(1, 2), 'b3': path(1, 3), 'geo': Point(1, 1)},
+            {'b1': path(2, 1), 'b2': path(2, 2), 'b3': path(2, 3), 'geo': Point(2, 2)},
+            {'b1': path(3, 1), 'b2': path(3, 2), 'b3': path(3, 3), 'geo': Point(3, 3)},
+        ])
+
+        # here a subtle difference with the test_raster_source_catalog_reader test, feed the DataFrame not a CSV and not an already created spark DF.
+        df = self.spark.read.raster(
+            catalog=path_pandas,
+            catalog_col_names=['b1', 'b2', 'b3']
+        )
+        self.assertEqual(len(df.columns), 7)  # three path cols, three tile cols, and geo
+        self.assertTrue('geo' in df.columns)
+        self.assertTrue(df.select('b1_path').distinct().count() == 3)
+
+
+        # Same test with geopandas
+        geo_df = geopandas.GeoDataFrame(path_pandas, crs={'init': 'EPSG:4326'}, geometry='geo')
+        df2 = self.spark.read.raster(
+            catalog=geo_df,
+            catalog_col_names=['b1', 'b2', 'b3']
+        )
+        self.assertEqual(len(df2.columns), 7)  # three path cols, three tile cols, and geo
+        self.assertTrue('geo' in df2.columns)
+        self.assertTrue(df2.select('b1_path').distinct().count() == 3)
+
 
 def suite():
     function_tests = unittest.TestSuite()

From 1e8b95a293d457e15bea37efc533192bc895c425 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 23 Jul 2019 15:14:51 -0400
Subject: [PATCH 238/380] Small docs tweaks lingering from PR 187

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/nodata-handling.pymd | 52 ++++++++++---------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 795771577..d9ef1feb5 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -10,8 +10,6 @@ RasterFrames provides a variety of functions to inspect and manage NoData within
 
 To understand how NoData is handled in RasterFrames, we first need to understand the different underlying types of data called cell types. The cell types are GeoTrellis `CellType`s, so the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values) is a valuable resource on how these are defined.
 
-Use the function `rf_cell_type` to find the cell type of a specific set of raster data.
-
 ```python setup, echo=False
 import pyrasterframes
 from pyrasterframes.rasterfunctions import *
@@ -21,23 +19,27 @@ from IPython.display import display
 spark = pyrasterframes.get_spark_session()
 ```
 
-```python ct_from_sen
-spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif') \
-    .select(rf_cell_type('proj_raster')).distinct().show()
+The `CellType` class from the `rf_types` submodule allows us to create a representation of any valid cell type. There are convenience methods to create instances for a variety of basic types.
+
+```python celltype_ctors
+from pyrasterframes.rf_types import CellType
+import inspect
+
+[c[0] for c in inspect.getmembers(CellType, inspect.ismethod)]
 ```
 
-The function `rf_cell_types` provides a convenient list of basic cell types. Note that this list is not exhaustive. 
+We can also inspect the cell type of a given _tile_ or `proj_raster` column.
 
-```python
-rf_cell_types()
+```python ct_from_sen
+spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif') \
+    .select(rf_cell_type('proj_raster')).distinct().show()
 ```
 
 ### Understanding Cell Types and NoData
 
-Use the `CellType` class to learn more about a specific cell type.
+Use the methods on the `CellType` class to learn more about a specific cell type. Take for example the cell type of our sample data above.
 
 ```python
-from pyrasterframes.rf_types import CellType
 ct = CellType('uint16raw')
 ct, ct.is_floating_point(), ct.has_no_data()
 ```
@@ -85,30 +87,30 @@ Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create
 ```python def_mask
 from pyspark.sql.functions import lit
 
-unmasked = unmasked.withColumn('nodata', rf_local_equal('scl', lit(0)))
-unmasked = unmasked.withColumn('defect', rf_local_equal('scl', lit(1)))
-unmasked = unmasked.withColumn('cloud8', rf_local_equal('scl', lit(8)))
-unmasked = unmasked.withColumn('cloud9', rf_local_equal('scl', lit(9)))
-unmasked = unmasked.withColumn('cirrus', rf_local_equal('scl', lit(10)))
+mask_part = unmasked.withColumn('nodata', rf_local_equal('scl', lit(0))) \
+                    .withColumn('defect', rf_local_equal('scl', lit(1))) \
+                    .withColumn('cloud8', rf_local_equal('scl', lit(8))) \
+                    .withColumn('cloud9', rf_local_equal('scl', lit(9))) \
+                    .withColumn('cirrus', rf_local_equal('scl', lit(10))) 
 
-unmasked = unmasked.withColumn('mask', rf_local_add('nodata', 'defect'))
-unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cloud8'))
-unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cloud9'))
-unmasked = unmasked.withColumn('mask', rf_local_add('mask', 'cirrus'))
+one_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
+                    .withColumn('mask', rf_local_add('mask', 'cloud8')) \
+                    .withColumn('mask', rf_local_add('mask', 'cloud9')) \
+                    .withColumn('mask', rf_local_add('mask', 'cirrus'))
 
-unmasked.select(rf_cell_type('mask')).distinct().show()
+one_mask.select(rf_cell_type('mask')).distinct().show()
 ```
 
 Now we will use the @ref:[`rf_mask_by_value`](reference.md#rf-mask-by-value) to designate the cloudy and other unwanted pixels as NoData in the blue column. Because there is not a NoData already defined, we will choose one. Note that in this particular example the minimum value is greater than zero, so we can use 0 as the NoData value.
 
 ```python pick_nd
-unmasked.agg(rf_agg_stats('blue').min.alias('blue_min')).show()
+one_mask.agg(rf_agg_stats('blue').min.alias('blue_min')).show()
 ```
 
 We can now construct the cell type string for our blue band's cell type, but designating 0 as NoData.
 
 ```python get_ct_string
-blue_ct = unmasked.select(rf_cell_type('blue')).distinct().first()[0][0]
+blue_ct = one_mask.select(rf_cell_type('blue')).distinct().first()[0][0]
 masked_blue_ct = CellType(blue_ct).with_no_data_value(0)
 masked_blue_ct.cell_type_name
 ```
@@ -117,9 +119,9 @@ Convert the cell type and apply the mask. Since the mask column is bit type, the
 
 ```python mask_blu 
 with_nd = rf_convert_cell_type('blue', masked_blue_ct.cell_type_name)
-masked = unmasked.withColumn('blue_masked', 
-            rf_mask_by_value(with_nd, 'mask', lit(1))) \
-            .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
+masked = one_mask.withColumn('blue_masked', 
+                             rf_mask_by_value(with_nd, 'mask', lit(1))) \
+                 .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
 ```
 
 We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` tile.

From ea5a93a6b0b537b3fc227a4f9669d43681da0bb6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 23 Jul 2019 14:34:39 -0400
Subject: [PATCH 239/380] Move numpy-pandas doc to pymd format; initial content

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/index.md  |   2 +-
 .../src/main/python/docs/numpy-pandas.pymd    | 119 ++++++++++++++++++
 .../src/main/python/docs/pandas-numpy.md      |  10 --
 3 files changed, 120 insertions(+), 11 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/numpy-pandas.pymd
 delete mode 100644 pyrasterframes/src/main/python/docs/pandas-numpy.md

diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 63a0d57a0..4dbe19c47 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -43,7 +43,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 * [Raster Data I/O](raster-io.md)
 * [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)
-* [Pandas, NumPy & RasterFrames](pandas-numpy.md)
+* [Numpy, Pandas, & RasterFrames](numpy-pandas.md) 
 * [UDF Reference](reference.md)
 * [Release Notes](release-notes.md)
 @@@
diff --git a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
new file mode 100644
index 000000000..7bfde9356
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
@@ -0,0 +1,119 @@
+# NumPy and Pandas Interoperabilty
+
+In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will recieve this data a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame. 
+
+## Performance Considerations
+
+When working with large, distributed datasets in Spark care is required when invoking _actions_ on the data. In general _transformations_ are lazily evaluated in Spark, meaning the code runs fast and it doesn't move any data around. But _actions_ cause the evaluation to happen, meaning all the lazily planned _transformations_ are going to be computed and data is going to be processed and moved around. In general, if a [`pyspark` function](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html) returns a DataFrame, it is probably a _transformation_ and otherwise it is an _action_.
+
+When many _actions_ are invoked, a lot of data can flow from executors to the driver. In `pyspark` the data then has to move from the driver JVM to the Python process running the driver. When that happens if there are any _tiles_ in the data, they will be converted to a Python [`Tile`](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/pyrasterframes/rf_types.py) object. In practical work with Earth observation data, the _tiles_ are frequently 256 by 256 arrays, which may be 100kb or more each. Individually they are small, a DataFrame can easily have dozens of such tile columns and millions of rows.
+
+All of this discussion reinforces important principle for working with Spark: understanding the cost of an _action_ is important; and use of @ref:[aggreates](aggregation.md), summaries, or samples to manage the cost of _actions_. 
+
+## The `Tile` Class
+
+In Python, _tiles_ are represented with the `rf_types.Tile` class. This is a NumPy `ndarray` with two dimensions, along with some additional metadata allowing correct conversion to the GeoTrellis @ref:[cell type](nodata-handling.md#cell-types).
+
+```python tile_intro
+from pyrasterframes.rf_types import Tile
+import numpy as np
+
+t = Tile(np.random.randn(4, 4))
+print(str(t))
+```
+
+You can access the NumPy array with the `cells` member of `Tile`.
+
+```python tile_cells
+t.cells.shape, t.cells.nbytes
+```
+
+## DataFrame `toPandas`
+
+As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samples), a pretty display of Pandas DataFrame containing _tiles_ is available by importing the `rf_ipython` submodule. In addition, as discussed in the @ref:[vector data chapter](vector-data.md), any geometry type in the Spark dataframe will be converted into a Shapely geometry. Taken together, we can can easily get the spatial information and raster data as a NumPy array, all within a Pandas DataFrame.
+
+```python spark_session,  echo=False
+import pyrasterframes
+from pyrasterframes.rasterfunctions import *
+from IPython.display import display
+spark = pyrasterframes.get_spark_session()
+
+```
+
+```python toPandas
+import pyrasterframes.rf_ipython
+from pyspark.sql.functions import lit, col
+
+cat = spark.read.format('aws-pds-modis-catalog').load() \
+                .filter(
+                    (col('granule_id') == 'h11v04') &
+                    (col('acquisition_date') > lit('2018-02-19')) &
+                    (col('acquisition_date') < lit('2018-02-22'))
+                ) 
+
+spark_df = spark.read.raster(catalog=cat, catalog_col_names=['B01']) \
+                .select(
+                    'acquisition_date',
+                    'granule_id',
+                    rf_tile('B01').alias('tile'),
+                    rf_geometry('B01').alias('tile_geom')
+                    )
+
+pandas_df = spark_df.limit(10).toPandas()
+pandas_df.iloc[0].apply(lambda v: type(v))
+```
+
+## User Defined Functions
+
+As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/s22s/raster-frames) if you have an idea for a function that should be included.
+
+We will demonstrate creating a UDF that is logically equivalent to a built in function. We'll quickly show that the resulting _tiles_ are approximately equivalent. The reason they are not exactly the same is because one is computed in Python and the other is computed in Java.
+
+```python udf
+from pyrasterframes.rf_types import TileUDT
+from pyspark.sql.functions import udf
+
+@udf(TileUDT())
+def my_udf(t):
+    import numpy as np
+    return Tile(np.log1p(t.cells))
+
+udf_df = spark_df.limit(1).select(
+            my_udf('tile').alias('udf_result'),
+            rf_log1p('tile').alias('built_in_result')
+        ).toPandas()
+
+row = udf_df.iloc[0]
+diff = row['udf_result'] - row['built_in_result']
+print(type(diff))
+np.abs(diff.cells).max()
+```
+
+We can also inspect an image of the difference between the two _tiles_; it is just random noise. Both tiles have the same structure of NoData, the white areas.
+
+```python udf_diff_noise_tile
+display(diff)
+```
+
+## Creating a Spark DataFrame
+
+You can also create a Spark DataFrame with a column full of `Tile` objects or Shapely geomtery objects.
+
+The example below will create a Pandas DataFrame with ten rows of noise tiles and random Points. We will then create a Spark DataFrame from it.
+
+```python create_spark_df
+import pandas as pd
+from shapely.geometry import Point
+
+pandas_df = pd.DataFrame([
+        { 
+            'tile': Tile(np.random.randn(100, 100)), 
+            'geom': Point(-90 + 90 * np.random.random((2, 1))) 
+        }  for _ in range(10)
+    ])
+
+spark_df = spark.createDataFrame(pandas_df)
+
+spark_df.printSchema()
+spark_df.count()
+```
diff --git a/pyrasterframes/src/main/python/docs/pandas-numpy.md b/pyrasterframes/src/main/python/docs/pandas-numpy.md
deleted file mode 100644
index 812825170..000000000
--- a/pyrasterframes/src/main/python/docs/pandas-numpy.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Pandas and NumPy Interoperabilty
-
-\[ be sure to discuss the performance implications; basically do as much on spark as possible and expect sluggish collect and udt applications \]
-
-* Small/realistic DF toPandas, 
-    * inspect contents of what you get; t.cells is np ndarray
-    * reference to writing page section on previews.
-* UDT on tile; log1p from test_udf_on_tile_type_output
-* Bonus : pandas 
-* Array to spark dataframe; using the Tile class

From d2be387cfe0d58fae205a8635f4f90bcc54b824c Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 23 Jul 2019 11:30:10 -0400
Subject: [PATCH 240/380] Docs on raster write: samples, parquet

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-io.md         |   7 +-
 .../src/main/python/docs/raster-read.pymd     |   4 +-
 .../src/main/python/docs/raster-write.pymd    | 131 +++++++++++++++++-
 3 files changed, 132 insertions(+), 10 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index d10bab8f1..3880d8629 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -2,19 +2,18 @@
 
 The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames provides specialized DataSources for geospatial raster data and maintains compatibility with existing general purpose DataSources, such as Parquet.
 
-Three types of DataSources will be introduced: 
-
 * @ref:[Catalog Readers](raster-catalogs.md)
     - `aws-pds-l8-catalog`: built-in catalog over [Landsat on AWS][Landsat]
     - `aws-pds-modis-catalog`: built-in catalog over [MODIS on AWS][MODIS]
     - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
 * @ref:[Raster Readers](raster-read.md)
-    - `raster`: the standard reader for most raster data
+    - `raster`: the standard reader for most raster data, including single raster files or catalogs
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
     - `geotrellis`: for reading a [GeoTrellis layer][GTLayer])
 * @ref:[Raster Writers](raster-write.md)
-    - `geotrellis`: for creating a [GeoTrellis layer][GTLayer]
+    - @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
     - `geotiff`: beta writer to GeoTiff file
+    - `geotrellis`: creating a [GeoTrellis layer][GTLayer]
     - [`parquet`][Parquet]: general purpose writer 
 
 There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 29cb55c14..aabca0ca6 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -12,13 +12,15 @@ RasterFrames registers a DataSource named `raster` that enables reading of GeoTI
 
 ## Single Raster
 
-The simplest form is reading a single raster from a single URI:
+The simplest form is reading a single raster from a single URI.
 
 ```python read_one_uri
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 rf.printSchema()
 ```
 
+The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.  
+
 Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_. 
 
 ```python unpack_schema
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 1c4e4edb5..ba4af68db 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -1,11 +1,132 @@
-## Writing Raster Data
+# Writing Raster Data
 
-### Overviews
+RasterFrames is oriented toward large scale analyses of spatial data. The primary output for most use cases may be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](spark-ml.md), or some other result that is generally much smaller than the input data set. 
 
-### GeoTIFFs
+However there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the process and results.
 
-### Tile Samples
+```python imports, echo=False
+import pyrasterframes
+from pyrasterframes.rasterfunctions import *
+from IPython.display import display
+spark = pyrasterframes.get_spark_session()
+```
 
-### GeoTrellis Layers
+## Tile Samples
 
+When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_. 
 
+In an IPython or Jupyter interpreter a `Tile` object will be displayed as an image with limited metadata.
+
+```python tile_sample
+raster_url = 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
+             'MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF'
+spark_df = spark.read.raster(raster_url)
+tile = spark_df.select(rf_tile('proj_raster').alias('tile')).first()['tile']
+tile
+```
+
+```python display_tile, echo=False, output=True
+display(tile) # IPython.display function
+```
+
+## DataFrame Samples
+
+Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
+
+In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](pandas-numpy.md).
+
+```python toPandas, evaluate=True
+import pyrasterframes.rf_ipython
+
+pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
+                .select(
+                    rf_extent('proj_raster').alias('extent'),
+                    rf_tile('proj_raster').alias('tile'),
+                ).limit(5).toPandas()
+pandas_df.dtypes
+```
+
+Viewing the DataFrame in Jupyter looks like this. 
+
+```python, evaluate=False
+pandas_df
+```
+
+
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>extent</th>
+      <th>tile</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>(-7783653.637667, 1082298.5058092135, -7754001.623809213, 1111950.519667)</td>
+      <td><img src="data:image/png;base64,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" alt="Image" class=""></td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>(-7754001.623809214, 1082298.5058092135, -7724349.609951426, 1111950.519667)</td>
+      <td><img 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fkDe5EJmFrgTZuE/dkk7E+D3VYIBeXRJi8ePMiYtUxLeUxFVaLEoB07GFLRv7nGhlKNY8sDnD4xlFnRCjFJxwI3U29bKx54KVvH53nx8EGOdof55vRG4rsrxEWN3xIw0WWir0YtyjyFPndyN74RXpSjSyZ1wN3GJ2/82AXznHrdGwG48UtvhVigygnHXv/Wc2krAx5xt0TzmpTcWRO7BVElI71yGJymwogUWgimni+REeSWFIkjCEuC3j1VPl/cjSkVZbvLj1eOMJhv88zqafavrGO6ViIpSMq5gN17Zrh3bj0TlTp5K+T+2VEsMyVODOLI5ORdY3zmegNTKlpni1iuRiaw/hWn6MQ2YWrynUObub5+C/99S5FGnLtQ04HLEKluuOEG/d3vfvcx7+09/EK+3djE2VaZ7eUF/vIZf7mmOq97/b5MDlRQPJuiBXzr797Elg/sJfE0zrLECDMRp3okofmaJuVcwFi+zvPKR4m1wZeXd9DndLguP0Wf0eaL9V2UrABHJlTNDq3U5XS3j6Wez/RKiSQxSE7lEWNd4p6J40foB/M4dcG1rzjCTLuE0oLp6Wq2hWMpxkdqPPjav2D+0IXFgcuypz4eVpIcO/JzAGsi9CHSTv/RG9ny/r2YXUFtm0FvSLH1fXtRNmBojCjTxZUFKxtNwsiiVK4TKZMD3fWc6VQwpeLLR7dzdGiQfVs+ydaheQ6E65mKquRkRCt1eVrxDPNekVOLfaRnfNJiilhyuenph1gOfQ6d9Rl99hlsmbCrOstCL89iPk/UsdGByeSZfpqRe9F2XVFSP37Xs6gONXna4PQF823aezsyEcTFFJFmHz0upQx/B3KTHSZfVgIgNwsr20CZ4C1ompsgzkPRC5EiU1PvWhwniCzue+mt/Kr1Knb4syyrHL6IuLO1ia+f3Uxn3mf4XyXdAYlb0+iXdGC0h5x3UJUYiebIzBBDOxcY8xt4RsQuf5q79QSbB5c4fGI0m5e9FBVeXLW6oqROvvqta86bjIYU9rscvC2THiZ/883wmw/Pc93r9zH0b4LE1SgTtJlta0+UalTsgILZY+Z0P1YpWzyeXz7EbmcGgAeiYb50dAfMOhhAfbtAJNDarDCBF11ziG8VNjBeanCi2U+11OGdWz7PdFyhpy2OBUPMBiUOH19HYaCNITSN5fya2nZFSV0r9HBIf6XN0u4LayqjX5iFRpPg+o0oS9LaZBCXFE8pTWGJlDvmrgVL4XkRb97/nxiyKqRIfqmwzP84+3QQGjHaQwPplEeSTzCbBjeMneX3hv+Fb5SHuLO9ieOdAQ7OjdBULgUj4CtL2/nO6Q3IEx6mDR3PJZ/vkT9sf08jvACuGKk3/Ne9LN6YPmyf5/GgE8niTDkzmlwIQnDHwkcf48Yb2Xv4hVScLqdDSXOuwBf0tfifKrL7dw7w+/szP2EBmSlSgL11BSk06bBkk7/Ep9tbub89xkKYZ7pd4kfHT/D3CzfwwPwwTx2ZJmnY2IAY77JlcJm5VoHegAYvvWj7rgipW/72Vp7xG5P8cukUcHFShZnJiqxceMv3jgfff+762T/3Qb75998zFH9mZg87y3PsjzJDTXvJJ6/gu3+1h6N/kk0p296zj8LTF3nx+kN8bX4reTubJiyRMh1V+PKpa5BSE6y4zD44iBEIBPDNFY/yuiYNO49npRydHGbssxI2CtZiBbgsUp/1pbfiWxEf3fIJfube13D8F95zSeVP/eKj94UuhIk//wD29Q9/1dOTA0zdO8qpN72BjX90O3QNwlfW2f/SWwHY9In38qqXf5sRq8HXGtdQ63rM1IoMV1qEyiRvhNw4fpq759Zn7kmlmCQnGV5fA6Dk9OjPd5hcrLDpbzQyjhlcjjnWuwoL1dHWWV5UaPDAwjCbx2Y5cKFQgysEv7/LwMb2ud+bbt9L34PWOUv8qde/8VFlTr7yFgDecf/LcWSKAAbLbZZaPv3r2izFeVYil6Dr4KyaAIWh2FxaZqpdRmvBiSOjeFMGzplZ0uOnSH/saWt638uwUiUMu00++dMX1qquJLYPzHP/9Ch7PvdOViZLjNwlKN0zv6ayf3Ddp/mnU9sBOLA8Qt4L+d/Hn0mz5jM6WsM8kiPxNd7WJkoJmrFLrCSJshn9Fyg8WGP6p0YQyQipBxy++DMv2Z6aanlOF76aePk3fwvI1Noji0O4bkwvsvDHWoy97ij8z0fr4LcefCmfO7n7YWkfP3ojm8wVhpwmcSrpz3Vot1xQMDNbIdzco7xzmf58h0q+y4k7NrHYyLPQyDP9As3x/1KhuTuiPaHojmQbmBfDJffUTf4EZ1pVJj56G0jWtNpfDnYWZ9nxu/s4fOv7H5a+6RPv5TuHNjP5q295VJnvNsbZ4s49LO3d//hzjO+aZant067nWFds8pIdB/nSyaz3CgETpRr3nRlDLduYRY15ME/qaXLtzJPRcFLSnMRqGIiLL/6Xt1B9/QW3wQsup+Ta8cXpHVSeO/eodCk0RjFk8217SSoJhp9gHfN48F038w/P+QhvvO/neeV5+U+97o3c8I/voNtxIBa4RsxXp7ZQyIUoDY0Vn6lWGTHtIkyNjASFSU2SyzQ5oSRx00UVNamnM9/Zi+AJ60l954veR7/XYcct+86lbbp9L3HHIqq56LEAo2mglhw23Lb/XJ7bn/LJR9WVs2JKxS4yl7DFX2R9aYVebFKbKqOXHRp3DSLSzAvRXoHYzxzxZKIJK5poNEb0hQzvWEDYF5f+fyga1Vpx6h82Y0ff+52WE8yahZaabR+oo5st7lj8WBY2dgF8/QW3AXD9F25hqlcmSCyiyCR3xiSsapKtXXK5kPBAme6oOufWbXYk6UiP3RtmaIYujpFk/lsXwROa1AO3P8LtRmjSoWyBUiWfL5744EXreOrnb+Hel7yXDR/5ILt2Nzm8PMzSQhFSQTKoEEM9HCemc7qEHQuikRgEbBhfZMBrE6UmeSvElgmtyF1TtOwTmtTzsf3T7wbDQYcGdinkyG+uzbgRpwYb/uoPoag5sdTH+vIKSxQxcgnKSRkot5mfrmCGAmVnvv3WgoWzMSFRkpl2kW5oU/B6a37XHyipGz90O8Vj8lH7WmtBeDaPqEa84obv0kkdPnr9Xz/s/nP+6S1ML5RRnWw/a9u2aXaWZrl2oMx36hupVtpYhqIW5LD9iKjhICJJzc5hNA2EBrMriGJJfmeNqUaZ7nSe8TsU8UaLxvMkrh1f3F7BD3ih0hLUT9Qvq6zyU/r7WzzYGuLbsxNM/NltD7t/9mwfKjDZsHmen7j+AGN+g1OdPo4uDyBaJrW5EkuH+hkr1omXPIxCjIwExuE85lgHuy4wg8w9qdnKER0pUjhhkHiS0smY5ESeZsvDDC7+rj/Qnmq1BAdedml2Ash6Ya4vR6Pl0eq6xJGJ199l44dup3xY0OsXsFUjWyZnFys0uh47+ufppRZ9fpdWr8qGnTP8zMh93NOcQISCwjdzNJ4S425s0Zn3CTcmTHxGM3BfipYSZ2aJpOoTDDooS1CYBONBl6Xo4pPqD4zUTbfvpTx5eWXLbkAQW6xEJskJH+UreiXB4LYluhsthvMdZuoltB9h2wmNmk+z5LKzNMs35jdx0/PuZ8JbxhIpnTQL5GiPawoD7czqJGDwWwaIBKMTkxRs0pJH6hgYoUJGir5DMWHFIs4/QVb/DX/zPoRhc8/HHm34WAsOTo7ieDFx3cEJBUlZUywGmFKRppKp5TIA0YrDyMYFrulbpGIH9Ftt8nbE6XaVIafJHYs7ue/0GGJbgJDQOV1CJEAppTMqUKZJeySPP5+S5AyUJUgtgRFL6iMGYRX03Vehp84Gx9ec9/ov3AKf6uP0X1x+6P/EX3wAvy+k4PXoWAmdgkul1CVVklo7RziVR7kKq9zL/Fel4t7TY5RKXb48tZN1m5b41k+8n48cuYmvdLciDY1q2BhNiZaQVBPsORN7RWP2NEak6VUMrK5CppqwJIkLkvaeHtLQSOviwv8lL1Qj3hY+cuSmNeUtfTCPP59c6iMehly1S86JWOl4dDsuKjBptnK0mh7BUg53XmK0JcmyxzXXzDBVK6PaFvWpEiKXUu943HzvL/Cd5iaeN3ScSqkDgJoI0Ot7+H3dLH5LkfXKUOMtJfjTASKFzjpBakMuHzLct4LWV2n4399ZmxH1xC8ZlPevLaDrsbD9XftAlFhclyC8BB0ZGPkY204IVlyEl6CeFpAueiDh2PQgnh9iV3skkYGKDYSAxShPO3aQaJZPVRCxgFkXZWmCCpiuRkawsgWClonV1KTXWPQGdObMtiBYmcoTpAUILt4PL4vUR8qIj4fHsiRdClJPE1VTrHKPuOFiNg0SqelFBlYuZl1/A60Fs0qQ+1qe9nMiDEPRXfCRgWR4xyKG0ETKpNbzWQryoLO4ry1/tYTK2XTG8zS2CpaemVBd16A+WcGbMTIfVgvsFYERaYa/KeiMyDWN7Se0RpXkNEZHkigPGWdD1C2G2FZCp5t59Y0XatS6HhtfeYzJlQqNhs/GT6UYcYz+bInZZ7nMDA3irW/R69poM7M2Hf/lfobvTJl5eYRf6DHm9Ui1QPsJYZ/AWZaYHUEwkpK6kvLRLEZ2LVaqJySpN37prdRaPqrgQCLBVmgpUQ6QClpdD7Fsc7ozyEyxRJpKTssqwT19OClAyPRzPHrbejxlwzGk0Nw/PYp7yCN1IVwXASZTP5vQV+mw0napKUmn5iE6Ju5iFm0TF9Q5b8PE5ZwX+MXwhCR1ZrYCPQPcFFY91mUos4AMmUW2yFRg1kzktEVaUHQ7HnLVgDz9YzZmBywn4cjCEJaZoqZzlE4qwpLA3B3gDMUM+B2Wuz4A3ek8mBozECQ5jTJAJILcnMRbVkSFVUKNi7P6hLKnbv7gXjb+8e2gBSIViLaJUAJigbY09voO5XymJ4o0O1Qh8TRWMxuqytIkOU1cVhy+9WaKfo+g5RDur3Dy5jeSOoLaDQmdhkdtrkQ3tunFJulcDmtFIoPMwzC1M58BbWv8aYXZ0ziNbOiLNZB6yT31VOcy1aILYNun3kOv6SAtMzu1IspOsFA5hdkwEEqS+BrXjrM4qVyC1bYxellouxFmLu/RUIJwUnQq2PXZd6GUhZuPSC0bgMZLOhScmE7LBQumF8uIeQenITGC7JQNLTNf2jivsZcNZKpQhiDxstixq2JPjdQlxL6sEQ++4l1A5oVtNg2SUuabj9TZlkkzC2mPEoOJap2alVD3PLQp6I3GoARWPQtEM2ad7OCFKZvE1+hqzMZnZA5zRb/H8okqRpARI1S2witLk7qZZ2HqgNXKji6RSWavTnKZfcHskvkIXASXTOq2wvpLLbJ2iEzDEV0D5aUYKyaI7EwALTVByyUtS3w7Yr4vBUPjlnu4XyugTKgezPIv7xLE1ezokUq1zXi+zgu/cjNBVIJSjE4sZChICgrtKIyemXmnpBmxZhfMribxBPXtMguZb0JnZ49C/uJmqifMQrXj//4eIuegg9Udy56BUKth8LEg7kvQkeTY9CD5/S6+Cd3xNOvlr4Br37GPzohEWeBtrzNeblDveSw28hxrDFB2g+yslTkrO5TB1BhdiTVnYHUgKmaEygSsTjZvtic0qa+wBgLCmRzjIzXuW8NIvaKe1N8vtr9rH3FBk5SyMKCHtoPtuiQYjxE9A28u09nTnCYaTDD9mCQwQYM9a5G64Gxq0l3xIBUIO8V0EuK6m1n1GwbWSjY/pnYWBWOsnjqUehqzLcjNaeKiIKxoEj8LZpNRVqb+p++ncWThB+dJ/f3ikaHgG/7kdrSX0vM0WBpiTXdTnIk1iQBz1bihgVQQVxRGR9Jd9M/5I2z40w+SCLCXDaL+9Bx5WkI6GpKs+kY5s5k6LVPOEWoEWdh8XFSYHUGyLsSRF7dlPKFIfQhPf9Ve7vr4G6gcEvSqFsoCbRikribVWXCZSARaZscyIQGVyZVosOoG29+1j6ii0L6Grok2IH/cJBhRiFQSDia4XkxsaNSyDUJjtQRaQJwDdU2HqGehNci6RbA+4dlbT/LlNfj9PeFI3foHezHXC7a+dy9iVCBjKJ7SmbXIyWKxIDs7QC4ZpJ4mLqc4fQH2YEJrtoCIsvhSqykzv/5QYrUEysq0ImVr3DmTHh5Gy7CBVsEAAAKgSURBVMCrZZHhSU6jPI0oRmwaqJMoyfRSmcQxMVsGd359Bytd76JteEII/1vevzf7/7e3okzoDapsznM1wWhCZ52guz7NNCYji8y22gKnAev/OWLLJ2Kqn/YxhKa6voHoC0lc/bBI7bAvq9OuC4rHwZvXiFCC0PSGFMZIF726/a21QGnBgNemXOxCkn1cZ1kgoifYxt9jYdO+29EyM0Yf/4Xf5cRb3oAqJChHoyyNXTMyxzCZaUvSTUBk8mRuXvHVL70Na65F0CdpzBSJEpOcH6LsbE4UlspO/5EglCA/o8gtpShL4J+VVB7IjCeWlaJTiQgFumvSiWzunxmlvuLjLkpklMmpP1D39MtF6itkPmbyfEdgkamg7rwBAqKhBK8SELQcXDdBrI8Z/z8CeTIT6s/+9CBagNk0CDpF0koC5YR0NGKk3GZGVbHmLGJfkzgCp66RkcZdzvb5UxfSUwWECWIohEjSChxMU6FO5LGb2WvZzSe4lWrLJ3+f4z//zsf2GhQaq26QOqsHMcaCsGdBYKCm8hy75Q3w8u9ltxua7ojAmxcEg5l+Lq3sgMXFegGjnh13V712iSWvSrNurp74I+k7GJE4FmYIyhTUr3WhP8YwFNcOzHNIKlaGfOxpi7Aq4J8u3rYfGqnHf/6dj0rb87l30mp7EFrYK2L1lAqV9VwlsvP/hhMm/tf7cYohR38uU2876wRH33kz171uX6Zqtk1SS6FiG1GOUDmF8BPGi3XiMYN41MCRil5UZuk6O5NLfUGcFyR9EYaT0qrnOOuWKXs9Wmke5UAq1+afesnCvxBiEbjyVpV/P5jQWg9cKMOTB31fBfzQV///iHiS1KuAJ0m9CniS1KuAJ0m9CniS1KuAJ0m9CniS1KuAJ0m9Cvj/V93s2dRQgWIAAAAASUVORK5CYII=" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>(-7724349.609951427, 1082298.5058092135, -7694697.59609364, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>(-7694697.596093641, 1082298.5058092135, -7665045.582235852, 1111950.519667)</td>
+      <td><img 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bHNErWrCbvSpzl3/ANIpx21s+pyrvW6gioTQdFwiHFM7aZyGCr0XOLtghOc7U8ySCPEUGM1yEJgIoeTDpkLrANbtZ5cK18yKYeBDykrhubykEpYsPbEHCoVmKolGPPX9FyDMPfLuWhZ9E7AVjEJwNTeDrVKzmCPJP5KFRCks45k3jue3rLEasimnPf8BejEO6ds0mErFjFU45KLw1QdwUBSVkBlgrzlsOEVYHS1oDrtNaesOpjOiCoFrmow6xWK3ZiyUKyuTlEaiauVlBWH1VBMWEzLXKYtQUfBTMbMwTbV2SHLy1vEUymt1ohmnGGdQM8lRId77D24TRiUmI0KrScF8c4zJsFUfMQjMkl7q0ktyjG5omg6TGX8nKVf1kXdL3nhfGJIjyDa9eNzyoe3ohSEu55XCyPoHzbeF8Azye5XkGvy/rIQlBVDFJdMNYbY+oj1C1NQCtzY6KejENXxl7eRw9VK5O44pb6YEddSAN6y93ECYfijrUNkwxClLFv9GmWpKIYBRaqphAX9XoXmSYlOHO3XWEzTEK0GmIoveSx+Ada+VZGVGnrjaTnQfUF103v7dM7iJOjEa67VMFwUJEcyZGiQgJMBwRDkrsBUwCpFdcNzWWF9LuP6g+pADwWtpxRFvUH4nT1ubm2yWOtRDzL+XOs0v7l2Jycf30NtVXq+OWHAePsV3dplpu6J+mKlh3WCr/f3sNZp4lLFaFiHeoGzgpsPrFPRBSc/eRNywVLWIFnwmld/MiDsOub/80mefPdB1ucBA+1OzZdNhj5OFw5MIHDKL+HmU56N5E3Ipv2UJqf7DJMI9dUGTvpAIOpZ0glJOiOIuhYnJXEOKr0BoArrHUT3ZiincsIs4ktry/R6FY7s2+BfbH8j7QsTqNQ/VScACbKvMRXH0kSHQR4Rq4LEBDzc28OpnWlqcU7eCLClwFnBkeV1nnh8L8QWudci83HpO/DVB28rHe37Vpj7E8HWX8wQ2yHqXIwwYGIIu1A0oIy9uZA5JHOen9rAkU+O4/onprABTLYdOgGVOZJpSd4U1C9a8oakqInL7OCV5JqaKUzsmL9jgz372mxfbCGE48i+Df7c1BnefvPv+1q5GgOgfGpNlAIXOi52WwzzgLPdKU5sLrLWb/INe86hlcGWAh2VUEhOP7CfoKsQypddwo5gtMdiIodKJNVVwfJnCoLJlM1v9MTTx/gCWQhUCsMle9nhXAovTcXhlFcMlUiibUXtgqR2UWIigRPQX5KUFd8DAB5kG+Dp1I2I/a0CPT/COMHWboNwWzNoxnzfkc9SOMWT6QJqKIl2JGXNF9Js4BCTGXPTfXZOzGKqltpSn8FGnaSnGH7TDndMr/L5fpWsG6O6mupFgYmhv/BMuk2WXvsvaeyp/1shMgeBI4wLslgB0mfBFATjpLMNwSrPTPIpgx5I5r4E6SSk06BHjrQiyOecDxLaPmwNRg5Zem6rUxDGa/t1B5XAMTsxIMkDTKYwcyVT9YRPrN9DPcg4159EjbwNy2YMrmII6zmLkz3OXpxGRp5CZWnAocNr3D11nvOjSX73kduI6zlIh53LyNsxo8M5aifwFCf2Szba8UA5jdcaJ8AIsk6MHCqc8AGDLEH2/JDTGd8kUbQcaiSJtwTVjYxgpJGFYrDsGY0aCcADKgzoxBK1c3orMaJ0VNruxmgqFqwTnnDHJQfnt/n+vV/klnCNf7n9zZR1yVa5SFF3uKqBXCIfqdO7JwMnsLHlTXc9BsBMOCCzmnP9SXRkSLcr3kzUnS+jWE/Si6anYrqvPKAKHxVJR+WJiNFy6Y+NHNmMJd6UBAOHU4J01rf/FC2Hk17Lw55juCdkuCDJpj1dinYEeuTzAOAIBhBtZxStkGROUluzCOuwN4RSWUGkDNXGgC1R41BjG4XldVHIWzeW6Tw6jXZgFjKq9Qx7ooXKoHtqkupSn8lawt64w288eSdSOm5fWCMrNWY7QmXSe+umQ0clYWgY5p5k1s74thwfy/sGB4xgdKBAFBIXGcjVM31dSlDWfChrJ3zgEfQklU1BNuUzYCr35iwYiMt8tbruNbW2npHNRIxmFfGOY7DXBwHu8zcAVGHg7NlZ4omU5ek2Z4ZTfGNTcqx9kLxUlDMFZV1Sb6bEQcnOtCHoSvRAcNfiRV7bPM961qIsNGao+Vq5F1MoXGRRXU+6y0RDVFKWEpQjXNfEO17TkjmvUWVVcOjf5BStgD/65DsBuOkjxwgGHlUbXiLrvqQTbyiijiOb9A9GZZBP+BqVMGP7WfjAoLKVY7Vk6w5NslT61aMclVV94zL/wbbGNASrvSaHprbpmCoHo02sE+zd0yYpNMMkwjmBmMrRe711f2RrgYvDFmu7Taq1lEwHFJlGrUVI4e2kE4ByFIMQGRtEaClXUoZHSpSylE82aa6DvuAQ1qFH3out/PrP41oKPdIIfEoPIBh4G6xTfFHQjJM0IeAEeugBxoFOfL9VOh1eXhUIh5wsMCNN1ObG1KiE8+m+6XqKVobSKXbLGp/ZvJ1k5Ov3/c06OMiMoLYwJE1ClDakSUhnvcHSyjaHW1tcHLU4vztBWUQII8jmS5oLfXqrDYSVWOV81gmoVzKMFYwWM9q1gMqaYuNbQvb+nvcczkG4oy7nPIWBYOh/O4nP8Nd8NiqfcKjEx/7qUnEiFAwXFSqFuGNoH9FkM75HzHQDZCovU8XrDuol2e1VadYTvn5yH/UjGa+bPMfGoMEgiRCZRE7lmIFmuFFDjSTlfEa1ltGc6dJJYipTBc0wJQpK0pqjsi4oUkn/fJOwKykaPvdqc4VLFV3pIwm9FqH7grADy7/yJOyZ9wOygukTjsE+HyardAxkxZdyjBakM46yYYm2fEU1bzrAmxOnoLJtqV0YkSxUfOowdqh6gTxboXkK6qvFFS3/qyb/NoDpA7vMT/WohgU6Lnnb4meJRMlNk9sEQYmLLc5BdWYEymEaBnUhZjSIWF2dot+u8VuPHuXE6h76gwpR23PLsCOJ1xUqEZz5kXcgAoszwiekH6njzlcxoSPqwNyv/Alu3yK//fCHADjwcahfSAl73k7GHUvrVI6N8PnV2Gtt5aIi3vL2FumplImgjAXxVkYyX6G7olGJQHelbxHFVzuq/+3UFXn/a4qotDJs7TbY3G2gA8P9F/8Sf7h1M09szzEaRczu6eBKiT3RonomACso6xa2I4LNAKEtUjuUslgrSPYXDJYtZcWRzVrSw35Nnvnr7yXYCog3JEEfTr7z7diK9cnryUl+58TPXh7T5377PXz2v/4kRV0Q71jKWLB1Z4QTII1f5pUNT9HK2ti2Jj76CnveUdlQEe1kFHV/zajtO7idBJ06MNbz41fC52oBFYFl/cIUIpdQK7HWsjZssrHTwlmwqaaoKaZn+mQTI+wDk4hKidyMkIWfWG9O4qTxVdTJAYM4omg3qK4Ksml48m0/fvl+xVwBKzmjXsRNHzlGWCge+9C98KHnjuvoe46jh0AEowXpbZ+DsOt8T2rgTUIyD0UTwo4gGPhzF/9wm3yuTvdARNGILudYlz69hRgkfObscW5/13F6b70V8S8+88oYXe3mtOrhRbf/F95KWShajYTtzSYIh+gFPncZOOL5IX9h+Sk+d/4Qo/MN9Mg7iEu1/HTO9zexN0FKi71YpXZeErd9D1Oy4LDaIUqBvmlAnmrsMODsD73rZcd25w8fQ5aeQdhAkDe9raxuOF8mkZDOCl+ITAVB3+dTwZuL7kGf2Q97jtpaycbrA2QBj33Q934d+vljXPjYcdKL569vz39VF8w0hihpubm1ye+u3o5IlS/CTRlUo6AsJdt5jeFmDZ0KVCIwE5baG7dJ8gCGEaYXoM9VPM0ZR0gm9GWLcjFj5g8isimB22qQHi6vrDXEeS9eVvE/44R03vQZLgTjqMmHvCAoa4LqhiWZ8XY9GDiirqW/X/sOPwe3vP84soRA3qB8qhaWwkq+YeYsJzp7UbUSm4beOgcWKS1lpvnSyRXUQGJDRyEBCYMkwhhJ+GSFdE9JOVNAKdE9X8JIpwUmdtANMJFfuoMl8RwNveWnjpNNWU7/6DueM65b3n8cMemroZeiKpV5MIUdc2A5rkVNGUQhULmi/qT1GTXtewEmTmaM5kLcuJU97DjSmXHLj+XGdKgI4dhb7/K13X2cWZ/G5op4cYS1ArFdoRxo6mf9U88noKg7ahcFyRuH3DK3wVM7swznDXIkcVMG2ZGUdYOpCGrnFKZmiLYV8a7lgY+/8wX3T/YXRK2Ub/rd99BLI6yVJOca6Irv7RcGwu64jj/j03w2giff93aO/Mxxyooj3PYPMZsyJNMKG0LzjCEYGIqar6vlLZ8dy6YFOvH3tpor6k+9au/fHtX4+uoe7pk+h000ejOAhxvIx+pULihaT/lkhsoc2bSlbBm6t5UUqWZt2CRNQqaXdxHzGa6Q2NjbTmEEyZxDDRXBQNB4esDBf/9cb3Tbb95H2MwQjzbYODGPMZKbZzaxkSWfNpiGwdS85qWzvsUINwb0p4+jEq9pKh97/V2FqUBt1RLvFETtjKidUV/NiXe8g9Oj54J5JU0q15SlCsOS33j4LkQmfUjYtOiBRCfj8M8Kn25bHAL++NEgIvvPc8g3D9nZqUM3IBhIwp5PDF+ydft/9XEAHvuFg4jScvt/+QBZFqCUxRiFO12juur46j/2/a4H/s2HIbBEzYziQm0cWvpUn+lrnIbDHz5GdUugCud7qho+H6CHgsbZZ5a/HOW4RsRgb4jKfepvuOiZhHDPFBGvP6jKMVivIzOJLC+VKQQIX1xTuc8i2dCR9yJkpeTWhQ0eThaRJcz/u5gL312iEklZcwRHetiHWnz9uPew3/arf4enf3kfsjA4IylLhXNQFoogLEn3pQxMfHk4p7/vJy7/ffjDxxDGg2b1uNVH+rTeaK8j7ArySYOw/kGqRBAMLbU/PQOtBmhFUQ+wSnheOrarZQz9A4bqqroxTb8wriqW0DjjWxMvOQGroWg4gr7AHExoVDPcFya5+AcH2bdRYgPD2hsULvUt5C6y5E83qfbh25d+FJcXnPpHe6lFOZ1RHRUZ8lxjS4nLFGWkcKXke7/zj/nxh77Axx95HbYfEExkVCo5NpxA5r7S4EJHtO5zAdIA1tf2wS9lVQicdvT3KcrKAcqKn4sNBZUte9mpBQPHYMlTxSuJpq4JVKUsopUjpyyDskrYFcRtzwOzSQHlWFO2YvphiFgx7P2DAatvamHCcZLDaZ5+j1++N91/jK9/9F74KNz9Q8eoxm2yQiO0D3WDwJClmqCeUxYKcsnARDzdn0VKx9RSh+4gJss1pmYpJ8ecRzmCMavIm94clFWf+behI94S4w4Z6EwqshnrG+V2ASSVLYuJBGXsAwVhFFFn/IBeSemulvxXDu1xc+/9MSgFwa4i7ArCnl/6RdMPdrDs7dSlMLC67uNxEwlE6fjar3hAD338gzz9V993Rfd9zb3HmfvyiM7NFSa+7wKNIOWrT6yAcMStDGsFZa6YmBjS61dxqzG1i5J0xj2zU896ZlBWYemzCSotWfumBmV13FcVQ7ztV2FZ9Z3eyeJz9ysIIb7snLvn5cZ6Tctf9ZSvFY2bYGUO2bKjmCqxWl/eSS2cL/wN9/jJmNjTnDs/9T5KKymz6ive65Jcsrn/xx++nZPn55BbIdWDPZwTJLsVVK1ASNht13FDTTT0y9mEgPCpvkuOprbq2Ly7gk4dUcenBL02+hxAWRn3Vyn8DuurlKuvpuZ+l7QP+cYti1VBZR30MECUeCo1ZdC1ArMbUSqHLDXpPr+ZYfDoFGFHcObD93LkP/4Meya73Nra4PbaBQySH7nlD17y/t86+xRnN6cIb0pJ0wAz0lRPBcjXD0lGES7R6L7CVBxFy2FDnxMNOxoTO181GJefVeYjOae8ZsY73m4O9zgEPnhQ+RV2+j5LrilLdYlWCDPeB6/9oIqGNyV65CucYVQQzCQQWNzRvq+bW3wCe9Kx/E8+4o8XlgujCf75qTfymc3bOfjvPsydn3pxs/CB2z9JvZYybFcwnZBDv2bQqd/RYlIFgSXoCcqm8c1vcDlik6VfLZf3ADQFg5VLSWwY7oHuEef7DHZ9SBp2rh6fq7ap0fKSW/rhe4m2BUh84rdpEOclVX4AAAcxSURBVEagBpKw68vT6YIhaEtsBOVESTSRko9CRCfAVgxBW1PuyWi0EkajCGsFYVSSdiNkXyMsRCt9sjTEbUYvCEu/5Tt/ke07AkZLhnhdUdlytO8uQUDzkeDy/nynHfGG9juhRz7qap729fzhgi/mhT3IW95USeOT1vGWj8a+8k+fu//rxthUB+VihjDR5S3caiC9trYMYVdT1ByuYgDf7Vw2xjbKCFyzRLW1zxplitEoIq7kpElIlgToaols5Dz1ve8H4LWffD97964D8Prffi9JHtDvVlhSPpycO7jDZr1FWQ05+7ffDcCRnz5O0NbI3GeiTAVwHtB4y3ec5E3BaK/1gcqCxWlH8wkPh0r9Bo5o9wpc/YvIVS9/YUFuh741vWF8Nn1TIjOB7iryCb/9u/pUSLztc5ayUTDZGPlXaqTSk2/tSxUTzRHTtRFlpnAj7fdQWf8Qfurr38VUbcRsPODnHvkO/t7Bz6GVwZWSi2+WJAdyth6fofFQRGXrGdtnQ1+eaZxxVLZ9V4lOfPtk3LEM90gfxQ0kpm5wgW9RH+3xDGC4zxcOs8YVEtPnyTVpqswEbiVBrsfULgqyFpi6xQmHzCUqHTfUpp6/2kSzfn6KylRCKkPCC36XSpFWGBwx7PaqkCluPryKkpanH1jm9nceR3zLaxkMYi6GLU5UFrljdo32hQl0K8d2NWd/4D0vGN6Rnz5OdUtgg3HKz+B7YgsIBzCal/Rv8mVzdaRHZAX5Wg0XWEzVku7LMCONMCFl7Rpc/7WAKiyowwPSfsTy7xvSCRgsCf/ymIkSVzVUvhYRDB2dI47ZL8NtHzjPo+/fT54FuERjQwh6PvZOkgA30hBYnl6bRUhHeHOf4DU5nW6NvbMd0lKzvd3gyWCWs3/nXaz8yv2c+bF3vOj4wq7XxqwpLvPhZ8vdP3QMOZnjZgzqS03SfcY7pJmEhYk+Zy/MvOjDuiqMrtpR7VtyR7/9Xp+0HVnyumT3Nt/eaKuGiYcC+jf56MSGfpNXvO03Lzxx370vuN6RnzlOUbfY2Zx6K2E4iLGFhFyidzX1W3e5Z+E8D64v8frFs/yze379Jcf22rcdR2WOMhac+AcvvNf1kBvjqBTs3u6wdd/Ie/ATOTuv02Bh4qFgnL906IHfdbL/dRdpD6sMz7de9HImdpi65ez3+/ctrvzrnyPYCFGJL3s04oxIlrxmbvVlAQV46B++EMjb33Wchz9yYwB+Kbkmm+oubbuxcPbbQhAWVzeoQiJH0Hpc+feQGFiptzmzNs2Zv/fchPPhDx0j2hU8ff8zS3T5136eYCPyW8RzH2ev7rTYHVVYnty9ouG9/m98FBMKRvOCxz50L4P9llv/03089t33XfVUr1WunvwL59/uoy1n3/ouv6m2K6mcDhnNC9pHfW9AvA3R0Q5/YfIxfuKez/Dnf+/dvOa/fODyZUzs7fNN9x9j5Zfv57afOE7tiYiw46Mdp6Dck3Hq//1JzIMT/Na3/MOXHdY3/M1j3PnDx+gelHz5V9/uu0uAMz/yDpJhxPKv/iIr///PXfV0r0WuAVQQhUS1PUm9tOsv2VOS7CmxscUq6Lym4Iv3/BqpDfj6aB93TK3SiDMOffyDHPi3H8ZWLCbySZfqBd8iGXZ80y1WUDT8q5huuv8Y6R7Dyi/dz63vOw7Ayj+6/wXDUoXfKyULn1eVueC1n3w/f/NLP0C17vsIas0raNi/DnJNyx8Hp+59B7e/8zhyEhhJwsN+X3/3fItHfvFefuLE9/D+jTdw/K6PA3D4Ez9LvZoilUUHBhYKuNAk6oybyQQYJ8gmfQUUASymWCtwiQYjSBYNN91/jNmvS1735WOozBEkji/8xjtJJy4lbLyG1s5JhmKSz5+ZYPJgm7SnsKcn4LuuF3QvLdfwsi9B7YwnxdmUo6z594/MNX1ngu5Jlv/xR8is5lNP3375NFMqOt0a1ThHCEd2vo5KfPSiEzz/zaG6KqisC1QiMf2A8OkKe39H0npEc+aH34mZy7GBf6lBMivJa5LX/eAxeocco9tSTt37Dp/x13DPNz/O5ME2d86u8pff/KDv/PsfIFdNqe655x734IMPvuDzdz30vXz69FGGmzUm93RxTvDQ//VMW87dP3SM4T5BdiCl1kzJH24R9nyThbDj/OXQRz1FDaYfMXzhP76wmnr4Ez+L/lqdsupj+cc+9OKe/a1f/n60MKwmLb5yaj86Knn6/3n/Vc31xeRKKNV1A/VaZOWX7qey5juV7Xiv02DZEnQlT9x3L0fuO07RtFQ2/M7oN7zpEQZFxInz+ziwsM257Ula9YR9jQ4fWf5P/NTFv0yviPnUN3/suff59Z/nzN/48ZcYxdXJDQFVCLEFnP2zDOx/cVl2zs2+3AGvvuj7Bsirb/q9AfIqqDdAXgX1BsiroN4AeRXUGyCvgnoD5FVQb4C8CuoNkFdBvQHy3wETfluoosBpuQAAAABJRU5ErkJggg==" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>(-7665045.582235853, 1082298.5058092135, -7635393.568378066, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+## GeoTIFFs
+
+GeoTIFF is one of the most common file formats for spatial data. 
+
+\[ See issue https://s22s.myjetbrains.com/youtrack/issue/RF-71 \]
+
+## GeoTrellis Layers
+
+[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames write data from an appropriate DataFrame into a GeoTrellis Layer.
+
+\[ More details see https://s22s.myjetbrains.com/youtrack/issue/RF-72 \]
+
+
+## Parquet
+
+You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format. 
+
+```python, evaluate=False
+spark_df.withColumn('exp', rf_expm1('proj_raster')) \
+    .write.mode('append').parquet('hdfs:///rf-user/sample.pq')
+```
+
+[GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
+[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html
\ No newline at end of file

From bd0f5b237a02fdef18bb116c35641878de4c81e2 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Jul 2019 10:09:01 -0400
Subject: [PATCH 241/380] Exposed geotiff writing extension in Python.

---
 .../rasterframes/ref/RasterSource.scala            |  2 +-
 .../src/main/python/pyrasterframes/__init__.py     | 11 +++++++----
 .../src/main/python/tests/PyRasterFramesTests.py   | 14 ++++++++++++++
 3 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 287bb8064..4221166e3 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -145,7 +145,7 @@ object RasterSource extends LazyLogging {
 
   object IsDefaultGeoTiff {
     def unapply(source: URI): Boolean = source.getScheme match {
-      case "file" | "http" | "https" | "s3" => true
+      case "file" | "http" | "https" | "s3" | "" => true
       case _                                => false
     }
   }
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 2af1677a1..713629f18 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -25,7 +25,7 @@
 
 from __future__ import absolute_import
 from pyspark import SparkContext
-from pyspark.sql import SparkSession, DataFrame, DataFrameReader
+from pyspark.sql import SparkSession, DataFrame, DataFrameReader, DataFrameWriter
 from pyspark.sql.column import _to_java_column
 
 # Import RasterFrameLayer types and functions
@@ -150,6 +150,11 @@ def to_csv(comp):
         .load(path, **options)
 
 
+def _geotiff_writer(df_writer, format_key, path, **options):
+    """ Saves the dataframe to a file of the given type at the given path."""
+    return df_writer.format(format_key).save(path=path, options=options)
+
+
 # Patch new method on SparkSession to mirror Scala approach
 SparkSession.withRasterFrames = _rf_init
 SparkSession.Builder.withKryoSerialization = _kryo_init
@@ -163,8 +168,6 @@ def to_csv(comp):
 # Add DataSource convenience methods to the DataFrameReader
 DataFrameReader.raster = _raster_reader
 DataFrameReader.geojson = lambda df_reader, path: _aliased_reader(df_reader, "geojson", path)
-
-
-# Legacy readers
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
 DataFrameReader.geotrellis = lambda df_reader, path: _layer_reader(df_reader, "geotrellis", path)
+DataFrameWriter.geotiff = lambda df_writer, path: _geotiff_writer(df_writer, "geotiff", path)
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 6fd925459..d6b9cd485 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -781,6 +781,20 @@ def path(scene, band):
         self.assertTrue(all([row.b1_path in b1_paths for row in b1_paths_maybe]))
 
 
+class GeoTiffWriter(TestEnvironment):
+    def test_identity_write(self):
+        import tempfile
+        import os
+
+        rf = self.spark.read.geotiff(self.img_uri)
+
+        dest = os.path.join(tempfile.tempdir, "pyrf-test.tif")
+        rf.write.geotiff(dest)
+
+        rf2 = self.spark.read.geotiff(dest)
+        self.assertEqual(rf2.count(), rf.count())
+
+
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests

From 04a0005225fb5d3a9c455dbf343192e76580000f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Jul 2019 13:08:30 -0400
Subject: [PATCH 242/380] Initial steps toward writing unstructured
 RasterFrames to GeoTiff.

---
 .../encoders/StandardEncoders.scala           |   3 +-
 .../ProjectedLayerMetadataAggregate.scala     | 177 ++++++++++++++++++
 .../extensions/DataFrameMethods.scala         |   6 +-
 .../test/scala/examples/Classification.scala  | 161 ----------------
 core/src/test/scala/examples/Clustering.scala | 108 -----------
 core/src/test/scala/examples/Scratch.scala    |  42 -----
 core/src/test/scala/examples/Tour.scala       | 140 --------------
 .../rasterframes/RasterFunctionsSpec.scala    |   3 +-
 .../rasterframes/TestEnvironment.scala        |   2 +-
 .../ProjectedLayerMetadataAggregateTest.scala |  59 ++++++
 .../geotiff/GeoTiffCollectionRelation.scala   |   1 +
 .../geotiff/GeoTiffDataSource.scala           |  94 +++++-----
 .../datasource/geotiff/package.scala          |  32 +++-
 .../src/main/python/docs/architecture.md      |  12 +-
 .../src/main/python/docs/raster-write.pymd    |  56 +-----
 .../python/docs/static/df-samples-output.md   |  53 ++++++
 .../docs/{ => static}/rasterframe-anatomy.png | Bin
 .../rasterframes-data-sources.png             | Bin
 .../rasterframes-locationtech-stack.png       | Bin
 .../main/python/pyrasterframes/__init__.py    |   5 +-
 .../main/python/tests/GeoTiffWriterTests.py   |  53 ++++++
 .../main/python/tests/PyRasterFramesTests.py  |  13 --
 22 files changed, 434 insertions(+), 586 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
 delete mode 100644 core/src/test/scala/examples/Classification.scala
 delete mode 100644 core/src/test/scala/examples/Clustering.scala
 delete mode 100644 core/src/test/scala/examples/Scratch.scala
 delete mode 100644 core/src/test/scala/examples/Tour.scala
 create mode 100644 core/src/test/scala/org/locationtech/rasterframes/expressions/ProjectedLayerMetadataAggregateTest.scala
 create mode 100644 pyrasterframes/src/main/python/docs/static/df-samples-output.md
 rename pyrasterframes/src/main/python/docs/{ => static}/rasterframe-anatomy.png (100%)
 rename pyrasterframes/src/main/python/docs/{ => static}/rasterframes-data-sources.png (100%)
 rename pyrasterframes/src/main/python/docs/{ => static}/rasterframes-locationtech-stack.png (100%)
 create mode 100644 pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py

diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
index badce6283..256da58d8 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/StandardEncoders.scala
@@ -24,7 +24,6 @@ package org.locationtech.rasterframes.encoders
 import java.net.URI
 import java.sql.Timestamp
 
-import org.locationtech.rasterframes.model._
 import org.locationtech.rasterframes.stats.{CellHistogram, CellStatistics, LocalCellStatistics}
 import org.locationtech.jts.geom.Envelope
 import geotrellis.proj4.CRS
@@ -32,6 +31,7 @@ import geotrellis.raster.{CellSize, CellType, Raster, Tile, TileLayout}
 import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.spark.{KeyBounds, SpaceTimeKey, SpatialKey, TemporalKey, TemporalProjectedExtent, TileLayerMetadata}
 import geotrellis.vector.{Extent, ProjectedExtent}
+import org.apache.spark.sql.{Encoder, Encoders}
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.locationtech.geomesa.spark.jts.encoders.SpatialEncoders
 import org.locationtech.rasterframes.model.{CellContext, Cells, TileContext, TileDataContext}
@@ -70,6 +70,7 @@ trait StandardEncoders extends SpatialEncoders {
   implicit def cellsEncoder: ExpressionEncoder[Cells] = Cells.encoder
   implicit def tileContextEncoder: ExpressionEncoder[TileContext] = TileContext.encoder
   implicit def tileDataContextEncoder: ExpressionEncoder[TileDataContext] = TileDataContext.encoder
+  implicit def extentTilePairEncoder: Encoder[(ProjectedExtent, Tile)] = Encoders.tuple(projectedExtentEncoder, singlebandTileEncoder)
 
 
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
new file mode 100644
index 000000000..20d827489
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
@@ -0,0 +1,177 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.aggregates
+
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
+import org.locationtech.rasterframes.model.TileDimensions
+import geotrellis.proj4.{CRS, Transform}
+import geotrellis.raster._
+import geotrellis.raster.reproject.{Reproject, ReprojectRasterExtent}
+import geotrellis.spark.tiling.{FloatingLayoutScheme, LayoutLevel}
+import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
+import geotrellis.vector.Extent
+import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
+import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.sql.{Column, Row, TypedColumn}
+
+class ProjectedLayerMetadataAggregate(destCRS: CRS, destDims: TileDimensions) extends UserDefinedAggregateFunction {
+  import ProjectedLayerMetadataAggregate._
+
+  override def inputSchema: StructType = CatalystSerializer[InputRecord].schema
+
+  override def bufferSchema: StructType = CatalystSerializer[BufferRecord].schema
+
+  override def dataType: DataType = CatalystSerializer[TileLayerMetadata[SpatialKey]].schema
+
+  override def deterministic: Boolean = true
+
+  override def initialize(buffer: MutableAggregationBuffer): Unit = ()
+
+  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
+    if(!input.isNullAt(0)) {
+      val in = input.to[InputRecord]
+
+      if(buffer.isNullAt(0)) {
+        in.toBufferRecord(destCRS).write(buffer)
+      }
+      else {
+        val br = buffer.to[BufferRecord]
+        br.merge(in.toBufferRecord(destCRS)).write(buffer)
+      }
+    }
+  }
+
+  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
+    (buffer1.isNullAt(0), buffer2.isNullAt(0)) match {
+      case (false, false) ⇒
+        val left = buffer1.to[BufferRecord]
+        val right = buffer2.to[BufferRecord]
+        left.merge(right).write(buffer1)
+      case (true, false) ⇒ buffer2.to[BufferRecord].write(buffer1)
+      case _ ⇒ ()
+    }
+  }
+
+  override def evaluate(buffer: Row): Any = {
+    import org.locationtech.rasterframes.encoders.CatalystSerializer._
+    val buf = buffer.to[BufferRecord]
+    val LayoutLevel(_, layout) = FloatingLayoutScheme(destDims.cols, destDims.rows).levelFor(buf.extent, buf.cellSize)
+    val kb = KeyBounds(layout.mapTransform(buf.extent))
+    TileLayerMetadata(buf.cellType, layout, buf.extent, destCRS, kb).toRow
+  }
+}
+
+object ProjectedLayerMetadataAggregate {
+  import org.locationtech.rasterframes.encoders.StandardEncoders._
+
+  /** Primary user facing constructor */
+  def apply(destCRS: CRS, extent: Column, crs: Column, cellType: Column,  tileSize: Column): TypedColumn[Any, TileLayerMetadata[SpatialKey]] =
+  // Ordering must match InputRecord schema
+    new ProjectedLayerMetadataAggregate(destCRS, TileDimensions(NOMINAL_TILE_SIZE, NOMINAL_TILE_SIZE))(extent, crs, cellType, tileSize).as[TileLayerMetadata[SpatialKey]]
+
+  def apply(destCRS: CRS, destDims: TileDimensions, extent: Column, crs: Column, cellType: Column,  tileSize: Column): TypedColumn[Any, TileLayerMetadata[SpatialKey]] =
+  // Ordering must match InputRecord schema
+    new ProjectedLayerMetadataAggregate(destCRS, destDims)(extent, crs, cellType, tileSize).as[TileLayerMetadata[SpatialKey]]
+
+
+  private[expressions]
+  case class InputRecord(extent: Extent, crs: CRS, cellType: CellType, tileSize: TileDimensions) { self ⇒
+    def toBufferRecord(destCRS: CRS): BufferRecord = {
+      val transform = Transform(crs, destCRS)
+
+      val re = ReprojectRasterExtent(
+        RasterExtent(extent, tileSize.cols, tileSize.rows),
+        transform, Reproject.Options.DEFAULT
+      )
+
+      BufferRecord(
+        re.extent,
+        cellType,
+        re.cellSize
+      )
+    }
+  }
+
+  private[expressions]
+  object InputRecord {
+    implicit val serializer: CatalystSerializer[InputRecord] = new CatalystSerializer[InputRecord]{
+      override def schema: StructType = StructType(Seq(
+        StructField("extent", CatalystSerializer[Extent].schema, false),
+        StructField("crs", CatalystSerializer[CRS].schema, false),
+        StructField("cellType", CatalystSerializer[CellType].schema, false),
+        StructField("tileSize", CatalystSerializer[TileDimensions].schema, false)
+      ))
+
+      override protected def to[R](t: InputRecord, io: CatalystIO[R]): R =
+        throw new IllegalStateException("InputRecord is input only.")
+
+      override protected def from[R](t: R, io: CatalystIO[R]): InputRecord = InputRecord(
+        io.get[Extent](t, 0),
+        io.get[CRS](t, 1),
+        io.get[CellType](t, 2),
+        io.get[TileDimensions](t, 3)
+      )
+    }
+  }
+
+  private[expressions]
+  case class BufferRecord(extent: Extent, cellType: CellType, cellSize: CellSize) {
+    def merge(that: BufferRecord): BufferRecord = {
+      val ext = this.extent.combine(that.extent)
+      val ct = this.cellType.union(that.cellType)
+      val cs = if (this.cellSize.resolution < that.cellSize.resolution) this.cellSize else that.cellSize
+      BufferRecord(ext, ct, cs)
+    }
+
+    def write(buffer: MutableAggregationBuffer): Unit = {
+      val encoded = (this).toRow
+      for(i <- 0 until encoded.size) {
+        buffer(i) = encoded(i)
+      }
+    }
+  }
+
+  private[expressions]
+  object BufferRecord {
+    implicit val serializer: CatalystSerializer[BufferRecord] = new CatalystSerializer[BufferRecord] {
+      override def schema: StructType = StructType(Seq(
+        StructField("extent", CatalystSerializer[Extent].schema, true),
+        StructField("cellType", CatalystSerializer[CellType].schema, true),
+        StructField("cellSize", CatalystSerializer[CellSize].schema, true)
+      ))
+
+      override protected def to[R](t: BufferRecord, io: CatalystIO[R]): R = io.create(
+        io.to(t.extent),
+        io.to(t.cellType),
+        io.to(t.cellSize)
+      )
+
+      override protected def from[R](t: R, io: CatalystIO[R]): BufferRecord = BufferRecord(
+        io.get[Extent](t, 0),
+        io.get[CellType](t, 1),
+        io.get[CellSize](t, 2)
+      )
+    }
+  }
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 43177117b..ed861fc4c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -219,7 +219,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
     RasterJoin(self, right, joinExpr, leftExtent, leftCRS, rightExtent, rightCRS)
 
   /**
-   * Convert DataFrame into a RasterFrameLayer
+   * Convert DataFrame already in a uniform gridding into a RasterFrameLayer
    *
    * @param spatialKey The column where the spatial key is stored
    * @param tlm Metadata describing layout under which tiles were created. Note: no checking is
@@ -228,11 +228,12 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    * @return Encoded RasterFrameLayer
    */
   @throws[IllegalArgumentException]
+  private[rasterframes]
   def asLayer(spatialKey: Column, tlm: TileLayerMetadata[SpatialKey]): RasterFrameLayer =
     setSpatialColumnRole(spatialKey, tlm).asLayer
 
   /**
-   * Convert DataFrame into a RasterFrameLayer
+   * Convert DataFrame already in a uniform gridding into a RasterFrameLayer
    *
    * @param spatialKey The column where the spatial key is stored
    * @param temporalKey The column tagged under the temporal role
@@ -242,6 +243,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    * @return Encoded RasterFrameLayer
    */
   @throws[IllegalArgumentException]
+  private[rasterframes]
   def asLayer(spatialKey: Column, temporalKey: Column, tlm: TileLayerMetadata[SpaceTimeKey]): RasterFrameLayer =
     setSpatialColumnRole(spatialKey, tlm)
       .setTemporalColumnRole(temporalKey)
diff --git a/core/src/test/scala/examples/Classification.scala b/core/src/test/scala/examples/Classification.scala
deleted file mode 100644
index 6f234e45c..000000000
--- a/core/src/test/scala/examples/Classification.scala
+++ /dev/null
@@ -1,161 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2017 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- */
-
-package examples
-
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.ml.TileExploder
-import geotrellis.raster._
-import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.classification.DecisionTreeClassifier
-import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
-import org.apache.spark.sql._
-import org.locationtech.rasterframes.ml.{NoDataFilter, TileExploder}
-
-object Classification extends App {
-
-//  // Utility for reading imagery from our test data set
-  def readTiff(name: String) =  GeoTiffReader.readSingleband(getClass.getResource(s"/$name").getPath)
-
-  implicit val spark = SparkSession.builder()
-    .master("local[*]")
-    .appName(getClass.getName)
-    .getOrCreate()
-    .withRasterFrames
-
-  import spark.implicits._
-
-  // The first step is to load multiple bands of imagery and construct
-  // a single RasterFrameLayer from them.
-  val filenamePattern = "L8-%s-Elkton-VA.tiff"
-  val bandNumbers = 2 to 7
-  val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
-  val tileSize = 10
-
-  // For each identified band, load the associated image file
-  val joinedRF = bandNumbers
-    .map { b ⇒ (b, filenamePattern.format("B" + b)) }
-    .map { case (b, f) ⇒ (b, readTiff(f)) }
-    .map { case (b, t) ⇒ t.projectedRaster.toLayer(tileSize, tileSize, s"band_$b") }
-    .reduce(_ spatialJoin _)
-
-  // We should see a single spatial_key column along with 4 columns of tiles.
-  joinedRF.printSchema()
-
-  // Similarly pull in the target label data.
-  val targetCol = "target"
-
-  // Load the target label raster. We have to convert the cell type to
-  // Double to meet expectations of SparkML
-  val target = readTiff(filenamePattern.format("Labels"))
-    .mapTile(_.convert(DoubleConstantNoDataCellType))
-    .projectedRaster
-    .toLayer(tileSize, tileSize, targetCol)
-
-  // Take a peek at what kind of label data we have to work with.
-  target.select(rf_agg_stats(target(targetCol))).show
-
-  val abt = joinedRF.spatialJoin(target)
-
-  // SparkML requires that each observation be in its own row, and those
-  // observations be packed into a single `Vector`. The first step is to
-  // "explode" the tiles into a single row per cell/pixel
-  val exploder = new TileExploder()
-
-  val noDataFilter = new NoDataFilter()
-    .setInputCols(bandColNames :+ targetCol)
-
-  // To "vectorize" the the band columns we use the SparkML `VectorAssembler`
-  val assembler = new VectorAssembler()
-    .setInputCols(bandColNames)
-    .setOutputCol("features")
-
-  // Using a decision tree for classification
-  val classifier = new DecisionTreeClassifier()
-    .setLabelCol(targetCol)
-    .setFeaturesCol(assembler.getOutputCol)
-
-  // Assemble the model pipeline
-  val pipeline = new Pipeline()
-    .setStages(Array(exploder, noDataFilter, assembler, classifier))
-
-  // Configure how we're going to evaluate our model's performance.
-  val evaluator = new MulticlassClassificationEvaluator()
-    .setLabelCol(targetCol)
-    .setPredictionCol("prediction")
-    .setMetricName("f1")
-
-  // Use a parameter grid to determine what the optimal max tree depth is for this data
-  val paramGrid = new ParamGridBuilder()
-    //.addGrid(classifier.maxDepth, Array(1, 2, 3, 4))
-    .build()
-
-  // Configure the cross validator
-  val trainer = new CrossValidator()
-    .setEstimator(pipeline)
-    .setEvaluator(evaluator)
-    .setEstimatorParamMaps(paramGrid)
-    .setNumFolds(4)
-
-  // Push the "go" button
-  val model = trainer.fit(abt)
-
-  // Format the `paramGrid` settings resultant model
-  val metrics = model.getEstimatorParamMaps
-    .map(_.toSeq.map(p ⇒ s"${p.param.name} = ${p.value}"))
-    .map(_.mkString(", "))
-    .zip(model.avgMetrics)
-
-  // Render the parameter/performance association
-  metrics.toSeq.toDF("params", "metric").show(false)
-
-  // Score the original data set, including cells
-  // without target values.
-  val scored = model.bestModel.transform(joinedRF)
-
-  // Add up class membership results
-  scored.groupBy($"prediction" as "class").count().show
-
-  scored.show(10)
-
-  val tlm = joinedRF.tileLayerMetadata.left.get
-
-  val retiled = scored.groupBy($"spatial_key").agg(
-    rf_assemble_tile(
-      $"column_index", $"row_index", $"prediction",
-      tlm.tileCols, tlm.tileRows, IntConstantNoDataCellType
-    )
-  )
-
-  val rf = retiled.asLayer($"spatial_key", tlm)
-
-  val raster = rf.toRaster($"prediction", 186, 169)
-
-  val clusterColors = IndexedColorMap.fromColorMap(
-    ColorRamps.Viridis.toColorMap((0 until 3).toArray)
-  )
-
-  raster.tile.renderPng(clusterColors).write("target/scala-2.11/tut/ml/classified.png")
-
-  spark.stop()
-}
diff --git a/core/src/test/scala/examples/Clustering.scala b/core/src/test/scala/examples/Clustering.scala
deleted file mode 100644
index 66e7e58b1..000000000
--- a/core/src/test/scala/examples/Clustering.scala
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2017 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- */
-
-package examples
-
-import org.locationtech.rasterframes._
-import geotrellis.raster.ByteConstantNoDataCellType
-import geotrellis.raster.io.geotiff.reader.GeoTiffReader
-import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.clustering.{KMeans, KMeansModel}
-import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.sql._
-import org.locationtech.rasterframes.ml.TileExploder
-
-object Clustering extends App {
-
-  // Utility for reading imagery from our test data set
-  def readTiff(name: String) = GeoTiffReader.readSingleband(getClass.getResource(s"/$name").getPath)
-
-  implicit val spark = SparkSession.builder().master("local[*]").appName(getClass.getName).getOrCreate().withRasterFrames
-
-  import spark.implicits._
-
-  // The first step is to load multiple bands of imagery and construct
-  // a single RasterFrameLayer from them.
-  val filenamePattern = "L8-B%d-Elkton-VA.tiff"
-  val bandNumbers = 1 to 7
-  val bandColNames = bandNumbers.map(b ⇒ s"band_$b").toArray
-
-  // For each identified band, load the associated image file
-  val joinedRF = bandNumbers
-    .map { b ⇒ (b, filenamePattern.format(b)) }
-    .map { case (b,f) ⇒ (b, readTiff(f)) }
-    .map { case (b, t) ⇒ t.projectedRaster.toLayer(s"band_$b") }
-    .reduce(_ spatialJoin _)
-
-  // We should see a single spatial_key column along with 4 columns of tiles.
-  joinedRF.printSchema()
-
-  // SparkML requires that each observation be in its own row, and those
-  // observations be packed into a single `Vector`. The first step is to
-  // "explode" the tiles into a single row per cell/pixel
-  val exploder = new TileExploder()
-
-  // To "vectorize" the the band columns we use the SparkML `VectorAssembler`
-  val assembler = new VectorAssembler()
-    .setInputCols(bandColNames)
-    .setOutputCol("features")
-
-  // Configure our clustering algorithm
-  val k = 5
-  val kmeans = new KMeans().setK(k)
-
-  // Combine the two stages
-  val pipeline = new Pipeline().setStages(Array(exploder, assembler, kmeans))
-
-  // Compute clusters
-  val model = pipeline.fit(joinedRF)
-
-  // Run the data through the model to assign cluster IDs to each
-  val clustered = model.transform(joinedRF)
-  clustered.show(8)
-
-  // If we want to inspect the model statistics, the SparkML API requires us to go
-  // through this unfortunate contortion:
-  val clusterResults = model.stages.collect{ case km: KMeansModel ⇒ km}.head
-
-  // Compute sum of squared distances of points to their nearest center
-  val metric = clusterResults.computeCost(clustered)
-  println("Within set sum of squared errors: " + metric)
-
-  val tlm = joinedRF.tileLayerMetadata.left.get
-
-  val retiled = clustered.groupBy($"spatial_key").agg(
-    rf_assemble_tile(
-      $"column_index", $"row_index", $"prediction",
-      tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
-  )
-
-  val rf = retiled.asLayer($"spatial_key", tlm)
-
-  val raster = rf.toRaster($"prediction", 186, 169)
-
-  val clusterColors = IndexedColorMap.fromColorMap(
-    ColorRamps.Viridis.toColorMap((0 until k).toArray)
-  )
-
-  raster.tile.renderPng(clusterColors).write("clustered.png")
-
-  spark.stop()
-}
diff --git a/core/src/test/scala/examples/Scratch.scala b/core/src/test/scala/examples/Scratch.scala
deleted file mode 100644
index 151fc2b34..000000000
--- a/core/src/test/scala/examples/Scratch.scala
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2017 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- */
-
-package examples
-
-import org.locationtech.rasterframes._
-import org.apache.spark.sql._
-
-/**
- * Boilerplate test run file
- *
- * @since 10/8/17
- */
-object Scratch extends App {
-  implicit val spark = SparkSession.builder()
-    .master("local[*]")
-    .appName(getClass.getName)
-    .withKryoSerialization
-    .getOrCreate()
-    .withRasterFrames
-
-  // import spark.implicits._
-
-  // Your Spark code here.....
-
-}
diff --git a/core/src/test/scala/examples/Tour.scala b/core/src/test/scala/examples/Tour.scala
deleted file mode 100644
index 0c2896949..000000000
--- a/core/src/test/scala/examples/Tour.scala
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * This software is licensed under the Apache 2 license, quoted below.
- *
- * Copyright 2017 Astraea, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not
- * use this file except in compliance with the License. You may obtain a copy of
- * the License at
- *
- *     [http://www.apache.org/licenses/LICENSE-2.0]
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations under
- * the License.
- *
- */
-
-package examples
-
-import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import geotrellis.raster.io.geotiff._
-import geotrellis.raster.{ByteConstantNoDataCellType, Tile}
-import org.locationtech.rasterframes._
-import geotrellis.raster.render.{ColorRamps, IndexedColorMap}
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.clustering.KMeans
-import org.apache.spark.ml.feature.VectorAssembler
-import org.locationtech.rasterframes.ml.TileExploder
-
-/**
- * Example tour of some general features in RasterFrames
- *
- * @since 10/24/17
- */
-object Tour extends App {
-  implicit val spark = SparkSession.builder()
-    .master("local[*]")
-    .appName(getClass.getName)
-    .getOrCreate()
-    .withRasterFrames
-
-  import spark.implicits._
-
-  // Read in a geo-referenced image
-  val scene = SinglebandGeoTiff("src/test/resources/L8-B8-Robinson-IL.tiff")
-
-  // Convert it to a raster frame, discretizing it into the given tile size.
-  val rf = scene.projectedRaster.toLayer(64, 64)
-
-  // See how many tiles we have after discretization
-  println("Tile count: " + rf.count())
-
-  // Take a peek at what we're working with
-  rf.show(8, false)
-
-  // Confirm we have equally sized tiles
-  rf.select(rf_dimensions($"tile")).distinct().show()
-
-  // Count the number of no-data cells
-  rf.select(rf_agg_no_data_cells($"tile")).show(false)
-
-  // Compute per-tile statistics
-  rf.select(rf_tile_stats($"tile")).show(8, false)
-
-  // Compute some aggregate stats over all cells
-  rf.select(rf_agg_stats($"tile")).show(false)
-
-  // Create a Spark UDT to perform contrast adjustment via GeoTrellis
-  val contrast = udf((t: Tile) ⇒ t.sigmoidal(0.2, 10))
-
-  // Let's contrast adjust the tile column
-  val withAdjusted = rf.withColumn("adjusted", contrast($"tile")).asLayer
-
-  // Show the stats for the adjusted version
-  withAdjusted.select(rf_agg_stats($"adjusted")).show(false)
-
-  // Reassemble into a raster and save to a file
-  val raster = withAdjusted.toRaster($"adjusted", 774, 500)
-  GeoTiff(raster).write("contrast-adjusted.tiff")
-
-  // Perform some arbitrary local ops between columns and render
-  val withOp = withAdjusted.withColumn("op", rf_local_subtract($"tile", $"adjusted")).asLayer
-  val raster2 = withOp.toRaster($"op", 774, 500)
-  GeoTiff(raster2).write("with-op.tiff")
-
-
-  // Perform k-means clustering
-  val k = 4
-
-  // SparkML doesn't like NoData/NaN values, so we set the no-data value to something less offensive
-  val forML = rf.select(rf.spatialKeyColumn, rf_with_no_data($"tile", 99999) as "tile").asLayer
-
-  // First we instantiate the transformer that converts tile rows into cell rows.
-  val exploder = new TileExploder()
-
-  // This transformer wraps the pixel values in a vector.
-  // Could use this with multiple bands
-  val assembler = new VectorAssembler().
-    setInputCols(Array("tile")).
-    setOutputCol("features")
-
-  // Or clustering algorithm
-  val kmeans = new KMeans().setK(k)
-
-  // Construct the ML pipeline
-  val pipeline = new Pipeline().setStages(Array(exploder, assembler, kmeans))
-
-  // Compute the model
-  val model = pipeline.fit(forML)
-
-  // Score the data
-  val clusteredCells = model.transform(forML)
-
-  clusteredCells.show()
-
-  clusteredCells.groupBy("prediction").count().show
-
-  // Reassembling the clustering results takes a number of steps.
-  val tlm = rf.tileLayerMetadata.left.get
-
-  // RasterFrames provides a special aggregation function for assembling tiles from cells with column/row indexes
-  val retiled = clusteredCells.groupBy(forML.spatialKeyColumn).agg(
-    rf_assemble_tile($"column_index", $"row_index", $"prediction", tlm.tileCols, tlm.tileRows, ByteConstantNoDataCellType)
-  )
-
-  val clusteredRF = retiled.asLayer($"spatial_key", tlm)
-
-  val raster3 = clusteredRF.toRaster($"prediction", 774, 500)
-
-  val clusterColors = IndexedColorMap.fromColorMap(
-    ColorRamps.Viridis.toColorMap((0 until k).toArray)
-  )
-
-  GeoTiff(raster3).copy(options = GeoTiffOptions(clusterColors)).write("clustered.tiff")
-
-  spark.stop()
-}
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 84f00d30b..5059fd6b9 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -35,8 +35,7 @@ import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.scalatest.{FunSpec, Matchers}
 
-class RasterFunctionsSpec extends FunSpec
-  with TestEnvironment with Matchers with RasterMatchers {
+class RasterFunctionsSpec extends TestEnvironment with RasterMatchers {
   import spark.implicits._
 
   val extent = Extent(10, 20, 30, 40)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
index 0600b4745..87ab2559d 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestEnvironment.scala
@@ -40,12 +40,12 @@ trait TestEnvironment extends FunSpec with GeoTrellisTestEnvironment
   override def sparkMaster: String = "local[*]"
 
   override implicit def sc: SparkContext = { _sc.setLogLevel("ERROR"); _sc }
-  //p.setProperty(“spark.driver.allowMultipleContexts”, “true”)
 
   lazy val sqlContext: SQLContext = {
     val session = SparkSession.builder
       .config(_sc.getConf)
       .config("spark.sql.crossJoin.enabled", true)
+      .withKryoSerialization
       .getOrCreate()
     session.sqlContext.withRasterFrames
   }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/expressions/ProjectedLayerMetadataAggregateTest.scala b/core/src/test/scala/org/locationtech/rasterframes/expressions/ProjectedLayerMetadataAggregateTest.scala
new file mode 100644
index 000000000..4d4949357
--- /dev/null
+++ b/core/src/test/scala/org/locationtech/rasterframes/expressions/ProjectedLayerMetadataAggregateTest.scala
@@ -0,0 +1,59 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions
+
+import geotrellis.raster.Tile
+import geotrellis.spark._
+import geotrellis.spark.tiling.FloatingLayoutScheme
+import geotrellis.vector.{Extent, ProjectedExtent}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.serialized_literal
+import org.locationtech.rasterframes.expressions.aggregates.ProjectedLayerMetadataAggregate
+import org.locationtech.rasterframes.model.TileDimensions
+
+class ProjectedLayerMetadataAggregateTest extends TestEnvironment {
+
+  import spark.implicits._
+
+  describe("ProjectedLayerMetadataAggregate") {
+    it("should collect metadata from RasterFrame") {
+      val image = TestData.sampleGeoTiff
+      val rf = image.projectedRaster.toLayer(60, 65)
+      val crs = rf.crs
+
+      val df = rf.withExtent()
+        .select($"extent", $"tile").as[(Extent, Tile)]
+
+      val tileDims = rf.tileLayerMetadata.merge.tileLayout.tileDimensions
+
+      val (_, tlm) = df
+        .map { case (ext, tile) => (ProjectedExtent(ext, crs), tile) }
+        .rdd.collectMetadata[SpatialKey](FloatingLayoutScheme(tileDims._1, tileDims._2))
+
+      val md = df.select(ProjectedLayerMetadataAggregate(crs, TileDimensions(tileDims), $"extent",
+        serialized_literal(crs), rf_cell_type($"tile"), rf_dimensions($"tile")))
+      val tlm2 = md.first()
+
+      tlm2 should be(tlm)
+    }
+  }
+}
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
index 3c3151783..3148a67d0 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffCollectionRelation.scala
@@ -37,6 +37,7 @@ import org.locationtech.rasterframes.datasource.geotiff.GeoTiffCollectionRelatio
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.util._
 
+private[geotiff]
 case class GeoTiffCollectionRelation(sqlContext: SQLContext, uri: URI, bandCount: Int) extends BaseRelation with PrunedScan {
 
   override def schema: StructType = StructType(Seq(
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index c62d1a7e3..a080b9691 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -21,16 +21,18 @@
 
 package org.locationtech.rasterframes.datasource.geotiff
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.datasource._
-import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
-import org.apache.spark.sql.types.LongType
-import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode, functions ⇒ F}
-import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
 import _root_.geotrellis.raster.io.geotiff.compression._
 import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
+import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
+import com.typesafe.scalalogging.LazyLogging
+import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource._
+import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate
+import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+import org.locationtech.rasterframes.util._
 
 /**
  * Spark SQL data source over GeoTIFF files.
@@ -58,42 +60,47 @@ class GeoTiffDataSource extends DataSourceRegister
     else GeoTiffRelation(sqlContext, p)
   }
 
+  def createLayer(df: DataFrame, parameters: Map[String, String], tileCols: Seq[Column]): RasterFrameLayer = {
+    require(tileCols.nonEmpty, "need at least one tile column")
+
+
+//    val prd = ProjectedRasterDefinition()
+//    val crsCol = col("crs")
+//    val extentCol = col("extent")
+//    df.agg(TileRasterizerAggregate(prd, crsCol, extentCol, tileCols.head))
+    ???
+  }
+
   override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = {
     val pathO = path(parameters)
     require(pathO.isDefined, "Valid URI 'path' parameter required.")
     require(pathO.get.getScheme == "file" || pathO.get.getScheme == null, "Currently only 'file://' destinations are supported")
     sqlContext.withRasterFrames
 
-    require(data.isLayer, "GeoTIFF can only be constructed from a RasterFrameLayer")
-    val rf = data.certify
+    val compress = parameters.get(GeoTiffDataSource.COMPRESS_PARAM).exists(_.toBoolean)
+    val tcols = data.tileColumns
 
-    // If no desired image size is given, write at full size.
-    lazy val (fullResCols, fullResRows) = {
-      // get the layout size given that the tiles may be heterogenously sized
-      // first get any valid row and column in the spatial key structure
-      val sk = rf.select(SPATIAL_KEY_COLUMN).first()
-
-      val tc = rf.tileColumns.head
-
-      val c = rf
-        .where(SPATIAL_KEY_COLUMN("row") === sk.row)
-        .agg(
-          F.sum(rf_dimensions(tc)("cols") cast(LongType))
-        ).first()
-        .getLong(0)
-
-      val r = rf
-        .where(SPATIAL_KEY_COLUMN("col") === sk.col)
-        .agg(
-          F.sum(rf_dimensions(tc)("rows") cast(LongType))
-        ).first()
-        .getLong(0)
-
-      (c, r)
+    require(tcols.nonEmpty, "Could not find any tile columns.")
+
+    val tags = Tags(
+      RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
+      tcols.map(c ⇒ Map("RF_COL" -> c.columnName)).toList
+    )
+
+    // We make some assumptions here.... eventually have column metadata encode this.
+    val colorSpace = tcols.size match {
+      case 3 | 4 ⇒  ColorSpace.RGB
+      case _ ⇒ ColorSpace.BlackIsZero
     }
 
-    val cols = numParam(GeoTiffDataSource.IMAGE_WIDTH_PARAM, parameters).getOrElse(fullResCols)
-    val rows = numParam(GeoTiffDataSource.IMAGE_HEIGHT_PARAM, parameters).getOrElse(fullResRows)
+    val tiffOptions = GeoTiffOptions(Tiled, if (compress) DeflateCompression else NoCompression, colorSpace)
+
+    val layer = if(data.isLayer) data.certify else createLayer(data, parameters, tcols)
+
+    val tlm = layer.tileLayerMetadata.merge
+    // If no desired image size is given, write at full size.
+    val cols = numParam(GeoTiffDataSource.IMAGE_WIDTH_PARAM, parameters).getOrElse(tlm.gridBounds.width.toLong)
+    val rows = numParam(GeoTiffDataSource.IMAGE_HEIGHT_PARAM, parameters).getOrElse(tlm.gridBounds.height.toLong)
 
     require(cols <= Int.MaxValue && rows <= Int.MaxValue, s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
 
@@ -101,22 +108,9 @@ class GeoTiffDataSource extends DataSourceRegister
     if(cols.toDouble * rows * 64.0 > Runtime.getRuntime.totalMemory() * 0.5)
       logger.warn(s"You've asked for the construction of a very large image ($cols x $rows), destined for ${pathO.get}. Out of memory error likely.")
 
-    val tcols = rf.tileColumns
-    val raster = rf.toMultibandRaster(tcols, cols.toInt, rows.toInt)
+    val raster = layer.toMultibandRaster(tcols, cols.toInt, rows.toInt)
 
-    // We make some assumptions here.... eventually have column metadata encode this.
-    val colorSpace = tcols.size match {
-      case 3 | 4 ⇒  ColorSpace.RGB
-      case _ ⇒ ColorSpace.BlackIsZero
-    }
-
-    val compress = parameters.get(GeoTiffDataSource.COMPRESS_PARAM).map(_.toBoolean).getOrElse(false)
-    val options = GeoTiffOptions(Tiled, if (compress) DeflateCompression else NoCompression, colorSpace)
-    val tags = Tags(
-      RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
-      tcols.map(c ⇒ Map("RF_COL" -> c.columnName)).toList
-    )
-    val geotiff = new MultibandGeoTiff(raster.tile, raster.extent, raster.crs, tags, options)
+    val geotiff = new MultibandGeoTiff(raster.tile, raster.extent, raster.crs, tags, tiffOptions)
 
     logger.debug(s"Writing DataFrame to GeoTIFF ($cols x $rows) at ${pathO.get}")
     geotiff.write(pathO.get.getPath)
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index f89ebc2d6..8c7b644d5 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -29,25 +29,47 @@ import shapeless.tag
 
 package object geotiff {
   /** Tagged type construction for enabling type-safe extension methods for loading
-   * a RasterFrameLayer in expected form. */
+   * a RasterFrameLayer from a single GeoTiff. */
   type GeoTiffRasterFrameReader = DataFrameReader @@ GeoTiffRasterFrameReaderTag
   trait GeoTiffRasterFrameReaderTag
 
+  /** Tagged type construction for enabling type-safe extension methods for writing
+    * a RasterFrame to a geotiff. */
+  type GeoTiffRasterFrameWriter[T] = DataFrameWriter[T] @@ GeoTiffRasterFrameWriterTag
+  trait GeoTiffRasterFrameWriterTag
+
   /** Adds `geotiff` format specifier to `DataFrameReader`. */
   implicit class DataFrameReaderHasGeoTiffFormat(val reader: DataFrameReader) {
     @deprecated("Use `raster` instead.", "7/1/2019")
     def geotiff: GeoTiffRasterFrameReader =
-      tag[GeoTiffRasterFrameReaderTag][DataFrameReader](reader.format(GeoTiffDataSource.SHORT_NAME))
+      tag[GeoTiffRasterFrameReaderTag][DataFrameReader](
+        reader.format(GeoTiffDataSource.SHORT_NAME)
+      )
   }
 
   implicit class DataFrameWriterHasGeoTiffFormat[T](val writer: DataFrameWriter[T]) {
-    @deprecated("Use `raster` instead.", "7/1/2019")
-    def geotiff: DataFrameWriter[T] = writer.format(GeoTiffDataSource.SHORT_NAME)
+    def geotiff: GeoTiffRasterFrameWriter[T] =
+      tag[GeoTiffRasterFrameWriterTag][DataFrameWriter[T]](
+        writer.format(GeoTiffDataSource.SHORT_NAME)
+      )
+
+    def withDimensions(cols: Int, rows: Int): GeoTiffRasterFrameWriter[T] =
+      tag[GeoTiffRasterFrameWriterTag][DataFrameWriter[T]](
+        writer
+          .option(GeoTiffDataSource.IMAGE_WIDTH_PARAM, cols)
+          .option(GeoTiffDataSource.IMAGE_HEIGHT_PARAM, rows)
+      )
+
+    def withCompression: GeoTiffRasterFrameWriter[T] =
+      tag[GeoTiffRasterFrameWriterTag][DataFrameWriter[T]](
+        writer
+          .option(GeoTiffDataSource.COMPRESS_PARAM, true)
+      )
   }
 
   /** Adds `loadLayer` to appropriately tagged `DataFrameReader` */
   implicit class GeoTiffReaderWithRF(val reader: GeoTiffRasterFrameReader) {
     @deprecated("Use `raster` instead.", "7/1/2019")
-    def loadRF(path: URI): RasterFrameLayer = reader.load(path.toASCIIString).asLayer
+    def loadLayer(path: URI): RasterFrameLayer = reader.load(path.toASCIIString).asLayer
   }
 }
diff --git a/pyrasterframes/src/main/python/docs/architecture.md b/pyrasterframes/src/main/python/docs/architecture.md
index 5dc5735a5..d8650689e 100644
--- a/pyrasterframes/src/main/python/docs/architecture.md
+++ b/pyrasterframes/src/main/python/docs/architecture.md
@@ -5,7 +5,7 @@ RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO
 [JTS](https://github.com/locationtech/jts), and
 [SFCurve](https://github.com/locationtech/sfcurve) (see below).
 
-![LocationTech Stack](rasterframes-locationtech-stack.png)
+![LocationTech Stack](static/rasterframes-locationtech-stack.png)
 
 
 ## Concepts
@@ -19,17 +19,15 @@ RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO
 > * Projected Raster
 > * Layer 
 
-RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As showin in Figure 2, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
+RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
 
-![RasterFrame Anatomy](rasterframe-anatomy.png)
+![RasterFrame Anatomy](static/rasterframe-anatomy.png)
 
 Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. 
 
-> Update to match components in raster-io.md
-
-![RasterFrame Data Sources](rasterframes-data-sources.png)
-
+> TODO: Update to match components in raster-io.md
 
+![RasterFrame Data Sources](static/rasterframes-data-sources.png)
 
 
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index ba4af68db..0097cb1b3 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -33,7 +33,7 @@ display(tile) # IPython.display function
 
 Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
 
-In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](pandas-numpy.md).
+In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
 ```python toPandas, evaluate=True
 import pyrasterframes.rf_ipython
@@ -52,59 +52,11 @@ Viewing the DataFrame in Jupyter looks like this.
 pandas_df
 ```
 
+@@include[df-samples-output.md](static/df-samples-output.md)
 
-<div>
-<style scoped="">
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>extent</th>
-      <th>tile</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>(-7783653.637667, 1082298.5058092135, -7754001.623809213, 1111950.519667)</td>
-      <td><img 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" alt="Image" class=""></td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>(-7754001.623809214, 1082298.5058092135, -7724349.609951426, 1111950.519667)</td>
-      <td><img 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" alt="Image"></td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>(-7724349.609951427, 1082298.5058092135, -7694697.59609364, 1111950.519667)</td>
-      <td><img 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VrLUpOKDEnuB4CscDaymNXk3pCHlzfuuzZ/ozJfuH/5045/9j/+dpyJPHr+BtNpS6wysZTA+dI3PEbXFrTrkhg1xiTa+wL162ZYLfWnnBTtG3vS0pFWFpYOM/IUDy/o9xLJSJ0IDaNyYOgtxYlGRYU7lrWyXZccLCUaGJUDpowUpefBR6+zt7tEm4QtAqoQM06DkaxrUBRzjVsqdFCYlSYNBld7JrsrjE6ioZk7yv/vWqhdtHzv5//9TztelwMPbh9zsG6YXdkiTwKpTqcOI84dyYtAx02HioqdpmWrllgTNgW7Ip3Ggslr/GCJu4H1w57QQP9Ix6ovyEkJLJc3BbpWkoJ2VXA4G+GjJgVJEhZ9QectvrP440qu0xpMFUBBmCZiIcG+7qWUAhAGS9c5fDRone9YWndt/iEaXv8P3kdTDWw3LW/ZeY7vf9uPMCo8VkdWbUl26TSeM1Xgoe/7IM01S/uGyKTuiUnDxOOT5mjV0K4LALSLjCYdXVsQBkNeW6JXKA261fTnAs2oZzmvUQeFvLQEbqYJkwxBY8tAUUQWP79H3guM93sa51l7x6I3UrmYWZ76xsd46O/8Ocnpl5owymCyVFM7hR4c6WKHtYnKBVaLTW3tDqL/VxRSTUcdv/zcZR6pb/CJ9Tm+4ifeiU8lzy63SFGjW4NdKvxWIs4KJpc1ts30LtF7y+ykYbLVYlSmKQf6zhFPCtIo4Exkd2/GlWs72BOD6QQsCY28pXZZSkD+0IK+c4S1JXUalRVFM1AUUmV1jy4Zu0hMimuzCd2yQC+NpKBl4sEf+G6oBHUKO0HCvawgGuIokevIqBmIUXN0MoLjAteqOwr+79r8b+bZbx49Q5ccKWsO1g1OJxrniUFq56lAyhY2s3w0kpwiXyvZblr29xcM3nK4GLFsS0Jn0b2ieLbg5Ild1t6hTEYlKOageyXpp01om8mtoSkHitKjVpYwiTRPG/iFsUCHOtMvZf1etSXdYY1aWDHrDNgsa2NrMCuNO9okJUESEzToMrI6rolRQO48CcTqHgX/zgZq5zkKY2ax5on5Pm1fULtAbT0paAyQTd4g85GsMrEE0ykuP7tPXhtBlaLUk+jE4SQLadtzPBvhHm+ort9yTjpAMRkYjisoEjEpiTTKhJ0Z1hcTplWEeQle5mtDTb3bokeeVGohgHQGdMaNPMEbcidLj5lb4iiK0BOklaO6ahm2LWF3EDBoeWdA9Ssyf60yT7X7PN9O6YKVjEMnrpxso1aWOI7oXqM95CqQsLTnEsVMYz9ZEuoMhyNoMnmUsAuDyuCnEVNGwrwgjzJ6SxHeuqSpBkoXuPbsDu7I4M9k5ouGNBiUV8QLPTlowlTCIcpI7g2j/TXnpgsuD7voGw60xKC6iviVg0Hj1pL7x52Ng0w318yMf8MaDYSFg6wYzoR7E6f6YPBJ85FnHiJGzevOHkANT59ss5zVmN2edK3CLhWxyeRZgYqS/oWRUIPCbjh1GGapCZMIRcI0gcmoY/V0TSwy6wci5yZrll3JycGY6opDZTDbknaudIGPopmmDsR5gR57cT61J0bNjeWIfL2CM714/V6QL7WyqAjDuYA5seL0bCKbjJlZklXElcNNetx0IGVFjsJBeNWF+ik/tomTTmpQi4MRem5RvRLzdxJMs8ns/E5ENQFXBnabjnVX4utAOi6x2wPaJIoicHI4RleZtOWZ7qxZD4720gTXK9F8izidaSIMFntkyTaTlhadgHVJ2grkpNC1Z7Ws0Gc7iVFtpFUlHJTkOsGgwCt0BHvN0p8NUrdqEtklmt21wI8qk3orAk33wvtnxbyt0DpLqNEXzJ/cRrlMGkVsK04nFcI/Sk0m24hppAoQoz6tE8XWosYeYyN70xVDsLRlJE7BlBGjE4tlTdrxmOcLVIZhIl5aKci9kQxIyXpdX1f025lhCkXtaaoB0/SEqCUrWlfkTpYa5cXs8wZ4Hh7qUFlhDw1Dk1BFYn1SU056grcCW67MHQn1rr2/0hm9odQs1iWrdUlqohTGiiRhVJ2JlQgZm0QIgDORadPRe0voLMoljEuEKyNuHE82F8iU454zOwuKTVm5ulSy/TjsfCJSHgookzcOJZuMHgQIiSWYXsrKOSt6b+mDYauWa3bzUpKERop42YBK4HcCRe1RNjGclZJJ7gzKJdLGmSqdyXW8NyB1TkoQGxNPmXOYTPG8E1ICgu6bKqKOC7IRLQVO+U0pKYrnHMN+pD6/oLsoFB/hPnlG1cC6L1guK/K8wK42194IASuY6lo1uIU+zYCyAj/O2DpQFOI8lcos+oLFlamUrfUmC3MJgiLVGTMOKCUZnCoSOYMymaoRrAIgB4VeWqFevtpCvQl95Q3fMyWNqSN+x6AnnqIKdLOSuLKSoVjhm1oXpYKaNP2qgP2ICqLBMWgBWrwlBEOImvlxw+gTJX6SyQbMkGn3NMtHA9omrl7eo37WShnZS6o6bGf0AyvqytN2jnUQaeeoYBJEu7NCn1hymU+1LnaGnBALCBrKeHq/RREYOiddW3BvNNXaxNZ0xdFzWxtKJNjpgF1q3LM1bgWmkTJvdy6yf2ZOOzimtZRglm2JLSOhlUKgDwZXhg0pF6yNdIOEO1mBaRUqQ7erCTWoQcFY0tZQZ/QA4aHulH1IVgzeitrmTZmljKSFFXJvL45IHiafhlEpSP2pfsbSXVBQhVuIWjPQzip0r+4NlfJmJdNOPGHpUGVE6YRdKYoFqJBJW4pYZdSgaAdHYQOLrqRrC+GdnhSYnYGyGuh7h1YZ6yIxbgCUThD3VArG6UeiidoLh9TaiG8iprX4ncR43LE4GEk21wjeG1fCoVK9JvdGSs513GR5CYI+zZ7ICLYbFd3ZSNZigbE1mDLik0Yt7R1p6SsSKsB8WWM2hTKlM8OyoF4CCYZtcQDJAgqWJzXTnTWVE3Zf6Tz6oqxV06ajnKyorKcLjuN1TUqKmCWM6e4T8oJeWGKtqA5E+0Jv2b04o90rUFEL2BE0ZjqQBkOOCtMEUlTkTQakeovaCFWXkeQ3xzcFSYG6wK414WKPUlL6GY06Fs9MMb1gENq8/KL6ioSakyIkg3WRYVFQPVOINk2hPxPJVaS46oh1ht6wXFYskqxXSmcm22ucyYRoCNGw6AvyhpJubYTJINDeogAUbqHw48zqvozuNGpb2IEPnj2iD5arh1L/0jqBU8TeiLBuWqrXpDqhkkIXkWYk5ZO+t/h1Ab2ktQB+N2BswrqImQo4fZOUFqdSkHzVhZqzIi2csOGcxG3dWWF7oEBtDTArSE5MVY+9lKUHWUNNFcWrb853ZZAcPEqDhNZZ2Hm9QTeB1Bv684Fyu2N73HLt0i7quOTRz36OtXc898wuqkjYkSd0TpB5u9G+m9yCBJiMLQPaJLw3xGAwNmKrQBgKSVKSAqXZmUo/w9HJGO0SacujysCoDMxMvAdC9UICM70iOUPY2sBmmwpknhdU1wx+kqXEGzSuGYjKoCxiklGfCtVvbkG7hDFCwUkrJyljUkz2ViwORwyd4zgrJhcXdG3B5Z+6X4CW+zrO7s9Z9QWxlEprtyxhYckmC+i98fTjUYdSGR8NXnGLcaIE6d5+9Eji22BYzmuMTRSlZ91aHjx7xBun1/nwvdBUFeWNDjsSCOsmkE8KeYCTQkoRLhO2gziJzjBQgNcS/40SyiZ0IdqbB00xERxUqczi+pjqqoUM3fnASlegoKikoaLrHMYmut0ozJVnKm5cL1EXOuqmZ70uQcsLtUuN3wHdaVK1KfyZjHNSFEzp1svN48CqLfH9LZEonejWcu+Xb+xwrl7ckYzuXlOLTGwSlBI856OS+qpm/YiHqAg7gdQaifk2QbWtPJSKGDTGivmkaCSgriJNteE0ZYVeCuAR64yeeFJvMJUIIUbNqB7ovcVOB4bCCmyYFAyG5dCQO4MeeVSAWG5iagXldYOfamKViHqDZumMHXtYGrLN9L6WJo8NwOP7El0FlFeEzvGzz9/HeihefaEqk9h6YCZp39URKsH69QP1tBNGXdD0toReY8pN1pVFoDcB7hQNcWnZPr9gUvX4pHE6sewLgeHKTDLI2p2BY0vxyIKtpuXLzj3B0+0uH/nFR2QdjAq1PZBWDuUVxYkmzQ1hO2BGAZbS4xULRXb5NAO82ZgW5gXKSRlFRUWq0ulz5iQ9Yjoq9PWC/tDdG5TK6oSPhvawxi41YS9Ap2lzhbKJHLRAehda7MbM/GDJUW+69GQZcFs9QzBcO5lQlZ7lsiL1BscmJo1gr1v6855sYXV9xLBl+dHDLyC2kt7oSkIk4yK5UeRBk9aaMI3YsZeXmCWsiuNNKBQVOOl0SWsrwMoGAMpOKhUkAWS8tkJnN0KpFArQy8vo7sspKFaziuaSdJmYJmC2JNfPa3vKQYpBwqOUBAnSLjGadrgibPJ8qTf51rFcCuqlWkOshJUSRkkSiE6iBt1pysrLTXiNOXbkpGRpQdogCUL4pRB2tHVBsNOgUF6Yf2jRwtQZ7LGVPgG10fhBg1eoKhKCwRYRVUYhFDeR8f7qjmR099VUfzOMSjDxxEFjN/X1bmMaUTtUVhiVsTYBAd861qkUflJU6FFAHRWkMqGmCfsLDXlLeptAHEcYKdTSgBKzXB41qKVF2UxqEq4MXNiZk7LiaNWwai0pAxlms0byeJfIaqM7N7OnhYDdcZRuaoogUDfJbdoSQ6LeXTPMS9zcEEYCCml9L4J/JUWzbLNo5qBRZwPntxacOb/kymKb59o9rIvM5zVKb9AfBSloQXoAmoA536KzIswLuosCEAP47QSdQQVFHkfsVcf4aUu3l4l1xl6QhrRR3dNHw2xV062KW0Ip0y1C8c2akgIiaC/XSFVicn7BalEJGzFoqCLJJcyJJTawvDpm++cssYIulyzjLb/wqgpVKeEWdSaj5pJf+2XBtXLC8bqmdIHx/orVohKhb2A/lJxv2g3tu7Uku3lgm6i3O0LQaJO5MFlxMBvjb0gVFCDUMJwJmHFg2PAEYimtPVujVpxmloQDr1DDBoS2SKpqhGl9s7Nbnx3YrjtCMLSDwR1YwBCm6RbwvdJ4YczjtyK7Z+ZcvwMZvYKMCulPChty2cTzwPljfNKcLBvipr0mB429Sf++XgmgbDN+P2yo6gplJe3LUUtDxHFB2PJcG7ZOu/NUVKQysz4TxLlEJQCyVyxoQGfitN1QcgS0xmZUMxCH25D6oCTvD4qsIKwcq8kGdDGZ/FB72rxGVIJcVYKCZStzp6RfsrPxFQtVawGZ6WW90jZz0lYymY2EqGkPGlQt0FlOG+JsULeauzI0ly0oS3c2kZuIPSyE+tkJMSyOkoQ+Rnj3bjxIU8aqEjpPHVFzS64Si+UENQlolyQyiHJdZTI5SASgBk1yEJsozsoleu+wNqJsIh6U4vkLAbFVHcidodvXxCqzc3HGqi3vSEavSKjduhBvOkqMRh27ozWztmLdFYzqnmE6EDqLLTy+E20gizllLaBzeyGSx1F4q70hXBjIwwZ59xpVRkbjnsIGYtIUNtIOTtjSpZUXtOXBa6gTrvZYG1n3NWotTW438VK90qc9Ur5EliIrGIBz0gakL3ra643MpzYbRBRJLKuWewiDeXHB/FKEmrMibwLiFBX9YGkL6XKejgSIzsiNx03jQXFgSKXQFFN1G3E2KtLSYSaenAX90kUkDpqdneXp9breSfoJhLVFFRFb3ELnY9BovblekHJ4MpqsoxA6jDg/NUholUcSbzoXGQaDn4uWSguQ4K1uJKGax6I00o14B04KXkGcGqNCrzVf+sUf49e96WM01cCqLySPzgqtMlUlKWsaDAyaWEHY89SftTgFX0RCgqSnqLAuol0kHZXgEsu1LCnzRcOwLAhLR1g4lEtsba/xraMuB3JG1vAMvreoMgoXym2Ke1YKdtlKcJ+bm2myol0XxE3JRa2N0IuqiCpkexA/K6E35AzrRUneVCtedaGOy4Ev/ZKPURvPx4/PUxeerVrQH6Nlq452XWwWeySP3/XimDY9++iMbgJma6CYCiCcLo3goMQuBYFPT404eWKXdFQw3VthZhY78dgi4qNwR09mI5yTpjFrhWFSjQbpzHNJnI7J8vJ0Jo8ixXOOctzjysDezlLKMHUg15FUZUwRyUETe5rbx7wAAAW6SURBVCNz2ETqjXC7/J2J667NP2bFUT9Cq0zaBPg+ycX2GyHIruaVOIMioWtJDPrO0feWtJLY1kxlGRiOK8xKox5ckTtHcolx01OdDxxc2YYisVxUFA8teXDviOVQMu9KwWCDZpWEiz+0DlcF/GBJJ8IRyKPwaSy9+EiHjprxqONk3hBXDjvy0o09ysRNe6Vpgji9pDBjj1YZX6Z700ihEDSp0IGzzYLGSdi007Qsh1LW1KAlhBo0rpAHLSuPXxVQSAOZn5X4dcHk/IK46/EnG+wgSxPZ8WyEHnuK8YArA19w3xXeML3OoitZrypZQpQkIGXp2dtbMqp7Cbk2GyUUzSat3aSgo08UooEqs+6k2mCaILtYFBGls3xK4RuYYwdJETsrxOTEvWFSGyV08oN2zDoULIeSxnn6YDle13KjlWCtamXpDmvGo466HCjGA+fPn8heJDZhK+GjsqFJKp3Z3lpxcW/G/WeOGU86diZrHn/He9AqEbJh8FIYzGtZAkZnVxiTODeWHSyMi9jpQLHTUZae8c76tM6/+uweBk0YLL63FKUnJ/ETvhOjTUGjbSL3hrgjNTJ7QyKYeq+9dx1/AJX1rH1BSJq2rQjRsFpVUoGso5hNr0hZE7Ni5ALV1pLCRLb3liiVKWzk+vUtVG/IhXCdQCzhbftXWEwrnlzsATAky3Prmu5GjWk1+VxPWQZi1IwrT0iabnCUZRBTDYZh0+2CS4wfL2jfumb37BxANmmwkWGzc4UpknReR0XT9ND0hGAw/3UqS9kb5f87GXct1JUvOGwbKhsobZAmA2Vlm47eoKtIOi4oDw16AL8FpY1cP56wPV2jgJNrE8mOWit1qCqiy4jWGWcSIWmudlMebI7YL5c8+EPfxeWv/QEAHn76A5x5eE43OHpvyVnho+by4S76f0xYf25LPRo2BTpFf1QzftKyelPHuZ0FlQ08e7hFXXl676THamOw2iaKsVR9ZYMwhX/bmhQValUJl/ZeaGqKmkVXQiU9qkZJF7O1kezUprFhA7hkJWUVlaXeYyLPPrkPZtOnWiSqekA1MKk7usExKgYmrufDD/87vvB//C6Ontjl8p94DJB9Ws6fkVv+X1/1PkC2S/rZ3/R+AN7w8Q9R1hLz9r0jbkz6o991azefL/u37xKS3dHoNDwqJsMmppaYN232tIqdVBaoBWxXdhPOvNpCJUlc6KMhJs1xV3M8GwmCXgf0jQIbhUyRLNLFnBVbo5ajZcPO/TPZMKZz/LL7n+X51ZSYNMuuPI0onl1s8Xn/7feyWpc8tREowFbRshq2WHa30sXbNwvLBoxJhCDV2ZsM7Zvj8/7Zn2Wx2hGiRQKKhHYJ37rTMkzwBmPTJvzz+F5iVb1JBu5k3L1QNVSFZ6dqqa3nSrstAm0kV44jqV+ptSHXCQV0XnaaAGj7QrQ6Kz5+/RzWJDKclo0Xmypm6A3afWqk3UUnfNXVZ64T/eK7v4lH/t4HsC7w1Nd866d93w0CbOsqCM0nS0YHnLYa3dyz0BixwMt/9E9JFwtsyuwvb/93vYVS+fD9+XO+7+uoC49RmWeeOIvq1YY2mVFOiAgpK7Yma0aFp/WOIRh8MLILROmFiOaN1OEHi3mmEtr6zkCOijNn5/z0V37gru7theNz/+l7yVnQpU/8jvfw6IffT/Ra8nqv0a2WjsGdgN0gYDfxX1MIgvbE7/4znyowpX4m5/z2l7ruK+KnKpWZtxVXj6YQhROqNg0K6qg4ze19NAzRYI3sn+I23r1rRdP8rKSfl5CFGZIngdGk4/LXfsvLCvThH3l5gQ+DvETvDa/70fcROrtZG28DrpUQPFwRNnsEwqU/8C3EoInhlW3ccdeaqpS6AVx+RVf7/2M8mHM+81InvLbR9z0Yr+30ew/Ga0K9B+M1od6D8ZpQ78F4Taj3YLwm1HswXhPqPRivCfUejNeEeg/G/wGiDM0eClamRQAAAABJRU5ErkJggg==" alt="Image"></td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>(-7694697.596093641, 1082298.5058092135, -7665045.582235852, 1111950.519667)</td>
-      <td><img 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" alt="Image"></td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>(-7665045.582235853, 1082298.5058092135, -7635393.568378066, 1111950.519667)</td>
-      <td><img 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HDgBjPkcExR7gY6FD67WeAj3OL/Sia4mukm1ASxgtOOiV86S5jMa3v0/j3oluL5W88usazXsf+di5dc0ojBCLqa8/WTq3lar0Qry9raDLEuaM56701yIe+EWCeY2QhlhNZa2xVDsP5De33B5nng1nWolvLz/3gOlkeo9sQFHSGPNRIZ3IY29oUyqivzdAbOCHmfRbpGuysZ/zCczBHtggt7HsGs4tzUzvnIBPxDckKplFMYjXNCoIVALH97YAsqsLhjJe9yWlDiiFa00drSOtyDjYDMp49t6Bh5YLdfRYEDI0ghqAFYWW/w9BIoWt6Sd94kyuuW8JRCHy6hn6miMkEV3vjVHW+Ii+nKyWqBxN1kWiZ+6SvngySf2smGT+/kmj+5e4E7euD2bYvSERv3eBm2cfcYIx89G4Ce+JXbmHjTrSSdiMIoOmlEmgcMRG1uvuER7KkYlQvEFl026MCSZQHN6bJfQeIQ58l0sQ+ySKWgZ6jpvaqOIggMSbG0ubdsO9T0FYQnQv8oxH+83dn120sOF3ZJU1A+FJEOWp81FNBt5X3lmsUUglQLjGg2PryDohkikUW0xa0QRu/fgxR+9hx8uyfchvMqH8BnOLVD1yDPNVo74rAgd4rHZtfhQotONKapMYnCRGdy8d5wV+2uh9QVQ+pEhAuA3o53tXsM5nQJ6q3LQKgDCoVO/EwzsaPosXMzznUJ1g2FKnwQOR3opgstc2G3OTIe2kVPvUMnCX21SNUTZRINuSJsKuw5+Zzxty0MrIgTBtdOM9Mo8+0jh/hGY4CZTomPfssnALj2f+4jWWlQAxlmJkTa2ru/XfvX1gzqdIDry4knYuJpmL0up92OqQ63CLVhZU+D/7DiAP/nkhPajdQkawpvhHcniqrn2EboA7EWbBlUorBRNz2byQKlcwbzE2JnMHr/Hn9u4Mi7KV09G7Bp1xgH3nFWjo48sJvqcAt6Yr5/7TOEypBbTVnnTJWrc+edKVoAb2K5yI9DAotLAp9WvipBi8OGDimE8uGQZ97rzbmND+9gff8UTzTWLImiZeeUpKNwkcNFvnMEOBkTtbplNCEUNYMtWe8b597wF+floKlaXORz5+eD9GXYRCOpN/AJfGyVcx5Iz6oGpbAg1Ia/PnItr101wev7HkeL5UMT38eP/MNbeOrLG7Ch4+DW7qzWjiA2OEApS2FDolOK3JQQ43XA1z949gFc8yd3c9XQLBtrk4yWJvnMJSf0TI5FOSiEcFrhAi8JTOyXpsohaGqfZnZ4YgMHmeL5t148DmoT7eVbZJFU4Yy3aVGqG4YTpGTQUYwr+0jThhVTtEzEF6ZfQVlniDieenElurPQ8wm7ZUFFJ8S2NNXDmqAFed3rgQPvWHh/z/3EnWz57N0wCN9IB5dE0TLz8n4pq5YmaGhs5L2LotdgagZXKzD9BUVvwcSbbkWMt//I/ew6X3p4EZygIoNkPmIu3Wv1yg4YofRCALMBPbUOee4VzHRS5pGJ9Xz+8VfwJ199NYcmB+jr6fDsb52dbaOf+AAHfvrdHPjpdyMdL0edQFZnLgO76QMLvaGND++gVk453OnnS8cXbEi5IJZph3Y1Y+JnatFXENZT4tCQtCN0YMkbERO/0l3OirkSlkNvfjubPnMPm/7ofSjleO4n7rxAHw7bCbraXXW1r8U5L4dVAfEpzTR90JuzYkWDtAgwRhFVM1b3z1KPEz63+a/YuGcFNnKMv3U7h37h9rkuJBck1WT9bsEM3rhnjNEP7+XQb/qZ+vzPvAuAV//luzj9zABLwTLNJm8O2cjna0r9CVpbrBXick7nVHnONz9D4hklM/K7u4kGHWH40i7cGUW1adfYWfPIaYpcUJmQ9TqCjhC0FXkUMDnVg4iDkzH9106SW8Ure48ynjcZfuVxXjzWv6gPMczJ1Q379voYQ7WgMhXj9GLlOVhpMdlfXxJDy1vyClzV4CJHUM/Is4DWdBnnhE4j9rKyi9EP72X0w3t9jeZwh7UbJglDQ5qEtGZKF++rW8zryyS9Daty8ZUjLQiaQmWoxStHjmI7ASoXTp7qQYCmifng5E18+fW7OLPJ5VWff9dc0+HMvESf6mYdQktnleXpe7yY+PWvvIEbPudXUUkvPUu3/Ih9rpBMMCdL3p4TR5FrgthQtANG79uDWL80Tc0S9SesH5rihdk6xih0YDDdUphNn7kHaxUH/+sdi7o5cLtfius/uRPXCglmNDZ2ZH2+LOaMK/rd/+vtXnl1g9U3rdpPKIZ/OjXKjX/xbnRU4We+/CasPVv5bK9vMnr/Hp+A62ZtTaag5yxxj55cR+eZPvgR+Nx33QffBfLLt3MxLDs4Iqk3m874ua5sCUJDngVIaJEiQGU+2UU3h/PCbB1rFVkSEEQ+fzTy4C7CKpQr6Ut2Wapk5IElD0KvsJp6zs//lUd/gf64xpGut+MyzaOnruba3uNsqE0RKcOpOOPh1z7A+k/uZOShXZR6UpxVqEQIG5qi6rCBo7w6Yainxeu/+DamkzLHj/QzML4sdl4GodrhymYuigSAFZLTJVSlwCV6TnFJIUhLU6RlnviN9yxuywnWCZ3O+UM8m3eMYUqg1vmAhy4bTEf7DGomPpK/YgvueImo41MW/StnWVuZYdvQF/lie5Rv64Ej2SCv/+LbGOivkhaa5nSF4FhE1PBxiZ5rTzHbLJMmERMnqhBaVMl4XfDG5ZEJLzNJd4YQ4m7ha2h9os36VIgtuTlzx0Xnr1S9kGF/BqoQDI5isuwj/1VDMB0QnxJsCLqjUAd9LY8NHLZsWdXTIFCGO478EPUw4W/2X4sODNVySr2UkhYVnPGp7XSFgxUpWjkOdrX5ls/eTZ4Fvtb/ZWJZhIpyqHKBbYU+mpR4MuOelCCwtK34iRtbnFYL3NOlYv1v70VnQjFovYtYeO3ulPaJvgLvPjq8tRE5pICrrzlOPUqYycs8OzlMs1WiaIT0X9Wiv9ThuQOrkUSjc4inFOkNHaJnyjTCEvwn3/ez/+UCptwysCwt76zgTkd+uXe1Z1ApUMqRJKHXqIJ3E5U3lsOppT+zkQd242JHUfMxAE+apRjMEYMXATnE0z6eGrSEoOmTaD951Ve4qf9ZIlVQinKiOIfQURjFockBVFsTTSsObtvO0zu20vNImWjatzl6/56L39wSsWyl5KpmrvIXAR0YilxjWiGSKaSezeXZD2576WV9Ls6kj0d+b7efmanC9SfYVGNrBj2reXyf1+5b7t43Fz5EwaOzo7ymfhCASphzqqhS6kk5fbIHckX/c+IrQfBV0hWRuTho5fBLJJOWiWUrJYw3M7xycqSnykjJeO2vfawyruTk2cWb3nTvGDbobnqILGTK15b2nkmpOPTRElHiZ6vTjvUf2sv4W7fz7J1b2bxjDHVNkxWVhNNphb+duo4jjT4Gym36e9oEynLi+Rob//A07fV1/uHP/QNThZD7QhPGb9l+wTL0l4Nlbe+OR9a51bfdgiv5YoFKJaVxsoaEvvRblMNZYd2QryD+u+/Z+5LtjTywG93Qc0EV01dQ6e8QBYbZ5/tw2keAzoT+xPjcf2lSKMre6C996xTDtSZTba/F2+2Y3p42Wjk6WQj/2EeQQFECBJ7cdfENEufixr94N1/7oXsQka84577lpc5ddvguWJFQZJpyOSPLuqEm5Y171/LNHdW9F/bV52HiTbfOVYzoWY0ZdOS5pj1VQTQc+s3tbH7/mE/0WfGBbKAz7B+ADSErAgbiNtfUT2Cd4vNP3MDK1U0OHF/BgZ96D/zoskYI+AK1aEbxzHu38tovvINKBK/564sb9bBMQqOwIIwK1gzOUFjFIz/qK4bXf2onNtWE0z6G+dyvLl1bxocjnnuPnzWj9+2haPnKuIPbuqE063edmKpBt5TP/be86ZT3GSLgB1d8na82R/i7FzagY8PEqX5PZhevevMYzau8VTA/kzry8V1EL4bsv+PssdH79hB2fCpnw8M7GOoX2mlET+mlHZAzWNaS77lmlfveB3+crx1ad8FquaVg0wfGMCU3l87YsHcMUy+QXM2VFwJc96d30TnSgysbglru467TkZe32lGpJ7x6zWHqQcrXptYyOVslDA3Zk71zJN3w9n0kKxzRrPDUTr9/c+Lm2xj9g/ejj8WojEXVKCMP7gLt93/OxyVf8lvqV/Fn33k/fOdyroLr//y3CJSl2SrR39viwO07F/x+cPv5U8RP/9hdvpbUCNYItZ6Ejjh0YOmrtSkFBbEyHGwOMtMpURSa/IUq4/NmnNPMkQkgHcWmP3ofojXFcLaINIBXbznE6bSy6PhS8G+yX945YeZ0led/5l08+gM7z3vO6B+8/7zHbdnn920jJC80qwZm+c6rD/Ka4Ql6opSptMLppExR+CIw27uwIOHJXVuJTzte9Wtek7uyxRqNTTTx4fO7vf/9dR+5qEK9EF6+67kMPPWf71rw/8bdfnDz5dn5qpfBB4PVYEqlklKNM7b0neBb6+P81cnrefbYMGFoiMOcSiml2S6hgsWu7mMf2cb3f/vdwMVd3n8tLjuh3/RndxFqg3FCTymlN06AEcTCNfeM4RRz8u5MtHzTZ+6hSP3LAqgZlPIkNTolptIKf3HilRydrVOKczpJiFaWcpSTnS5Rez7gVV8a47GPLBQjS93F8a/FZSc0yzRh2RBqw8npGseKXirXzJAmIfZQhbAhfNMd+0j7HOXNDb7377ZR5EO4tvbbXqxgjSZQKc0k5FRSZUW5yWM/uGNBP6/43J2EfQnttRVMabEk+8I/Lc9P3/SBsbmY7HJwWWXo+k/tpEgD/uWH3+cJGK/ipiOcE1YNzDJ44wnSG9pkfQ53dYfrVx5jqlXh0Bveie7LiCo5ElrCqKAS5azqb9DOQ9rFQtm35bN3k+UBzvpAc2edl6Pf/JZ9L/veizXZgpKfpeLyzlCH35jQxYF3bGP9h/aSjPdwPKlTVB3B6jbZQIFkmmcmh3niR7ysG+pvEGvDidka5ThDiUOJo7eUcHSml5/68q9yrFXHOqG3qom04ejJPqQQqt8I2bxzjP33LX+GnYFo66Nay8RlJfTcIlqA0nFF1u/LrgHsRBVqBqeEJAnZ+PAOHLBuqMA5oSgUpyb7OBUbhoZn6VEWrSxPn1yJdYJzwsp6g/64zVH60B0hHXAEzZcf03zF5+5EdIxWS3zrwDz8m2j5+Xh6x1Y27B0D5XceO/Gb+21sOfBT7+EnHvk1phJfSnNstkY+G0NkKdUyNvVNsqV2nP2tYY62ev0bd4zmRKPGiUYNHRjy4RwKIe+9OKEjH9/lI2cs3CjRbJawzfC8mycuhktO6MjH7qU62F5kKs2HLVuCbiGYqRof/HA+aZe31tM31CTJQvJDNSRyuKqPcX7vwNM8MrOJepjwXDbk7dtG2RfRdgKkmx2V4uI730Z+/15f52+FoLGQ/CguSHLFxod3oLRFa0tncmmG/iUnNKqni8jc8Omd2Ny/3+PQz78TVy0oCMD6VIYTEBOQWx+Ybuzv8+9bmhLSATB9loFqm3XhFC3zTTwzPUwricjSkKIZEk4F2KrF9RRIW6NbCrMqZf0ndzLQ3+L0TBU7Gc8VMABz9QNb7t5HUXb8h7+9ldwqeuOEIOiDVFNYgVR51X0e+/Z8uOSE7v/JhQm5jbvHsJUIVzZI0PWTleAq3dqnwKJDi8k0pIpgyu8hDWekSzQEcUEjjfnLmW8mEIsAnVYMMyHRjN8blQ/5DV66pZCRFgEsCJBcCM/euZXr//y3ONUu+7yTCSgKTdSfkE2V4Ezhr1uaTL7sMtSuSXzdfSNElTNUyaCU88mwROMKb+r0DTQJtOWk60c3NMlKSzijEANZJ2T0qqM8fnoNQ+Uma2ozXuuvcnxj/0rcjCI+Gvk9Rv0FtCKwwvoP7sVWzNnSoJdAqA0nZ2qYQmMaod+i2PH9i/Pb05eCy0ro+g/uhSEY7G8yNduPSTRGKV9SWCgk1QSDHaLIMDNTwTnxIUAHJmRuP6m0A56dHGZt7wwKLyfLQc74iUF08+ygVeE3vdoA/76TtmCs9jkjAd2fUrTCRe7nkz/63rP3C8QN8fvtwVfLCHN7CC6Gy2rY69VtRldNEWjj08lWoFAUrZBKPSEYSAgC/2KCcjUjKuX+FRtNYehRGHWLap8AAAGaSURBVHzSMPxVS9/jit5ywuHpPoZLDSZm+znw+FXYo2WClqBywUQOlfoMaTHk32aTDRpM3RCs6BAPt30N1DxZ+B0/uTA5ZysG21P491F1UZS724SW8HIEuMwztJgsc+T5qt9MG/m8kbT8Hsv2kRrVdQ06nQjT9lUncSVHZcLKRzOiv3kMAPnma7G6hvnYSorrFE/WV3P8RC9hS4hm/KwJmjB7jfO1/jWDLhe42L81bHBFg2YnxhSKUjmjr94+e3/lebX6D+yGyCGBpRjKCU+EmNhbGFE9xS1Rhl7WGXro198+t4cpaGhfl1n2W2xcxWCMwsx6d0QFDq0tg08YTElhv+MVBGtX4x57kp4//QrRTEHYhOcPD+OsJ9MpKMrQusr3MX7LdiZuvg2lfHlQra9NOw1JZmKsVTRO1BaED1urzg5fJYq4lvpVYkG3BVaklHpTynHOUG9zSWNeVsReRE4CE0u+4N8fRi72ussrL7W+xLjyhttLjCuEXmJcIfQS4wqhlxhXCL3EuELoJcYVQi8xrhB6iXGF0EuM/wsPDLlWt8+0lgAAAABJRU5ErkJggg==" alt="Image"></td>
-    </tr>
-  </tbody>
-</table>
-</div>
+## PNG Overviews
 
+It is oftentimes convenient to have a simple overview image of the data, particularly if it spans a very wide area.
 
 ## GeoTIFFs
 
diff --git a/pyrasterframes/src/main/python/docs/static/df-samples-output.md b/pyrasterframes/src/main/python/docs/static/df-samples-output.md
new file mode 100644
index 000000000..a7f56faf2
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/static/df-samples-output.md
@@ -0,0 +1,53 @@
+
+
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>extent</th>
+      <th>tile</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>(-7783653.637667, 1082298.5058092135, -7754001.623809213, 1111950.519667)</td>
+      <td><img 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" alt="Image" class=""></td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>(-7754001.623809214, 1082298.5058092135, -7724349.609951426, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>(-7724349.609951427, 1082298.5058092135, -7694697.59609364, 1111950.519667)</td>
+      <td><img 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VrLUpOKDEnuB4CscDaymNXk3pCHlzfuuzZ/ozJfuH/5045/9j/+dpyJPHr+BtNpS6wysZTA+dI3PEbXFrTrkhg1xiTa+wL162ZYLfWnnBTtG3vS0pFWFpYOM/IUDy/o9xLJSJ0IDaNyYOgtxYlGRYU7lrWyXZccLCUaGJUDpowUpefBR6+zt7tEm4QtAqoQM06DkaxrUBRzjVsqdFCYlSYNBld7JrsrjE6ioZk7yv/vWqhdtHzv5//9TztelwMPbh9zsG6YXdkiTwKpTqcOI84dyYtAx02HioqdpmWrllgTNgW7Ip3Ggslr/GCJu4H1w57QQP9Ix6ovyEkJLJc3BbpWkoJ2VXA4G+GjJgVJEhZ9QectvrP440qu0xpMFUBBmCZiIcG+7qWUAhAGS9c5fDRone9YWndt/iEaXv8P3kdTDWw3LW/ZeY7vf9uPMCo8VkdWbUl26TSeM1Xgoe/7IM01S/uGyKTuiUnDxOOT5mjV0K4LALSLjCYdXVsQBkNeW6JXKA261fTnAs2oZzmvUQeFvLQEbqYJkwxBY8tAUUQWP79H3guM93sa51l7x6I3UrmYWZ76xsd46O/8Ocnpl5owymCyVFM7hR4c6WKHtYnKBVaLTW3tDqL/VxRSTUcdv/zcZR6pb/CJ9Tm+4ifeiU8lzy63SFGjW4NdKvxWIs4KJpc1ts30LtF7y+ykYbLVYlSmKQf6zhFPCtIo4Exkd2/GlWs72BOD6QQsCY28pXZZSkD+0IK+c4S1JXUalRVFM1AUUmV1jy4Zu0hMimuzCd2yQC+NpKBl4sEf+G6oBHUKO0HCvawgGuIokevIqBmIUXN0MoLjAteqOwr+79r8b+bZbx49Q5ccKWsO1g1OJxrniUFq56lAyhY2s3w0kpwiXyvZblr29xcM3nK4GLFsS0Jn0b2ieLbg5Ild1t6hTEYlKOageyXpp01om8mtoSkHitKjVpYwiTRPG/iFsUCHOtMvZf1etSXdYY1aWDHrDNgsa2NrMCuNO9okJUESEzToMrI6rolRQO48CcTqHgX/zgZq5zkKY2ax5on5Pm1fULtAbT0paAyQTd4g85GsMrEE0ykuP7tPXhtBlaLUk+jE4SQLadtzPBvhHm+ort9yTjpAMRkYjisoEjEpiTTKhJ0Z1hcTplWEeQle5mtDTb3bokeeVGohgHQGdMaNPMEbcidLj5lb4iiK0BOklaO6ahm2LWF3EDBoeWdA9Ssyf60yT7X7PN9O6YKVjEMnrpxso1aWOI7oXqM95CqQsLTnEsVMYz9ZEuoMhyNoMnmUsAuDyuCnEVNGwrwgjzJ6SxHeuqSpBkoXuPbsDu7I4M9k5ouGNBiUV8QLPTlowlTCIcpI7g2j/TXnpgsuD7voGw60xKC6iviVg0Hj1pL7x52Ng0w318yMf8MaDYSFg6wYzoR7E6f6YPBJ85FnHiJGzevOHkANT59ss5zVmN2edK3CLhWxyeRZgYqS/oWRUIPCbjh1GGapCZMIRcI0gcmoY/V0TSwy6wci5yZrll3JycGY6opDZTDbknaudIGPopmmDsR5gR57cT61J0bNjeWIfL2CM714/V6QL7WyqAjDuYA5seL0bCKbjJlZklXElcNNetx0IGVFjsJBeNWF+ik/tomTTmpQi4MRem5RvRLzdxJMs8ns/E5ENQFXBnabjnVX4utAOi6x2wPaJIoicHI4RleZtOWZ7qxZD4720gTXK9F8izidaSIMFntkyTaTlhadgHVJ2grkpNC1Z7Ws0Gc7iVFtpFUlHJTkOsGgwCt0BHvN0p8NUrdqEtklmt21wI8qk3orAk33wvtnxbyt0DpLqNEXzJ/cRrlMGkVsK04nFcI/Sk0m24hppAoQoz6tE8XWosYeYyN70xVDsLRlJE7BlBGjE4tlTdrxmOcLVIZhIl5aKci9kQxIyXpdX1f025lhCkXtaaoB0/SEqCUrWlfkTpYa5cXs8wZ4Hh7qUFlhDw1Dk1BFYn1SU056grcCW67MHQn1rr2/0hm9odQs1iWrdUlqohTGiiRhVJ2JlQgZm0QIgDORadPRe0voLMoljEuEKyNuHE82F8iU454zOwuKTVm5ulSy/TjsfCJSHgookzcOJZuMHgQIiSWYXsrKOSt6b+mDYauWa3bzUpKERop42YBK4HcCRe1RNjGclZJJ7gzKJdLGmSqdyXW8NyB1TkoQGxNPmXOYTPG8E1ICgu6bKqKOC7IRLQVO+U0pKYrnHMN+pD6/oLsoFB/hPnlG1cC6L1guK/K8wK42194IASuY6lo1uIU+zYCyAj/O2DpQFOI8lcos+oLFlamUrfUmC3MJgiLVGTMOKCUZnCoSOYMymaoRrAIgB4VeWqFevtpCvQl95Q3fMyWNqSN+x6AnnqIKdLOSuLKSoVjhm1oXpYKaNP2qgP2ICqLBMWgBWrwlBEOImvlxw+gTJX6SyQbMkGn3NMtHA9omrl7eo37WShnZS6o6bGf0AyvqytN2jnUQaeeoYBJEu7NCn1hymU+1LnaGnBALCBrKeHq/RREYOiddW3BvNNXaxNZ0xdFzWxtKJNjpgF1q3LM1bgWmkTJvdy6yf2ZOOzimtZRglm2JLSOhlUKgDwZXhg0pF6yNdIOEO1mBaRUqQ7erCTWoQcFY0tZQZ/QA4aHulH1IVgzeitrmTZmljKSFFXJvL45IHiafhlEpSP2pfsbSXVBQhVuIWjPQzip0r+4NlfJmJdNOPGHpUGVE6YRdKYoFqJBJW4pYZdSgaAdHYQOLrqRrC+GdnhSYnYGyGuh7h1YZ6yIxbgCUThD3VArG6UeiidoLh9TaiG8iprX4ncR43LE4GEk21wjeG1fCoVK9JvdGSs513GR5CYI+zZ7ICLYbFd3ZSNZigbE1mDLik0Yt7R1p6SsSKsB8WWM2hTKlM8OyoF4CCYZtcQDJAgqWJzXTnTWVE3Zf6Tz6oqxV06ajnKyorKcLjuN1TUqKmCWM6e4T8oJeWGKtqA5E+0Jv2b04o90rUFEL2BE0ZjqQBkOOCtMEUlTkTQakeovaCFWXkeQ3xzcFSYG6wK414WKPUlL6GY06Fs9MMb1gENq8/KL6ioSakyIkg3WRYVFQPVOINk2hPxPJVaS46oh1ht6wXFYskqxXSmcm22ucyYRoCNGw6AvyhpJubYTJINDeogAUbqHw48zqvozuNGpb2IEPnj2iD5arh1L/0jqBU8TeiLBuWqrXpDqhkkIXkWYk5ZO+t/h1Ab2ktQB+N2BswrqImQo4fZOUFqdSkHzVhZqzIi2csOGcxG3dWWF7oEBtDTArSE5MVY+9lKUHWUNNFcWrb853ZZAcPEqDhNZZ2Hm9QTeB1Bv684Fyu2N73HLt0i7quOTRz36OtXc898wuqkjYkSd0TpB5u9G+m9yCBJiMLQPaJLw3xGAwNmKrQBgKSVKSAqXZmUo/w9HJGO0SacujysCoDMxMvAdC9UICM70iOUPY2sBmmwpknhdU1wx+kqXEGzSuGYjKoCxiklGfCtVvbkG7hDFCwUkrJyljUkz2ViwORwyd4zgrJhcXdG3B5Z+6X4CW+zrO7s9Z9QWxlEprtyxhYckmC+i98fTjUYdSGR8NXnGLcaIE6d5+9Eji22BYzmuMTRSlZ91aHjx7xBun1/nwvdBUFeWNDjsSCOsmkE8KeYCTQkoRLhO2gziJzjBQgNcS/40SyiZ0IdqbB00xERxUqczi+pjqqoUM3fnASlegoKikoaLrHMYmut0ozJVnKm5cL1EXOuqmZ70uQcsLtUuN3wHdaVK1KfyZjHNSFEzp1svN48CqLfH9LZEonejWcu+Xb+xwrl7ckYzuXlOLTGwSlBI856OS+qpm/YiHqAg7gdQaifk2QbWtPJSKGDTGivmkaCSgriJNteE0ZYVeCuAR64yeeFJvMJUIIUbNqB7ovcVOB4bCCmyYFAyG5dCQO4MeeVSAWG5iagXldYOfamKViHqDZumMHXtYGrLN9L6WJo8NwOP7El0FlFeEzvGzz9/HeihefaEqk9h6YCZp39URKsH69QP1tBNGXdD0toReY8pN1pVFoDcB7hQNcWnZPr9gUvX4pHE6sewLgeHKTDLI2p2BY0vxyIKtpuXLzj3B0+0uH/nFR2QdjAq1PZBWDuUVxYkmzQ1hO2BGAZbS4xULRXb5NAO82ZgW5gXKSRlFRUWq0ulz5iQ9Yjoq9PWC/tDdG5TK6oSPhvawxi41YS9Ap2lzhbKJHLRAehda7MbM/GDJUW+69GQZcFs9QzBcO5lQlZ7lsiL1BscmJo1gr1v6855sYXV9xLBl+dHDLyC2kt7oSkIk4yK5UeRBk9aaMI3YsZeXmCWsiuNNKBQVOOl0SWsrwMoGAMpOKhUkAWS8tkJnN0KpFArQy8vo7sspKFaziuaSdJmYJmC2JNfPa3vKQYpBwqOUBAnSLjGadrgibPJ8qTf51rFcCuqlWkOshJUSRkkSiE6iBt1pysrLTXiNOXbkpGRpQdogCUL4pRB2tHVBsNOgUF6Yf2jRwtQZ7LGVPgG10fhBg1eoKhKCwRYRVUYhFDeR8f7qjmR099VUfzOMSjDxxEFjN/X1bmMaUTtUVhiVsTYBAd861qkUflJU6FFAHRWkMqGmCfsLDXlLeptAHEcYKdTSgBKzXB41qKVF2UxqEq4MXNiZk7LiaNWwai0pAxlms0byeJfIaqM7N7OnhYDdcZRuaoogUDfJbdoSQ6LeXTPMS9zcEEYCCml9L4J/JUWzbLNo5qBRZwPntxacOb/kymKb59o9rIvM5zVKb9AfBSloQXoAmoA536KzIswLuosCEAP47QSdQQVFHkfsVcf4aUu3l4l1xl6QhrRR3dNHw2xV062KW0Ip0y1C8c2akgIiaC/XSFVicn7BalEJGzFoqCLJJcyJJTawvDpm++cssYIulyzjLb/wqgpVKeEWdSaj5pJf+2XBtXLC8bqmdIHx/orVohKhb2A/lJxv2g3tu7Uku3lgm6i3O0LQaJO5MFlxMBvjb0gVFCDUMJwJmHFg2PAEYimtPVujVpxmloQDr1DDBoS2SKpqhGl9s7Nbnx3YrjtCMLSDwR1YwBCm6RbwvdJ4YczjtyK7Z+ZcvwMZvYKMCulPChty2cTzwPljfNKcLBvipr0mB429Sf++XgmgbDN+P2yo6gplJe3LUUtDxHFB2PJcG7ZOu/NUVKQysz4TxLlEJQCyVyxoQGfitN1QcgS0xmZUMxCH25D6oCTvD4qsIKwcq8kGdDGZ/FB72rxGVIJcVYKCZStzp6RfsrPxFQtVawGZ6WW90jZz0lYymY2EqGkPGlQt0FlOG+JsULeauzI0ly0oS3c2kZuIPSyE+tkJMSyOkoQ+Rnj3bjxIU8aqEjpPHVFzS64Si+UENQlolyQyiHJdZTI5SASgBk1yEJsozsoleu+wNqJsIh6U4vkLAbFVHcidodvXxCqzc3HGqi3vSEavSKjduhBvOkqMRh27ozWztmLdFYzqnmE6EDqLLTy+E20gizllLaBzeyGSx1F4q70hXBjIwwZ59xpVRkbjnsIGYtIUNtIOTtjSpZUXtOXBa6gTrvZYG1n3NWotTW438VK90qc9Ur5EliIrGIBz0gakL3ra643MpzYbRBRJLKuWewiDeXHB/FKEmrMibwLiFBX9YGkL6XKejgSIzsiNx03jQXFgSKXQFFN1G3E2KtLSYSaenAX90kUkDpqdneXp9breSfoJhLVFFRFb3ELnY9BovblekHJ4MpqsoxA6jDg/NUholUcSbzoXGQaDn4uWSguQ4K1uJKGax6I00o14B04KXkGcGqNCrzVf+sUf49e96WM01cCqLySPzgqtMlUlKWsaDAyaWEHY89SftTgFX0RCgqSnqLAuol0kHZXgEsu1LCnzRcOwLAhLR1g4lEtsba/xraMuB3JG1vAMvreoMgoXym2Ke1YKdtlKcJ+bm2myol0XxE3JRa2N0IuqiCpkexA/K6E35AzrRUneVCtedaGOy4Ev/ZKPURvPx4/PUxeerVrQH6Nlq452XWwWeySP3/XimDY9++iMbgJma6CYCiCcLo3goMQuBYFPT404eWKXdFQw3VthZhY78dgi4qNwR09mI5yTpjFrhWFSjQbpzHNJnI7J8vJ0Jo8ixXOOctzjysDezlLKMHUg15FUZUwRyUETe5rbx7wAAAW6SURBVCNz2ETqjXC7/J2J667NP2bFUT9Cq0zaBPg+ycX2GyHIruaVOIMioWtJDPrO0feWtJLY1kxlGRiOK8xKox5ckTtHcolx01OdDxxc2YYisVxUFA8teXDviOVQMu9KwWCDZpWEiz+0DlcF/GBJJ8IRyKPwaSy9+EiHjprxqONk3hBXDjvy0o09ysRNe6Vpgji9pDBjj1YZX6Z700ihEDSp0IGzzYLGSdi007Qsh1LW1KAlhBo0rpAHLSuPXxVQSAOZn5X4dcHk/IK46/EnG+wgSxPZ8WyEHnuK8YArA19w3xXeML3OoitZrypZQpQkIGXp2dtbMqp7Cbk2GyUUzSat3aSgo08UooEqs+6k2mCaILtYFBGls3xK4RuYYwdJETsrxOTEvWFSGyV08oN2zDoULIeSxnn6YDle13KjlWCtamXpDmvGo466HCjGA+fPn8heJDZhK+GjsqFJKp3Z3lpxcW/G/WeOGU86diZrHn/He9AqEbJh8FIYzGtZAkZnVxiTODeWHSyMi9jpQLHTUZae8c76tM6/+uweBk0YLL63FKUnJ/ETvhOjTUGjbSL3hrgjNTJ7QyKYeq+9dx1/AJX1rH1BSJq2rQjRsFpVUoGso5hNr0hZE7Ni5ALV1pLCRLb3liiVKWzk+vUtVG/IhXCdQCzhbftXWEwrnlzsATAky3Prmu5GjWk1+VxPWQZi1IwrT0iabnCUZRBTDYZh0+2CS4wfL2jfumb37BxANmmwkWGzc4UpknReR0XT9ND0hGAw/3UqS9kb5f87GXct1JUvOGwbKhsobZAmA2Vlm47eoKtIOi4oDw16AL8FpY1cP56wPV2jgJNrE8mOWit1qCqiy4jWGWcSIWmudlMebI7YL5c8+EPfxeWv/QEAHn76A5x5eE43OHpvyVnho+by4S76f0xYf25LPRo2BTpFf1QzftKyelPHuZ0FlQ08e7hFXXl676THamOw2iaKsVR9ZYMwhX/bmhQValUJl/ZeaGqKmkVXQiU9qkZJF7O1kezUprFhA7hkJWUVlaXeYyLPPrkPZtOnWiSqekA1MKk7usExKgYmrufDD/87vvB//C6Ontjl8p94DJB9Ws6fkVv+X1/1PkC2S/rZ3/R+AN7w8Q9R1hLz9r0jbkz6o991azefL/u37xKS3dHoNDwqJsMmppaYN232tIqdVBaoBWxXdhPOvNpCJUlc6KMhJs1xV3M8GwmCXgf0jQIbhUyRLNLFnBVbo5ajZcPO/TPZMKZz/LL7n+X51ZSYNMuuPI0onl1s8Xn/7feyWpc8tREowFbRshq2WHa30sXbNwvLBoxJhCDV2ZsM7Zvj8/7Zn2Wx2hGiRQKKhHYJ37rTMkzwBmPTJvzz+F5iVb1JBu5k3L1QNVSFZ6dqqa3nSrstAm0kV44jqV+ptSHXCQV0XnaaAGj7QrQ6Kz5+/RzWJDKclo0Xmypm6A3afWqk3UUnfNXVZ64T/eK7v4lH/t4HsC7w1Nd866d93w0CbOsqCM0nS0YHnLYa3dyz0BixwMt/9E9JFwtsyuwvb/93vYVS+fD9+XO+7+uoC49RmWeeOIvq1YY2mVFOiAgpK7Yma0aFp/WOIRh8MLILROmFiOaN1OEHi3mmEtr6zkCOijNn5/z0V37gru7theNz/+l7yVnQpU/8jvfw6IffT/Ra8nqv0a2WjsGdgN0gYDfxX1MIgvbE7/4znyowpX4m5/z2l7ruK+KnKpWZtxVXj6YQhROqNg0K6qg4ze19NAzRYI3sn+I23r1rRdP8rKSfl5CFGZIngdGk4/LXfsvLCvThH3l5gQ+DvETvDa/70fcROrtZG28DrpUQPFwRNnsEwqU/8C3EoInhlW3ccdeaqpS6AVx+RVf7/2M8mHM+81InvLbR9z0Yr+30ew/Ga0K9B+M1od6D8ZpQ78F4Taj3YLwm1HswXhPqPRivCfUejNeEeg/G/wGiDM0eClamRQAAAABJRU5ErkJggg==" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>(-7694697.596093641, 1082298.5058092135, -7665045.582235852, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>(-7665045.582235853, 1082298.5058092135, -7635393.568378066, 1111950.519667)</td>
+      <td><img 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HDgBjPkcExR7gY6FD67WeAj3OL/Sia4mukm1ASxgtOOiV86S5jMa3v0/j3oluL5W88usazXsf+di5dc0ojBCLqa8/WTq3lar0Qry9raDLEuaM56701yIe+EWCeY2QhlhNZa2xVDsP5De33B5nng1nWolvLz/3gOlkeo9sQFHSGPNRIZ3IY29oUyqivzdAbOCHmfRbpGuysZ/zCczBHtggt7HsGs4tzUzvnIBPxDckKplFMYjXNCoIVALH97YAsqsLhjJe9yWlDiiFa00drSOtyDjYDMp49t6Bh5YLdfRYEDI0ghqAFYWW/w9BIoWt6Sd94kyuuW8JRCHy6hn6miMkEV3vjVHW+Ii+nKyWqBxN1kWiZ+6SvngySf2smGT+/kmj+5e4E7euD2bYvSERv3eBm2cfcYIx89G4Ce+JXbmHjTrSSdiMIoOmlEmgcMRG1uvuER7KkYlQvEFl026MCSZQHN6bJfQeIQ58l0sQ+ySKWgZ6jpvaqOIggMSbG0ubdsO9T0FYQnQv8oxH+83dn120sOF3ZJU1A+FJEOWp81FNBt5X3lmsUUglQLjGg2PryDohkikUW0xa0QRu/fgxR+9hx8uyfchvMqH8BnOLVD1yDPNVo74rAgd4rHZtfhQotONKapMYnCRGdy8d5wV+2uh9QVQ+pEhAuA3o53tXsM5nQJ6q3LQKgDCoVO/EwzsaPosXMzznUJ1g2FKnwQOR3opgstc2G3OTIe2kVPvUMnCX21SNUTZRINuSJsKuw5+Zzxty0MrIgTBtdOM9Mo8+0jh/hGY4CZTomPfssnALj2f+4jWWlQAxlmJkTa2ru/XfvX1gzqdIDry4knYuJpmL0up92OqQ63CLVhZU+D/7DiAP/nkhPajdQkawpvhHcniqrn2EboA7EWbBlUorBRNz2byQKlcwbzE2JnMHr/Hn9u4Mi7KV09G7Bp1xgH3nFWjo48sJvqcAt6Yr5/7TOEypBbTVnnTJWrc+edKVoAb2K5yI9DAotLAp9WvipBi8OGDimE8uGQZ97rzbmND+9gff8UTzTWLImiZeeUpKNwkcNFvnMEOBkTtbplNCEUNYMtWe8b597wF+floKlaXORz5+eD9GXYRCOpN/AJfGyVcx5Iz6oGpbAg1Ia/PnItr101wev7HkeL5UMT38eP/MNbeOrLG7Ch4+DW7qzWjiA2OEApS2FDolOK3JQQ43XA1z949gFc8yd3c9XQLBtrk4yWJvnMJSf0TI5FOSiEcFrhAi8JTOyXpsohaGqfZnZ4YgMHmeL5t148DmoT7eVbZJFU4Yy3aVGqG4YTpGTQUYwr+0jThhVTtEzEF6ZfQVlniDieenElurPQ8wm7ZUFFJ8S2NNXDmqAFed3rgQPvWHh/z/3EnWz57N0wCN9IB5dE0TLz8n4pq5YmaGhs5L2LotdgagZXKzD9BUVvwcSbbkWMt//I/ew6X3p4EZygIoNkPmIu3Wv1yg4YofRCALMBPbUOee4VzHRS5pGJ9Xz+8VfwJ199NYcmB+jr6fDsb52dbaOf+AAHfvrdHPjpdyMdL0edQFZnLgO76QMLvaGND++gVk453OnnS8cXbEi5IJZph3Y1Y+JnatFXENZT4tCQtCN0YMkbERO/0l3OirkSlkNvfjubPnMPm/7ofSjleO4n7rxAHw7bCbraXXW1r8U5L4dVAfEpzTR90JuzYkWDtAgwRhFVM1b3z1KPEz63+a/YuGcFNnKMv3U7h37h9rkuJBck1WT9bsEM3rhnjNEP7+XQb/qZ+vzPvAuAV//luzj9zABLwTLNJm8O2cjna0r9CVpbrBXick7nVHnONz9D4hklM/K7u4kGHWH40i7cGUW1adfYWfPIaYpcUJmQ9TqCjhC0FXkUMDnVg4iDkzH9106SW8Ure48ynjcZfuVxXjzWv6gPMczJ1Q379voYQ7WgMhXj9GLlOVhpMdlfXxJDy1vyClzV4CJHUM/Is4DWdBnnhE4j9rKyi9EP72X0w3t9jeZwh7UbJglDQ5qEtGZKF++rW8zryyS9Daty8ZUjLQiaQmWoxStHjmI7ASoXTp7qQYCmifng5E18+fW7OLPJ5VWff9dc0+HMvESf6mYdQktnleXpe7yY+PWvvIEbPudXUUkvPUu3/Ih9rpBMMCdL3p4TR5FrgthQtANG79uDWL80Tc0S9SesH5rihdk6xih0YDDdUphNn7kHaxUH/+sdi7o5cLtfius/uRPXCglmNDZ2ZH2+LOaMK/rd/+vtXnl1g9U3rdpPKIZ/OjXKjX/xbnRU4We+/CasPVv5bK9vMnr/Hp+A62ZtTaag5yxxj55cR+eZPvgR+Nx33QffBfLLt3MxLDs4Iqk3m874ua5sCUJDngVIaJEiQGU+2UU3h/PCbB1rFVkSEEQ+fzTy4C7CKpQr6Ut2Wapk5IElD0KvsJp6zs//lUd/gf64xpGut+MyzaOnruba3uNsqE0RKcOpOOPh1z7A+k/uZOShXZR6UpxVqEQIG5qi6rCBo7w6Yainxeu/+DamkzLHj/QzML4sdl4GodrhymYuigSAFZLTJVSlwCV6TnFJIUhLU6RlnviN9yxuywnWCZ3O+UM8m3eMYUqg1vmAhy4bTEf7DGomPpK/YgvueImo41MW/StnWVuZYdvQF/lie5Rv64Ej2SCv/+LbGOivkhaa5nSF4FhE1PBxiZ5rTzHbLJMmERMnqhBaVMl4XfDG5ZEJLzNJd4YQ4m7ha2h9os36VIgtuTlzx0Xnr1S9kGF/BqoQDI5isuwj/1VDMB0QnxJsCLqjUAd9LY8NHLZsWdXTIFCGO478EPUw4W/2X4sODNVySr2UkhYVnPGp7XSFgxUpWjkOdrX5ls/eTZ4Fvtb/ZWJZhIpyqHKBbYU+mpR4MuOelCCwtK34iRtbnFYL3NOlYv1v70VnQjFovYtYeO3ulPaJvgLvPjq8tRE5pICrrzlOPUqYycs8OzlMs1WiaIT0X9Wiv9ThuQOrkUSjc4inFOkNHaJnyjTCEvwn3/ez/+UCptwysCwt76zgTkd+uXe1Z1ApUMqRJKHXqIJ3E5U3lsOppT+zkQd242JHUfMxAE+apRjMEYMXATnE0z6eGrSEoOmTaD951Ve4qf9ZIlVQinKiOIfQURjFockBVFsTTSsObtvO0zu20vNImWjatzl6/56L39wSsWyl5KpmrvIXAR0YilxjWiGSKaSezeXZD2576WV9Ls6kj0d+b7efmanC9SfYVGNrBj2reXyf1+5b7t43Fz5EwaOzo7ymfhCASphzqqhS6kk5fbIHckX/c+IrQfBV0hWRuTho5fBLJJOWiWUrJYw3M7xycqSnykjJeO2vfawyruTk2cWb3nTvGDbobnqILGTK15b2nkmpOPTRElHiZ6vTjvUf2sv4W7fz7J1b2bxjDHVNkxWVhNNphb+duo4jjT4Gym36e9oEynLi+Rob//A07fV1/uHP/QNThZD7QhPGb9l+wTL0l4Nlbe+OR9a51bfdgiv5YoFKJaVxsoaEvvRblMNZYd2QryD+u+/Z+5LtjTywG93Qc0EV01dQ6e8QBYbZ5/tw2keAzoT+xPjcf2lSKMre6C996xTDtSZTba/F2+2Y3p42Wjk6WQj/2EeQQFECBJ7cdfENEufixr94N1/7oXsQka84577lpc5ddvguWJFQZJpyOSPLuqEm5Y171/LNHdW9F/bV52HiTbfOVYzoWY0ZdOS5pj1VQTQc+s3tbH7/mE/0WfGBbKAz7B+ADSErAgbiNtfUT2Cd4vNP3MDK1U0OHF/BgZ96D/zoskYI+AK1aEbxzHu38tovvINKBK/564sb9bBMQqOwIIwK1gzOUFjFIz/qK4bXf2onNtWE0z6G+dyvLl1bxocjnnuPnzWj9+2haPnKuIPbuqE063edmKpBt5TP/be86ZT3GSLgB1d8na82R/i7FzagY8PEqX5PZhevevMYzau8VTA/kzry8V1EL4bsv+PssdH79hB2fCpnw8M7GOoX2mlET+mlHZAzWNaS77lmlfveB3+crx1ad8FquaVg0wfGMCU3l87YsHcMUy+QXM2VFwJc96d30TnSgysbglru467TkZe32lGpJ7x6zWHqQcrXptYyOVslDA3Zk71zJN3w9n0kKxzRrPDUTr9/c+Lm2xj9g/ejj8WojEXVKCMP7gLt93/OxyVf8lvqV/Fn33k/fOdyroLr//y3CJSl2SrR39viwO07F/x+cPv5U8RP/9hdvpbUCNYItZ6Ejjh0YOmrtSkFBbEyHGwOMtMpURSa/IUq4/NmnNPMkQkgHcWmP3ofojXFcLaINIBXbznE6bSy6PhS8G+yX945YeZ0led/5l08+gM7z3vO6B+8/7zHbdnn920jJC80qwZm+c6rD/Ka4Ql6opSptMLppExR+CIw27uwIOHJXVuJTzte9Wtek7uyxRqNTTTx4fO7vf/9dR+5qEK9EF6+67kMPPWf71rw/8bdfnDz5dn5qpfBB4PVYEqlklKNM7b0neBb6+P81cnrefbYMGFoiMOcSiml2S6hgsWu7mMf2cb3f/vdwMVd3n8tLjuh3/RndxFqg3FCTymlN06AEcTCNfeM4RRz8u5MtHzTZ+6hSP3LAqgZlPIkNTolptIKf3HilRydrVOKczpJiFaWcpSTnS5Rez7gVV8a47GPLBQjS93F8a/FZSc0yzRh2RBqw8npGseKXirXzJAmIfZQhbAhfNMd+0j7HOXNDb7377ZR5EO4tvbbXqxgjSZQKc0k5FRSZUW5yWM/uGNBP6/43J2EfQnttRVMabEk+8I/Lc9P3/SBsbmY7HJwWWXo+k/tpEgD/uWH3+cJGK/ipiOcE1YNzDJ44wnSG9pkfQ53dYfrVx5jqlXh0Bveie7LiCo5ElrCqKAS5azqb9DOQ9rFQtm35bN3k+UBzvpAc2edl6Pf/JZ9L/veizXZgpKfpeLyzlCH35jQxYF3bGP9h/aSjPdwPKlTVB3B6jbZQIFkmmcmh3niR7ysG+pvEGvDidka5ThDiUOJo7eUcHSml5/68q9yrFXHOqG3qom04ejJPqQQqt8I2bxzjP33LX+GnYFo66Nay8RlJfTcIlqA0nFF1u/LrgHsRBVqBqeEJAnZ+PAOHLBuqMA5oSgUpyb7OBUbhoZn6VEWrSxPn1yJdYJzwsp6g/64zVH60B0hHXAEzZcf03zF5+5EdIxWS3zrwDz8m2j5+Xh6x1Y27B0D5XceO/Gb+21sOfBT7+EnHvk1phJfSnNstkY+G0NkKdUyNvVNsqV2nP2tYY62ev0bd4zmRKPGiUYNHRjy4RwKIe+9OKEjH9/lI2cs3CjRbJawzfC8mycuhktO6MjH7qU62F5kKs2HLVuCbiGYqRof/HA+aZe31tM31CTJQvJDNSRyuKqPcX7vwNM8MrOJepjwXDbk7dtG2RfRdgKkmx2V4uI730Z+/15f52+FoLGQ/CguSHLFxod3oLRFa0tncmmG/iUnNKqni8jc8Omd2Ny/3+PQz78TVy0oCMD6VIYTEBOQWx+Ybuzv8+9bmhLSATB9loFqm3XhFC3zTTwzPUwricjSkKIZEk4F2KrF9RRIW6NbCrMqZf0ndzLQ3+L0TBU7Gc8VMABz9QNb7t5HUXb8h7+9ldwqeuOEIOiDVFNYgVR51X0e+/Z8uOSE7v/JhQm5jbvHsJUIVzZI0PWTleAq3dqnwKJDi8k0pIpgyu8hDWekSzQEcUEjjfnLmW8mEIsAnVYMMyHRjN8blQ/5DV66pZCRFgEsCJBcCM/euZXr//y3ONUu+7yTCSgKTdSfkE2V4Ezhr1uaTL7sMtSuSXzdfSNElTNUyaCU88mwROMKb+r0DTQJtOWk60c3NMlKSzijEANZJ2T0qqM8fnoNQ+Uma2ozXuuvcnxj/0rcjCI+Gvk9Rv0FtCKwwvoP7sVWzNnSoJdAqA0nZ2qYQmMaod+i2PH9i/Pb05eCy0ro+g/uhSEY7G8yNduPSTRGKV9SWCgk1QSDHaLIMDNTwTnxIUAHJmRuP6m0A56dHGZt7wwKLyfLQc74iUF08+ygVeE3vdoA/76TtmCs9jkjAd2fUrTCRe7nkz/63rP3C8QN8fvtwVfLCHN7CC6Gy2rY69VtRldNEWjj08lWoFAUrZBKPSEYSAgC/2KCcjUjKuX+FRtNYehRGHWLap8AAAGaSURBVHzSMPxVS9/jit5ywuHpPoZLDSZm+znw+FXYo2WClqBywUQOlfoMaTHk32aTDRpM3RCs6BAPt30N1DxZ+B0/uTA5ZysG21P491F1UZS724SW8HIEuMwztJgsc+T5qt9MG/m8kbT8Hsv2kRrVdQ06nQjT9lUncSVHZcLKRzOiv3kMAPnma7G6hvnYSorrFE/WV3P8RC9hS4hm/KwJmjB7jfO1/jWDLhe42L81bHBFg2YnxhSKUjmjr94+e3/lebX6D+yGyCGBpRjKCU+EmNhbGFE9xS1Rhl7WGXro198+t4cpaGhfl1n2W2xcxWCMwsx6d0QFDq0tg08YTElhv+MVBGtX4x57kp4//QrRTEHYhOcPD+OsJ9MpKMrQusr3MX7LdiZuvg2lfHlQra9NOw1JZmKsVTRO1BaED1urzg5fJYq4lvpVYkG3BVaklHpTynHOUG9zSWNeVsReRE4CE0u+4N8fRi72ussrL7W+xLjyhttLjCuEXmJcIfQS4wqhlxhXCL3EuELoJcYVQi8xrhB6iXGF0EuM/wsPDLlWt8+0lgAAAABJRU5ErkJggg==" alt="Image"></td>
+    </tr>
+  </tbody>
+</table>
+</div>
diff --git a/pyrasterframes/src/main/python/docs/rasterframe-anatomy.png b/pyrasterframes/src/main/python/docs/static/rasterframe-anatomy.png
similarity index 100%
rename from pyrasterframes/src/main/python/docs/rasterframe-anatomy.png
rename to pyrasterframes/src/main/python/docs/static/rasterframe-anatomy.png
diff --git a/pyrasterframes/src/main/python/docs/rasterframes-data-sources.png b/pyrasterframes/src/main/python/docs/static/rasterframes-data-sources.png
similarity index 100%
rename from pyrasterframes/src/main/python/docs/rasterframes-data-sources.png
rename to pyrasterframes/src/main/python/docs/static/rasterframes-data-sources.png
diff --git a/pyrasterframes/src/main/python/docs/rasterframes-locationtech-stack.png b/pyrasterframes/src/main/python/docs/static/rasterframes-locationtech-stack.png
similarity index 100%
rename from pyrasterframes/src/main/python/docs/rasterframes-locationtech-stack.png
rename to pyrasterframes/src/main/python/docs/static/rasterframes-locationtech-stack.png
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 20fa381e6..f992b2534 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -167,7 +167,7 @@ def temp_name():
         .load(path, **options)
 
 
-def _geotiff_writer(df_writer, format_key, path, **options):
+def _aliased_writer(df_writer, format_key, path, **options):
     """ Saves the dataframe to a file of the given type at the given path."""
     return df_writer.format(format_key).save(path=path, options=options)
 
@@ -186,5 +186,6 @@ def _geotiff_writer(df_writer, format_key, path, **options):
 DataFrameReader.raster = _raster_reader
 DataFrameReader.geojson = lambda df_reader, path: _aliased_reader(df_reader, "geojson", path)
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
+DataFrameWriter.geotiff = lambda df_writer, path: _aliased_writer(df_writer, "geotiff", path)
 DataFrameReader.geotrellis = lambda df_reader, path: _layer_reader(df_reader, "geotrellis", path)
-DataFrameWriter.geotiff = lambda df_writer, path: _geotiff_writer(df_writer, "geotiff", path)
+DataFrameWriter.geotrellis = lambda df_writer, path: _aliased_writer(df_writer, "geotrellis", path)
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
new file mode 100644
index 000000000..7bdf1d1e0
--- /dev/null
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -0,0 +1,53 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import os
+import tempfile
+
+from . import TestEnvironment
+
+
+class GeoTiffWriter(TestEnvironment):
+
+    @staticmethod
+    def _tmpfile():
+        return os.path.join(tempfile.tempdir, "pyrf-test.tif")
+
+    def test_identity_write(self):
+        rf = self.spark.read.geotiff(self.img_uri)
+
+        dest = self._tmpfile()
+        rf.write.geotiff(dest)
+
+        rf2 = self.spark.read.geotiff(dest)
+        self.assertEqual(rf2.count(), rf.count())
+
+        os.remove(dest)
+
+    def test_unstructured_write(self):
+        rf = self.spark.read.raster(self.img_uri)
+
+        dest = self._tmpfile()
+        rf.write.geotiff(dest)
+
+        rf2 = self.spark.read.raster(dest)
+        self.assertEqual(rf2.count(), rf.count())
+
+        os.remove(dest)
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 2bfcbfc94..ccdc952b4 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -837,19 +837,6 @@ def path(scene, band):
         self.assertTrue(df2.select('b1_path').distinct().count() == 3)
 
 
-class GeoTiffWriter(TestEnvironment):
-    def test_identity_write(self):
-        import tempfile
-        import os
-
-        rf = self.spark.read.geotiff(self.img_uri)
-
-        dest = os.path.join(tempfile.tempdir, "pyrf-test.tif")
-        rf.write.geotiff(dest)
-
-        rf2 = self.spark.read.geotiff(dest)
-        self.assertEqual(rf2.count(), rf.count())
-
 
 def suite():
     function_tests = unittest.TestSuite()

From a5adce50270711d71eb1397e38a5b3f482d41314 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Thu, 25 Jul 2019 13:49:16 -0400
Subject: [PATCH 243/380] add picture, fix display for pyweave

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 pyrasterframes/src/main/python/docs/local-algebra.pymd | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index c90f24322..f5f215974 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -14,6 +14,10 @@ spark = create_rf_spark_session()
 
 [Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single `tile`,  between a `tile` and a scalar, between two `tile`s, or among many `tile`s. These operations are common in processing of earth observation and other image data.
 
+<img src="https://gisgeography.com/wp-content/uploads/2015/05/Local-Operation-Raster.png" alt="local op" style="width:200px;"/>
+
+Credit: [GISGeography](https://gisgeography.com)
+
 
 ## Computing NDVI
 
@@ -57,10 +61,9 @@ df.printSchema()
 We can inspect a sample of the data. Yellow indicates very healthy vegetation, and purple represents bare soil or impervious surfaces.
 
 ```python
-df.select(rf_tile('ndvi').alias('ndvi')).limit(5).toPandas()
-```
 
-**TODO** fix the display once issue #166 fix.
+display(df.select(rf_tile('ndvi').alias('ndvi')).first()['ndvi'])
+```
 
 We continue examining NDVI in the @ref:[time series](time-series.md) section.
 

From 17f7a2bac7efa9423f18c81a2cfe51b5e4028a2a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 25 Jul 2019 16:14:01 -0400
Subject: [PATCH 244/380] Added ability to write unstructured RasterFrame to
 GeoTiff.

---
 .../encoders/CatalystSerializer.scala         |   5 +
 .../expressions/DynamicExtractors.scala       |  14 +-
 .../expressions/SpatialRelation.scala         |   3 +-
 .../aggregates/TileRasterizerAggregate.scala  |  20 ++-
 .../transformers/SetCellType.scala            |   2 +-
 .../extensions/DataFrameMethods.scala         |  99 +++++++------
 .../rasterframes/extensions/RasterJoin.scala  |  35 +----
 .../extensions/ReprojectToLayer.scala         |  49 +++++++
 .../rasterframes/functions/package.scala      |  38 ++++-
 .../rasterframes/rasterframes.scala           |   2 +
 .../rasterframes/ExtensionMethodSpec.scala    |   9 ++
 .../rasterframes/RasterFunctionsSpec.scala    |   1 -
 .../locationtech/rasterframes/TestData.scala  |   4 +-
 .../geotiff/GeoTiffDataSource.scala           | 135 ++++++++++++------
 .../datasource/geotiff/package.scala          |   6 +
 .../geotiff/GeoTiffDataSourceSpec.scala       |  20 ++-
 16 files changed, 304 insertions(+), 138 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/extensions/ReprojectToLayer.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
index 921d0bb67..831411557 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/encoders/CatalystSerializer.scala
@@ -154,4 +154,9 @@ object CatalystSerializer extends StandardSerializers {
   implicit class WithFromRow(val r: Row) extends AnyVal {
     def to[T >: Null: CatalystSerializer]: T = if (r == null) null else CatalystSerializer[T].fromRow(r)
   }
+
+  implicit class WithTypeConformity(val left: DataType) extends AnyVal {
+    def conformsTo[T >: Null: CatalystSerializer]: Boolean =
+      org.apache.spark.sql.rf.WithTypeConformity(left).conformsTo(schemaOf[T])
+  }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
index ecc9698f2..a7144a8f2 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
@@ -24,7 +24,7 @@ package org.locationtech.rasterframes.expressions
 import geotrellis.raster.{CellGrid, Tile}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.rf.{TileUDT, _}
+import org.apache.spark.sql.rf.{TileUDT, RasterSourceUDT}
 import org.apache.spark.sql.types._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.model.TileContext
@@ -38,7 +38,7 @@ object DynamicExtractors {
     case _: TileUDT =>
       (row: InternalRow) =>
         (row.to[Tile](TileUDT.tileSerializer), None)
-    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
+    case t if t.conformsTo[ProjectedRasterTile] =>
       (row: InternalRow) => {
         val prt = row.to[ProjectedRasterTile]
         (prt, Some(TileContext(prt)))
@@ -48,7 +48,7 @@ object DynamicExtractors {
   lazy val rowTileExtractor: PartialFunction[DataType, Row => (Tile, Option[TileContext])] = {
     case _: TileUDT =>
       (row: Row) =>  (row.to[Tile](TileUDT.tileSerializer), None)
-    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
+    case t if t.conformsTo[ProjectedRasterTile] =>
       (row: Row) => {
         val prt = row.to[ProjectedRasterTile]
         (prt, Some(TileContext(prt)))
@@ -59,9 +59,9 @@ object DynamicExtractors {
   lazy val projectedRasterLikeExtractor: PartialFunction[DataType, InternalRow ⇒ ProjectedRasterLike] = {
     case _: RasterSourceUDT ⇒
       (row: InternalRow) => row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
-    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
+    case t if t.conformsTo[ProjectedRasterTile] =>
       (row: InternalRow) => row.to[ProjectedRasterTile]
-    case t if t.conformsTo(schemaOf[RasterRef]) =>
+    case t if t.conformsTo[RasterRef] =>
       (row: InternalRow) => row.to[RasterRef]
   }
 
@@ -71,9 +71,9 @@ object DynamicExtractors {
       (row: InternalRow) => row.to[Tile](TileUDT.tileSerializer)
     case _: RasterSourceUDT =>
       (row: InternalRow) => row.to[RasterSource](RasterSourceUDT.rasterSourceSerializer)
-    case t if t.conformsTo(schemaOf[RasterRef]) ⇒
+    case t if t.conformsTo[RasterRef] ⇒
       (row: InternalRow) => row.to[RasterRef]
-    case t if t.conformsTo(schemaOf[ProjectedRasterTile]) =>
+    case t if t.conformsTo[ProjectedRasterTile] =>
       (row: InternalRow) => row.to[ProjectedRasterTile]
   }
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
index 34e44b082..1d6697048 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/SpatialRelation.scala
@@ -30,7 +30,6 @@ import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{ScalaUDF, _}
 import org.apache.spark.sql.jts.AbstractGeometryUDT
-import org.apache.spark.sql.rf.WithTypeConformity
 import org.apache.spark.sql.types._
 import org.locationtech.geomesa.spark.jts.udf.SpatialRelationFunctions._
 
@@ -48,7 +47,7 @@ abstract class SpatialRelation extends BinaryExpression
       case r: InternalRow ⇒
         expr.dataType match {
           case udt: AbstractGeometryUDT[_] ⇒ udt.deserialize(r)
-          case dt if dt.conformsTo(schemaOf[Extent]) =>
+          case dt if dt.conformsTo[Extent] =>
             val extent = r.to[Extent]
             extent.jtsGeom
         }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
index c68b8aecb..a93abb15b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -21,16 +21,17 @@
 
 package org.locationtech.rasterframes.expressions.aggregates
 
-import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import geotrellis.proj4.CRS
+import geotrellis.raster.reproject.Reproject
 import geotrellis.raster.resample.ResampleMethod
 import geotrellis.raster.{ArrayTile, CellType, Raster, Tile}
+import geotrellis.spark.TileLayerMetadata
 import geotrellis.vector.Extent
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.{Column, Row, TypedColumn}
-import geotrellis.raster.reproject.Reproject
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
 
 /**
@@ -87,10 +88,21 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
 }
 
 object TileRasterizerAggregate {
-  val nodeName = "tile_rasterizer_aggregate"
+  val nodeName = "rf_tile_rasterizer_aggregate"
   /**  Convenience grouping of  parameters needed for running aggregate. */
   case class ProjectedRasterDefinition(cols: Int, rows: Int, cellType: CellType, crs: CRS, extent: Extent, sampler: ResampleMethod = ResampleMethod.DEFAULT)
 
+  object ProjectedRasterDefinition {
+    def apply(tlm: TileLayerMetadata[_]): ProjectedRasterDefinition = apply(tlm, ResampleMethod.DEFAULT)
+
+    def apply(tlm: TileLayerMetadata[_], sampler: ResampleMethod): ProjectedRasterDefinition = {
+      val actualSize = tlm.layout.toRasterExtent().gridBoundsFor(tlm.extent)
+      val cols = actualSize.width
+      val rows = actualSize.height
+      new ProjectedRasterDefinition(cols, rows, tlm.cellType, tlm.crs, tlm.extent, sampler)
+    }
+}
+
   def apply(prd: ProjectedRasterDefinition, crsCol: Column, extentCol: Column, tileCol: Column): TypedColumn[Any, Raster[Tile]] =
     new TileRasterizerAggregate(prd)(crsCol, extentCol, tileCol).as(nodeName).as[Raster[Tile]]
 }
\ No newline at end of file
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
index 772432d1f..6990e34df 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/SetCellType.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure,
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.expressions.{BinaryExpression, Expression}
 import org.apache.spark.sql.functions.lit
-import org.apache.spark.sql.rf._
+import org.apache.spark.sql.rf.{TileUDT, WithTypeConformity}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, TypedColumn}
 import org.apache.spark.unsafe.types.UTF8String
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index ed861fc4c..0e224a1de 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -21,20 +21,23 @@
 
 package org.locationtech.rasterframes.extensions
 
-import org.locationtech.rasterframes.StandardColumns._
-import org.locationtech.rasterframes.util._
-import org.locationtech.rasterframes.RasterFrameLayer
-import geotrellis.raster.Tile
+import geotrellis.proj4.CRS
+import geotrellis.raster.{MultibandTile, Tile}
 import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
+import geotrellis.vector.{Extent, ProjectedExtent}
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
-import spray.json.JsonFormat
+import org.locationtech.rasterframes.{MetadataKeys, RasterFrameLayer}
+import org.locationtech.rasterframes.StandardColumns._
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.expressions.DynamicExtractors
-import org.locationtech.rasterframes.MetadataKeys
+import org.locationtech.rasterframes.util._
+import spray.json.JsonFormat
 
 import scala.util.Try
 
@@ -91,7 +94,19 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def tileColumns: Seq[Column] =
     self.schema.fields
       .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
-      .map(f ⇒ self.col(f.name).as[Tile])
+      .map(f ⇒ self.col(f.name))
+
+  /** Get the columns that look like `Extent`s. */
+  def extentColumns: Seq[Column] =
+    self.schema.fields
+      .filter(_.dataType.conformsTo[Extent])
+      .map(f => self.col(f.name))
+
+  /** Get the columns that look like `CRS`s. */
+  def crsColumns: Seq[Column] =
+    self.schema.fields
+      .filter(_.dataType.conformsTo[CRS])
+      .map(f => self.col(f.name))
 
   /** Get the columns that are not of type `Tile` */
   def notTileColumns: Seq[Column] =
@@ -135,38 +150,6 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def withPrefixedColumnNames(prefix: String): DF =
     self.columns.foldLeft(self)((df, c) ⇒ df.withColumnRenamed(c, s"$prefix$c").asInstanceOf[DF])
 
-  /** Converts this DataFrame to a RasterFrameLayer after ensuring it has:
-   *
-   * <ol type="a">
-   * <li>a space or space-time key column
-   * <li>one or more tile columns
-   * <li>tile layout metadata
-   * <ol>
-   *
-   * If any of the above are violated, and [[IllegalArgumentException]] is thrown.
-   *
-   * @return validated RasterFrameLayer
-   * @throws IllegalArgumentException when constraints are not met.
-   */
-  @throws[IllegalArgumentException]
-  def asLayer: RasterFrameLayer = {
-    val potentialRF = certifyRasterframe(self)
-
-    require(
-      potentialRF.findSpatialKeyField.nonEmpty,
-      "A RasterFrameLayer requires a column identified as a spatial key"
-    )
-
-    require(potentialRF.tileColumns.nonEmpty, "A RasterFrameLayer requires at least one tile column")
-
-    require(
-      Try(potentialRF.tileLayerMetadata).isSuccess,
-      "A RasterFrameLayer requires embedded TileLayerMetadata"
-    )
-
-    potentialRF
-  }
-
   /**
     * Performs a jeft join on the dataframe `right` to this one, reprojecting and merging tiles as necessary.
     * The operation is logically a "left outer" join, with the left side also determining the target CRS and extents.
@@ -218,6 +201,42 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
   def rasterJoin(right: DataFrame, joinExpr: Column, leftExtent: Column, leftCRS: Column, rightExtent: Column, rightCRS: Column): DataFrame =
     RasterJoin(self, right, joinExpr, leftExtent, leftCRS, rightExtent, rightCRS)
 
+
+  /** Layout contents of RasterFrame to a layer. Assumes CRS and extent columns exist. */
+  def toLayer(tlm: TileLayerMetadata[SpatialKey]): RasterFrameLayer = ReprojectToLayer(self, tlm)
+
+  /** Coerces this DataFrame to a RasterFrameLayer after ensuring it has:
+    *
+    * <ol type="a">
+    * <li>a space or space-time key column
+    * <li>one or more tile columns
+    * <li>tile layout metadata
+    * <ol>
+    *
+    * If any of the above are violated, and [[IllegalArgumentException]] is thrown.
+    *
+    * @return validated RasterFrameLayer
+    * @throws IllegalArgumentException when constraints are not met.
+    */
+  @throws[IllegalArgumentException]
+  def asLayer: RasterFrameLayer = {
+    val potentialRF = certifyRasterframe(self)
+
+    require(
+      potentialRF.findSpatialKeyField.nonEmpty,
+      "A RasterFrameLayer requires a column identified as a spatial key"
+    )
+
+    require(potentialRF.tileColumns.nonEmpty, "A RasterFrameLayer requires at least one tile column")
+
+    require(
+      Try(potentialRF.tileLayerMetadata).isSuccess,
+      "A RasterFrameLayer requires embedded TileLayerMetadata"
+    )
+
+    potentialRF
+  }
+
   /**
    * Convert DataFrame already in a uniform gridding into a RasterFrameLayer
    *
@@ -273,7 +292,7 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
    *
    * @return true if all constraints are fulfilled, false otherwise.
    */
-  def isLayer: Boolean = Try(asLayer).isSuccess
+  def isAlreadyLayer: Boolean = Try(asLayer).isSuccess
 
   /** Internal method for slapping the RasterFreameLayer seal of approval on a DataFrame.
    * Only call if if you are sure it has a spatial key and tile columns and TileLayerMetadata. */
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
index 83f85fdd0..e0cec7a8c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/RasterJoin.scala
@@ -20,48 +20,15 @@
  */
 
 package org.locationtech.rasterframes.extensions
-import geotrellis.proj4.CRS
-import geotrellis.raster._
-import geotrellis.raster.reproject.Reproject
-import geotrellis.vector.Extent
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes._
-import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.model.TileDimensions
+import org.locationtech.rasterframes.functions.reproject_and_merge
 import org.locationtech.rasterframes.util._
 
 import scala.util.Random
 
 object RasterJoin {
-  private val projOpts = Reproject.Options.DEFAULT
-
-  val reproject_and_merge_f: (Row, Row, Seq[Tile], Seq[Row], Seq[Row], Row) => Tile = (leftExtentEnc: Row, leftCRSEnc: Row, tiles: Seq[Tile], rightExtentEnc: Seq[Row], rightCRSEnc: Seq[Row], leftDimsEnc: Row) => {
-    if (tiles.isEmpty) null
-    else {
-      require(tiles.length == rightExtentEnc.length && tiles.length == rightCRSEnc.length, "size mismatch")
-
-      val leftExtent = leftExtentEnc.to[Extent]
-      val leftDims = leftDimsEnc.to[TileDimensions]
-      val leftCRS = leftCRSEnc.to[CRS]
-      val rightExtents = rightExtentEnc.map(_.to[Extent])
-      val rightCRSs = rightCRSEnc.map(_.to[CRS])
-
-      val cellType = tiles.map(_.cellType).reduceOption(_ union _).getOrElse(tiles.head.cellType)
-
-      val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
-      //is there a GT function to do all this?
-      tiles.zip(rightExtents).zip(rightCRSs).map {
-        case ((tile, extent), crs) =>
-          tile.reproject(extent, crs, leftCRS, projOpts)
-      }.foldLeft(dest)((d, t) =>
-        d.merge(leftExtent, t.extent, t.tile, projOpts.method)
-      )
-    }
-  }
-
-  // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
-  def reproject_and_merge = udf(reproject_and_merge_f).withName("reproject_and_merge")
 
   def apply(left: DataFrame, right: DataFrame): DataFrame = {
     val df = apply(left, right, left("extent"), left("crs"), right("extent"), right("crs"))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/ReprojectToLayer.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/ReprojectToLayer.scala
new file mode 100644
index 000000000..c396deaee
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/ReprojectToLayer.scala
@@ -0,0 +1,49 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.extensions
+
+import geotrellis.spark.{SpatialKey, TileLayerMetadata}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions.broadcast
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.util._
+object ReprojectToLayer {
+
+  def apply(df: DataFrame, tlm: TileLayerMetadata[SpatialKey]): RasterFrameLayer = {
+    // create a destination dataframe with crs and extend columns
+    // use RasterJoin to do the rest.
+    val gb = tlm.gridBounds
+    val crs = tlm.crs
+
+    val gridItems = for {
+      (col, row) <- gb.coordsIter
+      sk = SpatialKey(col, row)
+      e = tlm.mapTransform(sk)
+    } yield (sk, e, crs)
+
+    val dest = df.sparkSession.createDataFrame(gridItems.toSeq)
+      .toDF(SPATIAL_KEY_COLUMN.columnName, EXTENT_COLUMN.columnName, CRS_COLUMN.columnName)
+    val joined = RasterJoin(broadcast(dest), df)
+
+    joined.asLayer(SPATIAL_KEY_COLUMN, tlm)
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 427dc504c..51c78a34b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -19,12 +19,16 @@
  *
  */
 package org.locationtech.rasterframes
+import geotrellis.proj4.CRS
 import geotrellis.raster.{Tile, _}
+import geotrellis.raster.reproject.Reproject
 import geotrellis.vector.Extent
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.functions.udf
+import org.apache.spark.sql.{Row, SQLContext}
 import org.locationtech.jts.geom.Geometry
+import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.jts.ReprojectionTransformer
-import org.locationtech.rasterframes.model.LazyCRS
+import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
 
 /**
  * Module utils.
@@ -100,6 +104,36 @@ package object functions {
   private[rasterframes] val tileOnes: (Int, Int, String) ⇒ Tile = (cols, rows, cellTypeName) ⇒
     makeConstantTile(1, cols, rows, cellTypeName)
 
+  val reproject_and_merge_f: (Row, Row, Seq[Tile], Seq[Row], Seq[Row], Row) => Tile = (leftExtentEnc: Row, leftCRSEnc: Row, tiles: Seq[Tile], rightExtentEnc: Seq[Row], rightCRSEnc: Seq[Row], leftDimsEnc: Row) => {
+    if (tiles.isEmpty) null
+    else {
+      require(tiles.length == rightExtentEnc.length && tiles.length == rightCRSEnc.length, "size mismatch")
+
+      val leftExtent = leftExtentEnc.to[Extent]
+      val leftDims = leftDimsEnc.to[TileDimensions]
+      val leftCRS = leftCRSEnc.to[CRS]
+      val rightExtents = rightExtentEnc.map(_.to[Extent])
+      val rightCRSs = rightCRSEnc.map(_.to[CRS])
+
+      val cellType = tiles.map(_.cellType).reduceOption(_ union _).getOrElse(tiles.head.cellType)
+
+      // TODO: how to allow control over... expression?
+      val projOpts = Reproject.Options.DEFAULT
+      val dest: Tile = ArrayTile.empty(cellType, leftDims.cols, leftDims.rows)
+      //is there a GT function to do all this?
+      tiles.zip(rightExtents).zip(rightCRSs).map {
+        case ((tile, extent), crs) =>
+          tile.reproject(extent, crs, leftCRS, projOpts)
+      }.foldLeft(dest)((d, t) =>
+        d.merge(leftExtent, t.extent, t.tile, projOpts.method)
+      )
+    }
+  }
+
+  // NB: Don't be tempted to make this a `val`. Spark will barf if `withRasterFrames` hasn't been called first.
+  def reproject_and_merge = udf(reproject_and_merge_f)
+    .withName("reproject_and_merge")
+
 
   private[rasterframes] val cellTypes: () ⇒ Seq[String] = () ⇒
     Seq(
diff --git a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
index a58f851a0..1517e8f0e 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/rasterframes.scala
@@ -125,6 +125,8 @@ package object rasterframes extends StandardColumns
   trait StandardLayerKey[T] extends Serializable {
     val selfType: TypeTag[T]
     def isType[R: TypeTag]: Boolean = typeOf[R] =:= selfType.tpe
+    def coerce[K >: T](tlm: TileLayerMetadata[_]): TileLayerMetadata[K] =
+      tlm.asInstanceOf[TileLayerMetadata[K]]
   }
   object StandardLayerKey {
     def apply[T: StandardLayerKey]: StandardLayerKey[T] = implicitly
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 3b30a38ae..4b917b86e 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -55,6 +55,15 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
     }
   }
   describe("Miscellaneous extensions") {
+    import spark.implicits._
+    it("should find multiple extent columns") {
+      val df = Seq((extent, "fred", extent, 34.0)).toDF("e1", "s", "e2", "n")
+      df.extentColumns.size should be(2)
+    }
+    it("should find multiple crs columns") {
+      val df = Seq((pe.crs, "fred", pe.crs, 34.0)).toDF("c1", "s", "c2", "n")
+      df.crsColumns.size should be (2)
+    }
     it("should split TileLayout") {
       val tl1 = TileLayout(2, 3, 10, 10)
       assert(tl1.subdivide(0) === tl1)
diff --git a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
index 5059fd6b9..caccd74ca 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/RasterFunctionsSpec.scala
@@ -33,7 +33,6 @@ import org.locationtech.rasterframes.model.TileDimensions
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
 import org.locationtech.rasterframes.stats._
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
-import org.scalatest.{FunSpec, Matchers}
 
 class RasterFunctionsSpec extends TestEnvironment with RasterMatchers {
   import spark.implicits._
diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index d47fafbc1..584a01ef6 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -124,7 +124,9 @@ trait TestData {
     readSingleband(s"NAIP-VA-b$band.tiff")
   }
 
-  def rgbCogSample = readMultiband("LC08_RGB_Norfolk_COG.tiff")
+  def rgbCogSample  = readMultiband("LC08_RGB_Norfolk_COG.tiff")
+
+  def rgbCogSamplePath = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
 
   def sampleTileLayerRDD(implicit spark: SparkSession): TileLayerRDD[SpatialKey] = {
     val rf = sampleGeoTiff.projectedRaster.toLayer(128, 128)
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index a080b9691..98e161b18 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -21,17 +21,23 @@
 
 package org.locationtech.rasterframes.datasource.geotiff
 
+import java.net.URI
+
+import _root_.geotrellis.proj4.CRS
 import _root_.geotrellis.raster.io.geotiff.compression._
 import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
 import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
+import _root_.geotrellis.raster._
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.rf.TileUDT
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource._
-import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate
+import org.locationtech.rasterframes.expressions.aggregates.{ProjectedLayerMetadataAggregate, TileRasterizerAggregate}
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
 import org.locationtech.rasterframes.util._
 
 /**
@@ -41,17 +47,15 @@ import org.locationtech.rasterframes.util._
 class GeoTiffDataSource extends DataSourceRegister
   with RelationProvider with CreatableRelationProvider
   with DataSourceOptions with LazyLogging {
-  def shortName() = GeoTiffDataSource.SHORT_NAME
+  import GeoTiffDataSource._
 
-  def path(parameters: Map[String, String]) =
-    uriParam(PATH_PARAM, parameters)
+  def shortName() = GeoTiffDataSource.SHORT_NAME
 
   def createRelation(sqlContext: SQLContext, parameters: Map[String, String]) = {
-    val pathO = path(parameters)
-    require(pathO.isDefined, "Valid URI 'path' parameter required.")
+    require(parameters.path.isDefined, "Valid URI 'path' parameter required.")
     sqlContext.withRasterFrames
 
-    val p = pathO.get
+    val p = parameters.path.get
 
     if(p.getPath.contains("*")) {
       val bandCount = parameters.get(GeoTiffDataSource.BAND_COUNT_PARAM).map(_.toInt).getOrElse(1)
@@ -60,61 +64,87 @@ class GeoTiffDataSource extends DataSourceRegister
     else GeoTiffRelation(sqlContext, p)
   }
 
-  def createLayer(df: DataFrame, parameters: Map[String, String], tileCols: Seq[Column]): RasterFrameLayer = {
-    require(tileCols.nonEmpty, "need at least one tile column")
+  override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], df: DataFrame): BaseRelation = {
+    require(parameters.path.isDefined, "Valid URI 'path' parameter required.")
+    val path = parameters.path.get
+    require(path.getScheme == "file" || path.getScheme == null,
+      "Currently only 'file://' destinations are supported")
+    sqlContext.withRasterFrames
 
+    val tileCols = df.tileColumns
 
-//    val prd = ProjectedRasterDefinition()
-//    val crsCol = col("crs")
-//    val extentCol = col("extent")
-//    df.agg(TileRasterizerAggregate(prd, crsCol, extentCol, tileCols.head))
-    ???
-  }
+    require(tileCols.nonEmpty, "Could not find any tile columns.")
 
-  override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = {
-    val pathO = path(parameters)
-    require(pathO.isDefined, "Valid URI 'path' parameter required.")
-    require(pathO.get.getScheme == "file" || pathO.get.getScheme == null, "Currently only 'file://' destinations are supported")
-    sqlContext.withRasterFrames
+    val raster = if(df.isAlreadyLayer) {
+      val layer = df.certify
+      val tlm = layer.tileLayerMetadata.merge
+
+      // If no desired image size is given, write at full size.
+      val TileDimensions(cols, rows) =  parameters.rasterDimensions
+        .getOrElse {
+          val actualSize = tlm.layout.toRasterExtent().gridBoundsFor(tlm.extent)
+          TileDimensions(actualSize.width, actualSize.height)
+        }
+
+      // Should we really play traffic cop here?
+      if(cols.toDouble * rows * 64.0 > Runtime.getRuntime.totalMemory() * 0.5)
+        logger.warn(s"You've asked for the construction of a very large image ($cols x $rows), destined for ${path}. Out of memory error likely.")
+
+      layer.toMultibandRaster(tileCols, cols.toInt, rows.toInt)
+    }
+    else {
+      require(parameters.crs.nonEmpty, "A destination CRS must be provided")
+      require(tileCols.nonEmpty, "need at least one tile column")
+
+      val destCRS = parameters.crs.get
+
+      val crsCols = df.crsColumns
+      require(crsCols.size == 1, "Exactly one CRS column must be in DataFrame")
+      val extentCols = df.extentColumns
+      require(extentCols.size == 1, "Exactly one Extent column must be in DataFrame")
+
+      val tlm = df
+        .select(ProjectedLayerMetadataAggregate(
+          destCRS, extentCols.head, crsCols.head, rf_cell_type(tileCols.head), rf_dimensions(tileCols.head)
+        ))
+        .first()
+
+      val config = ProjectedRasterDefinition(tlm)
 
-    val compress = parameters.get(GeoTiffDataSource.COMPRESS_PARAM).exists(_.toBoolean)
-    val tcols = data.tileColumns
+      val aggs = tileCols
+        .map(t => TileRasterizerAggregate(
+          config, crsCols.head, extentCols.head, rf_tile(t))("tile").as(t.columnName)
+        )
 
-    require(tcols.nonEmpty, "Could not find any tile columns.")
+      val agg = df.select(aggs: _*)
+
+      val row = agg.first()
+
+      val bands = for(i <- 0 until row.size) yield row.getAs[Tile](i)
+
+      ProjectedRaster(MultibandTile(bands), tlm.extent, tlm.crs)
+    }
 
     val tags = Tags(
       RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
-      tcols.map(c ⇒ Map("RF_COL" -> c.columnName)).toList
+      tileCols.map(c ⇒ Map("RF_COL" -> c.columnName)).toList
     )
 
     // We make some assumptions here.... eventually have column metadata encode this.
-    val colorSpace = tcols.size match {
+    val colorSpace = tileCols.size match {
       case 3 | 4 ⇒  ColorSpace.RGB
       case _ ⇒ ColorSpace.BlackIsZero
     }
 
-    val tiffOptions = GeoTiffOptions(Tiled, if (compress) DeflateCompression else NoCompression, colorSpace)
-
-    val layer = if(data.isLayer) data.certify else createLayer(data, parameters, tcols)
-
-    val tlm = layer.tileLayerMetadata.merge
-    // If no desired image size is given, write at full size.
-    val cols = numParam(GeoTiffDataSource.IMAGE_WIDTH_PARAM, parameters).getOrElse(tlm.gridBounds.width.toLong)
-    val rows = numParam(GeoTiffDataSource.IMAGE_HEIGHT_PARAM, parameters).getOrElse(tlm.gridBounds.height.toLong)
-
-    require(cols <= Int.MaxValue && rows <= Int.MaxValue, s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
-
-    // Should we really play traffic cop here?
-    if(cols.toDouble * rows * 64.0 > Runtime.getRuntime.totalMemory() * 0.5)
-      logger.warn(s"You've asked for the construction of a very large image ($cols x $rows), destined for ${pathO.get}. Out of memory error likely.")
-
-    val raster = layer.toMultibandRaster(tcols, cols.toInt, rows.toInt)
+    val tiffOptions = GeoTiffOptions(Tiled,
+      if (parameters.compress) DeflateCompression else NoCompression, colorSpace
+    )
 
     val geotiff = new MultibandGeoTiff(raster.tile, raster.extent, raster.crs, tags, tiffOptions)
 
-    logger.debug(s"Writing DataFrame to GeoTIFF ($cols x $rows) at ${pathO.get}")
-    geotiff.write(pathO.get.getPath)
-    GeoTiffRelation(sqlContext, pathO.get)
+    logger.debug(s"Writing DataFrame to GeoTIFF (${geotiff.cols} x ${geotiff.rows}) at ${path}")
+    geotiff.write(path.getPath)
+    GeoTiffRelation(sqlContext, path)
   }
 }
 
@@ -124,5 +154,22 @@ object GeoTiffDataSource {
   final val IMAGE_WIDTH_PARAM = "imageWidth"
   final val IMAGE_HEIGHT_PARAM = "imageWidth"
   final val COMPRESS_PARAM = "compress"
+  final val CRS_PARAM = "crs"
   final val BAND_COUNT_PARAM = "bandCount"
+
+  private[geotiff]
+  implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
+    def path: Option[URI] = uriParam(PATH_PARAM, parameters)
+    def compress: Boolean = parameters.get(COMPRESS_PARAM).exists(_.toBoolean)
+    def crs: Option[CRS] = parameters.get(CRS_PARAM).map(s => LazyCRS(s))
+    def rasterDimensions: Option[TileDimensions] = {
+      numParam(IMAGE_WIDTH_PARAM, parameters)
+        .zip(numParam(IMAGE_HEIGHT_PARAM, parameters))
+        .map { case (cols, rows) =>
+          require(cols <= Int.MaxValue && rows <= Int.MaxValue,
+            s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
+          TileDimensions(cols.toInt, rows.toInt)
+        }.headOption
+    }
+  }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
index 8c7b644d5..75bdc7e76 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/package.scala
@@ -24,6 +24,7 @@ import java.net.URI
 
 import org.apache.spark.sql.{DataFrameReader, DataFrameWriter}
 import org.locationtech.rasterframes._
+import _root_.geotrellis.proj4.CRS
 import shapeless.tag.@@
 import shapeless.tag
 
@@ -65,6 +66,11 @@ package object geotiff {
         writer
           .option(GeoTiffDataSource.COMPRESS_PARAM, true)
       )
+    def withCRS(crs: CRS): GeoTiffRasterFrameWriter[T] =
+      tag[GeoTiffRasterFrameWriterTag][DataFrameWriter[T]](
+        writer
+          .option(GeoTiffDataSource.CRS_PARAM, crs.toProj4String)
+      )
   }
 
   /** Adds `loadLayer` to appropriately tagged `DataFrameReader` */
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index aecf2d848..4c160034e 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -22,6 +22,7 @@ package org.locationtech.rasterframes.datasource.geotiff
 
 import java.nio.file.Paths
 
+import geotrellis.proj4.LatLng
 import org.locationtech.rasterframes._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.TestEnvironment
@@ -88,16 +89,31 @@ class GeoTiffDataSourceSpec
       assert(write(rf))
     }
 
-    it("should write GeoTIFF") {
+    it("should write GeoTIFF from layer") {
       val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
 
       logger.info(s"Read extent: ${rf.tileLayerMetadata.merge.extent}")
 
-      val out = Paths.get("target", "example-geotiff.tiff")
+      val out = Paths.get("target", "example-geotiff.tif")
       logger.info(s"Writing to $out")
       noException shouldBe thrownBy {
         rf.write.format("geotiff").save(out.toString)
       }
     }
+
+    it("should write GeoTIFF without layer") {
+      import org.locationtech.rasterframes.datasource.raster._
+      val pr = col("proj_raster_b0")
+      val rf = spark.read.raster.withBandIndexes(0, 1, 2).load(rgbCogSamplePath.toASCIIString)
+        .withColumn("extent", rf_extent(pr))
+        .withColumn("crs", rf_crs(pr))
+
+      val out = Paths.get("target", "example2-geotiff.tif")
+      logger.info(s"Writing to $out")
+      noException shouldBe thrownBy {
+        rf.write.geotiff.withCRS(LatLng).save(out.toString)
+      }
+
+    }
   }
 }

From c802d2887c7238c73dbd1d0f1654ef5e04a1b73d Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 23 Jul 2019 15:33:44 -0400
Subject: [PATCH 245/380] move example code to aggregation

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/reference.pymd       | 42 +------------------
 1 file changed, 2 insertions(+), 40 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index b4749aeef..620c306f1 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -399,17 +399,7 @@ Performs natural logarithm of cell values plus one. Inverse of @ref:[`rf_expm1`]
 
 ## Tile Statistics
 
-The following functions compute a statistical summary per row of a `tile` column. The statistics are computed across the cells of a single `tile`, within each DataFrame Row.  Consider the following example.
-
-```python
-import pyspark.sql.functions as F
-spark.sql("""
- SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
- UNION
- SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
- """).select(F.col('id'), rf_tile_sum(F.col('t'))).show()
-```
-
+The following functions compute a statistical summary per row of a `tile` column. The statistics are computed across the cells of a single `tile`, within each DataFrame Row.
 
 ### rf_tile_sum
 
@@ -489,17 +479,7 @@ spark.sql("SELECT rf_tile_histogram(rf_make_ones_tile(5, 5, 'float32')) as tile_
 
 ## Aggregate Tile Statistics
 
-These functions compute statistical summaries over all of the cell values *and* across all the rows in the DataFrame or group. Example use below computes a single double-valued mean per month, across all data cells in the `red_band` `tile` type column. This would return at most twelve rows.
-
-Continuing our example from the @ref:[Tile Statistics](reference.md#tile-statistics) section, consider the following. Note that only a single row is returned. It is averaging 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows.
-
-```python
-spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
-UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
-""").agg(rf_agg_mean(F.col('t'))).show(10, False)
-```
+These functions compute statistical summaries over all of the cell values *and* across all the rows in the DataFrame or group.
 
 ### rf_agg_mean
 
@@ -545,24 +525,6 @@ Aggregates over the `tile` return a count of each cell value to create a histogr
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
-Consider again our example for Tile Statistics and Aggregate Tile Statistics, this time apply @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean). We see that it is computing the element-wise mean across the two rows. In this case it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
-
-
-```python
-import pyspark.sql.functions as F
-alm = spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
-UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
-""").agg(rf_agg_local_mean(F.col('t')).alias('l')) \
-
-## local_agg_mean returns a tile
-alm.select(rf_dimensions(alm.l)).show()
-
-alm.select(rf_explode_tiles(alm.l)).show(10, False)
-```
-
-
 ### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)

From 8ddc1323fabbc3ddb68d39a95127674c2c21e93a Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 23 Jul 2019 17:11:44 -0400
Subject: [PATCH 246/380] aggregation functions

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     | 49 ++++++++++++++++++-
 1 file changed, 47 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 21dd89998..8fa04e35c 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -10,13 +10,58 @@ import os
 spark = create_rf_spark_session()
 ```
 
-## Cell Counts & Tile Mean
+There are 2 types of aggregate functions: aggregate tile and aggregate tile local. In the aggregate tile functions, we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the aggregate tile local functions, we are computing the element-wise statistics across a DataFrame or group of tiles.
+
+## Tile Mean
+
+We can illustrate this difference in computing tile mean. In the first code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to average 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned.
+
+```python
+import pyspark.sql.functions as F
+
+spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
+UNION
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
+""").agg(rf_agg_mean(F.col('t'))).show(10, False)
+```
+
+
+In this code block, we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) instead of @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the element-wise mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+
+```python
+import pyspark.sql.functions as F
+
+alm = spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
+UNION
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
+""").agg(rf_agg_local_mean(F.col('t')).alias('l')) \
+
+## local_agg_mean returns a tile
+alm.select(rf_dimensions(alm.l)).show()
+
+alm.select(rf_explode_tiles(alm.l)).show(10, False)
+```
+
+## Cell Counts
+
+We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 387,000 data cells and 1,000 NoData cells in this DataFrame.
 
 ```python
 rf = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B8-Robinson-IL.tiff'))
 rf.show(5, False)
 
-stats = rf.agg(rf_agg_no_data_cells('tile'), rf_agg_data_cells('tile'), rf_agg_mean('tile'))
+stats = rf.agg(rf_agg_data_cells('tile'), rf_agg_no_data_cells('tile'))
 stats.show(5, False)
 ```
 
+## Statistical Summaries
+
+The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance.
+
+```python
+rf = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B8-Robinson-IL.tiff'))
+stats = rf.agg(rf_agg_stats('tile').alias("stats"))
+stats.select("stats.data_cells", "stats.no_data_cells", "stats.min", "stats.max", "stats.mean", "stats.variance").show()
+```

From 5629390b4568d1e2f9ad74358ff4ebaba87ef8dc Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 24 Jul 2019 12:02:26 -0400
Subject: [PATCH 247/380] add tile aggregates

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     | 40 +++++++++++--------
 1 file changed, 23 insertions(+), 17 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 8fa04e35c..e10c5c505 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -10,11 +10,23 @@ import os
 spark = create_rf_spark_session()
 ```
 
-There are 2 types of aggregate functions: aggregate tile and aggregate tile local. In the aggregate tile functions, we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the aggregate tile local functions, we are computing the element-wise statistics across a DataFrame or group of tiles.
+There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the tile aggregate functions, we are computing a statistical summary per row of a `tile` column in a DataFrame. In the DataFrame aggregate functions, we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the element-wise local aggregate functions, we are computing the element-wise statistical summary across a DataFrame or group of tiles.
 
 ## Tile Mean
 
-We can illustrate this difference in computing tile mean. In the first code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to average 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned.
+We can illustrate this difference in computing tile mean. In the first code block we are using @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) to computer the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0.
+
+```python
+import pyspark.sql.functions as F
+
+spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
+UNION
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
+""").select(F.col('id'), rf_tile_mean(F.col('t'))).show(10, False)
+```
+
+In this next code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
 ```python
 import pyspark.sql.functions as F
@@ -27,7 +39,7 @@ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
 ```
 
 
-In this code block, we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) instead of @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the element-wise mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+In this code block, we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
 
 ```python
 import pyspark.sql.functions as F
@@ -36,32 +48,26 @@ alm = spark.sql("""
 SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
 UNION
 SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
-""").agg(rf_agg_local_mean(F.col('t')).alias('l')) \
-
-## local_agg_mean returns a tile
-alm.select(rf_dimensions(alm.l)).show()
-
+""").agg(rf_agg_local_mean(F.col('t')).alias('l'))
 alm.select(rf_explode_tiles(alm.l)).show(10, False)
 ```
 
 ## Cell Counts
 
-We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 387,000 data cells and 1,000 NoData cells in this DataFrame.
+We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,348,900 data cells and 0 NoData cells in this DataFrame.
 
 ```python
-rf = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B8-Robinson-IL.tiff'))
-rf.show(5, False)
-
-stats = rf.agg(rf_agg_data_cells('tile'), rf_agg_no_data_cells('tile'))
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
+stats = rf.select(rf_tile("proj_raster").alias("tile")).agg(rf_agg_data_cells('tile'), rf_agg_no_data_cells('tile'))
 stats.show(5, False)
 ```
 
 ## Statistical Summaries
 
-The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance.
+The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. You can use the @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) to compute summary statistics separately for each row in a `tile` column and you can use the @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) to compute the local element-wise summary statistics.
 
 ```python
-rf = spark.read.geotiff(os.path.join(resource_dir(), 'L8-B8-Robinson-IL.tiff'))
-stats = rf.agg(rf_agg_stats('tile').alias("stats"))
-stats.select("stats.data_cells", "stats.no_data_cells", "stats.min", "stats.max", "stats.mean", "stats.variance").show()
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
+stats = rf.select(rf_tile("proj_raster").alias("tile")).agg(rf_agg_stats('tile').alias("stats"))
+stats.select("stats.min", "stats.max", "stats.mean", "stats.variance").show(1, False)
 ```

From dff53a9b14f7f954867fdfb3106d81dec4cb820d Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 24 Jul 2019 12:16:59 -0400
Subject: [PATCH 248/380] add links

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/aggregation.pymd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index e10c5c505..862797c20 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -10,7 +10,7 @@ import os
 spark = create_rf_spark_session()
 ```
 
-There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the tile aggregate functions, we are computing a statistical summary per row of a `tile` column in a DataFrame. In the DataFrame aggregate functions, we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the element-wise local aggregate functions, we are computing the element-wise statistical summary across a DataFrame or group of tiles.
+There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[`tile aggregate functions`](reference.md#tile-statistics), we are computing a statistical summary per row of a `tile` column in a DataFrame. In the @ref:[`DataFrame aggregate functions`](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the  @ref:[`element-wise local aggregate functions`](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of tiles.
 
 ## Tile Mean
 
@@ -69,5 +69,6 @@ The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over al
 ```python
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 stats = rf.select(rf_tile("proj_raster").alias("tile")).agg(rf_agg_stats('tile').alias("stats"))
+stats.printSchema()
 stats.select("stats.min", "stats.max", "stats.mean", "stats.variance").show(1, False)
 ```

From fc270e15bc801f440d90c9134c93d82fbd0fe18c Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 25 Jul 2019 13:41:03 -0400
Subject: [PATCH 249/380] jason's edits

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     | 76 +++++++++++--------
 1 file changed, 44 insertions(+), 32 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 862797c20..7233fc83e 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -10,65 +10,77 @@ import os
 spark = create_rf_spark_session()
 ```
 
-There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[`tile aggregate functions`](reference.md#tile-statistics), we are computing a statistical summary per row of a `tile` column in a DataFrame. In the @ref:[`DataFrame aggregate functions`](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the  @ref:[`element-wise local aggregate functions`](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of tiles.
+There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[tile aggregate functions](reference.md#tile-statistics), we are computing a statistical summary per row of a `tile` column in a DataFrame. In the @ref:[DataFrame aggregate functions](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the @ref:[element-wise local aggregate functions](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of tiles.
 
-## Tile Mean
+## Tile Mean Example
 
-We can illustrate this difference in computing tile mean. In the first code block we are using @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) to computer the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0.
+We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 tiles where the first tile is composed of 25 values of 1.0 and the second tile is composed of 25 values of 3.0. In this first code block we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
 ```python
 import pyspark.sql.functions as F
 
-spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
+rf = spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as tile
 UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
-""").select(F.col('id'), rf_tile_mean(F.col('t'))).show(10, False)
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as tile
+""")
+
+rf.show(10, False)
+
+rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
 ```
 
-In this next code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
+In the next code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
 ```python
-import pyspark.sql.functions as F
-
-spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
-UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as t
-""").agg(rf_agg_mean(F.col('t'))).show(10, False)
+rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
-
-In this code block, we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+In the third code block we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
 
 ```python
-import pyspark.sql.functions as F
-
-alm = spark.sql("""
-SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as t
-UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as t
-""").agg(rf_agg_local_mean(F.col('t')).alias('l'))
-alm.select(rf_explode_tiles(alm.l)).show(10, False)
+rf.agg(rf_agg_local_mean(F.col('tile'))).show(10, False)
 ```
 
-## Cell Counts
+## Cell Counts Example
 
-We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,348,900 data cells and 0 NoData cells in this DataFrame.
+We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 313 data cells and 5,788,522 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
 ```python
-rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
-stats = rf.select(rf_tile("proj_raster").alias("tile")).agg(rf_agg_data_cells('tile'), rf_agg_no_data_cells('tile'))
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/06/11/2007247/MCD43A4.A2007247.h06v11.006.2016118065909_B02.TIF')
+stats = rf.agg(rf_agg_data_cells('proj_raster'), rf_agg_no_data_cells('proj_raster'))
+
 stats.show(5, False)
 ```
 
 ## Statistical Summaries
 
-The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance. You can use the @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) to compute summary statistics separately for each row in a `tile` column and you can use the @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) to compute the local element-wise summary statistics.
+The statistical summary functions return a summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance, which can be computed as a tile aggregate, a DataFrame aggregate, or an element-wise local aggregate.
+
+The @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) function computes summary statistics separately for each row in a `tile` column as shown below.
 
 ```python
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
-stats = rf.select(rf_tile("proj_raster").alias("tile")).agg(rf_agg_stats('tile').alias("stats"))
+stats = rf.select(rf_tile_stats('proj_raster').alias('stats'))
+
 stats.printSchema()
-stats.select("stats.min", "stats.max", "stats.mean", "stats.variance").show(1, False)
+stats.select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+```
+
+The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of all cell values as shown below.
+
+```python
+rf.agg(rf_agg_stats('proj_raster').alias('stats')).select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+```
+
+The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below.
+
+```python
+rf.agg(rf_agg_local_stats('proj_raster').alias('stats')).select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+```
+
+## Histogram
+
+```python
+rf.agg(rf_tile_histogram('proj_raster').alias('tile_histogram')).select("tile_histogram.bins").collect()
 ```

From 435af71644ca22914bc8f5ea2481033d9b62d6ab Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 25 Jul 2019 16:36:37 -0400
Subject: [PATCH 250/380] add histogram & pretty printing

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     | 61 +++++++++++++++----
 1 file changed, 49 insertions(+), 12 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 7233fc83e..f70f52198 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -14,7 +14,7 @@ There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, a
 
 ## Tile Mean Example
 
-We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 tiles where the first tile is composed of 25 values of 1.0 and the second tile is composed of 25 values of 3.0. In this first code block we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
+We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 tiles where the first tile is composed of 25 values of 1.0 and the second tile is composed of 25 values of 3.0.
 
 ```python
 import pyspark.sql.functions as F
@@ -22,32 +22,36 @@ import pyspark.sql.functions as F
 rf = spark.sql("""
 SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as tile
 UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3.0) as tile
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
 """)
 
-rf.show(10, False)
+rf.select("id", rf_render_matrix("tile")).show(10, False)
+```
+
+In this code block we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
+```python
 rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
 ```
 
-In the next code block we are using @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
+In this code block we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
 ```python
 rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
-In the third code block we are using @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+In this code block we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`. To compute an element-wise local aggregate, tiles need have the same dimensions as in the example below where both tiles have 5 rows and 5 columns. If we tried to compute a local aggregate over the DataFrame without equal tile dimensions, we would get a runtime error.
 
 ```python
-rf.agg(rf_agg_local_mean(F.col('tile'))).show(10, False)
+rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(10, False)
 ```
 
 ## Cell Counts Example
 
-We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 313 data cells and 5,788,522 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
+We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 19,417,342 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
 ```python
-rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/06/11/2007247/MCD43A4.A2007247.h06v11.006.2016118065909_B02.TIF')
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
 stats = rf.agg(rf_agg_data_cells('proj_raster'), rf_agg_no_data_cells('proj_raster'))
 
 stats.show(5, False)
@@ -64,23 +68,56 @@ rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0
 stats = rf.select(rf_tile_stats('proj_raster').alias('stats'))
 
 stats.printSchema()
-stats.select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
 ```
 
 The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of all cell values as shown below.
 
 ```python
-rf.agg(rf_agg_stats('proj_raster').alias('stats')).select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+rf.agg(rf_agg_stats('proj_raster').alias('stats')).select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
 ```
 
 The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below.
 
 ```python
-rf.agg(rf_agg_local_stats('proj_raster').alias('stats')).select("stats.min", "stats.max", "stats.mean", "stats.variance").show(10, False)
+rf = spark.sql("""
+SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as tile
+UNION
+SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
+UNION
+SELECT 3 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 5) as tile
+""").agg(rf_agg_local_stats('tile').alias('stats'))
+
+rf.select(rf_render_matrix('stats.min'), rf_render_matrix('stats.max'), rf_render_matrix('stats.mean'), rf_render_matrix('stats.variance')).show(10, False)
 ```
 
 ## Histogram
 
+The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the below schema. In the graph below, we have plotted values on the x-axis and counts on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram was just computed for the `tile` in the first row.
+
 ```python
-rf.agg(rf_tile_histogram('proj_raster').alias('tile_histogram')).select("tile_histogram.bins").collect()
+import matplotlib.pyplot as plt
+
+rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
+
+hist_df = rf.select(rf_tile_histogram('proj_raster').alias('tile_histogram')).select('tile_histogram.bins')
+hist_df.printSchema()
+
+bins_list = hist_df.collect()
+values = [int(row['value']) for row in bins_list[0].bins]
+counts = [int(row['count']) for row in bins_list[0].bins]
+
+plt.bar(values, counts)
+plt.show()
+```
+
+The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of `tile` in a DataFrame or group. The histogram is shown below. The range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this computation is across all cell values in the DataFrame.
+
+```python
+bins_list = rf.agg(rf_agg_approx_histogram('proj_raster').alias('tile_histogram')).select('tile_histogram.bins').collect()
+values = [int(row['value']) for row in bins_list[0].bins]
+counts = [int(row['count']) for row in bins_list[0].bins]
+
+plt.bar(values, counts, width=10)
+plt.show()
 ```

From 0a87705aa436b11b65faf0e6d214eea5d5fa119a Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 25 Jul 2019 16:39:21 -0400
Subject: [PATCH 251/380] fix wording of rf_agg_approx_histogram description

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/reference.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 620c306f1..e04ee2387 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -518,7 +518,7 @@ Aggregates over the `tile` and returns statistical summaries of cell values: num
     Struct[Array[Struct[Double, Long]]] rf_agg_approx_histogram(Tile tile)
 
 
-Aggregates over the `tile` return a count of each cell value to create a histogram with values are plotted on the x-axis and counts on the y-axis. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
+Aggregates over all of the rows in DataFrame of `tile` and returns a count of each cell value to create a histogram with values are plotted on the x-axis and counts on the y-axis. Related is the @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function which operates on a single row at a time.
 
 
 ## Tile Local Aggregate Statistics

From 413962d2ac0b13de7551d3e09a4d9965ade25a0e Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 25 Jul 2019 16:56:03 -0400
Subject: [PATCH 252/380] fix wording

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/aggregation.pymd | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index f70f52198..ffa26de91 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -40,7 +40,9 @@ In this code block we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mea
 rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
-In this code block we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`. To compute an element-wise local aggregate, tiles need have the same dimensions as in the example below where both tiles have 5 rows and 5 columns. If we tried to compute a local aggregate over the DataFrame without equal tile dimensions, we would get a runtime error.
+In this code block we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+
+To compute an element-wise local aggregate, tiles need have the same dimensions as in the example below where both tiles have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal tile dimensions, we would get a runtime error.
 
 ```python
 rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(10, False)
@@ -48,7 +50,7 @@ rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_ma
 
 ## Cell Counts Example
 
-We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 19,417,342 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
+We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
 ```python
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
@@ -77,7 +79,7 @@ The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over al
 rf.agg(rf_agg_stats('proj_raster').alias('stats')).select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
 ```
 
-The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below.
+The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal tile dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
 
 ```python
 rf = spark.sql("""
@@ -93,7 +95,7 @@ rf.select(rf_render_matrix('stats.min'), rf_render_matrix('stats.max'), rf_rende
 
 ## Histogram
 
-The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the below schema. In the graph below, we have plotted values on the x-axis and counts on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram was just computed for the `tile` in the first row.
+The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram was just computed for the `tile` in the first row.
 
 ```python
 import matplotlib.pyplot as plt
@@ -111,7 +113,7 @@ plt.bar(values, counts)
 plt.show()
 ```
 
-The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of `tile` in a DataFrame or group. The histogram is shown below. The range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this computation is across all cell values in the DataFrame.
+The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of `tile` in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
 
 ```python
 bins_list = rf.agg(rf_agg_approx_histogram('proj_raster').alias('tile_histogram')).select('tile_histogram.bins').collect()

From dd69c159f26dbd8eee91bf175fd888b60fb9d456 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 26 Jul 2019 09:19:59 -0400
Subject: [PATCH 253/380] Some minor style tweaks to aggregation docs

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/aggregation.pymd     | 32 ++++++++++++-------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index ffa26de91..13143d280 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -76,7 +76,9 @@ stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10,
 The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of all cell values as shown below.
 
 ```python
-rf.agg(rf_agg_stats('proj_raster').alias('stats')).select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
+rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
+    .select('stats.min', 'stats.max', 'stats.mean', 'stats.variance') \
+    .show(10, False)
 ```
 
 The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal tile dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
@@ -85,41 +87,47 @@ The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function comput
 rf = spark.sql("""
 SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as tile
 UNION
-SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
+SELECT 2 as id, rf_make_constant_tile(3, 5, 5, 'float32') as tile
 UNION
-SELECT 3 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 5) as tile
+SELECT 3 as id, rf_make_constant_tile(5, 5, 5, 'float32') as tile
 """).agg(rf_agg_local_stats('tile').alias('stats'))
 
-rf.select(rf_render_matrix('stats.min'), rf_render_matrix('stats.max'), rf_render_matrix('stats.mean'), rf_render_matrix('stats.variance')).show(10, False)
+agg_local_stats = rf.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').collect()
+
+for r in agg_local_stats:
+    for stat in r.asDict():
+        print(stat, ':\n', r[stat], '\n')
 ```
 
 ## Histogram
 
-The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram was just computed for the `tile` in the first row.
+The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram is just computed for the `tile` in the first row.
 
 ```python
 import matplotlib.pyplot as plt
 
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
 
-hist_df = rf.select(rf_tile_histogram('proj_raster').alias('tile_histogram')).select('tile_histogram.bins')
+hist_df = rf.select(rf_tile_histogram('proj_raster')['bins'].alias('bins'))
 hist_df.printSchema()
 
-bins_list = hist_df.collect()
-values = [int(row['value']) for row in bins_list[0].bins]
-counts = [int(row['count']) for row in bins_list[0].bins]
+bins_row = hist_df.first()
+values = [int(row['value']) for row in bins_row.bins]
+counts = [int(row['count']) for row in bins_row.bins]
 
-plt.bar(values, counts)
+plt.hist(values, weights=counts, bins=100)
 plt.show()
 ```
 
 The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of `tile` in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
 
 ```python
-bins_list = rf.agg(rf_agg_approx_histogram('proj_raster').alias('tile_histogram')).select('tile_histogram.bins').collect()
+bins_list = rf.agg(
+    rf_agg_approx_histogram('proj_raster')['bins'].alias('bins')
+    ).collect()
 values = [int(row['value']) for row in bins_list[0].bins]
 counts = [int(row['count']) for row in bins_list[0].bins]
 
-plt.bar(values, counts, width=10)
+plt.hist(values, weights=counts, bins=100)
 plt.show()
 ```

From b333438f5547fc5116fa5970868b7b4994e06968 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 26 Jul 2019 10:59:57 -0400
Subject: [PATCH 254/380] Remove stray paren from Tile PNG header

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index 771b8b19e..141e4aad4 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -43,7 +43,7 @@ def tile_to_png(tile):
     axis.xaxis.set_ticks([])
     axis.yaxis.set_ticks([])
 
-    axis.set_title('{}, {})'.format(tile.dimensions(), tile.cell_type.__repr__()))  # compact metadata as title
+    axis.set_title('{}, {}'.format(tile.dimensions(), tile.cell_type.__repr__()))  # compact metadata as title
 
     with io.BytesIO() as output:
         canvas.print_png(output)

From 935312a70086e10296c1d4e3038f89c892f09c7a Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Jul 2019 11:01:35 -0400
Subject: [PATCH 255/380] GeoTiff writing example.

---
 .../extensions/DataFrameMethods.scala         |  7 +++
 .../geotiff/GeoTiffDataSource.scala           | 29 +++++++----
 .../geotiff/GeoTiffDataSourceSpec.scala       | 48 +++++++++++++++++--
 .../src/main/python/docs/__init__.py          | 26 +++++++++-
 .../src/main/python/docs/raster-write.pymd    | 48 +++++++++++++++----
 .../main/python/pyrasterframes/__init__.py    | 38 +++++++++++++--
 pyrasterframes/src/main/python/setup.py       |  1 +
 .../main/python/tests/GeoTiffWriterTests.py   | 14 +++++-
 8 files changed, 180 insertions(+), 31 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 0e224a1de..3735eef37 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -36,6 +36,7 @@ import org.locationtech.rasterframes.StandardColumns._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.expressions.DynamicExtractors
+import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.locationtech.rasterframes.util._
 import spray.json.JsonFormat
 
@@ -96,6 +97,12 @@ trait DataFrameMethods[DF <: DataFrame] extends MethodExtensions[DF] with Metada
       .filter(f => DynamicExtractors.tileExtractor.isDefinedAt(f.dataType))
       .map(f ⇒ self.col(f.name))
 
+  /** Get the columns that look like `ProjectedRasterTile`s. */
+  def projRasterColumns: Seq[Column] =
+    self.schema.fields
+      .filter(_.dataType.conformsTo[ProjectedRasterTile])
+      .map(f => self.col(f.name))
+
   /** Get the columns that look like `Extent`s. */
   def extentColumns: Seq[Column] =
     self.schema.fields
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 98e161b18..1b639a1f5 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -98,22 +98,35 @@ class GeoTiffDataSource extends DataSourceRegister
 
       val destCRS = parameters.crs.get
 
-      val crsCols = df.crsColumns
-      require(crsCols.size == 1, "Exactly one CRS column must be in DataFrame")
-      val extentCols = df.extentColumns
-      require(extentCols.size == 1, "Exactly one Extent column must be in DataFrame")
+      val (extCol, crsCol) = {
+        val prCols = df.projRasterColumns
+        if(prCols.nonEmpty) {
+          (rf_extent(prCols.head), rf_crs(prCols.head))
+        }
+        else {
+          val crsCols = df.crsColumns
+          require(crsCols.size == 1, "Exactly one CRS column must be in DataFrame")
+          val extentCols = df.extentColumns
+          require(extentCols.size == 1, "Exactly one Extent column must be in DataFrame")
+          (extentCols.head, crsCols.head)
+        }
+      }
 
       val tlm = df
         .select(ProjectedLayerMetadataAggregate(
-          destCRS, extentCols.head, crsCols.head, rf_cell_type(tileCols.head), rf_dimensions(tileCols.head)
+          destCRS, extCol, crsCol, rf_cell_type(tileCols.head), rf_dimensions(tileCols.head)
         ))
         .first()
 
-      val config = ProjectedRasterDefinition(tlm)
+      val c = ProjectedRasterDefinition(tlm)
+
+      val config = parameters.rasterDimensions.map { dims =>
+        c.copy(cols = dims.cols, rows = dims.rows)
+      }.getOrElse(c)
 
       val aggs = tileCols
         .map(t => TileRasterizerAggregate(
-          config, crsCols.head, extentCols.head, rf_tile(t))("tile").as(t.columnName)
+          config, crsCol, extCol, rf_tile(t))("tile").as(t.columnName)
         )
 
       val agg = df.select(aggs: _*)
@@ -152,7 +165,7 @@ object GeoTiffDataSource {
   final val SHORT_NAME = "geotiff"
   final val PATH_PARAM = "path"
   final val IMAGE_WIDTH_PARAM = "imageWidth"
-  final val IMAGE_HEIGHT_PARAM = "imageWidth"
+  final val IMAGE_HEIGHT_PARAM = "imageHeight"
   final val COMPRESS_PARAM = "compress"
   final val CRS_PARAM = "crs"
   final val BAND_COUNT_PARAM = "bandCount"
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 4c160034e..866fa4966 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -22,7 +22,7 @@ package org.locationtech.rasterframes.datasource.geotiff
 
 import java.nio.file.Paths
 
-import geotrellis.proj4.LatLng
+import geotrellis.proj4._
 import org.locationtech.rasterframes._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.TestEnvironment
@@ -105,14 +105,52 @@ class GeoTiffDataSourceSpec
       import org.locationtech.rasterframes.datasource.raster._
       val pr = col("proj_raster_b0")
       val rf = spark.read.raster.withBandIndexes(0, 1, 2).load(rgbCogSamplePath.toASCIIString)
-        .withColumn("extent", rf_extent(pr))
-        .withColumn("crs", rf_crs(pr))
 
       val out = Paths.get("target", "example2-geotiff.tif")
       logger.info(s"Writing to $out")
-      noException shouldBe thrownBy {
-        rf.write.geotiff.withCRS(LatLng).save(out.toString)
+
+      withClue("explicit extent/crs") {
+        noException shouldBe thrownBy {
+          rf
+            .withColumn("extent", rf_extent(pr))
+            .withColumn("crs", rf_crs(pr))
+            .write.geotiff.withCRS(LatLng).save(out.toString)
+        }
+      }
+
+      withClue("without explicit extent/crs") {
+        noException shouldBe thrownBy {
+          rf
+            .write.geotiff.withCRS(LatLng).save(out.toString)
+        }
+      }
+      withClue("with downsampling") {
+        noException shouldBe thrownBy {
+          rf
+            .write.geotiff
+            .withCRS(LatLng)
+            .withDimensions(128, 128)
+            .save(out.toString)
+        }
       }
+    }
+
+    def s(band: Int): String =
+      s"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/" +
+        s"MCD43A4.A2019059.h11v08.006.2019072203257_B0${band}.TIF"
+
+    it("shoud write multiband") {
+      import org.locationtech.rasterframes.datasource.raster._
+
+      val cat = s"""
+red,green,blue
+${s(1)},${s(4)},${s(3)}
+"""
+      val scene = spark.read.raster.fromCSV(cat, "red", "green", "blue").load()
+      scene.write.geotiff
+        .withCRS(LatLng)
+        .withDimensions(256, 256)
+        .save("geotiff-overview.tif")
 
     }
   }
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index a1949f49c..d1a64afb9 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -1,11 +1,35 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
 import os
 
 
+def docs_dir():
+    return os.path.dirname(os.path.realpath(__file__))
+
+
 # This is temporary until we port to run on web assets.
 def resource_dir():
     # we may consider using gitpython which I think would be appropriate in the context of building docs
     # see https://stackoverflow.com/a/41920796/2787937
-    here = os.path.dirname(os.path.realpath(__file__))
+    here = docs_dir()
     test_resource = os.path.realpath(os.path.join(here, '..', '..', '..', 'src', 'test', 'resources'))
 
     return test_resource
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 0097cb1b3..458be4c29 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -18,8 +18,10 @@ When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster
 In an IPython or Jupyter interpreter a `Tile` object will be displayed as an image with limited metadata.
 
 ```python tile_sample
-raster_url = 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
-             'MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF'
+def scene(band): 
+    return 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
+             'MCD43A4.A2019059.h11v08.006.2019072203257_B{}.TIF'.format(band)
+raster_url = scene('02')
 spark_df = spark.read.raster(raster_url)
 tile = spark_df.select(rf_tile('proj_raster').alias('tile')).first()['tile']
 tile
@@ -35,7 +37,7 @@ Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column
 
 In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
-```python toPandas, evaluate=True
+```python to_pandas, evaluate=True
 import pyrasterframes.rf_ipython
 
 pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
@@ -48,21 +50,47 @@ pandas_df.dtypes
 
 Viewing the DataFrame in Jupyter looks like this. 
 
-```python, evaluate=False
+```python show_pandas, evaluate=False
 pandas_df
 ```
 
 @@include[df-samples-output.md](static/df-samples-output.md)
 
-## PNG Overviews
-
-It is oftentimes convenient to have a simple overview image of the data, particularly if it spans a very wide area.
 
 ## GeoTIFFs
 
-GeoTIFF is one of the most common file formats for spatial data. 
+GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTiff.
+
+One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
+
+Fortunately, we can use the cluster computing capability to downlample the data into a more manageble saze. For sake of example, let's render a simple RGB overview image of our scene as a small raster:
+
+```python write_geotiff
+import os.path
+from docs import docs_dir
+cat = """  
+red,green,blue
+{},{},{}
+""".format(scene('01'), scene('04'), scene('03'))
+outfile = os.path.join(docs_dir(), 'geotiff-overview.tif')
+rf = spark.read.raster(catalog=cat, catalog_col_names=['red', 'green', 'blue'])
+rf.write.geotiff(outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
+```
+
+Wiew it with `rasterio` to check the results:
+
+```python view_geotiff
+import rasterio
+import numpy as np
+from rasterio.plot import show, show_hist
+ 
+with rasterio.open(outfile) as src:
+    cells = np.clip(src.read(), 0, 1800).astype('float32')
+    show(cells)
+    show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.3,
+        histtype='stepfilled', title="Overview Histogram")
+```
 
-\[ See issue https://s22s.myjetbrains.com/youtrack/issue/RF-71 \]
 
 ## GeoTrellis Layers
 
@@ -75,7 +103,7 @@ GeoTIFF is one of the most common file formats for spatial data.
 
 You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format. 
 
-```python, evaluate=False
+```python write_parquet, evaluate=False
 spark_df.withColumn('exp', rf_expm1('proj_raster')) \
     .write.mode('append').parquet('hdfs:///rf-user/sample.pq')
 ```
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index f992b2534..dcc0da624 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -102,6 +102,12 @@ def _aliased_reader(df_reader, format_key, path, **options):
     """ Loads the file of the given type at the given path."""
     return df_reader.format(format_key).load(path, **options)
 
+
+def _aliased_writer(df_writer, format_key, path, **options):
+    """ Saves the dataframe to a file of the given type at the given path."""
+    return df_writer.format(format_key).save(path, **options)
+
+
 def _raster_reader(
         df_reader,
         path=None,
@@ -167,10 +173,32 @@ def temp_name():
         .load(path, **options)
 
 
-def _aliased_writer(df_writer, format_key, path, **options):
-    """ Saves the dataframe to a file of the given type at the given path."""
-    return df_writer.format(format_key).save(path=path, options=options)
+def _geotiff_writer(
+    df_writer,
+    path=None,
+    crs=None,
+    raster_dimensions=None,
+    **options):
+
+    if raster_dimensions is not None:
+        if isinstance(raster_dimensions, tuple):
+            options.update({
+                "imageWidth": raster_dimensions[0],
+                "imageHeight": raster_dimensions[1]
+            })
+        elif isinstance(raster_dimensions, str):
+            parts = raster_dimensions.split(',')
+            options.update({
+                "imageWidth": parts[0],
+                "imageHeight": parts[1]
+            })
+
+    if crs is not None:
+        options.update({
+            "crs": crs
+        })
 
+    return _aliased_writer(df_writer, "geotiff", path, **options)
 
 # Patch new method on SparkSession to mirror Scala approach
 SparkSession.withRasterFrames = _rf_init
@@ -186,6 +214,6 @@ def _aliased_writer(df_writer, format_key, path, **options):
 DataFrameReader.raster = _raster_reader
 DataFrameReader.geojson = lambda df_reader, path: _aliased_reader(df_reader, "geojson", path)
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
-DataFrameWriter.geotiff = lambda df_writer, path: _aliased_writer(df_writer, "geotiff", path)
+DataFrameWriter.geotiff = _geotiff_writer
 DataFrameReader.geotrellis = lambda df_reader, path: _layer_reader(df_reader, "geotrellis", path)
-DataFrameWriter.geotrellis = lambda df_writer, path: _aliased_writer(df_writer, "geotrellis", path)
\ No newline at end of file
+DataFrameWriter.geotrellis = lambda df_writer, path: _aliased_writer(df_writer, "geotrellis", path)
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 91d5cd188..dfd8b704b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -135,6 +135,7 @@ def doctype(self):
         'pypandoc',
         'numpy>=1.7',
         'pandas',
+        'rasterio'
     ],
     packages=[
         'pyrasterframes',
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
index 7bdf1d1e0..135d88a88 100644
--- a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -22,6 +22,7 @@
 import tempfile
 
 from . import TestEnvironment
+import rasterio
 
 
 class GeoTiffWriter(TestEnvironment):
@@ -43,11 +44,20 @@ def test_identity_write(self):
 
     def test_unstructured_write(self):
         rf = self.spark.read.raster(self.img_uri)
-
         dest = self._tmpfile()
-        rf.write.geotiff(dest)
+        rf.write.geotiff(dest, crs='EPSG:32616')
 
         rf2 = self.spark.read.raster(dest)
         self.assertEqual(rf2.count(), rf.count())
 
         os.remove(dest)
+
+    def test_downsampled_write(self):
+        rf = self.spark.read.raster(self.img_uri)
+        dest = self._tmpfile()
+        rf.write.geotiff(dest, crs='EPSG:32616', raster_dimensions=(128, 128))
+
+        with rasterio.open(dest) as f:
+            self.assertEqual((f.width, f.height), (128, 128))
+
+        os.remove(dest)

From f67ce464be89f3a602afb89266c50e8dce85a4ca Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Jul 2019 14:04:40 -0400
Subject: [PATCH 256/380] Added capability to specify GDAL `vsixxx` paths.
 Updated docs on URI schemes.

---
 core/src/main/resources/reference.conf        |   2 +-
 .../rasterframes/ref/GDALRasterSource.scala   |   4 ++-
 .../rasterframes/ref/RasterSource.scala       |   2 +-
 core/src/test/resources/L8-archive.zip        | Bin 0 -> 59826 bytes
 .../rasterframes/ref/RasterSourceSpec.scala   |  11 +++++++
 .../src/main/python/docs/raster-read.pymd     |  31 ++++++++++--------
 pyrasterframes/src/main/python/setup.py       |   1 +
 7 files changed, 35 insertions(+), 16 deletions(-)
 create mode 100644 core/src/test/resources/L8-archive.zip

diff --git a/core/src/main/resources/reference.conf b/core/src/main/resources/reference.conf
index ef635bb61..e7d3e57f5 100644
--- a/core/src/main/resources/reference.conf
+++ b/core/src/main/resources/reference.conf
@@ -12,7 +12,7 @@ vlm.gdal {
       //CPL_DEBUG = "OFF"
       AWS_REQUEST_PAYER = "requester"
       GDAL_DISABLE_READDIR_ON_OPEN = "YES"
-      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.tiff,.jp2,.mrf,.idx,.lrc,.mrf.aux.xml"
+      CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif,.tiff,.jp2,.mrf,.idx,.lrc,.mrf.aux.xml,.vrt"
     }
     // set this to `false` if CPL_DEBUG is `ON`
     useExceptions = true
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
index eabbac002..52b8d0070 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
@@ -34,7 +34,9 @@ case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSour
 
   @transient
   private lazy val gdal: VLMRasterSource = {
-    val cleaned = source.toASCIIString.replace("gdal+", "")
+    val cleaned = source.toASCIIString
+      .replace("gdal+", "")
+      .replace("gdal:/", "")
     // VSIPath doesn't like single slash "file:/path..."
     val tweaked =
       if (cleaned.matches("^file:/[^/].*"))
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 4221166e3..937587c38 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -140,7 +140,7 @@ object RasterSource extends LazyLogging {
 
     /** Extractor for determining if a scheme indicates GDAL preference.  */
     def unapply(source: URI): Boolean =
-      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal+")) && hasGDAL)
+      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal")) && hasGDAL)
   }
 
   object IsDefaultGeoTiff {
diff --git a/core/src/test/resources/L8-archive.zip b/core/src/test/resources/L8-archive.zip
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diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 439934f38..1e6d76f07 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -21,6 +21,8 @@
 
 package org.locationtech.rasterframes.ref
 
+import java.net.URI
+
 import org.locationtech.rasterframes.TestData
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
@@ -125,6 +127,15 @@ class RasterSourceSpec extends TestEnvironment with TestData {
         gdal.layoutBounds(dims) should contain allElementsOf jvm.layoutBounds(dims)
         gdal.layoutExtents(dims) should contain allElementsOf jvm.layoutExtents(dims)
       }
+
+
+      it("should support vsi file paths") {
+        val archivePath = geotiffDir.resolve("L8-archive.zip")
+        val archiveURI = URI.create("gdal://vsizip/" + archivePath.toString + "/L8-RGB-VA.tiff")
+        val gdal = GDALRasterSource(archiveURI)
+
+        gdal.bandCount should be (3)
+      }
     }
   }
 
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index aabca0ca6..522d7b6ce 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -51,19 +51,24 @@ display(tile)
 
 ## URI Formats
 
-/[ TODO Populate this with valid URI's schemes we can use with an example? including how to use gdal /]
-
-file://  (is this only valid for spark local? or if file is )
-https://
-http:// ?
-s3a://
-s3n:// ?
-hdfs:// ?
-ftp:// ?
-azure ?
-google cloud ?
-
-gdal vsi? https://gdal.org/user/virtual_file_systems.html 
+RasterFrames relies on three different IO drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used first if a compatible version of GDAL (>= 2.4) is installed. It is the only driver that can read non-GeoTIFF files. If GDAL is not available, either the _Java I/O` or _Hadoop_ driver will be selected, depending on scheme.  
+
+
+| Prefix              | GDAL        | Java I/O | Hadoop |
+| ------------------- | ----------- | -------- | ------ |
+| `gdal://<vsidrv>//` |  ✔︎          | -︎        | -      |
+| `file://`           |  ✔︎          | ✔︎       | -      |
+| `http://`           |  ✔︎          | ✔︎       | -      |
+| `https://`          |  ✔︎          | ✔︎       | -      |
+| `ftp://`            | `/vsicurl/`  | ✔       | -      |
+| `s3://`             |  `/vsis3/` ︎  | ✔︎       | -      |
+| `hdfs://`           |  `/vsihdfs/` ︎| -       | ✔︎      |
+| `s3n://`            |  - ︎          | -        | ✔︎      |
+| `s3a://`            |  - ︎          | -   | ✔︎      |
+| `wasb://`           |  `/vsiaz/` ︎  | -       | ✔︎      |
+| `wasbs://`          |  - ︎          | -       | ✔︎      |
+
+Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. See the GDAL documentation for the format of the URIs after the `gdal:/` prefix (which is stripped off before passing the rest of the path to GDAL).
 
 
 ## Raster Catalogs
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index dfd8b704b..a4811824b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -134,6 +134,7 @@ def doctype(self):
         'pytest==3.4.2',
         'pypandoc',
         'numpy>=1.7',
+        'shapley',
         'pandas',
         'rasterio'
     ],

From 51b5a2f9f2cc66d74b26eec1f6e241369a914db6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Jul 2019 14:12:52 -0400
Subject: [PATCH 257/380] Regression fix.

---
 .../org/locationtech/rasterframes/ExtensionMethodSpec.scala     | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 4b917b86e..05179c67d 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -25,6 +25,7 @@ import geotrellis.proj4.LatLng
 import geotrellis.raster.{ByteCellType, GridBounds, TileLayout}
 import geotrellis.spark.tiling.{CRSWorldExtent, LayoutDefinition}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
+import org.apache.spark.sql.{Encoder, Encoders}
 import org.locationtech.rasterframes.util.SubdivideSupport
 
 /**
@@ -61,6 +62,7 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
       df.extentColumns.size should be(2)
     }
     it("should find multiple crs columns") {
+      implicit val enc = Encoders.tuple(crsEncoder, Encoders.STRING, crsEncoder, Encoders.scalaDouble)
       val df = Seq((pe.crs, "fred", pe.crs, 34.0)).toDF("c1", "s", "c2", "n")
       df.crsColumns.size should be (2)
     }

From b7e38c289921814ad63126d268e71d87749cd99a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 24 Jul 2019 16:54:22 -0400
Subject: [PATCH 258/380] Correct NoDataFilter implementation in Python;
 supervised ML doc example

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/deep-learning.pymd   |   6 +
 .../src/main/python/docs/machine-learning.md  |  15 ++
 .../src/main/python/docs/raster-processing.md |   2 +-
 .../main/python/docs/supervised-learning.pymd | 197 ++++++++++++++++++
 ...ark-ml.pymd => unsupervised-learning.pymd} |   4 +-
 .../main/python/pyrasterframes/rf_types.py    |  11 +-
 .../resources/L8-Labels-Elkton-VA.geojson     |   9 +
 7 files changed, 235 insertions(+), 9 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/deep-learning.pymd
 create mode 100644 pyrasterframes/src/main/python/docs/machine-learning.md
 create mode 100644 pyrasterframes/src/main/python/docs/supervised-learning.pymd
 rename pyrasterframes/src/main/python/docs/{spark-ml.pymd => unsupervised-learning.pymd} (90%)
 create mode 100644 pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson

diff --git a/pyrasterframes/src/main/python/docs/deep-learning.pymd b/pyrasterframes/src/main/python/docs/deep-learning.pymd
new file mode 100644
index 000000000..5aba5e997
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/deep-learning.pymd
@@ -0,0 +1,6 @@
+# Deep Learning
+
+> Using RasterFrames and `pandas_udf`s to feed tiles/chips into Keras.
+
+_TODO_
+
diff --git a/pyrasterframes/src/main/python/docs/machine-learning.md b/pyrasterframes/src/main/python/docs/machine-learning.md
new file mode 100644
index 000000000..198cefca5
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/machine-learning.md
@@ -0,0 +1,15 @@
+# Machine Learning
+
+RasterFrames provides facilities to train and predict with a wide variety of machine learning models through [Spark ML Pipelines](https://spark.apache.org/docs/latest/ml-guide.html). This library provides a variety of pipeline components for supervised learning, unsupervised learning, and data preparation that can be used to represent and repeatably conduct a variety of tasks in machine learning.
+
+Furthermore, through the use of [`pandas_udf`s](https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs), `Tile`s in RasterFrames can be piped into deep learning frameworks, such as [Keras](https://keras.io/) or [TensorFlow](https://www.tensorflow.org/).
+
+The following sections provide some examples on how to integrate these workflows with RasterFrames.
+
+@@@ index
+
+* @ref:[Unsupervised Machine Learning](unsupervised-learning.md)
+* @ref:[Supervised Machine Learning](supervised-learning.md)
+* @ref:[Deep Learning](deep-learning.md)
+
+@@@
diff --git a/pyrasterframes/src/main/python/docs/raster-processing.md b/pyrasterframes/src/main/python/docs/raster-processing.md
index 3ac4c8ac4..2f236429b 100644
--- a/pyrasterframes/src/main/python/docs/raster-processing.md
+++ b/pyrasterframes/src/main/python/docs/raster-processing.md
@@ -6,7 +6,7 @@
 * @ref:["NoData" Handling](nodata-handling.md)
 * @ref:[Aggregation](aggregation.md)
 * @ref:[Time Series](time-series.md)
-* @ref:[Machine Learning](spark-ml.md)
+* @ref:[Machine Learning](machine-learning.md)
 
 @@@
 
diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
new file mode 100644
index 000000000..cf99a4a60
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -0,0 +1,197 @@
+# Supervised Machine Learning
+
+In this example we will demonstrate how to fit and score an unsupervised learning model with a sample of Sentinel-2 data and labels from the US [National Land Cover Dataset](https://www.mrlc.gov/) (NLCD).
+
+```python, setup, echo=False
+from IPython.core.display import display
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes.rasterfunctions import * 
+import pandas as pd
+from docs import resource_dir_uri
+
+import os
+
+spark = create_rf_spark_session()
+```
+
+## Create and Read Raster Catalog
+
+We import various Spark components that we need to construct our [Pipeline](https://spark.apache.org/docs/latest/ml-pipeline.html).
+
+```python, imports, echo=True
+from pyrasterframes import TileExploder
+from pyrasterframes.rf_types import NoDataFilter
+
+from pyspark.ml.feature import VectorAssembler
+from pyspark.ml.classification import DecisionTreeClassifier
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.ml import Pipeline
+```
+
+The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
+
+```python, read_bands, term=True
+uri_base = 's3://s22s-test-geotiffs/luray_snp/{}.tif'
+bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12']
+cols = ['SCL'] + bands
+
+catalog_df = pd.DataFrame([
+    {b: uri_base.format(b) for b in cols} 
+])
+
+df = spark.read.raster(catalog=catalog_df, 
+					   catalog_col_names=cols,
+					   tile_dimensions=(128, 128)
+					   ).repartition(100)
+
+df = df.select(
+    rf_crs(df.B01).alias('crs'),
+    rf_extent(df.B01).alias('extent'),
+    rf_tile(df.SCL).alias('scl'),
+    rf_tile(df.B01).alias('B01'),
+    rf_tile(df.B02).alias('B02'),
+    rf_tile(df.B03).alias('B03'),
+    rf_tile(df.B04).alias('B04'),
+    rf_tile(df.B05).alias('B05'),
+    rf_tile(df.B06).alias('B06'),
+    rf_tile(df.B07).alias('B07'),
+    rf_tile(df.B08).alias('B08'),
+    rf_tile(df.B09).alias('B09'),
+    rf_tile(df.B11).alias('B11'),
+    rf_tile(df.B12).alias('B12'),
+)
+df.printSchema()
+```
+
+## Data Prep
+
+### Label Data
+
+[](https://github.com/locationtech/rasterframes/blob/develop/core/src/test/resources/L8-Labels-Elkton-VA.geojson)
+
+```python
+crses = df.select('crs.crsProj4').distinct().collect()
+print('Found ', len(crses), 'distinct CRS.')
+crs = crses[0][0]
+
+label_df = spark.read.geojson(os.path.join(resource_dir_uri(), "L8-Labels-Elkton-VA.geojson")) \
+					 .select('id', st_reproject('geometry', 'EPSG:4326', crs).alias('geometry')) \
+					 .hint('broadcast')
+
+df_joined = df.join(label_df, st_intersects(st_geometry('extent'), 'geometry')) 
+
+df_joined.createOrReplaceTempView('df_joined')
+df_labeled = spark.sql("""
+SELECT *, rf_rasterize(geometry, st_geometry(extent), id, rf_dimensions(B01).cols, rf_dimensions(B01).rows) AS label
+FROM df_joined
+""")
+```
+
+
+
+## Masking NoData
+
+We will follow the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually masking we will just sort on the mask cell values later in the process
+
+```python make_mask
+from pyspark.sql.functions import lit
+
+mask_part = df_labeled.withColumn('nodata', rf_local_equal('scl', lit(0))) \
+              .withColumn('defect', rf_local_equal('scl', lit(1))) \
+              .withColumn('cloud8', rf_local_equal('scl', lit(8))) \
+              .withColumn('cloud9', rf_local_equal('scl', lit(9))) \
+              .withColumn('cirrus', rf_local_equal('scl', lit(10))) 
+
+df_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
+                   .withColumn('mask', rf_local_add('mask', 'cloud8')) \
+                   .withColumn('mask', rf_local_add('mask', 'cloud9')) \
+                   .withColumn('mask', rf_local_add('mask', 'cirrus')) \
+                   .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus')
+
+df_mask.printSchema()
+```
+
+
+## Create ML Pipeline
+
+The data preparation modeling pipeline is next. SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell/pixel. Then we filter out any rows that have `NoData` values (which will cause an error during training). Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
+
+```python, transformers
+exploder = TileExploder()
+
+noDataFilter = NoDataFilter() \
+  .setInputCols(cols + ['label', 'mask'])
+
+assembler = VectorAssembler() \
+  .setInputCols(bands) \
+  .setOutputCol("features")
+```
+
+We are going to use a decision tree for classification. You can swap out one of the other multi-class
+classification algorithms if you like. With the algorithm selected we can assemble our modeling pipeline.
+
+```python, pipeline
+classifier = DecisionTreeClassifier() \
+  .setLabelCol('label') \
+  .setFeaturesCol(assembler.getOutputCol())
+
+pipeline = Pipeline() \
+  .setStages([exploder, noDataFilter, assembler, classifier])
+
+pipeline.getStages()
+```
+
+## Train the Model
+
+Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model.
+
+```python, train
+model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
+```
+
+## Model Evaluation
+
+To view the model's performance ....
+
+```python eval
+eval = MulticlassClassificationEvaluator(predictionCol=classifier.getPredictionCol(),
+										 labelCol=classifier.getLabelCol(),
+										 metricName='accuracy',
+)
+
+prediction_df = model.transform(df_mask) \
+                       .drop(assembler.getOutputCol()).cache()
+accuracy = eval.evaluate(prediction_df)
+accuracy
+```
+
+
+We can take a quick look at the resulting confusion matrix. 
+
+```python confusion_mtrx
+prediction_df.groupBy(classifier.getPredictionCol()) \
+    .pivot(classifier.getLabelCol()) \
+    .count().show(20, False)
+```
+
+## Visualize Prediction
+
+We can recreate the tiled data structure using the metadata added by the `TileExploder` pipeline stage.
+
+```python assemble_prediction
+model.transform(df_mask.drop('label')).createOrReplaceTempView('scored')
+retiled = spark.sql("""
+SELECT extent, crs, rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction
+FROM scored
+GROUP BY extent, crs
+""")
+
+retiled.printSchema()
+```
+
+Take a look at a sample of the resulting output.
+
+```python display_prediction
+display(retiled.select('prediction').first()['prediction'])
+```
+
diff --git a/pyrasterframes/src/main/python/docs/spark-ml.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
similarity index 90%
rename from pyrasterframes/src/main/python/docs/spark-ml.pymd
rename to pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index d871a8831..13dd58a44 100644
--- a/pyrasterframes/src/main/python/docs/spark-ml.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -1,6 +1,4 @@
-# Machine Learning with RasterFrames
-
-RasterFrames provides access to train and predict with a wide variety of machine learning models through [Spark ML Pipelines](https://spark.apache.org/docs/latest/ml-guide.html). This library provides a variety of pipeline components for supervised learning, unsupervised learning, and data preparation that can be used to repeatably represent a variety of tasks in machine learning.
+# Unsupervised Machine Learning
 
 In this example we will demonstrate how to fit and score an unsupervised learning model with a [sample of Landsat 8 data](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/src/test/resources).
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index ffe2aa925..b3f743bf1 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -25,13 +25,16 @@
 class here provides the PyRasterFrames entry point.
 """
 
-from pyspark.sql.types import UserDefinedType
 from pyspark import SparkContext
 from pyspark.sql import DataFrame, Column
-from pyspark.sql.types import (StructType, StructField, BinaryType, DoubleType, ShortType, IntegerType, StringType)
+from pyspark.sql.types import (UserDefinedType, StructType, StructField, BinaryType, DoubleType, ShortType, IntegerType, StringType)
+
+from pyspark.ml.param.shared import HasInputCols
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.ml.util import JavaMLReadable, JavaMLWritable
+
 from pyrasterframes.rf_context import RFContext
+
 import numpy as np
 
 __all__ = ['RasterFrameLayer', 'Tile', 'TileUDT', 'CellType', 'RasterSourceUDT', 'TileExploder', 'NoDataFilter']
@@ -464,7 +467,7 @@ def __init__(self):
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.TileExploder", self.uid)
 
 
-class NoDataFilter(JavaTransformer, JavaMLReadable, JavaMLWritable):
+class NoDataFilter(JavaTransformer, HasInputCols, JavaMLReadable, JavaMLWritable):
     """
     Python wrapper for NoDataFilter.scala
     """
@@ -473,5 +476,3 @@ def __init__(self):
         super(NoDataFilter, self).__init__()
         self._java_obj = self._new_java_obj("org.locationtech.rasterframes.ml.NoDataFilter", self.uid)
 
-    def setInputCols(self, values):
-        self._java_obj.setInputCols(values)
diff --git a/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson b/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson
new file mode 100644
index 000000000..076158b56
--- /dev/null
+++ b/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson
@@ -0,0 +1,9 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "id": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.638383330417554, 38.391061314584398 ], [ -78.637172448825936, 38.395449766458547 ], [ -78.629215101908301, 38.396549823989744 ], [ -78.630786820138638, 38.392028667955493 ], [ -78.634228219464887, 38.390206983429579 ], [ -78.638383330417554, 38.391061314584398 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.625973620483947, 38.384086145975928 ], [ -78.619781683426993, 38.388373376437947 ], [ -78.613688820548219, 38.386918574305838 ], [ -78.611752103843472, 38.388980469097866 ], [ -78.612829995997004, 38.39138397517295 ], [ -78.604257166078241, 38.392259658270049 ], [ -78.609485512325875, 38.382139853466676 ], [ -78.625973620483947, 38.384086145975928 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.65213631116778, 38.386854807768536 ], [ -78.647983519633271, 38.385930966410982 ], [ -78.650653269705685, 38.383182700685978 ], [ -78.652397953903673, 38.384268357728601 ], [ -78.65213631116778, 38.386854807768536 ] ] ] } }
+]
+}

From 7f2e66825c90b515d82ec29d13c0b9a01970b4be Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 25 Jul 2019 09:12:16 -0400
Subject: [PATCH 259/380] Remove deep learning page, for now

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/deep-learning.pymd  | 6 ------
 pyrasterframes/src/main/python/docs/machine-learning.md | 3 ---
 2 files changed, 9 deletions(-)
 delete mode 100644 pyrasterframes/src/main/python/docs/deep-learning.pymd

diff --git a/pyrasterframes/src/main/python/docs/deep-learning.pymd b/pyrasterframes/src/main/python/docs/deep-learning.pymd
deleted file mode 100644
index 5aba5e997..000000000
--- a/pyrasterframes/src/main/python/docs/deep-learning.pymd
+++ /dev/null
@@ -1,6 +0,0 @@
-# Deep Learning
-
-> Using RasterFrames and `pandas_udf`s to feed tiles/chips into Keras.
-
-_TODO_
-
diff --git a/pyrasterframes/src/main/python/docs/machine-learning.md b/pyrasterframes/src/main/python/docs/machine-learning.md
index 198cefca5..075454ba6 100644
--- a/pyrasterframes/src/main/python/docs/machine-learning.md
+++ b/pyrasterframes/src/main/python/docs/machine-learning.md
@@ -2,14 +2,11 @@
 
 RasterFrames provides facilities to train and predict with a wide variety of machine learning models through [Spark ML Pipelines](https://spark.apache.org/docs/latest/ml-guide.html). This library provides a variety of pipeline components for supervised learning, unsupervised learning, and data preparation that can be used to represent and repeatably conduct a variety of tasks in machine learning.
 
-Furthermore, through the use of [`pandas_udf`s](https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs), `Tile`s in RasterFrames can be piped into deep learning frameworks, such as [Keras](https://keras.io/) or [TensorFlow](https://www.tensorflow.org/).
-
 The following sections provide some examples on how to integrate these workflows with RasterFrames.
 
 @@@ index
 
 * @ref:[Unsupervised Machine Learning](unsupervised-learning.md)
 * @ref:[Supervised Machine Learning](supervised-learning.md)
-* @ref:[Deep Learning](deep-learning.md)
 
 @@@

From 6502d0dc01f95da986effcaece68ddc84ce6807c Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 25 Jul 2019 10:01:54 -0400
Subject: [PATCH 260/380] Add new label data set for supervised learning docs;
 flesh out initial text

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-write.pymd    |  2 +-
 .../main/python/docs/supervised-learning.pymd | 72 ++++++++++---------
 .../src/test/resources/luray-labels.geojson   | 18 +++++
 3 files changed, 59 insertions(+), 33 deletions(-)
 create mode 100644 pyrasterframes/src/test/resources/luray-labels.geojson

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index ba4af68db..86f88a7ee 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -33,7 +33,7 @@ display(tile) # IPython.display function
 
 Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
 
-In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](pandas-numpy.md).
+In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
 ```python toPandas, evaluate=True
 import pyrasterframes.rf_ipython
diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index cf99a4a60..5c00a71d1 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -1,6 +1,6 @@
 # Supervised Machine Learning
 
-In this example we will demonstrate how to fit and score an unsupervised learning model with a sample of Sentinel-2 data and labels from the US [National Land Cover Dataset](https://www.mrlc.gov/) (NLCD).
+In this example we will demonstrate how to fit and score an unsupervised learning model with a sample of Sentinel-2 data and hand-drawn vector labels over different [land cover](https://en.wikipedia.org/wiki/Land_cover) types.
 
 ```python, setup, echo=False
 from IPython.core.display import display
@@ -16,19 +16,7 @@ spark = create_rf_spark_session()
 
 ## Create and Read Raster Catalog
 
-We import various Spark components that we need to construct our [Pipeline](https://spark.apache.org/docs/latest/ml-pipeline.html).
-
-```python, imports, echo=True
-from pyrasterframes import TileExploder
-from pyrasterframes.rf_types import NoDataFilter
-
-from pyspark.ml.feature import VectorAssembler
-from pyspark.ml.classification import DecisionTreeClassifier
-from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.ml import Pipeline
-```
-
-The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
+The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
 ```python, read_bands, term=True
 uri_base = 's3://s22s-test-geotiffs/luray_snp/{}.tif'
@@ -67,14 +55,16 @@ df.printSchema()
 
 ### Label Data
 
-[](https://github.com/locationtech/rasterframes/blob/develop/core/src/test/resources/L8-Labels-Elkton-VA.geojson)
+The land classification labels are based on a smalls set of hand drawn polygons in the geojson file [here](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/test/resources/luray-labels.geojson). The property `id` indicates the type of land cover in each area. For these integer values 1 is forest, 2 is cropland, 3 is developed areas.
+
+We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive but in this case both datasets are quite small. After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tiles_ are the `id` property of the geojson; which we will use as labels in our supervised learning task.
 
 ```python
 crses = df.select('crs.crsProj4').distinct().collect()
 print('Found ', len(crses), 'distinct CRS.')
 crs = crses[0][0]
 
-label_df = spark.read.geojson(os.path.join(resource_dir_uri(), "L8-Labels-Elkton-VA.geojson")) \
+label_df = spark.read.geojson(os.path.join(resource_dir_uri(), 'luray-labels.geojson')) \
 					 .select('id', st_reproject('geometry', 'EPSG:4326', crs).alias('geometry')) \
 					 .hint('broadcast')
 
@@ -87,11 +77,9 @@ FROM df_joined
 """)
 ```
 
-
-
 ## Masking NoData
 
-We will follow the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually masking we will just sort on the mask cell values later in the process
+To filter only for good quality pixels, we follow the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually masking we will just sort on the mask cell values later in the process.
 
 ```python make_mask
 from pyspark.sql.functions import lit
@@ -111,10 +99,23 @@ df_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
 df_mask.printSchema()
 ```
 
-
 ## Create ML Pipeline
 
-The data preparation modeling pipeline is next. SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell/pixel. Then we filter out any rows that have `NoData` values (which will cause an error during training). Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
+We import various Spark components that we need to construct our [Pipeline](https://spark.apache.org/docs/latest/ml-pipeline.html). These are the objects that will work in sequence to conduct the data preparation and modeling.
+
+```python, imports, echo=True
+from pyrasterframes import TileExploder
+from pyrasterframes.rf_types import NoDataFilter
+
+from pyspark.ml.feature import VectorAssembler
+from pyspark.ml.classification import DecisionTreeClassifier
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.ml import Pipeline
+```
+
+SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell/pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). Then we filter out any rows that have `NoData` values (which will cause an error during training). Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
+
+It is worth discussing a couple of interesting things about the `NoDataFilter`. First we filter by the mask column. This achieves the filtering of observations only to the good pixels, without having to explicitly mask and assign NoData in all eleven columns of raster data. The other column specified is the `label` column. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
 
 ```python, transformers
 exploder = TileExploder()
@@ -151,35 +152,38 @@ model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
 
 ## Model Evaluation
 
-To view the model's performance ....
+To view the model's performance, we first call the pipeline's `transform` method on the training dataset. This transformed dataset will have the model's prediction included in each row. We next construct an evaluator and pass it the transformed dataset to easily compute the performance metric. We could also use a variety of DataFrame or SQL transformations to compute our metric if we like.
 
 ```python eval
+prediction_df = model.transform(df_mask) \
+                       .drop(assembler.getOutputCol()).cache()
+prediction_df.printSchema()
+
 eval = MulticlassClassificationEvaluator(predictionCol=classifier.getPredictionCol(),
 										 labelCol=classifier.getLabelCol(),
 										 metricName='accuracy',
 )
 
-prediction_df = model.transform(df_mask) \
-                       .drop(assembler.getOutputCol()).cache()
 accuracy = eval.evaluate(prediction_df)
-accuracy
+print("\nAccuracy:", accuracy)
 ```
 
-
-We can take a quick look at the resulting confusion matrix. 
+As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. 
 
 ```python confusion_mtrx
 prediction_df.groupBy(classifier.getPredictionCol()) \
     .pivot(classifier.getLabelCol()) \
-    .count().show(20, False)
+    .count() \
+	.sort(classifier.getPredictionCol()).show(20, False)
 ```
 
 ## Visualize Prediction
 
-We can recreate the tiled data structure using the metadata added by the `TileExploder` pipeline stage.
+Because the pipeline included a `TileExploder`, we will recreate the tiled data structure. The explosion transformation includes metadata enabling us to recreate the _tiles_. See the @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile) function documentation for more details. In this case, the pipeline is scoring on all areas, regardless of whether they intersect the label polygons. This is simply done by removing the `label` column, as @ref:[discussed above](supervised-learning.md#create-ml-pipeline). 
 
 ```python assemble_prediction
-model.transform(df_mask.drop('label')).createOrReplaceTempView('scored')
+scored = model.transform(df_mask.drop('label')).createOrReplaceTempView('scored')
+
 retiled = spark.sql("""
 SELECT extent, crs, rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction
 FROM scored
@@ -189,9 +193,13 @@ GROUP BY extent, crs
 retiled.printSchema()
 ```
 
-Take a look at a sample of the resulting output.
+Take a look at a sample of the resulting output. Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow). 
 
 ```python display_prediction
-display(retiled.select('prediction').first()['prediction'])
+display(
+  retiled.select('prediction') \
+    .sort(-rf_tile_sum(rf_local_equal('prediction', lit(3.0)))) \
+    .first()['prediction']
+)
 ```
 
diff --git a/pyrasterframes/src/test/resources/luray-labels.geojson b/pyrasterframes/src/test/resources/luray-labels.geojson
new file mode 100644
index 000000000..bdcbcb504
--- /dev/null
+++ b/pyrasterframes/src/test/resources/luray-labels.geojson
@@ -0,0 +1,18 @@
+{
+"type": "FeatureCollection",
+"name": "L8-Labels-Elkton-VA",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.074908035755897, 38.325593965393153 ], [ -78.078776658402262, 38.327823897217776 ], [ -78.092819443861529, 38.32571146899194 ], [ -78.100161396610659, 38.313995346890302 ], [ -78.089376916671426, 38.31282539893752 ], [ -78.077634077459933, 38.317374423878938 ], [ -78.073898621221971, 38.322204512883999 ], [ -78.074908035755897, 38.325593965393153 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.369170650882523, 38.58065284389172 ], [ -78.383640370680567, 38.54472654334991 ], [ -78.327374251542963, 38.538843940894999 ], [ -78.314259254758397, 38.575748016690518 ], [ -78.369170650882523, 38.58065284389172 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.565452768282938, 38.692698239200176 ], [ -77.54853756075201, 38.687441203624303 ], [ -77.547985098699257, 38.68151785733005 ], [ -77.564314874957617, 38.685855270536351 ], [ -77.565452768282938, 38.692698239200176 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.625973620483947, 38.384086145975928 ], [ -78.619781683426993, 38.388373376437947 ], [ -78.613688820548219, 38.386918574305838 ], [ -78.609339103717076, 38.395958714056761 ], [ -78.600276924123435, 38.395057914051847 ], [ -78.598004559194038, 38.384358026518214 ], [ -78.600712105415838, 38.372726630839622 ], [ -78.61155677616398, 38.374617746567012 ], [ -78.625973620483947, 38.384086145975928 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.499872299697259, 38.265959576364672 ], [ -77.490962536589549, 38.262548509310811 ], [ -77.494072526256261, 38.251792574227593 ], [ -77.508081452628034, 38.250717051596098 ], [ -77.512193148300739, 38.258543877561848 ], [ -77.499872299697259, 38.265959576364672 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.896560416594369, 38.384489202082833 ], [ -77.885446228394855, 38.379901618618995 ], [ -77.886558613990559, 38.386926446793112 ], [ -77.892167110596205, 38.388029313414151 ], [ -77.896560416594369, 38.384489202082833 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.104587721849782, 38.230347976602772 ], [ -78.096623830061731, 38.232933469701152 ], [ -78.093733463904584, 38.227058631140601 ], [ -78.100422718389751, 38.223567355393556 ], [ -78.104587721849782, 38.230347976602772 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.09500918449011, 38.235516869468242 ], [ -78.084617397721388, 38.240823731215684 ], [ -78.079911154013928, 38.23752678606543 ], [ -78.084755595273549, 38.232321061869477 ], [ -78.09500918449011, 38.235516869468242 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.505050042952476, 38.306327713885693 ], [ -77.5048207527187, 38.300040419530752 ], [ -77.511898783174473, 38.289086070422542 ], [ -77.522735680615511, 38.292059924398963 ], [ -77.514377553898413, 38.300185554775908 ], [ -77.513706480506912, 38.310493467061114 ], [ -77.505050042952476, 38.306327713885693 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.89809802814473, 38.532542129891446 ], [ -77.901444154311747, 38.525317699576725 ], [ -77.893247775998773, 38.518264149754522 ], [ -77.897703956491853, 38.513453996400983 ], [ -77.901049016994193, 38.520378827806503 ], [ -77.916274443433977, 38.528730212529119 ], [ -77.909492522815626, 38.535864361205661 ], [ -77.89809802814473, 38.532542129891446 ] ] ] } },
+{ "type": "Feature", "properties": { "id": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -77.516032435253635, 38.794413501916893 ], [ -77.523118384311459, 38.785034143507744 ], [ -77.505102142982878, 38.775534328871899 ], [ -77.504411687954828, 38.760891842932352 ], [ -77.483604951868131, 38.747060201442238 ], [ -77.463488423830327, 38.747867575574077 ], [ -77.449558893048575, 38.757506997981018 ], [ -77.516032435253635, 38.794413501916893 ] ] ] } }
+]
+}

From 83e22b777ab1e1232ad3893f41915620ee203711 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 26 Jul 2019 09:44:23 -0400
Subject: [PATCH 261/380] Update link to numpy doc

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-write.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 86f88a7ee..31ea98402 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -1,6 +1,6 @@
 # Writing Raster Data
 
-RasterFrames is oriented toward large scale analyses of spatial data. The primary output for most use cases may be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](spark-ml.md), or some other result that is generally much smaller than the input data set. 
+RasterFrames is oriented toward large scale analyses of spatial data. The primary output for most use cases may be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set. 
 
 However there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the process and results.
 

From c26d33f335cb715833cbac8d50740d1c2f816abd Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Sat, 27 Jul 2019 16:11:05 -0400
Subject: [PATCH 262/380] Tweaks from PR feedback

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/docs/supervised-learning.pymd | 72 +++++++++++++------
 1 file changed, 49 insertions(+), 23 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index 5c00a71d1..c7ffaca75 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -1,12 +1,14 @@
 # Supervised Machine Learning
 
-In this example we will demonstrate how to fit and score an unsupervised learning model with a sample of Sentinel-2 data and hand-drawn vector labels over different [land cover](https://en.wikipedia.org/wiki/Land_cover) types.
+In this example we will demonstrate how to fit and score a supervised learning model with a sample of Sentinel-2 data and hand-drawn vector labels over different [land cover](https://en.wikipedia.org/wiki/Land_cover) types.
 
 ```python, setup, echo=False
 from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import * 
 import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
 from docs import resource_dir_uri
 
 import os
@@ -18,6 +20,8 @@ spark = create_rf_spark_session()
 
 The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
+The imagery for feature data will come from [eleven bands of 60 meter resolution Sentinel 2](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) imagery. We also will use the [scene classification (SCL)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm) data to identify high quality, non-cloudy pixels.
+
 ```python, read_bands, term=True
 uri_base = 's3://s22s-test-geotiffs/luray_snp/{}.tif'
 bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12']
@@ -55,11 +59,13 @@ df.printSchema()
 
 ### Label Data
 
-The land classification labels are based on a smalls set of hand drawn polygons in the geojson file [here](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/test/resources/luray-labels.geojson). The property `id` indicates the type of land cover in each area. For these integer values 1 is forest, 2 is cropland, 3 is developed areas.
+The land classification labels are based on a small set of hand drawn polygons in the geojson file [here](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/test/resources/luray-labels.geojson). The property `id` indicates the type of land cover in each area. For these integer values 1 is forest, 2 is cropland and 3 is developed areas.
+
+We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive, but in this case both datasets are quite small. To speed up the join for the small vector DataFrame, we put the `broadcast` hint on it. Spark will put a copy of it on each Spark executor. 
 
-We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive but in this case both datasets are quite small. After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tiles_ are the `id` property of the geojson; which we will use as labels in our supervised learning task.
+After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tile_ cells are the `id` property of the geojson; which we will use as labels in our supervised learning task. In areas where the geometry does not intersect, the cells will contain a NoData.
 
-```python
+```python join_and_rasterize
 crses = df.select('crs.crsProj4').distinct().collect()
 print('Found ', len(crses), 'distinct CRS.')
 crs = crses[0][0]
@@ -77,9 +83,9 @@ FROM df_joined
 """)
 ```
 
-## Masking NoData
+## Masking Poor Quality Cells 
 
-To filter only for good quality pixels, we follow the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually masking we will just sort on the mask cell values later in the process.
+To filter only for good quality pixels, we follow roughly the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually setting NoData values in the unwanted cells of any of the imagery bands, we will just on filter out the mask cell values later in the process.
 
 ```python make_mask
 from pyspark.sql.functions import lit
@@ -90,11 +96,16 @@ mask_part = df_labeled.withColumn('nodata', rf_local_equal('scl', lit(0))) \
               .withColumn('cloud9', rf_local_equal('scl', lit(9))) \
               .withColumn('cirrus', rf_local_equal('scl', lit(10))) 
 
-df_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
+df_mask_inv = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
                    .withColumn('mask', rf_local_add('mask', 'cloud8')) \
                    .withColumn('mask', rf_local_add('mask', 'cloud9')) \
                    .withColumn('mask', rf_local_add('mask', 'cirrus')) \
                    .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus')
+# at this point the mask contains 0 for good cells and 1 for defect, etc
+# convert cell type and set value 1 to NoData
+df_mask = df_mask_inv.withColumn('mask',
+  rf_with_no_data(rf_convert_cell_type('mask', 'uint8'), 1.0)
+)
 
 df_mask.printSchema()
 ```
@@ -113,23 +124,22 @@ from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 from pyspark.ml import Pipeline
 ```
 
-SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell/pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). Then we filter out any rows that have `NoData` values (which will cause an error during training). Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
+SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell or pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). If a _tile_ cell contains a NoData it will become a null value after the exploder stage. Then we filter out any rows that missing or null values, which will cause an error during training. Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
 
-It is worth discussing a couple of interesting things about the `NoDataFilter`. First we filter by the mask column. This achieves the filtering of observations only to the good pixels, without having to explicitly mask and assign NoData in all eleven columns of raster data. The other column specified is the `label` column. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
+It is worth discussing a couple of interesting things about the `NoDataFilter`. First, we filter out missing values in the mask column. Recall above we set undesirable pixels to NoData, so they will be removed at this stage. The other column for the `NoDataFilter` is the `label` column. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
 
 ```python, transformers
 exploder = TileExploder()
 
 noDataFilter = NoDataFilter() \
-  .setInputCols(cols + ['label', 'mask'])
+  .setInputCols(['label', 'mask'])
 
 assembler = VectorAssembler() \
   .setInputCols(bands) \
   .setOutputCol("features")
 ```
 
-We are going to use a decision tree for classification. You can swap out one of the other multi-class
-classification algorithms if you like. With the algorithm selected we can assemble our modeling pipeline.
+We are going to use a decision tree for classification. You can swap out one of the other multi-class classification algorithms if you like. With the algorithm selected we can assemble our modeling pipeline.
 
 ```python, pipeline
 classifier = DecisionTreeClassifier() \
@@ -144,7 +154,7 @@ pipeline.getStages()
 
 ## Train the Model
 
-Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model.
+Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model. We filter the dataframe for only tiles intersecting the label raster, because the label shapes are relatively sparse over the imagery. It would be logically equivalent to either include or exclude this, but it is more efficient to do this filter because it will mean less data going into the pipeline. 
 
 ```python, train
 model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
@@ -152,7 +162,7 @@ model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
 
 ## Model Evaluation
 
-To view the model's performance, we first call the pipeline's `transform` method on the training dataset. This transformed dataset will have the model's prediction included in each row. We next construct an evaluator and pass it the transformed dataset to easily compute the performance metric. We could also use a variety of DataFrame or SQL transformations to compute our metric if we like.
+To view the model's performance, we first call the pipeline's `transform` method on the training dataset. This transformed dataset will have the model's prediction included in each row. We next construct an evaluator and pass it the transformed dataset to easily compute the performance metric. We can also create custom metrics using a variety of DataFrame or SQL transformations. 
 
 ```python eval
 prediction_df = model.transform(df_mask) \
@@ -168,7 +178,7 @@ accuracy = eval.evaluate(prediction_df)
 print("\nAccuracy:", accuracy)
 ```
 
-As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. 
+As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. The categories down the rows are the predictions, and the truth labels are across the columns.
 
 ```python confusion_mtrx
 prediction_df.groupBy(classifier.getPredictionCol()) \
@@ -182,10 +192,15 @@ prediction_df.groupBy(classifier.getPredictionCol()) \
 Because the pipeline included a `TileExploder`, we will recreate the tiled data structure. The explosion transformation includes metadata enabling us to recreate the _tiles_. See the @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile) function documentation for more details. In this case, the pipeline is scoring on all areas, regardless of whether they intersect the label polygons. This is simply done by removing the `label` column, as @ref:[discussed above](supervised-learning.md#create-ml-pipeline). 
 
 ```python assemble_prediction
-scored = model.transform(df_mask.drop('label')).createOrReplaceTempView('scored')
+scored = model.transform(df_mask.drop('label'))
+scored.createOrReplaceTempView('scored')
 
 retiled = spark.sql("""
-SELECT extent, crs, rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction
+SELECT extent, crs, 
+    rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction,
+    rf_assemble_tile(column_index, row_index, B04, 128, 128) as red,
+    rf_assemble_tile(column_index, row_index, B03, 128, 128) as grn, 
+    rf_assemble_tile(column_index, row_index, B02, 128, 128) as blu,
 FROM scored
 GROUP BY extent, crs
 """)
@@ -193,13 +208,24 @@ GROUP BY extent, crs
 retiled.printSchema()
 ```
 
-Take a look at a sample of the resulting output. Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow). 
+Take a look at a sample of the resulting output and the corresponding area's red-green-blue composite image. 
 
-```python display_prediction
-display(
-  retiled.select('prediction') \
+```python display_rgb
+sample = retiled.select('prediction', 'red', 'grn', 'blu') \
     .sort(-rf_tile_sum(rf_local_equal('prediction', lit(3.0)))) \
-    .first()['prediction']
-)
+    .first()
+
+sample_prediction = sample['prediction']
+
+red = sample['red'].cells
+grn = sample['grn'].cells
+blu = sample['blu'].cells
+sample_rgb = np.concatenate([red[ :, :, None], grn[:, :, None] , blu[ :, :, None]], axis=2)
+np.imshow(sample_rgb)
 ```
 
+Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow). 
+
+```python display_prediction
+display(sample_prediction)
+```

From e62ef2edde809c63f5c0a9daa2f494ed12d8363f Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Sat, 27 Jul 2019 16:15:13 -0400
Subject: [PATCH 263/380] Remove dupe of test l8 label geojson, not needed

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/test/resources/L8-Labels-Elkton-VA.geojson       | 9 ---------
 1 file changed, 9 deletions(-)
 delete mode 100644 pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson

diff --git a/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson b/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson
deleted file mode 100644
index 076158b56..000000000
--- a/pyrasterframes/src/test/resources/L8-Labels-Elkton-VA.geojson
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-"type": "FeatureCollection",
-"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
-"features": [
-{ "type": "Feature", "properties": { "id": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.638383330417554, 38.391061314584398 ], [ -78.637172448825936, 38.395449766458547 ], [ -78.629215101908301, 38.396549823989744 ], [ -78.630786820138638, 38.392028667955493 ], [ -78.634228219464887, 38.390206983429579 ], [ -78.638383330417554, 38.391061314584398 ] ] ] } },
-{ "type": "Feature", "properties": { "id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.625973620483947, 38.384086145975928 ], [ -78.619781683426993, 38.388373376437947 ], [ -78.613688820548219, 38.386918574305838 ], [ -78.611752103843472, 38.388980469097866 ], [ -78.612829995997004, 38.39138397517295 ], [ -78.604257166078241, 38.392259658270049 ], [ -78.609485512325875, 38.382139853466676 ], [ -78.625973620483947, 38.384086145975928 ] ] ] } },
-{ "type": "Feature", "properties": { "id": 2 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -78.65213631116778, 38.386854807768536 ], [ -78.647983519633271, 38.385930966410982 ], [ -78.650653269705685, 38.383182700685978 ], [ -78.652397953903673, 38.384268357728601 ], [ -78.65213631116778, 38.386854807768536 ] ] ] } }
-]
-}

From 617053543e7e1ff27d537aaf026e20b0ac8ee958 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 29 Jul 2019 10:03:13 -0400
Subject: [PATCH 264/380] Fix image rendering in the supervised ML docs

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/supervised-learning.pymd | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index c7ffaca75..a13b39b7b 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -200,7 +200,7 @@ SELECT extent, crs,
     rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction,
     rf_assemble_tile(column_index, row_index, B04, 128, 128) as red,
     rf_assemble_tile(column_index, row_index, B03, 128, 128) as grn, 
-    rf_assemble_tile(column_index, row_index, B02, 128, 128) as blu,
+    rf_assemble_tile(column_index, row_index, B02, 128, 128) as blu
 FROM scored
 GROUP BY extent, crs
 """)
@@ -221,7 +221,8 @@ red = sample['red'].cells
 grn = sample['grn'].cells
 blu = sample['blu'].cells
 sample_rgb = np.concatenate([red[ :, :, None], grn[:, :, None] , blu[ :, :, None]], axis=2)
-np.imshow(sample_rgb)
+mins = np.nanmin(sample_rgb, axis=(0,1))
+plt.imshow((sample_rgb -  mins)/ (np.nanmax(sample_rgb, axis=(0,1)) - mins))
 ```
 
 Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow). 

From 7f71409fb2e82a5d95ac46af2df52fa98fb15c81 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 29 Jul 2019 13:03:48 -0400
Subject: [PATCH 265/380] Expanded the TOC to include machine learning
 usbsections

---
 build.sbt                                               | 1 +
 pyrasterframes/src/main/python/docs/machine-learning.md | 7 +++++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/build.sbt b/build.sbt
index 6ffcffa4e..365ccb73f 100644
--- a/build.sbt
+++ b/build.sbt
@@ -139,6 +139,7 @@ lazy val docs = project
       "version" -> version.value,
       "scaladoc.org.apache.spark.sql.rf" -> "http://rasterframes.io/latest"
     ),
+    paradoxNavigationExpandDepth := Some(3),
     paradoxTheme := Some(builtinParadoxTheme("generic")),
     makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value,
     Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value,
diff --git a/pyrasterframes/src/main/python/docs/machine-learning.md b/pyrasterframes/src/main/python/docs/machine-learning.md
index 075454ba6..aadebde8e 100644
--- a/pyrasterframes/src/main/python/docs/machine-learning.md
+++ b/pyrasterframes/src/main/python/docs/machine-learning.md
@@ -6,7 +6,10 @@ The following sections provide some examples on how to integrate these workflows
 
 @@@ index
 
-* @ref:[Unsupervised Machine Learning](unsupervised-learning.md)
-* @ref:[Supervised Machine Learning](supervised-learning.md)
+* [Unsupervised Machine Learning](unsupervised-learning.md)
+* [Supervised Machine Learning](supervised-learning.md)
 
 @@@
+
+
+@@toc { depth=2 }
\ No newline at end of file

From fca70aa5bce8309e5dc4d8f5394aeb06373720b2 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 23 Jul 2019 09:37:11 -0400
Subject: [PATCH 266/380] Fixes to TravisCI build to handle pinning of Python
 and Java version to support PySpark.

---
 .travis.yml | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 81febf1a2..9f876fb9e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,6 +1,9 @@
 sudo: false
 dist: xenial
-language: scala
+language: python
+
+python:
+  - "3.7"
 
 cache:
   directories:
@@ -11,30 +14,24 @@ cache:
 scala:
   - 2.11.11
 
-jdk:
-  - openjdk8
-
-python:
-  - "3.7"
+env:
+  - COURSIER_VERBOSITY=-1 JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
 
 addons:
   apt:
     packages:
+      - openjdk-8-jdk
       - pandoc
-      - python-pip
 
 install:
-  - pip install setuptools
-
-sbt_args: -no-colors
+  - pip install --user rasterio shapely pandas numpy
+  - wget -O - https://piccolo.link/sbt-1.2.8.tgz | tar xzf -
 
 script:
-  - sbt test
-  - sbt it:test
+  - sbt/bin/sbt -java-home $JAVA_HOME -batch test
+  - sbt/bin/sbt -java-home $JAVA_HOME -batch it:test
   #  - sbt -Dfile.encoding=UTF8 clean coverage test coverageReport
   # Tricks to avoid unnecessary cache updates
   - find $HOME/.sbt -name "*.lock" | xargs rm
   - find $HOME/.ivy2 -name "ivydata-*.properties" | xargs rm
 
-#after_success:
-#  - bash <(curl -s https://codecov.io/bash)
\ No newline at end of file

From 4aef6eababd3aafcc9344824000816a8273ca4f4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Jul 2019 08:45:28 -0400
Subject: [PATCH 267/380] pip '--user' not available in virtualenv.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 9f876fb9e..9df6d0290 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -24,7 +24,7 @@ addons:
       - pandoc
 
 install:
-  - pip install --user rasterio shapely pandas numpy
+  - pip install rasterio shapely pandas numpy
   - wget -O - https://piccolo.link/sbt-1.2.8.tgz | tar xzf -
 
 script:

From 430468f1bd95a71243d6260ff2af9141686e1b6b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Jul 2019 11:09:52 -0400
Subject: [PATCH 268/380] Added "concepts" page.

---
 .../src/main/python/docs/architecture.md      | 37 -----------
 .../src/main/python/docs/concepts.md          | 62 +++++++++++++++++++
 .../src/main/python/docs/description.md       | 26 +++++++-
 pyrasterframes/src/main/python/docs/index.md  |  8 ++-
 .../src/main/python/docs/languages.md         |  2 +
 .../src/main/python/docs/nodata-handling.pymd |  2 +-
 .../src/main/python/docs/numpy-pandas.pymd    |  2 +-
 .../src/main/python/docs/raster-processing.md |  2 +
 8 files changed, 97 insertions(+), 44 deletions(-)
 delete mode 100644 pyrasterframes/src/main/python/docs/architecture.md
 create mode 100644 pyrasterframes/src/main/python/docs/concepts.md
 create mode 100644 pyrasterframes/src/main/python/docs/languages.md

diff --git a/pyrasterframes/src/main/python/docs/architecture.md b/pyrasterframes/src/main/python/docs/architecture.md
deleted file mode 100644
index d8650689e..000000000
--- a/pyrasterframes/src/main/python/docs/architecture.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Architecture
-
-RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
-[GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
-[JTS](https://github.com/locationtech/jts), and
-[SFCurve](https://github.com/locationtech/sfcurve) (see below).
-
-![LocationTech Stack](static/rasterframes-locationtech-stack.png)
-
-
-## Concepts
-
-> * Cell
-> * Cell Type
-> * Tile
-> * Extent
-> * Coordinate Reference System (CRS)
-> * Projected Extent 
-> * Projected Raster
-> * Layer 
-
-RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
-
-![RasterFrame Anatomy](static/rasterframe-anatomy.png)
-
-Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. 
-
-> TODO: Update to match components in raster-io.md
-
-![RasterFrame Data Sources](static/rasterframes-data-sources.png)
-
-
-[R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
-[Pandas]:https://pandas.pydata.org/
-[GeoJSON]:https://en.wikipedia.org/wiki/GeoJSON
-[GTLayer]:https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html#layouts-and-tile-layers
-[PDS]:https://registry.opendata.aws/modis/
diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
new file mode 100644
index 000000000..19537376a
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -0,0 +1,62 @@
+# Concepts
+
+There are a number of EO concepts that crop up in the discussion of RasterFrames features. We'll cover these briefly in the sections below. However, here are a few links providing a more extensive introduction to working with Earth observation data. 
+
+* [_Fundamentals of Remote Sensing_](https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/9309)
+* [_Newcomers Earth Observation Guide_](https://business.esa.int/newcomers-earth-observation-guide)
+* [_Earth Observation Markets and Applications_](https://www.ofcom.org.uk/__data/assets/pdf_file/0021/82047/introduction_eo_for_ofcom_june_2015_no_video.pdf)
+
+## Cell
+
+A cell is a single sample from a sensor encoded as a scalar value asssociated with a specific spatiotemporal location and time. It can be thought of as an image pixel associated with a place and time. 
+
+## Cell Type
+
+A numeric cell value may be encoded in a number of different computer numeric formats. There are typically three characteristics used to describe a cell type:
+* word size (bit-width)
+* signed vs unsigned
+* integral vs floating-point
+
+
+The cell types most frequent in RasterFrames are as follows:
+
+| Name | Abbreviation | Description | Range |
+| --- | --- | --- | --- |
+| Byte | `int8` | Signed 8-bit integral | -128 to 127 |
+| Unsigned Byte | `uint8` | Unsigned 8-bit integral | 0 to 255 |
+| Short | `int16` | Signed 16-bit integral | -32,768 to 32,767 |
+| Unsigned Short | `uint16` | Unsigned 16-bit integral | 0 to 65,535 |
+| Int | `int32` | Signed 32-bit integral | -2,147,483,648 to 2,147,483,647 |
+| Unsigned Int | `uint32` | Unsigned 32-bit integral | 0 to 4,294,967,295 |
+| Float | `float32` | 32-bit floating-point | -3.4028235E38 to 3.4028235E38 |
+| Double | `float64` | 64-bit floating-point | -1.7976931348623157E308 to 1.7976931348623157E308 |
+
+See the section on [“NoData” Handling](nodata-handling.md) for additional discussion on cell types.
+
+## NoData
+
+A "NoData" (or N/A) value is a specifically identified value for a cell type used to indicate the absence of data. See the section on @ref:[“NoData” Handling](nodata-handling.md) for additional discussion on NoData
+
+## Scene
+
+A scene (or granule) is a discrete instance of EO data with a specific extent (region), date-time, and projection/CRS. 
+
+## Coordinate Reference System (CRS)
+
+A coordinate reference system (or spatial reference system) is a set of mathematical constructs used to map cells to specific locations on the Earth (or other surface). A CRS typcially accompanies any EO data so it can be precicely located.
+
+## Extent
+
+An extent (or bounding box) is a rectangular region specifying the geospatial coverage of a two-dimensional array of cells in a singular CRS.
+
+## Tile
+
+A tile (sometimes called a "chip") is a sub-region of  a scene, usually square, usually with dimensions that are some power of 2. It is usually encoded as a two-dimensional array, or a a one-dimensional array with width/height dimensions. Tiles may be singleband or multiband (multi-channel). The tile is the primary discretization unit used in RasterFrames; the bands of a scene are separated into different columns, and the band's raster is carved up into tiles for each row.
+
+## Projected Extent
+
+An extent paired with a CRS
+
+## Projected Raster
+
+A tile or scene paired with a CRS and extent.
diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 49df72744..64d6eba06 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -1,4 +1,8 @@
-# Description
+# Overview
+
+RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms.
+
+## Context
 
 We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis. 
 
@@ -8,7 +12,25 @@ RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings to
 
 > Of the 1,738 operational satellites currently orbiting the earth (as of 9/[20]17), 596 are earth observation satellites and 477 of these are non-military assets (ie available to civil society including commercial entities and governments for earth observation, according to the Union of Concerned Scientists). This number is expected to increase significantly over the next ten years. The 200 or so planned remote sensing satellites have a value of over 27 billion USD (Forecast International). This estimate does not include the burgeoning fleets of smallsats as well as micro, nano and even smaller satellites... All this enthusiasm has, not unexpectedly, led to a veritable fire-hose of remotely sensed data which is becoming difficult to navigate even for seasoned experts.
 
-RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
+## Benefit
+
+By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. 
+
+
+## Architecture
+
+RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
+[GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
+[JTS](https://github.com/locationtech/jts), and
+[SFCurve](https://github.com/locationtech/sfcurve) (see below).
+
+![LocationTech Stack](static/rasterframes-locationtech-stack.png)
+
+RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
+
+![RasterFrame Anatomy](static/rasterframe-anatomy.png)
+
+Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. See @ref:[Raster Data I/O](raster-io.md) for details.
 
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
 [Pandas]:https://pandas.pydata.org/
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 4dbe19c47..5045d3c5a 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -38,13 +38,15 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 @@@
 
 @@@ index
-* [Description](description.md)
+* [Overview](description.md)
+* [Concepts](concepts.md)
 * [Getting Started](getting-started.md)
+* [API Languages](languages.md)
 * [Raster Data I/O](raster-io.md)
 * [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)
-* [Numpy, Pandas, & RasterFrames](numpy-pandas.md) 
-* [UDF Reference](reference.md)
+* [Numpy and Pandas](numpy-pandas.md) 
+* [Function Reference](reference.md)
 * [Release Notes](release-notes.md)
 @@@
 
diff --git a/pyrasterframes/src/main/python/docs/languages.md b/pyrasterframes/src/main/python/docs/languages.md
new file mode 100644
index 000000000..25fb55909
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/languages.md
@@ -0,0 +1,2 @@
+# API Languages
+
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index d9ef1feb5..4689f306d 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -2,7 +2,7 @@
 
 ## What is NoData?
 
-In raster operations, the preservation and correct processing of missing observations is very important. In [most dataframes and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a null or NaN value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the NoData value. 
+In raster operations, the preservation and correct processing of missing observations is very important. In [most dataframes and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value. 
 
 RasterFrames provides a variety of functions to inspect and manage NoData within `tile`s. 
 
diff --git a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
index 7bfde9356..7edd65802 100644
--- a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
+++ b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
@@ -1,4 +1,4 @@
-# NumPy and Pandas Interoperabilty
+# NumPy and Pandas
 
 In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will recieve this data a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame. 
 
diff --git a/pyrasterframes/src/main/python/docs/raster-processing.md b/pyrasterframes/src/main/python/docs/raster-processing.md
index 2f236429b..a16b2bb7d 100644
--- a/pyrasterframes/src/main/python/docs/raster-processing.md
+++ b/pyrasterframes/src/main/python/docs/raster-processing.md
@@ -12,3 +12,5 @@
 
 
 Explore what you can do with RasterFrames to work with, analyze, manipulate, and summarize Earth observation data.
+
+@@toc { depth=1 }
\ No newline at end of file

From aa67956b541c00e4e423ec4831fddec8f45f8859 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 30 Jul 2019 12:06:50 -0400
Subject: [PATCH 269/380] Fix python raster reader strict eval test condition

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/tests/PyRasterFramesTests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index a3080b6a2..33b848368 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -714,7 +714,7 @@ def test_strict_eval(self):
         # again for strict
         df_strict = self.spark.read.raster(self.img_uri, lazy_tiles=False)
         show_str_strict = df_strict.select('proj_raster')._jdf.showString(1, -1, False)
-        self.assertTrue('RasterRef' not in show_str_lazy)
+        self.assertTrue('RasterRef' not in show_str_strict)
 
 
     def test_prt_functions(self):

From f69227a12f07d51613ec5ad2fff5de5c2f65acf6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 30 Jul 2019 13:27:52 -0400
Subject: [PATCH 270/380] Tighten up python Tile equality test for
 nodata/masked cells; fix test_matmul

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/pyrasterframes/rf_types.py         |  3 ++-
 .../src/main/python/tests/PyRasterFramesTests.py       | 10 +++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index ffe2aa925..3ce84b932 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -295,7 +295,8 @@ def __init__(self, cells, cell_type=None):
 
     def __eq__(self, other):
         if type(other) is type(self):
-            return self.cell_type == other.cell_type and np.ma.allequal(self.cells, other.cells)
+            return self.cell_type == other.cell_type and \
+                   np.ma.allequal(self.cells, other.cells, fill_value=True)
         else:
             return False
 
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 33b848368..49529bd98 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -532,8 +532,6 @@ def less_pi(t):
 
 class TileOps(TestEnvironment):
 
-    from pyrasterframes.rf_types import Tile
-
     def setUp(self):
         # convenience so we can assert around Tile() == Tile()
         self.t1 = Tile(np.array([[1, 2],
@@ -589,9 +587,11 @@ def test_matmul(self):
         #     r1 = self.t1 @ self.t2
         r1 = self.t1.__matmul__(self.t2)
 
-        nd = r1.cell_type.no_data_value()
-        e1 = Tile(np.ma.masked_equal(np.array([[nd, 10],
-                                               [nd, nd]], dtype=r1.cell_type.to_numpy_dtype()), nd))
+        # The behavior of np.matmul with masked arrays is not well documented
+        # it seems to treat the 2nd arg as if not a MaskedArray
+        e1 = Tile(np.matmul(self.t1.cells, self.t2.cells), r1.cell_type)
+
+        self.assertTrue(r1 == e1, "{} was not equal to {}".format(r1, e1))
         self.assertEqual(r1, e1)
 
 

From eb0fbf5354a0766b595774d77eee6f1320cd540d Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 30 Jul 2019 14:19:07 -0400
Subject: [PATCH 271/380] Simplify python test_sql that has been failing on
 travis

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/tests/PyRasterFramesTests.py  | 39 ++++++++-----------
 1 file changed, 17 insertions(+), 22 deletions(-)

diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index 49529bd98..f93383555 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -228,28 +228,23 @@ def test_aggregations(self):
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
     def test_sql(self):
-        self.rf.createOrReplaceTempView("rf")
-
-        dims = self.rf.withColumn('dims', rf_dimensions('tile')).first().dims
-        dims_str = """{}, {}""".format(dims.cols, dims.rows)
-
-        self.spark.sql("""SELECT tile, rf_make_constant_tile(1, {}, 'uint16') AS One, 
-                            rf_make_constant_tile(2, {}, 'uint16') AS Two FROM rf""".format(dims_str, dims_str)) \
-            .createOrReplaceTempView("r3")
-
-        ops = self.spark.sql("""SELECT tile, rf_local_add(tile, One) AS AndOne, 
-                                    rf_local_subtract(tile, One) AS LessOne, 
-                                    rf_local_multiply(tile, Two) AS TimesTwo, 
-                                    rf_local_divide(tile, Two) AS OverTwo 
-                                FROM r3""")
-
-        # ops.printSchema
-        statsRow = ops.select(rf_tile_mean('tile').alias('base'),
-                              rf_tile_mean("AndOne").alias('plus_one'),
-                              rf_tile_mean("LessOne").alias('minus_one'),
-                              rf_tile_mean("TimesTwo").alias('double'),
-                              rf_tile_mean("OverTwo").alias('half')) \
-            .first()
+        self.rf.createOrReplaceTempView("rf_test_sql")
+
+        self.spark.sql("""SELECT tile, 
+                            rf_local_add(tile, 1) AS and_one, 
+                            rf_local_subtract(tile, 1) AS less_one, 
+                            rf_local_multiply(tile, 2) AS times_two, 
+                            rf_local_divide(tile, 2) AS over_two 
+                        FROM rf_test_sql""").createOrReplaceTempView('rf_test_sql_1')
+
+        statsRow = self.spark.sql("""
+        SELECT rf_tile_mean(tile) as base,
+            rf_tile_mean(and_one) as plus_one,
+            rf_tile_mean(less_one) as minus_one,
+            rf_tile_mean(times_two) as double,
+            rf_tile_mean(over_two) as half
+        FROM rf_test_sql_1
+        """).first()
 
         self.assertTrue(self.rounded_compare(statsRow.base, statsRow.plus_one - 1))
         self.assertTrue(self.rounded_compare(statsRow.base, statsRow.minus_one + 1))

From 767624bd68d53577d7274554dabc23c77215d681 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Jul 2019 16:35:40 -0400
Subject: [PATCH 272/380] Changed ReprojectGeometry to accept either CRS struct
 or a StringType.

---
 .../expressions/DynamicExtractors.scala       |  14 +-
 .../rasterframes/expressions/package.scala    |   5 +-
 .../transformers/ReprojectGeometry.scala      |  29 ++--
 .../extensions/DataFrameMethods.scala         |   6 +-
 .../rasterframes/functions/package.scala      |  17 +-
 .../util/ZeroSevenCompatibilityKit.scala      |   4 +-
 .../rasterframes/ReprojectGeometrySpec.scala  | 129 +++++++++-------
 .../geotiff/GeoTiffDataSource.scala           |   8 +-
 .../src/main/python/docs/languages.md         |   2 -
 .../python/pyrasterframes/rasterfunctions.py  |  14 +-
 pyrasterframes/src/main/python/setup.py       |   4 +-
 .../main/python/tests/GeoTiffWriterTests.py   |   1 +
 .../main/python/tests/PyRasterFramesTests.py  | 118 +-------------
 .../src/main/python/tests/VectorTypesTests.py | 145 ++++++++++++++++++
 .../rasterframes/py/PyRFContext.scala         |   8 +-
 15 files changed, 279 insertions(+), 225 deletions(-)
 delete mode 100644 pyrasterframes/src/main/python/docs/languages.md
 create mode 100644 pyrasterframes/src/main/python/tests/VectorTypesTests.py

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
index a7144a8f2..6a7e6e421 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/DynamicExtractors.scala
@@ -21,13 +21,15 @@
 
 package org.locationtech.rasterframes.expressions
 
+import geotrellis.proj4.CRS
 import geotrellis.raster.{CellGrid, Tile}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.rf.{TileUDT, RasterSourceUDT}
+import org.apache.spark.sql.rf.{RasterSourceUDT, TileUDT}
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.model.TileContext
+import org.locationtech.rasterframes.model.{LazyCRS, TileContext}
 import org.locationtech.rasterframes.ref.{ProjectedRasterLike, RasterRef, RasterSource}
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 
@@ -77,6 +79,13 @@ object DynamicExtractors {
       (row: InternalRow) => row.to[ProjectedRasterTile]
   }
 
+  lazy val crsExtractor: PartialFunction[DataType, Any => CRS] = {
+    case _: StringType =>
+      (v: Any) => LazyCRS(v.asInstanceOf[UTF8String].toString)
+    case t if t.conformsTo[CRS] =>
+      (v: Any) => v.asInstanceOf[InternalRow].to[CRS]
+  }
+
   sealed trait TileOrNumberArg
   sealed trait NumberArg extends TileOrNumberArg
   case class TileArg(tile: Tile, ctx: Option[TileContext]) extends TileOrNumberArg
@@ -113,4 +122,5 @@ object DynamicExtractors {
       case c: Char  => IntegerArg(c.toInt)
     }
   }
+
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
index 776412e1c..8a8b70e00 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/package.scala
@@ -127,9 +127,6 @@ package object expressions {
 
     registry.registerExpression[DebugRender.RenderAscii]("rf_render_ascii")
     registry.registerExpression[DebugRender.RenderMatrix]("rf_render_matrix")
-
-    // TODO: Replace registration in `org.locationtech.rasterframes.functions.register`
-    // TODO: with this once String columns are supported as well.
-    // registry.registerExpression[transformers.ReprojectGeometry]("st_reproject")
+    registry.registerExpression[transformers.ReprojectGeometry]("st_reproject")
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
index 89ad88fae..9c1ab2234 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/transformers/ReprojectGeometry.scala
@@ -27,12 +27,14 @@ import org.locationtech.rasterframes.encoders.serialized_literal
 import org.locationtech.jts.geom.Geometry
 import geotrellis.proj4.CRS
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult.TypeCheckFailure
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.jts.JTSTypes
+import org.apache.spark.sql.jts.{AbstractGeometryUDT, JTSTypes}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Column, TypedColumn}
-import org.locationtech.rasterframes.encoders.CatalystSerializer
+import org.locationtech.rasterframes.expressions.DynamicExtractors
 import org.locationtech.rasterframes.jts.ReprojectionTransformer
 import org.locationtech.rasterframes.model.LazyCRS
 
@@ -49,19 +51,22 @@ import org.locationtech.rasterframes.model.LazyCRS
        ..."""
 )
 case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: Expression) extends Expression
-  with CodegenFallback with ExpectsInputTypes {
-
-  // TODO: Replace registration in `org.locationtech.rasterframes.functions.register`
-  // TODO: with proper Expression supporting String columns as well.
+  with CodegenFallback {
 
   override def nodeName: String = "st_reproject"
   override def dataType: DataType = JTSTypes.GeometryTypeInstance
   override def nullable: Boolean = geometry.nullable || srcCRS.nullable || dstCRS.nullable
   override def children: Seq[Expression] = Seq(geometry, srcCRS, dstCRS)
-  private def crsSerde = CatalystSerializer[CRS]
-  override val inputTypes = Seq(
-    dataType, crsSerde.schema, crsSerde.schema
-  )
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (!geometry.dataType.isInstanceOf[AbstractGeometryUDT[_]])
+      TypeCheckFailure(s"Input type '${geometry.dataType}' does not conform to a geometry type.")
+    else if(!DynamicExtractors.crsExtractor.isDefinedAt(srcCRS.dataType))
+      TypeCheckFailure(s"Input type '${srcCRS.dataType}' cannot be interpreted as a CRS.")
+    else if(!DynamicExtractors.crsExtractor.isDefinedAt(dstCRS.dataType))
+      TypeCheckFailure(s"Input type '${dstCRS.dataType}' cannot be interpreted as a CRS.")
+    else TypeCheckResult.TypeCheckSuccess
+  }
 
   /** Reprojects a geometry column from one CRS to another. */
   val reproject: (Geometry, CRS, CRS) ⇒ Geometry =
@@ -71,8 +76,8 @@ case class ReprojectGeometry(geometry: Expression, srcCRS: Expression, dstCRS: E
     }
 
   override def eval(input: InternalRow): Any = {
-    val src = srcCRS.eval(input).asInstanceOf[InternalRow].to[CRS]
-    val dst = dstCRS.eval(input).asInstanceOf[InternalRow].to[CRS]
+    val src = DynamicExtractors.crsExtractor(srcCRS.dataType)(srcCRS.eval(input))
+    val dst = DynamicExtractors.crsExtractor(dstCRS.dataType)(dstCRS.eval(input))
     (src, dst) match {
       // Optimized pass-through case.
       case (s: LazyCRS, r: LazyCRS) if s.encoded == r.encoded => geometry.eval(input)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 3735eef37..1e94ff3ca 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -22,22 +22,20 @@
 package org.locationtech.rasterframes.extensions
 
 import geotrellis.proj4.CRS
-import geotrellis.raster.{MultibandTile, Tile}
 import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
-import geotrellis.vector.{Extent, ProjectedExtent}
-import org.apache.spark.rdd.RDD
+import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
-import org.locationtech.rasterframes.{MetadataKeys, RasterFrameLayer}
 import org.locationtech.rasterframes.StandardColumns._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.expressions.DynamicExtractors
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{MetadataKeys, RasterFrameLayer}
 import spray.json.JsonFormat
 
 import scala.util.Try
diff --git a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
index 51c78a34b..87894188a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/functions/package.scala
@@ -20,15 +20,14 @@
  */
 package org.locationtech.rasterframes
 import geotrellis.proj4.CRS
-import geotrellis.raster.{Tile, _}
 import geotrellis.raster.reproject.Reproject
+import geotrellis.raster.{Tile, _}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.functions.udf
 import org.apache.spark.sql.{Row, SQLContext}
 import org.locationtech.jts.geom.Geometry
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
-import org.locationtech.rasterframes.jts.ReprojectionTransformer
-import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
+import org.locationtech.rasterframes.model.TileDimensions
 
 /**
  * Module utils.
@@ -167,24 +166,12 @@ package object functions {
     }
   }
 
-  /** Reporjects a geometry column from one CRS to another, where CRS are defined in Proj4 format. */
-  private[rasterframes] val reprojectGeometryCRSName: (Geometry, String, String) ⇒ Geometry =
-    (sourceGeom, srcName, dstName) ⇒ {
-      val src = LazyCRS(srcName)
-      val dst = LazyCRS(dstName)
-      val trans = new ReprojectionTransformer(src, dst)
-      trans.transform(sourceGeom)
-    }
-
   def register(sqlContext: SQLContext): Unit = {
     sqlContext.udf.register("rf_make_constant_tile", makeConstantTile)
     sqlContext.udf.register("rf_make_zeros_tile", tileZeros)
     sqlContext.udf.register("rf_make_ones_tile", tileOnes)
-
     sqlContext.udf.register("rf_cell_types", cellTypes)
     sqlContext.udf.register("rf_rasterize", rasterize)
-    // TODO: replace this with full ReprojectGeometry with String handling.
-    sqlContext.udf.register("st_reproject", reprojectGeometryCRSName)
     sqlContext.udf.register("rf_array_to_tile", arrayToTile)
   }
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
index d937dd290..5826ad09a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/ZeroSevenCompatibilityKit.scala
@@ -324,6 +324,8 @@ object ZeroSevenCompatibilityKit {
     def ub[A, B](f: A => B)(a: Seq[A]): B = f(a.head)
     /** Binary expression builder builder. */
     def bb[A, B](f: (A, A) => B)(a: Seq[A]): B = f(a.head, a.last)
+    /** Trinary expression builder builder. */
+    def tb[A, B](f: (A, A, A) => B)(a: Seq[A]): B = f(a.head, a.tail.head, a.last)
 
     // Expression-oriented functions have a different registration scheme
     // Currently have to register with the `builtin` registry due to Spark data hiding.
@@ -361,11 +363,11 @@ object ZeroSevenCompatibilityKit {
     registry.registerFunc("rf_localAggMin", ub(LocalTileOpAggregate.LocalMinUDAF.apply))
     registry.registerFunc("rf_localAggCount", ub(LocalCountAggregate.LocalDataCellsUDAF.apply))
     registry.registerFunc("rf_localAggMean", ub(LocalMeanAggregate.apply))
+    registry.registerFunc("rf_reprojectGeometry", tb(ReprojectGeometry.apply))
 
     sqlContext.udf.register("rf_makeConstantTile", F.makeConstantTile)
     sqlContext.udf.register("rf_tileZeros", F.tileZeros)
     sqlContext.udf.register("rf_tileOnes", F.tileOnes)
     sqlContext.udf.register("rf_cellTypes", F.cellTypes)
-    sqlContext.udf.register("rf_reprojectGeometry", F.reprojectGeometryCRSName)
   }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index 46805a441..2c5d801d0 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -24,75 +24,98 @@ package org.locationtech.rasterframes
 import geotrellis.proj4.{CRS, LatLng, Sinusoidal, WebMercator}
 import org.apache.spark.sql.Encoders
 import org.locationtech.jts.geom._
-import org.scalatest.{FunSpec, Matchers}
 
 /**
  * Test for geometry reprojection.
  *
  * @since 11/29/18
  */
-class ReprojectGeometrySpec extends FunSpec
-  with TestEnvironment with Matchers {
-  import spark.implicits._
+class ReprojectGeometrySpec extends TestEnvironment {
+  // Note: Test data copied from ReprojectSpec in GeoTrellis
+  val fact = new GeometryFactory()
+  val latLng: Geometry = fact.createLineString(Array(
+    new Coordinate(-111.09374999999999, 34.784483415461345),
+    new Coordinate(-111.09374999999999, 43.29919735147067),
+    new Coordinate(-75.322265625, 43.29919735147067),
+    new Coordinate(-75.322265625, 34.784483415461345),
+    new Coordinate(-111.09374999999999, 34.784483415461345)
+  ))
+
+  val webMercator: Geometry = fact.createLineString(Array(
+    new Coordinate(-12366899.680315234, 4134631.734001753),
+    new Coordinate(-12366899.680315234, 5357624.186564572),
+    new Coordinate(-8384836.254770693, 5357624.186564572),
+    new Coordinate(-8384836.254770693, 4134631.734001753),
+    new Coordinate(-12366899.680315234, 4134631.734001753)
+  ))
 
   describe("Geometry reprojection") {
-    it("should allow reprojection geometry") {
-      // Note: Test data copied from ReprojectSpec in GeoTrellis
-      val fact = new GeometryFactory()
-      val latLng: Geometry = fact.createLineString(Array(
-        new Coordinate(-111.09374999999999, 34.784483415461345),
-        new Coordinate(-111.09374999999999, 43.29919735147067),
-        new Coordinate(-75.322265625, 43.29919735147067),
-        new Coordinate(-75.322265625, 34.784483415461345),
-        new Coordinate(-111.09374999999999, 34.784483415461345)
-      ))
-
-      val webMercator: Geometry = fact.createLineString(Array(
-        new Coordinate(-12366899.680315234, 4134631.734001753),
-        new Coordinate(-12366899.680315234, 5357624.186564572),
-        new Coordinate(-8384836.254770693, 5357624.186564572),
-        new Coordinate(-8384836.254770693, 4134631.734001753),
-        new Coordinate(-12366899.680315234, 4134631.734001753)
-      ))
-
-      withClue("both literal crs") {
-
-        val df = Seq((latLng, webMercator)).toDF("ll", "wm")
-
-        val rp = df.select(
-          st_reproject($"ll", LatLng, WebMercator) as "wm2",
-          st_reproject($"wm", WebMercator, LatLng) as "ll2",
-          st_reproject(st_reproject($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
-        ).as[(Geometry, Geometry, Geometry)]
-
-
-        val (wm2, ll2, wm3) = rp.first()
-
-        wm2 should matchGeom(webMercator, 0.00001)
-        ll2 should matchGeom(latLng, 0.00001)
-        wm3 should matchGeom(webMercator, 0.00001)
-      }
+    import spark.implicits._
+
+    it("should handle two literal CRSs") {
+
+      val df = Seq((latLng, webMercator)).toDF("ll", "wm")
+
+      val rp = df.select(
+        st_reproject($"ll", LatLng, WebMercator) as "wm2",
+        st_reproject($"wm", WebMercator, LatLng) as "ll2",
+        st_reproject(st_reproject($"ll", LatLng, Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+      ).as[(Geometry, Geometry, Geometry)]
+
+
+      val (wm2, ll2, wm3) = rp.first()
+
+      wm2 should matchGeom(webMercator, 0.00001)
+      ll2 should matchGeom(latLng, 0.00001)
+      wm3 should matchGeom(webMercator, 0.00001)
+    }
 
-      withClue("one literal crs") {
-        implicit val enc = Encoders.tuple(jtsGeometryEncoder, jtsGeometryEncoder, crsEncoder)
+    it("should handle one literal crs") {
+      implicit val enc = Encoders.tuple(jtsGeometryEncoder, jtsGeometryEncoder, crsEncoder)
+      val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
 
-        val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
+      val rp = df.select(
+        st_reproject($"ll", $"llCRS", WebMercator) as "wm2",
+        st_reproject($"wm", WebMercator, $"llCRS") as "ll2",
+        st_reproject(st_reproject($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
+      ).as[(Geometry, Geometry, Geometry)]
 
-        val rp = df.select(
-          st_reproject($"ll", $"llCRS", WebMercator) as "wm2",
-          st_reproject($"wm", WebMercator, $"llCRS") as "ll2",
-          st_reproject(st_reproject($"ll", $"llCRS", Sinusoidal), Sinusoidal, WebMercator) as "wm3"
-        ).as[(Geometry, Geometry, Geometry)]
 
+      val (wm2, ll2, wm3) = rp.first()
 
-        val (wm2, ll2, wm3) = rp.first()
+      wm2 should matchGeom(webMercator, 0.00001)
+      ll2 should matchGeom(latLng, 0.00001)
+      wm3 should matchGeom(webMercator, 0.00001)
+    }
 
-        wm2 should matchGeom(webMercator, 0.00001)
-        ll2 should matchGeom(latLng, 0.00001)
-        wm3 should matchGeom(webMercator, 0.00001)
+    it("should accept other geometry types") {
+      val df = Seq(1, 2, 3).toDF("id")
 
+      noException shouldBe thrownBy {
+        df.select(st_reproject(st_makePoint($"id", $"id"), WebMercator, Sinusoidal)).count()
       }
     }
-  }
 
+    it("should work in SQL") {
+      implicit val enc = Encoders.tuple(jtsGeometryEncoder, jtsGeometryEncoder, crsEncoder)
+      val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
+      df.createOrReplaceTempView("geom")
+
+      val rp = spark.sql(
+        """
+          | SELECT st_reproject(ll, llCRS, 'EPSG:3857') as wm2,
+          |        st_reproject(wm, 'EPSG:3857', llCRS) as ll2,
+          |        st_reproject(st_reproject(ll, llCRS, '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs'), '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs', 'EPSG:3857') as wm3
+          | FROM geom
+        """.stripMargin).as[(Geometry, Geometry, Geometry)]
+
+      val (wm2, ll2, wm3) = rp.first()
+
+      wm2 should matchGeom(webMercator, 0.00001)
+      ll2 should matchGeom(latLng, 0.00001)
+      wm3 should matchGeom(webMercator, 0.00001)
+
+      checkDocs("st_reproject")
+    }
+  }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 1b639a1f5..92640654f 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -24,19 +24,17 @@ package org.locationtech.rasterframes.datasource.geotiff
 import java.net.URI
 
 import _root_.geotrellis.proj4.CRS
+import _root_.geotrellis.raster._
 import _root_.geotrellis.raster.io.geotiff.compression._
 import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
 import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
-import _root_.geotrellis.raster._
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.rf.TileUDT
+import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource._
-import org.locationtech.rasterframes.expressions.aggregates.{ProjectedLayerMetadataAggregate, TileRasterizerAggregate}
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+import org.locationtech.rasterframes.expressions.aggregates.{ProjectedLayerMetadataAggregate, TileRasterizerAggregate}
 import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
 import org.locationtech.rasterframes.util._
 
diff --git a/pyrasterframes/src/main/python/docs/languages.md b/pyrasterframes/src/main/python/docs/languages.md
deleted file mode 100644
index 25fb55909..000000000
--- a/pyrasterframes/src/main/python/docs/languages.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# API Languages
-
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index e342842d9..74d48369d 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -91,12 +91,10 @@ def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
                        num_rows))
 
 
-def st_reproject(geometry_col, src_crs_name, dst_crs_name):
-    """Reproject a column of geometry given the CRS names of the source and destination.
-    Currently supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
-    An example of a valid CRS name is EPSG:3005."""
+def st_reproject(geometry_col, src_crs, dst_crs):
+    """Reproject a column of geometry given the CRSs of the source and destination."""
     jfcn = RFContext.active().lookup('st_reproject')
-    return Column(jfcn(_to_java_column(geometry_col), src_crs_name, dst_crs_name))
+    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(src_crs), _to_java_column(dst_crs)))
 
 
 def rf_explode_tiles(*tile_cols):
@@ -514,6 +512,12 @@ def rf_crs(tile_col):
     return _apply_column_function('rf_crs', tile_col)
 
 
+def rf_mk_crs(crs_text):
+    """Resolve CRS from text identifier. Supported registries are EPSG, ESRI, WORLD, NAD83, & NAD27.
+    An example of a valid CRS name is EPSG:3005."""
+    return Column(_context_call('_make_crs_literal', crs_text))
+
+
 def st_extent(geom_col):
     """Compute the extent/bbox of a Geometry (a tile with embedded extent and CRS)"""
     return _apply_column_function('st_extent', geom_col)
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index a4811824b..a76031b6b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -90,10 +90,12 @@ def run(self):
                 print(traceback.format_exc())
                 exit(1)
 
+
 class PweaveNotebooks(PweaveDocs):
     def doctype(self):
         return "notebook"
 
+
 setup(
     name='pyrasterframes',
     description='Access and process geospatial raster data in PySpark DataFrames',
@@ -134,7 +136,7 @@ def doctype(self):
         'pytest==3.4.2',
         'pypandoc',
         'numpy>=1.7',
-        'shapley',
+        'shapely',
         'pandas',
         'rasterio'
     ],
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
index 135d88a88..6c4f21b2a 100644
--- a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -61,3 +61,4 @@ def test_downsampled_write(self):
             self.assertEqual((f.width, f.height), (128, 128))
 
         os.remove(dest)
+
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index ccdc952b4..8a0ab12e0 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -21,129 +21,13 @@
 import unittest
 
 import numpy as np
-import geomesa_pyspark.types
 from pyrasterframes.rasterfunctions import *
 from pyrasterframes.rf_types import *
-from pyspark.sql import SQLContext, Column
+from pyspark.sql import SQLContext
 from pyspark.sql.functions import *
 from . import TestEnvironment
 
 
-class VectorTypes(TestEnvironment):
-
-    def setUp(self):
-        self.create_layer()
-        import pandas as pd
-        self.pandas_df = pd.DataFrame({
-            'eye': ['a', 'b', 'c', 'd'],
-            'x': [0.0, 1.0, 2.0, 3.0],
-            'y': [-4.0, -3.0, -2.0, -1.0],
-        })
-        df = self.spark.createDataFrame(self.pandas_df)
-        df = df.withColumn("point_geom",
-                           st_point(df.x, df.y)
-                           )
-        self.df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
-
-    def test_spatial_relations(self):
-        from pyspark.sql.functions import udf, sum
-        from geomesa_pyspark.types import PointUDT
-        import shapely
-        import numpy.testing
-
-        # Use python shapely UDT in a UDF
-        @udf("double")
-        def area_fn(g):
-            return g.area
-
-        @udf("double")
-        def length_fn(g):
-            return g.length
-
-        df = self.df.withColumn("poly_area", area_fn(self.df.poly_geom))
-        df = df.withColumn("poly_len", length_fn(df.poly_geom))
-
-        # Return UDT in a UDF!
-        def some_point(g):
-            return g.representative_point()
-
-        some_point_udf = udf(some_point, PointUDT())
-
-        df = df.withColumn("any_point", some_point_udf(df.poly_geom))
-        # spark-side UDF/UDT are correct
-        intersect_total = df.agg(sum(
-            st_intersects(df.poly_geom, df.any_point).astype('double')
-        ).alias('s')).collect()[0].s
-        self.assertTrue(intersect_total == df.count())
-
-        # Collect to python driver in shapely UDT
-        pandas_df_out = df.toPandas()
-
-        # Confirm we get a shapely type back from st_* function and UDF
-        self.assertIsInstance(pandas_df_out.poly_geom.iloc[0], shapely.geometry.Polygon)
-        self.assertIsInstance(pandas_df_out.any_point.iloc[0], shapely.geometry.Point)
-
-        # And our spark-side manipulations were correct
-        xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == self.pandas_df.x
-        self.assertTrue(all(xs_correct))
-
-        centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
-                                                    g.centroid.coords[0][1]).tolist()
-        numpy.testing.assert_almost_equal(centroid_ys, self.pandas_df.y.tolist())
-
-        # Including from UDF's
-        numpy.testing.assert_almost_equal(
-            pandas_df_out.poly_geom.apply(lambda g: g.area).values,
-            pandas_df_out.poly_area.values
-        )
-        numpy.testing.assert_almost_equal(
-            pandas_df_out.poly_geom.apply(lambda g: g.length).values,
-            pandas_df_out.poly_len.values
-        )
-
-    def test_rasterize(self):
-        from geomesa_pyspark.types import PolygonUDT
-        # simple test that raster contents are not invalid
-
-        # create a udf to buffer (the bounds) polygon
-        def _buffer(g, d):
-            return g.buffer(d)
-
-        @udf("double")
-        def area(g):
-            return g.area
-
-        buffer_udf = udf(_buffer, PolygonUDT())
-
-        buf_cells = 10
-        with_poly = self.rf.withColumn('poly', buffer_udf(self.rf.geometry, lit(-15 * buf_cells)))  # cell res is 15x15
-        area = with_poly.select(area('poly') < area('geometry'))
-        area_result = area.collect()
-        self.assertTrue(all([r[0] for r in area_result]))
-
-        cols = 194
-        rows = 250
-        with_raster = with_poly.withColumn('rasterized', rf_rasterize('poly', 'geometry', lit(16), cols, rows))
-        # expect a 4 by 4 cell
-        result = with_raster.select(rf_tile_sum(rf_local_equal_int(with_raster.rasterized, 16)),
-                                    rf_tile_sum(with_raster.rasterized))
-        expected_burned_in_cells = (cols - 2 * buf_cells) * (rows - 2 * buf_cells)
-        self.assertEqual(result.first()[0], float(expected_burned_in_cells))
-        self.assertEqual(result.first()[1], 16. * expected_burned_in_cells)
-
-    def test_reproject(self):
-        reprojected = self.rf.withColumn('reprojected', st_reproject('center', 'EPSG:4326', 'EPSG:3857'))
-        reprojected.show()
-        self.assertEqual(reprojected.count(), 8)
-
-    def test_geojson(self):
-        import os
-        sample = 'file://' + os.path.join(self.resource_dir, 'buildings.geojson')
-        geo = self.spark.read.geojson(sample)
-        geo.show()
-        self.assertEqual(geo.select('geometry').count(), 8)
-
-
 class RasterFunctions(TestEnvironment):
 
     def setUp(self):
diff --git a/pyrasterframes/src/main/python/tests/VectorTypesTests.py b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
new file mode 100644
index 000000000..5f7946e5b
--- /dev/null
+++ b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
@@ -0,0 +1,145 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql import Row
+from pyspark.sql.functions import *
+
+from . import TestEnvironment
+
+
+class VectorTypes(TestEnvironment):
+
+    def setUp(self):
+        self.create_layer()
+        import pandas as pd
+        self.pandas_df = pd.DataFrame({
+            'eye': ['a', 'b', 'c', 'd'],
+            'x': [0.0, 1.0, 2.0, 3.0],
+            'y': [-4.0, -3.0, -2.0, -1.0],
+        })
+        df = self.spark.createDataFrame(self.pandas_df)
+        df = df.withColumn("point_geom",
+                           st_point(df.x, df.y)
+                           )
+        self.df = df.withColumn("poly_geom", st_bufferPoint(df.point_geom, lit(1250.0)))
+
+    def test_spatial_relations(self):
+        from pyspark.sql.functions import udf, sum
+        from geomesa_pyspark.types import PointUDT
+        import shapely
+        import numpy.testing
+
+        # Use python shapely UDT in a UDF
+        @udf("double")
+        def area_fn(g):
+            return g.area
+
+        @udf("double")
+        def length_fn(g):
+            return g.length
+
+        df = self.df.withColumn("poly_area", area_fn(self.df.poly_geom))
+        df = df.withColumn("poly_len", length_fn(df.poly_geom))
+
+        # Return UDT in a UDF!
+        def some_point(g):
+            return g.representative_point()
+
+        some_point_udf = udf(some_point, PointUDT())
+
+        df = df.withColumn("any_point", some_point_udf(df.poly_geom))
+        # spark-side UDF/UDT are correct
+        intersect_total = df.agg(sum(
+            st_intersects(df.poly_geom, df.any_point).astype('double')
+        ).alias('s')).collect()[0].s
+        self.assertTrue(intersect_total == df.count())
+
+        # Collect to python driver in shapely UDT
+        pandas_df_out = df.toPandas()
+
+        # Confirm we get a shapely type back from st_* function and UDF
+        self.assertIsInstance(pandas_df_out.poly_geom.iloc[0], shapely.geometry.Polygon)
+        self.assertIsInstance(pandas_df_out.any_point.iloc[0], shapely.geometry.Point)
+
+        # And our spark-side manipulations were correct
+        xs_correct = pandas_df_out.point_geom.apply(lambda g: g.coords[0][0]) == self.pandas_df.x
+        self.assertTrue(all(xs_correct))
+
+        centroid_ys = pandas_df_out.poly_geom.apply(lambda g:
+                                                    g.centroid.coords[0][1]).tolist()
+        numpy.testing.assert_almost_equal(centroid_ys, self.pandas_df.y.tolist())
+
+        # Including from UDF's
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.area).values,
+            pandas_df_out.poly_area.values
+        )
+        numpy.testing.assert_almost_equal(
+            pandas_df_out.poly_geom.apply(lambda g: g.length).values,
+            pandas_df_out.poly_len.values
+        )
+
+    def test_rasterize(self):
+        from geomesa_pyspark.types import PolygonUDT
+        # simple test that raster contents are not invalid
+
+        # create a udf to buffer (the bounds) polygon
+        def _buffer(g, d):
+            return g.buffer(d)
+
+        @udf("double")
+        def area(g):
+            return g.area
+
+        buffer_udf = udf(_buffer, PolygonUDT())
+
+        buf_cells = 10
+        with_poly = self.rf.withColumn('poly', buffer_udf(self.rf.geometry, lit(-15 * buf_cells)))  # cell res is 15x15
+        area = with_poly.select(area('poly') < area('geometry'))
+        area_result = area.collect()
+        self.assertTrue(all([r[0] for r in area_result]))
+
+        cols = 194
+        rows = 250
+        with_raster = with_poly.withColumn('rasterized', rf_rasterize('poly', 'geometry', lit(16), cols, rows))
+        # expect a 4 by 4 cell
+        result = with_raster.select(rf_tile_sum(rf_local_equal_int(with_raster.rasterized, 16)),
+                                    rf_tile_sum(with_raster.rasterized))
+        expected_burned_in_cells = (cols - 2 * buf_cells) * (rows - 2 * buf_cells)
+        self.assertEqual(result.first()[0], float(expected_burned_in_cells))
+        self.assertEqual(result.first()[1], 16. * expected_burned_in_cells)
+
+    def test_parse_crs(self):
+        df = self.spark.createDataFrame([Row(id=1)])
+        df.select(rf_mk_crs('EPSG:4326')).show()
+
+    def test_reproject(self):
+        reprojected = self.rf.withColumn('reprojected',
+                                         st_reproject('center', rf_mk_crs('EPSG:4326'), rf_mk_crs('EPSG:3857')))
+        reprojected.show()
+        self.assertEqual(reprojected.count(), 8)
+
+    def test_geojson(self):
+        import os
+        sample = 'file://' + os.path.join(self.resource_dir, 'buildings.geojson')
+        geo = self.spark.read.geojson(sample)
+        geo.show()
+        self.assertEqual(geo.select('geometry').count(), 8)
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 10c83fef8..34c708175 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -22,11 +22,13 @@ package org.locationtech.rasterframes.py
 
 import java.nio.ByteBuffer
 
+import geotrellis.proj4.CRS
 import geotrellis.raster.{CellType, MultibandTile}
 import geotrellis.spark.io._
 import geotrellis.spark.{ContextRDD, MultibandTileLayerRDD, SpaceTimeKey, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.Extent
 import org.apache.spark.sql._
+import org.locationtech.rasterframes
 import org.locationtech.rasterframes.extensions.RasterJoin
 import org.locationtech.rasterframes.model.LazyCRS
 import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
@@ -188,10 +190,8 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
 
   def rf_local_unequal_int(col: Column, scalar: Int): Column = rf_local_unequal[Int](col, scalar)
 
-  def st_reproject(geometryCol: Column, srcName: String, dstName: String): Column = {
-    val src = LazyCRS(srcName)
-    val dst = LazyCRS(dstName)
-    st_reproject(geometryCol, src, dst)
+  def _make_crs_literal(crsText: String): Column = {
+    rasterframes.encoders.serialized_literal[CRS](LazyCRS(crsText))
   }
 
   // return toRaster, get just the tile, and make an array out of it

From c14f60d2b21946fbc83298608e0ce9381bdc9bf6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 30 Jul 2019 16:36:56 -0400
Subject: [PATCH 273/380] Started work on SQL document section.

---
 .../src/main/python/docs/languages.pymd       | 99 +++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 pyrasterframes/src/main/python/docs/languages.pymd

diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
new file mode 100644
index 000000000..69c9efdcb
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -0,0 +1,99 @@
+# API Languages
+
+One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This manual is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program (computing average NDVI per month for a single tile in Tanzania).
+
+```python, imports, echo=False
+from pyspark.sql.functions import month, dayofmonth, year
+from pyrasterframes.utils import create_rf_spark_session
+from pyrasterframes.rasterfunctions import *
+import pyrasterframes.rf_ipython
+import pandas as pd
+import os
+```
+
+## Python
+
+Step 1: Load the catalog
+
+```python, step_1_python
+modis = spark.read.format('aws-pds-modis-catalog').load()
+```
+Step 2: Down-select data by month
+
+```python, step_2_python
+red_nir_monthly_2017 = modis \
+    .select('granule_id', month('acquisition_date').alias('month'), col('B01').alias('red'), col('B02').alias('nir')) \
+    .where((year('acquisition_date') == 2017) & (dayofmonth('acquisition_date') == 15) & (col('granule_id') == 'h21v09'))
+```
+
+Step 3: Read tiles
+
+```python, step_3_python
+red_nir_tiles_monthly_2017 = spark.read.raster(catalog=red_nir_monthly_2017, catalog_col_names=['red', 'nir'])
+```
+
+Step 4: Compute aggregates
+
+```python, step_4_python
+result = red_nir_tiles_monthly_2017 \
+    .groupBy('month') \
+    .agg(first('month'), rf_agg_stats(rf_normalized_difference(col('nir'), col('red')).alias('ndvi_stats'))) \
+    .where(st_intersects(st_reproject(rf_geometry(col('red')), rf_crs(col('red')), rf_mk_crs('EPSG:4326')), st_makePoint(34.870605, -4.729727))) \
+    .orderBy(col('month'))
+result.show()
+```
+
+## SQL
+
+For convenience we're going to evaluate SQL from the Python environment. The SQL fragments should work in the `spark-sql` shell just the same.
+
+```python, sql_setup
+spark = create_rf_spark_session()
+def sql(stmt):
+    return spark.sql(stmt)
+```
+
+Step 1: Load the catalog
+
+```python, step_1_sql
+sql("CREATE OR REPLACE TEMPORARY VIEW modis USING `aws-pds-modis-catalog`")
+```
+
+Step 2: Down-select data by month
+
+```python, step_2_sql
+sql("""
+CREATE OR REPLACE TEMPORARY VIEW red_nir_monthly_2017 AS
+SELECT granule_id, month(acquisition_date) as month, B01 as red, B02 as nir
+FROM modis
+WHERE year(acquisition_date) = 2017 AND day(acquisition_date) = 15 AND granule_id = 'h21v09'
+""")
+sql('DESCRIBE red_nir_monthly_2017').show() 
+```
+
+Step 3: Read tiles
+
+```python, step_3_sql
+sql("""
+CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017 
+USING raster 
+OPTIONS (catalogTable='red_nir_monthly_2017', catalogColumns='red,nir')
+""")
+```
+
+Step 4: Compute aggregates
+
+```python, step_4_sql
+sql("""
+SELECT month, ndvi_stats.* FROM (
+    SELECT month, rf_agg_stats(rf_normalized_difference(nir, red)) as ndvi_stats
+    FROM red_nir_tiles_monthly_2017
+    WHERE st_intersects(st_reproject(rf_geometry(red), rf_crs(red), 'EPSG:4326'), st_makePoint(34.870605, -4.729727))
+    GROUP BY month
+    ORDER BY month
+)
+""").show()
+```
+
+## Scala
+

From 66e53f3376e5ddca25727455aa7e34ca15c09eec Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 31 Jul 2019 10:44:13 -0400
Subject: [PATCH 274/380] Updated languages.pymd with three variants of same
 analysis. Added `doctype` option to pweave setup subcommand.

---
 core/src/test/scala/Scratch.sc                |  0
 .../rasterframes/ReprojectGeometrySpec.scala  |  3 +-
 docs/README.md                                | 19 +++-
 experimental/src/test/scala/ndvi.sc           | 35 ++++++++
 project/RFDependenciesPlugin.scala            |  2 +-
 .../src/main/python/docs/languages.pymd       | 89 ++++++++++++++++---
 .../src/main/python/docs/reference.pymd       | 19 +++-
 pyrasterframes/src/main/python/setup.py       | 14 +--
 .../src/main/docker/docker-compose.yml        | 13 +--
 9 files changed, 158 insertions(+), 36 deletions(-)
 delete mode 100644 core/src/test/scala/Scratch.sc
 create mode 100644 experimental/src/test/scala/ndvi.sc

diff --git a/core/src/test/scala/Scratch.sc b/core/src/test/scala/Scratch.sc
deleted file mode 100644
index e69de29bb..000000000
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index 2c5d801d0..23a3ddf25 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -105,7 +105,8 @@ class ReprojectGeometrySpec extends TestEnvironment {
         """
           | SELECT st_reproject(ll, llCRS, 'EPSG:3857') as wm2,
           |        st_reproject(wm, 'EPSG:3857', llCRS) as ll2,
-          |        st_reproject(st_reproject(ll, llCRS, '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs'), '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs', 'EPSG:3857') as wm3
+          |        st_reproject(st_reproject(ll, llCRS, '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs'),
+          |        '+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs', 'EPSG:3857') as wm3
           | FROM geom
         """.stripMargin).as[(Geometry, Geometry, Geometry)]
 
diff --git a/docs/README.md b/docs/README.md
index e8b3071f8..7c8209b6f 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -67,7 +67,7 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     sbt:RasterFrames> pyrasterframes/doc
     ```
     There's a command alias for this last step: `pyDocs`.
-4. To evaluate a single `.pymd` file, you pass the `-f` option and the  filename relative to the `python` direoctry:
+4. To evaluate a single `.pymd` file, you pass the `-f` option and the filename relative to the `python` directory:
     ```
     sbt:RasterFrames> pyrasterframes/pySetup pweave -f docs/getting-started.pymd
     [info] Synchronizing 44 files to '<src-root>/pyrasterframes/target/python'
@@ -81,11 +81,22 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     Processing chunk 1 named None from line 14
     ...
     Weaved docs/getting-started.pymd to docs/getting-started.md
-    [success] Total time: 21 s, completed Jul 5, 2019 12:31:09 PM
-    sbt:RasterFrames>
     ```
 5. The _output_ Markdown files are written to `<src-root>/pyrasterframes/target/python/docs`. _Note_: don't edit any files in the `pyrasterframes/target` directory... they will get overwritten each time `sbt` runs a command.
-6. To build all the documentation and convert to a static html site, run:
+6. During content development it's sometimes helpful to see the output rendered as basic HTML. To do this, add the `-d html` option to the pweave command:
+    ```
+    sbt:RasterFrames> pyrasterframes/pySetup pweave -d html -f docs/getting-started.pymd
+    [info] Synchronizing 54 files to '<src-roog>/pyrasterframes/target/python'
+    [info] Running 'python setup.py pweave -d html -f docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
+    running pweave
+    --------------------------------------------------
+    Running getting-started
+    --------------------------------------------------
+    ...
+    Weaved docs/getting-started.pymd to docs/getting-started.html
+    ```
+    Note: This feature requires `pandoc` to be installed.
+7. To build all the documentation and convert to a static html site, run:
     ```bash
     sbt makeSite
     ``` 
diff --git a/experimental/src/test/scala/ndvi.sc b/experimental/src/test/scala/ndvi.sc
new file mode 100644
index 000000000..483d89006
--- /dev/null
+++ b/experimental/src/test/scala/ndvi.sc
@@ -0,0 +1,35 @@
+import geotrellis.proj4.LatLng
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.raster._
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+
+
+implicit val spark = SparkSession.builder().
+  master("local[*]").appName("RasterFrames").getOrCreate().withRasterFrames
+spark.sparkContext.setLogLevel("ERROR")
+
+import spark.implicits._
+
+val modis = spark.read.format("aws-pds-modis-catalog").load()
+
+val red_nir_monthly_2017 = modis
+  .select($"granule_id", month($"acquisition_date") as "month", $"B01" as "red", $"B02" as "nir")
+  .where(year($"acquisition_date") === 2017 && (dayofmonth($"acquisition_date") === 15) && $"granule_id" === "h21v09")
+
+val red_nir_tiles_monthly_2017 = spark.read.raster
+  .fromCatalog(red_nir_monthly_2017, "red", "nir")
+  .load()
+
+val result = red_nir_tiles_monthly_2017
+  .where(st_intersects(
+    st_reproject(rf_geometry($"red"), rf_crs($"red"), LatLng),
+    st_makePoint(34.870605, -4.729727)
+  ))
+  .groupBy("month")
+  .agg(rf_agg_stats(rf_normalized_difference($"nir", $"red")) as "ndvi_stats")
+  .orderBy("month")
+  .select("month", "ndvi_stats.*")
+
+
+result.show()
\ No newline at end of file
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index a4caa89c0..f351f2e3a 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -59,7 +59,7 @@ object RFDependenciesPlugin extends AutoPlugin {
     ),
 
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
-    rfSparkVersion := "2.3.2",
+    rfSparkVersion := "2.3.3",
     rfGeoTrellisVersion := "2.2.0",
     rfGeoMesaVersion := "2.2.1",
     dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.58d4965"
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index 69c9efdcb..3be17b9e0 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -3,43 +3,54 @@
 One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This manual is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program (computing average NDVI per month for a single tile in Tanzania).
 
 ```python, imports, echo=False
-from pyspark.sql.functions import month, dayofmonth, year
+from pyspark.sql.functions import *
 from pyrasterframes.utils import create_rf_spark_session
+
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
 import pandas as pd
 import os
+spark = create_rf_spark_session()
 ```
 
 ## Python
 
-Step 1: Load the catalog
+### Step 1: Load the catalog
 
 ```python, step_1_python
 modis = spark.read.format('aws-pds-modis-catalog').load()
 ```
-Step 2: Down-select data by month
+### Step 2: Down-select data by month
 
 ```python, step_2_python
 red_nir_monthly_2017 = modis \
-    .select('granule_id', month('acquisition_date').alias('month'), col('B01').alias('red'), col('B02').alias('nir')) \
+    .select(
+        col('granule_id'), 
+        month('acquisition_date').alias('month'), 
+        col('B01').alias('red'), 
+        col('B02').alias('nir')
+    ) \
     .where((year('acquisition_date') == 2017) & (dayofmonth('acquisition_date') == 15) & (col('granule_id') == 'h21v09'))
 ```
 
-Step 3: Read tiles
+### Step 3: Read tiles
 
 ```python, step_3_python
 red_nir_tiles_monthly_2017 = spark.read.raster(catalog=red_nir_monthly_2017, catalog_col_names=['red', 'nir'])
 ```
 
-Step 4: Compute aggregates
+### Step 4: Compute aggregates
 
 ```python, step_4_python
 result = red_nir_tiles_monthly_2017 \
+    .where(st_intersects(
+        st_reproject(rf_geometry(col('red')), rf_crs(col('red')).crsProj4, rf_mk_crs('EPSG:4326')), 
+        st_makePoint(lit(34.870605), lit(-4.729727)))
+    ) \
     .groupBy('month') \
-    .agg(first('month'), rf_agg_stats(rf_normalized_difference(col('nir'), col('red')).alias('ndvi_stats'))) \
-    .where(st_intersects(st_reproject(rf_geometry(col('red')), rf_crs(col('red')), rf_mk_crs('EPSG:4326')), st_makePoint(34.870605, -4.729727))) \
-    .orderBy(col('month'))
+    .agg(rf_agg_stats(rf_normalized_difference(col('nir'), col('red'))).alias('ndvi_stats')) \
+    .orderBy(col('month')) \
+    .select('month', 'ndvi_stats.*')
 result.show()
 ```
 
@@ -48,18 +59,17 @@ result.show()
 For convenience we're going to evaluate SQL from the Python environment. The SQL fragments should work in the `spark-sql` shell just the same.
 
 ```python, sql_setup
-spark = create_rf_spark_session()
 def sql(stmt):
     return spark.sql(stmt)
 ```
 
-Step 1: Load the catalog
+### Step 1: Load the catalog
 
 ```python, step_1_sql
 sql("CREATE OR REPLACE TEMPORARY VIEW modis USING `aws-pds-modis-catalog`")
 ```
 
-Step 2: Down-select data by month
+### Step 2: Down-select data by month
 
 ```python, step_2_sql
 sql("""
@@ -71,7 +81,7 @@ WHERE year(acquisition_date) = 2017 AND day(acquisition_date) = 15 AND granule_i
 sql('DESCRIBE red_nir_monthly_2017').show() 
 ```
 
-Step 3: Read tiles
+### Step 3: Read tiles
 
 ```python, step_3_sql
 sql("""
@@ -81,7 +91,7 @@ OPTIONS (catalogTable='red_nir_monthly_2017', catalogColumns='red,nir')
 """)
 ```
 
-Step 4: Compute aggregates
+### Step 4: Compute aggregates
 
 ```python, step_4_sql
 sql("""
@@ -97,3 +107,54 @@ SELECT month, ndvi_stats.* FROM (
 
 ## Scala
 
+### Step 1: Load the catalog
+
+```scala
+import geotrellis.proj4.LatLng
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.datasource.raster._
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+
+
+implicit val spark = SparkSession.builder()
+  .master("local[*]")
+  .appName("RasterFrames")
+  .withKryoSerialization
+  .getOrCreate()
+  .withRasterFrames
+
+import spark.implicits._
+
+val modis = spark.read.format("aws-pds-modis-catalog").load()
+```
+
+### Step 2: Down-select data by month
+
+```scala
+val red_nir_monthly_2017 = modis
+  .select($"granule_id", month($"acquisition_date") as "month", $"B01" as "red", $"B02" as "nir")
+  .where(year($"acquisition_date") === 2017 && (dayofmonth($"acquisition_date") === 15) && $"granule_id" === "h21v09")
+```
+
+### Step 3: Read tiles
+
+```scala
+val red_nir_tiles_monthly_2017 = spark.read.raster
+  .fromCatalog(red_nir_monthly_2017, "red", "nir")
+  .load()
+```
+
+### Step 4: Compute aggregates
+
+```scala
+val result = red_nir_tiles_monthly_2017
+  .where(st_intersects(
+    st_reproject(rf_geometry($"red"), rf_crs($"red"), LatLng),
+    st_makePoint(34.870605, -4.729727)
+  ))
+  .groupBy("month")
+  .agg(rf_agg_stats(rf_normalized_difference($"nir", $"red")) as "ndvi_stats")
+  .orderBy("month")
+  .select("month", "ndvi_stats.*")
+``` 
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index e04ee2387..3859c1694 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -87,10 +87,22 @@ Fetches the extent (bounding box or envelope) of a `ProjectedRasterTile` or `Ras
 
 ### rf_crs
 
-    Struct[String] rf_crs(ProjectedRasterTile proj_raster)
-    Struct[String] rf_crs(RasterSource proj_raster)
+    Struct rf_crs(ProjectedRasterTile proj_raster)
+    Struct rf_crs(RasterSource proj_raster)
 
-Fetch the [proj4](https://proj4.org/) string representation of the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
+Fetch CRS structure representing the coordinate reference system of a `ProjectedRasterTile` or `RasterSource` type tile columns.
+
+### rf_mk_crs
+
+Construct a CRS structure from one of its string representations. Three froms are supported:
+
+    Struct rf_mk_crs(String crsText)   
+
+* [EPSG code](https://www.epsg-registry.org/): `EPSG:<integer>`
+* [Proj4 string](https://proj.org/): `+proj <proj4 parameters>`
+* [WKT String](http://www.geoapi.org/3.0/javadoc/org/opengis/referencing/doc-files/WKT.html) with embedded EPSG code: `GEOGCS["<name>", <datum>, <prime meridian>, <angular unit> {,<twin axes>} {,<authority>}]` 
+
+Example: `SELECT rf_mk_crs('EPSG:4326')`
 
 ### rf_convert_cell_type
 
@@ -117,7 +129,6 @@ Functions to create a new Tile column, either from scratch or from existing data
 Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
 ```
 
-
 Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
 
 ### rf_make_ones_tile
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index a76031b6b..a8881fdfb 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -53,6 +53,7 @@ class PweaveDocs(distutils.cmd.Command):
     user_options = [
         # The format is (long option, short option, description).
         ('files=', 'f', 'Specific files to pweave. Defaults to all in `docs` directory.'),
+        ('doctype=', 'd', 'Output format type. Defaults to `markdown`')
     ]
 
     def initialize_options(self):
@@ -62,15 +63,15 @@ def initialize_options(self):
             lambda x: not path.basename(x)[:1] == '_',
             glob(path.join(here, 'docs', '*.pymd'))
         )
+        self.doctype = 'markdown'
 
     def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.files, str):
             self.files = filter(lambda s: len(s) > 0, re.split(',', self.files))
-
-    def doctype(self):
-        return "markdown"
+            if self.doctype is "html":
+                self.doctype = "pandoc2html"
 
     def run(self):
         """Run pweave."""
@@ -83,7 +84,7 @@ def run(self):
             try:
                 pweave.weave(
                     file=str(file),
-                    doctype=self.doctype()
+                    doctype=self.doctype
                 )
             except Exception:
                 print(_divided('%s Failed:' % file))
@@ -92,8 +93,9 @@ def run(self):
 
 
 class PweaveNotebooks(PweaveDocs):
-    def doctype(self):
-        return "notebook"
+    def initialize_options(self):
+        super().initialize_options()
+        self.doctype = 'notebook'
 
 
 setup(
diff --git a/rf-notebook/src/main/docker/docker-compose.yml b/rf-notebook/src/main/docker/docker-compose.yml
index a093ca7fd..79d6c8dcf 100644
--- a/rf-notebook/src/main/docker/docker-compose.yml
+++ b/rf-notebook/src/main/docker/docker-compose.yml
@@ -7,11 +7,12 @@ services:
       # jupyter notebook port
       - "8888:8888"
       # spark UI ports
-      - "4040:4040"
-      - "4041:4041"
-      - "4042:4042"
-      - "4043:4043"
-      - "4044:4044"
+      - "44040:4040"
+      - "44041:4041"
+      - "44042:4042"
+      - "44043:4043"
+      - "44044:4044"
 # To save locally at './work' from the container:
     volumes:
-      - ../../../src/main/notebooks:/home/jovyan/work
\ No newline at end of file
+      - ../../../src/main/notebooks:/home/jovyan/work
+      - ~/.rf_cache:/home/jovyan/.rf_cache
\ No newline at end of file

From e8d117c75c4db3effc271cf89ce0b2d67d557fea Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 31 Jul 2019 11:04:25 -0400
Subject: [PATCH 275/380] Bumped to JVM to Spark 2.3.3.

---
 project/RFDependenciesPlugin.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index a4caa89c0..f351f2e3a 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -59,7 +59,7 @@ object RFDependenciesPlugin extends AutoPlugin {
     ),
 
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
-    rfSparkVersion := "2.3.2",
+    rfSparkVersion := "2.3.3",
     rfGeoTrellisVersion := "2.2.0",
     rfGeoMesaVersion := "2.2.1",
     dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.58d4965"

From de350b15a6e483ec58bf84838f7d03b567644b86 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 31 Jul 2019 13:19:22 -0400
Subject: [PATCH 276/380] Update release notes to reflect change at
 e8d117c75c4d

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/paradox/release-notes.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/main/paradox/release-notes.md b/docs/src/main/paradox/release-notes.md
index 96db8e579..b02526b9a 100644
--- a/docs/src/main/paradox/release-notes.md
+++ b/docs/src/main/paradox/release-notes.md
@@ -4,7 +4,7 @@
 
 ### 0.8.0
 
-* Upgraded to the following core dependencies: Spark 2.3.2, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
+* Upgraded to the following core dependencies: Spark 2.3.3, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
 * Build `pyrasterframes` binary distribution for pip installation.
 * Added support for rendering RasterFrame types in IPython/Jupyter.
 * Added new tile functions `rf_round`, `rf_abs`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.

From 92d9a1a63bd8f8912ff8ee78b6c5486a28adb5be Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 08:45:19 -0400
Subject: [PATCH 277/380] GeoTIFF writing post-walkthrough comments.

---
 .../ProjectedLayerMetadataAggregate.scala     |  3 +-
 .../aggregates/TileRasterizerAggregate.scala  |  7 ++--
 .../extensions/DataFrameMethods.scala         |  6 ++--
 .../geotiff/GeoTiffDataSource.scala           | 26 ++++++++-------
 .../src/main/python/docs/raster-write.pymd    |  7 ++--
 .../main/python/pyrasterframes/__init__.py    | 32 ++++++++++++-------
 6 files changed, 46 insertions(+), 35 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
index 20d827489..4af542d85 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
@@ -94,9 +94,8 @@ object ProjectedLayerMetadataAggregate {
   // Ordering must match InputRecord schema
     new ProjectedLayerMetadataAggregate(destCRS, destDims)(extent, crs, cellType, tileSize).as[TileLayerMetadata[SpatialKey]]
 
-
   private[expressions]
-  case class InputRecord(extent: Extent, crs: CRS, cellType: CellType, tileSize: TileDimensions) { self ⇒
+  case class InputRecord(extent: Extent, crs: CRS, cellType: CellType, tileSize: TileDimensions) {
     def toBufferRecord(destCRS: CRS): BufferRecord = {
       val transform = Transform(crs, destCRS)
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
index a93abb15b..e1b11ae3b 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/TileRasterizerAggregate.scala
@@ -58,7 +58,7 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
   override def dataType: DataType = schemaOf[Raster[Tile]]
 
   override def initialize(buffer: MutableAggregationBuffer): Unit = {
-    buffer(0) = ArrayTile.empty(prd.cellType, prd.cols, prd.rows)
+    buffer(0) = ArrayTile.empty(prd.cellType, prd.totalCols, prd.totalRows)
   }
 
   override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
@@ -90,13 +90,14 @@ class TileRasterizerAggregate(prd: ProjectedRasterDefinition) extends UserDefine
 object TileRasterizerAggregate {
   val nodeName = "rf_tile_rasterizer_aggregate"
   /**  Convenience grouping of  parameters needed for running aggregate. */
-  case class ProjectedRasterDefinition(cols: Int, rows: Int, cellType: CellType, crs: CRS, extent: Extent, sampler: ResampleMethod = ResampleMethod.DEFAULT)
+  case class ProjectedRasterDefinition(totalCols: Int, totalRows: Int, cellType: CellType, crs: CRS, extent: Extent, sampler: ResampleMethod = ResampleMethod.DEFAULT)
 
   object ProjectedRasterDefinition {
     def apply(tlm: TileLayerMetadata[_]): ProjectedRasterDefinition = apply(tlm, ResampleMethod.DEFAULT)
 
     def apply(tlm: TileLayerMetadata[_], sampler: ResampleMethod): ProjectedRasterDefinition = {
-      val actualSize = tlm.layout.toRasterExtent().gridBoundsFor(tlm.extent)
+      // Try to determine the actual dimensions of our data coverage
+      val actualSize = tlm.layout.toRasterExtent().gridBoundsFor(tlm.extent) // <--- Do we have the math right here?
       val cols = actualSize.width
       val rows = actualSize.height
       new ProjectedRasterDefinition(cols, rows, tlm.cellType, tlm.crs, tlm.extent, sampler)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
index 3735eef37..1e94ff3ca 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/extensions/DataFrameMethods.scala
@@ -22,22 +22,20 @@
 package org.locationtech.rasterframes.extensions
 
 import geotrellis.proj4.CRS
-import geotrellis.raster.{MultibandTile, Tile}
 import geotrellis.spark.io._
 import geotrellis.spark.{SpaceTimeKey, SpatialComponent, SpatialKey, TemporalKey, TileLayerMetadata}
 import geotrellis.util.MethodExtensions
-import geotrellis.vector.{Extent, ProjectedExtent}
-import org.apache.spark.rdd.RDD
+import geotrellis.vector.Extent
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.types.{MetadataBuilder, StructField}
 import org.apache.spark.sql.{Column, DataFrame, TypedColumn}
-import org.locationtech.rasterframes.{MetadataKeys, RasterFrameLayer}
 import org.locationtech.rasterframes.StandardColumns._
 import org.locationtech.rasterframes.encoders.CatalystSerializer._
 import org.locationtech.rasterframes.encoders.StandardEncoders._
 import org.locationtech.rasterframes.expressions.DynamicExtractors
 import org.locationtech.rasterframes.tiles.ProjectedRasterTile
 import org.locationtech.rasterframes.util._
+import org.locationtech.rasterframes.{MetadataKeys, RasterFrameLayer}
 import spray.json.JsonFormat
 
 import scala.util.Try
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 1b639a1f5..749f15827 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -24,19 +24,18 @@ package org.locationtech.rasterframes.datasource.geotiff
 import java.net.URI
 
 import _root_.geotrellis.proj4.CRS
+import _root_.geotrellis.raster._
 import _root_.geotrellis.raster.io.geotiff.compression._
 import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
 import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
-import _root_.geotrellis.raster._
+import _root_.geotrellis.spark._
 import com.typesafe.scalalogging.LazyLogging
-import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.apache.spark.sql._
-import org.apache.spark.sql.functions._
-import org.apache.spark.sql.rf.TileUDT
+import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.datasource._
-import org.locationtech.rasterframes.expressions.aggregates.{ProjectedLayerMetadataAggregate, TileRasterizerAggregate}
 import org.locationtech.rasterframes.expressions.aggregates.TileRasterizerAggregate.ProjectedRasterDefinition
+import org.locationtech.rasterframes.expressions.aggregates.{ProjectedLayerMetadataAggregate, TileRasterizerAggregate}
 import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
 import org.locationtech.rasterframes.util._
 
@@ -96,32 +95,37 @@ class GeoTiffDataSource extends DataSourceRegister
       require(parameters.crs.nonEmpty, "A destination CRS must be provided")
       require(tileCols.nonEmpty, "need at least one tile column")
 
+      // Grab CRS to project into
       val destCRS = parameters.crs.get
 
-      val (extCol, crsCol) = {
+      // Select the anchoring Tile, Extent and CRS columns
+      val (extCol, crsCol, tileCol) = {
+        // Favor "ProjectedRaster" columns
         val prCols = df.projRasterColumns
         if(prCols.nonEmpty) {
-          (rf_extent(prCols.head), rf_crs(prCols.head))
+          (rf_extent(prCols.head), rf_crs(prCols.head), rf_tile(prCols.head))
         }
         else {
+          // If no "ProjectedRaster" column, look for single Extent and CRS columns.
           val crsCols = df.crsColumns
           require(crsCols.size == 1, "Exactly one CRS column must be in DataFrame")
           val extentCols = df.extentColumns
           require(extentCols.size == 1, "Exactly one Extent column must be in DataFrame")
-          (extentCols.head, crsCols.head)
+          (extentCols.head, crsCols.head, tileCols.head)
         }
       }
 
-      val tlm = df
+      // Scan table and constuct what the TileLayerMetadata would be in the specified destination CRS.
+      val tlm: TileLayerMetadata[SpatialKey] = df
         .select(ProjectedLayerMetadataAggregate(
-          destCRS, extCol, crsCol, rf_cell_type(tileCols.head), rf_dimensions(tileCols.head)
+          destCRS, extCol, crsCol, rf_cell_type(tileCol), rf_dimensions(tileCol)
         ))
         .first()
 
       val c = ProjectedRasterDefinition(tlm)
 
       val config = parameters.rasterDimensions.map { dims =>
-        c.copy(cols = dims.cols, rows = dims.rows)
+        c.copy(totalCols = dims.cols, totalRows = dims.rows)
       }.getOrElse(c)
 
       val aggs = tileCols
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 458be4c29..b1af4b401 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -63,7 +63,7 @@ GeoTIFF is one of the most common file formats for spatial data, providing flexi
 
 One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
 
-Fortunately, we can use the cluster computing capability to downlample the data into a more manageble saze. For sake of example, let's render a simple RGB overview image of our scene as a small raster:
+Fortunately, we can use the cluster computing capability to downlample the data (using nearest-neighbor) into a more manageble size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
 
 ```python write_geotiff
 import os.path
@@ -94,10 +94,9 @@ with rasterio.open(outfile) as src:
 
 ## GeoTrellis Layers
 
-[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames write data from an appropriate DataFrame into a GeoTrellis Layer.
-
-\[ More details see https://s22s.myjetbrains.com/youtrack/issue/RF-72 \]
+[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames write data from an appropriate DataFrame into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both reading and writing of GeoTrellis layers.
 
+> An example is forthcoming.
 
 ## Parquet
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index dcc0da624..c9ddea6cf 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -180,18 +180,25 @@ def _geotiff_writer(
     raster_dimensions=None,
     **options):
 
+    def set_dims(parts):
+        parts = [int(p) for p in parts]
+        assert(len(parts) == 2, "Expected dimensions specification to have exactly two components")
+        assert(all([p > 0 for p in parts]), "Expected all components in dimensions to be positive integers")
+        options.update({
+            "imageWidth": parts[0],
+            "imageHeight": parts[1]
+        })
+        try:
+            parts = [int(p) for p in parts]
+            assert(all([p > 0 for p in parts]), 'nice message')
+        except ValueError:
+            raise
+
     if raster_dimensions is not None:
-        if isinstance(raster_dimensions, tuple):
-            options.update({
-                "imageWidth": raster_dimensions[0],
-                "imageHeight": raster_dimensions[1]
-            })
+        if isinstance(raster_dimensions, (list, tuple)):
+            set_dims(raster_dimensions)
         elif isinstance(raster_dimensions, str):
-            parts = raster_dimensions.split(',')
-            options.update({
-                "imageWidth": parts[0],
-                "imageHeight": parts[1]
-            })
+            set_dims(raster_dimensions.split(','))
 
     if crs is not None:
         options.update({
@@ -200,8 +207,11 @@ def _geotiff_writer(
 
     return _aliased_writer(df_writer, "geotiff", path, **options)
 
-# Patch new method on SparkSession to mirror Scala approach
+
+# Patch RasterFrames initialization method on SparkSession to mirror Scala approach
 SparkSession.withRasterFrames = _rf_init
+
+# Patch Kryo serialization initialization method on SparkSession.Builder to mirror Scala approach
 SparkSession.Builder.withKryoSerialization = _kryo_init
 
 # Add the 'asLayer' method to pyspark DataFrame

From 42016926f4c6cb669c729e949e9c681738f31c4f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 08:58:52 -0400
Subject: [PATCH 278/380] Regression fix.

---
 .../org/locationtech/rasterframes/ExtensionMethodSpec.scala   | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 4b917b86e..7652c010e 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -25,6 +25,7 @@ import geotrellis.proj4.LatLng
 import geotrellis.raster.{ByteCellType, GridBounds, TileLayout}
 import geotrellis.spark.tiling.{CRSWorldExtent, LayoutDefinition}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
+import org.apache.spark.sql.Encoders
 import org.locationtech.rasterframes.util.SubdivideSupport
 
 /**
@@ -61,6 +62,8 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
       df.extentColumns.size should be(2)
     }
     it("should find multiple crs columns") {
+      // Not sure why implicit resolution isn't handling this properly.
+      implicit val enc = Encoders.tuple(crsEncoder, Encoders.STRING, crsEncoder, Encoders.scalaDouble)
       val df = Seq((pe.crs, "fred", pe.crs, 34.0)).toDF("c1", "s", "c2", "n")
       df.crsColumns.size should be (2)
     }
@@ -70,7 +73,6 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
       assert(tl1.subdivide(1) === tl1)
       assert(tl1.subdivide(2) === TileLayout(4, 6, 5, 5))
       assertThrows[IllegalArgumentException](tl1.subdivide(-1))
-
     }
     it("should split KeyBounds[SpatialKey]") {
       val grid = GridBounds(0, 0, 9, 9)

From 54e7ef1a9ee6bae6997ad16166cf7fa1e8fb211c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 12:56:47 -0400
Subject: [PATCH 279/380] Tweaked ordering of scheme table.

---
 pyrasterframes/src/main/python/docs/raster-read.pymd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 522d7b6ce..1d3f8b252 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -61,10 +61,10 @@ RasterFrames relies on three different IO drivers, selected based on a combinati
 | `http://`           |  ✔︎          | ✔︎       | -      |
 | `https://`          |  ✔︎          | ✔︎       | -      |
 | `ftp://`            | `/vsicurl/`  | ✔       | -      |
-| `s3://`             |  `/vsis3/` ︎  | ✔︎       | -      |
 | `hdfs://`           |  `/vsihdfs/` ︎| -       | ✔︎      |
-| `s3n://`            |  - ︎          | -        | ✔︎      |
-| `s3a://`            |  - ︎          | -   | ✔︎      |
+| `s3://`             |  `/vsis3/` ︎  | ✔︎       | -      |
+| `s3n://`            |  - ︎          | -       | ✔︎      |
+| `s3a://`            |  - ︎          | -       | ✔︎      |
 | `wasb://`           |  `/vsiaz/` ︎  | -       | ✔︎      |
 | `wasbs://`          |  - ︎          | -       | ✔︎      |
 

From 8c641098d215f3e45e5974b1804c2a743d0adb2b Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 14:02:46 -0400
Subject: [PATCH 280/380] Shapely spelling fix.

---
 pyrasterframes/src/main/python/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index a4811824b..262cc7b74 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -134,7 +134,7 @@ def doctype(self):
         'pytest==3.4.2',
         'pypandoc',
         'numpy>=1.7',
-        'shapley',
+        'shapely',
         'pandas',
         'rasterio'
     ],

From 2c6df24b53d38895812e4e9eada5dfe9195fb1b0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 14:38:00 -0400
Subject: [PATCH 281/380] Separated out RasterFunctionsTests.

---
 .../geotiff/GeoTiffDataSource.scala           |   3 -
 .../main/python/tests/PyRasterFramesTests.py  | 175 ---------------
 .../main/python/tests/RasterFunctionsTests.py | 204 ++++++++++++++++++
 3 files changed, 204 insertions(+), 178 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/tests/RasterFunctionsTests.py

diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index b40bfa337..749f15827 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -28,10 +28,7 @@ import _root_.geotrellis.raster._
 import _root_.geotrellis.raster.io.geotiff.compression._
 import _root_.geotrellis.raster.io.geotiff.tags.codes.ColorSpace
 import _root_.geotrellis.raster.io.geotiff.{GeoTiffOptions, MultibandGeoTiff, Tags, Tiled}
-<<<<<<< HEAD
-=======
 import _root_.geotrellis.spark._
->>>>>>> develop
 import com.typesafe.scalalogging.LazyLogging
 import org.apache.spark.sql._
 import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, DataSourceRegister, RelationProvider}
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index f60b8bc2e..b4f121ea7 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -28,181 +28,6 @@
 from . import TestEnvironment
 
 
-class RasterFunctions(TestEnvironment):
-
-    def setUp(self):
-        self.create_layer()
-
-    def test_setup(self):
-        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
-                         "org.apache.spark.serializer.KryoSerializer")
-
-    def test_identify_columns(self):
-        cols = self.rf.tile_columns()
-        self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
-        print("Tile columns: ", cols)
-        col = self.rf.spatial_key_column()
-        self.assertIsInstance(col, Column, '`spatialKeyColumn` was not found')
-        print("Spatial key column: ", col)
-        col = self.rf.temporal_key_column()
-        self.assertIsNone(col, '`temporalKeyColumn` should be `None`')
-        print("Temporal key column: ", col)
-
-    def test_tile_creation(self):
-        base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
-        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"),
-                            rf_make_ones_tile(3, 3, "int32"))
-        tiles.show()
-        self.assertEqual(tiles.count(), 4)
-
-    def test_multi_column_operations(self):
-        df1 = self.rf.withColumnRenamed('tile', 't1').as_layer()
-        df2 = self.rf.withColumnRenamed('tile', 't2').as_layer()
-        df3 = df1.spatial_join(df2).as_layer()
-        df3 = df3.withColumn('norm_diff', rf_normalized_difference('t1', 't2'))
-        # df3.printSchema()
-
-        aggs = df3.agg(
-            rf_agg_mean('norm_diff'),
-        )
-        aggs.show()
-        row = aggs.first()
-
-        self.assertTrue(self.rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
-
-    def test_general(self):
-        meta = self.rf.tile_layer_metadata()
-        self.assertIsNotNone(meta['bounds'])
-        df = self.rf.withColumn('dims', rf_dimensions('tile')) \
-            .withColumn('type', rf_cell_type('tile')) \
-            .withColumn('dCells', rf_data_cells('tile')) \
-            .withColumn('ndCells', rf_no_data_cells('tile')) \
-            .withColumn('min', rf_tile_min('tile')) \
-            .withColumn('max', rf_tile_max('tile')) \
-            .withColumn('mean', rf_tile_mean('tile')) \
-            .withColumn('sum', rf_tile_sum('tile')) \
-            .withColumn('stats', rf_tile_stats('tile')) \
-            .withColumn('extent', st_extent('geometry')) \
-            .withColumn('extent_geom1', st_geometry('extent')) \
-            .withColumn('ascii', rf_render_ascii('tile')) \
-            .withColumn('log', rf_log('tile')) \
-            .withColumn('exp', rf_exp('tile')) \
-            .withColumn('expm1', rf_expm1('tile')) \
-            .withColumn('round', rf_round('tile')) \
-            .withColumn('abs', rf_abs('tile'))
-
-        df.first()
-
-    def test_agg_mean(self):
-        mean = self.rf.agg(rf_agg_mean('tile')).first()['rf_agg_mean(tile)']
-        self.assertTrue(self.rounded_compare(mean, 10160))
-
-    def test_aggregations(self):
-        aggs = self.rf.agg(
-            rf_agg_data_cells('tile'),
-            rf_agg_no_data_cells('tile'),
-            rf_agg_stats('tile'),
-            rf_agg_approx_histogram('tile')
-        )
-        row = aggs.first()
-
-        # print(row['rf_agg_data_cells(tile)'])
-        self.assertEqual(row['rf_agg_data_cells(tile)'], 387000)
-        self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
-        self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
-
-    def test_sql(self):
-        self.rf.createOrReplaceTempView("rf_test_sql")
-
-        self.spark.sql("""SELECT tile, 
-                            rf_local_add(tile, 1) AS and_one, 
-                            rf_local_subtract(tile, 1) AS less_one, 
-                            rf_local_multiply(tile, 2) AS times_two, 
-                            rf_local_divide(tile, 2) AS over_two 
-                        FROM rf_test_sql""").createOrReplaceTempView('rf_test_sql_1')
-
-        statsRow = self.spark.sql("""
-        SELECT rf_tile_mean(tile) as base,
-            rf_tile_mean(and_one) as plus_one,
-            rf_tile_mean(less_one) as minus_one,
-            rf_tile_mean(times_two) as double,
-            rf_tile_mean(over_two) as half
-        FROM rf_test_sql_1
-        """).first()
-
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.plus_one - 1))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.minus_one + 1))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.double / 2))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.half * 2))
-
-    def test_explode(self):
-        import pyspark.sql.functions as F
-        self.rf.select('spatial_key', rf_explode_tiles('tile')).show()
-        # +-----------+------------+---------+-------+
-        # |spatial_key|column_index|row_index|tile   |
-        # +-----------+------------+---------+-------+
-        # |[2,1]      |4           |0        |10150.0|
-        cell = self.rf.select(self.rf.spatial_key_column(), rf_explode_tiles(self.rf.tile)) \
-            .where(F.col("spatial_key.col") == 2) \
-            .where(F.col("spatial_key.row") == 1) \
-            .where(F.col("column_index") == 4) \
-            .where(F.col("row_index") == 0) \
-            .select(F.col("tile")) \
-            .collect()[0][0]
-        self.assertEqual(cell, 10150.0)
-
-        # Test the sample version
-        frac = 0.01
-        sample_count = self.rf.select(rf_explode_tiles_sample(frac, 1872, 'tile')).count()
-        print('Sample count is {}'.format(sample_count))
-        self.assertTrue(sample_count > 0)
-        self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
-
-    def test_mask_by_value(self):
-        from pyspark.sql.functions import lit
-
-        # create an artificial mask for values > 25000; masking value will be 4
-        mask_value = 4
-
-        rf1 = self.rf.select(self.rf.tile,
-                             rf_local_multiply(
-                                 rf_convert_cell_type(
-                                     rf_local_greater_int(self.rf.tile, 25000),
-                                     "uint8"),
-                                 lit(mask_value)).alias('mask'))
-        rf2 = rf1.select(rf1.tile, rf_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
-        result = rf2.agg(rf_agg_no_data_cells(rf2.tile) < rf_agg_no_data_cells(rf2.masked)) \
-            .collect()[0][0]
-        self.assertTrue(result)
-
-        rf3 = rf1.select(rf1.tile, rf_inverse_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
-        result = rf3.agg(rf_agg_no_data_cells(rf3.tile) < rf_agg_no_data_cells(rf3.masked)) \
-            .collect()[0][0]
-        self.assertTrue(result)
-
-    def test_resample(self):
-        from pyspark.sql.functions import lit
-        result = self.rf.select(
-            rf_tile_min(rf_local_equal(
-                rf_resample(rf_resample(self.rf.tile, lit(2)), lit(0.5)),
-                self.rf.tile))
-        ).collect()[0][0]
-
-        self.assertTrue(result == 1)  # short hand for all values are true
-
-    def test_exists_for_all(self):
-        df = self.rf.withColumn('should_exist', rf_make_ones_tile(5, 5, 'int8')) \
-            .withColumn('should_not_exist', rf_make_zeros_tile(5, 5, 'int8'))
-
-        should_exist = df.select(rf_exists(df.should_exist).alias('se')).take(1)[0].se
-        self.assertTrue(should_exist)
-
-        should_not_exist = df.select(rf_exists(df.should_not_exist).alias('se')).take(1)[0].se
-        self.assertTrue(not should_not_exist)
-
-        self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
-        self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
-
 
 class CellTypeHandling(unittest.TestCase):
 
diff --git a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
new file mode 100644
index 000000000..4436c6fc1
--- /dev/null
+++ b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
@@ -0,0 +1,204 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import unittest
+
+import numpy as np
+from pyrasterframes.rasterfunctions import *
+from pyrasterframes.rf_types import *
+from pyspark.sql import SQLContext
+from pyspark.sql.functions import *
+from . import TestEnvironment
+
+
+class RasterFunctions(TestEnvironment):
+
+    def setUp(self):
+        self.create_layer()
+
+    def test_setup(self):
+        self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
+                         "org.apache.spark.serializer.KryoSerializer")
+
+    def test_identify_columns(self):
+        cols = self.rf.tile_columns()
+        self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
+        print("Tile columns: ", cols)
+        col = self.rf.spatial_key_column()
+        self.assertIsInstance(col, Column, '`spatialKeyColumn` was not found')
+        print("Spatial key column: ", col)
+        col = self.rf.temporal_key_column()
+        self.assertIsNone(col, '`temporalKeyColumn` should be `None`')
+        print("Temporal key column: ", col)
+
+    def test_tile_creation(self):
+        base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
+        tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"),
+                            rf_make_ones_tile(3, 3, "int32"))
+        tiles.show()
+        self.assertEqual(tiles.count(), 4)
+
+    def test_multi_column_operations(self):
+        df1 = self.rf.withColumnRenamed('tile', 't1').as_layer()
+        df2 = self.rf.withColumnRenamed('tile', 't2').as_layer()
+        df3 = df1.spatial_join(df2).as_layer()
+        df3 = df3.withColumn('norm_diff', rf_normalized_difference('t1', 't2'))
+        # df3.printSchema()
+
+        aggs = df3.agg(
+            rf_agg_mean('norm_diff'),
+        )
+        aggs.show()
+        row = aggs.first()
+
+        self.assertTrue(self.rounded_compare(row['rf_agg_mean(norm_diff)'], 0))
+
+    def test_general(self):
+        meta = self.rf.tile_layer_metadata()
+        self.assertIsNotNone(meta['bounds'])
+        df = self.rf.withColumn('dims', rf_dimensions('tile')) \
+            .withColumn('type', rf_cell_type('tile')) \
+            .withColumn('dCells', rf_data_cells('tile')) \
+            .withColumn('ndCells', rf_no_data_cells('tile')) \
+            .withColumn('min', rf_tile_min('tile')) \
+            .withColumn('max', rf_tile_max('tile')) \
+            .withColumn('mean', rf_tile_mean('tile')) \
+            .withColumn('sum', rf_tile_sum('tile')) \
+            .withColumn('stats', rf_tile_stats('tile')) \
+            .withColumn('extent', st_extent('geometry')) \
+            .withColumn('extent_geom1', st_geometry('extent')) \
+            .withColumn('ascii', rf_render_ascii('tile')) \
+            .withColumn('log', rf_log('tile')) \
+            .withColumn('exp', rf_exp('tile')) \
+            .withColumn('expm1', rf_expm1('tile')) \
+            .withColumn('round', rf_round('tile')) \
+            .withColumn('abs', rf_abs('tile'))
+
+        df.first()
+
+    def test_agg_mean(self):
+        mean = self.rf.agg(rf_agg_mean('tile')).first()['rf_agg_mean(tile)']
+        self.assertTrue(self.rounded_compare(mean, 10160))
+
+    def test_aggregations(self):
+        aggs = self.rf.agg(
+            rf_agg_data_cells('tile'),
+            rf_agg_no_data_cells('tile'),
+            rf_agg_stats('tile'),
+            rf_agg_approx_histogram('tile')
+        )
+        row = aggs.first()
+
+        # print(row['rf_agg_data_cells(tile)'])
+        self.assertEqual(row['rf_agg_data_cells(tile)'], 387000)
+        self.assertEqual(row['rf_agg_no_data_cells(tile)'], 1000)
+        self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
+
+    def test_sql(self):
+        self.rf.createOrReplaceTempView("rf_test_sql")
+
+        self.spark.sql("""SELECT tile, 
+                            rf_local_add(tile, 1) AS and_one, 
+                            rf_local_subtract(tile, 1) AS less_one, 
+                            rf_local_multiply(tile, 2) AS times_two, 
+                            rf_local_divide(tile, 2) AS over_two 
+                        FROM rf_test_sql""").createOrReplaceTempView('rf_test_sql_1')
+
+        statsRow = self.spark.sql("""
+        SELECT rf_tile_mean(tile) as base,
+            rf_tile_mean(and_one) as plus_one,
+            rf_tile_mean(less_one) as minus_one,
+            rf_tile_mean(times_two) as double,
+            rf_tile_mean(over_two) as half
+        FROM rf_test_sql_1
+        """).first()
+
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.plus_one - 1))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.minus_one + 1))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.double / 2))
+        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.half * 2))
+
+    def test_explode(self):
+        import pyspark.sql.functions as F
+        self.rf.select('spatial_key', rf_explode_tiles('tile')).show()
+        # +-----------+------------+---------+-------+
+        # |spatial_key|column_index|row_index|tile   |
+        # +-----------+------------+---------+-------+
+        # |[2,1]      |4           |0        |10150.0|
+        cell = self.rf.select(self.rf.spatial_key_column(), rf_explode_tiles(self.rf.tile)) \
+            .where(F.col("spatial_key.col") == 2) \
+            .where(F.col("spatial_key.row") == 1) \
+            .where(F.col("column_index") == 4) \
+            .where(F.col("row_index") == 0) \
+            .select(F.col("tile")) \
+            .collect()[0][0]
+        self.assertEqual(cell, 10150.0)
+
+        # Test the sample version
+        frac = 0.01
+        sample_count = self.rf.select(rf_explode_tiles_sample(frac, 1872, 'tile')).count()
+        print('Sample count is {}'.format(sample_count))
+        self.assertTrue(sample_count > 0)
+        self.assertTrue(sample_count < (frac * 1.1) * 387000)  # give some wiggle room
+
+    def test_mask_by_value(self):
+        from pyspark.sql.functions import lit
+
+        # create an artificial mask for values > 25000; masking value will be 4
+        mask_value = 4
+
+        rf1 = self.rf.select(self.rf.tile,
+                             rf_local_multiply(
+                                 rf_convert_cell_type(
+                                     rf_local_greater_int(self.rf.tile, 25000),
+                                     "uint8"),
+                                 lit(mask_value)).alias('mask'))
+        rf2 = rf1.select(rf1.tile, rf_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
+        result = rf2.agg(rf_agg_no_data_cells(rf2.tile) < rf_agg_no_data_cells(rf2.masked)) \
+            .collect()[0][0]
+        self.assertTrue(result)
+
+        rf3 = rf1.select(rf1.tile, rf_inverse_mask_by_value(rf1.tile, rf1.mask, lit(mask_value)).alias('masked'))
+        result = rf3.agg(rf_agg_no_data_cells(rf3.tile) < rf_agg_no_data_cells(rf3.masked)) \
+            .collect()[0][0]
+        self.assertTrue(result)
+
+    def test_resample(self):
+        from pyspark.sql.functions import lit
+        result = self.rf.select(
+            rf_tile_min(rf_local_equal(
+                rf_resample(rf_resample(self.rf.tile, lit(2)), lit(0.5)),
+                self.rf.tile))
+        ).collect()[0][0]
+
+        self.assertTrue(result == 1)  # short hand for all values are true
+
+    def test_exists_for_all(self):
+        df = self.rf.withColumn('should_exist', rf_make_ones_tile(5, 5, 'int8')) \
+            .withColumn('should_not_exist', rf_make_zeros_tile(5, 5, 'int8'))
+
+        should_exist = df.select(rf_exists(df.should_exist).alias('se')).take(1)[0].se
+        self.assertTrue(should_exist)
+
+        should_not_exist = df.select(rf_exists(df.should_not_exist).alias('se')).take(1)[0].se
+        self.assertTrue(not should_not_exist)
+
+        self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
+        self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)

From 33e228b3825f71bece354f9abcf29ead008dd080 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Thu, 1 Aug 2019 16:04:11 -0400
Subject: [PATCH 282/380] update nodata docs to show cell type conversion
 behavior and aggreations

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 .../src/main/python/docs/nodata-handling.pymd | 84 +++++++++++++++++--
 1 file changed, 75 insertions(+), 9 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index d9ef1feb5..ed4043de9 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -15,6 +15,9 @@ import pyrasterframes
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
 from IPython.display import display
+import pandas as pd
+import numpy as np
+from pyrasterframes.rf_types import Tile
 
 spark = pyrasterframes.get_spark_session()
 ```
@@ -132,29 +135,92 @@ masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
 
 It's also nice to view a sample.
 
-```python show_masked
+```python
 sample = masked.orderBy(-rf_no_data_cells('blue_masked')).select(rf_tile('blue_masked'), rf_tile('scl')).first()
 display(sample[0])
 ```
 
 And the original SCL data.
 
-```python show_scl
+```python
 display(sample[1])
 ```
 
-## NoData in Arithmetic Operations
+## Changing a Tile's NoData Values
+
+One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a rasterframe from a tile with values of 0, 1, 2, and 3.
+
+```python create_dummy_tile
+tile_size = 100
+x = np.zeros((tile_size, tile_size), dtype='int16')
+
+for i in range(4):
+    x[:, i*tile_size//4:(i+1)*tile_size//4] = i
+x = Tile(x)
+
+rf = spark.createDataFrame(pd.DataFrame(data = 
+        {'tile': [x]}))
+x
+```
+
+First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a tile with the cell type 'uint16ud2'.
+
+```python
+def get_nodata_ct(nd_val):
+    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
+
+masked_rf = rf.withColumn('tile_nd_1', 
+                           rf_convert_cell_type('tile', get_nodata_ct(1))) \
+              .withColumn('tile_nd_2',
+                          rf_convert_cell_type('tile_nd_1', get_nodata_ct(2))) \
+```
+
+```python
+collected = masked_rf.collect()
+```
+
+Let's look at the new Tiles we created. The tile named `tile_nd_1` has the 1 values masked out as expected. 
+
+```python
+display(collected[0].tile_nd_1)
+```
+
+And the tile named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the tile by setting a new user defined NoData value to `tile_nd_1` the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
+
+```python
+display(collected[0].tile_nd_2)
+```
 
-local algebra example; same celltype what happens to nodata 
-Possibly use st_geomFromWkt and rf_rasterize to create something to work from
+## Nodata Values in Aggregation
 
-agg
+Let's use the same tile as before to demonstrate how NoData values affect tile aggregations
 
+```python
+tile_size = 100
+x = np.eye(tile_size, dtype='int16')
+for i in range(4):
+    x[:, i*tile_size//4:(i+1)*tile_size//4] = i
+x = Tile(x)
+
+rf = spark.createDataFrame(pd.DataFrame(data = 
+        {'tile': [x]}))
+display(x)
+```
 
-## Dealing with Multiple Cell Types
+First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked. 
 
-Quick demo of one ND tile one raw tile
+```python
+def get_nodata_ct(nd_val):
+    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
 
-Quick demo of ND in two different cell types
+masked_rf = rf.withColumn('tile_nd_1', 
+                           rf_convert_cell_type('tile', get_nodata_ct(1))) \
+              .withColumn('tile_nd_2',
+                          rf_convert_cell_type('tile_nd_1', get_nodata_ct(2)))
+```
 
+The results of `rf_tile_sum` vary on the tiles that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
 
+```python
+masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
+```
\ No newline at end of file

From 84985ad0630a787f7cb8f2c627f5791c7e9b96a1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 16:05:39 -0400
Subject: [PATCH 283/380] PR feedback.

---
 .../rasterframes/ReprojectGeometrySpec.scala  | 28 +++----
 .../geotiff/GeoTiffDataSource.scala           | 82 +++++++++----------
 docs/README.md                                | 10 +--
 docs/src/main/paradox/release-notes.md        |  1 +
 .../src/main/python/docs/concepts.md          | 10 ++-
 .../src/main/python/docs/description.md       | 13 +--
 .../src/main/python/docs/getting-started.pymd |  3 +-
 pyrasterframes/src/main/python/docs/index.md  |  2 +-
 .../src/main/python/docs/languages.pymd       | 12 ++-
 .../src/main/python/docs/reference.pymd       |  4 +-
 .../main/python/pyrasterframes/__init__.py    | 11 +--
 pyrasterframes/src/main/python/setup.py       | 15 ++--
 .../src/main/python/tests/VectorTypesTests.py |  2 +-
 13 files changed, 103 insertions(+), 90 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
index 23a3ddf25..a58294287 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ReprojectGeometrySpec.scala
@@ -33,7 +33,7 @@ import org.locationtech.jts.geom._
 class ReprojectGeometrySpec extends TestEnvironment {
   // Note: Test data copied from ReprojectSpec in GeoTrellis
   val fact = new GeometryFactory()
-  val latLng: Geometry = fact.createLineString(Array(
+  val llLineString: Geometry = fact.createLineString(Array(
     new Coordinate(-111.09374999999999, 34.784483415461345),
     new Coordinate(-111.09374999999999, 43.29919735147067),
     new Coordinate(-75.322265625, 43.29919735147067),
@@ -41,7 +41,7 @@ class ReprojectGeometrySpec extends TestEnvironment {
     new Coordinate(-111.09374999999999, 34.784483415461345)
   ))
 
-  val webMercator: Geometry = fact.createLineString(Array(
+  val wmLineString: Geometry = fact.createLineString(Array(
     new Coordinate(-12366899.680315234, 4134631.734001753),
     new Coordinate(-12366899.680315234, 5357624.186564572),
     new Coordinate(-8384836.254770693, 5357624.186564572),
@@ -54,7 +54,7 @@ class ReprojectGeometrySpec extends TestEnvironment {
 
     it("should handle two literal CRSs") {
 
-      val df = Seq((latLng, webMercator)).toDF("ll", "wm")
+      val df = Seq((llLineString, wmLineString)).toDF("ll", "wm")
 
       val rp = df.select(
         st_reproject($"ll", LatLng, WebMercator) as "wm2",
@@ -65,14 +65,14 @@ class ReprojectGeometrySpec extends TestEnvironment {
 
       val (wm2, ll2, wm3) = rp.first()
 
-      wm2 should matchGeom(webMercator, 0.00001)
-      ll2 should matchGeom(latLng, 0.00001)
-      wm3 should matchGeom(webMercator, 0.00001)
+      wm2 should matchGeom(wmLineString, 0.00001)
+      ll2 should matchGeom(llLineString, 0.00001)
+      wm3 should matchGeom(wmLineString, 0.00001)
     }
 
     it("should handle one literal crs") {
       implicit val enc = Encoders.tuple(jtsGeometryEncoder, jtsGeometryEncoder, crsEncoder)
-      val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
+      val df = Seq((llLineString, wmLineString, LatLng: CRS)).toDF("ll", "wm", "llCRS")
 
       val rp = df.select(
         st_reproject($"ll", $"llCRS", WebMercator) as "wm2",
@@ -83,9 +83,9 @@ class ReprojectGeometrySpec extends TestEnvironment {
 
       val (wm2, ll2, wm3) = rp.first()
 
-      wm2 should matchGeom(webMercator, 0.00001)
-      ll2 should matchGeom(latLng, 0.00001)
-      wm3 should matchGeom(webMercator, 0.00001)
+      wm2 should matchGeom(wmLineString, 0.00001)
+      ll2 should matchGeom(llLineString, 0.00001)
+      wm3 should matchGeom(wmLineString, 0.00001)
     }
 
     it("should accept other geometry types") {
@@ -98,7 +98,7 @@ class ReprojectGeometrySpec extends TestEnvironment {
 
     it("should work in SQL") {
       implicit val enc = Encoders.tuple(jtsGeometryEncoder, jtsGeometryEncoder, crsEncoder)
-      val df = Seq((latLng, webMercator, LatLng: CRS)).toDF("ll", "wm", "llCRS")
+      val df = Seq((llLineString, wmLineString, LatLng: CRS)).toDF("ll", "wm", "llCRS")
       df.createOrReplaceTempView("geom")
 
       val rp = spark.sql(
@@ -112,9 +112,9 @@ class ReprojectGeometrySpec extends TestEnvironment {
 
       val (wm2, ll2, wm3) = rp.first()
 
-      wm2 should matchGeom(webMercator, 0.00001)
-      ll2 should matchGeom(latLng, 0.00001)
-      wm3 should matchGeom(webMercator, 0.00001)
+      wm2 should matchGeom(wmLineString, 0.00001)
+      ll2 should matchGeom(llLineString, 0.00001)
+      wm3 should matchGeom(wmLineString, 0.00001)
 
       checkDocs("st_reproject")
     }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 749f15827..fe01d224d 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -40,12 +40,10 @@ import org.locationtech.rasterframes.model.{LazyCRS, TileDimensions}
 import org.locationtech.rasterframes.util._
 
 /**
- * Spark SQL data source over GeoTIFF files.
- * @since 1/14/18
+  * Spark SQL data source over GeoTIFF files.
  */
-class GeoTiffDataSource extends DataSourceRegister
-  with RelationProvider with CreatableRelationProvider
-  with DataSourceOptions with LazyLogging {
+class GeoTiffDataSource
+    extends DataSourceRegister with RelationProvider with CreatableRelationProvider with DataSourceOptions with LazyLogging {
   import GeoTiffDataSource._
 
   def shortName() = GeoTiffDataSource.SHORT_NAME
@@ -56,42 +54,40 @@ class GeoTiffDataSource extends DataSourceRegister
 
     val p = parameters.path.get
 
-    if(p.getPath.contains("*")) {
+    if (p.getPath.contains("*")) {
       val bandCount = parameters.get(GeoTiffDataSource.BAND_COUNT_PARAM).map(_.toInt).getOrElse(1)
       GeoTiffCollectionRelation(sqlContext, p, bandCount)
-    }
-    else GeoTiffRelation(sqlContext, p)
+    } else GeoTiffRelation(sqlContext, p)
   }
 
   override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], df: DataFrame): BaseRelation = {
     require(parameters.path.isDefined, "Valid URI 'path' parameter required.")
     val path = parameters.path.get
-    require(path.getScheme == "file" || path.getScheme == null,
-      "Currently only 'file://' destinations are supported")
+    require(path.getScheme == "file" || path.getScheme == null, "Currently only 'file://' destinations are supported")
     sqlContext.withRasterFrames
 
     val tileCols = df.tileColumns
 
     require(tileCols.nonEmpty, "Could not find any tile columns.")
 
-    val raster = if(df.isAlreadyLayer) {
+    val raster = if (df.isAlreadyLayer) {
       val layer = df.certify
       val tlm = layer.tileLayerMetadata.merge
 
       // If no desired image size is given, write at full size.
-      val TileDimensions(cols, rows) =  parameters.rasterDimensions
+      val TileDimensions(cols, rows) = parameters.rasterDimensions
         .getOrElse {
           val actualSize = tlm.layout.toRasterExtent().gridBoundsFor(tlm.extent)
           TileDimensions(actualSize.width, actualSize.height)
         }
 
       // Should we really play traffic cop here?
-      if(cols.toDouble * rows * 64.0 > Runtime.getRuntime.totalMemory() * 0.5)
-        logger.warn(s"You've asked for the construction of a very large image ($cols x $rows), destined for ${path}. Out of memory error likely.")
+      if (cols.toDouble * rows * 64.0 > Runtime.getRuntime.totalMemory() * 0.5)
+        logger.warn(
+          s"You've asked for the construction of a very large image ($cols x $rows), destined for ${path}. Out of memory error likely.")
 
       layer.toMultibandRaster(tileCols, cols.toInt, rows.toInt)
-    }
-    else {
+    } else {
       require(parameters.crs.nonEmpty, "A destination CRS must be provided")
       require(tileCols.nonEmpty, "need at least one tile column")
 
@@ -102,10 +98,9 @@ class GeoTiffDataSource extends DataSourceRegister
       val (extCol, crsCol, tileCol) = {
         // Favor "ProjectedRaster" columns
         val prCols = df.projRasterColumns
-        if(prCols.nonEmpty) {
+        if (prCols.nonEmpty) {
           (rf_extent(prCols.head), rf_crs(prCols.head), rf_tile(prCols.head))
-        }
-        else {
+        } else {
           // If no "ProjectedRaster" column, look for single Extent and CRS columns.
           val crsCols = df.crsColumns
           require(crsCols.size == 1, "Exactly one CRS column must be in DataFrame")
@@ -117,45 +112,48 @@ class GeoTiffDataSource extends DataSourceRegister
 
       // Scan table and constuct what the TileLayerMetadata would be in the specified destination CRS.
       val tlm: TileLayerMetadata[SpatialKey] = df
-        .select(ProjectedLayerMetadataAggregate(
-          destCRS, extCol, crsCol, rf_cell_type(tileCol), rf_dimensions(tileCol)
-        ))
+        .select(
+          ProjectedLayerMetadataAggregate(
+            destCRS,
+            extCol,
+            crsCol,
+            rf_cell_type(tileCol),
+            rf_dimensions(tileCol)
+          ))
         .first()
 
       val c = ProjectedRasterDefinition(tlm)
 
-      val config = parameters.rasterDimensions.map { dims =>
-        c.copy(totalCols = dims.cols, totalRows = dims.rows)
-      }.getOrElse(c)
+      val config = parameters.rasterDimensions
+        .map { dims =>
+          c.copy(totalCols = dims.cols, totalRows = dims.rows)
+        }
+        .getOrElse(c)
 
       val aggs = tileCols
-        .map(t => TileRasterizerAggregate(
-          config, crsCol, extCol, rf_tile(t))("tile").as(t.columnName)
-        )
+        .map(t => TileRasterizerAggregate(config, crsCol, extCol, rf_tile(t))("tile").as(t.columnName))
 
       val agg = df.select(aggs: _*)
 
       val row = agg.first()
 
-      val bands = for(i <- 0 until row.size) yield row.getAs[Tile](i)
+      val bands = for (i <- 0 until row.size) yield row.getAs[Tile](i)
 
       ProjectedRaster(MultibandTile(bands), tlm.extent, tlm.crs)
     }
 
     val tags = Tags(
       RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
-      tileCols.map(c ⇒ Map("RF_COL" -> c.columnName)).toList
+      tileCols.map(c => Map("RF_COL" -> c.columnName)).toList
     )
 
     // We make some assumptions here.... eventually have column metadata encode this.
     val colorSpace = tileCols.size match {
-      case 3 | 4 ⇒  ColorSpace.RGB
-      case _ ⇒ ColorSpace.BlackIsZero
+      case 3 | 4 => ColorSpace.RGB
+      case _ => ColorSpace.BlackIsZero
     }
 
-    val tiffOptions = GeoTiffOptions(Tiled,
-      if (parameters.compress) DeflateCompression else NoCompression, colorSpace
-    )
+    val tiffOptions = GeoTiffOptions(Tiled, if (parameters.compress) DeflateCompression else NoCompression, colorSpace)
 
     val geotiff = new MultibandGeoTiff(raster.tile, raster.extent, raster.crs, tags, tiffOptions)
 
@@ -174,19 +172,19 @@ object GeoTiffDataSource {
   final val CRS_PARAM = "crs"
   final val BAND_COUNT_PARAM = "bandCount"
 
-  private[geotiff]
-  implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
+  private[geotiff] implicit class ParamsDictAccessors(val parameters: Map[String, String]) extends AnyVal {
     def path: Option[URI] = uriParam(PATH_PARAM, parameters)
     def compress: Boolean = parameters.get(COMPRESS_PARAM).exists(_.toBoolean)
     def crs: Option[CRS] = parameters.get(CRS_PARAM).map(s => LazyCRS(s))
     def rasterDimensions: Option[TileDimensions] = {
       numParam(IMAGE_WIDTH_PARAM, parameters)
         .zip(numParam(IMAGE_HEIGHT_PARAM, parameters))
-        .map { case (cols, rows) =>
-          require(cols <= Int.MaxValue && rows <= Int.MaxValue,
-            s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
-          TileDimensions(cols.toInt, rows.toInt)
-        }.headOption
+        .map {
+          case (cols, rows) =>
+            require(cols <= Int.MaxValue && rows <= Int.MaxValue, s"Can't construct a GeoTIFF of size $cols x $rows. (Too big!)")
+            TileDimensions(cols.toInt, rows.toInt)
+        }
+        .headOption
     }
   }
 }
diff --git a/docs/README.md b/docs/README.md
index 7c8209b6f..d89d0ea60 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -67,11 +67,11 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     sbt:RasterFrames> pyrasterframes/doc
     ```
     There's a command alias for this last step: `pyDocs`.
-4. To evaluate a single `.pymd` file, you pass the `-f` option and the filename relative to the `python` directory:
+4. To evaluate a single `.pymd` file, you pass the `-s` option and the filename relative to the `python` directory:
     ```
-    sbt:RasterFrames> pyrasterframes/pySetup pweave -f docs/getting-started.pymd
+    sbt:RasterFrames> pyrasterframes/pySetup pweave -s docs/getting-started.pymd
     [info] Synchronizing 44 files to '<src-root>/pyrasterframes/target/python'
-    [info] Running 'python setup.py pweave -f docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
+    [info] Running 'python setup.py pweave -s docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
     running pweave
     --------------------------------------------------
     Running getting-started
@@ -85,9 +85,9 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
 5. The _output_ Markdown files are written to `<src-root>/pyrasterframes/target/python/docs`. _Note_: don't edit any files in the `pyrasterframes/target` directory... they will get overwritten each time `sbt` runs a command.
 6. During content development it's sometimes helpful to see the output rendered as basic HTML. To do this, add the `-d html` option to the pweave command:
     ```
-    sbt:RasterFrames> pyrasterframes/pySetup pweave -d html -f docs/getting-started.pymd
+    sbt:RasterFrames> pyrasterframes/pySetup pweave -f html -s docs/getting-started.pymd
     [info] Synchronizing 54 files to '<src-roog>/pyrasterframes/target/python'
-    [info] Running 'python setup.py pweave -d html -f docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
+    [info] Running 'python setup.py pweave -f html -s docs/getting-started.pymd' in '<src-root>/pyrasterframes/target/python'
     running pweave
     --------------------------------------------------
     Running getting-started
diff --git a/docs/src/main/paradox/release-notes.md b/docs/src/main/paradox/release-notes.md
index b02526b9a..51a126dfc 100644
--- a/docs/src/main/paradox/release-notes.md
+++ b/docs/src/main/paradox/release-notes.md
@@ -4,6 +4,7 @@
 
 ### 0.8.0
 
+* Super-duper new Python-centric [RasterFrames Users' Manual](https://rasterframes.io/)!
 * Upgraded to the following core dependencies: Spark 2.3.3, GeoTrellis 2.3.0, GeoMesa 2.2.1, JTS 1.16.0.
 * Build `pyrasterframes` binary distribution for pip installation.
 * Added support for rendering RasterFrame types in IPython/Jupyter.
diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
index 19537376a..62a40d64a 100644
--- a/pyrasterframes/src/main/python/docs/concepts.md
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -1,6 +1,6 @@
 # Concepts
 
-There are a number of EO concepts that crop up in the discussion of RasterFrames features. We'll cover these briefly in the sections below. However, here are a few links providing a more extensive introduction to working with Earth observation data. 
+There are a number of Earth-observation (EO) concepts that crop up in the discussion of RasterFrames features. We'll cover these briefly in the sections below. However, here are a few links providing a more extensive introduction to working with Earth observation data. 
 
 * [_Fundamentals of Remote Sensing_](https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/9309)
 * [_Newcomers Earth Observation Guide_](https://business.esa.int/newcomers-earth-observation-guide)
@@ -51,7 +51,13 @@ An extent (or bounding box) is a rectangular region specifying the geospatial co
 
 ## Tile
 
-A tile (sometimes called a "chip") is a sub-region of  a scene, usually square, usually with dimensions that are some power of 2. It is usually encoded as a two-dimensional array, or a a one-dimensional array with width/height dimensions. Tiles may be singleband or multiband (multi-channel). The tile is the primary discretization unit used in RasterFrames; the bands of a scene are separated into different columns, and the band's raster is carved up into tiles for each row.
+A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). A tile can conceptually be though of as a two-dimensional array.
+
+Some EO data has many bands or channels. Tiles in this context are conceptually a three-dimensional array, with the extra dimension representing the bands.
+
+Tiles are often square and the dimensions are some power of two, for example 256 by 256. 
+
+The tile is the primary discretization unit used in RasterFrames. Each band of a scene is in a separate column. The scene's overall @ref:[extent](concepts.md#extent) is carved up into smaller extents for each tile. Each row of the DataFrame contains a two-dimensional tile per band column.
 
 ## Projected Extent
 
diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 64d6eba06..33aac6cdb 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -1,6 +1,6 @@
 # Overview
 
-RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms.
+RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. It provides APIs in @ref:[Python, SQL, and Scala](languages.md), and can horizontally scale from a laptop to a supercomputer, enabling _global_ analysis with satellite imagery in a wholly new, flexible and convenient way.
 
 ## Context
 
@@ -14,8 +14,7 @@ RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings to
 
 ## Benefit
 
-By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. 
-
+By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. Because it is built on Apache Spark, solutions prototyped on a laptop can be scaled to run on cluster and cloud compute resources in a way not easily achieved with other toolchains. 
 
 ## Architecture
 
@@ -26,17 +25,21 @@ RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO
 
 ![LocationTech Stack](static/rasterframes-locationtech-stack.png)
 
-RasterFrames introduces a new native data type called `tile` to Spark SQL. Each `tile` cell contains a 2-D matrix of "cell" (pixel) values, along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", discretized into regular-sized chunks, but can also be quality assurance information, land classification assignments, or any other discretized geo-spatiotemporal data. It also includes support for working with vector data, such as [GeoJSON][GeoJSON]. Along with `tile` columns there is typically a `geometry` column (bounds or extent/envelope) specifying the location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when querying a catalog of imagery.
+RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called "tiles". Each tile contains a 2-D matrix of "cell" (pixel) values along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other rasterized spatiotemporal data. Along with `tile` columns there is typically an `extent` specifying the geographic location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering 
+
+RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. You can use vector data to filter DataFrame rows, using geospatial predicates (e.g. contains, intersects, overlaps, etc.), to mask cells, and to be rasterzied into training data appropriate for machine learning.
+
 
 ![RasterFrame Anatomy](static/rasterframe-anatomy.png)
 
-Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of (preferably Cloud Optimized) GeoTIFFs, [GeoTrellis Layers][GTLayer], and from an experimental catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. See @ref:[Raster Data I/O](raster-io.md) for details.
+Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of georectified imagery (including Cloud Optimized GeoTIFFs or [COGS][COGS]), [GeoTrellis Layers][GTLayer], and from catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. See @ref:[Raster Data I/O](raster-io.md) for details.
 
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
 [Pandas]:https://pandas.pydata.org/
 [GeoJSON]:https://en.wikipedia.org/wiki/GeoJSON
 [GTLayer]:https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html#layouts-and-tile-layers
 [PDS]:https://registry.opendata.aws/modis/
+[COGS]:https://www.cogeo.org/
 
 [^1]: [_Demystifying Satellite Assets for Post-Disaster Situation Awareness_](https://docs.google.com/document/d/11bIw5HcEiZy8SKli6ZFQC2chVEiiIJ-f0o6btA4LU48).
 World Bank via [OpenDRI.org](https://opendri.org/resource/demystifying-satellite-assets-for-post-disaster-situation-awareness/). Accessed November 28, 2018. 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 957eba2d8..df4f97676 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -1,6 +1,6 @@
 # Getting Started
 
-There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames. Let's start with the simplest. Python 3.6 or greater is recommended.
+There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell. Python 3.6 or greater is recommended.
 
 ## pip install pyrasterframes
 
@@ -72,7 +72,6 @@ df = spark.read.raster('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_
 
 Now you have the configured SparkSession with RasterFrames enabled.
 
-
 ```python, echo=False 
 spark.stop()
 ```
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 5045d3c5a..a9590a361 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -41,11 +41,11 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 * [Overview](description.md)
 * [Concepts](concepts.md)
 * [Getting Started](getting-started.md)
-* [API Languages](languages.md)
 * [Raster Data I/O](raster-io.md)
 * [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)
 * [Numpy and Pandas](numpy-pandas.md) 
+* [API Languages](languages.md)
 * [Function Reference](reference.md)
 * [Release Notes](release-notes.md)
 @@@
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index 3be17b9e0..dc7e93065 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -36,7 +36,11 @@ red_nir_monthly_2017 = modis \
 ### Step 3: Read tiles
 
 ```python, step_3_python
-red_nir_tiles_monthly_2017 = spark.read.raster(catalog=red_nir_monthly_2017, catalog_col_names=['red', 'nir'])
+red_nir_tiles_monthly_2017 = spark.read.raster(
+    catalog=red_nir_monthly_2017, 
+    catalog_col_names=['red', 'nir'],
+    tile_dimensions=(256, 256)
+)
 ```
 
 ### Step 4: Compute aggregates
@@ -87,7 +91,11 @@ sql('DESCRIBE red_nir_monthly_2017').show()
 sql("""
 CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017 
 USING raster 
-OPTIONS (catalogTable='red_nir_monthly_2017', catalogColumns='red,nir')
+OPTIONS (
+    catalogTable='red_nir_monthly_2017', 
+    catalogColumns='red,nir',
+    tileDimensions='256,256'
+    )
 """)
 ```
 
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 3859c1694..1017a2f35 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -94,10 +94,10 @@ Fetch CRS structure representing the coordinate reference system of a `Projected
 
 ### rf_mk_crs
 
-Construct a CRS structure from one of its string representations. Three froms are supported:
-
     Struct rf_mk_crs(String crsText)   
 
+Construct a CRS structure from one of its string representations. Three froms are supported:
+
 * [EPSG code](https://www.epsg-registry.org/): `EPSG:<integer>`
 * [Proj4 string](https://proj.org/): `+proj <proj4 parameters>`
 * [WKT String](http://www.geoapi.org/3.0/javadoc/org/opengis/referencing/doc-files/WKT.html) with embedded EPSG code: `GEOGCS["<name>", <datum>, <prime meridian>, <angular unit> {,<twin axes>} {,<authority>}]` 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index c9ddea6cf..e431b4e7a 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -182,17 +182,14 @@ def _geotiff_writer(
 
     def set_dims(parts):
         parts = [int(p) for p in parts]
-        assert(len(parts) == 2, "Expected dimensions specification to have exactly two components")
-        assert(all([p > 0 for p in parts]), "Expected all components in dimensions to be positive integers")
+        assert len(parts) == 2, "Expected dimensions specification to have exactly two components"
+        assert all([p > 0 for p in parts]), "Expected all components in dimensions to be positive integers"
         options.update({
             "imageWidth": parts[0],
             "imageHeight": parts[1]
         })
-        try:
-            parts = [int(p) for p in parts]
-            assert(all([p > 0 for p in parts]), 'nice message')
-        except ValueError:
-            raise
+        parts = [int(p) for p in parts]
+        assert all([p > 0 for p in parts]), 'nice message'
 
     if raster_dimensions is not None:
         if isinstance(raster_dimensions, (list, tuple)):
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index a8881fdfb..53b52401c 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -52,8 +52,8 @@ class PweaveDocs(distutils.cmd.Command):
     description = 'Pweave PyRasterFrames documentation scripts'
     user_options = [
         # The format is (long option, short option, description).
-        ('files=', 'f', 'Specific files to pweave. Defaults to all in `docs` directory.'),
-        ('doctype=', 'd', 'Output format type. Defaults to `markdown`')
+        ('files=', 's', 'Specific files to pweave. Defaults to all in `docs` directory.'),
+        ('format=', 'f', 'Output format type. Defaults to `markdown`')
     ]
 
     def initialize_options(self):
@@ -63,15 +63,16 @@ def initialize_options(self):
             lambda x: not path.basename(x)[:1] == '_',
             glob(path.join(here, 'docs', '*.pymd'))
         )
-        self.doctype = 'markdown'
+        self.format = 'markdown'
 
     def finalize_options(self):
         """Post-process options."""
         import re
         if isinstance(self.files, str):
             self.files = filter(lambda s: len(s) > 0, re.split(',', self.files))
-            if self.doctype is "html":
-                self.doctype = "pandoc2html"
+            # `html` doesn't do quite what one expects... only replaces code blocks, leaving markdown in place
+            if self.format is 'html':
+                self.format = 'pandoc2html'
 
     def run(self):
         """Run pweave."""
@@ -84,7 +85,7 @@ def run(self):
             try:
                 pweave.weave(
                     file=str(file),
-                    doctype=self.doctype
+                    doctype=self.format
                 )
             except Exception:
                 print(_divided('%s Failed:' % file))
@@ -95,7 +96,7 @@ def run(self):
 class PweaveNotebooks(PweaveDocs):
     def initialize_options(self):
         super().initialize_options()
-        self.doctype = 'notebook'
+        self.format = 'notebook'
 
 
 setup(
diff --git a/pyrasterframes/src/main/python/tests/VectorTypesTests.py b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
index 5f7946e5b..ba70f7520 100644
--- a/pyrasterframes/src/main/python/tests/VectorTypesTests.py
+++ b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
@@ -129,7 +129,7 @@ def area(g):
 
     def test_parse_crs(self):
         df = self.spark.createDataFrame([Row(id=1)])
-        df.select(rf_mk_crs('EPSG:4326')).show()
+        self.assertEqual(df.select(rf_mk_crs('EPSG:4326')).count(), 1)
 
     def test_reproject(self):
         reprojected = self.rf.withColumn('reprojected',

From c5c64eccb0ea4de3633e28c55623054f325706c6 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 16:50:38 -0400
Subject: [PATCH 284/380] =?UTF-8?q?Added=20detection=20errors=20during=20d?=
 =?UTF-8?q?ocumentation=20processing.=E2=80=A8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 project/PythonBuildPlugin.scala         |  5 ++++-
 pyrasterframes/src/main/python/setup.py | 11 +++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index e4383a7b9..36c356e55 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -93,7 +93,10 @@ object PythonBuildPlugin extends AutoPlugin {
       val cmd = Seq(pythonCommand.value, "setup.py") ++ args
       val ver = version.value
       s.log.info(s"Running '${cmd.mkString(" ")}' in '$wd'")
-      Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
+      val ec = Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
+      if (ec != 0)
+        throw new MessageOnlyException(s"'$cmd' exited with value '$ec'")
+      ec
     },
     pyWhl := pyWhlImp.value,
     Compile / `package` := (Compile / `package`).dependsOn(Python / packageBin).value,
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 53b52401c..693785d1a 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -78,6 +78,7 @@ def run(self):
         """Run pweave."""
         import traceback
         import pweave
+        bad_words = ["Error"]
 
         for file in self.files:
             name = path.splitext(path.basename(file))[0]
@@ -87,6 +88,16 @@ def run(self):
                     file=str(file),
                     doctype=self.format
                 )
+                if self.format == 'markdown':
+                    dest = path.splitext(file)[0] + '.md'
+                    if not path.exists(dest):
+                        raise FileNotFoundError("Markdown file '%s' didn't get created as expected" % dest)
+                    with open(dest, "r") as result:
+                        for (n, line) in enumerate(result):
+                            for word in bad_words:
+                                if word in line:
+                                    raise ChildProcessError("Error detected on line %s in %s:\n%s" % (n + 1, dest, line))
+
             except Exception:
                 print(_divided('%s Failed:' % file))
                 print(traceback.format_exc())

From 031f41f7b405bdf5bac9bb38ee6cb453ee94e8f0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 1 Aug 2019 16:55:26 -0400
Subject: [PATCH 285/380] Error message tweak.

---
 project/PythonBuildPlugin.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 36c356e55..545fbcb75 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -95,7 +95,7 @@ object PythonBuildPlugin extends AutoPlugin {
       s.log.info(s"Running '${cmd.mkString(" ")}' in '$wd'")
       val ec = Process(cmd, wd, "RASTERFRAMES_VERSION" -> ver).!
       if (ec != 0)
-        throw new MessageOnlyException(s"'$cmd' exited with value '$ec'")
+        throw new MessageOnlyException(s"'${cmd.mkString(" ")}' exited with value '$ec'")
       ec
     },
     pyWhl := pyWhlImp.value,

From 21c810eee0854cf2a802d36e9e282ee5df211596 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Thu, 1 Aug 2019 17:01:50 -0400
Subject: [PATCH 286/380] update nodata docs to show interaction between two
 different cell types

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 .../src/main/python/docs/nodata-handling.pymd | 47 ++++++++++++++++++-
 1 file changed, 46 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index ed4043de9..e5667b419 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -223,4 +223,49 @@ The results of `rf_tile_sum` vary on the tiles that were masked. This is because
 
 ```python
 masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
-```
\ No newline at end of file
+```
+
+
+## Combining Tiles with Different Data Types
+
+RasterFrames supports having Tile columns with multiple cell types in a single RasterFrame. It is important to understand how these different cell types interact. 
+
+Let's first create a RasterFrame that has columns of `float` and `int` cell type. 
+
+```python
+x = Tile((np.ones((100, 100))*2).astype('int16'))
+y = Tile((np.ones((100, 100))*3.0).astype('int32'))
+rf = spark.createDataFrame(pd.DataFrame(data = 
+        {'x': [x],
+        'y': [y]}))
+rf.select(rf_cell_type('y'), rf_cell_type('x')).distinct().show()
+```
+
+When performing a local operation between tile columns with cell types `int` and type `float`, the resulting tile cell type will be `float`. If two tiles of different sized integer cell types are added together in the same way, the resulting cell type will be the same as the largest of the two integer cell types.
+
+```python
+rf_added = rf.withColumn('xy_sum', rf_local_add('y', 'x'))
+rf_added.select(rf_cell_type('xy_sum'), rf_cell_type('y'), rf_cell_type('x')).distinct().show()
+```
+
+Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 3 tile columns: one without a defined NoData value, one with a NoData value of 1, and one with a NoData value of 2.
+
+
+```python
+def get_nodata_ct(nd_val):
+    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
+
+x = Tile((np.ones((100, 100))*3).astype('int16'))
+
+rf = spark.createDataFrame(pd.DataFrame(data = 
+        {'x': [x]}))
+rf_nd = rf.withColumn('x_nd_1', rf_convert_cell_type('x', get_nodata_ct(1))) \
+          .withColumn('x_nd_2', rf_convert_cell_type('x', get_nodata_ct(2)))
+```
+
+Let's try adding the tile column without a defined NoData value to a tile column with a defined NoData value. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has no defined NoData value. 
+
+```python
+rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x'))
+rf_nd_sum.select(rf_cell_type('x_nd_sum'), rf_cell_type('x'), rf_cell_type('x_nd_1')).distinct().show()
+```

From ac68fdc3f1da45e28bdd9e6b978630e0d190434a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 1 Aug 2019 17:37:43 -0400
Subject: [PATCH 287/380] Refactor construction of LayoutDefinition in raster
 writing process

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../ProjectedLayerMetadataAggregate.scala       |  7 +++++--
 .../geotiff/GeoTiffDataSourceSpec.scala         | 17 +++++++++++++++++
 2 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
index 4af542d85..5156c8270 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
@@ -28,7 +28,7 @@ import org.locationtech.rasterframes.model.TileDimensions
 import geotrellis.proj4.{CRS, Transform}
 import geotrellis.raster._
 import geotrellis.raster.reproject.{Reproject, ReprojectRasterExtent}
-import geotrellis.spark.tiling.{FloatingLayoutScheme, LayoutLevel}
+import geotrellis.spark.tiling.{FloatingLayoutScheme, LayoutDefinition, LayoutLevel}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
@@ -76,7 +76,10 @@ class ProjectedLayerMetadataAggregate(destCRS: CRS, destDims: TileDimensions) ex
   override def evaluate(buffer: Row): Any = {
     import org.locationtech.rasterframes.encoders.CatalystSerializer._
     val buf = buffer.to[BufferRecord]
-    val LayoutLevel(_, layout) = FloatingLayoutScheme(destDims.cols, destDims.rows).levelFor(buf.extent, buf.cellSize)
+
+    val re = RasterExtent(buf.extent, buf.cellSize)
+    val layout = LayoutDefinition(re, destDims.cols, destDims.rows)
+
     val kb = KeyBounds(layout.mapTransform(buf.extent))
     TileLayerMetadata(buf.cellType, layout, buf.extent, destCRS, kb).toRow
   }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 866fa4966..ad2754c76 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -101,6 +101,23 @@ class GeoTiffDataSourceSpec
       }
     }
 
+    it("should write unstructured raster") {
+      import spark.implicits._
+      val df = spark.read.format("raster")
+        .option("tileDimensions", "32,32")  // oddball
+        .load(nonCogPath.toASCIIString) // core L8-B8-Robinson-IL.tiff
+
+      df.count() shouldBe > (0)
+
+      val crs = df.select(rf_crs($"proj_raster")).first()
+
+      noException shouldBe thrownBy {
+        df.write.geotiff.withCRS(crs).save(
+          Paths.get("target", "unstructured.tif").toString()
+        )
+      }
+    }
+
     it("should write GeoTIFF without layer") {
       import org.locationtech.rasterframes.datasource.raster._
       val pr = col("proj_raster_b0")

From 4a77ce8a6654b02a403f3ec8a4e559c52ce9e179 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Fri, 2 Aug 2019 09:51:19 -0400
Subject: [PATCH 288/380] fixed code to match documentation

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 pyrasterframes/src/main/python/docs/nodata-handling.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index e5667b419..4505c32f2 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -233,7 +233,7 @@ RasterFrames supports having Tile columns with multiple cell types in a single R
 Let's first create a RasterFrame that has columns of `float` and `int` cell type. 
 
 ```python
-x = Tile((np.ones((100, 100))*2).astype('int16'))
+x = Tile((np.ones((100, 100))*2).astype('float'))
 y = Tile((np.ones((100, 100))*3.0).astype('int32'))
 rf = spark.createDataFrame(pd.DataFrame(data = 
         {'x': [x],

From 74436315b85aaf23fcbf3e4769942f3d6324e46e Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 2 Aug 2019 11:57:13 -0400
Subject: [PATCH 289/380] Add more unit tests for round trip full res GeoTiff
 writing

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../ProjectedLayerMetadataAggregate.scala     |  2 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       | 89 ++++++++++++++++++-
 pyrasterframes/src/main/python/setup.py       |  3 +-
 .../main/python/tests/GeoTiffWriterTests.py   | 16 ++--
 4 files changed, 99 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
index 5156c8270..0f1b4727a 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/ProjectedLayerMetadataAggregate.scala
@@ -28,7 +28,7 @@ import org.locationtech.rasterframes.model.TileDimensions
 import geotrellis.proj4.{CRS, Transform}
 import geotrellis.raster._
 import geotrellis.raster.reproject.{Reproject, ReprojectRasterExtent}
-import geotrellis.spark.tiling.{FloatingLayoutScheme, LayoutDefinition, LayoutLevel}
+import geotrellis.spark.tiling.LayoutDefinition
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
 import geotrellis.vector.Extent
 import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index ad2754c76..e98d82b2a 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -23,6 +23,7 @@ package org.locationtech.rasterframes.datasource.geotiff
 import java.nio.file.Paths
 
 import geotrellis.proj4._
+import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
 import org.locationtech.rasterframes._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.TestEnvironment
@@ -107,17 +108,95 @@ class GeoTiffDataSourceSpec
         .option("tileDimensions", "32,32")  // oddball
         .load(nonCogPath.toASCIIString) // core L8-B8-Robinson-IL.tiff
 
-      df.count() shouldBe > (0)
+      df.count() should be > 0L
 
       val crs = df.select(rf_crs($"proj_raster")).first()
 
       noException shouldBe thrownBy {
-        df.write.geotiff.withCRS(crs).save(
-          Paths.get("target", "unstructured.tif").toString()
-        )
+        df.write.geotiff.withCRS(crs).save("datasource/target/unstructured.tif")
       }
+
+      val (inCols, inRows) = {
+        val id = sampleGeoTiff.imageData // inshallah same as nonCogPath
+        (id.cols, id.rows)
+      }
+      inCols should be (774)
+      inRows should be (500) //from gdalinfo
+
+      val outputTif = SinglebandGeoTiff("datasource/target/unstructured.tif")
+      outputTif.imageData.cols should be (inCols)
+      outputTif.imageData.rows should be (inRows)
+
+      // TODO check datatype, extent.
+    }
+
+    it("should round trip unstructured raster from COG"){
+      val df = spark.read.format("raster")
+        .load(getClass.getResource("/LC08_B7_Memphis_COG.tiff").toURI.toASCIIString())
+
+      df.count() should be > 0L
+
+      val crs = df.select(rf_crs(col("proj_raster"))).first()
+
+      noException shouldBe thrownBy {
+        df.write.geotiff.withCRS(crs).save("datasource/target/unstructured_cog.tif")
+      }
+
+      val (inCols, inRows) = {
+        val id = readSingleband("LC08_B7_Memphis_COG.tiff").imageData
+        (id.cols, id.rows)
+      }
+      inCols should be (963)
+      inRows should be (754) //from gdalinfo
+
+      val outputTif = SinglebandGeoTiff("datasource/target/unstructured_cog.tif")
+      outputTif.imageData.cols should be (inCols)
+      outputTif.imageData.rows should be (inRows)
+
+      // TODO check datatype, extent.
     }
 
+    /*
+    it("should round trip jasons favorite unstructured raster round trip okay") {
+      import org.locationtech.rasterframes.datasource.raster._
+      import spark.implicits._
+
+      val jasonsRasterPath = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/17/03/2019193/" +
+                            "MCD43A4.A2019193.h17v03.006.2019202033615_B06.TIF"
+      val df = spark.read.raster
+          .withTileDimensions(233, 133)
+          .from(Seq(jasonsRasterPath))
+        .load()
+
+      logger.debug("Read local file metadata")
+      val (inCols, inRows) = {
+        val in = readSingleband("MCD43A4.A2019193.h17v03.006.2019202033615_B06.TIF")
+        (in.cols, in.rows)
+      }
+      inCols should be (2400)  // from GDAL
+      inRows should be (2400)  // from GDAL
+
+      val outPath = "datasources/target/rf_mcd43a4.A2019193.h17v03.tif"
+
+      // now take actions on the read df
+      logger.debug("Actions on raster ref dataframe")
+      df.count() should be > 100L
+      val crs = df.select(rf_crs($"proj_raster")).first()
+      logger.debug("Write full res geotiff from dataframe")
+      df.write.geotiff.withCRS(crs).save(outPath)
+
+      // compare written file to path
+      logger.debug("Inspect written geotiff metadata")
+      val (outCols, outRows) = {
+        val outputTif = SinglebandGeoTiff(outPath)
+        (outputTif.cols, outputTif.rows)
+      }
+      outCols should be (inCols)
+      outRows should be (inRows)
+      // todo check extent and crs just for grins.
+
+    }*/
+
     it("should write GeoTIFF without layer") {
       import org.locationtech.rasterframes.datasource.raster._
       val pr = col("proj_raster_b0")
@@ -169,6 +248,8 @@ ${s(1)},${s(4)},${s(3)}
         .withDimensions(256, 256)
         .save("geotiff-overview.tif")
 
+      val outTif = MultibandGeoTiff("geotiff-overview.tif")
+      outTif.bandCount should be (3)
     }
   }
 }
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 262cc7b74..b07696fd7 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -136,7 +136,8 @@ def doctype(self):
         'numpy>=1.7',
         'shapely',
         'pandas',
-        'rasterio'
+        'rasterio',
+        'boto3',
     ],
     packages=[
         'pyrasterframes',
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
index 135d88a88..fe2af7eb2 100644
--- a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -29,7 +29,7 @@ class GeoTiffWriter(TestEnvironment):
 
     @staticmethod
     def _tmpfile():
-        return os.path.join(tempfile.tempdir, "pyrf-test.tif")
+        return os.path.join(tempfile.gettempdir(), "pyrf-test.tif")
 
     def test_identity_write(self):
         rf = self.spark.read.geotiff(self.img_uri)
@@ -44,13 +44,19 @@ def test_identity_write(self):
 
     def test_unstructured_write(self):
         rf = self.spark.read.raster(self.img_uri)
-        dest = self._tmpfile()
-        rf.write.geotiff(dest, crs='EPSG:32616')
+        dest_file = self._tmpfile()
+        rf.write.geotiff(dest_file, crs='EPSG:32616')
 
-        rf2 = self.spark.read.raster(dest)
+        rf2 = self.spark.read.raster('file://' + dest_file)
         self.assertEqual(rf2.count(), rf.count())
 
-        os.remove(dest)
+        with rasterio.open(self.img_uri) as source:
+            with rasterio.open(dest_file) as dest:
+                self.assertEqual((dest.width, dest.height), (source.width, source.height))
+                self.assertEqual(dest.bounds, source.bounds)
+                self.assertEqual(dest.crs, source.crs)
+
+        os.remove(dest_file)
 
     def test_downsampled_write(self):
         rf = self.spark.read.raster(self.img_uri)

From 02284bbe3d557a8ba64c2cfc56b8a86074fb1f34 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 2 Aug 2019 12:13:10 -0400
Subject: [PATCH 290/380] Added ability to query GDAL version from Python.

---
 .../rasterframes/ref/RasterSourceIT.scala     |  4 ++--
 .../rasterframes/ref/GDALRasterSource.scala   | 20 +++++++++++++++++--
 .../rasterframes/ref/RasterSource.scala       | 16 +++------------
 .../rasterframes/ExtensionMethodSpec.scala    |  2 +-
 .../rasterframes/ref/RasterSourceSpec.scala   |  8 ++------
 .../src/main/python/pyrasterframes/utils.py   |  8 +++++++-
 .../main/python/tests/RasterFunctionsTests.py |  9 ++++-----
 .../rasterframes/py/PyRFContext.scala         |  3 ++-
 8 files changed, 39 insertions(+), 31 deletions(-)

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 1123c6a08..58fae9e0f 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -63,8 +63,8 @@ class RasterSourceIT extends TestEnvironment with TestData {
     }
   }
 
-  if (RasterSource.IsGDAL.hasGDAL) {
-    println("GDAL version: " + GDALWarp.get_version_info("--version"))
+  if (GDALRasterSource.hasGDAL) {
+    println("GDAL version: " + GDALRasterSource.gdalVersion())
 
     describe("GDAL support") {
 
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
index 52b8d0070..481155f24 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/GDALRasterSource.scala
@@ -23,12 +23,14 @@ package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
+import com.azavea.gdal.GDALWarp
+import com.typesafe.scalalogging.LazyLogging
+import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 import geotrellis.proj4.CRS
-import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster}
 import geotrellis.raster.io.geotiff.Tags
+import geotrellis.raster.{CellType, GridBounds, MultibandTile, Raster}
 import geotrellis.vector.Extent
 import org.locationtech.rasterframes.ref.RasterSource.URIRasterSource
-import geotrellis.contrib.vlm.gdal.{GDALRasterSource => VLMRasterSource}
 
 case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSource {
 
@@ -67,3 +69,17 @@ case class GDALRasterSource(source: URI) extends RasterSource with URIRasterSour
   override protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]] =
     gdal.readBounds(bounds, bands)
 }
+
+object GDALRasterSource extends LazyLogging {
+  def gdalVersion(): String = if (hasGDAL) GDALWarp.get_version_info("--version").trim else "not available"
+
+  @transient
+  lazy val hasGDAL: Boolean = try {
+    val _ = new GDALWarp()
+    true
+  } catch {
+    case _: UnsatisfiedLinkError =>
+      logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader for GeoTIFF format.")
+      false
+  }
+}
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 937587c38..6aa9ba441 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -23,7 +23,6 @@ package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
-import com.azavea.gdal.GDALWarp
 import com.github.blemale.scaffeine.Scaffeine
 import com.typesafe.scalalogging.LazyLogging
 import geotrellis.proj4.CRS
@@ -120,27 +119,18 @@ object RasterSource extends LazyLogging {
 
     /** Determine if we should prefer GDAL for all types. */
     private val preferGdal: Boolean = org.locationtech.rasterframes.rfConfig.getBoolean("prefer-gdal")
-    @transient
-    lazy val hasGDAL: Boolean = try {
-      val _ = new GDALWarp()
-      true
-    } catch {
-      case _: UnsatisfiedLinkError =>
-        logger.warn("GDAL native bindings are not available. Falling back to JVM-based reader.")
-        false
-    }
 
-    val gdalOnlyExtensions = Seq(".jp2", ".mrf", ".hdf")
+    val gdalOnlyExtensions = Seq(".jp2", ".mrf", ".hdf", ".vrt")
 
     def gdalOnly(source: URI): Boolean =
       if (gdalOnlyExtensions.exists(source.getPath.toLowerCase.endsWith)) {
-        require(hasGDAL, s"Can only read $source if GDAL is available")
+        require(GDALRasterSource.hasGDAL, s"Can only read $source if GDAL is available")
         true
       } else false
 
     /** Extractor for determining if a scheme indicates GDAL preference.  */
     def unapply(source: URI): Boolean =
-      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal")) && hasGDAL)
+      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal")) && GDALRasterSource.hasGDAL)
   }
 
   object IsDefaultGeoTiff {
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 4b335a241..606794bbf 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -25,7 +25,7 @@ import geotrellis.proj4.LatLng
 import geotrellis.raster.{ByteCellType, GridBounds, TileLayout}
 import geotrellis.spark.tiling.{CRSWorldExtent, LayoutDefinition}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
-import org.apache.spark.sql.{Encoder, Encoders}
+import org.apache.spark.sql.Encoders
 import org.locationtech.rasterframes.util.SubdivideSupport
 
 /**
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 1e6d76f07..d3963dedd 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -29,11 +29,7 @@ import org.apache.spark.sql.rf.RasterSourceUDT
 import org.locationtech.rasterframes.TestEnvironment
 import org.locationtech.rasterframes.model.TileDimensions
 
-/**
- *
- *
- * @since 8/22/18
- */
+
 class RasterSourceSpec extends TestEnvironment with TestData {
   def sub(e: Extent) = {
     val c = e.center
@@ -112,7 +108,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     }
   }
 
-  if(RasterSource.IsGDAL.hasGDAL) {
+  if(GDALRasterSource.hasGDAL) {
     describe("GDAL Rastersource") {
       val gdal = GDALRasterSource(cogPath)
       val jvm = JVMGeoTiffRasterSource(cogPath)
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index 5faa62a22..d18e1fb37 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -22,8 +22,9 @@
 from pyspark.sql import SparkSession
 import os
 import sys
+from . import RFContext
 
-__all__ = ["create_rf_spark_session", "find_pyrasterframes_jar_dir", "find_pyrasterframes_assembly"]
+__all__ = ["create_rf_spark_session", "find_pyrasterframes_jar_dir", "find_pyrasterframes_assembly", "gdal_version"]
 
 
 def find_pyrasterframes_jar_dir():
@@ -92,3 +93,8 @@ def create_rf_spark_session():
     except TypeError as te:
         print("Error setting up SparkSession; cannot find the pyrasterframes assembly jar\n", te)
         return None
+
+
+def gdal_version():
+    fcn = RFContext.active().lookup("buildInfo")
+    return fcn()["GDAL"]
diff --git a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
index 4436c6fc1..1a0696639 100644
--- a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
+++ b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
@@ -18,13 +18,10 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-import unittest
-
-import numpy as np
 from pyrasterframes.rasterfunctions import *
-from pyrasterframes.rf_types import *
-from pyspark.sql import SQLContext
+from pyrasterframes.utils import gdal_version
 from pyspark.sql.functions import *
+
 from . import TestEnvironment
 
 
@@ -36,6 +33,8 @@ def setUp(self):
     def test_setup(self):
         self.assertEqual(self.spark.sparkContext.getConf().get("spark.serializer"),
                          "org.apache.spark.serializer.KryoSerializer")
+        print("GDAL version", gdal_version())
+
 
     def test_identify_columns(self):
         cols = self.rf.tile_columns()
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 34c708175..4ae5c1231 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql._
 import org.locationtech.rasterframes
 import org.locationtech.rasterframes.extensions.RasterJoin
 import org.locationtech.rasterframes.model.LazyCRS
-import org.locationtech.rasterframes.ref.{RasterRef, RasterSource}
+import org.locationtech.rasterframes.ref.{GDALRasterSource, RasterRef, RasterSource}
 import org.locationtech.rasterframes.util.KryoSupport
 import org.locationtech.rasterframes.{RasterFunctions, _}
 import spray.json._
@@ -53,6 +53,7 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     RFBuildInfo.toMap.foreach {
       case (k, v) => retval.put(k, String.valueOf(v))
     }
+    retval.put("GDAL", GDALRasterSource.gdalVersion())
     retval
   }
 

From 917b0a4fc6a7fcde7a24dd2b1c373e0304f45272 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 2 Aug 2019 12:30:59 -0400
Subject: [PATCH 291/380] Improvments to GeoTiffDataSourceSpec

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/ExtensionMethodSpec.scala    |  2 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       | 20 +++++++++++--------
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 4b335a241..606794bbf 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -25,7 +25,7 @@ import geotrellis.proj4.LatLng
 import geotrellis.raster.{ByteCellType, GridBounds, TileLayout}
 import geotrellis.spark.tiling.{CRSWorldExtent, LayoutDefinition}
 import geotrellis.spark.{KeyBounds, SpatialKey, TileLayerMetadata}
-import org.apache.spark.sql.{Encoder, Encoders}
+import org.apache.spark.sql.Encoders
 import org.locationtech.rasterframes.util.SubdivideSupport
 
 /**
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index e98d82b2a..5e22d13e0 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -24,6 +24,7 @@ import java.nio.file.Paths
 
 import geotrellis.proj4._
 import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
+import geotrellis.vector.Extent
 import org.locationtech.rasterframes._
 import org.apache.spark.sql.functions._
 import org.locationtech.rasterframes.TestEnvironment
@@ -113,7 +114,7 @@ class GeoTiffDataSourceSpec
       val crs = df.select(rf_crs($"proj_raster")).first()
 
       noException shouldBe thrownBy {
-        df.write.geotiff.withCRS(crs).save("datasource/target/unstructured.tif")
+        df.write.geotiff.withCRS(crs).save("unstructured.tif")
       }
 
       val (inCols, inRows) = {
@@ -123,7 +124,7 @@ class GeoTiffDataSourceSpec
       inCols should be (774)
       inRows should be (500) //from gdalinfo
 
-      val outputTif = SinglebandGeoTiff("datasource/target/unstructured.tif")
+      val outputTif = SinglebandGeoTiff("unstructured.tif")
       outputTif.imageData.cols should be (inCols)
       outputTif.imageData.rows should be (inRows)
 
@@ -139,21 +140,24 @@ class GeoTiffDataSourceSpec
       val crs = df.select(rf_crs(col("proj_raster"))).first()
 
       noException shouldBe thrownBy {
-        df.write.geotiff.withCRS(crs).save("datasource/target/unstructured_cog.tif")
+        df.write.geotiff.withCRS(crs).save("target/unstructured_cog.tif")
       }
 
-      val (inCols, inRows) = {
-        val id = readSingleband("LC08_B7_Memphis_COG.tiff").imageData
-        (id.cols, id.rows)
+      val (inCols, inRows, inExtent, inCellType) = {
+        val tif = readSingleband("LC08_B7_Memphis_COG.tiff")
+        val id = tif.imageData
+        (id.cols, id.rows, tif.extent, tif.cellType)
       }
       inCols should be (963)
       inRows should be (754) //from gdalinfo
+      inExtent should be (Extent(752325.0, 3872685.0, 781215.0, 3895305.0))
 
-      val outputTif = SinglebandGeoTiff("datasource/target/unstructured_cog.tif")
+      val outputTif = SinglebandGeoTiff("target/unstructured_cog.tif")
       outputTif.imageData.cols should be (inCols)
       outputTif.imageData.rows should be (inRows)
+      outputTif.extent should be (inExtent)
+      outputTif.cellType should be (inCellType)
 
-      // TODO check datatype, extent.
     }
 
     /*

From 438cfa147d8ccb4a8874346da1c50b8a4e3b12ef Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 2 Aug 2019 13:12:06 -0400
Subject: [PATCH 292/380] Fixed `Add` expression to use GT behavior; created
 new BiasedAdd for Aggregate use.

---
 .../aggregates/LocalMeanAggregate.scala       | 10 +--
 .../expressions/localops/Add.scala            |  7 +-
 .../expressions/localops/BiasedAdd.scala      | 74 +++++++++++++++++++
 pyrasterframes/src/main/python/setup.py       |  3 +-
 4 files changed, 84 insertions(+), 10 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/expressions/localops/BiasedAdd.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
index e978d21f9..06741a98c 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/aggregates/LocalMeanAggregate.scala
@@ -22,7 +22,7 @@
 package org.locationtech.rasterframes.expressions.aggregates
 
 import org.locationtech.rasterframes.expressions.UnaryRasterAggregate
-import org.locationtech.rasterframes.expressions.localops.{Add => AddTiles, Divide => DivideTiles}
+import org.locationtech.rasterframes.expressions.localops.{BiasedAdd, Divide => DivideTiles}
 import org.locationtech.rasterframes.expressions.transformers.SetCellType
 import geotrellis.raster.Tile
 import geotrellis.raster.mapalgebra.local
@@ -59,16 +59,16 @@ case class LocalMeanAggregate(child: Expression) extends UnaryRasterAggregate {
   override lazy val updateExpressions: Seq[Expression] = Seq(
     If(IsNull(count),
       SetCellType(Defined(child), Literal("int32")),
-      If(IsNull(child), count, AddTiles(count, Defined(child)))
+      If(IsNull(child), count, BiasedAdd(count, Defined(child)))
     ),
     If(IsNull(sum),
       SetCellType(child, Literal("float64")),
-      If(IsNull(child), sum, AddTiles(sum, child))
+      If(IsNull(child), sum, BiasedAdd(sum, child))
     )
   )
   override val mergeExpressions: Seq[Expression] = Seq(
-    AddTiles(count.left, count.right),
-    AddTiles(sum.left, sum.right)
+    BiasedAdd(count.left, count.right),
+    BiasedAdd(sum.left, sum.right)
   )
   override lazy val evaluateExpression: Expression = DivideTiles(sum, count)
 }
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
index e0b6b6714..b7a3c8946 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/Add.scala
@@ -30,7 +30,6 @@ import org.apache.spark.sql.{Column, TypedColumn}
 import org.locationtech.rasterframes._
 import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
 import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
-import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 
 @ExpressionDescription(
   usage = "_FUNC_(tile, rhs) - Performs cell-wise addition between two tiles or a tile and a scalar.",
@@ -48,9 +47,9 @@ import org.locationtech.rasterframes.util.DataBiasedOp.BiasedAdd
 case class Add(left: Expression, right: Expression) extends BinaryLocalRasterOp
   with CodegenFallback {
   override val nodeName: String = "rf_local_add"
-  override protected def op(left: Tile, right: Tile): Tile = BiasedAdd(left, right)
-  override protected def op(left: Tile, right: Double): Tile = BiasedAdd(left, right)
-  override protected def op(left: Tile, right: Int): Tile = BiasedAdd(left, right)
+  override protected def op(left: Tile, right: Tile): Tile = left.localAdd(right)
+  override protected def op(left: Tile, right: Double): Tile = left.localAdd(right)
+  override protected def op(left: Tile, right: Int): Tile = left.localAdd(right)
 
   override def eval(input: InternalRow): Any = {
     if(input == null) null
diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/BiasedAdd.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/BiasedAdd.scala
new file mode 100644
index 000000000..10bd82e62
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/localops/BiasedAdd.scala
@@ -0,0 +1,74 @@
+/*
+ * This software is licensed under the Apache 2 license, quoted below.
+ *
+ * Copyright 2019 Astraea, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *     [http://www.apache.org/licenses/LICENSE-2.0]
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.locationtech.rasterframes.expressions.localops
+import geotrellis.raster.Tile
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionDescription}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.sql.{Column, TypedColumn}
+import org.locationtech.rasterframes._
+import org.locationtech.rasterframes.expressions.BinaryLocalRasterOp
+import org.locationtech.rasterframes.expressions.DynamicExtractors.tileExtractor
+import org.locationtech.rasterframes.util.DataBiasedOp
+
+@ExpressionDescription(
+  usage = "_FUNC_(tile, rhs) - Performs cell-wise addition between two tiles or a tile and a scalar. " +
+    "Unlike a regular 'add', this considers `<data> + <nodata> = <data>.",
+  arguments = """
+  Arguments:
+    * tile - left-hand-side tile
+    * rhs  - a tile or scalar value to add to each cell""",
+  examples = """
+  Examples:
+    > SELECT _FUNC_(tile, 1.5);
+       ...
+    > SELECT _FUNC_(tile1, tile2);
+       ..."""
+)
+case class BiasedAdd(left: Expression, right: Expression) extends BinaryLocalRasterOp
+  with CodegenFallback {
+  override val nodeName: String = "rf_local_biased_add"
+  override protected def op(left: Tile, right: Tile): Tile = DataBiasedOp.BiasedAdd(left, right)
+  override protected def op(left: Tile, right: Double): Tile = DataBiasedOp.BiasedAdd(left, right)
+  override protected def op(left: Tile, right: Int): Tile = DataBiasedOp.BiasedAdd(left, right)
+
+  override def eval(input: InternalRow): Any = {
+    if(input == null) null
+    else {
+      val l = left.eval(input)
+      val r = right.eval(input)
+      if (l == null && r == null) null
+      else if (l == null) r
+      else if (r == null && tileExtractor.isDefinedAt(right.dataType)) l
+      else if (r == null) null
+      else nullSafeEval(l, r)
+    }
+  }
+}
+object BiasedAdd {
+  def apply(left: Column, right: Column): TypedColumn[Any, Tile] =
+    new Column(BiasedAdd(left.expr, right.expr)).as[Tile]
+
+  def apply[N: Numeric](tile: Column, value: N): TypedColumn[Any, Tile] =
+    new Column(BiasedAdd(tile.expr, lit(value).expr)).as[Tile]
+}
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 693785d1a..b24eeb0c1 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -152,7 +152,8 @@ def initialize_options(self):
         'numpy>=1.7',
         'shapely',
         'pandas',
-        'rasterio'
+        'rasterio',
+        'boto3'
     ],
     packages=[
         'pyrasterframes',

From 3c1b728f2fc57450cd007b9d69478ce467347a22 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 2 Aug 2019 14:51:54 -0400
Subject: [PATCH 293/380] more docs and unit tests around local algebra and
 nodata

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/reference.pymd       |  4 +
 pyrasterframes/src/main/python/setup.py       |  2 +-
 .../main/python/tests/GeoTiffWriterTests.py   |  4 +-
 .../main/python/tests/PyRasterFramesTests.py  | 13 ++-
 .../main/python/tests/RasterFunctionsTests.py | 86 ++++++++++++++-----
 5 files changed, 84 insertions(+), 25 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 1017a2f35..0b4b0fe58 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -238,6 +238,8 @@ If input `tile` had a NoData value already, the behaviour depends on if its cell
 
 [Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single tile (unary), between a `tile` and a scalar, between two `tile`s, or across many `tile`s.
 
+When these operations encounter a NoData value in either operand, the cell in the resulting `tile` will have a NoData.
+
 The binary local map algebra functions have similar variations in the Python API depending on the left hand side type:
 
  - `rf_local_op`: applies `op` to two columns; the right hand side can be a `tile` or a numeric column.
@@ -536,6 +538,8 @@ Aggregates over all of the rows in DataFrame of `tile` and returns a count of ea
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
+When these functions encounter a NoData in a cell location, it will be ignored. 
+
 ### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index b24eeb0c1..d4a1491d0 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -144,7 +144,7 @@ def initialize_options(self):
         'setuptools>=0.8',
         'ipython==6.2.1',
         "ipykernel==4.8.0",
-        'Pweave==0.30.3'
+        'Pweave==0.30.3',
     ],
     tests_require=[
         'pytest==3.4.2',
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
index 6c4f21b2a..59ad0d0ae 100644
--- a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -37,7 +37,7 @@ def test_identity_write(self):
         dest = self._tmpfile()
         rf.write.geotiff(dest)
 
-        rf2 = self.spark.read.geotiff(dest)
+        rf2 = self.spark.read.geotiff('file://' + dest)
         self.assertEqual(rf2.count(), rf.count())
 
         os.remove(dest)
@@ -47,7 +47,7 @@ def test_unstructured_write(self):
         dest = self._tmpfile()
         rf.write.geotiff(dest, crs='EPSG:32616')
 
-        rf2 = self.spark.read.raster(dest)
+        rf2 = self.spark.read.raster('file://' + dest)
         self.assertEqual(rf2.count(), rf.count())
 
         os.remove(dest)
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index b4f121ea7..af14f5ade 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -28,7 +28,6 @@
 from . import TestEnvironment
 
 
-
 class CellTypeHandling(unittest.TestCase):
 
     def test_is_raw(self):
@@ -237,11 +236,16 @@ def less_pi(t):
 class TileOps(TestEnvironment):
 
     def setUp(self):
+        from pyspark.sql import Row
         # convenience so we can assert around Tile() == Tile()
         self.t1 = Tile(np.array([[1, 2],
                                  [3, 4]]), CellType.int8().with_no_data_value(3))
         self.t2 = Tile(np.array([[1, 2],
                                  [3, 4]]), CellType.int8().with_no_data_value(1))
+        self.t3 = Tile(np.array([[1,  2],
+                                 [-3, 4]]), CellType.int8().with_no_data_value(3))
+
+        self.df = self.spark.createDataFrame([Row(t1=self.t1, t2=self.t2, t3=self.t3)])
 
     def test_addition(self):
         e1 = np.ma.masked_equal(np.array([[5, 6],
@@ -253,6 +257,9 @@ def test_addition(self):
         r2 = (self.t1 + self.t2).cells
         self.assertTrue(np.ma.allequal(r2, e2))
 
+        col_result = self.df.select(rf_local_add('t1', 't3').alias('sum')).first()
+        self.assertEqual(col_result.sum, self.t1 + self.t3)
+
     def test_multiplication(self):
         e1 = np.ma.masked_equal(np.array([[4, 8],
                                           [12, 16]]), 12)
@@ -263,6 +270,9 @@ def test_multiplication(self):
         r2 = (self.t1 * self.t2).cells
         self.assertTrue(np.ma.allequal(r2, e2))
 
+        r3 = self.df.select(rf_local_multiply('t1', 't3').alias('r3')).first().r3
+        self.assertEqual(r3, self.t1 * self.t3)
+
     def test_subtraction(self):
         t3 = self.t1 * 4
         r1 = t3 - self.t1
@@ -541,7 +551,6 @@ def path(scene, band):
         self.assertTrue(df2.select('b1_path').distinct().count() == 3)
 
 
-
 def suite():
     function_tests = unittest.TestSuite()
     return function_tests
diff --git a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
index 4436c6fc1..699307b9b 100644
--- a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
+++ b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
@@ -97,6 +97,21 @@ def test_agg_mean(self):
         mean = self.rf.agg(rf_agg_mean('tile')).first()['rf_agg_mean(tile)']
         self.assertTrue(self.rounded_compare(mean, 10160))
 
+    def test_agg_local_mean(self):
+        from pyspark.sql import Row
+
+        # this is really testing the nodata propagation in the agg  local summation
+        ct = CellType.int8().with_no_data_value(4)
+        df = self.spark.createDataFrame([
+            Row(tile=Tile(np.array([[1, 2, 3, 4, 5, 6]]), ct)),
+            Row(tile=Tile(np.array([[1, 2, 4, 3, 5, 6]]), ct)),
+        ])
+
+        result = df.agg(rf_agg_local_mean('tile').alias('mean')).first().mean
+
+        expected = Tile(np.array([[1.0, 2.0, 3.0, 3.0, 5.0, 6.0]]), CellType.float64())
+        self.assertEqual(result, expected)
+
     def test_aggregations(self):
         aggs = self.rf.agg(
             rf_agg_data_cells('tile'),
@@ -112,28 +127,59 @@ def test_aggregations(self):
         self.assertEqual(row['rf_agg_stats(tile)'].data_cells, row['rf_agg_data_cells(tile)'])
 
     def test_sql(self):
+
         self.rf.createOrReplaceTempView("rf_test_sql")
 
-        self.spark.sql("""SELECT tile, 
-                            rf_local_add(tile, 1) AS and_one, 
-                            rf_local_subtract(tile, 1) AS less_one, 
-                            rf_local_multiply(tile, 2) AS times_two, 
-                            rf_local_divide(tile, 2) AS over_two 
-                        FROM rf_test_sql""").createOrReplaceTempView('rf_test_sql_1')
-
-        statsRow = self.spark.sql("""
-        SELECT rf_tile_mean(tile) as base,
-            rf_tile_mean(and_one) as plus_one,
-            rf_tile_mean(less_one) as minus_one,
-            rf_tile_mean(times_two) as double,
-            rf_tile_mean(over_two) as half
-        FROM rf_test_sql_1
-        """).first()
-
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.plus_one - 1))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.minus_one + 1))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.double / 2))
-        self.assertTrue(self.rounded_compare(statsRow.base, statsRow.half * 2))
+        arith = self.spark.sql("""SELECT tile, 
+                                rf_local_add(tile, 1) AS add_one, 
+                                rf_local_subtract(tile, 1) AS less_one, 
+                                rf_local_multiply(tile, 2) AS times_two, 
+                                rf_local_divide(
+                                    rf_convert_cell_type(tile, "float32"), 
+                                    2) AS over_two 
+                            FROM rf_test_sql""")
+
+        arith.createOrReplaceTempView('rf_test_sql_1')
+        arith.show(truncate=False)
+        stats = self.spark.sql("""
+            SELECT rf_tile_mean(tile) as base,
+                rf_tile_mean(add_one) as plus_one,
+                rf_tile_mean(less_one) as minus_one,
+                rf_tile_mean(times_two) as double,
+                rf_tile_mean(over_two) as half,
+                rf_no_data_cells(tile) as nd
+                
+            FROM rf_test_sql_1
+            ORDER BY rf_no_data_cells(tile)
+            """)
+        stats.show(truncate=False)
+        stats.createOrReplaceTempView('rf_test_sql_stats')
+
+        compare = self.spark.sql("""
+            SELECT 
+                plus_one - 1.0 = base as add,
+                minus_one + 1.0 = base as subtract,
+                double / 2.0 = base as multiply,
+                half * 2.0 = base as divide,
+                nd
+            FROM rf_test_sql_stats
+            """)
+
+        expect_row1 = compare.orderBy('nd').first()
+
+        self.assertTrue(expect_row1.subtract)
+        self.assertTrue(expect_row1.multiply)
+        self.assertTrue(expect_row1.divide)
+        self.assertEqual(expect_row1.nd, 0)
+        self.assertTrue(expect_row1.add)
+
+        expect_row2 = compare.orderBy('nd', ascending=False).first()
+
+        self.assertTrue(expect_row2.subtract)
+        self.assertTrue(expect_row2.multiply)
+        self.assertTrue(expect_row2.divide)
+        self.assertTrue(expect_row2.nd > 0)
+        self.assertTrue(expect_row2.add)  # <-- Would fail in a case where ND + 1 = 1
 
     def test_explode(self):
         import pyspark.sql.functions as F

From 695471909a60a2190f559272ecc346ba388af28f Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Fri, 2 Aug 2019 16:38:42 -0400
Subject: [PATCH 294/380] changed creation of spark dataframes. added explicit
 call to display

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 .../src/main/python/docs/nodata-handling.pymd | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 4505c32f2..5d801bc6d 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -158,9 +158,8 @@ for i in range(4):
     x[:, i*tile_size//4:(i+1)*tile_size//4] = i
 x = Tile(x)
 
-rf = spark.createDataFrame(pd.DataFrame(data = 
-        {'tile': [x]}))
-x
+rf = spark.createDataFrame([Row(tile=x)])
+display(x)
 ```
 
 First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a tile with the cell type 'uint16ud2'.
@@ -197,13 +196,12 @@ Let's use the same tile as before to demonstrate how NoData values affect tile a
 
 ```python
 tile_size = 100
-x = np.eye(tile_size, dtype='int16')
+x = np.zeros((tile_size, tile_size), dtype='int16')
 for i in range(4):
     x[:, i*tile_size//4:(i+1)*tile_size//4] = i
 x = Tile(x)
 
-rf = spark.createDataFrame(pd.DataFrame(data = 
-        {'tile': [x]}))
+rf = spark.createDataFrame([Row(tile=x)])
 display(x)
 ```
 
@@ -235,9 +233,8 @@ Let's first create a RasterFrame that has columns of `float` and `int` cell type
 ```python
 x = Tile((np.ones((100, 100))*2).astype('float'))
 y = Tile((np.ones((100, 100))*3.0).astype('int32'))
-rf = spark.createDataFrame(pd.DataFrame(data = 
-        {'x': [x],
-        'y': [y]}))
+rf = spark.createDataFrame([Row(x=x, y=y)])
+
 rf.select(rf_cell_type('y'), rf_cell_type('x')).distinct().show()
 ```
 
@@ -257,8 +254,8 @@ def get_nodata_ct(nd_val):
 
 x = Tile((np.ones((100, 100))*3).astype('int16'))
 
-rf = spark.createDataFrame(pd.DataFrame(data = 
-        {'x': [x]}))
+rf = spark.createDataFrame([Row(x=x)])
+
 rf_nd = rf.withColumn('x_nd_1', rf_convert_cell_type('x', get_nodata_ct(1))) \
           .withColumn('x_nd_2', rf_convert_cell_type('x', get_nodata_ct(2)))
 ```

From b342f21fd61cc7ca8362407d961ee5eca45abb29 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 5 Aug 2019 10:52:23 -0400
Subject: [PATCH 295/380] Fixed handling of literal CRSs.

---
 pyrasterframes/src/main/python/docs/supervised-learning.pymd | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index a13b39b7b..dc38ace05 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -5,7 +5,8 @@ In this example we will demonstrate how to fit and score a supervised learning m
 ```python, setup, echo=False
 from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
-from pyrasterframes.rasterfunctions import * 
+from pyrasterframes.rasterfunctions import *
+from pyspark.sql.functions import lit 
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -71,7 +72,7 @@ print('Found ', len(crses), 'distinct CRS.')
 crs = crses[0][0]
 
 label_df = spark.read.geojson(os.path.join(resource_dir_uri(), 'luray-labels.geojson')) \
-					 .select('id', st_reproject('geometry', 'EPSG:4326', crs).alias('geometry')) \
+					 .select('id', st_reproject('geometry', lit('EPSG:4326'), lit(crs)).alias('geometry')) \
 					 .hint('broadcast')
 
 df_joined = df.join(label_df, st_intersects(st_geometry('extent'), 'geometry')) 

From e89c4011c86e061110d5fe02d43a4cb29e8385de Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 5 Aug 2019 10:53:23 -0400
Subject: [PATCH 296/380] Allow CellType or str in rasterfunctions

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/nodata-handling.pymd    | 16 +++++-----------
 .../python/pyrasterframes/rasterfunctions.py     | 13 ++++++++++---
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 5d801bc6d..fcd16db50 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -133,16 +133,16 @@ We can verify that the number of NoData cells in the resulting `blue_masked` col
 masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
 ```
 
-It's also nice to view a sample.
+It's also nice to view a sample. The white regions are areas of NoData.
 
-```python
+```python, caption='Blue band masked against selected SCL values'
 sample = masked.orderBy(-rf_no_data_cells('blue_masked')).select(rf_tile('blue_masked'), rf_tile('scl')).first()
 display(sample[0])
 ```
 
-And the original SCL data.
+And the original SCL data. The bright yellow is a cloudy region in the original image.
 
-```python
+```python, caption='SCL tile for above'
 display(sample[1])
 ```
 
@@ -166,7 +166,7 @@ First, we mask the value of 1 by making a new column with the user defined cell
 
 ```python
 def get_nodata_ct(nd_val):
-    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
+	return CellType('uint16').with_no_data_value(nd_val)
 
 masked_rf = rf.withColumn('tile_nd_1', 
                            rf_convert_cell_type('tile', get_nodata_ct(1))) \
@@ -208,9 +208,6 @@ display(x)
 First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked. 
 
 ```python
-def get_nodata_ct(nd_val):
-    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
-
 masked_rf = rf.withColumn('tile_nd_1', 
                            rf_convert_cell_type('tile', get_nodata_ct(1))) \
               .withColumn('tile_nd_2',
@@ -249,9 +246,6 @@ Combining tile columns of different cell types gets a little trickier when user
 
 
 ```python
-def get_nodata_ct(nd_val):
-    return CellType('uint16').with_no_data_value(nd_val).cell_type_name
-
 x = Tile((np.ones((100, 100))*3).astype('int16'))
 
 rf = spark.createDataFrame([Row(x=x)])
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index e342842d9..0c9f3939a 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -36,9 +36,16 @@ def _context_call(name, *args):
     return f(*args)
 
 
-def _parse_cell_type(cell_type_str):
-    """ Convert the string cell type to the expected CellType object."""
-    return _context_call('_parse_cell_type', cell_type_str)
+def _parse_cell_type(cell_type_arg):
+    """ Convert the cell type representation to the expected JVM CellType object."""
+
+    def to_jvm(ct):
+        return _context_call('_parse_cell_type', ct)
+
+    if isinstance(cell_type_arg, str):
+        return to_jvm(cell_type_arg)
+    elif isinstance(cell_type_arg, CellType):
+        return to_jvm(cell_type_arg.cell_type_name)
 
 
 def rf_cell_types():

From 3a51158527db8fc728bfc507564365f34586125a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 5 Aug 2019 11:46:54 -0400
Subject: [PATCH 297/380] Add prospective links to getting started docs

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/getting-started.pymd | 29 ++++++++++++++-----
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index df4f97676..543ddb89c 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -1,6 +1,10 @@
 # Getting Started
 
-There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell. Python 3.6 or greater is recommended.
+There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell.  To get started you will need:
+
+1. [Python](https://www.python.org/) installed. Version 3.6 or greater is recommended.
+1. `pip` or `pip3` (recommended) installed. If you are using Python 3, `pip3` may already be installed.
+1. Java [JDK 8](https://openjdk.java.net/install/index.html) installed on your system and `java` on your system `PATH` or `JAVA_HOME` pointing to a Java installation.
 
 ## pip install pyrasterframes
 
@@ -31,8 +35,7 @@ df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
 
 This example is extended in the [getting started Jupyter notebook](https://nbviewer.jupyter.org/github/locationtech/rasterframes/blob/develop/rf-notebook/src/main/notebooks/Getting%20Started.ipynb).
 
-To understand more about how and why RasterFrames represents Earth observation in DataFrames, read the project @ref:[description](description.md). For more hands-on examples, see the chapters about @ref:[reading](raster-io.md) and @ref:[processing](raster-processing.md) with RasterFrames.
-
+To understand more about how and why RasterFrames represents Earth observation in DataFrames, read about the @ref:[core concepts](concepts.md) and the project @ref:[description](description.md). For more hands-on examples, see the chapters about @ref:[reading](raster-io.md) and @ref:[processing](raster-processing.md) with RasterFrames.
 
 ## Other Options
 
@@ -43,15 +46,27 @@ You can also use RasterFrames in the following environments:
 
 ### Jupyter Notebook
 
-**TODO** User facing quick instructions e.g. how to pull and run docker hub hosted container
+ RasterFrames provides a Docker image for a Jupyter notebook server whose default kernel is already set up for running RasterFrames. To use it:
+
+ 1. Install [docker](https://docs.docker.com/install/)
+ 1. Pull the image: `docker pull s22s/rasterframes-notebook`
+ 1. Run a container with the image, for example: 
+
+      docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest
 
-See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on running a Jupyter notebook server within a Docker container that has a fully set up environment.
+ 1. In a browser, open `localhost:8808` in the example above. 
+
+See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
 
 ### `pyspark` shell or app
 
-To initialize RasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip.
+To initialize RasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. 
+
+You can either [build](https://github.com/locationtech/rasterframes/blob/develop/README.md) the artifacts or download them:
+
+ * Assembly JAR: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes-assembly-${VERSION}.jar
+ * Python zip: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip
 
-**TODO** how to build or download those artifacts.
 
 ```bash
    pyspark \

From 9488923f5ec88e61c89f71786ffca712ed143b48 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Mon, 5 Aug 2019 09:42:36 -0700
Subject: [PATCH 298/380] cleaned up need for cell type name

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 pyrasterframes/src/main/python/docs/nodata-handling.pymd | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index fcd16db50..2acf32552 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -241,7 +241,6 @@ When performing a local operation between tile columns with cell types `int` and
 rf_added = rf.withColumn('xy_sum', rf_local_add('y', 'x'))
 rf_added.select(rf_cell_type('xy_sum'), rf_cell_type('y'), rf_cell_type('x')).distinct().show()
 ```
-
 Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 3 tile columns: one without a defined NoData value, one with a NoData value of 1, and one with a NoData value of 2.
 
 

From b4d92af08a89ca7a8c88bd2e745f7661eb8d1d69 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Mon, 5 Aug 2019 12:52:35 -0400
Subject: [PATCH 299/380] add fiona version to required libraries

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index d4a1491d0..661a2028b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -145,6 +145,7 @@ def initialize_options(self):
         'ipython==6.2.1',
         "ipykernel==4.8.0",
         'Pweave==0.30.3',
+        'fiona==1.8.6'
     ],
     tests_require=[
         'pytest==3.4.2',

From c09ee49b78daa626dacd840ec62b8d8f9e34e8fe Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Mon, 5 Aug 2019 10:03:24 -0700
Subject: [PATCH 300/380] update the examplanation of the fancy tile

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 pyrasterframes/src/main/python/docs/nodata-handling.pymd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 2acf32552..d778c42a9 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -148,12 +148,13 @@ display(sample[1])
 
 ## Changing a Tile's NoData Values
 
-One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a rasterframe from a tile with values of 0, 1, 2, and 3.
+One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a rasterframe from a tile with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 Tile with columns of 0, 1, 2, and 3.
 
 ```python create_dummy_tile
 tile_size = 100
 x = np.zeros((tile_size, tile_size), dtype='int16')
 
+# setting the values of the columns
 for i in range(4):
     x[:, i*tile_size//4:(i+1)*tile_size//4] = i
 x = Tile(x)

From 818cffbe73f8f34f50b2ddb5709f7fad0114fcf2 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 5 Aug 2019 13:57:06 -0400
Subject: [PATCH 301/380] Basic support for generating a PDF for proofreading.

---
 README.md                                     |   6 +-
 docs/build.sbt                                |  81 ++++++++++++++++++
 docs/src/main/latex/header.latex              |   2 +
 docs/src/main/paradox/RasterFramePipeline.png | Bin 0 -> 30917 bytes
 docs/src/main/paradox/release-notes.md        |  10 +--
 pyrasterframes/src/main/python/docs/index.md  |   4 +-
 .../src/main/python/docs/raster-read.pymd     |  26 +++---
 7 files changed, 106 insertions(+), 23 deletions(-)
 create mode 100644 docs/build.sbt
 create mode 100644 docs/src/main/latex/header.latex
 create mode 100644 docs/src/main/paradox/RasterFramePipeline.png

diff --git a/README.md b/README.md
index fa878e5b1..77795f2b2 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
-<img src="docs/src/main/paradox/_template/images/RasterFramesLogo.png" width="300px"/><sup style="vertical-align: top;">&trade;</sup>
+<img src="docs/src/main/paradox/_template/images/RasterFramesLogo.png" width="300px"/><sup style="vertical-align: top;">&reg;</sup>
 
  [![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 _RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
 
-<img src="docs/src/main/paradox/RasterFramePipeline.svg" width="600px"/>
+<img src="docs/src/main/paradox/RasterFramePipeline.png" width="600px"/>
 
 Please see the [Getting Started](http://rasterframes.io/getting-started.html) section of the Users' Manual to start using RasterFrames.
 
@@ -54,6 +54,6 @@ The `pyrasterframes` build instructions are located at [pyrasterframes/src/main/
 
 ## Copyright and License
 
-RasterFrames is released under the Apache 2.0 License, copyright Astraea, Inc. 2017-2018.
+RasterFrames is released under the Apache 2.0 License, copyright Astraea, Inc. 2017-2019.
 
 
diff --git a/docs/build.sbt b/docs/build.sbt
new file mode 100644
index 000000000..4e9ed21d8
--- /dev/null
+++ b/docs/build.sbt
@@ -0,0 +1,81 @@
+// task to create documentation PDF
+lazy val makePDF = taskKey[File]("Build PDF version of documentation")
+lazy val pdfFileName = settingKey[String]("Name of the PDF file generated")
+pdfFileName := s"${name.value}-${version.value}.pdf"
+
+makePDF := {
+  import scala.sys.process._
+
+  // Get the python source directory configured in the root project.
+  val base = (Compile / paradox / sourceDirectories).value.find(_.toString.contains("python")).head
+  println(base)
+
+  // Hard coded lacking any simple  way of determining order.
+  val files = Seq(
+    "index.md",
+    "description.md",
+    "concepts.md",
+    "getting-started.md",
+    "raster-io.md",
+    "raster-catalogs.md",
+    "raster-read.md",
+    "raster-write.md",
+    "vector-data.md",
+    "raster-processing.md",
+    "local-algebra.md",
+    "nodata-handling.md",
+    "aggregation.md",
+    "time-series.md",
+    "machine-learning.md",
+    "unsupervised-learning.md",
+    "supervised-learning.md",
+    "numpy-pandas.md",
+    "languages.md",
+    "reference.md"
+  ).map(base ** _).flatMap(_.get)
+
+  val log = streams.value.log
+  log.info("Section ordering:")
+  files.foreach(f => log.info(" - " + f.getName))
+
+  val work = target.value / "makePDF"
+  work.mkdirs()
+
+  val prepro = files.zipWithIndex.map { case (f, i) ⇒
+    val dest = work / f"$i%02d.md"
+    // Filter cross links and add a newline
+    (Seq("sed", "-e", """s/@ref:\[\([^]]*\)\](.*)/_\1_/g;s/@@.*//g""", f.toString) #> dest).!
+    // Add newline at the end of the file so as to make pandoc happy
+    ("echo" #>> dest).!
+    ("echo \\pagebreak" #>> dest).!
+    dest
+  }
+
+  val output = target.value / pdfFileName.value
+
+  val header = (Compile / sourceDirectory).value / "latex" / "header.latex"
+
+  val args = "pandoc" ::
+    "--from=markdown" ::
+    "--to=pdf" ::
+    "-t" :: "latex" ::
+    "-s" ::
+    "--toc" ::
+    "-V" :: "title:RasterFrames Users' Manual" ::
+    "-V" :: "author:Astraea, Inc." ::
+    "-V" :: "geometry:margin=0.75in" ::
+    "-V" :: "papersize:letter" ::
+    "-V" :: "links-as-notes" ::
+    "--include-in-header" :: header.toString ::
+    "-o" :: output.toString ::
+    prepro.map(_.toString).toList
+
+  log.info(s"Running: ${args.mkString(" ")}")
+  Process(args, base).!
+
+  log.info("Wrote: " + output)
+
+  output
+}
+
+//makePDF := makePDF.dependsOn(Compile / paradox).value
diff --git a/docs/src/main/latex/header.latex b/docs/src/main/latex/header.latex
new file mode 100644
index 000000000..a6fca2586
--- /dev/null
+++ b/docs/src/main/latex/header.latex
@@ -0,0 +1,2 @@
+\DeclareUnicodeCharacter{2218}{$\circ$}
+\DeclareUnicodeCharacter{2714}{$\checkmark}
diff --git a/docs/src/main/paradox/RasterFramePipeline.png b/docs/src/main/paradox/RasterFramePipeline.png
new file mode 100644
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diff --git a/docs/src/main/paradox/release-notes.md b/docs/src/main/paradox/release-notes.md
index 51a126dfc..89b52c718 100644
--- a/docs/src/main/paradox/release-notes.md
+++ b/docs/src/main/paradox/release-notes.md
@@ -11,17 +11,18 @@
 * Added new tile functions `rf_round`, `rf_abs`, `rf_log`, `rf_log10`, `rf_log2`, `rf_log1p`, `rf_exp`, `rf_exp10`, `rf_exp2`, `rf_expm1`, `rf_resample`.
 * Support Python-side Tile User-Defined Type backed by [numpy](https://www.numpy.org/) `ndarray` or `ma.MaskedArray`.
 * Support Python-side [Shapely](https://pypi.org/project/Shapely/) geometry User-Defined Type.
-* SQL API support for: `rf_assemble_tile`, `rf_array_to_tile`.
+* SQL API support for `rf_assemble_tile` and `rf_array_to_tile`.
 * Introduced at the source level the concept of a `RasterSource` and `RasterRef`, enabling lazy/delayed read of sub-scene tiles.
 * Added `withKryoSerialization` extension methods on `SparkSession.Builder` and `SparkConf`.
 * Added `rf_render_matrix` debugging function.
-* Added `RasterFrame.withExtent` extension method.
+* Added `RasterFrameLayer.withExtent` extension method.
 * Added `SinglebandGeoTiff.toDF` extension method.
 * Added `DataFrame.rasterJoin` extension method for merging two dataframes with tiles in disparate CRSs.
 * Added `rf_crs` for `ProjectedRasterTile` columns.
 * Added `st_extent` (for `Geometry` types) and `rf_extent` (for `ProjectedRasterTile` and `RasterSource` columns).
 * Added `st_geometry` (for `Extent` types) and `rf_geometry` (for `ProjectedRasterTile` and `RasterSource` columns).
-* _Breaking_: The type `RasterFrame` renamed `RasterFrameLayer` to be reflect its purpose.
+* Reworked build scripts for RasterFrames Jupyter Notebook. 
+* _Breaking_: The type `RasterFrame` renamed `RasterFrameLayer` to be reflect its intended purpose.
 * _Breaking_: All `asRF` methods renamed to `asLayer`.
 * _Breaking_: Root package changed from `org.locationtech.rasterframes` to `org.locationtech.rasterframes`.
 * _Breaking_: Removed `envelope`, in lieu of `st_extent`, `rf_extent` or `st_envelope` 
@@ -37,8 +38,7 @@
 * _Breaking_: `CellHistogram` no longer carries along approximate statistics, due to confusing behavior. Use `rf_agg_stats` instead.
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `org.locationtech.rasterframes.model`.
-* _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrame.withGeometry` for consistency with DataSource schemas.
-* Reworked build scripts for RasterFrames Jupyter Notebook. 
+* _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrameLayer.withGeometry` for consistency with DataSource schemas.
    
 ## 0.7.x
 
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index a9590a361..790bff5ca 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -8,7 +8,7 @@ To learn more, please see the @ref:[Getting Started](getting-started.md) section
 
 The source code can be found on GitHub at [locationtech/rasterframes](https://github.com/locationtech/rasterframes).
 
-<img src="RasterFramePipeline.svg" width="600px"/>
+<img src="RasterFramePipeline.png" width="600px"/>
 
 <hr/>
 
@@ -24,7 +24,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 ## Related Links
 
-* [Gitter Channel](https://gitter.im/s22s/raster-frames)&nbsp;&nbsp;[![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+* [Gitter Channel](https://gitter.im/s22s/raster-frames)
 * [Scala API Documentation](latest/api/index.html)
 * [GitHub Repository](https://github.com/locationtech/rasterframes)
 * [Astraea, Inc.](http://www.astraea.earth/), the company behind RasterFrames
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 1d3f8b252..25f6d0910 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -54,19 +54,19 @@ display(tile)
 RasterFrames relies on three different IO drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used first if a compatible version of GDAL (>= 2.4) is installed. It is the only driver that can read non-GeoTIFF files. If GDAL is not available, either the _Java I/O` or _Hadoop_ driver will be selected, depending on scheme.  
 
 
-| Prefix              | GDAL        | Java I/O | Hadoop |
-| ------------------- | ----------- | -------- | ------ |
-| `gdal://<vsidrv>//` |  ✔︎          | -︎        | -      |
-| `file://`           |  ✔︎          | ✔︎       | -      |
-| `http://`           |  ✔︎          | ✔︎       | -      |
-| `https://`          |  ✔︎          | ✔︎       | -      |
-| `ftp://`            | `/vsicurl/`  | ✔       | -      |
-| `hdfs://`           |  `/vsihdfs/` ︎| -       | ✔︎      |
-| `s3://`             |  `/vsis3/` ︎  | ✔︎       | -      |
-| `s3n://`            |  - ︎          | -       | ✔︎      |
-| `s3a://`            |  - ︎          | -       | ✔︎      |
-| `wasb://`           |  `/vsiaz/` ︎  | -       | ✔︎      |
-| `wasbs://`          |  - ︎          | -       | ✔︎      |
+| Prefix              | GDAL        | Java I/O | Hadoop   |
+| ------------------- | ----------- | -------- | -------- |
+| `gdal://<vsidrv>//` | +    | -        | -        |
+| `file://`           | +    | + | -        |
+| `http://`           | +    | + | -        |
+| `https://`          | +    | + | -        |
+| `ftp://`            | `/vsicurl/` | + | -        |
+| `hdfs://`           | `/vsihdfs/` |-         | + |
+| `s3://`             |  `/vsis3/`  | + | -        |
+| `s3n://`            |  -          | -        | + |
+| `s3a://`            |  -          | -        | + |
+| `wasb://`           |  `/vsiaz/`  | -        | + |
+| `wasbs://`          |  -          | -        | + |
 
 Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. See the GDAL documentation for the format of the URIs after the `gdal:/` prefix (which is stripped off before passing the rest of the path to GDAL).
 

From f617ad5f355cf2a5e28ffd1d96538b676fbb512d Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Mon, 5 Aug 2019 14:03:28 -0400
Subject: [PATCH 302/380] fix spelling

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/description.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 33aac6cdb..53961166b 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -4,7 +4,7 @@ RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling
 
 ## Context
 
-We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis. 
+We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis.
 
 Today, DataFrames are the _lingua franca_ of data science. The evolution of the tabular form has continued with Apache Spark SQL, which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables interactive and batch-oriented cluster computing without having to be versed in the highly specialized skills typically required for high-performance computing. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
 
@@ -14,7 +14,7 @@ RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings to
 
 ## Benefit
 
-By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. Because it is built on Apache Spark, solutions prototyped on a laptop can be scaled to run on cluster and cloud compute resources in a way not easily achieved with other toolchains. 
+By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. Because it is built on Apache Spark, solutions prototyped on a laptop can be scaled to run on cluster and cloud compute resources in a way not easily achieved with other toolchains.
 
 ## Architecture
 
@@ -27,7 +27,7 @@ RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO
 
 RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called "tiles". Each tile contains a 2-D matrix of "cell" (pixel) values along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other rasterized spatiotemporal data. Along with `tile` columns there is typically an `extent` specifying the geographic location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering 
 
-RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. You can use vector data to filter DataFrame rows, using geospatial predicates (e.g. contains, intersects, overlaps, etc.), to mask cells, and to be rasterzied into training data appropriate for machine learning.
+RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. You can use vector data to filter DataFrame rows, using geospatial predicates (e.g. contains, intersects, overlaps, etc.), to mask cells, and to be rasterized into training data appropriate for machine learning.
 
 
 ![RasterFrame Anatomy](static/rasterframe-anatomy.png)
@@ -42,4 +42,4 @@ Raster data can be read from a number of sources. Through the flexible Spark SQL
 [COGS]:https://www.cogeo.org/
 
 [^1]: [_Demystifying Satellite Assets for Post-Disaster Situation Awareness_](https://docs.google.com/document/d/11bIw5HcEiZy8SKli6ZFQC2chVEiiIJ-f0o6btA4LU48).
-World Bank via [OpenDRI.org](https://opendri.org/resource/demystifying-satellite-assets-for-post-disaster-situation-awareness/). Accessed November 28, 2018. 
+World Bank via [OpenDRI.org](https://opendri.org/resource/demystifying-satellite-assets-for-post-disaster-situation-awareness/). Accessed November 28, 2018.

From 39392d236617aab615420d7a7b97af975161c309 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Mon, 5 Aug 2019 14:29:26 -0400
Subject: [PATCH 303/380] fix spelling/grammar mistakes

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/concepts.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
index 62a40d64a..0ddfd6a30 100644
--- a/pyrasterframes/src/main/python/docs/concepts.md
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -1,6 +1,6 @@
 # Concepts
 
-There are a number of Earth-observation (EO) concepts that crop up in the discussion of RasterFrames features. We'll cover these briefly in the sections below. However, here are a few links providing a more extensive introduction to working with Earth observation data. 
+There are a number of Earth-observation (EO) concepts that crop up in the discussion of RasterFrames features. We'll cover these briefly in the sections below. However, here are a few links providing a more extensive introduction to working with Earth observation data.
 
 * [_Fundamentals of Remote Sensing_](https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/9309)
 * [_Newcomers Earth Observation Guide_](https://business.esa.int/newcomers-earth-observation-guide)
@@ -8,7 +8,7 @@ There are a number of Earth-observation (EO) concepts that crop up in the discus
 
 ## Cell
 
-A cell is a single sample from a sensor encoded as a scalar value asssociated with a specific spatiotemporal location and time. It can be thought of as an image pixel associated with a place and time. 
+A cell is a single sample from a sensor encoded as a scalar value associated with a specific spatiotemporal location and time. It can be thought of as an image pixel associated with a place and time.
 
 ## Cell Type
 
@@ -35,15 +35,15 @@ See the section on [“NoData” Handling](nodata-handling.md) for additional di
 
 ## NoData
 
-A "NoData" (or N/A) value is a specifically identified value for a cell type used to indicate the absence of data. See the section on @ref:[“NoData” Handling](nodata-handling.md) for additional discussion on NoData
+A "NoData" (or N/A) value is a specifically identified value for a cell type used to indicate the absence of data. See the section on @ref:[“NoData” Handling](nodata-handling.md) for additional discussion on "NoData".
 
 ## Scene
 
-A scene (or granule) is a discrete instance of EO data with a specific extent (region), date-time, and projection/CRS. 
+A scene (or granule) is a discrete instance of EO data with a specific extent (region), date-time, and projection/CRS.
 
 ## Coordinate Reference System (CRS)
 
-A coordinate reference system (or spatial reference system) is a set of mathematical constructs used to map cells to specific locations on the Earth (or other surface). A CRS typcially accompanies any EO data so it can be precicely located.
+A coordinate reference system (or spatial reference system) is a set of mathematical constructs used to map cells to specific locations on the Earth (or other surface). A CRS typically accompanies any EO data so it can be precisely located.
 
 ## Extent
 
@@ -51,17 +51,17 @@ An extent (or bounding box) is a rectangular region specifying the geospatial co
 
 ## Tile
 
-A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). A tile can conceptually be though of as a two-dimensional array.
+A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). A tile can conceptually be thought of as a two-dimensional array.
 
 Some EO data has many bands or channels. Tiles in this context are conceptually a three-dimensional array, with the extra dimension representing the bands.
 
-Tiles are often square and the dimensions are some power of two, for example 256 by 256. 
+Tiles are often square and the dimensions are some power of two, for example 256 by 256.
 
 The tile is the primary discretization unit used in RasterFrames. Each band of a scene is in a separate column. The scene's overall @ref:[extent](concepts.md#extent) is carved up into smaller extents for each tile. Each row of the DataFrame contains a two-dimensional tile per band column.
 
 ## Projected Extent
 
-An extent paired with a CRS
+An extent paired with a CRS.
 
 ## Projected Raster
 

From aa67b16158fa7f26a42b42353889d8642815bfbb Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Mon, 5 Aug 2019 15:36:09 -0400
Subject: [PATCH 304/380] remove extra parenthesis

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/raster-io.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index 3880d8629..b007ec291 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -9,12 +9,12 @@ The standard mechanism by which any data is brought in and out of a Spark Datafr
 * @ref:[Raster Readers](raster-read.md)
     - `raster`: the standard reader for most raster data, including single raster files or catalogs
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
-    - `geotrellis`: for reading a [GeoTrellis layer][GTLayer])
+    - `geotrellis`: for reading a [GeoTrellis layer][GTLayer]
 * @ref:[Raster Writers](raster-write.md)
     - @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
     - `geotiff`: beta writer to GeoTiff file
     - `geotrellis`: creating a [GeoTrellis layer][GTLayer]
-    - [`parquet`][Parquet]: general purpose writer 
+    - [`parquet`][Parquet]: general purpose writer
 
 There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.
 
@@ -29,4 +29,4 @@ There is also support for @ref:[vector data](vector-data.md) for masking and dat
 [GTLayer]: https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html
 [Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html
 [MODIS]: https://docs.opendata.aws/modis-pds/readme.html
-[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
\ No newline at end of file
+[Landsat]: https://docs.opendata.aws/landsat-pds/readme.html

From 4f92e99c42dcd71766d99316ad8b7afc8ac925d0 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 5 Aug 2019 16:14:57 -0400
Subject: [PATCH 305/380] Added `rf-notebooks/includeNotebooks` settings.
 Partial installation of GDAL in rf-notebooks. Version is incorrect. Docs on
 installing and checking GDAL.

---
 docs/build.sbt                                |  4 +-
 .../src/main/python/docs/getting-started.pymd | 39 +++++++++++++++++--
 rf-notebook/build.sbt                         | 11 +++++-
 rf-notebook/src/main/docker/Dockerfile        | 20 ++++++----
 4 files changed, 60 insertions(+), 14 deletions(-)

diff --git a/docs/build.sbt b/docs/build.sbt
index 4e9ed21d8..8e2325fa0 100644
--- a/docs/build.sbt
+++ b/docs/build.sbt
@@ -1,7 +1,7 @@
 // task to create documentation PDF
 lazy val makePDF = taskKey[File]("Build PDF version of documentation")
 lazy val pdfFileName = settingKey[String]("Name of the PDF file generated")
-pdfFileName := s"${name.value}-${version.value}.pdf"
+pdfFileName := s"RasterFrames-Users-Manual-${version.value}.pdf"
 
 makePDF := {
   import scala.sys.process._
@@ -78,4 +78,4 @@ makePDF := {
   output
 }
 
-//makePDF := makePDF.dependsOn(Compile / paradox).value
+makePDF := makePDF.dependsOn(Compile / paradox).value
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index df4f97676..795322bba 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -78,8 +78,41 @@ spark.stop()
 
 ## Installing GDAL 
 
-GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below.
+GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below. Version 2.4.1 or greater is required.
 
-__TODO__ Verify notebook env has GDAL? 
+### Installing on MacOS
 
-__TODO__ How to install it.
+Using [homebrew](https://brew.sh/):
+
+```bash
+brew install gdal
+```
+
+### Installing on Linux
+
+Using [`apt-get`](https://wiki.debian.org/Apt):
+
+```bash
+sudo apt-get update 
+sudo apt-get install gdal-bin
+```
+
+### Testing For GDAL
+
+```bash
+gdalinfo --formats
+```
+
+To support GeoTIFF and JPEG2000 formats, you should look for the following drivers from the output above:
+
+* `GTiff -raster- (rw+vs): GeoTIFF`
+* `JPEG2000 -raster,vector- (rwv): JPEG-2000 part 1 (ISO/IEC 15444-1), based on Jasper library`
+
+Do the following to see if RasterFrames was able to find GDAL:
+
+```python
+from pyrasterframes.utils import gdal_version
+print(gdal_version())
+```
+
+This will print out somethin like "GDAL x.y.z, released 20yy/mm/dd". If it reports `unavailable`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
\ No newline at end of file
diff --git a/rf-notebook/build.sbt b/rf-notebook/build.sbt
index 753f13cef..cac2f88ce 100644
--- a/rf-notebook/build.sbt
+++ b/rf-notebook/build.sbt
@@ -1,6 +1,9 @@
 import scala.sys.process.Process
 import PythonBuildPlugin.autoImport.pyWhl
 
+lazy val includeNotebooks = settingKey[Boolean]("Whether to build documentation into notebooks and include them")
+includeNotebooks := true
+
 Docker / packageName := "rasterframes-notebook"
 
 Docker / version := version.value
@@ -20,7 +23,13 @@ Docker / mappings := Def.sequential(
     val dockerAssets = (dockerSrc ** "*") pair Path.relativeTo(dockerSrc)
 
     val py = (LocalProject("pyrasterframes") / pyWhl).value
-    val _ = (LocalProject("pyrasterframes") / pySetup).toTask(" notebooks").value
+
+    Def.taskDyn {
+      val withNB = includeNotebooks.value
+      if (withNB)
+        (LocalProject("pyrasterframes") / pySetup).toTask(" notebooks")
+      else Def.task(0)
+    }.value
 
     val nbFiles = ((LocalProject("pyrasterframes") / Python / doc / target).value ** "*.ipynb").get()
 
diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index 0a22c820a..3b62ac84f 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -14,20 +14,24 @@ EXPOSE 4040 4041 4042 4043 4044
 RUN conda install --quiet --yes \
     anaconda sphinx nbsphinx shapely numpy folium geopandas geojsonio
 
+RUN apt-get update && \
+    apt-get install -y gdal-bin && \
+    apt autoremove && \
+    apt-get clean all
+
 # Cleanup pip residuals
 RUN rm -rf /home/$NB_USER/.local && \
-    fix-permissions $CONDA_DIR && \
     fix-permissions /home/$NB_USER
 
-# Do these after the standard environment setup
-# since these change more regularly.
+# Note: The above step takes an insanely long time in the CONDA_DIR, so commenting it out until we have perm issues.
+#    fix-permissions $CONDA_DIR
+
 COPY *.whl $RF_LIB_LOC
 RUN ls -1 $RF_LIB_LOC/*.whl | xargs pip install
+COPY jupyter_notebook_config.py $HOME/.jupyter
+COPY examples $HOME/examples
+RUN chmod -R +w $HOME/examples
 
 RUN chown -R $NB_UID:$NB_GID $HOME
 
-USER $NB_UID
-
-COPY jupyter_notebook_config.py $HOME/.jupyter
-
-COPY examples $HOME/examples
+USER $NB_UID
\ No newline at end of file

From 5b6e6cc42d049f51f72ddebe507aceac27af9dde Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Mon, 5 Aug 2019 16:18:56 -0400
Subject: [PATCH 306/380] Add more unit tests around RasterSource read tiling,
 round trip writing with GeoTiff datasource

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../locationtech/rasterframes/TestData.scala  |  1 +
 .../geotiff/GeoTiffDataSource.scala           |  1 +
 .../geotiff/GeoTiffDataSourceSpec.scala       | 73 +++++++------------
 .../raster/RasterSourceDataSourceSpec.scala   | 37 ++++++++++
 4 files changed, 66 insertions(+), 46 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
index 584a01ef6..1b6b373e9 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/TestData.scala
@@ -146,6 +146,7 @@ trait TestData {
 
   lazy val localSentinel: URI = getClass.getResource("/B01.jp2").toURI
   lazy val cogPath: URI = getClass.getResource("/LC08_RGB_Norfolk_COG.tiff").toURI
+  lazy val singlebandCogPath: URI = getClass.getResource("/LC08_B7_Memphis_COG.tiff").toURI
   lazy val nonCogPath: URI = getClass.getResource("/L8-B8-Robinson-IL.tiff").toURI
 
   lazy val l8B1SamplePath: URI = l8SamplePath(1)
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index fe01d224d..5ec4ffb36 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -121,6 +121,7 @@ class GeoTiffDataSource
             rf_dimensions(tileCol)
           ))
         .first()
+      logger.debug(s"Contructed TileLayerMetadata: ${tlm.toString}")
 
       val c = ProjectedRasterDefinition(tlm)
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 5e22d13e0..266fdb8d6 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -23,6 +23,7 @@ package org.locationtech.rasterframes.datasource.geotiff
 import java.nio.file.Paths
 
 import geotrellis.proj4._
+import geotrellis.raster.ProjectedRaster
 import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
 import geotrellis.vector.Extent
 import org.locationtech.rasterframes._
@@ -43,7 +44,7 @@ class GeoTiffDataSourceSpec
       assert(rf.count() > 10)
     }
 
-    it("should lay out tiles correctly"){
+    it("should lay out tiles correctly") {
 
       val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
 
@@ -85,6 +86,9 @@ class GeoTiffDataSourceSpec
 
       assert(result === expected)
     }
+  }
+
+  describe("GeoTiff writing") {
 
     it("should write GeoTIFF RF to parquet") {
       val rf = spark.read.format("geotiff").load(cogPath.toASCIIString).asLayer
@@ -132,13 +136,31 @@ class GeoTiffDataSourceSpec
     }
 
     it("should round trip unstructured raster from COG"){
-      val df = spark.read.format("raster")
-        .load(getClass.getResource("/LC08_B7_Memphis_COG.tiff").toURI.toASCIIString())
+      import spark.implicits._
+      import org.locationtech.rasterframes.datasource.raster._
+
+      val df = spark.read.raster.withTileDimensions(64, 64).load(singlebandCogPath.toASCIIString)
+
+      val resourceCols = 963 // from gdalinfo
+      val resourceRows = 754
+      val resourceExtent = Extent(752325.0, 3872685.0, 781215.0, 3895305.0)
 
       df.count() should be > 0L
 
       val crs = df.select(rf_crs(col("proj_raster"))).first()
 
+      val totalExtentRow = df.select(rf_extent($"proj_raster").alias("ext"))
+        .agg(
+          min($"ext.xmin").alias("xmin"),
+          min($"ext.ymin").alias("ymin"),
+          max($"ext.xmax").alias("xmax"),
+          max($"ext.ymax").alias("ymax")
+        ).first()
+      val dfExtent = Extent(totalExtentRow.getDouble(0), totalExtentRow.getDouble(1), totalExtentRow.getDouble(2), totalExtentRow.getDouble(3))
+      logger.info(s"Dataframe extent: ${dfExtent.toString()}")
+
+      dfExtent shouldBe (resourceExtent)
+
       noException shouldBe thrownBy {
         df.write.geotiff.withCRS(crs).save("target/unstructured_cog.tif")
       }
@@ -150,57 +172,16 @@ class GeoTiffDataSourceSpec
       }
       inCols should be (963)
       inRows should be (754) //from gdalinfo
-      inExtent should be (Extent(752325.0, 3872685.0, 781215.0, 3895305.0))
+      inExtent should be (resourceExtent)
 
       val outputTif = SinglebandGeoTiff("target/unstructured_cog.tif")
       outputTif.imageData.cols should be (inCols)
       outputTif.imageData.rows should be (inRows)
-      outputTif.extent should be (inExtent)
+      outputTif.extent should be (resourceExtent)
       outputTif.cellType should be (inCellType)
 
     }
 
-    /*
-    it("should round trip jasons favorite unstructured raster round trip okay") {
-      import org.locationtech.rasterframes.datasource.raster._
-      import spark.implicits._
-
-      val jasonsRasterPath = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/17/03/2019193/" +
-                            "MCD43A4.A2019193.h17v03.006.2019202033615_B06.TIF"
-      val df = spark.read.raster
-          .withTileDimensions(233, 133)
-          .from(Seq(jasonsRasterPath))
-        .load()
-
-      logger.debug("Read local file metadata")
-      val (inCols, inRows) = {
-        val in = readSingleband("MCD43A4.A2019193.h17v03.006.2019202033615_B06.TIF")
-        (in.cols, in.rows)
-      }
-      inCols should be (2400)  // from GDAL
-      inRows should be (2400)  // from GDAL
-
-      val outPath = "datasources/target/rf_mcd43a4.A2019193.h17v03.tif"
-
-      // now take actions on the read df
-      logger.debug("Actions on raster ref dataframe")
-      df.count() should be > 100L
-      val crs = df.select(rf_crs($"proj_raster")).first()
-      logger.debug("Write full res geotiff from dataframe")
-      df.write.geotiff.withCRS(crs).save(outPath)
-
-      // compare written file to path
-      logger.debug("Inspect written geotiff metadata")
-      val (outCols, outRows) = {
-        val outputTif = SinglebandGeoTiff(outPath)
-        (outputTif.cols, outputTif.rows)
-      }
-      outCols should be (inCols)
-      outRows should be (inRows)
-      // todo check extent and crs just for grins.
-
-    }*/
-
     it("should write GeoTIFF without layer") {
       import org.locationtech.rasterframes.datasource.raster._
       val pr = col("proj_raster_b0")
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
index a8ca3bef8..5f9716116 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
@@ -245,4 +245,41 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
 
     }
   }
+
+  describe("RasterSource breaks up scenes into tiles") {
+    val modis_df = spark.read.raster
+      .withTileDimensions(128, 128)
+      .withLazyTiles(true)
+      .load(remoteMODIS.toASCIIString())
+
+    val l8_df = spark.read.raster
+      .withTileDimensions(32, 33)
+      .withLazyTiles(true)
+      .load(remoteL8.toASCIIString())
+
+    ignore("should have at most four tile dimensions reading MODIS; ignore until fix #242") {
+      val dims = modis_df.select(rf_dimensions($"proj_raster")).distinct().collect()
+      dims.length should be > (0)
+      dims.length should be <= (4)
+    }
+
+    it("should have at most four tile dimensions reading landsat") {
+      val dims = l8_df.select(rf_dimensions($"proj_raster")).distinct().collect()
+      dims.length should be > (0)
+      dims.length should be <= (4)
+    }
+
+    ignore("should have consistent tile resolution reading MODIS; ignore until fix #242") {
+      val res = modis_df.select((rf_extent($"proj_raster").getField("xmax") - rf_extent($"proj_raster").getField("xmin")) /
+        rf_dimensions($"proj_raster").getField("cols")).distinct().collect()
+      res.length should be (1)
+    }
+
+    it("should have consistent tile resolution reading Landsat") {
+      val res = l8_df.select((rf_extent($"proj_raster").getField("xmax") - rf_extent($"proj_raster").getField("xmin")) /
+        rf_dimensions($"proj_raster").getField("cols")).distinct().collect()
+      res.length should be (1)
+    }
+
+  }
 }

From fbf5d9661089160c61888f69decf4ff27aa2e8b1 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Mon, 5 Aug 2019 16:26:27 -0400
Subject: [PATCH 307/380] spelling mistake

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/raster-catalogs.pymd | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 55d5ec68f..325ca0dd8 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -5,7 +5,7 @@ While much interesting processing can be done on a @ref:[single raster file](ras
 A _Catalog_ can have one or two dimensions:
 
 * One-D: A single column containing one or many URLs across the rows. All referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different places and times.
-* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row reperesents a single spatiotemporal location (or scene) with the same dimensions, extent, [_CRS_][CRS], etc across the row.
+* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row represents a single spatiotemporal location (or scene) with the same dimensions, extent, [_CRS_][CRS], etc across the row.
 
 ## Creating a Catalog
 
@@ -13,7 +13,7 @@ This section will provide some examples of creating your own _Catalogs_, as well
 
 ```python, echo=False
 from IPython.display import display
-from pyrasterframes.utils import create_rf_spark_session 
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 import pandas as pd
 spark = create_rf_spark_session()
@@ -21,13 +21,13 @@ spark = create_rf_spark_session()
 
 ### One-D
 
-A single URL is this simplest form of a catalog.
+A single URL is the simplest form of a catalog.
 
 ```python
 from pyspark.sql import Row
 my_cat = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
-# or 
-my_cat_df = spark.createDataFrame([Row(B01=my_cat)]) 
+# or
+my_cat_df = spark.createDataFrame([Row(B01=my_cat)])
 ```
 
 A single column represents the same content type with different observations along the rows. In this example it is band 1 of MODIS, which is visible red. In the example the location of the images is the same (h04v09) but the dates differ.
@@ -37,7 +37,7 @@ scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 
 # As CSV string
-my_cat = '\n'.join(['B01', scene1_B01, scene2_B01]) 
+my_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
 # or
 my_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
 my_cat_df.printSchema()
@@ -58,10 +58,10 @@ scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43
 
 
 # As CSV string
-my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02]) 
+my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
 # or
 my_cat_df = spark.createDataFrame([
-    Row(B01=scene1_B01, B02=scene1_B02), 
+    Row(B01=scene1_B01, B02=scene1_B02),
     Row(B01=scene2_B01, B02=scene2_B02)])
 my_cat_df.printSchema()
 ```
@@ -77,21 +77,21 @@ from pyspark.sql import functions as F
 spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/2018-07-04_scenes.txt")
 
 # The scenes list file has index URIs in the download_url column, for example:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html    
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html
 # Image URIs take the form:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF    
+#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF
 
 modis_catalog = spark.read \
     .format("csv") \
     .option("header", "true") \
     .load(SparkFiles.get("2018-07-04_scenes.txt")) \
-    .withColumn('base_url', 
+    .withColumn('base_url',
         F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
     ) \
     .withColumn('B01' , F.concat('base_url', F.lit("_B01.TIF"))) \
     .withColumn('B02' , F.concat('base_url', F.lit("_B02.TIF"))) \
-    .withColumn('B03' , F.concat('base_url', F.lit("_B03.TIF"))) 
-# ... and so on.  
+    .withColumn('B03' , F.concat('base_url', F.lit("_B03.TIF")))
+# ... and so on.
 modis_catalog.printSchema()
 ```
 
@@ -160,9 +160,9 @@ root
  |-- B9: string (nullable = true)
  |-- B10: string (nullable = true)
  |-- B11: string (nullable = true)
- |-- BQA: string (nullable = true) 
+ |-- BQA: string (nullable = true)
 ```
 
 [MODIS]: https://docs.opendata.aws/modis-pds/readme.html
 [Landsat]: https://docs.opendata.aws/landsat-pds/readme.html
-[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system

From fbf76089f2184d6b11ac6d5ae5ecf6d84f6ed0e3 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 11:28:36 -0400
Subject: [PATCH 308/380] Address PR comments; more details on pyspark shell v
 submitting apps

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/getting-started.pymd | 60 +++++++++++++++----
 1 file changed, 50 insertions(+), 10 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 543ddb89c..f83dbda9a 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -3,7 +3,7 @@
 There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell.  To get started you will need:
 
 1. [Python](https://www.python.org/) installed. Version 3.6 or greater is recommended.
-1. `pip` or `pip3` (recommended) installed. If you are using Python 3, `pip3` may already be installed.
+1. [`pip`](https://pip.pypa.io/en/stable/installing/) installed. If you are using Python 3, `pip` may already be installed.
 1. Java [JDK 8](https://openjdk.java.net/install/index.html) installed on your system and `java` on your system `PATH` or `JAVA_HOME` pointing to a Java installation.
 
 ## pip install pyrasterframes
@@ -30,11 +30,12 @@ df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/201
 
 # Add 3 element-wise, show some rows of the dataframe
 df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
-
 ```
 
 This example is extended in the [getting started Jupyter notebook](https://nbviewer.jupyter.org/github/locationtech/rasterframes/blob/develop/rf-notebook/src/main/notebooks/Getting%20Started.ipynb).
 
+## Next Steps
+
 To understand more about how and why RasterFrames represents Earth observation in DataFrames, read about the @ref:[core concepts](concepts.md) and the project @ref:[description](description.md). For more hands-on examples, see the chapters about @ref:[reading](raster-io.md) and @ref:[processing](raster-processing.md) with RasterFrames.
 
 ## Other Options
@@ -60,16 +61,23 @@ See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/
 
 ### `pyspark` shell or app
 
-To initialize RasterFrames in a `pyspark` shell, prepare to call pyspark with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. 
+You can use RasterFrames in a `pyspark` shell or when submitting a `pyspark` app via a Python script. To set up the `pyspark` environment, prepare your call with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. 
 
 You can either [build](https://github.com/locationtech/rasterframes/blob/develop/README.md) the artifacts or download them:
 
- * Assembly JAR: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes-assembly-${VERSION}.jar
  * Python zip: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip
+ * Assembly JAR:
+    * The assembly JAR is embedded in the wheel file publised on pypi. Download the wheel from https://pypi.org/project/pyrasterframes/#files
+    * The wheel file is just a [zip file with .whl extension](https://www.python.org/dev/peps/pep-0427/); you can extract the assembly JAR with a command like this: `unzip -j  $PYRF_WHEEL  $(zipinfo -1 $PYRF_WHEEL | grep jar)`
+
+
+#### Shell 
 
+The `pyspark` shell command will look something like this, replacing the `--jars` argument with the assembly jar and the `--py-files` with the source zip (not the wheel). To submit a script, add a .py file as the final argument
 
 ```bash
    pyspark \
+    --master local[*] \
     --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
     --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
     --conf spark.kryoserializer.buffer.max=500m \
@@ -77,18 +85,50 @@ You can either [build](https://github.com/locationtech/rasterframes/blob/develop
     --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
 ```
 
-Then in the pyspark shell, import the module and call `withRasterFrames` on the SparkSession.
+Then in the `pyspark` shell, import the module and call `withRasterFrames` on the SparkSession.
 
 ```python, evaluate=False
-import pyrasterframes
-spark = spark.withRasterFrames()
-df = spark.read.raster('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
+Welcome to
+      ____              __
+     / __/__  ___ _____/ /__
+    _\ \/ _ \/ _ `/ __/  '_/
+   /__ / .__/\_,_/_/ /_/\_\   version 2.3.2
+      /_/
+
+Using Python version 3.7.3 (default, Mar 27 2019 15:43:19)
+SparkSession available as 'spark'.
+>>> import pyrasterframes
+>>> spark = spark.withRasterFrames()
+>>> df = spark.read.raster('https://landsat-pds.s3.amazonaws.com/c1/L8/158/072/LC08_L1TP_158072_20180515_20180604_01_T1/LC08_L1TP_158072_20180515_20180604_01_T1_B5.TIF')
 ```
 
 Now you have the configured SparkSession with RasterFrames enabled.
 
-```python, echo=False 
-spark.stop()
+#### Submitting Apps
+
+Prepare the call to `spark-submit` in much the same way as using the `pyspark` shell. In the python script you submit, you will use the SparkSession builder pattern and add some RasterFrames extras to it. You have more flexibility in setting up configurations in either your script or in the `spark-submit` call. 
+
+```python, evaluate=False
+# contents of app.py
+
+from pyspark.sql import SparkSession
+import pyrasterframes
+spark = (SparkSession.builder
+             .appName("My RasterFrames app")
+             .config('spark.some_config', some_val) # app configurations
+             .withKryoSerialization() # sets spark.serializer and spark.kryo configs
+             .getOrCreate()).withRasterFrames()
+df = spark.read.raster('...')
+```
+
+To submit, use a call like:
+
+```bash 
+$ spark-submit \
+    --master spark://sparkmaster:7077 \
+    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
+    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip \
+    app.py
 ```
 
 ## Installing GDAL 

From b97582859256ca88416e39a1728d1cc9ed1a0901 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 12:37:53 -0400
Subject: [PATCH 309/380] Update getting started page, simplify pyspark shell;
 update notebooks

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/README.md                      |   8 +-
 .../src/main/python/docs/getting-started.pymd |  51 +-
 .../src/main/notebooks/Getting Started.ipynb  | 134 ++-
 .../pretty_rendering_rf_types.tile.ipynb      | 891 ++----------------
 4 files changed, 181 insertions(+), 903 deletions(-)

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index b857762f5..1961dd93b 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -153,7 +153,13 @@ sbt 'pySetup test --addopts "-k test_tile_creation"'
 Or to build a specific document:
 
 ```bash
-sbt 'pySetup pweave -f docs/raster-io.pymd'
+sbt 'pySetup pweave -s docs/raster-io.pymd'
+```
+
+Or to build a specific document with desired output format:
+
+```bash
+sbt 'pySetup pweave -f notebook -s docs/numpy-pandas.pymd'
 ```
 
 *Note: You may need to run `sbt pyrasterframes/package` at least once for certain `pySetup` commands to work.*
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index f83dbda9a..a67067c5e 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -59,30 +59,20 @@ You can also use RasterFrames in the following environments:
 
 See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
 
-### `pyspark` shell or app
+### `pyspark` shell 
 
-You can use RasterFrames in a `pyspark` shell or when submitting a `pyspark` app via a Python script. To set up the `pyspark` environment, prepare your call with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. To these you will add the PyRasterFrames assembly JAR and the python source zip. 
+You can use RasterFrames in a `pyspark` shell. To set up the `pyspark` environment, prepare your call with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. For RasterFrames support you need to pass arguments pointing to the various Java dependencies. You will also need the Python source zip, even if you have pip installed the package. You can download the source zip here: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip.
 
-You can either [build](https://github.com/locationtech/rasterframes/blob/develop/README.md) the artifacts or download them:
-
- * Python zip: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip
- * Assembly JAR:
-    * The assembly JAR is embedded in the wheel file publised on pypi. Download the wheel from https://pypi.org/project/pyrasterframes/#files
-    * The wheel file is just a [zip file with .whl extension](https://www.python.org/dev/peps/pep-0427/); you can extract the assembly JAR with a command like this: `unzip -j  $PYRF_WHEEL  $(zipinfo -1 $PYRF_WHEEL | grep jar)`
-
-
-#### Shell 
-
-The `pyspark` shell command will look something like this, replacing the `--jars` argument with the assembly jar and the `--py-files` with the source zip (not the wheel). To submit a script, add a .py file as the final argument
+The `pyspark` shell command will look something like this.
 
 ```bash
    pyspark \
     --master local[*] \
-    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
+    --py-files pyrasterframes_2.11-${VERSION}-python.zip \
+    --packages org.locationtech.rasterframes:rasterframes_2.11:${VERSION},org.locationtech.rasterframes:pyrasterframes_2.11:${VERSION},org.locationtech.rasterframes:rasterframes-datasource_2.11:${VERSION}
+    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \ # these configs improve serialization performance
     --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
-    --conf spark.kryoserializer.buffer.max=500m \
-    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
-    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip
+    --conf spark.kryoserializer.buffer.max=500m 
 ```
 
 Then in the `pyspark` shell, import the module and call `withRasterFrames` on the SparkSession.
@@ -104,33 +94,6 @@ SparkSession available as 'spark'.
 
 Now you have the configured SparkSession with RasterFrames enabled.
 
-#### Submitting Apps
-
-Prepare the call to `spark-submit` in much the same way as using the `pyspark` shell. In the python script you submit, you will use the SparkSession builder pattern and add some RasterFrames extras to it. You have more flexibility in setting up configurations in either your script or in the `spark-submit` call. 
-
-```python, evaluate=False
-# contents of app.py
-
-from pyspark.sql import SparkSession
-import pyrasterframes
-spark = (SparkSession.builder
-             .appName("My RasterFrames app")
-             .config('spark.some_config', some_val) # app configurations
-             .withKryoSerialization() # sets spark.serializer and spark.kryo configs
-             .getOrCreate()).withRasterFrames()
-df = spark.read.raster('...')
-```
-
-To submit, use a call like:
-
-```bash 
-$ spark-submit \
-    --master spark://sparkmaster:7077 \
-    --jars pyrasterframes/target/scala-2.11/pyrasterframes-assembly-${VERSION}.jar \
-    --py-files pyrasterframes/target/scala-2.11/pyrasterframes-python-${VERSION}.zip \
-    app.py
-```
-
 ## Installing GDAL 
 
 GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below.
diff --git a/rf-notebook/src/main/notebooks/Getting Started.ipynb b/rf-notebook/src/main/notebooks/Getting Started.ipynb
index 33e3c6b6a..e299b4b2f 100644
--- a/rf-notebook/src/main/notebooks/Getting Started.ipynb	
+++ b/rf-notebook/src/main/notebooks/Getting Started.ipynb	
@@ -22,7 +22,7 @@
    "source": [
     "import pyrasterframes\n",
     "import pyrasterframes.rf_ipython  # enables nicer visualizations of pandas DF\n",
-    "from pyrasterframes.rasterfunctions import (rf_local_add, rf_dimensions, rf_extent, rf_crs, \n",
+    "from pyrasterframes.rasterfunctions import (rf_local_add, rf_dimensions, rf_extent, rf_crs, rf_mk_crs,\n",
     "                                            st_geometry, st_reproject, rf_tile)\n",
     "import pyspark.sql.functions as F"
    ]
@@ -44,7 +44,7 @@
    "source": [
     "### Get a PySpark DataFrame from [open data](https://docs.opendata.aws/modis-pds/readme.html)\n",
     "\n",
-    "Read a single \"granule\" of MODIS surface reflectance data. "
+    "Read a single \"granule\" or scene of MODIS surface reflectance data. "
    ]
   },
   {
@@ -103,15 +103,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
-      "|rf_local_add(proj_raster, 3)                                                                                                                                                                                                                                                                            |\n",
-      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
-      "|[[[-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3408,3471,3110,2875,2798,2973,3255,3169,3,3217,...,3,3,3,3,3,3,3,2519,3036,3]]]                              |\n",
-      "|[[[-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [2337,2346,2581,2751,2575,2364,2223,2384,2618,2296,...,3,3,2688,2702,2967,3200,3257,3052,2914,2534]]]      |\n",
-      "|[[[-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [2602,2728,2784,2781,2567,2539,2254,2327,2436,2888,...,2726,2695,2788,2898,3139,3121,2939,2778,2859,2728]]]|\n",
-      "|[[[-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3058,3163,3036,3228,2877,3310,2885,2932,2931,2940,...,2299,2190,2180,2441,2563,2431,2347,2414,2525,2778]]] |\n",
-      "|[[[-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [3238,3355,3502,3055,3343,3334,3,3,3,3,...,2629,2841,2983,2839,3107,2762,2524,3175,3190,3181]]]            |\n",
-      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|rf_local_add(proj_raster, 3)                                                                                                                                                                                                                                                                                                                                        |\n",
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|[[[-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3408,3471,3110,2875,2798,2973,3255,3169,-2147483648,3217,...,-2147483648,-2147483648,-2147483648,-2147483648,-2147483648,-2147483648,-2147483648,2519,3036,-2147483648]]]|\n",
+      "|[[[-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [2337,2346,2581,2751,2575,2364,2223,2384,2618,2296,...,-2147483648,-2147483648,2688,2702,2967,3200,3257,3052,2914,2534]]]                                              |\n",
+      "|[[[-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [2602,2728,2784,2781,2567,2539,2254,2327,2436,2888,...,2726,2695,2788,2898,3139,3121,2939,2778,2859,2728]]]                                                            |\n",
+      "|[[[-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (256,256), [3058,3163,3036,3228,2877,3310,2885,2932,2931,2940,...,2299,2190,2180,2441,2563,2431,2347,2414,2525,2778]]]                                                             |\n",
+      "|[[[-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667], [+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ]], [int16ud32767, (257,256), [3238,3355,3502,3055,3343,3334,-2147483648,-2147483648,-2147483648,-2147483648,...,2629,2841,2983,2839,3107,2762,2524,3175,3190,3181]]]                                |\n",
+      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
       "only showing top 5 rows\n",
       "\n"
      ]
@@ -157,21 +157,6 @@
     "What area does the DataFrame cover?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "coverage_area = df.groupby(df.proj_raster_path).agg(\n",
-    "    F.first(rf_crs(df.proj_raster).crsProj4).alias('crs'),\n",
-    "    F.min(rf_extent(df.proj_raster).xmin).alias('xmin'),\n",
-    "    F.min(rf_extent(df.proj_raster).ymin).alias('ymin'),\n",
-    "    F.min(rf_extent(df.proj_raster).xmax).alias('xmax'),\n",
-    "    F.min(rf_extent(df.proj_raster).ymax).alias('ymax'),\n",
-    ").withColumn('footprint_4326', st_geometry(F.struct('xmin', 'ymin', 'xmax', 'ymax')))\n",
-    "coverage_area = cov_area()\n",
-    "coverage_area"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -206,7 +191,10 @@
     "print(crs)\n",
     "coverage_area = df.select(\n",
     "                           df.proj_raster_path,\n",
-    "                           st_reproject(st_geometry(rf_extent(df.proj_raster)), crs, 'EPSG:4326').alias('footprint')\n",
+    "                           st_reproject(\n",
+    "                               st_geometry(rf_extent(df.proj_raster)), \n",
+    "                               rf_mk_crs(crs), \n",
+    "                               rf_mk_crs('EPSG:4326')).alias('footprint')\n",
     "                         )\n",
     "coverage_area.show(10, False)"
    ]
@@ -215,7 +203,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So where in the world is that? We'll generate a little visualization on http://geojson.io."
+    "So where in the world is that? We'll generate a little visualization with Leaflet in the notebook using Folium."
    ]
   },
   {
@@ -224,38 +212,57 @@
    "metadata": {
     "scrolled": true
    },
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = geopandas.GeoDataFrame(\n",
+    "        coverage_area.select('footprint').toPandas(), \n",
+    "        geometry='footprint', crs={'init':'EPSG:4326'}) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
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    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
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    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_b901974daf4940e39ace962ea60a98d1 {
                    position: relative;
                    width: 100.0%;
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<body>    
    
            <div class="folium-map" id="map_b901974daf4940e39ace962ea60a98d1" ></div>
        
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<script>    
    
            var map_b901974daf4940e39ace962ea60a98d1 = L.map(
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+       "<folium.folium.Map at 0x7fd095675ba8>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import geopandas\n",
-    "import geojsonio\n",
-    "\n",
-    "geojsonio.display(\n",
-    "    geopandas.GeoDataFrame(\n",
-    "        coverage_area.select('footprint').toPandas(), geometry='footprint', crs='EPSG:4326').to_json()\n",
-    ")"
+    "folium.Map((5, -65), zoom_start=6) \\\n",
+    "    .add_child(folium.GeoJson(gdf.__geo_interface__))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Look at a sample of the data."
+    "Look at a sample of the data. You may find it useful to double-click the tile image column to see larger or smaller rendering of the image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
@@ -290,38 +297,32 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>(-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>(-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-6953397.249648973, 756126.3533735601, -6834789.194217825, 874734.4088047068)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>(-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-7783653.637667, 281694.1316489733, -7665045.582235852, 400302.18708011997)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>\n",
+       "      <td><img 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\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-7783653.637667, 163086.07621782675, -7665045.582235852, 281694.1316489733)</td>\n",
-       "      <td><img 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\" /></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF</a></td>\n",
-       "      <td>(-6834789.194217826, 163086.07621782675, -6716181.138786679, 281694.1316489733)</td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>\n",
+       "      <td><img 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CNdccw3uvvtuXHfdde56f/qnf4rDDz8c5557LsqyxOjoKG644QZ8//vfH2jX6Ogozj33XFx88cV44xvfiFe+8pXYunUrNmzYMIcW/sxnPoM777wTxx9/PFatWoVOp4M777wTn/jEJ/C85z1vgPL927/9W2zcuBFnnnkm1q1bN/C8a7Uajj766Dl9tHHjRqxatWoO7Wuxdu1avPWtb8UnP/lJtNttvPSlL8V9992H973vfTj66KPxqle9ao/nBPY9hgP+yPH7CvYG/OFjb4ImK07Z27+qKGbjxo107LHH0oIFCyiKIhodHaUTTjiBbrnllj1es9vt0vDwMP35n//5Ptv2V3/1V1Sv12liYmKfx73whS/cZ1stdu/eTaeccgqtXr2a6vU6JUlCz3jGM+i8886jycnJPZ77s5/9LD3rWc+iJElo4cKF9NrXvpa2bt26x2P3JGh617veRYwx+vWvf73Pe9iboGlPBS/sdaq49dZb6eijj6ZarUYA6PTTT3d/Gxsb22vfPF6U9O///u908sknU7vdpjRN6bjjjqNvfvObc9pw33330fHHH09DQ0O0ePFievvb307/8i//MucelFL04Q9/mFatWkVJktCzn/1s+uY3vzmniMQPfvADetnLXkYrVqygJEmo0WjQc57zHLrooovmKHX3dT97GstWrPWBD3xgbsdXUJYlfeQjH6HDDjuM4jim5cuX05vf/OY9jr8nO4YD/rjBiEzQJCDgcTjjjDNwxx134P777wdjbI9e5u8Ly5Ytw+te9zpcfvnlv++m7BNEBCklLrzwQlx00UXYuXOno1CPPfZYjI2N4Wtf+9rvuZUBAQHzjUALB+wTmzdvRhzHOPLII3HPPff8vpsDAPjVr36FbreLd7/73b/vpjwhPv7xj+Od73znnM+np6dx9913/1ZFGAICAv5wETzXgL1i06ZNTuVZr9efsMJNwFzs2LEDW7Zscb8fddRRQeASEHAAIBjXgICAgICAeUZIxQkICAgICJhnBOMaEBAQEBAwzwjGNSAgICAgYJ5xQCkrlFLYtm0b2u122P4pICAg4A8URISZmRmsWLFivy0nekAZ123btmHVqlW/72YEBAQEBDwJbN269SnVLP9DwgFlXNvtNgBg3T++FaJRQy+PEQsJAFi7cAfG+w3c+GeX7vG7R/6vKwEAfDoGk8Av3/p2/fnnPo5kty6uwAoGWSfIutK/j+ao1Qv9PU7oTNSBzKzCBIHl5mcCQNqTplTiV2e8A+s+9QkAgKpLQOm/iR5HNM3xs394q2vXs/9fc1xEYMQAfWnUdnHAOOfFEEE2lLsWNSRq7cy1qz+r689SVyCaEeCZ+SIjkBkhKgZUqs/BRzMktdK1IU0K5KU+sJ4UmJxqQEl9jn9/9blz+nLdJ3Wb2eIMsqu/J2YE0p26P4oGoGpexF6bYOgepJ9T1OEo6wRw/fd7/uffuuOOvuFS5FM1xEP63uJEIeubGyAGORMDAFrLZqF+PIL+Wl1vtz3URy/Tx/38L98zp70W5/7iLDyWtbE8nQYAXPacz7m/PfebH8ZP/sc/uN+fdfX/AxK6jb866+8G7/+rHwU3f0vSEtL0FSkGUgyy1P0QJRI/P8Xn8h71z3psikhBlhzM9EG1zc/66hWgvnBjhve5e57laOk+j6YEymEJZq7NhnLE5pm26jnyUqAw7RiqZ5jq6oL8y0emsW33MPJOAgCoD/dRi/X3htI+uoX+XDCFZpLjkYlh1zZu2ttMcxAxCGbGE1fIzPhJI9OGRD/DmbxmhzH6ZYROT5+/lkjU4hJFqd+9NC5w23+/AABw4u3vwyM7RkDmm7V6jjLX52eMUOYRmNDXVr0IvF66n0VD/0wEJGkBWejzK2JQu/V7wiQDCQLV9ZhMWjmiWLn7JAKU6eciizA60tV9zqW7z6WtGfTKGLu7urxknkXgwp+DMaAszPvQS4DSPM+e0O9w7N8PMauPq+1m6C/Wn4seULbJzQF8UR9lz/RBRCACID17J0wfiEhCFpH7mRRDlOj7rMUlokobsyICZ+T6v5nkAICJXh2JkNg1oedbpRjqzdx9z95XPl1D3M6glP6dJAOzTmrexea3XOHm7P0RB5RxtVTwVH8BOE/BBSE3Y+Xn40OIIoWhoaE9fpendQBAsjCHlNwdJ0QD5SF6YMY7Y6BGYA09GFlNoICelFTOwZsAHzEvdcGB0hvapcsm9efEsO6GT0IkZiuxdgkxoR+TGiGoGhtoIy3SBsMaVtbSbcmHYsQTZtAOEew7EXUYSAI0qwdtGRFiMxEwBSAFyGyAQgzg0vYd3DnUbB0Y6qCW6Gv1sgaGhvU2Z7WIoBLg7v9x4V6fw8iR+tjp6Tp4odtPiwlZS09kySRHPqwgerpdchHQnDSTSwmUBBTDujHP/tLV4H39N5WMgqcEyfQEvGjBFHZN6OfGt6RYcfRjAIBHdw1D/YlAY6HuHyZS1PVhOPFHl4AzgjSLnYnZBg4anQIAdIs1WFzvYBt0B515z3n46UMH60cYN9xzWfeND6B+SISsq+/t2Fuvwn/+X3rDgEO+9CHEw7GbUBBLxNwaGULv/mGwmunoCY51X/kMACBd2INo6jGRJCV63RqkmaTWffkzEB39s+BDAAP4Ut3HSjKQNJNvylBMGwNRRuANv0B65qHjYGaiJGJQUOiaZyNYhDzWY3CGjUA0EwyP6u/2+w0kLX0tFjOsGtWGZHunje+eeBX+5IYNALRBjc0AEkxAcAUi/bxLFWN8XBvhjiQ8cOr5rl1r//kCLGjpc8ZlhHpNf6dZU9g9PYIFIx0AwP93wlXuO7vlQkA2wJvGYDQ4oqa+t7IUkBOpW4TyGiCaegHMagyM6/ukXKBkEgq+Khlv6p8pUhDNElayUuYpag29UOvsaKK1dBaReaalYshUCwCQM6BmrjXJFqBgAjLSfVxO1yGGtAGS/QgiLcEj3eYo5sC4WQC3CfFuDmmMKy8Y5Ij+uT8KmEeIcpECKxnIzkX1FKJfc+1nBQNic2Oxgk3IlEwBifmZGIgxSDO2spqESPR9yU4E0SzRaOhnP9lvo8PMtWIgTgo0F5kFRiGgzKRS5gLCLtJVDCVikA3REQdLzVyammeyH4fvDijjaqG6MUAx+JRwki5SQJbsPeU32qFHYnJ4F/1egkO/cgkAgC1kbkWuDi7xUGViqGJs42UQzUIbVWBg1QhGmOro2T2KJJ5/+AN4pKMnmyNHtuOT/8cX934z9jyJAq9JKLM65TlDvkIPUGTcecZshkHFQDlkPMEpP3mQOay6KlZu6Uv2FGASkA+1MNPUL0m0sI+Zrn5xZ1kNrXqGU+/6nwCAybyOm1/48YEmTz46ZE8Obrx30ff9IVMCxQRlJsAH3/ku97cj33MVkmlApuZ+SgYykxArGHjGYNYD2DXZAjfMxMKjduC/LXkIAHDF8brQ/JFf1wZPKo481/0wlGYQXGFitgkAiCPvbeRlhM2To0gT3a+CEQ5ethuPRy0uQQDufd0HAABj11yGNV/+kO5jYihmEyRtPZHe+1cfwLO+oY+75+UX4pCPXYFNb9Pe/tH/cr5jBPI8QrFLj5HfvPlcrP6nDwMzxhOBnmQBoBxSQLNEzUxOnYk64ob+uZiuIXnMLNQSYPPZ57k2n/hd711P5zW0kgyF1H0y3kmdJwYA7WYfpflbrVaiNMZ7oqhDGKOye6qJQ770ISxeqD3QREg8vH1Un2AmxnOPuh9bZ0Z0H3PlmI4kLXHszf+A/33ihwFoo2wXOlIxjNT1ZB4LidEluzCb63G37hsfwMyknsCjmgAEgRtvsjeVOs+MFAMIiKaNYR8tIfuGeeoLzRQB4FMRKBJgw7rv6q0Mdp7P+jFqaYHOuL4eS+yIA3irQGc6xeiCWX0/XKFuPPtScbRruj8e2LoERACfMIvjhMBN30ljIUszJqngYJF/J7lkUGZdRBxg5mdmfne/DBUQpg/kVAJuvF+qAagp5w2zrgCZBR31IkA4SwtWL0F2kswEpGXbTHuyzCzABCE3P5czMbJ2gSiW5m/KsRs9nrhFZ1Qv0Whk6HT0olH2BBQ381GC/R77Z6Q4ICAgICDgDxgHVIWm6elpDA8PY80HLgFPU0172hV/U4GXzMWnKCLI1HikKSGeNHGChRLg5Cg12p2ALM1RL/HQawc34h77vI6TMaHABDnPEvAr3rQSj2jVMxy5cDu+d7/eo7LRzPCrV1zg/v5/3vIe5zV0sxhkY7XEkPViiMjQUX0fV6JepOO60PEinjEo0wzRY26JpSIdo7GeIM+Z91aVXxVTjVB7TDhqlpiPxwIAa0iMmJX7L1528WB/bLwM0Xjk+pVZ2rlgLs7KJEARHN37wHnn4Og3X2naqNuem1Be0SYowzhsevu7cMjHr3DnVHVy55CjJVqGssyyCItHZjHVrbt22begmeYoSoHpaf23Wr1Ab1qvrKO0RBRLLBvRMddHdo1gzVJdHrJQApM9fdz0TAND7S5+fvIl7vyr//Ej+ofZCJve4uPQz7z+QmSWqu0JMMnAlmjvbOWiScxkegnfz2P0HtX0YvX7j8fqT34U8eIecnNORMrFZikTLlYNABCEBYv0VnN/snAHdvb0+bfsHkV/d+rYDtEunGcJxVBv95H1dbtkziFqusPHluxGJ9efZyZuZ73ONC4hjffbTAqsaE2hW+pj792+BOUj2gtUIyXiRg5lxriSDI2WiaEL6WJ+UzN1jAx13XWyPIIs/Xewq4bGav2cikIg7xpXiBNIMucxqpHStd95ZYDuM6bjroCOGw4b+hsAdu1sA9bjlQxsODff02PJ0tW7f7MAYpmmjJuNDIeMjgMAfv3YMnCuII3Xn02mTitQzCbg0xHUkHFJc4540txbjQDl2RoS5OYwEoAytC1rlsBM7N57tEsdizfPHZK5UAIJ/85TYilvHZ8mALTDhLYS5WK4EATEyrEDqAwrZfQAzaG+67vCxrw5ufBDvVYgTQpMzJi4cy92nnY7msDdr7wCU1NTew3V/aHjgKSFZY1ANULUZRDmfRF9DkaANHNSWaElWO6Nbn1rBFUjkLBCGSA3k7sqB4mA1Z/6KGBk5MQZkEofFJHMxVSIGBa29cs42a3jC8d+Djh2bruP/pfzoVSMnqFflGIQVhgTl/pfpCeKbi123+sq5uK7LOMgBqS79O/9xRKiZ/5mRA48Z/53Z5QrDeloQ8gKb7iqAhrkHJPTmvJb8+UPQXZMXzEg3RpDGdqZKeYMNqu+nAmBUgXV0h+OXX05YLbhZBkHz5k35gRHowJmsWQN5SbuniffmWD2ENPGjsDOB5solusJkUfKPTshzIRnJpGy5C4+V/YjlNMJNvd03z5z5WNOwAMA3HxnyYJpTMw23OerP/VR2NWM7TOLYlsTzPQHNaQWdezUk9mWfCGYWSwJoRDNPDHRVNslUHaa2Pz35wAA1lx1BRqH6pjxzLY2YMbqqlXj6BUxnr/8QQDArqyF7TM6Dt+fqoGV3Ld1tobUxO/7SxS6OUd9gTYYGcUQxuA9tG0RRoxRmdzVQtwokNZ1H8dComEmzk6e4N5dSxwNGkUSxSJ93OErHwMRw7ZpP6HmZmKWkiMy47uYTtCv5yiNQW2kOaZn9YIorpVQyyVmH9OLBTByNKZIJGQ/hmp5+lemdhAC9UV6AdabTrXWYZU2rmUWYfY/9G5GIgM2f9hvxrD6Ux91YrC8F2PJ4mkn7vnp370LL7tTix/Hew1sm9WrwiKPtPF61GgrGsqFl+JmgTJRYMoaTQmZ2XkE4CVBGRpXdPyCKZ7iULHRH3RNjMe8WDIWEDPGQKfaSCrT51TyAaGli79meoFOTfPySwZmnQqz2LLPnoiBmeeZpCXKQrjnxhih2dALh1JxF2JQxCAVd9oNIZQ738zk/s8LH5Ce6zPf+SGIWoqySYhnrGsGyBgoho2hTMhN4CxnSCaE+1z0GYQeKyAGZIu816ZqBOI2BshdLFGmBGpIr0ZUcF6miBTu/Ssddzv+jnfg3k3LERuV8eLRGdx1vPZ+/9u3341Hd4yg3tQX706niMyqmwBwRmg19Wph4mGv0mQFd8aLIjJeolU4EVjmJxdIhqhr45mAMsaJBEGaGGsyLiBrBLnABnsI6On+ER0BRnBKWdlU4EbEoUoOKCRpU8cAACAASURBVAZuYoUkyBvykjmjq5oSvFlCzZoFgoJfMUMbcNv+dEeEbNT0Y8aQ7mTIRucOaRUDqm4VWeZ61kDHXn1ca2dQkkMZjyuOpVt1q/FEe/ZmsokbXoUKMvE8AHxSewxU2aEvMcxH0VagGjmvAcofp1IF0eMuHs5bBZRZmIhWiQdPG2RFVn/yo/qHduFES43hHro7ms4wUqrA7BhRDMw8l4OX7UYmBcaGJgAAv9m9yJ335ydfgrGrL3fnYAVDPGvGcU1P8Mkh2uMt8sjF1pK4xOysEV3VtAo6MewMY4RRI/qZ6qWoJwWkEXVNzaZYMKSNWhqVYIywu6MXJ9OPtQDjjW0+fVDJPfaZy91CKh7NnNpWPthCubhwegTeFb4/IkL7QY6pI/3YtcKnh14z2L/zBafP4AqFWZg1flPTc4hp/8y6zB2fNApIySGt0l0BfNprKRigFfPw4wrQToOs24Wr/szOb/kC5YV/KSEZFygb5uKcPCuVkO/TSY58kQRq3rja+UvtqgECYJbpW5RrIw0gaeZYNOyZoZFGzzm2M/0ahlJ9r7kUmO6kaKSeubNMRD5d4MEzPrRfe64h5hoQEBAQEDDPOCBp4bJJUKmOqXRW21WZziPlZgGpY5PapYhnPdVIEcBzgFsh7ghBGE9P1gksZyCzeoQglIusrM/8Z2lW4b0rxghjnzM5jI3FgGQuFjPWnqisfFsYHu6676lWjv64Xh2yjEPWJSZ3ac+BAYgWaS+2zCIo41la75TZYGrOwKxq0Hp0Nh6r4LxwlXrvTsU6VivtLZRce38w3leX+zzVmvT3mml62ioyWcldv6iEXNwHxEDjNaBug6fMU2SRAi8YhIkf5cMK3DohJQY8RtFnbgXPFMGtJTlBxeTOiZy5+9TPiDl6qiyEizdSuwSPFPh23cflTOTShSgi148iY1AJuWuDdEwf0G3jfQZuFuuyTj73dIaDSaC9bMa1RdZNukc5d6N6m2YhYgUYyrXII/B2Aewwcdx6CWVUp1G9RGwoOEUM41NNlwY0OdVEvaEbteaqK8ASTxWolkQeGVqyKcET6WL9ZTeCjIzyNhFQ5rko481m/dh2ATpTRhGfFlDEkJnc4qKbYNx4sbLg2PS6f8A6o6CGYq5/qhj77GVoLOmiu0OruovpxCvil2dAwbH5jVoNvfYDVyE/Qr83qh9hah1zf/uvgB1ZSSxR7tR98OuL34mxz1/q4uFsJnJ5s8X2BihWYIWnapXJU6ca06p4M/+ohAbCLFHHM2W8YN47BVzOuuhw5CMKIrO5s35+A9fZBPqEWlUt6z7kw3fpP3JllOkWMzFY26johcLEbMPNYZ0swdSEfk48kehU2I1aUroYLGdw36E9PPP9DQekcSWdiaPpQUNLJpPcDVhApyqURtBU1r1oQCaAqPj7ImcuHkmxoWsMZckS6aipWqPQhnCXiR81JMSwtuT9buLk7yoXQKQcxXLXfWtcCkCUKkxP193Aa7QzsNRQfokC+twVBVCtEsWU4XQT5WOnBQPVlDMEIC9mYBlzdC4A8MLHGykCYCZ4S1vaOA3POJSZ6KkhUUYVw9UTkJkXfhAnn/4kCCq1faccPS1mOXjBUDQtbcVA5gZ4VyDqMhRtH3O11BgrgdnDfO6mbHixVjzBseBX+ufuUo58uNIOBtc/WU33Wc2IaBqNDDMPm5zmPnP9qz8gSBMXjqa5o9NVrEOs1rjKlPTzMd+Jt8UufYKXlQmQA0oAM7v1RMSE0qIUAFCeXpQlx6a/+QdwY8Co0qZyqoZNbzlXx6kBoBMDkY+L9a2wp91B0U1QDJtnyAmdXSZO3DTPwkx6rCL0AWkDJawIj8EtumTJ0RjV1G93ZxNQwOb/++/dV8c+o9tUAih2p1p0A2DzGXM3vbe0fGvZLNLYP9Oxa/UilPci/PqUDW5RuvlMf45DvvQh8LoXCeTD5N6ZWiWl5rfB2Kcvd+8sb5QuZs86EfhoPkDh2zixUhwrnrETAHT+b950i7p4cc/lPkuzoLDpMUwyH7qB1j8oK7aMfRobcUDa94KbogylN6B2EWpDsdIugBVDbUIfV7TIzYMk9BgWXa/JUBWxI8sZ1Kg+OE5LyO164ZALLVqyi9LpvO60A7IfuTmxnwvEC0oI0wfTnRR5z+btVybj/RSBFg4ICAgICJhnHJCCprGLdSqO6APSeE5xh1XFdSBOjh5l0qfs8AIoW+TFKgQvxElIU5pto35LS7diVn2BZHuMYpX2iEhyrFihCxDcdfyleOb1uqLRaLuLqU7dKQe70ymaw8YbeGgIarRwaQRgAC3wHgQV3BeVUAzRpBdhWa+qGFZaUWq9JebYNFCkhQ125arpV/O9toJs+ZU0RUrTugAgAZi0AeoJrQK2q/q+V53KphzwdFjGtYAC2qN2qmWpvT0rWmLNUt8bAD4T6TaZ5pcrM4jt2ttUK/pQmXDJ8SC/cidBrmAGxVqUZp8hk77iE0UERMoLNwxNCRgBkQIa2/QXiyahWGDSsLrc3TOTTKdrWbGWBIqD9XNnu5IBupo4XCpRuoujrMMNwrIOtLboc2QL4O95bRcPnHo+DrvuIn3tCqWtlC5qkk2ZgV0w5yFSX7hnRpEC7wkoM1YHipqU2kN3qRpRpQxmojQTYj21egk2ob1hNVQCxsuNJ8wYs96vYlBGCJYMZchnExcu2PT697h7uf9V70cVa75yCR7cQ2GWsWsue0Jq11aHatczVzFp244RUMFRNxXFbOWsp4Kxay4D6wn3bpQj5YBnSREhXqDPX08LzDyimQ+qS/ApI44bKsFmIzdGZFP6UI1V7dv3sGRobDPCpLaef5zXyeHYJdVQnllpFaBuBG7ZoAr1WzbN2OT+3as689Y7jbomFc/cWjHkU3EoVrqEq8t4gE/5Ugw88scyBshZM4AS5SccyRC3M9QqpVQLU26y9xjh4bdt2K8FTQe0cQV8fmZky8fZFyYlFzes7eIQNsYhtKLYUsHVAWcHuQXPvKpV9DlkQ4EMjXv4mkfRL7WR3PKbpYjMy9hu9TDbTZFP6PaNLJ/GqhFdGvGBm9egGCYoF1OEG/j50gLgcFSh6keODkQmBowM73FEPasilL4ii2JIxoUb+6LPkJjwX38BoWx5JaKKyRlGCD+JiolYx4fsqGIYMDrV2CbgKceo59XCJDStHRkDl9ZzzE6anNSe0LFxY7CLJQXQN51QU0DOEZmUA5kq38aahNiljUA8y1AMKbd4AiNHLecjChQRotlK5SrbP6SNoa1gxfsVFbYgJLvNAqDUBjXqmW5NgM5KQ4vVFeJpAWbGk0x9iUmQNrRWlc0kc/nVKgKSKWNoFyq0D5t0FZT6vcRR79GuGMQJC+/Wx+54vk//SnZFLndbrOziN6/0hmzss5c5wxtNm3Qn+zwioGxVcrE4XIwXBfcpHg0F3vW5VbIt/d9qXoVKkabkbVoHFwRl4/6cIGLpVLWbT3+PrkYFuEUOoCnyMhPOyG8+yxvaNVdegaFnTrjqVo1a7mqIb3t4AaAYREs/gAdPPR9jXzA5yCV353nuTe9FVkTomZhxMVNzBpRnTKvgzb0u+THw2HH6FOlOjrJFLhbPpDZmgF18w/VHPM39wlbAZyAI/bztuKPIz0sy0VoRV4lJ+JQ4puDz9IUea/YC9ZUz6D2sU614YTQlZl2uYj+FETBYt7jPKotv/U7pk2gHxGkhOLnFAaUSYiJy9cyH7ouQG/vYX1m4i4lmgUYjc+rhiYpTMbuTsOXsi/Zr4xpo4YCAgICAgHnGASlo4gXAhfaWyKysi2EFFRNkzeddWu8oW6g8Dcy1ulNk3hO0VAw1JOKdsaP8ql6hslVVjKeWihIP7Vyov5dKrXYEMLE7BTFCfZt+NJNJC5NG4GLLEtgVbpVezBcx8GkBMjm3rMcHlVdmtcgzDnAtzALMCnbWex4k4FbdRZuQLfQUkYWKjFfOrWKHuZ05mIL2Quyqm5MXRQnSK23j9TChHG1e8tgv9UjTv8rkw2bLvKI23RGBFTpvD4Cui2pFFjUJJBLKUmFdDmbE1WWdQ9qKN1wY78N4Y5FCPqI9GeoL8FnhBEdlk5zXwHNN0WWLjNqZ4DwR22eAEZlIIBv1v1tvtP5IhGyhwtBW3cbeUnLPIsoYyran9ljOXD4jk174xAqGTi9BMWu+WHAvUAMg2xI7/kz/zBKF1Iiz+mUTm97sC2687Wevwb/e+yx9XJa4MRLNMk0DW1qvNMVBoPMgWZ+B291iYu2hPh5MMsTjkWN4auPcvUP2f1mzDAN5z6lGKBWcKHD1Jz/qBuvYxst83KZMEE8K3H/eOXOurVLC9GzdefP9JEHdFDFI2jmKvp/2xj5zOVhuFM2pxGFf1RXFGK9rpbhRWte2+eInsqHfeavMjXtSi/9gMgaKSqio4b3OaJY5L5Yp7REOzCM2hBQReAkU5tnzzAsLecEgGxVBomSuZjCTFQ+3oc9nKzb1t7ZdHzPFBsSbUZ+58+cjyp2PZzrPPTI5zqpGkIZAchWdUAmFWKZ6JgIY0H5A93PrEYkZYZ4FwamgVb+GmZkY3ZZm6RaMzLpdduKRAluwf+OANK7E9b+iSU5dR1ynUthCA7zHXewBHCAbOyKmB5tlTmICCm/EilHp6dhKIINGCn2cmVj+49GlbocSROQS5YkY4pE+2FJDH49O4pEpXRAiP6qAktydNp+NwbtGiWsruHRtbMOfE4zATMxD1fTkXTMVmjLhiynwPkPZVK7Ki9umDnBFIgDo1ICUnEGliFAOm8IKE0JTitZ4V1hgYgQxLaBq5uWqS1+OLya/K0gmdKk+W/wgi9BaoK3kLAOSbbFPF5rlumAAANqdIH1MuAWHiuFTgiJCZOJd0SwDI6Cw1865U2dHkwKy7mPqvPAxaJHpNIV4ylO1Lt2GVVKAMugFTOrj2tY49VbnACdMHWFuQALxjI81R7Pc0f5MeWV2NemfKYC2NmGKAOlUMBv+Tgi8K/w4jtVAXPH4O94BAHh0egi93hFQJl7KM+5iv8UQDRbtKLQqHtClBSmmAQqfm1g5z5krWmAXgDatoxgapErB4IxT1GW+r7rasJe260rm+qcsPB2qGgrFyFyjDuiNJIbbPezaYVTekU8dUpLrbf2sgeXkDFxZY26RC6njzu5dG1EuPqpSBd7jTq9B3AsX7ILcGiumfAhpgM5VOnzg1LeVyl1M6lCCjZeySqGRqnq3erw+CZDoYlzomeqVtvgNzxiEGS+W5nVhixgo7cq9opynSCuKy7bf5MNV7WLGAbFK5di/MzYtzW5/l41y96xY5kM6KiVAMhcS6GYJErPloFQ+Dru/4oA0ruXCArxu4jU2dlQagYvwE6LPryMw5Qe0ivzKDdVydiXXIh1rkzPu4hfxBEe+QCJZaHJPtzXQ2qbP+atLfSm1I27cgN6mttOX3NepudqqACBnYmck6jPemykb5sUznjgJ8iHgWPm4TMmQ7uBOoAIGJ/nnnUh7WFYklQkndGESSIxR6S/X8V1ZecfT7X4olQf1wXabSbvDBwwVcbhUCIr4gPWVHbtDiNTvt/E8UDJ0zBZ5ItOTHjezbzksXfUa4trbrsYs7cTDuz5VJh81ubJTVrHmU61UpO/VToJypIQyCxji2gAp61F0fcxVxkDR8osRXVrTC1TIpkjkXHtlJuZHBQczE2A+oqBS0nmH0HF6YYwARYRk2nhHNb1DkTM0NS++K+11LKkwHeOQL+kdeVQnBpLluu2JBHbWfPtb0hvJUk/GTlfQ9s+IKUBxgjU7vID3eDusshDRk7g1rjxn3hOW2li4xUgOwG0bSEAJxOZekylfIvO+9/vdkQ75+BW+ypjB2Gcv0+drcUjF0DBCwKIQfgebrtB5lTY0XDIXR9TeYMWTLHybyWylCJjFBMF57A8fD7DCxNRLXZWt2mci9zF7N9zJpLqUc/sRMIyUC+j7utsy1YuByCxioh5QmC1Py4Znmnipx6Ad1yKDS8URPc2q2PrcyZSPC4PBxWKLYQUMF4BZsMu28ouGQjsZ9hlGM9yxLFRolk5awVTkY8EqJZ3vDoDlAAODYvr8nZKjY9cm/f3fNIWYa0BAQEBAwDxj/18ePB2UXP9j5D2zkgPSU1yi76kqlRjpOUysJPbUCu9zkJPFE6imIEylErWr5oqtF8MKjJjbo1HVFH51qV+JW+T3DkEtKj3t3BMoU5/oDw7Uxu0qv0I9AlBLcl9QQDJPVxMGUl66Y6VrI38kdcnexWgJVnCoSb+DiKVtyzo5tltMRzpeaRnXWY58yKeyUCZANr6JyMVsZKpTfVydXcnc6l+0C7crCRUc8W7hVtplkyCHTWoME5Clr4Va3cUHAGSbXCoOcXIPSmSDKQa6eIY5hWQoRnx4wPYTAPDZyMVfVWKqMBlqQtUUEpPulI/6SlF6dxLC/e/28cA1V16h25fouDuZGxAd772TAKAAaSh2FXtvI57yXjIrgSj3XoN83O5CJLwTFE9z2Mqt0Yxw++BGM7FO57C71jwaeVqPk64PbftH+Z9lw6jjTbuiLhvwvpw3bTxW6xGJnq/Hbel0V3yF+fOryHzHMEhli7n+P/TyKx11XX+Mo7/YbIoAE8+31G/JMDnZ9Ju/764hMt6SbCr9njtWgYHZ14TDxw2lp+oB6M3JrY4ADGDk9oRVidcVgHQGge0TFZNLMaMIkFVmjPx4k6n3+pm04YYKjW496gptC+h0LRuaqO9gLn5PkdnBy7BNMvFjiTX1tZgv6VtRLROKBV4vwSrpbOCuGZpm7jNIw5xVWRvi+p10xSdUJXwC3w5SmhmwrCCbEe75Ut9vPLK/4oBMxVl1+UXg9VQPlMqgqgblYXIV7d98DiYzea/6sGKIoFp6tMTNAkU/8uX+pmOfxmGuY+OKnJEreN6bTp3QacXqXXj0/sWOqoUgJJUt6dJa4Xa+yUuBwlRNslVgumbT8jiWiO0mzaVAf5spP5YzxGMdR13nOxruntlorl94G3dSXpbPe9zFnIj7uJnuk0osCdpWuP5pk6vexEquq7rYAvq1Ssys7+M5UYcbYUiFcjeIZ7TxsBWaVF252BTPmE4/sPGpzIs9ZF05agpkxGX2mfQru/NIPdG46kp1b8ijHnMpNPpg+FxoeDpNphjY+AGAzxllgGpKl4KUbheu2LqKNG1m2ylTnyvLpafWRKb/VlpxSUwufglow2Yn7WosDwRf2F1qg8elv5alsVlpDEFaoSK7PqZYNrwxT6Z8fF3WCfG0byMxXR3J9kmVEmUSA222NKqq6TKYLk+6EloRHY7abj8G+4u9+Itib+DiWV3C094rReRCPHaip+rQMs+X56wiHIJZuNk+reS2A4CCu3aV0mUl9OK7Glu1iwjuBUzKtNf2SXU3Jxdjtf3FfDUligBWwI1re01AX8ctYIQ+1i5yVOSvpSJNLbtwTQbkI6a/88pCKtWLSVdClFCJJftFpG6D3wZPxTRgXKv3BmBgEVrNq9ffNf/3+3how/khFScgICAgICDA44CkhSkmvdJl5ArOA3CKYABOnAQYb9V6ANwo6Ky6biR3ydOlKURut5KTrRLCFLpmSq/e0qUVLsZg8xnvxhpTN3b7zmG96jPbtMlu5ApKxCN9zEzX3abHcTt322xFXGG2W3OioKGlPZdE35tOXWqDrAMo/BI8Wtj3RdinEu1RWWpUMrf8UjXliuVrr6Ii9qjokmzB8MyoA0WXDyihqe6pZuTceXC8z9GsVKEplhYQ4+ZeHvCeZFkH8lG4LdCyhi8EIvqmOlSl/a5oBcFXmCq04jU2QiLRY24FDgDka8DrcWL3ruwzKOGFLapW2ZawYBCuWLGuZFT1vN35FdNUpPlb79DKeChNnxq5MM8ZVt6hl/lbXyx8ShOH2wpQ32dFMUrMVMrxlKJLz6h7ZSxIe1hlzXss1iu3KlGrWlY1hdq431qv6mlUPSyZEMrlupHxFEfUYU5Rq8ePLxhCzAuaypbf5IAEIZrlA7S/FQ2SICfeYUpT0r4QCHw9awN3r5kviMEkG0yfi8lRxIwwoP6uVgIr2t5DZKT7y3pZxOGKsoABpOA8b+LucSLuMMSmKEs+zKAi3w4mfT9y+bj9pGXFUy2Nd1mpEFfRPaHQBBXScUI+5PtDF44w5yv0e+2UyhnzXm0y6E0z5dvPKrQ/RQCUZx94xtyzobbUGRDWc819SCCe9W0sW549stfzYRns9zggjStMCbfGcM+XGezUdAzAVhyplINjJv0A0LQdMXKpFRhP3I4VUAAigrTpMM0StFSPKpkJiMkIs0a1uGb1DnRyk7/3+UvBuDFcBK9YBiDq0r3VZR7pQu6msk3RjV3OaJFHUAV3E9HEzrbbN5UJ8jQzAargiEzJsWI2ATdpOiwmYLgAN+eUM7G/z2iQUpRN5ak26SkykTEtu7e3sLzvis/73DyvSq3Gekqz+0Z/hS6j13xE/77wni52H1E3x2naySoTESkXW1ZCv+R8xlJvcDGhdKdw3ymHNB9nJ4qy6WnOqKMnAqvCTsbFQKoD4A0lz5hOwzD3ZlXKoqvzTqPFWhle9GIsW673Td35q8VIdkVuUwJFgDBVu2Sm+9umvZRthc2nmD7v+MlcSE37WvVzbYL7GGcDAAMSU4h9IF9VehpbZGbitPdSenqXuN7nWPTN/cYRugeZtKiaAkulK0dZ5D7HlloSbNZv0qBqlXh4lSMj/eytIRN9+Gu3JIqm3/Aimoi8MWfM0eTFkDJlPe3izC/2ZDIY/2UlwJxE3bTFLrqUXyxU6Vxt0OCpWnMsoDUPTAHFkL1eJTWJ6/tKJs0YTPzeq6pC0zIJIPL9r2L/nGCMvG2LKJnbrUvVvHbBwpVQTMn1cx8MtQkgGzHXqxph05bC6iS4z6Nl3L+TTOl7dvH7Wb+YkQ0CyOe/E9eLB92gCOWCwiucSx9yIOZLzoKA2i5WoaE9Je2U9vsxDkjjGqc6FadR88ujLtVQ3fAagBMxQXG9iw58bNbFwkqgMIUnKFEAJ3ATI200+5jZblJI2oUufWgETQ9uWuKMKOsIHe8EAMkQtTMI87cF7Q52z5httQDISPk6sGWlfFrGIbrc5QTqQVx9cSviJk4oJ7WR55WwJzVKcKEgjYeqxTv6b6LDdTwTMIUmuCuJRzUg3m12zGkpfV82ZpkJlzqkIgLVyPUr73jlTTEq0Vur+yCplShzgfouu/KVGL3PLFJSgXxIYNc6Y4B45FbPNkXKeuK1SYaiki9k44+1XQJlnbvVdNnwHkTZItQfYy5WyJR/0fNhXRLTCXhKgM94w/7AO7RAbc2VV2DT287FmiuuBAAcdszD2LxjgW5/S6HXlo4xYZFywhuk3jjpZ+i9ZkoUWGYLN+i4tDVORct7VdX6se48pguSaS8OYhJAxati0nvkxIB+S/nCGLyyMCKhvf6WflZlFjlDCwKiJTr9RXWaOu5shGjoc5cvScx45nZHwYgcq8ATqXOOO4ZVeNwka40Yz3U83d6lqJRr5Ao6lmzOXza88XPlAatxVmtMCj+527Qut3Dg/tr9JfpebU5vnDEUw/788SwQ2YVJZeGpkorBzAGA+RrBGFz4VLcsVIlPtbKoepd2ASPr3hNMJpiLyQN2UervTaaEdKcfa/beQKwSRzXXsVv5Sb9GUqavipZ/Hk5cqaDHralbXaYcFJtrjftxa9knd2/kdQvVxcP+ij+CWwgICAgICPjDwgHpuRYzCXiZYEJxcGEDEZo+xbTNRwDIrMQoVeCGiqVcgBihNJtHs5ryK/eSof5IjLijf82H6ojqlhrRJQLVIu0ts07kVHMgQI2bIBojFA2OdJE+ybaHF7ji5AC0V1hYiTtzlZOsktLRTgCKSjzZVVKJ4OLAwKAKtSwTyLRS/qxkzlsth0uwjt9VBgt7QN/SzgrFEhN7jEgrf613XXBHl0bTHFT1FBSQ21KC9dLRzPlMAlGXPlVDcIiu6TciPPo8P2zjab8XpU7MZ+guMx7GYuU8OSVMmTcASpBTy9pzZIu80nH6cDngsVSLNfDcK4uLEeX6VdXIFZintsAh/+tDQKSf6YO/PMgrpCMFPhMhWqErTkWRdBuzz8y0NF1qxkUyLlxsVS3vozRsRm175ChbQHtEluWMutpDsalFrGTuPrMRvxlF1GFzSlo6qn24AOUcFOsbjae4K2CRLZRAyVxhfZEoiNRU1ioFjj/0XgDATcUReocii8pOOlFXx9ZcFa+K8lV1I/COL2lZtNWA4tl5nGZzBJeOZGhKAANxZX1+T9wARk0dex7UqWbLyi5QLZ1Ow6ohGqv0FfodyEfN/MDgQwaRoart6SVcyUDR94xCuotBxv6cMiU3PsuUNMXqPFdPXWv1uveooSqU67BXawP6XXcykhJutieOgeMYaeoZ0P1pNSSyRlAMYMrMYTU2EJNmqqJGZ3hcPF9ApbaCi6ex8yFPXcuabouYK0PxldX2YxyQxpXVJZjZTLnoVur0KTYgcXflA6HjlA4Fh2jZHT0kCsMZ14Zz0KNtR8cwvyEJuNniTDiKFM5wpTuiCqXCUBLDTKnp5Hi30FQrABDT9KCNdebMVbLJlpTghXA0Ey/Z4CRjbV1DQg1LVyVJzkQgu7F0znVem/leVZDCZgWYqdyUJCWy2ZoX2EjhBVidWE9K9mWNyYkUyiFl8jDNywr43MGCg/UsXcggBbmdRqYOa7rJJR+mwZSPHkPRct2DZKZCN3IfjwIzhgGGvicGcKvY8QZN1U36h60J3VAg4YUZNofPQtrdYiRDskVbwrJpNoq2tYsFubhqnJToc6B4TA+SYqRAe0hTqVG7gOwICFYx7LYdmfA1WWOgNuHrQwM+BQPQE55L52kotyG93YUGAJJHUtR3+Vra2fICcVs/X6U4FCc3/gsWQdhwQ0RgiQIZIZosOZQ5R5IW+N87Dtb3WSuRzcbgJp+alN9dJVtWQExHPnWjruO4WWgHuAAAIABJREFU9riow1DaZxqT0z+oSjlOVTIg8iEGEBBNmkpCZucYZ1wJlXq5mkZV3McAua3pKwCY95AXmmZ2YZfKtMBILwjcbk8ECFv2sc8ga96g8swLiYqmNyzd5QRWeqq2Gp4B9CJvIFc29wtBmcK9zyKvGDzuY9BRT8db3c5euxnIPv4aAO7HDCu1oQP0osVGUqoVxiweT9daURRTfjN2FWtjaoWXIAa21FSm60XOOUh2CZMa5Y25SyvaQxre/oZACwcEBAQEBMwzDkjPFTMxUMaQiQJLzIo5FzotxLItqacUxS6vCKZYgfU5YIruF02F2kJTwzSLwFu+tq2W5Zlz9HR6gVsJp9JvNk4+SdxVrimswMYXDAcxkPRJ9dojteIDXdwinrUVWcgLmip7cbKcA9OxrhkKgBrSe4yZFjDZqkmyDVecXw5LrxqMJaKRHqRZ4ma92BVCZzkHNaTzNpIdkVPeij7TtFZiKctK2k9HgIynRyUDFHN732YLgPRgncMQEUMxU0OWWiUOobbd7GoSEUpT8xQAeocUkFagVjLvufUisIxXaGdPidpNpFXq+zU1wqp4Bpg+vHRpNJRKvzF7TMiX2uKt2lO1FadqzdwxBVkvhtjtXzvZiTAtTd3bTqSLYlUKrOeLTV/FynmWZdZA0fDpE+m4T29gEkh3Acw8m34EsAXaRSHFQB197f5iQm0KSKZNuxYzJEZBnmcR+ACNqiBJu8nRtAB1vZAOHK7CF6sX6PT1cdlMTSuGjYCvOdJDblLVyl26ooTz/EqG1oimyXvdGspm5AuPVIpIUGUDbjSUrj1tx08mBgoaABUVcMUJKptKV5myRSo6YnC/XuYP5iVzgi9erTJEmp519ahjQtn2XiwrvZeuEgykm1T3b63+r+CfOy+MOM8JEskp9XmhRWnpLktfA73DTGEasxuN/o5XytvruPTBSvEKQHutrq8EBsJVvKh41cqngBVCi7FcxlSFolexbmf8mB/nea6/yEYKX31rrIDqxC7lMZnkrh3lH4Hbd0AaV2Lkclopq+wqU1EKs4KBzGhRqRqstMQrO8ZEhOJhPbNFHV3xhlVGhqoYzbLtY3msLkFmV4rMqA8BgEgbYkeAMbiJHmSoa6tUzr2qVXT1Lh0kfJ6hK+JeMp9rGhFYq9QGDACEp22JM6jh0m24HiUSckjfSz0pkWfaiM3uaIL3hN7pBwCPFRSZ8zcI6HkKs6yTj5+NaEpbzNp8WSB91BT4L4HuCnObqVZds8Ib/e6E5tlEo4TYHfs+kQzZCruTPUA80jsTGfC6LQcD/1mH61zKap6fDb1LoKwzX8S+8JVsiAPpYxGyBWbi7wu3UKimBIEAmXMXr8v7sR9bs5GeYe3m8rMC1PPjhZine1ml3UyQq8JVLMohOzUXM+0c5G+uNqE3NfB5nAojw8ZwZQn6hqqr72DY/SwFSm2AV6GemOfJFfI8giz9AsZR4QyuWpM+GC622mcpWku0VoB1hFavP6b/1numBO3WE2xtnKNs+qpSJIC+KXeX1nN0FvqdnygikH0ZKlXTUHKvSgV0HNKWCLTVt2zcnzw1yzgD73O/4GRec6BiH7clYWLzlZi9NabENV1t6VNGnr6XKQPPaXDTBrtBQWUjCRWb65kFQTLBnWo2sjsDGePIFXMGjhc6P3Z2zFPkNuUumeQuppuPAPGUp4z7C8ipm2GUz06fUQ7GUi1UrBcHjj5WVUUzTLqVve+KviMHqISrpsVzuDKhRUsgN3oA1VCoLegh6+gBz3bXXPxV7v8h10ALBwQEBAQEzDcOSM/VgeA8LN7XZYasspIin7dFHJ4TkloQkez0OYd2T9giIbCcOa8h6voVYTLJIOsViqsv/F6sJfPbZwmYjZPhzu8qIaVSq2+tgjT1FU3KGIhmBdQivfQrkPj6rEMFlPFYeLOAKrmjZuK0BNumV47FiEKUlij7lr6roTZuqN+1PUf1soJr2ty0XzKfr8pSXTfXeiUygVPKxlNanGW9TtHhukoL9B6rkaGgi4gA4k5oJYcKv5/stlTnmtot9BZnjpakgqMYrhSwyDkwY7ejqyyFhfYK3P6WFTWjrhxUqRjEBvMbZeLr/epnY5ViwvXp6JIZXS3LeH6qEzlvPZrVBfhtLV29TZsRFS1UejPyzDMTtR1mw/hEgUzR/fZQD7OHEIZHZ3W/ColdP16q+6oGZCN+yzPWkMgKfY4887QhL4xgbYFNfAVqkRc8ZVns8m/LXuw8bRWRrv1sGRmqUIyKodfTY4nMht8uv3Rb6o7LR5SubWspzIhc1TAZKYhIQVYU+I56r3qjTAthyNZvNiENfW8MYJ6C5RVlOC+McKkXuWfo6kMP5LiTZkIsLdyv1M5NjJAqMmOwz12xFZ4D9ceYEzTJyvurBNyMq2KtWE8mjEgtgWt/2SJEsz5vFwwojSittZmBGNPbwZm/RVM+t92yCoLbPtKH2aIh+mJauGdFUoAXYOl2elFk1K18Hvk2qlh7q67QPoffwARAvNsXNlERBpTVduMOvpOjm9QcU5YtlshtLfC+98z3VxyQxtWqWVnmq8toGpIhX2wrkitXTJ/3uNtFRuYC1K8UW19UgJvUFtlSaD0oMPNMs4PL0hJ8pzFcUm8sbIeM6EQVCopcsXnYyiw23psxKBfrEfpz+72ksmG5AOSyDFFiErcXw8XQlORQNhFcEFAwMEMflztTYNiX8CunvQRVLOuh19C/p7+poz+mrRGDNqLcxKtl5TvR9gTFsESeYg6KRQV4TWrjDqDkAumjJlarqtVeBMolBSJDWYpIIrOFLZROLVErKrkopgIUahKMKiUPOUEl3rA7RXCsK0wpq+jsVShiUxrRqiBFh7vN41VEJlXBXJd5RWfU4Sj+f/be7FfXLL0P+q3hHb5hf3s8e5+h6pyq6sHV3bYaEyxLSJAL/gi4QIhBhISIgIe0DSYohMS4HbubGIMgUZwQCSEugAglCKTcIeACELJDp9tdXdV1ajjTnr/pndbAxfOsZ71ft3PXFz6cvW5qn9rffr93Xuv5Pb+BJ6q2LzA0BfRtkXZDFgPVFb/IUj6wQmZ+tgr2SudeeRUF2p/st+hSUkgNnB4v8XP3PqHDdzWu3qfWhPtgDqMVhjO6XyfzTiD11NNNo7xVaOoqHQqafbbv1BFF4aE1s9knPVbPiL1OBirYZcQnyLLV8LaQ801cAvA1BTRfsmqt0d4LiGwygF7LxNa1hSwEAdBkmloyGjtQL8bv3x+xEox2l9mazqlpFGBHvU+d/25s7VjearhplGADV8cMmUaavMXkv899/nIFuFne/hguVTFP5KHkBV2SFg3ZEctPyK0oMZzdPPeahyn1ydNCVIW8cKiu8vEOC2IBJ0vCyXnep/YesZbDiPU+hqQTe1onNvPonkk9V1/SNvSIG5Lu8fol7VtzxvDvJKLkRXp1k80togYwKERWbMTaA8wJEDvJ13i8kZMrlSdqR1IRbYTxCuUFR4idRdibHCk1mVLjYdVNMX1q0Z1wj7E12QHn0mDzVpAGvV6E3G/paAK0VzyxTOPIozXLZoa9gFAFWLGRAyLf0HZFlZlOk/5a5dX2gxZhYzFwhTc92UpR1TqD+oCeXGs91o1FbNLTFKV6Tw9RdUTNYK0jBn65tPdzDN7sE4PN24A55QVHQH6xOaA4bOVJrie9EJ8OZg1ut3mJHPYUGsUsCxug+KVqlxpqbeBS5dRaKE7QUd6iulRYL1hnudGIiUDmNMxhh5AsLJ0GmERTXea+1TAFFDKhKVhItYi0sBk/2+Oit8tWdKoYVToK0letCge9H7FJlpA3WdMcLVBfRHSH+QUmOlqbe3oAVUEFVzatn2HynPtWezU2X/X4e9/5GQDA/sEW7S3dJIuv3qBpC5wtqOTY9gUME7l8ryUmrd+nfZYqvIq4+pS98kwEiojZAd0HpXUZ6dhwKk46bo7JA6h3mWQ5oaDnSSa1Vu24LdWP1phP6ER2g5U0J7cuqFJNz2ZEnlBNyF7gA5MP+R6xMwefQu0NqJcqnsTIXrVczUk/0ymxKgTy/++OyH9ccfNP+Vxppz779NP8+ky/6xfY8f7Vo6StYHMP13RqhyAUdSQNaNrHkTVodW4wfZGQA6C9N3Jf23fiwa1C7ulSXzgvfPpF3idEBdPIrtO1SmjJH+HpmypQjAiZpqfjSpWr3eSwdJK9hUzsVBG+zIvJkv2Vm3sRZmXw0S+Ss9n7f+HbaFkvP9bhvq7jrud6N+7G3bgbd+Nu/ITHG1m5JrkL9T1yxaI7CKUe3LcBqDfV//4hAGDWAKYBiqe0Lrn9aZ97rGtanTdv8/LvWS3QzrCIcLMgy5niRsvPplVoHlJ1UR036K7rzEZWEGbzcBZgLotsklBE+Pu0+rfGI06AwP3S9pO9ndzUMBD00qZwgpSl2hqR5djJAKUi9rhK752BP6aKt1+VwkrcPgxQh7249EDl1bx70sOAjAgAYLOuoV7S8rz/qQF9b+R3VTXg6NENAOD6ai4uWM7XKFYageEi/7CTpCH3sMNwT8u+2I1CwT93Rx7eaTE4qD4vYUcrdBHz75GXbWKJKq92kmPUYQ+dzBsmGvqC9sP0u3mu2o9W8q1C4FD7m8s50GuYFVfiTZaHRANsz3JKSHuSc0IVp6GkbQ7z3N+dvDA5F9QCm1WNyAERN36O+XFujt0/XGFaEIR/UDdop3SdPn11X6Dx7ihwnit/d6ugWTbjJwFRZ8/jm5uZsM1dModPRcoo19dPonzOA4g2S60ESgVVXYWOqLnH2/QFwitOfmqo+hrzHcZsbTF2KQIitEDvrjICJRYrhWER5RnSbWYf92cDYCMKlm+FMifthDLu5qSOkmqUh0hjKMgjZAOIYUTJKCJ8NYLKh1xN6jhinhsyhhAOhkL+uYgwFxblDe/zYUSxTjsS0R1ktrN9VaC8HSECV/S57SkxjhM6F72Sylj7Xca38lnWFUd91RRyIKEEo5aI4qQhkSo5CpAA6PkyrYLdZBOJ1KPfvB2kbWMaDX/o8OX/6Nt0LA64/3/Qn1w8wWs/3szJlUkYKWQbIMJCf+wFjlJdtu0rb4wYcSuG8QQqcUpcejoY2AYoLpgQNIsAT3Af/5lfBgA8+S//KgDsGHYPiwCzoLf2+2ev8IE5kT5ZuZdh1YfHt/jMHEJzL1UDmNX0d9ttBWODaDlDr0keAhAE3mXoV028EH10q5O/OfqgUO91cEkj2RXoL+mlZzb55a4GAOtaLPZgo0B3obWIt2WOW4v54bz+3hEZtCfiz9ESnz+nRYspA2o+ls2kQPG8QEF8HTR9heFdugBx0CifFzsxWwnKsluN0JcSXlBdZYjVTZGN6Q0Qpx56zT3RTok8AABm8w4Nw5TaRkk2GjoDe1nALehEVC8tDE9WvhrLLywRtPgF1h9E8FyHYR53TPWDBabPaSf7fWoVJDN0FbI1XH8w6gP3CupFjXqTIfv1BfVcde3QDxbHeySJmZcdrhmKV6cthoKOa/pgjc35FPXzdA7yufL3B9w7WWHb0QmrJgPKgg60m/XolpUkKdlN1oLGid/pj0aVe4O6H1lzeoXtszmKt+k8TsoBayav2bXdDR0wUfTPcEp4EMopWqCmtfFV/ju3R2Qhy/fBcOJEy42gUM46DHOObbwdgXeKe4lIC/AR4afO+2Fqj3BToj/Msrd0r/6YpGUEpY4X89GQFWB6phIpMh1be+ZhWKIVqojtfXZe2kT4aZR3jN0qOW7TKDT3ePsFyeDSIq4/yCQ9gu+zNEf3GFlRjp4Tza5JIoUakb4UQdvJ6pUsIfM5SHrctB15Xs1I3/xkA9XZHXnS6jFf31Er5nUdd7Dw3bgbd+Nu3I278RMeb2TlKoxblf0zdUdZhVFnOLZiuY0asR7DjFadyU1FeSUrWq8j/F52AlI6EqkGwJO/8ZtQEw/NsLNXhYR1+4XH2fEtAOCT2wMcz7fYY7KH1QHPXhLR5HI9xfHxGssNVZNaR5E+KB0QvCHPVYBcWLYJs8EoPk8hOgV9wGbriWkLQN9adCag29L/s88qmCSlGGVbRgMMx04ISNWVzhFtLVViCY5tj4DulKuSjSaoiuP1nl/sCwlIqYiq4Cq89hjmOUoOyHC33hgUGyXXwwy5ci1eElHNNiydOcpZkdFkdmSoA+q9Dj2zncOLKhNBiojN7QSzfTqAvrcomYEdJgrlyRo9S2y2mGW2s8/SiTDzCJ1F8z5V28VnlVQX0QB+32H2AV03N83ntbpWO5mf4zBwu6HWAsCmFzaie4fuEVN6MZjomwJ9a3GtqVp9a+8GP9ic0ndHoDol+Hjzaob6hRVylm2J6AUQOtANVohhbjAouHIdegtVBiGcqKBowwCKK0tZvmnYbM5fLEfQo6bYvxtDgahq6gQxGvYD7GZkqqEVXAqaL4I4l2FvwKAKql5BgQWpqhqSOXySoaxMJteUAd5rxDJ/XyotYxVGDGAN3WSEKcw9LBuSuGUJ5RQqdu6S7FdQJU+ZuiNCVqqu3Ti0HVAmG1joQo3MYghZSceteoWWgyX0Pp0/Mza+SI98OYLTLckKJev1nhOyZigjxfClEPf1CFX4EQUMBZhD/k7FXNnrTmV/az2CwlN1m0AMB4SUl7zWQhrzg0EctJy7fj+iS+fn9vUvXd/IyTVUEagJnhi7KZEpNk94cy8PgunyyxwAByXn/kvSe8YqYHK4lZdSd1tDr0cTnB/p8kwUQ349cTi/psaPO6+xvteKjGZzMxEdWLOp0LWlhLGXi04eYqUA3xoohoLj3GUpSKuEfeymESoYDPf5YKqQJ+FIbkue4dJgR1aIIw+5yWcWw6FCfT6C1V2GpmyTIcZkBwewLq4K1OcFAJezHX1jcMN9t3Kp0D/u4ScciHChMeOeYnu7x7ATZJ/Fdo1/TqHroYRIpnwd5UUGDfSfzxB4UWSDIt0uSJYTlEVc8Iu6KRA/5DzdA494upW8WDVx6Pf55d7kBCG9NnCHThybhnk2mFdRAV5h8y5d373vW4HuQgl0hyONLWueAaA/zj3iUEW4+z2KCZ2E/rqGuuX+7uMtjg/W2K9oYj+tVnhydgkA+OijM7S8T/bWkEUmvxxDoWQRVMwGrFc14i1bHh63cLygsIVHvy2yLtjkoHnVZ8lRtPR8hWM6zu3MyO90T9re5IJlDjzCqywJGlvpIQB6m+6zfL/Zywr6y2sM7Yyvb+5tIpDUSuRsrYZKms46khQsneMyAvx8qbXdYSmHcdB8p+Fiep60uEwBdM939fh85AlJd3nCGz8L9HfIz69XUCMnJD9D5kU4C8fB5qpnKdoon1a2ryLcfg6LCJWGfkUnsn5pBaYdyohYBeqfAyjWxU52bHai4uCCxD1plWhg6Vixo51NbGGZZJNjlhq3ZKJMpn5TQG81umPu009CTuSZ/cgs/xqON3JytWsN7TQHEvPFLCPCkRe7PHVdygprOAgwvJquzxU2jz1i8j5tszepvbQw31/AcxVns2afCAtOwzMho1x0MNyzaW5rRNaKRjaTaLki1aWHTvaEEWSYwDf/cDERgX0sAuoXliQzAKpzKxOLXSt0rN+NNfc826QfCiKABwA8r6HSar2MKK45aaSOCJwE1B1xHNfPUrXddQWm/zeVPW5KlVeu7PMLV/ScPNFXF1qo9+WtRkmbw/Z+RAxKDDH6BwFTRhTM21ts9VT8funa8Q8VqKecJuwqAk9oUp5PevRsphB7S+k0LF9xJ4P0jP3UQy8tmo9psVOPwp3dTMM5DcsLGFMElKMFRkrPCTbCXts86ZcxS0gqB3gFxde0PTUoOTIvVbHhXQ4c/3SSPV9thOcJ2gcFc1lgOEmlQaRjALA36TEve8wLelMfFxvcm1BD8OPpMfRnNe9rMjbJ8olk2qE+nMIqQH+Z/u7+wRIvbqjK7K9rzM/W2HDP3mw1ynWaUCHPiVuw1afnyt4pmUhCSf3pVFW53iAeJ1NZBdUY6Ver0fUk6z853dA67OhJBYXygFrpbM03DZlQpiJi6r8CTNrhc3DQk+82n++xhhdRQScjFkcyGjF9mObeqe7ZeCFNEhPImsDbrEVNpMpo5A2BOEmLP+YwJDJSFYQ/EWsFKCu6Wj/JxC3FsZbpWuhWSS/Y9EDPxC0/DTSZ9yONfJqgR3NaHJ0mgO5rkW6VdH7SO1KFXKyHkqrV9LtosmRHeex4o0cDmfTVqIqNo0XI6zrueq53427cjbtxN+7GT3i8kZWrryIis+CEqXbgUe53cGzDFusRXDp3cFNeZW+IjepSBmQdBLYqVgrVTWYEdscxw08VoB60InOpC4emZ7PyRYeW/c1Ur+FeTbIhfBmI+QvqTdnKYWBBdnQahr87KIXuLLM1uzMnq113AhiGmPzW0j6lhaHPuZS650pGekSAS5IgAzEjCK0BAvXfACC+rHN1N4ko1jmw3E+jVBt6oAzHMgnEI8Re0fSAaelv9j4G3KsSqy/Q980fLbE/IZhTzbb41Bk0puLzmpfaZmUQ7vWwVe55S780qtQahF8WwLqGOqVrMQ7EVjrC+1xl2c9LtMd5de4Hg70Z7Uu7rrBNiSRLK30wMppQsnQdVMxG5MsC9qjDlO+DpnLYTqjqjwVJtSa8z72NAFcKqlcoWNrjSzpfno1AzFrDPiba5rQc8IMPHsh9sP5ChedLqjqLDyfC7jQNJaokRMOsc8LM8LgDgsKUXbx6b+TcFQctmqYUnoFe6cx4tSO4vg7QtRP3LuV32fmIyJKy6xIx5SNPHEJQ4uKlvJKkJ+Vz2LibRAyrWkK+3XTUe7TEpZC2S5PTVoIvie1fZ1eyqFKbIluXqoHugVRB6yFbSgYb4etsHxhsts8MRUS/GLmNjUw2EDMTXA9kRzhWDSRmeCgUYKLcl/VeI62I9nLCmbTpWKOgBbbJ0HjKtE1wrK+BlhOWoo3QrZZ9DDZD+2PGNDQX9qOUnPCjVWfaeQ34hNTUAXrEwqbMVj7GTsEl1vtWUwUtxhdROBh4/QvXN3Ny1b2C1gphFMumtwZDNxFdmNlq8cBsSoPpGb2Vto8BtbEAk3LQmeyV6YlE0/8sYTHh0xn0WwRLfvjP//sAgJ/9+78GAFg3Fdpttg08e/saAPDys0NAR0wPCRpst2X2zt1YuKigOIWkepnv7m7C/bmdfC1+KQ0KYBcXzD299HV+gMIhvXlCryVwGmAKfXqwjjp4/t5EchjWvP9Tj81b/DcG6PfUjuwl6fBITwqUS+4jLlSGPTX9O/3cPPBQ/MJ1QWM70P7frtnhKZ3/QWcbxggU9SCwbWE9GrYMDEHn/W81ipWCY3scd5QXIhg09DpfUzeBEKuKtYJ+PkFraB9qDTRv5dQdeUGBXvYJ/lJOyWQ3ebDG9maCFWtKy2mP8gHdW/3LKRAhNodh4RF4IVVea+nzA4Qazj9MprjA5oRO+NoG6NmArz1+DgB4sd5DyWQn1+ZoOjcF/E9tUKSe39UMIfkwlxF20ct+PP9sDsXnW2lqTaQXfzR5m2P9hL610JMBnv9ffW7RcRtEBYJDpVc7KNSfsqZ5EqHVqGcfIKSraCETlQqAKoLAjUCeqJRTO5NteaMkfs0fOKiNkVaIcgqTz3jiOsn9btOQa1p/wMepRpKUqBALSFrP2MaQ5C9KvtuXuccYi0hewwCciju6aT1kxzi/R8Qq+4xjLXUFf9rL/op1IpjYlJD9ATLpQtNnW3aS83te9lFtDUynROtarii8HUgLk3xd6LzHfJw8TEu93jCCjtNxFueGCFVpsVBmtyxf5ecieiLDeXZ6I791vi4dXvtxBwvfjbtxN+7G3bgbP+HxRlau7niAnnDVl3yAWw110gM3vIKeBuAJVaBqW0qVaaYOPiqYV/lz4kd6GmBPGvH0ffufeIZ1R6vPn/9ffhXTYsCspKXeeltn16HG4uVz9nUNxCaN/88+/fPJkBmMQSFuDYols1AXYSQ1iTtMRAQI+WAHBnO06k5QkhrJA2JFzkXJVaK80TnNYlWgvGLh/YogrfYkswETq1gNtLIeSzVkHzXBU5uHDJ+2OSsSyCtfX1P1NJ3RL/cnrSS2XF7NoV7UYs6h97ObUvQKw02N4t4mX+sLzoHd6BRiArshp5zErNQTB8MQ6P69BlfXM9jnyZk+r9iDAYpVRHM6khqkH4soUJjyoFzbRJKaBIG7trcTCi9gSLe7mAhcqhRgNgpuJniypKYAOUHFtEBzP7NhowbqD+k+2963iCbiO58+oGPTUcIYZhHo7lOVUOx3qOtBPH2rtYJO2cYBGBYmByAUkCzWMHMoZgN0goznFhZpv3KLIZoI11kU+wx/2yjSreiJ8SyhB+tshOAWHnbRw/NzqG9NZrKaDAvHiYe6LjPZyeXvhqZjSDIdN8/MfHNtyZSfiVzVtUJ3lNoi2PHIVtlOeydbt1grDPPsE6w8eYKDP0PZypnAk5QGxVLDJtOFgqrM1DLxkyjbt2uN4kU+bl8DLu3X3oB4U6JY52c9kZvGBCTlFPoTLyQ1qAi1Sj7bGsN+kDbG9kH2FlYhCtytHCS1Ku2zQLg56pf/nVEz/0e4eMlW1Kgi1iD1ABPMyksjIQTNHK/9eCMn13LeQ081+utaJCrhQYv5vIWbEJZhTMBmzczKkaUeQDKGFAS8eZINqqON3C+lW+njdYlHj4ge+OpmjvPPpph9yg/8zzUIy5Tmkl++0BFmabIDlFdQCcJiS8bE8Iwm7jJxw4jqryG9qmGWbd2ijtTTShPe3IkcSTekdxOmZlDiYOOmOVw5OVQlGE4PEJhZeYqaMqPQmvZeMt1XKG+1MIm1yw9pey/KOTUdABXFJtEFDTh2pCk93IMWE75OAEmU0jkoDlpYfvEvr6eyEKEXGb/ApzyxpjXL1kpbrKkcwqqgfjmPJIGxW+DmayPmaRF35BKpf2nXJPUQR6VGSz/KHjeIn8wElVceGa5zNHnEFGCwMrJJzcmbAAAgAElEQVRAGuYRpmfYsFaYP9U5MFvlF2x5YRCqiCHtlw0Sj6gcZMIfNiXUhzPMX2Smb5+gfAu4vWzrSVFv/OPGYogKxR6vihYDhikdgF/anQB632kMDTt87fcIifEaFMrPytHCBKO3L+A2hcC2wea+IcKox+4U1JC1s8rliTFqehaH6WhRmnqFHS2sUt8/mfwD/D0x3yN6yH1QPShUL+nnfsH9Yt6V6blCE1KoAvXNk/tRexJFohVtXqlpR3+fFpfR5HNgWgoJkKCJvSAOU+bIwU08BkvbmXxuZHEPtXsedaOgNiwf6kdQO0fwybkLmd0PZKtClLvbM20+VylSMAVe9IdRFtGmxY5eW404HrpX8Em2VOWQEzonGb7H8euPC7+Rk6vSEVpHmEWPA+6JltbhdjtB12VThTT52YMeoeQXv/XAAdCUrLjXQOSen70k/V/gp87cFjif0xLMv5ii2Gj8w2//AgDgS7/+LXzw7/0iAODJ3/6mTKBEQojoxfQB+PjPknXiu3/tt/NLAsBYdjL51KI/iGLeoLySflSxVug5Vi7UgfxZ02Q+IjT5aST9rU6WdTlarz7PpBDtgeY0Sx9CGaFqNmTwCnY1iuSzELmTCuRNW7V5EpXUoDY/4FEDqtNiufe1oxc4q5YAgP+5/wo2qNDzdXJbi+KcE3I6hb6fYsMvpflLLVWJnwaRKRQbhWizvaVqsmlqVQwIp1v0vHQubjUmr3gblUL90qB5wOSbXkPxdQpTL/KmUNALNSXQxCJbvg1NASw8Zj/k/ubIkKQ/3EUfTAdJXwpVQGS0obpUaO9lWUQ6dgCoroHbL+fre3S4wUV6wXqLxfdTvi2wfhJQcbJPdZN1wO0JR+2ll2zMk1pxZYGNhePJe7bXigfxys1hlqmkAYrLLEdysRQEJi0ek4QkFjHnoTacFZxuz5lHr9Jxj6If16RzSxOqHvL9nvyIxWYvKFmEFita3CViUShHi0mHnRSfqAkdStdi+wCyfeXoXAN0j0u/caURVY7kQ8RIBpQJX35CGuP0dyLvARl6+BJizOL3HWwiE76od3p5/UHMXr2jXq9yCpOXKj9TFtg+YKQMeQIHmFOQiv4ecu+EMu5IZdLxpO1FDYSkbXV5MalKssSU98rertwv3beqM4SapXXPiQeYZxE2ozL8NR13Pde7cTfuxt24G3fjJzzeyMq1u6mhuxqq8ri8pAol5ZumCsxWDpFXySHklZ1rC3z8L/2qbOu9/+bXoQ2zMW1EHHI1o88G9BvuHY1dhQCpWgHg6b/8K/Lzu7/z2wh1gGHTcakEAPzw3/4lPPmbvwnFzjZ2rQV66Y+oB+erEfyb3KBilkFMPreYPctJLLdfBPVZwSbmAQLNdPccyktmJt9EDDOGgPZ3K4OoIbB5EqaPsx1TYLlyCnbEbmzOsom9bnd7s9HGzPSFwjkr4DcNM3zT9eoyRK8H6hN3h3ytZvmcm7VGfZl7ve29vGKGjRTUDCBGBaWyMB9BkREAgMkrMluXrF0HOO4NYuJhNtkuExo7Mo4+BRtYTWhHki2M4LP+MPetAaquUo+ruDWyu8MeQXI+9cNUhOHru34bwMKhZPemedVBMRN9dVxjycYZ8+Mtit4Cn3BzKwL1NVeSRgNKw7uMkgwjdrDykD531xbwL/kEVUHY8cOqQui1sD/tyhArG3S/lZdGoHLlFfXewNwHpwgJAIAyyL3VHUVxJ1NOU0/UZBhXUqAAxKmHStcjQEzwo4nwk8yq1X2uuHTMxhZQDNHz5/qpFyTCdMR67/chn03VXZhG2HV2TdIOo5YMRlCsggqjFJ6Y7wlX0+fS7+xlscPkVi5XnUHFnT5oumL1Ld1bJdsIupmSfmao6LlIlbFtsv2hbUduSml2SJCu23WGIkZ2vkcSihAMPb9GHNF05nx0Gmb0HowVxL3MLg2c5ZOQ2l2v8XgjJ1ezMtCDgd8H6s/ormrPHOwooHvwhcgN1FWBcMBQYOHx7n/967ItZSJiSLpTklxEhlX6dYmn/wpNnO/8Z7+F7/2lX/jH7tOTv/VN2t5RoPuVNY3xR67Q03/tG5Ks8+B/97j6Cn1gmCsMX2jEupB8WBkGPWgxXFOjY/IDw8QihmZiliwUK7Wj5PFTSJhze5x1ftFE+H2PyWEOVd+cs1Zz6tFPkMkMTufeXaQ0js0X+VyuDOqXIz1cekFVtBhwr+il/QezB6LzGzqL6BUMS4aKpc6+z5Z7wylcfp0lQdX1SPZjGZoaPcCB32w31zPY0mPOsK0eCAIHgM1DDrhXeTuJMKJXFhU7LXVH7LLFMYJ6NiDwdSluDRE8EkmqjHBPqEFdVQ7dxSRDb2UUuYceVJ7wmSwidohFfjmGKuLs7Eb8jyvjUPLC4WS6AU7oc5/f7mNoCmieW+0W0voo1jR59/uJoAKJoyMYW0H9kO4nP4koWKrkphoD649V5RF9ToyCAorzDIW7WYYzlVcoEoSuqe0ZWW9urjK07O93ck+bjUbUEZ4TinTtEPkcm41GCGYHMhay0JYmhXFwu/Tei8wrEJ/oNMmbCNPle6Lfz5ISPw2w/Lyalifz7sd7usNeQHvM36ki7CbzD0KBkayIJm95hmK+X4pbtl1M567XIxcpoEjymluadJfvgf+d90k7oD0NefF9lYlEUeU2RTCA9tnyUA2A+sdgndFkyRTAHAeRokHiF6OJYuWICCAoWZQSSYu13O4OFr4bd+Nu3I27cTfuxo+MN7JyJUhGAV6hWNH/m74w2DwCQsHs4UkAmtHqM6GETpPRQEq+QYYo4RWUDeJmo2yGTD7+s7+Md373t4Tc8MN/55d2d4pX5GppESZBIK6P/s1f/rH9T+YBr/4poH2LlplPHl/gYj2DYvnKZlWLT7JrC1k937wfMf9Ey6rYVzFLFqYR1bVCz6ogFAGhYEju/RYV5612bYGy8Ghu2FWqNcCMvstUnqC7FIgwc5lp7TlFYySZEMhpyPBoKIF4OMDy993czEQKEudkiC85nCpvQ0WCtFPFMiyCkKlMjx1pQLHM6RzuwGN2ShoJ5zT6poBK6UU1MgOWHawSGzzWXlicuhtVjycD3nl0gRmHuNZmwAUTpJZthfWmRntRyzYDQ53d1qK6MMKmVCP/WmKT8v04GwCXK3bVavi36br/iXc+wcPJLQ4twbMfbE7Rsr+v1Rk2XdY1NmVOo2hPlDjzTF8SIYU3geZ+yNC+V5g8MzmVxWY4Uw8KKnlRTzRUVEKEMoseg+XKb2VRLBUCQ4B+VEn6KgJBiUFPsVRoHlFptrdosLohhETIaEwmjF5LEEDaz2RcgJhJNJ7D6MWVbESEGrNt9aDg5wE6VVUrJefD1wRtJxc1NWT/6WJNJCLT0YbWb2eSVJjnvGgiEgL6cnQf86+qa4LAE6PfzYM4sUUFVOca7UM2TtnzmQi1NNmn1zPRakjkpLx9uwFU0OI6l9yiAIaWk0EFt2oEOZjm95mbhix9AiFNghAragGNpVEJpbAbBXfEphGdhr22O4hSaqX44g4Wfi1HsuNSXmH9Lj+clmUtCY0wUfoXxUpjqJjNWA2IJhJrGASJRm45laVD1xSI/LJMzMY0iqXe6bWm8eRv/iae/qlvAAC++BvfgmnMbnTXaHzl176N/jTf1MmS8LOLA9gfTGTyrjQEzrER8sIur8maUNIzyiBaVgDozrSwBYt5DzmCzqAN+YYfmgLlHk0ecaYwMCMVz2uAjcEBQN3kiR0mAnsDYlpIjMy99aByFNVZj+OTFe7NSGf8cj3HzTnDjSuL+mWeGKGzg5Jyu71ghLFdm0KxzexXP8lWd6HW2NzyCeo1ylHogX5ri2FVpc1BtRr6kG0TAcQrhntX2XFIn5e42J/h1tA2b5dTCV9wrQU6jfo+p/wsq5xk1FiG5zLLNfWu3cILH8AUAb7XMtmqoOR+fLXdw57tsHEc9q4CNL/1WlfggwvChZ0zCJeVsGbHi4/2BKgvgPXbPClUgdoMAGmJ9zUKdt1yh0767FAUJUf7pOnlzhaizldA6msXEb5UsE2GKcWtKRC0nSRUzSMPze5QgzMiU1LzAbEzcl7D2sLIREL9dHlpqywbiwWxoseh5hJtqHK/sT11UCEnP6mQJx3Sl0cJzVCDwsDP03AvL2AAQNkgTkt2azEcZNkecQvoc7qDcAcSFyHBtmajZR+jobB00Yqa3NaxWwWb3JUCMCyUSH2qqyh8DDdli8P0mIwM+aMetSwKCjaRCW+kxU0nVpi/Yed/C2ydtikWigrAMgW9a0zOM6N5+zD3aZNO/nUed7Dw3bgbd+Nu3I278RMeb2Tl6uoIPYmARxalB6paU+6jXVr84Fdylfnkb/wmfWxT4Omf+vPy/9/5O78hP3/vX/g1PPm9b+Lpv/4N+X9f+bVvAyDI8oNf361af+Z//A8AAGZa453//LcAAB//6o/DwOPx3b/yC3jyX9F32ipXt6E3ZGLAsJO9tJg9Y0i0y0xfN+UqKLEivRGmbDnv4SstpumuszC3DPMdOMTk4FN5xJhJB0pFMSeIOhtyA4CvQ86wLQK5V5lcTY6ZvglWffTwEkf1Bh9c3qP9sg4hkXlURHekoB4RmcoPGgMzdk1LhvkJYhz2g0QDbh8qMbYYFhHllZIcSdgIlaLGag83y5pPdz7J0FodqGJ4zmQeCygJv1Y70GD7h/sY3qPKe3+xFZYzAoAyiDZ0cbzB8gUxoXWr0TzwiIkE5BTKl8yO7RVUw+zp4x5mbxDiXH2vx7vHZFaiVUSAwsAkuxA1ln0tv2tW/PNlAWWAyTmf/z6zX0MRMcxzRaGCkqpZKUAddGh4X9Ta7jBxU6WtewBFxOQV/a71Gpar2JSTKpmnQ4akA0ODPfsQx5kThnBzWwhj15kI1WloJuopmz18E6EtRREqnw0Z4Ei/KoYTJsIxqWtYhPw+MORolD18MzyqnCLWa5e3IW5TBwOG1kpQBmJGNPoTj+JqpAP+keosfVd5S/dxijD0dczQdeDcaT42Mxi5r30dsT7MSND0eSYZ+YpD3Pn8lKv8b0TkNlGdK+GogaBjblPETDo0Ld/v6fVZCJBC1a+CPIfR0DuHTofOEP0kYv31DuqKFRV9NrBQW7z2442cXP/fP/1vYbFYkHkDu/noVxX8zCPW6cWfi/ov/ZVvoSjoVIWxiQOA2JqdybZ8UQibd/LMIrW5mrNAE+iCsKqP/8V/F8sLdjyPSmCUd36XJlnLPZYffGN3Qv7Cb30L4IBuB6D8nG7M8FZPvQ7uQenBork3glznI3ixz5BoVPlh6q9r6NkAdVHix4aCuPIUhd8x23CdEQwkTJKDEcNwNkIxDKxXhpx7UoD5wmHFJg+qNVD79DbfDgVCnEtAd/ODfYD7NObawm4VhgRRr4ucbFRGhKmHOaS3jfEa5im9QcxWoR9BcknOAoCkTaktDINipUjQDpAUJBkC6F1pVCiDoGShzC8e27Bl3Sd0fW/vW0oSAqBrj7JyWExpH283E7n2ulXwsww3Iij0jxjb7DUSRm9swHTaSVLQuitx3dJOzosez9w+Hs9ZfuMstgNdz8J4HJ8QyWD5+TFJXpL8Y5Hh9KhpkksWe/1pyFC+jqgnHsHzCduW8gLXXsvzMewHmFaLmUJ9oeRnN48UJp+gRD26by9K+JhtH4fTgPsP6FheXe8h7Ce3Mg202eiC7Pf4erJbU9p/mkz5lBYMF6cLp/MiAlGj516mPS9IHqPz340n2mKVLQ6TVAsAhm0B1RgJiYCJ8Pdze8jNM5SvHEa2g+T8BGTIOI3xz+Ut9fbnn/DibwZseZ+xcMCaGdkB2J5BGOzJVQ0A7Jrg6BQaYBuMGPBqJ9vVdKOA9NFaYJyiQ9vIAfF7TyP6fY3129yTjhDuQ6wiCu4zD++1CKtCso69jihWuWX0uo83cnL92t/+T6AnNVRvoJjs8dEvsVvSX6eJsVxqPPkv6Oenv/bn/+gNAXj73XO8/9//hwCA4cM9KA08/Tfo81//c9/G7/9Olt989e/+RWxe0Qv3yd/6plR7qtcS4RV7EsOle+trv/JtfOebvyB/f/i1HpuWfY29hrc8EeqI+dka6xt60mORe0n9ImY7wkh9p0TSUYMWf2UACK3NRKyYkzqUCWJHGEIkIlebRIBaVu66pTSRtIp180wqSi85ibrqLL1kAcwerjCw/GKv2rU+0x4oPqHj7I4C4vtrqM9Y+nOvh+GeaPKP7V/Q7/RxL9IEN8va3mgj/NwLAcZ2CgM7WBFxRSHwJDocRqgq9UQNfJm9V6mvx1XyXpAA934v7kSJhY2FYeeZk8MVfNAIfILcYDA9pmX61k5gzwuJp4t1EK0m9odcXQwaXW8BfqmfztcoWWv9nU8eIC5LXL9Dvzz/9BB6xnrtdUHuXABsARi3q73mNi2ltYRcqdXPC3Tv0g0UvUazLWEL+r7+aIC5ZgeoFdAf8yTZaphmPHFR0hEAWgR2mYAUqiCEl6hpv5Lsq570WFT03VfFFM0F994HtUOUKS+MuCkl6U0iBI2tODXoHkwLPBVUvi80xGULkSaMFDY+7GWpjC8BTEMmYU0DRfYBQK+YP0D7Mnt7LYvEdlXt6GiHRYS5yISjdM+Qa5Ea9SmjnA/l6DgT0VAFoGZ7S3evhZ7SCe9WFcy1RdQqbSITk1gql46NovDoRzciLdGxxh+bSAGqB9T4oyoT/26/RIiVEA1blfuPceSONVBkpri0NZljEI92e9ev47jrud6Nu3E37sbduBs/4fFGVq6UZ0rymCe/R+YN7/yd34AuPTQbjXfHHk//9B9dsb7zn/42IsO7Tx5FScwpW4Xv/4VfwM/+mW8BACY3UXquzUOP8kxDsTxGXZfiSmMancO0ma2X+pTr9z2++N/+ZfobbeALhbZhKHhVwKREm0Fj88kCJvWdbBQHllBG6ATNthph4QhmBACVpStmrTFOs/DzIBKbGBQ8V6rBaRSV20kUGseH+DKzB+1aS0UZbYQPWvqZfi9Io2ZW9dhfUIlxvpnh8f4NHh8RHPj9mxoDV8Zmv8fQWkkesp9nOYldargFqMoDoI1H+1bCUiPqBVXEbx3d4NnNAsMHhMOFIgoM6asIXwFT7le76wKbJ3QOyitOUuJjK2+0GLQrD8n+hCJ0IPUizaWFY1j4AsD941t4Pu69eYOKPZS914ifF7BcrXbHSnJrY2eguGeJwx7vnFxJtboZSrxaU+mx/7/ViEbhfEIN1LPHV/CMOFz0C/FhjlxpDAxFmiajDZH7ocmnujsKiEla5RVCX2AYUp9bS9+8OwkSZBA7DTcjaBigqknkJJYqMzWChQ0/F2FrMOxBZC59Z/Hdjx4CAKYHjWxftQWCDVL9DnsB9avs5KTjj0OXdK3JRWmcL5pMDaKOwnzWnu7hFOAQdYZw63Ng+zBX12ZtpKerm1wJA8DmdpJbTzdWtuHriGKVK+Pm3ggKjoq+P21TqSxZ6xMTmJGW5ail4TVODwn276YdLvw+ouQS5+1HndEjIMG24O3t9oGHvdw2GiM1wUZ6V6UOhony87DvgSKKk1y0kOxY6Iwo3P+fLJ7/yZG8Zy97cKsGr/14IyfX6VEDkwgyqZe01ijf6nD6tRcAgGdXC/zUf/eXABCckwgKvjVQsBKT9GqZs5G0A772jW8De3QjbR5lqMeuNbpiguKG45VuFCLbnXQnQcKoq8mAZlmLe5BeWgzJaWZjcLVf5omsCPDctjU3FmarxDKtO/ZZVhRzv0WddjDPakxfZPnK8qdpAlInDbSO6FiWoooAbJhQE0c9uUGhb7LXmu6UvFzIqH9E/oj5pRoqgmYTvGbW2aGmuV/g8YIm07PpEq+aPXz48kSOW9xkwNaUjGGGIu7AerEI8i7YISPtedEf37Y1thdTTBg2H+YR3amXfSLomj5crID2Hr9we4VhEQTaK24VHJ9/3Wezdj9jq7z0MjMQ2cmwLPBSLTCdchRbU+KayUGTH5YIRUR3Rvty9t4F9hkSfbnaw+1TmjD1qxKr0wo1x/Bd/Q9vybUwQ4S3dN8AwMX1CQxbEpraw7F5e9xYFEuzY4mX2ggS7p3eh04hsv0e6Xmz1te0wLCf+p5yiaAHuh7pd+N2AEDa0vSSRUDuUdoIxCjSH/dqAsWLpe3NRJy5EAGlMnGOFgq8Tx31JpMOeBwNqAKoxxvz70S6VQYhHNm1YtP6dBIiCsqOwDCncyXnbkfWAtFR03nOz+tYt6wc8R4SiSyaDKumoPbk9KUCJy0BaB8QZyJJeMKo/aB1lEXbzXKKydMsg4tqLHdSsE2+zv3eCCYfEfPS50XqFrL2eZgr9PvjIPjcJ1VRIfp8zklLTD/7Osp98PxPErwv4ekj3fi4z/y6jv8fHMLduBt3427cjbvxx2u8kZVr11poXeCrf/cvApbIL9W9NQrrUVtmpZoIz1CYqbx4vKqG3Gkcr8Saq4mwYX0ZJYgbILalSRFwEaiuR6c75FV9KKPAbn1X0IeTNMErIT4BxNYUx6ORqUPxeAM3GLiXtfy/BK1Vz22Ge15NdgLKtw+iuNAcPdri5dMjqa71kCULvsrEBs/G4gke1SOvXN0D0IDj8GhzpWETiWOr0D4ahb/3WshOXzq4wXqgUuOfPvwQ/1C9hVcc17fWAftc6fXOYnM9QdXkKiiRMIwHzHUBX4/Os8DtBh3TOi9eTaFCds7xdQTmdN2dseQ7zd7L/V6GzPqDQFCY5GxGqV6GeZRyKJFkJEhaMysbBCH6vsZynl290vk3HRDsKAIwaFw1dH++e3iJVxWV+c8v9tEOFuc3dH7UP7tGeEol9PQ5VWz3338FAHj2+RHuLch96mY9Rbukc2BXBsUyy5NUGAn9+bwllyEgGz6EEmiPgSEFDxSjfOGJF0mTCmpne3oY/TsCbg7JOdXHnZTJejLAO4PiQ9pPNwuINwxllwGePb5VaxCLgMgPm+7VToXYLzL0O64YiSmsMsvYQmRpEVrY0wnyTS2NYqWxfZgDKOqrHLI+9uMmYs9u1ZfuHzeLO9mrfhIwecHXXgN6lc047AaCbGmX998uNfwkP4tkns/XojNYblkmdlFBT7NEKFrASZUbAWgUm9H14XOQEAeArm2xVpJpC2TiU9SAbRVcCijwo3O8JfQnuWgB+VrDAMO91LtS0J3JKF1rxT87OYK9zuONnFyN9TDWo+8Nijmz69oCvbb4YUfO2t7not63RuQRelDQHaBLhjrrgMlnWbvWnkR5qZa3OZDYNAz9JKs4D8ye0c/92qD9Ok0e/rZEddzADawvbYzoQsMeWf+l7Ne458Rm0DsN9ckEOvUitxqeXaXMKHHG8Hts+5C2ufjqJRzHk7z85GgnccM0asQmVTnbcqtQX2adXHcY5QG0W4KLEoxVrLIEAwDsjYV+h57q/qZCcURv9weTJf65g38EAPg/1+/iqNxguaY/DOc1bg7Y9aoIsBdFZjE7JcbxavSAA7QgSJ+DhpzHCKC4MRlOnjkoPujpJxbDXswG68hwl4Zic/cMoSUoMhT5Jad6BX84SMtBt1pe9Har0NuY92XIfVvT0M51fG2avkCzpQWH0UF6p6E3uP3DI3kRqahgE8S3D3T3POb82Wqvw6sLaqy+9/ACT5Mr1WaKcpnlE+1xhvzKZUQ0Shx9TE9sWYDN3HuVVxw63+MxZI2xJLVIny/K/RI1LQK6Q26LFKX0Ur031AoZM385ncd2BvZFsleMQMxymDHrOSpiPIvtYEGwJZ1jOuY0yWXpGJnLp2NJes/0O1/niUoPgK+ylAVjjacCoLNOGsjf5evcwrBbxRaBvF9tts+kXNm8OAsFMryrCbJO59k29M6h31k0mtnUiaEvmvIc4I5IC/repu9Tu4zqBO8GWhCk+0D3kEQbb6nPKik/PrtbyUiKgWmU/QllkNSw6DVCHVEx29lXkYoJQDgir/N4IydXayOMDTiaZaXy88t9BK/QM1nIXhSy2lKL7I9brBTJOtIktjRYfMzko7c03H6ATi+YK4ViyQ8/rz7Xj5MsJeYUlcOImCrOmRcpCQBg6oV8pFuD6moUMxcV9D16KtxgEO8NsBcc8L4FJi/TRuhlCdBLsj+ICI/o7zZNhY6lLPUzS/003r6vIVXu2CJNe4XNW0FW/8orSgQCPWDlbZ50Nm976akk4odnGQ1mQSL5/ten7+EffOcrAIB33j7Hp6+OUH6XJlcVgIb9jl1rUTf5ZWC3eaFgWgWE/GKnxJyMDqQuiBoUgo3wLAPCoKFvExkmIJRRKtcw87CMOBQrqlbSeTDdKIR+32Ubw0EDJkolosJu3ypMAjSTXDCJIgcb9jiqL72wVJQ4w3Vboee34c//1Ef4g+cP4bh8dL1B3NA1bE973H90jZcfUL86FhFnT8hgYt2XqCo65mFLk4xoK3XM/T+tduz+gPyi1x7wBaTn6qyXhYny2TBBRZqIq5uEfGRDA2haBKSeq701Ii2JNqI+13Lf6YGsKgGKYksTlQoAVCYZgf8t2z/IFpymUYgx/10o4048XeIEUGRjriyVy1WbHpTIflJfVUhRNua1hgOCyRNrtHm/lFNgYIwWY+NJ0+QFgmIP7jQxjidX3dHzl55Rp/N+mK2GK63sB2JGnrTK91Us6HepSlcq39O+zvtRrIgrkPTy0SgkjliwwHCQ+Qe613J9Yx3hjp1MolhZMTxBETBlvfzmtgb2BrT83OgmN+1Ha+TXdrz+y4O7cTfuxt24G3fjj9l4IyvXEAg2GYKWHgU+m8AO2OkrJpek4kWVE1RmEaGOsCnMvFG4/SL35/YDWdbdJHZp/s7uUKE9iSSuBn1PCgofsyxVY3DwPYWBGcd2Y6CHKNuw24zIuTlgPmLodD/sMHp9peDLtGTOkF+w1Bs2n9BxBwDT5GSjADtKyDB9rjZUUPC8EvVVBE47MU33N6UEiPuaWKDCND3p4NmeUEX6/3sfsTxgptGxfOXBz53jvceXABFIUE0AACAASURBVICJGfD08xO0zJq1Gw11WaZDge4yVLt5lOFYXxEaEMe5lfd4P5yC6vL5SJBhOgnCXLWRbDGTE5dXcHtpvW4o1YNF8H0Voc9a2a8kVcJAbkphZGOYYPJQRNTPLYYF//vQCVNZdQo47ZAKn/XtBOAe5lAG3HvrBgAZ8ANUsQKAeVnBvEOajp9/61P8X//gK+AAGpTvL/HWHv3dR9fH2K45LP2KqiMxtzcK3o4qJeT7LJRZruUnUSoegKu+VLmOTqmbUN5p+TEfZ6nAeffEGC9yELm/3+V+3XmJ5n6QHq+bxOxgNXIWUylXV/qsSnqIvqIKN1eCmakcNW3H3uZWTuIOUBB8Zh9rn89FNDHfM5Er1LT/VQ49140i8w8xwh+xeYfROTXEkhUoNWZY1bT8jKUWrMcohSjCbrIxRn8AOe5QQNoNft9DeQudnNIURD0QNfdWk8layPu1Aw977LR0kkwQoOOffmYyIzudS3Cv1edWVHnaYEh2mTqiYeliUTsypuF3h3JK4ONupA54XccbObkOvYW3Fi/XBzll47SH8ypDGYCQjH7wjV+R//fu7/w2zFrDpv4FRr2RkgKQ5WHVEFipmQFu3+c0CwVo1omWNwqz57SRYa6AGDF7xnCjzVDb9iF5A0/O6d/VhR5NLASl9UwWaOsoRCjTZCp8sEB1pfNDpEYTS+q1jkgucs4WOWJKHfYIg4Z9yQ/J6HOhjAh7EWBHIhWUBEmXN5TakRYLdqvQfJ17rtMlrnt6kl+GPRSTAQPLP6oLhfafZGbYp/SZpOFVIVu8BQu0RxHT53lyTX2xWOYJtVhquPs9Dg7prbT9g0Pxo3W1h4o6azJNFPg7+R9LqHsdYb9HEHf3Xie6XzUoflHwd+u8aKuuFNrT3Hsbh4HbRgFPa/GNjYcDps9Y83qicLOk77q42MPJyQrDD/byOWfI+Pl2gcOfe4WXLwlH/+LhNVbsLXzz8UF2lJrRyzUtnupL7PhPj72efZU1r7oHbK/QFYlso+RZ8HX2oQVIF6xdmv0i0g3maurVpcWnvy1EE4wAFO2Pwr18H7dqZ0GqQn72bJNhTjeLPBHzjmjIwkEF0mGnVCjV5bDxYp17oHqj0e/ne57kJAxf9hrDPBOmgEzYMa0mjkNaXDqV4fDxYqDm3n2CY6cB1SW7gu1xahV/t1mZEfyqMMzH1yaKC1Ooory/bOUw2AB7zr69XZ5MlePzOnpvJX9lxNFzv8ztj/S7NGEmi8z0bASbNbJ2TRpn8L3gnZEFklIR8Zb2yfEiNvFEYCM0L4JU9/qDqq//EdyNu3E37sbduBt/zMYbWbku9shE4uZ2JnT3OGjoVWaQ6pMOiimYT37vm3j6r1L1+sM/90t48tf/KhR/0D3sBLKMMw99oyVUWDuFtshwjm5yqHIospi6fTSA2A2AbQkysmMvYF4ClTdEhR+ybwW2X0jLf4VQZylEtBH6gH5XTno0L+iP7FLvVCXACKoqiA0oFZcFGs6ODXOfHVduS0ATPA4AulUobzNhpz/pYZIJwG2JBZuMt/eAzUkQCVF1BcolBeCiRs32UhfNHHU14Gtffw4AmP6JAd+9PAUALNUEw14UU/YxMYMgxMye1EOGpqLNJJa+iIhOYcNMXHcUYDZcdfZctSYE2SsgZLYnohJzcaxGovrzMgvqNUFj9bPEgoRsb/sows09FFf28bqUNoJpaXvdUdpni+YtKquKkwZDx6bsncH5y32AM3+Lgxb7c7phfNB4sriG4ZLuD3//McKEDTI6LdcpGoZO+T6YXFDbASC3IzlGPsfi+aqBcOSzy1YZ4U8yXDp28wGAzUOuZtpc9RQbJgUK7JnJQqZTgIIQxcb5vNWV2dl/OgbaRnM/5H2a0vXUY5RoFJzuD30m91RBZHa+z05LwzzsBH77aXaDGiNVMkb+2THfPsLOT3+XYNSoI0HIqaLzOSwiambXJhZzk6VDuqcqMZ07X9H1AujZMxyAYAsP7zUctybKwWQTtYTkj3Y/EbmKlSb3JQCbt+kaSd4tRqhNCcDkYytWSt57RCzU8J5uLl8Hcn8DEO71IzRAI5ZenOrqj0tpkaB9/SlNb+TkuvzgELquEQ6dPAXVM0pXkRv8VSWOPsoA7/613wYAhKmH8lp0nOqqzEb0Q0EB0akf+K6DZnixvqCAb5G5bDW6M/qgWVp5eDYn9FArfnqq6ygwYbmk3s76Md+cHaDSjVqTFnf2FtnINNsKk2kO9U4T7VBYAFb0mdHG0QMPQCnwM4FQjvpvvUbF7jX9IkB7PWISYwcOjE5LP1AsA/nvjr58hZZlRtuPF/hn3v8AAHBQNGj4QKdFj4vVDBcNLQjuz5a4+pxgTjYsEphbubwYsFsFewW0bFnnphF2JA3x3Cutzhq0ywrxU3oz25FOVw0EJwpTdvRiri41+sNI7lcgWF+0jkUQS8wYFOKgyb4Q2JVlHA1QRUBZ02cHX6I/pO0d/CNafPX8op5+ruU4hyOF6ZxO+N69Fs4bXF2TttVdTHDBrOtLG/ByuoeeJ+Lp5xrN/XxtUr/YrhQx3/mFuD1T0h5QkV/+I/lQgmOhIrlYYbTNabbik+SYniaOdP90k9ELfK12Fo3KAWGSJzE1ZAsl0+gciLAf0T9IjVQAncb0E9rpyQuNgfcxSCh4gpCzfMquNcprLZF2agTtxiLCc8O7WGm4We41kwQvtxvG8Wh6UKPeNd2P7Skv5A59juRTkIVU4LZNWiT6REgAYHq944I1LHL4RXGrEeeZ0U/wep69XYJcK0ttIZ2u0VgbG4AqZDtNBVk49AdB9lF1DPVP0rXIsLmvIqobJZDxMI8jWD6guiDlBACU5yZroRsLs8fPyQzAYDD/Lu/zjBb/ABDXrz+o+vofwd24G3fjbtyNu/HHbLyRlas/dIgTh3LWo7+hsq07c7BLI0LxMA0wC6r2CpvzK8NNRd6e4hubV5zRRCDkIGO9zYHK7b2AUAUyuQeZ4tfHxBLpm9lIPE1ZlgnuctMc29WcBcSTTohW+tYi8urTawOz6GE5QNZYLwYEMSrZfnFhCS4dx2zxSPrUZE4Ar4RUYVc5q7NYK9iNkuiocZZjvx+htkZCyvtTh5IddkyvcPnxIU7eJd3l4Vdf4qjMWuMJs79K4zGre4lle7XdE5LYWHgPsLtVQgM6hWGGzDx1EIjLtAqaz1V55jBMDPxZRh9ElM8QWKrUDHvppmHX2VxhHFsXZ0HY0643UEXIsHmfq8LJQYu3D29w21Gpf3HfIDT0y/7AoFiRvhAgA3ohl6goSMrl7Qzx45kEtU9famye0O9iqdA2Vly2yDA/s6RFo8sG8Am1GOYxy0QHMoZIBhy+HEGhrMdMhgTBAuW1Sbso1Qs5N2UUJ5qsjY2aK2beaCxjDrS4LRBnTvTa0QCbtxginnhMPqKHq3kyADqiP8zXJl2nUAUg5Bs7TIMQaoYqVcajY01tkZGzkgr0WKf3wZg8FRXFxSVId3Ke70lX03kW8wZN90n6w2SuVp0bah9M8v6nv3EnpJlOZEuUAZGf376nnFnxaY65QifGMt+PTAgSbe7co+TgCgNCV0LNxh2dESWA6kckSTbSSM8G1AglahX6g+xQ1t/zEmSPAEx+/iIlIuJquofyWQpENxC/j0jH1i9ShT6630emIK/reCMnV1s56NphMWtxnSZNp+E7jZior5UXc3XvtZjZFzdm54Zzk5y1qN0OIZAcTh7RNh7fv8K7i0s0jLn23mDLHorffzGFYTjEGXrgEuREjjIj6GprheZf3moxAaA0GgvF+1KWDqtrxmqjEkNyaGL4iZOOy30fX9FElV8MGRZz0+xkU13x5J7cfmyGqeoLjWEv4vgLJKt5db7A5mfoIX734QVWXYWvHb+Q4/n+knqpU9vjhzfUbOyGApuXMxw+ugUAXD/bF6lPqCJJB1ze/6R2SM47CaLTfe5VuVlEPKRfWONRVQO2G86EPRhgmPkcKmJ+JihPhVHwQMHfnyDLrQbuM/R+VSKyqbzWwPy9Wzx5QhZcf/jiFJ5h2+amxg+HY7hLuja60SgZ6q4vI3RPrHDavpJ7y7cFLIvth2WFyUZhyg5fvgKql8wqPqVJcTjhDFdrBd79wa/8Ir78lymxqTnjRWE6eSGzWrVTHDjO/+4yDF9dqp1+fSgyQ1i7PFkrz+xwZhlv3goyWfdnDtWzIrskLXqo1Hs86VB+Z5pbCSNz+NBlmLy4smSNmNoivUq0BaD2iBMPcHgBmVTkSSbaCJMY7LfZkMTNxr3MXUZzsHkSSwtmy316N4VMENrxuWIYV/VATOHjvZL+t99S+6F+lZ7t0XlVZsfEIypOHOLzOn5GfY3M+i12+8C604j36D4oJ4MUCK4zQGcAm5wvIJnFGLRYGqpIxypyoqh25FYEE+eesZzjSYBWwMGELv5VWIiKgT7A52ZQUK0Vo5H+NCdtjVN7XtfxRk6urrXQymJpaqjPaH1VrZlkwVVhbDU2Bb014nUpgcTKA+UKOxaBqZIJJRFtUpXSPHHQV/Q2eNrew1NzIg/C9LDBwwPqj8YywCRJkCcPVpt6MaOHx3QKxdrkHm+RezhQQH/i0fb0hG5vJ0Jn18PIG9YScSbFo7n9TO6AiaSrSfFiMfdizFaJXEWzPdtYwyd2dwZQjzc58PzeUmz7AODrJ89wVJIEZuMqzAv6XJmYJCDnFrM2WH2PJltjRoHNkR1qeJP1xcjvNElYUj+qhYSgq0HBvKIPXDUHeOeLL4EFaUM/Oz+ETy8l1kGOq1Vxr5lEhDJKpTYsAuI1O3qttEiChjnQtAUa1qMaE/C19z6n8x00Xq3nuGF3JV8axIJ/LjTKZcDkpZZr1f4UnZ9qMmA+4XN6BLgLC8fSGdNmzahZa3z4jV+UfX/nd38LH/3iLwEAvvCb35KgJD0o+El25iHtZu5Pm576/bRfI6TDMKkmoTOlAtLkV+TrpB2ReYo1L/ZuNAYh/dDElaLq2qKQvwtFhJtHmcBUyH1R5ZSEsSdkRLF+0nQKw2EiwxjybO6ytjgN1WuYjUbJL/Tpi5EsqlWYvaJtXH/ZwIfcM1YxL6h1r9AfBJGe9AcRez+kz63eIZJXWhDoQQMbLX/XnabecoRdKXQn6fzkvq1lq9T0XG4fRUEKfMU95GQnyNFvAIQYRNdQIdQBxYQ2Oq17LFdcS3aGCHwxVbeQxUw0cbdyRd4PPwk7EqlQxR3/YO1Sr1bj4rMDeCazHZ0usVzRQtbdZI/EWJA96eqnR2Uq2x5298dG16/nuOu53o27cTfuxt24Gz/h8UZWrsVkgJ4a+E9mYvLQHwTEMgplPFYR9innmrrMqqxuFJrTDJdCZVZlfa5QLiOaM64obmyGEFkInnoK3Y3FhwWZANhuZDhgqF00zJNgXSGmipFZrELt94DlYmbYA+JNmVAVqLURhynlIKHMvgS2j7wEimNrc0pNJL1+TCkVnZZ+nd0qIMlOZlEgYfrgyJMVwN6sRcWymsEaGO4Dv7d3iY/XR/jucAYAaAaLtxcE/W5dieunh7QBS9CuSCk0cj6sZ6ZpMr6343NHPRvpoVVRYP5YRxgOaVjUA17e7mWHqesKZshVW9R0TQB2spE+Il33lJSiOwXLyMH80ygQvYrAsC5xu8cJJV7jB69O5Pz0TUH+w6BVfupbee7XpWpvmCvEJlW1HpdXVKLH65IABu7LtycjmLyI+NKvf0vuz71rgy/9xwQFo0C+b0tqDaQes+lGpg4M825Pc/VY0mVCdRsxTLM5im1ygENv87kqbgj2TJWf3UL6o6GIGOYBIQWRVwFmxtKkrUXoVQ5LGHIfvDsJ0nuf/LCEHsiXG6B7MlW4utXQbteHOFWdZqn5/uFrWkKczIY5sHmcnpnIXsnjni79100jqkstEKwagPVjyHcVayVs5OpSS0XaHcX/j70367U0ya7DVgzfcMZ77pxTZWaNPVWTTUIWKVqSYQiCbPgPGIKfDNiALdlCt1pNkBZtk1Krm6SqihRkCDJgeZBhGAb8QkAvsgUbNEXYpntis9ndNWUNWTnc+Z7pm2Lww46I/Z0m+dYPSuYJIIF7857hG+KL2HvttdeCCOfphIavFN4NKMOnfuUtFBd8TE4z690VzOuwAwe9Zn1xn3lmIyuB4owRNjP2MI/p5lzLQUIp8ppMDZLalUXPCEOy2YWj5z4Zohe+xwYHIEXKyt1+Bx+cpfwyg54rXIrwQOQOMkDQaqeDXUUz2lCWiJrCfSS4Bz8/q+O53FyLb42gihJ67bG+EeCVsYG+0gn2KC/4Tq9eYMiy9cFOqWSijA7yY8VVhLtCbWlKtHogkCMcEgkoGnsDgHMy9TaSoTi7avThIWFDjTHCcJrdSkRH8JmNJAjtMXzEn9EFU28z9BB7bSJIQHtkZ0E4ft9AXGYorv8ooNHO+n2JtFknSbacxfPbWYcyMyiDfUanFLIgPv/Nk9u4OJkmw+ud1y4Qrc3ffnIIBIq+OsuJaBIue3PToAhuKP3NEyAB+Fgjyq8FUPJC0RzZFDjIwmI0oB1BSoe20zBRsq4WDItpoDwTaRGvDxh2ky2o3puiGz6W1S3B7QYCyB9nOAUp4auBhV2zxRy9Ny5mIpUAvKSFXtdcvysf0yoqD9bo1sHIQBLc3R70ArewCbgghhOh0OoI6PZC61BvUWZLNL6WEV532aa839u/9EV84T+mDbqZCeiqR8KxHjrW0CyLvJshIDLAOL5WyTZtKclAPpY+jIDaCfwGLQGvk9ORGSCVI4QVUOH6dSOCL+OzYAufanmucHC5INeo8LWqycK50bNVXCJ8PhuWR84BAMhgANGXQ+TyDJUI+ptOsnbLwuvC39Z3TXL8oS8JG9wZWVe+/uW36NxGvUBk5WFngomFEuiiiUgjU5AHANmVTNeHoN5wXkNqP4pQsZ6rFGC0MxeCVz4s9NquYnAlLG2syZ5P8Hk1R1ROEqGG7I2Ab7nGLSw5YAGA2TNwTcTefboGwghACpTBdq8b9xyn2mcfVH32z2A7tmM7tmM7tuNfsfFcZq7t1EOVHtUhK+xA+qSQAxCEYxPsw4SOd3/+Sxuf9ZP/yVsJOooCEn1GXcwQuh0HN2DoCh7IrtnHMEJfrgVcJriNwyBBZFGvljMMJlzoitjDTR7E3FcSq5BNmj2zAf1KUDN3PO8Y7ZaPiOafyEOiD8fy/w+fCMjOY3mPfjc3G4Z0OolMOmqfAfDkcoJxYF1fXYyRnWYp67+8HONwRKlTURh0QdDbDl0yfwcArHVizRbnAvWhS4QWGJHYvOsXDY7vXOLk3WDGmjmMdomxaK1MikxCenR1z12g9EkfN79iSBKg6D5G8mbsyYLuPHiKThxsQAS89ql1y1Ya+ZMsNenLkwwuiI74jNpOfBDZ0GuRyGWyA+ojIL8MUFsR1bso8xIV31uA9Wyl7c0RSUb1kckKCSBo4vpWJNJJcUGiJE7FDJdbq3xGJJ8IV3/2F99CdydeEZqr+Tz81lPp6RsBwBFpp0+YikgHiecz8QwCbEkWBCT6/rGJT9tj0ArPzFkgZEHxPMcWMrPwYf6ISiWNaWkYpQDCnI7kxGZTDAIIHrsAuikSecciZHWRVe/Ehs8pCWT0CHEBEhXKwy4jdLDZ+mMGbAtpc7I2VDUTsiKx0BUOstcu43L+DOEYCkcopcSWrGwhWI87nLPvrf6JJW2x0X5E7Hv6ffCUNcrroUBxqmBeoznvtIB8FAigypPITrxXtUw/b5IgBZx2qA/DCcw6RtTOnn1c+LncXLtdRwvTpIMNcoHFJ/mG8bDLN42A46R45VffpIcw1JxUidQqo1ebdH6vPaoX6KkTPXgYoEU1+aF6Vs6hRQjQ4cEqzntsWAkUlwwFd1MAt+np98qhuS5SL2eElwEQAziaDwvALbP00LmMpQRVAzQzD+n54YoP7ugTARM2Etl66r8N9d7RtEYVfHBvHV9inDf43nu36TNWCusbYfPIHGzOAYZbazTBy/RgvEIXFkM9cyiyDnXQ5vNeJJnEbkqwuA0LVj7t4HaCClZm0VmZ+inVwGB1RjUnUcu0IeuhwWhaYzUPNfVOJOanquke2ECstEOGw13pIJcKNtRcMe4wmlIfR6YsVlXoK84t2l2ZamFm5IFpNPIU8CtNCyTo+pZnfL1Xr9cYfY4aXY2V6N6mOrQ418CN0Hf9uCDmcuy3zT10gtrCcYcAprizZLjuoxzT9zgQrA54Y28PDJcfOgFVyzRXuwmzVZtdD9XyHHfZ5vxMXsDKh37qXqtG7AazQN9hxe+yfZToJITrCehb7tONkof0vZvuPJBgg/D4XwGWVLWAavkZ7UPUqvUozrnmHVuHzICe5TgPVCWg8rjB0ZzIL3pOULGHV8Vabbh4Q5P6mPOdBn5IN8gWCsVSYHnfpWsXIe5uSmtCdNDqczV+lAHdN4iAY64AlUZ6CkpTXs+yJW+SADGO+xwS12un0pVI16obe5Y4XJAilp0H845LxS1wnUQL9ORRJTOZC58CfVc4yLmGC/wP4ZEU52J/+rM8nsvNVa0kpJWwIksLfTdxaI4cu6GMDIs11DJF4F4yuQMIG294yJt9AK5nJSdCNggiKLmezVm2EGiTwwQLN4hOYPQJo/W2pFouQNqvZtRroxm5VEes1gXZPMWoXDE5y7ostex45dHNXJq8/RouPPrmJVANMHzKtbyYrdT75MwRF/B2UWI8pVVpVla4M7zC4yOKAC4vxmiuwiZWWNIjDbXmYq/CILhHj7MGtQl9osLDOgmf0YFVDycYnPGiYgsBdR5s164yNot+ZY51XUCHDNI5kdqR8iuJLkTktlYo768hd+iY57VCDK1V42EGPSlAI+ALXiS86mURLsMyLJyitKntIchVpwy0utcl8pTUHrblxUY2OpGDTEn2cU04ruWiRBYy0HbXJhKU13TtY0Dg8l67UOmRXXNttboqUy3Sjx3anV6bz4GHvRG/nDM6HTY7+4gCEyc4KASA6pAX6ij3R+ci0rzSawGvOTCR8EyUCZrP0cpPZw4qBEsG9HkxeNVrkWQ2m12uc9JG3rfCc8kmUF5pQOjUsqbXPZGHIZJABN2s3sbVqzPHTTYhNw6YPqCfl3fo+GPATfKK8RoQmaoLLUPqLEutVdWRxuhVsv9bHkish4oFOAwH7PBIpLl4rrF1KGa8TvPfkum5AmRA3kweWqbCNR9+IlIQ1Ow76pHf4Y09IXi+l5GDg6Q44qauLPW85sHJh7JwET8C2ULCtjFaQ5JztT2taFlLchqK9eNhBxt6caMN3bM8tjXX7diO7diO7diOH/N4LjPXSHqTlYA7DOGiF8CafROL3QrVIwr1XM71AzigrXSCSmTLNTmizPdk2DKgiXCIBEQrUhToCiQ2bzdmA+5u16DZk6nWIzug3gvMyQkp+ESIzguJ9ntEdbQHdqN1xmUsHCHAkbhwArJVLJnWIZ2LV8S4jS0A3dSj2YtwqdisdxmGsdSDEutxUJt6QeODfBeFDixCJ0huDgAKCz/tMJ7RwYyKFo/nRNf/wvEneCGYer9zfojOKjRv09+U4FYfvRYYPfSoD0Kz+S43stcPJrAzg8+8RNJFWjq8W1ILTD0pkhrR8eE1pnmDwwH1J/3u/CWIkHpEQZBYjxIOiTnsc48NaTsgKf34SsJcBhh7t4OcdmgD3FVkJqnjdPOcWxDQqzuCRBu6CbC4DsImww71jVBzHRvodwfpmIoLzk5VzcfkNGXOMdMZf5Rv1Bg5YyN3GheOWQguAdjcYjhusD6ilNF1Ej64r6t1aNWITOuG5iUAdHsmZSFe01yMmV/Xa9VCEC2ItT0zz2HS8yTCXPPhfoAF+TOu6YqocBg0If3IQqiQidkMaiFTdtdOPcpzzvzaHQ+l+hclfLcB8muuSyohsLpNv48fCn4OxyT0keqbvWsng7G5jbKkI4flS8HY4EqhCaxllVuYRiaUqzjjDL3bCX60PQZvrJcmcYoo2iL5Z2GxoRbnBfM/6kPPx+sDCzhySpzA7g/o57OfwAYqYXOPeKmkQTJcJ4N4MuMAiCHcb31yGUsjmhGXniJHAaAyCzwgQodDl2tGZ4pnX//wudxc4emfV4CPFHFJC+fkdZLtW1VFgpmEEcmtAZ6o7MUpq5vEB95loEVrzrUZl2YmQr9p2PBsr8YyYVhYBtJK3MjMsLfQd1T7iRCObEWCFFWj0ey5DfcSfcom5XGYIX13Pu9BPRFiUtS/lnoCW4b5nPa8SDsgW/PmTWbgAb5UIzQTg3oYIFIB+NCKoJ8WcNpjGeqNSzFKTut/oG7ieEy1xnWdoz0vUTRcC45QvJdkXxYl61QL5IEYJn7qGrm2kGF1ePf0AGVOx9HoDPdv0L19ZXqKb568gI//T2pOnCz5Hkb5vlhPdgrJ9BkCEI3g1pxO9OYIuBbrgcGghQqL/eJ8BLHU8SPghza1I5mBR30jrI55ELCNZLNhh8kxBQCZsrjcpQBm93sS3/pHm8S6V/8etcroCmgOLYahdakbc0+qy3p1ThEkIXvlgQQfXxSoH5UIiD3MvTq1akR94rQRD3twr2FVpNgf2bdUi1B4N/HU2xps9+xKQwYYsDwl+cz4bKQNAQEKRjwXshCMWt1oJVTgTwhL94B1mfm+Rgu4uAm53DOh70Qmw3hb0HWLc3x122+UCqobjlq4gA3XI70iycdmN/6HT6WJbtdCPKUAyWtP9eVeSSb2AWdzkkKMm21fAlLYQDQKz7no+qUghrFNRgpY0bpyedfRZgYKkIQVyZy92XO4+Axv5LHcQ2pcfG42BwdVJrQghXlhpjYFS7KmZvyYdKiGa9CyFky2A9Dusqyqv85SbXz39ww+xrM9trDwdmzHdmzHdmzHj3k8l5mru1MDQ2B/tsTBkFpBPjjfgxA+tYZMywb1hFLEJ493gUAOimovkTXXj6xd4QDt4QJUqNYCZTB3rvc8uceEyLKduja21wAAIABJREFU+hTdmh1uMldrCTPwUFEJpRawMXvMg9G3itAvoE96J+YBfUzHbBoNGzLX4sKjOuKIE6YfFfcEyWuB1V3LQgfotU/0hC1cTlHt8EkgNE2YSSwMILQj+BOgbCzS/EeksBNhYj+0GAa2rRA+MYzlQifWIEDR+vJeD8YWTL7pZ+Wrx2OstcdyRSGzzvi67s1WuAwiDP/bo89APSowjkICQ0AFYQTlBWzBpDGAs73sssf+Df/vAgNZjUzS7bWNQrUo07mhk8ldSLQCYqmScbvq+VYOpjURkMKoTwfoVgFe3OuS9q8ZkPhAFwRwZAP4IITgHTHTY5bVz1b1miG/bkzwZsw6/cCmVh9fOOR31mjepy/IS4Mmo/vprSDCVoIiBaKZPHrCEBD0e8qUc8cawcGNR0pubYnXo7pBkHeck3rF91pYQo2AkHEqTyUbkEhCzFSFobKLjeWZkv1QvaJMMrWvmB5KNOYsFh6wQ84E4XvuNoLOW6y5ZBKPy5YeXSfY2KMEEDJ0sdCc2V9hgyVmB8zI3v2hxeVrKkHBhIaF57cjJCvBxI4BJdGCfRh0QNLCFBx/INFNZPouO+RMXBruVhg+EqkbIVtRVpycexzNEwCwnkoiEVXTC9kT9KDnspsyxN4F+PjG73qc/STDxXCbjPIiEBcvP9uD7Z/R8Vxurjo3ULnB6Ue7uD6jmpwtATux6HYImqy6DGeXk/SeaAitaolsIVIPrGz54Xc7DsOdClVLtVphBdqwmuXz0KM6ibU27m0VFwrdPs1Ms2OhVhI2PHjtjoMvGG4UTsAG1l92LVHvM3RaXEpY2p8wmtaoR0X4DJEWDaeB0aNee4lmKqgdeKiVTC1CQK++2GMRl2fUh2eGvYU0HKKqBOQnBbrjAAsrDx9hnwC9R4ahrSRGx4T3ruocgw9oAe9GHmbH9epuAmaPPm/wQY52gtRXuL7hNizo1FLCqFA/XZcwTbwXDHePl3Q+UbUq1qPjuQjXC3gcMDiigKVe5WT2HOus2kMHybfbB1eJ7XxysgPMNdai4I9tYzmAWJZ2PzByp7zhCeGp/jThelMkT6KRCaKsD+j+9ftGkx3gnOZnZIb2lXh63gjUkuIAGY4/LwzqeLyNhHMS9oCO0deaa7oDB2cFlIz1R5sUqxJEC1pg7cSmedGHhSHpX7cKMG4t06aj2tAGFxWk1sD6VvyQPkzpQyknPnxI32VLeh7i/bad2rBo60PB3Y6DD3CpkTyRhCW41/cDw7gJ1BJ6LTEOuGWziwRndrsGvqcuJAqXgqyVGcCGYEB4jaxnAOIKhq4f/3nqI09t2Kfcf9tNYhkmHNfYQoT5KFwP0hUE20YVsn4SYEuaP/F+CCuQXTGHIZY6uhEAwcYGPvMQwaauHLaoVInyo9CbPvCYvkevu34tqGrFS66QAqvz12VaS13mScrxLl2f4705TlqSRvU1nvnxXG6uptVkxVX15OAcIBqJBz+4CQDIDivYUCeTa5UmnBlb2FKkBu+4YQDA7sECSnpUsefQcdTnNEWCMQItT3lTXh97iOhEEzLjWIfIVgK2DBvtgQEGNvl6toWECG0h2VzCFsCdXeqXeXS+A3uL0tNVniMPkoZ6BXRDjk77ZJfoBtN3/OGwmBfwei8s7GnD9lDRB7cIi3m8BrnncLqVyK8ksiC5tzw08OHLmjoHYlai2eYNoLpePI76poXsIQD9ISsSIMhPdPqcmM10MwtE0Y4B1a/z1NvKC083BKojBxH6Ecthi2oRxCdWGrIVaaGWnUBYC+C8QB5kHvMPC5ixg3hK7zM7JvXeYtZAagsZnIIEAPeUstXqKofPHbm6hBE3ctWCyXAZZTlxU5YdELse7IA2nLjIZgvSuwYo443Z7g9++Yv46f/gTZz/bLhWmYXQkRAk0Dzieria53DTQFDTHmLSpb+JyyJlsa5wQCDyqAYQXrGcoKXWMQDA0JBoRHLhEWnjt1EUIWWTID9WAJAeOtauvUQ3NmlT7gcO2ZJqwvHZIyGQXsDYI/rQ+THSFFEJEuiQqQ4NC6ggYekzD2FIChIIpLqdyO7zEJVI9U1UCut5CPAENgJlp7FxIPG7y1NCHmLmVx15rpdWckMLuC8CsyGq4WlDNVGSU3uudbpgfRdfa0Qi1pkhp8I+I89aHZ4bJ8iuMw5Z2JSxCytw9me4L7fdtQmh0mvR22h5s5aNhM09XAiyHs0PgGCViKe9G/SMjm3NdTu2Yzu2Yzu248c8nsvM1VmKmoXsqYi0goQGQm2gOxlswG56ERh/MwtfOLiYqQ0c9A7BZ02nsT4dQfeM1PvZhbBIWRtCYAxQBtVorqOaoYeOLjYF0O5xWO5byZlgj53pZYC1Q0b0yo1TzBvKiJ6onVQz02uFdoYU0QI96r2kTK8845ir/zpoTnO9ZsN1lzPzOb8UWL/WQIQoXyyK9HkuI4jIBNWb6Y0FroPHpPyoZCOAzFN9J7KYBbgWNukwuFUnM4DLswlEqEt6takoA8HQqRkadBHW6yTyM5XUuGzBrOtu7GFn3FJSPx4lUQg/Mjh++Qqtoe+7fLALHeqG11WJxRWJLuiCWim6YFQttMfoqKeQD6BpgoRipyCOQ93ZC7jLHDKaswe0AwD0ssdk9sQYjxnA6m4PQt81cBfZHwsF14dcigCA9S3OHq1RKALDu65VYPtGgQOukwsH+CaHiZlrbx7LRiZTbFEpCN8rK7R8X3wrkT/V7OyieJ7JlkRNYhZnSqB8HBCktlf/M0D2NN9g2yYj9UW4j1GFSPokohIlHtO8llReAYB2z3Gm3ZK5QMo0HWeLXnuYcU9VzQF736BjvP43KshhB1dHU2du75NGoBtz+UdYxTVphd6DKJLwPl1MJLUvWzrITqTfndss+cThJUHrfWP3aOjuSpJQdKOARuQergffR5TOK0KdzC4dsxwatJdluqYid8lNyo4dMekRESPB7WaKhTramUvtPHGIgAJO3lOYfy48Mw7P/HguN9d80EENFWxuMQgKR3WTobks08ImW4HsHi2I9aKAPAn1oeDkEOtf7RAYBu3c5Qc7GD/ijak6RK/VIcCddYR6Nt1tkvxY4YiwcJvhl6j8010WtMiFh9CXTK+3ISi4WtFmpYRPLSmHe3OcS4KmmsWQFr3wdWbcl6sBBk9kUoQyA5EWKDvgN/nCoz/3+zZ4Zugh1hr6mhfjFGAYoPn0GnZB1/JwUGP99ozuyUJsbOTSCLi4YQw8G7ivFZZrjcEB1UF1aaDGdP3bJ0OYIbdGmP0O5auB4FXlMBHWKy28lAmqqm4bZDP6jFv71xjnDd75l/fptQLowobxwq0L/LmjB3hvSXX68jWDquVG1dij5xRBahFSFNqiqel15aDFqGgTHL66KhME7VsJDC18VKpque1KtQSR0vX2aHcEtydpDx+szNBEWJZrkS6jn/NrrvkDQH3gkEXjdw+oc1o4C0ffGzc4WszDfJ95+KHrtVbIFAB0Iz5nHyU3I6zqBEHe6BFh4saSebYJHDhA9khMHsm+Ta9E2rzzU43Rx1zeqPe5/FAfkEtN6q0sGRaWJrbMRVKaR7sXoU2GoOVKIr+W6X2u1+YiXHyWRTrGq0/T6wa/PySJyNgT3tMutiVfH2QOdsBrBZVg+BrDE7ELAJpdftp0j1gJAGZmUxCkWj4vGdyWyqDRu7rDLVNqTT1NwsRiKtd0XeGTvq8QRJCLUp1OcO1UNhJeMolMX0vSE0Z06hGp/1w2DHH7zKfaNYzagLVXtz30ZShT2K1Z+nZsx3Zsx3Zsx3b8yHguM9css1CZwb39Od5/yibWkB4u6J2KVqI+D5DlWibySzWiaDcqBolWYDGn12VLYhc2oY1D1UB7FKCXRkNf9QQbBAsVlCsgGMLA15Ki9wDXqRGTfuSkg2tVEuGXS8UZ9A2L8d4aJkSjT68myEIrSrXO4YIbR+4pA4jHkS2Y8p+gmB5BIkJ3dtTj/Hu6PqlhfeRgoq/sTksh7nWRPjNmv67wOJgtsXeT0rGH1zuwx024rlmC3j2AfA00AQbVCwHElpTSw44sqougVlRYmGv6AuHp+ie0YK2wqunAvHYJQkdDWavZp3B972iOV/fOAAAT3eDd+UG6v/5Gg+M9YpAPsxbvLA7xcEHZ9t5gjXFGyMeTxQTZhM6lkx5irRIZyToBNSPot8wM6jbDOpgGQLukYQ3licwU7kN5xshBN/ZojimaV5MO6v0BunGYIysJFzJor30Sj48jmbg7Rkhe/M034CcGXdFr6A8OPGKp0O2IJJRiBz04OUJ9ETHJPERS5PfMpM78hu62fGEVJJUAN5TwZzkfo0eac7KSG05QruD2HpcjZT228Gin5JAEELSZ3Fw6giGjAlF+zY4/ToMg3thW07BBgds1QIAohSHSXGo/G/qUFdqBAzLPQg6Ngg8s2urQwq00q5JZkaDl/FzBBlRCrwkNiEz1ZpcJX9L0ShsI2Xw4LunFRklGtHJT5zkSIa8Fhk892gmXWqLWdRKhiGpakp9zeKRzkWsFaSQGJ/S65YsuZc7EQu8ZLLQAZBTPIUg5ooDleY+AWCl00yhnRdl0Km0pvofR8elZHs/l5hrHULd4/TZJ5dU2ww/NMfwVrWakYkKvy5aCJx9AEzBeOQUWHb9fo+0kdFAkaldZWoxUJcKkDpN/yXVKM+B6GrHpGCqxK801Vi/ogeg/TLFl4TxDO9YYD2kRd07i+iE1P5ZPVFI0ggeyVQ+GUyK5q9RHBtlSo9ll5m80jV/uIB2HaAiajvVptdMhD3JlTZ2RbN+eDt+teRG6u041SgBYXIwgFvS6bC7Q3gibx7VCNxZpcewzk70EslGb7Ol8raAD21N4kmtjNiLfN9kpFlovHTrFtevr+QgfB5OA3bJCLi3Kl2hDfWn/HFc1beRPFxMcjFdQ4RyWbYFPPiJ7OzUyqW8TjhRpUj1/3MGFjeX0yQ7ESqWaWb8lwo8s5LiDij2TV6NUYzS9VhBbafh7deol7qtG+bFF9ihjo/k9oLlNAUDxSZ4WYjc1kAvNQvgTg3xEr2u7AmKl2PxdkyRfPDcvHPRFkENsgnE2sLEhqJXcaL+R0mNQUvAxvxhBN+zYUh8xs1S1ZJkXF37Vu4dmxySpPHhBzPTY5tWv0WsPq/hv7cwjC/3m1S1L0GbcrBoJvxfNC2SaS32rxfh77JWVrYSDgwj31BcuKW65kmrNUb3JZbzhtTe6ZEOozySZrsc6eguI8Bw6Rf2rdbyuatP0oF83VysJE2r7nVKJZd9NPNpKYPFy2ChruQHz6xUHJqrhEpVXAnIVbeok8jmwvBeOo3ToImv5QiNbCnQRCl5Ituh0tDnG8wZ4LgFstfmjwbzs+POie9CzPLaw8HZsx3Zsx3Zsx495PJeZa73OIVFgeNTh0+MnAICTdoIf4jh5NHrpufdrQDZtAMFqeik3/BRNGS5jR9F6JG5k4zYZfrezEOlGsYUGG72aydPTC1THgA9wqZA+GUm7ZUYiBNH+Tnu0e3Qcer/G4c4S96cXAIBvP7md2Hvt1KOIup89TWMAaA5ZfFsFSK7vk7m+2ROwiLBqYC/aG0HY3Qo0FZtAWyMx+CT2T3LUfXv/Gv/und/Db1++RseSW7giMokVimDPJ3xgDHuO5FMYKICuYiNy2F7jfO6gpl0iTPWNEjb6Yo1AeXOV7K1MqzaISdZJfPaI5sVOxt3sL++cYdEVySbvu2+/QDZ6ALLcJD9akTm4HnFIeLAJtACw08GCr1ckiXjp4a2EOwm+sOOexdrQQgSGrm8UeYRGYkje63sEscvNKECAlvoR44FEiHX6BzkWr9gkWMG5EIDMw+ceXeqlZLZ8eSawHoqULbm5ThZ8eiXQ3W3CbdEQrUgeusOyxbrO0/V3OZdWZMsau8IEKDYiFbdryI+iM4ZIilaqFjClT9rXqhYQ4Vb1DSboP5A8SotTRf60IUM1txrIaIGWd+gOw3srhU4KjB6G7FQzhNuNyY81EnZgGKXwIsCckfxX9h62TqI8CVnhnGDuP/zaFwEAr3z9zfQyr+iZT5B0LwfygjLovsKUOA1zyYGFaRywvuV7CIlHvMvd1KE5dvw8g98nO0I7ABKbKa48TBmu1ZJJgKoR6MZMbKtvdwnNgKfsND5z6xueyVn7LrGnXeHR7dg0/6GA/Cysv8ue4ewzOp7LzdWelfCDEr8/vIlvPyFJo7bRcLVOdQ9iBdPrfcHQjs89fCPgBrGtQ6B4SBPBjB3syMEFiKuctigDG3mx1DADZkGOHgHFordxhYVgfUBqNSvQamBHLqmiZLMa3byACIu4H3DXeLfMsRrm6XyE8BA3aLWxqwxVGRdACTMQPQk/xybKcwlVAzo29JfsEpNf6aTyYkeONs3ITB2xopC3AvKdIdfMRswk/uzuE7xXH+H3PrxHx/i4RFIo9EAbPDCLE0WL+A1e3KP3J3JiN8fNRO60UOFDukUB78HsW6eRBTUovQazg48djJFwNhx/ZtGGWpj3AmXe4bQiau6T1TS1Nz1ZTvDpvaf45jdfpg8qLFTY8JoqS8o86krTfQsLj3MCchBrAIKeumgQLT1mM2Klt0ZjtSiTzJ4cmiQDqYB0IW3nITKu1Xoh6LoAVI/v7ZReIpU62l2HNkhu+qElWDx8hioNe2ki1L9s3Fgcut3ARTCKVLCi4MRei/xB8Ov1QPYhzVuXU7vJbJ96yqyTiQ8gKpX8WOlDeEN0WVQ9C/PTAbgfKNNzhrW7Cc3bnXdC2eIFPmeXkfxefK0tPeobLPKgrjQHkJd5Yr7bgeWasSSoM3qsjj/2STRCrwKzvceqjcGAXii40veCBZmCbUiXHH7yK4FuBHz+i2/R993CBkPaa08KVwD6LkpOAMZKVmzaYS6BkGDmc6jb9qUFEzQrCE5OzGXBcqhOc616eUdgeVekkhKJV4TXKY/hY5lgbTPr3cPCQa9kgn171QKotUjlJC+RPJIBID+XKK7iIW2Ee8/keC431+x4DTl0+MLxJ1h2NDu+9e49qCud2gpMyXXJWEugPwgyZo71u5wXoWwuYXcNZnu0oGjlkmpPcdfC3hG4fEzsEltqmOgoUXLNw4wEqkOuuWaXCjZkd90uIHKXXGYEaHEGAG8l6jZLmTJAtTn6gRWfoupSovLvWGSB/u6VR7Pbs76acHbRJwrFerQPcnHWU80RAMRJgeaYa2PCA4O7VL/839/9FI52FzAhs9SeiSu2AMqnbLxsi96G2tMZ1gMDc5UDsRfPC3Qxa7YCrlFJaQilQ323TX+LNH87csi1QxMWe9NqmOBcPxw30NbhuguuNU6mOrEQHr/74KW0GOihgTsp072I5LKUSQcdVqEd3Cr0/dYKetrgtRunAICPr2aYhlrkxUohLztEz+y8NOjCcdlWJhm9BjncVb7h+pL6SR3d36jVK4MDCgBS5YlByVzDTU3Khm2lE1FOLYME5oyO5PbxVboGH5X7wIqz5rzsYEa0ectOkBIWALVUmN5YpDr0/GoEF1qhdENtGiLpVmNjuIFLGa9WHHmqhUq1U7cXpBh12Fi836jrtbs+1XRtDuS7dO3aVc4awQDEyPAztMw2ekX7hKx2smk2DsWOMOhpCcMTfyC2Aa0zgfJxsJlrWBN48bKDsIAZx2vAxx4lVUXIBO3YMcfD07Pb3qTdVS51Oh/Ry+whEHpdRTqXvsJXP6D3grW0233OJL0ScIVP7U/ZmU7XWBigMwLNPhNA4/qgl9TCxFrPIgUVwm3KdgoLkmAFBfPx+u9/+9m3nNvWXLdjO7ZjO7ZjO37M47nMXNt1Dulz/M47rzA0UhN7NIq5C4ekmQrRy9oqglMSLDP1LHTvAHWpsShDC49yKasSysFbifwsRKOlQCM4co/qMu2OJ2HtgiHYKHRvkMGVjnV3BVLdR5QW1gqYoNOJmL2FEY+/Pv4RFp5Aav72wm/4PHZTdk1xO45aWPrXINZ+nYIL7Mt8JVDcX0OGbKDMDK6/S4za8kzgWo4wCCW0+tDCkqgRyscqQULNjPRaU2vFtEutRPayIOb1LcpETKPQF8FAJ6GD4IfLPWxQl8l3GnQBnhtOahSZQR0Zx5WCCKzW+r0parCykRl4VulRHnKpUAaBDPVAp+hctlzXE1Zg8ZqFDExoEbwvAZpXo0ELF475aLLEIvQcrdcFhPA4PiR1COskTs8JxxWNRHNGx+sGjuqZUTFI+fRd6uYa3WXJIg9GwITWB1mppMrjZh1UYbk+bZgPIDsBO3a4dUw3ZFrUSe0LwkNMWwwDs3hYtDg7CMc11xABwZC7NYyVyfhdnhQQQaDBluSQw4LwjoUKRhb5tEnzp3k6TNlYcS1QXIb5WAjUI4/5pyJc3fc1pTq/y3owd0R0Wgnh2IzdWwEfUAW9UBtGFWbgU7bqCha0BwA9l1w7zHxiNBcXxGKujvj5i9nq4JTLMW5iiDcQhUaM2FAok61gLWbPZSlIQkZEw+pZ+Uql1yXBipzgcRWV0+Y91vWQXpdK0trDxp0gc4lr4kO9NkLlZtrzsDUCzYFNdeF+5wKAjVYwrzgzl4brxfmlQLPv0rMhG4GGlgo8/vMa+Od4psdzubmKpSLpsYGACA+1AOD2O/gAZ+q5SjfdS5YfS6+NUmtLkRalwalAOxGoAkyGHqTlGw1IoD2i2d5NJW9iI4ZQpKEeOBeVUDqGcwAPtd40G0if70HtKWFTneyusV6Huq1UEMHI29eKYOLYW9a/MIo2kshz0Ht1qhlnyuJgSLWv09UI3ovk3LN4MkkPYHOrg6xyzHaojrhYFwladpmAUz3LtoLbS6r7Di4SmlyAVsOa4WoFNQ0uMqsMVgPjqKyFPMHMxYlCc2hJdQqglpiw8LR1CYRNcn05QHuRAXHxkh4Im5M9aKmVKrbVNDot9PJxifyKlaTqT3VAmD/ZlUokN1ULZBcaXXDWEXsm1UdlaXA0XiIPu/cPHx6nuqoAtXUNM4bEzsex97TgHtPMwWuedxjYFCMOcgszNMCcCVp94kqUmvNDT+uwivPHJ6jdSEAMDA4HdA9L3eE6bK7jnQqFtqkd6enTHYiwOfncIw/C7n/ppbfx/atjPP4DagfrdhzD6SuF/FKkGr65H/q3w3APRrElFsWSpfPKC59coGzuyYWnF1dFZ5qoztSE+qMrPfwlPZPZitSJ0r1Z63Qd9Vqkza6b0qKf5Am1J9UpIPWgx9qk67V/NfsewrEhuCuYlFYd9mwbG9qco+ymbLkNBYLmUFLZ8tyWFs3SRVSS64Cbv0PXvNllxaTrFyXaXa5r1/u8wRUXZEh/8B36fX0osb4V5m5pIR6W4ZgIGk5tSYo3Taf9hpG6cEiGFj6IyNmSSaDxOKxi+zwz8gR/J9MPDxsCMP0Qz/zYwsLbsR3bsR3bsR0/5vFcZq7ShBaNriecvd8iKwza0OJBhB36m2oYBs6vBOp9zwIHNZsER4u5GN2pnoFzt2sgByZlguvrAZoQJWdLjg4HT0hlaH2TYUTWRRUwE8vRY+4xDBq7xii0FyUQmMWLi9HGObtIMGpUgnYAioAjBOoyCbNjMLpFBKTpoMZ1MBiX2qIyIUPMDHbLCt9/cIuuybliZZWRhM8cFkt6X7fKICLEHTsqYiQ8sJAh07ZrjXaXhcQBJqF4D5Ql3YBFo5ANusQ8NYsMaskMT32tYG8SQchXOkGKo48VwWHxOISHu0n3QpwXKaOQ1zmZSYdj8JqgYIDmRLPnuAVDeAwPg/70KIcLGXQ219AVoII4f2MFE7BygQf/zwtkoxc+U9wM6k2DFrd3rvHCiODY75zdgg/qU9KIJKCOzAO1YChV+WQeYa1EPujQxDYj7ROJSU8adMGMXSw1TNtrrVhKMiwAIGctvAd+eHIEAPjM8RPMK3pf1ylMywYmWuatdfICHf3EBW5MaO50TuHx5ZS1eQuXrMv0mpirUfh+Oq1wdUJKWvmJRnEheqpeSO02q9tIUEsfVQIo84twY3lGTOEmtsMoNku3ObUZ5UFT2RY+3U+9QjJziN0CIjz3ohIwwZA+En5ipubaHjM5eNHG9cGWPmWMas1eyYNHGmTIzucRn+tsLmDGTNDyPfKU7MgWMp6r7ATOX6d5Vx3zeVKrj4efBeTjgpGMH/wKtf/c+29+LRw0WyzirEAZTNvrG5ZKDj2f3j46Q/aG4TgakZ5veGzYBsIiMasRTA/oGIn1HNEz17u2ng/3mR3P5+baCEgh4DU/uEBwwokTybPCi6pFgjbXtx29Pzld8ANe7xHUEScIMeHCd1YKaqdJ36UKCxtVYlqdPqM6op5asUuLpekkVKy1FQ7ZlWI2auZwazZPn/ne2S2W0tOOa5EOyEIdMrZApP5PgOEd56GvNaoJfd90UKMNG0TXKbx8hyQCW6vwrXfvJThNGIatsr0ak1GNUU4P9cfzA2Shx62dkoRiFh7eZq3hYh3XIEFCctTBrTIcz2ihPluM0DR0/HsHi9SyQsfMgUJ7bJCNW8xGtFldYQQxL9J9ikN2QHPAC4MbWgw/4evT7tnUByk6pEWiG3uI/QZ5aP1pFznWsaZ4pRH001Hf7qgmHVqU/CJj/9OnBcyOS73KduDSQ1hXOexU4odXtKmdfrSL4Sf0ofUB9yX63ECMuU2nzA2yoDBlraRacmTYrnhB9yMJEVqCxNiTNF8Iuux+lz5PSodumQMB4j2rxsg1/bw/WqNQBh+cUTOknsvUI7mqClyH+16qDs3FACKwltVSpV5up4H1PZuCJyE8KzLVYqMH3Hl2URIeEC4cY5/5CtpIRIJ0yUAiqkPpuUpyel57Crh6lIRYGrIlzxOn6DNj0KUaARvuWX4poSt+7omJSz+bYBIQIWq1FjC9nufI4jZjgfKUNySXs6QhEGqpsTf0sMdudtR+dP1qYPfOeLOK7Tf0C7D7XYHznwvP+Q47Pd37r3+dNvKwjqhRl/qwXeFQ3Q01+pWiEkSE6M9lehYpQXrRAAAgAElEQVRcRmzsyNdwYF6HaAVUK5JUoi0ZUic/WvqudtdCOPa+lbVM1xvPvkDT87m5Ug8LkYFiuwSMJGuxUNfqdl2K2PquL8WZRLbgh9+WJGUIAIsXqe80SQvmnMV6B3RXJWyooW2QD3q1HTt08JmHDs2mbs3tQa6W1Dwfot3PvPYJvjCj4sTvX9+G3G3gLmgzyR/r9N3d1G1ICTpwg3q25MWrPbSAdhCh/nV6OYEJm6vMHN6/IrZB02nkoxbdE0oF2xfr1OMpOwUlPU6jQXTLD6TXHn7gUn1zg/Rw3GAQslMhPExusajpXLS2OJiEDNForFcF/Job1qNR9WBWI9MWqypsqGc58qtwDcZUswOorcJrDx9aldRCpWsgOxI1SCIGGS+iUB76QQnzYtgUtOf+w5JJOfpMw92vkivR8IFOpBavw/HaSEgRsOGeYdrh7ugSpw1du/OjFboZHWOuHYahZWeQGZxcTtAFW7PJuEMXPs97Ae9F2ijt1EJUIcuvdDovcakB7Vk+sJHYuUdEqsWqBLRLbV0Pf3iE7JgUFMrM4KMnR1CPg3a0B+pA3nFXBdoQ2LxzfkikpUmQtJzniSvgMpDgQZjjrdGJK2ALj/UxUIT7ll/xRtpN/EbmmlphQJty7EmNpL3UhuKB8pQzVXjOjFzu08NdH9kk8qAN0PXaRvQK0BULc9iiZ8DueVPTa4F618ANQvDdMDow/ligmUdCY3jmw9+E6wUKBelp91uLUiCYe8xfBlxsv2lkep2qZJqrbebRzqjlCqDziGuYHTp88Ne+nD76/v/wdWqvCtcqBhuQgJqz/Gq+CO5DYbjMs+G9QRLgMWO6xkkzwIp0/F5wHVi2VJs2sa9+5BCL7fJPQcHyT8EpbMd2bMd2bMd2/Ks1nsvMVXSAVIAvLYQKcNFah4b78JpW9qTzOGL2UsCMsFlnDfUhkkVkSrrvdYlII4C1hMtiwRGpPtrt+wT5CUNGyHZNWaHqRbTZQqDZ9ZCHlB0cDxa4XxJU+9tPX4Fda+x9N2RPFTC/H8+XI/z8WqR2Hzp+zoQBQF2zmLtzGXxsnygE5ivCsJrrEnrUcYuKFSimlFU18wJn5xP4ZYiEBUf4qpIwZU/Q2wj4KWWro1GT6qg/cfMRLushHs1JcKOq8sR8Bqh9IsKsEEhSiNWiQNVKyDU7s0QfyexaYHknfO3IwecOeTjm1hWshiM99Fm2wXxMWX9gd8faKnJHLjYAIJHgLdEpYjXHOtxtBxdVejKCQCOE6XLHRuTC49untyDD5Wnfm6Z6XXZ/kf7fOInuukj39VqMWF7RAyJ3DA/2XFlErbh21wrYWZfUxNSow73ZJQDgohzik9MZTDAGkLstXKjhPjndgXxaMLT/6Sp9lwRQB/GN1aJkZSIA3d0GNmSnUnjIpwN079D97fYZsizPBaojj2aXkRYVZAezhUiKQLqieRvh9VQDDNcAGbN0zaDH0g1IRLy/GHNdVVaSme1j4kJEVMoW4FaWKKQQUQADVi8LvsfJgckxXLp40WHwOLQVXVJrTKrb5kC74/hcSp+EI2QjEsLgtIfd6Ulhag8ROAHlac8QPXjWRqi5uOQ6NrzEy//zV5PbljcqrXWiEwm6Fh2J2/jQMlgd8jEJF/4eb7H0ZIQOWvdUzcI1fZEQN7Jc3siATrpkZO8Doti/xs/yeC431/YGkYuyYQcba5ROEMklFuUFQ7quBw02R6RgEs2jVUsapwBR/lXFf+srIXkBskQLC1Y3cXAHhGP11Y8wNTCtRP6Ub018QLqxhznscBjaXB7M9/H9i2MAwNPHM6CTuPgzARLr64bOOVBY33JwQ4tbd88BAJfLIcwirFiNgn5hhfYxkaF84aGCSo8AbaoAbWZi3KbPl9rBRuNl5TGZVlh4wln9WkO/QKSrrtHAWiciC2YtfurFjwEAH893sQoLeGsVCm3govG2Fcl5SCoH4wVcWFBkJWCDddxwWmN9OkoLqR1wy0V35JLpvD8vMdxfp+PvGglM2bDcFR7Fw7B5zDh4EtrDjh0mN6kWvFqUkKdRfUdg9ChshEuP61eyDbeYeB1VCOZ2wzV5+njGvZUXBS5WWZK7VHeq1GnSNBl2R7TLnHzzGJlkvoBc5On+tjMH72yC8lC41C6kVjK9R1hA5Ra2J9MTCUzNoxF84VLN2FU63QtkDuK4QT0M5/2whAmqTHLcUf0UwP7eEudnE/gYSCnWyPYSyG+vUF/SfMrGLdV4ASzvObiJ4WNeKsgeN0/FTdFRT2mzx6Sfbj/KfQmIwqZSBdqe5J7yyK9kCpgggDv/gv725GcVBwotaXJHOF+aXillQYSj8pQ3jy5Yu1nhkV0pmBCUlqcKk2/R38wQxM4DqTX9f//tl9J5vfKrb7IiXOlp042H6Bjy9lJAzRVkKNeYsUttP+3MJ4Us0ZD8oAg0j/UNljn1EnDLDOo6BMCFI1vHMPJrJgiaXpsgekGJzX3SUwYC1OviRk7zLAYE5HYDfm1MODpSgGr2I6FMJILXn4axhYW3Yzu2Yzu2Yzt+zOO5zFzVsIMcKsqCAnnHC2IYxahK9N1Wsh4DOJgoR3ikHThuZA/ciCSSoBlOBoD8WiYHGp9xJA8jMAntL8v5AKKRSe0nv2RllW7mIKTHrTExhP/qzf8bv3X2UwCA07cPCHqMBCrlWcB+z2E4pqxHSY/GKExyCmmXukAdyEFeehijoI8pq9LaJeionhcMo+oM1sjE9twZM3nn6ce7aFoNGTKk7KBKxBWlHNadgr9N77u5f43vPiSjgf3ZEkVgvI6zBmPd4klJ4W71cIIuEM/K3Rq2UsiuuUk/auIaoyBKCx+VeSS3qJi1TqpIXns0dU5euSBhAddQ5lRcCpTnHtmKw/VI2JGtgFcey8AQ9q2CD4IVquG2K+GB4ROfnGlwp8Lnbj8GAPzWX/iH+NT/+ivoLB8j4s/aA8InBq+pFcr9QCTKOzw5p16QwZXA8tUu3WvXiUS+U5WAFRI+KDGJpWIzasFCC/5eBZ1ZVvQ6VWg1TbrBUwkzErBBGGT8lB1hlvc8aT6HzNgc8HF4J7B4QvdsOTDwlWLCTmGhwjx46egc3gu81x0AALp1r+/C0zWJz5sdWxZKa5lNWt809JpIlstdMpCwtabvjh+ZeSC0pPhOoilc0r4uHyk8/rlwLiMWRvAqsMkjtF/4lDWrBvj+V7+Ev/jvUCvLb/+zr+D1L5MA/63/q8Pjny2SXi4ALO6GtcMB3/8qtcG8+tU38crX32R1IiHSOSsLwPVgZ8eiNbagrDYiFXolE7Jlhj2Sl/Zw0uOdv/G30nG8+D/9PQBIamfxHspGJkazlx5tRERqCdUIvPvzlGG/+Btv8Jq428J3MsHTmGdJkKTZt4AEskued5GtLaxIiKAdOWIRRzZ+yWQqnz/7GexzubnaZQZvMzjDdbwo0p1qrj2qvs/4ZnsA+lpyPaRldrBXBA3HB9QOHUbHBOFWqxx+MUjyc6KVkFdBpm9qUk3x6GCO+bBE+yExRu0AGzJxo2mNOmiV/YP3/1JacAFQ7U4wE8+19ETqWyvsj2jDvDu6xHvzA6y7AMMtyxBYEKw3HDaoK/pbvc6THVf5YY721SCmXlgMhg0WT+kYy9kCN0a04V/OhnBWRvQLWtsEKVbLAmKtYMNif5GPYM/pvE8uCxRHdIzfePQCnJPJwi27wRCuNRJixa1LuNFAfxx6MHMH9TTnmrdAMnH3nn021EKh/ECn+lS2FCguIvzHLToAMDxxkGHznr8I2vxijXSt0vXOloDN6I2rV6k1Sd2ngl33yQjfaajg++qTv7MRtKjcwV+GNqDCbwigwyMJ9+9PVqkXtP7CGtIJPPirvwgAeOnNN1Cec811dWCZI1A4jN6n67j61yoIzaYP9emAWagZz/n1yy2x5zuG+eIczOYCTku40BOLRqba2s6NBa4qxv/EwHIAeV7Ah+fi/QeE87qopKV8cvVxAwc4ZhmjUslcW9UCmh4ntHsCYq9Nyk7ZsGUDh+hoFTenvmJVFLMP17jdc2wXp9kIQNakJpZqopp5FnpNr//tf/aVdK5/8Pe/iP74zH9Gm20+5+Nod4BXvxas5cKjmspLDU8622srAuh10aRcmlDvDJ+paoEf/hf03S//2pvcr91TT4ojmkfoK7Xh2iQsWBHrXKXnwmce3WFPQF8yf2P4vRL1noe7Q4Hyg7/2Zdz7x7+errFcq1Qfd0MLEea7XsoNyzlhJNq9MCc9Bxj9XvxndWxh4e3Yju3Yju3Yjh/zeC4zV7lWkE7BDfvRGwlDJE/C3G+wHZPtmfYwHmza3Ai09yl6e/XOCR6c7sE9HKa31e9TJO8zgp2ivZstWcjBdRI23IpTN4G/yCEiYjz0cEcEbd67cYFMWdhwkOs2g70OqkA31tibrvH0yYzeeKmTZRgALEMWO9ppUCiTtGLdWrMCywRwTsKFMFY/LNhCrPDI3g+qSy9VpAQ0I5j4fDnEw6ckMK8zC53ZRBRbfzhNLNpsVmN2sMDFNWUu9fkA2UHwnDUyCVa4RkEWNqlZSenR1CHLdxKqEjBHIaKeZ1BR/cgE9Zo7dFxKWbRBfGIwbLD+gNipgzOB2XsWpz/Jfcw29iUuyLdz/hJ9vDDgfuRRgLCieIADVBOFQIDv/EOG/FwGmIsgMHHYQAUITmsL/60dlO/QNbn8Nx3EXsyuAXmdbTBb5Yd0n06UxeyISgdXp2PACrz4G28AAB586W+iP376P3wT5z8T1JbWCt99k47r9d/6z1EG6P30492N7MgPbUIwhPbASiYm98Vf9Jh+g45DNYA4boAIu0q/wQYdH1FqmSmLYd6lXuXFeoqg/4DhCSnzdOOg+7zn4GNPufaAVYkFLFu2czMDJr+ITpCFXXh+zWqQWKcuA5GsojXjZc7+qk6SF7GKrNper6Zl4t/7X6Rr+trfpUxz9gMgX9Hn/e7/wj2if9KIBgNeijQvAOBzX6GMtpsQxBtZ16oDZ9pWbCAJvqfpa3YMhNWIF1NagU/9l/SZMgOs4qzQ/0jylwV9bmMkPviPvoyX3nojfV8kI9mCxTdUJeEKzr8e/Keb8+ylt96AeEL395VffRMf/jxB0Pf/6deAtWJSnell4W7jIwhmjqWDVuBH7Qef5fFcbq4+Z9cZtaDJky0Euh3PjLqSvS/RSRb4Xwm4oYUNfoo3b1zi37r5hwCAh80u3nl4BB1rJ+cqMeb0kuomcXGgCRw26IVKXdN2IIEJC72Xsxqfv/kIAPC5yWN84+ourmpatC9PJkCoRf7r9x9AC5c2rs4jCc7nmcGrM/IPHagOmbJoA7tXrlSa0DqzEMLDBscNN2Lps27sk49snlus5yUQXtcNWC2oqzUgPLKg7tPuCV6IAQyzDl0QGrDDBj9z8yMAwLdPb+PsE4K4x0crjMsGy7AwZ8qi9hQc2EoBuyY530CwFF6508COW+Q5HWe1yuHCdVxej6GO6XvVR0NcvqoSk1g/VklJ6/J1DzcyULFefVKkBXfynsLiRZGCFp97dFFxZyTwyq8FyE8DZmohg7i6kxq7Nwg2vzFZ4PsvDNEcBBGJssdq9UGMIgY72gMvESQ+GrRYBbheFpYM0MPL7v/Tr8G3Ud5Swv+cJZcnAG7W4d4/+dXwvhJVVGGq5AYrFx7JGUgITxttrH8pj+oovK8TkB+WsJGZq3x6395ojd0iyHE6BSlcohzMpe+1dQWT7XiaS5lE64UTkBU7/gDciiY8kiOUAKDONS/MXRBBCccEI9LxC4Mk7ZidZXADl+6NV0ibgFrxRvLSm29AdgKTICCfVX6jjtofr37tzRSU67XAD375i2kdSRFFGMVVaNkpApciBvM9QwtbUBCT/Gh7m3B5RvNeRs/laa9tRbDpuVcUaL74m2+E1wt4TetGvt4MIH503P9Hf5/OpRIQTqUAI78SG/B3v4z27s9/Ca/9nbDJj3MSLwmBp55zi6PLADdi3olaSugFC3zE18WWqGd5PJebq1pKSCOhWta7rY8t918CgBfIRjRTTZ2lflipPG7uzROB56cPPsadnNpa/t/L+/CNYoPxnnyazcVG3ysyl3oT1Vqk9hThBfSTHN0eLQZ39y7x12/9CwBADouPqr20uX7m5UfpcCeaNo5IDikmDUYDOv5p2eD9ayKPfOPRC5DSY3lGm7CQSPUtczJAN+r10BUu1U6ROciwuZpWQeUOMrS2TEY1ruehX8JI5LlNLRntkyEQ6tWDsoPzAjsDOtZ5XeD/eO9VAMBo2KRgZlS0UMJjFYlDnWRtUk9ECxvqdaIVwAGdpxAee9MKi4pr6WmBbWVqg5m/3mL4bp6UYtrPr1OmXQ5bDIqWVIoAdFNDakagBWvyQAKe/maGZMoNAGZm0KU2F3IdEr1rt65pY3zvO/cw+twc6yUd4wf/3i/g3n/3q3y8HffLIndwQXnp6skkTZ7h4QqN8tzbes1Ih1pLGO0hxmHzu8oSuUkqDxmugbhRwa2ztDllFxrmVtB2vs6hagkXeytLt9EOJhwSIcgPbaqlPp1PMNjjGt28HpL6EkCbSECCqrsdsgvup9ZrdoCxA7+hpdu3Q5OtSHVPMyKyYOr/HHjeHD33igKhjSa0THUH1FMb743LGb2CZMeW4kpgeOIw+piYXOefH6I65vXhs7/4VurF9aVHNmf1ps/+wlsQoTXHjIHP/O2w6bQA9nr1RsfyqMoiZa6qpUA8koBULTD+OGbatJbEdasbebz3FW7piSPWdmOm+N5XvoSX3qD/e/tv/9HXx/Hib76ByE6IOsnx9a9+9c30GcIDwzOJ732dN9u3f4l+/vyX3sLqtmBlucyn2nW3w8QwO6T7y7KPzF8xfwo2123NdTu2Yzu2Yzu248c8nsvMVRr653KqYfAfehkSgPEwtKt4kTR2rQUePZ1BBChp1Wb4aIfqjR9dz5BNWnQ+WnqIRKGHo2g51Vm9gA/G0l3BWrb6UpMIeHSfkA6/8fAvAwB28wp/eHmc/D5vD69xpyRVnaUtkAmLvSOCHzurUjZ5cTpNGVy+W5NmbC8wlAFGdWMARmAc2j+GBx2efkQC7UL7xHDFPIN3rJl/ZVRyb8kWEis/RPEkTK0dD1GG+p8gzeGYQR5PF6n95uR8Ch1E5Z0X+OSDg5SJ2AHT9WVD1yppQnuV6pmv33iMJ6sp8sCIHe0tceYCe7WTaBd0jKJSBJ+FTMoDSaRid0ywZhOuyXi4wHJEWeZKDaGXAoMgHqBrwAa2pz22qYVGNBL5pUwwn9QOf+7uBwCAf2lfwupqAPRUs164TcjH7/zlXyPlHL41yPNw7QYtVudUy19fhJp+jwcQh9nviK0ZtXrHNs1V20kWgwBQTpvEyO4yndCY/HiNttGJjQwgZddyrolZH/kIDkn0YnU+xElBkPmqzrG+HEAGpS702sTgCcWJus/tjI9fGMrOVBTMagUbDwhwvdtiwzDCZ73anSElp+SD7Pl9EfmIk1ctVfJUtYVPDNfv/IM/Obv7yb/+FjKJhHQJ16sVCs5GASBbsAiJLWjOAKz2lODSHIxeGQE7sr1zVbh+hX7Wa0HXK14yAbz865RNvve3+Jjf+QX6+VO/TFnzq197E7qPzP0J48Hf+Jv49C/Re5pDuscRWi5ambySZUNKb5/9RXqtqoCWlkG09wjpGDwNPIM1MH85ZN4KaW0j5Sm+jspwiSfC/8/yeC43127mIAcOvrCYHoRWmTqDaTWODmhz0tIlMkaE0gDAVwqilanof7GaoW6D5dOqgM5smjyTvSUWJ9Su4nzYGGJ9Z64SWaA4XqMNIuy2kcALTTID/+Enx6n+6j21wcT2kknWpM31KJ+jFAZ/5c4PAAAfrPfxbUM9pLUVmB0QGWa+HGA6qSCD1Nriu/uwtH+inDToWo0srGyx5gkA8jRP0I4ZkxFAlNXzyyxtVN2eg77QvMAoXjjXdQ4pPdVrATx2ghxcAOA6w8ErJOW4U9Q41TvwoQ6dn6vUhycOG7jLHPqcrvnss+f4meMP6ZxXe1i1WYJgreUHdHR7geo9IjQVFyRwrkP9yuxIyGCocLUaoK5yuCDqv9IOx8ckaH/+0QjtDYNBqPeaEmhC7V14bu/QC4Ik9YvUijMbr/Hf/9l/QgfyZ3uuJKD2CXuTVqX7/+PX4HuG9/ACdbj3Ojfp/+VVBrdjkiB/agQFgIGDdwJSxroWt8MMxk0ieA2HDayTaGL7ivJJPaxeZYB2vKHmFghzfPhQYnWP+1zlWvGGXTicv0PmDrIWkLdr/psVaaPNLyXMkCUOyzOBwV+ge39xOYJfZUluUXrBqkySZQb1SgAC8HGPVx59gwgJmdpLvADcLt0ndZbDDVkeUtUiWR1CeRRhXr32d9/E+GPgm//4j26yy3seesVG52KDjARkFffBA9zCAwG0NAVRngp0Y26zM2XPRs5QgJmfcO9y7I/940YkNP1xg80FALPj/sTXxfHib74BGSDtSGC6/19RDbYbcQDTzSxEK2CLEGAYbnXL5tROtnwhnLbtBROOBf3pxR7qii7W7veB1a2QfKhtK852bMd2bMd2bMd2/Mh4LjNXuPDPSNQhkndWAU7g8/ukpPPNk9swIXq2RkGeUlSvGoqekx6sdgmmU9eKmHshw1hcjCCiZ2snkM8FYuhnhj6xIOvLMr0Oili7EYLdma4xDVZj06LGULfQoepvvMQ3ru4CID3ef//O7+DzJWn1fv3y30Z1GuBDAdy9Rwbc2LnCoitwd0wZ73deF9AhM7+8HiHLDa4C49jVLJJgxxY26gNkAROOMF9kZyKdXvJ5hACKEYX4WtN5xUyqqTL4aHUFYL6mjFYKTzC0ipCfSJmBu8ihKgn5/7P35sG2LWd92K+717TXHs9453vuve89Pek9CUmAkjjYZWewTVwVB2eg7EqIKRJsIqBAEhowlmwGDU96A8FYwYREDCbGrrIrRcWhUrgqVIIhDJZ4aHjjfXeezrjnNXZ3/vi+7t7nISB/qCpc7ul/7jn3nLP22mut3d3f7/sNO4w41DF2OaItj2pIATQPmAiVhHuRxS0WDCG2uYXtH3eNSTnurqkjCGkRMYlJNsDukqoxO9BQE4WK4a/yQhOqTGHhWEXtpRLybuZJUvfv8h/wIFiSvk6emPpKHlpAFkHrY3raX5+mkT4+zPRb3Pj2Dx875mO/9HH6mRZQsfFM8WYRe5cnKS3MLr3WNE2gBo03xEAlYflvRCNghfDPpNHBAao4ZWEz4xGJaCFQM8BB7QVGYPoGUlmcYw/ruw9GMFzOlRdaqHHkkZDijMHjfUIHlDTQaxJH/AzKa9lKS0BAcgVkI2LJOgJMNFNomf0tlxImN0j2mXSlEIIkDKit4ByACuGze01sUW2ybGwisTgDvP2DL/DP4CttnRsII/39FgaeLZwcWTTdEDBgksDghQ0EI8e6fYwZ5qukpLf90AsYvSxRbIfj/1HD+SY/9pngTwxBEKx1lavAMQb2HzaufV9gED/27PPo3RDoc9hIuWWh3TNSSTa04EpzJV7T9AyswIrb1cp7MIBasFKhpSrWGaDUfXgvZyz+2FP9Ez+EtfaPv+J/SsZ0OsVwOMT5H/8RyE4G2W2Q9zjNpYqglMX3PP1rAICjtot/fu1dAIA8rT1EPJ90YFfgRqEMMKaF1+aaelBN0Ox5vV4l0dkVHi41aaDQt/3gYGKFhRw0iLnX9vYz97CdEbyorcBOdoiryy0AwG/evuTPI0saXByO8V+c/l0AwK8cvAO/efUyACDtNBjk1OwZZQXKNsbF/iEAoNQxvnT/DF2DIoYpIiifOSs8Y1TdT9Gy3haG8kItp88IK0i6BJAVobLYPEOTZRq1uHOPFheVatjdzLtUpcMStYMlZzEwCExTn9cKHIOWVbeBnsVeT9m2AZZM0wZ50uDB9XV/ze0Gy2Zqic4NhqAtUK+FjNtoq8D6gI3074/QfTUJiSpd6yevaKtE1qk93LycdILBfJ/Oy4/YQPDCaxdBS3zt+4/LH3Z+6jP+/cWDGvZm7sPH6zWDhG0eX/lYgAV3fubTUN0W2iXyWKDP7Y2N7hKLOsF4Shsr3UoY7qve+PYP44lPPO+Po1dI1WarhnrAcHrHwOYaYhGyQFuXH9ptIWPje8F1HUFzv71zJ0LJYQU2tkg3CnzjedrsXZuu4+4N2qQgMYjvJ4jmfJHfPfW9d/dvxek6k9tDSO5ryyZ8flRNi8qqy5CzxIxKYHnG+MW7PtWg+xqdY37fYvIE0DBEKssVE3/AbyaTsUS9YfyGQ81JFw+QQ5tJrO8VpkcCX/lkuD9v/dgLlD0LmgNcaLhsQsKSidmo3vWT41VJjUU8C3OMiYGrP/DVe8BPf/gFZAc8j3QEFuf5eJIlTCvo6qoL1OtvYhg7drHr1brx7/3FT6Fao4tebEjf+/VaWNcbFQgFB2jRde9NlUGrbGJ7rN8ssPK+I4uYtcpVusStD3wUk8kEg8Hgq773P+njkaxcVb+GzCXyvPL/N+oXGGYl/uWDdwAA/sOtl/GfXf49AMDnxxfwDZs0SfxOehFVE+EtG6Qb/a2vPEaiewC9zQXaVqHkhA9UyidWxFOKi3OpDyYxSDjCTOQ6pIdIUOXEE/Pusu89cTfSJZQw+PoB9Rhvr43w9hHJcbaTGf5892WUnAL96tEWhsNgG+iISfPtBZKoxT1FmtLWSKwzied+NUR3c4m6DrrU1MXiXdQY8fVaFAna/Q6iZTBQ0FfoZzYRiBKNjZwm+3vTASRLe3QZUUoLv7dqkaA3oqpqMQlVLATod1wc2kQhu0I947efuodEtrg5o/fzYNJHs0+Vqh4prOdF6L2t1+iwnKo87Pt+nblYwjTSn5dSFvtjqn7lNCLCyCU6f90oPz91OjWSSGPeOGNpGUhXMvIxeGm/Ql3E6PDGTXcaH6d36R9/ErZSvnIVRnpJEK7nEALHUkIcunFstAK6VF4yJXC4SM8AACAASURBVKRFxD3W3WkP5TKBinmRWyEw7fz0Z4D1cBjRBKMIlKHyQ0Ie1j6q7lQJwTaMMjLQlULBPWmxUBhco/fW2TXYepHO99ZfFhj1Cnz+Ltk+vv30PdzL2OBkEsMk1iel2DpC4ap3AFunJhjv8/2oxLEFyS0QshJID9SxCdz1LNsO9SnrU/Te5DyCpkcEh19Hphf5HbaEvNR6VCQ+Un6TaxJAVALgDYiqAcPHaHscm+ikXMGB1J+n5MvadEK8Iy1A9KVs2AvYPQcNILnPmB1IqApY/wptDHe/PvHHvvLCc+g8kL7Ca89YLC7y8eWKuU0pcPWD7/dGESazMMa9tsDOT30GN76LTB+uPP8cFL/vJ3/4BX9N24HB9V/9iH/td733eZz7v+gi3/yPaGH1kqbIYtW1QhiETbEQftNi5Yo5hvNX5w2MTbXv4SZ7D7+bxEnP9WScjJNxMk7Gyfgaj0eycv2mS28g6SV4ZbztGe3DtIQUFu9Zo6pQCoOcQxT/7PpVXEl3+f8tcllj3BLslg5L1Lv09fwgp+0KV6HxoUI8d6Jx2vG6aiA+imAdPKKCW5O1gJQGKcttlDTII/q6tRKvL7fxzWtfBAC8Z+MGCk0HWZoE/0/xGJaM9V0cHOHlPcp6rWsVemaLBDYHiob+LpIGo4yqx3ZNYrs79yYVkyJDUbqTtN5YAZYCuF0YeNu3EC5dyAhEeY0HMypLkkhje4MY2HWrcHTUC+YHgJeC2K6GYimOvNmB0EEupDsWC7YS/KI9g+VRx/+9SAzAkiY9i3HY7aB3imB0goxZ3L9VoXHs2lmM7nZo6hgj/K7bKgujBCxXms7hCSBGeSli1ItQSdhtRj8W8TF50/Vv+0E8/k9/jL65kUOfYki9UoiPVAhgXwo0XPm0XaqIkokT8Yfj7fzsM+H4sYBMtE83iddL/3vdrEY3q31oeaMs6lvcQ8+MT8hRpwtknRpLvqdmHoWeXCNgpfCVuNGKWPAASdJWmL+yFsjvMwJQW195DF5VmOxto36Cnq1J1UHE8qYmiqA7xve11361g7ZLfze9YrAfBfN/2Qi0LiSgFbBcQadjoNi2vtfZ9I2HG21soJbSs8GlRshDVXTOrsodfjlCxWi1ziykMzvoW0SFgOYqqz5be6cx2Up2i+L7lAWjiGZgEdUCLb+F9EB59ynKcxX+nKwEXme498pzz+ONDwRI9ukPvYD5OTr/lz7+PrzjA3R8e5FCy93v7vz0Z8JzYYOblXu/euAsj4R357eCrA2dxGaVl9v0rK8ko7nEpc8+i+vvJbvH4fUWt/8Cm/9PcawSF0ZA8WNYDy2MsMeMXxyELpX1qI1WoIqW0RWxiI59Dh/28UgurpHUiKRGa6Qn0RgrsKwSDGJ6QjqqweWc5AEznWHGlii/u3cR//mFz+NX77yV/s4Esg1ayYQU/rZv0KyHPopIDZSDSPPWP9Wj4QJHhwSDwQpsj+YYpHQedyZDNOzSUzQRLgwmePYq6V6XVYK1nCavB5M+ylmK0QYtLFUTBT/eWYzRZSI0nerPcC6f+EX52nQdR2OC66ZHOSa9DoYcyl2UsU8aEfMIcp0+sbpUiPeiY6Qlp/GEstBaYsZEMdzNsPMugq7zuEE3aTB38W5x67WVd2epj6bTkvoxzSj0dZwdYVkkiPIWgz5B2UpaHPC1G52dY75M0XD4e9yryfkJRKaal8HzuVgk3qLRWoGIe331UCA61aBiqFn2Gy/L0ZGCqVVwJ5IW4FQfIa3fLElp8Y5f/hiaCc+w6y3gFmlhoSqBzd9jnfRpgeZC0FpbaaEbOqbuGB/ijmWIb4umCiZRADtraS2heeJU0uLy6ACvH5IjV72irbap8CksuiUP6a112vgsuwmWL9Nz0A41oAIsb5SFNhzRdiNBvRZ6kbAC9/8Sbf6y66nfNKSHQHmuQTenZ+awyP2zpJbksNVyyH09TNC/Rfe+sy9w9GRGkzxI12lYqrEatbY4R5Cqg1/rofGQqCgUpA52gmIZnH9ggfQgfF+csl42s9qHvPwTz0EsJYyzZZzGvm9olWV4kzfRi/BaJrIoTwcGkmhDahDB/0yKSmhhuvwTtMDhTSho2wUOLtJxnv7IC/jyc+xb/cnnYSLu1QOAQNgwFSs93Ai49Nlncfo3XKQmsP/uII4VWni/ZdkEQpYq4aFxVQqYWHho2f5l5e+706c6rbKJ4FtedJ2EvzfxbEXvb2mzDLA8R60swjLcC9N9+BfXE1j4ZJyMk3EyTsbJ+BqPR5ItfOXnfhAqz9DtVB427KY17txbQ6dPlc7F9SOMUoZLjfSEkdPZ1JuSA8D/9uV3eJMHsVTEXnTF6qnSQy5CkK+rk9jUh5mHX9K1ILY3WiFJG8RcxS0WmU+HEcJi2CkxXlJV1UkaHBxQ1aYepIjmYbdoUng4Svc10hFVwl939i6GcYm9kv7uxdcvQM6YFXqqQJK2WN7hKrrfesaruJfBnqFjSGnRzmMPG8pxjISDkYVhQo5T5jSCMjoBRJsFemxeAABlFSNlQwxjJEqutKU0wLWuT+RBYoDKba0tetshn3ZRJ5hxFm4cazSNQsW+vVk/ENasBSquaMFkFMXOUUoZtC4XNG1hrfBVlrXwFZx7fbEMLN3r30OQ2c7PfDrAWxUFWDuJQdsBMlKkoO4D1aaBds5gWkByVS4epEgm0lcfzZoJ8JhFwO+kpQqMw+oH3RL7+44dJOicXX5m1vrMXEggv8lEnp0Wne0lCr4mMjIeAs96NaQ0KPg6ytgQGQ1EYBJ6JXdTh6rHKgudB3mTTUxwjzLwhi2tlvjzF6/ilck2AGDyT88FeLcrUK7AvTDwhC/Rhmsqa2B2JfiBRwuB+lS4psIIiCF9bkypvIwJgiop93ev/P0/3Jzh677/Bcy5epT1CrEqI2jT+QmrMniDNwOmwPK9SvZCeIeJbVALuAxZ9zmxwPrv0zfLUwLlqeDnHC1EgFIzIkX6TNQI3swCCPmwOqUQkq/mI/yWH6Pq11XvJgkw7Or85a6VI2Q1IwO1Rp8pcz/jyjYcNz3k571rYZLg09zm4X7KJlSu6YH02cH0Q+sDUsy8wq0P/90TtvDDNprdHLqToUozJEN6WNK4xZM79/HqS8RufGU/x84V6rM+NthHn+HiRZvi9/bO4YilDraRkE6GMouor8VSFqsFIoYejSH2ZMFON21PeUZnXcQQh6yjLQWKUwpiSAu7EMHRaLQ+x6xMcWWDZuqv3D6DziuuD3pcb7fa/xOlhOYe4u/fPYs8q3G6T+xblWnfO+12atR1BLnBLNcyCpKYjQYd1oIW44x6nSu6y4YnNj1JoJbSTxpWWUSb9F50o7DRXeLq7S2+XjH0OjOOpYF2r5W3sOdLpO7aaYmGcSURG2gtccQbDGMFOrxA161CvUz8Qlya1DNq07zBYIMm96qOkCStj187mnR9KHwca7St8paQeiV9BpGhvjDf7zf+RrAHXG1c2Uzj6vd+CH/uPyHo7uZfAbKDoLM0iQ3HlBbqTuZvWXE2tAtEJT3bvF0xxBelQrRRos+Q9/5BH5aha1iaIF1gRNsPzjk2Nlhc4cUv0ygmGbJbdF2TMfxCUs0i2BXdok71ygQIoPHkTzTrwSnKZhqdm/R8108V6PcKZAmd93SZ+RbGzVdO4af+2i/gW379vQCA/n/1Bu4vaHNweH8EsVS+z7c6Lv/4c1icZ3lTZEkCBgcxCnRu0WtXa4Y0wu55agUMb2ZkolFlsU9i+aNG00WAgmXoL8IAsMIvEmol2Fu0gvvmYbOpk7BwOVtHCO6Peia0wf6f4Us8U4gn0jsZ6Sw4Lbl761jTpm+oPw46v4Z7rNluYKS/eTgbSb/QRyENTKxslmRFr+ukMqISsHdZi675/btNxJH00K+Nji/KbdeuaHWFl0jZCN6dCaDwBdf3/+O0vQ/DOIGFT8bJOBkn42ScjK/xeCQr1849BZUqJBOJekC73el7eEfMHrkqb3HviOCI3WnPe7JurU+RRC221qjyuz/dgG2dzswi31oEg3sEUTwAaCM9+1ZIeOgRlUJ0mnb1O9uHuHprO5izywBDHlUD9E/N8cWXybQzmkTHBOQ6JZNsAJidNj4YAMqi5exV3VIV+80XXgIALJsEtzjo3J21dlWQtEg5ED2OtfccjjapuitcXmPe+og5nRriJzDkHe9HePwUEcPO5RO8Mt4OL2ThmcPGKl8d2fsZTE+jWvD1SUxgMKb0Wg5armuFOUObopSIx/JYrqczra+WMXRCf9PvFVDSekZtkjbe47iuI9RHGdJ1uh8m195hqp+XyOMGdw+/CkwlQhUipxERTv4K/SjdUzDOBjgjmDNnZEJricppnCOLtc0Z5i7ubpLCuDJixRHLKgt9r4N9jhcUq8xKQfB7M6ZjiMR432cZGVhGSKwRUHOFmg0yBlcFpu9geDo2SLMG1djlmklP3pElVU7eYH2lAjStDIQUhtJbJuOdH01g+MZnZxZ4+y9/DBdH9MtHZcf7WMsJ/d/jz5CpgYktOuyxe+1TH8Cln3yWL7fglgMjDMPGO6qpowjJboR6m9+PCa5ghqtZB1+vjqf+zgueRQwLtCOL1/4OwaqXPvtsIHGBKiuXV2pVgEDjiUIzDMYjciUAXDb2mMG/bAT48iAZR4F5qwnWdd83w+CFLDQgIWBMqGTd7+nUegi9ONciGitvDgEEg4iIIy4do1jEwpMTDVed7vfqtVDVrr4XVQrIGj5WThig3OC5aKuBnoZIQaHh87ONBTSTlWxHA0ZA8T2Xy5Dj+6egcH00F1dVsmNfR6B8J4dRRxqNljh3gSDXp9fv4V/fvgIAJANxln39CPNF5nu1EBaSzb5NZiGlRcYymm5SQ3Gv9mjZwWyZhgUVQOcNZoWmFk1DE9n9rE8TAT/RIjaQLpj6Xgb7+hrW5mFBjYrAw287AvXIvUcBzWxb20rEOZ1TmjWeoQsQrJpyLmtRJGiWsWfDimHtDdS7vcKbWQhhsZhniBlSTxLt+6UiMpBZ6/t3zbrA6/cZBj4NlG2AmuO5hG4DM9ZneCpAdlrvLIRW+OvfjFNEWxoFG3XYOlg02lzjzJMPPNP6oMi9s1bx8gjNGjOOkxZPbu2iZMb07ckQV87SBmB/3kXbUx5qfsupPS9NGhcZJvMOmikdc+dnn/GzgMpbXD5Fz87rr52B7DVeKtN2bUhDii2QGFQcLjAcLNCwvCTr1Lg4HKPs0fevNqdgFMs/Oi1UtCKHKRLfC7YNvNTKZIbsOpm1Hq2VqPl8bSMhVuBovd4g4pzT3T+jQ197HqFeo3AJ+sPQb5QNue0cdzXif2qB8hL3uUtFRhDcQ9sXI5+ec+b0ES70x/55WqgEScSZwucWaO520fLiJGqB4hR9ffnHn4Ngy0ErOPeVF1dbS583rHsCb7zvQ/70dv7HT2P9d+iaLk8LQAbHq6//289jdonPvxNyXi33tZ1cxabWO69BUci3k0ypGohn3JfsC+iapC7+fvP1aXqhVwpBfcjr30uOXY99+nlvgejYuR4abUOPm04Gx6wGnaOXzqxnTMtWkD0k92Bh4N259DpZMrpjxAXlzgJAfjcYQ6RHxGw3Mf1ecdrAjOhzoZcRoqn0i/7ytPE46I1v/zCuPP+ct140qfXPJ0ToodOBVt6XDL1Z+9XMUx6y8Ugurp19A5UYVGsSmidA5BWKKvE77d+4cxl1FYKe1zkZJYk02k6N2RFXlomB5ApIr7VoWwXNFd7+vIsO95ySSKNN2hWrvhbVUxw5N0+8m8n8QQ9CC2xeInvC2TLzySVKk/2bznihX7MoiBOC4etseZaE3WOfq6NimfrNQCQNFkWC3zq4BIAWO+eOo1LaSboFw7YSrQ4Lapc3DQeLHKZWyNn9yBjhK5WsW0MIi6LmykkLtPv09Wt2G6NBcI1qewZijbfPszgQV1ZTXgAixXDLUS4VykVyzHlI8QTb7ZY43xtjr6CZ4u7d9dAcHGpI9jQ9//gEG+kSN+dUsfezyut+J0ddjNbn2O6RpMlYgZIDv2dHOVBLSEYSTGbQO02/t5hm2J2xq1AhSSrjot5yg8IlgfCknXe5h1+kvrou5ike5D1flQtpQ+rLNIbkaLZOr0IyWqLmPnpxv+d7rLASRkUAk8jMtS6ii1wl1wrxLutf+wCq0EdUCwXNsh9RAtnLGcptZxEI/15MDJh+kKEAYRHQqQUYIRGNAFoVbDG18H3sPG5wVOZoeSa91D/ENUti06PdPpCFukWuTL6mY7xMRNSCJuKp+/xq2CPmLdQCl3/8OV8tCSMxfsoRqyzMqPWVsX46LHhCBjcl0zEQtVix9wt+0CY3MKMajeHKfi5gIiYqrlEXWDbW3Q6/ALmFFABV4G9qyj3OPsPdBxKyAZZnLB9TA84iUNM5uf6mrIMlJETol5rYQhXCUy9UI3xcHDgir3bPpAXWX2SnsSQk2CzOCERLeHcru1F7D+5X/+ZHcOnnP4WGORM3vjNsZp745PNAImDjFUJiGd6sczWTtSKXKleVNxy1B8Cah39xPem5noyTcTJOxsk4GV/j8UhWrlaSA83irEV2h7Zpi16KPK98pTldZmg4XDvfWCLm5Ji3ru3iqd5dvDilvudvvPrYsXzCtlFoIq5sjIDbgKXSIE8a327sZRVq7sceaomEmbHVbg6baTRclTR15KU+8Ywo+skh97P61kNCR09TD8VnR5qQZ2qMwGhITNnNfIlpkmJc0K67qBLEDAtHsYbKKywmnCrTSMQ9qiwniw5qDsI+P5wAw4mHl1/f3fRsVdmvYExgSevI+MrMaIGDwx5Un17vsdN7mDccsr5tMGNzicPbI9hlBNXjY1QKkvvHotfCFMrDxABVrAC5Z33h3jkvIVFZiz7/bDrrwOb0N/vLHNd/8wLkk3N/zV3AAtYaCGExr4OrvevlwYpjMgso6/vQsALTe8R4VZr6ri4P87Ff+rjvST9+eg/LJkHD1enESNQMqVsjsHc4wHBA96qT1yjc7ZzGPmVna3MBYwX2plQp27yF4Gc1WgjYMgps0shCvkwoi3jbAkbx7y0lLICIYcNqU0Pw8c2ghRknvhqOFwJSB3cfnVvykgWxY13fU9QS8RH39jML02/9fYqHle+9/9vr1/Fru094tODmfM3fe0iq/H3vrWMDJBpZwFWZhg0m2I/X1ALZPp1jMiY3JJ+V2rXeV1r3DAXVXyIEJZIW+lbu34tn20eGkoEWAW73JVZkYIoIcePgUu2NHHyLwtPlg9zm0mef9VXsmf8baDsSl2uCgJNS+nOUNfDiP3gf3vXe5/m8IlSuf2xJ/uJ6vFbBV6Q2DklS0ZQ8sd09lG0wE6Fjhp+1mw0WZ+n6r8L9JgHK0xpqyOqHUqE5ILevy7/4Cdgm8vd356c+40s1lUmqqLklYHEc5rX8+bfSwk5j73ylSgHD19v+fwh2/5M+HsnFtenRg5MeCiwu8MLYks5yfofIKhs7R3jX03cAAA+KPoYJQ6w6xq1yHa8eUR/RGgHhzM9bCV0rGLaAk9JiNmfJSF4hjrSfgNtt6WUKALzZf7xeYmtthrp14ekKgnWo9YCo6uUl520m0EQOBwLaNR0WHSNwYY1cmV5bbnuXp41sgWmdQrq1pLdEwguENhKbnTm+cLgDgCZLrPRn3YTx9PAeNuM5fvPwiv9/t2CO8gK1VigU96GN8PFnRZHAzCPfZrlxsOYDCpQ0fmMjew1MGUFPGJ+KLSwnr6j1EjaD7z8qZZHzon847aLZ6yDeonu11l/im069AQD4tbuP4+iA5R67A6jLJd59lu5vcr71xKEv3DuH6SzHaJtg+aNlB7OXyO0+sjSZGYYt5VJh8UV2wj9foXuKFkUnnbryTz5B13URIx4QhN6LK6ynS7w+ZgelWvmet1OclyzXWo47EAxlR0vp1TD3jgao5wmym3RN5NAgnoTJKJkA43fysxXZ0NeapMj59/L7Fk1PYHGBX3TQ4Pq3/SAA4NIvfBL1KCTy6CyQa5ysxPX2olKgdn1KZdEMA6avOtprtNe7S9w8Ihj+5d+/SBsTPv5uzyI9dOQ4C9kA9ZkVHbBz/5Jh8TDscOQXjzpovF/8yffhyvPP+cVWZ4A9FWwq07XSBynoRQzh4GMdeq7RXoxmqMPGOTbBmcsKZHcjD4cn4wAAmkQck+2scBspyo/Pf+/dkgLHc75e09Qn37zze8jqUHe4p1vC6z+FAZKJDMHqDWBdsLhRAWK1rHV1zpEp/IbIRoDthcQuUQQ7ThscCNEONJAY2D0mDCoLw+lIqBSgBSLuy6uK9Nzuta0g4igAlNvhfduOhuD0KNtvIEwgx1X9UKRgjod+nMDCJ+NknIyTcTJOxtd4PJKVq6qIP1SNiGEKAKglmjL1sgUXmQYAt/bW8MaSmENnzx3iQufI501ubk+x1aVt1mGRo26Vr/C0FZAMJ4/yAveOBhidJS/Xogqs4e2NKR7skXt7FGvcvbOOmOUfMtZQbPqu7+dk/O82eMp66VC2VhJsuFJp3ptSFa4ig5QrvZuzNaRK43BK8M6gW6Kf8K5eEeGpu0aVn7XwEhWlLBI+xu/s7+Dy4ABfuXsaAHB5+wCv36NKvmgiZHGLvEtlhIf7AIjcohTWk6vqvRx132XHApvn6Jo/qBRFzvHTubU9xdkeXbf9oovxsuN9k8tFhD1mFetxgnR76Zm+WdTiXknXNVaGAxKAtNtiZ+MQy5bPLQLuzjmCr1UY9JeeVFQ1oUKRzXHHm8s/8Zzf5av7KUquDPJBibpWaI6Y8CItulzBTesMb+xueFMPUypoRiZMriG7LSp2i1LjyEN5/TeANmcnrX+/QrdTYRx1/Wuz9TVe/ej7sPO5FZN/i+Ac1Qq89PHjjkRXnidY8vq3/SCxnwHEeQtzyqJK+fqYUCG6PNrHP0WQZbvqFhSHfM/sUGB5RiK+Qs/S7ftrPvdYGIZgHZmqEismCUB9dsUwIzEwmww7C3gUxwxaxL0atYt3bCSWKb325f/+OUACiyfpuVaJoVYCSD5T389DWdHRx1oM0uWORkTyMiuSnVUzeZNaf41lGzx9TWyhu8bLdICVj+RKYdZ2LUSv9f7W9SjMB8szFm/7uy/gJfYTfuf3vgB3wm3XotrSSDmSTauVwPhlqLxVCVgh0AycsiBECMpCIjlQqFlNEE+kv4dtEiD/eKzQ5uHa2NiG9yCJ+BikSwKaWzeylognEsvz9H00U2g32Q1tHIUquZUUb+jCOxID4YhPD3/i3KNpf/juv/5xqCTD+Emg3aGenOFUk84W9WKqIoadstwjMR5aE3kLFRm03GNc25rhb1yhgPJJm+NfvP5OdBimnC0yZMyuK4sExkhvEB/HGl3Xw+yPUTPlb3fRw4Ob6/7DmZ2bo9jjnpARkEVwZ6k3tP/kym4LpYyH4YplCsPn3O2XaHgzcH59jFhqvHaPNgtrwwXmBcE+g7xEorRnx3aTGrf2CMrbHM2x4IVSCouiSLwp/mQcDPEBsst76tx9AMC92cDbE1bjDKrXeJlOkjUeFi7LGEM245/McugyQpezXs+Pxrh5SOeRxi0mR13Y2vVBw+uKWkKs1V7LCGlx7vyh//n9A1pAu90S5ZdHXnuaPDH1YfIHky5Os4YZAO7/m9N+ETCp9X1UN3Z++jP82gK2FzZq5y7t4w6Hg4tSwfaChtQuIw/J2Z5G/CBMrM1I+x6jKqTvAUZLQfcbAAYN0quZnwTrrfA317/7B7Dzs8941yS0waTepMZLRE4/vYvDee7DEmb7XeRv0P0tnypgtfD5uqIVfmK+8Z0fIiauszVUxxcdJ7mw0iI+VaC9R8/Gai5rPBOoNrU/l9VhBWDWGrDiBvmgRFlwP7CR4b4bQfabrjdnxDGrRdFKJPxZVspQsD1A1oiFhOLzbAfa5wtH+3HgLAhnrs+LZlcHXWtsEc2Uh6Ff+8H3Y+cffca/B7WU3t6QXpMPaVbg0chC5K3nI7i5BgBkIZAcSay/Qn9YDqWXC0HgmHUh/QHC+3ZwsRYUuMAsY6vgofemS9C725DJ5vgxVqU9wgS4XRjqLwP0bAoLD5vbyEK59piw0LMYMacemchSrxv0fLhnVfc1orFC/oC+Lzest5F88du/E8Ph8KG2P3wkF9envusTUGmG2SUT9GKZgayE9wFthpqCvQEIZSBdv8LSDlqyflVFxi+m46Mu7DJCwhFg1gg0zghBcSOCJwfVbyC519nvlvhrO78PAPjdox185d4pqJeIrLIapQVpoQrpxd8mIjICQP0ta+B1jJtrM8x40ex3KmxyJV60MW7tj9BM6GfdrSX6HTrfb9i8jS8cnMPhjCPKjPCeu3mvwjtPUbrNtMkwq1NsZHTM3WXfk1MODnrIujUurh8BAG4crCPmDcVinqHbC5Nls4j99UBmIOb0WumhQnmu8ZrjZR37nvFimsG2Ehn3MMu9TkgdiSwggc4mTarFOPOTxmB9gSG/zwe/cxqyWZk0vnHidZYFbyDKWUi7cRKSN/76DwEgvSUA6j+5NWYaeW/V8pSByTUE6y5tEYLURWSAeQzrdMzd1stJZE16XhcnduW55wOxJzNQ7JWrrnbw2g+FCvrSTz4LucE+ulogvpUGaY4EzEqf0kWS2dji+ncftxh0UpBmu4FKNbRbXEvpG3GyAWDFsWpMc6/MKuslOwAgUg25x6TAuxLLc6FSqteMv2+qhr+OVgLNmg4mJMOKpGoAmam4QsoIMklxC3uh/L0OvsZ8zZcR4Eg0rYSYRcGCT6wYSohQebtQbxcgL0vpe+1qprzPNgCYWgWpibJI9iMfF+g3IaBNipOaCE3kOG/3VwsvbzKpgaglNl6k7w/ebbx3t1sQvVwusn4hl5VYsS4UwWoTwM6vNBDMrtSJxK2/FPlnxEZA9xaTs7atlz/phHrtPhA9tv75Ea1AtBBo+0ygOrPEGm+OD8Y9GC0gdlN+PvRNigAAIABJREFUP5YMIwCgkV6Ks/qsv3m4ufphXlxPeq4n42ScjJNxMk7G13g8kj1XnQFIieXnrb0OFHRCIckAaBfLqSlWC58fupYX6MY1MkU74WWb4MtXzwEAuq8mWD5VetnF2c0JbmpmmsaapDGHtPXUiHH5iX1/Tq8uCKadNSm6nRozZ0e2kjrhhvs/oREyN2ODNG9Qc3rJ3mHfGzsU8xTjDsFio16BcxsTlEP6vXmZeknNft3FvEx9gHm/W0IwrLTRXSKWtPt89+gWXhyfx5xzR9ezJe62tLvs9CpUVYxXXqVrkqyVvt/b3ZiibhVmXM2LlWpDZi0U95n1oovu1Rh79ynsXT9W+MrJVgpyxW5RaOGPEa9V6HVLH0pwP+ljfJMsqxZZiuUr9HVckfBe/VtUXc+nHXTYGD+OW1RVCD6Psgavf+tHj11/13e89POf8vaNssWxfubjn34eJnZmEyHrVRwliGYC9fnav5+IgxLs3ezYTt5s1b5qHuQVaob2SwcP88jOLjzzfFkmEFWGei3IM1yl0A41bvytD+IPGy7PdOdzz6DzYgfFFlc6fY3kIJgMCA1/za9+6P3Y+blP8QkLzxWAFdA6wNpND76vV55tIVrhbfbaToBfe1cmSIz0yUzNOPOMVJ1aX6lGMwFbxmj7XAmuSDeEFrCZhWI+glGNl7PBHjdekDq4Kdko9FGjghm/iXM5E/61zVZNblcOcViBaG3EblwMCLbRSl9SBpcnYY6/HhRgogCdRnMBVdEPswehAVlvaI96AICspIfsbaNw7fuOty0cynLtr8Y48+v0d/f/XUFInevtF8IbVujcIj0IqIeNLK6+j5/3zz6LaO56vwbNTu1hbSmtz8bO8wrLZQrtUJJOQCJELaB7Qbr1p3k8koursz+0IvQurCRyht6kyWHn3AEmrAUt69hP5sYKHBZ5CPneH3nv32RiUd1PMXoHkW8mReY/PPVBBtFtYbn38B9/4xewFtOC/VsHlzCuqTf1YNrH8l4Piift7EigOO9SjS2wDBMWQF6mAKAriXIR+QlAdlu888ptOn8debmNsQLGCtwf02JYHnRQDWmSeHAwRJrV3ls4GhhP1trM5ki4IbVX97GWLnFQMSkqKZBw6siL83Mw+ynA8FdTxNhnCFp1WyRpEyDGFY/aONbBveqInGEsN970vSxEmbUCxsSo+cM6vDjx16KfVVjUsY8HHGQVxtzrNAcpROzIHdRPqvbY860VOAB9nXYaNFXko/baOsKVX/o4APJ9rtoI95h8dv2/XknFedMwkV1xP5I+eSiecnA0v1c1ibwHtEktdv7RZ3Djb9MCKMYxdIdu9rRS6K8xH8AKXP6J5zxMGQ8Vlny8tlWQufVJLa0ENBPiVKLx9l/+GADgS3/1R/7Qc+++miBaADFLQUwnEI5cIosj8Dz5Iy9ArNPzcvGtD7x+d3/SQ7dTorpOn6G2a8O9PqJ+pZNumNTAcvD7sFOSReZZeu6Ku70Q6i1BGlXQ51X3ArQMG3rLNiH5kdtcUgQfv7mGNtQ+mnFl8bNS+N4gFhKqAuJd+mw0oxWYfx7R67oNXiuDg1VqUJ62kN7ZKWwq2u4KPFoSicgvQNHqCs0FgAjfl6f471oBacUxiU+0krjkvIStpL+3rlgwwL0/x9expoXNuzmlwZnKSotyM5zLqqsUVhJ+bFd7bT4ARJFGwdwKawXaRRx64Fp4+9WmlsFG8qtIWR2HwU6qP/jDh2z86d46nIyTcTJOxsk4Gf8/jEeycp09piE7GvFU+p2vSS306dpXqA8mff/16dEU71wnw4H9qoeXDrYx4+QSM048rBSVQGdX4HBCFV3bKA+bICOnos0LBEVWJsL/futpOp9limqftvGi2wK9Bq106R1ihbDDO3auCmUdjLNFI4ChxuMXKYN2K5tj3oad5N6SzmktK9AY5U0rVoeKNMplQpmlAKpWYW9BFV3RxnjLkI59ezlCHtV49+gWAOByuoea41CMFfj8/ceh2FN20F8GIw0jMOwWPgi+d6GCcpD0rIuYST/lukXUEb5iSQ4lSt5Zbz92gEZLLwvSRuIta3sAgGUbA+h5Q/heUnn3qbXtia9od8c9oFXeBxdC+Hvdz0uUk9TvqqP9GO02VX639kewN7qwp2lXffkXP4H8i/TeogVQEaEZsgXM2QDd6W6QMDgiVUMeElS1zQLpTRYSV55jZx4ICMHnmBp//t2zM9RfGRKRBoDuSe/fDEGVmzOAMImFZmRFW2DG/sc7n3sGaKRn90YL4VNTmoGFlQIxC/mrK42voMV+jKZnPVEmWgofGH/pFz6JhK93U0bQWkLwW4sKATCJyCRk0K43XPkIX8k3RiKLWhSS/bQ3KpiKP2sdG9jBiSF41GWgW/iqlmBw6TNw00GFisMLICzaYesNISCDmYXQ8NI2MZOoR4aqYACIjH9GLOAhUwCwrQ1kpy65WzlHIllK/yzZXCPmKtP5gHtnMCsDOYv/6+hJRm5yE9yVXKiHM6mILODcuOJwTrIRx45vkwBB664BBBlLAED6IPLVu2iDO5aVJLnSzjmqDUqFFhHaaeR/t44sLv5LOsbtb6XrpXieMq30bnFIjJcqunO79D9Q0pHNNJy7zap39cM6Hkm28MWf/ihknsHMYqR7vCikFs1I+6DzvFvh9IB6d41WXp4yXWYQwvokE32/45l89cgQFDiiyThKQm9MSIumjNBjM/3lMoW41fE/d5OVMARVOcPzaBL5vlI0VYAF2m12Q9qYY/yAXIfe9sQdXOgeoado4v+1u49jyYkw1SLxvbthf4nxuOvPzWjp4VirBTCL0T9PsPZ6d4lhQqzIcdXBZodm25vTNWgj8LYNWmznTYqXuD/aFDHiToNTI7p2o6zAV26f4dcS2Fif4/KIWMD/zugafvnu1wEgPWzFjOPzozFeeeWc1/IJI1Cy/ZsaNji/deTP5f5igI5LIYpqLNrEs5jvzodYzwhKvXa0jvmcJuko1rAWIdnICu/4pLUkyN9F6N3P/aRkOgZyKWF40hi8HKPp8g20QHGZ7vuN7/gw3vKjLyAlgyzMLxrYNbaYvJsgKoQPoH79I+/3fbE3M3gvffZZ5Lf4GmhgeZZ7iH2ShcTsDLT1jQ+wZFenoztD6kNzWyE9kH5yNBdKL8/CJD6GW3kYE/TW46mg4HaQ5aRy/cCrHchGeMmEcxVy4/JPMJPauVi5fqZcgV83qFcn7jP/YKPxiTyyoOu7mnbjZUVY0aUbgexOjIr7z9FMeii8XqPr7aB9Ka2/v3URQ0YW9gFH3NUh9Nz1LgFiBOv1xm80+93ApVgUKYwmNzYAsEsVmM+lpEhAbmOopfQbh2QqUJwJNoarEqZj/diamMSqcAsNQcoAbcYg4OUsohUedrbKhoVc0ULr2cit8IuZ6WqChfln0VSFBVUEm0RhgGY9MLLVODoG5cpGoBmFOc49Qza1x99bqr3Foayk37BYaYHUIL4foHe32dO6wK0f+OhDzRZ+JCtX7w+rwiTq9FpyRUqwP6eZs9EKNX+Q2iqCrZT/INhco3E6wp6GyDTkSv/EmUgYTTvY+ZR9e0sFeZYWruhGBs2TVbKnYCKLlkWYVlqoeZhcVCUguA+07CbIN2jxGCQlvnR4BkuWkozvDHw/VqlgfTa91YG8UHiJjWkU4pwWhe3NOSaDDL2MFmhtJA5L6gUraTyBKYtaXBoc4OVDWlBjpfHEKaoej8oOenGNKXvzfuW3L/tdfOfCDOf6E2/e8PnpRe/Nm0QaT67TMd7ev4tKR7jRJZ2orSV9sAHEux0sR3McCLo3d+6u+42DaSS6wxIXu4QO1FrhtX0qEcsigeJ7oVuFfq/wFbUQFnGs/ddSWq//nEcWyQHfe5ZSRCzrWL6ngd4Pk7Scho/Tqx99H/mtgidSV5lttTBThXQ/LGaOIOUMHdy4/t7jUpknPk4V7Wvf/yFcef45nzW687PP+GsA+wf/7vL/QjaMAtQvBIAb3/0DZLbgCqLEehu/Nreo1g0ifu5MJdFyyo64WME2EtE+PYOPffr5Y4u0ixlzdoFeiiLhe67QEv3fS9C9R8e89x8I32d249I/pGommUroS6xF1wKYu1IVKM82XuLUDi30ofPHJZTILahNEaNlAwsIQEyUVwyZCLDxSr+Tf6AuLqCnKTTrT8fLCJ0RnUdbK6jIQPE1b1uBdJdfO7a0WV2Rr7hecHnKkGkFeCPbSC8FtAo+L1YI+nsnFxIaHvloLW0iHCHL5AZqGnqYXlqYWOrbluF75ewOdQSTG18dtn3tv1ZL6e+T2yD5Kn9lxBOJaisQlaCFf44B8hrOb7nCJfKbSRtZLw1TMwXMVXgulPUbV3H48FeuJz3Xk3EyTsbJOBkn42s8HsnK1TQKqBWwwrqTlYDODHqcoqKt8MYFVRn7HMNhr8TRpOthJSsFNDNlRaEQ9Wuc3SAGq1mh9N3dH0FGxu+ERSNgOKxbXynQ+zdcIVZAtRbMLExkobk34vo0fhe4SFDzjvZL4gyWt/oeSooQoObOA4mK/eVxaYELm2Ncu012hd1Rgcc3giSoaiPsHRHU3OuW2OoSxHqpf4AFN2mWUY39suer5LqKcOocwcBvX7+HnqrwK9eeonOMLOKzdIx3nL6H27MRNF+XU9kMk5Lp+3EDw2XUbx9dwpl8iukaB51XCao59QrL0y3aSRcHmr5PurWvMkskODWYoRtR5X1peIjf3r1Mxx+U3vijbRXGD/pIOOw9jjVShpZ7mcF0mWHJPXXb0Xj1o6ESvPJPPuHRiCjSaFiuIl7PUW/ReTz5z38ETR0hZjlMmjXerlG3EngTvObGG+//wB/4PxfWLVoBzbaAO//Tp4E+8NhnqJK98cEP49I/oN+78b1/8Bju2S0OOyG9BcC17/uAZ2d2rwfjg/RQoDhjvCtgPBMw3J9uQNVRzC4+5bYODNhW+kB0eRQTjO6kYqn27FGxUNApsPse+rMb/03IAgWI8do7ZNOQCwbKBUvMsuAAlRLkKVeqTpfOg4T6o85iErWE4gAEYUl+4z5Dx2DOQno4swYjEu71YoFi5j6IguYQRr1UR6NaD+lINtOeEb96fFEL2MjZfYH6tu78RfhdsaRrXA9c1W890qEz4PUPv9/f73RPoR6sXAMHmwsLuVChIi0Ch8Hkht6n+zMB30awIpx6NFHQnZW+80o4bbWpYZX1Gcn+dXnc+K4PevQhnkhywgLD2a7Y7ZM7lnDohkXIAz5JxXk4R/wghszI6sz1MmAE0Ap809lrAICbyzW8+oAWoE5eYXbIzbUR8PU7NxFdoifzd29eRHsYyCT6QQd3+XXaRiFiuDFOOD7NkSJieA2drhTWXuEF2li0HYm6Tx+m+QXh4SIH9Yh1WhSyToNuxjILabDIc4hFCHiPdzjIezNBnAXbxVv7I4B/r+koDDjx59WjbZRNFFyluiXusj/xay+fQ4eDwbtZjdky83rYNG1wb0G/F0mDNlK+jytr4claX1RnYK3AB57+VwCAmclQMb71oOij5s1MayUOqxyLItgmOmkPtIB8o+MhF2dfCRCkf+32Ft64SnC17LbeT3g5zdAfLf3vDbbn3j84jRufRFMJslgsdtm2r5QUbA0AwwbXv+24/ObSz5PGUz1WYMB2kFJYWAskSXCmMnM6fnY/Agzw0o8FTezlX2TYVlnoaYLOHbomL//w+7xuceenP4OI+3/6MEU0lXj9I6HfeSyI+x8+G3pvmUZ/M1y7N+sgne71rX/vBUScb6dToHtLodzgvnMSJGuiEYjm0k/Uttf6Bc5MV4CwrQqoFKyDIhMDy/BiPJGoB9ZDm48/87yXgqhKQACYX9L++KugpIdRYUkbzfe3bRQE90xVoiGERXvAn8tMw2yxV/dBDDWTaIbBts9DuGkIY4cFonGYHrWyvncqSoXr/91Kb/wffzIQcCzJq7TrOVcy2E+uxK6JhnzAXaA7jPDvLZ5K1EPjpVzCAgk7clZ8T5xGWLQhladeM2EzoCwgERbUxIZeaksrqHD6aiNgBUO4UiDm+9j0Df2es3qW1mtUEVvIcfDdFq303tR0QwQ6bOvZPLUE+PkXlQScgkcC0ApwqpvIHLOYfNjHCSx8Mk7GyTgZJ+NkfI3HI1m5Dl8DVAI0fYGCjJEICpESv3brcQDAYreL/mnaLlZfGqE3pl3f8myE35lkcOGaopY+SUNxKoVhEpBUQMNM3wYgyMztyISBYAgq71U4eHrI/w8sdlps/wZ/28Lv6k1mgMwgZwLSIC8x4ESbSpOBhK/Ehw1GHd6ZWoGcSUqzRYa2jjC6QFTWb77wkq8eP19egLXwAel1G3kfYPQb5Jw2My9SlNMUCf+etQIxQ65350Osd5aeIFRtNl5AboxEErf4jQld49cmW1Ar9jYPlgRHJ0rjYJGj5kSe9TMT/IWzrwMA/o8bb4M8bXweJ7QMFeI0Iycd59G6iEKWKYBpQ1DyaHuGNG49w/bw3hBqwhKJ0yVsXntfaasFLFchzg1rdSR8L6pFgsW9YfiBEaj43iMyvjoqt9s/YFjvWLMmNRDDBuUm/XznZz6NuE/H72xq8koGSSJMHAT3N/7WB3HlBYIJdV9D2GCOLosYRc5wprTY+Z+purjxHR8+dg7Fpdpft+x2DJ0c99V1VU+0kFBFYNhGu0kI2O5pn0lrFxFkoQJUa0RIihFE4PEm+StFiioETGy9xy+KBG2H7o1sRHD6GdQwjfKIAKRFl436tZZoGuUh3nyt8CiLVjHarvVMa5OwNAVg43v+LHP6jnufo52xRzpm45zCwp0ZyoM0XANlIUrhz19gpWKVK5V3TFXyKkPYXeNqQx+TztjEYPz14RrtfO4ZiAGzb5uQxQq74gZlaC7yBhw2GIGoMwXipMVyj9A4WcrAwobw7QFhiIm8CtEaTh6Sszfl1ioLOQlBD3qzRnGBS97VUIIqpCHJhs1InMPaOPLzl+4ch5kfxvFILq7zcwIqpYev2g4yF7tdoa74Aclb1Cy3qc82sBHr7ioBU6hjMJCDtzpTgaYHNCOWjfSboKVsBcFiDKHJxHhm8mK3C3GRYTBpgchi95sYTh4r/zAitjh95gg1LyzzMkXK/aj1bAFzLnwItBV4wCkwupZ4z/kb9Htnl/hXN57EgBfbN5abWE9oUlpMMqBSyLeoR1qWMTRDeTLRaFmrNuwWaOoIbePYyAYpG983WmFWpz7xp+gkaAqGhyxQRTH+z+lbAFBaj5M3KGGRssH/rcMB1EpE3LnBFDeW1DRW0qCfVZ5lvD/vetN926+wGHc8JHrjOz/kGbvIwvE2ewuc6sxwe052iPNq4Ptn1/7L47Dv4//sR5EwKzRPazz+z370mB1idUiQd//0DDPDEXCdltyB3MRZKZ+MAgG88b7j0KyzPNz53DOAll4bHY0jNPx1gxgxx/jZgxjtUCO/ET6+3rx9qWDXat8jlQtFjlkAOrvKLxaPf+p5vP6R9+Op//Xv02mpDL0Nuu/1MEJdRXjyHEmtHsz6GLObVbwfo9owvrff9nWYfBtB+mHQplMtBaxiHe3RCpQs+XLz38WzMOE2PeqlOvcgVQkPdSZj4e1J9YykRG4R033tQ+plbKi/zkEHxSINenC3l3N9v9R6dyTRhJ6r7lrWqNL3yzJBy/aTcaeBXQm10D3tF03RCggNtGthwfabMy2O94yF9T1w2jDxfV9KNGnrNztopJ83XGSbez2dWtIQgx3nVljQwiK0B2TYROSdGrOjHPERn3/HHtuEmk6AzE0W+s6yCpuGdqCPhb8DK31XLSDm0XE5Dl+Dtg8v7RGT6Bh26gsD4JhC4mEdJ7DwyTgZJ+NknIyT8TUej2Tl6jwCmsGKqflAA630rjdx3nhYEgY++1NqIJ4qNAPHygOUyz9MgHozwBmmFV5/KI5SyFrASmbXpREMEwoQGe+jC2UhIgs5WRFWc8WSZi32DgfQTlQv4Z1PhLCotcLeAUGr1gi85cIDAMC1vQ18aY+MHByU5U0x6gwv7REBqNOv0GbKVwBCIOj+IuNJP8NOiV639IHvVRnj1gFVgXlWY1GkqJdMYJhHPsLKKgsMDc5sEyR9MO0iZUg3SxrkzNjdbSX0IkZvkyqptw3u418/uELnXcWIlEY3DpC0O6+6itFfX2C+ki8bzehaXf2u4zrKb/n19+L2nrNUgqdI7vzMp3Hjvw3s1WjFmadsIrzr/B18y6+/FwDw4s3ziDkQfTlIceoMva+zvQm+8NIlT1g7+9geuUIBIUD9q42KfFedmUGbNz6ST/abYHpxvkT2Sgf1kM7rsV/6OKwKz6pdRB56lpVAfo++njzVehN8/5LMYhaR8RrsLK8Rpy0afujXu8FlSzQx0LdgQy6opQxOS1oEwwdh0awZb86uysDMF5ogYQdTZgcWL338eISeG8lYevj4y58KJLAnf/gFesnUmR8oYIs9mluqmLc3yAxl/6iP1iFImzXacexJQL2bEi27TxU7TdBtWkDHweGo3s2DQf4iBTZI7wsAiGyAw1OLOkGozlZc1AA2gXBDBEells0hAMAMDG58x4c90c1UCsKRxhJ7DKZVpQjuSga+4neQvv9dLXyrYPnaCBi2aDYc2ymQqdRSephZltR+cIiDsPDvE4LDKNzbiU2IjxTsG+54nrWA5Oeu3WpCXCQyahW4t2PhK/n0/sNf9z2aDk2f+jHILKOHOw4fJrVY+SCIIGVRpUB65CjtxKZcnucP2nroVVktkPSChWI1DTZ6Yqkoi5XnoTZf+ZCIECbcPz3D/E5wJFELiZYnr976EknUYjqjxcNagbecpQV0WmUomsi7MjV1hE0O/T6c5jDMxDXjBHKtwhb/LFUaR0uaOJU06KW1dzV6dW8LxTQsBm6j4Bxv3H5g1FviiM9Jt4omMic8jw0kyzPeefE2EqXR8iLx2sEWtvvEQF42Me7eXvfXHjKkmnTyCssFG1h0auRpg0FGLOGjZQdHd7nX6WAoZ+JRqT80BebP/uqHcJ8lR/pu7uE0PdBIBpWH/ISkcHWAjC5GncJLrK7d2fTH6/Qqv3GJI42qjvzGYTbpIHuNQyDOaMDAMzJ1GgztbWxpcl+xfnPSFgCwPPFkd2MIA0TfwGYZdRQ2gosI6LYQR9yLtOTo5K7PjW8/3mt1cgkMGmTd0MsHgIY3boOswvUbLN16LcHyrEGHJ7+2a2Efo+elmSdBmuEWC74X8W680hsUSA/FsRShNw9nqPHG+z8Q8nNz4zfDlu0H3YIBAZjhipF81qLlhCg5iXw/U3eMZ9LSQQHB4Q5R0nr5jllExI9wkGgtYZw1Yq4RZQ1wjU1mhsGEQbTE7j/Gdl1VlTiz/0Yi3Q0tn3o9ZAPLjZrmhJVepX+2W4KW3YIazyRaduCyMizeshFsc8iv14bFysYG6agM73U3CxCuDQvyte//AB5/5nm/YFsJv/mwAjCDNlxHSxtpALDdFp0bCap1vl4rvWx9ukLC0rDqkLKYnRtd3A0byOaBxu33fezEoelhG/1LE6i8xPSNUfD2FNSTcA9WNBdI2dZwNYGi2Lb0Qc6c1tH4XXHcrSGl9b3IuFf7oGcbWUpK4RVJVsLvLhEb6ssBmI9zyGWIwhMGx/ohk1kOvQwuNa/coaqz2yshQP1QABhtFJizS1InazA9oPNIxhLJ2doTkG7c3PQfkEsX9nChd+Sdl7Re2Wy0ApIrjSjW6Ocl1jJ6LQOB3Zo+ALaVkLHxPb+3XL6PUzlVENenG8jjGlf6ZH941MuxdCHr0y4kp45ECRFjuh2a7GfzDno9mvA7HK22qOn99NIaEfs1H066kMKiGTs9IjzRx0SAHdDf3vibH8Gv/8VP463/4ocBAM1ag/geHS+6G0MfRbCnnEwBWLDmdVpLmE3qAQPAaG2BqXd5ArqONLbMKILrPi3eItNIOLyn2hCwaw3sPNyPl38kLDI7P/cpKL4OeprAciWo5sHtqNrWkGsVMr6H890cco2ulZhLtEoBKyQX//xISt0BSFIjWgHhjWwTFDwBF9OMrDW5zzrY2fPPZ71mYVOD5eO0ICW9GkPefOyP0zBJC9DGit+LSSO/iRA1UK/hjxx6PSyUhq0E4yPlq6hmyDaALglHAWKlTye7NRLeLDSz4J0bLSSazcZXgrZUYbJfJBBMZFKVhN6sPQJgNppjFShsSAYSNvQi3f/5BTUxvrcsGhmIkJp+x222RSMg+JkzWsAWERTHu+leIMTFY4VmTSMeq+OvA5pjlLMP7BjAAIold6tWiFAWTRUFMlhm/CbFxjagOJ97BiqJArFKhusdlQKtiIKLUy29jjnei1FcqaAOOZKvCdW1nsWI+7x56zaQyobLKizsrtMXB4ndwzoe/tr7ZJyMk3EyTsbJ+BM2HsnKtWoUVBOhd3niA7R1bmiH7+DBbGWbauG3Ic3QkDMM7xAx7SB2BjVnLbS0sAynWatC2ZsY2CbQ7W1svdwDVWApynGMeB6YgzaipA2AUmV0Gfnehsi1r+jKKkae1R6ynNWpr06VND6lRjxBTNtbVwnmg4Rnne6vdXFrb833naMsGKpDAHqPKrjOzgSx0thfEhRc1nEwy8hrLGep7ze2ViLlkPVRVmBaZbi5oLLlaNnB0QOqeFW3QY93tFJYjA+7LkQFUhHMCgDzWYZOt/YSnknT8X0gpQysEVBcoepFRDt4Pv/kzvEkoJf/07/nv770WXaTOSLIzRkeJPvBFxWjFpNZ7uHfs4MpImdioKVnMLeNQpI2xBYHsD6aY/xNVOFe+9aP4tJPPuufL2GCiYQ9TCFG1hcjog19MpMbgEMlstcyVEOBoz2qjGUtgLss50ksGaU7lmgjIdlAxFpBUiUAslLHc01jwLjnyghMr44Qn2W4VysP3bVvYttGkfbs9XitRONcjCIDGOFDylUt8P+y96YxkmXZedh3l/devNgzMrOqspau6nWmZzhchhKmRWhwAAAgAElEQVQpYyBRECCBFCzDEgRDBmQYAiRAsGgaJGWS8hCyBZISyeFMy/ICGjIsmIYNErYMCIYtwJZNQRRJDcme4WzdPb1WVXdVZVZuERnL2+7iH+fec6NI2z+M+cFi5QUGU9kR+TLiLffe851vyc+25C+Fx4u/8AX+zNuVkxdAiNfFnV/6Wcgq8Q8YBvaCGoAxYcWmQHo1a8jL+R7dn2LrWbY9D7FR8Dr1S6PhCSrFz7kdWaBWVLEC6A0bZvdbK1Gf9SAiOmBSFev7lljTUTKkHLzdcmWKz/l2rxEgNCymXxkJuUkSG9EJ/m7moCVDpTycS+23ZEBbHQUnuK8MAO2u42cSAPw8Z2OWYtgw6x0OyFaBCe6ImRxbGF6knm62FOiGHuVHQVExS45eTgNirZP/ce7S/NZKrObhbxkJZ1O/1/YsEJnK7dPfrXwmF9ef/OQ/RX+k8BO/8xcYfk3JEuD/d1FC16XfFa2gh2SPIBzTSb6jpSJtmg+TVG+nRrMOE/pKQ68lTOiteuXTItlKdlyxJdkbyrhgX2u5d1dkBhvhMb1BsGTdZliHgOIss+isYmvButPYhNcAcoECAHeaw+wpFCcpcSa6vyzfnUAaoFjGc1GgCyke+YliEstmU6CucnSBtKRKw7pW74HJdINpP0DGXuD9JRnwL5sCJ6cj7OxQn7XpNF59iaL8+rrFB3PquRqrkPdTss7D0wkGJS28bZ1hfV5isEPHvza9QBWgZecFpPC8wTjDkCH032uxtz2e/wefZ8P5buqQLSRkhGPrpPuTFxpOO3zb/iMAwFg3DL03UqENJDHTKNhO4rmDMwDA4XyELvREX/jln4HserznsoVHFkKnzb6HCxMOQPpGHzWYXgAhxN72PPpfKfletTklMgHEAchyy+0IWAEX2wi529KaesgmbSaFA/JgOegywN+scC305asuY3gXFzpM9oEcY/uQcfPXKshl1F9qDmkHiGPQfyT4bzUTwYuL7SU9qWzDQhU3AaWBHaQNXnxmfOaI0NdPD6cI0Xplv6EFMOrDJx28CvBo2NCyE5D2MO3WNBgh9NxB9g1vDtom4/6lnedPptHIlCQjlxpeAt1OeB7W6dg+83wMr2jjFJ/z/FyiycOEEyBrHWDhbmZpwxSP4wEf5H7ZUcZpQN10C97NSfMut8hPEdoXnYCqBLqwgTddgn5V9XvcqzKHLsxvxblEF+QyzY5HeSi5b/4nv//ncO/PptB2WaXUHdnItPnQnqF3SAAunTvnE6zvLh2aLsfluByX43Jcjsvxe8czWbkOZIO+VLi5f477R8GgXRNRKRITZAOu1AimpX+rjYAZeXQ9erH3MOPdnNcabmKgAnwnpcN0RlXaeTtGt+MpZgmAnRqIKJdoxRMCal94jvia7qw5mL0+H+PVlx9gEcKjN3WOMpB+cm2gpGczfe/BAQIPjqdM9vCZh600EBiG+1/yWB9EODCwAEPR882//cMsi7A9wIbw6Ws7S5ycj7bYww43ZyRDeTgfo7OKQw9OlwN2hzo7HkMoBxUgs++5cR+lomO+v9yFCb9jnYRSjlnM42HFVaFUHqPJCuvgHLWoemw+oYTHSWAAAxT+PrlGcp7v/t8+i/MQYm8vsicizj74oR/FSz9PEGVxKtDseAzu0zlpZj4xiQcW6CR+7Q0ywZhdueBM2+NqiEdL+tvT2Rp7wzVkKAfGgxonwavYekBda+AuggRmYNAGOQwWGXzhEvHECSbA0BcKlcbAoxt5Dq62A4fBdaoyN6uCqqNojFB4NqVwqwzlo0BMmni4bMslCQmpgaP4vlWML5wPuAKTnqLQ2ht03dQW1IhGsuxn9VIHsdQpAq20qPbp3+WxgO17DmcHwAHx6cOE79YoJsqglcnoXpJJSy/c/12nYPJQeTuJepNDHwRYu9YQIWMZTsBvmUUASKSxKqFQMArWCGQ7gWTkBfsYY9rCzvPke28EzB6dD7FRyC4kxxTanmeDDy8TjB1fE6H30exb9hCHE/CZh1hFyFtABGmelx5CAiIYZNgykYqQOyDm5/YtPJJSBtJzpqqXHuZ6CxVJXVYAAfa3Akyk8lLADVO13ew4Pv/5uUKzm679/T+TSGORVcwkpp5jiY3LwYH0sd3mo6fyIt3rdiuM4Gkdz+Tieq/bRdlq6kHNgl6yVhQgnNQxbDDtsgRTCCNIHxYnulc25BYDCjZ3wsOdxfSYHOIKTe6ilvA9l4yuK7nlzpKe6ZjV6BX9h/nhiB/I/v4aDxYTtMEpxjvBLlKZssiVwTw4TCntcHhG/Uy3zFg2I0qLYtCiCZKJxV9YozoP+sYHGT28WzD43R8kg/Ln/9PPIws5skdqisnOmhe1WblhTeT16QUeLca4NiDo+uHb+7AbaqD1X75AkRm8EMLSB6rF3TVBwY9XQz6edQKbuqDQcgCrdQ9dlFVkDk2nWUZwseyj3DLMd06g7KcJN25MuirDvX/3J7A9Xv0saSXf/JkfZhZqvefx3n+YJv1P/chr2BzQ5ygeaZocwhxwZiZ4EDYOq6ZAFWD4fr/BTrHB42DneHI8fmLBfP/f/o940yKPigQ3DqhPiSbeDJ51oqITBOOCepS+cHDhg6idBqsjOsdy0MG0mjOFhQSnAWHQoQu2ee/92I/gxZ//AvfFsDXvu76DyhxvkMRpnu4fD5Z+AGHxi9Bj5rB6NSxinUB2pUIbzfOVRxsC77uJTPaG8TgxWaqwBMVGDakTyelqixMhxi12JmucndOGyW00P0RrIwEv0IZ7RjQSPpyC4mAD0ykYbEGT8dL0PGQgqToNIPNJWwwA79LfyiuB6nbHcht9qlF8QBsRW4bWUNyYX0gYl64v9x4bwXmtAC0s3TS1jIQRnLIEjy2tOBUC/W/QM1vvby1Cbdo0iMLSnBYXMuEZykfIkrXhvKq+gTjbcqCLc11QKkQFhVdJptNeMUAncOe/+3t0zF7GC69aUng8fxYnUl9Ye5YteeUhqxRsYHMqOgBAmKcfFn4mF9cP6n0UOsO4qFkIfuvOCTJlcXRBE2L3xph33XBgnatsAK8ydC9Sz89c5Emf1gL6OIe/Sa/ZeY76g1BJRVJFnKQsaf0AumlNCN3RawFdA8sXw4dVHtnjkFixKzEcVNiEyfjWlTnmdcnfa92mvlDb6FQd+TQxi3WO7jmLGDVSr3MmvLQTBb0WeOunfr/+MD+XyNbhIz0ocPHHHO/kjz7cwTT4MO8N16g/GOFLgdjitUc3DWSYh0OMXzrhsPSPTMYTeK4tZiVVGud1iRtXjjHOEh3/jROSB3VWwVrJfUTZN+wz7JyAlJ4IVQDu/jt/64nvEL14RSvgCwdxg67983//81BS4P9pqNozKSpbEcmEiSZGYlEFklfe4YWrFN1X6g57xRqn9YCPEyclUUuKCwtztrTgRUE1Cmbgk9GA8vAiTrhJlA/lAel5x2+WGUszuoLMUOJiIhrJ7yturfDWjyd7RwEgrh3CUdwhALg+YBY5mrt0b0kF7p+6RkK0EsWHITrRCtShLy/Xyc9XWIGuLXlRRqXSvW9oso0a27t/428yAUhqDwfHJDWRuUTCqmWyAj0pcHpa8IQutuRqkB5SO7KgBMmpoqe3EB5mmSWTB+W4r+qVh4sqrl7iEACkr25yugDZBV3H2HO1hYcZBX1pTVaOxXHsDSNVyQ7JNjW1vunHTnDlZgaeF1gAGNxY8ibabjKIlUb1SZpjhAQQNvfCCrYulMIDtUR2HHgAfQ8/iv6Tgu65iA5c5EB4zfRSL1x0QYq3tbHihbewkH3PhYVwSUvrMkAZ0owDgD5PVoi28FwZu5wkjXngf0gr4HrhNf/k5utpHE//N7gcl+NyXI7LcTn+gI1n0qHplf/+J6D6BdbnJVedn3zpAaZ5hd/8rY8DIHG/DpWa7YFNALKVRzsRWN6Jauqt3o3yyKY1yiBKXy7K1O9qFESjuN8g14pZfqb00MFCsXcqIBxQXQ3s1S3GZdYz6GqNckQlxmRQsWXg2bqPUa9JrkOdgouC9ZV+UtKx7cDmBFTIhzWNQnaUMyxczFOv2QugnQYofK+D6lnoAOPujJJDU3vUh15JuOeo6ry2u8CjE3JQGv9GieULDsXzVOVeGa+QB7rkzcGcP9OL/WN0XuFrF9cBAA9WE5xdUBU4G9NFWYd+oJYOO4GZ/Gg+RlNnbGBx768kN6LtjFMIwAufqpdOQi+39pkCfI5UI/DN/3gre/Xvfx4uQFdXr80xKYJTVF2yPGicN5gUFT5ckszr8HDKlZ7pe0quidemEVy9AAHyjZWO8vyZZWlYqrTZFDC1ZpMEpRzqwEb2VlBYRICFVWlxfY/O7dF8hDaw18VGEeQXK1wjUB7TH6v26TNG6YaQHlev0jGaTqP5V7tsvegKn5yLjODqSFxkJGli9ykHudlyb9rqeYpGPgl5Dy2XdEIk9j1MgiiFEcHRCnx8PmbsXUZoWXpmO6vMQmuHOpwHX22Bd5nDcLbhHzergmVXvlF8/P47OVQLrG4H2Fam6jTCoXkwxXcqMaG3h7DE5YgBCF6ndkM3csTYDs5RqjTM9kejoBcKZicoCKZJkdC7V6CLagRJlWyU1djCp546yJxf79L95L1giNhXms99dkp91AhxO53Mc8hsAuyYBS+49SEcACfY/INeThBx/0P6Ls3Mw4wt96e9AqNcftHiwx//yUuHpqdt3JqeQw8KHFy/h2+cXQMAnGwGuHe+sxXZlCQ4tkj9KF3RTbr/O0GKcC1NypsbDt28B0zCA+MEPZQAhUW7ZEfmxgZexZvKw2WBAJETXCLDmpqVHVsXducFVCXRhIlz7gWOgxuUdwKZtpiNQ+yWkzg9C324WibdnRXwmYMKXp92ZFMyTe7gAZSnSTIRe5G2BExwzZnurSCExyu7xwCArx0e8DkQluLIomZSCo/ZlBbE8+d7sEOHXNNJPuhf4DvGHwEA9vQSH7XUf924HJXN8FbwPF6f9llmscoNqipHEeLvDkZLXB/QzqcxGg+WO6zNffFzX0gLVSZ4YyOsgHBpMvAypbBkF7TgsCTr92A7bmJ4w7RpM6yC3aQQHpMgF5oUFd48vor1IthKnuSc5lKcSnRGkJc1AF96TgBxZdBaM4y4JaNQKTVojYIWRl6APCaTpElt6oyt/9x5jg9XZNOYnWuoqJ+O606TrvXeVyJJRsL0JOYv0TG6T2xw9DhYTF5k6LsED37wQz/KcLtsBVyQWQgbNnJxoduSrriBB7q0UKqNTItk7shyMJ73vkkWkPOMHZNsGYhf8TUjk+tSpZ7sFfYt6V5Bm07bqScX1bgR2bKarKscbp3xplRI8L/zJXDwvx/h3l+k+1NX6Tm5eNkCEmiDz7Faqif8iuPwGmgnQFRey0awBMb2HUQnmTAIgNO1RLA1FGGz0D4asHTHFkl6E/WvzSwslBcCYB/mwB1ZBwelnmVdtxx08IvoKodgqUi/ly8kmmjbrTxELdNmx9FGEQC6cXCUir147aFXaa6rrobrtNdCeKCNG5Mt7W+KwHt6xyUsfDkux+W4HJfjcnyLxzNZuQpBFVWhDJ4bky/tl+49B/VBL/J80OxZ1HfirlgAH9Iuzy4E6plAERmeBbgqcaWlDE5QNTO8usI6GN/7ThKsFVNsrEiw8MilillQWDKmVJlpL5CHKq2tCtgyhaV7L7iCA4DOKPTy9HM0azj34NJbHeWASAECwml0oYKbfjnHV/7B/7uZehy3/9ufxXe/dA+rLviASg/uLkST7rDTPlv38emDDwEAF9+zwFndxzySgFQHGXDJqdpgP3gQP+x2cGJG+MSVQwDA6+vngEC6qkPVGtOAdntrVJauzcPDHchFxuH1PktkJOET01TWAtNvCsxfCVXQbgcfKqJuHByDotd9LfDi50gmojcCuOOhg+nD/nDN6UJ1pzEOEPHheoxqU0CFyiN/4QJv/vn/BABV0y73kJEFbAG7E22SUng2ECDGUG17JxiJiLt7NigxEk1ITtIBFRjPAnx+NoELe+gIQQJU5QgjkC/ov1397Qa9u2R6sX51H9J4vkcOfiXH6oCuZ70nsLlhmSl7+xc/x6x34bY+b3TGio5KEjD7kUlMPrTxGL4TfJ3koIO3kitNv9EM0UuXJC8yt/RaHB6JSVw4+ErBBwcioTxkTJUxkghMW5B3JDW6TqKqtly8RHJ96u1WqM/ovu0feYiLFW7/Q7pfRa9A/QpVsfkyh1NAsxPhWKAJjN7t/FOvPYRLsje/Fd6BwsKXFr6OHuI+Vb9CELoRfIF9aZMhhkrnIPoWRwTGDFO7QdUUBjJ9PcjZvrdmdMA1CsVp9JG20CuZsnsDE5reSAznePzySGB1O7UH3MQ8gSRE9YNst3yGtUNRdKjCfIla8rWOMsWneTyTi+vt/jnyQYZ/Y+dL+GZDfb3D9RgfnV5J9oR50gd6I9GO6d8nn3ZQlYQNJF3qZQTY5Fhj5y2P0V8hBx8Jj0dhEa42BYzUeO4mMUqHeYMHC4La5g/GyC/SxOczDxWgKtMpDhuXEpjeuGC7uV7eoR8W01m5wUeLCes8s9yQqwyA/qhBXUWpgAu6WvpbshYMozoNvPqTr+HNn/7/XmCF9PhoOcVZ0I2a0x58cOnRLUkMbDhfV8dLXLR0st4+3ufeIABMb23wQk7Q8r6+wD85/zQA4N5mhleGj/H1ADf7eY7yenB1qun3D3ZoYpu3JZ9H/TDHtqWc6AREgM3NJKWO5OeSJrMg/yjHNapwQbNTzfpROgj495o9i6tXF3h1RklEy66ACXB+f9SitvQ4bUSO2XSFOkimqm9O8fLfowXa9wITNOo/c/9EVJeXqc/qRfq3axW/T60lpABkYJCavY776y7IkYZBIrQ+aJB9QIuC7XmGgVUtoNfA/pfpfb27ZxB1gIULAS8F+of0xU8/qVFdCzB23wJepInTAy5M2s6LlEhkJPSF4vaGLcAbmGgiL8qgrRwIbpd4K5H328QyBpiVXtcZMKfrL0tyTxL9GDOXFmQ0QQqit9oz8XMBtFmOSO32+mwEbNgskdefgBrTF8gygzo854//qMTF8y9i9+v07PV/630U92hjopdDiM7hoz9D92R9xSUt8db1hKRzZwPz3E0dnzupHcpBi/Vjer5Em2IIY685spnRSr6XvEBqdSiS+G0nb2WBV5AvBCqXoboW4Njz/AlORjdODk3djoWM4QUDm/pjEmh3LQeum35yuZMGaEvJcLUcGGZuA0iynNMC/ec32IT2CUHvUWOLp378IfgKl+NyXI7LcTkuxx+s8UxWrnF8pX4OC0Md+kIbTO/MsXgvZGHppLWDBOw4sCBbSSb/SLvwfJF2Wxd3BJ4riFzyfbO38av5xwAAX2uuQxUW/RDy/er4EO8+JqKJ8ImV240dRD959daNglDJN7bIDDaheisygz+yfx8AcH+9g9bolBC3pXUssg6TwKg9zQYwHw4YfrFbjL5ugCc0rq9+9jVmk25uWgxvUbW4M7NYNznkO3TudAaIUFEQ1OUZqvrodMrEG3t3iGIjID9FBKRH9QSHJe3wb2Wn+MaCKtVVW+CrH91gh6ll0eLscWAMNhI2M3h0Tj9bo2DC3+61VI3ZoAd96+88yfLl8IWRRzcCu2W1yyFEIG2YAZEx4i5ciC1R/bRF1WZYtPQHesrgpCKotjIZf891m2N/sMKmo8/VfHuLo+NECBJdagkAibFre/6JaLys7OCDENVZARffN/LUYgiviUqhP6PrmykL5wWOv0QwJbIUQiGNQEYAAEwJHPzGBnJDf8sfnQDB23Z4d40Hf2qMJrLDRxY+RotpD7+RQJuIS/E1ZA7lmKBxayW6rmRWtKoBF0wMrASQW9bfCpvIZRAebZ0xQ1j2LIdC2Epz2LsROcSw45AMPehgAkFHrSTcXpcIYa1MWtZaAYVFHjyJ24sC+jRoSIeOWwrwAnZogUd0rVe6xz7AduBQSYGHf4J+T3/6Y6wBd5r+1+6Ec97hSdOENp0D4cGMWtGz/JwPRjUORkvcDahLd5zOI1xi3sbB8Lr2T8TU2cwzZO+Vx9s/8qMAgNv/1eeCeUNqn0QWcH6s0AXUCWXIbK4is10mrWyjIKxgr3SXJX9006e/66Pnd+5THN3VDeqgQy+GDU5PRlBnQbPeivSMbpH5ntbxTC6u/9c//w7IXg9eAt/7x98EABxejIjduRtwrODyApDpgAh9MZdL6MfJOEJYgfpG6nMW0xpvPCYG8v2LHZaMmE5BCDKoB4A3L65xEoWqUx/CFR7eSNSnAXeWHnpEv6Mzmjgj/GWcxK89fAEAZZ52FzmyAGMNyha1opt7UxfJqWhRQFvBD6RwAtlZErzf+S9+gVMwlCRDCwAYva9Q7dPxDvaXePud68gj7uFD2DOI+ZmfS3hJL7Z5jw0O8guyeXQB1p63JZqwq/iFB9+PJsCqxklc2Vnir93+NQDAPzn+TvQCU3ZZF8iU5VB4e9yDihmfA8rLjQvIp370NXzt87TAylbA3aQvI7TFoGyxWATq43meoK+egx9auMBu1JuU2uG8wGywwaN1yK71IoUEbEpG/G5N53jnaJ83Z2WvY4s9AdqAWLbES71QrwAIj+E0GAQIT1AoACWpdwgAttYQPQsXWrD6YcGw/3PXj/D2oyvohTbD5rrlCXb2dY9snaQaat1CvE9sbeQZum+7DQB4+BkKBYgSEq/SZsnbYDIQzrkwIjn6WYEqcAzQCSB3cFmYmFUyTPB5YJNGmcuWLaA3EqJOUiK3zNAF0xFVJzN4KOp92wj7Nyk0QFjBwfJ0UEAe0zFc7uE9EAVuIkuwrbiQKbg+mNa7IuLyAK4ErsMqgx162OjSdmAR7U5k5mBXGTNl9UqlZ1uDF5l8LmkjHWHblYaLkO6wwVnVZ1cyMWk5qQpQbIgBAMiSCYYwgjfLekX9y3jOu4nHnV/62fAhJQYPJL7+OXo2bv/Dn0cWNhiuAKfpdDMHIdMGXK0l5FFgEkvA7Jh0X2TJgEMgsKdDn9XDc2626dKN0FwUyI+yJ1jU0f3LyaefLfxMLq7dxEKWFMv1tcdULQnhoQ9zdEE/pkcdxH1a4GwvJZdYFaK6olWpBt8camhQ5Aaf3Ccizv3lDuvkJuMN90cBoKc6Jink82QBlu1XFOMV+qWm0jDhwXJeYGElh7FXvkAdtJXWyifs2lqj+H3eC46zgiVSRVxcMe4grtPnGvYbqDbDuqTvrU8z6DDxyw7ozmnifHtzjfRtsf3SCdb15XOyb4y9694jzTIFpymqqv2AeklvLgq8OqZzJYXH/SOS4oxHFf78C6/jk/lDAMDoaoX/JfsuAMA7i310TuI8knu2hlce7pUNdLBD/Mqf+ym8/Hep16kc4EMV0s06FOMNdoPv8+n5jF2R/Fa4NUBks+jbe2W0wofHOxiE4xeZwSKEpbPvLIB754R+mGBFqQd1Wjy0h++SCw4t3uE8LgVcJvieyZTlmDMAnJaDllCJIkToCQ8UX6HPcffNO5geeVz5dertf/AX99DuxAVV4Nf/MVUvP3D9B+E3FcSMtLjdwQ4ef5qO4XKgHbt0j+gtwpqgfmec7L30TF6TtUybzuDCFI9he0mHKnsGOC7gIpFLCohgx+khISYtb1RcpVNCTgeYQB7MRi2ck2nR6ST1BAGICw1ZbS1qWdDtAoAD1KSlBRB0WaLtoGxEsoPUnirfcSKb8TUedHBd6oHLLLlBuVahnFWoFnSvGQneXLrSQS+j1tfDDS10IKJ5gI/RNBrVpoAMi+3N/XM00at7PkQ3L5gYCYetfmw6/zb3kJ1gBy5ZSdiw0YHyvLDySQgnXFUCzU2a3IQXEFsrH9myJvKmaCRvSiE92pAERJwUt0Xa25JhHfYgwvfqHSu0Y89aXL0B2r1wnbbId0/ruOy5Xo7LcTkux+W4HN/i8UxWrqKVEIqqxeUDgviKKxuUH59DhIqxOyoxvUfvX98QaE4Co80I9NaCKejdfgdZBqeiyRqv7h7id49uAABWi5Ihm8mNM9wczfHqkCq1o3aM/i71ZqtqxNCUbxVu7s4xCL3Z1io8XlGVVrcZ6qMBiuPocGKRX6Nj6F4HoxzyUGHPBhusQw9tPh8wlCwMidA5H7JSaIMH8UnwTmWv2zp9T5ODd92jGwt0Owqrw2DIf19z/8ZlQDv2DK/JRkDXsbcG5HPPMGjv4yv8xuPn6Xy8u4fb/yvtwB983w5+RXwar8+eAwC8OjrE0hDmdLruo201esGdSI4amHCOtXaoNjln3L70819IIdNji2KX4Nar4zWU8AkajOHaAFBp6MVWruRBDR3QgY9OpxDSM5wslWfnnN6oeUIGta41itDXcy6ZeMAKqpJja08K6Ghi7wmKjRAvSnCQd7cokom/B0Stk7j/XGD6TvBv7glM31jAlSFMogJ236D3De+t8af1X6LP3isgdHr817dKrvTaiYMbpXAKWIEsVFjWSPjzHFl05hEJxenGW/IaSZUm54l6yQxQZwQwMSnxR/nk7tMJCvIOQ5rkYtTtWIZRTZDFREYqHBApxuTLG4IQwnllo4i+ga23pj3pufKzA8cIgz7OULy8YslX16mUkSs9ZG6hgiRuOqo4ZGKxLtFUGURoA8hGsO+wGndgP3rtIVcKtknXPp474wW8T4lDi6qHKsxLfC+1qWKMJiS41jC6IU5y2KGDL6McyaUedKNw+xc/x8+saiQzeM3QcaZt/+0e2okH2277JO3xiryVIyKgxy1wQeenPJRY30nOXXraQoQEnaIwWJ3S89ONQn843Mfd2Kd7LktI0NM6nsnF1Q8MfGkglslQen+8wslywGkrshNMwNj7qsPihdD/MJGkEI4lNeQnaNKuO43f/OAFmKC/E7ViOOShmEFcB2pDD0llMoxKgheb/Qx70xV/vuPVAC9dJ4nKvdUM+wNiS7z73k3oSqA8jM0rBbMXFtqLHCgsZhN67/5658EAACAASURBVEdHO0wYgRNs/yYaCdsznJpSjBqGj52TsMsMeh77jel7Og3WIrZGY9hrsIpyDE02b3Q+aKJ0QafrGgX1WPHnsIVgwtHN8QVsmHGPdxu0I1oUr/yOw0m7i8f/Gp2fnXyDjw1J/nLvYgdLL5DroDUdrLEKxKFVXdBm5sPwBwRgr9ExxpMKf+Tah3yOf+3uC3yt3UZz4DdaQRFZofdeFB2nykyGFTZNns6XEQxZeg8Oqs+Uhe8k6yJrV7I5uRnRAsR2cCaRQoCwuMR7yyMFeUezdYT7zwompV35covyzUM+htsdQ1g6/uCRw+R1eu2fvvcL/J4fuPHv4+RPP8/XbX1dpAgxAUCCpWiQHjJAoqbRFMQdSXYiaXZd7pIdqBPwXnJfkmz0woarlvBGJCJU/WRKjivSgud1CmaHSC5JbmgB7aHCfW3XGjIsMqoJKSzRlaxLhCa7ziByi9u3KJmpswrL2L93ElWQipl9IHMCz+/R+966f417hUJ5jMcV99uXmx6mQ9rkmk4hKwyaXrQr1GytaZAjD/yGbkoOU9GJSBUWNgbB1wpqLfl5W1jBz2scDHnnnjZCoNaK32TxksEbwYu8z5PcSVYSvePUimpmSU4oGwEfIGhpaLO3vfGJ7R54wPVSL77odTDB2ckMwn0cF+xlBhkkU6tNjt4kzHs59YtdlDxutmw7Hz/9S9MlLHw5LsfluByX43J8i8fTvz34/zMsuZy4kcFojyo958mblw29p4aqQQCzNzqMJe0I11clpCVRPEAEgCr8zmhSQSrHGYq+lVCR6OAJ3okQ4zBv2AzCW4GzC4JKhACe3z/F3eUuAODD8ymqELSdrwim/d3/MuWNvvQrPw0AkMMOeWH4+OWgYXE2OgkXiSY9l7xaQWSnLKddZdNQZF107RE+iblN33MsVdtoPF4WkFs+spHlZ0YOvnDoB0nG5rRPvqbheMKBSSPTosJeTud/oFu4HwxwlG5Rne3hEztUcVU2w//07nfy5603Of7oK/f5OyyDU9Rbm6uQJxm64IF89dY5yhBskEsLHRiI91YzFIVBHWUuEnwOuhldg8jfqVcFM2CHOxvsDdeY7VFA+vXyAt+YEzP8cD7mWDCnJfJRizZUEWgUw2mqEjDaM/HE9dwT7E/f89DRxceLZCaSuS1zHAkpUsyZXhtEnY5vGsi5hHtMhKbp11u4j78EIJCYZiQJMs9fhdPA5mqAXEfJ4EAYQCw18gO6NsYoJlkJ7YgJHtmw6wShi05RoDyoIlcbwSYNZrdj9MS1CqglVz2ySixgOh9bz9Bqa4oqLERgwxfaoiw6rAOE7noCCP7T0Rua7/MmeeB6QY5M9x7S8zXdWcOG+6CucvbVFUaglgXePL5Jn7GW7Drlrcfi/oSRhPxgjUKF7y08TKeT2QJSlSlaATMM1zay9cP1tRuNV196AAA4Xg+x+OousvB81UhkNjFtIQeGK3FojzzA09YoIpWB/ryqBDsjOb9tMOGxeb5DHqtDnxjrZugYNVh+3AKdgApB83ajgYvoGkXkPxVCLDargu+f+hr9t/69YKpyyzDTfTjcYL3s8bmC9OxrDAGIdXiGsui88fSOZ3NxzR2QO4iVZqnDt195hLOij7figuQEmhBWfO/PZph9LUFTy9vJSsznSUZwcTjC6NoSLvRpfCWZCedvWpR5hy48yA8WEzRdulFVtCZTDu8e7kN9QJ8jXwjkIYFk8i7wO/8oLayvfvY1ZCDmrd13cDcsTDj+oNdiExYF0QogQJli2mI6XePsQ2KJqq+X2NwKfZO9CnYrF1N2QLMTF03PsFJXZZAXGvl5hAO3zq0A8nHDbGe5UVChJ6caD5cJVC/Qf/jyRzfxr7/0dQDAn9z7Jt6v9gEAa1Pg37rzZZwFG6m7qxm6Lk3ig1GNWRAWVi7HQUF6WOMU3naCe5+fufo+vnf0HgDgqJvimxtaCHNlcWOyQD2gz3ioRwwR68xSXuwyyD+WiielVTuEuOZxc0gJMaVqeWIGgDY4aRWTCsiAPEw8q3lJWpQwfN+yKbvbaIjwb28lhPQ8kVbLgielbNCy7MTlCuJxht6C7p/VLYnJV4nRLK5fhT89h7hOOlfZdMAyiDCF4F7s5noP6+uC5Tau8AwTyo50p11gO2eF4X+7SlPvN7Zji60+mSCGKR0kuASFay8qlSZR5YHccdvCAZzLOr22RNVkaIIWUnhBjGQAYqMZendKoms05ZmCJur4vm5CsDK3CksHUcfepgCQ3Nc2dY7mIuyU2y1bxAHpWHpH9L3LQ4/xPfqdal9jdVOg+hhtIMuiYzav9wJ2kyBqO3DsEqY3gvuLogm908B36F3dYNHQ86qkQzezsBEqDzA9APhFTudKpuOMB/Q5jh9MUcYcWUWWh3Gzo5cqyb8K9wS7F8pDxUVTASLmCV9o+MJxW8TljmVpshPwteSAjuxMor0aFsSwkaluhuNvHaNpdNpYbjJk54rVBF6n/quqn35Q9ZlcXOU8g6wzSCNYugJQdRNvdr2WcL20yzz9rnRjqlGHLHi4dq2G206U8AL4KIRMWwo+BgClLTqjsA4GEN6nVJPBsGbiRKYsXC5QTyK9XuGdv0UL6qd+5DW8+tnXoKnFize/sGWS8D/8XSjlsKxoohj0WvSGNLPVTvANXfZbnH005R6Xl8Ddf+9v8nE+/rdf48VQtuAq1rxUcQ/BbDQHOwPkORrDtKez1RPf7bzWWN0K53QjoGogO6afdw7mWAUI4HE7RmUDIUJ1mKgNJor6WN+UV9ELMX7eC/zxm+/z7x1VY3z3lbsAgImu8P75jAlHXypvoQhMsd+d3+TJy3mBftZhmFPv5zPPneD9gBRM8hp93bJEa6X7UPPk8RqRAYC0yucbutb1aZmkVdqi0A0ePCBpERrJVabXHlm/RRdtII2Aj47WHmQaMUhEoqRpztir2FuP8QfA6ia97/TbBKT5BABg/M/eAooC9j06J7Is4TaB9HbrJvdpz1+m4PeYxELHjaS3UOVE+ZAXkHEzWSnIDskHFz55/7YypSgNHGzpYMLGEIV9ItnFe8lxZdIkO87F+YA0qnEvO25TlbbUWz1uCS/TIgkBiqoDLeSwIuk/K8n9P4SFKVaoLfJkeao89IwWKqU8uk6hCfrt/kMJ048EnIDohM+4XPdwsQwypk6m+wVbkWyg5ySah3jtIZpkJlKXBfIpmbQ8mA8hhx1ckTYEHLsXNMe8uS8tTPQrzx37GKuNIEJZtDYtPGte9ZmCv9PC98JGZZ6nfnIr4IPeXHYCcq3ZHhpWsJGG6Tv4Xtq02L7na0PWk6kAkUsNRHKfF+zzbK2DsArjD+iYp9/tgHB81jM/xePp3x5cjstxOS7H5bgcf8DGM1m5+swT43FsoMLu/Lga4vv230H/08HhSLf41XdfoffPc4ackBPEkYceXZEbdIEZaK3Eal6iqJIoenMjsAGtpF157NMMWgz7tEt2TmITYNSmzqG0RRYgV9UQ/AsAMgeaPQd9//fviVyroAYNTIA3jZW4OSP40u0InK2pmqu/NMOVDzxEaMKqJvVfP/Z3XoNuwdWHKSmjFqBNeoQr1UrBDFwy4PYCwylVR7m2mJUbzIIF5L9alXBFqFhWCvUVBxcM80/nQ9wtqGJUY4/rPfq8tcvwG4sXcdCjnXzrFAZFCo2ftyWmOZXv+70VutDQ3NFrXB9f4Cjswh9fDPFFcQcA8P6717gf5foW5azCTmB4vvt4DzdmBC33dIe3Tq9whVqMGzQqfc9xr8Hb5/vhvQarkwF/rt4OXc/WKKyqBOl6iSfuH2ep5wgA2UJtyRt8OqcIspFY7WnHLHQ115h/3FP+K4g5vLxF7xu88hz0/ccQIStYlD3oq/R54RzUCX3P0YcjLJ9LFaJXnuUPzgUoPFRLbZdDBtgw2wjYnHr8AMHEskqVNxuLNJJgxO3+fvz/wkENO9g82A4akVjG0eA9VqgXORvOA0AXe5WVojBxvjZg5rDPLeW1RkP7vuV/wwiIRiXrv60Kyece3YIQkU576NJA7RG6Ue/24fLIeieWLvfDl1nqsWoHO+v42mOlU6DNll2gcIKQsWjecK7xYbnDnwUe6RjewwegQ1QKxYniP9fcaDGf0z3oa5UY2JAQnWBbQ71UMLGy7xRcq5hXoGrBn182qc0zfUvg7NtdMgbZtiRUoJ5zvN6twLbbvs22wikGFi6cV1EarlyFcuimDiffG35uE2u8+0NQuT6bi6sM/RkjkYeYsP1yhb5q8EMH/wwAcOoGeLAh8sc3/VX4ABdnPUMwTbgZx2XNk8bJYgipHUd1eQmGo5wVBLvJ+HO6EReLPrL7dPO1M4uD/yOHCZC0LRIBCALwIv3ed/6NL2D1J2iBmO6SzpITczKDoyXpUNtWo/drlJbTbz2kSTZ4tpD45I/R4t3edGj2k1WZ3giewAtt0cWUDgtIKzg9pj3o+Hw4L1CZDG9vaEJ3rUL/QZAL7VCUW4TylLZseXizOMfjjj7j2hSY5Rt8sKaF9weufAP/80MiNP3lm1/E2/U19iGWwuMdTf3Fr8+vw0FgFBJhzjclPjxOExZLQfoG7b0hjkDnxw4c7gYy0sW0hzLvcHpB33XUb3gjZa3Elf4SX3tESUpNZshtCCQ5akLP1XQK9rjH2mXZJr2wHQj4ucbgseRzwv3LgaPJJ0za3gkUQc9b5AbL4INt+w56peCC4giOfKEB4P73jzB4OISwL4TjC9z4PylW8fxTE4b8zz4h0M0MX2u1UXBXgvPUboWD6QUOF3Q9mjqHDYueuNBQ8OjiYiX9E8krPEkbwKvfD2cCwOC5C/Qyg/0Dkp+993gP7VnkB5B+Mi4KXnu2BWTdKugeFLnlBcidFrB5tDnzEIXl89jrdawd9scFvAB08MFt9x0nOsELvjdl5qC0RRs4GZvbBogEndsGEB7iLEQumtQXRhsSeYq0OETfcL0G+0FLA3TDJC2CAMTdkv8ND24lyN2GJaoYOthF2hShlUBoS6lawHEP3UEIyclDJnOMU9rCQR/m/DzYUUq7kbXkls/qFi2Q8dn2wqPbD5NbR313OaMbysicYWBID9k37GhlWpW8nTvJEiaVW2TX1nye6k0OEzbi6vDpB1Wf/m9wOS7H5bgcl+Ny/AEbz2TlisIBhYNQHn/q9jsAgI/1D2EhYMOW8I36Bt68G6qjjN4PAJNRhavDJc5r2mVu2gx1GyqWVkHf73F1YIuUvCInDXRumehzY7aADvTMxcWAEZX8VOHX/3FiBP/e8em/9gUUSzrG+ioZ3APAuslxeG+XqwOx22B/Rq8V2qLuURWyuuMxek/CBDLD6pZI3rOCROSxEml3LbIg+HZOsHerqjQZu0cXpjxBf1WbQUmHKpyT3rBBdTUwhw3BSDFsXGuHIrjuOwhMAlOrshkqm+F2nzIyT8wQf/nmFwEA/3LxMl4/vInVRSCNKY97A6pOL46GwWggmok7qFCViL5hGC8vOqgXaywf0jnRFyr60GNTZpiWFfavktxm2RV4HBCAzUkfr997OUGpN9a4cYWg7MOzMWzYne/tLPHYCRTfoM/YzBITtzhSlHcZE2e2cmYhPbDKWDZVHqyY+byuii3CESX1RF9Xn3nU15J3bv2c5W2zfpzhw++n8+MlfRYA8NdqZJllBrLMLMpwXep3Jjh80aOL0qKTAr1ofhAZqFWQYbUpBELWlOULAO2McoMjGxmdZNLMetmDGFd4dBEDEJCkMpmDkykswRcOdhoNPiQQYc9RR77D8dxlibUcDRlkqGQ3q4KfOz+lEIU2MlmNZDi5KDrkgag4fzRGsd9uHd9hcydUbdoDXZL3bKco2UFw34p+0VsBAl4lIpg14glEA8Iz01rYYLoRgxpWWWIHZw62v0UWGnfAcYBcreBABV1LureqLRJZQNHkJrwWhlopSgACIQURuh7cl5TyExGSzGO6R2jD4nwA7wEZJEhu2KEok0NZW2dwwdpJZi5y3uArDR9+xyOkJwXHLG8ku91tKZme2vFMLq5irSCcgs88phnBqmdmgC+e3cGbA1pQnZcQUce523I49NnbM6zme+g+Tr8HL6DCA+mMJL1YdDXKPEODzguUeYcrI7o5J3mFndA3zG5YvNER1OiNwJ1f+lmIc1qQ8oWECPfsWz/1wyiWHr/5y2S+fuc//wVE+/pJWaO7oliysjdZMfwyLSs8/GPUa/P3RnAF0JRh8pLpIQaoB8gTovTozgP01UjuHbmMekVs7G4kazyF8Dg82kURItBMp7nH6lcafmTYKu7qeIndHsFCEh6HDU22p80AG5OzzKXUHUDKIdxf7RDDO9otOoHV+wTfF0EH7HjCkqQpBTDdXTGLeFOV6E8r3HqRXLCqLsM86Iw38xLdaI2lp+9dG812bdmZhqoEX1NzXTDjWG4Z9wOAW2Wo91NPNI5u5Ilt20vszORSLyArwX3Xal2k32s1RNB8UqqM4MXEK5/6yWMT7HnCZN932ISbxEvPmkVvJDGWYz9NeYb/zNjCPhgm+cfIoI7pNl4QJB01k9rDDeOEuBU0H/qSUbfoC5f6m63E8vEw/e3SslNRNPBH6HVirfl7Cp/cq9wmLKw+/T0VpEmYa6Dv0MWFZWjQH9LxhPBYoQ8xp+dLdAJymPr5sa0iBx1J2ZotHW9wGZLawXrNYewx5g0AXwd2MkLahPrMcd9StoJ033F+KJCiDSEg/dYmUbjExF1puLFBHtoFzkq89x/8KP+tFz5PQRUuo/ZOtNa0eYq7cxmQz2ruuYqTAZxOcqG44H/ttR/Gp374NdjAku7fvuBCguIQk9rCNwpttK2ctiS7CnI2rwEVNx+F5wB6pS26RnNsoLzQgAh9+FlyrHtah/Acd/GHf1xcXGAymeD2z/w0ZK8HHDTQQaxcFh2qJsO1KVV7i6qH+WOqbGBFqkAtLUjugHq10+kaXdC4bTYF/FEPgw/DDd1Lk2izZzG9cQEdJuHFqofJkI5xejJicXZ+bQO7JdqX0rHdnjosKF0kWAte3V9gEnrGjdXQ0mFeUbW0qgrOTRRrlRoADsjmkuOhVA20k3QLFGcS9UGo9joB2UTiB55IGYHcIoMIz9q4rOzQKzoMQ9+z7jTOHtLiJ0qL3dkKTZjACm3x4g6ZHbw6PEzymmaE1mmc17SoKelw93QWzodHU2cwW/6wMQ8yW0k47Xli64bJus2V1M8EwKSy6YwW9sW8D8Q82t0GvbLFtQndB6OswVfvkle0rzTpXq/ROe/1W1TBX9mr1DuNNn06yjC2njCnqYCLFZ7N0was2UsVJwDSYof7RUhPlRoo1jDLLFbnITJvy4Yxm9XoNjlPvi9eOcHhku7j+dkw5aSGZJvemK7T7mjN8YjrKodpNcfkQQAikoWMJDJRtMTrWfTCwlWdlWkR8GGTGb/PqON+pmsV0CRNqepZrmrtIqf+bCBC+UYlMlLm0A9xfPUmh1tnaeNi0ybG9RyZPmzZ9rlhqtREI/m92YWAeJUmcucE2mAIIxca2VLwYtjs2dSb7ST5k3eJmxA31GZI9pbxNbH9ufKUmxpzaeOCCgF+TW0kZCN47oBMi7BwhFro67S5b9c5nUsQ6hWlf+Jqjff/0mfx4udosTUzw9dwMtmgajK0QQ6mH+W8oVa14M18N3VPbLxRODa9AACVOZhgtCM6AR+QLZlZeCeSrlkCarEVB3grFBWZRbPO+ZhKW3RxzsIK9/7qT2GxWGA8HuNpHJc918txOS7H5bgcl+NbPJ5JWHjv4ydQgwK3RnPcv6B+VGclsszi4RntkqxJJtKi1bC7IRXECgz31vieA7Lfe+9iDyer4JK00Zi9KfDlXyRzh0/++GsIBFjIcYdNnayMPnPnA3zj9Br/LGYx5JyqgPmCjqm0Y2s+/0IL5QQnpWyaHKtgOr43XOPe4xlDwe60iCEhtPuM2ygBdBPH1Z3LRMpsbYH6TkOMaAC422fUTVgk+DLzT0g3ID3bHa4fD2BXfawPgun+ZI0ypNFI6SGE58Nsmoyr0y+bW3i4onN/Z3KGV4aPMc/ptS8e3WaTDWtph+vy8Jkf9rbyYkNllCcWJENQWx9fZxaz0RqPw7X2lYaKsZ0B6lq3YVcvHBCyP7PdCq98/BjjjL7rb9+7zc5CaqGZJa5agXbikjLBcWEG4QC9FqivB3SgNPBBYqOX6olek+sLeBVgN50sAds6Q54bFKFi7BrNX84aBVEptG3I3jVX2RA+73f45AuPAAAfnO9iuSqZ0dmY5H7UbXLonoGJ5hZOcLaodWQ2L7NQWTqyo6QvA5bUeE1VTvxu6GRyWlIO6DuWZCjluGKEpPsq9u9a4eHqdIxqRfe7OiygW6Ddj83NxMyXww6+lPCR4dwpFIcBbiwogCLeM+1zLXxIvAKA/Iy+czchQ4YI8wojgAjLlxYYdfBLui/aPcusXL/WJCeJLZTCI+KvXifpjT1ooDML8TBmRjtkobqzBUmyXAgpF41k9rHX1NONjmJwgByFc6U89yxto3D7v/55iDJShAW3Ul6cneD1d29DBAWEyz0++KHQavrPPg8RpykJQk+2ZDEqS7yFepNzyIcZW6gwb9iNTolHAEQt+FmwQwsEAw8/6tAbNsyyd04iOlYMfzddk6d1PJOL679586voDTV+a3EHm9BDOBgt8fBijK5KfrBMWHB4IuLJe4H7a1qUH5xMYUJfpniQo96lRRUA8oXH6uXgs1m2eHnvBIdrWm1fP7zJCwYAXgilAAptcBZCoOu1xmA/OOwoS9BzmATrhkgYAKV7mFalyUx71iL2By3WoW8oOgm9kQxHdTOD0VWCxYxR6AvPfVuvSPcGAD4jCBMIMJcXDBmJtcJGpf6gyz3DrOuiw60dIv20TsE6CRs1hqB0oPjaPLjcPH/9FN/ev483K4Jj3RYyNSgb5Nri+JTOI8WExZMY+pERuVIeOvbDneBr65xA3dMMaWVnihfkl288xruP9vH4iGRAJ5UEt1OXA7zxuGQtoVwpIEyAruegA8HIlB6qEcgWqccVF1c4cu2KsKABOOLOZx52aiBD71BuJOwebQ7yfsfB6WXRop93GI5JB1ybjDdVR/MRzKRlRyvTKbgQlda2EvJ6IFI5CddJNCKc/3YrUs1IGJulBcOmXmc5aqC1RResO93bQ94QmOttsnU0EphnyZ5wo3mx0NOaHJBCn75d56z7FX0LoR3fg85I5jt4L7k/54LtaCR1udJxTxQeyO/nFGEWzmskkFFEnkjua51E+VH4HDPHwelw1GP1XZgPtnc9JqTsRMhU+5RAFdrdKsLCZutvaZd+Z57BdXmyE2wkcwVYvxy9l82Wxaj2uH37GB8eBYmZ8nDLjF/jHWSYv/zI8M+xJfD6O3cgVIqje/+v/hhu/zc/R+8bAOosEDRLB1Vabp35EIUHANWqgDjLt/gZwGRE85QbSKy+ucPPlKqT7SO0Y49pbySaOks910c5yvDMyC6Ro57WcQkLX47LcTkux+W4HN/i8UxWritboLMZbvTm+OJDSgypVlPYaw102FWZVkIEaMMNDe+2IIg+fhocj8wiR/kwEGouyOx+9Rzt0toJ8OrLlHTx0ugYzkv0NO3IapPhzpCyIq9kS/yLE/ocH55Psa5zht0cFFRwC8q1RaMtG1DsTVbIwmtCeOjCwEXfznnOcNq60iimBGXagYKRBaeLjEY1emFnmvUaLDYlZ4iKzLMAflvYLxuJ4voa9Txw9L1gT2a108IfF1CBsJPdsIkRLDxWXYHT1R6d83XBhg+FdPj0rY/4Gh2bMfqKXrs1XuBhqNp62sB6gckkEDoGLacSuU5R1RqtgCuFLkD2rrSc2dorW5SZwdXnSOpzhBnDx+8d7sOfFsjnWwzQQCZ1hUd/rmCjI1fukQcf2Wbfop2Fa9GJJ1iXQCK2wQG6EuhCOkqsvIBEfIlkp27qmK0qhy0T2y7aPqrc4KgjWLvfb7BaBd/kStPfCGS5ps64QunvVBhldE7/3J2v41cPX8bFhn6vaTKGDeEAYWVi7krPhCDfM4RwhOt2NishwnnVmUURK2ajUG80EwGFJyY8QO0BrS1XrnBAttPwMeLnBqiKiqHwwgNik9i2Xnu4wDIudyvUATL2ixzFqSCCGIDyygZVvFcNJURFJyB1quDDPe4yDx9zjzeK0JcIiRrw+cnOJBGHoh/vFrlOGPL0jc/NNiNYLTQjGBE5YmMMC5ZrqaWiwPdYbFvBphfd0OB4OeAKVYggxwFBwS4gXpGt7zjsPUHVYqXg8ySNuv2Pfi59/i12tF4oWCtQBgKS8wJNnBukR3FrhSoQkGAkEzsBwOx1jEbIRqWKWpDhSvwd79Lv9E4Ehh/SSVgPnn4tzjO5uErhoITDrx+9ABEeTtdIeCvgTkIyhQc/1FAeV/aCFV9g8l6sCMIsjhUKWiPRjYGLVw3++md+FQBwr97l9JZH9QQD3eAi9MJONgPc7BNc+sbqAMfrYGHmBZo6I1gNADxQBXeZfFTBWcmT7LIueGE8PR+iP2hQvUcTrpSACAvqznjDUGyuLebaMit61lvDhFXgcDWCMRI+yicmhmE+mVkUMWigbDDuNXgUoc6HI7S3gx72LEfv5or7cLPBBl8/pt7yt+0fYrdYYz2l1z5odlluUyjD8XMTVWEgG+wqgqv7uuUEobM39pA/v2S4t1+0aNsY3Sfh2xS75YYJYhWNxOAqnY87O+eoTIZFHaLkrq4S1F5nELMGZg88IiQqHpO7j4x6U58Y03qdoHZVCei14MmyGyUmsc8A5xLzWjZbOtFOAJ1EexAgMem5L9l+NOA2hcs9WluwtGJVRCEigCmxcpeHBJuLVgABnsu1xTtzcs76Snv9CQ6A1hYWobe8CPaW46QvjUzfttYoBy2ChBH9K8lhRynHcWJulQGe2OgAaaax1TNu1jlvHPKdGt0hbVa7jNynouzLS0CGY7gsyb+cXCoW2wAAIABJREFUD+zaALNW5yVUgIXt0GD5crI4LIsWfhL0t42G+qjHEKwtkwOUcKnVoTYymNQHeVI/SVmEDZIVma5H7CkqC2QrgSranq4k8xuESzIx2RHrN/ahi1O1FSAANLPUs/d6q/XRSdSbnO9XXOjUrtmS3jQO2Lt2gbPzwN0YdrCBbe69p2sRmd3Kc3/aS8/cgW7sgHGH+Skx4mVmOcDBGZl67QA7lQHAetUjk/82wdo23qJtgtOH72u0E8+bUNmAgwGyzdMvYrmEhS/H5bgcl+NyXI5v8XgmK9dvLK8jczk6++TeIn+QM3yn1xJtrNq0Y3H52fEYqmdgAxQkCqC6FnZzGTC7McfCUFX71/f/OX75/HsAAL99eAvjXsNaQucF3l8Raebe2U4y3A+m2szGtBKzCVUH1kko7WBCpbZ6MMbs5SMAQG9/joenE/4uftZhNg6+w2XFVZp1Et928AgmbKE/ON/l3xHCQynHRJa832I6JEhoVRcMv55XGbqJSvmlFsg+otfMnRrDssF3HhAc/u58j6FfLS0ap3G1pKp5tZNjsaZzdXw6wtv6CgBgd7rCjeGCP9dpPcAqVEQ4qGE6xaYVtczICB8gdmoB3HiezCGWdYH1IMBWTmLWr/iY7z3Yf8LjOObp2oucGLyhOh1MKg4NODISqspTpWPB1aREMiqPEBiTUHz6b6KjaldsVWbMpOx5ZNM6EYucoAoQAMonTSpgBaO4sk1sTLHUEFYgJuPJRqDbparCOInv3b8LAHj99LknKlfvBWSo/Nj/IHzGwZU16g9G6fwbiUYGVm2bppDWC4LmwzlQq2RIIrwgQwgA3rjENAfQHfbhA7QJK5AtNFX7CFVhP2lU9Wk4RtSHRqJhz8FFdvBGQa0lzJh+7/x0xJrL7FwBHk8w6d2WA5Rc0Pn22lNsXZTYLgVMP34mAbNjKNcW5Ey1nW+rtIBo0r0Qr43Lkw+zLcnrNzo4eZkIg2YU3henJyue0Ni6TWKHPwGeyid1s+eLAXSYw7pGM8zv+xZyoZk45bcgYwigGzk+HntDA8BpARO0rLACctSxcsGtMyy3GMLb2m5TevigWxaNTIZkHXD9X3RsxuGFwOB9QvOWnxniaR/P5OL6YDmBcgWen55huaZJ2ygN00/OKu3zNff1lquS39cbNahPSmYmmomFehx6cKVHri1+d36TfobEvzwiA/WD0RLfOfsIo4CT/Y/vfxfunRHjbzMv2YC8HNXYrAYQp/SQD1+e4+YopMXYDKuqIGE9gJ1bc5aMnByOIS80XOjhiJVGM93qgYSNxMWqRKENyowms5d3j/HWCS1qptNQyj1hZ3i+JLhuNl5jP7hLeS+w6TKsI1Q4dQkyE/S3VoYWte/Ye4jTho7xuBrh7smMjfAPRkvs92njsGwLPDyjzYEUHs4LrLuC/x7DUVbAdIrt7LpVYimLTkJOWrRhsd024MB5DnFlyyyj7NA20VUKKYxdeDLrCBPPejlC9IIQNtwfW/KkbpCOGSEtcidKPTRpE1vSq8AmDSxsW3iIWfhBUBC9CpsbITxwGiQJPk2cbmSBvuHWgewZhvxgCFpGdMTpee57Zgf/N3vvEavpkqYJPWE+9/vj0l5/b3VVU13VZhp192AkRmhggdNIg5BA7FnMAlgiISEWjEAz7JDYwYIVLJBaiB2tgaa7q6t7uvytW1XXZOZNe+xvPxOOxRvxxpcjlrUgyRNS6Z6sc37zuYh4n/cxDp9tKeSgdwrWqpy6I8AWe3ACejGw85gxinuDGhQK38eN1nTZoYh2duv1JK/MkswUUp8eMlAAOQB0CmKnIc86PncubiLUVr127iACB48HHWCPooTJRiOIdPpbxf1SNA6hz7BkMBLFYbTYKWSpTBPYxCCMjCfUIVr/Ffm+c/NRuPjEwkW2uS9E7r96WoQT1OmRM1yBbAwhB0GGIsmYRY8ycpP5Rnp8/agXGgAxN3nTFQQQbSuDlOzihUHCtwoyJgpJDb6+YashfN4YwgnaIADwdYCM7liu03RurqN0pgjZ0MMDbuye1cvcr5YB5apDX8TNW6e4JRCKABl5CsICrpEoNrGIMR52Sff78os3ny38Vi6uf+/dH6CeaRxcRQHpAP588wnELksyytpgf8gTdxVv4MO+QvO1hohRNd0dj+a3KXWkP5/Bjpr6P7x+yDvLi8ME/+fwMb73r/1DAMB//lsjIkEvSUICoH0xg3Ag/1bQHPDxjFyMPt3cIyed6DcrRMDVL8i5SBuaaHwkyohWYntF/ZbtizlU1MK5g8bTzSmmqVeWi11aWEWWFRiv8e13SRfZ2gLbPp+PSWFw/JD8dz8z91H/kh6kdlLiel3Axl7qew+vUcbJ9+WhgncSKtkfTjb41pQq788PZ9hEze759ZxClePf7boKk0nPn73f13BpEgkiJ4tIWiRuYj9ciLzzlh549Cn1foWPjjrxZcMsR5It7m1xmFcQr2gzNX0iufKwEyI3pYk/KEDHSWlYeMikF3a0AKeFV41e44uAoAGbiGI2k07kKOWFvn+AiYuJ3Km8gVkruKPAk6XdlFmT3UuqYKLsBdoTOQe0Sfzp19Eta+JQTgZ2q/K94h4cZIAuHJpYsXdDwbIu2yvASOj4b2MUqiKJhEd1lArwS8O+vXjaQL5Dm1XTkb9y+uxmMuAwpNVIo79jsyevGFWpI60my8DiOQkjy8ckYeE+98TBROBDtiraJoJf72NVK0euS5D0N0kDDgBiFILuN5rRBDkI8s0GbbDHzTa/sgjRRlWe9jlS77yE8DliMCwc5D4voKEIOXZv5G0OI2mxS6lBC8PJSabXsKeB/y6UnnXS0lJlCJAe2E18LntlQDC8GyCdKgB4gQCZUQYVmOQZKkf658hp0AeBoUma7IB+VzGqIDuJ4hX93eKLgP4o9rGnwPlva8wex2LhswNcQ383+fljvOnjtud6O27H7bgdt+N2/JrHW1m5/nD7Dgpf4uVhwb6roiUj/9TfKQoHH90D2l2F3UvqAai9hJ0FzlidfrhmZxtRelzdTBmyvPzsBCp6gM4mPVdsaejopoLzBs05veH2Y4virMdZTLu5O9niVU/f8W69xXao2J94sOo1558gcgqPrwN0ZPDJqScXH5DHp1s4ZthO9YCPjkiSctVNsKg6/Cxmpd452+D8kFnMKenmeHrA3cmWq9PZ0QHTf4mg64UIuD/d4DvLZwCAZ90KPzsnKNIYDe8lJ710rsCPtmQU8WhzlDNhvcTlzYydqT4+vWRm9WfrO9iuG+53ISAHROuAuhkwiRVXbzTsJEsTPjim43y2WeD66TLLYIzgSuBff/9TPG1X+OviXXqP7YxZp74MED57rwaZJTahCPCp0hCUgpNgYl+AqwS9l2S0zyYDgDGxX31myMc6JhHZIfvqCpsN/YWnvmLyclVzw/1GLCz1NmO1UUwMTKw23EUFRFOBajoghCyLEIXPXrAqwDmBm8g0Da3OXrmSjDpSUIEQAW3kEQjtGSkQ2iM4yd8rnA5w+1iuVw5io4DnVE7uZ0UOL4++vOMQ82QKIg2AxAwvCHZOkppQhOwm5kEPA6eBZ+jXCwAB3F+uGsNmLoOvoZITmIrPVLxwwgDlTYaW23s+u1GpwFV4QhcStKoLCxP5E26ncxiCDghlgDxKLi2CZXQhIh+p8hONzVK3vQTea9lbux6FDijtuectJuT8lSp8O/c5eShB3xHNCiPfFVgBke6DmNnMLQ3Jf0XHYUfpRQoZ+RAAjMiOU3XI8HoJLL+KLbVKYpgL7B/Q645+DhTXXfysWynOGzn+7Ee/AdnUUDvFkFN1I9G9N3DPqD1UPFkKmSOe6g+3OJ3t8fSCYlq8lzg8ocUvVB7N2R4mkpPCsUERH6wHiw0WRcdQsOgUilO6WZffuWCpTN1VcE6gS9KTboqfPKPFrqosptXAxvchCPiRLSNrcUF9JhcnopM7O7y8IvgYDzqI6wrzabQrtCWKuApU2uLiMMWHDwmG3nQ1f9akNFg29JpSOtz0DT6akwbpnfdu8Kqjc/D8sIAUAX9zQ4vTWbXD2Ywg6M5qXKxnHDawGWo8i7FjlXa8WD84vYELAtc76tVO9IBG5knk5GSHyy31q4UV0HP63btn16iU5QB2JT1Dy9NqYDeok+kB5p5i2YgA8DsfPKHPkgMOtsA37hAp6scXDRDJGPpl7D0lvCfknllxI9k0XfdkKWmZzEPyHIAmbDvJ7yE9OEnGGgloD/8ohtKrAJmQwiLfg6KTKDaCEUY3kjfQl/G5fypCdi6agO/HbldSnzUtar3i3qZfWgqPGC226d3VTsIpwaQ6VTh+T98phmSDCgRNqjThBnZaAqjXzCSXQXJAhDu2gBEE34I2H2oEzaZzDI8M4cZrwW6NZYAw4F6nvCgyyawgSY2NbZi+LbKsRUa4FKQtxSCydCaAwy5cHRBmLi/6NkftmRldp9RasUJx4AJUgEztDAHACu7byqnha1ZUFvZVkxdvIylSD4BdOWBfYHJMm3ZrFTt3mV2Z+6pe0PFEfbIwkhd24SncPUnMIIByma00p/PcC9+vGw4VEUMmWYmDIig+BS404Mi8YABICkUAYhhAfE76I2D1y6g3Nx7dWYnNJ3SSv/q3J3j4JzSf/fGf/FdYLv87vMnjFha+HbfjdtyO23E7fs3jraxcIeh/bmUhIjRopiHCWjmXNLhkTgAU85jbKT1qbfD5v/ef8dslX85iajCpDMt27tzJkXC7ocKXl8evkRSS1+35uoKKBJGytLBdiZv4HjeYom6iqX81wAfB7Mzr6xkjX7qxENKjiQ45XV9guMrymyqa5zeVwV56Np/49uo5VNyCP2mPXiMSdUYzxN0OBa5aqqiW8xbTMleSF/2M2cFaelivcBPD5E+qPTs0da7A+fUcLyMUX5cG2wi36/sbVJGdemeyxUm1R7ss+HV9tLzZ9hVuNpMsz9Ae947J4OO92TWe7ZdciddasAMRUPJx3ZtuIBcBfkHQ++dfn+Hnrwi69kFiayoOspdzgzKS2YZ1QQjyeaxCA1hG4IsciB40oZJ2luA0MowHiFii2gx7ugbcYhBWkAdvgtCmoyB1FRgmdDMHdamY4RlkZjCrTsCcGoYLV7MDy53adQ0bmZ+oPEIAVHL3aRUb30MGCAGOEQxeQG0SY5equuT+pUtH1Q1AZJu0XTeS3icxdgvPRgUAhRu4kfcFY7pOAKWHipIb1wSWNPkycBWrBgE/cjGyxYjkZMnAg31vfVYBhDIg3Okxjc/U/qpBEc+VrA36qyYeZ6CWQzwn3oPhBjv39D0ToWzhYKM7l9xqem1CBEbnTrgsmdItVZAp2CNMwYx4e1WTXzE7aeQKXW0V3NLicBHjBr1g436owBGFwRCakSDeoAK3QYgpLLl6f/edS1xs6dkeBondOf0sG0sV8MhTPKTIudJDqABXxGM7jOzIJEXqpbaI2oKNKYpdgI/PTPnqgNnVHu/EwI6bT0qogd7/3/k7/xBv+ngrF1e9VZBGsdYMoAmrqg3AhucaIkJTofQoozzl7nyHL16d/L++r3/a4OKoQBUXYuskG6qHIHC4nGQqe+0ZUlEzw5KIdlMClcsTkRNoLc1C7baGUB6LBS2UurSwsZfqvYASAjouvMeLAS/j7zY/OGFpwOGjPYrC4d0o77kaJvjOnDSpV2aKS0zx3pTYz1M9YG+zLvdJtKSrNC02m0jB7JzGpo82ek7hpm1w3BBs9dPLewyTT6sB81kLlSYlp7C6RwuccQpHk+zI9OfPPmB97Lar8Feb9+kArkuEqcXf+91/CgD42/NfYevps//v9TfwW6tn+GnsGTfacJjz+rrGux/SMd+pdnixX2AXe4X1dMCdBX32VUzp+eIpyZNgBLooe5CBJDT98QjuTRKPMk/gchDwJV7rGxZRz8OB2P9M/wsgaFn4ke7VK7ikwRR03wIUgO6aALdMRvUCxYXO728kJ410ZYH2IoU2CITEOo0thKqmhcVqxaHYLBxJiQW7khc1XwJOZUnGcCiABOFOHBB1ovW5QvfAAGni7xUnpaT+cQoDh8j9aXGp4eYedpalG6HIC2+IM5bcAcOdgPI6OwulFo8wpE9N/w4qIKTFVQR4J7C/jhsCCQ456LqCepqIOtQR0i684MVaHvVAEJCP6/jZgJvm3niQgIjm99z/jT+nvm1/HADh88YKI5ZuQYs396Fzt4eyahsLFaF422v+nqrO+uzJcUu8ivi95rMWmyirC89qBAUUkWU8OMV2kwiCmeBUYEiEJDMaZO6FOoFgJBafptcBm28mn8cAVwX0MVtg+rXA8it6z2Jnsb9HO81yXUFe71D/mFoy9z9v4i4GSHkJb/J4KxfX3/+jz1BMS/zw5QP0n5I0ISjAbWYQ71OVdfdkjeeW+qpSBdyNVc62ryBlwDf+5/8SAFBXBmpCN4sDoBrLsXHWKVycx1gzJwArUJ3SwjhtetbOLmcdk4W8F6gKiy7+u9tUXJ0KFdBMB+593p1vcdXmQPHeaPTxrhysZhtDXwCTb9LCEkD95E8jyejhco0nBfVjj4s9LosptrEKbW2BStED0zvN3r/LooUSAbu48L4/uWK50F9evI+rvsQQm1XLusPXV3Qel02HprCsQ620xSpGi53VO3SxV3rVT1Eohxef0gInPCB9nqB80HjZ03n9rLiPQyxt1qbGk/0KEx2TZJTDyZyu5+rsAndruoZT3cMFgV08/yerHeuF912JdlNzlVjcKJ5jfR3I9i7OL2NCk9e5cpKDiJ6yueJIr/EF4CrPE6fsyaAAoN29L1/v6abVxC0dEZ5A5Ds38TnWSwbYpG+2goLCqfhANxTZZ9YL7o/WpcHxpEVv4znfT1h/3LYl6towatGpANfnhUTYUSLSTo+qNA+xjAtVTcYEOr6n2etsmCCIn8Da0MbBqBy7p7ZyRN4SuYITAXaRGoUKqhUYUoqNAG8YghUcBZg+kBfeXtIGIMl7ArB9NTIsSPKaXlLSDiNNAYiLjhIh9sfBv6vOs9b9i3/wn+Kjf/yP+LNZsxuAxSP6x4sPHcXDpZQfK1Et6bk2vaaw8SE3fGXse/rKU7s2Imyh1eyLbVvN1fT+1RSidjg6oXu+NwXL2bZnZGeZCobzq3nWzYrARMJk1JJuXjkI+NTzDjSfFbvEWfHoj6LP9h3LPWgAaC48Jn/5Fb3scMDRMa267tU5MJsiOXr4aQO3jBv2wgC/xBs9bnuut+N23I7bcTtux695vJWV6/cfvQc5qVF8NkEZiXFmEeBVZlMapzCJ8O7dxRYHrggVhudThteGssyU+dpBa4fBjvq2I8nI+5+8gknylWJgyPLQF/yzUh51abhyFTqLvauGqokUzh4CcLiKvRcVoCrHDMAQBPe77MIxXDwtDQrluMK9aieYreg4N7bBe5NrPGupKjxrdvjsiqrHpjA4LmOurHDwQTKcrBBwrAlWvXt/g7+4+RDvNgQtPzocA5GoXEiH55sFZKT0W5H3dvOiwzxunoeIla09vVAO2cIPMqBZddxX/Wb9nGHhrw4nuOkabCVV3p3ROERjChcEQ9dSBLRDgXfP6Dsuy5aNLl7sF3jSFgiROWunnlmpsqc0m2QYIHtkDDVg5MQD2DpjikECfTLjd4JYo/vEhhXs1iQcyAgjQaQus1A7CXY7CiqyNBOq2mVz+CDo98naUYjAwdp2UFytTEsDHwSjCH1XwCWDgE7BasdB6nZdQr7GkBZQfgRzp8anybAtNJkgcMKSyWYfITFyExt5xHZ2NVX13GZUufcovGCnH7uMphGpgj4oDv+uriS6+wEispGx12zSIo57iBc1ghtdn8TKrbJZg28cZaCybZ9iJMhEaRK7T80tujo7fNF7xvcusrmIr4DL38qyPd8rZlCrIqCPoe16NUDXFv1NDpDnoIdOIsxel6mkXOIxjC17Ce+Ba8zz/xevvS4dKQ2S6UfAKJvWZ0vDQSG0OdHGLrLZClSAaiV/r+ra4ujnEfJ+rlDuAuZP6MYufvII0HmpCS0hKXI+g5hO8PjvR9nbcYCLFpPV5xL4E7zR461cXMOrGqGuIVyG4IIEmm/dsMevsdl55osnZ9k5Z11AHwTcMf3Od5of+Go6QCmP3asopTASiFZ2sIL6kVFeYiYKd2a0IH11ccwToJMe5+2CoRmpA4cVa+3gvchkBoFRJJaAmjrWs4a9BpIfKcB9ztYUOJvuOYXHB4F/ek0392m9x5ebY5bA9F/PcO9b5ML0R3e+xDvlNb/f99Yf4llL1o5SBDxoyAu4khbvNte4V9G/n3dL9jW+vJ5BaccbibvHG+7vrooWv9mQNnbvK/zx4buQDw/8eWdzeiAnhcGkGDBVBIV9MZwx2enx9giHoUAX+0e2KxAi0eLloUA1oWt2PN+jNxqvtgQHFkuHr27o5+tnpH/VfV6sylHoeX/mWDZSjjxZ1YAcMScCIPIiGY4M/93srIUxCmEERdoI4UoTiR/pPft8f6pBjHqPkWiTJnIVWCaidhIwAjYuNIMAE+IgAuaxpeCDQGc1+2uvFgdcPI12XUFACOrx00X1r5kvjYlK8ICKKUQ4SLZJDDr646YFWwf4uKnSJy0QBMx6ZF0ZIWPVC3gdoJLjUR1YYkPynrg4dQqh8q+5GqVYPDMIFMue+Q5ukLwo+E5DNJ57q/bOwBsRHHS2HDRkr6hTcPjM59kyABAjvWjpgLhwuXWJ9//7/wZFvO/sImuag0fuJbeKesOlTJc08z+G2IpY0XULVsJHr2XsNcJOM6wtW5kdrOaGQtwB6jGXGRpv5h1xPkBh9UIAQx838BIMJwsV2DGMGHv5d7JTfMxpQ3e4S8dWbTQWn0fXNymgtj3Qp/tOINwjnooYloCj77X99im6I4n9h3HRP+7gI1dg/5vZke1NHbew8O24HbfjdtyO2/FrHm9l5SqNgFQCvojwEvIu9N0jIv58c/kS/+RrCjCfrVrsHxNUWt5Iatgn8srUcMCzlASp6Gs6rfbYcsVbzAbMygGXliqWzb7GOnrgOqtQVlRVLSYdLm5m6A9pVxkYqu57DdsVTDDQN9lQPURpBe/CpxZFlJAUhcOujVIZ7eCCxCpWMI02WBT088EWECDJBkA7r1TF/rS+j//lye8CAKrPGugWaM/izv2jHUAEXXxr/hLHOmd8Pt4eYbNPoeqAUgEfnhH56TdXL7i6KITDVTw3V3YKKQIn1QgROHjAKonWFlhoqmTv6jVMxOD+xTuf43/9/LsIsTIWyiOISJ6aGIY9lQg4m+9Z0nTTNdjGa6E3Cl4HNoBQbTZb7+9bVMsO/YbOpTEFPKcoZaG8L+hYGRVJ8GccQ1tAxtdVl3l/y0SmhNaJ/LOwI6mJkRBWUi4nyBOXSVY6vEak0oVFGdnddxdbbincX2xINhUrp89+9YAde7R2sEZlt6XKQcZsV9dqqNrBm2zm7uL3KHYCNrGRAyBWBjiP4QtNABbJEUjStUikIyO5zeID+fum4AQiPsWD8cj+xSnIYT7KnE05vnd7KBkg4/fyZTbVUNrD2pLNIlTpIeN9YKzMLR4nIKxAsUsGE5LvidA4yIVhdq+UHjpKVPq5gD/kUPSgspsbvXG6ngKqk1A38RTMcsiBuCrgjsB5t6J2zNz2jeMQcoAMP5C8lFudvZanjqSF8Z5vdxWfs6ErILXPREmRIePgZG4PeAExtQgpB7YIscQmYqE5tjAxDGNYFbgjo1TvJ1eAdQh1Cq/fQs3od5vfvc/f/enfAUJjoFMa07bk6yvGSMkbOt7KxdVNPULj4e4bXozKyqIuLK6jPvOinuHhkqDN3VDhw98h67wf//Q9VK80XJRF2JWFb+INISMEdYcmEVk4dju5P99i3de8mHijufeoC8sm6aVyFGcXn05d5clxv61fm6jt0rFhO7zAfNqh1Jm3/+qCNgTeSfgknVAaz5zEOys6tkXR4fmB/q41BV6+WEFFKEwNQP+YFrxf/nyOSYRHi22AawT8Qzq2h6strmPyzZ+2H+O02eHVgXo9g1McW/eqW+BkvseDKX32RT/jhf1nm3uY6EjXlw6f/uLha65DfkGfvWw63J1scackbevaTTFX9P7fnjyF+Ujhz159CAB4cbHk+Lzj+R672H+9aWtYq7Cc0uv2fQkb2ZLpdKb+qS8D7F36Xs28h/cCMvbh3FwxK9VIiWI9smTUWQpCB0V/t3s5gzxITljpARS7aHDeA/1RYPawCFkDO9zLk5CNci15HSVCJm8AfBWAKvC56zcV+i0d925a8UatVA6bvsb1IUpSnMC7/yN92KN/X1J/P0KRpi0yLCyJTVq/Q+fOBcGtlGHQBK3SCaHvE6FaNTOc4uMGBXFTsG1lWhDSuXNTl5nFORUPam54nXXnNaQREPu80CQrPu9KDEuBxRG1Fap5y3Kw3mhg2yAZfhlfwUXNdLnoWRtOMLNFu4rP4fNypGMO8EYylG1sjbCkN/SdAlTg2DbZSnjecGQ9suwEpAFH66lWIDW2RQAvrABByGmTKGeGVOkjBjKb+luRz5ckS8asPx0l03gFZ2ReUBGhYQD+IFm7LSvqu5vEBrcSIr7GrwvACxSRgWynCodT+qzV9Qbnf/dDTF7R/dqojzHM4z34UPEmsry7xbTpmaNy8IJbIm4/enbe0HELC9+O23E7bsftuB2/5vFWVq7H791ATSo4L/Dbd4hE8/PrO3j5askw7tV6yvDrcrHPGaELA79RmZmnArvVwArM7u1y1qjRbNZ/1U6w2dfZYF0ALhIKZOnQRdjn6iIGCcSYKtMpXKXw7KRrTOWVE8w2FDLAOIWbdWQSe8GQUFH1cNFXNxgBNfH45pKi3j5d34OJx3Y62eOqmUIeot/pAN4h+zLCnQCCFvjxP/qPXzun/8b/9Q8AEMS6Hhq8WlPFa3qNMgYIlLXB1W4CkOwVhXR40dLxHmyJo5Kqoa/3K0Cz5Q3dAAAgAElEQVQHNvEQRsBPc76j9ZJJTBM54M83HwMAWlfgL776EO45VWPS5Op6fWjQtdEQwwoIGfAyZpJiXbALTSgCmZWnSnBhs2HCoCnQoR8xQ5lxKWAWGR4VPpu4ewAi3iO+9BQHN35diqYrALPw8AVdN30QGI5iBVQ5bh3MH7RwXmJXxCziQzYSkDGAoowMcO8k6R9B7QcXjd2f3KwoUjEZmSwHXP5H9JrKaJzM92jZ31ozYiL3Cr0MDCl+cHqFr2/ogrrtSPkfiByTGLveSjY40JWDbSSTooQXEMkPQwHFWuXQbycy0WdfsMNZsSdTh1TZq36kvTUC6BR2kZClV3vs47V3RlEebjz949xarT1s1LJKFVDVho/z0OVnXuwUIJFZzEbQNYjXEy63mfSdFiY+T+gzAQ6C8lyZcYxMTEqtBtbYqpCfeZBZRDKdKScmkyG7UQCCJIcsjt0rQyY/BjqvKWwgjOBkIcDthrDTlIsbX+cnHjLON4ufSZz+6IDujM7x7OeXMPeix/p8in4lsPkozm/DEYaIALi5zedgX1LLKhKt1HXBRNHg3vy6761cXK9vJpBDjeXigF/enAEAXp4vaQJJk8EhBwH/zsfPMI/w5f++/ecAC5jTSNE/qJwMUQS0v1ryg+UXll1vTlY7Eoe3+ZRzf2Tl0Xc5WFhYwRMzegkf+2JhZmlySZOSkfx9p/MOdWHhJwRPtfuSHVg+OrvEImqOXh7m2HQVfnBJge7PLpdsH/jo+gim08DdyE5eK0yepyZRPn9uBNUBwMf/9T+G/JAkO++c3OCoOkCe0Dn4xS8foE+We0FAnnV4tidWqpKeYXIAbE84LztgkMR8BUkA6sie7qzGemjwvesPABC8uYuh6lIEeC8ZvtN7AR9h0IfLNc41bTxK7dAOBTbnMenIiLzWSYKCE5RXzXuWpMybHrW2eBW/5zBoiCfRLk8F3ny4kli/yZDfmezug8ZBFh4+LkRjhyaraUFOi4TXgI4QcTjUaOd0HrvVAD8o6MvohnSTbfW6M3IWquNCrJVDX9PfsQsPgM2mAQQwm9F98f7RNd6bEHP7B5cP8c78BtfRrcofS6y/ikEVCwt4wX3nz3b3RxClyIEBoP5sCu8OE8cbQWclUPq8zhwUB4XLg8TRp8DNb6SFZcSGNWSQQX8Y+61xA2kz8Zh+PTW8Ub6+mqGIGzy/L14LWbebkjduZgBvBMm8RVCbB9T3LKLhyXBZo4i9eYAMPkRi5m4LwGW3LvH5FGKe+8cpD9WXgWbf+D187TMUXniSa6V7pMgOU6JxZKk4CunI0VhgW9DUbxXHNAfoUbvIbKvc747HFiIuLDoFvc4uVb4EdOo7HxTKz+meWH5loC92mD+7jt9Ro3hG7R5hHfbvBMh3iXvRH0o+d1IE3uAtFi3avuDNqxttZGUxOr43dLyVi6vSDlI7rNcTbFIx+KyC1wF8SYPgyulP/urbr+kZ9eihEC4TWfROYvYYsFN60813YpMewLaqIbWHiw+hqi0TItR5CbdIb0K6PhdlF8IKhCSpsRJqp3hnG2aWJ4P9tsZ2W3B0GpzAEG/iq3bCrkWNNgiVwJNX5JKiC4dVTRVjbzX6xlAQMgBnK8ikkayz1MQsAr75X/y3OREGAvgRLVRf3m3Q/8Y5pgV9nphYlK9o5usfGJyudlhHvemd6Q46vslFO8Nl7Nt+fnUKMbEQMbIKRcAq9kdL5VBIh0dr+v67Q41JtPAbrMJifsAi6le3fcnELRskR/XtugqH5zOek9wkx4fJiYXSjn2Ou7ZEKLKTDQBOIXHrEiISb/RBcgKJm3s4ESCiLlLY0aLTK/hWcVVCQdv0qyCjN3CcjH0Oc4FXYNJP+UWD5hUgolbTNcCQQu9VJg6l85OG3Re8EOpIXro3JwefX746wxfqhI/56fOjXF0bmaVoybO2TT3RQJFoIOJTcvoRQSD0mahElWxcQJ/WcFMPdRL7daVnAou6Urj5Ru5F4mhgz93gwWQbLwPFz6VFZtSvU42D67I+U9UWQ0J/Ck9JPYn0pT38OAovPlvFxMAOCvIm8g/uH7CISVJXRsFaQfF+8bNDQjNKsi50yXpRSq4m9XVGGFwdAEEEOgCwD3reRKOnql5FDb5VQBVladYoTKY9dtHP2W1KRgdm93bYb+L1XhdQvUARj9t8o2VJlvFUoSa+ibdZLxymFnYS554DuWDVF1kPq4Z4z1UCCAGw8fxvd8AxbcAe/bsP4eYWKRAJRsD0NAeIxpF0CbThvWwnSAK/fp8X4aXa4THe7PHm196343bcjttxO27H/8fGW1m5SkUyD3tR5pQaCbi5Y1G6rz27s8BnE23O7Uxi6kPurQUN2KngBAh1rRHu0u687wpiAUfox7cVQ0e+Dgzt1MseQ68RrmLf0wj42FgSTkDvBULKjlwCQ4pbiZ6yiabvaw91Q697dTjBqzkxgnXpABG4H+tl4BxVANQfisVG/UrBRjKp18QeBggmkmZktj4y41CPFdoPNUO8RWVRfpeqznuTFtu+ZAPxi+s5TmMofKkcfnFB0HLXlghGwsRqvpj3cD4ZtEdnrMgwnE063I1mHOu+xrOnx7jZUVV78vEVewaXyuHyehbPvSaD/ATJjqQa3khABK5Ofacw7DKcKoyATTDlOKmmCrlv1Wo6H6k9UGePWmGpwlIjOQUb9TcBei/4vqAX0H+kA8RNNue3k3xPDsuA/izCgY1DM+2x3US42olcdQYwj0BIjw/PrqDi+ex2JdDlHrc6yalHzmbYWe0l/Zy+4sRAxn6gG7U8gqQg82T4PzwwzEi1xxb6RnMvWDWWv5edeTo30UABAa95a7Mxx14DlefPHnvxup0m96focyyl5+rabkrIg6TEIQDVokd/E3N9BdDGVBy5V9Cj3rs5lLiO3ATfK4iFze5rrzlsICbGJGmRhox/53XA2MQ/SMCsIlO5sjBxOpbXmsw04qPtlxYmOl25g8bOSmTtFXJ2rB1JvgLNHd29WF1vC+xHfeHgBUI0bFALw6Y13a5khrEI9D4JFQkqoLqK13Mm8cV/cA81RTqjvvI4+hHpiuZPPPAHWY5nmhxEUjSGEYCvro7R7kvu/arKMWT86nKJN328lYtr8GT9FSaOA8aldsB1TWQHAGqaaf92U3IyhJ0FqE6geTGacEe0+P1Dj+kn1Hs4LQ3b6rWmwEW7eK13mR3hsxXcUGlykUnBzwszIpMIuBJsak5/kGbf6IgXJ3hpVDY/HwREhGb9gw7eCbJVBPCdd57i91aUSvG/Pf02qlkP9yUtQkEHdDEAqNgJXmiFB3QHlOt4ThpwjJSrgMP3T3H1Lk1s1bLD8YQW13dmN3gcjnAdH7TQaryMTy5ZN8aHTDuEetTK8zldSIiAVdmiPI7n1RaoFX3Wi2GOo7Mt1mV0mLIKMREL+77MUH7hySYytZO1B+ICGmSA6yVcNJKXW52t56yAOgiYaB7va1oM6Vzl7wsQvJui0kIVXoOBMZJuYiS3Ua1AUCMiVJUXk+oqw579cUB/kuPX+oeG+5nBCZqItxnq1LF14EQm3zmr8MXLU9j4d2qb+54cOj6KiEuxZq6REAed4+5uskxHSGBynzY6xigY12A8khwMAfD3OyBOpP565NQ0imoDSGNu4t/ZUYC7sAJBCw46lzrARRhVX2uSuUTLPfPPLH5B0fECwGAnQAqT3+ocUF5Si4gdiXYKLn1/ERCsGD17gWPlfBkQqvAat+I1Le4o+k64DMd6J0dWqYLC5OPGHPsCiNp5TDyC07xZoJi5CNU6wYtuqDzKd3awz7ITWLKAhBPkdJWC1c8rdFWE812OQ4SlzYVNsqJBYP4l/apfSbgmYPP7MWT90xq7d8iutD8KKAOYq1DWBr6k71VXBn1qNVmJFP8JxHk5wevdm99zvYWFb8ftuB2343bcjl/zeGsr18S4S7693pFjDBtpK8+7Zzkz8JvM7gxCvOZJXEWXFa/Jccd+SL+8+OkZs07Le0RIYPecERFKuCwux4saovLQd6JI3yggRrEFCUABvkkVRmDWn9hqEtWnBC43qgIUGaIDQKEd3L5CuaJt/t5U+NMLkrK8eHaE4lXBcK80Av2deH5KycHRsqVcRx8ZC8Uh8DbNawDf2eJv3XsOgOLoks/wk+4YP2rvZ0hLe5STBN0FPDiiv9PCY2dKvLpa8DW72VA16mYCIWTIyAeBZRV9h0sD6yUbd2yvJ8xWLe8eeIffTDu0hzJLlWqLIVUa2uP47iaHKsxLmGio7ucD7LqAOIqer3vNwv8g8rV1Mwe/9Ayv6Z1iqYm0GQYGCApOlapqBXwVYCcZxq3PI4vTAmaUjGaOLXSMklsVlti/ACAEzK7M95YOsLHyC72C4Fg2CbfVkC5/dlDxWFRAOK/yPVl5qIh0KO3JySiFcE/ca4Hch2hYEYxkWBEg9mdgZzFNpK7kzbuXWUbjBd2rkUy4Fw23TEKrWZIShIDoZQ6d77Izmq8CgsrB3kJ56OhWFmpyIEosYHWj4eM0GCoPGQMbqiuJ7m6unoIO7GtcrDXMsWUTCajAKFFoPJG+0nOpRuEdAlCRCGa1o2NOlavPc4CbOajlgGk0aNgDcFLzZ8nKkVkFQMYRETViyQ+Axd0dtpsmeyifGKjEmN5VEE5m5MYIIIIHQQY+LmkInWH3LCGx/ia9qLym37tI+BpWgV21sDJodxXD9PWqw8mSYGIpAg59/p5KO9hzunfFILJvjMm+6G/qeCsXV2spLF0XDkPsO4SDJpp7nHCHQ511ZqUfJZDECTCarZuVR38SIaxHkqjr34tQ5zLAzUL+zM3rpztNsiJk6n4ATTAmOcVI5MmlCpBCZueiAHZyUa2EKwMzYJO5OACCr+LPw00FSDDDttEGX14f898JR8cEAP64x4Mo07k7yS5MX//NAwiXDed9IaCjx377nsEnxzds5P+N5iVWin751+v30XUFQWogS8jTZYQRncJNS8e83dcoS8d64fmsxemE3qNQDr3TuIrOQmfTPbtBuSCwOdRZdmAkL1zDiwn0WdxQ3DQQMjA0HrxgmG25OpB1XtylaO0wpNaBDGge7jg1pD+vck5r4zOE6ASwz/0vYQEdIVwzDbTAppBsK5iF7Uty9mEZ1kg7axa5/WCnAcV84L7w+mbCoQTdRQO9HkG8MjDb1h8Un3sghgGM27tpY2Zp0UqTarHqyHYTAPaaws5TPupeM9vZewApzWlpINeaHKMAiMsq35sibv54M+Jf04xCgN2EZOm4pxiALEObke2fiIuhcAJFhOj7OsDf7XmBDk6yPWF7VZGGOr6/rwLj+aL08NEOtV2C3j9+R31QlAoDwCwd6ucF65Pd3GeXqVFqDF2cIgsNGsebHtVKWoySjrxXLJ3zjYRSPvdQBRiClhOLsrLoNqO2VNzEBR0wv0cchrYtgXWR9cJWwsX0HIGYzVqNOAEm33Np+DJA7SVsYtK3Mku+7ni4qYdMrHEJlhK5nUZx1ENG5m+3rRDrD5zM9/xc95sKolf5s/0Iqh5Jut7U8VYuruGgEYKGflrDPIgyAiOg1yWKDV3o9r7L/RGZbzjhBAIC992CDjyp2onE7HFAdxon9BOH5pQWhe7pDKHymDyJVl8fD6gWkey0rRDWkbTk6bvI5I/rcjXqy0C73TRBFpnt4SaefpcW7EHwze4rD31GVY7WHt2LKc6mmXBwOqOfnZcIx4CKD9i/+sFnTEyayAE3DS2uT99b4nBU5YkE4J16MTFQwuMvXn4AAPisvou/ffoFAGAz1HBWoZjRJPKH738FH5/WH758gH0U/R8d0YKbElyWVce9a+sl1l3NUXK/2kyxmNM53u1rhJD7cOWyh5/FXXgQ2XfZCoRR89sh9/i2uwb3Tta4iYt3u6t4wvGtRhdt5QC69i716GrHE7/akVlAkkxVNwI2BhlJE5GEWOkUO4HESUtEp+QXDR8YcYATXIX40iNc1DBpAi89E16gaYOVKsHV8sAGCv5oQBkruOWsxXrXwD2OCU6jUHJp422VrB23FVczQQcUawkX935qoP4gHZvI393SRMz9u1Flb5ekZyxu8uLhUgj30iAMCuU8ykbagiu/+qTla2YGTecm9T0HoH3H8PmQF+VYcouujTtBQf3RtPkQApxy5DrF2mq3dGQUERen5J8MAGJqKdKuT6hC3rwKF3XK6XejRnzwAuY090phsz2h7CVM7HGLiYW/qsDrXeMgZ4bfo7to+Nz5AnAn0TZ0MmC/owvj95Sck8652ipGpIKgSjZZVKqFyf31/YiUpgLQkfaYjg0ZoaoDJmd7lrdtns15voGkDfE8Wro+6TV7pb80C8j4GnFQVOkj6fgHNqzQXYc3fdz2XG/H7bgdt+N23I5f83grK1cyfhAw88A7Qr8vYGoLGx2VQuMQqrjr26hcvaS0jhHzrrzM1PXDvQzliYlFG0XdxUHATgXMd6lKfHC0xfk1wZnF+Qi+CQJqyOzbcW4kBMjsPP27k6O+aoCYZMYtrOLqJZQBq5iHenk9w+mHV3wuSmVxFg3zz5odnu2WbPX4xz/+Lr79IdlDrsoWG0PHspx1mB5vUCnLr0vj+WGBl9s57kZzguPqgE939wAAWlIfex6p+D94+TCbvn85B2JSyTDXeHd1A+MVv07G5t1uqHBxMSdJEYDptGP7RiEDZk3Hu2ljFTbbKEm5KpEUR0KA2N+x9eNHYQjNpEdrNCfmYFdk+cfcQutRZq4K7BgkjM6sR0nVX4L89u85aDbnJ+ir3ORerTSpMRklGqnvv1Fc2Ycy8Huk/w6RMR0kQXHpDc2xxewO3WfDyPKwrA1DydZJFIXDENnxvhHZHccSM9roDDeym5gMMHezSYV+UaC6oO9jlvl8+NIjHGXGvbgq0Lyi3/UrAV/mYGxfeYjI2E0QcDrHkFn2BgBD+v8vKmBhIeJ1ClqyW1P5tISdBLhlrBIFMlNcB5L5xCDyMHFcNYud5rScNFI1JkxGGIKVUDOL8mu6t7pTj5Bym1WgajrJsMY2noNAiJW9utavSa6CAiNl8mWFYivQn2RrU2bRGgkxsQjrPOek9oYzKj//XkBvVW5blAEuSgvl1AJGopgScuaszCzsuWHZjCg8xI3KtowgC0Q6yR7eS2yfExFALAzUcZwPFge8M7/BZUdowR989BXPFefdDD/9BbnDSS/IBCT1eHWAi3B38WiKN328lYtrkNEF6W6fo40CAC9Q3SeI0RgFPI0uKPMRRFx4mhGT1PEQda+g5BIEwaSRejKgLmki2lQTVGXWqykRmCTSLV1eoD0wrEZaDS+45yriQ5sepuJkwBDhFnQKujYwqYesAl9dvRgwr+hBurALnD9boT+jXxbK4ZP5OQDgD2ef48+qT3BS0MT8W598jf/pxR/QsXmFTXRWen95BR8Ep9gAwMdTeo9V0eKX4gwPJ9RzvV+vWUbz11fvAdsCVwdyclF7yZFrahqQLsbu+Qyfnk8xv0OL9t+69zV+8PIhAIJt6+lANH4A/aDRR20iClq870TtbDsU2c/2IJlUNJxZVEcde7LOjjr85il5Ld8MDXqn0Ucd7XrUG8ROY9jmSVGFTBpzI9kM634T32U3AohEIPvZCKXaee67ieQylGQ7VqC6ioSUSeDepuqiNWKCiXuRHb5AJJl9dPApJwPrS4UA+nh/DPE+ZEmHAETUVqtWvEa0Cl6wxacw5B6UJm1Xvd4iSecjvU5Gba602UMZMkpR0mLSSoTofQ0dKOUl9RQ9gLih7TYVaWIRPWo7ybZ91UnLvXBjG/ouqVdYeJ60gwsEmSZnp0HmczC37KqF0hIBLGnRJ/k6oZdwJhOe9EZCn0eLyWMPcbdnn2U5NXwO3DTktKCSSFC8eI/8iIMmQhb3qK8zydDNHbApssXqqUGVepvrCirJivaS7rHUOhBgL2cvNMQg4eI84rtRVF3QEMkRblNQuHv6HhJMKJssW7RPZ8CKPns67/j8T8sBB1vi8UvicnzyjQsU8QA2fU22rek4d5ptQsWTGnXU8FuV55Y3ddzCwrfjdtyO23E7bsevebyVlatqJWSQcJWEilKcYAVCETBrqMK7fHaMMB1R8csMhwgBZl0GkUkP5fMCIgB9zAM2L+bQv0E8ucX8AK08Wk072hfXc9jL6AYz5Eo4vak6ZBlNMiOYfrDGv/XBT/Dz7V0AwJc3xwjx+4Yg0PYFG3o7Kxnms5sSX39JlZ+KUbHzd+l1H0yvcBSpvi/sEoVw+A9XfwkA+LPuffwrJ78AAHx/8z6evCTno+2qxLuLNc5bgoT+8PRLFLFs27sS31y+xL2Yt/rz/V28isk3v/jyHopNlgDIAeyAA4zMMRqHcmLwwRG5jpbSZlKOFWj7mpnXVgWIKNIPnYJ70eBp9HOWE8vpGq4MqGIF2Twu0DqBWTQ8eH91jVVM5LnqJ3i5mWN/HSu/df6+dhoJZyMiFzNsAzi7FAdF6EiSMJSCd+uipCpu7EedzoDqBVWlVd7zpms//kwzDzDzkVuRRpaFSEAcBH8XrT2spU/orms2OwiKqiWZzDlGspmgAdvk8G51kIyeSBNJO0mKNsqsfY3Yoz3Ui4r9cV2ZWeih8hBdDlggpmkiJgm4EfnJT12uvkYmCXACajWwAUW/L/neF8sBdWO4JSMf12zKEgTB0FxWlJ5bDAAQ4s82wrA+GjkEJzgRxm8LiMaSFAvE3k6hDX5poQAOnhciZKJYK5BOkK8DyfESd60KnMvqa4dBZYTE6xExKpKsEtHtm++9YNLhp1cPIR81fE7DzDEJzi1tJk/F+zMhGmpq4BAr7cYiXGfIXOwVhwEEAZYqAvT+3EoQAdPoXfzR/BI/vHiAVZTf/OD8Id5fUitKSY+Pv0WtpscXRzC9hotOL7KxsE/pmrlsEPbGjrdycXVHBqFRWB3vKZUBIwZ9tIMr7++Zvi+lZ5eh/VDi6maKEKUJfmmZPj4oBejAbFi3U9hexN5BEFATy6y8YOTrkVLcsBMQPktq7NJiEeHRv//R3+Dvzn+M35/SIvc/+H8BP3tOC+3ZaoeqsLi4iT2QQTJNXg1AbF8SS3Ju8M3VKzo24fHTLe0GPpme4/dmj/C97l0AwKftA3z/8n06blPi2+8953PoIbjXOpEDL64fNJfY2Bp9yLfWo8so9TES5sjxRK22imUoxUbyAqIqh2nTo4w46BfbU9Q1TVa9AMyLyWtQFQfIFx5hGC1kz2vU27xJSb1wXwQUxx12L+lc/Whb46eazoHSDv2myov3NHCfSfQyOi/l9+ENWACHqPsA6LWGiwtBcl4CaHGqbgRfD7MISAgYSbLA9pnp9+n7hxHkqvcCw4OYfFNbhuTCdcm9dr5WEULX15p7+6qj5CVmujeZaR56vMbuJeZsfM+BNhvVq9jG2EmGuH2RN0jiqkQQ2YDfNz4nsTiybuQ0KRngY2pQKMj4PsQ0F4zi9IornRnSVsCtS9qsAXCmgJtnnoIrHcPr5shBJ3btxCPUHvWcFs3uqkao6e8W88MouJuSY5I0r5n3aOOGS0wtwoHYuABQnBw4GEA3lsLs0xCB2fdkqZp3Y3KjYedJVA5mm9ONkJ27APD9gwCURx2OIkO+dxpffU5zgBgEzDJuMCYO6PLzpS8L2FOT3gLFdOD5rduXHILeTAYcYntAtJHNG8+BnBnoGGJhrYKuLW9CtpsGfU1f+J9sPobZlZid0HcstYWO8+pRdUDnaO4sSwv3eMobGN8pjn706vXe95s43srFNQ3SU+ae09m719ybPG4OmBX081QPrPEEgE1RQ5/Rw18VFl3U9rWuhr4sOMKqfGfPBAzfKcpgFHlyyGkoAkOKsLMC8niAit/rP/n2/4G/3n4AAHBB4sfdu7iwVAl2TuPhCfU2d32Fy8sZp3OIADawCDr71YbK4TvvP8P1QBPFZT/F3tLD9DN3H5dmigfR9OF8mOHpmjS7QoQsyykGbIeKk3a2rsY8ligeAl/uT1g6sxlqyLRhOe4odi9NIg86+Egs6e85juMK8bymzzuqDvj6KmaGPp5CipAnZgeEe/Fa1AbyKHC0WmjrXPEJQL5PO+l502O3r1EsI6HjZQMfE1oW8x7eSSZ/BCuxWEWpz7aG2NUsN5EzA5UIQgcN/Yh23WZB/bkk6yr2AmaWFzw7iYsZYvXIki/A1XjNCCRZHNpp4NdIEyva5Gu8q7iCVIOIEphI6jIq+0hXAT7pR62CCMCQ+qWTLCWyEwk/yz7bYqte008LJzh20E3caMHIFXRwUfqUTr8RCIlRVnr4haU0FgD6SsPG3l09G+g7c4yjh4rXwqiAIkYqGluhXPbQd5KmWfGGF+sCwyBZK4subyLC3KJoDLoYmVcse964FcrjOqJJoVXQawV7J/YzRwYNCLSQiZQ9GoD5Cd1b2+tJNMWgP/WlZxKk7ESWHHlabBGJUHJd5E3R3EFMLXw0NpE7BT9JDX3ADgpX0VTFDhrlRdb69vezZAcA8wx8GXiD1MQYxWQrKWRAEzW2v3f/Cf7Kv0endNAU/Zqq+F2BsIz31aakQiBtiPcS/Sz1qz2KqUF7oHM8aI3LCRUZUz2gjf31O/MdNr9pcXU9yqCOntaiffNL19ue6+24HbfjdtyO2/FrHm9l5Xrv7g30tMLzVyt0A+2uZg83uFpPcXkVA7S1x7sxF/QSUxxVVL3cmWzxajPDyYz+XWmLRweCaetlDz8fOMWju2yYlVhMDepmwCSxhw812pTG0efsSSwNTo+2+I0jgm3/9OYbsLHB9fnhFM/6JWYqVlxBojW0C7x4vILejGjzGrxTFQ5Z6mMkFkWHKpXNAB+bCwI/ubqPP7n5BgBgNW/ZTaUpDcOcxivMioFDyje2Rh2xzB/evINnuwUjADdtzTvk4OVrjjVeB8wfELN3ezVFiHTMyaTHP3/2GDeGqoi/fPI+h3PrQBB3Op5QeRQRQhoGjaoyDMuVI4Pdxf4AACAASURBVPN8aYEhul6JE2JqJ1SheW+LVYT9N10FXTg2nHhw9wqvNjHIYF1SVRorDKU8G1ZgkNw/LtbRGSpuXc005EqmCAiGzCOAXMHSB1DVmtyuAMDM49ufOOgov9CtgEU2MQgTh+pZrNpETMiJ1UZ4XuL0l/E9FgKbb8VKVQaYKTj7dTLrcUgZoYGqJWZFt7k3OKyoEkvSE6E9WyHKvYKKhv6+CHQ8qXJqPFeSRWMgBGBSiECj2Ayi21SQa52r92qUsWwk21KqmcGkHjCv6T67PjQw65gZqshZiF9XhOxY1Sm4dc49Ntc1TEU3SXtRIESYVrjoYBTPgZ5YpCcmOAFlBPA8IhUry58NT8iBH3E0UkWqr0qICInaaFKRIFdfe852FfsS5tRwr8ov7GuwvFSBme7YFhgeUpVXfl1CR2mhvSkhrICL8pjTOxts9vR969KgGwp2p5M6YL+m371azXEU57bnn58hlB4y2iZCe87TZXlT+ryJgIrHfHq0xc2uyUk7bYnPn1DiVT3rWUHRxP8eH1HVfy2nOdzh/wfjrVxcO1NADQXunG5gIuGlNwXq2uBsTn3ER89O8GpLk2qpLa72BMPcme3wR+98BRMXvJ9e3OMH3g4SzbTPE+6qY11hf9nAdBpuQfCplAFFhI+NFzxhV43By5dLHCLUfDxpWXP5zvQGC93heUdQ7RfPTlFExx3ZS/g6ZA9bA3aWCjIncxRTg8ErPN3TeyzLDjrS5J/vF7jeTZiYcFS3OIpB6lftBOexn1tVFqezPcM732/fw7/58CcAgH/55Jf41eQOGknv8T37AbbxvNtNCdUL6uEBQKuwjdaFeqNgY/xWCAK/2p7h5Y4+bziUTKRwd3ta+EZ9rWXU8FoncfNyjpPvj2zjWOAITJ7F8Pjfm0AsB374hQh8jldNh4vtlO0hn10tuZ9WriXMwmNynHpJDusbui9SlBwAmCNPOtpkh9iOrBBdlF2M0sGSREUACL1AdU3/bu/myDO9HGBVsrwjTa06yy42Zh4X05K02wkWLLYaxSERq/JnBgX4xmEyo8VJK48q/tyjgpcg/1lQ0lHaDAQJyKMePvrGll9V7IUc5GjTo+ICkyBjIyC2IxLaCDIOcztql1DvnWFoj0yI0CMyjRe4eTXHdhodiYzk0HByFspyG1U5tv5D4RGmNkuGthohpOMM0DEtRx8EurvZP9hc13w99dTAHgP6PPYmR/GB8niAXHmoRACTAX1cuFwJuHsx6WZQlGiT7P6cyreqBUmEUsKNzLGBQQJ2XWbbwZVlm8fhnmWvaNnReQzRB7u3ihfk9WZCcqT4/q70fP7XfY0XT6lYgAqQjeU5TGmHfp/6AQJyZtha0+4KhOgz/MoJ6MLh/hGRGl/4BfrYehr6guWIplFYNB33uetmgIv3nDFvfs/1Fha+HbfjdtyO23E7fs3jraxcb66mkG0NWXioaPJ+vDjAB4Hn15TEInVgoX3blkz5v5ELPD494qzC8dCFgxDAnQXVaufbGe/EIAC0CqaJ5ACfjbTlIRMgugCoxqGOEqHOamYwSwR8vjvFEGmE33znJX7+KOp+TgaEVqGIySC2Cez0g8ahiezIT84ucLAluzC9PMy4Op0VA7a6wj4SEZrVNX5xfkbnYJszN826wu56gjKSIE6XO6xjduf71QV+u3mEP775XQDAB4srXGwiY1p7CK9yJSKAo4dEnrp5suL3b0qDz57ezSzRxmAaJUc311PgvAKqDKeuowuTVB6zXxXZ97YbMWAFcP3duPsXlLdp4468KjMyoST59CY43H85RZOqNgWUVwqHmv52mBqWY+h2tE91AZj61xyPWLpSBRgFDp0WAbCJjRyDE4ZFhlZtJE+FVnNlZo8A2SrOZi2nA9xpTOrZadSNwRBZ8N2Zx+HjUcIIk5s8oD3afbIdAmeQighpu1Ws7FsFFxN5gg5wm5KdmMwsIMTPhhfwiazmBCADZ8naruRq0VsJKI8i/i4UAuYmQrqGHMrMPMt2+H6RAS4F13sBFB4ungNVOUyi85cSAet2lJw0jKrCVr3mnyscstREAi46o9lFYBkLAMi9hH8vvr92ENLD3ov3015zJelaBScUm0WIQaDcjs5VIhwOEqH0TPKSB8nSKleBWPA+Ihp9Nu0QrUQoAoe9i4NislZz1HICkjvr4b3ILRgvmZikaws5N/AvYtVfeogYpP7iyTFklOz4hUXwgkPt/bZGmvaCBsQyMEysZgblcfIvdzjsa1zGZ2o1O0DNCfqdlT2+vqFnvT1UzNinF3qoJkkJb1Nx3sghdhrCaYSlQROZgoNV0MozBFJWhl2AmmbA+w/Iwednj+6jO2/Q3CFosKmG3FMMZDY/nqhLTTeJmWu4LlvReTPCBQWYOVw+KtGdeuwjLFlqx/KaL35FNoLTZG03KNy9Szrabiiw0zUQGbDLZmDrv9VyDxsX01f7GQar0JsIzQwah4YW+Uk1oO8L2Aj5fX51ks/HxGBIPa1BQuwFhrgAvTBLdnx6t7jElZvhG81LPjwbPwtDTAJJmJ8X+OjoEgBwUfc4xP7xvOpxoxuYCMcG7dniUBUergivpWa4l3ScVgX444A+rtPVjYCOzoxmAabhCi8QHIBnNLkcfMOSiCQ/aZdx0isCJi/oPfYPY/pP7G+6RgOrFMkXmJ0NHXB0tsV2F52jrnU+5EDQ7RBZl37iGb6UVwVsE5iJW94IyHgtglQZctW08LLzT20Rkv1h5aF1jnfrjmQ2tw/I0KoKUKV/zTYxjVAEck2KcLU86eEv6NrXzzXM7HWpWB0dgoZeZ1lUggsjG1x2OR4OTgCHIsf8+ex0JY3A8GDg7yNkyNZ8Ooyo1PSf1BYpS8sbWRsEijsthuuaDzstfsLTa9PhhjLw+VF7yWELorHwfYlyHXkG88AOTUp5VKWHSUk7Bw2/Ss1lYHrUon1M7Y76lUR3FjcKc/ta2IVaa9YBSyvY6UpYkV3B0hD5fq9O2pzmFV7/Wzna9JtdCUSJTaEc94/9uqZ7NW3qDiqn+kBCR/naUEqEJmTHuePDa5sxeMGbSycAHSHvvn/dBcx7ySEcWnru76JXlNp0SISEHAU4Xa3xpo9bWPh23I7bcTtux+34NY+3snINjSO2o5HYJJMHJ1AdZYKI1p4N5j8+umQTal06qIlhxptxigkvCMCLQ8kkBV1Z9tV1rUJxUcAsIoyyGhCKuFucAfNIylmvJygeV7CfEjzdHnmE6BdaHnUYtiWzXE9WO2wONX/nu8cb9lZwXmISjbk32wnvaEvtcOhKJhX4QcFEyHtTe5zdXePGU+XddwVHlE2b/v9h702aLVuuNKHPm92d/rbRN6/Tk/SUyqxKJVZgFBhQhlUZ02IAY35FjeDnwAAGYIAlUFhlkpaVypSUSqW6p9dEvBfd7U+7W28Y+PLlJ9IYakBUXDeT6cY7956zz96+t/v61tewJni3KZGVCarNtcUvbu4DAN60/yk+nZzh315+AAB4Or1in1W9VrCVf4s1ekDOSAd5g9+tAgTtvAgVPlU/wy7jqqSsetSVToJ76VPR5QMpJaOdNxzQhrcMfs1z0jOWocqxxPDJ1slDNlQyYFhP9gKrQJ6G3gWEIRYRdg+admPLO/ysNFDSM7RsJokxKkoLXysOpwbAhJTo5RtJTO0dx1WJcGCf4WEa3HWyGAZwMUJzP0KbHl2X8WfPDmoMRGQxRsIRAiClw1BnUNtYGeOtCkh4QJ2F8zPMLHtwCxteNAdhLqjKoo3zv1XBzYmuhSuSHtnNDJurDLs8wL0RqrVy72eB/FWeQgk82KzB96kKlwSP9hS0obVFSy5e1aiH1gPMmL7bdcFaU71REC4Rr5z27Frl9hGRQQKFgyP3oGwtMfhw0eq5CiHo8ZhLy+5NtlEhMJ6q9PrDVIXDCtYOe+1hJxaQBNPbEJIePsyHHNuo+3YiOa9ZAaUcE5zK45orxHaXQ56HY3Qjh/K0xnwc7i8hPLtGYTpAXBcQkdk9sfCR7GYTwz6/URi84Kp2yBIBru8VhnXB7QHZC9Szgo/XG4l+IBLTRmOXhefnG3WYIjMj45ihZg+1CHOkbVJe7bs63svFFYYSNGRgwwHAZNpiWnaY5GQiUdSoyDrnYXWD//n6BwCCeNqUlnsNZlBQ1OeUA6V90E0yuCwtApmD/niDU6K5RzYwEAwTIozne8mGEgA98OhB39cZ7j+8xppCxaXw+N5pgF+Pih0mqsNZFxblr9eH/IAdjTpedHe0sProwiKQEj2MgHWCXVi6NsOEpA7/7P5vWW7zME+pOgDw0+0T/MXrsJh+9bu7+Cv1EUbkznLTViwjsBMHv/cgyjKLZ9vg3pTJFI5+uR0jKwysov6RkZhMqN8lHZrMcf/xcLFj+ZRvFORGcXrJcN8FBy0AHz45x+MJSau6MZ7fHGBNsORw7BJ0akK/MQZXm7FiFyBpgO7AMzIpDeDo4aJPmz1HL4/VpuINgZj1UHFxFR5moyHJtEIpl+BLQsfyG7reB8mAAADqKCdhQweCMzMAxAafkuFFlHwBYLnKui2wuSFnMSVC/zG+lwDvGoQVUF1a7AHFvd/2noUv0vy0O82G8GorU0quJ8cm2tAI5VN/sbBwreLAcrSSMTRbOggn2RihfzBgcbTlc3fzOsxvN7GAdignyWwgtjD6XsG7DH5FkLRB+uwuuF75PRu/2FsGwPcaBADt4PJktB8Z0/o8x+7jHnqU/o4lKl4grwaMD8Mx39xMeF5HW0EgbIL+8PvP8e069IazPTj3cjmB3WbIKO/Z9JqvTTYPBhDxuzaXI17g/Mim1C4V+qFiD06OpizDqkC2lsGUH8EOMVpdFhcKhroZZuwh5n1gFgNwdYaSlARaC4h5hz4jOc9FnhjZ0gc3rtgi2LORFRvNPW54sluMvyfAc9rst83e0fFeLq6itKGC8Klns92UaJocn30ULP6U8Byx9rPlIzRdrE7p4kfN5JAaOMMB+aDG+6SVnHqRv9FotxqvRNjBfecHL/Bf3/8rAMD/8OZPcFmH/z45qmGM4l1xkRtsljFp28N5gQPSZCrpuKJ+s5uhGTQezUKvwnnB8pLlcow8EksGBbfNIKN368ixbZ+UHr3R3GuOlS8AvGwX7FL1U/EIu6HAJ7OQhPOqmTGdXjgB78GayaHX/JBwTgbnnbjjF8GxBQipOy8vQ7PUXJYQBz0+uR+0vqUyLPtZ9wV0YfCdu+GzD4sd/t4FUtdNPw2fr9MiET/roKjxLw7/DgDwP178cajm4ga6VuxtK6yAOe25Nx6SXsLvmbGHLfxbVnQxFeSfPv0KL3bh+H/38jT0QGnTogvLa5jZ5hRyHz6vKAYMdF69Cu9tiOOhOoF8RfF2Aix5sQuD8mUGSZdHOGCYh9/bmHG4nvQe07Jj8lrXZUwiskbCU/8xjmhBKGsRFkY6jzb3qfIeJGSn30pw4apf+6T1PWkgBsUPTm8FDJFmoEIfj0ljuU9952UWyF+09ywmHffb+05DxL629PA7jZbu0fKg5XPsTNg0yDYeV/p5mHn4PEXcqcwhL8I1bHZF6mPPHeD3wt8zB0tVMi4UIIP8JFxgj9E83JNDruCcwK4hfoL0vBC6g56fNwDwd88f8Py8d2eJeZE2kGd+joFcoYT0LK06oUU7+m5/XR3y+20vxsnlKXcwvWJky3vBC5ZsZUBgIsFsZHlz2T9OOn0hAllLxHmcWXZdqkY0+aJ/+cIwcuN3Oly/OLf27sPpkxXW52FyypEJhNI7lER2U6L4+/DcKFfvvt71tud6O27H7bgdt+N2/J7He1m58jCSmaxCAicHG/zlq6f88oIYbsumRBulKDr0O0ST3Gts9Iktbdip0y4tpImEn50OsFI0W3+5muO/+/m/BACMP1hxlRlF1ZZ2ksvlGPKKoJcHNayTuDMKu9dVX2JZh51epi1ybbEh16S2z7C8oh3iSqOtwnuIkQFyh/IkMI7LfEBHLN22ybG9GXF4cXa6Q0Pw9U/ePERNu1Zba8jC4mJHnqBeoH4W4DrhAV96NkY3g4KlHnEx7iFyj3ZLu3rh4egEvVzNWY6hWgkhPZZt+G69UZgUPX8vZxV+9Sz0eEezliugbNLDbnSCUguL6UHYFUvhgxk7AC0cMm3RxAvtwZWkVx4wEn4TfVJ9MklQAaZ3VYK1ohPSV5sjzHPq2TsBvVUsSzGFwvgOMbyFh+k0JF3vts2S+46T9N4EJ9uQHAQEA4JYLaq1Cjmq0ygtSnISVRp4K7jNcLGcwND5z6uBe3XmsoRq5Vt941jdFTcihI1TFepzDxWlRj6cB5+nConzBDzYNML0Kkhg4msU8g0AUjuU5ZCY9BPFfcNhq2H2oHDclOhjtSp86FeDKmif/JuH1YShTV84lK8y7p+6fC91RwLucGAkx1qBmgz5VWWZLYxBApkLweIApPIw1EPsTi1Eo1DSNfVeoGsJglaWTGXoWJxgn3ClweYk1oVnT5y7r88XmDwISE076OC7TOhSNu8xGbV8PatywOWKnOT2XMLUvnlIbt6SC2bawhYRtcjRzz38LCb3hPso/Jy8uZV2sIVkSHo6brFchXu+qYtAPqdrWlY9I2ovXh9CnedsaGPGPgTUAzgc11hHWMWTycZlOP+jbzW6Y+pdz959E4n3cnH1RoTIOOWhSU86HQX4KdLIzaBgiAiSacsB5b5TkK9KFFc0iXfA5gO6ITc6RNLRA9cuDOu2oC36dcEP4+2bCT7+4UsAwLOzI4wWYcGcZD1urEJ8Kh0dbnG5JdP6JkO+2GLZ0YKqLMa06BxWNc53E5azLF/PkF2HzxqODJMZvBPwQ3qg1KpMqReVIblGeuBut6SF6xUEkV9UJ+FKheU1ERgyh5wi8oIEV6K9iWLTJHVo1wVkkRJWtpdj/JpIKFFvHM8bNhncnKAqo/Hq8+Aaw7AvPdzlwrFDlh2C/SPHrw0CbUkPPeHxl+uPAAC/OL+H9ZspEzX0VrLVnxwE2sLzoqB2MkHoPgSVK7qGw8xB0HltjYYU4Xx88ugM2VOLz18Hyze3KrA7o3bAd15h3Rcsjbq+GcNP6KGXqbBY0XkQTkAuaaE1YPs9V/rQgojSkJ1KJv5DBiiPLbmLCScAen9nJfeBfeZhheNFxwsw+aifeQoH8HwOItxoFyacm3iOjWB40ecOgh7obptB1TKFuGcOnvqZttbYrXM0sVd42LOkQxoBP7aQ1Pf3TiRtaGkZaj9+coOr6wm6khaCjeLeI/rgbGX2e9TUWfG5h7ASfZs21QW3LQT0CVmBGgXbK+7bmtxBEllLeEDcS5s6YyTPX2sUppOGiYzrXQ5BPcx2XfBCcvhoiX/60RfobDiOrSmw7sI9o6RHVhrWYWttUdFzqlYe69dTTO8FLX2mLAeHzKcdb5SLbMDhqEEMNHy5mjMhcXsQJEH5KMzdyahl6L1tEiEzJi2ZSH6sJPdc2zqH22WQZH94MGrwvYOgWVuUDX5hH0IRxA7hMabFO5cW4+NwjnerEt1QMGFtmPlENJPv/uJ6CwvfjttxO27H7bgdv+fxXlauUD6QKnYapiAa+2AxDAo9BW2L3KElH83GiT25hISuBYu/vUAyKy88kHl4n3ZdcXdrrQyfG43A1wpf/ipAm+W5wsVR2LWezQzKaYeTWahkM2Xx6Adfh9fqKd5czTEmiZDzgh1OdvfWuDdb4wVFxEE7mAmRdArHEFExGrCpJ+x365Vnw3A7DsdYHAd45/5izTFSry4XsFFW4QVkKxP8qhKEmC8l4AXkedi3tceOiTIhSy4RWbwVnAf59M4VpnfDzvfVdo6H0yU2PZk8dDlXj7ITsMcOIwo6H+UDZ0UaI9GNFPQFCew14F6EkuUn/jHDZ3aTQdaS2bDSJBMP1QPFpWJHJVum3bQwAtIK9gKGDDF3ADDKBnRE6loOGr3RLHHSWwVDDPLtkKPUBk3M+Kw19CrdhnaciBx6LWFjvmUOJtdU97ZQymFzRbB8K1OguCbmc3yb2YDT4+DxWvcZBmqDtK0CKgccUqXZaDYc8F5A5AaCkBvrBObzUG0YJ7FbVW8ZQsgDCpLYZSwfESMDm8lkmmAlOx55CUAgxagNe+2TxRDMCWJkmvSJPNUp9nVuBw2pPFysqA8SO1rdZCHXNDKC9wwy4AHfKmhikZtOoyPyEZRnyZFvVWgBUdUvdjoY6AMYHzZo6pxdsITw8FQhfvVf/St873/6b5FHJEaApXPZaIBRdP77DH/14gkairiD9lgcJ1Z0vy7YML+tc7yiVko57iEqm+a8VRgT1Fxqw85ur745wnI05vve9ApP7gfDloNRgxdvQlA5AGx8iYIg46HV0ERMigEYEdFYbmfJk1mE+8GPwr+//eYYp6NQTTsvQq5tAsD4OfXFtoAiVCKrBo77A4BdWQKErEzzLd718V4uruoqhyxz2IllOKfeFUEeQPCUh2FjfeckJ8I45WFagf4pQWat4J6c7AT83OLwNDzMlHQ4O6PFzpL0Id68CpyD2c8Fu/uUMWuRoM7DssY1MYI3bQEpHUNO22XFUNh2V6KuWuw2JNPZpEvrVxk83TzeC8xOt8ysbNsMbk5wr/B4cLzE/XFgHI91j59fhg3AbFpjq+ghNA0PJUUPL7PJEryoyFIu3oNGJDwTAS7knE3lGZqa5w3+YPYKADDSA56OrvD5NsCq66rAioIGykuP/hioX9KmYlwxFOmNhFxr2Og8Y1NmLl5UsJTBK7oAAxtKDLGFZeixeJ0FOJR1l4CLpjTSo888dBO1pxLtq3Btvn4zBiL03ivI0gS4FoCtHOv3Bqtwfj1jSznRKug6bhyA3kkOMMAHNbt/Se1CQDeAUTEEy8e42Rm5vb6tCBDxHrS/2oUHeFX02MQkplrBFw42JvAIpPSWwiDLLI7moadorMKG+u3O/gOwq3CcvSoyl9jBXTCOjxsYl3uG3s3IBZ1r3BDsuW2p6wy2csxY9QLcJ54c7lhidPZ6Eb7rEBfOtKHw2pOEjeZ1Q85gSG2FgTbRsjLQ0Vay10mSVdi33KFQWZ5n9baAtwKOYO4oOwOAH/1v/wqzkWPYHx5pw5FZ/l71Tbh+ioICcL/F8oKCQqY91HhgGNc5ge46XMMWwEePzjkNS0uH589O+LPyRdjolAdtaG1dUntmPuAB3dc/e/0gsIDp+IeZYP2zkB52z7oRXoRrRf/2cUNtRehJx+St0uDL6+Pw2fkQpEl0KstZl3KtB4nxNBQHbZth+80M1YOwkKrM8nzfvpziXR/v5eLqTjqgCr6b8WGhlIMzGU8kWVhu5OvMcBSSh4T8YAdD5BtXiZS+MRrwX376M7xsqUfqBXYk4dnejEI/kH11PfQV0f7v9qwJM4PCqOxgaXGN/VUgEKxs2aGm9xRbDUc760xbXO1GwbcVgDICmhg7/Tylb1grYbzgya6UwyH1e0fZgEIZHBfh39f9GIcVBYX3yfVASY+1KNlE4madc/UOUCJP/JouSEoAqrxyx36zHknPdtWO4chTt5AGZ90ML7dhQb38xWksqrD6JMhC9DbGc0kM1NcTIwtXOt5wCJPSVbxA0jDSsUTpgM4tBB1TZwTyKwW/d2fE7yIt3goKlxbQ1Icb5hYiWv05wFkBQQ+i8o1CQ73Bi/VBePDHNJSpgd1RBRS9iOuooS5TRSeAnBCFy7NAHuPKzAqIuKgVFsIKjqczI4mB0lvaWYnpSbi2m6sxRLSdA0KfM55k4VHmAy6uw+fYXkKQTaIYBLJaYFjQhsY7+CbpN3kWGAE/G2CbFOQde6Bi3kNrx8Ym3abghdbObCDyxGvlkw57cz1GHSU7hQ3VbZelD6bTEYlN8RybiUX+Kp3j/jBVtd5Krn59L9/eUNdpAfLOw4ukWReZ44hEIYiYhrDx8V6gILJWMe24Gh6GtHmXjYQ97mGPouWh5NfsyMAZiYYC0fODFmpGRKhGY9lUuLwIi4/KHRtTQAD+Omz2ugcd/oOPv8Jf1B+H16zA37x4FF4jRE6Q1toPko1ewgtULBgBeTcZ65hl/lZCkdzqYOwB8MYSALpBYzxtsVuHhT3LTOpPa4kNHSMA3Pn4KiWTtTlzB+Jz+F0etz3X23E7bsftuB234/c83svKNStMgO2UQ0OsWTgAecq0XEwa3GzIBnBbMDtSOIHicIAntHdYFewSk2cGc10jo532/3P5EUtsVGHhtIOP2Z73B4ZmM+UYth1uCtz0CgdHMQUVKFR4rXcKm7riXaA87DnpYjZu4b3ANhqeu+CQA1B1FPtnNvRE3ZqceiRwGaFH5fHdB2/w61UICJhmLUr67Fft7C0mtZQedZscWXqqZOQgYEahqgNCsHesKMzUQ5WG3Wx8rZEtw2c/251i24Xq+N50jXVXYpIRlPrZJS7OQxVVTnocTnfMbry8nOLR3SCo763C9WrMvc6sSb1TMzcJ8tMeGBvovR7jEHfuMkDbEaoNtoB0HjOEcjtW5TZZIOaXis+3udNDrDOuDNqpYJa4nAzw1zmqu9Q73BaQH4dq0nw5CbIuqqTyK8Ww9jDx6DOSKk2GYF9IAfIwAsUFVYhGoTtyfMyjl4olO6rV2HhiEQ/BPjB+llCOHXZ8I7H0IxSPw3E1uyqZbDgBr0IgPACY8Z5px5DQC9UKGKUZavZZMpvIvi7RnVpkFDIh6hSWDuFD1RK3/UOwQwTCdTEVtW1KG6qoMqJGhuFpv8kCqzhWWSQfAoKF5cmPJc7/fboei57/DspjqOkidjLwJ6q9vnBUBG11+O/03dom5/bSrs3RbovUqnCC04ZkLRkBL68E2vsONiIOjeJq2vUK+aiHpmeRtRKODkMWFpev53wsdpAsf1J1klZNZg1e7ubJcnKbM7QMD+RHLXqqYPNxj54q9Gza8/356IMLtEZzZbk4vUYbs1e1wVe/u8t9dDuS2BGCURQGs6rFEzK6+Ga5QEuGGFhnAPXay2mHPul5MwAAIABJREFUaZECO272EB1+OL7D471cXK1R8Eah3+XsKwon4E471qE5L3AwDQ/AlfRo+zAxvfKo64Lhr+KgRUc+mHlm8JfXH+K70zN+jyKLMggBbDLuQfmRYa1dnhk0NDHFxMDvNFPqp0XPi2lvFQarMCL5TTXq0Irwd1fXE7ha8wPL3EverXlpMJD0AOscshPIiCA0HBoUlGqilMPL1Rx3pmFhn+ctfn4eeq51XaSHlxMw2wwm9ugmAww/GCREL1E+jw5HUZ4DqNmAw/kOVzekc7OCyUKyUfjkILgutVbj4WSJe2XoEXVO4//ug8Gvlg6lNjgiuPp0vOWF1nmBvDCwU4JqVZLUwIMfhp5IP/Eh4kwi3shGhkDqSMKSgIoyIxH6hjq0IpHtPLz6/wB/vi0AD6y/Q1KlPEHVzgNiPrAk5vhkjYvnJDMap4ckEO0W99JzaAGyuwxiPKAkL2z/qynyG/D5bpWHp+g0WwB6R9d67jnY25fUp40yGiM5OcZNDWRpOWZQDJI3G+x1HPlM+wHsEhybxn1rOudqMsCSrKU/clA7CTtJ546cNZHfKEijsHsa7o3ZbzW6IyJ16UQ88zsFN7EQcd4ZyT1pAGEzQ6/pqwzDIgapK5w/sQyzZpmFFXvzIA4Bjnfjf8f5X1rIysDR59lNBk39+77LgI3meY3coTinhXwQPK+aOw5wgCb7VVxVSYZSBu1qCg5X+0ZaKOYtQ81dncH36RkWe+1tm2GrC95UwySdse9USLiK98MeAc45iQenIWlrlPWBNNmSdj4zuLgJcLTzYWMjaXOMGw1DcsXJqMMk69lu0RjFRYVqBSzJorLMQMLjZhv+g1B7tpSpjf3OjltY+HbcjttxO27H7fg9j/ezcu0VvFIBCosid5BbCEkV1ps8eWxKDzke3nqPCclhMm1xRSLrftCoTY7/89WnAIDleoSBqlqxVYF5F2UFjQaocnV7u9S8HHBwssIPjwJz9qvNMVYkLr+8mUJIhx158MIDak2QkwOqT9YosnCcXZ5hHj2IhcfFOlSL/VUOf7eDf0Gs0UaxEP/gYItHsxU+moQKsnE5TsbkLGQ0JNH/R/mAbtDY7sJ7LGY1u+3UXY4lmasDobIx3w3b0INZjcOqRkm/++LqDtxRNHZ37NaUKwvjJS57cpiCx6NF2E1f1mMclTtoMvy9V67QER77V2dPUOUDf+9Xz44hojPPfGDWZkzj4EzdPkF+rnLwUqb4TJc8dsvLaOIeXuoygYwUA14ChnbkZuzhdTBHCCchVeh6PKAadWymf2+8xvAgHMfyYhJCygna17Xg7a/oBfRJmHNSBWlV85pcekoPW5JRx6mDPOphKMHFFp6PoxgNMJHMIjzkRc6ORF4nyQuMhF8pZvfuf2fpg+TMRS6U9NBkaC+sYFjSFT4cewQApNuPMoVXJP8BmTJQRWwmHvlKJBb8By45Yu29H6QPjlwm5iOnyhsA3NxAXWt6T8sMdXvXUJ5v+LxuyBK60SogEmlyB+/35EMKnLbUz0Wo3CLhca3QFeEETRYNWmLdA8BNXWFNSfNqrfh5I0uDx3evGXV5uc7ZkAGDhBkUV6fuKucKWowM+iZDQSx7XRiGbV2RUpTsqxGuTxWbPnQiGbUMJEeKpDGdWRw+uOFz93ASjn3ZV7hYT9hwo9nl3EqxuwzoJCsloBOzerOtsK0LlvcUmQGOwj3Z5AXLGjevp9htSshYQVsBRdeJ4fh3eLyXi2uwuwtQWHZJtmunA9wmQ7+XXhKN66dlhzeXocnqegWlDN8UdZtz/7JVGt/KBRrSpKm95I18KcNDV0XqoERLbi3lcYP5LKWZfO/gDF9tAq39m8sDFGQsbloNtBLVa+o7yQSndYcO9aaAqcIN84N7r3Fahif/X5895oi8w++fo+kz3MzJUrGRnNZxgwk+WFzjeR3MwLVMC960apHR4lpog3mRHGoWFRsJYrmrAO3QHZPrysICBAdebjNcFRO+CbOdYN1fcXeHF5uwaXj5/AhqOuDRSbjhT6otHo3Dz7O8xWmxgaEmmpYOX5KDVWQ2R9eb0cmOw52l8rBxobUBBosPY9WlRcFrD9UI7tHZseWHb2+C8w9Dn4VHTQ+D4o3mBciMXAjGjhIM6aHJDnI+aVFow8d6VOxwXgaozR4L/PD0NbM6h2HMloSuANtDInNQNxlKsv5r71jsZknWkikLT/3e0bjjjVu7yzmRCMLDzi27WYlBcF/VamJkb2hhAbh3HaP74uKkrjNmg3sF2ClB26R9jXNEKY8hMnQ1gImDWJG7UpNScbwDmlPHGlg5MkBchDuJ0bfEHfjRJizYEfaMXIY4rIAldnI271i+ppQLUHBkqO7yoBOmLypps8pWijFLXns4gtqztUB/3yEjPXV1JlBTZtvWCsiNxstvQpiEyz1EtIr0YOnQ0f0tvBdYE+QqKsPa3vG8hXOCYyGrB1uO04vPpBlZs55fz6D2rByji5qbWHzvwRk+nARt608uH7Ikq5w32KDCEaUNfXJwgSW1veohx4+fPwEAPDm9xqTqcE3KAr/M4WizoR818CVgI0O+k8hoM9MbATUf0NHc7YRPlHsjIYkR7MaB8R2fkr5TGM/DMS3fvPuRc7ew8O24HbfjdtyO2/F7Hu9n5Uqm2LJRXPkBQHaYiAJSenZB2bYF//esGjCdNDgk6PHLlydJC6eTCBoA7GUB6fagtRTLCHXQ8a6+a1K49SgfUEjDnqCHsx162p3LzEJeZujnaVcdySpeAxDBIxkAjFP4s2+Dl27fac5obfsseIZGKEalY3K1xk9+8wGePg0G4uOsx7YnpqYXWFGObNPkmE4aKGL2VXrABUXmNW8mgPAYjsjwvDTwxEJVawVhNUe42dIzkUgph8t1eA9hJOw6x7NdMJG4PB7j7iyQrP7k8Dmk8HjdBiTh33zzGRoyzpgsavzJ3W/5fC+KBu1BmOK/fXaP/7vaBcZ0hNq8BEObtgxVbTxGSCS95N0+VBe08xalhaZAcafBUKmwwQ83ak3tnR4ne97RF7sxXl+E41meVszAnJUdvlgecy5sX3gUVD26AXzdhfQorhMJBVYk/bT0cFZhTPm3dV0kos46g6Bq3Uws9KIPxuwA5FnBEyG/Vm8HwZcenqL1vA36cMZ4RUJjzMgHshiIxbrNIMk8ww8iMYIR9KUqOmT1e0YdiuYknWN9XkLSZw1Th90nBLVvc2TVwNCy7GVquWRhXkV2sntZMfyd36U2B0Gd3qbjkn3yUFYbFXyUyXcYqxzDYYQsAHSS5/j4exvkdI/6Z1MsfiOweUqXYyewz5OKEZSXFzMI7ZIjVK/YqH/XKKhpIoAdPN4xMTK2Yq5Nes5ogntdV3DFL3KLX391H09/GLKX13WZ8m4HjdOTNT9Xfnt9AknHcTTa4UdPvuH33nQFFmQm0lY9u1JZQ/GRscJ3AsNib84YAWvD784Od4wcDKviLR2yNxLVIjxLfTWwvv/fhbLvvVxcZaMgCfczH4WH0GzcQgrPcK+Sjify0GT84KnIASX+nlQeLkpjagXTJtMHuQe19QsHP7Yp89ALXlDvnKwwol7pUbnDN/UB55cWyuLsLMCeWTVguNMBBMX4iYF6GI7fvJhAnhVYU59jVrYsA6r7AooeekOvYWvNNzK0h6QHuGwV7NjhnPqzWjko2mB0g2aDfNNp9GWaOi/XM7Zd84UNrkMX0eQ/YwP4YeaQrSWHmfu7Hd/wp9MtztYBHp09vcZqWwVGI4DdqsIzgsWMk3gwXuGaMlDrs3GCmWYSf3d5HxWdy0ne4dWa0nq2ivtR0oi3oN94rQBAeA9b4i3Lw/h7fpCAdrxouloyRCxcWpDtJFznyKaGE8xubkwG7wVvOL7tj7j3pWY97DqHJgZm0QhOxZG9gCHIUu8EhAPa4z2Ho/gwGhvYXmI7hPMjc8um+LMv02Zy9X0B0+jk5CVSTq1TQLaW6I5prmoPET+7lcEYhFyq9A4YyExHdmAzC9EoqEZgUDHzNJl24CYHpE+G/3sIoMs9IFIKj/BAR4uaH9tg7gIAlwX8RQ5MaWEp9jJaL3Ngr4/rcg9P6TZDr2EvyuTcNXa8kAuDxJ7WHiK3vPgtHt9guSS7yesCfmShiIcxKnqMaE5fPFDYbifwkSl76KCp3+i/GfP1HCYS00WNnjgedp2lfrIVUF9XbGB1Wc3e4n9Uk44h488evMbXN6GN0+UOizthEyqEx3ozwl+8/IC/d7T/zIsBh1WNz+Yhu3rwCudtuIiLvMGTMkDJX9Sn+PdOv8FAPYEfv34SNs8IRiDeSM6xrX2V5vFGwU8HSHomWCfxkDgTXzZ5aG8BQe4kPRckcQMKAPUkmda8q+O9XFzj8PvB5ki7QoCkM3uVgaKb0xiF7bLCNW1H9ZUGYuScDX24bJWsz+Lu3MwMPnx4gaMy7AJvuhG+PjsKn9sWrKk901NMyo4XiNbokOADoK8zoFHQMQS6z9BEPkErYA4MNN3Uz88PYSnMWY1T/FTbk+wkSgWa8HfhFz1kYXEwDjfM2fUMnz0IN2CuDL4ge7OuzrB9PWHJyHbimIyhKgu/1sjXSW/bRwlpGwgQsUfn3hQs8Xhmj9gWre7ysKGhSqQY91xpXO9GOChqPL8O8hW16MNmAeEhd3eywXVD53I7wXZNEirt2W4SO/W2RaNNWlZlQv91v/+o53vSpzaDitF7VUqOkYPgvuTBnTWU9Hg0C33i83qKHcWVffUtWdVFPV8vWavpLgsg97D3CX3oFMoXVCkUHvlNWgTMCFxlyVby+e4OBc85IMzHfBOrcqA7iNciWHX66O4jBW8Uso2AGXt2nwrnJ11P+NTr93toTD93HB7vGgU3T5VZSZpNAOiucsg2bUy8TJsZaQA7Dok94bU9FtQgk6Sjpz459byrecvSIZeHQHRJv+vmA4fEmz5sMMQeu6p6GBak2ajlhJnepFQsIEhUmIAlwnMj+uIudxUvdkJ6DDOXdNLCs7+1tGm+YBBo2owTZ95KoyItcSTSYa0h6Lwq7fDJ8SU7t123I94AH95boScUZLcug7xqQhvbbc6yJRQDbtoKy1E4rqtuhB1FVdYmxze7MEl2Q07oVXitG/Z0y9sMcMAwIv7EaGC5n+skqlHPiNL8cI3DgnTdx0vuMy9vxpDnBUws7RdbfDS/DNdpN8VzvNvj34Hi+3bcjttxO27H7fj/13gvK1cviP3oBec1bi8KuMke/VulfqBoJVzsTRUhtSOyLO3IJXhLIuRWgnbMhQ/pHAjG6+u2xMvr0Cs8nW8xjv1RK1NqTTbA+RR2vdyOICI9fauhasmQlkuxrxBPakyrHg/nwXjh179+yO5HJ4+veEf41/YJbCVgLgi+FkBFsJVSDk2Tc6/58ek1diacn53JsaP0EO8EZC+TZ/DM495x+NybXYUmy9Hcjeb5CUKUFrAaLOiH9LAEKY7HfQpjX+dvGRCYQcHRrni9m+ILgNmeUvjg1oOQlPJyPcP1Jb1pp0IGKIDsoIN7Fb6z3glkG4F+TrKRDGhPyHCgFtB1qkLdg56NzDfXY6irDHK/hxaN6TNwStDNqzmyRQutAiR9tZzw7wvpUY57eEos6l6N2UsYPjhmZQTtq1EPQyxjZxXwTagE8pWA2gG2oH51JxiBUa0Iph2xf3ojucrsF6kid7mHrzX3M4UVyKh/r1vAqz2DDyu4yne5Z1MNIFTDJgaMS7wtdzKSUZemHaUSt3LQO8VJR/z3CPeTmg6cszx0Gvpl+N6+CUHuQLjXmjseiq5v32v2z5aHfYBZ4znfaNjIVK417FEKjT9Y7FhuNi8aPFsFiDX2CGMrZPlmypA35gOkdujJvEEIYCB/6GrRwt1p4V6EuTb7rUZPaEF3YuGjD/lSwwwjrlaFBTsX5aMenSrfcpgS9H38QY/rdoQ3ZOYwbAoc3g333p3JFqUKF7s/0GhMhjer8HuisGyy8R9/9iV+uzrFagjn9eFoiYIeKv/r199HfRkqWjUdUFU9tufkBWyTNAw6BC9EuU3fa3aqa51AfT1ig57X6xNMfxDm8a7PsdmGcyMzR/3x8JavXhzi3jiEnjyd3eBv8G6P93NxLUIqhzB7ziSjPQNsBBgsQjg+T+kPs1GLtskhr0i/avYWi9xBVRblpyQJaTMURCQyRqI3CkczIgcYDU09UecF60S9F7j4dhEmL4C32BBICxUAmCPLgcSDD5DRm024mcZ3dzj5ZMuf9bgKEOW9j9f4X776DEOEnDx4IR8GBTdIXGzCzeSc5L5wsywZVsqqATZzyJ+EG+bTw2v8+mWwTLQ3RTDMj3C7Sz97B7iRxfROOK5x0bP7i/cClhbQ8o1Ge9fgzsNwzHWfoaUFvyoD6SEGLphBsU6urouwc4rh762Ei73muoCgxaI/8uhOU9SergV8lKTm4aTYCPW/Kbk7kO0ETOlhyElpP+nFzk3qN1oBc1VhR7BhWfW8GVDaYeh1SsWZD2yBJ2sJzFIaysGoYRKZVi1WT+h79greg6PB+qsCmCfY0DYa+pIexjLobsNxAd0pLdzzPnwvgvPNMueNjs0VdCOQr6L2FEnPKILzUrymckiLcrFUaI/oe/YU15dauky20SuF/o5hcpWqZZovtJHs62T9FDc6wgH1A+q/Zp7IW7TYNhqKZCgx0N1N6bsuNbcOIIIVaSQBxZQdAPjy5oh/rvIBV8sJRyLCCdZeCunfuqZZZlEeh/dZv5lCrxROfxLO1/R3a3z+39DmajJA0X3ncgchfUiJQiBTxeAEKT2KecuRcDq36G/CPLCtwpvrGW/MG+l5sVpULUY6TORvlwu0XcZSJakdHLlBXXZj/PO7v8RUhjbM62GBr+vw3Y1RfA2m4xZ1m3NPGhLQRD6Km456lzbcnzwIREh3KPDF13c4iQgS+O1vH4RzV1kIvZdiddomopuR+O1lIDHurm6N+2/H7bgdt+N23I7b8Q/Ge1m5xuF1MhMXLmWqAoByad8hjAAuaId2GKDkGJYujIC4DrvD/hAoDxuuQnd9yVWhB7DdlNgRaUoIz/KYx4c3+MEiODL95OoxbpRn5p1wgpm9PnMQPlWv+ZeaTdnhNex4YDH7jx5+i2frAHH9Z/c+x7+Y/RwA8LftE/wnj7/An6kPwzFuSrTLROQqFy0b9Nt1DhG9T71gyO/7T88wyTqcNaHUebGaQ3wTds95j7ei3pwGR98NEw+90thQibSRyex9/ngFFePE/mCFD+cbfDgNrMVCGvx2HXa0Eh6X9YjP8bYuMMSM0sogrwbYWC4ddVDnRTz84M8LYuXWmsPAh2lgMQOA6gAvUzSg7BP5RdhQMcUduewk8iehCu97DU+M2ozY1w2ZW/hHDRxBiEfHG7RaY0tBAWKl4akd4Q4GjCcdw/I3dWKe123Bgn2UDuNFwx7Tl73iuTTssgDHRuKJTAEOtvDsQOSMRF4OTNrJDjpkVDHXcoTqVxlM+vhgpgGEqmapkW0TlB0ry2yb4gWb03AOx2fh3/We17U5MAHyj6inQqpefHAkigQqiER2cnOTGLWgv1lSruleu0QOGWzhmavojobkLGQkpHRcGb9qssACp/c7eRBYrU/n1+iNwoqMcPVh8oOejlv0RjF6YqXHeERkvFmP6d9WUH349OZhgv3dFLAU3QcJ3Ht0hTNB5jRdAUNVrPGBzKfo3uvrDNkivP+wzQHhsVqRH6/0PLde3czxrQkYtPMCttHICaq1RrGByC/P7uKnzx9zHCCsgKLoSqUtPnwUHNqudyMMrWbJmpwMyGiedZ2EIwQlHKTEF69P+JjUyAAVoTq1ZpTCC5Wi+7wIqA1Vsod31liuYhxdMqZ5V8f7ubhqF/7nBOIdrncSeLrDQDed3Xvwy07AEuV/tavgcweX0wTZD9Z2ArvrCg1Bs9IIGPo7KP9WKDQAmHXMVbzGaR4Yi1e7EfR4gDujPlPmua8nGgXhBBQRL/uFZ51rdz/Y+00nYVL+y5O/wfcfhACBn7YP8e0QYB/nJdam4Ieqzi2rcqpRj8NxjRefh4UsX0uYUfied757wQ5NB0WN02KDL1fhPXdNwcxGVYcenyGWoi09+oO4AQgSGE0B0XEBAILT1Ud3w039YjnHpivwhT/m16MJeGc1xntQXnzIAIC4zmFMwTCxcwIgCFe2ki31fFOiuk6bJ9/utQcyT4lCqR+rSSEwTAPkWM1TxuXxNMD8V9sROl/yd9Z1ktH0yxzVaWgVCOF5wwWE/q8jxqgcgFoESzsg2BxGaNB7wXpq0Um0RR6+HwBvBAaC19l8P8LaTnDP1SxS4LckeDpumFSVFlq5VWhO0+I3LCwzpm2vIHsBRc8+aZJ5v6nAMLAXABzQHtJ5HNs0/4cgwfC0YNvScctBSA+51OyuhNwxjCg8uFfqOkV9+fBrdupgo/nUIOAqh5K4BItJjf/iwS8BAD++eYqvrw9hox5dAMfEFzge7XBDUjotHKyTPJek9Lh3EBZeJR1e3cxh6NoU5YCzN2EjNflVDjl4nP0xbbiPLWSbWMBsZ9lILHcVCgoKqWeSk5Pi9Yjj8HiDhjKcTeYwbApekITwAM3r1hdpU15ajA8aniPm1Yg5AXWuQ/+UvtvseIs5OT5J4bEjbfvyagJ1o2EPyaJUIOlcG43Jb3JsPwpzVfQCnv7OFRQKYfY2YPsuVfFrWvAxAEHn/aM7Qaf+57+7j3d9vJeLq6gVhFcQVvCE8wIwTQZJOzPfpBgsr8G6DaVCmkUU2Qu3J1Mw4i0bNjdy3L9AL6GvNMwiLbazu2FBLZXBAUWt/JP7z/GvP/8OT0YxCCZxwAcCyeY7YUJ//MlrnJDF4TebA7y+nHOazu+6O/jHxRsAwD8uX2BKi9O/aR7hs8lrfDkJC1dvNO5NA4ng+c0BBiehj8ONZruK+1azvOMHz7PNIb5eH7GdWp4bdIwAhP7cMAvfu7y3w515+J5vljO430ySteCdgSUazarERZVClJebChfXMS3G8ENVSI+yHPDdk7Bx6I8Uzn3UsuZwlWPbPjdJGx+4FCMne4HuwKVNyz9o77gsoQPCgfuv7qRHllu0ZG9ZjHs0dL73ZRsiSlV8mhfNBcUXdpMgDSGrOGkENCWADDMPmVuuQgHg8CDMi6bPUMfvIj3cINFehfOl9mRA8k4Ls8qZKOO6PT2vE0z6cb0KFVuU0fQFcw5ULzCcGI4rE4OCjTF+S4l8mfrosk+yHFuAowaFA8zcpZQTAKJLvWU7BUBIhag1VJRx5D6cf0qtGY86DHtxiQNZhrIJSiQIVZa9cz84ucLL1ZwRgF2X49s2zKVlW0EKjxOKdIxe4kDQY8Z///TVQ7S7nD18pXTc/67bHGZQvAnyXnC/dHTm0ZwIlH8Q+AIfzNZ4sQwLb7PXR5YrheblBNWDcP+enK6xa9Prza7ga7NrihRwXuvExwCAywKIveXKwEraNJcDMmWxIomfXwy8eAvlIDKPCUnfnhzcoCfd/pPJNf70l98HAJTPcsg/XLH1olQ2EOsAZOMe9Q8t9xWL4wEtHWM5GtA1GVtk6tnApEDf7B2/VcFvmRbvs9WUN9FRN/8uj9ue6+24HbfjdtyO2/F7Hu9l5epHFr6yobCgHZU5NIFRR6zCfcarK1MF6pyAmg6c1OFqzckZqhHwMrGMAUBFVrENO/zovpN/smbx99c3h/jfxWcAgrmEUB4+qlW6lAEprUB3YnDncbA0+2env0FGSvxPJuf4v/ApDPWKb4Yx/rx5AgC4q5cYi1AJfJifo5QD/slJgK5/vb6Lz89Cr6Tb5ShODQ7J7mx6eo2vXoUK99nlIQaS71z6wLiW1BNyteYsUtkrCtQmGY2RvONvLyqIhYM+CXDdJyfXqHTYte4Ocry8mfN5m45b7KjKGjrNrMps3sF74Kdfhu8WsiipitGA2kqGM+VWsSWeagX3A4dxSICx0T+hTwYKbCAfqz0BrkB9rWGvcsg7odr+4wff4i+/Cg44fpVDRUcgHVyeYvVbnitm7OqdgGlzlrbYyrNjla08BMBQXrcq0VUZv3+ULDgKBRARAZgYjInFaa2EHRmos2h04Ti0XQEcAuGcRFdnUK/D7+lGIA8ABjZPHbsPAYGBnL2iqseFc8Lw715PFyBLSwS42Od77kddspu0Uws4IHtDlZpPf4eTDsKD2b0bIzGZhe/W1jlLjgQEfO74/rVWwCrqPRYzTMoOZ5+Hea06gX+LpwCA40mY2+eXAe2QyuPRabifLutR4kh4Ad8rlIsAKzgvsCG+hK011MgwCtDsck5YOv8PQz+ZiNforOa+pNtpCLMn4fMCI7oepTbICCZfNyV0bri/aYxKiBh9f31BEGyW+BlaW9hYnQqg6TLkBC8PL8bMOfCDDOHydIylGtjk5E9//gPuj7aPeyy0RRfvw3WRzHQ6DWwzlHfC+VTKoYoo1KYM92VET5wAIsfAAzBp2RH3Wl6EvBd49ppYy5se7/p4LxfXIHQVELlNDXkj4WrNri446vDx/dADXPcF34yHkxrd3uS47GeQNKFt6SGNYO9MOR3gTHh4lZcSw9jD0iKUa8syF2MlfvabpwAAvdQobgRDkXvtOTjtIbxguOvPrz7GvSo8Ea+6ETyAs5cB/vrvr/4YD06Dt/AfHb3AH45DL+NEr/H3zUN8vQuT+HevT5PcoJfYNgVmRM6Y5w0+exwcmn7x68csO3FV6Fe7eMM4AczCTdcVb0ua7OsR1obg3qmFmPX48DQQlayXeLMj6VDe84NtVHaQAixJcVYiO6TFwyhI6ROdv9YMN/rMwxtAE9nGZR7ZDW1mVnuLwDiQlPatC1kvbKLbEsHQfk9qJQB/OLCM42/fPIAja0G9Sbrf7sEAaTJI6o2bUepf9osgAYvkG1sAlhyyioM2xB62yTIw6rCzpYR9Gq5LWfWoNwUfI4RHTVA1NhlEnzaGaidhCpJ71Ar1JByvqoJFVZyPdgK0D8PvKhpCAAAgAElEQVTPo8Ma3gs056mf7RepDaIbgfYuHfO54knq8sQP8MpD7hTrav0+RjYfIC5zDAf/QFcOQBgJoRxLicyBgZ/G3rJkZzExCIhawe956caxeTXFdpDQTXSm8tgsw3fZ0oM/EpyKcmAr03vTDdZdgn4//OCMSWVNnQhl2WGLshzQx9Sacc8WflJ6mEFhIHj52Rd3mEQma5U27AUwf7TC1VWQ6XgnUIzJh9mLIMchaLS+GiUYHMG+NS6yPvOY3A3QcqYseoq4HDqNohr4uFzpmEOiZmFh3VyH+/Kvzz/E8f1Vug5sXQYsz6cQbbKilNdlfCl8/qngz96RkxOEh77Ik5Qx+weJRfGZKwKvYFyFG2V5PYGkTYPq3+anvIvjFha+HbfjdtyO23E7fs/jvaxcy0UDNfJh53kWYbHAdvR3Q3Xw9N4V+wD3TmFEkIf1As6DiQLwiWkqPBGdIv/ICuCAdqM3Japzgc0nBLFYhYEIDEOrudoTFm+FR0coLfwDUBuJl9+EqnN1ssPnbwL05Z2EWeWoXsas1wwvST6Ez4A//eq74bu0GaTyTP6wvUJFLkBlPkBJz8kUv7s6QUXM3MW9NVa0+5dX+VuyCD0eWMYxlAp+KgObE4C+2atiSX50vg27deskQ+NP59uUiAFg0+XYXJD4XjtIqlTz0mC3KVmCgZGF35NQWSGSjMOJENoNwGUCAzGYi2VALvweBByrWpd5kuMQhDxJ3sJZbjD0mnfaxkl8+kmQUH3+q4cwB1ShFBY217DHqdpjh6OFAfoU7O1Kx4H3fT+CH1scHAeyTW80CjInuFYL0KnCyXSLbT7g6pqqHivYnF+1ArJPjkrSAqOvqQoceQwiEodcmJ/RTER5hoL7LoNZ5VA0J+3BAFKdYPQ85NZGA45svWdqMhFoHxEJqlUoXyk0DyObVKYEp9dFYAPHqb3nhjY5qSEAbKYRMhbYnoXvWR3X7B+MyodknZhlus2gb5JRxH5rRrYCbplMX4RIluL+A4PXF6EdMXvYJmcmK+G8YK9hIFSsQAjyEAIoCdIdjIKJbk0SDM0Cb5smuEpCTYkgBWB5PeZw8KLsGDFa1yXqVcXGNaK0nIGMzMMVBm5EFeOsw5gkWdbJ1MJoFAbl2aAkO+gYGZLKod/mDCejSChaMekwRFLnLgtGLCTTgQBsbH3MQmvCUCXbVRl8n+5fszDcEpCtSOjGacdEMADwPsjpwi/6JJe7SfPqXR3v5eIahzOSI5P6EVDd3bLzCBBMrIGQVBOh2FfLGZyTb4UEM0biBFSbLODk3HIaTXuq4TLFv7u7GvHDIG8SDCmtCDDiHjSzr7OUBnD00NuVJUsF7DpDtkqwk608/FG46e5PVvzQuFEVui4LxvgAsNZoSXtnTxpYK9mQvMgGXK/Dgno4q1kn5087iFWOjOQsw2G4kQEAGUmc6GYVFkkvmfnwEKJ/G5vOtYTnG3xZV+iNwv1HAT5u+gw5bQbOXh4AVrB1XlggIzwK5OeaH+LDzLMhf3fo+dwIE3439jqFRWLsTsPbRdhW9UC3jjIIDZlbhhE/PT7Hz56HYHOvPONArldQDmlhHyUZSjHukWWWIUXTK2BGcW7rkL4U4cCni2u0FNu1mo7h6fi/+eU9uKlBRkxWpyTsnpOXMADimuDBIe5egxdTb4N+ujwOcPuo7HA8Cifhdy9PIfbi17LxwPZ+3bGDqgUsPQSbewKWdLrTO1v0ZNaOlYY04AQhADAPw6bEbzUgwfac5bnA9knkNAQdqo8s+0EwFNy1Ge7fCyzcy9UE3oPlMHKnUtINQk/YssPanp6dQtTH98MGpmly3jivuxLjLNwzf/joBXJpmUVrneR76MqoAP2SrMkMii0ru3UBudXQtGGdjJNsa3S84g1k02dYbycs7x2U4gi4XFuYcc+9d3GVM/w9Pq4hREqSGTrNLmejokdFDGBTKVgrWSs7Gbd8D1knsQbYaF8oj+Um3JRDnSMbEVP7both0Ohq2phID/0gvGZejZAvJbevBi/eki4KI1gZYYsoewRU5ljuCJJW5fR54jKHJ+Zwf7LXMnhHx3u5uNpBwQ8KvlG8LopZH3ajVKl1RjPZ5nI35j6rEIE8wb2fBty7UwMACfRxUm1zmBidhaAHjOSnOBEBIn9QfEh/6MIimkJEkh5UhP5Rdj9U1OOqY+nNrlUAFIYZLbaFx5TioHqruEgwRmE+rXHzNaXKtBJ4GB+wPZaXE9QX4QG526sIm3LAyUF4ILV9hmWtEU12ZS3ZVMNMJFzpUL6h3s8svYdcariJAIi3ZK1kYf7P39znRWWzrTAat/jOIvS8rRdY90SmGk3hL4qkmxMe+jKPP0LXycAi2wg22bATh+KM+k+UIEjJWhgmIeEGAIaph8vT+de1wPgZJY08NZgdp1is31zcCVIIAEJ7Psf5lQ7kNlrg2sxzqWSK0DP+9G6wiuuMxpeEPhRHDZRy2BABrBs02/PlxcASoGwjYK3GQB84OmjA02WpwwMvVq4QMLSg2srzopWVBnLs0FKsX/9yjOt56H9X0w6d8NzvHeoMs+Mw57qZxqjscUB5xoNV+Of3fgUA+LPLj/EtXc+hK8njGHTOfVhUw0EBysOQlnUz92FThrDphEyV7H7jajZtoGLucdmHFKs9UgKTqQYBn3vIw3BWhBewVNkLq+ALi+0VmTDkDh0t0DuZfLVdX+HLb09ZYuOdwHwWNh/eSKjcIqPFqu8yrgpF5oDjDk+Orvm4nl8FM5ddk6Q2dw820Pct1oSAOSd5Yz/YYFARrR1dlYhhfadxerDBmyZwQNwuQ02fvbsasc+w0xZFZnjhtU7i6TxySEpoZXHlAiLgvGAjFtlIDLRxHa5LyOkAT9e0+k2OnnrvygYZViS2la81+gX176cWaq3Zf92XFtmcojqXRSpGhIfYaPRxXhz1THxSu3d/cb3tud6O23E7bsftuB2/5/FeVq4qs1CZhalssoPrFWpfII9m6/nAQdtFZthkv8iA3dk4ZXDmMkFOEsHUnapaVVi4uFuVbzsSKUupNgjQZntvD9PygI1EzdIm5xYPQADdFUE47RjiTigZs/MMXoVeBwD853/098j23BHmefi9680Y43xA/yhUofWuwKgKsIxWLoRrU+9QaYcRGYQ/WiyRE+72eXMKZA7ug1C9FMUQGI10jMJIDNNIi0yVhZAeopdcjf2j05f4ch2kPm+WU7Sx1BMem4sJfpmFMIDvHFzg+TJU2lI6DPOBe1V2q5nxWlwpPp8AVaF0LbLrZI83TAOE6zISue9CPioQ5DPZLjGJXQaW80A7DFZxGsqwKnj+CCsSBCpCHzfu5EcnO2iCEEdFD+skXtLc+vTwAl8JMvRoM3xw/5KTk+r1BEvqx/rcsxWlHXnYqUVJ1UDb5EH6AAAlsZEjSxfJ5MHlwbwgnLcKw2JgFx1XuJAtiyB58bWGpF7zfNZw73FcdeiNxrM3oe9/uNjhagis08Oihj4K3/PzpxpNqznPVWYOiOb5RgQmdOwpNgqSpFaucAFNiRC+BwclLK/H0JRe5LzA0WLLOcjDKks2fYOA6FMQhC4NV8b2yCGrBu5n5rnBnemW33NJqEGVGYxmLT4+CmlS36wWWFGLxBsBnwk0dG87IxDLMZmFe6ZU4Vr95uwUmlpDWnv+m2+/OgnQO0lbsvHwVp607VVy05oM8NeEIjQZ1lXBxv1q0rBxv1fJxH86abDaVWipJwovsJwFZcH5doLNtoKh1paYGGgy7TA+Z/6HP+qRlwbueXiP6TcOiz/d0IURkM/PICbhnKz/0V1c/BGhcqsM/cylfj5SihUE9uwgLdzMQF/QtV94YEH9Y5WkYO/qeC8X1zI3UPmAodfc5xu2IeYsUtmv7IT7hvOjFufrAKEIAUB7TiRR456TJ4ZOBTgrwjlGsMUbAMiDRBzoWw1FPddhDzYUE4OsSBq3UdHj8iI8iH0nIXcK5QVpc8cePfXCpAi9tup5+Pf/0f4Q3/2DIL85LGrcKcKN9R89+QIAcDOmm2JeYknOS92gcXK04aD2ZsgYJvt0eoaLPpwDKR3y0cDEHuskslnH56dfFQxxyVYwZNyfOKiNwo6s3H5xdQ8HZYq7e8vT2ApcvAmLzMXreZCOADg5XGOWd3g8Cb23f/35d5BTlByHnLNOABwwHl4P/++etLA19cABmCotprbykDYt0PvJN9mkR99p7qMNhcH2dTgneiuRUYqMrTx0A3TcK8zRRJhzBmyez+HJVednBNsBwHja4no3Ys9a7wEZ+26twJzaAZvMQQEc0O2dAFbh3BVXKlhR0nPNTD3sKD3kon+2qCzQSyiyopQDMJxS77dRUDsJF23w2uSq07ca3kom2NRVhj9/EyRfo2xATW2KvDBQ2uHugqRiuxE2O7qHxoEMF72kbeYgCfK2pYM/6tmDV6808rsBjp2OOtQ0R3JtcXE1DbwJALoV3Ft2iuz26JybXcb3cjnv0K4Knk+zccvExYtmwjDt4Z0r3D9d4aajBdUndysYCbuWHGMncsc9V+9CXOTfPQspMCpzyYKw1xz7htICY8P6VaUdcmqL/MHpa9wrV/i2CRvKH3/5NDm2aYcPD64haeO8GUre+FgrYagX2xsNKfeZdMBAu/nBKoxGHdbxWDyY4OhnA2xO5KybHPiyxP/L3pvFWJak52FfRJz97vfmWllbV3VXd093T3NmOMMRV9ggaRmwDRgkYAG2bMOAZftNIiDLCwRQtmVAhky+WbAMG34wDEEWIIGUSZoiKQ7JoTgznOH0TK9V3V17Vq53v2ePCD/8/4mTxYFfjHlwuTNeuhqZee9ZI+L//m/Z/xYnLuUGdZ8JoI0OteZ++wdT9D7ipJwbfRz+tIeKN/pCGUdwBGhRBYjkZiPdWsSufedaF4RrPMaLPS5h4ctxOS7H5bgcl+OHPD6TlWsvLOBFBIP6DPeeogfP1w4uGiQZ/vL1bwAA/unJ510lZi0AYVE7L9QW9vSjGnXhwdq2UrhoSK487XaWqESbdWngHKCEtChnEcqmUuh4EFN2J8oEyv0KKYvqZVQjZgeWPAgQfho5Z6HwTOGDe7R77u2s8ReuUGV5Kz7Dny2vodRcGUiDcUyVwUzECFXLkBTCuipWQ+K1Dvn5vvTSOXyhETLeuNYRfuMxOUytsxCqWwGdhvavHdHn5mSBJ6cjB12ZWuBUU3Uq0lYYj8hAdCoHKSe9Ap/bIZ/klztEyvju/Cp9RmPgDpKhAM8bb5RjZjhLTlUBICqJeJQhEyyIP/dddRfOJKqucdWqrIQLJcCTBHqgAa5cu1EB74AriGGEkpOBZEXQs+V7KKVBFF+A/UclAq50tvtrdAO6N0erHrIicOSYuFcgb1jGSYVJh+7TdmeDuw/2XFoSfOtIXDq00KFtr8WF5CcTGlepai0gbOukZQTBswCgJgVqgfZ3Q0CylMUGfF5Nhu48RtXh6rdvkTIqIaVBWbTTSy8q4O3R9VinIercR91UTpWEPmB4Y+1D5woyoesTbKX4meufAAC+/vQlx5LtRCW2JyuHBB1hCDnz3Tmjd8FLt5IQDHEnEVWujUTl9KSP02N6Bv2kxGRIEPHVzhyP1iO8OiDi2X6ywLun+wCAeS3Jl5mlLKPdpfPCna46KArPOSP5vnaEKXvBF9jB5QwLGyPw1g4Ztvzk8B7O6h7+NL3e/h2PO1ePEajaZc9OT/uuorZGOGi/UAbDfortW1yVrzp4ck4ex3VFpDrnRpa27OZipRA84+D3Y5KrNXNcMK/h3SPpWfH564gCH/Dp2KZfnCA+48CJ2sJfCtRJU60qoHshVYmPUW0khS3wsysLiaUidCPpXGB0vqDjM7m4KmmgpEGtJSKOLhsPNoi8GlN2ZHmpP8X7KSUzbKoAMWvJNlkIGWkYfjlN6kFw/9WLyHS90eLZXMFjXVucFE5+AYDSPrIGkhMO3jJrCW+uXOqOPfMBlnSU2zXifo5RlyZZbSSOHxETsfupBxgg5cle7afoMNPxze0j1AwJfXtxncLZuSf0uc4p/uD4ZQBArdXziS3SYBAQbLvjrzDjxuT9zQQ/v/W++713ltfw+oQWv5Osh0K35zlPY+x0acLqBTkGBzlOM+rRHR6OLzjNGBc2Dt8g7pTYTOle5LmPP2W7w/d7e9jurfHoiM5bpMr1FK1kCQ2/rCYQzl1JDUt0ubecZwGy08RJnExoXf+vjsllqxpw/F3Z9tRNZCCjGrPztkWg+BoPeimmQ3aX2UhYAQSnDPvHGjF/txQWSbdwcXHGChdw3Q1LFJUHwRrbfpK7xePKcOkY67M0xu7+HGczYvfqle8myrprIHOJaqzdsTS6X1W0rHF/oVB3jesnm8C6Xph9GkMJ+3wyTRNJpmgDalwPzWLQpYWxFxaYcmTY9mgFAWDJsLcx0vWdm9GdpO7fTS9SVAIi8yDYASpbRfidT+7QdZxFrse9s3eKcbjBB+fUl0ehLlhXWnhhDdP0WZeB0wFPswE9M1tNG8PCHtImq8olTrnF81N7n8DrGcyZpT4vY8dmF5Kel84OLVybLIR/4dyqeQTJmmFdK8imd9qpYKfMLh8XpMdt7FaHNb5zSBvG0ijsRUsn+erurrFZ0HHcfbwL5RsX7uCd+dB7fC4AJKsRKhVgE9Q4fThC88PmObYBxfE5xYIEUp6z5MpDn/YySM5qhGcFvHNmyBsD0aHjiD46gh10cfZlWuRlbZGP+XnvtN8DUCKVLVqVRNMSqbf180lAuxsYLlrWs9Yd7EUdl7Dw5bgcl+NyXI7L8UMen8nKtRlXBwus2A/z5d4M6yrEmMkNtZVY1bSj9aRx4u+q8GAr6aos61ln5KCUQVUp+I1Zdh6hztiAfO1DhNrBRf1Rjs5Vqlgen4ydvkumsmWngv1yR/Q3W1sr9KPcVTDHj0fO6UeHQHZQo7tLu8wv7D3BcUpEqA/OdvHF3ScAgMP1AH/l5h9gqKhq6MgCd9eU3/rus33UVWs03unmGMdcLdUJ9gPS0H1vdoB3Nwf42QFlZF6Pp44xeq0zx3mR4JMpMWBf3TrBsqTrGKkat/vP8Gcg44VDMQKqxplKOOak9A3KUrWewauWdbrJPNS1dEb+qpAwDSfIkO6ugUirQQtfSmmec6iBAPwFX7sA4Ix56K4hqNRVvwZgofz4ygJZESBb0PmoU985Aa2EhewyIahLEF3N1fDt/TOsmRxkrUASlsjZe3ZRtgzR6TpBXUvUjHD0wgJ3RgRLPksHLgBhq7vBgwc78BnKMzpw5wJLkHTjoFT3NUbsG1vWHjYn7PMcGMfSBIA60LCN6YUwCKYKxudKVll47Ht7YzLD/ZNJey2ldUz62kjc2iV2bdcvkEc+zlL6vuPjvtvKe1EN4bX6UqA16kevRjJoK/ZUh06D6Q1KxMyS3Y7W+HC2gxkzeEUpWichLeB5BvmFEA6V8rPEz5HTvUYa8gpV3mIaOi3rN05vYrpJMGYofite43zOeuSogg0FNs1zEBjnNBYFFaq+Qo9bB1FQOcKR39XYuUYIT6E93D+eoLdD7+t6nsAywvBxsIW0H7j4tX6cY33K8YKdGkHYBrfXXY0Om2BsTjowE3oGr+1PscpDZEmr07WKT74WCGbK+UBXPYPgiBnBDy1G7xOpSC4z2MiHefgEf37IOMLZz17H+ip9RnQO6Ij+ne5byMI69rwJWh2zf+6jjC/UdI3pDKjlZi8gIi/6+EwurpVWsFrh1ugMn65pEZiXMfp+jpppomkd4DynB/pk1XVuQlJZdPob53YynXcQsA1aWXrEet008hvrHFKUMtBaIuFw5Lcmz/DdU4KdzTxwCSqyEtCRdf1BVMJZ/52fd7EIYtevS7ZSVPdoAc0OaoTjDDdGxKL9/skVZMys/Kkbn7pzH0YZPil28ZcG36L/lwZ7Eb1M9wKaPD63RxPA4+UIfZ8miS91HiBi/PVmbxdff/YSYqbLz6sYhq/bB7NdpBcclaSwGIYELY+DDU7Lnkvu8UKNmmc7L67wEhv6l0Yhq3ycNNmdF94zoQXKzG+hzhwO0q0GFuGZRDngHtSoxOvX6VzSKsCK+4HZJiCpRmMU49vWQtGjl10wk9XmCt6CIUU9hMok/KqR8Ahovk1l5rtNFQBU69BljT5b9BH6bRrNRXg0DirMuRUhJTlzNZu1VRGi0gR/10Y6K0pPGrx95xG+/5ieH9EvUTFV1oYGItQwfE12JyvH+B4lGR43MqLMh94t4bH0TB4lrem+4LD7ZpMnrAttv3t/jyBGnizrQuG4oJ5lr5+5hTbq17h/PnZZoMKzbvGo1z6g7PO9wmYUClIahPz8oAv4A1rgVusYKVvlfe90H/NFx9lulgquByq7lZMT0X2SrXNZz8BeyAe2lYR6xEzlicYmo3/3owJaS1ztUkB6aZRzweokBV6ZnOI0o/bAk9MRDh/QPBJvpfB9jde3iJ8QqwoP1nQPT1ZdZ1u4yClAoDH4txeC1BeLBOs0dCoE+TQCtnlT3ksxiHO30Zrc2uC1IW3AHvTHyHnjnfiUNdxhI5ki98nAAdTbtAooJvyMdzXEOd8nYzF9k9oNxuth9GEGz7uQYqOadB4LWVvH0J6/Zp3LmVUW5ZYhRjpIutQw+P21QHWN5wbPQq89Z5BRl3Gbsby5ELX0go7P5OKqLZE5vnN2zT2MDbGpsTjTRiLkvuS14dylZaw8DWOFIwH1o9z1RrQVOJ13nUwHYUvmMVoiDCvs9Wghe3+2iylLbMJz6RZX45ObjVhzRTcqXdUWdkpoLd2ufjNLINmVpre3ws3RDA9n1GO5MZo5XerTdIAH7BKz1dtAW4nXA9rx/8r0Fj5dU9/kpdEUf3n/X2CoqHr/x/GP4r059bT+ufcafn5IlerQz7DfW+G9BRE8pLAusir2K/TDHAu+Xp/MttAJuNdZ+6itxCcnNBHVmYfumK7jv37zXdyJaSH8h89+lIKTWaspqjbGT44K6NxzG9tyWyM8Zgu8AihHBh73nepK4hnrSW8MZzjf0DkrXzuP1B8YlaSXnSdqf9VKbIKZBx21m2od/LnddZNyVHgQcQ2ffW+rSiHg3r6UBtoIp/VdZFErzyo9xHHpenudoHTP5yoPMYxpo7PII6jYIGbbuK3uBqcJbQSFoD5ifpcWvOPcQ5fdlcSF/ujGM5DSOtKR9Sx0Y43YoWNvFq5sFcEwAiMCQ65Y3POTkXEbRiEsetxLLo2CUua5hVMwac8WCjACmh2gVL9yJWXDX9jt0nsyDjfOhvShHDspzmLZgedrKN6oqFHhArlNLYFaIhjRuZZBQIQ5AGJYotMpkKVNL9ii3G0r3ga18ZXGT17/FEOfns9P1lsuuL4TlDBWOO34g9Rz4e47/TVCVeMgpkX5WT5w8qTIr/F0Qfdlr7fCaD9DVtPP1mGJjDd/5ZMONAB/2UjujPNQDv0aoaodkpb47YZuO167d/6TxRbOTnuupykzCa9oe6x1xzgym1gr52S2vioRTtkF69TA+7N7gP3BKlJ0EsSnNfIx62971qUc+TMFs11jwJuii9V73veBQ3pW5W4KdAFz3nIVNKMnjXb9RR6XPdfLcTkux+W4HJfjhzw+k5XrIMzhhRa3uufIGmP0KsJLnXOMGNt4mI8d7LmqIjyaE43dWoG3dw9xMyEI87Ts4sGKA34tmd43ZggXRzmNYIoIH9+lSkoVAtw+otQURlvygwqilrAsRQijGrsD2sULYbEuQhQMF8WDHLpL+6Pt7ga1kXhpRJ6mT1cDx+B9shpizAzjN8fPsNYhfvGTn3U/e3vrKQDgSrhAZZULVv/F8bfw6fovAgB+56PX8PgqVcXrMoSxAjf79F259jBnc95SK5xOe86XFt0KU4b/0jF5sO6PyFjgzO84CQMA5BxNUxmFbBrDYzavTozbBu6Nl8hKHzPBjF1lUfW5NzguEcSVc7CSDYIA4KOTHRLVg8X8sYG3amVRwbp1V6q6tpWoXIiitIqDCLqtf/PuHZIGKWFdL1wngjM52+zeZiR+haL2MNuwy1bp4eU9+owGvn3C1U1ee1gx29aTBhlXQAs2Wf+JqwT3X4tmOBrR31RW4re/+ybQ5++UFpunBPOFu6mrfgF6nrbZnejBYtcltvR7KUK/xvEJfaatpIPJw6SCUgbdqJVKzFlaVdcK252NO95+VCDnbNpqGrVbeWWhFso981p5kE1luVMh9DS6PveTrcS6ao02HKQeEYrTSHPCqEIpGfJehQgHOV7bJbh0XsQ4nNJ7FzMM3uGe6PICK1UkNWo+3lf6pzjJuwi5EtxUIc4e0xwwefUpAqlxf05oUH9rg4L75I8OJ5C+cRXp66MjTBjlejgb4eevfwgAeDU5wif5DqYlVXEfzXdcNVqMS5Ikca9fJwZgmLTsKQSJxrUhVcaBrFGYdhp/tKJjWqQxtTca97i6RX+sR22Q5pqrTLTzz8SiQ9MBwnkFEQSwJRtGSOlkhkJK5GMPJfuEBzOJmiFd4wFY+VhYurbzzIPg+cxuPCc7xP0OwqVAepPuSd2rHUqH9YtfuX4mF9dQ1fCVRKZ9t4BWVmJZR0gUPUjnRQd3z6kHWdfKyWv+3S/+CX6icxfv5KRBW+sQX926T59hFH6neNVNkst17CQ28Cy88wsxZ6J1ARIWzsVIZgom1tjbm/Oxatf3SWtyTJIMNp+su6h4QlnmESbJBtOcHuhOUOKdY9K51lrijV1aaK+Ec8yqBE/XDE91VrgVEwlFCoPDauSu01E9cDA5BBzE+srkFB2vxIonvUUZO1h9lYcwK9/pLLXw3Hme1n0c7M/w5IwmqTCsHLz+KBs77eok2uBjz6JmckYyzByB5uh0AOVrJH26YNYKgOHR13aOcSVe4t0hwdVp5Tt3K3UcIJhzT/FaDZVK16s1gYXuNTaJCvGRhI4aW0ALzZwj45MhfvN3NjQ45rgy6RmMmAwTBxTAfXNAm4rAdeoAACAASURBVI+TtIcNE5omvTk8YbCTcAiC9nGtQ33yUNYwVjqSXSA1TZIgB51ms6S2LP7k8U383u//CN3fnnZ9aX+ck/6Vr7kMNAzDpUXq40zQZD7oZPClQSA5bWVn40g5njI4nfZhGyi4EpSiAyCvJTqjDBnDfMYK1xs0pxE+tkSO+6nbH7vnAQDklnWOXqt1jKoOXSqOLDzU7A7lC4vAq927t1wliBoJVe47mNnkCqilg5FhhUt5gRYwRuDpiu7NS8Nz1/cvjcKyiJyW248rhMxhSDchDnbpXmTax160wtcPXwIAzM+66N/lvnN5DTY02LpK7+Xt8Rnus+60zH3o1MPjx/T/8yxyEjxrBaZM/HtWDpFpH6uark/o1Rjwxmevt8KyDJ2T2eqsg5Ad0LY7G3xyNnF6/PVR1yUbSWmQsdOVrSSgBfwd+lk3yTGbtsHsQsA5R8lCPQfDNkS24Mkctiggmp5r4AM163Jnc3QPd2F8ejm0Dyxf4csfGXgLiYqdnmS3cpIjBAZqzolQfYNqbFpNc1Q7mH91/ucC1l/AcQkLX47LcTkux+W4HD/k8ZmsXA/iBYLEx6qOHKSihMV2sIbPJrMNiQIgz85ffPs7AIA/Or2Nyig8Y4bkVrDGvk87WCksrvZ28WRFldlXbz7AO8fE6FyXicsgBQhSbCQk/kK0Yd2hwXBv5QgAhfDwfr4LgKLeIq6KAGA9j+Ef0XGm+yXygefYmd1OjhULzzv9HBFX6FveCh+u9xzN/83BIW4EVLn+7vxz2A2X+Dij6uPectuRgEwlsVjQv+9hG0lYYpm1MpKtLsGBi3kCUcrWOF5JJ3+wwuJk3nXWv/0kR68hhSzHzg3KEwY3rp7hbE27fF9pLJfs6rTxYZQHzQSP/e0Fbg3o+H1hMK/i1i+6plhBgFyTGjg3mCqUu7VjvEK0ciqdS6iyvU8qbeG0umegNtJV5bJUMExk0YXCnGUhQ65gm2u811niiaFnIq0DVFphFNHv1FbhvGBWetpDNyjaa36BIfzFncco+VmdFQmMEdA7DNex4xQAZMuIHJsa0f5JCHBVonzjzFDKWiHVATaqbWE0sGRzHhdZ2mrNlXwgYAYCxrROSQ2qkKrQedQuyggfn27R8YA8cRvGsck8ik9k2BMWLlA8jktUWkE16ICFk8MsZITlGRO3CgXRL+GxiUeZ+pCL1qEpjipHpAukdh7BFccvFszS7XVy965JabHhf/+LRzdhPukiYDJbP2uJbMFcouoDZyeEiizWkcuVxTyANG1Ix+bTgWMqm8jiazmVd9UrClIY3EgI3fCEcezj7XiNQnuQDL37uxodfg6mWYK6Vi6P2Vsp1Cv6O90xkGx8b6WGKRXKFROOwgrKbzJzBRmBcJVoV63LGWRrAoHlGraqIXocTXd9F+rxSfNIwH/nU0w4LjG7OUQTIrw5UChHBqKJ66slAg6ZqEsFPeYvyxVgBQxD+4URVHHTHcWLPj6Ti+usSuCXAZZV5GQhADDwMwy45/poNnIMz9wC35/TInnQmeO786sOLj3cDPAT1+8BABJR4N/f/zq+2Scjc2MFQk67+f0Hb6KOrHtZdQAX5C0s3OIqKoHFg6F7k8P9FPmMF7FawGxlbpISS99lYlotsNNbYxXQin36eOR0Zqmy2A0JhvzjxW2c5R38hzf/CABwzT/HytDC9XOjd/G99DqWFX3ffrLA1Q5tHO4F2zhb0sQWBRVm68TpPZNhhgeP6CVDJaEquAlF5sJNomrpoTIC3T3q870yPMV+RP3XtA5cn622xNTe7dMxz9IYEUO/2SKAyCXA/dJVL8Q0ouPq+zle6Z5gHNBC/5unbzhHn3ILzg2qu7+GzgIHlwppW0mHH7j0ITqB1lZRhyRh0H7LaOxx49zvadQXbCOrWjkpV1b5GHJAwTDIcJp1kXNPrtQK04wmfk8anG66SDkdJYpLJ20BgGesW340HaGctRsb1S8x6NDnDzoZTmc9iBn3QccVeswW1lq6hUQpAwFgxd9VrQN0xvQZVyYLLPMQs4J6taLwoCdNtI5wCzQADOPMLWLnB9ZBmx+e7KLIfJfsApA5PQDYkiDLJkQg2sqcwf8yD5FXnrMb7fcyx5jOct8xjmWsoTyN6pj761rANCEZnoWxwoVtnK46LrXJm3moJxW29uj75svEadFhBKYL2gR171M7o9kY1h1y7wIuBHnn/Px04J6Xwc055sc9bB/Qe3N21nOQqD9XsBt6P7+e3gFCg+GE3oUkLLHfoWPqePRMNdB74GmXLe0rjbQIHBRf97TTV3e6Bfb4nckqHyeznlv061o5jbCvNMraczpjIy2CKX3e9d9O4R8SNG7WG8hBD+jTdTz86R5URs/E/tfOgaNT4Iw2B7G10BFB4cXEQ1ULqEM617prUFbtPNtsahtHsOYJtwEw2qHjV4MUP6iufbHGZ3JxPc8TeCrEoogQ8uRVGYkP5nv4rqa+32YRIeOHwNbSJUqcZD0sigiDkCaRVwfHkDxrK2HwQXaATzckNTnOejhe8gRlOD6rqZ4WwgV0exsK7AaAcEq7yKZPWeddCKanCwNUTzpuEvF3MudVPB6tkdceljxZxlspciZL+Pcj/NqDr9J5Dg1kIfBfvUfn2b22dCSXvPawzkJcH9PLNQ5T9AOusIx0ST1HhyPnDwoA6VnSRssJQA80NEePxb0CTW2UnSXPVUMPlhO80SU/1WudGb5zQuYSzaamqdAvEoKybg279iB5wUs/GuKDAzrnf+etb2JRx/j1d96mX7YUJA7QYjLmgO+teI1FGePZgharJCwxY9s+6xtU/QuRbaqtWISm9JVmxtU97T7z8cnI9QOLiCLNmgq0rD23Mdnqb3CzP0XJz1NpVNuj5/7kbSY4/bN/6Vfxo7/5XwAAHq7H0PxQKGWQbG8cca7fbUPET2Y9mNPI9eTkykMa0/UxtQA4Zsz2Kbav2VRURiDlft3T3IfyNHpjTuExHcRsWJFNY2yyEHXV9hGb6joOKhwzcahe+7TgMFojjECNxp/YIB5niC783dG8784NAPYHtNCsy8DZPBotnNlKuLdGngaQE1rYdycLnPM1ltKiLD0EjZQoC56LPoQEzo7p+2SgKZwdgJp5CNiMo+paVF3rLDKDuUB5hY437BUozmJnUBKGlUNL5k/7kL0KFS9+nX6OVLLsZ9Ou1iqVsN0Lf7eJ3fFP+htnywoAaek7VKfSir2q6bz9boYOb3aa5wigZ0nX0p1bXUuHMGgtURQ+DGuew5XE5F2WVqUVzAlzMLbGKF7axrMfp+en7liXHnX+pTHCxQDdr31E97fW8LLGyxQwiYHmzZQ/KJ7razf9e+Nbp1cHgGSQ4fqAJUxnL/7SdNlzvRyX43JcjstxOX7I48XfHvy/GHf6pwi6Pr725GXMy6a/JbAM6lZiYIXb2d25cYRX+lRNFMbD26MN5hWVndeiKf5w9SoA4I3kKVITYD8iu7kn6yGyB7Trjs4k/A0gS+4lSThHE1lbqKJxaKLg7ovB6t6mNZjwVwJ2xexPK3DlJu0yK62gjcRXrj0EQD1keZU+45/fvQN5yhVLpKGldDBQfTzCOW84/TVBXHcPaAfdOVjhDU6jEcK2aS2DnGz6GFbq9HNsDuk8rbKAZ3DjgKRKxgrHGM01pbA0vch1EeDXnr4FAFhmkYOpJoMNJnHqDDG6UeGq16SfI11327zVrRJdNkb42vErBAU21VLUVrzpJkKf7+0k3ODu+bazkFulEWquiEQl4a3bPWcxquHP+FpNamDuOQhQdWo8fMrG5TMfcpc+v8x9hHHl+oZl4Tk3Ik8alEYhZwlY4pUOFbnSX+J43cObQ0oe+bH/6z9DzSEIj2dDV40KYSGEcIHZQlgcccUohYXt1dB8jYVqnZFs5sFP+VnaouqncY7CCChO6F2ofQMdSZQsSxGBQZE1fQuuVvm7y9p7DlnYGVPFeaQHkKp9ZkwtHPtYJjWioMLVAb0nRe3hzHDfkNGKpl0zCHNI7tGt8hCixxKawx5kIaEZWp5HMYoGKlcWva2NQ/Z11npi6shCbBS61+g4jZGONZsVyvnIm9hA9UvoJlD81gZvbdEzPQxSfNrfwh7DuAM/x3eOG9P9BGbjI2dOgOdp1883u0XbI16E6HRKl3RUaYVXBjTH1Fbi20dX8aU9AkaPL4RhWABF4SNiV7jaSDw9JPmN9HVr2mEFnXNLeEDO/VcvqmGmIXb/mH42fHcKfPyI729bSf7m/V8BAHzxr9B/569b994tb0nsfssC+8TPWL4+xtFXGOa9ksPzNcYDbkcY6fr4dhrANgk5iQZ8A8HXOFtFeOecVBhKLfGij8/k4vo4HcIXAbLCJ3gHADyL0guQhSyD6LR9JSWNc2qJZIVElkgk/fzt6BH+SfYlAMCTcox9f47vlQRvztLY+bUKA9KEmcaLk0K5m9HALaqkRbSitQr+SkDxel/1rXNXAShlo1m4Jp0UyzzE4zUtSG+NDpGxv99Xb99HfYse/HeP9lE+7LoFwsvgUmWqDpDvGliGwzfLCI9j6kG9MjzFukPf9Xg5grGA32cdp9IYvczw6IMtSN+g4ElyN1njlHtfsIAoJFaPaSEIdtPnIMVm0T06HOHYG0Ads4zjztzZT0ZBhXKSOzjc5gprJjutnvUIxmM/1bhbOOhUSoPpml7wr6e3sDnqANEPaumsZ1EOjfM59jsVavZXlsog2V07yUpywbpwAwBLXow6NYwRTo9arQJE7BZ0vOhhlsYuFWcQ5lDcoF6XIQKvxrvc36+1ctdnEOetHGnew7CbOVJOrRXePCB4/elqgLVnkLPWF0nd9o99A82+rrYWGCSZgx9jv8bTxsO3U0F52i1WcqNaaNy3qEIPJVt8qlA7A59eXLjIuTCukJ3HTtPYGeTImrBxS+S8RlLW8Qt84SovJGkPWeU7iVDzTAB4Tj8ueKPWwIrpWeJ0m3Zc4rWtEzxYcHKSb1xrRfVLGCNdX9vza5QNOahfYnSNFoT5MoExEqKRMRU+nrIUrewodIPCbZAm4ca5anlbGao0QD6lz/cHhUvFeePg2PEK3gmuoKqUax2MksxpaqWV2O+tnEwwUpXjeMRRBQEgqxpnOYOUeRZWtzaD0jOwBjDcvjFGwj9hzfFQQghgcZttSLMBeg/apeC35v+z+/df+Lf+Luo9uq7UvuIN49BgvechmLK3s7aIzrktYkPUVwoHZS/z0G3GVj3lXKNUv0QnKSCHrb1lEwVYrVpt84s6LmHhy3E5LsfluByX44c8PpOV69PVEEqHKI8TF4ptFUOmoJ1Tr5s5h52Xe6f40ynBFedpB7FfIfGp8ri32YHPu+y7mx2sosgZdUthIdkzWNjnvWjzSVuRQsDtOG1NEoWmmpR1yyS2ghjGPsOWlY1QMfykjURZe5gzA+9BMHEs1K14g8M1G/wvIyA2kE3ijAAWP8IetTtL6DREv3Gv2UROnvHtw2uOwVk97MBsVXjpKsFYlVbOPai/u0bo1+gyg/TxcuDgRivJDL5hN0ppMV0RBK1rifqUU0ZKAX8lWznDvaGTw6S9mmDf5lLaluQiXT4le6OGFbZ7VImcrTuOsCPOAwjfosuEnfTeEFw0kLnH9cxVTPVZ5FKKFGe8NlBeUXsouXUgPYNwm26o52kM49wZazy0Y8fmNVYg8mrH/hyGGd4/JalVlhIJpWHV1oXC27eootuNl7i7IAhu1EvRDwosBTPDZz2YEZ37q+MTfHC+g9611mTjnM0DhGdgxxwa3i3gS+OYuPudJfybfLxPtwgqbcg3hSADDVBGpzwPHHqoOwI5AwB5FrhQibLwIGrpnHk2s9iZ5UNYKGXwcE4oyzBpTULONwk2qwjLhEk0tUTAUGo3yTE7J0hHjkvoXMHjYAw9DVt/bs/i3vm2Yy5bLdw11bWE1RI2b4LgpSPnJYMMX9im693dK/CN05s4kdzueJxg7vEx3VLY661wvKafNb7aAGC0IuMOhvqNkY5YdX82dseUpSGk0o7Q5AmDj5Z0fwdBhtirsGY5oLESwQXWuLzQotFGtgEIi8Ax4g3INam5h8VBCcOI1+gdhXIgMP6Qjqv7tXsAG/L/1vnfx8Uxf1mhYHKiqIVjztcDA1UKqJSfp6caJUt28m1B7YgL2dCbNUP2mQKaZ+k4wvjNGaZcvSedHBVfqzT7f/D+foHGZ3Jx9aSGUhro1cC0ZTDKpHaSj0orXO8T7r8XLHEScu+Uzfxz7oFMiwRvDAiSux5O8X8ev4l5zjIILV3gdL2SMD5Q8wTtLRREE9vV+pZDADAB4K3ah+uik5PQrVWiHtQwDGmF4xqeNNjnY/7+h9fQ5TBnX2kHq0ILyFy6xSRYWYy/wbKQZAvFqzXq36OXJJYC810672K3Rvy4gaKASvgY3W7DrhtdsCcN+n6Or39EciSU0iVdSC0Q3146qFNJg2VKL11x0nEvLiz1buvkgv1b4zi08Jx9m/vdxhBeCohSktQDwNmjIc7479RKoVGFKJYHbR6wu5IFqgldkGiUIz+PnTwgWEhnal6vfCzztsdYa+k2HN1ejh22EswqH1vx2kGAP/r6IxdfuBssUVmFJf//Ud7HPoc5PCw9KE87mFL6BkebnrvGN3ske7g738Y0S9AN6fO/evOBkx+dFV38pZe+jZ6kSfz3Z6/i7Ig2VhBwC5yvtIMkAWASp86I/sbBGaabBBmfp+kp4IhhOs8guJrhlR3aWC2LCMcLfkYy30HESafAOvVgG9ckA9f7hbLPhWEXlXchws5gd3uBOUP4de4jYsvCBbNpAUAqDb9fu6QaMShRJcrdz+Wq1TsHSeV0odYIWkybzZkW8Nj2Mct9HGd0rb45H2E+7bgXM6iA6IS+69oX5zhI5vj8iHwC51WC756SG5rOFERgXFwi5h6qxt2qa7AeMHO7VAj6lZNCrdPILcJp7GMSpzhh2ctHD/dcO2l7bwFrBdZN8IBW0PxdwqBtARhg/J5FfEbz2epagOSYn/GjNfL9BJ136Pit58Fu6Pn5V37kb+Lev8dWrxJIvrKEnnOL4cLCKEqJOgaqMf3MP08xepfmnmI4RJ7GOOaNVlW2y0xnb4MsZf6Hb7DIIoyYcf/ow11YbumIqg0keFHHJSx8OS7H5bgcl+Ny/JDHZ7JyPegt4HcCKGFxFHDsm6fhecYxB0edFFsRVSJ/cPay0xh+efwQD9KJ+6zSKDzOCN5qCERp2RIvFMefeSlVnJJZula2Fag/bclNVUfAWwuXk1iONfw56xtTgWLLQB5QxfjqzrnTtm1Ha/zC/nfwvQ2RqT7wriB9wFDwqyV+7hrp0dQ1g1+7/xbSJ1SdylLBNk+BAaJnHr77P/w1d/xv/bVfBQDoSKHYZoeX0KC3v3rOUP3lHrGWY1Xij45vOajNm3luC+cvBYoPBkgbZ57twkFawtLPAUBVFP7ewOZCK+iYq1OP/H0b6BdGOCgMgvx+JZvwq4KycQHARAaSRf9WWvgLAc3FU9W1rjIoUh8i0s4ppu5YgF1vlGexNVrh9TFldb53vueqhtL3nG72cztH6HglXknIzaayysGSPZXj02wbkklMgdRO89qJC2RF4KrLOCmdkcCH6x0H10tBAeVNpmdW+RjHdDJvDQ+hYPGkpNaEscLpMVFJly8spUH/gsvWyabroMeb/XPcGZy4QIqTdRfFDaokro9niFSNT6b0MyEsunHLWs7O6DgKgALb+d7Ufd1u5TWgkgrmnJ6fwvfR7bW+uqMoxfmc0ZO7ITSY2Pb5NRK+BkpYFBfMJgC4WLmgV8DaVidtLsDAAJGhGscp3RGoufr1BwU+OCKI3vc1QZvsdKVv5qjv0LX7/OApvrc4wEP+7ofnY0ewAwDM/bZFgZYwWCvrdMZQFtk8cuESplAo2bxljS7yq3MsV0wWktY9j1npYz1LXCQlrHAMcACtOb8E5nck/A393tbXj4Hlmu+TQPK4htV8cwIf4iaxnetOiN6n9F2bn9wg3YQYcvukNhJrJjD5c4nFHYsNa8z3vqEQnrWhELKAq9hHgw3F3wHYnCau+kUpsd5EmJ2wF4AAwPfCdi6wPV/Q8ZlcXGsjIYzEyayHrSE9cOM4xd3DXddnjf0KXY8m1TpMcbghCPH95T48qd3kstXd4N++8g0AwCvBEf438ePoM7z2rQ9uofuMA5wrYv42JvD+mh5AgKBZ43Hvt0/wjryAitQdXiB8slAbsfRkJ145xuIryQluBSf4J0dk5i6UdUYIJ59McK9LsodAaShpnDjeSqDstwvXz3zlfdz+B3+b/v9ZBPMlFqgHNQyzSTs7G3xu+9j1Ci+mmEhhyZijeYHa1h3yfY1odwPLMFEQVsie8iKvWzu87HoNtVJtb7mnHQwsKob0LvRzGpaoCQ0QGLDhFGSt3DUwlpjRdGKCNhR8iF4qnFuTzQJKENnmBXW/gOLFqa4UpssE30hv0P/Xbe6rMQKDDl2DvWiJUNb47pImLE8YGMbr/nhzi9JuOJTbWuGME7pRgWG8xAnb2W11N441u77Y17MCZa0c7DldJu7f78wO8NrAg0K7eLtr5RuMWB5xpbvENE9cqsxZ1kXI8o5uUCBSFU7WdBzDJMMkYkOJMsLD+cjJmDxPu57u7YNTfMwsZRFraC1gG0lQp26DAEIDUyqEe7RJDPza2Wf+zPY9nFQ9VJwC051ZxGd0LlOvi+INel+7SQ4hLDS7H3UmKQbb9IwPwhwPpyMHN55UCnWzAYsMkCnXnoFvEHTZMtDCnVcYVtjfmWM7puMyELjVPXP34OFs5DbiZX5hGq0l/KWEl7bvl08fAaE9lMPGusySS1VxYVG+0GacPhm6jbkd15C8KRLCIhlkKELux84CqGYDELTvvAktRA2UPQ549z2gpPtrdZOYxEzrL96ACenf/koj3ad7FsUlBFqrSCGsk7mV2zW62xsUH9G8KAxw/GPcHhgB+dXKmccs1tGFbOO2LwzPoEp9COY0CC0c+9uEF9xmXtDxmVxcA6XhKwo9bya5cZzCWjjt5vXODL8w/hYA4Dfmb7vqojYKiVe6yeDR0Rj/fUbxbaFf4+jpCIpJHCKTTssarKji4uIWF1KikE+ks0LUIS2szc/jZ6q1EqwAM1cIb9Mvf+PRTZRr+sBPdib4Xzc/hmrBpJ1Yo96il0l41vkdn09JhtOQhWRFJBUA+IUf/yYOwhk21+kz7/cnTk5ijYQ/pEV9kGR4vXvkKtdZHuPLQ9LX/tny2nO9PFkC1YAXxkEJpQxq3sBUpecWeVkId54QgPXQkmhi47xybWCAWkBk0v2uI6VJDjpvqqVhjc6n9IrXsXBaR6Fph9/0Uok01lwPlnhwpfnS9tSdixAWqzLE0RlNKJ6vce0GTbhXu3Mcc4/s7nIH1ornYsdqfn7G0QbGttVWVvqOPDVbJYhGSzdpz9MYb2yTzvh0s+smuUk3hVXC/T/QToDjfoqTvId5SffteN0FdNvQvzOiXqkvNWorMfPZs9kIctoCMI0LBBfINtNN4lJ9pvMudKFceHeR+a4q//jRDgRvgpRnYAal0+YmSYHUp+dFFwoi9ZCz73OR1OSiBOB/evhT+NKdB9i9Qk49s2fbsEy20bFFdU4bvOnieanG/mDpQsnvPttBENTOJ7jMfAQTenaDoIY3Ma1X9cpHxakvSa/AZspJNJmHPAkw495vP8nx6TltqJOwRJa2PsoAcOsKPQcf39tH3bUOeYpOBLwNP3e1QHRO9z7dp5i3YEb3pupZ1Ae0ObNGwDsJ3AbbrDxYj+0+Vz4lH/G9UeMCecBh7Ic+Irq9GNyvIcsK8btE0LJ5Dvis2R0NYEMf1RZtitZXPEzf5nfU+FC8CdVa4upwgRlzSOar1pUKkvgV8llLCF28wek2oxzDqETJLlVF4UMv+YII6zakMpMwiYFgDoZV1m14L8htX9hx2XO9HJfjclyOy3E5fsjjM1m5PlqMoOoQAsBXDsiZ5L3zPYRR5VI8xsEGRzXnjsrapWpMNwl2uj72E2LGvfX6IT5aEDz6ZE4VTQNV+Zt276KDRo7DVPYOVakAQ8CNtCF8vh9rlUXyrPm3QHJsXNC2edhBZ0p/uH42QTXWUAOuVoXFxa7FnCtQnNKXdg65j1sCJct3/tGffBnja3N8fpu+cLGOnPfssJM51Krjl/j2/Do+PiEP5WIZ4usxsYOXRQStJdSMroGXCqimL1mGWE+kE5GLst3JUzXN1yNVsNKi3OIz0MLJYURgYJ39ACDzNpdVllx18g/9qed2wnVinVG8DSz8QQGPf9EYiQacE9LgYLJwzFygNcx/Y/AM06qDswXt+AfdDF/eooq97+UYBrTlP0wH+Ph8Cz2WXXy62nIOX7VVOFr3sOCAcV0r9HsEb/zE9fvwpMbDZ1Qhef0M3z+hbNrleQf9CaElUlgESrt+bBRVTjJ1fznG7cE53mKXp9PNHUiuMk2p8J1DgqqL3MdwsHHQ6VHed1D+bJVge7B2lWutJVbMCJ5M1uiGhZPwzFaJY/r+y69/7GRoj8+GMMsIPqeheNKg10i8joeIrq8c6zorfNRc5YS9AvfOt11ggf/GEtmrDHuWHiWpgIwhbKFcr9xYgZyrd516yDY+yoSujx/Vji1crgL4nQqKfbLF0EIvmCuBC6OSGPQyaP786aLj/HxPD4fYuzrF50bUez8vOjhOub2RSdhJiZo5B/UmgMeyks6RhvEZqSkFdChcdRqsBDYsrWpQlAa9sn7LlveWCpWMoDiNyQLwOB916x2N3ntUQYuihF1vgIhTieIYlg34j35mG50TjaJH1zI505gxHC52CtTcF67mMe5tQmxNiM0eBBqGUbl+N0NtJNLrLHGKfcDU7hprLR0fQc5a6NcmF3JjRxWCpGoNYeK2L2/b9u0LOz6Ti+tOZw2/U+KL20/Q8+gu3r52CgmLhzlNDn94dBu/tiJrPl0rf9cPbgAAIABJREFUt8h04wKhV+Pf2Pqu+7xXE4Lu/t7ip1vYBETQ8TeN7Rr1WIVtHE6AqndhsmeiUzgV8FK43qywwlkmQlrUsUD8W+xw5LfEp2IEqEyid0CT5WodO7jOaOHg4mjZQtUA9XE7jxna8TyI70zwJ1do0ayuVkCPXuKFaF2FSh2TZeAFWPL7H9OkfeVgiuJJF8GSSSIbsnOkLxCQM9/9zEvbCcT4F3q/vqWA7qZvW8NdV1tJiFy63oywF4hhK0CW0umCASDb5d7jtQ3yC1DiwdYco5AuxCfTLdwYUVjBsohwq3eOiqHbXPuOrNWQx5rJICt9zCs6OSks3uiQtOFqOMM7H19Dyi4z3aunbtGZ5TE2eeB6exehRSksTvNua5WoNAqGOsNe4VyAXhmc4rxIoJtkpidj11ddxzVmqwTv+FfcZzYLiSkVctb6xr0c1/oL1+7QWsLjRXjc32BTBPC4F5xngTums6cDnMcadsM3LmqD68+LDnrcezdGAp5ByDKa7c4Gj2e0We3fmmN+1sXrdx4AIBnXwymRAqvSw2qeOFg+CGsHLYdRhfICzKyEhc/Xb1WE+NmrdwEA050O3jm74tJ6NmWAs6x9KKrUh88ObGQXWbnPb66PlQZnR31cu0aWh/2o3VBEV+awVmDDPZ6uX+DdKV1vG1hySrrAaahZQZRNpOu/ehngp9Y9/5srF7TbAjBXcphNk0MJR8YzPkGnjTVr8kGIre9zKs77x2i0UHrcx/rLV5Bu099Nvp/h8c/Rs6oji+kXAZkztJwrmC7d+9CvUXZ4hSskoAQ2OZ1nUbSyqDuTUzxZDZHu070vqti5WclhCVNJKJbI6cgAvLjKUDvCXhhWSBexc8OrjHDPmfn/gc71Eha+HJfjclyOy3E5fsjjM1m59oMMfqAhYZ0BfyQrpDrAb3//DQCAyBQEE2VMr3YSAG0EzrMEf7Aks/6jrIcPTwgWLjIfyBQk74RVAZz/CFdcnkX3vkLBJuQ6ALxt+szJcI3TPsdqrRIiC6T8e7FwMh0jgKoroArecZYWVjHLby2Q7xmX19np5A5ay4oA5QlDTspCBwKKYRdvYx1UFSy4Kjhuvttz8FaVaGw4n1R0aiS9ArhL1y5OW4j7eL6Dm28f4vAPqZLVERxBwt8IlAPrnKrq6IJ0ILCQI9rBmlxBpj68M/bqreFg82qkAYWWhZrLFi2wLL1pfndg3GfGYYmCiR9Xd2f4ytZDjL0WZv101kpLnmyGuNmjiiXXPs44uu+s7ODxauiqscivUXDpUVnlGLr/6AExtiPekRe1h+0usVyNFbBWuMq1Kj28fYMg3Ez7eLgYweNK842tI3zrCTGTL7rdfP98H1npt4WOb2C5FWHWPvQyQhm3rjqm8VAWAJiBnWytsKkD56wVxSX6CT0UmyLAehEDazZeULYl0PQqfP7qU3wyJXSjrDx3Lu8/23VuSmFYISvbOL11FeDaiEhKTxcDbO8tXKvl08fb6I8IRTCegbXaZazaoK1cP/g3f9lF8CU+xbU1RKtAaWfU0fx/4/yznHbQ5c+P/BpZ6aNk4qLy2vi2svSANU+JoYGMWiet003HwdjWCmx11q45EasKmslZKhcwte+8wmXVmsCoEih77KwUE9mvIQEBQHDe9IL4XrFMRy58clsCEE6BcigRcttlcL9C5wOSfNkwwOmPU67y5qqAvwR6T+gzDn86QbHdOMcYJKMMKbfATKiAZq4zAl7UxNvRtX1li5CbR4uhc5h6vBpimUUY9em6nndD966FYQUvMa5VsVm1ObwAXOQcAHSGGQoOiLC1hPBffAlOMz6Ti2uufWjtI2TGJACclj14QuON2wTtvffRVQiGXtTMRzYlSEtoYN4zLs91HKYOrsvOY4hawE6ZFRkBCUtxNjc01re1Y+l2d9duwjw+HgJLNofXwOaKRcCaz83tqmW5ehb+0wDemvu2iYXifIGqa2Ej7aDPKlHuwc83QSsxjAhayl/mRaeXA99m9msOfOd//CV84T+hFIz+J4DxGY7ylFvUl7cUUgB2j8578m2F7/z9XwIAvP5f/iqOfu8qPvpvWq3s5/8qaWXryMJ67WIuNVywgRWW8joBqLiG7pPbEkD9KceuDgxsKeGxzk9H1rlN6cjCKutcsURcO2nVTneNpAkJ4Alzy6Ne0k+OP8ZrPeqf/fqDN3GWttKWQGm80qXJ6/5mgrL2nJVhqLTrx26FaxxXdB37UYHB9dxllD45HmHNhvZvbj9zQekAwZ7fPSZ3n5+//iEeBaNGIYFHqzE0XxPPM0iaTE8j0Y0K1wNPNxFMzBOxbyAGBfxGB3y3g+ghXbzlnRqT29RLFsLibN1xz+4X9p864/jfv/cKrJbOdB+ecbDz1a05rndmOFzTuZYX3JUOJgu3oJWVh8nWyuWmFoMcC0mbsV6c43TWgz+mY752ZeqYvVJYLDYBumN6duOgwp/+q/8tAOCV/+O/hrX8GVsFOn7p7mXXL/ByTPfpo3QX4zh1oRZBp3Rs5LpWmPQ2KAK6JvMLMiajJeS48SQFtoZrZ2FZGwnP0vU4P6fe5dKnxWmVhw4GNgG5hynug+rQOjZ7PhbuWa0TIHu1Qm9n7e6hOG4Wdkuwb2O639HI+f4WLxkkH4Zu89353jOA3YyKW1uYvcEb467Gh3/rr+On/7X/jq7jqYTx+fPfXGHUydxmar5OXNCDMRIJm+zrSqHby/F4Sfd6uUpcj95YgcivsWa1RXJl3VqBSotNGrqeK3zjnLsEgJg3nUoaVLVCnbbLkDUvPhzcjM/k4vps3YdnQ9zZO8GznB6ceRnj1e4xioQuyXvqwBERvE1LPLASCF5dO/JKaZQTq4uckrUbO758By3ZZlhClxJiRZ+/PupC9ZmCXki365u8NsOT0xHqj5u0HoO/8WO/BQD4xvIWvrZ4AzXrQc2wRnDou+OKB7nrGRHJhY4/7hZIWZztxTWCsHJU95vjKd67SRNil+0N/+zv/dIPXLPP/9VfxfIW/TtYCKRb0i1+81eBV/8WLaDREug/qvHzX/pl+mUJpL/IcWgasKVwxCKdaCcaF5WA5arB9p+3PpNF+zewICs9r+1DN6Wq8S0giVQCADYSjshy73AHMVtbjicp9oM5NG85xmqNM44hGiVE1HhwRj1SpQzee0ykIrP0oYYlBkxA2hSBq8x+d3XHVXDZMoLIFBpGmUolZlzZ5OMzDMMM04D1oAAWM6qM/+Hxl5FMUuS8EMxEB4M+VdeBp10ySuJXOFl13cLY62ZYzOkeBmGFO9unbkKcjWOIpnSScM9EN6xQVB4WTI57IMcoG9OFzAOMgMekGV0qSLayq43EUd5vbSs3Aa7sUb+6FxQU+QdCcbQWGO+wJV7lu4VKCotOUrjzibwaPsttppsIthaO4GT9Gq//41/mJ0GhyxP/W6NDLKoYTzZD95mNMUdtFOZ5jJdGtJH4+HzLpfhYI3Bqum5BtQAy7o2LpY/wysbd90GYY1nQee73Vq4/LbeXOJv1cM4LatItoLhnabQAKomaY9VER6Duth7Nokmg6luIUmKzaoLsJdCkXZUCuhJkwgHqWY7eo3s4/YKGtwGyHfrMJ794HU3Bnt6oIWreiDPx6A/+6X+KZrz9638TALB4PMDTRdiSjArqjwNEBls1i10tsdz4LrrRiyo8OafrHYUV4qBCzDyMvPTdBizLAuiV7xAlFWto7tFbaZ1phxYSdaUw3qVnZLFK2pjA8sXvWL74Z3A5LsfluByX43L8f2x8JivXrPKhSh/fWxyg7xM04kmNZR3hvSlVKcPtNRYB2489CF2YeZ0AxSpE7yr3HjYjB4fAs5DDEh5XFEoZV13Mp12owLh+o+xWDjbZuz6F4p30IiP5i+sprjz8ximxlk/TDuC1GZYq1BB3aCffCyssZh3HsjRGYM2m4K9dOcYtDlVXMPitB6+7a/Heh9cw+TYdx/IlgZf/zq/g47/xg5Xr4guFk9DoPQ2x8lyFaZb+c6rv2R0PJ19gWVJknTmEUSwz6jVuM8TKBACUCoJNu03mESTZSGfSAILN/2EUZ3nS/4qqTeoot2v0dtZI2dQ8DGpkTSKPZzDmNJt5HuOd1TXshxxqnw+xZNOFfpjj3QdXHGux2PhuV39w6wyhV+P+0y3+cuoTNfdCN+k83QqmFgD3w422UAxjf+u9W5BJjcl47a5Xyn1KGVcoC999dxhWrs8XebUz51hNO/CiGsslm/pbQDSSlLjCbrxExHDpNx+MUOyz5WFUY8kSoOUqec4M4nTRdcbxfr+ArloHKEgLj49jtolxMuu5vvPtayf4sckDANQzvntMPb+kWyDPAqQMzUppnPFKWvkYd9K2H1sGLiP38weHmBWJQ4NmaewsDke9FF/dpe86K7o4znrOchIA/vcHX6ZzeTxCNMlwhV3JALieopUS5dJHuMXwZtnKeWy/co5V1gIPz0fQXEFvjVbYTdp7Zg0cuzddhw51gbIOwQJAzwi/82Y3d62a3e4KH59soVi30HXJ6IYJBGSmyDISAAKDL/4H7wIAHqzH+F0OLweAG//L33EVYzTMMeEUqKL2cOsf/G0nM3r4H/11Nx+442raMN3aPbswpDoAAN0x6O6usden9knPz/F4OeLPV1hsYvdcHIwWOOQ2SP64B68UqHvcow61c5gytUTRIAUZ2WMa5rMIwKky/jx69SKOz+Ti+rMHHyHs+qiscrDwSdbD080QW2x3tpOs8F2G64K5cKQEE1hEndL1pyJVoW6CqTv8kPZZSuEZ1I33qbCQjyL4eQNhKlQjmlBWWeR6g0Xhk6/pWzQx+AAezwmKWa5iRLsbp8Ud9VIonoinvLA2a1yetnZ5j+dDBHy8qzJC/qgHn+UwYqRx/hWefNcKVll84T+ml7dOBAp6lzD+4grzBV0Pz9eoPQPwi5s8kyiHdBzpFeDef/5LuP132wnA8DH6awltAKvYTq0WzkPU+q07y9Y+nXvjR2qClqQkS/JTNEljh9j2Y0WmkGeBg22tFQgPCHLKCh997pNXWuGdkyv4jiHS1eaTAXQDyaUStqMRjul3r+yfY8AtAE8a/PjoEzwd00V5mg3xjW8SsQ21D3a9RCksonHubPFM0C5ivjQIw7ZXlS0iN7l3JilKI2D5mclWkSOdjLdWLakplzB+axVnlYW34P7cROCbz25gsUjc9YFo+ubSEW1sLcgxjElLWgB1RJN7lJToJuRzDABxXLa8gtJHHLc9zFsHrSVgKGvsj+h6Pzoawy4D4JTejaJrcPgS/Z4xEoNOhh6n+izTCHsDmsBro7Abr7Bkn93GPxkA3hgf4YP5HgDgS+NHmJex0xKvy8DpXG+9fAQpLL7QfwwAOFwPsJk2npgWaliiYkKTNQKS+Q52XDoryjL3YbVwspHtZAOPmYXTTQLla+jGT/gshO3z/U0q1JFyBDNYtNpzAOdnPXcdhaBNGUDaUDDxzOYSNtDojOm5i4MK78+INNlorJsRHPmob+R8XQWOp7TA1bwR/r/Ze5Nf27I0P+i31tr96c/tXh8vmsyIqGyLrHKikqGEMK4agBiYESD+BhATxMAIYYOwZBmExJQRGINBKmaA7RJVWV1WZjVZmZERkdG89r7bn3af3ayOwfr2t0/U0MrJ07trki/y3nvObtda3+/7NYOTMJ997X//rxDFXT/WIMk1MrK7tE6i7GBYLQGS1xSpxq/eeYGjJGwqLtshB6CvNjmkcsi650LHGGQkqakE4o2Ai6g9Y5O+jUPXBOj1u0vyFo5HDaoF3afr139puoWFb8ftuB2343bcjl/yeP23B/8S49PtMWKfYNH07jLaSax2OcqM3EmshKBdrLCApg1je2RQCI/TKlS8N/UAPurSohVEKxjuPTm+YTbjx7sUemahDsJudJC2LNLPIoN5RnBRvsZb2Q0aiqr53bOv49vzINVYmxQ/+PQ93u2uVgXvrKfTEjenE0hKwYhqwYb/yzLCp3SekXRwqUNzd4/yTsefPdpBfzJGfUCG3m9rFAfhuBZfzhh6NKBKkyqnZtqzlm0GvP0//9eIqPKAB6LOgFwCSNHD2rWAy0gGdFBjQOkqSjoMkhY3caiUfeJ7WVTqg3dwh7qZXuoA4WHPciwOeknDo7uB1HJ/smIS2qeLI7R7Qed2aNnlxiUeIrN4MA+yke8fPMHLOiAH2in8aPUYHwyDacjvPPs20puuKgwmFgBgC4Xa5+yHjLw3z7dGYTgpcUPZpKJUvSHGoSfTBIJLlwVDxHlssCFIF4nDdFpiTn62Ox3jkiob/3SALQaIOxb51HFGZpTanpkpAGjJ11UYAdeFpIwrRMqh2ZEvc6xQK3I4KhPMDjcM1f7p2SOGG+eTEvOcJDVVhHheo85JTlUptE34t3MCuzhmhynvBdZ1+IyXNxMcjkus697wo6sm/+jFYxyPQxW1NAVKnWCchGfm8/NDWDJdGKQtjooSL5tw31qren9lGh0rVRBaAwC+UWjp+ggtIKYaaR7e3y9v5uwBXdUx9DJDtCQIWYH9ck2rgtFJ930umOsDgM8sk3zKVRaOqXt29xjwHRzcVeVlk+DqeTiX5Frhg//iH0HTu+0zjzsUWHCUl+wUdfHJEbKHG26FOC/QkAlGvUphWoX5PPxdl8cLANFQ48FRIKidFBtc1UPkKlyD1kX4cBae/UrHKOuEERiTSEY39MwiXkdwecc29xBJ9wz20qpmnUIkDrILxmgjho/Thxu87uONXFyfr6dQJoVzkvtKeRL6Le/dCRPWVqfY/fiQ/6ZLs0gmDX7jwRNUds8GiF5UNSa9G1m0SeHxahMeXKUcxLhlGceD0RIXuxH/Xpcqo4THTzf38OMXITouSQx+aB7x7/lWAVfUm5ma4KICYP0sR1YJ1CSPcTkQ08uPUmEjKNYpNxBaIlqGY04XgtNivEix+9DgzveCVd+xdHhxGSDQaCsZmmVXJYKx1Epx8Io50MAugia3paiUvPi10xCt1/VgbeERbcIxal1gRYtiXrQom4RdaIQHXLEn9oXv4+MUIFxvug8AjibIZFbjm7Ng5XjTFvjoKixAy+UgRJDFnRZUcovMxx7iOsGTswAZ//rffoqIDviyHeLlaoI/+Og9OlkJkXQuN33wQHYpoeu+lWCtAgaGz+2t8YIn6tNdzPF5dZXAe8GLq/fBTB4Azq4nsGt65jxw83yKZUOxcgMLkKxFGIFoK3sryXkL2clJrnJmY3rlQ1+7YwgPLdSg73OVdcLf7T14IzjIG3zz6BUePQoT8JPdAT6Rx+E8ncQXl0EvnM8rGK1QTMLDVScJ7IIWTAf89N//z/Gt/+vvAgiuQMs19ca9wMVy2FslbgoIuh5xbJlxrJ2CdRIvKUHIX2QMq14uRvBe4H2SVz0aL3Duw7XyVsBLgRn1vDdJDk3hF9m0RkOsYjmxkLKXkNBlD7+XabixhBGdDrsPi/Bt0IyqXe+o1PEnYAUUSWqcEYgy01v/if4zRGHw1t1rPKd3z2nJz7spgqtT18b44JvPuXd9VQ1YIig/uEAWGSwJVj8ZbnHWhjkgGbWYDCvewMyHO+xIpvbNo1ccLLEzMayT+NF5mIuckzgZhUUvjUwoLDoJkhOoaPMUT2vUVYGC1Af2uxvEpF/VWvXtDeUhpO+Z27sYgp7Be5MVPsHrPW5h4dtxO27H7bgdt+OXPN7IyrVuY6goEAq6nfD59QRJqtkU4KYs4KgJ7xKBqCTjA+mxaHN2l1ns8r6iuwphAJ4qos++PEH6ksTr71QQyuPigvIPhYehqiFSvYaxsRHOqxHaRdhx2pHG9rpL9ZYEsxLEOBKQTb8/aua9C4oYGNghVSLXKYdD+zrB5GOBZk4M24lHQ9nvNvF4672L/jqZCF+7G/77Y30HguK4pBZwkUe86GEx3owuI66agCCi737mEg/ovYg400dYJiuBlkKxy3UMxJ5Dz8PoKtXAvLXERBRWQBLsbEYWyCwEVa5ZqvHZJqAPUvieLekEROLwG1//HADwoxcP0agAucbTGnqXwFGl8I//7G/0h+BE2G2TjtlbAUfFmGp7D2VTeHiFPjNU+j5aTzrsTMKQqIxc0Dgi+AznqWaXLeQaDfm6OisYwoUKz1h8SBrYpHf5qesYOkmhCBFIUs0RdhjpYHZPxxROIvyPSC1XFwBQbVNEHZQXW2xJjzmflfhgcI73sgAPPkqvUdtwvb9YHDChrlpnEFsFTYiDKgyKe6HqiZXFW//TfwuQtljuJIbvBIhSyeAytngWqrZ4IaEfhgo6zzTOqVLtdL67bZd+4fsA9CTCxdUY//TFr4XPGDcY3QnfvV3liJMeYhfSY3wUqtiqjjGlivZ4uMVnZ0dsbOKcYF9dJTzi2EJ2UYoiGFAAgUgkN3FfySZ9+0S0Ep6c0uIyEPGysoPlgfo4/FExbBBJB38ernm66FGjduJgJpbf+8tyyExrbSUjIrsmwauLKUbjgBx8/OwOtxjef3COSDrWNX/26hhzyrW+qEZo6H6OkgZ5pDElePrVcoxn5AFtjESWt6xHjmMboG4ERymfeNSHhDgA/X0C4IjoF48aOKsgVT9vdcEin718gNd9vJGLq3MCwkkM84bNvYu0RWsidphZLQZI6MXSI8/9y1+/9xLH6Ybt8srPJigo2UVawOSB7g+EhaBjyfllAj8yUDRhXS5G/LIOM8fsyEHUci4lALhlAkGLZHQVQ+36hBi1UXAZbQAQcklFJ5cpDHIKgd6tEl79VCUQl8Bf/I/BQenb//E/wk/+u95N6fv/93+GLcFFbRvh/JLcm1IDRykBZmqQzWrUtCB1uYzhFx1gBfd+XeF4AYrXCsKiN8EoXC+xGYJ7g0CY9AT1iHzk+99TwVCiM4pIbyTLm0QmIKqYpQ+bdY66g6piy4YSf+sbP4fxEj96FeCuZpmx6D5cvH7TIteK3bIOfvMVPpyd4U/PAky/up7xBgxOoKY+dnqyQ1tHGI3IMtNK7JbEmm1ifPTiLn+VVB6u6Sfw9TrnPl+WGewIXhQy2HACQFJoRJFlqPZfvfcE/8/PfiV8XuwgUovRSVhMGh0x1CnKiKFrYQXcwPJGMI4thtTzboxCkmtm1Oqyt69brgv84PpdPLgb7CGv9AibNjy7dRsjI3jR5hLaCoxpA2CsREOfV9kUxecJH4v+1S1Kkk/ZZQIxNLxQCgdIWpDWreKsZCkdqipB9JJ6fgMHS5uZONeIYouKPDn1IoOOUr63LjU8oTebtIclnWQJ0GevjmFrxW0XmyiW5UwnJSaDCpoW1MViGNoMCHacwu+ZnFh8VZbTtQpS+r/pR9VdC0/SsyJt8fx6yoHryQrQpDiKSgGUCvqtcK+2Vcr8gJeLCVp6Zw4GOxwPt2x8sVnnvFnSTuHJ9ZyZufGo4d7pPN8hIVZ0Ig2k8FiSIUlTx4ie0Ka/8NCZY6vXbnMBBI6KsD2LX7dRHwq/1+f3NwNAAr4zW4k8LM0jHnsL7ms63sjF9d2ja8SDBK82YxTE/JgmFXYmwZeL0JuRiUVzP/xM7BTEPPz7ON3AeYktTQZeoV88bsLi2kVG6bnlHWy8UhCrhGPURnc2OCLd39lqhMMi/PvH5w+CTV9nPVcFggQQ1kcf9Qu9S3uiDzyQXktU98Pn/51v/TlmFH/z0Z27+OHvBW1rvBHYPAa+9Z8ER6W/2ltYAWD9+ycwBR3/xMF3zjMvCyh62aNtDHsdQe31XkXeL04ekvuxopGsQ5VtOIkulUenopfYNAKWqPvCCvjYcXWaXCkONu8IIYIWpPrQsSOWyC1kYjlpZjyokUWk41wPeaL8y6t7kMLj3XlYID7WinfgepVCNJInR1NYyLcp6cULlCbley9Mb2fXHDi2znswX+J8M2S97X5FqK/y0O8kElk6biDIrnE+3sE6yVWt3ZuwvJFMXvMeGOUNh1H/7j/7LnASJrn5tERrFO5PQiX40ZN7UFdU/SaeF1MoqqSoByik5+9tWxVi+Oi4vZVc8VoAP//0Pv7B4rcABK5CVzmlseHFSSoHeLBkJ89bVCVZ5Q0bDP/mhqU4Eh7rNvzsWg6QpgZuEH5WbVN48hkWOwVLz7uNye/4iCwtpzUTpqyRSFLTwyki9FMB4Gi8RWsVSpIZje+soLtQb+n5ObCNwvigxDDr7RDPr8Mis1oPgo6ZhjpLef2EI203/afUgjfAshZ8SD72UKVgb23hBD+fl09nQOJALqEoH3jEZfeihP4+nobrVd+XeCnCcVXXORO37ry/gYPAhPRhhwcbTrc5XY5RbVJ2WNPLDOlJqFy/OD/EAVWxs2yHp+sZFgvqa1cKZkhzz8gArYR6GRZbfWD4mZY68A0Ee36DrRyFEYzimOMWqBV81b/3PJLXf3G97bnejttxO27H7bgdv+TxRlauH338ADLPIGuJ3aOw292NElxuBtAt9QNii5hgpsm9Cv/2g+CQ8jeKz/FffvbvwJA4XvgA2wCh52ZTD31MvU4B3o1CgHd9ALDbpXhBO+12meJnF0GaURyXOBjsMJiGXomfgCnu67yAfZ7BTqlc0hIRmUGki+Ba1MkK/mp5j1mEn7467t1kHhoMnkZY/wo57Pyvf79nTP9wgPaOR7LkfTc0SYK8AvS8N25Qu55lbJ2EoTI2ntc4PtniksTu+jJHV552lXe3rU+uJbOwpRYc7u6VRzar+biaasiQGfxXd7hSg2UQNnE4ma/xaBSYrL82eYqUTKH/+dUH+FgEtvDNKrAcu8pvVDR90HYSGJwdhCxKxf3vMysDa5ecdCILGKo6o6MKk2GoEk7yDZZVjg31luym92dVM4Niz1d3nNf877JJUOxVgk0bsbwERmD+IMB/xirs2pgr23u/9gp/+87Pw7moGiub46NNgJ4nsxKrzj3I8a2AV46kIOEat9sEmqB8ddjAljHkqK/aOjjW2YA2dFyFPNZICR1IlOXMU6TAdRvBXIdrt07b/qWXAAAgAElEQVQTFGTGr7XCFikWG8rClZ5Zs7ZzO6NrkhYaPu9ZzA0ZtuTPYlQPNGLyP67LJIQN0LUqWwVBVbo3gt/rNDJII8OVq7GSnaNm6Q5nZcBfr4THZllAD8PxzIc75ORrPEhbLDYF7LPwzkpN5iYIOa0mF4y0uMzBk3RObOLOzwPpdajuduSwdv/ugg0gPAJHADLce9XfhsBKN0BSd37FCeoTum+NCFI1BFMN6ySuKEy23KWwnY+38sHTu+NkaMkV+9vH15iRLHAUEVt8z1hfnYSXPokc6mUGQ5nUMjNw276dZVMPedQnnkdT8qm+SqHuhs8fZBpaRzBfhrlCODA/QJT7fIvXc7yRi6uqJKSXcJnnCepsOYJuI9bUsfUbQrj6rxZPAAC1j3FSbHB5FB4Ie1qwcXZ1x+Hdb77kGK+z6wnL62wrgza0k38AaG/CH6o9rR0QYLK/9VYgov/h2duYZL2zUC0ydpRJVhJxMMQJfR6LXi8A4CQLfTd9rPCkI7DcZCjf7iclCA9NxxENgeJUcLhzO7eYPQg7h1lR4XJLL+omgx4rqJveaanTwHonsanT3gGnsDAEPaplFCahjuC0p2ayWQ9x+8ShXqW9bCFz3BP1ZQSfOlharDrjfyAEMR8XG+xMmDh/tr2HLzahN/54dIPZMLzUl8sh4tSyu9W2SlHTAiq07KFT9JpcAHA3adA/MpQHPuY80/juUUhUcl7goChRjsNxsOsMwnNVVQn3/No4wskw3KfFpsDmesDXRyiHfBYmsziyOCxIc1zniJVlyNt5gd+9/Hq4xsKhsRHeGwdJWZZoLAniFlrw8wflISPXp5AIwJMu0dQR4mGLvIseixyHkld1DJVrlgtdrIZoluHaxaMW790JMq5MaVx9Oe/Pu5HYXZLj2azGbpMybJjlLXK6v+X5BM4kvIFMD8sQBYewUUtWexKsVsLQYuyd4NBtKT3qZdZbEgIcGblpUjjfBwMkkWGSlHESLy+CntTVEeZ3VojpPr0zucKDk7C5uWxH+EQd4+XLzlpTwJFmx2mB5thC0GLSSXMAwBwYhm3N3EGUEb71TnhmhlG/gm7qNPQ6J9RWWChE5LwoHKDqEFkHAMkaqMOeES71YY5B2ICdDDd4Ru5uuopZtz+Z7LCyAo42NGoVoaGe8f/7d/4u3vsnfw/7owtwMJuYN4KTvEGWaJbfNDc5w8x2v/2AALFnxP+QJxXPrdZKWCtg6bmLNpJbPF70rZTXddzCwrfjdtyO23E7bscvebyRlWu0llCNhNEedtYZFzRolhmiSb+D7KKRvjk+xR0VSsTaR/jto59hQ+zez+sY0TuhojhONK7LAgvyDxWVYjG5aolJrIiFOnTctLcHDhF5z0bKYVHn+N3nXwMQmINXVDF6L2BPWs5+zS56MpXJA7nnww9ehL+LWnZWOcq3OE0C5NRGgUzTwXCTyQ76DlWIxwKVVrh/GHbov33nI/zlOlDiT8sJDgZ94LSxEuVLMjGIwecpNjnWs4SDnqEFBMG2qhZwMTD5Bd2HncfF98O/vQDvumEFYAViuhdi4hku8tIjm9Z4NA/Q7+l6zKQZu4vw5eIAHx4G84AvNgcsWG9shISgzO8+eInresAQZr2Y8D33kUO0iJjVKSz58ALAvRrFoMbmLNxffWDYhckDmMbdc7BG4yLU47Crf3pVsIxJFxayUnCE7N/IAW5A13FkIVKLIcknBMCB07OiwlaTl3Os0ViFL54d083v0YD5nRXqNsZy11fLg+MAe5bLHLPDUCUXiYb1AsttqL6Uciiv6N+Zga4jaIK1Hz+4wiVVd1J6FFnDVX+zzLhiMZcZVjMiuMQKct70MqNXKRsf6Dr6Cht5NqgwzcI5/6wYQhjJELIQnp271nPJMG2ca8yKmuVs5Z4pPQehd1DkNmZHqUg6NCbi52m5GuDuYUBn7g9WkHfDeZ2vR4iVw7cOggnJOKrw0bpneWsn8ehb4WerKsOaIG59nUBMWgyoRVBXCTSRtWQp4YggKNcRfOTx8z8Ohssu9nAT8icetDBNxGiNbSTajoxfBrOS4owMXDSwfrd3PLN36VqVGVabnOU3MD3ist7mGIxqbBakHdtjLb/3T/4edNeKcCJItGhughew9Ews/AAqsvyMiMxCUYXrrACqfmkRqjeKSFPD90KQpEl31fbAIyKoul3gtR9v5OIaqOIBpukC0bdtATXUePc4wGm/efgL/B/PvgsA+EbxEj9r7gEA/uHH/xaAEAYNAL5WWF+HxW+tJRA7qBuyeTtqIcnc3iliCHYQi1aQtABlecum16O0wVG+xYD8BP/84j73BneXA6itRH6xR3un514aQLUCn52FVJLf/tpHqGz47s6ZCADef/cUqybDjnpOVROzNlEIj+l4h7tF2Ej8ePUIW00szrLA9jxMsOM7mxB0TMED0VbC5J1VZGfVRC9QI4O7EwA9ClZ819+jiW6tIIn1a+ea+9NqJyGcQA3qaw9MgNcAqJGGUo4XGgDs5lPKBEXa4nERWMC1jfBiE2Cxrel1dqfbCW62BQcgwAlmdYNe9I6d2cx6NvK/+8FfIRYWv5+8G36mIxSUZ6mtwk8W9+k63sPFdsjG9yFppGNMh4Vb7dkTcrNZC4jMY06B1EXc4uUqLPxPXxyypGM822GS14ipF2m0QkRa19ZEGGQtbyrWZcYToIwtSzUaHSGJLL51N1hrPlvP8MGH53TdYjQ24k3d6c2kl6t4gU2ZwTR9Cgy3IrzA5TI8I/PxDpPRDjdk2+cSj+GD8FxpHWFU1Lhuw++mkcGWFiC1UXDH7V46jYCndKGT+RpfnwbYOVcaHy3uoNnTijd6T3a1x9C2AtxXvTNY45OrY+ICAHLW4s4gHNc0rpDS4ne+HmG5zfEHu7D4fffuSxRRuGmn5QSNjnBEDP8XlzPYFS1IAwupPPMdIDwkLRjOxYgJHtUA0tOYe7UuAdqY+oyDIKlyl+H5zF9JzpMWFkhXHqoNF33xdYWo6hYnx6xuIzyiZxmzkzG37HgGK7BpFC+8Lt/rJQnPxzga1GhNxK0zYQTQMZ+VRRRZeHqtwqLZScokRK5Z2lW1MUP7RitOVHJOoNrsJfUozz33hFogr/N4IxdX+2EJX1iM8hbvzcNiWkQatY3wAVmmvZ1e4L/54P8EEPqsP62CJrLTTXKvyqP3LY1d8IrtuDee5DgARCvgMwdHVZzcS4lo6gTvHoYF4Z3hFQ7jLWpqSH6cHHPCSTxpIM8HsN26UoBlLV4ALvJIaZJ9Uh7gugp/Z51kAsqqyYLco6v2rISjIGNYATOXeEVVbh5paNLJ1VXCG4P1+RCikawFdQq8gJqhg7CCtbk+ljCerllCRIpuMz01kDuSbmwiXoAggha2i6CLEguQRm9QNDgZbTAmiUFXjQLAyWiD7x88wZ/evAUAON8OeZd8uRvgatF7qErlEFN4upExQCH2opUQJKcAAD80HOP3ZHuAx8Nr7nWudxk2VDG56xQLkhSoSsArzzq/SICJJnYQNLp+T0LFPVYj4K3E8/Mg1L9/vIShia1L1QGAO6MN7g1WfE8vNkNOJ2mNwq5JMKEFWimHIRkjnAy3eLYIn93JVp5RhNiqzDmi7duTl/hid4iLvWq1I8NYK+Gd4OdfriM4SoSJcsNEsGRmEUnH0W5GR7zg7LTC5fMZb2ieXNxjbbIfOohFAj0kRCA3EGlPaOrMW362HWOWVTjMw7n92ReP+J2S0sEa2VfNA83a8SerObZlxprqNGsxodjJXLVsOlJeFxCVZCLd4GGLH58HFCeJLL5zfIqfXIZK1hrJKNT8eI31psCCkl7ETkGRIYkEYIpwHKPDEhs37F1UHNhGVa9SZK9iDJ/SNZGe+QkuESjvCjRzeveOa9YBCyt4nxMlFtEHa5Y/CSeQk+66KlN4I0NsHgAx1Nz/NnXM0j+TaXgveGOVHO8wIlJXEhlUbcyRiPub9A4V6wL6dBvx85MPGiT0/ixuhiF5iDYY+SsFPaJr9fBWinM7bsftuB2343bcjr823sjK1XkBeAEB4EtyWjoebvFbJx/hO/lTAMBLPQMIRflp9RC/fx3M2tNUszkAgLBj7ejjkYPPBTpFgFAeatU5JjnIzEBRFTSflLyTf3/aWw5WNoaOFD7dhn7aMG5x/zj0QE+vJ1B7iTBmAGjKjm2OLIrjkuHlUic9mzQVOHtJwaxehISKjvU3K+GGBM06yRUPACTScuU6GlacTVs6AW8SZjhL3YeXxysJHwEuoIFQmYahHnG0Dgb/nXDe55b7rLLuHV1UDYgUcF1VW7SYDsNx3RkEcfxvzIJ1YZcIAgCxsPhx+RiPhyEJp4hafHwZIHFTSwwJPm6Nwrio2X0qKVpmS2KsobOI7QPjQuPbD17yd3y2Oep7tXuWbgDYrUmqTkhPN0oC7qRL/PGwcQQcdi0BjYqyRrPTGPXQwtehTHlxPkNCENp8UqKhVsSXl3P84vkJw8Sz+RZHg1AnPF9O0dQxzsiVSUUOkpC3nU44H9a3EkXRYEPOPPV1jr949Q4A4C/nDyClZ+s/FTlmiQKhfRB1bO27Gr6D/KqI3wXnBa7WAzRrukatxIKsRaGINd8ZCzjA5X0wgy8sEoIm49jyMY+LGqfb8BmxsjBe4icvQ7tm/KMM5X2qvu42EIskpBEhyESub0IVrmLb20HSZ86TAO++qif8vEN4+NxBEBrxz/7iV5hh//BogUQaLJdEq/fAkNKjRmmLuo0xoud1vSlgqRKE9MjJHCOLDcy86lsT+6z3SsFLj+p475rTo1Ted/Cxg6AgCKxi/qGX/XzjFgM00mP2fngXxlnD881NkmNxM4QiRq9tFVenzgkOcOgCFbq2wnRectU5ShokynLebpG1WFL+ta8iqJHuGeabFCC4eldGqDtUqwrvWdciqe5aZqyL5vVfml7/M/iXGLaM4V0MNaxwh1IeMqXxV9v7eFKHxfaPzt7G9RdhQcrulfygCAQv106yY2LLD2bbRgEOFF0aTcQ08w4j6Hph702v8P3JlwCAr6VnWLvwkD5tD/E7L77DHqHvTq6wInu5b3/tFE/uzfHz5yEwOnqa8UIrK4m2ifFgSiHryjIsPE4aXBE5wmxiwAu2a1uvc9beDQY1NnXK/ZGreMBw4+LlpO+tJQ6qEmzPZtN+YbGZD/AmEXhs7KEI9hGW+s6k0RO7PR9i10Onek5+rHRczTrFjuDup3oG4yR+p/0OACBVBjd0nodFicfDG/zJWZciFOBsAJjtxaE9GCzxw1eP0I00MTAdGUNLqJEGyNbteLbBzy/CAq11MFHuYC9gj+yk9pJ6IsDtSbmcCn31buzqCEOC6Jo2YsKXTXyAWcnm0O8iNJ0zUt5gQh6vu10KlViMR31f6ozSl6pdCnedMMx6/NY1P3MvrqYMf8dbiU2WMwQI5eG70J1tDAw0HJFSXOQhCZZ3WgJGsnbT1wrxOCwYxsUs9fEInrs5OSM1ddxb4BGBpgvyLldZv7h4AE7whJ6mmrW4F1djTjwSuUU+aGBfkVbWeFjqHU5nJTaxY8TVaMXPu3ciWGtGXfKQQEk2iYOowaNhYNLYRwGe9ynB3zLwEQAgEg4/eP4On4/MLFs7vqimUMrxNbeV2ttECAwOwnxjnUAcWRjqm2sRIaJrajYx/DZi9y+b7CnshIeqJMSGbB9HFrZzQ8ssXGeKVCvACe6x59EW64YsVhMNPalQ0yYOQvICLSLHG+/rxRBKOSRFOMaySrAjl6crDCGlR0Lzg0foEwOA1Q62jLBuiTBlxV6k0F7vN7VwpvfnFvOW4+hS9O/L6zpuYeHbcTtux+24HbfjlzzeyMpV1BJCSKxWBb53EqQr7xUXiIXFn61DRbNcF8jvB6hNa4WKEh/SUQMpPVeWSjnsaBepYhfMvjumbGGYZJGkGlmi8c2jQN+fJztsbPjMZ/oAiegJK9YLTqw4r0b8XZ9tDvH0eg5Jfp6y7R1ZfOKB5zk+c4EtHCcGDcGNF3VPIopaItt0VYpJuOrcPc8QbwU7MbWpR9ntOJWHpB2462DvDg63HqoLUlfBhYn9flsJ2VV3HXmnU9xQlRsOrBfACy2Aw4bTXoqsZUh0s8iBVqKZh/++N18z6QcAnpUzTpURAvj6vUBQu1+s8OEgXPsf3LyLO+MNXhALe30zYHJNOm5wOC758z6cneNZHBCML84PYdve61nsQZuqklBkSOPjQE6yRUdisqgIQpb0N13KjHeCU1O8Ivb3XjWZUFX44cEFpnGAGq+3BXbrDDflpL83xI51dQQkHpKgvVWZo94RA24ZIyYTBuGDJ645JLLQniEGFOBahcFhqNSMkWjo+OOzGGa4l3OqPBNZhtMdmqZ3BpkO+8r6xkq0RJwbn2zx/uEFVk14Pl/43kd5OqwQSYcd3cMksmiz8HdtFTPsnBUtksiCUFssP/AASW9aE2E4qGGpAts+HzM6IzcqoCV0zHomGQqeJyVKYpUbJ7nKBYJZf0cge7GchCAG+nzXKja0Vx7Qjxq03T3ch3vnDX/G1dU0kIiuwvelK9nnAZeAGfa5yQC4KvcDi2jUsOOUMH3V6a1gYpJIHSB6CdtOJ5zfWu1SWC3757hSwAHJ3qRHQgQy3UahrUBwctvE7DftnEAU2b1EHgW77hn8iFwvAxKApwdGSM9IhLc0L3VuZaOK2fdZu8HHeL3HG7m4+sTDJx5xYvFWTrINF+OL5hAfDEOU1k+KuyyJaKzCTTfvKAfnJB5OyYrOSTSjcBmfPD/6ChvWtwrWEfyUCBgn8W4R2Mkf5n0f708278ISiHBaTTBJa8xTsgiLWvwJQZjeC9SbtFOLQE8c7IwW5VZCNYCkl7y5q3t5iQccsR59LKC2vQk8nEByTX2a2EO1QLIkmLLwwWwfBP3SF0crxf8/QNaF3cufOCDy3KsSVsCmfW9230FKtoKtFtsJ0GFaft4iji1D70lkGb7X0zVWdYZj6jEeZVs0g3D9b5oCJ/kGi0GYtKs2xvfnT8J9Ew43ZD11kO7w+c0hYoK0mrKAmIUTuj9f4dFwwbFbT7ZzpKq3/vM7BXRJR5XqGdNJL0mJNxLChpQkALASPNFI6WBTy5CiaBSHEZipDUEHXXRa4fDoMMCUxks8KYMe9u2DG6yHGUpagJbLAU96EAG6dRuy1pTgQGukDrrrhZcSZtIzoSF7pmlgcCuUtDlTixjFghaSCNCzPfmNEWipv+udYOepXZPgcFhi29LPfIBygaDZHUQtTrdd2krCRvhlbJHFhuHHVRPxwhilBoMpWShaFRZA6j2qwvRxaxsy++8WmpFhdrOzCsIKKJKADRKNjelh4auW+oYA4AUK0qt6L/DqjHgL6wjqoIWljRW0hB5TeMGNQvI0ZfvDZCU5BUrMPG421KddJoiXkt8j1ezJswYh5CImuq0eAeobQS40lB7lJuPnybt+QYWR8E1nfwZAAjX93pmViLp7DcAbieQsPCNm4GGXtDCmDodHYW5bVhm2qxxR2j//Br1N5b35inXYi13O1xsIbmkdW7vrt4aLAJaNmTYKBAW6b8urIbYUAGL+Gp/hdRxv5OKazCqowmM6rPDlLlDvR3GNaVzhj29I1O3kV6LfxkSGGaYNBnGLb0xCFfRudoF/cfMBAOBqNsCuTOG7B7XokzmE8CgSjZhsvTY2x7M29HeHUcO75zvZGpFwmCdhEjmtxlD08K2WRcjInIcHMJvXKGiBqKsEOB+gKynUIuoX3kb2PV94RLVAPSWZy1r1spPIQ1UChvwH8nMB2+WV1oBNeyMHF4OTY2zmefEWWiB9FaE4Cz/bvAPoMfXnIgFh+lQcsWd+AAHuE8JI6GUOSwuXaYHrzmL3UANGwNwJP/vi6gA57XYPix2O0w1AC8g8LsGdNy/xrAqT40U1wuGwxCQJm6cP3voZfvfsa3yvL+shaxqVcKzBfHTnBqtJxhN/UyaQa+rlWXCFbgYewoQeMxAWMknXWykP3SgoyqC1I8tSrmgVevZdH1poiS/PwzOyHOesd84TDW0lV+hZ0bLkCAg9rQ4R8B7cMzORgs/pGHMJETtImnBtFUER8cwlwYrSR4KPsUuO0cfh+guaOLvea/guwQlA63UOKR0WRCSKEst2hE+fHmF9knJkntOSF7/ydIRt5FBQ1ewix/IeFwls6DylIHkQHUeS9DrLKPUYzbesM3ZOoL2k3mwrYCcWtOfFq+sJV18SHtd1WPx2TYLpfIvjYVjhrJeBdwAAkcd8umWteHlZMPmxObKQO8mLt8k8LPlP+3UCQ0Sw7EqiOPNoR939CFpXAKjuBfvEYhoQlO26QNxVyQ7wWiK6DNckMn1VCydgB/ScTTRso7i/KTKDlv6d5S1K0+fkeiuhOv9j4UOCFID5qIT3gq+r3qSIzskvexryYDuim9YKh3f7TN71LmO0D5Hj++St4PuJVn5lwy3aCILOq5tPXudx23O9HbfjdtyO23E7fsnjjaxc08RCJQZCeJzXgWV5kq7x6faYja4BcFKKMQo1QV9X1RRyJ/GJeQwA+A9/6//D9yZBvnNWjrHbpsCwZ3vGs1DxZonGg9ESZ22QEijh8AuS2zwsFviyDBXKLNlhrTMs21A+rpreqMDXCsj7HZ01EoYclOx1iqwFZp+Gn59/X7ADlMt6qFpoCRd5REuCdwYO0WE4RmckNjLF+LOO3eihi97YoavEfLTXH0RI4+iwO1kL2Mxj8Z2u5yqgCCKWTZDssCtNDJi4N1fgQPQmQrISzCp2MaBIIZQsE7RTj0UU7ts33nmJKVWgn9wc43/70a9zD/MbHz7H92bPwvVoxwzJX9VD1CbCgBxxPt0e4+GIAqe3E5RtgtaE+7S4GOH+gyBn6JJUul6wfTJkOZKXgRUMUIiCAjsvucxjQJKazSpHvFQMsatVxNe160d3bGEI9FBn0weRX1yNgXXMjOZi1HAgukwsVGZgu9ZEGaEV5NBUGGBJVUPsgUoBVGGJ3HN+LoQPeHIH80nPcLLcRIHZTaHWcIItIG3kUJ6G++KVx1oVzCDVu7g3Bck0OUSRPCmxnCG63mVo6rh37tlGLKnZ789xy5euQdv08pdR1qC1CoYY/rtdykbyNgrh4GyHGFs8/SiYQTw/mnHl/Z17L/EwX+CfvwiBCJFyiKd9X/JmOeTcYNFIlpAMDnahkqXjwmnP6IcXnNzTjj2EFZydbN6q+1Qf6SGkx9Wz7qIDgtKjhkWD7HiNxS6gMKqUvbQtduyQ5q5SIHV8fZLYYPsiVN7tTgBD3ydNKd/3bb1ATUEM2WyFZFji2TkFMJg+7UcYgVpHvQ1mmcA8D1W/Sz1O3r2C/iw8C+ZYf8X8vwOT0vMIUgtMfxGO44//l/8U3XjrH/x9vO7jjVxcy6djyCzD7qTGxXWYRD96ehfFqGGIKIkMe+m+uJki/ZxIRDr4+TbvhAXpk+0Jnm3Cg352PYFfJ/AdrX1gQl8BgM00nq7muKoC5PJZfMTHszMJImrQVTbGos5xvQ4PqlIOdrVHFJB9X8MayTFP2bUKFog6/PzwzwXwH4Q+5fRehWVNi3WZw3w5ZCJLlJl+UkpbPG0P0fwb4dzsz0dIw5oDk4H7SnZsg1XZnl1hBxG7FIAHh3AfvLXmTcr7hxe4qoa4pL5Ts8wZ3hJG9NCyBVwseNERDn1Yugds5nB4TD2ouMF5FV7ixSoQkyQt5k9u5viP7v0hAGAwaPB7mwDfj5MaH47PeOH6wfk7bGXXmgjGSiwvw32ClkyukdJhs83ZbUYZQHRsHjpOICy0Nuv7kl54Jl15LWEGDtFRuOZZprF5FY5flTL473aGX4Vm2K2ukj48fZEg3gg4Ip6N8hoVkZYGRYOmjeAoeN4LiXQYFoX21YDlHfFVSGLqoEirXC+mjDx83MPVopLcvxckkWYimhCQ5CttZd/nROIgVU98ikYNJ9E4JyGExaoke0vpcHEZ3sPBuP4KsWVdZSxzkfth5kYiHzQ4HodFeZLU+PlZkEzFkUVZJyyHkcpBUW/W1gq6TFhapNH3A7NM49Es9LhrE+O8GbGUpawUIlp4myrIilxCC/3IsBVltUsgUsfPlkk9vwsytbwgG+mxm++1RbTqHcPaCHYbM2EwPqh4QzcrKjy9mLNtqNSAndBCvot4QydN4El039e0EeQJ6byrKGzcujZAbvkw7DJBsqD3p7wHcdSwXWFVK94niFpitSlwMg/v4YePX+HmJEDvlzcjNDqCeFzSsSi2NVQjzf3d4hQYP9W4/G54vx7/D/+QN96Y7eXsvabjFha+HbfjdtyO23E7fsnjjaxc00sJlUrgskAzIzjkqEW5yNkZpsharOpQrdbrFGnHECX2axcg/CdfPGbSgFxHUK2AOaYsR93vXayTcN6yy9HL1YR3t9syYyMKvUnwzjvnsLRDb27yHlaKfC/TQCCCdIkksgk71fPvhb8bvAT+5nGAq8dRjQdJgDb/xc0H+NH5e7xL/tcef4HDNOz+r5ohLqcDDmdf3NWQZJAfb4H6uDsZAblTff5qtFelJWGn3nnhdlUrEAzhr7YD3JuG3e5nNwVXvOFLKAhgsRf0ilAF+gdh1y2VhV9moUoF8GebHHoTjnF6ssH6YhpIEgiV5md1MNzIpMbvnQXD/dYoXOZD/NpBgIxPii02BK+3RqFuYs6S9Zll/9TlNodepYgJUhd7pFkAvSGGDxB4J5MSQ4OGSFAidkjGDTM3B2mLmuBGty0C/L6XhenPetlVcxQqCCE97Ds1SyTqNmZC0Pp0hMGdEgPyEzZW8f28THOAnlVT+IAQ0PWPF4qlH64IYQIda1yPv3qiwoMzSqNtD9/XRxaDSajIDwY7GCcxTMK5nW9G2HXGCkahvBj02caqN6nYXg1C8DYRfQ6HJXQejvnV1aQ3ohABqu3eoS8XczYguFkN+pB5BPlN538sdipUR3fD99kq4uzVNNZICEE63WxC62sAACAASURBVI7xly8eIT8I5+M9viJpQto7s6FWiMfh+RQCaNcxdOf4NTQ4PAoIUq0jlOTIJJRD3MHdoLD6qpeQydywgUWXCgQAL66nkNJBPNzxcYku5ABgOZuNAtTbrkhCNW76W2hC4lQ+D5Xl/ckKV7vwPl1pCTMhFKpREFoiJpJUW/Ss6+w4SKEOyJjluioYiYgTgzzRWG/DfzstIWi+tJVCtA3HW90RWH9NIaEEnMkvJGdjl/Weof9rOt7IxTXZ9hFw3fBWQN3EaLv4rLqf+JOV7MPA49BjNJ01XGwRE1W9jRz8ZcLMOFUYNuqPVZAYdIurNoong/Fox5FV8MAXn5/08JoHW50J5cNC3sF1sQOIum5XKYQVaO+HCfj9f/0F/vQySHhmWYXtOLxkiyZM4I50tA/yBQrSAzzdzSFEiCMDgIXyjBQKB6TXncBVwktwv9HEPQwMEUzmOyh7u07YuejnT8fwscdnS7rwomfGeuWD3g4BVoUAmgNa4AoL0GTlIgFID0tSE+sBEANzktcQ7y6wJXvKcdbgB9dhQc2UxoxizW6qINn52Sr02s63IyzWFHJQxoAVmN4NG4BGR9iSPrBZZlBbCd2xsH3PdJS15Jg6zFrIiwQ4CpOiFGArwS6+q4szbI1CTu5N61EKSM9uQiI1PSP7sA39RwBRKaFVAkvXfLlOUZD93vR4iSLWvDE8Ga445ODybIKIrrFNAD2zDEtaB5704mWAgdtZ31ftNizCIvQAu0TBmUNnqy8ih3IV7q21Enna8uJ3d7zmBXPbprhOBrC2X2xtt2hqAZ+IoD0GsL4aICWtb5a3qD3B36Max6Mtbihab305xPgobCjqyxzRtIWhlonLHSQ9I66VcDL0ogFADTVLvrwX+GIR+osCgBwYVBfhuRBDwxtqJB5yp6Cuu5ZPD1ebNiweXU/dJQ5LerbyouF0J1QSOKnY7SsetBiSKf4wbeG8QNoZ3O9yZj57J6AihyILv7u4HiGlkPhmL1ABWkJktteaArwZswjQczEJi+tlOcSO9Mkqdhj8UTjezfcrCNGHldgyxuwkvBdFotGYiFn1baLYRvVyMcLpF4c8h2WnMcZfhn/n1waeNjDCeuiBxCq8orAJEJOwPu3kY6/xuIWFb8ftuB2343bcjl/yeCMrV5MBPgWqk95HMzmLYVOPqDO+Tj1SMleAAxNBvAyaT1MQiWOsoGnXrZYR7MgiInccs42ZSFHuUmirOKdSX+UA/azNFJMvhJbwWW+sL7TgHbMXgMgNsKcT66BZlwSf0W6n+tGPHnMl5d+/Asgz/dVqDDltkZLB/z/+6NegF+QwVQZS1Ms5hWYPDVeWLgKSNX2eChC0ps/0kQ8sUgR9mqtSJiZILbj6hQeiawkX9wYTHVvSKt8zHTMPNzQMzQKAIFcqF3tA9vfDJb3jS6xs2FETFH16MUV+P+R/VibGv3f/zwAA//Tlv4IfPn0LUdS7zXRDFQZukWD5ik5OeOTPw/UufnWFg7d2eHkZWJxZ3mJchKrw7OUsGEIgVFWV8vz59MnhM7IWkXIoO62sjrmKVeMWtlGsPdVN9BXtb4cA6KkNiAZVspNHKwyIADTPd1jWOYo4PIP3ixV+8CwY8kNLNCcdxdsHhKUj33nJSES0kBwIAQDyqOaQbLGJiA3daSuB9G6omq0VQOe5rRVkBr5W798/h6UH8nI1hLMS9ppE1CPDGchqJ2CnLX++KyN2GoMHiuPePeu6LBiyl5lBSYhFPK+Da9Rey6Gr6IZ3t9ieDZERi3+YN8jpWi12+VdyZF2tOEru7vESZxT0EH+Rw8vey9innr2QvRNwcw1BfzcYNvyZ1S7FgBCG8mIA0/Y61DgxffuhymCtREFOSd4LjOg500Zhs82xIE9lSKDpEI3DCmnaISMK92YrNkDRTrGhx6VViJPedCNSFi1F+SWpxvrXw3fFsUW7SWANwdV5f4wewLZK8cPrx+EzEsv+2YOigU5MMOwHUJ8YyC7cIY/g4nAvJp9rSAsUr8Jx1AeCSYGD0/2Gy+s53szFNQ+Lq6p6ZyEXCcRryZZ1+7Bkfqp4gXAxUD62/eSyjdmlRxoBKwBLlmZCAY4M7N1Yw7QKvrNGNL15vnY9vBjtJJwWyCgQ3Qw9L1Tt1MHJPmDcZQ7xtk8W8bnlwHFhBQThdecvZvgdSsWRpYIwQEvNDTOxvVTGAKMngL4KL1MzjRjeqo/AvTVpwmJris59iuxgECwRRSt4ofQSzGBUbWAAu26uTD1vAIQVzEC1c43p4ZZ7hasyh+02N3m4Hvk5waoTD0N9c20Vlrve7k8qj5M89LvWOsNRFHYHX59cIJIOyyocyNXFGNkowGyP5gs8iebQZwVfV3w3/N0ga3G1GbDj0W6RBxs8UB4tXZ9dO4ZLHSTBqtPxjg0Hlq/GwJ7xAqzgHquMPPJRw8EJsGAJCTy4bwgrIFoJT72wuo253XB6PkU2aPH1o0v+io6xKyvZi/MFIHeSJTXpueJAblN4qEYw49iuk7CpQ3jGfOQBWujjeY32BUkwphrFXu9xtc2YYSuFx+cvA0M+zTWKrEZFE3VbxYElDcAdGUTCB+MBAD4TIeEGgDhqGMK9O9qgsRFn33or4TrWcuKgEscM5/ndFd6dkRObjfHTVc4bn1pHqIgN3vxizHC3GXikd8veFKPKGEJ3cehXd+YQkB5dFJZQge3dvdtlXfSpWZlF221mchMWVnrmm22Klja821UOrGPsku7ei/76VxEgPES3+W57WVSeaX5nSidwsRnypr2uEl78otig2cU43YZrlw7akGgEAM9z+APq7Wc+nLPq58SOOT/KGsxHJXRBbQYnUFLC0qhoUO5S3vzFK4nd4/CZu/f60ITV1xJkVxLZVfj84sIxLNxEt4vrazmaEwOZm+CcQwuXzT2M6OUUshH8AOuxZ8cVYYBoI6En9BbGHo7+yCUIleqQNIzKob2hxnz3UnSLqwUkufS4vcBgLz2EA3YPaSKV6KuL1EElFpYm3Ht3FzjNSINmBZJBy98n+uIXcqf6zQDCAtiRbaL1XsKLBJqZYEu24lygOqa+yVJ8JaXDpp6/QOYGLqIdeK6h6wjRBS1wpvdI7f6+69/pzMGRPjBaq16WID2yRLMjURwb1Hdo5m8kUEs0c+r3Fo57lkXc4uuHJV6koVp6b3qFmPo711WB//6LfxMAcPr0ALACMZ27KBxqiuY4ebDGVVGgvNvr97rK9noRkkA6PeLwYAf9k/Bd7dSFHiaA7KAKco0uqP3VBKKLz7MCyWkEM6CKPfMBqQDgBwZ1mfREOCNZQ+oTx/aBiIPjTTd5NbVCTc+EWkVokeKvKPFERRYZSSla5fmcvaS0G9pYtXPH74KqBPTI7TlFCUiSYXnloZ5lmHxGh1gMUZGqzJ5Ynsyngx12KuHJ/uVqwhvLepPCDXrbRAC8EEaJQZ5pli7FscWucxkygjcep+sxtsscghATjA2iUY8Y2UYxorFYDPFT2tw8nC2RDlpsF1T51RJyHP5OOUDPqR9IKVLd7wnlEBHpKvr6BtVNHnTDAPKiRUXn4prwHI+PQ/93u82YhOV3Cp2DeEitATov0ygzfV+1CZt52Vk7PstgqF8tPCBM79dtxhbpPHAJrJNYkybe6Cj4nHe9SyOwo+fKG4Fs0mAyCH+npMMpHb8ZWcRE6myr+KvubrsIdRx+b5Q1OCk2cLQ7rm3E976sE7ibFMUL2mA8NnyeaCXEiqp8Feapbs61sQDIHfLm3due6+24HbfjdtyO23E7/tp4IytXOdCQhYIVMVzb75Bs0qe5yK1CQgkiLvLQI5INGJLadKbUVvCuW000VGRhaKfdblOWG3TMYV9ShZrvwR4CQMdAXcSISgFPjkeucFy9yK2CSyNEc5JueIHhLPRwdmWKdpH1pvKy9/6VbZ/Q4nIL+B5aNgcmhKcjQE5eSWRXBEkX6H+v6Bmi3H/uIEb71T2ajBzDfOpGIurSVlyA27pKVjgwfGwzD08VxK9/7QmO0y0+WQftz80ux1sPgruSEB5PXhzCk9OQOuyNBN4ZXuMPTt9m44VXuzEHRNc6wnZHKIIXiDYKtutn7vmKb3WKk+EWv/h5cMyKH+5wQCk52ipYJ7DQwWCiqhK4x2TsbiTGB+QFu8kgrhLO2Yy2fdCBqkMLgA0nUscVUAc3d71bGMHXWA40xAW5cU0MxvfXbLRQxBpXW2LXulG4pp1fceawfRn6xz7xcC09Hyq0FRgtiB1cVyUrCRd77vdGawW8IPj74Q76jsbahuuvZ5Z9kt15hoqga6MV9CZBct5PMXLYP5u6UvzVXvT9dr1NYJoIEzLon+Q1OoB7dzFAu6ZAAgBq3rIaRijHjOzx8Tb0BNckDXmaoh6HiuuJlRDCIz6P+TpwlOzIIab82ePOMSrpvbv1lqq71Aa0hu5T5ZM9d6XAmu7YzmKrEBE64FIPSYxsMzMBLu5a6o2C6eRDTsCPDCMO1R2FiFjm1ki4XcTeyJAeroOkY8v/djY4tHWKBGdFb6qxjdEkDo76uE2TMJzfJAnePgqyvdP1GE0SA1+G6t0lHpMHododxi2MV2xOA4DdmqyVwNDAS+IgPI0QE19j87aHnZL6oZGwKWDI7zrRvkcO69e/cn0jF9e00FCFROUFFOnATAfzdpNe4plsI5vQBwQoMkxL7pP5qA8JlsrCWgnXvSQeENRfs9sYQsuv9Eu72UUYAax6pyIvAU/f10lQgM5ST8ASvLPeZWx+Pp2UuNlOWfdqC8e4hGh6Rx1ZK/ho73y1gCCdaFz1rkgA9XH3nvEOyozXISjd7Hoz7q6P6LyAii00QZ2mEFAk05M+SDp8lxCjRd+3yi3u3Al2UJ8vDvAX2wfQdG/SecVSJdeFXXfr+iJFNQ3X7sVuikg5ltXkieZ+2madM1FIDjWM9BypZq1k6PdiN0Iea94cyJc5ksNwXHcHazgvENM1v1n1Ws3ffP9TTOPweb/zybdDwtBuT+ecdtfQQTUC+qSPFeoSbCARCDR07USp+vBxL4ATupCtwt3xGjvSIJ8uJnz8Rw8XuDydBrceAJUTvZuPE3xDXQcxd5vJxELRghNVgWhmyUPRDC1kZ6MXOehaQk96LS47aVmwnMpsI2Bo0B7tkdLyPVvHyLHrkHMCzTVN0spDRh4b0kiu1zkvXOwKBWD4cI0i0dgN6P4uCo7/2/gcvpXIXlBrogE02Qe2C4qG6zS9uYOg652Parw1D6LLRZ3j/GLC73aWaSRMFpIwOkJKfXrdRNzXFtLDNwqKNkxOepgRHbQX8HR9ZKmAw4ah8tF0xzGEro4gygg1tXKGsx3r4Os6hkgNWlro5SqCjiP+bh4+9L27nreBCvcEpM+uFW6WYZNoK8Wh9oezDW6q/v3JE43VuySPiSzD9R9Oz/DZ5gjX2/C7HWkLAKLIQa9iJiVtHwrYbl97p4ai18JVCYQFkhW1YOYCg4/Ddbv/xQ6f4/Uet7Dw7bgdt+N23I7b8Useb2Tl2jYRpIrhK8UVnBxouG2MqGu277HV2gcte4fqMoFcR+yEgsRxbKrbRRC1BMh4XaxjSGKuekGRZN0O16r+OwTYz9PkRPHvdplWQFCQsYos9CLDgHbMxkjsLgf9iQnfu8b4nmmqZpohM7uNEa8Uu7pA9u47euKgdpIrkRBmTucWB4kMACSrkP2ZrEkaMnLAjoTmRkLNahbtexn15gpUOJnRXtXTmRMMLS5vAnxp1/FXjiuKHMNbQvjgs0sxdumrCJc6xAZeRAfwmWVZ0OVF1ocLOAFDfzO7t8J7j6/YjecPP30XIJ/V8fEVzrdDJGSY0ZwYrKqw7b4uC1grWY6wzwL+xeoIp9dBquFahajsUYrmnmZyjSgVvOwzOL2RX9niin3nq9jje19/AgB4tp4xWeXgcIXaxJznGscGjyhf+MnNHBAeskM8GskEMh/1+bzYBlKMIeG/0wKOCFn2oYY8zRDtiKU+ciGEHUBNVXYHGQsr4KjwFlYET2IEaQ+cQEYORwejEksyfEhjDe8F+/Y2VQw5DO9XnIboOEN+wkmuuSr3iWWP2s3FEFstmYmOxHMVnhUtbCphCgpcbwSjCIbiGruQAtFIvjcHwx2GcXi3Pjs/BDYxLD3HreyfQe9FaP8Q4U5FDoNRqPyMUag2Kdw1kbUmGnKPTJiRg1VLxgwx+T4r6fDevQCAf/rsJNy38wB3lOve7F5OWwjhuWq21zFAjmatlohHJIcZ1xxzCQDXyyFD0lEl0A6CqQUAVEj53HZtzK2UIjE4zEtsiQVcVwnScUBnWhdh3WTYndP8I/sQhapMMPtY8HtvvrlFTUQ0qTxLgJwEijMPT6vQ9HODehaOcfl+AfwRXuvxRi6uUoQbHM9rpvbXdQyRW/z/7Z3ZjiTJlZ5/M/MtwmPJjNyqsqqa3c1uks3hjDggpRlAGkCCIF3q7fQKegFBgCDMCAREAboZUppmk9Psraq6KmvJzMhYfTU304UdP559JwkBjUp9PoAAszMzytO3Y3aW/7f3gmpCyjAoYrSUThq9jFAv7gWH0iC+phnPqBdsp5dB6jgdqCsd0ms0wmCaoUPYRd8VxU9mNSu3uMbgvfOQqjofb/GpueRa27bL+OVVlzG08fjR5RsAof7Y0t39h7sHOCJ1otpGuKtG7M85zWqsykFqbPtmAkue0DAeZtnPr3p+6XdJEAzvVxXBlYNSvXkbDJz7GD9kIuEVUJ9bTnuGA6JOym3ML7nJgx1mo4p9bP/i7CnKLrysfnf3EPu0xfJt6JJup547oVWjEO1irqV6DdzT1UdyE/6tyQcN1vUIG/JpjbKWuzifLY8R/Zc52l+G86U6xYbQi3GJx5MVFkmorU5MjS/3oVX2ajfne6nrItixg6G6uUo7+N5YW4eOTE0vX3Va4+FlqHFZp3H76Rm/lD78s5c4Jsef5zhmGbyiTnA0LjGiTtwsthwUTid77G/GfE50MyzoTKFYaczF4T9HNMrljA4qUKBZzdSjozIAvBrGMWiRpmmWWxkPN6XyQGWGxWOrAGjUNJP9crPA+Cj8LUnUoagTFDQH6Z3CiIKF1h7TUYU3b0IX9iht8WgefEK/vjmB7evRVK/02TAO049TldsU89+mMBWlJd/z6B72alkebhfzc+jGHUuKbqsUj6lMkaYW2GqoftE7jfqpGfhZi8lRyUE/S1pM6Zm83Y/x4ZNrPKP78/Ro9x0f3mtKxdoqQpI3w8LBK1i68ErRee6nBNrB1MI7wEOhW1K3c+Z44ZC8jFE9CJ/3V3/+FVJj8R9//1MAwWj+5MdhHOlHx9f4zdVj7G5pnn0VoXpIp7XVYZwIQJlZbKrBtDxOLHsI/+rZR6je5Ijp/dOWMTraEMAreKOw/JdkZfVqDH9E98vVCO0JPW29+hjNvd78SYSyn5JQJd51vpfB9XhWwOQddlUK9xvabVyEwNfvSN3U8mA4Wo2YxCVUB2TXmuuP0V7B9fefC6uwPnTk53vMx+EmKeoE6/UYnupkcOCaro89W1SN8gZdpzGbUnu9V3yDX+ZrfHB6i89fBPcP1xieGVPG4/HZHT6chAfoo/Eb/LdVEA9YlRluyImmn+/rV6dGO250AMIsnqab/vHZHYqL8DBt/vYUphx+zsXgcR44xfKHCqGZpa8tqcjzzt4uLFSl4RV9r1VILsJKuF5nPAIQmw6xdqhoZ/Orq49Zni0yDh8cL1F/SG3+u4xnCtPbJBi3067E5C3LMEYbw+f725cn+Isff408Hl648zTsPL54dY7uGBwkLi9WyOOhPppHNbY0I+y8DnKS9Bm/ePwtAODz5RmWV3PY3uXnLkHUnzsNdCPHtnLGeNbfLdokzFDSCXt2fYxvb0OQsdZwg0tB+rSnkxDkjXLYNKSDbaPQ+Jb1L+Ohju4N2J6sm3Shvt8HJzeIUnjjgcjz4s/sIs6yqC4Eta6vKZaGZ6ujnWG3JW+CWxJOaMFxb8H19mYGEw/Si6gNSoR7/BcfPUNmLC7GoaFo3WR4syPrMquR0T1S+RQemnsadORZEtAYh+JBgnhHO+/LBsdkPL7djeCM5+dcWQ1Pz8LpZM9Wj7s3E5jYo6NaLRI36Hp3CrvbMfdduLHG8oYKq43GSV7g509ehC9dhFuqYcbaIe+lLluDzhqWR52Oa3zzhxDhdK1CXZ7q2pPHG1RluI8neYXOaWz7oLyP+Dl0XdCIBoBf//WfYvKPbrkx7JcPvsWrMmSGXhUzHE8KttHsTlpM83D/b19NeexqRAsGp2ksKuowoXO83Obw48H1S2kfegQAuOMWmw+HdIybWl5hd1MXsnsAoIH1x0AUDhFROYxApf3Q9TuM1FwFQRAE4cB8L3euWdwiijXefnkC/4S6gMcW0EPn5vHZljv0li+PBiUhj6GGCKB84L4jsm9KzSu4Kh08OJs6ArbxoHLUDjuKdgYWEi8rA7M1WE1J5WnUISMHm8+uH6CsYu5GPn28woJcKdLI4vVuit/ePgIA/Iff/BkU7ei0Hbqd4RWyaY2mn+m2ER7OQ5/8qhxBzUu8T56Wb/cTTh+3E887V9OQ3CLtBE2l0NE8i7MKo6sI1cWQ+mVVIE/jO1Qb8ynY/Uelg2G2Vh7WaT7/F5Mt7qKwoyibGFUX8UoeAEvNNT8scXS0R0E7/ZPpHq+6kJno2kE1Sq8j/N2rS07JddZgfEE1v8TCxr73fsfNJsc2CZ/35GiFvU3xdLPo/xy0ZIBQtxGcD/n01TqHau51hqeesxs+8fDaI6HaWFtH+OLvnoSfG3dQixauT606jb6i/x0pReWx3WcoSELxcrHGKKKegE6H4XzaEZlCDVmWcpCXgwIwa5GNw+9VtyNOL7p02NkB4Xr7XmYzDZ3o/bXx9fAweON5FwvtoX5Q8PdG0xYVCVv4uwTupOF7HpHnTu5Pry7Djq6/VsZjTLuqLGvRtoMMY1Qo2N64ojRY+ZCdGec17GUTlM8AoFO4u5oPp29suWNd1QaaxlyWxYjTnrrQcCPPHcpe30uN97tGer7qTQzdizU44Hqb4ygNmaeXmxmLiSjlOUVsIofu1QigXedNdYRoP6ituSiIigBBjao3PSjaGFUbfUfprRdica3h8S+feNRtjP3rcE7+Zv1j/OhxKBk5r1A2MS7OQ7o9NR1u972c4nDdV8sJfGlYvGQ0rbhOPhnVGGc17m7Djt0Xhp8ZWFL+ouubLSqWb7x+PecMCXwo6fSdxHbM/zSK63tfvKN8L4PrshjDIIU+rdkpot0nUJHjxgqlPDKqaU0udih3IaXiovCyso9CesS3mpsLksRi93KGiBp91CZD3SvbjLrwwunvXXXv5VUrWGoW0mOLTgFRX+ssDEp6UTYZmS1TKuzm9Qzb2SA51liD27tQ01Gt5peBvzeCgVah2iVc49ovR/j6eXhA3KjDk/dvsKnD3b6vE5T3glj5sNcxBGCHFzWP0wCAIsnGvqaVDiNBcAqIHb9I/SYZamja4/W3i+FztOeguUxzfHAW0t1ZZPHHT59wvSY9L3gMYJpX2GzHbP5+OVnjchJeIL8bPURFzRfJWcG6pwBwtNjj9Sacg6pIkNYK6ikpXdUKe7qGX79vEEcdDN0zm+2Y75+fPbrCsgqfr68yeO25kSg0g4WfSxYVjqcFNjRz21jNDWuoDS4uVthSDXz/NufxlXTcDmbpCKbW/Vzwy9s5ProIc8Cn4wKLn5Us+7hqRvj9Z++F44gwzBgmDpNZOZQEtOc0rSlCzawfoVJO8fe8dvDGw9MCz8zvNcupiBcwmLVoi4SDbQsMWr+Jh/dAfhFStXUVszTf7m4cGrJojlnVCsV7lPZsDDwFu/iiQFvGrKI2OqpQXYXz3yYWUWrZ1H5xvsd6R8pFTQRsYtb1hgfG1FQUG4cNlR/ccZBy5EXcKuFgNzsuYDuN8gUFllnLKeI4scizBmdZWCg+Wx3zuFxRx2gpjeqchrks0JHMpk8cbF+LND7MtZKrz/PlMWpK4XqSy4zzXkZNwV2Fhac9a5DQYsk2JtS07zV5VaQRfFeEnou+l+C2umfPN7ZISNO49jF8BnbdaeqYpUUn0wrb1RAAVW5BaxuYtykFfFLrmhTcPxHnDbsomayDv05hyUoRnYLa9tf93jvlHUXSwoIgCIJwYL6XO9emMTBRhOheO/34qETbGlhaxd09P4aakYnyqOWmAZt7dKnH6UnYGWSRxYyaYTLT4lnUYfW7oO7Tj64AgM46qNEgrhBdJ9zR6Q049dKnfNlL9k5D3YTVYnXZAcc1DH3d5R13qBZ1DGMcTo7DinmpJjzYDgC/+EFotnm2XuDmxRFq2uGO7jTqk77jEmg6gzsSbAiKT9SW/4Mtyj2lqrWHvsq4W9hmjj1nsaPRmz5tOHKDvqkK/0soDWfyBjWdb1tG3ER0dLZD5xU3Xc2yGv/6/A8AgP9882O898lr7MnhY3mX8+7LOo30nnh5ojtUXbjFL+ZbnD14Fc5jF+Ob5YJ35XUbcXMH1nEQrv8gnMdpXmFNggZNHaP9cjosSY1HSzvBZZXjmprGXBx2rb1Wrxp5ThzYxmC5GaNdDR3avTKSyi2M8rwbzs/3vEsGwK4vynioaYvzk5DO75zGmrINk7jB+5MlRmYoJUQLclQpYs6y9Cnx2IT7Lj8tUNMoVN981ZctXAy+V7t5BxU7Tolmo2YofWgP1/ukXodxqdkfw9fNFOg+CTvVzmp4q7FfDsIRu5r+f63DeE/fXZoO2silU9z+7ToDtJrHzcrlCBgPRgBA8CYFQimhT82GDuOOMzfpYo/zyY7PRUYeqpWN4LzC2TQcc7WI+H7UyuPbFydAL8ThFPJJNVynKsGvvvo4HL/yrBg2Htdo6bnQ2mGUtZj+5C0A4J9efI0vtqHz/K4eo2xjtNSF3dgINY2KqVnwlW3vZXwUpfCTcYvjaUjF323D2FifLYijDlsaG2vaKIy00S7dNQojhgAAFLJJREFU1hFGtHvPRw2nxqO4Qz4reIfbNhELeuyLNGTE+me7U9BUVhh/vMJiXHLpoHMar++mfN36JsxuHSM6rwaP28ixEEivAvcu870Mrj84uUOUp0hMh4ZqZstyjNpE2PUjMMYHBwoAVafxw59eAQDKNkYeN7jMQ7rxJ/lrPKtCMH1dTtFYw4E43ujBUq0ySGc1NHXetYt7NZxW800a3xm0847nXpUD1+vMVsO1WQhYCCk5ljdzCtu7MTtp/OUH3+CX86cAgPeTG/zb5/8i/JwHZg+2KOhFXU5j/rfHiwLtfSlD5aGi/vM1PD3QamSDfFs/7pF0cJTCijZBdLzr09y7wTTAT4IlXr8gSOP2O7XTXtWmqJJgQNDXhR/s8etlcFT+q5MvkeoW/+7pPw7fvEn5fOz2E8Rrg/Evw8JnWY/5xfZwvMHIhJf0880x6jrCnF5Eq3XOwQKRh513OKIZwNV2hLa3+KsMTDyMJOn3CjhK8728OUK3JOu+JqRRWcXID/O8i+MQMFd03ZoqZqF+EznUNuImWms1inIIqI7ux+yowuXxGmejEBSudnOeHUwji+tqgpsyBPp9maBd9TeQ5/vlyckKbWf4/LSxxf6IFgeZhj9qYUgWs2vMcH5AHcj97KwHm57HiUVNf0t34vD430d48a/CNY2PK7ZDK8sE3nkYum7dLuIZya7RQKPx6CEpJe1H3KGqlMd+OyxKzKRlyzakHdchgRBY+/Smc4oDtBp5tK3hxVStYxxRR39pY+6qn4xqtNZAJWGRcjlZ46YMC9I3q2lYKPY1XadYanGU14iMgyf1qaaJMKZ76SQv0I0oYHYGi1GB1oXj/69vPuRr2DqN5l7aFgDOPwplEecVEtPhNc1UZ6MG9Sjcn588eIPP3wbJ0PbtCNFpBUvXo6pjJMlwzeK443G/09Mtz2v33weApojRFPEwzqb9UGOPHXTcwVGA1tMWEc1JF0WKLLbcr7EvUr7PtOnQ7WgE7qgJMo29/Gqn+keeFdLeZSQtLAiCIAgH5nu5c70YbZGMa7wqZtzVF2mHmyJHk9FutYgQUXOTNg7bmkTTncYnx2/w6W2YSeu8wt9ehW7Pukrg7hK2g9ItuCnEVQYu15xCU/toMAkoNWLqoGsnHrrWaMgLNN5pbnZSCKm6fmdsoo7Te20bTLYNdZVepBs8iYM4wV+v/gRFS0LrncE4abF5S41PpUG8pYaUN3Os5o6bb5A4XqlWbcZdlSbqkC0alL0W6i6G2X1X07hPa3t4JMe9x6fHLK+wpWae5WoGvaJ0lwnZAoDm6lLHnqH7dYbfrn4AAPhqcYq/vHzK6UwAbHDfjR3ckwoPxyFdel1OMKfru6zHnNYrmxjtPsGyTwU7FbReEVS07NyyJjH8IHBQmQQoEvYAxu2I7eK6dTyYvSehC7LvuE1uDDwNyhd1jDS2vBtIspbN49sixs3maPDurIZOTVb/AlCXY7zWDh/NgqLPLk1xtQkp3aKN0Tk1dL3qQRQ/7DKpiYWMxhM6j0Z5HD0M562oEvz54xcobDg/X9+eYN/SPO/OBHGOxSD60FNv06Hb1Gq8/DcW+axXLtJsF9etE0TzQUABXqGjXfnZ4xVi0+F0FNKx1+sJauoQjrTDEc2r7ssUrtNsdjBZlPz5rtNQ9+a3myZCes94vL7LEM/D8Z8f7ViB6yzfc1f2tsjQVBHvuJb7MfakkNXVJmgo93+6B0ANWLXxKJdDySc6arj0tC4zVkxqO427asSNPtZpnOchE7FvE+zrhDuj49himobjncYVMmNxPg7ZmaqL8fV1yJx9+vwS0bNwjLEHzOsc7SPaQZcRmv7aaA+Xa+S0o7ad5t1q1cRoqGlpPKvCfDWdu+16BE3z69lZgUlW462f8X1wOg/HX7UR6jbijMPq1YzvwfTBDqOL8HOtNbBWc7Ol7QZ/W26Me4f5XgbXq2KOSKXIowZf3QXpPKMdOqfDyAyCmEJKqaSL2ZZrfLbT+HJzihXV4X79xU8HcYXYw+dDcNK3BrbvOk8cuteDgwQ0WGTfJx5dn7I5aaBjxy+bqBg6+Wz/UqegrJRnGTWlPE4WO4zjcMzXzQSvbUgdfb074bRPpB2yyA7D91vDiwG4IIrRv4C7tIOmOtZkWvHLxVYx2m3KDwLM4HSh0w5uH/MwuLea009Ke6x3I3YNgsfgGerCAgQggfdGs/coNoZlDLfbOf7T9c/YHxX3WvlHlzskUYfn2zASc7sb45s2dCDbJuL0q95ESPaKxdxNoeF6tZg81Ih79aAotVw7UsqHkaZ7D76hxcF9F6J4pdEuPKfNm0ctq94sRjV2VYqKhNd7UQSA0usA+4bqUcuqTKPY4u1/D+IhyUphP83w2+vHAIDH0xXXmXdVirqOWD4QGOqlo7MCMQWPu9czRHcROjIQiFL7nd95ul5wYNm/ydn315tgcN+nY9vWsJCAGVn21vVecT0dCBKW/ViI8oqfLQDYb2PutgWAi/EWDVknvX+65Hpy22kOmN6HOr2m+3i/zXi0R5ca6kGFikoVxydbHiHpVYn6Y65txG4umyblQOioQ7o/B7ubnOUD9WkNrKOg7gQgnjUskN/UMZKLAhHVFZ1TfG0eTLfcib/ejpEklhcEs0kJTdE6Ug5GeRS9ZKAO5QIAGEUGO2vwzTLc17u78WBcvzHsO734Q4flJwbZW/o7P6j4vhqNmqDkRe+029UEMyqRNDaCTYa/5XyxwdWr8DyZxKGbUzey1Xh/vuSFz31i49DYwYQeAOJlOMZC5YMBw8zC+8FwwFcGMb1LrZXg+k5yvZvAuBTjtOGZwrqJkcWWm0Q+mC3x2fUD+vmcH7q6jLFa5rzaikrFOxTdqqDORPdFb+gNAKrSSFaax1eauYMnlxPXGkQkzRd9lcGlHkk57H57Pdhu6kITEd2M1hp2FvFeoXMKiyys7I/iEp/tw8zrNzcnvCLP4u9KmulaoSaDaJ8Fe7tetcckDj959Doco1f4qg/kLzPYI8t/p8o6DkBuFwPGs0KTmVg4elBcZeASx7UqVRjWr3XJEIyU8chOC6iz8HW5S6Gvw4vAlAo2VjzJ5A2gj8N5jIxDHHXf0fjta4XJcYWGsg/RTiEqFQxL6QHNMR2/Cm425pbckiYJ3Hk/kwLEmeWXQXOXDfPPDhyg2ycNjk+2rEG8q1KUxZD5SCILQ1aBTRvx4gNOAdpzjVdpjyMaK/r46BqTfxKO4/PPH0HtI9Z8faVn2NPiqSrJ3H1FoxtZx8467y3u8HwZXpQJSXb2knXJrIKmBcAsr/DmzRwm7T0GPesywwPZ2SD955ziOeymjrHfDLZ+UB57mp9UpR5mQQEUVxP4cT/a5XkHCgTlq/fzkHWpXYS/+f3PAACjVwbNjBZBD2ug0Vz2RKu4ecflHXSn+DN3RcbG7KowiM4qPuf7KmElon2dYEKLmbqNUCuPguqDZj1YULZtBuWAiJyHbG7Zjk7VGuZhw7+XTytuUPv50Qv8cRdqomlkcb3LsZgMs8B9nTyNbLA3pMV3qyO8XU/453Z1gh1pikeriBvP+sAKAMtPDNrcwx7Rcz9ueeOwXY1hO81WjZO8gu6fZeVhN7TwOynx+nrOi8mu0XxPeK9gnebg6v2wiLhZTcLm4N7YYXMyXOv+89Qqhp/Y4dqnHbpHVCffD4vOdxWpuQqCIAjCgfle7lx74f5NkfFQ94PFBv/s/Cu8qUksAgo7Gjz3dqhJBHP0YZXcjTyvGNtZF1ZrE1KN8RGyt5RKaoMBdZ+CdTFgXoXPTwrFaU/lgOZoMGfXrUK87TVNAT/uENHqMU4sr7S18khMh7dFaHm/KSe43oTVbnUzwvRhqNHkSYO7YsSdldoGw3EAuFhscDlZc63t5XrOuqi36xyWPDeTUiEqYrSk/NNpP+iu9h3E+aCcw27UaQdfmyEdbjx3FUOBx35cY9AocLfn+dkG14Za+W9TxIuKuz+rMsGcdJgnaY19k3AtaY+UdY275zlrGPTXy1HGoZ16qH53uo2hrAodvwA6C6gXVGubd8CkxScPw27+s/0jrrnCODYh6BWUeh1Z5xTv7Df7DFp7VL1/aezuOeZEULMWI1Ikck6joNTaZ7cP0NE2bfpwG+qI91LB/ViRAkJ3MglT+Nrg8lHYBUbaoaIdlco9XOYQ0bjZP3/yJfIo/M6nq0tsJxnvdManBTKqFcbGYV8nnM5UCvy3pVnDwgowHj7ySK4pzZoMusYudUDs2fvV39vNXL+e49od4X+oJ3ThFJdB6sUghuKtBhIHQ30ALtLwtAuPFxWUAhranTZ3GYvDYNGhLYYOeTN1WK3CLtA7hZJ2+cfzPTqn4b4M1z69U0hX4fjrSqGZDxrQ3inWxXZjjTS2iGZDmntB3cjPywVe72f83xWGzZ3ziruFn71doNskwbEHgC0iZCS40XQmHC/dC6YcxD5cjGFnv2ihkw5ni7A7vbmeDXrfrUKpUywpUzHJahyTsccXb84401HdjoDEISGf32SlsPsxue6c7vHp1WXo6geAxA1CF+R37fsMVTzolXsHZE/DvV+dk7pdXw4qFGd/vH33Z3G+l8HVeQXlFbT2aCkF8vi9FWoXoaZaz29fPWLx/CxpcbsOD6CtI5jY8diF6sCSY9AAYscqMkoPNUUXA3bWIVoNIx/JG82f0RxRqnfmkJ4XqDbkGJJ0qPJBkk1lHd47Dy/L1Fj8/fOQuo5SC3s94qfVx56DmHbgtHYWtbBdjuiG5mkVYCmFOHtYYR5XuKaRg9XtkIpShWEh927k4RI/ND7df0vEDvBD/RQINTAA0JswltPP/yo3zDPqdcwpp26TwGmPBmRjlzWs4FOfAmdHOx5XWYxL/HAW1Im+2pxiHLfBxByAu7coigoF1gJX4brwtck7fsDN1kC3w0vKpY5rqWbWoCsjlKR0o5OOhdfhFaJbOt7codMeqx2dP6thKIgliUVsOkyfUKNPp2Fp5MIsgkl43yRUFUPK2CbU/NFDaTkgLKz6hp22ieDXKUsvYuS4Dv9qO8V7lzTS8VBhFLU8zpNHNT7fhJrushyjLmLEFLhm4wofH4XmqaO4xPP9As/XwVDg7s0MLdXolPY4/jDcm8ubaQhg2vD5r0jUnx1tqFlLxx1b2qlKAw48I9w3yAGAOx1q12gMVOzYmAEKmDwMf0tdxYgTi5hceO4beWvtcXSy4XRmUSV4dB7s+jqSBQSC0UCxyRD1af9uEDlTDmjOLFJq1PObFJYCSZo3KKoEJ7PeVMHj5R31PlydcsCJMwv7aox9Oyhkub4/I+tIMaufg/esjBRHoQSj6X5q8xYNvYt86vh3js+2OMkL7JphxKYPmt5rYBehSkmNyivsqFSUpS03MG2uJzB3EY+Umcpwbby1Jjxb/XPfamBP7z2v4DOw/CoiD1DpxsQO1QUtojOHyVEZ6sYApg/3mI/COV3evvs1V0kLC4IgCMKB+V7tXD1tY7bXgB4Bj8+XMNTav14D9X6MSIUV4c/nX2Ieh1XUV7tTrPp0rjXoWgUX996XBqYfnbijFnza0anCwPadtzXgVY1mTDvIb2L4mjQ8jz1wHhobHhxvsa4yjEfDyndtc/63VNHgm6dhpTceN+h24TjU0xgKzdBgoz2Px9iZg92TVRQU9k8jpNeUS9JAP7r95fMcy4nCLA07AOVGcP0Ove5gJ5QCjRBWrNSLoRvNYzS+ocYk2n3pbQSn+85h2olY+l5p4FpaTXcW5ks6+KSBB2DpM2vdwjtSEjJ7tDuLt9ckxL6O8fJBWIGPsxZ1G0F14dzpbuhUrpMOCZ0PNnHu05S2gV5SE9EudOza3lxAdayPO/J7nJ3usFvTLqsu4PpxnlbBkfk6CkAlzSCsMWuQIlync7PFvk3wy/lzAMC35TEsCQl89vwh1J3HToXPdJMWo3lF59ihWVMqeRvBTyxAnqp/+vAVd7V+fnOOWlt0ZOuHEnj6bUjdfXR+gzEdR2Isvl6d4ItvQubjwWKMt+Q12u5S5CdrdLT7ffUsRUfpTKWm2NUpmqYXiy/AYrG1QWVoXMtv0ZYJKmp2wsQDG2oC3BrYRQvsh4yGJkddH3noelC3as8sKyGptxF3PucPNphmFa6pjNNtYzTb8G+N0z3qIkbb9IL5LUZURoh1h6SpkdEOz6gUqgq/N4tbtE14tt68OoKqLPwm/J22VbxLKzMPV1vYHTUkrgef3OJO4eLJEpOORvyUw0sS8bBbzeIbtVeIinowUnAAXfbQea/ACmjGWGgbzsH678eYfa1QHdP5OXI4/mFQHsuilu+DqHNYLmOst9TQF69RUQMcNjGUB8yc3jGw2GzDvdXVUWgOC1+ELBz93cUE8CsyyegatJvh3PnU8c5YlQbe+jBOB4TP24frG2UtLN2381mB1Zsc2Iav09kW9Y5KDLbvOh+aQt81lH+Xj/5/kxcvXuDJkyf/0IchCIIg/C/w7bff4vHjx//Qh/F/xPcquDrncHV1hel0CqXe/Zy+IAjC/49477HdbnF5ecmSse8a36vgKgiCIAj/N3g3lwSCIAiC8P8wElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgwElwFQRAE4cBIcBUEQRCEAyPBVRAEQRAOjARXQRAEQTgw/xNSCNbubnwG+AAAAABJRU5ErkJggg==\" /></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -334,26 +335,23 @@
        "2  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "3  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "4  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "5  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "\n",
        "                                              extent  \\\n",
-       "0  (-7427829.47137356, 993342.4642358534, -730922...   \n",
-       "1  (-7309221.415942414, 993342.4642358534, -71906...   \n",
-       "2  (-6953397.249648973, 756126.3533735601, -68347...   \n",
-       "3  (-7783653.637667, 281694.1316489733, -7665045....   \n",
-       "4  (-7783653.637667, 163086.07621782675, -7665045...   \n",
-       "5  (-6834789.194217826, 163086.07621782675, -6716...   \n",
+       "0  (-7783653.637667, 993342.4642358534, -7665045....   \n",
+       "1  (-7665045.582235853, 993342.4642358534, -75464...   \n",
+       "2  (-7546437.526804707, 993342.4642358534, -74278...   \n",
+       "3  (-7427829.47137356, 993342.4642358534, -730922...   \n",
+       "4  (-7309221.415942414, 993342.4642358534, -71906...   \n",
        "\n",
        "                                                tile  \n",
        "0  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
-       "1  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
-       "2  Tile(dimensions=[257, 257], cell_type=CellType...  \n",
+       "1  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
+       "2  Tile(dimensions=[257, 256], cell_type=CellType...  \n",
        "3  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
-       "4  Tile(dimensions=[256, 256], cell_type=CellType...  \n",
-       "5  Tile(dimensions=[256, 256], cell_type=CellType...  "
+       "4  Tile(dimensions=[257, 256], cell_type=CellType...  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +362,7 @@
     "    df.proj_raster_path,\n",
     "    rf_extent(df.proj_raster).alias('extent'),\n",
     "    rf_tile(df.proj_raster).alias('tile'),\n",
-    ").sample(0.05).toPandas()\n",
+    ").limit(5).toPandas()\n",
     "pandas_df"
    ]
   },
diff --git a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
index d0541a821..03d645546 100644
--- a/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
+++ b/rf-notebook/src/main/notebooks/pretty_rendering_rf_types.tile.ipynb
@@ -23,6 +23,8 @@
    "outputs": [],
    "source": [
     "import pyrasterframes\n",
+    "from pyspark.sql.functions import col\n",
+    "from pyrasterframes.rasterfunctions import rf_crs, rf_extent, rf_tile\n",
     "\n",
     "spark = pyrasterframes.get_spark_session()"
    ]
@@ -43,14 +45,12 @@
     "uri = 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/' \\\n",
     "      'MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF'\n",
     "\n",
-    "from pyspark.sql.functions import col\n",
-    "from pyrasterframes.rasterfunctions import rf_crs, rf_extent\n",
-    "# here we flatten the structure to get the tile type \n",
+    "# here we flatten the projected raster structure \n",
     "df = spark.read.raster(uri) \\\n",
-    "        .withColumn('tile2', col('tile').tile) \\\n",
-    "        .withColumn('crs', rf_crs(col('tile'))) \\\n",
-    "        .withColumn('ext', rf_extent(col('tile'))) \\\n",
-    "        .drop('tile')"
+    "        .withColumn('tile', rf_tile('proj_raster')) \\\n",
+    "        .withColumn('crs', rf_crs(col('proj_raster'))) \\\n",
+    "        .withColumn('ext', rf_extent(col('proj_raster'))) \\\n",
+    "        .drop('proj_raster')"
    ]
   },
   {
@@ -63,8 +63,8 @@
      "output_type": "stream",
      "text": [
       "root\n",
-      " |-- tile_path: string (nullable = false)\n",
-      " |-- tile2: tile (nullable = true)\n",
+      " |-- proj_raster_path: string (nullable = false)\n",
+      " |-- tile: tile (nullable = true)\n",
       " |-- crs: struct (nullable = true)\n",
       " |    |-- crsProj4: string (nullable = false)\n",
       " |-- ext: struct (nullable = true)\n",
@@ -97,7 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tile = df.select(df.tile2).first().tile2"
+    "tile = df.select(df.tile).first()['tile']"
    ]
   },
   {
@@ -107,7 +107,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "Tile(masked_array(\n",
        "  data=[[1225, 1244, 1247, ..., 1305, 1245, 1206],\n",
@@ -164,6 +164,49 @@
     "str(tile)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And access the tile's `cells` member which is a numpy ndarray, or more specifically in this case a numpy.ma.MaskedArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "masked_array(\n",
+       "  data=[[1225, 1244, 1247, ..., 1305, 1245, 1206],\n",
+       "        [1166, 1188, 1190, ..., 1381, 1251, 1193],\n",
+       "        [1156, 1110, 1122, ..., 1248, 1245, 1270],\n",
+       "        ...,\n",
+       "        [1485, 1749, 1761, ..., 1034, 996, 998],\n",
+       "        [1780, 1777, 1663, ..., 1008, 1027, 1174],\n",
+       "        [1728, 1647, 1562, ..., 1189, 1297, 1382]],\n",
+       "  mask=[[False, False, False, ..., False, False, False],\n",
+       "        [False, False, False, ..., False, False, False],\n",
+       "        [False, False, False, ..., False, False, False],\n",
+       "        ...,\n",
+       "        [False, False, False, ..., False, False, False],\n",
+       "        [False, False, False, ..., False, False, False],\n",
+       "        [False, False, False, ..., False, False, False]],\n",
+       "  fill_value=32767,\n",
+       "  dtype=int16)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tile.cells"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -180,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -204,8 +247,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>tile_path</th>\n",
-       "      <th>tile2</th>\n",
+       "      <th>proj_raster_path</th>\n",
+       "      <th>tile</th>\n",
        "      <th>crs</th>\n",
        "      <th>ext</th>\n",
        "    </tr>\n",
@@ -214,135 +257,69 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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D8f9998PAE8oBtnh4IBL/uDwqJiZmcELX/hC7N69G1u2bMHMzMwQ/bdu3Tqjtb7rXe9ClmX48z//c0xPT2Pnzp34zGc+g1/+8pe4+uqr4Xme+d2FF16Ia665Bq9+9atx6aWXYt26dbjuuuvwjW98A5/4xCeGkjCce+65+PKXv4wHHnjALOT7wyc/+Ul88IMfxEte8hKcccYZQzQuALMgbt++Ha973etw1lln4aijjgJjDD/4wQ/w6U9/Gscdd9wQhTc6OooPf/jDuPDCC3Huuefi7LPPxp49e/CBD3wAhx9++LIsO4+EzZs345JLLsGtt966LDHDSqHRaGDDhg34+te/jhe96EUYHx/HqlWrTJ/deOON6Ha7aLfbAIDf/va3uO666wAAp59+utFQL730Utx44414yUtegs2bN6PZbOKqq67CnXfeiWuvvdbc7znPeQ6OPvpoXHjhhcjzHGNjY7j++uvxox/9aKhdY2NjuPDCC3HZZZfhzW9+M1796ldj586d2Lx58zL693Of+xxuv/12nHrqqVi/fj263S5uv/12fOYzn8Fzn/vcIWr37/7u77Blyxacd955OP7444fedxRFOPHEE5f10ZYtW7B+/fpl9K7GMcccg7/927/Fv/zLv6DRaOClL30p7rnnHrz//e/HiSeeuN8QMJ3o45EEtcZPf/pTTExM4Pjjjz/geQ4HMZ4iW67DHyEeyVFJO5080r+ys8uWLVvo5JNPpvHxcfJ9n8bGxui0006jm266ab/33LFjB5111lk0NjZGYRjSs571LPriF7+47LxXvepVVKlUaGFh4YDP8IIXvOCAbdWYn5+nM888kzZu3EiVSoXCMKSnP/3pdNFFF9Hi4uJ+r/2FL3yBnvnMZ1IYhjQxMUGvf/3raefOnfs9d3+OSu9+97uJMUa/+93vDvgMj+SotL9EFfo+Zdx888104oknUhRFBIDOOecc892GDRsesW8e7mz061//ms444wxqNBoUxzGdcsop9M1vfnNZG+655x469dRTqdls0uTkJL3jHe+g//7v/172DEII+uhHP0rr16837/qb3/zmsuQPP/7xj+llL3sZrV27lsIwpGq1Ss9+9rPp0ksvXeZ5e6Dn2d9Y1k5YH/zgB5d3fAl5ntPHPvYxOuqooygIAlqzZg299a1v3e/46/V6NDIyQs9//vMPeE0hBG3YsIHe8Y53HPA8h4MbjGgFAwIdDmqce+65uO2223DvvfeCMTakVT7VmJ6exhve8AZ8/OMff6qbckAQEYqiwIc//GFceuml2Ldvn6FKTz75ZGzYsAFf+9rXnuJWOjwV+P73v49TTz0Vv/nNbx4xv7TDwQ9H/zoMYfv27QiCAMcddxzuuuuup7o5AIDf/OY36PV6eO973/tUN+VRceWVV+Kd73znsuOtVgt33nnnk0qe4HBw47LLLjPFHhz+dOE0VQeDbdu2Ga/NSqXyhB2FDmXMzMxgx44d5u8TTjjhgI4rDocGFhYWcOWVV+Jtb3vbfj2OHf504ISqg4ODg4PDCsGF1Dg4ODg4OKwQnFB1cHBwcHBYITih6uDg4ODgsEI4pDwohBDYtWsXGo3G466T6ODg4ODwvwsiQrvdxtq1aw/a9J+HlFDdtWsX1q9f/1Q3w8HBwcHhANi5c+fjyi3+x4RDSqg2Gg0AwBFv/yC8SCYCZ0J+J3yAfMDvyb/9HqBr+IjQXiPoEoIOmd8BMCQ6AchjhqwmteB0BCBfOleznCFvCvNZhAQRqr/rOfwgBwBkizF4wuH35DXIA7KRAgAQjvUxPdpGL5MNGmQ+koF8hRMjXRzekIWPfzc7he6OJvy2vEbWFOATMuF5vZYgzT30F1W1EwawrkzyEHQ46ioaRHhAVmfwdQEUAtIGTJ+RB2QN+WxFTYAC1SEeIZwJEM+oe9fkuQBQVAhFrBKyZwxMyGMAQKMpKJMdyQYe4t0e4nn5nQgsq+AlBBGo6wUM4PLd6WfpHSb7ikIBr+shmpXXLCJAROp6PsCUzzut7YMzghfI31WiDP1E3iBLAhQZB2vLG/CJBJzL58wGAaLtkWkXhTTUDq76jWcMrADSMXlDv8NAnuqDnMFLAK5y0edV21fEAVaoz77s/7whzPXFRCr7YyZCvJchmZDXzFZniB6U4yNcBLon9uFHcmxRwSFI9mUU58hSbp6FdXz4Pfk3lUgcERBErQDvefbeFdWwQADqGn49gxcUCFQ/9nshvO0yzSRLGZLVuTU2MQJLVEL+vZ6ca/q5PTvv/L58bkDOQZ7K+wNAEQKFymLJcoDn9hrCs++XCfs8RSSPhy3bx/q9ZA0BCgj+krwIBWTuDWLwUpT+lu8VALw+EC3JL7xEzQs1/wfjgFDjXc8ZPf6DRY5Q1SkP+oS0ruZLXb53/SysgBnvPJfrkl57yANy1QfpuIA3kNcIF5j8rqrGWcpQmVVj32NIR+ycYcL2NxhAnMx6lzcKcP1+2wyBWk+8FChi269Bz7aDFfK4Xhtku5l9T4V9R0VMIPVsIiD4bQ6RDPDApz5s1uqDEYeUUNWUb0QxPIpBDMhr6rsQqMwBfl8JipiZgccJCFvyeNgtLZ6QCz4TdsB6PpBMyvvwgMAK+VlUCVwNVlEnMEbgoV7ZAaGEaNzj4DlDXpPfxc9o4aTVtorHTK+BIJcNGPQjnP4MWbFjIuhi69zT5PHdqxH3ONJVcvb5BSBCNUsrDOlDTYR6AV03gGioNs6FZqElDlAAqLUDIrQCkeUMfpehogVHSwoEAEjGBQLiYKpf/QBI1MJVNAswoTYLDBCMwJQgDeYrdmH1CWI1Q2dtSQBLuYCwzeCpXPE+gLQJs1iLgBCrNgUzUmjndZh3YdaOnCE5Up5YrXsYDAKouY6w3gVFsn+LXgTq+WBTSvCnIYpUtt9PObyYG8HHCrvmstwuaKjJBQR19S4Gnt2Q+bLpmuXiAHI1Rpgw8sM8e9CV/w+mBHiiNoUVhnwtUJtVY26uAi9VgqLKwNIAua8amXIw9ULTFgMmpTSPKgJZEKIYUfdLOLyBGgcRgQcCTI0RquXgegIQUJ3Sifs9JEmM/rzcaAQLHJ561wgBygj5iO5lIF6Q12BVQNSAoCOPC7WBAICAE4pIPRcBHif0p+TfeY3gqXcdLjIwVtoEe6WNXEzmXYjVKfxdEXiqN5sA1fXGVoAnHLyqN7N2nrMCQKAEKwBvwOCpR+EMQFOd1yVQCNCIEiJNAlPtCJcYeB+gUp0DpuRGMgF4mfwcCSmw9LgCL20AA/kbLcySCQGh1hBeMFT3yQ4QVbnp0PvcSosANa6KEQbUAOi1p4BVCric56Q2n7XdHgr1GRWAQnnjJJZrgd6wighg6hqDSWE2kgDg95ndTIUYmuN+weykyQCUqhoezOa5Q0qoaggfYL76Xw28aFEO5EJpRawg+GoShR0CVwtbEQDErcAlDjA1yokD6YgdKCy3A4rlDH4hv8i53H16XW6+0+1gAshjuwBtHGlhbWUJALCUVbDQq6DTlqPP8wXuXpoCAEzEXfN8xIF0VJjFI5/I0GzK2dza3YDX4yjUYkLtwPwu6DMjHFkhFy4z6EsaYbzAEHQBb6CEDWdSmwIQLnKIwC5wRWwnkt/xQFx3DsAKK5TyGoGaamUpGAoOQPVJkXEEWoPgVoAXsVwE9Hleyszi7PUJRWyFMSdmWIj+agL1ZIN7gqE+2kO/J4UBEUNXfS56PuKRBKFiEVrzNbMJYIV8V1w1Wfh2sctHCEIxFOTJZ4332JSPQiu4JLUnvVkJF5nZyRODkap+Tz6r1qrIJ5BmOfo+wiWGSGn1XkZmDGerAZYxkNooIRTwlrTaA2iZ5/kCGSdzQ4qE2WSAA/DIbhhagWElWD1HorR6sRAhnPWMoONFaQx4UvPjSlCHC/a9y/dm+47nVqAUEUOhFlovAZJxZgRk0JZaPqDuw6xGl9XJbEpFLFCZlC9eCAbKI3QPl+0XlQK8L9+L35JtNxtmYojadgPIU6tlFaHdNHl9hkCNq7wq26s3keTZZ0lHCayAYaDKWriX2vuKCPC79lkkg6bnGRB02NC9tXYaLTCrLZIcM37f9mNWt30j7JQfkgCsAMIWN8wJIwxtgtNxy7TxASvt+qSAN9chDG109csVod3g+D2GtCHAE3V9n+ANGEgc/GkTDk5LsIODg4ODwx8hDklNNa8AFEstw2hKMcADa98KWzA0Gs/I2PXymKGoWG00q2N458fsNZmwn0UoqREAoIra1SW+OU8EajfKmNzV16UK1E4i/HphLQDAZwKDJECtoajLMMO22XEAwD0La42dCh6BfEJlo9ReRTdEe/uIbp78X9OYA7uvSicKqV0DAAGrjphHL5G8Ub8Xgu+VKpbfk8+VNey5QiliWVPAS5j5rogIIpbPywfcaLR+l0mabkNfPRshT/SXBB4WIEWdU+KZ/umtE6BIXa/rSa1HdWdeI6QjWpPhRpMB5C6/UBqiiAjBqOzDejVBtx+BKVtpPw1QKDt1UMuQ9AIknarpV78l+4unUgvW2hj5kh0AAFEvDG9e2eUjrxKypjYOWs1DBHIMhovyInmd4PUV3ZlZSjSvK+1oRKndgiGYlW2MZxmCLoFrFiRm8BL5OVpiAOPImlpT4GYbnY/kiO6Tqk1WF/CVnR8ARLUAVQtzL3BCPC77K0t9kLbpFRw0I1XJoMvgDaxPAmAZhaxBIC7tyYC0o2sWxe+pPtEUeDr8zow2qujdoGXtesYW6ANZBUimNE8u4FeUHTnnGOyRtohwzgO4tBEDAAsE+KLsR78rNS+muFZvUGKZBOT7U6+wrIWTV2IoakB/SqAYNfQIWEdfX2rn+nfJGBkGh2clDbarWAk1XkRFGEaLK7OC37fao+4fEcjxAyiNM2Ole9k+ZUKyBkLPoYSb8RcuSVODnqNZ1V4zGy3gt61dnRX2XeU1MnZTqDVPPw9YyfwzngHqvCxgYNUCuWIKqjt9+H2gSA5e2lfjkBSqPLcC1TiDcClQg66l0TTyCkemFojBpHQC0LYGr8+ssw2XA01TJnpRBCSN46vJgdV9CMEhInnzIuNAYQVUbbKHyYb88XGje5Cp2XH34hSK3EM3lQOx3avDa0jhO7Z2Cavr8jexl4MzgXvnZXUUWggNTUQeQUwl4L6cVPm+GNSQi0B1pI84lNebqnewfX4Mgx3S8FPfYSm7eF6A50BWUfa5JrOOCUzTmWpRmEzhK+eVrBuAKYeXdEzAH02NMMsTHzSwFGmRlYxJjBDNKfrXA7K6svf5hMFUDijHH5Zxs1kgLhcdfYm8SsDhUoAfNTWLfq6ckQRHVnjoLCoBsxibPhXEwGdDRHOW8tWCmWdyMdGLjqgUph3eko+iqhya6iQ3MrZ7DNUnaVtLcVLGjJmBZ9ZBLh0hpBMF/CUl7NvMLGhhSy7OemMRtgWIK8GTEMhjhooGwdDv8UOBeWcslwKF1MaF13J4yg7r+wKcE/pd2RjRCcziGrSZpW0zKWwMtV0SlN5A+h5kI2pTGZAUcJCbUr/HEC6p/hFk3pnfB3I1xrKGpA8NXchh+i0dEaCIUJ2Um8iJeg/7WpLvLBYisxEij5CNCfBYdTKs8xr68ln03C2q1qxAvp7XMP2lhZnfLzlCKecdpo2ZglmBmAKsYEaQgkobhMReO20C2Yiw/hx9bt41E2yINuclJ6a8RsbUUZmRG6tMOT/x1Jop8hoDywnMV7TxLDe2YuEBIrZCtf+01GwO/QXfvpcuQxER+lOKDhYlZ6RCbUj2Y/f1Fn3TV4wAkTPE87JDvMHwenww45AUqt5Arn9lj0EvAYIOGe20CBkGk1po2J0YcbvDBKRmpj38KBBgAw+BrP+M/hSZARUscWSjasQsxmCVHCOjcluf5h76yk46Ot7B6noHmVL97m6txt62XCDaczWwnmfsaes3zuKZ47sBAL+aW4vdLekxsbrRgccE2gtqJ1AvMHbkPABgVbWLZjjAXXvXAAC61QBMLbRCcDQiOcN2t5rI7m6ivlc+d+Ohwkz8IpSaut548IJZDStlSEek4AYAxglZWy7I3pIPobT06lQXnBN6HbUKtwMEba7ehbQb6QlW3c2MA1kywozQED7AU89sakQsrJ1qAIRtIBmVf+ejBaqRXFmWkhiFUNphwTHoh6YPyBftGNemAAAgAElEQVQoEu3lwuBnzCxcjNvFr4jV/dUiL2IGvyqv748kKOakkM6bBRAJ42Ht9bjR2HjGzHUAuanTTljkAa2jrcYDgvEazisAdP12Jm3F/dAK0v5qLYgIrCDEyoGlqJBZ1KjkISsiUpq3epauDxHI9uZ9z3p2Q2p7YctqE5qhYHofpQVMBGPXZEd20agkps/bDzZLdnaGeL7kTS9KttEGM5sY8tT1tE21wyH0ZooANFNMNqxfQf6AnDPV0qYlmShAjODpfuz7iOe5uX7WEHYfV/KH8AZSoGnhJvzS5qrEVqRNuTZoez0I1jmr7YGnw85+GoxgbJ4iJLDM2mm1Vgqo52cACXtMXw9MClNAjoF0hBlB6iW2T4uKvEY065lrai9qv8OkE9xqZRjv+wj3+aZ/tCIhPbRpyKZq/BqUQDWexxkzxnvy7XtmGUNQerZkXPaNGJTDKg5OOJuqg4ODg4PDCuGQ1FQ1iAF+T++oJO3Rb8p9xmCSDMUTdKwXaRExZE0yWgMTDPEeFe9X50OxaEVdGHUgrzFDLU0dMYuRaID5vtQku/0Qa6YXAADVQG4vG4G8+drqEiq+PPbrPQ1QrUDckN/9n7E92NYZN89TUdRtzU8xFvWwb5W0JWWFh1ZXbtfnl2oQsxG4sr+ySJhn8TyBB/dJA0zRDhAS4A90eIbdf2lqVWsAvakSVV4niJEcUZyZa2ozSThu6eWJWg9z3SpEXw7BaJ6bHT8r5K45VBo/EzBepX5k7+unknbWNjK/lhk6uZgiLLYiQ8VFlQxCaUrdJARX74UzgucXKHLVHz4ZGppVc+DwDANlY0XKESxYL2QREISieb1milxpuHknAFM2PcYJ/KHYaHdhC0hU6ErWJOU9rp6nx5BoSnO8xIMRwPse/I7uIDJjCSTtrdozdfFIAqkLRnOS2tNaFRNM0poAyBNGg/C7TGoJys7GYntv4hxey7MxkC025KnrlZvpSd8EeREgm5bvOuaEVruKoqvsix1u5pOXPsy3oao86PVzK22rv6YA+WTiJouQDE0JAH5YYF9bjvfeXNWEkyTjli0ijwCfIJQdOGwzY6vPxtU80PM6EOBzUr1jfT7kkZvXrF0za1gNLh8ppD+AYiXK8abCJ7CcIZrXbIxte16x1yZPaadaAS2ZHHR8rDYRZCXzQ7jAjS29iKUnMGmTDDFkTUvVBi1uNMm8URjP+rxGoNEMhQqLChfsnBcBmdhcESobubKben1LhzMhny1T5qy8SuDa56FKxh4PHxDC9kMRkgrFcTbVgxKau+eZdIMHlI0mtMkIonlLn/h9KoWIMARdZn4n6RM1ESHnQtFQi3wjg9BxqgyYXi8p2IlKD/fPTiBL1SITFAjV6pQLjkT48NVI/O3CNPYsqoC2UKA60jc018/2rkdWyAnxsg2/MYJiPqthMa0gVHaxvPAg1AIq5iNQrUAR6JWcwDx5r+5sFb5y3AhToLqHIWwrm2fEhmhFsFIYRGZDgkRUYP1hc6YtueDwVXuX+jFSFWO7c24USScyISpFpJxFIK8bLsEsEpU5SwnxnIGY3sSohb0mXxTjwgjOImeojA4QqXCYfhJAFzlMUzvs89QDFRy1Ecm7FgVHX0ltyjjylAOqjV6Xm0UyrxcqyFQtZO0AlZ1qES4AEQSqvdIZR9tHkxFLi/pdhnRcgGvHE06GFmQZg9ezC3BQolzB7NgMlwiDVQyt/6P6IC7Q/JlqPwf609Y2KAJrN/V6zJgjaE0GxoBqLPnNsVofiz3JCXb7/pDNtohgBGKZ7iUG9CfJtKuoEDwViyoeCsAiu9gw2OQJrJA2ef07npEJDzIhaup8ypn5XZly5IIhXYyQqY0Rz1iJqiTjyxDO+0PhK8mEMOYIRIVJZAEAbD4wJg2/L9cKY/v2yHzmpaQQfMAhagVE3dpsmdposVSGv2iBWKaQTUwz5OaoqFiHNSZgErDkXArbrKY36mRMCdGiXctYIf05yNfOglZA6ufQtuRgyTNjK1klwGet1yV50hEOAPLE9inPGIKO3VyBSd8B/V0ZjOzzUWz7mPcZvNSeG++Tm+o/BUclR/86ODg4ODisEA5JTVWEAAsl1WQdE7Szkt4Z291j2TGJ5UBRBXKtnebMOnyAUIxbb9RiX2w0GQSEPQ9JanUmaoIKjolVkt98+tg+7BtIT4V7/7BGZr1RHrlrphewZkzmVQsnCqTCQ2sgt7jzM014SrP8zwf/X/irpLY1PdbG0aMzqIdS85i7c7XRAvOjBjL7jA6/KZihO4MFH8GS1cCFDwhfa6f2OcMWG0qcAbLaF29mWOhVMN2Uz5YJjqW+3NYnmY9+S37mLR+c7M62MmM9I1kBBD2ZDhKQlHvYUeEqPjOUdFFhQJ+heFBqVUVgw3cQCSDOTFq+ICgQ+rJPe4MISU/uyL1AYGpyAanS+PftGoWn6DAmGPKRHN6I7Ed/okCtIvmqhYU6uC9QtKQKGu/2hzwX9WeeS21RO/T4fel4BUhP82KGGW2sHLZR3c1tIg4BgFkqFDTsMNV5Wg6u6GZ/W4ysVmpHbrVk7fAElLxeAcSVFI1KguPGZeauuxdXY9BXzmUdmeFL03LeAOYagGR3AKB3VCrpyX2qX/vMjLmiqu6rf1ei/WSYiGUlvMQ6wBGD8WBlucyapJ2FeGa128FqlYvQePKTcXrzO9wkQfAGMjSut0Y5tlULE4ZGjIPl3IR8eCkb0rqyRkmjtOwsiggmfZ8ICays7ebMhE+BqXemqdAGkKsELF7Pjv28KrXPslOUfn88VeFJ2rlywBAtaprVar5BW95Pj7my968IZDuNJpxbdo4VUhvV2nTQto5iIrBzPFziyqPbtlGjqBLSUQKFdpDoLF685Q+tGTJ5hXoW/V5LtPjBikNSqJahB6/fk+EHPNe20lIcXHkRq8nByzWtG5BN/bYqB4sKeCqek6dWKBV1G+og+j7G1yyZmLh7F1cZm2dzuo1+P0Sm7Hi9JERLUZqd2ZpcGNW92YCj9qDOygR0x+Xn6VoL97ZWYdu9MttSPLAhL5R4YCk3lFjeKOCNqhCSRoa4Id0O5x8aBROeCWnwezKGTqOco1WEJdpnX4QizjCjPJYFMSQq/rTIPXjzKiykwyF8MrZGr2/jgnVGpmRU08HM5EblJcHlDWSKO5M7d6wwmxjmCaSpj1xTgpxM1qSi74GrcKaJ0Q7agwjtlspT2/OsB2tNYGr9grnfUreCxaWa6Uf+UIRgYOlIYRYgG/qRVuQiqcOrWGHDtmSaTJuSMl2VG/qdOExsq9eXdJsWPOTZVb2zXoYS0YISgqnN16wFns2fSzYXdaVAXJODf7LRxbFje3D30moAwEP3r4LXt9m+/I4N5dA5svVz1k6ck21vV8F2VIwglfdT76VCqMxYYZPVrTklbcgUfXoh9zK7qGcV63nt96Sg0YtvHtu4b7ORKsGE86T22nmVYTBJyMblAd73DB2uw1+MSSO1tlK92dF27LKw9UoerKxgIN+2hQJrtw7npXDXG550rBiizst2dS8dpon1GpWOyvenTQThkvX1YAyozdlNaNqw45G4tcEXyh6q/T68xApwkIyZ1vdmVJrjgY0tjmcBVpRsrIE0iwFA1ihAHsFrW7uy6Z/SZszvqQxnuvs9SXOTZc4PWjj618HBwcHBYYVwSGqqpBJu+z2rHYFJCqqcy7W8syo7NPECyLXHX40wcoR0QIqJodsPkU+r3/R8Q/ewjGPyMFmW4oVr/4AqT3F3R2qSG6tz6Klt/fd3PAPiwSo8pXG150fNbpcHMs5Qex7yhJmdb/+wHEdNSa1hT7eJVZUO/GfIGNb5dRUsLKhAuETGuRYqwUFczTBSk/zYQruKdkdpbJUceZUjXLJUpd5Vki81UCplktKOChQS+jNV6LzhQcszVXbgEzy1O0/GhMzkorufhuN/06aNLxSB3Z1LD1D1/KrqRTamYl/He0hVLto88VCppBirypYs9mPjGAYmvYEBICs46nGCU9ZuAyALE/xyUZacyoSHdhKhl8prDvZVABUjDJU03NCRJS9NL7NUuZfIz+XsOe0NSosdL0ClZ2M5RzynOkhYNkRTrvpe5NnAfkA6gOjEEMStB64IlGbpaccobrS0oJJh3bgcjyNhH//fnsMxv1fGOfMBt/RwTPD7nnXUqxKETqIxvQ/tVD700i7p4azpPFlxR16juosjWVXSEKs5UhXLyVIutXrFxgRdwkAxFO2n2WQVskLNw7RgrRJwklV0NGmQW2ZJBFZjH0wJ0FgKKI9zUc9lIm4AvnLY0dq1iOx4Z7nMTmSTP8j3CkhNrL9WOXxVCrCOb7RXEZGt7NKXc9XElXowXtoQDNGMjZclbjMq8cxSyEwAfttWtynHyDICUl30wJfXESXquSjRsfL+lhUy8ymVFypnVdN9ELYYogX1G18m8zAe2xWbEc5vcxVTbdtocxxbrdhLYSraAIr2TwlIH9bOgxCHpFANlwheKOleTW/KCUOW+gtL1Wd8m3wAYMh9SwV5faD1BxWGUhPwm6kdRM3UpNqLKpkJl9neG8feXgMD5Qn7P9s3IFcT3Vv0Zbo3Rb/5fRsYntVkMoGsKUdzc6wHT3kJjwQ5di2q5A/NDuYHNWNPDP0CoQpxyRhhfKyLI8dmAQCtNMaOBdl+UXDjJUyFpIiNq3yJxik8KfBMRpumpfNYykAVMjSs8IDKQ2oRU/YcQAqKcvJ08mzSb+Er934tA3NAGBu2/Q0TKhWe8rZkDAhCZVsMCuS5hwXlxdrrRCZoPmokaFbl6r+usYixsI/72zL71O0LRyIZSCFaqw9kgn2VmIMnHL7KBMS14NQy0B+2o9q0jYSsTiYVnJgg5DqzkKdKoI1qacnQPVx+jPd4JhFEtCDHpV7wBxPMUJ/+VA+Yj60titnk5kVd2OTnALKJHCyUjYyiDL1MPmc7ibC4VAOPbdk80l6rfY6sTtajvZnipMN3ApBjZ8/vJWVcnZGL6WBS292YSRLW3VCAagWiumxkOcyqKAIQt7a73mpu0uplo7npX6/LgR6Qm2Qe1nfBnw0QtK1AzGs2I1FeJSRrlWd0KOR81H4OmQ2R4rm1GRqQ7n/ZPi04QNanIq9Zc4y/L5AhR3ouLNkCCXksMyUV2tvYIyNReGkTACbTXRqhTTBZwoyQVChvGHRpOUBt7pi1lUoKWa1XiQ5RU8+dYijrGCt58us5Kn83vOmV6QdVNzaGUy6CldIUojQvSlmpANVenbWKq4xO5XscpDgkhaoGcQy58hcVpjeu8PrSlgfInZ+272mbkomzg51EXttDTiE8VW1F5NxoRHFoF7H7Fyewb/cIYlX3UjQFULWjzUuZsaeJkCFRMYtUKxDUUhy7Zi8AIBUekkLFeXo51tSUQ5NXoJXGmO1JNXZtvWVy+FbjFE8f24ddXRksueP3U4hUqjDuEUhnrMnYkEZUhFbokadiC011GDuh04YAQoFwt3zWaN5OrrwkhPIaoYgIg9XabsgR77MLCU8ZmHZmyUqOLQkQttWCVgEAhkTlV00j34TUiIyDep7RZng9Q6jeRa2SYO9uuTrP/GHV0EQXVbt9HvgFsn5gFCQRC/gqwwwrpJDT7fJKJb36U0B2hDwQV1Nk3QgpUw48vVIqxZgA327k/ChDphY78oFwbti+r8ueZXUhwxMAZEsRwgXPaLXJZGHsep5yDNEpE1F6zkE/RE/Z8YvEA+t7VnMqGYWoViBY08OR49K2zBhhpidDvHb8YcrYStMRubBW9jDzbgx7Uc8R1VJ4KnQrS30Ts8oy6dCkN1DddQJiUm4yao3EbHCKKkfR840gBSf4Ko60MiPjMo3DTSn7UTKVw1P1X/0gR7IYI5hTmzzfsj7Eld+Db8ejXh2TURnrWk5TqLOoeX0ZYqf7t1y5qKxxZg31rk3HMvs+hMxkBAAiEvA6nmGIvH7JPqyyFRnhGZbsxUPPD8VslFkPe2ue2v4uYjJjh+fWlgsorVk1uYjsOiczJsHGPPs2FpjnXKbY9K1A1/1TVKz/g3Z09EqbwazBXEiNg4ODg4ODg8UhqakKn4GZuqnyWFFhSEalZxugtFOVrEh4ZAKVhY+h/L95Uxj6h0IB+IRCaU68liPP5IlLgyo8lViecwIEM0HcYjRHoLQoPi4wmAjh75Ga5WA6NwneORcYqQ+wqyNp3n4aYERVCl9TbaHmy23fPUursXNm3FChd/diVGP5XSXIsZhWsP1eRdvt8Qz1lo0Io6V5A9kHJum6TpQNgCWKslK72njW7qbjGR+8AIK29kQkU7GGApsrt9Jm6E/bbE5FRBisUu+iJoB6bkJ9+KBkexUwmYVk4XRh7G5EzCTboIRL+6em4vZGSOryvSRLsfWgFjJ7k7GlxZYezGcrUjNSYRL+wAa8C0Wx6bAflgODVUpbXJuZ/LK9uSqQM8Q612q1lI0r5YjmOVLlRRmsTyDUs7HMMiVghM56GJ4uXOIglSRe59xNVK1LYgSmCxNwQl4TVkMqmKF1s75v6pv6qaT8tL2PfNtGMCDPPOzrStbD44TZGWV7TZhJqu4NGCZ+RYbNaB8h0DhcMicEoN+LkOfy3iK375MaOXIRIFyw2pLoSA20W9JMWSZtr+XE7VqbyysyrEbnxOYJtwXGI2Gy+CRLMfxF31Cr4aIdt9mY9GbV5xY1YZJv6HaZWsDcev3KBBWqD/oMGDAIlUs3r5ZCvBiAnJmi5V4pq1TeENYLGXJuaVq3XOSjiGS9YxNqlVsTTF63NXyjBWm20VSqiKxt2u8yU38WAPykpJ3qsV3Kh17O9KTbm4zaikz6d7yUyIE4zPv1+zZcSHgExi3l7SU2lEr4cgyWWaODFYekUAWT//IKzEACgGgeJrm0SbcGG5cJAEVdUh0mE0sgTGybrE8Fk+ZNDDxACVhRLRBEclRmD9XglUptBdUUtYrkq7LCQ9qyryUYSTDSkFRimnsoBEM/kVJw0IpQUangZgc1PJDKXUB7EIFzgaQvZ4QfFIhVZqHpWgt33H84/JZa4EpZpCgksEQLVRnqoSdLtADwkhMBL2AXRs/GExaxoitZySakJqMuGQdYO3E4b9thaLOEgfLAhhUEMIKBhApHgJzIfpeBVPmoDKGlB4mB9blZGEVIxpmnnJxeO2Voh5JowTfxiEWo1sKKFTaZSm7FSC7quQolGEwS0mn5Dlkg4KtsVlRlYLtj4/gjIjJVgSr3RXIxVPRkf3cdoQp1qsza5PeLz5Dt1SEMftemB8wrShgaswWXmxIAyDj8tmf+Jp+s09uAw2/bwc8LS+8JApiKzSXBUMxGWNR2kXZgNjWMgGhOh1kQiohh/kTZsMqqHlrzOn6Eg9cy+MqeW3QDGRMKIH4wQNC2izfLmfmu3DfkC1AAY/PnfW7tsg1Jh5vqMHVCRYULBV6BJJPvvZ950myhxhXxkkNNIjd92qHH6/GhmEkRyvR+gByn+nfJhDCC00s85A1Cruh2CslmfUq5PE9n4ALs/CkVcKdApa5U77dcbSYZk5tdkxZxws5HVgCVvTYDF3mASjwmnRu7Jd8RZoaBTBeoPuqSdmFL3TulEr3MkMq9lCzUIEoOTiXHPBPioy0OIUppLZm1kQ/UhtSE7CgTSqn038EKR/86ODg4ODisEA5JTbU/TeCxCk8Z2N1/1rQUWJleYoUsQAxIjUHUCvCq3u4Cgqudu0cYGemhUFuzLPNRnZLbvvl9TXi/kGpOdUE6RfVkRA2yboilWakie12OqM8w+v9IZ6Rjx/ZiKpLXKMDx3w8ch8GsrtVEmH1Aaqez9SbWrZGhPYN+CNpeBVPPEqxvm7Jbd9x/OMJtsdGSi6i822WGQkvG5U5UO3xkNcBX8TA8AQpfBu4DikLt2c+AzUiV15gpUlxOwh3vk0n0NTOQjNnk4DxlAJHJ4sMJhvbz+jJfLiC1vnKNSb/tmftnYzI3r/U2ZmZnnTWsdsGIIedkvEDLSdCZkL83eXYXbO1JMPk82ts1awpU7w/VMxMKXyUAgbxGOqYrMjD4M/K8dFSACYZoj1TTwqVSYggOdA+TPxFVSZMzpSGGbWaYlKIiHb6MhlAwBEr7D1sMQQsYrFKa/IgwiUoYbJ+mYwLxDEOs3k3aBNBT+YOZov73WepGlKhKTUUm4zKxgmZp+rNV06fZWAGRekiVhzvveqYcnd9XWpn23G2QZQZqucm7jEiAhwVEW/YV+YRCsw3NFBNjXfRUOBURQ6BU+STzMVDlBfmSL+erTrBfYql0CEo5obvJZJTJ3Ms2MxMwWA3TDl0bFqScGPU1Sk43xKXWypTmjVCYrGa8a8ctT7gq8i3/zuoMuWJ1grbUQnWfA7bAfbm9uoiANtfo/wHpOCVK+Zv9UjYngeEi80CJWSoVDuAZAM4MjS5CQmEoY6m15qEdxyZbUqkYgAhLhRM0fGOVOahxSApVzd37pTRZybikIphaQFlu3fzzGtkk6B0Gv+dDhCpLUERgE9pbcYB2pwJRooOT3XIGVHd7JtPNYIIhmbC1G4OZYChWrH94hrZK7XeftwrPWfcAAOBHi09Hd2/N0EbV8Z5JJ7d6omXCR7y7awhaQGejnKlxmKGjUhvy2dDGymGYnmUZA/QEqwj4HW/IxmG8fxvSJqITnwN2ghAHKvusHTVrWJoo6FpvP1GqvKG6yhYwzgHGrf2yaBQ6Ax2yVTaEQRfd1hSu32Pm2cJZbyhezkuZqb8paoWJN/WiAqJgyBS9HM74Q4uF37Pp9nRsHSBp1zKt5g08cy9iQD6pq0JLro23dYUW63kZz/KhAtVBh2zav2lmQqcgJE0+cq/ua+ltC0j7F8tt6AbBZvzxErlRNAsl48b2WlnXNt64yYNNm1UHcuOiY5CLUHona/s/WCnMYmDNIr01QsZl7lSZnRKGVMUPs4zBWwyMFyjL7ULLCjmu9IYqL3ngslAgHpWTJs88ZN3A0KdUFZiclpXNiRjavchQw3niA2oTnGceoPqefJmm0KQADGHGn0wjaL8bKrydyQT1sYrT7K5hxkQQ7/XMmEjGZYF1LcREQJaKjwQgAFLUtjcfmHdWxGRDbRhQe8A3ArK3VpgqTPFDwVBYC08YkolC9aMNneKpzFg25IWsN7aQm2JTs7ZORrHwBvL+Ju0gMaSqolJepdKGmSnhrj2bS74YJkOT9kGxwp3nsHS4iusub2x4AZArUn5wgqUMnMsdmt4FSscbZjURsnFrwocZsF6fARwm7VzaADLl0NTZU5e2kUDv0gjV3XYWDMbVYkGyKoNJbhAphxIAxZSAV8/xnLU7AACroza++MBzAQD7dozBX/Ig1smFhohJpycAe7ZNSEECIBjICe6tSsx5xa/k7AggF0ozQXLr2u8N2LL0ZbrgOvkA02kKhbQ96xAP4jCbk3BJTVh1TZ3STF7TJkjwM0B4Nn9r0GXGrslILnhZQzme9PhQ7llbKUbIQPeSo4XWhLMaSSGY2YVRaGVLMHBlf/P8AnkvQmWHrTCjBRYr5MZLLzLlXTfx0sYA0h6kw6CyiRyVphIGOQeIQbSrpl+9JdUHA2mn1qks8ximElBeJWMPDtoM1RnCYEI9WxM2uYTKwZpNqFjdlMNXNubeFCHoMkTqHfbWCniTsl2DXojgAdkhIzNsqGoKeXbzEM0zlYZOf2fHDjGrvfg9hup9dpM6mLB9w1MGv28ZBX0PAEhjlS9WCQevmRp7dBTa+I6kH4AlHkhpekElM6k9AcD3hSl9uEQVDHpSKlHXN06G3oDJvMuq75Kxsu0SKvbabhS1fdXPGLyU0F2jNPkRMs/JCvveKSB43eGcwUJ91pWVtO1Ul5oEZKiW2Sjv8OElQF+VU+RTA7OJTFbLcCztz8FrmSmIToLBb9sY6mi+LDitY5UI5CZJC1KUQoB0WkKTwKNm35PfK70/kgyJ3gj5HW5yEJNicPRGPRiwoT7WfhlZnQ3HdmdyPjBnU3VwcHBwcHDQOCQ11bCldk0l1/W8Kvl+vWvLK5YGKWIy9tWwJamh1pHKw288A9q6Moe0h2jPOl4w41nLM2aCrFkmd5G6EDUbTXH4tLSHHtWcxVxSxf/skql1st81zb3jCpCsKlCtyu1cd76CUNnj4qy0Ax8nBEe0ESgtNv3xBGqKqk2bDDy0GrkIYGyeYHbnaJM0qL/JBpBnNWVrVBpAuGT7Nq9Lz99AabV+j0xVGZDV9JIRPlycmcNu8YSyaSt7kTewoQ9FBcaWZuxPpYob+hp+jymXfds20//CQ6Grk3RiVBLr+Vqu0qGTEhQlu6wJdSgAr2W1u2SCZJYfSG2x+H3D3LOctcZUmoHW9KxWk4xZ+1bQYYa2DVuEZMSmbSwiMkk1kgmCOGwArhnxQWg0fFbIMdFdrxiXegH/PmkiCHvMeHnynGSxB02/U4mh4MNjFYLZ/mZkNBRtCkjGlKZdt57V+j3od80zm1g+GxEQIxnWr5Xjf119EZ1cdmo7jUyFIxIMFBWA0vTyXmwSYETNBGnqmUQReScwHvnBomcYkMoMgQkybICm9wFppkhHxFClJM2OFFVCL7Tvw+8ylJOYaBut35GmgnI1G+N9nkvzkq5/qguOA0DQ9hEt6PsSumuB/DBdJYKb9cVvyXq+QifVWAjhJZbNMJ61OhmL+tvv60Qp0qziDaz9lXypXcvnIvDCvkNRYizIs+F1xg9DP2Zhw3AYYagGrNZc5e9s4v0iVMUOSpqpCADhQmoOTvACJielHlxFJJ0U9MQHSjFTHqEI1WCIgc4xKQLlsp8tRSYG0e9KgWZsG4UdUHmF7MIKFaOq3ckTD7vmJT272KvA9wR6qkRafY4hHdWUCYGqBQb3ywW7ssSH7UMmQwsw2F1DoIRSWABZ1QqesG+dHdJSuTFGpZymSrBoAcNT21dBZ5iGBUrXG5HCVucJ5Zntg6TJjZDOGvI8UxEjt4JHV68QpbJT5fJShs5LJe1abqNJp+opmiBoJNoAACAASURBVLSU/s1QUm1mii+DSdpVZ4d5eNUOlJykypVoCk5DC2heLxCoCjz17TB9KgJriwYwRIHqTEk63VsRk+n/oCMXOUCek9dlJiVACne9wOUVAuUcfFE56TRzFDqOseMP5Uoe/VUApqow+QMbsiMzQxH0KpzVSw5YDQGMZCZmGMSQq4Eb9bgxg2R1pmK7tYOaMOOPFQy9tYUpB8YSa+9jqwdYP7GEF03fDQAY8XvYOZD2lLsW16KlclFTxhE2UqQdRetyQnNMSrnQL9BLAvR3KckRkKFCgzazGysOdNfaqkCVvXYzldXIxPzqd1N+Vzyz4z+vlfousKFOIBVuohlkgSEuMGjZEnrlAu8st+M7G2WyJJyqOhTvs30lfPmbQAlZ4dvycVnTFmOXmeIIpMctt0KVp1AlLnWrLD07mJC29HKlHj3OREQlGpcNhf1EC3aTTgxDFazASuFSZM0ITOUt1u0C1GbgT4A7/RN4BAcHBwcHhz8OHJKaqkZWL1Gfykhfzo+qnRaCRW4opP6Ucu1XISos4Sa7SDIGk7cWALyOpWTBgcGkCmSPBSAYKqvkTnvQDVHskCrcYiMGq+TwVEYlSUtZrS/cE+yXkuG5raPJlhjYojcU5mLrUsrg8nTUtstqeqUgbu3AUEp4bTQ4JkNl9O5a+FbTk/cAhMpYlZaq/fSnZC5WQDoQRbMeCk3X9q2DhKYc9bspa7Hk2WcJ2lLTrcyQ+U7naM4asu0mCN23jkrErdahtddQ0ZhDITUFwBJ7bpnF4Inc4evCCvGMb2jwIipRXrF9PnlzSefqz4NVrFQkuhzqIAs+AHKcpiMCItKhRMwkYAg6DJnnw5uWN2DEgN26AICszVl7UGvhBKaSuRKzXsjCZ0N1UvMKIVVJRVAwoO9bD+sWt4kECOivJnOvbMRS4MiZ1eqrQiYWUYXUiXtgyjv6ORu2D1Vp2tUdwb6BVO1nOnXUqlLNjEa6KASDiFWCDUYmTKyXBOjN1EyyA3BCoTS4hHM0tql+rDIUIZnsTWnDvmteMPizlqnKq2QSdrC+pVYBOQ9MrVqGYaanzww9GnTYkPd5OfFEXrOmAL9nmTDyVa3RjmVjdJtMLVnNCk0UJo+01B6tmaL8ftMGhqjgoFNyjqvZ/MTaG1o/qmaTAElbG2/ofNiswqjUpgahiO1c9hJmTAlF5WF0csUWSw866t5/AvVUD0mhmtUkjTeYFCbZeLxHdoWmhrKGjU8s023FWI7x0a4pzbav74NULF24z0PQZSa5Os+kIAFUJp1S+aXDNs4aeburFRupESx6yHNb7mlweAauMix5ifKY0/acemEWu3jWpowjLrOr6FjRIiqFEQQy1ZmJF2NkJq0ILcUTtJjJviKvUaK/qor61GEQNUK4wM136ShZylQA6ZhdkXQMJc/lxNOTsQhLgudhdpWiYtNClmNdhS+voW1kncOFeWYTe6c9ioXd4PAM9nPJo1F/x/u6P4bd/ssTnjwM9eOyhRal65XsqKywVF9eY0hHrF3P79lNQFa3NrwilvZQkw4TJNNjAoinuqhHqSnZ5/+havpR+PKapmhEQciVDbds3/IGhLgN9CeVsJnKwZQHsd+WKf+0XZ+XCornlZIndk0tkOrhioo1KwQtDq/HkNeU1AsJ9ZryQi583LGwHqGOK83tkiRK8RbdQQghOLiqyhT6BfoqnCxbiADPFmCHgCkqIGJgMKnGXCK9zI2vRNWm9uMpkxVa9H4nKCXQ1+OR9Lux9+IJNzZmv69shSXzj1/2N6gChTIFeH0gLhVM0BfnCYM3YEP3LhdryBoyJlqfq+lk8q2NNh0BWMP+RmYvUn8wIB1lKLQvQ2mjSFw+tynHNrDFAlgpg5oJi1O/HYzZkEOeSrrd9EnPjhdKh22vfoeZdmnP43K5zYMVjv51cHBwcHBYIRySmupgSoDHkk7jyntOe0xqyoQqBdjAN8e0pgoGLC7WQC25S5Y7M+0ZzBDP2dqi/dUMyaQqCr3omcT4YyNdpIWHfqpVRLu7zesEioShl70Ff+g7v2N3sbWdnqFZ2k8T8NbILW22FCHo8qHEDboYOzFANJildcsevyWkY4R8JDeZo0Q3MDF2LJd9op0kQKUE2wzwe9wEjUNY2pi4LQPFU6mBRargcrhISJtKAw3kP+MIRWXt39JcIiR015cyveSWeheB1K4M25BapwhipfesE6+XarQWvv3McsDTmibHUMyq1Jrtd0WJUtYasPDkfY226ltqPGvIvtPexfFsia72mHHqSsYFWCAMHcyXfASHSffqwC+wMF9H5feS8o0Whr1bR+6n/Y8DjwEqmbnfB5IRhs5GVSJtwUeg6Me8QhBVgb56v+FeH9GC1nbtWA9akm7UbEa4xIyKzwoAAUzSePIISwvy4X65WEVjpG8KQxTEkKn0PB4X6PQkh5z1QoS1FKGKL+50Y4i9iluvCPCeTVQic/qqZ+tZZz4vlSYFw7g0CjP/vTaDiMjOc9h80EwwiIBMKTsmpNlH912Z2gdK8aeFdUIToWyTpjurM/Y+eQzpfa3eX1GxGnq5OHrWkEyQzkFcrqFajkstQgDMjrlyfLg2TZi+EjAFALT2rbVMWTxefsez0jz2pEmibLYIWpY9GorTDe29ZcIdmN/IzEzqmppG/xPQVA9JoUqKKmJpSVEnuQD7yrZWVGwihHRECjoAQMLhzQTWNlInk7aNCYZkjCEZ1R6hAqwmTxzfOI+eEqJLnRhRlJtUaqOTHXSqcoEQfR/+rLWb5jVCUZV/hPOesWfJNgOdDSp8YnqAYo+cHfVdHHkMiKZ22YcZrP1pafMxEyK3gievEURDPxgHGEDzckbEC9xW1QkJIhSm4ozwYT4HXWm30gsvYOkiVthwlSJm8JYIlVlF0wUMg5KHtU4Nt+zd8VIYDlMLgTotXOBDC0naBAaryPxtquzYWHtVwLm0yHiW7i0i5ZFYWqxZeZEp0cjkAb7OkBMOC18wu7B4CUxSfv+ERfT7IfgO+d7yKjN0eF4hZDqMhRGCKEfWltKgsqGN0JcdubhYA/V8633p21CZoK02TabzUKLzGZiQje9PMnSOyI2Aqe9gpmZtayNDNlXAm5edznObWL2IMTROeW43mN7A9o0I5T9mTAIMniroQBxoZRy9quwgxgVClXhfCIZMefuyxENaREgzOU+Clgem5p2/5JmwFkCGvLD/n733WLIsy67E1hFXPOk6dGZWihIJoAB0NwW6SbPutqbRSA454w/wT2gcckCjGX+BZpz0sMkJMSENRkKQ3YUCSmRmZEZkuHZ/6sojONj7iJc1rUEH4h0zoDzS3a/fd+655+y99tprcbHe1v53lNGic4x2kMwStpUngZMQG46pfUp39Pxypao9PkEQzeD1GA5O4VJwRUbkIrYe5etYOKB6DLKZgNshWwdijwWvm9Su5kVWuxxS6cYV7H8afFdr7Jl/S5MOt3GW6v/EoxB7zP/w7uZtOYGJXnBdPzcekJnXdLjHyPS22Avw6Gb5fzX+QRyowAd6qAor6DDpRFQWCQsj1AmHMxsBfjk1wI57xVaKpAx5IQanFPqaNsxIbJlafPHqBgBw30ywXdGqFMqj9wJPLqig8NjW8LyTT74uYWYe4zmtzPJGQwdDZ8tZBSs4ta9sdBNxmwJFUEkJpa1YAwL6oMYz25dSKx/FnmqKW/HGfRUk3dImgPN06AmbNlDd7pN7inXalOWImIG2z0QMQLzwWHyT6i3bVzJuJHKg34ukBpkIP/FeAJQ/XqN5nKC4omdTbBIJyFYAjlLAAJ9tYhkZySsPiMyhJWspEJ7l+ThjLDfJGkzw/wsbai55l9eiveSDjX9v+7nBi0/JX/BissNVM8cNE4v6Y2D8hFWwOkUOSCApxXFVoTzp4hw8vKOTrXjQqG9EtNordh5mmrL1ocgIbC49CzMFtp+yOtFxD2kkpl/R+pzcOaw+ownpnhuoLHN1pccYUAmPpFjlBbxFOtyy09zUPgayAPVzx5q+ogPTcGZZzSyGntb7uKpQPGa6ukLGbNfWqfaHwkFvU23TTFLvqNM+qSQJkrQMDk1yVPGZ2dpH0hn48YXrD0tHRMAmEXVyZGNv7T9m+rY61wwH9BYxCx0VoFsOCnqkYEczWYrvSzc+qjKFd2KPSOcyklte8xQ+IiJ524pu6KAPhyzVYsOHJtKcjHtDuq/uArH+bCckPRj2TNWnz6k6nhP+PWn23+PwnnkF6NHDlWmtwv/D6FM91FQP4zAO4zAO4zB+T+ODzFSrawlVyb1mbzMjMXNzFOiv1DoDUBYYols1gI3K+cdWicEWzHlDNianBt8/UkahlMNkTqHdOCqUpcV3t9TXMj7U0dHDVpSlqseUnYYMq37kjC1Ecxbwj5RZ6q2M3pahJhkhTTZWB4DJjYDehmZ/oFpbmJrryvM0H/Ujqc8Ms8Au9Ci5xmRGyn7yqLNYp7qmmYjY2kIsU77dOtXgdEtZ07hQ8Z5jRKsZfQ6Q9XNLbUgA5Fbh4z96BwA4qRr8ze6jKAaRC014RZmSysyTQ2TtNaK4foD0fFbbifdRBBYrf5YhY1F6ZkRnb1De9pNnu/BAf85Z59EAxT94303xsJnCP+HstLDwDzRZeq32hD2KT3b4+Jxkd3775gKyoXmrbwQmNx5FQ9c3dcq6g/h9WAfdaVJlap47yDmlKOOuwPzvS1SPdF+bjyXaZ6zF20hMv5eJIa6BgpnePtNu1Tuxt+ZyJjkkzUkoEcgxrWlzNgIeUYu5LA06VkZSO5lKKyOhDj4ItTtEj129k1RrD+0xBjCZuXYU4Z8beCOhbgN2C4ws+u9ri+K2QHXPCkgq1UYBEevrAH3GPKEKa0kPCQWg/03rXRqB6jFrnakQs7ShoHcG2K/RhxEY7fDkWGNj3ToxuF0J1PdBSYtqtEHoY7rL6q01oU/jKa/pzM3GVR7lo9wzCDDMNdH5s2CkLwqtFKneKgcAInUoePW7Ag8AzWVAVMKcmhrROON9Hh/koeoVF9unqR4XN/yA6wvAsVh1+ZBgonHhyVS4lfFaoW6nG4H2mUsbi5E4mVHx4sV8hfVAO9p2qHD9sID/lt6++U1yGenOPMorHSFlr9NGLhz1LkYpu1V684qtiDZqkbgTDpsee9R4V2Vw5EuF5llqQ5m+Y6hWC/QnQMebqy8d9AO39nQARAatuvRy92z8HpWSPOJpI3xydZE9AAn0ywTXxvrTQPW39hN6A/XEwPT0oY+/WOGjOR0u36zPoLTLNoFEAvLS79HzZZ/m2AGQUdcP+xKJSAei4fURelhzuURXpq8B3mizulX43nDkYWsXjclL5fDdN+f0O6NEfamgAzzpgTkHLuPcYzijh3T80SOWdY+73TTeYCDRzN+wEUPYrIXIoEM6BK//KR+QRwPcwBZ3ykG+pfW4eEu13IYNEoZTh2LDqkn31I/rwp/OzBLEmNqbACL7xV5Gm0H2DpDd/iZs5izR2SnIRqL6jN6TQllsVtR3IqrkQKQaekbBABxeREhaDmKvXmdrAccuRL5yEMGmsVfQDzrCwW7i4vWnX5XwKvWEe5GISpLVfwIPQbVJQchMEIPN8O9Y/snWyOQaoP5uvscq1dadSm1VXjGZLEDIhcDIge24pLnMW1pkVr80UQJQ7JVLcicqORDkGpTAhEvPRe9obwl9q/RZOcjQHircY0EXDYFA3rIj/A8C5B/QIqLMp8zISqB9bjj2cJ3H+z4O8O9hHMZhHMZhHMbvaXyQmaorPATDHomwAtjaxTBDbHRseHclknFy6ShL5fDOTlNU1nw+UBM6Ex5mRx0Mt8as+glaxqQ8AGslBMNX4yIZIEdVo1Dozwg7ZkaMxBCpmmkG8crM3/DcACZZQS1/ixTtnlIW51iUwpYukj+KR5mEthVlvHoT9FtljFTz5n+A2JE5kzEnJOSsSZl5h+qOMzsOTKVFwtS8wPCTFscLmpTHt0vMnhHl8flig/uewn3rBUyjoTNIWWTXCPcT5sdlzzrqqZYEgYUsJCfUCCNQrGRkWOb3KPk64Zr96b4nZhQ3byRkL+Ekv2pvS0yDByvPR8juvEAyH6+AybMo0IqbzSzamcmbEvNv6b8XjcMwlwm+lkC5pftvnkqsfz6gXtCCUcqheWA295ukuGUnQPckAZqTSxlhbdUT/OhYNGIUiIIBQgEGCeXwKjM7GBOxxxV+r2QiHDHZw9fDiYtw4f3tIhqdewFUt2zJN5K+bWDhq21Cj7wmFZ/YylL6CA2LiYFngQR9pwmBmieQsXqn47xRCYiz0UGkdg9F6InOWL2hZcxnuuCeYe60fhJCREz01D4lkM2HSdmt8NRO5TL4OsLaww80tzMCnBx9Nr+e2L/8bzUCJtyvBKq79E7aMqkrCUHPKvfmjVnsVqa/O+7rdhcbRHtHrwnBCu9F+FzAfgYb7j/oYpspdwYcFJXezyGcIEPiEbD8AhQbAdWquIhs7eOhYRcWkt0m5E7CnY8RJbaNAjKPR+ckpiyt1jYVxjFhgv3IrEarUFYGg6ANTvVIkF2HPfWSvG6nWMovwLUQicULkb2kvYRqJBZf0b+HhUB/lti4qk0QoTBA9ZD63grekMcZQYyhby9AoAAd5maaVI6KXfLRpEMqvYxyQGzxUJ1Hdx5YyAz/8IEyThwsm3K//PgOxklcvaOa8+LFBhdzOlSdF9gONG83DwuIwu0J4IeDIt+M4jzyhq+69PlJuD/v5UysVTkI3iT531nwMM4IDgw+pgCgVjrOcQzWNM1TgOpVl+Tj+mloQ0n3HKTxzJMBPzqmiVsUHb5+OEPL7U3n/x/VwgHATOhAHRYJits9p7+1+dmIybKL/qTb10eob/lZ++TZ6Qr6u4H1aWZAya4pw4JaKILfr6tcnB8xitg7C3AtL8zVmOr4ToN9WPmQ1R6SL+IdBR7tt4SFipMhGnkXdzq9BxMPs0g7sp246FEKVgKKQZkAPNdo0er47rrSw80tBHMl9Nsqzv24IDZxgJTpF9JnCQYcAHnFRgnDzDRDsCxmcH3xIjOTD4dpmAOZhPhdkVpjunP+m3xfxRaQbSoPuFLs1XNDH6xX6R304gds+YKCEgCo1oJ6TIPTTDaIAZ28V1WfwbcCcCF43Yl9o3mXJDWd4sA6tJQJYAwyq/0+Gz8EIXT/rHCl3n/494M8VIP1G1wisoTsJW6apwbzC9rIt3dTqC7t0PKyjJkrliMELwTnJIrCwhjOEBcNZsE4ua2x3bCNlZUop0PSCO4yKS9BNc/wMqo2lyzzewep8ElWkayq6OcmbxXKdYoC+9M8ihUYl5kEm03EimKTNufdxw5uYTD5iolQHdBe8PUuDMTMYPL3TKrZpcMSoA0kkFI8O2sA1E7QPk/tGNWtjE36dmlx/HQTvoW7xzmePidliFobTDTNY2sKXN4T+cu+m6B+SNm1cEjZi9jvIYTwMWsAUm1nXDjORNKzCJG7137PIad5moyZfUFtIuVNqLtnh+Vx2hiqOwn4vN6XNdFPALVL1+/PHapXlJ0+m7WYafrBd7slHm/nWH4VqjUem1ccoC0okAjtGcOxwObHrK9cOBgj0V1SZp8HSWZpU60RgG8VwHXrceZTbfFkhFAObhcyRhHnKu97jZtvlFkkR6Xw30SnYvsHteLEx4Jh4eDndC9Sefghq/9z0GtmbNqeMcAc10b91EI06f2kOqmMfytkjkFzW14HNYJM1MXTe5XqvulZqwcBV5KuLUCBY942E4lKHUlLhsCuP864BY5a0EKm6ov0XpMtHl+7ojUY9iXd+RQAW8pIhyPmL5yldiG9E9FGcDhyEHbfASoIPIxLxHcuzJVK7dD0DEKdUyaEDjIdtkHcJEqBZu1TwmPPpUZa7CFcsWc1azsLv1fdymjJ+D6P9/8THMZhHMZhHMZh/HsyPshM1SmuBx0liMpMCZ4KrEQ4ge0ji5TfFjBHzCZUHmqX9Xv0KkZ+RydbFMpFcXDnBe6Zsdm8m0eRbxQew7sZwExGW6nk67ik+lNQJCLojL5HpuopArV19r0xuUHohuHO4EzTpgi5WAPmKSJsefHyEesdZZzrxzqKtqN00DdFVG/JIaryXgF3KrZdmD9pMbKkIzYF+WjyZYptqq8MJ+kZzF9Tm0bw/jx9vsIxS9VdrRc4O95C8Bw3Y4HbLWVbu+sZ9IrbcM5GdIXC9A3X53yq4QTFmhit21RjIqebBMd5pGyA/iPP74wi/jaTrovMVyOgutR+0D71sDMb/1a4J9XvM0JzBEHvBAtbcEb0rMHJnNKcdVvj6orgb98oHP1SR+bow5dc/wdQbCSKDfD4E77+p2scF6y2dDeHuZrGzHI4329YkGE9egHfKliGeGWbJC49iMUe21JqD8cPVJz2mGZtYv1jHVt9pAEcy1r6qYWvbYTHgQRPuop5DGxK4aWH3PLctSlzDIImAUb1CknlrJMERevA6rWAy7LpUOdtFEH6/C2n9zOnwPIF9pWFzJRrj5l4SEAXykex5wbF0xn/HQ0HpvRel6wSJrYZKpZ5GhOqIaKXLnm0hhumeYvGGdrvuRWFenx1J+GqdL/DmU3pUygzcYtg4I3Q3HPGmS33UEeGT8bjqqM6cqxhV9jfk7JSlMneHblOGa1iA4p8vtUAIDMtf1/HB3mots9J+1d4RJcalAwNhRe6Exj50JCf7uC4NuobDdUIlAxTDCcOpz+7i9fetBU6ds/wRkKwvBuqRJ6Qjxq6SbT38SjBswBBiTkkqxuGSCtBOrIBenJpQ7a1p80EXFNtJeavaQVXawfLrSaPX3rYZz1OTwhmHI2CNcGfKr3cxVWB6bvMtUPs0/eHCxu1jMe2ALa0lCZvqS6dy7iJcPkOqK+4vsXWbuXPqG7YjwVuWfP1eNrCA7h5pNPHXk4jhKQ14F7w270rMH2j0t/KarlO+ygRCJDDz77pdIDQiXSRrNoSrChbiWItY+BFP8//a8n5J9SL7PkAwT9XflPvHeA+C4zG6X6Q5CWAH1PkcrposGookNtdz1De0g9WDwLDMdC+NPE51d+xitQW2HzmoD/a8fxYtD1DtcoBJwOKCT2nAkC3IehTFhaTCe1gu3UNP7VQ3LdqfQm1pK8lgLIa0d4EuSsPH9pVWo0dH17VdASMiG5FeU+yqCzU1IVzE6KVQEBr5yPgRTzgXadQcf1ZZj3aP5S3U316R1A5qveFZ9PLGEh4mer2cATbB2cXOe6hyXuyf8Kn1pLQWhU1bUVqJSpXgAwkHUm1zZGDn+HYUx8uAPVIxMeSA9/+GGhf0fPUK7XX1pL3n25e+NgepFoBM3PR7SpocQN078F60BVUFw9ORsILgMsx0FS2CEGlnWAPzs/fEZ9JDuaSnK5gIlWYjh/UlW2dtMB96ZNZvUvzC7+vSQxBwXfelva+jgP8exiHcRiHcRiH8XsaH2Sm6gvyNhW9IPIDALFSqB4Slb19aSKENHYaPjTNDxLj0mNgw2U1M7jnjMoZAayK6CIxnFv4k2SmKVjDNzRZu5hlmkS0kIAZJabfp0jbcDuMrQD41Dpjpx4uQI6lRT2lvzV0GqbSwGv6MONU4PFLhjufdtDC4+E1YbF6m6BaWfkYmbqSCBF55B6yhXIlIKyG+J7ZrgbQQeRbUZQcIbYiiXc7RdE7ACz/4A5n1RBh3VIbVAxbrrsK22+XicggElwNL1D/dhK+xO6zERVr4va7EtV3TKxqyBDBZObpkSntM9ahYFMEhhLFKGJkrRpyLgmC5nApW6/uyOh9ZAUuqR30Nyz2vs3ajmoWmw/qSHWCgjUE+h/18A09p6vmCGJNX1crEYkh8EB/4pIW8IOOUGd/QiSvRUXPvh81BlYkktqjKA3GgZ6TGyWKKd3Is5MNbta0buVNiepOYjjm5/miQ81ZbLOt0G4qSEYl3ChjVqkKG9ue2qFgiDbDDsMa2BYQG4WaRRLsxGM84XlTnpxSgtDKlY7MV6ezVjZF1wt6014hlSocPbeoQ2wTKiF9goxdTelocB2So0BOpVVDYrS2T3xkyJYrcrqJnqEmXb95nqEYTsCV2ZrTPiqj1TfUmmUygZbgLUy/y/fQEuM6vCfSJHSnu+D2NxFITS4SKIVLWX33xBO6Elq1SgeEdi/tITY6ZuvCJMIUPKMn4bYytyWvgDE43dSUuYeOAN1metOx/Ye/l7WkqRzaZZg57g2s/esz7+H3dXyQh2rxQPCol2mDKzaC2aJ8+IwiUva9FSgWtCKq8xHWSrR39HbYVqFa0i4w2AJ+4uCf8Eq5r6GvGQpWqQXF1oA9GaGZfWlaDRzTavKNRvko9pm7H7XxPvy6jJuHq1yEZHL7rO9WZzj7fzR0S2/L9pWMzEvbaPi1RpFt2C4w/DxiXQ2lwwCN+oaDjhGJ6SkJutmT7OPNYliQOowM7QgmHbDdE4cf/fx7AMBEj1j3NY6maVO+vKQaolgX0TEjXF+0vDldC2x+QvP29ON7DEah7dnJZKP3RNHlIOICzzcPM01qS05xXT1vHQgm1sf8WfpUf41tC5pbEHhzUm/qJIno880TBDuq9PfCgTscO6jLMm1qIrMbs6kf2bKHfc5wHU6DbBfBq483tGDUSsNz60lx0mE+6eFqutDL5Tr+/uuHE3T3FARMuD/ZvaQPp3QStVfaQZQO3qWaZzB/mE97NDz3npnAYf5Vk6BE3yjiA/A6G48sUDHvYJTAKKNaV4Rqgb22KK/oQJQZPKm4D9uVHpA+buTFJtX77CQ9I8QyAT/8RmQOSthTR/MCmFxzcNVTcCRzOcwI4afaq6tY5D8oiA0CZQZ92jori+TWhkgciv6cjNPDPKhWxD1KDQJei9gnLFsZWfZmmuwr7dTFUhP9MRH3CfWgUT4kLsBeqab0MDK9dzozRycDc75GR05I4RrjLPX7xz70Nq3jCOF7QGfM4rx3OXx9kCl8T4eZU90sL/QHm6bQz+kXJjakz48bVyBLmgAAIABJREFUvDqigkUzlvj2V0/Ti3o0woyhKQuA8hh5s6qvdTykbY240H3hASNhmNyjJwaGI3W1URjn6cW3z3sczWizG4xCM6adRnYS80/pvl4erfDrd0/oevcFdi8EwgcQNvVJ6m25V9vozzxs6P+zIskvWkEkHW6B0RsRJQzLwWNciNgA3z2xEMfslmMkzEMRNyQAaJ/RNZ59eY1mLOJ/74zGrqMdo7lJNcRIEgl1GpvqRes/a/HsnA6HbVfBOQHJJ7ifJK1AOTIRJTvoQvuEVxmpRJKuqeBalxzTBgRPaEDYuKIuK6iZX7UC9Q39PTNJvqtmhr16kx5EPARd5QnRAPXt6Tb17uUHqbDpGqpn0k4MpnzM0vzc0B7ONoZ2aTE9pRt1TuJhPY1905fbRbQbLLVFfdrxc5Dw2uGC6+ylsli1tIal8DidNbhaUR+pLgQmFQeYhYlksvubJX/2oD+bedn2Ak5nGZxAtJKzU0dZZtYTupc5Rf1jyg5l1m5jp9nzNCJ59WY8JWGT4AIEve+hHirHrAZf7FukERqTvidNIurk/ZXdeeqfFeZ3iT7hM5PwgYjZriuSBKNuRCIfzQzkVqO6zshJ0QuVAgvN73KxE3s2a3G9tJIDEh/vK6AeRSapGH8tkxS0dQr+VZ8hVTJ9/jDnPjs9cunEPGvONZNVlwLsEDDlYjByJH3t930caqqHcRiHcRiHcRi/p/FBZqquphpDea/2KOWGfUIBoLgsY71Mnzq8WVFatrmbkQpdQN8GCRvqE1uN+j4ZZXuFvag4yJmZU6rXBhccOygIro3YkxHuiSM2JYDjaQfDhssj+z+qnYzXOWH49O/fPIVfU9ZXdpTpRlm+wkch+1wMHGDG4yZliOMxi6/vgmEAt+JsJaoVZ1vs+RijZA+IS8pspvdiT0Rj+6nFySckz+O8wMs5pZxvNse4+fYkZsbFkOj2QSwhsC/tzKH7km769GSHDWdRQ6/3aoZyoyLECIg9QQyTmSdAJi9OOYq9iDxvYSIkI7VMeJ1kBKvHdJ80P0kgHSJjUXpqKwhsS7j0vfpO7LEdnU5QXC6lGCT4Qh01b5qXa70negEA/ZpupLqVKDWwfUKpzulPrzAvKaXobXr1hyONp2dr/LMnXwMAzoodLtmJ/M/ffIF2LPDZOTHcH7tJ/FP3m1lUDCsmI0ZfwDFrVQyIaYnqOSvhz63W6nc+i96m92RvZPKLxOQN2anYz3w3Ccq1RcoQzdLFNaxXas8AwCuGrXmOXeFR36XvB4EQzy4sYU0HOBgAzMJGprQzAr5TkDtmbd+n1qTg5KJ9QBuSuYQX5EEMAHYoMLlK74LXqS3FK49inV/TQ/HeMy59QjJKMmKPNeasSyA8h9gqk9VQhSc4O6IBWcYJ8DPl/+5Vmh/FzjTh+jlTOBd8CMI2AK1zWyeIOag3Zcv4vR0f5KGqN4oMipF6vpwmKC3gRnLR45RhV2MVBt64ReHgRwkswo4noYM+qaUaToBr9C7VQ/onNlptYZSQqwLo6AfnP1rh1Sd02Kz7GjerObpHPjjezJIaz8RBb1SUUrNTidffsa6ZESjvMyk8BejA7dFir2VnTz6tcrEONh6PEEyGsU6jvtao72ieppc+1gLbcyJ4BOeS/EU3c49iI9B8Qj9cnbb4w/NLAMCr+hF/8/gKAHD1+pR65bK3KN6jpPpr8ZLYT0+WO1JtAfCwmUbFqouTDQrpcPVIh4g/GzAyjC4HvWfobCcJ1pLjvsOOUx6oE9wWYcsAlWWHWIChhxMy347PovbxQJQm2YA5DZiFgxcJigtQvBcJLgsjkmoK7l/k+VWNgFSBvJY+izly8EzUAYgQE2A5MyVN3HAwvf7mAmpGz+X8ZINlTet7+azDT4+u8ayiD/cwzvBvfvslXWNU8HNEe8DTSYP7lh58v0vCrraXe/MkXNK9NRMfZf0ACg7ivwtHjt2ZfnOoR7vSZ0GGZxnArJ81wvJkRRgOulzRBw5QreJriz1Yfa9mKz2q+3SYmVnqZQ72ZQniFLF+CQCWCYiiVSg3yUHIZYd7saFgNsShxU4kZS2V5BGlpc+RH2axl7OXBB/zPRarpNHsAdg5Q8g196WGNkCBqO+NUUB1WQBSZvMhKOjOZT9zpbeowuYpcMmh86ioxb8T+5BFRtqbZW1KpwZqlQhTZkq1b5tBzO/rOMC/h3EYh3EYh3EYv6fxQWaqQfjaacCeUBg1O25hrUTJbR1Hky6SNdbX86jKoh40CeqzTmp5pWOrxnDq4CUiAaF7auGDb6R20VBcjgK+8Pj4J1cAgHnZQ3MY2I4a4+U0ejQKRzqqAKC3CsLRdQH6O35McVFg5zmTCB4AZVG5eHdOtJC9hA0qUoOMYuPFg0J9na7RHwn0J5wpaQ/Vpmg9d+aQncB45FBym8t/89O/RM2p35/f/hi/+quP6bOwqXRkIZpEfLCVh5s4HM8JGxqMxralrP7Z8QbPZkRU+qPF9zjRO2xf0HP6366+xNd/9xwAMC4dReT8bIL5NIB939wfjFxVx7LvbCQPGWqjAYD+zKJYy9Tknn0WJ0TMIIZTB7XZJ8cEyMtM6evIBj5Ka8eX6ZnFbIczP1shtqQIJ1DdKlT3fM0ZMOZ6rQOgWCRgPAasYnET6SA51WjHAtfdHLfs/vOXX38Mx+tbTQ2ck5Hx+9hN8O6GSiG6MqhqerbGKHSbCoIzrsml3COr2KlL5L5BRAEJoelFrG8TAz0R7ETU1Q4s9KDHCyQIUw3pHQGYRRrXpodg+F439F4E0QhX+JhilSsiS/UnCVKORKId3VcQ+nDaRca8GNKztRVlkel5Jp/U8N+C+EOeicoR0YjcK0DaJJo/LH1s+xGSuZAZszZC6r2AnYVfkoQ+5UL/nNULjz3DDuFS25IaxB7MbTKHGTnsk5F+qEAms1JQTkAKykkAQ9Sf0MMQPZUtorjMPxTsFx/ooTpejJAThfOna/QMJQ6DxvG8hWYm6UMzwe4tO2cIoOBesfHIwRuB+ntaeeVjco2xpyMt6KBsJD18l5SYItPYkytLHxSEhMNv7gjGbb9ZEHQY9p+TZKRcrIg9GP7tZhayptXsjMTIG74c1J5SihwERlZXUY2ARKLA22VGH3QC1a8Ieyu2QHeB+Ht26uI9TV9rqofsQg0HGM/4M5+O+PTJPf7rF38NAHhZPODvOzrofvXuSYTXXU09sQXPideACbZbP9rip+f3uNrQ/B9NOvwnz78CAPwXR/8WHUcPU9Hj3s7xlq06eqNRXNCJNZv0eLhaQrK1lxMi24Cyfjqb6P1AOkgB3ihEarsaZ2QmAABilHsBFNWRffx6PEo6f2pIsLFi5nS4vm4ypmQmf1dsBXo2KReO+omjGP7CJqbnltojtj9ilvZORCm80L4TReMlMD2m+RmtwiUHjYWy+FV3ge2a+393GkGWUEqH01mD7UiTcHV3hPmC2eijRttQsCOEh74uYmuIrbO65sLtHVJe+9hzi1WB+lrGg064BOMWW8RDz9Z0aOV2aQGmH46pDSVno4YezfJRxnqtboi1PZyktpn6ln7clXQwh8NBZe02ECxVGOQkOwHLwafqVPzvchQoVyIaKuhdeo/bCw9IUksDgOrR78H+USWN6/GRqV74+M547anHPshOnqQLeJkCWzHIvdq0z1RV9UaQdGiRvhcO/h/aNroytSnpJoPUBV0vnIHS7DOIIVK73ThNSYA8G6AVBzujpNp0XvsuHVz7/jeqHuDfwziMwziMwziM39P4IDPV86drqGmPbVuhXVNYXM4G7PoyQmLb6xnAZslyq5NWrBMo3hWRLbp75SKELAQg5ynzk8pCBvWjQcFVTCRqBcQgcPk9ZVjXq4soHCCzgj9AJJUA//hiH/LR9xquZEWVwkNtg+4qiHATxQMSrmIWHq62Ec6GFQnaXidh//aJx3hkIdi+azLvMXxNmaOZ+z3PVDtzKOaEN//k+TX+25d/jhn/ceslpoxF1/WI4VXqGRx3JVrO2r744hJHJWVRjSnxbr3EyJl8rUf83Ypc1f/NV1/GvlRjFH7+4nscFfQwns3WuN9SyrPeTAHpY8Svd4Di7MWWaX694/48Js7kGsFeEMEpkLpwNALBmsr7PTTAzBwKhphl1nepWoHqwUMNHJFPEptYNx7DQkRiTrHJoMNFhgQUlAUHSL+6UZGkIy1ZtYWfLdYJhSBYNCkBHb1aoWYrwqfTbURHdm2FuhpRMpTbNzpmR59cPMA4iRUbQzw9W0VNZnM7ifcBy6bk4Z+1j9qzvnKAEUm1yorYp1reS0Am5rRTPpH7LhJKE0QUArtb9gLNOauaLUfYTkXCYM4+VUOabzOltZu8StP9BgPtQLhRQ+rlHhdAd5FE6c3co7xO6mgho1UtWatFERABtJ/TN4XywLqAZdLUcCQiobG+Q6Qhe8EqWZkgS7SuqxzURsEsk0SC4P5kaQBvQxpPyE+cbyNQXav4ubzOiESljyWSYEsYTDR8l0hL0iAaOgQd8OiZ6pGUqfgWulO653Hp4YPqm/AYWtasvtWk0sRKadOzBn1fQIj3P1P9IA/V22+PISc15HKMYuNKUY1p8xhwqPTz1V1yva+vJKRlOAeAOx+hGAoKm71l1p2DguVFX1yWkTY/HBMUJFeZbGGosQjQAo2UdBEF3b2k38thNDdJ8nphQduZg+ySYL/q0uYxLgzEKJOEmfRRlMLVDn2A3tYSeqtgOUhoNxUEv6S+EL/jBPLkhIpFtRrxP7/5F/hsTi0YX23PsCxpd/qPX7xG7/aX3B/M3wEA7sYZrnpq4/j58ntML3r86zd/AgBY9TWmBbcYzRtc39LP+XWJv9z8KB7oH50/4jkbez80EzyO8+RcUqR5JEiXvq5uJSkW8WcptiloGedcqztitatWxVYjeIJZQy2vulHQDHl1FwnaK1f090ydH5D0vf5Y7AlbSOPRPAtrxEE8pQ1ZflfvPcNcCN4LUuOJG5xKYgHjkYM9HfE//Kf/CwCCyx8DZRvA/17/EQDgz7/+HFJ4aJYf7IXH/CntrKuuRqks5qzK5LxAyet98ckDSjZAv/z+BF5phPPELSz0jOfNC9h1kbWJeKjAgFYUnOUtR9G0PWOtDycObmpju4pZpsDQNhqS+QY0P+nl1U0W/NX0vSIIS4l9pjGw7xmaKwHl1/RqvwUomiUsgO4svZPlkwZFYNZvS6C20anJVRLVbbiGz1q/SF4zFy0Jph9iICeeKDE4ioyTIIDoyeoBI1A8JNZzzugloQuGim2SZo1s5Ogcg7gP2TrB8rpl+J0vaUtABEesHbXNxPVYOBQzurB3EmBeibC0PhdP6KWptKX2LJlh4u/p+CAP1WKlIHuFsXYoF3RoTKoB/VjA80sgShf7SNvnEtPvmDbvge0XIyRr/05nQzxM+77A2GdT6nw0fi42qa/Ozh3EcgDuaPfzWc+n5Mwp9KaVDyJlA/yzQbXGzQ1E6PczOvXjbUirNPXZeRh+MWWjyIw5cJNOLWB4g5vY2C/rCsre9FuumZmU8fjTkTn8dF/TkzaShz6f3+IfTV/jl90LAMCzsxVG3nUqYbBiPcO37TG+WZ/i2w3tMnfbaXT3+eXREzzcLWJd7+Nn9/jyiEhdrS2geJecvRjwbrOIdb2r9QI7tuubHbeQpYVldEDsZAo6So9ynYgvXqU+QVemgClIS8q7wAATkWQkLG1+8dDWQPMiBSfVbXjuVEMNdXdf+GxDFvutShdJeUk/b5KdXuExPrOQfVDFSu5BqhUYjj3KPyZD93/10a/xp7NvAQBvhlP8B9OvcWmIWPQ/vf2X+OmS2Gf/2fEv8OmECop/+sff4i9Wn+KXd8/ozz3bRMnLF7MVOqvxzeMp/T3pcbGgjdB7gZvNjOdX0b2ccoBZG7hQCzQSqBzQhftXiXxj91tgpE0HWLERGDjbdUsDVVvYsN4HGR2lJB82IdAwU78n9xgOTmEom4yEuCIj+rSCrRX5e3VW51QUZAZins5ViWQiPnUvRhTLHudLtu9r6th2JGsL12po1hovHwSqR94PKgHDmXp34WGnLjnQqExmEbR+YmCHlMVGty0AaiOZEJeRpDIpTFKICnV3Ty42SM8gZqQyISDkzIN4PVeln8+z+rx3O/ye4T1RKB/t+uyg4CcWI6vRtU1F6lw+m9v3dBxqqodxGIdxGIdxGL+n8UFmqoH9K6RHzw4hfVMAmyK2XggrouNGmWWKu58MmCy7aCbdDgV23O4xrCtuCeAfLlLE2Z07uJAtzkfgpoptAvAp6vMaGI9dDNjGpYteol4D49MxZtBlYWM247WnbIwuh/7cxgjXT7JsFB5iVNGTMbj0AICrBAr2wzQLilInrEFqJsB4kph7opX4/A9JHP8Pj99hzpjdqd7hv/vb/xIvWLx91ddY7ShV6NvUR+BHSXWmIIrwUMasYfOmhvYpe7+ezvFtRRntXPex7v3m8QjNahKvYXoVYe3d/QQwMtaZXaZQVD3I2EYQourAtHWFTzClIrg9ZC+yk3BlKnqPS0tQOgB37IBQT+8lCQmAov32wkeWtdqqOKe6pSw1QL7NSwvP5uP2uykqvvf+zFJdjNdmf+KgX9L6+/GzK6yGGp2hdfCmOcYfTOm5fFFd4S92n+N//epP6V68wKantfrvHp5HHeY/e/oNPp484JegTLUuR/znz34JAPib9Sv89W8+IW9WANNFj1lJcF5vFZobSmtULzEuXcx6cFdFwQFfOIhW7bUVRXiTWasRxrRJz9aWHu6I5622JL7P5RS1lRGq9QXIT7UIa1okswdmowIBZk5wrc+0R5z2UcsaYJ/RTBnNlim7020S9u9OPdwrStOq0qAoLDn2AOiuZhAstiGlp2QxCs4neHlckHEGQMxuYUVcL8VyQFnR1822grqs9pi9oVUGXiatX4c9px4yd8/mNMvQIQBwO0/RkbJYmAc5JmgYHlBBySkoKYVacpfKHU5R2cl/QpDOYtqjZ3/foSninuRqguSCdvTxskE3aliXixm/n+ODPFTDEMrF4r5vFaZvVZTws1WCjcalI7UlAPWih7US1/dU15PKYdhynaBw8K2KLTUAYh1P9RLykSHBNxpeJ3WRvDcy9HzGvs9WRTjMKg/0Eortu4ZdmYgzpYOhW8Lkew3Va/TP6e0TjYqwip8Z2COfCA7bdKhWm/R1eU+GymEDGo59PIDNzOBnP3+LmhXkN2ONy47++F/84s+gHzW+dkc8dzbWK2UrE6RWeMhXDZ5xLXbyasQlt9Csb+ZkmcWb5NiUeOzoYcja40/P3gAALmdL/FX3MdXrAJSnXQwy5E1JEPY8YVHyQcW/HXtRp35PLQZewFeJqaR2MkFrIlm9QVGrQ9xMSh+fRf2uiP998ylgLkaU3IJV3wioPpFQxplIh/YgUASyzZCMCMTJAPVdjY5JL//8Z7/CTUdkoV+8fY4np2tcTKkG+np1iv/x9l/Q5b2g4CL0es4H9Ay39aPGjIXx/+r2I/z0+DoqLD2frvF1SySmh34KXRssuGd4Xg14c0u9W3ZdJgMG5dmWjD/a1MTSh1rpPXcZ+FQrdRUpI4lMjjBwAXwB6JuC51cDVVJRyiFR2QvoTKGoXPkIyUYTbFD5wukE60YrQABSCAwLk07ZRsKrH0CRGay5e8n/OBliwNq3NXrpITcc6JYu8i2EdIBHlEX1kmDfH94jNNVDA4Y4m/ZRTcx3igKSAN3qROqicJkv0bFiWCCNVVm7l+HDMgTVnUBJKqK0ZgVQ3/AVNfZanVTWX636rI1GZs9TEznJPtCH2qzLWMaBE6T2BEBWFmVpoPikfljN2AIwkSrf13GAfw/jMA7jMA7jMH5P44PMVGVpISsLty1iZKrXCuPCwwQXtBqwTAXHxGK6pCheCMAaDcum5XZXRW9IP0hAJcEHUSeOfrFNtk/9qaPm6ZAtTdK9CQcUj0m/VbgUWYtWwMwF7DsKH4VOItr6LmkCm6nHuPQob1j4QAEmZFhWQIwyCUiUCboxNUW59Pk91p8OEBlj1rOu7uyow20zizDX9moOzVl4NdBHDm0F5UNaYrkWqnsy4OnxFi9YYF/C4zfvLvgBESwcsszz0y1G1nyt9YhnJUHLL6tH/L/6JQpurSgKg/GGJlMYAT9Nvp1CeYwMNZU3Os5VN3UUWgaCmgfQJYVxO0miFzkxTGgH/1DGeVSNzAg2wPZjhnGPDaa/LvfEHyITe07wWrAwU21q/+jOPOqXhGEOfQGzcKQ+BOBXjxe4WxPsWk8GlMrimweCx7u2jCpHzgnoaSIMaW3R9gmCb9h2b9iWuH5Y4GcviAz2N5cvYTj7Op63UNri4ZZQhAcjUNyyoXiTyHdeA+glXBlSd5HattgwO7dtixrHLPsb14VOJDJSPwrXIMg39+mMbVGKSgXlKrxfCbb0yifCnvbQO5lEHbJ3cFiy4lNmyxf1iT2SrRuA5mOD4ijIbHk4Xpt2kJAbHbNprwQcZ2xoBSYPMr3zmc3cuExteehlJPMAwOPtPPro6h2VLfJNO/wtW/skks98n7CHuCKR4Yo1aRD70LuX/RwMlSSCxdvuRTJ8KFr6HpDMxiO6JkT0azZLIlb6k8BYltDM2B6PaN8FgKNlg6YrsWVioZAeUvuIWLzP44M8VMVtBVFXkAKxZmPOPL08ARY9GTCpaHOqyzEqgVgnaMORGUzB61PPR5RVKniMgwYa3riOHOwxr+xRQnYybjJeItLwVSNJEJ83BVsngX5XeKhOxlovnEiGwo2Im4wwArID7I/o7XCjhAiQVJAUDO/UKKAZ9lVdelHm//ge03LEmkX/Nw9T6CW9TbvrGdpuEYXh5xlMbCtgOHMYj3nT2Raxf1O3abPTpcHz2RoPHQUI39yewjMbWne0oYUDa9NU+OiM2K0fTR7wRU0C/f/HikTfg6F2t6miahUEIGYmsrl9p5Ibj0mtMFCgTahIG2iA+sHPWTPT23Q61ha9kYBA9KLNPSs3n5vYprT8dyWqBx/dVcxU4OHLVGssH2SEIWWf6lQAYLk0YXtFBxTPz5VcogprszDojI5KYKdHO2y4xm+MhAfiISuFj96/7rGMUBwE4JzE374h5StnBF48pfkulMV1cwTBTNtiJaNqUt7v6D23eATRfysg2gxuz2qbckjrJUjVhTqqrz16ZhDrbQpUHNhYPiDxmem5bqgX2ExT21I8LI/Se1psib0djeYVBS90U3QAhzWd+6J6CfjKJ2hYO+iC71Fb7Dahd81DPOmotg8AWeua6vdPC6eB/pzr+KWL7XUQ1GIXavK2wp7kpVc+6ORD9QJFG+Y0lSK8prlxmcxg8ZAO0XGW5ke36XMS8zprncn8g4ttYlcLB9giQdTCeNhJ+Jx07dDH6wvAvKBFPZkO0Yu3HwsK9sbEA3HewZn3/1T9IA9Vf9HDTwWKMhmR920Bv0ttKVI6VEUQdfDRfm1SjujHApazBjszKJhIcLxsMC1GXK+p3jU2BYpQ15i7uAjVVqFciz0XjPDS6YZ0Y/O+vfhzkiL82AyuPQRHgblN03DqgLMeWqemazPjjbu0cJ0ChsDWSPXE7qnD0WdUYPmD8yvcdjO0vAmfnG3x8I7qpuWNgjQC03ecGZxkJsuVh59YzBb0IpnJCNNRVmXmHo6z/5N5B+MU1gMdACeLBmJJdcGmL7H7zVGs4Q6LIpJq3rbHeMmkpaflGnU1YuAAZ7itIhnGzD3J7ZUp6g96rX1pk/WY9hCjhAwZ+sxBsoSeKB2enq2w5cBiB8BuuOa5UzznqW0hygEKYP5r+rnqkQ7Ukee/eS7gWFBj+lrDVcD0n1BrSzcUGP46Y8v8huatMtTuEaUUL4AX3I+76Sssyx4DWxLdbOZod3S/ktdyt6V/98pFTV+oFFjNTxp4L9Dwz3kn0DAKURUCRyc7rDVlFENZwH5Gh7TdFCgek3edm7hoVu/XBXyYRyv2zKpzKTyvPYxIh2wIcsLPxQKVF3SgRgm9VJ9UHT2HmJEqHx1+oJIoSmhlirKT8yRQIU8GiPsyXt8jk/KbUH+sqwOkI+Oc6spEXkYxHeFcCmB1IxMSthWwk6TnbCuftfOk4CGvcQKA0MDIJLpoVxikA7cpkAaQbNUKv6f3nQcxXlKWH52YBsQav7B0iIf6d05A8omWATOlmm00T1+kmqrugGoF7OY8x0/GWHPumjLaNCptYY2CCEiS9ERcNHnx/f0c739YcBiHcRiHcRiH8e/J+CAz1clsgJoKOCeiN6cfCM6TrAJTT4bIutMCUIoNxZ2ElC4GiKq0mE0pK5uVA7Z9Fb1Xy8sitm6EOiFAIvZ9oWITuepSG4E0DIkF2FJmTdcWaJ4DJjh8ZHWecZG8J1UrMG4KDBOuQ86H6DLijMCzlw942BDsar6bZXUxH1th7vspamWgOAu8vZtDcMTpPm8x7ApUD0kWrrpjCHkEujOJjoX+/VUNFWtMiB6sSjp8v13GZ1IqG7PiSTmiedFFdZVaO6xZ/L2ZlFgxJfFU7/AvP/o1/s/LT+ke5TRG/8VGwFYyRtf6KNn22CG13ohGQW9lVJhZPtug47rjyaLBaBXajhmot1WsFZPTTaqBCgNIzgYmX2sUO4Z7J8B4LhOT90ULwTDu8T+/xH948S3+/M0XAIDh10vYHxM2WU5G9LfMeOa2LM81vifHWxiu483KARM9Yt0kyDcMqSzK0qJjuRvb6VifgwTKU1q3y0kH6yRaLlX4QeLhDWXMF5884OkiiUHMX/W45jTkZnMSIW8z9RBHA3xg5U4sBLOyA3oQ4cislgknyG82IArKp3sUKaMShl2RQka0y7I0QbKOIVMzs6RIJHdiT4ZP2IT8jEsPxXNgGYYMKnm+QMy04QQhLPzZ9KOCL+jnx6mKPslmUPCdinVBM3UxWxyO6fWPvrpskgBw2SXsxA7oz5K8pi89kCVvYkztPHbq47rNBSnMxEMaETMmQPysAAAgAElEQVRzYdN8C0t/L1xD73xSnKoArwUUM3BtgTjf4zR5QwtHoIFq0zVzoYzVT2xUIUOvYknNawk75X3BC+K0hD/OpQifsbrf1/FBHqrNpoI0NR1IDOdBeYipwfExQZD9WEDxpl4Gt28A67bGMOjYiqoLGxWV7nZTbN4tUN6zlNrMR8hIlC4RmIyAqx1Gfvt8A1R3+UuRVJOkEZHIFFo/9CZBt/HnhlTTq+8FypVG8xH9u1ce52fUuvJq8YjNWOPOzuO9BDUhrwW+uj0DALw8WWFe9Hgxp0O21gaTl/SifHt/Aretsf4pQzdHA3BNO1qxkRCf7HA853ruosGEYXQpfCQmGSfxdnuE24fgBORhAznmeIc/fPkuHroPqxlafk730xa/bajd4/nxI77anuPmkg4AaQXaj7ivcUeWXKEuiSOkWlefIEsIQH6xRcG10vXDFIJNp1d/O8dw7GJPL45HWH7p5aqA3qa2q6IXkHzAEizGRKhzj/HjDq+eEqb35vIEf/BzUjx6OX3Ev/6Lf4ya60/2zKJk2cz+fpKUbs4H+EZDTFM5IugiS+Fxb6dYN3X8SKHedzTv4DyiUpVokxm4PBnw5IiIUJ3RWG0nkZSmpwZWpzkqlUXJQaXzAqcT+qCbsxqGSx2u8PBOROjcX9dxPYYR+yt9nB6YGZUzRNhUt2lLsjMHG6DgytHhFmq4UJFcNi5Za5hbn4pVkhUVNsHOEgT5BpKUn1iYltV+thrFJilVQSISrYSRgBXwXPIxT0ZoDhrR6GSTN7HAInEq7KaAU3xYD6TKFJ1kehE1dscFonKUcPS5Iz/CihhgC0sBc+w5rV1UaPIqwdXSCJQPSZPYqzTfcgB0l3SkIQDDwYjqATmQHnX43u4lByAnJgZ3xVqg2KYWJtUl56X2mSOt3zawpJDkQZWPVpV2yzKo/My8EyQ1eTApP4zDOIzDOIzDOIwwPshMNaqsrIvUjFxb1JMBbU9hoFIJ4l01k6Rj6gWclVBMAppPegwMITe/PYLywPCEWzwWPSTDdO6yji4p7kUH3ybPSq9EJFZ4sR9ZmolPLjJbCdUkpRSzyHRBvUi+iIZg1oKdPfBY4ZE1fPsvNeZ1j3HHRJScaXxkYJhauCg6NKbEMTvHnFQN3jXLODfyaRfnYOw1HLNg+6XBRDvc3VEGo0uLh/uURY2f0T01fYn1wxQnZ9w2YjQUM6Cdk/i7qyfoN6w73KgYrW/7Ep2lG/7v/6//CqJRmL2gazS6iqQIOEXROzOuXZucV1C4COXNXm3R9xrDDd1vdasiO7Q/9cB5H/1DjVHo3hF5SPYC/ZOEy1UPGt0F3//JCMlZzXLZoioMHhr2KnUCv/j1KwDAb77/FLUn9Sua9BH9feqvKs7ZpJ2VugLxaN3UEc6fT9htaRNYIwLzM8okS22wbmu4B85UjYA/pfRCaYtLRgmcVbA7HfWsy8kIw8LnN5dHWM0nMExsc0Zizp6sdTliXSQ4E8rDcoYiXYJ9PTNrAxTvyox8p3l9MxIhLKKqlOglFItLWA/4adKmzl1SnPYwE4+CWey+SK0mxS4RavpTao8K7i0oXEQt9I60nCM5qXKQDEP7kqDtUAoxvYK9D+mdj2IvbpQEZ25ZjORB7rGcc31ilZmB5xq745K7EDijy9WnpCEIO5Api3XKiej69HVw4Yl6ykPWlmfp+maSGNy6SURI4cklBwDaj8e49+ibZIhA2T7QPg1tUNjTxFb3Rfx7ZpYhPVZAtLEPB077BIG39O9/CJnqh3mobgrAFMBixPKIVoN1EuOoIMNB50XcuKR0GJl9aq2EUg5VSatmta0x8qEhNICTAcslXXO3q4Hv2VpuJ9DxYSs9bxhBItDnLxho8brEbJy+4/rNPPSVpl66UAMimJhf+jldM8BvcqQ6DQBsHqfYlUnCZVy62M5TzAf88UuSuOtsEeUAAVLWCapGk3JEqU2EIM+Xu1jj2/Uldt8tEkuzrZKbxcJGuztIj+VJgyW7n9R6i5sdHVj3l0dQKwUZflECjo0PpuWI7zak6CMLi4vPHmO/7GTeR6jTTRRtwjrQFxHhTb8qIww3jgpFYTEytGorGRVrQptNw/VKd1tBsxSk/agDRony+9AylST1itpAMpz8+G6J8qSDe83C8097eJfmPpfRw6oAeAOaPdmh51pusegxqUdYnuO2KTGd0bw1fQnvBcopt81IH6XfVs0Eu/tJZLi7ykWZv2Eo4wEV+iIDi7X/do6aW0u6ZxRoOT5Up8ctTqa0vq9X81jeUEcDtftGKzKB7hlDuhWbqgeG+0bFOqdzgBMy9lvDAZLLGz5D6X3pIXY6HtRkHJ4OA5kqNMidbrrzdJDpncBwZhMjfJCZIhRfNjBf1yod/FMDOAHNLH9nRezHFYOEeOTa8YYC21jbnfo9IXuvsxqxS3+r2IpoleZLB7VV6ZByIjkSaT5wAx/AiiRkDyRIl68f+knl6KN6k6/FnoGBapHWnwAsKyIBoADtN0Wc4ygfOREwEw8XnG5GkeQeC4LzXQZfSw529lS1QAh3WAd7Ivzv+TjAv4dxGIdxGIdxGL+n8WFmqjMDTAymix5DaIZ3AnU1xuxstApPl0TusU7iHTNCtbYoCpuyl+sa4L5DfdpgUo/Jk3Wj4RkWtQsAnDXJ7+s9UWz4xIz0mnRpA3Nvcp0YvkEIIlifSStihK8GwJah+d1TxMjRNymq0PfK1xXMzKHkCHF4OeDiCZGRvji+xcASKrUaMTiNm5Zg0d7qSDJqTImvb85iRqRnDitm5/a/PELVJcJHUJECKMKXLBR+smxwPGnj9+6aGe5ZtUduVRKpAEXvIeN8+/V5sr6SwPW6xMc/JiWgJ9MN/v72CU29ocZ7z3DwZNmhvebnoj2Wz+jZbrc11GSIMPSjmgHvuF9TecjrCiWbDPTnDj/7Z18DAH7x3XNMvqqSnd/UkV4xAH9TYGSmru4F1Ns5PM+DepdQAnNCGVDMFEqPZ6/uaY77Ek9O6bksyh6dKXC1ovlR2sZ12nYFzKgi3GwtokmEHxTkVu1p3CqGZ83M098HoGcj9WUypKl3CdrWRwNMo+M9TqsRLaM2QiTSnOtqEiXhDM696BLpZ13CCx+t63KikteA1y7q7Oq1gjkK4rw+ye2OktSA+gSThsxJeACZ+Ht9LaLpeYBdw++gdBCBTDXqSAhSHRGCQnbqCiTTicpiOu1Rcbnjdl0RdAzAGxEVoPSO+nFDlqy3ItqojctgksHv3dKjvuHsUSQSrF4pqD4jTGVzFb6Ovqfax+urXuz1ASPr/bVSYGCivZ16qDZluHYCBCjDlST8UNCrgdkbDTkGpljqyXYF6QdXN4woyCQ0IUcB0YoIZ3uFZG6AlJ2bKd1jYM9Xj9S3a7Ns+30dH+ShOln0UNN9xmlRGlb1oMVwsdjiiyUpS//i/jnmU1qFg9HYrWtIZru6IwPFThTzaY9uKABmj6pWAnxueOkhGUJTvYh1kTCiIHXtodcS9W2qRwVRa9WS0XaEijNvTvgMAutoUe+p1gRllw6QRmL4klbzP3r1NjI732yP8fGCWKr3/RSrvo7fezZbozH05rx5PEZZmtg69Pq3T1Axg7XoWCYxyJSZ7GVXHosZzaNWFu9WSzRv6dCWnUREajVBS6H9AE7E+VYD9iwX5asdfnxEz6m1BTTfbzUbMJ/06Dho2j5OcPEJf7bVDJvXR3FuunOg9XzYbYoY8Mi1RLERaF7QNf/oT15jWTCreVAQJm0S5b2K7Q25sHyxoecQfs5OfRR/gBdQjYRdsuvQso+tTmVpMC9YSMELOC8iy9x7SaUFAHarIWoK9AA+ULmVRRqqv0fRgBEJlq8S47YoDbptFWHg8Si1kwjporQcQOUOw7C0dwKC2edqI2HmFpMTWvDdroS65Jq4pb8XD6w6q3muBQahUrvZzMU6XrXsYZmv4C5rKnGEFpImHYCWFcJCjdFOkgm3OTURTpajgJX7710INkPpJJzirvTRWEFKh7ap0AUDbemhbtkn1aZ3K3zWcKiaKZnNA7wmXGoZmVwnbgQATC7DfQTYOF1zPEoM/xwm1VuRJAMF4rMdph5yK2KLHX1O/qKlvxuUsOCT6pRZOBLKCLGPEvB84MoBcU0PJy7B9aBHFw562VOQGF7dYpO+J1wSvJCGWMzh3+OU3hObOYK9r+ODPFTNKOFGBTMqFFwbLbVFVRjU3D5zUjX4zZq0aNtRx/rq9moOeETJQTVNxZzBaJhRRQWXnJiQvwxE/08LLBAhAMC3GqpXsc/RaUT5tf6cqPZB47NYi6h3Oi5EfFGCzmyUHxtTNuRKYPO5xVN2h1kNE3z1DWV3Qnt891vW360dnj57xPNFknf56o7abXaXM+i1ijXH6W2qIpiZJ+u6sHlpRBLT5KiLKlWbtsbuahbrfb7wcR+RBqhuFMrHMF9JTcjr1B/XPXF4cbxBy8Sl9VjH9p3jSYfOaLxc0GScPd/hr69f0vV2RbRpm560aN7MUTKpS9jMTH7m8Ok/fRMlAJ0X+NvbpwAA9aDhioQwmKMRapHkAMdHOlDshDZCG3Rkz/vUgzxK2CkiguGsjO0wdWFw39IBa51AP+pooWV3RfydYA3Wshk21kWs1ZOTiIhBgtNJZ9jViazVdwVpGY+h/WjEdE4nQ1UYNF2JgQ9SM2j4sK5KC7C8XmgDaW84KLhVez3UwQEF2M/M+lcjlqe7WC/uuyIiIN4LGHYd0oMAfCLj9EsbM8lcahPgLPw0FABF7O91lSVd58BXaGScmxgQZdJ+0S7x2xlpCNehUOijLrjvZewRDQdXMKu3tY98BWHpHQwqUGQlF+YH8V31Emy5Rv+hf2ogYoYvoBoRtXnhETNyeCZigfYFiBSo5wpWwtL/hcNevOgSCbPRsC9G6tkHyBko3FfhgSVNlt9qKJcIfSKv83ItN19zsX++T/9djh5okUmwCia04b0fh5rqYRzGYRzGYRzG72l8kJnqOGhIpSGkjyzeRd1jXvZoDUXk361PInRojEK3YihrFMByjELtdqejOk9jBTfpM+SjPCRHfbAiqvGM7L4S0U3tIa/o+vVaotim+qiZA+1zFllwApPL5LJBhsspQgyZneo9OWQErdgaMUton3jMX63RcOvQ5eUxlv+2jPez+Y8oDZxMB7RDgf/79ScAAP2rKSxnBnLiYWcO9WViabYfh9CU67yh7Hk0RDTAjAq3jywWMKR6DAD4mYNmR5vyUex5zAqH2LAvnEB/Ql//7J+8xmm1w3dbYhSfVA1+fExQ8LfbE9w8zmM56te352jWlMqrqYntQP1vlqh2WS1Ne0x+ShN5NmuwGSp8/y1l6KKyUAyLqlcNhqcq1mx1aaOZwtAXseY2HjFKwFG+AFAxU1cIj/axhrrnDPQM0bd0NCpm9YMpMA6aMlQQ4zQIEUjlSEUpICImNeV7xdAbr7PhxMEdh9RMxDpsWXErFbfULBctFszKvrxfYtyUEIHtql3M9NxOR9ceCNDzC4hIMsOJ6zyqJnkR67nzRQetbGSPegA+tKENApLr1AW3vESYsRBA4BM0tN6i803h4/fiRQHo+4KzXV5LFlHxCCA1oAD/+sJHZrBqBYZjT1wMMIucGb/VnQTbCmNYAsO5JbYwABgJ0YV6K2W0gQHrCqB5xQjF1EKyYYFuie07ngTJIw/BjGfdUK01CN7bOhOy0BmT1pMoSXi3hBN7WWX7Ry21/wDAYxlFRtQxvauSIf22qGJ7ECxIGxns1zpk2WmGqAvPcH9AljKmMYSISIPqqSYe7ivoD9thH55/H8cHeaiGIWUy9h2dxG0zSybOQ4J8x05Dhn7WqYHZFpAsZO9qBzFPEDDKRLoQbRLUdhOHjqXHZEsWVkHCUK9UfNHN3EP1CbIbFqmFpr6SKNce4zwRBgLkm4tmByhloM4TqJ57LgHYpz20dHh8YLH278r4og8LQL+mC3ZVRf1xoU5z7OCOErtKPhZoX9LnfvnZLa6559Hc1PRZ+FCxvYINSlKdihurKBwgMyWdPrUwjHOyjgtuPK4AWbCBpN38R/Qm/nRxhSflJh6qg9Oxl/bt/RGEAN7dUu1UFzap/XhEM3PlgP7zLsmjGQm7pTkYBo3+fgLJcKq+1rH2NswcRC8TIatWsD39nt4J6BBLzaltxrchiIjGHNCFAUYZWxNUaeOa2+xqjOwuY4zEuKoiQctpxAPRWQkhPVwwat+l1iqANtrghiKfdjEI8E5ER5Dg8hOCH+cFrh7peY7rkg7xcDD2MhZpi6yOLEeCZ8Nzkn0K/uSOSEQBXvalizaCjaywHabpEBSIcKfeyBiImqmHHDN40KdDxJVcIw6ONoWPB4UYMpnCCRup82FQbVJLmhACQvl0EBmg5LYiW3EfKUPswTaQvpdae6QBxHKA5+BHbWW8RrElp5dwaHcXGclwk6lDHTnIViYVpZVOLkYMq8aARSRCk8sIUsKQzWQ0Y2+T4b2vHNRVBREMO5YmGqkrbTGt+9gqB4FYyqrvkg2cV/8/e2/SLFmSpQl9R1XvYMObffYYMiuzqrOaaoamF0izYAes2fMP+E/8AUQQgQUrEAQBaamGRqpIuuiqzIqMzIjwcH/+JpvvoAOLc/Totcha5qIi3XQTz8PeM7t2B1U93/kG1qVmrkcyk75p4Nep7J/KSAD5cm+yHWt5z+gmC/CPePwRfIXTOI3TOI3TOI1/HOOTrFSdCzBVQN34QkDqGuxWM/XnhUmozoWsMR81Rm0cLbyfsBls0l03msCVaTYPaKJmkAZv1Ejd7Ykp9speLLvW+oGzIfurLJUhtF9LZbBJR5T6qTF5NgsHGPqJNQpb7yKpxyaZhO2uhfnI32FcFulN/UhKpOhuGIJSx6al16obnUU883j2iiUf7+8vELJna805qPZOTOjrVBxs5JgBwLUjYmU1Ss2t7ZFEor8O6pBjRiBMpAOjQOoP4wIBBgthyY7B4tfvmGgVpVrQkPLaF8irszh7zUQte7nHejPTa9h+7xBm/PNwxtV0XMq1r4p3K5qAREkzQ003MTAw0O0qDcTnUEKhwzwqEceP7OfrpFJwVcDuMHH00iqEuKofS4XlRcI0vhiBkbTNkBw0XDtVCV2dYJ7xfWxsRDvjc/X2YoWtxO69v78AUVLjEwAYD5lizvB19SCEpFkq5BVXKscUJf82s4tnESYUk/pkkjp8hRlpKDcRAE+lOt0ZrV7MQErwSoYJSPl+rDZGoc8wjyBPapgQxxIGHusEvywMXArFIzuZBF8yHUCpMIhnHxJILkCoCcaXGLRxWRiziQqE3L/0wN7pdzF+YtxAXIkdnsvvPg9o7q2+2N/IPbA2GG98Yf86wAqEzIhN0vKPgkDfAOzEnCE2QkiSeah7EfXY7doys1nu6WY+KFmQKGG1XmhrxL5vCmu9nphJdIKWTIws1C2rAqMIWjXjiCSZUTzbc9VqQiabgcPOf/zJb5/morpcdrDzhNoFfPjAGGnzmwbppcf8Jbtcf371hE76q7uhxoPApdVvWix2hP3n2Q4lFgZrx9IAIyzQuvGaH5hWtfb3hhce8KSwIlCgptnHhFgXzZ09lH4D5MHMkA+V+Q0m8MMOTJxm8nFNQslTMIg2wX3B39OPDviNwJ0XSS0LKbBMJOtM6b7ShzS86vHyxQrvv7vS46ou+CmK0SD0FnGYOLFkdmG2pAMwbmvUHyq4nJPtSZmp46uhbG7Ak2ktE1C9AjpZHP/y2y/x+nKtFoCb7Qwk0htDMrELTDoOTk9Ifd0hSt9uL6xWK5ufeG+LUfiOZSI5cJzmQ14bkRKzIHUhpRKeEIkXUwCqq4xOJsJZKMb+A8sXNHQeAQtZ9MLE0csPDnSwmH3MUGhZOPk9CmwZWoCu+D2axqOqvN53F7MO1y1LqQ6+wndfczCB2RvEmwGV7BW3m7bA4TYBfWHJ+rneSsz0nPTxyJdAleRSgQsbuf/yJqFOMMJyDr2Fe3K6IABlMfCLpOeG7x1g/o0svnvg8EL+IDLEm+97tiLMG4uoKSl2YxkSlS8Q6glD1vKi2Qq0W29KBm6smYGvC8xs4oBGBfIGJVBXdMEpHze4JbO/BPo3cnONRr+b8aTh6GHOi6aVHqvtUPBEdWfKbOZyzqrNVLoGxGXCKGHvNJBqhEOTAJeYuQ1g3g7YiuZ+1rJMbvG/LuR3CeMif+fETm2Qaz5xhDIowRLAcSsqp+LodcnWkhUwLglxokuNjhDcFC/+cY5PclHd7RsYtBg7p32e2X98j1ezDk8yQT91M7W/26xmIPFPZQp4UjkC7Z1OaO2zA95crVTr+mF1Vio4myZEDa7MpkScmos++AWhu0loHmVSPkAno9CWXirAN7J6bFbHN3Jok04spjOoHuRmfagxfj5gzDvhVMK12zsDynKV57z7z5FuZgAOr4Wy33p0Q4WzZzs9p3mRAgJoNsBd82vzZsDtA5cD4a4BRHdodxLMnCcKUx7StLdwG1t6xG1ElImqvwZcrpouDL76+oWmysAmIPeKDJPKct+wWQyws6L7HMXjOAUCGa4MAWD8rNcF3a0caDR63VIoC13qLHvdnueZjNTL2eyt+rXqtbsSuY2LZVNUR7jKq6TBuQAnx9GYiM2hhI2HKuLwopDSdDQBiMTJIABQRX0PP1qMg8P1JavvKxuwGfk9v/7+BrWI98friHo26gYwelMMEvYO1ZbQP8+7H44+A3hBytWi6QmwUGlLmlxP3/KiOgrBzFwNWoWDmGzj8vmiIm/JPXb+zoTZd+ZIqqFxaTbBnyck8eCtZiOiXF9zVywp4zxy73FXEBBoWLdB8wjYg/TMzwheFtVEWdZViD8F0Ull09VZtB+L3+/USzhWYj4h96M5mNIr9YCX/WmsE9zKlhDxflK9ERCpLDqxKhF34yJhFElNmnve6Gf0ogIw5t43EJYBr5+JkUtfa1Gw2zeIDw2663JcuT9vRoKXa+3nCbaDLrLJFATBDAClUhSYhLKZmshrIH3YH/Zcp6HrP9Zx6qmexmmcxmmcxmn8gcYnWam+vNzALQYs614dcl60G/zV/WdYZYvBRGqU3TyYI1tBO0B3Zv2LgJ/8nG3yFtUAQwm//HtOIUFnio1blRClqiGXEN92MN8x7Hr+91CHpe0XwOwDob0vcJ4mSlim/WfBd8iwGnjXl00hxmVCOA/a75vdln6TbwFKtcI18UWvST2HN7FktRIAk3D4QkwuFiMqwWqJgPV6VnIk514lMuQibq62eLXknqWjgHfvrvk967IN9c9HxDroObBdcYCyB4NqW9xzmpsDZm/4AjzdLzH/FaMG/lcL0GcDSIKmySbUwmD9k2f3mLsBjz1fz/frMw2kJ0oYMpPzQ8Wf84Zh0eXFAZvDmZxvQRcyRNhZrYTrK3HY2ghmOpa+IIUCkdrcF8wylMGCpJJcnB3gTMRqxce4OVQa7j6blV6XMRG0sZiL6844B8bnfD6q2YjFbFC3pVnl8e5WAgdswqubFRpXWL3f3YuT1IemyHB6YnOHXOQ/VdoHd1vL4eC5L94bzfBNLk3chBh+zNfQ7aZWlYQ0LyHfobNaCSMyWqEB2BN4FoAaPNR39iiJZThLeg5AANmIZpJFm6twhm7ls5oAs6qU7W778lwky8+Gbydwdu5D9tJeWZQDy7KuVJVet9sS/DLp+cGZBz3yHDJeBYaH+2zMUe7vflFMImASAoD6+wL/KsPXAkgT2JdKnnKyKK2IjQNFOjKwz6/58wB4wrvfskzM7G357IpD1fucttTEck/0pdddPxn4RUnWSUYC5OU4kpucO1PyX4GCrGU3MkVtIvflk//xl6qf5KL65dkD6mWND4czLBxDgqtxhrvtQpMoxn2NSm4a1wFi2oPDWw9asG8wAMwBdYMBgF/+6jO034rucyy+l6Gl4krztkc8OLRP/P6HF0D3UlyH3lnM7qL24MYFaa80WZ7o1fqsTjp52J7Q38j/F2JJJmSECWkJiYlSeRF3v20KBEPiNQx+UPavk9711d/NFYYaLiJMJGTXNkoVSCZTfxZwn5b4+eUd/zsZXD9nbHu7bzGKfMN8aFCv6yNLt1Hs+qq1BQVJcQFwM+d4MwCgg9UUjeYXK1zUIx5WEsdGCZ9fscZ0WfV46mfYq09twnLGH3b33YVqYv1ZxOLtRpNXNl2D5lqSi84tTCBEITGRN4DcHxfLAz7enit+1dwcuD8NID7Upf8sG5+8wUnzoNZvXVfBuQjrMomMF9P8XXK/N962cCOwfyUwY4ImzLTPRrT1CC8yiE3X4OKCNwgXsw7zalBC0t1moRpQvOkwrGW2iywvi6JJNLFsGv2l52Pvit9yvndopAmZjBeJfM/VG178ASYcpWQ1aYiqIqlxawu/DEhyvtzOlDZAAxiBSG0HhobVI1viyMDWnnHp0YukyeyN9ufDIqq8yzxWqLakbZf+OiK37qs1od6QLiLk2Y8WAOyQsHtllF8w9dVGgkKwTMRJvIEAQKsK9jVfi7YKOGzLsxbCMaw73ZwsvrFHpEO/LL1iDv2Wy1ZPoh+JzwH/kYF7LFanflbclmAAu7VKyIp1wijuU+nAOmO1Pt1OXLEuo6YrDZcR1aoUGofPQrknpP+ebSiTLRAy8yvkMHpCvYFGvRmJwvtjSKs5wb+ncRqncRqncRp/oPFJVqpdrBBChbfzFVYj727/3d0LdIdaCRT2vio75v9khZdnXHI6E/GT5QO+PzD5xkeD1vJO76//9gvMvqnUe3daIR6FEh8cyBMOb2VH6wkzgXtmdyxyDxOCQ65ik02gedBjrL+tldA0npf81DBLGF8OGHM188Ghz7vsOsHtSan4U0ituQdcJ2SEGSG5BCvwVXtXxN7ZS1gZm2nyPfcG6dDgX40/AwDMLw6Yi9NQ04xIX3FV6SYCeoBJIJklHJoE/zIhyc77MFRKJEqzgNlbrnyfLXf45uMV4qOEG5yNWPSlzMUAACAASURBVA+FEnm3n+vPz5c7POz43zQLCEJQo4Hg//oS3z5jyPeLP/uAf/aCM2Urivhf/uYXqD+KxOalx7NnGz0m81TBvOSt9tXZHtuOP3u7qXTH7fbEVYIwcglAEti8bUe8PttojuzgHUaBqLvvlmgeC2MzOfyDJI7t0xzD3OHzm6fyvbf8fp13iIkwSBVrTMJ8ztV67QJW2cQ+GKRVPTFJiMpgT7tKSuN8oTAh5piCciSG8HPqSGgK4ah+InSvY2GgD0V+FNoIilQqLkykGhOJiwlccamHMqCStFQlUG+VcU2BlBgGlIq22hHGxUReFtihDACax4TuWZHKNA+kFej2LaNAMcPIEeqvTJOUoWQTM4GzpOZZj4tlDuKwoAlL2/ZFbhMuR5iNSLpuDWx37EiVn+tcIauMyQKxzVh2IUEBDAsfLP/bnxec1W4sZh+MkhP9ImkAAxH0fACMDuQ2UaJS/SNJ9Sz/dOtJeywVSB0ADi+jIhTwBm6dQ+H5PGa43Vie9/4YiEqf5KL6k9k9mnmFPjrcS88tRoOwdTB7vujh3KP6CT8QX1w9YlnxZLQdG/zN40tlBr85X+PX9yxNqG8dKAC7zzJLEKiygXYLhGthgNaBJTY5ju3BYHYrMOs+wbeE4Zxf278uelZEAKsKZ78rhun718JWvAql7zUaYDDKcuyfFXjGHAybeQvsGtoCuYQWiBJmvPkJs1QzHByrYn5NXky/c4+UCiRlc0LOPeN03TOHfY7ySmxxmIcZSCU8w+sR1xJB9/Z8jYfDXJnYTTVi3+UPT1iKhV7nHcZ1jeZOFo3vLT7Ie+/PD3A2qATm3eOFsmxp4qSVmoT+Tzr86dtb/izn8S8v/h4A8P14CdsEDG/4d+vFoP3JfV9h8dMVKknF2RxaDNJXNhMnreGCrQFzqPg4OPziS160z6sOf7+6UUbxrB5xfy+pPV5Ys+DrE11hEDfnPUhg9LirYJYJVi7iqm+19906j8Z6WMHpz5oeRg6sDw77Wvp9t4sjQ3rMoYHlsIklMXkFsAmkzkhJYVs6CPNzYsOX3ZDCnHuv+XezrIq/W3E14v9R7rP6yRTtaV5IJoeZ2cE08gKoi9S5181gtaNyr88Sr3/6XkktQA9vGJqv34kz1Rq6WMYmHfWP3bpoZKNLxYbR4IgtXtUe9w9yPS1vTJzot01fnh93Xx2lViVbFlXjJwR5YgaubiaaqDrszPTlg+INQ35/u50wjUfC4VVUZYDdGXVlI1EC5HPtunLumgdCkrmsv+IUKTORjbldOQd+XpJ16HmvUZXxvtENQrUBjE/a5kqOEAwQJuzmH+v4JBfVX++eo0KN73fnWIuIfrdqQd4oQePy1Qb//OW3AIDGevxft58DAO6+ukZaBMzOecH96u4G3TsRc11FjFfQp6BamyJ/WUb1p0s7C7czqmG8/HURZ28+Nzi8SAjZo9WT7u5sx72f/LurPwtIsgs0VUSUytRuDZIDQk7qoDIpRpuAvUXIGrOJHaCfkxIJ6idzZIk2XCQVuRtPbAtXEsxK+kTPfalMkqifDHxOV3nTq5azfnBsO5dbdU3A23NeVH002HSN2ueNo1Wv5etnG1SySOzHCtQV+UF/XYwVNuuZTGT877NFBy/9xGFTlw1IAtBZfPV/8vUNs4Tlv+CZ5L+8+SX+67/4S/ybpy8AALe7JZ7NePaobMDHzRJPYmkYt8Xvl+qEmHuqi4hqNmq1/l/87P/BTxv2J/43259g19fYrnjzYG4bkM2TNVDlHT8B4SxgecNloDNR+7c087hYHLCSfqIPFsuGj38IFj7WmrJTmaDJN+t9i/4bnvBtZAMFJb0tR13oYVh7qZN+JBWjmqH05pNlQsoo/b/xPB31z6amKDQYuJzkRBPNLXiRUkOTWHpzxoslp7zmzwsLJ6UEGq1Kmupbh8U7fRH9deEW+ItQpE8Hq4hJvSb4ueXnA7ww5P5taPhA8wKT3HElOS2uUhO16u6eWiVkhYNF+32FWkKfzMjJUkB+Bkj/f/yBzV9+JlNizkJGdMzBAPts+pE4ahK82c6LV3l/HnZgPWg+xjCL5VoY3pxb1c+XKlyNJyD3igVH7kE25iJhoig99xsxHDER/p7vzebeor2T4+gShgtSH+P2XirVPwLv31NP9TRO4zRO4zRO4w80PslK9W++fwUz5y1SDnc+v9pjW7V4ecPV0uvFGr0olR+HGVZbriaqFwdcLA8K66THGilT0l1E9b4udmEVML4QI/6OVDTfPrA5dSWQiW8IndiXbf/JAERSw/50OSLlfMzOoL9JGF9y1WOboIy8uHfKMAUBuOmL+08guGyWHg2w8Bga3mrPvnOwsVQDeagrSq4kx8JWTE76KRoWm7Q6j1UCzMSezSQ2lQfDrlaq7nrDEobudTHU/5tvX/P5mQ0YR4uF9P+IEvZd4eWvBQpeb+awPeHwOZ+Py1cbhT43+xb9oQLJ8R/++kr7ZdWLA2ph8RoTsdu0oNxYvhzw1YrlBv/t/j/Ff/Xmr/DfvP2fAQAfwzl+07MN4n//238fu9uFogipjZrbSSMp1P/m7QOu2gNez/i++q67xP9+y/3mD6szdPczfY9YFWmF3RmVIoQ2wp6NKgnqfYUg7Fk793j//RXOxWzj9fkac2G0x2TgTMB3W5bRfNxc4bATbPWpKpaC8wBUCU7kPK4KHHYOYRlPEDkKhPY2H++EzTkKdCin0fgi40hO4NNsMbgy+jMzWAv7lQC9l+xAynzNkGR+jcKEwb6xqFakFVzzWCDZ7rpIOuwAmHuriAsFaJnZXSeEVwOwEoetvrCEjQcwqfxgCjIzzf9MNvF1y7D0wShrmiIHaDQPpZ2icpIwyRXFEbAEdyjBGP11gOkKlOsORYaGOJGr5Oc0H38o1ylWcpLls3PPmv+QpTP5evtZQhIQjlIx+oAFEqVitBJJn2MkqVSzdeveYnZr9LuoGUrNyTnaVcgZrBOHpR/r+CQXVT9amNGinQ0a7pwS4cWzNV7OmZC097USkOZuxD97w3jSb56ucfeba+3jjWcRUaQf5q4SuQp/TqyS2o8hQiU6FAA3FOLP6i+8QlK0dUhNRPMZH0d/qDD7nt9j+zPPBBKRPoTOlDQOQIk9aAOsiRxRBcDNgjoepZG4tyiwVPc6aO+12pTkHIC1hnkym2rKUiJQTMWpZjLvUgCCTUfnII+0qvUh2r9iwoh66fYGIcN3Zx5fXD3CyAfe7paqP11v5hpcTS4iXXlUy7yIkP7N2bxDt61hbvn6DpcRyy94YWtcwCrDtqFGeqyLtWNn8fGeSUvPbzboksNXA/vh/Q8f/gPc7Rd6HGY5ImSf5xlrIAHeSFghkGy6BmOwuN3xJqyyoaib5KvnGDS7GLVfGftapRnmbERVe1wuGf69Xy3Ueo8eWuD5iFdnTKBqrVe4d9U3+PX3b1RPjLEQhFIblXAESiCbYIQM1h+qElg+6fUBgFsZtZ1LopsEGO4dr72SuiiQSp/imWerw674E6unr/Qq833gtkZtIsdlcSQyYqud+4lushhUaybHaHxYS+izQ1GVtLfbPBKqbcIgOm8/K25ilAA32RBn+Qy/OPm+4MUlZR3v+VD6894gjAZmlXcW5XhxMSA+1Kp1DbOk8DIvKGVjGyeeyt1NKjrP7OYlX31cpKKH7oociJ2uyndIVLx43Y5Qbano1Kvj61s/mYk15ERv2hPShDAV64RwIUTLNmh7hqQYyF7R7UfC7E5aAoui9U1W5sF9+ezogHiS1JzGaZzGaZzGaZxGHp9kpRpHCwwW+81CpQOzeY+H1QLrPVcwn189oZFKNUwMKtvKIy08xkOuQAn2vZg9gHePOc1hmrs4TV9IhuGe/c9ymjSUvede7rGc93i84+304m9rdM+FWHE2Im4rVI/F8CHv+MebkiIT9g6xLh8YnFXiDFZsjF/lnMpFVENw8iUfFAlHTiigAsFRBECFXUhpIoMQ1mE+LgqlIjM9aUUeZgn2uldiUd14BGGcpkRY9y1eLbiyNJTQSXqLX9cwC/6wdj7AnJXXdvsGs5pfm1cjZmc9BmFiNrXHKOSe7lBjzPDm3jKcJXCwnZc0m+vZHis/xzeeHaH+s2e/wp04cexeNHheb/Bv1wxZ/9v3rzEIE3Ox6PWYtusZ0lmHi5lIK4LV1kFO0nHyfWKkQlQ6H1GJQ9CsHeFswNOWoY3wbg7Xl+t3/XytDN8hWvgJM+X59UaD4euLEvDgD27CsCEYFzioHNwuyPIMGgipSuq0ZcaSzUmxhLunKsFsrV73MI+gbIKwdqifCmzZPY/qlJQoAQ20GqRIsNkTO0HZv8MFs00zXJlsUnRhvGD26HApVRBN/IMnrmPREfpL0urUdoR6LdWXk6ptIhHSZyECcZYQzor/ca7gw2D1F1NvQYMp0ptUpDfR16CRMFwVRneeH6oNtLwJDVdxigYY0pQdSgyJ57mEfCEtjmcRNJsgYXtS5UGypSIfz4MSk/JxZFle/ttY87/HZToiPOXB8i5ieBtg1Cv7DDcRZmv178xYEASKQH85aTWZ8prxXMmGP4Iy75NcVJvf1bBtje7tiCRp9vu5xfXLNa7FWeflfI3dJOJtkGbRVXvAu3itN321ncBcFd+URpNLoBPXlC2YDe/r9yJpWCaY5/yGr642+ObrZ5h/LRBvO0nBWNWoVgZun2U6ZWKhNsBJfzgnxeT+UxiIFw+IQXcExotM4UzqLmT7kvRhOwIoHbktlUapLKz/EFRDAl/lydYVBidi6TUur/e6iAIcxN0LFN9TjXhJSGAt8BiMfrfRRWb1gnV1zkR9zZiIi4bP491+jsoFjeyzlDTo4ODrMnlejEjbkmCTIils/tXHGzx2M1y1fE98u7/EamDc7KI+YGYG7OUeqavJgvVXlwg/ZbzNtSNiJKyEZQ5Ao96CTTAmIuTFbLKo2qYk1jSVx1nT41FgaUxdaqqEh4/nWG1kwe0tKumNjp2DcRGLMz4nIRhdR93MoxZG8jhaxGg0wJz2rrB6XYLdF4s6Py8LT7JFZmJG3uDlBcXuDEh6xc0T33P7N2J/d+6VCWwlOCGzwkNbLIOSKwsZjQbU0xE0mZ8DM3D/M2tAp6H2vHjz++0+58U8w9CxRolRBI7clqZB76EBwjIWKdGsMIhd47W1EkY+H9VjcZ/SAPMRR2x3xEmalC0L5XCRkGyCmbDHjcpQWGKkrOQ2IQiMawbS82F7/hzVs5qyObEP9siCsVqX+LVEvKBmaDjUxQbRHopu1x64H+rn/GXG2sIsZWPYW7g9Yf6+fO9xXvrIGkd5znr50BSWeXSEGH5/Ef+xjT+CfcFpnMZpnMZpnMY/jvFJVqphlpDaBBoM2jfMmly0A/706qNCvgs7oBGx5e+2V/jdAzMf/GhRfSxl5+FVPIJj3LZAMu5wnDOYYeFkADoUR6XFm41GkX33/73E4vsiyO5eet36uLX9vSiobIZvKMGL+YBmkWYiSoTurJMF/HkpMZv3lZIFuhdR2ZsUmUySIbBqVQK6KfKOXlmU8Qc7fk8wUvUMN0H1bGY5YjHnX/Tegigp/EtEMDluLBA263Li+sGhf5QTYqAhykQJi2bAiyWTutZDoy5KbeUxmqTmDDERNlIt+sFqnmcKBqhj8eYdaq3ShnWN99sGm8tWPy/Dy/uxwt9+fIEbIQ+dtT12v2aW7XgW0Sz4e9aVx6weFZ6tbVCHo07g6MzSHrxFlzW93mpg+RgsPry/BGnGZmG+GhikwSBI9VWtLfyc37d63qGeVFLWRjXeD6PFYcuU06r1CNuqsMfdFPsUmD9n4pryUnTQkic1EXbp9cVQO7jb8px0NwlRotnQG9ZY5vewhRlcPHAFQhZY0UiVqhFjI4oLk1ReR5SbScGTq2DqDezGKGnHHqgwoKvEVaCwcL0t7Q5/ETg3eZkDVUnRgKoKairSPYorVSYSnUet7sjTVPaJWBdoNbRJ4ersj5sr8urJoP1YvnMogAcoQolVGYHS/x8AyGtuB8QqfxYQTVEehJZyzgFCzZWyZijHiV7WlfcLFf9OzOfDJaQnvlfn31nMPyR1ShrOy7Xwc+hFopSDCgrZceoW9WMen+Si6p+NMDOLejlgkElsOevxNMxwXjNU9rP5R9wKTfDjboGQDcb3Do4AyHzhdqRQDRLfrNrnWBTYdwqX5h5HnhgPv77QfMb5IKzEzCieOKVon1LWG7+MoLb0eTJjk1qOSUmS5UqJii3cIgKR00cAPt48kYSzEsjtLyOwrdSwn8XlP+i9TH7OZg9RGIPj5T/AjgyE/WaSrEzcFwV4wcrG9QiEVz+5Vybv+nYJEralqQOiwMZUezTOq8vREoQDSWhBNKhsUOcrH42aSdgqlgzTxoNmCWMlYfKjUTeeek0Yzwz2Ap2bqwG9mNz/2etbfHn+iBcNs27/x3/9H6EWmHu4CTiTxff1+Rpv5yt86PheOvgK257PwWozRxysBkaHzsEKFB/bBLri2X+/dbD3lW7KoiSB5OuCyIHygDjZnPNnX5wdMKtGbPvSxqjrnDk6oJVjfFgvWAoh/WcaCdVaDO+3nHaS5Ui+TUDeTKWpq5FB2DlQfg9bnMD2n0ekJoByqtFmskF72SMMBiRWeWYkNXagSMWIIDLcr2xVIjgJqzCBFwS9j9up0bx+daR5YCmHbD7DPLETGTgZKWxIv2eoSwvD7A1SRUhZ5tZGDPJd4nKEFzY+gTeXuX+ZqqRh7ySUB134AsHL78W6QMtuR7ADcY4yuLeapTKhKQlVAMR0pXxB/VxDvGjmZ7LhVB9AYG2CZsVOg+bNwD+PN/KBvYEVy0U3gcZN5E0dCYcgHRza9yKVW+f+NP/u1DQmGWh/2O5J7p8yd4ZWg4x+1OOTXFSrjxVMWyHeV6DP+O61JmKMVquIu3EJn0pUWJT/T3sOF0/ZS9OT7hbdQSq8HP5Ro+zMftB/pMjOR/yPpDvQ0B73IZNjF5U8AiYpHlUsc0YCrFQCMRDSwarDCgWCv5SEkJ41grk9Gu0kwaKK2tNLW4f2dhKkHopUKMySUuIBfuDUnq7liT0HRvvOaYWV6qSbgDQaUG/QP+TYEcC+4EVkNu9xv1pgPFR6XLnSjt5o7ywlwsftAo+Gdxn7rlaP4OdnO3ZckuvkTEmD8ROHJljwIp39TyMpgSQ67vFlaUtKwI0Efj9vt/gX51/jv/vunwPgzY/KMOpSEcZE+KuPb7GXzZu1EZv3PDOa5QiYhCBpMWbipVs/36O/4+9lD7wIDVdZAE1qp2k63tTlBaV5tS99ZBOx6Rp0srFo6xFnQpha71usBQ1IoouuhQREI441lG2ZDCmUiggRcH1x0gkNIcqiZGcBQe0pCdRZvd9TlRCfD3pdwugKT2BW+rTkCxnODCIZmRKJMmegERnJVL6lvdfy/6gJQEUYXhUikUbQXUZ0oaAjsYmqe83649SU59DIz8EbWCVxMSdhGqWmOlXRimqw+mTxsAejXty2pyOZiZ9Do97MwOc8WwAmKpIUPy89ZttBBK/Qz9YebQJAabK4l+fYDrxByRwL46Gub6BCRhwuWFJkHvm+nX0w6neeHDA2hN1ncv/PI0zeGE04JmaURVqJVjwXxT+CSvXUUz2N0ziN0ziN0/gDjU+yUvWzBDNLSAuP5xfcXDCU0HsHS7z7+uXjG83iHINlNi2A9p59UDPtfLpjzlIZrVRdgT6myRPDRYK/9kc73DxCy4kkxVN1AmUFYeCZyY5cINNkUnE4EUuWIMw9GgjVfcYL+TMyjT45qLwBgZQB2nxkBqhWtBXKFiyBd5i5UpglJFP6Q7EqECErz8u5T9K3c2sL2xW2sftyi0YMHrq+AhHw7DlDq+tdi0F6qtQbuFd7vWYxEQ4Cbza1x7PlTj+rtqGYpwPqZzvaiMNe+pWbGgglUNuMRTTvz7iXNr8osRtvl7wlv6gO+D8ef67IxvM/u8Oi5urrt7fX6OXe+er2BuO6UQnG4uqAZ59xosyuq9F3lUoTzEAIr7la7+9n6k8cgKMUEvI0SWgB4tmAy0v+3j4a/Z6HocKy7RXmTYmw2nE1NvSVBsu7uwr1E2klMmWqRwf0LwKSVHTVR1fSluzEAIREViMVnHMBJOYmfl3D9OWYw6XXQm3c1DA7i/FSyqUqohLTeQrlWKJL7GTU5+ep+FQj8TXLVZU/i2rOMLs6qMvWbtci7Bxmz/n++cWLD3g143vsf/p//z04FNcg0xtEQU5cHeB7p6iHraKyVM2HRpm1FAnhYtLZ7Y0eb/4uJWEmHclO8ghtwnhW2MvtB6tm9fnvM1xbP5b+bZq4PFGQ53pSDSv7tycAVLgTgfOi9fNraCvqaFDp5yaXMP+dKxVuV+R34wIYrqNW6+ZgNMycUSD+PeO5KtXkKy8uXP3vf/SPbXySi+rLP/0It2hAAM5FgrEfa4zR4HbLmj4fDZpsefc4R/tbcV4yfLMqOSmWhQHEvYL8sJstKSmC9V/8c2zZrk97lKbo6pITYkWGmgIhZfecdNzINweLKD0ss/AqBQkHdwTlml2lhInxPPIinp/9WBZwqiLsB8aaeMIsRIjs9MKvHZ/PafjwhN8CAHBng/YvsaoLFJQYuspQ6/ayhc/603bEsu01/N2YBAh0++XPPqhj0H6sMHiHlfRK++SUaAbwZigveg+rhUpqpqQcsomh6pB7x0AQecDyZl+M5cGSnV/ds03hb56u8eXFI/7sks3xQyJ8teK0IueC6lTDzsHMPc7PeFbz0eDhkV2ZUjBIkQrsvegwiCynOT9gK65P3CNPCutWVVACVogGMZFCvBeLg8qK+uCwOrTYScC7cwHdPt+EpBFllGQSnjJ95GuPFwmJiuwKscCssYkFYowEd93pDWBMQp9DzztOdVEodjQwWcbVEff5NE7OKunN9ATIsxVlkXCH0gtU6UcrJvzy9va6x9tnT3IOLN7/lm0nqyeL8GxU6VNrPf63b9gykrZlkQAAentAK5u8vquQPIHkFPjOofmGz2O1K4vXuJRNdPbGnFgHhhaIs6jcBnsghYKH5YRllQi2I3VscxMrv0R8enPEXWgni+WhPHv59/2yfJ+84IaGuRl2stiPi3Ie+bmUxd4XCVAOYAdKUEje2w9XhahISXgmUoTUK0LzmPR4c8Hh9kn6ufIe59LCOGKb/TjHCf49jdM4jdM4jdP4A41PslK1lDTjbzqedjM1LV/MejytGZuo39WFPJAAmIlAuklHRvR2ZGEzwKSOXKkan3QL2dyxaXWu/BCBWnbgw1VkqC8zefuSz5hcAoyBEfgn2rLDT4E4fkw+FxFKVDJjgbViG5FqwihQor8oVXHauSO4lyZkhyl0hciEBpXUjJNKmwSelF9t2hF7kW5QT8pgdTs+N3mHe/OvHfoLPv7tnw/YmRYk7GWcj/j5Tzkp9SfLB3y9ZYejw8CVaiaRzdpOo82ciRyrtuNqLwy2VGKRSmTe2oHWTit5f1m8hAE2TOhWcvwHq9vQX/zTb3BVH/DVhqugs7rXCjp4C1fx+9fXI4iSxs6FUAzLXTti1o56rvq+UmbzQMBSAq6dDZhVxdPXR6PM6HnFx5rzfh/7Od6t2TRju56BTEKVK66+4LopkDLLSUwJFH0hNpgHxJd4InOJNTQekRLBiG90WLK/dJY7ddtG/y4Zhm4zQ9ke6Ii4F0TeBnB1Om2HKFEuspvQVHoSslQmEuIi4Ow5Y4tfXD5hlHvi42aB+XPGTw/zBk07YiamF7+8fY3dey7TjGfEKS0keGJVI4i5QUZ8kIPEP5a4wWhRTP8r/q4Z7oy2ROGlKsJ0BvUqQ/3l7/xZgltJZSpIV54b/HzybAn0qyEGHqWyo2OSj5+V12hC8IouKdEQAKpQGM8UubKcSnPy8zmFnSmKcYacg+YpacXZXxMSFfYyBaC/yihZmlxP9gTWaMzZ75M5f6zjk1xUz5oeVZNwu1viUYKws040902enhZw3/Fkag+E/kagjxWhXheIhSYc8GrLLLb8EMxuCxTcXx7frMxYlB5NV8K6kwFPMPnB6QxSpqQ3DMUdNUsyQ7G3gCy+2Fq4vSkSnEgFUuuYPayOMREgmSxwMSJko/CPFUNE8pax/iFjcDLRTvIwYXjyyz2n3aosjhyCzG8yfjYi7Ryqp9JXzq9V8xHBG5AwJ3ETNVz7m90lVh0vlDeLPQ5jhQc5B5unObbC/q0aj7F3hc3cWWWVLj9bY58X29aABuLNBQC4hFEW0dHVDFXK5qReGfTPeNG42y/wcJhrH/WfnH3Abs5/13mHR3E4AhLaekSdF5uhUojXmARnA7ZijTlsa+1J1pXXhXOznWE76aOHYDRdKbOdM9u47yuGywG42qOug4anx8FqULjZumKuXwGYJJkcnifWZgKc57uxCi36WSr4VuD+KACYmUfYOcQxM5kni6NCm/KfiR4xtxnys0DeYFyURUR7nJ4XhPQPzFixibh8scGi4WsxBos3C+59/4fX3+KbPWvM977GZmzwJHrl7fuz4q40Y4a56pWbEkgRO8vsZbkPwjxhzDrStdEFMNbMQdB+YkeTfqKFmSgFQgu9H9v35UslyzD8UViF/F5zL33H/Exa6GKZaPL/J85FPxzNkxjqyznur4v0qbmzoADte/pFgWunLa/Y8IbeySLbPiaMs8KYj3W5buNVULi62hQ3OBDPiaGovfiYT5KaH+d4vzmDDQ0G79CJ7rCuPVIi9CLjMDYhfsnbrWreo1MzAjYqzbrMaseBuwAnMAyXEfVTEbYPE12Z9lSrJKkShSykTf8DwU96lCZAST8xEOI8/oMTCzwp8SlZmYDygoJSWYOkb5sXYIKSaIxNSPKg2AEwfVl8WQhe+jChTroRAIrtoduIPaJMoFXrEaQfGrrJgR8c7MGUxSyVnk1be/i+hf2M4YE3NyvMHG+LP+yXaEWX+pPlA67rHf4V/RQAcBtJKz1rwfXz8gAAIABJREFUI6j16G95cTM9qYXe5nEO5OSWCCCRVv/mySrBC7ORe9NbsSJcAX7G7//xwwVevnrS4/rl0xtsB15UN4cGrVRDz5Y7OIrYyWLz9OEMJL3M6rMNxmA1gWf5otP+52Y1440SGK2AgS6CsY2a6AOXUM1GrUadC2r/OO5qjI+mLGqzUISAqSSXNA8sidp9LtdpGXUidzIR5kXQL6IePyVoKWnuW9hUKktgck93bGGoySvXXjdlefHISUl8f2ZUZXIPS4VfZFymkNxebTF4C2f5/ppVIw6Bb87vDpdqJbkaWnzz8UolTNWDValQTm3CBV83iqTezHZr2IZR/YSjxvVNLf9oJJgITY6yXVlE/TJhnKBa1abI2jCRA2kAeSZkjdBwdNsngErSiwkT0xUq535a3edjzL8XGhx5epuR4Fblok3JYUD52c+TzgVm4GIhz23RGf09fv+EUfS/NPOwa76n20mREWupTvP17ETjPulr/1jHqad6GqdxGqdxGqfxBxqfZKW6+fUlTNseQQ1pI3DpW94WtrMBXvqO69slmu95K1bteNeXxfbjElj/ReHE08FqJimFwlbEZBefDcD1s02p0qIw83SX35cdclgE3gZNXFmUuRsIUX7PbVhMnmHZaCf9Ci/s40NO+wDSpZhh7xzqO2FlHrJJOfT3yGd65bHEJhG0JxYWbPzQLPk8WhsxCsRutvZoJ2pGKvC1S1qJHTYt6vkAJxVu5x3+dsWZpkQJ//lP/x0A4Fm1xeM4V1ZvjIR2VkwFuqFSk440Two1k7fazwK4wlDrvTopHGZdRNhUaB74u/VXSc8pmYQP7y9xa7l/mYJRZ6Q0sbF7NDNYk7DaZOfzBHNdXKQIUAvDh4/nmvYxZUGmKh3Z95EnzkMFG+8DpX0Rg0VYFdYtpQLPp1AkGG5Hyj5PFuhvON82H2O+7rkaLM5JxUGMIdty/cKkwsk96jz8PCFmM4hYeu40mOP+6ixO7okIM5e/WeAooWk8j3Bv9vKdDcbeqWXkTbvT6nTdt/i44b7p/m4OGo2mtCSLI0YyzX1xQFpX5dkVdn7uKc7fuXLMqRgkhErMK1RilzBeyvmr0w/CMApESr6YM+R/Z+4BB3TkF4BQT34v/ICdm38WV7Pc1+RKWH4MAPXQa19vEvM9AFBM6M8Nhgs5JZMeqD8LWoFHm2C2pTqNAeV+NcdhBKm3+hpD15l7wab8UznR1Ijixzw+yUV1uljkB6B7PTLElmnivUPT8hXvq4j+Ff8cHh3HFGVXk6UvUKJjanyY//7DMl04DXGPU6GVRdKgaqSyQPFrKDpSk3jCmwAM6vpSJZWrmAH8XXJb7CYUb9WejjcTNikBYxrPlfsmU/mMws6JH84wCTDPDkrUcAj3vC39LSVd+bIJ4P9Rzv+0j+TaEcFbLGa8MB+GSnuGf/7iA7wwMn6zf4b/+8NneLpf6nGNTyJDmXs8u9ng6k94ov1wf6H+uGYCy+WNSb5mSNDEjXBwMHtbUogm54KeKlSbCaGmArJ3ZawShqsCQ3f7WvXEaIKSmPLCf/go/VeXQDN+Le1cuU4RSG04Sgwyk2Seqva6qBobgDyRjwZxNCDxgq4/VEdSMIVjnyWERdRJE4cfRAC6cu1pnMS9GaiuIjkwySfDuskBWTc6T+xKNOkLkxC3UsMWhvkZIl/SVjwZRFmp3daCbIKXvjvOR5wvmB2z2Tf48sUDrhoJce8W+PbhEgAwDk415rAJmHhUh3mRBJmzkRfU7JttUjkfAaj3BvWTXA7HGyyAiUVKRiSguivPl58VopLbG5hQepnRQVtIACbQKs8ZGa61Qzln0bEMR/WocSJzm8C/JhwvVnxe5fcMUO2hC110gJd+6HjGG+n9n/CH21lAFNckGCChbKzYT1yOvZku6AluQ8A2Tw7cXgB44dd5KPB3y/r2/P/+GMhKJ/j3NE7jNE7jNE7jDzQ+yUrVvd7DzCO79OTdcyDARVxc8m43JMLmlisgsy+wJYvYC2mEVpVKDNhWpuwKpzBvculIxO1npfE/TeOwO8MQajZ/SFSglUi8284SmyYV4ok3WhUzESFpFiJMQnLZ/sjCdBOSRCSFr8wEBsUImESIVHbWSsuvJfMxE2cqqCE4OoM0Dxg831qHXa3sv1QVCA0xewhn3j9w+SWXAvN6xMN2joU4AVW2UyZsTISvdyyp+bvvX8DftwU6iyhMxtmI7aHBYcWVq7ur0DyVKnzKoB4uUpEptelImhSbCDOWEjX/Xr0ixHoCdaVSNZiBgAcJRN8s1d0HAIiAQchxw75mD+IcijAa1F+XGBIl7FQAotXvFquEJIEFVe1xPu9UIva0n2GXqwvxV84MVKTStkgmKSyjbNGM7vfl3ACCZkxIL+qxmwrMG+aRHb8E9XD7wv4Nls9vGjMsTfDioOTOB/iDUxZu/WjKPWGhsp9kE8Iy6D1IBHghZL263GDhBvxuzSzfwVs1ygCAZ1fsmvTh2yuYzijMndoIuyioBHVWTUZSVQIszEDwbULi2w5mKOHmbIYhxyQ/ZzIORWB2K9K1eeLEqXxqU5EYVVs68lr+oUtbHqGV2zbnTkzaOnbiRETSalJnt8kxuQNghqRG9rGmiQMccPhiLL7GB3v0bE2ThDg7+gftIHCV7PakZKg0RSd8YX2zR3pRRDSrhOGc/ijKvE9zUa0ibBVw/eU9zmu+G99vzrD+sMTm7/jBjG0EFgJpvuiUMWi3vHiFidF3/ZT7kAAI6J6JbOSZV1mIGYqDShokfSJPSEsoLMUwTlLIFJjAwVYYt3mtbCOiaA/Jk04C9kDC+JP3b4DmO3H4mSWWx8icYzpC/fT7PPas3Dnqd2nPDYAvyTdAQiIqfxdJ4Voy0KSRKbMwu+hkDe6bz+91YbjfzmFtxKWEg//m4Vr1wwA0zBwbd6RrTAbwcs26VQP75FBn2v99MZ0HysLgl3IuMrN5LOc3tRGghFjJhqcrQc3JAsNF1EnHdhO4fZkmIe2R7SMzLHqwZWFoA/dQ5fPaW1dgOquHVHpSeRPjyvGmRLh7PAO+lb71ALT5fNh0tBkKs1T+rkqTPhj/PL1OU13jVMPql2WxMr0pbZTBAOMkkq43qu22HSG05bXhqqQr+XWN6tEqRDicJ004SibBTnr1buX085fnB1xIOMBuqHG3XWhverdrVRp3dbFTa0ZqAvB2RMos9MEgfpRN14E5CApH2oQoQRb2jq9L3jRVWxzpVLUtIL1MPXeTMPmcDpM14fV3tUa6Ab8Pe+pCHajYQhK/N2X4N+Go9679SMPMXO3/+2MOBzXFkjIRt5gAoPt5B1dFeGE9m50ti2KiowXceMIgSVqZVQ+IBHAStsEtH/45tCUAIBnATFQCfgZ0N0CYWCb+WMcfwb7gNE7jNE7jNE7jH8f4JCvV/aqFGVrO12Q+A3wwqM4H2GveKtWVx1zE5NuuwU4qx3SoQQfApgyFQMXqwyXv+FX8PfeITrSjKwcnPrfGE6ItbiKYaEBhxF0mh3eHAv/GKrFfq8CJ1kakzI5cV+qjm4gh2rxTrT84hX/GOoF8gXLcjo4gmfJZbNSgxg6TYpZGFrVr9WIJEBMCOhjQaHA9F42vDVgbrgaiBeLLbFLuYW3CvOV/x0R49xv2zoVN+PM//Q6/umWf3e5+phAyTIFFKQHhLCAOBQ0wIt6PNZNQNPKqKZVGnMSBmZ6r+qyzpUDl/QcD6ids14wigJ1uzFjOQSJgEGMIsxyRxHCBnJgI3DXlPcTNCTt2csrEnOgSglQNaUoSs8z+zciA8UAQQk3/uMynRa/bVPMcz1PxprapVDkjaeuDBmaOZ4eiamUKs1sgx7iYVKgSOxfbqPcR60wLScdNckBHlnbrcSRXKncaiKH0fG2agrDQwWhkISCowpL//Xy5w28/sJtV+tBwJF3u5Dwf8PIlmz/s+hr9XiovJ/mmgh41H90RPBlmUIcyeML8d8W0ZEoESkbCtyHEnBy5NkAi6OS1uoQzZCKhy56+u4JcTQk6OYv0yEM7IxuJq1QlOKXCpgUK4ai/PkYXUpUAAXf8AmgeCe5QyGadPHZkEvzeFVSlKu0ZGoD6MRu1MFs8h7+bzqBaZ8SG55N8rat96bRUuwIZ257P1/4Fv3h4xRreSOX7/FjHJ7mo0s6CokWoA9biZjOOFk074lwgpTFYNXQfhokzj02IzSSPdF5YjVkWkk0YAlDceB6NQo6hTVPUBuSLvIEigBqIuVfnC7xCgSfWHEY+Ek/aAJCqqDBlsvxQ5c92h9Lj4Ae4LNTda68LevVkCpRFDLllKYc5lAmTPH9/helcQiVi+Fgl3Hz5qPZ57+4vSh5sHVE3YnQw7+CDVcP6u79+AZLvfPOzB3y3ukB3P5PjsgpRUwS8tB27Nx5m7pFaWThHU2Bz4slZ+9sTNmQMpP3mZGWyFDjStR5eeqhp74AKBervSXNYp+HOwDGkSSbBiaTGHxxo4yZC/wSzcvoeyUwWG5t0MSNPZWM18rmfTrzZLCEbyWd5htsV5mhocSxTSKV/ngzUVCRVCZjYTh6FNuT+Z95o7EqIA1xSK8LcGsimBdGVeyw5Psb8vuZs1L612/I9lxnWYR6Pcle1Vx/lvpYN1G9vr2G/khuBgPYvnnAmG7TGebxfsTNB982Zyo9CHYHesEEJ5JxNNpFpUXqezW1VFq+Io6Sl0DIzPo+82OYA8WzZZwagvS3ynWmvlGK5LlOb02rP3As/L9dp2l+liNL+CaU32l+VzX1sZR7SnNSS0cpWisVAonuekIQhn0ax4Zys6FnC5PYTa0kCqnUxzZ+GqkdpT2VmPGey5hN1zHjefFGg7XplYEYg/KCf/2Mcn+SiWr/cw84j6soXvRwBhpIupLUNmnByfb7D05ZlD72peYLMVnB70n5imMejm9KtrU42/fMia3EbAlGx7DOHUvGEBgjnHiQa2djGYzJVKg9h6E1ZnAnFfUYkQznSiXtw/LOVxTF/Nuqou/UxFes68gS3sqrVSyYVJ5bE/ZppRaE9m1c9nA2420vJRQkk1TSZpL2u1XqBMBjYB9H/dqXPc3d7zpsFcbmpHx2aJ5kwLKF7Jt/acfB4lGsWEYusYOWO+k3JTOQHJhVSFzg9xchn+cFq/B31/KCrk5THkS9tmCftY8c2aupLOFiduK1NrL2UhcjdV5huxjmmbDLT5P65KZ9re04uUbLQtiQjhTYxeUgOyy8KIc6MnACT+7sUChcABogSvm7XbGuZq9ipbWb/PPD9l/W+bYSRDUMKRv2UaWA7xzyZuv0k1mvgRZMkjSdNEBG358+aSq2U6DMQzFPuZxe3Hv4utVpG/uyfvoNBwq/fMbKRnmq4vOlYlueHDhbVgzlCXbSqbCPsypWotqNdr2y+dFMJJHkWbD/pGdYJdiCV0Uz15lObP6Bshn54HH7GZJ04QY+KZh1wXdGORkd6jsPERtT0vADme9zty8IcWmA4j0g3IpupIkLeRPYWCKRuUdXEBzi58h5m5OuRiX++hVbdrNMluI0c82EiCyKgvyjVNEMA8nsd61bxRxD9duqpnsZpnMZpnMZp/IHGJ1mpjr1DsA5+tMXrOxLIJGUUztyIITD8+LiZoxdTAbcTQ3pBnsxYZAqwDNWplKA9lozkimE4j3j75gGbjv9w96tLjGdSCcxYNpAlMGZeICkOlbagzNL0hGTzi4RwLaVGb+GeCh1+motoDyRbKYG2q6Tev8kVaUm1JcmAlUrMlV4k92smcFCaVMm3Dd6vb2DOxKWptyCpAuNgMWS5hwVQx8LCbRPoS4ah3l5tsOka7Pd8foaLCvVKvmZTdu7zqwOsjdhtiwyFtsdJKCVQe7Jbn6cif/FAmge0YvThvcHYFZpwbJL2i8x4XOmFNiGK6YVdeoS1MLFHghf3oPqyh0lA/EbSUIZy74RlrgJRhkCVaTBIwmaNy4TRG1QiwRifFZMFioQ4eQ/3ZNXIwpvcGpjcg7lnmCVWkOvpi5SGInB4JehCG0CU2HsXOCrhTB0QJlKwaltgwHGJAq027Bmc1C2KVE6SJWjVWu7pVYGJKUDh+5y/G+XZ6m8SGgmr348V3n39DO07vqjDVcR4I39oJ/3bkWBCqfiVkQsxk/fQ53AqP9JzNJHVadU5QYg0R3nI90v5e/LlugNAvS5QKIUEgzxncFVrJyYVGobhE6KFPvPdc6C/kms8C9rrNh0bdOR7fDxPCOe5ZAaojqhnfHDDvga2Wb1g4A5lrgizgkbZfvL/GzmmSd83V+dmICy/Tcc94py4syCFyGnSbtBzPAla+DGPT3JRTYl7Y8EbtSUzTTgKfx6ixUGCn2lCFqg22dWkPDhq4TaI5CLDbz2pxtHfFP3XYtnBS7g0AMTnQ5lwKrZpsxJBlSIV6zTHsW0pyzqaCDMrDZlsAE6BjauzA4xfFGp/JhKM5wX+Vewwle9JEZIeIu9dTbRoAx3JLo7SJeTnnJQSPcF9bPTtVZazHIFISGK79vYX3+PFnDGjx36O86bDN4lZZHE7R3cjx9EkjNcy4XvLbkUrXqjd1pQ+1UiSaiIPe1fIMOQJlUz+oU16XQDA906h97TwMGunk3CsSkpKsomJQI1cw6daF4bxKuDsJUd9eG9x+DhHNQ2rVghTevOyUNnWq3SL2qD9W3gCecL4Uszeq7JApcGgfrCw2YVokQqEGcRqUuC3cOULFGoS0qEEhScDdC/EZvGsBN7ryP/0BtGXPmeGud2O+3bTFJbpucp/yy8W6017oOOJvEEhU8XSb6YkvystjfqJ0MlG5X67xPkDYf9K7rl5YPkMWK+cIephvyibIvD1rB+F5CZyr6w/jXVZDPLv2smGMy9041IkceC2jgnl7yiURSJWvDDlBJgjC1BDvAmR82z7siBnzSmfG259DLJhMGec9AQA1UORY/ml3PdnfJCzZa+OW+OhQrMYVJaW9raEG1QJHoU4Zydks2mYgRl54dfNYVNsD+cfEmyfMC6FgPRi8tzFMlcmK0SoybxhD3SS1JzGaZzGaZzGaZxGGZ9kpYpIReyfoclNhf6pxp2UFMu2Vyj4frOAlRBhhYEyeuVK5ZHqhBSLEwsNVLYtk43/ftfisG8QsxB/NHo85rFCvPCIWcITjDJ8ySQmsuTdXSBQ3u0mFFbg3mD+rjB+h/Nils4xTYW8kt8H4ApOc1Iju69kFqJfTAgfifgQMrmqPabXA8LEBcsvMukivergxLPWf5iDrga0z3L6O3u2Asy89sZge88sDHoTtNqZvXNwYrYxJK7gMmTtZ0mJVYGSmIpL5ZFD18GVhkKAbUI8OBykaoNNygSmzsJOjOGTKdfaCAnF5Bg0IQUBAOqI7YpbB2nvYPdGPWvTLBSGcr4Ecs7jQ1PcsnqjzNdkE7syZWnP6DTUutowgzJLn9yB0N7JMY7A4Tnh8FbunwkZiB5qNE+FKT0uUvGY7izHxAFoFgNCIIy9ZAvvCiltaoYR64QwEiolO5UqbTxjVjNJ4ADZhCTQfma05orFDjgi99QTf2WOIuSf++uo17N+IqkYCys5V9rBG4y5+reJvbTlmTeDQS9GLbYnDmufEISKaQG3ceocJL4rkprhogR0wzAJK/+b3afkZ/HjLVDoBOpMBeLNsK9G7bXlPcZFgj8vrlK4ayAIPxPe5L61bQBibhcBh++XBWm77tCtS4A8RdJnhkYJAMndgjippiftE/JAbMtrZiLz2b8kUCrB52ZMWHwn72EI2y/y8QY2RZHhHh0/41Nv8B/p+DQX1Y0DvBP9lkw4gwFsUleWm9le2b/ffXyG+b0sEp71qBk+dTtT4LaREJZRbenIW+251WeD5lwaSvC9Y3cdgCcBmZzjpUc1HyaMVg7cBsA9YBdLH9Um7WnR3ik8Va+4d5FvbACYvZdNAQHjORAz5Dlx9EE6ZhqGGTCIbCaee33YggHsxmgqDgxgdznLNTHsKItU/XanaTPjaJH+nhdOB6D6rMf5XCRM0aCX833RdBijxeVzxsrW6xkgEO+4KIxBszeI86iyglRH1kSCZThuW5xkpu4zoU3aA0pLz7B6Xug8TRY7/jEzPfm7yUuLCD+f0Dk9qeykmo0YdxkrTwhXHpR1vNtKz3eqmAFp5NyZngDpl0dX+n2mI1AsjlmgpJOwX7LJvBPouXko3zNWPOEtfiuT60s6kt4cwYVV0p5kbBJGYWl2OQyhK5sHN2HIZjjPdpMeO5j5q+lKZwE0C6hzQMWqRTYuMl4WnMkx5wVluEj6Hm5r4Aw0Fzg2nPqSP2u4jEf8hZwv68cSTJDaANo51XknT7DyvRIAVAV6NmM5V2Yk1LfFIYq/G/9s+9JmJl9yQQEcaVtzW0Tvx4llH0vgZDPY8MEom3aSQ0uQTVze9874vAKs+w7iOpY+NqhEogIA/U0EnjGtNnjDX1bOlV0Z1c/bIbez5LOrchxuN1EdNLLITr53dmXK7HOFrwNh+7ncB/OoiVi0cfzM5I1F3gCfJDU/zmH3BiYahNcT/rZJmL3e4l++/hoA0JgRf7t5CYA9OnMvx8+kz5O1nWvSqLepDywAxIsRlRACjEkgmZGHfc0Lu5v8/mUWFxqM+7pIKwD4rINNUvnK7GVnAUFIBskkRHnQ/VJE/pP+aH7QQyuJODm5AyjV9OR/dc9ZCpI1fqYOiGLvZjciNckh69P+UJ34YZFqYOwd+iwveXSIshl58/OPmFUjHiQWbndoMJNg75vZHiEZTrgBELsSteW6IlMYAaSKirduHYv2MssgJnIHRRQck5P4BULqXekrzYNKY0DSA86T2jDZxQ+E1ALNOd9DfnS4vuRNQIiEJ1+uX/SmBI5jojF1vAFRyzgHrf6btdHj9TMhx+TNRG8wPJeq5HzgXuWGdxPRAUnQheYpsc+rCv+N6iuNJ42/83OOG6xFBkFPhCQQyPjaA4PRfrEZjvWKNnMLAvcLm5XcLyNhPJMqtrEIBGjgyjg5j+EYRYhV2QzSlztANpfh2xlXk/lSPxr9Xt3zyHaXk8q/v5vpMermZO5RnffoH/lBmX1vddHIIdxWfnlsy/3SPBi47pi4VCrL8l2Mx1HVOVwe64cxqVzTJJA7G5DwC0ws0j7kZDOoul/ZyJi9RfVe9L6TvvS45M1IL/cIzUNBwu5qUFWiH6cmLqaXczXV0ubK2QE+m9UQH4taMNblmDSN6w3/4dn5DlvxAki3M5iPYvfaHXM2psSsH/s49VRP4zRO4zRO4zT+QOOTrFT9eYCZce5n7jPZZx3O5x2+nN0BAN73F/jbv/wJAMANhO669KyqtVEoZJo2E2cRZjlqy6NuPGrp0R4OtfZ2EIghqMxMrSZMT0ogm1RgH73RvmkciKtA2f2G3pYeBEEhZPIFtgF4N513+P5cPneK1WVnlI0pu2Erfdeq9OPcI79p88iwqsLLEwarORj+u9wPrI1ie7EC7LVUdtHgm/tL7SsDQCupNO/W55wzKo5K9Z0rDi2TvlR4Hn+fAS3pOGERkVzp+fl5qfSSTZOKk1NRsrE/bZ1mw2IeAEpaZbIqX96jSqiWgybO1O2I9U7cuYZy8mNvYZrCRg3bCl5MC2jnQK5kudqD0R1797r0kSkQG4TIufLXI2aXDJ30hwopEEZJfYnOaDLKuCAM5zhyBtJ7qS7MS0pcpeSKpbsB4pf8/pYS4qZU8jS5daZSLbcDFu+Dhmh318SwI8COYAmaKWsGo73qcclmAbkn7BcR9kYQpERIt3xO5x8Yro5SLR6el+qIIkuJFOYllIQfB7WFdFXA8P+z9x5NkizZudjnKiJSlWx1xUiADw9GAI97/m+uuOKCZuSCNPI9ggMMZjBzVcuqyqwUIVxxcY4fj7xGbmizeD2dbjY23beyoyJDuPv5zifeL7H5U2lVACM/w2GTzp7/eB2kujODPpOGACSJKb+7WO+lhkIbiiFDWFaYvrCJBcqdcSDM7DoWO81S4c4lOwDxHsyePuxmED7ZivK80CVyoyoKgqAJ9gZgTgrO6yqtmo3I/VvJfHUZ7lCZ3vX9Of9/hWpWgURohmYVxeP3N2g/0u9ejDhnAucq2SFDmlwTkD7j8UUuquZooKOBejaIbFcXNh7X7YDfHb4CAPzP3/9GPh+XuRJbWoJaPfcQUjMjeNhE5ACegIbRILIdmD+6SjyxGaqNsE0RqmUEnohtG5GSQiw9uagQy0tVbAoZYFCLWN1/phrxlTVANn3gv2f4a/qzvhuBrKQXi9GIneF8kswWQJtgeTEIJ4vuVBe24WWuKpoJMuEnWxA7/ntQaJjan7sgveIP/3aP3CWsXx4BAJ0L4lo1PLcwTxYtw6T+Kp3p4EQTy9B0mkWzlWusvDqP3nPVbQkuVX9Thg2LjAYmQxWyUFK04SgNv7UX56WmITvDzPD76DU6hoIXy4l8pQGk3tL5BSZa3fWykUjriQhb5VxMRmSJTve2LhJ+RRKJktBilwHDgVYXZYjlU+wkKSWJJ9eWfGnbJ+61PwP9az7m9awfrIH40mO/Ygh5keqzejKk7SyX7lBJRclViUjznOGXGuNNkWdAyGvIoAWqLHq6+l7nBrRxKc94UoiPdB7Ng0HHi1dWwPBVFuKZO1QZSlok+FWUNCf3pKt94msvrRT7b2u0AwQ2Hm+q5MrsaUNTvlv71sl3K6QlgZ53+WyjUu5Z6bUWiN32s7DxkWHSmb5VdNMNxMHKb+h5FAtMdQ47m6FKXkhHzseYwdW5yYDOsokBILwPM9E5qXKMXPvU/po4A+X4SEqKh3lQfWJf6rIRiou6QS+66ci6frebxWbO4/S8wnRb5XxlwU74/BfVC/x7GZdxGZdxGZfxFxpfZKUal5EgvvmWorf41x9eY/dyBuEVB6Fcd2+Kd90lmDxtwrlsZtQCySqT4ZkUsfzeCjzj30ywbagGCUnJrjLYRP++UN6jEvG6HtnNqeS8mgTN1UscLEIhjfCutOxAVQQS+65Fill5AAAgAElEQVQ6k+B7J/Cs8rXKoTxS/s7LCKWzSGPUYIjIAGC6ZgkC72Kn21R3yS3tPkv1i6TguWrLXkMVYtU6wHQRw++phE5PCi0VrWgz7cALjJa6JDB0dLom+iRFEo8ZYVAYiZ6MFURmsYjVfGCWVQuX4JYe/uTkZ9oUdqihn3OQdfnvANBvOzTvHNRVkcpkjO/phHNTzfXt2iM8N3JiMWq8uKYSqLMBfwoG+YHlJS4jF+TkPtV7FBXU9YTVisqe06FFHovkykBPWkzu0wsPlJD14j50w/dmVl0oX59TfTPiat1jcU+2VQokIwOAIbSwT0aYr35Vq424zFDvyzkCsaletMnlM+P6tIy1DFQVOkRiI5WZC1GBHFWoxJ7xljxv8yLL58SP2FCyU2FAQwH+mtn5n5wYOrg9zkxM3EEJc1w8dsuzlOr7UyQjZlZ1lqptulaYGAWK3bmMS4dZNdoyBFzaTTOC09xRy+31GckwLPOZHzE5uFVoOxYP71TvN1yCbmtLSX9o5Jpqfy5bmkPaeuK5Y9YWKH82HvClygwK402U84IGFLdg8mSgBk0BHCjVdD3fUuH7a0o4sl/RgzU9dmg/WMTh86/zvshF9Re//Qi7arEfG2GmTYcGq82Apz3NCiko5AJraECFEnuU4e8S9C2tMCYrieFCUmjve6LxA1A/dBK+HFYZ/iVNzu1qgrVJYMDgrYQXw2tg0lUvCojBdWoy4ibCLeskz17+iKORhV97MlyvfZOabDMlB3Wy8h5TMkrpCaH2aKOC6hIS9xOL8wxAk0pqak81riM0928IFrUAw5P6YJBKX9Ym5HVJ4QbMn7qaatIAB9YMNo8UdFx6cqvXRzSW/t3+sEBgec3ie0vxbwyPEZRVvktGWsfaO93bym5ehTPtr3URfuKDtEkmErfySEmTDIFHWXz1s4W/TtLXMw9OJtrFVydsFvR8HMcGsQnSL25slDD2T4cVusWE06ZoSLRsHkhuwIuGS8i9xZE3Xmow9XMa5CDE99d938pEPn7t4b6uDPfx2MimptwDgDZk27DCiSUv1iaMfRFYUt+zOBuZoZr550XEJDIumrCtOOIo6X8GRRvH+WZobqQ+13POIfu0gGzcCO6tOmRafPnZ2WpmnfI9uqrwvtvXhJZ5v5Oua5ZNZLMl+DfyeUw3NdLNskWmQKFN1W9P13UBT20iZjd/zanJNflHg2xES6fosUqrdDhPPJoPHc4XvtSgso0jgGNdLMt/nxYaaCPMT61cO8sOYllR/zPxhiEbiMtUdiyRmjGz585xZVOUTUb33mB4yV9gtoAjUntGzdOi5BrU651NBpJCeEs3pDnSOaqLof5lXMZlXMZlXMZllPFFVqo/PVxD9x2S18L+dUuP22WPha0M1JOmnd5yPeJ0IguVuMwUlTbDHDf3hFveLAa8327gfk/bQD1ju8UGQoBJUWOKmmBYHsUBJidFekuuRLLXCLPoLrMKYiKhdEYoRKVj9ailf1gh62zIsAEA1MmSqYGtxxSizIzFazaejNQLa3BUAt2EJQeYF6aeyUKYmpIDtk4Yyrmd2eXsrZi2N6z9618y+/Iqipl8v1HQJyOVQX9qEBrWK04GzUOp3BkKk7K7kiaEPFa+W5uwfkX36XbZ4/sfyExYN5HyU/n66ybCMMybfljCTMD0KvA1GaHYtFwlAF6J3k8lILxk9MIkibi7X1XHKIBYz7ue0JHnj2vogxEeFDlyFVheV+1iIrhQPKa9khD0bPKZljGsuEIHGY6EYBCLW1SuGlloCPGp5LuOC4bmbwdsrqi06QcH7zWxa0GVZPEx1o+OzCZACI6KGeLq0M3MH5YJKlXnq2xQc3qZHCcoS6hoQ/tUY9VOr5PAqgBXi/w5Yi/PKugmE1mJfzbc83+3BM1KeHpHz2H53HibEb7heLrBoOGABDqnWk0npzDd8u8OFe41A3kwF92xtar6Ka9JG15QJ2Kc823xM61yMU/heSksyDyDbmiCOhm5h3pSsGO5/jNY+8lAPZgzZq0wdhWZukj26kz/nLoEfdLy7+yx6mf9phqftFsFvwLyFZ/0jIQGAPZg0Oxm93NZvtusIk+kSBAP6JOC21dy5ec8vshFNe4a5KlBcz9g2dFdPPYN3j5cU/8LgDlo3PwdzRh3yx7hH2lCfjguYXXCyxX9XamMgVXR3/3LayzemipLsagG0jYL0y1GMvI3DPlaF0SGkZ2C6SJaPi8/WXg2XdANTdaGoUtjEsJU0z7mi4vbVwce/8YD3KswR01uPeLsoiR9BgBy6f2NBnpnseSemT1Va7bYEBVeWmTPVhJxSkC6sDltPjOyKMbvdqDz7BgeTzsrcKFfEfQmpuWhQyoWg3EW6t0QVGv31RWnQE1q0GSFxue4fHHCqw07NA2d9CvNR0eTc3HBetvJ3iSuIuyveix41es/LGHLIsq2cwW+Hr6K2FzTQvTN9Q7PIy2cu75DPzqMJXB9Z+B4wlmCnpHCbI42yxuZXSJJD6inD5vEfEOdjMhEKKVAwa65JaAylgzjKpXRn1qovj4jEiY+qeqC5eh+mjc0Q1sb4bndEbgfLs/STYRm1yfbA7Gr91P72rYOq5r7CU40kuclovaYG5q4Cys2G4jc5vCrKjtTbBhRJuGsIeHXxW2q9DzbR1N7mYu6oLg9QYwl5L59Uoi8sPVfRej7ER07Mfm3G7EbTA5wxyxyofG+SmV0qJudbOg9L1Bo7LLYdubgSGpSkmvUbBVK9fqGdQJ0fa7OmLVHYoQHXmTDFcQm0wyq9l6ZrVzc0KBqAlEGzlzfoDIlYQHAYJGXAZbD3ymTlX5kTxWyH28ywiYDfdlo1Z6+PVAbSqwVZ0YW2tdebmVG1+sQVkD8K1iR/gq+wv+P0SagTbhaDYj8AvhjA9VTWDNAO7ji6PNPNz/iTUuNlV1Y4o/HF/jpSOyEk3d4+FeqehYfNO2+eHGIXdUgpi5B82KYJvL40oab+7k+sIvrAa0LOLI3qh+sVNNaJ+SskLjsTLEmhri9ri+OYr/S8jA/27OYpWyzEIlUrrvDsMwS2abet1i+1ZWMdFUXMzrIOe2/clAUEalKz2/SYqWWByP/ZromfWL5d25fSUaGXZPKdSw9XOBnWr8im5hJCeYTizlpNL+ihfTrm2ccJjrI43YlMWrhKlGEXpnk7zxWt7Q43ix7/PThhipvAHao1m+F0DHd8WRyP2DiXvrvvnsDcJVjTvRMCCaRKxlM80IhiIJVMomqqLHY0Kx+vz7hpuvl0v/74x2OP1GGlj6RLjgUGcciSCIJAIS+ulGVqhqgzV66Lj52Guk6whXiT1bon3nlSaAg+zLxjmYWYRiQnKv37GdJRqK1NJm0qmWj5RXMc3k4gbBIaD8Z+X3jy+LKRFUXXQ+6dtLvm1fny3y2wclm9qxqzNJlZv8WRZdJf+k+GOgfljLpd0dyoyrHO/wSKIxB6vvy89LWCo6eiSptQao+wM1OQ0eck5rKsxQVJibUFQRJtMsHg1TQF+73t295cxXrgq5nPuPTLfsA88bRPhvZjBTSn2FiUYk8BIABgPnDAu0D37YxY2K7Sr/OsEN5tyjizzCXJGySuKupCCSbxa1Lzg20WbKHcmzaGE03/KFMBKpcH9HPdvwVfIXLuIzLuIzLuIz/OsYXWalu7k4wy4j9qcXILFVkwL7oYXg37f+8wv4tVQP/I/4O/839RwDAP79/A2sjvr2myrWzHts3bFqABQnneReodJZKsl1N1WnHK8ACiSthbSKur6n3NkwOx1MrsBuChi6QFPdg3XLWeGC8JDkgYNZjmhQUFzdup8QAPCzzWcUyp82ndQS4ymk5jUT8hFf5LCfSHWoSRXZZdu7CmC7+uS4L0zYbjeE1Z8p+ZxAXQMM7V7/CWcZmWnAyDrgyLZv/6yCVe+oJape+5lh7cMEl5CYLw/r7hxsEhjRTb4GuVhPrX+/wZkPGt85E9Azn//EPr+EebWWqThWSzoaCvJevqQ1w2i0QuTc5R/aI/Tir5JMS2PIMsgfv0guikIHTE0HGp6cFvvdaJCrh3kvVJz2x4niksrDPU1DApGEPFZUQs/cmS38YAPKkEQzLugYFzcYkWmdCUnikXlc2Z7TSOkgNw7PN7LsX7wdNgRGlF6hyZfWqqNA+1pZJ/ybJs9TujFy32JA/seSTzgzdtWcIucxmuf4MoUKuydE1TrPPSQB9IMTGDqUCBY7fFGY6+ScX43kVIUxm7evvjQ3Dmmd5sPV3hyXku6kAMdUo5h50QJAkpinZtr7e24OD3euafKPzmeHLyKiJupqo1XSo0H3xulYtMeILb0CpjGNBJZ4duqOSaxC6WdpMrHKg7Oh2SeBAr86QsNTU6jRsolS0xA0o94yMTsq1ii1fy1mw++c6vshFtbERxkacTi00R5HlpBAmi/jIPVWvRAt4PHb43/78twCA9Z81wgr4lzvWV14FgVncVycsmohpqoHApW+akhJ5CqICbJZ4N60TDgz3hsFxFBx91G68LAymjYRAFahF55lNYZ71NdnsuvwoVlhF+6LJ4wnD1d6XfbJVsjBxX+a6Tt4FRjM99bAcv9Chm8FVW4u0qBOjaiISk53szp6FWKtQtYDF/YfOieHJ0j+zGWsO/b5fnfBuS5udMZHzUhnkblWlJghKFpi7mwO2zzTDmfdW4EH3usc4WTwNbOw/NOiP7FY0kgZWYK8IDK/o+Otf79DphO0j2fqow4wYgtqLEhi0QLDDfDGge+F54rUHBVUWzgVgmZDVPtI1Of66iAsVNPdQExwwaiHBQWVaTEHPtO51XWxmz4E5aNGspo50tSUNydrqtqQUMA6ucg1m39GcVG0B5HNHrthB7p9KBJcGlpRh0HAco9Y80wJ5+lVZZSF9yOk6yfG1V2eJLQKxguBz3Ssh/swhXz1BNi7ZsGSmtPuH2uNTifqv+7+pxy3ParNVULP2xNz0RwdAM8RrLIWWl8SWZOu7JdKYsrkKgGfIN64TUCReLsG4JBuZOBiZX5qtJjvAEm51UjO9bBI432ggDabyBJYBi9lGPAQtZMfDuzXMge/FTqHZ1oDx4S7P2i6V0FT0sOV5J0vQ+r2yrkTB5sFIW0pPPyN7XVU5UvtJ02bzr4CodIF/L+MyLuMyLuMy/kLji6xUB29hvEXbeQw9bcXS0UJNihySAKSlguGqofs/F2ifCusww0wKjhmnYdEIvd67Bv6EKm1JCoaNGnzv0PxE27RQjKOZzBO8QS5mD6MB2ojlFeeMegPNe580E80DRFQqVSuxOqvgWnlVqwoO7AYqbJVmhIgiCbDHSo2nYOZZZXOqnqNQOGP4mQlQJf+1ycguVS/dbQNTwgfuZlBWtmi2FZbWvh5PRaqIMsOW4T7Acs7tEKxUn2Cjc2EytlGQgaYJWHWT5ON+2q5hf08lob9Oki8ZJoPUW2x5555zdbeyg4btK+Em//aEBVdzKSsYAIZhuqhtNRxXFcr2Vwl2r88i0oQdrgn2Ltm83Sct8KmZKgEmLIHTL2poALyCLpKINlI8Jp+HtQlNw8YTT9SOyKFWl+K6oyGuYOjIh7rl6L3OBfQT3Yy+b6qwH/T8FHJMuE7ImtmnE0Gz09WMCVtaAk2Cas7Po8Cu0QH9ryucrbdWIE2oWnHGBVX7ga+VCkqY5CpSQHyeVaSlisoWYvauEhFkikFFMsTkBTiPtcnyzreP1TBBYunKbKlwhrgU8pFU6+XznGcLEInJVK4ZmaewM5rudc1IVgbBZbR39GGtE+K7EiILmFDDMWKT4W/5Lxtf1QV7B7UIsG19Zor0LoyW5G/FrWvSYlBjT4BfK/SvK/pV5gsz1LkhNZk803WdjMq91SByo90yChdqy0TFyryGBsbXQSRGZsIZEe1zHl/koupMhDERx76lpBcA7maAtUkePn9s5MV0Rwp4BgpMOXvRilYSoAXDJYB7d8or+I/0QpiDrm4idxO0zjAMsxmTMR5pNrXXI7RJOHGfQ9s0S6kxpE0tJtQmE80eBJmdhTT3NScxm6p7S4YgndLD0cP8czOdWFZnCRzISs5fFYlEOS9br0cx8y9sWriMdEsv92o9CjNV/+iQbJVFpFm2ovZAKC8uACRg+yPreWxGywkt+pYsFwuE33QBdxvqcU7B4tPba0nusfvK3F19p5F/pPsyXWVM33gknpHTVHWAlG2ZgK/o93VtEKP8b15sMUWDp56gaJgMz9/TrINYS5ofO3L+KS1cWyF1MyhMdxFgvd9xaWWSUblCnOM9Wf6Va+xf+drn5Oe39u4DxrLp4PZA2XQoWyfCeHCUD8ufu7k6yTH3pw7DfubinnCegMTQuRqVtIT9KlOvrUy8bZ7JNgD9sZFFqkhbAOD4DaUf6Zk8pjybWVe5kWbLwsJanye3UJqKqsbznGsK0EJRNioqA6av7PGwrIvv4q2BO+BsYzTXCftNfT5VAtQ8qaV8Z5fhN9VGFHp+vnTtykY3a6D9yPfOAp7lL+52RBgt/A8rvlYaatZvzTqjZO5O9xGKHcqMTeL8pTceOVcOBmZpVpTolKC4/9990pKjG5bA4dcBKPrzoAQa1qmm4Kj7kY5Zbr7KMOxyptnErLSi9IzROw+g95tEoRdF03sChpdAFEeuz3d8kYvqfr+Ajh3iwaK95YgrkzCcGul7qsHIDT9+m+Gv6El2zwZ6Ok+6l76mzjizYDtVy7LsMvILWrEWqxFdUyfGp48b+TfRaITJSMWlAIRDifQA1DJCF2H4rkGz403AbZIK2e3o90qqxIziXqoHKWpVfdHnaXAqg9Jxyvqia0yTLKCmTn4ysXQJatS17/JmwD2Hdzcm4sePxKFX1wntJ32+My3XoD3vPyFWuYBde2yWNAM/Hzsom+r18AYPz3Rj/GShF0EqOu8cDMe0xZYSOQA2EPAKac+bGF+1rv4mAl1Ex4v2ZjHgmhf0mDW+e3cni5S58liu6GenYwf9Ix3PntSZFGl64+W7xKOBe9Wj4wqxbxp4NhyxWwN/z5uuo4bb1+OowQhpBC5BDQaWz/E0NCK5cguP1Bg4Jro1LmBkstwUG1m82oWHD0YMK5SaMX089Wuln5ZogwigJrWAnrWMes/O4spOBmbALGEm103qIrPhQdFRV60i6SQrWWj+/3pWuBcJVrEjNON5dSlkoSYDN7PnPdRK0kz4mYlG/S70PFbLQdvXzWeuYBHignkA5bvnukEQRKlIW0z17c0my6YFf1rCzdCMsE5SITc78umWDfGoJZBeqSzvQRwMacNni15BiNLRovloa8GwBybeF55+FXD79Q6RUafnTyuoWI1W5Jq/7aC6atHpHo3EXyIq4lrMqnX5WZ7dl0UmEhn3W0+vyf419bOH6jMdl57qZVzGZVzGZVzGX2h8kZWqettBdR3UKknINJJCDorMykHVQen3ZV2dUexBYbrJZxmHxX4tRUq2KfBharPQ8MMKEnYdWoOnYytZnHqsocHZJSw31VW6PzaSE5mbhOQNEveSzEljelEYoYAuphAOZ5mV8SpAn4oxPgnBRTQeFOaWi/PqFhq16j6ziGMIubSBYu03uWeDsEwAp+LcXJ2waTln1HrEF/SPHv7whgzMGZqbV3OmV/AvaylilgErrgIbG/HpQ7F2UjDL+rmbqxMCX4NlN5HbFfcGY9AYflEw2IomqMHAHIxUIaZX4kTT3A14cX2A477s42mB3ZFg4+HtisOY+ZC3E/afVvWYZeO+yMRavWWz+iaiZcH99dc9nvtOzrntvOTN2vsegV2YTEkOKajBIoqRSB4cECmvFwB0FyX9qG0DvM8CRcekRUJWZDYAMLYtYqgVbdd4pFV5pjX8YGt/PlcIL7o8Y9nW9ghA7YXSV89tgr9RsPwMnt4ojF9V7YTZVfvKsK6MWRUhDOXsfhZgnbQE15fUFend5dnnzMxQP9J1LO0O6eOBKs5pU6uquZWiGYHmuaYy5Vm2qJpZfiIpYgInK/9OGPlsbVraItnUij+5GXNfAbnBWeujZRa4PXLlXd1NYflZit5ICwAZFFBevlvQVaqVlMwPAJlalNzb1asjpmDRcysKfoa06cq81oECNso7E37Oy9CQfNg4Uw1kneG5524OdGy53n9F5d0XuaiGOw+9MDS5Fgs9z8kwBcpZJYEm9KSEPFGSGwq8ZUYlUOd0l5CgxEaMLAD5gWoqBOMHi3y0YhOXlgmrr6mxse5GdDag9/RUHh+WVRdoFMGN5RyvYiULjLq6vHTU9yiTZOodHMPc/oVFTkq8evFs64I5g2fspM7g4LlLTbKsjyxITVRCdkgtENbAek2/b/AWe4Y0f9peYfwTYU3tUOn1ADC9iDLJNP/tDrcm4vlAi0pOCodDVz9cVDNHg/aPTno9T21XdZgtyRRKPyqfzBksJxrKNgFey0sd/u6EK17Alco4DC1OLHdSf15USURLk3yBwPRTIwtD7DLCDT8Di1DD6AHcXR/xakVw+MfTCp0LWDW0GqSssGrpz9vDAoo3DFMCYDP3w4CuC6Iz9I5kViXk3pgkNpbTZDEdGplcRwW4j+wOdajEsHgy0OtJ+AT7aSHPTijwa7mOStVnLqm6mepZHnRV++5yrSaFxdtZT3iThFRjtwZqdnw9zvr/DepCmuk9bJgA0z5Vx63UAHYC3KkupmWzpmNdALNjyDfWz8xhYncCykknVxdfPbH37/8LUWm6yUivedO4GjH0DRK7aaloUHY0KvH3mreD+HBmxmtItnoyA4A7mGphuCS4WvTir2sDMsfqY67biHic+Yq7hJxK60AjzZzHoKpG1ntDxD0m6plTbc9or6hXDVo4Y1MTZ/RUNyCkma4bbttXYtt0Xfvs2SjYgz7jUaidRvoriH77/L/BZVzGZVzGZVzGfyXji6xUf/ntJ9hVi+8/3CFtmXW7J6N5MRz3+gwSLE4gyRAjMfHn4jzUN4GdXSqUKwkzHDAOAJgcdFSI17T1u/tqhx3nuB6fO3SrSUgj0BmpsAktN/eLEbqdMSxthmIW6f3tATkr8TV+c7OHM0y00hHfb2/k79OtxfH9iq+BEa/S9oHkLkX87VezXbbNUAFCklKx7v59k4XNCtDu9/37O7qOB422pIIklhX8R6KBrpcjxmt2jDIRy8bjaKgUmY6tuP90H7VUF8OrhP7rKND83EfXPFikpuaAKg0x/W4ejGwn9UjED9l1/2mB5yVVxZQLqcQQY54CpIwiUhb7GicA619XtGEK9Gq1NmCKRq53ygrfb4ms5WyE0QnPA4eUZ4XdrrBqlMhk8rVHCkoqka7xmIrh/WSQ9k6q8DgLXPCjPQ9kN1nkJWE1k6C0CXFGVHJNkD/nqIDBnMGY8YaeW91EpCd6QNzesC9zJawUdMcMlAwzvOBjNhkNM1/jMhOyUxjtcZZmoyosaI90H5pnPnxbYWi3B5p9huJqbNooMTBItrr4NFvQM1ygfz0j5ymuTsvPZoHl4lNdKmgHBGZ627VH5qqwf16TrzajX3GVEGytVPWgajrPLKQ8uQxdDF0SoCctFXlYZIHAAfrOJbXGqSwloTIJRoxsNNrrQdCM08eVML2LF3UhfMVlEr9vP1jk3sLuKgO9XH+/iTXNSpM5TSEuZTOr+B3/uTB+VTV4URFw7PFsBoWwzufKAw/kGQHtcx1f5KLqo0GOBlonoPRDDTnASDh4qhCPqs80sf3mtnzLytxrdhqhy0htkSpUKYI9qaopMxl65fG3r8m5etsvqrF1R5oyPzKEZBO6a5oVgjfwz80ZhFRSPNzCC8sWAEI0CNyfO04NXq9pwh+jxZurPVoWj47R4hNP+NtPa7h/p0nSr1kWUaQDNiPe8zlOGu7B1t7XLEg5G0DpjP131Pe0B42uXGOXRSKRWpLMFKjVmogT43kxaTwelgRd8vXCDV2D4QYwrO81KiOMFt03dIxptPBP7Ey1UchtguKFqV16ut8Ahk0D9YE+lyw7wvDmJNokE5X96BAXSdij4voC4HgdcfXVXkz0b16f8DfXdD+HaPEwrOReLJyXe6FVlpDy0+RwOLViazjtm+rOdVP76mFPWpXIvcHtwcEU6Y0CsEpwV3R92s6LHWMZMpEftGx+sq6bP3c1wrkobHRrozjupKODOWppEUz3dWeRjk56ramhe1sj3WatlC5jfJHqRmaY2Q1yKLb8u5keEqg6VTMoNFuStgC0qJYNoPa0QfAb7vEt6vEpuJ7+7PZkEp9V6QXizAkouRp2QFZ7fA49HW98XZqxmez+AITnRsIZ5LoUpnKXxbI0nyyyVsilR6yrBlSFukHIJqN5mjN3K5SaXQIWEQ1r31PUSGWhi1p68zkoBFhpbZlTDduQiLxcFnctige9txQGz+2U3MXKSlY4Z3YrVYn7CTO2L0mryoYqrGaRcY8V7oWiz9RwDFC/fubY9bmOL3JRTVkhZkX+uuUhNwkwGab4t6ZapamUkXXZpdGOS82E7N1HnjC53yELc1CSK1i0iQCgu4BvX2yxH2li359atNzDiklj3HWwq8L1zxhZb2Ys2cmVBWCxHvHre4qnW7sRH0404zwPLYbJib3hqp1kUr9uelidRJfZ6IBTS8ffmSq9sXvKWfVM+9evB9xtaFbfHxbITxaWJKFn+YzhKgIHBzMW8XdNcknLiPXLo5xTMWYAgE+7tZgMPL/bwG0Nmt9w/unmhMddXaR8WWy9gr7yZ8L2kpaRO4p9y0w8GyZTK3ydkbjacksPeAPL1V3YOzi2Pkw2Q4+adKY8Tv8dVdb3N0eMwchC/TfXD1hxuOW/bV9IJfnrm0d8u9ziw0j35vv9jUgWAKoqpFiyGSWsNHzsKlKSzxfBuVxCRerplx4oAHle8mAAm5CLeUgHGH4ejVfiFdu6iMZGvFjT9fbR4O3TVT1HWwlDyitky/d2qIttbIlfIPIJQM7RX8ezDaZKiiLOwFyDrKh/BwC5LkpnVWUCTt9k6b2aYYYgKK2P8uQAACAASURBVOD4dTUSMUNdLGM3I8oY6uWV9Bl3BAJLUsYbSpQqhCs91s10XBCSIcSlrYHiqVOFOk/ERaIs1FKdjhpqqPpN5VUlXtksPUroWrU2T5o9g7Mcs1hvmnWAa4LoUeNkxEoVsVbBylAfW9Jhxrpoh2U+qyTzi1F6gHkwmO4S3C09x/7Q1AZhhpCsklWwx3p9kqtSIe0V9KkWJO6o0JBNOvx6RmDidJ955mtY/4yM9pmOS0/1Mi7jMi7jMi7jLzS+yEr1YbuGnjoyrp9ZbelZasfcQD4rFiuDqpe4SDWlYlDCeEQG0jdD3cEdHMZXzAKNqkLBtxG7vsOJd7FKVSZgf2qouhC9SpZeiXMR2iSRRTgbxb7vYVhhjKUqc8hZYbWgHafTST5XqlTNJ9mYiK9W1KjyLw0eP74AABgPRA2kG6qY/9O3P8q1+S+HBZkRkJc8hjeVvq+WgaBKhsdiVGL2rgBcLQizW7sJUzJoGI98MgnmXyrDd/z7Hms2RThNroaxHxzAfUyz8khBYzrNLHO4P1TkAALNb7XAaP4mS3/S71qyiSut6aDE6AM2Q58MRjbR714f8XJJ5/+wXSO963D/dwT5noLD2xNVd6tmwt/ePPO1j3g3XAmKMAWLkavY46FDjrVXmjOAvij9a96pdglpNFAlo7VX5y0JmzHwNchPjVS4cZWADDTM/A6Tgeeyypy0JJesugmvVgc03CT74DdoSjJSF5G9QjKl6lHITX0v/C8II81JwX50tR9qanWkBw0zVYarX2cxXAf4HpXnZ9YXj21lUYcXidjzpXcMIOvia0n/sOSThjWkd2wHNYOJ6b+Hq9qeicVXxYJkdaWCa2aVapurpSOoapderKn3QnlCd2qua4V4kfiaFI6F17X/P6gzZ7HpOhPiA5LGNGw3WPrc0jMfULFhl2G33BJglE0Y7asMzAvAqKB+QajTsvPoC7v9pJG6BL+fRQ2V72khtqoZQOx0vb+e7BPpu/B/K4ESp8ryT7ayuTP3s8VlzlRZ0ec+vshFNewb6MBvDT945qAlTQMgGKNAGirO5B+K+phlEo73QaziAEAFDfXIsoUZycWvMiwHZt+se7xYHvHc0SLyfruB597cYknShgIHLxoPz72SKVgMvYVb0Oyx6UaJKTt5JxrKGDW0zqJ57L2DL/1V36A1AUtbZBwagd8+H4xo1o7fAnkZ8Z9++wP97mTxz//HrwCQb2u2NWXDHDTSSzqebaJMAgAwTUZ6hr95+YCrht66lZ3gk8H/+mc+5n9eCeQ13UdgNNgnltScLDTD4c2rkxx7+LgANNDdEySrVMZQNHYT3U/HZCo9KerrAcAmADu6bk2RlvCkY3sFlWr4MpGR6Pv0n5aY9rQ4ptcjfvkP73Dk4PMf9jcijXm5OAgUPCWL//27XyAMJU5Ew3L/c7GckJKS/qWPSqD9dj1i2dHnRm9xOtkqu2gy8l3JF8skSeHjKz2ToZiM7mbAsqNz2ecOnqHJ8MpjeU3XbdVM2A4LPOwJYh8eFnVFMRlmqBB4gV8Btg7cs9a6xK/VvWA9X83xY8VDuY0iL9MnAzXzh01NhQjTywnLNZ370DdIJ4v2iv4+HloETjhxW32WEGTGmW76AJih9EkV/NVMkpIo3QUA0iqSJV/xxm5nBMEM2K2d9TZrDKL2FTK2J00bgVVdgCUGjm0/iz7Y7WYevi0EuvbrhHjv4TruxWaF6VRWforkE4espcdUgsgfbO1hLwiqLsfXkzrzRY6vJ3x7R5u+d49XsH+gX54caY+l0Agamb3QtUuUfMPfJTe5Omv5uhEiyeHs3je19TX3EodiKLqQN7NCsvns+fpcxwX+vYzLuIzLuIzL+AuNL7JSVV0kws9gxLghtZlMvXlLF+bGCklVw4FRQx0NEkNnGI0kW6Q2QR9cdYdZZQwvaKu6vjthzRXDy+URH08rvP94LcdvV7RV708N0mBg7ypcWxJDhr5B8lqyETMgZKfnUych6CkqaJNx4l13TpXIolc9Vm7CwLiX1UlITM5G6Hs6x9hb3L16xnc7kn88fdxIfqrK59BWfDWJC9RmMWDpqqRmDFagZq2y/HllJvwPv/sHdH+g8/frLJmsALC5P+KqVFhDi56dr4ZDA81VplJAXtWqOCUlrlXiw8zbxuFVRObKQz1bgfZVpOOIy04/YzK7RDDdj8woXieob6i6++WLLXZ9B8ehCG/We4FPO+Mx8UH+r4c3BOWXyq+NiFxdpEbROZei0CUYRiFSUnh6R3CyPhoKT4j1nNNT9YM204z4s4zC+lRNxMurg1zzu2WPDx1h9s+fVsISzpmYnGJEZLJUkmZnyOu2hEkvABRCTFQw/EwUz2RxW5pVbOakoRS1EwAArlJas81nBKfUVkMNeI0Ty73cs4GyGdNEVOw5VFigxEKcSw3QfSpSFiB2TEa6RXUKAyfTFI/tnq+FGIRw9ccjLlMliinIvKFCha5jkxHYKJ6uSZ037EApNQXmzRpnrmzFyzkt6NoXlCl5DRSjFpugdRYXOPXQoH0u7NzqoKYCoQYFAk98zgAQXni4NuD77+8BAMs/NnIeoVTm5ffNWmPpZOWZAADd6wrxhlnClKbvVhjzsatQuRkVJk4ZSl1iBl6Fx5snAzXW3/G5ji9yUV1eDTDLjLjW6D/R3VcTveixuJnYJJaFyJAJOZks1nYAvWzFUUXpjPRY/fv0pAB+aV9tDsK4/fPTLSZvkcvkZ3NNBQkKZhXQWNbBzRiyobdQNsni7HQSPWRKleKuTYZSGbGk5aiMpiTi8AS7dlWycfQEYY7eIhY3lWXAdrsS6YmNSha9CFCPqJibu1Q1jjphCFaM7cddJwHMy6sBtytalP6XH3+LxR8bOaZ/GbC6P8n57p+WOH2iBV3Han+nc9XE6aAQtcHYMNt10jI52hP1x0Jho+o6capY+2CpIXehsmj4dUb8DTeG9g65SfB3hZqZsFmzrrYZ4ZOWDcTrbi/Xcx9avOce6t2C0l9KQHr0GiuO9bOarpvhyWsKBvsdWxO+a0sLi+C8qNBseSFyAPoqZYmzRBi3nKCZwd00AV+vd/LcfThtZHOCScNxr36MBsPkRNfY3QwY39P5ZkVs3RLpFl56WVRzmOlXLaAm+h9AC8ac2ZnaTIxsgCbT0oM7kuaxcBbaVydht/p9A3uoK4PbayhmkiZb+3fZnqfPmLGmSlHYQ5XJzMFFPSjkqbL9KS6x/lw2CB0txLowbfP5dyttomanoYOW91/leo5mZNleCf2eqXCyAaY3oR67i5KehaBEvmPbgJw0wL11e6ps4gK7ArRZ0JM6cznyxeHrYIEPDlfv6Gf966qDJSb2TL2gVNU5h1kfPxWIl59Bl6vcRvHzys+uitXeMXazUI6sqxUjgMVbi+5jRryElF/GZVzGZVzGZVxGGV9kpVryVGOsewqV2OGowD/TLJ+0iwLPjocWOaYK3ZgsodYItDssTfvkMhRXIR/261rNmUimAXOzGyYmLBYTrE5oWa/40+OVGKSvbnt8c73DbUcVHcG2BOeNwUgkmjURo7diJr9oJ3R8vNYGdMYL/PvhuEbD5g9GJzgmBAVvYN62AvEki1nkFPl5Fvbo3IHn036F4A3ie1bctwk3rwiXczbi7e9e0fW2GeEfD1jzOX/VjeIstP3TDeyghbQQZo4ymhmWAOsZN0GucQ6VsJINE3oKmqQglVLWWszdmy2xQ0sFETYZiYMO3PWI+5sD9j0ROW6WPV4s6LskKFy3g1SBb/sr/HL1BAC4ayqZ6of9DZ52K8Q9V4ht9XVVKuN+dYJn1vah36D5M5tSNESUKte76BcBJpMI5AjEdZLQhfCwwOINEeJ+ffuElBX++cMb+Z2CXiyjuDw9nzqEYIQtfjh2AOd05r1FbGu2rZrpXrOpBD63J+2iZ3kreUNXFnJ2tSpRhwq/A4C/jWjuuHq3UVAb+2RrQDybqrSfuFVRgRaA/Xw9Q47DfYV4tUdlsHJBVSpLZJxVWGbmWQsNYVGroJD7XLWvba4RdJMSrW7mf1PCyG0/07A2OAuhgIZAod2v9njBTPfnY4fp0AhKll1CZua7L1prft79Jkn13MzQFtMbSnsrz/Q6i0GFmej8xrtyfWpOsh6I/Sv3Kik5vqqPLX3nmSMUaaj5dy2pSi4tsDlhKiED/LyQu5RB94k+1zxnhKVC1Bf497McOSvkrNBvO3nh8o0nqvnsYS5uRe1qqrKNoIBFrBO5n8E9nhbUAlXGr0eAX4hTasVkXivKtpwaZk7qjCUvLrdLeiMLq7RpYu256QRnIv64pX7I024lWav6ZLBn5u7Vmz023Yhrlq/M2b4AMEQnfbZNO2JkCPlwaitdf9uewaf5xSSbhxQV1N2Ellm+49slIjMBw9cTTBORmDV48+KArqG3/d2Pt/gP/0hs4j7QOXSWfvanT3cSzKxABg0iVZiUTH7IdXFPXWLm6wxL4/tCDlmziXx2b7u3Bst3fI9aoH8FeGaBmte9NP+Uph7wN9eEOToTsZtogR2CJWYmL1JGZ7nGL9ojLM8kr5Z7fNytkVl+tF4P2DB8/2G7xmloRL4y/bhCZtetvA5Q+2IwoDB8FevioCFB1bnJQJMAhm5zqv243394Ca2TsK+NTnUizGTiUYZrAvqR3bQGK301Myq4ZyV5p/EuIxfLxaCgNLNPPxgxRgF4si6ykGWA1hmq+BQkJYz5rOmeTczano4NwBN5njNBFWiSL308jzP27Ol1df8C6s/mcpJ5yDgApEU6M9HIQw1dzxqSVpRa+pxjyYrb142dLjmsfNxk6t/9pv6uzL3Gcn3Gm4TEdoPz9kBKCnYREEo/02vpZapI1pjlF6ix9jWhKlyN/DNDhqk6U6UmY7yfSVtmmcnJZGrVzCwjE8vX1KjRPLHz0syeEKAFVSRMPA8U6Dm5Wa93ZmeqRzKFEJnVSmG8zUjD58/+/SIX1d2HNfSiA3SGWpdSjPoHpTpVTZLqdNmNuGU3oY9PG8RtIz0htEnSRHJ0ABTCHf3d2CT2g4vGVz/VrPD8tJRJMzQZzyyJyAC+vnqWxeYBK+z+Cy2i+lHhT/pWyA7LBIz3fEwFBH479j9c4bmLWN7SAv2b+0d8/3wLAPj0cQPTRqx4Ed/vFgD7H2eX4MtCZMnj1ch3q/1hczXB2Fi/zzqS5y8AnAxCqrvN7bsNFix5Wd31+Nfffw0AWL8+YN2N+OmByFrx3UJcjjBomcAAJubwnmY+0aqsYN418pL761SlAwpAIj0gUIgs3MebgP4l/Xm6Je/ZxUu6v9PoakrHnUdnA1rub787bMTZKU0GuolyfxsbsBtpkXoal3KOIWm0rcea9a0vV0e8faZyzj+3gALG8pWWEd1tTR6JTFryu5YmVt6smbVHLBKa3sAuJ3GEUiqLxtnZiHU3njk47TOdo9IZkatb7RK8N1JN252VPpgONGmWagYJ4nylvMbibelTkx1gcWlSCQD3eY1LCNtGPHHtWJNL8GrEzabH7omu6xl50EIcybJW5NYzm9An5vmFFQWdl8Ug24pQUKIK5DuTfrYgLlo2Kop1pEWmk00W1Er1Guaka1LVz8LNy8gaULPqfV7dZdZoJjYGS5uI9pru9Tg4SY3KUSHNA8aBahXI5ynVY6AND8C8g5nmNrV1sYxNxlSiFF2GdhGppNgk0KYMALKCGo1s2KCrdt896xrMXuSFs82EbFSafK6jVpBNQLYQn24z0mJfjtm/yZjeBKT+8zf/vfRUL+MyLuMyLuMy/kLji6xUoTOgM8yiVlsAEDLt2gHKpSyw5aadxK3oenPCtJhweM94Xq7QZF4FBO/QXFMVeL3upW81eIuB4bX+3RpmqIJs7RXsHVVzV92IMVr8yMxX/PsS7baIy+k/qVigS3XmSyu93AbwVwonRVXJP++/BopIPygk7fDs2JN0mPUhg66yB2YBRjaXUIvaV26agHF0GNiDVz9bcdKJy0TGGMXM+6Zu5fvvNlB33JseLQ4fVgKdm9cDpW4AiM8L2vkW2G6WgpFzDXA2Jw13VGLS/3O4kBI5ZpIPRhfGuyr3UF4BJiN4lkVFJRXPODh82K8xMUN02rZSvdhlwC9fPcqvS1lhjPS5znoxufi/P77GNFkEQ8f/t+cV4qG4JiXYmcSmWXiB+nNWUgUrBUxPHTQzYdWjlZCC8TbBbztE7oUv1yNebYiJfN8dsRsXYu7xYb8WeD8MFprDBtLRQo0aLSejmL6aqgMEIxamt3po5ZmzRyVw3hxuBIB4U3vd8aFF98nUZJQuI12xgYGL2O2W4tGsJiW9zGZbK8lMIJAk3Uz3sVZKXsH0Wqpf09c+XljOmKnmZ05Os75uctQCKshMemwltECPQLut2bGxq9WonvdhFVXXEsSRyO8WqIzYkrW8eXnAwEzsNJlKS/YautdnvAHhNXAOaoFW7awijDOTBZWoai6qhNVmqAYjk0V4boh1DWZll4tgE5nflFCHranB7zP3o3mCD8A+vuKopNDs69wWu8oSNkNlAlPwe8bwgp/jlxF6b4B5K+czHV/kotpejTBLBe8NLMsKGheAJSR1JHgj/bKH41Ls9f7m9gFWJfzevaSffdoIdKN0RlokhLcE/z2GZQ3hDVWzZhQtDKX/4F9PeMFSjYXz2I8t/JbIGnqR0Rc4KdGD6fYVxiwP6c9jq5RXsA/sGvQ0x5LoxS+EEuQKqUWLs7SQPP/79SQOP4vGY+gb6IcahBzfsHY2A3rn0P2CJnZrEp7fcrTIJkAxxBiiA0o/EEAcLBJDTdqTbrFcHyAjMTEsG8iEoL3CeJuoNwYQaaz0D9khS+zkcjX9T8tKxlDRAEFRHxEARgPd8/G3HU5tW49vE8yWYddPDv/+8BWa17TT2SzHaiOnE/60IyZISgrGJJFuUcJJlRhNY3XCaZqAO+6pPxyX2D+x3EvR/RQpkYdMwtf/ojG81Bh+Tc+xVhnfrLYAgA/9Bu/2G5FgaZURH/hCugzFwQHNSUHPXI2ygZxT4sXM7vkZ9xCXMJXr8xeu2BCdF0vdRoqkA0lhysYH4EWIb9S0baFPVZoj1xoEW5YFNrkMv0ni8IOghZylPW1sy+YwtfU5KAsMfZD780Wl0yVU3RLLR36inrnrK+Gw2fF1KIjp7LxiU3uMyc0WWABhNbPh0xnxLqBjPXeMGr682FPVupoDhblL3JupcH7plZrSJfnGC4egee/QPtZNY7wOQnDsTy3pXQHkURPxqqiDbJYNMACYvoYk0HmX/5/dCyYAyl4iVGJSs6NrPt5VbkOByvVE5C0AsKcMv1YYb/idOWnaMMw2Op/ruMC/l3EZl3EZl3EZf6HxRVaqxJCkCqII3mPSMDpJXBoAgUy6ZhTThOepw8k32O2ZOXmwYvBu2wAfFTSH/IbrWCU6RyNG21BAbhISm5GroPDhA7EuPqgMbBuB1VSuFagOOHO3yTPGX3GVKZ/LtgYCaF93iDpkHL6tYnsgQxeBd1YwxcycHWQM+9S+vnvGuqFd9nag717gNu2V+B2jzbj+7ZMYGnx6fyXEEyQDrIqnKYDeQB0q3FN2wtAEJwmcmyrpwwSFwFKEuInQywDMJE0SWK6YQFEKm1mlZA4a2VTKf9okaIZF9cemZkEuMwUK8DNhtlYqKn8bsLjvYdlUo5+cRNnNWcE3qx4ft2v53c3ViLsrkuU87ZcI3sAxi/pu2ePxRNd2HGrZkzNXqyU70wCRr810rTC+iri5o2P+46uf5FmNWePF+lhdtx5XNVfzWIkndNBZ3FgHgWqbbc2QBbhymbFpi3PO8CZALYJk3cbnRgxSUpthTtV1J83i4XRvqBUyazsUmDVdV3ausL4LPNhUtIEQFg19qgQePTtHYcvy70mFcLcOAsWbPXsQC3Rb3aH8FcenFfLTQcEWPtnsfAshqsDgYZPEHcrdDFh2Xjy+T7sFVEFmBlUdojI/t4XxmyoyNV1TBF38iqVzLgE/8LtogeE1P393E7OtuVVhI6YHRj0ytTvO4ukKI3dSZ8Hk9IFyPSAtk6wZFSgAUagmF8mR61a512YAuscZU5pbV9ONwvEXUUhjzZOBe1aIF0elz3PEYJCDgXVBFtGUFKZopbemTRZT+84F0SP+sL0h6JMnUH07CYtS/WGJzZNC/5IXm7WH4UnXmwZJFg3ArSc5ptLU1wLI/msOWSUDgRW1V2easLmsAKgTnPbA8q2WBx0KMJwhOW1osbJsNG+G6soC0GIKAMiAPmlERW/HcdVIf9hqStFxbJE2vQy4+7rIThIWzuPTgeUxgxE3qu5qhOfrG0+WNGyhbjTSTD+oDATytYeacBJbVGvCoJF3TfV+76uWM7lMciBTv5uY63sl1yrcB7SbumpMqyTZufaXRyAYhLG6ZykOjL9ZkUbV8+KpdXWtAiB/7icHparp//3miB3rXlPUuL094MT63O9+upe+9Xo1ABzgvtstocb6qmZb73v/dYR70cvz+PvtS6zZ2P8wNXg+dTgx9GwOBm5fTdDLYlDMzefOOmd2cbOJdv68TVcZ4Zbvhc3IXkOzpWM7u2dZc9ZpYYEuovTtUpuQlrnCsPPkKIW6qBYGbNlo+Trzq6igZ0kvUOfnW56xrIF4XZug+X0LWxx+CoO1PII2A74uBshA81yOmWVT5lds3QiCSP0mIzNvwLZBfpe1CfuP69o71flsk12ka7and6IYqc0DzHWga2jfsm3mKuHrf3oPgNpGved3dWzgbJRN3vvfv6h5tZr1suW90LleD8Mw7yycoNzDsKxsYhXotpTNpzvVDUdhZBdWcvNMwfDle57Y6Wr6+56UYX3ZVBsqBj5/8u+XuaiWMQ1Oeqoxaoqv4r+XXR4A7E5Vz6d1wmo14MhxSXEw0BxsvvoBmG6AWET7swe2XY/S44hRw9qI00daeMzeSJ8tuwy7rbFKqamJGADOtHjJ1d3x8DLJ4tJsNcbb2gNxe8hCUaQlZSeZTZ100iZKILIaNB2Pzz9mJTFtH57XCIOFuq29TMcVv9EJPz1eI3GVb+8GWiB4RHbNyG1EdjNkIKsqU9IZOWi0H+qXLVVUWmSJQCu/W812+YE3BXkZYZdBPI9TUhjbTj6HGalpGhxysTBMQHpFk+JmOWIMBt5xNXOdZPEaJocYNBYLJm/ZKCk1u77DbksLWbv0uFoNuOrqNVgwAS5Gjf1hIf1ztQpCVJqClQ1ITgq5y2g/8ELkspiP6LsJt5uTGHhM0eCnLTXMT9sF9N7KwjGXMOgImeDDmjTBpYpNsZK6TD1t/t11ASg6VIA2T+1Hg/ahfnbivn3syNc5ls97LTKR3CSoNopMBzYLgQoZSH5mLRkUcqyktFKZ6sAEmEKOaSAL8zzcPZtM17hsYGf6Z3rW6zGywczsgaUzghBV0tI84chzT1mK66TR8fMxDg56EURT7T450bOnVRT/cDrn6qudLWla6RrQxrBYe/7Hf/geH4+Egvz43b1sINvNCB8NjsWCFbVHS9rcLFIzxBpmTr16dVaplj+bSZ0nFM36o1nT5qJcR9MD7bZe/3Ktjr8AYpnnPrTITZaNrp0RLj/3cempXsZlXMZlXMZl/IXGl1mpcqOyXQTpiU2ThR+t9FFVzjgeqbIxNkrlBZApuux2Ry2Q2ngL9F/FaocXFTzLTtBFWO7lNK2HNTWnU20NTF/NuotQHKAdplRiBSHjFt28YlGpVhrTVUazU1h/z8dwQFiWvgxR4Of2g/6OK4N2Bo25jNgmWA79VgD+8J4CzBeLCTf3B6nGFqsJ1y2VNH9+uMW0b2THv7gexPRf64SbDW1vR28RopaklDAZZF2vsX6qPeiwymeWcXOJBCYtfatwE6TiVyojHJw4GaWp2rspl9Bx0s/YO6TeSqWgkkLzNeGIIeq5cgBD3wjU77qA600vvdP9qcWnD1SaqaNBZlORRTtRuAEbMHw6rNCze5CxidoNjA5om+Q5yyaJpWDuLZqnWW8zK0xf0fl/c79DBvB4pHsxDE6MIcwT2QFOL9hOcjuHOSAs2KzpWSrV6Txom/4DzpATYX2yYQpAci57rM+mv6oOR8llqZDK+WOW+JJHI0H2ULUXqFVGfmr4+MSQLSYgGGtFFRcJ5qQRi6WhyxXetFkCHRAV9MdGqiMK867VV3IV3rZHJRUoJeiosxzW8t7FNmN6xS9Tk6glwS5qUXFvHIA2CX6wsJ+cXJNyjvapMsCzose05Lzi5ShVvP2hpSr1DT0Iv/vPv5C5B3eUvAUAfrQYPzm5xnlmKYqkoCZdk3RmdoMAzT3lr/N8XAp1oD+7g0L7VK+/X0PO3x4Ad8wIJRnoPktWrBkqAmK357+XQjOq78TnPL7IRbVpA0zr8XJzlPDuB78CkpIHOJ2MuC0l76qLjE2kqSvz/8sR44K1nAwnlTQUPFuo8qInhZZ1h+tuxGly8vKpGREnrKnXIi9wqOQhe1Jwx0oC8OsZsSJW4oAKAHJN6jB9hWeSzQjXifpaIFh6wZPY2Du07OJjTIJWGc/vSA5z+mEBfMt9wdUJD8clLMN068WIQ0m6OTbQBysBz8OxLrC2CSIPSDM7PfpZRHml/KcF3F4J/Ob2SvqrfpXJNg6gRWHmbqO8Jucn/rMJENmIzYD6DZF52pYkQXRvjXg2l3vRv2M/5dsRTRMkUi+eLNyaFrNXt3v03mL/PWOcGkJY03cT2q5IjOi4xdc4BI3r6+oNHJOW/utxaKRFMI0WkWHu5tEQWYWvY1hnbO7pu5wmhylY9GyjmU5W9KxZA9NdrDFlfkYgmeoCogKgUb1jCfrkDVmjkE21miNZR5WJCPFpUgiLukidJZdoUM+0pJ/YjFxmT17cs0D/QGS3H/Vo0T1XGNRfpUqOm+lZ80z/SV8uw5R3NyhoTloyEy2WSjanWSZ2HRSsr89SWOYqhyvXb67JZTh1eh3qZu1koXtVWyYbBd+WDbGieYH/StK4ye8dFAAAIABJREFUQpyoRLpmq5AaClcHgDR08m/W//RA7YIdfZ9mr2fuTUq0vvDUupH+ZJcEUs+7Bsor0gCD4vJkY54Zwi5EstmipxLO9MmxgciktFdi+9nsM/p7Pdv4195r1kDL0u7Y/mxD0+LMD/pzHhf49zIu4zIu4zIu4y80vshK1bKU5uN+hXGgHX7sDZFeit/nujrCpKigimH5YKAGg9vf0pZr1Xh8fyRY1JwMsstnpADDBgaxqWHUp8lhmiwUQ77zKgQA50NWGFZYdwk4fZVFUpJ1liokuywwohoMp0DwDnu24/T35OebuGrwk8XNNVU9LzZH+dzDYYnD+7VQ++1vDtiwf+1PD9cI3kDztTI6yem7LsDPTMBzb+W8zCIL3DvsW2DQUAxL6SaKQL19T+474qJkK0lCzWBLMKIorGEFyd/MmiRFpcoYX0YYhlYPzwvJpCTWpxIIOQNCgElo0etGSCOYEZ/ePVxDfd9BFxjzqxENJ42suknycJ8OSxz7RtyR3tzs8WJBZYhWGSkr7NmkHwB2h/rnwtgsOapl9x+uolzHpDUF24/FkUhXYo7LUJOuZiHDTCaygQRj66iQUA0Tkqvs0GxwJhvJNotBgxq1oCjlZ2WopBBe1LB6zJyq5oxsghBmOONYZVHZ5plJCWBOCk1J41nUUPvUJDpmIR0NGmrXya8q52UPGtqTKQN9ELVSnRgGL5msQ61osybmrUiC2kpisk/2XJqkZs5DiyAhFHrUxIjNteot0hszVQ/fxBWghJlbevcAQCvA907MSc4MNVYz2mxviN1d2PPLSUIVJpPRPRh0DwVtmPkpayBZVZOBNJ1bOd9ScepAz0+zLwgazW8AcHxDlXa5N24/g3lVvb4osqFy/f05Yvc5jy9yUd1/WEEvOtiNRyyQiQKapZeeVjg6ZBGIArnIYYLC4tu9ON+83V2JHs8OCkEDufRwmgQVyiymZCL03sCfGliePFJT4aTkaDEWHdkyCO08bDQwY4jC6zMHF/1cMGNmPfJCoeJM8+kV4qRheKF7efcsusbj5PD4PdsjugxzXSfFlBQ+/sCh4b0hY26O6zqOjVj5+d6RzrSbvR38q4ddC/PEpu2ZJu7Sqo5RwTwVrRC/wDMm5lm6dP7/+P+oEDhk3u4NYgtML/k72IzIcDv2tsosbKa0m6n+vTAj7Z7i5wpr1S08+iMzdR8b5G8HLFc0++WshBnc2oBYJk+VkZPGONL3/mlw+GBpdVy0E05DKzKuPLuZadvAFc1wR5NOmYTVMsj1Tl6TlKVYHybUxSXSNfRXM/covtdhWXv17gDkparWjbFuRmKbqSdXIM6k5Fo1W13h0lU+Y42G+9mCWhZN8dSrk6maNPRQ2eg6zCBkm+FY810WvWLYnxY1yMKYjHiwMMzCN2O9BsiAe6wyorCoGwY6gXqK5ecAkPQ8fJzgzrJYmqkGxpNlIS/aRwV/nZFK/7K3sPtqdZgcRKeZDWbQee0Vxy6fhY8v/8MWy5ZW7Xc/3AEJyLd8bfe2Boofa1+29IpLcH30Rp6txY8WKgLTFS+IPeReNPuM2NZnUMUaa2dPlQnsV/T3EnFnhixSmezY1rFoU2fX1K+rM5r2VDhI0LwhaVKczWef67jAv5dxGZdxGZdxGX+h8UVWqmgS0CTJLgSI4Tt9XIpOE4AwG9UyYMEm+W+u99i4Ef/8EwU/x/cLZN5Vdn/7iP/+63/HkcWj/9Pv/xapY6/YqOA5uFsFBTXb/ce/P0p016KdKBqOsy7TZGCYgZs8aWmL6X/WmeBVkDG+VAqGYGEhW7pcs0XbCG3Z9B7A+/c3YrafTa5wrEtQKgtMPD10VTvHu0lhNgOiRc1ZwY9W/JCR/h/23mzHsiS7Els2nOlOfn2IKSOnmouq6iLZaglCC1JL0AM/Ql+gz9AvCHoU0NBD/4QAAYREiC2SrWaBQ7OYWVmVGRmjz3c6kw162Nu2nZvqJ6EeGBVuQCLdw+89gx07ZrbXXnstkJ0UmIigc8QMDWFAx86I1ipA7NHUlMtQlp+HHGmoCDQeMWnA7qygBr6OxMKWqvQs8m2GzG6NVkHtc30rbIRi6M0cFFQBKGYvuzazeuefbcT4HSBz6ZFFHQ5lJfWr42CPIlAA5OMLoGvniI2HZlZ4DEqep3KKWacEpSmffXpjb+C5f3XpEUNWyNFdhhijIag2walukcd2sIBNms8NG28nw/FuAvcWkaP3TCRK1mOhjNkSjpuMwV5nhm9SYZrAvwkNiLWHL5RYjAU7QVj6bFQfLDNTT6lfP3pyhwMbVNy9XhEkmqJpn5nS9jBRPOJ6U+1zlCljosOR3+m4ChKlhZKiyQSjk1hGut9M1hrWEe7EZ6RjM/E7jQrw2bg96ijwbYqSAarXLPbA9nuMHu1rtG0KYxVgA9RtKjLPrOzY+FznzbGS4ueuthVKEXshNEDIfT4eKWapCCFQlfcT4tY8E5rMQP2kR7rG9lFOK5T3gKtzdIomQ+VQE/vFjs41sPavr0CM4gnA8b62D3JRLWcDzEzj8clO/u3luzX0ehD1Iu80qjU94bPlHj8/ew0AqLTDn7/5nohhn/3gBr94RH9rfYE/ffFDKdpfrVrseAFw2wLlmkbvsC+hS4/PnlKl/M/Wr/Hz+UsAQIgKf3b3I/zSk+9oi1KkFNP/0zUObRZBkLwSQOzKUong+FRu0LeWyhTSy1h7qCRSHiEQXQyA7+3RRDhdtOXzIIPrA+em3WAQ+wwzqr2VF8k3E7cTQy/mNOeUXkxfcV56sikQn1uvBFqerTo4p9HveQPSeISUZ2LzeIHHImTDlAyjgcyAFVbmvZWidiiC6GQxWg/4/CN6Zs9mG1xUO7S8gfp34WPcM7wcRo29S+VYAVU94LCh3/VtgTKxIS0wLhxC2oC0ubTKz4O48ajOQA2QsghVeclnxwjyCeV7C0GLNGMsIrkQ9Tm3Lj6mOuaJL4Ag90PKmU0WcBNpsWLxEH3ISl3jSZbhAzgHLP2qZOMGr1j2jz/rlJSdAYC9m0xDCmJgnky/072ElcNiRSdvhwL3X5N8jz1oFh1gWHqTnyv5p/Itj3Sf07xedc9/m4hhALQwp42cYnnQJLQCBRRMPxhWufwlVBF2Y6Cni9SEVW432aVl/0kU9bJiY4T5Wm7IW1SdsQmC8ZIPRaDKAtkg1z7LNirkHULEEY5a3maYXjZLSaBqn/+tPyWYu9jmvhvSGFHI0ozcJ925kvtO53YNnX8K66pk1KCVQMaalaIsK1NVt/ysHhSV3s/2dL2FnQ8YvcHVhlgL9WzAHz97KdKBv9mc4UfrSwDAx/Udfty8AQDcuAVOPzng2wsy/X7drvC3V88AAD4ofLy+w/MZvamX/QIlG5afV3t8vSPnki+Gx6jqEZoXnq2r8b9+/V/Qd26XGO8rITiFKkKd0wtWViO0jmhZzel7z69wUtIofXdYYmB7utv7OfyocxToNPyGZwQTUX+0l3pcNxqq4QQAFbNZ8qg5FzuJ3NOk6BXUmIlX7baG4s/FoOhzSVZOZUm3MPPQe84xOXWUd1P7HGmnGj7JA08Mo+dnh9xvmwbR6WwS3xkorh9WkTYakhe3UTYZUvebzu1o4gFoUhhYKSraiPnXVia8MShYzpt+PrvGIZT4aksG8v1oMV/STJL0XQFa9MbRHC0UKQINBaDvCymFAo7LDMR0W0eghEQiUeUILnpF5DqRncupy6RzG9jGTbUmnytiIgeo6N9j7n+ZuCP9l8ZBuTGThZmkOAFCVEJQub+rSQmNV3QPE8JKckaxW8PPevI8Jm4oGWEJKGYDdlzupHzeMCmw5RjLCAYL2SQFC9HRTg5NElVtJ58rKMLKta8xu9RsWB0qDceBiDoA5aYTmcfuKUIWtaUq1+rShgYY1+nhKIlogwEKvvbD84iTn1/je2vavLmg8eUNESGHokJsPBTnz9V9kcvLOi0kveQik67f9Nk2Mt2/6P06wNcJ2cifA6gvSl5gw0Rhaqr5TPd9XMOrhwmpq8zRrh4nzwK5zC8dM5jJhuc9bg851Yf20B7aQ3toD+131D7ISPWi2aNoRsztgO+fXAEA/tnyJf5x/xRf3JNP6k9P3+GvXn8CAPiH8gn+evYxAODNdglrAs4a2tLddQ1cUmFSEZ0r8Ku7xwCAdhKxHBYlHje07fu6PkP3coFfv6Gt2pfzxzDJOcMEFCc9HJdZNK8MRnbE0X8wYF4N+MnFOwDA03qLv7qka+xHK7BwjApxn/OLsBHqaSfH77ssZhFHLWIVkjfjFssM61CEE+XnKaM4jlrymmpkv0am+KulzxFRl0UhQlQEa6bC+qCyqEAZgYVDs2Cz93mLWUHb3cvdHFtWcoo9QbxIJggTHVPfBHLkSNGRy5HNNKfkS8CtA5CUo3TEkiPO7bcrKA8svqHPDncV2me0Bf/7zTMEKNxxHnUcLA5clG92uSjfPm7hhrz9jhoIE9OCaKNEoGrQOadfZA3csKUyCs+RfTkfMKR8dqT8N5IBOIDAZulqVGTuLcdXso2OKhfb64HYpmIiPgt5u11S3ZK+Yx/cJpeMqZMBMdG3nUJsPApWqnK9RUzjf1K6AuScLMAG1zFHTtHkaGZcBKgmhzb+zUygUBUy3Fts2TVmwhbPPsb5vKGg3wW+nuMoQk760gBQXWcxjP4E6J76bOxdRslR1pc5bZGcfiQ69ZMSkXBsbh5NzsXWl0pKh+qf3uH58h61oZPf+Rn2qczKK5hNIePYz0NOW+yzmYQeFHSPI5eqaf/7XLWFUCnJlYrgxETog7MYrBnOqaw55VDT56PJ7GVXRfhTSJ5WT7gMykOQklBQ36f+UYH0gv1wnKN/H9sHuaiel3uU1Yjf7s5wdaCF7Zdvn2NZ9+Ig8ur6RD6vVMTIuMSnJ3d4s1/i1Ybegu31XGC6et1hVo7YM4HCmgDPf+u9xVlJC/G//OQ36J9bgZo3Y41btlP7bHmLZdHh6ycEFX/z0SniV4Q17d4usDMR+ws6/r/ff4KQ6i2jkgEbZx5/8AffyjV//e4M7pLuy9cBxXLAyPJp9qo4mnhSGUHUoIVIclOTBVaBYLn0Em+NqK24WUR4NKDgBaEoHYZ+YsY8gTp1mxe6UGWoTzW0oH5ySmbbMSq82xHsd/jyRBYQzHjBTotqr7KyTuOB1qDYcLmKmdTZjZkgYT7f4fl6K8/70ekWt7vEQiGYK0mruXnE9ZbGy+PZFvuxxr6lGSm8bKASrNvkHGeMDM8m6craQ6fcuInwrYVhOC804ThffUXHLlqF8cRL3fSwqaTGOdaBpBP7iSVayld2hjY5k5yqlGPpnCMPFRO1pvnQ5BrjaPOTFpGoJiUvrYVl+NeNJCM4brnkqNUwqSxn4ckUnaH/qQl2OmZaiMwAjGdZ9k8Usg4GoQwCcdq9kkn9yFwdnK9L92YzHK5ZRSo5qbh5gN1nAk+zVbKIQOW6YN9EQOUqneadFpgyFMjuPgZAyIuZcpA+DSVtoAT+dArlLacVFkDxCxrrn67v8On8Blu+kHf7BUq2Bux2BZfKeDmGnZQciYRjzGMWINnA9N75msqX0gbWtAol55VNHzHOJxvRmPuu3OS6cRVI8D9BwMHm+1ReQQ+55jltmgBSXpLcbsVlObzBsS0dX01rft/T9gD/PrSH9tAe2kN7aL+j9kFGqv/Xq+/BzCooFfFHT4h1+ydnfwcA+LJ7AgDY+Qq/2tLPLmiJaF/s1/DOZCGBoEQP9ul6g9PqgH1Bu/X9WErU+uJqja/fEqmlmfW4WOwxL/K27MdMihqDQaUd/vnpCwDAJ/M7/P2CruPN1QnibYndW9pCzx/vsTgjSLm2Dv/qyRcAgOflLd6NK/z6QFD2N/pUokw1aLjLmgTKuR2RRNIOt4iEJU7LQSZM2aijMFWL+6zi4+eB9Gt5ezoOFu4wOcGE7ATkKCNM7LqePbnDeXOAZWzoxeYUd6xBjFUuWcCo6VqSoP7UFL6nKC1BesUuK8WMC6D4nMKcJydb3Lc1np4RU2Q/FFICU9yTgXOKXoKFWJHddHOMQaNhFaX7R4NoIceoxNIuAgi9kbIZYz0ce6OGXYHyxmTYMUz7OzOe3fMBq1WLAxPU3KCz5+VgEK3JggNeQbn8bFWvjhjOwtZVEI3X2DOcn9IHVTiCpO1BCYIRzIRMpQB/y5EpQ/66Zs1dY+DTMSoPKEAzHGz3k3KhiqLAdD/j2osCVzUf4Pg7oTWwm4yIFNscRbk5oQ8JtvRlhh+jztGQcoQ8CJfqelrywoIESatljonGMakxWS6p8dXkbwbC6LWsWJXOTapJ9HMoCV4XK7VO4fA5jR09czA8Xp41G2xdjS/u6N292cyyRraOiLNw5CWbfVGVvEtmULAHoLrlaHSIGNlQI0HSySpi6t08nFCUOo38U//4Wokvqoo4JtdNGOB6VDBDZjmrOGEcTwhkeuDIfkIU69cPJuXvbfvjJ9+iXJT4tLlBxU//y+4Jftue45sdsXpvD43kKLuhQLult4PqRD3WZzQpl9bL587rPQ6uxGagiWZwRiBeawM6VsE57Cu8CwoV1zk+mu+xHek726FGpR1OC4KKfzx/gx/OKIf65eljvNivcd/TtRQ6YMYL809P3grc+++2n+G/v/hznDB//S+LT+F5kJuNPhrcoYgCCaoJDV85rnOVHFz+W7QRKiqBudwsyiSpW43oS4wpFzbxlIWJspiqUSOUEZ7hWlV7nJ5SncKy7FFqh86zo8c0H7czWZiHYbkkK0gv++QeYp7wokGuebwYcTJn1vRmgbPFQRSQuqHA8II2UBYEh0+FxNsdKyOpE4SgRZpQF+RCQh2SZRoBQM2c5LDdVZMdQnz2Jk0tbdDKymHgxXc+69H2BRwfX3VaIEfTavjSH913mmh1TznmtJCGKoqRAnqdn7dmCcPEup0YDHy3DMWfj8LsttdZxcex+XeYbDZTHzSLHqX1uOPc/QDkciGvYLca4wlfVxmEGTzsZplx22mU99mIXI8Z7i229HPa/OiJtyrURDCezd0Tw3VqAB4KYvSm2vHxLG8sTEumFjHlQ1VOJQCZzT0uWBqTFyUVchlO8ipNY9f8ZAu349SB05LqqLTDdpgLH2O8q8V7VilSt8oqUPkdNF2GbVXInAEA8IVCKFKaBbzB4e6+B1yCuUvumzQ3lLk0CQCGpZK+UgETxm8ul1Kevp826kHnMV5u1HHNcA9ZnN2MYenJ+d7X9kEuqqfFAVUx4t/ffYI7zmW2YwHntUyuzhlxJ4lRScmINQFl5WQhndp6fXH9SH5ObZ80SCe1hf5g0TqNMKe376tdI9HcxdMNPprf49d72qlWk4Tny8MJtkOF05oWBKsDHlW0uG/HGobzYP/Doz+lf+Nt8mFTY8YG174Bgs5kCj1mIkHUk5foO3yB6Y5W6kdTvqiOkoNTgaJHWTyLAMUqA2Ew0MlizZNGaHITWS0PGLkkaDtUuD7McXtPi5vfFBK9hJWD6lIeiS2sUoSlIXlH5ehF9yd8sXOH+Qn1W2E8DgPNkBfLPdqxQMu/H25mAEsd+mhQ3OtcIlFGzH5Ln/NVgfGJg7dZuCGVOhQbjZFrF2MZgE2RZf6Q0QDtuF/TwrSMMDzOxtFgvaLZ/6w54FVcwV8u+HM+k1XqQOUlKW86eU4qUGQqUZWNsmCpkCdh3SsqbWRNX7vVR+5HwWbnkqIZAY6s3TCTsg27MfCzST5XRaDO9zKORhb+UOTyGrPVFAWnaGeyGFNnpevAkTZstJN+mwH9aSZQ2cPEuk4B05rVqbasr477Srtc5qG77A5V7BV/NkrfpUXDzXP/UpSX/+ZtXnCjAfzHHT5/RqUy+6FEy3PD/LTFsxkhJeflDmPUaPtMRJPyl1Ej+Pxi2sN3DMbTLTuSDhQhi7maGMZTza1NMt+T6Uqi+7TRsIA75Y8N0w0I2QSK4X0HBJMXXF/nEilfRVTX+STDenI8nRdfXx4TzN7n9ntwCw/toT20h/bQHto/jfZBRqp/9ub7MLMK3VCIl6VSEd1QiBqS1kGgPaUya1Ip+tujOW31xmDQuRzRDg5SYnO4awRCKpe9RL4IgK1HEWD3Qym7+NvNDP/n5geCXrnWouCI9nuPbnBSddgw/HvWHPCkoh2uVhEfl+Scs4kVfjtc4K/uPgUANL+qRCXFV7QrlB37pNzhyCEiBYBpl68wkYFTWXWIjykShoZKRHQSiR+0uKFMHVSggbh0EmXe3c0ROVfkTzTGwcJz9K7mDg0L14+DxZgEKooAZbLhcrRRSnbUwVDUkJx7AJw0lFx7PNsi8Ja48xZ3bY2O2dBm5oBXjC50QPfMQXF5UPTH0ajqtUC+ugioPyLUwD/RiLd0DNUauoakgISImMT7OVr3Kyd90rNK1nLV4uMlQYI33Rz936yRPNyVU1mZyinoVufShAnkncT002dJMSs/t5QThwL8IsBuUhSbHVBC7aEHLddvbEDH92bchC1eEMM0idr7WQAMlwQFDX/IJgZ2N1HPaiLiapSSMr8vRISeREDyvQQL6En5SvYVjtAOYlAxhXWVw1HeFApHDF/JHzJMKezcUaFKovksYJA8X/0sIszyMRMUXGzVkYB8fxYymjML+Mnzd/hsQe/ov331ORSjO8umw9ywaH6/wp+/+hztNR9EZ7jX1xHQUWQnESf52wgkh+9yS0zaJJofJjlmPbJ0IwfCY5NzzIkVLKIaIUf5fhazMhejHNL/y4l4R6Rnn9S/zE0h10+RaI72yUgEco2hiIiTSPx9bR/korrdNtC+plIZnvxD0NP5BjFojGP+WfECaG1AVXhcM3HJ6JwEmJcDrDG4vGGsJSisntBEO6sGbFSSrvPQOopzCfYW1RVr1jYWvsmDspgP+IhJNAEKN/sFKoZT/+HVE/z9t6TmtFy0eH5C3PjDRYWtr7HnPG0oIZJr0RIsNHWwmeZN5AVAKnXgybWMsnAmibGULwqzkOG8ZNCeIFk7IT9NJgh13mM179DzRqPfFqIV27cFSffxy13WI5YNzQr3QeP8M4LQtIp4/W6NeE/9GCd2fbFW8EbJRuDi0QafLm+pr4oOe1fJLZ/PD+L64kaL8JjOVS97DPc1It+TXY7w3B9DozE7bfGjC6pzvulmeHNLZKoYtNRXppIQyXlGSOlQtApYOakBBQCzpHN/sr6T5/f1l49RmAwlqqCAyUKhJoslwaRpMqUJMnJJRjkbsyPOXZmJPouA4kbDTrRuRWZyJJnDlNceXsxR7fJiE1jC0e5IESvlSlGTxjRAOUO1t5KbRmBCEgBzMiI4BbykRaQYp3BndklRke51moJwk3pL7TI5JlqIVOA0R+iSDm06RsgLg1vQpqC454W/y4uj6Xi8p5pNp6B5wNudkhytdkB/OlHkUvlcqnG43M/x6zeU1vFXFZaf0ns9OIO/eEcb4Mtv17ShnWhnh0Xe7apWC+Re7HCUv53WfE5h1DiB0W3H80FCly3gk/F7z7n0STpIuBJD3pyoARhWeSENRRRXo7RxUazlbNsJsS1mlSdf871V+dymU8DvAVHpAf59aA/toT20h/bQfkftg4xUTeFhCg/vFSITi7SKsosHAG0iAvufxqBQTgTA7+7nouzSzHqcsrfq9X6GcTTi7FKvOyEu7fsSdUmh7+gNdvcNIuvUlrdaIsJEFkmEktFX+Hp3wRfORJNEjHAKmnVvn5/co+St5J9e/xhP6i32Y3aR6ZkgkHRRRckIGSojSCeTWuLSoeASCQRNEQUI3tb3Nke4AXJNfknKM2kHKmbEfLIEzxrr0Q9W+rE+67BeZOHRy9sl6pNO7u20pr/95Plb3I6Evf3l5acwbyr4OveZlIzoCLMecbEmpOBitsfAFNa/vXkm59l2FcbRwvOzns070e49vF7AHLSYcrv7Uort/TJgNevwYnPCz8ng8TqbjyelpV2YATsrHqRhFoDkMlQBamMlyi8/3uPRio5x2zV4c0nHNgcif6Wozc8yi1o7ZqMmU3sTj8s9PEQYIlQegdnLxS4Lccy/NvBlhnKHdRaCsDsNX0YENl2wV4UwTqkMRcu5qGQkjU0t74G5s6gvM0Tdn2diUrgrYe+1REd6PEZOpsIK9P1JNJ3SFkwwTzC3HjNLGDpHtFGDo93Ehs7n0QPBuAn+dXU+ftK6nZbYpKhNe0hf9WfJM3QCzT+mMWwLj5vXJzAsRmKCwuaaWVGjzgzf5DZ0yGYYaS7QHUVzScx/Sh7SHlATmvy4zB611Cf874tJX4CjzAnJDTpHriEjt0eqScmAIZuZRyCZmUegujK5VM7m/raHTIoUJCGN6ZLJZ78HYd4Huaj22wo6wX9p1Aw6v50AfBlgeEHROoojTBwpv1TOWDLOOlHj8ZuSjsF5vP5QZDjSa8QuL9pmOQJsAu5WI2xBb3NjPbq2BO6S7lcuiYADHZ9fMlQe56c0Cbug8atXzwEAP3v+Gq/bFV7+hhbjysQM9/D5ZbIqcz2kno8oGCpsqhGDMwKP+0ET8xMEb7pJX+nSZyF+o+BjhnzNIS90oY7ARHhf24iCy4o+Xd/hSb3ND+n0LW7ZIuNHy0s8YcXxXx2e4Kst3de7f3gEc1TbmW/w7PEGhQmYl/R2t67AN3e0sxhHi44lBXXtsVh0ch27u5lILsIGhBmOco2peadwfbeQ5+ZGg92GrtfYkO3eOAcbL+g6rA1SGoNRIS48zh/TvfXO4IoVmxZNn3NpZw6+05MZDqIUZToFNWF+BkU5LgBUbxogi7jbFiivGZbb5by48lQykxWnjLiJjEug+emd8AT6+0y7ra+oFAIAuguS3VM8K8YnvSzgi681muuAmz+gzyboFyBJR2Le8qQ8KZUJVRTmtenoeuUZDFlWUAVFi1zIi4OfLKRij5ZSkRPXmrThMyxzGCbZi7QJcHPO/fKp1WTMKZcX7e6RR2yy0ljz7CCcje1+glXzue0lM8nnQQzFlWM/kiiSAAAgAElEQVTmcoLfPaBZ9ckeCOIW554mL5ZRASkTNS6odGWcTxYzycfT9SdoW/cq16I2QDDZAjCx0+W60rl0Tg/RvShZHJXLDGCAy5H6zAxOfWp6hqknAvqhjIjT+rn3tH2QiyoGDRgmX6QX0SkgKqnj02X24pzWThWLAU2di8AOXYXVgmag285CHUxepCoPn+TjYs7vqdpjuWjxeMELYtTomex0s5sh7G3O706iSgBUK8eLdlE7lEwGuT7M8eyc3XHaObrRklMIgP6Cyk/oALz7Tb8WQaLd6BUcR2z3bQFTBPhUaO50lqArAmzlsVpS9Oi8kYjcOY04VxjfzOT6ZUc7cyTZB7LWUzri0zWRcf5o/S3OhOcPVHpEraifD6HCa9aW+8f7x/j2knj+eiSLsqkHbsGbnVXdI0aFw0gzxOAMbVYAjJscwSsdsd/XCCni6gyyewtpqoq0Wpl9V2MdEIJGx9J72BXi5BKXQUhov/jFV7A64K9fkHb02FmU8yz6sZh12LNt3jBYWJ6EC+OxPqH+CEFjt6/h2uTRpY/EGEwHcUoJUz3VViOsXBZyGLVYupkuR4TDmiORJKLRRKifUijx44/ewuqAd3sKcQa/mmjz5kl99oaikN3P+N4GI1H94pVHv9LikaucQvOCCV4jLVpCrppGKnGqT0z3NtXOTc30yLtFfm5T/dpE0rOHiP5MwSVJxDZHpsklRcnClAk740lAPBugruk5lXca5R1/bgW0n9E969qT2AET7KwJQo7bHyqEXkvuMRpCOwAg2iB+siknLotPp7IjkY6AUSJmMe0r20b4Kkd9vsl9V+wn+eBA/ZU22cHSZwFaUKGOiV0iVjFmQpOrqV5Wjr/J/UgSiZBrOUIUJsQnmheO88DB5trX97n9HgTbD+2hPbSH9tAe2j+N9nuwL/j/0TRR0xGyckw0BNvO1rlWJMmDaR0k92d0IOk5/tt6cRApQgyacmepHGHIBe/63gpEMpwq3Ic5es7dnS0OIlk4NAaLj3sR4h+9EQ9PABiczSbo8078YIPXuLoleFN5BX02YH1OkfD+UGWnlKhEFQoATlatKDs9nu9EDOPbN6fwoybDcVCUI5FYJCjx9ooixjjxAFW1p/KjBDkWxPAEyA+2veFtcRmwmO3xekuM2WfNSkqCnhb32Pgaf3MgB567cYbLjiKly+0C/oYjZssKMyL+rsUn9mo3x7waMHBYst01EumpKuTynYMFRg3N5SWTriZYzkT4knfdVTZZN4sR/mChtwzv2gjLTjdVPeLZSS51+usXH2NkuLlYd2Jwb0qPfiyk/1ezTkQp3t2sJCcJRe5CFTODh6KQsMEfLEKlgW1WlUpwW9SAus1+sMOZF8RCjxBBAIDyqJI3bRxOOUrWKqJ3Fu9+c86/E9QLHAvjaw+0j0P2je011v+Qj9+fKTRv6PPNu4hyTx3drRWiVkeCAyLYXwBFkhiMFEELdDvkiE1gySTIUOcIaPl1/s7hCQuOJMH7SXQbLEVw4m4zTqQZVw5wGiWnAUzPeWFwOU+V3YRQBOhUgpUPTyYKKsOdbull3Kq9zezZQM8voyOZMY9A9yyOME1W++rXWXRCeUYvOG8ZCqDY0tUUO4J+p2INqdmWSmXcYoIC8DOemribQUEnj1wA4yp7H5sD/c1P5gQRCJkYvY9Lutd0b+MqECL3/qO/H+iiyk05NambjFAmSi2psUGIRUpFbHeN/HyybHHOCihjyCU0qidSSyoliF6Jg0pYOwynfK7OIHYGHWii3Vgvi+rFbI+ZHeBC0pidiSl37w12+1om6Ntv5qhueNBbQDG0Nzwd0cx67FkrtqpHfO8xlaFU1uHlZoWB4eb727koDX3VnaPnek0oIPbmyNg7ybQhKNh7K5Oar6jOEWD41Gaii1mO0AxRt+9meROjIzbbGf7kR/8BAPCT2Rs8soTT7UOFb4YL9IwFtb5A6+jk+7tGYLloSXYvQcqwXhw9SutwGAoxdHf3ZdYF7gz0CfW30hF+U8o4UH0mjSmn4OeB8t+gDVVpc74cLpOY6vNW9H61DrjvCG/7zZsLhNsSipWjxrtaZvPTRzSGilTPGVUu7dkVR7DtOHd5clUA2OmmWA3wbxtZKHwVZTFIC8vwiPvk0krfdeeTibDj9yDBe51F5BrH3lu8vD0BeKHAqZecsG8Nirs8PhAVLv5vGrfrLzuUvyU969i2OPm3FWLPs/z5KcbHtJnaP2ugIqkPAQRBS05vyHBh+re0YUgKSwBEXSktks0lUN3xc1ppMRTXqSx6QkASi74Db0KShHId4ZapSFPB3lhxc9EOIvunQkTgTV790R7eaZEm9V5jyxvu0+UBl84gsKWj7jQ0w+PTIpJUuiILpFNQk3wowfT586nO03zH3WWae9Uul96QbKM6IlMJqQu0QOa69bzZUd0EqvUE2QsBzEZRdjKDInJiWlkmN0cbpvQLQ+BVXkXV8cff2/YA/z60h/bQHtpDe2i/o/ZhRqpBAUkEOm2UTISyWRTdKUiketJ0WNW0nTuMBUrjsWXR/BAVLk4pwnrnFdAZhEQo8UrUT5KKDkAkFxRBFFq2bxfiQPLkbIPdUEl06qNCO+at6cmyRWBi1HZf47BkQYm9hj9l5u6qg9UBz5+QMIFWET2HAm3XQCtg/w1H1wHYJZLEPIsngHf0CRaNRTyKnAhiyoy/tJ12Kw/VZ0pfGDX5fQLs9Uk/FrMBv3j+Cg1vsV8PJ7hxBGUXyuNFd4q/eP0ZAOD+fibMaX0wOZqYRRSzUZi2xnoYjuDcd8qWYCNMUuqpo9ynMQFqNcgxfGcypFuR92zS4O1GK8/Jvq6IBfk8sUYoGgFIBOTVLRGrwm1Jyk/MAlaARP+XL9ekkMPwYbwrUTDE2LRKdvGhBPw6iLbzbNaLaIZ7MUe5yeUqwX6HDFJE1C9p/OgR6J6wWMOzA4qUApi1ePXbC5g77qtnPa6vCW6/GqgMJCZRh+sCNjG7u0yosQfg9FcRszfMEDcKw2fE0i5fXCPOahEEuP7PLrD9hO4zFARTCgN1hiO3lUy5pShViCw6Q7UqkKhBsc/krfacS9JWE5YqM1OF0eqJgANQxEcEG/69yXrWxZVFfakE9g6fdbDJDGN5wElFY6BzBfZDiQOLulgTcMbldvddDbxsYFOpSYHM2NbI5DioI5OFcqOgJjCuryMcs3qLjT6KUCXijBwRTlMZTBwal4RSZP9TiC9qQitEKF9H2KT+ZfK/h4Kh+IlesxDNwMxfvje7m8x7XNKU+vfY65Yg7/h74Kf6YS6qk5xqYsiW8wFjW0ipzNlqj1lBP4eo4Cb1rFpF7BjW8UHLBBf35NohCkIRMrC1V1RSAlAuJai8+EQgvmVz9ARTMjS3OD1IHayPCotiEEWlfdPhdkaw9P1mhs8ek2KQUhFWB+x44W9HKwLdWkfs7xphEMdqWjoUpF4TAFRrZNTHs4Es3QDEm/KIDu/mMZuDg+rphPEblPSBmTn8/PlrAMAPFpd43Z3g11yD+1GzgebZ7maY41c3j7HjMgSlo+SpPPKiBBsRo0Izo9mkMF5g+vFQUC1nUo4qPeyaJr8YFQaG0EPlSQw/Qb42ZKnDyqOqR1HNsjpQ2RQApWgDAV7sy2rEDRsAvLs+lzrS2HioMkAz/OtbA51yqveWJ0JmKD8dMa64zOq2ROBc9Pykg3Na8vqb2xnsW74OUD5UJABbQO94gzOLKDZZHP/w4x7/83/1bwAAa33Av7n+lwCA/+3/+COYoGA/4zpbHdEyfF1cUj1ywRsN3Weo0XQk3A4AtiN7seufU7+aPiJY3nT9vIFtIXlUV+WJttjRTbgEw+7VZPKOUDp/1ldZuWfqwhI1HUNKZqw6gpCnddhQeZFVE3ZrKKi/hmecF1/16K9Y5WmjsP35gNUZ5ZnbrpD3pLZONqzbvsTuUIvZxrwecNfSMW6+PEPR5nIn87SFu8obYrkOQ/yOaS1wupdQErSfSp/K++ONRdqMuBmXSCXBe4dcw5o2j6mkbqq8xAxw2ZT5CUwc1VFpEslh5ty0zAUNXV/arCBAWN/TkhpysolSv02lPkrUwN7n9gD/PrSH9tAe2kN7aL+j9mFGqpG2q2pSN1mWDo9OdhIFds7i3ZYgsHE0YkY9ryhSTJBs21v07IuYfAWnNXTgyENdlTBcxB0PpLs6VSQSIsaBatmkHnKd4d9F1aOyDucV7ZjX5UHqW92iw+tbgnS1JsJVTCSP3sDcZghZ1QExKRtVXmoqEZVA13pnEIsIz5/DwYoaTHlHsF+qb9MD5N70QPWPfpYwtohHz4iQ8y8evxDVp52vROEIAN71C9z2hENd72foRyvCCiEo8SP1eqLaoyhSTbW1XVdgTKbZnp5FYh5rHRCYGeyuaoC1laPTqJa9RIFuNOI8JsIObEm3byuB6dyZowicWcT9iwVmr/n4n3qoc8binEZ0CoFPYBov+sHoDaC1qGjNz1qcMCpxWS0QuC74sKlhKyf3VmxMHh8FCayXmwkUz80e6Dm1T+mz//q//tf4b/hc/+Plz/GnX/+QPhgU/MrBp3Fsg0DgqW5yavSdVHl23/fyLCjNEYXhqweguaZzLb5uUby5A0a6OPWff4z+NInms32a1E1OojTketRkS5gEH6LJ5JgkbjAuMutZIrjI8CoIlgwlRZ4AMVrTEAxlxPB0xOqC3q1+sChv6Y+HTx1mJ60IYARvcLKiz11uF0LuK6sRp8sDlhU9e6MCvmXBkXAywvclQhJ5eNXAJP/aKkrUF8pIc8NkChGykCe7tcT4VSF/Trks6ODmx1q9ps1w7/5RxLgOme0+KLhEMmTVKmFADxC1pSn8S+IXk3P7iXBIGn+J41VBnpNykOtwBcHHybLQdAq2y0pL73P7IBdVNXNQjcNs0Qvzcl4NiCCfQ4DEAhKMU5YOhc3wZu+s+G92NxMTYa+glEJIYvjLQYQGiGmYkxxTIftQZCam8lREn2CQ/Zs5hlMaaaV1+Hh2h6ueYEatIi4aerkjgIqvsbIOry9PEFmVyR50hmdrehMF7pwoI437QnIeYeWAUcNecX4oKffwyaLOE3isqCAeIGiwO48CLz96vMF/+9EX1G/BYsYY1cL02LsKly17hEaNyx3dV9cViEELm9ZYL4xKXXuAJSOVjvC9EahS7WxOTZ33MCbC80IUvuPAkxaNsHDwzsCnkiMVoVOpg6ZFu09ylRGoGEIe2gJxMAJf6UGhO+MJv/bADT/3MhLTMfmflkHKrKBZMYgnmnZfEjQPUP6ar6Ne9Oh2lYyJKWOyekfw7NSZJjNlWQyeofO/7j7F//L2cwDAX774FP4FbWJCGVG+sxgusglAWtaGcw/daxL/53OnvG/9yogqUyiB6jbi5Ne8oIwB9pb+qC/v4J+eo/2Ynm97qsHpc7gmkqtJ8f9dRaaLXjRUzjRdxGXh5HKP7OGJo9IMEc2fRVQ3Of88LqIsRH7lCPLlVE749QLjRzRWZ6sO3aFEw6IdP33+RpyiQtBYntKYOKk6rMoOS0t9cFbuJVf/Iii0fRbE0OME1q0CVJFLanSr82JmsyhHtCylOOb7FvMBH0XdyleR+o3Pba2STYtvAovX8zFiXvRCGSW3CdCCmvpUDxnW9GWEW0Txm7X7Se470rhLYhPa5cV4XMaj52x3GvW7vBirAHKwf8/bB7moPjrbwswH3G5nGHouD4gKMSpR3YkAalZOmlX5Sd+3NZwz6O5o1Nh7KxPauPbQyxFNkxM+1WNa9PabWqQHVeQ6uPlkkU35iVSWwS+V7gxGLr05zHr88vojWWCezHaobT5Xqsm0hvKEsit8lK21ACIPpQ2D643UFgLINZ86onpnjnI7qaUcVtZrzT/7is73n/7otwCAn61e44Q17wrl4fmDr4c1Ku2wLOnt/s3dmdTjFoXHOGRdZtdb2QRoHWWx9R3p5uqSo+4nAyouqXHOoLtsYDm/SNfMuZ0mIqSFuQhUbsO/V7MRVck5ZhXho0LNpJS6cLjbcmmVoQler2lsuDJr+GLQ+XlyxCylW+NEbtArzp+nEiwt9cPVaiSpQgB32wbqppBowNcTkhjX+olFVz3Z7ChgOAHsDc3e/9P//ieobvhZFxFhxZuH9YDy0x7P5kS0uj006Fac591TdDUW9L1Hf6Fx/ldEgMO3b6BOiZAF56lkZmAewg8+wfU/pzrm/vQM4yxHNjJ+QBN5tDlS09MazWm5RQAw0KYVoHU3qfhQDStkvOtxkiu1E3s0T4pSspjVE1TJK/Q3jYz/xU/v8YjVkEJU+Mmjd3hUU875t9tzbDt6J5d1Lzn3RUEnqgw9gLf9Erdc9x0C1fyIzZqFbHTVmPPewVKuMdXBAhB+RbQBfq4B8Ht+gDjkuEYJ4cjXtDiqfT6mFySM5oWkwBX1ZJOmQJsTk4+TxpnpqawGADCjWtSk9OSamGUm+VmkhdTX+Z5NB5FcDJYUq9I165Gesf89SEj+HtzCQ3toD+2hPbSH9k+jfZCR6rs3J9BNDTgNs2KIpxyxOdQUuYEikRTN7bsSPeuz2sJhHKwYDLuTaRKLqOTzmo75g9MrHBx97zc4Q/uORdwd7w4TnbxXRzmVo62OgkCpPircHxrs3zH8+3lEzTng291MjI03QUE1XnSM4RQ8l7woHak8ZULbF/Hu2gMb2tJWl5PoFZynmuZUQv59qmffnwU8fXaL05Ki0y92j/GjxTsAJJTx9xtyiLnpZvh4cYead/XfW99gy4XxrzYricYBQNuIivv0KFItj/eETTXC8/f6fUl5vsT09LnwXHdKFLFwMNQnSeRiAQxj8lY1KEqHO86Zuf74dVEmwqdSn33WfDb7HHlEDUCrbH/KcDBA6ESsg4hS1IseF8usZPTN6zP63G0Je1ASDdjbDPtFTZB7EjhQYy4TGRfx6NnYXdb+dYuA+hmd6/npPUJU2DCMfjhU+MFTEm74wx+/xJ+9/T5esTnD4tsBuCSWud/tgc0mH//ZU4w/IRWst/9ihvZJzn2bXqGgr8HXQHtGnRBOR6itnbB6J8zxSQkGDP0sJTz7iVC7ZWYwQ+zDLGbvWQfYxGWwEcO5Fy1tu9cCxY9nHj/80WucVtkpyXL4r1XETT/D37G7UQT53QKUCjqpaKx/eXOBH59fyhj8h5snuE9s9HcNTKeFoTsuI3SfFJoUxpP0QLPaUWoJDQlOofqqEC1j5ZFRD5VF7PXIZukTwwGZa7yCGiZoRpHzoYkTIgiUyymqhIjQ9QIh5O91F1GuP6UGRKBCTYQnMIGyFd2s6o5ztr8HHuUf5qKqWgMFg9h4LOe5ztB7LSuEtj7XLnqNJdeGuqARvIZLMO3MkRwhiOCxWHRYN5xLQsTLDZGHdpdzFN+BeNLLHSxgntF3rAkY7iqYQ0pmQOr72kMFP2roBc2au67Chgs/26uZkA9CFcgdJZkDzHL9qT9YwAZxlSFzAX75rktUtxkuTebM6TqyxQ3/P1UI7VVWbxkVBmfwy6uPAADLqsezmqRo7sYZrlraEOz7Epuqxop16KwOCPzWjqOB1gG6yJNykmZczHpELrMYQDmtVJtqTUDX0sYljmQ6nyYPFQHdTvpblJ1oYlFsKtAfCliGkBfzDm1fYNwxbG+jQM1hMMDGomY41TVRrmtqEu3rSHZvejJb8LiKC4/5usXHPEHP7IC/+ZachuKrGkUybE4bmGQ3NlHLcYuI0WTyjekm4umeHI5kYbWZQBZrj188e0X/rAJ+fX+O281MLvE1j9sxGFzeLiUtsfmsxOPfzLi/NeIpfe72j8+x/VRjXGQikZSugPJw2x/yuedeSo7MDeXxRWlLTcacyvk+UfnJvSj1muOjEcVlkYl52UiHuABJWeo8CdhzGuCTTjZrn5xs8aTZ4uWe4Ozzeo+OMcy3hyV2XfaJm1WDGAzU1uG6ozF9Pj/grm/wd++eAgD6Lo8d7Um1in0hiNzDOclpzWpaAFP6x594BE4bNd8amCFvZrUDFK9C4zo7xQRDG3OxXPzOQqXcZHzOomy00rHEuedA9b8Aw+28WSu6CF8rkbmMNorLzsj3l9IuarL5pnxrHtOhiDJWQxHhFgGhnZIf3s/2AP8+tIf20B7aQ3tov6P2QUaq9bM9zMzBmkwU2vclic7/R+CHZ6cbKWsZ2orMzFOkWgaUKyIoKEU2Yi+ZMPQSJ+jbxIpQAn3Q7lvDryn0KBYDCW4DVNphIhyTSGIZYDkyrZsBu90chn8fncnG6iYK6xhRAYsR4Gg3sC4rkIkzYoJcRChWfaov9RHcE4opnX9C8+fDJRjK9JnO//iPXqMwnvRiAfz07C2eVhSpPq3usWdF7RdYY/QGneYSnkkNgVLAuC8lmoGJArf3hRcBemsC2tGg5L8NU+GKIiBWWg6hhxyxpGsGgOE0wD5qEbhsJnolZKdDV2JsCzGOhooi/mC2BnoExmTfJSbaFCkm4f1oIlB5IYoFp0Ub+mK9w+cnN/h6Q4SeX335Ecw924YdsmB5sshiS1kqH2GmsRpp9y8s3Il9WYLsU2RWXmmYlv1xf9Tj7YEw46vdHLvbmfj0ltcGu8+YfX6o4A9WIFRfAf6CQhS3OMfNT1hg5DEJ0Mu5TS6HcU0g7eMtG6S/m4jXpmtGvmZ5hqPKOsyJPZ3gw1lOk6i9IRWipNUbMgypvJKIVo7LbOiz9U7G3bLocdkuhGRUao85qydctQt0fSGKWTEqGWv7vsSqyezfN9slWiY7+tbKu0V9MoV8M9lsPPVZl5r9VBPUH3cG5T0/ly3p7SaHxHIT0T6awqdRfp6WxlA/5P9HO/HcNVEiZuVJ8F5KZUJGRKABvU+wvIKrITC9nwdoHlfmoMhoXghZ8ajMS2Dokcb1eDJBDyKOoYj3tH2Qi6oxAcYEjJOyGecMYlAwDUv9NYN4IS7LHhuGfw43M5rseZL0TovnaBzIi7NLTDgdEZnOpkaV/RnPHczCYd4wc9RpOM7j+QO9iAlOsYtRcoj7V0ugDFI/6b2GZxhXTfxfASAeJo/2O0olalCZRq+O3Ua+u/AkV4npYNdjZusBtPgu/ru3AICfrN9i7zJUti5aFHzQna8lV1roAKUiZsxeLnQum4kRUBND9xiDjFSlgI7LmUJQKAqPlnOeSXYPAJQJCI2XXYGfwO3QgH/KOVobURQeA+dY60UvUPOwK4mtm/xaVYaJPYDYaVn4YhEF4lWNl4UzDKRKFSf9n7xbbzYzvLtcQXOuveyRc4tDrgOONqK807JxCVWUZ1ZsOafKtaPTMgs3Dwh1lBpiUr7izZtX+OZvKUeonEI5AMuv6HvlPqL/mnC57lxh/jpi8Yqf02aA3jMr+Q8XaJ/wmF6SqlNm3WZHHzXziL2BTU46IxBEDpDdWxLMywxU+jkeOa9Em/N9xb2WxTJa8tRN92narKjkFxMD8JkDOoMnj2iT54NGw6ppX92ewTmDkzntTj5b3OAf7x8DAF68OIcqAnYFl2cFhYYrAv7Vx7+W5/rb/Rl2+xo+uSEdJpvvQR0L1Aege5QWlEnBKSA+wQCNq1S76WZgCJX+NqyUjIlx4iyj3fEiqwJEHhSgxVxKanzeSEeV1/bUpBZ9zIb0oaS0Q5JTtPdmUuZDm8E0DlxzrII1nTPGVTguJSwionv/k6of5KLa/voEuq7hJ/ZL9rqAKiM8R3t23mHFmp6tK6jwH4AasosJgCMnF5hIUVWaaCeiBea8B8g9CwVXd6fJOwaVzcwdDa5Eo3d3JQwbYceacnOpnEfCMCDLLgKwG0MvZdq5zhx0IjsdLMy9yZqbk1q0cRmPFtVoc07SfsfQ2Vc5ij18PuK/vHgJAGj0gCFYPGkojP35/CVKPkgfCokM1lWLR/VOpAnvxwYhpuJ6TXlbmVwVSi5TGgYjconGBAx9IR87bIqJdrECRiWTcLS5Rk7V+SbrZqBFnMsivNcY9smfSgM25OfplNSbpryy2N7xswdoTES+T10EGOsxbDm3trPyXWcL2F2um5wuKHFiyG066vuYIohuQjxxwDifbIYixA3JLwJgAxyPA7saZHNlf1ujvsyTXTSAGehzs7cDVl8w+uIjolEY1zTmrn82R3/OoiiLmC2+FC8AqW7XZfuv2BmgDFCfMQlIR7hbri0eFUekqQ9i3gBOtGuhUrkN/c3NQ85DOgXTZhJQKFlCEqB3QGzVFP7ZT15gP9Kz6J0VBKrrChgTxcbxz158H+2Wr7E3iF7BNTQ+P7+4wR+e0ngPUeFv7og/8JvLczTNkG0WYY6jvjKKVKmvVF5IbX53i3uqUU1EK7vVOe/I9cgDOwj5KksAKj+RbtTcd0dWcHlDGYpsh2cOeS9OEoXZtSbq/I6PVTwiJmoHyeFGlTWToQPlxZMDUqvl3NFEIU+Nj1kUh/kisYgyFt739pBTfWgP7aE9tIf20H5H7YOMVP16RGwMMGrJ7/g6ItTkSgIAz0/uJaq6aWfCBI4zB21D/j3k0hiUAdWil0iqsF7KM8bBQpmc8/TOSNQTOwOzYwaxV1QUnnIvrUaoc97hCBZtJnDn3giDMplry0541FL6YbeUN3ZJeEIjMzGhjvJxQGYoTv8NkXbeCXL64Q/eYMk0wSfFBufFHjVvm58XN1hq+tvX/QU0d9ajeoetq9CxkrrVHjcdsUrdu4ZKYPi+zXoQhq93BXp2nqmWPWazHtv7hIsqMVWHiQTDco5smleGyrD8/q4BIhlzAwTFp77XrUYsVYYPS59N270CQs71xZD7Uc0cKo6sQ1DkJMP9GCcuMtEojmC4r1t1lJMsthkKVgFioB5sjkpCgSMf0HGJoxyi6g1iEv540WDxJrOeUyQ8LrOIAAC4xuDyDyknvn8eiQUuyjpZnGGas6MUw3GeTRjPCjC1E96AdyYzUwHE01FY1b61UBuGT4MCD51cvvGIVZ/KKMfQB4IwU0TnFz5D8b2W98TWo7hLAcBHi3v5edtWcCzAIlEAACAASURBVM5gt6GxFFsjyMPioy2errY44TKxn61e48B5kS+2j/Grr55xR2qMvoHiEhh1NiAytB+KSH3Cz9C2Gkkb0wMoGBq3O4LQy9uUMEaO+FnIPrL60lRYQbmJoAML44s7z3dylXrIuepos0RkVMdGCGHizQtMxrqJGOvMzPZzL37B2gaE3ogrk23VkbRkOGdv4tKTCM2pT7dJCnDvf6D6YS6qem+hvSV1kQR91AFmNeBkSS/O6A1uWprku9GKbmySsEsLZ9gYGCb9uCqgv2nE7q2b5oAuWpykshyv0Y9W9GaHymA0XLbR8oTDY9mvvEAkxZ2mWq6kx9vrI2u2tAilCV8nGCpOSCMLf/yCtVomLEyUbmxLk2eY5ICmFlrDSYT/Ad3PsuiwZcfiM7vHz5sXmHMiqFYjLj0RWwrlYRlDvuwWsNqLCo1WAW/Y7B06kppOUhoKisqdQDnsmolhZeFoQeXaWu0mk0UJIChRKFKFkvwz7gpxJ4Im95xpy+pWAAaFZGun5yN0lT/rnQFSTltHFJO/9d8QRKoHhaLNzzMgj4moSN0pQV5RZTiZFry8qE5LalSEyPwlycjhhPNsj7zkFotbQ+S4A9fZrjw2DA2beysG97PXEYs3Dr6i713/rMD+E+63oKB3GobJMeM8oj+b5u8mi6OJUrJjNybnAucR3inERVb/Sgu9rj3JTd7z+B+09JUKOd/vS5r8swNUhk+jjXBPBiiWhlRByT5Xn/V4ckYMryezLX57d4ZHc7qZi2qPLzaPqN/6AmU1CkwPAA1LUjbliJOyxQmXf73q1vjbGyqbefPy9MhtCqsx58+vS0k5UJlLhErziMv3Vl9ngmSw9DfNxLNUf5y+owd1pDSViT8qw7gmw7sAb7z4O6bj9FJKM+i8aCdoXeqETf43X+ZxiwiCrC+SfKoX1x6/KVG9MzJZ+CpLS/pV5hrEoKBthOZAw4+GNqX+/V9VH+Dfh/bQHtpDe2gP7XfUPshI1XQKGrTjS3DE+nQPrYNQ5X/99kLcW8Z9KW4kMBHFMlsp+CrAJeJSpJ108uPUTY5c3GBwfUvRS/QKsbWwrOakVCSheBAUHBsv5Q32xmYSigago0TCUCBhAVB0miDkWESYfrLjj4BbZpZdscl/mza7y2SkZFasM2Kd9WUrupa0I397WOI/Wb0BAJzaPYwKGBkvvHQrdJEipZ2vcNUxySUYLIsOBZ/gtp/BCGNWQbVaIqcQFUa+3tmiR8WMzdvrJYpvS4xr/t56hK2S2H7AuC8xsm8qTIRKZuBFhE4ECRPhbfYqjV6R6QAAX1CEqRg+vFjv8HROBKzWFXhxuxb2+Hre4vUbKo1RN4VEKN5GhPJ49x1WyT4kwr4rhdQxzLPYvulzdDuuyKMyPQuJUpHLnlJpAnQmw4TzEc2yE0Jc6G0W/VBR3E6amwBfalz9nN1+Ph0k+iqvNJmbX3CfLEKGAYuAkCKLaRTDLaUOTK8wnALBJ5OBAJUY861BjBA1JMQc/SqfVYJCQfelJkzSkBitZz3qekSf4GanUbP4/aLpsSzpff3Hq8eIETitCev+fy4/xts35CLz+SeXuNrNpf9ho5TDnS0OcMHgmz09329v1uhu2At1OWa92kEDXSYuhiazepXT0HuN6prTOkOG7H2NIwcY8nbl51vmkpQknCAk/wlpOOqcEoisMJUccaAg16QCjsRJQpFJRQEcmfLxp25ByuX3X3mNbjWKIE4/Wvhv6YLrbdb2TdeaNM5VeSzsoI2HS17UrUFxUAjd+x+pfpCLqq8iYkVi2ss1vWCF9di1FVq220JQCDzxopq8HEWAUhn+9Y2TiSp6hViTKTUAlBM40I0mGzncUzGY47IXXfs82S1HYMg5p2gnjMpOoRiV5I7cPMBuknfV9A7Zfq1OL7RCdcVyegPDjuJaEYUFipj/PQlrJzW/qUg5FEmgpfv73uoGX+1Jxm6MBqVyuHSEWz2x9/i7A6kE/eXVZ9gyu/LJYofTssU1O+58eX2B8Wv6WQV15MSCUaFgOUilIm6/OeWLB4bHuY/rryv057xwVgHwSuoCwzxLMNqDlomJ6uMUkJ51keuCl4sWozfYvaGNwNtXa4xP6Hj9WKDbl8IMfnWV1YiOFIMOmvLkZ1PsNqlZUd5KcserQcox9K6A51yUYWuz/5jzB5XT5PIVsxhlMxiCziVg6dElOcwqShlOt9bYfB8YnvCs7DSqt0bO1T0OMkHrgxYoETbmPDLnLlNdJi2IfM5AzF2/4EnVRCqTSsdwSjaRutMTGBMZ6ow0/tP1u8cjmhU7Bg0WfVvg5ITe5fP5QepNW1fg+kDj6rCtcH6+w7dbWkjfXa1QzGjxfXO3os1H2iDrKHlxFzS+uLrA0OdSrqef3gAArm6XUFyjGTXBqmKeECBwr3LEcJ7mQONk9k2ylr7ihTR1VZ/rq6VPVf5d8soDMu6omA2cNt8656TtjnKcPilf2ZjrfYf8ObofHEHN03IpfTDY/iO/hyqXeEVNhgWSW29CLkkDEHjOU5XH2JcZvt4byvUO7/+i+gD/PrSH9tAe2kN7aL+j9kFGqsm2bPnpBiV7kO6TYH4SdbiphPWJiSB1jFRHOXYpOtW5Di4qoAjQvNsdxwnDd1L8H00kIhFDvlpHhAR9jhqq8lLnqA4FyvsMebkZRJlJeZVNgWOGZ4Jl26Y2E24yjDYhe4A0Y4VJOmGVAoCrmGABJkLodP0EoT5dEwHky7sLPJ6TLdbeVfiye4Izln35u/Zj/IcNkTpCVGhYDWnT1xiDwW/eUISrX9QCwfp5oKhnQgrr7ynsGXoNLXZUCsXLCRtaA7NvGcJ8TMo0Alm7TJ6YFqdTB0VSoAKwWHV4vNzJn95ulhIZLM4zRbbdE1Mx7JlCWwQ0JxQ5dfsS5nUl1zh83okX59AX8Kz2o3vNAvJ8jZ1F9W0myiTYLxTxCJYLeoI0lBHjuYNd0vX7wSBoPr4NxGxmNKN5q9BdMKP1G8B09PP+ucK49qLmVN0qYXY7C5T3WrRcfQWJoPWlkSiqPwuILguchCLDliQIgAy5T7RiERRUZwQqngrBh/oYgvRVRLigfqxmA0aGDuezHou6FwT6rm0Elv9oscGLy1Puj4jSOty3NJa0iUKwCSGS2hW//81sEFvIy+slZoseNYu1xKjw9h2L3G6zOpRyCmrUmSRkj6O85o0WFbKpWllUUziWx2bqnsl77JaRmN0CKSuUdxl+T+hOGi+p/7VTcjxfk/CGaPC2+b1OHq/ZRjAKIgLkqNV0CvXbqfhGrh9O3/ENH6TK9cRxYjEZOwM15nFV3tE85B9Myt/PNv90AzPryX3ijqAhwwy2pHZjTgcR6I5eIzKMiFGLk01qU6q5MpHKNwAg6ryYTuTK1MxDlx6rRWYa77nQvKjpTRiTxGBJ4tV0fJpwxkRDdwpqP4HbGLmOzP5LJRN6hGwKUt40TG5BJuhcbYBxHuFOPMprlh/rcy5vXAasP70TQ3ejA+7YN9IqjwCF1x1NOn9/+wT3e/rbybyVMqVNW+Plt2cCH/qLESYZAIBEKmRDMkz6blRSbmA7zkFN3XO41ZcawUCK6BGzigzURHHnbETZjKgrWpRK69E5OuCjZo8bM8PqKc2EPmi0DF+XtUN3X8FsmfV44cUlSHW56N/XEeqmRH/L4g+9kpdOO9qsaB5PprNH30vPI4nlZ8PuvEly8wBVe1HZ0iZk6cqo0Hxd4OQr3py4gBNWTbr/vkFLgkGketMryc+7IRfsD+tAG7ns4X7ksduf5XIvc9ATsfes4hUKKv2Q5+Myoxqe1b0SE/4sZG/VLrN43ZrH/D09uH7QqNkcfFYN8EGL6P2i7kXE4ZdffCJ50p/9+FvMLH0WAA67CiFB8U5D19l4Yv9uLlBwsRgwDJYUtgDoiavOVFSE/ohczjNkJ53iXqHYAyrwQqRVdm+xWbkInI1w6yR/GTGep+NpEspI471XgjX6KkpeXXe0iEp/x7wRz0bufEyG5gEu1zHA1NBAnICqzP4N9vhzejg+vuRyAer7lCIYlZwrNJHSWckIomdI/MGk/P1sXVtAqwmJBUBAgfq8RcPkIcsyhgBw926Zd529Ppb8G9VEjs1BqQhTZHbPuJ1GHvl71nrs9kx2sAGzBZeW6ICuLSW6KG/zd9w8koJKih53RgYpzXZ8Wk0vQypp0G66a+UdaSrtLPLiO5wGIleAykfs61p2ye3jKDvVeDpis2skd2dtEFLH7aLB63olhK/doZao4fJ2Cbxk0+ZZAGYuE1wGLTnsaCPUwgn9XoGUpQCgfpdt1ZI26RHpIu19TmhyTrkde8h6pOMy6yQ3ix5KRWy/pk1ALAOWz7bS57NqwNvrZC0SUbKBeXdTQx9ypFa8LrPijM71fWHmUdzaicH4JHpRgO6BcuKeI/Z0Q94E2JbuS0yoZxFjUgwqIhSAjhd0lIEWLQCz3xSYvcmT4TjX2HyPF8vPOykPm816LOseby75Pm9rdB/lXHUIgC5TdJ01id08m69HExGqXLs4zccpzwSZtElSU8IRmV0nE234XBblZzlXDBsoF33CcomVw6ymAX6/b+BGg6bJM3LS31U24qOnlP/c9DVeb5e4ueTSLaeE/2AXA9xNjZi4NGUmtrneIh4M1XiDiT6TKFMWDQ9A5z7QQ140yi2ACNkgTzewqb8AWpRImpA/d+6gUplYKpXjTY32eXGMJi+OiLzwJgRN4Sjf6qs8h2CyoZk60QCMyiW98l6huD+ei0TqtMjjPaYSmimZij9nOp3rdDd5w5H6MVRUs/u+t4ec6kN7aA/toT20h/Y7ah9kpDr2FlpbVGctnp1SXrAyDtuhEghpt68xcr5MlZ7E8kGMOtOrvDOzEZFzo8YGlKUTtaV+X2RVGYOszMPXkPJsRgfZWRsbgK9nqK8y5JtFuBVMC6h9wn9y/kmPGaoxPUVoKaI+osMzZV92knpiZuyU5GtCaxFOHRzrjArLGAA2Fn4JNOscGSTmZduX2LY1+mTsvSlRsuF5vVci0O+ChtqWouwUZx4xsZW3FtoGgYrHtkDz2vK95V29r/5f9t6k15ItSxP6dmPd6W7v15+/LiJeRGZkVGZlFqigSjUoVAwoCZEIIYGQqBkDBqikkhghhIQYw19hiGBWDYJKICozSpH5MuLFi9d5f7vT2bFmNwzW2mvbSQRiEIPy8Lulp+fX/R47ZtvM9l7rW9/6PhwJH9CB+P+B6s0q+eNOoLJog2QobjQYN2UWg6g8treEc2/1DMpELFcE01eFw+0dUWbtvaVMIUHns6yOoQIyetFrjKde2oNMrwQ+1T1noUlfdZVrWHrMGqwpA09N+m4ZRMFGqQi8raQlJRotGcS4igi3wMAtPd15zgKrX9cYuSa2L2p0ByUZc3/lM2PT07wFztBDpbNYRRVyWK4jInImBq9EhEJH4iSkDJfOk6+lCpQ1pWd1ggLBI9NlTUSx6MQ0YV4POLCxgnMa2gRYFhLYdRXCHfvqlgEvbygDD6Omdye1SD1bg9FY3N0sKRtOz6CJmZEPUAtZEn8fstACYhZIiIYgzWOVI/41BWifrxPIqES5BsaJrm6cfhczivPETbJkP5lThcyk5fLONIvNQhnIv58OyX/WTh3dI+Uz89h0+VYAhPwkQf3piCXV8SV7d/rI11WQowPB3wkCTyblIfw/DvnOjfdyU/3Rx69h5ySRd1ISxvbt5uQIqvReo1wmYgIA7rEadYHYmqMF2vCm6nuDPijghsX3A4R8E6oAs8htFSfLFgXLtt0+LETQfXg+x+w2100QIS9LgtES/Bk1xMhaeRy9fCrkjd9PCR+KflcW8nnMKkSGNgGA2k7cPC+aocyQlFsFNMse7Y5rWMsOmy1hk74z0Buboe4TJ4vHYLI1WIKZDCtCeQWUd9yu8uFI0Dm3lxRvC9lsxlVeBFQA+gsvi1qx0QJXYeQa0QQdT4L0sQrSShWChp/rfM/2Oi+SGgjPson9w7ZB3GQ4f2pvFifEHEQlhuh+EchmL82jAYohz0Eosm2eayYLl4sYTvn3eu7jTK0PvUJgcXe0htbTp8l2BFDfclmhU+gugcNTL/OTYGjtIZujr4FYTIIrrxD5/pm5gy0chpZ7TFUgtSqAnrcxSToahDpAJfvBtsiQd0k1tNQ6dGTxpSJQBixOueexKySYrb8r0V9B7sXUhekwFLD8zsxXLXzQWG9pIsd9kUskM4fAEp2qM4gTQtlnZzdiu4ee+l1EBtRNerZTi3qySIuTWn36C+R3LN3D6n5iN6go4GDhMYzLKARE1+S5B6itZUyBYsymHNRICqgE0YZ8bjGjuHDJiH5CaJSWOwOa/0mQLeuJjWTDl9abEKWW7mY5QPBVJBMDqcVG2Qz13hytL2pU8rlgjnuXzYSU5EscBw/v8HiEfx/H43gcj+NxPI7f0HgvM1UfNVTU+ObVOb5hNiGWDlgXiEsK25bnewxDxkoKhr9Olgfc3i4QOYuCjSKaLcLVE2g18PFUkQUkisKj7Sp0zCZUCgCLTjSviZKfG61zVBz52K7MEahEmQpCVPIl2zSliDNOhCCSJmfS97RRyElmr6E58xBLL45Aywedo2kFdK/mBPUA2BwWwoItW4XxLMh1E1OS5+MoQyG4WbNUkn1j5Bzt2wIKhfyqryPcgr6rWGvYXc5Y6tdGqP1udmxdN9Ur7ZsI+4yy08YEjIy3dbsScBqGszYz5CjbnTpYHQWG9qNBmRRxOnVEjAoT1XLTTdoUDvpIkxg6IuqEtzFzmX80Q57vUEDun68BDBkGjAYiOKBGhXg5IDLjV+0NRvaKDXeU4SfUwLZKfEydiRJSj8sAParc+K8UArfU+L2FVzY/S2WYZE4TKHMWyGN4m2D6bA4e6gh9OmA1pwwxAuhZSGHe9Lia79Gwr+7XD2fYsjpPf+WhmDhYlJ4t1Ri9KB12qTVm2WJV92j5mN5r6FmGx4NmAlxDuteJHf0vX32Aw0uC881BE0EuJdN6UjJxnHEmwtciE7TSvwNErFIT2F7FTAKEBoZlzAIeYzY+CBOLxXJP4i7CklVWsm7l1JFVYDST5F3lRE/3GqHKylfToTt1JMgQTgI8l5Ps2sAM+ZyDgWg5xzogJFUmhomnrGdpoSniEYFSRRzph0uJ6oBjEZqKftlPMeZ3dLyXm+qXv3wK3dQo70xWENqXgAb8Gcux+WwcnliuALDeNlB3JWx6ODorkKNraBEPqR5VByzP93K81BO73TWIryuAH1g4hYpbV0IBQAP9eRY0T4tdLOjhTQt08WDg0maJDAslFqKasGS19NLSJjXy8VEGWaCDRXbE0VFeMoBgYs+qRrolqE/eYhtFiixOWiXoOJMNPWQYKgUDwiCsMrvY7hUQFYY0B6cDkhbcYA10CnbS9wt7MeO9gWXakhyeuugxq+liRm+wT2zZAKjGwyeC70PeQNJitmHnEvO8lnlUgZxdUiuE2VhZhMfrAYaZo6GziAcjUGt1o2XR9TU9M9IiESCBkd3lHlsA6C8C7Mf0LF0vW7x6TrCl3RbwNyUiqxWV1y0GrmcjUo3LcD3czTOEr6MSKbn6tTn+risnNWelonAEAFCd0U021WltcV3IM+ebQP6/AOYXLT48yY4w675Gzf3KPih8e38qNfiwKaASA/10QGBYfgRgr7P5Z98XOFlykFQ4HMZC5BiL0omrkdZR5C/7tkB0Wlj/ztU5WAjMdr3iB35diBqXivROpWdVNVHmDoAEKnarYVslvahTyBSOmfZcuw+lEj9SPWRxfUSG+7kOrl2+T+ldEph5qqg0Ye2qEYBW8IkhHgmWBUjU3syctA5Gp+UeUvCdN8hYhyy16icmBb0hb9jJHEg9N22o6REpYjao8MiQr+LWnBRfjnwtE0bwuzrey03VdBoa+igbcCtPNz8laEHD8CZorcfARKWxs0RxTy/3WZB2GzUSCcVwa4h75tExAenidCeL01ZRlJcIPNNMRoEIAKnWqIe8IfqKRBGSzF2osv2VGjLJI5TUMG7Z6UIPuW7nG6a/p8Vkl8Nd5XONM5EKhkSEGlS+zqse6A3Mnc3/xu+Cm0XaqCYas7lfIN+DUAJRRalbjSdeZNWoDy6fY9gX4lhjNyZnDMuUSvDhFbIxexEpYk9ZuI4oktBHW+Xrrz2MDXAqBTUR4TTZUwW4zqL6lu5hscvBgDlE+EZJQBXqKIId6A18yhJGDbPPtcypfN84p2xRSDunXrI/u82b9LCKaH6wwe9dvQYAfHF3Ofk9hbgCwItfUWTHELfyKFY9wooztW/q3E9ossl0Iq9N22gi1xYJHZkQbEKWCiQj+ZyRRBMRkqWgiSiZiFdah2/uztC3abOfbtIKykaArcJQBsQUDH5XQyeD9Y/3sDagTYjOokfgftO73Qx9V0gGbQuP5YxW721bSZARvYYufc64xlwXDEVEmAWoB/rd6s7kIDXSO5QCqFAek4eSgIHdUyA0nOV/k3t4GglxSqhEq2QjtS2Os08N1Ld5k0219USKS3VJFYD2QyY7Vl50mGNvoBsnz7tSQHFJf+67At5p6f1Wg4bZpXcV3OM+CRgmPfkSfBeRUCp5BnNrmfL8/qa1YrKm2paIlkB+B6TeWvCffwv6VB9rqo/jcTyOx/E4HsdvaLyXmao/I5NypTIjFL2hxvJUg9uXUDZDwQIBs0j7eMKhWR1EDBwAxqUTeb04aISC/q0bCjzccjvGmwLaRnGOMYcc0XqOgnVOGqSmGmqKdBNDF2HioVhAsjR7ULDthOG7nCieaPov+bCqmJv0QxkRlqmQp6BbLV6xoYww9+woYSyMywmon8WjyNQcdG6VKSKCFKpy/SkWlI0nWr4KaqJSQxGzwG0HDaicOfWXKSwGt13Qj3rISkmp4Tw5AdmCDBMAkCBHYrcuAi5Od1gXlIb4pQJ7NGPYl7BvC5TUdQXTAWZIhU2gPYsClZmDluzcnTlYrum53gAbI60DKgAjZ/9+EYCFw/yEwvdhsAhfzeXaUmYyXo/4dLWF48xss2tQ3KbeG4IEZ5wV9r2Fv6frLB809PMCFasrapeP2V9EMfyGBuLc5Qyyn0C8GgRJp0xVR+gzSuFiIHgbAOLISEbKckYl3qTDfU2EgyLh4/l90S3BhS5BlToiUebduUPFTN2xt+jXFmZO360UhKnvnEZordRRi9LhYUN1WWOz2pSdDfBeI7Jakbup4Sd1asQM8bo6IvKtqO4JiZE6ea/g+Xkv1iZDt5rqhwkCL/YQWchQ0vOcMmM9qqP6v7xLNY6E95MBBl00ZYIds7nj3ImsqjYBMUyE/QHMmyxk06VSlgmIUQmSYnZa0CJfgkwPEmrWGeFNwETExOw2kVp9ErowKR2IIUASeeizIpgZJtc1kUZNP0d1BGK8s+O93FQXpweYWUDXFRhZCQiVP+pLUyaKE00E4F/RS6oD4JeeSBmgl1vzwm1MwLAtYV7QohbKiMgP3vq2FGU2KHp5BebymVIfLwboNxVUoq/XgFvmTUSv7ZHOZpw4QBTrbJZMhX8+RpnrdqGglzs93G4WUVwd5Py7l3lRjzYKPR865p5bDWDI/Ypqoi2KyHXCWV4xFC+8UyUazb1+EjyM+RihjEe9dXqEfBeKCLvV+Rg2Q3FunnuG1Z5g4uQE5HyR31gToVkr93RFO05SJCpvjFiiWUWtJ1OCkLT2LKhlSUhAOy3n67yC29FzZba8oabgpwTGS7ro+UWL0jrsWq7xvZ6h3vLxDrn/tlr2cEHjX/zqEzpIbxA+4PpwJGnLjiFO/1CK/rGKtMCnQGNcRikDhCrkQCgqqJ3NLRiTIDE2/vjeK8ByPdQ5I3ZeqokwJjCZCMCuyMdfONJ9ncx/CtDMQWG49ADfNwSF+TXVjptyxIZVx2JQ0I0TRaumHIWYFIKGXYzw/N1dW0odFYCQDIe+wHJxkODkcKHgb7mf1ZClYiqn+HmATRKgAUcauMVWo7pLwSZyWwvoEi13N7k6k3RSy0uytdMDwb4AYPoIX/H3NqxxPCELTZ1iQkOylAAwX3UwHOx3fZGhWq8QYPDANou68ijYUWpYV9RDzDaRChNiUkOQrj5MoP80JoEQFPXwyvxEHJVBEDIkbg5K5iMqQHEUo7TCcJL7spMbjz9WgH0nxyP8+zgex+N4HI/jcfyGxnuZqRoVYVQkdZbEFg1aGHEAYEoPnwQBpu4bVaCMin+3OusEQu5vG5i9RvzkIN8jqkytyQSPyKpHHMm7iRatvi9h+qwN6hZeoNrEBExMW32eu6fj6xrVTaLxHUfWYQ75DDQQVUBxRp+9WB5we0Pep3heQs1zVKzcJNKekBdUaygsDwmujZNsjvVa068P+kjYO0FToY7Qh9weoAcl7iehCoCOKDibUWOGwJXPOsahIJgpCYmrsyGTtYIi4lMySGi1qLmU5wdcrCgb2nUVup9fYMGN+NPoPLE+U5bvSiAUmaQTiijRuumAyLixmfi1pjlP8O/+ex6zc0pRZtWA0nis14SClGuFcp2uDRi/TyH+qhrx1a+fyHw3l61An31fkIAFIyLF2mBkCD85jriTKbTK52SiMLQxqCPiCQDEYoJQRORMxURYzgKr0okSGMDQImfdKCM0O//YwiOWDo5dX/BQyrwNpwFq5qRFrTjpUVq6wfuuFPKRthFFmck3vTNwyWHGadLc5qzZD0bclaLLqc/l2RYxKmzuWahkU8Ak/85ASENiqutO57YQD5hxgoiPE+3eAHmuoiGos9gwvF8rIS2FOkAPWt7L8l6heuByTRex/ZRh5yaSbjc/L66JiIaz2BNiZWu+zhAUxpHWl6LwiAyNj5uSIHwRHIkYEhzekul8Omdp+QGVhMIsCGIRdX5GlFf5mYgKxa39K+1HCbGh/xfJv3UPEbwgc3T6++4yws2izL+bRfi5RzhM6l7v6HgvN9V+NDCjhfcZslMqwtYjRmYXxrcl1GGK2AAAIABJREFUzERRJT2E5rLHYt7JorbezESNxzii5Ket2d3WmDMUNy4i3MQYuH8SyGUC/NJyn6dmFwrZOHs9efGB2ERU17Qony/3IvZe3elJ+wvBxgmS8fOQJRIrj6oZ0W/onNufzbFipzMSzeffGxUUlECtgJYNljYQSO9oNFFaV2JF8JSwCyd136mANnraUMWcuYmIzLpFBMxNmdWQdGZAm0Puf/SzQBAwy84dlWNMhPmgReT2mzADlidZbvBmTdBY+GqO+j7XYqPNLUDTXkWAAgyblKk0KRSlYGMqMejn2WnF7DTKByXM0VgGzNgRZ1aMeHF7gvILhp7XebHe/c6Imh2LHm4WMGsLz7XMEBRUkup63tDmz+0rvskBjR6IVS79ihOYXo8qGyTwBiuC92U4MpZWB5OPeTJkZSrwAg5QNFLSBik/8ygKYsHrG3b4udfoL/nZmTtgMOIENDqDh3s2q9dRNtV4sIgLiN1g56ww67WJeHqxRptkC4OWHnNrAz44oc/c7OZ4eLkSJrny+dnUgVtjONANABRD8VNpTIBg9GmdU0wQemKIp02w/SDCnXMNeG8x+04LFFquM8S7/TS/W7OXzCBmzf9YQBjshB/H3AavIwybsc+rAQeGgsdDQfV9Ka3Yo5fDN5P33Gc2d1g6sp1MxuqtyYGXjXJPzYbacKbtMEnuMWqg3GQziCk87osJ8/2E1Npi4lEUkb5j2qbzjo73clO1NsBYj8J6sYHa35XQG4uyTbWNSV2jDphdUWZzOj9g3TZSw4oPZa6XLT3UpkDBPad6ICo9AIyXozxctnawk/4/11rYWz6eBtzlCL3hmlOn8mJ94qHmuQfvbjuHZ/EK10RElTdpPeYMK858dro4WODrCotdihCB9Q94M7NBZBVVX8DutbwcbhalN1cPvLCk5m8TIYwPHclNI70bPvfjSdEJaV4jfDqvOgBcy7Fbg6ijCFVElSXgpu1Adqepb2+dMiCLwPXn82drNIWThTZGhSW7mmgVcdNRdl61ijxCOROmBWfyYqucpetOp24mmI6JHYmQNVkQdO3EZ7W6UYAGDp/QHJ9db0Sj9psXFzCvyqzxa4H979MPpgjouJ/S3hREFHvg+vDa5o1SxyM5yaiCEKbUQVFmsEgsKYi1WXBaiCZUE1d5QbMxk+081a1F0nBXSNdDUTpUbL8WgkIMWjxH0zwDwOZmjupFQYQfUJYiNXINXD5ZS3106AvhNkQFsUy0pwOuzzfyzqz3jRCQqoJqzinQjVHJc1xahy++vqav2mTdX4AyMdvn74p20hZVB4B9aV3Dm0F6dKOC8BbLKFllsSNv3JGBn1BF2pjA7kojJKh5+HHeqJvXQLXloJ3b3wL3qcZJyxUi3b8Ztwudz1tZvwBgHzNqAKekDh5tlJ5bv/BMeMrvpGzaQQHb4qinPabAS0VpeSs2+ijImPoTG9bznXovj0zTgIpClFNeweyVzKnygBq1BOfv8nj3r+BxPI7H8Tgex+P4V2S8l5lqZT2M9dh3JQ73bCzdGuhRYfiAs7aJW8PqrMWsYum3qFBahyVH6IvrW7zaUGi6XzeIPrs3hNUkmxt1hpY8RfVSL5pI+Q1PR6i9EWq//dE2K9owpHXYs7yhBpBYd1WmsRc7hWAmDjabDP/EKqC/8ujZoFovRpQc8Y+dhWIzbdMqQEUxJic5Pc66l9QonzJGs8mKQT5kOBY4ZvzGfAhq36mDfM5sjGTkbulJuCETTieqTLk+GQqGhfn3fBOwuiYs2+iIbVdh4FaCk/kBgefx268uUT8v8rVUQZSM6MB8vgyDJlcgPeQ6uFYEn8r9LYJkd6G1aL7LLS+7HzgYhkUPfYn7N/S82NsC9pAhyP1nTuqa+tsaBWfnriYIN0F2MTm7AATbjhqxTCoaGnbDGdYqkBrVmp+XUeXzDTiq8UNRiwwdQyGmCQl008S42kRok9WWAtM1tQlomk7meBgtuht6t+qXFm4RsfuU56cOuP6EPE6XVY/X2wWVYgA0sx4HRefr94W4By3mHQ6jxXpDD2QYNZolvYMJcbqcUVnk7X6O/YZwxu6mORKr0BNug4oT1n1Fz3OqDUZEad0ye31UCiCJypQFQkQifE3yj+Mqw+rVTRJ4AcGd/FjMnisxLNcuZoUxF2EPWepwOIu5xaXyUDoK41eriA+X9wCA3Vhhz6iM2htqe0nXOSqEk4lcKoCYMsJRSwYq3IiUGFc5c7cbI6ITot4kjHYlH9KjOoLEfXXczpc+k1xu4rQEUQTEmNeOd3W8l5vq/cMMeqgRDjYb+Z4OuHyyxqKkFXs3lOh5QV5vZugYnvrsyQ1+cHmL/+nznwAAXrU2Kw0d6GEWuNCpXH/yCoFXrdiV9ADr/EAluKd8TXqt8SPWSY0K3T0XIgJtdAW38DTNgM5yW8HtPD/0A6Ammq/VDzZYNvSWLqseISp0jq7tYd+gveV2ob05MksOGpP+uZChW0uLvJgPH1SuF5esMDXRCZWXSuFIL1QNKtdbZiHDa0UAei2SiWpUR/KAiYiDqx7xrsz9rGfDkdFFP1gh1RyGAptXtJmVbzOZx88JUgfD6LbVGM9TrxOAg5FeXT/LbSij0VQ/ZiWj2bLHgZ1cqheFwObtRwHNZQvLBJt2XwuMW+yIdNVd8Wpde5g73uzPHOIy11BDn5Wk4BQUt0/og2IVHiazdVmjWQ0K+nmd+5CTSwxAVoSp7cmprIAF2lgUE4mi5lpaioa8wWj4HFUO1god0XYlhjYR8yzNK4DZ33zA9WKH3cBqSHbE2z1tjq/6JbXm8HH6rqDyAYDV1Q6fntGm8XyzInnPVGUoAr5/QRvzm/0C3WhxB3qO94cKUQIhfdRHqftcS/bNJDgJ4HaSVEKxWd9X0aY4JcjJnNocNEorjMiIalE1U3+l1ExILQeiKhOGXEN18HE1KSukjb4zKGYjFhzgXzU7OH5Bf31/jj7Jd65G4CFDweHECcsqelJbSnVlFAEF1337dY2pSbw5aIGNTTd5Vxtuy5vsf8J56ID+LAoHBVHJJqzH3FI0nASEuZdj6toh7Iqjnv93dbz7V/A4HsfjeByP43H8KzLey0zVmABtAurzFpahrEXd4zAUeNhRtKtUxIKzu6vzrRBeXm5W+IvPP0LJsM54FqBmLP4wHzDuSgSGjaJXGc6rHcCC37HTUIOGumDizLoWZR53EqAaL5Flt25y6FMG2NoJxLb78gTlAxM5JoLa7bMAfzFCM9RTl6N85lffXYmeKkBQ9JTQINEomNWXHHIaL1T+6DTiqIUeP9TIcHkZgM5kHd8qZ6CxyCQjNVLkK+IVk3YOPSXi8Hklw2W/HLFg03AFYNMZ+JK+q66dwIibocbYWyhOEfa3MxR3TIRqs1h9qBWRt1IGekXwO8DsWU0KSQBg56MQcXZ3M2pb4NPsugJgtyLlKUMFAPsBtb9s7ygzK18UIi7hK6D7KDuxYFtkH8oqwG05e36wZOA+cSsRiFrT95V3k3MWD0zKGBJsDJ2zHsUlCACErhThSOBBbTOJKUwyOj1zIqagFCmPAayaVOSSwMWn9/h7H/4CAHDwBb7YXmFVUfp+085RsYCENQGxcDhvCLr9ZHGPv7Z4DgD4+e5D/NnNMwBA2zErnzPqatHj63vqV1k1HWbzEfct+6l2NpdF9OTc94ZY5kVGQASpSkSt9KxO3pFoIuwut2T5JqNRAi2DoWMbULAwhD0ouBkfvohHJgnTVq3hJAq6UGwUYpHJSUSAY9WuxuN00QoB7OvNmaBp/WClxSg6TYSsJKjhcnsNVIQ2ESWvWd2hRM++yFLOabNww9SHWchJUSGUmXRUrrOYTH9BgjEpw9Uuw+baKQzc/hZm1CWwYEb+7vmK5rib1mHezfFebqpjW0BHMkNeXhKr9/XdCjEoRK4/xTIIy5bYhLzoBg0UAcMFHau+OqDgBaJtK1SrXlRf+j6LfIegxbnk/HqN9a6B/4YWWnc5YsYPV12OeHiYIyRlmiJCJdbd2wIqlPmF0+ReAoAWxBPaWZcrqh/23Be43s5EZnG+6hCCQrvmHo9ey6ITTUSxmaoyRekrLZpRFtNZNeLufo7Aiz40ROUFm4I21LRRF5BjAHkR0j21o5jEQAVk44Smz4RVdshI0oxVMwoKutvWwKBFHanbVLkFICiY1ZAZtPdW6lZunvsRlVewE8H7aLJQf2gilh9u4HijrgqX69tBwbQaXnPLjlOwDEN3H3hcfEqw5arucN82OPBGXd8qYUN2nw6Ynx6kRm5ORpyd0PP4sJlBvUi9GuxiwkFMmAQ/5b1Guc4Q5HCSWetShkiyllw7BYAw87DcR+pHTYFWsrjrJtCnZ4ZmgiBvK4xsyxLqIDVIqqppLLk15t+8/hpLxsC/bs/xw+Vb3A104TftHI5rsdZ41NZhWdLvuqjxvz/8AADwlzdP0A3Jzk3Be416QYHok9UOi5L+/NA1uG8bqZ/TyfH/iyDM97CIUL05Yr6qSTAXVERIGpUm5vajQWFchdyPWk6MLPpsZh7YtN5PpDfTZlNsCepPrVXjMlJJBRRspqAoWtqAUwscTJRgFgBu13PqrwetS3rybgUOOHSSLkxMbxWB9CjpiOAVDi8W6aJl89UzgmBTD7FtkZ8XM3k/FdC8zrCuqwGXDmdJflQgX6egE7u9pLkDADVzsKXD7lvqHarfGJqf3wKXmkf493E8jsfxOB7H4/gNjfcyUzUPBXRXIFQB7RcknhAtQZUJTlVeY3iV/018ImcR6nJAuaLw65PzeznuMG/ROYs7ZigO+1Ki//PLLZYTBnFbePzob34FgFh8B0cR+bqroU2UHsLmVe4J664CPvn9F7hqiOH652+ewnAWNY4GqwVlu005Yt02omAT9xY+mRJ7A99a6Z/TDrmnVE2EDwyJ5NeXdMxF02O9I8LUzaYGOiMhmVkNQi4JluHGFK75HK3DEREISNyXTH4IJgqRRieSksqZK1ZMpmgLdD2dhyoD9HIUUXdEoFjSHJ+uWhgdcKsohB69QmBrs7C3kvmalpRz0hy7JkI/o2v+6GINowNudnw/nRXEAjrCr7wcRzmF8YKOf/XsAcuKrcf6CvevVnj2T+hj24+B8ScEdVbW49CWqBmKO50fRPg8vKkR5xl6V52G6ZKgBoSc5ZuIQ5WJIr6EkGiSIlAmmASB21Xt5Vrmqw6F8Xh4YNGFg5kgCgrFW5uJNPOQ1blsRGQGq6o95sseP2F7uo/rO3y+f0rz5g3e9Et8uz3le6HRJyUgo/HmdoW7BeGk5/MWz1nQRCkI0mNsQFkOuFxQJl8ZJ/Bv31toHTFOnndh8aqQkZLIqkBJr3mbRS1iwQImie2608LqBajMk4+DnAmrbPxgR4XRZ2/aUGTUYFjx853emZ0WD2Xljg3LQxUz4lJMyicBgAEOW9ZDdpM+zyKgatjoILIuM1+LSUpIAMaHGmavs/JYkwmIYVegvDGi6uWrCTt64n06f6Ggxwg3o78YV7lPVSf7SybqKT/huNXIPssHA7yoMJ9Y3I1LKju96+O93FTD5QA0GrYepa7kdwXVi1jMexgyI9H1VkTyEagGNzD0+bJ0Iiqw6ypimE42g+sPHwAAv3f+GicFK/poh6/bc/zlDfW11OWI2wfGT76aodwoqbe0P+7xd39Mtakfzd5g7Rv89O5jAIDRAfv7JNUDuBnDxDqgtA77QC+fHjQUb2a4L6hZQk9qQmmtsDEbrFcBxXzAwAvVzU0zYUryy8y/q3VA5NUizhziYLKmm53AaL2W4ER7YgW75ImqIXVY3Suo0cBPN1We72gDNLNKtY5wu0IaxvUZqV0BwO5QEZOUIbHLD9fYd4SBHbYZ4tKDIlgr3esnA/7gGW0MC9vj89snwqpsqskqC2rPkHHVY7mg7y6Nx3e3vIG8aXD251qgMzcDfHKz6Q3QGwx8L9YK4tsbVk5gv+Wiw+ZmLuozCLm1xy+oFmrvJ8bt6TaVJBk5bXFKo6xH8RwFCG6ObXJLj4jz1P6iEKosuxirkBWJJou1thE/efIKf+v0SwDAy+EUJePtWkUM3uCqoXdrdXLAytJ3/2+vPkXdDCJi8PXLCxHDr5q8iZ6UHW4OcxHD3/YzhMRX0BHjYKV0o11uP1J1dqlxgyGv0Um9b6qCBSB7i05o5L7m90TllpIE/043kFCA2kmqvPnKn4OC3RkULIdpuyzf52dxUuNnhnkKfibyoDEojPe1vCfQkHtbnwwY+Tn1o0Z0WvZltTMSzJYsCpM2SyhIW1vzkoKMJFjj66wkV2yy/2s0gLO5jBFNdj8yw/FGCmRjiHER5e/LV8SQl2B2xs9rmHzwHR3v5aa6OCGXmqfLLb69p8Xv4qMdautQsNrNWdVi3dOG9YsvPsitAzuF6s7Kg9f3hdT49vsaiEB9QW/ZxXIvdZ+DL/Ddnr7r67dncIMVB5sjfb1zB3y/x4cXFC5+b3mHt+w28Rd310fneDiUME3aYAJOG/re/VDi/nYB1SayCXI0WpG2Z3oxzQhZQMZFFIKHaRzGTZUVTuqJKlNQR4uOaLoCtGhNN5vJIq+H3HoTZo5qWhPDaJEEVNzqkvoyg5IsYXbZSq370FaozzohlFXW4YHJKn1XwBYeV5c0j1pF3N6xa8chbxKiH3xOc/o3PvsGjguUn98+weAsVrxRO6+x2yedNaoJp9qX0ZmMtNutZNE9/7lCuY24/etM6roa84IzaCiXHW1s4cV4G4MW0+nNfgGzM1L39RXgOCuuT3qMgxVFOtVryUb13FE/YprjxgvJ64o3KwD49uYUfjAoTpg4Z/JmWZVOFKDSaJhD8PpuBc/Px08+eomn9QZfdZcAgJt+gd3ItVcoLGyP781uAQAL0+Mfv/0RAGBZDdi1Nca3HBx6BfWEzvHJcodlQedUGod5MQiiUzDBCQDWhxnibSUymH4WMkknTqQUTYRus1VbKCZ1wvQsxowApAAwDeklHXM9Ufm8MYSC0B1xLmoV9B0fngllSUqU6pCTzXeCEKEM+Z6VXp73sS/o35hDUFVOAovDdsInUPyfyE5GOOZUmPb4uYWOKG4YHSkJqfET+dEkn2r3Slx1QnncfzouQ5a49EqulY4BmVOyTkwICG28SbYwvYdTWdB3dTzWVB/H43gcj+NxPI7f0HgvM9U0vr49E6buvi+x6yo0JUWBD4cGpw1FzL/7oxfYcuP6/W6GrisQ71j15W2DoU5u0hooA7FQATxfV8c+hAyX6ZoyhrHJ05+Ewk/KDi5qaYH5X3/9A2oRAFAvBizPH3C7p/pT0wxCrz+ftxiZUXn3MCeYmlmA3mgRKVA6Qr+qs2foARiWGQpKsLDfFUAkuBUAKU6lOt6po98dUn1USVYJcETOQhRqkmXGItem7b0l0YuJvrLdJAYkaZUK01ZH2JNcd0yJ/bLuYVQUJZneWewfKOMpmhHPztcomZr5crvE1FXHc5StBwXfBCxYiennLz/AsGMFoiLgyeUGJWdq90MDN6Y6mCK9Yp6f4vMZkoGNryJmL+mHk1/3+O7vlXCfsvqPCQJv6kWPri0Fou5uG1j2xLUqQ28AZUdJbN/PcybTv57B7DRUan0qcwtJOFjonUFY0PkvVgdcL+k6z6oWz3fMGQgaM66rAsB+4jwzOoNl3ePZgjL++34mCmKutTh7Qmzfv3f5OcZo8E9vKQO9PcywKOiezYseQzD4Z28+A0A11vR8b9saw30t/qTDaZC2qG/enEsm1swGLJsObc/sfEBE86NXiDMPz4bFsQhA8hZ1kzYWR/X9zM7NMKV9IHENX04yxpgzP90pEVc5yjhnwHDGWWCv0LzMbVahyBmb6cFs43RD8+Ghc9aHxtO95RMLg0FIRsyKdMNTW1eMCt0uYcgqZ6peASaLxIybUhAnPwuwFx0puwCkHc4f6688zd2EEZ2g7SlUGzVn+eI+M2kr8rSmiOpZOZkDn5WiktNPbrchhvtvA/v3vdxUtYrQLGjfslVVGDWUjiKUX9ejtFIs6x5/9/oLAIC/1vjp3cf4MhLMhZsKpk1i+BFh0NkiyUyEw09HzBa5hhWCxumC8JRNW4vC0ZvNJbq2FHo8eiPOHxfLPb67O5VjxAipi82LAV/tiLihdYRqnKj4aB1lMxjXFapWoWBnGpIf5HnplLi6xJJ78SaQbLxmsfdE1ddGziO50qQ6UiLJxEnvHwJyLZOVhMTI2qns6NEaFBsNzzBa/dlGpNnePiywmOXzUCpKe0aC3wFqm3q9XsocFCb32eIiwLPijl8EFM2YzQ1GI7XCTz+4RWUcvnhJtW+/LfL9XDqonUX5guHIPdCf80Y9gbLf/mGF4cmIgiG8snJSJ1Qq4vlwKmQcuKz2o0aVlaKYS5J6mdWoYFJAYyPcmaPe5nQruf6sBq4t8gK9e5jJRnRbzyQIUzpQm9XIgaIzYnjdXOzxbLEWKPdmN0e7591dAb9/9RIA8GZY4Z+8/iG23E9aWg/Dfalfr89QWi/3aXBG3jvXU09z/4xN4692wlG4bxvsufXr0JbkSpPaflorwYOtPFxnEOcTmDop/LQ615iriBgyqUu5DOkCtFGIuLxMJpULVMgbn9YK/Lqiv/AwbM04e0llkeQwg5gl/abymgD/mYX33cpnvkJv6J1L74XJRCVbjjAmyj0kuzs6X9+ZvBlWAab2GLmdTHdaruvs2RqHvsQocqQa4xnLMS5H2MJheENBu91qCSRCBVkLxmWEm0eZD9tqsXyzB94wGc0PJs8jMIGF03KRnK8OBC+rvES+s+MR/n0cj+NxPI7H8Th+Q+O9zFS7wcLYgrK3xBVwmnRFOcw4BAVwq8NHiwdUHNLeuxlCVNnncZ69Q2PjYZsMz+y/WyKmyNdptN8u+TMO9UmPm3v+OSq8ZW3h4ChjTvBecdrhfMUZbVehKDLbOMQcAv7F86fCHbLWI0aVFVa8kkzMronyPzLZ2NdsHYbcmA0AYmg9aUKXiH/UiFMt2ois1MNs4uS1mkyRAabbcyQ6LonUkY4fRy16tqbTCCVQfp+gRe+1CCScnu6FkKVVRGWcQOD7RYlv3pzTvJUk1DD93dTGMQwGYObifN7hpOnwhv1Vy8rhfEmZ5LwY8Hx9Ar/hzM8ryYZs6bH6dIs7Q8jBcK6E/LV8DlQbuq67v6YAG3DOog79aPHynlKZGBROlgfcr+lkfBkQk/m1mmT4A2dbKQR2EPg61v6onWLoLZHAAJgzT8pXjHrMTw6SJW+6SjKe4A1cVEJQsoXH6jRn07fdHB/OicX+5dsLBIYSi9mIGybRFSpgXgxCFHM+4hec4YdRo2xGKbWMo4VP4iZRIZ6OsIwi7NtKjMgL41HN6V3SOuBqucf6QJjjepzJZyJ7uSZfPtVp2F1uP5I2McVEmUQsKqMwepWnnxPJKNrc1qId/674iWaCk/JKSil6IOOBcj2BjR39XrllQhy3obQfHMP7qTULYOg5rS9OT0zJCb1JKAIixKihmI1QTHwatiXCTSXev27lhYS23TVwDyVpIoPZyZwl+00B7GqUSa940g4XNWerYEPxWUDBpQpzyMStaIBx4kAHYML4hwhljAtCyZI2sm2Z9PVb0FPzXm6qw6GARkEvdIItgwKqfEfDpgDfb7xsV+g8L0BRo7Ejan7ZD30jEOb1Bw8IUeH2lyS3pD3gU6HNTcy0bUR3VwvEU8xG8bmMXqEoHZ5cPci5HHgzMCpiWfek6gSg7UvsuKYSvZJzqssRbVfCTxSPZERi+Ik6TDFxWlH5ZVZlQDwYqAPDwUuXZe0SzJQM3ntzJD4ORc4eANetQm4jsAw7m0EhagN3kucnQU3RkDTgzCa/TId75u93Q4ENf2RVd3BBwzOW9HCoUdUMISuChndcg9vsGumlNTag5PaY3bbG5n6G62uqGZ7WB8wszeNdN8fDq6VsbouPNvjklO7LH51+hz99+Ah3ijbVWAfx/mzuAh4+47aZDzt8fH0v98xHNVHcsrj59lQY0FOvT0QlbVDKsfRgmm+nSIwcIDUrE6VNx1gvc+BGg9haKR88We6wH7itqC9FnH427zArR2lrGZyR9qMfXN7iw9kDfnZLcoFaR/zhD74DANwc5vjyLT3rz8sTaBWlHrpf18InMHOH1bzDhpnT/V0jl2lXA7OeaZLH3mJ3qOTfr08osPpsdYOXhxXebHgTr11WDAoKwMRQW+WFPFQ4gtSP/HH7LAofStBzPek/TfyCYInRKr2XDggM3ZqDQsWt6kbg3byJi4zlqUZ/nttL/CLLCJoyYM4M8+3DjHvAMzM4BcShNVBtbvXDSVYXG7elsO6rjcK4iHAnvN54JVCwcuQ1O5VhlGi8DIhWiy9zfZeDgnF5XOvUvc5GGSAPWADCwE5jyuhV/aSODJpPYRRbQGkg/Bawf9/LTVXpCGUiws7KQqXnI9UxhywKENj093W9xENBT82y7rGqOsxrfoPO84vvg8bNdo7mE/r5fN5KBuSdkZfD7y2U09DLXs4nkZF0EfDxxQMMP4lv93Ost6xHDNowa0srxmLSDP/d7amc03rbUGtGkV/M5IyiAvWppY1W+UntblRwp7zK7C29ZClTnegYiwZtItzMHDy3Leg2kyzo+PlHX+WainLsfMEkjGmvqB6BYjHgX3tKi7ePCn86fggA2G0amUelInaHShbkGJXUUAGFEBS2bxdyzs2KFq5hsNnyrwi4vNqK9mw7lvjmjmrTh9sGatBonlEk8A9++Cf43YpqiP/Lwx9QJsaBWPNFheYtXejrfwOykmgbcLOdZ9IVSKgDAMJdRfJ+qSdx6tAxyRJUaktKx7jqYVJ20VkiswyJ8QWMqT5nI1B7lJzFfvXyIgtleCXktWrRYfRa9HWHzuLqgp7h63qL/+PVJ7h/Rdn13/n9X6LltpY3DwuxfhvaEsqGLJvnlUhXzuYd3r7IXADoiNNrOv4JIwlJYKOqRsxZJOX7qzt989cAAAAgAElEQVQ0hs79l+sr3LfNUW9qCgqGtqANPLVuddnVKFQTCzRPJK/krkQ9pXy67JIUJ4FL4gUoT5tFykB9DZHeK3ZZOCRYuk8iE2kVunPIMcZVJJIfgOqkk2eiLJ3wN6JTR6iQKoK03llub3EXNCd1M4q9nt0YeV6GE+oHTYEtghJhlVhEFKcd3E3ub5dAutMo1xrlQz7n7pLfLZ0z/lCS4MXUOUpQrIK/m9uWEJH5GwXEEs4cOIYXSU0APtdY3+XxWFN9HI/jcTyOx/E4fkPjvcxU06jODzl7dAamDPActfl5du043DfQF618bvQGN29W8vNL/szwdoZoAz78lJrcX92tBJaDgsCPMiYZ15IF9WfVgG1f5XprgMjY/eHTF/jR4g1edBT1f7U7l+b4WT1QKw2oLggfpcVA7SyqW4Yjmwi3CNn/8KAkQxxPA/Scpc48R/7p/EeVxdNPBtSzQWCo/X0jUbF2gFtFYfElJReAIt0Ef0XDrTOcNYRZQGCpNrNw+KOPnuO8oCz86/YcPdeclYoY+M+9pcb4yOHtvOmw4nrzQ9tgs2mguB6li4DuFc1PtBElm8x//4ru1fM1tZdsH2aI7JSheo24dPhPfvR/AQD+09Wf4S1L8Dyt1vidD97gi3/8fQDE/l3/LkfyTRaaRyAzhppbtba7RmB57ajdKNW3oCHwo5q4B+lRAWOuU8chM5TRaZi9yS0YVYRmY4XrizXutnPJjG3pMXJGVJ4MImqhVcT9wyLb2VYOlzOa+3/+4lN0Xy6hn9K8/vTFR1KL1XrSk5Lu8R2nfkUQI+zt/YzYuAw5VqcdBq73v7xfIUaI7+2HJ1tcz6i97JvtOTacPRsdURiPPUv02dLBJf9QVvgSZnZUIvvnlplZqzibTc++m03UpoIiaDSJ0o/IDjQagAK6yyj/lo4xFTrQjn4eF/xMlxkS9jVJeKb5GroCzZzmtClH3HFdHU4fuf3E3kCx760rQ4a4QQYSkr2eZxqzGjTMJr93oYhkVg8gjgb49RzJNyAUURDv6tagus/X42YQDgQm0pgqaEQDcbCxbVamSnOQMlAVMhMYyMeGBvxUI4alYP3x4/ROjvdyU1U6QumIsnQwk0Wh7Ur4xJW3kcx8ASwWncBU267Ci/2JfEavLcIbfoKaiOJ8wOs72nBdn+sfth5ltmMRUNWj9AWezg6w3DLycGiw3jZgP3NcnO/w43OSzQtRY2E6zG0mKv3Fq2s6vvX4+AkVd8agaTHlhb1+m59et4gwXVYU8k0k2UaQ0XbHPYrRI/f6AVBRQXGvaNrk929oIbBrIzVatwyIRYDhlpXuqROItKidkGZCZ0k/t8wL4Yp7Rc/nLYZg8XVL2NnNYYEZQ9v7WMLzgry5mUPZiDOGKpfVgMpkhamicjhlPeT77QyRF5bPPrjBjmuLLzYrHNpKVI3UoGXxt08O+I9+76f4ry8/BwD8fDD4r776D2iu7IBfvb0UgsbhOtepVVCwFwc+j4hxNHle70roBFfrSE4maVWLmbwSZoF6J0GBTyhze5NpHMI9t0sMDBOnuvXlAUu+5te3J/C9gTowYW1UME9YSrF0spD7zqJa9ALdah3x8y8JbtdrSwS2pMFrgtSEh74QEhcUgDGblputEdtA3SmqmbFetjEB7Q2XNKqA+Ul+/td9LQbm/WgnTlER+7bKm034K1B5ryebYKQ2FQCq8fLM6TW1iEhwMtEBJvu7CBVTcJjfEflOUU7K9VU9Aoql9caZQn+RW2+Uy6QoN4tH5KfZoscHJxQ8vHhYAS8oWDAKiEoLDBpXY+Z9MDScSjlQyG1oZYaJdZtlMek6I+x3dPyyo2cpbYKIkJYg23LrFu/vvsq16VhkkpHpFPXzJojaA0XqZ+0jfK1E4cv0EDnD4Sy3BymvhBwGkNayHfCoqPQ4HsfjeByP43E8jjzey0zV7wpEX2B7sDlS1SDnFiZXVCcdLlcsru8NXrJzhr+rYFqdxJHIYSJpeJ4OCFFhxuIEoZ5AMEGJ4LVSEUYHnM8JUh69kVaBGBXGbSluK0/mOwQOkT+bv4VBxN1AUf7zuxNxovjodC2elG/aJfq2gL1hL8o6KxeZg0IoIIzfWERpD9qva2ibMgMQVJXC9eUoSkDdgVWA+Gd/7QXqg1NQg0Z4RufS1KMITyQ2ahqq19BnnD0+fYuzmubj1X4Fqzxqzjr3Qym+mjFoEU5HQy0XA6MLt86iZBJXYQKenm4FDQAgkOOb7ULakcbRwG1KuZY48xKB/63v/Rp/fPJTfOfofv4Pr/4+/vwrYsFiY1FstTjJ+Dpmw/UiSjbtUpb0ls95KoahiYkqxuReCStTH7RAhwQpRqhz+gv/UErLSLFVGE4i/CWnzPsCd7tJT0OAKGthHnHK7Vk3z09g15zZfNDh04t7vNpSyWFzP8vaswGIOrPk211FpQEAsTMTmJsY0Ils5Rche+yeUkaoXtIz3l4Y1Of0fBgT0JSjqFatD7X4AAefnVaS52s1Y7eiQwE8MCt+VIgqQ7LiogMAm0Kcp/So2GCc/snsM7knZZcJ0tQjcnkmAuM8Z362zS0kpod4sPqaMlIRx7cRnoXrw5xanxIjut3U+PIbmm+7V7AuHSMizCYEwSlaFBTMzghiEaqYyzO9kWfC1wQzp3fcbsyRXzDCMfu3eZVg3IhQThjQMatPBYtjPV8AirPTqClDTf/mGkxcnzLBSY9KMuRoCKUpGKa2B3ZY+i3IVN/LTVV3BhrUoyoPigXCyYiipkV5OetxwxZu4VcLmNRrmdAiZga6eYRmSK1uBjxdbXHHvXqd1+i5NcHv8lTrmUNTjllF6W4l4ulF5TC/bPHDixsAwLPZGktLx3/Vr/A/vvjrUleaLXr8wZOXctyHgb43RIWz8x3uGIKtbgyK1LM2iwhNfmlV5eXFVUXILNVRQ7FoPECBwMjKUcVshKkcPJ/z2Galm7RRJJirPxSyCGNdwCQLvaVHdX3ApxekOP7hbI31SNf1u6evcfAFfvlwRR/bNhgfuLa210BqkdARWgcsuI46OIOaRdZL41EZh46Zqj5oPFkQvFwbJ8Lsv3p9SXXkW1bWajz+9u+Teta/f/GnaEOF//zrPwYA/PyLD2HvsnoWSQemWmYQ2JiCEcifzctqAg3jaGEBd4MADNOxVGOxzcf2bIMnpvA2H//w8Qi7HDOkHBSKiubAe435xInGeY3bG1rIVe2huHfx4/MNdmMpKkdKR4DbcDwsdJefg9AbqLTxd1rqbO5qwPKsxSEpJW1K6NtC7jW8QmBFrmR2DwAlG0Qc2PJuv6kzRKiiGAyYIkCbIM9cbC0Uz5svA8zWUP2U50fvUs9zZre7WUSoo8hmKo2sZqVwVDdMizwA9E88MYrTxtdEpJN08yj126nJBMB18fR3XiH6bKmHTmfVsaxsSEPTGgEQDyMFomanoSLE2SnO8zyat6W0DsWK+CDSYnOnRDlqXBDjOW16s5catk2sXnaeSRWZSc3TN1GCFkQKJkRicAR8xXXkgp2YmjwnSUlKDwp2n+dGu3wMN+P7Y/7KJL6D4xH+fRyP43E8jsfxOH5D473MVNWooAxBbWK5VAZoG9CwGtJ6V8N8Tj2OZgKXhIIUU1IU55ZByDAX8xbbvhKosl03Iuowv2pFv/Zyscc3b87FNit2BmZBoeSsHvCTy9fQHMJ9157i7kBwbzsUKIzHx9dESLpsdnAccrqg0bNAxZPZFt9tT1Fw1qNCPl/fsO3TxBtVMTwYvQISPBsoG0u/F70Wm7kQiQksZBEVgTFhQvSzZKcxTrLYCM8N6adXO3z/7FYgXq0intSUSR58gS/Xl3jY0XWP60o8MMVoGgAcqUal+Z5Vg/Tw3uzmOGk6UVsyOuC7B2JNax2ETVyWDt2+FKWkf/ePfob//tk/o7mKEf/Z1/8OPn9OZtt6YwUaq+41CQskVRwNYeSW9QjHmRduSuqBTKfvM3ToSxDszOhA802R+/3KKAL6oQBiGaEX9MFwsNLYDxvhDpkgpAuPmoUtQlQorRfYu+9K2CobK5wts1LXZtfIeZye7elnAKH2OHm2Fhj27maJyPci1gHqir5rVg8IQcMlZvPhmDhULAdRbIpRoWBEYXAW+0MlJYIYFHS6vxFiN6hUhNZRMlWELOKgeo0wC+JBSoL0fAgDgWAjW7Oldzl6JQhU0AQb96dJmCOfv+4IphSrs1UUP1vtcr8mgCPBBxVz5hsa+os4y89ucJmBr3KVArEIWaysN6RfzMPNA5UoAEGTAEIKBCrWEXAK5T2TAouMjoyrgFBH8Y1VjvppgSyQn7JYKPJRBYD6Jq8hid0rSmkT+zsV6b6IzrECyi3P40AZKUDM6FABrs6ZabF/FNR/Z0f1/S3MjAy4p1BUCBobFgtovioEThnOgyzqxVZhXEWMT9mB46QTWNFHDR80Kl4w+srD2MzwrXjB/+bNOdxDrnvp1Yirc2ICXs92+LPXz+Tfnq62Ao25oPFstcGPT4gN/EG5xpuB4Lw/vf9ImK9v2iXefnuGpB8/VVAKdWCYl7+79CLcQMo9E/ilDGhOCXruDwU8qyuZxiF4nduFgsoLGsNomaWpoJkhqgcFzV6Zn53f4MfL11LbXJgev2oJ7v3FwxV2XVbVqc4PGA4s4+i1BCrlbEBVOmKFgtxVyjJL3A3eyHH261rEx5eLA56dk4LS1794in/4b/3P+D/X3wMArOwB/+2bfx0A8LP1h/jzn30i7GlfxqN2jFDlkrPqNfSKju+cIYFzADjh54ufH+UnovlOQR0s6rcMcQ55cRpXk7oXiwGkWrLyQGTnGVM7MhfiDauqRgneEDR6Z8QkIowalmHFqnQy91NhCgDYbGdy/ovzFtYEnNV03+7iKou9Fx6emcWDKkjFKtUCDwaON9x62SNGTNjFedMojCeT+/S8OI2wzwz8NGJBSmPiymJjVh0a2Qkp1a8HnZWSiih1UoCC58SGLjZa7oWfB9hVVrz3bSXwbLkmJnBMvIQuw7rTzRABQIHcFhWy2AEiUJwM2bihtfKu+YbKTwBJCuqZk64BNWhhXicINjCsG8uA2Qe09oyjwbhl9bMCKDZGINjxzEEfsrShXWsU2wx7S/BmiB+Syg56VCSIz38ut+n6Gf5NaksrJU5XplcoH3KbXrCQYwxXuWwRNXvWThjFymW28bs83stN1egAowNGXqgB0jvt38ww/4aj+vP8QtdvDRw/oN3HI4rFgL/x4Qv6nA74dksZkPMGvTPY84vfzAZcsdXWzW6OQ7LUelkDs4CzD9d8PlFetj/71cdYnLX47Jz6J28Oc6krXS93eNJsseDmsS8Pl7jtqe47LwY8dJRdvHlYQA0kVQZwjSWpH1UeYTSiJ+o3pWjWhjL3V0YbcPZkKySg0GcXDH+wUCbKMaKKRwbmenI9fl3IIhM/OuAf/N6fAACeFBs8H86wZDbO190FvtyS5N3bhwXcYDFf0oZOtbVUM8yb+bzpcTXf4yWohenQ5fvpgsZ+Pce45Y1o1PAdneMmAn/27/139Iv/NgD8l/K5P/6n/4Vk/3/5z78HbSOGVc60zT1H3V4hhChKNXo5oihoPrq7WtSsdOlpk2DSTiwCFG8a5MxxnLn2l3S8UOXaHzS1SaT6k18ElPO8AYiKEWiDTPesO5QITpFOM4BiOcg5njQdZmzN9nx9Qq5Mqd0pKJxdU/HrvGmxH0t88S8/onMeFfwZ9zKPOhNlAMrEEsHpskclWr8GWgcMyQFqNuCS0Z3CeDy/P5FrKJoRo+bNYZJx2sJTf/I4CeSSUtJE/UjmLpGRWj0JKCmDS9mXyiABzE7D6UJMuatpn6cFm49D5iCtB9Ec914qn2uxsZg8H/MR3uuJbq/KuuCdFs1b5RTCLrshTfuVVaRriEnbuQwSMKlvm/SK5xoytw7pvREOiB65DUt+FxhZchGRMlNdp3nNQcG0vuoa2ojdIn0uk7P6y4Bio2QOiOiW50PWkAX1Dydim/IKbpERgHd5PNZUH8fjeByP43E8jt/QeC8z1e2rBXRTQzVeBMDHhwrFRh9lqCnC6n7YCZX/YtHiR6dvUXBo9rObZ+IyApCg+Qm3LTTliDs2FG93leiu6ic9moZE7wGGC5N27nzESdPhuiE4+KLaI4FlVgW4qPGLHbl/dL5AzUUhFzTumXU8bipiRk7kSRLsN5v16AcrDjaIyOLsAGLyf2z8MSw4hYW9gq69eL6qaetNBPlz8vHNyYiTjynr+dtPv0bBYf3L4RRdKPBLvpYhGLxeU8jsX80w+2QrusaHsZDzdaPCk0uam985e4NCBezYQL5tK+xf1/mUW42G60r9ZcBX/zBnpP9v45c3lwg/o/YpG6kGlKL08i7DhW4eEa4GzFgVZ+gLdLcsHWMDNGeEYTAUunKGpSam9SoC0EB3xcc88ZSZgLP/JEh/UKQRrPMHE2s1wb7dljG8QWclpj2Jj1z8kBjWlXWiUHS3n2FfMCwcFIa+EHelw6HEliH1D5YbvNwuEZacpjkFzTV4ZSKC5ZpeZ4FBwyzpAarqQVrIgtPUFpayQhOE+f78hlCeVIYJgbJSAHCAMJljUFQ7To+hiVCpbq8ARJW9UCMEfYk2yj3DYoRaF0c1UDHTrhTsg0Wxzm00AotWBIlmpupxNvVXReRTK0s4GaU+rHWE67MrFiIE3o4zj8glHjVS1p0yVT1m2NlzGUCv+b4p5FYZm1/BUEYSYakmmeohZ+d6BGav6bsPl5MMn03VE3KCOBG5cIDLrxb7w3LWWeXyEnjeLH+fL2PmEyhI6cZuDGLqFADdp7HGbwX7973cVKGpHhM7A8XwiY78siRSzemIxYogqlXdw/MTezXb46rc4WXHfatBYcWEj6pwWJU91j09fW/vl7Jpx02J1NyqNNUo04Ixm/U4vyQV69qO+PHJa3xQEjTso8YtS5L8xeYprA7oeUH6aP4gbSjrvka7q/L1TdxhYuFxzTXbw2gxKBJhpy/0WSh/ArdpQ+0qmq+7aEaMbL+mKg8/6CPIN3QZHwqDgWG4c7Vs8Xc++DUA4Em5xcuB5QDHGrf9XKDW3ZDJKuFkUhcEtcckOzBrPZZVUpTS+JNXH2H3mgllew0wEaS6OAAXwOf/6L/B/9f4R//iP8Zfbq/xDZOYxl+eYrxOXlV0zNTnGE3eYIMlqbwEW1b1KGSeELT8GcqTVdrExD1ZhYUC6K88NCs9ldZjSHO8tbln0hHBROqolZcNUAZDeN5pjBygRQ1cfHYnYu1t3+CKbe1qO+I196X2XYlm1kvgYq3HvKE5/vbhFH5CUjMmIPDxgs8WiAigNg6+byFoKVsoFaFsDgQOh1KkDutmkE0dYBUyfq6WqwN6/r2hLVG8LrIa0tzn/ltnCW5PilOTFiBECIyu70uUD/q433KyH0Yd4Wdy22Tz9IsAs8uG3VFn4g+53vAUWNp4I/cTF4WH455b57jOmIIqG8VIXVUBkVXN1E0J5ZXYtoUi1yGLNc97ap1BPv8wkfz0daR2G66jqlEdkYqCAbbf4+ObKK1CiAxnp011evwiO8oA3IuaiGIBYlKgR4KWpc0oQOYNUWVRfsvtNAnN/y3aiR7h38fxOB7H43gcj+M3NH6L4oP//0MNGspQ47qfTTKi8w5/8IwISC4YlMymbV2JPTvvnpYtvm7P8XJP5JjSemnj2HQVfv2wmH6VEBP0qAS/89bg+ukDfnBCZCSrPRxT5M7LFl/tLvCNIvux//D6p/hJ8xwAUGmHbw7nqDjDvR8avG4p21i3TXJRo2hY5Qjd/N/svUmvbst5HvZUt7qv2d9uT38bkpcUSVOUTCGyDcVABgk8ysTT/In4N3jsQYAMAmSSWQJPEiBxgMRBRhIkJaZ6kRR5+3u63X7t6qrLoKreWlvKyOBAR2cXQPCce/Ze3+q+qnqf92kqizbKTnb7GmavKBQ5xIplqMnGWKmz4x0W5QAet8mXbI5DIh/ZXHWF/2cTF5Yg0TlZBYLWj89e4UUV4MdrvUBnw3l8tjvFohhwUobKyXmWhf6FQ1OOKKLZ6OAZBbM/X6xRRCX7n719hv2bOXj0LrUzh9WTUJH/p88+BWce/+Zn/zkA4F99///E/9/43cWnuBrn+NV4Fp7FgcGWsSKoHZzy5KYTzMFj1XM2YN4M0NEV/LDJ2BhXLoQaIFR9w6AwRvRC7jn0UXZv4oUlaQVezZCS66Y+9frY4ej5hljl3aiIuauExf5QZUTEcJJnLZ9scegL8s99fLTDk2hW/zd35zhE4tzTszVOqhbrIZqWGInr+B4rZQNzN56QlBZ9RDmcnmSYRjgznYc1nBy4qtmIvldk1hCY4/F7UWp4xkheFt7F8Fm7bQ0fqy2xlTALBx6rQC4s9Lqie+X5pAWR4g0R0JokjRE9A9PZw9aKbPyeo8vi/88mFeKdgNwzCtRmFmifRMj+TJOUyrcyhDBEWZqxmTgH5cAKBxH9s51l4LHSM3sFkeRvhkGMIMKOU5kRy2yASeU+Q76pmvbSwywTRM/BWgkeq10x8dRNDN/EyGUO0LF6zEY4mcmb4GXmgDFmiLjC3zMxATL6kshfCW5O8DEQP5fiKKMHcZIHKR9Y2f8AiErv5aKatGr8vEMZ+zfPVxscVy2OVPjmCOaxN2HSGa3Es1mAY18eVtgNJaXZAICOVLdtzD21CeqzjNiRbmGwOA0LyOmsxfdXb1HHhs7r/ojckG76Gfa6wL98/qcAgFszx3/3zT8HECzcXqzWMHHx+Wa9IsbmsC3zBCc8MPKcY9pKbFw4N9dKsAlUy8cso7HHBipmbxbCYiZHCmeXwmEeU03W6xn8QeaQ9cUIp8J5VPUIwR2FAPyT5ad4rQO0+rJb0XX+zulX6GyBX26DjOar22NaXBZHHRqlUctoSWcl5kVYVBdywF08xqhlkKhE2cpPvvsFjovw/J6Va/RO4b/59/8CAPDf/q//BngR/u35+R3+q+d/BAD4Xy7/Kf7yy6cQr6KbEAMxSV0JYKVhVOx3WQZ1HtOEqhHaigy5G54n5pHDxNXRWg7zqkEVe7vjylEyCi8svGVgNxNbwcl+xcTrml8csKgG6kMCIPh0v5mBlRY8TYSVRh1Dyvdt0PykhJyP5rf45hC1uszj4/Ow2TmtDvhqd4x17Mm3u5IYw5aHXqmMEHW7rvN7NvkOqKMBXDhiacMz0ny3bQnvGQXDWyMIJtZGYOgV9R5lrSnEnA0ciJOuf9yH1mlS0WxL6psCYfGcMoAJtiz/ViITJppKm6FJW3nYuYePq49oOVTUcsoDUGw9nEpyqtw3hc89YA0ZGa3xcwpPqU9SuvAuHFKDlMHFTQfrRD4nFnSw6fi85ZAJWjUAbxmxdb3MfV54FnTUSN9pkIuSFxmOVTsWe5nx/kx0omBxEY/HlAdGmwxTe9pQqm1grU9zV1MSjZPhc0V0TnIib3D4kDc4TgUmtudRHgQLzyeM93d4vJeLqliO4A1HWRo8XYadO2MeN/0MV13YoV/tZ0RXPz064KUNvUBjQ79siMQCIRxJaODD7p1SQSoLFa3gitKQ4UBXKPxic0Hnc72foYiT8GlzwG+sLvHvr34DAPCLl4+o2hWVxV+vn9LEIkoLmywGJx6hvGcQLaOKCNKRxR3zLGj14sToCmTPWs1h4jVfbeeYFwORlUYjoGN14Q0PNmOxQtSuRHkSFptF3eO0bvG6Dffr33Y/wU0besKCOwoDX+sGL9sj/OpVWFTr2Yjf+faXAICLco+1rvGHn38cjjnv8MFFWADOix2u+vCMhlYBC42PnoaK/25o8CYiCH/4b3+M+tpDfSdc2vzHN1jFpKGv//gZ/vWn/yU9Mz5wmjDM0oIvw0R4frzHettAsyjxKB31Go3jGHpFz4Z1nET5YqIz1Fc1hGbon0aTi7mmnosQDva6zn1smzV97FmHeVyUKpWs/KKpSFsGv2KExUwqey+8eyCP6bCoHzfh2Xx1OMZMhmP+y6c/xZ/uPwj36vWH2GwbIgtx5UiTDMPAZoZSfCByehMAkj05xzEOMmuXAWxvI4HPAc1xR33TaWSccyzoZ9+G65n/CkhYz90PPSUcuSHE3fkJ4cuVGWXiA6e+oVPI93TM/rJiCItJtolEtoJsHLzwkJts7VfdTkiLUz1nDCMHAgFQx4VSbkSIUouEKTYh8/ltAc88RNycNM2AXbw/vsxpOVZE2VWqrscQ6J2GXkzQkomumet8zbYO2tzp5iHJiGQP8EuG4TgcQwtkcpz0KNac4t6cyn1TMTCqRkMyTzZyIEtChPeXuUxUcipru7kGSYe4iclF8f7LOE+6clLavqPjoaf6MB7Gw3gYD+Nh/JrG+1mpyuB0VEiDu2iYsGsr6FGijgbkz442OLsIGMZMDvir2ycAAoNyNevw/CjAwXM5wJxFaM9KXHUzEt/v2pIYle22gohmCVe3C+zqMfcmEaBMAJgVI+7GmmQibsw0fLuPvPnUw5nAy7zLTj2hV+yBo+QqwHPA+MjAJ7aFXLOQzoPocBIlEefLPTqjMMaeoTYC3VXYmlZvJFzhMZ6G66lOszH+YCWcZ1jEgMXeKjyaBysW4zjOohXhVT/HZ1enqKOJwaPlDt+dX4bP8gKdVcSglcJhFWH5v9lfEGP40cUGR2WPZUzn2esSn/88PKfH3zi05xznv/MGAPDyizOsY2VdfLKHjhIUvpbBvi5V68JjNg/Hu9vMgu2eSDD3gEMXfs9aHu5/gkKXBk2s2oa+wHgX8zE7HvpjEeoT0lK4tnvdBDek1MviwOyTgKnZCYP4dj3DtV6QlMJLD8QqLTGm0xDCwMf7M+4LFPMRbaxwK2nQxEr1f37zW3i9XdLvrY4OdG1Oc3LIEqUNqUqxLcB5NuIAAB3fW60FGELIAxBkYuMEje27ItwvBHP8ZELRbSo0vyow/yayqLcW2w9TKempMlXzEYwBY5QEeeFz+Hhh4WeQ0PkAACAASURBVJ2CPkqMa9yzKcxJNLhvMMByog2z7B7Dt9h6WJX6hB7M556jXni4ixj6fVD0DphzDdZOTPM3isjFzAOQIWEIANrXFYVzTEsbr8KzVTGMoNgxkv30px6umMiAJrO3F7gnKbKcEVu4vGNQ+1iRFwzjIisD1I7RDRLJBCa1gtnEHGPMcDJz4R4kW8NpkICdh4rfHSI60DO4dLsZqKc6nluI5Qgp8nvsLYMb3/0l6d2/gv+IobsCFgX0KEmasJq3OK1b3ESf3be7Bc6qRKLh+BdP/xoAcDkuYLygfut5sccypsi0rsB5NcNf3ISJ3bmJ5ABRy4fgz+o9g0wTLXekySyFwae3Z9i+WdDvpQgtXocJrow9s8NNQ2kcdpatFL0If08QJMaciMEMg507IjHINqeheOEhIwx9GBUE91hvYy/2ZQ1EOUP/TEMtB3w39uQEdyQjUtyhM+re38/qsJBK5fDtJqTvfIozfOfiGtv4c89maywiq8F6DqM4fvuDrwEA351fQsddwIfNLbYmbIS+s7iCcQK/2gWS0a9eX2D1l+Ee3PwQKH90h8u7cB+ffnSNm5g6NFw2ULs4gQiESS32wX1jKQXIHVSYcFLP2QhaGPwoQs8v9kdVrXMi0chp4+NqBl9Z0nYK4WGvo2wmus2YRJZbaezjZ6vSYIj+u2AefK3InQfKQcTPtYOA8ciRaH0BGx2b1HLA0bzDoybc/6fNBj+9Cs5I631N0H5Tjfc0yUI5yHg8IUIvMJGdrOW0UXSa5wDw0qKZ9zidZT/h1KZwjsGM8p62NvWEMXK0Hxr46KJU/hVQ3YTP6i4YxkXu0Y63JS1YvnKQsV9phslChginxk0B24uglQQgxgCJJk2lb2yW1GgRXH0ilLl/kUk2YgzRcmQl2HgiGdVnhxywfjcLvuLJ+WppCdblBw7e8+w1LkDfQeYz4QhRCkO9zArwCUUvA4RKulgfrDKBoDUvo6Xo2CuI1yWKqLl1EuhPIrxc+6AXpXSeHAvphb+n4WUGkJM+9b04txKTfm7u0TIdNbbxcsbjyT1moOfELAP/qoZPlo6Fh5s7+O7dX5Ie4N+H8TAexsN4GA/j1zTe/W3Bf8TwvYBnAs1Fj++eBcjxL18+xZs3KxKef/z0mmQLAPBqCKzJmRjgLMc/O/oUAPBar/BpGyql0Unc9Q0ZFyxmPUHBWgvUkf1YKgNtOf1b25a4jRAM5w67b7JpuVyO+NHzIKnprYJ2Al+8DR65MIzckNjIid1oGg8UE1LHwLPaoArsxESVhwdMNIIvL1qqSHb7GnpfkDenP8nMYC5CFXy5D5SSR4sdjiLB4M1uAeM4RLyeH56/ocSd35i9Resy03WuBjxrAty5Uh1s3ApXXKMWGt+ZXdHf9zpsyb86HJO8YyYH3I4NpfMw5rH5vXAeJ6sDnAd8rOrfXB8Bl+EYcmRUvbilASscSTfAfTbGQHRAirCXswzyJpmWe9iFhayz409iL8vSUj6u0zzIByJRbNgU8LHidE0gzal4z8dtScb79lCBJ2eeykE8aRHDRCClJdIPEw5loyn1RY8SzWmoFpdNj3kxooos6r++e4xdhHi9ZygTG9czKJ4hWaXuu2mpSJQCgJFJDNGgIlXt6c+SO1zvQ1mltchJPcyDSwcZEZemGslNrDhv0W0nciSdGa3juckowdsak7x52JmHTc9p5KHcSyWCA5EF5YFTxaaXHubIgMcc0qrS6Nv0PobAcjI+4JmAY5vog5tmy+MRR8uALG22s/CMAWCjIA8M41nC83NlFtKVsn8wn/gVjysHH2VQbCtR7PJ3GWyS5EIwdpwrLINOrkm1yS5pWwW1Z+RZ7YoMc4tDML8w8e/cZDmcGINsiEeikuw9VafjESNHpeSglM6/usuG+ubYQs40oXJTMqW8VmTkbwt/z3yDuUh87N/9Ou+9XFT/8Q8+h5oV+PTuFH/yNx+G/8iAZtWR48y3FjfUu1Pc4uM6TPAvh2MMTuJ/+PKfAADeXh2BJ2o8grOOnvS5isiorEuNbsgRZYI77CK8Z29LbMY8qWOhqdfIucerfWDSjkbg7moBFgPPGffUzpB7PjGudiEEOb6gCb4CAMd9gI8Sq/fYQZ6GfqXWAvrLyNQdGPhsYgiusqXj+fEO7VDQ+W+3NUF7QjmUlcbTVVgsjef4ThPZuabB54ewISi4xbeaa4J1K67JjtGC4VbPSNO60yUuox7XekZo0ugkBiNJevPRoxuIOCMPVuLm0ODwdQzldtnGzi0yg1UJB3Nbgc0nNjIRPuU9CzB6XDzYhCXsKws519RfdFrQe5D+DkRmpWUEA0L4rMXzYdG1MX1GtJzs6Vzh4aLuUM1HzOqR7ve4zxuT+ihsInbRVQrCg0co8bRu8aja4efrwDTf92V2gCo1bXxm5YhNl7WuKWYNCL3RkCLD6O9V7JsOvSInLW847t4uqXfMeA5ZUMqiLAxpjQcjaQE/bCsc/z8K9XX4+913FXa/Ha5JSgd3GWfymHxEunI/WdQdA7MM6dvAGKNoM3lgBNt6gSBliefc2xwz51hYOJMloJPZaUh00SVonq/t9jL0o8WtuufgqVeOIvqEcDBdlAf5LAdKx9fLrNGchgNg4kjkOciFiLn70iEz9+Bn4Z56j6wEQJDAkKSuye0eWwX+QHrPYLLbkpMepmGIKhfoJaNNgF7kMHa/1MDI4eKiPXyoqS+r4nvC4kYRb0qoqaQpvfoiQtHJlS1tcpG/Q+/qeC8X1Z/+/CPwugpfxuiJ+8HjW5xUB3yyCIun8wxfticAwkT++zdBmzFXA1qjsIy6yfLxLW6iv68SYYc/RmsyazktqoOWVKmORqLXEnoTyTKG4eRsF/9NYHszw4vjsCh9srzC1oSf+39ffhC0XYlUU7p7Ozv7JJxT3YzoX86J1g5kPRofw4uddqpubuC24fhiI0ng7hngCoePvh30pqUw0FHv0RsZdJNjFrfXZZQKFBondUuJOXM14Is2LKSv2yUtgM+bNQYnUfIsz0hGE39zeAzjOe6GcF+vuhlFuA2jpAn+7hBs95rI3Oi0oip2tAKHXUX+p9DZE1cthnxfXjbA+Yiz03D/tRHYxE2MF4BaZw2hnWeNKSuCJjNN7Fw5+rPdFDlHcxbSOETcuXvHqDnFew7XCyRHRqbzhsEtDaooM+DcYXeoKI7t0ZM1kZi2bYX2KntPq/mAZZQOnVd7/Hx9cS9Gr0qxhEYQEjNagUIamOjO0PWKyFQAMK8HFDLnnyapmVQWLt5T7xm4sJSx6z0jiU5VaChpyeqz15I2nr4X6E8Z9h/GHveLDk3yIG6LrBlWHnZpwVPs2dRwhMdJOVVc+2xFCJYrTFsGElDqb8/nfbZBRFikkk5SthzNq/g8S6B96jIqNHm+zOZ+olc+9A3fRjIbBz1bz4MHcXof5Vxj0UxIXfE8LAe04lnmwn0mXfFwLvoial9rk98Xw8HX4bmU1xzchoUQCJu1ezGCLBs88GFC5JIALDCcZvJWqopd48CSlKoXYLWlTaQZBUTcnNhRhHzofeZ3kExM+RCNlx7bwCgFh7n4PP8BVKrv/hU8jIfxMB7Gw3gYf0/Ge1mpqsUA3gT2baqwnGf4/O4Uf/U6MHet5VjMEhuVEatxPdR4eb0idyEpHLH/pHCwjlFyiN4X2EfR/OLxjlxkrGPY3zbBAB6AeN6ij/2nri1RLgZ8ML8DAAxO4qevX4Q/fz0P/cBYZfqBQ0V7M32m8eMPQ+/1y/Ux7D7v5G3jabfORHBrIcPxkUNGBjEfQDR5/uEB/8XHvySI8LKf47rLFoyrqqPe8UyNWZrUlzAuhLUDwJebE8yKaDAuLB5VoSL81S6YPnxrHtjAcwz4agjIwNtuEQIChoLua58M10dBzMvFooUSlipozjy5DhnLIQuLMQrzfWWp8rOGw9yGC+WPBzw62eIuumENm4q27nLgED3L5uCT4S2D9xwiVj22l9TH40OGueRaBreeiR0m9QnrwIyk3rfjsOk5DSLksgKAZ2jOD/jtj74AEOwqv9iHe/Xm5uReP/FitcfTeZB7/XJ9jl1f0vtpHUcX0YV7jF7HsG+rDCsbRmHjR8cHDEZgswvPVyqLJlZYDMjV7ihD7m186ZR0uFgG1vFZvcc3uxW2bbieYVDgX4TjVSMLvc7jaDzhg/wMALCXMKvw32fnLQ5vZnRfeZ+rKKrmkjm78mDZuyK7B5Ueojbk9OQ8wxClVcwG1moRKyxXeByexedUeWA15tzaKjLDAfBJ/52PDHyXqy9X5SqTDwxeOqhluHdKWezuwjsnCpcDGGxwHUpwaDhQ+D8vfbC2TO/cKIDYCpJbjuomwqw9YGaZrRvyZSOErBl4z1BEuY3n+efUPlapPv9eqs75ToDF98OLAPfzeAw1ZAtHJ4DuiYWNz5MJT6HqdpVh7uJWBDRh4rDkCw9v/+537V0b7+Wi6qwArIBlwO4QvsB3t3P4UeQwb+kwpvgy4fD6LvRQnAuwVnJU2h8yHCmVwWrekVRmkA4i6tJ2fkEyCzUfUS0HDGly3RXQRWr8AE8utvhF7IN9/eoEImrWxMgyPR0BmkzygMfP7vDmEPqH69dL4MxkklHpwCPMXZQGRgty5Jkeb3xssDwPE+GT5RZf7E7xJiaZdF1BmwUgQHjJoei6nZG3sHMcm0NNm5W60KSTFFbgMxeg4I8Wt/ikucQ3Q/A4/qw9wxebsFBcr+fwllGKjx5kDndmWWLkPAOHRxmhyXVXQ0Y2i3UMepDkaOU8oygyzh0Q9bh1M2DXVRjWmSyTItc8B4YzS1AZi5ImAMGmkU8sKT0LvWwAViLfe+XCO5UkDDG+K5wIwoKY3IUqD56caARQHof76z3D+eJAhK+dKfH5y0COS8ddnsf2gRX402+eAQDMIFHNRmj/d7/mShnaMI1GwoyCZEWsttQ37QYFo2XeEE601VNHKVkbcOEg4ju9bLIzzl+9eYKxl7QQNV9Kghj1qYGYG8h4LqbLmxMvPfkYdym9J/UePYAiLxTT4ZqJI1GXNdm+sZDSEvQ83NYobkT+OZkdhJwAzCJiyIUD8yyEzSMuwJHsZLiHuo7QPo+bUgqX99RH9TIcx0XddzfKvCFr5b2wd688fJqHDoJ6u577EP+Wfq4X5BYFZE2pqYHxyGeZDvO0gS82oT9M3tIuELgAoH1hwxwVz0VuBIoIKTvpCU6uLvn98HGRg9lt5cEmbRFWWCA6L/mRQ+4Efa6Z5UUbLLSfHuDfh/EwHsbDeBgP42HQeC8r1aLSEBVHVWhKRlnHnXDKO2xWHZEztM3yADtyzE4OxOS1o0DRhKrseNHieAKL9l0BG3MSoTk5F8mVw3fOryn55vNvzuncLs63KIXB19sg4RE3inajXIcdaNp1ega408xaTVWUWvXQuxKijY4qz/Y4iv6vlTS42s+QohHttsDy+4Gd+2i+J6jwpmtwt29IcjA10K+PO3z7+Aaf3oWqsxuK7BEcq0p5Enagj8sdSV4GI/GPT4Khww+aV/hmPCETjVf7Ixwi3FtVOjgWxd20Kg1UrJycY2RqUQiL83qP1oRncVK3eLk5ovPg0pEXrZt4I1utMH8cKvJKGVy/WZKJBjcMNrKEzYkBPIOIJgMMIEP0IAFg2Ud2OWEPewZPJUqAcin704PgO7dTYIaR4N4VHvYo60aG62T+ALwGcBhjcMOhAr/OUFz5fI+LRbie27amBJjT0/DfUuA4kNnolTKUszr0Cq6ThKTUi54ScfZtCSEtsXVXTYe3txG10RwqmkSEXFSOJiIUoxF4+zq8w6wXEAeO6i5cZ/fY0f2q5iM9LyC2Jyam6v4qkuhaBs5BrmGO56qSDzxAh4lEJhywjRWWAsxJ+Cw1CxBuFxOFihtxL/HFyWyEQGkqAPhOBnP5NFt+0BFqMw4lzCJWlQODGBgZ78N7+FiJ+dICvQhVKUIFl6Q4rHDgycxj5EAFQiB84Sl3Ve2CM5o7xHlkZNnIggPjcTx3Ed6lRGhUG0Hh69wEstO4ii2kRwOqOH+pUUJvC6i1iL/HcoYqZ5TvKwZgOM1yGy99htiLYLyRzEm8B3ystNWtnDCXHdzcUjoPEJ6nn1Kp39HxXi6qR00HOXNolMbXN6v8D4ZBxmim01lLmrtSaRwfBalNMoR/7QMsWpcaL6J8BAB2Y0l9q+OjAz5ehQXri80JbjfheMumRyU0Lg+hR8kLCxvh2NvNDIOWaN+Gn63vOFRMfNCL2OdIjNbCEXq76ypKrNHrKmi+UirLao2rQzjepq1x1HRkL+dqgyeLAB2elAd8tQsQ7LarAqMzyUSORurBVYXGV9tj3F6HeyAKR7pMbzjAgPYQJsNfdhcUkP5bpy9Ri6iZbJ/idX9E5vi3h4ZgOcY8iiJrI2eFvgfxpv6zB/DF9gTbyG5t2zIbwVsWYLhJcs886jfnVQ4KePtmBXmdKYmu8JkpCQDSkVSBrxVUn/W9noPsAmE4fRbvOUkYzNLCNwYg+NrDRwkNQ5g07aR/xidh4LqNC/hBQF/XuI4sbXUl4RO0dzrgg5M7inTj8HRt+6hJTc+NMWAZZS210ni1Douj3hdghSPId9n0uIpOVFJaLJoB8xgM3xtJjl5FqWnxFdyjlAPuDjHp5q6mkAd1G97L9pMYELAYSFc7DArWCNIGs70AFpkR7imhhQVD+3T7Z1nD6nh4Ts1ReN+71/M8eS+yo4/elNCWEQ9BdJP+Z2Tuion0hBzKODCe2CyF6iUtdLwTmWnsGUyTDfXFQkPG63SWw3mZ2wIptBxBi066YBM3k0mitufE4mcmLHRepI2Ah440B9lNAsCVh68c1G101toygnj1kYXcCbpOfFnBRYcyacOCkBZLvfS0UJulpbaLLA3MKIgLEH44ufcjtEXS+z5xujJzBx/PgxmG6pW653bllIfr88+/q+MB/n0YD+NhPIyH8TB+TeO9rFTnaoRUDJ9fncBE2EnWBs3pgG+fBDaq5A4fLIK37aNyR/rHho/4tD0jY4i32wV20fz+2WyDSmgK1/7e0VvMRc4B/aMhGE0MWuLV/ojCzR+dbPEqEnb0usT6kCun/pGFjjCunblQRRHJgIHFau5wV4NvoouJZbAzixdnoYK+qHf41iJUzDUfsbclfr/9FoAAESZ4NrkWAUAhDYZBEkQlC0vm7abk2G8qiAgvBYgnQlkHAV86uHjMZx9c4/vHwdT+w/oa1zEM8mqc4223wG3U+AruIJI7DPPoR4U+hmiLZSYjSWGRKEW7vkQbM0MBBB9R2jGnHXE8r9mIWYQmt22FPplCKA9zpqnKZIbn6pN5sFZCblK+VvY79TwQixLkGJ5H/DflYVL4csq2HbIZQSKJmNoHFms0Z39ysSaXLcY8zh4HiKLVBS63c3SRTKVPDdQy/M4/evYKj+sdfrZ+BAB4s15mDWj0ni6jE1ZTDXQfN32VqyMRyFIJ6u8uG/LO/fjjGyxUjy834f3c7hpqM5wcHegdNo6j0xLtJlQ9fC/JOMApwFxoFNHQRIiMsMzqIRwzVtN+huxZfZBULZo6GMaTRttkpyt4AAzoXkbkZ2RE7kHLgQiXqgODEz7DmDprWJlh93JHvXLkfMW4B1qB4m18Nj5759rKkwuTK+KpVDlpPl2LcxxypuGq+K6OAireDylddsjqeSh1Eiu28NAyQ7zTSDduQB7WnmffXtEzFF8JmChfbj/WdDy5E+BjPn/mcI+s6JTH8Dg55+f/ziuTK/42hAiwMpOM0GZyGWtzeL3ssoGEmXmIIRnShHuXNKy2csHl7d33fng/F9X1UEOIEvq2wixOXN89u8Rp2aIWiW7PiW05FwMeqSBT+Hn3BM+rNX528xhA6AXpJrwZ67HGdqiop/qzzWPoyPa7a2ucx77Xeb3Hz64e0UJdKoMyfsEGV1LvBwjm2DHKNZhNT9M5WgEe01Ckyew/5gD1UYdVGeCw3ipKeelcgc92Z5hHGHDXlRhSkk6laFIfjQyB3bHPxoWjPtJ+WwfHnDRh9IKgPh/ZrU8/CIv4RbPDn988BQD8ydVzXMzCPXi7X+DmZk5G8NbwzIJ1HJx5LObhnM+aA6WrOM9wnXqvysAUWTjv9gWxC1lUBtjogqMKQxZ97dsZkCbMxgS3G5MYnJ5632AAbO7jiTbPMvrYBiecRLCUPrtK7TlcGSfFhQHT2SrPs2AyAARjiPn5gTYPb66OyIlJNSP1POflGBa8ePz5xQHPYkpSJQx+tn5EAeNSWmLq9m0BLjyFlHdDQWx3ZwWZmRezEXqQ8FGegdLh/PGG7veff/2c4LxqMdDxdl2JToTFoI8wbrJZdJWjCZIvdehBx3eaMU/fEes4zo531BrRnYSPi9Q0dDxtJlPvm3mQAYOtPLzJAdhimPQ/We69esYClDoxOxhX8VlEB6XUz+UDh1jnz3ITSz14UNKN6BjBv9wAw8yRzMVWDE5NwEDPMytWeFhqd4BCECA8mGcENXuW+7tMM4Cze8HqxEzXgNzlhbJ75GFjyD0bOGUf28bBNrgfsJEkenUMAEjzyOT3wAT9HJQLzObYamGaAak1ysKxi9v0eZ4crVzlIOJ7NB4FI4zED6mug2GHnQbNv6PjvVxUbz4/Bq8rLF9s8clpcFAquMVVP8f3lsFBSDGLJzHa3nmO/+P6BwCAp/UWpTR4sgg9rNPmQIvjuq8xUyO+dxSOMTiJuzFUYox5IgFdtgscNtnar5wPeXfO7sdrLZYdTa6tDX07cZfeUkZxTE4AMu7Ah3OL42qgaroA8Ko7ouuspcbrTbRZE9mCzjqOZZX0twoH5onIVSpNEzIAuL2i6gvKZdLITOP7L96QbvXPXz6laztZHvCryyAFGTYVwD0Riap6pD4v5xaMZfefVhe4jelB1+s56Ye1ERi6SLJB6GWm8wjaQk2aVmsEhttwTuIgiEDGmIe/K4NcCQjev9myFnKXiURegILfmWXwwkNFwo0+KPBYEfkXHZbxHNe3M6gNp96deTpApHQi5tFPpEqMMSJUmesa2+Pw56O6h1QWi+OwIZkVmlKArg6zez69AHJvmgOzWU+LrBAOQxcJPAdFbmLWcvhWkkyEK4frq/B+XPlQqSXfZ++DLWU6fpskRRPJUxosVutur4DShlB5AGhAPrV1OWLflzDXMSpvmEzyKutvfVocU5W2z5aRrnDBNSg5X5WeHInkgWWbSRcWUiIhcZBFH3MM+uw+2YzFjZbsAAgQiYn5vMESA+jZpkUuOQ9JNeEa3JYx/i0ulpUFS3aAXY4XDL16HypWhI1D+jPi4m6it7DoOSEnomeIYVlonzrYhZ1Iwzz488AnENzDjAJFfJ5Dp3IyjGXgBwFPm8i8ITDHBmqW3gEW37n4fDXPaVw9R3HHqb+rV+4e8Sz9d24AsZvIhVgki02An3d1PPRUH8bDeBgP42E8jF/TeC8rVXXegTceR3WPz9eB7TpohaOmw7fn0SVEWHw1BMnI5bDAeQzX1p7jm/4YP1l9BQBUzQLAL7tHeFbe4bEM0NlnwwV5227HinqXSlh8+OwaV7vQAxpHAR3dSmYnHc4Xe/LIdZ7h67vIUDYs9Gtiz8PuJUiLbxlsrDxWqwNOmg5F/Oy9Lim3tNMS1TR1xAg8PgrsX8UtJcUo7rCsBsxUqOiu2wZFDFl3jsMKD34etsbJoQYA/tmzz3EzzMAQqhnvOIrIFr3ZzKAjg5UVDrLSdEwAVLVay1GUBtsunDOLFR0QdsXb6O7jLQuGHXG4OrvBNBcHWMtJXmIOCuou5oAWngK/mQ5QWwoO8NITQ1N0DNyy7NRT+GxMPvdgpYOOrG02cFQvwn1cNj0uryMScF2AWVCgO+eeepKMeyhpskRIGpxehD7+zaEhKH7bl6jqkcw2AEBHBGFRjjiMBdapemSenocUPQppycmo3dRkrACV7xVnHosnW3JHMpbDR+mNMRzOCuqn+11B1RYvsv+rQzBM8XexbPMsMz/LaCiQzA4mRgetLzC+bTLCcGomchKAx/6k1zyYU3STOmDSf7PzbPjA+vsQbzpf5gCusxmErSaV8MmIuhnpOnUv6ZxME/qZiYXLx2xC7zkog5UQjhRGsFdkDCF6FsLNo5lFqvoAwHiWWeqeobgUmH2Tr03PohTpkQe3gLpOYQ0gZEC1oe8MpCpaEPwLBmKSA4CoLPokIQSyWYUO/Vw5MZTQpxFCVtkxwhxUeEaxNSQKB7wK71h5G3q+KZhAbniWKdWOYGe1C88otVZ8waCPHFz/7jdV38tFVd/U4G2FrzclFhdhsVw1Hc7qA15UYVL7VnGJKxMmxt9svqY0lROxxyt9jCbmI32gbnFpA+nlR83XWIkWn43BDWnwEhdFmGjrY02T1tfdMb7YnJA2T3cKi7PQ2/1gtcZ2yAvwusuEkvn5AWfzA1nx+WMGHVfV6QK4KjpsdYW3Mdnlbt9AxfDxZ0cbFNxijFFtg5VYqHAtB5O/aNYzzNWIb9YBNnaOE4x4ttoDq2xRtygHPJuFzcXr7gg3XUMLYtMM0Glhe9vQhIPKAp6RObvtJE2EaqZhTf68UlnadMAhE4mA+7KZ0mJ5HGCuYQwRZclkXI55MgVy/9k3cXFJC0zHicgS4EJPkJX/W301+aaAPom6z6cHWhzffHlKvSJmQ/9IRmJRNdmAKGGhraCA+hfzO/QxmefVekk9SOc4TpoOR0XoMb9tF2QD2XuGza4Gm8QN1gk2HxW2bUVOUkw4IBJxyma8p1m1jmGcBCSkz5bSoesUuf/wZW7eT6MRmQX464qCvc3M5/6hEfAMkDHIoCxzTFl/VQMqQ6Ze8ywnmev8oLgPkre4sNmJtIzH2MPU2/QiP0+wTC7jI8O4crSBYp7Rc4IMYew6kQQNn/Qrg/409XC5zlDwcGHJ0Um2DL7j4NFmkUcCFRDs//zcQMTrtCZDpox7kqcc/5nA6V/3kJtwr/Sqwst/sTGafwAAIABJREFUXtE9lnuGSPsIdoTRrUg3GVrVRy6Q6lLaks5OVLb22QUMAJQjPgQ8AJdvuSszfwMM0MnSceRBd5tsOV8XFOnGHOBK3CNvpVG/EvRcxiMP82ikTVki1bliAsG/o+MB/n0YD+NhPIyH8TB+TeO9rFT5wMAZQ/WkxUl0Gno62+Cfrj6DivzvP9x/Bx9VQV5jPcOPyoDHCHicij0dS8AT3FvA4g/aT6Dilvm71Rt8W4UQ9K2r8O82PwYA3PUNRiMoGuto0eHFMhjoH3QJwR0uIzTMWWZvBtmCIuehs/kBPzwJLNuvDsdkJjFYicvdHF2sAmf1iFW8TucZ1kONp7NwzgvVkwfsVlfkTnRaHXDVzdHEuLpeSwyxsq4WBq1WKCKEfFod8OnmjI6vuCOS0WZXw76JcO0yi/qF8KFqTVDghE1sYjZpqrD0KHM1CsBHglR10mMcJPxt3A4XDofoHmT3ClAOLhoJ8MZkv2MPMvnwCLFZPjrdyAPPvqgI5AkyHBA+R7oVHuzjA84iIenQF2hfh/svWp7h5BON2VEmC3VtSdWqjSznMTLEnedUqRojsqNUM2BeDNjpTBRL7NlBSxwtOpxHVnWrC0Iy2r6A0TKHpxeWSFFCOGJNt20J73JUW1kYenfMKFHNR8iIdIyjJBKdd5lYJW8UnPLkLsR0ZpHCMeBIw8Rj6kORYWjhgdJmskzhgl8sgoFAQjbYXaiG9HG8r6ULkCSi2YHkRKxhJlentvK5shYB5mfx7+qWw0Q/W9dK8L2kIHRmMoTMTahCE+TbPXZEpmKG3TOMCJmn4edCfGI6iAcGDheNSthyzHFpNyWe/H4kLd5pOMmx/1ZAma7/kSCZj4y5rkmiYrOaDGBZDsQcAwxypFvp4aOPMTPBlSk5fLFeZClYdKVKcX6icJhwjMgbGjYEjhMcrjGJcENwEUvv7i5/b03jMT6K6Miqh9fZ9CN9pzG++3Xee7mo4mIAGoZ5NVBv8XZo8Iv2Mf6zo58BAH5QvcQbE6DPFW/xtQm914YN2Lma4OCtq1GxMEl+OjyC9Ry3OsgDUrA5APxV+ww/WwcZzl1fYzQSMk5w20OFSxm+REpY3B4a6iFy4VDFhW1Wjrj8+TnYRYCGiuUW//fPvxc+gHvKBN0NJbQRmMXJ+6juaaK92s0xrwZ8sw992rN6Dxf7qHM1EEN5PdTojcQ2Mn71ILFYhoX5MBYQ3JHm8efXF3Sdq7qHdhy363AP3E4Bq2gTV2s6p0NbwlpO/cXVyQGbdWZKu1EARWYX5jR2jzIGcxstgKuSFmPPMuRYrnpw7jGrwucZy7Huc2iBTczIToKNDGoyIfv8Y4FhOnFY4s/CPfj2xQ1OqwPednHye31EM4k9GwkuXc47nM5a2iSpwtDmREqHqtCUd/ozfUHh12wvwU5iqLeWeL1bEKx7VPbUHlgWAwR31DPf9iW9O8n1JvUlhXSw8f44K8kcXxUGx4vkRxd0vHXsg4tmwKErcdjl/jYFsAtHi6M505CVCc8bAPhkEzQz8Wamnqe4xwhFL+5B+j5NrMID+9SQi0zSyFBWlaG2iBMO2GeNtW1CEhMA8LgQhQ8G5H7igITADgYALQSKNc/MYIeM47EAKfdRfmNnuX8LD+jzdK8M7EGBxz4qwyRgXLOw2Uo2i6MgCdPjPwBMHY63+UkBp/JizAwQ44LBh8xWBsI5pVQpp3IouRMeDIxkP/xkIL25t/6eftZzD0QHqGo5oC6zu9PuUMGl78zAUb2NG6gu35fwefnPtvQQA7sHN9NGqLb0Hem3ZdC6xsWURaicPSyq7+b47Q+/hpoVKLjBeRF2+B9VN/jd5lco4rZt60v0sXHyS/MYv+yCuH6ja3xU39Cx3o5L/FnUYQLAx8tbfNSEf3ee47//6vfCz20WGOJOXRUGw76kXfjp2Y6qlevtDGVhSKNpJzFql784DwLwuPD9zRePIdZRo3mqKTmnLjTOFgcyVnh1u6R0DDuGhayOfbyDLimJ5mKxx2ByLzf1OwGgng0U6Cy4w/msRRerWiUszaG3hwZdW8DFQG1ITyYRdaVpI1HXI9q2pMppu6vh0+9UNlg3xgmVjTz0YBESflI/zu5V0IfGCZkBJKGRMoSIJ0tDa3kmzigHPySyB4v/C//kedYg2rkDn2mw+D0/P9niN09fhXvAPP7k+hnevIkkMu7zt8kzkv0kW8A0lLSU4OI8w6EvSAKjFKeem1MOIvrtSuHQjwpdRChudjOcLA50zG5UFBivR4llXCBvL5dgypFNofe5V+osw3wezqPrFa7vFiji5q1UBm1f0PFUYaj3ZQcBHs/LdZKKUdYJ+DsJTjFrDmo10L33hoPt824lyTbYyPOiC4RnlBY2kyVS/NGIk6MDJUfttjWqX6ScQkDPPcwyr5Ziomf1E/OE5PULAONZtjCUW4Hh2FHVKdtcadvCY1y5HN/ncK+6S7pmfqVQtIyqx/HEUXKRFwzyINC8iijLnSLjg+3HDMNJ0qX60ANNxxceIlWENnv3AuGa06JtF9lGke9k6I0m8lcvyXhDKQtrOYY3YW5Qew713fwurW/n8EmnnXSrCOhLWtC5Cak0VJ3asOADWf+aqubxic595IMiZIp3PBt0ICzurvD5PXiHx7u/LXgYD+NhPIyH8TD+noz3slL9yfIrVHOJI9FiIUJFeGWW+Iv+BV7rUHlsTI1f7EJ1etAF2fw9KTcYnMRnbeghfrk9pirhk9MrfFDfYq3DLvB///r7OEQXH8499ayc5YBmqM/CZxvLyVhBSot6woDbdRLdLu3I484z9ijlyGh33ix6+r3bbQO9rkj07llm8cmFRiENmdB7z8idhzOPTZ+DpJ0VZAgOBEs5IJgPrIqO2J+CFxQo3rUF3CCo3yULS1V3IS1V0x7AfNbDJhnHRBoDAO6qgppki9pYjeq2yH0X6UIKSBTHeyfhomH/YVOBCU+ZrOO+yE5U3KOO5vrGCLgvZtQrHU4cfDIOX2icHe/wYex3f9jc4nUf4Nmf3z7C1asVwVeee7B5ljSlivz1XWDxzuO9k8LSvRp1YCgniz6lLMpYLWouKJe3kBalMjj0mUp5GZNi0r1M43jZ4vpltOCyLNrmTd67BLcvW+yi1MYMEqoyZI6/3TX0c8GUQ8Alu8dJiggbOcqr2A+WHmaRYVe1yiYXznG4gWfidOEh9/n3PEPuv3IQpCz2HPZxuG8/eP4aX94dY/9ZuLbq9m8ZDDQWIj43u1PU0wYDfLKMHDnGmYecR9vGUpOtolkG85B7rOFkDCEC5JuylqE5fbfEelLB2dCHtWR16CnH2PUCci8RHUzRnzK0TyJqoLLURJgQHpBY1GIAoShOhio15ZrqlcvhD2xy36K5PgUCGAaTWit7BqcAHuFlM3PQ65quCyr3nDGx6BQ9QxH8boJlpMq9XXKcQujfmpnPVb3msDGNi7c5HMAWgJnbewYTzDKyT3yXx3u5qL4ZlyhHhT/uPiLYdTPW+Gh+ix8tAiFJT/QXv3P6FYxL+k2LZ8UdfnUIcW2VNFARjr0bGvypfo6rSBjqRxWcgwCwwpIln74rwQxDexNe9K60NFk11QjJHXZx0TNGUJ8QpQMbOFycJE6/u4aNL+Fm16C9jvx65QPEGZ+ubPLCWDcDVnVPkDIAOt/RCHJv8o5Bqix9YAz4eBXkRr+xeAvtBb7cBT/YQUt00TfWdRKQDjL2Q1OiSfq5tAERwqEqR1pgVGngIqyoNyWKbdaHMgaKiHISwFE+ph856ou0QPKw6KZ/6zjG2Esq5iMt3M5wSgEqrwXUyNCfh2c4+2BLMWq11NBW0ObhZbfCV/Gar16uwMacRuMfjwSnGscJqvWOwTpGvWlnOUQk/TjLUc0H9LEf2HcFBWHDMuA49lRlPO/4zPSYQ8OL0mCcbEiurxYkSUHpoBpNWl0uLPVK911JelGhHM6O9qSLPT/Z0vux70p4xyHjhtA7DnsXzrd6LbLOUwXSDIsEsLLS1Dt2hoG3IhO+SgsTF0DWiTCZpw3PZGGzTwbMFuGe/sUvn0NdK2oz9OcOWEVIszJwlsPE9J+0mACRyBO/P/NHezDmsXsdfZ83NXg52SSY7FAmDxlmHc8t+EwDKYZuZLTg8gkhy9QervJwKYJu4GCvo7TsNvxQ+3gie4kfLfc8Wx1ahmKT4VQAsHHN8zzYKupV7JHvRXaEskHKAoDCyUkCNDDq5Y4nDmxktPnxpSO/bGYY1JUiWJZZ0OLOTdbB2irKe2Ib3hWASSk48dzSM5D77MNsak96bfjQZ07SJLUN9ot2ePfB03f/Ch7Gw3gYD+NhPIy/J+O9rFT/6OpDyLbEad3ie4vg03tRbPG98jX6aAVyZ2b4T46/AAC0tiBDd8Us/qeXv0MkIG0FFtHpxmoFQEFGRnG7XVA+ptM8e5/WNpByInTTLAYyuL9Zz7Ee5+Cx0uPMo1qGfztZHFAISyYPxnF8fhVcn9xNSTtCN7dgZXa7SbAzEIT+665Cm6j9zJOLjFSW2LhHiw431wsiD/GjgTyOAeD3334rh1WbXF2wwkIWlkhY1nKYaHIquMPxPGxvnWcYjaBjes9IcgF336jBlZ6qczEzmcloGNRipBxZM0j6XO+B5qwjt6jdUGK9Dc/M9RLVZayilA/m3hG6Zczj7TZUMmfzA37z5CVMPJlv2hXexGtmmsMXDvw05u+u9jAR9eh1riRVbQILNFZ+o2ME687nXWD+piCBXlCqDhaarsU5jkXdYx2h8ykkHxjUyNWodHCxW8Ciz6uMELhznMhmznJi/56swnuVJFKjFWjJh9mjrHQ26dgpVAnyLT2RcvTKQh0NdM5dW8JGOJL1IpgCTAhIqZXhpQ+JOAmNWWbDB685Dl+F+y17Blv6ENoNQJ70uXLvZZBExcMHV6xkrJAR3cOugvymhIhVm608EY5c44LxR3QT8uK+HIa/qhADpzLzFwA4MJxE1yflwXuO+pvMWE7DlYCeOzomHxkRkMAidAxAbaO/b0TwuQFdgJ4HuFZu4vdpYj7vlM+B5YUDsww2yZsmlanYi+ALnNCriaRG7jiKHZvkw2a2sa0Bk7pQOpxzgq/NqckuZId0bqBz7B/Fyn010vvtx0AcVNt43QcG0QHsPq/vnRzv5aL6exefoZwrXBRbDBF/WPAe/+Pl72IbtYAFN/hkESQxO1NhH92G/sOrF6SfBMKC1U+caPpRoY/uP6Jw5F7ChMeHT0JfVnKHUhiClIGQNgJEKFUqqAiFPl7uICIVMGkQkzxGMB/6ZIhMv6jJrGYjjmYdzprA6uPMkyH9ri8x6qw1tEZmKYjl5LJzezeDtwxiEb5VVT0SDPrvvvoB1rfzPDEanvuchYOQmhinQGYrC+6xiWkqWgtw7kniYUZJUgq5zUkzAOBXGnUM0GbMI2W/ce5x2FU5LJl7grLEXON41tG92rUVwb98J8hajrlobxjvx+5qDhWdf5xn+On1C9IFH7qCLADLj1oMWlIf8jAUNMkrEYK9gbBAWccxxDls1gxZszqqoLO1E7gy9e0so3t4MmsxWkHPjHNPulHvGYTIrN5xkJBlgpcZuPAQEVqd1T2lJvVGEDQvhYV1HHfx2fzt0XcFbNxcyY0kdrSTgIt9TZQ2WOHFZ8gHTppPPrCg602w9DR9prLAyEM/EwDbiLxo8bywmaUNGtZ4f3QvsxuPDT3OxAJHkTdeMJzYufJKgnnA1o6OTxGBmt+LVTM1MEYpiNgGmJUM+2d5c+mKvNCLPYc8TIz967zQOQVAeKhNuj8Zak7RdUCAnKc6aSczy9bLcA9TkIBXPt//RW6JMODeJkP0HDzQJrK8pU2JRBaIGzJ5YHAC4LkdjZjUGByy4kZ3TG5WiXV/ECjustzGNJ6gYlfkDY7bK3KfEj1Hsc73IElqpoEM7+p4gH8fxsN4GA/jYTyMX9N4LyvVv9w+hbIFXm5+QCzNfVdi0WTDciUsLqN37uV6TpUj5x6zZsim6MyTWPrQlbBG4Pg0EF2M4zhELO7k+EA/x+Gx7msyWgCAmzYQZ6xnaKqR4MJX6yWdowdQSotH8wBprvuaTAxQOYL5VhFiTQYBtdREfOpjGHK6Hmt58N1F8GZNWlq/KcAAuLjL3+0l/sN1OEfRGIjCUuUE6ahqlaWB97hXVQ0TreiQRPosOColVxl4QGwyJIsS8CehKpwterpXoxF0P3bbOpgbxMqsmQ1kfMCYx81uRqiC7UUmZAD3wsa9yDCgWGicHoUKv5QGg5HY7WPwtrDk03sYCxw3HXaxinWOE4QvuSNTEc48Bpux7HRMIFTwXLgMWRtO29x6MeAkPsfRCuy6klCJ86MtoRzdqNAPiv5NFQY6EoS8Z6ib6b3Lhg9NM5CGdjdpBQCBHJf+PA4KduSovk4koEmQt8iOPqyVUdwf76v04LFqtU10orIJnpyWYuxedWLn+V2amrYH6yt2L8OTsj5lCOjOnvSe3iV43AsUd5XLpYR0VFmLPSevZzrnxMjtGGyFezpNCgrXDCxWesWWAQ7k0mQWORJRbgXqtzyb+atsxO9ldh5iPhCB0r8l3SwQGLNB4xs9sZ8fsJoFZv1oJLHDu00VnJ72uWYighAH+EzTXOQPEuVVSgII15j8hE3jyWfYVj7nqUoHdpCkBeYDoCPUrI9wzzjDT/5cXgqCe7kO15nIYGII1/YPQaf6Xi6q1+0MAiU+Pr7Bq31oXpwv99h0FQ6RpblctCRRqSo99VGHFI4mnW5UNFGNrULRZBhGa4kffxjYxM+bNd5EOcZ2rHBStzQx3nYNnM+/044SZXQCOp516HSeJKWw5J7zeLZFexwWwc2mwYcx4WQ/Ftj3JS1mbIInecfAuKfF040iGw5YnlNXJtICINibeZ/F5Ix7koL4TpBg33kGZwRBnJx7gib1ICGjvMOOImcwIizGeB6++Oamgp8ZzCLkOwwSPEKVWgu0UQLACwtRGzIt2L+e0wTqCwc3M3QeqGhOB7OCYDk7D8b+tJhNDO+dZwF2jbP149M9ZFyNT+oWd31N0L/37N59TlC5dhyN0pBFuJbrtiGY2DgOZ3kW23NAraJpRNPT/DLo4L4lI6Q8GEnvy6glvGPEKDZa0oS5Wh3QFJoCzMdBkkXirBzpv2stUBSG3onUpwWi2cNaUj90OHEUSO1Lm20mi5BikuA9PmYnI2ailWDqJ/aMFizbhH55spNkIgSOAwGSZdHRJy3eaZFJfT0AULvghCTjfWRGEPPd1h42uROlBbbJ5v0kO5Fhc0xwqs89S6cAMYJ6oE4Brk4LP6BSeL0PchdzknwEGYrYfy42jCQxQIS10zV1GQY1dThOcnoCy2xeOw+bFjZh8ydeQ1nmhZIpBwiP+YuwAVzfzMEOSfMSXM7kLkO+08ltPMqh4l4A42l8iDNDBv1iEywK08Kvj3NmqjgIsioEor1jPF0xTGQ4DlC7zC522RDrnR/v5aK6LHvIyuPV/ogs4vqugDOMqr1lNeCsDhVnJRuaJAtuYTzH9T5UbcYIcsRZHLdYVAP1rX74+DWe1qGZ0dkCbezLnlUH7HVJQd6LcsDbaGPX7wvI0pLf7/eP32KIIrBPN2fY9yX1KDmbYx3tAI+OWrxahy/Y0Bfw/n4iSurPlVX48h02+S2mPp7wJG3wyoM1JlvGTfpPAIJXZzoEQ5ZBDAKytPcq+1QpKWnJmxfMQ5UWMv6cEA6HbUXHKxpN9xWeoUvWe1pQZcq4h9kruMtkoZc1iXySBgKEzQPfSbo2SiAZohfq8RjvncIyEs9uDw0OXYGqCf/WaUkbIckdRiOop8qYJ9KV5I6QhkZpKG7pWU/77+OgQoxWvMVq1WO1CJWH4A6LuBD3WuKwr/HiPOhlX6+XtIBz7sCUxzjk4z5/FH5uWfZ4tV3SfeTC0QbwdjujTQ0XDkOfN4fwYdOT/uyODPTJRHqS3pfbiXyJ540JEGQi1K+0MQ4s9dZKDzOPG7LC3+uFe+vBitQrzbIw3oZeK7kjubxoyzYsltEdFF7mato2ecJnjgWHo1TS2hxAz2xELZJDYp+dkcSI+NmgnyXJC8sELK8Ac2Tpu6DWgghIToVF6v49ytVpIgHZOjgoJQmMXjnI0/BOSMdhDopcw/q2yAtpBXz7PHiVO8/wervE+k1A2vhBUIXMfOyxJmWL9NTD9jLeo3gBdjFxqLoq6HcAoH9scrKQ4RC34f0rNkGrO11I02ebKkiC0nlMPZqBUOlP/vrOjoee6sN4GA/jYTyMh/FrGu9lpdppBRnTXhI8eLQMPc8k2n+7WVB1erNvaEf40ektZmKArsP269Y1VDX0vUJ7qNBE39efXT7Cl2Uw4p/KR96KBQ59QUHBb1qZTdu5x3zWUyD1SrXYxm1sozR6LUm68ep2Sf24ti+gk/n1VsFLj9Qhnro5SWmxv2kyrFta+rdhVxLEwzzgR0Vm21wZMnSwlsMrF4weALAqV5zeM0hl6X5NmyQBGo4VSRtYpCpKldo2eyHz4wFmFJSAco8RyD059ZhtAWYY3DJTCHlyclIWYzTtBoJbThLDe+nhE74pPJoXW3x8EqDz0/KAV4fQEmiHAlWlsYi9x9FItLHSTAkv6TBSWjK8Z8zf260OVlJQ+JQ5zpi/l74Dn72KC2Gpwl+UIxbliG10uyqVwWbT0O31lkNEWP38eEf93JebI/SjorQbaySG1Gu07H7vsrSo5rFa7xR5vrLK4u/0ua7LdLvhYlgC4x6+k1Dr5NjhCer0PHgBk5nHyHIFJwAvGMHeZWnC+4kg/0hVlCt8gA9jdW1mnhjbpkHwuiXmrs8VUPR2BmKaiuNAel2GiedzZJ6m6pFbRtWol8H/N52zFyDTAnBQJWyPLFjHUb9O3ABQZW3m+V6kkY7hRT4G08C4chBPQj9dsdBWoufEPewmemJ7Bh4NQn736ZeQsZT849cfYn25AI+Qr0uZwQCKKwHR5UzWAFmHP45LF5CDxBreZ3/e5JgFAMV5i8IzjOvwgOuvFETqN8d7RkYUNdA/is5acwNxp+jnvMhVLNd46Km+y+PN1RH4vkLZaJxGY/JaabzZLjDEha6cjXhzF942vS0JFv3MnWJWD5NFAwS92TYYWe/GaAnGPfXPvM8Tqt8U4B3LNnwqEjQQEiUW1UBSkP/tsx8SFFwXGoW0BCHaSXycHrM0g/Ew+aRFzytH8gnnONR8vAfP7u/i+baCUkA8C70ZRhChnwRXa/RtQbZt3nC4ZN59kDBM0WKhVj106qmOkuQv6rjHybKlkHU7CuqlobTw23x8udBkvF8UhmBKW8eg59TXcyz3Mu8KoHBEZpGHLJcAY+BPAqT2X//W/4Ufli/xB4dPAACftucUFNANChdHe1qktl2FLkXLtRKssJgfheM8WuzxPAa1//zuAudRzjRaAW0VEUpszekc910JYxktYLpV1NN9vlijj+chuANnHqs6HOOb2xVNPlx6iGrEk+PgIec8o0SctPCnd9DuZXZbmgxWG8jSoN+lVRCoT8JnDb2CNxwyvj/WCPhV5g0kopm7K1DeZEKWaXyQKiEsoswxIu1M49d8ZcGkg76NNp2vJJYxWdELoDv39PxcJOoAoWc7jRWzJTIpaGKG7+RE1lJGAtXknaAQbRZ+LzkleZEhZKcCnOkSLNrkY6b7BQBiLVHeTuRC/x97b7JsWXJdiS1vTnvb10abEZmJBEAgAXZimUQjVaYqSWaaaapByUz6CP2O9AcyTWQy0Sgrk0QWe4IgCBIJJBCZERnNa29/T+OdBtvP9vtUUwwYjOeDtIh88e49rbvvtVejA3/+EKo+kLdUm+By1QE+o99rH1igcvDRGUzoJPcSrYLoBV/X/HiP330yOMBJ/PSaUrCWr6ckR5pZPq58Eb+3j2SzuCfrTnyCeVUArGSCkzRJvoPTDmV8BqxRCC9r1Ms4N1gkbaughXT4ve7ccRB89XWeoOAqINukd1L1EcZPHav3dtzDv/fjftyP+3E/7sevaXyQlarUHjLzCAG43dKWzVoJe1ORLABAd1kDQ2CxAEQkshS5RW818pgl6pxkQlALkjQchjhzdeqRsglVgDuxCBEKOSTOFIXBqik5JLooLHKdGAJ1ZrBX0Vzi4P8LAcAkFuKdbMLMp1DrXkNrzzmj+y5HVkUIb9QzoUmIgP3FiKGzAb4CAGczBCdYfO83GXwkAQ0GFGw+jlSheyPZ9Hw2btEZzRmqMvMQg9B/mQMyEPQIQEiPPiII/W2ZsjdFALpUHalpD7dKQeRyp5hb1T82LJs5Ot/g3zz5BQBgZWv8if0OLntCJX62PMf1ik62LAwak+Ey/n1UdWhEIudkpWWI9mZXY9kQGWlcdBhrwsHe9lNkymFaELzZWc1GHH2v7+RZVrOWq9ifvHvEEXHjvEcmHb5ZESzdrgr2c9aZg1IeFys6fu/FnXAC32ry0wVIdjXp+d/pA1mVvaoYjZETw2iA7xQFE8R/m2WOUIr42X4dGaGthJn6VCEWPgU6ZDGe78CMn5nfETYeWLhmHIj0BIJ87dzxv0Pm+X5nC8XwMvUqUtVjZp7Zs0HiDrtV9CmEPuhkLqEaAVuHg9DvcAc2Dhqw0/huOMFG8NImZrJu6BjseKhw0/mqRqC41YkwdFDd2TqgPzt4l3caiA5foZdJHuQF3JHB/IxK+ePRnnORF/sK26vI1Co8YAXLoIQBw7NBkBOSPR1Mg8FG/HJLrGyuJh/13E7xRsL8MhKfeiBrBOeqyh7oDxygzCQxiOuvNUz0BW6emQPnJfI7zjZ82lB9APr3H//9IBdV7yRg5R0j8tAo0l9FWn6QATLCZj73DMtp5WCcwvMZMSwxA14syCown+yhlWeT+LbNICN0WI0MqpOYoNLl2F6MqdcBkAtR7IlNqo5kOlH7hsIyRPp4vELrMraaM8uSpAwAwa0H0N5hCPRHj255Ub10Y5S5YU1b32UMrdZlck0EKDxpAAAgAElEQVRaX46hN4q1aYdSIbvNIOsUSC28SCb3XkCXlmHpEATMQOd3AqMj6hU5L7C8GaOM7kUhAN069uqsQCg9ZNxomE3qjUKA75HaSUCAk0zYRQc0ydu5xdFDgkWVDDiJkOxpucPbdsb/dmlq/GxJQevLXYU6bjg22wph1OJ0Sr+3aQv+DlVbcnSKKURKecwixPvReIHW0bX5zuwS3xu9xZd7Sjz6y8tnyVzf0vHnMzp+KQPDy9PJnrNDb20F4xQ2tzRp6jpdX2sUrFGoo9wm1w5bGRnt6wIwCXalcGr682zasP7Wm5hbGp+l2WyferZOQBSOgxXCga5UNBKqGaB4n5i88fd4MZuQJG2YoAGSZQEAOoI0hwVGt4It7g7hXtEo5G+ztFBJglQBSpBRu5QWI6zgiZzTagDA0+LmWW7j+Ph9EeA1SbHoRJOzV1BRc8oOToJTdrJtYgnbES0mw3HJXhxoW2nBZSN+l3qq/YlLetxe0oI6yFcawfCsqBxOj7csp3rxzRmx8AHiZAzZpxuF4lomtrIHp8iYKeXOyu2wGRIsUwoC6B4bZs6HVsFHiV1xozgs3efJTB+gRTq6u0LviFHt4jNn6wAb5xDRSt6MFAsBnWJcAQGYWsCp/7g98b6ND3JRDTuN4CM5aHhpTWQqMPU8ael8TjFRANBZhWZf4EtQ9FvbZghx8R2PWoQgcBZTTpaqSmki2rF8Z+h1DRNQNW25KjkfbfHWT3E83/H3DSJ9HyQutmP0V7G66wX3MvXIsBFE2JFF2ehhMjEYIt208lAysMxCaceRblIAi+iPK7cKduIgp7GX5iRcfMGgAnyj2VrOzw331rLcQimPfZTHiNsM0foX9UdpW7q4mkCoZFPobJLKeIAio4ZQawEmrAiT7ovwVD0M8W5Hkz0ur0hWJI9bPJltmRzmgsA4o/Pc24z9fF81R1j3Jc//zkk0g0HCqMXZeMf97UHDPFw37wVmMdbufLTFg5LOT0uHB8UmXtOAn+8f4osVLdqrbYV+QZ8jW4kwtuy93G9SFexGyUwCADa7kqUsSjvu45dVj0nVYpxH5MFkaLcxhWWvgIlNemInYaMxxKI9hB4E5NSgHiUryCqGWreSUBbW4EogXEekZC9TMLgKkAe+zICEjIk1UnooFdAPqTWdAuJiwL3VQc0jUrSZ7AWK6NE8GC4MvTpfp1BusdWQ5sAoQQS48YEEKCIeYa9oYRtO5QDJcJUnw4RVIsexHWB1sOgB0Jc5a1jNOFDiDIBQUoWYLZMGdNggIMTKOD7ShwHj2XGLsznNGc5LrHYV2ljRhanFxw+JRDfLaY748T89p0u+UXBH9H7Ktb5DsmqeOo4wlBvNlbvPAmlUh8sTkg7Wzi3xKN7RPJW3AuVNfNcseQ8DgxUheONiRz7pkwfDjIHkJQEde6pBJuJWPwNsJbhS1S1d80OJzfs67nuq9+N+3I/7cT/ux69pfJCVKhs3W3HXUUUG3jlny2QpBh044SS2Kzm1w6wKZBG+Ox9v8W4zwc1ArQNQxCph1+XcpzKdhqgs8tjLrIoe85Iqnlr3OK72eLWgXomUAXmkyr9az7B4ecS7Qj83yCJsLGTgzacrPEZHDR7N1nwco1jJfDq/QS4t2hkdf+syvN1QdbfvMvgrqqLCyEFNDFsYwosENXsAVrKUReU+yXKaDGGv2URbPmzx+5/8CgBw243w0189oc+QAVmVendCkHEEAKCX5KYzhDOXHno9QPGBt4J27CGPO4xjlV9lBrN5lCJox9cUAEqV4GspEkKx6Gq8W08YdpXKIY/SISUCdn2O6yVt0V0vGcJUyuPjk1v8m7Mv6LiCxKuW5FO/OX6FPFrH/OXmU7xrJikEQR30Fk86SJAxBQAIHbi/3RuN9RDgbhXMNudK3nQaRfx3p+MdOqfwdhXv4bpM2bxPtqhygyayxZt9Dt8OOKCEjGHdIicIeLeJsp/ScJKONxJZZRj+9VclQ5r22HK1KLMovWEzAsd90wAJh2THCBkYGlZbckMaqkzhwYYM0pKVHRAru5FP8GzmUcRqGpMOXV0iu6ZrbOvAVRpkAAYJigwIKvDn671gEwphyUxicFTyZZKhwAuIjSZDCwAQQPMkvneV5WfCRaeioSrU6fEjFyYNtOfxuGaGr0GwCottmjMeztfAnN7dWd5CxvLtzXaGizdzfv8BILuOEpU+5Z2aBy3gBbd1fCXgogxPOEHX8cD8hI0aNgrCaZbFZLsEy5vJga3ikJ073IqlTE5mOlDlO4AGyfwJ7shyi0peaegGbCihGpJByfue6vs9RC9xoIxh6y4g9UXoHyJBq50ix5dBfpAFnEfoRkmP50cLthF8t5xgt41Qnwz8kJc1EYIGqYaSgeUTv9iNsFyNmIR0MtvhckuT+mpZI5QOMpI3RlXPGsQQBHu+ltMOj2ZrjKIG9NnoFicZQcG/3J9ib3OGRa2X3KPZX414QkYWyI5wWER0kuUo6eGDYJ/dAQoFwIHq559RIs8fPPgVXjUU7P2PXz1OaTYiwBWSj8Os8tSDk9TbGnqBwsg0+TkBFyUdxbRDVRhMCjrPXDo8mZGDlRYete7Zxar3GvN8iJ2T+KdL6nHub2tABuhq0PF6hux9EFjtqgRpmqQH/YOPXqBSPRbRxuer/Ql+f06bBxMU/nH/GADwcncE6yW7I2EM3kjYXkFI8EKa5ZYdmra7Ev1g0RcE8kl/Bx62cdHbxHScIf6umrTcqtDKYdOUd4hzIk6metzy5sF7gR4Z2z16L7C7rfgaeyfhYzC58ICPJBcBsAwqeIHQKojhWjnNa5KNnsBy2AAK6scCZFPns7SQHuoU+2mAOTpYHA8cv0iuFiHYJgNUgHkQj2uvDp4zsExMZB6h0cgv6T23dSIjAYGi7OL1UTvJUC1AvdPh73ZueYMpr3LeSCjQQj0spkGQ7R9A52iP0iYkyx3Ojgj7vFmP0MT5pBz3aK1GFp/Bq2aEy2hF6KLPtY/n5iuPMB9iIAVCbJfImxwhDwjcp5bJ0/ishxCAXEa51l7ckcPIPkl9ggA62tvDF8l5SbZAvkzXxtYJsndDTOOAgTqRNpFGovomtXT6aUCxiNfOkLfxYeTj+zru4d/7cT/ux/24H/fj1zQ+yEpVdBJCUn7isEuTlpxXhqa9sGnHJg5Ct5F7yNzBxyb9fL7nvM0A8mkdWJW201y5+QO4TWuH7W3NfqdybNCNCcLcbkvkhcXnD98CAI7yBj+5eUS/lzvIyqDiSsTDxqqn7TOMJvQZnxzf4qzY4llFBIcbM8KPVwS72qCQy4TJlMpgvYnmDy56owJQhbvjHVzlBl2EAHf7Am6vUcTw9KowbFCxL3JMyg6/f/4CAPD3iyf48s0ZXZ9WMUtET8iD2Cxj9SWRqgYHhIm94zY0wEt+YllSUGT2jon91uRwEc5f7SoKYI8V3OPjNXS82Zf7CVWoAOABOUCFIERBxc/sjKa80wiBq2mP33v2kn4tCPx8fY7fPXoFAPgfHvwJ/iwaSPzV4jm2fWQFS49MOrjIxLndJM/dvKREn+F+dkYzm9h7yaiB32m4qxxiMDofW743Sgb0VnJ+6yjvsevpmm4aQkmGayBk4O92RgGxUs20g9aJ4dutSpYfWaPhNllSpRwfqPMFoKIcyA/GHUPBWXgm98icoOAB5lYLze5WrgQQgO5RJNzsU6niS895vsGTqQnnulaGCYLoJaADy7VQODJiAYi9HCUj2GkoS1AyEBm4gwQoD3TOA3Fp4phNmy+I4OQeRSJXiM8yyFt4YBCrhoK2XXyk+1mAexxJgNpjXPU4GRFaclpt8ZM3hGb0tyWzYttWYRHA98K1OlXd2gN7QjfoogDiIrYtnICLagI/IfP74V22x4Z9r9Ep4CAgvbgVSWKkgP1DD5xHRv5NwfdJdeB3V1iq2gf2ss8PiGYFyZtEbAdhmbMfd7ZIHsSuCtA7gfImPuOKDCDwL8D84YNcVKURkEpA9kAeXVm8pp4Bm2MrsGZVAJhOY8+z6FFqy4bpe5NxGszVYgLvJUOyQntUURcoRIL9lIoL84EEZh97eoPO9eWaINNVWbHMZVR3HHsGALs29Wln4wbnI1psPh1fo5AWFz3BRr9cn2Jr6E2f5ElDCQBf3J7DDtrO3GN8SjBxri2sU+jjud0uRlCDC5MIkCODeWS+zoqWY+ZQ9Hg8XuGLDcGrL66OERZRL6fAsVW21UQtPbBnRJwUQi/ZLhGg3xGDFaFJCTjLbQVnFUPlWnu+9k+Pl/BB4OtL6nNersccwbbrcoYis4Ku9wCFZlEyBVB/2HcK+RFtVr738IIh9Ur1+NenX+J3668AAD9un+GrhqRVxqk7AfS3uxrLS2LklvOW+89tk8P3Cn3sz+vcwg665k6xvlRaQezUqF2cTBtuHWTKYTTuue++7guG86X06HuNIrKjewGYuFGpRj1D5auuxL7PEisd4Pvi1hlJnOK7oHRIjPYibc6aXU68hCEgobSJeW0lgpFQy8j+LQMvAHqj6H28jeetkpH7+MGWF5e+jYv7kKjkZYLlgwDcXUkVj4DkSOSJITzAuP2JS6zyXiDkASKeZ+gkB2/3Jw5ybBC2cRWRSX6EIAY5O4KUkeEbNeefbNjGclY3ME7xhmexP0MRN1P1456fD/i4qRmGTDIltdJ30m1kK9iJTdQWWMfjMxJhZKHiubitRojzml4r6D0t/vQhQB83Gd0DC0hAXg5RgODfM3VAiPezuNIQWbIidGXgPjs2EvbM8KYDuYcYeCviIDZQkqOSLeP1DwFdJeAOAuzf13EP/96P+3E/7sf9uB+/pvFBVqrD7i/bSYZ7fE6QxOCreej+AgGGdNs+g3OCzRmkDLADQ7aXRH6KxAhVJUgNSMHgWpK2chBIj0ctw6eFcni7mGIbYcD1ruTvUtqhyC1M/LsQgT1ff/P4NXzEYAppUcsel452v71XzACtM4O3+yleXxMDwV6XwIiOq5x0XD33VqPvNX/3bLZnv+PeKtSFQZXRMa+6kv/8aLTG3uZ4uaTPN00GGd2opApcFQsnEEaW2ctSeXSRHCN6QRDSUAxIAW9StNntJVXg+bjHeNSmQG2r8fyYTDnmeYMfvXnKkKOuk1+zlh7jCJUbq6C1Z4csIQL6JhGCVOVSdq7N8Pn4NQDg28U7mKDxk/YpAOCPL76Hq92Ij2Oo5ozR6HY5snHCtcyBGb/QngMCvJcUVA7SCbM7UeWhpun32zaDzyX/Tq88dKxc3UGFvN8XyDLHaEZZGJzNCM0IQCLArWpqUwwwqUxEIkTEZqh6APB1dC6hMlIFFLMWOqIGxmjW3yrtyX0puhcFHSD3iUgkXHIf8hmYOb69HvFxyF4AY89QfLiRCLHaFaVDMJKrI+ESpHkYQxZE9CQeUA8vklxTBQgvoN8Oz2cK9pYjA7/NoCKR0c1Sa0JuNZOb/NQin3b4zz+i1seDYo2r6NT152+ecz4zQIjX7To+L7dl0stbevYHRAdOQEftrD22kJXloAlXgXNSxW3OkD2UoOsbHdsyl7JgVUtQ7SBQ6E8cw8Ryr5AvJfrZYIDv7oQuVC8HvJegc3PgRjXcP3tioStLSBQAWDDiorqD++IFXAH2Rg5SoDsK8O09+/e9HAP8208D9wKGAGO2H+sE7CDqDmQeAABOaggj+UH0h+y2PMKPAxOz3vOE3HVZkuEoBe8kfucZ9eNKZZDFFeQv3z5D8AJtdDwKnWLHnY9OlpAId3JYP50Qy9YHiXdttKoLU0gRsOkJRuqd4uPY9jnW+xImsjkxclDFYFcnYOPileUWwQuGKpUMyHVkqWqFWZks9SZFh0lGi1TrMlxsx7yZKEY9OyWF3ENHQwCdEfw92A+KA1vDUEU5Q3cApAwtu5FFPU4uTNZLnuSl9FhF5vXb9RTPT26hz+hz112Jbex975qCobcis8i0g4ubiabLuVcXvEAIQB0h/FnRoIyWOP/P5jfwYneCF0uCfPddxszdoUUAADeuRlaZg8VGsT2gtQEhqLRINQoiLgx+dGA4EFI/EgCykcMkphjtuhxdm6OKDOjtvoS5jtqKiUFed5yy83i8wqqjn626ku8fNhn0RvI1dmXgSV3UCcYFqHcqB5WVF7xw+l7BGckmIECy0bRWQeQOYCesBPG5kpyYErNUIERXnWBFSqk5NdQfHXquZx3k0L91IsK89HdhkkuQKwLLZEJkD8sI9UsVYIfeq5F3GL9+4qHis+q2GUQn4WOvEVZCRymLbgTax/RMTE52+MMnL3CWE6v3j99+F32UUuXaYr0r2a1rtR2RIxoAFB4yWhFKQw5NPp7c6OkG4Tyey7aAUh5lZPyaXsPEW6MvKDgcoDaWqwKzkLPNgUuVjozkQdK0kxCDrM1R77S8iv1dgF3f9FolO8aObEuHubOfe2L8ApCVJZvROGQv2RDCjj3/OcSebBYNK8yIWm/e3S+q7+XwOgCadqaDDV/IPEQr7wb2moM/D64sgV72LJJ08tyxO5F3EqePN5hG+UTvFa5iYoiUAWVB///50QKPqhUTYkaqw09XREbaLmraBQ+LjA7I48I20j0am7HFYNNnrH+c5B2TY55NFmid5r5bs8uYIFFpg94qNMNLcJMhxFnSjB1NfgDq2kHKwLvradnidkfb21HR481yiu+eXQIgLd0qLuC3TQ0pUjVjjWafWrvLYCPt33mqVAeJSnAiWSuKQH2hoToSYBLKaNRyNVZkFtODRJ/ltsJNrAKfnyxwXOyxtXRNjJec7jMdtUzw2rc5rFHcH1TK82JQVj0+mi/x2eSKrp0y+JvNxwCAr7bHHCwP4I4/c6YclkwSUhjXLX+3PJCCSBlgvYCPE7swIukwdWCiks4NEahiP/2Tk1v+rk1TQoiATXR7MrclPzujcYeHkw1vqL5aHTNiAYDTifKFpOolbiIxsihiMHu/zxEgIGLlXU86dMPm8LqCGHpglUfIkBy+Mse9YqVJo+qjO5ffaIRRurdoZaqyBBgt0tMetlT8z9BJJr0c9nO7VZm05yDfYT8eNNUiyXQkAA+OH/SNZAmQbKna4iq2l8Br2oAoEeBPU59QHaAIZuy5gm0nOf7i3TO+F7Nxi3F85z+fv8NXu2P88oqc2Mx6MC4GxCYRt6Shajo7pxVxXHZMOAudgtlp2DpuLJY58rgoeUWJM8OQfQpVN+OA/ij++czQcxU9m2Un4R5E0lvu0DgB/RV9X76S6IfNrAOiKo80qgf6U+GRZDhekL3pgd536L0KK6jKB6BvM+QbwaSu4Zj5C9/jcd9TvR/3437cj/txP35N44OsVEWElUIWUjhwJ2nXNo6C+IAkND+QXMBJqHmP6sAIYehZjes9lAh4tyEYtsoNvnNK1dw46/Ct+hoA8Ly4hgsSL3uCDv/P19/DIspadGkhleeqJATB8Ns/vH5EKSzxeM7O13g+oR6iDZITU97up8il41zQx9M1+96+2c6oJ8nByqk/JDKP8zPq0eqY4fksfv6yr5h5vG4LTKqOvUgB4KslsWy3uxK2V9wrPUztkaUFIjL56HSF83qDH335nP6HEXf72Dowa1OPDSaRaayVRx37t49HK1w0E/ZUljLgwSz5C3+9OeLQ78VizP3KquiZnWtaDaGS0XwIYAP9edngrEz+wS92J3izJSP+VVMi1xbHNR3Xtk9b7sU2BdeP6xadybiPOh61DBPfrEZkmDA8XyXYIETI5LdblgajosdRdIjamRyvIqvZrTLIXlIIOAA1M/jkIT1n351eYmkqvI6h63VmMImV07ot0dwOGp0I+XJlnJjpMvPQmWU+gDEqmUnUFojBKOgppMJHCNIJYjMDxMrWOvV25Tww07i/KSGCIMgfgBcSxUVEM1YKIlY8OOohxpbvYQiCTB8Aem48IGL1K7W/wzz2kbUregFMLJvQ64XmfquZeUpYGloOIVXMCAA2Gtk2uXqxab5NhgX2tsRtkyGLVX5rNB5N6H36u5sneP36mOFr4RNEHUN2+DiCCnAv6cJeh1FCcEYe2UpBRj/kIENKvQngatdOHHwdUkj5kWNWM6xEaBSyM3p3j2c7DtsQAC7/7gFDskGCc1iBJBWCiGlCWZpDBs6AWZYQIfkwy7VKfs1nPdAMMDdgSyBlygo6iYMK+H0dH+SiGrL0QAw2hbIX1MTPYn/hrMNoRA9e0+SwN9EgHkA+axnu663idJJR3mNvMjZCz6Rn8tB3Rpd4mhNsJ+Hx0+YJ/o+vvk/HE4DjOJEXyqFzqc+23pfYRlcm3yqoWc/9wM/m19BRStEazRNJpQ18EBjngxG/wNbEhUcEPHi05MkViKHXACZ1y71AgPqVX63j5B0Ep6aUmcWnsxvsolvR1hRJm7vO72xCJnXL/UpTpL7gm8s5Xu9PUzxY5XD2gCQezgvcXk6Rxxd1Nm74xT+pUjzGRTPBYl/xz/7Tx69w1REk+81qhmnZYTFoiBuF8pjuZ64dmi4tgofuPNNRx9dtntN9/aclhT9f72uoIQQhNzitdwy5hyDYzlAArCPdbCs4K1HGidY4lbSoTkHWlrWjUnueXb0VUIMMQnp0RuPK0ecvViP4m0EGRZDl0Bc/mu1QKLqH/7R6gLNqi8+mtMg2LsPfvSO9cvPVBHLQlko66MFusygNQ7xFSe5fh3aSAzwuZEi93gBAe9b8ZrlFFjcPmXJo+4zJeJ3JYN5FpkzlgPJAkxwkSTtAn6di6+DsaIO2z1J6DsC9UW8kpUEMGtxVDndAsGHt7Ij6uvltJBn2giHTUHqgVWlj5xJhiqyjwO5OamL4eN1eM7ys9hJyncGVNK025x1+uqO2jl9nyFaK+5DSJts/P3GpH2wl9FrBDwk8lWf5jlpo0tWLYUEXMLHn6WYHGXQqoJ41MJO4+N6WCLH5qkqH0fGO5VS3TY2r+NyaVYEsJNmLsIlYJA42D/0sMIw7DBO5EdktSXaGzYrLkQIHmmRf6hXgx+GOgX4QgHd478c9/Hs/7sf9uB/34378msYHWakOg/INY6UkiMXGldNVgc3gqFIZTJ4mc/ois/it0zf0Z2lxGaujf3j3CH2v8dlDIrY8qVc4zqkC/Z36K7yKcO8fXX8fX96ecjza0cmGK7AH5QYX7QRfXhKhoVuVKWy8dAhOsKD85eaIWclKBDaQNzE8cRNJKcaplOHpJObTPVczXy+OGGquM8NV36YrkCvHZCdn9R25xov1MTtJbRb1HdcXoT1krBR2bZ6C2l/WzFAM5xZqZphd/N3zS/z8ipyXmtsKamwwjUjBYUX44uaYY8+enC6x2VbsSXzRTpjR+nS2Qmc1/9t83jE0vGqSuL4ad+SFOoS4G80QtQ8C3+zmkLEUOa4b/vxKGxinUMTKftMW7Dp0Mt8ynO+DIPlUrPS6Nr1yIUQ/3sGpBmBCkA+aq+emyyGlT+Hgu4zNMB4+WKK3ipGCxbrG7ZKgwyy3kCcBC9CxfHV9zP7QYeQYlbE6AKVjM4+2z/iaCkEZr3yMMmGVwYskuQhEMmGHoszBx+el8xJKeSZrNW/GSS5lBWUZD8jRSc+V8KOjNdYR2r9aTKj1EdnFxmfJZGWdQdoUO3YYEReqJAtRC43qQnK4efPEojhOrvfdtkgs3APCjJ05ZLMO1WCi0SvUI7o+5YnFchNRGlfB1SkGLdzmTHzUkZE8ZJIKnxyJ1LXm6+EV+ewOcH62UFDDMyMIph9gWDc1d8xTxjFMotkXaLYFIyBibJCXKR7x8XjFbYzbH59xZJyoA3wG9PXwmR7VmwGuTv6+/qynFsFlnFNKzxKpbCPgC6CNnsfCJzc0WCDE9pqbGTKrGFoJFTlYef3+l6of5KIaZKSDB6TeYoRZilfRJL4DdkVkZdZpscm1w9PJEk9L6jWaoPDNnuDTUdnjNx+9wZOS3Ka3rkAWrfH+cvct/Pn1JwAIcul7jUl8Cb59fM2T9U03wqvlHN0gNWlkgnV6CVE6zGIfr7OamcCzUYPLXWKjZsoxnX+zL5iJeXqywSjv8fO3lO8ZgsB8SsdhvORJLASBTDmGlKVITOBcOayaEtuL+H2CsjQBYs/qzCGLL0fbZbCXNOmEiYONEpesMihKg4dTWuh++voR632LeQutPWtfG5vhXTQVBwjiBMglyTYaVZ2g7KGvvDM5fvX1Ob+0x4+37GZjnOJFX0ofXbDo351Ntmwp+HY/RaEshx20VmNW0EI/OEjd7unctrsS0wldx9t1nc45D8DEsI2fzJJhv1kVpEmMkGMokm+eLg3mE7rPzgtsthXkAE2ODbsHdUZDycCLYNdp+AhAae2xNxmuV3Sf+n0GFaFEJ5JbDlRANemw2iRolWFoJyBV4MUeAEuChAzJ5D8A0AFZDLOfjlrWzvZWoe0ztoaULkllMDlIPorjKF7HvcmwXNAGgaBDmXSlNobUgzbGB6ofckOrBzd5D3UTNdpvJVwONM9pZRsdN+gjfO2sgtiru1m9g1tR6aC0x2cnQ6/6gt/Xv739iN+RGy/hrsrUkxRgGFfvBbJtYs36HJwGY0cB+TIuSj0lwgyLuzBJZ7v/yEHMetY1u13G0PZHj28xjc/mSzGHtQdSLSvxnzwl+d6b3Qw/+sdPmLEsA9A+jMqGIfggWhpWr1O7Zv/MQR3F9kDm0O0zzpTNVho5ockw0wRr071IrTYx6xGi61NQAuq4hxuc05wg3PRfAHb6QS6qqhOQgsKuB+G2G9MOfLDb2n1i8Nln7wAAEoH1jz88eYv/6eEf4crRpPnvt9/nvua8alApAxefjEoZbOO2eNlWWMdqa9fmpNGMJJqfXj7EqBh6bhKbVZWqgcqzB+nRkxV+4+SSrfLeNlO8DUSKkiIAceJzXqI/WHCdVXhwRk/9k/EKL5bHPDGeTnZcje76nEk0Z/UO665kuYrzkg0SbjYjdG9GTJjKZh0vUpO6Q2s01pdxwfUCKA4mzqGirahX/eXlQ/6Mh+DjLlgAACAASURBVI9po9IZqopNrHSu1mMczppX72b852yU/FS18PjZBW0WuuuKyGjxu6+vJ6gnNCmogwWiaTOUpcEPzuhe2yCxjvIgExQ6p1FGfe5xucPG0M82XYHltoIfrCe140XJG0nRXiAbxOAlyljZ9L1OcopA5AwR+6E6c3xfRmXPJh8uCBSl4eu/2ZVs/dhOMjyar3EcSTVf3xwhi5uRXFts2oKrU5V7+EFDKAJXh6q2ZPQRTQWk9iznUYOxQbwXWZbMMIIXHEeH0mE8vss1GBbV9aaGXeeQg6FESAu6WmSxagt8rt042hkGAf0mSqKOHMLUsAd36FXq63lBUYGDvOdAlZG/yZEv6X/sHwaEhy0yPRhzKD4v32ioRvJCIQKAuGiXz/b4/tkFPqro+cyEw9uOnsFR1qd3xKa4QoAWQxk3P8ICrgAcV3Cp11vcSvK9BZF31D79rJ8HDiKHlYCTLHsrZx2OJ7TB/Hh6w7yJ3bYkRCteq48f3eCnV/SeNW2G559e4k3cpHqneH5Bp4iwGUlGPktRdaF0bNzCrfh4zKoFV/9exwi3eGtkJxGiFMftSvIGBqDmLW2ihw8rPK3w/wKaqv8C9gX3437cj/txP+7HP4/xQVaqshVQEDCjAHcc0zFyB3FZoDuindST5zc4K8nSbWsL/HcP/xoA8N+O/wEA8MpQf/Rvlx/h778hRuXjkxXOiw2eFeRy9IvmAUPDb3dTrHZU3TarEkJ7iNjnOBntuc8nREBwMjkMaeDBU6oyH9Qk77jpqCIyTuHRhODTWvc4LWhLuOwr/OTyEUOwR+M9yzFal+HJdM2h3YfM4ONqj9N4ztYr7EyOTeybHtcNXlzQOeN1BaECGxVIGTglZdsUaNcF93qEcpBDtWMkQqxU7XWJUHpGA5Tw2PSDkXfAUd3gakPQn3OCUlUABCe5OvJ7jdm4xUlJ5/3F9TncV1Qh10uBfAnkUQZhao3NJ/HznyXLxelkj+O6QR/70Ll0/LNCWXgIhnq19JhFRvCyqQjqjvepP0gkykrLlXuZG1gvuafqjUxGIr2MhgSC78XgjLRrc+51z6oWUgS8WxAqYbZ5yvOM3zNYDua5ZdTjdj2iSnpgxe40s1vlyHCqkQChBukzPcOb3gn4RrNJQhfA6A4ATpFR2qHtMkYBJmWH62jDF+J/hszUoAKbRPgiumcNPcRRj+0X5FTgKo9wHt/PgkLQfXwOoD2nPHEjd2D56oD6RZTROGD3lD67eLy7k2rU95rTbERDYd0DUxUAS1k2byZYTDaMYGjp0cfQhS/fniPEpJh8IyFCMsAIgnJCh2MLAshXwzU4OGCk5BxbUY+SzTE6yTKcUHp88vQKz8ZUMbsgcNnQMzHVHX61Ih5G8ALjWYMivv+brsBHc2pJ5dLiy9tTdg1zO82G98JHGF2mY2KHuaWEG3qtj1q4vYaMFbrwB7mxBbnUDT3pIALbU/oMjODYRgNOID+Jz6AgcxN3EPbxvo4PclF1ZUAoA9zU3pmgMXXIo1PS944u8LyixfHfjP8RzzVBjBsv8T/f/gH+9199DoBINUcPicT0rx98iZne46c7WmR/tTnhhUKKpM2TueMJDSDpyreOqV+z6ius1jXDgNNRy85LHgKVMpCR1WCDQs2pKQZ/+pp6tptVhXrSce/1tNqij/5rO5Mjl45/TwvPGlYA/JK+vD1C32Xcr/zlN2dQ72K/VQf4iUM5HhJ4AlZR9mO2OVH6p3R+k6rD7Zo2Aa5RvNjqSYvRQS/0Zj/CvkuuQxdtcpyxvebrMZ7tWdaijj3mZYNf3BDBaft2jNN/ot+pFhb11zvInjYu++dTLH6LXvRRYZnkMsoNjoo9b0he7o544tXS43o/SpaDTY3LmwibNRTgHtIcTZIYkDb1wZg2J3uT42ozYpIRRwgCkJWDzixDyN5LrOJCVJQ997A7q3G7HCX5ykEkXtdm+Gp3wnFg08keN6sRXzcAHCQOEdNMAPY+BoD19YgWpmGh0wk6FCogm/QwA1kmpJ/pwqKK0qEnsxVq3eNF1CtfLiaww/GuMqguBWX70kNHqz1nJLLSwr+IC/BVlkhGI8ebS7/TkI1iaM3nPpF0RACcQBYTp2Qvmfiz/16H2dGOz9U4xe2OEASci7acG3KVGugLJF2Jf6ksrvc1Rgfevd+8pfNUFzlDt0EHuAwpTtKA02B0CwgX4GIqiysSUclMPSfzQIBgUNayACJ6Zz89X+KHR2/wX0x/BgD4o+UP8JOoie+cxjL29yfTBo+ma+YCnFZb7KP87cvbU2yuxtzDlo1KWv1A9pg+j5vKa8Wewf1RIN4JAHlRolxJqPgImUmAnUbZz3EHAcBdlfyZQyM8FJ7Tp/RRd0ez3RnNkr33fdzDv/fjftyP+3E/7sevaXyQlWp40iLUQF32aF/STk9Zgel3b/GvHlII9afVNX5QEWPuWLbYxy3sH+++h7++ecY7rN/+/Ev8wdGXAMjU/n9989uYRPMALZP7z95kvA0e6O1nY9pBP6g2DD++XsyQF5ahv+mBcb1EwMYWuGlpV7/pCs5y3e5K2Ogco8eGWKwR4tmYElUk22jpoaTHMhqrl8py5du6DL+6JIjXWYXxuMX6lr5LX2aws0hMOmqhZWAzdWcTrAsVUI57vj6X11Ou4GTpmLWqM4cis7iM/rltl90h/QC4m/ATpTezquW801VT4mY5ht3QeU++1LzrXnxHY/HZDLMX0f/4REJR8YhdGOHJp4QMPBytcV5scd3H8xQe5ZC+00eZSqwAmjZDOMieFdon6BHAfL6L9yz5MN9sazSbEjrKMQYYGIgwq5MpcFwElkiFILDZ026/3eZUNA1uPzLwdtjdFAilZ0eb1bpm16G87on1PYSUG8H3IgDYRPa26Mnz2kfIMUiBwCbrAchT6ELwgn13nx4v8fn8LQDgtq/xejdn6NzsMw4HF1bA5wEhev9K7Znpnb/LgFAgJGAiGQe0EiGW4GqrqAIcAjCcZCMF1aQqFQDsOMB+SshSXVg0EQHxXkDKgCLe33ZZonwb2agikqEGRnTlSOoTx3HdsElKuyyhFxH6dGCPXeGBfCm4UlV9qlopGUagPYkwb5TpYDil+K6ORi3qomd0KlOOGe3fnV7iv5n9BD/ryFDi5e6IZUpfXUxQzKl0/P7jd5joDqtIqvMhtTCsVaiP92jeDkRCcEXuSw+ReWTvcr6uA6xr5zaZ8K/I9L+L5+Jqz8lcQgSYRcGwty8CS4yEScRQ7wXaPuPjzzKLujBwLqEB7+v4IBdVt84QTIb+mxIhGur/u3/7/8J4hX//7tsAgC/UA2SPI6u3Ti4+3/RH+M7sEt97TJPJp/kl91f/t7e/hRAE3m1poQ5BsKF2azRPns+OFhhnHfdortox3q4IVpyNGsyKFq8W9AJfbUd4MIl9ziDxxfX5nQDsYeEJAI4fRYbvdA0fBNZd0mPeNtEMP+txsR0zPDnOOizj4vF6NcM4QrLGKawXNadx2COLLMaP2V4j7PWBRhH8wumaoNVBNxm6ZGunS3MQKO7Q9BkxneOQUYPorIJ3giFNnVt865Sg+GVb4SpKRGyv4HsFFc3BXQEsfhBf2qkBjITPI9y0B2R0lZHHLcsPtPB410544prmDa5b+vxh0zJsLGAEOQABKMYUJTdoAefTPfcT1wfQtZQB5bhDux2kSpTcAwCm01BZmjSLzPIks96WbK+HAKC2KbVGglmTQQVk4z71bFvFC0+/zyGWGdRgXzd13MXbLOrEMFe0SLOtXSWSK5YIcK1GMR4CJCy+c0I67JNih5c76n9+vTqCFGAJDEwKp/Bjh6OHaz637uUYxSbaDeoAn6fFJ9sJmPgRIk9yIzejSV3FGDRhBPKYdqMaWtD2j2If+1vrg3QozRaLdWlhnMLtRYwOfJc2Yf0skAxnuCaNYsmO7yW+/PkjyNg/Fr2E3qdFfOih5uu4oMYfuSJZ+3UnHsXjHSZDmtCuhL2l93P8ZI3fiHamrcvQ2Iy10ZfbMc7ju/qb41f4cfMMP1p/RJ/RF3BDK8AL3pRqQTaN55Ef8a6ZYBJbPLPzC/zoi4+5v+0rjyyGsZsMEIuMf2amAfZooPEGZHEuEBbojx25UAEoX2fo4lRgOgW9SYED8MmJKWQBeppSqpwT6XoHTZtK9/6Dpx/koprfKKhSIfvhCv/us78CQDT5/+VX/xmaPU1+//0P/oI1pn/bfIJbS2/623aKf3v0MzzWRBb4UfMx/mLxMQCSpBxXeyb39Fbzolpllun7667Eoq14V9/0GR5EveZZtcU3m/md4910dEy3rsKuybnVY5qMF7bxtMFpXPzHusPalDzJr9oSozwSYEyO57MFymj+8HJzxD2mXDvOce0uashe0A4VgBob6pcCkFuFUDuWgqjMJ/0gSB85HKOYJDKP1u5OP2uzrfjlEyJweggC+c8OfdTz6ZY3KutdyZNkv82hFpojruwoUAYkqG/tAXTH9N3NkwAZdXaj0nAfuXUavdfoYv/ReslpPEp6rNcVL2Cicnh0vuT70luNeUVf7oNgacUoNywHsk4ByjOJSVWJxDSeEAFpkKF0RmMXs1z9JqXJyAlJSZxJ12foq6p5T/62ETUQmec0FcT/Z2O/rjhu0A9+uZ3iCkJ2ghKbzvv0+Qc2fzJ3fD/nVcvPzl+9e8b9beMU2jZDGCQkTrCBAXTA8qs5W4KGyjN5RW8FwijZ1bki6Ro5U3Q4ppB0n1mb4swAYP/YIzyg/2Gt5OPNc3fH2GO/rKCiBtTnVGUBtLjLrWbP2uErES+Fy4EQ5Uh6nVJ1hAcnxUACdgROaDHjAMyHayrQ3pZoPW0i5cTg8bdoc/Lx9BYvoh1oazQ6k6GNz8HpUUoZ+qPr7+PnN2dMQHow3vAmeO0lnp+n9KIv16esy/5sfs0yvP/ri+8CIqD+mHgg29saPqPnJFsqNm8AAPuoT5IXI2FOaVMxPtljpBxbRrZP0n1Qa0WV61DxywARCXB5ZfhcjFFEdoqaWFN5hCMB3x7c8/d0vP/bgvtxP+7H/bgf9+OfyfggK9U//K/+Hvk4x++MX+KHJfVNM+Hwe7/1K/ykJWhl73N8WlwAoEDqHy9oO/aHp7/ExlX46xhQ+NfLZ2z55YOA8xLzGCD99cUJXu5o25fllmUnm7ZAbzQbjH80X3Lf9NVmDuMUJ8KoA3lDCGRRyMHeyqOoosmASqk0t12NEATvVA+dkZ5OltiagvuFhbacsHJzM4a8jMfbA/0Dw706APCDgXkNYiUOKT4icNWtlEeVG2YDn823yIZKwSmq3AAsNxW8E9yLDQeMVj0xd1yZbnY1w0In0x07BMFIZOsknO+OEmsyBAFYATfkah6wbmdVCx1Lo5UhU47huJwXnLXqvIRvE8w9n+9wG2U+ZW7w0XyJPDYAW5eybW+2NbrYxxvVlD86mL+P6o4RBOslfBC4iYbmdq+RRZMI4QUQ+1R+ndH2d5BZecFh71J5smIcnK8yD3fAEvaVw/xhtGdc1sAq9rB2yejAjALCk5Zh6MPYlBAElHbIY4/8YjnhsHchQkI2djmnvwAAxgYyGlRkVwrdAws3VKcrxSYrNlrfDXdHHEpvdorhXwSSewzGBHqX+n2uDHBjxxUC9U3jfekz9J3mDxcqJbt4HSDbQU5CBzH0afVeMBPY1gF6paDjd6s2/bugyAFp+AxXBJaehNxDrAYIn0xQihNCNj46WXIb589ffELMeEQ3o7MO0+imdb0cY1/TZzyYbKFE4EzcL948YDTnkwc3+HRCPIGvNid4fT3HyZzg31+tTnDxDc1XIvfQtWVmeVNZbpGoViBIoDuN3InKcPBBthEwczr/7aKGUJ7bNcWsRRfbG25ENo2DTCdkHogQeL/ND5wjANHKFG4e20aHbPr3dXyQi+qTcomyzPDb5Us8iJz3TADXzkNFXGemGvzp5jsAgFf7I/zeMRGYNq7Ei/0p/nHxAACw2lU84Svl0RqNfYSQdZZiz7xPKS9HdYNSGyazfL044mPrOo2qNPzQn9Y7XgB+eXuCfp8mUAjwZJcpz9CqFAGrvuCFzgeBLLo+fb0+wnpfYlLFIHWruA+m3xY8u/Wn1MMyq4gF5Z4JL9ABkIH7iWZboDol6DmLvdLHxwQv3e4rPq4qs9jHayCkh98USSNnBXzs0dhNBl8LFNHftmkzjGI/+moxQXhJL3q5F9D7JE0IKqRj7BQgAy8+w+QDkPuMjcSwnckRgmB4vLWaJ7vtsqZJIC4G603NyUXfOr7G59O32Mem2V9dP8dV1JF6n8hHAeDYOoASWgaoWYgIgy0T+WmIMxNjQ31rAKgcstImWQsCL0L9JqdzHnqPneIkkFB4HD1aM2EtOAl1sPB1J9FucN5DqQTxkmYwSrpictGw4FZHhjWav3h9jnAbtb+Ozqe4iRuSSqOPqS79Mwe0CtWr2PsuA/r5AezayrSQiqQPpUl+EJ/SojVYAPospan40uMwpLxZlejiJkZpz3F63XUF1cgkmwHueBdLQ4s1QNDwQKopbgWkSxaDrkh9QmHBs2gb7Q8HXSlyDzFJgffn0y1fu29u50xGnIwbbKLhdHncINeWLUZ9r+BLuqZPR0tcb0cMDQNAu6DN61s1xSSjZ/PVYg6zy3AV3dbcOufNQzASXnveDLlWc69etRSFN/TC+3XBXt1egx2x5FrCjgJcXBB9l3TpQLSNHDbJPfEjAEBIcmYCYptCBIj4e0p7FIWFc/eOSvfjftyP+3E/7sf9iOODrFR/vHqKzOb4pLjCY/01AOBMFfiT5hx/sf4UAGV1DpBpLi3+dkGw8JN6hcZlXHU6J1nkbqFQTC1OI+zS9BmTUH7z9A1G0S2k8xobU7IBQ2c1w6VSBkgR2LhdioAvLsnPtl2WgBOQESKsxx2OosFDoSyTAJyX2Hc5V1zdtsSVG7w+BapRx9XLal1TWgQAe0AwEIUnaHbY1nvBnqNSB2S5RRerqmzeMvnGeYkqN+jijlwAnF262VZsBj6YyQ8JFkEHiLjzrSYthCCGJEDQObOJrwoMka/CUsXiIsnZFyFVHqXD5x+/YSnB29WUoaW9zXG5p138pi2Qa8eOVk2XcYUPESDHhv+uM4vPo0fwp6NrLEyNP3tHhhuL1QghoqdlnYwblPTY9xmT1A7Zje26gGgVM4rRyXQ9RGLPoiZYe6gkAcANYc+lQ3AeoUmB4wPBR00M2j5Dc0vXTq00XJTNOBWQzxPTJ88tTCQ2SRlwMibkQYqAdVtgKBgbneHdTWx39IormWwjEDJwBerqlAMqrzMIK9i4HTPDZCrRSkCAiUsIKeBa7wR8LMp8ESgQPB5Hd+LZNF+0EkGTIQd9YaqaRnWHZWRvCyPhy/SzIJNj0MDaNdP4zq8EV63Ck2xm8Lcd8lUBoHnsIAYy0mD6H4+rnHR4dkyExkobaOnYhOX5ZIGXG0KoVk2JBycrPi7nJRPAMAUjPf/3z7/N8P1wHEOF3+wn+LuYehU6CTgBFwa2ruAqG6c9styyHC6rDKyiP/dzhepKoLyIFamRaM+ipObYclqW9xEVit89frpGGwM77CWRD10kDIraMkyslGeGsjOSEJYolctyQvX8gUTtfR0f5KL6Pz78U9QThU+zW0yibuNHncR/2HzGUOv3pu/wd4unAICv1sf4V48J/pXC44vbM9ZKHk93rCmrMgOJwMb5zkv84PQbAMC36issLMGWjctx29XstlRoe2BYbiClx9c39ML1bcYwoKgshAwceP2t4xv4iHk1NkM29An7IhqaD6xbjzL2c8/HW7Q2w9tFTH25TpFlvvQQFT3kwUR7uwNnnaNj2ixkyuN2NcJ4Sgs6OdNEtquVmNUNVlHb6Zxk673FooKN8KNqiU7vI/tSTHt87yNasC53Y1y9nXHgtfeCQ7mznbjD0LTjkEKQR5YXntnRDvO8QRsX1W2V8ybpxfKY75+WHrm22DbREtEq2GgsPyS6lDF0/lunN/j2mKQPLkj85eVzjlnzmwzZUXKRGmL4lm2FvtecEnRokwcnMHq8wW4TJ0Ob+niykfDRMD4riDHMzki95A0IAsG6HFmoAmR0TQpOoLmtGGJ3E8eOSqNJe2cC+/9PaI2JyTxGwxjFUGVrNGuG8aaEHPYDJx6yT5sktVMQ2xRg7h510HFTZlrN+l7eRKiDxU6n4xh6r9IIuCKkRBUV+LwG6Few5Cjg7IT6yMttBbFJi22obdIW9zLZ6UVrvZx+DXp/IAXRgOgilwBAPw2wc5c+c4DpB45BhH9NqXgueLOe4mS05wXypkmJQA+nG047Mk5hWnY4jlGQ5dTguqGe++qrOX1HlPYgCE4/ggoQ20E7KxBKx9dHdgLumO7ZZNzCGM2b2/4gitDNLBqhEoRfHlhSuiRvQhZI2xxbK85JmMExTADhyEDl/zGM66y6+/yLgBDftU7lpADo7uHf+3E/7sf9uB/3437E8UFWqlpYZCLgF+YE/8FSxfbT/ZMYOE6w4J+9/hjjWKH89sPXLMb++5vH6K3GeTRkaK1GF0kFVWawM4lEcDpOnqMvmlPYSBnc2RytzZgVe7UZQQ9axS7D/qZm8oAQYOcVpT0mdYvfOCZW8tNyib9fESt5bzL4EKs56TGtW4aoJ2WHpxPSV17sJ3h1cQTxLnpzysC+o6KXwKCPzGPVOiChMuD2kq6Vriwm44bF/N4LDiIXMuDidsraQASB3SB8U4H1fb4MwKTnOLbT8Q5v1vT5y8UIqk6+srtNiWyTNJp2MPYWFOrMWZwxSg0gAtbLzRFOK7pPZ/UObzf0+RQunnbEt5sRH7/bas6GHT7raERV56NqhW8aQhB+cvMIV6/niWg1NVCDd24QbHC/2VZwveKdu/cCPu7O65M9hAiYzagqWdox5eeCDBMG31uzKSA3CkyElWAf3eCi3nQw/9GeNauqcKiOmztEugHK7nuNoojZp2WHXZffgZdXMXg7BHHHo/V2NYJZRnLSWQ83FB6dgvCSvWIhEoHMjShKboAcATACEnSgajtC4GKnOVLs8DN8Eej2Drm0rWQdbMgD8qOWz/N4tmO/5r7N+NqIaQ8pAUQPa9lT9QsA+UoiX6fDg0xmIcEJdMdJ8xyyAL2OpisqwEUDGUTHoAENyDLH5LWyNJQPGyPXbKcxntFzZZxCFa/vcd2gMRm+vqXnbL8u2Wu5/miDZpfzO+l7yWxx26WpXDiqlgdo2BWBn6XdtiS970GFOrR1kHnIXjP60I/uuiHxXGAlfUZse+27KjldzXuC4QeimPJErgSgbjPW8IppT9dpOJdoBMFa9fd4fJCL6h+tfojCZbjopsyK3ZgCrcvwaknGC49maxwVQ1/J46ttNNAWAb/36BXe7KivlCvHVoTWS2TK4dOYItG6DC/W5LY0LxrkUTS/NQU6l0KEQxCoS5pJmi5HqAVDbFp7lmCcjnf4/dMXMBGX+pvbZ+yUlGuLBzUtIFfNCPsu537rw9EaF7GH+NWXDwhaHETvlUsZlQEJwsp9goBBk4eO0HBRGmy2FTNqq8LAxQU8yy2KzLIMqO0y7jmLRiWpxshgMkus2LeLKfdlZ/M9pPSpj7rX7ExzaNANAO7EIKuTwH74XudIrmLjtXq1nLOxQlka3nA0TX7H5H7yYMtM1+2uRF5Ydl+6aKZYRResbVNAjQ3K05RdOkDK613JcK/SHsWIDBoAQAiBx09IpJ8rh8ZkuLqJmoxecvKPqi3nm4o2wpTDvkIEtsmzU4cwdsmgolUMDyrtIGWA91FuIwNEbBGEILhtsdxXd6Dfrs0Y7pXKY7srsYos3/HTNc6PCCN9dzuFW0dLux0xeAfLviBxJ1vUNzqx1q1kNioA+LGFjNCl3gqW+tDnxOfRgaQqAxk4C2wmgdMGUgbUJd2nTDmyBQVB53FthMoc3Nuar6OtA7OJsy3QT8FGIkECbhI3IEcBdux4IZV7wc+gzwQ7L9HfAxBN+kNpeGO4XVbYoUx5wrlnN7TZZAt58FBPixZF3BQ8efwKRZyjXm6PoI78Ha4HG+W/PQeaJA/yReANgzxrmanPST9xkc1Kywz/+msNacjAAiDYe7AmJQeM+Bk69u0Hf4fCISsONzEBeZy/TK8hhz6wT81ov8sgepFMIkpiuHv7/sO/H+Si+vPNObQv7qSzlMrCeoVvRwu262aMn10TQajvNR4d0Tb28+O3uGrHrAmVInA/ZN8WmI/3uNjTbrSxGU/ypTb84gxEouE1KnODXZNiz85PkgzCOon/8unPAZCW9k+vPr1jgzeQgFTtcbGPPrp9htPxDg8qmvxuuxoXqwn/ji99Cg4HUt+0cskb1gnkkxQ+rpS/Iw+yneJFdb2p2H5QygCtPAdU90andBUV+GUuxz0K7VgTOq5bZHEi3/cZMhm4z4zCIQwV3EHvTZ70qMse81hJLncVaTZBFoCXiwkuhjDmA89a7yUTKwDqQQ2yFyk9ttFztygNnswSgWTVl7xwFpm90x9qupwXTh8EJrHfPC470hXGWzYtO1yuoy7VSk6SASKhZBwD0edbXF9P4vFpBAV2HdJ7gT7KYYqTBtZouCG67nzPjlPTqsW+S8hJlaeg89W+IrcoRP2zDLyQIoDds0QvoY46PPgWaSDbPsPb60hU2us7fTvhqMc9jMOIONFL4HCRjc+f8AJyrZnwJG1asLpjz33l4/M1pAAT+tybmiVYsBJdrxht8EHwRtS0mvt2bpFTX3BIj9tI6Lio9lMK3faDhrVKzkiyB+pXmrWpEOlnABjpyW418qVkPa5fjzHsHbK4KfBRJpaVljfVq7bEJDqvfXd2ic9Hr9nN7a/Wn+BvLojb8VvnbzDLGvx8HYmLLoOLBxU8WNYSApH4QnTICj7ZaQYrARGQRX27WReov47XLaPEmcG1yo08EF3ThADrkIMAoUPDd+vAm8jQkpRtIMcBAIb7VB3shgOhK7wxmhjadB32jefTUgAAIABJREFUXN/T8f7X2vfjftyP+3E/7sc/k/FBVqpCUIX5sFxjqgkyuurH6L3CF7E6tU6yHOZosscPj94AAHa2wHUz5jSRZVNifyD9cF5yFbvYVwwl/sP+EerIgg2BXHuGKnCzrTCOpgJaeVin0EVo6L/+6Av8RkXm/T9rHuF78wvOTW3alHyz6xK79fOzdzjO9/jFmnJGv745QrdI5vooPDMlDz13hUpOOmXdQ4jEVtXSQ8ff6a1CdbrBMuZ2Bi/gYwW3X2fo8owF3wc5zEDuMZpTBVflBq3RDIGNqw7LCPdq7dlxCfj/2vuyXTuS7LoVQ45nvPMliyySxe6q7rYltS1ZNgy/GDb8If5FA36zAKEFybJlWLJ6qJa6q4rF6c5nzDEmPUTkzks9GLBwDYlgLKBRVX3PkJknMnfsvddey7uaDA4qsIx6Vk9OVvjJwQW+2QVnHctx241lV2M4ObY4B2geBOMto/JUIg0EtzTC0PQJ/W1RtDgtdmhDOW/fZ2S83W0ziGLUldVKEKPyvmrSPlQVhsz9ajtFN2TJzvegbRhpYjOF6dSvg/W2HMdkuB+nYYMDzMIiCeMwqpNwjiFfjFWXobdYdyk6JXEYxmMyqbEeRoeqdOyruTDSFEq8QjMgG4UVnGO4Cb1B3UgyB2eKUfZsU4dkz8HDYTCA+q0udWAdgx1696mhtoJ8n34gOGBTh/5p6PUeVdTC2HUpqjYlHWk1NWSc7hoB3giY0JdteEpVCQDgoW1hme81JquhpQF0h6ES0wOGAzysOSscla9lxT5Yx6Idx366Iwe+H3vF/dISozjZMmIvtycOtjDEiGZvE7Qng9D/OOL2y9U5frU+w3e/8eIyouaQTz0342ffvIRpJalzZbmCDutbSAt9FJ4vrfAnO0wNTBRpMoP5KtGgMiU2kjgKLgGYwtgjzu5VrhzzvVTAr9uGwxZjVesDo/h76mgss2BDBYSNDlSMAf0mA8J3cYbwvMFHj08yqB5ne6R5iu+qIyIZXe2nyFNF5CTBHPXSnk/uqDf3fXUAzhyuQwlP3SvfFVmPVBh6cFnLSMrvcFJTn4TDIZUpye2dHW4xDXZxnDms2gL/8tTLJ/6wuMR7taS//fH3L6nEuZzXMGEx122KLASDu67EX108pmNT25QWOisNmLBUknUOZNSe5hpJIEzkicYk7alEVfUplQ4naY+7uqAAcH8DkkiD29WUrMOcYciD8XuSaDya+ZL0JOnwZrckSz0pDPWVlRFwltMNbbYpjYzwZY8fPPJjLf/5yZ8gZwr/xf4L/9vcHZBEH5MWQtiRSOMYbRCEsJiE76q7JEhBjutjIKEBwNd3Z2SvV9UZTBUCYLhmQ+9bh+AG+PKjVuO6SKXBehi96QRdby4czE1Gik35pEcV1LiGfqp/IfwMZehvsck4ZziMc7Th+iSF8mSccByPlluyrnt/s6CHFhcObiCobBNkb4XvB8IrFbEz/9ueLfe4vpvB7Mbe6X27u6GMy3sfULNQxm2PHVwIzKxnsAcKCFJ88iajYAzrxeptMLm2U42DE79GDsoG1/tguB7GMergasT3gngBkM4bn69DGXMvQHyp+8FQeyIblajdGMwRKo9D6ZZZqm5Czb3gf7oaA4ceqpsM5FjTf9YjyTW5z+g5RhKdZRA7QZ/ZnxjMgnwkZ8C70KYw70qkGw4WpAKLL7a0Ma8dAy97aotozZGFzcM077ALz52qLT1vIpSareZEVHKdQHtTUIldACSgzzTQn2qI0JPnzOFw4QP69e0MNoy48Z2ES+/dMD2n0jNLLJzz5W3A9+SH2VTrGG3ak1QDi44UxKxmsHcFbPPxR9VY/o2IiIiIiHggfJKZ6s9vHkHUGaSwWG89ezbLFXZNTko4Lw9u8Kz0LM3OSny3P6L3v7lZUjVIJqNQ/mFRY9UWH5SDhwwuu8cOBfwQ/ZA5acvJTqvSKV7M73CeeWLU/9o9RxfYE1/fneFg0uD42JNG/ub6hLJRh3FX/m47RyoN6lXYTktHXqiMO+/jOTB5M0VZ2+KeIXqrJe6qksqY/T3VpzxXmOcdHk83dPyrzl/Hd3cLX9IMGUB50GBe+qzn2XyFi8rvyG/qEvs6xyIQhKoupTEXbR0Yc9CBlZhdSvSH/m+LeYV/f+KJW0te47U6wlm4VkXWUwZXFL1XSgqjIVqJUaCiF1SCzXIFKSxl/MfTikpxN/uJJ2VR2YtBhBGGJPhBNkG4gUpc8Hq+xOy2DF2bgoeKhZyN3qdmlcGVBuksML+3OQkH8H40dOY9gy0cEDIF13MqwQ5etkNFZFp2NP4ymCoMylR50aMJBg9un0AE/9Rk58uU3XGw6Dqric19ebkAek4MV9EwKhfahaaMxzgGNR8Vj7gBrQFbeu3foVyrJ3YcAZpoL2oR3pfPO9K9fXO7pFGzdp8RCQYAcNqN+rL+p4E5H0u+ZKj9LiMVpYFQlL8PalT9KPCgSxc0fUPGdU/ZSdTMG6eHz2xOHZU+ecNhH/v7/9n5Ha62U+iwFJJ7FnFOAHo+mnmj59jd+ixczTvoG79OGQf6HzbEGtZaoAljegx+vQ6ML+c8GQ3wz5P63TScF4Nd6NHwQtqR/WsZWGGA4EEsunsayksDcEdqXWef3aEdKi4MlO3a1PoMfyAgOUaVG5kYOMdGkuHfIx4NTHtimw8GGJ0ApPuA3Pax4pMMqk2XQIjA1gw/al1lOFxWOJn40t/vL17hUPjSxy/rx8Ss3TU5OLdUdmHMkRj7ts+wq3NikiojsAus3lnWkXTfel9CSoOnR352NBOaJAs5s1imDQm1v2/m+D6M+SyLFqflDq82frzHGD6WHDWDGdxgqhLYSWAaSrnTnoT397scMhnHaIzhWIfAs94VOJzXdF6LoqVSdt9J6lM9P1ihUqPUX6sl6jCz2tcJklwjXfqbXQhLkouvd0u8e+uPHZqBlxqr0JedTlrqI/e9hNqmyC/C2EjhaI5xnnfkbftNf4raZvimOgYAbLaT0Zy6l6iqHFaNN/fwYElyTQxZwS02+4KCu3UMt3u/QbCWeweee6bww0PeOUC3yeiuwxgJt99vCTAGlJPRAWa7L0hAnzlgdlyhH17fcfBufPiR2fXc+OA1lIS5G0XQSw2rOJ33dldSnzfLlfcHDn9r3sxGNSoGkq6ziUN3amm9dOucJOlkzcHMWAo1uaPeKCyj1wGAKdzYT1yN0nhGC7jTjloOrBY0KsOlBU/GMroxDKofXXDqbahNSovssKFSotqN85pyoiAKRZvKPFXYhY2EKRyVJuVGeJH+EGz65fjMd9xf1+G/aS4a3sCcWUCH8SlYRmbpZqFxfrKh47WWIV3592a34xhUe+TgJpoM6nueUL+/vctJ0jE7azEtxv74Zp970XvAl65bSRs4Jhxuhntmn0KGjY8p/bjLIA8IAC44BmGq4XqOJLxWT0dGMhTz406BgZ5wi50ZpSv1PrQ+DIMr7Nhn5g5p7t8jpUXbJpDJOBI4rM22SdEP3A7ulb/ycD1MztGvsw8kJj9WfJJBtcgURMZxfryDCllgIRWsY/jdxVsA3qVm6GVe99NxmDzxYwmD8IGUhoLlSVnhs+kG32/94HbTJfji5BYA8KjY4s+CmXnfedu3R6W/Geeyw3eVDzZDpvi28d9dqRSTQHCaZy3+6vUTktGTmQEf3ERmHfahH4dKwk0MZgc+QArmqFcnEgOjBXgQwuXCwgwG18JRlpZKTVJ1gO/fpuG7Xq0OkEpDs57a8DEr1hzKMOTH/pg/X64pQ9/UxTinx/zwuCj9zThcTwDoL0rkN2IkwWSOiDhPpysS6BCw2OoCv7o5C+dikN6TRzPaEakDAB4HfdVCKuzD7t9YDs4ckbyGTQTgx2v6LqFd9ST0kAFAqfBwC09ycc9yjYlRgIEJC6Uk1HBM+4QkBst5C+dG4QwkFjYNGa60EPOB5SLAEkvylJw70jFWvaQABQCm5zAh/VJN4rPMfSAW8dHkGxZIg+6tmvoHJJmb34NjAPgYVG3hxkBqQDKDNrdeuzg8n7sjO46azH0fcMjCmWFAOLc00+DcUgadlz0FPWMYykO/hstMoe4S9EEOjxcaaT5ubJ1jWITRqk7JcX62Z+ChT6dLC7201BMWe0HEKm4YTD5qEHfHZswqFYdcS8jb0P+To2CKmGjcho3h6XLvq0Dh5+yXoFlRvbB49tntKFNYldgGYl6SGJwvfbXlB/MbvK6W+O7GPw/ULhvt0lLr/z30IR0Ae+BPgJxxEGZIOUjaE47R/DNLLJAAehbWhGZILv3nmdzBTg3Kif/Mm/0ETbje7i6lGOoKC4jx3mIMRLrsOwmrOfqwme0sIz4HzwzpljMOeD3r8L5divRafiBK8bEi9lQjIiIiIiIeCB//tuAfgJ+evEU6TTGRHSrtd8jvmzkOswplcLzemxz/I2SW1/WEGKCCO2z2OQkfnEwrUmUCgN5Keu2Pzq5I8P7P3jxHG3Z9Bwd7fHV4jRelz2K/bw5JYHvX5Hi7WdAIBgByCPmbixM4AGnI7vJMUWa5uppRQ2v6eIeDIHcGeIWfoa5ljQBjo7/o54crytoAkDpUIRUanUCEdNE4TiIX/jvN2H+tU/IxZaXG5+d3eDn3fd+dzvB6yLovJqOAOndgicWLU38NOiPx6js/ApSEkmMfZOHYaYvHBz7LXPejEHkhFBqTkBxk1aS08y1S5fuyIZv+4ek1GYqvupIkItdVASkstkHwQQhLPeCqS8GFRRJ2+UpJyhCltCgmPfVmrWEkRegAIJTX2MTAOYyC+qlFPvWZQFOnJIjvP4TBhRIsTyy53nzx7Mp75LajmtPQOzaGgwvnR10A8K0cvUkHoYyhv9gzEjfgenSUwVEHzkGlctfz0cg7tx+wZJliJF1nZgY8iCfw1rNNVTpWImRgkaoq8YzR0INzU40kXKsk0WjqjMQymjaBHkaOmIOq/Nqs1QSs0CRacDivyFi+6RMYM5q9GzU6/HAFqMNRhg8dhxzGWuzYU+2PtD+34Tx7Bt4M2a7vO1KP+6Cn9aP2KY4/8yxe4xjcNiVpxeSrDc5mfm2eljtIZnHd+mN0jqEsxzLvUe5f9/O7c9ysZiQkAsNQnNT0Oq0EKSDxlsO9D/yNhhHvAJYBCqMClxjFXrhw4NxCSb9e0lvhS+QA7FJheVjRuOB+VQJBnCGpOAleOOGQTvoxy6xTNN0oe4pKUi+ZOcCFcThnuOcDwGf4zjLUwVkHisMmgP34BZU+zaCacIOEG+xUjusujMYYgVqn+MuNVy9pTUK2Yds6p35NU/sZuKHUNEl6TIOl22Dt9GjuSznrrqBAdLbY4eTc92tnssNEdvi29uSnv1md0rGdzPao+pSC264bR1lenNyhMxI3Ycyg7RIilIjCYD7zN5+2HG+vl/SZnDvq9yGUOoe+zdvNgsaIDosauRjp9GXeow0zt293C3qIMeZgLBt7gQCywyCJuNzh3558g33wyfrL68c0TlKc1kSK+vHRBf7V/BW+a/01+KM3X0JswsalZjCFI4LJ8aLC86knjR1neyr/3vUleiPwfhPGEZRAP1TKpLeSOpn7a36W72gTAIBK9Pc1kgGvADU8nK1jKIqeAnPfSVoHaeoD71B4nc8bcgW6uZiT3OPhQeXnK0PP0FlOlnlWczAx9nrBHZFLykmHP3z0PR3XZTuj/rkQlq59liuoXnp5QvgyLZWDLfN7qeGnN2NpSh0YTE79g7zMetzeTe8Zdt8zpNYMuBd8HHcwQeWINcL37wBMzisYw9HxUC7sOezl8KD1ZKUhyLp7Gs1+o8KwDaQdJi1k6N3rXoxuNqUmjWTAbz4HVSzTCa89PGju5hr98L51iuJtIPMJ3z8egoOVoAsit37cZRgREg2nTYl6GgLIB64+Q3BQNMNrWt+yOPnnfuTr3519g9dBK/qimmPbZjiZ+Gu+KFq6j4+nFW5b/++rXQm9T6jEfvhsRd9lHcO8bHEXjsFMOfibYBnZMyShz6tLB5cb4iHAsJFQ1gjYZLz+unQwoVWT5BpVnUENY2O1gAjrQM8tudIk0vjWQiA7ccWoreC48+toMB+fKbiwDmxpSMd82DgNY3M2YxjZax83Yvk3IiIiIiLigfBJZqrGMRjH8M3uiEqk5xOfyQzqOcoK3Fa+1Ki1IGGCnzz/HgdpDRuYG9pxymgBL3I9MD215fjpmSc+HaY1GRQbx7DTOX5xc+5fZwSVMA+yGl8urvBqPxKXdkGVZ10VUL2kgWxZ2g8ccgaj7d311Gcbw8491aM5tmPQvcCt8tnYo9M1joJ341FWfXAue5194Ps46PRKYdHdGwmSqSGlm2na4X23wG3nd96fzbd4eeBLvLVOiQj2H+a/wH9d/x7+9OqFP7fLGbIgZm5KBz2xKB/5LPMHyxu8KH05uTYpHaNkFq/rJZ2bbSTOTj2jutVerWm4JmdnW8pUV31BjO19l6HpUirjMu6IGTxJFYxjxJzm3JKRd9smSBKDJ8drOu/fXh+HE2BgYfyl7hKoXkKH3T8vNET4/Lzs0XcJtBrLv8PO/ffO3kGFNfb97hDbNqO2Qlun5E2qlYDpBVz4PraXNGrCXBgbuc/cDXqwT89WRM662k5J/xcAxFqSmpApnfcuNWPZnodRHJc48JB9FqnCZleM5b2doPfoo4Gc4s+tyBWRXrpewvT8A8UiclxhDnlgkS8mDQS3uNuFKs0uI9Ib6xn4UY/DpV8vzjHchd9JVoxcV9ojB1OMikdchVEZeN1bc29ESJcGGYmWGHSd9GL8AGAAM/Xnsny0xdOlXwONTlBIhR/PvS/wn18/x+tLn6k6yyASS+NCzo3knmnaYdUGP9Vw7vIojMr0Ca05xoKKWoDdJWCDA0/qoGehvC6dH5vB+LeBOAcGzBYNtv10uMRAqHLYLCiEDb8FA1UimBrdleA8AU+c+htWtwm1Rbi0vszbDqxhC/bYP1/mk9a3ogDvQ6v4B+ocLo7UfLz41eocss/QG0FC1q2R4MyRxKC5N0bx4uQW//roOwDAr/dneLU/pBGYXGhkYcZUWYG7rqS/fbXc4bL1paG9ykiVicPhopqRcPvBrMYfHPlS3/t2jr+4fIo8PMitY9gONlya4w9+8B1OM//wuGhnNF5zV5UUGHiufe9kcBrhDs4NM7GedTqUfHstcVX5G2zb5eNoRpt527LQ20kyTYpN7SAFGD7fGIazue8r/XT5BoeyQl2Ofdqv937z8HR2if80/zkA4K/bp/jZxUvcrsJsXSVIpcYWFi43xJQEgFeNLxNXOqXrvVMZUmHoGM+f3mEfBOS3+wJwDOfHvlj2P2+f0WdZMNow1XUGqzhEMo6hDNcGCGMd4TOlsBTAGQNOZhUF6lerg9Hw+d44TFun3pFjYEBOHH5w5jcIpezxvprj/aUv1WdzhcPQg3uzX0KFNdgqibZP0OwCu7sTcEPZzDE4xWnEQ3SMyr2iZTClgwq9U3HQ4fOwCRDc4mrnr33f+w2Sux2kdUB9tuEhN/yTFIgAuNyQetPN6yVExZGEgGUzB/sojFVx4PhgR2NX1nKSyTOaQ2RmtPyy7IOZXxOu2zCa1t4GRaWW03iQnlhwblG1/nc6mVW4vT2kc+kOApt4YsEU8yVtePF8FQIR6zxLdRpaNFoLtMFUoOs5xHa0RHPSoTzxv9NXx1dYd/6YnkzWKITCt5Vfqy/mt6TKdrGfgTNHUpwAyDHo1eqAGOZZocAnPdrQz23rlFoCtvdzusMoDm84RFiq/bGBCLPoUvpN7hCM+01GUor8vIXgFsk0jLJUAiKwwA1SGOnA7jkIDeVf+7il2WjbSkA4uDAa5sRY7nU76bkdgTmtrwvaxNxM7kmlOiA5aMm4QawSpGsG0338xdOP/wwiIiIiIiL+ieCTzFQzqSGlQKcltoFReVjUUI6jDeWZhFv86MgTDmZJi//2/isAvqR7OtmTj+FdOyHd3lwozNOW/vbr9Skx6aZpT5kBZ56BN9jJPZ5ssNP+OP52fQLBHc5Kn/l9fXXmvSgBlIc1btsJkRqM5VSSbdtkFMbnfv7LkrC6JEWf549uMUl6yk6LRJEm8b5PUYWsrG0T6E4iDbORB7OasvemSWEtJ5Prn5xd4EnpM6C9yXCnJlhKX/L5Z+VblMF1OuMK3yufQfzx3Q+RSw0btGs5RpIIAMAxIhC9qxY4n/hr9Vm+JhJUyiVSbohBXKl0VK1yDOdHG2x7/9p5Oorcb5qSSC7OMEwXDWYhO23VyN4GfKY6KBRVbUrZ+bxsUfUplbN0L2AHg3c2Ksc4wwBpwcOA/dFy1BX+fnuA66s5RGAXLyYNsUqtYzQX3CrpM5bgOeoyO9rMVYknFQ0KTgzgIetRcwd9rJAFI/iDWU3X4O3dgo7DtBJ8LTH4ypuJpXIyNAMSR2VR1/OR0CQcTBV+v46Dawb9xH/XdN6QX+uLg1usutJb4MErTg1Emfm8Ab9naLCrc3T1aCNmk0C+CeXCIVN2DFCnwTc282ImA5Hr1TenEMO87KGlUq3YczDLSN83fVqhDeISTPnsayCldRclRD9m5eZAkSG4UQLN3r/vry8e4WSoLlRL/MfTr/H70+8AABtT4i+Mr5Aclxy5UPhi4dfqXVfi/dYT7LarscWSlgp9z2GGmWHuwDGIPVhPbrs3k6qe+utdTjtam0pJNHUKG8Qa5Hpk+AppvLDLzJdu61SjfR0sBvU4U+p/HAVdhGeKYRBBNcluE58x32eX/70KBvneZhYmufeZocLCcwNVp3QuyS5Ud+699GPFJxlUv788BC9zwAG/9+wNAM/2vdgsKEidL3ZY975U85v1MT2Mzic7WDDswsNaMEflXgC4rGd4Fx5YWaZIeu9mP6GHc5koPJmuqXT4Zr/E151nAGeJxvl0h3d7/xlacyrVqF5ilnRkdv7t+mgUTXAMCA91m1pYOQaobNLjp5+Nvd039RKnQTnq/W5OguvOsdFBBcDycI8yyDZKbrHpx1GHouzwJHiN/nTxhnwdv2uOwOHQcb+0flF/huPEf9eJ3OLPdy/9NZA9GpWAh5vKCffBDTU5qqkUXSY9fnfmj/9dt8TrKjB30xafT1eo9FhqHkqMOtXotEQRWNSJMNQfvttM6AE8mXm1o+09E4SBoTxJeygjyJnGGk79bM6cNyOvB6akHB/4+Vi2dppjcthQedw6ht9c+t7rMC4yYNdk2LPRK3coNXd1AuyS0dBZOLDLsLFoh14n6J/diabXyULjYGCFG4HX5CzExzGcjRdIF0dDuXbsK7dNGuYihvJ1DxVGwxgfmcu8ZcDzCqdBkUtwixdzX3p/s19i0+TElM4yNZ6bkuiaBHZgnHZ8VLaTDi7cd8N40fDQd4WBDOIPDl7JaxjrEhWHOfS/+7PPbvHmxpfXjUsB6ZAt/HnmqUJrQknSAbAM3YVfI3LHoRf+S599eYFMaHx7fUSvTYvBnN5gFzaiy6LF19U5fmfqr50Bgw4/TKP9NMFV2LQ3SmJ7EYJZqcnztd9kXvJv8DjmH94XrJL+WsP3dYfRJO93HK5plQJ7iXQTRqTukWq7uwJ9mVDPU2wk0v24hpx00M/89SknHeqwebBVAheUoiCcX4t8DKqDE5CzDHmhaORLb9ORIp9aMpOwlQSvBfLrwNNoAht77Lx8tPgkg6qzDM4wvHxyjUr5RfPt1RGM4ShCZjbQ3QE/cvAoZEraCSzTmgzHObMk1/f66gDOMEzmflE2dQYTsjnGQFlUpyT19IbXDcpIFctweb0gGzGHsXd5vNjjN7fH9EAq844IAvY2HR+6zqsjDbN/Pz65xBeB6PPL7SOs24Ky02nWYVn4113uptTbWS4qHJYN9S+15ehSf/x/+OgVHmcbrLQ/h5L3+KbxM6aVTtEbSb3k55M7Cqq/aJ7gVe0z1VqnuFtNSIGHOU6uI9lpjSJVeDTz1/zH8wv895UnNFnHMA/jLHuV4dX2gIhW73cz7K58Bv702Q2sY5ThbrpiVMFKDJFEEmEguCPN5y+X13hX+Q3N69USXZuQhN5yXuMwfNd1NfWbpvD71llGKj5MceDGr6viyR6p1KMb0mrmJfYA8Fr4OdDwQNJa0HFxbkcnmkb6TfxAQKol9amc8A+jwbCbHfSU9QlhIaXBxevQX7TjaAVvOWSYN9VzP17TB0WoJDG0obGawVmOImS7WnOIEMzMPgEP/bjkBzvkqcIyD6pGRuL/XD3yn2E5rGXkrmQaARYyXGYAlzmarb0/vsM0A+tHgg2zgAmau2Kiac6zaROYLoVchwf50mB56Dc/AGgjtE81JnlPM+AXbw6p1yg6Bq4EaRfrzzqcnfpNI2cOuz5DH/p/MAz9vfEsym5VglnS4Y9aX9Xa9xmqILnYaQFrOd2vzS4fdYE7AXkbesyF82MpIQv0puLDC33QM8FlJ1m2yPNx09uG43CtADIL+8JfH7PKfM8YgNgIyHeSrrGaW6gv/f1kFQc2CdiVX7v7PKH5VthxjSG1SArlR54AuF7QM8pxhnabUVWFGwCnQfWJO5h6sDP0OtKhiOVney3GAPwRI/ZUIyIiIiIiHgifZKY6XzQQpcVtVWKzCeLpmmN+UOM8+H02OqGRg12X4nUQC/jq8ArrvsS2G5lsgxfiZNp6MfiQYSznNfUFlRakqJQVCp8fruj912KCOjAX+yYBmGdLAsBRUdN3bZocTT329Xot0Q3uFsBYMsoNjo92+NHhJQDgSb5GF8Z5OLPIpCalJCEsXq98eUwKSybCACCYxVcz/xl/evWCDLff1ksI5kg3+U+ql7hpRsGERBikgSr5q80Zfr7yGYuynPqy13cz2H0CPpSmnANf+m3ry5MbnOR7nGb+Gvx6e0al8nnSkipNwg0eTbb4zZ0vp+4upxBBkSiTGl/Mbmi0Z9WOmWqR9fQ7c+agLacxqL9dn+Ay/J4yMVjMaxxNfHZaSIXX66GUGLIuEJCFAAAJKUlEQVS88PuaWoK3Q/8JJJAghEWRKrwLDF/skw8YtHyqyBR9yFIBX/olo/e/t4MXPaNyr14YsMLgNGRV/+bsO7xrfKb97foIm31O7jCsZyNjdmqhl2NPsm1SqpbMipb0bAdT6/sYXEzkSgJPG7qmqTT45sqXSK3lH5xXW6VAYItyN6o92cICg0A7AJZZKk2yZtzzc8VgUwc2mEQUPdRw7S9KcDuOfyAz9PtU/aiy9fzoDt9cH6ELDGKxH3u78oc7zMuW2gdNnZE/6b7NsL+ZjCL6cwOZj25FPzq7ouOsdIpv3vn1mBWK7tUiVcilJnZ6wx2xbplmVGViBkAjRq3ezBIfIkk12k4gCa5GaWqo5NsoSZWN9KDFV+dXeLPx62BzlSO7DYxh4ysb7edBT/x4ZLB3bQK9AGxoyeQnDT3LjJNUhmbceY/mcFy8EsC1f44K7SUc+oPADUgcEHq7yAzY4Km748hvx0yVaweuAdN//KnqJxlUpfAG1tsqpzLro5MNHk83uL4XHPpQrp2kCi8Xvnx6203wNhAMAGC9mZC8m+4kuHT0MFxrQWMtuhcogzzdo8UWi6zBPpSey1QROeb0ZItJ2uOs8A/9rcpJ5L2pM09CCg/frpNAMUrjDUSCJ8drfLW8xFz6sk5nJZF7OHNIuKEbbr8pSMGm7xKYcC5bW2Ked/jZxUs616E0/HazwLotsA692K5N8EUYE7FgsI6R7FpvBRG3apV6UX34h2LdCzIHTw46LEM/rtEJ3lULXDa+hJwLhZMslHjrOZXerWO4rGekLjQ9rfA7p+8BAM/LWxhwvK19MFNaYDn1nzFNe9RBmlFbDuMYyRT2XTI6/1gGYzmV6o3l9CC3lqHbjG4uYCNxw+YOxXIU379ezcaAcp/8MjNIE4ODcFzWMVKtultPiKAGrwoJGZxTHPdkIgBIlh2+OLvBNPFr649e/5DaA22dAtcZ5PCdLtiuAWDLnsZhAOBgMTo0ff363M8QAhD5OA8LeMnB4cFYfrUma8NdnWEbjMQBIL03G93sEj+OMRkt6QaegHMMRnPqKaKSRGaxU0PlWTj/v+HzObfQw8wn8wbkOojczw9qmh39YnqDJAzd/uzipZ8DHfYqucXRC7+5PSorvFkvUd2Etsy9kTT9vkS6Y9DP/W+aZaPL0TTv8GrtN9y7KoftBY6Ow8a8T1AFU/UmTcGZgwpjV+nblKq6Zmyjw2XBkSls0MRVSjPD1gJsZmEnYQTGcBrjcoaTK02WKfz25gjdt/7+Ka5H2cnuwEEfauo/779dwAYZQZYZYC9pPKbdZOPojWEjGc76WeV0G44jHfu+JgumDUMAnmgaV9P7xNvhAcjuvIuRDRV10fkRp0FG82NGLP9GREREREQ8ED6pTHUQQFhfMfCCQXWGFGxYUeNNnaENxJmm55gHAs/LgwsgyI6uthO0tcb+LuxolUERdqaHEz+ycFeHzMZwtEH8mgmFNGRbrm7wbptCh8yvNwamC6QC2+FJdoF9sMKq2gT7a/9VXNSAY+Q76jQH4373zKXFYShpfpm8w5HaQQUiQWcS/OXNo3BeKcq0R2Z9VlIbgz5o7haTHo+X/jOudjO8fZfiOIwLJMLgKrAfu8ShkgKHU58NPF02SFVgNssez4o7tEFV/G93p7gL1lv7jmOa+fcUicJdWWKj/LVSO4eNCmlrabHMG0wCIUkaA6v8blpVOTEqd10OYy2g/O/0fHaNU+szZt60eF0f4f3KH8dhdottMFm/1JJIY4NtWCL8b3OQKiKX9I1A1zsat7GWo1mHdkEjAdaScpLTnLaok1mDifM7/PW6gLpKwFXQCrYMOpSG06xDgQ7tLmQGSqIP7EfmKrhQsneVhNwLDIV5mzhY4//rOFlhtQK+2R+Ev2p0myCyfgsw0yGIhMGmDjYww0SnsQhEtkXeYCo6/PK3fo2YtidbL9s4tHsGWw/M7w5ffOFbApu2wM2Vvx7OGTjlaLSiEwAGz3CnfOYSMpakULDhflJtAlYJDMoKNjdgA9V3k8AFgo0SAMoeMqwzW2voJmS0insO1j6sn1mDc+ePsd0B/3vjmfWbrYOpO7h6UHEArr7zv/VVlyPZCiCUddlJi/634TpueuiJfy8AMKUxmfnfs98DXShhTmWDF4/uIEMK+n23xG7l7xkDTzRjvf/uLh91jOFAJgWsAeR7hiE9Zb1GKDh5I/aJgghryRoHHSwXWcdJ/L5yOUzqkGz98XbJ+Pn8FpB7BzsUQTSgw8piK4b0jiriaB9pdN1AVGpG5m4t4TQH/9I/K3QvoIapgU0CdIALmTyrHY0HpTcarPH/f89CGyB8fD/16l22C/eJ+3jLwMx9zEf//4g3b97g6dOn/9iHERERERHxf8Hr16/x5MmTf+zD+Afhkwqq1lq8e/cOs9kM7J7mZERERETEPz6cc9jtdnj8+DE4/zi7k59UUI2IiIiIiPj/iY9zKxAREREREfFPEDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCDGoRkREREREPBBiUI2IiIiIiHggxKAaERERERHxQIhBNSIiIiIi4oEQg2pERERERMQDIQbViIiIiIiIB0IMqhEREREREQ+EGFQjIiIiIiIeCH8Hya1/TH3RqosAAAAASUVORK5CYII=\" /></td>\n",
+       "      <td><img 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1Vfja1/7Gj7ykY+MFGE455xz8IUvfAH33nuv3cgPhI9+9KN4z3vegxe+8IU4/fTTR2hcAHZD3LlzJ1796lfjzDPPxFFHHQXGGL73ve/h4x//OI477rgRCm98fBzve9/7cMEFF+Ccc87BWWedhX379uHd7343Dj/88BVVdh4K27dvx8UXX4ybbrppRWGG1UKr1cKWLVvw1a9+Fc973vMwOTmJNWvW2Dm77rrr0Ov10Ol0AAC//vWvcfXVVwMATjvtNGuhXnLJJbjuuuvwwhe+ENu3b0e73cbll1+OX/ziF7jqqqvs+Z7xjGfg6KOPxgUXXICyLDExMYFrrrkGP/jBD0bGNTExgQsuuADvf//78YY3vAGveMUrsHv3bmzfvn0F/fvpT38at9xyC17wghdg8+bN6PV6uOWWW/DJT34Sz3zmM0eo3b/5m7/BFVdcgXPPPRfHH3/8yP1OkgQnnnjiijm64oorsHnz5hX0rsExxxyDv/7rv8Y///M/o9Vq4UUvehHuvPNOvOtd78KJJ554wBQwU+jjoQS1xxMcfyBfrscfOR4qUMkEnTzUv2qwyxVXXEEnn3wyTU5OUhiGNDExQaeeeipdf/31Bzznrl276Mwzz6SJiQmK45ie9rSn0ec+97kV33v5y19OtVqNFhYWDnoNz3nOcw46VoP5+Xk644wzaOvWrVSr1SiOY3ryk59MF154IS0uLh7w2J/97GfpqU99KsVxTFNTU/Sa17yGdu/efcDvHihQ6e1vfzsxxug3v/nNQa/hoQKVDlSowpynihtuuIFOPPFESpKEANDZZ59tP9uyZctDzs2Dg41+9atf0emnn06tVovSNKVTTjmFvv71r68Yw5133kkveMELqN1u09q1a+ktb3kL/dd//deKa5BS0gc/+EHavHmzvddf//rXVxR/+OEPf0gvfvGLaePGjRTHMdXrdXr6059Ol1xyyYrI24Ndz4HWsgnCes973rNy4isoy5I+9KEP0VFHHUVRFNGGDRvoTW960wHXX7/fp7GxMXr2s5990GN6PHHBiFYxCdDjCYNzzjkHN998M+666y4wxkasyj801q9fj9e+9rX48Ic//IceykFBRBBC4H3vex8uueQS7N+/31KlJ598MrZs2YKvfOUrf+BRenh4rCY8/evxkNi5cyeiKMJxxx2H22+//Q89HADA//zP/6Df7+Md73jHH3ooD4tPfOITeOtb37ri/eXlZfziF7/4vYoneHh4/HHCW6oeB8SOHTts1GatVnvMgUKHMmZmZrBr1y779wknnOADVzw8nuDwQtXDw8PDw2OV4FNqPDw8PDw8VgleqHp4eHh4eKwSvFD18PDw8PBYJRxSURNSSuzZswetVsu3YvLw8PD4IwMRodPpYOPGjY/bkp+HlFDds2cPNm/e/IcehoeHh4fHQbB79+5HVU/8jwmHlFBttVoAgCPe/B4EiSr+zaT6TIYAhUDYV3+HfcD08JGxO0bUI0Rdsr8DYEl0AlCmDEVDWcH5GEChCq5mJUPZlva1jAky1n83S4RRCQAoFlPwjCPsq2NQABRjAgAQTwywfryDfqEGNCxCZEN1C6fGeji8tQgA+M3sNHq72gg76hhFW4JPqSLnzUaGvAwwWNQdThjAeqqwQ9TlaOoMEBkARZMhNE1PCMhbsHNGAVC01LWJhgRFekICQjwTIZ3R526o7wKAqBFEqouwFwxMqvcAgMZzUKEmkg0DpHsDpPPqMxk5ViHICDLSx4sYwNW9M9fSP0zNFcUSQS9AMquOKRJAJvp4IcB0zDttHIAzQhCp39WSAoNMnaDIIoiCg3XUCfhUBs7VdRbDCMnOxI6LYhoZB9fzxgsGJoB8Qp0w7DJQoOegZAgygOv682XdzRVxgAn9OlTzX7akPb6cytV8zCRIH2DIptQxi3UFkvvU+ogXgd6JA4SJWlskOCSpuUzSEkXO7bWwboiwr/6mCokjI4JsCPB+4M5d0wOLJKCPETYLBJFApOdx0I8R7FSlJVnOkK0rnbOJEVimi/A/EKhnzVx34J67cKCuG1DPIM/V+QFAxIDQlStZCfDSHUMG7v4y6a5HJOr9eNnNsbkvRUuCIkK4pA5CEdlzgxiCHJW/1X0FgGAAJEvqgyDTz4V+/oeTgNTr3TwzZv1Hixzxop7GASFv6uelqe67uRYmYNc7L9W+ZPYeCoBSz0E+KREM1THiBaY+q+t1ljPUZvXaDxjyMffMMOnmGwwgTna/K1sC3NzfDkOk95MgB0Tq5jXqu3Ewod43e4MaN3P3Sbh7JFIC6WuTESHscMhsiHs/9j67Vz8ecUgJVUP5JpQioBTEgLKhP4uB2hwQDrSgSJldeJyAeFm9H/cqmyfUhs+kW7BBCGRr1Xl4RGBCvZZ1AteLVTYJjBF4bHZ2QGohmvY5eMlQNtRn6VOWcdI617ljpt9CVKoBDAcJTnuK6tIxFfVw69yT1Pt71yHtc+Rr1NMXCkDG+imtMeT3txGbDXTTELKlxzgX242WOEARoPcOyNgJRFYyhD2GmhEcy0ogAEA2KRERB9PzGkZApjcu0RZgUisLDJCMwLQgjeZrbmMNCXIdQ3djRQAruYC4wxDo+vAhgLwNu1nLiJDqMUUzSmiXTdh7YfeOkiE7Un2x3gwwHEbQzzriZg+UqPkV/QTUD8GmteDPY4hcjT/MOYKUW8HHhNtzWek2NDTUBoKmvhfDwClkoRq6Ybk4gFKvESat/LDXHvXU/8NpCZ5ppbDGUG4EGrN6zc3VEORaUNQZWB6hDPUgcw6mb2i+zIC1SponNYkiiiHG9PkyjmCo10FC4JEE02uEGiW4eQAIqE+bYv0BsizFYF4pGtECR6DvNWKACkI5ZmYZSBfUMVgdkA0g6qr3pVYgACDiBJHo6yIg4ITBtPq7bBACfa/jRQbGKkpwUFHkUrL3Qq7LEe5JwHOjbALUNIqtBM84eN0os+45ZwJApAUrgGDIEOhL4QxAW3+vR6AYoDEtRNoEpscRLzHwAUCV3gZMy41sCggK9TqRSmCZdQVeUQAj9RsjzLIpCan3EC4Y6vvVBMi6UjqMnltbJkCvKzHGgAYAs/cIOKOAq+ectPLZ2BtA6NeoARSrE2ep2guMwioTgOljDNdKq0gCQDhgTpmKMfKMh4K5h6YAUOlk+Hh2zx1SQtVAhgAL9f964SWLaiELbRUxQQj1QxR3CVxvbCICiDuBSxxgepUTB/Ixt1BY6RYUKxlCoT4oudI+gx63n5lxMAmUqduAto4tY2NtCQCwVNSw0K+h21GrLwgl7liaBgBMpT17fcSBfFzazaOcKtBuq6d5eW8LQZ9D6M2EOpH9XTRgVjgyoTYuu+grFmG6wBD1gGCohQ1nypoCEC9yyMhtcCJ1D1LYDUDcTA7AhBNKZYNAbb2zCAbBAeg5EQVHZCwI7gS4SNUmYL4X5MxuzsGAIFInjDkxy0IM1hGorwbclwzN8T4GfSUMiBh6+rXoh0jHMsSaRVieb1glgAl1r7gesgzdZleOEaRmKChQ15ruc2UepTFwSVlPRlmJF5nV5InBStWwr67VWFUUEsiwHIMQ8RJDoq36oCC7hot1ACsYSCtKiCWCJWP2AEbmBaFEwcmekBJplQxwAAE5hWE5sqwEa5bItFUvFxLEs4EVdFxU1kCgLD+uBXW84O67um9u7njpBIpIGITeaIMMyCaZFZBRR1n5gD4PcxZd0SSrlMpUorZW3XgpGahM0DtcjV/WBPhA3ZdwWY3dKszEkHScAshzZ2WJ2ClNwYAh0uuqrKvxGiWSAnct+TiBCVgGqmqFB7k7r0yAsOeuRTFo5jkDoi4bObexTpMF5qxFUmsmHLh5LJpubqR75EckABNAvMwtc8III0pwPumYNj5kFa1PCXh7HMKIomturoydghP2GfKWBM/08UNCMGQg+fgvm/D49AR7eHh4eHj8EeKQtFTLGkCpsjKspZQCPHL+rXgZlkbjBVm/XpkyiJqzRosmRjU/5o7JpHstY0WNAADVtFaXhfZ7MtLaKGNKq28qE6iTJfjVwkYAQMgkhlmERktTl3GBHbOTAIA7FzZaPxUCAoWE2lZlvcpejM7OMTM89b+hMYdOr8qnhLKuAYCANUfMo58p3mjQj8EfUCZW2FfXVbTcd6U2xIq2RJAx+5lICDJV18uH3Fq0YY8pmm7LQF8boczMhwQeC5CmzikL7Pz0N0lQoo/XC5TVo6ezbBDyMWPJcGvJAErLF9pClAkhGldz2Kxn6A0SMO0rHeQRhPZTR40CWT9C1q3beQ2X1XzxXFnBxhqjULEDACCbwvLmtT0hyjqhaBvnoLM8ZKTWYLyoDlI2CcFA052Fo0TLpraOxrTZLRmiWTXGdJYh6hG4YUFShiBTr5MlBjCOom0sBW7V6HKsRHK3Mm2KpkSo/fwAIOsCVBf2XOCEdFLNV5GHIOPTExw0o0zJqMcQDF1MAuAYhaJFIK78yYDyoxsWJezrOTEUeD56z6w1qundaNn59awvMASKGpBNG55cIqxpP3LJMdynfBHxXABw5SMGABZJ8EU1j2FPWV5Mc63BsMIySaj7p29h1QqnoMJQNIDBtIQYt/QIWNccX1nn5nfZBFkGhxcVC7anWQm9XmRNWkaLa7dCOHDWo5kfGan1A2iLs2CVc7k5ZVKxBtI8Qxm36y9eUq4G84wWdXfMYlwg7Di/OhPuXpUNsn5T6D3PXA9Yxf0zWQD6e0XEwOoCpWYK6rtDhANAZI9f2tfgkBSqvHQC1QaDcCVQo56j0QzKGkehN4jhWhUEYHwNwYC5YBuuFpqhTMymCCgaJ9QPB9YNICWHTNTJRcEB4QRUY20fa1vqx8eN70Ohn447FqchygC9XC3ETr+JoKWE78TGJaxrqt+kQQnOJO6aVx1RaCG2NBEFBDmdgYfqoSr3p6CW2gTqYwOksTredLOLnfMTGO5Sjp/mLkfZpfMSvASKmvbPtZkLTGCGztSbwtocoQ5eKXoRmA54ySckwvHcCrMyC0FDR5GKouJMYoRkTtO/AVA0tb8vJAynS0AH/rCCW2WBuNp0zCHKOgGHKwF+1PQsBqUORpIchQjQXdQCZjG1cyqJgc/GSOYc5WsEMy/UZmI2HVkTdhzBUghR1wFNTVKKjJseS/Up2tZRnFQw62bghQuQy8cI+ZRAuKSFfYfZDS1eVpuzUSzijgRxLXgyAgXMUtEgWPo9vT+y94yVSqCQVlx4o0Sg/bBhKME5YdBTg5HdyG6uUYc52rZQwsZS2xVBGQxV7EExppXKiJSAg1JKwz5DvKTnR5K9Z+EAKPUaK1qKPrR0IYedt3xMghJCfa1SIqeafexfVnynWEisIkQBoZiQ4KmeZLjgNQzUtZhnV9SdW4FC81zDzpcRZuGgEgilg3eYcWZK5gRiDjDBrCAFVRSEzB07bwPFmHTxHANu7zWTbIQ255UgprJB1tVRm1GKVaGDn3ju3BRlg4GVBBZq2niWW1+xDACZOqE6eFJulcNwIXT3pccgEsJgWtPBshKMJLRCcgC/b7AY2rliBMiSIZ1XExIMR/fjxzMOSaEaDNX+V40YDDIg6pK1TkXMMFxrhIbTxIg7DRNQlpmJ8KNIgg0DRKrnMwbTZBdUtMRRjOsVs5iC1UqMjSu1Pi8DDLSfdHyyi3XNLgpt+t2xvA4PdNQG0ZlrgPUD60/bvHUWT53cCwD45dxG7F1WERPrWl0ETKKzoDWBpsDEkfMAgDX1HtrxELc/sAEA0KtHYHqjlZKjlagnbO9yG8UdbTQfUNfdul/YB1/EylI3igcXzFlYOUM+pgQ3ADBOKDpqQw6WQkhtpdene+Cc0O/qXbgTIepwfS+U38g8YPW9zAaQZWPMCg0ZAjwPrFIjU+n8VEMg7gDZuPq7HBeoJ2pnWcpSCKmtQ8ExHMR2DiiUEJmJcmEIC2Y3Lsbd5idSfX69ycuUIayr44djGcScEtJlWwCJtBHWQZ9bi40XzB4HUEqdCcKiAFg+2lk8INio4bIGwPRsZ8pXPIidIB2sM4KIwAQh1QEsokZ2U6NKhKxMSFve+lp6IWSkxlsOAhfZDWXtxcvOmjAMBTN6lBEwCaxfkx3ZQ6uW2Tnv3Neu+NkZ0vlKNL2s+EZbzCoxFOjjGZ9ql0MaZYoAtHOsbbm4gvJe9Vhg3hsAACAASURBVMzUK0pLNiVAjBCYeRyESOe5PX7Rkk6Pq8RDBEMl0Ixwk2FFuaqwFXlb7Q3GXw+CC87qBOD5aLCfASNYn6eMCaxwflpjlQL6+hlA0r1njgemhCmg1kA+xqwgDTI3p6KmjpHMBvaYJoo67DIVBLdOO8YHIeL9oZ0fY0ioCG0a8anauAYtUG3kccGs855Cd59ZwRBVri2bVHMjh9W0iscnvE/Vw8PDw8NjlXBIWqoGxICwbzQqRXsM2krPGK4lS/FEXRdFKhKGok3WamCSId2n8/2afCQXTTSlNQfKBrPU0vQRsxhLhpgfKEuyN4ixYf0CAKAeKfWyFamTb6wvoRaq9361rwVqCKQt9dn/mtiHHd1Jez01Td02whwTSR/71yhfUiECLPeUuj6/1ICcTcC1/5Ul0l5LEEjct185YEQnQkxAODTpGU7/MtSqsQD60xWqvEmQYyWStLDHNG6SeNLRy1ONPuZ6dciBWoLJPLcaPxNKa461xc8kbFRpmLjzhrminY2PLGwUlk4W04TF5cRScUmtgNSWUi+LwfV94YwQhAKi1PMRkqWhWb0EDi8w1D5W5BzRgotClhFBapo3aOcotYVbdiMw7dNjnMDvT611Fy8DmU5dKdqko8f19fQZMkNpTlZ4MAL4IEDYNRNEdi2BlL/VRKYuHkkgfcBkTlF7xqpikilaEwAF0loQYY8pK0H72Vjqzk2cI1gOXA7kMhuJ1A2qwwxUbII6CFCsV/c65YTlTh2ip/2LXW6fpyB/UGxDXUfQm+vW1tZggwCFZPMmRUyWpgSAMBbY31HrvT9Xt+kk2aRjiyggICRI7QeOO8z66otJ/RyY5zqS4HPKvGMDPhKRWzacX7NoOQuuHBMqHkCzEtV8UxkSWMmQzBs2xo29rLljU6CtU2OAVlwOJj/WuAiKivshXuDWly5SFQlMxiVDDEXbUbXRMreWZNkSNrK+bBBovIDQaVHxgnvmZUQ2N1fG2keu/abBwNHhTKprK7Q7q6wTuIl5qJP1xyMEpHTzIGLSqTjep/q4hOHueaHC4AHto4ldMYJk3tEn4YAqKSIMUY/Z3yn6RD+IUM+CaOlNvlVAmjxVBqzfrCjYqVof98xOocj1JhMJxHp3KiVHJkOEeiX+emE99i3qhLZYoj42sDTXTx7YjEKoB+LFW/7HCor5ooHFvIZY+8VKEUDqDVTOJ6CGgIjMTk5ggTpXb7aOUAduxDlQ38cQd7TPM2EjtCJYJQ2icClBMhHYfNicHUspOUI93qVBilzn2O6eG0fWTWyKikh0sAjUceMl2E2iNucoIV4yEDNKjN7YG+pGMS6t4BQlQ218iESnwwyyCKbJYZ67ZV/mAUhwNMYU7yoEx0BLbSo4ypwDeoxBj9tNsmwKnWSqN7JOhNpuvQkLQEaRHq8KxjH+0WzM0aJhjyGflOAm8ISTpQVZwRD03QYcVShXMLc24yXCcA3D8v/Sc5AKtH+ix8+BwXrnG5SR85sGfWbdEbShAGNAPVX85kRjgMW+4gR7g3DEZysSWIFYpXuJAYO1ZMclaoRA56LK+yOwxG02DK54AhPKJ29+xwuy6UE2RU1/n0pmf1elHLlkyBcTFFox4gWrUJVkYxni+XAkfSWbktYdgUTYQhYAwOYj69IIB2qvsL7vgOxrXikKwYccsiEgm85ny7SixXKV/mIEYpVCtjnNUMqRqLmANSZhC7CUXAnbomEUdbKuhGTR7WVMqHgOCk2woBOQ5jqMLzlaCuzaytZI8FkXdUmBCoQDgDJzc8oLhqjrlCswFTtgPquCkbs+St0c8wFDkLvvpvuVUv1ECFTy9K+Hh4eHh8cq4ZC0VGUMsFhRTS4wwQQrGc3YaY/VwCRWAqIOlMY6LZkL+ABBTLpoVLE/tZYMIsK++xW1OpO0QYJjao3iN588sR/7hypS4a7fbVBVb3RE7ob1C9gwoeqqxVMCuQywPFQq7vxMG4G2LP/jvv8X4Rplba2f6ODo8Rk0Y2V5zP1inbUCy6OGqvqMSb8RzNKd0UKIaMlZ4DIEZGisU3ed8TIbKZwBctYXbxdY6Newvq2urZAcSwOl1mdFiMGyes2XQ3Bymm1txkVGMgFEfVUOElCUe9zV6Sohs5S0qDFgwCDuU1aViFz6DhIJpIUtyxdFAnGo5rQ/TJD1lUYeRBLTaxeQa4t//55xBJoOY5KhHCsRjKl5DKcEGjXFVy0sNMFDCbGsTNB0bzgSuWhe81JZiyagJxyowCtARZqLGWatsWraRn0vd4U4JADmqFDQaMBU90kluKabwx0pikZlHKWzkk3AE1CJegWQ1nK0ahmOm1SVu+5YXIfhQAeXdVWFL0PLBUPYYwCK3QGA/lG5oif363kdMLvmRF2f1/yuQvupNBHHSgSZC4AjBhvBykpVNckEC/HCWbfDdboWoY3kJxv0Fna5LYIQDFVqXH+DDmyrC5uGRoyDldymfAQ5G7G6ilbFonTsLEQCW75PxgRWtXZLZtOnwPQ9M1RoCyh1AZag79Z+WVfWZzUoytw/nuv0JBNcOWRIFg3N6izfqKPOZ9ZcNfpXRmqc1hIuHTvHhLJGjTUddVygmIzcMx4vcR3R7cZoIOqEfJxAsVskpooXXw5H9gxVvEJfi7mvFVr88YpDUqhWYRZv2FfpB7w0vtJKHlx1E2uoxcsNrRuRK/22pgRLBAKdz8lzJ5RE06U6yEGIyQ1LNifursU11ufZXt/BYBCj0H68fhZjWVOa3dmG2hj1udmQo3GfqcoE9CbV6/WNZdy1vAY77lLVltKhS3mhLADLuaXEypZAMK5TSFoF0pYKO5y/fxxMBjalIeyrHDqDao1WGVdon/0JRFpgRkcsS2LIdP6pKAME8zotpMshQ7K+xmDg8oJNRaZs3NDBzNZG5RXBFQxViTtbO3dCWCWGBRJ5HqI0lCAnWzVJDAJwnc40Nd5FZ5igs6zr1PYDF8HakJjevGDPt9SrYXGpYeeR358gGjo6UtoNyKV+5DW1SZr0KiZc2pYqk+lKUuZrSku/E4fNbQ0Gim4zgocCt6t3N6tUIlrQQjB39ZqNwHP1c8nVoq4JpA21+Ne2ejh2Yh/uWFoHALj/njUIBq7aV9h1qRymRra5zsaJc2rsnTrYrpoVpOp8+r7UCLUZJ2yKpnOn5C1Vos9s5EHhNvWi5iKvw74SNGbzLVOX920VqQpsOk/ujl3WGYZrCcWkeoMPAkuHm/QX69LIna/UKDvGj10VtkElgpUJBgrdWChyfut4Xgl3o/DkE2KEOq/61YN8lCY2e1Q+ru6fcRHESy7WgzGgMeeU0Lzl1iNx54MX2h9q4j6CzAlwkMqZNudmVHnGI5dbnM4CTFR8rJFyiwFA0RKggBB0nF/Zzk9FGQv7usKZmf5A0dzkmPPHLTz96+Hh4eHhsUo4JC1V0gW3w76zjsAUBVWt5VrVrKoBTVwApYn4axDGjlABSCkx9AYxyvX6N/3Q0j2s4Fh7mGpL8dyNv0Od57ijqyzJrfU59LVa/91dT4G8r45AW1yd+XGr7fJI5RmayEOeMav5Dg4rcdS0shr29dpYU+sifIrKYZ3fVMPCgk6Ey1Seq9AFDtJ6gbGG4scWOnV0utpiq5Uo6xzxkqMqjVZJobJAqVJJygQqUEwYzNRh6oZHy4HtsoOQEGjtPJuQqpKLmX4azf/N2y6/UEZOO1cRoPr6ddeLYkLnvk72ketatGUWoFbLMVFXI1kcpDYwDExFAwNAITiaaYZTNu4AoBoT/HxRtZwqZIBOlqCfq2MO99cAnSMMXTTc0pGVKM2gcFR5kKnX1eo5nS3aip0UoMq1sZIjndMTJB0bYihXcy4KXGI/oAJATGEI4i4CV0basgxMYBS3VlpUK7BpUq3HsXiA/2/f4Zh/QOU58yF39HBKCAeBC9SrE6QporF+Pzq5uuilPSrC2dB5quOOOkZ9D0e2pmIh1kvkOpeT5VxZ9ZqNiXqEoWYoOk9yxSpUh5oHWcHGJOCkuugY0qB0zJKMnMU+nJagiRzQEeeyWapC3ABCHbBjrGuZuPXOSlWdyBV/UPcVUJbYYKMO+KoJsG5orVeZkOvsMlDPqs0rDWCjtCEZkhmXL0vcVVTihaOQmQTCjutuU82RZQTkpulBqI4jK9SzqNCx6vyOFbLPU64OVK2qZuYgXmZIFvRvQlXMw0Zs11xFuLDDdU61G6Orceys4iCH7WgDaNo/JyB/0DgfhzgkhWq8RAhiRfcaelM9MOSov7jSfSZ0xQcAhjJ0VFAwAJZ/p9NQGhJhO3eLqJ3bUntJrbDpMjv7k3ig38JQR8L+984tKPWDHiyGqtybpt/CgUsMLxqqmEDRVqu5PdFHoKOEx6ISexZ18Yd2F/PDhvUnxqFArFNcCkaYnOjhyIlZAMBynmLXghq/FNxGCZNQFLENla/QOCJQAs9WtGk7Oo/lDFQjS8PKAKjdrzcx7c8BlKCoFk+nwBX9lqEO7zcysASk9WG73zCpS+HpaEvGgCjWvsVIoCwDLOgo1n43sUnzSStDu652/02tRUzEA9zTUdWnblk4EtlQCdFGc6gK7OvCHDzjCHUlIG4Ep5GB4agf1ZVtJBRNsqXg5BShNJWFAt0CbdxIS4be4eplui+whSCSBbUuzYY/nGKW+gyn+8B86nxRzBU3F03pip8DKKZKsFgNMkkK9At1nZ0sweJSAzx1bfPIRK0OOIomuYj2do6TDt8NQK2dfb9VlHF9Rm2mw7XG78ZskbDeFgFqCCRNNchqmpUQEYg7311/Hbdl9Yrx0s5v0ONAHyhtMQ8XuxDORog6TiCWDVeRqKwTso06MjqW6nk0cQ6FS5HipfMZWpCZfzU+IzhALqaibDh3TLg/UilH5llYcg0SylRVShIm2jggK1F4RQkAU+UurdAm2CphVkhqVBUG01oO0Modc75SRSHr/SozKWr6unOMVB1jlUh+84yq340qvar8oJ7G1mjJRbBKmUJUnotKVSpAj9dUreK6olP1HI9THJJC1YA4RkL5RY0ZxRXBQPnyAKX5Gf+e8SnZPDu4hyjoBCgpRqC7rciSW4sojd0mds/iFPbvHUOq+17KtgTqbrUFObP+NBkzZDpnkRoCUSPHsRseAADkMkAmdJ5nUGJDQwc0BQLLeYrZvjJjNzaXbQ3feprjyRP7saenkiV3/XYaiS4VxgMCmYo1BRuxiETshB4FOrfQdodxD3TekkAsEe9V15rMu4errAihskEQCWG4zvgNOdL9biPhOQMzwSxFJbAlA+KO3tBqAMCQ6fqqeRLalBpZcFA/sNYMbxaI9b1o1DI8sFftzjO/WzPyoMu6U5+HoUAxiKyBJFOJUFeYYUIJOTOuoNLSazANFEeoN9J6jqKXIGc6gKdfKaWYEhA6RS5MChR6s6MQiOdG/fum7VnRlCo9AUCxlCBeCKxVm60V1q8X6MAQUzIRlescDmL0tR9fZAHYIHCWU8UpRA2BaEMfR04q3zJjhJm+SvHa9btp6yvNx9TGWtvH7L2x7EWzRNLIEejUrSIPbc4qK1RAk1Ggepsk5FqlZDRamVVwRJ1D9EMrSMEJoc4jrc2ovEwbcFOpfpRNlwh0/9cwKpEtpojmtJIXOtaHuI57CN16NLtjNq5yXatlCk0VtWCgUuzM/FY7F1UtzqKl77WdWObuh1SVjABAJhJBN7AMUTCo+Id1tSIrPOOKv3jk+qGZjSrr4U7NczffIiW7dnjpfLmAtpr1kEXi9jlVMQku5zl0ucC85KrEZugEupkfUXPxDybQMagog0WL+ZQaDw8PDw8PD4dD0lKVIQOzfVPVe6LGkI2ryDZAW6e6WJEMyCYqyxAj9X/LtrT0D8USCAlCW068UaIs1BeXhnUEurA85wRIZpO45XiJSFtRfFJiOBUj3Kcsy+H60hZ451xirDnEnq6ieQd5hDHdKXxDfRmNUKl9dy6tw+6ZSUuF3tFPUU/VZ7WoxGJew867NG23L7DUWzEmrZUWDNUc2KLrplA2AJZpykprtems06bTmRBcAFHHRCKS7VhDkauVW+swDNa7ak4iIQzX6HvRkECztKk+fFjxvUrYykKqcbq0fjciZottUMaV/9NQcQ8kyJrqvmRLqYuglqp6k/WlpY4eLGdryjLSaRLh0CW8S02xmbQfVgLDNdpa3FjY+rL9uTpQMqSm1mq9Uo0r50jmOXIdRRltziD1tbHCMSVghO5mWJ4uXuIgXSTe1NzNdK9LYgRmGhNwQtmQzkISzNK6xSC0/U3DXFF+xt9HoRsjGFAWAfb3FOsRcMLsjPa9ZswWVQ+GDFO/JMtmdI6QaB2umBMCMOgnKEt1blm6+0mtEqWMEC84a0l2lQXaq1imrFC+12rhdmPNlTWVVmNqYvOMuwbjibRVfLKlFOFiaKnVeNGt22JCRbOa74qGtMU3zLhsL2Duon5VgQo9BwMGDBmkrqVb1ispXgxAyWzT8qBSVapsSReFDPVsGVq32uRDJKrfsU21Kp0Lpmy6Hr7JgnLbGCpVJs43HfaY7T8LAGFWsU7N2q7UQ69WejLjzcZdRybzO14p5EAc9v6GA5cuJAMC447yDjKXSiVDtQarrNHjFYekUAVT/8oa7EICgGQetri0LbcGl5cJAKKpqA5biSWSNrdN9aeCLfMmhwGgBaysC0SJWpXF/Q0ElVZbUT1Ho6b4qkIEyJfdbYnGMoy1FJWYlwGEZBhkSgoOlxPUdCm42WED9+ZKC+gME3AukQ3UExFGAqmuLLS+sYzb7jkc4bLe4CpVpCgmsMwIVZXqYR6WZAHglSACLuA2xsDlE4pU05Ws4hPSD6NpGQc4P3E878ZhabOMgcrIpRVEsIKBpE5HgHqQwx4D6fZRBWJHDxIDG3C7McqYbDBPtTi9CcowASXJQmjzEUWs98KaEzaFLm7FSG3qpU4lGK4l5OvVPWSRRKirWVGdge1NbeCPTMh2BardnajNUNOTg71NxDrVqTbrit8vPkWN16QwhD1XHrCsaWFo3RZcKSUAUHCEncD+TSG5oLchR9hxi58LR+9JApjOzSXJIGYTLBq/SCeySg0jIJkzaRYEkTDMn6gGVlvTx/K8yR/h4I0Cofbnil6kckIBpPdFiDpu82Yls59V54ZCCYpgff58wJ1ftqXocNsdpkmo6XShKBDICnXfB0Wg3BZ6XRGvBNRkSukzAT1Bn4/kTMpYlfcD1Do1v8umpBWcQRagbBFKTbdTTK7qU87V90wFLsA9P5UG7hTp0pX6/la7zWQTStm1ZRGn3PPIBFB7wFXgogDQhcdUcGOvEjvC7DJQ5QL1S9PSLl7W586pQi8z5EqXUo0aZCXAqRKYZ1N8jMchRqWsJXM+8qFWSG3KjnahVFr/PV7h6V8PDw8PD49VwiFpqQ7WE3iq01OGTvsv2o4Cq9JLTKgGxICyGGRDgNeNugtIrjX3gDA21ofQqllRhKhPK7Vvfn8bwc+UmVNfUEFRfZVRg6IXY2lWmchBjyMZMIz/PyoY6diJBzCdqGMIcPzXvcdhOGt6NRFm71XW6WyzjU0bVGrPcBCDdtbB9LVEmzu27dZt9xyOeEdqrWSRVLVdZim0bFJpoibgo2gAoc6H4RkgQpW4D2gKte9eA64iVdlgtklxtQh3ul8V0TfMQDbhioPznAFEtooPJ1jaLxioermAsvqqPSbDTmDPX0yo2rwu2phZzbpoOeuCEUPJyUaBVougM6l+b+vsLrjek2Dqeky0a9GWqN8T62smiFAXAIE6Rj5hOjIwhDPqe/m4BJMMyT5lpsVLlcIQHOgdpn4i64omZ9pCjDvMMimipgK+rIUgGCJt/cfLDNEyMFyjLfkxaQuVMLg5zSck0hmGVN+bvA2gr+sHM03973fUjaxQlYaKzCZVYQXD0gxm63ZOiwkBmQfIdYQ77wW2HV040FaZidxtkWMGGqWtu4xEgscCsqPmikKCMGxDO8fURA99nU5FxBBpUz4rQgx1e0G+FKrn1RTYr7BUJgWlWtDdVjIqVO1lV5kJGK6DHYfpDQvSQYzmGJWgG+LKamXa8kYsbVUz3nPrlmdcN/lWfxdNhlKzOlFHWaFmzgHX4L46XtNEwLhrzP+ACpySlfrNYaWak8Rok3mgwixVGgfwAgBnlkaXMUFYylhZrWXs1rGtllRpBiDjSuMEg9B6ZR7XOCSFquHuw0qZrGxSURFMb6CsdGH+ZYNcEfQuQ9gPIWNdJSghsCkTrThEp1uDrNDB2V71BNT3BrbSzXCKIZtyvRujmWgkV2xweIGOLu13d7AGz9h0LwDgB4tPRu+BhqWN6pN9W05u3dSyTR8J7mggWga6W9WTmsYFurq0IZ+NXa4cRulZVjDAPGA1ibAbjPg4bPRvS/lETOFzwD0gxIHafudHLVqOJop6LtpPVjpv6KlyDYxLgHHnvxQtYSrQoVjjUhhM021D4YZ9Zq8tng1G8uWCnNn+m7IhbL5pkAhIwVBoejmeCUc2i7Dvyu2Z3DpA0a5VWi0YBvZcxIByrekKrbg23jEdWlzkZTrLRxpUR11yZf/WM5s6Balo8rG7zFyraFtA+b9Y6VI3CK7iT5ApRdFulIxb32ttU8dG42b3tV1VHSjFxeQgi1hFJxv/P1glzWLo3CL9DVLlZe7WlZ0yhlznD7OCIViMbBQoK91Gy4RaV0ahKisRuCyWSMfVQ1MWAYpeZOlTqkusXa86mxMxdPqJpYbLLAS0ElwWAaDnnkJVptCWAIxh158qI+g+G2m8XagC9anO0+xtYNZFkD4Q2DWRTaoG60aIyYgcFZ9IQAKkqe1gPrL3TKTkUm0Y0Lg3tAKyv1HaLkzp/dFIWgvPGLIpoefRpU7xXFUsG4lCNootlFJse9Y2yRoWwVCd35YdJIZcd1Qq61RRmJkW7iayuRKLYSs0mRgUJ9x5CUeH67zuqmLDBUC+SfnjEyxn4FxpaEYLVIE3zFki5PLWZAi7YIMBAzhs2bm8BRQ6oKm7r6l8I5HR0gj1ve4pGE7qzYJUVwZb3CDRASUAxLRE0CzxjI27AADrkg4+d+8zAQD7d00gXAogN6mNhoipoCcA+3ZMKUECIBqqBzxYk9nviV+qpyOC2ijtA1K60P5gyFaULzMN1ykEmClTKJXv2aR4EIdVTuIl/cDqY5qSZuqYrkBCWAAycPVbox6zfk1GasMrWjrwpM9Has+6TjFSJbpXAi2MJVw0SAnBwm2M0hhbkoFr/1sQCpT9BLVdrsOMEVhMKMXLbDJVrZt4RTGA8geZNKhiqkStrYVByQFikJ26nddgSc/BUPmpTSnLMoXtBFTWyfqDow5DfYYwnNLX1oYrLqFrsBZTOlc35wi1j7k/TYh6DIm+h/2NEsFaNa5hP0Z0r5qQsRk20jWFAqc8JPNMl6Ezn7m1Q8xZL2GfoX63U1KHU25ueM4QDhyjYM4BAHmq68Vq4RC0c+uPTmKX35ENIrAsAGlLL6oVtrQnAIShtK0Pl6iGYV9JJeqFNsgwGDJVd1nPXTZR9V1C5147RdH4V8OCIcgJvQ3akh8je51MuPtOESHojdYMlvq16axkfKem1SSgUrWsorwrRJABA91OkU8PrRKZrVPpWCaegzcK2xCdJEPYcTnUyXxVcLrAKhkpJckIUlRSgExZQlvAo+HuU9iv3D9SDIlRhMIutzWISTM4RlGPhmxkjk1cRtFko7ndhXoemPepenh4eHh4eBgckpZqvKy1pkroellXfL/R2sqao0FESta/Gi8ramj5SB3hN1kAHdOZQ/lDTGQdF8xG1vKC2SRrVigt0jSiZuM5Dl+v/KFHtWcxl9Xx33tUaZ3iN2177rQGZGsE6nWlzvXma4i1Py4tKhr4JCE6ooNIW7H5D6fQ0FRt3mbgsbPIZQTr8wRzmqMr0qD/JpdAXjS0r1FbAPGSm9uyqSJ/I23Vhn2yXWVAztLLxvhoc2YOp+JJ7dPW/qJg6FIfRA3Wl2b9T5WOG+YYYZ/pkH03Njv/MoAw3Um6KWqZi3ytdukwRQlExS9rUx0EECw76y6bIlXlB8paFL9t2XNWq9bYTjMwlp6zarIJ59+KuszStvEyIRtzZRtFQraoRjZFkIcNwQ0jPoythc+EWhO9zZpxaQqEdysXQdxnNsqTl6SaPRj6nSoMBR9dq5DMzTcja6EYV0A2oS3tpousNvfB3GteuMLyxZiEHCuweaNa/5uai+iWalI7eWI7HJFkoEQA2tIr+6ktgJG0M+R5YAtFlN3IRuRHi4FlQGozBCbJsgGG3geUmyIfkyOdkgw7IuqEfuzuR9hjqBYxMT7asKtcBdVuNjb6vFTuJdP/1DQcB4CoEyJZMOcl9DYC5WGmSwS3+0u4rPr5SlNUYyFGkDk2w0bWmmIs+u9wYAqlKLdKMHT+VwqVda2ui8CFu4eywlhQ4NLrbByGuUzh0nAYYaQHrLFc1e9c4X0R62YHFctURoD0KTWPT3ABW5PSLC6RqCAF8+ADlZypgCBivRhSoHtMjkiH7BdLic1BDHtKoFnfhnALqqyR21ihc1RNOHkWYM+8omcX+zWEgURft0hrzjHk44YyIVBdYHiP2rBrS3zUP2QrtADDvQ1EWijFAijqTvDEAxfskFfajTGq1DTVgsUIGJ67uYq6ozQsUDnemBK2pk4oL9wcZG1uhXTRUt+zHTFKJ3hM9wpZaTtVbS9l6bxc0a7VMdpyqoGmSSvl3ywl1WG2+TKYol1NdZgHd+1AJUiq2olGcBrZQMumQKQ78DR3ws6pjJwvGsAIBWoqJZlybyIlO/9RV21ygPpO2VSVlAAl3M0GV9YIVHLwRR2k0y4hTB5jNxyplTz+ywhMd2EKhy5lR1WGIphduGhWArBaEhgrbM4wiKHUCzfpc+sGKZpM53abADVp1x8TDP2NwrYDY5nz97F1Q2yeWsLz1t8BABgL+9g9VP6U2xc3YlnXoqaCI27lyLua1uWE9oSSpvL2NwAAIABJREFUcnEo0M8iDPZoyRGRpUKjDnOKFQd6G11XoNoDTpkqGmRzfs29qd4rXrj1XzYqcxe5VCeQTjcxDLLECBcYLbsWetUG76x067sYZ6olnO46lO53cyVD9ZtIC1kZuvZxRds1Y1eV4ghk1i13QpXn0C0uzagcPTucUr70aqces85kQhUal42k/SQLTkknhpEOVmCVdClybgSm6xabcQFaGXgCcKdPgEvw8PDw8PD448AhaakaFM0K9amd9NX6qCZoIVrklkIaTOvQfp2iwjJuq4tkE7B1awEg6DpKFhwYrtWJ7KkEJENtjdK0h70YYpcy4RZbKVitRKArKilayll98b7ogJQML10fTbbEwBaDkTQX15dSJZfn425cztKrJHGbAIZKwWtrwTGVKmO0axk6S0+dA5C6YlVe6fYzmFa1WAEVQJTMBhCGrh24AAlDOZp7U7ViKXDXEnWUpVubIfuZqdFctNTYbRJ66AKViDurw1ivsaYxR1JqBMAy990qi8EzpeGbxgrpTGhpcJFUKK/UXZ86uaJzzevhGlZpEl1NdVANHwC1TvMxCZmYVCJmCzBEXYYiCBGsVydgxIC9pgGA6s3ZuM9Y4QSmi7kSc1HIMmQjfVLLGiHXRUUgGDAIXYT1MneFBAgYrCN7rmLMUeAombPq61IVFtGN1IkHYDo6+hlbdo50adrTG8P+oTLtZ7pNNOrKzEzGehCSQaa6wAYjmybWzyL0Zxq22AE4QWgLLuMcrR16HusMIiZbvSlvuXvNBUM465iqsk62YAcbOGoVUM+B7VXLMMr0DJilR6MuG4k+rxaeKBvOFRD2HRNGoe412nVsjBmT7SVrWKEpYetIK+vRuSmq9zdvYYQKjrqV4LiGq09soqHNpRo2CVC0tY2GLkfdKowqY2oRROqe5SBj1pUgag+ik2uuWXrU1ed+AvRTPSSFatFQNN5wrbTFxtN9aioMNVS0XH5ilW4TEyUmx3u2Ndv+QQjSuXTx/gBRj9ni6rxQggTQlXQq7ZcO2zpr5e2e5dRKjWgxQFm6dk/DwwtwXWEpyHTEnPHnNIXd7NJZVzKOuKquYnJFRVJJI4hUqTObL8bIPrQydhRPtMxs9RV1jAr9VdfUp0mDaBDiBW4/y8fJUaYSyCfcjmRyKHmpHjzzMIq4Inge5FcRNVcWsprrKkN1DOMj6x4u7TXb3DsTUSydgsMLuNeViEbzGR+Y+RgN+68+8BRgZB5XbLSoHK/iR2XCUX1lgyEfc369sO+UgKLpfHgiVf5QWw4TpMpjAkine2gmuW3ZF/6ubudRhuqYtmmEIJTah1v1bwVDQtoBBmu1sJkuwXQEcdhRJf+MX59XGoqXtUokdkNvkPriRM25FaJljqDPUDa01IsJzYaOQhYhblvYjNjklZZuS5KVfIveMIaUHFx3ZYpDgYFOJysWEiBwDdghYZsKyBQYrtVrLlNR5jZWou5K+/GcqQ4tRt+JKgX0zXokc2/cuXjGrY85HGhfYcX9E1bjDeqA0K6AYACklYYJ5uA8YwiGbOTc1WYNRUvlRJvvGjqZQuejzccA1nK/UdWL9B8MyMcZhIllqCiKxNV123ZsQ9csgFUqqNm0OP3b4YRLOeS5otvtnPTdeqF81Pcadpkdl4k8rrbbfLzC078eHh4eHh6rhEPSUh1OS/BU0WlcR8+ZiElDmVBNgA1D+56xVMGAxcUGaFlpyUozM5HBDOmc6y06WMeQrdVNoRcDWxh/YqyHXAQY5MZEdNpt2SRQIi29HCyEI5+FXafFNnYHlmbpPEki2KBU2mIpQdTjI4UbTDN2YoBsMUfrViN+K8gnCOVYaStHyV5kc+xYqebEBEmAKgW2GRD2uU0ah3S0MXHXBornygJLdMPleJGQt7UFGql/NhCKqta/o7lkTOhtrlR6KR31LiNlXVm2IXdBEcQq99kUXq/0aBWhe81KIDCWJsdIzqqymt1nokIpGwtYBuq81loNHTVetNTcmejidLZCVwfMBnVlkxIskpYO5kshosNUeHUUCizMN1H7raJ8k4XR6Naxe+jA6yBggC5mHg6AbIyhu1W3SFsIEWn6sawRZF1ioO9v/ECIZMFYu26tR8uKbjRsRrzErInPBIAItmg8BYSlBXVxP1+sozU2sI0hBDEUujxPwCW6fcUhF/0YcSNHrPOLu70U8gHNrdckeN8VKlE1ffW19V0wX5Arl4JlXFrCPv9Bh0Em5J5zuHrQTDLIiGwrOyaV28fMXZXaByr5p8IFoclYjcnQnfUZd54yhYq+1vdP1JyFXm2OXrQUE2RqEFd7qFbzUkUMgLk1V80PN64JO1cStgGAsb6Nlamax6vPeFF5jgPlkqi6LaJlxx6N5OnG7tyq4A7sb1RlJn1MQ6M/ASzVQ1KokqaKWF4x1EltwKH2rYmaK4SQjylBBwDIOIKZyPlGmmTLtjHJkE0wZOMmIlSCNdQXJ7fOo6+F6FI3RZKUtpTa+NouunW1QchBiHDW+U3LBkHU1R/xfGD9WWrMQHeLTp9YP4TYp56O5h6OMgVk24Tswy7WwXrl87EPROkET9kgyJa5MA4wgObVE5EucNdVJybIWNqOMzKEfR31lN/KbLyAo4uYcOkqImUIlgi1WU3TRQzDSoS1KQ234t7xShoO0xuB/lq8wEc2krwNDNeQ/dt22XG59rqBc2WTCRzdKxIdkVjZrFl1k6nQyBQAoamQE48KXzC3sQQZbFH+8IRFDAYx+C5138o6s3R4WSMUJo2FEaKkRNFR0qC2pYM4VBO5uNgA9UMXfRm6VJmoo5UmO3mo0PkMTKrBD9YydI8orYBp7mK2Z+3yVoZiWiCYV5POS1dYXaQYWae8dApmMHRzI2P1j1mXAEOgGzoQB5YLjn5dTRDjErEuvC8lQ6GjfVkWIBcJ8kI9J9FyAKafu3ApsGktgEp5YdpZL1JaURnNdo4JJbiOEhYJqQInRjcsXPpUOFT3r1qpaiSewBTN0OvRCE4mnXKlGpEzm3pUXcdMAsmiKZsJyB4q64CNRMGHfZeuRqziu8yd60ZGuv+p6buaYqT5Ny+dcCsazv+v4ijYSOS/eXaraTkmEj3Sfv1q4wFe6TVtxmgjvQVGFDw1WP1/iCeEQAUOUaHKBFPCZMhsZRGzMIyfMJ8SluDn9RLo6VyxpUCVMtQL0XRKUa/VhmkDW+oCR23aDwCY79fQXVKrkgWEjBjWrVUOhcVBCtI7ee3eGP8/e2+ybFmSXYctb05z+9dFH5lZ2VSTAAoASTUgJTOSRplM4pAz/YD+RKahBjKZ6RdkpgmHlCbCRDKYhEYiCwVUk5mRGZHx4vW3O603GuztzU1Ma4BAXDcD6kW+9847148f973XXnstM/MYL2hlltcaOhg6W84qWMGpfWmjm4jbFiiCSkoobcUaENAHNZ7ZoZRa+SAOVFPcmjfud0HSLW0CuEiHnrBpA9XtIbmn2KRNWY6IGWj7VMQAxAuPxTep3rJ7KeNGIgf6vUhqkInwE+8FQPnjDZqHCYp39GyKbSIB2QrAKgUM8NkmlpGRvPKAyBxaspYC4VmejzPGcpuswQT/v7Ch5pJ3eS3aSz7Y+Pd2nxs8/5T8BR9N9njXzHHNxKL+BBg/YRWsTpEDEkhKcVxXKE+7OAf3b+lkK+416msRrfaKvYeZpmx9KDICm0vPwkyB3aesTnTSQxqJ6Ve0Pie3DuvPaEK6ZwYqy1xd6TEGVMIjKVZ5AW+RDrfsNDe1j4EsQP3csaav6MA0nFlWM4uhp/U+risUD5murpAx27V1qv2hcNC7VNs0k9Q76rRPKkmCJC2DQ5McVXxmtvaRdAZ+fOH6w9IREbBJRJ0c2ThY+w+Zvq3ONcMBvUPMQkcF6JaDgh4p2NFMluL70o2PqkzhnTgg0rmM5JbXPIWPiEjetqIbOujDIUu12PChiTQn496Q7qt7hFh/thOSHgx7purT51Qdzwn/njSH73F4z7wC9OjhyrRW4f9h9Kkea6rHcRzHcRzHcRy/o/FBZqrVlYSq5EGzt5mRmLlZBfortc4AlAWG6FYNYKNy/rF1YrAFc96QjcmpwfcPlFEo5TCZU2g3jgplafHdDfW1jPd1dPSwFWWp6iFlpyHDqh84YwvRnAX8A2WWeiejt2WoSUZIk43VAWByLaB3odkfqDYWpua68jzNR/1A6jPDLLALPUquMZmRsp886iw2qa5pJiK2thDLlG+3TjU43VLWNC5UvOcY0WpGnwNk/cxSGxIAuVP4+A/eAgBOqwZ/tf8oikHkQhNeUaakMvPkEFl7jSiuHyA9n9V24n0UgcXKn2XIWJSeGdHZG5S3/eTZLjzQX3DWuRqg+Afvuinut1P4x5ydFhb+niZLb9SBsEfxyR4fX5Dszm9fP4JsaN7qa4HJtUfR0PVNnbLuIH4f1kF3llSZmmcOck4pyrgvMP/bEtUD3df2Y4n2KWvxNhLT72ViiGugYKa3z7Rb9V4crLmcSQ5JcxJKBHJMa9qcj4BH1GIuS4OOlZHUXqbSykiogw9C7Q7RY1fvJdXaQ3uMAUxmrh1F+OcG3kiom4DdAiOL/vvaorgpUN2xApJKtVFAxPo6QJ8xT6jCWtJDQgHof9N6l0ageshaZyrELG0o6J0BDmv0YQRGOzw51thYt04MblcC9V1Q0qIabRD6mO6zemtN6NN4xms6c7NxlUf5IA8MAgxzTXT+LBjpi0IrRaq3ygGASB0KXv1dgQeA5jIgKmFOTY1onPE+jw/yUPWKi+3TVI+LG37A9QXgWKy6vE8w0bjwZCrcynitULfTjUD71KWNxUiczqh48Xy+xmagHW03VLi6X8B/S2/f/Dq5jHTnHuU7HSFlr9NGLhz1LkYpu3V684qdiDZqkbgTDpseB9R4V2Vw5AuF5mlqQ5m+ZahWC/SnQMebqy8d9D239nQARAatuvRy92z8HpWSPOJpI3xydZE9AAn0ywTXxvrTQPW39hN6A/XEwPT0oU++WOOjOR0u32zOobTLNoFEAvLSH9DzZZ/m2AGQUdcPhxKJSAei4fURelhzuURXpq8B3mizulX43rDysLWLxuSlcvjumwv6nVGivlTQAZ70wJwDl3HuMZzTQzr56AHLusftfhpvMJBo5q/ZiCFs1kJk0CEdglf/lA/I1QA3sMWdcpBvaD0u3lAtt2GDhOHModiyatId9eO68KczswQxpvYmgMh+sZfRZpC9A2R3uAmbOUt0dgqykag+o/ekUBbbNfWdiCo5EKmGnlEwAIcXEZKWgzio19lawLELka8cRLBp7BX0vY5wsJu4eP3pVyW8Sj3hXiSikmT1n8BDUG1SEDITxGAz/DuWf7I1MrkCqL+b77FKtXWnUluVV0wmCxByITByYDsuaS7zlhaZ1S9NlAAUB+WS3IlKDgS5BiUw4dJz0XvaW0LfKn1WDjK0hwr3WNBFQyCQt+wI/4MA+Qe0iCjzKTOyEmifG048XOfxvo8j/Hscx3Ecx3Ecx/E7Gh9kpuoKD8GwRyKsALZ2McwQWx0b3l2JZJxcOspSObyz0xSVNZ8P1ITOhIfZqoPh1ph1P0HLmJQHYK2EYPhqXCQD5KhqFAr9GWHHzIiRGCJVM80gXpn5G14YwCQrqOVvkaLdM8riHItS2NJF8kfxIJPQtqKMV2+DfquMkWre/A8QOzJnMuaEhJw1KTPvUN1xZseBqbRImJoXGH7S4mRBk/LwZonZU6I8PltscddTuG+9gGk0dAYpi+wa4X7C/LjsWUc91ZIgsJCF5IQaYQSKtYwMy/weJV8nXLM/O/TEjOLmjYTsJZzkV+1NiWnwYOX5CNmdF0jm4xUweRoFWnG9nUU7M3ldYv4t/feicRjmMsHXEih3dP/NE4nNzwfUC1owSjk098zmfp0Ut+wE6B4nQHNyKSOsrXqCHx2LRowCUTBAKMAgoRxeZWYHYyL2uMIflEyEIyZ7+Ho4dREuvLtZRKNzL4Dqhi35RtK3DSx8tUvokdek4hNbWUofoWExMfAskKBvNSFQ8wQyVm91nDcqAXE2OojU7qEIPdEZqze0jPlMF9wzzJ3WT0KIiIme2qcEsvkwKbsVntqpXAZfR1h7+IHmdkaAk6PP5tcT+5f/rUbAhPuVQHWb3klbJnUlIehZ5d68MYvdyfR3x0Pd7mKLaO/oNSFY4b0Inws4zGDD/QddbDPlzoCjotL7OYQTZEg8ApZfgGIroFoVF5GtfTw07MJCstuE3Eu4izGixLZRQObx6JzElKXV2qbCOCZMsB+Z1WgVyspgELTBqR4JsutwoF6S1+0US/kFuBYisXghspe0l1CNxOIr+vewEOjPExtXtQkiFAao7lPfW8Eb8jgjiDH07QUIFKDD3EyTylGxTz6adEill1EOiC0eqvPoLgILmeEfPlDGiYNlU+4XH9/COIl3b6nmvHi+xaM5HarOC+wGmrfr+wVE4Q4E8MNBkW9GcR55w1dd+vwk3J/3cibWqhwEb5L87yx4GGcEBwYfUwBQax3nOAZrmuYpQPWqS/Jx/TS0oaR7DtJ45vGAH53QxC2KDl/fn6Pl9qaL/49q4QBgJnSgDosExe2f0d/a/mzEZNlFf9LdqxXqG37WPnl2uoL+bmB9mhlQsmvKsKAWiuD36yoX50eMIvbOAlzLC3M1pjq+02AfVj5ktYfki3hHgUf7LWGh4nSIRt7FrU7vwcTDLNKObCcuepSClYBiUCYAzzVatDq+u670cHMLwVwJ/aaKcz8uiE0cIGX6hfRZggEHQF6xUcIwM80QLIsZXF+8yMzkw2Ea5kAmIX5XpNaY7oL/Jt9XsQNkm8oDrhQH9dzQB+tVege9+AFbvqCgBACqjaAe0+A0kw1iQCfvVdVn8K0AXAhe9+LQaN4lSU2nOLAOLWUCGIPMan/Ixg9BCN0/K1yp9x/+/SAP1WD9BpeILCF7iZvmmcH8EW3ku9spVJd2aHlZxswVyxGCF4JzEkVhYQxniIsGs2Cc3NbYbdnGykqU0yFpBHeZlJegmmd4GVWbS5b5g4NU+CSrSFZV9HOTNwrlJkWB/VkexQqMy0yCzSZiRbFNm/P+Ywe3MJh8xUSoDmgf8fUeGYiZweRvmVSzT4clQBtIIKV4dtYAqJ2gfZbaMaobGZv07dLi5Mk2fAu3D3M8eUbKELU2mGiax9YUuLwj8pd9O0F9n7Jr4ZCyF3HYQwjhY9YApNrOuHCciaRnESJ3r/2BQ07zJBkz+4LaRMrrUHfPDsuTtDFUtxLweb0va6KfAGqfrt9fOFQvKTt9Omsx0/SDb/dLPNzMsfwqVGs8ti85QFtQIBHaM4YTge2PWV+5cDBGorukzD4PkszSplojAN8qgOvW48yn2uLpCKEc3D5kjCLOVd73GjffKLNIjkrhv4lOxfYPasWJjwXDwsHP6V6k8vBDVv/noNfM2LQ9Y4A5ro36qYVo0vtJdVIZ/1bIHIPmtrwKagSZqIun9yrVfdOzVvcCriRdW4ACx7xtJhKVOpKWDIFdf5JxCxy1oIVM1RfpvSZbPL52RWsw7Eu68ykAtpSRDivmL5yndiG9F9FGcFg5CHvoABUEHsYl4jsX5kqldmh6BqHOKRNCB5kO2yBuEqVAs/Yp4XHgUiMtDhCu2LOatZ2F36tuZLRkfJ/H+/8JjuM4juM4juM4/p6MDzJTdYrrQasEUZkpwVOBlQgnsHtgkfKbAmbFbELlofZZv0evYuS3Ot2hUC6KgzsvcMeMzebtPIp8o/AY3s4AZjLaSiVfxyXVn4IiEUFn9D0yVU8RqK2z743JDUI3DHcGZ5o2RcjFBjBPEGHLRy8esNlTxrl5qKNoO0oHfV1E9ZYcoirvFHCrYtuF+aMWI0s6YluQjyZfptil+spwmp7B/BW1aQTvz7Nna5ywVN27zQLnJzsInuNmLHCzo2xrfzWDXnMbzvmIrlCYvub6nE81nKBYE6N1m2pM5HST4DiPlA3Qf+T5nVHE32bSdZH5agRUl9oP2icedmbj3wr3pPpDRmiOIOi9YGELzoieNjidU5qzaWu8e0fwt28UVr/UkTl6/yXX/wEUW4liCzz8hK//6QYnBast3c5h3k1jZjlcHDYsyLAevYBvFSxDvLJNEpcexGKPbSm1h+MHKs56TLM2sf6hjq0+0gCOZS391MLXNsLjQIInXcU8Bjal8NJD7nju2pQ5BkGTAKN6haRy1kmConVg9VrAZdl0qPM2iiB9/pbTh5lTYPkCh8pCZsq1x0w8JKAL5YM4cIPi6Yz/joYDU3qvS1YJE7sMFcs8jQnVENFLlzxaww3TvEXjDO0P3IpCPb66lXBVut/h3Kb0KZSZuEUw8EZo7jnjzJZ7qCPDJ+Nx1VEdOdawKxzuSVkpymTvjtykjFaxAUU+32oAkJmWv6/jgzxU22ek/Ss8oksNSoaGwgvdCYx8aMhP93BcG/WNhmoESoYphlOHs5/dxmtv2wodu2d4IyFY3g1VIk/IBw3dJNr7uErwLEBQYg7J6oYh0kqQjmyAnlzakG3taTMB11RbifkrWsHVxsFyq8nDlx72aY+zU4IZR6NgTfCnSi938a7A9G3m2iEO6fvDIxu1jMe2AHa0lCZvqC6dy7iJcPkOqN9xfYut3cqfUd2wHwvcsObrybSFB3D9QKePvZxGCElrwD3nt3tfYPpapb+V1XKd9lEiECCHn0PT6QChE+kiWbUlWFG2EsVGxsCLfp7/15LzT6gX2YsBgn+u/KY+OMB9FhiN08MgyUsAP6bI5WzRYN1QILe/mqG8oR+s7gWGE6B9YeJzqr9jFakdsP3MQX+05/mxaHuGapUDTgcUE3pOBYBuS9CnLCwmE9rB9psafmqhuG/V+hJqSV9LAGU1or0OclcePrSrtBp7Pryq6QgYEd2K8p5kUVmoqQvnJkQrgYDWzkfAi3jAu06h4vqzzHq0fyhvp/r0jqByVO8Lz6aXMZDwMtXt4Qi2D84ucjxAkw9k/4RPrSWhtSpq2orUSlSuARlIOpJqmyMHP8OJpz5cAOqBiI8lB779CdC+pOep1+qgrSXvP90+97E9SLUCZuai21XQ4gbo3oP1oCuoLh6cjIQXAJdjoKlsEYJKO8EBnJ+/Iz6THMwlOV3BRKowHT+oK9s6aYH70iezepfmF/5QkxiCgu+8Le19HUf49ziO4ziO4ziO43c0PshM1RfkbSp6QeQHAGKtUN0nKnv7wkQIaew0fGiaHyTGpcfAhstqZnDHGZUzAlgX0UViuLDwp8lMU7CGb2iydjHLNIloIQEzSky/T5G24XYYWwHwqXXGTj1cgBxLi3pKf2voNEylgVf0YcapwMOXDHc+6aCFx/0rwmL1LkG1svIxMnUlESLyyD1kC+VaQFgN8T2zXQ2gg8i3oig5QmxFEu92iqJ3AFj+3i3OqyHCuqU2qBi23HQVdt8uE5FBJLgaXqD+7SR8if1nIyrWxO33JarvmFjVkCGCyczTI1PaZ6xDwaYIDCWKUcTIWjXkXBIEzeFStl7dktH7yApcUjvob1jsfZe1HdUsNh/UkeoEBWsI9D/q4Rt6Tu+aFcSGvq7WIhJD4IH+1CUt4Hsdoc7+lEhei4qefT9qDKxIJLVHURqMAz0nN0oUU7qRp6dbXG9o3crrEtWtxHDCz/N5h5qz2GZXod1WkIxKuFHGrFIVNrY9tUPBEG2GHYY1sCsgtgo1iyTYicd4yvOmPDmlBKGVdzoyX53OWtkUXS/oTXuFVKpw9NyiDrFNqIT0CTJ2NaWjwXVIjgI5lVYNidHaPvaRIVuuyekmeoaadP3mWYZiOAFXZmtO+6iMVl9Ta5bJBFqCtzD9Lt9DS4zr8J5Ik9Cd7hG3v4lAanKRQClcyuq7x57QldCqVTogtHtpD7HVMVsXJhGm4Bk9CbeVuS15BYzB6aamzD10BOg205uO7T/8vawlTeXQLsPMcW9g7V+feQ+/r+ODPFSLe4JHvUwbXLEVzBblw2cUkbLvrUCxoBVRXYywVqK9pbfDtgrVknaBwRbwEwf/mFfKXQ19xVCwSi0otgbs6QjN7EvTauCEVpNvNMoHccjc/aiN9+E3Zdw8XOUiJJPbZ323Psf5/6OhW3pbdi9lZF7aRsNvNIpsw3aB4ecR62ooHQZo1NccdIxITE9J0M2BZB9vFsOC1GFkaEcw6YDtHjv86OffAwAmesSmr7Gapk358pJqiGJTRMeMcH3R8uZ0JbD9Cc3bk4/vMBiFtmcnk60+EEWXg4gLPN88zDSpLTnFdfW8dSCYWJ/wZ+lT/TW2LWhuQeDNSb2ukySizzdPEOyo0t8LB+5w4qAuy7SpicxuzKZ+ZMse9jnDdTgLsl0Erz5c04JRaw3PrSfFaYf5pIer6UIvlpv4+6/uT9HdURAw4f5k94I+nNJJ1F5pB1E6eJdqnsH8YT7t0fDce2YCh/lXTYISfaOID8DrbFxZoGLewSiBUUa1rgjVAgdtUV7RgSgzeFJxH7YrPSB93MiLbar32Ul6RohlAn74jcgclHCgjuYFMLni4Kqn4EjmcpgRwk+1V1exyH9QEBsEygz6tHVWFsmtDZE4FP0FGaeHeVCtiHuUGgS8FrFPWLYysuzNNNlX2qmLpSb6YyLuE+peo7xPXICDUk3pYWR673Rmjk4G5nyNjpyQwjXGWer3j33obVrHEcL3gM6YxXnvcvj6KFP4ng4zp7pZXugPNk2hn9MvTGxIn580eLmigkUzlvj2V0/Si7oaYcbQlAVAeYy8WdVXOh7StkZc6L7wgJEwTO7REwPDkbraKozz9OLbZz1WM9rsBqPQjGmnkZ3E/FO6rxerNX799jFd767A/rlA+ADCpj5JvSsPahv9uYcN/X9WJPlFK4ikwy0weiuihGE5eIwLERvgu8cW4oTdcoyEuS/ihgQA7VO6xtMvr9CMRfzvndHYd7RjNNephhhJIqFOY1O9aPMnLZ5e0OGw6yo4JyD5BPeTpBVwUek0AAAgAElEQVQoRyaiZAddaJ/wKiOVSNI1FVzrkmPagOAJDQgbV9RlBTXzq1agvqa/ZybJd9XMcFBv0oOIh6CrPCEaoL493abevfwgFTZdQ/VM2onBlI9Zmp8b2sPZxtAuLaZndKPOSdxvprFv+nK3iHaDpbaozzp+DhJeOzziOnupLNYtrWEpPM5mDd6tqY9UFwKTigPMwkQy2d31kj970J/NvGx7AaezDE4gWsnZqaMsM+sJPcicov4xZYcya7ex0+x5GpG8ejOekrBJcAGC3vdQD5VjVoMvDi3SCI1J35MmEXXy/sruIvXPCvN3iT7hM5PwgYjZriuSBKNuRCIfzQzkTqO6yshJ0QuVAgvN73KxFwc2a3G9tJIDEh/vK6AeRSapGH8tkxS0dQr+VZ8hVTJ9/jDnPjs9cunEPGvONZNVlwLsEDDlYjByJH3t930ca6rHcRzHcRzHcRy/o/FBZqquphpDeacOKOWGfUIBoLgsY71Mnzm8XlNatr2dkQpdQN8GCRvqEzuN+i4ZZXuFg6g4yJmZM6rXBhccOygIro3Y0xHusSM2JYCTaQfDhssj+z+qvYzXOWX49G9fP4HfUNZXdpTpRlm+wkch+1wMHGDG4zZliOMJi6/vg2EAt+LsJKo1Z1vs+RijZA+IS8pspnfiQERj96nF6Sckz+O8wIs5pZyvtye4/vY0ZsbFkOj2QSwhsC/tzKH7km767HSPLWdRQ68PaoZyqyLECIgDQQyTmSdAJi9OOYqDiDxvYSIkI7VMeJ1kBKuHdJ80P0kgHSJjUXpqKwhsS7j0vfpWHLAdnU5QXC6lGCT4Qh01b5qXG30gegEA/YZupLqRKDWwe0ypztlP32FeUkrR2/TqDyuNJ+cb/LPHXwMAzos9LtmJ/E9ff4F2LPDZBTHcH7pJ/FN321lUDCsmI0ZfwDFrVQyIaYnqOSvhz6026u98Fr1L78nByOQXickbslNxmPluE5Rri5QhmqWLa1iv1YEBgFcMW/Mcu8Kjvk3fDwIhnl1YwpoOcDAAmIWNTGlnBHynIPfM2r5LrUnByUX7gDYkcwkvyIMYAOxQYPIuvQtep7YUrzyKTX5ND8V7z7j0CckoyYg91pizLoHwHGKrTFZDFZ7g7IgGZBknwM+U/7tXaX4UO9OE6+dM4VzwIQjbALTObZ0g5qDelC3j93Z8kIeq3ioyKEbq+XKaoLSAG8lFjzOGXY1VGHjjFoWDHyWwCDuehA76pJZqOAGu0ftUD+kf22i1hVFCrgugox+c/2iNl5/QYbPpa1yv5+ge+OB4PUtqPBMHvVVRSs1OJV59x7pmRqC8y6TwFKADt0eLg5adA/m0ysU62HgyQjAZxjqN+kqjvqV5ml76WAtsL4jgEZxL8hfdzD2KrUDzCf1wddbi9y8uAQAv6wf81cNLAMC7V2fUK5e9RfEeJdVfixfEfnq83JNqC4D77TQqVj063aKQDu8e6BDx5wNGhtHloA8Mne0kwVpyPHTYccoDdYLbImwZoLLsEAsw9HBK5tvxWdQ+HojSJBswpwGzcPAiQXEBivciwWVhRFJNwf2LPL+qEZAqkNfSZzErB89EHYAIMQGWM1PSxA0H06tvHkHN6LlcnG6xrGl9L592+OnqCk8r+nD34wz/7rdf0jVGBT9HtAc8mzS4a+nB9/sk7Gp7eTBPwiXdWzPxUdYPoOAg/rtw5Nid6TeHerQrfRZkeJYBzPpZIyxPVoThoMsVfeAA1Sq+tjiA1Q9qttKjukuHmZmlXuZgX5YgThHrlwBgmYAoWoVymxyEXHa4F1sKZkMcWuxFUtZSSR5RWvoc+WEWezl7SfAx32OxThrNHoCdM4Rcc19qaAMUiPreGAVUlwUgZTYfgoLuXPYzV3qLKmyeApccOo+KWvw7sQ9ZZKS9WdamdGag1okwZaZU+7YZxPy+jiP8exzHcRzHcRzH8TsaH2SmGoSvnQbsKYVRs5MW1kqU3NaxmnSRrLG5mkdVFnWvSVCfdVLLdzq2agxnDl4iEhC6JxY++EZqFw3F5SjgC4+Pf/IOADAve2gOA9tRY7ycRo9G4UhHFQD0TkE4ui5Af8ePKS4K7DxnEsEDoCwqF+/OiRayl7BBRWqQUWy8uFeor9I1+pVAf8qZkvZQbYrWc2cO2QmMK4eS21z+m5/+OWpO/f705sf41V98TJ+FTaUjC9Ek4oOtPNzE4WRO2NBgNHYtZfVPT7Z4OiOi0h8svsep3mP3nJ7T//buS3z9N88AAOPSUUTOzyaYTwM49M39wchVdSz7zkbykKE2GgDozy2KjUxN7tlncULEDGI4c1DbQ3JMgLzMlL6ObOBVWju+TM8sZjuc+dkKsSVFOIHqRqG642vOgDHXax0AxSIB4wlgFYubSAfJqUY7Frjq5rhh958///pjOF7famrgnIyM34dugrfXVArRlUFV07M1RqHbVhCccU0u5QFZxU5dIvcNIgpICE0vYn2TGOiJYCeirnZgoQc9XiBBmGpI7wjALNK4Nj0Ew/e6ofciiEa4wscUq1wTWao/TZByJBLt6b6C0IfTLjLmxZCera0oi0zPM/mkhv8WxB/yTFSOiEbkXgHSJtH8Yelj24+QzIXMmLURUu8F7Cz8kiT0KRf656xeeBwYdgiX2pbUIA5gbpM5zMjhkIz0QwUymZWCcgJSUE4CGKL+hB6G6KlsEcVl/qFgv/hAD9Xx0Qg5Ubh4skHPUOIwaJzMW2hmkt43E+zfsHOGAAruFRtXDt4I1N/TyisfkmuMPRtpQQdlI+nhu6TEFJnGnlxZ+qAgJBx+c0swbvvNgqDDsP+cJiPlYk3swfBvN7OQNa1mZyRG3vDloA6UUuQgMLK6imoEJBIF3i4z+qATqH5F2FuxA7pHiL9npy7e0/SVpnrIPtRwgPGcP/PZiE8f3+HfPP9LAMCL4h5/29FB96u3jyO87mrqiS14TrwGTLDd+tEOP724w7stzf9q0uE/e/YVAOC/Wv17dBw9TEWPOzvHG7bq6I1G8YhOrNmkx/27JSRbezkhsg0o66ezid4PpIMU4I1CpLarcUZmAgAgRnkQQFEd2cevx1XS+VNDgo0VM6fD9XWTMSUz+btiJ9CzSblw1E8cxfAXNjE9d9QesfsRs7T3IkrhhfadKBovgekJzc9oFS45aCyUxa+6R9htuP93rxFkCaV0OJs12I00Ce9uV5gvmI0+arQNBTtCeOirIraG2Dqray7cwSHltY89t1gXqK9kPOiESzBusUM89GxNh1ZulxZg+uGE2lByNmro0SwfZKzX6oZY28Npapupb+jHXUkHczgcVNZuA8FShUFOshOwHHyqTsX/LkeBci2ioYLep/e4feQBSWppAFA9+APYP6qkcT0+MtULH98Zrz312AfZydN0AS9TYCsGeVCb9pmqqt4Kkg4t0vfCwf9D20ZXpjYl3WSQuqDrhTNQmkMGMURqtxunKQmQ5wO04mBnlFSbzmvfpYNr3/9G1SP8exzHcRzHcRzH8TsaH2SmevFkAzXtsWsrtBsKi8vZgH1fRkhsdzUD2CxZ7nTSinUCxdsiskX3L12EkIUA5DxlflJZyKB+NCi4iolErYAYBC6/pwzrav0oCgfIrOAPEEklwD++OIR89J2GK1lRpfBQu6C7CiLcRPGAhKuYhYerbYSzYUWCtjdJ2L997DGuLATbd03mPYavKXM0c3/gmWpnDsWc8OafPLvCf/viTzHjP269xJSx6LoeMbxMPYPjvkTLWdsXX1xiVVIW1ZgSbzdLjJzJ13rE36zJVf3fffVl7Es1RuHnz7/HqqCH8XS2wd2OUp7NdgpIHyN+vQcUZy+2TPPrHffnMXEm1wj2gghOgdSF1QgEayrvD9AAM3MoGGKWWd+lagWqew81cEQ+SWxi3XgMCxGJOcU2gw4XGRJQUBYcIP3qWkWSjrRk1RZ+ttgkFIJg0aQEtHq5Rs1WhE+mu4iO7NsKdTWiZCi3b3TMjj55dA/jJNZsDPHkfB01mc3NJN4HLJuSh3/WPmrP+soBRiTVKitin2p5JwGZmNNO+UTue5RQmiCiENjdshdoLljVbDnCdioSBnP2qRrSfJsprd3kVZruNxhoB8KNGlIv97gAukdJlN7MPcqrpI4WMlrVkrVaFAERQPs5fVMoD2wKWCZNDSsRCY31LSIN2QtWycoEWaJ1XeWgtgpmmSQSBPcnSwN4G9J4Qn7ifBuB6krFz+V1RiQqfSyRBFvCYKLhu0RakgbR0CHogEfPVI+kTMW30J3RPY9LDx9U34TH0LJm9Y0mlSZWSpueN+j7AkK8/5nqB3mo3nx7AjmpIZdjFBtXimpM24eAQ6Wfr26T6339TkJahnMAuIsRiqGgsNlbZt05KFhe9MVlGWnzwwlBQXKdyRaGGosALdBISRdR0N1L+r0cRnOTJK8XFrSdOcguCfarLm0e48JAjDJJmEkfRSlc7dAH6G0joXcKloOEdltB8EvqC/F3nEAen1KxqFYj/ufX/wKfzakF46vdOZYl7U7/6fNX6N3hkvu9+VsAwO04w7ue2jh+vvwe00c9/u3rPwIArPsa04JbjOYNrm7o5/ymxJ9vfxQP9I8uHvCMjb3vmwkexnlyLinSPBKkS19XN5IUi/izFLsUtIxzrtWtWO2qVbHVCJ5g1lDLq64VNENe3aME7ZVr+numzg9I+l5/Ig6ELaTxaJ6GNeIgntCGLL+rD55hLgTvBanxxA1OJbGAceVgz0b8D//5/wKA4PKHQNkG8L/XfwAA+NOvP4cUHprlB3vhMX9CO+u6q1EqizmrMjkvUPJ6X3xyj5IN0C+/P4VXGuE8cQsLPeN58wJ2U2RtIh4qMKAVBWd5y1E0bc9Y68Opg5va2K5ilikwtI2GZL4BzU96eXWTBX81fa8IwlLikGkMHHqG5kpA+TW9OmwBimYJC6A7T+9k+bhBEZj1uxKobXRqcpVEdROu4bPWL5LXzEVLgumHGMiJJ0oMjiLjJAggerJ6wAgU94n1nDN6SeiCoWKbpFkjGzk6xyDuQ7ZOsLxuGX7nS9oSEMERa09tM3E9Fg7FjC7snQSYVyIsrc/FY3ppKm2pPUtmmPh7Oj7IQ7VYK8heYawdygUdGpNqQD8W8PwSiNLFPtL2mcT0O6bNe2D3xQjJ2r/T2RAP074vMPbZlDofjZ+Lbeqrs3MHsRyAW9r9fNbzKTlzCr1p5b1I2QD/bFCtcXMDEfr9jE79eFvSKk19dh6GX0zZKDJjDtykMwsY3uAmNvbLuoKyN/2Ga2YmZTz+bGQOP93X9LSN5KHP5zf4R9NX+GX3HADw9HyNkXedShisWc/wTXuCbzZn+HZLu8ztbhrdfX65eoz720Ws63389A5frojU1doCinfJ2fMBb7eLWNd7t1lgz3Z9s5MWsrSwjA6IvUxBR+lRbhLxxavUJ+jKFDAFaUl5GxhgIpKMhKXNLx7aGmiep+CkugnPnWqooe7uC59tyOKwVelRUl7Sz5pkp1d4jE8tZB9UsZJ7kGoFhhOP8g/J0P1fffRr/PHsWwDA6+EM/9H0a1waIhb9T2/+JX66JPbZf3HyC3w6oYLiH//ht/iz9af45e1T+nNPt1Hy8vlsjc5qfPNwRn9Pejxa0EbovcD1dsbzq+hezjjArA1cqAUaCVQO6ML9q0S+sYctMNKmA6zYCgyc7bqlgaotbFjvg4yOUpIPmxBomKk/kHsMB6cwlE1GQlyREX1awdaK/L06q3MqCjIDMU/nqkQyEZ+65yOKZY+LJdv3NXVsO5K1hWs1NGuNl/cC1QPvB5WA4Uy9e+Rhpy450KhMZhG0fmJgh5TFRrctAGormRCXkaQyKUxSiAp1d08uNkjPIGakMiEg5MyDeD1XpZ/Ps/q8dzv8nuE9USgf7frsoOAnFiOr0bVNRepcPpvb93Qca6rHcRzHcRzHcRy/o/FBZqqB/SukR88OIX1TANsitl4IK6LjRpllivufDJgsu2gm3Q4F9tzuMWwqbgngHy5SxNldOLiQLc5H4LqKbQLwKerzGhhPXAzYxqWLXqJeA+OTMWbQZWFjNuO1p2yMLof+wsYI10+ybBQeYlTRkzG49ACAqwQK9sM0C4pSJ6xBaibAeJqYe6KV+Pz3SRz/90/eYs6Y3Zne47/76/8az1m8fd3XWO8pVejb1EfgR0l1piCKcF/GrGH7uob2KXu/ms7xbUUZ7Vz3se79+mGFZj2J1zC9irD2/m4CGBnrzC5TKKruZWwjCFF1YNq6wieYUhHcHrIX2Um4MhW9x6UlKB2AO3FAqKf3koQEQNF++8hHlrXaqTinuqUsNUC+zQsLz+bj9rspKr73/txSXYzXZn/qoF/Q+vvx03dYDzU6Q+vgdXOC35vSc/mieoc/23+O//WrP6Z78QLbntbqf7h/FnWY/+TJN/h4co9fgjLVuhzxXz79JQDgrzYv8Ze/+YS8WQFMFz1mJcF5vVVorimtUb3EuHQx68FtFQUHfOEgWnXQVhThTWatRhjTJj1bW3q4Fc9bbUl8n8spaicjVOsLkJ9qEda0SGYPzEYFAsyc4FqfaY847aOWNcA+o5kymi1TdqfbJOzfnXm4l5SmVaVBUVhy7AHQvZtBsNiGlJ6SxSg4n+DlcUHGGQAxu4UVcb0UywFlRV83uwrqsjpg9oZWGXiZtH4dDpx6yNw9m9MsQ4cAwO08RUfKYmEe5JigYXhABSWnoKQUasldKnc4RWUn/wlBOotpj579fYemiHuSqwmSC9rRJ8sG3ahhXS5m/H6OD/JQDUMoF4v7vlWYvlFRws9WCTYal47UlgDUix7WSlzdUV1PKodhx3WCwsG3KrbUAIh1PNVLyAeGBF9reJ3URfLeyNDzGfs+WxXhMKs80Esotu8a9mUizpQOhm4Jk+81VK/RP6O3TzQqwip+ZmBXPhEcdulQrbbp6/KODJXDBjSc+HgAm5nBz37+BjUryG/HGpcd/fE/+8WfQD9ofO1WPHc21itlKxOkVnjIlw2eci128nLEJbfQbK7nZJnFm+TYlHjo6GHI2uOPz18DAC5nS/xF9zHV6wCUZ10MMuR1SRD2PGFR8l7Fvx17Uaf+QC0GXsBXiamk9jJBayJZvUFRq0PcTEofn0X9toj/ffspYB6NKLkFq74WUH0ioYwzkQ7tQaAIZJshGRGI0wHquxodk17++c9+heuOyEK/ePMMj882eDSlGuir9Rn+x5t/QZf3goKL0Os5H9Az3NaPGjMWxv+Lm4/w05OrqLD0bLrB1y2RmO77KXRtsOCe4Xk14PUN9W7ZTZkMGJRnWzL+aFMTSx9qrQ/cZeBTrdRVpIwkMjnCwAXwBaCvC55fDVRJRSmHRGUvoDOFonLtIyQbTbBB5QunE6wbrQABSCEwLEw6ZRsJr34ARWaw5v4F/+N0iAFr39bopYfccqBbusi3ENIBHlEW1UuCfX94j9BUDw0Y4mzaRzUx3ykKSAJ0qxOpi8JlvkTHimGBNFZl7V6GD8sQVHcCJamI0poVQH3NV9Q4aHVSWX+16rM2Gpk9T03kJHtPH2q7KWMZB06Q2hMAWVmUpYHik/p+PWMLwESqfF/HEf49juM4juM4juP4HY0PMlOVpYWsLNyuiJGp3iiMCw8TXNBqwDIVHBOL6ZKieCEAazQsm5bbfRW9If0gAZUEH0SdOPrFLtk+9WeOmqdDtjRJ9yYcUDwk/VbhUmQtWgEzF7BvKXwUOolo69ukCWymHuPSo7xm4QMFmJBhWQExyiQgUSboxtQU5dLn99h8OkBkjFnPurqzVYebZhZhrt27OTRn4dVAHzm0FZT3aYnlWqju8YAnJzs8Z4F9CY/fvH3ED4hg4ZBlXpztMLLma61HPC0JWn5RPeD/1S9QcGtFURiM1zSZwgj4afLtFMpjZKipvNZxrrqpo9AyENQ8gC4pjNtJEr3IiWFCO/j7Ms6jamRGsAF2HzOMe2Iw/XV5IP4QmdhzgteChZlqU/tHd+5RvyAMc+gLmIUj9SEAv3p4hNsNwa71ZECpLL65J3i8a8uocuScgJ4mwpDWFm2fIPiGbfeGXYmr+wV+9pzIYH91+QKGs6+TeQulLe5vCEW4NwLFDRuKN4l85zWAXsKVIXUXqW2LDbNz27aoccyyv3Fd6EQiI/WjcA2CfHOfztgWpahUUK7D+5VgS698IuxpD72XSdQheweHJSs+ZbZ8UZ/YI9m6AWg+NihWQWbLw/HatIOE3OqYTXsl4DhjQyswuZfpnc9s5sZlastDLyOZBwAebubRR1fvqWyRb9rhb9naJ5F85vuEPcQViQxXbEiD2IfeveznYKgkESze9s+T4UPR0veAZDYe0TUhol+zWRKx0p8GxrKEZsb2uKJ9FwBWywZNV2LHxEIhPaT2EbF4n8cHeaiKmwqiriAFYs3GnHt6eQIsejpgUtHmVJdjVAKxTtCGIzOYgtenno8oq1TwGAcNNLxxrRzsCa/sUUJ2Mm4yXiLS8FUjSRCfNwVbJ4F+V3ioTsZaL5xIhsKNiJuMMAKyA+yP6O1wo4QIkFSQFAzv1CigGfZVXXpR5v/4DtNyxIZF/7f3U+glvU37qxnabhGF4ecZTGwrYDh3GE9409kVsX9Tt2mz06XBs9kG9x0FCN/cnMEzG1p3tKGFA2vbVPjonNitH03u8UVNAv3/x5pE34OhdretomoVBCBmJrK5faeSG49JrTBQoE2oSBtogPrBz1kz09t0OtYWvZGAQPSizT0rt5+b2Ka0/A8lqnsf3VXMVOD+y1RrLO9lhCFln+pUAGC5NGF7RQcUz887uUQV1mZh0BkdlcDOVntsucZvjIQH4iErhY/ev+6hjFAcBOCcxF+/JuUrZwSeP6H5LpTFVbOCYKZtsZZRNSnvd/SeWzyC6L8VEG0Gt2e1TTmk9RKk6kId1dcePTOI9S4FKg5sLB+Q+Mz0XDfUC2ymqW0pHpar9J4WO2JvR6N5RcEL3RQdwGFN576oXgK+8gka1g664HvUFvtt6F3zEI87qu0DQNa6pvrD08JpoL/gOn7pYnsdBLXYhZq8rXAgeemVDzr5UL1A0YY5TaUIr2luXCYzWNynQ3ScpfnRbfqcxLzOWmcy/+Bil9jVwgG2SBC1MB52Ej4nXTv08foCMM9pUU+mQ/Ti7ceCgr0x8UCcd3Dm/T9VP8hD1T/q4acCRZmMyPu2gN+nthQpHaoiiDr4aL82KUf0YwHLWYOdGRRMJDhZNpgWI642VO8amwJFqGvMXVyEaqdQbsSBC0Z46XRDurF53178OUkRfmwG1x6Co8Dcpmk4c8B5D61T07WZ8cZdWrhOAUNga6R6YvfEYfUZFVh+7+IdbroZWt6ET893uH9LddPyWkEagelbzgxOM5PlysNPLGYLepHMZITpKKsycw/H2f/pvINxCpuBDoDTRQOxpLpg05fY/2YVa7jDooikmjftCV4waelJuUFdjRg4wBluqkiGMXNPcntlivqDXmtf2mQ9pj3EKCFDhj5zkCyhJ0qHJ+dr7Diw2AOwW6557hXPeWpbiHKAApj/mn6ueqADdeT5b54JOBbUmL7ScBUw/SfU2tINBYa/zNgyv6F5qwy1e0QpxUfAc+7H3fYVlmWPgS2JrrdztHu6X8lrudvRv3vloqYvVAqs5qcNvBdo+Oe8E2gYhagKgdXpHhtNGcVQFrCf0SFttwWKh+Rd5yYumtX7TQEf5tGKA7PqXArPaw8j0iEbgpzwc7FA5QUdqFFCL9UnVUfPIWakykeHH6gkihJamaLs5DwJVMjTAeKujNf3yKT8JtQf6+oA6cg4p7oykZdRTEc4lwJY3ciEhO0E7CTpOdvKZ+08KXjIa5wAIDQwMoku2hUG6cBdCqQBJFu1wh/ofedBjJeU5UcnpgGxxi8sHeKh/p0TkHyiZcBMqWYbzdMXqaaqO6BaA/s5z/HjMdacu6aMNo1KW1ijIAKSJD0RF01efH8/x/sfFhzHcRzHcRzHcfw9GR9kpjqZDVBTAedE9Ob0A8F5klVg6skQWXdaAEqxobiTkNLFAFGVFrMpZWWzcsCur6L3anlZxNaNUCcESMS+L1RsIlddaiOQhiGxAFvKrOnaAs0zwASHj6zOMy6S96RqBcZtgWHCdcj5EF1GnBF4+uIe91uCXc13s6wu5mMrzF0/Ra0MFGeBN7dzCI443ecthn2B6j7JwlW3DCGPQHcu0bHQv39XQ8UaE6IHq5IO3++W8ZmUysaseFKOaJ53UV2l1g4bFn9vJiXWTEk803v8y49+jf/z8lO6RzmN0X+xFbCVjNG1XiXbHjuk1hvRKOidjAozy6dbdFx3PF00GK1C2zED9aaKtWJyukk1UGEAydnA5GuNYs9w7wQYL2Ri8j5vIRjGPfnnl/iPH32LP339BQBg+PUS9seETZaTEf0NM565Lctzje/xyQ6G63izcsBEj9g0CfINQyqLsrToWO7GdjrW5yCB8ozW7XLSwTqJlksVfpC4f00Z86NP7vFkkcQg5i97XHEacr09jZC3mXqI1QAfWLkTC8Gs7IAeRDgyq2XCCfKbDYiC8ukeRcqohGFXpJAR7bMsTZCsY8jUzCwpEsm9OJDhEzYhP+PSQ/EcWIYhg0qeLxAzbThBCAt/Nv2g4Av6+XGqok+yGRR8p2Jd0ExdzBaHE3r9o68umyQAXHYJO7ED+vMkr+lLD2TJmxhTO4+d+rhuc0EKM/GQRsTMXNg038LS3wvX0HufFKcqwGsBxQxcWyDO9zhN3tDCEWig2nTNXChj/RMbVcjQq1hS81rCTnlf8II4LeGPcynCZ6zu93V8kIdqs60gTU0HEsN5UB5ianByQhBkPxZQvKmXwe0bwKatMQw6tqLqwkZFpdv9FNu3C5R3LKU28xEyEqVLBCYj4GqHkd8+3wDVbf5SJNUkaUQkMoXWD71N0G38uSHV9Oo7gXKt0XxE/+6Vx8U5ta68XDxgO9a4tfN4L8YPn6kAACAASURBVEFNyGuBr27OAQAvTteYFz2ez+mQrbXB5AW9KN/encLtamx+ytDNagCuaEcrthLikz1O5lzPXTSYMIwuhY/EJOMk3uxWuLkPTkAeNpBjTvb4/Rdv46F7v56h5ed0N23x24baPZ6dPOCr3QWuL+kAkFag/Yj7GvdkyRXqklgh1br6BFlCAPKLHQqulW7upxBsOr3+6zmGExd7enEywvJLL9cF9C61XRW9gOQDlmAxJkJdeIwfd3j5hDC915en+L2fk+LRi+kD/u2f/WPUXH+y5xYly2b2d5OkdHMxwDcaYprKEUEXWQqPOzvFpqnjRwr1vtW8g/OISlWiTWbg8nTA4xURoTqjsd5NIilNTw2sTnNUKouSg0rnBc4m9EG35zUMlzpc4eGdiNC5v6rjegwj9lf6OD0wMypniLCp7tKWZGcONkDBlaPDLdRwoSK5bFyy1jC3PhXrJCsqbIKdJQjyDSQpP7EwLav97DSKbVKqgkQkWgkjASvgueRjHo/QHDSi0ckmb2KBReJU2G0Bp/iwHkiVKTrJ9CJq7I4LROUo4ehzR36EFTHAFpYC5thzWruo0ORVgqulESjvkyaxV2m+5QDoLulIQwCGgxHVA3IgPerwvf0LDkBOTQzuio1AsUstTKpLzkvtU0dav21gSSHJgyofrSrtjmVQ+Zl5J0hq8mhSfhzHcRzHcRzHcRxhfJCZalRZ2RSpGbm2qCcD2p7CQKUSxLtuJknH1As4K6GYBDSf9BgYQm5+u4LywPCYWzwWPSTDdO6yji4p7nkH3ybPSq9EJFZ4cRhZmolPLjI7CdUkpRSzyHRBvUi+iIZg1oKdPfBQ4YE1fPsvNeZ1j3HPRJScabwyMEwtXBQdGlPihJ1jTqsGb5tlnBv5pItzMPYajlmw/dJgoh1ubymD0aXF/V3KosbP6J6avsTmforTc24bMRqKGdDOSfzNu8fot6w73KgYre/6Ep2lG/7v/69/DdEozJ7TNRpdRVIEnKLonRnXrk3OKyhchPJmL3foe43hmu63ulGRHdqfeeCij/6hxih0b4k8JHuB/nHC5ap7je4R3//pCMlZzXLZoioM7hv2KnUCv/j1SwDAb77/FLUn9Sua9BH9XeqvKi7YpJ2VugLxaNPUEc6fT9htaRtYIwLzc8okS22waWu4e85UjYA/o/RCaYtLRgmcVbB7HfWsy8kIw8Ln15crrOcTGCa2OSMxZ0/WuhyxKRKcCeVhOUORLsG+npm1AYp3ZUa+07y+GYkQFlFVSvQSisUlrAf8NGlT5y4pTnuYiUfBLHZfpFaTYp8INf0ZtUcF9xYULqIWek9azpGcVDlIhqF9SdB2KIWYXsHehfTOR7EXN0qCM3csRnIvD1jOuT6xyszAc43dccldCJzR5epT0hCEHciUxSblRHR9+jq48EQ95SFry7N0fTNJDG7dJCKk8OSSAwDtx2Pce/R1MkSgbB9on4Q2KBxoYqu7Iv49M8uQHisg2tiHA6d9gsBb+vc/hEz1wzxUtwVgCmAxYrmi1WCdxDgqyHDQeRE3LikdRmafWiuhlENV0qpZ72qMfGgIDeB0wHJJ19zva+B7tpbbC3R82ErPG0aQCPT5CwZavC4xG6dvuX4zD32lqZcu1IAIJuaXfk7XDPCbHKlOAwDbhyn2ZZJwGZcutvMU8wF/+IIk7jpbRDlAgJR1gqrRpBxRahMhyIvlPtb49n2J/XeLxNJsq+RmsbDR7g7SY3naYMnuJ7Xe4XpPB9bd5QpqrSDDL0rAsfHBtBzx3ZYUfWRh8eizh9gvO5n3Eep0E0WbsA70RUR406/LCMONo0JRWIwMrdpKRsWa0GbTcL3S3VTQLAVpP+qAUaL8PrRMJUm9ojaQDCc/vF2iPO3gXrHw/JMe3qW5z2X0sC4A3oBmj/fouZZbLHpM6hGW57htSkxnNG9NX8J7gXLKbTPSR+m3dTPB/m4SGe6uclHmbxjKeECFvsjAYu2/naPm1pLuKQVajg/V6UmL0ymt76v1PJY31Gqgdt9oRSbQPWVIt2JT9cBw36pY53QOcELGfms4QHJ5w2covS89xF7Hg5qMw9NhIFOFBrnTTXeRDjK9FxjObWKEDzJThOLLBubrRqWDf2oAJ6CZ5e+siP24YpAQD1w73lJgG2u7U38gZO91ViN26W8VOxGt0nzpoHYqHVJOJEcizQdu4ANYkYTsgQTp8vVDP6kcfVRv8rU4MDBQLdL6E4BlRSQAFKD9pohzHOUjJwJm4uGC080oktxjQXC+y+BrycHOgaoWCOEO6+BAhP89H0f49ziO4ziO4ziO43c0PsxMdWaAicF00WMIzfBOoK7GmJ2NVuHJksg91km8ZUao1hZFYVP2clUD3HeozxpM6jF5sm41PMOidgGAsyb5fX0gig2fmJFeky5tYO5NrhLDNwhBBOszaUWM8NUA2DI0v3uKGDn6JkUV+l75qoKZOZQcIQ4vBjx6TGSkL05uMLCESq1GDE7juiVYtLc6kowaU+Lr6/OYEemZw5rZuf0vV6i6RPgIKlIARfiShcJPlw1OJm383m0zwx2r9sidSiIVoOg9ZJxvvr5I1lcSuNqU+PjHpAT0eLrF3948pqk31HjvGQ6eLDu0V/xctMfyKT3b3a6GmgwRhn5QM+At92sqD3lVoWSTgf7C4Wf/7GsAwC++e4bJV1Wy85s60isG4K8LjMzU1b2AejOH53lQbxNKYE4pA4qZQunx9OUdzXFf4vEZPZdF2aMzBd6taX6UtnGdtl0BM6oIN1uLaBLhBwW5Uwcat4rhWTPz9PcB6NlIfZkMaep9grb1aoBpdLzHaTWiZdRGiESac11NoiScwbnnXSL9bEp44aN1XU5U8hrw2kWdXb1RMKsgzuuT3O4oSQ2oTzBpyJyEB5CJv9dXIpqeB9g1/A5KBxHIVKOOhCDVESEoZKeuQDKdqCym0x4VlztuNhVBxwC8EVEBSu+pHzdkyXonoo3auAwmGfzeLT3qa84eRSLB6rWC6jPCVDZX4evoe6p9vL7qxUEfMLLeXysFBiba26mHalOGaydAgDJcScIPBb0amL3WkGNgiqWebFeQfnB1zYiCTEITchQQrYhwtldI5gZI2bmZ0j0G9nz1QH27Nsu239fxQR6qk0UPNT1knBalYVUPWgyPFjt8sSRl6V/cPcN8SqtwMBr7TQ3JbFe3MlDsRDGf9uiGAmD2qGolwOeGlx6SITTVi1gXCSMKUtceeiNR36R6VBC1Vi0ZbUeoOPPmhM8gsI4W9YFqTVB26QBpJIYvaTX/o5dvIrPz9e4EHy+IpXrXT7Hu6/i9p7MNGkNvzuuHE5Slia1Dr377GBUzWIuOZRKDTJnJXnblsZjRPGpl8Xa9RPOGDm3ZSUSkVhO0FNoP4EScbzXgwHJRvtzjxyt6Tq0toPl+q9mA+aRHx0HT7mGCR5/wZ1vPsH21inPTXQCt58NuW8SAR24kiq1A85yu+Qd/9ArLglnNg4IwaZMo71Rsb8iF5YstPYfwc3bqo/gDvIBqJOySXYeWfWx1KkuDecFCCl7AeRFZ5t5LKi0AsDsNUVOgB/CByq0s0lD9PYoGjEiwfJUYt0Vp0O2qCAOPq9ROIqSL0nIAlTsMw9LeCQhmn6uthJlbTE5pwXf7EuqSa+KW/l48sOqs5rkRGIRK7WYzF+t41bKHZb6Cu6ypxBFaSJp0AFpWCAs1RjtJJtzmzEQ4WY4CVh6+dyHYDKWTcIq70kdjBSkd2qZCFwy0pYe6YZ9Um96t8FnDoWqmZDYP8JpwqWVkcpW4EQAwuQz3EWDjdM1xlRj+OUyqdyJJBgrEZztMPeROxBY7+pz8RUt/NyhhwSfVKbNwJJQRYh8l4PnAlQPimh5OXYLrQY8uHPSypyAxvLrFNn1PuCR4IQ2xmMO/xym9JzZzBHtfxwd5qJpRwo0KZlQouDZaaouqMKi5fea0avCbDWnRtqOO9dXduzngESUH1TQVcwajYUYVFVxyYkL+MhD9Py2wQIQAAN9qqF7FPkenEeXX+gui2geNz2Ijot7puBDxRQk6s1F+bEzZkCuB7ecWT9gdZj1M8NU3lN0J7fHdb1l/t3Z48vQBzxZJ3uWrW2q32V/OoDcq1hynN6mKYGaerOvC5qURSUyTVRdVqrZtjf27Waz3+cLHfUQaoLpWKB/CfCU1Ia9Tf1z32OH5yRYtE5c2Yx3bd04mHTqj8WJBk3H+bI+/vHpB19sX0aZtetqieT1HyaQuYTMz+ZnDp//0dZQAdF7gr2+eAADUvYYrEsJgViPUIskBjg90oNgJbYQ26Mhe9KkHeZSwU0QEw1kZ22HqwuCupQPWOoF+1NFCy+6L+DvBGqxlM2xsilirJycREYMEp5POsKsTWavvCtIyHkP70YjpnE6GqjBouhIDH6Rm0PBhXZUWYHm90AbSXnNQcKMOeqiDAwpwmJn1L0csz/axXtx3RURAvBcw7DqkBwH4RMbplzZmkrnUJsBZ+FkoAIrY3+sqS7rOga/QyDg3MSDKpP2iXeK3M9IQrkOh0EddcN/L2CMaDq5gVm9rH/kKwtI7GFSgyEouzA/iu+ol2HKN/kP/xEDEDF9ANSJq88IjZuTwTMQC7QsQKVDPFayEpf8Lh7143iUSZqNhn4/Usw+QM1C4r8IDS5osv9NQLhH6RF7n5VpuvuZi/3yf/rscPdAik2AVTGjDez+ONdXjOI7jOI7jOI7f0fggM9Vx0JBKQ0gfWbyLuse87NEaisi/25xG6NAYhW7NUNYogOUYhdrtXkd1nsYKbtJnyEd5SI76YEVU4xnZfSWim9pDvqPr1xuJYpfqo2YOtM9YZMEJTC6TywYZLqcIMWR2qvfkkBG0YmvELKF97DF/uUHDrUOXlydY/vsy3s/2P6E0cDId0A4F/u9XnwAA9K+msJwZyImHnTnUl4ml2X4cQlOu84ay52qIaIAZFW4eWCxgSPUYAPAzB82ONuWDOPCYFQ6xYV84gf6Uvv7ZP3mFs2qP73bEKD6tGvz4hKDgb3enuH6Yx3LUr28u0GwolVdTE9uB+t8sUe2zWpr2mPyUJvJ81mA7VPj+W8rQRWWhGBZVLxsMT1Ss2erSRjOFoS9izW1cMUrAUb4AUDFTVwiP9qGGuuMM9BzRt3Q0Kmb1gykwDpoyVBDjNAgRSOVIRSkgIiY15XvF0Buvs+HUwZ2E1EzEOmxZcSsVt9QsFy0WzMq+vFti3JYQge2qXcz03F5H1x4I0PMLiEgyw4nrPKomeRHrufNFB61sZI96AD60oQ0CkuvUBbe8RJixEEDgEzS03qLzTeHj9+JFAei7grNdXksWUfEIIDWgAP/6wkdmsGoFhhNPXAwwi5wZv9WtBNsKY1gCw4UltjAAGAnRhXorZbSBAesKoHnJCMXUQrJhgW6J7TueBskjD8GMZ91QrTUI3ts6E7LQGZPWkyhJeLeEEwdZZfsHLbX/AMBDGUVG1Am9q5Ih/baoYnsQLEgbGezXOmTZaYaoC89wf0CWMqYxhIhIg+qpJh7uK+gP2+EQnn8fxwd5qIYhZTL2HZ3ETTNLJs5DgnzHTkOGftapgdkVkCxk72oHMU8QMMpEuhBtEtR2E4eOpcdkSxZWQcJQr1V80c3cQ/UJshsWqYWmfidRbjzGeSIMBMg3F80OUMpAnSdQPfdcArBPemjp8HDPYu3flfFFHxaAfkUX7KqK+uNCnebEwa0Su0o+FGhf0Od+8dkNrrjn0VzX9Fn4ULG9gg1KUp2KG6soHCAzJZ0+tTCMc7KOC248rgBZsIGk3fxH9Cb+dPEOj8ttPFQHp2Mv7Zu7FYQA3t5Q7VQXNqn9eEQzc+WA/vMuyaMZCbujORgGjf5uAslwqr7SsfY2zBxELxMhq1awPf2e3gvoEEvNqW3GtyGIiMYc0IUBRhlbE1Rp45rb7muM7C5jjMS4riJBy2nEA9FZCSE9XDBq36fWKoA22uCGIp90MQjwTkRHkODyE4If5wXePdDzHDclHeLhYOxlLNIWWR1ZjgTPhuck+xT8yT2RiAK87EsXbQQbWWE3TNMhKBDhTr2VMRA1Uw85ZvCgT4eIK7lGHBxtCh8PCjFkMoUTNlLnw6DappY0IQSE8ukgMkDJbUW24j5ShtiDbSB9L7X2SAOI5QDPwY/ayXiNYkdOL+HQ7h5lJMNtpg61cpCtTCpKa51cjBhWjQGLSIQmlxGkhCGbyWjG3ibDe185qHcVRDDsWJpopK60xbTuY6scBGIpq75NNnBeUV9q4Hp4mdVNLX1fpPgpDQ8Ik9YmybGmazqdHcDv8fgH8BGO4ziO4ziO4zj+fowPMlPV2kIWFmVlEgGpq7BfT6I+L6RHsWSyxnSMNmrjqGBMxmZQPkbdqCxlpkE8oHLRg9QaGYXUdSOIYh/ZiylqLe/IG7I/Da0yAvU3nBls/QGlPhcmD2LhAEE/rkRi66181NgU0mO3ryGv6TOM89R6U96LSKTozgmCiopNcxOzbnQKbmFw8ZRaPi5vV7BBs7UkH1R18/+z9ybNkiVZmtB3VPUONrzZZ48hszKrOqupZmh6gTQLdsCaPf+A/9R/ABFEYMEKBEGAlmpopIqki6rOrMjIjAgP9+dvsvkOOrA4R49ei6xlLjrSTTfxPOw9s2t3UNXznW8QE/o6FQcbOWYAcO2IWFmNUnNreySR6K+DOuSYEQgT6cAokPrDuECAwUJYsmOw+PU7JlpFqRY0pLz2BfLqLM5eM1HLXu6x3sz0GrbfO4QZ/zyccTUdl3Ltq+LdiiYgUdLMUNNNDAwMdLtKA/E5lFDoMI9KxPEj+/k6qRRcFbA7TBy9tAohrurHUmF5kTCNL0ZgJG0zJAcN105VQlcnmGd8Hxsb0c74XL29WGErsXvv7y9AlNT4BADGQ6aYM3xdPQghaZYKecWVyjFFyb/N7OJZhAnFpD6ZpA5fYUYayk0EwFOpTndGqxczkBK8kmECUr4fq41R6DPMI8iTGibEsYSBxzrBLwsDl0LxyE4mwZdMB1AqDOLZhwSSCxBqgvElBm1cFsZsogIh9y89sHf6XYyfGDcQV2KH5/K7zwOae6sv9jdyD6wNxhtf2L8OsAIhM2KTtPyjINA3ADsxZ4iNEJJkHupeRD12u7bMbJZ7upkPShYkSlitF9oase+bwlqvJ2YSnaAlEyMLdcuqwCiCVs04IklmFM/2XLWakMlm4LDzH3/y26e5qC6XHew8oXYBHz4wRtr8pkF66TF/yS7Xn189oZP+6m6o8SBwafWbFosdYf95tkOJhcHasTTACAu0brzmB6ZVrf294YUHPCmsCBSoafYxIdZFc2cPpd8AeTAz5ENlfoMJ/LADE6eZfFyTUPIUDKJNcF/w9/SjA34jcOdFUstCCiwTyTpTuq/0IQ2verx8scL77670uKoLfopiNAi9RRwmTiyZXZgt6QCM2xr1hwou52R7Umbq+GoomxvwZFrLBFSvgE4Wx7/89ku8vlyrBeBmOwOJ9MaQTOwCk46D0xNSX3eI0rfbC6vVyuYn3ttiFL5jmUgOHKf5kNdGpMQsSF1IqYQnROLFFIDqKqOTiXAWirH/wPIFDZ1HwEIWvTBx9PKDAx0sZh8zFFoWTn6PAluGFqArfo+m8agqr/fdxazDdctSqoOv8N3XHExg9gbxZkAle8Xtpi1wuE1AX1iyfq63EjM9J3088iVQJblU4MJG7r+8SagTjLCcQ2/hnpwuCEBZDPwi6bnheweYfyOL7x44vJA/iAzx5vuerQjzxiJqSordWIZE5QuEesKQtbxotgLt1puSgRtrZuDrAjObOKBRgbxBCdQVXXDKxw1uyewvgf6N3Fyj0e9mPGk4epjzommlx2o7FDxR3Zkym7mcs2ozla4BcZkwStg7DaQa4dAkwCVmbgOYtwO2ormftSyTW/wvC/ldwrjI3zmxUxvkmk8coQxKsARw3IrKqTh6XbK1ZAWMS0Kc6FKjIwQ3xYt/nOOTXFR3+wYGLcbOaZ9n9h/f49Wsw5NM0E/dTO3vNqsZSPxTmQKeVI5Ae6cTWvvsgDdXK9W6flidlQrOpglRgyuzKRGn5qIPfkHobhKaR5mUD9DJKLSllwrwjawem9XxjRzapBOL6QyqB7lZH2qMnw8Y8044lXDt9s6AslzlOe/+c6SbGYDDa6Hstx7dUOHs2U7PaV6kgACaDXDX/Nq8GXD7wOVAuGsA0R3anQQz54nClIc07S3cxpYecRsRZaLqrwGXq6YLg6++fqGpMrAJyL0iw6Sy3DdsFgPsrOg+R/E4ToFAhitDABg/63VBdysHGo1etxTKQpc6y16353kmI/VyNnurfq167a5EbuNi2RTVEa7yKmlwLsDJcTQmYnMoYeOhiji8KKQ0HU0AInEyCABUUd/Djxbj4HB9yer7ygZsRn7Pr7+/QS3i/fE6op6NugGM3hSDhL1DtSX0z/Puh6PPAF6QcrVoegIsVNqSJtfTt7yojkIwM1eDVuEgJtu4fL6oyFtyj52/M2H2nTmSamhcmk3w5wlJPHir2Ygo19fcFUvKOI/ce9wVBAQa1m3QPAL2ID3zM4KXRTVRlnUV4k9BdFLZdHUW7cfi9zv1Eo6VmE/I/WgOpvRKPeBlfxrrBLeyJUS8n1RvBEQqi06sSsTduEgYRVKT5p43+hm9qACMufcNhGXA62di5NLXWhTs9g3iQ4PuuhxX7s+bkeDlWvt5gu2gi2wyBUEwA0CpFAUmoWymJvIaSB/2hz3Xaej6j3WceqqncRqncRqncRp/oPFJVqovLzdwiwHLuleHnBftBn91/xlW2WIwkRplNw/myFbQDtCdWf8i4Cc/Z5u8RTXAUMIv/55TSNCZYuNWJUSpasglxLcdzHcMu57/PdRhafsFMPtAaO8LnKeJEpZp/1nwHTKsBt71ZVOIcZkQzoP2+2a3pd/kW4BSrXBNfNFrUs/hTSxZrQTAJBy+EJOLxYhKsFoiYL2elRzJuVeJDLmIm6stXi25Z+ko4N27a37PumxD/fMRsQ56DmxXHKDswaDaFvec5uaA2Ru+AE/3S8x/xaiB/9UC9NkAkqBpsgm1MFj/5Nk95m7AY8/X8/36TAPpiRKGzOT8UPHnvGFYdHlxwOZwJudb0IUMEXZWK+H6Shy2NoKZjqUvSKFApDb3BbMMZbAgqSQXZwc4E7Fa8TFuDpWGu89mpddlTARtLObiujPOgfE5n49qNmIxG9RtaVZ5vLuVwAGb8OpmhcYVVu939+Ik9aEpMpye2NwhF/lPlfbB3dZyOHjui/dGM3yTSxM3IYYf8zV0u6lVJSHNS8h36KxWwoiMVmgA9gSeBaAGD/WdPUpiGc6SngMQQDaimWTR5iqcoVv5rCbArCplu9u+PBfJ8rPh2wmcnfuQvbRXFuXAsqwrVaXX7bYEv0x6fnDmQY88h4xXgeHhPhtzlPu7XxSTCJiEAKD+vsC/yvC1ANIE9qWSp5wsSiti40CRjgzs82v+PACe8O63LBMze1s+u+JQ9T6nLTWx3BN96XXXTwZ+UZJ1kpEAeTmO5CbnzpT8V6Aga9mNTFGbyH355H/8peonuah+efaAelnjw+EMC8eQ4Gqc4W670CSKcV+jkpvGdYCY9uDw1oMW7BsMAHNA3WAA4Je/+gztt6L7HIvvZWipuNK87REPDu0Tv//hBdC9FNehdxazu6g9uHFB2itNlid6tT6rk04etif0N/L/hViSCRlhQlpCYqJUXsTdb5sCwZB4DYMflP3rpHd99W/nCkMNFxEmErJrG6UKJJOpPwu4T0v8/PKO/50Mrp8ztr3dtxhFvmE+NKjX9ZGl2yh2fdXagoKkuAC4mXO8GQDQwWqKRvOLFS7qEQ8riWOjhM+vWGO6rHo89TPs1ac2YTnjD7v77kI1sf4sYvF2o8krm65Bcy3JRecWJhCikJjIG0Duj4vlAR9vzxW/am4O3J8GEB/q0n+WjU/e4KR5UOu3rqvgXIR1mUTGi2n+LrnfG29buBHYvxKYMUETZtpnI9p6hBcZxKZrcHHBG4SLWYd5NSgh6W6zUA0o3nQY1jLbRZaXRdEkmlg2jf7S87F3xW853zs00oRMxotEvufqDS/+ABOOUrKaNERVkdS4tYVfBiQ5X25nShugAYxApLYDQ8PqkS1xZGBrz7j06EXSZPZG+/NhEVXeZR4rVFvStkt/HZFb99WaUG9IFxHy7EcLAHZI2L0yyi+Y+mojQSFYJuIk3kAAoFUF+5qvRVsFHLblWQvhGNadbk4W39gj0qFfll4xh37LZasn0Y/E54D/yMA9FqtTPytuSzCA3VolZMU6YRT3qXRgnbFan24nrliXUdOVhsuIalUKjcNnodwT0n/PNpTJFgiZ+RVyGD2h3kCj3oxE4f0xpNWc4N/TOI3TOI3TOI0/0PgkK9UuVgihwtv5CquRd7d/e/cC3aFWAoW9r8qO+T9Z4eUZl5zORPxk+YDvD0y+8dGgtbzT++u/+wKzbyr13p1WiEehxAcH8oTDW9nResJM4J7ZHYvcw4TgkKvYZBNoHvQY629rJTSN5yU/NcwSxpcDxlzNfHDo8y67TnB7Uir+FFJr7gHXCRlhRkguwQp81d4VsXf2ElbGZpp8z71BOjT4l+PPAADziwPm4jTUNCPSV1xVuomAHmASSGYJhybBv0xIsvM+DJUSidIsYPaWK99nyx2++XiF+CjhBmcj1kOhRN7t5/rz8+UODzv+N80CghDUaCD4v77Et88Y8v3izz7gn7zgTNmKIv7nv/kF6o8isXnp8ezZRo/JPFUwL3mrfXW2x7bjz95uKt1xuz1xlSCMXAKQBDZv2xGvzzaaIzt4h1Eg6u67JZrHwthMDv8giWP7NMcwd/j85ql87y2/X+cdYiIMUsUakzCfc7Veu4BVNrEPBmlVT0wSojLY066S0jhfKEyIOaagHIkh/Jw6EppCOKqfCN3rWBjoQ5EfuCqpgwAAIABJREFUhTaCIpWKCxOpxkTiYgJXXOqhDKgkLVUJ1FtlXFMgJYYBpaKtdoRxMZGXBXYoA4DmMaF7VqQyzQNpBbp9yyhQzDByhPor0yRlKNnETOAsqXnW42KZgzgsaMLStn2R24TLEWYjkq5bA9sdO1Ll5zpXyCpjskBsM5ZdSFAAw8IHy//25wVntRuL2Qej5ES/SBrAQAQ9HwCjA7lNlKhU/0hSPcs/3XrSHksFUgeAw8uoCAW8gVvnUHg+jxluN5bnvT8GotInuaj+ZHaPZl6hjw730nOL0SBsHcyeL3o496h+wg/EF1ePWFY8GW3HBn/z+FKZwW/O1/j1PUsT6lsHCsDus8wSBKpsoN0C4VoYoHVgiU2OY3swmN0KzLpP8C1hOOfX9q+LnhURwKrC2e+KYfr+tbAVr0Lpe40GGIyyHPtnBZ4xB8Nm3gK7hrZALqEFooQZb37CLNUMB8eqmF+TF9Pv3COlAknZnJBzzzhd98xhn6O8Elsc5mEGUgnP8HrEtUTQvT1f4+EwVyZ2U43Yd/nDE5Ziodd5h3Fdo7mTReN7iw/y3vvzA5wNKoF593ihLFuaOGmlJqH/kw5/+vaWP8t5/POLvwcAfD9ewjYBwxv+3XoxaH9y31dY/HSFSlJxNocWg/SVzcRJa7hga8AcKj4ODr/4khft86rD369ulFE8q0fc30tqjxfWLPj6RFcYxM15DxIYPe4qmGWClYu46lvtfbfOo7EeVnD6s6aHkQPrg8O+ln7f7eLIkB5zaGA5bGJJTF4BbAKpM1JS2JYOwvyc2PBlN6Qw595r/t0sq+LvVlyN+H+U+6x+MkV7mheSyWFmdjCNvADqInXudTNY7ajc67PE65++V1IL0MMbhubrd+JMtYYulrFJR/1jty4a2ehSsWE0OGKLV7XH/YNcT8sbEyf6bdOX58fdV0epVcmWRdX4CUGemIGrm4kmqg47M335oHjDkN/fbidM45FweBVVGWB3Rl3ZSJQA+Vy7rpy75oGQZC7rrzhFykxkY25XzoGfl2Qdet5rVGW8b3SDUG0A45O2uZIjBAOECbv5xzo+yUX117vnqFDj+9051iKi361akDdK0Lh8tcE/ffktAKCxHv/X7ecAgLuvrpEWAbNzXnC/urtB907EXFcR4xX0KajWpshfllH96dLOwu2Mahgvf13E2ZvPDQ4vEkL2aPWkuzvbce8n/+7qzwKS7AJNFRGlMrVbg+SAkJM6qEyK0SZgbxGyxmxiB+jnpESC+skcWaINF0lF7sYT28KVBLOSPtFzXyqTJOonA5/TVd70quWsHxzbzuVWXRPw9pwXVR8NNl2j9nnjaNVr+frZBpUsEvuxAnVFftBfF2OFzXomExn/+2zRwUs/cdjUZQOSAHQWX/2ffH3DLGH5z3gm+S9vfon/+i/+Ev/66QsAwO1uiWcznj0qG/Bxs8STWBrGbfH7pToh5p7qIqKajVqt/xc/+3/w04b9if/19ifY9TW2K948mNsGZPNkDVR5x09AOAtY3nAZ6EzU/i3NPC4WB6ykn+iDxbLh4x+ChY+1puxUJmjyzXrfov+GJ3wb2UBBSW/LURd6GNZe6qQfScWoZii9+WSZkDJK/288T0f9s6kpCg0GLic50URzC16k1NAklt6c8WLJKa/588LCSSmBRquSpvrWYfFOX0R/XbgF/iIU6dPBKmJSrwl+bvn5AC8MuX8bGj7QvMAkd1xJTour1ESturunVglZ4WDRfl+hltAnM3KyFJCfAdL/H39g85efyZSYs5ARHXMwwD6bfiSOmgRvtvPiVd6fhx1YD5qPMcxiuRaGN+dW9fOlClfjCci9YsGRe5CNuUiYKErP/UYMR0yEv+d7s7m3aO/kOLqE4YLUx7i9l0r1j8D799RTPY3TOI3TOI3T+AONT7JS/ZvvX8HMeYuUw53Pr/bYVi1e3nC19HqxRi9K5cdhhtWWq4nqxQEXy4PCOumxRsqUdBdRva+LXVgFjC/EiL8jFc23D2xOXQlk4htCJ/Zl2380AJHUsD9djkg5H7Mz6G8Sxpdc9dgmKCMv7p0yTEEAbvri/hMILpulRwMsPIaGt9qz7xxsLNVAHuqKkivJsbAVk5N+iobFJq3OY5UAM7FnM4lN5cGwq5Wqu96whKF7XQz1/+bb13x+ZgPG0WIh/T+ihH1XePlrgYLXmzlsTzh8zufj8tVGoc/NvkV/qEBy/Ie/vtJ+WfXigFpYvMZE7DYtKDeWLwd8tWK5wb/Y/6f4r978Ff6bt/8TAOBjOMdverZB/O9+++9jd7tQFCG1UXM7aSSF+t+8fcBVe8DrGd9X33WX+N9uud/8YXWG7n6m7xGrIq2wO6NShNBG2LNRJUG9rxCEPWvnHu+/v8K5mG28Pl9jLoz2mAycCfhuyzKaj5srHHaCrT5VxVJwHoAqwYmcx1WBw84hLOMJIkeB0N7m452wOUeBDuU0Gl9kHMkJfJotBldGf2YGa2G/EqD3kh1Ima8ZksyvUZgw2DcW1Yq0gmseCyTbXRdJhx0Ac28VcaEALTO764TwagBW4rDVF5aw8QAmlR9MQWam+Z/JJr5uGZY+GGVNU+QAjeahtFNUThImuaI4ApbgDiUYo78OMF2Bct2hyNAQJ3KV/Jzm4w/lOsVKTrJ8du5Z8x+ydCZfbz9LSALCUSpGH7BAolSMViLpc4wklWq2bt1bzG6Nfhc1Q6k5OUe7CjmDdeKw9GMdn+Si6kcLM1q0s0HDnVMivHi2xss5E5L2vlYC0tyN+CdvGE/6zdM17n5zrX288SwiivTD3FUiV+HPiVVS+zFEqESHAuCGQvxZ/YVXSIq2DqmJaD7j4+gPFWbf83tsf+aZQCLSh9CZksYBKLEHbYA1kSOqALhZUMejNBL3FgWW6l4H7b1Wm5KcA7DWME9mU01ZSgSKqTjVTOZdCkCw6egc5JFWtT5E+1dMGFEv3d4gZPjuzOOLq0cY+cDb3VL1p+vNXIOryUWkK49qmRcR0r85m3fotjXMLV/f4TJi+QUvbI0LWGXYNtRIj3WxduwsPt4zaen5zQZdcvhqYD+8//7Df4C7/UKPwyxHhOzzPGMNJMAbCSsEkk3XYAwWtzvehFU2FHWTfPUcg2YXo/YrY1+rNMOcjahqj8slw7/3q4Va79FDCzwf8eqMCVSt9Qr3rvoGv/7+jeqJMRaCUGqjEo5ACWQTjJDB+kNVAssnvT4AcCujtnNJdJMAw73jtVdSFwVS6VM882x12BV/YvX0lV5lvg/c1qhN5LgsjkRGbLVzP9FNFoNqzeQYjQ9rCX12KKqS9nabR0K1TRhE5+1nxU2MEuAmG+Isn+EXJ98XvLikrOM9H0p/3huE0cCs8s6iHC8uBsSHWrWuYZYUXuYFpWxs48RTubtJReeZ3bzkq4+LVPTQXZEDsdNV+Q6Jihev2xGqLRWdenV8fesnM7GGnOhNe0KaEKZinRAuhGjZBm3PkBQD2Su6/UiY3UlLYFG0vsnKPLgvnx0dEE+SmtM4jdM4jdM4jdPI45OsVONogcFiv1modGA27/GwWmC95wrm86snNFKpholBZVt5pIXHeMgVKMG+F7MH8O4xpzlMcxen6QvJMNyz/1lOk4ay99zLPZbzHo93vJ1e/F2N7rkQK85GxG2F6rEYPuQd/3hTUmTC3iHW5QODs0qcwYqN8aucU7mIaghOvuSDIuHICQVUIDiKAKiwCylNZBDCOszHRaFUZKYnrcjDLMFe90osqhuPIIzTlAjrvsWrBVeWhhI6SW/x6xpmwR/WzgeYs/Labt9gVvNr82rE7KzHIEzMpvYYhdzTHWqMGd7cW4azBA6285Jmcz3bY+Xn+MazI9R/9uxXuBMnjt2LBs/rDf7NmiHrf/P+NQZhYi4WvR7Tdj1DOutwMRNpRbDaOshJOk6+T4xUiErnIypxCJq1I5wNeNoytBHezeH6cv2un6+V4TtECz9hpjy/3mgwfH1RAh78wU0YNgTjAgeVg9sFWZ5BAyFVSZ22zFiyOSmWcPdUJZit1ese5hGUTRDWDvVTgS2751GdkhIloIFWgxQJNntiJyj7d7hgtmmGK5NNii6MF8weHS6lCqKJf/DEdSw6Qn9JWp3ajlCvpfpyUrVNJEL6LEQgzhLCWfE/zhV8GKz+YuotaDBFepOK9Cb6GjQShqvC6M7zQ7WBljeh4SpO0QBDmrJDiSHxPJeQL6TF8SyCZhMkbE+qPEi2VOTjeVBiUj6OLMvLfxtr/ve4TEeEpzxY3kUMbwOMemWf4SbCbK3+nRkLgkAR6C8nrSZTXjOeK9nwR1DmfZKLavO7Grat0b0dkSTNfj+3uH65xrU467ycr7GbRLwN0iy6ag94F6/1pq+2E5ir4pvSaHIJdOKasgWz4X39XiQNywTznN/w1dUG33z9DPOvBeJtJykYqxrVysDts0ynTCzUBjjpD+ekmNx/CgPx4gEx6I7AeJEpnEndhWxfkj5sRwClI7el0iiVhfUfgmpI4Ks82brC4EQsvcbl9V4XUYCDuHuB4nuqES8JCawFHoPR7za6yKxesK7OmaivGRNx0fB5vNvPUbmgkX2WkgYdHHxdJs+LEWlbEmxSJIXNv/p4g8duhquW74lv95dYDYybXdQHzMyAvdwjdTVZsP7qEuGnjLe5dkSMhJWwzAFo1FuwCcZEhLyYTRZV25TEmqbyOGt6PAosjalLTZXw8PEcq40suL1FJb3RsXMwLmJxxuckBKPrqJt51MJIHkeLGI0GmNPeFVavS7D7YlHn52XhSbbITMzIG7y8oNidAUmvuHnie27/Ruzvzr0yga0EJ2RWeGiLZVByZSGj0YB6OoIm83NgBu5/Zg3oNNSeF29+v93nvJhnGDrWKDGKwJHb0jToPTRAWMYiJZoVBrFrvLZWwsjno3os7lMaYD7iiO2OOEmTsmWhHC4Skk0wE/a4URkKS4yUldwmBIFxzUB6PmzPn6N6VlM2J/bBHlkwVusSv5aIF9QMDYe62CDaQ9Ht2gP3Q/2cv8xYW5ilbAx7C7cnzN+X7z3OSx9Z4yjPWS8fmsIyj44Qw+8v4j+28UewLziN0ziN0ziN0/h3Y3ySlWqYJaQ2gQaD9g2zJhftgD+9+qiQ78IOaERs+bvtFX73wMwHP1pUH0vZeXgVj+AYty2QjDsc5wxmWDgZgA7FUWnxZqNRZN/9fy+x+L4IsruXXrc+bm1/Lwoqm+EbSvBiPqBZpJmIEqE762QBf15KzOZ9pWSB7kVU9iZFJpNkCKxalYBuiryjVxZl/MGO3xOMVD3DTVA9m1mOWMz5F723IEoK/xIRTI4bC4TNupy4fnDoH+WEGGiIMlHCohnwYsmkrvXQqItSW3mMJqk5Q0yEjVSLfrCa55mCAepYvHmHWqu0YV3j/bbB5rLVz8vw8n6s8HcfX+BGyENnbY/dr5llO55FNAv+nnXlMatHhWdrG9ThqBM4OrO0B2/RZU2vtxpYPgaLD+8vQZqxWZivBgZpMAhSfVVrCz/n962ed6gnlZS1UY33w2hx2DLltGo9wrYq7HE3xT4F5s+ZuKa8FB205ElNhF16fTHUDu62PCfdTUKUaDb0hjWW+T1sYQYXD1yBkAVWNFKlasTYiOLCJJXXEeVmUvDkKph6A7sxStqxByoM6CpxFSgsXG9Lu8NfBM5NXuZAVVI0oKqCmop0j+JKlYlE51GrO/I0lX0i1gVaDW1SuDr74+aKvHoyaD+W7xwK4AGKUGJVRqD0/wcA8prbAbHKnwVEU5QHoaWcc4BQc6WsGcpxopd15f1Cxb8T8/lwCemJ79X5dxbzD0mdkobzci38HHqRKOWggkJ2nLpF/ZjHJ7mo+mcjzMyiXg4YZBJbzno8DTOc1wyV/Wz+EbdCE/y4WyBkg/G9gyMAMl+4HSlUg8Q3q/Y5FgX2ncKluceRJ8bDry80n3E+CCsxM4onTinap5T1xi8jqC19nszYpJZjUpJkuVKiYgu3iEDk9BGAjzdPJOGsBHL7ywhsKzXsZ3H5D3ovk5+z2UMUxuB4+Q+wIwNhv5kkKxP3RQFesLJxPQLh1U/ulcm7vl2ChG1p6oAosDHVHo3z6nK0BOFAEloQDSob1PnKR6NmEraKJcO08aBZwlhJmPxo1I2nXhPGM4O9QOfmakAvJvd/9voWX54/4kXDrNv/4V/9R6gF5h5uAs5k8X19vsbb+QofOr6XDr7CtudzsNrMEQergdGhc7ACxcc2ga549t9vHex9pZuyKEkg+bogcqA8IE425/zZF2cHzKoR2760Meo6Z44OaOUYH9YLlkJI/5lGQrUWw/stp51kOZJvE5A3U2nqamQQdg6U38MWJ7D95xGpCaCcarSZbNBe9giDAYlVnhlJjR0oUjEiiAz3K1uVCE7CKkzgBUHv43ZqNK9fHWkeWMohm88wT+xEBk5GChvS7xnq0sIwe4NUEVKWubURg3yXuBzhhY1P4M1l7l+mKmnYOwnlQRe+QPDye7Eu0LLbEexAnKMM7q1mqUxoSkIVADFdKV9QP9cQL5r5mWw41QcQWJugWbHToHkz8M/jjXxgb2DFctFNoHETeVNHwiFIB4f2vUjl1rk/zb87NY1JBtoftnuS+6fMnaHVIKMf9fgkF9XqYwXTVoj3FegzvnutiRij1SriblzCpxIVFuX/057DxVP20vSku0V3kAovh3/UKDuzH/QfKbLzEf8j6Q40tMd9yOTYRSWPgEmKRxXLnJEAK5VADIR0sOqwQoHgLyUhpGeNYG6PRjtJsKii9vTS1qG9nQSphyIVCrOklHiAHzi1p2t5Ys+B0b5zWmGlOukmII0G1Bv0Dzl2BLAveBGZzXvcrxYYD5UeV660ozfaO0uJ8HG7wKPhXca+q9Uj+PnZjh2X5Do5U9Jg/MShCRa8SGf/00hKIImOe3xZ2pIScCOB38/bLf7Z+df4b7/7pwB486MyjLpUhDER/urjW+xl82ZtxOY9z4xmOQImIUhajJl46dbP9+jv+HvZAy9Cw1UWQJPaaZqON3V5QWle7Usf2URsugadbCzaesSZEKbW+xZrQQOS6KJrIQHRiGMNZVsmQwqlIkIEXF+cdEJDiLIo2VlAUHtKAnVW7/dUJcTng16XMLrCE5iVPi35QoYzg0hGpkSizBloREYylW9p77X8P2oCUBGGV4VIpBF0lxFdKOhIbKLqXrP+ODXlOTTyc/AGVklczEmYRqmpTlW0ohqsPlk87MGoF7ft6Uhm4ufQqDcz8DnPFoCJiiTFz0uP2XYQwSv0s7VHmwBQmizu5Tm2A29QMsfCeKjrG6iQEYcLlhSZR75vZx+M+p0nB4wNYfeZ3P/zCJM3RhOOiRllkVaiFc9F8Y+gUj31VE/jNE7jNE7jNP5A45OsVP0swcwS0sLj+QU3Fwwl9N7BEu++fvn4RrM4x2CZTQugvWcf1Ew7n+6Ys1RGK1VXoI9p8sRwkeCv/dEON4/QciJJ8VSdQFlBGHhmsiMXyDSZVBxOxJIlCHOPBkJ1n/FC/oxMo08OKm9AIGWANh+ZAaoVbYWyBUvgHWauFGYJyZT+UKwKRMjK83Luk/Tt3NrCdoVt7L7cohGDh66vQAQ8e87Q6nrXYpCeKvUG7tVer1lMhIPAm03t8Wy508+qbSjm6YD62Y424rCXfuWmBkIJ1DZjEc37M+6lzS9K7MbbJW/JL6oD/vfHnyuy8fzP7rCoufr67e01erl3vrq9wbhuVIKxuDrg2WecKLPravRdpdIEMxDCa67W+/uZ+hMH4CiFhDxNElqAeDbg8pK/t49Gv+dhqLBse4V5UyKsdlyNDX2lwfLurkL9RFqJTJnq0QH9i4AkFV310ZW0JTsxACGR1UgF51wAibmJX9cwfTnmcOm1UBs3NczOYryUcqmKqMR0nkI5lugSOxn1+XkqPtVIfM1yVeXPopozzK4O6rK127UIO4fZc75/fvHiA17N+B77H//ffw8OxTXI9AZRkBNXB/jeKephq6gsVfOhUWYtRUK4mHR2e6PHm79LSZhJR7KTPEKbMJ4V9nL7wapZff77DNfWj6V/myYuTxTkuZ5Uw8r+7QkAFe5E4Lxo/fwa2oo6GlT6ucklzH/nSoXbFfnduACG66jVujkYDTNnFIh/z3iuSjX5yosLV//7H/1jG5/kovryTz/CLRoQgHORYOzHGmM0uN2yps9HgyZb3j3O0f5WnJcM36xKToplYQBxryA/7GZLSopg/Rf/HFu269MepSm6uuSEWJGhpkBI2T0nHTfyzcEiSg/LLLxKQcLBHUG5ZlcpYWI8j7yI52c/lgWcqgj7gbEmnjALESI7vfBrx+dzGj484bcAANzZoP1LrOoCBSWGrjLUur1s4bP+tB2xbHsNfzcmAQLdfvmzD+oYtB8rDN5hJb3SPjklmgG8GcqL3sNqoZKaKSmHbGKoOuTeMRBEHrC82RdjebBk51f3bFP4m6drfHnxiD+7ZHP8kAhfrTityLmgOtWwczBzj/MzntV8NHh4ZFemFAxSpAJ7LzoMIstpzg/YiusT98iTwrpVFZSAFaJBTKQQ78XioLKiPjisDi12EvDuXEC3zzchaUQZJZmEp0wf+drjRUKiIrtCLDBrbGKBGCPBXXd6AxiT0OfQ845TXRSKHQ1MlnF1xH0+jZOzSnozPQHybEVZJNyh9AJV+tGKCb+8vb3u8fbZk5wDi/e/ZdvJ6skiPBtV+tRaj//1G7aMpG1ZJACA3h7Qyiav7yokTyA5Bb5zaL7h81jtyuI1LmUTnb0xJ9aBoQXiLCq3wR5IoeBhOWFZJYLtSB3b3MTKLxGf3hxxF9rJYnkoz17+fb8s3ycvuKFhboadLPbjopxHfi5lsfdFApQD2IESFJL39sNVISpSEp6JFCH1itA8Jj3eXHC4fZJ+rrzHubQwjthmP85xgn9P4zRO4zRO4zT+QOOTrFQtJc34m46n3UxNyxezHk9rxibqd3UhDyQAZiKQbtKREb0dWdgMMKkjV6rGJ91CNndsWp0rP0Sglh34cBUZ6stM3r7kMyaXAGNgBP6JtuzwUyCOH5PPRYQSlcxYYK3YRqSaMAqU6C9KVZx27gjupQnZYQpdITKhQSU146TSJoEn5VebdsRepBvUkzJY3Y7PTd7h3vwrh/6Cj3/75wN2pgUJexnnI37+U05K/cnyAV9v2eHoMHClmklks7bTaDNnIseq7bjaC4MtlVikEpm3dqC100reXxYvYYANE7qVHP/B6jb0F//4G1zVB3y14SrorO61gg7ewlX8/vX1CKKksXMhFMNy146YtaOeq76vlNk8ELCUgGtnA2ZV8fT10Sgzel7xsea838d+jndrNs3Yrmcgk1DliqsvuG4KpMxyElMCRV+IDeYB8SWeyFxiDY1HpEQw4hsdluwvneVO3bbRv0uGodvMULYHOiLuBZG3AVydTtshSpSL7CY0lZ6ELJWJhLgIOHvO2OIXl08Y5Z74uFlg/pzx08O8QdOOmInpxS9vX2P3nss04xlxSgsJnljVCGJukBEf5CDxjyVuMFoU0/+Kv2uGO6MtUXipijCdQb3KUH/5O3+W4FZSmQrSlecGP588WwL9aoiBR6ns6Jjk42flNZoQvKJLSjQEgCoUxjNFriyn0pz8fE5hZ4pinCHnoHlKWnH214REhb1MAeivMkqWJteTPYE1GnP2+2TOH+v4JBfVs6ZH1STc7pZ4lCDsrBPNfZOnpwXcdzyZ2gOhvxHoY0Wo1wVioQkHvNoyiy0/BLPbAgX3l8c3KzMWpUfTlbDuZMATTH5wOoOUKekNQ3FHzZLMUOwtIIsvthZub4oEJ1KB1DpmD6tjTARIJgtcjAjZKPxjxRCRvGWsf8gYnEy0kzxMGJ78cs9ptyqLI4cg85uMn41IO4fqqfSV82vVfETwBiTMSdxEDdf+ZneJVccL5c1ij8NY4UHOweZpjq2wf6vGY+xdYTN3Vlmly8/W2OfFtjWggXhzAQAuYZRFdHQ1Q5WyOalXBv0zXjTu9gs8HObaR/1HZx+wm/Pfdd7hURyOgIS2HlHnxWaoFOI1JsHZgK1YYw7bWnuSdeV14dxsZ9hO+ughGE1XymznzDbu+4rhcgCu9qjroOHpcbAaFG62rpjrVwAmSSaH54m1mQDn+W6sQot+lgq+Fbg/CgBm5hF2DnHMTObJ4qjQpvxnokfMbYb8LJA3GBdlEdEep+cFIf0DM1ZsIi5fbLBo+FqMweLNgnvf/+H1t/hmzxrzva+xGRs8iV55+/6suCvNmGGueuWmBFLEzjJ7We6DME8Ys450bXQBjDVzELSf2NGkn2hhJkqB0ELvx/Z9+VLJMgx/FFYhv9fcS98xP5MWulgmmvz/iXPRD0fzJIb6co776yJ9au4sKED7nn5R4Nppyys2vKF3ssi2jwnjrDDmY12u23gVFK6uNsUNDsRzYihqLz7mk6Tmxzneb85gQ4PBO3SiO6xrj5QIvcg4jE2IX/J2q5r36NSMgI1Ksy6z2nHgLsAJDMNlRP1UhO3DRFemPdUqSapEIQtp0/9A8JMepQlQ0k8MhDiP/+DEAk9KfEpWJqC8oKBU1iDp2+YFmKAkGmMTkjwodgBMXxZfFoKXPkyok24EgGJ76DZijygTaNV6BOmHhm5y4AcHezBlMUulZ9PWHr5vYT9jeODNzQozx9viD/slWtGl/mT5gOt6h39JPwUA3EbSSs/aCGo9+lte3ExPaqG3eZwDObklAkik1b95skrwwmzk3vRWrAhXgJ/x+3/8cIGXr570uH759AbbgRfVzaFBK9XQs+UOjiJ2stg8fTgDSS+z+myDMVhN4Fm+6LT/uVnNeKMERitgoItgbKMm+sAlVLNRq1Hngto/jrsa46Mpi9osFCFgKsklzQNLonafy3VaRp3InUyEeRH0i6jHTwlaSpr7FjaVyhKY3NMdWxhq8sq1101ZXjxyUhLfnxlVmdzDUuH/hu7fAAAgAElEQVQXGZcpJLdXWwzewlm+v2bViEPgm/O7w6VaSa6GFt98vFIJU/VgVSqUU5twwdeNIqk3s90atmFUP+GocX1Tyz8aCSZCk6NsVxZRv0wYJ6hWtSmyNkzkQBpAnglZIzQc3fYJoJL0YsLEdIXKuZ9W9/kY8++FBkee3mYkuFW5aFNyGFB+9vOkc4EZuFjIc1t0Rn+P3z9hFP0vzTzsmu/pdlJkxFqq03w9O9G4T/raP9Zx6qmexmmcxmmcxmn8gcYnWalufn0J07ZHUEPaCFz6lreF7WyAl77j+naJ5nveilU73vVlsf24BNZ/UTjxdLCaSUqhsBUx2cVnA3D9bFOqtCjMPN3l92WHHBaBt0ETVxZl7gZClN9zGxaTZ1g22km/wgv7+JDTPoB0KWbYO4f6TliZh2xSDv098pleeSyxSQTtiYUFGz80Sz6P1kaMArGbrT3aiZqRCnztklZih02Lej7ASYXbeYe/W3GmKVHCf/7TvwUAPKu2eBznyuqNkdDOiqlAN1Rq0pHmSaFm8lb7WQBXGGq9VyeFw6yLCJsKzQN/t/4q6Tklk/Dh/SVuLfcvUzDqjJQmNnaPZgZrElab7HyeYK6LixQBamH48PFc0z6mLMhUpSP7PvLEeahg432gtC9isAirwrqlVOD5FIoEw+1I2efJAv0N59vmY8zXPVeDxTmpOIgxZFuuX5hUOLlHnYefJ8RsBhFLz50Gc9xfncXJPRFh5vI3CxwlNI3nEe7NXr6zwdg7tYy8aXdana77Fh833Dfd381Bo9GUlmRxxEimuS8OSOuqPLvCzs89xfk7V445FYOEUIl5hUrsEsZLOX91+kEYRoFIyRdzhvzvzD3ggI78AhDqye+FH7Bz88/iapb7mlwJy48BoB567etNYr4HAIoJ/bnBcCGnZNID9WdBK/BoE8y2VKcxoNyv5jiMIPVWX2PoOnMv2JR/KieaGlH8mMcnuahOF4v8AHSvR4bYMk28d2havuJ9FdG/4p/Do+OYouxqsvQFSnRMjQ/z339YpgunIe5xKrSySBpUjVQWKH4NRUdqEk94E4BBXV+qpHIVM4C/S26L3YTirdrT8WbCJiVgTOO5ct9kKp9R2DnxwxkmAebZQYkaDuGet6W/paQrXzYB/D/K+Z/2kVw7IniLxYwX5sNQac/wz198gBdGxm/2z/B/f/gMT/dLPa7xSWQoc49nNxtc/QlPtB/uL9Qf10xgubwxydcMCZq4EQ4OZm9LCtHkXNBThWozIdRUQPaujFXCcFVg6G5fq54YTVASU174Dx+l/+oSaMavpZ0r1ykCqQ1HiUFmksxT1V4XVWMDkCfy0SCOBiRe0PWH6kgKpnDss4SwiDpp4vCDCEBXrj2Nk7g3A9VVJAcm+WRYNzkg60bniV2JJn1hEuJWatjCMD9D5EvaiieDKCu121qQTfDSd8f5iPMFs2M2+wZfvnjAVSMh7t0C3z5cAgDGwanGHDYBE4/qMC+SIHM28oKafbNNKucjAPXeoH6Sy+F4gwUwsUjJiARUd+X58rNCVHJ7AxNKLzM6aAsJwARa5Tkjw7V2KOcsOpbhqB41TmRuE/jXhOPFis+r/J4Bqj10oYsO8NIPHc94I73/E/5wOwuI4poEAySUjRX7icuxN9MFPcFtCNjmyYHbCwAv/DoPBf5uWd+e/98fA1npBP+exmmcxmmcxmn8gcYnWam613uYeWSXnrx7DgS4iItL3u2GRNjccgVk9gW2ZBF7IY3QqlKJAdvKlF3hFOZNLh2JuP2sNP6naRx2ZxhCzeYPiQq0Eol321li06RCPPFGq2ImIiTNQoRJSC7bH1mYbkKSiKTwlZnAoBgBkwiRys5aafm1ZD5m4kwFNQRHZ5DmAYPnW+uwq5X9l6oCoSFmD+HM+wcuv+RSYF6PeNjOsRAnoMp2yoSNifD1jiU1//b7F/D3bYHOIgqTcTZie2hwWHHl6u4qNE+lCp8yqIeLVGRKbTqSJsUmwoylRM2/V68IsZ5AXalUDWYg4EEC0TdLdfcBACJgEHLcsK/ZgziHIowG9dclhkQJOxWAaPW7xSohSWBBVXuczzuViD3tZ9jl6kL8lTMDFam0LZJJCssoWzSj+305N4CgGRPSi3rspgLzhnlkxy9BPdy+sH+D5fObxgxLE7w4KLnzAf7glIVbP5pyT1io7CfZhLAMeg8SAV4IWa8uN1i4Ab9bM8t38FaNMgDg2RW7Jn349gqmMwpzpzbCLgoqQZ1Vk5FUlQALMxB8m5D4toMZSrg5m2HIMcnPmYxDEZjdinRtnjhxKp/aVCRG1ZaOvJZ/6NKWR2jlts25E5O2jp04EZG0mtTZbXJM7gCYIamRfaxp4gAHHL4Yi6/xwR49W9MkIc6O/kE7CFwluz0pGSpN0QlfWN/skV4UEc0qYTinP4oy79NcVKsIWwVcf3mP85rvxvebM6w/LLH5t/xgxjYCC4E0X3TKGLRbXrzCxOi7fsp9SAAEdM9ENvLMqyzEDMVBJQ2SPpEnpCUUlmIYJylkCkzgYCuM27xWthFRtIfkSScBeyBh/Mn7N0DznTj8zBLLY2TOMR2hfvp9HntW7hz1u7TnBsCX5BsgIRGVv4ukcC0ZaNLIlFmYXXSyBvfN5/e6MNxv57A24lLCwX/zcK36YQAaZo6NO9I1JgN4uWbdqoF9cqgz7f++mM4DZWHwSzkXmdk8lvOb2ghQQqxkw9OVoOZkgeEi6qRjuwncvkyTkPbI9pEZFj3YsjC0gXuo8nntrSswndVDKj2pvIlx5XhTItw9ngHfSt96ANp8Pmw62gyFWSp/V6VJH4x/nl6nqa5xqmH1y7JYmd6UNspggHESSdcb1XbbjhDa8tpwVdKV/LpG9WgVIhzOkyYcJZNgJ716t3L6+cvzAy4kHGA31LjbLrQ3vdu1Ko27utipNSM1AXg7ImUW+mAQP8qm68AcBIUjbUKUIAt7x9clb5qqLY50qtoWkF6mnrtJmHxOh8ma8Pq7WiPdgN+HPXWhDlRsIYnfmzL8m3DUe9d+pGFmrvb//TGHg5piSZmIW0wA0P28g6sivLCezc6WRTHR0QJuPGGQJK3MqgdEAjgJ2+CWD/8c2hIAkAxgJioBPwO6GyBMLBN/rOOPYF9wGqdxGqdxGqfx78b4JCvV/aqFGVrO12Q+A3wwqM4H2GveKtWVx1zE5NuuwU4qx3SoQQfApgyFQMXqwyXv+FX8PfeITrSjKwcnPrfGE6ItbiKYaEBhxF0mh3eHAv/GKrFfq8CJ1kakzI5cV+qjm4gh2rxTrT84hX/GOoF8gXLcjo4gmfJZbNSgxg6TYpZGFrVr9WIJEBMCOhjQaHA9F42vDVgbrgaiBeLLbFLuYW3CvOV/x0R49xv2zoVN+PM//Q6/umWf3e5+phAyTIFFKQHhLCAOBQ0wIt6PNZNQNPKqKZVGnMSBmZ6r+qyzpUDl/QcD6ids14wigJ1uzFjOQSJgEGMIsxyRxHCBnJgI3DXlPcTNCTt2csrEnOgSglQNaUoSs8z+zciA8UAQQk3/uMynRa/bVPMcz1PxprapVDkjaeuDBmaOZ4eiamUKs1sgx7iYVKgSOxfbqPcR60wLScdNckBHlnbrcSRXKncaiKH0fG2agrDQwWhkISCowpL//Xy5w28/sJtV+tBwJF3u5Dwf8PIlmz/s+hr9XiovJ/mmgh41H90RPBlmUIcyeML8d8W0ZEoESkbCtyHEnBy5NkAi6OS1uoQzZCKhy56+u4JcTQk6OYv0yEM7IxuJq1QlOKXCpgUK4ai/PkYXUpUAAXf8AmgeCe5QyGadPHZkEvzeFVSlKu0ZGoD6MRu1MFs8h7+bzqBaZ8SG55N8rat96bRUuwIZ257P1/4Fv3h4xRreSOX7/FjHJ7mo0s6CokWoA9biZjOOFk074lwgpTFYNXQfhokzj02IzSSPdF5YjVkWkk0YAlDceB6NQo6hTVPUBuSLvIEigBqIuVfnC7xCgSfWHEY+Ek/aAJCqqDBlsvxQ5c92h9Lj4Ae4LNTda68LevVkCpRFDLllKYc5lAmTPH9/helcQiVi+Fgl3Hz5qPZ57+4vSh5sHVE3YnQw7+CDVcP6u79+AZLvfPOzB3y3ukB3P5PjsgpRUwS8tB27Nx5m7pFaWThHU2Bz4slZ+9sTNmQMpP3mZGWyFDjStR5eeqhp74AKBervSXNYp+HOwDGkSSbBiaTGHxxo4yZC/wSzcvoeyUwWG5t0MSNPZWM18rmfTrzZLCEbyWd5htsV5mhocSxTSKV/ngzUVCRVCZjYTh6FNuT+Z95o7EqIA1xSK8LcGsimBdGVeyw5Psb8vuZs1L612/I9lxnWYR6Pcle1Vx/lvpYN1G9vr2G/khuBgPYvnnAmG7TGebxfsTNB982Zyo9CHYHesEEJ5JxNNpFpUXqezW1VFq+Io6Sl0DIzPo+82OYA8WzZZwagvS3ynWmvlGK5LlOb02rP3As/L9dp2l+liNL+CaU32l+VzX1sZR7SnNSS0cpWisVAonuekIQhn0ax4Zys6FnC5PYTa0kCqnUxzZ+GqkdpT2VmPGey5hN1zHjefFGg7XplYEYg/KCf/2Mcn+SiWr/cw84j6soXvRwBhpIupLUNmnByfb7D05ZlD72peYLMVnB70n5imMejm9KtrU42/fMia3EbAlGx7DOHUvGEBgjnHiQa2djGYzJVKg9h6E1ZnAnFfUYkQznSiXtw/LOVxTF/Nuqou/UxFes68gS3sqrVSyYVJ5bE/ZppRaE9m1c9nA2420vJRQkk1TSZpL2u1XqBMBjYB9H/dqXPc3d7zpsFcbmpHx2aJ5kwLKF7Jt/acfB4lGsWEYusYOWO+k3JTOQHJhVSFzg9xchn+cFq/B31/KCrk5THkS9tmCftY8c2aupLOFiduK1NrL2UhcjdV5huxjmmbDLT5P65KZ9re04uUbLQtiQjhTYxeUgOyy8KIc6MnACT+7sUChcABogSvm7XbGuZq9ipbWb/PPD9l/W+bYSRDUMKRv2UaWA7xzyZuv0k1mvgRZMkjSdNEBG358+aSq2U6DMQzFPuZxe3Hv4utVpG/uwfv4NBwq/fMbKRnmq4vOlYlueHDhbVgzlCXbSqbCPsypWotqNdr2y+dFMJJHkWbD/pGdYJdiCV0Uz15lObP6Bshn54HH7GZJ04QY+KZh1wXdGORkd6jsPERtT0vADme9zty8IcWmA4j0g3IpupIkLeRPYWCKRuUdXEBzi58h5m5OuRiX++hVbdrNMluI0c82EiCyKgvyjVNEMA8nsd61bxRxD9duqpnsZpnMZpnMZp/IHGJ1mpjr1DsA5+tMXrOxLIJGUUztyIITD8+LiZoxdTAbcTQ3pBnsxYZAqwDNWplKA9lozkimE4j3j75gGbjv9w96tLjGdSCcxYNpAlMGZeICkOlbagzNL0hGTzi4RwLaVGb+GeCh1+motoDyRbKYG2q6Tev8kVaUm1JcmAlUrMlV4k92smcFCaVMm3Dd6vb2DOxKWptyCpAuNgMWS5hwVQx8LCbRPoS4ah3l5tsOka7Pd8foaLCvVKvmZTdu7zqwOsjdhtiwyFtsdJKCVQe7Jbn6cif/FAmge0YvThvcHYFZpwbJL2i8x4XOmFNiGK6YVdeoS1MLFHghf3oPqyh0lA/EbSUIZy74RlrgJRhkCVaTBIwmaNy4TRG1QiwRifFZMFioQ4eQ/3ZNXIwpvcGpjcg7lnmCVWkOvpi5SGInB4JehCG0CU2HsXOCrhTB0QJlKwaltgwHGJAq027Bmc1C2KVE6SJWjVWu7pVYGJKUDh+5y/G+XZ6m8SGgmr348V3n39DO07vqjDVcR4I39oJ/3bkWBCqfiVkQsxk/fQ53AqP9JzNJHVadU5QYg0R3nI90v5e/LlugNAvS5QKIUEgzxncFVrJyYVGobhE6KFPvPdc6C/kms8C9rrNh0bdOR7fDxPCOe5ZAaojqhnfHDDvga2Wb1g4A5lrgizgkbZfvL/GzmmSd83V+dmICy/Tcc94py4syCFyGnSbtBzPAla+DGPT3JRTYl7Y8EbtSUzTTgKfx6ixUGCn2lCFqg22dWkPDhq4TaI5CLDbz2pxtHfFP3XYtnBS7g0AMTnQ5lwKrZpsxJBlSIV6zTHsW0pyzqaCDMrDZlsAE6BjauzA4xfFGp/JhKM5wX+Vewwle9JEZIeIu9dTbRoAx3JLo7SJeTnnJQSPcF9bPTtVZazHIFISGK79vYX3+PFnDGjx36O86bDN4lZZHE7R3cjx9EkjNcy4XvLbkUrXqjd1pQ+1UiSaiIPe1fIMOQJlUz+oU16XQDA906h97TwMGunk3CsSkpKsomJQI1cw6daF4bxKuDsJUd9eG9x+DhHNQ2rVghTevOyUNnWq3SL2qD9W3gCecL4Uszeq7JApcGgfrCw2YVokQqEGcRqUuC3cOULFGoS0qEEhScDdC/EZvGsBN7ryP/0BtGXPmeGud2O+3bTFJbpucp/yy8W6017oOOJvEEhU8XSb6YkvystjfqJ0MlG5X67xPkDYf9K7rl5YPkMWK+cIephvyibIvD1rB+F5CZyr6w/jXVZDPLv2smGMy9041IkceC2jgnl7yiURSJWvDDlBJgjC1BDvAmR82z7siBnzSmfG259DLJhMGec9AQA1UORY/ml3PdnfJCzZa+OW+OhQrMYVJaW9raEG1QJHoU4Zydks2mYgRl54dfNYVNsD+cfEmyfMC6FgPRi8tzFMlcmK0SoybxhD3SS1JzGaZzGaZzGaZxGGZ9kpYpIReyfoclNhf6pxp2UFMu2Vyj4frOAlRBhhYEyeuVK5ZHqhBSLEwsNVLYtk43/ftfisG8QsxB/NHo85rFCvPCIWcITjDJ8ySQmsuTdXSBQ3u0mFFbg3mD+rjB+h/Nils4xTYW8kt8H4ApOc1Iju69kFqJfTAgfifgQMrmqPabXA8LEBcsvMukivergxLPWf5iDrga0z3L6O3u2Asy89sZge88sDHoTtNqZvXNwYrYxJK7gMmTtZ0mJVYGSmIpL5ZFD18GVhkKAbUI8OBykaoNNygSmzsJOjOGTKdfaCAnF5Bg0IQUBAOqI7YpbB2nvYPdGPWvTLBSGcr4Ecs7jQ1PcsnqjzNdkE7syZWnP6DTUutowgzJLn9yB0N7JMY7A4Tnh8FbunwkZiB5qNE+FKT0uUvGY7izHxAFoFgNCIIy9ZAvvCiltaoYR64QwEiolO5UqbTxjVjNJ4ADZhCTQfma05orFDjgi99QTf2WOIuSf++uo17N+IqkYCys5V9rBG4y5+reJvbTlmTeDQS9GLbYnDmufEISKaQG3ceocJL4rkprhogR0wzAJK/+b3afkZ/HjLVDoBOpMBeLNsK9G7bXlPcZFgj8vrlK4ayAIPxPe5L61bQBibhcBh++XBWm77tCtS4A8RdJnhkYJAMndgjippiftE/JAbMtrZiLz2b8kUCrB52ZMWHwn72EI2y/y8QY2RZHhHh0/41Nv8B/p+DQX1Y0DvBP9lkw4gwFsUleWm9le2b/ffXyG+b0sEp71qBk+dTtT4LaREJZRbenIW+251WeD5lwaSvC9Y3cdgCcBmZzjpUc1HyaMVg7cBsA9YBdLH9Um7WnR3ik8Va+4d5FvbACYvZdNAQHjORAz5Dlx9EE6ZhqGGTCIbCaee33YggHsxmgqDgxgdznLNTHsKItU/XanaTPjaJH+nhdOB6D6rMf5XCRM0aCX833RdBijxeVzxsrW6xkgEO+4KIxBszeI86iyglRH1kSCZThuW5xkpu4zoU3aA0pLz7B6Xug8TRY7/jEzPfm7yUuLCD+f0Dk9qeykmo0YdxkrTwhXHpR1vNtKz3eqmAFp5NyZngDpl0dX+n2mI1AsjlmgpJOwX7LJvBPouXko3zNWPOEtfiuT60s6kt4cwYVV0p5kbBJGYWl2OQyhK5sHN2HIZjjPdpMeO5j5q+lKZwE0C6hzQMWqRTYuMl4WnMkx5wVluEj6Hm5r4Aw0Fzg2nPqSP2u4jEf8hZwv68cSTJDaANo51XknT7DyvRIAVAV6NmM5V2Yk1LfFIYq/G/9s+9JmJl9yQQEcaVtzW0Tvx4llH0vgZDPY8MEom3aSQ0uQTVze9874vAKs+w7iOpY+NqhEogIA/U0EnjGtNnjDX1bOlV0Z1c/bIbez5LOrchxuN1EdNLLITr53dmXK7HOFrwNh+7ncB/OoiVi0cfzM5I1F3gCfJDU/zmH3BiYahNcT/rZJmL3e4p+//hoA0JgRf7d5CYA9OnMvx8+kz5O1nWvSqLepDywAxIsRlRACjEkgmZGHfc0Lu5v8/mUWFxqM+7pIKwD4rINNUvnK7GVnAUFIBskkRHnQ/VJE/pP+aH7QQyuJODm5AyjV9OR/dc9ZCpI1fqYOiGLvZjciNckh69P+UJ34YZFqYOwd+iwveXSIshl58/OPmFUjHiQWbndoMJNg75vZHiEZTrgBELsSteW6IlMYAaSKirduHYv2MssgJnIHRRQck5P4BULqXekrzYNKY0DSA86T2jDZxQ+E1ALNOd9DfnS4vuRNQIiEJ1+uX/SmBI5jojF1vAFRyzgHrf6btdHj9TMhx+TNRG8wPJeq5HzgXuWGdxPRAUnQheYpsc+rCv+N6iuNJ42/83OOG6xFBkFPhCQQyPjaA4PRfrEZjvWKNnMLAvcLm5XcLyNhPJMqtrEIBGjgyjg5j+EYRYhV2QzSlztANpfh2xlXk/lSPxr9Xt3zyHaXk8q/v5vpMermZO5RnffoH/lBmX1vddHIIdxWfnlsy/3SPBi47pi4VCrL8l2Mx1HVOVwe64cxqVzTJJA7G5DwC0ws0j7kZDOoul/ZyJi9RfVe9L6TvvS45M1IL/cIzUNBwu5qUFWiH6cmLqaXczXV0ubK2QE+m9UQH4taMNblmDSN6w3/4dn5DlvxAki3M5iPYvfaHXM2psSsH/s49VRP4zRO4zRO4zT+QOOTrFT9eYCZce5n7jPZZx3O5x2+nN0BAN73F/i7v/wJAMANhO669KyqtVEoZJo2E2cRZjlqy6NuPGrp0R4OtfZ2EIghqMxMrSZMT0ogm1RgH73RvmkciKtA2f2G3pYeBEEhZPIFtgF4N513+P5cPneK1WVnlI0pu2Erfdeq9OPcI79p88iwqsLLEwarORj+u9wPrI1ie7EC7LVUdtHgm/tL7SsDQCupNO/W55wzKo5K9Z0rDi2TvlR4Hn+fAS3pOGERkVzp+fl5qfSSTZOKk1NRsrE/bZ1mw2IeAEpaZbIqX96jSqiWgybO1O2I9U7cuYZy8mNvYZrCRg3bCl5MC2jnQK5kudqD0R1797r0kSkQG4TIufLXI2aXDJ30hwopEEZJfYnOaDLKuCAM5zhyBtJ7qS7MS0pcpeSKpbsB4pf8/pYS4qZU8jS5daZSLbcDFu+Dhmh318SwI8COYAmaKWsGo73qcclmAbkn7BcR9kYQpERIt3xO5x8Yro5SLR6el+qIIkuJFOYllIQfB7WFdFXA8GGOs69zqwLo5R72Z/Ho/g8XXqs725kjaQjAkpj82dl6L9Yc2pANGfy8wPSZTaxQ7oQDYSfnMdtp5gp3KtkBmPdgN/zL1QTCZ1tRmRfayG5UWUHgDcPeAOyeUI2mSKsmI0j/VjNfq4RqW5je5fk5/i+hmFUgMpphREXx8M0lmo/82bMex0zgVCQ7bEiTSgLSj3h8kouq3VmYYEFriyB2df5sxEXT4W+3rwEA/8c3P9XfD/NUiC0NQ62j9BBiPSF4uMjkAJmAut7+/+y9R5MkS5Im9hlz92BJiz3SdGZ3dgQzs7jjf+OEE27AARBgF4OZ6Wn2WNHMjMggTozhoGpqHk+AC6QPW11hIi1d9TLK08OJmemnH0FkOzB/dJV4YjNUG2GbIlTLCDwR2zYiJYVYenJRIZaXqtgUMsCgFrG6/0w14itrgGz6wH/P8Nf0Z303AllJLxajETvD+SSZLYA2wfJiEE4W3akubMPLXFU0E2TCT7Ygdvz3oNAwtT93QXrFH/79HrlLWL88AgA6F8S1anhuYZ4sWoZJ/VU608GJJpah6TSLZivXWHl1Hr3nqtsSXKr+pgwbFhkNTIYqZKGkaMNRGn5rL85LTUN2hpnh99FrdAwFL5YT+UoDSL2l8wtMtLrrZSOR1hMRtsq5mIzIEp3ubV0k/IokEiWhxS4DhgOtLsoQy6fYSVJKEk+uLfnStk/ca38G+td8zOtZP1gD8aXHfsUQ8iLVZ/VkSNtZLt2hkoqSqxKR5jnDLzXGmyLPgJDXkEELVFn0dPW9zg1o41Ke8aQQH+k8mgeDjhevrIDhqyzEM3eoMpS0SPCrKGlO7klX+8TXXlop9t/XaAcIbDzeVMmV2dOGpny39q2T71ZISwI97/LZRqXcs9JrLRC77Wdh4yPDpDN9q+imG4iDld/Q8ygWmOocdjZDlbyQjpyPMYOrc5MBnWUTA0B4H2aic1LlGLn2qf01cQbK8ZGUFA/zoPrEvtRlIxQXdYNedNORdf1uN4vNnMfpeYXptsr5yoKd8Pkvqhf49zIu4zIu4zIu4y80vshKNS4jQXzzLUVv8W8/vMbu5QzCKw5Cue7eFO+6SzB52oRz2cyoBZJVJsMzKWL5vRV4xr+ZYNtQDRKSkl1lsIn+faG8RyXidT2ym1PJeTUJmquXOFiEQhrhXWnZgaoIJPZddSbB907gWeVrlUN5pPydlxFKZ5HGqMEQkQHAdM0SBN7FTrep7pJb2n2W6hdJwXPVlr2GKsSqdYDpIobfUQmdnhRaKlrRZtqBFxgtdUlg6Oh0TfRJiiQeM8KgMBI9GSuIzGIRq/nALKsWLsEtPfzJyc+0KexQQz/nIOvy3wGg33Zo3jmoqyKVyRjf0wnnpprr27VHeG7kxGLUeHFNJVBnA/4UDPIDy0tcRi7IyX2q9ygqqOsJqxWVPadDizwWyZWBnlxkS6QAACAASURBVLSY3KcXHigh68V96Ibvzay6UL4+p/pmxNW6x+KebKsUSEYGAENoYZ+MMF/9qlYbcZmh3pdzBGJTvWiTy2fG9WkZaxmoKnSIxEYqMxeiAjmqUIk94y153uZFls+JH7GhZKfCgIYC/DWz8z85MXRwe5yZmLiDEua4eOyWZynV96dIRsys6ixV23StMDEKFLtzGZcOs2q0ZQi4tJtmBKe5o5bb6zOSYVjmMz9icnCr0HYsHt6p3m+4BN3WlpL+0Mg11f5ctjSHtPXEc8esLVD+bDzgS5UZFMabKOcFDShuweTJQA2aAjhQqul6vqXC99eUcGS/ogdreuzQfrCIw+df532Ri+ovfvsRdtViPzbCTJsODVabAU97mhVSUMgF1tCACiX2KMPfJehbWmFMVhLDhaTQ3vdE4wegfugkfDmsMvxLmpzb1QRrk8CAwVsJL4bXwKSrXhQQg+vUZMRNhFvWSZ69/BFHIwu/9mS4XvsmNdlmSg7qZOU9pmSU0hNC7dFGBdUlJO4nFucZgCaV1NSealxHaO7fECxqAYYn9cEglb6sTcjrksINmD91NdWkAQ6sGWweKei49ORWr49oLP27/WGBwPKaxfeW4t8YHiMoq3yXjLSOtXe6t5XdvApn2l/rIvzEB2mTTCRu5ZGSJhkCj7L46mcLf52kr2cenEy0i69O2Czo+TiODWITpF/c2Chh7J8OK3SLCadN0ZBo2TyQ3IAXDZeQe4sjb7zUYOrnNMhBiO+v+76ViXz82sN9XRnu47GRTU25BwBtyLZhhRNLXqxNGPsisKS+Z3E2MkM188+LiElkXDRhW3HEUdL/DIo2jvPN0NxIfa7nnEP2aQHZuBHcW3XItPjys7PVzDrle3RV4X23rwkt834nXdcsm8hmS/Bv5POYbmqkm2WLTIFCm6rfnq7rAp7aRMxu/ppTk2vyjwbZiJZO0WOVVulwnng0HzqcL3ypQWUbRwDHuliW/z4tNNBGmJ9auXaWHcSyov5n4g1DNhCXqexYIjVjZs+d48qmKJuM7r3B8JK/wGwBR6T2jJqnRck1qNc7mwwkhfCWbkhzpHNUF0P9y7iMy7iMy7iMyyjji6xUf3q4hu47JK+F/euWHrfLHgtbGagnTTu95XrE6UQWKnGZKSpthjlu7gm3vFkMeL/dwP2OtoF6xnaLDYQAk6LGFDXBsDyKA0xOivSWXIlkrxFm0V1mFcREQumMUIhKx+pRS/+wQtbZkGEDAKiTJVMDW48pRJkZi9dsPBmpF9bgqAS6CUsOMC9MPZOFMDUlB2ydMJRzO7PL2VsxbW9Y+9e/ZPblVRQz+X6joE9GKoP+1CA0rFecDJqHUrkzFCZldyVNCHmsfLc2Yf2K7tPtssf3P5CZsG4i5afy9ddNhGGYN/2whJmA6VXgazJCsWm5SgC8Er2fSkB4yeiFSRJxd7+qjlEAsZ53PaEjzx/X0AcjPChy5CqwvK7axURwoXhMeyUh6NnkMy1jWHGFDjIcCcEgFreoXDWy0BDiU8l3HRcMzd8O2FxRadMPDt5rYteCKsniY6wfHZlNgBAcFTPE1aGbmT8sE1SqzlfZoOb0MjlOUJZQ0Yb2qcaqnV4ngVUBrhb5c8RenlXQTSayEv9suOf/bgmalfD0jp7D8rnxNiN8w/F0g0HDAQl0TrWaTk5huuXfHSrcawbyYC66Y2tV9VNekza8oE7EOOfb4mda5WKewvNSWJB5Bt3QBHUycg/1pGDHcv1nsPaTgXowZ8xaYewqMnWR7NWZ/jl1Cfqk5d/ZY9XP+k01Pmm3Cn4F5Cs+6RkJDQDswaDZze7nsny3WUWeSJEgHtAnBbev5MrPeXyRi2rcNchTg+Z+wLKju3jsG7x9uKb+FwBz0Lj5O5ox7pY9wj/ShPxwXMLqhJcr+rtSGQOror/719dYvDVVlmJRDaRtFqZbjGTkbxjytS6IDCM7BdNFtHxefrLwbLqgG5qsDUOXxiSEqaZ9zBcXt68OPP6NB7hXYY6a3HrE2UVJ+gwA5NL7Gw30zmLJPTN7qtZssSEqvLTInq0k4pSAdGFz2nxmZFGM3+1A59kxPJ52VuBCvyLoTUzLQ4dULAbjLNS7IajW7qsrToGa1KDJCo3PcfnihFcbdmgaOulXmo+OJufigvW2k71JXEXYX/VY8KrXf1jClkWUbecKfD18FbG5poXom+sdnkdaOHd9h350GEvg+s7A8YSzBD0jhdkcbZY3MrtEkh5QTx82ifmGOhmRiVBKgYJdc0tAZSwZxlUqoz+1UH19RiRMfFLVBcvR/TRvaIa2NsJzuyNwP1yepZsIza5PtgdiV++n9rVtHVY19xOcaCTPS0TtMTc0cRdWbDYQuc3hV1V2ptgwokzCWUPCr4vbVOl5to+m9jIXdUFxe4IYS8h9+6QQeWHrv4rQ9yM6dmLybzdiN5gc4I5Z5ELjfZXK6FA3O9nQe16g0Nhlse3MwZHUpCTXqNkqlOr1DesE6PpcnTFrj8QID7zIhiuITaYZVO29Mlu5uKFB1QSiDJy5vkFlSsICgMEiLwMsh79TJiv9yJ4qZD/eZIRNBvqy0ao9fXugNpRYK86MLLSvvdzKjK7XIayA+FewIv0VfIX/H6NNQJtwtRoQ+QXwxwaqp7BmgHZwxdHnn25+xJuWGiu7sMQfji/w05HYCSfv8PBvVPUsPmjaffHiELuqQUxdgubFME3k8aUNN/dzfWAX1wNaF3Bkb1Q/WKmmtU7IWSFx2ZliTQxxe11fHMV+peVhfrZnMUvZZiESqVx3h2GZJbJNvW+xfKsrGemqLmZ0kHPaf+WgKCJSlZ7fpMVKLQ9G/s10TfrE8u/cvpKMDLsmletYerjAz7R+RTYxkxLMJxZz0mh+RQvp1zfPOEx0kMftSmLUwlWiCL0yyd95rG5pcbxZ9vjpww1V3gDsUK3fCqFjuuPJ5H7AxL30f/nuDcBVjjnRMyGYRK5kMM0LhSAKVskkqqLGYkOz+v36hJuul0v/x8c7HH+iDC19Il1wKDKORZBEEgAIfXWjKlU1QJu9dF187DTSdYQrxJ+s0D/zypNAQfZl4h3NLMIwIDlX79nPkoxEa2kyaVXLRssrmOfycAJhkdB+MvL7xpfFlYmqLroedO2k3zevzpf5bIOTzexZ1Zily8z+LYouk/7SfTDQPyxl0u+O5EZVjnf4JVAYg9T35eelrRUcPRNV2oJUfYCbnYaOOCc1lWcpKkxMqCsIkmiXDwapoC/c72/f8uYq1gVdz3zGp1v2AeaNo302shkppD/DxKISeQgAAwDz+wXaB75tY8bEdpV+nWGH8m5RxJ9hLknYJHFXUxFINotbl5wbaLNkD+XYtDGabvhDmQhUuT6in+34K/gKl3EZl3EZl3EZ/22ML7JS3dydYJYR+1OLkVmqyIB90cPwbtr/eYX9W6oG/if8Hf7D/UcAwD+/fwNrI769psq1sx7bN2xagAUJ53kXqHSWSrJdTdVpxyvAAokrYW0irq+p9zZMDsdTK7AbgoYukBT3YN1y1nhgvCQ5IGDWY5oUFBc3bqfEADws81nFMqfNp3UEuMppOY1E/IRX+Swn0h1qEkV2WXbuwpgu/rkuC9M2G43hNWfKfmcQF0DDO1e/wlnGZlpwMg64Mi2b/+sglXvqCWqXvuZYe3DBJeQmC8P6+4cbBIY0U2+BrlYT61/v8GZDxrfORPQM5//h96/hHm1lqk4Vks6GgryXr6kNcNotELk3OUf2iP04q+STEtjyDLIH79ILopCB0xNBxqenBb73WiQq4d5L1Sc9seJ4pLKwz1NQwKRhDxWVELP3Jkt/GADypBEMy7oGBc3GJFpnQlJ4pF5XNme00jpIDcOzzey7F+8HTYERpReocmX1qqjQPtaWSf8mybPU7oxct9iQP7Hkk84M3bVnCLnMZrn+DKFCrsnRNU6zz0kAfSDExg6lAgWO3xRmOvknF+N5FSFMZu3r740Nw5pnebD1d4cl5LupADHVKOYedECQJKYp2ba+3tuDg93rmnyj85nhy8ioibqaqNV0qNB98bpWLTHiC29AqYxjQSWeHbqjkmsQulnaTKxyoOzodkngQK/OkLDU1Oo0bKJUtMQNKPeMjE7KtYotX8tZsPvnOr7IRbWxEcZGnE4tNEeR5aQQJov4yD1Vr0QLeDx2+N/+/LcAgPWfNcIK+Nc71ldeBYFZ3FcnLJqIaaqBwKVvmpISeQqiAmyWeDetEw4M94bBcRQcfdRuvCwMpo2EQBWoReeZTWGe9TXZ7Lr8KFZYRfuiyeMJw9Xel32yVbIwcV/muk7eBUYzPfWwHL/QoZvBVVuLtKgTo2oiEpOd7M6ehVirULWAxf2HzonhydI/sxlrDv2+X53wbkubnTGR81IZ5G5VpSYIShaYu5sDts80w5n3VuBB97rHOFk8DWzsPzToj+xWNJIGVmCvCAyv6PjrX+/Q6YTtI9n6qMOMGILaixIYtECww3wxoHvheeK1BwVVFs4FYJmQ1T7SNTn+uogLFTT3UBMcMGohwUFlWkxBz7TudV1sZs+BOWjRrKaOdLUlDcna6rakFDAOrnINZt/RnFRtAeRzR67YQe6fSgSXBpaUYdBwHKPWPNMCefpVWWUhfcjpOsnxtVdniS0CsYLgc90rIf7MIV89QTYu2bBkprT7h9rjU4n6r/u/qcctz2qzVVCz9sTc9EcHQDPEayyFlpfElmTruyXSmLK5CoBnyDeuE1AkXi7BuCQbmTgYmV+arSY7wBJudVIzvWwSON9oIA2m8gSWAYvZRjwELWTHw7s1zIHvxU6h2daA8eEuz9ouldBU9LDleSdL0Pq9sq5EwebBSFtKTz8je11VOVL7SdNm86+AqHSBfy/jMi7jMi7jMv5C44usVAdvYbxF23kMPW3F0tFCTYockgCkpYLhqqH7PxdonwrrMMNMCo4Zp2HRCL3euwb+hCptSQqGjRp879D8RNu0UIyjmcwTvEEuZg+jAdqI5RXnjHoDzXufNBPNA0RUKlUrsTqr4Fp5VasKDuwGKmyVZoSIIgmwx0qNp2DmWWVzqp6jUDhj+JkJUCX/tcnILlUv3W0DU8IH7mZQVrZothWW1r4eT0WqiDLDluE+wHLO7RCsVJ9go3NhMrZRkIGmCVh1k+TjftquYX9HJaG/TpIvGSaD1Ftseeeec3W3soOG7SvhJv/2hAVXcykrGACGYbqobTUcVxXK9lcJdq/PItKEHa4J9i7ZvN0nLfCpmSoBJiyB0y9qaAC8gi6SiDZSPCafh7UJTcPGE0/UjsihVpfiuqMhrmDoyIe65ei9zgX0E92Mvm+qsB/0/BRyTLhOyJrZpxNBs9PVjAlbWgJNgmrOz6PArtEB/a8rnK23ViBNqFpxxgVV+4GvlQpKmOQqUkB8nlWkpYrKFmL2rhIRZIpBRTLE5AU4j7XJ8s63j9UwQWLpymypcIa4FPKRVOvl85xnCxCJyVSuGZmnsDOa7nXNSFYGwWW0d/RhrRPiuxIiC5hQwzFik+Fv+S8bX9UFewe1CLBtfWaK9C6MluRvxa1r0mJQY0+AXyv0ryv6VeYLM9S5ITWZPNN1nYzKvdUgcqPdMgoXastExcq8hgbG10EkRmbCGRHtcx5f5KLqTIQxEce+paQXAO5mgLVJHj5/bOTFdEcKeAYKTDl70YpWEqAFwyWAe3fKK/iP9EKYg65uIncTtM4wDLMZkzEeaTa11yO0SThxn0PbNEupMaRNLSbUJhPNHgSZnYU09zUnMZuqe0uGIJ3Sw9HD/HMznVhWZwkcyErOXxWJRDkvW69HMfMvbFq4jHRLL/dqPQozVf/okGyVRaRZtqL2QCgvLgAkYPsj63lsRssJLfqWLBcLhN90AXcb6nFOweLT22tJ7rH7ytxdfaeRf6T7Ml1lTN94JJ6R01R1gJRtmYCv6Pd1bRCj/G9ebDFFg6eeoGiYDM/f06yDWEuaHzty/iktXFshdTMoTHcRYL3fcWllklG5QpzjPVn+lWvsX/na5+Tnt/buA8ay6eD2QNl0KFsnwnhwlA/Ln7u5Oskx96cOw37m4p5wnoDE0LkalbSE/SpTr61MvG2eyTYA/bGRRapIWwDg+A2lH+mZPKY8m1lXuZFmy8LCWp8nt1CaiqrG85xrCtBCUTYqKgOmr+zxsKyL7+KtgTvgbGM01wn7TX0+VQLUPKmlfGeX4TfVRhR6fr507cpGN2ug/cj3zgKe5S/udkQYLfwPK75WGmrWb806o2TuTvcRih3KjE3i/KU3HjlXDgZmaVaU6JSguP/ffdKSoxuWwOHXASj686AEGtappuCo+5GOWW6+yjDscqbZxKy0ovSM0TsPoPebRKEXRdN7AoaXQBRHrs93fJGL6n6/gI4d4sGiveWIK5MwnBrpe6rByA0/fpvhr+hJds8GejpPupe+ps44s2A7Vcuy7DLyC1qxFqsRXVMnxqePG/k30WiEyUjFpQCEQ4n0ANQyQhdh+K5Bs+NNwG2SCtnt6PdKqsSM4l6qBylqVX3R52lwKoPSccr6omtMkyygpk5+MrF0CWrUte/yZsA9h3c3JuLHj8ShV9cJ7Sd9vjMt16A97z8hVrmAXXtsljQDPx87KJvq9fAGD890Y/xkoRdBKjrvHAzHtMWWEjkANhDwCmnPmxhfta7+JgJdRMeL9mYx4JoX9Jg1vnt3J4uUufJYruhnp2MH/SMdz57UmRRpeuPlu8SjgXvVo+MKsW8aeDYcsVsDf8+brqOG29fjqMEIaQQuQQ0Gls/xNDQiuXILj9QYOCa6NS5gZLLcFBtZvNqFhw9GDCuUmjF9PPVrpZ+WaIMIoCa1gJ61jHrPzuLKTgZmwCxhJtdN6iKz4UHRUVetIukkK1lo/v96VrgXCVaxIzTjeXUpZKEmAzez5z3UStJM+JmJRv0u9DxWy0Hb181nrmAR4oJ5AOW757pBEESpSFtM9e3NJsumBX9aws3QjLBOUiE3O/Lplg3xqCWQXqks70EcDGnDZ4teQYjS0aL5aGvBsAcm3heefhVw+/UOkVGn508rqFiNVuSav+2gumrR6R6NxF8iKuJazKp1+Vme3ZdFJhIZ91tPr8n+NfWzh+ozHZee6mVcxmVcxmVcxl9ofJGVqnrbQXUd1CpJyDSSQg6KzMpB1UHp92VdnVHsQWG6yWcZh8V+LUVKtinwYWqz0PDDChJ2HVqDp2MrWZx6rKHB2SUsN9VVuj82khOZm4TkDRL3ksxJY3pRGKGALqYQDmeZlfEqQJ+KMT4JwUU0HhTmlovz6hYateo+s4hjCLm0gWLtN7lng7BMAKfi3FydsGk5Z9R6xBf0jx5+/4YMzBmam1dzplfwL2spYpYBK64CGxvx6UOxdlIwy/q5m6sTAl+DZTeR2xX3BmPQGH5RMNiKJqjBwByMVCGmV+JE09wNeHF9gOO+7ONpgd2RYOPh7YrDmPmQtxP2n1b1mGXjvsjEWr1ls/omomXB/fXXPZ77Ts657bzkzdr7HoFdmExJDimowSKKkUgeHBAprxcAdBcl/ahtA7zPAkXHpEVCVmQ2ADC2LWKoFW3XeKRVeaY1/GBrfz5XCC+6PGPZ1vYIQO2F0lfPbYK/UbD8DJ7eKIxfVe2E2VX7yrCujFkVIQzl7H4WYJ20BNeX1BXp3eXZ58zMUD/SdSztDunjgSrOaVOrqrmVohmB5rmmMuVZtqiaWX4iKWICJyv/Thj5bG1a2iLZ1Io/uRlzXwG5wVnro2UWuD1y5V3dTWH5WYreSAsAGRRQXr5b0FWqlZTMDwCZWpTc29WrI6Zg0XMrCn6GtOnKvNaBAjbKOxN+zsvQkHzYOFMNZJ3hueduDnRsud5/ReXdF7mohjsPvTA0uRYLPc/JMAXKWSWBJvSkhDxRkhsKvGVGJVDndJeQoMRGjCwA+YFqKgTjB4t8tGITl5YJq6+psbHuRnQ2oPf0VB4fllUXaBTBjeUcr2IlC4y6urx01Pcok2TqHRzD3P6FRU5KvHrxbOuCOYNn7KTO4OC5S02yrI8sSE1UQnZILRDWwHpNv2/wFnuGNH/aXmH8E2FN7VDp9QAwvYgyyTT/3Q63JuL5QItKTgqHQ1c/XFQzR4P2D056PU9tV3WYLckUSj8qn8wZLCcayjYBXstLHf7uhCtewJXKOAwtTix3Un9eVElES5N8gcD0UyMLQ+wywg0/A4tQw+gB3F0f8WpFcPjH0wqdC1g1tBqkrLBq6c/bwwKKNwxTAmAz98OArguiM/SOZFYl5N6YJDaW02QxHRqZXEcFuI/sDnWoxLB4MtDrSfgE+2khz04o8Gu5jkrVZy6pupnqWR50Vfvucq0mhcXbWU94k4RUY7cGanZ8Pc76/w3qQprpPWyYANM+Vcet1AB2AtypLqZls6ZjXQCzY8g31s/MYWJ3AspJJ1cXXz2x9+//C1FpuslIr3nTuBox9A0Su2mpaFB2NCrx95q3g/hwZsZrSLZ6MgOAO5hqYbgkuFr04q9rAzLH6mOu24h4nPmKu4ScSutAI82cx6CqRtZ7Q8Q9JuqZU23PaK+oVw1aOGNTE2f0VDcgpJmuG27bV2LbdF377Nko2IM+41GonUb6K4h++/y/wWVcxmVcxmVcxn8j44usVH/57SfYVYvvP9whbZl1uyejeTEc9/oMEixOIMkQIzHx5+I81DeBnV0qlCsJMxwwDgCYHHRUiNe09bv7aocd57genzt0q0lII9AZqbAJLTf3ixG6nTEsbYZiFun97QE5K/E1fnOzhzNMtNIR329v5O/TrcXx/YqvgRGv0vaB5C5F/O1Xs122zVABQpJSse7+fZOFzQrQ7vf9+zu6jgeNtqSCJJYV/Ceiga6XI8ZrdowyEcvG42ioFJmOrbj/dB+1VBfDq4T+6yjQ/NxH1zxYpKbmgCoNMf1uHoxsJ/VIxA/Zdf9pgeclVcWUC6nEEGOeAqSMIlIW+xonAOtfV7RhCvRqtTZgikaud8oK32+JrOVshNEJzwOHlGeF3a6wapTIZPK1RwpKKpGu8ZiK4f1kkPZOqvA4C1zwoz0PZDdZ5CVhNZOgtAlxRlRyTZA/56iAwZzBmPGGnlvdRKQnekDc3rAvcyWsFHTHDJQMM7zgYzYZDTNf4zITslMY7XGWZqMqLGiPdB+aZz58W2FotweafYbiamzaKDEwSLa6+DRb0DNcoH89I+cprk7Lz2aB5eJTXSpoBwRmetu1R+aqsH9ek682o19xlRBsrVT1oGo6zyykPLkMXQxdEqAnLRV5WGSBwAH6ziW1xqksJaEyCUaMbDTa60HQjNPHlTC9ixd1IXzFZRK/bz9Y5N7C7ioDvVx/v4k1zUqTOU0hLmUzq/gd/7kwflU1eFERcOzxbAaFsM7nygMP5BkB7XMdX+Si6qNBjgZaJ6D0Qw05wEg4eKoQj6rPNLH95rZ8y8rca3YaoctIbZEqVCmCPamqKTMZeuXxt6/JuXrbL6qxdUeaMj8yhGQTumuaFYI38M/NGYRUUjzcwgvLFgBCNAjcnztODV6vacIfo8Wbqz1aFo+O0eITT/jbT2u4P9Ik6dcsiyjSAZsR7/kcJw33YGvvaxaknA2gdMb+O+p72oNGV66xyyKRSC1JZgrUak3EifG8mDQeD0uCLvl64YauwXADGNb3GpURRovuGzrGNFr4J3am2ijkNkHxwtQuPd1vAMOmgfpAn0uWHWF4cxJtkonKfnSIiyTsUXF9AXC8jrj6ai8m+jevT/iba7qfQ7R4GFZyLxbOy73QKktI+WlyOJxasTWc9k1157qpffWwJ61K5N7g9uBgivRGAVgluCu6Pm3nxY6xDJnID1o2P1nXzZ+7GuFcFDa6tVEcd9LRwRy1tAim+7qzSEcnvdbU0L2tkW6zVkqXMb5IdSMzzOwGORRb/t1MDwlUnaoZFJotSVsAWlTLBlB72iD4Dff4FvX4FFxPf3Z7MonPqvQCceYElFwNOyCrPT6Hno43vi7N2Ex2fwDCcyPhDHJdClO5y2JZmk8WWSvk0iPWVQOqQt0gZJPRPM2ZuxVKzS4Bi4iGte8paqSy0EUtvfkcFAKstLbMqYZtSEReLou7FsWD3lsKg+d2Su5iZSUrnDO7larE/YQZ25ekVWVDFVazyLjHCvdC0WdqOAaoXz9z7Ppcxxe5qKasELMif93ykJsEmAxT/FtTrdJUysi67NJox6VmQvbuI0+Y3O+QhTkoyRUs2kQA0F3Aty+22I80se9PLVruYcWkMe462FXh+meMrDczluzkygKwWI/49T3F063diA8nmnGehxbD5MTecNVOMqlfNz2sTqLLbHTAqaXj70yV3tg95ax6pv3r1wPuNjSr7w8L5CcLS5LQs3zGcBWBg4MZi/i7JrmkZcT65VHOqRgzAMCn3VpMBp7fbeC2Bs1vOP90c8Ljri5Sviy2XkFf+TNhe0nLyB3FvmUmng2TqRW+zkhcbbmlB7yB5eou7B0cWx8mm6FHTTpTHqf/nirr+5sjxmBkof6b6wesONzy37cvpJL89c0jvl1u8WGke/P9/kYkCwBVFVIs2YwSVho+dhUpyeeL4FwuoSL19EsPFIA8L3kwgE3IxTykAww/j8Yr8YptXURjI16s6Xr7aPD26aqeo62EIeUVsuV7O9TFNrbELxD5BCDn6K/j2QZTJUURZ2CuQVbUvwOAXBels6oyAadvsvRezTBDEBRw/LoaiZihLpaxmxFlDPXySvqMOwKBJSnjDSVKFcKVHutmOi4IyRDi0tZA8dSpQp0n4iJRFmqpTkcNNVT9pvKqEq9slh4ldK1amyfNnsFZjlmsN806wDVB9KhxMmKlilirYGWojy3pMGNdtMMyn1WS+cUoPcA8GEx3Ce6WnmN/aGqDMENIVskq2GO9PslVqZD2CvpUCxJ3VGjIJh1+PSMwcbrPPPM1rH9GRvtMx6WnehmXcRmXcRmX8RcaX2Sl+rBdur6RTAAAIABJREFUQ08dGdfPrLb0LLVjbiCfFYuVQdVLXKSaUjEoYTwiA+mboe7gDg7jK2aBRlWh4NuIXd/hxLtYpSoTsD81VF2IXiVLr8S5CG2SyCKcjWLf9zCsMMZSlTnkrLBa0I7T6SSfK1Wq5pNsTMRXK2pU+ZcGjx9fAACMB6IG0g1VzP/52x/l2vzXw4LMCMhLHsObSt9Xy0BQJcNjMSoxe1cArhaE2a3dhCkZNIxHPpkE86+V4Tv+fY81myKcJlfD2A8O4D6mWXmkoDGdZpY53B8qcgCB5rdaYDR/k6U/6Xct2cSV1nRQYvQBm6FPBiOb6Hevj3i5pPN/2K6R3nW4/zuCfE/B4e2JqrtVM+Fvb5752ke8G64ERZiCxchV7PHQIcfaK80ZQF+U/jXvVLuENBqoktHaq/OWhM0Y+Brkp0Yq3LhKQAYaZn6HycBzWWVOWpJLVt2EV6sDGm6SffAbNCUZqYvIXiGZUvUo5Ka+F/4XhJHmpGA/utoPNbU60oOGmSrD1a+zGK4DfI/K8zPri8e2sqjDi0Ts+dI7BpB18bWkf1jyScMa0ju2g5rBxPTfw1Vtz8Tiq2JBsrpSwTWzSrXN1dIRVLVLL9bUe6E8oTs117VCvEh8TQrHwuva/x/UmbPYdJ0J8QFJYxq2Gyx9bumZD6jYsMuwW24JMMomjPZVBuYFYFRQvyDUadl59IXdftJIXYLfz6KGyve0EFvVDCB2ut5fT/aJ9F34v5VAiVNl+Sdb2dyZ+9niMmeqrOhzH1/kohr2DXTgt4YfPHPQkqYBEIxRIA0VZ/IPRX3MMgnH+yBWcQCggoZ6ZNnCjOTiVxmWA7Nv1j1eLI947mgReb/dwHNvbrEkaUOBgxeNh+deyRQsht7CLWj22HSjxJSdvBMNZYwaWmfRPPbewZf+qm/QmoClLTIOjcBvnw9GNGvHb4G8jPjPv/2Bfney+Of/41cAyLc125qyYQ4a6SUdzzZRJgEAmCYjPcPfvHzAVUNv3cpO8Mngf/0zH/O/rATymu4jMBrsE0tqThaa4fDm1UmOPXxcABro7gmSVSpjKBq7ie6nYzKVnhT19QBgE4AdXbemSEt40rG9gko1fJnISPR9+k9LTHtaHNPrEb/8h3c4cvD5D/sbkca8XBwECp6Sxf/+3S8QhhInomG5/7lYTkhJSf/SRyXQfrsesezoc6O3OJ1slV00Gfmu5ItlkqTw8ZWeyVBMRnczYNnRuexzB8/QZHjlsbym67ZqJmyHBR72BLEPD4u6opgMM1QIvMCvAFsH7llrXeLX6l6wnq/m+LHiodxGkZfpk4Ga+cOmpkKE6eWE5ZrOfegbpJNFe0V/Hw8tAiecuK0+Swgy40w3fQDMUPqkCv5qJklJlO4CAGkVyZKveGO3M4JgBuzWznqbNQZR+woZ25OmjcCqLsASA8e2n0Uf7HYzD98WAl37dUK893Ad92KzwnQqKz9F8olD1tJjKkHkD7b2sBcEVZfj60md+SLH1xO+vaNN37vHK9jf0y9PjrTHUmgEjcxe6NolSr7h75KbXJ21fN0IkeRwdu+b2vqae4lDMRRdyJtZIdl89nx9ruMC/17GZVzGZVzGZfyFxhdZqaouEuFnMGLckNpMpt68pQtzY4WkquHAqKGOBomhM4xGki1Sm6APrrrDrDKGF7RVXd+dsOaK4eXyiI+nFd5/vJbjtyvaqvenBmkwsHcVri2JIUPfIHkt2YgZELLT86mTEPQUFbTJOPGuO6dKZNGrHis3YWDcy+okJCZnI/Q9nWPsLe5ePeO7Hck/nj5uJD9V5XNoK76axAVqsxiwdFVSMwYrULNWWf68MhP+x3/5B3S/p/P36yyZrACwuT/iqlRYQ4uena+GQwPNVaZSQF7VqjglJa5V4sPM28bhVUTmykM9W4H2VaTjiMtOP2Myu0Qw3Y/MKF4nqG+ouvvliy12fQfHoQhv1nuBTzvjMfFB/q+HNwTll8qvjYhcXaRG0TmXotAlGEYhUlJ4ekdwsj4aCk+I9ZzTU/WDNtOM+LOMwvpUTcTLq4Nc87tljw8dYfbPn1bCEs6ZmJxiRGSyVJJmZ8jrtoRJLwAUQkxUMPxMFM9kcVuaVWzmpKEUtRMAAK5SWrPNZwSn1FZDDXiNE8u93LOBshnTRFTsOVRYoMRCnEsN0H0qUhYgdkxGukV1CgMn0xSP7Z6vhRiEcPXHIy5TJYopyLyhQoWuY5MR2CierkmdN+xAKTUF5s0aZ65sxcs5LejaF5QpeQ0UoxaboHUWFzj10KB9Luzc6qCmAqEGBQJPfM4AEF54uDbg++/vAQDLPzRyHqFU5uX3zVpj6WTlmQAA3esK8YZZwpSm71YY87GrULkZFSZOGUpdYgZehcebJwM11t/xuY4vclFdXg0wy4y41ug/0d1XE73osbiZ2CSWhciQCTmZLNZ2AL1sxVFF6Yz0WP379KQAfmlfbQ7CuP3z0y0mb5HL5GdzTQUJCmYV0FjWwc0YsqG3UDbJ4ux0Ej1kSpXirk2GUhmxpOWojKYk4vAEu3ZVsnH0BGGO3iIWN5VlwHa7EumJjUoWvQhQj6iYm7tUNY46YQhWjO3HXScBzMurAbcrWpT+lx9/i8UfGjmmfxmwuj/J+e6fljh9ogVdx2p/p3PVxOmgELXB2DDbddIyOdoT9cdCYaPqOnGqWPtgqSF3obJo+HVG/A03hvYOuUnwd4WambBZs662GeGTlg3E624v13MfWrznHurdgtJfSkB69BorjvWzmq6b4clrCgb7HVsTvmtLC4vgvKjQbHkhcgD6KmWJs0QYt5ygmcHdNAFfr3fy3H04bWRzgknDca9+jAbD5ETX2N0MGN/T+WZFbN0S6RZeellUc5jpVy2gJvofQAvGnNmZ2kyMbIAm09KDO5LmsXAW2lcnYbf6fQN7qCuD22soZpImW/t32Z6nz5ixpkpR2EOVyczBRT0o5Kmy/Skusf5cNggdLcS6MG3z+XcrbaJmp6GDlvdf5XqOZmTZXgn9nqlwsgGmN6Eeu4uSnoWgRL5j24CcNMC9dXuqbOICuwK0WdCTOnM58sXh62CBDw5X7+hn/euqgyUm9ky9oFTVOYdZHz8ViJefQZer3Ebx88rProrV3jF2s1COrKsVI4DFW4vuY0a8hJRfxmVcxmVcxmVcRhlfZKVa8lRjrHsKldjhqMA/0yyftIsCz46HFjmmCt2YLKHWCLQ7LE375DIUVyEf9utazZlIpgFzsxsmJiwWE6xOaFmv+NPjlRikr257fHO9w21HFR3BtgTnjcFIJJo1EaO3Yia/aCd0fLzWBnTGC/z74bhGw+YPRic4JgQFb2DetgLxJItZ5BT5eRb26NyB59N+heAN4ntW3LcJN68Il3M24u2/vKLrbTPCPx6w5nP+qhvFWWj7pxvYQQtpIcwcZTQzLAHWM26CXOMcKmElGyb0FDRJQSqlrLWYuzdbYoeWCiJsMhIHHbjrEfc3B+x7InLcLHu8WNB3SVC4bgepAt/2V/jl6gkAcNdUMtUP+xs87VaIe64Q2+rrqlTG/eoEz6ztQ79B82c2pWiIKFWud9EvAkwmEcgRiOskoQvhYYHFGyLE/fr2CSkr/POHN/I7Bb1YRnF5ej51CMEIW/xw7ADO6cx7i9jWbFs1071mUwl8bk/aRc/yVvKGrizk7GpVog4VfgcAfxvR3HH1bqOgNvbJ1oB4NlVpP3GrogItAPv5eoYch/sK8WqPymDlgqpUlsg4q7DMzLMWGsKiVkEh97lqX9tcI+gmJVrdzP+mhJHbfqZhbXAWQgENgUK7X+3xgpnuz8cO06ERlCy7hMzMd1+01vy8+02S6rmZoS2mN5T2Vp7pdRaDCjPR+Y135frUnGQ9EPtX7lVScnxVH1v6zjNHKNJQ8+9aUpVcWmBzwlRCBvh5IXcpg+4Tfa55zghLhagv8O9nOXJWyFmh33bywuUbT1Tz2cNc3Ira1VRlG0EBi1gncj+DezwtqAWqjF+PAL8Qp9SKybxWlG05Ncyc1BlLXlxul/RGFlZp08Tac9MJzkT8YUv9kKfdSrJW9clgz8zdqzd7bLoR1yxfmbN9AWCITvpsm3bEyBDy4dRWuv62PYNP84tJNg8pKqi7CS2zfMe3S0RmAoavJ5gmIjFr8ObFAV1Db/u7H2/xH/+R2MR9oHPoLP3sT5/uJJhZgQwaRKowKZn8kOvinrrEzNcZlsb3hRyyZhP57N52bw2W7/getUD/CvDMAjWve2n+KU094G+uCXN0JmI30QI7BEvMTF6kjM5yjV+0R1ieSV4t9/i4WyOz/Gi9HrBh+P7Ddo3T0Ih8ZfpxhcyuW3kdoPbFYEBh+CrWxUFDgqpzk4EmAQzd5lT7cb/78BJaJ2FfG53qRJjJxKMM1wT0I7tpDVb6amZUcM9K8k7jXUYulotBQWlmn34wYowC8GRdZCHLAK0zVPEpSEoY81nTPZuYtT0dG4An8jxngirQJF/6eB5n7NnT6+r+BdSfzeUk85BxAEiLdGaikYcaup41JK0otfQ5x5IVt68bO11yWPm4ydS/+039XZl7jeX6jDcJie0G5+2BlBTsIiCUfqbX0stUkawxyy9QY+1rQlW4GvlnhgxTdaZKTcZ4P5O2zDKTk8nUqplZRiaWr6lRo3li56WZPSFAC6pImHgeKNBzcrNe78zOVI9kCiEyq5XCeJuRhs+f/ftFLqq7D2voRQfoDLUupRj1D0p1qpok1emyG3HLbkIfnzaI20Z6QmiTpInk6AAohDv6u7FJ7AcXja9+qlnh+Wkpk2ZoMp5ZEpEBfH31LIvNA1bY/VdaRPWjwp/0rZAdlgkY7/mYCgj8dux/uMJzF7G8pQX6N/eP+P75FgDw6eMGpo1Y8SK+3y0A9j/OLsGXhciSx6uR71b7w+ZqgrGxfp91JM9fADgZhFR3m9t3GyxY8rK66/Fvv/saALB+fcC6G/HTA5G14ruFuBxh0DKBAUzM4T3NfKJVWcG8a+Ql99epSgcUgER6QKAQWbiPNwH9S/rzdEves4uXdH+n0dWUjjuPzga03N9+d9iIs1OaDHQT5f42NmA30iL1NC7lHEPSaFuPNetbX66OePtM5Zx/bgEFjOUrLSO625o8Epm05HctTay8WTNrj1gkNL2BXU7iCKVUFo2zsxHrbjxzcNpnOkelMyJXt9oleG+kmrY7K30wHWjSLNUMEsT5SnmNxdvSpyY7wOLSpBIA7vMalxC2jXji2rEml+DViJtNj90TXdcz8qCFOJJlrcitZzahT8zzCysKOi+LQbYVoaBEFch3Jv1sQVy0bFQU60iLTCebLKiV6jXMSdekqp+Fm5eRNaBm1fu8usus0UxsDJY2Ee013etxcJIalaNCmgeMA9UqkM9TqsdAGx6AeQczzW1q62IZm4ypRCm6DO0iUkmxSaBNGQBkBTUa2bBBV+2+e9Y1mL3IC2ebCdmoNPlcR60gm4BsIT7dZqTFvhyzf5MxvQlI/edv/nvpqV7GZVzGZVzGZfyFxhdZqUJnQGeYRa22ACBk2rUDlEtZYMtNO4lb0fXmhGkx4fCe8bxcocm8CgjeobmmKvB63UvfavAWA8Nr/bs1zFAF2dor2Duq5q66EWO0+JGZr/jjEu22iMvpP6lYoEt15ksrvdwG8FcKJ0VVyT/vvwaKSD8oJO3w7NiTdJj1IYOusgdmAUY2l1CL2ldumoBxdBjYg1c/W3HSictExhjFzPumbuX77zZQd9ybHi0OH1YCnZvXA6VuAIjPC9r5FthuloKRcw1wNicNd1Ri0v9zuJASOWaSD0YXxrsq91BeASYjeJZFRSUVzzg4fNivMTFDdNq2Ur3YZcAvXz3Kr0tZYYz0uc56Mbn4vz++xjRZBEPH//fnFeKhuCYl2JnEpll4gfpzVlIFKwVMTx00M2HVo5WQgvE2wW87RO6FL9cjXm2IiXzfHbEbF2Lu8WG/Fng/DBaawwbS0UKNGi0no5i+mqoDBCMWprd6aOWZs0clcN4cbgSAeFN73fGhRffJ1GSULiNdsYGBi9jtluLRrCYlvcxmWyvJTCCQJN1M97FWSl7B9FqqX9PXPl5Yzpip5mdOTrO+bnLUAirITHpsJbRAj0C7rdmxsavVqJ73YRVV1xLEkcjvFqiM2JK1vHl5wMBM7DSZSkv2GrrXZ7wB4TVwDmqBVu2sIowzkwWVqGouqoTVZqgGI5NFeG6IdQ1mZZeLYBOZ35RQh62pwe8z96N5gg/APr7iqKTQ7OvcFrvKEjZDZQJT8HvG8IKf45cRem+AeSvnMx1f5KLaXo0wSwXvDSzLChoXgCUkdSR4I/2yh+NS7PX+5vYBViX8zr2kn33aCHSjdEZaJIS3BP89hmUN4Q1Vs2YULQyl/+BfT3jBUo2F89iPLfyWyBp6kdEXOCnRg+n2FcYsD+nPY6uUV7AP7Br0NMeS6MUvhBLkCqlFi7O0kDz/+/UkDj+LxmPoG+iHGoQc37B2NgN659D9giZ2axKe33K0yCZAMcQYogNKPxBAHCwSQ03ak26xXB8gIzExLBvIhKC9wnibqDcGEGms9A/ZIUvs5HI1/U/LSsZQ0QBBUR8RAEYD3fPxtx1ObVuPbxPMlmHXTw5/fPgKzWva6WyWY7WR0wl/2hETJCUFY5JItyjhpEqMprE64TRNwB331B+OS+yfWO6l6H6KlMhDJuHrf9UYXmoMv6bnWKuMb1ZbAMCHfoN3+41IsLTKiA98IV2G4uCA5qSgZ65G2UDOKfFiZvf8jHuIS5jK9fkLV2yIzoulbiNF0oGkMGXjA/AixDdq2rbQpyrNkWsNgi3LAptcht8kcfhB0ELO0p42tmVzmNr6HJQFhj7I/fmi0ukSqm6J5SM/Uc/c9ZVw2Oz4OhTEdHZesak9xuRmCyyAsJrZ8OmMeBfQsZ47Rg1fXuypal3NgcLcJe7NVDi/9EpN6ZJ844VD0Lx3aB/rpjFeByE49qeW9K4A8qiJeFXUQTbLBhgATF9DEui8y//P7gUTAGUvESoxqdnRNR/vKrehQOV6IvIWANhThl8rjDf8zpw0bRhmG53PdVzg38u4jMu4jMu4jL/Q+CIrVWJIUgVRBO8xaRidJC4NgEAmXTOKacLz1OHkG+z2zJw8WDF4t22AjwqaQ37DdawSnaMRo20oIDcJic3IVVD48IFYFx9UBraNwGoq1wpUB5y52+QZ46+4ypTPZVsDAbSvO0QdMg7fVrE9kKGLwDsrmGJmzg4yhn1qX989Y93QLns70HcvcJv2SvyO0WZc//ZJDA0+vb8S4gmSAVbF0xRAb6AOFe4pO2FogpMEzk2V9GGCQmApQtxE6GUAZpImCSxXTKAohc2sUjIHjWwq5T9tEjTDovpjU7Mgl5kCBfiZMFsrFZW/DVjc97BsqtFPTqLs5qzgm1WPj9u1/O7masTdFclynvZLBG/gmEV9t+zxeKJrOw617MmZq9WSnWmAyNdmulYYX0Xc3NEx//HVT/KsxqzxYn2srluPq5qreazEEzroLG6sg0C1zbZmyAJcuczYtMU5Z3gToBZBsm7jcyMGKanNMKfqupNm8XC6N9QKmbUdCsyaris7V1jfBR5sKtpACIuGPlUCj56do7Bl+fekQrhbB4HizZ49iAW6re5Q/orj0wr56aBgC59sdr6FEFVg8LBJ4g7lbgYsOy8e36fdAqogM4OqDlGZn9vC+E0VmZquKYIufsXSOZeAH/hdtMDwmp+/u4nZ1tyqsBHTA6MemdodZ/F0hZE7qbNgcvpAuR6QlknWjAoUgChUk4vkyHWr3GszAN3jjCnNravpRuH4iyiksebJwD0rxIuj0uc5YjDIwcC6IItoSgpTtNJb0yaLqX3ngugRf9jeEPTJE6i+nYRFqX6/xOZJoX/Ji83aw/Ck602DJIsG4NaTHFNp6msBZP81h6ySgcCK2qszTdhcVgDUCU57YPlWy4MOBRjOkJw2tFhZNpo3Q3VlAWgxBQBkQJ80oqK347hqpD9sNaXoOLZIm14G3H1dZCcJC+fx6cDymMGIG1V3NcLz9Y0nSxq2UDcaaaYfVAYC+dpDTTiJLao1YdDIu6Z6v/dVy5lcJjmQqd9NzPW9kmsV7gPaTV01plWS7Fz7yyMQDMJY3bMUB8bfrEij6nnx1Lq6VgGQP/eTg1LV9P9+c8SOda8patzeHnBife53P91L33q9GgAOcN/tllBjfVWzrfe9/zrCvejlefzd9iXWbOx/mBo8nzqcGHo2BwO3ryboZTEo5uZzZ50zu7jZRDt/3qarjHDL98JmZK+h2dKxnd2zrDnrtLBAF1H6dqlNSMtcYdh5cpRCXVQLA7ZstHyd+VVU0LOkF6jz8y3PWNZAvK5N0Py+hS0OP4XBWh5BmwFfFwNkoHkux8yyKfMrtm4EQaR+k5GZN2DbIL/L2oT9x3Xtnep8tsku0jXb0ztRjNTmAeY60DW0b9k2c5Xw9T+9B0Bto97zuzo2cDbKJu/9717UvFrNetnyXuhcr4dhmHcWTlDuYVhWNrEKdFvK5tOd6oajMLILK7l5pmD48j1P7HQ1/X1PyrC+bKoNFQOfP/n3y1xUy5gGJz3VGDXFV/Hfyy4PAHanqufTOmG1GnDkuKQ4GGgONl/9AEw3QCyi/dkD265H6XHEqGFtxOkjLTxmb6TPll2G3dZYpdTURAwAZ1q85OrueHiZZHFpthrjbe2BuD1koSjSkrKTzKZOOmkTJRBZDZqOx+cfs5KYtg/Pa4TBQt3WXqbjit/ohJ8er5G4yrd3Ay0QPCK7ZuQ2IrsZMpBVlSnpjBw02g/1y5YqKi2yRKCV361mu/zAm4K8jLDLIJ7HKSmMbSefw4zUNA0OuVgYJiC9oklxsxwxBgPvuJq5TrJ4DZNDDBqLBZO3bJSUml3fYbelhaxdelytBlx19RosmAAXo8b+sJD+uVoFISpNwcoGJCeF3GW0H3ghclnMR/TdhNvNSQw8pmjw05Ya5qftAnpvZeGYSxh0hEzwYU2a4FLFplhJXaaeNv/uugAUHSpAm6f2o0H7UD87cd8+duTrHMvnvRaZSG4SVBtFpgObhUCFDCQ/s5YMCjlWUlqpTHVgAkwhxzSQhXke7p5NpmtcNrAz/TM96/UY2WBm9sDSGUGIKmlpnnDkuacsxXXS6Pj5GAcHvQiiqXafnOjZ0yqKfzidc/XVzpY0rXQNaGNYrD3/0z98j49HQkF+/O5eNpDtZoSPBsdiwYraoyVtbhapGWINM6devTqrVMufzaTOE4pm/dGsaXNRrqPpgXZbr3+5VsdfALHMcx9a5CbLRtfOCJef+7j0VC/jMi7jMi7jMv5C48usVLlR2S6C9MSmycKPVvqoKmccj1TZGBul8gLIFF12u6MWSG28BfqvYrXDiwqeZSfoIiz3cprWw5qa06m2BqavZt1FKA7QDlMqsYKQcYtuXrGoVCuN6Sqj2Smsv+djOCAsS1+GKPBz+0F/x5VBO4PGXEZsEyyHfisAv39PAeaLxYSb+4NUY4vVhOuWSpo/P9xi2jey419cD2L6r3XCzYa2t6O3CFFLUkqYDLKu11g/1R50WOUzy7i5RAKTlr5VuAlS8SuVEQ5OnIzSVO3dlEvoOOln7B1Sb6VSUEmh+ZpwxBD1XDmAoW8E6nddwPWml97p/tTi0wcqzdTRILOpyKKdKNyADRg+HVbo2T3I2ETtBkYHtE3ynGWTxFIw9xbN06y3mRWmr+j8v7nfIQN4PNK9GAYnxhDmiewApxdsJ7mdwxwQFmzW9CyV6nQetE3/AWfIibA+2TAFIDmXPdZn019Vh6PkslRI5fwxS3zJo5Ege6jaC9QqIz81fHxiyBYTEIy1ooqLBHPSiMXS0OUKb9osgQ6ICvpjI9URhXnX6iu5Cm/bo5IKlBJ01FkOa3nvYpsxveKXqUnUkmAXtai4Nw5AmwQ/WNhPTq5JOUf7VBngWdFjWnJe8XKUKt7+0FKV+oYehH/5L7+QuQd3lLwFAH60GD85ucZ5ZimKpKAmXZN0ZnaDAM095a/zfFwKdaA/u4NC+1Svv19Dzt8eAHfMCCUZ6D5LVqwZKgJit+e/l0Izqu/E5zy+yEW1aQNM6/Fyc5Tw7ge/ApKSBzidjLgtJe+qi4xNpKkr8//LEeOCtZwMJ5U0FDxbqPKiJ4WWdYfrbsRpcvLyqRkRJ6yp1yIvcKjkIXtScMdKAvDrGbEiVuKACgByTeowfYVnks0I14n6WiBYesGT2Ng7tOziY0yCVhnP70gOc/phAXzLfcHVCQ/HJSzDdOvFiENJujk20AcrAc/DsS6wtgkiD0gzOz36WUR5pfynBdxeCfzm9kr6q36VyTYOoEVh5m6jvCbnJ/6zCRDZiM2A+g2RedqWJEF0b414Npd70b9jP+XbEU0TJFIvnizcmhazV7d79N5i/z1jnBpCWNN3E9quSIzouMXXOASN6+vqDRyTlv7rcWikRTCNFpFh7ubREFmFr2NYZ2zu6bucJocpWPRso5lOVvSsWQPTXawxZX5GIJnqAqICoFG9Ywn65A1Zo5BNtZojWUeViQjxaVIIi7pInSWXaFDPtKSf2IxcZk9e3LNA/0Bktx/1aNE9VxjUX6VKjpvpWfNM/0lfLsOUdzcoaE5aMhMtlko2p1kmdh0UrK/PUljmKocr12+uyWU4dXod6mbtZKF7VVsmGwXflg2xonmB/0rSuEKcqES6ZquQGgpXB4A0dPJv1v/0QO2CHX2fZq9n7k1KtL7w1LqR/mSXBFLPuwbKK9IAg+LyZGOeGcIuRLLZoqcSzvTJsYHIpLRXYvvZ7DP6ez3b+Nfea9ZAy9Lu2P5sQ9PizA/6cx4X+PcyLuMyLuMyLuMvNL7IStWylObjfoVxoB1+7A2RXorf57o6wqSooIph+WCgBoPb39KWa9V4fH8kWNScDLLLZ6QAwwYGsalh1KfJYZosFEO+8yoEAOdDVhj9fZHiAAAgAElEQVRWWHcJOH2VRVKSdZYqJLssMKIaDKdA8A57tuP09+Tnm7hq8JPFzTVVPS82R/ncw2GJw/u1UPvtbw7YsH/tTw/XCN5A87UyOsnpuy7Az0zAc2/lvMwiC9w77Ftg0FAMS+kmikC9fU/uO+KiZCtJQs1gSzCiKKxhBcnfzJokRaXKGF9GGIZWD88LyaQk1qcSCDkDQoBJaNHrRkgjmBGf3j1cQ33fQRcY86sRDSeNrLpJ8nCfDksc+0bckd7c7PFiQWWIVhkpK+zZpB8Adof658LYLDmqZfcfrqJcx6Q1BduPxZFIV2KOy1CTrmYhw0wmsoEEY+uokFANE5Kr7NBscCYbyTaLQYMataAo5WdlqKQQXtSwesycquaMbIIQZjjjWGVR2eaZSQlgTgpNSeNZ1FD71CQ6ZiEdDRpq18mvKudlDxrakykDfRC1Up0YBi+ZrEOtaLMm5q1IgtpKYrJP9lyapGbOQ4sgIRR61MSIzbXqLdIbM1UP38QVoISZW3r3AEArwPdOzEnODDVWM9psb4jdXdjzy0lCFSaT0T0YdA8FbZj5KWsgWVWTgTSdWznfUnHqQM9Psy8IGs1vAHB8Q5V2uTduP4N5Vb2+KLKhcv39OWL3OY8vclHdf1hBLzrYjUcskIkCmqWXnlY4OmQRiAK5yGGCwuLbvTjfvN1diR7PDgpBA7n0cJoEFcospmQi9N7AnxpYnjxSU+Gk5GgxFh3ZMgjtPGw0MGOIwuszBxf9XDBjZj3yQqHiTPPpFeKkYXihe3n3LLrG4+Tw+D3bI7oMc10nxZQUPv7AoeG9IWNujus6jo1Y+fnekc60m70d/KuHXQvzxKbtmSbu0qqOUcE8Fa0Qv8AzJuZZunT+//j/qBA4ZN7uDWILTC/5O9iMyHA79rbKLGymtJup/r0wI+2e4ucKa9UtPPojM3UfG+RvByxXNPvlrIQZ3NqAWCZPlZGTxjjS9/5pcPhgaXVctBNOQysyrjy7mWnbwBXNcEeTTpmE1TLI9U5ek5SlWB8m1MUl0jX0VzP3KL7XYVl79e4A5KWq1o2xbkZim6knVyDOpORaNVtd4dJVPmONhvvZgloWTfHUq5OpmjT0UNnoOswgZJvhWPNdFr1i2J8WNcjCmIx4sDDMwjdjvQbIgHusMqKwqBsGOoF6iuXnAJD0PHyc4M6yWJqpBsaTZSEv2kcFf52RSv+yt7D7anWYHESnmQ1m0HntFccun4WPL//jFsuWVu13P9wBCci3fG33tgaKH2tftvSKS3B99EaercWPFioC0xUviD3kXjT7jNjWZ1DFGmtnT5UJ7Ff09xJxZ4YsUpns2NaxaFNn19SvqzOa9lQ4SNC8IWlSnM1nn+u4wL+XcRmXcRmXcRl/ofFFVqpoEtAkyS4EiOE7fVyKThOAMBvVMmDBJvlvrvfYuBH//BMFP8f3C2TeVXZ/+4j/4es/4sji0f/5d3+L1LFXbFTwHNytgoKa7f7j3x8lumvRThQNx1mXaTIwzMBNnrS0xfQ/60zwKsgYXyoFQ7CwkC1drtmibYS2bHoP4P37GzHbzyZXONYlKJUFJp4euqqd492kMJsB0aLmrOBHK37ISKA4KTARQdeKGRrCgM6DEa9VgNijZahQoay4SrXSUBlYROTiAXuwghrELhMLW1Tp1eTbTJXdmq2COlZ9K2yGYujNnBSUAxSzl0NfWb2rXz1L8DtA4dKeTR1OTSv6VT/ZswoUAOX4Ahj6FfIiQjMrPCcl91MFxaxTgtJUrDm9eTSIfH11E5FTdcjRQ4UYsyGotsCpYV2f7WQBWzyfFxy8XQLHhxnc6zJX75VIVKLHUpP/H/bea8eyJMsSWyaOuNJlqIxUVdUlmlXTYjgkiCHIIcGH/gh+AT+Dv0DwkcCAD/MTBAg0yAZn2N3D6UaL6erKEpmRIT1cXXWUCT7sbdvsJueJqIeJCjcgke7h9x5hx46Z7bXXXitbwnGTMTjozPBNKkwF/JvQgNh6+EqJxViwBcIyZKP6YJmZekb9+smTOxzYoOLu9Zog0RRN+8yUtodC8YjrTbXPUaaMiR5HfqfTOkiUFmqKJhOMTmIZ6X4zWWs8jXAnPiMdm8LvNCrAZ+P2qKPAtylKBqhes9oD2+8xerRv0XUpjFWADVC3qcg8s7LjzOc6b46VFD93tW1Qi9gLoQFC7vPxSDFLRQiBqr4viFuLTGgyI/WTnugau0c5rVDfA67N0SlmGSqHKuwXezrXyNq/vgExiguA40NtH+WiWs9HmLnG45Od/NvLd6fQp6OoF3mn0ZzSEz5f7fGz89cAgEY7/Os33xMx7PMf3OAPHtHfOl/hT1/8nhTtr9cddrwAuG2F+pRG77ivoWuPL55SpfxPT1/jZ4uXAIAQFf7s7of4a0++ox1qkVJM/0/XOHZZBEHySgCxK2slguOl3KDvLJUppJex9VBJpDxCILoYAD/Yo4mwXLTl8yCD6wPnpt1oEIcMM6q9lRfJzwq3E0MvZplzSi+mbzgvXWwKxOfWK4GW5+sezmkMe96AzDxCyjOxebzAYxGyYUqG0UBmwAor895KUTsUQXSyGJ2O+PITembP5htcNjt0vIH6t+FT3DO8HCaNvUvlWAFNO+Kwod/1bYU6sSEtMC0dQtqAdLm0yi+CuPGo3kCNkLII1XjJZ8cI8gnlewtBizRjrCK5EA05ty4+pjrmiS+AIPdDypkVC7iJtFixeIg+ZKWu6STL8AGcA5Z+VbJxg1cs+8efdUrKzgDA3hXTkIIYmCfT73QvYe2wXNPJu7HC/dck32MPmkUHGJbe5OdK/ql8yxPdZ5nXa+75b4UYBkALc9rIKZYHTUIrUEDF9INxnctfQhNhNwa6XKQKVrndZJeW/WdR1MuqjRHma70hb1F1ziYIxks+FIEqC2SD3Pos26iQdwgRRzhqfZthetksJYGqff634Yxg7mqb+25MY0QhSzNyn/QXSu47ndvN6PwlrKuSUYNWAhlrVoqyrEzV3PKzelBU+jDb09Mt7GLE5A3eb4i10M5H/PGzlyId+OvNOX54egUA+LS9w49mbwAAN26Js88O+PaSTL9fd2v87ftnAAAfFD49vcPzOb2pV8MSNRuWXzR7fL0j55JfjI/RtBM0Lzxb1+J//fq/oO/crjDdN0JwCk2EuqAXrG4maB3RsZrT956/x0lNo/TdYYWR7elu7xfwk85RoNPwG54RTET7yV7qcd1kqIYTAFTMZsmT5lxsEbmnSdErqCkTr7ptC8Wfi0HR55KsnMqSbmHuofecY3LqKO+m9jnSTjV8kgcuDKMX54fcb5sZotPZJL43UFw/rCJtNCQvbqNsMqTuN53b0cQD0KQwslJUtBGLr61MeFNQsJw3/XJ+jUOo8astGcgPk8ViRTNJ0ncFaNGbJnO0UKQINFSAvq+kFAo4LjMQ020dgRoSiUSVI7joFZHrRHYupy6Tzm1gGzfVmXyuiEIOUNG/x9z/MnFH+i+Ng3pjioWZpDgBQlRCULm/m6KExiu6h4KwkpxR7Nbwsy6eR+GGkhGWgGo+YsflTsrnDZMCW46xjGCwkE1SsBAd7eTQJFHVtvhcRRFWrn2N2aVmw+pQaTiORNQBKDedyDx2TxGyqC01uVaXNjTAdJoejpKINhig4ms/PI84+dk1vndKmzcXNL66ISLkWDWIMw/F+XN1X+Xysl4LSS+5yKTrN0O2jUz3L3q/DvBtQjby5wDqi5oX2FAoTJWaz3TfxzW8eixIXXWOdvVUPAvkMr90zGCKDc8H3B5yqg/toT20h/bQHtpvqX2UkerlbI9qNmFhR3z/5D0A4J+sXuIf90/xi3vySf3J2Tv85evPAAD/UD/BX80/BQC82a5gTcD5jLZ0d/0MLqkwqYjeVfj53WMAQFdELIdljccz2vZ93Z6jf7nEL9/QVu2rxWOY5JxhAqqTAY7LLGavDCZ2xNG/P2LRjPjx5TsAwNN2i7+8omscJiuwcIwKcZ/zi7AR6mkvxx/6LGYRJy1iFZI34xbrDOtQhBPl55JRHCcteU01sV8jU/zVyueIqM+iECEqgjVTYX1QWVSgjsDSYbZks/dFh3lF292r3QJbVnKKA0G8SCYIhY6pnwVy5EjRkcuRTZlT8jXgTgOQlKN0xIojzu23aygPLL+hz453DbpntAX/+80zBCjccR51Gi0OXJRvdrko3z7u4Ma8/Y4aCIVpQbRRIlA16pzTr7IGbthSGYXnyL5ejBhTPjtS/hvJABxAYLN0NSky95bjK9lGR5WL7fVIbFMxEZ+HvN2uqW5J37EP7iyXjKmTETHRt51CnHlUrFTlBouYxn9RugLknCzABtcxR07R5GhmWgaoWQ5t/Ju5QKEqZLi32rJrTMEWzz7G+byhot8Fvl7gKEJO+tIA0FxnMYzhBOif+mzsXUfJUbZXOW2RnH4kOvVFiUg4NjePJudi2yslpUPtT+7wfHWP1tDJ7/wc+1Rm5RXMppJx7Bchpy322UxCjwp6wJFLVdn/PldtITRKcqUiOFEIfXAWgzXDOZW1oBxq+nw0mb3smgh/BsnT6oLLoDwEKQkV9X3qHxVIL9iPxzn6D7F9lIvqRb1H3Uz4ze4c7w+0sP312+dYtYM4iLy6PpHPKxUxMS7x+ckd3uxXeLWht2B7vRCYrj3tMa8n7JlAYU2A578N3uK8poX4n3/2awzPrUDNm6nFLdupfbG6xarq8fUTgoq/+eQM8VeENe3eLrEzEftLOv6/23+GkOoto5IBG+cev//738o1f/3uHO6K7su3AdVqxMTyafZ9dTTxpDKCqEELkeSmigVWgWC59BJvjaituHlEeDSi4gWhqh3GoTBjLqBO3eWFLjQZ6lMzWlA/OyOz7RgV3u0I9jt8dSILCOa8YKdFdVBZWWfmgc6g2nC5iinq7KZMkDBf7vD8dCvP+9HZFre7xEIhmCtJq7lFxPWWxsvj+Rb7qcW+oxkpvJxBJVh3lnOcMTI8m6QrWw+dcuMmwncWhuG8MAvH+er3dOyqU5hOvNRNj5tGapxjG0g6cSgs0VK+sje0ySlyqlKOpXOOPDRM1Crzock1xtHmJy0iURUlL52FZfjXTSQjOG255KjTMKksZ+nJFJ2h/9IEOx0zLURmBKbzLPsnClkHg1AHgTjtXsmkfmSuDs7XpXuzGQ7XrCKVnFTcIsDuM4FntlWyiEDlumA/i4DKVTqzd1pgylAhu/sYACEvZspB+jTUtIES+NMp1LecVlgC1R/QWP/89A6fL26w5Qt5t1+iZmvAfldxqYyXY9ii5EgkHGMeswDJBqb3zrdUvpQ2sKZTqDmvbIaIaVFsRGPuu3qT68ZVIMH/BAEHm+9TeQU95prntGkCSHlJcrsNl+XwBsd2dHxV1vx+oO0B/n1oD+2hPbSH9tB+S+2jjFT/r1ffg5k3UCrij54Q6/ZPzv8OAPBV/wQAsPMNfr6ln13QEtG+2J/CO5OFBIISPdinpxucNQfsK9qt76daotYX70/x9VsitczmAy6XeyyqvC37EZOipmDQaId/evYCAPDZ4g5/v6TrePP+BPG2xu4tbaEXj/dYnhOk3FqHf/HkFwCA5/Ut3k1r/PJAUPY3+kyiTDVquKuWBMq5HZFE0g63ioQlluUgBVM26ihM1eo+q/j4RSD9Wt6eTqOFOxQnKMhOQI4yQmHX9ezJHS5mB1jGhl5sznDHGsRY55IFTJquJQnql6bwA0VpCdKrdlkpZloC1ZcU5jw52eK+a/H0nJgi+7GSEpjqngycU/QSLMSK7KZfYAoaM1ZRun80ihZyjEos7SKAMBgpmzHWw7E3athVqG9Mhh1D2d+Z8eyej1ivOxyYoOZGnT0vR4NoTRYc8ArK5WerBnXEcBa2roJovMaB4fyUPmjCESRtD0oQjGAKMpUC/C1Hpgz565Y1d42BT8doPKAAzXCw3RflQg1Fgel+plMvClzNYoTj74TOwG4yIlJtcxTlFoQ+JNjS1xl+jDpHQ8oR8iBcquuy5IUFCZJWywKFxjGpMVkuqfFN8TcDYfRaVqxK5ybVJPo51ASvi5Var3D4ksaOnjsYHi/PZhtsXYtf3NG7e7OZZ41sHRHn4chLNvuiKnmXzKhgD0Bzy9HoGDGxoUaCpJNVROndPJ5QlFpG/ql/fKvEF1VFHJPrCga4nhTMmFnOKhaM44JApkeO7Aui2HD6YFL+wbY/fvIt6mWNz2c3aPjpf9U/wW+6C3yzI1bv7WEmOcp+rNBt6e2gOlGP03OalGvr5XMX7R4HV2Mz0kQzOiMQr7UBPavgHPYN3gWFhuscHy322E70ne3YotEOZxVBxT9avMHvzSmH+tXZY7zYn+J+oGupdMCcF+afnLwVuPffbr/Af3/5r3HC/PW/qD6H50FuNvpocIcqCiSoChq+clznKjm4/LdoI1RUAnO5eZRJUnca0deYUi6s8JSFibKYqkkj1BGe4VrVepydUZ3Cqh5Qa4fes6NHmY/bmSzMw7BckhWkl724h5gnvGiQax4vJ5wsmDW9WeJ8eRAFpH6sML6gDZQFweGlkHi3Y2UkdYIQtEgT6opcSKhDskwjAKi5kxy2ez/LDiE+e5OmljZodeMw8uK7mA/ohgqOj696LZCj6TR87Y/uO020eqAcc1pIQxPFSAGDzs9bs4RhYt0WBgPfLUPxF5Mwu+11VvFxbP4dis1m6oPZckBtPe44dz8CuVzIK9itxnTC11UHYQaPu3lm3PYa9X02ItdThnurLf2cNj+68FaFKgTj2dw9MVxLA/BQEaM31Y5P53ljYToytYgpH6pyKgHIbO5pydKYvCipkMtwkldpGrvmx1u4HacOnJZUR6MdtuNC+BjTXSves0qRulVWgcrvoOkzbKtC5gwAgK8UQpXSLOANDnf3PeASzF1z36S5oc6lSQAwrpT0lQooGL+5XEp5+n7aqAedx3i9Ucc1wwNkcXZzhqWL832o7aNcVM+qA5pqwr+7+wx3nMvspgrOa5lcnTPiThKjkpIRawLqxslCWtp6/eL6kfyc2j5pkBa1hf5g0TmNsKC371e7mURzl083+GRxj1/uaafaFAnPl4cTbMcGZy0tCFYHPGpocd9OLQznwf6HR39K/8bb5MOmxZwNrv0MCDqTKfSUiQRRFy/Rd/gC5Y5W6kdTvqiNkoNTgaJHWTyrAMUqA2E00MlizZNGaHITWa8OmLgkaDs2uD4scHtPi5vfVBK9hLWD6lMeiS2sUoSlIXlH5ehF9yd8sQuHxQn1W2U8DiPNkJerPbqpQse/H27mAEsd+mhQ3etcIlFHzH9Dn/NNhemJg7dZuCGVOlQbjYlrF2MdgE2VZf6Q0QDtuF/TwrSKMDzOpsngdE2z//nsgFdxDX+15M/5TFZpA5WXpLxp8ZxUoMhUoiobZcFSIU/CelBU2siavnarj9yPgs3OJdVsAjiyduNcyjbsxsDPi3yuikCb72WajCz8ocrlNWarKQpO0U6xGFNnpevAkTZstEW/zYHhLBOo7KGwrlNAWbNaasv65rivtMtlHrrP7lDVXvFno/RdWjTcIvcvRXn5b97mBTcawH/a48tnVCqzH2t0PDcszjo8mxNSclHvMEWNbshENCl/mTSCzy+mPXzHYDzdsiPpQBGyWKjCMJ5qbm2S+S6mK4nu00bDAu6MPzaWGxCyCRTD+x4IJi+4vs0lUr6JaK7zScbT4ng6L76+PiaYfcjtd+AWHtpDe2gP7aE9tP842kcZqf7Zm+/DzBv0YyVelkpF9GMlakhaB4H2lMqsSaXob48WtNWbgkHvckQ7OkiJzeFuJhBSvRok8kUAbDuJALsfa9nF327m+D83PxD0ynUWFUe033t0g5Omx4bh3/PZAU8a2uFqFfFpTc45m9jgN+Ml/vLucwDA7OeNqKT4hnaFsmMvyh2OHCJSAJh2+QqFDJzKqkN8TJEwNFQiopNI/KjFDaV0UIEG4spJlHl3t0DkXJE/0ZhGC8/Ru1o4zFi4fhotpiRQUQUokw2Xo41SsqMOhqKG5NwD4GRGybXH8y0Cb4l7b3HXteiZDW3mDnjF6EIP9M8cFJcHRX8cjapBC+Srq4D2E0IN/BONeEvHUJ2ha0gKSIiISbyfo3W/dtInA6tkrdYdPl0RJHjTLzD8zSmSh7tyKitTOQXd6VyaUEDeSUw/fZYUs/JzSzlxKMAvA+wmRbHZASW0HnrUcv3GBvR8b8YVbPGKGKZJ1N7PA2C4JCho+EM2MbC7Qj1rFhHXk5SU+X0lIvQkApLvJVhAF+Ur2Vc4QjuIQUUJ6yqHo7wpFI4YvpI/ZJhS2LmTQpNE81nAIHm++nlEmOdjJii42qojAfnhPGQ0Zx7w4+fv8MWS3tF/8+pLKEZ3VrMeC8Oi+cMa//rVl+iu+SA6w72+jYCOIjuJWORvI5AcvustMWmTaH4ocsx6YulGDoSnWc4xJ1awiGqEHOX7eczKXIxySP+vCvGOSM8+qX+Zm0qunyLRHO2TkQjkGkMVEYtI/ENtH+Wiut3OoH1LpTI8+Yegy/kGMWhMU/5Z8QJobUBTeVwzccnonARY1COsMbi6YawlKKyf0EQ7b0ZsVJKu89A6inMJ9hbNe9asnVn4WR6U1WLEJ0yiCVC42S/RMJz6D6+e4O+/JTWn1bLD8xPixh8uG2x9iz3naUMNkVyLlmCh0sGmzJvIC4BU6sCTax1l4UwSYylfFOYhw3nJoD1BsrYgPxUThLoYsF70GHijMWwr0Yoduoqk+/jlrtsJqxnNCvdB4+ILgtC0inj97hTxnvoxFnZ9sVXwRslG4PLRBp+vbqmvqh5718gtXywO4vriJovwmM7VrgaM9y0i35NdTfDcH+NMY37W4YeXVOd808/x5pbIVDFoqa9MJSGS84yQ0qFoFbB2UgMKAGZF5/7s9E6e39dfPUZlMpSoggKKhUIViyXBpGkypQkycklGPZ+yI85dnYk+y4DqRsMWWrciMzmRzGHKa48vFmh2ebEJLOFod6SIlXKlaEljGqCcodpbyU0jMCEJgDmZEJwCXtIiUk0l3JldUlSkey1TEK6ot9Quk2OihUgFljlCl3Ro0zFCXhjckjYF1T0v/H1eHE3P4z3VbDoFzQPe7pTkaLUDhrNCkUvlc6mZw9V+gV++obSOf99g9Tm916Mz+PN3tAG++vaUNrSFdnZY5t2u6rRA7tUOR/nbsuazhFFjAaPbnueDhC5bwCfj94Fz6UU6SLgSY96cqBEY13khDVUUV6O0cVGs5Wy7gtgWs8qTb/nemnxu0yvgd4Co9AD/PrSH9tAe2kN7aL+l9lFGqqbyMJWH9wqRiUVaRdnFA4A2EYH9T2NQqAsB8Lv7hSi7zOYDzthb9Xo/xzQZcXZpT3shLu2HGm1Noe/kDXb3M0TWqa1vtUSEiSySCCWTb/D17pIvnIkmiRjhFDTr3j4/uUfNW8k/vf4RnrRb7KfsIjMwQSDpooqSETJURpBOJrXElUPFJRIImiIKELyt722OcAPkmvyKlGfSDlTMiPlkCZ411mMYrfRje97jdJmFR69uV2hPerm3s5b+9uPnb3E7Efb2F1efw7xp4NvcZ1IyoiPM6YTLU0IKLud7jExh/dubZ3Kebd9gmiw8P+v5ohft3sPrJcxBiym3u6+l2N6vAtbzHi82J/ycDB6fZvPxpLS0C3NgZ8WDNMwDkFyGGkBtrET59ad7PFrTMW77Gd5c0bHNgchfKWrz88yi1o7ZqMnU3sTjcg8PEYYIjUdg9nK1y0Ici68NfJ2h3PE0C0HYnYavIwKbLtj3lTBOqQxFy7moZCSNTS3vgbmzaK8yRD1cZGJSuKth77VER3o6Rk5KYQX6fhFNp7QFE8wTzK2nzBKGzhFt1OBoN7Gh83n0SDBugn9dm4+ftG7LEpsUtWkP6avhPHmGFtD8YxrDtvK4eX0Cw2IkJihsrpkVNenM8E1uQ4dshpHmAt1TNJfE/EvykPaAKmjy0yp71FKf8L8vi74AR5kFyQ06R64hI7dHqknJgCGbmUcgmZlHoHlvcqmczf1tD5kUKUhCGtM1k89+B8K8j3JRHbYNdIL/0qgZdX47Afg6wPCConUUR5g4UX6pnrNknHWixuM3NR2D83jDocpwpNeIfV60zWoC2ATcrSfYit7mmfXouxq4S7pfuSQCDnR8fsnQeFyc0STsgsbPXz0HAPz0+Wu87tZ4+WtajBsTM9zD55fJqs71kHoxoWKocNZMGJ0ReNyPmpifIHjTFX2la5+F+I2CjxnyNYe80IU2AoXwvrYRFZcVfX56hyftNj+ks7e4ZYuMH66u8IQVx39+eIJfbem+3v3DI5ij2s58g+ePN6hMwKKmt7tzFb65o53FNFn0LCmoW4/lspfr2N3NRXIRNiDMcZRrTM07heu7pTw3NxnsNnS9xoZs98Y52HhJ12FtkNIYTApx6XHxmO5tcAbvWbFpORtyLu3cwfe6mOEgSlGmV1AF8zMoynEBoHrTAFnE3bZCfc2w3C7nxZWnkpmsOGXETWRaAbOf3AlPYLjPtNv2PZVCAEB/SbJ7imfF+GSQBXz5tcbsOuDm9+mzCfoFSNKRmLc8KRelMqGJwrw2PV2vPIMxywqqoGiRC3lx8MVCKvZoKRVZuNakDZ9hmcNQZC/SJsAtOPfLp1bFmFMuL9r9I484y0pjs2cH4Wxs9wVWzee2V8wkXwQxFFeOmcsJfveAZtUneyCIW5x7ZnmxjApImahpSaUr06JYzCQfT9efoG09qFyLOgOCyRaAiZ0u15XOpXN6iO5FyeKoXGYAA1yONGRmcOpTMzBMXQjohzoilvVzH2j7KBdVjBowTL5IL6JTQFRSx6fr7MVZ1k5VyxGzNheBHfoG6yXNQLe9hTqYvEg1Hj7Jx8Wc31Otx2rZ4fGSF8SoMTDZ6WY3R9jbnN8tokoAVCvHi3bVOtRMBrk+LPDsgt1xugX6yZJTCIDhkkTWVqUAACAASURBVMpP6AC8+02/VkGi3egVHEds910FUwX4VGjudJagqwJs47FeUfTovJGI3DmNuFCY3szl+mVHO3ck2Qey1lM64vNTIuP80em3OBeeP9DoCa2ifj6EBq9ZW+4f7x/j2yvi+euJLMpKD9yKNzvrdkCMCoeJZojRGdqsAJg2OYJXOmK/bxFSxNUbZPcW0lQVabU6+67GNiAEjZ6l97CrxMklroKQ0P7gD34FqwP+6gVpR0+9Rb3Ioh/LeY892+aNo4XlSbgyHqcn1B8haOz2LVyXPLr0kRiD6SFOKaHUU+00wtplIYdJi6Wb6XNEOJ5yJJJENGYR6icUSvzok7ewOuDdnkKc0a8Lbd48qc/fUBSy+ynf22gkql++8hjWWjxylVOYvWCC10SLlpCrykgllvrEdG+ldm5qZkDeLfJzK/VrE0nPHiKGcwWXJBG7HJkmlxQlC1Mm7EwnAfF8hLqm51TfadR3/Lk10H1B96xbT2IHTLCzJgg5bn9oEAYtucdoCO0AgGiD+MmmnLgsPr3KjkQ6AkaJmEXZV7aL8E2O+vws9121L/LBgforbbKDpc8CtKBCHRO7RKxiyoQm11K9rBx/k/uRJBIh13KEKBTEJ5oXjvPAweba1w+5/Q4E2w/toT20h/bQHtp/HO13YF/w/6NpoqYjZOWYaAi2nZ/mWpEkD6Z1kNyf0YGk5/hvp8uDSBFi1JQ7S+UIYy541/dWIJLxTOE+LDBw7u58eRDJwnFmsPx0ECH+yRvx8ASA0dlsgr7oxQ82eI33twRvKq+gz0ecXlAkvD802SklKlGFAoCTdSfKTo8XOxHD+PbNGfykyXAcFOVIJBYJSrx9TxFjLDxAVeup/ChBjhUxPAHyg+1ueFtcByzne7zeEmP22WwtJUFPq3tsfIu/OZADz900x1VPkdLVdgl/wxGzZYUZEX/X4hP7frfAohkxcliy3c0k0lNNyOU7BwtMGprLS4quJljORPiad91NNlk3ywn+YKG3DO/aCMtON0074dlJLnX6qxefYmK4uTrtxeDe1B7DVEn/r+e9iFK8u1lLThKK3IUaZgaPVSVhgz9YhEYD26wqleC2qAF1m/1gx3MviIWeIIIAAOVRJW86czjjKFmriMFZvPv1Bf9OUC9wLIyvPdA9Dtk3dtA4/Yd8/OFcYfaGPj97F1HvqaP7U4Wo1ZHggAj2V0CVJAYjRdAC3Y45YhNYMgkytDkCWn2dv3N4woIjSfC+iG6DpQhO3G2mQppx7QCnUXMawAycFwaX8zTZTQhVgE4lWPnwZKKgMtzpVl7GrdrbzJ4N9PwyOpIZ8wh0z+IIM8tqX8NpFp1QntELzluGCqi2dDXVjqDfUqwhNdtRqYxbFigAP+PSxN2MCjp55AKY1tn72Bzob76YE0QgpDB6n1Z0r+nepnUgRO7DR38/0kWVm3KqqJuMUCZKLamxQYhFSkVsdzP5+WTV4YIVUKaQS2jUQKSWVEoQvRIHlXDqMJ7xuXqD2Bv0oIl2Y70sqpfzPeZ2hAtJY3YuptyDN9jtW5mgb79ZoLnhQW8BxdDe+HTCbD5gz1qxTTvhe4+pDKWxDi83a4wMN9/fLkRp6Ff9BQau14QC4mCOjL2TTBuCgr23Mqn5huocAYZPbSa6mNUEzRB1926eNzE6YrOd409++O8BAD+ev8EjSzjdPjT4ZrzEwFhQ5yt0jk6+v5sJLBctye4lSBnWi6NHbR0OYyWG7u6+zrrAvYE+of5WOsJvahkHasikMeUU/CJQ/hu0oaptzpfDZRJTe9GJ3q/WAfc94W2/fnOJcFtDsXLUdNfKbH72iMZQleo5o8qlPbvqCLadFi5PrgoAO91U6xH+7UwWCt9EWQzSwjI+4j65stJ3/UUxEfb8HiR4r7eIXOM4eIuXtycALxQ485IT9p1BdZfHB6LC5f9N4/b0qx71b0jPOnYdTv5NgzjwLH9xhukxbab2z2ZQkdSHAIKgJac3Zrgw/VvaMCSFJQCirpQWydkV0Nzxc1prMRTXqSy6ICCJRd+BNyFJQrmNcKtUpKlgb6y4uWgHkf1TISLwJq/9ZA/vtEiTeq+x5Q332eqAK2cQ2NJR9xqa4fGyiCSVrsgC6RRUkQ8lmD5/PtV5mu+4u5S5V+1y6Q3JNqojMpWQukALZK5bz5sd1RdQrSfIXghgNoqykxkVkRPTylLcHG2Y0i8MgTd5FVXHH/9g2wP8+9Ae2kN7aA/tof2W2scZqQYFJBHotFEyEcpmUXSnIJHqyazHuqXt3GGqUBuPLYvmh6hweUYR1juvgN4gJEKJV6J+klR0ACK5oAqi0LJ9uxQHkifnG+zGRqJTHxW6KW9NT1YdAhOjtvsWhxULSuw1/Bkzd9c9rA54/oSECbSKGDgU6PoZtAL233B0HYBdIkkssngCeEefYNFYxaPIiSCmzPhL22m39lBDpvSFSZPfJ8Ben/RjNR/xB89fYcZb7NfjCW4cQdmV8njRn+HPX38BALi/nwtzWh9MjibmEdV8EqatsR6GIzj3nbIl2AiTlHraKPdpTIBaj3IM35sM6TbkPZs0ePvJynOyrxtiQT5PrBGKRgASAXl1S8SqcFuT8hOzgBUg0f/Vy1NSyGH4MN7VqBhinHVKdvGhBvxpEG3n+XwQ0Qz3YoF6k8tVgv0OGaSKaF/S+NET0D9hsYZnB1QpBTDv8Oo3lzB33FfPBlxfE9z+fqQykJhEHa4r2MTs7jOhxh6As59HzN8wQ9wojF8QS7t+cY04b0UQ4Po/u8T2M7rPUBFMKQzUOY7cVjLllqJUIbLoDNWqQKIG1T6Tt7oLLklbFyxVZqYKo9UTAQegiI8INvz7LOtZV+8t2islsHf4oodNZhirA04aGgO9q7AfaxxY1MWagHMut7vvW+DlDDaVmlTIjG2NTI6DOjJZqDcKqoBxfRvhmNVbbfRRhCoRZ+SIsExlMHFoWhFKkf1PIb6oCa0QoXwdYZP6l8n/HiqG4gu9ZiGagZm/fG92V8x7XNKU+vfY65Yg7/g74Kf6cS6qRU41MWTrxYipq6RU5ny9x7yin0NUcEU9q1YRO4Z1fNAywcU9uXaIglCEDGztFZWUAJRLCSovPhGIb9kcPcGUDM0tzw5SB+ujwrIaRVFpP+txOydY+n4zxxePSTFIqQirA3a88HeTFYFurSP2dzNhEMemLB0KUq8JAKozMurj+UiWbgDiTX1Eh3eLmM3BQfV0wvgNSvrAzB1+9vw1AOAHyyu87k/wS67B/WS2gebZ7mZc4Oc3j7HjMgSlo+SpPPKiBBsRo8JsTrNJZbzA9NOholrOpBxVe9hTmvxiVBgZQg+NJzH8BPnakKUOG4+mnUQ1y+pAZVMAlKINBHixr5sJN2wA8O76QupI48xD1QGa4V/fGeiUU723PBEyQ/nphGnNZVa3NQLnohcnPZzTktff3M5h3/J1gPKhIgHYAXrHG5x5RLXJ4viHHw34n/+rfwUAONUH/Kvrfw4A+N/+jz+CCQr2C66z1REdw9fVFdUjV7zR0EOGGk1Pwu0AYHuyF7v+GfWrGSKC5U3Xz2awHSSP6po80VY7ugmXYNi9KibvCKXzZ32TlXtKF5ao6RhSMmPVEYRc1mFD5UVWFezWUFF/jc84L74eMLxnlaeNwvZnI9bnlGfu+krek9Y62bBuhxq7QytmG4t2xF1Hx7j56hxVl8udzNMO7n3eEMt1GOJ3lLXA6V5CTdB+Kn2q7483Fmkz4uZcIpUE7x1yDWvaPKaSulJ5iRngsinzBUwc1VFpEslh5ty0zAUzur60WUGAsL7LkhpysolSv02lPkrUwD7k9gD/PrSH9tAe2kN7aL+l9nFGqpG2q6qom6xrh0cnO4kCe2fxbksQ2DQZMaNeNBQpJki2GywG9kVMvoJlDR048lDvaxgu4o4H0l0tFYmEiHGgWjaphzzN8O+yGdBYh4uGdsyn9UHqW92yx+tbgnS1JsJVTCSPwcDcZghZtQExKRs1XmoqEZVA13pnEKsIz5/DwYoaTH1HsF+qb9Mj5N70SPWPfp4wtohHz4iQ888evxDVp51vROEIAN4NS9wOhENd7+cYJivCCiEo8SP1ulDtURSpptravq8wJdNsT88iMY+1DgjMDHbvW4C1laPTaFaDRIFuMuI8JsIObEm37xqB6dy5owicWcTDiyXmr/n4n3uoC8binEZ0CoFPYGZe9IMxGEBrUdFanHc4YVTiqlkicF3wYdPCNk7urdqYPD4qElivNwUUz80e6Dl1T+mz//K//pf4b/hc/+PVz/CnX/8efTAo+LWDT+PYBoHAU91kafSdVHl23/fyLCjNEYXhq0dgdk3nWn7doXpzB0x0ceo//xTDWRLNZ/s0qZssojTketRkS5gEH6LJ5JgkbjAtM+tZIrjI8CoIlgw1RZ4AMVrTEAx1xPh0wvqS3q1htKhv6Y+Hzx3mJ50IYARvcLKmz11tl0Luq5sJZ6sDVg09e6MCvmXBkXAywQ81QhJ5eDWDSf61TZSoL9SR5oZiChGykCe7tcT4VSF/Trks6OAWx1q9pstw7/5RxHQaMtt9VHCJZMiqVcKAHiFqSyX8S+IXxbl9IRySxl/ieDWQ56Qc5DpcRfBxsiw0vYLts9LSh9w+ykVVzR3UzGG+HIR5uWhGRJDPIUBiAQnGqWuHymZ4c3BW/Df7m8JE2CsopRCSGP5qFKEBYhrmJEcpZB+qzMRUnoroEwyyf7PAeEYjrbYOn87v8H4gmFGriMsZvdwRQMPX2FiH11cniKzKZA86w7MtvYkCdxbKSNO+kpxHWDtg0rDvOT+UlHv4ZFHnCTw2VBAPEDTYX0SBlx893uC//eQX1G/BYs4Y1dIM2LsGVx17hEaNqx3dV99XiEELm9ZYL4xK3XqAJSOVjvCDEahS7WxOTV0MMCbC80IUvuPAkxaNsHTwzsCnkiMVoVOpg6ZFe0hylRFoGEIeuwpxNAJf6VGhP+cJv/XADT/3OhLTMfmf1kHKrKBZMYgnmm5fEzQPUP6ar6NdDuh3jYyJkjHZvCN4tnSmyUxZFoNn6Pyv+s/xv7z9EgDwFy8+h39Bm5hQR9TvLMbLbAKQlrXxwkMPmsT/+dwp79u+MqLKFGqguY04+SUvKFOAvaU/6qs7+KcX6D6l59udaXD6HG4WydWk+v+uIuWiFw2VM5WLuCycXO6RPTxxVJohovnziOYm55+nZZSFyK8dQb6cygm/XGL6hMbqfN2jP9SYsWjHT56/EaeoEDRWZzQmTpoe67rHylIfnNd7ydW/CArdkAUx9FTAuk2AqnJJje50XsxsFuWIlqUUp3zfYj7go6hb+SZSv/G5rVWyafGzwOL1fIyYF71QR8ltArSgpj7VY4Y1fR3hllH8Zu2+yH1HGndJbEK7vBhPq3j0nO1Oo32XF2MVQA72H3j7KBfVR+dbmMWI2+0c48DlAVEhRiWqOxFAy8pJ8yY/6fuuhXMG/R2NGntvZUKbTj30asJslhM+zWNa9PabVqQHVeQ6uEWxyKb8RCrL4JdK9wYTl94c5gP++voTWWCezHdobT5Xqsm0hvKEsit8lK21ACIPpQ2DG4zUFgLINZ86onlnjnI7qaUcVtZrzT/7hs73n/7wNwCAn65f44Q17yrl4fmDr8dTNNphVdPb/eu7c6nHrSqPacy6zG6wsgnQOspi63vSzdU1R91PRjRcUuOcQX81g+X8Il0z53ZmESEtzFWgchv+vZlPaGrOMasIHxVaJqW0lcPdlkurDE3w+pTGhquzhi9GnZ8nR8xSujUVcoNecf48lWBpqR9u1hNJFQK4286gbiqJBnxbkMS41k8sutpis6OA8QSwNzR7/0//+5+gueFnXUWENW8eTkfUnw94tiCi1e1hhn7Ned49RVdTRd979OcaF39JBDh8+wbqjAhZcJ5KZkbmIfzgM1z/U6pjHs7OMc1zZCPjBzSRR5sjNV3WaJblFgHASJtWgNbdpOJDNayQ8a6nIldqC3s0T4pSspi1BarkFYabmYz/5U/u8YjVkEJU+PGjd3jUUs75N9sLbHt6J1ftIDn3ZUUnagw9gLfDCrdc9x0C1fyIzZqFbHTVlPPewVKuMdXBAhB+RbQBfqEB8Ht+gDjkuJkSwpFvaXFU+3xML0gYzQtJgSvqYpOmQJsTk4+TxpkZqKwGADCnWtSk9ORmMctM8rNIC6lv8z2bHiK5GCwpVqVr1hM9Y/87kJD8HbiFh/bQHtpDe2gP7T+O9lFGqu/enEDPWsBpmDVDPPWEzaGlyA0UiaRobt/XGFif1VYO02jFYNidlEksopIvWjrmD87e4+Doe7/GObp3LOLueHeY6OSDOsqpHG11FARK9VHh/jDD/h3Dv19GtJwDvt3Nxdh4ExTUzIuOMZyC55IXpSOVpxS0fRHvbj2woS1tc1VEr+A8VZlTCfn3Us9+OA94+uwWZzVFp7/YPcYPl+8AkFDG32/IIeamn+PT5R1a3tV/7/QGWy6Mf7VZSzQOANpGNNynR5FqfbwnnDUTPH9v2NeU50tMT58Lz3WvRBELB0N9kkQulsA4JW9Vg6p2uOOcmRuOXxdlInwq9dlnzWezz5FH1AC0yvanDAcDhE7ENogoRbsccLnKSkbfvD6nz93WsAcl0YC9zbBf1AS5J4EDNeUykWkZj56N3WXtX7cMaJ/RuZ6f3SNEhQ3D6IdDgx88JeGGP/zRS/zZ2+/jFZszLL8dgStimfvdHths8vGfPcX0Y1LBevvP5uie5Ny3GRQq+hp8C3Tn1AnhbILa2oLVWzDHixIMGPpZSnj2hVC7ZWYwQ+zjPGbvWQfYxGWwEeOFFy1tu9cCxU/nHr/3w9c4a7JTkuXwX6uIm2GOv2N3owjyuwUoFXTS0Fj/6uYSP7q4kjH4DzdPcJ/Y6O9mML0Whu60itBDUmhSmE7SA81qR6klNCQ4heZXlWgZK4+MeqgsYq8nNksvDAdkrvEKaizQjCrnQxMnRBAol1NUCRGh6wVCyN/rL6Ncf0oNiECFKoQnUEDZim5W9cc5298Bj/KPc1FVnYGCQZx5rBa5ztB7LSuEtj7XLnqNFdeGuqARvIZLMO3ckRwhiOCxXPY4nXEuCREvN0Qe2l0tUH0H4kkvd7CAeUbfsSZgvGtgDimZAanv6w4N/KShlzRr7voGGy787N7PhXwQmkDuKMkcYJ7rT/3BAjaIqwyZC/DLd12juc1waTJnTteRLW74/6lCaK+yesukMDqDv37/CQBg1Qx41pIUzd00x/uONgT7ocamabFmHTqrAwK/tdNkoHWArvKknKQZl/MBkcssRlBOK9WmWhPQd7RxiROZzqfJQ0VAd0V/i7ITTSyKTQWGQwXLEPJy0aMbKkw7hu1tFKg5jAbYWLQMp7pZlOsqTaJ9G8nuTRezBY+ruPRYnHb4lCfouR3xN9+S01B81aJKhs1pA5Psxgq1HLeMmEwm35i+EE/35HAkC6vNBLLYevzBs1f0zyrgl/cXuN3M5RJf87idgsHV7UrSEpsvajz+9Zz7WyOe0edu//gC2881pmUmEknpCigPt/09PvfCS8mRuaE8vihtqWLMqZzvE5Wf3ItSrzk9mlBdVZmYl410iAuQlKUukoA9pwE+62Wz9tnJFk9mW7zcE5x90e7RM4b59rDCrs8+cfNmFIOB1jpc9zSmLxYH3A0z/N27pwCAoc9jR3tSrWJfCCL3cE6yrFlNC2BK//gTj8Bpo9m3BmbMm1ntAMWr0HSanWKCoY25WC5+Z6FSrhif8ygbrXQsce45UP0vwHA7b9aqPsK3SmQuo43isjPx/aW0iyo235RvzWM6VFHGaqgi3DIgdCX54cNsD/DvQ3toD+2hPbSH9ltqH2Wk2j7bw8wdrMlEof1Qk+j8fwB+eHa2kbKWsWvIzDxFqnVAvSaCglJkI/aSCUMvcYKhS6wIJdAH7b41/CmFHtVyJMFtgEo7TIRjEkmsAyxHpu1sxG63gOHfJ2eysbqJwjpGVMByAjjaDazLCmTijJggVxGKVZ/aK30E94SqpPMXNH8+XIKhzJDp/I//6DUq40kvFsBPzt/iaUOR6tPmHntW1H6BU0zeoNdcwlPUECgFTPtaohmYKHD7UHkRoLcmoJsMav7bWApXVAGx0XIIPeaIJV0zAIxnAfZRh8BlM9ErITsd+hpTV4lxNFQU8QezNdATMCX7LjHRpkgxCe9HE4HGC1EsOC3a0JenO3x5coOvN0To+flXn8Dcs23YIQuWJ4sstpSl8hFmGquJdv/Cwi3syxJknyKz+r2G6dgf94cD3h4IM36/W2B3Oxef3vraYPcFs88PDfzBCoTqG8BfUojilhe4+TELjDwmAXo5t8nlMG4WSPt4ywbp7wrx2nTNyNcsz3BSWYc5sacTfDjPaRK1N6RClLR6Q4YhlVcS0cpxmQ19frqTcbeqBlx1SyEZ1dpjweoJ77sl+qESxawYlYy1/VBjPcvs3zfbFTomO/rOyrtFfVJCvplsNp35rEvNfqoJ6o87g/qen8uW9HaTQ2K9iegelfBplJ/L0hjqh/z/aAvPXRMlYlaeBO+lVCZkRAQa0PsEyyu4FgLT+0WA5nFlDoqM5oWQFY/KvASGnmhcTycFehBxDEV8oO2jXFSNCTAmYCrKZpwziEHBzFjqbzaKF+KqHrBh+OdwM6fJnidJ77R4jsaRvDj7xITTEZHpbGpS2Z/xwsEsHRYzZo46Dcd5PH+gFzHBKXY5SQ5x/2oF1EHqJ73X8AzjqsL/FQDioXi031EqUaPKNHp17Dby3YUnuUqUg11Pma0H0OK7/O/eAgB+fPoWe5ehstOqQ8UH3flWcqWVDlAqYs7s5UrnspkYAVUYuscYZKQqBfRczhSCQlV5dJzzTLJ7AKBMQJh52RX4Am6HBvxTztHaiKryGDnH2i4HgZrHXU1s3eTXqjJM7AHEXsvCF6soEK+aeVk4w0iqVLHo/+TderOZ493VGppz7fWAnFsccx1wtBH1nZaNS2iiPLNqyzlVrh0tyyzcIiC0UWqISfmKN29e4Zu/pRyhcgr1CKx+Rd+r9xHD14TL9RcKi9cRy1f8nDYj9J5ZyX+4RPeEx/SKVJ0y6zY7+qi5RxwMbHLSmYAgcoDs3pJgXmag0s/xyHkl2pzvq+61LJbRkqduuk/TZUUlvywMwOcO6A2ePKJNng8aM1ZN+9XtOZwzOFnQ7uSL5Q3+8f4xAODFiwuoKmBXcXlWUJhxRcC/+PSX8lx/sz/Hbt/CJzekQ7H5HtWxQH0A+kdpQSkKTgHxCQZoXKXaTTcHQ6j0t3GtZExMhbOMdseLrAoQeVCAFnMpqfF5Ix1VXttTk1r0KRvSh5rSDklO0d6bosyHNoNpHLjZsQpWOWdM63BcSlhFRPfhJ1U/ykW1++UJdNvCF/ZL9rqCqiM8R3t20WPNmp6dq6jwH4Aas4sJgCMnF5hIUVWaaAvRAnMxAOSehYqru9PkHYPKZuaOBlei0bu7GoaNsGNLublUziNhGJBlFwHYjaGXMu1c5w46kZ0OFubeZM3NohZtWsWjRTXanJO03zF09k2OYg9fTvgvL18CAGZ6xBgsnswojP3Z4iVqPsgQKokMTpsOj9qdSBPeTzOEmIrrNeVtZXJVqLlMaRyNyCUaEzAOlXzssKkK7WIFTEom4WhzjZxq8022s5EWcS6L8F5j3Cd/Kg3YkJ+nU1JvmvLKYnvHzx6gMRH5PnUVYKzHuOXc2s7Kd52tYHe5brJcUGJhyG166vuYIoi+IJ44YFoUm6EIcUPyywDYAMfjwK5H2VzZ37Ror/JkFw1gRvrc/O2I9S8YffER0ShMpzTmrn+6wHDBoijLmC2+FC8AqW7XZfuv2BugDlBfMAlIR7hbri2eFEekqQ9i3gAW2rVQqdyG/uYWIechnYLpMgko1CwhCdA7ILZqCv/kxy+wn+hZDM4KAtX3FYyJYuP4Zy++j27L1zgYRK/gZjQ+v7y8wR+e0XgPUeFv7og/8OurC8xmY7ZZhDmO+uooUqW+UXkhtfndre6pRjURrexW57wj1yOP7CDkmywBqHwh3ai5746s4PKGMlTZDs8c8l6cJAqza03U+R2fmnhETNQOksONKmsmQwfKiycHpE7LuaOJQp6aHrMoDvNFYhVlLHzo7SGn+tAe2kN7aA/tof2W2kcZqfrTCXFmgElLfse3EaElVxIAeH5yL1HVTTcXJnCcO2gb8u8hl8agDmiWg0RSlfVSnjGNFsrknKd3RqKe2BuYHTOIvaKi8JR76TRCm/MOR7DorIA790YYlMlcW3bCk5bSD7ulvLFLwhMamYkJdZSPAzJDsfw3RNp5J8jp937wBiumCT6pNrio9mh52/y8usFK09++Hi6hubMetTtsXYOeldSt9rjpiVXq3s2oBIbv25yOwvD1rsLAzjPNasB8PmB7n3BRJabqMJFgWM6RlXllqAzL7+9mQCRjboCg+NT3utOItcrwYe2zabtXQMi5vhhyP6q5Q8ORdQiKnGS4H2PhIhON4giG+7pTRznJapuhYBUgBurB5qgkVDjyAZ1WOMohqsEgJuGPFzMs32TWc4qEp1UWEQAANzO4+kPKie+fR2KBi7JOFmcoc3aUYjjOswnjWQGmdcIb8M5kZiqAeDYJq9p3FmrD8GlQ4KGTyzcesepTHeUY+kAQZoro/NJnKH7Q8p7YdhJ3KQD4ZHkvP2+7Bs4Z7DY0lmJnBHlYfrLF0/UWJ1wm9tP1axw4L/KL7WP8/FfPuCM1Jj+D4hIYdT4iMrQfqkh9ws/QdhpJG9MDqBgatzuC0OvblDBGjvhZyD6y+lIprKBcIejAwvjizvOdXKUec6462iwRGdWxEUIovHmBYqybiKnNzGy/8OIXrG1AGIy4MtlOHUlLhgv2Jq49idCc+XSbpAD34QeqH+eiqvcW2ltSF0nQRxtg1iNOVvTiTN7gpqNJvp+s6MYmCbu0cIaNngLfSQAAIABJREFUgWHSj2sChpuZ2L31ZQ7ossNJKsvxGsNkRW92bAwmw2UbHU84PJb92gtEUt1pquVKeryDPrJmS4tQmvB1gqFiQRpZ+uMXrNMyYaFQurEdTZ6hyAGVFlrjSYT/Ad3PquqxZcfic7vHz2YvsOBEUKsmXHkitlTKwzKGfNUvYbUXFRqtAt6w2Tt0JDWdpDQUFJU7gXLYLRPD6srRgsq1tdoVk0UNIChRKFKVkvwz7ipxJ4Im95yyZXUrAKNCsrXTiwm6yZ/1zgApp60jquJvwzcEkepRoery8wzIYyIqUndKkFdUGU6mBS8vqmVJjYoQmb8kGTmecJ7tkZfcYnVriBx34DrbtceGoWFzb8Xgfv46YvnGwTf0veufVth/xv0WFPROwzA5ZlpEDOdl/q5YHE2Ukh27MTkXuIjwTiEus/pXWuh160lu8p7H/6ilr1TI+X5f0+SfHaAyfBpthHsyQrE0pApK9rn6fMCTc2J4PZlv8Zu7czxa0M1cNnv8YvOI+m2oUDeTwPQAMGNJylk94aTucMLlX6/6U/ztDZXNvHl5duQ2hfWU8+fXtaQcqMwlQqV5xOV7a68zQTJY+ptm4lmqP07f0aM6UprKxB+VYVyT4V2AN178HdNzeimlGXRetBO0LnXCJv+br/O4RQRB1pdJPtWLa4/f1GjeGZksfJOlJf06cw1iUNA2QnOg4SdDm1L/4a+qD/DvQ3toD+2hPbSH9ltqH2WkanoFDdrxJTji9GwPrYNQ5X/59lLcW6Z9LW4kMBHVKlsp+CbAJeJSpJ108uPUsxy5uNHg+pail+gVYmdhWc1JqUhC8SAoOM68lDfYG5tJKBqAjhIJQ4GEBUDRaYKQYxVhhmLHHwG3yiy7apP/Vja7y2SkZFasM2Kd9WUbupa0I397WOE/Wb8BAJzZPYwKmBgvvHJr9JEipZ1v8L5nkkswWFU9Kj7B7TCHEcasguq0RE4hKkx8vfPlgIYZm7fXK1Tf1phO+XunE2yTxPYDpn2NiX1TYSJUMgOvInQiSJgIb7NXafSKTAcA+IoiTMXw4eXpDk8XRMDqXIUXt6fCHj9ddHj9hkpj1E0lEYq3EaE+3n2HdbIPibDvaiF1jIsstm+GHN1Oa/KoTM9ColTksqdUmgCdyTDhYsJs1QshLgw2i36oKG4ns5sAX2u8/xm7/Xw+SvRVv9dkbn7JfbIMGQasAkKKLMoohltKHZhBYTwDgk8mAwEqMeY7gxghakiIOfpVPqsEhYruSxVM0pAYrecD2nbCkOBmp9Gy+P1yNmBV0/v6j+8fI0bgrCWs+/+5+hRv35CLzJefXeH9biH9DxulHO58eYALBt/s6fl+e3OK/oa9UFdT1qsdNdBn4mKYZVavchp6r9Fcc1pnzJC9b3HkAEPervx861ySkoQThORfkIajzimByApTyREHCnJNKuBInCRUmVQUwJEpH790C1Iuv//Ka/TrSQRxhsnCf0sX3G6ztm+61qRxrupjYQdtPFzyou4MqoNC6D/8SPWjXFR9ExEbEtNendILVlmPXdegY7stBIXAEy+a4uWoApTK8K+fOZmooleILZlSA0BdwIFuMtnI4Z6KwRyXvejW58luNQFjzjlFWzAqe4VqUpI7cosAu0neVeUdsv1am15oheY9y+mNDDuKa0UUFihi/vckrJ3U/EqRciiSQEv39731DX61Jxm7KRrUyuHKEW71xN7j7w6kEvQX77/AltmVT5Y7nNUdrtlx56vrS0xf088qqCMnFkwKFctBKhVx+80ZXzwwPs593H7dYLjghbMJgFdSFxgWWYLRHrRMTFQfp4D0rKtcF7xadpi8we4NbQTevjrF9ISON0wV+n0tzOBX77Ma0ZFi0EFTnvy8xG6TmhXlrSR3vB6lHEPvKnjORRm2NvsPOX9QOU0uXzHLSTaDIehcApYeXZLDbKKU4fSnGpvvA+MTnpWdRvPWyLn6x0EmaH3QAiXCxpxH5txlqsukBZHPGYi565c8qZpIZVLpGE7JJlL3uoAxkaHOSOM/Xb97PGG2Zseg0WLoKpyc0Lt8sThIvWnnKlwfaFwdtg0uLnb4dksL6bv3a1RzWnzf3K1p85E2yDpKXtwFjV+8v8Q45FKup5/fAADe366guEYzaoJVxTwhQOBe5YjhXOZAYzH7JllL3/BCmrpqyPXV0qcq/y555REZd1TMBk6bb51z0nZHOU6flK9szPW+Y/4c3Q+OoOayXEofDLb/yO+hyiVeUZNhgeTWZyGXpAEIPOepxmMa6gxf7w3lescPf1F9gH8f2kN7aA/toT2031L7KCPVZFu2+nyDmj1I90kwP4k63DTC+kQhSB0j1VFOfYpOda6DiwqoAjTvdqepYPgWxf/RRCISMeSrdURI0OekoRovdY7qUKG+z5CXm0OUmZRX2RQ4ZngmWLZt6jLhJsNoBdkDpBkrTNKCVQoArmGCBZgIodP1E4T69JQIIF/dXeLxgmyx9q7BV/0TnLPsy991n+Lfb4jUEaLCjNWQNkOLKRj8+g1FuPpFKxCsXwSKegpS2HBPYc84aGixo1KoXhZsaA3Mv2UI8zEp0whk7TJ5oixOpw6KpEAFYLnu8Xi1kz+93awkMlheZIpstyemYtgzhbYKmJ1Q5NTva5jXjVzj+GUvXpzjUMGz2o8eNAvI8zX2Fs23mSiTYL9QxSNYLugCaagjpgsHu6Lr96NB0Hx8G4jZzGjG7K1Cf8mM1m8A09PP++cK06kXNafmVgmz21mgvtei5eobSAStr4xEUcN5QHRZ4CRUGbYkQQBkyL3QikVQUL0RqLgUgg/tMQTpm4hwSf3YzEdMDB0u5gOW7SAI9F03E1j+k+UGL67OuD8iautw39FY0iYKwSaESGpX/P7P5qPYQl5drzBfDmhZrCVGhbfvWOR2m9WhlFNQk84kIXsc5c3eaFEhK9XKoirhWB6bqXuK99itIjG7BVJWqO8y/J7QnTReUv9rp+R4viXhDdHg7fJ7nTxes41gFEQEyFGr6RXat6X4Rq4fTt/xMz5Ik+uJY2ExGXsDNeVxVd/RPOQfTMo/zLb4fAMzH8h94o6gIcMMtqR2Y85GEeiOXiMyjIhJi5NNaiXVXJlI5RsAEHVeTAu5MjX30LXHepmZxnsuNK9aehOmJDFYk3g1HZ8mnCnR0J2C2hdwGyPXkdl/qWRCT5BNQcqbhuIWZILO1QaYFhHuxKO+ZvmxIefyplXA6ed3YuhudMAd+0Za5RGg8LqnSefvb5/gfk9/O1l0Uqa06Vq8/PZc4EN/OcEkAwCQSIVsSMai7yYl5Qa25xxU6Z7Drb3SCAZSRI+YVWSgCsWd8wn1bELb0KJUW4/e0QEfzfa4MXOsn9JM6INGx/B13Tr09w3MllmPl15cglSfi/59G6Fuagy3LP4wKHnptKPNiubxZHp79L30PJJYfjbszpsktwhQrReVLW1Clq6MCrOvK5z8ijcnLuCEVZPuv2/QkWAQqd4MSvLzbswF++NpoI1c9nA/8tgdznO5lznoQuw9q3iFiko/5Pm4zKiGZ3WvxIQ/D9lbtc8sXnfKY/6eHtwwarRsDj5vRvigRfR+2Q4i4vDXv/hM8qQ//dG3mFv6LAAcdg1CguKdhm6z8cT+3UKg4Go5YhwtKWwB0IWrTikqQn9ELucZs5NOda9Q7QEVeCHSKru32KxcBM5GuNMkfxkxXaTjaRLKSON9UII1+iZKXl33tIhKf8e8Ec9G7nxMhuYBLtcxQGloIE5ATWb/Bnv8OT0eH19yuQD1fUoRTErOFWaR0lnJCGJgSPzBpPzDbH1XQauCxAIgoEJ70WHG5CHLMoYAcPdulXedgz6W/JtUIcfmoFSEqTK7Z9qWkUf+nrUeuz2THWzAfMmlJTqg72qJLurb/B23iKSgkqLHnZFBSrMdn1bTy5BKGrQrd628I02lnVVefMezQOQKUPmIfd3KLrl7HGWnGs8mbHYzyd1ZG4TUcbuc4XW7FsLX7tBK1HB1uwJesmnzPABzlwkuo5YcdrQRaumEfq9AylIA0L7LtmpJm/SIdJH2Pic0Oafcjj1kPdJplXWSZ8sBSkVsv6ZNQKwDVs+20ufzZsTb62QtElGzgXl/00IfcqRWva6z4ozO9X1h7lHd2sJgvIheFKAHoC7cc8SebsybANvRfYkJ9TxiSopBVYQC0POCjjrQogVg/usK8zd5MpwWGpvv8WL5ZS/lYfP5gFU74M0V3+dti/6TnKsOAdB1iq6zJrFbZPP1aCJCk2sXy3yc8kyQSZskVRKOyOw6mWjD57IoP8+5YthAuegTlktsHOYtDfD7/QxuMpjN8oyc9HeVjfjkKeU/N0OL19sVbq64dMsp4T/Y5Qh30yImLk2diW1usIgHQzXeYKJPEWXKouEB6NwHesyLRr0FECEb5HIDm/oLoEWJpAn5cxcOKpWJpVI53tRonxfHaPLiiMgLb0LQFI7yrb7JcwiKDU3pRAMwKpf0ygeF6v54LhKp0yqP95hKaEoyFX/O9DrX6W7yhiP1Y2ioZvdDbw851Yf20B7aQ3toD+231D7KSHUaLLS2aM47PDujvGBjHLZjIxDSbt9i4nyZqj2J5YMYdWZQeWdmIyLnRo0NqGsnakvDvsqqMgZZmYevIeXZjA6yszY2AF/P0b7PkG8W4VYwHaD2Cf/J+Sc9ZajGDBShpYj6iA7PlH3ZSerCzNgpydeEziKcOTjWGRWWMQBsLPwKmJ3myCAxL7uhxrZrMSRj702Nmg3P270SgX4XNNS2FmWnOPeIia28tdA2CFQ8dRVmry3fW97V+wZHwgd0IP5/oHyzSv64BVQWbZAIxU0G06bOYhCNx/aacO6tnkOZiNWaYPqmcri+IcqsvbUUKSTofJ7VMVRARi8GjenUS3mQGZTAp3rgKDTpq65zDktPWYM1ReCpSN+tgijYKBWBq0ZKUqLREkFM64hwDYxc0tOf5yiw+XWLiXNi+6pF3ymJmIdHPjM2PfVb4Ag9NDqLVTQhb8t1RESOxOCViFDoSJyEFOHSdfK9NIGipjRWCxQIHv8ve2/Sa9uWpQd9s1jVrk59q1dFxIvIjAxnOtMgY1sWMkqEQEIkQkggJNyjQQNZMqKFEBKijf8KTQS0XCBwAuGMsCLjZcSLF6+69al2tfYqZkFjjDnm2kkhGtHwjXum9PTOuWfvtdeea605x/jGN74v02VNRLHoxDRhXg84sLGCcxraBFgWEth1FcIt++qWAS+vKQMPo6ZnJ7VIPVuD0VjcXi8pG073oImZkQ9QC1kSfx+y0AJiFkiIhiDNY5UjfpkCtM/fE8ioRLkGxomubpx+FjOK88RNsmQ/mVOFzKTl8s40i81CGcivT4fkn7VTR9dI+cw8Nl2+FAAhP0lQfzpiSXV8yd6dPvJ1FeToQPB3gsCTSXkI/7dDvnPjvdxUf/DRa9g5SeSdlISxfbM5OYIqvdcol4mYAIB7rEZdILbmaIE2vKn63qAPCrhm8f0AId+EKsAsclvFybJFwbJtN/cLEXQfns8xu8l1E0TIw5JgtAR/Rg0xslYeRw+fCnnj91PCh6LXykI+j1mFyNAmAFDbiZvnRTOUGZJyq4Bm2aPdcQ1r2WGzJWzSdwZ6YzPUfeJk8RhMtgZLMJNhRSivgPKW21U+GAk65/aS4m0hm824youACkB/4WVRKzZa4CqMXCOaoONJkD5WQVqpQtDwc52v2V7nRVID4Vk2sb/fNoibDOdP7c3ihJiDqMQQ3S8C2eyleTRAMeQ5CEW2zXPNZOFyEcMpv67nPs7U+tArBBZ3R2toPX2SbEcA9Q2XFTqF7hI4PPEyPwmG1h6yOfoaiMUkuPIKka+fmTvYwmFoucdUBVKrAuh+G5Oko0GoA1SyH2yLDHmXVENLrUNHFl8qAmXA4pR7HrtCgtn62xL9FeRaTF2YDkMBy8/MfNXCB431liZy3Be5RDJzCCzRqTqDOCGUfXp2LbZ76KnfRWRA3aRnO7WoJ4u0OKnVp39AfsbSNazuJnaDigIOFh7DuIxCQHRNnnuA2lrGFCjGbMpBjaSAShBtyOcWM4oLl4zoJ4RGabkzoPmfBNmynthINnxpvQlRaululgMEX0UyMZBabJTNUO/N0fqiRiXvC+a4d9lMSEm+xHHw8A6PB/j3YTyMh/EwHsbD+A2N9zJT9VFDRY2vX53ja2YTYumAdYG4pLBteb7HMGSspGD462R5wM3NApGzKNgootkiXD2BVgMfTxVZQKIoPNquQsdsQqUAsOhE85oo+bnROkfFkY/tyhyBSpSpIEQlX7JNU4o440QIImlyJn1PG4WcZPYamjMPsfTiCLS81zmaVkD3ak5QD4DNYSEs2LJVGM+CfG9iSvJ8HGUoBDdrlkqyb4yco31bQKGQl/o6wi3os4q1ht3ljKV+bYTa72bH1nVTvdK+ibDPKDttTMDIeFu3KwGnYThrM0OOst2pg9VRYGg/GpRJEadTR8SoMFEtN92kTeGgjzSJoSOiTngbM5f5VzPk+Q4F5Pr5GsCQYcBoIIIDalSIlwMiM37V3mBkr9hwSxl+Qg1sq8TH1JkoIfW4DNCjyo3/SiFwS43fW3hl871UhknmNIEyZ4E8hrcJps/m4KGO0KcDVnPKECOAnoUU5k2Pq/keDfvqfnV/hi2r8/RXHoqJg0Xp2VKN0YvSYZdaY5YtVnWPlo/pvYaeZXg8aCbANaR7ndjR//zVUxxeEpxvDpoIcimZ1pOSieOMMxG+Fpmglf4OELFKTWB7FTMJEBoYljELeIzZ+CBMLBbLPYm7CEtWWcm6lVNHVoHRTJJ3lRM93WuEKitfTYfu1JEgQzgJ8FxOsmsDM+RzDgai5RzrgJBUmRgmnrKepYWmiEcEShVxpB8uJaoDjkVoKnqxn2LM7+h4LzfVL375BLqpUd6arCC0LwEN+DOWY/PZODyxXAFgvW2gbkvYdHN0ViBH19AiHlI9qg5Ynu/leKkndrtrEF9XAN+wcAoVt66EAoAG+vMsaJ4Wu1jQzZsW6OLewKXNEhkWSixENWHJaumlpU1q5OOjDLJAB4vsiKOjPGQAwcSeVY10S1CfPMU2ihRZnLRK0HEmG3rIMFQKBoRBWGV2sd0rICoMaQ5OByQtuMEa6BTspM8X9mLGewPLtCU5PHXRY1bTlxm9wT6xZQOgGg+fCL73eQNJi9mGnUvM81rmUQVydkmtEGZjZREeHw8wzBwNnUU8GIFaq2sti66v6Z6RFokACYzsLvfYAkB/EWA/onvp8bLFq+cEW9ptAX9dIrJaUfm4xcD1bESqcRmuh7t5hvB1VCIlV782x5915aTmrFQUjgAAqjO6yaY6rS2uC7nnfBPI/xfA/KLFByfZEWbd16i5X9kHhW/uTqUGHzYFVGKgnw4IDMuPAOzjbP7Z9wVOlhwkFQ6HsRA5xqJ04mqkdRT5y74tEJ0W1r9zdQ4WArNdr/iGXxeixqUiPVPpXlVNlLkDIIGK3WrYVkkv6hQyhWOmPdfuQ6nEj1QPWVwfkeF+roNrl69TepYEZp4qKk1Yu2oEoBV8YohHgmUBErU3Myetg9FpuYYUfOcNMtYhS636iUlBb8gbdjIHUs9NG2q6RYqYDSo8MuSruDUnxZcjf5cJI/hdHe/lpmo6DQ19lA24laeLnxK0oGF4E7TWY2Ci0thZorinh/ssSLuNGomEYrg1xD3z6JiAdHG6k8VpqyjKSwSeaSajQASAVGvUQ94QfUWiCEnmLlTZ/koNmeQRSmoYt+x0oYdct/MN09/TYrLL4a7yucaZSAVDIkINKn/Pqx7oDcytzX/jZ8HNIm1UE43Z3C+Qr0Eogaii1K3GEy+yatQHl88x7AtxrLEbkzOGZUol+PAK2Zi9iBSxpyxcRxRJ6KOt8vevPYwNcCoFNRHhNNlTBbjOovqGrmGxy8GAOUT4RklAFeoogh3oDXzKEkYNs8+1zKl83zinbFFIO6desj+7zZv0sIpovrfB7129BgB8fns5eZ1CXAHgxa8osmOIW3kUqx5hxZna13XuJzTZZDqR16ZtNJFri4SOTAg2IUsFkpF8zkiiiQjJUtBElEzEK63D17dn6Nu02U83aQVlI8BWYSgDYgoGv62hk8H6R3tYG9AmRGfRI3C/6e1uhr4rJIO2hcdyRqv3tq0kyIheQ5c+Z1xjrguGIiLMAtQ9vba6NTlIjfQMpQAqlMfkoSRgYPcUCA1n+W9yDU8jIU4JlWiVbKS2xXH2qYH6Jm+yqbaeSHGpLqkC0H7AZMfKiw5z7A104+R+VwooLunnvivgnZbebzVomF16VsE97pOAYdKTL8F3EQmlknswt5Ypz89vWisma6ptiWgJ5GdA6q0F//xb0Kf6UFN9GA/jYTyMh/EwfkPjvcxU/RmZlCuVGaHoDTWWpxrcvoSyGQoWCJhF2scTDs3qIGLgADAuncjrxUEjFPS3bihwf8PtGG8KaBvFOcYcckTrOQrWOWmQmmqoKdJNDF2EiYdiAcnS7EHBthOG73KieKLpv+TDqmJu0g9lRFimQp6CbrV4xYYywtyxo4SxMC4noH4WjyJTc9C5VaaICFKoyvWnWFA2nmj5KqiJSg1FzAK3HTSgcubUX6awGNx2Qb/qISslpYbz5ARkCzJMAECCHIndugi4ON1hXVAa4pcK7NGMYV/Cvi1QUtcVTAeYIRU2gfYsClRmDlqyc3fmYLmm53oDbIy0DqgAjJz9+0UAFg7zEwrfh8EifDmX75Yyk/HxiE9WWzjOzDa7BsVN6r0hSHDGWWHfW/g7+p7lvYZ+XqBidUXt8jH7iyiG39BAnLucQfYTiFeDIOmUqeoIfUYpXAwEbwNAHBnJSFnOqMSbdLiriXBQJHw8Py+6JbjQJahSRyTKvDt3qJipO/YW/drCzOmzlYIw9Z3TCK2VOmpROtxvqC5rbFabsrMB3mtEVity1zX8pE6NmCFeV0dEvhTVHSExUifvFTzf78XaZOhWU/0wQeDFHiILGUq6n1NmrEd1VP+XZ6nGkfB+MsCgL02ZYMds7jh3IquqTUAME2F/APMmC9l0qZRlAmJUgqSYnRa0yJcg04OEmnVGeBMwETExu02kVp+ELkxKB2IIkEQe+qwIZobJ95pIo6bfozoCMd7Z8V5uqovTA8wsoOsKjKwEhMof9aUpE8WJJgLwr+gh1QHwS0+kDNDDrXnhNiZg2JYwL2hRC2VE5BtvfVOKMhsUPbwCc/lMqY8XA/SbCirR12vALfMmotf2SGczThwginU2S6bCPx+jzHW7UNDDnW5uN4sorg5y/t3LvKhHG4WeDx1zz60GMOR+RTXRFkXkOuEsrxiKF96pEo3mXj8JHsZ8jFDGo946PUI+C0WE3ep8DJuhODfPPcNqTzBxcgJyvshPrInQrJV7uqIdJykSlddGLNGsotaTKUFIWnsW1LIkJKCdlvN1XsHt6L4yW95QU/BTAuMlfen5RYvSOuxarvG9nqHe8vEOuf+2WvZwQeOf/epjOkhvEJ5yfTiStGXHEKe/L0X/WEVa4FOgMS6jlAFCFXIgFBXUzuYWjEmQGBt/fO0VYLke6pwROy/VRBgTmEwEYFfk4y8c6b5O5j8FaOagMFx6gK8bgsL8MdWOm3LEhlXHYlDQjRNFq6YchZgUgoZdjPD82V1bSh0VgJAMh77AcnGQ4ORwoeBvuJ/VkKViKqf4eYBNEqABRxq4xVajuk3BJnJbC+grWu5ucnUm6aSWl2RrpweCfQHA9BG+4s9tWON4QhaaOsWEhmQpAWC+6mA42O/6IkO1XiHA4J5tFnXlUbCj1LCuqIeYbSIVJsSkhiBdfZhA/2lMAiEo6uGV+Yk4KoMgZEjcHJTMR1SA4ihGaYXhJPdlJzcef6wA+06OB/j3YTyMh/EwHsbD+A2N9zJTNSrCqEjqLIktGrQw4gDAlB4+CQJM3TeqQBkVv7Y66wRC7m8amL1G/PggnyOqTK3JBI/IqkccybuJFq2+K2H6rA3qFl6g2sQETExbfZ67p+PrGtV1ovEdR9ZhDnkPNBBVQHFG771YHnBzTd6neF5CzXNUrNwk0p6QF1RrKCwPCa6Nk2yO9VrTywd9JOydoKlQR+hDbg/QgxL3k1AFQEcUnM2oMUPgymcd41AQzJSExNXZkMlaQRHxKRkktFrUXMrzAy5WlA3tugrdzy6w4Eb8aXSeWJ8py3clEIpM0glFlGjddEBk3NhM/FrTnCf4d/8dj9k5pSizakBpPNZrQkHKtUK5Tt8NGL9LIf6qGvHlrx/JfDeXrUCffV+QgAUjIsXaYGQIPzmOuJMptMrnZKIwtDGoI+IJAMRiglBE5EzFRFjOAqvSiRIYwNAiZ90oIzQ7/9jCI5YOjl1fcF/KvA2nAWrmpEWtOOlRWrrA+64U8pG2EUWZyTe9M3DJYcZp0tzmrNkPRtyVosupz+XZFjEqbO5YqGRTwCT/zkBIQ2Kq607nthAPmHGCiI8T7d4Aua+iIaiz2DC8XyshLYU6QA9ansvyTqG653JNF7H9hGHnJpJuN98vromIhrPYE2Jla/6eISiMI60vReERGRofNyVB+CI4EjEkOLwl0/l0ztLyAyoJhVkQxCLqfI8or/I9ERWKG/uX2o8SYkP/L5J/6x4ieEHm6PTv3WWEm0WZfzeL8HOPcJjUvd7R8V5uqv1oYEYL7zNkp1SErUeMzC6Mb0uYiaJKugnNZY/FvJNFbb2ZiRqPcUTJT1uzu6kxZyhuXES4iTFw/yiQywT4oeU+T80uFLJx9nry4AOxiage06J8vtyL2Ht1qyftLwQbJ0jGz0OWSKw8qmZEv6Fzbn86x4qdzkg0n183KigogVoBLRssbSCQ3tFoorSuxIrgKWEXTuq+UwFt9LShijlzExGZdYsImOsyqyHpzIA2h9z/6GeBIGCWnTsqx5gI87RF5PabMAOWJ1lu8HpN0Fj4co76Ltdio83omXhWAAAgAElEQVQtQNNeRYACDJuUqTQpFKVgYyox6OfZacXsNMp7JczRWAbM2BFnVox4cXOC8nOGntd5sd79zoiaHYvurxcwawvPtcwQFFSS6nre0ObP7Su+yQGNHohVLv2KE5hejyobJPAGK4L3ZTgyllYHk495MmRlKvACDlA0UtIGKb/zKApiwetrdvi50+gv+d6ZO2Aw4gQ0OoP7Ozar11E21XiwiAuI3WDnrDDrtYl4crFGm2QLg5Yec2sDnp7Qe653c9y/XAmTXPl8b+rArTEc6AYAiqH4qTQmQDD6tM4pJgg9McTTJtg+jXDnXAPeW8y+1QKFlusM8W4/yc/W7CUziFnzPxYQBjvhxzG3wesIw2bs82rAgaHg8VBQfV9KK/bo4fDN5Dn3mc0dlo5sJ5Oxemty4GWjXFOzoTacaTtMknuMGig32QxiCo/7YsJ8PyG1tph4FEWkz5i26byj473cVK0NMNajsF5soPa3JfTGomxTbWNS16gDZleU2ZzOD1i3jdSw4n2Z62VLD7UpUHDPqR6ISg8A4+UoN5etHeyk/8+1FvaGj6cBdzlCb7jm1Km8WJ94qHnuwbvdzuFZvMI1EVHlTVqPOcOKM5+dLg4W+KrCYpciRGD9Pd7MbBBZRdUXsHstD4ebRenN1QMvLKn520QI40NHctNIz4bP/XhSdEKa1wifzqsOANdy7NYg6ihCFVFlCbhpO5DdaerbW6cMyCJw/fn82RpN4WShjVFhya4mWkVcd5SdV60ij1DOhGnBmTzYKmfputOpmwmmY2JHImRNFgRdO/FZra4VoIHDxzTHZ483olH79YsLmFdl1vi1wP736RdTBHTcT2mvCyKK3XN9eG3zRqnjkZxkVEEIU+qgKDNYJJYUxNosOC1EE6qJq7yg2ZjJdp7q1iJpuCuk66EoHSq2XwtBIQYtnqNpngFgcz1H9aIgwg8oS5EauQYuH62lPjr0hXAbooJYJtrTAY/PN/LMrPeNEJCqgmrOKdCNUcl9XFqHz796TB+1ybq/AGVits+fFe2kLaoOAPvSuoY3g3TrRgXhLZZRsspiR964IwM/oYq0MYHdlUZIUHP/w7xRN6+BastBO7e/Be5TjZOWK0S6fjNuFzqft7J+AcA+ZtQATkkdPNooPbd+4ZnwlJ9J2bSDArbFUU97TIGXitLyVmz0UZAx9Sc2rOc79V4emaYBFYUop7yC2SuZU+UBNWoJzt/l8e5/g4fxMB7Gw3gYD+NfkPFeZqqV9TDWY9+VONyxsXRroEeF4SlnbRO3htVZi1nF0m9RobQOS47QF49v8GpDoel+3SD67N4QVpNsbtQZWvIU1Uu9aCLlNzwZofZGqP32B9usaMOQ1mHP8oYaQGLdVZnGXuwUgpk42Gwy/BOrgP7Ko2eDar0YUXLEP3YWis20TasAFcWYnOT0OOteUqN8yhjNJisG+ZDhWOCY8RvzIah9pw7yPrMxkpG7pSfhhkw4nagy5fpkKBgW5tf5JmD1mLBsoyO2XYWBWwlO5gcEnsdvvrxE/bzI36UKomREB+bzZRg0uQLpIdfBtSL4VK5vESS7C61F821uedl9z8EwLHroS9y9ofvF3hSwhwxB7j91UtfU39QoODt3NUG4CbKLydkFINh21IhlUtHQsBvOsFaB1KjWfL+MKp9vwFGNH4paZOgYCjFNSKCLJsbVJkKbrLYUmK6pTUDTdDLHw2jRXdOzVb+0cIuI3Sc8P3XA44/J43RZ9Xi9XVApBkAz63FQdL5+X4h70GLe4TBarDd0Q4ZRo1nSM5gQp8sZlUXe7ufYbwhn7K6bI7EKPeE2qDhh3Vd0P6faYESU1i2z10elAJKoTFkgRCTC1yT/OK4yrF5dJ4EXENzJt8XsuRLDcu1iVhhzEfaQpQ6Hs5hbXCoPpaMwfrWK+GB5BwDYjRX2jMqovaG2l/Q9R4VwMpFLBRBTRjhqyUCFG5ES4ypn7nZjRHRC1JuE0a7kTXpUR5C4r47b+dJ7kstNnJYgioAY89rxro73clO9u59BDzXCwWYj39MBl4/WWJS0Yu+GEj0vyOvNDB3DU58+usb3Lm/wP3z2IwDAq9ZmpaED3cwCFzqV609eIfCqFbuSbmCdb6gE95SvSa81fsg6qVGhu+NCRKCNruAWnqYZ0FluK7iZ55t+ANRE87X63gbLhp7SZdUjRIXO0Xe73zdob7hdaG+OzJKDxqR/LmTo1tIiL+bDB5XrxSUrTE10QuWhUjjSC1WDyvWWWcjwWhGAXotkohrVkTxgIuLgqke8LXM/69lwZHTRD1ZINYehwOYVbWbl20zm8XOC1MEwum01xvPU6wTgYKRX189yG8poNNWPWclotuxxYCeX6kUhsHn7YUBz2cIywabd1wLjFjsiXXVXvFrXHuaWN/szh7jMNdTQZyUpOAXF7RP6oFiFh8lsXdZoVoOCfl7nPuTkEgOQFWFqe3IqK2CBNhbFRKKouZaWoiFvMBo+R5WDtUJHtF2JoU3EPEvzCmD21+/xeLHDbmA1JDvi7Z42x1f9klpz+Dh9V1D5AMDqaodPzmjTeL5ZkbxnqjIUAd+9oI35zX6BbrS4Bd3H+0OFKIGQPuqj1H2uJftmEpwEcDtJKqHYrO+raFOcEuRkTm0OGqUVRmREtaiaqb9UaiaklgNRlQlDrqE6+LialBXSRt8ZFLMRCw7wr5odHD+gv747R5/kO1cjcJ+h4HDihGUVPaktpboyioCC6779usbUJN4ctMDGpps8qw235U32P+E8dEB/FoWDgqhkE9ZjbikaTgLC3Msxde0QdsVRz/+7Ot79b/AwHsbDeBgP42H8CzLey0zVmABtAurzFpahrEXd4zAUuN9RtKtUxIKzu6vzrRBeXm5W+PlnH6JkWGc8C1AzFn+YDxh3JQLDRtGrDOfVDmDB79hpqEFDXTBxZl2LMo87CVCNl8iyWzc59CkDbO0EYtt9cYLynokcE0Ht9lmAvxihGeqpy1He86tvr0RPFSAoekpokGgUzOpLDjmNFyp/dBpx1EKPH2pkuLwMQGeyjm+VM9BYZJKRGinyFfGKSTuHnhJx+LyS4bJfjliwabgCsOkMfEmfVddOYMTNUGPsLRSnCPubGYpbJkK1Waw+1IrIWykDvSL4HWD2rCaFJACw81GIOLvbGbUt8Gl2XQGwW5HylKECgH1K7S/bW8rMyheFiEv4Cug+zE4s2BbZh7IKcFvOnu8tGbhP3EoEotb0eeXt5JzFA5MyhgQbQ+esR3EJAgChK0U4EnhQ20xiCpOMTs+ciCkoRcpjAKsmFbkkcPHJHf74g18AAA6+wOfbK6wqSt+v2zkqFpCwJiAWDucNQbcfL+7wVxbPAQA/232An1w/AwC0HbPyOaOuFj2+uqN+lVXTYTYfcdeyn2pnc1lET859b4hlXmQERJCqRNRK9+rkGYkmwu5yS5ZvMhol0DIYOrYBBQtD2IOCm/Hhi3hkkjBt1RpOoqALxUYhFpmcRAQ4Vu1qPE4XrRDAvtqcCZrWD1ZajKLTRMhKghout9dARWgTUfKa1R1K9OyLLOWcNgs3TH2YhZwUFUKZSUflOovJ9BckGJMyXO0ybK6dwsDtb2FGXQILZuTvnq9ojrtpHebdHO/lpjq2BXQkM+TlJbF6X9+uEINC5PpTLIOwbIlNyItu0EARMFzQseqrAwpeINq2QrXqRfWl77PIdwhanEvOH6+x3jXwX9NC6y5HzPjmqssR9/dzhKRMU0SoxLp7W0CFMj9wmtxLANCCeEI763JF9cOe+wLX25nILM5XHUJQaNfc49FrWXSiiSg2U1WmKH2lRTPKYjqrRtzezRF40YeGqLxgU9CGmjbqAnIMIC9Cuqd2FJMYqIBsnND0nrDKDhlJmrFqRkFBd9saGLSoI3WbKrcABAWzGjKD9s5K3crNcz+i8gp2IngfTRbqD03E8oMNHG/UVeFyfTsomFbDa27ZcQqWYejuqcfFJwRbruoOd22DA2/U9Y0SNmT3yYD56UFq5OZkxNkJ3Y/3mxnUi9SrwS4mHMSESfBT3mmU6wxBDieZtS5liCRrybVTAAgzD8t9pH7UFGgli7tuAn16ZmgmCPKmwsi2LKEOUoOkqprGkltj/sbjr7BkDPyr9hzfX77F7UBf/Lqdw3Et1hqP2josS3qtixr/2/33AAB/cf0I3ZDs3BS816gXFIg+Wu2wKOnn+67BXdtI/ZxOjv9fBGG+h0WE6s0R81VNgrmgIkLSqDQxtx8NCuMq5H7UcmJk0Wcz88Cm9X4ivZk2m2JLUH9qrRqXkUoqoGAzBUXR0gacWuBgogSzAHCznlN/PWhd0pNnK3DAoZN0YWJ6qwikW0lHBK9weLFIX1o2Xz0jCDb1ENsW+X4xk+dTAc3rDOu6GnDpcJbkRwXydQo6sdtLmjsAUDMHWzrsvqHeofqNofn5LXCpeYB/H8bDeBgP42E8jN/QeC8zVXNfQHcFQhXQfk7iCdESVJngVOU1hlf5b+ITOYtQlwPKFYVfH5/fyXGHeYvOWdwyQ3HYlxL9n19usZwwiNvC4wd//UsAxOI7OIrI110NbaL0EDavck9YdxXw8e+/wFVDDNc/f/MEhrOocTRYLSjbbcoR67YRBZu4t/DJlNgb+NZK/5x2yD2laiJ8YEgkv76kYy6aHusdEaauNzXQGQnJzGoQckmwDDemcM3naB2OiEBA4r5k8kMwUYg0OpGUVM5csWIyRVug6+k8VBmgl6OIuiMCxZLm+HTVwuiAG0Uh9OgVAlubhb2VzNe0pJyT5tg1EfoZfecPL9YwOuB6x9fTWUEsoCP8ystxlFMYL+j4V8/usazYeqyvcPdqhWf/iN62/QgYf0RQZ2U9Dm2JmqG40/lBhM/DmxpxnqF31WmYLglqQMhZvok4VJko4ksIiSYpAmWCSRC4XdVevst81aEwHvf3LLpwMBNEQaF4azORZh6yOpeNiMxgVbXHfNnjR2xP91F9i8/2T2jevMGbfolvtqd8LTT6pARkNN7crHC7IJz0fN7iOQuaKAVBeowNKMsBlwvK5CvjBP7tewutI8bJ/S4sXhUyUhJZFSjpNW+zqEUsWMAksV13Wli9AJV58nGQM2GVjR/sqDD67E0biowaDCu+v9Mzs9PioazcsWF5qGJGXIpJ+SQAMMBhy3rIbtLnWQRUDRsdRNZl5u9ikhISgPG+htnrrDzWZAJi2BUor42oevlqwo6eeJ/OXyjoMcLN6B/GVe5T1cn+kol6yk84bjWyz/LBAC8qzCcWd+OSyk7v+ngvN9VwOQCNhq1HqSv5XUH1IhbzHobMSHS9FZF8BKrBDQx9viydiArsuooYppPN4PEH9wCA3zt/jZOCFX20w1ftOf7imvpa6nLEzT3jJ1/OUG6U1FvaH/b4Oz+k2tQPZm+w9g1+fPsRAMDogP1dkuoB3IxhYh1QWod9oIdPDxqKNzPcFdQsoSc1obRW2JgN1quAYj5g4IXq+rqZMCX5YebXah0QebWIM4c4mKzpZicwWq8lONGeWMEueaJqSB1W9wpqNPDTTZXnO9oAzaxSrSPcrpCGcX1GalcAsDtUxCRlSOzygzX2HWFgh22GuPSgCNZK1/rRgD94RhvDwvb47OaRsCqbarLKgtozZFz1WC7os0vj8e0NbyBvGpz9uRbozM0An9xsegP0BgNfi7WC+PaGlRPYb7nosLmei/oMQm7t8Quqhdq7iXF7ukwlSUZOW5zSKOtRPEcBgptjm9zSI+I8tb8ohCrLLsYqZEWiyWKtbcSPHr3C3zz9AgDwcjhFyXi7VhGDN7hq6NlanRywsvTZ/+urT1A3g4gYfPXyQsTwqyZvoidlh+vDXMTwt/0MIfEVdMQ4WCndaJfbj1SdXWrcYMhrdFLvm6pgAcjeohMaua/5OVG5pSTBv9MNJBSgdpIqb77yc1CwO4OC5TBtl+X7/CxOavzMME/Bz0QeNAaF8a6W5wQacm3rkwEj36d+1IhOy76sdkaC2ZJFYdJmCQVpa2teUpCRBGt8nZXkik32f40GcDaXMaLJ7kdmON5IgWwMMS6i/Hv5ihjyEszO+H4Nkze+o+O93FQXJ+RS82S5xTd3tPhdfLhDbR0KVrs5q1qse9qwfvH509w6sFOobq3ceH1fSI1vv6+BCNQX9JRdLPdS9zn4At/u6bO+ensGN1hxsDnS1zt3wHd7fHBB4eJ3lrd4y24TP799fHSOh0MJ06QNJuC0oc/dDyXubhZQbSKbIEejFWl7pgfTjJAFZFxEIXiYxmHcVFnhpJ6oMgV1tOiIpitAi9Z0s5ks8nrIrTdh5qimNTGMFklAxa0uqS8zKMkSZpet1LoPbYX6rBNCWWUd7pms0ncFbOFxdUnzqFXEzS27dhzyJiH6wec0p3/t06/huED52c0jDM5ixRu18xq7fdJZo5pwqn0ZnclIu91KFt3znymU24ibv8qkrqsxLziDhnLZ0cYWXoy3MWgxnd7sFzA7I3VfXwGOs+L6pMc4WFGkU72WbFTPHfUjpjluvJC8rnizAoBvrk/hB4PihIlzJm+WVelEASqNhjkEr29X8Hx//OjDl3hSb/BldwkAuO4X2I1ce4XCwvb4zuwGALAwPf7h2x8AAJbVgF1bY3zLwaFXUI/oHB8td1gWdE6lcZgXgyA6BROcAGB9mCHeVCKD6Wchk3TiRErRROg2W7WFYlInTPdizAhACgDTkF7SMdcTlc8bQygI3RHnolZB3/LhmVCWpESpDjnZfCcIEcqQr1np5X4f+4L+xhyCqnISWBy2Ez6B4v9EdjLCMafCtMf3LXREcc3oSElIjZ/Ijyb5VLtX4qoTyuP+03EZssSlV/Jd6RiQOSXrxISA0MabZAvTcziVBX1Xx0NN9WE8jIfxMB7Gw/gNjfcyU03jq5szYeru+xK7rkJTUhR4f2hw2lDE/Ls/eIEtN67f7WbougLxllVf3jYY6uQmrYEyEAsVwPN1dexDyHCZriljGJs8/Uko/KTs4KKWFpj/5dffoxYBAPViwPL8Hjd7qj81zSD0+vN5i5EZlbf3c4KpmQXojRaRAqUj9Ks6e4YegGGZoaAEC/tdAUSCWwGQ4lSq4506eu2Q6qNKskqAI3IWolCTLDMWuTZt7yyJXkz0le0mMSBJq1SYtjrCnuS6Y0rsl3UPo6IoyfTOYn9PGU/RjHh2vkbJ1MyX2yWmrjqeo2w9KPgmYMFKTD97+RTDjhWIioBHlxuUnKndDQ3cmOpgivSKeX6Kz2ZIBja+ipi9pF9Oft3j2z8u4T5h9R8TBN7Uix5dWwpE3d00sOyJa1WG3gDKjpLYvp/nTKZ/PYPZaajU+lTmFpJwsNA7g7Cg81+sDni8pO95VrV4vmPOQNCYcV0VAPYT55nRGSzrHs8WlPHf9TNREHOtxdkjYvv+8eVnGKPBP76hDPTmMMOioGs2L3oMweCfvPkUANVY0/29bWsMd7X4kw6nQdqivn5zLplYMxuwbDq0PbPzARHNj14hzjw8GxbHIgDJW9RN2lgc1fczOzfDlPaexDV8OckYY878dKdEXOUo45wBwxlngb1C8zK3WYUiZ2ymB7ON0wXNh4fOWR8aT9eWTywMBiEZMSvSDU9tXTEqdLuEIaucqXoFmCwSM25KQZz8LMBedKTsApB2OL+tv/I0dxNGdIK2p1Bt1Jzli/vMpK3I05oiqmflZA58VopKTj+53YYY7r8N7N/3clPVKkKzoH3LVlVh1FA6ilB+XY/SSrGse/ydx58DAPxjjR/ffoQvIsFcuK5g2iSGHxEGnS2SzEQ4/HTEbJFrWCFonC4IT9m0tSgcvdlcomtLocejN+L8cbHc49vbUzlGjJC62LwY8OWOiBtaR6jGiYqP1lE2g3FdoWoVCnamIflBnpdOiatLLLkXbwLJxscs9p6o+trIeSRXmlRHSiSZOOn9Q0CuZbKSkBhZO5UdPVqDYqPhGUarP92INNvb+wUWs3weSkVpz0jwO0BtU6/XS5mDwuQ+W1wEeFbc8YuAohmzucFopFb4ydMbVMbh85dU+/bbIl/PpYPaWZQvGI7cA/05b9QTKPvtH1YYHo0oGMIrKyd1QqUing+nQsaBy2o/alRZKYq5JKmXWY0KJgU0NsKdOeptTpeS689q4NoiL9C7+5lsRDf1TIIwpQO1WY0cKDojhtfNxR7PFmuBcq93c7R73t0V8PtXLwEAb4YV/tHr72PL/aSl9TDcl/rV+gyl9XKdBmfkuXM99TT3z9g0/monHIW7tsGeW78ObUmuNKntp7USPNjKw3UGcT6BqZPCT6tzjbmKiCGTupTLkC5AG4WIy8tkUrlAhbzxaa3Ajyv6Cw/D1oyzl1QWSQ4ziFnSbyqvCfDPLLzvVj7zFXpDz1x6LkwmKtlyhDFRriHZ3dH5+s7kzbAKMLXHyO1kutPyvc6erXHoS4wiR6oxnrEc43KELRyGNxS0262WQCJUkLVgXEa4eZT5sK0Wyzd74A2T0fxg8jwCE1g4LRfJ+epA8LLKS+Q7Ox7g34fxMB7Gw3gYD+M3NN7LTLUbLIwtKHtLXAGnSVeUw4xDUAC3Ony4uEfFIe2dmyFElX0e59k7NDYetsnwzP7bJWKKfJ1G+82S3+NQn/S4vuPfo8Jb1hYOjjLmBO8Vpx3OV5zRdhWKIrONQ8wh4M+fPxHukLUeMaqssOKVZGJ2TZT/kcnGvmbrMOTGbAAQQ+tJE7pE/KNGnGrRRmSlHmYTJ6/VZIoMMN2eI9FxSaSOdPw4atGzNZ1GKIHyuwQteq9FIOH0dC+ELK0iKuMEAt8vSnz95pzmrSShhulrUxvHMBiAmYvzeYeTpsMb9lctK4fzJWWS82LA8/UJ/IYzP68kG7Klx+qTLW4NIQfDuRLy1/I5UG3oe93+FQXYgHMWdehHi5d3lMrEoHCyPOBuTSfjy4CYzK/VJMMfONtKIbCDwNex9kftFENviQQGwJx5Ur5i1GN+cpAsedNVkvEEb+CiEoKSLTxWpzmbvunm+GBOLPYv3l4gMJRYzEZcM4muUAHzYhCimPMRv+AMP4waZTNKqWUcLXwSN4kK8XSEZRRh31ZiRF4Yj2pOz5LWAVfLPdYHwhzX40zeE9nLNfnyqU7D7nL7kbSJKSbKJGJRGYXRqzz9nkhG0ea2Fu34teInmglOyisppeiBjAfK9QQ2dvS6csuEOG5DaZ8ew/upNQtg6DmtL05PTMkJvUkoAiLEqKGYjVBMfBq2JcJ1Jd6/buWFhLbdNXD3JWkig9nJnCX7TQHsapRJr3jSDhc1Z6tgQ/FZQMGlCnPIxK1ogHHiQAdgwviHCGWMC0LJkjaybZn09VvQU/NebqrDoYBGQQ90gi2DAqp8RcOmAF9vvGxX6DwvQFGjsSNqftgPfSMQ5uOn9whR4eaXJLekPeBToc1NzLRtRHdbC8RTzEbxuYxeoSgdHl3dy7kceDMwKmJZ96TqBKDtS+y4phK9knOqyxFtV8JPFI9kRGL4iTpMMXFaUflhVmVAPBioA8PBS5dl7RLMlAzee3MkPg5Fzh4A161CbiOwDDubQSFqA3eS5ydBTdGQNODMJr9Mhzvm73dDgQ2/ZVV3cEHDM5Z0f6hR1QwhK4KGd1yD2+wa6aU1NqDk9pjdtsbmbobHj6lmeFofMLM0j7fdHPevlrK5LT7c4ONTui5/dPot/uz+Q9wq2lRjHcT7s7kNuP+U22Y+6PDR4zu5Zj6qieKWxfU3p8KAnnp9Iippg1KOpQfTfDtFYuQAqVmZKG06xnqZAzcaxNZK+eDRcof9wG1FfSni9LN5h1k5SlvL4Iy0H33v8gYfzO7x0xuSC9Q64g+/9y0A4Powxxdv6V5/Xp5Aqyj10P26Fj6BmTus5h02zJzubxv5mnY1MOuZJnnsLXaHSv7++IQCq09X13h5WOHNhjfx2mXFoKAATAy1VV7IQ4UjSP3IH7fPovChBN3Xk/7TxC8Ilhit0nvpgMDQrTkoVNyqbgTezZu4yFieavTnub3EL7KMoCkD5sww397PuAc8M4NTQBxaA9XmVj+cZHWxcVsK677aKIyLCHfC641XAgUrR16zUxlGicbLgGi1+DLXtzkoGJfHtU7d62yUAfKABSAM7DSmjF7VT+rIoPkURrEFlAbCbwH7973cVJWOUCYi7KwsVHo+Uh1zyKIAgU1/X9dL3Bd01yzrHquqw7zmJ+g8P/g+aFxv52g+pt/P561kQN4ZeTj83kI5Db3s5XwSGUkXAR9d3MPwnfh2P8d6y3rEoA2ztrRiLCbN8N/enMo5rbcNtWYU+cFMzigqUJ9a2miVn9TuRgV3yqvM3tJDljLViY6xaNAmws3MwXPbgm4zyYKOn3/1Va6pKMfOF0zCmPaK6hEoFgP+pSe0ePuo8GfjBwCA3aaReVQqYneoZEGOUUkNFVAIQWH7diHn3Kxo4RoGmy3/ioDLq61oz7Zjia9vqTZ9uGmgBo3mGUUCf/f7f4rfraiG+D/d/wFlYhyINZ9XaN7SF339r0BWEm0DrrfzTLoCCXUAQLitSN4v9SROHTomWYJKbUnpGFc9TMouOktkliExvoAx1edsBGqPkrPYL19eZKEMr4S8Vi06jF6Lvu7QWVxd0D38uN7if3/1Me5eUXb9t3//l2i5reXN/UKs34a2hLIhy+Z5JdKVs3mHty8yFwA64vQxHf+EkYQksFFVI+YskvLd1S0aQ+f+y/UV7trmqDc1BQVDW9AGnlq3uuxqFKqJBZonkldyV6KeUj5ddkmKk8Al8QKUp80iZaC+hkjvFbssHBIsXSeRibQK3TnkGOMqEskPQHXSyT1Rlk74G9GpI1RIFUFa7yy3t7gLmpO6GcVez26M3C/DCfWDpsAWQYmwSiwiitMO7jr3t0sg3WmUa43yPp9zd8nPls4ZfyhJ8GLqHCUoVsGfzW1LiMj8jQJiCWcOHMOLpCYAn2us7/J4qKk+jIfxMF9SJdYAACAASURBVB7Gw3gYv6HxXmaqaVTnh5w9OgNTBniO2vw8u3Yc7hroi1beN3qD6zcr+f0lv2d4O0O0AR98Qk3ur25XAstBQeBHGZOMa8mC+rNqwLavcr01QGTs/vDJC/xg8QYvOor6v9ydS3P8rB6olQZUF4SP0mKgdhbVDcORTYRbhOx/eFCSIY6nAXrOUmeeI/90/qPK4uknA+rZIDDU/q6RqFg7wK2isPiSkgtAkW6Cv6Lh1hnOGsIsILBUm1k4/NGHz3FeUBb+VXuOnmvOSkUM/HNvqTE+cng7bzqsuN583zbYbBoorkfpIqB7RfMTbUTJJvPfvaJr9XxN7SXb+xkiO2WoXiMuHf6jH/yfAID/ePUTvGUJnifVGr/z9A0+/4ffBUDs3/XvciTfZKF5BDJjqLlVa7trBJbXjtqNUn0LGgI/qol7kB4VMOY6dRwyQxmdhtmb3IJRRWg2Vnh8scbtdi6ZsS09Rs6IypNBRC20iri7X2Q728rhckZz/09ffILuiyX0E5rXH7/4UGqxWk96UtI1vuXUrwhihL29mxEblyHH6rTDwPX+l3crxAjxvf3gZIvHM2ov+3p7jg1nz0ZHFMZjzxJ9tnRwyT+UFb6EmR2VyP65ZWbWKs5m073vZhO1qaAIGk2i9COyA40GoIDuMsrf0jGmQgfa0e/jgu/pMkPCviYJzzRfQ1egmdOcNuWIW66rw+kjt5/YGyj2vXVlyBA3yEBCstfzTGNWg4bZ5OcuFJHM6gHE0QC/niP5BoQiCuJd3RhUd/n7uBmEA4GJNKYKGtFAHGxsm5Wp0hykDFSFzAQG8rGhAT/ViGEpWH98O72T473cVJWOUDqiLB3MZFFouxI+ceVtJDNfAItFJzDVtqvwYn8i79Fri/CG76Amojgf8PqWNlzX5/qHrUeZ7VgEVPUofYGnswMst4zcHxqstw3YzxwX5zv88Jxk80LUWJgOc5uJSj9/9ZiObz0+ekTFnTFoWkx5Ya/f5rvXLSJMlxWFfBNJthFktN1xj2L0yL1+AFRUUNwrmjb5/RtaCOzaSI3WLQNiEWC4ZaV74gQiLWonpJnQWdLPLfNCuOJe0fN5iyFYfNUSdnZ9WGDG0PY+lvC8IG+u51A24oyhymU1oDJZYaqoHE5ZD/luO0PkheXTp9fYcW3xxWaFQ1uJqpEatCz+9tEB/8Hv/Rj/1eVnAICfDQb/5Zf/Hs2VHfCrt5dC0Dg8znVqFRTsxYHPI2IcTZ7X2xI6wdU6kpNJWtViJq+EWaDeSVDgE8rc3mQah3DH7RIDw8Spbn15wJK/8+ubE/jeQB2YsDYqmEcspVg6Wch9Z1EteoFutY742RcEt+u1JQJb0uA1QWrCQ18IiQsKwJhNy83WiG2g7hTVzFgv25iA9ppLGlXA/CTf/+u+FgPzfrQTp6iIfVvlzSb8Jai815NNMFKbCgDVeLnn9JpaRCQ4megAk/1dhIopOMzPiHymKCfl+qoeAcXSeuNMob/IrTfKZVKUm8Uj8tNs0ePpCQUPL+5XwAsKFowCotICg8bVmHkfDA2nUg4UchtamWFi3WZZTPqeEfZbOn7Z0b2UNkFESEuQbbl1i/d3X+XadCwyych0ivp5E0TtgSL1s/YRvlai8GV6iJzhcJbbg5RXQg4DSGvZDnhQVHoYD+NhPIyH8TAeRh7vZabqdwWiL7A92BypapBzC5MrqpMOlysW1/cGL9k5w99WMK1O4kjkMJE0PE8HhKgwY3GCUE8gmKBE8FqpCKMDzucEKY/eSKtAjArjthS3lUfzHQKHyJ/O38Ig4nagKP/57Yk4UXx4uhZPyjftEn1bwF6zF2WdlYvMQSEUEMZvLKK0B+3XNbRNmQEIqkrh+nIUJaDuwCpA/Lt/7AXqg1NQg0Z4RufS1KMITyQ2ahqq19BnnD0+eYuzmubj1X4Fqzxqzjr3Qym+mjFoEU5HQy0XA6MLN86iZBJXYQKenG4FDQAgkOOb7ULakcbRwG1K+S5x5iUC/5vf+TX+5OTH+NbR9fwHr/4t/PmXxILFxqLYanGS8XXMhutFlGzapSzpLZ/zVAxDExNVjMm9ElamPmiBDglSjFDn9A/+vpSWkWKrMJxE+EtOmfcFbneTnoYAUdbCPOKU27Oun5/Arjmzedrhk4s7vNpSyWFzN8vaswGIOrPk211FpQEAsTMTmJsY0Ils5Rche+yeUkaoXtI93l4Y1Od0fxgT0JSjqFatD7X4AAefnVaS52s1Y7eiQwHcMyt+VIgqQ7LiogMAm0Kcp/So2GCc/mT2mdyTsssEaeoRuTwTgXGeMz/b5hYS00M8WH1NGamI49sIz8L1YU6tT4kR3W5qfPE1zbfdK1iXjhERZhOC4BQtCgpmZwSxCFXM5ZneyD3ha4KZ0zNuN+bILxjhmP3bvEowbkQoJwzomNWngsWxni8Axdlp1JShpr+5BhPXp0xw0qOSDDkaQmkKhqntgR2Wfgsy1fdyU9WdgQb1qMqNYoFwMqKoaVFeznpcs4Vb+NUCJvVaJrSImYFuHqEZUqubAU9WW9xyr17nNXpuTfC7PNV65tCUY1ZRul2JeHpROcwvW3z/4hoA8Gy2xtLS8V/1K/z3L/6q1JVmix5/8OilHPd+oM8NUeHsfIdbhmCra4Mi9azNIkKTH1pVeXlwVREyS3XUUCwaD1AgMLJyVDEbYSoHz+c8tlnpJm0UCebqD4UswlgXMMlCb+lRPT7gkwtSHP9gtsZ6pO/1u6evcfAFfnl/RW/bNhjvuba210BqkdARWgcsuI46OIOaRdZL41EZh46Zqj5oPFoQvFwbJ8Lsv3p9SXXkG1bWajz+1u+Teta/e/FnaEOF//SrPwEA/OzzD2Bvs3oWSQemWmYQ2JiCEcjP5mU1gYZxtLCAu0EAhulYqrHY5mN7tsETU3ibj3/4aIRdjhlSDgpFRXPgvcZ84kTjvMbNNS3kqvZQ3Lv40fkGu7EUlSOlI8BtOB4Wusv3QegNVNr4Oy11Nnc1YHnW4pCUkjYl9E0h1xpeIbAiVzK7B4CSDSIObHm339QZIlRRDAZMEaBNkHsuthaK582XAWZrqH7K86N3qec5s9vdLCLUUWQzlUZWs1I4qhumRR4A+keeGMVp42si0km6eZT67dRkAuC6ePo3rxB9ttRDp7PqWFY2pKFpjQCIh5ECUbPTUBHi7BTneR7N21Jah2JFfBBpsblVohw1LojxnDa92UsN2yZWLzvPpIrMpObpmyhBCyIFEyIxOAK+4jpywU5MTZ6TpCSlBwW7z3OjXT6Gm/H1MX9pEt/B8QD/PoyH8TAexsN4GL+h8V5mqmpUUIagNrFcKgO0DWhYDWm9q2E+ox5HM4FLQkGKKSmKc8sgZJiLeYttXwlU2a4bEXWYX7WiX3u52OPrN+dimxU7A7OgUHJWD/jR5WtoDuG+bU9xeyC4tx0KFMbjo8dESLpsdnAccrqg0bNAxaPZFt9uT1Fw1qNCPl/fsO3TxBtVMTwYvQISPBsoG0uvi16LzVyIxAQWsoiKwJgwIfpdstMYJ1lshOeG9NOrHb57diMQr1YRj2rKJA++wBfrS9zv6HuP60o8MMVoGgAcqUal+Z5Vg/TwXu/mOGk6UVsyOuDbe2JNax2ETVyWDt2+FKWkf/uPfor/7tk/obmKEf/JV/8mPntOZtt6YwUaq+40CQskVRwNYeSW9QjHmReuS+qBTKfvM3ToSxDszOhA83WR+/3KKAL6oQBiGaEX9MZwsNLYDxvhDpkgpAuPmoUtQlQorRfYu+9K2CobK5wts1LXZtfIeZye7el3AKH2OHm2Fhj29nqJyNci1gHqij5rVg8IQcMlZvPhmDhULAdRbIpRoWBEYXAW+0MlJYIYFHS6vhFiN6hUhNZRMlWELOKgeo0wC+JBSoL0fAgDgWAjW7OlZzl6JQhU0AQb96dJmCOfv+4IphSrs1UUP1vtcr8mgCPBBxVz5hsa+oc4y/ducJmBr3KVArEIWaysN6RfzMPNA5UoAEGTAEIKBCrWEXAK5R2TAouMjoyrgFBH8Y1VjvppgSyQn7JYKPJRBYD6Oq8hid0rSmkT+zsV6bqIzrECyi3P40AZKUDM6FABrs6ZabF/ENR/Z0f13S3MjAy4p1BUCBobFgtoviwEThnOgyzqxVZhXEWMT9iB46QTWNFHDR80Kl4w+srD2MzwrXjB//rNOdx9rnvp1Yirc2ICPp7t8JPXz+RvT1ZbgcZc0Hi22uCHJ8QGflqu8WYgOO/P7j4U5uubdom335wh6cdPFZRCHRjm5c8uvQg3kHLPBH4pA5pTgp77QwHP6kqmcQhe53ahoPKCxjBaZmkqaGaI6kFBs1fmp+fX+OHytdQ2F6bHr1qCe39xf4Vdl1V1qvMDhgPLOHotgUo5G1CVjlihIHeVsswSd4M3cpz9uhbx8eXigGfnpKD01S+e4O/9a/8j/o/1dwAAK3vAf/PmXwYA/HT9Af78px8Le9qX8agdI1S55Kx6Db2i4ztnSOAcAE74/uL7R/mJaL5TUAeL+i1DnENenMbVpO7FYgCplqw8ENl5xtSOzIV4w6qqUYI3BI3eGTGJCKOGZVixKp3M/VSYAgA225mc/+K8hTUBZzVdt9u4ymLvhYdnZvGgClKxSrXAg4HjDbde9ogRE3Zx3jQK48nkPt0vTiPsMwM/jViQ0pi4stiYVYdGdkJK9etBZ6WkIkqdFKDgObGhi42Wa+HnAXaVFe99Wwk8W66JCRwTL6HLsO50M0QAUCC3RYUsdoAIFCdDNm5orTxrvqHyE0CSgnrmpGtADVqY1wmCDQzrxjJg9pTWnnE0GLesflYAxcYIBDueOehDlja0a41im2FvCd4M8UNS2UGPigTx+edym74/w79JbWmlxOnK9ArlfW7TCxZyjOEqly2iZs/aCaNYucw2fpfHe7mpGh1gdMDICzVAeqf9mxnmX3NUf54f6PqtgeMbtPtoRLEY8Nc+eEHv0wHfbCkDct6gdwZ7fvCb2YArttq63s1xSJZaL2tgFnD2wZrPJ8rD9pNffYTFWYtPz6l/8vowl7rS4+UOj5otFtw89sXhEjc91X3nxYD7jrKLN/cLqIGkygCusST1o8ojjEb0RP2mFM3aUOb+ymgDzh5thQQU+uyC4Q8WykQ5RlTxyMBcT76PXxeyyMQPD/i7v/enAIBHxQbPhzMsmY3zVXeBL7Ykeff2fgE3WMyXtKFTbS3VDPNmPm96XM33eAlqYTp0+Xq6oLFfzzFueSMaNXxH57iJwE/+nf+WXvivA8B/Ie/7k3/8n0n2/xf/9DvQNmJY5Uzb3HHU7RVCiKJUo5cjioLmo7utRc1Kl542CSbtxCJA8aZBzhzHmWt/SccLVa79QVObRKo/+UVAOc8bgKgYgTbIdM26Q4ngFOk0AyiWg5zjSdNhxtZsz9cn5MqU2p2CwtljKn6dNy32Y4nP//mHdM6jgj/jXuZRZ6IMQJlYIjhd9qhE69dA64AhOUDNBlwyulMYj+d3J/IdimbEqHlzmGSctvDUnzxOArmklDRRP5K5S2SkVk8CSsrgUvalMkgAs9NwuhBT7mra52nB5uOQOUjrQTTHvZfK51psLCb3x3yE93qi26uyLninRfNWOYWwy25I035lFek7xKTtXAYJmNQ3TXrEcw2ZW4f03ggHRI/chiWvBUaWXESkzFTXaV5zUDCtr7qGNmK3SO/L5Kz+MqDYKJkDIrrl+ZA1ZEH9w4nYpryCW2QE4F0eDzXVh/EwHsbDeBgP4zc03stMdftqAd3UUI0XAfDxvkKx0UcZaoqwuu93QuW/WLT4welbFBya/fT6mbiMACRofsJtC0054pYNxdtdJbqr+lGPpiHRe4DhwqSdOx9x0nR43BAcfFHtkcAyqwJc1PjFjtw/Ol+g5qKQCxp3zDoeNxUxIyfyJAn2m8169IMVBxtEZHF2ADH5Pzb+GBacwsJeQddePF/VtPUmgvw5+fjmZMTJR5T1/K0nX6HgsP7lcIouFPglf5chGLxeU8jsX80w+3grusaHsZDzdaPCo0uam985e4NCBezYQL5tK+xf1/mUW42G60r9ZcCXfy9npP9v45fXlwg/pfYpG6kGlKL08jbDhW4eEa4GzFgVZ+gLdDcsHWMDNGeEYTAUunKGpSam9SoC0EB3xcc88ZSZgLP/JEh/UKQRrPMbE2s1wb7dljG8QWclpj2Jj1x8nxjWlXWiUHS7n2FfMCwcFIa+EHelw6HEliH1p8sNXm6XCEtO05yC5hq8MhHBck2vs8CgYZZ0A1X1IC1kwWlqC0tZoQnCfH9+TShPKsOEQFkpADhAmMwxKKodp9vQRKhUt1cAospeqBGCvkQb5ZphMUKti6MaqJhpVwr23qJY5zYagUUrgkQzU/U4m/rLIvKplSWcjFIf1jrC9dkVCxECb8eZR+QSjxop606Zqh4z7Oy5DKDXfN0UcquMzY9gKCOJsFSTTPWQs3M9ArPX9NmHy0mGz6bqCTlBnIhcOMDlR4v9YTnrrHJ5CTxvlj/PlzHzCRSkdGM3BjF1CoCu01jjt4L9+15uqtBUj4mdgWL4REd+WBKp5nTEYkUQ1aru4fmOvZrtcVXu8LLjvtWgsGLCR1U4rMoe657uvrd3S9m046ZEam5VmmqUacGYzXqcX5KKdW1H/PDkNZ6WBA37qHHDkiQ/3zyB1QE9L0gfzu+lDWXd12h3Vf5+E3eYWHg85prtYbQYFImw0wf6LJQ/gdu0oXYVzd+7aEaMbL+mKg8/6CPIN3QZHwqDgWG4c7Vs8bef/hoA8Kjc4uXAcoBjjZt+LlDrbshklXAyqQuC2mOSHZi1HssqKUpp/OmrD7F7zYSyvQaYCFJdHIAL4LO//1/j/2v8/X/2H+Ivto/xNZOYxl+eYnycvKromKnPMZq8wQZLUnkJtqzqUcg8IWj5GcqTVdrExD1ZhYUC6K88NCs9ldZjSHO8tbln0hHBROqolZcNUAZDeN5pjBygRQ1cfHorYu1t3+CKbe1qO+I196X2XYlm1kvgYq3HvKE5/ub+FH5CUjMmIPDxgs8WiAigNg6+biFoKVsoFaFsDgQOh1KkDutmkE0dYBUyvq+WqwN6ft3QliheF1kNae5z/62zBLcnxalJCxAiBEbXdyXKe33cbznZD6OO8DO5bLJ5+kWA2WXD7qgz8Ydcb3gKLG28kfuJi8LDcc+tc1xnTEGVjWKkrqqAyKpm6rqE8kps20KR65DFmuc9tc4gn3+YSH76OlK7DddR1aiOSEXBANvv8PFNlFYhRIaz06Y6PX6RHWUA7kVNRLEAMSnQI0HL0mYUIPOGqLIov+V2moTm/xbtRA/w78N4GA/jYTyMh/EbGr9F8cH//6EGDWWocd3PJhnReYc/eEYEJBcMSmbTtq7Enp13T8sWX7XneLknckxpvbRxbLoKv75fTD9KiAl6VILfeWvw+Mk9vndCZCSrPRxT5M7LFl/uLvC1Ivuxf//xj/Gj5jkAoNIOXx/OUXGGezc0eN1StrFum+SiRtGwyhG6qT1abjvZ7hq4XSGmyGQrlqEmz7ZSl2dbLKsemsPkN2qBfSIf+Zx10f/VRIWFWnTOT4mg9YeXL/BRTfDj9bjEwdN5fLG9wLLscV5R5hSiyo3+ZcCsGlCy2GgflRizf7i8R8md7D95/QF2rxbQrF3q5wGnTykj/1c/+BW0ivgHP/83AAD/+e/9z/h/Gn9j+Su8HRb4fLika7FX8BVnBE1AKKKo6ZA4OGc9lz0Ws/+Lvffo1W05z8SeSit9cccTbyJ5SZFsKsPqbggNeGL0yJOe+k/4P3jsgQEPDHjimY2e2IDdBow2PJIgyW5JLYkiKfLme+JOX1ypkgdV9dba0qzBgY7OLoDgOffsvb6Vvqp6n/cJA3R0BT9uMzbGlQuhBghV3zAojBG9kAcOvcruTbywJK3AyxlSct3Up16fOKyeb4lV3o2KmLtKWByOVUZEDCd51vLJDse+IP/cx6s9nkSz+r+7u8AxEueenm9wWrXYDNG0xEhcx/dYKRuYu/GEpLToI8rh9CTDNMKZ6Tys4eTAVc1G9L0is4bAHI/fi1LDM0bysvAuhs/a72r4WG2JnYRZOPBYBXJhoTcV3SvPJy2IFG+IgNYkaYzoGZjOHrZWZOP3HF0W/382qRDvBOSBUaA2s0D7JEL255qkVL6VIYQhytKMzcQ5KAdWOIjon+0sA4+VnjkoiCR/MwxiBBF2nMqMWGYDTCoPGfJN1bSXHmaZIHoO1krwWO2KiaduYvgmRi5zgI7VYzbCyUzeBC8zB4wxQ8QV/p6JCZDRl0T+SnBzgo+B+LkURxk9iJM8SPnAyv4nQFR6LxfVpFXjFx3K2L95vt7ipGqxUuGbI5jHwYRJZ7QSz2YBjn1xXGM/lJRmAwA6Ut12MffUJqjPMmJHuoXB4iwsIGezFj9cv0EdGzqv+hW5Id30Mxx0gX/z/C8BALdmjv/h238FIFi4fbDewMTF59vNmhibw67ME5zwwMhzjmkrsXXh3FwrwSZQLR+zjMaeGKiYvVkIi5kcKZxdCod5TDXZbGbwR5lD1hcjnArnUdUjBHcUAvDPl5/hlQ7Q6otuTdf5+2dfo7MFfrkLMpqvb09ocVmsOjRKo5bRks5KzIuwqC7kgLt4jFHLIFGJspXf+/6XOCnC83tWbtA7hf/u3/9rAMB//7//t8AH4d+eX9zhv3r+pwCA/+3tv8DffPUU4mV0E2IgJqkrAaw1jIr9LsugLmKaUDVCW5Ehd8PzxDxymLg6WsthXjaoYm93XDtKRuGFhbcM7GZiKzjZr5h4XfPLIxbVQH1IAASfHrYzsNKCp4mw0qhjSPmhDZqflJDz8fwW3x6jVpd5fHIRNjtn1RFf70+wiT35dl8SY9jy0CuVEaJuN3V+zybfAbUawIUjljY8I81325bwnlEwvDWCYGJtBIZeUe9R1ppCzNnAgTjp+sd9aJ0mFc2upL4pEBbPKQOYYMvy7yUyYaKptBmatJWHnXv4uPqIlkNFLac8AsXOw6kkp8p9U/jcA9aQkdEaP6fwlPokpQvvwjE1SBlc3HSwTuRzYkEHm47PWw6ZoFUD8JYRW9fL3OeFZ0FHjfSdBrkoeZHhWLVnsZcZ789EJwoWF/F4THlktMkwtacNpdoF1vo0dzUl0TgZPldE5yQn8gaHD3mD41RgYnse5UGw8HzCeH+Hx3u5qIrlCN5wlKXB02XYuTPmcdPPcNWFHfrVYUZ09bPVES9s6AUaG/plQyQWCOFIQgMfdu+UClJZqGgFV5SGDAe6QuEX20s6n+vDDEWchM+aI35j/Rb//uo3AAC/ePGIql1RWfzt5ilNLKK0sMlicOIRynsG0TKqiCAdWdwxz4JWL06MrkD2rNUcJl7z1W6OeTEQWWk0AjpWF97wYDMWK0TtSpSnYbFZ1D3O6hav2nC//m33e7hpQ09YcEdh4Bvd4EW7wq9ehkW1no34/e9+BQC4LA/Y6Bp/8sUn4ZjzDh9ehgXgotjjqg/PaGgVsND4+Gmo+O+GBq8jgvAn//a3UF97qO+FS5v/1g3WMWnomz97hv/ms/+SnhkfOE0YZmnBl2EivDg5YLNroFmUeJSOeo3GcQy9omfDOk6ifDHRGeqrGkIz9E+jycVcU89FCAd7Xec+ts2aPvaswzwuSpVKVn7RVKQtg18xwmImlb0X3j2Qx3RY1E+a8Gy+Pp5gJsMx/83TP8dfHj4M9+rVR9juGiILceVIkwzDwGaGUnwgcnoTAJI9OccxDjJrlwHsbiOBzwHNSUd902lknHMs6GffhOuZ/wpIWM/djz0lHLkhxN35CeHLlRll4gOnvqFTyPd0zP6yYgiLSbaJRLaCbBy88JDbbO1X3U5Ii1M9ZwwjBwIBUMeFUm5FiFKLhCk2IfP5XQHPPETcnDTNgH28P77MaTlWRNlVqq7HEOidhl5M0JKJrpnrfM22Dtrc6eYhyYhkD/C3DMNJOIYWyOQ46VFsOMW9OZX7pmJgVI2GZJ5s5ECWhAjvL3OZqORU1nZzDZIOcROTi+L9l3GedOWktH1Hx0NP9WE8jIfxMB7Gw/g1jfezUpXB6aiQBnfRMGHfVtCjRB0NyJ+ttji/DBjGTA746e0TAIFBuZ51eL4KcPBcDjDnEdqzElfdjMT3+7YkRmW7qyCiWcLV7QL7esy9SQQoEwBmxYi7sSaZiBszDd8eIm8+9XAm8DLvslNP6BV7YJVcBXgOGB8Z+MS2kGsW0nkQHU6iJOJieUBnFMbYM9RGoLsKW9PqtYQrPMazcD3VWTbGH6yE8wyLGLDYW4VH82DFYhzHebQivOrn+PzqDHU0MXi03OP787fhs7xAZxUxaKVwWEdY/u8Ol8QYfnS5xarssYzpPAdd4oufh+f0+FuH9oLj4vdfAwBefHmOTaysi08P0FGCwjcy2Nelal14zObheHfbWbDdEwnmHnDswu9Zy8P9T1Do0qCJVdvQFxjvYj5mx0N/LEJ9QloK13avmuCGlHpZHJh9GjA1O2EQ325muNYLklJ46YFYpSXGdBpCGPh4f8ZDgWI+oo0VbiUNmlip/q+vfxuvdkv6vfXqSNfmNCeHLFHakKoU2wKcZyMOANDxvdVagCGEPABBJjZO0Ni+K8L9QjDHTyYU3bZC86sC828ji3pnsfsolZKeKlM1H8EYMEZJkBc+h48XFt4p6FViXOOeTWFOosF9gwGWE22YZfcYvsXOw6rUJ/RgPvcc9cLDXcbQ76Oid8BcaLB2Ypq/VUQuZh6ADAlDANC+qiicY1raeBWerYphBMWekeynP/NwxUQGNJm9vcA9SZHljNjC5R2DOsSKvGAYF1kZoPaMbpBIJjCpFcwm5hhjhpOZC/cg2RpOgwTsPFT87hjRgZ7BpdvNQD3V8cJCLEdIkd9jbxnc+O4vSe/+FfwnDN0VsCigR0nShPW8xVnd4ib67L7ZYrQEowAAIABJREFUL3BeJRINx79++rcAgLfjAsYL6rdeFAcsY4pM6wpcVDP89U2Y2J2bSA4QtXwI/qzeM8g00XJHmsxSGHx2e47d6wX9XorQ4nWY4MrYMzveNJTGYWfZStGL8PcEQWLMiRjMMNi5IxKDbHMaihceMsLQx1FBcI/NLvZiX9RAlDP0zzTUcsD3Y09OcEcyIsUdOqPu/f28DgupVA7fbUL6zmc4x/cur7GLP/dstsEishqs5zCK43c+/AYA8P35W+i4C/ioucXOhI3Q9xZXME7gV/tAMvrVq0us/ybcg5sfA+VP7vD2LtzHpx9f4yamDg1vG6h9nEAEwqQW++C+sZQC5I4qTDip52wELQx+FKHnF/ujqtY5kWjktPFxNYOvLGk7hfCw11E2E91mTCLLrTUO8bNVaTBE/10wD75R5M4D5SDi59pBwHjkSLS+gI2OTWo5YDXv8KgJ9/9ps8WfXwVnpM2hJmi/qcZ7mmShHGQ8nhChF5jITtZy2ig6zXMAeGnRzHuczbKfcGpTOMdgRnlPW5t6whg52o8MfHRRKn8KVDfhs7pLhnGRe7TjbUkLlq8cZOxXmmGykCHCqXFTwA4iaCUBiDFAoklT6RubJTVaBFefCGUePsgkGzGGaDmyEmw8kYzq82MOWL+bBV/x5Hy1tATr8iMH73n2Gheg7yDzmXCEKIWhXmYF+ISilwFCJV2sD1aZQNCal9FSdOwVxKsSRdTcOgn0pxFern3Qi1I6T46F9MLf0/AyA8hJn/penFuJST8392iZjhrbeDnjyeQeM9BzYpaBf13DJ0vHwsPNHXz37i9JD/Dvw3gYD+NhPIyH8Wsa7/624D9h+F7AM4Hmssf3zwPk+DcvnuL16zUJzz95ek2yBQB4OQTW5EwMcJbjX64+AwC80mt81oZKaXQSd31DxgWLWU9QsNYCdWQ/lspAW07/1rYlbiMEw7nD/ttsWi6XI37yPEhqequgncCXb4JHLgwjNyQ2cmI3msYDxYTUMfCsNqgCOzFR5eEBE43gy8uWKpL9oYY+FOTN6U8zM5iLUAW/PQRKyaPFHqtIMHi9X8A4DhGv58cXrylx5zdmb9C6zHSdqwHPmgB3rlUHG7fCFdeohcb3Zlf094MOW/Kvjyck75jJAbdjQ+k8jHls/zCcx+n6COcBH6v619cr4G04hhwZVS9uacAKR9INcJ+NMRAdkCLs5SyDvEmm5R52YSHr7PiT2MuytJSP6zQP8oFIFBu2BXysOF0TSHMq3vNxV5Lxvj1W4MmZp3IQT1rEMBFIaYn0w4RD2WhKfdGjRHMWqsVl02NejKgii/pv7x5jHyFe7xnKxMb1DIpnSFap+25aKhKlAGBkEkM0qEhVe/qz5A7Xh1BWaS1yUg/z4NJBRsSlqUZyEysuWnS7iRxJZ0breGEySvCmxiRvHnbmYdNzGnko91KJ4EBkQXnkVLHppYdZGfCYQ1pVGn2b3scQWE7GBzwTcGwTfXDTbHkyYrUMyNJ2NwvPGAC2CvLIMJ4nPD9XZiFdKfsH84lf8bh28FEGxXYSxT5/l8EmSS4EY8e5wjLo5JpUm+yStlNQB0ae1a7IMLc4BvMLE//OTZbDiTHIhngkKsneU3U6rhg5KiUHpXT+1V021DcnFnKmCZWbkinltSIjf1v4e+YbzEXiY//u13nv5aL6uz/6AmpW4LO7M/zF330U/iMDmnVHjjPfWdxQ705xi0/qMMG/GE4wOIn/6at/DgB4c7UCT9R4BGcdPelzFZFRWZca3ZAjygR32Ed4z96W2I55UsdCU6+Rc4+Xh8CkHY3A3dUCLAaeM+6pnSEPfGJc7UIIcnxBE3wFAI77AB8lVu+JgzwL/UqtBfRXkak7MPDZxBBcZUvHi5M92qGg89/taoL2hHIoK42n67BYGs/xvSayc02DL45hQ1Bwi+801wTrVlyTHaMFw62ekaZ1r0u8jXpc6xmhSaOTGIwk6c3Hj24g4ow8WImbY4PjNzGU22UbO7fIDFYlHMxtBTaf2MhE+JT3LMDocfFgE5awryzkXFN/0WlB70H6OxCZlZYRDAjhsxbPh0XXxvQZ0XKyp3OFh4u6QzUfMatHut/jIW9M6lXYROyjqxSEB49Q4lnd4lG1x883gWl+6MvsAFVq2vjMyhHbLmtdU8waEHqjIUWG0d+r2DcdekVOWt5w3L1ZUu+Y8RyyoJRFWRjSGg9G0gJ+3FU4+X8V6uvw97vvK+x/J1yTlA7ubZzJY/IR6cr9ZFF3DMwypG8DY4yizeSREWzrBYKUJZ5zb3PMnGNh4UyWgE5mpyHRRZegeb6227ehHy1u1T0HT712FNEnhIPpojzIZzlQOr5eZo3mNBwAE0ciz0EuRMzdlw6ZuQc/D/fUe2QlAIIEhiR1TW732CrwB9J7BpPdlpz0MA1DVLlALxltAvQih7H7pQZGDhcX7eEjTX1ZFd8TFjeKeF1CTSVN6dUXEYpOrmxpk4v8HXpXx3u5qP75zz8Gr6vwZYyeuB8+vsVpdcSni7B4Os/wVXsKIEzkf3QTtBlzNaA1Csuomywf3+Im+vsqEXb4Y7Qms5bTojpoSZXqaCR6LaG3kSxjGE7P9/HfBHY3M3xwEhalT5dX2Jnwc//fiw+DtiuRakp3b2dnn4RzqpsR/Ys50dqBrEfjY3ix007VzQ3cLhxfbCUJ3D0DXOHw8XeD3rQUBjrqPXojg25yzOL2uoxSgULjtG4pMWeuBnzZhoX0VbukBfB5s8HgJEqe5RnJaOLvjo9hPMfdEO7rVTejCLdhlDTB3x2D7V4TmRudVlTFjlbguK/I/xQ6e+KqxZDvy4sGuBhxfhbuvzYC27iJ8QJQm6whtPOsMWVF0GSmiZ0rR3+22yLnaM5CGoeIO3fvGDWneM/heoHkyMh03jC4pUEVZQacO+yPFcWxPXqyIRLTrq3QXmXvaTUfsIzSoYvqgJ9vLu/F6FUpltAIQmJGK1BIAxPdGbpeEZkKAOb1gELm/NMkNZPKwsV76j0DF5Yydr1nJNGpCg0lLVl99lrSxtP3Av0Zw+Gj2OP+oEOTPIjbImuGlYddWvAUezY1HOFxUk4V1yFbEYLlCtOWgQSU+tvzeZ9tEBEWqaSTlC1H8zI+zxJon7qMCk2eL7O5n+iVD33DN5HMxkHP1vPgQZzeRznXWDQTUlc8D8sBrXiWuXCfSVc8nIu+jNrX2uT3xXDwTXgu5TUHt2EhBMJm7V6MIMsGD3yYELkkAAsMZ5m8lapi1ziwJKXqBVhtaRNpRgERNyd2FCEf+pD5HSQTUz5E46XHNjBKwWEuPs9/ApXqu38FD+NhPIyH8TAexj+S8V5WqmoxgDeBfZsqLOcZvrg7w09fBeautRyLWWKjMmI1boYaL67X5C4khSP2nxQO1jFKDtGHAocoml883pOLjHUMh9smGMADEM9b9LH/1LUlysWAD+d3AIDBSfz5qw/Cn7+Zh35grDL9wKGivZk+1/itj0Lv9avNCewh7+Rt42m3zkRwayHD8ZFDRgYxH0A0ef7REf/FJ78kiPBtP8d1ly0Y11VHveOZGrM0qS9hXAhrB4CvtqeYFdFgXFg8qkJF+Kt9MH34zjywgecY8PUQkIE33SIEBAwF3dc+Ga6PgpiXi0ULJSxV0Jx5ch0ylkMWFmMU5vvKUuVnDYe5DRfKHw94dLrDXXTDGrYVbd3lwCF6ls3BJ8NbBu85RKx6bC+pj8eHDHPJjQxuPRM7TOoT1oEZSb1vx2HTcxpEyGUFAM/QXBzxOx9/CSDYVX55CPfq9c3pvX7i5fqAp/Mg9/rl5gL7vqT30zqOLqIL9xi9juHQVhlWNozCxlcnRwxGYLsPz1cqiyZWWAzI1e4oQ+5tfOmUdLhcBtbxeX3At/s1dm24nmFQ4F+G41UjC73Ok2g84YP8DABwkDDr8N9nFy2Or2d0X3mfqyiq5pI5u/Jg2bsiuweVHqI25PTkPMMQpVXMBtZqESssV3gcn8XnVHlgPebc2ioywwHwSf+djwx8n6svV+Uqkw8MXjqoZbh3Slns78I7JwqXAxhscB1KcGg4UPg/L32wtkzv3CiA2AqSO47qJsKsPWBmma0b8mUjhKwZeM9QRLmN5/nn1CFWqT7/XqrO+V6AxffDiwD383gMNWQLRyeA7omFjc+TCU+h6nadYe7iVgQ0YeKw5AsPb//hd+1dG+/louqsAKyAZcD+GL7Ad7dz+FHkMG/pMKb4MuHw6i70UJwLsFZyVDocMxwplcF63pFUZpAOIurS9n5BMgs1H1EtBwxpct0X0EVq/ABPLnf4ReyDffPyFCJq1sTIMj0dAZpM8oDHz+7w+hj6h5tXS+DcZJJR6cAjzF2UBkYLcuSZHm98bLC8CBPhk+UOX+7P8DommXRdQZsFIEB4yaHoup2Rt7BzHNtjTZuVutCkkxRW4HMXoOCPF7f4tHmLb4fgcfx5e44vt2GhuN7M4S2jFB89yBzuzLLEyHkGDo8yQpObroaMbBbrGPQgydHKeUZRZJw7IOpx62bAvqswbDJZJkWueQ4M55agMhYlTQCCTSOfWFJ6FnrZAKxEvvfKhXcqSRhifFc4EYQFMbkLVR48OdEIoDwJ99d7hovFkQhfe1PiixeBHJeOu7yI7QMr8JffPgMAmEGimo3Q/h9+zZUytGEajYQZBcmKWG2pb9oNCkbLvCGcaKunjlKyNuDCQcR3etlkZ5yfvn6CsZe0EDVfSYIY9ZmBmBvIeC6my5sTLz35GHcpvSf1Hj2AIi8U0+GaiSNRlzXZvrGQ0hL0PNzWKG5E/jmZHYScAMwiYsiFA/MshM0jLsCR7GS4h7qO0D6Pm1IKl/fUR/UyHMdF3Xc3yrwha+W9sHevPHyah46Cerue+xD/ln6uF+QWBWRNqamBceWzTId52sAX29AfJm9pFwhcANB+YMMcFc9FbgWKCCk76QlOrt7y++HjIgez28qDTdoirLBAdF7yI4fcC/pcM8uLNlhoPz3Avw/jYTyMh/EwHsbDoPFeVqpFpSEqjqrQlIyyiTvhlHfYrDsiZ2ib5QF25JidHonJa0eBoglV2cmixckEFu27AjbmJEJzci6Sa4fvXVxT8s0X317QuV1e7FAKg292QcIjbhTtRrkOO9C06/QMcGeZtZqqKLXuofclRBsdVZ4dsIr+r5U0uDrMkKIR7a7A8oeBnftofiCo8KZrcHdoSHIwNdCvTzp89+QGn92FqrMbiuwRHKtKeRp2oI/LPUleBiPxu6fB0OFHzUt8O56SicbLwwrHCPdWlQ6ORXE3rUoDFSsn5xiZWhTC4qI+oDXhWZzWLV5sV3QeXDryonUTb2SrFeaPQ0VeKYPr10sy0eCGwUaWsDk1gGcQ0WSAAWSIHiQALPvILifsYc/gqUQJUC5lf3oQfOf2CswwEty7wsOusm5kuE7mD8ArAMcxBjccK/DrDMWVzw+4XITruW1rSoA5Owv/LQWOA5mNXilDOatDr+A6SUhKvegpEefQlhDSElt33XR4cxtRG82hoklEyEXlaCJCMRqBN6/CO8x6AXHkqO7CdXaPHd2vaj7S8wJie2Jiqu6vIomuZeAc5BrmeK4q+cADdJhIZMIBu1hhKcCchs9SswDhdjFRqLgR9xJfnMxGCJSmAoDvZTCXT7Plhx2hNuNQwixiVTkwiIGR8T68h4+VmC8t0ItQlSJUcEmKwwoHnsw8Rg5UIATCF55yV9U+OKO5Y5xHRpaNLDgwnsRzF+FdSoRGtRUUvs5NIDuN69hCejSgivOXGiX0roDaiPh7LGeockb5vmIAhrMst/HSZ4i9CMYbyZzEe8DHSlvdyglz2cHNLaXzAOF5+imV+h0d7+Wiumo6yJlDozS+uVnnfzAMMkYznc1a0tyVSuNkFaQ2yRD+lQ+waF1qfBDlIwCwH0vqW52sjvhkHRasL7enuN2G4y2bHpXQeHsMPUpeWNgIx95uZxi0RPsm/Gx9x6Fi4oNexD5HYrQWjtDbfVdRYo3eVEHzlVJZ1htcHcPxtm2NVdORvZyrDZ4sAnR4Wh7x9T5AsLuuCozOJBNZjdSDqwqNr3cnuL0O90AUjnSZ3nCAAe0xTIa/7C4pIP23z16gFlEz2T7Fq35F5vi3x4ZgOcY8iiJrI2eFvgfxpv6zB/Dl7hS7yG5t2zIbwVsWYLhJcs886jfnVQ4KePN6DXmdKYmu8JkpCQDSkVSBbxRUn/W9noPsAmE4fRbvOUkYzNLCNwYg+NrDRwkNQ5g07aR/xidh4LqNC/hRQF/XuI4sbXUl4RO0dzbgw9M7inTj8HRth6hJTc+NMWAZZS210ni5CYujPhRghSPId9n0uIpOVFJaLJoB8xgM3xtJjl5FqWnxFdyjlAPujjHp5q6mkAd1G97L9tMYELAYSFc7DArWCNIGs4MAFpkR7imhhQVD+3T7Z1nD6nh4Ts0qvO/dq3mevBfZ0UdvS2jLiIcgukn/MzJ3xUR6Qg5lHBhPbZZC9ZIWOt6JzDT2DKbJhvpioSHjdTrL4bzMbYEUWo6gRSddsImbySRRO3Bi8TMTFjov0kbAQ0eag+wmAeDKw1cO6jY6a+0YQbx6ZSH3gq4TX1Vw0aFM2rAgpMVSLz0t1GZpqe0iSwMzCuIChB9O7v0IbZH0vk+crszcwcfzYIaheqnuuV055eH6/PPv6niAfx/Gw3gYD+NhPIxf03gvK9W5GiEVwxdXpzARdpK1QXM24LungY0qucOHi+Bt+6jck/6x4SM+a8/JGOLNboF9NL9/NtuiEprCtX+weoO5yDmgfzoEo4lBS7w8rCjc/NHpDi8jYUdvSmyOuXLqH1noCOPamQtVFJEMGFis5o53Nfg2uphYBjuz+OA8VNCX9R7fWYSKueYjDrbEH7XfARAgwgTPJtciACikwTBIgqhkYcm83ZQch20FEeGlAPFEKOso4EsHF4/57MNr/PAkmNp/VF/jOoZBXo1zvOkWuI0aX8EdRHKHYR79qNDHEG2xzGQkKSwSpWjfl2hjZiiA4CNKO+a0I47nNRsxi9Dkrq3QJ1MI5WHONVWZzPBcfTIP1krIbcrXyn6nngdiUYIcw/OI/6Y8TApfTtm2QzYjSCQRU/vAYo3m7E8uN+SyxZjH+eMAUbS6wNvdHF0kU+kzA7UMv/PPnr3E43qPn20eAQBeb5ZZAxq9p8vohNVUA93HbV/l6kgEslSC+ru3DXnnfvLJDRaqx1fb8H7u9g21GU5XR3qHjePotES7DVUPP0gyDnAKMJcaRTQ0ESIjLLN6CMeM1bSfIXtWHyVVi6YOhvGk0TbZ6QoeAAO6FxH5GRmRe9ByIMKl6sjghM8wps4aVmbYvdxRrxw5XzHugVageBOfjc/eubby5MLkingqVU6aT9fiHIecabgqvqujgIr3Q0qXHbJ6HkqdxIotPLTMEO800o0bkIe159m3V/QMxdcCJsqX2080HU/uBfiYz5853CMrOuUxPE7O+fm/88rkir8NIQKszCQjtJlcxtocXi+7bCBhZh5iSIY04d4lDautXHB5e/e9H97PRXUz1BCihL6tMIsT1/fP3+KsbFGLRLfnxLaciwGPVJAp/Lx7gufVBj+7eQwg9IJ0E96MzVhjN1TUU/3Z9jF0ZPvdtTUuYt/roj7gZ1ePaKEulUEZv2CDK6n3AwRz7BjlGsymp+kcrQCPaSjSZPYfc4D6uMO6DHBYbxWlvHSuwOf7c8wjDLjvSgwpSadSNKmPRobA7thn48JRH+mwq4NjTpowekFQn4/s1qcfhkX8stnjr26eAgD+4uo5LmfhHrw5LHBzMycjeGt4ZsE6Ds48FvNwzufNkdJVnGe4Tr1XZWCKLJx3h4LYhSwqA2x0wVGFIYu+9s0MSBNmY4LbjUkMTk+9bzAANvfxRJtnGX1igxNOIlhKn12lDhyujJPiwoDpbJXnWTAZAIIxxPziSJuH11crcmJSzUg9z3k5hgUvHn9+ecSzmJJUCYOfbR5RwLiUlpi6fVuAC08h5d1QENvdWUFm5sVshB4kfJRnoHS4eLyl+/1X3zwnOK9aDHS8fVeiE2Ex6COMm2wWXeVoguRLHXrQ8Z1mzNN3xDqO85M9tUZ0J+HjIjUNHU+bydT7Zh5kwGArD29yALYYJv1PlnuvnrEApU7MDsZ1fBbRQSn1c/nAITb5s9zEUg8elHQjOkbwLzfAMHMkc7EVg1MTMNDzzIoVHpbaHaAQBAgP5hlBzZ7l/i7TDODsXrA6MdM1IPd5oeweedgYcs8GTtnHtnGwDe4HbCSJXh0DANI8Mvk9MEE/B+UCszm2WphmQGqNsnDs4jZ9nidHK1c5iPgejatghJH4IdV1MOyw06D5d3S8l4vqzRcn4HWF5Qc7fHoWHJQKbnHVz/GDZXAQUsziSYy2d57j/7r+EQDgab1DKQ2eLEIP66w50uK46WvM1IgfrMIxBidxN4ZKjDFPJKC37QLHbbb2K+dD3p2z+/Fai2VHk2trQ99O3KW3lFEckxOAjDvw4cLipBqomi4AvOxWdJ211Hi1jTZrIlvQWcexrJL+VuHIPBG5SqVpQgYAd1BUfUG5TBqZafzwg9ekW/2rF0/p2k6XR/zqbZCCDNsK4J6IRFU9Up+XcwvGsvtPqwvcxvSg682c9MPaCAxdJNkg9DLTeQRtoSZNqzUCw204J3EURCBjzMPflUGuBATv32xZC7nPRCIvQMHvzDJ44aEi4UYfFXisiPwHHZbxHDe3M6gtp96deTpApHQi5tFPpEqMMSJUmesau5Pw51XdQyqLxUnYkMwKTSlAV8fZPZ9eALk3zYHZrKdFVgiHoYsEnqMiNzFrOXwrSSbClcP1VXg/rnyo1JLvs/fBljIdv02SoonkKQ0Wq3V3UEBpQ6g8ADQgn9q6HHHoS5jrGJU3TCZ5lfW3Pi2OqUo7ZMtIV7jgGpScr0pPjkTyyLLNpAsLKZGQOMiijzkGfX6fbMbiRkt2AASIxMR83mCJAfRs0yKXnIekmnANbssY/xYXy8qCJTvALscLhl69DxUrwsYh/RlxcTfRW1j0nJAT0TPEsCy0Tx3swk6kYR78eeATCO5hRoEiPs+hUzkZxjLwo4CnTWTeEJgTAzVL7wCL71x8vprnNK6eo7jj1N/Va3ePeJb+OzeA2E/kQiySxSbAz7s6HnqqD+NhPIyH8TAexq9pvJeVqrrowBuPVd3ji01guw5aYdV0+O48uoQIi6+HIBl5OyxwEcO1tef4tj/B762/BgCqZgHgl90jPCvv8FgG6Ozz4ZK8bXdjRb1LJSw+enaNq33oAY2jgI5uJbPTDheLA3nkOs/wzV1kKBsW+jWx52EPEqTFtww2Vh7r9RGnTYcifvZBl5Rb2mmJapo6YgQerwL7V3FLSTGKOyyrATMVKrrrtkERQ9ad47DCg1+ErXFyqAGAf/nsC9wMMzCEasY7jiKyRW+2M+jIYGWFg6w0HRMAVa3WchSlwa4L58xiRQeEXfEuuvt4y4JhRxyuzm4wzeUR1nKSl5ijgrqLOaCFp8BvpgPUloIDvPTE0BQdA7csO/UUPhuTzz1Y6aAja5sNHNUH4T4umx5vryMScF2AWVCgO+eeepKMeyhpskRIGpxdhj7+zbEhKH7Xl6jqkcw2AEBHBGFRjjiOBTapemSenocUPQppycmo3dZkrACV7xVnHosnO3JHMpbDR+mNMRzOCuqn+31B1RYvsv+rQzBM8XexbPMsMz/LaCiQzA4mRgetLzC+aTLCcGYmchKAx/6k1zyYU3STOmDSf7PzbPjA+vsQbzpf5gCusxmErSaV8OmIuhnpOnUv6ZxME/qZiYXLx2xC7zkog5UQjhRGcFBkDCF6FsLNo5lFqvoAwHiWWeqeoXgrMPs2X5ueRSnSIw9uAXWdwhpAyIBqQ98ZSFW0IPgXDMQkBwBRWfRJQghkswod+rlyYiihzyKErLJjhDmq8Ixia0gUDngZ3rHyNvR8UzCB3PIsU6odwc5qH55Raq34gkGvHFz/7jdV38tFVd/U4G2Fb7YlFpdhsVw3Hc7rIz6owqT2neItrkyYGH+z+YbSVE7FAS/1CZqYj/ShusVbG0gvP2m+wVq0+HwMbkiDl7gswkRbn2iatL7pTvDl9pS0ebpTWJyH3u6H6w12Q16AN10mlMwvjjifH8mKz58w6LiqThfAddFhpyu8ickud4cGKoaPP1ttUXCLMUa1DVZiocK1HE3+olnPMFcjvt0E2Ng5TjDi+foArLNF3aIc8GwWNhevuhVuuoYWxKYZoNPC9qahCQeVBTwjc3bbSZoI1UzDmvx5pbK06YBDJhIB92UzpcXyJMBcwxgiypLJuBzzZArk/rNv4uKSFpiOE5ElwIWeICv/9/pq8nUBfRp1n0+PtDi+/uqMekXMhv6RjMSiarIBUcJCW0EB9R/M79DHZJ6XmyX1IJ3jOG06rIrQY37TLsgGsvcM230NNokbrBNsPirs2oqcpJhwQCTilM14T7NqHcM4CUhIny2lQ9cpcv/hy9y8n0YjMgvwVxUFe5uZz/1DI+AZIGOQQVnmmLL+qgZUhky95llOMtf5QXEfJG9xYbMTaRmPsYept+lFfp5gmVzGR4Zx7WgDxTyj5wQZwth1IgkaPulXBv1p6uFynaHg4dKSo5NsGXzHwaPNIo8EKiDY//m5gYjXaU2GTBn3JE85+Y8CZ3/bQ27DvdLrCi/+VUX3WB4YIu0j2BFGtyLdZGhVr1wg1aW0JZ2dqGztswsYAChHfAh4AC7fcldm/gYYoJOl48iD7jbZcr4qKNKNOcCVuEfeSqN+Kei5jCsP82ikTVki1bliAsG/o+MB/n0YD+NhPIyH8TB+TeO9rFT5wMAZQ/WkxWl0Gno62+JfrD+HivzvPzl8Dx9XQV5jPcNPyoDHCHiciQMdS8AT3FvA4o/bT6Hilvn71Wt8V4UQ9J2r8O+2vwUAuOsbjEZQNNZq0eGDZTD8TBpuAAAgAElEQVTQP+oSgju8jdAwZ5m9GWQLipyHzudH/Pg0sGy/Pp6QmcRgJd7u5+hiFTirR6zjdTrPsBlqPJ2Fc16onjxgd7oid6Kz6oirbo4mxtX1WmKIlXW1MGi1QhEh5LPqiM+253R8xR2RjLb7GvZ1hGuXWdQvhA9Va4ICJ2xiE7NJU4WlR5mrUQA+EqSq0x7jIOFv43a4cDhG9yB7UIBycNFIgDcm+x17kMmHR4jN8tHpRh559kVFIE+Q4YDwOdKt8GCfHHEeCUnHvkD7Ktx/0fIMJ59qzFaZLNS1JVWrNrKcx8gQd55TpWqMyI5SzYB5MWCvM1EssWcHLbFadLiIrOpWF4RktH0Bo2UOTy8skaKEcMSabtsS3uWotrIw9O6YUaKaj5AR6RhHSSQ67zKxSt4oOOXJXYjpzCKFY8BKw8Rj6mORYWjhgdJmskzhgl8sgoFAQjbYXaiG9Em8r6ULkCSi2YHkRKxhJlentvK5shYB5mfx7+qWw0Q/W9dK8IOkIHRmMoTMTahCE+TbPXZEpmKG3TOMCJmn4edCfGI6iAcGDheNSthyzHFpNyWe/FEkLd5pOMlx+E5Ama7/mSCZj4y5rkmiYrOaDGBZDsQcAwxypFvp4aOPMTPBlSk5fLFeZClYdKVKcX6icJhwjMgbGjYEjhMcrjGJcENwEUvv7j5/b03jMT6K6Mi6h9fZ9CN9pzG++3Xee7mo4nIAGoZ5NVBv8XZo8Iv2Mf7z1c8AAD+qXuC1CdDnmrf4xoTea8MG7F1NcPDO1ahYmCQ/Gx7Beo5bHeQBKdgcAH7aPsPPNkGGc9fXGI2EjBPc7ljhrQxfIiUsbo8N9RC5cKjiwjYrR7z9+QXYZYCGiuUO/8/PfxA+gHvKBN0PJbQRmMXJe1X3NNFe7eeYVwO+PYQ+7Xl9gIt91LkaiKG8GWr0RmIXGb96kFgsw8J8HAsI7kjz+PPrS7rOdd1DO47bTbgHbq+AdbSJqzWd07EtYS2n/uL69IjtJjOl3SiAIrMLcxq7RxmDuY0WwFVJi7FnGXIs1z0495hV4fOM5dj0ObTAJmZkJ8FGBjWZkH3+scAwnTgs8WfhHnz38gZn1RFvujj5vVrRTGLPR4JLl/MOZ7OWNkmqMLQ5kdKhKjTlnf5MX1L4NTtIsNMY6q0lXu0XBOuuyp7aA8tigOCOeua7vqR3J7nepL6kkA423h9nJZnjq8LgZJH86IKOt459cNEMOHYljvvc36YAduFocTTnGrIy4XkDAJ9sgmYm3szU8xT3GKHoxT1I36eJVXjgkBpykUkaGcqqMtQWccIBh6yxtk1IYgIAHhei8MGAPEwckBDYwQCghUCx4ZkZ7JBxPBYg5T7Kb+ws92/hAX2R7pWBPSrw2EdlmASMaxY2W8lmcRQkYXr8x4Cpw/G2v1fAqbwYMwPEuGDwIbOVgXBOKVXKqRxK7oQHAyPZDz8dSG/urb+nn/XcA9EBqloOqMvs7rQ/VnDpOzNwVG/iBqrL9yV8Xv6zLT3EwO7BzbQRqi19R/pdGbSucTFlESpnD4vquzl+56NvoGYFCm5wUYQd/sfVDf6g+RWKuG3b+RJ9bJz80jzGL7sgrt/qGh/XN3SsN+MS/zHqMAHgk+UtPm7CvzvP8T9+/Yfh57YLDHGnrgqD4VDSLvzsfE/VyvVuhrIwpNG0kxi1t7+4CALwuPD93ZePITZRo3mmKTmnLjTOF0cyVnh5u6R0DDuGhayOfbyjLimJ5nJxwGByLzf1OwGgng0U6Cy4w8WsRRerWiUszaG3xwZdW8DFQG1ITyYRdaVpI1HXI9q2pMppt6/h0+9UNlg3xgmVjTz0YBESflI/zh5U0IfGCZkBJKGRMoSIJ0tDa3kmzigHPySyB4v/C//kedYg2rkDn2mw+D2/ON3hN89ehnvAPP7i+hlev44kMu7zt8kzkv0kW8A0lLSU4OI8w7EvSAKjFKeem1MOIvrtSuHQjwpdRChu9jOcLo50zG5UFBivR4llXCBv3y7BlCObQu9zr9RZhvk8nEfXK1zfLVDEzVupDNq+oOOpwlDvyw4CPJ6X6yQVo6wT8HcSnGLWHNR6oHvvDQc75N1Kkm2wkedFFwjPKC1sJkuk+KMRp6sjJUftdzWqX6ScQkDPPcwyr5Ziomf1E/OE5PULAON5tjCUO4HhxFHVKdtcadvCY1y7HN/ncK+6S7pmfqVQtIyqx/HUUXKRFwzyKNC8jCjLnSLjg90nDMNp0qX60ANNxxceIlWENnv3AuGa06JtF9lGke9l6I0m8lcvyXhDKQtrOYbXYW5QBw71/fwubW7n8EmnnXSrCOhLWtC5Cak0VJ3asOADWf+aqubxic595KMiZIp3PBt0ICzurvD5PXiHx7u/LXgYD+NhPIyH8TD+kYz3slL9veXXqOYSK9FiIUJFeGWW+Ov+A7zSofLYmhq/2Ifq9KgLsvl7Um4xOInP29BD/Gp3QlXCp2dX+LC+xUaHXeD/+c0PcYwuPpx76lk5ywHNUJ+HzzaWk7GClBb1hAG37yS6fdqRx51n7FHKkdHuvFn09Hu3uwZ6U5Ho3bPM4pMLjUIaMqH3npE7D2ce2z4HSTsryBAcCJZyQDAfWBcdsT8FLyhQvGsLuEFQv0sWlqruQlqqpj2A+ayHTTKOiTQGANxVBTXJFrWxGtVtkfsu0oUUkCiO907CRcP+47YCE54yWcdDkZ2ouEcdzfWNEXBfzqhXOpw6+GQcvtA4P9njo9jv/qi5xas+wLM/v32Eq5drgq8892DzLGlKFfmru8Dincd7J4WlezXqwFBOFn1KWZSxWtRcUC5vIS1KZXDsM5XybUyKSfcyjZNli+sX0YLLsmibN3nvEty+bLGPUhszSKjKkDn+bt/QzwVTDgGX7B4nKSJs5CivYj9YephFhl3VOptcOMfhBp6J04WHPOTf8wy5/8pBkLI4cNjH4b796PkrfHV3gsPn4dqq279nMNBYiPjc7F5RTxsM8MkycuQYZx5yHm0bS022imYZzEPusYaTMYQIkG/KWobm9N0Sm0kFZ0Mf1pLVoaccY9cLyINEdDBFf8bQPomogcpSE2FCeEBiUYsBhKI4GarUlGuq1y6HP7DJfYvm+hQIYBhMaq0cGJwCeISXzcxBb2q6Lqjcc8bEolP0DEXwuwmWkSr3dslxCqF/a2Y+V/Waw8Y0Lt7mcABbAGZu7xlMMMvIPvFdHu/lovp6XKIcFf6s+5hg1+1Y4+P5LX6yCIQkPdFf/P7Z1zAu6TctnhV3+NUxxLVV0kBFOPZuaPCX+jmuImGoH1VwDgLACkuWfPquBDMM7U140bvS0mTVVCMkd9jHRc8YQX1ClA5s4HBxkjj7/gY2voTbfYP2OvLrlQ8QZ3y6sskLY90MWNc9QcoA6HxHI8i9yTsGqbL0gTHgk3WQG/3G4g20F/hqH/xgBy3RRd9Y10lAOsjYD02JJunn0gZECIeqHGmBUaWBi7Ci3pYodlkfyhgoIspJAKt8TD9y1JdpgeRh0U3/1nGMsZdUzEdauJ3hlAJUXguokaG/CM9w9uGOYtRqqaGtoM3Di26Nr+M1X71Yg405jcY/HglONY4TVOsdg3WMetPOcohI+nGWo5oP6GM/sO8KCsKGZcBJ7KnKeN7xmekxh4YXpcE42ZBcXy1IkoLSQTWatLpcWOqVHrqS9KJCOZyvDqSLvTjd0ftx6Ep4xyHjhtA7DnsXzrd6JbLOUwXSDIsEsLLS1Dt2hoG3IhO+SgsTF0DWiTCZpw3PZGGzTwbMFuGe/vUvn0NdK2oz9BcOWEdIszJwlsPE9J+0mACRyBO/P/NHBzDmsX8VfZ+3NXg52SSY7FAmjxlmHS8s+EwDKYZuZLTg8gkhy9QervJwKYJu4GCvorTsNvxQ+3gie4kfLQ88Wx1ahmKb4VQAsHHN8zzYKup17JEfRHaEskHKAoDCyUkCNDDq5Y6nDmxktPnxpSO/bGYY1JUiWJZZ0OLOTdbB2irKe2Ib3hWASSk48dzSM5CH7MNsak96bfjQZ07SJLUL9ot2ePfB03f/Ch7Gw3gYD+NhPIx/JOO9rFT/9OojyLbEWd3iB4vg03tZ7PCD8hX6aAVyZ2b4z06+BAC0tiBDd8Us/pcXv08kIG0FFtHpxmoFQEFGRnG7W1A+ptM8e5/WNpByInTTLAYyuL/ZzLEZ5+Cx0uPMo1qGfztdHFEISyYPxnF8cRVcn9xNSTtCN7dgZXa7SbAzEIT+m65Cm6j9zJOLjFSW2LirRYeb6wWRh/hqII9jAPijN9/JYdUmVxessJCFJRKWtRwmmpwK7nAyD9tb5xlGI+iY3jOSXMDdN2pwpafqXMxMZjIaBrUYKUfWDJI+13ugOe/ILWo/lNjswjNzvUT1NlZRygdz7wjdMubxZhcqmfP5Eb95+gImnsy37Rqv4zUzzeELB34W83fXB5iIevQ6V5KqNoEFGiu/0TGCdefzLjB/U5BALyhVBwtN1+Icx6LusYnQ+RSSDwxq5GpUOrjYLWDR51VGCNw5TmQzZzmxf0/X4b1KEqnRCrTkw+xRVjqbdOwVqgT5lp5IOXptoVYDnXPXlrARjmS9CKYAEwJSamV46UMiTkJjltnwwWuO49fhfsuewZY+hHYDkKd9rtx7GSRR8fDBFSsZK2RE97ivIL8tIWLVZitPhCPXuGD8Ed2EvLgvh+EvK8TAqcz8BQAODKfR9Ul58J6j/jYzltNwJaDnjo7JR0YEJLAIHQNQu+jvGxF8bkAXoOcBrpXb+H2amM875XNgeeHALINN8qZJZSoOIvgCJ/RqIqmRe45izyb5sJltbGvApC6UDuec4GtzZrIL2TGdG+gc+0excl+P9H77MRAH1S5e95FBdAC7z+t7J8d7uaj+4eXnKOcKl8UOQ8QfFrzH//z2D7CLWsCCG3y6CJKYvalwiG5D/+HlB6SfBMKC1U+caPpRoY/uP6Jw5F7ChMdHT0JfVnKHUhiClIGQNgJEKFUqqAiFPl7uISIVMGkQkzxGMB/6ZIhMv6jJrGYjVrMO501g9XHmyZB+35cYddYaWiOzFMRyctm5vZvBWwaxCN+qqh4JBv13X/8Im9t5nhgNz33OwkFITYxTILOVBffYxjQVrQU49yTxMKMkKYXc5aQZAPBrjToGaDPmkbLfOPc47qsclsw9QVlirnEy6+he7duK4F++F2Qtx1y0N4z3Y381h4rOP84z/Pn1B6QLPnYFWQCWH7cYtKQ+5HEoaJJXIgR7A2GBso5jiHPYrBmyZnVUQWdrJ3Bl6ttZRvfwdNZitIKeGeeedKPeMwiRWb3jICHLBC8zcOEhIrQ6q3tKTeqNIGheCgvrOO7is/n7o+8K2Li5kltJ7GgnARf7mihtsMKLz5APnDSffGBB15tg6Wn6TGWBkYd+JgC2FXnR4nlhM0sbNKzx/uheZjceG3qciQWOIm+8YDixc+WVBPOArR0dnyICNb8Xq2ZqYIxSELELMCsZ9s/y5tIVeaEXBw55nBj713mhcwqA8FDbdH8y1Jyi64AAOU910k5mlq2X4R6mIAGvfL7/i9wSYcC9TYboOXigTWR5S5sSiSwQN2TyyOAEwHM7GjGpMThkxY3umNysEuv+KFDcZbmNaTxBxa7IGxx3UOQ+JXqOYpPvQZLUTAMZ3tXxAP8+jIfxMB7Gw3gYv6bxXlaqf7N7CmULvNj+iFiah67EosmG5UpYvI3euW83c6ocOfeYNUM2RWeexNLHroQ1AidngehiHMcxYnGnJ0f6OQ6PTV+T0QIA3LSBOGM9Q1ONBBe+3CzpHD2AUlo8mgdIc9PXZGKAyhHMt44QazIIqKUm4lMfw5DT9VjLg+8ugjdr0tL6bQEGwMVd/v4g8R+uwzmKxkAUlionSEdVqywNvMe9qmqYaEWHJNJnwVEpucrAA2KbIVmUgD8NVeFs0dO9Go2g+7Hf1cHcIFZmzWwg4wPGPG72M0IVbC8yIQO4FzbuRYYBxULjbBUq/FIaDEZif4jB28KST+9xLHDSdNjHKtY5ThC+5I5MRTjzGGzGstMxgVDBc+EyZG04bXPrxYDT+BxHK7DvSkIlLlY7Qjm6UaEfFP2bKgx0JAh5z1A303uXDR+aZiAN7X7SCgACOS79eRwU7MhRfZNIQJMgb5EdfVgro7g/3lfpwWPVapvoRGUTPDktxdi96sTO87s0NW0P1lfsXoYnZX3KENCdPek9vUvwuBco7iqXSwnpqLIWB05ez3TOiZHbMdgK93SaFBSuGVis9IodAxzIpcksciSi3AnUb3g281fZiN/L7DzEfCACpX9LulkgMGaDxjd6Yj8/Yj0LzPrRSGKHd9sqOD0dcs1EBCEO8JmmucgfJcqrlAQQrjH5CZvGk8+wrXzOU5UO7ChJC8wHQEeoWa9wzzjDT/5cvhUE93IdrjORwcQQru2fgk71vVxUr9sZBEp8cnKDl4fQvLhYHrDtKhwjS3O5aEmiUlV66qMOKRxNOt2oaKIaW4WiyTCM1hK/9VFgEz9vNngd5Ri7scJp3dLEeNs1cD7/TjtKlNEJ6GTWodN5kpTCknvO49kO7UlYBLfbBh/FhJPDWODQl7SYsQme5B0D454WTzeKbDhgeU5dmUgLgGBv5n0WkzPuSQriO0GCfecZnBEEcXLuCZrUg4SM8g47ipzBiLAY43n44pubCn5mMIuQ7zBI8AhVai3QRgkALyxEbci04PBqThOoLxzczNB5oKI5HcwKguXsPBj702I2Mbx3ngXYNc7Wj88OkHE1Pq1b3PU1Qf/es3v3OUHl2nE0SkMW4Vqu24ZgYuM4nOVZbM8BtY6mEU1P88ugg/uWjJDyYCS9L6OW8I4Ro9hoSRPmen1EU2gKMB8HSRaJs3Kk/661QFEYeidSnxaIZg8bSf3Q4dRRILUvbbaZLEKKSYL3+JidjJiJVoKpn9gzWrBsE/rlyU6SiRA4DgRIlkVHn7R4p0Um9fUAQO2DE5KM95EZQcx3W3vY5E6UFtgmm/eT7ESGzTHBqT73LJ0CxAjqgToFuDot/IBK4fU+yF3MafIRZChi/7nYMpLEABHWTtfUZRjU1OE4yekJLLN57TxsWtiEzZ94DWWZF0qmHCA85h+EDeDmZg52TJqX4HIm9xnynU5u4yqHinsBjGfxIc4MGfSLbbAoTAu/PsmZqeIoyKoQiPaO8XTFMJHhOEDtM7vYZUOsd368l4vqsuwhK4+XhxVZxPVdAWcYVXvLasB5HSrOSjY0SRbcwniO60Oo2owR5IizOGmxqAbqW/348Ss8rUMzo7MF2tiXPa+OOOiSgrwX5YA30cauPxSQpSW/3x+evMEQRWCfbc9x6EvqUXI2xybaAa5WLV5uwhds6At4fz8RJfXnyip8+Y7b/BZTH094kjZ45cEaky3jJv0nAMGrMx2CIcsgBgFZ2nuVfaqUlLTkzQvmoUoLGX9OCIfjrqLjFY2m+wrP0CXrPS2oMmXcwxwU3NtkoZc1iXySBgKEzQPfS7o2SiAZohfqyRjvncIyEs9ujw2OXYGqCf/WaUkbIckdRiOop8qYJ9KV5I6QhkZpKG7pWU/77+OgQoxWvMVq3WO9CJWH4A6LuBD3WuJ4qPHBRdDLvtosaQHn3IEpj3HIx33+KPzcsuzxcrek+8iFow3g7W5GmxouHIY+bw7hw6Yn/dmtDPTpRHqS3pfbiXyJ540JEGQi1K+0MQ4s9dZKDzOPG7LC3+uFe+vBitQrzbIw3oZeK7kjubxoyzYsltEdFF7mato2ecJnjgWHo1TS2hxAz2xELZJDYp+dkcSI+NmgnyXJC8sELK8As7L0XVAbQQQkp8Iidf8e5eo0kYBsHRyUkgRGrx3kWXgnpOMwR0WuYX1b5IW0Ar57EbzKnWd4tVti8zogbfwoqEJmPvZYk7JFeuphexnvUbwAu5g4VF0V9DsA0D82OVnIcIjb8P4V26DVnS6k6bNNFSRB6TymHs1AqPQnf31nx0NP9WE8jIfxMB7Gw/g1jfeyUu20goxpLwkeXC1DzzOJ9t9sF1Sd3hwa2hF+fHaLmRig67D9unUNVQ19r9AeKzTR9/Vnbx/hqzIY8U/lI2/EAse+oKDg163Mpu3cYz7rKZB6rVrs4ja2URq9liTdeHm7pH5c2xfQyfx6p+ClR+oQT92cpLQ43DQZ1i0t/duwLwniYR7woyKzba4MGTpYy+GVC0YPAFiVK07vGaSydL+mTZIADceKpA0sUhWlSm2bvZD5yQAzCkpAuccI5J6cesyuADMMbpkphDw5OSmLMZp2A8EtJ4nhvfTwCd8UHs0HO3xyGqDzs/KIl8fQEmiHAlWlsYi9x9FItLHSTAkv6TBSWjK8Z8zf260OVlJQ+JQ5zpi/l74Dn72KC2Gpwl+UIxbliF10uyqVwXbb0O31lkNEWP3iZE/93BfbFfpRUdqNNRJD6jVadr93WVpU81itd4o8X1ll8Q/6XNdlut1wMSyBcQ/fSahNcuzwBHV6HryAycxjZLmCE4AXjGDvsjTh/USQf6QqyhU+wIexujYzT4xt0yB43RJz1+cKKHo7AzFNxXEgvS7DxPM5Mk9T9cgto2rUy+D/m87ZC5BpATioErYrC9Zx1K8SNwBUWZt5vhdppGN4kY/BNDCuHcST0E9XLLSV6DlxD7uNntiegUeDkD94+hVkLCX/7NVH2LxdgEfI16XMYADFlYDociZrgKzDH8elC8hBYg0fsj9vcswCgOKiReEZxk14wPXXCiL1m+M9IyOKGugfRWetuYG4U/RzXuQqlms89FTf5fH6agV+qFA2GmfRmLxWGq93CwxxoStnI17fhbdN70qCRT93Z5jVw2TRAEFvtg1G1vsxWoJxT/0z7/OE6rcFeMeyDZ+KBA2ERIlFNZAU5P/4/McEBdeFRiEtQYh2Eh+nxyzNYDxMPmnR88qRfMI5DjUf78Gzh7t4vq2gFBDPQm+GEUToJ8HVGn1bkG2bNxwumXcfJQxTtFiodQ+deqqjJPmLOulxumwpZN2OgnppKC38Lh9fLjQZ7xeFIZjS1jHoOfX1HMu9zLsCKByRWeQxyyXAGPiTAKn917/9f+PH5Qv88fFTAMBn7QUFBXSDwuXqQIvUrqvQpWi5VoIVFvNVOM6jxQHPY1D7z+8ucRHlTKMV0FYRocTWnM7x0JUwltECpltFPd3niw36eB6CO3Dmsa7DMb69XdPkw6WHqEY8OQkecs4zSsRJC396B+1BZrelyWC1gSwN+n1aBYH6NHzW0Ct4wyHj+2ONgF9n3kAimrm7AuVNJmSZxgepEsIiyhwj0s40fs1XFkw66Nto0/lSYhmTFb0AugtPz89Fog4QerbTWDFbIpOCJmb4Tk5kLWUkUE3eCQrRZuH3klOSFxlCdirAmS7Bok0+ZrpfACA2EuXtRC4kPR0/haon8pboM1wuBsCp8Hv9IwPUFi46gzGZ5V6sF2Ajo/tanLb43WfJAY7jp9chBWvzYhnkSCtD51Xcxc8dI9ks7smGM5dhXuEBw4ngxHWW7+B8QBXfAaMF/NcNmk2cGwyytpWFhTT93nBpKQi+/qrIUHDtofb5OynGCOPnjtU7Ox7g34fxMB7Gw3gYD+PXNN7LSpVLB64cvAduD2HLZgyHuamDLADA8LYBUmAxw//P3ns0W5Zc52Jfmm2PvbZsV1sABBqgeeILiUHqhfgkRWimqQZPEdKP0N+R/oFCE4VCDCpeSCIfPUEQBIkGGl3dVV3m2uPPNuk0WLlXnqs3xYDFujnoqOpb95xtM3N96zMQkchS5Ba91chjlqhzkglBLUjScBjizNWpR8omVAHuxCJEKOSQOFMUBqum5JDoorDIdWII1JnBXkVziYP/LwQAk1iId7IJM59CrXsNrT3njO67HFkVIbxRz4QmIQL2FyOGzgb4CgCczRCcYPG932TwkQQ0GFCw+ThShe6NZNPz2bhFZzRnqMrMQwxC/2UOyEDQIwAhPfqIIPS3ZcreFAHoUnWkpj3cKgWRy51iblX/2LBs5uh8gz988ksAwMrW+BP7XVz2hEr8fHmO6xWdbFkYNCbDZfz7qOrQiETOyUrLEO3NrsayITLSuOgw1oSDvemnyJTDtCB4s7OajTj6Xt/Js6xmLVexP337iCPixnmPTDp8uyJYul0V7OesMwelPC5WdPzeizvhBL7V5KcLkOxq0vO/0weyKntVMRojJ4bRAN8pCiaI/zbLHKEU8bP9OjJCWwkz9alCLHwKdMhiPN+BGT8zvyNsPLBwzTgQ6QkE+dq543+HzPP9zhaK4WXqVaSqx8w8s2eDxB12q+hTCH3QyVxCNQK2Dgeh3+EObBw0YKfx3XCCjeClTcxk3dAx2PFQ4abzVY1AcasTYeigurN1QH928C7vNBAdvkIvkzzIC7gjg/kZlfLHoz3nIi/2FbZXkalVeMAKlkEJA4ZngyAnJHs6mAaDjfjllljZXE0+6rmd4o2E+VUkPvVA1gjOVZU90B84QJlJYhDX32iY6AvcPDMHzkvkd5xt+LSh+gD07z7++14uqt5JwMo7RuShUaS/irT8IANkhM187hmW08rBOIUPZ8SwxAx4viCrwHyyh1aeTeLbNoOM0GE1MqhOYoJKl2N7MaZeB0AuRLEnNqk6kulE7RsKyxDp4/EKrcvYas4sS5IyAAS3HkB7hyHQHzy65UX10o1R5oY1bX2XMbRal8k1aX05ht4o1qYdSoXsNoOsUyC18CKZ3HsBXVqGpUMQMAOd3wmMjqhX5LzA8maMMroXhQB069irswKh9JBxo2E2qTcKAb5HaicBAU4yYRcd0CRv5xZHDwkWVTLgJEKyp+UOb9oZ/9ulqfHzJQWtL3cV6rjh2GwrhFGL0yn93qYt+DtUbcnRKaYQKeUxixDvB+MFWkfX5ruzS3x/9AZf7inx6C8vnyVzfUvHn8/o+KUMDC9PJ3vODr21FYxT2NzSpKnrdH2tUbBGobEvMeoAACAASURBVI5ym1w7bGVktK8LwCTYlcKp6c+zacP6W29ibml8lmazferZOgFROA5WCAe6UtFIqGaA4n1i8sbf48VsQpK0YYIGSJYFAOgI0hwWGN0Ktrg7hHtFo5C/ydJCJQlSBShBRu1SWoywgidyTqsBAE+Lm2e5jePj90WA1yTFohNNzl5BRc0pOzgJTtnJtoklbEe0mAzHJXtxoG2lBZeN+F3qqfYnLulxe0kL6iBfaQTDs6JyOD3espzq+bdnxMIHiJMxZJ9uFIprmdjKHpwiY6aUOyu3w2ZIsEwpCKB7bJg5H1oFHyV2xY3isHSfJzN9gBbp6O4KvSNGtYvPnK0DbJxDRCt5M1IsBHSKcQUEYGoBp/7j9sS7Nt7LRTXsNIKP5KDhpTWRqcDU86Sl8znFRAFAZxWafYEvQdFvbZshxMV3PGoRgsBZTDlZqiqliWjH8p2h1zVMQNW05arkfLTFGz/F8XzH3zeI9H2QuNiO0V/F6q4X3MvUI8NGEGFHFmWjh8nEYIh008pDycAyC6UdR7pJASyiP67cKtiJg5zGXpqTcPEFgwrwjWZrOT833FvLcgulPPZRHiNuM0TrX9QfpG3p4moCoZJNobNJKuMBiowaQq0FmLAiTLovwlP1MMS7HU32uLwiWZE8bvFktmVymAsC44zOc28z9vN92Rxh3Zc8/zsn0QwGCaMWZ+Md97cHDfNw3bwXmMVYu/PRFg9KOj8tHR4Um3hNA36xf4gvVrRor7YV+gV9jmwlwtiy93K/SVWwGyUzCQDY7EqWsijtuI9fVj0mVYtxHpEHk6HdxhSWvQImNumJnYSNxhCL9hB6EJBTg3qUrCCrGGrdSkJZWIMrgXAdkZK9TMHgKkAe+DIDEjIm1kjpoVRAP6TWdAqIiwH3Vgc1j0jRZrIXKKJH82C4MPTqfJ1CucVWQ5oDowQR4MYHEqCIeIS9ooVtOJUDJMNVngwTVokcx3aA1cGiB0Bf5qxhNeNAiTMAQkkVYrZMGtBhg4AQK+P4SB8GjGfHLc7mNGc4L7HaVWhjRRemFh89JBLdLKc54if/9CFd8o2CO6L3U671HZJV89RxhKHcaK7cfRZIozpcnpB0sHZuiUfxluapvBUob+K7Zsl7GBisCMEbFzvySZ88GGYMJC8J6NhTDTIRt/oZYCvBlapu6ZofSmze1XHfU70f9+N+3I/7cT9+TeO9rFTZuNmKu44qMvDOOVsmSzHowAknsV3JqR1mVSCL8N35eIu3mwluBmodgCJWCbsu5z6V6TREZZHHXmZV9JiXVPHUusdxtcfLBfVKpAzII1X+5XqGxYsj3hX6uUEWYWMhA28+XeExOmrwaLbm4xjFSuaT+Q1yadHO6Phbl+HNhqq7fZfBX1EVFUYOamLYwhBeJKjZA7CSpSwq90mW02QIe80m2vJhi9/7+CsAwG03ws++ekKfIQOyKvXuhCDjCABAL8lNZwhnLj30eoDiA28F7dhDHncYxyq/ygxm8yhF0I6vKQCUKsHXUiSEYtHVeLueMOwqlUMepUNKBOz6HNdL2qK7XjKEqZTHRye3+MOzL+i4gsTLluRTvzl+iTxax/zl5hO8bSYpBEEd9BZPOkiQMQUACB24v90bjfUQ4G4VzDbnSt50GkX8d6fjHTqn8GYV7+G6TNm8T7aocoMmssWbfQ7fDjighIxh3SInCHi3ibKf0nCSjjcSWWUY/vVXJUOa9thytSizKL1hMwLHfdMACYdkxwgZGBpWW3JDGqpM4cGGDNKSlR0QK7uRT/Bs5lHEahqTDl1dIruma2zrwFUaZAAGCYoMCCrw5+u9YBMKYclMYnBU8mWSocALiI0mQwsAEEDzJL53leVnwkWnoqEq1OnxIxcmDbTn8bhmhq9BsAqLbZozHs7XwJze3VneQsby7fV2hovXc37/ASC7jhKVPuWdmgct4AW3dXwl4KIMTzhB1/HA/ISNGjYKwmmWxWS7BMubyYGt4pCdO9yKpUxOZjpQ5TuABsn8Ce7IcotKXmnoBmwooRqSQcn7nuq7PUQvcaCMYesuIPVF6B8iQaudIseXQX6QBZxH6EZJjw+PFmwj+HY5wW4boT4Z+CEvayIEDVINJQPLJ365G2G5GjEJ6WS2w+WWJvXVskYoHWQkb4yqnjWIIQj2fC2nHR7N1hhFDeiz0S1OMoKCf7U/xd7mDItaL7lHs78a8YSMLJAd4bCI6CTLUdLDB8E+uwMUCoAD1c8/o0Se33/wFV42FOz9j18/Tmk2IsAVko/DrPLUg5PU2xp6gcLINPk5ARclHcW0Q1UYTAo6z1w6PJmRg5UWHrXu2cWq9xrzfIidk/inS+px7m9rQAboatDxeobsfRBY7aoEaZqkB/39D56jUj0W0cbn6/0Jfm9OmwcTFP5x/xgA8GJ3BOsluyNhDN5I2F5BSPBCmuWWHZq2uxL9YNEXBPJJfwcetnHR28R0nCH+rpq03KrQymHTlHeIcyJOpnrc8ubBe4EeGds9ei+wu634Gnsn4WMwufCAjyQXAbAMKniB0CqI4Vo5zWuSjZ7ActgACurHAmRT57O0kB7qFPtpgDk6WBwPHL9IrhYh2CYDVIB5EI9rrw6eM7BMTGQeodHIL+k9t3UiIwGBouzi9VE7yVAtQL3T4e92bnmDKa9y3kgo0EI9LKZBkO0fQOdoj9ImJMsdzo4I+7xZj9DE+aQc92itRhafwatmhMtoReiiz7WP5+YrjzAfYiAFQmyXyJscIQ8I3KeWydP4rIcQgFxGudZe3JHDyD5JfYIAOtrbwxfJeUm2QL5M18bWCbJ3Q0zjgIE6kTaRRqL6NrV0+mlAsYjXzpC38WHk47s67uHf+3E/7sf9uB/349c03stKVXQSQlJ+4rBLk5acV4amvbBpxyYOQreRe8jcwccm/Xy+57zNAPJpHViVttNcufkDuE1rh+1tzX6ncmzQjQnC3G5L5IXF5w/fAACO8gY/vXlEv5c7yMqg4krEw8aqp+0zjCb0GR8f3+Ks2OJZRQSHGzPCT1YEu9qgkMuEyZTKYL2J5g8ueqMCUIW74x1c5QZdhAB3+wJur1HE8PSqMGxQsS9yTMoOv3f+HADw94sn+PL1GV2fVjFLRE/Ig9gsY/UlkaoGB4SJveM2NMBLfmJZUlBk9o6J/dbkcBHOX+0qCmCPFdzj4zV0vNmX+wlVqADgATlAhSBEQcXP7IymvNMIgatpj9999oJ+LQj8Yn2Of3X0EgDwPzz4E/xZNJD4q8WH2PaRFSw9MungIhPndpM8d/OSEn2G+9kZzWxi7yWjBn6n4a5yiMHofGz53igZ0FvJ+a2jvMeup2u6aQglGa6BkIG/2xkFxEo10w5aJ4ZvtypZfmSNhttkSZVyfKDOF4CKciA/GHcMBWfhmdwjc4KCB5hbLTS7W7kSQAC6R5Fws0+lii895/kGT6YmnOtaGSYIopeADizXQuHIiAUg9nKUjGCnoSxByUBk4A4SoDzQOQ/EpYljNm2+IIKTexSJXCE+yyBv4YFBrBoK2nbxke5nAe5xJAFqj3HV42REaMlptcVPXxOa0d+WzIptW4VFAN8L1+pUdWsP7AndoIsCiIvYtnACLqoJ/ITM74d32R4b9r1Gp4CDgPTiViSJkQL2Dz1wHhn5NwXfJ9WB311hqWof2Ms+PyCaFSRvErEdhGXOftzZInkQuypA7wTKm/iMKzKAwL8A84f3clGVRkAqAdkDeXRl8Zp6BmyOrcCaVQFgOo09z6JHqS0bpu9NxmkwV4sJvJcMyQrtUUVdoBAJ9lMqLswHEph97OkNOtcXa4JMV2XFMpdR3XHsGQDs2tSnnY0bnI9osflkfI1CWlz0BBv9an2KraE3fZInDSUAfHF7DjtoO3OP8SnBxLm2sE6hj+d2uxhBDS5MIkCODOaR+TorWo6ZQ9Hj8XiFLzYErz6/OkZYRL2cAsdW2VYTtfTAnhFxUgi9ZLtEgH5HDFaEJiXgLLcVnFUMlWvt+do/PV7CB4FvLqnPebkecwTbrssZiswKut4DFJpFyRRA/WHfKeRHtFn5/sMLhtQr1ePfnH6Jf1V/DQD4SfsMXzckrTJO3Qmgv93VWF4SI7ect9x/bpscvlfoY39e5xZ20DV3ivWl0gpip0bt4mTacOsgUw6jcc9993VfMJwvpUffaxSRHd0LwMSNSjXqGSpfdSX2fZZY6QDfF7fOSOIU3wWlQ2K0F2lz1uxy4iUMAQmlTcxrKxGMhFpG9m8ZeAHQG0Xv4208b5WM3McPtry49G1c3IdEJS8TLB8E4O5KqngEJEciTwzhAcbtT1xilfcCIQ8Q8TxDJzl4uz9xkGODsI2riEzyIwQxyNkRpIwM36g5/3jDNpazuoFxijc8i/0ZiriZqh/3/HzAx03NMGSSKamVvpNuI1vBTmyitsA6Hp+RCCMLFc/FbTVCnNf0WkHvafGnDwH6uMnoHlhAAvJyiAIE/56pA0K8n8WVhsiSFaErA/fZsZGwZ4Y3Hcg9xMBbEQexgZIclWwZr38I6CoBdxBg/66Oe/j3ftyP+3E/7sf9+DWN97JSHXZ/2U4y3ONzgiQGX81D9xcIMKTb9hmcE2zOIGWAHRiyvSTyUyRGqCpBakAKBteStJWDQHo8ahk+LZTDm8UU2wgDrnclf5fSDkVuYeLfhQjs+fqbx6/gIwZTSIta9rh0tPvtvWIGaJ0ZvNlP8eqaGAj2ugRGdFzlpOPqubcafa/5u2ezPfsd91ahLgyqjI551ZX850ejNfY2x4slfb5pMsjoRiVV4KpYOIEwssxelsqji+QY0QuCkIZiQAp4k6LNbi+pAs/HPcajNgVqW40Pj8mUY543+PHrpww56jr5NWvpMY5QubEKWnt2yBIioG8SIUhVLmXn2gyfj18BAL5TvIUJGj9tnwIA/vji+7jajfg4hmrOGI1ulyMbJ1zLHJjxC+05IMB7SUHlIJ0wuxNVHmqafr9tM/hc8u/0ykPHytUdVMj7fYEsc4xmlIXB2YzQjAAkAtyqpjbFAJPKRCRCRGyGqgcAX0fnEiojVUAxa6EjamCMZv2t0p7cl6J7UdABcp+IRMIl9yGfgZnj2+sRH4fsBTD2DMWHG4kQq11ROgQjuToSLkGahzFkQURP4gH18CLJNVWA8AL6zfB8pmBvOTLw2wwqEhndLLUm5FYzuclPLfJph//8A2p9PCjWuIpOXX/++kPOZwYI8bpdx+fltkx6eUvP/oDowAnoqJ21xxayshw04SpwTqq4zRmyhxJ0faNjW+ZSFqxqCaodBAr9iWOYWO4V8qVEPxsM8N2d0IXqxYD3EnRuDtyohvtnTyx0ZQmJAgALRlxUd3BfvIArwN7IQQp0RwG+vWf/vpNjgH/7aeBewBBgzPZjnYAdRN2BzAMAwEkNYSQ/iP6Q3ZZH+HFgYtZ7npC7LksyHKXgncTvPKN+XKkMsriC/OWbZwheoI2OR6FT7LjzwckSEuFODusnE2LZ+iDxto1WdWEKKQI2PcFIvVN8HNs+x3pfwkQ2J0YOqhjs6gRsXLyy3CJ4wVClkgG5jixVrTArk6XepOgwyWiRal2Gi+2YNxPFqGenpJB76GgIoDOCvwf7QXFgaxiqKGfoDoCUoWU3sqjHyYXJesmTvJQeq8i8frOe4sOTW+gz+tx1V2Ibe9+7pmDorcgsMu3g4mai6XLu1QUvEAJQRwh/VjQooyXO/7P5DTzfneD5kiDffZcxc3doEQDAjauRVeZgsVFsD2htQAgqLVKNgogLgx8dGA6E1I8EgGzkMIkpRrsuR9fmqCIDersvYa6jtmJikNcdp+w8Hq+w6uhnq67k+4dNBr2RfI1dGXhSF3WCcQHqncpBZeUFL5y+V3BGsgkIkGw0rVUQuQPYCStBfK4kJ6bELBUI0VUnWJFSak4N9UeHnutZBzn0b52IMC/9XZjkEuSKwDKZENnDMkL9UgXYofdq5B3Gr594qPisum0G0Un42GuEldBRyqIbgfYxPROTkx3+4MlznOXE6v3jN99DH6VUubZY70p261ptR+SIBgCFh4xWhNKQQ5OPJzd6ukE4j+eyLaCURxkZv6bXMPHW6AsKDgeojeWqwCzkbHPgUqUjI3mQNO0kxCBrc9Q7La9ifxdg1ze9VsmOsSPb0mHu7OeeGL8AZGXJZjQO2Us2hLBjz38OsSebRcMKM6LWm3f3i+o7ObwOgKad6WDDFzIP0cq7gb3m4M+DK0uglz2LJJ08d+xO5J3E6eMNplE+0XuFq5gYImVAWdD///BogUfVigkxI9XhZysiI20XNe2Ch0VGB+RxYRvpHo3N2GKw6TPWP07yjskxzyYLtE5z363ZZUyQqLRBbxWa4SW4yRDiLGnGjiY/AHXtIGXg3fW0bHG7o+3tqOjxejnF984uAZCWbhUX8NumhhSpmrFGs0+t3WWwkfbvPFWqg0QlOJGsFUWgvtBQHQkwCWU0arkaKzKL6UGiz3Jb4SZWgR+eLHBc7LG1dE2Ml5zuMx21TPDatzmsUdwfVMrzYlBWPT6YL/HZ5IqunTL4m81HAICvt8ccLA/gjj9zphyWTBJSGNctf7c8kIJIGWC9gI8TuzAi6TB1YKKSzg0RqGI//eOTW/6uTVNCiIBNdHsytyU/O6Nxh4eTDW+ovl4dM2IBgNOJ8oWk6iVuIjGyKGIwe7/PESAgYuVdTzp0w+bwuoIYemCVR8iQHL4yx71ipUmj6qM7l99ohFG6t2hlqrIEGC3S0x62VPzP0EkmvRz2c7tVmbTnIN9hPx401SLJdCQAD44f9I1kCZBsqdriKraXwCvagCgR4E9Tn1AdoAhm7LmCbSc5/uLtM74Xs3GLcXznP5+/xde7Y/zqipzYzHowLgbEJhG3pKFqOjunFXFcdkw4C52C2WnYOm4sljnyuCh5RYkzw5B9ClU344D+KP75zNBzFT2bZSfhHkTSW+7QOAH9NX1fvpLoh82sA6IqjzSqB/pT4ZFkOF6QvemB3nfovQorqMoHoG8z5BvBpK7hmPkL3+Fx31O9H/fjftyP+3E/fk3jvaxURYSVQhZSOHAnadc2joL4gCQ0P5BcwEmoeY/qwAhh6FmN6z2UCHi7IRi2yg2+e0rV3Djr8Gl9DQD4sLiGCxIveoIO/89X38ciylp0aSGV56okBMHw2z+8ekQpLPF4zs7X+HBCPUQbJCemvNlPkUvHuaCPp2v2vX29nVFPkoOVU39IZB7nZ9Sj1THD81n8/GVfMfN43RaYVB17kQLA10ti2W53JWyvuFd6mNojSwtEZPLR6Qrn9QY//vJD+h9G3O1j68CsTT02mESmsVYedezfPh6tcNFM2FNZyoAHs+Qv/M3miEO/F4sx9yuromd2rmk1hEpG8yGADfTnZYOzMvkHP9+d4PWWjPhXTYlcWxzXdFzbPm25F9sUXD+uW3Qm4z7qeNQyTHyzGpFhwvB8lWCDECGT325ZGoyKHkfRIWpncryMrGa3yiB7SSHgANTM4OOH9Jx9b3qJpanwKoau15nBJFZO67ZEcztodCLky5VxYqbLzENnlvkAxqhkJlFbIAajoKeQCh8hSCeIzQwQK1vr1NuV88BM4/6mhAiCIH8AXkgUFxHNWCmIWPHgqIcYW76HIQgyfQDoufGAiNWv1P4O89hH1q7oBTCxbEKvF5r7rWbmKWFpaDmEVDEjANhoZNvk6sWm+TYZFtjbErdNhixW+a3ReDSh9+nvbp7g1atjhq+FTxB1DNnh4wgqwL2gC3sdRgnBGXlkKwUZ/ZCDDCn1JoCrXTtx8HVIIeVHjlnNsBKhUcjO6N09nu04bEMAuPy7BwzJBgnOYQWSVAgipgllaQ4ZOANmWUKE5MMs1yr5NZ/1QDPA3IAtgZQpK+gkDirgd3W8l4tqyNIDMdgUyl5QEz+L/YWzDqMRPXhNk8PeRIN4APmsZbivt4rTSUZ5j73J2Ag9k57JQ98dXeJpTrCdhMfPmif4P77+AR1PAI7jRF4oh86lPtt6X2IbXZl8q6BmPfcDP5tfQ0cpRWs0TySVNvBBYJwPRvwCWxMXHhHw4NGSJ1cghl4DmNQt9wIB6ld+vY6TdxCcmlJmFp/MbrCLbkVbUyRt7jq/swmZ1C33K02R+oKvL+d4tT9N8WCVw9kDkng4L3B7OUUeX9TZuOEX/6RK8RgXzQSLfcU/+08fv8RVR5Dst6sZpmWHxaAhbhTKY7qfuXZourQIHrrzTEcdX7d5Tvf1n5YU/ny9r6GGEITc4LTeMeQegmA7QwGwjnSzreCsRBknWuNU0qI6BVlb1o5K7Xl29VZADTII6dEZjStHn79YjeBvBhkUQZZDX/xotkOh6B7+0+oBzqotPpvSItu4DH/3lvTKzdcTyEFbKumgB7vNojQM8RYluX8d2kkO8LiQIfV6AwDtWfOb5RZZ3DxkyqHtMybjdSaDeRuZMpUDygNNcpAk7QB9noqtg7OjDdo+S+k5APdGvZGUBjFocFc53AHBhrWzI+rr5reRZNgLhkxD6YFWpY2dS4Qpso4CuzupieHjdXvN8LLaS8h1BlfStNqcd/jZjto6fp0hWynuQ0qbbP/8xKV+sJXQawU/JPBUnuU7aqFJVy+GBV3AxJ6nmx1k0KmAetbATOLie1sixOarKh1GxzuWU902Na7ic2tWBbKQZC/CJmKRONg89LPAMO4wTORGZLck2Rk2Ky5HChxokn2pV4AfhzsG+kEA3uGdH/fw7/24H/fjftyP+/FrGu9lpToMyjeMlZIgFhtXTlcFNoOjSmUweZrM6YvM4rdOX9OfpcVlrI7+4e0j9L3GZw+J2PKkXuE4pwr0d+qv8TLCvX90/QN8eXvK8WhHJxuuwB6UG1y0E3x5SYSGblWmsPHSITjBgvIXmyNmJSsR2EDexPDETSSlGKdShqeTmE/3XM18szhiqLnODFd9m65ArhyTnZzVd+Qaz9fH7CS1WdR3XF+E9pCxUti1eQpqf1EzQzGcW6iZYXbx984v8Ysrcl5qbiuoscE0IgWHFeHzm2OOPXtyusRmW7En8UU7YUbr09kKndX8b/N5x9Dwqkni+mrckRfqEOJuNEPUPgh8u5tDxlLkuG748yttYJxCESv7TVuw69DJfMtwvg+C5FOx0uva9MqFEP14B6cagAlBPmiunpsuh5Q+hYPvMjbDePhgid4qRgoW6xq3S4IOs9xCngQsQMfy9fUx+0OHkWNUxuoAlI7NPNo+42sqBGW88jHKhFUGL5LkIhDJhB2KMgcfn5fOSyjlmazVvB4nuZQVlGU8IEcnPVfCj47WWEdo/2oxodZHZBcbnyWTlXUGaVPs2GFEXKiSLEQtNKoLyeHmzROL4ji53nfbIrFwDwgzduaQzTpUg4lGr1CP6PqUJxbLTURpXAVXpxi0cJsz8VFHRvKQSSp8ciRS15qvh1fkszvA+dlCQQ3PjCCYfoBh3dTcMU8ZxzCJZl+g2RaMgIixQV6meMTH4xW3MW5/csaRcaIO8BnQ18NnelSvB7g6+fv6s55aBJdxTik9S6SyjYAvgDZ6Hguf3NBggRDba25myKxiaCVU5GDl9btfqr6Xi2qQkQ4ekHqLEWYpXkaT+A7YFZGVWafFJtcOTydLPC2p12iCwrd7gk9HZY/ffPQaT0pym966Alm0xvvL3af48+uPARDk0vcak/gSfOf4mifrm26El8s5ukFq0sgE6/QSonSYxT5eZzUzgWejBpe7xEbNlGM6/2ZfMBPz9GSDUd7jF28o3zMEgfmUjsN4yZNYCAKZcgwpS5GYwLlyWDUlthfx+wRlaQLEntWZQxZfjrbLYC9p0gkTBxslLlllUJQGD6e00P3s1SPW+xbzFlp71r42NsPbaCoOEMQJkEuSbTSqOkHZQ195Z3J89c05v7THj7fsZmOc4kVfSh9dsOjfnU22bCn4Zj9FoSyHHbRWY1bQQj84SN3u6dy2uxLTCV3H23WdzjkPwMSwjZ/MkmG/WRWkSYyQYyiSb54uDeYTus/OC2y2FeQATY4Nuwd1RkPJwItg12n4CEBp7bE3Ga5XdJ/6fQYVoUQnklsOVEA16bDaJGiVYWgnIFXgxR4AS4KEDMnkPwDQAVkMs5+OWtbO9lah7TO2hpQuSWUwOUg+iuMoXse9ybBc0AaBoEOZdKU2htSDNsYHqh9yQ6sHN3kPdRM12m8kXA40H9LKNjpu0Ef42lkFsVd3s3oHt6LSQWmPz06GXvUFv69/e/sBvyM3XsJdlaknKcAwrt4LZNvEmvU5OA3GjgLyZVyUekqEGRZ3YZLOdv+Bg5j1rGt2u4yh7Q8e32Ian80XYg5rD6RaVuI/eUryvde7GX78jx8zY1kGoH0YlQ1D8EG0NKxepXbN/pmDOortgcyh22ecKZutNHJCk2GmCdame5FabWLWI0TXp6AE1HEPNzinOUG46b8A7PS9XFRVJyAFhV0Pwm03ph34YLe1+9jgs8/eAgAkAusff3TyBv/Twz/ClaNJ899vf8B9zXnVoFIGLj4ZlTLYxm3xsq2wjtXWrs1JoxlJND+7fIhRMfTcJDarKlUDlWcP0qMnK/zGySVb5b1ppngTiBQlRQDixOe8RH+w4Dqr8OCMnvon4xWeL495Yjyd7Lga3fU5k2jO6h3WXclyFeclGyTcbEboXo+YMJXNOl6kJnWH1misL+OC6wVQHEycQ0VbUa/6y8uH/BkPH9NGpTNUFZtY6VytxzicNa/ezvjP2Sj5qWrh8fML2ix01xWR0eJ3X19PUE9oUlAHC0TTZihLgx+e0b22QWId5UEmKHROo4z63ONyh42hn226AsttBT9YT2rHi5I3kqK9QDaIwUuUsbLpe53kFIHIGSL2Q3Xm+L6Myp5NPlwQKErD13+zK9n6sZ1keDRf4ziSar65OUIWNyO5tti0BVenKvfwg4ZQBK4OVW3J6COaCkjtWc6jBmODeC+yLJlhBC84jg6lw3h82XObMQAAIABJREFUl2swLKrrTQ27ziEHQ4mQFnS1yGLVFvhcu3G0MwwC+nWURB05hKlhD+7Qq9TX84KiAgd5z4EqI3+dI1/S/9g/DAgPW2R6MOZQfF6+0VCN5IVCBABx0S6f7fGDswt8UNHzmQmHNx09g6OsT++ITXGFAC2GMm5+hAVcATiu4FKvt7iV5HsLIu+offpZPw8cRA4rASdZ9lbOOhxPaIP50fSGeRO7bUmIVrxWHz26wc+u6D1r2gwffnKJ13GT6p3i+QWdIsJmJBn5LEXVhdKxcQu34uMxqxZc/XsdI9zirZGdRIhSHLcryRsYgJq3tIkePqzwtML/C2iq/gvYF9yP+3E/7sf9uB//PMZ7WanKVkBBwIwC3HFMx8gdxGWB7oh2Uk8+vMFZSZZuW1vgv3v41wCA/3b8DwCAl4b6o3+7/AB//y0xKh+frHBebPCsIJejXzYPGBp+s5titaPqtlmVENpDxD7HyWjPfT4hAoKTyWFIAw+eUpX5oCZ5x01HFZFxCo8mBJ/WusdpQVvCZV/hp5ePGII9Gu9ZjtG6DE+maw7tPmQGH1d7nMZztl5hZ3JsYt/0uG7w/ILOGa8qCBXYqEDKwCkp26ZAuy641yOUgxyqHSMRYqVqr0uE0jMaoITHph+MvAOO6gZXG4L+nBOUqgIgOMnVkd9rzMYtTko67y+uz+G+pgq5XgrkSyCPMghTa2w+jp//LFkuTid7HNcN+tiHzqXjnxXKwkMw1KulxywygpdNRVB3vE/9QSJRVlqu3MvcwHrJPVVvZDIS6WU0JBB8LwZnpF2bc697VrWQIuDtglAJs81Tnmf8nsFyMM8tox636xFV0gMrdqeZ3SpHhlONBAg1SJ/pGd70TsA3mk0SugBGdwBwiozSDm2XMQowKTtcRxu+EP8zZKYGFdgkwhfRPWvoIY56bL8gpwJXeYTz+H4WFILu43MA7TnliRu5A8tXB9TPo4zGAbun9NnF492dVKO+15xmIxoK6x6YqgBYyrJ5PcFismEEQ0uPPoYufPnmHCEmxeQbCRGSAUYQlBM6HFsQQL4arsHBASMl59iKepRsjtFJluGE0uPjp1d4NqaK2QWBy4aeianu8NWKeBjBC4xnDYr4/m+6Ah/MqSWVS4svb0/ZNcztNBveCx9hdJmOiR3mlhJu6LU+auH2GjJW6MIf5MYW5FI39KSDCGxP6TMwgmMbDTiB/CQ+g4LMTdxB2Me7Ot7LRdWVAaEMcFN7Z4LG1CGPTknfP7rAhxUtjn84/kd8qAli3HiJ//n29/G/f/U5ACLVHD0kEtO/efAlZnqPn+1okf1qc8ILhRRJmydzxxMaQNKVT4+pX7PqK6zWNcOA01HLzkseApUykJHVYINCzakpBn/6inq2m1WFetJx7/W02qKP/ms7kyOXjn9PC88aVgD8kr64PULfZdyv/NW3Z1BvY79VB/iJQzkeEngCVlH2Y7Y5UfqndH6TqsPtmjYBrlG82OpJi9FBL/RmP8K+S65DF21ynLG95usxnu1Z1qKOPeZlg1/eEMFp+2aM03+i36kWFvU3O8ieNi77D6dY/Ba96KPCMslllBscFXvekLzYHfHEq6XH9X6ULAebGpc3ETZrKMA9pDmaJDEgbeqDMW1O9ibH1WbEJCOOEAQgKwedWYaQvZdYxYWoKHvuYXdW43Y5SvKVg0i8rs3w9e6E48Cmkz1uViO+bgA4SBwippkA7H0MAOvrES1Mw0KnE3QoVEA26WEGskxIP9OFRRWlQ09mK9S6x/OoV75cTGCH411lUF0Kyvalh45We85IZKWFfx4X4KsskYxGjjeXfqchG8XQms99IumIADiBLCZOyV4y8Wf//Q6zox2fq3GK2x0hCDgXbTk35Co10BdIuhL/Ullc72uMDrx7v31D56kucoZugw5wGVKcpAGnwegWEC7AxVQWVySikpl6TuaBAMGgrGUBRPTOfnq+xI+OXuO/mP4cAPBHyx/ip1ET3zmNZezvT6YNHk3XzAU4rbbYR/nbl7en2FyNuYctG5W0+oHsMX0eN5XXij2D+6NAvBMA8qJEuZJQ8REykwA7jbKf4w4CgLsq+TOHRngoPKdP6aPujma7M5ole+/6uId/78f9uB/3437cj1/TeC8r1fCkRaiBuuzRvqCdnrIC0+/d4l8/pBDqT6pr/LAixtyxbLGPW9g/3n0ff33zjHdYv/35l/j9oy8BkKn9//r6tzGJ5gFaJvefvcl4GzzQ28/GtIN+UG0Yfny1mCEvLEN/0wPjeomAjS1w09KuftMVnOW63ZWw0TlGjw2xWCPEszElqki20dJDSY9lNFYvleXKt3UZvrokiNdZhfG4xfqWvktfZrCzSEw6aqFlYDN1ZxOsCxVQjnu+PpfXU67gZOmYtaozhyKzuIz+uW2X3SH9ALib8BOlN7Oq5bzTVVPiZjmG3dB5T77UvOtefFdj8dkMs+fR//hEQlHxiF0Y4cknhAw8HK1xXmxx3cfzFB7lkL7TR5lKrACaNkM4yJ4V2ifoEcB8vov3LPkw32xrNJsSOsoxBhgYiDCrkylwXASWSIUgsNnTbr/d5lQ0DW4/MvB22N0UCKVnR5vVumbXobzuifU9hJQbwfciANhE9rboyfPaR8gxSIHAJusByFPoQvCCfXefHi/x+fwNAOC2r/FqN2fo3OwzDgcXVsDnASF6/0rtmemdv82AUCAkYCIZB7QSIZbgaquoAhwCMJxkIwXVpCoVAOw4wH5CyFJdWDQRAfFeQMqAIt7fdlmifBPZqCKSoQZGdOVI6hPHcd2wSUq7LKEXEfp0YI9d4YF8KbhSVX2qWikZRqA9iTBvlOlgOKX4ro5GLeqiZ3QqU44Z7d+bXuK/mf0UP+/IUOLF7ohlSl9fTFDMqXT8weO3mOgOq0iq8yG1MKxVqI/3aN4MREJwRe5LD5F5ZG9zvq4DrGvnNpnwr8j0v4vn4mrPyVxCBJhFwbC3LwJLjIRJxFDvBdo+4+PPMou6MHAuoQHv6ngvF1W3zhBMhv7bEiEa6v+7f/v/wniFf//2OwCAL9QDZI8jq7dOLj7f9kf47uwS339Mk8kn+SX3V/+3N7+FEATebmmhDkGwoXZrNE+ez44WGGcd92iu2jHerAhWnI0azIoWLxf0Al9tR3gwiX3OIPHF9fmdAOxh4QkAjh9Fhu90DR8E1l3SY9420Qw/63GxHTM8Oc46LOPi8Wo1wzhCssYprBc1p3HYI4ssxo/ZXiPs9YFGEfzC6Zqg1UE3Gbpka6dLcxAo7tD0GTGd45BRg+isgneCIU2dW3x6SlD8sq1wFSUitlfwvYKK5uCuABY/jC/t1ABGwucRbtoDMrrKyOOW5QdaeLxtJzxxTfMG1y19/rBpGTYWMIIcgAAUY4qSG7SA8+me+4nrA+hayoBy3KHdDlIlSu4BANNpqCxNmkVmeZJZb0u210MAUNuUWiPBrMmgArJxn3q2reKFp9/nEMsMarCvmzru4m0WdWKYK1qk2dauEskVSwS4VqMYDwESFt89IR32SbHDix31P79ZHUEKsAQGJoVT+LHD0cM1n1v3YoxiE+0GdYDP0+KT7QRM/AiRJ7mRm9GkrmIMmjACeUy7UQ0taPtHsY/96fogHUqzxWJdWhincHsRowPfpk1YPwskwxmuSaNYsuN7iS9/8Qgy9o9FL6H3aREfeqj5Oi6o8UeuSNZ+3YlH8XiHyZAmtCthb+n9HD9Z4zeinWnrMjQ2Y2305XaM8/iu/ub4JX7SPMOP1x/QZ/QF3NAK8II3pVqQTeN55Ee8bSaYxBbP7PwCP/7iI+5v+8oji2HsJgPEIuOfmWmAPRpovAFZnAuEBfpjRy5UAMpXGbo4FZhOQW9S4AB8cmIKWYCeppQq50S63kHTptK9++Dpe7mo5jcKqlTIfrTCv/vsrwAQTf5/+eo/Q7Onye+//+FfsMb0b5uPcWvpTX/TTvFvj36Ox5rIAj9uPsJfLD4CQJKU42rP5J7eal5Uq8wyfX/dlVi0Fe/qmz7Dg6jXPKu2+HYzv3O8m46O6dZV2DU5t3pMk/HCNp42OI2L/1h3WJuSJ/lVW2KURwKMyfHhbIEymj+82BxxjynXjnNcu4sashe0QwWgxob6pQDkViHUjqUgKvNJPwjSRw7HKCaJzKO1u9PP2mwrfvmECJwegkD+s0Mf9Xy65Y3KelfyJNlvc6iF5ogrOwqUAQnqW3sA3TF9d/MkQEad3ag03EdunUbvNbrYf7RechqPkh7rdcULmKgcHp0v+b70VmNe0Zf7IFhaMcoNy4GsU4DyTGJSVSIxjSdEQBpkKJ3R2MUsV79JaTJyQlISZ9L1Gfqqat6Tv21EDUTmOU0F8f/Z2K8rjhv0g19up7iCkJ2gxKbzPn3+gc2fzB3fz3nV8rPzV2+fcX/bOIW2zRAGCYkTbGAAHbD8es6WoKHyTF7RW4EwSnZ1rki6Rs4UHY4pJN1n1qY4MwDYP/YID+h/WCv5ePPc3TH22C8rqKgB9TlVWQAt7nKr2bN2+ErES+FyIEQ5kl6nVB3hwUkxkIAdgRNazDgA8+GaCrS3JVpPm0g5MXj8KW1OPpre4nm0A22NRmcytPE5OD1KKUN/dP0D/OLmjAlID8Yb3gSvvcSH5ym96Mv1KeuyP5tfswzv//rie4AIqD8iHsj2tobP6DnJlorNGwDAPuqT5MVImFPaVIxP9hgpx5aR7ZN0H9RaUeU6VPwyQEQCXF4ZPhdjFJGdoibWVB7hSMC3B/f8HR3v/rbgftyP+3E/7sf9+Gcy3stK9Q/+q79HPs7xO+MX+FFJfdNMOPzub32Fn7YErex9jk+KCwAUSP2TBW3H/uD0V9i4Cn8dAwr/evmMLb98EHBeYh4DpL+5OMGLHW37styy7GTTFuiNZoPxD+ZL7pu+3MxhnOJEGHUgbwiBLAo52Ft5FFU0GVAplea2qxGC4J3qoTPS08kSW1Nwv7DQlhNWbm7GkJfxeHugf2C4VwcAfjAwr0GsxCHFRwSuupXyqHLDbOCz+RbZUCk4RZUbgOWmgneCe7HhgNGqJ+aOK9PNrmZY6GS6Y4cgGIlsnYTz3VFiTYYgACvghlzNA9btrGqhY2m0MmTKMRyX84KzVp2X8G2CuefzHW6jzKfMDT6YL5HHBmDrUrbtzbZGF/t4o5ryRwfz91HdMYJgvYQPAjfR0NzuNbJoEiG8AGKfyq8z2v4OMisvOOxdKk9WjIPzVebhDljCvnKYP4z2jMsaWMUe1i4ZHZhRQHjSMgx9GJsSgoDSDnnskV8sJxz2LkRIyMYu5/QXAMDYQEaDiuxKoXtg4YbqdKXYZMVG67vh7ohD6c1OMfyLQHKPwZhA71K/z5UBbuy4QqC+abwvfYa+0/zhQqVkF68DZDvISegghj6t3gtmAts6QK8UdPxu1aZ/FxQ5IA2f4YrA0pOQe4jVAOGTCUpxQsjGBydLbuP8+fOPiRmP6GZ01mEa3bSul2Psa/qMB5MtlAicifvF6weM5nz84AafTIgn8PXmBK+u5ziZE/z71eoEF9/SfCVyD11bZpY3leUWiWoFggS608idqAwHH2QbATOn898uagjluV1TzFp0sb3hRmTTOMh0QuaBCIH32/zAOQIQrUzh5rFtdMimf1fHe7moPimXKMsMv12+wIPIec8EcO08VMR1ZqrBn26+CwB4uT/C7x4TgWnjSjzfn+IfFw8AAKtdxRO+Uh6t0dhHCFlnKfbM+5TyclQ3KLVhMss3iyM+tq7TqErDD/1pveMF4Fe3J+j3aQKFAE92mfIMrUoRsOoLXuh8EMii69M36yOs9yUmVQxSt4r7YPpNwbNbf0o9LLOKWFDumfACHQAZuJ9otgWqU4Kes9grfXxM8NLtvuLjqjKLfbwGQnr4TZE0clbAxx6N3WTwtUAR/W2bNsMo9qOvFhOEF/Sil3sBvU/ShKBCOsZOATLw4jNMPgC5z9hIDNuZHCEIhsdbq3my2y5rmgTiYrDe1Jxc9OnxNT6fvsE+Ns3+6vpDXEUdqfeJfBQAjq0DKKFlgJqFiDDYMpGfhjgzMTbUtwaAyiErbZK1IPAi1G9yOueh99gpTgIJhcfRozUT1oKTUAcLX3cS7QbnPZRKEC9pBqOkKyYXDQtudWRYo/nLV+cIt1H76+h8ipu4Iak0+pjq0j9zQKtQvYy97zKgnx/Arq1MC6lI+lCa5AfxKS1agwWgz1Kaii89DkPKm1WJLm5ilPYcp9ddV1CNTLIZ4I53sTS0WAMEDQ+kmuJWQLpkMeiK1CcUFjyLttH+cNCVIvcQkxR4fz7d8rX79nbOZMTJuMEmGk6Xxw1ybdli1PcKvqRr+nS0xPV2xNAwALQL2ry+UVNMMno2Xy7mMLsMV9Ftza1z3jwEI+G1582QazX36lVLUXhDL7xfF+zV7TXYEUuuJewowMUF0XdJlw5E28hhk9wTPwIAhCRnJiC2KUSAiL+ntEdRWDh376h0P+7H/bgf9+N+3I843stK9Serp8hsjo+LKzzW3wAAzlSBP2nO8RfrTwBQVucAmebS4m8XBAs/qVdoXMZVp3OSRe4WCsXU4jTCLk2fMQnlN09fYxTdQjqvsTElGzB0VjNcKmWAFIGN26UI+OKS/GzbZQk4ARkhwnrc4SgaPBTKMgnAeYl9l3PF1W1LXLnB61OgGnVcvazWNaVFALAHBANReIJmh229F+w5KnVAllt0sarK5i2Tb5yXqHKDLu7IBcDZpZttxWbgg5n8kGARdICIO99q0kIIYkgCBJ0zm/iqwBD5KixVLC6SnH0RUuVROnz+0WuWErxZTRla2tscl3vaxW/aArl27GjVdBlX+BABcmz47zqz+Dx6BH8yusbC1Pizt2S4sViNECJ6WtbJuEFJj32fMUntkN3YrguIVjGjGJ1M10Mk9ixqgrWHShIA3BD2XDoE5xGaFDg+EHzUxKDtMzS3dO3USsNF2YxTAfk8MX3y3MJEYpOUASdjQh6kCFi3BYaCsdEZ3t7EdkevuJLJNgIhA1egrk45oPI6g7CCjdsxM0ymEq0EBJi4hJACrvVOwMeizBeBAsHjcXQnnk3zRSsRNBly0BemqmlUd1hG9rYwEr5MPwsyOQYNrF0zje/8SnDVKjzJZgZ/2yFfFQCaxw5iICMNpv/xuMpJh2fHRGistIGWjk1YPpws8GJDCNWqKfHgZMXH5bxkAhimYKTn//7Fdxi+H45jqPCb/QR/F1OvQicBJ+DCwNYVXGXjtEeWW5bDZZWBVfTnfq5QXQmUF7EiNRLtWZTUHFtOy/I+okLxu8dP12hjYIe9JPKhi4RBUVuGiZXyzFB2RhLCEqVyWU6onj+QqL2r471cVP/Hh3+KeqLwSXaLSdRt/LiT+A+bzxhq/f70Lf5u8RQA8PX6GP/6McG/Unh8cXvGWsnj6Y41ZVVmIBHYON95iR+efgsA+LS+wsISbNm4HLddzW5LhbYHhuUGUnp8c0MvXN9mDAOKykLIwIHXnx7fwEfMq7EZsqFP2BfR0Hxg3XqUsZ97Pt6itRneLGLqy3WKLPOlh6joIQ8m2tsdOOscHdNmIVMet6sRxlNa0MmZJrJdrcSsbrCK2k7nJFvvLRYVbIQfVUt0eh/Zl2La4/sf0IJ1uRvj6s2MA6+9FxzKne3EHYamHYcUgjyyvPDMjnaY5w3auKhuq5w3Sc+Xx3z/tPTItcW2iZaIVsFGY/kh0aWMofOfnt7gO2OSPrgg8ZeXH3LMmt9kyI6Si9QQw7dsK/S95pSgQ5s8OIHR4w12mzgZ2tTHk42Ej4bxWUGMYXZG6iVvQBAI1uXIQhUgo2tScALNbcUQu5s4dlQaTdo7E9j/f0JrTEzmMRrGKIYqW6NZM4zXJeSwHzjxkH3aJKmdgtimAHP3qIOOmzLTatb38iZCHSx2Oh3H0HuVRsAVISWqqMDnNUC/giVHAWcn1EdebiuITVpsQ22TtriXyU4vWuvl9GvQ+wMpiAZEF7kEAPppgJ279JkDTD9wDCL8a0rFc8Hr9RQnoz0vkDdNSgR6ON1w2pFxCtOyw3GMgiynBtcN9dxXX8/pO6K0B0Fw+hFUgNgO2lmBUDq+PrITcMd0zybjFsZo3tz2B1GEbmbRCJUg/PLAktIleROyQNrm2FpxTsIMjmECCEcGKv+PYVxn1d3nXwSE+K51KicFQHcP/96P+3E/7sf9uB/3I473slLVwiITAb80J/gPliq2n+2fxMBxggX/7NVHGMcK5bcfvmIx9t/fPEZvNc6jIUNrNbpIKqgyg51JJILTcfIcfd6cwkbK4M7maG3GrNirzQh60Cp2GfY3NZMHhAA7ryjtMalb/MYxsZKflkv8/YpYyXuTwYdYzUmPad0yRD0pOzydkL7yYj/By4sjiLfRm1MG9h0VvQQGfWQeq9YBCZUBt5d0rXRlMRk3LOb3XnAQuZABF7dT1gYiCOwG4ZsKrO/zZQAmPcexnY53eL2mz18uRlB18pXdbUpkm6TRtIOxt6BQZ87ijFFqABGwXmyOcFrRfTqrd3izoc+ncPG0I77djPj43VZzNuzwWUcjqjofVSt82xCC8NObR7h6NU9Eq6mBGrxzg2CD+822gusV79y9F/Bxd16f7CFEwGxGVcnSjik/F2SYMPjemk0BuVFgIqwE++gGF/Wmg/mP9qxZVYVDddzcIdINUHbfaxRFzD4tO+y6/A68vIrB2yGIOx6tt6sRzDKSk856uKHw6BSEl+wVC5EIZG5EUXID5AiAEZCgA1XbEQIXO82RYoef4YtAt3fIpW0l62BDHpAftXyex7Md+zX3bcbXRkx7SAkgeljLnqpfAMhXEvk6HR5kMgsJTqA7TprnkAXodTRdUQEuGsggOgYNaECWOSavlaWhfNgYuWY7jfGMnivjFKp4fY/rBo3J8M0tPWf7dcley/UHGzS7nN9J30tmi9suTeXCUbU8QMOuCPws7bYl6X0PKtShrYPMQ/aa0Yd+dNcNiecCK+kzYttr31XJ6WreEww/EMWUJ3IlAHWbsYZXTHu6TsO5RCMI1qq/w+O9XFT/aPUjFC7DRTdlVuzGFGhdhpdLMl54NFvjqBj6Sh5fb6OBtgj43Ucv8XpHfaVcObYitF4iUw6fxBSJ1mV4via3pXnRII+i+a0p0LkUIhyCQF3STNJ0OUItGGLT2rME43S8w++dPoeJuNTf3D5jp6RcWzyoaQG5akbYdzn3Wx+O1riIPcSvv3xA0OIgeq9cyqgMSBBW7hMEDJo8dISGi9Jgs62YUVsVBi4u4FluUWSWZUBtl3HPWTQqSTVGBpNZYsW+WUy5Lzub7yGlT33UvWZnmkODbgBwJwZZnQT2w/c6R3IVG6/Vy+WcjRXK0vCGo2nyOyb3kwdbZrpudyXywrL70kUzxSq6YG2bAmpsUJ6m7NIBUl7vSoZ7lfYoRmTQAABCCDx+QiL9XDk0JsPVTdRk9JKTf1RtOd9UtBGmHPYVIrBNnp06hLFLBhWtYnhQaQcpA7yPchsZIGKLIATBbYvlvroD/XZtxnCvVB7bXYlVZPmOn65xfkQY6dvbKdw6WtrtiME7WPYFiTvZor7RibVuJbNRAcCPLWSELvVWsNSHPic+jw4kVRnIwFlgMwmcNpAyoC7pPmXKkS0oCDqPayNU5uDe1HwdbR2YTZxtgX4KNhIJEnCTuAE5CrBjxwup3At+Bn0m2HmJ/h6AaNIfSsMbw+2ywg5lyhPOPbuhzSZbyIOHelq0KOKm4MnjlyjiHPViewR15O9wPdgo/8050CR5kC8CbxjkWctMfU76iYtsVlpm+NffaEhDBhYAwd6DNSk5YMTP0LFvP/g7FA5ZcbiJCcjj/GV6DTn0gX1qRvtdBtGLZBJREsPd23cf/n0vF9VfbM6hfXEnnaVUFtYrfCdasF03Y/z8mghCfa/x6Ii2sZ8fv8FVO2ZNqBSB+yH7tsB8vMfFnnajjc14ki+14RdnIBINr1GZG+yaFHt2fpJkENZJ/JdPfwGAtLR/evXJHRu8gQSkao+LffTR7TOcjnd4UNHkd9vVuFhN+Hd86VNwOJD6ppVL3rBOIJ+k8HGl/B15kO0UL6rrTcX2g1IGaOU5oLo3OqWrqMAvcznuUWjHmtBx3SKLE/m+z5DJwH1mFA5hqOAOem/ypEdd9pjHSnK5q0izCbIAvFxMcDGEMR941novmVgBUA9qkL1I6bGNnrtFafBklggkq77khbPI7J3+UNPlvHD6IDCJ/eZx2ZGuMN6yadnhch11qVZykgwQCSXjGIg+3+L6ehKPTyMosOuQ3gv0UQ5TnDSwRsMN0XXne3acmlYt9l1CTqo8BZ2v9hW5RSHqn2XghRQB7J4legl11OHBp6SBbPsMb64jUWmv7/TthKMe9zAOI+JEL4HDRTY+f8ILyLVmwpO0acHqjj33lY/P15ACTOhzr2uWYMFKdL1itMEHwRtR02ru27lFTn3BIT1uI6HjotpPKXTbDxrWKjkjyR6oX2rWpkKknwFgpCe71ciXkvW4fj3GsHfI4qbAR5lYVlreVK/aEpPovPa92SU+H71iN7e/Wn+Mv7kgbsdvnb/GLGvwi3UkLroMLh5U8GBZSwhE4gvRISv4ZKcZrAREQBb17WZdoP4mXreMEmcG1yo38kB0TRMCrEMOAoQODd+tA28iQ0tStoEcBwAY7lN1sBsOhK7wxmhiaNN12HN9R8e7X2vfj/txP+7H/bgf/0zGe1mpCkEV5sNyjakmyOiqH6P3Cl/E6tQ6yXKYo8kePzp6DQDY2QLXzZjTRJZNif2B9MN5yVXsYl8xlPgP+0eoIws2BHLtGarAzbbCOJoKaOVhnUIXoaH/+oMv8BsVmff/vHmE788vODe1aVPyza5L7NbPz97iON/jl2vKGf3m5gjdIpnro/DMlDz03BUqOemUdQ8hEltVSw8df6e3CtXpBsuY2xm8gI8V3H6docszFnwf5DB8DIN5AAAgAElEQVQDucdoThVclRu0RjMENq46LCPcq7VnxyWAUk2GBBV4wT2rp2cL/ODoLb7axGQdL3HTJdjVOcmJLSEAVkbDeC8Ynsq0g5KeJQxNn/HPZlWL82qDNsJ5277g4O1uXUBVyVfWGsWMykPXpG1EFYbK/XI9RjdUyYF60D5KmsTEYDym52C5rpNMRpKcRgwJMDOPLMphTKcRgkA5S6jL0Fvcdzk6o3Ec5TGFtlgO0qFdnvpqIUqaIsSrrACKZKwQgsB17A3aRnM4uPj/2juzHkmS7DofW3yNNfes6qquqq6Z7hlKJEciRQmCXgQJ+iH6iwL0JgLEEKQoURApztIznOnu6tpyj4zFV9v4YObXs/ggQEQIZKHsAwbdPRkZ6e7h4dfs3nPPVYx2zzZ1SHYcPBwGA6je6lIH1jHYoXafGioryPfpB4YDNnXon4Za71FFJYxtl6JqU/KRVlNDg9NdI8AbARPqsg1PKSsBADyULSzztcZkNZQ0gO4wZGJ6wHCAh3vOCkfpa1mxD+5j0Y5tP92RA9+NteJ+aUlRnGwYqZfbEwdbGFJEs7cJ2pPB6H9scfvF6hy/vD/Dd7/x5jKi5pBPvTbjp9+8hGkluXNluYIO97eQFvooPF9a4U926BqYKPJkBvNZosFlSqwlaRRcAjCFsUacPchcOeZrqYC/bxsOW4xZrQ8GxT9wR2OZBRsyIGycQMUY0K8zIPwtzhCeN/jo+SSD6nG2Q5qn+K46IpHR1W6KPFUkThLMUS3t+eSOanPfVwfgzOE6pPDUg/RdkfVIhaEHl7WMrPwOJzXVSTgcUpmS3d7Z4QbTMC6OM4dVW+Bfnnr7xB8Wl3ivlvSzP/n+JaU4l/MaJtzMdZsiC8Hgrivx1xeP6djUJqUbnZUGTFhKyToHGtSe5hpJEEzkicYk7SlFVfUppQ4naY+7uqAA8HABkkiD29WURoc5w5CHwe9JovFo5lPSk6TDm+2SRupJYaiurIyAs5y+0GaTUssIX/b4wSPf1vKfn/wpcqbwX+y/8J/N3QFZ9DFpIYQdhTSO0QJBCItJ+Ft1lwQryPH+GERoAPD13RmN16vqDKYKATBcs6H2rUNwA3z6Uavxvkilwf3QetMJut5cOJibjByb8kmPKrhxDfVU/0L4HspQ32KTsc9waOdow/VJCuXFOOE4Hi03NLru/c2CHlpcOLhBoLJJkL0Vvh4I71TEzvxne7bc4fpuBrMda6cPx90NaVze+4CahTRue+zgQmBmPYM9UECw4pM3GQVjWG9Wb8OQazvVODjx98hB2eB6Fwauh3aMOkw14jtBugBI5wef34c05k6A9FIPg6H2QjZKUbsxmCNkHofULbOU3YSae8P/dDUGDj1kNxloYk3/WY8k1zR9Rs8xiugsg9gKes/+xGAW7CM5A96FMoV5VyJdc7BgFVh8saGFee0YeNlTWURrjiwsHqZ5h2147lRt6XUTIdVsNSehkusE2puCUuwCIAN9poH+VEOEmjxnDocLH9Cvb2ewocWNbyVc+uAL03NKPbPEwjmf3gZ8TX7oTbWO0aI9STWw6MhBzGoGe1fANh9/VI3p30gkEolE9sQnuVP92c0jiDqDFBb3G6+ezXKFbZOTE87Lgxs8K71Ks7MS3+2O6Pff3CwpGyST0Sj/sKixaosP0sHDDi57oA4FfBP9sHPSltM4rUqneDG/w3nmhVH/a/scXVBPfH13hoNJg+NjLxr59fUJ7UYdxlX5u80cqTSoV2E5LR3NQmXc+Tmeg5I3U7RrWzwYiN5qibuqpDRm/8D1Kc8V5nmHx9M1Hf+q89fx3d3CpzTDDqA8aDAv/a7n2XyFi8qvyG/qErs6xyIIhKoupTYXbR0Yc9BBlZhdSvSH/meLeYV/f+KFW0te47U6wlm4VkXW0w6uKHrvlBRaQ7QSo0FFLygFm+UKUlja8R9PK0rF3ewmXpRFaS8GEVoYkjAPsgnGDZTigvfzJWW3ZejaFDxkLORsnH1qVhlcaZDOgvJ7k5NxAO/Hgc68Z7CFA8JOwfWcUrDDLNshIzItO2p/GYYqDM5UedGjCQMe3C6BCPNTk61PU3bHYUTXWU1q7svLBdBzUriKhlG60C407XiMY1Dz0fGIG9A9YEvv/Tuka/XEji1AE+1NLcLv5fOOfG/f3C6p1azdZSSCAQCcdqO/rP9oYM7HlC8N1H6XkYvSICjK3wc3qn40eNClC56+Ycf1wNlJ1MwPTg/v2Zw6Sn3yhsM+9t//Z+d3uNpMocOtkDwYEecEoOfjMG/0HNtbvwtX8w76xt+njAP9DxtSDWst0IQ2PQZ/vw6KL+e8GA3wz5P63TScF4Nd6HHghbSj+tcysMIAYQax6B54KC8NwB25dZ19dod2yLgw0G7Xptbv8AcBkmOUuZGJgXNsFBn+PeHRoLQntfkwAKMTgHQfiNs+Vj7JoNp0CYQIas3wodZVhsNlhZOJT/39weIVDoVPffyifkzK2m2Tg3NLaRfGHJmxb/oM2zonJakyAtug6p1lHVn33e9KSGnw9Mj3jmZCk2UhZxbLtCGj9vfNHN+HNp9l0eK03OLV2rf3GMPHlKNmMMM0mKoEthKYhlTutCfj/d02h0zGNhpjOO5D4LnfFjic13Rei6KlVHbfSapTPT9YoVKj1V+rJerQs9rXCZJcI136L7sQliwXX2+XePfWHzs0Ay81VqEuO520VEfuewm1SZFfhLaRwlEf4zzvaLbtN/0papvhm+oYALDeTMbh1L1EVeWwavxyDw+WJNekkBXcYr0rKLhbx3C78wsEa7mfwPNgKPzwkHcO0G0yTtdhjIzbH5YEGAPKyTgBZrMryECfOWB2XKEfXt9x8G58+NGw67nxwWtICXM3mqCXGlZxOu/NtqQ6b5YrPx84/Kx5MxvdqBjIus4mDt2ppfulu8/Jkk7WHMyMqVCTO6qNwjJ6HQCYwo31xNVojWe0gDvtqOTAakGtMlxa8GRMoxvDoPpxCk69CblJaZEdNpRKVNuxX1NOFEShaFGZpwrbsJAwhaPUpFwLb9Ifgk2/HJ/5jvvrOvw39UXDDzBnFtChfQqW0bB0s9A4P1nT8VrLkK7872a3YxtUe+TgJpoG1Pc8oXp/e5eTpWN21mJajPXx9S73pveAT123khZwTDjcDN+ZXQoZFj6m9O0ugz0gALgwMQhTDddzJOG1ejoqkqGYb3cKCvSEW2zNaF2pd6H0YRhcYcc6M3dIc/87Ulq0bQKZjC2Bw73ZNin6QdvBvfNXHq6HyTn6++wDi8mPlU8yqBaZgsg4zo+3UGEXWEgF6xh+b/EWgJ9SM9Qyr/vp2Eye+LaEwfhASkPB8qSs8Nl0je83vnG76RJ8cXILAHhUbPDnYZh53/mxb49K/2Wcyw7fVT7YDDvFt43/25VKMQkCp3nW4q9fPyEbPZkZ8GGayKzDLtTjUEm4icHswAdIwRzV6kRiYLQAD0a4XFiYYcC1cLRLS6UmqzrA12/T8LderQ6QSkO9ntrwcVesOZRhyI/9MX++vKcd+rouxj495pvHRem/jMP1BID+okR+I0YRTOZIiPN0uiKDDgGLjS7wy5uzcC4G6QN7NKMdiToA4HHwVy2kwi6s/o3l4MyRyGtYRAC+vabvElpVT0INGQCUCg+38CQXD0auMTEaMDBhoZSEGo5pl5DFYDlv4dxonIHEwqZhhystxHxQuQiwxJI9JeeOfIxVLylAAYDpOUzYfqkm8bvMXRAW8XHINyyQBt9bNfUPSBpu/gDHAPAxqNrCjYHUgGwGbW69d3F4PndHdmw1mfs64LALZ4YB4dzSTINzSzvovOwp6BnDUB76e7jMFOouQR/s8HihkebjwtY5hkVoreqUHPtnewYe6nS6tNBLSzVhsRMkrOKGweSjB3F3bMZdpeKQ9xLyNtT/5GiYIiYat2FheLrc+SxQ+Dj7JahXVC8snn12O9oUViU2QZiXJAbnS59t+cH8Bq+rJb678c8Dtc3GcWmp9f8e6pAOgD3wJ0CTcRB6SDnI2hOOUf8zSyyQAHoW7gnNkFz69zO5g50alBP/nje7CZpwvd1dSjHUFRYQ43eLMZDosu8krObow2K2s4z0HDwz5FvOOOD9rMPvbVOk1/IDU4qPlVhTjUQikUhkT3z8y4J/AD85eYt0mmIiO1Tar5DfN3McZhXKMPF6Z3L8j7CzvK4npAAV3GG9y8n44GRakSsTAPRW0mt/dHZFhvd//uY52rDqOzjY4avDa7wo/S72++aQDLa3TY636wW1YACgCSG/vjiBA5CG3V2eKdpZrq5mVNCaPt7iINidAd7hZ8hrWSPA2Dhf9PPDFe3aAJA7VCEVGp1AhO2icZxMLvzfNGP9tU5pjikrNT4/v8PLua/7bnWG18Ou+2IyGqhzB5ZYvDj116AzEq++8y1ASUg59sEWjp22eHzgd5n3/WhEXgiFxiRkB1k1Ka18i1T5umzYTf/w9JoGiq+6kiwi76sCUlhsguGDEJZqwFWXgguLJKzylZK0Q5TSopj0VJu1hpEVoQOAkF5jEwPnMBrqpxb51O8EmjolQ3z/JgwupGB5YmnqzRfPrvyM3HZ0cxpqx8ZwcOF8qwsAvpHjbNLBKGOoL/aMzA24HifK4KgD56BUuev5OMg7tx+oZJliZF1nZgY8mCfw1qtNVTpmImRQkaoq8YrRUINzU40kXKsk0WjqjMwymjaBHlqOmIOq/L1ZqwlYocm04HBe0WD5pk9gzDjs3ahxwg9XgDocbfjQccihrcWONdX+SPtzG86zZ+DNsNv1dUeqcR/0dP+oXYrjz7yK1zgGt0nJWjH5ao2zmb83T8stJLO4bv0xOsdQlmOa9yj3r/vZ3TluVjMyEoFhKE5qep1WghyQeMvh3gf9RsNIdwDLAIXRgUuMZi9cOHBuoaS/X9Jb4VPkAOxSYXlYUbvgblUCwZwhqTgZXjjhkE76cZdZp2i60fYUlaRaMnOAC+1wznCvB4Df4TvLUIfJOlAcNgHsx2+o9GkG1YQbJNxgq3Jcd6E1xgjUOsVfrb17SWsSGhu2qXOq1zS174EbUk2TpMc0jHQbRjs9mvtUzn1XUCA6W2xxcu7rtTPZYSI7fFt78dOvV6d0bCezHao+peC27cZWlhcnd+iMxE1oM2i7hAQlojCYz/yXT1uOt9dLek/OHdX7EFKdQ93m7XpBbUSHRY1cjHL6Mu/Rhp7bt9sFPcQYczCWjbVAANlhsERcbvFvT77BLszJ+qvrx9ROUpzWJIr68dEF/tX8Fb5r/TX44zdfQqzDwqVmMIUjgcnxosLzqReNHWc7Sv/e9SV6I/B+HdoRlEA/ZMqkHyV1MvfX/Czf0iIAAKXoH3okA94Bang4W8dQFD0F5r6TdB+kqQ+8Q+J1Pm9oKtDNxZzsHg8PKt9fGWqGznIamWc1BxNjrRfckbiknHT4o0ff03FdtjOqnwth6dpnuYLqpbcnhE/TUjrYMr+WGj56M6am1IHB5NQ/yMusx+3d9MHA7gcDqTUDHgQfxx1McDlijfD1OwCT8wrGcHQ8pAt7Dns5PGi9WGkIsu6BR7NfqDBsgmiHSQsZave6F+M0m1KTRzLgF5+DK5bphPceHjx3c41++L37FMXbIOYTvn48BAcrQRdEbny7y9AiJBpOixL1NASQD6b6DMFBUQ+vaX3J4uSf+5avf3f2DV4Hr+iLao5Nm+Fk4q/5omjpe3w8rXDb+n9fbUvoXUIp9sNnK/pb1jHMyxZ34RjMlIO/CSMje4Yk1Hl16eByQzoEGDYKyhoBm4zXX5cOJpRqklyjqjOooW2sFhDhPtBzS1NpEml8aSGInbhiVFZw3Pn7aBg+PlNw4T6wpSEf82HhNLTN2YxhVK993MT0byQSiUQie+KT3Kkax2AcwzfbI0qRnk/8TmZwz1FW4LbyqUatBRkT/M7z73GQ1rBBuaEdpx0t4E2uB6Wnthw/OfPCp8O0pgHFxjFsdY6f35z71xlBKcyDrMaXiyu82o3CpW1w5bmvCqheUkO2LO0HE3KGQdvb66nfbQwr91SPw7Edg+4FbpXfjT06vcdRmN14lFUfnMtOZx/MfRx8eqWw6B60BMnUkNPNNO3wvlvgtvMr78/mG7w88CneWqckBPsP85/jv97/Pv7s6oU/t8sZsmBmbkoHPbEoH/ld5g+WN3hR+nRybVI6RsksXtdLOjfbSJydekV1q71b03BNzs42tFNd9QUptnddhqZLKY3LuCNl8CRVMI6RcppzS4O82zZBkhg8Ob6n8/7t9XE4AQYW2l/qLoHqJXRY/fNCQ4T3z8sefZdAqzH9O6zcf//sHVS4x77fHmLTZlRWaOuUZpNqJWB6ARf+HttJajVhLrSNPFTuBj/Yp2crEmddbabk/wsA4l6Sm5ApnZ9dasa0PQ+tOC5x4GH3WaQK620xpve2gn5HHw3iFH9uRa5I9NL1EqbnHzgW0cQV5pAHFfli0kBwi7ttyNJsMxK9sZ6BH/U4XPr7xTmGu/A5yYrR1JX2yMEUo+MRV6FVBt731jxoEdKlQUamJQZdJ70ZPwAYwEz9uSwfbfB06e+BRicopMKP534u8F9cP8frS79TdZZBJJbahZwbxT3TtMOqDfNUw7nLo9Aq0yd0zzEWXNQCdpuADRN4Ugc9C+l16XzbDMafDcI5MGC2aLDpp8MlBkKWw2bBIWz4LBgoE8HUOF0JzgvwxKn/wuo2obIIl9anedtBNWzBHvvny3zS+lIU4OfQKv6BO4eLLTUfL79cnUP2GXojyMi6NRKcObIYNA/aKF6c3OJfH30HAPjV7gyvdofUApMLjSz0mCorcNeV9LOvlltctj41tFMZuTJxOFxUMzJuP5jV+MMjn+p7387xl5dPkYcHuXUMm2EMl+b4wx98h9PMPzwu2hm119xVJQUGnmtfOxkmjXAH54aeWK86HVK+vZa4qvwXbNPlY2tGm/mxZaG2k2SaHJvawQowvL8xDGdzX1f6yfINDmWFuhzrtF/v/OLh6ewS/2n+MwDA37RP8dOLl7hdhd66SpBLjS0sXG5IKQkArxqfJq50Std7qzKkwtAxnj+9wy4YyG92BeAYzo99sux/3j6j97JgtGCq6wxWcYhkbEMZrg0Q2jrCe0phKYAzBpzMKgrUr1YH48DnB+0wbZ36iRyDAnLi8IMzv0AoZY/31RzvL32qPpsrHIYa3JvdEircg62SaPsEzTaouzsBN6TNHINTnFo8RMco3StaBlM6qFA7FQcdPg+LAMEtrrb+2ve9XyC528FaB1RnGx5ywz/JgQiAyw25N928XkJUHEkIWDZzsI9CWxUHjg+21HZlLSebPKM5RGbGkV+WfdDza8J1G1rT2tvgqNRyag/SEwvOLarWf04nswq3t4d0Lt1BUBNPLJhiPqUNb56vQiBinVepTkOJRmuBNgwV6HoOsRlHojnpUJ74z+mr4yvcd/6YnkzuUQiFbyt/r76Y35Ir28VuBs4cWXECoIlBr1YHpDDPCgU+6dGGem5bp1QSsL3v0x1acXjDIcKt2h8biNCLLqVf5A7BuF9nZKXIz1sIbpFMQytLJSCCCtwghZEO7MEEoSH9ax+31BttWwkIBxdaw5wY071uK722Iyin9XVBi5ibyQOrVAckBy0NbhCrBOk9g+k+/uTpx38GkUgkEon8E+GT3KlmUkNKgU5LbIKi8rCooRxHG9IzCbf40ZEXHMySFv/t/VcAfEr3dLKjOYZ37YR8e3OhME9b+tmv7k9JSTdNe9oZcOYVeMM4uceTNbbaH8ff3p9AcIez0u/8vr4687MoAZSHNW7bCYkajOWUkm3bZDTG577/y5KxuiRHn+ePbjFJetqdFokiT+Jdn6IKu7K2TaA7iTT0Rh7Matq9N00KazkNuf6dsws8Kf0OaGcy3KkJltKnfP5Z+RZlmDqdcYXvld9B/MndD5FLDRu8azlGkQgAwDESEL2rFjif+Gv1WX5PIqiUS6TckIK4UunoWuUYzo/W2PT+tfN0NLlfNyWJXJxhmC4azMLutFWjehvwO9XBoahqU9qdz8sWVZ9SOkv3AnYY8M5G5xhnGCAteGiwP1qOvsLfbw5wfTWHCOrixaQhVal1jPqCWyX9jiXMHHWZHcfMVYkXFQ0OTgzgYdej5g76WCELg+APZjVdg7d3CzoO00rwe4lhrryZWEonQzMgcZQWdT0fBU3CwVTh8+s4uGbQT/zfms4bmtf64uAWq670I/DgHacGocx83oA/GGiwrXN09ThGzCZBfBPShcNO2TFAnYa5sZk3MxmEXK++OYUY+mUPLaVqxY6DWUb+vunTCm0wl2DK774GUVp3UUL0467cHCgaCG6UQLPzv/c3F49wMmQXqiX+4+nX+IPpdwCAtSnxl8ZnSI5LjlwofLHw9+pdV+L9xgvsNquxxJKWCn3PYYaeYe7AMZg9WC9ue9CTqp76611OO7o3lZJo6hQ2mDXI+1HhK6Txxi4zn7qtU432dRgxqMeeUv/hKOgiPFMMgwiuSXaT+B3zQ3X538tg0NzbzMIkD94zZFh4bqDqlM4l2YbszoOXfqx8kkH1+8tD8DIHHPD7z94A8Grfi/WCgtT5Yov73qdqfnN/TA+j88kWFgzb8LAWzFG6FwAu6xnehQdWlimy3rvZTejhXCYKT6b3lDp8s1vi684rgLNE43y6xbudfw+tOaVqVC8xSzoadv7t/dFomuAYEB7qNrWwcgxQ2aTHTz4ba7tv6iVOg3PU++2cDNedY+MEFQDLwx3KYNsoucW6H1sdirLDkzBr9CeLNzTX8bvmCBwOHfe31s/rz3Cc+L91Ijf4i+1Lfw1kj0Yl4OFL5YT74As1OaopFV0mPX5v5o//XbfE6yood9MWn09XqPSYah5SjDrV6LREEVTUiTBUH75bT+gBPJl5t6PNgyEIg0J5kvZQRtBkGms41bM5c34YeT0oJeX4wM/HtLXTHJPDhtLj1jH85tLXXod2kYFtk2HHxlm5Q6q5qxNgm4wDnYUDuwwLi3aodYL+2Z1oep0sNA4GVbgReE2ThfjYhrP2BuniaEjXjnXltklDX8SQvu6hQmsY46NymbcMeF7hNDhyCW7xYu5T7292S6ybnJTSWabGc1MSXZPADorTjo/OdtLBhe/d0F40PPRdYSCD+YODd/Ia2rpExWEO/ef+7LNbvLnx6XXjUkA6ZAt/nnmq0JqQknQALEN34e8RueXQC/9Hn315gUxofHt9RK9Ni2E4vcE2LESXRYuvq3P87tRfOwMGHT6YRvtugquwaG+UxOYiBLNS08zXfp15y79hxjH/8HvBKumvNXxdd2hN8vOOwzWtUmAnka5Di9QDUW13V6AvE6p5irVEuhvvIScd9DN/fcpJhzosHmyVwAWnKAjn70U+BtVhEpCzDHmhqOVLb9JRIp9aGiZhKwleC+TXQafRBDX2WHn5aPkkg6qzDM4wvHxyjUr5m+bbqyMYw1GEndkgdwd8y8GjsFPSTmCZ1jRwnDNLdn2vrw7gDMNk7m/Kps5gwm6OMdAuqlOSanrD6wZnpIpluLxe0Bgxh7F2ebzY4Te3x/RAKvOOBAL2Nh0fus67Iw29fz8+ucQXQejzi80j3LcF7U6nWYdl4V93uZ1SbWe5qHBYNlS/1JajS/3x/9GjV3icrbHS/hxK3uObxveYVjpFbyTVkp9P7iio/rx5gle136nWOsXdakIOPMxxmjqSndYoUoVHM3/Nfzy/wH9feUGTdQzz0M6yUxlebQ5IaPV+O8P2yu/Anz67gXWMdrjrrhhdsBJDIpFEGAjuyPP5y+U13lV+QfN6tUTXJmSht5zXOAx/67qa+kVT+HzrLCMXH6Y4cOPvq+LJDqnU4zSk1cxb7AHgtfB9oOGBpLWg4+LcjpNoGukX8YMAqZZUp3LCP4yGgd3soKddnxAWUhpcvA71RTu2VvCWQ4Z+Uz337TV9cIRKEkMLGqsZnOUowm5Xaw4RgpnZJeChHpf8YIs8VVjmwdXISPyfq0f+PSyHtYymK5lGgIUdLjOAyxz11j5s32GagfWjwIZZwATPXTHR1OfZtAlMl0Lehwf50mB56Bc/AGghtEs1JnlPPeAXbw6p1ig6Bq4EeRfrzzqcnfpFI2cO2z5DH+p/MAz9g/Ys2t2qBLOkwx+3Pqu16zNUwXKx0wLWcvq+Ntt89AXuBORtqDEXzrelhF2gHyo+vNAHPROm7CTLFnk+LnrbcByuFUBmYV/462NWma8ZAxBrAflO0jVWcwv1pf8+WcWBdQJ25e/dXZ5QfyvseI8htUgK5VueALhe0DPKcYZ2k1FWhRsAp8H1iTuYehhn6H2kQxLL9/ZajAH4IybWVCORSCQS2ROf5E51vmggSovbqsR6HczTNcf8oMZ5mPfZ6IRaDrZditfBLOCrwyvc9yU23ahkG2YhTqatN4MPO4zlvKa6oNKCHJWyQuHzwxX9/rWYoA7Kxb5JAObVkgBwVNT0t9ZNjqYe63q9luiG6RbAmDLKDY6PtvjR4SUA4El+jy6083BmkUlNTklCWLxe+fSYFJaGCAOAYBZfzfx7/NnVCxq4/bZeQjBHvsl/Wr3ETTMaJiTCIA1SyV+uz/Czld+xKMupLnt9N4PdJeBDaso58KVftr48ucFJvsNp5q/BrzZnlCqfJy250iTc4NFkg9/c+XTq9nIKERyJMqnxxeyGWntW7bhTLbKePmfOHLTl1Ab1t/cnuAyfp0wMFvMaRxO/Oy2kwuv7IZUYdnnh8zW1BG+H+hPIIEEIiyJVeBcUvtglHyho+VTRUPRhlwr41C8Nev97K3jRM0r36oUBKwxOw67q35x9h3eN32l/e3+E9S6n6TCsZ6Nidmqhl2NNsm1SypbMipb8bIeh1g8ZppjIlQSeNnRNU2nwzZVPkVrLPzivtkqBoBblbnR7soUFBoN2ACyzlJpkzbjm54rBpg5sGBJR9FDDtb8oweQJl0wAAAhhSURBVO3Y/oHM0OdT9aPL1vOjO3xzfYQuKIjFbqztyh9uMS9bKh80dUbzSXdtht3NZDTRnxvIfJxW9KOzKzrOSqf45p2/H7NC0Xe1SBVyqUmd3nBHqlumGWWZmAHQiNGrN7Okh0hSjbYTSMJUozQ1lPJtlKTMRnrQ4qvzK7xZ+/tgfZUjuw2KYeMzG+3nwU/8eFSwd20CvQBsKMnkJw09y4yTlIZm3PkZzeG4eCWAa/8cFdpbOPQHQRuQOCDUdpEZsGGm7pYjvx13qlw7cA2Y/uPfqn6SQVUKP8B6U+WUZn10ssbj6RrXD4JDH9K1k1Th5cKnT2+7Cd4GgQEA3K8nZO+mOwkuHT0M77WgthbdC5TBnu7RYoNF1mAXUs9lqkgcc3qywSTtcVb4h/5G5WTy3tSZFyGFh2/XSaAYrfEGIcGT43t8tbzEXPq0TmcliXs4c0i4oS/cbl2Qg03fJTDhXDa2xDzv8NOLl3SuQ2r47XqB+7bAfajFdm2CL0KbiAWDdYxs13orSLhVq9Sb6sM/FOte0HDw5KDDMtTjGp3gXbXAZeNTyLlQOMlCireeU+rdOobLekbuQtPTCr97+h4A8Ly8hQHH29oHM6UFllP/HtO0Rx2sGbXlMI6RTWHfJePkH8tgLKdUvbGcHuTWMnTrcZoL2CjcsLlDsRzN969XszGgPBS/zAzSxOAgHJd1jFyr7u4nJFCDd4WEDJNTHPdiIgBIlh2+OLvBNPH31h+//iGVB9o6Ba4zyOFvujB2DQBb9tQOAwAHi3FC09evz30PIQCRj/2wgLccHB6M5Vf3NNpwW2fYhEHiAJA+6I1utolvx5iMI+kGnYBzDEZzqimikiRmsVND6Vk4/7/h/Tm30EPPJ/MDyHUwuZ8f1NQ7+sX0Bklouv3pxUvfBzqsVXKLoxd+cXtUVnhzv0R1E8oyD1rS9PsS6ZZBP/efaZaNU46meYdX937Bva1y2F7g6DgszPsEVRiq3qQpOHNQoe0qfZtSVteMZXS4LExkCgs0cZVSz7C1AJtZ2ElogTGc2ric4TSVJssUfntzhO5b//0prkfbye7AQR9qqj/vvl3ABhtBlhlgJ6k9pl1nY+uNYaMYzvpe5XQTjiMd674mC0MbhgA80dSupneJH4cHILvzU4xsyKiLzrc4DTaaHzMx/RuJRCKRyJ74pHaqgwHC/RUDLxhUZ8jBhhU13tQZ2iCcaXqOeRDwvDy4AILt6GozQVtr7O7CilYZFGFlejjxLQt3ddjZGI42mF8zoZCG3ZarG7zbpNBh59cbA9MFUYHt8CS7wC6MwqraBLtr/6e4qAHHaO6o0xyM+9UzlxaHIaX5ZfIOR2oLFYQEnUnwVzePwnmlKNMemfW7ktoY9MFzt5j0eLz073G1neHtuxTHoV0gEQZXQf3YJQ6VFDic+t3A02WDVAVls+zxrLhDG1zF/3Z7irswemvXcUwz/ztFonBXllgrf63U1mGtwra1tFjmDSZBkCSNgVV+Na2qnBSV2y6HsRZQ/nN6PrvGqfU7Zt60eF0f4f3KH8dhdotNGLJ+qSWJxoaxYYnwn81Bqkhc0jcCXe+o3cZajuY+lAsaCbCWnJOc5rREncwaTJxf4d/fF1BXCbgKXsGWQYfUcJp1KNCh3YadgZLog/qRuQoupOxdJSF3AkNi3iYO1vj/Ok5WWK2Ab3YH4aca3TqYrN8CzHQIJmGwqYMNyjDRaSyCkG2RN5iKDr/4rb9HTNvTWC/bOLQ7BlsPyu8OX3zhSwLrtsDNlb8ezhk45ai1ohMAhpnhTvmdS9ixJIWCDd8n1SZglcDgrGBzAzZIfdcJXBDYKAGg7CHDfWZrDd2EHa3iXoO1C/fPrMG588fYboH/vfbK+vXGwdQdXD24OABX3/nP+qrLkWwEENK67KRF/9twHdc99MT/LgAwpTGZ+c+z3wFdSGFOZYMXj+4gwxb0+26J7cp/Zwy80Iz1/m93+ehjDAcaUsAaQL5nGLanrNcICSc/iH2iIMK9ZI2DDiMXWcfJ/L5yOUzqkGz88XbJ+P78FpA7BzskQTSgw53FVgzpHWXE0T7S6LpBqNSMyt1awmkO/qV/VuheQA1dA+sE6AAXdvKsdtQelN5osMb//z0LZYDw9v3Uu3fZLnxP3MebBmbuYz76/0fevHmDp0+f/mMfRiQSiUT+L7x+/RpPnjz5xz6MfxCfVFC11uLdu3eYzWZgDzwnI5FIJPKPj3MO2+0Wjx8/BucfZ3XykwqqkUgkEon8/+TjXApEIpFIJPJPkBhUI5FIJBLZEzGoRiKRSCSyJ2JQjUQikUhkT8SgGolEIpHInohBNRKJRCKRPRGDaiQSiUQieyIG1UgkEolE9kQMqpFIJBKJ7IkYVCORSCQS2RMxqEYikUgksidiUI1EIpFIZE/EoBqJRCKRyJ6IQTUSiUQikT0Rg2okEolEInsiBtVIJBKJRPZEDKqRSCQSieyJGFQjkUgkEtkTMahGIpFIJLInYlCNRCKRSGRPxKAaiUQikcieiEE1EolEIpE9EYNqJBKJRCJ7IgbVSCQSiUT2RAyqkUgkEonsiRhUI5FIJBLZEzGoRiKRSCSyJ2JQjUQikUhkT8SgGolEIpHInohBNRKJRCKRPRGDaiQSiUQieyIG1UgkEolE9kQMqpFIJBKJ7IkYVCORSCQS2RMxqEYikUgksidiUI1EIpFIZE/EoBqJRCKRyJ6IQTUSiUQikT0Rg2okEolEInsiBtVIJBKJRPZEDKqRSCQSieyJGFQjkUgkEtkTMahGIpFIJLInYlCNRCKRSGRPxKAaiUQikcieiEE1EolEIpE9EYNqJBKJRCJ7IgbVSCQSiUT2RAyqkUgkEonsib8DcTFkQHuyL9QAAAAASUVORK5CYII=\" /></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14455356.755667, -2342509.0947641465, 14573964.811098041, -2223901.039333)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td><img 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1VVX4aabbsKznvUs7Ny5ExMTE7j22mvx3e9+F9dff72732//9m/j1FNPxetf/3rUdY3p6Wl86lOfwle+8pWxdk1PT+P1r3893vGOd+AVr3gFnv/852Pv3r3YuXPnKrPwhz70Idxxxx145jOfiW3btmEwGOCOO+7ABz7wATzpSU8aM/n+yZ/8CXbt2oVLL70UZ5555th4p2mKs88+e1Uf7dq1C9u2bVtl9rU47bTT8Md//Mf4u7/7O/R6PTz72c/GXXfdhTe/+c04++yz8YIXvOCI1wSOPocDfsPxq3L2Bvz644EITZac8kD/mqSYXbt20ROf+ESamZmhKIpoenqazjvvPLr55puPeM/hcEiTk5P0lKc85aht+/3f/31qtVq0sLBw1POe+tSnHrWtFvPz83ThhRfSSSedRK1Wi5Ikocc85jF0xRVX0OLi4hGv/ZGPfIQe97jHUZIktG7dOnrJS15Ce/fuPeK5RyI0ve51ryPGGP3oRz866jM8EKHpSAkv7H2auOWWW+jss8+mNE0JAF188cXuu+3btz9g39yflPT973+fLrjgAur1epRlGZ177rn02c9+dlUb7rrrLnrmM59JExMTtH79enrNa15D//7v/77qGZRS9Nd//de0bds2SpKEHv/4x9NnP/vZVUkkvvrVr9JznvMc2rJlCyVJQu12m57whCfQVVddtYqpe7TnOdJctmStt771ras7voG6ruld73oXnXLKKRTHMW3evJle9apXHXH+PdQ5HPCbDUZknCYBAffDJZdcgttvvx0/+clPwBg7opb5q8KmTZvw0pe+FO9+97t/1U05KogIUkq8/e1vx1VXXYVDhw45E+oTn/hEbN++HZ/85Cd/xa0MCAhYawSzcMBRsXv3bsRxjDPOOAM/+MEPftXNAQD893//N4bDId7whjf8qpvyoHj/+9+PP//zP191fHl5Gd/97nd/oSQMAQEBv74ImmvAA+Kee+5xLM9Wq/WgGW4CVuPgwYPYs2eP+3zWWWcFgktAwHGAIFwDAgICAgLWGCEUJyAgICAgYI0RhGtAQEBAQMAaIwjXgICAgICANcZxxaxQSmHfvn3o9Xqh/FNAQEDArymICCsrK9iyZcsxm070uBKu+/btw7Zt237VzQgICAgIeAjYu3fvw8pZ/uuE40q49no9AMDjXvAWiCSDqAis1t8xReASUCZPgigJKtLabd3mSJYlAIBXhHJKQJrvREkg8xviDNFQwdYaKnscxZTXkEkA1aQmZ2cHGbr3mmvWBF4Z0jYDVMTcvQEgGioAgEoYZOp3cSrS97S/49X9rhOb70h/1j8CGOnn1c8NVB19zaoDRENA1KsJ5MUEQ9Xx16sbedyjERCZQiyKA1yZewIgDt8m00eqMetE6dtbZ41nzgnpsv5OCYZiUn9XTAGUEKqe7hMmGZIF3X7ZIsiU3LOKgoGP9AeVEsjclwRB5AysYq79yZIZlwUFJn2fLD+KuWeJB4BM4MaXIoB4o/1dMs9IoF7d6DzhrsEUAxRAmWl/yZEc0hOomlZQsUI00J97dwNxX/+wmOTIZ819Y30sXtbtH25WbnhZDXTv4YhG+py64/tU5H6uypj5OQEgWSZEI2X6h4EEwEs/R+y5xRRHMcnApLmfBIr15rkF0LpPn1h1gHyDQncPd+ep2LSfAaIE6pZpSwpw01280v/bOVJ3Ccz0nRgwiML/xvY7oMfejmc8gH7+xri58xJg/myJzoYBAKCVVBBMuX5YWNETuyoi8EghSXXDlGIoR/oBaBiBF9zNu3xjDST6GlGrBu3PIIb6hpQA1aT0/TynB8C2lSL/bkT2NxyQbUK5znRKRGBmTlBCQKQAaR4oUYAyf3PSnQtAzMcQOSDM/K+mCCr240kM4KYNKtHvAwDUPYV4dqj7LVZgjJAPE92MpEZh/sZyDJFzyJZ/D901WgrUlkBtxr5dI22Vuv9rjmoxGxu7aEW4dtCkngBcDrD7j//WrdnHIo4r4WpNwVGcQcQZoprATA8wSWDcCx2hCMIIVJVzlBN6UsmEIZJAbN6XcoK5lz/tK8SlwmhWT5Z6Iwc3i1k0BKoW0NLzFhOHJZLSvDAMoFS3jSmzKNi1mQHUMi9dxhBBL34AwBoJk5gCWARw2AUesO8tI4AX/iWuWwwwfSETgJvrJQQgG19U8xkjSRJAxP5+UQX33BQB6ML0m14oWW3bwcCMAFIRg0qByCyQoiTURmhCAd0FU2Gl1hsblpgbRAxy1owdgKpH4B39Uos+B7f90yJA6IUbMONtNgGckVvcWQ0IMEc44AJIzbhnVQ3iDMWM7txUNgSJAASDW7Ql80Kg6hF4bDsEYAMGu2YzaiywmQQTBMbM3edSyM2mIb0aYiAQGTMYTTAMzXfEgdhcL+prQTnYYoZGAa1Dug+yOd2GasI8W8TcBkZwPd4AIMw6bDcwoiKQ6R+ZMHAJRDALpwJy0x/oMcQCUOY6+QZy/dM+wBBLM9YpQ3cRSM08EAXATO0AFQH5OgZu+o4DcHvESM8fbjYHUjFIsxZzBlDHXKMF1G0CN2Md1QzJiunHCMAE3HcMvo35ZgberjCzrg8AGFUZcqmfTUqOuGeEU4chjgFpbl4sZeCZeS/yGAIMMOt+rFLQlN5dchFBbuOopREsA4HOfn399n7CaIMVYvo9jwr9OV2EQzGtX09pJhCrAOrYDQwBiQKLzGQgBgx0R0aLHMmSea9bgOwSqnWm/0uGyGxcKAJkRlBT5hoxQRphzSIFivXLPFqJgVQi7ujFqOz3EC/qezFl1h+zyKiegkyNstBPdDuN4M0mCfkhXYpSjDi4FcjE9EbUKAHRTAFZ6v4WpBt7LLvvjivhasErAmcEpvyumEuvxQKAzBjqll5tmCK3UMZDpRdP87KKAm4HRoyhnBBOIKlYa0WA1/Ts77h0m0xQxMBL5domW9xppEyR00AZGU3V7uRLcoKWuG5bNNQ3IMH8NWqFumMEfsYhE+Y2ESpiTltnpNtln6fqMJRmkZYtcjvddEFrDpXRPEQF13e81s9g+4uP/N/DjUbjMHKl6jKki7od0QiorDbMGJIVgqj82HT26b+LaYZ8kwJq5vrTapJMAqL2O3JW+3vJFGPaI5N+8QUD8mnb3wK8BpIV3z8WMtPXkWaRFTlzmgeXAJkFKsoZeOEFkMwIrNQNaW0YoJOVGJV6blVpDVkbbea+DOlh7rSz0SYvFLLDDPHALLBMz8/OPi+4rAYtnXZo20IQxqpQt31fxQM9t/IpbsaNnNYoSgIxoGobzWyau/kSDbWAGJxsTpYM0ZLRPCJguNEIjw4hXfCaZpT7zR2vtTDJZ/zctWASiFfIHZOZHydR+s0Bq4FkiY2/h/b6lX4ep+UmwGC7sXycUOLsx+7Gvv6k7uMigRDm3eOExEggpThGRYzY7KKziQL5IT3h4z7TWqZ5pHK2Rmo03HIUQ8TKzQt2OHabvWJaby4BrVGKAkgX7ALk2y9yhtEGgjCClwRBpUYgJRL8YKq1VAAqI0Qr+sd1V6HaZDpEAWwknFarIoKcMNdoS2TdArXRLKvFDDzXf4uhgIr1JIorQLY4lNmVxoXv73JWgrUkXJaEXAAjs5AoBsReiy4OtCFGZk1sKUR9M98VILf62sN1HoGZsSjnxusUH4s4Nj3FAQEBAQEBv8Y4LjVXURAipU2P1gRKjEG2GZj0O+y4r7dpolROC6xbQmt2VuvkrOHbJNQZc74koOGbJL2jdhqR8GZbXiinucqUQ8V+h0icQRTeJ1Rn3JlR9Pf6f6a0Kda2n0mv1RbTkfaxQWtidUubZADdpqiwv9Hfy8Q+q78PLxiS5fF72h251lbNsZH3v+n7aS3LHmueGy97/2g5wdx100WCKAh1ZrQq6f2l+XoCk9pvqdvPUHes+ZIhXvLmWIow5mfiDW03Gnltm4TXrIgxMKlQTXL3rNacrGKtwVuNpe4YHy8AMKB1QP8mWdTPZZ+7ZAzVBq8RFVWEfJSYzwyqr2+Q9jmSZd+/UZ857T1d9PclASSHlJt3deb7mwQDq3X/AUA8IvS3+D20tRTwilB1mJuPUUHOxE1Mj4e0rooa3vqwiWG0SWqtCEB3D/ccg2lCbcyXUd9oZivGfF/49irBkE/7NvHaa57xgMb6vM4a7gfuz5s4qFD2mLfcMK2t2j4AeZ+uipjz6c5sXMbhURfLQ60ZEfk5HnEFpYz/nhiE0D5HAMgXMrT36g5KF7SLYbTB9GXBUe3T9mpKFepYuf7hDdeBSgFhLFnJEiHpe2tBVCiMpvVvhpsVaKYC1dZlwsCNzxV9AZUq7bsHQC2JSvg1S8zpjlMJga/P0WnrDqslR1no76JYYjTfglgyJt5UaTMutDnZjrVKoTVmYftVgJeW0EBaex6aa0jvwyfzm2hef1dP1yDzOz7iUGbhE1uGEIDzwbKKObM/Gs90rOK4FK5Vh0MlfHxBEQyioDHyjUqMuaUtPImFG0KQtb5EWvjq7xh4TYgHZlGd8IteNDBCxU7cmLnvWE1jZlsu6X4kINsepr8zi4hMvGkqWZZI53LnvJJZ5L7jFUFZP60ibRJ0JlLfL3Wq/VvWFMqUIYNAL3Z2wYuGBFazMTKSXfTqln62MSHrCEZGkBnzpoq92ZkiIJ331yh7Ap0DxkTU9ebF7m5gtMm/oHVXuX6MFxjAPSkNfNxEbJ9VlPpzc2NiF7lsQYIpcoKlnGSoM9sH+rlV2vBrj7zAtuBSm9SHG00bZysw4zDN51ooSqaJKYAmpxj/WbGpQrmOg5mFKDvMERvhV/YYoqH+O+mPb+JkypwNSkXarMjNJqtqM2d6jHJvalcCyBaVE8J1i7txL7sMsuX7BEbY2vFM54QzSVddv4EkBsRLZlMizLtl/dNpQwh39N/WXB0PyT2bihko0v1u7+2JSeQ2BzLVGwNnJlbeJJ0sGXeP3aCmQP4YfTNRR5hqeVNkMYpRCb0IdDo50lhL4bqMURYxlDH1t/bGzqebr2OQGWm/6f2RKLChQDJvhGvD1RT1gdYhs+kZKj1P2vr6S1si9E80xMWWEerWrTMQjiBVbykBRlCWxFRyZyIWy8JvymMFIRRaifVdEvYvmgmzp4WIA/WUbpzoViBzvTjxDa6rCIKR471RtwaZhYMrBlKeFMhqrxDILoGvCEiz6eV94dYb2ZPobdIdORymkIuJIzTVUzWYNRnXx75R9bgUrsQbjE/LpABQdrkTGE1GrRJslYZoF3Qw5v1c9mUwCxsv/Y45GpoduVkMKjBExs9BzGu/TBlWZ3NuNXz6SowziWPD8EyWKpDgbicPBhQzenjLjn8uQG8kRsbH2GT9xn3D7LUvQsvv/nntF0Mw3Te2jbxu7M5jNtY/5SRzC2CU6wV7cILRYFLv56ZIM0zt8ewQQ18YzbUC0iVDjljUwqsw7c8jrw2ohMyibfyNfa9dy6ShTceaMGKFByPPWq7bHLwm5zcvmwsoB2RKzleezq+2IADAYAtDOaWgJvTDsViB+maHXzOtYc8ZwksdOR+u7ChQSzryyrAtUBhtprfbb25Gsxwq8nOJyfG/eU1uE5cMyFktZMJQGhIOl0A0RygmjC8sYqi61l8K7bMzi2XV9Zsna3mw16zaQPs+7/sdrdfnFdN6vljhYe8J6L6OB/55mPRWEplay4pp/wIh7vu+tWMoCi1A7buXr/MbMPu+WKY0q4F4j3Z2sseNMD/wk55HCi3DZO1lBbi5cVULZK0SxY+1bzZe8Zpquc5oepYpLsixd6O5GGLkSY5QjbnBgWLGvp8cow3MzXmZEVIzJ4pZgC+niFfsBk+h7poN/FCAEQOrveWps1f/brBNod6on4UJgpQch+b1gIufZzByFnWPwNYXWtuEJvsJQ0aSNYc0gi1KJJKkRsT1vUdFjKrU81hVHCg5ROmFq2xbBjxDssQ1ixoAUiDdqHfpE+0cyrxA/aql3ykjhOM5/y5E/WNfuB77TxAQEBAQEPBrhuNScx1tYBApg1wEsgVv4uO19wHKyPslAb+Lt+zdMZOijZWVBEggXdKfReF356LUpjirYcSjhkbE4PyqvCbIhIORNf1yp9US8zt6QIdP2BhY4gzVROw017rNnWk5W5QoJvSWv+wx5BvgzKq8Yk4zEKXWWmu7m051CJF9Fmeejn2fVNgAACAASURBVJn2cTVM47a9VmOy/egYudB9UU6Sj1EkQNoQg4gcwzJeEEabMb+bBArjo8vmaIwhHI0YqgkbnwlEAz9mdQeojKYmct8mVunxtGMaDwmtQ7Xrx+XtEQZbjNUiIednYsZvbjVjXvtrQnorQDGttEn9oB582SLth4I2myZL3qJRdeEYpJQq8FTaKCkkP0k927zlTdzxgFD1GMpJqzEytA6Qa1M0ImdFGc1w1CZMqqldx8vA0qPEmDnbms2TRc1HKCdN35Vw1oxoRMgWvNuifQjOhF6nDO2D+iLT/6vjvW1cdtlhUNZPLgnxkJxWK1OvuTLSz9e0klhtldfeqlJOaE3bmYUJiG2IV96Ig4V2z1ST5uZFDM7J+VI77QKR0J0wLGMs9/Wkk6MIfDlCZNmxU0Cx2QyaZIgXBJQx7csJCWFMm9lBZkJdzKntxgs71CxyAFg5iUFFhImf6c/5DMNoo2eh8xIoJxpz0Iw973PUE8qbYNdVWDFxxlEikRqzbhrXEJzcsx2QDFlHv4xpJFFVEarKhAISQ76iO0xkNWyUmJQco2EKaUJ9WMFdjLVumNc6o2UBMTSWplKvL6wyDPmtK5ju6IksiaFv/N1Jq0K5HCNetCYweIZ0hWMex6VwTRcJIiHEfRoTfrzywlXAm4UBbwazgtUSYHjlfaB1xrVZK/UhBi4kpdYEBrs4NAUqqwnSkHdkxo2Q9j5S70cZJzfxhqm3WBdjZUvk28waE1XAJWGQLSOkbWjRCpDNe9JP2fWEoGikTdQWKvGLoSi8HIXwZCHv5zXftRsJAdoE2SIXLA9iqG38Z6IQHTbCKCMMJxTIBOZDMqQm0UK6SOifwJ1pVsXeBC2GDLz0vp/moqQST25KlhjioQ/VyOYlyJja82mBqstAsY3Fg/exxnr8bChBsuz7ZrSeNRJbaKFft8zi2JIuFEelQD7r/VggeFIUAexAitiEVrQOkUvOUUx5H6uOUwa6pkxs3Cf3Hau1idfGU8rEX58EvBlPmjlgx6shZOsOg4rHw1vshosphqoF2JBnFflNSvuwdCRAEnoDZoWrdrOYcSq08LZjKHI4n6vdPNo548zc0H5sa+qtM4y5S8QIzj/NJQGMoZg2Y7OthpjQgoVIk8gyEzqTRBJ5pW/WP9RxRKR4wFBNS5SWnCh8OFU8z1FNexJQ70cxJu/Wzz3YRKjbzAlGpuBiT6ORd33wEujs9X7i0Rbp5jsNBWQKsI168KkfQ5oEJZIAkUnIwvgmOYFMvHxVCtSJ5W4IcE7OjCti5f6WkrsQIwDI5zOwrDEBDGQ/RrQQITWbyWJjDVZYAcoA7l0k9VTtwuPiJaHfy9SvVUsjLVD7C23Xx6xgSJe5cw2pxPvQq9nV7TnWEMzCAQEBAQEBa4zjUnNNlghRTGZ37neYgNcGKfKB+GiYY6OcwEvyZlAAlWH6FhMNhiOgNZFmYoqEobLmL+W1Qk7U0ELJpZ8DbDII/Xc8UIhXaqcNkGAoe/rEfIYDzJuoi2lvMkuWvQZUTRBkpkMlAKOB2nslALjXRIh5c52KPTkoXjYZfaz2GDWYzyYJhXKqzTiRKBpwd/1inQJr6Q9sIXYmJjZZImt5u9BoKUO8rKfqaL1mxro0fq0GeWeJIRp6kyCTzKWbVJEPyrfPZvu1bjFwYymQsWZMu/6PSKdyg9YSxP7E9SVxoG77/nHP2ZYopxsp6iru3Q2ZQrLgU2vVbd+u7GAMLoHssHUJjKcItC6F7DChc0C5cas63I2N/sxcOkcm4UKreO3nrYwNw9ix1xvaq9FoyylD4JkkxH1vbWDSs5HrFkM2b1NRAYNNPo0XcX9euqRcesWqyyEqQnLQssH5GPEJjZAbEDDYbH0wjXlFhlxliVYVXHrOaoJhtFEBs3qgmGJQhqSTtGu00hKDkZ4ky/MdZHt0x2bKWzfKLZUOgTFWCl4wFwUw2lwDMaH3YxNCtUAYzVqGpNFWlxvWKzvfp/zfvALyWYZi1mirMTmGrMoUWLt2IVosk4iMpsmFQrGc6jkFgBj31oecQxkCnowEypK7xA5MeWsMItLfNbvcfKcqDhoZbXdFaGuWyeQkVoRnPzPtOlMmjScfCrfW1RMKxAi849/h/oL2mbCRQLa/kZkuIucWAffXE63GhD5GcVwKV4t46M20jLT51S4+rAJs+pFm3CmvtPCrjYCTiWdZQul4QevvUn4NRdVmOjewWSDTBfL5fSW5fL7SmJYd65e0iQsAiOuQnbGsQXbhLAElyLFoReHNZDKFCwuRLQKvWSMshdwLQ2SYtDbbkiJnLmUN9qjtK2v3IOE3H3azYhdB2TICENrUFve1n1H3JRCZhU3FQD2tX0ZSzOUz1V8yFCaNW+uA9lu5WGICkhWfvxYNk3c5SU5g642OGZdICwXL6q6z8YUGBJfxiDg5cxdygXpKoij9wFqTaz0hAWNaS9oVlOSQh40fa8gbZnim8+Ca0Ifebm8erbr6uaypMMrh2L0UeQHHSz2f7LiJwrszdDy1zzxWdr2Ak6kPn9LZwwjllGed2tAVnd/am2bTOe58v/YdsZuuZoayoic84ztmiHJCe48xvy4UqLtmrIUOzbDX4DUhHja6n3v2dj7DndCXSYPhbcKn7EYqX99g8MOwsu8zsawMzoQ7akUYRS10fqonaNbYjJVThHK9YXgPBcSAO8YuRcBom89KJRYj5xaoesxtlBlp029tfK3VjARrGX++YmCGNU6pAsukSw3KFAOWdJv4ZIV2p0Bs/KVEDKXJ4lXkiQ4x6+m2tNolBnNacKnJ2qVFlLnQ5le7wYsIdqIxky3JchzERAlpBCobCMR9u3k3uYaXLGufOa4GALBYm4B1fwOVEcLRTI4krTEwoT/57h4i445wmzRAzxvS5ncAYFMlZmc0AWRpruHbPUYRzMIBAQEBAQFrjONSc41yhUiqsWo0ulqLz8erIua12LphJjQZmazGqCIfX8eUDu6339ljAJzWas8l4ckevFQuYYVKNFnFVSjhAK88MUAlbCyzkNPSJtg4u7ds5mdlTvPgFXOZkvRF/DUYwROu7Ne8cV6T9NJmLjk/r33+YBLm2RLPcLYm6CjX10gMmzpe4Y50UvcaeUpLnRPVV63hSOf0363Dmilpd8DDTdyZaS0jeLjZaGozlasmEg25M/lVEzo/smVsM+kZr1WXafNrIzaRD03u3J7WDIpN1g5HiNq6I2NGTn1nJsjfJoqQQoEbJmW+XiEa+LzA8YAwMBmURAkkK3Bzspzwcys94N0IyUDnt25mCbNaOAkG1iC6pdLnTZYZfDYx6GxL1pxM3Ck2kClQzHjtt8lQ1/OxkbUq0ox2C5t0JB5KJEu1I+CV06kj7XGpk5q4xAIp8xWRSFt/rJm1nGRjpmA3PzlQt7xJndcMZNnffd82fQ3fWa19EWKb4B9AMQMUM0ZDbOkEEACQLHKX9ATQ1pfkkMnQNM+MZq8bk80pp4UX0/pdk9aywglU2ewZ3M1pKAE2FE57pFjpfLwA4liirgUGKyZzESNEiYlDzQWSbolOS0+MUZEgMubXZrYpOWKu+pO5nTPTxmmNYpAgzszcTWoMl72lyMaa8pJBDLjvA9bIGV4x1B3SFj7o7k026MVH1gKDpcxrzQxQ5ppl7EmG8TLD8MQS2aR+lo2TKzi4rM1a+TKOeRyXwpXXJnF/7TMXeTOs/k/F3uwm277kWdxvmOCgTWTKZUXSgrXpj/UJ+E1lEEsM5T7AXsWxWwhEQRC5T2MIwfyiZEJlHGs3Zu6lrjtAOu+T/DeZykxiLHWhzLzQrDo+iT8JjNkyCI00jBFcCkIonTbSmuiaYQ8kbNEDc/0euWQLxADVyMfNK2+uE0MOMunZiAOUUCP7C1zYiUq0j8maCuOhT35OHFg6BZCT1vHMES9bFjbBJjGP+jqTVrpoFqyUOwG08mgF1ZYuN2UzO47KuE5JZ8e/5qB9+oFkQogG5l5MVw1yQqHh3+UVQ3s/cyXhwIDeXpMIZFlCJcyFTQHkSs5FuRd2ymTgtEK4mbKzbo2HlI1muWPYNpN91Ka8oHVdJEs6vAfQIUXpPHMbNZ21y/yuS5CZF8piBOdzzeYqH1JW1FBp5DaNTPqSdnkrcsx02y6Xci/ShQIsO7lpnibhn0vFgC59aDYwbeWSz0dD7fawvjxewVWZYlKH5hSGjUpd6V1DQ4HEhIXwWgtvm4VJZQrCmku53hzZdsnEz4l0Xmf3si6IaCFC9li9m6xr4UJe2EAAFXMCSEzX6HT0LlEqjuFK6oSflBzlkvldxVFSgnLZPFzNAMPKbU3kyAc2NglAROBb9SAmkUInM2XfFEOS1BgN9TWGc22XFQwcoNS6EbRLxG5gmPL9TalCcthnXso31cBcy7SRAb3aPZtqKXd9MeJus11OElhEmOrqNu65b8aZxqOF4HM9JlG3OBBrYk2z/mmzAk00Uk77YspniVExM3VUzedGD/J6nOjDpBceKmJgrKEBN/xDolRuIbOC0/5fZ9xrDlYw24VzPfeL46J5qZV9RuYIMMTh4iAp0hsJ+zteed8Xcd0PNiwiGpHXQIXX7qywKBsbDl/5hqHq+fCh5iJdTMHk8TVtzOBiSFWD2EDdGiwXLmxElD6sZbi+1lp06RdBqzlprYkQz0Wun8rN1kkHxAf0i5ss6xzP1nJQtRlWTjL9363HCEjgvs5muhBpgWQ3+QquUs3KoxrVhdzuHq79Nt43myOAfAhYPGyGmuiKRS6GdIVcak1eKMjM+LciPW+t7101arOmCzVkJlB1PcHG3lu2msJUf5ca4V23fZrHZFm307Zj+WSOquN99qxmjnTV3S8R21rHUo2Fr/GiRjVhNx/cvSu8IpQ97vgHKvLvQ91i4JWfr6MNzG3qqu64WYUEufAVVvk0gDI1sdtmHKppcrVvLUnL9ld8IEay6P3HrpTeOu2vd+Ekko357IebvDUoWSKQOW20niHfQKinTWrBdu00Ss59zmEwQHUkRFef1+nkkCavcZ7H4JGPxZWFr25DnMD7EZQNU2tJpCZ+ta69+YGVHHy6RGVr0KYSc4bHQNKsRTb/ccFB0/oaIlaQllOwFAOMXKwupQrMaND8UAKR+3UwXhSozDPzQoDlTU1Yufc8WdTrAwDU6ypMz/Rx4JAJqF5M0Pm5eSdtesdjGMHnGhAQEBAQsMY4LjVXJgFmwjkUs+YKoyHYUJxGGIGM2bi5VXqNtZm9hgTGKneoBouWKc3MtJlnROE1HABO0yCuB6VZKMCZ/0gzkW3eWyYJMNqL0yoNeOU1p2xBOfblcFZr7MnAsxsdE7rWz2LN2lwC1GAS2wxEKgLkBBvLvtTsg3jF+9PKSa/9xkM0Mk8AkfT5WlXmzczx7kRr1GbHnG+qwYxvkxFANXcaixoJ5BvMRQ9xJMs+dGm0UUEYBq8cRE57EYX2R9t2HT6LgW8zzsJhguSwQGZM2VM/qUHGP7BwaoTRBrg8r7LbMB+PhEt0oUyYlw3dSBa96VSmzNxb367q+oQYyTIh6fuxSpZr59dWsfd9KcFcrVVAm4zTBd15MuEYzQoXlqJiz6xm5P2R1kJhq+JUXd9vMtVjbENgtLXB+CwPMcR9wuRP9eDbwvYAUKWR5wd0IsjU14HV1h5jjWkxkxEN7t42k5aKtLm6vH9YGzS7NVrXqP9ZCV1HFAAlCso8QDmjUKwnl02IlT4kiyLSmdesvzcj5/cXpa6zas+j2FeEQTMZRK4/27m8eJrXqqlbaXOo/VnNMDKmWjWIPalbEHi7hjLacF7EXss0GqU0qTpYzfyzSF1DmJnwMJFIFP3UtdFquPHsSFf4sSFmpQA3WmfaqVAWMeSKyROcKSTGBF3lkfcRtyVY6hnNNIpcmI7sKhSskWCFe5O9nKwRzUe+albBkRrrQP9REpjwTOe8jEE2TKrPUEyb+dLgDRyrOC6Fa9U1VXFGyglQRkbYNsNcXHJyX9GDIn3cLoDarKq/07GrXjAyAmpXvk0vbMKVNvOmMBWxhn9Uh+ioyGe2sW2SMUPVYa5dljxkwZTnezTDipRg4NbnV5tQGntiI30jMWMWY/5+zetbqAT3q3jCkM+av1uaRGHNuEzB+8lMNh+3+Yi9kImXuK/AIzShRnYNRT+T7gWEgs6Ws2wKOK8wlx2qbukk8vb65QQD7tVmSR6RS0lXdfVmaeVkKyRrJN/XRIreIUK6RBit09dceGzkfMrDkypMbOhjwlROWR5kKIyfKT0ofKrCDkM0ALr3+gXCCbvG/AI0GcaOhc72BSRLxrxWSiiTcceWItT9oxfvdEn3TzSSKKZ0fww26eINPiuWnyPMhx2CV4RsQaFsmI+t+Y/VDIMtfuz1vNV/J8uEbEk6X6rMGAabjLm6kZYyHhGSFYV82n/XLC9YZ94nXXfJx1rHBCa5W7RlRpCmegtPpEv6TpJpIeTM9wQyMdPIBXjOkN3rJ69zpWyWwGTlCUKSozZl02rAC1MCWESOZJfuT/w8FkBUAsun6PlzwhkHcO/BKfMlQ9ytwW2IDSO9CQC0KdcUA2fQwtaasqvFxPk2ZVtpwW6HhuCS7JMgsE7t3gfbdpi2MxsqQwyqFO5z3KoQmVyO+TABO5iCbKJ9yVAu6JdDTJROCMtau0eU2cCwRDrhCq7fT5v2MRoIlMaPzSZLyJKDGR/15P9410e+nqOKTUap/T3wErD8z6rrCVg8D2bhgICAgICAgPvhuNRcmdLaoSg98aZu8TGWb516MxwjoLZZf5Qptm6SJZRdTyYh7k3D+rMPI2BkmMCWXJKiUe6LXEk1FesSdo4UpXTSc0CzNpnyAfYy8aZZElhF0Gqaai2jNBoB+TpviqQILptPPCTEI8/irKcaOWWbqT5Jk4eaDE8bHiSGmknKLcOQxgP/NclLfy4n4YL0RdkwlfZ0PylDWqJYAWanziqmGYeGZJSfUDnTV7RsdtFmbHr3NJJxMF9ujYR+rpnv6+/ahwBAN3Jla4SVE7kbt3KCoLZqte2E2SXUiuPAz7UdsbU3Rtdoq808vNFQ91GTke2KBkjN+G5qcdZykM1rRq3IfVqvZoiWM9+TzmctclMKbH3iw21SQ2SzZPPC9z+vyGnXoqAxV4IoaCxrFyNfdzdb8OS+ZKAgE47BBm89cXVZR+OlEvNpn1SiSbaTKVxdZP2ZICeM1lNxYLkRAtOVPonHcuqzgmVK18G186Lkbs61DnCducn1vw8viZc4yi6HMgwkVQqw1GZHASKT+F5JAbWYOPZwNuffgWIKGJ6gIDbpztw3N4nY5CoWQoFMoXUAyOIay8ZkEmU16tzGB3EgVoDJ1UuJglpX2WaAlmNES9Zq4bVya8Xhsc0aBteRnCvUuZ64lWEXcxemAwwXtJWF93WtZ8uupph0zBYAUszlII5bFcAIwiRiqQaxqz2MikMMuM9fHhFgEluo5RjpnMDkT3W7REFYfKzNKEXo3WWIhYuEaoJh5RRj4h5wHzp07FuFj0/hGg8UolJXLrFMWQDg0jM+wbxZuFlf0oWfOCYxxvyxzYxHVdf7ZpNlvai6Si+xz8wDeJ+pKDXb2C7uVeozQFlfoWUSc0lgVlhnOu7UmXh589qAGvnFl9WA7No2+kVWzWuhmRuTaNUjV2UmGnnhJ4pxoaxDavwGwAoX2w5nWla2mLr+GA0x5lNzDE5zz7pjef46W469lxgx5JsMa3FFIDvMXRvrjq/jKfJxNrcFcZshS38ebIgc87mYBsoZiXiDXjinuyPUUl9///wEkh+20bZCTsKZmhn5/gDpeMdqwrfDZ7fSY+/mU0no7DcLZU2I+hVUasxmnQh1w7fqNlxmozZarzty+STuC5ZzQuvguEnNhVNxhjprCMyUo2q4NFqGARwPNFPY1TaWjQ1dylH0mJszyYqucAPo+eEZzLpYROU2ND4m1VZ3Kk2lGtUiMMt+HQmoiJx7IzkcubhINH7D2jWwGLvwJ14yXyzCbF4cWzv3LGF+wggJJ18cvFcgMWZ+ArCyTzc4XhJQPeX89MPNBLnV+3ujREKZeSGEcpWMShNOFrf1bmtlmDqzMEkGZs3CHQmSHGTS/VlBBgBVPwEjhrpnBi4it4GkkkO0JGRpd/AMwvpLl1KXWB8xAakCLemXVlYMUSO0TaUENWWEuSCXJlFVHKLr/QeyEj5+VrGxxP1M+oxzvNZtA7SZOWps0osTGXKT5nH22wzW1j7czDDcrCBMhrWx9KTi2Jeux6VwJcZATFfYcKQiIhS9Bmkk8kKMSXI5TFWiBaur4tHInatz0voJwqRPVMBtmTMjDGv4Xb4ofQygyBWqnhgLqrdp1mQKyJbXYGSjsgiXWjA2iVBWiPESiE1pOi51PmLvJ/Z9YMNCLOmF1/dLWGGVYqnJMHZRLSaZ80smyzrtohU6dXs8UQGTQJz7NrvC8rUuhwfoJA/FtGc+pYeFJ0+RFrqt+3Snd/YR4oHuEBUzlL1GOUAJ+NSRjb4RelNlS6rJrFHeK1XAZOXIJYcWMifYW/dpjdZZI5QJrQGQLvmY6f5WhnKaHOGlfYA5nxMz4TtWWGULcixelWKOumUqm7S4IwGN+egFQ9nlKKYamxCywo5BFI30hKUnHMkWMHG3EWg2raUd0xpIVpQbC5n6hbPqcPS3WZ+x1qQsUSnKG5uuUidoAYDBBoFqglBNeEKQnSPxCtPaWGPTBVvWTOmNm50zTDb4CDVcxSBZpohG4wJVNErO2ecFdBFxTOkLTvdGKGuBwmhnZRGhvlvvNLPDDNGsmQfbR2hnFZITjUaqxj1oeRGDmSLishYww4solohjidJU2okiBW4amQ8SRw5SlU5NyBr+TWVDYGo+XoxdMle1hsdKFyov7GQAlIlXFQVzyRqYYuALkSM4NdNUMqljgKNDRvBuLNz1k1blNNdacUAxR7ASfeEsRiTIrJFmbZqSfoNUCAxOrsBN2kcRKXT/S5swlCAsP8aOJyGd5z5WOfUbXjb06+ixiuBzDQgICAgIWGMcl5qrbDHAZGCyO/Cix3Wwut3JU8N/SeNaAyO4bUkzOT8xk5Te/Cwe+vADFRMQeZOlTOHSsDHpk/3XbYGywxu0X880rbve1GVhE+XbWrEuA5RiENW4mQ/QfjBREDr3mvMamZy02Zq5Si8y8QkfoOCzBY10m4frTejDBJAummfuE7gkVJFn9zbN36LwzEFiPsOUbLBHixmp08/lXnt3TM0UaB1kaB02mn5JLixFxqYYQO5Z0pXw/mmrRdUTEvWicD5GmfgwoGgoIO4T7jNFvs91UXj41IVDr60WE958X7cJKiL07tbtah9UzjxtE9q3DtkbwCWzgGBjlg8S2v9uf+eSLiSabWs1QZF7a0Y01G20NXp5TTh8thmbZeY0Vp3xiZAu+/Pssww3cMMcN4zUBuNYGe0iXdDnZvPeGV+3GPIZ4cZTJnDaO5PMpaxUKaGcloBl90rmfKdcaf900wVhNVEV+7/TBc2I9jViMR62I4CVR5vC4bMjJMaXWkuOVlL5LIrf7blrjJ4wAucNNq9irnpONYq9Kbldos5jV0mGC6nDXqC12DiWjplLxBCbv9OpIfJSD2heaFMQGdMyDRp6TkTGz2weKJXOnAzoUB1uzbO511blpE6+AgDRMjccB32NclZ6UysnsFS6DFARJ+cjrmuONNNzc7ScgQ0FIsP6rdfVoMiYjzOlm9fMkWkLD5QMfF2B2mrAc5kOwYHWeJN5ownnTIcwNVxurjoVjn0cl8LV5Sglv6Aw0hYUmw2mWeoN8EKMS53u0BOCGuXWlDZn2Wo07l7QC33dhhPK8QociYk1hAeThHjkQxOKKeYyF7H7+VPjFYJomMV4I/6Wy4Y5MIXzW0W5fmZXtaZBapGJFsSWyNI65H1t6n4bj+F67vxp2RyNhdHkkw0yWCO1nTefW/8moZwxQrLvy6Z19gpnRgf04u4KuA/0c7vYyk4j+9GiDq2qM0sS8ebPfJacD0sMONJ5n6KR1wzc5mQujL9w1o9hbOIbVaIzG1lhW7V88e6q49cZUTC0DzC3GWmmxxMFIe4rqNhPLpeFqVYgxlBOmkUp82OjIu8Lrzs6ZCFqVpJxqfh0v9WG4LR8qgLZHLLLic/glbGxsCAS3ixftzFW9UinVDRtHTGomJDON9wi5ryyx1CYiBSZaf+/MEJApeR86PW6CixSLqwjWhYuvpRJvTmxfZws+fSWdcdnkXJZncwjVI2NZ90xYR0mr7SaYS7VX5JWWOm3wPeaC3UJ7GQ9eakSkNKPS11ETqCCERKTCUlJzcayWZkkCWfu5VyhLCIXMpQkNaQ049n0X3KMxcqytgTZkJeSa4KQMbPySGkzMqA30BVzG0+ZkivTxgqBeNG3v5yVLj6cKu7IU71NfRAxcDNhIyFRSeMXJoHRojEzL2mzcj0p3b2VzVhluyX32oXbhK4vwRR35DA5ScAhfc1ohbtsTTZTk3tvcuY26aNGmtRjFcEsHBAQEBAQsMY4LjVXJgHOtVZjk+eTzRfcYLk6hqckr0WY41b7Iga3++SV1qqc9sv8jl9mjcwu0BqkMwWnQGyIPSphKHoM+awnoVjNLBpqzccSnHSOX2vaZJANhiRXhHhgmIh9T5qhCEBHOI2rnPDPLAogXSYk1lzd0ErsZ0Br0+U0XM3T5nky088tRtYk3fgu1f2Wz+jvyvXShQBgkLiwHPu8qkGUcWNRm+QQZjiiETnzt65q5NurBFyC/GSZgde24gkz2ry5pucDoVhHptqHPpDOs1V94AoVMP9sttIPYEhhQ699EYc30a9IXUVI+jY3KxHVLeESOyjhMxUV6wjChNHoxBl+PFTk3RPZIiFfZwhVgE4SsmRyLUfA3OOM1jClwGo4NniTbRuNtLbqiyV4oolKCMmCQGk01HTZh5RpK4u18WHsucSQubq+Or+urxsqcp+5rJwk1Otqlx+aEVyNYsuIdmMhmEnQDQAAIABJREFU/bFia+kKiLNKV4RJTDYnIRRq8x7mu3vIDnEMTzIVYiYLx+ZlDJ6Fa7VIo/m1ewUKE+ZS92MdEmMTWgwFmNHoFDQr2LJ7R4gdqQgKOjuU6e+muwnwVm2XGcomvicBYchBchCDFz6nOHG4uZoscBTrDfGpWwE1B7ekpekanU1aQ08jCUU6TAgAKinQH+pnKxd9FY7sUSsoSwG5aM0FzJnyGSefUAIAOCE6UZtSOq0CRRW7zFQ0n0JUnlhoLRg6YQpDbNYzUZJzrdjQqWMZx6VwJaEXo2ZmJMUMm9eFLRg/JrQgtOaoYkqbVH2ZuXFWbrPcGiOgblRGiUY0xjK2/kYmfTiPMtVn0kXjC2bMs3crQrrYKITd8SEdoiLna7SwzE2VcJ/EnzGUE8wVLK+63lHb+bFm9tbGVKxN4Obeta/iMzIxjjZMoeo0/H8FIRo1fJi5chuAdIWweIrwDNJEoX2X/mE23yz/Z8zV1iXXqLqjjA/Umlz1McOIlDrVYr7Op+1zhbAnffJ21Si6DehC27WJs+Qj7et1QqeRRpIiHTrTTBnoSvc10lTa0np2bOK+ctVixLCGyGtXOJwEg2wZ33UvRjHhTc1125vO4mXm/N/E9SbDbuJU4jdx+Tpdpq67x27+hDPfy8yzuKMVHaNoy/CB+fbKVLsYunuNK2HkTdQU6XYlZn5Wba45DNC/cektzabTcQCiBjt+yA3j3DNZbdq7alqitSd2Ju8685ssFZGztfFSv4fFRsNIbdWQxkHNZyrESe38p/kwQfYj7QNoDYHlU2vHjq3zGJHxMcpauJAUXnAd/2kkXj5KfElEBtAwcoIXDM6ky0fcZ3kysKUHERPIXcTMURs3Kv2GUT8s3HV4KiFNJqbkYAQx8sUMMGDITR/YvgAAWkiAhCBNFiaeSLc5kJJjpjdAbPpHEXNClbUkpqf1gtNLS+xf7Lli7+1e4fqAiCEHIEx1nnpd7cKRVvotVIupY9mjwdRnEohGdv0wRT8aa65z1VTNzjg2EczCAQEBAQEBa4zjUnNVEQOL2VjsI6+gd1iNuqpoaIh2x+9KxllLT6vBWKxojH1LAi5DTTzQv3FB9onXBJsmYp1v12vU6UKDdFXrwGyb8IAR0D7gWbPN4u+8UFCGhao1Qf33aB1DMQNvoiNg6oeG+bmkTG5jozll3mwuM6ZNyNCkIkaN5x6t1iJdEoye8EzlWY5inXJZb9o/ij1zuBGXqyLm6nXa72yC/2jIkM43xkEAsIk0euO5kMdyFy8zVwPWXnewTT8AtaTODARAdSTUFIHv1T9MR14jZaTjWZvsZ2veFYW3ekQjpck8xnIQr1TuPCYVqokUxbSJs+w19rekmezWHEsMyA4ZrbCGdymMdP7k4SYzDxqMbFHqZBDNgusls+f5YhS81u1NrSm+AoZb/Dim8770HZNAa85k6ekwgBpl4FLWyMLk2Z58NG62JdawCglNWqo2esa0WNAXbP08Gksu0nTVcMmgzIe6TdoEba3fFUdn1jO88p/1XFKCiX2+/GL/RIVosoQ0480TiapvVWPmaqMqrrMbWdOvatTxZbECVQLUt+xBuBy7uviFP1emBNi5Gin/t5AQmYRcsUHT/pl5p9KFRez8n0uRLZhEKSYz2spjjJaq4JjDFPlMThTZzFam/blwxS7aJsFFYUhMS4MWehu1ttpJS0wk+vt+laCVVpgyacik4pBmbVheaQOLiY8/LzlyW8+11gkmbAwsxeSKWoiRfw+t9ckSGfNZ74LhxgVyLOO4FK5MWp8Xc5Pdmi58qjj4xPcJc+EwJEyqvpa/njNRGna6FRLDTd6EW3d1kW6bdEA1kv83WZ9gRtjaF02SE0CjWY6y54trRw2hJkpd/N3aImRrPOO+vQaIIVkC4n36Y/de6YttGyFsTYD5NHdCjUtd3QXQxQeIjxf8dqEa0M9sfScqAsrNpg86Eum8wMTPTB+IJjvbM5ed2d0JQenHiTjaZSNTFvf3AnTf2eIIddv7EWUG5Ca7TzRgKKclIpNYoF5OXJgCKzjiwxzt/fpzukSuCpHOWqS8Gbrw1xcFuUVDlKT93JYFXEqQCU0q1meoM47RrBHmMXP9X06w+80rXQAA0P1k2eLpMiGf4n6zgEZIGOk+t/PV+u4BHXLkmL0zuoC1TZSSLDV8rgOGZMnP3aTvC1yIiiFZ0XMD0Ak/ZIPZaf3CTJrMYMbsTALOH6sy5Rn7AJJ9sQvt0QxjzwbX2YRMm3u+eDlxLbSmT9DObsYI8/dqR3Dn7ggZ08UfAGD5MQrRZv2SbZ7s477Dk8CiFqjxHAdsQfS2ArkdjIIqhN+1ELy5t+SIVnzRAJkpz6KFDoGRHetv8n3DCv9OUqSgFpOxAhrU9gMqCwFmTcaMnOkXiUI2UYCbNIq0mACzWhhGQqE+qCdQNNDCjTbq7zq9HJExA1dSYFgkLiwojmvEwjDpGaEw7OayjjDTGWJhqK+5tNyGMpsBnus607WpBhStCMfBkDM1UDGIvhGoK9xt2NNFQmbY35WpjmQ3ic1satWUX9uOVRyXwtXuEsc0kBqIyka1GDQII8JrRLzWi78TwmWjyksE5DPMvdQkyFV3YMqkCHOasq/eAnhBnvSN39XukgkunpQiE2NpBXbGkBjSEpM6vtSGB0CgoQ0zrGy1WX9MXmC78W3krCXBUPQ4Kpspqed9H+0D3jcCBrCGMNFZezxpQwk4P1/d8v7X7JBA60CjAkrjdzLxAppXemEebLN5BgFWWP8fA0DON1N1vVbCKwDMa1wqapQCi/3AltMSvFehNgssq5nL/BMNGNIFcnG0+YyviJQukk4daUlwDT8rl/B+1VyZEnHWT8lcQXeQtkxYa4SOJzWWgg7pvMojO/g+BKzoMHDTP/kMRznRSA+p4GJIRa4LdluNUeRe+ImcoZwy2nrHxCyasmNVx+d5TpbNe2AuX3W4e05R6bhiO0fsM9n+sPe1JDG7eaqna5+XtuYAJ2TGOpAseI4BUyaNok0TmviNg0rhwk7iqRwzE0Mc2K8FavsnCaw8K6YJcnMBbirCTPaGrvD4fL+NKJYoO/oFyFPuiqA3BaEujs5dXmPKpBd2/WjMh0wJuWxKCtAbQdn4zsTDsn7ktEfeqUGSITI5ies89vdaSHSsq33wqcpV2QGA4mDbXZ+vK9Eymmj/QNcXWD+xQNYqkZgYW0kMS8t6cEgxiEg57Z1aQGT6tagjINJt6qYFFkeZztQEQPVjiKEhjdV6M2YtYLKjfBxtwcEq5mJseeFDbEj4HN8y9u8uAFMBTP8t+sHnGhAQEBAQEHA/HJeaa/3/2XuPJtmW5EzsC3FEylJXPd3dD43GQHI4RjOSCxq3/NPc0LiDYWjDGQwaQKv37ruydIqjQnDhHu4nB9yQ1gtc3Aqztr71Kivz5BER4Z9/ojHIXC0UvB+gKqpUCn4gr2FAoeAyTFKjiHqf0bPH67Q17GbDkOKNVdhwpN25sOZmu2QblJVIryXYDwCGS60sq91pyHe9T6cpOCmLHSlCFjOCh184qaZTlQFjRTpCzGCunBYEfw+XfD7aLD0/EyCVquWAAIVxjIbMFzMCPg5/nP+bqps5VKlypznjFRivolT9c3akHakiKoza+XuU/mqRbsz732Yy0pvN1iDfNrLLB0gqAgCbHxKah6SuT7XB6l3x4wX2Xyq05ztlw9I50fezUxK4N7ZOKvT9F/TvuU9z6WXHmnb5y3dcpR+yMLOnTT4xcogNyWkAwD9YOQfT9jShZ7jKAtfZySBtVEphOiuVB+X60meNZ3TPSbsjK5s9NtQPn3twz81Fik8sEjPiS9pNlcRlKIGrOL6E03rWqx0zhstT+ZMgNdsIv6WLOB1q3P/jChXfF8NlgvmS6NTtYoQxWVyTjn2NVHgEfUXSmVj6LoBhU4N0FvQ5TIDdK2KVs4G5YaSjIFoM49pVELOJDFD/fi7fKkYLNsOf0fFvN0c1lAAQ2wm7By7XnYPJBpmr69V6QM9MX7xpkZ9NWGz4u9ZkigGAgtT5mK7OD6h9wHGgYx6GSj6r4WzXid2Wlu0o1Wk/Ouwz4fD9roF98GJY0YxGbngTDDuR8fmZMaTtYOEP5sRhragTTNTnc1rxvCfPkM4p2ev7farjs1xc+2cGriHJgr3TCYRM+PnCL3VCj41O/GFBi0QxrR83p5O8DaQ3A2hBnRvp9xdGZBwUP8e9wVYJTHbKqHYBx2f0UPgj0N7ypOQIBhYyVaWfbYeEVFuZ4JMz6C7KAqG6MdcZLN7PZDuGeh8ATZjZQyaY1WsNKJhrOk0iuz+BuIOen5zZ4rBoVWcQen2fSTbCvzuRbgDYf6cQkx3MzCRcX0OwvPYHw0J7ayR/0T4lSVR0oS0Ei2pnaSHm79ncWNSP5ToZTEsrD/n2h4DjMzqA4crADhqk4IcsULANWd12ymKwKCHZRhbl2NLCVfrCw6VCYyaTtWO5F46vDPpf0STq64CB9Yar33lMZwn+odxnBsMVL6AjOVYVeDZsIqr7YpcF2EK8GT3sLP7P9UYWRpOI0FQW7Dm5rAQeKMEvi9wpNUl6a6WnWhaunByyLQ5EZaHm89jpBqy/4km7yK/m/UubYX9DC0k7GfTfTHAcqeZcEstBAJgmL1Kc4VCjescEtcEg+SwSp9xGWA5jx8GzBpcvRoZoSP3gFP6uKCi8OCjVTaBgcVAvdb6wx87BcH80LSMcH2PtI86aHjdHYoPd3y+kD2xHg/hsxGJDu6TDQwvHevD2FzvUfhZJmCwm1qhiGeRvurHC7tjIpiJOjtJvQAuyMRkj2xPujw0mltTAZ1Tv6P3W9wbHVwnTs8DXzcI/8j1XYO+ie+2cLqbmlMAGqJwq23zSo88uK3wcDCZ2g0pujtF/muMJFn4aT+NpPI2n8TT+yOOzrFyrA+AmrR4A2qjGSiHX1ABTIYVMKm6uHylurbAlU6VGBdM6wwYDz7BcNjN3pZ7IMNVBP7NUyfU+oX6k3aEdIu7/dClV1fJDUu/ZgdjAhRxjJ/XR9aCdYqlqu2ce/XP+LnXG4h2/7giYlDGcFYaeQuGJXaraD/xd96eyE830NIitQmaTVygy1qcQuo1a8YYlsWGLAUF26pdbiDYA+Qz73kjMX/ZEjKLP5v8m7wk5V27gaqscc1bilgHE0zS2gEMWSVD9qA5E49ogXWnFnp3DcFVgWtqBi7vVmYFJei0WzAT3x4Q48+6NlZH7KjZ0jY6v+PhXSSQjzY2BP2Q8fs/H/O0RSya8jKPD4jU9rsMFBZuXSmG4TEjbEiJsMTyHVFWmc+o3PTPOCOcR9mixfKv3RYGuswNn9Op5LCSu/hmxjgsSkrZBb7rJQJJYqwxTR3HxMZNB84FzatfpBPadZwoPVxE4n+CYPBQGh+o9lT3uo8P4S6IjN4sJTTJk/ACoBzCIOTx0lRg7wObZ/ZIR1kRoA6jqnIbCosnIW66EbyvYSSMXY5H+AOL7Wy/ptWFySFx9OZ+wWg7oGIZNN7UyiW0Wo4XWB9wcV7h+TYQs/+jkNMarCRgcupEmnfWLg/gAT8Fhik7dokYHzyYP59ujZA8/7hZIBw/H33OxGoURPAaHbtciM6rjljM4nFnAALD/eYA7G2WRCKGWe2K8SCfQba4TUi5kJ6A6WJXjbRPiovioGzWXSIDvjdyfw2UUdKB6/PTrvs9ycfUHsswrzF+A/j8sNHkke4U6k1NYw6RMf2P074rjUHaAf1Q4xGr+MZI3iCtIgHZywOot3XDrH3q4370DABz/w3fIBmKEj5wpJQdAZcmGsRyX77SvR5+X8PgtHejxlRE2qeuMJPC40tPih2QeGp4dTSYFWp6WulDOM0LHjTlJSokLZRK7jt67nJPkIQ8WACzeOTWgX2dM3AO0g9U+tEgc+P9nOY++A/qrfMIsru8LK5o3OGWi63TR98eM7qWRv7GdnkeTtNczntH7FFes4dyguVFGcEjA4Sv93uU4lq8tVqw5Pr5wGC6M9OWPXxrqL6G0HvLJhqAEA8QGuPurBHtZmsMGxz2bqL9pZOKBofMsvb9VhGN4Lk4WzUcvE58JajsYNgl5zRCfzcCx1j7u7PqWISqUWvXaJtKzIRNrsPCPDH/7jMSOQKa3wNGhYuZzc6vQ7/ItpSHJRslp28KOBmlXwXJrZfvR4PHfsU3fiwTH0GbJRnUVfe/1coDj3vIYHDbbDv6Cfrc7tMjspmRdwtJHSYExgISep8kBbH8YFwlxmyWTNEcj7GNjMpyPknuaJgvPsO3zix36yQscm5sk1ogARLv69m6LafQCiYbzgGpLN+vzs4PIZACSzmSeSJoq0M8lGGAxyYKdMnB/w83NBLjNJNC4tQmHTmza6Dj4u1avlyifNm4Tpq8H/p5Aum30Xj0fkThIPR0q0gEX68jRnlqGOt0wp0WSlontVf9qBwpAEc190nskf/pr62e6uI4ZjhNxpDLL3JssCTRJF4/s1DvWBuqRzv1yy+v8gXZhXoKeVaMnE0m5UUe1P/Q/3SKfb/mzjCz8AE3opXXRXVByTOnzmRkRyiRg/2UtBJjShwSAxXuguef+07lFbE61tdIDyTTJlg2BP0Ju9lSZk3NjA8Qy1R1VGG4iWQ2OV6yb247ADSdi7C0Rc0ofzqjUJ7ssFVzp2RSShA1qlbf/NiEvIhxP6PWNVV/gVxF+b2FmEWXlOk0btfqzk+FFmTdFVr2ibWRZSOmfziphk6jKjYwWjGcGsc7yWdd/VeROlFRSZCN0oPpv/7HC6ietove/pAnLtgHeJ/G6zftKzC0MgKmYS7gMNxrpG9rOofkDTY8Xv46YllmMRvpns95XBtrf07VIXjdQAFWt5Z5obxP1hYsRxdpqcPqaCFJFkuHuIX3W5CHVYmoyqkclWo1nWSL+kAnRKajOtNHrW2wey6Z3/7OE6owm+zA5RO5fwmSKfOOL31QBDVdm6yZjjA63O9oRGAO0LFdxJmPVjLjd0xcfR9W9GZ8Alq/VyxFVFTFwVRvh4NmLN0WL8VhrBN1iQrugL7PvG0zBIcWy+wMl0gBAAiZXjh9wPsI+p5O+WQ4YuQo/DDXGURfoplEd6rGnPmrd0D2zXfYI/FnXb89gxkLCzIiDQ5zoRE6dRuQBAAYnm6JsM/KvaDf/YnvA3Y7OzXC7QH2vUjRz26oV5fMJaXKoPrDudSa3GS5IKleecxOcSpVmMYGUnGSkSh3Pk/LJ/g2sTP8G9gdP42k8jafxNJ7Gv67xb2B/8P99RG8Atj8sFeK0IYtDzw4zc2tEt1cxf2HBCbU8AXVxl5lwEtoclpQtCXBO6Cx/0nXA1X8i7DGdrRGu2Fj89QHr/3IAEkOMv3qB0NJlqo4ZfsjwB64KhyR92/7SY/+1Qf8iyfGXPivBnlop2UmrhlgZ3WIl+v6lt5qtGranSt8jO/r7srusjtpLTp6rXGalpqmRsGWT6DMKLBTWSX5XPZ66KSVFxQhiOlNY1T46eO7bTOvTTFgbFGKMLRDPCoae4W7Z/PyBerOSbbpSa0cYYlqXap5YxKV0zYi1lf67mYDFnVa8/aW8BWxvkIvL0NkoMJ593+D532V0V6WyzMJ4BYDpUMGysUNzZ2fnKkv/b/Wa2MzF/ODZ30HC123MHJbADj7GSBJLaJ3c39NG82wBup4lNzXWBOuFIleZnQI3GJikYRLzRKTstH/m7qh1UEw8/FHlR4evgbBM8t1MMmg+0ps0e3I2K8iHXU8II1dYnRcT+WZJkOd2STBFZZPY+R2HGjFp3bBsRwksP/Q1dvszsjPk9ymSrO6hhWNDf2szDo9Ka3VVEvh46ujmPL/a82sT9kd6bWEsS7rOzPwBPsMWNMYl1HVAz1CtMRnrls0g+gbOJbjSdokWpfNpbYL3VH0DQD9W0t81TaREHhAj293XIoVCcmJm4XwEjg3ihqVcL47S073fLwXuru4d2hsj9qbT1uD4BaNmR0efwbB//aAIWKqJUV1Y4SYDoTyHLiMzI7u+Iwb28Ko8+IokpvTpx6V/lourZTcjRCUwDWcA8inWP++dFrcjIGPa6ORfHZQoYkLpH9HPsdH3KGSh5o5+Xn6MKtk4b+HvaZKw93vEn97CXlzwmxpUx2Kjl1Hfjahe3wAApm+fYeB0leNzh3FmGVY/GLS3KjMqjjrZAXaAOELZGR/FTiQvKak401pD1c1MtmFH6rsVK8d5JJwDk5oY4h0u7Ey7BknpAMAuLnx+KoXQw5LJX+wY5Ea11RvPCZ4uMg4bIL6lyPS3xY0pNZl6f6Akljmhp37QfulcNpAqYLhKMmn4o264UkUbprKw1I9ZJFmhNYKTx9bg+JUSPvyPrXyX7e/TiZY41Rn4QDdMfWdRzY4ltlng2ObaYvmepUR7smCsumKDmWCSXvvQWjQ7/l1v0F+UzQHJaAByd0oOsvDW7/SzSC6UYcrmcphpXo2RQPZyHsVlJ0M2FNOKoMmm9MP9LMLOMw9gX7yvs0ihjq8yxudKsEmHShYotyWoFiDpTeWiLKK3jytxMSqLZVkkrE3Sw4zBEhxbyHiTQyykqypJ+kz3UAGWFncASNEgFa1pBprzXhbsfdfIBiBFizA4gWBzNHD879D7E21r39VChLq5W2O7oRvNu4iur1DXUb5PWfxqDiA/8vcZhkrTenbqoGQjHWfZOC9mvsvdrgGWSTTDw7ES6NoMDtWtSrySA/rCMVgm3WeOBr5T2VtsgJF5FtNF4IWykOUM6vcMT6+z2EhO64zczliYFirNm29KPtHxBAs/jafxNJ7G03gaf+TxWVauJ25Al1zReapSJNy8UejUZN1FJfbDnLNQRYbSsF/mzIx+Xgkv32VhlK5+PMqGLawr2JHhpK8u4Zsa5pHKo8UPD0Bkw+0XG/iHDvEl0ffH8wr3f0If1j8j0XtzTR+4+UFNKmBOiSvZA7H4DgcypwdIThIrg+Fcz4mQDxqIAUa2gO31XAFU8QJkLtG9ygL5+cMM7l3mE89Rk6DmBCslcdV3xIQtjOlqp17CyAbZql9xdTAnMotUzwhZezsLmld2bXtN56YEkZus1elkgfajPfG6Lezm+iFj82MSNnV/YTEU9ncF9M8hf7P5jZWq0A1KQjt8aSkbuDBnf7Ly7/GcqmnxlQawesNw8sgQPgBb8/nmv3Ndgi33yNIjzoMmLBTS7bXiLDB4kV3NJVM25JNkpnqXxLCkVPn1gxK5ChkseUjoef2Y0dwry/j4VURelQ+3iCvIffDsby3230Dew995xDV/+GZCvWDZTK033PHY4LBfwjBRrFpMWsHBwLmEizO6gbqxwnpBD+nd4xKpd8SWBkt4Cs+qd2rO32a4JiIeuKrtrbBy8yLC2owpqLtJ3bJX8c0CaKI6Ng0OoRj2V0lMJMLkkB9qCkYH4FZBCE3dbgVbJYRiCJMMakYHHq9XMHWSzNVUWRzeM3ErMTMXQHSZPo9Z5E0V8HBPk4AxwNXX9+gnlnZZj8RuTfnRn0jZ+ud6n5lghO7senMCGXcvDCaGd83BwfZWJXkJmC64kneZfJOhvyvD9lZZ6HMN4Cc6PsvF1U4ZFhnHF3Zmas6LYYkNO6j8JrZKEU+e+gulB2snTWWZVgRtmpmpv7gT7TK2v5+kRzpcNhjOubdxSBie04FUDxPMOCG8e0/HAWD4d4TL9FcVpo1HYGu+aWFPwoUXH9Q9aG7HR/F59DdhyVF7BYkJamFW7Mfy7K4oWtPmqIHx2RjAZUyzSbUsQIevCDoqDMDl2yyT72GRKTOhuLtUWRZ5fzRYvuUFLfPCV3qiS8hCmCqScpTvFla6cRB3qSItwmxkYPGBobV9wrBRJqsbNPLPjXQeygbD9UDD8PrqfUBYWnRXyhYu565+hCTpVIfMsq5yzBmHL4rEI8MftGc5ngPjWTlgQ2EP3Kuq7xSWH85n0rDawL+l3iqAE0g4thbTSv/OBpV/2CnLvWrAffPC/hw1SSe2vIDyZqo/t7LBNJH+VywsTdbzjaytg3FpcPwyAWz3Vy0mjAd2HXt0uPh7iNZ693NguJjPshmGE4ucS6Jl3e0aMbe3y4BqM+KSF1Bnk1j9rRqy8yvQaXescZz1T20dkUJpFOskbhcBFeuKjSG4VBbeRRKoslqNeLHd4zDS54WUVDK1mQjq5cXVrSeBsoeuwvSRHtjcJJiN/g4mI3JPt1mNqKoga9yintCyK9OyGWFNxvU9kT7iYy2blLyIMLw4OYaUIy/sDz+ciQzr2fMdUoZ8njHQMPajbn7H84S0yMgsM3J3lWirbTAcKcivvUiw99znd0C8nLTvDOiCbTJQNtfRIJssDmJ2MtLuicuTp/eTHJ/l4pqdoUUu6047NoWQpBe1TES+055TYsOEIj0JC4i/7/IdyReKxCY2ZAoAAM//j3fI769hXj4DAOz++oVMzNPKot6zLdqbe6QP1/BfkMvA8a+/Fo9gABg3GnWVZlpcO1E1If3ZoJPuuLHor4rAm4TsOvGrL3Dy1M+cV+Vl7hk32h8NS95wCG9eo6dMJNvC9obP68JIuMfqNW1merbqi2cB/oYmwMX7WboQ2+2N5/weLUTn116zjrbVhaA8iLHNyD7DTvpQF5mIn+WyJkfoQ6nMprUmdaSGqsfiNby+TmgeuRpo7UlVOK/UXKf912wp/7RokK//0ovmuPjv9i/551lSku9oU1Iq9uFctYJuMADLljavI/VZg25GpjXdI6E1qI6aAVw0yeWzrWwezUmsX3+lcXf1A2nBRf9cK7GNZEs4qW5Kv9ROwMBUge7bCdVmQOC+53jfYPOPdK3r+4y7PwciV1V2NGKjV0g3Rb4VbIbr+ASdB6xYurJsRoRosappEW5ckAXo7rjAoWvUYIJTeACqJP1yhOUqeOgrnfeN9mnTvoJpI8xMvlK8kVeLETeHpdgH5qyV69h7WJexPaeLGJLFvngGP1byPUWXzAtcTgaL1cDfbTptTlhpAAAgAElEQVTxvaZjY8LRboGpq2C4osYqALxvsDPLwHTbYPHGofuKz+uZJutcf9jCtQGppOL0DvUHlQhNZ0WfGmEWUc5PvJjg3jJ5KhCpU5KZoBsrczXA2gxbnstghcgVg0UuciFYVHdOvLTHLTC84D7z4dPvWH763+BpPI2n8TSextP4VzY+z8qVZTbVEcidVhthqdWZ73FiWi99h0T9rlIZpMpI/ym2wLCy4sxjx4yr/4uV1Q972KsLDN/Q1n5aGoJXAbgpoX3PTb+uR/gffoXDq0aOq4ySkVms4opRAMBZo4kE/wDQvVQjdjsaYSmDq9ZSzfhOqxkKt1Y7xGllZjILADODeTLB4L9bZbEnbG6tVD4AVdAVn+NYAbtvLdJzqjbq1zWWb+k7VAftER+3ZJ0mbO3BoDqW60LnvpyWaavMbddR9VXkB7azcPfaLy1V/rgxmNb63VI1r7yZUT3LuC3n1GQKrBfTkJkEyfezgO9EvevuFbvXJJUHpWUCMkTAX+1mPT9D71ng97hQiLjaqcuWnTLdO6XX3DqxwcyOerNz56XISEpoZwEOfbmH+D3W2scetwahNYIkjOfaOmjuDLVQionKIkt4RViTtSBAUg37dlVuGbTXCqE/fk+BAiVMO54HtOcEAfQfF6juHUKBylcB/hUnybSDsG2n4GBtwm6g52TyDol/N0weOSskitEK69csAnI2CFy1OZeE6YvbWivydYBvJ3GEsjZJlbnbL0gSU81kO++4udwmNOtB2MlDV4kFJFyGZYh1ueoxTR49G+YvznqpLIfgkJLFkuU2D4cFPhzZZGZ0MHWEYbh9sRzR7eiGaX69EEb5uDU4vlJIF6MV2LY5GoSlh2eI3UyQEPq0UPau6R2qDx71g7ZkhmfcO+Xns1SuqU2wLCmzNiNOVsIMYIDMQdNVE+BYMdAfa+RHh8OXPAf7DP/ARiwXMw/VT3R8lourSTzhVdozKPrP0nuLM9LJ3LXI9TS5lYl08TGpk42xWL0NAge2H47IFfeL/pdfkMUc90tJAsML0t0E9/aWPqCuMK28xpfN+vpEljpNoykbgGltcPwCYrOX6oTFB6bCHxQiTt5gPFeylutVguFGcrApcHiqFDot8hug9JbzCRmhSE2qHdDeqnfxuAXcVGByoH8ZYe4YHnxQ1yda1Pn9NyRRKZC0P5oTWH7azlplCScSGwBy3doPlsLlAZioBKb919TbKZNG+QyA3re6g2hK919pOs+0oeu/esNpKPuE2HAvdWaJGWuDaa3n3PdGfIzDRN+zLG79cw2Z9nuL4UIJYNWjUVJIBpYfGEpsLEzSzQKyIWkZFAYv33XckrQIYPnUe9YV74HhCui/4Q8YLNyBNwOB7v/+Of9dneF36qUdFhmZNzQExWuTu+LJsWwMyqYuLJkcA9r8mMEiFj/kaDD+yIvTKsJ8fzjhvCwanWiL9Ma7SItk6Q8ah4kXv3GoEHoPy31HvxpFo2pdQlVFTOyCNe5ruNIrNEDa0DG5RYT3SW0GfUTgxW8aPIzP6LnPip0HZraS0+Dh+bNzMmILmNcByzXd5ONIspzNFcPHwaH7zZbPv4H9+QHBM7dicsix6KIycqeh68ebJVa/5YX8MuOe5XjZUYvEPTCvY2eRXOEp8Pc4KztIKOnt0WPBMZPNHcm1Dl/z6zdZJGoAb9TKNDVYIUUlC2BmhwgDZN7MT9lj5EXXPnp6zvm8th+dyHlwV2KRPt3xBAs/jafxNJ7G03gaf+TxWVau45aYnHNmrA0Mi87Yn57hTDeSUB8gcpDdA4uPvMMdVMC/+S+PMFNAeEE7UPvYof+OYOBUGfTnFq5UJceEak/v2fzjOzm27lcvMZw51HstC0twd4GkWXUBN2Z1UPKUjlIMFKoHK9UXMKvQa6paCqvYBGWMTitDebKHLJ9XdqbDuWaGpiqjvrOoGabMVqtH32UcX6jD0bTOGH2BjjJyk+D2LG+Ieo6rg7JHi5lHe00/Lz8k+Z7DM/LUFR/irMc4bTJgM5Y/0vsvrmeZuc1MYuQylm+NOlHNINb+Ejh8ncQH12Q1RrATsP5JkYrQWvji8xwNCrxR2MRz4lmRJtUPBsdvgsoRohGnpdRktDdGnJLcqNUpuS7xNaxwcn+EhdXzszWYNgbDhVYpJUAbwSCsC7sTCFcTaY8AOWcAVa1hnVAz47t+UKSge0WIgkhujlag5mwp5QcA7GBR7bR1sP8+qAQrGuTtBMfVVwoGyRc4BhgPNTwThJyPGFgy0taTQKXOZByGGr4Y8JuMiR2aUjJwbZiDPuLF6xzBuwIFYxYsYZVoZB2Z+xfXJGsyHju6qdebHvt9CzxyxdskMe4HgOVywONHYvOa3sKykYl9ph7BORukZMShKb1ZYPUTV95bgr3vrollV7+p4OXcUSunMN+HS4PdL/g9F1HLpWDg772gS+NZEpmOLUYTA7cSqgzDSMrZP2mr4P5PARg1W6n2BuMzdTzDZFHdFn9iIBZnDkPGICKr8YpyZVjJlU2LBHu0cHwcYZEVqRnnV+/THJ/l4jqcU1i6nSA6rbAiCK6Yl88XluqQMGxVytIZi+pYLr5Tp59vn6M6JtSPfLPbDUaW2yTPizTLJ5q7gPY3H/kDPPZ/Rezg4Yz0YcK8ixCIOFte3EvvcHvaN8xO4bv22gj0O7dh9EeCkkV+A4iGsTChC+vVBoi7T1jpAuH3Fs0DhAXs+jzrUbJmcsZizjzNxWWAffDi2mOSspa759oPzA5Y/2AEBoUxmshzkTl5R2HiclyuM6gfrOqOK4OJJ/ewmlnz9bSYiiSoBh7+lFmczwbkYDF5gtqqe4v2o26ypqUB+B5p7qPC7WsnMYShJTZygdvHc9VCH78N/0KOIIHcB7J/231n5Di3f2Cmcm1FU1PvIkxI6K/oGMeNEbmTP2ZMedavDkAs8JwF6j9/4FOasbtbov6ok2OBC8MmwXVW3HfKdyjnuLmf6abbmdOVhWwUNr8naPzwK4V0S7+RwsSNyD8QjepLJwB1EhnVoplwtqCVPGWDiWHhw1AjJCsuScPkJaDc+YicLJqW49bqSTSk3hH7tcCz3bEB2FJxsRjFCcm7iLYK0seNycJzwsz97Qrm6JE5mMGttS9R1QGHQyvMbjsYRNb3xmOFuweCO5d/8BTE8C1xLZKH9B5Xrw3qv12o81vMwoMYzg2mLSSgI6xoIw0AIRiBtU1vaQPEx59qlbyYyNA+w7Fnf++kvRRboHuhPVCTtGUS2yyLd7sdEH+zluLEJGik42rWL+LrayS8QJnh5V5RJzzliUyrgE99PMHCT+NpPI2n8TSexh95fJaVaxmx5koEXEUt1Ce1ejBYcoUSFupW4zqC6oqe1CSt/EJLVWbzhhwChlcrDclmM3TPcGZ13yNtaft/+MVWIL96R3FfqfxdhVkwOzFLC9PXDqpzHc9JD1hIOnbUmDs3KGxrAzFgS8WevbJt7UQwbflu3SXlkgK0uyzuT/UDAKvVdXYGOReIVT+TzpfB8Quuem4qyno05bxqNF6uFDpavLdYvYvCij6+tMKgba+Z6cyVX1pkRGYH1+89kdWE2Z0FDh/OSehejslORqHUq4T2SyrTrM04XC/R3tCXW75VZnisM3ynJK/sDaYFx4S9suJGNFyRkUaBQf3RSNC2f3AImyjEo+rezs6/wXieJYbPjVrZh1azdX1vcHxZnbgwtbcMsXqK/yvVhkka6RWWGfuGSnlz8KgerbwuLPVzF28cfA+Blu1oBKIv161oi1MFtB/4766zHO/+24zxZRAnJFdHxGLAHwwQrJofuCzmEKgS5bQy3Hu+7NAHdiEbKwSuTsfRY9mO4pKUspHs0s1qgrPqxzsGd/K6XPJMAVycHSRgvPIRm0Zh4G6q5D3e327FrUnK6oLsdx71mi5OjBZpsjAMxefNBFeKtpsaLSMF/YuEfDXKOVj9pLF+3auM6TwCTdLPKVVek5CPTrS/cRv1hh+t6F8X78hLu8wB7UcrVeZ4nmCDwcv/k9sYzzL654wgvdAge8qB1lzoeDmhYres/Pcb+BFyX8caEsLRXFuM51l9xNPMQ/w8SFWfbYYLVsh9c3e16v7TX5o+/W/w/2OM5xm2zZguiLUIALlOJxDdotOeXPLqeJMdgKhQRnM3YtyWCBeL1T8/YnpOq1/2VizrsgM2P0xYvKZGpbnfofuLLwEA1S6Kc1NYWFlYAZLWyM+ZF9bSIprUci9b6ucVuUa1V6tFZJXvxNoAWRfUNFto6x2l7vRnzKQ8MzqBtFmdW8BOVLywVwcNSx/PDYZL/RkW8DMTfMrDpV/1z5NaFe6MyHLa+4RYG+nBFokKQN/fRKB/zpPXKsIctNdTXk+vNbJ4h2VGc1MMyQmGK/KbuE4YXlOPzB0NNrOFZFoZ6S9WR2KOl55rd+VO7ouStxpXdC/Vt4UlrZNGXGS4gxN2dVjquSqG9sWUYflRjSjmiTPD1rI9pPbGCzu4ML3LQjxtNU3H9Qb1P56yMEtQdfZAfVdeR4zwstjGNmPPfbj6gdonyzfldyAnJgD775MumCVY3KlpvRGTA3YBKpugaERa4jxJQYos5fawlGNN2YhxQ4wWh65Gw45K0+SwbFni5SNaH2RRBiDvN00Om+WAl2t6UB6HFhOvfpVNGPhvKhdhTcYN577mDLFaxMGf2PjZWb/VuYTtsx06luIcH1sk7rmizui/58W7jsD7Fs/+I7PGL4HDt/w+6wDrkgTBW5sxHmnlde9r6m9uZqkxxSjl0aG50/lmvJg9h9CEIhjA7w32X7Hd4suMaa3mM7ZsdAw952WBtg8VzE+1vt9W2cJ20jkxLDPC2WzRT0bYzcZmgOF0c1Mj24z+a75Bg5HcY3vZ4VMfn+XiGr/skZeAiRaGtVl5cLBHj+0/8eJ6qxNbWGh1agJJcRZv2Xbt3R18oJsjP7/E9GyJx29LIDW0R3Gb0L47AO+oz5q+eiEyDlrstAoBZo44HnIc/RXtAJfXPFHMtJr+aPgG50ktq6xobtPpxoxxbRBWWuEWX10bifgj0VFe+3DZQhbu5IH2WgPYfZ9xYE1n/yzTAsIVut/rYuE7qmw8L1Zn/6R6zOS0L3yseFderClH3QzEBUmHysSf9l5dqkayFZTz9czI4tHcqfMRTPne9B5X/8nKwjwteAPD80K9y6gPmkoUGyP992yUkJUcsPlteX+H/bd67vzeCtHHHwl9KE40bm81HWmZsTgYLG7KRKeSr2mhkW1uUPckgPpwxX6yEF6O3L8LZwFLDlI/sTv0YCtDRkwOUH32RBVv6b1N50n6Y9nSPXj4holov3xEWa6Pt0sp6swyoF2O6IrtYG+RbSF6cb+P490wOIDlGdPoYKsoFe84ealiQ7CYOLw8Dw5YBgxc9RTNKUD918NQo5cUGyOaS1dFhGhxfaSbrfUBy4rmgJgsHFe/ORtc71YSWE6fyUSg7Yjce7gSf9dMqLn3e77s8HG3FjtEPHqASYZ2MyHf0wVY/NDADsDtn/N1Og+ysDi2ZyxexsNdK9dw3GbE8wDDC3o+eixeK0FQXN82dI8UyVRYaUrT8rVHWGQ8/pJvIgNJ0zFhJr+bDPqXQY7fPTgNMZ9JaABC2KamSL4y9dD5farzQWL6cu9gOyVS5U2QzcGcgTYnnH2q46nn+jSextN4Gk/jafyRx2dZuabggImSMRKLye2jhx2M9BirA2WuAgCMJjy4IVEFs6Qd6PgXX8IfaAfbvWzQXVkN/K3VW3j5pkf2Ft3/+D39rtJtmh0zXK8V57RSyY59zNh9x8L5Dli+S5JAE1onjGCgVBx8nKPuKk0GUIqEc4Ow1KBsn7VCLL1lMePeZqnuqlH7u9WOYKfDKzqu45cJmasL1xsKaS8ttP0sCxTUB9XQAN1px4U5yYvtn6s70TyAnmJZMpLIPzIMO/H4jqrWcvzZa4DA3GjCRGI4l/c8vLJS0ZlEFVxhTNe7JEzN2Bp0z6z0FauD9mN9pyjYw/dkSF6MI2KTFZGYVRAAUD8a+ez2mnx3T0ZB8rIiELExqA4qzcnOCGMa2WC8yOJSVd14Nf+/OP3c+lGPOdba3yUoBVi8L8xfK/dVfwns/mKUHuM0OfEPhsnaa4wG3e1CqhKTDTIbK1SLCVUdcLxWyLfIWuAzUEXx0h26iqpUPqxSsVmuGkvFakxGx8b9MRqE0evfuSyGEqGvcADEQelYBVytqOfgbMLbe5LR9ccazWJSr+HewTKDNScDtwzS4wWg2a5Dg2UzouOQglRnOO5Frv5xIdeiv8qIqySpODAKoWeGzMd7qn5N74hljuI65mG/IXg5AOhfKhRf5DXl2Zn4PvB7J+2B7quI7LK0wJprh1QVlI6CJQBmB9cJtkjngs5Z/kj8AEHFnD5PZjTI64TmjI5x2DUCnacEpILo3HmkUKH6ks5/00wiTZriDJr5RMdnubiazsHAAVlDvQsEWghBVZfQXbHbTG3gefGz1qC/tOiv1Nouv2DT9MYgzuwDz343Yfnra/Af4vGvn8lk6fskRvImZkS2rxs2jibi0q7wRiZcN3CYedG2NkoqsoEgynpfXqupONkBRz7e/pKg5LJ4h4VGhiHTIlGIFfWDmZGWAFMWckMwbV8o+1UWOUBzZ8iMnu+s7mUWfWn9SH9bHvJUK4QbG4XE+6/oQa3v9bMliHmbWc/HsOTHCs0NQ9wTTjYOyWUh7MxN6ks6TDl+ZJ1Qqj29z/KDSgHCgg7s+i9IPlHkNybghEA2D5b3Qc/ddD6bRAEgG7Rv2IruNouOlq4F0D6Ua5goqAF0f5a+eb3L/4JIV0gh2dB5LTZyFELPZKdaJ8ApEoHGMixc7fSa7b4CwtmE6o7+w+a3QFcILz8fYZuIcV92I5AV2i2DWA7Wb2q5dnQtgFRsNquIMDW68M5iCPOG4OTioGRsFjN/3wTU3GNtqgk5Gxw6WoCszfI3eNMCbYbZsB1flZAYZjQ+AdkglcSl4HDDfd39/VIThKpE1oR8XPCZFj0+yXG0yIz8ep9wc0c9+xwNfBMo5B3A9h+89CKHC2Da8gJq+XtPCn8K7HywCM8nOHZ9igawbBXpemp1BIa8czQSal/fOLmGYZmQmyTX0ERgvCwxUBmmt0KumjZZtL4mGiHbpe+OwKES+De7uSVmQmpn93TSxTcvEnwbBFKvlhMmvl/cgxPuw3SWYV72mnp028pnm9tPH1T99L/B03gaT+NpPI2n8a9sfJaVq+0sbCaRdWn4J5/heiPknmxnBJIxCxTbX1i4MWM4V1JLCRsnQ/uMFZsftO+OwB2J9vHyGdrrSaA8ej2TNrZezOHDwqDeKYzopozCbLATEYJK5Yo0k7ywAUYh2Ngpw420s7z7kxrDJX+vUk01pfoyQvQxiXbXAqHOqkw3KLwblkR6ErbwoxWpxuI6IVYGx1dG3l/MCdaUUVre3w1ALM5RjbINFx/pNUXaElb5xFTD7J0wGl1vdLde0/crEJcbzEkkX8fM6lQD09Uk8obFG39idL/5aYKdmESzcPjpf+VrfdlTPNdHzr6cVfZxoX7NRapUAskRDeALFp5ph1+u7wj4BzY17xIzuwvRTdnmw6XKGeIAJGcF/k6Vwus2EOO4EKYAkvvQOdD4v7hOyIOBu1PkpjhwhasJpneo+Xf7bzJGDsL2y0AmEJE+0B6dsp1rj4qjwkwkBKTAyVVnMMmXzsjBCLHFd0YMRKo2wLkkhhA5ZaCesXEZih2DhzVZGML9WAFvqb9hR4P8chC27XSs4W6ZCOX5srNpxbiMWJ7RQ39xtZPPfXxc0AsL1LnzgnJlS22JAu93960QsvyDw/r3jUDsxy+zyFXmiE42OAkNz04rdHQWpnOIpVKOBomNKHJjScLykbNxj6eGKuGcYfLRoHlXybMwPlckZvFjRZFxbNafDeQ98jpgKub8o4MZrJAHT4z6N4GQoEJGSgaZQxs2lwesGjXkePOHK1VlvBxwXPOkWydglkdrJyPVcFrPaM6f6PgsF9cyUg15eOp7i/YGWLJ8xU5ZFqpYG9HDEnSqTLlppTBq8pSmM624x7IfkPa0cpl+QD2+QNoQBBXOGgwXdJPtv3CiXa325J4UZ3KccozjxiKsMNOhQazybADqg0LN09ri9mvuF1W6YJqRjzdrr7D8juQXs3zOUe3IpqVBx72d8TzDHY1AqW7UhZcWaCMyl7l+sv1IC0SxV+teJYEz5w4vqSLW8VxaUyaobDPMTDdnR3WKyh6YNspsdZ0yoXc/T0jPOPtzOSEcajRvGj7nFDYAAOufRmQDcT/6+N9ZJA7uti6jflehYbi6e67H2NzoZiys2DmqTJ6zbE57cLCjwfoP3Iv/GAUaT7VBd+k0mCGqBMz1M32to/ujLN7dq4TqsfTJgLBN2vO+Vx0hDFlkAuTqs3hvTu7xwkxuP9YwUXu003cDVuxoVLmImA0mhj25BU7n4KObuYdlhMsAt5vZ4xX5yOAopYW/z7RJwDlDuCZjHD3CpH3c0ttctJMsrg5AUwUx1t/tF6gZSkxNRnrfwPBiaDYJcV1WeYaYuWf77GIHV1yZbBL5jvMJCYDj9CJ/sLKYJm9gJiBzL9L1BqvXLOc5ZDx8TyxtgKHfpByA0ue0kX5OzGY3bUQ+anBC9hl2Se+RRgfHucfVwRAEy+d8uEikHQWAyUjSkD8YjBeRQt4BmMGiZu12dtRb1fspw/E9XtcBfQm1f1cj+Rm/YZV0A2AATGrtmA1QP6OHLQSH9/fnAnNXZ4NIpoa+Enjdv6s4AISf+xcD7B1/9vWnzxb+LBdXk+l/9b1Be8MPVkfVafEQzlbj22KjxgdVR3KMMqH77rRqyAZif2j2R/nM4X/6MwyX1Yl/bqnuwlJ7fsv3J3Mx/X7WE612WTSxJs5E14cMZODwBWvXnilRxh+oTwdQny45rRLrh4z+mR7H3OShhKcDXO3Ng5F9ViOEfcZwSf++/7OMuApKxJmsEGP8MeP4xSwQeTSSskG9HvD5hiSzANozLMN1RhaMsFbKf/bkX7xgj9bmPuPwNf/Rq0FJOR8XaK7drNLMWFzzRFZZHF553P0Zn/vnE9DTOV393w18l7H/ht/TAA1rWefkLDtRVVBIRiYZ1JxQVD0aLD9ktHdRrmFJShq2RAQrPfDHbzIsLxD1w1wigZOYvMU7K/KmcQO4wZ3qDwtpqVUjkOIvLZKepPKpsOQJuFQ2wUo/09mE/Q9buLFUcdBrmCH2deEswjQRsRzHKsBwdVf6mvFMe4Cl8gupgq0SfMWEJZ+E3BSThXda0Xy82yBxekr71s2MXgzsYNC/YjLPsx6bNU38tY/Y1oO8x2GqpcJq/SQmFdOxgj2otenwMqhFIwB/49H+VOYHDYk/fE3PheMKPjsoGS/NNrkJiB5CNMyjFTKS7wzSziHyQtb8VMl9QOYkswpymTRK7s6K/nv4ZoIxEFOJ6sFKQRBrum75Fd00600vns3v3lzAdBz7tk6kT509y6bE52XSw5a5wpgM/FfyQu5eBFTnAyL7RcfgsL+nh9mMBpmfi/htjxwNMh+jf9fohrpYB3zC46nn+jSextN4Gk/jafyRx2dZuTY3ZNy/+ikJCxiGqs7CDI3VqbFDeV1yxJpTiz2FaX0PztnkHf844fi//Q0Agn7nr+1eGLHjs6OGgacK8ANgqtnPM6vC0M4g0aDpJDZQRTvNZCjld14LaLJonNTBp79S6NGOhhm8kHNiR614S181VfRdy+/6K4Pj9/yGycAenFTi7bW+/+Frtst7V6j9JFMp/y7nxmQAFifykrnTjEmQHa4/mBOZTTuDJvsrI84z+aHSPufZhDEYbP+JduiLuyi98P7C4v5XWiWuf10L9J5cRlhpX765U3lMmuW5DhdUERYpjj9qb9wEuj8K4zh7o/eZ4/uCmdFxkdEU2DyqIxMyoQWlWh3O1UVn8SELqxwg8wnpSbdGAhbsRCHnxbgjNtoz9h1JodLyX/a9Dv90jvVb/bvsAMPnPy4zpq84xHs9YOgrMYrI0SAX83Zm7NrSn50MYumxLSKqOsCztCVGi8Bs0umxwaH0IW1GdeNRFbOSo57/ck1WPzCKMy4Q+P78+fkt3h22UqGmbARqvjmucP9I/Rl/XSGcRQkAN8mg+W3p6RKsW5yp0oWiG+5gkV2WXieyIjIwwHRRrN3SSYh7niyaW3pQUkVBGNWPBD3ZQZ20AK1aAWDx2mM8pw/oX0TYK63K4+BEiuZ6rTLj90csFqNA6s4mfLjmLNneaUpTnWB6q22W0SjTn1Ei4QGsEuzXNNFU2WDqPdCVoACjVo7GSCoPuoalV6Xlo/NSKpX6Jzw+y8XVdYBLp7CnGzNBpl4X1HLj+D4LsaQEiNe7QngxEr22/DCh/niAvaX/0P3Nt9h/cdo7mDbctzxLcqOuXhtJpbATMGyMkFV8N9Nt8iLv9PmR6LFUGey/sDLpldcDRCQqT1bRwhayUJq54WWXMSwBz7Dk4r3ChnOo2g303oevCjSbYFmnWB7AsrgOF8B4UXxWA9A5Oh4AfqfwpRvUPjB5slosGtsMyKJggjlxlap3KvXJtvSQuZ/5DrA8oU///RENx47tHxfY/M7h7PcsdWgMdt8Vz9dMcqKyrmRg9105PwTPLt+Uxc8gsuxnOM+6eHcG/qCLYfIGA/eZq70BcsbAFpPTTN87bg3LIspJyUL0sUHtD6etwXCur8t2pk2sebNR7H3y6bXry8RsIEHmAElxRGZxBoTnI0xJrekM8IYm/nZHz0xZPOoH7f1OXw+4uiJf7ZvbNfybRvrOJhrR3rqHCusfrJDs+pdBJl/jE4ZDjbEsNOsoi6ZdBCEpxbdLVHsjyT2pmtn0ReIilPM/nhvxFv5w3KByURbX1gdc7+hh6PcN+R4D2PzqHiE4jH9Y83WzwkU4fJUQryZ5pszea++xUqJg+d5lk5ibBFNcqWyGbQPiIy2g7eA8z0QAACAASURBVDuP7guNczOjPssmGiRJkrGwnZEecn81SQ/T+CRSKIyWiEulzX2ZZfGrq4hh8Kj4XHZDLWHsbm/l2ppEcYjSmzWQ/qsNBvWdRf8lE902KrcBAFNr/GX2WVOgJiO960KAKv1kmCwkq+Q/fULTEyz8NJ7G03gaT+Np/JHHZ1m52pBhbWYoj3ZU04qIPgWerWaZp3PmbmyIKCOwcATaWyZf/O9/B/vVl9j/+68AAMdnTqqBAu2WKmvzeysM2+ZBjfuHDcF4Jcw81Sqf8V1m1ihXDfskfzeuWHbCx5VmTEQ7kecuQL+PrRoeZKMkCxcMqkecVMbjjIBV2J02ZBxfGQy8azWjhWcTiWpH1Vf3ZSk1oQYKycCdTUi3DHcFCFFjeJaEuOQf6bwVwhTJU5gos0lobqyck+MrI0Sr5t6g2mtAen9pcfg5O9uMDvm3VAo8+zWhDMW44/DSYeLqa/GOKknx5t1GYXhufuNQHTIiZ8uGFjNim1a7biDosJy78TyLrMUNmSrU1YwExMStbLkiZOKM31uc/bpciSx+0LFimPhRHaBKdRfWSqSi762s7lSppGn1k4EJM2QiausjLCzqn2qRO5mg7lyxJbi+oCK7P51QbRkKrgNubqnSs+8bll3NGOYf6IFaviN/6ILcwGWYwpS980iXE8DMbmuywpdVRBg0iNj1eh8fvkly/pdvSKZUEIfpixFrdj966FqEaCVk/fq4VvZqExDZxH/3boP6o0M9cwl7/BUzgIOBeayEyJXarKQ7A2qNlEtgMwxDtcvliMgV4jQ5xF0lGavD8wjDphF5ssgrTRRKvYOdhVOkFyPWm16ucfExTpODfWCZGKctRW4x2G8OaDnfNkaLzarHMNHJ628WwuoWVjWU2VxSp2AzwEiQ/+DR/2yQwPsMoGLXLmMgHtDlmOtLOt6cGDIGka1MmLUEjFb8/xbKvs9ycW0eMnxFUK/IHiZ6gMrEbENG5oXXJYWFqwNNQmXhrXcJi7/7A/1wfo77//kbHJ+XXlue9eQMTMpYfFDJSvvAsEiXMK3ZUadmC0KGG/srIzdaWBIMtmCG87S0koZSpBnCIqyzTo4RuuiyJrJAPzbOFoWOJtFpw6/16shisk5k3RfA9FIfrJi9mLJ3X0aBg2QU1xUfke5rLN6w5WQERoaCs8vwjzPXKzPrv9SQc+APBm5S+RMysHxH73/+G5qcDi/pffZfZ3Fyqv95gbN/puNdvaOFVbTFS+1PuzFj/60GPpvBYvmWXlc/ZoZE+bNnbSFZKED3Uvb639oPep9lbzBs9LuR+T/9e9oQpLz9zey6yQcozF8/AnikMHJ6T+0zIhFMjKJndVmcnZZvLOqHct2zfGeA7vf+kj6gf0aLcOnVxwbCvLWXA9LtrJdQJ1n8utcbsr4Es2Eb1VJWOyPvlyqD6qgMc1MnNfH3CY2P8p7eR0m06bsaZ+f0Jg8AdisLM0vfKX+zvzBwtbKMn58dJKVmZInPcWAp0cyYP3xs5Zq60WB8FmGK5eHgYLpCCqBrUyLVSBo2Y/MGyM/m2yP+5BWFdbzfr7EbaScVH2v4e4fpihfsOiIf5wuSbmAQZ+z4rwcsVoN8t+7YIPHf+Xsvz2t2BLEmljgtfJIggxgcUrLoObjd9haJv0t2WTSpdjSILwe4IkG6qdF+4D72z0e4OlGKESgNqFhA9kMFX0WszpTsEcv1tAl3Pd+4CQR3l3P+4KltAqgm+hMen/43eBpP42k8jafxNP6Vjc+zcr0P8D7AjUkg1mnlEZYWgXf54EoTIHZwYQD7nrSwbqBdWvP7Gwx/ScLHu182iM1Mr9lCqt/sMtyoGbGhNTg+L76x6nLjjwRRd6+KJk2hO58NqkOWYzx8OYtsq0hjKE5GwQiLc651RIZo1MrvCiELSQ0eAK6u+HRMK6D/mt7QrQJw9IjMBvQ7J84wcBmmSlIx4rFSzeXHGtVOw8HjQhEAf5jt8wxVqwML4P3Bor5XeA7/TWF8+Q90XNPa4vFbJ+cuPB9RvaUSd/1jxvIjnaywcDi+cEKAARR6NxFY/mRw5H2n3xu014VkwSQ4rppjo5D63IwDmSo3aSu0eszB0jUuRChk5R5VO8BbI5rJ+lHJWbGBhECYTHGDJas2riKRw0DIQwSAlUKM1Y2X91diGGByFnZ58nr+60cyZe+LU4/PAPv0tu0E+/WAZU0/p2zw8Uc64O3vFK4/fJWw+NkOIxsSTHe1IBruYGG/6vD8gm68291KtK/eJ+RMBhEA8Gqzw8NA1d6z9UE0qdYmLKqArlSifaNa3CZh3NVCork3SzjPzk69R9pXCr3fqfcvaXvZYP4Z6UQLZGz3au4Bk0903oB6OyfPumw2ZThbDvjd9RUAgl8L8WlxbdH9bESzZXP7faN60kWk81HiAG+9ENt8HdAdGqlybW8h0l/DGaugY6gueizY3ao71kgM6VZtQH/fwozMmG4TDJtqmGwkzDycRSBaNP/APtEe6H9JcMdqM6D2QVnXyeJwpAfqbMPowiO1YXwVcM4645v7tWia8zrCVhHuR7q+/mCU1Dj9Nw/5Jzg+y8U1LB1QOWSvkGhYcNpJQXeyyjGAjKoY948J7U972Buy6Tn+zTc4snG/nWgRnU/+Bvnk/eayi8ICdr1CQP0VGcoXlqWdFBZ2PXB8aQRK9T1EhE4G6ppR6veYQUTQyX1Ji1rphWHW84PhRZhfO24zJpYL+cse4JQO/5sWVTCIbBgwnSl+aQYL9Bau0zQacWEKBAOXCfhkzAT22bMJQTEC77Kwm2NrkGYJHKvXEObt7huL7pW68Zijx9k/0+sWtxHjtjB0Ldvz6YRY5CrVMcP3GTXDU9lkgeizNRiu1EyjvtdetokEtQJ0jcftTFoU9bu1txn9pTJ/5xKS45dkoN6+40ViyBJ+bqcspiD9pcHwPMG9okks7msYPqnjVSS3H5aG2N6i/WDku8liXRscvrQnGwxhq7YJiAbL39EuKKwypnP6VVNNsAYCsz5er+DvNct3fMnvkYHjmzVygXtXEe6+uC4BLy528NzEr6qAyyW7+ySLu8NCYc9w6iYQeTLfHVo8JCvIfBictB9yHeGvK7kPUrJIvJDk2wbVQaV0sYawmFFySAHkaFEtJkyctJOrLBaBtrdkwmCVGVz63HGRyNaPN573h63OKVGtCvPf7GC6CsP1Qt4jzxKF3L2XZ3S6SBImH4ND3nt5vpAMIlsjts87sUysLFlDlvOYWoMt9z3fvzkniQ1vAJo2yMYkTZpoZSaLi7+zOL6in/vvRlxcED27G2rsx0YCBGAyPLswpWTx8LDEYkUTkDHALUuc4mQl2QgZMB8aOXeThxjCpP+3OeITG5/l4po8SW6GjUd15Af8mJCtRWCSS/IZVem/9lkWRRsy4AzCdy8AAGFpUXGY9rihSVv8e1dGSEvZUl+sVD3kgVv+baTP2b+iG1SsyowuTuN5xnSWZg+WOuKYxMSVUgEc9ThSrQvotGWNYelVzcyPirSjEBnC8wkou9vXS1z8ll7XPSfv2szVEQYnO3KAejWWF3ZZxEELa1xolNxc9+t63egkk8n/mf/WH9VtKjZE2Fm8t3I9eu7dxYYXSf5OzUcHN3BfuLaysDS7iGHrROowrTPam0JSouMok2926phEyT2atBM2JNsByAO69D2HC+1pA9RHLf7T2VL1WN2UilQr1WzISadYMWajfddUG+y/5cXiBZ3ckiaC0WJ6UWKODPLRSc/P71UHPG6MLNbjRUa4HGUxqdqAugSUHyu0P9XiXjYGg4llV6t6Qh88Oo4Gw2jFmjI1VAkCtJGaXqhcxe6dJK988/1HVC5KFfp8fcAUGQWxCZvFgOt76suF6CSIvJ+8pOBMDw0tYlyNuZ0GeWfrKM6NSUbOR4w3LV8nYDqfbQaTPjNmsOr2VWXEWu/pvAqiZc01baALGW9uzwlL50R8g+1swc66cA1vl/QcFi7EKkpFV906qkJfaZ5kcTEyE4WoixvY8w4vz2mHN0WL/ZF7usFhig5fnZG3+fbiBn/7zz+T9/PPelQ1Pb+bxSCblps/XGD9W01UuvvLKItwVUU87mkzYE1GGJ0gDou1HusUHc7PDxIBOBwrkfoYn2D5fMS9R3ZQac4yoFqw7vfDqSvbpzieeq5P42k8jafxNJ7GH3l8lpWr7xJ8SLCTkWSa0FrYCPV8zVl2sWGlYdH1TQfTTzj+gh1NkuZxpkohQYAqRsM792pPcGipRExWuc3xpZEK1O3JEaX0WADIFihVmbMv6efVTxmL21KZGXRXVgwa6Fjo33O5jR3pvUNh21qFu9yR4C7zLUF0ZrJo/kAvDOuMu3+vlFi7DKh+R7vk7GcOMpmg8cR94blsIy0SzKguQciQyi8zrA1QteuO2k82GchFZvHWYHGTEavSKzfyncfzjHQ2wT7QF6/vlf1dP0aEFUN1v/DoXmYkZjsv3llxYQKoQi1mIqmBBNnbySA7vVbVg94/AIT9Sn1tMwtAILMRgFjglPij90y5Ns2tgRuBjkARuF4lMN2LLAxmHP2JsYDdTkhc2bidQ3NrhYGMDAmFiI0iHXGVYAaHzBBdGJ3AesZlmEnZzq4HwOf0x/45zDIAu4o/e0Rmb+TcO5Rw0fQsoFqOyFwRmfWI52cEKS6rEfuxwZGh5ZyN9FjH4LDbLxA5GOBxV8EstNIsvUYzWORsxOHInQ3Io8pVrM0EFQNUtZZqaZlgJivnNRvAzAw3EvdcLafzoGWos/PSo4TP8I92hihpWIQdDD2nvfYzPcPhJilSFBbU2khczVu+bgA5LeVlFBMPM6m7Uvn8ilsCy3bEwH3nbqgQCrw7OlTrDpEfvv/87gvUS6ouUzIIfYXA52scKvh/oJukajL233Er6OURLlnEYsTy2IgJRuJ7vJ1VrMVVa9sOyACOvZpKiIlElYWrQS5sSUu8BGF8C3T8CY/PcnF1Y4JLCcPWi91hWfhW77UfEDjdJrQGzT0/BB/vcfyrr+S9sjXozxQA8J1aglVH7R0UWFhSZtaQlJnhKkq/BaOF2zvR0M3JRyYSfNp+5Mk+AIdXrHUsWtxZckox6w+twcjQ49w6DSBIpiRppCYR5PWOFs3FtcXxW56F6iREBwDAdaPEpOVscin/z98zLLMYdSOaE7egor8sx1VgYDvixOKQwsD5nO6JhDNcMOzZQcLkj19mYNT0EjupdjMuLD78BzpX41WAPVoJbV58UNJYCSWXqLSdSlnCgs5rmVTrBz3HsTJoGM4NC8PWlNpvL4trdaAFtfSQ505g2QHH74JEsbUfrMhtwkb1tv5AxCG1iPOoJeydPru0AVKt12K8SGJp6O882lsDw3Bsf5VhJ/qjyx8y/JDknrJTFjlSNhbjmYP9OS+U7UhxbwD61ACrQnwKWC8G7JjkkpLByDD2zXGFh32r/TqrPIU8OthF0Ml4sLqwBANbFrgE5AyBNlMySLPg8TSotaA5S0j3pR9DF6Tow8tmECAouMQQ5sg2hkUek43AzCYYhG1C4FZOrrIY9dd3FtM2CzHKDlZ777M2CAxBwfUHNcLvfjbTZwUrx4IEeG6lhGWCv+xp4QdF7UWGk6e9krjcZsKinvCbnyhn0ZgsoepxcPBtQOANzPI/tuhe8DPzJ3vkQkLb17BNhOHz0152GDreVPkMY5MEonufsG2pXdF4uiYlDjAlg4lh4dT5kwD65lkn74FskF7zIn8/3018muMJFn4aT+NpPI2n8TT+yOOzrFxjZWEqi+TV1zVVBi4qcckkiHGEG4HFW9qp52UL5IzushgQGPEwdUeKrRu54p0z3mJjqIoqzNAvaBcK8I6Zd+71DVWthb1qFXWRHNNCsJk2MwlJAvrLLJCr3xskrjyGC/WodT0ROAqTsrp3CCvdkdd3TsT+/YsZqWCwcEcVl89NDUpUVjleEw1CYWouCQoGmE1tIdW1DeoHmz0oGBtqpFGKXN8BFUPo45lBWJLvMQA0jwlHNo1org2mjTlxE3r4OZ3Iw9cZgXMvzWCx+b1Fy5D6tFSTBwqdz3Al43Y5O8eZ5TKdksEGRi1SrZm2yw8JJikknWZmJcOFEZcjAHBH3aF3303kycrnczybRcIZCiUAmBi3zLI1rm+tMq0tM8KZYOMPBsOVxii2b6nyWP2YUXUJ3RVXXA9GDCb654acnUpghFOG+nQWgTYVr3Ucjo24Di3POnE+ygD2XYPxyGzbDDxMCtum0QlZzgSjlerZhDQ4MTJwg0HKxQAeksn77P9h702WLUmOLLFjg093ekOMOQLIAsCqruomW7jgl3PBb2hu2F1d1WABKGQih4h48x19sIELVVP1m+xeUCQXjIpnIpCMh3efX3dzd1PTo0fPeblD6wNOk7KJn/i/3idsFr3Azrt3a7WLqxJM74Tpm32WNhRMKkqRR4swWYFBjcsCveeG3lfDZQXzVAmqMK3Y67nwmQbtQIiNKruRB2yFY2m5W0edg55MPqSElAymayYfvd6jclHVlXqPWLJrl3H9tswC8P7uQljj2WchC1XrgPGHJTpGbna/ScAFE4keW7nmejmiqiJWnJHeb5eoGkUKwuilxQk+4o41mr2PeLk6CBGtaye85bYrazIq7h36sCNYZvuOIAC/VdvANEOUP9bxSQbX8cIi1pZ60py2w3R3SfpZh42VF8GfEuyRXurwco3dl5WaWI9ZFrbqmKUlBmAoUJSFqJWieLgmn4VJCZ+kTcFEqomW4Epw9QwijjNI0c1aPB5IvH3/FUOkX6kYOoKRNoVoDdp3TgTtwzLD7xkevSFR9tNbDowXAeDakcnKqpT/liAJZU6mJmNaJxLpL989kzLyJyN/N15qy1FqMjzX56qepqa4vpBxt7JcV38xqIso+0oDuxuA7A3GC2Zvv4gS2GEAf0+TuvqONjolsJwFQs9Bv7zcRm9TtSfThHLvS48oQAG5BGtqu8rouUVouDLSP1w2S9XOyDkX6NdtHWzwCGuuo7+dJNDORdjjKsPvDaonOkZ7n+U5zp6h61OBEaGM3ZBRs1h/dsD936lq0rTJ0h+cmohq66TmOr4MGmRsxsXFUfobgVmdzGTxW+3HCv2ugb+t5B6W4+UKZLxd4NLRSK0TwcDuvcx5NpDn5ep/ukfNkGPrAyobsahoQh/7DuslPTDb3QIf9hfIJw3mhZZrnzzVOWdBU9x6AGG/mt4idwkV11xjcCq6nw0FrRJsu4jIzxx2HrlJsHs1iS/D9UaY5tMmY/9NFEgagJRnTOB3rawjLyZYrkF6mzBMlagtpdGhYii+eNYCwP7YUltNaReyWWqs6YcOsMDpG6b0T1aY0PAJngX96zqgrQJuH9Y8B1aCq7UZdTuJelbOBtNI89E1I7Z9I8/F9fKI2moN9ftHehnGP26IZd0W85EsJZe509XHOj7J4BorQyIRWWtyi5sIOybRm/W91tpMyhjf0AN2fFvDRiWaUG+o1jZt1B1n+5iEGBMb6mENhfhjeLcKwG69NqHXHKxnQhSGA74bDMIyy8JPdUkOOmsyfi/PcHU1oOto4RlGj/gXWsGbO8r8Su21vdHWj9NLoP8sSs3SHJ3WcAYNiqVvtogC2JPWUrMB1aZK8/3RSeA1PDeljSD5rPrHs4UnttSgn0pCcZnFvae9NXBTxsQ1zNMbtbRDBhFxSr30UV13Vt9RvZPmLWFczWwDZwt/ub6COsRWNxWZs/UiOWkipF2lOqnOc782iI0G1Nhqz3FsKfgmPsfhbUZ4TffJNRHTyUutrXpXS4+q6yGtQ+0Hes5KMB/XRo3laxatKBmv1bYiTHpOu98nNB+c/t2vTiJfFyaPsApYbWixvm5GHLmtYr9v8fh+LQv/5tUejgOqMRlrNt0+nBqqf5fF1wGRe6bbFyc4AMM7qq+5wcCx3my2RI6TjcQm4OUXlI2FaOFYZnPbt3jat0hcM47HWS3PZMpUy94nQzLhtEhw6wmp1FZ7p0S6LiDN220SkDgrr+qAYPi77hu4k5X31y0D4qE0LmcgqiF4faPksmkJnL7kB62JwNzU/qQCHMkTYlHITourE5Zcv+yZvJQ4UOZkkHijMxfSSA8Nsk/wLP6RooH9kW52eDFRhs6bCjNZMkUHgGyEFBVri5uHDSxnpxcXR6mbh+Ck7gtQsH3J2akxGY2LWM1M6R96auH58cdrMXf3IxEGBaXotW0snlNDPsrxXHN9Hs/jeTyP5/E8fuHxSWaubsxwyGgfI6onls5bV4itxem61Ci0hoYEHN/Qzj15ZgTzr6ZOd1v1jtxtiizatJjBTYYzsrIjC0DzwckxRSHoybBwgTJeRTHpgqA0K9CJCuv7Y8bhc4Pxih16ssFuy+ovtzXaInYwUgZXaP/VMeP4ZtYWMhjkSevOBV4scLWcE6DZ6ax1SATvT6WepudPBgIzM4CEGUPYSJ3QToahWvrcxX/JkkkOF4QQSHtJrVlh4pagUv9yvcH6L1qbFaH+RlmwdN+yMGOz08waANzsXo9rg2HW4mQnhatjY88y6Lmvruu1blvtKbM8fU2TuHx1xBVne/dPS4L+T1pDLqhCrCGtPamibPr4hq+nU+g61dyUP2dlMyKQXCYREQBmILWg8VesopMMRoYa63bCixcH7HuCC24f1kh8r/O2ht9bBK4BWpMFIo7RYsciA84nvPn6Xo5xeGphOLvr71uYyaK+dzI/BUmZLiOhFsxH2LzaS7Z20fX44c8vAQCL7z1cBYRrvp6LSRnACcRML5lshBgZABBXJgDAKsCXdqTezysYNKl/pevpLyI2bykz2x49EkAtSaCsEOz85FaR2Lgsu+kG4MTchdRllQUdiklFgfO1TJSbBCwCWq6RLls1Nk/J4rhvpJYKl0WFaRq9sKJzF3H1cofHR3oQu39ucfps1tKUtM6djbKkTRfhmYEdo8Xl9UH+JiQr9fUYLSEVC3p+lvUoED0AtG5CH+l5er9f4f57kviqHhymazqPydmzMhEwg9HT/EZ8nOOTDK5FirDaBtEPNiljXDtUTOLwp4T2lh6W8bISaTsbaHHrL4z8XHoW+2uP7Iz0r9pJoWWASCNlkbWT1tqyVdWi7LhntMBFo/ZL5nujKkQAkCBtObE16F8neFZTiZOF/aDadqW2trhJCI0q9RxfK+xJcLQhtRgQXGPKd3k15LYTwTmxyJatkxpCdxHm6ITklOos1lkmUdAsgXguyxibfAa1u0Gh2mGt0O+0IXJQCbZ0PyDfFRvVz7WDBtBxZRWOrqmFqhDRkDHTAabju0Hvd/+Srru5z2dtNXbK2g/rIWQ4ZIU36XM6x/2rjPBqRL2ghXMcPA43tFMwo4U/Wfi9znORXrSjXicRlmYbsFFl9VJNcyMwvc0Yl6VWkCR4mJPDtALMwyzQsIPKsGvw410nrSwAkLhtw04G8c0Ix8Fkt+/EGaVtJrRc90vJ4v2319IbWh11A5Yban8ZL5n8tIhiV4anBpgMHAeu06kmNSYAp90FqtKxdpnJlWZm06ZcAJrvXMg2XRbylN86xEWS1jdz9EiPPLFdguX6ZTJk4l1zXTtsgO1P3HvjM7CZ9PuMVGeQHhrU91bdXdZZHXJGyAbAtBE5GljecKQqix5xVUe03SjEH2cTDgzLH3cNcjJCwqq7STZFeVBCEAA8/uUSl/9M1334XAlw1Nyr+t92FaR2Gu8bTHy+V6922LQDBobe97M6KgAsuwFfrAmyb/2EIzfP358W+OPDK7EHdB9q4IID9lc9Kr4vYXKIqZbNH2abbczKNB/reIaFn8fzeB7P43k8j194fJqZawSsBU5vajQPvEN+6eGGLD+bBAxXtBMb16oJSvrA2loRa82UursMG5LAjqHTz6XKwB8Vq4utmQktAIm3wX40Z/T9VGcxWHc9QaUF3BE3Gz6GSUDY0jm377yQSfxB2yqmzmC8NDi94l1rhAhATGuCnee+quL7OmmLi51YHEL765GZ8We3nrSAi553VFiSMiyFMOftBmdiGYlZuwXKMwq/ZkfwaBFocKMysKc1KeLYHbeXbKHz6BSazc7ADWqqPi2MtDeFjklBjbJ5HRNS/InOo8wlEgT5SM5IljytiGBUiGfDyyCMSFtHOAOMxUvz4LD6UVnLodU2hGkDTAyPuYmEHgAgXKuOMN3sKGxPe6JntbCk08sJnp1RMoD8nkgtxdt1Ws/gt+/onGJLbNaSt5plkCy2faWG2AC1ZBRiS1MFIT4Nf9zAGSByxoLXI9JOs+TNZzu8XBHkGJPFt9+S2IE7kFZx5OuJQyXCEfbLIyYWMTBHR6YApTRxmLXXNAl+M4m4AjIkQyw/+w90HMeKSgCQVhmpGHmPlH0WAqLfOUEHzGRgo5f3xiRIFutP9OxOK75Xi4x0Vfq6LGyBkicLc3SiSZx9Fs3dJUOtLatWTdHhVBxnLo/YH1oYW7K/n3nAlvd16+H3Bvuv+ZJnGr6mjjA2wxX1qWwQWHHLXw9Y8PePweOnh0YY1MhGUKjN6oTfXd/i5kTw2z9+/7nA04vFgOmpETGU4nsLkOF90cS27xtCwUon0aBkxDN4/iMdn2RwzZ5uaHVUpZbuLiIsLE6v6SGrDgn9FcuDzRYgMVMv7TenfAbFxNpo/c5oT2qxeiu9p/MeWDtBWZvsrFMYtu1WRfBLnVFqjA7IpR3EEPPXsJJOd5s1wIUs/aTDlcG0zvAzp5oiiafuHXxeo5E+TDspQzoscPbwZ59FsN31dOGW30gTZkbPq0wdBrM+11Jzcv285YWkIUtbVP/Ciul2Cdzl77bfZMSibpUNqq3F4ieFbQtkT9dD/617OnYJoLFWuN5E+v5Sb5872sSWfp6r7JR7UYUkPa/HzxJB6+VzNksQSPuKei25rmoHrR8XBZ/yff2rhBd/cw8AeLPaCePyjq28Xl+QYLuzSYTXf7y9RLptpCZqbEY4sWvT1qP9wPDoka6l+8Cw+dqgOXOjxAAAIABJREFUf1muk+QPi41dXARRKjqePNrLXtxXQl+J4fh+tKhv+boMMF0mbF7TOR6PDeyROQabAGsydgM9lDfvL+CemOvg6JmRDVg2IlOZJ+2NpS+3Ul/PPivj1dNzVoJV/tDIM0gbIov6YR4Y6d/+UGk/+EiOR6VcExtguKZDTBtWJuKNleuNuFGFjnqQJ+Y+wGtZBE0Ulq/ZMTu6tPfUSebxcKpxserFzGCYvAS88v8VWcl49CIVWK1GpB/oYUoeZPLBylGl5Q3g9qOnGlPZfF+MqC7o+M5lLJgDsO8bpGgFCrY2oeZ67EXX4//89mu4f+Ua+28PWHZ0jKenBdzOIa4Yeh8tDL804a5DfaeqeLGZyaNWEBjeKdH4ox2fZHAtwx+TOF8cX5OrRgks02LusaotLtniLJgmZ5A4MIYFZURlF0vH4xdwoNpsyW6IIMTftdYdpzs52FHl90IL5EKOyeffnb0u9P4ALN9lcfnJBuhZ6MIYSDDsXyWuKdLPZvYE+KMhH9jy3Qt1gAHOSTomGtihZMbaEwlL5yQBdZllF2+j1rvlGIUDkfUcw5I9T3ljcnyrNnWpzohJSWTrvwDjJS/oE0shSsHxfL7m0osAMHDdPCwo8AO0sC9+0t7lbFXo4viGxS0s/V291dpsNqopbUdtLQGA6TpK3c2eSNwjs15u6IBYehjriBgsLi8po/v95gmJJ2U3NlgxYWT9coC3CX+3eQcA+PeLv+I+Ugbx5xev8L/nf4D7nh6u1Dg4DhD+pBZ57UNCNtpWBKtISAikn1xq7PV/awWlCMuM9G6Fhvt0Kw9pNYkNMFxqJob1JEE/Z4O0KubDwNP3F7AsSuLjrK1rMhQIikVcF6VXtvnOS5bTv42AzcjcrmKqpCSYbBAfa1SMwLQf9F2zI1njnd5wW9CtFWlNN4B2BSDEJhsjvIjj50meJXcibWLZZFnI5s8OjPaU3t+jRV7NHjzpGyeURdxzsoGvtKf29qcLOK7/GpyVdykjZ61ndz0K2cz/lxXGa91gILMna/lDrpPjQyObawCYbCUZ9Xp5EPThsGuBDLRLeu6m0Uurz1++fwlkg/QVLSSv10eROCxthiVzpQ2q/rvMWzaMVvHFVTtde07XP3tZP8LxXHN9Hs/jeTyP5/E8fuHxSWauzX2ErwgGFoWfwlptyy5fs1VEoN7TlsofE4ZLJ3VVkzJadlTJW6C/snqMQz6T4nOjskuzI+YoQDu24tFqeNM7bfiYs0y51ChLVugGMkUHKBPpbiZMa7qlj793auQdsjBeYTLcqLCbm2ZMUw/4AWcG2oUFnCqdI5MMHWfQa0kzYYv6SZmt88w3VQw5lixu1Ho1AJji3BMJbt/+imttIcNxpmQjSSE2W4VtS8tO8qDt4mzTO2dnqyMJMFyr01H2KkNoA9Vk1ZwdGK4LqkDfV9pGxmxQ7fS7CxpQb2elAACpVsGBeJ3RrAa82FB2WruI2z192dBXaLpJZONuTks87Anm8z4KEj8Fh1U34H+9YmGBXGPgCf/D0xu471usvuVTsGqI7o8Zq3ecMTeWpBj5OR43Wl93PdX8RxZ9yD6fub5UJyPPnY0Zp5cK+zcsuO5OBuG+RX9FX+4G4jkAwPQiINcJsXjJ3jv4LcOcTUZ+M6rAwdHJM3j4TdByhMv0mZm5uTnQzV5+61Dv1F8XyOJOdfgiIb0aJbM8VV4E+e0iCGyL0WJaqbCDSVADioHKJeNleSEyKj5/es7MWStJZsP3PPP4TaugkowAEA3i+04/V6UZOSEjllqwzcjBwLBcYbptBOo/fRVUkQwA1hNwmi3xzOCv9lRamV4y43gzCKS7O7SYHijNry4HXG6OAgu7RY+3S3rgEwyG4BVZGRoRs0jbShAsgL2aZ8hWabkrJhj14wx6f8P8gKePP3P9JIPr6ZWHqz3DErO2kNlzGWsIkYh6Tblm0Bquq9Lnktcaa1iaM7mz2AJ5Zm0VWiX6xE57SN2A2ULMdadZQP25JJgsGhmouTbYPEWElcP2V6rDWlpNDp8Zga3oPJNqofosRKLqiRSgpGWlygKBulHP0fUAjFqvZQcJtNXuZ/VZo7XibDPcyaC95WvLquXs+ixyh3Yi55sCN5rZQuUPTGJ6ZWWOFWZmCKpYpQ1ZXGvqbRT1reHKUZ17ZlxfvtsfAFglTE1rIypJ5GSUpA63fqd17WllJIjZietJfHwbjIgHVd2EZTfg8UALaekbBIDfvL7DQ98hcF0tZeDNBS1mp6nChxvCnXOgfsP/44e/A0A9hn2gm/bT+0s0vZ6L6zO6G/73mBF4Du7/nRHzd4CUi0r/dHZsKM7BwBydPC+xSzgtIW5SdjKYWPovtRnTa66zHRyR4MpzdpnIcBwAIkkcVk+6oSzv3nQVJbCWIdKarZH6IikrZVIRAyi48fNJmtuzjU8F7L7hv1vEs4BjL0eR+wMgLSkhe6QFZCOYvFrHLd4b9C+1/OAGfabDChhnGsKxycgFjm20Fap8wN/QQ1LtVLYzLROWL444bls9Lz7HFIiw1vyZfmciBVWAiYkbDpiLESE4xOLM1Mw0q78e0a4G1HytTTVJP2wKFpvPaOK+unzEqhqQeME5hhojk0VW1YCFH6XH2SLj//7pLV3T3iG2SerQbjBCCpyrh9lIQbfoVsdW7S/njlkf63iGhZ/H83gez+N5PI9feHySmWsZfshnbSGxNpLJmqCZX7VXOPd4bRFb1al1p6xG2C1lTXOSSyyG5b7Q4fn7orZ42EmzUpMyxoufwUq8BRovM9xRM7rmPqPZstrJymH/uRXRgcW7LP9ONYRxnBpD+rslCx1msExL0F5hZ9pBTaX9QVtxhDpfSBFZ4d3sgeBxJthQmIom6rmX0d3wd0UVfAgdwZIFkhuulVhV7bQtp4zyXcYCLqprTb1XxvFw6dCzUH+qOLMsJJdJSTnlb8vvTFD96ePnxc1ESS8joxtzQ3oYbmcpzkBNlvsZ33W4v6hQM0nk65cPaBxN7IfDCsPkJVMYBo+He2LU5Ggle7l6vYU1wM0diRq8n0N/GZh+d8LASj2L75xA9uPaCHErLEgkvdzT6sbJ8zi8IG1nd8fM1K/6M9/U6bZTMt5FFJMGVyds1qRWclg1GA81UmGkthMCC7vnySJXqhZlB4PELN2iemRvWWnIqoOTmQwcX1dcJPi905a4RklEbsgYroDhBf0urCMsa+zmkyfItWTswZ75wErpw2Vkk2B4zu2HBu1dYZcrtAkQAlVMD6Y1+RJLW1CdxEDDuITM7UFm78XlCAD6NxHuuvihRoSgovvWZSEtYbSoHrygRuEiiDZvXkZ9zoJD3NZCYsqjFZ3hbjFgHL1oCB+3rSg+ff7rW1y19CC8aXe4GVb49uFKpkbag1qHzk+wPGE/7dYCO6cqwx2tlHnMpGuYG2alokxGHLEgYFXG4jt+lrcfv4rEpxlcuS3G9wnDhh6qaWm05xJc++T3p7+2wpTNBmgetJaaaq03lgW6BGLfZ3UCsdQ7ObeSk3aPpH2VsWFoudDTvTJl7WDQ3kH6ZW3U7zq8sRgvNYiGpcFwWc5RjyGs3BJcj0ZZugtqAXL8os0DuRs0iOWK6o7SUjMaab6N1c8YwRNgTKljU1Araleu15duqg3ySmHJ4dJg3GhdtYzjZxn1blbzc7MXd8wkL8iQ6NQZgbVjO2t/yvQZ2aQ8KDs41RS8y8ak2gFPv89ynd17I89CWBipBzb3MxWpJTkgzeUQy0iXExabHg0vUrf7pYihe5dwPDZiYu0WAXVXhNctanYkuV6c8HjqyHga5PbjvqEJ6ZoJw+RxcqWPE7DcPuZPRpjD9aNBe5tFkSxWwOFzczbf639HbUC/vrzHv9xRH+oweFQvTwJn22zIfQVACgYjw9N1HeBcoiABUH9qqbV3AbkxIoqfEsgcHATL5m0tbRyosgry+4Tg+dncO4RlRv0Z7XKHbQN/x7VNSzKcUuOsE7VAlXthDFDadmbD2Kw9nSbDnDz8DywJuTPy/Ewb6vGet5gpH4E2ZFk4CAYo7jzJod6WFxsYXiTtPb0YxTQ89hWQDAwrGeUEYKt9udNm9kJEo9KOQd9lYxO6l0cJoFUdpHYao8VwqEX28cXLHf7h5U90bcnhcaSH/w+Pr3HztMLESku+ili32iPz2Hd4OtEua/duDcPPgR3JWEI4GR4qcRiAamZkMG20RNW+c+hYcW74+FHhZ1j4eTyP5/E8nsfz+KXHJ5m5ZmayDRsnsK0/EZFHyEmdft6OBLOWvw2dwZEb7lOdBU7LK9r1Nw/aZykEppYYqM1jySIg+r7J42zn60aFGFNl5G/cQBnzTN4Th9dqNOB6CIljuKYMin4380mMBs2Dkd6+sADG0qM3cRYqkPEcrtYMMTUZqc6o9ipIMGdVukFJRf40bxKn3X9o9bpLljRcK+mq+0DG3SXrHC+z6PT6A5MjlEipak3Fa7UQW5YzH9VJWcWxpuxiDsuLX+yGrqGhpA39S2XKdh9YG7mwqTOw+LGQ2XAGw2efpYcxV0ngOVsl5GwwsLKOc0lINNsPK8BQZlfG8FTURYDQ0N/88bFD/UOFondk/3aP6zVlcKexIl9NzsyObzM2/0qfq58y/KBoSVEbA4D+2ggCE9YJX/7uAz5fkW7sGB1+dUVGsEP0sMgiaPHwtDzzTT2Uckb42b7dZkVjBgczzASQDcRfNWcDf91rlhUsHKtbhaca7U80B8PrCHMxihm7OTrETtnNZwo/vVVSXCZSk2tK/2cmkhAAY7IqTx08Vv/qBKUYLrOgFDCA7Wfqa3VGYui3eiB2s6AWgDB43UnFSsImAReT2Prttx1yUXyyGXmw8O/o2ppbg+Elw85vRuoBLn20Qdn+bhXE07WpApUX+JkM0YoAxTQ5+CbimzfEwvpy+SjnegwVbo900bePK3gfsVhRttrVkyATp7HCdreA+YGez3pUuHe85OtPOl9S1tlDPKfDOtI7wpBAtYOuPdMzW/ijHG7M8DkjVkbqaaniOh+/MM2TCjIAEDPq/prg1vKSuJM2qGebUW8VigwLNTZP7GpS4NLhQhezWOvxhDHL6MvifdZjeIKa54zk0vifGGItogCxyVLHQlYReddTAJVAUEEWomwI4hL/1aS/S9CgEpYUrEtwMoqKkerSzB81dKpA5I8EZQeer+FaNx/de1VnSRVw/Nxg4FaQOWO6fuQG/FJnTbNaJ3BmiDC8gMBRJOXI/2Zoqqht9S8U0nU9nWc55+kioXun7kXDa53/5v7cnafU2WwAQgVVDAoGJbKkbDDZLPMVTx6O1a1Mk5GbhFwYqgcvLVqpzlLTIhcilRZ0o8eP7BaDKqNajbAseZgaLwzh6pAxXBbWO51S2VyGVSamLgC7mvDufoObLU3koh3QVeqC83DscHjiB2jr4QqTeJmU6ZvYlaY8gj5LPRVPFfmhzoKhKBbVGbGvhblcTQTJAiQpWJjJuYto2iASh1NrYVigwR0tUjdj6cKQ8wsAZMD4JFJ91iWJw2Fbo2KnnvVPxKYuAiiwWjfPln2Iy+bJ5ZnUXz5TizIn9TPOXqUA7dWIqg44sqyh8wmJI3s6ejTvPRpWkdr+XuUnzWN95nqUfYZbq1m6ZVnEw1DDmowpzOq6ZSNiM7549YgXLW3IOjfhX7YM+0cvTkYAlSPA8PTu2IjcYnyqqC5fSkOTEWEOOxG/oqw59ZOVd2b7+4hcxEQmCySD9rY817MNvP/4ceFPMrjCUCCh1gQmj1yS6k/JgmIFpLVGsbAon6OfSw8poBkRcK4fnIxB/cS/4WJOkVI8czWZtN5biE5l55YqDR6lHUb6M2ekn2zpsxOrBCWPM2m40j7S3hoKTCVosik0QFktAiSrHTezbKPKWgfzGXjwcPwC2KD1y9JGpOpT+l0m0O+mdZkToLvhRfSQVT/YUq15rkksPbus41oU5ZLXTNj3VAcuBDMzO68ytwAhALE1YrWHDNF4JTNzg9PbJN8tdnoN6dCKlRn/Lf1dPs/sV6p7CwBGJPws6bhm/e7IUnmm4oW+PEJtAg5a/y73PbY0AY7lCdM0I7/sHXC7QFsUlHbKCTi+MUIesYPBtEnwX9MCO9138hykwSFZi4kD11DVuC91+D0HrhKs6oxYCDvB6v8PytgNZ6R1E6T+ZwaDcBXgiwLRtx26d0pGyt5g5GdkeJGwfzPr6yobFpsxDl5qqWYyom+dNolqpmX6kxFpQWMzrFeCkDEG8YGCyeJ7J6jTeAkcv4qaFe7crO+dnok0O5dUelYz/VwUptzJSIIemkxuOqAaqDEZ6yW9bIdjA8f2dhffAac3Btt/YB3nkwNu6Bxzm2gOeKMFm/Hqmmyz+rGSzNLbhGHyGAeanxQMLG82LtZHjNHhxwPBOv/15q08cstGbeO6jtp5jjsOtjutm7sJgNE+eGrpo485bgWrH1WxSVSZqix2e+5gUT9aWSORIQTBaVZW/ljHc831eTyP5/E8nsfz+IXHJ5m5Th3pu6Zas8D6iXZehcmKrBlQ/0JhVM8qPQKlOmWhksODQs31Lv3M71PLTKnmTBGUkS5/5EzV6e/Lz5oZU5ZW4EyTNXONrUH/AgglG44QT0T3aOUYphhHF7Rr0vor1VxVQzg7IFxyVtUkcV4xR6r5nLni2HJ+dJ3yfUmzfHeiWpj4WyaFZkl3GTJXdtQdv4kK6YaOMmXN5rOcf33I6C+1FjbPWrPTvwlLg8OXSY3dg4FnJ53Vt7zrZojaDgpvxS7BRDV7D8uZctFoMLwo4rMZ5uRElN10UTPVYOB3Tjxbp4uEVMzdTxVBpZsCmxnVnN5pGcFEygp6VrNxByuZ38R1/3J/979KMh/11oogRqoBGyzSllJEv8yqQ2szrE/YsFF2SBanirOXNV1DSQrz4MSzNftM1wr6fuuzeL2G0cH+WPSOM6o7j/afi0mGZsJhaXB6kxHbMpcQFmr2SWq5ZjBILouvqb8M2urTO0JX5GUzMCWztBnx5OQ4zfdeFsHUANvf8r1+McI3EYFbf2xQ0ZfsM8I6i6AFkrbeGEPfX/yMDdQYw7wYpKWpwLdPT1RXWP7nVlr/nv42I9VRzB1MMISEgOe+Sli/oHvjbDqDcYv2bx9qxGCl9ce1ARuuxz6x8UM5F+8jLM/V06FT1nJwSB9aWev865MIbuRsMBwrymYBYDKCNBXexZzlX9jTzQcn8+h7XRMA8k0uHtExPNdcP8phg0qxFYKHuNxwnbW/dNj+hv6/VGWRtCt1SBGS/5m7jckzJSVrZmpHBD33r3VB73ihW/6YJMikyqC/tEhFYeeUpW82Niwc7wqmq32605Kh1JNCkSLOHzELoCRILp9RJTiYiLM6U7gMWL+hyDgMFWJRk9mT4L7Ue53WKMOS4Fyj67QERjvQxqP0niavLUhz0/MCLQvM2uZze69ZT2x1zGjv9S1OTg0XwlJrqaSyxXO1IhixemQixczEfrygeat2M+i3tPM0BIOJc0pvhUAWmywG2bE2JMrOE5t7B8vwbnUida/+cw6gMxg1VBScTClkBY/mrrQ3kKoXAPi9QVgBngll9ZMSt7LNSAuoEP5ocPkH/qqYsf2Gz7HNCOskxwjrKDXRPLgz0py3CQuGLwGgP9WY9oX9ZAQqNJn+FgD8ckJKBuEnNoIPQPyMHkhzV6N+VOu+VKnpQViqSg9A9cxS84OFtHzlKsE2Ue/pU6M4nMtk7ceBvRDGAGA81qg+VFjwZuT4Wcb0muHp8j0AXJUQZuL/9eOM8FXTeZg7tdArBgK5ph5aaTG7jGhf6C5vvaB5PI0VTqca1bf0opsEHD6nzzQ3FnExU75aBdkM+MsRq2UPz89ITAaeA/Wxr6WVKAyOOo64n3q16LHbsyrYSLXOxBuHvk5iTr9Y9mh43m7fb+ACkNhhaXpskC7oeN5HuHeNvGtxoe9otlQ6kbbDSdcfM3+wEpXbSimLSnV8jGn2uY90PMPCz+N5PI/n8Tyexy88PsnM1U0ZDiTMX1jAQIaJwPZrmpLDF6oP2t7O1IlKdvU/2lhlyG46eYWWpxXZvZXfXf4BaB/UDqpnL9Dhmr6rEKtSbUTjGJnOvdi0mRnyVbSRy47ZzJBIuBmsujBiDgAAySoEbaeSndHP9eWAgQkR020nNmOUuREMDRQmLmeSO3MGY2dDkDtAWWasjXisxk6Zw6nKApM3dzRfhSxhgpkpKHE2z9+x/GkSw/LxwpPyUqdZrpKztD2oeaCMrn/FKMWbjMWPKryOpDBWWKrSUrbcID+UbNuIiL+ZIDt1mn8jJDIzGbX4SkCYmUcjGbUd88QUrri9oblXtm21z7CsEbz7lcH4MsIMJaM28iBkTyWBmhmYm2+j6AAf3yo5K1UZtjcIBRpfBXiGCaeTQ85ALJl3NgjMOo3RIAZLFm8AMHgVz19EMeBeLXs8flijYqhw+mzUZ9XTu1bagKYVENYFFk5CTAIA22pbUk4Gtk3y7zQ4yTZNG5GL6H5mUtaxwEYGti9ZvkV7B8n0p1cTZcAA0ujEezXdN6j2FhWXB0grmq+zKBCV67FAKq17A3nMGlZb8lWk1igA1kU8cfY4HmrYrRdI9PhFQv3A2r+XbBpSCHHZCCP4cnPEEBwS34/aRzxu6Zih90qGiwa5Smga+ruHu7Ww0BEN/KNXlakui91dSha3PxFL3B4c4lWAZU3lXGV4zmrTH1d0jjNx/rkucJpx9tKs6yrZWZthQ7rCUQEAJUbOlVc+0vFJBlcYMGNY5Q5DZ6jXkiHR5tGg2il8mWYPwLxFxR+0bpsqCoqiTpTJ9Bughb1+slKPnTOVk1cIxR8oCEktsiPxeIBrGNO8DcUIw3a4ol67IoZe7bSGCavBFbxel2PETmn90yZTza8E6NsODUu0NVA3mNglNI8O/gidnxlzOLbailPvVAUoO7qW0tIzbrIc86xntJjCc0DyRw2MhS3d3XLtKiSElk2l145bYvS8CjMZZuae44gJ6thP9Oo/6+YgNnR+RR1qfh6xJchboPh10voxnxtAwc2M5gzKLvBZ9mCol+HYVZYAZ3uL9s6QeQBIiaucsxszbv9nDkaXAe5k0d6UDVmWFg9Eg4phYwB4+sbJc3D4h57k/wDUdw7TKsNe0xdcbA7Y7mji7ZLUlcrGCoDAjdaSq8/+PS/Ak0FaK8RdcU3u6S+XaO8sxks6r8Wmx7G07zgKroV9m2oglA1MF+G6IOzenIxI85nJIooheoJr47ksYznXaCnIl17Z3kkPaWoyhmuD/m0hJGQkPr5tIvITlz4eLZU3yrtdQ0o1JhrAMvuXz9lICxKQV1FqkylZUlgCkIz2p7p7D2SD8Zrr5kcrtf3FO4PhStWnLlcnUfE6jRVqHwQW3h1bYT7bmQEBnSiw+8APQoZ+986RKUcxag8GwyNt6IYMkVNM6wBzckj8bLnNiOlHqtf6SOuPnXE55utgqrL0FqdKzUFM0JLasCFnrYY5GWeGJTM3rY91fJLBNTkD4w1CC9WbrWnX1N4Wgoo6tsxrrNlQTc5x4DVRxSCqHS2G0odqVJfW95QVlqBZXHIAJktwfdRlavQvJuvhpfaILT5o5luOX16Y+skg1TOUf/bPbM5rw6nWvlcTddHIhrLEQsaoDurUAUDFFN5RXbNknXPRi+JSU65nHhj7K2qxEJnAdZYWkva9k3MurjLlxbUzgo6JGdVRUYXkLUaWsMwWGF4YsYgLqwTHNcWLP+q9ePqtRX3n0H2QGVIpx4nadAqhyWS1BnQ9ES7K3MHM2qJmJDGTWIyjJAqtHt8daSNV9JYzaCEHFCERwlqCZOV3/0E3YOs/eVT7jP6aj98oKcTy4mV5wZpWGdXfU6sGtq26vDjONDkTaauAsFBpu9pHxNK+YrK4piw3JxyOjQgj5DrDPdGJtTcWyfHD9Dai/2pCtaKbeLxZyvPTfHAwGTi94nP8m9N51nnfiA2caaL2XQHcMwzAGpUqBIlxJM6mo6Gfi2yf3XqxhMs+Y3gVpY7bLEepyfbHWs7RH4kvkbqSrep3Re5HPssSS524IjhpOtEfdOsBQ0//jiePxR91lz68SFL39wdtNwtLwPx+j9cXLGnpJ+wt/V1IFmPw6IuMZRWwYEnCEB12vIHJycDsvdQwkYHIhLX0aoRvJzQ8B9PopV/Y+YTEyFLeV9RTWwLeXztBIrI9J84lP1sjGZk44xPMN9E/GbnmM5lQg7N36GMfzzXX5/E8nsfzeB7P4xcen2TmWu8ItomNO4Njm5lB77QwZ/XMAnP68dwEPTugvedMI1NNsUCu1UxQYtyQd2HY6M6v1BdszMKC9CcS2S9+pdkB3W0++10ZZMxO/y7Zi8gcDrojTDMFqALXFL/EVGtW1TxYmIAzA3N/UPWUcl0wlLWW7LS4zNAc0HcXJigM8PQNq+gsqZYktWCjalDzthl/yudqLVIXB6oj4AdtL5lWTmp3x7cG44UyqJffObQ3eh6HtwrdNTt1PcoWsx043adyf4drTVCM4XkIOnelbuVgVIxjIK9bkcsDJHMynPDMywyFiV7tyWWnICH1U8bD3/N3B2DzR/p385S4jMHnXyd4FpRwJ2JqT+yROb2a4DgDrboJI0Og4SqgvexFNvF+t5Ta4HLZ4zRWIh5gb2tc/p70IGsfsTu0AvllZClFxFrNrrGe4FwW4Yj61oniEED+qu4FPUCVjxiLPOFTRVlgQQQOXqH3YISViwzkYKU2nrMRKDYMDuNjg4qVr/xB1b7SJqBajXCcacZopPUkT1baSaZVPpMxJDa71lxhs2TRJs8UoDyJV6wuj3x8KwYL3XeVSJn2Lw1cr+0rYZnRs3DJ+qstrtoBnSc46P60EFg4JYuUjGTDo/WqMHWo9JwmQk6KSlu2WRSscLKYJotQl1qFEYa6MRkN17kHAPjQCAvenRSNKdKusq7UWjt1Pd2r4sCUanLzAoDNn7TVb7i9nqhAAAAgAElEQVSm4xSEyp0gpaY8Wzs/1vFJBtfjawdXk11Vc0f/n+/pRSoLNbXV8MORDWyB9Qb63NzdRgvyhvs49bPjihfVyFBKKRmdcGbUXoJRtsC4tGpS3meBM7MDYmWkvhkrrf+50bC0If2cmpkZ+IxE4KI5P//BoGWijD9mcuWZBXCBS0OWRT8w9KsuQBp4ScM3n/WsjpcM0y649aPAv6NB947nZ2bVZ6fy4upiXKBSN2YkZxALdGUN+msNXJd/0HOOs8UxLLWVotoTKUrmbshy300EjIOQtcarCMeLkj0ahl31fgh8lXTdCQtSsyoKWdWTtuy4EZh3GThFYtG/MtIPDQDHz3Ueq522qyRvcPgyI665j3awWH2n9+b4eYJ5S0yuyib0e1b3maycE7JB+NMKP15yHXTGcjuaDOu0laj6+iAG7rt9h/yuFRP0uMxqY1e0YgGgd4hVEjNwfzToX3DAv4rwF6PUSwGgW9JEpG5CP2+riZAJywaw3PuZlhGuDqItnCcLf08PYdNTL3J5jk9fBFGKQjKYjjWmYlbeRESul/qbCnM3KuJm8BowM5aHAczJ6eb7coKvtf9zuewR2UT8eLPE6k8MT4/A4Qs+fp1ZqpPn5DrKOaZs8HRqcfNE9dIYregf58mSOlMhEm1G0WGGybAsLZgGR6b0x6LTCnUJMgB8kt7fNFp4huFbVmUCgHT0qA/mTA5V+u95k16+OjYz/fJgxB0IAPyk5L7+1WytGHH2OTurx5YWvY95PMPCz+N5PI/n8Tyexy88PsnMNVYGqEnpqGSP45oII6LxmyEKR77XzGZiYYICnZLReWEVU+baPCb5uYhUTAvKSoT402iW7IcsWVpROyrnVR0y3JD4HO25ktEMWiSHE4VSw1JhGj/zZQ0d7RjLMfxJ2378oMSYMkpmFhbK8q0O9Dclu54rXZF/qJ7//d8aTJecYUWCLIsou6l+ZlheGs0jaZcWyDXbuSBGZqIPX7fL4osbFga73xiBltpbvW+pIuMEgGH9qGQhk9WUIDVA/0JFANzRii9ltWeyiZBecEYwE4j+IgA+o2J/0e5G5+70itR9CpTqBmVHmwCMVwmRVbH8bYX0BU1K+CLhxBq4trdI64j6A8O430MIcPHKIF0GYa/GvpJWGZgsbS5mtIjrJMQeM/M3DbsKZrLISzqP9aKHK0IFxwa5yghFRSoZpEKasVnUmtzeor1R79vj1wHVJV3Lpp0wjF68Xr1PogMcmYkraaGFnLNbBmHeYnQI21qYrW5UiLV+pHtxessiEssJ+bGI1jNEykpScbIifBGXSVAJO1iCnku257L60Z4sssvIl/QS1d0k3rTGJuz+usHiezrmwkJ0qk00ZyWR5AzdAwAvv3zEsibY6f7YiaA/wIzpOJvjRRSdYABqpG6AdCi1IP5Pp58TYtJkgNEjLZjMdjHAMSzsTMaJywNu62CjMqbDMp8ZZqRKSx/0bisj2AYVYgmrn39Oj2GnGUM4KWrzb0Ba+NMMrm6kPlc3AqdXHNQczmBVal/hh2MxsxlDCaj8Q1YxfljA75WVahIFVfpOhTsBbtspASOpO0m2JN9X6oH1PmFalJqlAbKyjAGgYdFrUlYCpkUpHmrryRwi8/05yy87kk4E+IU45lmREQiN1ojP2mGyws7kgqFzW50y7v+O526TZAGsHy1da2Hb5nNW4HCh98Ifsxq1zzZBbqKgPmzomIfPDE6fae2oe2fRvedA00KMwtvDrPVjbhwPukcFhupfElwsJbSDBuuwVEcggNptRPB/7kJkAIxWFpvD51mkFu1gUG1VQhF51uL0ZsDXn93j/RP1D/WjxYrh0mUz4tgx8/bYoP5Th+t/4g3YKWFYcy21y3D3M2WnSAEdABkvFFS4TrBdwOWGLu7h3QbF9cVEg9zqAn4calSOjbUv91i/ucOPj4RRH5865FJ33jttBdvTRq7/NZ3/1Ys99hwwdk8dMfB5foLNItNX5q8YeTfthP5ED1rsnfSkwgBwWTY+JkAF4AH0rxMySwbaD42cIxIIIuVNgF0EedxzmxSCtiCIuwSx0cKOWn5IFxEVQ8FdM4nLzsP3F7j+R4vjGz6PzxWStrcV4kof+OqyR8ubm37y6NlxZhw9sX0ZhnY+omlpwcnZYOw9YunhnZcm54YS5dx5k2VGLQmkywnVvH84Y9ZqlchYAkA1GnquS5TIkPku9Wdpl9vrd2fLVpbchuVORngF44VyPOpH7rmf3/pSNpuVSz7W8QwLP4/n8Tyex/N4Hr/w+CQz16ItfPhMvVjrLWeWs57Pud+qEEv2lEmVTCfP1I+ah4x6N9tKZmUMJ0fQ78BZYnUAmqcClwIjZ2KBBRiElFOpolFp1C5wphuIqAP8v4UXXD/rnR3yGeN2LiphAsQmr2SH897NkvFOS5wxq5NXeGfe7+l64PTCKrx2tKQ0xMcbL5NkVYAeMyw1M3YnYgUXElO9iwJpwQLjyoqRQnebsXhfINaMObPInIzMMQAhl8ERPJdmpImi2JNY4F+IG8t87i2Z1YQ+LbTX0YxGej9hWNzhFZseZAMvMDCVDooxvNEEAstNj91Qo7+ji6vvHLqv6cHrqgm7Ez2c8ehhK2VhZ6/zWe2A7kYh6t2vFWaGy2hYa3bRjjAm4+HP1Cy7eG/Vu9QQxF6m/Pi0gmXBjX0GPjQqQFDde1RbRT4KG3a4MujfRlQdnf/+0Eom1iwmxGhEaL98p94Mi8z3Zhw8LGfNaBUCNTYjT0YyKJuM+PjGhtiplkUw4kLP1xRBiqLKtJ81sNqsHq02MSO4EO6M3Ot4GdCsBnxxTalyHzze/9fXAIDLf7WYlmTmDgBmEeA4Ow0bi2pN80+WcxBEoPIRoUDLTYYxGQMT0ZJPiFx6clWEdRmZs2Zjs7CRzczoAcHATF77ggGBgV1D4huFuLTsBlx2BNkfp4pMOvie2Ok8sxQz85PBtMnSdQBAiG3TJsG/OiGzMIU5OgzXXCqrICTG0oHgZuUgITT9G0j7PsngWmp2zYMyYOstM2WtfqbcdHeatW04/X35b7fT+qjJyno1MSO2+pRMnZH64Ly+eXw9E0ov70ZT6pLmbOGJNbn0AByAGIKudgbTEjNxBcxYvxoIxVmlrLeD1hfDwrDEH/08XhiM7F9rR3W3STX9XIJyqnWuYMgLc+7tWOZqvMioH6wcJ7ZALI5CMx9ZN2RUh6TqWa2F77WOTWxkI9fpSvnPs1A/z0F3r8cYNg7DldW5mTGowxIC77a35myzMJdmC6uM6VphvhysGJ1XW0sN9yBkMVdZYEQ7aetTbDNSo/OfPRA2XP+LFg8/XGHxI03K8deTyOUdq1oWw1JDLQG63hqsvtf6dGy0Bnv1TwaHz+gipr8/ioLS7p+uUT8ZmCIB+ToJK7TaG7jBwNzS37mTQup0fIPMzhf+BHmmswG239DnptcjXBuRmTUbJ1UnMlxjLVCqsUAsbPb7CmmhZuYX6xOe9rRIx94pDBos3MGi/WDlvKaiqlWgdqk1A3avS132WUUrABWpqBIsB8IcLSlDlUOuIgzPXVVFeJ+wGyj43T+upNVk3ADHrwIcC9zHo0d84gBeaWsSTIZzGU1FFx6TEYbxNHmM7xfa9VJZYKG7MGN07UgnlTx0vUUedZ3KFjPDeAhOGQeHPhphOFcuyXe/+/4azY8VHw8ytwCVmcqmdrxOiFcB+StmJ88ceDBapB8WqMTtSUVZqp0eL7vC0eD3fGl0E/3xk4U/0eAaIbZxZdEY17Solmwv61pAlPn1rB0gaOuMGzTDsiGTwk9p60i62wVo1zZx9tTdJZxYHWq4mrnITHRsqckZJmCBMtbYGlQsj2cDVKe3oYXazIhK8wx0Tpqxc8s8q7XZUncpG45xQwsrQEF7bq3nelVJmu9uP/xvXF8qPXXISEx+sU8e1U9qmRc71SG2g0pFto8R2Ripec/JU7E26K+saDbXO11oQkeIQNFsnpYW45rrWGt1yJkW4FaiEhSM1oxYeUbbdFRCMdZUV8ynUsDWmxsXGaGoXiXAHawqTAWtzdL3AfGLUnAnQg8A9IcaF//s5Dy7v1Yw/8pm4BHwJct/mRDrjPpJM4C7f88H/7xH3Fa4/EeWOdwnTIVY8m2HtKUHYXGi+1s2BHCA4+M196A+xjmKU0p8bhbEQBuk3W9pvldv96i4bjs9dlQj/e/0UuZkSI1vpnVb3bCG9asJ7cUgGrZPu07UjhCM1IUBkpE8/j23HDUBkQOmBMa5yk+JpRYURGvNXAuZq1mO8g4MTy3VdEtQMxk1tw7lbNDVE3b/iSSmmlHbzY6vAswikIE9f19Rm4Kb8RmygfcaMFOy6IuO900HJIPMbTW2iZL1T6eK6qjF4q6N1J4DcrEq/IZcZaCJahhvMrKQruidKdfjbMJ3/0JF4vWfdNNR9MotB9kzlbeWiBf1P9Hz1Dz+bH2Y5uSnLJtJk2gTX0asz9tu5jaTH/v4N5B8P4/n8Tyex/N4Hv//Gp9k5jpcGriGssUCaQEAkkKpoTOy9ZjrxjrO2ubN/8J4zUB2CoNmSypK5RhuIHUdgMUVLoz8nTCHw3l7T+g00yNGs353bIGyNU01AJNhZ9qfc91PzaLO2cJzpm/5joEFFOyg8K6Jeoy58wxAGfnuN3SQq8+f8PiwRC4uIdkIY7G5s7CT7lzHtTrhrG6y1KezMSKYARA7utQ9TQLqfUZ3z+1JSyswuYlU4z69pJtIKluzLKtA4qlkpDp3JfO2ezrlkunHRRYdYDsZ2MlovXQyolQUVuqNaib6rBgFVAqnmV8fsFkMmBgP398vYEpGlBwe/8OEjv0/mypgy44neV6E2nrUj5oZ738/4tVnVP87DjWOM5/R0Bipg7pexTFSRVm+ZajZn6AlieX585MdGUMAQHo9kDvNI2eTXwy4uiDGcUgWxxsufJqM5qoX9aMpWIFiTZVgXSZfUQD1TxXGV8y8vezRv1siFp/ZLgNSA8yCiKy+tdj+w4iWa7r9rgHG2Rz5JBlpHq1kd2gjmuVImSlAjFpOl2KworFramJLz7PVMpxLePrHF6j4PRwvM8J1qTFYQjYUuIFblmNAMlCYTIbjE9eFoxUt5GwzzHoSzeOcjBgZ2CphcalyZtPkMIRi/pyRF8qmNjaLUISxGZ7nytqM4VTh8FeCZManC6zvCwv4PEO1UVGL09tExh4A1n/0aO+tKFO5GWKXHf2Niuno8cJihoIU06JZFCrTk/8NRKZ/A5fw/31kQ//zMyUhk2jxLXAjLM7w/+KQ4xgCLe03qdJa5OKOCC7DBS8iKZ/1dPk+k9QhgMNbha2o947+3eyp3iiQ6wz6DQsy2i7EDTPbDLieYGj5vhn06wacw9NeoWxAF/7YGm5F4WvNMwWoMCM8jQRlS19nm1G/pQX26c9XsBHyEsJCWgCmNbWxlN5HWKB9x+ebIbi0nTKi1VqznYDqkGROYVQSMXT6IlYHguWl7WgmqZitEpMyqyDZGVQ1V1AaLiHWbGGZlCSyt6xuxYuez2pqf7QyxW7iVhs+/+E6IXOfn//LEo/dAu4Vw5mLSWDhWCWkYHHihb/3CeC2iOwz2nfc1/pDxnBppH8SFrj7F4qarjeoR2D7N/S7atYiEVugfqB/dzfUclRKAMMVCckDTPgbVP4zLDPcG5rMuK9hd04UoDark7TYTA8tulf0HHx2ucVfb64U0h2t1KqdT/BVhP2WrnNeJz89tTCrALygm7NsJ0xcpxyPNbo/0/F2v40wdUL/QMfwD17uS3o1EtFnKOwYo0bwiRxgSuC1dZTAFSatsVZtgDFaI227EX0R4P9xgeWNwf5XdD2LL/conXrHfUN9szx51WqaOfdY2WxUVaRgW5DxqK03ZjMpZM7z1XIb1svVAfeHBQ4HrkPPvsu4LPWMHA1tyEyRNdTvOn1YoPvBq4Sr1TXg5yM2EPOObIDr/4vmo9rTGlXWpqme8Tp86VvXMkl5X4loqfyAbCHv5bxvv3zmYx7PsPDzeB7P43k8j+fxC49PMnOtDhluUmYhQC0wsVHSCwCwbjbslOH43yXrLZmfPwEdE2iSN5g6qwLWo7KPqyM1Tx9es27nTJSivc2oCzydyepOoBNrlABAHALJzOyopJxx8zN6nVFYpuwgATUkOCc7cfZynVncQslbpQvCjpqFawbNO9CvTojcPuLHsoNVOLwoMvkT/beIPAAz+Gd2TmTxpwb182w0NhanaytZ4XihrMbmKcOfEqYlt71MM43juWVYa5A7hbnrEXK82BBhpzB4USVp38ie73+rMKXh7MhEI/MGQ9leYTS3H6wyk1dA/LIX8YYxOIwsHlB8RQtpx97U2PyRd/yjWvxtvyE0otoxY/egztSxzRivlflrghFxBRsgrWKxInbm4UtGCwZISw1A5uWJ4Vh/MSJ9Tzi5z4D5+oCLFU36/eNSTMnf/voOLzq6ru8eLwGT4VmswCxUhWkaPKlAlYyoi1gy1DkOHs4n2KIIdWiAD/SyVEeD4xfM1G4j7H0tOGK4DPBsKG7iuZE63IxxPziYLmCxJqyyP9WSuSIDjtm1RXHpzfW2/ArHfyHqfHdnsP8mYvk5wR0hOAwHeultTe1H5fxrH5H43kzZoJqxd40hOBjAWVuSsRmuSmJEUMhdAPDucYPhVIkIBizg6pKdqsV47D09n8X8fbLoe5pHv3OYNlkyfX9QNq+dZkx2Zhw3D0UYJJ9poId2pi1c67Njw3nZLLSqUJadkjyLBrlorI+6tsV/A4SmTzK4Gg5SpGqkdQIbVIIvORJ3B84p6NTXauQBXP8YhEqeLQWd8iJPC233sBEYNkaYpyYB7R0/3L0y68aVPWsTgYHAx7mGyCMCFDBK3TYbOvf577Q/TRcXk4ralMLaxa80OZUsK9dtZypS8xplajLCNU/Ctobbl8BAi3SpRY6X6hwzLBOydeIxGVu9zvqgi864suSuMzM2OLyl4z/9LsGdMpZsrN590N7ieheRaiP16uqQkPilH2o3a6PKaB5nLGmrmxNaULIo85ijQoWp4d5Hhuz8o0JrwMzlaKJFpDwzp18pi9MfDez3LXZ/ol+6QTcwV08Zp7dGNguxJrgWoOe1GLhnQzB22RxMa4Pjr0p9A6jfe7RcQxsugeNnpcYA1A96ncNLDcJu0I1gqjM9czwHYVsL2Ta+HWCjw+07Umjy9xXSBU3C/XaJ48BC+tmgaQIsQ5ZTcBg5gOTJYvHyKME2JYPDA2/OOoJjT9zjiX2FituYxtdBZAux90ityjfCsisMQPfHKsPc+iQ1a7eecLE5YH+knYp1EbY8utGRYQGoQrFaDDiOdMz+P70AiyShf5mw+Gwv7ULT6CWQG1Ad9PqCbmJMBpHr65WP8E6jxm7fYToW250Mx723ziVslio5uT22GBheT8GSOxN/1roMw58Lo9eNWU1M4dIXnDOkJh0uIpCB6onPazvrWc9QrokHTq8zppd0f7tvK6z+KqcPG7WMlq1ulGNnEBaQzaWdMrLRz817u+c6AaKOh/9OsvARjk8yuI5rIjRlp9kAQLXTKEQc/f9TpVKIxdC3EGpIAIKl5wqlfFYvLYtvaAzGC32I610++45+1oNZb7O2CK3MWZab3Szw4rx1CBkaRLPWEU1U0YvYMMGGCTvDyyiOFaUntbT6mADJ8LPVY/cvM1KVseD6lxuphxWgrG+ostpzLSNqNswetw269w7tAy8GrVGRDW+ESGEyzWuRfRxXBo//Xi+6/WuF9l6Dcb3nPtHGwp8S/JEXSGsQuM84NrorNpHOuUhODtfmnGSRjSxEeRVguEeyvndwvZH2JABQF5VzWcXTm4TlN5Qy2mgx/IV2Va/+43vs+wb9P1IWVO2M3otrg2mVJKD6g8HwJskxJRD2ZK0XWOpSRCIA2K1H82iw+4YX3+sR8cC9uPde2miSB2kEc9YTl0ZbRkwGRovqltuAAhB+TcHC+4hpX6us3ldH1JwdWauZU1sFEUUAKICWHlvnElKyGDiw5KOXQH65OeL+cSm15ubWof+anh8zl0ls03lrToa+TzWLe/A5pmSE2LNZHzEfdR1Rc0uMMRmp9JpGh4ebNZrvWZPYAYfP6XO//f1PeOpb3D8ReSA91LLhCk3C6s1erj0mK8fMmTJZADiNFaxL6EoQjhbLbpC/6Scvm5Fx20iGbhs2uOeUMWWDxHVhY7N8zrmEMDlqSQJgj07Wnmprqb+0oCkt9Dl2KkAyvozwL07AEyMHO0XsQmswdfrshsXs/UoUKMtmLdYGYaVzXt6ffCQ99/Lu+dmt+R/VgD+m8VxzfR7P43k8j+fxPH7h8UlmrkX+MBlILdXvz2ni1QGy9ZhazTjdmImZy6zfcW2VYXjK7ExT2kY0Ox03VGsoSk/+CJExLI45gMolFlhkmu34iqD8XD2oMDrLTlQycaNwS54x+RybqBexeDsagW+KSL2Vz+YztaZpoefij0ay1fAiCDzn7ivKuNiRZPPiIDWncWoxXgDdPR9jyGJsMFe2qvcJoTV4/B3Pwdc9OpbtO32gkxgu6OYsblSCsN4GIFMGCwCxs9LOMy2NoBLtXca00FozKRrxhSW6NsNQnrtxWH3LU5poRz1t9H6cuAaIqDXiuIroXpzwYklb8b98/xLV57Rd//HbFzDBIl+z0MXazhjeBtXWInJGOl1HVA9O7lMRophWeW6/ivp9JTWz/jrj8B9PUmKM2wrdj/RwxTrLseM6UrZYasa9AZJK/VV7dTnpPwuyCw+Dh1sEqQMS/Esv0fXipPea5+/QU+YXghMGrPMJ4+RQc2vI+sVeINDbxxXcX1uZk+FVlPtrXBLfUZiMMHptsbFaX8yJiRFcUzQ+YbmmDNHxZxYtPU9tPaGdiTncHej5Ov51jXrGtK7/lwe8WdA9/LBbYRg9IksnupMVb127mnA61RgZXvY+Ctu5ayecGGYeRwdrM0YWjsgZeOIXzNhMikeMOKBOMJzZF/eakg0bk2Wdosybn/dTBddExGKeHiCuQYt3hGRJe1jCjCcCmCIc88HBfr/CxbtSHktirpEqylrLWlQd9BjjZYazBnlWuxVD9F4zXBG24fs7X+tSMff4iMenGVynDGsyqj1QMYRY6nxzl5biPhObn9uhqTrRfEivZCFLGXV6mVZk+1agmGwVah4u1UYO+Wfkmwai02vY/kmUo2awMxEMZoH3Z/1q5ydKjhR0UCUYmMxShoVclc5rkaWmmJ3BdJHE6cXfeXgmLQ0vI8JnPZZcoNreL0Ui8PJPFu1DkvNKlZltDhQKn5YWu68sRq71LNcDRu4BNIHIEQVCSk6DaaoMstPaYWiN1qS9QujDpcF4qdA10ozE5TKy19pzd6OQ+uHLDJOyBLLUAo77MeMiaXCaqOXih7siWGwQ7rhlZG8JCl5qPbn7oGSS7IDEc1k9edVe7jJCcVSJBvWjRceL3v5rYP8bfhDaSC1Zpa1jMqLripeD6NC6nUOqrLQIGT5muYfTBjCvud/WR0wMUdoqEawbC+RqsWCodUpWg2twCMnK38V9JRBuArB+u8M1B6u7wwKH7wk2dydLcPcFu8BEA9exkpBL2jbTV8i90xabaGaBFhRs+b1Zb04SvMfgEKKF5yB10fQ4ThQVfri5RPMH1nX2QPzdEV+8fKR7P9b4sKXV39qMYdeIvV58ManiU+/gFkHOcxwqCXj7nRPyF3yG7wISB78crPakch9w4vry8uKEVUsP7//D3nt2S3IkWWLXVUSkeqokUEADLaZnR+5yyXP4A/jv+YGc2ZnlcntaoQEUSjyZKoQrfjBz88ie/dgfWFvp5+Dg1ct8kZEeEW5u167dux9a9H0j9d5SIwaY0MTkONPROdgdtxJ9VP+O3DcnEBaPtzR7TrrbTPq/pXyy0hIQCy+kcEiSzSe107BKYs3Y3dZNiorVYSysMv3M92pzr2s7zv8EhKYzLHwe53Ee53Ee5/EXHp9n5sqwsMo1e+yeEtIeovebdRVJINEC+jEsFascVSKO7QtUpeAOWdxXwrKy6bRn2JO3M3YgIQCAoRiBeonAJBCJIuFroEK/ZedXiAP0c4ZfKVHSgQJ0gYQ8TgQZoKo+bFhmgYuggMX7aqw+Zz7bY5a5ih7UOlNYv+sE9S39Ud62iIPFgc2pV99ZdLc8Vy7j+FJjccsN/L5mqyqRsQFAAhXjTYXUj99vpB2g3ROKUOBzM9U2nWltiHBRWKi2ZrFmJOYsQK4pWVco2PRqRgwj7ebCAI+dwsS78/ZeYfk+w3g6//2XM5aX0pKFR5NJAYjn3z2YajTQUEvW4mOBq6kFij6biVGcTS7eacSmQsTr31q+FsD+Zxn9/0HqJWmykiXkyQAqVUGL56P8LHq3YKhu1LCUmGF8GRGfl/RFodmMQkDq+0YyYa0Tcoa0iaSUsGXmbUpKGMDOBUyTRXpPr3UPtR3J/+MBKWm8e6SJ9T+uKoP81YQ8aoCJOPZiEgGF/tjWTG/SgE1C2FFeI4vhe4Zto5CX+rFBz9mpNglX6x43i8qe+f6PzwEAq987DGxk8OrvP+DVcofvt8Que3ha1fnb21M1JEAgb2UzclIIc9hW3qOrmQDIzKB45maTqwqTiWjbgEVTSXx7bqMZBwetssy/m7X6jKMlshOAMBmkwQoTPTUVjsXAxL3ZmlCU6uZEJCgmQLLIQ1hAOhd0BMarmWDLQcEyETK1gHmqmXKYdU3MdamzBeIqyr3rN1lKH3k94FMfn2VwLcog00ahYUZtssC4qT2lydWbkVp06KK3j2TcXQIoqR3xA6+A4UqfmqxzgO4+Uo9YCaJhoaqcoMrSMlJ6y4rqE0GWHEhYsk/glwikgmxyICkBlb4jB7UlhBGcI/dxvqYP1y7BsxC9ebQw/SnsnGf9vJE3FPmK6nElKCsPqHtiTq53tPlonkrtGHj8W4a7gsLNv2aZy3nwi23thTNDRnunoJryZCoAACAASURBVHhHo8dqWK4yLQRFRrKwtwFq5wmdOjGoL4vG4YuZbGEkxm1hU7tDDeQq0fGKgbyKVKMCSHbRTLRBKH8nLOwZDNbcG/g3E/S2sKlPYbHxJuPI7SuqN1j9WDZ0wOFXk7Bcj18n2Ywtv7MYXvJ98O0eb652GAI9vrfThlSBAMBkaFddZbrFhJEh9aQ03C3Du542Uce/5VqkyWLA7TYjmpkl2WlQVrAuihxgihqe24zSZATiG3xHqkkMZY8vogQ/kxX1s/6/dM/YJmN6Voqs1MOsl9yzqjL1uvJ5lOCtmkQwcFEyWnt5bbEa0diIft4WxGWKVTth1Uy4PdJnf/zhCu6eoev/bYdrZuy+WBzw3dM17j9ygX0urbgKUCbDtXQN/WBl/lXD/bw8/znXskvOEGUopbIoVZX3lTq2MxExaalXe2/EXcjYiLapBu+Tt1LTBahVBwCazmM8uFl7VV0PwlLB7ckVDOBnhOeOGMCzNhg1q42muukP6wTllbRJze0p6RcUfAEgLnLtWR9PHaJUVsAVOwNdKDHFyPczuaZPdJxh4fM4j/M4j/M4j7/w+DwzV95lmTFXoQJ1uvNSqZKFmqcqKg+wkHxhxl0oeCb9JBaiKNmpDoBjSFQHEqIvKFH+s22NqKI4gmZKP9nc0q5/ToQCsQKb9buWrWyBdJOBaPiavorIIwFhk8Q0O3gDxazE1Q8aKmcxYE8GWL1n0tKQRB+02SlMayVZmx1q5meHhOQU9m+YafoiSYZ7/d8o4ywknWmjZySwqh7klwSvZ1vh8MJEtAcSlxBbv1wher8iE3UxNmiq9ul0nSpEHEkERLx7A9Acav9n/0JL9t7d1+8WG3VC+BieKRy/YbLNxSSsWQCY7pZobzkjtfX6hEuCpPWRbrb2VgvU1r9OgNfiBZomA/c9s23XGYtfP8rxf3q4wFQECJKqGRwoKypEl+O+BW7phll81EKkSi7j8JUX4XvvDVQh5SSN3f0KKLaBM53t7DKiAgL/e56tuqVHuCWoxvQa8YsR3ZJ7nCdL7wWAHxfQM6MAf5Hk+LAgG7UZ61Vey6q2hifQd2b2cB4Nmku6MK0LlK26ygIuBCajEx6OCzz8QGQz5TXwLcEi1kasWzrGbb/C03ZVGcgz+7biRztx/6caNPXdAjBNFOs7gLLVYhgvdH9Qf+3lssftE6WC06GRMoJqEi6vD3IMrTOMK5k8CXIUklQIRtSgtM5YrmkBeNouYXZGtKXNgPo8DdxLz9Pj10rKVwQD8xSX8tOMSV8ETponIh8JadLOxCEy6WmnRUWXilZ3skYY68or+s4PdI/bY1V88v8TGLp+lsHVDIBJgI5VzD2sCaYUxZ0A2EOp61VoNnQKsamBJSwgzfxupwAFtPccUGMNtMSuqzBiVpAgYwdIbS05YgSXG1X7LEFNPGZnwXvOADxxt3EEfQI4aRifroD25VEgqOFugfUPmj+L38cC+t0uw+1Zxu1xguYV1V80MN4Im7p/psU4fbpQOL4iNjFAwvEv/ol+7u4mqJARFhxYdBWO0AHiYONXBv1LJfPaPM5qpz3NR6mDmnHmupFpcyEGJgoVVn2rBSKOLV2nUlf16wqF2WOGGbLAwsdXSiB6t6f7pWw+/BqyWK6Wo6gR7Q4dCTYwDO2vorS1KK/QPmhRvtK+sjHNoJCWAH4kxurmrZJNhf/Pe3hmgg7bluDTGdu5KGJlFscvbi7BG4HhkoOYevuLDOUShsfivlAXs3gEzN5IewlUBrgW5lYTctJwRYR/NWHiYJKigbrmFpevRsSo0X8kTLG5NbCFQXsdMW4y9JHvu0GT2hKAXODeGRQ9F1sR2xTD/AWGpNEkEdl/8ksYk2C4PaxxAS2327y7v4D53QKutL79eiuw6jg4fMwc7CaDPP/crCoj2FObjDzbi6oUlaKS4AsQLF8M0bUitnIZb79/JgEVGVBcw728PmDZTvDcO2ZtOqndBm+lFUebCDOr4z7c0oNhbx3sfub9PFaeAjKtJUVExa9qWw7UTNThQOuXQMu2riNyOmWDGmsycvxZPFH4Uq4qZGVEubZU7lHVJGOZsfmGhFe+7X7ETAzqkxyfZXDVIUNraqcpi1fWHGx5x1R6UMvPZWeXjIIdstRS23uF9qGQllgrM9a/k57XIhVWniVVe16zOu2j1YF6QOkYp2ol5BBTfs6z31OgKrvNuMwSZGxfyVl6BPI/byBCNzNij+1JP7hoKpspwx45CL9s5fNiqzBxlgjQxqNsGsZrYPjaw91x+81vsihXjdcObhdJXxSA20fZOOiYRemq1D27u0r6aR/LOQHHL1QNTlNti5pWtWUK4MWgvC9myeptnzFtFIYXHFAPs01KQ8G2LDDNE9BuaZHY/Uzj+K2HKnWhNmH5nDWCvcX4diXn7y+iLJaYNPSB/qbZqhOURE21vr74AOTv6uK7/yrDX3PQ2baygDfriXp4++JGDWRmrpiGHFRKTdSYhLAumUJtMQoXkZxqOGDkpKC4Rux2CtPNbKdmaltLigar1YAb7uGNSeNJ082121poPsdxcMB3y7K+wn9Ts9i87eA+1H6z2GTRa9ZtJLPzXCLXLCufBREkBdUbUZXSXc0sU1RIWuFmRRe8MRE//tdXfD01htcBX/78FgCw7TupZwLAccsPimKCUXGxWXohDuWB1I7KOas2yvuaLqBxp2Qky5nl43GBQ7G6mzQFJt6k6BPN4Yzd0J4Gd144ptEieAPNcxEGh8R/G7dOWm+aR9oUyiY9ZHl+kyV+Q7n/Y1d1sIHZJv3PULZ5KyEyvc8zOtY8AccvZ1m+nfEYTIZmFMGuI2LZYNwArvUossTrbsSGkYO395f41Me55noe53Ee53Ee5/EXHp9l5poNiw1gJpKgWai+sGMHnLTYFAjUDpQpiQ6myjNRBDqOKkbnbYWZTcgwuwoBJququXDRBUY1rS5aw9MFTijzWVdIRqXKREQmmHl4xp99XcXip6Cl9qISEFrAc+1N5apZW1pvJEt/0PBLSu2NrztRv6BaaTFM1zMHjOwAe2+x+oEh4+dKmMPtNrPbTWHtZiSGiP3awAyFjUyZqqhW7SrUDpCaVskmu4eI4ZrZziOrXRU2dajZvZqZPhc/13INY1trSQWWl+urge3PaR77rz2QFPIFw3w2YfiOYLjFBw3NbRzxIsLeWbgfuaF/qixLv8mwB4XFR4a8t1m8gQ9vCCIr7OLpWcDcdHtzTSelAGwfljBdhW0Lo1cbYvKOQ1H+UUgXLLz+h0ZqrtAZbj0JvJjuWoGPwypTllhg5+J0AaDtJlwuBkT+uyFYbJkpDq8QFYvBv3UkfMFCIG3n0d+zWcGTRXK5ev42CZq/C2WfqfxwahIfqmqUmhQJYBQXm6OV58QsAzarQdSQ3r+9kWl0/8sDlibKaykp8VsdBwfLDOAYNHKswg4pK2FTA0BusjB/m87jYkU30Kal8kDJcrdDJ+L/02Qku8Yiol3U7NaYJEIXPhp4b4StPRftUCqj6by0SY2jFUcdc9TibJQVcHxdFbnwfERiRan2g0F3O9PTVoDfJPm5uFepoGCHWnZRsZbHkIGoFYaXPP//+YCvrp/k/O/2S0FPwozN7FzEZkkHDFFju10IfP8YNB73dI9M2zmh5NMcn2VwHa5YuN9WCMQd8knNVcXa1+mXqpqST/T3AiPaKqPXPOUTI/WwULj4jm2jjgl+rWVxsCFLwJ42p8dPhqzUAFro5gboZDlX673VVu6UhDUXNQ/LCvtoDyImMMHJ7mftSE6dCNyHlYLiQBi9wvQlK/hcZ7SPtZVIx1ydXHYKfgXsv6516LbycGD6iOR4YX7mRMbQ9hmJ25t0qG0C9N2SQO3RKbRPWeZg98bKZmC8ofe0D/XaFIUme6xzkyz97Isc4n2d4xxAlmXFYKWdwWIPtICX809dApa0MBz+uraCoDdoH2e9s6pCv+vvgfapQuNPP9fS5xovKXinA7ddHDXSc7rR1ldHWayOxxZISszG47IGQrMkc/FSowtHi+a94+uZEd+wAL/JSD8s0TxxT/YiwzMUrFeeZBFLO896Etm95+sDlMq43VNA3b9bQ4/lfJVs4sYbUvAqxK34sBaIOFwkZJ2J0AK6n4XslIkwJHyWDIGFVVQwx6qIpXsNe88byMsMXHG9dzHh6WkJ85YJR1+MuHhDG5OQNHb7hQTUnJVIEGqd0XBwNYsEH4wEhjBaGFZMSiHBuIQNk4cWjcemYQWlqcUYrNRW+8EJ7Bz66p7TdB7LboTjed0eO0xT0StVSElDkHENLDgg5aygVcbzNdWG7EXC9w/UwO19K326uPRQJlXVJwCKLfnynSE7SdkUK7Ss2DZ3plF17yaj2lMC0696vLihxUPNNhS9dxj62kqjdRbrvot2wH1PdfjbjxvAa6g1nVfXBIxFDrIYMnzC4wwLn8d5nMd5nMd5/IXHZ5m5lqEiKtS5pmy0QCUq1nab2Co0W4YsNWVBogp0MTNYV+pEPH/1Lgn0O9xYYuKXDKyb+We6SoKK7akgv9uqE0Ye2d/Rv7OpPrBhxfBmUWwKqopqs8UU/REdp0CAc3ZtUXEp57X9ZZoJ/ithNLu9QnufpUWo3VaDclKYSiJa0T4qNMw4TlZhunJCFnP7msGNF1UHuL3PtNNOlYDRP+MsxypicfM5h4XC8HyW5R5nULmuhu7TRc1c3V4BQ4V+50Qze8hoH5MwK8dLLe08qc0Er3HrUvveCqQ+XWtRl1n9aCgD4PmfnmWZ5OQUxmsjbVKxqwpZbucwXSekF3QR24UXVvc4OvgPlGov3hpc/yZCJfqA/rnG/T/y/ZmpfaUoBiFWY4b4y70YEjT/soTtK0JyIgCggPWzIxyTpFoXMDJT2SeNbd9h/5G+gHs0Qprxlwn+dZEEAvTOymerXJ8tfdSsWcsZe6OAIrRhMkHB5e/Guv9XsbZqNPcaKiiMzxjOXEYh+RyeOrh3Dfw1izI0VcUoBAM1E8xPqWbNzWqSthYfDELQkrl261Fae0Zv0bog/3Y6STYWooaPRjK3DCCOdXIL7GxMQogGh57t3FwlkBVIv2Sdq8UEx/dBYyJaGwSW304ddGmF+tUOYcfZuspIBwdzKGpxSZ4LNxFSVhSVbF+JT8lVoqU7UMtbIX1ufwngDWXrN5cHNCaKEpVRGQ0vYleLHg/bJSL769qLUdjaf7y7kTaiV6+e4KNGN2uZMrzwuThLoT/R8VkG1+QA5ajeVaDfnLnfcV9bbsowfQ0kyRKTd2KTdTPOGKpMcW8fa1AoMxw67nEtZdxUoc25v2pqM9yu9vNRSw39HDtiVs5rimXEjgLOxIuNHrTIJs5bdMrflcBbAnb5bn6dpY2Gvp+W71Yk+9o7YjSXAKRSZSK2jxn2aLC441acQ5QgGRYabp/kvdPGYOSa9+ErhdWP9HsdabGfLuiz/bou/s0jAFVN7qfrKqnmtgrtY22vGq+q+49KwPI9Q9DHfOKJO/fIbbcJbh9xfEEXbvezCtmnLgFHK4o+yArj8ypfVwJtshRLp+uyIcsn1zDr6glrj1WZarrk3sBYmaED91KaRwtl6zHu/6b6cx6/9Wiv6QPGXQs1GGHWKq+AvyPozu8bdH9s5ToNzxUmvtbpKmB1RScVAi14xdjbqIyJ1aCetkuko0XzkY3PNTC94PddTBXe3VNgLapAgDq5X7NBbfWZQ4BeQR/MrGdSyU7TDEo2banNmG4iFEPB1kZ4nqvF9w7TVZK/u9z04q+aM+BHWzcfQUN3QV47HukYOSn6j5WZhtwgsEHEojutB4akpV46eEsyjeX4GcKg1ibD8s8hGIyjk83TNFkJksZGaJ2ljutMhOPjOxNxu18J7DyNTiBuYxI69k4eOMiWDczyeyuKZCKOz6c4F9N3hzwzxaBrNjznOX8x4oqhcGcixmDxwJ623WLC0dJC8vDuAvbewpb93ZPBn/7ICm7/4QHfXFHdZudbfNyvMDAcPu9Lnv/8qY7PMrjakdae2NWs0+1owS36ttB1wdW+ZohECKoLru3ra36tTnSCVaquONNF6Zflk5jV9ZodpLYJRZlr2aGbASf1J7dXJ32cpUcs8jmUnao91HOcg/9F+lF69Ex9sGJHGar5wC0xsdY+bV+1eUv2bMeSlSipnfYvFRbvq8xj/7wKMlBPqsJ4aeW18VmpzQL9i0q6Ugknrj7lPLJW2P6cyT4A3JPB8i19dndP5LKBpGIxXSV0LORw8fuEsrPRc2IGiDxW6uR+ZTBeGZlXqIzpJUsVeg13Z6ogxKoKU6hU55vm9DSbFkJZIEJV+Tu/BMZXpdCfoboIzVZpsTfQu/qIlmCqA4liFEKQWkT4sdyEGrpXiJy1LV/2ko2FD2shrgzfBOhHK6446+uj1PH2Y4sQtUhrPu2WlczjNdyTqQSYRbVDS15LMNJR0flx4CTxjzJvGfnlKLdlOjhoFqzIijYjekZkKj28KtE1BWijo1ZBRB7C+yXWP5b6Meldv/4ZeRveb1czFx9F9ejSX936iiokLdmWPhiSFy1at7NnSKuMZTudBLzdSA/R8dBxvZvPWWfJqJVO4hKU+Tgl2LbO480F1SU1MqyOOAaKgmO0OEz083ZoJXMGgGfrIx6OhGgMfSObcmWID6AYuelNg2nHsp07dtcqG9an+nzNJQ4BWiNLHbZbj9JitO07TJNFw8Iph8cFwg80d5tHhfFZhv+KFjvjEq4uKGIbnXA/LGXejMqY+Nrsp042Cu5ccz2P8ziP8ziP8ziPPx+fZeYaFkBuQYo+x9nvl7X2ZvpcBfN9hl8UKbgMv4bUUtunPHNU4f/zjnBc1eOVUeoXKlUFqLk7T6nXzv1ji1hD80THLhnvdJnFQUUFZqdKbS+LQpPboTaEFxJoRX4FxkYGUqOkDtdsIWpE4/WpQIPKVeFlvDC4/09JjhEXGg+cOalRo72trTL9i5qOTpfVVWP/iyBQ5vo3Dm6fT7LrUvf0GxLMuPg3FqCfSUVOFwq7XwZhzq5/49Dd1QxSnHSGBGjg8JKOcXxdIcvQkZ9pyeh0r2Ef6X3NAzGr+xflvLJA70XAHCDoPqzq/Hcfq0hIcoQUFGGHUhOmE6PabNxya8XeVEGSWDPX4WVE98UBcZbdlcZ8FRXiOmFxSRdr6BtEFofAy0nuD3UwyAZwLwjm0yrjqaebz0eDvm9qrTAqgpoBuCcNf5GQ12XCam+Y0rk68KwifR/OZPNoBF3JL0coU9uHYBOSSOJpqEkhXEb5PsV9KaxyFY1YBeSgke4oY1z/qYqa+L8+wgL4cEui+8bF6n+qZ9krSL5RIGKvRTUKij16S4uQyQLbFinFo6d53T1tpDZrXaDWGWaUx6hkzmMwAptrF2G6iGVLaeHSVZGKPjpoZAReULZDJ7C2jwY5V9eddw8bhNJ25bWgCNoRLF6Uutx6wP739GDHjsRmHPsS65Crb3Bba6xFPaw85/3dQjLv9WqAW4y15tpGjK8Ypfs2EoLBIieb6yc0XHPtJyff0+iEy8WAjrPh49gIvL4ys/6+T3R8lsE18/0+NwIOKwW3q/U6ABIY/bJCm7FR8Kuq1ZsM4Bn6JUUjnLRxzI3Pk53BsRaIpVVmMYfMqG+0tgSdyvvFph6TAjQ/ICMRsASO1LONQ21ThB4hWrwA4I6VOBQbBZUyuqnOyTzYF4nDZktBcbzih/+vEjTL3qWokJ8lqHte9P5oqt3UGqyERf9uHxX18YJs2UpN1wxUjy61oXlN2u15w8HnZXRtD+hfZqBJMA+sNLSvtfLuPsIOka+LwfHGyjy2D1nIVBMrIjUfjXx2uWZmpGMON6VYNesJjNwOAlqUlj9puF2FFIeXPAerDOhaSsiu1gapgdXCMbSfmlz81xEvkig+uYXHNDrEXfkCEfYjT1aboS8m6SlVgwYYZsaMWJNdhrkYseSL3Y8OU18XaQRdFXaigt3Xlp3cVrhXuYTMcl/Za6DlYKSZmFSem0USA/Ru4eEnW3V7gVp3jQT5ijTiRJsFgEhLho8fRwP3zokR93idEX9JGwVjEmLUWBT97KCRynOXNHLQyCI7qKoCVAJygYGLe02BwxMw8s/TxyW58Oj6HJWgGWDRXYwYj/XBPzFxn/W1KJVrL2jSOLAtXkwaMSsMTCI7Dq0E7wxA64TIO5W5A4/bjFX/WNFmoBx/+9MGxVedyjoZuVhIzsiV5KxVTpDKGIG5A2oRxV3oZnWE0xGR//DYjdit6JnfPy1IPYs3uU+/v8aB16nptYfhzerHy4DN8wPpWAPQT7VM8dieYeHzOI/zOI/zOI/z+LPxWWau7giYcCrC4HbM3i07v4QTk+CiS1vNz3mHa+pOLytiNs6VgERL2AG6nzVmp/rznOGabIGJ6bW5qEM2tNEuJAvT13absCS2cMkS3b5CncQO5t0nC1GYGcQbXWU2mhEYbypEPdfjLZ81XpG6VMksuw8a4DaR/lWCfdBY3dFrZqzQr/aKtXuzfLdyjiVrBYDhRtE16pmsslQniknzORmvskCs2WRc/NdGyBntNqF5LBAAEJbc2H9jMV0ogf133xI5qQzTa1G2SRZi1K49nVvZkrYfNVoSpcHumyzQsOnp+m7/io/RpcqaLVyXksDYTNklAPOoCYmYCaXHa7qgZhERWdN4emyhewPLcKnbWow3TIz58kDQHUPseVFbMGCyiM+vNgNipAwJADbLET2Ta4a+QYqz1o0/tfU+XlPmXbK9PFZhlALZAkAOmqDk4i984SXrmUaHOOmarSpALfk6LQH0RiD1cJmQSzZsE1rOtI/bFdxO4fA1Z7XXXgThc6Tn+LivbSk5/1kmVD47KfHhVaHC93pr2UCjkqnKHCSbYd52GL7kN68C7C239rQZw1QzdthK+FI617KTSUhJYcfs5NvDBt+8oYfG6ITD0OL+PcM6M2gZOiOaDLtgkp1J0t6Tkq6wv8oIADKb1TdHJXOaDRBQVZmQtYic6GkGpEQiMXpW2XIu4s0l3fAH3+CYnZhV9JPD7i2pla2+s7j8fRRv6cOrKrqj/83h+JpLPIPD8fFSDB0W72oGHeISn/r4LINrbADwwi5OEYnbKWai9aXdQ4VaKy2WY4VBl1xl15mB1Jz+R3hAd4sT5xozVMF5JKCodLePXF8sSFIASmlNT1zXLdhPrm0oypPQvUgIKsyo9vV7qcwBuKBwLU563MJixmLenpoDlE2DsKz5peapfs+woAWq1HByQDUvYIZicYuxR4XNH3k+xiw1M3uk71nmH7kymkuAL7XmuMxIvKiv/2jRPmR0j9wT+Bik3Sl2WmqsqaEN0+7nM0bvoS70plcIhTHpEkaGimOnsHiv0LACVFiRuD4ArH6oC8h0Bex+5SvcqLMs5nrQpKQ1lk2aOtnoJFelKfMXg/Q3+l0LvZ8ZG2DWotVmJFaKGnuH7HV1cBksNAeu5WqUWuM0WRiTJAaEqNEf6CZPnhjRy9+y9OUEDC/4nBxDwrF+HzmI1lVsPnMbzpqh7K6ar8eRg27ZcGggc01XDRp2trlBRDUqdwn9W7ow7YPG8ZcTOraZG3t3UkeF1wBDu7BJGLvGMrN6BumWB2yuMEUlF7ZTBLP+5Zlhtm15gHtD9dkyMioUP2cZmyys2YftEv6xg3lieHeZ8Ke3z+i7rSYM+0bq3FgHOd8cFUxTTdZjOF1sNLdPRW+A21bWm+ZBnfRWh4vq1GRGnPTxFz7J/puM9OWAm0tikTuTsGfoej+0GMY65+NDh+UP9F2ufxOplY4V11Q6bXEsm9/Fe55LnirbZ1lT4mzd+VTHZxlcCzlk8bGKPKQGdYePGiQA6mstAaJ94OJ/uafVrCaqmfy0rBlMCbx5llXOzwPgnlo+3vF1BnIWObLYnBKJ5jq42ZArD0DH1hNnzyALvZFrJXpSEgCbJ8qsS9Y5D4SFVCXesrr+bp5tFWs6EZ+wVXpQB+rhtDN7K/HMNdT03zxy68wHwB2SfJYQPzrWPS31xq6eE8kPznr1ksLiLX2BxfuM1fsAM7KTzzMn/cq7n+m6CdDUg1oWF7er1zprej1wD6ZaBWSutamtwXRx2pbRzea/EMGGF9SrKse/NeLwYwbqrS615vxnT2BYZrS/oJYMpYADW7bpoxFbNihg8YOV++DwdarZ40D+qqm0lKw8Li8qa6+IQSxaD6WykHM+friA2tNrttdoHmqm17/ISF19OJTXUNKGNWtXsQm6OAYpALYSnGLQSEPpZ8ukCczXWz9ZEdKIXaa5L7wir9DcMSHurRUEY/frAN1Fst/jkec2dU2idhSUdhjuJ33o8O9GqVnuNFY/8c+HdKL/Pe/RLe1xhp2OimQp6NcIiyyb5awyMt+rzXqUTDVyr3D4GV3E5WoS8YjhieQtwaQx23khLSmdJXunFyGiDAiagioAfGzR3ukq+ekoeQAo87Y7g4bXmMWHXC0dR+D4Bf28/PUjfn59j4Hru7upxX6g8+/7Bpt1j4ff04O/eqtx89/rJEWjxdlr+THO5F01Ul/XNh1ntoqr6vg1dwL7VMe55noe53Ee53Ee5/EXHp9l5tpsM0xDGYSIAMRTNq+eMVJDd6ouowJOIN0iF2aHDL9QyJyVjNdV2i4sKessO7OsKkwzXdRdpRmopaZkauE6Y2LxBh1IaH9eS5UMyGQ025ptjNcZgWsq7ceZKwXXTieu8WZd65epYeiXvxu19pQvfcoyzrbCOWFRxfJ1oL8p52+mKp7fv8xoHjU6stJEu63IwXiphBFc6tHFK1KlmTwk73IX72aM3UPZIQfEVmG8pDf5ZfVs1aF+T78GkCBMU9vXnXJsSaigiMojA3bLzj0XGXkZoBim7N4ZKQ9MlxWiF5ejWSuRmBxwrb8oR5mZ1274qyOciyKSv3tYQhXY7/kIU2qxdw0dh+cnz7xAERRU1rA3lBF1i0my1Wl0cjn7d2voodYbdVMVphTo/ES5Q0OS8gAAIABJREFUq8lIi1KMLJ/JWVBTFaXM3sjx4oJrpcXE/aGp1QyXoWxEFjMJJSz31GVgEdH8wJD0UM3qYwcc33A2ugzIdy35hvJ1KvOgukhs5Tz7bDYoUNeJnrUCAvjKpr76bcTFbwg1OH69wf3fGrkn7QFYvuPSzS6SGAhLZh2+rkYD0bE37azOXebnuO0q3GMyln//gK+vyNXi93fPKnCWFd13pZYaq3G7NvnkOlob4RkR0C4hP1Bm2eyJ31BQBT0paRuzB0JqCp8iq8rxOHypMHxDUNxl4/Hj7hJ7lmjMGVX0ovPY/eszXH1Hf7f6EIU3okOGOyRZL1RIiC2zqTsl7/NrWgc1v699yiLiI2I+n/D4LIOrDtTvljXE5NsOGYv7ah83PNMVKhlqLUCBA1DR7e2zQJtkV6YqfDpVWyewjKGoN2UgzKDZosGaNS2a/lVRosnS52d7jZn4C4bn+UT9KBkmNgHwzwKa9/Th3cda80BmhamCJK3qTRwWOLVbUxUC1b6SMWKrYPv62W6n4Pj4dszQIYsJ+uG1rvU6A7iHSlTyy6onrBJm7jy0sSlybeV3QD230uayvK3ajmGpuU+vPLwKjmtJ/esKPxlPpuVlcUGqx2uegM2fFPoX9MWnCyPG4dllNO+t1OX0RP2/AF1Dy7/3VxEqVQcXFYHhRZk72giFK1o49dWAZxcH+Q795PD4wFEzaHSvZq+xkbdOCuNVJYqpqJC5TefF6ycsncfHHR1jmixWC1osv7l5wO8+PC9fGcrP+kmXQfph9agIblzwdWtjNSqfNAWnZg4Tz2rji9o2g6DlOdG+PhexSVQXZsecbDIiE8pUVLj450qgmjbVhDu88OIqk48WOkE0iLPNVfLRZCidkZ74+8SZ+bvGSY93bhPcPZ3H+/894+7v6YL6C5LpLPD41e8izMg9pGPC468aOa/U1fUhmww1aTofEAmrOLwUM3oAUDcjrEl4HNiGTyfpt1WTRnbphIQlRvB7B6y9GM+HYETRy/7bUp6P7j5DhXqPzMsZdqDn6fjFrA5WLqHOWLMM5sJ53B9OiUWto9rW7l+e4fk/Z7gj8xFaddLGmNrK3cjaVhW7VU1odKQNeynJ+IXC/hv6uX/26WsLn2Hh8ziP8ziP8ziPv/D4LDNXlTJUypg2ukIS20T95CzOPYeIU1Mh3GQAqGrebThTA4rAxGkxfg7hIs2IOXlWvO8w7y0nwpErBJi6G9QT/ecv5BDVV7YBxhcVu15853DxHe+0fZbveXyukbUSApJKmO26ObPij7R9zehUrm1IbkeQod9UtmE5vpnIp3bP3q9xASHbLD4UswQ6vl+rE7GMueBG+5irG42bkcE04LaztqALI8QJgLxx+5ecQW6yZKfdbdUTNmMha5X5rhC3yiTWX9p73JNC965kWJQhDSzWH1dJNHHtQcFfzSDLySCwoD3GGaM2AWoZ0bBR9tW6xz1nmUplXK56mALzuoTAsLBSQLdhZqzOiAdLTFoA6nrCYkE7/dFb7I6VtHO17rFq6LXfvntRIcSZChIA6I+NXKfkMhkIFLWfNhKDGODvUaFOJEiWBlRh/Zws0jLKzZQNhNVdVJgKw9lsPMCCGMvvDWlycylk+DKQvyyoFaRhFvQxA3lyNeNqa8tL0wYM21Yg6rQ8zbxVUPUmByENAIA2Id7Ir3HxfzVYfmD94G1AXNAcPP2iwe7nNUufk/1UVsiLCMOtMrG30mplewXP/rzfvL7HdXvEDzv6oiFqMQ0PqwDjkjCB897CbStRLK29uPqExwbukV5b/lTv99gB03VtlyuiOQCtYdMFUFuLIMiBfTaImtLdYYmUtGhTA8D9B1p8FgeFZGtmnPUcmVAE60o2jBORivKsxZYQjeL9nA0QnhVv1xmL8xMdn2VwHa80TEOwr2XoUfuM2Gpp+dChMgCzqW0oRTGpUPSzpgUeIMijBByAgqe0wESA+lA5WFWyJBpf6e9mzNj+iqBW+btZ4PUbwLNijUoVejEjsP6dRbObsR1LG0oDeKbFjze0eJb62pytGtsMM9aAZ4YaxOYyjmY6NT1AhnxW6Kh+Ghd17ko91h6IqVyCpoqoNbxZXbL0xs7bA2qLFJ1LkWTTMWNalbo2bYLKOXd3SqDlUhcHaPPjN0qub1hW+TdiKSdRJGqeqryiXymEpRIVJ/dg5Pz9dULmuqTKCmqY9X+6ChvqXgMHg4mNDt7fdQAHndV1j13fiaOKa4NI7uWsMBW27c6SwtL1v4fOrIlobJX7u98u8f5AC7i9czB8PBUVzGBOFr0kdc9EmwAOVnmuYpRxEpig6+Ypa4jCTl5E+ptZy4vdzZjEOouaU/Iaix/rjThtgOE1O71svDB9rU3wRaloMkCXoFieUKsqkD/smzrvAJ1HgWYDlW7ktUzXiyY8AQwltx8Nt86V2rLGkaU7t78gNnn53gBE0QmaoeDZfJX+X7+OskG6Oyzx08MF5sOzQpayidqVuLZvdwapmDmsE/Rti+YDvbbeogb2lDFe181MUaIrQxKEKyp/1edcITBfwD+1uH8ofW90nQoD3AwK7Vhg8gQ7JPTXLOcYZl0BfyYDm9xMUjHU15MFdn87wfA1TLFucota2Kc8zrDweZzHeZzHeZzHX3h8lplrWAK5JXiz3bEX5ZgxrWv2lFUGXCWu6Bmc2z0myVyTmbNQyXassBvn4vNAfR/AmZNY1VW2MAA0j7XvUqWatYUFML6I0v+GgxWyk57UCXwaWiVkLb+BZJJ6ZJEE5in4y8psVJE8UcsxslXQhwJ5c5M3OMtpap9u+1Sh8WmtyDquZOVPNZMsVmtzeKrA5CeEqUaRTvNQs/xyjMgM7wJ37Z5rYdsiEynKzKCxcca0LuhAZNKmkL+uKryrMiU55bX+lRLlpdDR/JesVsUZqJCpX7Ocb2qyZEd2q0WkQkVi4sYCpZoMxcSeg1/BbDxUyYZHK2SYOXJgX/ToOg9fDMwnS+o8IB1aYxIO7yhNaT9alBbV2GWZt3hN5CDpDY1KDA9Ub4C1F81g+H+/By+Qa7a5qjC5eg31zjIrtzJUhRHsAPVsFF/U8fu1TOR0mZF+NohRe5gsIveGhiaKnq9m2FUyRA0E7kfWbUSaKnM5u1TJTsdT5aWsIVA29q2IQfgNoTgf/tcC+xvx7oUi1EgUqUxFJsA+sHoGqTfsFzuHV2PUSFEL9JuCrvM4WsqiORvORkt5yPQG7YM6WROseAXPuhpY+KYqtmVZD8KKGNiFhEhsf76X1rWP3h0ywqKSFQGgfSqoWUboNMwciSsZKSvdlecNqva9Z1NV5bZ/HWAXQWzmtM4YuJ83HD/9vO+zDK4FLrFHYFrxQrkg+KLAg7E9FRYo0GCzpUW/MAfzUqN/Rsfwa4Iti+rTiTm0Jui2BIJkgMM3/AEbD/2BjZ4/qBORh+YJ8uAOL0ggv9yMg9do39PNuPopI5ksIvbaZyRGd8IqywJSpBXLQpdNPgksKs8+e1uby+2Ya9vMNdWW50GyiDVkQy0LhTl44l26OlVvok1LpWEXNSW3I8H9cgyFeh5Sx1H1+/y5pKTMsasLj1/XDYX2JME4PSsM0gw1Y/YqA+TX/IdtQP/dWv6uvTOy+KgITFd8rUclxtRm1Gju9eyz6xwg0xyp4tN66eFe0m4gF8i1YGOmskT1DF4N3mB3t5LFGLG2f6W9w+qPFosiD/kskUMNj+aquo34OfTmsnxu8+KI6dhQkAVDnmUzkOgciyQheiNBRkV1whw2B31SK5fA+2KgQPNPdNJdqvecfx6gE+CnQjWt85GjhuKgmyayd7J8HmE00paT9o7apaRNR8lclTpvgavVpBEL8zlXeDfajONSiVhGaus9mNsEtQiwHEDD3sGUmqgG0gbQXFNXBtJaBUBq6Clq5KxkI1XabwCQcMmkYT/QHKz/pKQdL7Y0j8LEtTOOh5q5avUEuxYGPvlMl0CoWN61vDdDUwcSujtA5Sx/42f8ETtXlSsfWXgpZsYONlxbLWp0YxYeRP86Qb+gB+NiOaKxET1f6/7YyHHb1dkV55Mc3QP1ueqQq/whAHdMGC+5frqE3Oz2mE9qrMkCWdNT8fQLg+GGF6UnkvbSs1JYqeGWwFpMuQ/fRqhLeqP+sUPD9k9+TWvJXFKwZJ3drUboO5FDVJuE9pHflIHpSqF5mpN2mKx1p06yx+kyyw49N0mcV8yepPlWb3nXPas7J6ckcMcWtZcT9BDK/AzcF8xPYXBK5iDZU5JUNjWgqpilRaeQo+zA7Uh2ZvyemdRRMjAHcSjKmnppZUNwVCd9qP23swuTlPSrIqhKyskK8SJis6EF4LDr0D2UDQsABUylPrXJ4oqz/FFLXVd7mvvpomRtpAgFEFJgn/fQPOlaZTHy7haTSAQCIGeVQgjKoNYWoNY/eehJw/6B23SCQv93vSzaaW+l+KMXtYY77BvKSEuA1pD65dQ7Unoqc6IgAc4uIzIgjjwqKyFWAagqUjojNxr6sWw8M8A1YvvdAu193ewMX0RBbuydRVxroPTVRiXEKgSNdKAly12OcC5iHBp5DSPPqefrWRLNpKj3FIDuApQGcgkKycE+GblugSU300VAHjQ0Z95xWbPfxbMeXeOx3RWrpoTVrwiu6vsGKmmxAMxJIbBjTgoa3ZrmoO08UlJit5aSxqFn+ckfl9CjEt5FWM4UycB7hQ2fv61ohOkVGg6SKgLdXZU9zRpwU3muai0ZoGdbOCUJgjZMXKMtiQUUZK0QWdGmIgAlmMZWYdrMNtV/10tC8LOLvVjOLd2Ex2EhsptQwKK0GNUOtE92fPq593mcx3mcx3mcx//PxmeZuSLxf7O6gErEFi4QC4kpzOBd3uDbnjKjwyvejRqIqIA9Ur2iYVGJ4bLuXaZLglJLBoONR+IakcnVPFsFEnIv7Lqsq5LKeJPhL6ru6uoPVrLA8Zr+bryqddaSGWRV235IsAJVu3UwovXrdgQFF5ccv6oiGyoRixOgeqT2MybuLPuPLWWZpW1Hpaq97A4EEQkLWyuoUGs2hnfZdiARCtF9nmWu00bh+GVGZNEEPSoR4HC/3qIzCfs/kMjv8qeaHfVvAtySPsA/tVBTzVb1tUe6LxY/5FLT/3fC4TY/1FJB1jQnRXWru6si5GZM1annUmO6rNnGeJ0qdH1FhuWRtX/zIlS3mMlCqVx9VXtbBQ80AG7VgM1Qo4EudbijEqH7/OYApzPGHTvCtNU1RZskZtdUF8lQY0kvVBXdL2zeUu/NgJpxAtJM1SjnqtGcVZZaZnNn4XYVogcA9xNn1x7Y/yKKipTJCoGNtcOrCAym1nmbVH1oE+S+DYMjWJvRh9LeAwC5zcg6wzIy5JogcGxOGm3nRcc3TxXd8DcJ6oL+oaIGskLi1plm4dG2Ncvc7pbouP3p6lmPdUM3+XHR4LHvcOQ6cRqM1K7VMmBRzNEbD2cifKTv9vFpjeb/pvLDvDsB4Oe4sM0jK4XN+BvzzoLyrJk+Y7xS8u/uvqpIIZNWeoF/ta/a6SSsA/mZUBheD5rTNhqVqrlG/yJXM40rD9UkXF9TNv+LzQ4j6xObGQllP7V4OixEc9qsvOgk97uqGf2pjs8yuLpjhvXUFlJqhVBEFy8LujtUMpIdq+k21cxqq4kOQMM9r+6QYfuM/Rf1zh9IEEeo9HMbr2LJlE1tjYEG+pdJFvD2XqF/xZDiTYB9tCLbp6faa9rdU19oZqOA8TJLXdWMCrF8luL6L9cYwzpifFlgYAt9V1WbwmxhpL7W8oQDSWWBY+f1m9TQ4lDceXSsgXZuUg8AeqgLtg6VFJIVTgL08EzJZ0+vJyiXoB643cIC7S8JQ1+1Ez7cXghUG7tKItO9RkilIJWQl7UXKu0dbM8bjC1Ba9IDOytLZgV09ziB0ArMP72pvbH+KkB5BcPHVAlIrMikokJ6v4CW+l2UhUSZTAvxHPblH9ubHg1Da7uPa7hHLZsKv05QX1CgSkkjeiV9ljkpgeHiaMnAHKCglIFc6rGh1iUluBbyUBeROMCFA5Ntyr7RJuTyD5Ox+AOL/x/pHpCNz67WDYd/6NE2AZG/Z9g34viTHZuxl1tj1KR6BgARZKEHhslTJWHlnKUVyi4CtEkyXzkrIXxpnTAMDnFbiAUZ4duBX+ONA/j7b6ohunMRkeduHBoolWGYoLQdWjz1dG8plcmonQNvar1A18ZGPP6JJuHBZMBmMQ7X8fS+yjZjvKzlE3k2XIbda7htLdEIUcxWOVQz8UZahPtrO4wdqO+9tAUmW1WT/LrWTlWkpGNa1sArrXsJ2P5NkHaqi+ujlIKulj18NEJKuz2uZK42zYjO0El99CuMvYNjqNzYJHOMOGOCfqLjDAufx3mcx3mcx3n8hcdnmblqn6GR4ZdadnOko6uEmDO3SjNjpbFHpzBezfQxJ0mAEDqF/pmW3Z1fVWPw1GTkdRB4LaosqjTNffWv1KNCd6srQ/hlFhZq897CHZQIKCx/mjFIl0yGKuSSPPMoRfUINRO37RTt06AEFm6LwXkhFk1VKWpylAEDlJW0D1kEK7KeGa6DoSSBSCs02+wAfZgpJXlq3aHzU0IISlbh+EJjeMmZ4EWUthbdRvKpfMl418wI+/2fbtDcGizeV5isaP+aUcEvKqyKSVdlm7dq1gbErVoFflxlxEWBzxTibE6TJQ1aAMhNhmVx+O4nCztUWD7+7ChCBXHbIK6jwKpa56rMs3f0+8IMVll29eNDh3xLN1bLWc7EilB4PlIrByBCCul/0D4Dhapf6xXyOsAxq9X3DmD4DyYTFMvnleYwbckYZ4lFIUItftPCFiKKoiyoIDxhUecxB4WgTG2d2ZnKAm8SVK8rkatJVZB/HYU9XUznSyuR6iIMqzc1rYefLA5P/OFPTo6RVoGyd/5ubj0hc1YbBis+uNZFaJ1h2U933Y04jDUD9aPF9iPDRFEJJI2SZZZLGFVla29bKH4u3JNGd1uzxNjWrDAs8qn6kZ3D7cRWH29qOaWgXDrUFrLUAM3jXIWsMntL20zomGy2AiKjRNNM14IgZQW/qX93fMPz8fUWnbcC42ZAhEv2Y4uYFIxmuFcnuFkb0seemFIhGixWowiD+MlKu1K5Dp/y+CyDK7EfT2ETgFhxJ6bc/IBMm6rc1D/nGlxh3+pa5/BrChDH1wxnfhlguIaz7DyGvkEUBRgr7jC2r5KAx9fEZJSFCEB7y8HvgWDnIveXLNA/Z6jqmlVSGKpt76vbSrJAw4teUUgpbTp51MJUNj6LoTnAtZ9C5MtVaakE8JM2mmWtz80XFzNVdSQzcp/nDO4tPXTtY4bl9qb+ht1ISiw8aPLIBGBvO0yvAtQtO3W0CUdeYNsPBot3VarSb+q8hk2SoLX4YwO3hWwOps0pDDxdkpk0AIIdC1x60CRvWV5rkjB42w/UfwgQnD48z8BXpcVGIfYFNwdU0Mi80uWkKKiCFuJsIxZ/oEkPywz1E8sCHpU46YzPI8yllzlOWwfNtcc4Uy0qI5d/7l3tnewS2tUkrFZEVQPoBAryJZDNA3WbCL4u5u8uwnxPN5oZ64YirIibMPdptV+SqrzWGdNopV6aFrUeaB8tMWB5I6RWQTYEube16MfPcAmSOSqEJ5437aBCrcGqhFqTjpo+t6hDRSML+vKyF7a2Uhk5Q6DN49jIJu5y3cN3BgO3kEyjReLrq0YNc9Ri4qA9oDh4xxaCFcYmo39ZSzdup+XZdU8kT5r4HNV02idtJmD1Y+Ej1EvTv6obs2yIOd/elz5dYMHlq8C8iN23tayQ+L7Qk5LaKf0CuPyayi5fXz0i8He5Pa7QuSBwr1YZSxb199HA6CTliIX18vN9v0TkeydEXTkAALSJwmbXsxr/pzo+y+A63JD8oYq1hcRMfKPyTUB9nfT+8UqdEDPUTMKL+iprNjNeKwkEatRQ39PuWX9YYjNWolJY1IWof1Wzwu6WZMoyLwwmQkgz3QMteEHEIep5lcBVzmu6rDVjt68uONJrONa6bSFu2SMdv2TpWalaOx2VLAzJkVDGtJ4tGuXwc9NxAHqq7QDJAtNzJcIRl3+MaB7pH8fXDv2NkfkO67pb18fqODO8itB7I+efbjz0W5qEhhelqdSqXJZFQ0UFd89tHBxYS71dZTKXB4Dh64n6XtnBpHtbUQR3YIcWVxf45Xd0zNQAB3ZJiRvSslVT0ePNgljkRaRgUXb8R4uOzd7tAGRtpNXHjNXk/vB1QnrOrVuGtIWLq4xOkOzIbri/khc9pSB6wlj7Sg7SuQZWgFl19WfVxioiMSM3KZtgm1p/ND90WHxgEsqrDP+a62e3DZptRXjSVZCA7KOmGq70Lmc0H6sAR3iZ0D2rvb9+ZHeni0Fqp94b5KRq60bQGJ+YMLUzJ+ItcV03VkXneXNNgX7TjaLt7L1B4MXeNgFd5xHL5wUjbTMhavhoEIoU42NTW4e4lF/4CGaY2Rl2lfSTzWnWaYbq+BQXwLyhVMVaV7UHhWabpdYPXaVAyXKSr8UXASpX4Y64UNh9zRudhvpNs2MBnV6jvava0eYf6aZ7dbGDVQmJj/luvxHdamsj1t2IwISsVTNJADU6YTe2UoPNWUlW+9XmET8dKD0+Di2Uzliy+XvKCgue4wmlQfrTHeea63mcx3mcx3mcx194fJaZq/ZUXsp6rjBCTOHaAqOEIaz9TC2IN4dCZnxeW0s6Fmto7+m9F3+on3l8zfW8dalnKoybyqgtkOu0odpLgZrbh4zlR1aNceQ2UTxQ/apucAvcWwy7oahFA6itMwAQLWfoRY3nSZ34tyZb648C+wLIDaRtpt2R0EPZPWejatuSp/Mq9H0zVhjbrxTsAbj8Pe1Ks1V4+DVDeas6qeOzjOlFkPNYfOekvpgNsUkzqyuZHztpRRhvMvwmnWwZi/pU86hljpMDjq8VIusCTpcZ9hVPwq6F++hk7pot0N3RZ+++0YirJO0rdqfRf8VerzPTcNgEt54QSrtH0AKBQgFoEhRnDXmqGdx0QQz28n10BLa/ZpbxMopvZ/KaPD9LNryJVfw8KRhb4bWUVGUIJ5wI8DetF6gzek2Sn4BAwqXulQYr5Y3VcsT2foXlv1VVg+1/pJtV24Tut5SmqUSZWnEKMm2UzCYFRgP489RgRC1LLQM2l301EcrqRKVKa3rfeGiAvcWBEQbTa7jCuAdxHEobjW0iAmfseu3xxYsn9Cwd+bBfigRhDLrKK/L/LxfcLqQT3j1SxpUzMG5bqIGPeT3h5oraTh63S6QfF4JmHb+MJ6xxqY9OinkdkNdKbdNvMtKiIjfZ1L9rH+geked+gNS5s4F0FgDUGuUvaf6nl4mM4UHtYEZnZEZ8Vj9q4Vlsf5XhWczi+/EaTRsEpQjewPJ929iAmDRadtBRKktb0VPfIWUl7jopO4GCD1MjP68XI/rJoXX0vquux1VLiMX/+eMrfOrjswyu04WCaQmerPVDCiZFsi6Z6t6SrKq6uiOpmahSo2sUGu5DVYHbUBjR8GvqbQWILDQ+m6uYVLi02Sppe0kN0N5DrK6abRRHG79SGG6UQJgF8qEPJ4i31P2QKzxK34H+bwag3c+DX4WtSNmm1kSBWou0fYW67JgR2tof6I7ppA9VB6B55FrMbGNy+buJrevozcOVQWrqAiLwWZth7y3DXCQxmF7whwVNEB+34qQmS+8wLVgKZlafKvPRPlbFpP4VmXNnDkhXz/ewLFF37w3s3glZrNnXuu3iA9WwivNI/1XE6ktq9j3sOqkNapsQbhcCXbe3WnqV9US17dKPnBwkyJuR5rT/OUOry4AC3Ba5P/owhi5fFLd3CFyadg1SdkKYMssZqSApqAJfqsxBi+HGNiDkCv0aGxEYjjXLIG0tw79cofNK7q24TgIZuz90GG9oHu2rI5yL1C8KIAYjQQyJSwyFt7AK6C4pQBcCUanlTWMjbkBjdsh7hvaf2NFnRnAq8wgAuc2yOQijkdq4WXg8HBYyXwCEUBajluBxte7RmIgh0Oc97hZSMvKDBaJCyxsyayP2PdVG/L5BM9SNmz1otHe8gVnU76y5hFMIj/RL/t8E5KNGvOBrlxUyS1GGJekKl2dP+0p8OrwBwlc0j01L16wv/bZRQa1qe1bYOTR8fx5f1WPE6yBB3S6oNaZcN6UTGlfvp8ASjjz9ODLha/LUr20KBD6z5olJSRDOWeFy2ePFgnYHazfi/7nloPrh0+9zPcPC53Ee53Ee53Eef+HxWWauoQNyS5mIiOxHUgUqBIzhRp9Q0Ausag8ZbqykH6CSYTQ3bpfddOjmBCPOVpmoZD1R5QFKSAopaP19xuIuSoY7XRgh3hy+UMLCBDhLLtnkXsH2kEw5NfWcgVmLjqoav2WUYyRHbGjhUuTq8GOPda6yAtpdEjg8tgojq1GZMZ94pzZPoZrJtxqHl06g98PXCuN1Ya9G0frt3htqYOfDZJ2JKQoANsF+bCQDOBFUt0D7oAUmc/vq1rP/GgQZg0sBCVhe0SRonXD3O3LJvvyNJrUuzt6PyzofhZw1sOjG+s1W2KXaJoD1VvPHFqsftBDRith6uS79a0h7ldtWg3G/zgjPAjRn1MYkIfNA5yp8oDNUUvCPhc2WAV/IWQrZJijO2vIsMYLJUDN27eF+gfaiZIwJ6KopuVJAx//e/3gB/MTEPAMMX/vqSnOwIoQwvvFy/JQ0ht4IiSnPhDGUYQY2t8e0q0nIQX6yaFoPw/MzHh2xnEHXrBwlXMyEJkD3SHlRTN/Zl1QliLZw13ncLHthve6GFj3D91eXB7y5IHFejYzvt5fSnfT6eicQ6GO/wBQMDsWtxxuBnZGB6SYKe7u905IVNo9VJzx03J4npC7IfRC7jLiOsOyyZPfqRBkpNtWQIiyqyEzz8x2aoifuPI5De3L904yVjmXA9CVD8YquAQA0KuNmTRl5SBq7vkXgL7BejhgZTtfPv06SAAAgAElEQVQMA1cjgoUIZ1yuevSTQ4g1dyuEJh+NoAaLxmPpPO4HYnb+l3/9trp8vX7Apz4+y+C6+okgi3nbSdRETx+vSktDVSQyY2Xbtg8UXDwH1GTnEA0FrfLe1EIcN1RSaLbVoLt5qjXN1CixSeseEjEml3Qew41C/6rAowlIEGWn7q5Cs0W2TNpo9J+1FRURnZ5kC4UFHGpg71+SElJThOpD7UltdgndPS+oCggrg+E5fVhs68Mvcmu3tDAbn8TBZve1w+ErJWbv+LJHwzDguG+R2bVjeJFgBiVKS1CAYaNt2xuC0spLCQJjXf6G2ql2P2PY9hdAvGH2rEsA1+fUQHZhpQ1g+G6D5/+Fv7NPePzryvg2A4QNmxqF/dcZ7g1F75S0BIXktRx/8YFs8B7/A51jvJ6wuKLVsGs8XDQ4/Inqd26rpE6evhgAX+XypoOdMZOzOM7YAzm5OFbZShYIr+hGePnqCUYn3D7RDWptRC6ORVnhb169AwD89v45hsFJPdPZiBWzNhfO44f317A/slPT/By/6aHuW5hbCuzpKsJ+TfOhZtBvjmS9JozjDOnZRVZIJsMyxBi8RXpkxa1FhFsN2B/o+HnWb6t8rTPTl8sipi81bVBfKJokgb5bTrhY0vwrlXG7X8liDwArDgqbdsLB03kcvYObudn4pPG4ozk9HlvkqGBcqXNrsZhLAMwqIfE96ccWzWMJmhUGDiu6nmVj69cZ/rrWnXGw8myrPFNHy3TPlKB8/DIhM4s8Ro1VR9/zODoEb6rE6tZU8/IVbZ7K/NDfMsegSXg40A4gZyX3NwDsDx00z4nX1MJk+OVF66Wlade3sCbJs210Etb11WLA0dNzMnqL3/7mC5hDkbrMaL+m3fwaB8woK5/kOMPC53Ee53Ee53Eef+HxWWaudsiwMSMrzMThQT2esz7UAtPoEVj/UDLQjMMbPfMMzWJkHLvTnk+kGfOTM8QTK7kZtLy8q7vI0CkMTHghXd2ycyfjbiFhzRzUoFC9M/kXhQVsxmp0DmRRtwFYUMIU9vGpXZ7bZVx8x1Dh1mN81sp5T+sKd9ljRrMvmXCG20fJyrNWGJ5xs/0lKUMVuzsVdHUrUxko4geJUISS5c/hv7AiE/Ky27W9QsfKUk+/pmOfZDec0eSZ2Hy8SNCLAM+w4fW/VULZ9h+A5BIsZ4XLd5XF/PTXCe2XB9mhj4ND/NjxXFXbruFlgnnV4/klZXQL52H5b1oT8N++f43VD5U9HJisVXa6+Vh6UQG3JCggegPFzGf/wsPcO2Gep00QWO8wNhhHKz2sX9484R+u39LhVMY/3X0FANi92wBNgr2gTGe/60Q/WB0s3E6JuMj+50HEJ7BtYMb6XcHMWfkCZb4zCUDYRTUNmIq12Ehs4djy9/QaYOLV5vqIw7EVCFx5JTAvEpBZDKIIZYhC0zLANpUABJXRsmHB9bLHnsk2+0OHGLRkna+vd3i+oGzpflhJVlV6M0sf52FoBAItAv6lJGBtFNvAtHPQPzWwRT/bV7vH8TojvKHr2S5JRWosKlWLAHBfdGbCmjB9bTVH6N4Z2AE4fsGZ8mUQ7WWlMnYMVcdgEO9aLN9y7/hNhn5F8JhRVRWs/N3EKI739fsZlaF1FhUmrTOulnSM4+QQkhamb84KE5O/GhvRNR6G0YGXyx3e7slM4w8/vACeimlFhFoFRMU3k0v41QtS1nl6nNczPs3xWQZX5AzkjOSUBAE7AsPVqaG2Y9i/faow5HipkcwMCp6UqB35VUbzpE48FEVGb8HtCcXb1FHNFwCaWY0yLBSSVSeBt72rAIMOOFGHmgdzM+KkrUacNDwEmi1eqQJdNyyMwO+zRyXntXhIsAMtQsOrFsfn1fcyORa1AOAOCW7HDxm7bUxX7HLSaTz+koPCJqN9VLC8oPRfKKg9y/Z9NVQPUuORkpOasQ61GT4tEtRU59jtgcNXHDRXXIebQakCF7YJ7ZoWthAM0kMLx3KFw7O6ALZ3s9YkkNDHyGzk3CaMvZPFAbrWsqfnUYQPAMC5gI5X2N67/4+9N2mydNmuhJa7f+1po49sb+ZtXv8klSiqMAQYVWYyMMxgyN/g3zCEMcYQgxFVxqSETCAhifeedPsu28joTv913jDY27efEIywO0nLcLN7LTLinK/xr9m+1157LZyMKNB+dXGK4qtaFmfdsRdIMS8sUFp0/Mdy3t4ReogG7tmrSvYPAHqVoYtqR0sDBGD+mxsAwL86+wp/dfMcAKnjXH5OThLFVgHaQH3NcOxDh+lTqjcePG3x6t0B7CXdXLrXJEmIVMcXAmgWCHIHCWLEF33IPZQOsi7qV6Xct8ophLEVSDooj7yiueq6qBrPc26QFksmyI7DoKEKh/qQ5jwKPACA0QFlZuGiKtB2hC0LTKDX0CMrUPBB1aBlFQYflARTpQKC11I3VCpgwgudwRn0fXan3SnCp/nSwHRJKCXfQBTD8MkWh2O6hovFGH6bI+MWJ+8VNNdYQxEQRhb6ho5L7wn15xuQehMvyPLSSm1cAQK/GuPRHQO7mrY5O9kKTJux6lT8rHUaPc/7sLcw804htAbjkx3Pq5cWpijMH7e5i+b2AFxQuF5M8PSUXqB/++UzUR2btKxeBpIOPTjYws3oOB5M1/jq3SltY9fifR8fZHBVgf7LumS15NhOKZoN7xN4aJWdstN9C7ruMGn/moZs30TJSKefxfKNXzDjNx56Twd4iM4T7NTTHfPn79DuWY4wxloP2YZy9PvYNqL7PZEXlY439rH+U/s4gPrltE1OO4DG4hOqv3QnQV6O9aXC+I1H/W7g4/LCnFFGYfO4xO6MDnLz3CMccq/gRSnnAQDjHw22z6Kkk4Jf8yq+0Ri/1PK55jzJ4elGo1hqIYO1xwF2zC/32iEf90TOAZFjbOAJaTX6Ha2cdKtQX2tBEXy5lyUrYPcgOf6YRknNdfw6g88y0Svun7cYP6aXu91T8In1vOiUUhUDrhva99DkyIuQ3EVOOozGKei3q1KIXd2ygooSeCqIfjAetwhBSYYz+zKDN/S37VOPR794h+czCq7/w5f/HO0VXUOz0xi/4iBp6V7e/REd/7/85AecsEbm//HuI/jBCJkn6ES8cTMinsX6r+o10MabPN0j8IrqfQKYpAw0FB4680J2AvZca5Sj+nW8x3Of+mEVRD+4rAbUxSCBot9bhBzUDZohx82KNWz79DczHXA42+F4lNy4Vx1dp3VbyuFWub1DyNE6CAmq7zOEAMnoisKiX3LLSx5QLBKHwueJAOfXJW42FGT0JoPSAe6GZTyzgCKqNV1rBJ36n/et5IYp7nAOhibHfELPV50nmcEAYKkAxxm6VkHO+furI/SXo4T+7BRK3r7ea0+M+27PWFFp6oXf4OcWpnJyr/t31R2JxqxXeAOa/5EDmoe80YMBOSMK2BRoukLq9F/enmM8p3P57OwdvsH7Pe5rrvfjftyP+3E/7sdPPD7IzDW64rhSJyp8YAWhPaWhWIt0VRJh0AMtziO04TPKbgCguiEWX1z9miGppwxTMiMvlxEbA4Y6ZatxeMPawnFRnwNxAa0tizl0KaO2nPF2R0B36qC5Vli/U8iXCVqLAgo+pxaAyD4MGhi/jJBTgHYpU27OkwOPaZLxcnkTkG9SX4QdZ2hZF7ifKDSnCs1TWp1ms16ayO3cQg+Z1KFdQSpHAOBsIczPmN3Hmq7ZKYxYPCAYYmy3nNm7URAxBe8MZUh7TNAoup9tjFwn5Zh9qe7W2wFiZAcTUL9jx5x3XjLobq6wOwfMr0g44qTuMHDGNBq1kjXsOvL7jKLvs6LDNxcEx1Zfl+hOPKqntI2DqsfNklb4bsUtRjFzMEFEE0LlMebWoe3FmDLHkiZq9ZsehsUhPn1whW9enOH116d8fQOqC66FrZJH7/oXA559fIlPWRFn0dX4q999Rp+7NKicgmUhBDvzmLFYRl0MePfdsWQ3vgrSUoNBS8sL/VsllaGMoE4AAoNHvWVlHAJ/zdoMwas74hfRlPxovIfXg2DceKW910n7l5WDJswQ7otM7sG6GDAueswK+ts8b/FmTZnZsJf9OkN1w+jmcrurZRtZRmzb7S0hAnY1QrmO6A+JqMRSUXcQxNFJdTq1kI0coRLRyOOdkRq37qkcEe+7cpF0wtfPFGlXMwowmrWihNQMudRcu+ua7hG+r80rg9c9tZsVCggHKfvVXTpeO0kckmJJ75rp9zwpQQuiA+RQPhOBGJ+n7Lqbk3dvFNJwBTO4wZWa2yTU3t5Wd1qyPjokKHnm7mHh93IEoxAMkTIioWl3rlAugsiRBYO9vjkgSuX4gl5QJgrrd/+ESBRS3VO5INs3DTC+cCK6bysluIHLldRCo7NNvDD7AuS7R2RFFVtxTJtafXwVkN9o+Vu+CUlE36RjpGNLUKdLKnZwtYLaBllI5OuAMrYKTFPbT7nyMK3DMKVJaQ6NOAHZScAw8dBco3NdBXXM8nhji6HTCAxhBgD2mCc88wDLyZklmSpEx5xiAZGibM407HhPjedBJ8SVttFQ34zQRqJPlWCs8KRBzi/p7apC+W0lJQCfp9Yq3SlMf1DQvL/tg2R9Fx63mE13OB3TiskGDfBLaTfk6LjulPOLOdbvvnpxDn1JE92eOZQPd6lW1RUC95qtRvZ0KzUvuyokgBbjHl0b65HA+OkaTw8IG3+1nEtg//rrB9A7A8/fMzst99DqZxaf/eKNHO+qLfHDjxT09SpDyQsdO/HoRy61uXiF1QVN0Cooqu2NomVRUmiimigfYqsBAwksyJJ5gTEeRWmFIDT0yVUGWcBo3qDZ0sTmpb0TPOLPu67AZltRnRpAs6xQM6Q4P7qFC1qg5n2CVwgEGy87CozXbSyI8qnyudS5xYPxCpbrBb1LgffixSGqVzm4JfbO4lt5+i8uYoIGVBcXdYmMqM6oZ88auqbZVv2/CIr7/exRarRYglfjXPusCryZ8s0bgOqSjnO2uRs0XUG99gBgx56CIS82afHN0PXEicFFsST+hyzmFQVOgMpmwywtFoZpWgx3xw7ZaSs118fjBa7aiZxahOGXTYWwJ9Z/VO9wwHJ3i+UI7/u4h4Xvx/24H/fjftyPn3h8kJlrN9OwBUHCUYkn+oyqffP06MLkUoYXraJyJsMMI+yRNqjNR8wAciWkqPEbR9vbYwELIzhQFgrQtsvbpPXZnqS2h+JWYZghwTlDYrZmW4KAojhEJDjFnyNS6gtA2URoclVIRIRAq+t4rlkDKCYqFcsAMwQ5blvlGNjovJ8pIVa5kkzhYxaqdxqa/T6HYwtl9jSRFYSFatpMGJHlLfnTbp8m6Dqu+E0XYHYKw1HSgF0uaJVbf1EhXwOxk8XXAfUjyjLPZhvcbHk1vCiQr6k8ABB6Eec73xCDvDumY2n+2U70brPMo8wtOscWaNmAizVN5HI5EtEFO/ZYzgeE2MC/1WSQDmpPGQYjGdLQ5FC3lL24Q4t53YlP6ORhKyQapYJkYkU5YBgMbpgkFUDZGQCYVYaQB9Rv+N9dKmE8/uQKDbeavH55hOpFAeZSYfvMojuMotiaDMCbxA4XAYLKk0lBvIRZAKK9nQ4iGhH4+u5bp4lKUxFQFwOuljR39belQIrtcUCzMZLx9p0hpjFI47iOjN3BwG5z2G0Uv/Yypy8XB3BOo72uZd85i3h4r/Hj1aG0BZnK4oRF9x8erHC7o+/s+hyvwhxrJqUNg4F7QfNdbagNbfMRE+mKgHzB521IKU2Y3ANguihME2A+oX0VhcVmVSNnv1XTp3tchQDTKbTHNAfdUYJcfRFgGoXyhj/r0/ZNH9Bzuac/UNg9CLDHTDpcZwLN6l4h5F6M1Q//aIETRgTeLSfoNEPLTUGWeayxbkcQFvT604Bw0It+s1ukso6ZDnh0vMR5TaWEVV/jtqV5vVmNRXUsLwm6N2KP+P633+yPDzK4Er2flW32fFp9BoQI5+z1jYZirw6oCVoVab4MMBEG9hRQY7003wVMXkWcGfC5RpA6roJiKLhcBNneMFLoDtODlW/uShBmu6SGpFzyYrU1eZnmTPHMt0EYyHaE1NdKLYDysO73LCpPc1Bf8UPSB4GE6Ds0Cc1Rhu0jJUbqQOrfnfyo4d4W0tJUXSfIrH6bw44Sc1oPAcOYA8YqKUptn5KJtHjO4i5E1nw0oDqil+V2V5KKD4i5vfvIIWcR+FwFnE5pZ6+uDlD8HUGA8w0dUxTgH8ZBYP7qKqA5Veh+Q/BUXffy0BsVsNiM0K7pQLOrXKC8bK9ua1oNW2Tybzd3SY6w1/BewbHzCEqPUO8pATmDRweEN16sJ+JIonXA9oZZvyOL6bjF4BJ71b2lCc8baqGIClN2BFh2h3l3O8Wwpv1Wr3MMU4/+M5or5TRChOW3GkElSNfN9moTXtFLNKLCpRXVnr7NZRsoHUzphBHsBy3i+VnmqTbI1628TpClaRWU1wL7hzzVBn2usbml81SNgdlpWdQNJ4O09vRdBntTQXObSzXqpdUkK3tsX0+RHdJ5/+bxG8wYimxdLqLyF4sptk3qc3PWwPOisCsdQhak7QhWiasPTCDDdGbihjyguEmsaPuC7sE+IzeafB0Xw9QaCJDhhzcQ4wrlkvdzUPQ+iF0B0EpkWrtDyDG6CblKRVWs8ngnvdaZ8bBOS6vSclPjNjrftBnUNr4Q2KGHb1VfBIBLPKNJB60CWp7X/LSR/u+z2QbH1VYg97fr6R3m9WRG8300atA5g1lB23w8WiYP2GKPsvyejg8yuHaH5IrjcsCJzB1lgTrW0RVSRmpTgIg11khCqt8FCZg98SJQcXCqr7z00XYHGffX8nb6pMFr9pxjAKp1xoAaVJBAmLX0sxhQ5xD7ueacWgCSJnFyLlGOJA+B9N2ocapc0jiubzz0EKS+qWxAvouShwrNKT1I6+cK7bmTc8kXySlGM4nL8ap4M9prF7LqjohGc06iEgARs5KWMGfbcfofdiItGCqPbDyIXJuzWkhLKtD/BhYgmJxtxdXEDgbc1YLuALDT1EJV3GgUPD/docLu2YAZ9yNmxmGxoJdE9Q81jAeyIz7mkwHRhFtnnpxrAKitAQoPw72b/qaUrE1ZRSQjJmGNqg7LNb05XWdwNGpQGq4ht7nUCtXbEuqMXpSjUQcfFBYvD+j4rwwqrp/1czLkDtGRpPCofij4mudQLD05/IxJYOvUnxjt+ZRV8FUi4ojROEDBozESXG0W5O+hSVKFGDRcZ6RlB3mAmSZyQt/mqF/xtZmkzDgYytrivWWzJPuIIReyTbmgXufYg4xew3/Oko+tgj/yCAMFx53V0IwONKFCOLRCkmpsjtNqw58rcL2ha2F7A98Z6NXeKzJLAV/5dIzquJO6c3AKamTF+QmDBm6i4AlpX9MHafEdt9HPE3qiB8oQpRf63MOzxGFoDaATyqNtmgOfB+jHtNKfj1tUucXlLUVe5zS2y5TJhz49N9CAnrBwROnk2eoAut5zthucthhxfXTTlmj7TJ6T/ayztRl+9/qRZKg68xJ4i8LhiIUojPYYaS/Ev+VQSXBthj03p/d03Ndc78f9uB/3437cj594fJCZ6zACfEW1P9NFGBUM3dJn4ioSoCwqQsSmo9WmtJeMUo0SgT5XrmI27NEzo9blgHZKjMNNn9SS9uX9ojRhrG3A3IVEodKKNjITAWD2DYAQJIO048QGzNq0D+Wp7hqz4dGbgLxhP8tcsQNQbEfycDlDj/MMu3M+lyIgW2mULEi+feqAafKKTF6hpKSTXbO7x0AZdnhKK1e3y1C+Yg/IA58ypQBh+QJA9qokFxQAMIHk6/jO9VZDRcPvBx3QGWnjIEk2vpCrHJtP2dh5bBE2GerXXJcckkqVfdjh8HAr2Wr2usTkbYLnNs88zFPKDnKvMLAoQFjlsfQI86DBdNLg5oKgDNNo+Cq6/3gcnK1FNq7pc8wmvJJncfw3XMcdViUKbqPxRapZ7nYl8LpCteJaW7fnCxoAnHQIXIusf8jRntK+f/5HL7DpKZu7Xo/RvxojiwbjfN8BjBwEauMBQJlNhOkCQ7zVXp0syu9VToQt6B5OjPgI1QPE7K1+LJLqVqb2ni9S4xrO+BdewTCTOF8lZMZVBOdHyLJ+nSWmvwYmP2jhHwzTMsGoARh2ObaGHiI1X+Lr1Uk6tg1D/oWDbzMRvnATJ9kdvIK63XNmWuWJ32AViisjiIxpVSqD9HffK/1USSlE771jmjMWRpmz2Xtp4dgBqXxnUF4reQe0xwHqI7ofzw/X+JNjkrocvMHvrh9Khm5UQNPX6fidgmFo3GQOfcMTdFsA7COrjjsoHSSbbNsc6zdTOc+QB1zFtprCQ3F5IHiFvLLijauNR8ms7mg+DxBjfZQPGGV0jJnyuGho+xO1wPs+Psjgmm8BY5kQFBXrOLBKIFMJ+tUhkQ18puDqJDuoLOSh1haoLz2ylmnthRK4NwbCsPcC84h/C6nn1XBgVWl/8ebuKk3SZxyEqkuF6oZvaKXQz9NDp/u0jaCSrZxlz+fJK4ZpNh6bB/TEF+sA03vonreZpfpxe6Sx+pTbOxqFyQtg8xHDZLVDxi/fvLDomlxaK8zKwB5wvXHWI8sc+jcUuMxAsoEAtSuIlnOvMH6V9H43vxikduQ9vfkNk4xcbxAY79U6QI8svfwBlLMB621ciSTYs3zJL1aOHe1xgHpGb0DtNVZfHWL0Lmn/Ng848D7oofaUhbzX0m+Yne/wy4fvAFCrwcVyKpCim6Y68LDN0bGZNEB9l0t2IXFO4bqbIH9JN1c1JNlHe5T0g4fXYxSb1HM4zILo0IbSo/y2FsnA/tc7/PFjar9pbI4rVi3q345g9mQk96U5pS8z6gQrf0fqDx7IL1kyMA+o9uZq95Sv56xHcEruCx+U6AWbrb6jyU1917zvjOzW4o1XvczlPPuDADuN9ztBs1LbNADzzOCKQDrfkZjXUlsKEIOYQjmiE7/cjjFmqPNiMRWt4sGlNhPaAeB5waJ6hfJK77W6aWnriqSfqHq2z0sAUo+68gB2iVC5e5A4B37kgekAHfuAvx7j4E3iT2yfBDFSV5XDhAPonx6/wnFBEPdlP8V/+eT3+KGhhvB/+8Uv5BjUyCIvrZQc+jdjgfbHz5dSm+0t3afbFT0v+qqAKveg8V4lklSeaqQZbzs+o6OqxyPuW6pM0tk+KnY4yHYYeMUxzxr8dkqLg++v77ZIvY/jHha+H/fjftyP+3E/fuLxQWauegC05mxyT99XuURcCnvLjn0hh/aU2mTMLmWkkfFXrAOyLogAga11MtoeAmylREQCCuIkE7QSEpHP1Z3WE4Rk3D1M6NirS/5TRhll3L43iYkbtZIByqjbk3Tu82+9QL/eKNQ39HPWeOguKS8NE4PNQ1pVbp6lFqP6HTGMBT7eZIgodnhTId9oIYqppzuMeGU9Lnu8u5qljKUMMFG4v08kqPJGoT0G/K9pFV6qIM4rKvfIK4d+V8TpEa9U5zT6TYHHj6lPYVJ0oo97tahQLlmcYUJsZGFMnXQIsT3gRQlXUmsKAKiRk2ymqAc8PFxJe8y2K3B0TpY8zyc3+IbhxR++P0V2m0HsQM8GHLFDzjDRKDInRJMsc+gYWlNdzBTpa8oCjqFiXVsMr5lYdaFhxwH9+R4ZjyFi5TW6cysQ5s8eXOLHJRGflqsRPMO2ivcT0QLlkyBJUAD2FJUwaFhGItTOYPTGiPKY6ZMAh60h6lJ+l0EVXnRjs8IhjOmYHHIAOkG1SPeq6hTydS5s4f7QQ53z9e2MMIyVVdCNvtO+tfgtHa+ZD3Ae8Cym7xoj91m+IeKi/T3N/+3ZCLc8V2HQYvyuHCmSKRFsMUmDu6Vyihy7D7D8jGYNwb1RrH+YBFEkUxWEYewKyraHWczEgRBb1DyVQuqLVBqJetbtKbdCRQKVV3jOqkYeChtLz8lvxq/wP735E/xwzV8MEJ3qMGgM6zoZgEwtpkd0QY3a11A2GLYFFPe2+dMeNfdutU2BYDX0nrtOLD15ZzCZtJhW9NlMexGOOJ1vcFYSezDuZ87wYa4czrnH8U+P/hH/Hd7v8UEGV1cCKP9JcNVsGVclhm2seypHvWYAiVxnuwShmS7VWIuVpweNjc5doSQAddNkUwdQwIti8fRiS+y/oFJwVyG125gGGGYKLfdgagfkDEcFxbJrWfpe3EZ3lFSjJi898o1H1jBcl2t5mRerAbY22D6gt97uXAsTUXfA6G1kPQZ0B4ndmK0VKna9yNqAxS88Zp9SzWRet1i1NFnvrmf00o1SaEOClWxJrF2ArkN35BFi0LEqSSNWDv22kB5MNR3QcItKfdTgP/n1l5hl9FD/5dtnaP93Cnj5NMD9gl4geWGBPhNVI/WyFkPrfhbgT3uY6PTiIabSH53cYnAGJzVvxzi85V7N716dQDG0Vsw72I2BH9McPz5JUNujyRZ/+4/PoViusNvm0E1sIuVgx9eteTYg44CUfT5Cxb2Nu/PALWF8H3QK3TEbG5x0+OT8GnVG3/vq4lTk5nSrpRYOExCyIEEgmGQFCA2o2kJtuM3iNil/2VHA7tkAw5B3sVAomQFO/dT8Is4MhrkXpSjblhJk8g1Byx23mw2HLrGMFWAbgzDmh89qaLFUwx13nmynpCSz/KWDntE5+0EjK624LDmnYHmRUnDwjOWg7AdDIvl87eOchowWNxGSLl8B+S4FEnKu4kP26ZnsjgPsfJDgl60Mdk9j3QiJX2GCwO60Q6B6HV8I1Pfe05oI3YmjgLo/opNSZfHNFUG/zUEu9/T//PVv0W+KxAhWVKIB2D1qMogMZZY5sYdr+xwt8wjUOgNKj7NPaQF5Mtri3ZZWUl1bwHslbXAq96hGNFmzUYsqs9j2zFI3Dg/H9KKaZi0a1lRtXIHLdoKM2wn+m8f/Rk7vYq8N6n0dH2RwjcNnKUtLT1IAACAASURBVLhqS8FI9Gd9ClTtSZJFVFZhGKc6a7EKKNZM7vCUDYmdm1ESNEU/mB8uV6R6prZJ3s/lCrZOgcuO9mphInSQ9h23McyoNy7W0Fye9qksMH7N+p23DvnOihuQ8kHahVxpYMcGAzeNuxIob7n2dR2EpLF+phBMSDZY60Syuv2POjx7eI3DkqL5TTvGcsVNelclzKCSPqlPLUG+8KKTXCwVyhuNoFm+7nEvJCW3KmA2mjRtAZi3Jdw598kdLrHoR/iLbz4FANS/q1Mw+XiHkq25nNOwvUHxPR20q0JyLplZautgTV9MB0zm9Ca+2dWYVR1+/+ohXQ+nJDuCUyhYfq9vcmSPdvijhxf0b2+kbr4eKqg9N5HgIa0m5FQT4OepN/rg39AE+Sxg+5ivS0ULK8uZZvmLNX5+RNlL5zIsmgovd/Rm7q8rIeXckfHMgkhUAnSvZBt+UXpAXRn5Xn+0R1izCmaRkc8tbzPqPO8T8VQAioWGipZtDqm9ZkQBY/SIbuRJZpNYBkjjV6QRm1yybb0zUkt2GoAHhliaMxA/WugAOxghV2XLJErRPOCgtOF7baFQXTJys1XyzLiCDibjZy1vgkgQBgN0B0rQGZ8Bds5ZYUbEirh4CrMBmbj6BAyROMQLy5hR1+/0HR6HHdMc0TYTyqIKB517OEYShm0ui7pmyMWJqb+poDstCJudJiLe0aSB0V7Qk25bCKGpnnRi/3f8YIF52Qp587oZ4fqWgqvf5EDmodlX9+hgi6czugcrbiVb5HTvTvIOZyVPJIAtZ9fX3Rjn9Qr/xeHvAACvh0NcWiIB/v3iGMA/4n0e9zXX+3E/7sf9uB/34yceH2Tmqlz6L2ZR3rDy0p7Yfawx5jsl9SFfcLvNgle7bRLn1y456QAxk41pLO60O/g8yR+aFmiP0qrY9HsrcpUgrMgAjoL2rlRouO7mCjIil15unbKIoy8cqmv2GrXkBpRqwT65/9QG6yfJr5TM12MtmKQY4/GOLpKM4fpjYPpLwiwfFQMGZ/BiRRu5enkgTj26V9iXlQwZxPQZTiNwJmXHXI9iUX+Ve2GaZisDO0nMRDfyKGo6t2bI8c0/PML8C9rBMAHaP6bVumfVHtqGhioDuie0b9Ua6OgE8jrHcJDk/UKT4fwJ136NxeAM5lPaZtvnaF7TSj5MLI5nBBf//KNLrIcSO0vHfLUbi/MNABwebUTUf6srUcrxVkF3CqOv6HvjN0FQhO3jgIGVllRjUD1d4z98/D0dl7Z43dCK/3I7hnVG5P2Q7Xmx5kHg9eIdea2KOEqT0JJYGvHRS3ZsRcQj22gUK4XuIBZJ966nSsiJdgT9RmZ799DRdgDoLCAzTtSn1m0NuyhkX8FqYVobDzhWsAp5kBqoGihzFLhUB6ioDuWorBNbo3wRUhqRBYTaoReYuEwIj6OSBwCEOT3z8dxWz1KNeJh7uHESUdmXeVSlhykSMmEyLz83t3UqLgeFbGUwYhZwtg3yfLVnBKeLApQKovBVjYlxPzA713slYg2vvj8R9rQJzKB+SmjKmBWVAGCxGmFYp9IKco8nnxKRY5T30q6lVUDnMjEtePvmkKBiACg9ylmHBwdUPz2qtpiz01CmPBqXi/NQbQZ0fKOtbSnKTUZ5fLs+wX+7/tcAqDXnY/YhPsmYWPIejw8yuMZhegj5SCuqo8Ta6jAOAh1pm7R/y13ggJcIDbHdJjBJKio22ZES/d2gKFBFONmVEIWm/XaeYaqweeYlMFaXWiT2VADUkIJyP08QdHVDL7PY0qA7YPIq1oKdwMC+VIBW0B09nOunpZCitk8CiltgdEHfi8cHEFw9+/5u3ac9YQLO40Z+9+aKdeyuuF+wVci3acHRzz0sqwSpyiFE2HBnkDMpxxdEGtNMpFDLpItqDyzUoBH4RTF9shIJtsu/PsdorbD4DQfl2pFCDgAscwSuMelPN4BXCJcEW+lWy/V1n7SoygGW4V6lA77+kmBgNbYIrRFiCBTkuKpJL/Wuv/juEzirUyuF9hixJu7pZItVW2F1xcG21/ISzRca5W2yPdw8VhhYZcseWjx7Ri+co2qLR/VKoN9/eP1AiEN+MMiucpgIn9YeakgAVezrtnWAnybnG73MCBLn81KNkZpgdlsKmWeYe+yOLTRDyKEIAtVmS5NaT4ZAwYh7msvMifavez2CD8DAJvfFtUHFxCo7JvWnWP81Dxu4uFBoDXwMpiOGSqNC1jpDxs8rAi2ChT8RAD9hstPIwpjUTrV9liG8MPK5NTsgxb5kWWSZAEggp+sWYXU/8igPo1IRoLXHuKKHNjcOC2610pWFuqDnolhoFMu0mL/9sx4hrlfWGXSvk01b5VBPWHawHND0ucxlf1uh5F7okAXRC8ZJB6UB9YpLH+saQzQeGgCMA4YjmpPPPnsrNfoXiwORgByVPYz20iqGAKgjOo6Tow2O6h0Kk8zYlz3t691uijofMOb+1UVXYxmdcLa1LDamdYc/f/QFzpk4cpqtMGWX+eUqvTPe13EPC9+P+3E/7sf9uB8/8fggM9esJbgp3yaSgs8p6xu4Sd10ShrD9ZDaUMAZaMxOgw4Cq6pAWew+8SHS3ZWm78Z/F6vkfBMMhG04zAJ0q0S0e79dwXS0nX0R78hu9IYW8TVnnfV1QL7hVeWw52ICQAWP3TmtoJefahF5n34LjC6dEJyUS7CYyZUo3oQM6KZGyB7eKvSs4asU4K8LyVaVS9CgKwkGjmvS0Bs5rmyVMnQo8rbMrnhOxmQEDwB6a5DtlKzQV2+movlqPmkIYo3iB52W1H7y0Qoti+X3b0dASKIV0AH9OUOgtwX8TQUT2bGTAMUZlroqEMYOh0d04ovlGIdPiBWdG49rhn6HNTmE1Ie0Cp/WLbYdQ8SbMQZrYJYsMFF7yfwQyEhi/SmnM6UXD9R/9csvccR9Vl+sz/G//J9/Iktjs9WiJYzSk+Yxk5H01ggLWzmgecIZ3GyA3mtXwUlqW/I9ta5kTbrHu3P63uGjJRa3Y+DEyvUODBn7IkiG2584qJGFjupZ305QX6bMkhjnNAfdUcDAhCCzo4ytfEjQuzEeNoqa9FrKKtABpnZw7DyUL5PYv6tCgl8B+NonBSUQoS0SglQAdk+YOGT22rNMoPsq6horJJhoAFSn4aPTUeVgOS0sqx4H4wZVdJlZT9C8ZPefCy0a390xsPr3W4ymlAmGZZVUzUpSKxsfsP502SdId1Oju66Rrem6ZZ7UzeL39DiqggDqRSWExO44AE9YT1oT6erpEb3gcu3w9SWx6rsmx4NTon9PWVA/ljCs1TiY0jYK47AbChScaRbaSRnkpN5i2Ve4auh5sF7jekHlE5M5/KfPvwEA/GcHf8BYd3A86UPIcG3pc1v/T9jR7+H4MIPrLsBYYtrGQNKe0t+qK1bxuQ1J/g0JvimXHj5TYsWmHJJoteJev/jOHvjfiMpLyYR7n408TPcsz1YKWZv+pu0eo3kIJLcojhiQYG06WizUVwx92iBKS57N4el4A0JmxOHm+A8B5Q2/KD0tGqLoeD/VUvPLmmQusH2ooYdkW1f8UKKZ0YNVLKi1Rx742sHwiyA6dkgLQD2gY7k5PSTbujCQelOcA1cF5Mu4EqFzji/x8ukOvzwjVm7rcnx7eQy32bM2YgjX/u0BdAzyz8gIwHOADoVH9SrWFGnxEOUhlVOwLC0Yxh75vMPJiOUPjZcWm9ttjWFZyn7NZBBBcgDpc2+oNnr6S2pvuPrmCJPvuUY8A+xvtzhgCPnpwUKk4TQC/rdXPwMALL89RLFOalzuaIBh1maEFsOSrkd5raXE0Dx00sLhVjnywxY5G8136xLmhufAA1Cppj7MHR4/vwIAvFsk02uApRDzCKVaGK5/jwqLdleg+Ad+AHRyoNIWWD0E8DgFUESD+NqgnHaoS4YUbyZQi2grF5AdE/xaVgN22xI5S2sOU5+kBVVA8WAnqlJDnxSxvNd0zHGe8vSM3xmBShV36qrxT1lAKD3yGbsv5U72Na9bBAAvrgiy969GOPySg8cEWP2cF4nHHQyAJkLeQSE2RufzDlU1oGSFo11XoG1ZHerHGqOVErnL4dTCxIAaFLKvab6zHS3MY792fbbD0STetw6lsQLpfnt9jI7ZwrNZI7DwbihgtEfDFohZ5uFify3PdazHWqNhubi/6UtcbRLHYD5qcHJIq4p//eAr/MvJtwCAqW7wajjEjaN7audKnPDqo1ar/+/r8h6Ne1j4ftyP+3E/7sf9+InHB5m55ruAbAhojpRYtgVDSiplVCtqkzYpFDB5w0zHIWAYm7srWc4Kh0pxJpu+F5m90UM1ZrLtTAmL046Sn2jWMhQcuRMDxPc16LsMRr1HhDItZa6SoXb70HViEftcQbuQWJEt4AvuKZxoNEdaSFjKk0k6HaNKWeyWGKg69k/ulJBybBVgj2wimmz2brEAYJmjYAPzLHOw1xOZ/6j0EzTpMsc+wqADcu4HDJpg5skvqKfu48NrtJzKf3d1jG65p9RReLEac3XAwBrH2GSE8nFWm93kd8zqi9uUzdhKCUM1O+zw2fkVvr4gCM12mYhN2GUSkT15ukBvjRCc3l3NRNDg0bNrFMbhhzfUHDr9NpGA+k8bPD+9xT87fEnXIxghLf319z8D3iSS2HCQoE44BRet4zSLE0SI0ZAgB0Dko+jVGcYWts0l0zU3uVwzd9aTOACf9+GTpcDatsuIOXxImWUxGsRMvtmUwsgOG41ip6SconuIYMX22QBdWzl8YxIEqEceQ5fhloXqzcpIT/P48RpnU4LkW5thczOCP4305CDqTflBi8/OrrAd6JgvllPJzIJXUMYjyMOHuyNeemHq8q8LD1UlDet61MtX+i7DGWdmN5sRmlWF/C3tb/paYfuIPtedWyFFuTbDvsGFqWwycODrcHNLE+aHZH2X7xTaUw8/4ns583Lti8tMYGdXA+25w69+RfdSY3MhLWXao7E5rlmoQWsvCIZSQTLV9a5CCBAN4v2hFdkxxh7Y226EdUfbGxc9Pjm+hubJvGlH+POHXwAAPiqu8aKne/+r5gxnxRo5Q3NT06JSUcT/3s/1vRztoYIpyJS855aCbKtI7Yhv9n6q5OfpSyvQbHvIL6dYw/GJLewL3DFLN11AxS07LldoTxRa9gI1bYJJTZsMBFxB286iAUcGIN9nHENqkz5P7UJmCEBIhubKBbgqKUXFY6yv6SGydarDRfbx6plBexKEiVsuFHYs6+YqkpijY1Kwe+hg0BCBCVcHetHxy8k0SmquutVwcysCAe2bsdQU+0cWYGH9fGkAlWB50+wZCDy0ePz8SlSSbrsRvv/ujD63MtAGyB/H4O2xazh4DwrluyRf5QqgeBUNC9K5ZFtiucYa+O7nHR49ZHm5oPD5iwfkWwogW2SptHdkcfSQalWdNXBOi8NKNe7x/JhaDDLt8cXbMxz8Jf1t+yRgeEwX9LcfvcF5tcZFR9CxDRq3HXu9rnNUa5alO+RrzAuykAGq5sVf7mGMx8Dm6cpB5CZDoch7FFQnDQMS47X0yM8b/g4ZaWuuNa/WI5IeBLOID3pxW+m7HN1L2ld9paWtxTTMY4hs51EyhTcji7yw4htqrUZgEQlnybQ9MpzdJAW1Zlfi23cUcPTWwITEKi6ujahIDVODry5OhfG97zqVlXejqXdKTNxRulT3tIpciPjezSormylLgvxXK1opl/WA1+9oERR6jeJtjvod1zoPIEbqqtNSww2G2oMig72qBrQc1LqWhDOicld1o2UR3R2SyEgUyg9OI79k56QM2PyGru+jh7dwXovs4JPpQoQ6rDcYnBH4VyElBCEoWA6mw2AQnBLWu8mcLIS66DMcDSiMRce8i1/M3+H1bo6LHT17/+L0R9wOdI+87Wai0DTJeox0L8L9a1dJ/XUSkvfv+zo+yOA6TBR8qWDHISkmVQH9gZJaHhRQXsfgF+CquyvZWKu1tRYCUz8jbd/Y61dsAnrOAptThWEaJNtwKogCDoA7fbTKJWlEX+z12655vxxpoqqUDAVkW36QPdDPEhuqXKaVYHOcSRa6n03383BHtcrWSG0We+007SnQHTshBJXXWv7uC8Bpg+KGpdbKIJ8bjhyQBQyc3Zhewc04G1hmci2GA+ohNFtu98iAjjVYR6db3GxGeHtDAchfl8g56ORrhf4wEGEJwOCVtJpkjdp7ySpUN6kP2FaphUmdkBON/4gCzcl8i5s1vdBDAKazBp6v90aNoTmonRxu0Q6R1EUZSWDz9I+e3qLggvLff/4R5n/IReu5f9jjP/jZdzQHCPhqeYoX76hHOFiN+ksKwrUCmsccQKcDwjoXS7I8dxJIisLSz3zezZMBiiUIs9Ji2HHtzhLaEI5oG58+fYdFQzfCrsuxWxRwKvVBKs64Rg8bnE43+PEt9ayVX9RCLrPjpKNrR0RCCzHDCpAg5noNt63kWVK5J7ISIAuzuHBQSsmiy5WJmBflD4vbKMlJut8AaQm7kCVXmyzIIm4YDHSeWsAAwMy4rq2CZGI+Uwg+BRbvVVJCWldotpksQrvrUoCsYqVQLBRWn/FzeNRLJuoGjcAELNVruka84Nje1oIcKBOQ32QYv0wL281HSd7S8BwCgNpl6B8nFOEp951qFeC8llv+th3htqXr2/F9apgHYJ1GVaRgFpEIpQLyzMnnAHJxAgDHgfqmoWdtWnb4aHornzutNvj1jNyYLrqZOIDVZsCEeQSnxRo3dgzDL7iPy9Tb+mI4wPs+7muu9+N+3I/7cT/ux088PsjMtT8I0BW3wsSlnb6rMFPeBlSLmAUG+X0WPFyh0B7GOmVSb6quA8G7vIztZlrUjroTj5AFZNvoXpKyQhXUHZP2oJIPpBqSitQwBZRXIsIfnXwA1jctFTaPC9lO1Cuubp3UX9fnGXYP9nxsEQQCtWMPV3sYhgqDg+gHFysS6wcImlWdljqoCinzVpbEEOLI10qWcGFh4Astmbh61MLETKF0mM/pxBY3Y9Rfl3KMdgzx8dytKuhFjoL3ke2SgtUwAZu984r/0ALsNDNMPQmWgyDU/qZAyczw9syj5Dpw/2KM8kbD/kDZ9dWZkaxtPt9BqSBasYcPVlitaeV+fTPBdEYHolVAbzUePSVGcGEc/v4fn9G1eJ3BVUD7GRV5//TjF5iy0cBfv32K5Y9zEdOoL1RyQzm3OH9O0PK46HH6yUaUbl5cH+D8gGqRRgU0QwZw/W69rWB7bvuxGopr4LpXcIcDnj8mFvA467HL6CJevZoDOmV0VTVg4My4/WaGt5s5TBVZ9imryTcKu8cMV9YeqnBiZI91Mh7XC3NHgEP3WWp1C8zQ5mvlDmyCbQEUV3QcplUwe8hhd5C0rzFoqm3GVqXKiccvBgXvTTKPKLy0uQxtJuUMlSV3KIDERGL7jo4qRYKlAuVNuh83zzzqx3Q9tA4yd94pgYFDwB3HJdUaBIa/6y9L6GHPCefcYfyEMtLdtoR+WUExHwHzARX7/B5Ptxg4o7xejpHnDqOSJulqyKRdrsgs6tyKMlgISiDiEEh4HwByzlgj6915jZsdZb/rywl0bXF8SOd5Xq/xszH5GT8pbvCyPxK410Mh4wv889FbbJjm/jBfoNKDHMfSjfEjq/g03T8thr9/44MMrnbqoWuPfKmFrKLZoDyq42RdEIgVAIYxv6AKoD1MtaVsF6TuljUUiNtjfqGPU83J1x66SULawaTaadB78K6nAKFiu1pIwv3acq8rQ9Jhz5lDW2rNiKSgfAVM2RB98yiDrVJ9Lt8m0+bmVImYeZhYwCmpBe8bQm+eAcMZywXuDEyrxJFH7QV55ZRIS9IvUl+urwJMkwzAjfECQQ2DweKCbdhuMkABLavl2KkXcXtzUUCBIEgAGKbJdLs/dkDlkL2LDCF95wVppuyawscYez6rk6Qw5WvPrVFBthGhwsWbGalKxQWZ1dIGMZ832HIf7bju8fBwJW5A//j3H2P2WslcLX874J9/8iMAYJp3+Hc/fgIAaNdlIsMB2D0KsA8p8FajAXVO+3o8XgiJCwBOZluU3Fe5G9hxhAlIwWuEGJy2BoEF+H1jUt8mgBerOW5es7qW4wXRK3qR9kOderLLAPfLncDL5iZPxuzHTmq/6AzVTqPl3EbfWchSLZ7nfI8faMcB4ahL5RcVRARfvaik9zZrCM6PwT2UJGsok+wUNBPWvFVyzbLpQHBnwUYEQZEsIZ93rO9mhYPJPHqWFgy3pSw6lY+SirFspKV1qzkNOPvZlczruikT1LwH16PwwCqTmq466DH6PR2HrYHmiQP4fq1GfTIsf1fS+4a3M543OB7TonTdFVhv6XNFQXCutCA5I0EyNx6dzWAZGh+ckQXAuE4yib0zqDIrxLzBa3gO3o+eXuPnB5f4ckE9jJ+MrzBlVuDL/gh/Ovoev2+eAgB+PXmN84z4CDtfYpQnMpgLCheW7ruNq2TfP+yO8L6Pe1j4ftyP+3E/7sf9+InHB5m5qkFBGcpeJHs0pKlbX9NKuL7qhRC0eWiQc0bbTxRcnbSG+wMlFnCuJFH8jmvx0QgZwB39T4BguQg1E0RMP/scgNqDWQOEXp/tArI2nUc/SfBxdxyIGcor+2IdsH6cCEExM+7npKITMwA7SZl3/jpHeZvaJ8g0nGG+kYNiglG21Siv0ueCSW1L2nLmHbOUkAhZxUIj6CDemkObYWDGpdlqxFzMZwHNR1Y0fM2bUhSr2hOC62K2ZBpNRDEAqBy1pURfUg+AyTzqYZ/4TMuCBD0YMm4va/k5YyswSeo0hOGpZwPKakiZiFcYsYbsZlvB7BFBfvj8AcY/cmtICfSxPHDsMDnd4vPLcwDA9mokGspmIH/SyC7V40GyS++V7PfL2zM4rzGr6GbQKuD1La3++zYjAYnYJuUhEOjh81t0nNnuMiLhRGKS3+aSWVYXBNM2z3uZ18gOBoDm+ymKJjJz9+BTD4DbQhT/L16n4dAlizuFO/aIvghkKgCQtd0ql/KJqwIUIxPZTsnz2pwHDOc9wGL9amSFfARQe5yoTwHIWdwiBCImdS3dsP6mEJaxn6YWMmc12bnF7Q/pfrczMiGIlnb5SqFlb1r9fIvCODQ8z8OQEIJy0gky0a5KhDzZ/p3+RYntA/pb91GHetpJ1tm1hRCwgglwOZCfsjJSZtExpNv2OXJmEdfFAOdVItkBqHO2bfQavTWSuXqvUTJ8XOZW1KU6m8FoL5Dxcb3Db4+IpPT16hR/+eNzfHJKpY/FMMKKVUKeV9f4m93HeFwQwWmsO1wMdH9+3x7jZzXBxyNN6f5Up5fajw3dj517/0PT+38G/z9GttYwg6agyDd7tgDqa49iTTdne1SgOblrZgzQQ2taYPcwtdGIT2VNMG1U9Mk3Gnz/ULDc84s1bdq3rVIw1QP9HGGmfAuRHQSA3YME5bkS4u5BDjYKFTOclUvQr95BGM26VyjWKRjmK4WavTldCWw+dlIbC3liU6tdwu5sHZAXSo4xa4KcF/YhbrC5QCSCdmTcHp1M1DpHtt7TpePhuZ6U/0iwav/Awp5EmiiATt/xIq3esRPIy5L7YOn3wyzAjVLAs9uc55helNKiorTUaYMJwMMWY1ZJanYlLDM8z45XsM7g6h0xlZXxWDEj2OROWhjcvz3GVAO7Bzz/Fugf0MtrckR12+2LKV83DccyevpkwNCkR9I3GSoWhK/LHrdc7ypzizofULJv5ncvT6GvWEWnUfBlUlfyc4vZEdWTi8xhsaA6bf6ihLKK4E0A9TJdz/YkwD1vo/Ur/KJAw4LzplPQGugP+Zh7LS1Tyu/B6ToAWUj1SZ8WllBAtl+LLwP5oAJULy09PBsMVBfpOmct0sL1yEJtsnQv6SDwd+g1Ob7Ev40shnjtNxm8T36uwZAZAf0RwuYNHkAe5JjNTlP/NqiVyK1z1K+4BaYE8k9pBXw+X6MZcmxj8HZGIOg8t2h2PI+1hbqsMf2edt2cKCmDmMKjzC0WN3StsssCReRdzD0wHzDhuui0TIueIktuPNZpdEMmC7Isc+j32meKzCFjmNjoIAG1zgbphy2NhfMaNQfeTyZX+HxJi8KrzRhaB3w2JYZv5zP0/FL5dzef4j8++gZXTMH/71/+GU5GdA8+H99Iu42DxtpN5HOfr89FyezxaIH3fXyQwbVcUnAzbUDJIgn51kN5wI44mznUe/2qSSgiawKWP0vZ2H6vs61pFS6BBvTgAVRDzdfp80GlbNV0EB3joOn3+ZpfzC7JxrUn1EAeg4Ie9tp+Vgr1uyQO4cokHNEdpj7RycuArA1oD6IDDQVAAGg+7qG2yXlEeSVmzm7i916idPzFKgaPcEfvuDtSkk1mG4UsusoV7DbCmQKcwnCYHGZiHcmsDPzEwfyKVuf5YIhsAuaQ9BqBg0fwgON9d6fk0FKcUw2qzDwKfmncvplJ32DIAtxoj1DmFOwhfe788S2O6h1eLWlSJuMW5w9SI+wXXz8St5iQp2xSrypMuHVi8zQAKggxqTtOGsFKBWxezlBe07F0H3cwbCcmvaRx+7VDy5KKrSoT2Sb3CE4hu855fu7eV74I4l6S5Q5brsOtX8zEkswV1PJRXaV2j90jRimmFljnQh5CldrGggYRw/h8gg5J11gHqVmOJh2c0+j53HxvYBbcAtJSvdVyGxZqh2pCN0nX5Mh/LKVPVFmShQSA1S8H2b7JAsIICBsWh9jL1rUjp5qMCVl2k8s9F91u+uhkVey1xLU6Od+YAPRaEKf+wLMuJPUcZysjC5j+3OKTg6TT2zuDkrNEY7zcI7lx2PHP5rsKplFY/DbpGucHXF+vBiwuJyIcYY+H5O6Ue4zGHSrevg9KWmXK3EoA7YaMW8L4GVVBWoJiLTbWN6OGMAC0dk/MHERGioHOB4VnEyLVvV7N8F99+nuMGEq46Ge47mgxmN8i6QAAIABJREFUcF6t8cXuHC+2BNf80fEbnHIzuYPGkl8WS4zw1fYMl9yLfljtMGb25g33xb7P477mej/ux/24H/fjfvzE44PMXPNNgOkDTL/ny6oo24usWuWAwFlnP1d3BPhNl9i8drLnmAMQ2zdu0kHaZnR/t/YJlUQMTB/utCJkbWIIN4cK24+Y5bfUyDdJNpEa6enn8RsP0wVYVmWyNUTa0RtqLQJo/9sHiQXcHgf07DijWgPdKwRhUCtpA8JW70FkBC2LkEa1p6B0rKgOzHPQPHSI+GIoPMGFLmXehn1BXZFgQzd1yHIvwgg+KBQjmvB+XQDjRNP3k+QqE7OEWAfdtQWa72n1PHuhUUS1rEph8RsvbRGoHU6PaWU9LTu8XM5FKP1g2uDNilKn9WIEmKQcVVxl0qrkSmD9nI0Spg56bfCrP/8KADXUf7sm1YhvPn8ENSRXH7XIAZbpU6c9UDgRhM+ypBi0fTdGxuYFyhFSEefcToLU9UJBUH5gc/mhdFBdqj3aeRJ1GL3IRJKwO/LQJ7RfP2jozZ6Eoobc1G7qYCYDNGdL3hlxpsGeKMr2ekTfYajWrI0YM9hRgK+8sIDraYvdJR1I+c5g8mNCFdYfA+pjhrUBeK4T2lUB1SuR4ERIWWngti3Lc6AnA6ktAeTj2pmUVvhUpsBezVZtM+S3WurfmFjJHnVDDk4DK2U9//gd5gWhLDtboMqsVDk6m2HFyEHb5vBsCo8S6B93MLzvk8M1xoz9fv+7R8hbheGY7/NeI1vxs/B4gFLAhpnoxZ7IQzdkSXWJhUxKZphXuZW/Oa8xKRMr2AUtf7tYTiXbPZttkBuHi4bu/08nl/i/F48BAMfjHTqf4Sija1PrBE+/2s1xVO4EMj4r1jjj1oIDs8Xfbakt7W9uPkKZWRxW9BI7KnZ4vSPE6G8/P8P7Pj7I4IqAO3VSALClJglBHq5MsoDdYbJNM62CGgDLsKfPg7i3mI5ISiyPiWKRtIVtTUSove4HkTRrpylImhZYPwM6fnD92IqrjAqAy4ARt3WMLzwKVl7ypUI7NxKUh4lK9nY7CFHLZxQw28NEaIpQWOg0oBSKJb9EhlTT9UVS38m2CsMYaM4SaSZaW5meYWP2L6sujSwiQmYExgbIkL4/oeMvjloMrP+qb3NYp6AnTELxStpCdB4QDobUH3hTkKUYgPyoQbctsOhZ8rAxqJaxsJfIX5unFMgNG2ifHq3wcEwP/8VuimHIRE912xZiVRcGDdVq1K/5RZdTjywAqIctZmOqg63WNf7Fr7/BSUEvnv/121+gX5VyDUnYmI5FWcCxSXk56qEUubgAgO0NfISK9wJX1ih0h0GCn2mUBHzL8pNR4QjOJNjWQGD+YqEpyEUiWq9gvqnjx6Tvm3ae7n9kAW6XwTMJK9toITe5IsRvw40C3MSJBZrLfIq9ivpIMybfNG8mmH5H3ytvAzZPFdqPuO0r9wgLnrs9C0EV5QMjPG2C7CsrHJzT8IZLH3t1bDgF5ZT0lJIHHR/YoASS163GcOCTwpRTAi2bjtTdjp8TYWdadPC84i2Mg1ZBpAAzk+qgblUIx8DOHeAVZtOdfO+7zx/Svh2A5zuoS1Yy22nYMzo3owP63mA+if/2AgVbr6XlyFojbW4A0PS5HEe1R2wCgPWQY73jFp7c4uGMnoUQFBqb47QixuYX63O0fF7/3ulL+KDwN8uPZB/xnGPLz2FO5/ar6rWoML0d5lhxpjLOO1TGSpD/vy6fiDXdn/3mc/yPeL/HPSx8P+7H/bgf9+N+/MTjw8xc98YwSkQNn6ukeJQr9EykcFUQJqW2BGuJw0ruxQfTNArlbWrvMX1AP+MMcZQgZdph0rP1eXLFMV1AtlPwLNbv20xMoPtjj+LKpJaAUiFnYkY7N9g8Sb6wwSTlonwT0DGByeX0GbvPF+AVeX5rkO2JPAzTIGIZuk8wZHNO7Rcxw822kCw5awN8rqRVafskQd75hjL79a9pgkxtUXH20q5LaCa8+Im7A23qhvRaAaB94BEGDc1tQbpX8AwDdssKqteSlehOy7kEo7D6mK/nww5PH9ziAWerPigRZagyi09PrzDJ6UK9WB/gNYsMqEbj8A9amNbLPx5wwmL9j6dLESr/9NkVvrw+xV+9+kyOI8qz5muC10VAIYdo/3arErBaiDjKAIpN1bONFmi2+aQDvCI9ZgBqj4Oie4LlI8Qe8oRM5Bc5MhYx6Y49/NhRNghqhYrlDFdSdtyyQbrqtWhF6wtD2td5vKapDGJyhZ5FU9zEIZ/1cAzj6jI5qCgVoDTgLmhep9+lfS9+AdizLqkyLXNplQlZkFYZafNhaD8f92Lk3Q0ZVrcjKDZxVzqRltSg4A6sqG4FpwTuzW+NzE976khUhYdqMihmMPucxPOjqIf1Gg9rvpeg8P36iFSyQLCtZyJa/SoTlAtrAzzp8YizxBeLA+TLqPoWoF6MAG4pswdOdI21CphPWhENsV6LID8AmeOisKiLQSDjEJQwcet8gPVa1JwK43A+X8v2IqnJB4XeGfzjFTGEB2fwX3/6twCoZaZBjkc13f9bW6Ln9pnCWIyzDjt+UL7tT4URfNVNcNnRc3JQ7GC9wWrgDF17/ItnP9BlX9Z438cHGVyLtUeWew5gDGnlEc6kz/giyQ6aTgkEN4wD1csi8+4mR3XNN/SC+lBjfXaYpGBdrOmlNey5ycQgXCyVwMchIzhW2mNqoCWHJqidQn0BjNgQPWs8mmN66TUnxNAVBnIPgXHbYyXM3qwNd1WZci+BKttSe4TuE/wVIV2fpxd2/Y4hRYYK25N0LtmOejWjkbcfeQm8rgbCxJFyEgDc1PBxDkKa+1BqCvi8v/I6uYKYRsN5Ra0cIPF/zS+DYe4RxhaG+2OxyETJqR+UGGOfnKzxaLLEqqeDfMnMYAAYFdSKsBkIily3pdi0Hf6BGNjLX9PBHD1Y4phbDL66OhHZuJfrA2x+fyTE02EakMUXcxnuuAiZ0zb13xbAbNJgteFg/mONcpWCoZtzsDMBodcSdKD2FLL83b5p3WgUb1hdrAxof0bHWI16NDe1wKokDRkhY7qGsy/4ZbkMskhxpYLpg1yPoCALQVek3u7ysMXZfIPLFd3ww2DguS0nv8pQ3KbWn92jgPCMAqPrDfWuxh7qiZWAAQ9hHCuv4B+l/sjgtdQ2+3cj1BepzuzztJixExb/58Cre4XyOvXUNk+4bpETWzi68yirUj9sEXBytsKTCbFop3kLy04bL7fUK/SYg+b3t4fAd1zYDnu8gsoj0x5/+JxUjOqXGXT0SdjQPIbYo517kmMEUNc9Wb3xufXWSAtYCEoE+EdlT4g3z928bHE+omP6cX2EzhmUotjkRIWpNBZLdtK5uJhD7TL8+jekJvafn/4Dlo7lD22JP569wtzQdbuyE2heRZfa4jDfSXD9i+vPZOF5PtrgvKJA7oPC310+FtWnJwcLvNlRRqNt1Hh9f8c9LHw/7sf9uB/34378xOODzFzj6j5CpACRCPZJTH4PauvnAcNDtqXKPLzVAjkVSwVevEF5ylZjX6oeEqN2mCjxagUg5um0Deo9BYBurli7NG2zvuQsrSECyuJTho9mSgTtQxagBiXwnfLA8lfMxnydSEVx31GhyTktouP5hjKemJXoIeypNSmZm/Y0wNV7FOkABJ1IUK4O6B5wU/pRgzGzd69vJsAuQ/mOhRcaJefp6iDZl24V3KEV2BN7PcEAZdiGM0E7Thl0yAJ0noTYbQaMXyb2dDhnL9Og8NXNieiw2i5DYNLL2gTMP2mFlLH9bo75N0yMyYDVnzU4nNGq+uF0jWu23Oq6HG8jfNwZ4FEvDFXVJKKZMMnPUx9q1HV1bYblxaH0SWtLGSvNjxfj+aCJCRqvge6VEKQQFLKNgmZYlUzKOVt62oqKUXNTA3sqVcoqQTpGFzx/XDIZJolMZfqAoBOBypVKoE6fARkrBz08XGHbF3Ju+vsa43dpO3YEbH5J98XsZIshCnD0BtnSyLNRv82kNKFcIgh2Bwr2thYYulgqUS8z/PxJL7pN4ha61VD/D3tv1iNZkqWHfbbczbfw2HLPrMqq6qV62LOQjcGQAIERCAgC9d/0KwQIepEeBOiJAgUJFDUakprpnp6e6qqurNwjMjbf72pmejjHjnn0o9APk5NhQKM9KyKu393sfOdbdlqIdaZRko/r5kNicXEkXPQrVksrwQ+Hz27weLrEnBnCGgFXPbOdzQCjNNYdIx83I7DUlxjZbFZSXBnkv5ug4Pu/OQ5w/Cxj2iPUBoqj9pA7HMxSJdcNBjru5mAFelcqCIvbqIDCDhjbxOJ9tU5+vc5r7GK+a1OIP/GbxQG6b7kfNnf48z/9Fn8x/x4AcN7P8B8uyAf754fv8Cy/xIuWWL0X3RTzLHl0/+3iCV4tqYqfla1UyaXt0XKV//3yBLtdgaenKaoukqyMWLx9vOOTnFx3DwxMbkiS06YXSXewF3TeAOsvWeBdeOnLhLVFtks5icpjL8iYJouCHMHgC2D3kD9zny3KBap3RtyUTBuk9xshtrhN5fYYzJXC+gsHfcRpJVWHgQ29p78qYHcBm2c8md/vpYdpkkYcQ0WyIrcX1G7S80ff3Qf5O79PtIwTYelhdlpcbkyLJL25F+CmTnJOh0Hj8pweVnuZEdOZ3xlxEUL7FRIMVlKPKRzSG1CdFclqUQcMpw59lE20WowRorl/lPBkD3bYsIVlDBEHgOvFGOZVKXC+GoDNc/rZ6Y8v0XmDl1ck4SmukplI85drHI8bcbO5qkc4e0e/p1cWKsLaE5JiqDqaQqRJxVUeepZ6Yc1lheyGs1h3ZK7gb/W8WbLT68SMzcKtXr/ykL68rYm5HRcj3SyIoUfo9hzydYBuTDLC3yqx2RxKJbnC9AXpOgWtoFyyh1QD9Ybp2AKGK1pgvLyqUL4zYKQcQwmsf8QbHA+wxQDDL9LVhwlGL+hYDq8Csm2QRCeXK3EXc4USeZnpAoJVwlJXId2fQfM9GXG5AAnMsFu6p+M5caWXyRudTn+Tk6YutgT8wQATU2uyASPbwfIJ+tBO5fxoFWC1x4sLajWYq0wc20yjkPP+tkcB3TxIcIibOmQH9KD2jQWCkudhMmtu9asBYLlm9rzx0LzQGZUtMr7HM+NQ2R41t0xaZ8XqMn9RwrQskwPw8EcXkuVbn02AB/RC+Jc/+h5fjC/xn5afAwDebg9EsvNqe4QftseyfaM8Zjm3HEyPi3qMP7v3FgBw1Y4lz3XwGr/+8AQA0NQ5np7eiIlFaXqMeDFw0378oOonObnaXYAZqFqMulaXkxwmPqDNvXTjK5fKpuJSkz6WWm3oDpIlm/Ikv4mOR1GmAdCDla0VNFeW4/epWsXvL9ICoLso9VFCfGqOA+xpjQlb8y1XI0z/lksiT6v5GOCMTmP0nm7Q8jL1YptT3AqJ152SY45+r7F/qhzgOFTdlWQnCADluUF5nbbp8mQH6Q57qMzDs3NOflahqON3Ud819qR1l3qPyrHlHMhWMCsGtAuafftJQIgEoHsdjPVwbGenOi12iZj0eHiylNO4agrRRc6mO6lU9ZsS+TKRb4Zx0q5eXE3hewP7gbY/OQ9ChPK9Rd1l2HHizPa6Qsa2g0HtOQ7pgFAbqa6DAvyYKyCuhHoma0XpB0CyqGHspeeNKNsBqIril20+a9GfjWRiCQriaARQ3z5WysEilcudlkpVd+qWk9ieTBHZNsANQD9N+3FrsadI0gUQwhO/y24Vsk3S4nYz8ogGgOyghY3pKssC/ipDxfdndZG0cRH92R5wVTUCfAy836X9GCqqmAUJGBKBz24Dsjo92/1kf7FBkYlxQUYSrahZ80nqszEwjcYwTXKWaGNYZT0elkt8u6GqrR4yvFvQAtJ7jWZdwPJ9oQcAEQnqgN0TXrBnAVABObsy5cajYwmWtuSGVoySH3LT8fOUDaibTPqUYD1r/L2Dgia4SHiKk/GbyznUFWufZx7D/QYnB/QSO622UmXmpzv84slrAMDjaoHrfoxLjjbcdrkgDIUd8Hi8wCnfk/uxct8s7qG0g9hzHhdbITH9l++fiYzu/qMFrPY4LUjq03orMYrAnon6Rzo+/uXB3bgbd+Nu3I278Y9sfJKVqx4ArYG+UklqMpBsJrohIUDYpcoplMw+zLbk6hT7UcFCeq7BUj8yuu9kKy1wnfLkHzz6wD2Rbq96dAERU3QF+67GijFPzNuQBWgdsFxSn2/ynyvky8jYVQQ/8XKpem3FnzgYYPuQKw0boDsgv0mG+RIQvwgwDaBYie6tQn0vQpGpT5ttkvkGQFV+NEJQrUHxOkdBFqQYxlQRAwQf7qeh5GsgMLRcfAdsH9HFqJ8otCbbYxkHmKfUExplDm2TElwmny2lX7TtcqybQlbXbZMJZLZaj6BfVHwNFbrDxIAFABPdiZxC/j5DwZDx5gngntIFriqSljRvJ3y+ElQ7HHgxvfCdgfJKenR60ouCxO/sLVN53aX83BhUHyHXYCDoiZ8MyNgkobsukW0TE71chFuBDtkaCIwOeEt5o3RsAZqraQXu+/O9azqgvGbpR0nwq9x3KrUWwpifmXjvDpBeZ1BAN2d29twD8z71v5uM8ksBTF4R8sFtSjQnSj77nGHs2DvcauRL2ufyag8tKdWtgIjiJqBiFr0rFNZPUsZqUMD2MVdY9zqoVSbnPIyH5Nm8SaHtumdJGiMrR/dW+PqY0lwKPeDF9hgvb6glUDcZhpj76jTUxsj9s88VqB852GM6WaeHa/igsGLzhq7NktfvfEeZqyzniVVr/C7XG3gOjAgFUJQp0SamyQxBQ6sg6TyHsx06RrwOR7Wwg+PvxkCHX3zxEgXDA9+s7uPdZoYls9e1DvhXT18AAP757CWOzQave4Kad67AOWsXH02W+OPpW+lDf7s6xffvyaEsDBpff/mOrpPXGILGLy8fASDDjVh5B7Nn2v6Rjk9ycg2K/mebpENtjhTqhy4ld2RetHb5lREYWA9sk8gvr2GUXhJu5FFcGUxeJSg19iXzZUC+9ongVGmZxIZSy34ES/1ezZCx7lLUVT9WaC+rJJFYBzSnDH2NWS7g04QaYb1OQyZdu6OIvCgJ6ubJecllCrb24q7kCojuj6BGJpMck8xH4N1WoXpFD/HoA0GsLucXlsee7R39/+wF94V2QeDF9dOkdSzODdqTdC3M0524zXivxPoOANZXY6xvaMOaTc2jZu/SjLF9Q5h69c7IZNHNA9wo2R9+9vxCCEwvLw7hM+qJAYD9eoWMZ8btugTWWYImRwGeYTHM9qynvALGA4oRG8f3FuBUmayn2LRg6OXYT9IL3GxJQxpfez4P8BGWNAH9mmC96o3F6ENAtqXvro+13J+mo+ShfsKTU6NIYgWSrwjJh0l9cZIk68woNaFJuZt7OZ6eIXTdK2R75CHlklSsO3LpPCggNAaabftMp4SYp1tg/Tkw8OLDjb1I21SrMXpjknZ2Hx1UkOdk+/kA5RQyXiRm24DFl7yoOgroT/rkvKSQrA03lp7tGKSwtTC7PZ0o/8lw0gMqYHxAOzArW5zXdC/VfYbrzQj1Jd13ZtrD8L3kABTX+S09+/oL+ln5YCvkPqMCrtdjuGgPaV2Ki8vItSg6L2ntUW/p/gkD652jFNA6ZPxsNF2GBmkiViqg5hbGdNRgUtJORW1shIx//f1jTGNykna46ejB7ryB0V4SeB7PVliyJvVte4jj0eZWXNzXI4qj02OP/7j4Ev/3C+qnuJ2V+MjPnl7ifEMvH6ODxN4BwFHV4qiIC+WPf3K9g4Xvxt24G3fjbtyNP/D4JCtX0wZYH4h9yCvhbs6QaiRINEYcU/IlkaAAgqPIFD+J7yN0V34wyJepQnMlBB7N1xRKXh/HqDeFoCLhIsFuloPXI+y6n086fqdQXhn0owQFx/0IvO8RKjMN9sKo9xjMGZM6HrCn77W55eoUdJISNccK7ZGXv8sXfD5WuEW2yTaRlMIkqBwIUQ7hQD64ILP5yWsvjOh2mqpf4Lbvst1q4HNaxWod0sq917fE+Oi1eMr61mDTjWRFbnXyht09C6kkLBwm8xqHI8JEr3eVyBlcbwiR/BGt5L3X2HHsm9oahNwLycvMOiiGFH2vEZroAhBgiwEdeyWrq1wM5rMlMXFjHmrIgpiyB0Xoh4lZtRrinlW8KzB7QcdVLB26iUY7S0zZeGzbJwHwQHnB7ll1unehkiOZHugaRj/toVSon8V7PMDNe4FLo8kIfWbiVUQ+HvQwzAzPrRMCWb8skF8Y5NHso4E4nrVPPbU4mmSEHxGS2XfUltg8TohMjGPcPgGGY3avqg3GLw2Ka2bPPtdov479GQC9EUSAdjQ+GPSc5+d0bdxeyIQrA3DIzK5Bw+T+lsNRxhDMDhm6NgO4GvODQuAq015mFMPHEZW7hz7F/1mHXcth9W0GrT0c1zchKPH81Spg22UCC5d5L+HubkeVayTGBa/R8DZHZZcCFbzGti5S4HpvJTh9GAzaJoPn+/X+owV+cUokJo90nOiA05HHPKfncNVVuKyTC84Pu2Mc8c++Hr/DhqGh//PqK/zmzQOBruf31hLBp/ai7wankVsn7Ptx1gqreMthFh/z+CQnV2o4EcQkk5MN7HQTX4JaGJh2l8z5oVgnyi+DbG2SC9OSJqH4u/kSKG9YXhLoBbafAxuhr9FZgKsS+xK4rTW1FC5Bk/pMCQxHO86/x/CxWA3uMSsRUq5scxzQPeolK7K4SbmsygH9iCb++HfRvaZYkEQCIGcoKCXm/z5LcolgeILlybWf7tkfrgkujj8bSrXnnAO0R5zVyZOL+0Az73DQp4lLBaguaXPb5y2ODjmZI+tvafsWbQVQq4deLhuGZivqBX5Y0oui+zASQ/Vip1A/71DyC6C5rAQ29JYlMfxiy/NBtqm2Vti8cArDZSm4kD/sYc7YypH82pMd354GMyhaVEQYd3DA/O+517/zwi73meLc38QWjozdbKlg671rj717qqNFnoyQNKz1/YD+KeuAdxb5Weo7BwvUp/xC/LLBaNyg5Rd65pVIn9p1Bsvyr2qlUCzSfu0eBsnu1bWG3aZkpuk/KBheCLoCWH4JDFHDOwAtS0ayWQv9nlaus29JNnP15zwhjQbprw+NlX59HIoXAMFpjL7N4arEbtc8SWa5k2OB8ciLXvr3zWAlrH6zLuF7k1j+JohUTzmgvFTSdvGnHSy7K20WI0zmNBnNqwa911iBrf9Mmsg3bY5dk4ttYr0pEGKAQ+mgjBe9cjVqMebA9H396xAU8jy5W1V5jx33bpt3Y4TC4/MvqIf8R/MzcVeyKkgyDQD86+NvJZRgOVRYcOXwv33/Y7jB4E+f0aT8tCzx14vP5O/+6Ol7+fz3bx+AawbMpjvMGJ6ubI9dnwur2AeNHZ/vcfbxOzR9kpPr5pGGKTTa4z2P4NwDnUa+oIs7OlMwTaoKo4HCUNJElS94Y4EmHoB7VYWSlXaxCPD8AmxnCqYPqZfUJgs5VyQ5jysCyit1y/QhVcL00ApJiqPHALJvhE7pJUOloPmlamqaVAFQCk2nUZ5z1TCkl69S9PKNiwPllOgN94lO66cGQafKuJ+khYLP6CWe8dOUL4HAfdV85dHONWpOkxr2qv594ofPAuxWwe3FslkOiAe79E3/nFYcc+3xz47OAFBQ9V+fP8NLFsv3jRWdot1ogHt8nVPoLypBJopeoTvgntnTBlXZo17zpDkoqeBMw71nXnz0nYViXaHPPewV2/ttFdpDj/EXJAtaX47lXNkt98dZk6v7hDZo1kznrCaavgIU99ARUmUfFP1N1EC7QiHjnqs3tMCSa+oh5BXlAqKCo77H55evqakVqt/wi76lY41xdN2MtMsAcDrfYL0r0UcCT22Q3cRzrATBGEbA8ifu1oIjpjvpAZi8Ss9NP1bYPuKK5Ucd9UrjczJvgehP/Nsxxud87mfA9rMBepxCycVMQQeEoFJAvfWUrARg9G0OPQC7e7yQGw0YM9FnsylTgo71qG8qKO6l9p1NNoxxmzHU3iloXoAV13syKt6XnpGP6YM1Hk6JD9B5g22XSUUHAOuafs97Da0D9ep5G3bKvVrrobXHfExVejdYkYbl1kmf1mgPq4JMtleLCQaOu1MHPY6ONvh8ysHn9QwPKtqvTZ/TohTA89kVlsMINftuX/dj/PKChPvdooCZDHjAVoavmyP53AeNzlv85zPSs5ZlL/3ecd7hIdswFnpAW1gsuMerlUdpYnj83gvwIx13Pde7cTfuxt24G3fjDzw+ycq1nwW4kmCqWEGYdQbdIQnr92Alb5Ws4odxQHmhJHxcu1SdukIhW6WKtDlOEoPymlxtIjtW+YCee3fdQXLDyVe0apc+qE4wqrfsiBMh40ER2xSxX0rsUIAcj2K/K2iIBCNbGNhNgqfr02TUPzoja7sI5eXr5MRz8zWgviSopix6rM+mKM+4YtkirdQ972NUNBklLlj1CaMFMSO2SCxRwkS5v9VpdDOf2J6tRn+PzfJPVzgZ7XBcUqlWmEFW1n/1/hk2Lw8wfs0M0nyvyh8U4lrSnhtkW4JCAaB/voPfpnB0rT1qdr7KllqOjVyLlOzX0BpJK4FTKVUmD1APGglcH32fC9LR3CPz+Hg97C5dC9Pw//h89SMku7w9ONflBAtHZy1XpPuFIOHEwt5PQHKlwvZpyjEtLo2wcU2brtkwospwKON1CtJfvLyaImxt4hlcGBQ3cRsByx9zNX3c0TXlvqqptVR15RXt39UfyyGhv0cHY6sBSnsxRuiWBfJzRgRWCi2pX9D+pEaWOakKQ6sRxnsMU68k2zTUWUr1ccDmx73cawrAekEnKAwKir0KdeaRT1uBZt1gEjwDRdUqOzap8wLlh2j+D2y+HFK/tzM4fUonaJx3IpVZ1iXGRSeMn4gwAAAgAElEQVSJNuu6kMrbWo9hSGxeWzg5H8Z4TKsmMcpDQiaa3grrPQSFbrDYLtk4ZZEBE4a/S3qWXqyovzQvalx3dA42fYFHY4JOfjZ5h50r8KSiCveNOcL/fvlj+mIN/PTxGVYDnf/7xRotQ16Dy6AR8NOTD7yPCgcZ3WhaBYwtVbHrvsRuyMR8QqsgxxLP08c8Pv4j+P8xgqH/2aXZ07WRbGaf+h/humEEecFWZ+pWD3aokudutgvQDjJpBp00gDEhRyzldNKQensbTnJlkiD0M0h/SDl2AprEfwd5+arh9osaUGjn6e+6Y35pdBp2vRcNVgZU/GKw/FIvGNbuRwrNUZRneDiWgnTbHHqnBb4sbtL56A5YfxnXKFnqDZKzD27JndQe6SR68WYcnB7tA8PYYTwnGGxWthhnrcRUvTg7Qv8NMWXylUK2R7YJKhHPwqyTtJzuXYl+qjCcMla7zWAiKSooOGegNinS7tb5twnSDVoLVGjXRhY23aEHzkqMv2PY0ACLfxZXRHTfiXbzMsX6BU2To1g9GiCr+TplEP/mYNK5BG67AClPfe19b+x4z3XzIASsbKWQLyCwv/IQrWxzfwA0hHAUdEiEoF6jem/kvibJDn2uHzuoGELeGKheobhka8clpM2y+QzoHnbQLD3xjRVrwaLsKZz+HV3faqXEG3nz1SC9U3gF/3YEGy0vD35PuhEAxYQas9aymNk8H6AKB8tuS1GfCgDZJPVHZ6OGdJhRKlL0Inmpu4yCxH8gaU5xpdGc8kV72BIcy+2Ck4ONTISXmzHJuUAuTNZ4LFY0qWX5gBHLdAavkece8wPqrRR2kCDy3utbvru5ddiwVtY7DV+wZaginTeYqxCOOhQV/exwusPT6UIm9vPd9Jbu9f4e1Jsph6/KcwDAL5ePpTcxO92gdwb3i5Rw82ZHN8JhXuP56BIfOpYuuVx6usu+wnlD/10jIDcDYiVzS3vr9/pEH+m4g4Xvxt24G3fjbtyNP/D4tCvXnUpuQavbUhBXAV00QtgjWPZTgtcETuuCJHX4PBGfAKoGotREBaow4iq/PXbCGJ281CKX6KcKLsMeizakwBMOwY4wrq2TS0+EiiM8qAagWiYGsmk403PEkiNeVo3eaQkQUI7ZrLGSUthLJ9HwV4keX10G8T8eRuRrHI9ZKdxy94mer0PF+8LkD3Od3Ih8AfFx7UNOsB67GJnSYces3B/Oxnhda6kSda/QP6KLmP90h0J7DH9N2KHtgZb9nSfzmlbyANAxrMrwWFn2AuHGSiVWeO2xF49gBGA47iVbU60yFFcswdglyVHQAfnSkCwGQH+vlyq8+GBgN8D4PUPgRiHsZeZ6A0QfgHyTmNbdgZL2gwqBZVd75Jn40RB6ESHjoCBQqvIQyNw0THDKU2simoTYNRn6K342uiMPw+xy3SvUz3qYKf3Q1VZIP6gNzAdmRXcQ1jPAKTifcyvlXoMyc2h3kXUV4LjC6n+YodgoQWfq5x2qGT1suQ5yH4StJZlaTJJRSG0ER2k/0j5xQBsJTLMeNhsE7g1ewRaR/e1wMqF2QzNYDE4nyLXNCBoGoI3DcFWhZCb97umA8X36O6UC2tbi/pwqukne4mxNldpmUYkxjc4cVtsSJ4f0e0YFdHse5kYHyWK92o4kLcZz6HmU3DRdhiJWqyCXIwDY7ErYzMExs72atJL7+rPDc4xti+83RKVfN4X87OF8haOcjuXQ7jAyLf7nD38GAPi7l48wP6Zq+rP5DX42ey9uTr/ZPMCHXQowWPZPcK+k3820w2VL/bHBp3OamwGdsyK/oWtM23tYJY/wj3V8kpOrCvQ/n4cUXVbeZqy6Ik2qYW9y1QO/OBiuMx3QsZxhmCSrv/g9kcWpPIWK9/wyKM8NJm+YwTsDujg5DWCZBX+3hvR/TQfobXppuCJA80sCbEmXercJhts9UBIiUF5oZBugvKTfy7ZBXKO8pX5rdP4ZbT26adRLJgcfKILA46y/e2BkMRDZyhFKNbWSBYUbU5pO3H+7TRZ2g0rbM0sL5RR8zpPVzkhQdbZR8DbIy9dXCYo8GNVwXmPxJ/TCanY5soIlGEFR2giA0UahfuTw6Ggl1yqSv+tdjnCTJ3g9CxLrpyoHbTxUdFtaJTlJcxLkuIprCvt2B6kfm9Jt6JzHiVH36bwGzf3x6ABVqluuSaJh7iiZJkK6t0ZASnkB0E3TecyXSmRX/UihO1F78H1ismcLSkqK8htTp1g597zGfFJjs41CZkBds3PUeVqoDSPqj4rkZeRhWO9ZjVpSwzHcW69KmGtOn7FA/dMGms9XmQ/Se9wuKpibvYMLBPnSH6Y0ITf2UL2S89gdOtg5rTaKskfXGVlJ56Ne0pImZYsd2wVu6gLDoOFdDCInNyQA6K9LZCuN+kva5nhei4zGaI/H95aw/O9VW2LN9oFZOWA6YStNZglHaHbZFNJiCAHYLZIAXFkP+66Q6xQMYB8R/+FwurvVcx14f5+d3OB3b07lJda2Gb56QAz7wgxY9JX0NZ3TOJywXvXgHBNeUb9sjtB5i5x1r88fXWKa0c/uVytk2uE/Xj0HAKy7Ap9Nqbd8UmxwmO3Q8EvhQztFx9/VOCvJNwAkVQegJJ943v4pjE9yco2juE6Sl2FELyXJWx3SSyqY1NvMNvRC3M+wjEN3VBCKDMKlarifUbrH6G2qHOKLrZunSUl3VI1GGco++ShuN5KYtEsvFOXIkjDa4GW7IGk32qVcU8vysVixuxzyAleB/ruYPMw0KvabDTr1AouVg7cKu1N6kLvDgPCMXhrupkB2rUUfCwBDJP1sNGyT9tHW6fzYRmH8hjM9C5Icxd5pf9rLih8w5GOcRcKIFg3g+dUBQiAzCQC4/3AhD+uyLpG/oxPSHnk8+/G57N+7qwP0C5bedBqwAX7Eur9ZJ8iB22bA1kpCDzTE0CMowPT7ffM02apWJ/30js5vjCLcr+6yLSUlRaOHoVIixdGDkkWWrSn1JS6KlAe6Met+50Qgc3HCNkixbA7Y3eNK9chDOQXNyIS3NAHSZ0IG4mTbzz00myto47A4m8q9W77N5J4axsD6OW8vo+zSmDk7frwWPeZiU6F/NxZ/6zwP6B7SDTk/3mDwGtslTS7tYkSegqBFhU/FLuCThWLIgvRjzYcC+VKh/oK+r5o1ooENAZiObyeuRFMDoz3WW/revrPwTsFkyaxhYHRDOUrZiQiG9wpzlvNoFbBsSzGAqNscZcURkdmAqKy63oxkUgfI8GS7o3uwX+co3mfiUW53qe/vxh75UYNnJzSRLepK4ucOpjWOKroYLy6OKKM4iwhJwHdnp3S+jcNPpudYMInp5w/e40FJC83LdiIkpUI7rPsCS5HKBDQ8SR5nW/y/i6fouZr4bHqD3UDP10JXuGyT2cRVM0Zl6fpOslb6qVo5eChZHExNI6YUF9s9d5mPdNz1XO/G3bgbd+Nu3I0/8PgkK1e7VgStDUiwWEHSg1jRKZ36mflSSV/VtFQdRKgTYc+6MAr5WfowHKT+aL4ETJtW3j6ncHWAoZ5YMTvaFxtt49q9nm9NcLVRsSpJKSFmp2DaBFcrnyBis+/Yo6j3Gb+7uE5GBdSHAzaPub+TAd0Bsz3XQfrC2/sWzbHC5gva6OjeVuwJASBkybKunSeIe5g5uLESCNwXXhip2VohsPOUKwLcwSB9SnuVwbERfXjQAL2Rn+lGJSjaUVrOyVMCeadFi2VD8OXmbIIRV4ndscPVdiQpJG7QYuQeRg523AtEN6yz1MuzAZj20B9iAC6SuQWSsYhpA1flLOMoUm+/nwGbz7wgHsW1xvxbDjJgGD72r4Hk5GS6IH1+7djYJJpZVEoqVeXp+0Rm9LiDZnmJW+XI5rQRf1Uhv9RycykPkasEG9A9b4RRW1qPZse91JdjHJwpQRx2jwKaz+jgbDmQOxKop1hMWsxz+tm2yfHhBV3g7FpDjQKaJ/Szct7gPpsirOsCu4uxBMhDQWB5dzhItSiGGuyMpNcW2YdCzln9aEgOSqCWAQBk2sMHhc2e8UKEdNdNgZ4zVYfWwORe+qx+a6HYEMPsNIaZw2xG23x8kPqD71czDF5LNuuo7FDmKZc12kNGqLup+V56U6Fig4zphsIvakYZ2hOHyVN6oO5PNzDKC6Sb2wFPT6mKrWyPt0tyV4oSpcjCBiC9WY2AF9tjfDGh3tCPqnNk/LL4Vt3H//WB4Iemt9AKOB1T7zTXDs/GJMu56sfonBEZzWYohNG87gtcbMYiC7o/2cAytDx4wwxhSKUbHZpy7aQSHsLHX/d9kpOr7ilyjiwQ6b95G2B3KVSZ+qr8eUiQaDcl3WkM13ZF0hECFDweZQrV+W1Idz9Y2ucpjD2olJ7jM9aJ8otBu9tkpXyVSFL9NEl4si3ZE4pWsUr9NN3TZA5QYojyCtU59+HWQXyMh5KIM463ny/2iFVleqEGTRKX4phOkPda+m6mVqRHjPKMJwMMJ6UYAK4xsBwCbVTAcD6S447QFwLZ2R08oBeKNR4Lhr786zFwMIhloHlSw0cP360FJr2Qkl5+OEL2KyJSjH0iWelaYxgMmnUh35dYY6DUnTih6gAVD7zWsJcWJdsCZutEODJdailQ33Svh9xBIgq7GUXL5UyEOvyHNNGuPrMC5cdrGidoW+8tljqChNsZQ8FHSZscbTXjvVW8KISo5B526Plc6Y5sF+P1zVZKNMHueY3pqEXLE0T7ZiJyLd0D20eBUmMAqNzfWqREklieDxgGg0vWC+c/lFDsgmW+XmNSdJgyTLxuc5y/PuRrY2AbJZCopNkAQK+T42Dp4DYW+YWVc95PeaHAi+YRy7fm41qizHZ9jmVdouZJrao62f+mzuX8KE3EjHDFJ7by0Bf0N8Nxj6P7pLcGSJP5Yc1Wmp2Fc1ooOoPX2DS0Da2C+AATTO1p8QZgtFCyeO2mCtvPHA6e0aR9kA0Cnb66PES+14cGgKentKp7uTrE+jt+8Eb+1qI3OIUH94mL8HZ9AB9IMgMAl/0Uhm/kf/f6JwJpax0wrZpEptIev12RvZrVHrO8waqj63uxG8uCVKmAadnKOb9frbHlidRDYdmm1J1p3gqJqXEWc96nXO9d9490fPzLg7txN+7G3bgbd+Mf2fgkK9dsG2D6gHaeIEUxkNhbMEWnHN3j1jJEuSTVUC6ILEQ5oLyGeL4GlSphV9K2xbh/SN9tulQV2l2E9ujfYU82Y2tyTBIJz2uIHMYzWzhKgXyWqqX6XsDAsOr4pSGHnKhKmSgxeci2AfnGi+Rj9yDB2MGkSj5o+r6B3V/yDxYFn4N+HDCM0/bNVsNZvs1MgKnSKtxflCgvkyn+MGOW8sMN5qNaLsW6KTBccjl92qEYdQLXdasCxTv6su7IITiN5RuCxuZ/p6E59WXx0yCVvPJA+2EkblzKUcYqQO5dofAIES7ttEC62Vpj8jog20WXhwTNuiJ9DoYqfe0S2Ux+poHRGytpLlBAO2d3H0Y90jVVQloaypSLqwLQTVMGsGlvJyDRPcInTwP9UXLmifmhCIQyxOOuv2oxYslLpoDttoRbchVnAnZfdfLlSkH8lpUO8H3cphKJS9MT0Sy7ZJj1qxqPT6jCqvsMSgVcbghV2F6OYJexAg3weXI5C14hMNsZuRdSl3pbQtuA7kE8cCXfZXcKTalgmTB0WNZ4vaCKrm4yZJlDxSSjtrPoGPIOTokBfwDgulSB2hsrrOtq3mBwBi8uyMPaey3Vr9IB1jo5D8NgUImxfnrd9r2Ba3NxcqofOslmPvrRFR5VO9w0dM/XXYbNho0iGgM30vB8HQ8PN/irX30FgJ614nOqTmejBleLiRj8P7i3kO9+OrtBaQasmbj0P734E/nZ4BKkTfuvsdS0H1+cXGGW0z1y045g9+BpgIIzAKpqp1mDCTOLW2elwt31ucDF46zDxLZSrVa6w0VHCMC7rcCBH+34JCfXdq5gCoLBTBclESRdiROICnsOTSXEfF56tDHguk2m6XZLDk3xRTpUBN3G7avU/gAACSm3u+SUE4y6JanZj5HrpjTZyYvUAJFwaJtwa1J2uRKz/v7QoXzH8VX8UheZkUrfHbWTNT/kwzjZHyqnxHnKZ6zv5YD05sTD/IQe6so6rD9MBK4bMqQX4s4ie5UL5KfyIIHcvgiwR/TghgB8uJmmhBKvoA46/hkFxmt2DCpv9lY9XsHcWJz8F/qn6QMu/jnvc5lgeOWAbJFce3we0B/yTk4GaOPFDlGFFLVXfQgob7wsmLxJkYX1/SCLJeXoBR9dmLI24c7lJfXy4zXcneoEV/++VtknG0NoQPF1ag80oPaCxHUKLPc5LSLivoR7LQy/YP27ShYzwyigeeRw7zllET4pWrxbUI+kay38oKDGKUlGRqdpEuL+Y/BKLCBDo6F2ycQ/GCD7CUH7uXV4c34o1wmbZKGoQ+qhmpZkP9FxSbnEkjY36W+GA4fJ/Y1MBOHFWFokzUnA+MkaI57UXi/m2HxPC64v/uQtNAJeXdO+tFeVbBMmACVb8eUO4TqXcIfu1IkFZNdmYo8JAKZwqJgtnFuH9a7AECeofMCWDfn7xkKxVlx5oHi2xYyZy4dlLbaDN+0I57sJNvx3zS6HuuS/ywJ85oEN3Z+LqyPggK7T6WdXyLh/fLOroHTA0xPqkX49P8e/mPxA2wsZ/sfXv5C+80HVyOe6yeBW/F3VgPF0h3nVyHmM51SrgHVbCGRstEfDi4fT0RZaBbzndJ1p1ghk7ILCiFeJp+UGGkHsS3+zuI9tx9F6fUyb/3jHHSx8N+7G3bgbd+Nu/IHHJ1m5eguojEgdsVrwllaTEYYLRgm5JFglcLHPAIQU9Zbv5ZwOlUI/SVCqKxOTWA9UAccqN1+GWx6yUWtqunDLGUn3+1UtfXckIJmWNKe0X4pj7Jg4M9XYRReml1YMK5QPpGON7jV7GbDaBezu6cRAjiEGILgy7oetiSUbSUswQWAwBcrdHHZGtqF5FW9rBVcEDLzSVoNO2a2VFz/Welsge12g3MXKMlV1kbSSLVNFF/c3WytMXieS2vYRmejTyUMaXjErm6/baO+HK4vyzAhcmq9CYlO3QDPXAs8OZfouu1PoxwkVsPVt5na8ZvWDAN0qCVVQbEpC2+d7gg8tIiDxxMb7MV7DqAMeRskzWHcK/cxD3WdW8CpH9ZoJKgNVdQDlmH727BIzJp387vJYArmB2xWpzpwEX8MEqNLBMgNZ6SDxc9l1ep10pw7ZQYvdTdSrWuTb6I0c0M+8GGvYdYLevaXrYrZRD56MRlwVoB/TA/TZ0QqZcfjuGxIM5w5o2Ks7/3KFMhtw/i05EJla4/TnZKCw6XIsNiM0i5LPv0IYpTCDOPSbEsVCoT2KaIQS72vXaejMoRqzjjbvkXPg97op4L+boGKC4u55CgnQywzhiA70wYMbHJY1TtnFaDvk+IcbIgut6xL1NhfEQFkP9aCRXQwfShx8QQzh3DpcLehGuLiewXHEouo0wsjh4RNCDqa2wd/vHgEAfr18iMv1GF+cEGqhVUDN5hmuN9AT2sc/evoefzZ/LWYQ9TwTQtOmK2C0Rx/3MShMc4baB4uVK7FiItfSlJgW9EL7V/deyDm+7CZY9iVeLgleH/YQkn8KVd8nObmalpirykEkNtmOYNYYHh10eull6yBs2/0QaoBepPt/A5V+Jz9PYn6XUzD59C3bsDUe9TG7lkyVBGErRxOeTAaK4E0AsCsPbxWKZfzuIJBZtnZwlYYrmEF6mMwaipsg/b+hVLegZoKx6XM307Qw2HvJCGO6SzCkK2liETZyraUH2uuAfpsj4x5stlK3jOkpQ5Qnln7PFH8yCER2sSmgeyXnfH/YHfWqo5GDqSEJRcEA9alCtk7nx5fJHk8Yxk7DFUoC74taSf6saUjetB/MUPL28y2dfzEOUQqj89TzjoxvNRDTNC6YhjHQsNuRbmmBFZmhyidIdKhIshNbCbvHLhntL7Us6IDbPdgo96J/0CLDMyPTdomF3Y4C8qd0UzyYbrFuc7y/odVB8AohMtuH1EMFyJkownraerjWUBg8OG2JWcb9ySDsXlUbqO9HmEQLTpMWRjE0Pdpg0gJDpfOzVrcWMM1DNoc46MQAorQ9vnt/T5jc3emA0TGxd2ejBue/O5H9v/cn52iYAbtYjBGWyYErjBwsm0+4ziB/RTddeUGLZEl3KpyEqkMBeTGIBeHgNHZ8vvtvZqguUlg6nILhtJ7ZFxtMGFad5C1K02PMPpVvtnM0HGa+vRpBlwMsu4sNvUH2O97+gcfjr89lUfTr7x9DRqdhNixt22i0X/T42zOaUPEAeDqiCbl1FsZ4bDqa/FZNgYrlQo/vLTAv6aG/X65x1s5wzBXBEAxy7pe2g8WmLqSHf3SwFVnOYbnDVTOW3drPwV0NJbbc671uR3i7PBAp1LjoxATDbT9+WPiTnFyzLWB6qiziinmoyDM43gd6gExIkSQC0P/30+QtHO3k6G8C7IVHcZ1m4O6ATnE30SjWDs2cJqH2qYHn/qjdJLlNVodbGsZYUQOAKzSGIk3EegCyLT+AI0MV6Z6kJCXkQFx/AH6hRzs7A/GX9dlt60VgfxIOaI5T39nlkO8qrhXMeyIgtMcBepRIXv1BEI9aFWiScE2s0vccrh4MuF7SA1m+zJGt0yTjqiDyoGBo2/F6jN8F7B5GmUuAtyH1MDsFs2WdYh6QreL3soY5qj3aRF7zuUI3S05W5VWQ8+2tgjcQwpetOaSez2mxYD1mRnKtfTJYHl2SeG0RyWbwyW86unbFfvjBb43sY3sIbJ+wlKUlIlLkx+heia7YNMD2KUUOAgCmASp6UT/a4XRGq4hlXaLrreguXWcQGG1Qg6L9iP3N4yAuWOEqhxkUQpbu+/hCdz71p4MivXNMZnKjvYVOACbfW1nYyn8E8SFUSGlG3bHD4SNaTSoVZAL65sNDqJ2RMPY4sQLA+bcnyNYasz+hysyogOWK7i2/yYDCS7ScUhBtbv4uQ3kRr21AW6i0EKjcLRvUoTeC1nRXJSwHwZsB2D0MGP2YCESfHyQrxHxvZb7pC3xzeQ+/0yfy36JDE5yCeV3K4i/zQP2A9uOzr8/QOYNf//ZJ2pn4WI4G6DnLWrY50Bj85HOKfftvT36JuaFJ8n/94WdoW4sLT+ekyAY8ndL+NkOGkt2UAGBiWqxY+/ehmeBiR38TFyuPjuna/OjgQoiKF+0EZ6vkMzzKG5xWG/l3dHm6aSocVA0KrvqnWSPnSmUf/+T6T6H6vht3427cjbtxN/5RjU+ycq0uHGzmCNKtYgrK7Z6rHlLlESsRILFxc+4/mj7smSsoNAcKLqc/zLZeKkY9BOxOjVR/agCyVZTipH6mwI5RfbAK8nkoibErPdEufZcKQH2sxTvWlSksfRgnh6AoTYnym6DSMdudksSVeDwxUaWfJdamLwJ54kYWZ5vOy1AF+OmA9pjZpFuL8j33XBuqhIUl3QANtVvQno+kssw2DMv3aT9iduz4LKC86qW6u/x5ThA1XxufB9hrLfsVj9P3ST4VqDBLiEAJtNEdKifGuOFKM5gUzCDVrU3fl3HBZJsgoQ0GZDwhlasiqQVA1bTdKeln6wECY1P2LbUh6N9pG9ka0CwTqe979DMvVXm+TAjD6l/WOJpvcb2gCkOdF3CcNpRlDucLqii6VYFs0kGJUbpJ7N09j2QA0O8LgbyD5nN8k85xbCv42kj2KnzkCzA3YaUkjzZfkdwoVu/9NKEl0es4Gl8g81itCRXR2qPnJB1VGzEbAShE/IhdmM4eaMy/Smb6F6sJPJucIAtQhROJimsN7CVtszpX8nxc/zwgjHtY3n4A4Lm3rHYGodZwfBMWu9TCGL5s8BfPX0h19n47w4J9ckdFLwzg9nwETAYU3LftewP9in6v6ID2/gD3jL77+HADy5Xi9XaEzYuDVBWdtNL7nVaJlWvnGzyb3uAvj74BAMzNFv9hTUHnm6sRRoc1Hs3Z9ala46ImHFupgB9NqdqdmBYX3RSLnvbrm8t7Ijk6Hu/wx0dvURl6SD+0UyxZbjOyHf784SuR+vig0LBBfzNkYihhVEBle/Edzo2T34sw+8c8PsnJVTtyaBqK1D8bRioRShAnWvrsiuSKBIBtBulzN1ECqypH+jwx6x/plMwxVeimqbenhiBOTtkuoGVJxzAit50IO6s9LWU/YUhXx+/zYrLfTWj78SVld0omHbtL2skoMYoTga2Tq5PPAtrD1O/yBoJt6A4SCB2ygKC19Mn6gyRDAYDsQyYv1aASiStoOq4IB1I4OMsnco+ebf/szhAsz+du9sLj4Fs6cf2sQHtkcfUztknbIyMpT33P0dkexBjJWgOSA9HEA0Mis5kmLRSUo8k2QtIid0EirMV7wXRAvo5QZ4DLk2aX4gfpR+2JR8bpLcpxGhCf/+0zJxDr+IWFafaITCr1ZmlfuUd8paG8vqVtbf6YbsjRqMW2yRGWedwENPcUd5siWTfOHElDdukVEPfDjR3QabGm1MN+22Jv8gXLzWbczx97uSeqDxr5Kkifux8rOG7D1ac0mcbzrwbI4slbgvfjgsCUTtyIlA4Sbxd6DT3uMZnQBaryHme8cOg7i01TUPoNgOGyEtJSMW3RtxYuSoauLCav0iS/e8aw5KyDNkG+2zsNREe1kYPLUs/FPG1FU/vs6AarvsQso/2aZB1Wmi0462SvGHIP+yFHxglL+QCsfkInxM47PDhcSyh6O1iZ1JaLEUJJ5v0A2SsesFTmZlfJ5y8PLvHzyVu8557Df/erfyO8iMN7azw5WMLyg/9qfSja08N8h0vWmvaZQaF7XDYEXT+apRSpZ5MbPC4WcLyqK6seb8BRj9rhopmg4Z5J7w0yfqn03oglaWbIuL/jl1HdZNj0rDnubocrfIzjDha+G3fjbtyNu+XkmNIAACAASURBVHE3/sDjk6xcXamATN2SOthtYEYp/dtbiHG2K9PKXTmAPIkjhBaIHQr67y5TcAeJFBGrQIAq3lgp66DQcXW0X3EGDXL+4eqoLpQEX8fqM0JXrlBojtL6SNi4APRKofrAsNUy+Qf3EyI9RZKObRKs1x4S/G0Xcb+S05Ibe5HNqJZgZqkaddgjgnFFGMlOy1Qle0vfFV2k7C4RVwBiDwPkfwwVMP2Bvm/6qkF/wLT+5znvZ5QFKUERshUhD7sHiTwUrxuUwjClf2SHLZzT8Owba92eYcic8mFry9D+Jrk8NccKuycDdEu/PPtOy7H0YyWQv8/pPomIQLbWyHjRXywCyZGqWIUagUKzDZ23CD8HrQSZCIauI22Dvar5Xto+BdRrqga2k5zIbHwu3dQBbIiha53ILz0F1Ydo7K4DDLNhfa8Reo3RezpOYmMzwWim0MzkkqGfBngmFWVLjdH7dO8TzM3IymEKp6jOFLRLEGx9T0nQfAw1jzeUawxOH1BZOziNTWRPn7YYl5344J6/ORS3o8nhDm1rxbxeTXsY/lm3y2E+5Ji+TvtZUxIb6scOIVbGO0uxhuyV/OBkiZrJVMvVCNW8w8/undGpU0GYsrOsQe0yCTov7IBDhqvXTYH2HZXv1YXG6CwIq3jzixqzKZuoABicEdi57jJsWdKEVmP2aI1JSTeNUUHMG6ZliwdjutF+MfsB//7qJ1I9/unjt/hiREb9mXY4a2fC2n02vRF4V++xIE+yDV43h7c8g2MFfdWOcJpXcFyf/c31EzwZL/hndIyRjeyDQqs4OzYojNjJqfcadZ/Jz3xQ4kesfI2PfXySk2tfEZRrWsDEoPCM4Nj4AqhPVTICd0C2vc2oNdIbUugZ7orm6vGl4bMUgu5yZinzBOlylQz/Veq7mYYYzKK/3ZuMopY1OiW5PLFmociEf8QxpdqlhySYPS1rFxCMEuef+jRNrsEQDCphBlnaD3uZKMS6Iygw2sHpPSkFhdADqk/HKfuhiUGqeLEQLFII+sJIL7a8Cjj6dQOf03de/ryS8zh6Ty/pYpGkGyJj8r/Hit6TTak2ST/6PIO9zETaElRiT5u1gQOQxxDuAKw/o4/9aQ/VakxfxD59wOIn8dwFkW65UUC+0HJN9RoJdvZ0H5Q3LOO4Svvrc5q8ooRq/xhMuw/fU/sgujKZBujvc+7oeABag5AzvJl7BF4M+NmQEn68glkZuHG0+PLwN2wwXytUZ1oY065IwQDDCGxvyRK2jUL1kpmyTZB7fxjR/Rqfp9HbxHKPKVDdNPEPhjhhFw6oDRTDuPmow3ZPfzthKc7xeIdMO7xf0R+OT3Yo+KW9WI4RFslaEKss9YhrahuokCb27ud0oCcHW/muts1wMt/Iy95DYc26zYcnSxxVO5lQh6Dxn94+pc+DwXTcoBvS87LjbNr8fYb5G76uPqCfKGz+BU0iR4dbtPw3Vnt0g0HP/66vK7KxAvDzP3oFqxw8n9iz7VS+62Dc4F8ffgcA+Jv1MwDAV1PS9/7bg7/F/7H5KQDgN+sH0CrgcUmT4XG2xc7HPqiXZJrfbu/hb84fy+Ra7wrJps2Mw8vloYSzH413uOHc1yEYfNhOsGXXp2jkDwClTfank6xD542k4jwcrYRR/aq+67l+lCNfB9iMwsRjBRq1n6JZtSniS/k9iU5P1V6cGIcKe727QOkoe5VafDnagXqNqeoE6QB4G7FShVKpjwfqD8f9aI7Jbi9WMAc/9EJG6sfmloRnKNOLzeeQGDOf0cIgSojyJRKBydLfxXnZ7pRUOkGlhQdNoCG9vPrUvyTDBEBF4b9LJBXTAXqTjrUbQ9KFjn4dMHlLv9gdWLz9r0ZoYhWjPA5/mYhnuie5AwC44x5gK8TZN5auI+/XUEAkQe1x6geWr3LpV8bjjr1B5YDyzEp83O5hEDmG3hrkC42azQqGzxp47t2ZjUH/kGfXxsDUWirQfpriC5UnElTUwAaFJCU6CCiv0n7pYS/laKRQ30/X02dByENh3kNFH8xFDrPVGOZ7kEmcaE1A4JehWRvYnRJ7PyBJgExN9117lM6P2CkqRgsuI3KTFme6A7aPeeFXkK47GmnsHiZ+w+gdGWBEmU4wgJtxv/Eix3CUjmcYDMYV3RdHoxo973/G8WQxzs23CtdvqL9YntlbC+B8BVkA95OA5jQtFopfXONPjqii80EBUWM85DhfT7HRdHD3R2sccArUpi/w/dUxdpuUqqSjHap12NY5PNsCutag/IG1s5eJj6F9wDACjE1a2cLSNbPGYXBGJqHTxwv8+JAmyaN8i8t2gm+uqdxerkfik/xkvMC/vyLS0uvVIR5OV/hvDn4FAPgfLv8C73e0EPk39/4BB6bG2456pDfDCPdYy3Vq1/iuoRvtb88fY301TpK1yklEXqMCglM4OKCFiVYB5zs6eR+uZ9DGS8TduOjElnGatSL1ib8/L+i8ts4KKUpjb2X8kY67nuvduBt3427cjbvxBx6fZOXazhUGhlRFqmF55R5Xlt2e7VoZwARdmA3lLsYqqLgOyNe/x8RlyFV3ABPt4HJaSa8/Z2hymhI+8pVCfpV6oL9v/h9hYd0B0zduL7M19dCCVminCu1RZBKnirqdJ6Zstqaggcg4DjrICl91xE6NlYgrFboD3r5JVXjQZDCQRWi2S6xcsuK77WQVP+uOGM8Dw+3FlcLsBzZeyBUu/pj7hk8CfOXk2Mr3JhlD9AHdPEA/oRWz0QH+kmUnA53vGAZgtxrNaQxBBdDFnmiAL1LFBZVsI6FJBrL+intvpYPiqsSXDq7JMTykExZaA1VxEDkAe84VypXivnnqLeciuwoUZn6aGOARmRidKeTLvUShLiEr3qYw9s1nAcPMiXk+eo38ZZHO8TQI0xe1pt4hALtJYQUxPCAy1rF3zdojoJ4FQQD2rSP1oOBUEAh/n9G8e6SShIzZv9tHXJWfOGQskVr9iOwZcw5jb19NUL1mqcZ9ckLybOwwPd3gF/dfA6Cg7SjvKE2P75cnOHtD5XX1Q4YZw+nZllonsTqtT1PlbRpie7uviX1+r2rwu2t6SJfLUUJxOgMMCku+vu/VXIw04BVUrySdqrjSgiK4pw28U8CKjqe8MMi5PRBs4k0ghmwwXDAuOoFRF6sR3KBxeEj76LzCmw1V5a/CId5fHWBg43476fFgRl/w/foY+R7J4y+Pf4t/aMkesjI9/ut7vwEAfFWc4dpNMOEb76Kb4r//7s/p/LeZVJxNk8FWAx0PgBAAXzPLetzj/ulSzNyutyPsGFLPix5FNmAUQ+KRAtEBYMEym22XY9fmYv5/Um1wXNBz3fUdPvbxSU6uLldAwYSKyMnpCa6UqDeTIMt8jxyUbzxPuklOIn3UQmHY60/1kz1pTEPwYIRIq52GiQk8LkFy9SmRWFI0XYK3RpcepkmWiiQlSqQrIFkBxu+njSC5vWzoZRj2MIv4MgfohS6chpBgLFcEGCQok4R/kG3Gbeg+LjD470ovspbiUqO8CshfJohx9dzwNdlzhtJ0XaLzD8GSaV+7g4CMr03fWIyuEpFo2LNXHKbJvxaFg+PbPRhFvtJR+zsodMdpMs0nHQreftfsSVUaC1MrFL/iRcBTD8162PE7mhjjudqPi8u2SUs8lAr1vRT1NnmTvKndnq4V4Ik1QnIN0NzjHygguzHQfYxYS22KkDE/YB3bGwnyBtK9FB28olxrGCVnK1956tnGvwuQz2FjYbYJGtcDLZIAYPQuRcUtvyL4uJ8lV6bhOb3M7x2vcLMeoX1DbJ5srYUcF8YOYdAoDuh351WDtzuaWEa2w6anh+vvzh4i/HKGEV9DVwVwCAt0x/KhPp27mFAEAOsfDTAML798fSKLOKgAxZMiJgOQB4GnfWtgVjGuiB2/IqQeAPeY9tcYB7+oUL2j3y0vg2x/+zi1VspLBZ8HzKf0Enh/cQD7A10Ad+ihj1psdil55/otnQPVkxeyYiLaZNzg/ZLg3meHN3hYEbx7uRvhf3n/c3w+pVSco2yLA0MT1y/rZ3ixO8HvVrSo8EGhZYlQljnpjxoVyDWK21d+Z0VX7AaN86sDGJsmcx3fl9bheLzDlOVInbeoWb/6ajmXydQah/uzNabsxlSaATNmJ67/CWCq/wQO4W7cjbtxN+7G3fjHNT7JylUPQVZZcXkxVKBqiWEy00BMwe0uEUuUCySRiG48ffIgHrRCP02Vq2l/j7HqgzAw9ZCSUprTVFnaHcmCIqToTWIf6yHA1g7BcLU31UKGQSAozvgkmahP0zFGiUR1SXmkQ7HH4hXHIYV+mqpO5SAhAa5RIptxJVf1Ue6RJzelbM15nFyJZAudiCVLgpzXTzlTdBL2pDIJDlW9Qr5VAmGaFoIcrJ4rqKMG3ZYgqOJ1jo6JMcOjFqEx0OyR6ysPywkfNnNousjwsjBtqgp9nswOEIDw/RiR7BketBizRGKzzTA6CwLxHvwQYFqqbLqpRsOh577gc8j73xwmdyjdB2SbBMXHqpVOEEl64nmwO0jV00+TtKe8vM0oNm1AF69FwX8TC/ardG1cobB7wBU/k69i8g0AhL0gdbUzyT9YAcVbzuedeJJlMfM6v1EYv+dzMFMCA9stsYrNQ7rh+zrDCcOcbW8xDEaufTf34hGsM4ei6nGPPZBHWSeh4b9dnEJ9y/7TV+zfzNfJ5cm/uZ8S8tFxRm+2TrBtdzpQ1XfGMp08ILCxPnqNMOHEJhOQVT26Nd1nemPlOQHIBCL8lKoskw+wXPl1ywL5jZY2TDdT2H7m5G9iclBzHDBMPdZ/RcQkmwf0RzFTuIcxXvKMw00uxh1+6gCfEno22xJfPiCy0yRr8WZLFa73GlZ7kcU8Lhf4f9bPAQC/uXmAD8uJ+ErbzMnnprdw0W960PBep/vCK/JmBghdGg/IMm6LOI0so3NXZMQIfr+linrb5sI4zu0gmbClHZBpB8tQtlYBbxva//erPQjnIx2f5OTqrYLKCLIVizpOwYmQqM9vu/Psh5kHBXnpuQxwmZbtujy5ObXHQSAz3QHapQmtneKW+Xz8rmEE1CdK+pJ2q9Bw/7U9MmgPtbgauTygP6E3p1la+EJJYLS3yf0o2wCmTg970HvHaZREqqkB6KZ7L6WVhuY+n6mBTMUXOEGvMZmmnwaxx6s+EAwW4a+gElTYjxWZ1PM7225SXztYBfEL9yT1iZNJfhEE9mzvO4TGovqBe1rXIYW7rzJ6kR5wysbJDj2/oJp1Abtkg/YbcqGKDNLu0AmjFk5Bf7mRl8HJbIsNSzDKdxamCchqngi6gGHEL6UjLVaO0EkPDfDnKKMBw8V8DlyezkE7p4XZ/j2o9/rcUStLGtLE5HalwjCOn2nbsSeqXErnWX/loI847NrTBBKdiuCU3JCqp88R9lR7zGrdK2SrJDMyHbC7F2MD0763xx7qYYOcX755nuDD9aaC22TQcQFTeCh2kSqqHpl14rbUXlaoeGIvuhSZt/rpAN1oWD7OYrHX9596+CL1RNsvG5QjxuFXJezbguw7QbKpPq4vZoNIXmw+YOgMMCRnrbjoDCMHvTHwHDTR7rmJ6VlAd+IkZq49SbyI/IOVxXzIgOxGy7MWDnto1tiOxg3aNpOJTM166fvH6DvN25/PdnKvLtsKLz6wPksFzEc1BoZg/927n0gwBgCEoGD52tw/WEvk3OVigmYT/SwV3Rc+yrdw6zlxrUHNEh6lIW5W26DgvBZdcG5T/3XXpUlzmrfQew39zhssuR+76T5+UPXjP4K7cTfuxt24G3fjH9n4JCtX0wUYEKMwEoyIwLRnJtAkhqceQiIO5fR7ifikbhlD9NPEVi0/pFWrdgwpqwSbxYrRFQo9V2b9hOO5GJKrnwxptThomJWRbexrTd1xj2ybi9E+AIkhy1ZBXJ4kqzRWVSoIc7ibA/WzXtilpskEzjR9gOPPtlZ0jMywrS4UETd4uCLFskED7PtNRhprSNU2jAkajiMSmoYZVZJuzZXmUqO+z8iB9dBrk0w29io4s9PojyikGyC/2RgT1u9SADq5S1GcGQDkxw16Ji7p0mM2bnBQEpTQO4OrhoOk5x7uXMGcJzJbN2HUwiS/aTqxCZlQSJCuCvQ5ZvQqt6dBtr9X8frUEpi8SWiAhDfwse0eJS0uPFBcWLnezbGiewiAGg3wN+zdagNgffLq1VrY1Loh3+KIfAiTHhRST3pw+rfrE/ozjAOGA4b4xlSpDIyvG+OlcrLfVcg6hfox7Vd5nE7cuOxwvRij/DXdNJM+3Re7hx7+MKY5KKjN7YzbaPpSXBq09xyKe4Sdj7JB4tz0wsIXAXU8Xzo9Q6gN1JhdvDIHBAUfvYWLAMssm+w8g3JA/YAjAJ/t5Fr7VUHPxRBNKzSKqwS3Rx9mbwA3d5QnB0AbjzyydOscbtByXpxTopsNTiMrBxzPCTY/rnYYWbrY31zeE93s8XSLzhm8/kBwiv3/2HuzHsmSK03ss+VuvsWee2ZtJJtkT6tHGgF6kAQJkqDfrAdBDxpAEGZGQvdomt1sVrGYVbnH7tvdbNHDOXbsBnse+ZJIN4BgZIWH+3W7i9n5zrcUDkdMnupGi/2ukjzaq+0cPYcShFEDY4aBMVFQwESpYlXtYYogsXsxIP8cDfZBCbx8NBsxLzL7N2W2uqBRai+uT7f9DJueztPu87cW/jIX12GpYCpibKY0i1DQw6y8Y8bbZvLQn9T3RRuBGNFzwocrkB2TmNw3e5cTQ5KIXo/EFE3Znf1pkBQS32RmZiwD3fAJQi7yBZ3+W/84YVD5deUnS3BtWmi6vHGgjFC+qZWSUHeAGaO8sKfeZUoJqa+VMIKnLkbBRNSftEhDgAyxRkNB0WInOI/U3wTJIXyVWbpR5UVHO4Xu6eQXAQLdDStyR+IvABXysY6LzATVo4LpLHrejGxtQJ9SVMbcC9/+eoBpvDxExstGeq5eAZfbAve8QCsVMbb8HmXE/rHG/IOS75qgyGIfMSY2taWvkAJn9Dj5np4CG3KOsJLFld5zMs8FZCPVneZzVt4B7eOYs3AjYHb88OXUmt1z/j6r/ADHpsgPygDCrRIUvDfQKWe357nn19phAvNrPMC7oslthNBkeJd+qaSXN141mLOTk90D6+8CzBE9cE0ywABw/eMJ5q+NbFTcLJuOqCDEVURH8LT022cRgV2d7HLE2WoP3rtivZnBs4QEK8dSGp2mDirNnY1o5r0cU+c0zJaOef5z3ihvvvNQx4PAqm4wIlFRgyaZDt/boQoZMh6B/asMjyuvEHkyA4Au5OugbEaBZ9XOinJBzR2gIh7PaHE9qfa47Gin8x2bYQCUxnN9t5B/WxskJSgEhaJ0svFxo6FFFaBNwUSWhjLkZxEwYVZTTzZBwcaGSSg6cVqO57S5qa2Tzy6Mx+tbqgA0OzelxbX3BmtmSNdJv/QZjy9ycXUNECvAmLzbjQaoPylJcCnavLgSaSJXmVFricty8/zgpIcS+cfSewSM3JPbvFLozwNCmR8kyaPWtEr6sb5W8PNAPrAA0f6bXPb4pZeLXbVGep3BAO4s29u5QaHjG1mvrSz4dkuEm+RrrCeVR3mrsPipEC1eKJAJX7MsH5m9JzLJ/lmeu+SElN5zXKRKExjOk4VVRPnRymJrBiAJiNunDnaZdXHhqpJ57b/tpccUdhb+yKcWJopriy4RtyIl3pQcweWdJo9YALAxE3Q04FuTHxRFyJuStSFpDi+2uvTiuesHsgR0dV5dUn99WJLrEMCOVBNSkfLZL7jYxQdJR1Fl+dS4VOhXGbWornWWaD0JsI9px98HDb8uYVkaUqwVPG+QguFNzIrP/cxlF6mdztKqlYOyQeZHj0rOSznpXwIPpVoqAnC04AJAfxIREiEoqByqrgBVBJGylNcaA99r26897HGuZHa3DerXrBEOJLUa2RqxvoKcm+5JgL6lgzG9Qn/uEdmCU/VaKtCyGnG7nsEnXWpvoPeT+6kKiClpJ+RNhJo7DH8gEo4ZgLpV2HPs2/jf5T685f50ItVh0h+cOn0BwPyNkY3U+lcOmgl2uKbrO/CGXC28kMuC0xjfzlH06ZkQoc5pvl49vsHj2QZfzUhi8+PuDL9Zkcdx60v8P1cUov7x8ghxZ6WH3EZgd0MXq+bIPdGvhgnMEvkkpJ9HLRtslAGGpThaB5RlTizqeys/VyUtpomrgJqSfQCydrScwDN6g5vdTNClWTXifEkP4GF7cGg6jMM4jMM4jMM4jD8bX2TlGjXEcSixeaOGmAAAQHuicyJMkxmdrqadZOAwZ9Mq2l2De7mpagFw9TdGJCn+vAcGDTUk9uEka9TnvlIoI8xWi2NQsFGgqajp81JWqvJZ8uLOiOmY3h8GUsWGlUN8nwxgs3EAQFVsknUoD4Jjud+htwEjS4JUmZmg3ali8/h/WbkWG4JsUzqPfbrHgp14uj8cQQ/ZY3ZcRozcn9arEW5DEz57XWA4jhi/IohOT0wQUBAUrrjqHx8PKDi5ZLyvgCJIbqVSEZahx+AVos+9L0BheUZfvOsKjJvkcMTflz8zbIrsdrT06M4jweXgqpwdlHYvPc05AL3XZDKwmcDVLGlKMq5pZZiq02CB+jJn/oaS3KoAQJ/3MAm6dgr2Llfew3FEmD3MITVciYRRU7A4j/AoJU5oqOvyAYqQzqGvGcpOgED+c/JGXucWiG/yuVF9zviFZjkZn+xQZAZzvDdwsUL1kd94HtE95dDznUZ5qzFj6dWwVMIQtvcms6ybKFUrQAxew8YK7bYih6UJnBma5MpCxybOZohQjBIVP+dQ+N0vB6zOdqhYYtPeNtKfBoDYGaghv39CRZRX0K2W89s+DghcdWoTEbiaLh63CFHBMlwaw8TjWdF3cwlpWTj8Fy/fAQB+u3qPMRr8fk3+v98tLvG2pV7T728ucM/B8kpHkjcl5nlvoBmBKSqHcbBUsYIqV/GmtlmelRCP5ASmiiAQ7mLWwwcN5/NxJ3hfqwhrQs7hBaUDAVTBJrP/gefWMirlg8KO2cS7/eSi+0zHF7m4ml6egwLrqQC4uRI3oagztKPHTMM3IwCd+4ZmeKhV7E8UNt+yrq0K8jDAoKmvyrFnwSnYG7qQQsHyFkBkLEm/p9yEsDOy1aD0KQGTyE33ht2iJv3N9ODzSnpTwwmg+wx/q8nCHi3B3PK7kBNnHBT6E5X/ewto7tPUNxGGX0cB5VmHqX+3gE9B25okGmmocfLd3tRYcQzY/a8C1FkOoB43JVQyn28NYhEo/QXA4rgVuY1dDXCdhS0ypJQW2mY+oE0w3m2JWOUekdYRap83MKGM0CwdCSrCnjEhSEX0tsTVMS/ejZMHURwMwAu+P3LQjcNQMRGqMujZBtNuFJY/54i+FDEIkE45Wkh0XfvUC1yn31cYNEd4NQHh0QjFD6WicuJYpXVAu6vk+0NHxGTRaDVwz/FzTsG2SvrVU16B6Ul6Ja5PJl8jeqBrpD/NULy95X6jnzhp1RF6OVI6DVibyzaJ6W/jv6K+mrutRZtcXWsU2zwH++c5TN7eGXHcio2nRXJy3F50zIruNZ4fM2nF+EEDvZGeqF1r0ZgjEmkKAFRnsPl5lVsJJkq6UJ7XdM9GKF401aDgz4cc3+cmkLHTKBe00LrRQOmIwAtNOi6A7vlYBCEynp5tsGAXo/94/xwuaDG7/3F3hjcbWlydN9LpOD3ZojAeV/ccfN4WIkfSOmLoCtlsKhNQsdVVCApDkuIUAUpHCSUoqxELDlEYnYEPWu6hovASorCsBoxBYz6RX+1Z6jM4g4rvT+c1jAkS1G50RM2L8DZ+/ovrARY+jMM4jMM4jMP4C48vsnKNlqu0imBXgCrCYZUrSNPl6k6PUXyAQwGYUglBJViF7YvE4vWIJgpkae6tEBxiFaF2CsUmufhEjKtUgkKqlwRBBiYVxCpAMyysokL02R92ytw0vcpG9GBCyT7vphMkZ1tkpigSi5l+9jXQXEaUO/rs9lSjO8+7+mkkWbHL/sRmQIbZDH223ar8/kz0mYYVAIBfepmr+pMSqDERU8a0g7YR2CTbKEBVXmDb7ftFrn5XI8r5IIzSobcoGJIeegtcUUXXfNJonwO7NX2gus3wqG8iYKJUANpGzGra8c+qAfc2YHdFJV35p1qul3KvxKw+lEB3YRH4/BLUn6v+7lTJ+Q5FZgcPK4LTE+lNOSUyjvqKDPsBABcjxcdN2KX9Bz6mW43izypNme9JQVusFYr9JMtXZQazHuha8JP2wTRLdv/KSRg7ggJbyFIecpK1BEC/raUybi8iml8TvfxJ3cNHhQ+vyfCguDUwLPtRERiOgPYZR9BtDPyCyWxVFHhXzxwxXCf5tCoFGejIUCffo6OWuVKtoWPj813d5TjJ9kmOJYQD/CxI9atsFOINggKCgmZmdAxKXIzMkx51NYoMZRiM/J0uAobEXt9YqF5L0LyKSlyqUoD9nP2V5+WIgaGsV/NbjMHg76+eASC3q+TVO/QWrx4R0emk2uPt9kig2mLV4YhD27ddhaJyMNxKqMtRzCZC0IjzpNtTMDZIRQoA99ucsxqDEulPXY44n7HpfjDoRivxgM5rlOxBXJggRhKl9agKJ3m0MQL3Ld+TU+eSz3R8mYsrQ6/JzQaAWP1lmUhEwVaFYSKPiJYkHS25lmE4d5KMgo1Fsc62btCQ1B0PkjcMrK+LsywF0fvcY3XHDmbuJDFkHKzQ5CPf+AniBeghmb5UKHJgd1QKLrE4vZEFWY+8+O2yRCjpXE1P/+5XfLMeqQdazWkwu5vlnnE0UdjTAC0Ektl6khdh3SvqVy/5uCJQ3WTZ0u4lb0RmxHpWCVbaFlnasHKAzxZTauEmcgCPosiZk6E36JPLjVMoGP4j20WFGRvwDyeRFn4wu3MAdi/YRH0WcJdYp2db7N8tMHtrZO5Sj04Pk9D2LVDdv0wAPAAAIABJREFUAVGxO9SZQveIFwgLQCn0J9w/XXphhkdDfbLUN7e7HFgOPenNjgYYIRuVYV3Aspyk2CrYbYZ0Q5nPm3ITm8odHYucG5/7wMoTqppToSDWlypSFqxsGk9HOLbtC04D3N+1G4Iou7+mB/q3T6/wYk6La+sL/Mf3z1B9mkB//FndOS1wxR39bnw8ihQtVhHVii5kN1q6fxgON/PcCghecXZtXngTZJ9tITNol2BqPSjhUuhHHZazHlsOOo9T/acCUAZZUGNrcl9SBwx9AcW91HKWdzf9uoK5z+zsUETRkYc6CJO4rOi7HPNieN5s8ZslMYJ/2F3gd1ePZTHc3M5gGYJ+fLrGUUl/MwQLoyIathpc1T06ZuzOqgHLupeOVaGDZMcO3mCvWYpXOMyKETd7TrHZ1HkTHcjhKVkeltaLOf/Nboa+K0SqtJx1co/u+5x1O3qN7a6W30FF4RUs6y0+9/FFLq7DqYduPFRQWds3AoiZoKLCZIFYAv15ji6LRYDiC9pclig+cmN+Rzdq0s5CsYYVAILC8CgbQqh1kR/Mo8LAln16ZxA6ja7hPlavReMWyiixdukY26eTUGyd49ygIxTf/PVl9vcNhnq1qbIZJ1W4belBnOwEQ5EJK8FCjAnKtQLCVI/50A93XAL7b+hBoSqPOOmFmcUosiPzpsbiZ77pniiMj7knlIwNuF9n1xpukecRNuQH7qizRtVEBK/h2ZfUro3MVXmvZA58mYlsAC36R5xikgxDSrb+6wpAs2xk84dj1HeZ6Fbd5c3Z9hUwnvK5qDyK92WOBvymlQe9/bFGsNkuDxuTqxenUGysEItMC1l0tq8i3EnKhNMwu7whiyp7R2tH5+UBGYl/th1k4+dmtKGZpijJ/FT5PgDoOig5XjAaoL/wwIptNyckn9BpVJd0UfSPHX71y3c4qwn+eVRvsBlpw/Lz5hj9zwvoFG3Y5P6o3mv4JtImCgBcrkiLZqRFFYDvDSEYfG/4brJQR5BeM321/FUAAPOfjWw4+tOYNy0GKJ/T8V6stiQTSYQdr3JKkI7AoBHTV68CZke0qMWoUJe9VGq7vswISWekMk4b/MAcjOXZTnqPtzcL1PMB//rsLQDajPy4pyr/n28v0PZl1sCaiOWCPntR9ij5PXZdhabI5g2NHXHE8z2zA6z26DxdyHtXYt3TMYaoJBWnNB7tWGC3reWz0kZfmQg90Sf7oLHjuSqtg5oQ3UZn0PHDQgHCkQDIPEMW18m4m1TIn+s49FwP4zAO4zAO4zD+wuOLrFxjRSkcap/Zh+3jCLtT4goElaGwcTEJjm481N6ieku7vuomuzxF81AeY7psHh4LkskkWIggOoYpmyiVEkBMX3XLfQiTZQO+ilTB/We2RNFExvL48ActUKrpMvsYoB1zYv5GBTQsJQoGaB/l9Bu3zC5S5a1GdcvvzTaO0sOLWc7jK66mE4vW6QfCc+8MzBvaCS9eQ+Dk9olH0VA1VBQepfW4412yHwuYNjliRcBnZq5SUXpV3hXQOwOTWtlDhm3NkCHR2cf4gJFt91R9A4QGzD96zD7R725+a6A/EsY6fxcxzvP57o8V2uds1nAyYDGjHf/244KMIrhnGX3uyfmarjPpsTvA9MkhiM6HZ4lJ1CojB8Vkd68IMpYEp37CWGfkIR2j7vGAGZ5GdRth+/ggHSmdi/6UPk9CLQzQJ6nMYkRpvfS1x8FKwky5V7B/Q43n355/Qm0cOk/X+//14RtcXZJBg74sARvhTpmF3WkUd9zzM9RbFWOG5FgGYrzamivmuYcxAe09T7KO2cks/X+a417nEIgbzdaFPK9VgEk8hyZIf/TDzYqM+6cG8mmKRy2tCwCAV9hf0zVSrHqsh0YqvCQTA4j7IKzsSPdswSzdeTVgz2HpR8d7/K+v/kn+7kO3xG1P77/rSnivMFtlf8ALdkJ61GwQ+AO+Xl6jMSMqhkiWpkPBk7APJS6TiwyA626OVUXv92Q+SEV7uZ9j15dYLOh3bVdIm0spIHgtoEA7FDJ3xhATP/07KCVuUDEqWK7qQ9DQOrP2p05d+/WEIPCZji9ycVVOQzlND9gmn1AEI8Sl4WhCOEImGs2/t9AjxBVo822UBai+pj5kovabViGkHqglCU1avLXL8p7CKYFY+3NPVHyGSrQjWQNAfWDl1YPw63RcalQIVV4MlVNoPnLvbrK4RkOQYIKFy3UmvfQntLCOx2wjt8m9WruHBH6bjiRM4u9r80LiGqC41/CJIDQPAP9u3JdQG5s9hGcqyz2WTh7YpfVYb5oM/bI8Jn3PtDkCqP+nud9IXrwT20fkBXWcQ/qXUZNjkrJ54bUcHVfsPEwXMC7Y9u7txGYQFD+o2bO5vVCidfSDxj7yA6EMGJdKNgTYFLJA6IHg3rSxUi4Tjdw8wgxZxhSKfI0oo2DuuZ+505h9iKhveE5C9oduzyixJp0rFSC+2KHM5zpqYGwynN+fqAc+z74EPOszbeVRc/+sKBwq60XfqHXAfkUH+exfXclD+uN+iXfvTmE/0Qe4lc+bxAV5RyfdaHFphWDkVwGxiNJ/VBNyWVE68a8NQWEcbPZGdjovhIqufzO5LiQlKNC9m6B402lp3aijQWDnMGrEQUs/XDkFnzgMy/EBTKzr/JwYb2uoucP5OcuMvMZd8u2dBeFNxDKy3pY3sj5LVP77Jz/ABY0fthc8lwv0fFzOGRSFF2h1Oe/QcJzU03qNI36ABSj80/aJHNfT+h47vtC2rkSIWoLnE3QPPPT3Hb1GPSUcBS1RnQnOTSHrALDkzeVps8flbo59R5uFcTTSSy0KJ+ezcwbeWTQNa9GjwvYyJfd8/ubCB1j4MA7jMA7jMA7jLzy+yMq1+mRgKoNxGQVG0T0RXhJzMMyCSELqd4VUtP1ZhJtNYOKgUPDvogJUUJJ1WWyAUGZyUDCZaDIdw0l4CPtFwCa3nCoKK9SwKbj4CRfAuGKm5ow8hxNrVDlkpx87MQRQVLUmcpLpItpHDAeekPGE2bGEJ0zYsOMk9SWCyFpJKbPP5KZiB/hRCdnG7hU8My5VExHnDpFZi1HnxJZyNgpD+u5mDow6m6vPPOySdsXeGeC2lEqnvlIPGK9T4hXJXjJcmr5Lf0QyqxTOMK0QfamJad1o+Z2vkkwHKDYRieNi24jFz/SPflNRjieIiVrdZtOE4cjIXKXUm4QIKJ8rahWokhxW+ZgTg9d0gG0Ti5iPtUrQfoaBATJ5SNXqFAqGAnRivWv6mxS40J8FuTjj3EOXHkUyqSgzqnA6a7EbSqzXdE5N4XHxjFjAd22Nn39itwyvYLZaUBe9HMVpyztyh0ooy3gcKLACAFSErigwHcADeDFGJZVT8BreaUE3lA0Aw4qxMwSxp2ppozM6sAh0/e8TQxtisoGoEJIUxyuoQct15iYJNukzE/EubArxCcfM4+mjOyEWdc7i+JhO2L2aQa+yIcO8HvBiSXP38/oE/8PT7wEAR7bF//butxhYyvJovsVVpBM1FB5DnwPN15sZdswqrvWINxyL9X+//wqDs2hYRvOjPZVjOio7zOyAFTOL10ODnaML6L6rMXKlGjiXtedUKKWiSG+MCdA6wEweaMmc/2o/gw9agtSdMwIFA8AwJmcmj+N5i/vERr6aiZmI/XgIS/8sh68jYh1h2kmPFRQVl/Sl89cWBetL+6OJtMGwZCLFsu1yAkaxA9QamZ0YJxrShuHFRJwtojAHp8dBD9jc7zK9gkoLnFecLMMPQQNJAkERYdZ2EiKgHiyoqV8ULX12fcULtFVZJzooCsNO+t4emF0yi9NBQrejItgxRdoBgDeZTRoqyIagusu92c4Aqi/ks81AsC4ADEWJYeDJshGwEXpBK1BVjWg39Dv7vkTzIWtih+MoTNbqmiE27h0Oq3wumo/qQTJNuYkY0+uOlaQhKU+65hT1hqgeuBf1x0rsIYttRLHNMo7ZB96oDTQ/KVbO7snGDyANp68gf9dcZ6mSb8g2T/paPkPveqDNA8AuW0OWSZkxykK7exExnjiBLO1NIfOjPB5Iz6IBuif8AXU22YfXiDcVype0S1w2nUCDr//4CLrVaL6i38UIXL7lqCevgASRagW/AMyKdjoKEKN4dVMSLJsIoWWA4d5kZBmN4x61tUHgxqO6k1Dvu30DpSNOlrQzUSq7+/x8eYL4sRa5TSghGx91NNB9uZs8vJOsaNSSCGPvLLm2MUtd1R6xS/Aow7l3dGHbURHsDWD5aAsfNK7ZtN7qgJZ7qabwEhq/qHvU1uFPdxQJ9+uzT7jj3Ml/++E7bLtKFsar/VyCx73XcL0F1gwTNwF/fEcbmj++P8/3uSeZ0ljRMT89WeOkork6KVuMUePjnkMKJlIcraJE0XVdhaGzMLxI1vUoMhqtA2blKH83HaX16IYCQ8+Lsg6Y8fktTMB8QQ+YpMVtX1P/t9wpYcq78C/e9rMbX+TiWt5S5Bx0rliGowjdK8zeMeEo5MxKkr/kBU0FBfAzyZ+O8EkXemJQ3eTkkeoW0hcLhcL+aYRJ/dhOiSxC+5xSEy29Jjn4+TrLYVwRYbucTtOdB5EpxD31R1N1FlWuStwsothlclN5rx5kyQrZ444e4tWaNXrbIMc4LLXMVSJuperIzXK6kC+pyq1vc58v9VibS7KNTNmvUQNzXkjWRwrNBd38hfXY3M3EOm7f16h/ZNLMGuxrTO9ZbJTk7lZrSiFKm+nqFijepLOeF7Hm1kMPEcEaOTfJbk872sR0x0ygmuh3yfYxyudFreQaiRO/YN8o+ABErvSLdqKZrhTK+4hyk8wDgH6ZF2WASHJppMW1PwFG7jvrnhbW7hH9ztWQnq5vGHEZc79RJCmTsf2KCUW8sJjaIdzQHKvliKOv77BhOcT+56VsBIuR8oa7H5dyLGDtthryta8ah+OLjfQRr+/nABtdhBlxA9I9hUHD86NImYAwGkRehJp6lBzhd3crqVyVooU1Lahvr45R1dMsQ2D/Lf379Mk9HrPecz8W8EHh3tCx+H7iQTxqlNeJSMhVbtq8jpkgGAGSlaWFYBUke9WoiPW+lt7w3mUm4WrRikQnRIUPd0v8T9/8AQBwNzT43S35BQ/OwHuNW86/nSY4qc7ArjVp9AHKUU0ewEGJbjbqCDVzYoBS24kRxFjj036JkRfKVdmL4cPoNQZGlsaBFtZUgYagMQz0upfcU05/B0Deb9+XGEcjBi5NNYjBRGUddiNtNn64Ocfmn0/QsCyu2JLkjKZ7Crl8nuPQcz2MwziMwziMw/gLjy+ycnUzIFZsp5aCH24VinWWNETzMARdpdxFz9VYsk20QZIuqmuSR0gFU5HLEb0hCfGF4ekn0oejfGy6V2w+Qf8ONstrzKCAkM3v4+lI7jAAyhuD5pOSIILuSYa4zTb3joo1oCInlgBAgPQGo1FUmXFVZXc+mxFYMqCnY6JqL4UcuCYzXos1wabjKn+nYp2OP9LulO0ix2UUNi9iTskY+gLlbCCZB4Dmn2qp6sYFvTb1k00H1BMjBBUglaUZ8tyFAjD79GWA9tyiP8pzIA5BpwqDz+YT2kHybaNWqO4j9hfsMLOEyGGmcC4CUHT5mPojJZB6sSHIf/8oBwWkufM1X188r/2Zz5UTcoi9mwf4Y/eAKZuSb2LlgZjhdrvJsPa4ihg5W1c3DoXJfdZxXYlZhi087t6vMP/RynlK7xEqaokk9nZ8NEJxxRurgJNnJMU5m++x6SuxOFSDQlxwhTtqun/SFOkIpOSh0ZKhy7N/med5PG+lx9c5i9J4cQ+qm0ESW/TrGm4VcPyIqiujo7gTAUA/FoKKaBsQOdDBbDWGC/7cqGhuUk+3y5WrGhVMq+DY1cuetSIj2bWlmPGnUdX/0iXp+maB//YXP8hr9q7Egg0fru4X8F5LHq3qcxqQ3WhAIScCTVo+MFH6zPPzPUrrpFK+6xph7e/HEoM3ksjTukLg3X4sxOGsKN0Ddu9uX2HFhhU+UFB6qla7oZAqNkbgaJFdmWJUYt94v6uxael6Gb5fYfWnHJ6yfTFpeYV8/3yu44tcXKOlQGq7y/Fe0VDvSqLfbCaDBJuhzWjYJi31xT5WKPtM+lEh/52vHhJKbJthaNh8Ufk6irdtLAAEZC/jZf7s1P/xKV7svkDF3q3NB4J626/TQhlRM8Q9f5dvOtcoeIYmAaCY9ANp0YSQeWwboPfs8aoAPU8kHwoGn5J0RE868vdKRJxJBJ8egd0zhf1X+cGZYLj6bYH+jBeERy2C12j+UyPvITF5kVNbeLG1+yjB492phmuAkgll1V3MPUuXv9f+EfWB08JerrN9o4qAchHjUYa8xTFpAHbNBPrMa5MssvQe9H5TG8xkjQhFvet0XFGTpy0A+BMHXXqyN+SDSYkuwSv4J9R7tJWDBkTe0G0qgVhVS9rtJDVpX0ZY7ntW9Yg5k1o2uxpjZ6F2bMfnFPUcAahrjdM3QMfcJDePME9aPiSKSUu9w/3bRbYMPRulv/jDT8ewWw39giYm9Eag6mgDQbHJCxgQ8lp1qzEuAuIHOuGbqkL1iCDF89leZCfvtit8uFll/+kAlOwOFQtAn/a5dzhmAlCMCu1tIzC6Wlu599yRz1he5IU1fbciZtJVUAjziPk5HdesGrDlBSMEDd8aKN58L4/3+M3FRwDAP19f4O6OoN5fv/yAEDU+dQv5PnebRt4jjNnvMqqIYjOBX0/dA15HIl4qE8VuMUbwuaB//+rsEnc9v39UKI2XhTECD3q6Cc49XuxR6CB97qdPLuF5l7UfC9zvG+mNe2fgk52rpci5tLC3Q4Edy3K6XYnZ7+jcnnyI8HXeiLsTB7m5us9/cT3AwodxGIdxGIdxGH/h8UVWrrMPgCmZiMOkn3ItPAgADO0mVn6dq0ftSR7TsHn71PlHsRQkQbp/noPpK4jYX4VsvKCnoc42wtcRQ6qUy5gNJToNu1Mob1K1keUxoQK6U9r5AxA3JQAYlwpMRIQZuaLjyq+9UFIVFvtcEQK8iVSJ5Wozi9URKUlYrqXCwITR/bNAW2Gp6DIT2jUK++8GcWwKVxWWf+JjXNBxArR7xmWVA+qbPI/Kscl8qvwMsLvIbNhiC5g2Gygoz7DnTEP7zN61bT5G1xAcLuelUhOjiyjnU/dcxfJxpaADgKVICYLWXJmm17VRmL7jQqG7iBiesRtVM05265bgyhTuUmfHoHo2PGDQDoOFf0cnrtwpMS4JBUG3A/scq8bB88W0v6nyBR7oWplmmVbMmB6WCvunmUinHmVXDj8YYFvBswlGbCh7FwDCYDAkE4AyYnw0AhycMDVMEAOGVJwEJSb+tgPsXgthzdURXUVV4Q/DhXgI2+sCdqtS0Qblc6BFeNrh+dk9Pt4Rvj7c1jnFZzUSnL7lKtdGuFRBT44pSUISG18NWuRmAHDyZI0Ve/C+uz7CeM/YfuVRrXohAc2qAf/u+6/pPUzE00ckvVn3NT5slkIac9siE6siHrC39V6LVC9cDFBB5WxZDZgmO6AFrtCLwmPZtPjbc/In/tiusB3oGH2k1kciivVjIQQsAGL2XxmP3VAKvHvf12JmMXqDYTCZYKYDjo4JpagKh/tdg/UtB7crIDKkPvuhFD/rYaWw/SrAH0+IaPHzr1jT+CIX1/6I2MLFDgKPTpmwADDOsnaQIL/Mwpvd5AcguQLlXh5KJW5IZuoU5OjhHP6svyafnRZhp4A2G/A/cGMaFYpdDmoPBbB/kuHGYqNQ0r0LN8+wMzTEtajYEIw6rPJnzz7khSWq/KwPhYab54VxWGSodDiZ9IJ7gg4BtpPb62xQPsFGuscBxXzEuOYA7RuD9TfpaZYlNc7VsHv1UKPJQ0U6Z+l3/UnWFWsXHwS86zHKhqDYB1ngjAX6lXpg72f2k/fweRMUtZLvlqIKEwRc3kfpr083FL7m906L92l+cO1eeeizHoYnOQSNwA8eXXjEoKA5FMJaj5HdfYYfl2g+Zfh+NmQ5zzhXsm45xS5V/BAcjvIJmM6n8nSiJS2pB+7+is9h48kyMDlw3ZZQrJ+2HhjP8s6hvDLQ79n6zwPdBfeBZw7ojLBXMWEHq5mnh21K/1mbPI+gDa+wYW2ETgvhxmJ2OXHjGvI1Pi4m/brB4NP9QuYOZQBmiVVvqf+bepYm5kXNRijWeIeGdOOKe8Gm1bm98dUOPmj89I76yXHUULzA2ZKsIbefaJOxjQu5DorTTnqbu75E3xdwLbOkh0nQAOhHfVnKuXIpSWrQD9yhLp7eC2M6RIWmYHtIFfDro48SlnDTzaTH2g8l2qEQ5q8xWX5TFQ4zlgCFqESClY4pwevWBCzn3QNpTsGbgf1QoN8X0DanJVXvsvRp/S39v196qNUAnZ43nckPH/P5L7IHWPgwDuMwDuMwDuMvPL7IynXxNsKUkVi5yaxhYOISb5zKzYTcZMhoAAC54RggprisSFAfvZDeJ5kwBK50APKaH44mG7MOuXLSUXSi0VLGY2L6IkCyLZtLcv1Jmsz+JDtFFWvK/uxP+fOqnBUpmaAgEwQVctRYuYnCiqaM1khsTVC1GnUyVM/GDf0ZBQgkqC1awD3jEmjUMDcG4zFrZa81kbQAhDIgXtYodnlP98ABiuet2BPhKM2PryOqG3rd/H2AdpnE1Fzmn7UjhrDteMdcqOzhXmRtb9T03gkBsG0UL94/z7stNhmuHhesA2a0w3TMSAagQhTdr+0jXK3kuvAl0D1hg/9TYrUmSC7elOJBHIoIM+ZM0b4OKK/o3M/eKzRXTHyqFNbfKOxe8DEP2c8aChjnkRzGACBAwiNCmY1LbKtQXwEdFV/onjoUDOvFwUJtK6l06485snD/JEDvjZC8gHx9Tf2m/aChgsrXiI6CzqibAuD7DyDDk/R345yv3QQGBSXXY32ZjVGGI2Isp+tf+Rw2jkFjeD+X6kctHNQVV4Ga0CDRrEbk120NYrrvCoJxEpwZB0U+2QBwW2PQVUZuNgYhOXq1JbwFTr6lvkxVONxuMjy6ZXOJri3h7ko5Nw/KHAWoTX4YRY0HOtdYe7x8eQ0AeLG4w91A5fu6r/FhTVD4yazF3988x5o/r7ReIN3troYbjeStlsUIy0Q3rUiLCgCfNgvEqMR5Ccjm+qX1GJwRtyVjApzn+y5oFLXDcEsPjOaNFfSnfzYIMqNNhFZRkCxyp+F7q/7PwFaf2fgiF9fUC6WeIv23YOginrI45fU2GzfokZx/0oNfRUg/JKqHDwoVACbawS3owSYPnxrZmKLI7ahQRkRkyNWuDeZvcy9mnCuBzPQIMSdHBLrzmA3hQ3ZaMm1+/+SulGC47jR/Ue0i6psgi5WrNcodQ0nHkxQcxezJJEd6MlB+JgBzU5BU5i4ZIyj057mnVdwYmDY/mIWF7SeQecVQOR/z7H22EqSNgMoPtpEWMwAoNwF6CPDN5OSlB/iQYaZgSb6T4Hs1gXRdk48N4GuCYf9yrdi9aTIP6fi1mmzG6DyL/eE0G3XU8IMVdmwKzaZjJChcJbecIc/VsAK2L3mRrOMD83+7Uxg5DzicDzBFyAvqrpAFQ/Va/sZ0JNgPT2lBbZpRerrqpqRNT7pmTDbtqC8JHhXWuwZ2LxhyrUKG8yK3NJKzYJnnP56OxIq+ZgmPzhukaGlTm2BQe2dQrOl3m186cXwqOKh7+5b6GzFGeTDrvYHulVxP+mMpC6NYHY75vrHMYXArj2JJ7z9uShQ3Fo77zv7YZdvDLcHY/owuBP18wIszkiApFdHYEfecj/rupzOBlmORNxRqVNSLTAzkqHKryRHsqyc9WHOTZXBu7nC5JpbxzW6Gdk+LU9hb1Cd0Po/rFm/uj0Qec7+ZwbfZbKJYDFjM6bXWBDxZbOT8/HhDO/RxNDhetAJlt0Mh/dd2KNiQnw76aNbiJVs5/uHmHO2flqjvkrkOSccAwDZOFnVjAlkrJpvK+SAZsUu1xWt83uOLXFypn0o6zdRXLTZUwaSLn/SHXHnUE+/ZihbDku4lqJgfyFFRJZhkHeNi4hMLwG4ziSmUuUcU6pBD1IuA2FrU7+kDl6+jRNj5mvqt1W1+4KbfKY8H9nblOveTbRvlGMeFgrcKbEGKYpM9dotdQLBK+o1mjBhYfuMaJf00XPQobMDYsu5yW4iusrxXsG3Wbra/7sRTtvyReqmpevd1FA/iRAZLc48INFd5E9E+5uNfRiAAR9/znHYRdp+eSsDmZSHaYjfL82F6oGKNcbGPiCrbOU4XCxXJeSoRoaJVUC7tfKgaToutChBJli/p/AC0GA0Tne9wGvJ1lUK9J+lFyaiouFeobnPCTX/Cf8tzklKO9KiwfxIRmGgUXwTpmRkFjJ2Vfh2OPKojeoj2uxKRH8Rt0kHzgtre17QxAmB7oH/kSTIDkslorlTDCTB/q+W6dscees59PhPhumR5RvZ7mhcgP2QfYAAI1xUqlmGZjnqmADCcsT/zR+5FegD/hm62Y+tRcz+wGwryoE4OU8cj8L6S+bE7heJdqtIjFO+WuzODcR6F06B7Jf7HKEMmJkW2SV2m3bCCvc2L8OrpBi+PaTGxKki03swOuGoXuNlQz1V1eUODyUYq1gGq8pj6FEf+2diA8KHOPfKYe9B42kM5hYH7yc1swGpJrMa//vaDLIQ/bU6w70r020o+O83V6mILowPO5gQ5nNU7lLzr//dvvhJy02JGWlU36buOXKnGCMzqAU9Xa/nef//uOc3xZQNlgI61yno+wiR3tFkvfdq+K6B0QMOISV2O0u/t81r/2Y5Dz/UwDuMwDuMwDuMvPL7IyrV9GqE5tDoxZX2pMM5VDqAeojjzICrYVBz5iPo294TaU/WgpwlgIlGJkhdbXVIKj4RIREjVFhqfzQJuS6y+N+IU1Z1neE55qmZS/wKg3hkABA5VT6byymfouq+1GD6YgY4v9TdNH7O70g6Yvx/g5rQRWagRAAAgAElEQVRT3T22uQqs82e5ywrRKYghURnFAcoMFDxffEtbT+s0wlvuOXliYKcw8GKTWa5k3pCPXbvMbm0vlFTowQCLdwppUjYvtPTDx2VELIIYUzSXQHuRjzGhDdQSyBA9nSuuQCvObE3Q7wROdg1VuNJjjxludzUzqEHnJ9QBZsuyqCstlZkCyXlcgvYd5QCn79afTAwmCmDOqTvFLuL2Nxk5KGsHz7DhuC+pMgQoJ3Vn4NlwpFz1lCQEEBSaKvlWITRR4NFUlQHA+LLHfNVhe8cmHstRiKzqU43tL0aohLQAUnGF6wpg2NUsR8wmbNK9q0T+oryC3SjM33If7kgJjF1ekRtR/4ROwPnzeywqqtA7Z7Eo2cXodkmmCafsGPTjAuV9Qpoijr8PKNe5b9eesXxtANyjSGxggFKrUuvgzmb53TzAPNkj3BHUZLYa4RndXItFj9J6OP5udTHCp1SZocEYtLiLxSYgmJTAk3NqZ7MB3usHypMkoxnvKpRtvuarl1scNTQH17cLxMHALmgeHq82+HpBZtR9sHjPZvz3bU1s6fT+JuIxpxcpRb3OVOW+2Rzj8n4hx3HELkzzckBlHJCK32Ak63VWjKgn8Pc//vGZIB9qFoCjESalCKko0iTvdTb0ABCDhuIb/ajp8IvVFQDgf3/9Cp/7+CIXVzCEavcPe6vVxEYvlDmFxLZRenYE+WW5TSgmN2dH6SRJNxotBYcDDOsoICZugILAwhg11Ee6OZfvFcpNlEWNouO437hU8LMohBe7UbSoghbq6ibLUIalEpgSQLZkLBnOTISsoHD0Ixtsf9jDNwV9JzDBKYWcjMgkkDJATfrHplcCfe1eeJjzXh4urrMo+rT4BYQmCEFLj1kTjAjUDGOX/H3bM15YtjxhIDi+PwE2LOE5/+0l2jt6MIS7EqgDqh+ytWD63s2n/L5moH5rInJtvlKiC7Ub0hKLbKeb9BcZPk5zN91UUch8lqHYysPzg8e2pK3OI05kQLmX7SvSGhcM05f3wO4F/W79GwfVJIlOgPcKI9v26XsrKU0xsrVgcuQaDeI9va661nmTUgH23kqI+HAc4I/ohCoF9L3FnOHk3X0NyzBzqAMQFGJazHUULXRc5d2K1gHb+0b+HXsjsXVmT6b3u2d5HpOcKhQK3eOAJ69owbjdzMTd52jeUvILaEFfrlpsOS0JZcT+F7RwNT+UKLYeemACWG2kZ9w+jsCjHklIrpxG/Y5+dvNsDwkVEd7OoB/TojZ/0mHDMXspgu2mpTf909Vp1peWDuNgRV5lZg7lCUtbgsLAf7u7aUhSk4S6XokkSHmF+O0egSH29mqGFjOZS+iIsqLj9EHjH24oFL0dCmy3tcyPUlEsMn/5/BPmBeuRo4aLGu83RH7ad5VEwp0t9jit6cK22uPDbgXPi7AP+WFZGYf9WOL997R71U7Bc5/crAZoHeDZaczYKM8D+jcvtL0BOoO2ode1TY//4w+/omN0OWjgcx0HWPgwDuMwDuMwDuMvPL7IyrXYKMounbgR2TY+cGUyXYb8AIjMQrsI0wFxspFMBB03JyekJD1BzP670UQor2T37lZePqv6YIWkZIaI/eNsFGE8sGHR9XAxAjqKIFv5bMq+fE1ykvacIReNB98vEakS9JqyT09+30JPwhP70wLbJ5PKL3nzGqB/zP8oA0YTMfuJq9NZxMBVG1YOISiE5Fm7MxgfJwsroP6pzNKNWYZ+TY/MTtWAqxQcm/3sXnlEpuaX7wvEAgiP6Mt9+v5MgqptANS9eWCskSQ8oVASkNAfa/I3NpOqgeUBY6URSoNiw9KKiWlE1EB/HgQ5cDstx++WQZiopgxw2wKLN8ktKwdEuBnB/MkNCYgicynXRLLaPaNj7p5OPGTLgJh8XFVE2BUPTAfSUE7BbvQkB7YQhGQ4C8IuL24UumcO8Tk7RZUOhuHduqL/tvmZIEYV2feV50rNHCz7z86bAS3ndo69FfKau65hJs5CplewTIpSyYObLwtf5+ts/5XDL375Hu/u6LPL0qFgmciHN6dyzsr5AGs8AjtAmUe9MLCLLUmjkjuBm2nc/GuWfyxHKB3RrOik+ndLQTfcLApjd3be4uRFK3Dp+sNSrhHTBHRtiR1X5tEp8RL2XsMWHssjglat8eInHHYFFF/Hqggkc0sPGR3x/BXJd86bHX73/jEFIAOAIpg9nYwX53dYVXT8b+6PsGaXJ99NHucqopg5nB+RefpJvRdv4c1QYdtVcvnM6l4i4aav23czgb4BgoIXJV2s//DH50Cvc35vEcStCVEhBg3L/tNKRTiuYqNX8JukzaPvVs3pXr65n5OnMgBTHqQ4n+VIUhwzRLG9S1KKtFiZISKqJGuZyAg0/S89cF2dc1+h0+/5RptF2E1ipFK/0Z0yg25nUH/MF277iB9CrQI0uRkBQFg5CZKurEf4YSF2gnYPnPyB2XXHBv1KwbAspdjmPmKocoaqHoHm2qHY0HuaziFUrH972WD7zIikpNhGgZZ3L4K40ACAvS1yj/FshEqB014hdoXoG8PcC9xVXFsEm9nUdp8DCobj3Ms0HekZx7+iX9bVCP97grCqO4X+NEJ/ZKP0OuSHY9qwSI9aoS1zf1Tcro5GGBvlRo7Q0i8qb0iXO3VekvSfGQU+pP4yGeTT73SvEJlJOXtborqJwvDsTpW8X7GLmH/w6FeGz1uG3snCMmJ4SudUFXlBtZWH49zO0FqYnc6Wfgrw/HBv3huBzgGge+xFBqP3Go6TacKzEXXlhHXaXTfy4AeA4a6CEhN7ZJlI41A1I8oiuwIlSFSbKMdYbDRMp3IghcrXtHrUwbcWJW8S7T6HOfzql++gVMSLY2qQv70/wu1r3hWVgVivAIauxuaHOcrviDwwdhblT7STLdcR+0cW2+e8SflVhwUvpk05wuiAj5f0nsYC7TMOA3+yw/MT+tw3N8d4+/4E4LQhVAGaIdaht7SoS2pDlN5pUXis5jkR5vLDkThRoZhYGpoIFOFB5ulxTdd75y2CN9IGWBzvZYPxm7NPOC5a/N01MXM32wY+6UT5WABAFRFVNeKrJS3Y67HGnnNU73acJMTM3Lpwklrzcb9E5GrB6IDjYhCz/uvdDG//36f0OSsPezxAMUvdO4O6oUVSKWITJ5nOMFiUnLVblg49b8bcaBCDQr9PukbA8AZm3rT43McXubiajq5BCkRPUgoi16TFibSK/FCabKJICxvRs62cm0EqmWAf9vmKtRKdYigAXwfYa06qeavQs4B/+K6FYcZU31ssjvdo0kUbtOww9384RrVWWP5EvyvaADdjicEJJfykXl6wuVq1+ygB6NWt44WGjuv+lwusv0kmBrQoL97yTTHPYeChjPKgN7cWvo5Q3I9SXiFKYHPWadKEKRS3Ews1nSsW004q1y7LloYTrvR48etu5mDgAJu/HlDOB+lHxcHAr3gxUgA2hchG1KiE5KJHoLtIhCMN7wHD+sbqWss5i6wYSfKM8SjIZkkPGqbLG6LyLhPg7D5bLSJShZoMPUwPNB9TPznANRr9cSZQTe0sh3OP+ojTb6xHz4ufu27EuEGzHjYtXMEC5X3uY2++DTk5KSqoZEVZRqgFL4rrAv1My8O+PmtFgjHc1g83IzYIgSk6jb4t4PlacL2V2EMKac8bpPoK6JhQ1r0csDil6mj7foHZGyvX6v4rh3/z138EADxr7uGiwf/583cAgPb1Mvdyey3GKGav4V70EhdX/VzKudi9ALoLj5NvaGF5Odvj+3d0INvdAtOIQXfi8OwrMmQodMCPH6in6zcFdKcnWnQlVTJMJPlROt1Ow3IPtK4o3H2dpDg2ILEhtY2yAGkd8Or4TirDj5slfvcnbkKvLdTpgONTqjpd0FJZuqjxZn+M+5ZubrcpRN8r/w/gxdMb/ObkA657Og6rglS75CusUU5sE+84us9HhfnEW3gzVHj/iYzD7bsqb7xPOxSFR8s9b2VzCo5SEYPLSURV6bBgH+ZutNh2qeIngl3qC794nA3RvzLv8J/weY9Dz/UwDuMwDuMwDuMvPL7IytV2ESaQkUBmfFJSTLazy31WuGyF6AsyZR9S/+4sZHPy1LMVicrEgFxHNB+0GDZsX0SMr9gEoDdQDHOWO41Nlys9ACjYnWX1EzC79Cjvace5eVUK47XcxAdGBqHM0HWxA5rLgY9fYzyyuPuOeyAG4kITLRlUiMlDpTCuEks6Qm8Ylqwi9Glu6MZBQ3HSjTYRYVLJ6lbn4PGBXI7ScekxGyaMyyhbPd0rskrkjfjs2RZPjohOuukr3K1nKBsq92LtRGriewPFf0/vn1m/hBww9H5Du/0k/xiXkzxdTa9LaAQCVawAoYB+HqD32d1HbPtW+XtRJm5mwBa7fP0MSw3bRQl0VxHSY929cmgu9lJB7ncVcEXXhW0ngQ2WbCPTvMYy5iCGirJcEyNbhZxu44+d9PHM8fAgZzvBwwBVsQBQMPTbtaVUIarw0CYKdBiDIqtAAAhK+t/RRGy+jQjJxcgEtN/TTVOMdM0N3O/92+9+xszS9fRv336H+9dHsDzH4cRBr9lir1UYj5lp+riHui6xfM3oxkXE/mWi90eURzk14w8/Pwb4PZShLx35mL/++hM+sdvR/nIujHi714gmIp7mnnS6MZQmGFjYsKUX1yGAEmMsz90YrSA+QJR5VjpiO1RiLdj2hVS/7jgitBZ3d8cylztmARvrMa4rcX1SCtmyUQNPnlL195uTD7gfG2xHun5Oqz1ueiKKDM6gKpy4N613tdxDzaxHwcd0u2+wvp6j+MQw7ipItu7pcofLuwVV5iAzi2RkMnpDJhPcu5+Xg1Ta63Uj86FMhLIBp8cE7XfO4tWKjl9PJHCf6/giF9f5B7oZVJg83EeNYCAyFF9myDIalYPBeQFNnsHFWk8WUCYuhQT5ZQ/T+RuNUABrQrsQioijf8c3TJ+lGd0Z29QxlFdsCF4DgNXrEeXdgLu/opvE1Ur0sNpFdKc6WxQCYEkgzBhx/00t38tXSgg1/YnCeMIQ4kDynQRZdhd5oaqujBCa9MzB7yw0J4io00GcZpQlT1bNN395lxdX0xEUPLUiTD8VWyUJJ91XA0ztpJfUvl/g9Z+o8amcQrQRniHp46OdaCk3749RbDOJZpq0EgqgvmI7thVtrFIvUgWyHgSAcUXORanfhZ2F2WX9ZCwCaSMBuXYAct8Kk9ZXfZWJSqGASLesixhnuQfrGoivtF6N6PYlcFvKnEiMXQmk1n/qucviajLvqVhrNB819s+5VfFtmwkkTklfNSQpCj8ck7QDoP7fqu7x/pZWbO+09F+beQ+tItZXzDYbdQ499xBd93g+UnweQ8bFdSn+x8NRBL7d4X/8+kcAwPv9Cn//jyTBqN4UqIJCz9Io3Rpxptp97VDwouluapheYfNLel2svcDOWkXs9xVuruiaMWVA+Ywe4O1tg5PHazxjZ6F//nABd0kXXrHTAr2HIsKrfH6VgljzGRMeRLRNXgZrPAZnJZEnOC0bGt8ZeO4pzlYdNn2JwXGPuvDJtAvusobtNELyTb4YREvsP8xocU8SnolGvqoHVNyb/cfbJxi8kSSc7VDJAmd1QDcUsrhqHVHO+H5qOglR3/y8IgvGr1Mo/EN4dzHrpbc8egPH76dA/dy0cbjZN+LypDTEWzh6haoe5T2+PrqRzcDHzRk+93GAhQ/jMA7jMA7jMP7C44usXPUYoRHRnRgh8yRTifqWd6dD9p4d5zlRxTUECyfIb5wjB3kHwK51hjcHhSrlqzbAeJSdfxZ/0lAxV7xJUD+cORT3Od+yXAPLt1wxjgGX/9VcqtzqLoppRL8iM4Lk3mT3SirEYa7kdeNCo30cMZxzZbAYc57lTUkVWJENLBKxZ7jw0CnkfFcQBH6eSjMlZIrwqUZ9qSXEPZqMAJRrMtlIcHu5jrA9f9HLiO2zZE5eQIUSusjzkxyHdAQQIVKfm2EFfZ/C46nSk/ACl1OPKAT9z4gf6Z+9EilIea8xAJKGUtxnb9hQRuh9ZnmX95lJ7iuVpVW3LOtK7z/mKjMYgt17znjdP4vEcgaAHRn6J6N6qOwdrV12s9KeSFLpPVUECpZy9ScRw/+8hubqY1hXgjCEJmRv46BQnjKODapO51y9DM7ip/dzxMQCLjw0V0T7XYWwLgA+N4snWwxsEBC8lirEXdaor4wc87CK8HUKFxjx9HiLv/tIjNf7N0do3mcYu3uUWy31R02VLsicIEk64EEQcUJES4+Rf9dvK5KJcEU3m3fYsgHE3/7qJwDAP354DAAY7+pMUptcI76JwHmPiiv6BJEDgHMG42Dluxrr8e05kaI+bpfY3TdSaSaCFwCo2sMya3YcLPbXWc+nBg3L8i/MKFDhyQUxlzddhc0lMwtNJMOQKpGHqGIFgIvlTqrObVchRCXHCFBFDxBpqbJeYNxlNQgb+fXlCfTv+bPOPeJRnnPvci3WlCOM8ZL3WhdOkp7C5BhovrRU+lNHKlMEeK/x7THN3V8tPuJdR1D4j9tsQPK5ji9ycd09sTClxfarDLtVW1pYE1sYAHQK2g5xEsoMQOW0m+HcCxvT7gmyTA/j2af8Xq5WWP4IiQ0LhRLt49V/CUntUCNJglIw9uonD8MRaptXJfXyGAouNwHDkk2wTxT6syCfrYescy3aKBuFUNAD23K6SPAKkdMybKtQ3wbsnvJ7nmeZC1QUDaNaDajrUR5mbjAwbwhyKtYKdhKnp1xmw45zdnVKUpkQYVv6gN0jk9m8s/ggXkw9b3PKi+LoMO6D6o0S6D0xg4XFqXKf1c2j/PfyThO0L73lHI5gBjJ9T1if8hnCLe+I3WjzmiTQsPaA2WaYmQLX+TWThJ/+hKL7dt9xL2/ZQ7FrT9wbstlL2ugJrqQ8ZNE1PR6EsSsP7H5F57Ne9ejaEpGhZRUUQkokqV22MQSxgjXH3cUi4m4i6VCDlgU07gzGdO4jzzef0927JSI/tO3aoLriNgKA7W8HaNYrxvsys5Zbg3d/PEf9gc3nx4l15MKjujKiT94/jWI7qKKC/pScoiJ9fz6u0FkM/J21Vwgrh8dPaWf76XKFp4/p57u+oZBztmI0nRJJzXgc8sZ4MT7QEYegc/rMqAlq5p7io9UWf+Ikmd1d88BtCSrCHtG5KatRFp7ujjY9liMXVaT0GAB49Vcf8Wx+j9/fEMN58zFbE2LpiFfAPWOtA5ZsjfjhbikQdYx0CadNQV046ZM3xYgXizssC5rX19tT/OHvXtJ8DNmtTB0NJFfjRVXPnEC9PigopWAZKu9Gi5o/q7Qjru/n0pMOQ+5bKBsQe/7vAP6b3/6AJzVB9NfDAj/vuM8cJyL9z3QcYOHDOIzDOIzDOIy/8PgiK9dkBFF/yl68xZ78gx/kuPLP40TvqR0HNTN8ZLY6uym1pDUVJmjI0WuzjwQJJy2kqxTaR4mYkp1zogGWPwKL9wxHbTz2j6iy6U4VRafdZShy+4LJR489lFMoeMdvhlw9jlDYfMOve+Kgag/9I5fiBqi5Ipq/i1h/rTEyDFfeazHZ173FyLtxczxgdz0Ts/fmRmXyjv2zqipOiEVMT51x9a7HiGGRK+/hCe1W7U0B30TJyyx0QLxmE/mWXZF4J6z7/NlmIGRB4NKQtaC2zV7IwUDMHwB6zbhkIs4xGVGYLe2urZvA/hwmYNh4RI95jhEhW9WxIvQhoSDjQj0gmu1fZneo8abO+a19rmLoQHO4Q3WbURbKmM3zun/pRRPcv5/BbrUwhPXjDi8uiIG5G0rc3hMRyW0LwAaEFALhFNQE9otlyNWXJl0pQJUeOYPxNVgreNahRgPsnzMyc+RQL/vMjh2V+B8jEiLQPWUv46BEU9u8sSh2+bvZnUKXNNQmSliE2esH6AC9EX+VlcfqbIePb0/k7/Y9Gyh8XFI8YiKKFUBknXTRjBgn2agwER3rqVWvczB77VFWI/7myXsAwO8uH2P3cZ6/W1Ti1lXOB8ybDLcnwwQ7HxEah6FJFxDw8ptLAMB5s8V/+OkV3BVrSDWAOc+VJeOJYzbX90Hj8pqIW2E0KJhFb2yAMUEi3HzQOG2ImPS/PP5H/NP2Kf7hhgwhPvzDo0zouxhx8oh6Xt1QYFAFReMBmM16ITSVxqMdCzH/V4DM8TAYuImXsK48zSeA2Fm59s+f38NOWIdWe/xq9QkA8Pt2ktn4mY4vcnGt7wJsEeDqnM2ZH7y8CJ0odGdKfpckO76hf5drtlrb5OzOYkf9trTgQeXerOlp8U5Q8OYbhZH7iGHmxbpt9XuD6j7Al2x1d6bRXjAsM5JZQbI47M8grMpYBjTvipyK43Ie7fV/nUOmsS5RvCkFZj36YxSzjGFFfcMEVUWbmamhBCxntqp1A+Py76Y2i5Ed3ZL5fHMdsH+UEsVJSiS94KWWsPbtLxwMP5D8k4Bz7jcBwPX9PLsR8WKdzDlMlx/ElLsbJwtqfvjqEWjZFtEtA6LKrO5o4wMIECqzXn3Q8v2MI8hbEnP4tQBBtSkzN7DDU3uRYdx0fXXnfL2xNCRBzQCzyycbAuXy30WTv6cZKO2nfzSJ9Um9x1GRHGfOD8RmwM2O8PX15QJqz+eiDFCtkZWcQsp58WgcLRD8WrPTYkrhTxxl0XI/s5iwuv3JmN+/0+hUCXOV4OnsIqUitU/EOm9jYRie7k8DxkXeCI0ve0lX8felMMF1oO+Z5srNIvQFXfzPT9e42c6gEuRdRtxxqLruiU2emOInz+7R8AK07SqMm5znqisvLl6qcWgmDkQAcn7pXZ3lSCaSyUliz6soRhHO6SzTCwrzRSeyu+OLLWYFvf//9+7Z/8/em/TqlmTXYSua037N/b7bvv5lV6zKYlFVbEATBA1LgmVAEmDPZMATe2YD/hH2RBP/AM88MAzDA3tkw4IhEaAEU6AE0kUVKZLFysqsynzd7e/Xnj4iPNgRO85leSTUwA/vBpDI+25zvnPixDk79tprr4XhKo+lkMIyU3lxsMc8b9F4lvFqUzLzO512LEGotcHRpGK5wmnS4pOS2g5+sn+Ef/XmJZpXnoFvBcQnxKY+mdYcJI2voYc2o8FIZqXvuxT7JmW1Jb9vpvMNcxYg+0qzQYGaDCi9Sft/8ckfYWdyXPvM5f8+/xS3fvPXb95/WPiDDK7O19tk77gPMgSFIBlYP4rZTLqS6Hw5qlv4uqYPtt2cSEcAkN/SiyG8BK2mYAhQBttPJTbf8gF1GttXMAh2zxHGE6jGL9XgTGMc2kOS/wOA/mjgXWX6OoMciGAFAM1HFs7vdtVKQ11T5ic0ZR/TV36x947dZ4aJrxcGqbvmb5C1QkALymThSXOxNUnXQH5jkd/Ree0fRdurbEXzXR17F4xTgW4Ro9rEP3SnM1Kmud75LOuiRLaOGww2hodXz/KHUK3PgkIclrHNxWTgzEPWJEXJmYgRrF2MjhYC2waWFvmVDzIdOR+FNWPS+8EvDJPR97O7cI4OO+9uYxOH7Fpxy4cwEd3QtaBs1cTjBDKP7En6EaA1154NHFChHeQu6kHbwkJ6qcqu0+j2wQIpEs8CUmL8GpeTgV/SrqEaWXi5m4mFXHp4wE9u6ONsVzmkD6hUq6ZfGyaOSGE+eNsEseaaOSC10JehuBw5B9m1glMO/ce0FpRyMKFtqDBwfsNoFSBawRm6Omkw9eunHTTqyxK5tx7sFpbvtS0sRDngs6eUJUo4XO1pnW3ezbiFzGaW2m98q5LdJaj2o2ysVjET1xYICE9CbTrWt6Vo3bMp+ZWdovXB1CUWu1XJbSmJNvjZFbWf9Oe0GbKlJx2WJDkJAJO0x6bJsPJByBkB5deus4IVtyZZh1z3+GhC7kKPsjV+vCP3nH/5l5+h+DqFO/PG9M92fF27OmOCWpoOSNOB/13mPYqEzmPfpujahElRzkoYz91AS6ROF2wJMwPp5z/Nevw33/0/AQBfd8f4F9ffwuu1739WBn3QRxbvf3B9qLk+jIfxMB7Gw3gYv+TxQWauwtF/sosiEhCUtQYf1X5qOZvUFZgtnKwJ2gmiAEkV2zH2T6gOFGqiY9/UfirQHTjOqtBJhoJ1LTD/0n/fOZgcyLexjSZAyd2RQLe0sDPKGmbHezQ1fcAwIciZM7oBEDfBNDSeR3YjkN861h2uHkfhCdUQtB2g8n4qODtSbTw2BGVqfRn/HdSJ0jXpGHdemF4O0Uc13Vs4ETPj/MahI3IgFo83WJSUEt/sS+zrDN3GqxM1glsknCQ3IzVCHMIwGWWq4XcDRAqAzbEB0LyPWMBOu3tZeHKnIxyeuDh3K8po+zLURh2MbysyWay/qo7KA+HcqrMIc2Y3AqrHvfopn4cCIIG+iBl14iH6ZBfXYPVRD1nGTBPw2SAATAYkI0GIbptGZaE+mpIDgJmbyLYdQXf0Q8HKP7IYSAwBlMlJ4ajdBUD+OhkZDzi0p6HvykJIB+MzNdnIeA9SC7FJeE6G0nErzlA49HMHF9xuFi3S0quLDQrWBJ6Cg80FJktaM2Jk/n19OUeyUbH9qbBsUn683OF0EjO1n5yfYLighay9aUa4MXZIo/oRwM+rGEhBLMxXvmzYDzVRBo9mW+w6mp9m0Hh7600ClEUyb+O17BJMH1FWa6zA8M4/UImDXLZIvah/32l0Hn69MDP0jWbGrZr2/HtSRgMBLS1+ZX6JrTeX/h//7e/AvaOv00qgeWSgvRLX0CuGfrtO86PQdxrNIJH7+R+MwuvbBf+NEA59HdRvFK8tVxBvofTOQHbkrPODx2/w05baoP549RKbNmdoedcUXDNOsqiw9b6ODzK4wtF/VsfeQQCoTiR2L+PDFGpryc5xjQ8S6GbAMA3tMNGUvD10SLYiCriPjgHAG5oHODDWX9IVWI3FSQrm2rffWC25dto87ZEctCizCPkNt/TAlOcS7dKh9zXY5C6+XO8+cIAAACAASURBVFQX21TyGwdhiEAURuhJFV7hbayoFNphfsHCbqRGZJMIjWdbi+pE8SZF7x2m7zxspci9J0DIJhcYvJH0YCUu1lQDavYpXKsYbjR5nH9h6HzD5mAo3T2I1QnwvJrSch0xuYvwtLAUeEP7jckc9yPqvYSqBNpD3yP5TiLZxHtjsig7aNP4tXBAfhs3Vd1BVJwSDsAw+hr3bewYgs4chqljdS7pYlsXRGxXUdMezghuc7F7DeVh21Cb63b+Bg2xputSGzcjg28XCaUJ5agGCwrCNreQ3o3JWcFtWM4IDHcZci/JaROH4TDWbUNblIMXvQ8lkpmv1YLkJ1UVnz1dCTTHfr0XFqKTSBaeODNy7nFOwLahz1UgXbToAmQs/EYCQHKdQNUCzUvfAjPpMC1pwpd5jcFKfHlObS7mNuO6rTTxPsEJDOU4sAomnJmJRbJoGYYGgKXfGC6zCq+3C2zrjM858YHrbL7lXtPL7RSVdBx41lUG/dgrIeUd2l4j8QF7XjZYeycbYwTSooea+vnRhn/PWMGKT6uqwP/108/hvvEGAgO4ztydGCCxcP6zlTZcI86ynlt2rBXQieE67mBikMyLDvtv5pDh/hYW8LZ4i0WF54sVVl6g/3Zf4sWSaiTfm73FT/YUXK/rKTZNxhuCYtqy+L/sRnyC93Q8wMIP42E8jIfxMB7GL3l8kJmrSQVEQqpLEQKV6BaIVl02kiWqxyPPTQ045ZD7ZnmrgfYotKsQmSaQiiCA3me4ZjFAFgPcDe1oZz+TSDxcalLB1nfJzkE4YPsseKxGHd206GGMxG5FO0J9mSLbBxjbAc9rIBACnGICkhgpBPVTAb13UL4FyWoBFTJSD2MHqNMmEQpO9u4eycomURQ/vSYyFwA0BxLdLBrRl9eWYfNupoiZHFpPlGOordpnkWVYaQ+pe1iyk5zlmJys3RiG3kRB+yDwETI/0URimK4EM6mtpv+8HgX0ILjlpT026A/AAgeqBpyfj2ZBWWlggFNrVTiPOD/DRMCODBEgIgM4WVM5Ypj8IpzsFJHNAiSdraLQxe6FA54GQX2DAQqDv9fLxxtstl6UfZNBJJY1g10fbdpgBLOuxSBgMxtJOb2AC3+TOWCQbOsH6WCMZ6FekkZwtwhYNhgdcNoxi1jnA4ZGM+HO1RqpJxgFNnpz6j9v2cN5wX9ZKcizhjPw8TCbhDNjzHvkWc+ZzmpTQngIWgxA91mN0sOZWTKg8ASs66rE7ZsFE5dEXI73SGk2pSycM30JpB954/FphbtdySxjgETuAeDry0PACVa0ktLh+ZIELNZtzopGj2Zb7POW9X7n0xqnUzq+cZIhboCg5alv51ltSlhYLGfePm5QrIwUslYA2F9MULyJtn79zMJOo7FBPmthPIybpTFLlMKh7SNSUGTdPaGIziMHzZ1nSHvSVZL3+OSUlJZ+bfEWf3z9kq/hxfIOjwuCttYmKi8lyqBpEs6gnbOsd/zpbIV/g/d7fJDBNdRcVecwFL4FYC7RnBoWIVdXCT90ArjHSJVdfGkP09HL3JKqUajzOeWi1J4gP9QAbzoF9LPYXhLqtE5SS0znDbrhwI4eg7JwTnB7g00cmue+3pIP5EoSfndqIVee+VhEpq/eO6T7WA8UFmxeTg45YFhV9hiJzwu0p/S1yR3yqxh4s7VjScP2UCDZOkzfepUX52CyUFt2XK8EKAghlEKvM2YE93MLt+zZZNr14BaM0Esc6tBOAvVZYKR6GDXAoJG8CmFHbTm+XzVArvVjC/eRj7yNRnKe8ObAJnHzBJCHazR0iC1HJhHojv3fZFQTDAE7XY3qqpIcdHht2Qi3CyOQ7GNrV3M0kuObGH5YA4w2XXp/1CpCazIfYPcJhA9W6UawYH4/c9EhZ2ri5ABRfB+geU9sXLuDhL6it7TsPRM4lDEmhuHpouiQ+SBWtyl5rN/RZjK7kbwhNTmw+7xFPvW1vF5hCIHxrEGSDuzCs18VEN6NSQ2AOYzQY5YMuL4LPZ6Se3H7ucXRcg/tA9yuyfDma5pUMch7GzcxxPViBQVV+oH/z9dczx7fYe9bVDZ1jjzt0fggtK8zbs8pyxaPZ1vceUj0uNzjlXe3GQaFg4mvQ0KgSHr2WP3do6+Q+V3iq+YQg1Mo/ML46+0ZK0DpxOBwtmfnmk2Vo/ds3n6VxXa5zKE9GgVU6dg3deFdaAL82/bRe1VKx0H3dEHB/nJFrTJDk/AmCMpBLfoIeR9skfr5/v1X38bpdIdnfrNgncCzInq1bgdaE9e7CdLUIFHR4/bTJQXorBnJoL2n48MMrsZBSIchlywL2B5RY3pyGfRt44vNJqPeyQqYvLOcafaIL9th4shUPA2WJ4Jl+lziIFcS6SZmhWFXKUc6xsOE6pxRj1dwraPLNTXA+2EXA0TQ9G005C5qEqtG8pY8vyaiEUCtN0MeSVImE2zvpGuqC/c+sFM/qc/eHwluwUk2AqqNOslD7ntkEXVvu3lonXFMXBHOwSnJddx+Bu5vdMqhexlJDM4IJtvIHph6azEnKXgFktFQuli72/kXZ8jGVJzjbuGQ+lYWOdAcc5vOzABBAvI6QbqOZKfm1EFVMZNUrbunjRyynaGMAVTvCfkI8yWsi1aACQAbCVmBAAYA3URi/Rkw+PMqziVnscNcRIPyDfUp79bBqomnjUhLLq4fYSOZKkhBAoDwaEDoh0Uneb4h/abQZ0TJTaxXm9LBFBb5CV3cYlpxHbFMetxWI03Ydzmmb3w/+N6h8ebx7a9VOFvsuI7oAO4TNRWRaLD2/ZMOcD7wJ09qzL2Rt1YWF2+WUBsVT9lvUoaFxfXl/F4N+V5tUIALYi5xQJBbVo77wXPv1hIC0NXtnLPRPO9RtylLCyplMfc13V8/foNVV6DQ9HBs+wwHBf0siDgAwE1d3hN2+IPLX+FML5UGR/kerwcKym83c5ZeFAJ4+/aQ69eQDsm1n6vCIfuMMkRrBZwTHHidBYpJnDuArObC76qAPjgg9ZulbZOhbhL0nlgIK6B8XTVIP4aMujUKPwsbAGkxSVq+HguBK58tXLcTfHFD9W7rBJS0bM7+0fyW/+aHl8/wvo+HmuvDeBgP42E8jIfxSx4fZOY6lBIulaQU5Hfk6YrqYiGrsomLsn0y1tnKC4d+ItjgGhgp5aSWMoCwq0wtZWAA1EYzUxigGumY5RrM103h23V8xmV1bLCHFfR1yDAcGLoTiYVLJLSHggNUDXhmrWfzWuXdc4rQThKZvgEKG7fVBDWl8hxcJ9S1u8cc7jPBJvDkaQtmH8phVJNz5M4TPttJF69tOrCTiNhq6EYww9lqcMuOSak1hmvjfaxRCuMhXz//poz3UNiYrfdTh/7AcsaCQSC5pR9m1wJOg+Uuk41A6sWiVEd11SBrWJ9EWUPVRta17BxUFwUmhmJUu/aZL5eeHLB97k2rf4VkEfM3vg2ljqpSLrNIX9GHpXcC9ZmjjBug9eDXWbKRUZLRnxf7xXaChShMSvApC1H41hn6RXounM/8ZCsw+PtkpgaTkwpPD2hS7poCdz4D2iTRmcZ+NcX0jeC52j8D2qe0aCZ5Dy1Jng8AmquCEQx2WAqZ8swg9+49XatRevm966s5srcJr61+RibxAMhIXMZ1ADsSQwnSjeG5SS0OlgSTllmHux29AIRwyJMBlW8TyfIOSw/pvruihzW030zyDt9dXtB5tRO83R2gC16pwuHAQ79T3eKipgxuXRVI9YCL3UiU34953mLT59EMYJ2zeL7oJCM0AMHYgdU8fbHhLHC1K0lqMWTDZcei+wCJRQSP1SQxfC+ktKgqWmdBeStZ/CJESyzoAbmmRb5rU7Se1f387BrfmV3gx1tiBW/7HG93NGfnt3OmzGfZgOeLFeYpHb8aEvzoG8pYze6hFee9HLJzUI5e8qElpZvRyzj+TlTOye4EJuf+5ZIK7J9F9Z1uDiZxyMzAOjCxIn2bMJkk2d4/Zn4TST8mj60lwhCEFdpEbO4gFsFVY0C7yqPOmBFwwcXakRZw6Is0Gbi+lt9EYsZQUEAI55FugW4Wa2FOA9ltrDv1kxAIwfAxHMGbDHWOpPlkF4Kc45+Z0lP+/WaGA7MTwDxqobo3FHGKi3g+9HeRdCKFAISDuvM1xfWI0DShvwl14mQba5smB/q5h4ELC1dYlsfLVorrgcJS4EkC96OOKltyoKDbHMcaZtiIyEHcU7Pqp4LPX/aI5LVMoDuIm7XmRGD/gj5MrxWKy6iV3E/jPRRJDISm8POtQyFXIFkpnm+bOFhfKxzmYPJOdiOZYNedDRi7ymAQ3CKlEks13GDQnTm2/MuWDZS0eHVHu516lfPv9cUA9Srn+9JPgf1Tf84zy0pCAHC7K1Gv6XdlNwLQPBwdNibYKvRbr0Z00uHujoKRfpcCwnFLnMtsfC4A2tiGfw8ybiKUo83yjCb50XLLEG5nFXJPUsqSAU2XoG28FGCryMgetFZ1Ylhn1zmBf/nNxzT/RuLp8QqLnAKxFhb7gf7up6tj9D6gFWkP46FbAFDScfBzTuDVaoH9nd+BGRHLQVZANfc3O/Oz2Ld7dUu1ZTsIyMQi9xZ3Y6u3uqMNQ+iPzdMee9861FU5ENqdhIOe9aw2BeG4AqGzDmXaY+fr0FWT4tGSFvUPFq/xx7cvceNlN7tB87UI5VhFyqUG87TBTUP398s3J1wP/vT0NV7h/R4PsPDDeBgP42E8jIfxSx4fZOYa4M/6RKCbB9ISfTt4ZvbzKGSe7AScCBmuQHER1XKax7E5Xp/nGCaOWw2yu8gqFvY+Exc2km3o+4G8Q1lmIFCZzLESj3mTQxQOztPf03nLWpzqIoOqI0zZzy0TcQgODdCpu09ySaNQhE1JBCNkqzaJ3rFOiGjurqjdIWRjNsFIWCE6DdHxBQtODLmA0wKth8DbJz2TYfCqQHE1ylhHJB2rYjZaXDqCbcM5pnEexz6uNJmjdhgZmaAudRCNhPTMx2Qz0iPWdJxwbaohEhgAdFOB3UvyHAXIEWksbsEZ9Iwg6PwmzldgSduUsuHOG7a0Jwb5W9+iUtNns+LXt3osTigrGaxE+9bDdFtvABBg9CYKRdjCkRJS0ADeKCbm1c97TI6JQKOsQL3JmZENbaF820xRtthvc2YEDweGocEi69EbRX8bJjvwx97kvG6bU2LN8zO0aLGY02fXXYK+S9jJZ+yYEzJu2wZ1NIH+OODrEnJN6132hO6Ec0Rq+TmUBXmXuuDyoxxr/wplkeQDQ7rvbg6YnKNVhETbrEe1yQEPjUM7LtUURYfdLsf1VwTxqlpg8Jm9KS3eiANojypobXgpK2lZ8ME5AWNl9NcVBq3PKK9uZ7C7BHrtVZiaSLBrjwzaI4PUk5OWk4Yz0aZJOEPNyp6zcACo24SzZCkdEm2wuaMHv9VJbLsaKXjJCc2j8ufcNxozr7o0zVsYK9H4z/7s9Bq/ufwGAPDl/gTWCW7pqXcZM8qFBOYzz3Lf5/jXf/4ZI3+HJxs8nVONqtqPVGre0/FBBtfbXxWQuYDJLdcmVUdBlBmHJVBcB+ebaJaerRy2Hwk0Z/TAZ5eKlVsgqR4W4sVQxLobQHBoeAHbFPfaYcJL2iYUGAMMp+YdlJcty+4E6qcNnK9rdfuUX46h1YSDiR2dRxlf9Ca/b39Gfbv0dbomSHKsOtQu/Py0sXUl2XuHH69E5QTi1wpojgTb7jkRVYmq06iKBHjG6irn44fzCkGwfEv/DkEHoGO3C4fwRuceTvj2l97LCwK0gfFtLaZwbAMG6YBWo3wXryecvymIBRygYCcFGxtQ3ddBb+nf+VWEpJ0GWl8Xzu6Aya3lDcBQjlSdEqB6NrAReXquuVa7+8QgWUvqaQWQzVt+efWdZnu4zfd6qMJEKcBBoD8KTcikRpWEenUGDL59RRUGrTdmH2oNGMGygDoxDBM2dUr1Vh8g5sd7brFpe439bcE1QL1R3OojO6A9ikFGFAaJhyWTxHDrShCCx6g/NkiNYi1pLXhmc3tquFdWXqbkIgQAgtZrPbJig5cyFRKAdWziLguDAx/YhXDYbEvUPpjoNNYbjZUQHppt24SCuS9bSOkwbP3c/dUSB7cOvWfIdweO3wE2Eej3KWwRmcQTDx9L4WB8T+euTT1Llz7v7m4KV0V7vvxa8cbcFA7DZ97IQDgoHVWTqjZhlScpHDIfdA+KBoOVUdlpUJj4oLavM+yvJ4xb2jqamROT2m8stcGk6LC69SpPKkLXIaB/dETGAD9YvMZNR5C9dQInxY57f8UM3BOslcH5uX9QOgk16/HxI3Lr+WR2ja+21M9Wysisfl/HAyz8MB7Gw3gYD+Nh/JLHB5m5DhMLWVjoSvIOX9eUVQaW6OxrsIpRP6UdKUDM13QDSL/7lgYs4u+khyn9rOY3kdgzlATthH/DjQgwJpJ8Vt/voWc9nhwSPHKzmaD1MGSVOOAuRRKa5Q8MknVUvUnX0ZZMjKzYhLsvGjEWVLAZKTjx+Y/IScKCmbKQ8TqFpUx1DM2O9YilcdELtwPqUw+rHg9I1iqKMNQxu3YCrPg0+4LITyFrbo4ii5sJLH6nTX2n/loSAMKhV+G8Rmxkibg7dwLJRkCPTM/DZKm1N5r3vaz9JBKTxAAkaxJ6oGt1LASiGqD02brqiDgWWNFDDrbWG2aGhAmCzm4jsH8RlJEs6UgvaTEoZVmcoJhVUAf0wYORaHuNOniPFoZJKGonkd3GHtvdoWOPVVspaL92EgD9cmAT605o9K2HZywgFj3OvKfu0+maSSdXl3MkVwmSbWSD1yce0j2JWs5q3mE2aWLm3SsWn1fakFl7gJPbaJZuMqBbWji/5pMy0tL7A8Vcm3Ql0R4b9oR1leaMK4ggBDu3R8drVv4ZPOGo6v25SMvw5b5NeL4FgHzRYPAoEV4XmHi2f3Hp7gmIWCVQfeL7P+ctsqxH7jN9YwXPQZ726H3P+jBI9K2+B8eWX9PXyZ66B7rDSFoKRF+pHcygAA/VZqmB8p+VTgxnxu2g0Y1EKw6yBq2h499ezwDtuAcfs54h9GQS5ztNDTbbgssP2bRjb9pF0eAwj9nlj7dnGPwL4ijb46KZ4WRC63WZV/irK68nfDlnVrqed/jB89dYpnScm3aC3zsmB5OfXM3wvo8PMrgmOwk5SCTbKKM3FABKoLjwkNzWsbADqTIFyj+1QYQai5OOa3eqJWH+wAS1OqowOUEvogAjSuOgg4ze4HD1W/H8TC/x7sJDJ74+DJAjT7oR3FqhdopfUMmeWK3Gv9B1FdtoTCq4LmkUHS8EK2HB8ofZiti8wSjAWUQGaRuZsSSwIXijIOsYkOGA7cdgIwIIYFh46K4nUfywqdBDDK6TNw7Zil4Y7Vxi90yOIGQH6+OIyRzuadaNRDx0RbB+YH2PIWiXGYjg33qboHwnuJ5sVWyjcYLWQgiM/WzUxjGQ201oOzKZQHEV4OPRHAivduXnuDtwfA9FL8m55l3G8x9aSGQ+wACsetN3ml/u5VHHMnf7KsNwk0fBkDoauue3pJC1e+HnazFAT/pwa9B7sQxVDMA2YXOEZBOh6+7IoCg7flH/9fUpdjd0Mem7hNyGglPTQWQmO21j/exgj35Q7PdpBs1yjUObUuvPnj67X5jIfBYUFMMcKGXJZByASA2cpq+bj1pIbZF8TTuf/lmHwksC1vsMQjoUPjD3RrETTq56XFYz7AML2EiGwydFi7oNtT6L7tUE2S19XnEVJUOdomcgrJHq+YCTJyRxeFxSQLmtab4ao5EFxxxpsfYbmK5J4CrNcpSqluzTbArifIR5RWp5/Vgr74lbpNpA+Ieo7TUMi+5LJMqwalJrNL7+6jQeTztoP1/WSqTehEAIF9Wx7gqIWkH6bgWtDUoP7zaDxnU9wXFB17vrM3w2I3i3UB20NFgk9FD9b//212HDupsM7Nv8n37yQ7xtF7hsKaP5vcOf4icVec7+bOMVR97j8WEG142AaumlHGqpva+bsPLSLL5sslW0HWuOqa8sECmSO8V12/yaFIlCljuUMXDZBICLDySRcuhnJhNIgqvMtYSTCbqlr7k2AuXb0CbisHvhMMxi32volU02pFRUXIegD/RFrCeHTNKmdB7jWjC/KOeROAFQMAkBVDWROFSfCNiM6st0/HjN3YxeliETEZ1gpRzVCug6Zrm6jprEwgCbj4PtGCEELmjuylGwVu5eC4lwI+s46WAKUsryP4ztGZ0EvEF4dieR7N09CcJgn9cuBSttAYBswTXF/JYCK89RG4OmkwLe3YvbcMKa0Y1A789Jn1Xo9ymTzYaJ44zLWQGRWPRrCrxqJ+FmUTs3KDTZfeLr9GFTB0zehHvmsPrcQXod4snIVUYIh8kRvQyNlahuMm5R6WeO1IsA5Ec1pHS49YbcZpuQqxCAZC/QTx3Xx83BAOEDalZ2WAaySpOh2mVsVQcbTb2NcNAXKYa5DzqLJjqv9Bo6GdB6QotKLSsjOSuQHHu1o4M9Li8PYD+h63SNRnVLN1GVA9KsZ3LPMq+R+gX/ervAel/wz5LEcPC7vpgjuaRFV1wKmhtGO+K6dQJwSmD3Mf3d4umGg2quenRW485rPTsr4HzdeTCSa97iljSaQ6uVbOMmbpj4wMp7y8grMJ1EWvZ4ckiogpYWtc/Cj8s96iE2ORsrsW3oRq2up6yClZQd8rxH5fuTZ9Mape+PXVcF2ptg5wSkpxWKEeEr1FxT4XCYV2h8NlxqCqgA0DuFm3aCf/qn3+PzD8pOh4sdfvXonO5Fu0RtEvzq7B0A4M+3z/B6T0lFFRCD93g81FwfxsN4GA/jYTyMX/L4IDNX2dKuwmZRWAAOKC5jG0S7jO0qNomtHyZzXo/UZ06G9IYBaiEJQgMAoEfZnhO+Pz7Asxnu1RvDcBLEOvasyPzGcfa4e04sygAlpRuJg5/GVDPdxd1uX8qYNWvwNkp29HXY4OqG5oMuBvfUfVwaBf+7g5hpO+n/RoZriVl+/cjC5QbqbrS0GNYW0FWsZY+h9/WnggUOhPXuNz7TEYOE3nhoMKgxhTYUG9WPIIBuMar71VHJxmRRHzpdE6oQ2quEc9h+HH7PIwZt/Jwwj6qB94KNTOUuIB1TgepRaE3yOsb+s7uFhXriW2CUAy4ThtutBvTK1+8H0vDtl5Fxq7wRA+40nK8fi9kA9DrWxgeBmuRaMUwdZt9a8dSvVyWcb+Vanm249li9mdI65hYkx+02UlrUVQpxQVnP9DzuwfspoS72hCbl5ZMb5F6EoTWahQP225zZrwCIlewXfHJBov7Kw439Lo0ZYi9hhIYsPdtWG4bGpbY48mLyd9sS6iKF8dk2MsvZkbMC9TZjY4B1m+PSKyH1RkFrw+pEzgnc3NDPJj9JUV5EnsVQxjIORMxcgWiuEEbIGDdtjsvVFP2dhzG0g7mgTDC7kQie9jahOc8vPdN3GKFovvUozIGzsa1oebTDyWSPztc3qz7BiYdmp0mLIfVs5D7DTV1yjTSft1x/LZIer6+W0L5Wezip8G5FF1Rfl3xNakbersHJ5zfOXqP3k3DdTNFZxXXcUnf4nSnVS//xX/597G5KiKAJXfT46JhYxf/oyZ9g4l84/+zuV/E0X+HPN08AANsuZzg903u87+ODDK6hjmlV7GvVNS345oh+JdlTfQ3wFnXhRWa84s0ILgoBSddAcyiQ+lqqE2A6feLrktbX6XUTHyZhYoBwMtb2AHq4g4qUyellWFzQAp99Y1Fc+heKoh5S2fo+wURw7dSmUbh/yAX6eYSFk20Ule8nVKcM5gLpOorz2yTCo8LROQZC0FAK1L6cIwfA7nR0CnL35zjZORS3dLGb5xrVY/8yW0T1HudrQsHhR7Yi1pxImAd6HT+Pe3unBPXqXVStYnOEVtzrSaVv0v82H0XCl+yoDhwIO+k6XqdTkRAGUO9qgNurJ7Gtq3wnUJ86GA/fu3JA6gN+92aC8iYGbLJ+iyStfmEh/HHSreRjNmcG8pD+yN6mEIsO4pQupNmnSN/RhU4+v0OWDNwqkU86SF+jv7uZQmzo+27mFZqGwBCK5tn1TiP9OkN24z/72MVNqHTQxw1OPblqkdWw/uasmoKhRrf3984HvLzoUF3R7sw+brFY7mMf5CBZJlTkBjKxsEHC8lUJdxImC7j5N7TQkq2AWTjIY6+SZAXLZyptkKQDUh88qi6JdU9tIIVD72u3q/UEWBEErStEDsPOAYiblnYhokwogG7ukD3y5uZZh31Hx2h6jX6doXjja4xdJCsKC7S+lOiUQ3onMPgNa3Nm2UQhWAaGOXFWcA/ybp/j7maKck4boU+P4+amGhLuG2+Nxu1mwn3wzx7doUzoRL66PEKW98gS+rvXNwvewBTHFRPPpLSYFC2+e0zSjq3V+OmKWmW0tNi3KX5wRvWIVA747/76PwIA7NcFkkmPE6/YdFRU+Acnfw4A+IO77+Cqps3MMq/wB+ffws5D11oZJEGGMWQe7/F4gIUfxsN4GA/jYTyMX/L4IDPXbg6ojHaSxaWHEFtg84mAClnDKppfKztquehIw5fNuzPwbncovKLPyIg8wK9yIC3jJMqAMmwoh5hJBt3fwPYUDoCNpKLyncTsG78L39uo4Qsg2Q2waRC+MGjnUWAikJZ07TB/ZbmtxqQCW9/y4uR9zeMxuUlXUbAe7n47T7a2nNWaXKBd3DeZDp8te4eksti8HBnBl4Fp6kgFByDRgM2ImDGzkGxyIKB30fh8yD0b1x9DtvdJWeMMcfKGfq+8NrBaYPMiMGVl9IttCHkIbRZJFS3t5ECZcYCy+2nMgrNbyfB+fUZavMF8HINE94ZSlMM/E1Cd5XtjCjdqiyLIP+hDq1rwtcEKSK/ba056FGUX20ZeJ9SWAsDsc/R5z7q3g5HYrzxE0kqG2mVi4IwEZsE3UGLwFnaTrzVm31hsX3jS1dTBH5/jSwAAIABJREFUBTLMssV00nDW+cX1MVuY7auMhS2SRQudGExyugGbfY7s0MOSWY/1puTz1+mA3ouJyKCh6+Fee9xzFix6yZBp9p09zCaH85lZUkQ1Iq0tsmTg8xLCMdMaTmBT5xEWvo3CFOmG1mcY+ycK7jv0wJZ5x9ccSElBASnXA2df2+sJyp8n99jzAVlxCvzM60pg/7GBXHqBCUQwzHYKdq8hRgpT1s+B0Q7psuFru6lL1IlvdzKKM+j1tsBsWuPslM7/uprgYkWw2cG0wdl0i+uK1qRSlhWrjJGsWJWnPU4me6w6Wj+7LqrP1L3G88UKX6wotT+/OYDxrT3ZpMN80mDqM+W/ffQT/K9vfxMAmcoHC76frw9RdQkST1g7LiuGmbfm/Q9N7/8V/DsMM3FwucPsqyjvt38iYdMIicohyt5ZJaC5t5FqsQFuVCPDCJMTjBt6JIOTC0A9nenW8ct4zMwVZmSQfWrhlh2cF/8vv074s9I1HX/zkTcGWEnMXvv65WWN+tkE9aH3sHQxOJJTDH09uRgAAdRP6NZXZ7GVJbujABgCl01GrGIRg6uwJAPJG442esKGEaBOIKpPyR6ozhR2zzycvBii6YGO7QZukAQNezYp7tLYs+uDes+esFFiL11L2BHLeNyzm20pqALUVtUsFL/0hIuQd7ZyyO8chInXGu6nSQgyDlKG9YlgZvIwM9A7L1OZeU9fD22KYkDylk7KalKRCipMbkSKlJ2A7GJ7WH8wkvfTDupTWoRPDrZY1zmqPycNRTeuqQsHKS2ubr2J+F7Ht3ZuAL+ubKcAE5nc6VYwFC4ssHkpuT0pu5bovktBYFK22NcZes9AVtqiCspClUL+hM7xk+Mb7LoMry7pHPMi9k+2vUZedAw/ip9OoD2z2j5uoLSFCf2fg4Dwcp9C9ZD+OnfXE7pxob6uLbekTPIOuzpjhx4gtjeZQWLoFNS5rydfCt5gd3OBzae+D7UAvvvbX+FxQfWHzmr88JwcWxrPol55+cbVuznSKz+PoI16uIftUVSb0rvYhlY97VEc1Rygmy5B7Vm6eq39ugtrUGI4oAdRz3oylw8M5/WU78VyuWO1ppPlFko4/PyGcGjnBF4ck9tIoXusmgIXF16H1AqUC7rZ80mD0sPFvZWYpQ0OErqYedLgXUUvqkU+oB4SvHlLxxfSofBmCMtphceTDXJF5/y/v/1brNZUZrHFK1UGKrdYepODfZ9i4zcpudrifR8fZHDNLwRUJjA9H2BCC8mBg64E1yYBoD0IZIP4vVDjC/J42cph+4J+zwlQpjZqIQkiDJMLQ1nic8k/C6IJFkD1ES3o5aMN6jZFf0mLzKbgNp1k66BbB0HcAHQzgf0jL8lWKlQnkcCTryz03u/+01izrI80unk0RE92GPXvCZhccH+sk/cz3nAMtqsLgddFkY36zMHqUbbXC77O5kigPXL35PjCMJsEehuDk1COsxhVR8cWYYgwZGbxnggTa9LDJBKa9FYh93XD6WvLRCTr+365rj1CDnRDgXW8qQjXYhWw/kiiOfPzOu+ZuJXeKHSnoxdgo1m2D1ZwkJQDsPoOnzpk5Plw1t2z3rUDHtEL6+xww/Zeq6rAel3C+c/Llw2LFjRNgu1dyTU6PetJ6hCgwBqs1hoFWHCWbFOgPfKZ/VuSwQzEvPqjHieeSJTrAdt9jonvkUz1gNstvaTd1DBpph4SvL5awnoEojYCSe6zzqzH7usDzhjtt3c4W0QN5dv1BCKQoRzYFcpuEqR3fvOoAJs7yFM6j2BcDgDrXQ4pHWdjQoDbmPp9gtlfpdyfvP4UvNGZvVzhP3jycwDASbrDf3zwQ3yi6bP/yf4l28X9+PUck68VnPSkoieWN3TZtUC2cuycJHtgSpK7aJdA9dL31J5UkNJiu6OgM1SaRR1MaSGGaKVocnKnAUDoQmLRSfrAF6e3OCsoEK26gglBxkps2pTlFT8/ueD5+dNvnmPYpNxbPD2qWJ4w1wPXZn9j+Qql7FDZqPP77Skd53/+q99Cv8pZPpNkHjs+xrorcDl4G0c94HBC9WnnBBuiS0FyiquG5mDbZPyz8qHm+jAexsN4GA/jYTyMvzk+yMzVpoBIqRWjn/ga5U5QJuM3TN2obqj3f6OON/Lx7eYRxnOKMtpu/jdYqQC6ibwnLAAAzdJniM9q9kLMlMHd9QyZh+T0buTQ0gHp1kLXvl2kU5xFmpQEGtKdb/JeD3z+u6cps1oBypTSlc+kooEKnCKGcMgS+0kUplctortNKX7BCSfUadOVQPU4Gnknd4pbeGxO4htBKUknA7uH7NcJu7eQMEacf6dGjEsjYBN3ry2Cmdb+OtLb6CaifY1b1w5D4dV9DkjRSHtEQrh4DNU78qANTGsdYeDtx0B/2pG3KrwQu/eqdac9dJDwu8qB6QDtoUj7usTs53T8dknnHyDYsQdpYIMHZSmXWYZxz785hPD10rToodMBiyNaM22vsbn24uqphVAOJmSrVkR42gqojeI5s6lDdxQmDyhe+yx8TfKVzee0CI+Xe9x4H1X1KodZGByfEMRY9wmSA3oghAD2XuHo4mIBVytmCyttSbYPwO6yQHYrUf4WKfosigarmmquq9UE4jKD8u1mZmo5+y3exdeVnTnY0rCLy36Xs6xe5muGlW8rEYNE+Y1vR3rnUD0C7v4+ZVLfe/oW04TO/x8c/jluDF1nLnr8ZqqgBM3rp+kl/s7JXwMAfvV33+GfPvsOthsvTH+dojj3ZYU7MvnIbmJZYf/Es66/W2Mxr/me7W8nUKENS4KdgVxhgL1i2U1TWsBn8smyQZYN+K3H5Hb6UXGDLyti8F7tp8wSX5Q1Hs23OMoJpr+qp/jqK5IgFI0ECouTxwSrOSe47vlsusK3ppcAgANV4yxZY+/l0Sqb4n/4ye8CAIabAmrRMav7YFHjsKA5vWsKtL1mswfrxP8n+7fqE/SDiu1CyYAjL8bR7swv/P77Nj7M4KoBoYFmIaOKTuXrc6y4E+thcogtKd1CoJ9Eh5ixEt+9vlJQ4EpZb9aheizvv0w9mUcBLGf29nYOdashhsBWiZBremWQ7AyaI28xtRCj+qhDtjFcF22Ok9iDORFR3s/L9IXgobbuHjnLZAQPA76+GVRjypG+sg4tBvFiwoaiOXH3lJH6w4HN4+EApJadO/peoQ39gMpB1vFahInnnGwEYyxD6XuEgzWWjIEcAEQr+WfJDiivo6xez6btdE8D/D2+731BNXXuv/0WOOibo57Mnv2EuXUKMfUvkEFCvqHF5E56iJ0GLvyL7ssoI7l/7slZo81akGs0GW1sguqWqBX6Y0/KKQzKKQWBIu0xSTuuY21fz6H3gSBlARnXp6pjv7ZsBcOXzdMe6byF9ESf4bLg2uDqc0A833NbxPWrBfJzuoB+6vDJt87ZYHxVFdzCYwHs1gVfV7JoGfPu1xkbfksnsPidC2wquvdvmwzWn4czEnZi+e/0nWbouj2ysNPYrpIWPQ6mtEiUtKzhu7qeQl8nmL+N0Gy4v7ffA7LP1vhPPvpLAMBxssNvlV8BAGaywVWoKeoKb0yFf7L7NgDg928+xw9/RizD7MscyQ7IgxRz6jgQNkeCZEjD+vmOwZNvXQEANk3GMLCpFTsLAX7TGHp2ewlXGhi/WTs83uLlAW1mSt3ju9N3aP2C+rI6xpdr3x6jDIIvnhQOSlh8cUs/u/t6yb3b9qTDt59d8KYCAJYpzeNZtsHjhPqkv5u/gXUS/8vNvwcA+Bc//wztLZ1/cthgaDVOTylAf354gZuWNiK7OoNSlt9pxgm0HiJuB4XeE8OGXiHNejxZUN0rU0PULg7ZxXs8HmDhh/EwHsbDeBgP45c8PsjM1ZTEFh4KwYpM1htwR51axBabUpA5NSJ8yC01EW2FGCiDC5nC9LXF5B1lafVJAmGA3otzD1PvjgJAKovbDUFYQ6uRtNF5BW5kSq6B/aOE2askPuGZsjuLZGvQHfgMo4jXMn1rEPoe2jmRlqSJBJsA90IQ2WfsOBMVjqJYe7px9/6uPRQsiAEByiSDglWrmLgiLNAKoM/pQHYQrOKCnebPlYOAbCPLuJ+BW2WE8Zn3ZNTCE2DajYbeCyaRTd9aqJGoRpgr6edNN+FmA4PPaoUjwlZ94nf5yYg4tUngpgPENkATjlnd0BaDF8LIXqfQ9YjwBYf9U3+MgdjVnK3m7h6z2iUWzlNiXWYxP6aFELRfAfL0HKzE3gs2iCEyfZOtgpOxdEEoBX09TBz6QzrH8rCClA51nfLndt69KF20cFag9+1Dk3OJ6gnN1dGnt7jcTqG9xuyirDHx5zZJOrzbEuknTwY0vcb2kmDW7DxB+8yrOj2/hhAO2Yyu7WI9Q3cPwZDIroPzk0P91Bs/FIYdWpwD+lbjxrOu3CAh7jx8fCuRbEeEwQT8/E6/fYe/++wn+IcHPwIAPNcbBDvzrU3wSUaEHQWH/+rLf4Qf/8VzOuYbhcVdZHivfrOD8Jm9axXsrYfUVwLGCmz+Fl3r8emGs7ZqnwNv6TrzvYBVDv0BHUPvBZQvZ3SHBiKxOD2hjO63T79mWLWUHd62C/zFigTuN02Gto9ta3VF93OnB6zqHLtXvg3BCujnNN+/cnaFqW6ReTbvabbFoW+HeJle40TT5/6ofol/dv05fnZLjOB2k0F6E4ihV5gc1Hgx9wxk1cffaxLoZKyCFZG5bpMxkUplBrOiZRJTbxTD2NfVg1n6ezmEb01xCggGy4APqEEJKImB1ORg5l4/d8juBLOFrY7MWynoZTZ5R4tnct5hKGmBbZ8rtEvHECMAZpr2VTQrTq8VZBfh2PI8yh8KRwLxrCZkgNyrHeXnFYZFhuqUftjNiLVI1xXVmnTjkOxjP191rNisQPbwovjhumNANVlkPqsuMIlj3TDM1TA3kOXAgu2ij3TbbumA+Yge6yTcEFqfBEstyl5AjNqd+N6AWnysRoTXk4ivylZg+sq3G4HucQiaVkdGsLAO0kSYuF1IDkZWA/ungk3nnYzBz00HwIhYG8vjyx69RHbpexFzX7f251g/drB5uGagPegjLGxE3IhkFrAC6ZJqV7OyRTFqi9j6umTfK3S7FMXPo7JQWC9W0caPW74WQP04yHE5JL5dom1SmE4yRG8Kg7Skz+o2GdUC/XXuXw6Mcd18tURyVmO5oHrvMquwDn2QfYoXC4IUf/LuFMNVjsSrZWXfv8NvezWfVVfiL149hluH/htAeig/vZMwWQyoSBy/jGU6UvG6yGkT4deM3sf+7GRHG8NQymkXAupzOt/T6Q7P81u89AFk6zTOB3qAeyj8TxdUU/yTf/UrSLYSs6gkibvf8LKExQDhBJxvxZHFgKGIpY/Fv3+Nz33t8GI3JRUoAOp1juw29pQ3Lw2vX1dJ9I/ohM8er5DrAY8mdI7X7ZSVkXI9YF3nvCmyVsL5tVROW2TeJODuakYKZ96AQh01WHgIXcKhMZolFL8zOcdnWWQT/6GHwv/47iV+dnOI1jsIicRSbzSAtOywLGuc7yl4/8lXL3m9S+2QJIbNEep9BueZ0KocuC1rmre+B9mvkWTA11tq3bpdvf+g6gcZXGEFhCVSDrdZJL6nMdzTUW3UKvDLRbWU2XVeeGHIYx9bfiNQnlu2TusnGqvPvF/mAQCBKAvYqJhdrCW7YzjtoHYx+wWA7M7XmayDSQTXPnXtkGzpYTLTFJsXGapH/uFVjutuTkaNVGWA9kCgmwcHmkha0jVlqsHdRXRAd+Brd81IBtC36GQbr4GbS1Qv/ItPOtg6asPa0qD1bTPFQYN6nXNbBB0sHD/229rEwUziOQsXNY5tD4gMPF/oJJSvN05fC0wuhthyk4w2IkPM8p0SMCll8QCQbl0kLX0C9FM7iv+OgymMoPMN9fBOcm08v4j2fzYF+mctpCdumUrHYKodZGqiW4wR0H6u0qyHlA6d9wre1RnWWwpc/S7lVg1ZSeT76Nnq5IiUpqmua3yPsCwGJDq4ykgWa0huFeQne+7/7JSD+4qCwPxCYPupgTzyC7TRED7ATc4aPJlvsO/phft2d8C2ZlWb4u1ryl7UXQLxqMEPfoP6UGa6xZ9eUvq+upsgyQf0nuwkblNorqsaiGUHhB5VI6gHGuTOU7wO/cJAP7dx05W4kZAJrZ3Qp75/4eC88MMsbbBQFb7olwjjRw3VUv/7f/13MP8zuq4iBdqlw/ZbPvhlht8Btibv2IAqAIA6oA3R0aRC3Sf45o6OX+0yiNuU79PuM+/7etAC+5TXhXtR4/kR7V6VtOiM4npp1WTofN+vM14qMmhrZwYvHlFvnnUCr155/VZLNdzQKnN6uMF/+JgIWV/sTvF6t8CLma/jyg5fdSQG8bP6BJcNoQEXuxmMkRwkZWLZQnA5q3CzK1F5K0Iklu8ThIO1Ao13NnKtYjJeXnQscGKdQCJjf/LFbsqWf9aOBATe0/H+bw8exsN4GA/jYTyM/5+NDzJzlT1R3+UQ4Ubj5RBDW41E/NlYZN8qB6GjqAFEhIxlB6ge3N6zf6xGtUh3z/2G3FWCgIXl1KO48Fmr/91sbbg+Wp1ogmIjqop+TjvydqFQn4ylC6NBeq/ESAiBarFq5ITD6kcGXonJ/91M3FOgCtCjswQZ16d0ktVnHdLAAK4TOCOgQgP/BLzbbfYp0Et29YEEZ51ilKkDuJe5kw+uzwLLWCcFgPxcY/a1b8HYkKyhSeXoOL7O2sdWHKsJKg+m5/1U4Pb7/rizHkI62H0SP0eF4ruvhXpVqfRaxbalmcNw4FPv1ELsEgh/bZgZ6AOaHyEc+n3Eu1VhGMrrWg1jJJt8GyNhX1NmkIzQ9GRDWT5D2UkUfOhetihmDXqf/VonyHUGQPYmhfU1vuTbGwgBtF7dR16lyK/8epwDmPeYlF4Uv2zxeEaw6kFWY9dnmHihgV2f4vU7ylb1eQr5jBbMb//ul3harLDxMMjv/+Q7LHeY5AO6XcosctkJ9ItRDX2VMvuZRDaCoER0ThqW3njAm73bJJYRgmjD5mW435bvXak7fN0e46In4Yvfv/wOvvozyqiLW4ndR0EgZCAY2meIUjqGREVhUEw6Fl5IlcHOZ1yvbhboWw3tIWydDlBP6feOZnuunb5+dwi51rBeeSnPezY6N0ZhXRVofLZte8muOLDENC+PKVP++OgWFzuCtW/vJnydIjeYLirMfZb49x7/mO/F02KFTA2Q/iH64fYFf32WbfDzwaMPktqnwn0zmxSY032/vJ7Dtio+G07ABvciYehZD2IR5cBrepq3jHTsuwT7puDjj2uzbvyyfE/HBxlch3mogcVWFggfbEN74EiWziaxHUP2Avmt4xpmP0GUGewdqhPJThdiAGwwlU4pOI1fkL3XjVWt4BfbUFCgDy/O/SOFdBNUnTw06ktVauNYp7c5ooAf7ONsGjcHEPH8dSWQXzsOmraPQbOfiXsQuFXU+gIA2SpCj7e/5uAOI+yJRqFbZf6aJZBYWG9tZq8z2GAGnjgkt4ohXpOTFjCAKPMHgohNGvttTeGitZgAkrXiDU1x4bj9aMho4xDJWvGYQy7vtUyZ1GHn1bKqRxZuQi+5JDXo9wlEO9Jl9sQz0QuoSiJd04HqzxskGf3d0GiInZfz22jYicFQxh3CsEn5GFCO65nOgrV/RaXglEN97V1CdgJpG18yQYJzKIDmiWUtZrWXUfUqNVxPBUC9n/6F2z6NPbrVbUm1zNC21AkO0M3THp+/OGctWTvqg7xpJjhfz1Dv6Geu1tD+hfvo18/x7cUln+8Pb5/jekcPg1ulLBHYHlpIQ3rMAG1oAzehn1vYdLRRyceN2KAWJ/heTQA2EOJSS/11oBo6EAlN028kdt+nuXpXHeC6meKrK4JPu4sSbk73qTsbmJ+oADgroP39NYPimm+aDnCO3HYA4LaeYPC2fs4KCOVYGWk5rdB5QtOb14fsXpSI+7rkxkgm9lzfzTA08dUspGNY3vUSybzH56dUI/3y9hi1l5Espy0Gr4L17dNLaGnw8eQGADBTDb5XvAYAfJRc4x+/+oe4a+nh/tunXyD3NZgfbZ6h8pD/xfmCYGtuILewvs7sjKQabBfh+wD9SungpEPpN2ezvGVJxbpPcLWhhTb0CsM+gZ5EXWi+5l/4zvs3HmDhh/EwHsbDeBgP45c8PsjMVbbE7M3WUVdXNQS3joXUg8iAVYKFCYJxesjodDUSj0jo++HfunLMxFUtKfMEgQA72rWmdwLtYWDUWggj2ERZ1+BtnHCkWDG5oB1ivVQwaSQtjf+vWqDxx7S5gwrC9LcEX7NThxaxFQeR2AWQbvLBz+gf/URGs/TUQb/JImHqIOr5wgqIiYXxGQZG+sFQDv0SGDyRSO9kzJKjNr8XkRi1PW3lveuTHVC+85l448bkY6Q7wySRoZToZj7DUUDqW5r6UmD7IoqoJ1sJeeszb5NhRFIm0Q3/4bIjslD3gphErtbovBCCaCUTakhRKpqZWwUWCHCJg5z0Efa6yVB6f95gVh9QC9mD10V/YCEOYjsORgbaQ2Eg157wsvFC/V7sXk4HGJ+tikZBekUgmwBu4pDN6cPS4z1np4fJgLumYKeXYVA4vyEYVX2Vo3/eYbYgWDI7HjDLPAN50PijNx8BoGxc7BTDu4kZoSd7AV3F+R8mlM0CQPFsi+WkZoj0ZjVFX/s70kl25xFWQAyC/w1J5DOA0KfZz6O+7/6Z5bn62fkxE2gAQB21EL5skSQG0h+jbRLofGB4XYhY3nDOk3V2sXTA6185uA6QHr6+2UwwvJqEH6FfhkUBYD6g8MIgSllc3xG8m2Y9dGLYeMAZwZB0sWjw3bNz/OnX1CJktwll9wDUvMGnJ6R6VeoO35+/xtLDHb+d/wznhpi9//Vf/Wf4/vEb/N4hmZu/6w7wxZ58cl/vFgzzi72CWHasfDU2bccg4DodBVuOa0wKv5Z8SWiRRyGIq70XmKhydFt64SRXCUTpMPj7ITMDEVTOuvc/NL3/V/DvMgT950b1RoJ+Y9Apz6ODjcui6pJqCQruZ+Gl52If50DBMA/SZ84hv43wsZMiSprlLoqmJ7FnVBiB9FaysHj1KLbRyI5g5W4a+iwjjK0r3HPrEQYoPNR83+zdMVwGAGaEXfQzgmDTlX9gdo4hVl1bDqZnfyiw/kSg/phe9nKn48sltZDKYnZGL99+UGh8z5qrqeUoMGxFD3b+oPmK5zIOMkPhYqvSWiC7if3J45FUFkMu+XerExlbkGrHc2MTuk/hXpvCQY6M1K2OEoSmdFwjNoWDOGrh3hCMq6Rj9rfso2qU8IEkwPdOx2PIVsAWAjL05jYjlS11v61m++2eWKUAcmVRe3Uctdawxx0mS3p5VZucA7vsBbGdg/tNL1mZJ7uR6Bb+RTnpsTzd4nRKDdvvNnNuGTmYVzgoGjReHL7+eo70I/q9p797DQmHXR93ZNuWJvL6ag7na6DCCrjUofcvWrVXzLq2qUV/apHOfH9s0eJjX9PV0iKVAy4rCjRDr9i5R/QC1m/WnHbAzLAsYPZOY0qKgFCNg1MRzZStgPH3pihbVPsMbhUeuBgYh9ww/Lo42JPIfDBgV5bXcb/2dnB+HbvUAh6eTooe00mDXeUX11cTpH5tNceWJC0B5IsGxah3ue015jN6ZpR02NUZ1TgBIDN49Ih6gg6yBv/PFx8BHvZHZtkQYV42OMwomP6XZ/8c3087bC397L89/3v40TXVlhd5jURYrA2tp9Zq/PUtBdeb2ykQNsbzHs4KclACaEPA1n2gsoLnQASTBAB4NltBS4vXW7IF2zQZqzJ1uxTSH99qwGWWDTycQ6zTypGE2Xs6HmDhh/EwHsbDeBgP45c8PsjM1Sn6z+QjvVxJmVPu7dwgSGQ9/H7I9sLvBeuu7Foi9ZZwQ0HZXlD+6WaSM16bek3jQGjaRi9QvRc4+IK+r1vK1ILYtyliBm21IGKR30gKF1m2wgtgsJZxEwk97ULeE54YClKdAihjDtm0U2QQH855KAR6TzQxhUDnxTLaYwsz7SN7dkxG2mhkXydoJrk/Z7AHpzkYABFN6MesBdnFjNbkpAAlghqPskjWvpf1FQlgqJGucbhOkwhYTWbtAKlKyRGS2s0CO1ugn8V51TsxQg6AYeYwzPyOfBuJUMmjCvbnk3hMGZnbcALWN+yb3MGmDqoKjFfBkKg9GIBOQb2izCZdCeqBjodB+9jbfy0bzrjquwJyr3geF4d71tLFOuFyg2oAWDlSfRJMwNo/tZDHlAnPJw0225K1btOsvydAcNceQHiS1+yzNV/m15eH6Bsdjc7zHr1XV1KVJKgfQHdkoOYd+7KaEYkrKXo8P7lj5uyuS9m421iBbtDYb+mYbqcZ9nSlixmbFdBvMhSXPiu/i/q++ydkMt8f+vN/tMWJZ81er6cEpcrQvBnnHtJhOiUYYbvP4axk+N4aS88YSOxD7CPRsDmzyL04R7PKsbrKkWzivQ8oiD6rMPUkHyXJbk35DG3uBRUA4HIzRb3KkSzoXF6e3LFy0Re3x5Qxe0vBJB/4pfLd5QX+89M/pHmExO/Xx/jnG/I3/Hp3iNMJoQ9/9/jH+LX8Ff6P1a8DAP7o/GNsfabtWgUxp3XgQnYestWRBrcoCdIOxK1nB2s8Ljb+1wT++O0LmMCuFo5722Vmoqy2ANJJhzT1ZEJlWFDCiF8kOb1v44MMrmovoAbhoTz6Hgdc/5LtJ/SAAlHAIIx+6rgmmuwjFCsHIKmiZKBuHKqzEduzisII228N/GLWO42kDp/l0B5IhqpkhwiV9D64h7U+ejFYBZSXbiTnKFCfSL42VlAqqNYVasYmc9E03FLtOUgvjg3dTe4VlsK1rHVsizGCmatmalDNHcsaOnN/7jAImGlou5AMqSfbkXiFI+Wd4GuabCJMXl4ZDLmEDeYCDjDsn0vXFgQrT0J7AAAgAElEQVT/TQ60h35+0gj1CgPAAdrDpdLE+9IekopW4s8r/e6a5f0uvjmEUlGUXQ5RQMQWhqFfl1nIneIgPFaaQiOR3imG2PcvTdxkuP+3vS9ZtizJrlrenOa2r4+IjCbbqixJRQlJgGgMAxkDPoBv4V8wPgAmTJhgYGAmYCATZggMVOoqqzIjo30Rr7v9Pa27M9jb3c8rJiC7hioIX6OIeC/OPd317XvtvdcC3MgEmqytNSzX9WQl4bzazoQ8Vb3zja4F6gsOQJlDtoiNA07xZwB4+OVNqKOubymYHZ/TgrvZlbBcd4ZyePbVdQh+Ly9P4fizxIgWQq+sZV+PIfymYm4gPqGAoKWF6RQ0U5auRHiPdWbw+vY4LL5w4l5NwDQq1PkwMgB3qIpKIb+laxu9ByCjMEj1UKBin10z7yFyg8ksCtPfrul6u+sR8qUMZR036eMmsVFYL+iLkY87yAHVaa2AaWJnrBtbdA85UAKo13TvsluS4PQb4PpRj+kj9sJlSUgAaDoBKR3O2Oe07jWubliqcJ3h9LMFfuviLV2byfDSKxe9OYasJXDeh3vpPXQ/Hd2h4w9+1Z3hF/VDVPzl+Hp+hX909OcAgJ0t8M8vfw/fLqhjerUZh3dJZDYoT9HYT+w3ccoF+UOAas9//8lzAMBn5S1e1HS833/+Q7qHHDSdE+j9PR6sBzKnGrffwNCGg52kBv68Hyo+yuBarAGVA6J3ZCQOyrBsFhdZU8ages86bu7uzYkK49BzRqT3LIfos8IizpfKmrKK6hEv8J0I9TqAVJMAap6SvQujOUAMEMJQcB3WVX2WWSwd+hLoeca2GyocGYQdupOUuXrN3OHCT7O38ZgQcb63PbGxI38poWrAciZVf9ZiNKdFVQiH/WoUVF1gRRibUHtJ+sqswesEjcQAgMoGs8MA6gsbbeAagZJ1XatTmi3197+dizCK47iW7jctzUmse6p9nNntp9RY5o+RrWPjmTBUm5z8HWoMmeQtViw7KEoD7GT4ADMCwJkgGgXHN1kvNGSLwExkWxHk/UwpYTOgPfWUA2ixBNUi0UsYPx7mELJfVYkgDGXWGWQtUbCecPNJj/EpLdIXsx3qXocMYFo0YfF68eo8SG5iYiFnHTbf0KJtS4ujT0kh6LPjBZ4vTrG+Yxs7ZaFYTckarhf7MalTQJVxIbS+7uwUdG5CNtZ3GobrwNUmg5x2YawDjYoLuKYRI6/ZLKyA2sRdZHjuR2yDyBud+sJGBqUTcFJif0kjH3Ivg/by0R3QHA3Yn20WGuBsAfQX3EcgHZwDLG8Aun0Wmm2ccIAS8fyFC9ZxqhaoPmtx/oiyuCmA2xuifPbahoYpIRwgHF6u+cu9yiB5/AtPKkzyDj9bUh30ajGDe0FBX2kH96gJc6NCOJyx1dvT/A5/uKPA9kVxja0poPj+f17e4h3P9v7rd7+Ft+s5apZQNI2K9WOHMJ4FQ0p2YUyt7NFzk1VW9Pi9z36B35xQofvfXf81/Mnrx+EY5agNe0ZjRUgIpHYAB04BkvL0Rve57sPITt1/+JlrqrkmJCQkJCQcGB9l5tqNaZeq92LgSUqZjM90ZBs7gs0ojtAISx2rQ1FwryakWsciEPS7/YiyWYB2yu1xrCvmSxky12IRBSJ2Tyi79K47snNRV5ep66ATXDuAP2v7TKC8iT/L1wjXNuxS7ubUoRsoUkui+XQ8ugeezqwvHNpz1gQdGFWLnuqu9ow+TOcG1dIXbgWKoxrNiv6u73ToPgaA/bM+0KBDPWHKVOM9VlV0eplcRnUr4QCYWD/Nti52crdAfS7IUxZ8H7ne6LKYSeZLiXyFe+YLPoM3Ywf9G2tsK+6AvTyCYjEIVyvACpgTuu5i3qBh/VS0MmTM/cwAmYPgztl+7ILWLx0IgUYfKlapXt7zpkUvoyC/RtDfxZ6cb/qvKMX9ydPL8F9uqzGurudBIWi1GaFjgY/iSkfGYiRgdxr5p1QDmJUtjsdEL/7Z20dkoD1i04BdBlN5uTJ7n+rXLmRwKrNhtKTMepyO9kGD+P3dPNZLMwu7zVC8ZypycMnCKLTH9z16fQowrJ978whfAph/K8Mz3HxOmsOCv1/5SqBY+OM7SCNijbqhLl6A/HrLacwInROQXFPUox79xr8kAnqtQmZmVew7aH9Q4eJkS123AOxeBzrc5QLW60NXksazuHaKsQksiNlkeLU7D9ea3epQSjn5bIEfn7/DT68oS3ww3eIfnn8DAPi2foAjviH/aflryKTBjydklvCmOcHvX/4OABK/6DodqfcmlhHQirBGucxBn9VBaL/aF4GJ+HufPcdptsO/fPW7AIC3N8fBOL2ctCjzDsqP8EAHhxwhXFBvs1ZAK4eC/17qPjj1bMzgnD5QfJTBNduRTGG2c/dcX8xoUJucDsY/FjL8u5dI5PICfam8NdqUapRDa7ZiRS/Y5hk1xvgFIl9HylhYF4Kr3nl62tO2MQC5HChuXTjG/rEg6UQAo3cSso/1XgoYHOQnsd5ItUcbF6y9RHEbKVHhgNZrmgsEVR1hY/NUc0bC8H7h6QdyfjAC7udTZPzdKK/EwCicxM+lX6hF3Mw4AQy777OtwOTSD7BG83LaDAxMBETcUOw/oXlhX6/OFzKMC0E5zP+Y62IbqpsGg/czh/JHRImWTqD5Zh5LgCcGlsc2shW9B5Ypy6YfBSUnAGHMArlFdpUFytuO3P2mkMyFOmJ5qcNGqpsImKNhpHFkrA4qUbQsETj6bIMvz26DZN2bzRFuXtLYg6okUDp0imUN9xIlN4N1UwfHNdHJpMFuNUK7onvSVRnWax710RZ50cWaqEAQgLetAjoRmp10Fs83L/ogCfjl8S32fY5fvCVBeLfKIZhaltrBWRNKGLIX0LyR6o4c7MMm3v9tNlBAi7XMbkIbTd9Ehi92OJ5RYOnrHPvryaCpKNLAphT3vof9aDDb3kvUvEmUox5K2aCaZG8K5HwfiwVtVv1mDc9qTCZ0X9tW4+ZmBscjQsIKwN8iowBPsZ7SXOgkj65Hiw1Rv7YpIKwIKmTticGjL0hp6Yv5Hb5dneMZuw/97sn36HjB2ZoCf3jzBQDgx8fv8NvTF3jdEu38+5dfo+F6b7UvMJ/tcfeOaGJhRJgRFk4EVywohyyPlP90Ugfrw/959Rj/pfk8ymx2EjlT1WXeIVNRkF8JF0Z1ZN4PAi0F2Kb3sokOtxXdg8Xul3o1PkAkWjghISEhIeHA+CgzV9k5SMGiAr7hyAB20PiTLwbCC27QHNQ77J7FruLyVgTNV1Pep6qybTy+99j0x7E5guKRauNIRz8mmth37FrlBlQ1GZ3vP2G6Onco2VQ63/CIi0+QRrEZqZ8MMvSRoyaRwc7QX4s0lOH6HflwEB9roH3sO4wc5EZBX0W1n0DtCboHQ0OE+iKqE0E4WFYPQqWiuEIvQmOJ3glMX8fRiuFzsrmA3rl7usltaCCj3/NG6sUPlqiuiZ6b/UmBfBmz9+qBC7S/nRjsdzwa880IWpD1Gf2QOngBoCsNjXDwTRE7Fah9VQOQrI87lTTK4xWmljLcYzMxELVEzsbY2TZ2SbfHpPXrs+H8VgU6c/fMwp1F8fMXixO0nInUyxLZcsAwGBGydzO26B5RVjWfVqHRabspSQeXTQiEiqMmk3GD33n4Gh6vd8d4cUt0hpEOs/MKv3ZGGsK9k0GjdpbVeMzjGFJYvDbHwQAh20o41iMGSOXLZ522cOhY3EKU1ARleYRHb2Sgw52MDXA2B/ojg/yEru38aBs0fJvvZzh6IZFtYunDd8cLQ99r/zycdIEG1Xc6npNysEJCcAaq9zIwMPsnFua0C+INsBIb7r4ONL+n/addEAwBgJM5W9ONd9i2BTZ1vCe+lALtcPbpAi1ndCd5hxNWO9r2Bb4+vsYnBTEtG1PiTUWsxffrU/yTp/8DAPAwW+EX9UP8m9c/BgAs12NYfvazaYXF7Sw0LjkZWTqnXBDjn57voAd0knEidF2bXsF0MmgLy7IP3cFaWZS6R8+f1xkVGrkAhIxWCOoonvGY1LbNsWbFs7b+8EUkPsrgajMBkYnQIQxw0JMIC/w9hxzjwlyoBPloFsTSoFi6SDmN6GejGw4mCqEL1WZ0fP8l9z8HKAj6uq3e8zG9jGHnoDggdxOBbh7ddMprYPI+voTdWAwW6ugk089N+LLnVxrFnbg3guTpXpvRAubrUbIRIQC1ZyYs4JM3ZBbv78n6Kwv3KBbEXC+Dqs5+PpiBnXVQ2qDluUggCq+rStImA8D8e3uf1h5TRy9AlK6wCAbR1YVAfe4XXAd70gXT7+W7GfIrb2zgQte1KRyEEcE0wI4E5Fs2Ip9HOT36QDeYZQWNbQS1KxGkLvuZgzv284E0wlDc0MuVrwdykyuNbhaNvE3OJQhQwIG2UEv6f+2JQfOM6bTMIOM66t2aXqKeXVPyKx3OW19UgJWhLqqVC/T9ZjsK3bxZ0UONqTYG0LjE58c05P3F5BaNzfCLDdX93qyOYHihf3K+RKk7aB6ZONZVEIcHgJ+tHwIArqsJ3v/0IcaLWO7whhY0xyzAAkFwKnZFiwWZMvj7Chc3q07Efobm1EHuJRzFFby/PcLkj+iAx1sH2cbvJc2Y8wZsRKNbfsMqHDB+F0fdai51NjMAVkLx3G4/tsAR3ysrgE6ia+kgcqcgPJV61EEfdyh5ZliATMAB4POjO+R8356vT3G7noT72m8zKJawNFODxXqM82OqFzydLXGSM+XtJE6zHV7XdOHfrs5Dh+0//erf47uGOoz/7e1P8N/fPkXD74iQLtROl3cTiI0O5+yki+/02ODsnNSyJnmLutdhfAuI3dN9owAjkHGNOsv7MLJWKAPrBG44EDsngm9wpkwI2FpZFLoPBgjbfYE+yB5G6cQPFR9ncFWA0NzUxPUuX//ztR5duaDb2x4JjG7iHKrVMmgSd1MRMkS9B8q7aODsZMzGrAbggPKOg9UsZo5OxkCrWg7IfB7lwqGb8IJyQlmnD8TCIYz96NqR8w4vIrIFBMewbKXil6enAOyzctUi7FrNiMZefHZAdWeet22irKDo6bPWX/Im4qQLRsnOCrhWxjpT7pCzfm1ZdNisvQcfBW+f+WU7gaPv+BiCNjQ+29bRk5r+XYlgCl89snH+cyMhbjN0KmZB9iuuFb4rIxPRk7Zt8zDWC3tuOBK9ILnCwThCEBxw9Dwlbxz0JjZC2cIG+zPRC2RrielLPq8ujlpVj2jj1HATjZMObmriZ/USPY8qyUkX5kv1ZRk2VfQfATeKGx+PrsogBGWiAGUJPlPI8micfjKuMM3jHOhvHr0Jf75uZ3hfz/CLd1QvNZsMGcswPhhv0FuJmucnv1k8iNlGkwVNab3QkJbmlwFqjgsZ4l7ct180IjT+jd87cufhW14sBxmMiI1/uhZoTgQcO6wcvXTw81/dVMBNBuM2NrIDFGgdHD+3fmzRce3dHnXQ3GCkAJhdBvOQAoZrJSSbnstewI5tYDSypzuMCnrPjkY1eivRckPOtipwwo1iWljccZZ/t5mgezsJ77+2sSlKjAxO5vug2VybDNdc55/nFX6+fYBrFt34zbO3+MfHfwoA+K55gH/x/G/S/2kzNFUWGABIhz3PMeu9pI0osxbILDLekJajFiMO1pkyeLecBQnIvtFRW1iSLrXPSCdli4yDZm8lrlfT4BSktA3HMDI6LGllsNiNojzqQIs1L9MoTkJCQkJCQsIv4aPMXFXnoIQjWbpBXU+Y2EUoDGWDAHXQ+W5VXZF/qJdGVA3ttgGgWFvAITixVOci0IH5mmq9np5SbaSkXRZ3+E7SkLvPlPtSwGTxs3y3LACoKkr4mVzA5tGTsx85eAUxp2NXpc2AvL9vgu6dV5oLAygXvUyHXYRGhLEWm9MQf3AkEXEcw9WKsioe+ZAbhVYzzbksACeQ36hwXl5cYfYiZijemcezCN1UoOEOZqcos1Sc6Uxex07u5gTotYO4jaLygqnffC8Gwh9A9dAB3AGbvSlgfDd1aSHrKHlIPqH+YA6iVVDcHe50zHjhEOtWmQsuSACVC7af8vEvWmSjDnPuLlXShi7Ozig0dQbDfq7521HI6Lx8JkD3pnrah1ETtVGBXneZADKLJw+om5QyTbrfX02vsewoc5LCIZMGx/wBW1Pgz1ePAADvN1NU+yLUlouTGl8/vKZnaDR++vIxrFf0KaLClOtl+D/9eQexV9h8xedYiSDCYHO6d92Jr0cIqJd0vN1jqokWzPDkuygmsnuogrRlc0bMhpd2bE5iqUP2rF7G748pBkYMJT2f0CPgBCz7ucqMuqQBwFoJIzTEHb27asAa9DOD7KQJZvKZNjCcmd3uxmGMByDmwDMEy3aEn18SbYvXI8jBO+4kguKZzg2qNsMkp8XoKK9Q8ojKn1x/AucEfnhGz+NH4/f4V9eUrf7s5gE6zpjrfU7PyE8kVCqIVNjcwU4MPTtQieBoStn1JG/DOMzVdgrnBLowZodYSxbe45b9bq1A1TG9WxWwRqLk7uGh8bmWFiXT5FWb0Xl6g3eBMP5l9x9+3vdRBtf6TEAVVB8dX/GXrqB6jqePmrOoSyv72NhTKUkyh0wn670LYzPFokN9lqE+i4tvsYzjJKYcaNjagV6xiQ0WNmclptBgEM9bGFoc8lX82e4JBzxNtUQ5oN78xqEf2VBfDKbs/mdTh+6EKdFRTyMEXtM3c6RGw/ByhP2YjKXDF63SoTZlTnrAIKgyQQD6ii7OZg6QQMcjJXotg3WckzSpAJBGsNMI+sr1s2jyXTwvMX6FsCp5mhAgOz1dxWBrNe6pPvlLqR/QHGX5LdOZpzZsIuRewhUu0NpiQHHLVhDlzx/Zz2zQP1YD+zxTOFRPelRf8uL1Pg+U9Hhe48nRKmjKrpsSOTd0vLk+hv6+DMpL/RjYfcEfPumD5GCQwzynF9S2Co4XzotHKxyVdXCq2XYFfnJMMnqN1aFWap2EhMMtd9W92p3gckXye9Uup5osU8tSOnzznihiaySmsxpNQTc2y0xYPIusw9ZTxNscOOqg2GxcKRd0kq1RpPTE99zu9UDqUmDy1kFXTGsbYP2pN0GP9WlhgMk7EUsVNupqm4KVzvx7kAPtRaTaAcC08YvlF3SlLRo2Hjf+exBKJhZuws9z0mIyagPdLoVDbXhzsC7hWhUUs746j/XoP33+GPq9p5a5NMP3rpuTYhYAnJ9s8HC8Ranp798tz3C7mIZn8etP3oVj/rM//gfhz3nRYX9DmyfRSWpM8l9fEfsboABoC82BcVzGfoltU2Dt6N2pmhztooxzr4OmJAAwvYRg4fAKeZA4LMsOZd6FMaauV+Fd0spisaVzrHc5IBA2XXAIVPKHH1r//7iGhISEhISEXyl8lJlrcUfawroa7Owsde0Gv0sB+G1rviQqGWCatsA9WjXQlxONZiZDZytkpPJsRlmrp3RlG7Mqqwcdo4rHgPgQqomCEsLRcXzDlD8mQDtfp4HiRoTjeFH5fCED7Sw72tn7hg5TxHsgbnOoLo5xOCviGyJojMffK9FJgGnW4lqhfsQX5vyumf/qEMedPI3Z+GF8IN/QjrYbibDV68YCu6cO9mEdrnv0F5QR5Wtq2vJd10MRCVNSU1kU0ogjQVY7NCdx9Ca/0mgeDI1tRfgZnIDgZ++EC2bp5rgnkQztO9aiIIauBBpWTHr8YAnjRGj06S7zIJCxv5zieavgXlLGaPPoCTt5T9e1/zRmqyGrUhb1e+6+zCwePl0EOrktFP76Dyk7LVSP17vj0OH5eLrCn6+J7p1ndRDjv2vG2Hd5yC42dRFEI4pRByldEAxoOh19TbVFVeXhd6V0QW+22uWBLgaAYt6Fn/U2Zouup8YttWYR/luBgrWjfWNeO1fh+QZ96A7IL+NYTj+J3yc/dgOwGIoFts84s/x8h3Om4bcVqQw5HqORMmartlOhvCE3mnS8PWVcmCCE4DtffRfwcjtGvaWTFAKYXWzxg1PSpn61PsHtLWedi+zeuyksQrOcnff40RMabzovd7BO4Gd3xBYsV5OgJfzJ0RqXmzlu3hPLkI075MwO7G7HxLQApOkNxPUNiOUNEAU+4e7hus2w3dG72jcqCp4ApMilPD3gIJitcr2AqxQ63+A0bjHiJiRjJfrBMSZlzPJX21EwS4ekLmZn4/H92FK3+/BD04d/BX8JSEMm4E4Dm8exjpjtENSKnKTZUYADoVd/2zvYgWGDKQV67uatTyVUFc3IjYoLfbZzyDeR7rUZ0PL/62Zx9Ka8EchXLnw2Ce3HUQHZuVA7bE5iEIYgcfrhaNFQ4s2fRz/2Vnv8pR5Fo2ebW7h9nN10MorKCzMwMrAUlH29qz53pOwCqruRsLmn72SgTnVF3bZ+hre8c+h5tMJm0QZv84OeZmm5O1PVItj6+Tna0IU9ovobAMiG6PBgRiDic2sf9MjmLG33egzhgGzBYwUzBzv1mwMBaBOE4/Nbhe6p14NUKK7UPWODjhWVHv7kBl8fUx3stpngpz97Fizi9ODe6Z1CNckgHtNib/ca5RWdcDflWVdPw/UC7Z5HKTYa4IX+5HSLTVVgv6YF8UefvUPLddW3uyO8eHGBjB1hvl+ehm7VadZgxs7s76sZNk2OqqF7rLWJHZ29hu0FdteDuTHuLO2A0BkOANXdKNTu4ASZGwCQTAN7ythZhPGg4m0G0cc5ciDaO1bc++DfEdm6UHdWHULNtXrg0M8tRm/8Li6OO7VHArtnBvlD+o953oeZ0VHRYl42aLgGcbOYxYB6k0Navx44iLMGk0nsqA7X4oCqybBaTMLPvGzijx9dYpY1+PmSAuPN9Qxywc/QIVoBOpZNPKX/9+ufvsNY05/vmjFe3J1gf0v3f3K+x8WM6lAvrk9gjcIPPn9Px99OsGSDBf++Af87hQuH0FUvpx3msyrUZ9smui+JVsSN8bhnwf3BsfxxFSAyi3wUu3obHqMRgijj2TSO0yw3NCXQVVkM8g73AmpW9GjraKP4oSPRwgkJCQkJCQfGR5m56gpQBvfmPWVP85TeH7I5iSIGeu/uNSINM0FhXZyhywR0HXWC1SDjBYjyanimrp2LkHE5kF4pQPRWtnODDtVIQducMlI/V9tNojJStpIkEO/9RfNIwzoR1aCcoqy1m/odqAuC6l5M3JR0cfl7jdF7zmZGCGIN/TFlUN2MqbvMhszGe2NmbG0m25hBOwnoGphcxpTFcVbenIo417pWsHkUAcjWAh0btVuNoIHsr83TwsWSGlB85tqPHDrWXhalQX/L1PKeqG9vSiDKqJOsMgPTKcg1PfD23ATKWK8VssFsa/3A4vgJpdSZtPjPf/E1nZMRmF7sYLmxbb8awftDFLMG+rspjv+Cr0cBm8/jXDEpLNHP5E2G7pwuLn+0D5qsyxfHJILBQu8vbk+DbdduU0JNOnz5gBppnoxXGLEKiZYGd23MRpsuC9qw1aYIzw6eyvc0i3IhW1WZwWTURqP2sg9CFFUTjwfhIKWD5qaceltAruJyI7uhv27s5Pa2j57mzXeR3WhOBBoWjehODIQVqD6hm7X/zAWvWSEdpHKhYep4VGOcUVZonMSr22M03AGrVhoZf79cFu0Qxxc7jIuYlfVGRm3eZXmvEef4dBsEOADgD55/GcwShvPhTsTs0RUGsjB4yl3d27bAqqFzWu5G2N+Mcf6EOheVtHhzSzrARdHj1y/e4I/fknB/czcKpQlhBw2QTpA4jPXXFtmkouxgnAgCE7gqkA3WijDzzWpkfmZaqpjBCma+uobuibMiZqCZwfF8j55LB7smC++SUC7kwf73PaPR7PKgiCVsUmj6ICEsmYrLXkB7FaaW/t13+pqRQ77mgLe3cIopxBHujV30o9hRmK9J8MGryJhRXPglj974nwEIYuWqIdqYPou6iH2Qt5lAd+RpYXLdcSIeI1Cgjgbrgz9qK+4JZIRu5IIccVw5qDd6AYjTHsIJqHdMFe7EgGqLzjRyp2BLGynpLo5gwAGwQHE3+DtD9kC+JEoeAJq5vNft7BfYfC3gVBQWAOImxZX3XYpUGylXL8bh70k3d6HDU6wzjN6qcM9sFuul1gKOBUPcIiehDXb8EbmFY2EE87DBbpxF31Btg9h902k8fkRahfs2w2o1DjRiPmnhntPuZvxfM2R7YPuUA+8XXaTQtYXQNpiWm0l0aek7BbCKlBRcq73kzlzl0PBzzy4qTMc16p5uwsvdCfY8IlF3GhkvlNYJEpjgYnAxie+D6RWsFcE1xXYySCNao7DdlUHQve8UNu0o/F7JrjiTskXTK+y2dI7OAY5HTWytYI6jAYWwJCkI0Htw9HOJ0YI9f6XwqpKAE2jOvEamg1M2nKPKTagnQzj0jcaEywCdlfjuipw2tLYQApDsdGQA2DEHp6MWp0fERx+Pauy7DKvdiP+fCRsHNTKYTatQk86kxeWOaqC3qwm6dU7CLeCA6qUopzbQ5lnZYTYZNG4A98ZoytM6BKeb18eYP6KmiUezDf7o28+CMYDaxbKLzd09Oli0ctBVDxRH9Hll3mG7K9EzFazbOMZkc8TNgJEhsAKAtSIEQiEdnJGxXgpAM0U8Klp0vQobMGMEzGDjFt6rhsb2/Pufjzv4iSeb5A8/bDiBoLRkNbmk+PlSqrnSA25nMsgY9iUFCZ8Jyh4ob+IL3c9jQIIbNNRwYPXjMUAc5wFiXdVJHt/hedh+JCCsz0apZhmcZGQM3k4AsEDupQu7GKysjrOs/czSbjYUfxEbGLSD6wXMjJVWuqinquo4G9seO/pSc23QTGxYQEQvUF6rkBlbzRmlP6fWoT6hm1KfRpcTvR9ciwb0in4XoI1J9dAHYWrACjZzw8Yy1oz1co5DE+tsJUNDltVAP4/zq7KNzTUOQH/RhdqSznuUj6l2tLma0lrl2Y7C4OyUHuKDyar0sFEAAArvSURBVBbblh784u0RyncaGdeJh2pf3RxY/K0WJatWzbIeY8782l5hcT0jZxsAduai68gyh/ZjVltBjWihfi8gn8SdiLUSu9Y32LhgnyeEw2hC1+IVhJRvXnMImbHtJVwVhaVlLYFwT0nVyPR0TJfFjFGXXRi52NU5qnUZbPfKa4WCk7vqgUN3ERsXjh4v8UlJD/K7nz6B7Bza6cBtiJmg6pEDjjkiV5qs2J5G3WTtXZqMRD7fh0xzsZpgxDOpQjg0TUYLOwBkFpqfRZ4b5Ny0tNiPsLidxu9JJzA6o3v38GgD50TQzvXPDgC6RUm60bwkmGms54+O63B/TsYVjssKOXdkXe1n4TnZXqJeFWj2zDKUFtsNLRzfvJ/Sc/fPvnThff9l1SunYy+HOGrDZ2/3BbptHmu0EuiLQQ+Gn1+3gLMy1NSHgdRZQZtBv59RLhiiOyewq/Ig7agzA8UbeGtlqHEL5SC0DRZ0fafiZ2S/VDP+AJFqrgkJCQkJCQfGR5m5CsND3AM3F6GiKtLw9wBADFhP1QCqcUHnVQ0EwvuxuOfgImyszTrJQhV+PGZguO40K8qAMmknxcBnNo7eqJoUnoJLztB/W3Pd2CcwLtZmrY61GFkLopG4kc8WA5ca6YBWIVvySMZS3Dt/n63rnQCcCKpPshUwXhe1E6jP4442W8lwLX0J4CiKLdAQ/f374+9xP46ZbPVQoPEjUoquc3Ttn1HMCnvWk/Uet2ovkbH7j6/vAVRzzu8ksoHbijfMdhqAEZiyCIBzAtsV3chs3iDPTTAV39RFoO4ao/HiJam+ZwuFvox1bXveYXLEriZXE0xOqjCaYK3EhjOWaltA3WUhGzeVDhLNwkSGvR+To5N//v1ZB3FN5yiOW9hCBMWgts+CnvAn8zUaFjtY7kcwRkIOXE985uo6CdFEulHW8V0VVkDtRXROmvaxRHI9guOs25QW+Vpi+pJ+pisXnm/3aYOffP4Wv338CgCw6Mb4j69+CACYfUvvSzsLpxVYnW5q79Gh3YMOFyfEHJS6x2JP92C/K1CUHU6m9AzLvMMdi8i32xyiUtCnrHc9aoPWbd1meHdJUmDqTkOULqqQ5TaIJFRdhlHWBQH6t3dzmEvKMvMdfXd77iKX0w6TCWXbue7x9IjqqPOsxl0zRsVf0nd389ATEJgLptFFK2FZyxmFpR4A/9jsoDPYxRKVy5gi5u+lVja44phOQVQyiErYibs/fuNfNCcgsuiWJAft3dYI2FYF2ng0aVFkvvZOC5QfEZLSousGajjeRL2TcEYFqhkuliLw4SeuH2dw3T+UUAW5m3jTbe+2opnrN7kIs626djCZf8MAWMT6jgWsr6MyZai8sL6lUQIAYVYsBOUGECw0boUIozfCkai//1m+jtRONxbB1g7g4M+nIV3cDAB+tIX+3JyZUCfUW3WvscKOTKSHtgqqjWL6TsVj2izSzGpFCkqeKocAsJPh96AcBC8Q3uAbAIo7SQHVL8ZjRFnGPNZphaXjBKUrGallWGqKEn2kyn2NtT1yMOOoRpWvRQhwTpJsHQAU15pGPTx1V5BhAQB0pcXkfI/5qA6Xdj6lOtztboxqX+DNIpoPnD4g7rfpNSTXZp0CnvzWZVDYud1PsOLao5p12L+ZxlqwdETBggKXsHHmWW2otg0AbtbDcb2xbxTKl3lYVPPLDJYDlz0CmSPwtU3mNY74Wt6u5/fUlGSOQEXWtyNopsZx3tH70cT+gzjraGEmFuU5bRaktNhf8ShIHwX5R5cKo5v47rZHAsu/QS/Q3/3Bc/zG7BILlmL8D89/Deq/UTT1DU3+JTEF08EAoFxwzzHHPYppE6jfu9UEX39Cc6JPHq9w24zx09dP6HcX0dUF2kKetPdM3n0wqNdFoLHNhJv0/AZDx7lQYyWu1tOghqSXGtorOeY8O17E8RKPadGi4lq4hMOqKfH2e9qQ5TcK3D8JUzqie/33cECRiorlRf28ae6CbSOki/0AAJVF2Jw9L/qwOXDrnI4ZgiiiCtOgaQnKAcJ5PwQqF9gYCFVhgtPOuGhR87OwVsBaib71soaDEkYnQ4MgDG0GnDdPL2wYgfPmGB8yEi2ckJCQkJBwYHxUmavjLZipa86AHPTSCygIyNqFaQTRUBOs/5nbx520zQa0jAJEw1lUL2As4J2TZA8UQ7WmWdQ7ReXCMbSjkQyA9XUrBD3bvo/NO66iLs+GG3PybfS9FNzkw5Ky5BPKlJbdI3QNdgDtxp1vZHGBlpG1gLqTkL4RCvEy9SbudK0WJNzPv+daAGHEwMEWNvg+ut5Cc2bsNgJ9H+lqUSHQ03qPIM4hHNANqfIKKK4H91HFJhfngDr4VBtgJ5DdcdacO/SeftUW2XveFd/1xKxxxitWQMbXUo96jO0KuuHxFWHxZkVjEG1j4WwdNFm/fnCDmg26v/v+HGJLGWL2xQaLO2D5ngxk9UpHwQQL2HED1/I/lPZe96SoFDTrP9uRg2ErsFw0ocu624zQdybapj2KQgclSPf26YxGPFqrccW2bKt3Kmg0Ky3R7zJMv6GDnF+3WP6QP3fSUUbNTS6ul8E8QhSkNr+/8kpGGWRN5ygNUPC5SwtURaR3beEgDD3s3cbgD1aP8fN3lLW5VxpZy0yBBaDj96GfORi+ea63MH7Fai3MvsV5Sdf5tz+5DbrJz2/P8GffP4DgDnx9ukTBnazbuzHwUoeRj/05ML0gZkK4DpYZKmckROsgOcM9G22RW/rzu5s5+jsN0TFXvu/RB8UzCxQdRiy6320lxtxB7aoGLb+P3y/mMK8nyNc9358O7Uksz4h9HOHBkJFSjmgq/29WxAxUALCxk72YtHgwJtp8krf4fsuG985BDAQnRC/C8ezEAL3vXneAit9lDDJQKR3m2R4zrmHcrCbYbzhz7R3geghuYhLSBXF+NCrO0Um+Vt/stFRQfAxs+Z75tPkDhHAf8tn/X+L169d49uzZX/VpJCQkJCT8H+DVq1d4+vTpX/Vp/KXwUQVXay3evn2L2WwGMZgVTUhISEj41YFzDpvNBo8fP4aUH2b18qMKrgkJCQkJCf8v8GFuCRISEhISEn6FkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKBkYJrQkJCQkLCgZGCa0JCQkJCwoGRgmtCQkJCQsKB8b8A9aMjF2bt2CoAAAAASUVORK5CYII=\" /></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14573964.81109804, -2342509.0947641465, 14692572.866529081, -2223901.039333)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14692572.86652908, -2342509.0947641465, 14811180.921960121, -2223901.039333)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
+       "      <td><img 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\" /></td>\n",
        "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
        "      <td>(14811180.92196012, -2342509.0947641465, 14929788.977391161, -2223901.039333)</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(14929788.97739116, -2342509.0947641465, 15048397.032822201, -2223901.039333)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15048397.0328222, -2342509.0947641465, 15167005.088253241, -2223901.039333)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15167005.08825324, -2342509.0947641465, 15285613.143684281, -2223901.039333)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15285613.14368428, -2342509.0947641465, 15404221.199115321, -2223901.039333)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15404221.199115321, -2342509.0947641465, 15522829.254546361, -2223901.039333)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td><a href=\"https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF\" target=\"_blank\">https://modis-pds.s3.amazonaws.com/MCD43A4.006/31/11/2017158/MCD43A4.A2017158.h31v11.006.2017171203421_B01.TIF</a></td>\n",
-       "      <td><img 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\" /></td>\n",
-       "      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\n",
-       "      <td>(15522829.254546361, -2342509.0947641465, 15567307.275333002, -2223901.039333)</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                           tile_path  \\\n",
+       "                                    proj_raster_path  \\\n",
        "0  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "1  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "2  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "3  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "4  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "5  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "6  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "7  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "8  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
-       "9  https://modis-pds.s3.amazonaws.com/MCD43A4.006...   \n",
        "\n",
-       "                                               tile2  \\\n",
+       "                                                tile  \\\n",
        "0  Tile(dimensions=[256, 256], cell_type=CellType...   \n",
        "1  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
        "2  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
        "3  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
-       "4  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
-       "5  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
-       "6  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
-       "7  Tile(dimensions=[257, 256], cell_type=CellType...   \n",
-       "8  Tile(dimensions=[256, 256], cell_type=CellType...   \n",
-       "9  Tile(dimensions=[96, 256], cell_type=CellType(...   \n",
        "\n",
        "                                                 crs  \\\n",
        "0  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
        "1  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
        "2  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
        "3  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "4  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "5  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "6  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "7  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "8  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
-       "9  (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...   \n",
        "\n",
        "                                                 ext  \n",
        "0  (14455356.755667, -2342509.0947641465, 1457396...  \n",
        "1  (14573964.81109804, -2342509.0947641465, 14692...  \n",
        "2  (14692572.86652908, -2342509.0947641465, 14811...  \n",
-       "3  (14811180.92196012, -2342509.0947641465, 14929...  \n",
-       "4  (14929788.97739116, -2342509.0947641465, 15048...  \n",
-       "5  (15048397.0328222, -2342509.0947641465, 151670...  \n",
-       "6  (15167005.08825324, -2342509.0947641465, 15285...  \n",
-       "7  (15285613.14368428, -2342509.0947641465, 15404...  \n",
-       "8  (15404221.199115321, -2342509.0947641465, 1552...  \n",
-       "9  (15522829.254546361, -2342509.0947641465, 1556...  "
+       "3  (14811180.92196012, -2342509.0947641465, 14929...  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "pandas_df = df.limit(10).toPandas()\n",
-    "pandas_df"
+    "pandas_df.head(4)"
    ]
   },
   {
@@ -354,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -362,14 +339,14 @@
     {
      "data": {
       "text/plain": [
-       "tile_path    https://modis-pds.s3.amazonaws.com/MCD43A4.006...\n",
-       "tile2        Tile(dimensions=[256, 256], cell_type=CellType...\n",
-       "crs          (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...\n",
-       "ext          (15404221.199115321, -2342509.0947641465, 1552...\n",
+       "proj_raster_path    https://modis-pds.s3.amazonaws.com/MCD43A4.006...\n",
+       "tile                Tile(dimensions=[256, 256], cell_type=CellType...\n",
+       "crs                 (+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=63...\n",
+       "ext                 (15404221.199115321, -2342509.0947641465, 1552...\n",
        "Name: 8, dtype: object"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -398,16 +375,16 @@
        "7    Tile(dimensions=[257, 256], cell_type=CellType...\n",
        "8    Tile(dimensions=[256, 256], cell_type=CellType...\n",
        "9    Tile(dimensions=[96, 256], cell_type=CellType(...\n",
-       "Name: tile2, dtype: object"
+       "Name: tile, dtype: object"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pandas_df.tile2"
+    "pandas_df.tile"
    ]
   },
   {
@@ -419,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -564,710 +541,44 @@
        "      <td>-80.943126</td>\n",
        "      <td>10490</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>SLC</td>\n",
-       "      <td>Salt Lake City Intl</td>\n",
-       "      <td>Salt Lake City</td>\n",
-       "      <td>UT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>40.788388</td>\n",
-       "      <td>-111.977773</td>\n",
-       "      <td>9898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>TPA</td>\n",
-       "      <td>Tampa International</td>\n",
-       "      <td>Tampa</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>27.975472</td>\n",
-       "      <td>-82.533250</td>\n",
-       "      <td>9182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>EWR</td>\n",
-       "      <td>Newark Intl</td>\n",
-       "      <td>Newark</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>40.692497</td>\n",
-       "      <td>-74.168661</td>\n",
-       "      <td>8678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>LAS</td>\n",
-       "      <td>McCarran International</td>\n",
-       "      <td>Las Vegas</td>\n",
-       "      <td>NV</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>36.080361</td>\n",
-       "      <td>-115.152333</td>\n",
-       "      <td>8523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>PHL</td>\n",
-       "      <td>Philadelphia Intl</td>\n",
-       "      <td>Philadelphia</td>\n",
-       "      <td>PA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>39.871953</td>\n",
-       "      <td>-75.241141</td>\n",
-       "      <td>7965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>MSP</td>\n",
-       "      <td>Minneapolis-St Paul Intl</td>\n",
-       "      <td>Minneapolis</td>\n",
-       "      <td>MN</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>44.880547</td>\n",
-       "      <td>-93.216922</td>\n",
-       "      <td>7690</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>SEA</td>\n",
-       "      <td>Seattle-Tacoma Intl</td>\n",
-       "      <td>Seattle</td>\n",
-       "      <td>WA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>47.448982</td>\n",
-       "      <td>-122.309313</td>\n",
-       "      <td>7541</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>LGA</td>\n",
-       "      <td>LaGuardia</td>\n",
-       "      <td>New York</td>\n",
-       "      <td>NY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>40.777243</td>\n",
-       "      <td>-73.872609</td>\n",
-       "      <td>7392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>MDW</td>\n",
-       "      <td>Chicago Midway</td>\n",
-       "      <td>Chicago</td>\n",
-       "      <td>IL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>41.785983</td>\n",
-       "      <td>-87.752424</td>\n",
-       "      <td>6979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>IAD</td>\n",
-       "      <td>Washington Dulles International</td>\n",
-       "      <td>Chantilly</td>\n",
-       "      <td>VA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>38.944532</td>\n",
-       "      <td>-77.455810</td>\n",
-       "      <td>6779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>SAN</td>\n",
-       "      <td>San Diego International-Lindbergh</td>\n",
-       "      <td>San Diego</td>\n",
-       "      <td>CA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>32.733556</td>\n",
-       "      <td>-117.189657</td>\n",
-       "      <td>6233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>STL</td>\n",
-       "      <td>Lambert-St Louis International</td>\n",
-       "      <td>St Louis</td>\n",
-       "      <td>MO</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>38.747687</td>\n",
-       "      <td>-90.359990</td>\n",
-       "      <td>6204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>DTW</td>\n",
-       "      <td>Detroit Metropolitan-Wayne County</td>\n",
-       "      <td>Detroit</td>\n",
-       "      <td>MI</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>42.212059</td>\n",
-       "      <td>-83.348836</td>\n",
-       "      <td>6044</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>JFK</td>\n",
-       "      <td>John F Kennedy Intl</td>\n",
-       "      <td>New York</td>\n",
-       "      <td>NY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>40.639751</td>\n",
-       "      <td>-73.778926</td>\n",
-       "      <td>5945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>MIA</td>\n",
-       "      <td>Miami International</td>\n",
-       "      <td>Miami</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>25.793250</td>\n",
-       "      <td>-80.290556</td>\n",
-       "      <td>5907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>BOS</td>\n",
-       "      <td>Gen Edw L Logan Intl</td>\n",
-       "      <td>Boston</td>\n",
-       "      <td>MA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>42.364348</td>\n",
-       "      <td>-71.005179</td>\n",
-       "      <td>5627</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>SMF</td>\n",
-       "      <td>Sacramento International</td>\n",
-       "      <td>Sacramento</td>\n",
-       "      <td>CA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>38.695422</td>\n",
-       "      <td>-121.590767</td>\n",
-       "      <td>4943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>BWI</td>\n",
-       "      <td>Baltimore-Washington International</td>\n",
-       "      <td>Baltimore</td>\n",
-       "      <td>MD</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>39.175402</td>\n",
-       "      <td>-76.668198</td>\n",
-       "      <td>4749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>SNA</td>\n",
-       "      <td>John Wayne /Orange Co</td>\n",
-       "      <td>Santa Ana</td>\n",
-       "      <td>CA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>33.675659</td>\n",
-       "      <td>-117.868223</td>\n",
-       "      <td>4616</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>MSY</td>\n",
-       "      <td>New Orleans International</td>\n",
-       "      <td>New Orleans</td>\n",
-       "      <td>LA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>29.993389</td>\n",
-       "      <td>-90.258028</td>\n",
-       "      <td>4432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>191</th>\n",
-       "      <td>GTF</td>\n",
-       "      <td>Great Falls Intl</td>\n",
-       "      <td>Great Falls</td>\n",
-       "      <td>MT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>47.482002</td>\n",
-       "      <td>-111.370685</td>\n",
-       "      <td>108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>192</th>\n",
-       "      <td>MEI</td>\n",
-       "      <td>Key</td>\n",
-       "      <td>Meridian</td>\n",
-       "      <td>MS</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>32.333133</td>\n",
-       "      <td>-88.751206</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>193</th>\n",
-       "      <td>MQT</td>\n",
-       "      <td>Marquette County Airport</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>46.353639</td>\n",
-       "      <td>-87.395361</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>194</th>\n",
-       "      <td>TEX</td>\n",
-       "      <td>Telluride Regional</td>\n",
-       "      <td>Telluride</td>\n",
-       "      <td>CO</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>37.953759</td>\n",
-       "      <td>-107.908480</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>CHA</td>\n",
-       "      <td>Lovell</td>\n",
-       "      <td>Chattanooga</td>\n",
-       "      <td>TN</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>35.035268</td>\n",
-       "      <td>-85.203788</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>LWS</td>\n",
-       "      <td>Lewiston-Nez Perce County</td>\n",
-       "      <td>Lewiston</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>46.374498</td>\n",
-       "      <td>-117.015394</td>\n",
-       "      <td>102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>CDC</td>\n",
-       "      <td>Cedar City Muni</td>\n",
-       "      <td>Cedar City</td>\n",
-       "      <td>UT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>37.700970</td>\n",
-       "      <td>-113.098575</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>ALB</td>\n",
-       "      <td>Albany Cty</td>\n",
-       "      <td>Albany</td>\n",
-       "      <td>NY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>42.748119</td>\n",
-       "      <td>-73.802979</td>\n",
-       "      <td>93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>BTV</td>\n",
-       "      <td>Burlington International</td>\n",
-       "      <td>Burlington</td>\n",
-       "      <td>VT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>44.473004</td>\n",
-       "      <td>-73.150312</td>\n",
-       "      <td>91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>FCA</td>\n",
-       "      <td>Glacier Park Intl</td>\n",
-       "      <td>Kalispell</td>\n",
-       "      <td>MT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>48.311405</td>\n",
-       "      <td>-114.255069</td>\n",
-       "      <td>90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>MLB</td>\n",
-       "      <td>Melbourne International</td>\n",
-       "      <td>Melbourne</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>28.102750</td>\n",
-       "      <td>-80.645806</td>\n",
-       "      <td>74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>DAB</td>\n",
-       "      <td>Daytona Beach International</td>\n",
-       "      <td>Daytona Beach</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>29.179917</td>\n",
-       "      <td>-81.058056</td>\n",
-       "      <td>72</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203</th>\n",
-       "      <td>ABE</td>\n",
-       "      <td>Lehigh Valley International</td>\n",
-       "      <td>Allentown</td>\n",
-       "      <td>PA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>40.652363</td>\n",
-       "      <td>-75.440402</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>DLH</td>\n",
-       "      <td>Duluth International</td>\n",
-       "      <td>Duluth</td>\n",
-       "      <td>MN</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>46.842090</td>\n",
-       "      <td>-92.193649</td>\n",
-       "      <td>58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>205</th>\n",
-       "      <td>CYS</td>\n",
-       "      <td>Cheyenne</td>\n",
-       "      <td>Cheyenne</td>\n",
-       "      <td>WY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>41.155722</td>\n",
-       "      <td>-104.811838</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>206</th>\n",
-       "      <td>RKS</td>\n",
-       "      <td>Rock Springs-Sweetwater County</td>\n",
-       "      <td>Rock Springs</td>\n",
-       "      <td>WY</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>41.594217</td>\n",
-       "      <td>-109.065193</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>LWB</td>\n",
-       "      <td>Greenbrier Valley</td>\n",
-       "      <td>Lewisburg</td>\n",
-       "      <td>WV</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>37.858306</td>\n",
-       "      <td>-80.399472</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>CRW</td>\n",
-       "      <td>Yeager</td>\n",
-       "      <td>Charleston</td>\n",
-       "      <td>WV</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>38.373151</td>\n",
-       "      <td>-81.593190</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>BLI</td>\n",
-       "      <td>Bellingham Intl</td>\n",
-       "      <td>Bellingham</td>\n",
-       "      <td>WA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>48.792750</td>\n",
-       "      <td>-122.537528</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>MMH</td>\n",
-       "      <td>Mammoth Yosemite</td>\n",
-       "      <td>Mammoth Lakes</td>\n",
-       "      <td>CA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>37.624049</td>\n",
-       "      <td>-118.837772</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>211</th>\n",
-       "      <td>ATW</td>\n",
-       "      <td>Outagamie County Regional</td>\n",
-       "      <td>Appleton</td>\n",
-       "      <td>WI</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>44.257408</td>\n",
-       "      <td>-88.519476</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>BKG</td>\n",
-       "      <td>Branson Airport</td>\n",
-       "      <td>Hollister</td>\n",
-       "      <td>MO</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>36.385913</td>\n",
-       "      <td>-92.548828</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>213</th>\n",
-       "      <td>PIE</td>\n",
-       "      <td>St. Petersburg-Clearwater International</td>\n",
-       "      <td>St. Petersburg</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>27.910763</td>\n",
-       "      <td>-82.687439</td>\n",
-       "      <td>52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>214</th>\n",
-       "      <td>SPS</td>\n",
-       "      <td>Sheppard AFB/Wichita Falls Municipal</td>\n",
-       "      <td>Wichita Falls</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>33.988796</td>\n",
-       "      <td>-98.491893</td>\n",
-       "      <td>50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>215</th>\n",
-       "      <td>FAY</td>\n",
-       "      <td>Fayetteville Municipal</td>\n",
-       "      <td>Fayetteville</td>\n",
-       "      <td>NC</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>34.991472</td>\n",
-       "      <td>-78.880000</td>\n",
-       "      <td>50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>216</th>\n",
-       "      <td>EAU</td>\n",
-       "      <td>Chippewa Valley Regional</td>\n",
-       "      <td>Eau Claire</td>\n",
-       "      <td>WI</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>44.865257</td>\n",
-       "      <td>-91.485072</td>\n",
-       "      <td>48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>DBQ</td>\n",
-       "      <td>Dubuque Municipal</td>\n",
-       "      <td>Dubuque</td>\n",
-       "      <td>IA</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>42.402959</td>\n",
-       "      <td>-90.709167</td>\n",
-       "      <td>48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>RST</td>\n",
-       "      <td>Rochester International</td>\n",
-       "      <td>Rochester</td>\n",
-       "      <td>MN</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>43.908826</td>\n",
-       "      <td>-92.497987</td>\n",
-       "      <td>37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>UTM</td>\n",
-       "      <td>Tunica Municipal Airport</td>\n",
-       "      <td>Tunica</td>\n",
-       "      <td>MS</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>34.681499</td>\n",
-       "      <td>-90.348816</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>220</th>\n",
-       "      <td>BIL</td>\n",
-       "      <td>Billings Logan Intl</td>\n",
-       "      <td>Billings</td>\n",
-       "      <td>MT</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>45.807662</td>\n",
-       "      <td>-108.542861</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>221 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    iata                                  airport               city state  \\\n",
-       "0    ORD             Chicago O'Hare International            Chicago    IL   \n",
-       "1    ATL        William B Hartsfield-Atlanta Intl            Atlanta    GA   \n",
-       "2    DFW          Dallas-Fort Worth International  Dallas-Fort Worth    TX   \n",
-       "3    PHX         Phoenix Sky Harbor International            Phoenix    AZ   \n",
-       "4    DEN                              Denver Intl             Denver    CO   \n",
-       "5    IAH             George Bush Intercontinental            Houston    TX   \n",
-       "6    SFO              San Francisco International      San Francisco    CA   \n",
-       "7    LAX                Los Angeles International        Los Angeles    CA   \n",
-       "8    MCO                    Orlando International            Orlando    FL   \n",
-       "9    CLT          Charlotte/Douglas International          Charlotte    NC   \n",
-       "10   SLC                      Salt Lake City Intl     Salt Lake City    UT   \n",
-       "11   TPA                     Tampa International               Tampa    FL   \n",
-       "12   EWR                              Newark Intl             Newark    NJ   \n",
-       "13   LAS                   McCarran International          Las Vegas    NV   \n",
-       "14   PHL                        Philadelphia Intl       Philadelphia    PA   \n",
-       "15   MSP                 Minneapolis-St Paul Intl        Minneapolis    MN   \n",
-       "16   SEA                      Seattle-Tacoma Intl            Seattle    WA   \n",
-       "17   LGA                                LaGuardia           New York    NY   \n",
-       "18   MDW                           Chicago Midway            Chicago    IL   \n",
-       "19   IAD          Washington Dulles International          Chantilly    VA   \n",
-       "20   SAN       San Diego International-Lindbergh           San Diego    CA   \n",
-       "21   STL           Lambert-St Louis International           St Louis    MO   \n",
-       "22   DTW        Detroit Metropolitan-Wayne County            Detroit    MI   \n",
-       "23   JFK                      John F Kennedy Intl           New York    NY   \n",
-       "24   MIA                      Miami International              Miami    FL   \n",
-       "25   BOS                     Gen Edw L Logan Intl             Boston    MA   \n",
-       "26   SMF                 Sacramento International         Sacramento    CA   \n",
-       "27   BWI       Baltimore-Washington International          Baltimore    MD   \n",
-       "28   SNA                    John Wayne /Orange Co          Santa Ana    CA   \n",
-       "29   MSY               New Orleans International         New Orleans    LA   \n",
-       "..   ...                                      ...                ...   ...   \n",
-       "191  GTF                         Great Falls Intl        Great Falls    MT   \n",
-       "192  MEI                                     Key            Meridian    MS   \n",
-       "193  MQT                 Marquette County Airport                NaN   NaN   \n",
-       "194  TEX                       Telluride Regional          Telluride    CO   \n",
-       "195  CHA                                  Lovell         Chattanooga    TN   \n",
-       "196  LWS                Lewiston-Nez Perce County           Lewiston    ID   \n",
-       "197  CDC                          Cedar City Muni         Cedar City    UT   \n",
-       "198  ALB                               Albany Cty             Albany    NY   \n",
-       "199  BTV                 Burlington International         Burlington    VT   \n",
-       "200  FCA                        Glacier Park Intl          Kalispell    MT   \n",
-       "201  MLB                 Melbourne International           Melbourne    FL   \n",
-       "202  DAB              Daytona Beach International      Daytona Beach    FL   \n",
-       "203  ABE              Lehigh Valley International          Allentown    PA   \n",
-       "204  DLH                     Duluth International             Duluth    MN   \n",
-       "205  CYS                                 Cheyenne           Cheyenne    WY   \n",
-       "206  RKS           Rock Springs-Sweetwater County       Rock Springs    WY   \n",
-       "207  LWB                        Greenbrier Valley          Lewisburg    WV   \n",
-       "208  CRW                                   Yeager         Charleston    WV   \n",
-       "209  BLI                          Bellingham Intl         Bellingham    WA   \n",
-       "210  MMH                         Mammoth Yosemite      Mammoth Lakes    CA   \n",
-       "211  ATW                Outagamie County Regional           Appleton    WI   \n",
-       "212  BKG                          Branson Airport          Hollister    MO   \n",
-       "213  PIE  St. Petersburg-Clearwater International     St. Petersburg    FL   \n",
-       "214  SPS     Sheppard AFB/Wichita Falls Municipal      Wichita Falls    TX   \n",
-       "215  FAY                   Fayetteville Municipal       Fayetteville    NC   \n",
-       "216  EAU                 Chippewa Valley Regional         Eau Claire    WI   \n",
-       "217  DBQ                        Dubuque Municipal            Dubuque    IA   \n",
-       "218  RST                  Rochester International          Rochester    MN   \n",
-       "219  UTM                 Tunica Municipal Airport             Tunica    MS   \n",
-       "220  BIL                      Billings Logan Intl           Billings    MT   \n",
-       "\n",
-       "    country        lat        long    cnt  \n",
-       "0       USA  41.979595  -87.904464  25129  \n",
-       "1       USA  33.640444  -84.426944  21925  \n",
-       "2       USA  32.895951  -97.037200  20662  \n",
-       "3       USA  33.434167 -112.008056  17290  \n",
-       "4       USA  39.858408 -104.667002  13781  \n",
-       "5       USA  29.980472  -95.339722  13223  \n",
-       "6       USA  37.619002 -122.374843  12016  \n",
-       "7       USA  33.942536 -118.408074  11797  \n",
-       "8       USA  28.428889  -81.316028  10536  \n",
-       "9       USA  35.214011  -80.943126  10490  \n",
-       "10      USA  40.788388 -111.977773   9898  \n",
-       "11      USA  27.975472  -82.533250   9182  \n",
-       "12      USA  40.692497  -74.168661   8678  \n",
-       "13      USA  36.080361 -115.152333   8523  \n",
-       "14      USA  39.871953  -75.241141   7965  \n",
-       "15      USA  44.880547  -93.216922   7690  \n",
-       "16      USA  47.448982 -122.309313   7541  \n",
-       "17      USA  40.777243  -73.872609   7392  \n",
-       "18      USA  41.785983  -87.752424   6979  \n",
-       "19      USA  38.944532  -77.455810   6779  \n",
-       "20      USA  32.733556 -117.189657   6233  \n",
-       "21      USA  38.747687  -90.359990   6204  \n",
-       "22      USA  42.212059  -83.348836   6044  \n",
-       "23      USA  40.639751  -73.778926   5945  \n",
-       "24      USA  25.793250  -80.290556   5907  \n",
-       "25      USA  42.364348  -71.005179   5627  \n",
-       "26      USA  38.695422 -121.590767   4943  \n",
-       "27      USA  39.175402  -76.668198   4749  \n",
-       "28      USA  33.675659 -117.868223   4616  \n",
-       "29      USA  29.993389  -90.258028   4432  \n",
-       "..      ...        ...         ...    ...  \n",
-       "191     USA  47.482002 -111.370685    108  \n",
-       "192     USA  32.333133  -88.751206    104  \n",
-       "193     USA  46.353639  -87.395361    104  \n",
-       "194     USA  37.953759 -107.908480    104  \n",
-       "195     USA  35.035268  -85.203788    104  \n",
-       "196     USA  46.374498 -117.015394    102  \n",
-       "197     USA  37.700970 -113.098575     96  \n",
-       "198     USA  42.748119  -73.802979     93  \n",
-       "199     USA  44.473004  -73.150312     91  \n",
-       "200     USA  48.311405 -114.255069     90  \n",
-       "201     USA  28.102750  -80.645806     74  \n",
-       "202     USA  29.179917  -81.058056     72  \n",
-       "203     USA  40.652363  -75.440402     60  \n",
-       "204     USA  46.842090  -92.193649     58  \n",
-       "205     USA  41.155722 -104.811838     56  \n",
-       "206     USA  41.594217 -109.065193     56  \n",
-       "207     USA  37.858306  -80.399472     56  \n",
-       "208     USA  38.373151  -81.593190     56  \n",
-       "209     USA  48.792750 -122.537528     56  \n",
-       "210     USA  37.624049 -118.837772     56  \n",
-       "211     USA  44.257408  -88.519476     56  \n",
-       "212     USA  36.385913  -92.548828     56  \n",
-       "213     USA  27.910763  -82.687439     52  \n",
-       "214     USA  33.988796  -98.491893     50  \n",
-       "215     USA  34.991472  -78.880000     50  \n",
-       "216     USA  44.865257  -91.485072     48  \n",
-       "217     USA  42.402959  -90.709167     48  \n",
-       "218     USA  43.908826  -92.497987     37  \n",
-       "219     USA  34.681499  -90.348816     32  \n",
-       "220     USA  45.807662 -108.542861     23  \n",
+       "  iata                            airport               city state country  \\\n",
+       "0  ORD       Chicago O'Hare International            Chicago    IL     USA   \n",
+       "1  ATL  William B Hartsfield-Atlanta Intl            Atlanta    GA     USA   \n",
+       "2  DFW    Dallas-Fort Worth International  Dallas-Fort Worth    TX     USA   \n",
+       "3  PHX   Phoenix Sky Harbor International            Phoenix    AZ     USA   \n",
+       "4  DEN                        Denver Intl             Denver    CO     USA   \n",
+       "5  IAH       George Bush Intercontinental            Houston    TX     USA   \n",
+       "6  SFO        San Francisco International      San Francisco    CA     USA   \n",
+       "7  LAX          Los Angeles International        Los Angeles    CA     USA   \n",
+       "8  MCO              Orlando International            Orlando    FL     USA   \n",
+       "9  CLT    Charlotte/Douglas International          Charlotte    NC     USA   \n",
        "\n",
-       "[221 rows x 8 columns]"
+       "         lat        long    cnt  \n",
+       "0  41.979595  -87.904464  25129  \n",
+       "1  33.640444  -84.426944  21925  \n",
+       "2  32.895951  -97.037200  20662  \n",
+       "3  33.434167 -112.008056  17290  \n",
+       "4  39.858408 -104.667002  13781  \n",
+       "5  29.980472  -95.339722  13223  \n",
+       "6  37.619002 -122.374843  12016  \n",
+       "7  33.942536 -118.408074  11797  \n",
+       "8  28.428889  -81.316028  10536  \n",
+       "9  35.214011  -80.943126  10490  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import pandas\n",
-    "pandas.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')"
+    "pandas.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv').head(10)"
    ]
   },
   {

From f6f6c736bbbd5d31682d27b40698307d4cd8da42 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 13:30:15 -0400
Subject: [PATCH 310/380] minor wording tweak and fix import in python unit
 test_agg_local_mean

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/getting-started.pymd     | 2 +-
 pyrasterframes/src/main/python/tests/RasterFunctionsTests.py | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 795322bba..db279133b 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -115,4 +115,4 @@ from pyrasterframes.utils import gdal_version
 print(gdal_version())
 ```
 
-This will print out somethin like "GDAL x.y.z, released 20yy/mm/dd". If it reports `unavailable`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
\ No newline at end of file
+This will print out something like "GDAL x.y.z, released 20yy/mm/dd". If it reports `not available`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
index 58da7c89c..7989b5405 100644
--- a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
+++ b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
@@ -98,6 +98,8 @@ def test_agg_mean(self):
 
     def test_agg_local_mean(self):
         from pyspark.sql import Row
+        from pyrasterframes.rf_types import Tile
+        import numpy as np
 
         # this is really testing the nodata propagation in the agg  local summation
         ct = CellType.int8().with_no_data_value(4)

From 9e07cae8c644625ccb5fa200bbc3ea4dae7f1302 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Tue, 6 Aug 2019 11:40:53 -0700
Subject: [PATCH 311/380] Add NoData sum section. Fixed abiguity in adding
 tiles with different NoData

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 .../src/main/python/docs/nodata-handling.pymd | 107 ++++++++++++------
 1 file changed, 72 insertions(+), 35 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index d778c42a9..02639d370 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -146,6 +146,37 @@ And the original SCL data. The bright yellow is a cloudy region in the original
 display(sample[1])
 ```
 
+## NoData and Local Arithmatic
+
+Let's now explore how the presence of NoData affects local arithmatic operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0. 
+
+
+```python
+tile_size = 100
+x = np.zeros((tile_size, tile_size), dtype='int16')
+x[:,tile_size//2:] = 1
+x = Tile(x)
+y = Tile(np.zeros((tile_size, tile_size), dtype='int16'))
+
+rf = spark.createDataFrame([Row(x=x, y=y)])
+print('x')
+display(x)
+print('y')
+display(y)
+```
+
+Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for arithmatic operations, Data + NoData = NoData. To see more information about possible operations on Tile columns, see the @ref:[local map algebra](local-algebra.md) doc.
+
+```python
+masked_rf = rf.withColumn('x_nd', rf_mask_by_value('x', 'x', lit(1)) )
+masked_rf = masked_rf.withColumn('x_nd_y_sum', rf_local_add('x_nd', 'y'))
+row = masked_rf.collect()[0]
+print('x with NoData')
+display(row.x_nd)
+print('x with NoData and y sum')
+display(row.x_nd_y_sum)
+```
+
 ## Changing a Tile's NoData Values
 
 One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a rasterframe from a tile with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 Tile with columns of 0, 1, 2, and 3.
@@ -191,36 +222,6 @@ And the tile named `tile_nd_2` has the values of 1 and 2 masked out. This is bec
 display(collected[0].tile_nd_2)
 ```
 
-## Nodata Values in Aggregation
-
-Let's use the same tile as before to demonstrate how NoData values affect tile aggregations
-
-```python
-tile_size = 100
-x = np.zeros((tile_size, tile_size), dtype='int16')
-for i in range(4):
-    x[:, i*tile_size//4:(i+1)*tile_size//4] = i
-x = Tile(x)
-
-rf = spark.createDataFrame([Row(tile=x)])
-display(x)
-```
-
-First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked. 
-
-```python
-masked_rf = rf.withColumn('tile_nd_1', 
-                           rf_convert_cell_type('tile', get_nodata_ct(1))) \
-              .withColumn('tile_nd_2',
-                          rf_convert_cell_type('tile_nd_1', get_nodata_ct(2)))
-```
-
-The results of `rf_tile_sum` vary on the tiles that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
-
-```python
-masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
-```
-
 
 ## Combining Tiles with Different Data Types
 
@@ -242,21 +243,57 @@ When performing a local operation between tile columns with cell types `int` and
 rf_added = rf.withColumn('xy_sum', rf_local_add('y', 'x'))
 rf_added.select(rf_cell_type('xy_sum'), rf_cell_type('y'), rf_cell_type('x')).distinct().show()
 ```
-Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 3 tile columns: one without a defined NoData value, one with a NoData value of 1, and one with a NoData value of 2.
 
+Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 tile columns: one with a NoData value of 1, and one with a NoData value of 2.
 
 ```python
 x = Tile((np.ones((100, 100))*3).astype('int16'))
-
 rf = spark.createDataFrame([Row(x=x)])
 
 rf_nd = rf.withColumn('x_nd_1', rf_convert_cell_type('x', get_nodata_ct(1))) \
           .withColumn('x_nd_2', rf_convert_cell_type('x', get_nodata_ct(2)))
 ```
 
-Let's try adding the tile column without a defined NoData value to a tile column with a defined NoData value. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has no defined NoData value. 
+Let's try adding the tile columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1. 
+
+```python
+rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_2', 'x_nd_1'))
+rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
+```
+
+Reversing the order of the sum changes the NoData value of the resulting column to 2.  
+
+```python
+rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x_nd_2'))
+rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
+```
+
+## Nodata Values in Aggregation
+
+Let's use the same tile as before to demonstrate how NoData values affect tile aggregations
+
+```python
+tile_size = 100
+x = np.zeros((tile_size, tile_size), dtype='int16')
+for i in range(4):
+    x[:, i*tile_size//4:(i+1)*tile_size//4] = i
+x = Tile(x)
+
+rf = spark.createDataFrame([Row(tile=x)])
+display(x)
+```
+
+First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked. 
+
+```python
+masked_rf = rf.withColumn('tile_nd_1', 
+                           rf_convert_cell_type('tile', get_nodata_ct(1))) \
+              .withColumn('tile_nd_2',
+                          rf_convert_cell_type('tile_nd_1', get_nodata_ct(2)))
+```
+
+The results of `rf_tile_sum` vary on the tiles that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
 
 ```python
-rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x'))
-rf_nd_sum.select(rf_cell_type('x_nd_sum'), rf_cell_type('x'), rf_cell_type('x_nd_1')).distinct().show()
+masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
 ```

From 4a14d3cfee948275a322f58b79bb4cb76d3a416a Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Tue, 6 Aug 2019 15:01:44 -0400
Subject: [PATCH 312/380] spelling/grammar mistakes

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/raster-read.pymd     | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 1d3f8b252..8bfbb0187 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -3,12 +3,12 @@
 ```python, echo=False
 from IPython.display import display
 import pandas as pd
-from pyrasterframes.utils import create_rf_spark_session 
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
 ```
 
-RasterFrames registers a DataSource named `raster` that enables reading of GeoTIFFs (and other formats when @ref:[GDAL is installed](getting-started.md#installing-gdal)) from arbitrary URIs. In the examples that follow we'll be reading from a Sentinel 2 scene stored in an AWS S3 bucket.
+RasterFrames registers a DataSource named `raster` that enables reading of GeoTIFFs (and other formats when @ref:[GDAL is installed](getting-started.md#installing-gdal)) from arbitrary URIs. In the examples that follow we'll be reading from a Sentinel-2 scene stored in an AWS S3 bucket.
 
 ## Single Raster
 
@@ -19,15 +19,15 @@ rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0
 rf.printSchema()
 ```
 
-The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.  
+The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.
 
-Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_. 
+Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_.
 
 ```python unpack_schema
 crs = rf.select(rf_crs("proj_raster").alias("value")).first()
 
 parts = rf.select(
-    rf_extent("proj_raster").alias("extent"), 
+    rf_extent("proj_raster").alias("extent"),
     rf_tile("proj_raster").alias("tile")
 )
 
@@ -36,7 +36,7 @@ parts.show(5, False)
 
 ```
 
-You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on managable amounts of data per task. 
+You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on manageable amounts of data per task.
 
 ```python count_by_uri
 rf.groupby(rf.proj_raster_path).count().show()
@@ -51,7 +51,7 @@ display(tile)
 
 ## URI Formats
 
-RasterFrames relies on three different IO drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used first if a compatible version of GDAL (>= 2.4) is installed. It is the only driver that can read non-GeoTIFF files. If GDAL is not available, either the _Java I/O` or _Hadoop_ driver will be selected, depending on scheme.  
+RasterFrames relies on three different IO drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used first if a compatible version of GDAL (>= 2.4) is installed. It is the only driver that can read non-GeoTIFF files. If GDAL is not available, either the _Java I/O_ or _Hadoop_ driver will be selected, depending on scheme.
 
 
 | Prefix              | GDAL        | Java I/O | Hadoop |
@@ -86,8 +86,8 @@ modis_catalog = spark.read \
     .format("csv") \
     .option("header", "true") \
     .load(SparkFiles.get(cat_filename)) \
-    .withColumn('base_url', 
-        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',) 
+    .withColumn('base_url',
+        F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
     ) \
     .drop('download_url') \
     .withColumn('red' , F.concat('base_url', F.lit("_B01.TIF"))) \
@@ -100,11 +100,11 @@ print("Available scenes: ", modis_catalog.count())
 modis_catalog.show(5, truncate=False)
 ```
 
-MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using `(h,v)` grid coordinates. 
+MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using `(h,v)` grid coordinates.
 
 ![MODIS Grid](https://modis-land.gsfc.nasa.gov/images/MODIS_sinusoidal_grid1.gif)
 
-For example, MODIS data right above the equator is all grid coordinates with `v07`.  
+For example, MODIS data right above the equator is all grid coordinates with `v07`.
 
 ```python catalog_filtering
 equator = modis_catalog.where(F.col('gid').like('%v07%'))
@@ -115,13 +115,13 @@ Now that we have prepared our catalog, we simply pass the DataFrame or CSV strin
 
 ```python read_catalog
 rf = spark.read.raster(
-    catalog=equator, 
+    catalog=equator,
     catalog_col_names=['red', 'nir'],
 )
 rf.printSchema()
 ```
 
-Observe schema of the resulting dataframe has a projected raster struct for each column passed in `catalog_col_names`. For reference the URI is now in a column appended with `_path`. We can take a quick look at the representation of the data. We see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent from within that coverage.
+Observe that the schema of the resulting DataFrame has a projected raster struct for each column passed in `catalog_col_names`. For reference, the URI is now in a column appended with `_path`. We can take a quick look at the representation of the data. We see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent from within that coverage.
 
 ```python cat_read_sample
 rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')).show(3, False)
@@ -142,18 +142,18 @@ spark.read.raster(uri, lazy_tiles=False) \
     .select('proj_raster.tile').show(1, False)
 ```
 
-In the initial examples on this page, we used `rf_tile` to explicitly request the realized tile from the lazy representation.
+In the initial examples on this page, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized tile from the lazy representation.
 
 ## Multiband Rasters
 
 A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands may represent intensity of different wavelengths of light (or other electromagnetic radiation). The different bands may represent other phenomena such as measurement time, quality indications, or additional measurements.
 
-When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader. 
+When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
 
-For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting dataframe still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
+For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting DataFrame still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
 
 ```python Multiband
-mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif', 
+mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
                        band_indexes=[0, 1, 2, 3],
                       )
 mb.printSchema()
@@ -180,4 +180,4 @@ mb2 = spark.read.raster(catalog=spark.createDataFrame(mb_cat),
 mb2.printSchema()
 ```
 
-[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system

From 563e67b64117caab521be383ea05fb1ae190b413 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 26 Jun 2019 16:42:45 -0400
Subject: [PATCH 313/380] Breaking unit test in python for issue 163 tile
 exploder with ProjectedRaster

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../main/python/docs/supervised-learning.pymd |  3 +-
 .../src/main/python/tests/ExploderTests.py    | 59 +++++++++++++++++++
 2 files changed, 61 insertions(+), 1 deletion(-)
 create mode 100644 pyrasterframes/src/main/python/tests/ExploderTests.py

diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index a13b39b7b..ca6d9d53e 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -71,7 +71,8 @@ print('Found ', len(crses), 'distinct CRS.')
 crs = crses[0][0]
 
 label_df = spark.read.geojson(os.path.join(resource_dir_uri(), 'luray-labels.geojson')) \
-					 .select('id', st_reproject('geometry', 'EPSG:4326', crs).alias('geometry')) \
+					 .select('id', 
+                   st_reproject('geometry', rf_mk_crs('EPSG:4326'), rf_mk_crs(crs)).alias('geometry')) \
 					 .hint('broadcast')
 
 df_joined = df.join(label_df, st_intersects(st_geometry('extent'), 'geometry')) 
diff --git a/pyrasterframes/src/main/python/tests/ExploderTests.py b/pyrasterframes/src/main/python/tests/ExploderTests.py
new file mode 100644
index 000000000..05ff958ae
--- /dev/null
+++ b/pyrasterframes/src/main/python/tests/ExploderTests.py
@@ -0,0 +1,59 @@
+#
+# This software is licensed under the Apache 2 license, quoted below.
+#
+# Copyright 2019 Astraea, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# [http://www.apache.org/licenses/LICENSE-2.0]
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+from . import TestEnvironment
+
+from pyrasterframes.rasterfunctions import *
+from pyrasterframes.rf_types import *
+from pyrasterframes import TileExploder
+
+from pyspark.ml.feature import VectorAssembler
+from pyspark.ml import Pipeline
+from pyspark.sql.functions import *
+
+import unittest
+
+
+class ExploderTests(TestEnvironment):
+
+    @unittest.skip("See issue https://github.com/locationtech/rasterframes/issues/163")
+    def test_tile_exploder_pipeline_for_prt(self):
+        # NB the tile is a Projected Raster Tile
+        df = self.spark.read.raster(self.img_uri)
+        t_col = 'proj_raster'
+        self.assertTrue(t_col in df.columns)
+
+        assembler = VectorAssembler().setInputCols([t_col])
+        pipe = Pipeline().setStages([TileExploder(), assembler])
+        pipe_model = pipe.fit(df)
+        tranformed_df = pipe_model.transform(df)
+        self.assertTrue(tranformed_df.count() > df.count())
+
+    def test_tile_exploder_pipeline_for_tile(self):
+        t_col = 'tile'
+        df = self.spark.read.raster(self.img_uri) \
+            .withColumn(t_col, rf_tile('proj_raster')) \
+            .drop('proj_raster')
+
+        assembler = VectorAssembler().setInputCols([t_col])
+        pipe = Pipeline().setStages([TileExploder(), assembler])
+        pipe_model = pipe.fit(df)
+        tranformed_df = pipe_model.transform(df)
+        self.assertTrue(tranformed_df.count() > df.count())

From 9362d1402f0224f2d1f524cd6b5d6271c4ba930f Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 16:08:12 -0400
Subject: [PATCH 314/380] Update concepts page based on feedback

---
 .../src/main/python/docs/concepts.md          | 26 +++++++++----------
 .../src/main/python/docs/raster-read.pymd     |  6 +++--
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
index 0ddfd6a30..f367a1310 100644
--- a/pyrasterframes/src/main/python/docs/concepts.md
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -6,9 +6,13 @@ There are a number of Earth-observation (EO) concepts that crop up in the discus
 * [_Newcomers Earth Observation Guide_](https://business.esa.int/newcomers-earth-observation-guide)
 * [_Earth Observation Markets and Applications_](https://www.ofcom.org.uk/__data/assets/pdf_file/0021/82047/introduction_eo_for_ofcom_june_2015_no_video.pdf)
 
+## Raster
+
+A raster is a regular grid of numeric values. A raster can be thought of as an image, as is the case if the values in the grid represent brightness along a greyscale. More generally a raster can measure many different phenomena or encode a variety of different discrete classifications.
+
 ## Cell
 
-A cell is a single sample from a sensor encoded as a scalar value associated with a specific spatiotemporal location and time. It can be thought of as an image pixel associated with a place and time.
+A cell is a single row and column intersection in the raster grid. It is a single pixel in an image. A cell's value often represents one sample from a sensor encoded as a scalar value associated with a specific location and time. 
 
 ## Cell Type
 
@@ -18,7 +22,7 @@ A numeric cell value may be encoded in a number of different computer numeric fo
 * integral vs floating-point
 
 
-The cell types most frequent in RasterFrames are as follows:
+The most frequently encountered cell types in RasterFrames are below.
 
 | Name | Abbreviation | Description | Range |
 | --- | --- | --- | --- |
@@ -31,7 +35,7 @@ The cell types most frequent in RasterFrames are as follows:
 | Float | `float32` | 32-bit floating-point | -3.4028235E38 to 3.4028235E38 |
 | Double | `float64` | 64-bit floating-point | -1.7976931348623157E308 to 1.7976931348623157E308 |
 
-See the section on [“NoData” Handling](nodata-handling.md) for additional discussion on cell types.
+See the section on [“NoData” Handling](nodata-handling.md) for additional discussion on cell types and more exhaustive coverage of available cell types.
 
 ## NoData
 
@@ -39,19 +43,19 @@ A "NoData" (or N/A) value is a specifically identified value for a cell type use
 
 ## Scene
 
-A scene (or granule) is a discrete instance of EO data with a specific extent (region), date-time, and projection/CRS.
+A scene (or granule) is a discrete instance of EO @ref:[raster data](concepts.md#raster) with a specific extent (region), date-time, and map projection (or CRS).
 
 ## Coordinate Reference System (CRS)
 
-A coordinate reference system (or spatial reference system) is a set of mathematical constructs used to map cells to specific locations on the Earth (or other surface). A CRS typically accompanies any EO data so it can be precisely located.
+A [coordinate reference system (or spatial reference system)][CRS] is a set of mathematical constructs used to translate locations on the three-dimensional surface of the earth to the two dimensional raster grid. A CRS typically accompanies any EO data so it can be precisely located. 
 
 ## Extent
 
-An extent (or bounding box) is a rectangular region specifying the geospatial coverage of a two-dimensional array of cells in a singular CRS.
+An extent (or bounding box) is a rectangular region specifying the geospatial coverage of a @ref:[raster](concepts.md#raster) or @ref:[tile](concepts.md#tile), a two-dimensional array of @ref:[cells](concepts.md#cell) within a single CRS.
 
 ## Tile
 
-A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). A tile can conceptually be thought of as a two-dimensional array.
+A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). As a scene is a raster, a tile is also a raster. A tile can conceptually be thought of as a two-dimensional array. 
 
 Some EO data has many bands or channels. Tiles in this context are conceptually a three-dimensional array, with the extra dimension representing the bands.
 
@@ -59,10 +63,4 @@ Tiles are often square and the dimensions are some power of two, for example 256
 
 The tile is the primary discretization unit used in RasterFrames. Each band of a scene is in a separate column. The scene's overall @ref:[extent](concepts.md#extent) is carved up into smaller extents for each tile. Each row of the DataFrame contains a two-dimensional tile per band column.
 
-## Projected Extent
-
-An extent paired with a CRS.
-
-## Projected Raster
-
-A tile or scene paired with a CRS and extent.
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 8bfbb0187..9be4f906e 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -21,7 +21,7 @@ rf.printSchema()
 
 The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.
 
-Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_.
+Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a @ref:[_CRS_][CRS], a spatial @ref:[_extent_][Extent] measured in that CRS, and a two-dimensional array of numeric values called a @ref:[_tile_][Tile].
 
 ```python unpack_schema
 crs = rf.select(rf_crs("proj_raster").alias("value")).first()
@@ -180,4 +180,6 @@ mb2 = spark.read.raster(catalog=spark.createDataFrame(mb_cat),
 mb2.printSchema()
 ```
 
-[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
+[CRS]: concepts.md#coordinate-reference-system--crs
+[Extent]: concepts.md#extent
+[Tile]: concpets.md#tile
\ No newline at end of file

From 02244bc7130603340d1e70fe33b8c60622e8e7eb Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 17:36:19 -0400
Subject: [PATCH 315/380] description.md page feedback partial, some docs build
 fixes

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/description.md       | 23 ++++-----
 .../src/main/python/docs/getting-started.pymd |  2 +-
 .../src/main/python/docs/raster-write.pymd    |  2 +-
 .../python/docs/static/rasterframe-sample.md  | 48 +++++++++++++++++++
 pyrasterframes/src/main/python/setup.py       | 11 +++--
 5 files changed, 68 insertions(+), 18 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/static/rasterframe-sample.md

diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 53961166b..7e5017821 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -1,38 +1,39 @@
 # Overview
 
-RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. It provides APIs in @ref:[Python, SQL, and Scala](languages.md), and can horizontally scale from a laptop to a supercomputer, enabling _global_ analysis with satellite imagery in a wholly new, flexible and convenient way.
+RasterFrames® provides a DataFrame-centric view over arbitrary Earth-observation (EO) data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of [Apache Spark](https://spark.apache.org/docs/latest/) [ML](https://spark.apache.org/docs/latest/ml-guide.html) algorithms. It provides APIs in @ref:[Python, SQL, and Scala](languages.md), and can scale from a laptop to a large distributed cluster, enabling _global_ analysis with satellite imagery in a wholly new, flexible and convenient way.
 
 ## Context
 
-We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis.
+We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent attributes and measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis.
 
-Today, DataFrames are the _lingua franca_ of data science. The evolution of the tabular form has continued with Apache Spark SQL, which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables interactive and batch-oriented cluster computing without having to be versed in the highly specialized skills typically required for high-performance computing. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
+The evolution of the DataFrame form has continued with [Spark SQL](https://spark.apache.org/docs/latest/sql-programming-guide.html), which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables data scientists to work with DataFrames too large for the memory of a single computer. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
 
-RasterFrames®, an incubating Eclipse Foundation LocationTech project, brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. According to a World Bank document on assets for post-disaster situation awareness[^1]:
+RasterFrames, an incubating Eclipse Foundation LocationTech project, brings together EO data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. According to a World Bank document on assets for post-disaster situation awareness[^1]:
 
 > Of the 1,738 operational satellites currently orbiting the earth (as of 9/[20]17), 596 are earth observation satellites and 477 of these are non-military assets (ie available to civil society including commercial entities and governments for earth observation, according to the Union of Concerned Scientists). This number is expected to increase significantly over the next ten years. The 200 or so planned remote sensing satellites have a value of over 27 billion USD (Forecast International). This estimate does not include the burgeoning fleets of smallsats as well as micro, nano and even smaller satellites... All this enthusiasm has, not unexpectedly, led to a veritable fire-hose of remotely sensed data which is becoming difficult to navigate even for seasoned experts.
 
 ## Benefit
 
-By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. Because it is built on Apache Spark, solutions prototyped on a laptop can be scaled to run on cluster and cloud compute resources in a way not easily achieved with other toolchains.
+By using DataFrames as the core cognitive and compute data model for processing EO data, RasterFrames is able to deliver sophisticated computational and algorithmic capabilities in a tabular form that is familiar and accessible to the general computing public. Because it is built on Apache Spark, solutions prototyped on a laptop can be easily scaled to run on cluster and cloud compute resources. Apache Spark also provides integration between its DataFrame libraries and machine learning, with which RasterFrames is fully compatible.
 
 ## Architecture
 
-RasterFrames takes the Spark SQL DataFrame and extends it to support standard EO operations. It does this with the help of several other LocationTech projects:
+RasterFrames builds upon several other LocationTech projects:
 [GeoTrellis](https://geotrellis.io/), [GeoMesa](https://www.geomesa.org/),
 [JTS](https://github.com/locationtech/jts), and
-[SFCurve](https://github.com/locationtech/sfcurve) (see below).
+[SFCurve](https://github.com/locationtech/sfcurve).
 
 ![LocationTech Stack](static/rasterframes-locationtech-stack.png)
 
-RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called "tiles". Each tile contains a 2-D matrix of "cell" (pixel) values along with information on how to numerically interpret those cells. As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type `tile`. A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other rasterized spatiotemporal data. Along with `tile` columns there is typically an `extent` specifying the geographic location of the data, the map projection of that geometry (`crs`), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering 
+RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called @ref:[_tiles_](concepts.md#tile). Each tile contains a 2-D matrix of @ref:[_cell_](concepts.md#tile) or pixel values along with information on how to numerically interpret those cells. 
 
-RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. You can use vector data to filter DataFrame rows, using geospatial predicates (e.g. contains, intersects, overlaps, etc.), to mask cells, and to be rasterized into training data appropriate for machine learning.
+As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type @ref:[`tile`](concepts.md#tile). A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other raster spatial data. Along with `tile` columns there is typically an @ref:[`extent`](concepts.md#extent) specifying the geographic location of the data, the map projection of that geometry (@ref:[`crs`](concepts.md#coordinate-reference-system--crs-)), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering.
 
+@@include[RasterFrame Example](static/rasterframe-sample.md)
 
-![RasterFrame Anatomy](static/rasterframe-anatomy.png)
+RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. RasterFrames vector data operations let you filter with geospatial relationships like contains or intersects, mask cells, convert vectors to rasters, and more.
 
-Raster data can be read from a number of sources. Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of georectified imagery (including Cloud Optimized GeoTIFFs or [COGS][COGS]), [GeoTrellis Layers][GTLayer], and from catalog of Landsat 8 and MODIS data sets on the [Amazon Web Services (AWS) Public Data Set (PDS)][PDS]. See @ref:[Raster Data I/O](raster-io.md) for details.
+Raster data can be read from a @ref:[number of sources](raster-io.md). Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of imagery (including Cloud Optimized GeoTIFFs or [COGS][COGS]), [GeoTrellis Layers][GTLayer], and from catalogs of large datasets like Landsat 8 and MODIS data sets on the @ref:[AWS Public Data Set (PDS)](raster-catalogs.md#using-external-catalogs). 
 
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
 [Pandas]:https://pandas.pydata.org/
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index db279133b..812bb162b 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -110,7 +110,7 @@ To support GeoTIFF and JPEG2000 formats, you should look for the following drive
 
 Do the following to see if RasterFrames was able to find GDAL:
 
-```python
+```python, evaluate=False
 from pyrasterframes.utils import gdal_version
 print(gdal_version())
 ```
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index aa4aac7dd..139f916c2 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -77,7 +77,7 @@ rf = spark.read.raster(catalog=cat, catalog_col_names=['red', 'green', 'blue'])
 rf.write.geotiff(outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
 ```
 
-Wiew it with `rasterio` to check the results:
+View it with `rasterio` to check the results:
 
 ```python view_geotiff
 import rasterio
diff --git a/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md b/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md
new file mode 100644
index 000000000..026537851
--- /dev/null
+++ b/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md
@@ -0,0 +1,48 @@
+<table border="1" class="dataframe">\
+  <thead>\
+    <tr style="text-align: right;">\
+      <th></th>\
+      <th>timestamp</th>\
+      <th>crs</th>\
+      <th>extent</th>\
+      <th>tile</th>\
+    </tr>\
+  </thead>\
+  <tbody>\
+    <tr>\
+      <th>0</th>\
+      <td>2019-02-28</td>\
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
+      <td>(-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667)</td>\
+      <td><img src="data:image/png;base64,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"></td>\
+    </tr>\
+    <tr>\
+      <th>1</th>\
+      <td>2019-02-28</td>\
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
+      <td>(-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667)</td>\
+      <td><img 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SeeeGIXjQvALojr1q3Du971Lpx88slYsmQJGGP48Y9/jM9+9rM49NBDuyi8vr4+XHLJJTjnnHNw2mmn4ZRTTsGWLVvwqU99Cvvtt9+0LDu7w/Lly3HxxRfj3nvvnZaYYaZQq9UwMjKC73znO3jd616HgYEBDA0N2T6744470Gg0UK/XAQCPPPIIbrnlFgDACSecYDXUSy+9FHfccQfe8IY3YPny5ejp6cGNN96Ihx56CDfffLO931/8xV/g4IMPxjnnnIM0TdHf349bb70VP/3pT7va1d/fj3POOQeXXXYZ3v/+9+Ptb387NmzYgOXLl0+jf7/0pS/h/vvvx3HHHYeFCxei0Wjg/vvvx/XXX49XvvKVXdTuRz/6UaxatQqnn346Dj/88K7xLhQKOOqoo6b10apVq7Bw4cJp9G6GpUuX4h/+4R/whS98AbVaDW984xvx+OOP44ILLsBRRx21yxCwLNHH7gS1x0scfyJbrsefOXbnqJQ5nezuX97ZZdWqVXTMMcfQwMAABUFA/f39dPzxx9Ndd921y3uuX7+eTj75ZOrv76coiuiII46gr3zlK9POe9vb3kalUonGxsb2+Ayvec1r9tjWDDt37qSTTjqJFi1aRKVSiaIoogMPPJDOPfdcGh8f3+W1v/zlL9Nhhx1GURTR4OAgvfvd76YNGzbs8txdOSqdffbZxBijRx99dI/PsDtHpV0lqsjuk8fdd99NRx11FBUKBQJAp556qv1uZGRkt33zbGej3/3ud3TiiSdSrVajYrFIxx57LH33u9+d1obHH3+cjjvuOOrp6aHh4WH6yEc+Qv/5n/857RmUUnTFFVfQwoUL7Vh/97vfnZb84Wc/+xm96U1vonnz5lEURVQul+nlL385XXrppdM8b/f0PLuay5kT1oUXXji943NI05Q+85nP0JIlSygMQ5o7dy596EMf2uX8azab1NvbS69+9av3eE2Ply4Y0QwGAXq8ZHDaaafhvvvuw5NPPgnGWJdW+afGnDlz8J73vAdXX331n7opewQRQUqJSy65BJdeeim2b99uqdJjjjkGIyMj+Pa3v/0nbqWHh8dMwtO/HrvFunXrEIYhDj30UDz88MN/6uYAAH7/+9+j2WziE5/4xJ+6Kc+J6667Dh/72MemHZ+cnMRDDz30RyVP8PDw+POE11Q9dom1a9dar81SqfSiHYX2ZWzbtg3r16+3n4888kjvuOLh8RKHF6oeHh4eHh4zBB9S4+Hh4eHhMUPwQtXDw8PDw2OG4IWqh4eHh4fHDGGf8ppQSmHTpk2o1Wq+FJOHh4fHnxmICPV6HfPmzdtrU37uU0J106ZNWLhw4Z+6GR4eHh4ee8CGDRteUD7xPyfsU0K1VqsBABZcdAF4sQgKSedbAQClNVdmPofjHBTpD0lVgUoKAFDqb6FW7GCsrtPmJZMFsFjvqKigMPBggPr++hrFLQydAZN0PGXoDEl9XkiI+tqIx02VkUjZNhY2RGApoCLnlM1T3bZfnfcPXc/zvz79fyDL+ry0osCkPo+3GZiCJfd5myGt6nuQACojkwiEbsv4WAXFJ3Q7ZIHs9WRVApJBNIX5HUG09fWDKf3/By7U7Tni89cjq3fEJAOXgGjoc4gDYNl37rmCBgNPYfs/7nHPxQhIehV69h8HAEw80wsq6vYOzZ5EJ9HTtr61CiaZ/Y4JArVdkgre4aBA34D1xLDkBKOsSSAy7TRDoNoBYPq7sC1A2ABas8n2ATPnlRdPIk4EOpM6v25YiZHGul3UCABhHowYWMogWrqDZFmBAjfevJRCpabNkul/ABC6c5AysJSDzOTkMQeZ61NJAjEHT/T1o50cadU8swJY6saLEcBNrnumAJ6asS4yQLlxintg+00V9G8Ko9kg5ppVAlSk/056FUAAj80JXD8rABS2CvDE/S77DQBEEwAIdv6kZYDMqsRSPV8zyJKbg6pAMEOHoMGQVgi8456T8nOupJ8lnGBIS0BnVqq/K0tgKjDfcagQEE39w86sFEFvrNvUCsBChcKTRdt3gSkGJAv6HwAQJ0AAKsg+A7JfP3h1oIkFvRNYO9oPAGiPubSbrCAxZ3gCADDWKKGztWLnS2kLgzT91VycYM78ndi6vRcAUOlp2+GIAgmWzQ8QQiGxaXuf7p9AQpkOKZVjFAKJcqifLRQSjVjfgDMCZwTB9bgFXCHk+t1qJhHGWrrNzakCVCzcXOUEUdJ9GkYpGAPamyum/5kdMyb1GAJmHZAMPDHXmNtCT60F2ezgkdM+b9fqvRH7lFC1lO9ACJRCMOHePtbiCKY4StudMCA9n1CIYYXj0EENcBZgh5mwsw8cw5I+Hc/ZTCNsP7iC8U26BFq7GiAaMwK3BLAe8zIDSNtFcCNTWcpQ3KZXjyDWJ3SMcBMthsTc+/Cbv4hoa2AFH82N7KQMGIGbZwkks4sKAMg+AjeLUzTB0GgF4JERRAiRztFfquHYCiXR4Xp29Op7rfvwOTj44pUAgJDpbuvp0ZKQVYoIm24xpRBgGXPDAJEttAJg2YIaAIgB886i3HLCK5mVIKqmkGHFnFoCM9efTAIUS3pBOOiIMazZMoTACKAgIHTa+sbpZIQgESAjtSVFCHq0RBGCEIZ6LKYmS2CcILjp0xogxyPTHxzNxdIJEgZkAWiN+iCqsxqQItTXb5QQ1PQ1JQ/sRgtMPyszO5xig6EzbOaBZEDi5G9Y54gHpR03ZjYL1BbgTe4WIAHIUrZJIgjidm5TP5BnzUSbAWbDwmPAyErwmOx5LNDzRcSmr0oMadGcGBE4Y1CDTqgy08R0SEGFZpPU5hBtgJmJpwTZsWVVBt52AlKw3N9tLZztxqsAvdkCIJQWsrqvgKgFqNB8joEk2zxUGAIBsKK7ZnYNWQBg/lb9AEqEyEhBapAVuAQOxgmB2ZwoRSgPaYHYagZIOwKh0L+LBwkd03fEAXC3AVaB+0zVFGGkG9ysD+LxpiuNyAvc7uBZUWJC6Q6JW0XwSNj3R87iSE0beRRg2+g8sGyzzwOEoe7ksbGSXcsKvW3EkxGY6WQVpiDzXM0Gw8iSjUiM5OcQiCM939tJAK6npMVwZQoA0McIYVXfa227Ag4GUdVnBoEEoJ8zDDk6nQCRIQTVxiJkr3k/pwRQ1H9zBogGhxwwm8dGAY1yEUrq3crebJ7bp4SqBdP/WEdYraS4jUMksLvC/G43qRKKB+mdZCWMsbleAwrZ5CD8bttcAMBI/xj6iy1EC7cDADYUBtAxLyJTgJgyL2yRQJUUrKm7v7Je2EWAmG5DNGl2llsJ9UVmoZoSoNyCKpq51ZOY3QWSAHgCPPHPZ+l2ffkq+zCtClDua6EY6Rdi51QflHkWtjMEVcxCXpJgHXf9g5evtFpmWmT4w6UuU9Cafzzb/n3g5SsgYmYXW54whJP6O1l0O1UmGUSstREAaM4jqHn6hRroayAUCrWCFoIb9uMY7GkAAA7o3YHYLAij7QoCIxwBQHCFwCwyVEsgO9xJkZCwcEhrvpPtIsYm9WrNAwUQgzJMBaUcMG3vPXin7qOtRipJhqCm+y1tB0hTAVKZukRQZufOImU1PX1RvWkDgKRHWQaBGIElzDIMTEJrjNnPjGBmKUPQYk6YlQCWbaYaHKLthCAJ16f6M0EYDY4YELQy9oUhyd5+0psb4obp6ACBkXIUA0GTWWEm2kBrjm6krErwljBt1O0UmSYsmV38WU4LBoC0TIjG3TMz6YQscYDMOyhL1NU3nQFCMmg2JDFHOG42rAGBuNaqAa0JZ++xiJ3myLQy7eZmhyGoM9dGMCuoZU2iPmo2daUUiDmSHtfHlBOk2TohOgwqzzBIZucEIgURKfs5qCRI2obZSDmSRNiLBS0GI2MR9ykoI4iCnhjpVIjCM3owgsNaVjsF02wJAHR2ljT7le3W6qFlw0r9LexoVjDV0p01VGtAmGtEgQRnhKm2/i4QEhMdvVNpJwESaRrV4QjHBNSEbn86r41CUd87SQSSegHMbG4Zh30HZUkhaJg1MCSoiMDMhltVJLC+BLT3XmGaYe+0BHt4eHh4ePwZYt/UVEMFhAqkGMIJs6PSCovdkYPpHTUAsMUNuyNkjFCOEiSGSty6od9uVR+ZLCIopCgU9G6aC4mkrNWLcGdg7Yeiw0CTEUpb9e9kCUhzu2me6B02ADxw01m22QdevgKiw5BWze4uyopw6PZm9ize0RTsAdes0J8DoXeCAIb2m0CjHWHnM5q+ZgmDMLtK0QZikzifQqU1JGOPS8vk7Ft7SMJFASBDAjMcEo9zdjEGZxOLCHGv+x0jRxlHgUQqBfoLTd3HURWhsfM8PTGEbWPa3rJ49g7Uyh2MG60zKHVQMJprsSfFhs4gWKZRtAQ27NDPXCnFmD2g1efRyQqSOADJvCpl2qg46o2iPVzob6MzZZgHodAeLTltvqDACrqPVcyB1BwPtGaa9mUcpLsPb2vbaGYflQUGbrRTFSlLIbNU07Pc2v3JajIqJKjAjU00zq1GmzELGQuSloBOn9P8KE905IY0mgAe+rxmIg68fAU6AwTRMn1ShbVdilZg7xs0mKb+rW0dYKa9KiDw2GnaQZPZ36UVQAmnZRInUJj9DTCZ9Y1+J1gr440JSc2ZSGRZATn7KxnNTCbMUtIggMXMan5grq8yWyqzcxxgxhRCYwG40D4H+oC2k+uxcJpv0i/B29y+h5IAGJ8EMdSBSjgKZf1iDNYaiI3mN9koWtt8tF3ofpyvWRsRSsBosbMGJqH6GbZXNHOSNCLHlCTcar6IFJAw5yfS68w6UnIUgxTbJ/TLtyURiMx6NVybQiWMnUYKWFtsKoXVaCuzG2h1aiiP6Hfo5bM3gRtngx3tKtQQw5NbhnVXbS3mfCoY0ophxRKG8v6TaDbMwMcCFBJI7v0J/vZJoconQ/A4RDTOwY3wUhG0IM3oSYKjmloh4jG9uD5RL0K1AoQ7dNdFBMRD+rxabwvVYgfjDePENF7M0XQM5S0ZJaoFTX3EtSljcYgTCqMMPJk+uUrbGJrzyb60KiLrTAVyzCFLGFSBLC17wFUrkK04O7bXwBggpjLhSRDt7N6uHZAMYG6xg3K2tKQ3R3EBWLzyWktJswoDUwxMMPus1ramgMDUy5aSIWgDqZFZQRNoDBu7UiowuzqFgLn7NBNjsxHO6WLN9kEoySF36EbunIjAOzmhlHC7uIIBSUtfo8UJvSX90JVSB22h0Bwtm77jKM7SwjxV3C1aAJTkKFT1ZoqIIRUExfU8YJGCapi/20I7EAF6wxU7ihcps0KOpYZWzRzKErcBYYxb+o4EAZxBiRydyt0CLzrOmaWLaiXjsGa6QHQcFcoZrNMVCUMpZ85aAbD0Qm0/F4zlJoU+J6xnzkIAM3RvVDdOO5G7pnUWou7/K+7mkhLPcixSDFnmVBUQhBFesqqgCgxUzW1OTJ+mJbIbGkDboLPv8s5qVJGgEsDrphOUM6XIGgEpsyaVYELYcVERgQKymwSeus0h4EwaIEBVU1AzE/xw59RD8HKKvqqeW+PNEgqhFrBxM7KbMFkmBFMcfKNxHsw5LIpZhHKYIDRmjE2jvVCdbFeTc36bDLRTXZ9xRopSCOOH0JwsgvcTyn36RWQMqJl3oTdqY2O911LD5WIHOyecw5AQ+h6LhnZi7MDYOgw+PjaMSqSvv3msB/HWsl2jRMwQmv7uDOtNBwCo/gRScrvpFcUUssKhePfasjdinxSqos3AwbSXremBuE9pDTJzECgp9M3SxZvrjaK2zwFQjRCljW6HrkJCdZa29xGAHRNVK4Cj7cJ67ooO8OANTut8vjjkvJXWc7Izi8DjnDbdkzrNp83thCWhteElV2lNVRUI1fX6u6lZCmoytIJUKeeVzKTb3UI5e5P+0vXVNCiGYNLY1owHaCY4oHILbYCuBR6kF2MAmNqPtO0KQF+pjZ6ojfV17SlZ31lBZe4YAEAqjv1njwIAhotT+MVT+6O0xWwQOCCLmTc0hyopMHNNxkl79gLoTBWwznhelwa0fblpbGGlwaa1bxUjbaOlQPePEORsr4pBtQIIY1eSFYmgntnFAJl5AREg2tza4JhkzhOWAUGTOztekez8Ex1mn4UnDFCwGhxLYcc6bOjvonE3HNk1SBjhlfmQJW7uiJbzuDUlgU8AACAASURBVFaRHo9sgyOdcq69Z0MgzGyleXstdxosGT8Fa1DKy2Ij4JKKcWpqddvNlHDadFIjSKPNQAKp0UZZojd5dn4mzN2LgKiUWO/uDoNdrFUkHFsRc81CZPssAgrbA/uhMye1fUUhuY0iMSB1fgIggIpZ35HrkwqZjYzZdJRTx1hEClExtYzL3N5JPLNTMyfUFIDZhMlIQfYBYly3q7yZ2/HY9Ns5YBJIe/S50WAbYa8eGJlyu2mkgMAHOqiU9XetdoiC2VzOnj2BjhS2r/rKLYQmEmBLo4Y4FRio6fVsx0QVyjg4lWsdVIv6epNxAZUoRirNekMMgXku9VQVqCi7VsiStPZc1hFW2FIjAGodqFEtwEWLgxWVtbHuzfA2VQ8PDw8PjxnCPqmpZpAl6rKhgrS9BwDYlEBzWKtYcjxCeYPxdCsTZERI+oz3b8wwtbUKAIj620g6AXhGIbWYo9Sy++Twsgs0xfboZdNrbmZ45NMfw+Fn6/N4ytCe52Iw+FgIPlurnE+fdr79zQHXrEDSJ+2uj8oS9aNMCEasaa18rGDe5pHRcDxmmpYz267yFoagoe/7q1XndPdjTksjZuiyjMZUQDiZ0dXueiLWWlBzjtEsBxMM9WhqrJ0GeHp80NJL5d4W6i29XW9OFdDq0dTVk0/PAWtza/tOas7TNZhiUIkAGY9r2SMtRUgdgdDYxDNbeblH9yPnZHfx9akSZEeAGdorkRxhyXj/jhYRTXBr2wymhLVhMuns20wyBE3WRRdmc0JG2rM8+xw0OWJDrWvbujMdEIfT8lvMMg1Mabt1qqcgiDlqlaXd4SU8yd276DRfHhsKNdPCA7L35ilQGHPzOO512nTQdIxEWtZzPAv3ApydEuiON828jQFNGasg3y5H/1HJebCuPU3Xzx25wRSmD8nanHmf1lKzMCmlGOKmebjUsU8ICLwhuswnSc1oWP2J1mgns7hsxwaAGareMAeyrE0LgGa9Mg2WxdxSwHYwDGbPnsC86gQaidbMHn96LsKqpkxFQ4Bl9w2oa61ozVaOdk6AZE6CyHj59teamFPRVM/DG+di4XzN4Ehi2FmvWDMJADSbxmYbSLRid4PxZgnze3Vkw0RaxIGD27G5oSmMMJR4z8v+GwDw4+0HYrzlYmuDKEaqdB+Mba5iotfE8EogGhPWREQcVlunUCE1IXoIFNLUxVunVanjhnPe/Hsr9kmhmhYJvEQA0/YLACiOc1AucDvuV+AyM3YBSa+jflRIIBOGIouwL76UHOGGgr0PT2GpvtTNRwDaZiUNHbb/Z6/Fmn86G7vCyz+6Er/7nBa6h567EpU1oQ02T0sEbChN+81T55yFxdeugDRxsWE1BpkXgK8v6LCFjKkMCOGYiWeLnJOIrEmoonPIeOhzZ+HoD6zous/ildfqawoOZZw4WKqFg7Uz5eyE4RSQ6CgFqABoLJY6AQeA2mADVRNCs37LAKq1Ntod3ZiBnga27dAvOjUCNEzwO8oK4QS3DitBy1F7YNoWKQf0ADAGu8jxpkCaGLs3I8R9MaoVLaXqUyUXBlEPAU4g09+iIJFM6d2IaGtnm2jC2PWqLnEGiDmhCh2zKbJ4aIJtY2ZvTnqyEBVYJykew9phSZhNQpZoIbd5CI0tk3I2Vkv3pvq7vCDN214zZzJV0As5M05vPHa2PB7r8I6M7mTOdAmWAg9f3b0hPOBqPUdExznwMDL0bc52nAkOEub5M9o1Z0MMqzGefOcFXdfPkmLwgoQyL6s0zktxR39WCQeSbPdG1v7MOEFV9bwGtF+B3YDsiCCHYks98w63/ZY5imXvAgD7/vcdMI6xx0z8abYRMSYBHrtNajKf4zdrF4KMDZQ3BGintuOLBPb5FWMmfj7rZJdEgwK9ZkmzLm1/bAjbxKDttw31IQBA1NeBktyGzUWVNkpGWDXjEOVCDGkEteCEVqoHo6fYwa8eXmzXg2R2gpt+tMw+c89ibWMIhEJBOOHHEo50u36fWEU7T4o5eoNcCBTaTT3pwkJqHeKSsSLkprKN+pH9qY6dT3MTbC+Fp389PDw8PDxmCPukpkoFBSoosA63ThPE9K4507hUTwrV0Ds43uZdlBfgMnRVnwhttqW0FABRzsM1dJrqNIqXYJ082LM2ZwddtgJDv9W703LObl/dpDC5yGmBPLfDBZyWECyewrwjpjBQbNrvHv7l/uY3DEmPsrtuEkBaySgZgjDB2U995OM47JyViOpOc3jwS89ytMpc9hWsV4roMB16YLUxZh1WZLHbwxQECJNMYUHvBLg5UXUE6lTSiRgAbJdc794BiN4YKjZUU6o9X1mOFnWhGqTbZzQWVnLjKWJms2URY0gnItSNSiHroYtSUugO08hpmapEkEKBTAxG0GIIJg3VXCLtQAStzWkHKvO7yNGnLNXXy0JUrLeueZYs2YAKAJRz1HbbaeQq6qaKoXLetgqIGt2OS1l6vTzFyDtM085ZHze4S85AJmzEXCOpEsJ6LlvUs5C1P6k5JyZIrflaj9/Q3T+pmeQG2TQLFHqGtKNMKcrn9tFY9z5NAx/wzcvBzdwplmK0WxFUlkChJZzTXkg2oUmlp4VGvQiSgf0u0/55ClT7WmiakCkqM+fNrRhYwixVCfdKYHS0akPBVFEBgYIoay0uHS24jGrEQB0Bbrzuw7ob33QwhTCOSmo8AqRme7L+ce8qATGHMpp37+JxHDioE8088JslqC3QVHAqOXiUWlp3tFVGo6MfNFUcExNlKPNuFSoxeFUvWJ0kQG1uHQ9/4BIAwDF3/jPkbH3exMPOHJMqPciNqaLpKwlhTCuyrRPqUFsPcG2wbvuqVS8AmbdypKBCBZYly0g4gkBCBnu/prpPClUmdYhD0HD2EGIAlwxJnx5UHkmoXB7ZTJhm9K+Y0N91Bqh7EWZkM9PIsrNT5XHEP61EMSb85gu79gae9aBEp6av/8uvuXP+6+u7pogB4KBLVwKGCVaKYV51As/UtXfheLPUFTOoIkLUyGxmzGZeWvSFa6xdcOSGq8H343j4o9PvOfLFq8ESbmP6WD7Gk/RzZwuqLLqsODz3viQ1TaFnmVik4thpsrcg5aCUY94inf5xolmy1J4IFOIBYw8VBGwtW1pXhbDhEzxmSKsS3Hj/qo5w4ySB1NjSUJRAR4C26cWUM0D1Jtmj6PPN4wmhrHChotSZZSaz0AE4ejnJx2RqwaeycJIwJ+RCQ41bm3aOqi25eSVivbDnz7P0bMp02FNGe4cAMnulAOKaE/BB091bFlwcNgkAglDcbGIZy2Tz7/K2pnLbZuNFuXCYZ28GD7hqhaXzKSJQnFHUuo2Z929SVV02VpYwS/vygrTCdF51ArsD4wrS5ExuNQuQ9dCGrzECaFDPK0q43QFP7aiAFZyvAc95WLOEYWpn2XqgS8l1Wj1ob1viZLOoocOtRyvFwuYFhxF6bFS/iDSUIDECcbJeRlBJkGZjul8TZNqfxgIqE5yBQjQe2HHiuXAsEtrFOhjTgzNBDBsLJmxmuIXJzTr8RTQ4+PwWfv+YTkjfP3cSoRFWkztLKJZjm/O6UozRNDbWh998SVcf//cbrnAfTpg+BktuvlS3a7QAmVHjimnvaLN2Tj46aOdmlADxsInb3x4iGUxBrSxRMtCZLEC1CHs7PP3r4eHh4eExQ9gnNdUMyWCK0Oz6mASSsqOhVFtYaoJJuOMBaYrYaqcMKgsuDwmiw/HEeVrzW3rRyq6Yv0PPNV68HIh7dx+P9dP/+PiLep7MKeKJt38KgNY8AaC4WYBn1WfKOoNPbByvonGORZ/X5/HYeTiyhIECst+Fk+65ys8EaM9SLnlAQF0aFkuZS/ofElSUqVim+g0AhIRKXwvcxLet2T4AbmgtXk0QFVJUTSWNYk9qsyF1mqGlhdlUgKRKVjsCYDNkqUiPkYqNY0hRApPGsaVArjKQ0WAttQcX4yhCBRkLkNmFp3DVZ1hL6H7N/DUILpFIjt5UYeZIZK4tGURGg5p5xPLUbY7WtfGUJqeuuyZZrVUWADByWYOU00BVSNrz2MSYEgfag047zSoXgYBwjNtnCbe7uZk52Nn41pgh1DnWkdR0xiXdSCBqMCS1rP1kTR8qNJq1rbLDunIjM8khjYe1Srn1Wt2/MoqTV/+deSyGm//3l+xvSsUEk6OmukoktTaXy6ls41lTp1WCAL4tcsxG5BwOU5P8xVYtagukvaZDst/niiQwo8XymEEO6PPEWAAVuFjp/uE6xrh2yw4YoVZtYdDEWD/1zDAi40k+f9Y4Npn5rYoS8QBzNEmoPZYB4zAVuLWHFMOm9dpRSUwKzH1AH+enbUMrCUDG+a6n2MH6TYPmS0K7GUFNGVPIHGUdn14o1DPG0UoypAXDEph1I5vT2lPa/D0nseOSzpH675zzF58MgPbeL5L2/id4MTBZZnhLuEwuMGWzzASg/WIX6M9gBScjoOdxgSQLYRAunEFCL1wHflovNE9c3E3vZhU3KCDrhflCsOSqFXjyXHfNgy5b4WjdIkGVXTjCgd++FBQYm4exXwKA7EvBWsLa3eJ+6WxkBHs9WVQQLW5Lfj1yhbMJt4eVTpxRdAtjeZ2hw/tJX7uTXcfZqcGdDVU2AzR2lG3yBAoICw/fBADYPN6DMJAYa+sVfVZlChWTEWbW8E48vmaO/k0theTC0c8KtuxZOMlAggOztL1IbSx3eW9mdCwCBV4PHKU5r23DbGTKNY1lstVQS7hMRoaqtWW+Cu45ibmMOzITSpGzOWcmB2WSM1iamcNStyx1Alb/3v3N01x2JYKmNzNat5i32Wrv08zjmoLuMJosIX00wUABXGatwFG7QVs/jy2EkOpk/ID25qaa/ru0ndAecGYS4ty2WRZ1DtCMUlaR6grfoUAh6tc3J4ItPTaelDGVOm96AHjZrct1mwPCnHk6IciWDQMAI5viESFcMYKYAXFmp8jodjdOtvM5gYfSFUVIXXt5oKDaAVgho3wdpUwBEJb13JwzfxQb1gxbITK2pQdF81yze+tIFMeT62br9ldiLBzU3rSpyqcyY1i0ZCt2TOlBa2ysQRmhXtwSIC2T2wzF3G68ZFVhx1tNZqQ0wES9jJoRqqniqPTq96DViqASjoMP3mj6m2EyLti+TWJXBerZdHCGkRuv0n+YecA63L1bBEQ7hQ05pEhpOwT0RiWrvMRDpb22cwUIFANUftLvpdg3haqBKiqQ8TJgxsklW0zCQoJOJ1MN3GKkONBYQHb3L3LZYUTMbJjCrvDY8t3Ho+4Oh35ypRXGIhd3BugFPWtHWlGYtf+o/S7dXEZxzO2sZU4AstgtwuGEy9gC7nbBPGZQEaGtvfT1RsHcPko4WJrXiIC4zyy6mVDIafKhcXqJh6XNsRtWEsgtJWdnXdDqSksouMLLh7SQTYnjcZNLdAvVMG+Brh6zaeNAt4apnAMPSAuwdG0l6wK7CSAGm9eVt7jOimN+l7YDG5eKeqjtc1k4Um4RY0o/d5ZNh0mT/9aMi7U3cj1G2aKT9FB3+r5c/Ckot7aSs1VneanzjkE8W9S51lKz8dXhUkaAt5n2E8gyNpXIXl+0OcKGaweRE9z5zFnRBKAKQGFH5jjj+iqf1rI9pNuQOUyJDsttMljuwfQzZ9VcmGJA4OyjxVKMpnGqUWAoCvdCLf7m5SiauVoIU5RNmj/eEF0aHEiHTdn+KGdGZobCqEBsQpi4AlTgNs4icFobSWZt6TQR6fehL5vUcNVV+hP0msxF+9XG0JgfYmy9zgTGy6nNTrS9XkGt1LEsyILBcXDT4PFmCXJSP3PQE0MqbrMXpbMFSsZuOtlbRvhYCWlWLk0xm4mJCbLzdHyyjEIhwcSYnvvNDf0YeMU2ANr5q9mJsNHk/q0U4q641flD4zY70mG3XwhlrlkuxBivZzvEYjfDAv0eAXruhXWma/QCUMRtvWMIN8FV3gYLaFt3UaKr8O5eCm9T9fDw8PDwmCHsk5oqhdoOx9vcurwz1R1ekrbyfJvzthRtnUUmNNVbZMHZ0kjoczNv2hlrL3f3zuPJT7r7LLlyhfXiG7nhaix82Xb89PVX7faai67XiRtUCEeHCThPvUmOtEg28Fz2Krv7D+raftYZdCEGlCXQz6qfZBpvBzYUhHU4YKjIpKWLxMv5ekdeKcV4fL2mxoJtEWJB+MlDOqg+rRDEPB0eNLdnEs3EJGAoSqimsEnQVUhWO2pXdUIB3s48J10/ZhlyAD0XIChfPEZnnYLx+lWw9VXRduPOUh0qZMOnCujyaM0ULJZqSjwrTCCLjvoUbWYod3Nflv3HzMcsVCMg8JR15d21CHQYTpbZSScIyO6lqeBs81/axi3rEU3mwnzCzMSRu665RtJjqGKj1bZnKZC1J8IWnY/7CDxFlxZuKxUBIM7AMpszuetDAawjIE3oUywkwly4z7yS9gB+emoI/T1N1E1moFYcYrKpH0DMaSGZiiwLsu7vdu+TsHjltTlq3tXz5IohCUOwLDF9QK4YA0HXeM282Bu5gW4LjBuN8JliHzpJiHCoZb/OapOmqcDWrb3oH9QG6f5CE5GhaTbXazZ3dhoEmKpGkEZDjNsBYmNnVClHmGcsCi6UhXMFaUKKmCDM65vEqEn4MFUs2HCYRAqUC7FNmr91U58N5yEFbOj027aTZIiK+hqNdmS1VijdP9YeysiqZ0wZu322NAg3H3Wif3NxQUCobJUn4gQmnuVOvpdinxSqosnBFZ9GqQFuUaB6aIvrRk1Hr4X17kUMcDGCJLXtLLOp5qu56GT15toRYe3fd6f6y+Ogy1bYa/7hM9Mp4yxVG4sZxJBJqD2PW9rmg395L+7ccshur7//ddfaRP/5cl1BnVkBKIuEp3L220XXX+vsskUg5mQpQV3hh2xfiDZs1icRd2fxCSL94sSNEHMP2m6vv3lbH9ikSQguqCs8BoMdpCakpplEmFPRJadOnPcw7h9dgkc3aBsr31SENPYmFuswjSwpetjfQVLXDWEJhzKUFGvpZPfCvNxsR2jpUxUSQAzlp027uBOA3BRjsJRp6ASsFtrumYujzPYHk86e3RkgR/1CmxiyTYFousT72W8zG58sUo5q1n2VFZoXTY601yy0bZ02Lyt4n42H/p1zQpIFMpsO87kiUdyqbxZXtMNRRvWLJneVU5izO4KQT+aEtOQ2ZCxl4B3mwm0CgpjKmSZKCus+pAXhK3/wCfz8uCvxbJy8+u+wY2Ovtck9/jcX2u8OuHoF1n38LBz6yZX22EG3aHtgpxG5hPoAaICBbzUZfuqsK9SMuCvPmPdPACedwalu5gGDLk8FHb4ime6EHVMVHDS0Dcp05KapHvQX9aRIqxyTlSIOHtA07MZGLyZM6k0iHf6V9UecBrZyzOSOijVVBE2OuE+5IHnG7EaCCdjYUzQEnmzOtukBeZtjXOjB5oMddDqBpdsBQO00Dl8d7ahIFT15h4brmDCUb29vC5212guNRQTW5l3mCes3QQAJ5d7dnEkJcBtsWSKwDs9V2WJQzQCqtfeLJE//enh4eHh4zBD2/m3BiwHpf0Ezpz0aepAyB4qStHkqZYlQ2ur2H0p0Z6TJe16qMOfOz3IG/JBAZje39kO711IBvbO3BZF3AdFjEiY0QuebITmmmlkCA4VFtZ1Wo43620g3mULeTQbqdYWxkaNq49nSZnyhAmH/zzmqTHCn7TCpd7XW6aWji00DAGvr7zNHneZcBQxrbVoIhf2GtMfmM7wPq3MayaLPX2NrZfKtYZcDErYXEBiabkNrGD1L9S7+yeYsLK7uwJqypokf/fh5u+2zJZ9ZAcw2uVD7mujUTV91OFjTJaYQbWbDjZhkiMZyOWBzlGxa1rQ0cwyhTbKgT9b/aw8TRNtRt0mP0zK1h7KjjRnlEmUkTjPlknUVe4/GneYbL4jBpgJXUisk9DzmAupl0Y2FiIE0cO23oWDC1BXNlJcg50EsdRalvOYmTPGE1mzqSgChQlhPVZ4y5Dl1WVOOTpUMcraew2vf+0ks+dZlOOx2rXnO6+nglT/QWZN+ftyV+LcnjtXtpSOx7sxz8ab7PwIAOPOB9+JHTx4MAHj647qYxO9zrE5ek130f3RYGFUk2JSwhbJRdt7uUAAYEM7XPHenEYFP6JdcFZWOBMjq20apLZauSmSzNzU3VdHoncBfDK7T4yRSrBnXoSyp5Dhy9kaMdvSLUgoSPDOlqVYiZk0MPJJobK2gUTA0Qm5Kqcg5uQEAawqbZCEOM282PW8pBMrD+lmk5DaSgTEgilKQYYwaSRFiUL+fT52s+zFbN3Y+PmA1y639BbBsWgV6/chMKMFUrjB7QVMWlqmJ3bpJAlCWCmBai83yModKa91i9+ve3oJ9U6hmyE3Q9mxTd9NQH9QIrEAMJ7mlyrKMMtZRleVCIhLg6XO7MxDt/zljuyyoLhpkT3i2l/CBV2g6mUkgHumAG4EebQmQ9GVlZKSNG/uXbf8f+FDH1lpMY1eZIy0ZG0dmgwuVFaQsEVC1LONJAFkmJIZKLG5xU4UZu6mjJR2NqaLu/hFtZouIF0Ymsa2u44+yl/zQ71wEAOCDBbuJSWsC0U5hbaBcMrT304swnwzw+0cWAgB+L0iHOfDpHTvyxaux7oMfxwHXmL5jgNipO67DYNMXyppEOCacTbJEdnHiHZMRyi5Wzu7IlPH4zUJI8lVY4KrvMAmgSPb6PHEmBvBuD24mWc5L200s3jHJ8c29O/2EdDgrsqtDikqbnW+ASwQPBG2CMvZuVYD1BGaK2TAZWTEpCrN2JbnKK0pvsDJBCuYEbGGnsLSwLjTuMjFJTjkPcG2vyy+Ya9/7Sft3Pmn+Ybdf2BXK8eDUCABgVmEKf/tfZyBVev58+RX/CrwCzwvFbWYTsEBpCjqzfLQ4lKFdeV0n188L4+zdhYKuoJKFAcUc3Agi2Q4g+o0EWV/C9kYF/77JNSwzd1RKHUzERWxr6PYXgxTceJnH9cj1DYM2O2VdlXC7GaEQgGKOOocW6oDe6KghkwmsLcAL0tYFTsaLljYvVGIwRmg9rQtUCAKEebde/t1PYXxbDcIUFX/67LNw8MWaUk9TZt8zMSUgcnWdZZlsqs2gpcsy2GiJAFZRAYd9x7OKPtY+XUoRliRU5qCyF2OfFKpZmkJZJFvhpDTcRJoIpNu1nSNocp1jFUYTy4RqYLwssiIYbUCYYPgubxeDTONi0hmcFn3+GlN4Wh94+uzdOzaN3HA1YOK2i1sChOsLbiEPgDhLncacbVeVCGq0gCOPfAoA8Ou1C91izQk8FwbE64HVilVJ2Z1lWlUY+C3HqFkfOkMSgckLHNQZgpbrEybdAs06unJKJmQeueIsZ2NOBJKNeqfet2QnDrltOXrLWut87cIncMfjh+p7FxUo4HZhSYuEdab01/NF3+9NWkPzrI9/6mNdi2S2MIV1XTrOJV3IFQqPGXhOwHAwG2+aaams7oRqY4Ebi8A4NGUxpSpL8xc4DRTIipFn/ejCaIqjhKTmximpAp2BbHFVtog1oMNdspy+PKacNs3AWo5FAJx9G5xs0gLEHBBkbXeiwV1aTgawUVdeT5YVmGEU2rOZ1UyFCU2y852YnWc80f02PFs7HW3f3Ivd4dmxkSuP+tZuz32+sDZgDgRjIh8SitKIdoDrjPUgaDKbPxsEIOsrQaCWsP2DorRJSxQnpJkdsD9Fo1XQKTEBQDEUa3pgokDiD9tmI471uWEokRg/ARYpcNNvQZii0wyc3VTBpkfsH5zSVZRMfChNRhD9WrhTu4hovW5wZ04KxmBju1GKbXiNTDlkGkGaxBbrTt/ze9UZMk5M3G0us/Vq/8/q92nNR8/GAVeZd3wg1etaxviJHHPX5i5BR1lCVNwOk3OFpB1AdfZ+keRtqh4eHh4eHjOEvX9b8CLAO4BgQGe2tB6+7UYEsbmAMBd7nNmLZMFllOExAOZsG2mJWRokLRGWXLXC2TkDAhNux++qXOh6m6kJQj/44pVd1WyWXLXChiPUxgPUD0xtu1FwlVhk0aUwU1VpbUUUKhy8ZBMGC9qmUiglaPea/dNEqCmjzLzVK20dSBQkMGVqQXY4OgPM2ouCKdblfSpLcJRyxSXD0J6kDL+9TlPYiz5/DWjIsAGcwGbpE3fuqIEJQttUs/jetsNBzYxb1UXgs4w/tjYrdEhE1ve7qkG76F9MysVFHIuuvxYF5faN2XgWN0QucUVEuiaq9cgFmEm3pISmNDMbKFOAyNkMgiZDZHK+xz2OCo7GmaWJgzbQGnZjz9vcMgM8ZkgqjiYN6wyPXaT77Yh/XGntUrIEdGZJcKOVYHPRpmPUGXYIZJIYhFPu3iSAOGQ2TOzxC/bAiKy6KseqOApZBbq2cKZtBH0xhvp15ZFtvMdlK2oIXZHHFqhQkD2m/cZ+lqUAPGj/Lbttx/8ErN1Usi6P9jwOv/1CTG6p2RAb0ebWFMIiCWoENqUhGGz4iogkZK4PZCpw+IHPAAA2TvZgbk331abJHqQpRxjqawqhrFcyTURgZmyTTtCVug8MKJpi5hOTZUSFFJ3EecmnJmkEysqmA2WRAucKgaGXJ6aKEIaGlpKjXOkgmejOVLU7rP2H3ft/ZO/f/tddC9Xn+gaBss9QWB/ZesEqQK4PCWEo0Z40VYEigBQD7YLt29uwTwrVpFdBlhRYUVpDf2FdUaddy0qHldzfSVVZwZlVjbCp5kKyKe8yc0BW0om3GWTFeXKs+4AOGxj54tVgSoB1nMDNJxLJpyI85LyVKJjwhkcv33NGppEvmVCbosSa7YNYXNMZljgnwJSxopouBkwTxo1+Sli6sNsLgtCa7arNBE23WL+gzFA5LiQMJOSj2i3/6fPPwsiNV4GyHMGSWQcstbMAVFOkZvEQHYbF15ri1zHrdhJ7FgKTclE0dWWU9lwX3JllVOI7gMGn9N87D2NIy05wFkcZgFPtXAAAIABJREFU0pKh1zh1ORKFTSDNYjtrgFSEJAvJCGHtSmGdEPeZDYc5X+Qc1lTRUGNkKG5zjdb8FC+7wOSHLjiauNNPKM2ZQmuztsdFU7wr7SFPYAW1CFwojiITR202Tft/7lqs2UXVIQAIxgLruJT0SiQDbgMoco4oKYrYakwkMEWzAePolTKoUu6a1axAPEGmHB0TxhEKidf8SC/WnBGGS1P2N2snBrqrozxP/O1/nYGBSFO5nzvq37u+ixcZbjzZPTE3ub0KhMpmJZJF52uA4RRUVDb3L4s5eDWjayVSQ/ey3gSHLNiMxVVdXWnLVA3rx3VO3+FqA/v1jmO8ozsovLwfa08omnsRUruhNI5G2Qa8oBCYCjNRmCKV3Nppk1ZgNwFUkSDzsrF6gKQtUDdCNe0E4Fl4YCFF66keBOnMCa+uzIKcEJYTG76W9DhfEtFikKaNFCm0p1wuAJVyvf7Q3i9UPf3r4eHh4eExQ9gnNVX0JkBJoFTtoDWld//hFMwuUZ+S1xxDcOuJmiWMyJIn5DOGWCeQbJMfkvUgzlOV6z44PeNLVu3j2dpEz1qF5vDu9z6Z8w0J0rU0oXOYvn7xY9gRa+eEI2Zvwi/bI7ZpsiOsViVyNSUVI/Aky3PLdNIC80w6p+xum9GFpReuRGfQhFZI02EA4t/044kLc1ouJxu8DskgzM6aNTlUGiKYqzWPeKJg+zTYGtgqLwdcvQJPffwsLL1Ia3cqJLdLNEkt8tl1QpM9qzNAePRy3ceLvnANKuuFSybPnQMWTxlkQJaVkAVmcxxLkzdamIQPomMfE0mN5TIcGc/djLIfSMDH9UBVnuH43bUfw+KVegxZ4kJl0gohnqc1dxEptDfUUN5ikm0EOi+u/pEuXp7Rv51BQmGn6e8aQRYd9bw7LfWYO/8Z6XAv0MpclAFmEgcEExzJUApmqjkVRp2ziWZwzDxqMnQGlfUypZhDGA0rbodgOfo3YMpmE2KMcPP//pLVXJ+tpZ7636cDAJ5p9GFRdac9fuas+/Dz5oEAgO9sejkm23Pcj47qfr5CWb/MnS3lXT6/RcJdov+IQJmrake4EBDoMS0U9TXbrQgs0zJThofXzMf33ns9AF3UImkZj/NOiJfN2YrRhm7D1GkcPDRFBOqhDdGhQFfOyT4jZZjqVF0bc47ueefHqBrbKk+dVohgcwHYZirkBC772gFXr4AcTJ7TQen5wIYqlajLezlpuMQ54SRHZ5YZ646jf8OS0WbzdK8g56C1F2OfFKqFp4oQhSLSYgEw3r+dQR1PmC2uzEVWgLirKkMBgXJhETqRt1vgWMps/JaIXQhDHkuuXIEnP9Ft23ni/F3bepqzuK0ysiuoWmZ4cy/97IFJrG0MYOuUplpLYYID5+pMLn9YrxefzMs0GUzBbKozgpjUlFR5C0Onz1E7f7jkY7oQ+nPg4ItXIq2RXnyhacks+ftjF06njW2R4lBBmpCgsMFQewTYYbLA1NYIa7NtziMbO8sTLcBnP6AXuPEDQpuGTxZhC4gDwOJrV0AOZ7XNCAd883IAQDBI6EyVbTUe0XaZkZIaabt3FhGQCCuwiAsdA5ptoHjOqzd0YTKItTDOBFu4NXIF3Au6v1jF3Lvh4pPTPmnpRr410lWBMhNELoYVUnsPZ/ZXHgPtITfnZEV28VGZ8EqkwOY/zAIAqFI/IAjcVBBSzQDZap1WdbhN2mMWRh1sqM/LpbiMD2whjCTi0aLt4yxrz+DAFOrNAiomMbwCs5WAeqI23vWLM/GKQW2cftP9H8HOlh7Enx93JV7RsxYAsLRSxH9uOgxlUw5w1fZXY21du8Vv2N6vq0wVdPsX/dsVWPuef7bP/NjbXJjM7sCLKVQsbL+K8cC+x7LCgN6kKzNTFq6iUmZt3bIZAG2386Q1FSDL8FWOsaVRsxuLvoEGCiaN4HhUQmfM1Yhk7VxsZy7tJOtwBA1nI6daCmZSh0rJoTKFYGsBPHG0f14Qq0hnh5oJ2JKLoULPoPbfaDSKkBOhLfDenucWSyqRrVKVtAMteM27zIjp0KGXQJyqp389PDw8PDxmCPukpppHvq5oaWOQy9GKLgeezCOUJZpysYHtCbNetzz7Lpelae1HplNu7FkbxQOuWoHAaC+Z92eGuAb84dLdOwaxrICxZFAmz+2W0V5s4zXXyKp2DgEATOlyZlkGIdXm1uOO150W0pyjy3hllzjwihU2oHtPaM9NDQVoyneFZLXA6W0PUNymO7l9UNsl769KBK3Qeh4uvnZFV5IFGzdqqOtsnKJJQnOu/vuJ887CQZetcEW0i7D1MINiAmX4WLWtiNK4ixWNe135MhUAUM7LWUWErB6Y6OhxzOKcoZxmIMtkY0OzzFM2gT8jV5tX6vhN6/RWkbZoQTguEJmSeZ0+Aleu2APB5fCVRa3IZHSzLkmXSyQgCMwkTH/Nj85Bb6SpmFaOXk8TAdUWVouCyFGfJQVRkHj6fY4utLRf0WkWQhAKhQTDi7XTUSxzGhsxHDZ3M3oN3bm+0Q9hXpKxThmlIEHbZLaIpcBQSWs95/32/0dvoOeRJI45lUk8ul0XXXjt8OM2x24l7OCRLXPQHDNOQNUXnkBANQOwhIP3Z7GdEsJoktLQ1yVTNzVJBOKmeRk6AlJ3I4JJAVlWzmFwbmqdiub01aGIITW0eij+L3vvHWbXVd4L/9ba/fTpozozKpYrxgU7AS65DoH4I/kuJKQQbkLCJYRcQuLYNFeKjausUQKE3gNcQghJboAQSgiQQAIugJskq8yoT585dde1vj/etdc+ZzQjyUbmw+i8z6NHM3PO2WfXtdb7vr+SaNH8KDB1NmcuGuAR2SQCStRBAdvSVo3Wz+0RkMocPLWOAwDbB+xa1oLY8/Zs/BCVCJP/c3XlsScU6tpX+usaaVwNDMBLEKt70C4FEMpOL26a5KEKQijbbohAPXiSCxqv2NM/Uz0rJ9VUdcZoZUhSYQOtkQi2QtqmNy6g3DjScmmZnOzTay+cTFou9cPUlJrV6gDL5hirzlbtV55sQgXaSjyCESUGpF6TSK57LAYX2L+fBiMYEvaskS0eBINUA1zSF0Goz5jHbDU4t5HQ1d2yacc4mMwGbxazbKFQAdxCAEOZInMm0RymLzvnc7fCUyW6KDHABywYquRrHHMylaCI45G7rsW2t1G52Q0Z/AFFF2LQIhTgpNxy+PnpwiJb0Gy5ZxxJQWphb2lLXZqLIq4h/94MecNKvZhiuvxr1RnCMs8I6jEyJGNA90XE0gmYXFoAgC9lF1g4Sg5Qtwug35d4kmgGdiqcwTtEI9KWg3CIwpSWvYUlwXRNECQuobYhrWwSBQBuChQKNJn5sYlvPv/d+rVN/+cO2l5ggJkSXDm0JO2+sfzEgc5Sxu9hzdZ0LJ4XiCIT04oilfdCWKqnWvFaqEcOBl2acIuWDz+h9+VMH0JyHK4RSlZIhrLr6++aDkv673N+XpeQ3/ed/64XScwUEKGRYRui02z+t0Wq8JMoSplZjPRkAMHADIlQCRPYTowwdVJgEixJjSUkSSGq82haCTwlwBALjrwVwrHp4jd9G3G6n3MOoEQ0GGhRxtocYFajtWz+zO1YM0Syn1OLRcSK2hO3OKw6y7yK22Ly968/8Y+nGSldTXoJDDeGW1Hqa2aiF+2l3gbCKJtWOBcoFmnVMSOKEMo0A00DoWFlMoURp3Ky/8Sv3U9bnJWTauJIko6zobMBq2aCR5nrSHs2ySMg7MkmEGnJDqut9AHgEdFONMfPknrVqvlbACZv6AQJ7Lr12iyjWhbbbt2pe5Hb3r4TUUlg/7VZ9tv+c6rZKQBAMC0PVnQCjF5A6kr3f3sbeMQQqR6ZtKUGFSDkGWd12cQvTeiBO6WmQPVHMGvDVPZrsUVGz5euJa7e4XoFXA3KGyqLmjv7wPH1GBmcx+ESZRdxJYGhwDBmg2PzveOw1GQWlqVWgGFNI5MKlHQt0wWQZMgmgwRgJrQtXMwEeK/STPYN2PNKzUaoiU4lKHFBakqNMIlD2gFeU4ceu5TtW4oNwmKm7x0gW1AZLQBOBijhYVsmKemfezyzUkuBUOlKH8gWeIbO1jNlJ3AgKSXgOTVTxxymm1JZiA8ZKBWfKO4csPb/DmUsV3/zGuw9PgBD3Z9GUeDx37wFgFL/GsxQexf841uxsZ+4l85QjD3HqC+bgsyKeeovcgZtfr1Y8zA2MI/7pkle0jVjeCZtM+dEeGRmGP0Fui+eUTmCPuUzNxfl4SkPvUga2H9wMDOQB8AWaPssAVBKdOZ0YFkmNvIxpTGdTlRtYJi0j8x7gaSWlWJi39SLCcYlkpDrfmtjKa+t2hIn0z82QgbDi7RFWqXURK9HE8pS4GJithdDFTp3Ld/Si96kJ9SUt6iUAK4g9SFAA9D0sXzoHq1WNDRUx2yNSiKP/+YtGvC2/9rXU6/+DDupSXV/uMUAOTdAUS1wLCNBPaSbf3PvLK7smcCkT/3u3UuDODRLCybGAJ7SkmKlLuZm9/Hka96IarWK8rWn7oH/NEe3p9qNbnSjG93oxhmKszJTjUsSwiU/UF2iVcwPrZErMtcRYWdC6lJRNXJHlIlwWWZiBIzQo+nn4hyZEwPkzyhVqXDkA9vBmzxTnzEkWA+9tvmecSrbqAX542/JUMHC6izDAdCC8cKUQEq8j0jLNS3nRYmBiSVycpGWBF9iuoQqfUDYbWhDtXoWpnJX0bXsjFJjz3FIA4iK6rBF5rfJl0zIfIh6RPVUy0gQqf7aTDMPW9U+E8FxeK4Cto1SPdayIGJFP+gJgYgjcTN6gzmnhCB8Bncuuxa735Jl6ltvH9fZhDRVVSF9kVFZHCBRfq59Y+lv6f63n2dp0DlIkbtmKyvT8wiw6pmSUWJn5ydxskw1FZAwUvEENytJ85iKuJrSZGYZbVIQWhzECBlY1NbHjzMzA2FKKp0pT0xnTbMD7fq8r70RC026MRpNR6OeK6Um7n8R/ewaES5cdxTHGlRq/a9fvkt/XuYT3X8GgEde/HZc9iVyM/GsCOUilYJLqmSbViViwXWmalkJjlZLukQ42juHXfPUjmBMYnPvLBYCagP829EtOK+PkOobvXkUFRz/M/svw9Dwot6mKDE88uK30/b+6l4YVUN76Y5+4i5IlZVahRB8kbYtiklHlspi1qHTa5ZCfb9YVqIRyoFvQQYG2Kzy4x0IYUyn1YXMg1gaQCwZoYABzMmCPh9RwmHbMaaXCuq49SMO2TI7aHbnvCOrWun2Tvp73YDooU9Oz5S0eE26LwBw7i07sfsUbaMnE5OvfhMAYOxTd6Ccb0GoSlIrsnQ/3k8s/M2BS9EKlA7x8ZzeLwiGAyfROf9ZibNyUmUx/TPCDOCROJKoK2kbpa0PBmQSdDwiHmNqD9YOWtq7ggTa6LupD4FilPULDAHRRpljAdd9NrPJOsA47fH4jSvckOluDAW6hLR24xzma3mEPv3+rV/art9+wb/uhN8vibIBAlekFA+ylkopIzTR2Ko/GBcFvKO0//YSUN0itKxg0heBq5JULh/gvIEp3TOba+S0M0XBDtGMldJKZCDnhrrsFyUGgkHafjOwUa+7EOp4nOMWTAUGoV6uOo1Z643230J2zVQ7WFNeTEnG8yDT+dRUmUcMiZ3JLPIom0SlsrtLJ+qwlNGDeABEBaavm8xarxC21NSYJC/IyFpJLVq1zFg6sYnywlJucCyzxRuTYDyd+CWskMHfTDs58Yrrce7naUIJfAsyNHQfbzl9xDISGEr83XUj5FTJLi1LAsA/PPc9eMm/v1YDZ9pj8pWdrYrxx14AP3o2AOjtAkAjtMFYxkXtzzVQVZqfm3vm8Oj0sAazbPQWUC/SquDyykF88egFeuH1zIGj+MWexwAAj7XW4lhI4vtRYqA338Q5PTThlsxAnwPpeEjyUtNLZNNA/8ZFOj+xgZpqHfCqSaYRqlQuI65pSzAlkjlH9y/HPn2HohYBRiHqsDAzjtt6AdUu3ylMiXjR1hPkyEfuxkKNHvRwwYVV8WGkCzQzm7RRCjH6CVrITLziepKvLKX3S3buR997L8yQQ6q+rzNr6AXZ6HvuxcSbT24p+eNEO02pXG4ib4fwlY/gzEIRieorTwUVMN+ATGk7jgBTtLmfAbGk04pu+bcb3ehGN7rRjTMUZ2Wmmuq5tgZFVl5R/8VKRNuoG5mdWcKQKFg7D9XKNC0lxtnPK8XE61ZfPaZ2SUy06QyXFXJQWZOd+9ad2PX2zlLOpnECJCR9EVCkD16w7rgurcbSgG0kqAcnimY/ck/ntrQdGtRKMi1NBhyG36mdq0ufsUTuGMcjd7eBpD54DwDALjdwsNqDKIXRCw7Xpoxl/7F+iDb91SQ2UMlR+TBnhVhqUfmx2XRIcLyqMgU/+24pob1gl2sAC1tqpSuAjLe16XeT65+dOQaVAJE5gJv5QTKRCX0wACzKqgbCA6SViiAwuMcNyFRn10CG8DWgl+XWggEeMQ2EEo7sAFPxgEOUU+oG1+efRRwinyrRcIQbI1gefdm2v7tVf1mh4MM2E9SaKwukB7GJRotei/zscb/v/7mj433/8Nz3rPj55XHdeV/FY41XAAC+fXAzNvRQRuiYMWLBEShqTM4MUatT2bmad3HuwBQmVQtiT30Q12/4Eu2TNPCDwnrMtKgs6hgxPnTwuQAIrXxJ/xE6Dt/CwFAdlkIBfufYKPqLyoS70EQiOAKFOl2cK+jzEfoWuKK1SEtRxmRWjkyD2WRFNvZpOi+mlSBK6SqBAVaMIFS7QwYGrFkFLCpKTYfjIeso47arFm17+074jgWoazhUbmCpRb2HRsKxv81fdtety553pXuNoQhYcvTY4A/FGfPgDGaBWz57GwBg72/dokv9uaKlvY9Heqo4tFjJKEGLDqxF1eYaikjEQWX2RovDXqCdO5V2+c9KnJWTquEz8sa0ZActBAyw5+nmMBtMl0KFTU4h9DMpKKW0T2lkaN9tt+5E7GX0hgPXrCwLl0asDMB5wPWgLpkqVaYlyBVku8QgjdCmKSBU2XXXkWGMDFGz8fK+gwCALx4gf9JnfuFmtO4jNJ6wZId6k+RSUzpoZ9KeMoe9xMBUPZK8PumlB9/XWYYe+ejd8PqoFjtUqGPIq2EuIFTio0eGIZRJubRktn1bIAkMTC3RSLWxdwFlT/XluECUGIjqiuzJMmUqbyY7H2Gx87zwdmoP1ISVUk1sAWtBlQeNrI9pBMT/FNpwPZtEDZ9DOplQvnCFVoqxaqxD5SjOZVwqyZEhRwWViFNUr2hHWwvo/irtMLS6lRRMK+uwhOFA26B7+T/fSM4vAB78lduxPF74b38OAJiqFWGZJnIu3S/VyNA8yScbz/vaG+EYdC9xLjWK90WDD+Hzxy7R4vh+bGFskO7HnBliIcjBNLKL896pXwQABImJhSCHq4cfpW0ygTFvBgDw7bmtmKjTRDzUW8WCn8MCqJxacEIkanKstlzEsQFfqRKxkIOr8qmMONiSKj+6tMiSQdrwZvpayIDOjaFkFsOqo8vJrBzBcmPkPSq/twIb8rjqjcZMq1n56xKMvH87cdkBoBLBVIuwZAuHYUjEQdaSGf1rkmSUp7gmGttxyEHYm2hEu1k3sO8N9Cym3OEzEWrYw5bP3gZT8YQZk9jSR9fzaL2E6tEi1n9NtYYuNhCso3uMcUmdl2bWc26O0Aka+dA9evKffNWbztj+/rTFWTmppmYIkmcavnaVepkpwCRx2kQG4qynGuU7+3rCkroR//gNT2wlxtXDLVlGV+EB9dw0YIqRtRJAk5pwJa24QSAbqTLCUm9D9zjum9sIx4h1z2ZhqgTFdMDut3VOiNIR+kZnPoepHobcUQazKREpQ+TYk1o/dHlMvvLNGPmooi30ATN+AQu+IuJbCfycyrhMqa3lpCA5x5akgXBvMKi1YqNpDxN/8gZsfVhl8jGDVU/3o43GIhVQq811SvdtUgqLGvAEyybcR+6+FlvuGtfnkAeZtCQTaJscCahk1dJ+upH1Sm3qcxoN9XVJtv2wJ+Mus4R6tlrSjcus3xQrPmiaTecjvd9mOdB9KhEYGP3rO2G5dDCFHFtxMk3jaJUy/of/x6247Es3aUPtUrGle9hPNiwj0TKCvfkmdk8TpeaHu38F5209ogFxL934ID47cRkAoMdt4sh8GcOVrEGYZrRHG2UM5mrY1SD5zEfmhrGlQi4v63OL+n17jg7BcUNU8lTZiBIDTQWGaSx4KD1spzRtLJ0fI1I0ImYJwhsAmPydGzHy8btgqDcSt1VNqjHxVNNKCq8ZWq/7/Bt2orGZI2zQ903+QZaBbr19HP5adX+7CRBxrW/r5ULk1YJmdqpE8oBLWXklxSSk40Aa59+wE61hVRkLGPa1gRW33jGu7609t2Tjzcks2k43tv3drQhaFmSdnkm7z8dgiR62pZaLIzUq78wergAcOHKV+iBPtAMQcyNMvLyT0pTiSmRe6Mn2Zzm6PdVudKMb3ehGN85QnJWZKg8YDEZlOS1JZ1OWk/ZR27NHgPqIABGrN98zrlGlvA3SNvYXO1Y0zl410ozIETAUmVzYJGaQCgmEAzGMGr3mTXMEvRLJeiVM7ht6WeQHFnlCAmAtctsQBcpszHyER+/sLLeMvodWj5Ovzf6+efs4krRcyhjCUuYba4QM576FFI6W93xGPngPDCXsP9/KYb6aQ14pyQRH8yiNkFh6veZqcQmjzpEUhBb35lxoRZh01Z1mp85Cdh1YAjTWpRQGSb6XaqUvjKynmngCLGbwjqfC/p2nXpeJOR1r6krEI6bpLxBKOSnKPpOihk2fw52V+vy3nEzqMLGl3vewL6ESYbu4eepAwgAUYsgUWcqgBQE8L0RdZQz5nhYC34Kt1HhOlqUClKGmkdJmAOCSL96EZ/VOnvSzq8U7Hv5VAEDZ3oA3jnwZAPCh48/TFBG7HGCqXkCv6pF/Zep8PKP/KADgm/u24qote8BVn0RIjopF6OMrKgfQTBxEqml+1C3jMz//AQDkUJPiBBw31GVJAPAjU2fdjXkPtYsD3SrhSyZiSw1tCYNQ99im8R3ghgXZ3waCSNWynASibgFt7k0XXE/3+6N3rV6Bevym67JMjHFAMLgV1Qop17RcI/MN8Dmr0/1qiV5bzhoQNrQ0Ybs/rX7NyqhDaZ+9kPOxuEjtkuXiFyeLkQ9sh6l8jIuFEEHD1sjpVmihYFOW7xgx9j1A4h3cJFqYtLPz6PTQMcfLBEZGPrAdk6870ZXrZzmY1CKfP/tRrVZRLpex6S23g7su9crUeBaWBPHYFDUBLUMrtPCG0TFZbt6eTaqSAfveSA/FyAdVz0ANjKeyV0ofRpYwTWtJtYNT3qTwBEz18HlTJKuoVXdKAoZyyJBo0/80JNyKrwFC9174t/iTT76GtmetXsbdctd4m3oQO8GMfNutNMgk9urb2PSZ22Hu9fTx8Bi6lJXkBHp+pNwr+hhaIxF6hqp0LIKjNkeDQm6vDXdeZk5ABsMP35XtS6ocI2zqPetSa8T0z0YIOPNtJdlyZuQtLGiqkLb7S5WS8m2yk1DXIgWsiax/brQY7FrGcw16JCLVI2ciKydDAKKQdFJ90vvKTiBirsu65WJLl+wtI8G8UsuJIwNeLuiYLH/ScctDLwEA1GMH/7TnIgDAC7fswlEFLts1PYRN/XPoVYpZU80S1udpcN6cm8GuxpCmU/3hmm8jUSfEQoKvVi9EWWlD1hIXlrpoi1FOT777G/3YNTeIgpPp+h6a7AcAMFuAmQLlEr1Xti10/dBCoHR6ZUS2db0DdM/Nz5Q0oGZ4ZA5T02UUlLymZSQaaBWHBiZ+7wbdA213wBn71B3IPaR4wOcHMI7bwEbaxkXrj2Dep2tYC2zUGi72/tYtpzzXF75hJ5prUwFxBkcBfVrrElJJawMn5QbofAvBER5Rk6qy+EsV1gCAebHeXr6c3WdCcN0eMJhEo+loW7u8E2rA4cKBHm2LlyqBiYJ6uEwJS+kiM0b91+gw7Yu0VpdZXCnSMXppaQmlUum0P/fTFN3ybze60Y1udKMbZyjOyvJvqookbJmp2ZRjMENqxxa4AnyRTs/yku6+N16X0WFkpmqEHgJGnG7u3063SY22seyzIjI02CYqKr9PBZoSFkMChaxlUiOX5eYmhGDYUKFMYSIcgFDKRfHMsnpSW7Ck09d16+3jugyd5AWKGj27On4/lwtQL7soLGbAnxTIZdVMNJWXdDvIAqDSnKEyjKBPgCdcZ6r+oNBi3gDAWNuKmQOy7aSlmSQPOYJKJ9I5Vaox24wUUmH8FN0NlpV7E5dQwWnJl9ScMsBUlEenmpYypJcGMmBSPoZhibbMgGm0Z6FIJbMU9RwmhqaFxAlHQaFNA8tA7dgyqPNTEM/7GpXprhrag7df9I8AgC/tvxD7wkHsrpMhuMkERgbJLHyi0Ytf7N8NADi3OIVzvOP42vz5AIAjS2X8t4G9AID19hw2OdP47NTlAICPTT1Hl0VtI8GgU8NkixDFQ05VK/VwJrDFnaLt+RUM5BuoR20Cy6oMioYJwSSatlKVsmPEKsPya44GFo1+8k4lXEAXOOxpotWiz9RaLjaumcfaPLUqplrFjjL73Y9ejYnfo7L3a+//XRz31fWY2ZI9s0wiHozQU6BMdd98P4ouXcNTlezbQ3LAqmUi/X5arpZAcbCuS6yGIbRKlR9aQB991zmfuxVB3UF7vTwFNHI7QX3RQ7GHsvpmzUGuSJ9jXEBIhmadxpQ4NmCaKc0ha2HQMw0wJaYSjvkwVYUvDFLTX3VKotXHip/VOCsn1diT4J6E0WJIFC8VEQcaDEi5gQ2jk+6gYsvd4yTZp3qPcTmBU1b9hIYNGfAO9ZXTDv1gqkk/5WWyrO9rNmlSTdQzlmyK0NOn0Hl7e3Q/0V90YPf6CBQa+LNHL9dlpxR6nzqUpKLJjIrzAAAgAElEQVTqANlFdQRrk9szJfwB2slU2en536D/9x0e0CUxU5WSUgnDYDABKysh+2ln1Z7z/uvaOK8fvgdhwPD4Daqs/v7tWTmVQ1vc8ZZCWvKs1KrR26pkm5as2w3So2JGe0p81kHx40GbFGHMIJiEM684dwEQpUpY6ty0U3hSNSQwCZbaxVkcgkuw1FiHSwwOk7NIK7SQsyNtvN2K8ogV968v30QzypCiRqmtGfcUhWPQ6u1oUNZ/e9GmhwEAP7rvFfpvz+qlSTUQJiaUcPpv9H4f7zt2Ffoduh+vXDuJQ34PAMAXFiJpYGOejvtoq6wVtziTWOcs6pIvADhqVXMk6NGT9KOzQ6jWPT3IBwuupsO4Qw3EMdcOMJxJtBo0MXCr7QJJoL/QwKYioYsP2T2Yd+iCtkILfmzi/sPUNxwbmMNL/v21dOyH1sP1fg7N5MUAgK/sfZZ2n/n5n9uFB4+tAwCYe0oQJlBaT5NUu1XdcuP0k0U7l3zkY3frEjWLGUwu0FuiSbsZWWj4Sg7QtyAaSuYzNICYgbWhitMun+QShi0QKnR0X18dSzVV5p53wX2mx5EgJ+Cr8ZAnbTKZQqHmE9UWYRKtWTqPVjnA3t++GSMfJt46VqAE/qzHWTmp8oiBGwRUsuboFJBXJpAoaLh0BLgClKQTKUA9wtgDEtU/K/Q30Kiqxppg1C9rm1NTUQRIhsk/Wr1h3w7+GXnfds1Fs+o8Aw9xILFZhx1cKtVmhAzCyMQqzhue0sCQffP9mtQuLQtXfPkGSHli5lM/J9QZuDQAbmSTBgsZ4kLnImNiigZUZ5+r//aDX33HCdsd/aTi47knqmSk4hOiGGsyvjVrQhpEmAcArMkyv8k/eqOmGPGQQVo8WxWHTGeqxDPOssyxv9gBS6a8T6kdXyjTzTJcMJk5FcWU0RpK0S8qZDKCiSthL3I90Jg1pif0uJJoGz5EHLJpajeUUjGTByx7PjwzQqS0Mk0jAVdZSNV3kFMZ1dxCAQdefvrgk9XidQ+8HEdbNGF+/jnvPeH1QwvkJrKxsIB7H/1lAEBT2EgkR8Gga+zwGHWF+lqMPPxy7yMAABsJqpGL0RxxGf/j2BhKKktzemJcXjiAYxFNsqEw8YwSiTpYLMFS7GmN3FZiYVGtXBqJrbPWnB0hyXEMFmnSnnNzqDdonwpegDjh8NT5qvmOvpeE4HqA557EviMDWOqjz+WsSC8Cy56P6aWCdudZHlf+y/X4t2nK1p+x/oheFBxplNGcUyutwQj5nkwT9792b0JO9Wi9QnDCNlNMBQ+4lsk8QYo04BqIZzYY6k1HL7yChypaItHeUkfQzndtGZnwScggIzU2lEIIwRAqi77ZuqP3MRYExEzpZrkjBpi6N6OCRDCQ8eqlJZGkIEOm6GAAoqpNY408cWoZ+djdHXSkn9Xo9lS70Y1udKMb3ThDcZZmqpSFtXufsphRyTDt00SZiwyrM12CTdxM7QgAGjU368MGHLAkJv63EuV+5w4oARgwN5P7O2V4CZJUUclEJprfZB3LoOd97Y3wHEqrmq2szMIihn3zfVhbIpRjf6Ghxc+bto3vXX0nRt5HyMDRd+2ALGiFBBgKXGnVGaI89LHGhQSO6r1c8I9vhd+yIY6rTOHQyZvIE7+7etkrNd625+0T0MbnfI7Qrmwm19FrTpWqNo3vACR0qZVHGdo3sUn8Pu3LWvXMfDxmyJCMUOYJbdvXqGNbdtBo4lwb0lhQOV67yqRm5ABYy9AuKeAAuNSiAmFsanSq4Ui0Ygu2cpN2jASxKm+uKVZxRIk4WHaMX/32n2p6yUpZ5unEuy/9NK7+5jUrvrb1b2+D/X36vscKQ1gIqST4zPJhbHGnMB8TdebTBy/HS9b/CABweeEApiLKfP+jSlnct6a2ACDVoV8bofctxR6ORT2YjWgbRdPHtJLDWucsYiYsopVkpe7UQ/XC4lHsqlMTvuK2YHKBw/MV/b61fdT/ZEzCj01dxvdbNiF9gY7yo1ywMfEnb8AVX6b7MUiMDkee1eJF3/ozjJV81GPK0JdCDy1Vmu/3GiT6AAAS8OyIMmUAphsjUOVZkTBc+H/fAkdd39mjZcCmfdv/utWdW1jE9DOStkN0/I/sx4v/6RYIdU/HkQHWE0Is0HdLkfkuj773XkhDgqtKGBdAq0HXxWxwbHvbTnCFmXj0jux5PP/GnSnbCJJLWP2tjl1JqWB+zInapMZRvmTpShUzMiEbe4mET5Y/8z8LcVZOqokryRyXZZOqVD+nkmbCy6y3JG8bMBPlJKN6EnvfdBK5LQldhpKtk5/q825SdBUXwJps5hROxsOEpAXBlrupPBP1DMKeVbJ5AZWlAQADAXpyLfzW2vsAAPfXRwHVJpsN8rjgH98Ko6Ie9ohrwIE7ZWg3mMQBos0t4u4BQMgRTdDDF1oSwhGYfCKc3FVCy7Cu4Mqz5zfIcaWdF7z19nENPkoKAkbNQPEAbaS5Rur37bmZ3nP+DXReJe/UCk4BajxUvVN1fYVJVCUAMJe4VlNKI86ntBypWwEA3Tvp4CdNIEndfjwBc8FE3EMTYqPmIqdKgZFFZt2OSa999aqdq56nV37vlR0TzxOJX/g6LfLICcZd8T3P2nAQ8Xq6zxYDD6Gqcx8NKvjewqguOY6UFjAfK5pI4mIqoIk4EAZ2HR1CWZW3hWBYb1Pvtd+0cDDow1RAE+lYbg6X5Igv+/36GFqJhQeOrgcAbBmY1W0LgABDAGkYJ5KhT+n9tiJT95wXlvLghoBI9aartp7opOAn9PWmDlEZ2ihGGhfw9avGsTzGPkUtk+EBD/XQQb+SYPzCVe/q6LdK5aZk9fioNR0ES0ou0Um0TKEUHGFo6pL45B++SU8wo39176q0k5Nph7fH4pFSJn8pSbs8xSmxmGkaGuMM4FmSwGNovAIPU5oafW7z9uycxBsTrVwmPYFCztf2bnHMkaSl58BAYbiGpirNi55QK0fBFroF5LsCMKTGeJwJRaifluiWf7vRjW50oxvdOENxVmaqqSF5e0iLnErSbIaE0FWpT0KDV3is1JZWyKyWx6kE9Tt2qQ0oY86b4Kk8rCdhV2ntYy9StpVmRNbB7PJJnokb5As+DC7woYnnACBAxv59hEKEIWF4CSyFlAwirleqwpIIaBFPwt0MHeAqre8b8Q6BhCcTm3bugNFksFXZ+rF3rF4G2reCTy2AFYFfW++g1fWWu8ZJ9L7tNY1kZtDlXrquUp/TMNdZoWAy89wVFiD6qDTJZ2y0BqUuN5utzKwaATSoy1wwwTY20JsnhLjBJS7qOwYAyJsBhOR4vDqw6rGn8dErPrrqa9f/8KWw1A3DITUdBiBx/RcMEbUlkgYeXNyg/z45Rzq9w5UqyrajM8Ra6MBV2XMrsfCc3n345iyVd+f8PO6b3AgAePG2H2nB+1hQ2S81GL+0dBA1xVM6Fpaxqzak9+lAsw/HVYa7v9aPqVoBm/oJ4HRJ5RDWWEQF+87SZjy3fx8AYHd9CMeaJczUVLUkaLv3JUO44GplINhC+/sKJvXPiW9i9K/vRK6fssUk4ZhW21seF//TLZCSejeLDQ9xbGC6mr334SNr6YfjDpDPMuvQtzrE4q/8FzJCmF0oQiQcTWWyftW/vh6FMcr4HzoDoh7utNlmdk9VOJmKnSBjE0z86etx6WvG4ffSvRqVpL5vmaBMVd/vbc8CkyzLVJkSzkiHwITBKtK5N4oR6kteZhIgMkEWKx8iMui62V6EcCanW2yjn7gLuZKPpHkioOvpFmflpCpsATgCRsPIeI2CQVoSMk+DCTMlEpGa63IIJZItAw6etAm3/xiRTgA8ZEh62iQRk4xGYzAyxAYUEtdGx4SQIoN5DJibqTzlWjEaoY25eRoERNOEodxVsMaH54Vo7adBzQwyo21ngaG6TYm27zMhzM7bY7lh9Y8T1hKH2SJx+zMVY3+5AybLZOYSL1Nl4kmGjgbLrh8T9HuUz+QY7cV0kUH/0nMcFwQp5oAGHxZkZbSwnE3MwpLaeDzujeAaErbqpZXsQPcrjzTL4Exq84Etn70NV2wkhyHOBD555YdPecxXf/Ma5MxBjZ5tj7c+9GJc2mPiaEB9yL21fgx6JGrPIweVApVq5xo5zNQK6M3T764ZY7RApduK1URT2BqhHCUGSoqH+fkfXIoN62kynK3lsW39FM4tHAcArLUW8IW5iwFQCbfHaWIpoOPcWpzBbJBZvW3pndWT1MHFCp45RMjgWBjYXafJuB47qPoOHGV8LgRDq6a0J2MOno8QLypHlUKszRkcN9H0GkhAhgZaNSpN9g9UMTPZs+J5XZwrgClT9SCwkLTMjvt/72/fvOLnlsd8lSZOy47hWDFCRXM7NFs5LXWl9th87zjMZiYd2t5jbaeMjf3FDnLPauOIOnN0P27aMY6eGHDn0meB6RZG4sgOmjxvMc1ygCG1xCjsttkVpGglVHsgSemEaU9bQMsZRr4JW8kqBvMeWD4mgAMAO5cpZT3do1v+7UY3utGNbnTjDMVZmalaVQM8NJSZNP2NSSDxEr3MkL6RlQ5ZhvqUhgRvMZiNH289smnHOFhqH2dKXe5NATVxPjUrz4TanQUJIwQUiBJmg2m1oqgk8ZwNEwCoBLhrcRCGUjkRtoCwFEdTMNRn87DTEnKNdaD8Lv0jxVPlEs21TPNWVyvBPtlgCYF8Ui7q7rf++BkrizOkZGrvl/qkIsw4psKRWpWKQGikE5z+rqsQApBWJiRhNLm+TogZzCbTPq8sadNrdoU2ko7LEjk3K2kt+J72HAVILCOnypaj5Xksqiz2vNJx/P73/hcAYN9SP/zYxGCePscZoYYBIIgdcCbx7N79AIDpKOMfB8LEw9W1WgTE4AIHa1SutYwEdYVSbSx5kAnTPM+rhvfozBQANtpzuN+gku/h+QouXkuZZFiZx4EF4io/Z8MBODxGTrmx7w8G0Ws31P4KODzBWo/QupPNXr3t80rHUTACFCw6R4thDi/u+wEAYPveF6I/l1nVDeQbmlf6yNH1GphjFUIwLpFkmhUatORHRoYEBgAmteLZwlJem4pv+extiENTly2t4xbitUrPFsgyrycYqZJWb6EJz4pWBEQtj8v/+UZdeRCSofZ9ag/EQwnMpqnLsKuFyAlCxKsbWdrytFC2m3bugFXNOLN+H7RONZgES5XFbAJgaU3lpqn5/TAkDaaphjoHHAXMi2MDkSrbs1wM6Rt6/A19C7EhIVqnOLinQZyVk2riEfqXxapXAEUdaStx8BbXE1ZcTPT7AGDPGUC97n99NkltvnccSIXsQ4ZgIAZXZZQ4J7V/6A/eoxCtN2YoUU3/YMDji/TwbatQXytFHkZ1G1Ipo2DOgREDpX3qXLQ58Zx38054aVlU0mSfO/bj1bk33zOuFyrOHMOjd9LDnYrTP3b7mSv/EpKRfk4KElFFQKrByZ02ocZ7iCgTwmcCMFvtPe2s/wQGxG6mvsRiIMlnE6ywMycdYdJ9AgBGX4C4qCQdWwYW5oowHdqxTUOzqFhUPj3SoPLvN35xxwnH8mcP/g7WODQJrRlcwvfnR+AqqsmBxV40WjQhFnMBntl3GDdf+AX92bc+RMo/Xz50EUYr89hYIiWj6aCgJ8vZeh7nDZAE4GMYQrPhaOPzrxw9F7GSEdzaM4N/Png+qlWa7NcMLIGrkXB9bhEXlcmJZiYsomRmNIvpqIjdS1S63VyahcdDOKrPYPMY5+TpHv32zBZsK0/hmcXDAIDvLGzC3oA+94kLPo4fBFQW/uzUs3CkVcbsAi0amCOQK1Gf2jISRImhJ1IhmKa59fY00FJ9TN+3IAXTeALHjjUyOu95WJrz9GQQ9SYaNSwly0qep4gtn70NnhKkd6wYjk1D7FwtjyjKFipjn7pD4zpsJ8bul75Fv1ZrunoyDgMLKCrB+waHOwv88J0nX+CaSwaEIyFKqpXVyr538/ZxiHW+pnihZWh2wYFrX4+ff9kOMEHfPVPgSJK2AUYhqpOIU3k9HRokdBkXhgSrWiT8D8CohKR+BWDy1aszJUY+eA+c/iYS8dQrhz3VcVa61Gy86x3kUhO2mYFzBVRKV2MNrgdXkROQKRct4idVRloe296mKB2m1L0LqYzNUyeXuCAyhR+k9mPqF5kBbOKC7OijnHfTTjS3KICAF+tel8ElFqs5eDlaIfotG0zxyIyHCxj5+3m0NtLg9K0vZDf6+TfuhOFn+/7Q+LU472ba/5MBiU4V7dnupnE1gUg6xhO4d08iUgUYgLJ3gJx8hCu0Hq+1xKAMT+D3Sj2pGgE5zpjquBMnyzghFYUmlRgMWAdHWdikqgTQ+4TbplqjJOPgJDCdRA+S6/oX0ePQjtQi96SZS+oMc9tF/4BnfuFmnN9Pk+AjM8P6Pa3A0tQjAHjJv78Waz3iJx/3i3h8bgCJ6ncZXHToDM8tUskjiTn6euuwFThJSIZnDVBvd3+9H5MLPfq1l43djw0W9VsXk5z+3h81NmDArmFJ3eTzYQ6OSusrVhPzYR77quQqM5yv6on5hX2P4EAwAF8hBA81e7DGpcXEYpSDUCP3gWofJg/3a6uzqGZrNyirEMJxYq2JG4UmklqKOJRt9BoGxiWcHG0j5wZ6cSIEQ7TU6Q/IUhyFYDCcBEmVtslitioF5Iov36CvtZQM07OEXeAGZchC2RsiZmA+XZd22symHeNIignMRXpfPBAR/x0ZWCitkok2GVVpZgtKgFTVkoLQ32WqsUYoXerUWo5FTNP+wCR4lMl0inwCrvj1zJBZ9iw4pGg7ljawozQV1z+dtJnMtu8mehxaLtk48v7txOdu+Th8zVu7LjXd6EY3utGNbnTjLC3/GgEDZ4zQbmo1yrwYct7WJY24J86WHBLIVVorb+wUEaVCAg0OrvQ3eYshKknE/YqeYSXAJKVOkgMCMhOJR6a/azQZLn7dTp1lcQPa5DuXC1BV/o8i4pAtE80UncckYp9WlbkFYObnexC79NozrtmJH/0lZaHtvdU0nmiGOvbpO3Dg5Tdi9BOkVCMFAyp0IkfffS8mrstW5VvvXD1Lu+LLN2gvx/mj5ZOWjrypTPwh7Gk3oM6M5qXFECiEddiXZL6rAWkHN9eorMRAZqTASK0p7dOyBEh4RrNiPQEipbIjXKFbBIxD0zviwMBQb1W7sgBAzqTrXrZ9XP3Na7ClOAOAFI/a47aL/gEAiTcY3MGnf+6DJxz7JV+8CS/4xrU4XqPKQ08urzOKnBnigoHjeFAJKwjBtC6tyYXWIS65AWyeaIeWPYsD+NE8icQfXyxSH0z18XfV12CbogRNhv1aCCJvBMjxEDOC9qNitdBQ5+ZAow85M8R1Y18BALgswqGIerGTQT8ONPtQVb3klww9qFWafrCwXqOQhWS4ctt+HKoRkvlYWIFhZdc6STiChkL/miLTXg65ptQwkBOLZVH2VWu4WiAhWnIAW+jfZcJQrND5iWIDrUVXq3DZCyvnIpvGdyAp9KJ3HVGC6k0Xsk5DbGJIMEfoTA22xMQf0j098oHMMIKvCwDfhDTU/dKm/SsNCe+YgaikzCByWSUGIpPblZwUwnianboS0UBqdcUpw0/LujzLcFnCwATTTAfmc8hQqTIVInA7MydghtSVAgC6ksdUmTztY5sLJmlhA+ALlkbLL4/J17wRm3buAPONFV9/OsVZOalGPTG4F4NFPCsNxUocXauQcBiqzCIZ0OT00J/KeHx5pO4ro+++t80CjdPgn/YhFh1dctn75utw7i07wVZp4QR92UMgDWhgRc6OMKTExoVkOPDYGuQUN7I2XcCkeoDxe09o91eNc94xrtWGHrn7Wmz57G0AANOivhJTg4IMTBg1dXBMapUWHpLN3Lm3pOVxaIBE7AHxlqJ20Bl914k9x/Z4aCdN/Jt27kCiHGyYpFKtqQbAoDfRzkLMi8FnaLAwG8RNTku30pTwjqiB0JYQFpWOAer/btpB+58/zLBUMWGvo4E3bGTlSJEOXABYzcQxq4y+Hro2jhFrMFLBChAkJqaV0tCrvv8H+O+VXQCAry2cj36bPnNk9hkdxtsAcNmXbtI/H5zv0UbTlpFoNSQRMTRjG9sGqX8ZS45qQCuyVmSRXRhIOjFJOBrKVm2p6WFTH1Fl5qwchGC6/zqWm8XegMrPD1Y3aKUlziSqtotLClQ23tVag1qclVMdnuCf5i8BAJyTP44Rm5xi1tvzGLKWsMmmfWxKB99c3Eb7LxmKFt3Dz+w7DM6kNjqfcQqwFGbAMAQ2VhYh+ugc7ZvuB9RrobQh1DPCmIRpAJGy17OsJOtzWhLcFLqkyQyJQKmmiYQDMde4h6TNfW70E3dhaIgmUckHwNwEi0t59X3IFuZMTTSqfdAhLG8JSNXjETEHfK4Xg2atjfYXcvgDQi/6zLqhS8A8ygwdRF5g8o87y9OpcpG6WBprAGRmFZIzyHyE0sNKKSkHNEdoAWi7sQYZ6QNKy7pcggXqeY/VMar7Py63TcQSWUl6WYx8YDvgAeJUKKynQXTLv93oRje60Y1unKE4KzNVWAKwBJweX4tQp5ZPI+8noXkedOq+7nr9jyd84B0zOpCuW+4eh1SZsBFmWrUAIVFTsEyifF/pBWVppq5aVMzoAZcPHMLeGgFB9h8bhLQkavO0YtZZqorLXjWO5ppM4GAlOsvou3bAm+Irln+33D0Oq8X0in3kA9thlRXoKuIw7YTKQwCQML0Sthe5Vn2KShLnvmUnpMKT+BtD9A9W9XfMzWXqNRN/ujraeuwvdmiwkOwBEcpBJH/W4poOI9tQzmze1iW8qCix9/rrtD2YW/Gx648I+DPyoXvAmwbiNgBIitoe/cRdsL0oy5ZKPgwlFpACZgBgaHgeOStEyaaMqxq6SoMX2FMdgMEl8spPdc7P47ucBOnzRoicQqs9Y/0RzPl5vOAbdC2+etVO3P+izPR6y9+8A+sHKVvyzAg5kz7nxxYSyTX9xuACkcosZw9WMDBCqGDOJLZUZnH/EVJbCpoWpl06/6/c+p/419ltON4g0Mg5hWn8qEql4UGnjopCf1VjF88u7sVkSPdgv1XHF3dfSOdqiLLeX+h9HABwZW4vJiJ631KSQ69ZR01Q9n4o6sXrhr4OAHikshZ/cM536TP/cj2CyNTWgs/+ypuRaDNziQG3jgeOU5k7alkwVKnSLQbwlUhECrqJVXa672UZwGvko3ejUmmg3qRMPqzbsBVKmAFkCajO3b6XZVWC/oEqZhQiWeQEZW+qtOrkQzgKPJhIhnrVy8q/7cHQUUplEdeCCTLMMtVgOAJvGaetB5zGpp07IHuyjJFbiQbuipaZKSNZ1E7y+1SbZCizYwznXMDJzEY6AEiWoJKzOk/tmudoq/4leaEBWCecgohRKfpMqOr8/xxn5aRa6W3AyMVYPFTRjjJpTL7m9JG9q8W5b0nF8aX2R3zs9mtxzm3097ggwBhbkfs58vG7YJVsPfmIfIKkoPpgVQN+QWgEqswnWLuGBsbHloZwZJ56UcI3wLwY3u4TxdMv/tOdCNYxTdN5aPxaLex94JrXY/Q9VCZy5jMO2fLY++brMPbOHfpBOhUaOlWOivISqrqJ2iiDEQD1dTRwFXsbmJ1RdImmCaPG9Xls95rd9radeqLMHWMwBzKFGcNniBVC0+gLIByuJ3eWMN3nEbaAMUqTQRKa5HmrtuFXs5Ll8sVIezBTwDQTJKrva1kZ+tRzQu2F6scmKk7Wj9871Y9yQZXl6x6SiGuT6OUqPSlPNWeGWGA5zDYJbXvZl25C3qaJ0zUjrOs3OwzNDy1S39GxYlSbLtb1UK+0EbhYrNM2etYvaaoJYxLfeXCb7ot5ZR+byjQRfm3mXLRiS0/GgTDR7xB3NJIcQxYthFKP1ZSnOuH34c+fSZPjdxc3o2K1MKucbnxpYdAgZSfXiTBo1PDp+Z+jcxK7iFQv5M3nf1kfUxCZGK3M69+XWi4C5Qm6ZXgGrcRCq5XVZVPeZNwydS/QNBMYhkDSVvpMHVQADssQWNtL5yqpcGwtU697f60Phxq9kCtUJvtzDeSUylO14NA1VSVk00zww//3thM/tCzsfIhYfUb4Joz+QCONjTCTyUTCwCKGsb9Qz+tpUvuEK8GaqqwdMQiPZxQhU/FK1fZFy0Q4qBYTttCvyajNmMCQYHaiaWJRywJXUqqimAAR66QqKprSyVpnE3/yBmz7u1uRNP1V3/N0ibNyUv2Pq285Y3Dt0U/cpeXMRNOEWYyw79abVnzvnltODfqZ/P3rsfWO8UxbN2FgypotZhIwJSxFCTCtBJbqpc3U8wibalCJOcxpa8UsU3LAqtNkmkYKxBn58D2kiwgg6E9O6hzRu2UeDd9e9fU0Rt63HehX31PnEMruavABgWPPYXqF3my20RkEAA6ElWwU23YrTbDFK2cwO0HiAS3BYfiZAEPcH8FYVLf0EQ9s2NcDIeOyQ1oyrVBICfB8rAEqScg7FhmrhWklKOV8PaA2QhuRoSYlK0bFpYm0YAU4VKtgqUkTZ9SyMJtSNzhQHKjj4VW0Xz9+xUf0zy/61p9hWk1KeSdEr0sT27FGCX1eE5NK5m/zhmnkPkmLq+lfCyAEQy2g76s2XfzypscAAA/MbtB2gAeP94KVQlTKtNBYW6pq2k4QmvCcCJcPH6JdZhL/dZyEIPpzTcRqxK9YTTzcWq+ddNJJFiArtz67DkeBAWwkmJO04Bu15nEormCDSxPmQ7V1WIjy+rOvvf936fg3NvHw0lq88nuvBACUvEFA0YOqgYtmZKOgMAROJdY96LrvZLaHTQci4YhVb3DkI3fDyNFrpUoDw/kajjdU1ikZ/uvoCADqUychB4Isy0r5revyTQ3+MnICA/kGYtXcXIl/vFJYVoKwTs8Sd2OIeVsLW0RFoQFSLGYQrnjCji7STWDMqwWUBA5cc/JFcFq1mdBkT14AACAASURBVHjF9Rj9a1p0GF4C26F7PY4M7P3tm7H1b2nBUOxpwvfUoi42IJsOpEfPwskWpstj90vfQrRH3HnqN/8UR7en2o1udKMb3ejGGYqzUvzhTBKLRz9xF6Bg8xOvPb0V5Oh77j3pezeN79AOOZJDU2+cQoDBch2eomRM1wuaaF5vOogaqZWOBKuZq/ZeLv9fGZXlvo9cpzMz4QlwRUh3p/gZUzvS7jaCgSthf3eGw64CixeohqWbKdikZaIUadsuuMBDBkcJ3j96x7UkHt6nBDCOOxoByaRyEkp1HCypV88QGXKxOFwjwXRVxk18E/AzSUdpSk2PEYLpjDafJyegFP1qcqFLvoO5GgZcqq8favTgWDW71wYL9ZP6pq4Wv/Xd1yCveqytxEKoMsSFIIcj31kHdh59X84N8OCvUL/1ki/eBNtMYKh7pOT4hIwFsHlwVm97cr4HRS/A+qKigkQODs6T0HzQsnDZ2EHsU3KEW3tn8fi86pvmmnhmLykhLUae/h4AON4qYvfMIADgsrWHMNUsaX/fddYCcsolYlewFo8216IWUea63luApThku+rDGiktJMOlPYd0afjx2oCmDu2eHUQYmroH2pdvYmqJMk7OBRxFoXGsGBYX+nMlx9do6OOLRfQUm1q6MQxNlFTmG8QGags5MOWbKvOxFje4eNtBNBQiebpWgAS0Z2qv10SfKpXvWhjE3GJBqzk99mtv0+fqnM/dCs/JlIRqdQ9JqoIUc1jzqnQrlRd0mxlEirdggp1QCRv5ONHaWMMEVLVLJuy0jTG23jmOcI0Syii3tClEo2VDCq4rA42WDUM9o37dhjFnQagxa+IV15/0O0Y+RFkxLIHJ37/+KRmjf9JxVpZ/z2SsdNOkerZgne4R+jOnmHx5zBApbhfzYliqdzFcqWFreQYPTBNQZHG6CKY4eNwSMFIZsZaRcS1XiPs+ch1eeGVWcnwiFnWnG1o5CYAdKI1QnunoCgsoHEmQ3oJL27KJbuud4xBGuztf9lPiSTS97NgO/Pnrda+6PeKCIA3equpVcWCiraez7e/o+EtugGk/60caTqKdNqQXwysGeuECAM1FVcZ1DBQLgeaf5u1QA5CGvRqChI7ryFIZnh1hSpWsf/jHp+6xpfHcr1LprBVZuP9F7+947cYf/ToA4IXrHsK91tU4vEQl35eO/BDnfI6OzbENrCnWNFVmql7Qk+lju9fjyovIEu68wSmEwsTBKk2ktaare8v5nhb2LfTpiXrkQ/dg3Ubqt44W59BvUW/08doAbCPRgCbLSLChhyZpiwmUnRZ+OU/ft94s4O0z59N5lAaEZHhBzyMAgK8vno+DddqPdPEIAJEwMNHsw0yLSuBBYqJg0+Rlm9TPThdGxxZKGnh40f/NwEhziwWYVoJIUWWOcoHBCi1G1vUuIREcjVQecMHFguqRi0WbwDiqD+nkQz0JTi72nNA3Tfu0R6JenDNKrj1DuTo2l+d0abg9wqatQW6+6gtrvicD9l7fWa5NwZTMS2Dtp+uU5E583id//8SxaeTjd2HkY3dnC9hXnVie3Xo7LWbD4Vi3tpqLHprqMyzkpxzDRt63/aSvnxDBygCmp2N0y7/d6EY3utGNbpyh6GaqTzLS0go3hQZFmHYM140A0EqbJaTPC1CWtuu2TkrN3jcT+nfkg/do/03e4hB50eYOAcQqk6r6Du5rbUBNKSdxN4bwFUGdSy2gjyUHhQmOy15FK877P7yCvu5TXPVPFWL2vek6bLmb9qPd2D0qCjSGOWIlH8sDlkHvHYkkLzQSe+ud49o8XnqJXsWncToAsOWRkv6PzZYh6pZ2/mBtJV4pGVpzHrii6RiGAMJMtL1gB9o1pRVbGpzUSixdEjS4wNTRir6+ALmQAIQc/fIv/OWq+9hQ6NwoOXEVf8czPq9/fvfRP9ZUkz++//c0CGu4WMOmwuwJSk0A8LfrLsNjPoGbHljcAJvH2pgcOV9nczdf8CXsDwbx7K9Qlj+8gWnDBs+IsKdBgKacGWFrfhoPT60BAJw7MIVBVQL/7tFRXDR4FI+G9FzMJnW8ovI9OjYwfG7pUhyKetU2QxQV/eiR42v0fjhuiFrgaHOAMDHhq2qAlAwbexdQDR39+/O+RvdOb16ioVSBhGSQkmFNHyF8I8HRUkIQ04sFuG6kEdy8xZGY6rw7CbiTgKtnMvQthOqZtJxYg3lSPVvzMO1H1JPgwAwd10m9U5lEc049CCkVRSFm05Jze7QzFNKq2N7TdJFiXEKGXN/v7VmrPW1i+LsJ1ihB/YNXc0yskMmeTqTuP6d83xMAMj1dottTfZKRIuS4G4Op8TJpmiSUrXp3MuTwDtHDZzYzucGwDPgjIfJK+rB5rKDVmxKX0H6p4DUvRBBNerDcHr+jZ7Y80rKT9A3YM6Y22z6TRuDtsRLl5YnEtlt3IiqmhsfQk6pwJSDIyBxQUo/lrBS4XIz7x43R994LFGn7MuZa6Srf34QQDKbqJTWbjlbckQnD0JpFTT1pxjZMJSB/QekY5kIqU86HOUzWejBfI0Rr0LQgW8r+yk1O2nO6sK10uRpCeHn82YO/g28c2goAKLgBvvvCu1d836vve4V2jdm9NISi7Wu1oj3HBiHVQHvBxmNY4y3hq7vOAwBcsXkCIzlC6h7zS7qPGgkDDo8xUVfWcjzBlX0TAIB/OXIefm5oAud6JG8YSROWQgIfiypotkkURdLA4SZRgj7/nPfqv//Gd/4YBxb7YCqE9fRcCbk8lX/zTgjXjDXtp+Y7sNT7hOB6UdKY98BMCXcfTXr+YCZXKfIJcr1N3X9dmM0s9MAkLC9CkpaD6xacXnp248iESN1sQt5BGxl75w7dhpFesmovc+wvd2SqRo4ATLFi6RYANn3mdq2e9URNzgGaRFndQGWUSvN+aCFUC5fENwEm4Ryi6xFWxBPmxP44MfbpOyCaPg6++randU+1W/7tRje60Y1udOMMRbf8+yRi5P3bwdSq+MCrbtR/P/etOxHlJWIFGHIqPuKiQmwuOXCP0un218QYGFrC3AJlMxP/+w2kfQkAhoSMGKk+gfiQrJIpwUTWyg19Qtim4p8MwshE4gHg0ldTCfaBD545s3F/iI5z084d2H/tqcFOY+/cofWP973hOiS27LCM0t6wloDRoXnKgFlVwnOfgsJKzCBVNQBOAqsUZi/FHL0F4m+uKWYApG/90na89v7fRUNlWX5iYn2OVv+P1Yaxe5aQr/2FBpaanq62e4UAsdsm0XSSON3stD2EZLh4iEzEjzQqeP0Pfgv9Sunjhgu+hJd9948AAP/5+PnwiorXacVY8D3MHK6ojWT84QGnjr3VAb39aujii1MXAACetfYg9igP3+n5ElwvxBVrSPu336lrQ4BE/jrueMbn8Y6HfxUAYDCBRPFUHRZjQeTw9clzAACmIfCQOu6r/vX1WJenc/q5Z3/4hGMd+/QdABSPVHDNRy25AeaVyEUYmigqlKpbDuA5IX6gqgMjH7lba2czSyDvhvBTXWAv0mIco391L/K9DfQXCMm75LtoBgpQxGJdok7FMzZ/RlWS+hhMVcZ13NV9Qp8IUJAziahlnfqNq0SH5rCKdOwZ2LCAufkCgiHV7ij+ZL1NRc2CaCWnfuNPeXTLv08iRt63HUaTHsZUMD+NLXeNZ64yPiM/TgDRQAyuhOWlJYFS1FHG3PI31BMzTAG/5sBQLhumlQkTGIZAq2Gjr5cGyVrT1YO1SDi4QuoFcx7seUO7t0SFH88PdbVIEX4sWd1fcuydO8BTlxcB7L2eJvW0dGwrZcLmsIRznup1RQbiQ3lNF0hVqZ7KSMv5zEmQV5ONbcZoBTZG+6jcucaranGDx2aH0Jtvwo9pQL2k74h+LRDmio4yP6l43QMvBwA0Ygf9Tl27xbznsk/izx78HQDAP95/CXqG6eR7doRj0xUUS0pST3Dtn7q+vKRpQwAJLQzn6XO10NXUnvlGDmM989r/9Av/7V36u462yvjcs9/XsY8v+ffXAqBe7MFaD/79BXT+n/+N6zBVUz6vCUcpR9ciTgwsVHNIqjSZOb2tzGEmNOF6od7nnB3pfa42XY15iCIDnAtdNn3uV9+k0dtLDQ+hb2rxfeu4rUVR/LURjGKEoV46bj8yEarrzpjU2+dMQkimKTst30KcTrgRP20qy/JIKWncJmPw1IB94nfPbBvkpyG6lJqzNE7WhPdmGEJiNyDxpFYAc49YWs9XcEAEBsnjAYBk8PpVH1Yy6uktKL6cZcPop94RYxJgwNw8DToy4XpSzZV8uIonGcCD4Wf7YTw517qTxugn7wS4GjBW0DPdfK/imLoSST6d+du0lJf1YbfeOa7pBEIwCE9k7uxPcYx96g5Yyj2jWGjpAdM2E3hWE8/qnQQA/OfsmKbXcC4w4NV1H/I/jo5hUekVr5QNPJVx/Q9fqvujAgyBoMGolVjYfvHf6kn25f/5apxboAXZr192v/78f86MYs3gIuaq1Pct5gKU3EytaLaeR0FxL4PIxGFBGa2QDEWH/r61bwZzfh4tJZf4sT0/jxwnIFQ9ons5zeD2vewm/MNz3wMAeOG//TlyVlYZ+PpV4xpkNJirYdCh/f3GwS0Y7q1i1qR9FAmHrahmVt5HGJqo5LLMKjVmN81E31eOE4FziXM//3YAgGvbqNYoo00CA7xqgvfR8UQ9CUQtBS0Z2P/qN2kFocd/8xZNW5KPF8DPoX0s51tYqOU0PYZzqe8XMxdhy9+84wQpyjTSscAqhdj72zdnlBRbwMjFansCnEvd2z3deP43rsOST5WBasNFUHcwNEwVgO9d/fRWL/ppjG5PtRvd6EY3utGNMxTdTPUMx0M7MuF8yTNdXTDyEAWAxCEoeUqtaPi2LiH5dRu8bmj6icwn6FME9bWFJRxrlLDUIEpNHHNItSJv1Vy0mNJbNST8fgGetu5KT9z5YfRdO8AjaETu8p6pbJhZCXyFfmraHzV8rsvhLM6MySUjA/FEaa+iD+AqkzWtBJG0TyitP1Vx4H/eiCv/hfpsjpHAyVEPtey08IND6/F/Fi8DAHhuhLLSm+3NtfDDo+uw+6VvWXmjP6F43+5fwBrbwlJC98RUUNF6vL12E8//xnWwOPU9Xzr8AP5+ijxN51o5lB06lh63hV1HhpEog+grNkziSEOJ8hsx+gsNBKpM2ptv6vJmj9PEQkCZ3myrgIrTwqYioaGn4jIOtggJ3O/WcfU3r4Hr0Tav/uY1bUfAsOfwkP7tvL9/G35hIwkmHG2W8e0jmwAAaytVHK8WtehCo+noKo1lJBAm16VcgwvESgjCMROEqYDBfA7D6+dRUGYEx5ZKSFLKSqoFPa+eIUsgUVgGGBJjn7oDhpXtY6RoOnyshaipfGmdEIYhdHYqJTk2pZEKUqwUKeUqzWTzw9S/bSx6utwLxmAYCfb8Bt1zo+++F+dceFid4waacdZrbcY2InUf1AIX88rjVQqGnv4ammH23rFPUW9aLtmnrQp3puJXv/2nAIAgNsGYRNwIfqLf/1REt6d6GjH2lzsge05PdgsANt9DE4fVYFB+0VQKVvNH4koILwFPpcj6Ak3VcA7bEJbMFFL6AvzSNrJ2eWxhGJaR4OA0DVYSQFJLCZwMLFCFB4MoKUZLKQOZ8rSARAC0A4YRMEACVl0ZdC/ryY589G7tfCFtkdU8DAljzgJPxxKBzPg9wf/H3nsHSHZVd8K/+/KrXNXVeWa6JyuiAEIIsDCrRZb5ANs4YS82BtbY+AMMtkHYYoVARgiEpF3DYhs+rwNYnzHYBBNMspCEJSQklGc0owk9qadzxVcv37t/nPtudWtmNCMhwkCdv2a6ql69euncc84vqOMhTAE+FPcdMgRTdl1CMPAV65SFw8//Ij2IttSWjpndnUq89Pa3oCbl5Bb8IsZzNDu758gGBD0L+/9bH4yWzQIB4JLavjVOKj+qeMUdb8LLRx4EAKykeezoUtvVYBwPLE4qN5rMeBwAHpsdRaVEiweNCfQiExsq1BK0tARctt6boQvGBCx5Qr3YWmMWkInJO3qMiBu4/zDZr71syyPqu1biPBphTlnSPTg3odSWzqsewaOtcSz16KG/pbKkpAmPdooIZTs5TTXs+uWrVQtZCIZSkfbfDy2kKVPWbFMjK5hvEyUmSTQ1e60VPXQDWy0K2vsqfScmDeAOV9Q2PhQrd5s0MMA0otUAlByzRIRFW9FhzBEfpXywxpbvySIDWkEAPDNLT9lxVY4Aad7RMJWN4cwb/0Q9a7DBV4L3UWhi+8Q89i7W5bFjiJvSsUoTKI501SKec6Y425xrYIA6riei7j3deM5X/kwBweLYgGkmGMrTd7V8B0P5HhIvxO0v/+hpPVMdtH8HMYhBDGIQg3iGYlCpnkJM/c0HT7h6nPrYDWSwCwCMdH2n/zd5kuqeplRr2Sp0utCpWs2WNGabqXaps8wQVch6DQDc8S5+ZcsDAICvHD4LzY7bh9SHOjTZgrUbmgJCJQVacRsSPRsOpwpMpFcipC0LZoNWpxka93hx1p/dDOXgdU4HqSS5O06MbsuFltFcjH6bW2iyCpUC3kg06BLwkeZTaBlMPztkcr8MI1UADKZxJEvuD60VdcGXrsKoBPA8mcLRj1O8++FfAAB88dA5+N3N34bDqAqcT8rY0yM6z38e3oRXbbkPn9p7IQAyT89JwFGrnQeXdCY7F6OYC1CW4KRebKrqNmfFqLk9RRfqJhZsWbVqjOOQRypJlpZia3FB7d8Rv4IDHXptSArLZ1Z2L/z6O2BLpG7RDLDkF1TFu67YxN27qOXrlgIlTJBRSQxJR3LcCNWcrJitEIeaFfQ8ulh511SKRNAFNElP44EO55CFYJy2YbT0/miCA3GVA5nJPWfQl6VmtS6I8iUFWcAZmCuvbwbVOjbsBMWCD01e26dasa6O6Y9+iJ4jq3yeVxtLGD2mqmvDZwjqEhQVM6SFPoWuMNlGz6PqNG2ZSqHNLEUo5IM16OVel46bk4swXPSw1KWuwY5fvOYp7//Gf7wOlYqn/r+yVESuRNfVVK2hAGS6xjHXKfYrZsFQzflIvBB3/eKHT+tKdZBUn2Zk8mCJKxR3UuiCzLC1fktThSGU2wqLGbSIwexkvFLA6vTfGhcAf730TC3E2DRKIujtyEajk0O0SHMso6PBbEs4fwz0JqUItyFgL+oIJvsJ7ESLgpNFJsEGAKZ8oBlGSsLy2cOkYayRIARIoYZ+q6YoNYKt+nukgVUiZSIOAFzOlVjDAsQPRuj/Jy0u+dqVeO/Wz+NQTC4ytzW3466vngsAyF2wjJFCV83W8maETQW6lvZ0hrHjAEkK2rkYtaKnZAqDxFAPv7IdoB3ZeHadZncPrUyoBDhRaGFLnoy8f7lyL77jb0ZL6k7u6I4r8XhLS1AyQtw9T/6ky8sFaFJpyHYiOFYMRyoZxamOULYIg9CEnc1QOw4KxUDNSi0jVbzRIcfDol/Avj0kmaj1tFVKRhxmgRYcccsGCzU1hlmNeUhGItj5CLH0WsWiDWmIQ4vTaU/RY9Ax++5HVl9SVHcTlAq+kox8KpHJnuZKAXb+0jU463PX0HH8xWtw5mfp372GC71hqpFM9rwBgCQvkAzRsWImhwh1uAckxavOwUt0fN1SgFqhPxdfbBcUz3ZqdPkYD1iFchZMLeZnfvuduORrVyov50MLVWjymVfIB6gXPHTlzHnI7am2fy+xMOrQg64VO9CYUHzu7kwZ5oSHtBdg32veP0iqp0s8ozKFq1wYDFmJJeWUbrBsThhr0OScUzApQQaSHtQ0ATFLK0mj15+9mh3SwPXHKPkMbWooM/CCG0IIho409GYMSPYRjcNsM/Ww4BZJjCkbNE7fAVCFfKrzVUBa24FccNJMICFlJM6Q8XEjqJubcbrBUzn3gdZfWwg3VRKAcNM1DzEe6cpgXIvZKWuZAsDbH/xVAEAncXB5pT/LW0hK+P3tt53ydn4S4vXf/R08skLJ5e6fux5vu//XlT7xgV4Nz6vsAwB85tCFaEubs3XlFgyN4+Fd6wEAVjlEuUBV4NlDc7jnyAb14J2sN9GVpue6xmFJOcBvv+SD+P37fgujkni8t1dXgKnHG8Mo2KHi9C7sGkZuit6XphrqRU850nRjC7Mz0tXe4jBz9EBmDMg5EaYqpP3LwVCxaB6nM4HlMK/2X2/riuMcDfeBQnpHB89xtbjNbA4BwN3QOaYy23yDnFeu92GYKcKFnPwcU90ptgozAAZom7oKSPTE2PaZ96r5rmGkapapaQJ+g+bIpeEuothAJO8Lw0wRSUtH56AFLerjG7a+7yZw2UlKVs2AwYjTngGozHyEZIG2L/Ip8lVfSR16XQeuPMZJoiGODGVPF3ZsFIdo4RKGprpX60Md1NxVidkrKOENPzLhBybKBQnoc3so23QtZdQqgDpCG2+5DsYBegZGQymsagDeC7Dvd647rZPqYKY6iEEMYhCDGMQzFANKzdOIre+/CSAALvRyBLRpFchCDdCEQgOyhJH7CoCkmijpQd42IQSD3ZbtTgFw2a5inGauGXJ3eaWAglS60TVyxDGlG013MQ/H76+YswKZJWQCnjm7pK6A3svU6iU6Wf43qSTQJIr3eO3W1WjnjA4DAEaXqfYY0NdpEJI6lBRWCYRnvRBDgEmlKJFqiHwTQrbzWNdQ29vztqemoHTrLAnIm3qKVJbFu5qjuP2/PkVPx2coLv3G27GxREjbbI74g46X3v4WAICjuyhJn9Fnf/kqnFPP484DGwEAF6w7jE8dJHpQx3eUKMIe6bhSGpFG53aE0Rz9W4PAizbsxV89+xNP+v2vuusNGHci3LlE89AVP4fRArX6GBNoBzamylRljl7QVa3hBa+ApU4eiVQycpwYupSJzEQUAMCyEoSxAV/SRqpOT71WNn3EXEd9ghS5lvQS0rK8L0JNSWEaPpAKDUlJiu27KaY30Rw45cfWF3vfTtfhuV+4Gp2Wq67jNM+RH6EKTgggJ1vUeStCO7Dx3H//U7XNrF0dJToY639HHOtKwpAxAU2OVpJUg2GkMGU7PE01cPnarqvXiopwC+ATVBGuH26qtnzDc9Fbya0Z+Ux/hHAeTilEzo7QyCg2KVtlPsAwXm9hdl7SqYohuvLZphsc5bLsDGgcqdAQpv3fs7hMaOuRehuWkWJjha5/S0sV1SfmOopmoD6zfqSBdkmK3Bwpo17uIjFC7DvmTJxeMUiqTyOi4QS5IbrA4lhXrU7GQSAGeRPzSozEkhfeKtNwlkshUgYm22OGD1R2S8BOmIKlQG9Ycsw2EigIAMbXdbB/YQhsH7Wh7BQwMkwAAzKzD13yYf1Ruml1nyGqyXmrDvBc2reWS5hqh62OF/7SDfj2Z9cqR6VqdkwtWrZKwjYzH2cc4Dr6Jumr2+GC9UEjHRNC11RyN0Z7T8t1A4AC2HzzxTet+fsVt/3hjwR4dFZ1DsOrh+Q/hMicWL586V/gtfe8FgCw0C0g5rpq5TaCHKakdVrbcTKHQjx2aAyFko/JMiWlduigFVFb7oLKIbzn3M8f9ztfe89r8bfP/VsAgKGleKg5iTlJZTH1FHsWqI1rmik0JpDIVRMXDLNtau0lXEPBDdHp0fcNFzwEEky1sFJSdobdpovJ8QbOqcwCAFJomAtoG7vbI2iFDobztBA4/5wjuH1mMwAyG880eZPYBM9xFMfo3OSsWClAzc9Vjvl9r7jjTQCAh1/xETz/a1diJU/3XeibakFiWokCXXHBoGtCJTfG+opKus5hm4k6T2FgIpJJybQSuHm6aYtugJbnrlFlKsrzlyXrbPbNJ6ooS1pUJ7DR7tBzYv9v9ilgKqTLk6Zx+JGJepWOgRBMJUQ3H6HhuWpek8SG0kYuVbuoSI62F1loBQ5yJm1zpZFfQzsDaEEHEAc/lIu2J+7X3HfGwbfS/tfWNdH2HaQZr/80jkH7dxCDGMQgBjGIZygGlerTCGPFgDgghXXzIpPABbeozZpIaLs5ZyGZoFW38A0FjBCmAJxUAX3qD4awv/2o2r44czO4QQAk559yaGyl1e2RYhnJsgNHtnJXF0NGT6BNnTekLpDkhAJGJVbfNFwbCgHfUMbEzE2P60aROGvXW5tuuhE8Ax/pQFhLYS/JysMWqlLlxYRQwRmlIelXo+BMCaIzBsAQCpmZRIYiw/NIBzP4KQmGv+TWt+GbL775uK/9oKvUi7/6TuUneucq39KTtUpf/B/UZg8S44R+p08WL/z6O1SFtbGyjEW/gNsu6x8DW6dK5hXTD+Px7ogi3P/M+j1YiKgq4YKhFVJlYzkx4thQx2vjLdepCujTey/Ae849/n7kjRCvvvv1AIBxp41eYmHFoG1qDBivEhhpU3EZj66MYVEKPJgaR0lWozXHQ8EMsZKn13JGhAcOTwIAkqalcG35UQ+2kWDSJmrPTDCkRPNjriOIDUwWaJuuHvVbxyaHLitVbgicNT2rhCaWGkXYjmzrMIGpv78emrxuh2pdbKrQfXHFbX8I2zAQSMCQiDVY0n0qTTQckcCq4lgHOStWlWrKmfJnNbiGe3/+uuMfSADb/4VQto1ODrt/5Wql8DXk9jDfpXPGmEDLc8Fd+p26maLVll0rJyYq0QniiR7Emb7y0UYJpbvpnEVFF8FZvlJwSgMdOen5/FSEIF511xvQ9UkEJFhy4Q7TtXTuF65WDkQAsPtdf6TEZlYcG7lRD5yf/pXqAP37NGPrdbLVKABN3vmpK1Gvq9CAGZRdb/RnhtzhYE6Kyc/Tw06POKwm3cDmkRWkR+bATHotfP6ZaGynuUPrzBTgQGmPVDLSoHik3ARSm07l41fRLGjq//ug2g+WqRVxBq1l9k3Qfe24s9Tz3nIzHvyLvorSlutvQjzST75a10Bulh5q3ekEyJDNVgrDStWNGTfsfqsZAJPtJKEJigAbCwAAIABJREFUmlHJ3K3lYyXpxlMNiW88bVePH1Y88SFxKvGib/4JJvPUZjW1dM3M9bX3vBYVkx5AN1/wKfX3l93xZhSMEK5Oxz9LmgBw/9IkHCPBQpsWYVdM71SozMwxZb9HdJup3AqGTJoXPNKZwLxPD+uUa1js5HH2KMkDGowro4CI6yhZARZ92n7RDPGCoT0AgG7qKDP2fd0hOHqM3ZIisaHaQDeii/PQoSGU6p5qLzt6DEf+hp1LozizPo8VKXfYCh1Fqel6jnJeqpc8XDGxAyMmJeqluIgdXaIEtWMHZ5bmlBuPl1rKks6StnAA8VkB4FCTWr29ro0NY+RANLNnFDA5aqPSgcdzMCLlQUt2gJjr0ORQdd98HY4rk+qquSJjApxrCllbdEN0/D5Sv17w0Oi56tzkbdpGx7cVJSVNNXDOMFKm7x7Lt9W58GILZStQrflDi1Xkc/Sb2vMFxQfn9Rhukag5x4vzv/guNKT5gzlnofYIfff8ixPi9TZkch4Oj2nrPtU463PXKDOGk4n3T3/y/eB+gENveO9pjf4dVKpPM+yGTBp5KElBrgOiHilvTiUbCCCtxUDU9ztlKyaCiqweUw3OPK0I+XIDOG+bwvY0ttsIJMPAqPtIEx2GJHXHBaaSKkvJIWd1HPjvJ+emZkIVT4zVCRUgkYjMggqcAFjdjfRgZG4KQzqGpImGODDUXEYrxeB+n4qTkdqnP/oh5Ca6ajZVLXuqokoSHXtfddVJ9/1HEb921+8BAO7ZO42ReqwcYD5y4S2n9Pm8GWHSpWprKSzgD+57NT767E8CABbDgppRAlBVoAYXtp7gnqMbANCD92enKLGdUV3AvUfXY/swAW4O9GrYmCeQyOrEDNCcKy91b1eDuC752pUYLnqK8jDfLWA4T8m3ZlOSH3bpIX/vgQ145Aglsyu27sRdc8Q91RhQcX3kZKIYd9sA5Q+4RoyI65hZJnRfrdBTUoeT5RaWg7wCy9h6irnDEgWoCUDeQ20rwd7eMHak9N0R19GRyWVdvom62YEOSmZ3Hp1GQe5H1e6pWe5ct4hGK6/k/IRgmP8WVcXYGGFy3QqWpFNPEphqn1KuoWgGipokAHSXaRHATK641rxrgsWMqGMgvEXco+u7VPMw1yyq5GkYKc6szQMAGmFOAbf2Ltbh2v1qF6BFDkB83ITragHF4z4QqjTahSu57R3fhrEK5AVAzdm/O7ceneWi4pjvubIPCpz+xPsx81t/ii3XU8EQ+99/engqAhKGlYAnp+Y1/OMcg5nqIAYxiEEMYhDPUAzav08zzng3zbCikkBalqsrkwOBDnuxzzUJR6SkWz6G5fSruTTRMPxVqgxyczGc+ySQ3DDAp0YRjNAyvzdiIKjRqlJ/0Qp6D1WRk+bjYRVIzqYKQszkldLKE71Kn25suf4mRZXRUvQpOnkOs6FByIUs2+gpx5C46ZBilPSl5IkGw8p+t74GATj9D9dTNQIAAihXqCoyjfRJ508/qrj8W29VsmrtyMbdP3f9ST/zpu/9Jj5y4S2qwtUgUJOiBbvaI9AgMCHbwcthXs0dS1aIr8tZ8Sv/843ggqFsSfRlYmE5oPc1fQedrovnTpHnq60l2JanCmjcbOB3tt2FX7nz9wEAD81OHFeY4LJb/wh7jwwrc/b1laZSwSmaIRaDAkqSCvFPl3xszedCKegwkusQqrdLWIOObyvR/LrjIREadi6SG41rxdhWpcp6vlciD1i5neVuDkFW3ZV8dLp0H6SyyzO9jhShXjC8D13ZpjFZirLhoxFT9fgfh7epFvKm+vKaqi9nRPjeA4QMNuoBYjnjH13fgK5xNKUDlKYJ1Z61VlG1AODoQgWarE7TSFNSm8I3oPmaUg0zCrESUijlAiw1C2rWaxgpNtepo9AMXNUW7nk2hmudNaIaT4zz/o1Q8q1GHkxuj8f6Kfn4Tn/i/dDnbKW+lK/1zZbLOR9lO1Cz9Yu/+k5EUjrUDy089sp3n3T7328MTMp/iuOx9/QT19T/kW3RSAPjDFqmBcyB3AE6xL0pIJUgCN3I9DrpbUwAwqeLm7ku4rKNoEoXc5xnKpk98vJrgZcff3+m//eH4K5K5ieKbdfeTGpLUpv3wGuvVDep17PXmCgnExHQlZeIt7apEY3FalZqpBpsR7bUShElniNSwaWYYs8JAEczv/1OJYPGOVM3cHOxcNLfcaLIaBCGluJfX/CXT3s7Wbzsjjfjiz/zYQD0cG2H1HIMohODQlaHn1p49d2vR8asqlk9jJiEMHsgnsRIroO6RQujWa+MjSWa8R3sVJVUXb1QwES+hcWAjsuehTo2DdMDueIGqLgB2jHt144jY/jbV1EL+dJvvB0fm7kSd15Ozj3P/9rah26mlgU2DG3JQiCvS6PGlRpSKhgONitYJ+ehQB9UUylYCqi1LteEn5p4YIZUjZ6/ZZ9KZvN+Ed3IIq4ngNpED0vyt7QjG46RoCPVnap5H8tyBtrxHHAJqEOsQevpOGxLHtAwmbADZNH3SGcCexs0J9leX1Az4IIZqgVImBjYVFhW9JLV1/rWT18Lnmo4bz3JMVYtXx1vDoYFr6BmsxMjTaKeAGAu4MtFgFPvIfAs6Ga/TZxFo5MjJyqzn5x3zdH82bFjNQZhmsBKOwdrlWXc6iDJQqnEZHJAqrJZQV/l7cDvv/24nwUIsDT1dx9AXgKQyjlfLSTavoP55bJ679xcBboln1X68fdnEMfGoP07iEEMYhCDGMQzFIP275NEpiD0+J8eq/Az9TFaFbKUKT1bYUtZo8ygu633Eb+lRL1P7+gwpzyMf1y6Q3xvP7ovlGT1ggYt6evl9sY0PHzjU2vnnvE/bgbjwM73Hf9zUx//oPqCA2/or2o3/dP7sE8ChLZ86s/BD+dUe1aLmEIX73/ribWDN/7jdWsQg2d//t149Bfec0r7fdmtdJyfKOLwVCKrVM8qHUU3tU8ZQPRk+5SXrdADzapqA9pGekouJJd/662o2at0Uv0Cdj9Ofqdnbj+MUbeDhxYJfDNVbqi2X16PlPrMnXMbUXV8BZwJU0MhUUdzbext1TG3SBXG8FAHy02qsC6enkGQGnjkKG1f1/lxEaFTf/cBuPstnH/FTvpuI0JLVr69xEI3srEi25MXjx/EIWlgvq20gKpEK5d1H53UwYMtAv6cUZzHv+w+HwAQeRam1y/itss+pI7poWXahmUlyFkxmt1+2zVD1CaxDt6WHQFdgLkp2LKktegCkJSXDMGbAXgYE/APE7J5fPsCXjd1JwBgzGxiJhrGve1pAMB3ZzfAk+YUMAV0O8VEnVrWc40ixqRAwli+jZlWbc0xi2VXpes5SKURhJ2Loetc7X+aMnWfmVYCv2srUFMmapFF9hld59B1Dl/6n2odA2NnUqu80c3hsVe+G2f863vUserN0m8WufSU2r8AoX9/e/PdAIBbZi5CKkFLYWyid7QAoxaofQ6l7njaM/Arz74XANBOHHz8Of9wSt/1VGPQ/v0JjqmP3QBHzmXOuPrmY+aURku2YGKoPk80Koj/mc1bagmYbKm5uxwleF88xFH4ognrUZqDeZdsRnuDRAynJD+m+7SNzqan3nYJxtInNfg+8LvHRwXvW4W43fPr78LUx26A0aTfabUYdlz31OQDATxpQn3h19+BiYJU8YkcbJfSflfc9ofIGdHTat9ms8BmksP5hYNP+fNPjPFcS6Fix0ttZdb9hZ/5yDHv3ZQZaKcMtSqhZwu2gXbsYNmnh/dKOw8meZOLXgEHG1WFmJ3tlhU6NzBD3L9ACWrb0CI0CEWt8AVD3pRSfkzgvKFZXD5ORvbfbUzhFeseBgA81JmEpaX4rxt3AwDuX55Ujic5O0ZbosjXT3aw+awlPLJEyfdZ9VnVPgWIwpOp6WiM44X1vQCATurAlrJaE2YD90XTKJg0S//KwbOwUbaotRGBlGsqGdhmHjxLIkzACy2ES5RUzWqo6CpxYChDCrPDEA1p0CLJ0W5qiKSUZzKioeb2wMr9+sDbQtv41fXfw0XuDADAYSkORUN4dJkMB3zPUgvgiYkVLDYLmF2iZP/c6RnsXBqVWyvhjNo8dsrPdXp9CowQDHaOkrtrR0i5tkYoP5NfjEJTuTwBQBLpKsFaVoLRCiXwbmhBCIbRqUW1/WyxkM00swQchTqQccxX8Tun/uqGJ20B56wYD3fWqf9PlohGlI05VsfUx+VM1xT47GPnAQDOmTx6wm0PYtD+HcQgBjGIQQziGYtB+/dJYvt7CX256+pj26gZ+lewvu5tPCK5qHK4b+YjtaJ9+ZZH8G9ffB4AwPAY6o/EaE1Taysq9UFLqU3auuGQFNsWgDUp7Zd65jHKKD8p8fYHfxV1CeC5v7VhDcr0RxUXf/WduPvnrlcKSAbjWPQkj5Fr6KzklWm2AJTgxZbxRcx1qP24ubaETuzgSIPas/6Ki+FJajE6RoJUMHQlSIcLptDFF44fwoIUZwCAbmQr8YKa3VMgmrLlY3NhCbcd3QIAGMl3UTDofXfvm8aZ6+fgxVThBomBZpcq5nLehxfS3ydKbYzm2kpcwtVjPLhCVfLBuRrO3nBUmZRvy83haETV3A3nfRq3PP5cAMAZ1hwunOp3Bn7trt9DI5DVuZ8DF0CrI9V/7BhRSNd+0jXJc7dClWW55KFxkMBIwuJg0uzB6DGY7T5oz+j10ejBCEdhY0v5q5YtX3FKp3IrmHYIMVzUfHBouOHRlwCgat2V+rV1t4vDnQpWpND85tElZWf27y/6X9j0T++DI8F4vY6tqs5ywVfdhXZgo9N1wSWgSYGsQAAkXefqNcNMYEtkcL3gqfHAQqdAgK6WNEg3hbKZm3kTdZ82y45IEhiK+85Wc8A/8X4YVoq4K8FPVkoesAB0T4PVZIhL9H1jF84pzvDxKtUt/3wtACCN9e9bCOJUYtD+/QmP4yVTANj4v26EGOsbjmcOMHrDQJrvk8EhGC6cPAQAGLHaOOfFjwMAHr5tK1a2m0q4QeiAFINBXOAkPF+nByNjAsEytcaMto7t77lZGYI//mdPvR374xp+auJAQuhNjQmc/8V3wbXoIeYYyTHmyc90bPr/icKTBjpqI9QOa6xQIpyXIvHkLEKtW8eKMTmxgp5EAa8rt1Ty2j0zBlMKpB/uVLDSLEDPPlcNlHi8p3E4VgxDzkrruR7aUoVoKSigGfTVd+JER82l+WWWUAFgOciTOo98sC/28vAt2qetkwvoRjZyslUMAJVC34M0k4SrOj3cc2hKLQD9hRymttAc78z1cyiZAe44TBqYD7kTSkAeAD6zIF1vIgdfn1p7TDMfzUbgQgNDVUofDue7SipwMSxB6+gqAXU8B8hLab95izyKAYhARzDClYpXXBLKI9hZ0NCu5FRSjbiBSI5a2omDpZjO3xFeRTPJKW/YfNFTx8bRE/iRqc7v44+sw8yb+9iBtG3Bl8dramIZS10puainygvWzifodF0lfRhHhpqdGkZKCF/Wr2GyRBokBnpygZOkGrRFSy0YAAFW7Z8/AGuEUTL3GWEJlWydfAJ/2QULpPJawtRCPzEEkjLgHqFjcFH9wDEiIasjltKMWttQ35Ul90EcPwZJ9WnE/j/8YwVUErqAJi9ec8qDCA2ct4Fg+cNOF/ccpSfNPQemUP6WtFEaZvBHBHRpC6clfbeZqARgJIAuH7TmQwU4F1GV4OUciHkb+/749EqmGU9yNV8w4saalfG+bl3RVbhgqOZ8Jcf3w3CauWwrzSRvP7AFK3OUTA+8/h10nm3aL6YJ1KTlVxgbsI1EJbqcEanf55RC5U7S6TlwcyF8SZkQXMCVM8MgNNGSQBAA0DWhqqNW6GBEuq40AxdhbGDBo2Q6lu+Ay2GgF1tohDllHJ6kGiblnHpjfhl7u3V87oUfPenvv/ir74Qtr7nbX/luvPvhX6Bt2Av4RuMslbDaga0qXAAnnHv3Ektp84axgbIbYCRHnQhHT7DzcalkxMnw25il/be2hUqFiNsCzhAl5oA50DwdXD6xdJ8pLIMeAaxnKOWhXbOj4PN0zsJnGSjotED1uYXDvYpKbAUrVPtoaCkcM1GdAt5jSm1saGMDB97QB6VtvOU6WFJBbH65jEWdzksa6zDtBKGswnWdK4eWILUgQh16IVavld2+DVoWYWhCjIZrVNn4qSgb6QKJ/EzasKUFpVxZaAK6XKiIRRtGl6lO2Ge/cxFuvuBJthtnsqJQwLBBPHkMZqqDGMQgBjGIQTxDMahUTzGmP0ztR91n4LaApqSGgKQiVZM4g2GmSlT8gfYGoE2HWBuKcP9fUYV5zp/cjMeuXVttnnkVzWh5jsPUOdzv0Oq3NyHA99NsQRsLYG9q/wB/5TMTF3/1nTBlxcUFQ8WhyuPR3evwGxcRlP++lQ24/FtvxZ6jdKyet9HHsEOVWSYyn9FjnhiZIXczcPGG6dvR4dQBePMZ//GU9/UDO67AUlzEckit5/MmjmCnSajP6Q/fCK0Ww3Zle89M0Jat217LhZe3UcpTtZGk2hrVnXKeKqxeaMH3LaSBvNWcROm1pqkGIQAeUqdjca4MUyJJa2VPqSt1AxuWkWB+niron79wB+5aJuPxpW4eAn1EqGPF2LtCv+W+3dP48mV/gZfeLgXqI/uExu1TpQbuO0Dawq+957WwdbrmetzCEa+Cc6vkY/rt2U2o5ak6f9Vdb8C9h0jsAYIhnwuVKEYSjyEnxd4LTggvMvHgMiFOq2VPVWwAkOY0JYhi6immpGoSYwIzM3R9WIsGhNHHL4hVOiepI2DWfWVcbpgpAllV7T00ohx9LhvfhR2NMSQhHY9DzQo2VomO041t2EaCld1EneHVFEabvmRlTw1bPngT9ryD7lnnURe9Kar8tHwMQ5pJ8FRHFJiqykxXtXrBGRlsRFLUReOKpsRAyHIAKDohZpfLYJlBRTFeg8pfc84+dgOY7FjobV1V7tylOaww+2OoVF5jjAHxhhCmrLQf/9UTexhvu/ZmsLqc5775j7H9Pcd3gzrnC1crERAuGAw9RU/iBHb98rEKXj/pMQAqnWJkaiVaoEHogCjJNo6dolam1pilp3DNGHuPkEOG6PbXLFqkYd036Ua54/MnhrtvvOU6kj2TSikQTFFx9r799Gr7PjG2fvpavGIr0T24YLhveYOiC5w1OqeoK1/72f+55nMvvf0tylkkTnRsGaaH7kuHH0aHO7ivRS32f77kr/HeR0hyytZiXHnWv6/ZzuXfeisAYDzXRl7vz6naiY1I9hV7iaW+q7mSB9MECmWZIPeUwev9ZDAx1lDtw5RraHXpnBXzAXzZIo0iA7Ydo7dACRIM0GRCEYmGctVTlJrFRh+YFPsmNKmq47gRem1HgWMK+QAvWb8LAPC9lfU40ijDldso2pGa9/VmC8gd0hE8i/Z/89iiaksfbZdOyWHn1Xe/Hlz0G1oPzE2qf7tWjJ+dIJzAvzx0Ic3uJIWkONZRMn/dwAa/pwJ/OyVZzeSoynum1XER9/rqVLmKrygpcdtWfzeLEQFvMsvCXKKSyMhwG2U7wJ6jdN/xuL+/dj7Cc9YRruGIV0Y3tNGT7VnHirGxQkm1EeZQNEPsnKMFVRwYEHKsw0IN+UM6/FG6Eff90Vqe9nO+QgCeZjsHzjVwyaVlKQN35c2b4Szk4oExKLqd4AxCJr1crQe/4yiDilrZU+4ur7779fjkxX+jvnfTjTdBMrzIsSonv0tI3qp0eZr62A3QpSIatwVm/uCZnYm+5Na34fwajbzuXd6AIcfDZ57/V09rWz8JQKVB+3cQgxjEIAYxiGcoBu1fGRv/540wO5oC53FDIK7IlR8DDrxxbXU5/UlaPZpmisVDRAFwjxiwG4Ax0W9RZdsbekgoPd8z/sfNeOza4yOLNU0gjTVomXyww5/xleWPKp6s1QT0bdWyuERq1Ta6Q3CsrC3aQ5TScexxG3t6I7igRJXI5/aeh2/OE11iMt/CW+7/Ddy9QFVszoxRlgjZmunhkF9V35NwTdmD5YwIm2tUCT8cWIjaNvzHqHKFDlg52kYpH8ALLZgSLeqaMfwHqO3ae7bAuhqBy4YcDw/OTiq/WQj0VYIcDtNIMexS1bbYLJB5O4B8xVct3TgyMDHewOwstSYbTRufXiHULZhAtdZFY5mq3KjsKz9Pc9jHhrOXFABsqZeDJ/09K4W+kPqTRZCaWPQLGHJoH39zy714pEOKUHft3oTbGFF5RKYWJsUIOrNFeMV+VS/WpTAPSXUeB1jKqsCeDi1hpJAEoJfmlDgGc1KYB2l/40ADK8cYHSEQ1mKjiHKR2r3rik1oTCix+uFaB45EKBesEBtcqkZved7HccGXrlLXUpLq2N+kYyoEw4HOEHiD9tEa6YHl+vv/6JuO38bc9pn3IoklUCnUAQ5ACurD16FJgQpeSlCo9eC16VyIVIOQpuJaoMHw6fgFXhFgQJyjzy2vGik8ujS29ss1gbjW1/Ce/mjfxpF5OrZ8gJTJ7FhXTAMMxdh4y3VrjC2+34i4joebdE0sdArY9/gYtn5LqqIxIC7SeTkZanjTjTeBB8eCt063+KlPqhmKVxcauCmUGL7hMQhDKrbkOTZ/kC4SLQWSqQAbJ0gtZv+jE8gflQg5BiQOqSIBxKPL6C+tzRqk+MwJEypAcPlNN92o3GHc2ZOL5P+kxD9f8tdr/n/X5R9Q/87UiqLEwM9NkZxeCgZXj/GfKyTx+F1tCr+x7rsAgIW4hC32vOIo7u6NIZYi8d3UVupEGhNohw5avmzdOiFqcjbn2DGSbk612KJKirRF71vskOTc1CRtf1t5AXglmXx3Ykchlx+Yn0TYM+GW5Ow10aBLjmA5T3zKTAVK1wUS2fqMOENBzmtL5RBBYihOLLeYor9USj0sL/XbxheMHcZjK9TC7B4u4YBRVV6rE24btiREx+LUrqsxp2+SDQA644oKMjzSVm5CZ/3ZzYiLAvaKTJwWoB4vDAjqXHEjmYCSv2Qpg7PQX8z21gMI6OIf2tTAsuRrspgByxYWlmWL1+FYkbPLihugF5uYlBKDZ1fm4Mr2vp9a+NSjtAC59YM3ovMLjlq4JJEO/Qidz8QVYAL9c72YAyvRNpgmTTMyTmisQVjZgtvtHyyTQ7NTcNkCZ+VIoYndXATHTBDL+XzUs5TgvtAFkoJUV2poiKockM+hxDcw/ZeULHPjJp795auUPGZaTDE63lRf/8TF99TfkBqSUwmQLkg5Rs/A/jc+M4v0zAzDMApKQjNTUMsSPIv6Uo1PFtMf+RBgA5zzk773xz1+Kmeq62+4FprrQDj9E2jNGQCDsk9LbdDFDXlNGFID12cwpzxER2hulZvVFB0mdYAkB0ivZMRFkamgwegwJYmmJUBYETC9zOhcYK8EQUz9/fVEM/DooXe6csIyMFHF8pWowN9c9Hff93anPnaDolkksY6t4wsoSUu0RpDDO6a/AgD4cvM8GBrHEZ+qzF5iYsIlMMjhXgXzPaouqo6PXtynf7QDG35P0lO6JvSO3rf2Y1BiBBCkh7zlQslDdjuomLRfO1tj2HNQAmxyMSwrQa9L2zSsFJYk/eftCHGqqQdvGBvwO7KS8XVF+j///H3Y3xiCJ2k5hsFVhdztOOChjmkpa3fF2A78497nAAA6jRzcYohrn/UFAMB93rRKiNc961/xZw+9EgDw3eUprMs3Ecq5sq0lqrorGz3EQsddK8RT1ZjAUY9mXbOzNRRrkmIUmuD784o7CgBC3jN6wJDkBeSaBmmRwx0jUJo/WwCLGcyOrOhMoTia3OX9Gaqg7ZgtSUOLAX9UdoTGAqwfbaAtwTHryi3F8V3xcvBWJCAo1CF0Aeb2ebaa3EfbidBbzKu/gxMOApC0uYghzcsb2+Jq3q0fdBBLoCI0mpe7spuRJBpykrM6nPfAwRTt6shKWSV3w0jhHaGFkRYycEdQMgLomTRBx1gIhqhjKUlCFmvqGjHbDDmpHuitA+IyR30jncPVNorT/3A9ipUePClRue83+hXri//jj7HYzSOW8qyj5Q5ieV/M7qsr6cPpT7wfTBeKgxt1LBx43Vrd4c0foiJk758cHwdy9jtuhrcxVQsX0bYw88Y/GcxUBzGIQQxiEIMYRD9+Ktu/LCHCubAJ2ZeFljDVqRC6gB7IqtUSyGpOq6XBvq0IR1WjgB7I1W5LIDUZemOyhez3Z0WPXfs2nP0OgqQXZjl2XPfHiqazWrmFdQwIQ6ypon9cI5t5DrveMQLzmal13ghxoFs75rNPNbLKd3RDv5rgguGxh9ejurEBgIyUb6+fseb1IKVLfM9KHUsuVaeNnqvEDApmiBU/h6Idqs9l9ABP44hhw8xTpc25Blv6UOo6h6FxHFim3zYjakqpRzdSuEXanmtH0DUBqyIpGAwIpcOJYyRIuQVLSuXFqQ5XGoXbtUQhdXuJhV5gwZaI0LIbKD/PUslH3o5QlBKGf3r2l/H3u6SJQagj6OSxZzu1g79y8Cw1b32s80Y4+hAA4FnVIzgalNU5qxg9zIZE35nxh7BzZbQvZuE72DREow+/bqInxSsYE2BTPQSSOqQ1TYV8NVo6hCHAsrlrV0P0uKSJmQJpOUFkSjnCrgZdjtW4Q10d+reAPdVWFT8WbXAnk/JkOLxUUVX4/lRHt+mqY4DM39RJgZRBZGIKBgdkxd+bzwNuCt2m/6ehrjyH0TahxQxahXYsbtsw5Nw0LnJkD4N8xYcQUJSjIDHUPo3m2jjUrSp6TxSYyhTeMhJ48rvGtjSw8MCoou9s/5f3IufQue14DqGrM9ELrz9SiosCbVdW8et6OGdiHhWL9gMgJyIAKNV6a1Dfb7n/N3D7ERqfJDyPMDSpGgYQFnrIyWvTbOnYcj1Vn2JIh1ZIYNuyle1Z2PKpP6d92pUHNwXSwpM3QFMHcI7qYIdlF4EPZqqndTz8B29GqVTClutvgh4FfqgXAAAgAElEQVRlrRRpFi5zbFzsz1dFLYY+TxeauyDAEuB7H3/q9JZsjNWe0ohjJvs/mz90k2qTaPUQImVwdron2syPJM787DUYkhJ3Y/k2WqGLqSIlpuPp9H6/2r3nfuFqFOXDZCTXwZcvPVYV6KW3vwViE4MlQSmXjB3AN49uBwBsKDZQs3qoWpQEK26AIKHL/eXTj2Bfj0BFzdBFxfHRlfKAhsZRydFnwtiAPeypeZGhc5V8m74DIRiCjvyckyCXp9dKboBFab9m5VNoTKArXWrqRU8l1YRrKNihevD6eqqUkcKwL5mXpDrSVMPGGrXzDJaqedzquXMWmZsJAFzwpauwJIE0RSdEV1J95rwSpqUh+t2L0zg8X0W5TOeXC6aO/excFVYuUq1K00wU8AlAfz4Z67DsWLU39YlYzYetYQ+9xbxKqmZLQ5mYOGhtZhDMUD0zbtAME6D2evZDhUmWarpsOfLREMgAX5xBpAxMzjnDwESxKhObbyGR3FDTSXDe5BEE0tx8x5ExJZ24nBYAAaQy4W6Znsee3eTaA0MgXRfAkL+1Nt5CJK+lyOIwszlpaCBNNfgy2bhmjAvrNB4wWQqTcXSkHulYvoMlnxaIncBGoUL70ey5yB/ut9D1+4pojMuFZDmGXoyRSqoetwR02QrOWu0AELds7L9vE6IK/W3qoQ8ClrSc01PctPMl6r3jlonL1pGL0YFeDd99dBNY0ncQ2reXFmT5Zn98ZbVNsIs9NSZBypRKWHuDCa1lgA31F6nHi6jKaSyQk7N1CVbLipDTOQbt30EMYhCDGMQgnqH4qaxUs+C2UMuKtCxgtTTknk1ozlEnVMIEALDzt9/5fX/fjvevRf1m2qJJtQ+cSNsWdE/Dzj9/asbkP+iYqjVUFXKwXUU3sJUTy8kiE/pmqxRm9vz6u570M0FoYn2FkI2ZMMMT41CzgslySzmScDC8RPqKPtyewMFeFdN5alWeWZ1TnzscVJTQxKFGBTm7T50o5Dz0pDC+a0dIUh2tFlWZlYoHL6LXinYExgSGp6haP9Iqq5Zy0QoVpaNohti9OKxoHLrGFXhtpZtDveghb1PLa5HnocuVernQA5eHK4hMiJTBT6RaEbdRkELwL7vjzce4i7zkVrp2Dt26ATjfUg42WcUNAHXXQ1sakResEJWK16++Ih1XSIT1o844lv2cUkoqu4FSAgoCU1WqupEqBxaAxBN02XbdXF9Gu+ip8z8zMwJfonrj9SGEgDKhEE1TeaZyA4qTprkJ4shAuiSrIwboshoyzRS6zhFlakU9E7Evq9hIA5NgpyjUcX+6HoknX2MCS21Jl7I4dDsF79I2Dq9UoBX7Ih081hDLCq4Z6cAi7QcbitTvtp0IFSdSms0HmxVF6Xp2/TAOelU0fTp2y0tFnDFFyKINxQa+c982+i5DYONj/Spv3ddbYAkdR3+igPmLbGy4M3udo7Et48roqO6mv3cnLbS2iuMabrz67tdjzGip/5ssxaGARhit0IVZipS6VZgYMBt0TcQFgcev6m9v8z+9b42w/+rYet1NiA3ruK+tjrjIwWp0HaeeAaQMXPz4j71OFj+V6N8MWbblAzchtaW8ViVBvuLDl2LeWLBhNelm2XXNDybBbfwLmqnyYqLUT37c4rJb6UbigqEjW5NBbOCRU1DjyeKCL9HNd///876TvPOpxeXfeitG3A6O9mj+N+R4iv4x7rZwpFdRdJUgNZXsnxda6CzSgskuB6iXPPhxP3Hrku6RcgbHTDAqheANjavW4YJXAGMCy7LNq+kcZ41R4s4ZsTL5fnzHJNyjOvIvIHSuF1jK9qwouaLZ99lGoji4lp5iqd2fH1tWgmGZtIPEQFkmYkPjx7XsyuKsz12jFJuqTl/Kb7ZbVpZfw3YXITcwnaMFyB5vWInTz7Rr2FRewsEOPXgXO3mVRNJE66shRRr0coSS/E2ebyOW89UN4ysIU13tcyo07JnJDMABtxyoeXTiG9CktCdLiTZCB4CvmSciYdCkqpkhk2rWbo67llIuQqT13VqcFEYxhinR12mqIZL7zzoGGGcqoadjIQwrs3cLFI0FALZfczPCUblfhVhRRkRPh1GJUK/S9TKa6yrLvHZiY6Y9hDmpmqXrAqFM/KJrYP3X5Ob+cz94owHo0uh80wbwnJxbJxwQAuyghPlyAb6dkrZgQPMMKW06xsD1vqRjOBmjNETXTjZPfdE3iVUw7HYRSdyBl1j45otvwqab6LnkLGkIa7SNE6F4nxhnXnUz/DHe5/uXY9TqdDwaB6owJMo7KXEgBRjvOw1FJQEeBJh591UD9O8gBjGIQQxiEIP4KW3/Xvr/fgS65cAcY9jzvrUrsAyR6yxrsBs/2P3gGYE8/PFd24y6tMo82ispz0drlZ/mqcTKIq04pz/5fsy8+pkzWX+iRjAAZSh+IK2hE9logtptCdewKFWH0MeBwLVjzM5VFe/wieLlv3bX76lWcSc2sKlI4wFHj3GwXUVFAl2qjo+ObKeuBHnF73Mnuhg7s4NU/t/UU3Sl8k/CNTzyiveqbsCh5QpC6V/pFCKlEJSmGiZKbQVoKpqhsn4LEwPP/9qVqrV7ZLmsWsiMCYxXOmhKYYvZdgm/NH4/AGCHPaF4qcshVcRZe3/E7ihucTe2Md8rYb5Fxy6cz0HvyUp1NEROoqF7i3kYe1ykDWqVx1tSJZAwe+84hA7Mj1F7cmpiGUVZOUWRgdJnC+iN0TYLRzgWL5TCB/UYuhS8SAMDLNQUIIclDEKqH8XlGKnBlVg9VoNdBIMz2VXHI/At5CV3dHX1+cS44EtXYVKK3I+7rTWvRRWOynqqQDtdF3xBWgMK6lhk56kVOZjI0W8pGBHG8m3MrdC9EPYMMKkRfMZfLoD5dGwEAG37ZrAV+k5xdAEspmPAXPk9bh/EqPWkhnWSYug++nfnlUMIhlOIHF0T+eqx6lm3XdZXX3r2l+maX9lTw6YHblTaxtuvuVnpjp9qJHkB4aTQMuR0qGPlCLXYGQPiMSl0YqdIewa0Fl2DQqfW92rA1ekaP9Xt30GcPF511xsAAM3IxWSObvQXlB9HUQuwLyJ1m5wW4WBI9Iwbzvv0MdvIWk0rXu6URNwv/uo74YXWKbeYM0HznBmjIxN/3opxdLkMS85Lk0RXRPyiEyrf0qrdgxfb+PqLj+/AsTre+eAv485FEkFIuAbbSFC1Jco0NfHlS/8CALWlVyTad6zQUeblAOBFlpoBl+xAzUkBoOU7eOBlf67+n5k4wOEo1jwMSarGC4b34e7laQBAN7IQJboSkGiuFDCzav7/mntep8wDdrZGMV0gxO+u5ojaj15souqsffBGUqlBCIaYa/Az9xmuIdgpzQ2qKfSOpML0GPQAMFZtRpPPey7HfokEDYd1DmuKEl2SaIg7Nqx5+XA1SMEMAIx6gGRRzn03tNHbW0aaCdRrUMnTyMdIfANIsgGvUK/lSgFKOSkO0qFzsvtXTu6c8pp7Xoefrz0EADgQ1fGvh85Xi8rNtSXcv3MagFRXkonAaOtISikgaTn5cqDmyKaeYqLUhiU5Qg/s2wB9gba37cOHILpS4GH9KILxAuwlOpDs0b3H7BvbIE0NhAC0DBSS4ujlhFZub+UQGpSfqjUUqAXaamT4Mxkv/Po7AABHHxhbg+oVuoC9LOU2iwL2trbcXY1a/nN0cXBTAIKB+wEOXfmu0/oZ/VNZqQ7i1GPepwrFiyxVyXwjPUvxPwFp9yQf0E900gBINg4AeOXkcmUAAXPWlVt42R1vBgBEqY6WpHEIwfBfxokCcNCv4ZbnfVxZqS2FeeVw0mzmwSMdSZMeXMNTDcSSyjLfLGLzCFWc+xtDCCJT8WCzxHi82Nerq2Q8lm9jvldUVWzMdfzmd34XALBr71ZMrqf55N6lIYyUujiyTHNf14kVAOvRnesxNr2MzWV6b3YMs6hM0gNofaWJTYUl3HaYAEe7uyPQ5HDRCy1EkYFnTZA1285Ux9ZPXwsAqJU85MyqSvwG4/BIPxDrik31gF8J82gELsbz9H0LvaKi3owVO9g9O4qS1NntzBWRa2ecbQOhpG2YHtAb5zA7fZnPTK4TAgjqAtGQ1PRNGPwlSnDMSQFdgG2jJBu1beiupBKFBoSkgnTmC8hv7CCStm1pqqnKNGlbNHPNlIYiTQ22etztu960bFTGOmuO8aabqTMlpJpaRnsT2jk4eBHpQx9ersC2Y7VwuX/PlFJ6EoZQrkOJJmAU4lULOQ221e/qXFQ9gCmbrrv7d2zE8H0y8QQhkEre61AO7r4VsFSC73QdIpEcZ9sGdB08L1cnOqPECmD5vBKa59L7zKZOFaF06zHNFMNFOr5nf/7dePQX3oMzP3sNbVMT6Eolqdp465RwD6+553Uom756Hvz3+h14+eTZAIB/6LnozhdgNiWNqdHnHZseQ2eM9n3r+nksdAtok/AYGADumRA/ATKFP759x0EMYhCDGMQgTrMYVKo/pJj62A0wyzQ3WT/cwK3/5cYf8R6dWpxoP3/x23+gZkcb88tKoP1wr7LmfZd/6604u0wr98WogNfc8zoAwKTTxLfmtipjb8dIMJmn9vJzxmJ4iaXmhst+BbacGa70XNy1RAbdhsbxijveBNsk1KPn24i6stXKGcxiCHcocyTprx8dO0bBpHORrbYfmyU06icefx5+a+t3jvub//mSv8al3yD907weYcjx1Oc/98KP4nfv/W0AwIHXv2PN5879wtXY82vHcei5FHjub92IpdcHa/blibHj8DiSCQ1TFRry541IaRU7poucFWPXEi35vY6DoRpVJRtLK7D1BL5ELJdtH4FsNy/6eaXCVLN72L9cw0XDBwEAE24Lsz5V1p3IAU8YGvPUitM7Omq76Fz4Qxr0ULqr1AT0sI+eTfIC2fDaH+fgbgoWZ4L0DEY309XVoU15SGQXoVT30F6QVDbOoEttWNNMsa2+gJlmX52ruULvM6sBklhXFCTBtL5vaddAkjniCCAVTFXyUdNGbRtdc6OFLg41K+jKmTDzdOzfRe1UoQlEmg1T7ovWNlYZgnPoUqSDGzoYE6hKg/rlTh6dJm3PKYQo6gE+PvNC2n7CUHmIOhSII6QdqqCtHYeBSgnclYhffRRaR7aG4xjMNJEW6DV/1MLi+bK1uiFEXZ53tkGQcIlJlWsvtLDYoWM1Wurg1Xe/Hr0WUXgQaTjwhhP7O6+OjKq10J1E2Q1Qsel3PlYaRVmnf//65u/h1uI2HHiAXGu4xZSRghYyMNmG3jtfR9q0VEWrJUSh4sHpbyAymKk+zTjvzTSDCyuA/hxq56WpdszMYvt76H3BVISSFB839BQr8yVocvbCPfOUL+zTIVY7aZz/xXdhXZkeXM3AxboCHauiGeCRlXE0uvTQmR5aQUWqH9Vtejh05BBu1iurGd+R5TJcp88rBYC2fBAi1qDl6EHi5iKkqab4oY4Vr5kbZrPMhXYBOTvG5iq15Z7olLM6zv78u1F2KQHeeRwlo9VxMkHxk8Ur//ON2LtCqk8J11ByA5V0NSZWY63ghRZaDWrhjY81FHXFT0xcNroLdyxS2/jgSlVZ0s3MD6mW7nDeQyo0jMuZ+d5WXc0Pa/ketpcXFKf17jvOhDtP3945KwLz5UOQgVqicgHlPG4TDxw0bw2rArqkV0ZToaKhMIMDAmAL1EZPK8ka825L2q8RD9VAPkcbiWKjT0npGdAKMRyp6tNrOyhIAJV3qM+ldhbJms2fltePJlCo0TE4e2QOhzsVRWMKOzYMKby/91VXkeOLpD5NTK5gQQKOBCjhA0QxOmdyFnvkeet5jjIsF8UErGvAbFMSLD8ODP3zg2rfhAQjaa4D6DpYnq5pUcqrFq84PIf03E1IclQLHXiZifx0Sx3+jDqVpDryVqTAZUmsY3yI3hdzDWFsKHnJyDcx81unBh48799oYbixugxLT9WsXlvFP18MCthxdBTGo5JqFvVV6pKcQLQuA1ZpgCbApDqU4TFAA3gQYP+AUjOIQQxiEIMYxCCAQfv3aceDH6ZWyOYbbgK7h1qeRgjglWvfl2lZamYKT2pllgo+2YjJ1/Ij3g9np38I8Yvf/gNsrHBlOL6ubKFgUHXh5GPV0l0MCmj7jkJHznWKCOUKfE+zDi+wMFKiijVvRjgq1Zs419BpEaVgZLiNhYUytKzVt2RByOqCMYGCG6qWYBgb0CQZ3k9MBTrZ+UvXnPQ3ve3+XwcAVHNT+PZLPnhKx+HpVqhZVK2+2fhIvgdTS7FvniqgQj6AKyvwKDHg+RY0sw/w2LlrHQDysvw/u8ZgjVA19qyJWVXtOm6kDMs7noO8G6EoBfVzZqwUocZzbcx0a9h9lNrL/+2K2/HJ//gZAIDWMSCqsjIthAg8S4nVx2WhLBCNHmC1GLjszGuLFkypRDVa7iBMdcz1qK2rOym49GRlGhBJ+zVYHEwXCuS11MtjXrb6WS4B04QCJBUqPs4dIYGEu5ouDryG0NAZXY7JYzUx1lCVaTtySHxDVr9mLlYiF5tuvhE6N5BKU4SjCxV13TJdIJVKSxduOIQ5r4RAWvSZj7vKoFs0TRg9honbpS7w/obSseC9HphJn+F+AAgOTQKX4PXQez51GlxTx4GX5hDV6TVrkaGzTPs/ObmiaFUroYVOr4i8S/edJ2x0pC1e0QkhjBTlKoHSguKpp4BOl+67GVbDOcNHlY7xZ57/Vyf8zBlX36wQ4WFdqHuVC7K3zB+W51cA/hgH/wmg1AyS6vcZe9/+5A/P3e869vWzPncNEGvgJl1Q4+X2D2LXfiRx7YYvoMltxIIurc81L1QygzHXUZJ32CG/iq1DizjcoQVJPechlIjisUIHR3gJB44STYdpAmXJB01bFvQytZDm5yo0KFuRsPxCqlovmVnyiSJTebrgS1edFPF48wWfOuXf/0wFFwzPGqbEULe6eLQ1Dku61AzleyoBHmhVYZopejIZHN0zDKNGD9NCPkB7fwXFrGWaGmqW/KJ1ezFk0aJlsz2PDeYKPEEP9s8sXaQM3ROhYX2+gb17SbnnloOXAnafK8olx3rDhgZ2tcdVIk1KqZprpkUd7lgXua9SOy/6+Taq0rSgE1oIIhO5et9RxS9LZG2kUZsQgGZQUs2MDyYKLaUI9fjsCNJQh1mgpDJW6qjFW5ZQAUDYtD+mPI5L7bziBe+OR+G4EepS/Wf1qODxpXUwuwBbzjiVhnLPSR2BDLD6iD0Oy0gQt2gfhSuQ5uhF97CB8e+EsOfomIvDR8EM6eiTy6nvYjkXwg+AcVrEpGUXQVWOPi6tgqWALuUT9XoPBTmjjFIdR5doDu64ESwrQSzVuUwzVc45hxar2DK2qPjnqWCK2z2ea+OW530cq+OcLxD9qHuwhJk39b1Xz/jX9+DidQdwsgjGUuie5DVXEphZUvVMCIvDl45e3BJwxj2kvdPfpWYwU/0RxTlfuFqtaF03OiX+5ukez//alRiTtI1EPrQPtelBMJz3FG1mYakEHuuo1vv0h2wGZJopIilpZxgp8k6EpSWqYkWqKa3Y1Q/T0zEu/cbbMSGBWxoTmGnX0JSau4wJVWnn7GiN9OFQpYufGSNu45f3n4UwNJGGdKzP3XQEWwsLAIBuamNLjv69EJUw7SzhjsZWei22sb04DwCIhY4vPvIsxakEJ0vDLKKt0gWo7KHiBljqUuVk6KmqbP4ve28eL9lVlgs/a+25ds2nztjDOX26090JSQgJEIKA5iL8lA+VDyfA6eKE8uOicAUvKhFBI4QkeEEBEfVeJ5BPNKA/5IKIIoOEzGN3ejqn+8x1Ts1Ve17r++Ndtao7SScIuZIO9f7T1aeqdq29a9da633e532eNDLx0kvv0e+5tzmnpR8f2plEc6MIw1c1RSNDMhh9lqFq5JwL2HaGgqpph7GlkR8wCdMUWuqQcYE0VP2/XRNT+4kQ1AsdlHMBVk9Sxm92DF2bTucisB0buQW6P3sbeVhlJcggGbKmA0OTqySEQj3c6T6CoQl6wun/ql0IXMJU2rnTtwrkT/aoDQaAsd2B7A9TuEjLDbYO+rAGEr1dqjXsyliLw5ilGIaZ6e9+utxFVclOCjAsNakFqOhG6Efnau/GigjGuYRtprAVuUpKhkhJdKaC4yULD+BUnzazG/0inlWjhfMTR56OTLkCLb/mP8b/eNr/IF5JbzHV/cMs4tRHa6n/Bwb8uS6yQYSjr3rXk2KO/kZjXFMdxzjGMY5xjOMJijH8+y2I/TfeDMlLWg7s2ym68ciLs5/YsBR8td4p6haAcrmPVsvXu2khGSLlWxr3DZQXqK5mcAnLyOAXKXuZKvSelK1Kb7/v+3DdpX9/3uef/WliX15c3dSiDpdUUmwr+UATZIg+FJ4wzhKJyARDwYtQKA8RAK4zjaliD0tnJnWmc6pR1a49AHB3Zw8AoGr3sRTWtDThd5RPYCmkY3xlYx+MTRtOU7XK5EYsXmFBQ9Ke+u6GcoxntweZboLtKK/NDsLUxG1LlJmJhIMPDEDJ1aW2HJJsIVwB0VSsYC6RWAKBq2qPA5OEIwAs/fhbMP/hG+AoE3EhGEHHAGBJnbkDwIOdGayp1p78CkN/TjFrUw6nxR+1bHDo42+HDEfernaTQxKKi7BgayUnaZKov6lab8rFAaIpyhDXd7mY+KcieKIMPC4toP5sVTc1Jaxtdf5zMcAlZKKEMgwBf4ay0UHPQZZYun6+3fU187be9cHVhWv2PTA2uk/ixERZeSFngmMQ2RioGqvnxNqF6fCeDeR4jHZEmXcmGb6gHI5MK4UzRee18Oe/Cxkbo6zTECPjdy6x/LNvxqW/Qtmp3ZHIFMQLAe3rKv2U2OLqmprVEEliIEsu/DxvvKh+C+KbJbFcSPG6O16Fz58hWHG+EuA51VMAgJrZxT39Pbhtiyb2znoBbk1BifkBHC/B5prSDI0MQMGA0hHa1eWxtFu/lfHa238c1xSPAwCOh9P4rcvOv6C+7IuvxYEyLWYlK8B7n/ER/dwPffkXAACNKIc5v60t16LE1NJ7AJC3R32rjjFS8GkHLp5xYBkPqR7WXsvD0SL14x4qbeJkQDDoSq+MfcUdPLO4BAD486Wr0WjTgp50bJiAditx60wr5AgLiJSK0RUTq1gLimhZtGmyzewcc3cBhs0BtVm4Zoqhw5cUDNLLYCldYCa47vtMExMnXzmq4+17702QfTVl5TK9cC68/0bAZIh6yo6tb8CapHsp6dm4p7lLH6MTO9pAu+WaGn5EzHH0N8/vRiVsCbehNgw2tCaudcZBPKWuOZdIE0OPqyl97VizuKeB9o+58JVlX96K0F4hQpnjJOiadL0NS2Cu1sLKJkG5jpfoHl7HS4gMptScOJeYUjD6RquAYE3BzoUUpptAKPWsLDQQKmLbfLmJh4KRFaFjpZo8dOT0DI5vTGJOtd9kgqPdpXuOG0Lb/DFlJu6sEBycudD3BI8YFn7/RhhKy3mwSyLz1QbBzZArju7bQcfVG4TJCuljZ+yxzc0vhLjwtwXjGMc4xjGOcTxJYpypjuMxY6gSdCi3iTmLFH1u7y/gE0cvf1yjcQDwjAQ/tHgXACLHDKOR5nFHfbeGqA4cXIdnqjaRzMCEN8BxQZlUPLB1k/jSa3/lCTqz/3txsjeBo0rU9PLy6nlf96p//zl0kwJued77H/X5tR6RuNY2y5iY6GltYN+JtU4vAAjJYasMNRUc28rL9fDEFnqpMzInTzgeOk2ZajBnaTWrODWx0i8DWNDHdJTAhhAcxuZommAZtD4uTwF5kjKsr+TmcfXMae3Uc/XEEr67cD8AoCNcfGLnSq2KtXRiGk6VMknTj1H1B+grwf44NdHbpmN6lQD7/up6+rBtB0bIkBTpGLxrgCvY9cSvvBH7330zMuUIwzKGNBrCkcDxdTJ+ED0LLOLwdlN2FzHotqVUOfkcvk6JukwIoEZZ01RtgK3MR6ZE4oUBeFv0Wb39AqZP1ypLOdC0MUTn98zX8QNzJPDQTH18rTGv2cu92MFclTLCfmwjKSiFL8GwWh+17FQLfW1mYJtE0huK4wvB8MDWDI0/NcBDlUkzE5khIJQZe2Gmq4UhWpGHPZWWblF7Rm0V93FSjoozAwYX2pTi8sl1tPN0Xdb7RWxuKuSoReb00Qzdc0bb1L6oRgzklwwIxRPr78vAAxqXUYoghv6pdgJnoqtRpz2FFsLUQgIlDnEBx3hRHcdjRj2kCXrR28ZnmySa/c93X4zln3vzY71Nx9muNS/+l1/WpuHdxMWt3/O7533foY+/HfMTtIgHJevr7g/9VsbCX9D5XLGQ4LIyCdy3Eu8Rrxu2KRyuGViuV857vI6qe120m1i6vmqHKdmhrr2uDkqo9334Cs4zuMAgoRltkNpohZ5eOCAYrHWaMNd2pnHt8+8FAJStAW7b2Yv7tmlybTR9CGUwPpRcPPg3xE7v7ni6lpY/biGZps9tnq7gHitFQylkfT65CCf7tCkSYNjoF7HeJDYnH3DEqjZqFkOs75R0LW16qo2BS+d95OW/iYU/eyeN3ZJI8imgoFUmCZIdhhEBaUX1hKYMULKUbGDolhcecOTWOXq2Mn93MxQnqNbYig1c9Ls3QygBJuFnsFYUG33HAY8Yqf6ozxrMqc8yJLKh5RyTsFsc6QHaMMzmOritvQAACDMTUWbqDc6wJQoAbJ7BVn/vhA6y1MB0jRZc10zPUc/aP72tXY+6oYNeb8RREENDd0PCsjNIBet21wpgpBqIODHx0v33oeRQX/A/HT9Epu4AYAo4fozFSaq7f3VlfsRktgVYj87TX2ij1/Zgbqp6ty8QFlTP/ZIJIyQDBQDgfgJh0L00Ve7hkgqxyu/dmUWYmBio++yBZBpCcGSDMfw7jnGMYxzjGMc4VIwz1XE8ZgzFAm5ZuRxFJcD+cPusX7j9J9BTzM7b1/acV6Vo+X65UJYAACAASURBVAvz4JfRDnxQ94Frz/+50VYOgVJUCtML4za9dJ6yU84k1kPKygapjVff+mptBP7J5/8+FivkadqOPKTxo5/by7/0izhcG2ViZ7plneWc7lXQj0d9iL6daLZtEDlwlBpSI8yBMwmuUh2rHCIRKrMxJb68sgAAKPsBNndKuHQ3jf/q6WVsRYRQvOjzb4BjpjhIqDHuC+Y0uYSlFrwTStx9f4z1I1PIKy3aVt9DWxGrLp1eR9kJsGXQMePSKPvq13OkLqYYyhvBBKCOv/+Gm8FNyrr5rgCFfKBVoKKWq83mF953E8pXNFE5S0B+oHpYUwFtCWd1OKyuhL2trOAqDO0WIQVGzJAUBQwFVZotU/u62jMDRE0XqRKlcDdMSEWs4tsOhMrcjbaBNCdxeBdZHZ5sT2AuT9cjlQaSzNCWeoFtIa9+TwJMs3iLboQ4NrUC0nqrggnVr+1ZCfqJrZWYMsHh5Qj5iWMDUv0GJZfIMg5HWdAtHt7G8Q2CwC0rw/3tWay0lRWhF+t7hwEo+iFOq35X08wwvZfu1a16Ee4cZdfdeh68ZyAtKYKWLWDW1XlNCRgR03q/xooLtpfeN5nraxUmgwtIyTSU3Wt7kKEBEVz4ed6FMVuN41sSr/jKz+MrR8nNYnK6je0BQUGXTa2d87pMMtxXJ+jw4qnN8x7v6HXnZ1eeHYc+/nZM7Quw1aZJ+OsxlX4yRKQW/65w0Oa0oLRDF92Bi2sXjgEA3nT3D2OtR2zoRisPwzzXP3Lo4jPhpNgMaGGedHuoTKzpemXRCnHbNrGm09iEaafYN0mTn2Vk6EU0cfl2jP3Fbe1SU/fyWLfVYn+moMVHuoZA1rW0EIdrJrC5Mtq2Iqx0yyi5NDFese8M7l+n77o3L7QBdfFeG8G0RGeTvrPCdA8LFYLvD/h1JNLAoSLdG/+0chAtxS6WoQG7HCJdp3uLxwyZQszT6VgL7++ptdAJHc2EZRGHqRi+wueY8Aca9u5sFHTNNhs4kMrYXJoSwmTw6nR8nhnaTFvYEnwiwolXkNLWvr+8HoUCnXNnxwdzBCyPFqnQcMB2FNNYEsQMAFk1hbFjoRXSCVS9AcLhte/n4ZgpJn0lz5jrYH1A13u9XYRjDd2UDAjB0FtVrkCVCG0F8e7EeUzVRrV0xiSKSgyjkfngSo7ScxL0+i6CLr0vKRmoFEeKVWdaZQwUU7pc7iNr031g9AzUO7Y2Wc+XAy0MwS2BaIW+WyNh4AlgzNKmwDAkkoYqojISxw8WaLFnAwNcGZE/yGa0icNw/MMwLAGvFDwl4N/xojqO88ZHr/kQcM35n7/qUzQBmcYcYrWg/O13fOCb/tyrdp1BI8phxu8+/oufJPG8z74Zh5Q1Wzd1ULNp8jzJqd9zaAh+R2OPlsCrzAT47LXv0cd43R2vgm+o1hiewMtTbWtowzbMZjwjwbWLtEhvBkU8VJ9ESdlwxWeZx/djG83YO8c1ZdiuUt7XROsMEU8yL0Zlro3GBn3OVzdK+N4rqN5atfvY5+/gRI8ynbVeCULVK3kC2OoriktA5khM76FrUHYDdJRC1scevBJgEq4iP933/W/HvvcqHV4uIZZ9MEVYyQqZXkjL1b5WAlo6MQ2zbWj93epCEwVFvlmpV3B6u4Kcq0gupoCpNiuRKcG7dIx4JkGSN+HWVTbaA4bcOQ4G08pw1c+Su9CpD/+aJklZfozDs1tY7dBC11rNwW4N22uk1uK1NiwkUwkCtRB5loGT66quPDAxvbupVbJ2Il/X0y0r0/3aaWZQjVNl61nKoUqSYEyi3fcQqzqkbNoIarQglgoDNFbp+0yLBmQ2slnbaBcQKcWtrGfBKsR42l6SwGwEI4lEYUt4tYF2/+nt5LSTlrnsgqlWnriSQRoMfJk2RmKhj8oh2tTtLNM55RSCIRxo6cp4mul5Iu9GKDgxTq/R78PxYySJiSwZtYRdqHHh59rjGMc4xjGOcTxJYpypjuMbjrxDUM1WJw/TEI/z6sePl3/pFwEAE+q4Dxf3/s+ON9z5o2gkvs4yb12f1yILWcYR9hws/SRpDHtmoutFvhFjO1ZqSFxgd6GFh1SLTWPgoeiOrtvZ8c+nD+KSKarH7fGa+u8FM8Jur4m7W7v18YeQbivy4FgpZlyCBU/3q1pgwDFSuEaKwzViD9+bzGnIrdXIw2zTuQQyj4EptRKQBNCKCcKsOT30Mkeztmu5PnbuUy0qnkA4QcfLrTGE03SuACkqDbMgIThyfoi54lnGEep2YWBI8xl4kbJYLgHPp8/qDxykdcp2WTmB8BMs/9hIDGIY8398A07+zK9h4QM30h/cDCUl2C+ngYFyNWI9AzKXwXomZYvdVg5SmWLXdrdgmykqP0m+us/8x18DU1lmZkm0Kh4OVQk3/iqqWvxBWABs+k9SBZgtMFug9P051VOoOAR3rvZK2JVvYzMgevH2wEfSonOTHQNbSq3IPm3DZtDtO3Y10JrG+UKI+UpT10NZuY9A1Wgbm0VtYOA4CZLY1KxesxBCKM9XbzKGyQXOtCir7XQ8rXG8q9ZCJji4MhV3zQRnmvS6fc/bwMltyiply4VkgFDoQtFJNKIguUSwJ9Fog10JdduVc8TDy3/4VgDAn33pO2CUY339w8yBU4jwVFCiHy+q4/iGo9GnSdOx0nPqI99oPBHQ8dnxki+8HlWHYNi/uPqPz3nuN+/9AQBAzoiwGlXw2aVDAIAfOnAXNqKhkLcDkwn01WLJucAuNWHaRoZuycGLPk914ulcB5PK9eVMUNEwbNEK0UlcDd1ePrmuJ9acIpI88x9/Tf1/BN9+tb6gjdBff+crsRJUsDtHC9bRzhROrNDC5uUjpKmBPS7Bb3kjwpEusYqSzMG9W7PaWN00M/09mes2MpceW22OY295IxY/qhSqJNNjHKQ2XHNEhCpaIZ529UkAQD3wNaEm7lfAg1GPrGOkCBUMKhKOQd/FcUUe2vd7N8Hq0fGi6RQs5VqtR0oi3QBA0nTAhiSaHRvSlFj4A1o4vbkeBjsKuhQM8390A5Z/kdp/Fv7sndpE3LQybaQuC7Q5GloHspYF6SrJv7US5vbu4OgaXTv3rhwKQynCGkPN6+H+OvWEGhHTtVhzsQepYFCWMshijKsqpwEAHBKXF6lPecrpYS0ooqnqrfX1km5NEnMhHCX3yC6JkJwsIFP9uHFk6YVmptBFfeAjr2DvRj+HxRqVCB4MZjRxa9D0wGwBrmwQc06MeCianxqw3RS13EA/N3Sz2e75qOQCPG+KZB1XwzIyxTg6cjvJSgIAN4BTrz9XYnX+Q+8GADhbJpKCQH6RNi5BaIGpTh8mgI/84wvocU6g4IdoK4cp5AQ8J0GWjeHfcYxjHOMYxzjGoWJs/TaOp2xc/Hdv02xkm2dY6xNs9ordX8NKTM3vrSQHz4jRSijr6aaO9nidy7cx43Y0dJtJjklXGaebBMGe7ipihhXjQIGgw/tbM3oM/diGZWRY31JG9laGS+YI4k0lx1KjisuniU19sj2h2ZZT+d45JKaz4/v/7XWY9ghK7acOOokLU6UDOTNBR7GEz7TK2FNuIVaZSOtP9mgB8/bL+ojWfH1MM2DIlE9qbqGDXoOux/yebeSsWKs5mSzDRQWCQYVkuKNBLOTlu+cwd+kmig5lxb3YwYoi4mRtC2bXGLryQRjExAVG/7KKgj+dFGFHZS8pG2nzMgnGJdgwu4sNIFGpWcZgNw19bsIGhHqf5IDMj7IfbmcjpaFjJvp7RszgSy9bRlcpHm1+YZdWjspcCeuijoaREXGtIFTY1UG3RdfKzsWIWi7MPCEQe6caWCxQJlk0A9zXmsPyDl2TeGCTSAXI7F0or9e5xW1sNorYP033Uiq5bilzzRSd0EU6FLZgUpddBpGNSNnd2U6Csh9oUlCcGmeZ2lNrz9A+0XUSfN/CfQCAS70VfHTj2TjZpN9GNRdgdVuR2VIOtq1admoxln78LTg7Fm8m4pnd5IgvCZDz6T5YrDRw32376BiFDEZHWdAlDEklAx8M7fRI11sEIVZ++boLeo4ew7/jeMrGZKGPnZAWjk7oaIblR1eepV+Ts2KcaZXxkoUHAAAnuxNYr9Pi61sxbJ7p2mB9rQy+SKzJTVnALr+lF5F+YuPWrb0AyDlmCKkdnKjjVKuKKxbOAADCzIKp2lXCxIRtpjiyQ4v2XS/9bT2ua//5v+MlX3g9AKAZevjKi9+F7/wcSTQWbAZDrVAX+Vv48vYi7l2iemu+OkCS0GcPewDrfboGwct68BVDNlwvjZR6KjESywRTk3yvmUNtuqOvj5BMq/ic2S4jnqFpo2wHuma4sdDFVjsPQ6ka9WMbrjesjZpgErrvUxpytFgaEgg5yqrlI2cnWFfsVjhAzqd6X3CiCGlAt/BEE0IvREbAISxdxoPwBH0GAJZLYSombS4Xodd1teduVJYQChI2dywc/cI+2G1lml0AMuWZmuYzZIENruqSIuawJ2m8ErRQAwStsoRDKJZsK3Bxa4/uiTTjqPgBDk9TfbsR5nBmdUKPdyjlt35sErwa48Swr9ROtSKWbWawzLMMx7fL2nuWcQm/NDJWbw88pMrg3bZTDJTUYe9MEeX5FmI19XdbOdRjguyP82m8sHYEp5rPpWMELrBCGwl7oYe4Suf18AV14Q9uhKyoa7M3gqjn0FXGB61coI3a7VIESzGlByt5+EumZl9HiyF409aOShdyjBfVb5MYSuN163ktPfdUjy9897txxT+QPnFzq6D/nqsE2lbO4AJ7yi2dcQ31hwFajDa6BTx9mupiBSfSz3/y+b9/zmfN/8m78F8uPQIASCTXJuzL3Qqm8z3d1iIle1R7sYdHN3Iwm6OFzVSsmKGtGgD4yn9tJaxg0uvhIbVG7Sm3cHSV6oLXHDiOu7fm9PsMQ6DRogXWLkaIMeq1/IGr78AnvnYlva5poleg5yZyfbRCD75NC+RkuacX2EOFTfx7fQEAOY44/iijdcxUa9b6JyyEExKWakMBB+JqpseRtnNoLSviTDWGVFmgaWfnvVb7b7wZXJmvyz0h5IYDe55QhKDrwNxULR0hB5uhxSbNOBgfCU3EswmYIvekhQwsxxDPDQuA0G0+5pYNVg2RqAza6HH4d9JCdOcHRo5TV7z2ZrQuEXr83Z6nrfGyjGOjUUTfVyIJkaUXd1hiyNuCUUggWjZ4ma63aWboqww5cYkAtHsX1da7BQf1oaF720IwbMMJDYBLLdKxOLmDDaX1a9ZCZIIj2RgZqf/zceITTFc72NgpwVdZZqfrQaoFMavn9EZi8SPXIwsM8I5aPgwGrxTq86zsber2rK1OHs+/gn4XXzq+H8d++K36el38G+8Z1fVXHKSe1AS2Czku/G3BOMYxjnGMYxxPkhhnqt8mcd/3v/1bPYRvSQxrlLlKoNm2rU5OQ6P92EZiGNho0k4+abra1zWn6lCrfcqiKs4AU6qm+vDYtbuBr21Qtnuguq3Zvklm4MFTc5jfTTWyqyeXvq5xV70BljpU2yo6IRY/cj1OvnJkKvDf7/oRAMCZfhnH16dgKL9ZkwtIBfsFmYVuz9MqNpYhdNYT9yzkp+lcpGT41ENP0/Cv5EBymjLaEyfyqFy2je2eahEyMs2srXfzuo6HyMCuPW3sKEZ4b+CAHaP3ZDUBYQFZWflqOplWkjLNDDJgEBZ9dhYY+lyyjGsBhlOvOreVRphSC8jn7/aQWUA8q+qEpxwkSuCdTYfascbzYgg7hSqnA00btVliqZbcEMtbVZ1Z9us5QMHQaSEDq3uaocx3BfAvIxThh778C/ib534QAHDX+9+I+Q+9G1xBzwdm6pj2iC3+Lw8eBG9baBuU7UpXkEk3AAiAqbqvaNiQfgahvoswGNVek64DZAx39hb0dRyGGTCwJdV+ZADYGyCvMs5EoSaAgpOZ1AIYqS/hKxnBmteHNZUR7AvK1GVhhNwMHWbM4x64I2EqBnfmSCSKLZ6WMkRWBkM594RrPv7i5Yp5f/U5XyEGe1JAGc0v/xS1pnU6HZTe8vjuV0/mGC+q43hKR6jgMTcXawhTFIDegCaOODbh2Knu6WMJw6TSHO6GDqYLXd2jKSTDhpo8XvKF1+NTL3iv/pyhWTQANKMcAiWZ1wscuIVIk3aWVyfw7qc//rhdI0Ve1RPvWZ07x6z7F27/CWSSxj+b62AzX4CjdG8bQQ7791Dd7v6tGTxt1zpWuwTFtfsepqZoEdlcrSA8qpSaMiCppXDrqpWlKJBVaVLMPeSg/8VJBDOqHuqnYKovMzxVgLFbyc5JoN7z0d0o6P8bqiZZWOJoPSPWrS0IODLlMDOQNnhOQqjWFsPL9Ibn2CtGUOHDo3xRAy0FZfcWGKyWAfsBWtDTvISYomtncgFbLVgGF4hDUxOVeCVGLdfX1820MvQbtOkwmyZU6RuSG0iLAvDpOI6bYFURz774Y6ONzqWfvA4w8piaoAX3O2vH8PHTV9Bnb9uw2wxxmc6N12IkLdXXPNNHoO5TkcsANoJu085I49kqRcg2PUgFS0sJTc5KipnGHY0eRxaaYHnVkxx4aNbpe5mZa2Jru4j8UAnrkgAF1TfdCHO4uLKBrwVUBy7mA8yoFrIgtfSGqTuVwt0wYT5TuUhtFGC2VB1/wDFYz8NRkolHX3d+q8bl17zpvM9dyDGGf8cxjnGMYxzjeIJi3FIzjqdUvOyLrwUA3PK89+N5n30zVtYJ6zPsDKkitrj5GHlvJNzNmERXZa6mmeGQUiA60aiBMYndJcruNnoF3Pa9139d43jeZ4kMttkqgHOps69dpbZ2kTm2NQnXTnD3973jEe+/+cEXIVJOz2952qdw8d+9TYvJ76014ShT8iNr0zAMoQkgh9/6Hhx5BwlSHPjYO1AujBihtVwf9T4RbNr3TcA+SBlVFFo48Ypfx6G3UQuPGQL9XSpzjBjMwUjsIJ2MYajWCpYBYhdBhxPVHlwzxXaXskfLzBDeq7Ro8xKl/U3d4jHoOlqoAF0LMpdqKJT1TUiVESLlOhMbZjXzf/Ku0UVSHqwsZeAR0y0wws+0dq7hZMhUtmeVIiR9S2fapp2ioCDSxmoZLJcCbbrmTt04J+WIDwSYUR6nncA9h0D1rJ8iveDmJcDMlRt48eyDNDxp4C/ueTaNw8pweHYL9x4nljYyBqbGb08OEHUVDVYwOmc1fmvd1obfciaE6I9g+96WD0Nl4VJCE6Scky7C6RRcQbf5fIhOcySUMT3b0spXx3YmkVeZapyaEBKaJR+nBtodet/URAezPt0vrpHizrXdKPl0b+0utHDHCRKHMN0EScuFqVpnzAH7uo00gKfGHD1eVMfxlImf+dp/1Y/P9MuQkqEdjUychxPJaq+k/yYlQ5wa6Cm1H8dJsUstoq3Qg5BMW64lmQFDsVv3l3bQSxxs9AlWO5/h+qWfvA6OmSFTE97ZbTOPFq+741UAgEiYWOpSy8Vnr30PvudffwlFmxaA+zZndX0rCizMTra1+tK+v7wetksT7dEfvA4H/+btSFV9VNgSvDbaTBinCOrMFkLk/BDdHXodb5uwumpFUbXhTF3GtJrAqtMsL0zgymseAgA8p3wKADAQtID9+YPPRrpKEzLLGORMqGuNjEl9XCk40r6lnV6W/tu5Sj3DWPizdwJ9U7fKmG0D2bSSVYw5YEpYygbNsjJdPx9EFoJV2kiwSowXXHRcy05+8cwiAuXWgo4FaUk4W8oFRzBt6Za/gnpNhwL+7cDVClPpVyv62kSzCQw/1S01MuUoVgle3ldp4MjmFKK+kg50MmTbqr5qSkD16y7s20Kjn9OM7f72SPAegmHidgN3/CExjg++4z1I9tPCxjYciEl1/m6KLON632KYma4HDzd3L1u8BwBwR3OP3mgFsYVB1wFrjKDoIVN6/uINvGSW+lkf6M3h9o3dWm6z2fe0Iw56JqQjwFSL0NJZ8O/C798I6asa6qt/FQ+PH/nKa5D0Y9zyov91Qc/R45rqOJ4y8cfP+l+6l7Mf26jl+jhQJoLQ8VZNS+gNIls3wy+UGnhG8QxuWbkcALWtDBfiYQvN0G1lX7EBz6D3XVVYwpFgVvecni8GfRcDQPdsLt50MwncAjjwjDP4zHf93jmv34pokf7YNX94zt/LdoBZlxb7o8YUYpWWnXqYFq4YmEi2aLI+8Ne/DdsRSKbVQtpwIJr0nL1tIJpWGeHARGRaYEOf1Izpup3VZ4hLEs7OUEfQglD10NLBhpZVXI3K+M7iEQiV3onsObDbw4UZCHsWjEqox2moBStsWzC6Bk6+8dEX02HIhINlpBUMkENOGo4IOLnSAFyNOUkM2AoNSDOOsErXfs9UEyfaNb1Jmiz0cVotqtKQqO5qoeHQ9ectC7JC37WUDAaXeiGt5fs4vU018mR3htn9JIaRCQ7PSnTtMRg4ul55fKeGqOGBKWJOGprIryj3nIJEqohVy+sTcHMx5LDp1pA6q2cBR+MKgSt/njJjPs0glW2bmEgosweQJga8XKx1pOtBXreCnW5WUMoFWA0JRTjTKmtS3WA9j9pCA/VMaQt3TTgNtTiemcTdPhHxBqmFKLIg1bk9fWYNt6f0XDww4KybiHar39cHbgSL1SajlI3aiFQs/D7JTtb2N+CaJaT9C9/6bVxTHcc4xjGOcYzjCYox/DuOCyredPcP497WHADgsvIaTvUncGSbFIkWKw1wdm73uFCC4BNOH/2MdvWD1MZGjzKSw9VNGEziqytUE6rkB1qtyDFT+GaMWLUkHCjUkaoMkUNiLSjilue9/3HHvPiR68FVdnT8R96K+Q+S+LhRSgAmkala79UHT+n3bA4KmFaM4qV2FQUn0m0tSWagv0WPc5N9PPj/vk2/75Jb3oYHXkb/n//wDZTlDH/hhtQMXLPLtZTfo2WJF/0uZUNOgyGzoZmwYU0irak2i4zpto6pWgcGF2j2KEuT9xa1kLqwgGgqhVFSfqeSIYtVPbRvAgxY+sXzs0QBymiMkGspRavDkFRG33VurqeF+MuFAFWP2KcmF1pasp/auGdtTkPnlpVpo/aFmR0s5Bu4Z5vurVYnp5m3e/bVEWeGZo87Rop15a0axtY5bPE4NfCMWRILuXV5Hn6OMi/XTtDq5c757CHTGILBLqm6ZtMl9m5eoQYxg1TKUbnaAIOOq9tPDrzrZl1HTotn1ZHdDDk/1NmuZWTnCIcEgQ3PGzkBZQMFWGYM7CwJRh4xpCVlVj/V19l/nJpa5QkAfu6iL+ODR54HAIhOFiEcoWUnRWjCKoyyz0yJTrhbHMFsBl4dylMmMM0M2SDCkVfecEHP0WP4dxxPSAxrgak08MGr/vwJPfbr73wl7tgmeOlQeaRX++nlizFb7GChQtT+vBVp3VsOCc6Eboc52prS5tFFN0KjRVDwljfA8Y1JZAE957sxLq+RFu9d9Tm0uYuFEjnA9FMHiVqkp50ucmepL50v5j98A8z8yKD7klveBqtKM2G1OMBiaQehglBbkaff149tbEiaVObybZxo1HQ9d1epjVX1uuECirP+P1TPYkkRrBhDKNlCbmfgqh6auVLr7i584EawhEEo0gsLDTBfyfUZgNVjYGpezLyzNi0Z0/2mBSfCRcU6/rlzEQAgrmUw+iNdVxgSmYIqIaENr1k5huha579+f6xaVjwgdQV4T0Gm06neLJiFBDknhm3S5/UCBzmLrvec3wbHKG+IOo6GSRNbAGpx50wikRzXzNDGZmZvBwWD4Oq7unTvHW1N6ePkVH214I5UtvLFCMfWp3D7Kr0+aTto9YcsI4a5PTva1SdnJ4jzdC6WlSHLRjXsrJrCL1OtdLCWH7nPbOcAS+LAX1Ndvngih52r1XcWc5hKhYlziW4zp+uolpeglKfjtXsekr6FdEf9TioxuIKkRc8CGGAryDdzpYar+w0Pk/N1ff71bh6DDh3jw8eei0Fd/S4LGYwe1w48uepAvycMbCKRAQgOJmSkrjawR17+m7jsk9eNpDMv4BjDv+MYxzjGMY5xPEExzlTH8YTE6T61rszl2k/YMff9JbWvyOZVmL+YSBeeEePioiJg9PPE3lXi9cIcPY4yE9O5LoTa+xadEDuKTQtAu2gcXZ4BAkO3H2xvFuGrbOWle+/HLacux9ceIpcNpAwsfCSr8bHCrw0wW+pokksndDFdJFjXZAKD1NZkp6NLs1jcS+08FTfQQv4AMFXo6YxopV3SJtzXfOZX0ej6mtVJBCwF8U4E8L0YfcVsTro2nL5S6ikIQJ2zHJjg5VgLSKQ5A1JlkmLbQZgTMFXWaTc5rN00/kouwMoGEXbaoYvTRgWLk8SUPRqZyEoq7+ha4AODhA0AwJRgLeW1GpowgnP39vtvIOj5xJvfOIKuBQNsoVWUAGjCl5QM9c0SoL4buAKiQKzb462ahoI5k2CmhFQotNEwkSl48/jpKQSzFp45SV6oA2HjthaJIAxSG3krwoxqKXloZxJ9xXY17QxFdS+lGYcQDNG6Yj0bAFOqTMIVaPU9uIogZxuZJmsFKwVI1ebjTQ5QyQ90+9FPfdc/wVAX4X1f/G4wI9NKSXd86DfwtE/8Jn12auAZcysAgAe3pxEHFqQStTcLIVwF3dZbDnLLlm7TSSrQGS1MAWfDhlAQO8sAqVjZsAXq3by+9P22C65QCiEZqkqPuHmyCiNiiBV0Xl7o6Oxzte5riNrNRwi6DnIKHt/3V9dDpjmI4MLP88aL6ji+6Tj08bfj6A+eKzA/NO9OJYdv0Sy20SugH9KPzTAEFisN1AOCjb7y4nfh4TE7SQt0cXeoBcFvTeaxWKKJ2zFTNIKchuzWghJsVbu0jQy+EetF9VivBlstGp6VotmkCaJYGSDOm4gGNMtYuQSfOnopALIsgyvAFOSIUoJcbQRnnR3P/Mdf06zSs88lSQx87tqbsfB+Yjka1Qj7K8RIXuuV0IkdytoF2AAAIABJREFUbG4R27JSG6ky+VaEIKUxzfgdhKmFdkwT+aTf172uncjFRKGPfkTXtd31dB2v4Ifo9jwN0aIYISnQT16aQv/dneojDC29ODTbPvyCEkj3YxS8UI+RbzlITxIs3SuPFv3t7QLad9bwwy/9IgDa8GyvqvfEDMITum4IAMlQXYlJnHzDuTXdE28eidQPa8A8AYTLNGTNA65l8pi04AQMg710TS7et4anlchNaHlQRcmic/nyygLQMfUknzvQ1o+T1MDqUg3NvnJlOatfOWf2MUgt3HXnfnoxl0CRFsdk08NOkb4nZgrIto38Mo05mJLaoQWGxGDTx0DVR1tOppm7APRiE3QdVPMDvGD2OADgSzsHsJCn+/3lz7oNf3vXlWg16Tez8IEbAd/Tnz10ZEoyAzIyNMQerOexWla9xaFB/cWq71hkDPt3Eaw7SCxstqaQVRQrXDBtdC7b9qgGDMBbshHsoWsQGBK2cvuRuQxxDnoztL5VxuwULbhGn0O4qs47kSF2Mm2eLgITyz/zZuK9/MLjG048mePC3xaMYxzjGMc4xvEkiXGmOo5vOvZPbj/ib0PGrJQMD6zMAgDmai2UypQ1OEaKjX5BMyqv+tSvY1eBMtNLiuvoZQ4AInyU7QCZT/s/34p0v6Zvxvjq+l404hF5ablJcKRnJ+CQONOhbKkfOBrmGuWDpAc7WewhK9CT2+08uKKtZq6B2lQH+w5SpjAUTh/Giz7/BkSKZFRyM7RDFw+PodLR0msJLl78yPUYpJQ1hKmJRttHudJXY5GY9IhJuhP62qt1J/TBIWErmHjS62lyUyI4DCY1rDgIR9lPGFs4/qMjcfL5D98APkfHtAyhrc0AIOtZqHfUtTsrG88yjp22rwUBWAbwcNizaoKrHkThSAgT+OvPEAs0m4m0SpK0JIxCglQRpuRZSkm8a2LhfTfBiNRrGaBc7ZCUxLnQsJAa8hWOQKpIY3IixtRkBy9V2V0/dXBPcxcA4FS9CqnEL8wBAypCj7l3uggUFdHHJE/TKKKsU0qGjT5l5N3IRpyaGqI1OwZSQ4n3zw40EzaOLIhSjKSgskdB5w6QlrDZZ4hL9NrqwQ46yp+VMXmOOINnJXiwQ0b3eSvCJ+4m/WBuZTC9FJ5CIrJ8BPPf6P7Of88Gjq0SkUpGBuwtE0LN7jwFEnV/Sz+F/R1d7M7TfVZ2AhzKbwIAbjl1OeR0BCgWtVcJtJm5PTXy6c1CE8KWMJv0HCtFmPTpHt51sINGkEOmyh2OkWGzTSiTs9DVjOS8G2niHQCE2SgLvtBjvKiO45uOZujhms+QQkq9lcelc+v41xc+stXke/71l9CLVX0vM1D1BshbNEFcVlnD1+pUw/o/ncOo5gJtrHy8VcOUT5NAzkxwokcGzqfbZUwXeljt08QSZwZ6PcVqLEhsB76uTVUKA8RK5i9OTbzw0FEAwL8tL2KxsoN716iVwvci7Wzz4svuxT8euQS9wHnEubzsi6+FZZh6YvfNGJ/7XqoF7nvvTbp+uPyzb8a+v7wepjKyzmILD6zShJnLRZipdtAa0IRyxcyqXiw5JHZUTfVwZQsFM8S9TTVGM9IiFKkwEKQWwpCOITKu67fsYVTK5Z99My76/0gSMefG+jzTlAOCwSjSBqeaH2h4+Uy9Av9LPqyuEgiYYRh2LQ32hShX6Hvp9F3IY3lAIYf8tAM5tNvc30O86o9qqlwCw8c9Azxhur3H6vCR8ESbI1Wi8MIVJByg4F/mZvDnlJoQAN+OdavVILFx+ih5ypp9DkvBxNIEjJBpBrO0JCpVGr9nJ6gbBVwyR/X6MDN1Tbvd8ck1ZsiSDTlyZxSMvlbAYB9dN2YKsKaNuKgu0FSkIVjZNRBOpyjM0ZZOSGCyRJ/d6OW0oXjY8XB8ywdUq5LhZHDy9Bux7RQ5O8HmBgk3sIEBXEmbpF1OiE1Vo53es4M1XtUyjrXdLexs08I2PddCvVHQEPJfPf+PsKHEHu6s7MG927vBVItXaDhgDfoNRtYI8kbCEFdGG56k66BVpPtvu1mA68WYLNC5ZYLj7KbN4aIaxBaa60WwIfs6ZTj09vcgC0cCIRdqjBfV/4txds/g2fH0v6fsZUgnfzzpuidrvPTf/hsAYNZPdd1q0uuft3fz09/5P/Xjw3/7W4gCCxNqUgszCyWVmTG4OLEyiXyJJk0pmV4o7t6cQ6B6C6ulPgwmkKhmvUxwagsAcGjfMna5Lf15/7J2AFwtMhP+AJbK+q7ctYJYGHj6LmpS6SUOZj0ipPQzGy+46LjORuc/fIPOlJZ/+vz9qZJLLe8GkOrRwvtuonPjEpcuEqGkHbvwzETXYoVkqNh0ziYX8NWGo2b3EGQ2FgrU2rPUnUDJodflrQjt2EWnT5uJrOmgrz6XgTSI6x2qHwvBkGzS5NeNc1oZSboCLOIQDdo8rDUmgaGmbGCgt1eCK3KP0xiJ4siMYaehjt2zYLgSmSISsYhrK7njP3gdDnzsHZDb9NnSy8DbikQzYIAAEuXeYoTQWWtSAHRKyyRm9jawrSz6spSjtzySm+y4edhlul5JZOq3ib0B2L20OGauhNVjsFSNXFgSTZuO1zJIOvH4DpnJe3aCgc5aAW4JVIqUwXd9B4PCSKuXKWUnHphwdjiEeiqLXaSq31TOhLh6cRkFUyksdWpoKOWlNDF0Fg8u4U/1z6lfDhei7kYBXUlOMABg9hkilx4fW5/CZJXu23orTwtuiRbBXuBAqgX2sol1HDNTnD5Cm44/3X4eXly+HwD1csOQWgry4vl1PBiSVrHZNpAqtGioWZyW1A7KkDrr3D3ZRCK4dmmKUwOJynaj0NDoRRTnwFOmVbeiaobEl1oa8kKOcU11HOMYxzjGMY4nKMaZ6hMcL/z8iLkoRAnPVbDol1/8Lhz8GzIKNwwDc+UOPnftzd+SMT5R8Q/Pf983/N5gxwNLOIwa7XDvPz2LGcX2tbiAYQndEO/nQg0Tm1xorVLOJOTZgveCY2E/1YdI/EHqLDaMLVxUo9rvA+vTuKhEjMdD+U3c3d6lRSKaUQ77cvS6durh/5w+jEC1pEzuan1dLjXmgCNT7i2HPv527K024cxRvZJzgWPblA1JyRBHFkpF5enZn0HJo/dN5brY65OoRSd1EQsTDzYouwhiS59zzAQWCztYWiZIHIbUrizLP0/OLovvoSxZWFJDdpknIFWmymJOqj3DpDDmkEPWbUQs22F2mjnQsK4cmFo8we4yJHsi2GeGIvHA8bNYvMd/5K2Y/0NSkkI6wqUzV0K4EixRsOCM0ApCENDb/uWfezMu++R1SJX6D++a4CqjFbaE0TSRqIzR3dVDquqVk5UuxAsIct1YqcLcMTV8LRYCQCEb1roF43AX/ZYyGShwXSuVgpE4vkvfzUWVOk4VqvqYZ5+PcKCh7MyWGspmAB6oT49eJ7hWV+KG0HVMbgrE0WhazgYmMpUZgkuwmMMIhpm8hLlN40+rDGmRjrFroo0NQ2q2u5AM+RrdY6d7FZy5fwbGNJ3L8c4k/n3t/wEA9HouWN9EfjdlvO3IBVdtSnaLQXLFct4VIA1NYIjGxGzEolbKTUODifkPvVvfhwduuFn76EoDsLqEUgAExctsJEhyIce35aL6yq+8EZZvY6Vb/rqtvL7e+Ny1N+OqT/06AMCxUpx9i9TU5Lm2VUZpKniUd1/4ceCvfxtCcC29t/xfH+lGAQCl+y2437OFq2oEhd7FduGKCYJg72/NoJAP9AITpibu2yCy09lQ8CC2UHYDTDgEyzmGA1fVGg/lN3G0N427N6jONlnoY7lFC5vIDDzYpAmuOBlg0u1hPSAo0TMTbET0OJEcByfq6BVpodjsFnTt+NFagIYxecUm6kqxKerbOI3KWSQPR0Ng4BKTM22kavNQ9UcEIc4ktkKCJo81J7GzXIE3Q1B50HewrYhJW9tFLP34WwByGMP8B9+t4blhDFtWLv3kdbjv+2ljt/iem5DlaeGRgsPocQh3yB6SGrqVhkS4O9H9ubk1A9lZs8ZQipAJQPZNJLuVc0zEccktb6NrcLwIlgIWp2uQFoQm8Ag3o3pu76yDqgWdSSCp0sKw8IEbYU+aWg1JGhKZagsZ9gwv/AG1Lbl2grkakcvWuwUNn1Zn2qguBjixQhsQtuFqqC7NSchjBXBlrJ7lYkRrdP2tHgOPGU6mRARqTnnaDnDLLUKq+qfcthFNZho+zU32IdQiE6/46PYsuDX63dtWClu1NGWCa4iUc0ktUYqM1Ou7SJUqEws4eHRWjdgAkilV52TQJYDuwEGaGrp3OY5Mjdlv9fLg0yHMhwh6bvghEsU1wLYDnkFvIrsNH84czVlR6kMOTdBPeXAyQBkSIXMkthQsn4QmuCkw/0dKCcsezYDmgS7kCXodi4DUw6jObgvIXAoZxLjQYwz/jmMc4xjHOMbxBMW3ZaY663ZhexYe2p56/Bd/A3H7S37nUf/+5cfIbp4qkWXULmE4j22J1nlmiH4rj6RG+7qFYgPHOpRBTOe6aA08dJROav+OGnAxQXh7JlqIFKPXMxOEqYUdtYW2eIYpJZ5eNftIJdfi5g5PsalIKafPUmE61SeVpaG9Wzey8dDmpP6sILGwvk2Zq2EITBdoHD9160+jHbuPSsqa8AbIK8GLExuT+r0AtJA8AJSn6FhDJaP1ZhGe8gF1jBSnG5RZ+26MqX075/VsXfjAjaM2l9woe1z4gxvh7+6ip8T3IQoaRSkdBBpniEUKDhIpGA6NjTRfZcbhLtkI91Hm9MD15ypJ7X83lTCi6RQs5ZBDOzZToL+hDMszIMuNCDAQGLXiSAM85JqRa3U5UpUt2m0OY12xTycyxPWcJukIb6QINf/hG2A1TMgSva/b89BR2rxBZGtLuNlSB1FqoqAIcJ2Ew1MtXuKBAmWrCn5MYxOGuqbODjGew91KFavnYV0dc2ayjYES3mgFBoyOqQlgcWQhU7Bubr4LxqQm2aWJgVShOdwSIwUrAO2mrzNLJ5cglZb+noyYIHgASH0Br0TjNwyh7Qy7Axd+LtKIznMWTiFVLW63nd6LtGODK6RgENqanSscATCg8gWCwJtXpkjP0HfobXD09itiUt+ANICp2xQ8zhk2r6b3mAmDd7gFoUhNQd/Gvr9SymiZDfgjowAA8IdsaMGQxCZE+tjzxoUQ35aL6lK/AhMOovD8Qt7/2bHwFzRhyj4xFw0lju04CcLTtBic+qXH9px8MkQ+H8KzE92+ctWnfv1RNxn5YoDuVh63rs/rv3UVfHVwdgu+E+uFL9kfAKrOtNXN414FYV7xD7+BSi7QtUfPSDBp04/0nt5uCMnQipVCDk81i9dgI7biAxszsO1Uw6IAtPTb0uYE0o6t+zbDdR/z+4mBe2d99yNKB6/6958DAPSTonaUKRX78KxUC6knsadZme12jiZTVVuz8zHYp2khPXplAdY2jXe7lAGW1PCmtCSgoMPln/5V2NuGnsiFy7RziTkZIEkMrQIVRZZWOVrcv4mGqya4oRRdOnxfpGFLv9RH302x/Ipfx8Nj4c/eiaU3kWPKwgduBIsYuBz2rTK9iKa7aEEeLhyIDGSKXYzIQGan4AryTXZH4MoPNprINEw8VByCqidCYLSAS4bMlmA5OuZFs1u6HWa61EUylK5MTVxUqqPrO+o8RypEwdV9nFmuaZjbdhPw/fQbDOc50sgEU+fm5yINtU6Xu+h0VF9qxAlSV2NdmN5BRZUmHtiaIUF55WcruQRU36vom6NNTNsG8okW0Q+7jlY1Kk4H6NdsxKqUkPNjBANapF0vRsWlzYJvx+jHtoZ1O7Gn1bnmJtpYOzULu6lYuBJIFSsbuQxXXbSEB1YPAgDyxyz0LlbtQqs2mK8Y4eUIaWRidULJYbY4uEKhjQFDt54Hd1U9t2fp83SLEUK1kZAWUJ1rj+BxMfLGvdDj23JRfWhlGtxzYXuP7zLynxFX/MNvwDDpxk4t2i0OXU3i2IAYUte/znjh598Ix0jxqRe89wkf6/nixf/yywCAIKghik38l33H6P+ZhTfc+aMAqE44ZRMJ4hWLJtrzHv5phX7AzWaeiC8A2lUXrplq8QODC7SVfFxw1kZo2Io0JIctFrZxu9JrDVILG50Cnjl7BgAtqg01gZa8EBst2qhEOx4ees2bzjmX+3/g8WXShud7dgwzgyg1dS3ds1Kkguv+WXtmVDeNt3LI7+qit03jylIDvRfQ8yw2kJSVpFvLAFvsQ9KQYRgSR3/wOn2czJHI5hSRy05JQQFAlhiAKWAONVpTDm9CuZ8kFmyfJsw4c8igfGjy3bEBtZh1kxxYw8L8nxLKwkyBpZ94CwBg6Sf/hx6DNCRkXuh6rlkNtTG7Y2aIUgPd+kg7dohkVKc6eO7MKUxZtBn6yImr0B9qS4QGeF8hCq95ExbedxPkjFqgEw5Hjd+xU3RbOd1zanKhrc7WGiVqsQFgmAK9swzq81aMfmKrMabgfUOTtwDAMlWrk9r06M+zUq2nfGZ1AoZaQMzpAHjIx97nkkRilJroMPrec06M/nYOtqqpOk4CxxyJP7S6amF2U8Q9G+5pW3+3SY0+PzcZo+oP0I3os9OMw1EbtKIb4cQmEeBMM0POjVHLUz00SC2stmkzFZ4qwIoYDFW6TDdzI2JYSoIXh19Ev90TjRqwQTdd93CiuQBpy4VRi5AOM1yXj0hjloS5YyKdHFoISd1yFEoHVlEJVyQGeoORWArnEklgQYQXfqY6rqmOYxzjGMc4xvEExbdlpoq2BcQWHlI77m91PJr4w4GPkfKNbWfIkq9v93b4bynDmioaaAXfnOzXj3zlNfjYNX+ohfGjzMSzassAgJuu+NgjXt9VSklQkmt37xDrNskM7C6QCEMsTHwtJrh3b6GBpc4EItUkDsE09f7h8f3/9joNJ9/58nOh5Od+5ldRVYjD/Y1ZzUK07BRhYON/P/tPHvM85z/07sd8/nzxme/6vUf87aPXfOiRx//f74TlJdgzRRB1N7JRVqzmrh9ip5HXQhF829VsTm5nw4QTmSchN3JaJi9h0AL90hbABJ0vAIgzvm5/EVWCEQc9+m64KbScIWcSUmVgSBgkOIy2qvHFDElZ3XP5FLKa6CywWS+Mzu0P3w1LvccwOYQFSGvkXDI0ey97lJ0NW6SSxNBIjGVkuL81i65PWUsY2HDU9xkmnGqnABZvvgnwJNgmnQuzJCKVOdm1FM+56CQuKVCGeKQ3gy1OWbHjJIj7lPVJUyCMLa0klXkcc3li8a50y7B393XtOxjY8D+XV9eNIbosRUEZHuwutDCTp8enGlVdRkpXc+A2cPwYMdUXD2ygpZSuBpFNWZu6Jr2OB7NMmWRzu6hbYwSXYJMxhKrtJpMpCpN07Wf9DlZ7Je04c6i2hXt36LN6kYNaWbHDlRLZMKPNBIej/GWtAy30AxudLSXEkR8JcQhPYPVkDfnDlE3uKrURKgGMuO3ArKusvsHQ900NUbOpkFpsAECZnNuqFp7mJYRH95KZGyFuIjYQdy04KnNPlvIwALCnQKbKpDxbROqpHZ1OB6VS6UnnKv/0v38r7v6+d5zztyGhpBc4o3aMvgXeMyCUXunyq89tV9F12ZjDLsSaoFHIhdr+yjYytEIPX3zRDY85phd+/o3wzET3b3LIRywanz55iX5cz+h63tGbx5mggvs3SYrPtRO9iAwSC47qr9xX3EE7drEd0MQ1levib7/jA485pseLxY/+jq5ZGZUIppWdA5M+GeKaz/yqdtJZWqmBty3k5gkSF4JpacH4SBFygSYc6/4c4orQbS6ZP3psDhiSooA3RxPqYCena3AiNMHdFEzVNkXKYavFbKLY120/SWDBdDIsTFMbysm1GoSqfRm5lCzTTipVooUQYtiywySMjWFfqoTk0PCpdDP4FRr/D+6/C7vsJv506RoAQJoZ2DlFtWNpSTjVADmX7rMoMRFs0LjsbUPbkEkOZFOxrqOyjAGqxleb7OC2771e94TXvL6We6z3fTS26N7kbROiksBScO2+qR30FPybCY7tZkG3grGBAV6hMYmMQSYcTz9ApYSTzaruE/asFDtdgu/DHQ/2tqFr2ml+1DrE/QSGlSHpqT6UlOvaK7cz8DWliOUJoJjCVdKEvhvr+r/BBXqho0sL/Z6rN1OWlY1gaitF0Y0QqVry5mYZTG0WFmZ3cHqrCrHhjq6jWuitHge7tKP1fh0nQcGjcezcO6kJcGlRwJoMEHfVuYiRJaL0BLX+JCM4mKlxyWoMU/UPp1sepJtprLQw2UO3mYMIQqz84m896ebo/0iM4d9xjGMc4xjHOJ6g+PaEf79F8epbXw0AONmdQJwZCJPh5X/k3iYbqq1wgYJP2UUrNElYXGUKix/9Ha1HGqcG9u+ibKUdukgzrpve04xjZ0C76X5oY2+1+bhjtXiGVIzG1Uudc9ShACBUVP96WkQjo+NfkltDPc5jUplENwceJlwletGcw7ULRIL49P1PO4fo8kTEyUdhqD4R8dZ7X6Yf12OCPz941Z//h45x2SeHGbODvEu7f2ZIyGqMwRLtyKUhYSktVCtgEEcoOzzyjjc84niH3vYeAKRIZPY4BnXV+lAbaHF2vxCiu+NrdjFzMkwpEfeyG2hGsljNIbd/oIlWe2caWD5CSIOxYYGnDEevozEs3nwTMEUZnHXa0RlJ6gGiILSCELhEqGDRvzt1OaLIgqPahbr1vBbUZ4ZE1Hbhq4xICI5LnkZG4adbZe0aw5mETExkQ91hQJsUdPouXvj5NyLOVFtU4mCxQFn3qfqEhil5wsC3bBQuIeh2e5CDr+DwRKqMbdjyJJWfLgBeSMCcDHcfJRKcVYwwUAK//kQbWTpURaBsfajYxCOGTJGAZMZh5WIYylM27DqE4wMQwoRQ19RoWDC8RItBdAXTWXynl///2XvzeMuyskpw7X3mO9/75hfDexGRQ+REkswoIlQrpTgU2l2WbWn9irK1LYpCERmUTMViSBrITLWEn2PZpXQ5VaktFhRKiQKKCZlAkmRGZkZkxHsR8eY7T2fce/cf+zv7RGQmmGAghn2/f+LFe/eee86+5+y9v/V9ay3YtkBMcL7tZ2bceueaKB3W361rC0jFTMmkVIswGeixOb/fQjZwYRF07nUZEmJWRcsZ3EeqII0OTOccJD19b4qSNOpK3tIU9XKIETVaCcGR7Op71RpaeNbzT+O+s7rMo9IiI/dLhbBD6klqqFPme2/NjyCmMS7i6o7ZonoF4sUf0bXA89tzcILU1I6eOMlvjLW02fndFpRk8Er6gb5hafdJx7y0znrid3QdkXEF5UpYBO/lkxQANEuhEYk/WuthnHrY7OjPU0oLxwPAvefWcaHXMAtkww+NCtHpzoKhq7hcoOaGeISk8W6Z2zFt+Tf84Vtw6rvegs1kwXz+G2/8H+bn3/jTNxqOaRw7uO9x/YBZjsQnt9f1WPwtC+rL/uLHjByhzaXhpjKmnrKe+dWM02PNZ2650y97Mc0jN2PmXKIz1AugCi09EVN3t9W1DVQWLovLbc+eEClZiElfAdUUjBaAwEswSnW9zLMFpr6AGOm/Lc4PjfuMzQRKtKBEh6bwnMx0zJ7fbZkJ9PRP6c7q3BCAMwA9gv0UDKeUAUAlBRuSCH1kGfsvqxzBsiSmRENhMQena5OeBEoCw4c1XzhbTHDB0bP88WYX22M9qXd6FbinA4AcYNRSbKBPzhXO7c5DEDTsHupikOrPchyBhKDybC6FV4uRESy6XB0Z+HccerBtAUFQ+dzhPtp7BD92PKhGoSolxiVgQS9me59ahk2drygruEOG6RGixzAFi+qVVgyEh3X3NACwiQV7geqJoQNnR5+HM2LIpmVki/rahGchpgWRJRxJNQWnzUS1EsInjrM1H2PSIYH+OofILNxyWHO0l4MRzjb0+D56Wvc65KpMp95ebNhe/JHXY1MtGslI/6KDjFSrvOUpXnT0LACdFMSZjWZZn/9utwZV1udUWxjh06fX4Z2nDZunkNZJRtSWRe3VlXD2HaRz+vzjyIHFJYS4+qk1M/h3FrOYxSxmMYsrFLNM9e8YL/3z1yEi0+lyPcSxVtco8jwxPvpP7nrK3+ddu08Va7/2rkJk0xPwazHiqc4GGq3Q2KUNY9/Ad+eHTUwiF9WS/lvepAQAMrQRKWBKO1ybS0yYPv4LVjfwQ/f9KwDAc5o9bMcN3Dq/DQDoJQGOV7XQ/DMaegf8ozd85CnPOc5sZAQ9nVhsX2b59nRjnHgmUxWKYUyZL2cK3/vJHwag9X3/Yv9aQ+6/EopVv/ToN5qfU2XhM8M1jDPdOGNzYT674YY4P2maRq4nGpg/Mb5Uw9Tx39YiEjx1kJHijAok8k37yZ+5B9IGHrujyCrEJco0KrYMnOpYEhnxMtvTmk4hCTIdTAJc09Df4S3VLdyLdfP7/ihAJ9XXybouzr6OuL93acUkThhq3o0LAGlDwR5RxukA3jkfgvSDeQLEq/q108hFpRQhIqEC5UrIHMYtCzBLmiYYABiRmMKg7JuGoCOLPVwAoPp0jMhC1KEmqWoGrxKjXNb3e5zZxmC8VZ7iuasaTt4Lq2hPy5hcYuSeC8GHfR9W3wanNKNXKheiGNDCFdY8QdQZh0WNRcoBJPV6Ck9pKJWupXWsj8EjczQeDLztQDSpA7YksEjdursXlmCTqIUVA0kD4PTZlaWx4Tir2IVKCjh1MChhRJl1tRJisK9fl07KYBlwpqR5q6Oqb57dwVEfuxdbqDxX///FH3k99skacLEG+Lu2MVLPSso0XVmWxCjT4z3nT7A/rcKzcqRAghPqNhwH8Dc92PmUM2ZICV7Oxg6cqn5eso4PlgJWnzx9BcNYMMhiqrpqY7aofgXx7A+++ZKOPNssbNc02jjVWTQ1kKcb0cTFcYJ4xcQBm1pQpDjDPGm6N5WUuu1KAAAgAElEQVRkyFILS4uaBuDbhUrQKPKw29UTCQOQ9V2oRRKej12cTfTDXZ6bIgpdAy/f8IdvwRrVWP/q4nGst7Ri0CAN8Oz6Ji5EGkK+v3cYjwoNg962fBHf/NHX4s9ees9TXk97qw6bJOS+kgUVuNzV59LxvH5+Hw3yHB1mPnb7NdMdfSXCgjT1YQsKB3EF62Vdn3OYQIX8MMeZh29aOIWf//jLAABrj/xf2Pw3T20eAABr/4kWfAZTEwdXeoKk+pnyL3HpEAyMYMXSrsJovTjWsf/nHXo2h+66ZVPLmHfv7TQMxLh2pIPuNEBK0PPaXNeYqn9g6xZzv8iMw7IlJMHELFcxAnD2dT+OtV95NzjJA0IwcFoXpK+QVWlxF0zDhsTnT5oSbKI/152bwncy03kcR5aBC3PDhdzBho1sI5Cx3a0bOgxSpnE1uk4rEIZ2YjkSzeoUCyVduz+Ylg3MnUmOlNR6xomH7qBsqEQXVAO2nRunA1iNDA1IKlZ07kYczJEGRgeArFXQQ/LF1x0wqOsnuHZeP0OZ4ugtkdjBkQwWAxTJFnqPBdjf16UVb1iMaVLXpd28u3sy8cGoTq2UPs9SlXxjL7nv+/0y0NDja3sZ5HaA8UBvTqp+jINIL5xC6muJadNrcdv0Xuz/5SriRQlFXdW87RiRiPB8FQc1XYveG1axUB3jaEXPG4/vzaNKG5rhKIBigKSVJWko8Io+LyUYWnVyZAJg7ZRhxTSGPRvTawWkuPrB09mi+hWEkAwBtYYHdooz2zozra5HiBLHZGlf96dvfFrZU7UeGqeSsOdBVTOwSW5abEHRTen4GUp+IUe2M6yZLC2JbZPZOW6G5trEPCyDsa/VdQBMYxvlWmQ+O45cPLZDGsjbPh4ic+Qfet7H8en+usl++5sNU+N7PykO/cj9PwAAWHKHxmLtHc/4A2z+0BuezjD+rfHY//bU2d2rP/N9AAABDpHxK9qg9Hi0iB1yqdme1NENS1gJ9CYGHDgf6k3GIPHRT0tYO74PAPjYNxV81/X332moH+6+jWQ1hdWjBUsBWZ6tMKXpHbmwTKeEPHeSnjLZ6GTFBhPKUKYYR0FTKGXwF6cID6hRxBewaKHoTQMIwU3N/vHuPM7FenOlJDP3yzdcdxofP3uiSNsyWrgB2Bs+eJUXzTcJg6CFVFnKSPdBaj6t28+pFMzU5iZbVWRLFqrkvJJdKBvnnDw2/8+n5ijnsfaf36l/oE0BY8rUFsXUxu5OE/uu3iQ06hN0M30fL1XG5h5uj8qQiWU2HVnGTVOX5QmIsQNOij++l8Gt6OdMKYbBhfolFJ4RDlX1PdGPA1xs61RMhgHUZhmniQO6dE0bFn1WOvTAfGE2Fo/87Gtx8qf1pjSrKFO7dPsc0oIxe7d2vULGMgTUkCMiStDaYhcX6LMZV5ib04vewcUGUBM4fkhnowfjMvqkSCYlB4Y2xiM9VuNqZpS02JwETxgcqpkLX8G7SXPMa7bA2U2aJwRDmlqwKbFYW+yarJUxhd6CA0bIiUo57Iv6Bs9qAoNz+hjpkQQ2h8mE47rUik25lOVVHFf/tmAWs5jFLGYxi38gMctUv4L43Le/zezkVddD6YjeIZ7aX0KWWVDnNHzYiSrAy576GDf+0VswoazQ6jVMDSuP/PgytI3CTLUU49rmAcZU2+BcQhK0pxRDlXbWJTcFZwon6nqnet5vIsoKmPhFh86Zz6lWQvS39K7Vm3Aorl/3193j2B7WTEeoNRdjvqmv85l/cjvmylMcLuvs6/RkEf/lBb/65Qzh3ykmdP3nJ01zzU8V3/nxVxuh7m5YMko6TxTaAIDvv/cHAQArPjddq1uDOhxL4MJECxW0vKk5nmsJPNhZwfhvqH7+TcWxcg1jAEgWMrDQKlSObK2NCgDZQgqpmDEV574qxBNspYn5AKIjKZxKgptXCwP22gl93RZTON1fQEiuLL1eBau/pzODg9sqSOoSpykTqQWR8ahdrQywOdTX9cnz65BdDw6p22R7JdRqBLGvU1ZLdbxs4pjz51lhWC48haQlIB1SZUoZGDWnCwZEQ89QT3L3l0tj/bfIgYcBil7HXWGEJtjEBgQzik3iwEd1TWeLpeYInCksBBpajIRtyiLjxDMORI4tENvSaBLPVabY6WhUgjEFq5zBolLLzUs7uG9Dd607pwO4DMhIDOKGuV2MU30P1rzIwMnpvFawyrvz99s1gOq+8CRUZCGlG2H9ve/Bxn/Qjj9rv/zuQkC/JGCNLNg1/VnCt57UKZ/Tqc4OXWMioAQrKHqOwi0nLmKzp7/f8YVCRIGlDIzDGJ2L1EHjWg1XD+0SSveWMD5GkH4GIwThWMLU5sGA+frYUO4WgjEGsZ7LAjcFDg8wHFNmPLGNgAfLGKJVuikkQ7iWmO5oZSl42w7EPwJFpdmi+hVGXuc89++/MueY6FwVQVfflE/kIT7/w2+CpFpmZWFiYJbB2Menh2uQNNk2GoWsWgKgQhOrxSUCO0Uv1pCgVAwOHeO6uQMAMA1JJ1plPEiNG1mDo3y/fs/Dcyuo1IuWfeFzdIkK4nkpBpGPOV8vql/feNw08GxP6pdBoV+N+I3n/QYArfpkcfVFX/fH3/CL5ucX/dkbUPXiJ73mRX/2Bnh2hgVfLwb7cRUrvlY4+vrDZyEVx4Qa0R4fzJn6+cV+A1IyWM/S8Nj1/+0/aDNoAIBjpAetiEFUpIF8+diCRbVSZ8NFWnXMRG5FDCnV8eyhBe+aQmlpsTY2ziucKcx5ZB4tbRyvt3Gqo3mlkAxJVX923JS6lEATeZTacOh+2Rw2DY9RCq4VkTbIqu3YBCs1/dnzlQnO7c4bo2woIJvXEyMLLQPxMgGAAxnRJ/xd28gs8pCBDxxYZJFmXfI1rP/iezSUbdHxq5nhijJPoNHS14kWMDrTMAL7TBWm7mHqoOpFONvTz0zJTVF29bMwTh0EJNHn2gLL8wPz2fuDCjiNh2VJBF6iKUMAHtg+hGPLelO65dURHZTQoA1cYKWIhD7fQRTgmhX9TOWG6BOqZTZaY/SiYorlQYZz3/dTeGJUV0aYjPTC758KwFNg3KD3scJezz9gcEcKWKH7p2sho/sMnsSoRyWAno0HHz1iGppYNTNmAPHEBe84ELTQ2SHDlJoAbzm8hYfOXQObHA2SloBFX2J3rwbk1n0pg5AcihdNawuBbrqyeAmpsFCjsUqDBJMh1TdGjmnAkoGAU0tNewESruvyM/h3FrOYxSxmMYtZ5DHT/v0HGDf/8U/jcJ06fK0UPjUB/M4LfwXHf/sdBr4SghvlFcYUXnJCqxV14hK6URklMsoeJ56hJthM4ubGNrqpzkpOdZfQHeodbuCnaJb0DvNErY15b4zTIw1vXhw1MCKrpqOtHnpRYI45F0yxQVDTpb6kX8t4xSdehaYbGsFxpRhqPgnZx96TvFBzAY9McpPZdCZ6XHKRhMBJ0Z3qLGQ0DrQYRw799TxYlEWJigCPC3EDljIwgg4r9wUIOkQ1WeQY3ZTA6jrmfXnXpwoErFIh8mFb0qBvNpdYJrP0eX+MSDh4pK0bQPifNzG4VX9WUI8QtkuAl1uMwRhSM65MF/nemXnwVgJBsHVjcYS1hu7sTKSFc+05RB193TzikCTsb4Xc+LgqW2l4kLIZf9uBTUnm5KhAsGsZZpi0lfmZZVoj1z/QYxceS4zSEAsEHI+6eHdKcFYnkGdJr7iVYfP/eOqGuB/89L/G+bG+Hyepa+5TzhQcLjAgONizhKGT+G4KITliep7idgCnpe+XdOrimrU9A3fu9muQj5LYfspg3arHMbeUa2/X6UIZrBHdE1UB2NJ0iH/zR19rkIdjjY6hAO1+ZhlWyIw/aVZWSOapKXLLRngoM9kcS5kRq+flTHuXArBrCRw3Q4XUllYrQ7g0h1TtGJ/YPI70gAT1LYXSov6iGAOmF6oGGmaAoXiVD48w2dJqYrwVg3OJMs1DZS8xxgTDxEdnWjbsCN/OzBgn56qmo1q1EgTlGNMe3VcjG/aYQUYRzr7tzf/g5+gvFTP49x9gfKmFyfVSc8M6ljDwb6MUGhUZm0ukkuMieSjaXKJG0ngCHLF0YFM7p5DcPBzjiW+O/flkFbYlLukgDrDa0g/O2b15ZLGF647qGt+ffMN/vHIX/2XEj9z/A0ilhTLRXM6O57E71g/+0Zqmw+R1VM/J0A/1+Fhc4YWkKBUmDmxLok4m0YcrfVM3DVMHw8jDwUBPCpVSZLqtgyDBcLcKt001xIrSEpLQkx1PclNQDa3mdBVvoDC4Rk/O0VIGq2dDEL3kUk4pFDPmAKH09OS3pCc/pZihFQVWCqm4adwd3JhheVUviJwphL3AqBytXL9v1JxKfoKbW7or2D+ZoTMpIeJ6pWNMmUl4krlwnQzphEAtyRBQTRUKSBp0H9kKbs9CWtEXkDQl3L5+T+tBDnsqoQhbHZ5gSGu0GMQcXscyx0HCsbymKUwWU0/qnr+Z5B4X/CdD+Xl85P6bUF3Vm45Ru5BprC6OEU49I+peLUWGUnNybh8r/gAf3rwBABBzmI75SnOKMHWwvac7bd1Nz4w3kzASgJO0hOriGIzg6/rDFpypHo/2cxg2frigXPWiAJ0NvfDXTkZo+RrKvngoRppy2FSXrVZCzNNizdaBra0WQOclywLzK/qZDBMH4QGpGE19JBJo+/q75scVbmjpZ/XR/qKukVNdW/pAuK3vb2UrwJOwVvS9FXcDsyGrBxEmNYKo/RRJbJuyQndcMopnQjIEboqE/l/zIkx8fV+FVWE2BGpqYxpaZqwA4PSbf1wnPm/76siN/n3FbFG9ymKlMcT+iMQILIElqivV3dBQJDYGLfh2donVlo1JqB+4NLaxWBqZmtD1rX3shXohGg6Dy+QRX/YXP4YpybgpybDxuObVwda6nV+Mp/rVjFfd//2o2fqhX/US7CdV7Ef6/BNhGWrSKPHR9Ke4fl5TXrbHdXDiNW536jhGjiw7ew1wRxqT6D2/ghXKAjujMtLUAqeNRq9XQbOpa0dR4sCqpkjymq5gRtpPetLUrJQnAQkzmbSfI6Eou7CCDMJ2YA2p+anHUT9LziWZZWqSkxVNaZkO9c49XUpw2tEIQpTaWK6NjAwibIneiJAHL4Vbi5GQTm1nWDac38XKGA/39PdZdWPYXGKOxAg8S5jMqeQkGHbLAGUsUADdLrCHFuwpach2bGQlZerFpS2GB+95smYxAFzzrrux8WrdpHPiXXcjXhBgDSJEphy7O3qxyTmseaz98rsBRy8UI1HBFwurkRhDdGtoQRFXdLRbRWlhgmmfsjSlZQwB4GVzD+Fi0kJIAhVwJSRxSsOzNcSHbLjUMDj/vL5BMwaRj4TcfiRXGA8C2NTwxVQZgxP6cBuv+onLznHw2XlskATksZ+7C9aqfo4bjQmmkWvOC4CxenO4AHcFAnL/SVML7dyKL7YA2pw5tRjHFzs4va3Ri+P1Dn50SQu1vHb0PRB7gRGbgGKwiGGX1hTAFNIt6p04NEFAUqienZmGyUYpRB8BxmTUnvZ9ZA2aa3o+1OoQEdnPnYnmzcafCQaHNlpMAMKD2UTyYxNc97a7IaKC7ne1xqymOotZzGIWs5jFFYpZpnqVxe6gisMNDflU3Qg7lFEEto2Nvu5+nIQeHKeAbjmXiGlXySyFz2wehaLE49y/fHI3Yh5/38L1Tyfe9+z3m59/6vPfjVC4OD/Smc00cUyt8UspOa2//06c3dYSbosLQySZhQHVlY82Doz04G2rE3xu5xA86oBmpRjqg1o8IX5RBM9PEFL7osoYJHVbIuVQRH6HApxGjLRLHZCuRHNBn2OvU4HTtg3FZO5hgb/6r5dnNABw0xvu0ZQc2tXXPudhUqPvMwMu+A3Eh6nzu29DkqvRJHShJAfPa7NuhsFAX2cqLCNSsrI8RKUZ47GOzn4tP8aAoPLEs9CYG2PQ1++bnx9h8Dk9dmmlkLGTDsBTGJWjB+8pKGJrv/xu+Ds2Hn2LzlzPvKH4m3K0SQRyp5eUw60/NbTLMgZeKzK43PlHQWeh1WU9rqJfBa/pMZARB2vTWHGFcKcCENRf8hNTLx+JAI9NFiFJBYp5EvY2Za0cSCeuGX+7MUJg6/ftpRWISU5lkeC2NJ6pTBrhK1z7+2/FUmNkfIxzowJzbZQ4vmBlE59tH8Jt81oKVFxi9p5KC5t+C5P9cvFGqm+7B5ZxDIrWFVJpGSWmvzl9HG+Kv1uf76AKKMC9Wc8hk4tViDJRXhSDNSpKCcl+CbmWWb9UwfXr2gT+mmobH4uOI6V7pLY8Qkr3EmtFmEw9Q3UbjEookeiH1YwBQgmEr72ATd29HKAQj7y6Y7aoXkXx8o+9Bos117jFJNJC1dU37LlOCzFRY1qNMW6a28XfXFgHAMQXK5i7RnPRXDvDzm4TTpA++QOusjg7mcc0c40jDsPTl0V0zuqHG3MjlNwU3pymkGz2mnjeitaKvThpIPASdHd0bdqpxUifS+M2dJByadR5VGpj8wd148zxe+4CVjSMZdsCzeoUHdrgLDRHaFONltsSZ954+eR6adz8eoLXLSCuF9Dqg/e8trB+KyvYEwa7o++JrCLQrOoJba40QcWJDbVqp18zfMjxQRlBS78uErrGnm/Cti7MgdEGIXJdKMVg0f/v+9Z34Lm/qykeijH0T9IpxgxeF3js9idfjzXRdeVbflyf84N3vxY33K5/VotA5bSD8XHauCQc9UP6vL794//eqFl14jLs+UPIcmk+ycxEHtGCl8vy8YgDib4nvEMTo5WdSwTmlJ3edh1DaiT6pcmLoD5bh0eNV0lDIl3RSwqb2nD2HWT0/2Hk4fyO3sBajjTNWasrPewe1M11Mwm0vkAL4tkytl/y1MCgLEmkRMf62IUTaJRCPDrQ0O2zWhfM6/7gkWcCbc/IEfK+A28r53kCzpiOt+XgbLRsJB0hGbaov+KZK1t4xF00/HMAYDkXWGnVI5vGxOlxA+8DFtKj+vfnp02MB4FpsBs7vmnY45ZCrTo11KQF4rYDwNFGHw+Fh/R/BAPLbDhDUovqcV3uuPoZNTP4dxazmMUsZjGLKxWzTPUqig+++BcAaKEBQGdBJ1q64eZwY2BUZCquzk7yHfrqyX0D5+W0Epe8KI/93F0492NfmYDFlxs/f+qbcDHRUO2SM8RD41WkpOX6/uf/+pd9vPsuHIFlKUMTyelFf1tUP+tjvFa0/APA1uOka8qAh33dwHPvP33nZe9b+6V3A9Th6zRiZLENlZPVbYnb/rvuWhSlhnmwcv3i3DBhGPpYaeqseLVcCBE8VYxO6oyk/nkHtsWQNPWFXnvn3UhWNITpdi3dYELBY25ciZRiONdvGSPuaxbaGFEGdzCq4PicvndKdoKNQQthWABwuXYxD1J4XmqgYgAYHdXXHLek0QROmwKP/vTl+r25SD4qgKgAj91e/P3U2zQUfMvr7sGDd70W17wrz36Bdls333S6FcSreiQHsQ8wpU2vAYDB6B2zhKNydGiOHe57yG1wkgtlqBKdpCuBrEiF7L5lxCqm7RLsmkLWIqGIaoKlBjWsjUuIpzXY5Hk6dEqGjjSNXYTUyLbbrmN9pYOqo9Gj9uEytvd1x/DG9//k5WPza+8CH5OJeNtGeIh0kjOOKHTxPTfeDwB4SfUU7pseBwDdQV4RYER9UrzovvbaHOESCTpMtflATudhgmHs6LH65N61cOoxUhKlYPUU6BNGLZlWfsp9fFOG6RH9fJTP2zj3qKan8WYMfuAaOhUGLppHdcf5aBygHkRwyds5FjbmSRhimrlF6WOnBiu55LuYME2zSq/+VHW2qF6FkduIXfv7b8XWiGgzlkCNoOAwc3Cx0zAm6ACMKtPZ08uAIzHdJEebv0cFk1PTFYxJnSgUDibCxakDvYC98lOvxB518faiAJ98GkYE5SDBWqNnaqDbk/qTXvPSP9cbhjl/YoTVWy8vQ1AHdZTZ+NS33IkXKQ3dbu03sLvbMO9f/813GuF2uwGjZpURlJgrazlBqsXKAQQLU8Rh4WjyLX/5o1idI3pM6hjJyHvPruPYuXeYehpjytSfwsiB39DHHl7LwVMGe0z1W65g1YmrPPFhxcxAwydesoEKTeqP9+bR3a/BWdGqT1vDGgbUGSzGDpKG/n0/qmMceRC5+k/KwEg8PkstiMwCI1H0a++8G+QUiNaDDO3nU8dp38Kxn7sLMl/AuALLZQmnHIoXRuf+fiHQny4o7YLjFaCZc1GPVRYoPM51nbdZn6BeiTCiOmGa2GBE9+BcwncyCIIg4yMTBCRc/+Al9LS1X30XWMp1RzYAsRKDk3i84grCl3jJzY/qYwgbP7LyUQDAr+y+BN1WD6fOaOjy2657GJ/c1RKGK7UhHt3Vv3caEc7vty5TacolF1/xiVdhlPr4ny+9m75smLEKTyY4tlqI39tc4sxEX/fLal/Abz36PH38VoQ0dKBo4eGKGfUj6cKMaWlXIWnwQtFKwsCquayhTWOXjRwwGvrSkSGWayO0x7pmOzzXgE0Lf9xSBQ2n7cGJWO5tgPp6z3TMS8lRdWMcLvXNOE6EHuNUWugRh7d2yjZ1+Pz8w8MpZHj1l6Vmi+pVGMd+QU9OwVGBaawnb9fmxr3mQqeBE4ttjCkr6Ye+4alOGjFExgtN1dwx5e8YN/7RWwAAK/UhLnYLS62H/lnhFcuh4NEOtpuUcd/mUdx8SDc/7EVVPHxe74Tr9adnqjhfmSCRFjhl6PPBGC//2GsAFFl93lAyTHxTVwK09B+gm5tOvPtuiGWdQavQxuYPFxnVxr96k/a0hZaZy2tHQSVGsxxip62PKQTHkHxAZWwhaGhE4KY33oPx9avG7q1MXFMAWF4YwLWKxptUWMayrNxMcDDVk1v2uQoUB/onqSmlx8GJcpFYQLDLjJjCQ+dWcdMx7YErJENzYWSy8ap3CU2jlZkaqlQMJS9B5OmDyIkF1sm9TxVkSYDRAuDsW0iremHLSgwe1WWDz1cxPgrIcpE128RtZalu2PF2C+9Vnp8KB+BI+At6XDwnQ79LlDE/NYtjmDhIUwvN6tT8f0rNd46bodOtwCb0ZbExRtkp7ALX3/ce/br5GFnkIKjQxmXkwVqk2rcjkMQ2dqZ6s3mk3Mc7Nr4NAFBzI9xQ28Xprl5I/2r7GJZoERnEPtymPkbS98ASjoxsG0u1CEvLenH5oxe9D5dFxsy9EE5ds+GbK0+hFMP+VG8wbz/9CqOZnKUWEFtgdPxz3/+TuO6tujYtXYWsor+j7jMV3I5l7NcUg3F/OXyoh/aojPgCjXHCzIKbZRba4zL62yS64EpI6q9jWfE63kjgH40Mpz1wU8N7X6yM4fLM2O1JMFSJRz61XSOdaCUK0mEY3EKLKFewAgHGr8x89LWMWU11FrOYxSxmMYsrFLNM9SqMc695cg305R97DR46q2Eoy8/w2PYSOGVBxxa62BkRSZxpab1gQe/4n24d8tJ45adeiZY7wSf2dK2nMygD0DvTnUENQjBEnYKc/3WkXtQKmqamJyTHMw5v4XMP6mOgml5Wd1p/73vgkVm1bQuUySzgU99y52Xn0nBDrPi6vnXPbb+Lb/yfmpLynR9/NTLFjSBGPwrMzxaXxq1FKAbrZA/jCW3J955seH6pHF6u6KMUwyjy8JJrtDTkvdtriCONGgStENFZPd52VcHqOeBHdFaSxDayuJCWZEyZjPFQvaiLx5mNPeokZc9QsCIGGdD5n+ib+qdzaIKkXTWZKiQzqk81P4ZvpzhZ12o6oXCwUiJlJy4QkAN1O67gkc4iankW6GbIzlGH8oTB2rMg/KIrFss68+jPWahRdsi/tQ37/nk450ly8RljpCSGAcWgPAlF94jfZYhalO3WBSqtQqTj4qgwWb9u+QBn9jV9p+QnUAo4XNWZ3yjxcY7GO44dMKYM/N6fBki8S75HyuzEbgk8ZXjmtWcBAA/Zy0aUfxx7CC2JI2V9/D9/7DrYBPunkY1PZ8eART1enEtTctga1E1/QhrYWhieIOrpQRlTqdGGtV97F6xyhrP/u6awua3IKBK1GhOEaVEuCBMHLiE9UsHAyWqgn50SZdprv/xuuBZdp9KSgwAAS/uz5qUdWZLGM3Xr4SVITwIBiYwsFRl9PHU0rJ5LHzrSzCFSWBCkzCViC9W5GGFaOF8NSL7TsQRkhaEd6vun5kWYkICMzSR88qvt3cZQecyBt6uPkazHEEMHMrz6XWpm2r//iCOHQmNh49ye5leKgQswGI3ZJyq9PJ249QN34Hkrm/hCV8O1JSfFHtUob17YxV5YxUf/yV3m9a/97L8wP3+mewQA0J2UMBoGUMTxu9Sk+js//moMEt8sgoGd4kJf1zkf/BISjq9/4J+jm5DiTuZhe1zHXl8vbuvzXZw70DSI5eYICS08e1tNQAGMuItqbMPt6L+dfvMXp7sc/523a23UgZ4wKstjszhOx555nZragCOx+cpCHWjtP+l6MZvYYHMxPII4b17ewZmuXkROzu3jINJjev6vD4NdP4agZiHXS5E8ru9faSuUdjnIkxvqphGec1jTMM6Pmmj4oZGQi4WNYzXdnFS1IzhMX/NOVMenNtaNchQApGRHxxKua6JEzxCBNPAjt5QpIwDakBp0CKeWmLpz/h177XwBYPD02oXMB+SzRoYq8+y18wZKPAgruNDT37tSDEIwLJLqU9lJIAmPPPvAIchmCoQkK7gyRkrHi3s+Nn9Ib4que9vdkC6QzunzbyyOYNOCNfjCHCBhbMpEK0WtqRfcCcHMgpx6WMzROKov4HizY/i9434JzJZwvaIencX6PGxPgFuyoAQdeLBJRSmNbbNQLlTHGEYePvttb8cXi7Vf1+UIp1M4ATFRGH6D6e9J5bKZrkD+Qu5IyL4L0No1f6KLg209xtbQAlYis0lwbGGuHYB5Hj0/hUXQ4ToAACAASURBVGMJQ8uJExsevUdIjvVW17y25kTYGBJ/PnbNeLcvNuDvXmKRWNO/l1GE8z95+1U9R8/g31nMYhazmMUsrlDM4N9/xNEhF4y9g3pB/eAKdt+GKMsv8c4vHSdabfzFuWuNwP40deARXHVjVTce5YL1APDJl/3uUx5n/b3vwea/KzLlb/6oplmsV4cYJD48EnXvRcGXzFDzuK9zFJOEBDCCKaLMNrqjm52med0o8jAiE2UAYBHHpXhNRqLw6++/E9wuNGBxiXfr2koPibCQ1PQjNJz4SEhJp9QIMSUPSSYYEF/+mOVOJU+Ma99+N/xbdAb0aHfB+Fy6Nw+QJDYEZYWRcCFbhadpuCghqEnFERyf2T4MAGhWptifVNCna62VI5zq6m5r384M1QHQgiHzJQ1RD2If05K+lge+462aGkOZKvezgqC/65kZxG9zhCuZyWLP/IvbcfIO3UST1hWs9TEkaeTaE5jMWtoAe6CK5gu1J+nGoIXFsj4vCWbKE1lka4iTGrPDzMFuT6MQz37eaWyN69gh+sr0XA2yQWISUQEDZyUFrEYIKKsaDgMszmsqTvWmDlxbGAOGTHJMqCTALYFsPwCnJjX30MS87vHuvIGdm3MjjKc+EuqiPvcvfwo3/b8/AwC4dWkb926sAyR67/Y50CN94pJCdlx/nzu9Gmxb4jkf0jDxE92U9ADq7zpdSMGmhRhG3gmsO8UtxAv0ekei0tBZ8VJ1jJ2ghoigcyEZqgt6vEeiCowcJOTtKmIGsUDj6Eq4lE3XgghSMRzQ+CvJIElz++TqHsLMgUOdaHthFd2xnoeirYrpvOaVFPF8IezfepBhcB3A4qufUjODf/9/Fuu/dScwdozk2lcC/578g5+FbQv806OPAAA+tHEjnruqVYj+5sI6ksg2ll05TxMAnv3BN+P+lz81rPX8D7/JwEnXNQ/AmUSF6laf3jtqOg2TzLpM9H/9t+40tS/LlqarVynd3p+bJcepbaCnZik0tm7jnYpeJGjRsEoZ6jW9uKTCQhQ54LSYWpbElFR7mvMjLFbGiEjdanNzoTBxVsVkzlL2lDXwp4pr33634e6FxxKj/ANPgNuF4pGKLaN4hImNYHlsJvZo5JmJVpUEgloEhzY8aWahVdGT6067jiOLmlt4tNpFwwnxUF/D+ef3WxC9SyBsV8Kp6O8iHXjGgF15EuUNgpabCnI1Ajr6faqRGk6pU41xzVIbZ/Y0tM0YCusxX8DbdpAe0zXu29Yv4GxPlypOzu3jCwfafH08DBCUYwPrVssRxmRFuNQY4cLZhctKCNf+/lsBAMnIhTUgXudSjHIlMmM13a5cJifJbIm5Ob3AJJllOnInQx9gMPf0SnNo6qHb3bq5PxqVKXa3mnBIJtIPEjyf1LkW3BE+dP5GjE6T002P45G30ibyve/Bxr+7/DnMec1KMtRIIYsxhdHDLbC1ojs+r88ryUwt19pzwQSQN9LylOkNBYBrXrCJkp3gNEHWw+2qgeUTUu3KF2cowCX67/CWBLU5/Vy4toBnZ9g6r78niKIzGFzBqSbI2tSjUEvNefEdHxnJTPpzIaJOgOBCYcae1hRkFGHjjqvb+m0G/85iFrOYxSxmcYVilqnO4suOHKbNd+uPnlsxHp65ClHehZsKy8A/npPhge9469P6jNd97nuwF2t4aT+smm5Lm0uc6+sdcvugCm4XMLZlS6MUU2mEqAUR+hOdEU2HPlwSw6hXQoRkTTUZ+vDLCTLiArquMKbWYay7IXP41wlSYxBf9WNUnMSoIn1m9zDGBJupngsrzDmZCspTQFV/tprYaB3WEG/ejHLbj2hBAOkyMKHHdHQMxpIrOZoAIxuo62M45z0ky7mLNUdteYRhj9SFJrZBIZStUF6aGL9P2xMmk2/+pY/JCukRf/2OEaQAgE6vAjksulGdVgSfmqlG21VYU31toixhkwhFtRKi4iXoEgIQTlzMtXTWt17v4r9+3S+ZzulWKTSfx5jC/qkFsGV9sceWOkbvNpMce7F+Tv/q4WvAJjb4vIYgW42x4d92JyVEoQuRZ/YZ1zA1AMsRSMnOza/GSBMbgrxtna4F6tVC2pJg9QQWZfVZ5MDP+awDX/OM81vNUWgt6+89FZYR5V8sj/Hw1jIENSfVmlN8/aFzAICL0wYeb89huqcb6ayJpY3LAfBLGsE2Xv0TeP6H32TgZYsrw12eJg5qfoytdiFO8lSTNz8fgGUw2aM1ZUbYP16PtZgJZeFiYsPf1n9MywqiKuBv6892R2TPBkA8e2RKKUoB4diDvUWa20qbqQNknL6QGD43sySadB/0z7TgHqZs18kwbJf1/QpoY/ZMNyqdfevVnanOaqpXeeQL3OlTh74iKPcriT976T34wU//a2xNNeXjlmsuPsmoPBdd2NyZg0ULH2PKOIssV0dkhk0LWOaY94wSDwtBC3WHiPlcokR/e2BvFcs1Tbz3VjLEmY0O0Xf8IEFAi54C4HBp4ML5hRGGVCMbjAMkQ5otUoZo7EDldSoGOFVSK8o4ZGSbGpZlS4Ny5UYGuVj9ofoAF+lvqhJrWBmAM7QgAHDqCFaHI3RJVeblH3sN9sZV9L5Obx5Y1zHYkSwLCIJx7R0XigE2QWo8BezHiVIzAdwjGVrzJP/GK+AEDTPoxc0+0K/N5lM4e3oC7dwmjGqSVAwHFxuYP6QXCsuSua834EpkiY3RRB/D7VpI6zQetdTQPY7W+yjZCRokQNIvFxSmQUyiGH+j69qNb+1iYunjBXaK3WoL1pZ+zQbmcOaMhnyXj3Zx0NUT68LKAAcXG7pzFcDQ8ZELE2apDZFw+ARRM6aMopV1qoJ0XX9XUd/XnbAUoqTMYqY8Ac4ARdAwd4pSgh1kEDYHaDGGm2GYK1MlFmIyp+j0KxCRDYtENuYrE3Ba9uLMxnTog9PmCvXUbN68pcwont36gTtQ9hR6JNW4driNo1VthnHf1lEMRyXY5JrEuULYJSpL1za0J6xNkQw8cNrY2WOGlOhYrOdABBasGrkalTNkvl4GFm7ex+7GnOkpyCoFTSewhXmWSn4Cuy4xyTvC48IRh3UcqNAy/QcK3FC8grUR5it6UW2Py3D3HAM1WzGQlmHUoq7mmC2qV3G84hOvwkZb18H+vt0dPJ6ZSXPBG+PVn/k+AMAk8/Abz/uNy9xirnmnzsSmSzYa83rXGmYOGn6I/YlefOpehLMdnYEu1Ua4MGrAbxyYY5RJlcW1C8WV/jSA56Smpjqd+DixrN+TSAs2l6beVfFik1m321VYVPcKguQy1af1974HWc5xZNC1S3pfPHXAaEeuFMPOqIqlKu3Cw8DUPLPMAqghI20qBOcdoyA0mbNMlnC2PYd6OYQielNpfWToOI36FP02cX2nDqSvwMZUU2WAQ2W1pAYMN1qoH9YLopIMgugw1oRDeQrZPDU1cYV0njK4vo0m0ULizIbXjDCJLjHfovNnIxvKk7BIIpFnDD5xeUPbgU88xwvDOspuarKqzrhk6s8AcOy/vAPXfLP+blwrw7VVzUu9EDbRWBxhEOuNRvBgCdk6LVIPLEIs6J/7wxKaK0P0N3WWFo88IxGplK7T1st6QR9OffgPF59dqunNWTjxdMMeLXruXAhBDXwiteB6qblHGIOhGE1iG4wrSOJvMsBktCKxkHRos1NLYfuZ2WgsBGOsBVp+8K931gHFjCQg2/KRUn3RXZxgcF5fv3IU+q40WfE0dXCqozcZUjKIyDJcV0D3IgDAnlc3KljsTAmsIaFImzdcEVj5BG0QphL9EzbwUr0JywTHdEmf1P4jC2gc72OwQRSbkOHsTxT9AMd+XtPk+i0HzdYYzTotkAdVgDJOxQC3bRnetAgkRtDZuVNKcH5X02tY14U/Lmq907UMlTMO2FO7/l1VMaupzmIWs5jFLGZxhWKWqV7F8Ucvep8REtj8t38/0G8egZVgvaxhKZsL7EZklm6lePFHXo+PfdO7zWuzKmU9oYXBRb0jf+Df/gR+6vPfjQ8NbwCgM9fpls7Mzg19rCz30Cbxg8e2l7Bd1ccvualp179xcRfn+nMmM6hTlyQATBIX/VFgtGM3t+dMhqhibugpleYIt37gDvR3qH5TluCUAcmxA15OTQaKoQNJUN8g9iEEx+NbuovSdjPcvKrpRJuDptFrFRlHtMqhSMic29KYZEvJsHuhBbtCpuK9wHRY9versLuFG4k9ZkgJlnPGhdZvPCfBaglEfo6SgVEHsWhmsPzMgBjZ1AYjSFDMF0o6h6oD9N0AGUGfCkCPXE2yvSpUaGHpPj3Go0OAJOQ82LYRkUbt1FPo+gKtJQ3KlrwUEQlvSF9C2dKoeg0TD1MyVqg5EeYrE2SHqHt5Ukf5nIZZpysSjAQdUsEQOQLOov6OL+12TWMbK4t9rJT1Z19EA/sn9fGr9RDWh3XmFd4kYTUTOESpifdLhsLBE4ao6prviY1seCQCcvRFW7C5RNPT8MDpzgIGPZ19KQWAMljHzeC5mVGHqtqxEdgYPdJC5doBBFFPokUOd4vMJaIqVJ4FZxzehotoWZ/HaOojIDUxKTjY1DKdwWe/982ouPpv9/7A5S44x++6Gw5Bq14HiMnJ6OBZFphQSKgGz3sOGAlesIzBczKovCwgiuXhhjffA6tKSAwA38mw/3miZ02YgYmFB6RNCbebmz9wiKo+XvODhbl6OM+Q1mBKCW4jxvTWDHIa4WqP2aJ6lUfOeVz7z++Evacf0sff8GQloLVf1SosubrM3yW+95M/jJbrokEY5JnJgqGW9KLSZQsqgCfRBfL4062TWKvrBqeKE2O7pSc/MbYhJDeSfavzfWx39GLcm9TQrusHL/DSy/4VkiEhIe/+KEDa88H39cKsljJTRyo1pqaelWQW4tQGC/SDr7JCZo5XUqiBC3tIgubVwkwaFU3ZyV1IpGUZabaymwKFSiMmXoaEanxKMqiQaCi2BWQMGf2fuwLDDkFlBzasKOcdAtGSNPWnrFxI0DEBYN9DuKMhSIuhaIBxBWTKzefZtQQZTX7NubFxdQkzBxU3xtm2ht+zjCMlzq2zPkU2cvHXv6fvmxd/x7uw9eJcqR2Fok/KoHxgEpLIvSOMc45sCnBL4Si54txU38HmVMOANhdYCkYFxakmIF2i4owYMv1r8GaGsF0ytTrmSjzjmK5iZ4rjaLlnjL33tpqGcjTslLH53uJ5eM4r78bgOv2z5QCc9hZZRcHfsSAdff9kJYV4nswfJiUoaIgfAJ6zfAGP+vqzxrFrJC6l4Kj4MS6M9H089CL0Ev2epWfsYXvvEucoprDyXL0J2+1XEVNt1B5yREcT1D+vx3/g+IiyAsrefMKztN3Xm8Hv/Pir8cff8Ivm98pR8Dp6/EfPD1H6HBm4JwrS1YspoA0TFI2pqGeYJg44bfowKpYHESiII/q5u+7QPh67sASPDMyThoQk43Rnx9UuSmG+lVMAmZkPTjCktMF2BwyKKzC6B1eaQ3hWhmwS4zyu7pjBv7OYxSxmMYtZXKGYZapf5XjV/d8PAFj1+rj95j+54se/4Q/fAgCoNSVGl9AgnhhXIkM1xxo1kZQtk6n+3gt/+bK/f92fvhEvXjoDAHjnrf/tKY9x4nfeDsf1YBP889fnjpuGoJuuv4itQd2IQXTaVWOaDQAJmVM7qyMoxQwdZr42wXZXZ7SuK5AFAmmN7McyZjqDa36MCnXvRpmDwbCMxQUNHaaCG7i3166CJwwpGVnbYwtsLm9ecVHyYzjUiRlFDsYxNYowZQTRe6OS1uylzlHWSMApYxZj6vYlKoiaWHAH5EEaMdiXIGH8Ev9K6xLVmdoZhqzEEC5S046vCnGGxIU1H8Mh0fiVxhDntrUAQ2+/iuuP60ypG5Zg8aLb9cz33IHv/PirAWgE4bM7h8znXXypBWdEvqWLAvZcLkwANKtTdEg1SZ2uQFDHKXckyuUIw1hndB/avAHhY6TpezhEszY1Cld2PYFICV9WDMqhzBTADddfxEWy7xvtVPHAWa0c9cLrzmp96615el9hss5KmRY8gdZhtm8o7iMRSGRNMvnescEEYJz4GBCTxd2wXYYVCExzCpmzaIQhhORIp/TcpRx7iuHEkm5Osrk0IgsWl7BdYaBcx8nQJRH6JHKM9rQ4kgJDB9MXkkXg2AWn8oDrZ1h//53GeGL9P94FVdbH+OPvKrLU9d+6E8qzMXgmNaXt+IjnSRgi1Bli7uXKUmZES5grEUUO5Fhfj50yYx6fNSRADXBndo+Cr0aIqataSQbElzT3KYbpMX3OfGpBEvwrStw0VKqR9nvlZL3nWAKBnSK9pBHxao3ZovpVjphMDX/78Wfj9puv/PFPfddbrvxBv0jkkmuTYQODaoCHdnXn8TueUbzm2R98M1aqU/QJt3vFJ16FCU2S49TFN9Jie3jhGJZKI4zpbydX93Dqgu5ybE/LCGOnMO+2JVguq8hgeKNJYqNeCZH8hZ64dl9gQRDlImEAXInyFol+zysExCfc71cw8PQEXwsiWLYwVJDOtIwoy6XfAFkRpg6Zzaew8rqsYhhPfSM+PlcvfFKHUx+J4WECoucZ5R4V2ijP6UWOl2JEkQNBVAXV9Qx2FB5PDP3FnjLdwZvzXDoFNGwlwOfe91rz2cd+7i5s/FjRsXnyD34WHi38DheYn9Ndn+PQw6OP0WLpSFTnJkYUHYCBEt/3yEvQcieGCnXzc7sYJHrstu9dhSJRHSk4Dh6bR+0xfQGD66WpzfF9D+N9D8MSUYcyBuRm5j0P/QsBBDnffMuND+Nj/gk9HoeAbLNwV9rsNs09AQUzkT+wuwrLkoZKJDIO5pJso5fBp/JA6DlIXRdOmdxmtkpQRPdgkmlFIYKDk7UYzo6+N+s399EblE23+06nbjpfJxPfqGcppnBiqW24yxcnDVTIxzjJbJRLsalbT0Y+ZC6un3BY5D1rH5kg5o6pybOIQ3kEs26VsfGa15kuXBVIWMGTXV28sz6iw6nx8GWHQqS0UPqLY6jERr61iHs+eK7A5UpIyfVCC02nyXmqrFVweFPf1eYFRvoUhjDLhObdyiU95lIyM95nXnmHOcdjP3cXrNWpGYPHv3AI9mI4q6nO4m+PX3/u//21PoUrErd+4A489M++uHBDLvZwpDbFnDeFSxppNpcIqd56srGPIdWHjlR6GCaB0Sd2aCcPAPvtms42aBGpNafGLYMBqJGU3HxlAocLbFmUDVjSZBB8yiBshehZegETXQ8HFzRPkmUMmNMPr1UKsT7fvcSwG6beyn0BxhUCauqRkqFEE/QkcpFGNhwyCxeSG7cPHmS49aiu97XDCnqfqmKi/a1RWi30dufKU+yLiuFGZtUM3pqerO17m0ia1DDlKrDYwvy6bgwbHMxrcj+AyQrT2rxEgakdLxZ3QJunj3KeqGJGJMK6xLCcTSx84ZWXayuvv19nd6XKS/D81U3curQFADhW6uAjO9cDAJStZQsBgAUCPAMy6kVpnGIYvZjcWiQDi7hBIuz5qNhI9F1YEQMjCcDPd1aNLq088OFQ3S7teEimAcQKcS4cBZdsxKTksC1pvgslmbl3ssTCiOrg1VqIxYZ2gQGAUVVoZxYAma8ADjx2u66/3viT9xjXl96gjFI5Mg1fw3YZ7Z6uCStPwJ3XGzLOFXpRYNxzLnQamCPJy1YwxTDxDO9Wjh0wQii8Lkd6XXEMuBKKssIn9iNc+/a7IRdyKyAJmT2ZSxctZ2CeuMxK8W1f+HYAwH/fvglueYqL96/qz/MU3B41r0UupK3g0pgH+wzZ/6Lr4L6TYTDOHcsBZKwoHnIFThuLZF6gujxCRNaEqc0ht0rmPHJ6nVzMgMwyWbGVMKSTfxzWb7Oa6ixmMYtZzGIWVyhmMoVXcZz4nbfj8e9989f6NAAURuTrtS5W/AH+x6amytSCCBZlKDc0d83PHAq7URWbA73jH13i2ygF13KDcVEXy8XMS36CKkFq3UkJaWrBo+xRKYb4UTL2znR7fy71pyxAHKXs1BZGdm8y8rGyMMCUZAunkWt8S0Xb09KAeTIwcMCaiTmGkhyiq8/bW5oadxJ0PCN/x1YieH5qoOdMcGO4Poo8VPwY3ZFO7+KdEvxVndk88t0/g+f8G72rbz9X6g5NOg/3wDLwL5OAdArx9OiG0AgMiKkNvxEh3tOZguIKLmXorpuZcxpNfTz6vxbGB8/8k9uNCEJ3pw7mCyM5+PrrPoyPDU4CAD70V7cZKoWqZHD2XNNNK23AuV7XqePIhRg4YASBM7sQ5RBtDzxhELmrjCuhKINjsQWrqb9rthEgncvgUD0aTMF1C2pSEjuQeUc0UyZTnZsb40RT1zg/v7OKZmWKkqOv+8zpFVi5wtGOB+kWCkuwFTh1hPulBEJwc194foqQFKaUZKYD3K/FcJ1iXA86VQQEfTqWwHAUmNeqlINP9PFkLUOLavrzpSke21hGiRSz0qqCIMoLTxh4WtBQVFmg3NQZbhS65juzHQF1qgKqPOHMm37ceLBef2Ib09TFxccW6YtiplPXjhiko5DM50r8MGpiXjnRoiag8svAgcq7hBWDNdJ/4ykg1yIjBQloOUhA36ucMuu0KsFXQ0hS0pK+0hKHUYTzb7q6/VRni+pVGLmKyij08fAr3vK1PRloR42zlyzu67/5Tti0aK0t9HBTQzfEjFLf1JgdLhALG2f6urkkSm0Duw5HmsKQr2WMKWPkvVobourqhWGU+GBMFbVBwCj/SE9BlQWsXqH0ktNQ5EJidF3r5RC9Uck06aRTF2ySW70BEDB6syKywWiSUYLBPnDNoh2vZAbOY/UEkiYVXk1Rq4ZGHckOMgiqCXNXgAE4vKBpRZ6VwSe7u61RHd2+XmyPLnWxP6zgSFNDcWc/dRSC1HKckYZVqxv6PEbr+lwAIGiGCDtBUYtVgL+gJ+EkttGgumD/TAs8ZSBvcKSLKUDNTizhUJUMPkGt4nTFNExli2mhh6sYwJThlSpLIVik2vGna8iePTK0IgycwjQ71d+LOhKacc0hQfCCs1N53Ia0geQWfUzbEXCpVmxxiTi1EZHGb70+NY5EmeA4XNc1zp1RDTU/MrJ5k8RBnJIG9H4ZbjPC6X9e1P3yeMUnXgXOFC4SVUZIbr4bJRk84i43KlNMYxfDrv6bu+PAPqkXS8aA6cgzdV/PS02tezAsGbN3ZikEj/hI6gT9ewqS7PRq8xMkaVGxc5wMqzVajP0JHthbNX8b71ZgD0izesQQU4OdrFOdO9+QRLaBwFnGIEoSqOnrKVULeaModI2mMQst2ANuZBHtkBlqFRgt/vkmb1EYBS7pFhSapCEh66mpkfO2Vg2TYYQLP3HHVT1Hz+DfWcxiFrOYxSyuUMwala7CyJ1gvpZx6wfuMD6dNx4aXfa3cj1EQubSZ3fmcbisM6xQOJh3dXY0zDxUncgYUj+ytYxyWad9np9CSmYgwjhyTCPRwaSM/bHO+iahi2MLXSMcLivCmHWzjAERL7I0W0GQsTfvOggpEzvUHKDUSjGKqUPZlggVGYxTVnGEMslx7KE31FCq7QuIfbcQP0g47JY+/3ToGpoC5wr9g4rxOBWXuOooweD4GfYGusO1Xg4RUbbeCMLLIHEhOB7d0N3Wll2IPyhbIa0oDK4t4OD8s8N2Cc3VAfqUVbGuayBqvuNjvKGvMxgzTFcFrIiypakFRu4zMrVgtx2kfZoqXIWFm7SG7zjyEFNTUTp1dHZL23SecKgHa+ackqmrdXcBsFoGjMkJpW0haUgIOi/LzwDq3GVTyzjiZGVAWgoZZbHC4xBukRMcW+zgwNHXWfMvybCYja2hPo+ql0BIbtSnGkGEPcpUnUaENLKx/pv62bJcgQrdj2WvCscSSAj+VIohKOnxce2s0HwWFpLPNHOBJdghMOqT4H05Qb0xNe4zaWoVutSVCCkdOxz4mB7NDCyteq7pZH7B6gY8nuH8RJdM2mEZ875+nrpxCc2Szva3O3XUVkYYSX3d2bwAJyEHNrXAWwkE0e/soQVB+s1KarQh7xqOIwcZZaf8Eg1s5Uqky8Lc00kg4RLEa48K6hcAeB0LyQ36vGTK4W3oezrY4eAXPEyOFM+rM2CQ0dWf580W1SsQx35Bt7g/XTPqfwzxpSzcjrW6hpMIADbhilIxXJxqCO3m+jbOTBbw/7H3ptG2bFd52Lea6nbt/jS3e7d9naSHhIUEMtj0A4cwCIM0jtOMmPyIMzABE5pALDBgGplGCJwE44CdAI4BhxAMwxhChi170IVuIAk17z3dd9/tzz3dPrutfq2VH2vWrHMREkJDNlzrzF/n3nN27apVVWutOefX3Jv7f1/enbFtWLaOgFXApdxmt0Kv5yfK84MVo4kHcYmHyyH0RV8SFMKx4pF8EPvyMa1hTeI6Pui0RkST4v6qjzedv48Xa99j0tqw6P8gLvGG6UP8/rEvLxe1xmjgv6sxCpvLOWoq5TojmAahBjX3bKtKQfUaBPdJV/BYodyl0luv8SLpRL8pggZr0gC8NFrg6pZfzBNd41D3UbfoVq1JHw+oRsYbPBPtQq0U0xtkJjGfp3A0MepznYxj3TcIqOxX7FhgVMNt02AdRZCk0ORSC5PYjl8YWpYDXAQJbN//Yp4lmO8NIVeEJB0YlMSdDS4t4WYpUyvqeQxJtI06dbAakGQkbtcKgtR5xMSg0bSIpgLhTHoUMeDdcwhBrLTF4Sblkq91AhvqkVsrmQNbpwqBMoip7Fo2mlGqvbTAYLzGwz2PELdGsFJSK6x/Qu5C00tziFbS0QmcG/hN5SzvoUkdwjlxWCNgskOOSkEDY2W3MFuJ6n3+eLIWngID4PxTM7xh6yEaUgZ758vPedcXAC8vdnGut8J+5jeVs2WKFSGZl0cp+kTVCgKD9SpmpSQRWNjUPzuqX3u1qylZ3JUdMldFBtYI3kw2ixCSxtuFgze9zgAAIABJREFUjjdrCLx8pKb300QOsuzK+eW2Y243Isvv3fA9EYot6qWPqfR/ypWmOl/D5jWe9HjytwVncRZncRZncRZ/RuIsU/04xJOeoT7/s56jWCwjBum4vsGd//obP+xnvuCdX8M6uzcGxwxAur8e4y+df5XVo77gnV+Dq8kxAOBlu4vdyGeBD4oxllXMhPrZpgfdytk4D3RhJCbAiMqjLOVS2WLeg44aaCqpBtqg2vMlQJNaBHPJqj5OOy4rukZwRntte4ZNE6KgzGacdOTzSDf49YfXOQPaSjOUplPkiaIarSx9swkQU/bbNIqzG9OEcPMQ5Q55Zx6oDjWZK9jIMFJyvgy43HnwrMEWKSE1TiLUDUrl/66JLAM+0lcC5OcsbEzf1zcsRmAntSfoU8Yy6OeYkU8nAofmIiFrpcOfv34bDdXt3h+fR7Zsjc0bRHGN0alxefnIZ/WXRgssSbwj0Aai18C212YFo3hXewOoYc3oWbVSrJRkEgcIBxt195oeCZzfXgAkkjTfJMiShIFnplCsQjQeZihqzaXVQnWgN60sCzUkQY3KKEZb95MSQ6o8nBwNkMcRA6Nsrvk+ncz6WAYWwdyf//pwCxXdT5nWmB/6zFGUCm63QkPA2rc89yreNLoDAPid+TW8+8ElVIQaju6FbHvmlPNGvgAe3Z/iLbt3cDv3pdt0UMBRNaB9z+Zrn13Wpe6y9bnGuiHB6cBCxQb98/5dC3XDYiRXJycojcbyx70a1cGfN+iPWlUsx76uADCfj/m5CmfKV3sA2B4QzCXzeKOZhKKKu9Meed/b9WOulMWaLACXzzZI9uj+xQ46Ewx2anZrBL0KVnRGD09qnKF/P8HjmX/ynWwiXh70uC9o9hJsPX/Mxt5VFuI1Vz2K9+ajHdz8K9+MT/tlTy43VrIXZ1VpDPs5y/RNk4y/66RIENFkp6RFbRQqWqSyKujoNtJitU58bxKAXmimbgBgqoAwACTgpqSQoxyCDxJEP3AQjUA97Ho2uhUA3zJ8vJ0rJ4h1g7xuF9Uc64omvtZEmhC5ia4xJyGFO3d2EI0KVlRyTrADidYGmxWVv48i9O9IZBepFGeBekzUktjAZQrBokXMAoYmsehChnHfj90kzvFgMcLykV8Q09uae7k2ACCA4I2+VJxGFVY53bNKkYcnLWBGMtXErgMu7ekLGbQ2eHbbU09ePZkip7JoEDbecYYWrDwPWbUqPb/hxddlCsGk7CT7qlNFMO0AC5ZPDOcSJZU7k2GBfBlzyVonDS5t+x788+N93Fn7/uHBuo/1Joa9T+bgowbnnvLXbKxE2Sgu62ZliE1r3p3WCImOVZVe1ajdQJWbEGGvNaRX/h62cpKlhNjyK4WUDtYIVvKCQLfyFwq7v+n//+BzK4hMQxINaDTIcH3sBTsKE+D9dy4ANHYuNpDkiBO/P0E8o+N98TEWqx73n1Vg8BeuvQoAmAYb7BUj/Nata/4ebgJoohg16wCq33kEJ2H9mD9uVfrrurI7wyAouYVyfz7ie4hSIZwUGFIvebGOYdtNpHDo/a4f+2LbebEGwi+YScM922ApvLzka/yCHkU14yvqhynifWrp9Bxk0zkvmVHjJQ2LAne/8cmm1Jxlqp/gEScVVnudfFw7Kapzpd/9L/wLF48L9LR/gS9tz/HmX3ornhp4qsLD9YjpHidFguOTPgM5Yt1w1rDYJOgnfsI5WaRMLQF8RlSvaBJwXjNUEG3ESce9F1UKntBM6Bei8Oap/m1LcZkC9cjAUXYaHmpYOh4GNcZjv2AdHQ88544yp+N4AEV0g9deeoRlGTP/b1HFqKmXNtxZY3ewxt0j34ObDje8mK3vjPgcw5WnqoQntKBPus1BlFYoRQDQJBwEBiF97uJ4iUHgL+b+aoy6UZhe8mO8PtriHT7gJ7U+bWJWedxNkisNuVWyabmMG7gT/3M8U7C0sWjQQ9Vv8L7SA6EubC1wfeIXg00TYl2FSOi81sc9JA/9tLHRPaYR6Y2EPOpBUfZl+pYXUbUUnMkAfkKN7vvnrJgoCAcE5ARUX7bY0KYmEBYzUtzKyxDWStgdymQKhSPSGQ6IXtNmqtkqgqA+shqVODf0fc2sDjBfd/QpAFyxCKIGQgCvec0jAB44J1tlLekgZGcwLqVFU3ZUrd/5Ma/C9Kb/5h2Yvd5B3PcL+kkSYRZ4zEAwKuEqBUHVF9lrcO2Cr+DsvfgU5p9JHOpNDDxIMH2t/10aVlhRNaA0GndXE94YAYB9RKC60MEe+p/zHQ+E6sV+rE4eDRnsNApzLKoEjwgclz/sI5zTZmclkJ3v4ajvv+/85RmWmT9m/fIQ2QXKVHdLpOMM1aukILbUTMdyyr+v9h5l02vJlRIZAzVRhfRKQFWAeL2/N6M0x/4r249tnp/UOOupnsVZnMVZnMVZfJziLFM9FX/x//0GLkf+9hf+nT/ls/k3Gzd+6m0AALPuc7bRu7xiioS1Aut5l4kMegUqqrvuLwZwTmAR+R35dm+DB+Qecm6wQl4FXAp9NB+gXPmdb2+c4/DAl3SitPJqRC01pAiB1ge0kJCNYF0BWQvWupUV0FBbUJUCTjtoqjCHS4dqRPSGvoVLDCRp3Fbb6ITDc41NSJq1gsj1LTK4UIiGPmt45WgLxkiklF1nRQRD53t+ssI8T7hPWNa6BeRCODCtwERAvuu49CwsGJFc1wo6atBQeUxpC00Z56v7W125UTr0+iVmD3xmEIpOmSZ6fgEpHC4MyBxcV3i/9ObRGAOrRwOM3+OPv76qmXJUXKghWnWfwAKN5Awu0g0jTI8XKaKoweYeVTOcQJNSj/alAJtLVF43PmuTZJBua6/+04aJuoqp3khGpiYPqfx/mUT/Q4NhTH65quL2waYIobTh7FGlFWLqsy+XCcpNR9mRC+3RqhRMl8ojNLWCoL7h9s4Sa/J/VcoizyKsK3pW0wI9Ov7Jqod6GXXuP7WApuusx4ZNw81bAohCoRlSeT9p8PzlfQDAZ23fxKNqiF97eAOAf17uHvjSdnOtBsjz1mqJi6/fxyjyY6ClxSsz31i2TiDLOgGJoJejXvn7ZAMDScIOZhnCAKhTQsKvFRx5w77r9mVIbSFvk0frpRIV9WzD90UYvSxgEvJGHvcwINT9+pk1v6tpXHsxE3KfEZlCQPrBNvDZaosWL68XrCKFWrK6kkkc8isNUnoONmWI0eUFTHaqpPGExtmieirWZfgYx+3jEa18X2UUDvdJQk/bx8Su/zTi1n/+Vv65XWCtFexE0WxCqMiwkfWmCHGr9JYk036GBw+m2FdUQro7wPhpXy483KQYJgVy6ls1ViIg+H6ehyw9WDYx7nzF42LhN97hqUmmb2Gl6xZcKVn2r55Y/v+glQKk+dPEnSpQ/5ZC8xkb3iQEs65EVXXVYoxGGbIiROlocj1R2Bz70lU4LBGGDQICKg16BdLQl9QWeYxhXCLc8hPL3sEYSUrPznYJHPvj6dxLv5VU9jXTms+/7cfuknn3lcEJ9jLidgJIiFqipYVxAhvnATbVlkE09eCSNKqwLiIcZqTw4/oMtmqN3ZfPEEArclxeaxcIAEgvZNicJN29t7KzcLvTw2ZgGUDltENDC3MWCFz8VX+8g08RqKcWspW8W0v+OzgHKHSlPdspO2VHCfRKQZKKz2iQYyfx/bijqo9juq66VkjiGhlxd7fGSyQkN9hYiez2EDrvjNtrWlTLTYhiSfc2NiRh6K/lcH/EG5zt80vkIsSCSviBMjg4IMrLcQCpqF8Iv3HQxE4SjUJFADKZNti5fAJFi3Y/qBj4szAJElUz1gAAMgLwBMea++yf/Nx9LKoYQ1IN08JyDzgOa4RRzf3RutKwu/6ZS9KKQUtiWMEsQwh1ql9Ji5ddBzAOUE97INH4X6VYfSapbE0cyi2gnpAZRqOYKy0EkJJ1YlEFgBXdJiO0aJJ2g+bgphXLio56BV/z/sEI+jm/SbJWYLtXMjjx0cMJ5ELDFk++S81Z+fcszuIszuIszuLjFGeZ6qlYLHtsjP2H45PIT1JLi3d98Xd+1MfcO/K73dEww/auL9G15a0/zXjdP/02AB50sT3xO/7FJoFSbTonEQ0LpK1vZBFic+Czho3qQQSWdYev/+TbMGvLuoMSq00M1WZEwnFZ0SxDVj+Sp0jfAHDth94OISkTKCTsoGF0pF0Hj2VYrTdps9XArgMoKp0pCRQ7rfg44GrF2qJWgyk6sl9jNOiEEILAoKTyrw07eoNzPkMK6fsi3WCfym3OCSzyGIsTyhALhXxOIK/8VGbdt3DSqz35kxZcdq1vDRDcWGFD5ubHQYpDOn6ehVhSCXawu4YSjmkKkzTvMqA8Rl0rrBDTGFgcrnwFwZYKkI4zedcIDF8m5Z7zDg151CZhjWYg0RDQ5+7+FJYUd0Tf+myOXgvRCCDqwEi//rP/AwDgjV/+DswuGViibtjQcnkwHeXI1hGjcJtG4ampz84XvQLrPEJOaN2FSpAN/Hjcmm/h6BE9V6Q93FJgKqPw5p27AIDnhwf4DXWNxRlkIaEJ+NRYjZBELyaDDLNlr9MdDgzSgX8XszKAAJBTFtjUGtEtAgjtGLjIolI0CK3yEOBVqCgLHA5yDKISkqDMpdGMsn33ySX0g5Kz64ODEeskNwOLZNtfV2E0NlWIE+mrJcsqwqDnz3ES51jXIfbW/rycFehRq6Iqg8eAf/1za6yp4iIyxdrCsl9DSIf60I/3u37oa/HRxF/6V/89aqLQ3S/GEIFFOOrMGdaNv08uNpDS4U1P3QMAzKsE+6SANhjlbIahpUWgDI9VdPkIjwYDmDM/1X+34tX/4q0f9nctgrU5xVP8aGJ3yy+k14czzCv/IP/yZ//dj/EMP7a48VNv417Uzf/UC4b/UUL8b/6ltzKCEkagrjQKWmTrWnG55/ZXfSgvtzXUriP/UtVtf0sZ9s6EBburyD8knHL7v/t6PPtd3pWl2m285F3YUU+6L7JMEbGFglopRvWaBFzOc8p7aTpacOWFgmkb6aAr48a6xkuvXsD5qx5tuX9rG7JdYK0EnMODQ9/LHI82TPvJygCbUkOc+Ot2PQPXcu4iC7X036s3EiZy7KAC5QAy7zYp0BwniM57BGQalBhQ6VZJC0V0oLZ81grBL/KYlYCs9ZJ5DS3UlVVs/CwyhWRPMVe3t9f1eutztZdxBLBYJWhOTcjqUcQoVUNm4g1tCmQlvXMPAK0tbzZ7X1rjsjL4tS/wbijXfvjtvNikUYV+XPKztd7EfC2xbpDJkEviTd3RrADg3EW/+F7sL2CdZG70g8UIv/C+N/D9zDadpCO2StRx55JSk5D//skUMpf4rE9/HwBgVUe4u/To7dwF2BqvsdhQr1E4FJfpIdUWcAKaFmDrBPOrlbYYU8/98vAE1klc6/tn6eZqhxfVdRXh4XKIkhZtHdeoiU8cjAumnWR1iFAZnm9GUYFLPY+yf2W5jbwK4AivIEPDEomDfs5ylKNRhk0edo4U45p/tBsNEVkEO92m8qOJX/mcH+SNeJ0HiPslEnJbWq56EKSo5AKBa+ePmYZ2lKVIqTddNA57h37jYyuFS5dm6IeEfFcG5SqCzZ989O/ZovpRxLWf+G685qrv83ykBfH6P34bXriyxy/Sv/jcd/wb1el99me+g3slp6272mhFHZQWDKh5+qe/C02u0Rv7lyp/0Mftr/S9zdki5Uz1zpf7DKTloirhMO/7ifyZ734HmpHF7b/uP+eOI5he17PRk4KPE4c1Az72MII8JA1SA1z/u9+PV7/aL9BPv/0dUJTRBjONetItpG2mA5DVVqshWyiIUwt1Nezs0Zq+BZYB+ndICCHQsMTYCd68wZCAID1d4cbVA9x6lcA9gYVT/iCTfo7zgxVOCj/RNkbhU877Hfh+McB771yEuuAzDHU7hb1CE5UTCPYo02uAeuh4gYEV0JSxCenQKMUG7OG2wVt2vVjAB+bnkWg/bncXYwjhmBpirMRKUxZvJUZJwZOYhcCtd3tZRaeA8vUZLNnTVROwFvJ4a+0nXvhJcjjJ2MDcRo5J/1C+t33xuuewHi36mFC2GCqDiM5jEBR45f9+Fs8t/DMnqhQ9WoRi3SDWNe6RRGW9iHAQUEZ+nHgeK22c4l6F0vhn5Hx/hQuJX1ByE2BWpvy7LIvgMvr5aOjdV4hvbctTG1/h4FoHGCNg+4aPcVykmK/8vb2xe4zjLMV0sOFzLinrnhEN58LEb5AfHI+Yv1mvAtR1931bvQ0mhJwLZYMPHu8A8JaF016OmjK142XKHNOt0QYHxz7Tu783hQoN3vDsQwA+232h73+WwuFX5zcg6N26cm6GGwO/gP/B8QU4WqTn+QAiNojIZSYMGxYSuXcwfQxP8cdF64jlw1dD0lHubRaJ26204U3XYHeNw3XKICx7EEPu+uegWYbde+A8N/30EppOcpjoyQcqnfVUz+IszuIszuIsPk5xlql+FHH7r/6PnLF9pHj1v/zwO8C/9rt/FT/65p/4uJxPiygOgoSRhld/7HsQ085UKev9D9ckCJ42TBh3pUYwKFFTuckNGlz7e28HAIiRQEWelK//hW/BH3zJt7PwgXGCS3T12EDUEtf+F/85iK6sKGIDYySXEy9NFpxxmW2BfZYCEnBW4Orf/z5/LVax+pEd1xhOMi6xaWWxrkn4u5Ls0CILAVl5ugadBtNaZCFgUovNVRKhTwy2z/tMY5zk2NT+OhsrcbhOAaKoILIQZLi8XPUQBQ2LOtS1wvu1z2jXRYTBMGeVI/lUgenQZwPHsz6q1khawqvRUM9MSseSf49mQ+8dSo4kl+I5Sut3/4muMSv8NW+lGVJdcQVk04TISEjdGIGy1owIFaLL1mUD2JOIj++MxGTbZ7vrLOJs65krB7i9v8Vo0eipNfI5QaQbCWiLkMqRF6cLzopDZdgQ4Vj0sPm0DIpte4D8pi/1xW9aYDveYJ36c86WMfdQ4QRELtG76CtB/bjEhZ6/T8s6xqbxn5HCIlY19td+vE9nOLIREHMNQz1iRAYhZcl1qTmjhfFuQr9794r/ZyNZ0KE0Gus8grX+uqOo5r51FDSIgob/fXl7jlu3/HMgSglHDj7HjcRbzt3Bw5J6u8Lxe7A2MbSyCCmzT5OSS+CRMuj3qT+pDZ6dHLKTTu0kHpR+jO9txggCw0YLV/onsO3fNQogqo3YaOhhBUtl4iIPcUR/d9o8/I+LN/7iN2EY+/PN64BbDjvTJY7nfW7DVIsIiloaGSmwGeoXi8BBEH0nEOg8ewce1dwQhkUJ543g2zbJExxni+pHGafl9j6W+GgX1Oe+4wcgKyDyVS+8+3/6msd+//RPfxeSnp9oRr0cPQI+LB8OIGkiz5YxIBw0TabNOugk0SY5iocptm74F/OoGCA656+tn5QoqUy8oZejLV+//he+hV8cOwtZeg6Ar3eQRBpWAdROjenEl9HOJSs8nXqrsA+o81jTYpBtImAWMuionjr0d/xnhok3km5l1rIiRErlar9ZIDBMoBCsOilCq0lxCZ4vJxLTAUq0Y3pQXge8YK/KCOtlAknn76zA4Kof/CyLcHQyYKpJmpTY2/cTXH+UI9KGVY4uDFZ48cF5f4xScQ8rPpTIr9TYIsnBSDdQrWtPpaA2Eo54lD//L9+C+Gm/oLSgMAAIJ14OseX+TvsZTz72fg/mcgZDpT+50FAth3LaID5VirdWICdQVL0J2Y1knidoVgEmF/x3h9qgeEA6stJBpAa37/oyZjrJ0aNeWlFr5FS6NrMIwULyMyESh1tf50EwV3/0e/GSduhP/Riko4KpTk2hIVLD19YCeQDgYNPn0vtnn7uJedNjziYAtkdrhPOygi3FwwKW+MmuVHxOspTeGWXuNytBI7jf/ECNIAT4PAJlcIkMwJdljEAZXEnJNUjVuJN6epk67Bx3iqHEL7779QhSf151of2mBN715mgVICYZ0IuTBZeXV2WIKZVnR2GBxkmWZyyaABd2/Hl88uQBerrCrPDPxnHZ45+FcAhoAWykQ72IEIw70M/6Ed3PP8GaVVSd5eK7vvg7mY+7fziCKxRz2CEdb2bdSQgX2w7sFxvoPXJJyoDVs2SS3q8ZBwAAcVKisRLilDXikxpn5d+zOIuzOIuzOIuPU5xlqh9l/NtC7IrXrVDVCi/+Z9/0R/7+0vYcD2ce1FBVGo/u0041dGgoy+wNC2xOEgYqSGVxZdvvsu/Pxug9tcb1sQc4/N4XfRcf+/Pf+bXYW/hjm+rx/dZylkKGnYsMHFgvVw0rFjKYnptjniXd5+oYH1j5DG5ddwhNW0sgtqx4ZCvFpbEl0UQCUneYDDJkRICP4goZAVFc0MAuAwYqIXLe+5PCWQFFAJher2S3mUg3OFr7cbPOA5/kkT+3/rNzLi1GcY1sHXmABQAz7AAp5rcmOHqmgkpo5y26klc8LlCQ0XkROkSPNPZO/BjoXKAeUGl44GWIBCVnpm863V5tEY/8/SsOE7hpgYQ8Wh2AhjK921/7dbjxju9H0CoZRR1VBtqhOEw6H8zQYkrl32IVMUF/dpIiGHXlSAAIzhN1ZRNCHkQQhGw27x7h6IbPgGRgGQQVHSj0Hjlk57tKQRt3/to34OnvfQeylX+2gosbFs3vBRX2131GOA/CEmsq+c5XCV/zbx5dx7Lonp+nzx3hIZmP96dLHHxgB3ZMsluFREOob1hAkfOSKoGm71jrVpaEGIf3Nx0MMs7kAeD9970W8mS0gZIWt53PHjdVCLPwF7j9xkMuC+/PhjBN1/qI0orbLHAAjiNUpBl873DC+sHbO0vWO55nCWqjGJR2MV3g1Y3Pih+sRyyqAvjsdLfn/+5wlfrMGABKXwGpJT3TOxtYEtfopQWu/vh3486XeQDStb/39i57dcDtr/h6PPN/eq/kpoghhl222zovpcMSemIwOyTBe+FY63uzDnybqHWYkg4vfntXbbv6D76XPkO3itpNgTYoSw1bNnjS42xR/Qhx/Se90lDSq5CTZZOQ3rnj/NiXZNKg4r7Ar3zOD/6Jjv/pv9JZq7VUjV4cIEj/6BLIG3/xmyBFyIowZaahz5OrzFGEauFfoqbXYOf8Ag2Vsk7TZ77kV78SdxdjvDDwjjNf+K+/mjcMD+dDfOA/7P72dET3AzQkX+ZiC6ccU12UsiiJtpCFAaS0eHTPT0CH6ZDpJDpsENLCPJhkqGuNgs45OAgwD/1C1+uXSKKay4yjqICSfrF5uDfxdBvA95AE2CC57jvmRrpcQQYGA3LcCLThvqB1AnnR9iABFVrYc8SfU4YneCkchHBoEiqtSotnL3kU7Hv1RYha4ilaHACg2vbl67wIIGmxFSlQigDhMXESY4f4up8IN7MENrIdB9QIxH1/HtuDDQ6XvmSnRjVG/QLPTw8AALeXUzx3xQu/f/qvfCNMfxuCnG6EEXAt19jBT27ULxaZxvyD/r4gtqgV0Y20Q50HTD3pj3PEkZ+Ez41X6F8v8dK7fB/SKQd5QCo7DnAkyVfuGshGdejrcYfeft1bfwChEHAr/8vSpVjQ8Wsr8fz0EAUhck/KHh6QEtTWaMPtjaLRmPZy7MS+93pY9Jl2kpUh7MBAU/nTaMlIWAj4MQZgRhYyaVBO6CQXAffnxYMEs1GAYECLg40QhK3rTQClLGp6n5R0+LxPeT8A4IOLHVZhEtJCabAMonOnnW0cVAOAzN/rYQPZ9iHLENWLfoFqLlboDQtuT+znA94AOgAXh0tMI7/hWTcR3v3AI70ngwxPjcngYjlEnkVoXdzyPESP3oOiCAAn8Nqf+zZ/78tBt8l44wJf8/t/BU355/wHpUP1sj+vp9/+DtjQl803OyWketwa8toPv729TG9i0S6qpWLshQsdl4yt03BVCNfem77EeJDDqCcf/Xu2qH6ESPud/mZBUmQCwPnxknfN8yzBu/+D7/gTHff6P34bdGigtd8mPzVeMDVhGmfsa/mHY7HqwWyCDpZeSxiimohJBUfQ/jipsFgn+OBf/lsfcoz9rI8oaPC3X//zH/K77DDFtf+DNI+XAW6fkhF8+Zu/Fld/xIOK7vy3nm5z9cd9v9VZAUVZ5clx3wsutIIPlYQgoEjjAtw8dczX/dNv6yzCpLeyAgCXVjBO4JCk8uZBwllmf5xjQ9mcIMcVdp8RDo4W3NZvdE294TCqAXLtOJj3mWLkjADmISY3vMyitRJzGlMpLdK4wqztG5YKD+Muk1eRYaDYbNNjuzQI11msbTSQGNTtRD6ssdmn6oIRcKGDWtM5XyzwhguePvFoM+SN297JEFWjcJj78bg2nPEY7i2HfmGgwzvleKFwAhCRgWu98mrAtpm8AC88tpZQpWCgT56HePqc3zxM4gz3VmMW32hScDXgxnOPcHvPZ1E4jFDs2k6KUFtc/Yc+KwkHGsFSMKUpXEjM9z3g6PprbgMAfv/mVf/LSjLgxgxyaOo/f/a5m/jS8e/hZ+efCsBXQJjrGjSIxwVKcljSoUEyJBnELEJCUn7WSuSbkAE2YruEoaqHILGLtqdaZwHiYbvRskjCGrupX9C1MNgjv9PDZR8lbbhdpjC8uMKaAIJRXEPSeyEkkD6zQET2dI2RnBXndwdoawQ6rlEUAW5l2/Q5xxWb9nn4wBGBpIRjmk+kGmzFflO3rKLHeMHDuORMuNEKtQOyuZ97tBEot/wYyyLAP7v5SZAtR3nVUV6E6WQaX/4bb8WzP/P4nNdqOUN4MJizXaYK2cpHCi8eAmB0dYWsCDkRyJYxMhHB5h89kOrPapz1VM/iLM7iLM7iLD5OcZapfoRoe5S10zDUo+mNc+R1wCXCdaHxJb/6lQCAi70F3ju7wKoyHy6UtpgOu9JW4ySSUySBnq7wzD/xUojPnD9kKsVT2wJHccq9x6rRyB6SbN4pYnV2kGJ4YYVr/8hnnWFSs7cioOGcYBH9x4hxL72hAAAgAElEQVTgkQFox97KCbbx3He+A2Lqd5nPvu0dqHYagNRtbGgZfYpSQi0VzEW/yxfSIbnblia74139se+BjgPe1tVj478ffgfunGCpwzissVr7nXW97tR3ZEMQ/VZ2rhYwIe2KSwGbayjKNupaYdF0vV5L2XM8KVCuA1ajSdIKxZr8R08CZLFFsuvLbbs7JzihfrEMLGwjOQPIipB7ZFFaMUNHDSukvRJrTdn1QQRHpeZWpMCMfaYwTgvORvthiWVJBuBOYJNFeEBl/7wJ2Ne1H5e49Lp7ePn3fHlWNAKKkJdGAi5B5zggBBC1nAYB1Sc0txEwpUJMaNG60rg780pDN4sd2EKzK46TAEgYomw0Lp/zvfo7m12fIdNXBUcBTKt0FTvYAGi2/POukgYxZV+bOsJ77l9i1KczAunEZ5mzeR8LquZc6Z/gdr2NC6Evt79z8yy/B8dHAySDAtepNJ/omlHDVa35PRbCYXd7iXM9KiHnKR6RjKiTvjRpaUoMhiWjppcnPdSDEleG/loXZcLvpJSWsQYiNOjHJc4PfHlfSYv75N7knIBWhl1xjJEsgP/q3+gUyl74+W+FEA6rhz6Tb0VYAF/ZqSrFCmU3Lh7h/syj0UdpzlUTJRzKRmND39WPKv5dUyvIyLAMpb1YdBKG8EIRG3qO9VzhlW/4o2UMP6QK1raCQgt73PWloR1c0z4UDq4tmhiF6iSGJFUvV0m4wHZl+yc4zhbVjxDbpK6ydzziF6fIQjy/c4D7K/8wD5OCbbJeOthlaHkbrEgkLdNJTBNj/3CE8dgfvxfWmFCfZDde46XFLr7wWd+zOSgH+MBtD5gQ0nMQe1t+gTTWQoz8z27R6eransXqwbDjXiY1livfD3GO+oi0Kbj+k2/jyV2ffhn+0HXISgA0CVeJeIy6YQrZVnigcgGngCjxE2i5CdFWHxF6NSYAEAOFpu40VBFaXhyrSqMXd8o6XPYGIArJhuXC+pKiIQNmO2x4kbYWXsKwT2NyGMMlNPlFBinxRrN1BKQNczQ3Byk7wJjIAFagoHLww0aiJseTeFLAWoH9u75HKfs1Lwx1reC4d+QVkK6c9yVbecGhICrF06MjVFYjUX6s9vMBL6Q7ybpbGDYhBtMN1kv/73wTQtF3TUcb72hykVRr1sFj7iGw6CzeYgNFfcfetGTJPJNFHT0KfqN19X/7Hhpv5SfGtsRuBMsbHi1TBqi10ZYBo5nA5pP85mE8WeOF7UeYlX7jooWFlqSgpBqkT5Xc7waA/cwvKPt2wFZ4N5fbuLX6bAT0uTSscPvWLgAgGFa4OF7y72qj+Hh1pbnEi0oiCxIsx9QS0KYD2BQKcq1ZTevidImLqe9R/tbmGsoiwD165wPSrQWAQVKyBd04KVA0mls5WnRUoVAbxEGDndS/8/fnY+aRAp0W9/u/9G8DAI//jZ/+Ll7ctfbvbpL6c15XIc6N/ALeWMkL/cODMXTQjeeDwzHrLptGwpYKF2/4DYgSDhsCP63WCdbzhPWyTy+o137o7QxMRGAR9GrUVG6XSw19zn/fC5f2cDPZRkZ2j0m/RK5JDa1UkATmy+73gdRAkKqULRWm2yuYrMR9PNlxVv49i7M4i7M4i7P4OMVZpvoR4srAl3ukcCz8/PxgH6+sdxgEsMpijFKf2cRhjShoGNXr/RR99pcENY6XRONYBxBWYEWlrclujqzxf/f33/SPAABv+ueeUnN0MGSwg7MCQdRgtiCgCwDbEqgTAzklo+e7CeppA7Q0iEY9Rs4W0wr1vv9uFzgGhjR9A01qQq2Qeht138G1ma8REOdK2Id+x28Ty5mtmzaQyqEieL+rJeoBucM0QENCDU47QDsIojs4Cc6WoqhGVoRe0J6uuy1ti6YD5ajCZ0QtmrOxulNbany52dylcq0C0KpIaYWMsuKwV6Ha78EkdNDQIiFkqogrjJICdwiMY/Z6AIF0yk0IFRrOCoNpw5lHU2ouUSeTEtnDPu5PaVevLFMfJnGOG/0jHuO0X+E91UUAwAcOz3HJLuhV6IU1tkhn+HDVZ2rGwc0t7OtpR5uRYEENf+MkZwO9tCsDbjZxl8EpB5kYRmJf/V+/DyD2yu2v+Hpc/ZHvg96i0nAWMLisnCUo2+8VzpuU03jUfYfwVf98nDjgIB0w9UQKy8+7FA5aWqTKn2PtJL7ownsBAO/Uz2OW+wpLXgfQ0kJStrQsIlap2h2uURvFKPxBWLL+7nIY4eSYXO2TBpPpGkp2ZZjWNaUsAjSNZLeYp4dH+NThqwCAOzsT7B2MWWx/2Cswjv07f+dg2gl9BA0CaTljbGxHAZo/GqDeydAP/HUGyrRSunj+Z78dFYGWrv/k2zAaZgj6LVjOsQBD0yhU8wiazvF43ueKhWkkOy8laYkoaDC/4zNrF1m0zR9nJIbbG1we+DL6y8c7XK6WAjhajhhdf/V//x6u4EBq7Fz28+Hh/TGi30tRv9YfVRjg3NQDpvpBiUujBfakH/Oq0jw3iMhwQSQ80KhiyyCsOPZes+ZUxeJJjbNF9SNE6yozjAr0tH+A3jO/hHmesFmyVJbt4qpGIQoaLKjvFg4MV1EbK/Hyf/KhovcfLo4OaSKoJOQR9T+0QxXrri9mOqNgCMCRM4rKBESt4WjOrHfBpVW1VcIZwehYVAKClGlUv8LNL/9QxDDgbb64l9lrIJXBzVYx5x98byeNpxyiuOPn2dNrswMfo3VqcbRIyaTpeoxGolqH3FsTwqGJ/O8KKxiWr3IJYf0CCgDRsWS5xHLHwmnHVBadgY3Cw7nEi199WigcjHpuxdcBX5a/83CLry25umLx9KZWGPRznJDsm9YWm1Z6T1v+TDZPgNCiWRAlK20QUmlcC4vGKaxpgamsZocWYyWjiZta4Wjex7Dvx0NKi7RHi8FFg3wVccnuNPJYZBoY1MxHzbOQ+2fJoIRpqUPSeccRoh2WtUbKPXjvotKn78uU5XJkVQZMl5J7MWTd8VPrvoNsD7EKcHc2QZ/cXAZRye/MSZGgF9R4btvThWJZY21iHoN2A1mvQ6i05sl7nBR4RGXLvZMhrBVMNWsyzWpRzkgkxLXMZwk2ecTI77dcu83XOI0zvNddRH7kF/EX+7tclg+khQ4Mb54bo3g+eO7CAdYVmaBLy1QYwNvTMU/VCmwOUuSknHSaMXDjp96GrcmarllwKwXwKl6MEl5FkL2Gn0EhHSOeg7jBgswBnBNoGoWo5RqXmp+JsFdh0su5xL5cJzg58j9H9wNg1yAgq7xeXCGgZ2R5Zwf1+0lVawAEG8ebK7NVd4bxTiLRNd5ywdvyfXCxg33hj1/MY353q4nB+NyKaXMP72xhoxPY/Mm3fjsr/57FWZzFWZzFWXyc4hMyU/2MX/pOqF4E4wTe+yXf/mH/7phKT8ZKHJ8QypY4gZLKIn8UF7QVvI9Ug3/9+W//mM4xfEAarSOLcN6SEAXypxxbn8EK3haplUSwbvlgQHHeICKbNdVrOEPZHq9hncBxuxtehJzlhKHBCz//rf4Ywis0tSAdFwv22PwQ4wDZibgDQL6KGbSjBjUaOl+Vd4bOrVB7m1HYXHP5t8hDCOVYXUiFhoEXIjQseN/0HJwWEJSpCgf+Wa8FmhQMhLKBgCVz7epKzkAQtdAwowaKNGChDBtVS2kRpxWDTaKg5izBrgIsDifoX/OZ0/nhCvda8/FKc9bdG+fIH/ShM/pdaBEO/Uk+P9xHYyXevfAl31AZHM78rl5pw2VisQwQzCRy+OexuNTp48qkAaxgZadmE3SesoFFFNdcPlTKIk58ZjDsFTgmHrCgakubY8Vh95nXf+0PYOvf2+DLrv5/AAADiR981+f771qGUFRaNrF3Mm9GdAMCB/3Aj2N8R2EjUuSxv5/ZJGff2KwMUVQBXo496KgyGldSD+o6KRIWYLCpQK9Xcvl0U4eMwC+sRBLXWBGQS2jXcVGlYSBRrmKUswTPP/cAAPBgM+LKwKqIkPRKrAkV/nBvgn1SLovjGjowyChjTMKay9ehNHwM7QR2eyscEYL7ZN3jFkYwKdBUmgU9rv3P34/0in92IEL87r/v0fjP/sx34IN/+W/xe7haJ2iO/bgFSwmTODRTPwY6NOwX/MH/6m8yb1xqiyYPPcgMgFpL5iD3CByZ0jiemy6x98gjvYUR0MMKKVUUNnnEQLHySgmnSFkscdhctejdI+u9y8CMqnPzPEFWBjhMfYXhYDbkdzeZ5FzlSMKaPY0BYPvSAus8gtFn4g9PZCznPcgyho7rD/s3vi9KpS3hMCIHkqIKUBbBR/QkbEsh7cT0scQH30ql1R/5PhQ7VCKtBYKZhLDdotL6XprUwpCRi8olZClhqf9k5iEmT3kk48X+AgfZgMtNR3bAC24hgq60lwWI+xUUoZ7dKc/jZ3/mO+BeTVGTmo6XmaLzqCRgBSIq71aFhqTyspEB/x0EoGYBL3RQnaemLZRfwGlBbFYBbOq//TVXHuEDxi9CVSow/EDAJcfsUic+oDfS91/pHhS7Bmrbv7BRXHs5NRo3GRmWYLMPEjhC4M4uRFCRQUKlz+vjGf4gu8j3SF/MsN33k9T+qt+V+gAMt/z/a2WQxbbVVYdQjnuLr262UJiAnUbuPpryYuyyEJrQ3P07AuUUKAhhqfo1o26bWiEdFDx5N+uAhTdEr0FTa3YaUtowvaSsNSso1XmAJqkZIZo3CrtD/3yUEjicDfG9H/xiAL5n2jqNyMhBT2nSDR1c5bgdIfKu9201EMwkahJz2hyk2Aj/sMpMIb6ywnse+nFNohrvf+glHc1+wmpI6bkNBMCORwBYyahHfVHZbuSURUWCDKgkqpTkAM8tkVcBMnIoqozCnKha1grEcY3JOb/QNVbyfcqKEJNBxu9zEtS8kIbKYEOLbVlqzLOEn4kkqpDTxtAqAXEKayC3S0YQB/2czd6BAE//9HdhQP6k8aDBUYtPWIfQG4HG0YY76ZSLrv7D7+XZXIcG/fEGMyrrNqGEyDsUci+o+JkbhCWWIzKreN5gPMzYeKIudScVurXG1hVyWoLDug7xUJGpQL/GmDZJZaOhlcGS7g0A9qhVwnFPX0uLeZ5gduIXX6WtRyYXT/6S9ORfwccQ0601VK/GOo9YGeTidAEpHPMOtfQLKACkUQVNtI1QG+T6IzfT/8XnvuND/u/P/bNvZi6kDrx7iqZJ4MX/6Fs/5O+v/mjLdRXcJ3TSQTjBPUSdA9T29QvIlp9cmsDLr9X9Tqv35IB6KC9NYRML12riTnJWK3IA1ouOh9mLS6zJCssa6VWJAJRlAPdUgYA+1xzGcK39GlEV24wujBvu+8ACckW91kEDM63RJ/eZ5t1j6I3/3fp6AwcL2fYrcwF3QhPj02v0Rm1v0WEpUu7Til5ncVcHCsIIhI+o/+QU6i1aYPOwk+6zfvKvibKjrUDTZq3CYWe6ZCrUcZGyhvLN1TmEYYPZhqTbVjH3Lp0T3HNvSuVdPOj5UYHlHlllNF56tMtasUHUoKLeq97N0dAiPR9pyH7NAJvT/b3DkwFWjwYdBUq6DjSWadjE8ObRNIo1cat1x2tWoYVUFnnWSnE6HNN1yc86gdnvI2hVnyxYGUlWAtWef6an7xOYvbmBIMpEsJAod2nhTyXsqGFQWvJQ8zPc9PwieJHUgm6+eo6dXZAY5tICPrtp5QwTXfNmwVqJTzl3Hycj/+xmTYjwvH++783HXHlY1jGaWuE+LXQ6ajijbTnRF8mZJlY19jY+Uz15MMLMCTy15cE9ozBHRTyxVRVhvU8Zfy0gdnMGTE17Odap/65+r0Aa1nj0e37DUO80WJP0Zvw7KdZX/fNx+6u+Dm/8xW9Cw711z3sGAHutRtMoOBofFVgGICptENHzFyiDqtG4dME/q5VRXGnLqwA2FtwTjlWDh8Jfp1QW1nUqZDgJkdP4vOH6Q1zreb3w+8UYr9x6ml2rpLSPKWtBAAFJUjYXGhwp/92fevku379bJ1texpHmCa/KZjvA3RMcZz3VsziLsziLsziLj1N8QmaqZaOgGgUpLa5MfVk0bwIYgFGJ1gnuxZSNfszN5U8SrTJSvRwiINcRUyv0+iWbVb/xF7+JsxdjJCaDDOPzJHxwd9QdTHhxhezqKcJ9a0y80pBEcUHkECwkmvRUNvaUz2z0qPSZI5UZqzJgFGi13+PSIRwwyzVE60t5yvtQKQsLyeVZWQtY1ZV1hXAs1mDziEWzkZiO7uH8Dnx97DOiXvm4swmc4NK2CwQLN9xabOO1u/sAvGtHlkTQVKKuNiHUjDLh2GH8XokgoxJyLNiHVTTSi00AcDXgNppRifLqhneaUVSjahRnRPcOJ/jsGzcBADfVLvIsYlpKb5Rz77VaRCyoj1Ih2c46Ar+0fN8XVYzJIMMBuX1URSe8X6YBZ/HrTR84imAmPrtoco1Wxl9KC2wkXKtvW2jIDT3DiYUzgh1tIBz3uqV2XPZ3FigXcYfMduDeIqyAzBTrK8tKcM/WRg7Dm7I7dmSYdlWNwSh1qx1gBKNRzbKP+rq/tk++fB+NVXhxf7e793SO+ihAuN35GMe6wW7i6R+vLLa5qrK1tcZ+MWAt4E0dMuXl0miBgw31OBcp7CqAIsGU6WjD97ZqFK6PZyiMH6sPHJzjkno4LWAaxf6nB82Az6mxkr14lTZwVmDv0L+zSVrhhQveuEJLi1dOttD02/fEsaLSe9/eubhc+4nvRm8YYEPavNu7S6b9DKMC9xcjLkNbKxilHSqDnGh+g6jCbm+FuBUVyYbYJGF7a3Gw7mOLUMgnpcSNie9hP9oMMFv2WNRBl4IFat48usPn+BuHN6CDBhW1D5xTbPbuBFUwRn5cg0HJ1R0AnOFvpRlWZYTxlm8z5GWAcpbA5k++n+on5KL6W1/0TRgO/UTWLnqjQY40rFBRzykJay7BNOZjT+hb6bpzl0/YtqmUvq922sqqha6fG62gpcURgUgwaIBle5sEyqmDICWjcC6h2nLeyEGTBVoVOZjE8SIVrMG9HGsE2Y3RxGsF0IrL9xsWv0cpIOOG1VucAwOH5EkMOzCdIXXcAZV0QCoyLaVBdRO5W2vucaLXoFxGUAv/ffl526kr0STcTsqulFBz/3cP5ATZlr+wqtHQoUG1onpkI2FGtGjXAsungZgQWfXAdXKGmQQmJKN4N4G5UDL464ULe48ZPz+aD1m2ELXEv/yD1/rfBRbWCASkyJMd9TA+7yd8vW2xOWob3N7pZkgLqQC4xbDKIkRhg4vn/BL56HiE5tmMPiZYhk8Oashl7EvJ8ILldUxyemGDZrtCdNNPwsW5Bv17/t4uX2ggtGWakNBe9B0AjHYdaEwID2abkw3XXKImJSqdCeh1hw2wIVCeJx5vYvCer/aKYS/8/LfC7fUhqCSYDgusD/04hvsa9bSbLJ/9C7dxb+45lKsqxroOUZICj1xrz2GGp3G11mAvXNmDhGNVpl5QY4sm5CSocbDps4zgpg4ZZNgLau5dhlENOfUSoQAQKYMVbWYbo/D7dy5z+yBJS1yc+A33JMrwweMdGHp+dnobrIhGk9ea8RZK+nvd9g1PVj2899ee8deSONjUQFNfP4xq9Kid8sW/+lV43z2vmnb9qWPcPx7jhRseTHXraAuKOOPGSjgn+HoaIbHdI4WmxQgp0VOGUQEtLF5ZeFH+2SpFcUw0v2OFLHbAs/5ejJICi4paPE4gDA0iUneTW46dkX57cY37yLGusT3c4ND5OUpKB0PvTx1ZYLvrz5erCK9UnorzCnbgqD0gew12tlaMZSg3oZ8zmid/UT0r/57FWZzFWZzFWXyc4hMyUz0d9Ynfca5fSnF0pWLgghtnvPNT0uLNv+TRvq2C0m9/4d/5Y4997YffDqcIbi8tI+6EAC4MlzjK/K47rwJG3D03OkAgLK71fUnm1+9dR0O7vnodQq4VggUhPROg3Pa/U+MKBZlpi1pA5RJ6449Zbhmo4xbtKiGHNdBqbtYSgkqhYq07b08H2FmEjOzVXGw5s3EB+SK21A3lAMqG6lr6n0+R4NnXSpyyiKqkL/G2T+C46iynTonzA4BeKi8+AS9osOkRzccKXwprM9C0hiVwEyRgtitkEdGKCgmZEio7l9Cv+J17M/BiCc+/xmcGPV1DJj4DevHQW2z1SDxASue1ggEuOddUiehtZwxsM41EMOyoAdZKHC38rt4YycINUdhgu7/BIfllmlKdKs9aDNuyX1xiciXjDPr+0Zh9YrXyqMniGmXeAli/qW6HwBsHtInmRnFpFcohoCqEEEAQGGxaM4VaICC2h868vnK5RW2AwEFQKX5A9moAYH53jOSNS1yi7O5onUJSViysgFMkMAHv97khMMytF8eITgSoOgkngeIyUS0E8JqnfKk/VjUGusStlQfEDMISZUBVGqPQC2qmslRG8fu0KUPUVBbdGmzw5p272JAJ+t31BCv4Ly7KAEHYoDwh2o+JYQi1GkqD7f4GD2a+rLvYJNwack5wtvXs5BBSOAbjAMDsaRr8WmFnskZNVaG8DHCu3+n2DkkNaVOF6PcK7K1IkCFo2McVAF6/u4ebc5+BDuOSxSZ2+htktb9/j9YDTBPFhh3xZIE7BHpr+j6L/IwLtwEAbx68in9+9Hp/7MkS97Mx04XmRYI/OPAZtAO4ZLzIYyyWPa7uTKZrpDQeb3j6JeyEKywbP46/cXAd89/071Fy6NCQctn6usT+eorBBT8Gra65+HcAqPQJv6iedoG48VNvY16cOrUoxLphV5A4rB97yD9S3P7rX89uMAfzvndYAZBOczxaDbg3EiiDhpoSgbC4td7Ci/c8SlDqTsqrNgJ21KAmZaDesEB5k/w9Uwk97HhfZRBCk3E1hg1iWhiyvT7cPGRjbxE42LYfortrtlstktX/W2w6uTFIL84uqXQrC8k+pqoUMOEpAfZBDXlIvqeneqYuNp6nSuLs4iCCIf4mlOvMAOi82pK37RumgkA4/xLSi+iMRLjVKbLUeQBBnD5jBEAbBJ0LkGgP+q9K1J/ZLYB72ZCdaIyR6Ccl992Wm5g9XxEar0SUdp9tpQOFAE+0YdSgaSRTPJJhgQ0hg6OoxqYKEZPHphnlHaeyDBlZPIhLrOqYN2UA+Bj1JoCMDCNmRdzwZOcazwuW1Be3Sj3mBlPTOQnl+3utY0jdt8xrrCILNAKaetXCUokPwPrWqFOiuqyBox7unFKcajdhJnGQmWQE+tUrRzg5pvbG0CBYa1RDx/c6nXZKWi395XDTR155KUE/Vmt+J8taY7bs8dhMhxmbMTwzPcJLh75fm9caldUoiZJWWYXPueh75OejBX5nfg0HY3+Osa69UQGARNVIgwoRzQ2hNijqU/SpqHvvhkGB/dyXO58eHWMr8QtRaTQCabC/9tdtrcCrx55jFIc1LhDqeBgWuL2YMtWnF1eMsr93MsamH2JO1xpPl7h54BdYKR2PTRzXkD2HCz3fVnj3wUWe15KowlaaQRIvamUSDIgbOqtSfGD/PJ7f9SXftowN+KSgNf3I6wDDQY4VbYyqRjNnWEuDgSpwXPsN4Osm+3jXp/rxnu2NIHv+PLYna3bsAXyP32TqMUWzJzU+4RfV02GPIhQ92tGlnZZo3N8wCKKxEs4ovPbnvg0AMEm73XrbI20FJV77c9+G6di/cLN5n2W9Nsc92Ek3gTZWMn/wfjbGyw/P8WRtjOS/613ykmVt/9UBaHboha4lJDmQGCOhhhUa4ReAMDlljA3AJQZGn8oCSSpQrRXMgI7d2ni12JVTCy5CCxU3sJxRgvmhVvuJl7WG8842TGfdwtpoCacM7MCfs9goBsoExxrCeam79kLbvp5cBLD0XfVVWtBoQZdrhXqX7OLSCvVSM1c3OtCoB6SluxZoUgKo/EUvlTaOCMglLN75ed8PAHju//p2zJc9jKln9rrzj/DBwE+Y2TpCGHeScVI6dv6BE7wA5lmI0TBj+lT2sI+QrOScEyhrjfks5c/liuy6RjmqVl8aflI7aLPdVcDOPKilrza09AbVTa5Bzz83rYiEOrXgyuOQ74VsPF+31TUGwBs0B7/457lfbPRGILrXmbGb89TvMwLb12bYFNSjPI752Wl1pAOS27zjdjo92LRBNemqKlYL5Hf9d7lp5elPdB47kxULMBSN5iytqAI0RcDOQIe14krCqydbyKi3W48L/D8vvZa/+/zOAoeVH9MHxRhXe535+3GRYkm9xuMiRRpU0NTLXG5iljqMk4qxEsdFipdmO7xZro1CTLxaJSxCafDp5z3g5z2zi7x5W6w6kYhXqy3uTwIe29E+S1UZ4KAesN5vomtsUX94/3gETZuzNPI81PsbsoVLCgbH5XWAxkq8b+4z0HcdP4Xnx34RvZTMMb2+wf2sc+NpZQSHYYlnB4d+3JIVHmYjPFA+cz+6N0Y58c/Yr5sbOJr2cX/tjyGEw7m+r/w0u4qpZkWtobU55ZSkydD8yc9Un/xtwVmcxVmcxVmcxZ+ROMtUT8Xtr/o6FoNIkxIVQegPV30uAUrh4ITz7gsATjYJq8+cG6yxKruSRrafohr7nVjSK+FIIi5bR8hPEjSDztVEEVr2ME8x6OeYt+Ux1ykzaW2wPdjg4ZHfBaZpAUmfe/XLPlTh6doPe4nEah4xZURnAk0joLNWOlDAtQre0rFRuDwJvD9pK5JgBByNgcgV3FKzLGKTdsha0QjvfEM9Lb3oeq/lhQYgL1S1UbDOI00BTxY3JGRRXKmASnYSeKWCoBJyNa0Q9P3/J4GBVhZLQkCbrRqTid+5r9ZJR+UBEKyAartVWwLyS0S2/4+/BZ/2y38To8Bnqj/65p/gz6RJidnhEPN7vo/3ls+7g83Q399F5MuxE6JuLMr4sRJkiyJ1VqCxEklLz5qW7GYThRW0shhOSK2rDFgWTgmHVjtotUhQVprlJPWgxoUt37s8Jim8ttxcZIZQ4HAAACAASURBVCH0PinujBVkWmN7x/etjmd9NoEWseP+thkaRGnlkeAA3CnJQjuLkMUashUWQYRknzKnEVhMAr0GmyJk6pAsBBs62NhCVBLmKUJAL0KoFZVxz1uYSQMzoIsT4IoL2p45fOVjsUm4SrMpQkbPTgcbiMEGJ+QiU5bBY77G20/5MmgaVp7u1UoHKsMUmmUV4/2H5xhHsS4inB/6cTNW4tbxFiLqUUrpULfUoUpxtWKTR6hOYtx45hEA4FI6R0Qer5smxLxKkNP3vXbyCHbsz+PWaovpOmWtASeR0Fzxpu17mNcJ/Xwfd7MJNnXne9xmya38JAAcL1Kswvj/Z+9NYzXb0vKwZ621528886np1nDr3tsj3dAO3SIOjgE5g2IpshAKjjwgO8KRFUc0ARqjYIcAgm4GQ5QE23GIE2EwsuNYUZARsrGNsd0YSEP37b7zrbnO+M17XkN+rHe/+xSDFSn9I+V7ltTqqnu++s4e11rv8z4DXtjybOiiDXFKaoK7Bz4VqXOLmukMmtxlTuoR3jc4wp3UV6T/4PiDqAmhK3WIN9cepWlsgFWdcOU+PNxgM/PP/oZQpI/teEP9x9UU91feBrEsI8RJ34uuq74flG17xyoTPv+G+sK53xmr/W/uWK1WmEwmWC6XLKn51433/e//DQDf8+hch7rmf0fFjwLDkVDOCQzTmtMi2iLkcPPJpOCJ9cm9Xd8j7FCecYv9Pd9TabTCYj6AmPnvj29sEBOsszvMsShTnN/zD+nWzTkWb/u+jDCA2fKfu/+nvwt3fvYHYco+2ov1p9JBFAEvkMFGQW91k1gfaI1RC1cE3Je1kYMqCdbaNlC5hJlSj+lpCEmLZXVFQ41amGXX9wTbDwoBhujCWeBDawbdJCxZlyobAVUKmDt+wXJGwJHcIzooen0cOTDFu/5zW6MC79vyUNar54dYrDK0C78IDu4HyO/4658+CFFeo2v1rb6nfvOnyQt4EeCdb/t2dOP2X/lRKJIqfeIPv4qzyk9OifLEGPaOLWPuK7VGoWkuwMLKYkR+qrPlgMlwbUnPBy0cplIISN86HPS913YZQ41aONoj7O+uMCTf1MfzCeoqhF31E1QHBb90/QSDsMa89hPek9mkt+/Tgi3uVGxYYwl4mU4nn0KtcPelp7j/r677Y4x6bWq1BzQv+WvfeTcH8773qkedztgBFsgO/IanXCcI4l5r3a4j/nd6bFhHahvVa6EDB+fANo4isPjDr7zhfyYczusMb577Sd9c4DzEoWYpTF2HMFpB0mJwdXvF5B4pHBab1Es7aAS0wdyZbrCb5YjIBuq4GOF0ThaAdcAbW1MrwAIfvOtJb1+3+zpm2kPPv3p6B/ef7CAhctjLe6f4wmMPwV7fXfBxnB5NgFbi6i2/+N0YLRj+LXSEQFjkRCQahRUOSbf7+nIfJ2v/bLatQhBcCFJPekvESgeo2oDbWXvDnHulidJobMcqBDZtjIg2BaUOubAwTqK+EMb+1r0DtuUcJjVCaRn2rk2AWPnPPVmNuc0lpUOoDPYJGj5aj/x8UNR47Zs//f96jv7/47iEfy/H5bgcl+NyXI4v07iEf3+f8Qd/6Ttxd8/vwtdNjA3BuqfrAco8ZqeRxSZDNaNdfWQZCgKA4XaBkqoN5wQzBmUhkZxK1NvkOnLY9sYQTeDJPcR5qR8NMbjrCRRnmwGME8ge+d+xKnZgyd/3/p/7Dtz63zwT8+bf/CGoWdwTT2RPPLFGwEYWguQOeqftGaFWMJFFnUaAAyfkmEQw6SecK7RbBsEpkQxiHxgOADExN+2Qview0GQo4YzgSlinDqoWDP+qSiA5J0gwFhAWKEEIQAD+3U0ZInjsr6lwgHsxZ8jRWImnhd/dTpIKRR3BTWiXf0MgJAZreauByi64UgE9s/lKhZf/zvfx78K2gKHrs2gyTCPy3K2Gfqed+UrhXbPNOZjGCShFbkXOp9YUdIwqsExEkaFndnd/d3EP4w3ihj1rRaZhGomQYNEk0D15pQngtERK5CdrBYesWwicFCOsiUDXVgHC9AJ5qe6E+A2adQRBf29D2+f0AnhwtsV/DnLh3ZIA1LdrHOyQAb1ROHs6YbN91Qh0V1iNmz5ZCcBgUqLI/TFZ8kbuyHKyljAkDVOjlmHtjr1aS7omAF5feFZvGrYYBA1DmsZIfiaAnslvjITVAnbun6sHdYCD/SV/Jo40GjKbcFpwZdZdb+16E/3uPhVWcqasig1MqfDFez4c4NV3r0IRoUxKi+lWzjKpLGj4GIdRzaTIINWIty6Qk0w/n5yXGbKwRUVQ8SBoOPO51gFLXjrIdr72CEUc9uqFo8dbmO5tULzpb+K70QSjj77rz8UJBNLitTN/Xb/pzv+Nr87eBgD8WvEiXtt4RcK91TaHhgCAjPrc6FWZIAo0zkgm1hmYdKOD7wNlsDfI8dZTuoe/laIZOdjq+Yd/LxfVf83oHuxlmTB0O4ganAmwubkxgiUdMrCAExhdCJOuBAUM1yFkF0dlBNSFhKM6j+CoHTfKauiHA4ZTdWqZQq9X3nVEXDX8PR2E/OJnfgz3vuO7+Ttv/tRnnjGn7uKXXABo1TugQPTB3KJQiGcErxnP5JX07gQF0NJCqYcO6aMAghZ+GwLldZIitV2iBv1/riCy3l1JUgRaPBdoxw7RkhbttP9dJgV04j8DeHTUJAQv7wi89SlK8PnpH4Y4T2HJHUlrhbNzspBzwPXDOR4uyOg7tDDXKDLrSQJLx/fiz/0A0qyGCGkBNwJ13idsHFyb8+R6Xmb4/MMbAIDBTgGtJZ4Iz4DcHuWYb/x9Opis8Zj63lJZtFXAzlrZuAJJlxEFvm9vTA+JdX2mMtDYGvlJ8qQZIx3U3AaY5RlLMEaDCnJkGc672Nd/cLaFZhH30L/wkhsA2N5dYz7r5B0S0JJN/2WiIYh5bVYRdKtg9+jmWAFLet80bvHS1PffLPXlzlpacReKcTDbKB8vR7BscZL2rQ8yUe8ShPQmRDylxJM8gnrk70X5Qo39vRV2hhS8bRROCIKdjEo8qccoyPJyuJfz+xoqw9dGwLPCB+TEtNwkzCbeHebQkURzwUHsGlmYGishhcNe7P/dNCrYFUsNSzagN62EqBVcS/PBsOW5IFAWjVYsU2msYoP+iz3S6bhArRXCrkdu+lSddZlgVSTcSniyGfP3KeH4OY2VwaPTLf77fJ1hd+KPHY3E8tEEIFtLVyv89rse2v+qF+/jrdkuS7yWOsUvLD8CwKsSXhx6SHrVJlhUKS/ucdqioudW1wHEpMQ0I91tHfX6fPRzqrUStekTlKzyrHxT95uv53Vcwr+X43JcjstxOS7Hl2lcVqq/z2iM4l3VYj5ASZo/YwSclbzD0k2AkHbu1np4qRNF20Yx+UPbgOE1CaAdgatV+TSCvU2i/yaE3WsAYru62LDna7aXw1qJ17+ly14E7vy411SazPKfZS0ghoK1pdODNR9vUUUIQ4N4z8PXxemAoc9wKVkDGq89BNvFzkGC47rCFcGXXaUag/VlUlqowEJMqdKxMVenAFgHq2sF0XroGABG9xxWL3ZMY58V22lT25FjtrKYhbj9E/480xsVdKawM/W7cCkc7/CXZeJhTzo3eSGoPby9gaGd9WhY4tZ0huNkxNf/c//p9/PxfvwXP8WkoLfeOuyrLyvQFBG7Lb08PUU98q/TeTXAkByPjJWII83wmHMCNZtEePJNFzsmw7Yncog+3EEpC+d6ODLPEzygz12ZrrCqEiaiPFn05I5mRX66ZPhu1yFXLOsqRjr0D2BVREh3CzSdZnAV9ujLsEVwP+k1w9OGIwCvT5cYk3HAWTPA2aMpB1c7BbQ7vZY2TFtv2g+K6KOWQEKaV/OKfx5FYtgHWC0CKKpczFmEehowKRAAG9Iv1ymStIEgQlwSanYu0qZnXg8HFZKoxTjyx7xYZf07XiYIlWXjg2FYs0HCrB5gVScYhl2FOGE9bqAs65ivjZZ482yXc3X3t9ZM5llVCeomwNHSP2eD3QajsIer9hJ/Xx5stpiN2w32Lg40WqNwRhX6aFiicoSARC0b6mujYCqFeOS/PwgsAwMQADQYKXj/S49Z7zsKa+wPN/z3zy+uwtCxSLieuTw+wttyF0cUjTfOKg4cf+etQ6zLEQY3/d9vTBccaNBohc3SownpoEGsND9LTlKb598A2uzlovo7RhcWHCqJI7IKu/cnvhu3/5Z3RhICgHDIO3bkOkSb0oMXG6TDumeBJophETuLER9Qz6MM0QSRz0CFZ9bKLh3HWsRpizoi44ajkAPLX/4734c3vvF7PbQL30e9yFTtxoc/+eMopYQgO8PqyTYbHwS58L2LTm4i0AvxXb/IlfsO4UrwwmliMNxrEoc3/+In8dIP+tzYZk8jogSetgqgG8WMSIxbmIQmCSN6aU8uEK2BmtDCzQ2BxCOJ0BnQDoHyBTJubyVcSH2rSqAlt6e6CuGMwNFRJzRHb4/oBFRkEJH8ZpD2fat8nSDueotG4a3ZLvI1ZWxGz2blhhfsJWEFhvt+8tvMMySjmuG3L5xd4b747rBPP+lE991YVzEqOg4hHIQQnI06iSsO4c51D5sl2xpxoHnBNKWC7ozIKRT6fNmFAIB/N6RDMNAsYyiM7M0TqhAD6guKQYO2VXjnm70s6+b/8sPPJNbozHFK0GhS8iQ/iUteeH7z4XXEJ4plNHBAds9fj+LFBkFg0HQ2l0Yi3CLrx30D5wTSTirjYoinXTgv0H7AvzOfuHUPn713q5f6nCYApfZIZdG2ATLaJJRNiPICi9eMyE2siLHepMhpw5OkDW5MPQQ7r1KMoxqzyk/6lQmwaXp53KJIcbwg68C45XccAC8olQmQRC3D9A493JlXEdomQE0b5NfafRxu+X58Fjb40txb+U2TErHQeLr293pTxiyzqqsQ9sKGvmpChGHfs+xMFqRweHWecX94vLfihTLarrA72fDm86u2HuKfnbwIALiSrjAMa86RBcCOUIlqWdpjncQf2n4T/0LeAQA8WG/hlYnfjDycbKFdxDid+e9wTuBw4M/zuBjy+5VGLY7XI97IyczPN65XBj2343JRpXH3b38/dB3g3p/8vt/z5+/+cT/hvPJ3vw9COJSUbB8UEjruSCgGWve6NSGAiAgWONTcpDdGwJo+JDpcC7TCv8DmwPY2fADaSU+4eOMbqUK9oL985hx+yC9yYgfehYgkMOEaCChkut5xcIFDsKHZz4EXVRv2a1J8LtCOHEd5hRugOKT+B/3+brG/+VOfQUOeqQgtUEuYrp9rBEATcjyufbwcgGbLodp3yB53ZsMAqQ9gQjp22oUL563uAE+AQSe/SAzUcQxDUhxMW07BgBNwkXlmcTsgUtG9aNvrAeEX1XydYGfbT0i/M+JvXcVMlhFaIH/gJwu1W0Op3nFmthrwfXtUhdii6sU64StoWoiMExAd+UYZQBkm2Ezikn1Xz/MMH9l/AgD4yOghPr++jrcfe8kIJHBttwt/A4oywZS8Y5eblPWmAGAahaarRC74WRdVhCVVDc4IbO+uWVYk1wGHCVkHxNdyXNnyPdwnswkq58/zcTjBKfntAkC9b3qdc2BZ/hIOGxSrhJOGzNigpWrUDVroWsEQEmTPY4wfUy9dAXjZL5SPNlOvYyQZFQaaK/y2CSCk60mCrte3Al7nC/h+alOH7F40yUosa0peEg7vHO9yD/QijyKvI0hp4cio2lrJkjQpHJOAQiehjcJN0ofuxDme5L7nLoSDlA6ONpu6CfDwse/3B4lmzsM0KfFfXv8l/MPVBwEAp80I//zRLX8iwmE0LPnZBfoqdhA1vAmwTmC8VWCz8ec2Xw6Qkt/0i/tneHl8gi8tPOnojc0+u4l97vQa+xED1IOm+LhRUGNAqIR1Er98/jKWRJIKpGVbyDsHZ7intnE4pYV0OWJbV+cEkrjnpsyLFIbQIzO2aLYdbPk7CITP4bjsqV6Oy3E5LsfluBxfpnFZqdJI0wa5lbj1P3gXovhM4fXv/bbf9TnnAKN7Y3I91RC0+2zXsQ/1pkphMCmZSVc2IUNBe9MNTgFmA9frmHfxzTqCyjRSChWXwuHjv/gpAEBRR9hsEoiih7Yujq7nKWvfD1UEpZQHDuHa/+54LiCMhM78705PJCfFCAOWRMD5yszG1Mfb6vuagMTtv/Kj/FkRCTiSRKSTCtXRoO/FRpbNDuqzlHs7emwA6ZDfpHCAx4r/TVgA9ZbDgCqW8sCxN2w7cFxOB09iCA2EZFBRRo7Dx6EBqyXuXPGw1B/Z/yL2Ar97/pnm42hjko8YhQ/deAu/8NmPAvBypPt/yl/vu5/+MSTvEyg74+9xy0k6gBfZnxBUZsuA036u7ixZClHrADtZjkXV9wITgmOjwCAJNCZk3L4fb/BO66uXLGq5ygGAJ/mkh6ybgJmXobTYH27w1tEef7aTrwy3C9R1wD611gnMiKFsjYSjShKtwPx8xDIrVQkYuu9qFqBpMhzTPYzjFmvyKn4y24Uk6Y3baqFGbc81qHquQVuGgBMwE3pAtURI7QKpLNzjFCA2bboRUHXHkgeWr3lof3WQ8XPUfYchV6aQ2MKWrnkY6T4/OA+519o2AXa3en7BMGy40guExZNyG2v096nry6ZxgygwuDLyz09tAob9Kx0wlLoVF9BW4vHKPxNlFuLRjFjg0npDD/IylhfORQiwvGbTxPirT/9dfGziHYmO63Hf624UCoH+fraSf7adlcip/WCJrdzlvH7s4BFimhxS1SA3MSZUnb493+HcaCkc3jrZxT713dOgZSTixI3w5HzC9/P9t59gj9KcVk2Cp6U/5yerMfYnG77GSlksV/55ca4/b02e5n/olTf9eZYjDMMabd7gEZ7vcbmo0qibAEnaIOzgn3GGuz/vLQsv2rYJKA/ldJNroBke1HniH5wuiWGYY0kwcagMw79VG+Dm3hznlEJSz1KGYIPzEFoAdae5O8qwpt7RvT/x3bj51z/N5I2L48XP/BgskUnEwHm5DX2nHhk0PswCspII1gLx3E9I0cKhIS2ncL2sxUk/qXULnWwE2nGve4Xt88ZdaKFoAtVaYnh9xYvnenVhMdkrnzEL102AYEQr/+MB93Nl6zcGFR0z3IXjMOBedJALVPsW9sIe4+DQw6KLTYo40gzv/driNj4y9q/rpokZsmu0wq88vsMm9/uTDW7+9U/765EqrE+GLPFwjYLpyGWZ9nDeBaeqKUHIizLBZuHv7c7uGtO45EU1i1oUNPltJSVKHWLd+hP/humr+PvmKwEAZ8UA2vpjn5UZpmmJw6mHYMs2ZAhwYxQenu7yRk6NGyahFJvYp+rQpq9wPgQAAPa3VzBkk3dyOvaQKcHobWz7PrtRcJFl8k0QGKguuEGHsLTQq9BCyr51EaYtZKcVdcLbTq4VXyud+T+7PAR2G4CiCYNcoDikc6kBQzrs8ChCOzV8XCpXMNsXFm0tIdPfDR2KTGMw6vva22nBXImNcDg784vG9jjHzRtnrK+cZCVvgp0TMFbyAryVFOyudFYNsUsko88dX8N21m+kH88niOjP3SI8JBjWXLCutE7g6tDfWyksGhPgV859uHneRqyJLR3Bu/T8ywv9/3ePdlkvCwfs7q65jzoNC0wCP68NVYV/NruLNYUFrDcpx18iNRhtFchC//sWVYrTTqJ2FrPjmTACtQn4+mRBg4Z25u/bPcFpOeS5ramD/ngDB03RjOYsBq5t8I9ffQUAcOuFU7z++AC2eP51qpfw7+W4HJfjclyOy/FlGu/JSvXDP/8jkFmCwbDiiiIeeHPsovI7OFcp2AtOQ1Hc54tqrZgKbi+YfmPUeoYrQRwPT7YxJgG/thLrZW8qIAWwKXp2YQezYtQClUK7ICJUI3CRj3r/P/vO3/OcbOg8iQfen9XGDgMiAW1G4HBqO7YwJuDoM0Ay1CeMYJN1PXCIlpKdjJwEVCn5d7nIwXWB2qXiY4x2SlgrOVt0f3eFnBinW1mJw4Hfkc/rDLUO8PCBL0cD1bsyVbueCRgTF6cZC2jKZw1K0QdmSwmnXJ/TqhyTInQbwFqB3aGXamx0jM+vrwEA4kBjTH6krVHYtBFXEg8f7LKbkDD+3nfEn2hcM/GsrkLvE0vVQDjoWZ+r2ZgNNZbrFJthjP2Br2buz7eYaXztygP8xvF19j/9ldXL7NML9KzS1iiMwgrXKR/z/3rtQ0xsaTaRP96xfx63xgUaggc36wTxdslB6jYPEVLVtpfmGJM71HydIQjMM0Sf7hluwqh3m4Kv2jo2qst6By5ngbYKuS0yGpZI6XqcWIEWEey+/3uc9TCxiaV/p0JySrLg3+cEEKyIWa8pt5eeAxe4PiZMS8hcAUS+qwYB+xpv7624clxXMUodso/3+WqA9swjCOeOckg7SVaRsoG+tRKBsiypCYRlo/xr2ZLzWZ0TyJuI3YYOJmtmc3eVXdcWKJveSzeSFi8MvGvavzy+hXWRYIcC0hujmEGehBrrImaISCnLsHH9YIgu/tQd1N7fl9CMz57ewlOCbl88PEVtAq7IL5IihXS4u33GzlHHD7YRrHozmI6B72ILYyX7An949BhrCih+K99D2YZMiGuaAAEhSWnaYD33MHE8UyiSjO/1w9+6Apc6uPL3JmE+T+M9uah2oyhihu+qTQS5DJGc0oTqgMKb50BNGjbads4zGzuNXBRrnpB1q/xiTBo8WGBJD83O1oZlODK0MFZAk9xmsJ8jP/IP+XQrx+J0yDZ6OlZsJ3fzr30GkA73/+zvXlhd4ICrlNN5mvoQ8q7nmRlm8aKWcAp9j3LU9yubiYXNun/k4NYSIfWtmomFvTChucAy5ChaARH6z5WrBCo2yEi20BrJsoWj9Qj3tA8AyMIWj0+mfjIEOT0Rw1c2fpNh65693EzoZ0uB+Iz6oSMLYQSCa/68t0YFL6r2PIKzAjOCzq7srdgs/Gt238GD0h/Hm4s9nM5HaNf05lvBE7fYajzDlCaxF/ZmbFoO4XgBAfzkNNynhJOsxoakQ0ZLvHO2w72kg/Ea+yNy9GkTTNIKT6gH11r1DOS4oNbBdlZiVg0QEfx4ZXfJbkJCOWTXNrzwz1cZL3RCAtNhiTz057apAmyIvfzbi4zh6izxx93l9tZtyAuKjltvdE7Xta4D7l3Cgdsgo2GJONQeSoZfiAYEI7aLBCLVyEad85XkjagxPg/WZdT3HAmIl/xxNVr5fis8M12VAi19ziUOMbkf6Tbwmzq6H9m4Yri60QEqgtuLWfYMnB+GBtkN/2yuNynqOsQ25ZOuioRZwlncoDWK81WnUYk3l76H3cm0/HkpINQMn66b3pD+D+w8wKvLK4jJvjIJND685dnd1+I5Hlb+edyUsZfeUM+8qENIclBKwxa5jHjO6nqrAGCnPfSdJBpV2UuyjhcjhuIfLydIo5Y3GsHOBotjgngF8PlH13oXqHEDEHPaCQBDymsdVwiVwYbaFverHexFvt+8qFMvaaLe8WhYsjUjAGQv+muz86ECj5cTvn5uT0BKC1NcsJp7Tsd7clG1VQCIAMGg5YDreKdEoxzKMb0kWiA8oz7SnkYnn0pTr+nrRPpNHaDNaZddKiC2EG1vr9ftJE8f9v6pYVYii1rUVDUYI3Fwy+9UF5sUMjaw5JGrIssVoQoNrJW4/TNeM3uxX4ZQ9rvOxPokmu6dywMIMgBApeCk69NolOPFUjYCitJVbOjQjiwsEZAuhny71ECUCju3vHTAuj6ebrXOoIJeJjJ7PMWAjC3iC5q6+w93ISrFEp4g7yVAqgRM4q0RAW8u0RlZOAE0084nVsDuNsBbfkNyvJMg2fETgthuYFvJVaB1Ap+Yeo/TtUkwCvy1PxyscLYaICHdZP1kwJpMqyWEAUKavAdBgyFNELvDHEfLEYqFn3RErvB47quBcp5CdFV95FC2CqPtnI+jSyTRViJvIibAhMqg1LTRihqecHaSHNtRwdduHmaMnLRliHyecrqNncUs20i2K6yKhBeY+GmA+qCrFhuu2JwTOBiu8c6ZJ0k1TYBi4yfMbFijrkIOYG/zCKAqHJHlSn29SbGyAKhntqhHPKmH0wrTUQlNC3NTJ5iwBCiBjC2SCd23vZ4gZKVl6YrJLIxyTHCyVvJGYjwqsJEJ/76q6BeeG3tz3pwUwmG0l7NH7qaOMZuTvlc5bI9zrOmzg6RhTW+jAxwM+x7lo82USUFF1aNNUnorwk7a0hoFHdAiKlsEwuLx2j8jdRtgRajYOKk4ySWJWlTHA5zPaRMfWQjiFoTKa3q5lyocxwiq2CCmzZG10iMNnc92q9g7mUlacc33PrnRx7GtioQRNNMoONoohmmLIRHsPnrwGFeSFe4XfiPw+nIfJ7HfbJ4XA6xmAyiSVmmjeHO1aWI2roiVQdWEGGWkVxYO1gmY8FJSczkux+W4HJfjclwOGu/JSjXbLqEyi3yZQI4I8pon3nCcYLvpCwvMhYeyIukwJkgkCgxsJJgJ+PRkylIKsQngjPDh3gAgHDP3LgZJj7IK5+sBQ8oyMtjQjrcpQ6jQQlMFbWoFQTvTJGl9Koki60MB7n3F05bp9bpWEBZox9SHXEtoEuyrQiJcCTZTsImDpWI3KASyp7Tb3xUejhV9T8tRf1XGBi+8cIojcvgZZRWuDr35+FnU4vG9XYwJ/k13Czw+9bKCOGmZAR0OGrQuggs6k34JQcV0M3JkBtHLaCJKs6nfVzK87iKJ8FGEdtqdgGUphVQOCPtMyVS1+FLu8ys/NrqH48Yfe2V80EF3XHqvgumyUM9CmJ2+/1eZgEPJ8zZCHGqUZEDgEovynJjORnBAejip4azkyj1WGqOoh7hmeYaPXfXyiVhqvJn71I6zasD/5igfo9ARM4jjC4xzUSjA9Ek68UHBAd1ZUqOsIxbYIwIzg+t1jJqqlw5BSC/0xTB4swAAIABJREFUwdKBP8Y7O+c4LzMcnZK8p73A4hUSZof6jlrAFQFk0zFQJQqqeMJIY1PGKIknIALHOaNBZNDWAaqOlyAc7GPfVw7XEu0NCkE4D+Gkg076KcsQRLo48dfl4CVv+H46G8M2fb3w8UN/ff9xfRfr2QBFZyM6i/h6uHGL9QXDh/3BBtGIjOWbFJHUeHfuK/m8jNh4Igw1p+BUTYj1OoEjl7C2DTBJ/Hvw+eVVNFZxtmsYGO6ZGysZAfnK/RXW22d49dibMzgn+nstHHSr2CFLt4rbUC4PUHbVLT37gtAdQTaXgJ+HNmXMqIGSjo+jaENMBiVX67oOOGWnXSQIdol3sNnCvdUO92l1HkJR79UMLLauLtlxarFJ8UaXRJM2fOwPnuzgq196l60OlbR4/+QYzabF5/B8j/fkoqpbCdsqCOXY9QXKQaUGhlx8AmWRUO9llFZM/lDSW6LNl2TDlQcMwcr9CgHAUFkat1gcebgjGLXsB1u3AYyWSAn+LTcxcqKgR+P6mViu5GmAep9gnEGD5jiD3PYvgQoM2g3ZzhnhFxKAe6mG2oRmaJAcE1lFOejM8aLqQgeVdwtnn3oTlOSwRP+hmVpOMdmZ5jhajFkeEymDLz71NmthaJDt5Xy9toYF2rSPzDqiF9FZ4MMvPcKaoLIH2TbczP85KARs5KBpw2DHGuKMIPbjGKqbgKUnKQmayF0o4LqeqvD62PkFfWhAwtovqqvsAPNkNcbBZI2Hpx6et43iIHWTWYy2Cob6H86nnBCyzFNIadn7N1+kvbVfZPj+wQkfAE7HfF4MuAf/wniOYVLjhfgcAPCo2UZIPbg7o3N8YeY3AWnQ4rQYsBfwvEgZnnXKQW3X7GtsjGC3ImsFwtAAFGDublReagW/GasJFnYA7s22eQMiAJ4Us6DBSibY3vLw9TJI0dpO+wS2o3SWnp1ubdxqeEPZNAGaWdIvYNJCUcuhrX10naTJtilDqCv+mlbjkKFmpxyS22vmHoiBxtVrvmWyKhMPUdPi5iyQvusf/nfzK3gn9AsUQgu0Eq5LbIosBEH9QWiwPSg4ym/ZpJwekzcRWqNYxqRPUmCPZFYuYPKa0RIyMryZsFriaOXf/3OVYSsrmcAzjnqLy6erMY7IAjGN/caT20vrCHtX/IZ1uUk9JAvwfQq7NoAVPXGru2adhjgUEPS5zr6x2ygKARiCyss6wt54w/10k0m2QSzRb7pao7AoUibLmSbm6D5Ih3/r8AGOSbc6X2fcz83zhPu1w6mH4F+ZHvuf6RiLNmXo/3kel/Dv5bgcl+NyXI7L8WUa78lK1RoJGInJpEB+IURcCIfrt72r+8lqyBKDSVwhI7LKrBpAhBqzBe34MsNkDXseQ+3UuLvvYSjrBBaP/Y5Nr0NoEn6v5xnQSKQHZGiu3AVpgoAKLQwltOjU4d5//l8BAG7+zz8MqQVXwrpRPfSsHO8I490S7SiAfEJwWyNR71HSxZmCcA6Cvl9YyUQlEzlIYmTJUMAGHh4GADvUvAVbrlOYo5QN9p80Ae9apXQIpMWI4Kx1FTO0OttkLEUKA4PzMuPqK4gMxBVfDb3xjd+Lm3/j05AEM8pVAFXR9ZE+z9Ufk4EILVeWCBxXTrcPz5AGfdjzw8WUiSbaSbx/fOSP74vbuPU1byE68Mf4eDnpd/MSyDcJ0qyruBTOak/IkMKhMQG71mBasoxIKoutQ5JSGYV1ETPpKAwMXt7yLk+fO76GUVLjb97/BADgK3ae4MHGV8zGSrww8kSws2qAs/mo9wwOe5GVaCRMEeDrPvQaACA3ET77ujc6b5cxMG4Q7PnqIElalllUmwh14Z+PNtUIIoMrEwoct5I9jVdNggdH2ywRUolBtEPM7jKELWkKUd7NanzFf8cgbvDkeMrHCYE+ySg0uL7nz601CssyYbaoDFz/jEQa5QWP4HyZYHDonxElLROJ6ipEnLS9W5SWjMTIWsISdOOE9FIbqpjTnZJZzQCQNyHeqT3E+4kr9/GomPK9aG0ffD65ucTiKZnORxbo7I+Fg5LOZ9gCQNDn4yLx55oEvRlES2z0OOxD5zv5jCKjlaMi5EScpiCTC6o6pbJM1nJWsFl9mDVomgCGSGOwYHN9CIcgMsxGD5XBB3Z9tRhKg1xHqBIyuR/3Wio9lYz6aCuRn2fclsJAA/QOhuMa99Y7OCv8vWjLEOFjSiI6bLEWCR/vF+wVDjH/2MEjvLPegb5k/z6fI4g0VKSxXGYQM5pA9ys4K1GTXCCJWnz8+n0AXupwb+WZboOwwbIcQk0IIlmFEANyULozg5KWYaMnswlPQEI4Xmg2mzGCvZIX9CA0aNv+oVfKQhMspZXzUhoAiL3dYBeP5KzgCC1XSzRdL7eRXmpS9AuRzHsno3AjoDvJrOydkYQD0pl/mY9fJA0oHYeMDb+0TR4haPvvbw4cT07WCgSh5d7RIG4wp7guYyReOfQLyrqJ8U++/kfwkf/zv/afS2vMz/3sdOfnfgD3/8z3cAqOSR3qPWIvtpL7lZDOG+h3DGgjeCIpdkLESnPs1HqVIiKIMQsa7IaeFfwffMOv4588usvXRkoLRYtWu44hGomKJqAkaZGf+nsW7xYYhhrrDZ1bK+E6RnUrcXZvSMduAQnU1PMsRy1+vXzBn+fuOV57csC/+1VxhXWMUjgclx4SbI3CeFQgpEm90Qol6QIReJbnP/rcB/wx7pS9nV/ipV8JbQrubM3wcOXh9yRq+flr1xGMcuh8w7RWHLh+fD6BKwKIjl3cSpimS9nxgeYAMaWl49jD/IKeUixCuMQi3faLe9sEDLePBhWkcNw3NK1CxQsAGF43RuLfefkdjh97sN7C2ZqgYOlQlhEMsfDT+yEij5ii3gHaa10EmkFThqyN/KqrvSHe67N9rIuYNb3/sHy5D8OAT1UZ04K3rmJMafMAAC9u+030o7VfhM8uLNSK7nsUaEjhONRh08Ysm+HrBTB0nNEGfHdvjQ3ZZCbDBrpVHDWXBJod2+K4P9bNIvXPQO8qymxoETiowLKkpmoDtjD86OghfnXxIlbkQhYrzS2B8zK7kDwDDHaK3qqxDtFQG0efpXhjdeUZDXHXhpLLAJYCL5x0KB1Qwv/wrXQXlQ5g2kud6nM5qnUCqROEaQu771+Uu4enCKTFdkzV0nwfby89l3033WAc+8+9dbyLILAwS9oFBg4He9TzyFPsjnI8OPILsJDgXXc5S3FKL1uwWyEILCpK3LCzmF+AQdpACIeaSC/hQqE9pB5qZOFeyWF136/r+1QOcknfl1jIojd1kLqPbVMVoJPebEKYXsrSTC3++c/7qvhr/+incf8/hpcdwW8KOvLRrFUw+w4BVe+xcLxjtlZCyT6/cbbJuJ8YhgZ3hn4C+smv/Fn8wV/6Tryf0la+cHrIi4HVEnf/9vfD3u4SbByid2jxShzMsM/pFE1vIYm474N/Yu8eZu2Ae6fLOPXRcAAerLbYWzWWGqujkdfkAdia5FhTHxwA4lOFhvSJRRQjoP6zezTB/EaL+IheoQgsYbJ7DbopTg5aCAmYDSW0rCLIfSJrLKaYjApsqGKcJiXOii7CzbHVW6Asbk7mbD7wcLOF1al/xt759k/i1n//Ixhc95P1OK0gyDjg8YMdiMiiod7pu/NtbMh4Ih000LSAxNMKUaRxmvvfLWVPbAkjDTF2z/TxDG0eJpNe5rM4H3pi11bXXxRsogEHILLPJMd0Vd9qnSKMNNv3bYoYLeXAIrQoyKQA0uHX4xtc8VflhWi38xiiFUiWfeJRtdebmHRmFUYrCOnYQ/k3n1zHgGQo3YLaLYJbo4JRlKIOUbUBMuo1fu21d7iKXbe9FnUQNZDCYRUlfI5dYg3g+/e7FBNZmwAlSauqNuBFtKJ4t9WZP+/hTsHXyloBqSyTjM6rjLW0zvl+LOA3O05LKNrsOwvgnPq8Q41GBNjZ8f3oZZ1grf3PzvQQk7DEXJHFoPXGKN34iht+EyKFw7pJ2O6x2UQQhGTISviYyK7XLgBc89fYtRKOkI1wriDOFcwd/74eL0colwlseWlTeDkux+W4HJfjclwOGu/JSnW6s4HKvK1ctxtdNzGMlSy+j5TBjCqF1kqmmVujUBYR7v+57wAA3PzpH0ZMEEkYGDw63oKjqkRkBhUleohJy+YMg2mJ7UGBhoTPx/k2Q8hVE6KpA0QzEsAHgOgqWtnCNL2tGNwF1yQrgF2/2xVOwA1b2CNycDJgqDYoPKvXtj00zBaJDvjon/eQ6/IbFOJJztVp148EgP2dFfezAC/W73bTe6Mc+oL5eNerBnz19ZNf+bP879KgxaunnpmpdR9sbmoFDYWvfvEeAOA37r+AdkTMzqg3ew8nNfa31mxQvylibHdVWjXF/dUWhyw3mwiK0kr29zd4I/ewayAN1LiBJrbu+WyIkK69bCmUYI/6PIsIepeE8tc1sA45BAAA1Av+d9/eneN+xyY2CraVEARZD6cFapI+7U1KDMIG25mHRTVV+YBPJOog9CgwiC442JxtBkjvenTk7s//t8Ao5N7d3Ejul4nEIIg0dib+uA6yDR7IKd+XzpEoDDWKIuZq6Wuu3+Pw8V967f3+/jGU6BhRaI3CNhkp5FkE+SiEGdEHlbuAonhzgq7CTbMaJcGcQjqMspplHVnUoqRnrtEBs2VbI5GEmu/nST7sDUaiDPXDIUTX/Zi6Pqte9f1EGXpov5P2QPQs2DjSjGQA/r93/b4o0MjClpnZb672uL+4lZQ4r/w7XusADj1LFvDuS4CHq6PAsISkMQpFJ4HTCjFVmUnS+neHwBJrZW9/WSQwWnHL4SKrXErHZh7xwMPEnXl9MGphaW5QAiy5AjwL+aTwv+woH2MY1ew8thUXqAhuP0jXeCnzrZtM1bjHaRe+D/zwbe8wZRMgnlTMXk7HFc8d60WGYEHzWuTg4j5RSSkLEVoIfQn/PpdDSU8o0Eah4rSPGOVJhuzAT0CddALwurd3vtmHlN/+yR8FBuaZ77tPYcODSYWXrx/jbYrh0ouok3n6h4eel6YOsFYxluTmEmx6/VotgOBRDEWLYGiAnAgDcdqiPkuhpqRvqxUvxlYGAOkrs2kJpSzW1GMKnwYcC6cH8PZydAo6AsfABbnAynNcYKctnBO8eOaLFAnZzG2PchyO1kxGKKuQ+2fLMsFyPuBeWNsqpCSt2KLFAwBu/60fRDYY88un235hdqGAbRR+7e1b/Hk36HLtPNEF8DDk6XIITecdpy1DdH9097fwq9FLWFCQcjho8FUEX71/eIR/8OT9ADzl3ywiJsBMshJmx0OpZ1/chdSC5SrCCES0EEWRxqYKmOQlDDiofetagQ++9BQA8MZqH28d7eH2vpfNzKuUe+uHgxWyoIGlCJ5Yavx2cdVf7yLmCXNnUGBeZQzP1lUftC2EQzKsMRn4a7suExRkh5kMa8ShZu/l+80WE5AWecqT/zipMU5q7n1/7vQaarovMrB8/v5GgFNf7m6fIaeFPo418u1+w4NWsgsWQv8dllatdZUxsUWNWpwdjZETbL89LNB2EW7KYL72z9iA3sdTgscbrWAkQcFPBggqgfIFf+9lpnut8TpgIqGUFmnSYkDP42qd8oIVBQZlFfLC2mjF1/jmdI7XT/b5EtzcmWFEjkR5G2FNGvMuzoz77EbiQUTEMycwiBpcyXwv9rgcseZ5WSfsFyyFQ2sUR/vVrULREZU2EWRkYAjORwomCDrXbxCypEYlQ94kxaHmvqxuPQTePUsHww0EkTSWRYrGKEyT/j3djv0m5rQa9j1+q1DrABt6ruZnI9b7R4lGXYYILvSju2sst3IYktJslukzHtAAMBqXMMHzT1S6hH8vx+W4HJfjclyOL9N4T1aqswdTyDSBGDfePxegMNF+VzWKaxw/IMJR2+NCwVrgzb/QG9rf/5bvwn/0K/8FAA/p1CZg15Bgz6AicwbUCoh7SDQvIza510PTs4lrBRs7qDmZZifgpIjGZpDjhuEUK8AVYrRVsRn40dHU//fOlKIBNHkg2NAhWgsOJnfKwR1SxqO8wMLcRGhmCVpi/4pcoabKdB1qFHWE1ZrMzlvJXp8AIALvVgV40kSX+1xdYDu++8f/Ir7+lz/JaRmlA/uYunkE2Qq4HV9RyJMYZqvl7+4MO2Ri8P7DY3S0qHdn2zgmw/ufeOvroI3EatObP3TM2n96ehcvTnzl+Jt5CjHsd9XLIkV+QuSYoYVN9TMCe/OAApdfXAFawNFxBY8iOKqc/tWX7mD70MOzobLY21ozqWN3mPP9y4IGmzbGjODDog0ZcoRwuE4krq24wBtn+yzKb6ugN5rQEuGuxphcmqRwDMlK4XC8HKHqSG/TCmV9IZWEquxZkaKqQgRE0lkXMXboWQqVwTpP2LzdaYnrE39u17MFciK53DvbBkYtBEGOLnAeAga8w1Sl2PC+Y6cDgCl8uPtrf+wvAfDMb3Qm+qlFSHKgxb0pVru9yQWcgFiQ9MPAV8ida5CWHPYAACExlMPQQBvJ6MhF+LRoQkQX8knXecKM2jdO9xAEBpu5f/5P4iGjLo1RCFTncyuhZO+iFkjL1VygLFqjkFOw8d3pGcOsADj39+pwib1kg/PaPxPLJmUDCTWxGGc9kWeZp0wQ7M4N8C2kahMjoHmoLCOuaIV0PhWoUzmolr9ve1DAOYEpBZhHUuNK4ivrWZ3xM9xds26u3N1fMbJRFDGwDKEltbquL7kiPysy5MQOj9IWQWBYNRAFGtO0ghbPf6X6nlxUnXD+f+uQa/Vs1/e6OjjCOtEvgjFw58d/FIDXb978qc9wTxUAU9DPNgMYI9k6bDTsQ7n1KgTI+UcDPpalwwlCB0sWY+FKIsgFh3I76RmvADHrUsUPtWtlH+lmJDu+xIMG9aqXNJjUcbA3pF9gu+9vXmhYOjAdlpwy4j/reOEXI82QblVGZJ/Ww3sXx2BYcTLFMKrx+rveGejVb/ku/sx/+E//AqwLGY6MQ43TGblPrSX00GJEJut6WLMpfBhpVBScHDwN8Xg6wY2xZ1gmoeYJTgqH2XIAs/TXNdvP8YUn/jjaeYKTQ7/4ai2BdYhy1qXUgO3dXGawu7vGgjYPMjZwyh/HKK1QphEc3TcbAhHFlFVjjYY2EGnoe0pdr7TWAWJaOI+LMeZVitNjL3MRgcX1A38uh1tr3oS8udlDqAy7HO3urbFYZXw92ibAg5mHGaW0+MihTz+5kizx944+isE+mflb8cyzOSBLvtb6ya2TslyE0U/nI7SLGOke6W5bhS/d89fxtdULAG0G01EFLRXcsDdu77StIvHG++6CpjWlNouUDkXeN6bf+U++h//8wb//l3jSNbs1nOm1mKNhCT2liLtNAjxMEJ3T5nPfcWC5Vb3pfGMFjJFsLi8luO0CUAIVPdNp0vKzNJuN/SbG9otJF4owXw64VxxEGqUMWa4ymw+eCcaYjHN2Pfvi2QHet3PC1+rWxLNxB6pBaUKOAAylYevE5SbFfJ3x5lkIh+s7foPzdDHGiqDyNKsRxJo3Qvyewvcu6zLELm3YrJPPJCNpK1Fo/xxoKfH2xm8CWqu4Z1u1AXMmAO/I1G1UBABMWkial+o24Dl1djQBqEAJt2rISMPSz+o2xFETwhQ9V+N5HZfw7+W4HJfjclyOy/FlGu/JSjXcqiAzH2MlaOdY1yHcUYIN7cZ2swLDqa+UintjZsi+823f/sx3vfJ3vw9C9FVPu4qxTV6djQ7gOh3ZuO0hu1ZCppp9aoOTkHWksN4xSBIqY0Ow2YGJATUPeo3pqOVqbrNJsFz6napQBL1V/vvbkYMiVMUqANaxK5Ezgh16TmcjPiYYARFb9ie+sbXgc368nMBawcQirRVrAbO4QaMDZuTGgcYrtz1p5+t/+ZO8K86bAdZl/MwuumMCmtQBCmio8p4MShyfejhMmwTygHxXDx1m8wGzKKsiwv5uL8pXyiKhiqipA/7dSAxXE0Uee9jyhCrO2HHg91fefohIGXz22LO37v/p78L7/95fBgAMowbHF7JXDSxsREzMVYCcGJX5NMFgVDFTtTGKwwekcHjr6R7v3pGHeKiJ9DYt8QJpHDt2ed74Y1TSYnfLk6nyOkJdhqhWBHMLh9/QN+j8r2F/d8VknyaPEBAUmoQa86KHxrt7CfhqtIsiO9xeYRZn7Lb0ZDGGPk26X+W9rwFUQQTbKIiKYOLQsk7SGjIp6dy/coUpEavmv3KI4Cs2uDhe/Lkf8McU9OQ1U3nCkaVKe+UEa8DtOkRwgTt4/8/07ZnbP/OD7Lmdl5EPvOhC5yPDpOY0ap/RVwNg4tZ0d+Od0QjBWBUJV/VSuZ5BHHuDhw4KjRLNVWwYaeRljGvb/t7vDxq+xpUOAYJc502Kw3SNu2Pv7BZLjXDiT+7tzR60652NwsDgCcUN1kWIdEgmF9KiBmA71UB04d6WgY+o9GADLASM6zJeW0TK9BF6JsQ4pOxZabAuev2tDAyjL4ESrIWW0iKMLSsdtLS4f+7baDLREANC3ZSFA7j6tU6gWCew5QVq8nM63pOL6uf+2HdhPB7j5l/9DJMV3SKEU0B75Cegd98dQHeB3YFDsOsf+lv/44+wbSAA1EcZp7f4LwKWK+q7WWBIi1KOGOZCL9C2EqMtD6mVkYE5pl7DXEKHDs02TUBaICQaerAREKaHbs1SYkWOSmrc+tQNeBmBsOghXwG0Y+qpOAEzctzTkpHB5tyfs4wNQnLfaWcJnPMSGQA42QwZfmxbhSDorQhXZcKLRqTMM/KDp6sxDigvtGhDDClbUQjnjSJoYimKmAOv3V4NgV6aMFsOICkP1s0jmA310kb+5yXBh0Gk2ci/NQrDrGJIzFzss2mJs1O/6N194QRv3TtgO8bJjSX3JANpcVoOuV/8sV/4HpTk0PT206uUSyv4PimC94ONYAmQjhXKIMIg6/Mrd8lg5N5m2/ecO7zI9czmr73xNrrxpcUBNnWfLKKNZIZs2yoMRhUa6v81x5k3r6f7LvYcL0zZpOT7uapi7t92so0P3PF2dYGwKEhaNsszCOE4nL04HSC54q9PGGqsz+lZX4WQ4xa22zg6Mh0AACsgyAoRANSo5sB1/eENdBnim/7Ft/prstzG9T1/r0dRzWxfYyTqVQxJcjUkuoccBxqtFrj35/v3shviJIahdxBOQJ5FaFMyxrcCcqtnpudNhL2BX+Dvz7YYes5bhb2bc5RdhuomZi6GGLVs1bhpFcbbOb8Lg2HBvcs40NhOCxwk/l0oTYglmYrcHp9joPxxPCnHKE2I3cgfx1ZQoCVXkXFU4sOjx3i39OqC3zq/iifU51WxYVtVayXaTcSZyc4KDsOAFcCot+/M24jvbagMhnHDkPjToy2IjpG/iHz6DQA1bTBKK4SyXxA7mFsEPtQBdJ9aLYAhmdeEvT3ieFBhN8vZqvE8zxBnLQz6Hu/zOt6Ti2o37n/rd/Cu2LTer+/en/wU//zjv+j/fHw8he48PA1w+yd+lCOu3Nhx7/X+n/oUbv61z8CQFVfyKEKlSN867isg20qgVuwK45zgB7YdOe+ARJN1NJfsxxsQ0737e2MFbEiRS63wWlUA1/6RxcN/HzxZy1JAdb2pgfVVLE1+tgww2CVii+zdd5AapKMa9+/5F1jmigPLx4drNG3AL5W5QP7YykosyhTXRn5H/tGDx3iU9x6qnQxnsRjAOcHxd65QTNaC80Hy68d+hxvPBDpXPhs6xB/yVVNdh/5lrrsFRiKd+sno/TsnSFWLzxpvCbgzKDhy7Wgz4kk9DVqoxMDRhF81IcJR35e9kq1g9/21muUZV2ZCACpXfRyeFrxxcULBdv99oJEkLXu6vjg5Q0CwxNF6BJMHkBn1IQf9AliaEP/y4S0AwMFkjUHUYE2kl07qAfiUl7YMPREIgDQCkhAKM9XYGRb40LZHCn7r/CoTw4yRXEW1RYhw0PCGpNHBBYcsX9l1MhTRCCY+VaFlOzox0LCbsCcjCTChTNQKTjnI+37zU+0bBNse+diZ5FhHCR6Sxd/10QINsehefXCFz1NIPCPTMa2EXpHFaC0Rz6WXuwHAbo1bh56I5vZ3OOpNCAe3XwO0+KBUvAk4zzNYK/BuSUHtVYCIqvp/+9a7yHWE335ylY8lJstFayQuCuwu6rmHUQNFet/3TY5RmhCnlV/ATooRezuXJuTFpTIh9uSGA8DrJGD3pnuLbbx6dogbY/9uXRsuIegSrauY3bIA70fezSm4cEzjvQ1ub80wJv3scTHm0PZxXGFVJ3j01P9u10iA0KJgIxlNa4cSVRPChfRerDLesOo8RDBoYUhiI5Xrnx3Ra2nvTM9R6ZCv1/5wg7c3e4xEPM/jsqd6OS7H5bgcl+NyfJnGe7pSBcDJH+ppire/85PP/Oyz/94P8Z+7XbAqBN761Cdx678jNnAtmSF763/9ISDoWYLVvkawoR5TZCHW/eV2ifUpIoBnz3Y78MjAWLCXpg0dOkW6WjjYQEC1BJMq0afKNBLZEcE23+RzXXUHDc/C3pc2FHADy7vYdLvk3shmk7B4fzCpUGxibB36qnCTJ0gJYtwd5tiKC/zGG7cAAEGqmVFZ6wDbaYFF7auZZZNgU5PvqAP3Mq/sLXG2GrCBuZOql0vkAdIjxX1gnYGPSw8cQqrU2irwLMM5OcdcqxjOl8LiYT5lR54PTo9wGPsd/lE2wRfmV/h4pTK4uufP8/HZlOUNiyqFEA7HJxTQvQ655+YCXwkwedQB2UPqYcVARX2wKDSoyghLusbnyQDboYdgr45XnGEJ+JQUSd/3q/duQxNq8LDawnSaM/xe1P1uPgi9LKQLtXeBg6PqMdsu0BiFXz/xPdZ1kfDvaozyFQYAtBLmKMNKE1S+0/Zm+NIhHVW9aURqEZ7644pWAfKb5C8bWY/YdJ7BWcNZv044QDqWdUH1cPLhaI0Fciq9AAAgAElEQVRVkWBB/cvz1aBPUIk1M9ObJoCJFRTl6upEIpx3zldAdbXFaJ+u4yZhdu4r149xj3p6zgnUOmRHKCd6xEYIhzg0HOoQZS0qcl76VXMbo0HFLGKpyJifro9oOv9di7IOmZ0/Saued2AV3lnvPmOi31Vpx0XvNT0Kazwuprg/92zu18Q+o0A3thbQTuKLT7wL2e39c4ZqW624sk6iFsu6z2SGFYyemVRiJ845mKA2Afd2A2Hw0uQUu6m/jq+f7aN60z/7qhZoqR/qigD71zaYldQ2kg6KkB5dKuhaYTDpgxC6kAVnBQZD/987hvELA1+t+4SaoE89eo7H838G/x9H56DS7v3+WP7N/+nTiK7QDP+Wh8/CNT3MU8t2bPIshLxW8kuly+BZyv6wj2bSTcCuI+0m6jED5YDY8sLsZL9wmsTb5hmCXaIlhRPDz2UNqWHCVzMyEqd/lzpeVDFpIZwACP6siggNQXZOS7YKLIsIcdqygXlwIW4sbyKsqgQpvThJ1HKPb56ncAPfjwGA6fYGednF6wGjbb94FW3ICyoATkEBABcbNGPJ0KoqBdpd0hqeBX1QtRawQwN16I9jPKhYE7dpY+wmF/pbQY3bsSd/XI9mOGv8d9xfbeHazrKHobZXOFv6ibVZR975p4NyG+GD3AGYwxo27Pu0ch2g2qM+uOnN5OO4RSMcL4gfGj/BkHYLH5w8xcPFlO0wpbR4Yeph0Ve2T7Ad+cX38/OrWFUxh3BfGa+wILLKrFEeAqfHLD5V0DT5NXUINSjRUC+wbRWTkyT1tPl4NZ4JKsh2PCQ4Smv8gb2HuJf7hWmxXUAf+Qet2rWId/z1VsqiKqN+wbIS413q354MAeVgD/x5R7FmOFw738/d0MSrLzwTybBm6UahYwxejy5IzQIMHtI7OAbe+LPfyRtfR25ggNd/dgtbXQfelaygVk5s+XirMsL+1hprauVkScPyI2slNkWCQ3p2zzcXjOyNgKN3Jhi1GA8q7n2/PDlhvemjYopFmWJITkyxtHic+wUrkobJTp1Nage/ax345xDAm8U+rh/M+V18+3gXYwr9FsJx6yYjrkN3XFAOks5rkpU+FYh+3zQuoTuiktIoTcjwexRo5HTPdCt5A3Lrzgm06y0140ijU5ea1EBFvQxoGNfcctBaMdQs4fDS6ASfm1/3592GSCY1TPj861Qv4d/LcTkux+W4HJfjyzTe05XqB/6Pv4yWoElo8ft+ThYKDYhhqjxRSd0lBqTwMVEAcL4YQrcKGVHbdRkgWBNUuaMhcvK5LVJ84EMP8OrrfpcWTmqYblfcEW86X4XNs8flpPeNALzEpiVpDCQgayKXxA7CAO2ECDCxZfP05tEAwUZAEztVaMBs0+cuOCo5JxCGms3et5OcjbdD5SHHThxvrGQ5TxhrlE2Iq4ce1hlGNWqqSgJp2WheV6EnUGw6Bx7bB7vS6PyPzY2KZT86Mhzd9f+w9+bRlmV3edi3hzPf+U31anpVXVU90BISVgwWgxeQWLaz4jjGZmWFEOKYOFmQGDMIxKAoAiFwa2gCeAoBFkkcGxub2DKLLMwQBgPCMkgNanWru7prfvO78z3z3jt/7H1+51WjlvGy/nCl316rV1fVu+/ec8+w9/59v2+oSglMfUh3vtfiFUWn9YMcHa/AlcSK6re8OVbaXsOZivBlw5fdabuOm9N1TGb297Rm8F9ykoXIoOqrNjNUaiIqmUKAnXKRKs8b8HkDZRusX7dEmSdHBwi4wud3rO9wzAvkxn7nj06uUqADYP1Qp67K+Pk/+SP4qt/8BgCWRLOcxqidb2pRS6oSPE+hWvqQk0Y+YVBvOLalYfCFwt5kQMfc+PH4QUUZm2LFoTZLCrkHa8lQR+Mu/t/sOoZOApMeJBAuAFz1FE0gjFmHosZwAAwoXOUUjTIwZoilXU5C7DdEKMOszKWRnoQ1KldJloVH0Wxq5aEYGbCm+MoYcsuhI1JYE2SPsKIw8EDUD3kJx36FI+2ybhWnlkMnyTHPA6oCs8Jr2xGaQQuDQyedK05J8UwuCMnodjJkZUu+eX68jSOHelS5BOMGo/MOAfBzyl/d6C0xL0+RqQwj56RFFqJ2QRBCKhzNO1RBq1xiw0nG7o6H9OwulpF9lppQ+ECRXO1g3ENZSzwxssYTvqiRuVYN9w0SWRDz+1Jvhq6D/fcmPYLlJdcoaomqIT9q3j6fnn5objCmlTAxZrA/tSjHiRfjKEuwndjq/3DRQVVK6PLRX5Ie/W/w7zCMaWHNm1/7Xa/7Or6ZW2YjgHpYu2BotwjOPRyc6o12hyliB32seEiMUKhTtHZp7ILq+qjVNHyINclCBTFt5TFNb5FXADRQuRaMClu3JW/JiOQnVwzFSBNcY3IB/cA+3HqkgFTQAlz3FdD04wJN8FvUzRF6NWKvTdxoMmX3F10slhFNwuXSx/qWfTgCaYONm6zIZRmQPeB8FdL7e25hLB1rVXYr6stm8xDBWCLbdudOM1ROF+gnJQYO8iprgdSvUSzt+f/0+AL8oct8nSUo1wXWQjvp/OIv/DFc+5I7AKxJeOKMu/fSHrLSg2h0tn6N+TnXo10IhIcS2vXuXv6uh3vuj/3Qhy2bGgBPOeE+3pxBuYlkWQXY7h7huLbnf10Cn1jYHufNo3UoxSmhhAlDcOfXfPSv4pyDsn8/Pw8mNRYzO/klvRzrndZVp64FymaBTGpcP2cza4dBigfLPmRjs1hxqIk9V+JCgThxm780AnxFbFrZK+F5rTOS1hyHU3v8iBTWr9iNSqU4waxlLVGtfKBZiLiBEk3uL4CaQzRZtAJQHQc5RiXqWthNFR62YNQVx/Ge62dLDSMNmLtvq0slbQq8fXu9br6GE9Fco/CqlbEIrtHxC5imRZ4HBJnayZ9j0HEG8uMeuQIZxZH0Mwop94dz6kMeLLqYO40wYwZJUJLZ/uGig7KxKS055DAnqHVZBQT5fvp4k65nWnkYhhn67lnLSw86bFtTSnFKn2GhwtyFg58bzAlmXU7awAJ7/JrcrFQGTAzDfd8u6NowktcwlsCLFe7N7cY3rySdH89T6IQtNBt7Jf3eRrJC6nJXm01Bs7EQXJPUpy4kuWzxocb+UR8Hz7ugAgPoyEBnZyk1j/RIZxG4a7Dv/MQH0NtYPhTG3Iw6TcDcgih8ZcXsjTXZUqBuFs5CYLUIkXvupl9JeBNnkbZVt9UwB8SiJZtoD2BOBsFL20PV7lms41YLqQVQd4Gq76wUpYF2WlpVC0qb0Jdy6EIguOfsxhJNi6h/IqA9UIQZ9xUl6URxgczJfKpS4mgS4di3K3gQVfRwqIUHHteoG3s5AzJgmM5jMN6Kv+NeTkL/KKjaWDJHr+9dsJPHIMrwygNXepQc6aWaPHn9sO2RcW7IznDQXyEKKhRjN6nVrJ3EDHAcJnj7+i0AQPiVNcbO+m1cxOS72vULyJ6mQPDIr3DhKUtoevGTl6A6gHT2g0+9+4fwwvd/C123V7/l2/D499uovDoCuq+6Cb8D6iP7I4XDogvfxQR9fHoJ92d2Vi9LaUO/3Y7/y6+/jFnlAsu9DL/wsk3SGfRSHO/26R5cnsRI3UZC5xJep3xIFrVw6EsgaowXCV0L1O1EG3o19f6yjoZaeCS9qFOJJn783GiOtPSwdFFtqPhDmuSpi0Q0Bgi6RUs804yq+uBY2P63My0pz5ekoUzn4cMIRd16VoMZREN77xS5Z/2gm56qZtQPvfmdDy+mj//j70OxsMfLNxXpcRkzqLQgOdXF/ozINpXiCLgmWZExaHW1ooIva1zq2n73yE/xwtRGB073ehRw7wkNbRiWzqTDABCOK6CMRBKVpN8ORY2paav1g8bfl2uSqgG2f96YPaSFj36S4dqluwDsfdwc/ywL28QXZbXtTQVvEoNky5KPODPQhmHmrlvoV+i42D1jGO4uh1i4a61qQUUH55rev+9nWAtWuLdyCTyao3LIRuxXiLwKw6D1n5aOa3C47BBq0I1ypJMIzi4bImNWX1+dnnkfzXHWUz0bZ+NsnI2zcTY+R+MNXalyT5FAHdLuMptdbVb4ZL0X9AprUA9AxApBqOl1Cz/CnVMm4KfH1R/5MCrnjISakYuRCTVUR5NInylAu4xWM9AWinNws+xUyBq3kmMfOtZgrsdiNCOYx5u1fVJzGACBRt1pd7xV31UXU45yo6KekF559H7LSUzWhuJEgPUMvINmNx3AXHEs260lqlogd/Cb9BViB2H24hx56SF2JgxH0w4Ox7aP8vSFPaoQtWFW4qAadxveZjD6NTYGS+wf2YpO1YKg4c3ekmCn+y9uAcJAjFzKTi7pfKxfnEJwjd85uQIA2I7nlMgxKWLqvR49GMAbSyhXRWUXV2Qmz4Yl9NIjZnh9QePpf2bTVJZHCeRUWlMPWCs4w+x3We3UGLpe4MhPsZv2aed+MZ4So7ppEzSQ7x+cbJOxOgBsO7P0rPIgOy08vt5dUYZn6vvg3BCKICVaiVQZoMxbaQWLa6o85quQmO/at7KQ4Fxb7TZs5aySmE4SazMIAMz25QBrP9iI9eO+zfBtYGijWji8uFCBZYKkPmwlIVJ3Dh4wlEMgP+eqScXIzhCstU8UQkNcW7a2hcuA+oTNePJnvxeAtR8Ug4bx24ZkJH6FjWiJOw7e9LkiqHaaRdamsEkQSlqoc3kSw1tXeHlszeV9qbB05x++ptbBiYPIm7934wLCWSRWobDtCgeTVlqQQ5PkrTvZIMywqnzqtc+KEG8a7QOwVoF3V0PcX1roNhA1LrswiXkZ4qVPXaRzqD0D7qq+eu6jcFB2J8lhNCcThrTwkbvjKGuB0K/ISrFSAvsTW0HXVYDAfa9lFeDufEi2hd04x8plviZhidu3N5FdsMc1XbZJOkZz9Bz/pB/k4BfH2D+2z3g99RFspVBpm8LzqI439KJ667/87of+vvOTzzzUE2oWm7hbUO9PjTtgK4bJjp007/zld+H1xq1van2Cb7z/WcKUX3Z2ao3W1XQU5Nj1PDoatddOSKriMLTw22NqSEf6pQ4RmmQG+HPXx7sMcMMJVRMlQ+V0k6UB4tsezNtsDzRnp2QQmUdJHzJjkCkjD+I6AbSbWBd1jKSfka0gF7ql1wuFWghaEKOkRF23coFI2jdcVAE4M9iMbb8rFBX1P0/yBHktIdwCMOqlFOI8XsVtD2itgOfXiN0CxroG4wM74S/SwE7yp2wLm99bj1Z0HNNehEoYuu5aM4ra0qVA79yCeplmJbGCnUh6m0uUQ4ncbbagGZZX3AKQctI77vbtgtpsHroyx3poNxxKc+wvurg6tAtpqQQ+uW/1s1908Q4tnIFXo5Pk8Nyk1kB3ALDRX6JUgpJ0GANNVKZ2GmrZbq4aFyVtmN2EAJAZtwQ4dy/lmY/6jv0M5QOmV4O7vrvutQtzZSTZKkqhsVyE1BZBxXE6sJxn3OpfYYkzV56yfd9ffuezuP7Ms/CPHOwqAe0WRD+oSJrBmcFiFrXe1Kd6hld+9MPwpxyG2+tWdA106HrdSUWkq1kWYryKKULv7nJI4e60mLqf9YMcN/ddO0IYZLmHjYFd+H77Hc/YiDqAbPya4y2KNkJPcI1e6Eh6zD4jzX1wOgYxKzyCRQdhhiudMV5dWGenRR7guWPr5DQMM8zLVlolOMeDpb3WHa8ktylmADVUROAzizbwvlmsm5QjYxjZpzJuMDiX0bFtuWcTsGk+DSy9Hc/R8Qq8VNnzc3RvSC2qzACs3xLFpNQ2CQpAkqR0LaZ5hHka4qmLdsPwoNuHFBrKtByOR3Wcwb9n42ycjbNxNs7G52i8oSvV1454mCGduh3/UoI7MlFxEOD2O/8ws/CPMt7yz/8nAEB5MYE8sBDJlb/5IciUI5q5/M0N0C4TAMJRTnBeEFSoQ/vn0aUV5mmIzJFUWGIgnQmFCgHnyY3+TaAYcBRDJ30IDVUNqqNRXUyhnJfx9taUsj9PTAeeo+gLrpF9aoDBS/b3xAODg43GOcYK6ZtRla3E43iZQOtTuZdRjgNXtb78wgWY2P77lUtHUJpj1+20x8uYpALSU6hrTjKjSnH4oq2wGs9kITQYAybOHJ/lgqBDVVvT/2Znvns8gHSM1qySBG9qJfD4lX28dLP1mb2xZk0iOucK/Patx9pKPtCEGiwXoXWwaSDNpYRcOajcB7a2Lfw1DFJ0ZY4nE0tyUYZjWtl7LJIVvvqxj+OO85t9YX8Lbz6/S8fRQIKcGZR1CxfmmY+uY0AbWMJQE6i9mkVUcaIQlvTj4LftzSkOTuz51qUg1nfdVWBJTbKIqpDEKteBAc9EyyUqeevXrBiZCijNEXcKrOauihaGSIBGMyipH/KgbZyF3vyR90Bclyj2nDF8xqFd1VPudjE+b7+zmnuAaNEjU3K61nzFUQ40TNhKnKTznlU1x3TPohfhroSRBqtte64eu3pAVdnUOTkljrRTKGnJWwCgGCrNkCftPd/kvl776fdTZerLGoFXE7yvzcPw9INZn5KXjAHqqvGRNohc+8TnNe6vBsSY3+ouW8P7RKEf5Lg/HdDvNaSxtPCJ+a5q62ncEPjEkoMd2u+y2gFdZwDQU58ui0lqdLwSTVbPOE/Q8dvkm8aY4lefe9LKAOu2rdA8I19+/WV0vZwcmw7n18gRapUFBAVPjzqQcY2bhxZSL5aBZQBnZ/Dv/6/GC3/hvfTnnR//AJm43/mr3/GZf+F1xs6PfdD+gRnAQVJiKskGcefHPgilDUGyciEgnSYzj2tUdxOaxNKBpJSU8TwhGQFgNabaPfd8AVS2TYjwBPDnBpVTQciMI3d2cl5cwmiO0JlcPz44wq9+8gl7jBMP6cwttjeOoJ+aQd9yfc2AYf1j7hjXBZZXOeAmLukrXHBxZss8QFn4BDedzBLcPiVXesevfjMA4EI8w4O0T+HanmwTLAZxhsf6x/jdXSs98U5BbJ5QqGhB5KgqAebYooYBaFRLnkJZCijXw4qSkvrgQGsR98WPvYKXpxuInDNQ9WoXe702qP3y5pgWgFIJzJ3zTzGOUAKt/nchKT6o7ih89aXfs+cUBgsV4tXMTh4P0gF25/b9I7/Cv8YOxrldUKRU2FvZn828iqDxzXCJ3bSP48xe4CQoafLvegVyJbFyZvtMavB9BxuPLYvac5NtVnrUly2NJEP0ncvHKJSgzQ8b+1BNQlOowE88oFHH+BqDkYWv89KjCLTIr5CVHkzpJtqcQ0VNYg0DX7Zsd7NeEIS9mEXwwhpy0wXS5x6EO/66r6jfnGznEFy3qUOnFmgtbaA8WQ5GNcm9jGGAk/LowEBfzQAnHUqr1lJQK45unBGbOc0D8Ca6LpPojFro8ul/9j8TXBxGjDbA653CHmNu3yOvJL3/KErhS0UJSF2/wEsHFj7d7Lc8gbT2USiJE5d0lYUloejzIrQSGJoDGC2qpxfwICxt2ETVbPIMuWWFnkIUlMiZ04fnIW0GzdTHqweXaIOvPWDtmtN5dxa4dWA3f2IhoOcJtIPzo15OMPqkjPDidJNaF1UpyWoSAIpJm6BUz33UTf+8YmA1p+jAR3mcwb9n42ycjbNxNs7G52icVaqvM+78t3+06vTK3/tBYkbyjMMMKzBX2fCcQzkNq+rVuPK3PmT/fVjBX6vJsLteq1D3HMM3rqGC1ssVNYOZul3fWgnvXgDuHG2YZnDyR/BTlpnaA3gNhCeOPLDVGod7nkKeCnLIub8aUEaljjX5hS7zAKu7PfDEVadrhpyceAn4myntOrujJSZOA1orjrXBkqqeMK7xtp+3UJk1hrBv8sp8HZXmKJyGsjMqUDmYaDNeIOAtRDWeJUSwYcJAOSE7EwZ+XEI6cbwxVncHWEcgKTTGE7vjrypBAvv1aIkXDq3O8FWso6wFkSnqfo2xqxKkVFjy1iCgE+d46S+9B4AzfvAE3nTlHgBgv9fD7suO2NKpIVx9cTtfw1IF+KXffIs9rlBTNuxk5eFklCCJ7MUrch8HjkU5GizJQGNWRvC4IrLWogjILzgPbARfQ3Rh3CAYO6ebAACzrEsAtspjjU4YkC5y7niZIFv5gEMpRM5ghLsBFxJGAsbljl46N6HPLgsP3LHIj+8PEN+VCBrY2AeqhiwU1jAlh3GfN+ynBMsbzVCmHkHUYiKhXMQgqxhw216LRWTARgV9HnwN5u5bXgGqZi2ZSksIhyAYzVCNHBRccnSiErXXsnU3Xdh7v5uhrAXFpwVRRezo3saS2hsAcHU0xqHL1R3PEqpGY69EodpWiFIeAgfhl1pgEGXou8i1d6x/Cn939mUALDnppNGbFiHGi4Squ2Gc4cHYtUi0zbYdOoOK0+CygSW0ARaR6HdTbJ+3+bgv3D8HTJrYQIbJcZfgWm7amEkdGfAKpEooB5oITYJrgo3ztQp85iHqWQTkK3dehnTP66/cu4E891pnLWOjCwFgmkaQTi9bFNb7u3FHMwao73RIffAojzfkovr0T/4weBSCKYbb39gGG7/pI++hG1VpjtS5EH2m8ONmcKlhmgDtiEF4GsrJLF79xu/Czk98wL7Q04icQD89SlDMfISb9u/5NKRMVsYM/G5BD5UnFE2YWnOU1xT0sbM0q049EB4gnWJfS4bVBYOmkVrHhqz2pNC4fv6IpC03726is+OSaCYxwq6dBJYLCwulX+geYK6pxwlPIQkqxNt24gpP9ZGMYwk2DwtjBid3LMS7ebVN1diKl/jU7jmakIAWwro1WcPHd6+S4xR4259ThQBrnG4KgZJ7xHLc6K4wCOykNS0iVJpjbdSK3kPH+D1MuzRhPrizBtGtSKrxWqj/yo9+GHALyse/+v3074YBUT9H7npHnBmga8/x45f3cb+03/lSOMavn9zAjbfYxffVwzWwVxxOr4GCh9hZs/3XNA9IxlUrTlDzztoEPlfoOLPx3WkPXbcQV0pgeSpwvOQC6UX7Z15wMMVQO5Zv0ClQOrgWKwnWb0T/1l6ysoeMusvhPbCv8+YM6dM5/tSTLwAA7q2G2HN92WF/RQuIWNPY9UeQR84QPjBgmVvkhIBccmhnEDKbDGHWm4xNAMyAuaQhI9p7OjrkdmMAa3QCYQj65J5GcNhChZViqF0OLhhos8a4TZwBABVwLCcxuXk9vn1I0qqs9FCW7fuVpSSIdzGNYTTD4zuWqXoxnmJvYWH6jeECa5HLI/Vy3F8OMHOJO/lJRL37QZxhWfpIpP3en1xdwNBZgN6f9ZEVTbayPSdNutJ2PCdjCMYMLvRneLxnLQYXdUi9y4O0S+zc+SqE1oxCF8KoRNpI9KRCWTHAScj0Zgns2POx1klRVJLmm0goOq6s9NCNm+QZhqrmdH40GI6LU9aPSlB76W3n72E7tPPLzzz3NrCpe15KBrNR0nt04wL86QxqVeAOHu3xhlxUwex/cj3D1b//AwBsD04IgZHbBXpC4cHGvxnfV4V4KCaLcY0wrk+9wM3eSpD3qZwL1GsVEUo66yuSDgzDDLcO13Cxb3uUrx6toczbmKk4KQDbnkM9DhHdaXd2LrkLdWK1avUN+10k17SILGYR/sT52/Qwdh8rELsH/V9nl5G76pP5GtVIYWfdEmzuHQ4xGtpKr6wF+lFO8VqzNKJJnXODSFb0sKQHCZjrvc5WEdnAPVj0kcQFnvtz7wMAvO3nv4f6nFLYak6fiq4yM9czVIwWW6YsWWblKhb/Yk3OSBf6M1RlgMKRQSK/eihAunEx6m0twZnBypGfXjuYbqVEp0d8dQ5f1uh5dqK53j3CwNkKZrWHj9x8MwBg0Elx+OIGaQbRhhrZyl8avHTXVs1i7CEfuHunZjh32fazEq+A5Bq3pran1RA/AGAzWWJ33iPHI8bQbkZy4PY3vJM2dlozsolD1PYgq9L2/syJe4+1AqoxphoaxJ0CB5ldRDgzJKn5ku1X8dLc2swdrTrY2JpBbTgiys0R4ELnxbEP7Rnc+mYrMbv2oWdRuygyr1cCzECxxpazdRBLt7VNbYLt5XrMWL9nAHwqwer2GsHY5woAdGgAxyfQFYdpisyTAOjVRIh7aX+D+vicG+tK5j6PAbThM4WVuA1De+8qw0gqA4A2lKUWuHcwtJ8DQNTtz9483MXvjy/g5ol9eG+erLf6zVN90roUWBstcbVnr/395YAkQJ7QkFxjUdtn9EI4xVFpv+hchhg5LXShBE6mHYyNfRayRQBv4JzLkgyhXyF1PU+lGHyv8Um2RKgm+rBWgvrFxcrHonl+GOB1StqI7qZ9sl9sNkmFQ1xeHG/hk2rbfU/QvVmfSuICbP+5FxbAKUTgUR2Pfq19Ns7G2TgbZ+Ns/Hsy3pCVamdrCRFXiP0Kk4ZNqGzSwjxvsj8ZfAc5Xvvp9yNwfbsit+brBKMZhrjvDN4LD3UpUU3b0yqWDhbdKnBuw1afe3oI4SvqSV65sYtLTnIB2MrkzsxicTtrE7x6YHe3W6M5ODO47/o+JlTIzjkP4pSR8T5Ym9wB2LzWkTO8z6WHX3zhKVzetjthxgzuOYr+O268iI8dXAYAnNweQqYcv/4ffZDep6nqGTe4NjpGXlsY8PJwQj2yOydD3DpcI/Ylqxk533SiAl+0acEdjykclx08/o+/DwDw0l96P576v99Ln9XrZZjfcs4xR22PWfsG2ndOTkuGumNQbdlKu/HbBYBX9jegJgE8119cjDSW2/bn3Sin3usqDazJP2/PVzM+75++F/EVg8xVgW/+yHsIsltO1hF2CzJWr7Ugf99PH2+CvWgriIP1GF7KqPqquprOx6vf9q24+iMfptxYbOeEDFSak4C+4xXoyQKTyN6rRS0IYqwUx3wRW8kJAAgDzxmJNDm8VJFO2hD6eJSSNKs+Cu11cvApA6Dcn8UwR7bycYvbPNVOWCB3vro/9+k3kezEHxTYHs6JrWuuTdBxEOasH2J+1MHVH3ZmJ7EBcxWzOnWUHd8AACAASURBVIggtjKwLVtJ6UJA7jtUQoOCzU2iLfvW9dyMb6AaboFiqAcKrHR92ZRDjZ3370YOc2ifF+1bWU9TrSsm4MWt2QDjBl3ngFTWgioxE1untSZM/IXVFvX/OTfE6N2b96BXHnwncys3FM71bD/xUjjGR6sr6DY5sqpFG5Rm5GoUdCtc7k0wdx7Qq9KnkG/OjZ2vLLCBa/ER3t57BQDgsask1RqGGQ5Vj57BpJ9TtVspgbz0qDo1Hkjmsyp9HMy61ILwhGodrLSkKp4LgyQuKLg+V5Kq29VhYuF8J286Pu5SEhV8TZK6oFPA89ocWcEMilpCqZZL8aiON+SiKpiBYAaLLMD20C02tcTxpIt+r9FpGjIcL1l7mrRi0FpaSAgg2j0AvPIau8Kr/9cPwKzb1/lBjd27Fr6TnQp+UMH/l3aSPDqfEDRZKNvTuNSzC/DuskfWXgbWFSZ0E4FSHKaRdNxLKCau6miYcwVCp32TcUEPcVUJmELg7r6dJONOQTD0/XSAP3/59wEAvxFdx+6895AtH3OQDis4dgd9smU72B/g+o4lRRTLAMP1BcFeweU5nUfGDFa1nexWtY/7ywERfwDQJF94GrriFOXlrYA/eLY1sn/zt/2QvRYCyC4onDtnF6JFFmI1cxPQ1EMwbRdjVjGa0AQzmO7bCfLr3/4bmFQxfugL/iFeO9567gFuzUcoHPxelBL3Fq7xqK09215qYVFlODacU1IclijdBie675yCHCbUQKDNOO26BQD/wf9jXb6WWYDLI9trHXgZAl5jK7IT9KIIcHjfBYWLCKxs+1u3//tvx86PW7i3IdtxZwnIK0bhD3nmk+E9EgUZV+TUA8PQOWc/izODlfGxcJ839zSEg/NhGOlBy6WPO7ONFtuWGlnH3puRm3x1xy3USQXlJlrWrxFFJeZH9l7iyzb2EAYt6SrzrRG/gwyDIwGZuY9KgWKTEe5W9xW9zvMU+CX7XVLnjNX05C9tTaiPrwzDfuZTuyDwanLjUoqjLCXpuZufN6PpK0uh8cTjDzC9bD9nkQXYju1zfCdbx8XulOITw6DA4dx+52GS0aIKAKWWdFxSKIx69vnXhiH2Kmo5TKoYk8rOIRv+An92+BwA4Hs//efAWBt03gkL+i6LNEA+CbF+3h6XL2vM3LNa1AKca+rnVooTVA7FSFbV61p5ULORPlh0MZu63pOnwVJBlxCGQQ7twyBku0jnqY/iOAIcma07WsFjpg0FeITHGfx7Ns7G2TgbZ+NsfI7GG7JSXS5DcB3CGODI/Vvg1QAzJC4HQK412jCK2oJmxEQFbGM+P0Vy2flfP0jG+TwLkdywVZTWHA06W8996A6D+UpbJUslyK0kkhXmeYiXlxbyrSqBc472n1UeAq8m2n/SzUn8PemGMM51yKyVYAZIDx3LVBowd8wysHmwwu3WPaGoqrw3HeCV47cBsG5FkmtMD114q6cBRy65+JY9FEoQJN7tZLh7bCu4tz/xCqZlhJsOsg7DiuQ7q8InT1PJNAZhhs/7p+8FYHfTcdfBkTXH2miOcezkMNcf3r2m284parOE8BURSjyhyBNWpsyynq9Y5CEQGhux/fPPfdmP4przbn3Pm/45ABA0iY0Ct77GVotN1mSVn3pMGvnEMENeSdzLLUSdhCX5CVdKoHiTY03fj+DNGF58X1tpv97Y+alnEHbttS0LSbFet8UaBn6KlyZWslNUkqo5nnHoroKYtXDi68nBjLDG+QBgxj6Fa0MzVJMAzH236NyS0IXAq20OppNaiW5FXrHC120AtTCAp4nE5Pk1MV9nsxjhICdYsaoElJNWmXGAxdy3xCm4YAnm3j9nyC47FnmobEtA2XtCPZWhOHLh2iUDzClXMmbIPEXK9v5oTP9TB6funvRx3oUWjFcxPL+mqjrNg9bXueLw+gVB275UGDupCWMtaz3yKxSqrTIvDmY4zOzzc2B6OFolEK7NMIpSrEI7b6wKH9mJy8rdSDEvQozTiK5b6KpiwTU+f/QAnxdb163fmT+GxNmorXtH9Pr1eEUVNwA8NTrA3eXQHa+B6nLk7ueTeUzXM+yUMAZIXdW8OOi0hhqGAc0cIjS6QYFpZo9xetIBHBGPVRzGMxTtJ4YFrmyd0LHszVwYQ2XD3Rvv6OujY2jDUbFH3/v3DbmoekEFEQgUdzt44Zu/+3Vf9yd/6dsB4KGFFgB0zalnoGsGuH7Wzt+1/ccmjUMNK6yW9gEWQoPHTa5jY01mb6DFKsRu4Yyx4xy+VHDPG5TiFBS+LHwIDty44Cj1ZdA6qfga3E2szFeoT0J4rp9bbZUUVO15CqLb9sUWCKFdj0l4GrXTgF64MMY0jeA5CC+JC8AixghEjaNFQg4zinNi0/7eg4vg3JA2sqokJu5ngySjHmTPz7G77OPKyPZ2JdO4517XdRPsabPz//jXvwmAhSMfe5s9xmXlI5Q1End+9pddWijkk3OUhUfH9dS5Azz3/I79Al9mJUKAXcj4VML4TahA+0jspT2sCh/cyQB0rMCCRoPIkWc+tHt9amIUm/bP2/05FqE9v//qdRKMPtO485ffhcf+ge1be36N1MkZ7qs+FlFAZu/TeUzM2lun3Kpeb2in+wRvF1UwELNWJq4dIVtZVzN6YYGFF9GmzA9qlA1D0wV7A4CMKmjFETkpRa04pscW3gx7BXbWxthzTlJaM4jm+VlKyBlH5a657FTQLoO0nvqUISs9hbKS8BLHhPUU9Mi1QWYeWMXbzWwF0lRnaQDlXHqCboH6VOKR1gz7U5fC4uDRyC1g290FdgN7vLFfYbKKMF9G9NnNqm1gZS6AleHdOhqhmtlrf+T38NSVPfszw8CYwZrb2G0ES3LqunW4RvNJWQrsT7vE+Jeeont4rZPi7mqEjx1Z3sONwREq11d4OdvCCytrvH+c2nZSE8BwUiTE1OfMyoCae8vzFPX4i5UPGdpQAAAIH3jEzSg3FG26RlGKWRHSxss78CAy9x6bCqLXLoydJKd83+kyImvGK5eOILk+ZenI4YsaTJxSTjyi4w25qKpKwFQCYjv7rK87TdJpxpM/+72oSklmBLdfM6nt/NQzCJxuslgGtGDpTNIk5sUlqtSH59I4wrCiVlSaB9B+Be4mLsZshBdgheDDIMXv3rSLQ5CUrbyi5MAlZ/V2EiI4Esgv2Ztb+NqScQD0OznSwgNbOMMHqSlZpNaMemK7hwP4YUXV6CoN2oSJow78fQ/8iSX9rDneJCpQKUE9JytVsb/XD3L0fNsPurcYwOMaLzurtk6c047c4xpHi4TOaS/OKaj5YNwjq8bRYIVh2F7DUNZIBvbv6TyEF1XoOG2dBiNyDGDt8OxF4sBGayqgUonr//D7AQDGDOH5NRGJ5Eyi7js9KPPseXMbqOHGgqLcxmmE7W6b8PFvGlf/F1slMw2w8+2C9qn/7L0ArMHIdNwhe8BqFuDOf/ftf+T3bxZSf8rhucNa7Siwxre3FKi4pntJKU7nOCt8cNFGohkDiCY2UAORMzdYS1LM8wAbiV009hddso+sKwFjGDrutfP9LpGKjK9Ryxb96XYySvhBoig9qDISVcUhHIehKiVpIWuvRjGOiBTIagblCDGhX9PrONcoCg+lexZ0KWwoOmCfTc1o4wgAf/ycDQP/5HgbUmjSerJOuynbHCxR6RYl0Lr1JPbCGvuLJnzcgLPWHrPr5Vg5C80kLpA4LfSNwREqLfCpY8tGWqYBIT0dv0BWe7jWt5XfQdrDRUdwvF8kuL+wqElWeuDMUKwdh6H+5yINkERoSUalB89tuI1hqAoJNnYLrsBDDhNNL/rW0RqkVCS3UaGxaAEsGuJ5ykYOApgvojYKL8nRcxwWnyvMy4B4GVl/BV8o1KtTLjaP6DjrqZ6Ns3E2zsbZOBufo/GGrFTjuISIGYpKkqRDKw69G+HVb/m2z/q7dc0RxwXmB3aHtfOTzyAaNE4jHKgCksqwkhOFHKbd6dWlRNApqB+63V3gaNVWZsowwO0su3FOiRW9IEfXKzBccxViFrTGE1GNemV30sn5JaIrFXxXeQReCyXmlURReDBBY2TdVlsP9YtPApTCA7Zsb7Ba+OArJyNYL1ANBbqusqwrQQzFshZgAPV0pNQEX82KkCpVXyjMspCsz3phgY3Ifq/DtIvQr5C4yg8ADmcOSgwrcr4pa4FlGVD/aXHQITSASYMqlxhPncXbUQ/RsT2Ox/7BD8BzcGNvfYHxJKFKWxcClavseMmR9WpK8aiHNVV98oFE501jPDa0VcOiDElWYAyjyvqPNNzW1my0u3St2v3ua9GQzzSaalcPK9z5r7+T/v3ah56FbEwGpEHunBTlet4yO6ceqlxQ7ipLBYJjd46H2vZiHdSqpcJa31ajh8c9urYHtYDv1Xj5gTWDkJ4ilronFW7ubUC7Si/ZXBHjnDFgdRyj07H3xUbSGqHs7o4oIIGtJKAB5RIkTFIhdQiOyS1j2DReIUFrGqE1A3POJ/24wCDKce+O7ffzpYB2yAOTGlh42N23vcdsTeL22Jatq+MYLOdNqxcA6BxwZshwpIHPGza6YAbrkX1dVntk4gEAMS8xck5M69GKbP7uLofo+xmuDGxb5MDvohfYc5NWPnpBjqPMPgvHaYy5OwexV1GoAmBb7o2d5LFJCKoF7HzTfLemzw0Ay+PEnm9XnRZbbQqTnErU7v6Q0Sk3LADiXAZccNW5stajTbVruEGyM3PXgrepUcsESnGyS1SaY1kLqPLRZ/++IRfVS/0pvMTHiwebKJz/rnci4eUMN37gWQDAy9/9+lFvgmtcumJDlkslaAFZVQG8Xom6sTvLRRt6rgG564gPBqh3NDL3e0+P9nC5Y+UTe1kPz79yAU9fewDAxkUtV23qxQsvXgTcgshWghZH0akIbmPMYDxJ0Onah5EzY/VjAM5fOUYSlDhxljOMmTa4uhLAkT1GHWv4RwKF52QIJaPgZ+9eCHMlI31bFBek9wWAMKja9A/NSNrT9QuCqPbHPSRxQYsZZwZpbR/EReEjK3xKp2HMYM1JnQCg4+z11sIV9tIeuU/dzNp0HFVx3P6v2sVo58c+iOyC69esJIRLCJotQzBh0IntgjYtJfixPY54nyOrJSW23Pn6lgD02IefhTFtSkjXb+G8KGo3A9d++v0PSa12fuyDD0G3V3/4wzBrzi5w5VFfUP9bGMvs/MQH4F+wx19XAlf+zx8EAIi9AHVPQbqEGO0ZKLeIYNH2K437f3P/oGdQuAmT5xxGGLovkDEcefZe0l2FcN/eA8VIoRyVbcSg4vAdrFjVAlFcQrm/57lH+tZoLQMLFBbHzuN3nFAkHaShHjmLS+iFR/IgU/uk/TWxja7TzXQmDDn3bA/ntCjdngxRPjdEx+7dUPYNShdIZCqOYDOlnp8vFSbjxpaJ2Z67W1TUqTZCL8gp2SZPPbz10n1yKNtL+2QHGsgaufLwdMf2WD2uMPDtonpvOaT76EIyRU8W+MTJBfqM0xu0VeXT5nNZ+WQpGIiaXM2ywkfg1fSzRhIGAHUu8Qpbh+e1kG/Tdkku2Dmokd9Mxx3Aya4MNw8FwwOgFsGgl9Jzt8wC1EchvLnT6o4MyYWE0JRONIwzZJUH5d6jrG3L6HTo+6M6zuDfs3E2zsbZOBtn43M03pCV6oNFD8I5dZPEQAAqMrj5HZ89jNzcTTCTMZbnna+u1MTirXKbfeo58/qqz9o+/0KSIFrmVqYxcYGnv8cuUQB1tvIR9XOS2DBmUE9tBbQsLczFHAwLBvLP1DUnE/uqEmCntktFJRGMLNlhb3+IJ3f2sO5YiIerDsYvWpkLC0y7zWIG1VDj3I6FN2erCJljDNfnFczCw/ply3pZFT4lreSlB20Yibw5bx1bZkVIu+B+N0Xk1cSA3IiWuDO30JvgNomjMQhflT5mjkV9feuYPE63Q1uhNtXAqL9C5WAuXyq8+SPvIdiLJz7BWlFcot84I+UBstwjIs7m+hyHxpYvxfkakhvgnq2iHv/+Z/HSu9v7Y7EKcduzMNr1wTECVyn0/YxSe25sH+JtP/89JGHobOGhoRMFOG9hphhuf0Mb3tC0JhoCyfA37fn/+N95zT3KDcF4YVCRGcFs7FuJQ+NGtV4CjRGH1DBOIsVqS1DjDdIBQDklldYeTKThuYqUGVCAOc84audqJDcsnNzc+3UpCKbfGCxR1hLHB/a8+rsedN9+VnYcgykGf+KeQwlUI1dNG5BUQzcSkaaCLhiUM5OAMDY83v0sGOSIQyeNqTxivjIA1bUMpXuevAlvqy/Pev825i9FJXHtopWp7M+7KEtJiAtnbfV1c7GOwLGJ+0mGw7RL5984SRYAzAuJXpDjD6aWobssgzantxbkynRvOcRWvCAU6z+5/Dw+NrbExKy2z1bu5oY3jfYwKe19dph2CX4exTMsSx/KwQ29bkqoz/G0gyLzCH7f7C/xxODQfS+N544vUIXLPQ3lNXmtnNJr6lo8lOs8X4UEBedHEZhoc55NfMo1iWuaA+/vDx8i+jEG6H4GVT76leobclG1LDxAvdqxCwmAV9752RfTZtSDGl63ROU0bKzf9sG4p6FWHlRjoi819TwRamsiDYD1CiS+Qu5gmenzaxQizHOGPPRx545dcEXKIB5z5t17AYy00g4A4GkL/26uz+nmPZrYGTFz7x+GFUUxcU9jkkfU25ivQqjEfXbauhgZycELRi47WjPSqRoOgBnq4QyiHAdOmsC5lRg0CRmTRYwnzrmHFgYPFrbHuZEsEcsStVsQOQylfUim0fEKvH1gLdg+svcWCuW+EM+wFVgG4SemF1FrjtTBrpXiJIlYj1a4V/chRautaxb3TlgQa9jjGlIoFG7iLSqg62wnO2GBvJKYBC7WDu1QgxpcMYK9b/ERLZx5Len8/tp/+CF8tiFmkuL7qrWH5QSn3aYAAF/7md8j7BXE3I2DCuOp688bwCQ1hYUHUYVq10G3kSKI1DjmazNN+kGF3Fn7MWVN142bJJVnSKKzc+2QjsETCnuzHlZTl4zSLfDmbaunHPopnju+AJa2m8HwyP45u1jBSAPDnTwrNLRR9KYCddxA1AbhRoaNnoU+7+2O6H4Uc2ljC10ftSokSgfVBl7eSpEW9tikYwnXXUMLhQ5r1JnEgjsz+UJSbzf0aizHMemVpa/IArAqJKT7rONJF2uDJZarthXSLJaAXUg7vp0vlGG0CQOAg5l9fnpxjpfH62RNqAzHUz2bjlMZAW04Dl0izKIKMSvcc5ZG9FmJLNH1crxS2N7xYhUicX3QfjfF+KiHct8e44NC4u2btwAAT0W7uDnfwDxtuAEgON8kNbauTel7GoA2zoyBrsv9lQ+TCtRuTmHcUO/emDbQ3Whm77lT90Tu+9DZo7+onsG/Z+NsnI2zcTbOxudovCEr1UXqQyCA9g1u/fXPzvZtRkP+YCFQTQMKmg6DCrwpTD0FHtRQL7tswUtVGxvGQOYP1SRExQDWRMRFGswJpk0RgOcM3rwJmjZgD9zuuavs7zhYTXdqCPceyzwg7Vmzg3zLBUt22l32W3F7UD7ktlKmfnscmY9q1FTBHEYAuweWWMSEIfF3vLlEP85w7Bi5944T2p55cYnbxyMUi9bcvnEG2oxb7WYsS9xfDIgBeTAfYf+BhX+/7o//Nv5gdh6/Pb0GwELDDeNxUQeYlBZDXVU+ODPkZLTIA0wdE1gZBsENsZ4DryIT9MkixuEtC3lvXj3BKM5w68AFK6QS3Ok31zsrG4U1dNXd2Mf1D1giGwYMxjCcH1kI+mQVE+FjGGbErnzrz70bgVfjd/7038Brx85PPgNPeeCO18SKf/s97lt/7t3wPU4wY1Z6pMvMeQAIA+GYznUlLTMW1vnGmzpv6zX3c3ev1kuPErjkkkNcTFEa56lccCIIHc47ePvF2wDsddlDj4gmvldjPbBQ6nGRYDyPbeg4AG/BiKkLzeCfCIrG8xYM2Tl3PpSFlQGACw3ONe4fOO/lXIB1WhMHIwzdxzqXWLmKqCwkVu58dOICeekh7zhmqjT0HPPDwELb7tp3+xmhDYJreElpAwkAVJoRA1/5devT6ykcnXTJEEQkNZRrMzBY2LhQ9mdKc3CHomgDCh6/1J1if9WjOeVjJzt46+g+AKCoPTwR7+OxyH63TywuksvTdm9OWcIjP0WmPNIFC64xdeiFUQzhXR/KmZ3odYPnJi0pCrDuYICtLGPHct7utn7eZsBwPOlSBZp0cpwsncOU0DABg2nCDhZWXwwA1anoN+4piyy4882FgSqFJUs+4uMNuah6nobwFL7kiz9J/3b9H70Po16Kf/VnfvAPvX7np55B46nPTnyYWMHM7YM5zyX1fXjBoSON8IZ94DyuUTloqJoFxIYsYYXnxtmDsV4NHLsMxpxB5Az5trMm61VkqA8A82VEtmLMMwTZdcKCHu5ekqNWHM8f2NmpF+eUQ9mLc1SKU0/IaIY7X9dKME6PK3/nQ5QwoQMFb+SMFDRDxysxc3KYqhC0KUiiEvN5BHlif09tFW3yD+wEAgB350Mc3R9A7jjWY2eGA+lSaViNJ7sHqNzMOy4TWji1YZiVduEslUBa+BR8DoDYxMs8QF0LgsQHUU7GBJ++tY2dGzYAIK8l0srDuZGFlB/kI5KCFLVEIGqyq4zXZgRRryofRd0GOr9pY5/com7ELSz6dH8PryzX8eTPfq89V6Vsrf08BlaBskvjBwI3ftAu2vpqBuU2T7dfc33+zK/99VPZsD1wZmjD4EmFza69//YYsJqFtMiWhQQfOvnXXkxMYwQaXlSRPEZXHNItDLwCit2YAthNXJPrVF0J3F/Za3a47IAxg9iF3MdBiV+/ZzdFZSlQH8QkaTKn9g7JbQntWSYuAIgCBIcDdgML2P5tMaghjx0OrYG66ccBgDCQrrcZ9jIyKjEGbVAAgGwWQrheI0s5zMptXiMNZhhid+0jv6L+f5r7GPVSHDorQXOK/at0G2agFIOQmvgLw07a5gtwDW0YzjnmrtKcmLCzSUKuRou1ANdHx1i4zeDuvIfaWAeljWiJSIzw5T0bGL9QIdkg7s17yGN7bl6druGxwQmlHPlCYbJr2y6sZjDc0DUw+yEWI3uOn59to6hl28ZYBKQM2OoscW8ycN+T26SuWROA4ZE0jnGAzyT8WQvnm6wJdADqy/b+k76ClK1bVF1JmIo/dG4f1fGGXFSZS0N4fnyO/s1ojsOjHq78H7aiuP1130nVKSoB4/oppqOst6hbHcRMwHc7/uxSBfgaHUfamc5j1I1vrAHUzY57Dw3GbXQbfb7XutRUI0UEoVDWyFy/b56G6CY5Vm7hqKYhhT0vgoDizJ5e38ers3WMnPvMncMRrm05CZAWWBUJXvgL7wUAfPG/eBe+9BetVORf/qkPPHSejN/q/cJuQQ9Y4NUotaAHIjgSqB2RaKYZsPDIqtELa6yd6is1hKzZMgSLFHY/bXWNv/XXngG+5DNdLeCbPv5fkB0bYKU0AHCy2sAq86EbHW83ReYmo7KQYAwI3K47ryUCt6nprq1wd89WkowbDAYrqgwAUN+n4xdQmmNnYKUGPT9D7sLd95ddfOHmXWg3be5nXXRdeshHJ1ex55x07k0HGMYZchcVyKUmmzzPU1huc8RD5408jkm2gUIgcgvUl/7id2CWhei6ymNV9GjzUCkBX9ZkSVdWErtTSwj6wgt38eur68ju2mPR/QrCyWf8iyuK/6qWIarUg3fgNlDrNSrn9qVCtwC5CkMGNVSjBeYGL91yz5AwEL4mAsxqHJFkBwbAsEQGew5EziiWTuQMdWJa396S0WJvTlkpMg2IE48oCuZCTsgJQgU/rImcpAxDtrKfpWuOeMMiJEUtwDwNfsHej90kx6yRgi08sIwTzyFLfdLVyl6J2SrC+hWrHV2kIXn4AkCWtZ/FhYEQrYTsjw3vAQA+Nt5BJCvsxPY9vmrjd/GR4y8AALwkN2gh64UFxnlCm7XVLMItl64zXQtxFHXwsWNLXDpaJBQGrgpBnAHONW7PRug7KZEnFS30qhYoQkkELV5wzN3i3g9yHM06KN25CzoFkbBefP4S5HrrXlatfCTrKX1es3FZ5QIcFmEDLAG0cSHDqTjNTpwjK3zqR5c5Aw9rQD/6NoWP/rbgbJyNs3E2zsbZ+PdkvCEr1dUiBK9DiL7Bf/ob/yMAYG3QRTco8Mtf8Sy9rsmbZJqRM1J4x4f2DckK9HqFrNcYPFhG28lt2/cxgba7L1jxdNVUpoWAnApg0cgITJv7qRggNfbvuUoq58DA7nyF1ODcENRSRzWxi9c6KfWbXuRbWGYBVczBH8S4I+3PtOKQnsJbf+7dAIDL/RXuzgaf8Twla2nrNKQ5sZqLysKeDYwJaVA7GUQYV8hrDuWCxHtR65T0yf1tqm6rowjG17jz12xPe+fHP/C66SqAzZgEgGUVUG6pFBpCGGh3WqVo5U3dOMdklpAofbqKMHF9n7rmMGXDSAQG2znJLlAzkgfsznsYRDm6zgXKYxqjyPZQa83xiZMLeGpoYeSOV+Clma26b7+y1fo8dwss5hH5q1aFROXOo5QaMq7JJzkPa+opcU9h5Pps5zszVJoT21oKTaJ5X9Yoa4nn//z30rl6+794FwDg1166AZNKyqz0ooqCDgAQu7UKBV78mu/Gjffbez/c9ZBfsD8zpYBMGaomRlYJ65kLF2Di0IvwvoRhoOBwUQMqdtWKZ2B8TdWu4Qy6abm7RJmGUSyPJJzaDeVIwT9yPUjfBae7yqbXzTEfO5azBsoyoHtrazTHloPAd6c9LF14vZQKjBuqliaTDvihM/o44Tbw3rVh5GaGNz+1S+fq3ryPhWPFFmkrSalPhZnrus0NBmwPddO3bYUvXX8FhZb096O6h2VlP+tid4qV+/OsCFEqgYXL+2WhIic2wLYkGrmNZlu12gAAIABJREFUFBrabyu73MGxo605rg+OcZzb83PrZISeC1+fLiJ7HZwBvh5UhMxM8gg3to6wFdrK/vnxOYxnrhcbKugHjncwquB3Svo9zjmWrrr1911bqZkSPUNJN2DtuclLD5wbCk8vPQl1EAH5maPSIzlMyWEEx+xuH/dOw2iihWOv/O0PtWfHWOgJAKqOgbdgKLccuyQXkC4dRnU0+EpCNRPEWEL7bvIO9Cn6voa3ZG1v6RT2aLgBO/JQ99xEOOcILzsCQpLC5wqJZxfLcTchtxjJFBZ9139iBsYwzFyqhn5zitrd9Iwb3PzP302f90W/8J0PQVnNuP6P3gffByVW1KWw8V4AqpJj76hPCxYulmQ3ls8DsFyQS08/yimYua5E6zbVq2hCBv5wXNk7fvWbyWXmrYMCfc+eg76XEdQ0ilJ4QrWJG0KR9CavJYRUtGDNprG1swPs5qf5zr7G0TJp9cSaEQGjLgVGcYaLsZUSeKy9P3yh8MTg8KFIraZ3bC+Cff/HNk+gDMetfStvMIUgx6DVygPvVLS4lUlJEYOdbo7fesczAICv+s1veIhcVitOQdLnOzPcdfreZjQSkuFwiVUYoMzcvZv6JGGoY0XRZnf+m3e5Y7b/K/oK8S2n7XSqsLIJhlhKCg7nZav9zc8pBEeC3KeMr9uwccXgnUjUTjamfQPWBOcUzEaFOUP2qmdIRgMA5bq9fnwlAE8jcJD4/CR52AwfwKUNC9OvSh8P3AYq9CtMJ3ZhyMsQMEDlNqVmKaEdYccGnjOU244nsAjwlqcsQWhZBwhlRWS528XaQykyJC0JK5SZR/ctAHS5s18MF/jt+XW8mtr74P5iQPajp8cqt25I8YZtccRBRU5JsVdhK1rg5tS+xyoLSCrn+TVkc6/PEzxXnycS0zDJqH9rw0Q4bRxRCKxv28+62LX3+W/cfgwAUB9GRCALLq3gr7sYRamQFh49W5USMK4XbeADGmTtaRlazZ8NcTvg1+jGBYqGQBmVqC/VUOmZof7ZOBtn42ycjbNxNtx4Q1aq/q4PEfooBxrjI2f+qYFP/pXvo9fc/sZ3YucnLHHHeBqvOqebt/389+DklRGYIwjd/h/eiSf+if29V/7ie3D9A89CNdp7aciowTuRqIaOIVdyVB1DuzleAef+hPUE3R33oOqWGVyeU5AO1jpZxchzD6GLYytLCTFyIeVpRNXMYhVCnQrWDjpFS46RCjd+5n0omwBmPmwrlVPDsicVwVx+WKPrYtSm8xib6wscnthzJ7xWDF+kEUykiBGdVR4WDn4LoxKLpfNTVQw8aT1yXzs4M8RmPio72Msse7Hn5YgcEeTp/h7uZUN8KrVkmfxUNbfIQgw6GZ2Tbj9DETmf2pXfVsmGYTmPwBsz+bKFf+OOhYV/6c4TACxSMApPifmrgIg6LBPtFjVSWN+0MF/oGMFNFRGuleQ0pCoBU3PsORP3qJvDNIb9QuFrf+fr7XFI4HJ/ilsT2xKoFcdmp82sPM1Y/8bf/VpwWNf8ybiDbj/7wyYSrzMa9OXOX3kX3vytP2Q/KwLUFywQu+ubHsco19zXvC/IOSfcFdZbuTkHovWKZRV/yKnssQ8/S0YNRgA6MGRiogsO4dii9bCGHDuoM2XIk/baBN0ChbuH+dJCz/eO7HkMw4paE5P7/Zbl7BnA01B37QPKAk1GEMXQVVfksKTx0eOr9qtwjXvTAVZzZ4pQCHi9tqJqkA0/qOH1ciLm+Vxhoe3v7FUD3E2HWHMyoxuDI7w0tdfJF4qOXQiNsvAoWm6c+ogSe62HowyRqAhRG3QyIv5xZlzMom1p9KMcA2dw8srROlXTm+tzyzx2KFZVSOwd22drM17gOOugGjvjD2avDQCUuUekpaIWqErZuijlHgLnNpVu2GvWmNmYQJPsT1dtpGBdC2Slh8/fshD7Ud7Bzdtb0NkpxuAjOt6Qi2rd19ChhvE1PAdbVOMQ1376/Vgf2n7C8aQL6RZHIRWu/n0bHt3pCPjbK7pJAeDTf7GdtOpYU7KGSjQlu9Q9TTo9f8rBNFAnrveSMtz7lJ2c5YJBXyzB566fWzNUR/bByV0fF1dtXy8OS2zHdvK+0hnjVz71JADg0oUTeELBc8kXD2Z9MtUuC2vJR/aMzOCxD9temhrUhNucvzjHZBkTO68TFQT3hlGJ6TJuEyZqjjddsA/H79cXcG40x96RfVAXWYCeW4wnixj+qcmIfwbYuRn7iy7B0j5XOErtxSh8iePUQnuRV+O33vEMbvzM+wBY1m7uWLD5vS5WfkLQYDDM0XWm+VUpkQztxJfnHqKwIuelzK/pe1ZuQW5gtEGYoeO1x/+J/Qs0YWAmoZunqWKYuF6U0gy15jQhLZchpIMfcRQAwrrVAHbBIi2zEvjonSsAgJ2NCX75K57FV/yK7T9zZsiNp+nLNeNvv+3vPfT3K3/rszs6AVY6dfsb3knnaud//xu482wr43nin3wfspXrNU4kgmnLzm1CrF9+77fiyt/8EIW9M6kp2FynEjs/+QyFS0gjCO5l2kHADgY00tB78qUgDS+vrI63cFC2kBqe21iouYSJNclSykJSi2Pnf/tA29P7/9h782jLsrM+7Lf3PuOd75tq7qrqqu5GaiEhkEQImGUtGYizQMEEszDEJsuAgQTHQVgKNBGSAY2twTgEELYhMYYlvIxBYJtBLITNrAhLanWru6u75qpXb77zPdMe8sf+znde9QA46X/K/fZavbqq3n33nnvOHr7hNwBAKRlpDye4zQIA6Gio+ppLhev7/qALQ4OiCCCpJ6ySgufu8cGUudHLPMJ6f84o8K/f+DS2tV8Hn9h5EMN4iVndOy1TbmNE0qBFc3O5SOAsYKntIgPL/q83xgPcHDf4hzjUTYXdCV6rOg9RGoUb9Fopm/c4qNpQyjY9VtOCJorUYzdPo9ddQnb9TRfKwWSN21T9WdZ6d5lajnGlv2A50OjYDGPZaYJW5dCmz3IA972X+y0ssgB/vH/Rf1Ymke4qmOLeV1R6WR6qtmWA1CAdZrxIZa+C20wwfZwg9gMHPSBbqK7jBZsXIcp59ILZ3fkf/yAQgV1B9FoFMSOhhlTz4nZSwgX+5wCgO5INtKueQ7QZNfqqGtz3kaXXaJ2R7dzaqQluzQ+BjKjHdGtrCMxDNm02xwu0ew3H1FoJQwtH7kcwbcqgC4kHX+n7SLcnfWS7LQTkCLN7kAKkA3rm5AH2PnUC5ox/j+P3HbD7zPpwhtEiZYecQBkm0dcAK8D3IENl8A1/+D0AgH/z5T+F1/z62wF4IJSUAevzfvbSfRycbHX1Xff+3M+/B9F1OnBPVwhIYhBrhadJUAYUBJY5sv3ekg9slTr00xwVXWMaauyQcbIuAqTDEhdXPB3JOsGByqRMPG1jnzi4sYMLmw2hrgwI4ZBnEW8mcI04R+vCGJPr/buk2hxJCj7+5qZqUo9Z4T/rdRs3cXnq+2off+OH73rNq976YSg69ycPGoQziXP/h7eFS/YkAkq0H3/0+/h3ar3h+GZDeanHxfd/CDpt83eTplHedMpr9QI++5RSsKiAyxWvGWEFoD3HFQBMJaAIXwABqExwxis1GOQmKyDZp3W35iCsgCBgUXR+xlq3U7R8IFs/i7K5/s7GAguipEALyNjAhsTzDlzTZxcOYVKxheH2tRUICqovrOyjtIoNx4VwLPQRSYOsaFxgsirAtbGvKPya+CI8s++z0TSqUBnFAiihNDjd9z3MzUWfsQtBqFEVAVdObB4AtJ/MwwQuNdxXrihjBIBhf4GQKFdvvPhZ/NatV6BFvfqiarJKrSW0lgzeMpXkfUMFXqe35mnnWcSSkUI2Fo7OCXRaOd//eR6jqI3frUTYrljSUQYW01GL/1xnqiI2cEZy4OWkQ5VJ2ODFA+17ZRz1VI/G0TgaR+NoHI2XaLwsM9XWSgbVsrBWMNWhmMdA26IOoLFRcMRxGC37mu/9MJ75ie/DCw2b+BJvjf4VcwV7zEePUjqWL6vOFsAk5L5stC9RdZsITZaCVXaEAeJ96jG1HMoNzb9XaoU48D/bOuhxVOkKhZMXdplC4soAvdRnjgLA5uV1JMfIPHkRNOjllsXTl7xkWdgvIErJvdJT9+1zmevm7VWIDQNF2W6sDBJCISdKY1FEWJAg+Nlj+3jjxiUAwKXFBu4sfTksDSrsZy1cn/io/is+/jZskGTctb0VZNMIi1FKz2uJJ7/9nS94z6/97R/EhVo6sJQssgABhMMcikqa51YOWCLu+s4Kl7mPD2dYSRbYnPvr8lly8yzmiwSTtr+OUBlsEJ0nTStsr0+wufQNxnAUoBoQjWaQ46ENr6pUWoWDvS6LLlz5lke4Bz+dpQiW3rgAAOxDcza/PjzO/8K7cfVbH2EVpY9/4rUvagDhFJCREbnKpSfh0++VPcfz6sIHPgQ9pGxrK8AzP/QWFKTiJeeKr9GcCoBpxMh1EwGuX1NjAN0jmbnMyxfWcwlo3GxcbCGMYAS9sIKzXaGFL/fWmVTquAcqHLA87vjPLnSwJNQeBQanyEfX3C8wv9Hj9oo5hB7OlnEDRdUKcj9EPKcPs42xeX66QpJUGFOvUZYSOfUWP5ef5H0CAL741C1+FtemK4zA7bS9oEHdz718sMa/U1TeZKF2L9qbt1GRmcQkTxDV7YG9Nn1ZugeZhBtSVYn9kv17tpMSGc3Vogpw/8ALxlxbrsI6oKB+6yDNMSNkepGHcAAEwa9XV+dYFiG/73TWgibRC9nS3K5xziORAd9TLXWABWW7dfULANqtAkpa6JT66WjM24VwnCFney0gtMwoUKGGOW5hs3sf/fuyPFS1FnBaPg/Ace5fvofLb24ashn44fHZFzlQAQCRhZMCgviocieGvOUnUbV6qPyrJBA6Ls86Cd5YAaC4kAMTkvnrOui8BnwIXz6kZv50q4uZ8WAh0Stx8QEPduqGOfpRjv+46/sVK8MFtp7yHEqsFXCBhaaeh+xUcLXxc1c3tnV5ACQG59f9Qq1sY+XVXVkgOaYxrc3TdYCdqS+Z9lo5iiJkJaPRMsWnRl4B5iBv4eYVv+O3jy2wnMXcpzLdJS98ZwVQSIh2XUJ98VsOAMe+yHNFA2m5z3NjZwVSOrSoJDYtEgY+Ma0HwN6sjawKsbdLLjuBhaxBNLRZ1ObjqMAcPsADTOrIy8QOEUkAxnGFJ26f8P9eSW9yfWgqHe7Bv9i4+L4P4dn/jQ5OAbziV96JbE7POnrxElm27ljmz0YO0YFksIn/N///5x7K537iAxADmrejgMuKNg+8wyAFiul6A9TKFxHfKzlNEMwFtyqcbMzPAe++VB8UKgfKISnpxECwkKywZCNAUVzhAvBh++zb3oJz/+K9CKhcWGqFS3f8nHY3WlAOMF36vLbGuX9J4C0nmo27V0KtGFYeMtOQsQWilAikxWzs57Qy4OsNIgPnBHMstZOIiF5lnOBSf1YQ97KWS4wq7ptubg/gjETnjJ+PF1b2sEcUnZOdKS4f+OBMtTSStGQd78k85ZKpCix0pZgfmvYq7O/5edsfLvDZTR8QW+vLsye6fq5Oy5j55s4JQLi7AseTA4/L0FZiy/QgqM+cJBVrhmfjBHu1nrdwCNsVFJWow9CwilQaVWhHJdvatcOSKYCP3z7Jh68oBWRP4xjJg06zBDkiiOre76kelX+PxtE4GkfjaByNl2i8LDNVZySskTj/C++Gq02blYUQYL1SlVZMOzn/Tz4It+Ijx+B2jGpgmNgeHip3ia4CjGC3DHc8Z+oAphFkTZc4UIimAov7CGmYOrSvE6ApAJarCu1Tvsy4nMWw9JjCTEH2ShZFP9Gfs9D8rXGfgSxb064Xk9/yUff+KGKyfXglQbFhYMl4O8gBfd6/36m1CQsYVMsQEMDT14kysggghzWAQWC5SBBROWg8T3GK3Fpu7KxASMslH20lHrvmI+hWt0Cbys6nB2MMjmV4hkpkk2mbI/5qGQKh5ZJbv5Xhm//47wEArk5XsLPnM7ar3/oIADB1QFvJxPsHTuzchZS8s9/H4SEOSCM49BSGGq3opGsE+gsFU0kMTvn3P9Uao0MooMop7M46OHbKCw5YJ1jlKAk1I4eD0KAqA1z5W4/wZ5//cQ8ceiGHpIvv9aXsGhkOAFe/5RF86W/9ACNwa0Pvw+PszzwKAEinASNmTS6gW64ph0pAkYD8uf/zAwjWCLy2mQIxuH2g10tgTujzUsJFFi3SeW3FFWbLQ4jjGkirHHSrUVSCcAxsE0uFcCL5O9kQ7HQjtIDuWiiqlgQzAUk/s+LujPza3/kBRuHny4if//l//EGPIqb3MC1AjKmkaQRst3Gz0TJoUonAwaGmUkksspjR19FKxoj5KNIMTAKAZw/W0Kd2CgDWXY4C41sy9NqzvRGe2PHrJ0w0ykWEhwfeG3VcpXj92nUAwAPpNv7p1AtfFzLEchYjozaGUpYzQqO9GlptSH/z6jrCAbV1hENYizFUAWaLpFmDRjF1UIYGqysL9hPeXbQxyf0+MZq0YfZirg7MRcr7l7ACcljw/RCHns2w3WgCA15847AAxhf2PTPg8sEaZreIwih9JWjz2ho/JzW890u/wMv0UDVGwhmJKNHMw2wlJRZZzJyzsgi5XGvbBoLKsUL7CVDz6vSpgpF6bhz5hToj2H+/ghkRFWEhEc6o5xM5L8VGPUnMQ+51OQnACDZ79vxHUn15YAatJVZ7/mCa5THmoll8O7RwaheRGmxrYgtHiEoxDRDvKFQdUkcaNDJiW/t9YNMvsM6uQDFwMGdo400MLCGU5WaC41+8zQtne9Jlnl2rVWB60EZr4IOCblSg6vjrlcKxfdS8jLGsIpRUJkrShr+pEgMpLdoks1gZhQPqRc2yhEuA9RiRIXqkDCaEQtze7SMIDXRA/WItce1/+EEAXi2rVrMSd8iknQ4AVyq2YBPw5tg1jaYX5NxLe/zgJD7/9e/ka3jz738vntr25cjxuA278HOgtb5gl5t6/Hl2g6w09BwK7yuH29h93L//0+94fgtCkUtQNVYsTq8KAZM6tM/4MmA7Lps5UjaGCO0LE8wnKdSWn6t6oBmN7lIDWIHlrg/Czj50i1Wrro/Xm6CxZb0MHR2ILjVI+37uZDJBHjmEByRrVwn+nk4ALhQwJKUorISl+f7MDz6/b1yL3ItDCF9I7zIj6bkdPz7GPj0LWQIlcbkvnN5FZRTujPw96LQaQX2lvBnAYT63bNXuMxKFUHywaquwKP26293uIyE0bjcpWDYS8IHWf3XSH5z7RQuv7G1hu/CfHUuNrxt8BgCQuxAFyWRWixCiUHCELwhTjQEdWv04x/a8w5Svhx68jVnpn9nuqMv/Xv+/5mhHQcObLrSCsQK7iza/xtjm0Fb53aV4ve7/ErYqXo+BsizZCADjZcr7qFIeJVwj3ENlsF/6MrcUjoM6vaKhpINLG7F9M4phs3sf/fuyPFTdQQyXxTBZwtJpbl3cZUUklGNeVrQT8GZhEi+p5gLqc2zG0MfIx3QpEZ+f8YSa7Xa4L6iVgtDNplIOLGfJCC2yM03ErGYKxrdKoGYSq6/eBeBdak6uTJHTop3NU44Y14YzzKcUBSYW0ILl3oJuBU1WdWXPwsWNLJyoBNLPk09kxwEPUkProkZb2sYto0g5+q/WNW5vriAiUroxknVPZ0ULcOD+6GSZcg/otadvIdcke+gknrx1HL2u3zCWecSarGudKaZ5zFScRRbjU3/93fz8vvb3/z4A4I2/+/0YLVM457/bQRnyve/3l1hpL1l3+F992Uf499VSQjWJhu9xU9863pPckzQxkJ+02Fr4+7rUEfdXt6ddHB7Xx0MUB/5wj7cDlORL63nBwMMfewcAYLW9xH/8az6rvPDRd+HyN//QXe/zzCMvDEDKTAicyl7wZwD4sK8Gh2hLViGcCpY0TMOKrfwgHFSX6AxOIEorSBI3COchilVaFwK+j0/9/6ceu4/5rEgMa72WGxpypuCofyvCpm+PUkJ2KlTknRluRQhqsJDwHqp14lO1G1GUB3/0wxwgPPODb8HZjzzKWZSshLcmBAAJ9E7MuA/5wGAX2y2fAclQME5gf9HCdJ7yXO2tFHcdRK24bNxWyvAuO72yCrj6UFUKsxmJmDiBla5fMxutGaRweGrX+/1+7s5JnF7xtJnT7THmJoamaE7C4anC993/7c6rsZjQIaUl0KmYA54tYu5DVkZhrbVk3rS2kjPOrB1iSiArk3s3nhn9fW04YznAbBmjUBbrfb8uWlGF0YIs7YheVFG/Ww4LrBHFSEnL69E5gVZSsH1kVSnu0db/X6dAujIK/+4zr/bvMQ5genWkL2CNAMbk3rRWwBXi7mDpHh1HPdWjcTSOxtE4GkfjJRovy0xVFQJSCKhMwFFcofvU06zdSyrJ5apwJtg5w7S8tFldTtXHSiiKwN1x35etI0sYAUdi+cIIRmEKR240RaM6IlL/HqpbQSoLs19nDQLbt31pVSYam7rXsO8BGCK876JBW6qWhjWCDX8dwHSD1Yf2PaLwqi//VAODBRmFh8MCfVI/KbTCdJoyDej6d7yN+3aQXoJOk1kA9mLIDVJN2U4gHDCrfCYX9Eq85owXlBgXKZeMCxPg4TN3MIh8JDyvYvYqNVZCyQhFLYhRBGzy/dQ3vAP7mS/ZlTqAc4JLyk//9z+Msz/nRegfvnAbgyh7nuIQAEQXpqiepqxeAFf/l+/HuZ/wWU9+sYDaIrm30FMbbu7UovlgdRhjJM79i/eygbhxAtf/3lsBeFN7RdmRzr2w/9lV33vNdCMUECcVLv7Sj0HvkvxdanH925/v1PONf/TduNjex0e/+Wee9zMA+MrfeSs6bf8988+3uXxXdR2kFti95LM229WoPQGCTAFDPyfacYm9Z9dx5Ud9WfncT32A/XBlbCASoP2kv+7FGcs9WjUOGtGGhXezqTuPYhlDJ9STnEiUseF1onsWNiRk7VKg6jpWOXKBQziq1x3w5NubzF0Y0ZQPN0qAMivZq1CUAWZjPy+2bq4gpNfZ0KEkakwZRzh+YgRN1ZftSZf7k8ZIhIHCsO0zuFke889acYm1zgJ7c1LJMgJ1PUB1K87gljpCpAxOD3x2emfaY5zDDTdEoddxX9fPg41khmcyn9GuJ3N89cOfBwDsF210w4LpNuMyxbUDTzuLlIGSFgkhba0UOCAnmqyI2BRCBBYuU5AkXqONuksNrZMUaId+khQmYKrM0np2QbDqs9/D9K5QWiyosiYF/ceKagpO1VUPhTA07Hm81BFCchoSDixkYbrGo5oHNFnveHUx3PuJKoRz7t4vYv8lx3Q6Rb/fx/l/9C7IJKHTxv+sWtE4dnqEOZUtlbRYEiDDbSZeFQYNBy4kZ3vddkjv8z2r2sbo4A6BYrTgsvHhflOwFCg3NLrrfgEr4TDe8+WkwdocUlpMpn6xWCOYtpCk3sVE7lLJpBSwZ/0C6LZzBFT6LLVCFBgckDvHYLDg9wtCjTA0XJLVVQBDoJT2+hILKmHK2EBIQNJiieMGrGGd57OJ6/61+kSjQ+u0gCgUA1jWzh/geGdGvycQUDPt5rQPbRQ6RHlRopE9c05gWYUM/CmqEEsqTcqwscBotQpUVcD9HKMVl+wFvGn5wZZ/FocPq6/+vf8VV3Y8haEaJbj+XW/F4XH/R9/l7wf1h2sdYxgBUPAD69V5YtJhtlYwRevCR9/FvbmN3hzWCXYkCaTFSuoDiUyHWFYhDmYU4FQK/a7/WWUUHlzd5fvWj3L832/4WQDA637jEWzQ5g/4nnJdbq+qAOZZP5dULqAKz08FvDynSw71o2lTXFmbYTJrwVBpeOXEhA+e2eTu0qCaK9hVsiLcjrg3LbUH2dXvKUuBYFmDkYB8wyJY1JQyML4gO2l8ibfGDXQqXxYE4CZRY15O3M1wTMGvIis4kF6zEQ1mIWnUygBwm0II5wF89O+mVGyrFkYag07Gc3BZRJjO/HdXgYEumvxDhhaWsBJyWGBIJdJekqPQAT/rSiskpGqUlyGME6xyNFmkrKU9mrTR6zZUpb92+hK+dfgnAIB/NX49/v2NV9L7F7gz6nFvs9RBo9QFMFBpttcmdxh/744dm/DBHwUaxkruixc64EB3Z9qBEI4BTmudBSStqNIqbjvlZejXMu0N1kpMrxIocK3AxRO7+BsnPg0AuFGs4vNTX+a+Ph7iAZrTX7P6BC7nG3hs4kGM2/MurAPMssDnvumDmEwm6PV6uBfHUfn3aByNo3E0jsbReInGy7L8Gx0IqFggO26baDc2yMoQa9Rgv7G1wibl0SHnBJMA8b5iDVVhgOVNX+pcxBZqplhX1gZAcMy/0FQKvZ7PKkutEKsmE1bKYrjus7miCtBODE6TN+SNzVXOJEtloDZjj1YFYFcruInPWqdGQFCI1O8tMEgz7N0a0HXFDGhyTqCTFDCRf/HeXheStGcX223IGnUYawgBrPd8RlQahREhJY1WiOIK+hzdhEpB7FPJdFBB9Er+rtpIjnbHRYrjbY9CnC8TVHnAmWWoDELKtBMSi69RlGURcNStAsOOGIG0iNOCxbzHiwjDVX+9n/naH8Nzx9f/wf8EAPjtv/qTbNL+mW96+12vufhLP4YhXfup7gRZd4abI38frRUYEJL3YNJGlQfItX+GdRkY8DSaWlVnl7LQIvf3J20VTNnZHXVh8gAdEvfvpAXrE4fKYFb67Lwb5bg0XucSuHMJK9MkUQVtJZf3pLQoWk3xSRWCS761xi/gTeFrCs1k2ka/t0Ae+0x1NOqwbi+sQNQp2U9VLQVSQgkHS4Cq/MjXrTeQIIcZGzm4WkM48NfBxtVBQxkSlfDZM1VjorhisY90uODvmY8TwBwCYinX6MYigJMWRa8BSdU0FDjBakVFEdzl3oRCwhE+qDQhjh/bwbjw2els0RgfKGXhrGWYE20ZAAAgAElEQVQKWVmECNcyehZgilEaVsiqgMF9y2XM2W67k6OqAkxq4Qkj+JmtDWdsQA8ADyZbWNqmTVBnjp2owLHBjOdPVkT+vgAIOyXKwv+5t7bAbJoy2Gmex16nGt5RKjqEnr+/v485zeG85VWfUqq4STjMCZiXVwFfb6As5lmMjChH1ko2ERF3EtxK+/ioeb2/B1XzPd587nNoUW/i4fg2Prs4g288/mcAgE/O7sfTkw1oe+/Tal6Wh+onf+R/Rq/Xw/kf/yAEoT6dFpjaFqa7hOoTDqoWxm87KEKlBQsJOCBfp35C7Jj+EI4C6NTBnfQTI460txkD0B005R1jJNpJCRP7zz4zHDPEXUqHyTxF2PcT/+ypPRwsCPYvLeaujdXz/sAdPbWCK2/x9IyzP/MoH4gHOz2MozYEwdXjSKOiBXZmSCLehBQerswxi2rB8aZw4VVZChaaj5ThHspwOMP21gCCRO6FEexOIkMLpyWLjMeRRkJc2lZY4oltz9sbdpcokoDLY7MswZwOdykd+u2MA4FOO2+QhmXA4uO59eow/Y4vo/WGS2SHNqfnjl/9ip/kP7/QoQv4Uusg9RvmpEwQycboPA41bzhGe6UkHBLRr0cUal82BiAjA7mZcFCg5ym21yiQ65cQgWU0p3Xg6w8Cgzsk4L4jOkiiCidI+eba1mpTXu8uECqDHUKjVmXAdK8qBmwoeY4cHte/42143d/1nNhsLcT4i9C45wCMmLVOeAWqmscbNtJ+y5MOVb/eTdGYksPPCX3ocBfmEF3IeDlCwNPVkn6BTtpspnUJ9mDagr1NBhc1ZecQDoExCS3jFbhqDIEDgkNl+fowEAL+oKF5JXsGhrjjtbLXIPbP/o7qMZJcKQujLLIZ9b5zxc89SDViamHsTDqspgR44+06uBIATg4n2Jr4Z5pEhgMobRTioFGK+vCTb2IzibwKcGHoVc2+qHcLSxvh97Ye8M8o1CjogBTSoUe/M52lgAMHRsZIbiGlSQXrBNvHAUAn8Ne/5brIq4Dv/zhLGPHcSQustHzCsbtoo8yDhj9bKP98aAgBNkWX0nKS8cx8AyuEofgV/SXYKzr4Y3sBAPDJO2cxGbVhs0Ow/Ht0HJV/j8bROBpH42gcjZdovCwz1XqEU8mi33q9ugtV+9oHr7M25+0nj3HZKdkMEC7AmasTAuQGhmzdoXtNYEllu3zNICIE8bSUWDnhVYesFWiFFYMF9pdtzlCKLIQzEsvE//3M2g6rJt2Z9ho9WADnx+9mRG44yFGNG0K2TDWX1OazhDmxz8yPYbg6Y6umw8ooUjlYUmGqWhZlZDClkm+clCwwno8SBJ0KyefpmlccNJXe3CSCS5qstp/mjaqMMpw1zLIYQgB9IozOAFRkw+ec/+9+0h2OpMEVMuiuyoBRmUUeIQg1R/w1r/D/zzh/bJ91S0ujUEmFLmUif/BV78dXfcIjZIX0KMu6hHp4fO6Qbdu5n/wAVCYg3CGecy2QYCTEQYhxrZ/QLdgQoNPLGp1UadGPc9yeeNCVCpr7uyhDOBdxtpvZmFV2dBHAhJLnSI1OrkfVqYFzAHZjVFRtCFZy1klmqy7Keqq+QVULVVk0YXnkAWS1N6xeNkYNwgLBQrDakm5buJa/3jOn9yGF4zLh3l6X1a7CqQSiWoVJAMrzGQHALANIyqYFXUqNuHbwptqAn98LWhfBWMENDAtl2N0EcrXge/r47RMM1kqGOVLivbajCoOVjIUWrl0+BkGZqgoOZfehgQOY62oBzhCHrQzb0y4/Uykr/nMSl2hRBeTK5hrsLERO3Gi7XuIyvf9jmyfxyuNbXCqOA4PuMV95WhYRiz1IWetENYPbP/T3BVV+Pr15Gjlp+srIQCnLer/ONXaNZ/sHuEqWdvNlDBUaNquotGz0zkNfeamrO2utBaOVt7Murkw8QHCQZNhdtJv9YEHVnD/HY/leGS9L9G+NLLv/Qx9Eske0jY5DeaLC/We9u8iJ1hSXaQJs3RkyAjQYB1C5QEA8fKGBeEKl4FAAsnHWgAPK1ZrD4BrSvBVAYNm5xCyanmFvbYFT/QmeeuIMAKBzeup9DUFKUFawgpMpFAYrC/puaSM2Pou8VyR93PrGlBecNhJSOhzv+R7unUkPy7lfVIPBAqPbfuNO1jKURQBLfeVwFCAmb0sngeyEZdpF+4ZiakXZdzAnC9x3/ACALwPVpdso1NyHMVphY3XaiBYYhd1N2q2NwIMPbOJk2wchm4s+duY+wCmqgE3Px/MUWis2eI4Dw44bRRHiyjf/EM7/gheNSNslS79J6Ri1W6sifeXv+ANnmsdYbS/53hgjGVWphMNnv84bor/pE2/BJE/QT/y1dMMck5JcfPYGcOTxahKHwfkRRjvUfHQC0TYp7pBgfOshvzHO58ldm3Qd5HXaOe7rj3F97Kk9xgne+ILAYEalZgC4/j82XrMPf+wdODsc4cmbPqhJn0jRvU5zJxIo+xQQOCDbcI3E4Mm8QchW3pC6Fkw4HHhCC5b1dNrPfVZuMKKhR1TSC6bU5uCJgVgSXmF9CecEymXIr617/MIAloLecCThFFCt0UFaSKaJOekVnEJyVDFacTAQhIZ7i7pUWBku+IDZu7wCRYe0kBa6DFgac6W3QEn3WBsFbSQfgvk44e+Z9IumTEyGDjXSVh9C5vZaOUrdUFv866kkG1bokcvT9dEQi80u9yghfJsAAFqdAp2kYGT2ie4UPTJEf3bcOOJMFimqMoDd8+taWPJiBqD7BiLVkHuEgVCA7Wj+LgB43yizkO9d7bEKANks8W0eep2UjlXDNjpzHGQtDhLu6x5gSutCO8n0oGwZQUjH618qi26rgFkW+OzfvLfRvy/LTPVVv/RByDSBMnHDP42BqFOiS5J0txd9HBANJWyVDKl3gYJJvKILAKQHDv/pIy+sgnPxvR9i6TSbNC41MtEQ0iufAD6rrPmx090O5tf6nP0aI+8y/HVaeucTAJCO1XIWwWE9VgG5G0Ke8QduEmi8YeMGAODfPfaFCFKNW9ofYFWl4GiDK9rBXT3ClcEC85jMjA86KMnarFzTUDMFS1KH5cBB05/DkwsEArh+yy9yFTfyhlWkfP+FrnE8b3HWeXizgQUuXT2Ok6/yh2ocaO69tuKS+0G2LZCVIdMK9vbafI9F3QOkn2WLiKUDrQM0AbzO/+K7oZRFv+v/Ph61Mb5Jh3unQruXY4V6VUlQ4Rv/6LsBAKuJw33tEfYLP0dGRYs3iDjWmBPtRCYGG505NjoeQPX05ZOo7qPNq1CQc4WCHGFUYGBpwxTC8QGbRhV+7a/8BJu4n+5POGsFfIZRz59zP/8enD3pM/y1juch1hq5APCGv+N1h4PCoSBEkEm9tqslqUA3CzkAHBDta0GBXbWImIbiKgVXSzpqCbmUHGw6CRjSlHbTwIOUVG2JGPABWy4jBLHmAFPsRixZ6AKgJoSaGIBwnCE6gFXNRCkgSolqRJWayEISJ1YqC3vocMurABEB4Vxquec56OZIBhp9OtwKE6BDlaTNcQ9SOhi6FhEZJIfUxATdRyV9cFi/Z5FHTK1aFL6aUAdsWRXigKwZe0nOXOtemiMbRnxQS+mwSopNhQ5wtjfivuS4THFtusLfbfsa/Tl0EIXk3np8J/AWgAAQWO+AVTvfVICjdZ3vp2itN9xUoRwfqkUe+eAfAIyAyRVjODrtnAOVXIeIlMFfP/EEv8+VzO8FV2ZrXFHprk6xmi5ZYnRv0sF0kcA20JN7dhz1VI/G0TgaR+NoHI2XaLwsM1V1J4ZMYkhzyEiZyqWf3/SlMlxvQRN1Bb2KDYJN2yKYKpBZCSvDvNCwZ3M46g/FnQJV3cdUzpdmqK9ktYScULaSC1SDphe7PGhBkXNGGGmYsMlmzDLAnc974XB1otGFHR6bYrIYQu9ROXKUsJPLuft2Mc1jjPZ9OfLV52/hMe0J2FUZIKDPKvMAc4ANBmziWK81uR2i6lu0bpAW6qvnuErUlFf92g8jVIb9ON1ODEcuIaYIIersfK1AWTTCDeUygqDsAnkIFAq3Fj5j1FZyn6o0Ct3I3/zV3gK5CXGjdqPR0vf2ADjncPafvR+iqEutTS8TQCMq4LyyUY3qdVmAgHRutQog+o6j69/+q/8Yb/qEr0oM4yWkcIgUOYMYxSo1a50FU7MAT/156rafV731OYaU+W7u91GZmCkkUWRQUpXNWYkhCQL88Ve/D1/x8bfhs1/3fgBe87h+/+1pF0pZWNHExzWlQwqHNKg4w51dGmKDwKlFX8DUcrNth/Js0Tg2BRbBIY/QKNJIqFxeZSEbNojE8LqwlYQrQq4QmI6DoQoIuhqdpyKUpOBUdRxE/T2FhI0OU9Ys0KNy+07Egv0udLD9CmrPVxuuvOX7G+1f4d13ahR+1ZVNdWQZcz84KID5cdW0Yer/AzjRneFUa4xnp97vd1mFGFMmCQCtuMDJnkdfX95aR0ZiJEGiue+4WMawWqLdJdWtQiFtNahmbRQ7wphDqOSbu0NEVHYuiwBSWrTIj3ilveTqWSANSqPwW0+/wl9+YKDrdVYoRKREZUPAnsg5G7WBYuQ1tIQwgvWt4fx9BrwpgdaKv0+SlkwFM8vAt7DgK2tRornKpK1Eu0bIK41YeRcnAPjNzVdijyp+g07G+syhtLg5HjBdCGiwFPf6eFkeqlL7/3TbMXdOFQJ6J+WNV6GxydIt2bhxAIAD5vfTrHwBI/Oz/9RvfhABb5jFQepLL/BWWPGeQtWlDz+Vof+A73GOn1mB7FU4cdH3dm/tDZiX+fCxLfw/j19gLmDn3BRFl3qeoUF22/cdgwsZolOLBoAwDnCLyoVRYJCXIeS+35yevH0/QKVbrVzzPUOLwki4jDZoAK07VNZaAVqbEotTVKKyEm/4Te8Ao0SA0ZUVLj0htpDEs7WJ5X8PQo213gI7Y3/NrpAID6jMXQHFhsE20URaUcUw/7XWAoXxr5uUKULZgJ9Ur2yk2lIHXSqE1Cd65m/ezUc9+8/8MwpjjSjQuEkWVHIpWTVLJAazvTZmWz4AOXfzvQgS39e8otfw2rM3MYz8AWnbAgkdsJdG66yatDntoUoUqwTN5wnOD32/edGNEA1ndymzZSF9t2kbf/o17+V//4Ovej//eWvS5Xnl6RKWe52uEiyQLoSnedSbpN0o8Ce/6J/T2Y88yhaFEL5nlpGaVtIr0aZNfZFHyPMQQa08lFZ49tsaTu4FUp9SkYE4WaEyRCUyXsbQv79DdsKyTKdaSF/aBeCGGgKetwkA6Gnuh7rTGZdBhXCoikYW8f4PfxBC1VxaL0doWs0B3LQxJKIxzds167EGFNhBOWys+oOyMgpXZmssr9ePJPYn/rtEkcHrj91AN/CH5a1xH0viH0f9Cim1JpS0yIuQLePSXo6MOKy9ru8d1+DEOvAB/JqsP0uXCoPhgtXRtiZdzBJax6Lm8lIgVwYNUM4C5ZBARRd3fOC86de8DcH9baF9md/W3SIBhGQlmYbG044OcdqDWkUtlFz2F9JBCMdl7kBZVmW6ORogX0a4dNmrKIlCwrUJRBdpVlDLtafu1DzkchoDlYB9cc+Ie2YclX+PxtE4GkfjaByNl2i8LDNVWI+Ii/ckR7fCADYSCKnEaSPg8tteGIB04f0fgiJE3pXnWHf5X36+3q9raxYAd22Dqmp0gd1mgvw8kdUTCywDzAg2byuJjEThPzU7h+7xWaNJ7AQDqFZ6S3QvkomwMlCdJQyhZPfDDkbb5CG5tkAcamRrBLRYHpoC0jUEeiHhpMXws1RSigQyQjUnewLB0iHboOsHsL3lS7BiFiA9PUOXyPy7B10uxbV6OWce54YjbypOXpejJMWiG9ePB9IIRluWWiGgrGRaJFxmXVQRhp0lR/CtVsFZ32IZI0z08zLUetSl5qoM8Olv/GFc+ACZg/cOoW+XylNh6J8cFHQdxWcBHrt9krPA1564jUHow+yLgz1WqZmNWpjLlEumTjrcnPoM4kxvgoWO8MUrNwEAe0UHt5f+Z924QVsCwGt+/e1shr3erZiCkpUho7frUQO3rBWQlFUAnrLzLX/ynQCAh1+RYnvuM3Bjhc9qqEjx6hObiKg++JntU9CVQlmXGY1go/BWu8DqoNHVnSxSZJSV2Eqie4VKwYlAvubgCJ2upUNAwiqQHkxUo7GjQDONS6BpP0SxhjMSjpCqLjJs9iAqqiRRdhq2Km90D8D2NBskBEuBMlFsx+i0xDoJGpRGoRsUDBh6Zn+dS6thaPC7Vx9gU4TlflMWXo5TuH6tZuXQaeV8/UUWQtH8mM1TDHpLbl0EwuAO0WbyMmQ6nDAC8yhBmjbPf0l7QZ6HSJKKQXu6arSLO4Ml7qcKyOO3T0BdTRHSLTaxg201lmsQQDjw19Fp5zg38L93fTJEUYVN5UdZXoNhWjHgy5QK+SHdZCkdU2iEcN7Io26vlICgZ70Y9VFO/XsU5wu0ejlXKDprC1grYJZHikr35BDGC3B3bjt86mf9wfngj34YKmtQvZf+9+cbQdfjxQ7bcz/1AQQTCRU2pZ1aIs7GCslaU9vonxhjl/qaEOBDqFpROLs+wum2p1l81p1k5OiV7TVfiiRE3vIgRkwyiO2oZEcM6wRiZTCixTgYLLjc1IoqVEZC1RvQ+pK5kUGkGx6dcFBPthHkVHLaM4jHdWHDQSfgvrK+nQIr1JPsVciXEZYkxK5GAaLT/hqldKzQcpC18MBgF7vCl3hDZTBc8yXveeUNzFeoFLeXdbC5TxxNZXGbVKpEYHH79gpLPA5bGW4fUJk70neJjT93xC3qEVYKZ3/2fQhAvMbEsPpVWQXQVVO6dYsQjnqGsuN5zfVnXJuu8GF/obeH3DSIbf9BtPGWEtORp2o9+4BHLt+Z0Oaah0wPqhHH9aipPPWoKUB5Ffjgjcr0TjmU9Dyd8RKDdT/6wfVthAQrH0QZnp56lxQ9DxGM1V3zui7nn+5PcNMNMD+gg6SSjBANpGXOZKgMkqhCSYdst51jeZw8fEfevILVy2LbmJI7Py/q8n4aVRiRyhEqyWXLvFCewkOGBrZUkFS+dtb7sdZl42oWNZu6A+bnqMyd+h5wbXQu2xU+d/m0v45Z4Lmz9KzDfoG1VWrJTFsQ0rGXbtgroHdqFTKFTPp11+7n6CUFelTiPFi02DlHht5I4XTLr+vL0zUOBscHbW7puMihWoRc4jVGopg3PNI4rHgtp62CA+z5NMVjU4+NwDSEVA1eRGzkDRqe7u35DY8QtxBYavIjvtNHPGgUjcKwKfEeRqPrRQgRWkYGWyPuMkS3vYIxFe2VBaabfp+78r3/EBff74NXMQqBXo6Ngd/bzvUO0AtzlPMSz+DeHi/LQ7V700FFzYEKAJfe/uKH6F80/uvfJm6gXYdpOSTbtCkI78IBAAgcim2/wMJ1L8H38H13AABXD1a4B7e734USlkE6bzp9Cb/8R28A4B1fDpzAF5zcBgBEUuMqbdDr6ZxdJLSV2GjNMFr6hb/IYo4+jZFY7rcQMSdNIDok6VYvdOsEqvMFdJtkFq82QBBVkewcZdrpDpAJ0jRd0QgiA0cRugkNU4LKSQxFWcLOqAspHJPQJ7MWrgtPCeh3M5Ra8UHaaec4uerpNVkV4qC257MCg7U5XrtxGwDwc2/4OTz4r73wglKWBRKeO77qE9+HIPAHWVkEEKGFSf2mHrVK5hlWZeApQLVLjYPf6OEzMXuIfmS6DT1gK+zi8oHv0QaRF2rIWwQuU9KbyAPQ43pjpvdXDgvaxB5/84/ggff4DSgaCzzxvmZ+fu3v/30U1Lt0TsBWEqJLD6dsel+DlSWOd2d4dttfy/XJCr78xBUAwL996gshbpNurAXCicCDP+Y/rzimAeF7xye/YIJXrG/jM7k/fKyVbBWWlSGDuEodIM9DpkwtROwPN/pe0XbAsoUuAffWXalQWMEAFQdwRSfaU42LVNfBxbbRJDYClqosskWUtPpxBK4JYlqacQGYB4j2FfLjlFXtxKjDXycAOVEIz/qA4fzaPtv0LeMIxkiW3ju7PsJVmiOmUEwHCpXBeJnyAZPGJcLU36skrrA57bGWblaGLPgilINtN+5HInCs1euMREgcUWslxpM2H2ZRpFnXWMQO2YIqWNKxuXw9auCZSh2O9WcYJn6/+dzWCbZBDAKgCOLm/QdLBiNJ2QD2yjyEKyUMrV0ox2Ih1gnoSvHzzLKInYVe+ciHURvUyVLgq77ySfzK418EwPPDu0kBvbj3M9WjnurROBpH42gcjaPxEo2XZab6H37ye19StY7NZzwMP1hI2LAJD4uhY+Pw1soSGZVxqu0UW6MEw4d8ie8LN+5AEwz5gVM7mJWxL+sB+A/5RZy86D0IO1GBvApYIefMsREL11sn0KHy49asi2GU4ctPXQUAHJQtnEl9P+gzo9PYko57ZEZLFsZe7S+4x3R5bxXZXszRbr4qEPu3wPI4EC4bgXQTAf2HfF9mdNBBFGlWgYoSzX3U5aQDG1MpVUncvLbGJTwIsIk14Eu5dfQbKMtZ9xP/3T+6696/5tffjicO/P340t/6AVz6xvfiLxp3Zl0uZ4btElHLIKPoXAgw7L+qlBfoOCRMIfNaWYh6v22fiSyKiIX+p4uE0bJBYGCMhCIZN1NJzqJEanz/iSHLgKUs7ez/9T6I00TeP2jkJwFga95lAYwk1DDdgvutxkp0U1/CO9aaY1ImGHT9PBtN2vh48ZB/3TSEIDRuNGmUiQAgGAWM1P30pbPori1QTahvKx32qO/L6kmAFxxJGjF5vZ+gd9oja5eXBqhOlwhvk4BE2AgToBJwWsGyk0zIdDJVCl5PshQwsc/qAMBVYAUlXQReGIJ2s8HKHNM5+cBuJWhR5cjEQH6mwmDDl3VP9Se4r+0n9W8+8TBUUkFSGTrTIcY054yRkNIyFWoYLxGe8Pf7yu4qNK3VybUBpG4MB8JzE/Q7tZuNv8e10tA8j/mz4rRCXgsrKAcRWEZ3O+dQUTk/apc4uT7BjW1f0dFaokPtAiEceyTbvgPmCopcgoSy6LX9nIgDjVZY4pPPnvP3dSeGbR96jrlE+0Tj1VvSnO60c/4OcVqhsBFcvRbCRkSjqhSMlmh1/HUtttoI6WVlzzGDoOoCv/7xL4UbUgY9cJjlMcy9n6i+PA/V1/zrRyFbCfQ0ep4e6n/uuPD+D0GJGtzjF1T2Cj+Bk1bJ5ZPZQRtiRmCP2AKB5XLhnWmPpfzedO4SPr1/CgUBUaJA3yV3FgcGK8f8RqCtZMPi2/M+dkfkgJGW+JPbZ3F2xb9uWiS4NvELcW/cwbGVKXYL38u0VkLTgt7Xkm3X4lCjWs8ZKKJzBfU6f+BWT/ehu4AmN57gVozsT9fo+oDFSsg80PTkDOvEqbzaS7jvJQMHBO4utZ/D4xv+8HtwobcHAHh6vIHd7SH/7BW/8k4AQCuucGYwxdVa+ix7cYcaAFwaBgLenI1WyLTiPlteKaYiCem8GTkpxzgtGwyaFhBVY7dnjGQgRxAaBgdprVBMYwaviUIC/cZAGwJ8OEXtkst5y0XM4J3HH/3Bu77HPIsZGPKFG5tQwmFU+CBhXKQoqOdWWoXtSRcZgWDUJEBh/ZwLTHOW67YvF1ZDOujamst3YhZgfrMHtBs3FOYSasmALxlYmENKTHDAdETuSqczoFRelhFA67aCE40ucHbcopIRX2NNXSwHFjZqNnyRGO5pwwo+bISyCGIDIRv6TY8Os+kxAUd87apvsXZigjM9vy6skxiThN4bHriKW7NBY/ZuFPoUnNTAoDcdf9q/p05wE34+SulYNalaA8wsRPe4P7Q3us3hdGNvCCkdFsT7zMYJ928ROqA2jy+UB2AdkntMV/13qcoAq8kCp+73rZCr0xVMDh38tRavtQ66YyAI1DU4ZI9Y6hh3ntxgxTa7XiKI68DWoZrFWBAHN+0U7FwkpeU9ifXC6/87geUe9Y4XCvJYgeUdv79EI8lORuFEIic1RScdqoHhoDpQFnkWw1RNYHavjqPy79E4GkfjaByNo/ESjZdlpqonEWQZ4fp3Pz9LPfvPPcm+uz7H/IYvESc7Ck/9iAeKnP3Io7j+XW/FuZ9/DwDA9QLIsvaadJBLCZBYdaVjZOsEja8EXA1sCS3CVoWtGz7DErFlxOkfbp6HlJZRpYsqxrGBj3wnWcIoScALq9eKJLujLhKC4c9v9YCOxm7so8WDcQNuEAA2r6xhcMqX5qazlPVbkThcu+wRoYgsWv2M37NMAr6m+19/E7fGfTgqDR32m626FiqXXBqeJe0GURxaNly38wDB8u6Y7sKjRGsZajx4IcHJ1EfkoTIc/b/uNx7Bk3/j3Xf93pf8e09rWs5jFiO4/M0/hFf92g9zNva5N/8Il+nCSHN0nqSlv181WvSQ6tLJ+w5QGsXAE+sEjp/x17E17WIxSZmSlDswdcgaAUfV0nK7hXRbsVi9DQFdazcLB5SSQ9s4rphEL6VDkTUGzwDwbZ/8uwCAb7g4xkHlgUrTKkFuFAJKPRZlhBEJCWxXAy90cMjI4cr3N+C8i+/197uusLi4AcvUWYiLLRAdKkeWEnJRq2xZqHYtCGA986h+Xds0ZXMnMFiZY5GSjnTWgaCExMYecV+XD03XwNSdmUog2m9KwbkV/DxdaP29A4BSQcfWZ3wADrIQAX2XbifDfEDenpWAsQLXyG2llxTYXnb4fpRacUbXiQpU1n/2mDxQf/nqawD4knttpKBUUxbOqhBFW+FE18+RC709SOqfXN9ZQVk1Qv+QQEBiJ3q95Psb9AuowKIsGj3oU0O/DpZViHkVY04Uu4NZu7lvRqJLJd5WUqBMS55LRRVwGdcBsKlFe4OoREXAmXZRhIh6BQMLs0XcmL0f6kAUeYggqWAjUuACYEjEIVgIaAsGjem246pVebzCax+8DgD49BPnIQXj4ioAABTcSURBVDsVOuSFXGiF6loHNr/3j6R7/xv8fxhPfPs/ePGeat3vAnD6IU/xOP7FUz5sETuc//EPQhoqPRnBvSgnANuy3Ed1SwWwgDwaDqgE1FNt2If8QWqmEffxOlGJZ68fwyvObwLwpdu6tzif+/Lp2PlNU4UWD5zw13j+vgNcJXFtc2KOKDA40fUH5zyLuQw93+5AdDTG236j6G3MsSRKQL+TY0GLKF9GkNLhwVXfz93P25gTZefGwdBLDI7pHlgvAwgA4azmJlIvbBJADKnUOg2hemRx9bcewdmffvSuW3/5rc2G/w1/+D24sfDfZ1FGvFEBwP10cJplACiH69/2LrzQWG0vcXPHv8eFj76LNy5dKVw47r/XzrwD0S/hqPwLK9jGamu/D1NKpsU4KwBy9+kkBULVWOO57RgY+M3p4sld3ByRdKLzzj02PaS8RQdW0NJwqUCbNsON7hw39ob1r8HS3Dn7z98PERk47fuh68cnjBavN/7aHjANK+zV3GPnqT91yVQ9p19V902DpWBDBADe/LsOACOLVi/ncnNlFOaCXHG0YKtAYQEROYD6eK5lWEHM5Aqi5zmcAHBwPEE4ataFrIBwjwKeeRPUVD0HTf0+G5MSWkF9vYMAVbe5nyaSTYvXeYlQwMv8jTt+rQstMLrdZ455ZRQfPNYJzOdJE5iWMUtXlkUAW0nkFCnlrRInO35tteOSA93agrBGyR6ULT4Ak7SE1ooD09KEDSc6VwBRhapJjOT4jG3n1tsLTMlyzliJedlwklmFCp5LW5trhMrgXPcOdnMfMFzaWufSeCupMJ1GWBDyPGyVjAVIkgp5FnFrxJYKMfVG8zLkni0cEAQWgviCVaXg6PrLU8a3BUia9MTxEZbU2lptL3Fr5tfF8NQEpQ7YtaqaxIiXAiZ/cdnXe2UclX+PxtE4GkfjaByNl2i8LDPVFxtf+Ttvhej4zGa63cHU+VC481CBHllgTQ/asG3DZPt42kT5wdIjFEPSPNWpg6OAXxjR6N72ShQtDbHjo/yHXn0TG6nPgJ4Zr+OvvOISexButGYMQgkjjQeP7WJa+N8bxBmXr860RzhLAIzHsxOIQ41zbU/wvpP2GE2cri2RTRN86cPe+jhVFf7gygUAwGwZM1I3SipEgcZ+TmXGPOYybnWjDRcc4hoGDpriM1l5AYJaRCOcSpj/5KNTObSgW4rz/+SDwHNwRed+0gukv/nL/gzWDWGp2LfII0wX/jtHkX5hFSsar/zVdwLwPqmTLGGfR+MahaY4rFg/eJlHnuMYHMrUCFBmlIOwAm1CsSrhWGDDWIlAWS6d5T2FiOzALm+vcVlYGgHIpgQGgO3LzG6C5NSc9VBnReyFEgAPgqqrp/T69tB/l2URsSF7HGpURuLANio/cb8pTQbKNiIDxQvH0LrtYGOLlIj/cahhqDpSlgGWBy0UBCgxuWoANs5XZngo15hMVxKo1ZBWc6y3F5xltdYX0Ptkwq0AlQnOop0Al8rrUiXghSxkLnhe2bBBnz/9Dl/hqNsHTjlYMrIY9xL0Tvq1NT1oozNcIqGse7W1wPV9v96d86XWOhvLqgAdUj+679gB9hctzHb9WhMCWE/8fjCMl7w+K6O8+AN5nLaDEtvLLr2/gBBNtl7FFSPkq0nMXFphBU50Zzz3R3nKz3q2SCClhas5slqyb3ErbBSYVuIlUlUhoBsUx5pL1DdvriKYKJiUKgq7bahzvhScZxFMpiBqP9tBhZI44dZK9tQV0kEpywwCaxQkAZqCyKDaaqFFbZJBkvFc2pu3GeRUC1jUqlUInPdn/i/ApPxleah+9e++HUE7vkukHACu317FcNUvllHRQ3vVT7ZlFWE+o039Tgh7PoOhfk6xZtgAWFYCKhegipwndNcUAC2ZfqFnIcJeiYuv8R6ngyhjibs3Hn8GhQ1wOvHKK88u1rG/8Iu2XEaYlTFubPmNYL+bM1Lvd0ddnCCBhDyLkGcRfq+8CAB41foWHt8ll5RWjk5a4LE7J/l7D3q+lLgsQu69mFKhmMcY1YdsWqGoUbGR8+U9poI4b1IN8l9fCEZ6msShpI1XrhZNT8kCvUt3T7/hY/6efix6LYbHp0yOz/dTLwQAYHa8wp83FtuNYXcrqhATmtkeosXcGfUwIGSnEPALuT4orGApPJRe/q42BP+S09dxaezpU97AWnBZfeXYFGnYIBdnLX+vplUXohKIdgmRe1xzf1uu57BWYEwC+FUZ8ByxlULSJdNtI/DsNzVyiw/98o+wOECUaJjLHW5B6L5G75ifw2lUYTRrsdvN9e98G7/H2Z9+FOjXtBYJ1W9E9A9GbaZE1N+9nu+He86I7V33TZTy7p/T9ywnMZ6pNjgAaSUllqu0CU8VdOoax5yWa1TIUsuo2LBVoVyGELox137mkecom9WXkjhceIVvn6RBhYDe8BknsJikqEhMoZfkiOiZZXmIahIj7JEq1nAfufFr4Qv627gUbGBE5dX9cQefvHMfAKCf5uwAZZzAA90DfM3wcX+/yzU8M/HzZa2zgLGSS8O782aeBr3SmyLAm6MD3u0FAN548hn87u0H/fV2MhgrWZzkvuEI23MynQhLnGr59X9zMcCz4zVUplFH2yfHnaCloQHv6ARAxxpBLWu5F6N9ZoaU1tho0oam8myQVJBhI7RfFAGjr1FKhIMmkDPrOQcnz+6ssQhLr5UzFWx77N2V6rXgrE9ObHB0qN6TQwrvOH/+F98Ndcuv5mpF4/p3/MBf8Jt+vPoffBiP/XijcPPwx94BwFM61oczzAmWP9/u3LXJ1DqgcpJApwZPXvUH27HjY2xHPqL9srWrOKjaDHDQViKniX3s2Bhb4x6ryvTTnLOQ6aiFnQktsHaO2W4Hs4X/+5PiGGLa/JW06IdNH2i6TGBqw+JDmZcKDHQVNBZat9tYe8BHxXs7PQhlGwswI6BW/CZsjUAl4oaH6NBkgZsJHPVasVZiOry7f/Lpn35h+cezP/Moqp5/v9oG78XG9e9qwGe9OOfD1DmBj7/xw897/cMfewdUbFgJSBQS155Ds/qCf+O5sX984xxbci3zCEFgefMIpGU6xlpnAUeHxDRuwyUOZUCRViUA4ivrloHrgsEmgHfNAYDSCuYxRpSp1iCsXrdg2k+5H8O1LTdyrn/n2/C63/A0pU/+N+/Bwx97B9+Dcz//Hlz7256eI4xotKgDB5MH2Ft4/mmwH2BBSlroaojQej4tAAQOQjXZI2cWzkvsocZWWcFgFbnwZubhaT9HsiKCSP33lMMc5X7S6GB3DAStkygynLnr0CLplCjrPu1BjHM/4Ssb1773H+KVv/pO6PX6ZJbscPTK9W3W840CjapV8ia/OeojrHvF4wSyXXFlIxCWZQn/dOcstnf7DNpptQo+2KRweJJs/TZWpgiExR/NfDB7ULb5sztRASMltqZ+nbfjkgOWbJIgoPuxN+pi1/VYEnB73mXwVFEF6LVypr1lOkSb6D7LKsKfbXvVq1IHZA1I1oxGMcdU5x6HgDqxDX2QDwBypWBsB+ArY5qoT/IQlUpJz0tls3rXZJzWWpgsQNRv6Eg10M9YiVtXvAIcuhXCbsG8bDUO7qrm3MvjqKd6NI7G0TgaR+NovERDOPdfgi3sX25Mp1P0+31MJhNG/9YCz8++iEj+X2bU9BohHR44tYMbBx7BmS8itKiEd6w3w0N9j9T91M4ZzLOYKRNSOR9BAnj9g1fxcPcOnpz76LcfZmycHEqDTljg09fOAPDqS08/5UW0k/UMHRKC0EZiNk9x/rgXT9BWMsoR8KWlw36OWzMfPadRhQmVIss8wCHfa5hF4Mt7AIK1DHo/RUgC+zp1UCd9idFZCaMlgk2fSamlQEgc+Pn9hjNYmXiN4FowQU8jVm86nG2+0Ki1lgsdYLpI2InmK3/nrTjf833k7ayLQFrcmfnn/Gf/7QsjhAHvf3u4NPqir/vIo03JO7SQhxSg2p2cqwHtqOQe17Nb67BWci8pCA2K/7e9+wmRK6njAP6tqvf635vunp4/ySSbkGxiwuKuiBdXvXgRr7LiRZAFwYPeRMQ9iKKXyAqCB48iIqKevIgeBK+7Bw/LroSYZHczM5nM/5me6c7r1+9P1fNQ1dWTgyto6Rry/ZxmoKe7p/t1/6p+VfX7zeq6GuFnfQDQutdC2be3+8Rn7uHuoW1An6Yt9Hspxq5KUBRr3yTbH8txz2vja9/x12N/cYKVhRSZO7Q/TNuYHLi0Y2TQW0n9/dcGfje3LAR6N+z6/M3lA7x/soxj1/dWvd9CseKyHv3Cd+lRyqDbnuJy1y5b3N5bQ+aWTOqpeiJjIWKDldWRf60eHS7OMwpGwIzPHCVyO+m7yymuDoZ+P8HGe+d88/Hm+QmKaQTpllqkMr4Hcbc13/J8NEp8oXcAEDst6P6sdq5G1NC4vGr/734j82ulSVzA1MKnV5Mox2ZqP+OH2YJP/x5MErx8bgPKTdHLWqGqZ/2Ia2xOBri3Zz/LjUbld+uOJq35sksl0WiVvqhL0ih9yrjUCmkRo6hms0LhZ7viTHWrqlJY6qc+A/Vcb4T7+/Zxy40EpllDLtv7b7XmvYqneYwyn7+OZ+9z9niAvf6MlrY62Oy1nBXXzyKIUqK9Zj/0d175AT7+B/v5PNnt+tu3lqYQova7oXWlIJWGmUzx4Ku3nviOfto880H1P3X11z/y608rCyk2d5ew/hWbYvv0n1/Dm59/3d921lnkYJxgctJGdGA/SOraY1t1x7l2dd93PHk06uF515ppqmP04inuHs/W9SJM0nn1n7PJ1JXB2JccNLXA37ft+dNBb4Lnuqc4zOyXa1ZGON61ab/WmQ4VjbhCUUY+BTnZT/yXYm/1MbSRKG/PX8PyiivQnynbmNjddmE98mcSs7Ua+ryrwtTQELL2FZtUpKHdz/+syhJgA+rOHRtsTKKhkgpmaIPB+euHPs01HHdseblNt145qBC5ovPnl0Z448z78vxvbvm13tl7N/PC7384/8J7PB9YQAB1W/u0XQ34ov9pEWOl49YxjwYo9jr+mBXq+RlQ0daoz7Tek1PpC6uL2Phi7EZLRLFG4QZedS3836luCRXpeYeWNPbBPm5WuPel7+Ozf/k2ANs1ZbTnlghWJljr280kW0eLKIYtKLeeOLtGAGBvsgAlal+yr7jb8wG8upAjcu3ckk4OKQ1iN0jQRuJw310fhXxyE5MRiN1g86MX9nA87WBrx+4TwGk8PxqzkmNpMXX3J+ygzz2PyeOm7yKjpMHRaeKvH6nmHVWEwBODzZODBf/6mCyal8msgXrUgBzY264Oxojdtb877KLZrLDmjlPlVYQTV1x/mjX8noRYaQxaGU5cpbTkzOahG70D7OcLeGvTDoi7C5lvbt5t5Nh3a6N+Q5MLuGvJCC1lb/f23nMoysinrJuR9oFTG4mpD8wKrXbhg2CvM8WxO7tstjrQXe0b1LeT+aBDShvkzi5HzAYhtRG+ulIUaxRFBO2uR5Eq2xgerupVpnw3ofVX50tqV37+Y39USwigP5h3zzJG4Hx/jCrN8cYXfvZUB1Wmf4mIiAJ5pjYqzSblo5FNPX35zW/hnft2F9/tV7/5gX/70u9+AgB4+fo6NkYDHLp0mBQ1xtt2pqQXI9TTDFdet6nGv33ju/6xPvnHWyjcRpZOZ4JPXXkP7/bsjPOl5W38tboCACjeWcRRV2DDNTCWqsbYjfAfnbbxsXNDHN2xI2GxWKDr0m2nu10o1wxYaGB0s8b2u67QphZoDuyI/+BOB+bqFGO3bf7q8hGOZsdLdiWUm3n1B2MkqvZHT9TixI8qJ0MJqUrk5+xsRogancjN1tdSNFWFu+sX7G1vVrjkUmoia/uNG+lJG+1uDuVSeAo1it3kiffnxV/+FAAQLZSoXNPpOIkB9z8PehmSRo6tDZsq31lvIenbVNz0oAaMAZr2/xZDiXJkX8eHww5e+K0dQRfTCELUePuLNqV89Rff8zN+qQzeeuU1vPgr+zxQV76ABzQQtaco3PEp1dDYcTMFIWscbrim7amErObN03W/8scPzMTOSmYpcKMVImFnKFUaAZU7xpJFqJoaxrVYlZmCadqsgowKRJ1ivmFKCZ96vrgwwuf+9HU03IN/pHOErRWblYilQU/bFN2ldoaxamLo0sutaoy7D22qbnLYRmspgzHueSFDlM76sWWIXNGJ9MQeuei4ZtidRobrqzbDsnk8QCPWGLjZe15FfoPdg502Si1xY3Xdvh9LyqerY6WRun6hx8cJVCQgXNojigpEuX1vH20uIxopSDfpEpVEfs3+oiKDzNWTTcdNmCxHo+E2Iy0YJI35bHLvdBGLkTsKUo/xwBUOyccaumHwwDVhKPNo3p5ukCGaXWOixv39BHJW6Kllm58DwHC4irRoIDL2Nc8fG+jZRrROiWzkKkC1pii0wq7LvuyYZb8jOD3VADR6fft7V+U4dkfedKXQFPaaMEKgGCufvj0aS9S126FcCqgdAe0Kf6TLtW/9qFSNqpS+l6sAfJeFfBpDClcD27V9lO6YQ2ViwCW5RG3QTHKfMZp9lgHAlBlQuZrg3RzNKkPPbboa5h1sPWrAZLMdxk9vAvWZSv9ubW3h8uXLH/bTICKiD/Dw4UNcunTpw34a/5ZnKqgaY7C9vY1utwshxL/+AyIi+p+p6xrj8RgXL16ElE/n6uQzFVSJiIj+m57OoQAREdH/IQZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgokH8AAtW9bvriMVMAAAAASUVORK5CYII="></td>\
+    </tr>\
+    <tr>\
+      <th>2</th>\
+      <td>2019-02-28</td>\
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
+      <td>(-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667)</td>\
+      <td><img 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"></td>\
+    </tr>\
+    <tr>\
+      <th>3</th>\
+      <td>2019-02-28</td>\
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
+      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>\
+      <td><img 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"></td>\
+    </tr>\
+    <tr>\
+      <th>4</th>\
+      <td>2019-02-28</td>\
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
+      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>\
+      <td><img 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"></td>\
+    </tr>\
+  </tbody>\
+</table>
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index cc4b0ec01..f2f3ba76f 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -126,14 +126,14 @@ def initialize_options(self):
     },
     install_requires=[
         'pytz',
-        'shapely',
+        'Shapely',
         'pyspark<2.4',
         'numpy>=1.7',
         'pandas',
     ],
     setup_requires=[
         'pytz',
-        'shapely',
+        'Shapely',
         'pyspark<2.4',
         'numpy>=1.7',
         'matplotlib<3.0.0',
@@ -143,15 +143,16 @@ def initialize_options(self):
         'pytest-runner',
         'setuptools>=0.8',
         'ipython==6.2.1',
-        "ipykernel==4.8.0",
+        'ipykernel==4.8.0',
         'Pweave==0.30.3',
-        'fiona==1.8.6'
+        'fiona==1.8.6',
+        'rasterio',  # for docs
     ],
     tests_require=[
         'pytest==3.4.2',
         'pypandoc',
         'numpy>=1.7',
-        'shapely',
+        'Shapely',
         'pandas',
         'rasterio',
         'boto3',

From 8dbeb34318dc1dc2216fe98988bc7cfa976f5275 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 6 Aug 2019 17:50:38 -0400
Subject: [PATCH 316/380] Links from raster io page to raster write page

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-io.md     | 8 ++++----
 pyrasterframes/src/main/python/docs/raster-read.pymd | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index b007ec291..9f6edc824 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -11,10 +11,10 @@ The standard mechanism by which any data is brought in and out of a Spark Datafr
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
     - `geotrellis`: for reading a [GeoTrellis layer][GTLayer]
 * @ref:[Raster Writers](raster-write.md)
-    - @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
-    - `geotiff`: beta writer to GeoTiff file
-    - `geotrellis`: creating a [GeoTrellis layer][GTLayer]
-    - [`parquet`][Parquet]: general purpose writer
+    - You can write @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
+    - @ref:[`geotiff`](raster-write.md#geotiffs): beta writer to GeoTiff file format
+    - @ref:[`geotrellis`](raster-write.md#geotrellis-layers): creating a [GeoTrellis layer][GTLayer]
+    - @ref:[`parquet`](raster-write.md#parquet): general purpose writer for [Parquet][Parquet]
 
 There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.
 
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 21e7f531e..d730d98b6 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -182,4 +182,4 @@ mb2.printSchema()
 
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
-[Tile]: concpets.md#tile
\ No newline at end of file
+[Tile]: concepts.md#tile
\ No newline at end of file

From 42354f4b526b41b0c53efaacb0b00eae1c4709d0 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 08:54:04 -0400
Subject: [PATCH 317/380] Minor wording tweak on index page to match
 description edits

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 790bff5ca..25d6548ea 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -1,8 +1,8 @@
 # RasterFrames
 
-RasterFrames® brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
+RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
 
-RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
+RasterFrames provides a DataFrame-centric view over arbitrary raster data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is both accessible to general analysts and scalable along with the rapidly growing data footprint.
 
 To learn more, please see the @ref:[Getting Started](getting-started.md) section of this manual.
 

From 84c533dfb809ff5d253f5664dd859421557b81b8 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 7 Aug 2019 09:16:40 -0400
Subject: [PATCH 318/380] wording

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/raster-read.pymd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index d730d98b6..91350dbb8 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -146,7 +146,7 @@ In the initial examples on this page, we used @ref:[`rf_tile`](reference.md#rf-t
 
 ## Multiband Rasters
 
-A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands may represent intensity of different wavelengths of light (or other electromagnetic radiation). The different bands may represent other phenomena such as measurement time, quality indications, or additional measurements.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
 
 When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
 
@@ -182,4 +182,4 @@ mb2.printSchema()
 
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
-[Tile]: concepts.md#tile
\ No newline at end of file
+[Tile]: concepts.md#tile

From 0e061257eb49c5aae4a0e35c87b36210d96903cc Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 7 Aug 2019 09:21:20 -0400
Subject: [PATCH 319/380] grammar edits

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/raster-write.pymd    | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 139f916c2..4bda0a78e 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -1,8 +1,8 @@
 # Writing Raster Data
 
-RasterFrames is oriented toward large scale analyses of spatial data. The primary output for most use cases may be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set. 
+RasterFrames is oriented toward large scale analyses of spatial data. The primary output of these analyses could be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set.
 
-However there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the process and results.
+However, there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the current state of the process and results.
 
 ```python imports, echo=False
 import pyrasterframes
@@ -13,12 +13,12 @@ spark = pyrasterframes.get_spark_session()
 
 ## Tile Samples
 
-When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_. 
+When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_.
 
-In an IPython or Jupyter interpreter a `Tile` object will be displayed as an image with limited metadata.
+In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata.
 
 ```python tile_sample
-def scene(band): 
+def scene(band):
     return 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
              'MCD43A4.A2019059.h11v08.006.2019072203257_B{}.TIF'.format(band)
 raster_url = scene('02')
@@ -33,7 +33,7 @@ display(tile) # IPython.display function
 
 ## DataFrame Samples
 
-Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
+Within an IPython or Jupyter interpreter, a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
 
 In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
@@ -48,7 +48,7 @@ pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
 pandas_df.dtypes
 ```
 
-Viewing the DataFrame in Jupyter looks like this. 
+Viewing the DataFrame in Jupyter looks like this.
 
 ```python show_pandas, evaluate=False
 pandas_df
@@ -59,16 +59,16 @@ pandas_df
 
 ## GeoTIFFs
 
-GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTiff.
+GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTIFF.
 
 One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
 
-Fortunately, we can use the cluster computing capability to downlample the data (using nearest-neighbor) into a more manageble size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
+Fortunately, we can use the cluster computing capability to downsample the data (using nearest-neighbor) into a more manageable size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
 
 ```python write_geotiff
 import os.path
 from docs import docs_dir
-cat = """  
+cat = """
 red,green,blue
 {},{},{}
 """.format(scene('01'), scene('04'), scene('03'))
@@ -83,7 +83,7 @@ View it with `rasterio` to check the results:
 import rasterio
 import numpy as np
 from rasterio.plot import show, show_hist
- 
+
 with rasterio.open(outfile) as src:
     cells = np.clip(src.read(), 0, 1800).astype('float32')
     show(cells)
@@ -100,7 +100,7 @@ with rasterio.open(outfile) as src:
 
 ## Parquet
 
-You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format. 
+You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format.
 
 ```python write_parquet, evaluate=False
 spark_df.withColumn('exp', rf_expm1('proj_raster')) \
@@ -108,4 +108,4 @@ spark_df.withColumn('exp', rf_expm1('proj_raster')) \
 ```
 
 [GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
-[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html
\ No newline at end of file
+[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html

From 59d10b8037d67657cb2f555fdd72d6c445c84da2 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 09:25:57 -0400
Subject: [PATCH 320/380] Minor format things: dataframe sample to include and
 L1 header in function ref page

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/reference.pymd       |  2 +-
 .../python/docs/static/rasterframe-sample.md  | 88 +++++++++----------
 2 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 0b4b0fe58..81f20cba8 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -10,7 +10,7 @@ ReturnDataType function_name(InputDataType argument1, InputDataType argument2)
 
 For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`][RasterFunctions]. The full Scala API documentation can be found [here][scaladoc].
 
-# List of Available SQL and Python Functions
+## List of Available SQL and Python Functions
 
 @@toc { depth=3 }
 
diff --git a/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md b/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md
index 026537851..c7ec4ed5d 100644
--- a/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md
+++ b/pyrasterframes/src/main/python/docs/static/rasterframe-sample.md
@@ -1,48 +1,48 @@
-<table border="1" class="dataframe">\
-  <thead>\
-    <tr style="text-align: right;">\
-      <th></th>\
-      <th>timestamp</th>\
-      <th>crs</th>\
-      <th>extent</th>\
-      <th>tile</th>\
-    </tr>\
-  </thead>\
-  <tbody>\
-    <tr>\
-      <th>0</th>\
-      <td>2019-02-28</td>\
-      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
-      <td>(-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667)</td>\
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>timestamp</th>
+      <th>crs</th>
+      <th>extent</th>
+      <th>tile</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>2019-02-28</td>
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>
+      <td>(-7783653.637667, 993342.4642358534, -7665045.582235852, 1111950.519667)</td>
       <td><img src="data:image/png;base64,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"></td>\
-    </tr>\
-    <tr>\
-      <th>1</th>\
-      <td>2019-02-28</td>\
-      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
-      <td>(-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667)</td>\
-      <td><img 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"></td>\
-    </tr>\
-    <tr>\
-      <th>2</th>\
-      <td>2019-02-28</td>\
-      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
-      <td>(-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667)</td>\
-      <td><img 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"></td>\
-    </tr>\
-    <tr>\
-      <th>3</th>\
-      <td>2019-02-28</td>\
-      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
-      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>\
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2019-02-28</td>
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>
+      <td>(-7665045.582235853, 993342.4642358534, -7546437.526804706, 1111950.519667)</td>
+      <td><img 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SeeeGIXjQvALojr1q3Du971Lpx88slYsmQJGGP48Y9/jM9+9rM49NBDuyi8vr4+XHLJJTjnnHNw2mmn4ZRTTsGWLVvwqU99Cvvtt9+0LDu7w/Lly3HxxRfj3nvvnZaYYaZQq9UwMjKC73znO3jd616HgYEBDA0N2T6744470Gg0UK/XAQCPPPIIbrnlFgDACSecYDXUSy+9FHfccQfe8IY3YPny5ejp6cGNN96Ihx56CDfffLO931/8xV/g4IMPxjnnnIM0TdHf349bb70VP/3pT7va1d/fj3POOQeXXXYZ3v/+9+Ptb387NmzYgOXLl0+jf7/0pS/h/vvvx3HHHYeFCxei0Wjg/vvvx/XXX49XvvKVXdTuRz/6UaxatQqnn346Dj/88K7xLhQKOOqoo6b10apVq7Bw4cJp9G6GpUuX4h/+4R/whS98AbVaDW984xvx+OOP44ILLsBRRx21yxCwLNHH7gS1x0scfyJbrsefOXbnqJQ5nezuX97ZZdWqVXTMMcfQwMAABUFA/f39dPzxx9Ndd921y3uuX7+eTj75ZOrv76coiuiII46gr3zlK9POe9vb3kalUonGxsb2+Ayvec1r9tjWDDt37qSTTjqJFi1aRKVSiaIoogMPPJDOPfdcGh8f3+W1v/zlL9Nhhx1GURTR4OAgvfvd76YNGzbs8txdOSqdffbZxBijRx99dI/PsDtHpV0lqsjuk8fdd99NRx11FBUKBQJAp556qv1uZGRkt33zbGej3/3ud3TiiSdSrVajYrFIxx57LH33u9+d1obHH3+cjjvuOOrp6aHh4WH6yEc+Qv/5n/857RmUUnTFFVfQwoUL7Vh/97vfnZb84Wc/+xm96U1vonnz5lEURVQul+nlL385XXrppdM8b/f0PLuay5kT1oUXXji943NI05Q+85nP0JIlSygMQ5o7dy596EMf2uX8azab1NvbS69+9av3eE2Ply4Y0QwGAXq8ZHDaaafhvvvuw5NPPgnGWJdW+afGnDlz8J73vAdXX331n7opewQRQUqJSy65BJdeeim2b99uqdJjjjkGIyMj+Pa3v/0nbqWHh8dMwtO/HrvFunXrEIYhDj30UDz88MN/6uYAAH7/+9+j2WziE5/4xJ+6Kc+J6667Dh/72MemHZ+cnMRDDz30RyVP8PDw+POE11Q9dom1a9dar81SqfSiHYX2ZWzbtg3r16+3n4888kjvuOLh8RKHF6oeHh4eHh4zBB9S4+Hh4eHhMUPwQtXDw8PDw2OG4IWqh4eHh4fHDGGf8ppQSmHTpk2o1Wq+FJOHh4fHnxmICPV6HfPmzdtrU37uU0J106ZNWLhw4Z+6GR4eHh4ee8CGDRteUD7xPyfsU0K1VqsBABZcdAF4sQgKSedbAQClNVdmPofjHBTpD0lVgUoKAFDqb6FW7GCsrtPmJZMFsFjvqKigMPBggPr++hrFLQydAZN0PGXoDEl9XkiI+tqIx02VkUjZNhY2RGApoCLnlM1T3bZfnfcPXc/zvz79fyDL+ry0osCkPo+3GZiCJfd5myGt6nuQACojkwiEbsv4WAXFJ3Q7ZIHs9WRVApJBNIX5HUG09fWDKf3/By7U7Tni89cjq3fEJAOXgGjoc4gDYNl37rmCBgNPYfs/7nHPxQhIehV69h8HAEw80wsq6vYOzZ5EJ9HTtr61CiaZ/Y4JArVdkgre4aBA34D1xLDkBKOsSSAy7TRDoNoBYPq7sC1A2ABas8n2ATPnlRdPIk4EOpM6v25YiZHGul3UCABhHowYWMogWrqDZFmBAjfevJRCpabNkul/ABC6c5AysJSDzOTkMQeZ61NJAjEHT/T1o50cadU8swJY6saLEcBNrnumAJ6asS4yQLlxintg+00V9G8Ko9kg5ppVAlSk/056FUAAj80JXD8rABS2CvDE/S77DQBEEwAIdv6kZYDMqsRSPV8zyJKbg6pAMEOHoMGQVgi8456T8nOupJ8lnGBIS0BnVqq/K0tgKjDfcagQEE39w86sFEFvrNvUCsBChcKTRdt3gSkGJAv6HwAQJ0AAKsg+A7JfP3h1oIkFvRNYO9oPAGiPubSbrCAxZ3gCADDWKKGztWLnS2kLgzT91VycYM78ndi6vRcAUOlp2+GIAgmWzQ8QQiGxaXuf7p9AQpkOKZVjFAKJcqifLRQSjVjfgDMCZwTB9bgFXCHk+t1qJhHGWrrNzakCVCzcXOUEUdJ9GkYpGAPamyum/5kdMyb1GAJmHZAMPDHXmNtCT60F2ezgkdM+b9fqvRH7lFC1lO9ACJRCMOHePtbiCKY4StudMCA9n1CIYYXj0EENcBZgh5mwsw8cw5I+Hc/ZTCNsP7iC8U26BFq7GiAaMwK3BLAe8zIDSNtFcCNTWcpQ3KZXjyDWJ3SMcBMthsTc+/Cbv4hoa2AFH82N7KQMGIGbZwkks4sKAMg+AjeLUzTB0GgF4JERRAiRztFfquHYCiXR4Xp29Op7rfvwOTj44pUAgJDpbuvp0ZKQVYoIm24xpRBgGXPDAJEttAJg2YIaAIgB886i3HLCK5mVIKqmkGHFnFoCM9efTAIUS3pBOOiIMazZMoTACKAgIHTa+sbpZIQgESAjtSVFCHq0RBGCEIZ6LKYmS2CcILjp0xogxyPTHxzNxdIJEgZkAWiN+iCqsxqQItTXb5QQ1PQ1JQ/sRgtMPyszO5xig6EzbOaBZEDi5G9Y54gHpR03ZjYL1BbgTe4WIAHIUrZJIgjidm5TP5BnzUSbAWbDwmPAyErwmOx5LNDzRcSmr0oMadGcGBE4Y1CDTqgy08R0SEGFZpPU5hBtgJmJpwTZsWVVBt52AlKw3N9tLZztxqsAvdkCIJQWsrqvgKgFqNB8joEk2zxUGAIBsKK7ZnYNWQBg/lb9AEqEyEhBapAVuAQOxgmB2ZwoRSgPaYHYagZIOwKh0L+LBwkd03fEAXC3AVaB+0zVFGGkG9ysD+LxpiuNyAvc7uBZUWJC6Q6JW0XwSNj3R87iSE0beRRg2+g8sGyzzwOEoe7ksbGSXcsKvW3EkxGY6WQVpiDzXM0Gw8iSjUiM5OcQiCM939tJAK6npMVwZQoA0McIYVXfa227Ag4GUdVnBoEEoJ8zDDk6nQCRIQTVxiJkr3k/pwRQ1H9zBogGhxwwm8dGAY1yEUrq3crebJ7bp4SqBdP/WEdYraS4jUMksLvC/G43qRKKB+mdZCWMsbleAwrZ5CD8bttcAMBI/xj6iy1EC7cDADYUBtAxLyJTgJgyL2yRQJUUrKm7v7Je2EWAmG5DNGl2llsJ9UVmoZoSoNyCKpq51ZOY3QWSAHgCPPHPZ+l2ffkq+zCtClDua6EY6Rdi51QflHkWtjMEVcxCXpJgHXf9g5evtFpmWmT4w6UuU9Cafzzb/n3g5SsgYmYXW54whJP6O1l0O1UmGUSstREAaM4jqHn6hRroayAUCrWCFoIb9uMY7GkAAA7o3YHYLAij7QoCIxwBQHCFwCwyVEsgO9xJkZCwcEhrvpPtIsYm9WrNAwUQgzJMBaUcMG3vPXin7qOtRipJhqCm+y1tB0hTAVKZukRQZufOImU1PX1RvWkDgKRHWQaBGIElzDIMTEJrjNnPjGBmKUPQYk6YlQCWbaYaHKLthCAJ16f6M0EYDY4YELQy9oUhyd5+0psb4obp6ACBkXIUA0GTWWEm2kBrjm6krErwljBt1O0UmSYsmV38WU4LBoC0TIjG3TMz6YQscYDMOyhL1NU3nQFCMmg2JDFHOG42rAGBuNaqAa0JZ++xiJ3myLQy7eZmhyGoM9dGMCuoZU2iPmo2daUUiDmSHtfHlBOk2TohOgwqzzBIZucEIgURKfs5qCRI2obZSDmSRNiLBS0GI2MR9ykoI4iCnhjpVIjCM3owgsNaVjsF02wJAHR2ljT7le3W6qFlw0r9LexoVjDV0p01VGtAmGtEgQRnhKm2/i4QEhMdvVNpJwESaRrV4QjHBNSEbn86r41CUd87SQSSegHMbG4Zh30HZUkhaJg1MCSoiMDMhltVJLC+BLT3XmGaYe+0BHt4eHh4ePwZYt/UVEMFhAqkGMIJs6PSCovdkYPpHTUAsMUNuyNkjFCOEiSGSty6od9uVR+ZLCIopCgU9G6aC4mkrNWLcGdg7Yeiw0CTEUpb9e9kCUhzu2me6B02ADxw01m22QdevgKiw5BWze4uyopw6PZm9ize0RTsAdes0J8DoXeCAIb2m0CjHWHnM5q+ZgmDMLtK0QZikzifQqU1JGOPS8vk7Ft7SMJFASBDAjMcEo9zdjEGZxOLCHGv+x0jRxlHgUQqBfoLTd3HURWhsfM8PTGEbWPa3rJ49g7Uyh2MG60zKHVQMJprsSfFhs4gWKZRtAQ27NDPXCnFmD2g1efRyQqSOADJvCpl2qg46o2iPVzob6MzZZgHodAeLTltvqDACrqPVcyB1BwPtGaa9mUcpLsPb2vbaGYflQUGbrRTFSlLIbNU07Pc2v3JajIqJKjAjU00zq1GmzELGQuSloBOn9P8KE905IY0mgAe+rxmIg68fAU6AwTRMn1ShbVdilZg7xs0mKb+rW0dYKa9KiDw2GnaQZPZ36UVQAmnZRInUJj9DTCZ9Y1+J1gr440JSc2ZSGRZATn7KxnNTCbMUtIggMXMan5grq8yWyqzcxxgxhRCYwG40D4H+oC2k+uxcJpv0i/B29y+h5IAGJ8EMdSBSjgKZf1iDNYaiI3mN9koWtt8tF3ofpyvWRsRSsBosbMGJqH6GbZXNHOSNCLHlCTcar6IFJAw5yfS68w6UnIUgxTbJ/TLtyURiMx6NVybQiWMnUYKWFtsKoXVaCuzG2h1aiiP6Hfo5bM3gRtngx3tKtQQw5NbhnVXbS3mfCoY0ophxRKG8v6TaDbMwMcCFBJI7v0J/vZJoconQ/A4RDTOwY3wUhG0IM3oSYKjmloh4jG9uD5RL0K1AoQ7dNdFBMRD+rxabwvVYgfjDePENF7M0XQM5S0ZJaoFTX3EtSljcYgTCqMMPJk+uUrbGJrzyb60KiLrTAVyzCFLGFSBLC17wFUrkK04O7bXwBggpjLhSRDt7N6uHZAMYG6xg3K2tKQ3R3EBWLzyWktJswoDUwxMMPus1ramgMDUy5aSIWgDqZFZQRNoDBu7UiowuzqFgLn7NBNjsxHO6WLN9kEoySF36EbunIjAOzmhlHC7uIIBSUtfo8UJvSX90JVSB22h0Bwtm77jKM7SwjxV3C1aAJTkKFT1ZoqIIRUExfU8YJGCapi/20I7EAF6wxU7ihcps0KOpYZWzRzKErcBYYxb+o4EAZxBiRydyt0CLzrOmaWLaiXjsGa6QHQcFcoZrNMVCUMpZ85aAbD0Qm0/F4zlJoU+J6xnzkIAM3RvVDdOO5G7pnUWou7/K+7mkhLPcixSDFnmVBUQhBFesqqgCgxUzW1OTJ+mJbIbGkDboLPv8s5qVJGgEsDrphOUM6XIGgEpsyaVYELYcVERgQKymwSeus0h4EwaIEBVU1AzE/xw59RD8HKKvqqeW+PNEgqhFrBxM7KbMFkmBFMcfKNxHsw5LIpZhHKYIDRmjE2jvVCdbFeTc36bDLRTXZ9xRopSCOOH0JwsgvcTyn36RWQMqJl3oTdqY2O911LD5WIHOyecw5AQ+h6LhnZi7MDYOgw+PjaMSqSvv3msB/HWsl2jRMwQmv7uDOtNBwCo/gRScrvpFcUUssKhePfasjdinxSqos3AwbSXremBuE9pDTJzECgp9M3SxZvrjaK2zwFQjRCljW6HrkJCdZa29xGAHRNVK4Cj7cJ67ooO8OANTut8vjjkvJXWc7Izi8DjnDbdkzrNp83thCWhteElV2lNVRUI1fX6u6lZCmoytIJUKeeVzKTb3UI5e5P+0vXVNCiGYNLY1owHaCY4oHILbYCuBR6kF2MAmNqPtO0KQF+pjZ6ojfV17SlZ31lBZe4YAEAqjv1njwIAhotT+MVT+6O0xWwQOCCLmTc0hyopMHNNxkl79gLoTBWwznhelwa0fblpbGGlwaa1bxUjbaOlQPePEORsr4pBtQIIY1eSFYmgntnFAJl5AREg2tza4JhkzhOWAUGTOztekez8Ex1mn4UnDFCwGhxLYcc6bOjvonE3HNk1SBjhlfmQJW7uiJbzuDUlgU8AACAASURBVFaRHo9sgyOdcq69Z0MgzGyleXstdxosGT8Fa1DKy2Ij4JKKcWpqddvNlHDadFIjSKPNQAKp0UZZojd5dn4mzN2LgKiUWO/uDoNdrFUkHFsRc81CZPssAgrbA/uhMye1fUUhuY0iMSB1fgIggIpZ35HrkwqZjYzZdJRTx1hEClExtYzL3N5JPLNTMyfUFIDZhMlIQfYBYly3q7yZ2/HY9Ns5YBJIe/S50WAbYa8eGJlyu2mkgMAHOqiU9XetdoiC2VzOnj2BjhS2r/rKLYQmEmBLo4Y4FRio6fVsx0QVyjg4lWsdVIv6epNxAZUoRirNekMMgXku9VQVqCi7VsiStPZc1hFW2FIjAGodqFEtwEWLgxWVtbHuzfA2VQ8PDw8PjxnCPqmpZpAl6rKhgrS9BwDYlEBzWKtYcjxCeYPxdCsTZERI+oz3b8wwtbUKAIj620g6AXhGIbWYo9Sy++Twsgs0xfboZdNrbmZ45NMfw+Fn6/N4ytCe52Iw+FgIPlurnE+fdr79zQHXrEDSJ+2uj8oS9aNMCEasaa18rGDe5pHRcDxmmpYz267yFoagoe/7q1XndPdjTksjZuiyjMZUQDiZ0dXueiLWWlBzjtEsBxMM9WhqrJ0GeHp80NJL5d4W6i29XW9OFdDq0dTVk0/PAWtza/tOas7TNZhiUIkAGY9r2SMtRUgdgdDYxDNbeblH9yPnZHfx9akSZEeAGdorkRxhyXj/jhYRTXBr2wymhLVhMuns20wyBE3WRRdmc0JG2rM8+xw0OWJDrWvbujMdEIfT8lvMMg1Mabt1qqcgiDlqlaXd4SU8yd276DRfHhsKNdPCA7L35ilQGHPzOO512nTQdIxEWtZzPAv3ApydEuiON828jQFNGasg3y5H/1HJebCuPU3Xzx25wRSmD8nanHmf1lKzMCmlGOKmebjUsU8ICLwhuswnSc1oWP2J1mgns7hsxwaAGareMAeyrE0LgGa9Mg2WxdxSwHYwDGbPnsC86gQaidbMHn96LsKqpkxFQ4Bl9w2oa61ozVaOdk6AZE6CyHj59teamFPRVM/DG+di4XzN4Ehi2FmvWDMJADSbxmYbSLRid4PxZgnze3Vkw0RaxIGD27G5oSmMMJR4z8v+GwDw4+0HYrzlYmuDKEaqdB+Mba5iotfE8EogGhPWREQcVlunUCE1IXoIFNLUxVunVanjhnPe/Hsr9kmhmhYJvEQA0/YLACiOc1AucDvuV+AyM3YBSa+jflRIIBOGIouwL76UHOGGgr0PT2GpvtTNRwDaZiUNHbb/Z6/Fmn86G7vCyz+6Er/7nBa6h567EpU1oQ02T0sEbChN+81T55yFxdeugDRxsWE1BpkXgK8v6LCFjKkMCOGYiWeLnJOIrEmoonPIeOhzZ+HoD6zous/ildfqawoOZZw4WKqFg7Uz5eyE4RSQ6CgFqABoLJY6AQeA2mADVRNCs37LAKq1Ntod3ZiBnga27dAvOjUCNEzwO8oK4QS3DitBy1F7YNoWKQf0ADAGu8jxpkCaGLs3I8R9MaoVLaXqUyUXBlEPAU4g09+iIJFM6d2IaGtnm2jC2PWqLnEGiDmhCh2zKbJ4aIJtY2ZvTnqyEBVYJykew9phSZhNQpZoIbd5CI0tk3I2Vkv3pvq7vCDN214zZzJV0As5M05vPHa2PB7r8I6M7mTOdAmWAg9f3b0hPOBqPUdExznwMDL0bc52nAkOEub5M9o1Z0MMqzGefOcFXdfPkmLwgoQyL6s0zktxR39WCQeSbPdG1v7MOEFV9bwGtF+B3YDsiCCHYks98w63/ZY5imXvAgD7/vcdMI6xx0z8abYRMSYBHrtNajKf4zdrF4KMDZQ3BGintuOLBPb5FWMmfj7rZJdEgwK9ZkmzLm1/bAjbxKDttw31IQBA1NeBktyGzUWVNkpGWDXjEOVCDGkEteCEVqoHo6fYwa8eXmzXg2R2gpt+tMw+c89ibWMIhEJBOOHHEo50u36fWEU7T4o5eoNcCBTaTT3pwkJqHeKSsSLkprKN+pH9qY6dT3MTbC+Fp389PDw8PDxmCPukpkoFBSoosA63ThPE9K4507hUTwrV0Ds43uZdlBfgMnRVnwhttqW0FABRzsM1dJrqNIqXYJ082LM2ZwddtgJDv9W703LObl/dpDC5yGmBPLfDBZyWECyewrwjpjBQbNrvHv7l/uY3DEmPsrtuEkBaySgZgjDB2U995OM47JyViOpOc3jwS89ytMpc9hWsV4roMB16YLUxZh1WZLHbwxQECJNMYUHvBLg5UXUE6lTSiRgAbJdc794BiN4YKjZUU6o9X1mOFnWhGqTbZzQWVnLjKWJms2URY0gnItSNSiHroYtSUugO08hpmapEkEKBTAxG0GIIJg3VXCLtQAStzWkHKvO7yNGnLNXXy0JUrLeueZYs2YAKAJRz1HbbaeQq6qaKoXLetgqIGt2OS1l6vTzFyDtM085ZHze4S85AJmzEXCOpEsJ6LlvUs5C1P6k5JyZIrflaj9/Q3T+pmeQG2TQLFHqGtKNMKcrn9tFY9z5NAx/wzcvBzdwplmK0WxFUlkChJZzTXkg2oUmlp4VGvQiSgf0u0/55ClT7WmiakCkqM+fNrRhYwixVCfdKYHS0akPBVFEBgYIoay0uHS24jGrEQB0Bbrzuw7ob33QwhTCOSmo8AqRme7L+ce8qATGHMpp37+JxHDioE8088JslqC3QVHAqOXiUWlp3tFVGo6MfNFUcExNlKPNuFSoxeFUvWJ0kQG1uHQ9/4BIAwDF3/jPkbH3exMPOHJMqPciNqaLpKwlhTCuyrRPqUFsPcG2wbvuqVS8AmbdypKBCBZYly0g4gkBCBnu/prpPClUmdYhD0HD2EGIAlwxJnx5UHkmoXB7ZTJhm9K+Y0N91Bqh7EWZkM9PIsrNT5XHEP61EMSb85gu79gae9aBEp6av/8uvuXP+6+u7pogB4KBLVwKGCVaKYV51As/UtXfheLPUFTOoIkLUyGxmzGZeWvSFa6xdcOSGq8H343j4o9PvOfLFq8ESbmP6WD7Gk/RzZwuqLLqsODz3viQ1TaFnmVik4thpsrcg5aCUY94inf5xolmy1J4IFOIBYw8VBGwtW1pXhbDhEzxmSKsS3Hj/qo5w4ySB1NjSUJRAR4C26cWUM0D1Jtmj6PPN4wmhrHChotSZZSaz0AE4ejnJx2RqwaeycJIwJ+RCQ41bm3aOqi25eSVivbDnz7P0bMp02FNGe4cAMnulAOKaE/BB091bFlwcNgkAglDcbGIZy2Tz7/K2pnLbZuNFuXCYZ28GD7hqhaXzKSJQnFHUuo2Z929SVV02VpYwS/vygrTCdF51ArsD4wrS5ExuNQuQ9dCGrzECaFDPK0q43QFP7aiAFZyvAc95WLOEYWpn2XqgS8l1Wj1ob1viZLOoocOtRyvFwuYFhxF6bFS/iDSUIDECcbJeRlBJkGZjul8TZNqfxgIqE5yBQjQe2HHiuXAsEtrFOhjTgzNBDBsLJmxmuIXJzTr8RTQ4+PwWfv+YTkjfP3cSoRFWkztLKJZjm/O6UozRNDbWh998SVcf//cbrnAfTpg+BktuvlS3a7QAmVHjimnvaLN2Tj46aOdmlADxsInb3x4iGUxBrSxRMtCZLEC1CHs7PP3r4eHh4eExQ9gnNdUMyWCK0Oz6mASSsqOhVFtYaoJJuOMBaYrYaqcMKgsuDwmiw/HEeVrzW3rRyq6Yv0PPNV68HIh7dx+P9dP/+PiLep7MKeKJt38KgNY8AaC4WYBn1WfKOoNPbByvonGORZ/X5/HYeTiyhIECst+Fk+65ys8EaM9SLnlAQF0aFkuZS/ofElSUqVim+g0AhIRKXwvcxLet2T4AbmgtXk0QFVJUTSWNYk9qsyF1mqGlhdlUgKRKVjsCYDNkqUiPkYqNY0hRApPGsaVArjKQ0WAttQcX4yhCBRkLkNmFp3DVZ1hL6H7N/DUILpFIjt5UYeZIZK4tGURGg5p5xPLUbY7WtfGUJqeuuyZZrVUWADByWYOU00BVSNrz2MSYEgfag047zSoXgYBwjNtnCbe7uZk52Nn41pgh1DnWkdR0xiXdSCBqMCS1rP1kTR8qNJq1rbLDunIjM8khjYe1Srn1Wt2/MoqTV/+deSyGm//3l+xvSsUEk6OmukoktTaXy6ls41lTp1WCAL4tcsxG5BwOU5P8xVYtagukvaZDst/niiQwo8XymEEO6PPEWAAVuFjp/uE6xrh2yw4YoVZtYdDEWD/1zDAi40k+f9Y4Npn5rYoS8QBzNEmoPZYB4zAVuLWHFMOm9dpRSUwKzH1AH+enbUMrCUDG+a6n2MH6TYPmS0K7GUFNGVPIHGUdn14o1DPG0UoypAXDEph1I5vT2lPa/D0nseOSzpH675zzF58MgPbeL5L2/id4MTBZZnhLuEwuMGWzzASg/WIX6M9gBScjoOdxgSQLYRAunEFCL1wHflovNE9c3E3vZhU3KCDrhflCsOSqFXjyXHfNgy5b4WjdIkGVXTjCgd++FBQYm4exXwKA7EvBWsLa3eJ+6WxkBHs9WVQQLW5Lfj1yhbMJt4eVTpxRdAtjeZ2hw/tJX7uTXcfZqcGdDVU2AzR2lG3yBAoICw/fBADYPN6DMJAYa+sVfVZlChWTEWbW8E48vmaO/k0theTC0c8KtuxZOMlAggOztL1IbSx3eW9mdCwCBV4PHKU5r23DbGTKNY1lstVQS7hMRoaqtWW+Cu45ibmMOzITSpGzOWcmB2WSM1iamcNStyx1Alb/3v3N01x2JYKmNzNat5i32Wrv08zjmoLuMJosIX00wUABXGatwFG7QVs/jy2EkOpk/ID25qaa/ru0ndAecGYS4ty2WRZ1DtCMUlaR6grfoUAh6tc3J4ItPTaelDGVOm96AHjZrct1mwPCnHk6IciWDQMAI5viESFcMYKYAXFmp8jodjdOtvM5gYfSFUVIXXt5oKDaAVgho3wdpUwBEJb13JwzfxQb1gxbITK2pQdF81yze+tIFMeT62br9ldiLBzU3rSpyqcyY1i0ZCt2TOlBa2ysQRmhXtwSIC2T2wzF3G68ZFVhx1tNZqQ0wES9jJoRqqniqPTq96DViqASjoMP3mj6m2EyLti+TWJXBerZdHCGkRuv0n+YecA63L1bBEQ7hQ05pEhpOwT0RiWrvMRDpb22cwUIFANUftLvpdg3haqBKiqQ8TJgxsklW0zCQoJOJ1MN3GKkONBYQHb3L3LZYUTMbJjCrvDY8t3Ho+4Oh35ypRXGIhd3BugFPWtHWlGYtf+o/S7dXEZxzO2sZU4AstgtwuGEy9gC7nbBPGZQEaGtvfT1RsHcPko4WJrXiIC4zyy6mVDIafKhcXqJh6XNsRtWEsgtJWdnXdDqSksouMLLh7SQTYnjcZNLdAvVMG+Brh6zaeNAt4apnAMPSAuwdG0l6wK7CSAGm9eVt7jOimN+l7YDG5eKeqjtc1k4Um4RY0o/d5ZNh0mT/9aMi7U3cj1G2aKT9FB3+r5c/Ckot7aSs1VneanzjkE8W9S51lKz8dXhUkaAt5n2E8gyNpXIXl+0OcKGaweRE9z5zFnRBKAKQGFH5jjj+iqf1rI9pNuQOUyJDsttMljuwfQzZ9VcmGJA4OyjxVKMpnGqUWAoCvdCLf7m5SiauVoIU5RNmj/eEF0aHEiHTdn+KGdGZobCqEBsQpi4AlTgNs4icFobSWZt6TQR6fehL5vUcNVV+hP0msxF+9XG0JgfYmy9zgTGy6nNTrS9XkGt1LEsyILBcXDT4PFmCXJSP3PQE0MqbrMXpbMFSsZuOtlbRvhYCWlWLk0xm4mJCbLzdHyyjEIhwcSYnvvNDf0YeMU2ANr5q9mJsNHk/q0U4q641flD4zY70mG3XwhlrlkuxBivZzvEYjfDAv0eAXruhXWma/QCUMRtvWMIN8FV3gYLaFt3UaKr8O5eCm9T9fDw8PDwmCHsk5oqhdoOx9vcurwz1R1ekrbyfJvzthRtnUUmNNVbZMHZ0kjoczNv2hlrL3f3zuPJT7r7LLlyhfXiG7nhaix82Xb89PVX7faai67XiRtUCEeHCThPvUmOtEg28Fz2Krv7D+raftYZdCEGlCXQz6qfZBpvBzYUhHU4YKjIpKWLxMv5ekdeKcV4fL2mxoJtEWJB+MlDOqg+rRDEPB0eNLdnEs3EJGAoSqimsEnQVUhWO2pXdUIB3s48J10/ZhlyAD0XIChfPEZnnYLx+lWw9VXRduPOUh0qZMOnCujyaM0ULJZqSjwrTCCLjvoUbWYod3Nflv3HzMcsVCMg8JR15d21CHQYTpbZSScIyO6lqeBs81/axi3rEU3mwnzCzMSRu665RtJjqGKj1bZnKZC1J8IWnY/7CDxFlxZuKxUBIM7AMpszuetDAawjIE3oUywkwly4z7yS9gB+emoI/T1N1E1moFYcYrKpH0DMaSGZiiwLsu7vdu+TsHjltTlq3tXz5IohCUOwLDF9QK4YA0HXeM282Bu5gW4LjBuN8JliHzpJiHCoZb/OapOmqcDWrb3oH9QG6f5CE5GhaTbXazZ3dhoEmKpGkEZDjNsBYmNnVClHmGcsCi6UhXMFaUKKmCDM65vEqEn4MFUs2HCYRAqUC7FNmr91U58N5yEFbOj027aTZIiK+hqNdmS1VijdP9YeysiqZ0wZu322NAg3H3Wif3NxQUCobJUn4gQmnuVOvpdinxSqosnBFZ9GqQFuUaB6aIvrRk1Hr4X17kUMcDGCJLXtLLOp5qu56GT15toRYe3fd6f6y+Ogy1bYa/7hM9Mp4yxVG4sZxJBJqD2PW9rmg395L+7ccshur7//ddfaRP/5cl1BnVkBKIuEp3L220XXX+vsskUg5mQpQV3hh2xfiDZs1icRd2fxCSL94sSNEHMP2m6vv3lbH9ikSQguqCs8BoMdpCakpplEmFPRJadOnPcw7h9dgkc3aBsr31SENPYmFuswjSwpetjfQVLXDWEJhzKUFGvpZPfCvNxsR2jpUxUSQAzlp027uBOA3BRjsJRp6ASsFtrumYujzPYHk86e3RkgR/1CmxiyTYFousT72W8zG58sUo5q1n2VFZoXTY601yy0bZ02Lyt4n42H/p1zQpIFMpsO87kiUdyqbxZXtMNRRvWLJneVU5izO4KQT+aEtOQ2ZCxl4B3mwm0CgpjKmSZKCus+pAXhK3/wCfz8uCvxbJy8+u+wY2Ovtck9/jcX2u8OuHoF1n38LBz6yZX22EG3aHtgpxG5hPoAaICBbzUZfuqsK9SMuCvPmPdPACedwalu5gGDLk8FHb4ime6EHVMVHDS0Dcp05KapHvQX9aRIqxyTlSIOHtA07MZGLyZM6k0iHf6V9UecBrZyzOSOijVVBE2OuE+5IHnG7EaCCdjYUzQEnmzOtukBeZtjXOjB5oMddDqBpdsBQO00Dl8d7ahIFT15h4brmDCUb29vC5212guNRQTW5l3mCes3QQAJ5d7dnEkJcBtsWSKwDs9V2WJQzQCqtfeLJE//enh4eHh4zBD2/m3BiwHpf0Ezpz0aepAyB4qStHkqZYlQ2ur2H0p0Z6TJe16qMOfOz3IG/JBAZje39kO711IBvbO3BZF3AdFjEiY0QuebITmmmlkCA4VFtZ1Wo43620g3mULeTQbqdYWxkaNq49nSZnyhAmH/zzmqTHCn7TCpd7XW6aWji00DAGvr7zNHneZcBQxrbVoIhf2GtMfmM7wPq3MayaLPX2NrZfKtYZcDErYXEBiabkNrGD1L9S7+yeYsLK7uwJqypokf/fh5u+2zJZ9ZAcw2uVD7mujUTV91OFjTJaYQbWbDjZhkiMZyOWBzlGxa1rQ0cwyhTbKgT9b/aw8TRNtRt0mP0zK1h7KjjRnlEmUkTjPlknUVe4/GneYbL4jBpgJXUisk9DzmAupl0Y2FiIE0cO23oWDC1BXNlJcg50EsdRalvOYmTPGE1mzqSgChQlhPVZ4y5Dl1WVOOTpUMcraew2vf+0ks+dZlOOx2rXnO6+nglT/QWZN+ftyV+LcnjtXtpSOx7sxz8ab7PwIAOPOB9+JHTx4MAHj647qYxO9zrE5ek130f3RYGFUk2JSwhbJRdt7uUAAYEM7XPHenEYFP6JdcFZWOBMjq20apLZauSmSzNzU3VdHoncBfDK7T4yRSrBnXoSyp5Dhy9kaMdvSLUgoSPDOlqVYiZk0MPJJobK2gUTA0Qm5Kqcg5uQEAawqbZCEOM282PW8pBMrD+lmk5DaSgTEgilKQYYwaSRFiUL+fT52s+zFbN3Y+PmA1y639BbBsWgV6/chMKMFUrjB7QVMWlqmJ3bpJAlCWCmBai83yModKa91i9+ve3oJ9U6hmyE3Q9mxTd9NQH9QIrEAMJ7mlyrKMMtZRleVCIhLg6XO7MxDt/zljuyyoLhpkT3i2l/CBV2g6mUkgHumAG4EebQmQ9GVlZKSNG/uXbf8f+FDH1lpMY1eZIy0ZG0dmgwuVFaQsEVC1LONJAFkmJIZKLG5xU4UZu6mjJR2NqaLu/hFtZouIF0Ymsa2u44+yl/zQ71wEAOCDBbuJSWsC0U5hbaBcMrT304swnwzw+0cWAgB+L0iHOfDpHTvyxaux7oMfxwHXmL5jgNipO67DYNMXyppEOCacTbJEdnHiHZMRyi5Wzu7IlPH4zUJI8lVY4KrvMAmgSPb6PHEmBvBuD24mWc5L200s3jHJ8c29O/2EdDgrsqtDikqbnW+ASwQPBG2CMvZuVYD1BGaK2TAZWTEpCrN2JbnKK0pvsDJBCuYEbGGnsLSwLjTuMjFJTjkPcG2vyy+Ya9/7Sft3Pmn+Ybdf2BXK8eDUCABgVmEKf/tfZyBVev58+RX/CrwCzwvFbWYTsEBpCjqzfLQ4lKFdeV0n188L4+zdhYKuoJKFAcUc3Agi2Q4g+o0EWV/C9kYF/77JNSwzd1RKHUzERWxr6PYXgxTceJnH9cj1DYM2O2VdlXC7GaEQgGKOOocW6oDe6KghkwmsLcAL0tYFTsaLljYvVGIwRmg9rQtUCAKEebde/t1PYXxbDcIUFX/67LNw8MWaUk9TZt8zMSUgcnWdZZlsqs2gpcsy2GiJAFZRAYd9x7OKPtY+XUoRliRU5qCyF2OfFKpZmkJZJFvhpDTcRJoIpNu1nSNocp1jFUYTy4RqYLwssiIYbUCYYPgubxeDTONi0hmcFn3+GlN4Wh94+uzdOzaN3HA1YOK2i1sChOsLbiEPgDhLncacbVeVCGq0gCOPfAoA8Ou1C91izQk8FwbE64HVilVJ2Z1lWlUY+C3HqFkfOkMSgckLHNQZgpbrEybdAs06unJKJmQeueIsZ2NOBJKNeqfet2QnDrltOXrLWut87cIncMfjh+p7FxUo4HZhSYuEdab01/NF3+9NWkPzrI9/6mNdi2S2MIV1XTrOJV3IFQqPGXhOwHAwG2+aaams7oRqY4Ebi8A4NGUxpSpL8xc4DRTIipFn/ejCaIqjhKTmximpAp2BbHFVtog1oMNdspy+PKacNs3AWo5FAJx9G5xs0gLEHBBkbXeiwV1aTgawUVdeT5YVmGEU2rOZ1UyFCU2y852YnWc80f02PFs7HW3f3Ivd4dmxkSuP+tZuz32+sDZgDgRjIh8SitKIdoDrjPUgaDKbPxsEIOsrQaCWsP2DorRJSxQnpJkdsD9Fo1XQKTEBQDEUa3pgokDiD9tmI471uWEokRg/ARYpcNNvQZii0wyc3VTBpkfsH5zSVZRMfChNRhD9WrhTu4hovW5wZ04KxmBju1GKbXiNTDlkGkGaxBbrTt/ze9UZMk5M3G0us/Vq/8/q92nNR8/GAVeZd3wg1etaxviJHHPX5i5BR1lCVNwOk3OFpB1AdfZ+keRtqh4eHh4eHjOEvX9b8CLAO4BgQGe2tB6+7UYEsbmAMBd7nNmLZMFllOExAOZsG2mJWRokLRGWXLXC2TkDAhNux++qXOh6m6kJQj/44pVd1WyWXLXChiPUxgPUD0xtu1FwlVhk0aUwU1VpbUUUKhy8ZBMGC9qmUiglaPea/dNEqCmjzLzVK20dSBQkMGVqQXY4OgPM2ouCKdblfSpLcJRyxSXD0J6kDL+9TlPYiz5/DWjIsAGcwGbpE3fuqIEJQttUs/jetsNBzYxb1UXgs4w/tjYrdEhE1ve7qkG76F9MysVFHIuuvxYF5faN2XgWN0QucUVEuiaq9cgFmEm3pISmNDMbKFOAyNkMgiZDZHK+xz2OCo7GmaWJgzbQGnZjz9vcMgM8ZkgqjiYN6wyPXaT77Yh/XGntUrIEdGZJcKOVYHPRpmPUGXYIZJIYhFPu3iSAOGQ2TOzxC/bAiKy6KseqOApZBbq2cKZtBH0xhvp15ZFtvMdlK2oIXZHHFqhQkD2m/cZ+lqUAPGj/Lbttx/8ErN1Usi6P9jwOv/1CTG6p2RAb0ebWFMIiCWoENqUhGGz4iogkZK4PZCpw+IHPAAA2TvZgbk331abJHqQpRxjqawqhrFcyTURgZmyTTtCVug8MKJpi5hOTZUSFFJ3EecmnJmkEysqmA2WRAucKgaGXJ6aKEIaGlpKjXOkgmejOVLU7rP2H3ft/ZO/f/tddC9Xn+gaBss9QWB/ZesEqQK4PCWEo0Z40VYEigBQD7YLt29uwTwrVpFdBlhRYUVpDf2FdUaddy0qHldzfSVVZwZlVjbCp5kKyKe8yc0BW0om3GWTFeXKs+4AOGxj54tVgSoB1nMDNJxLJpyI85LyVKJjwhkcv33NGppEvmVCbosSa7YNYXNMZljgnwJSxopouBkwTxo1+Sli6sNsLgtCa7arNBE23WL+gzFA5LiQMJOSj2i3/6fPPwsiNV4GyHMGSWQcstbMAVFOkZvEQHYbF15ri1zHrdhJ7FgKTclE0dWWU9lwX3JllVOI7gMGn9N87D2NIy05wFkcZgFPtXAAAIABJREFU0pKh1zh1ORKFTSDNYjtrgFSEJAvJCGHtSmGdEPeZDYc5X+Qc1lTRUGNkKG5zjdb8FC+7wOSHLjiauNNPKM2ZQmuztsdFU7wr7SFPYAW1CFwojiITR202Tft/7lqs2UXVIQAIxgLruJT0SiQDbgMoco4oKYrYakwkMEWzAePolTKoUu6a1axAPEGmHB0TxhEKidf8SC/WnBGGS1P2N2snBrqrozxP/O1/nYGBSFO5nzvq37u+ixcZbjzZPTE3ub0KhMpmJZJF52uA4RRUVDb3L4s5eDWjayVSQ/ey3gSHLNiMxVVdXWnLVA3rx3VO3+FqA/v1jmO8ozsovLwfa08omnsRUruhNI5G2Qa8oBCYCjNRmCKV3Nppk1ZgNwFUkSDzsrF6gKQtUDdCNe0E4Fl4YCFF66keBOnMCa+uzIKcEJYTG76W9DhfEtFikKaNFCm0p1wuAJVyvf7Q3i9UPf3r4eHh4eExQ9gnNVX0JkBJoFTtoDWld//hFMwuUZ+S1xxDcOuJmiWMyJIn5DOGWCeQbJMfkvUgzlOV6z44PeNLVu3j2dpEz1qF5vDu9z6Z8w0J0rU0oXOYvn7xY9gRa+eEI2Zvwi/bI7ZpsiOsViVyNSUVI/Aky3PLdNIC80w6p+xum9GFpReuRGfQhFZI02EA4t/044kLc1ouJxu8DskgzM6aNTlUGiKYqzWPeKJg+zTYGtgqLwdcvQJPffwsLL1Ia3cqJLdLNEkt8tl1QpM9qzNAePRy3ceLvnANKuuFSybPnQMWTxlkQJaVkAVmcxxLkzdamIQPomMfE0mN5TIcGc/djLIfSMDH9UBVnuH43bUfw+KVegxZ4kJl0gohnqc1dxEptDfUUN5ikm0EOi+u/pEuXp7Rv51BQmGn6e8aQRYd9bw7LfWYO/8Z6XAv0MpclAFmEgcEExzJUApmqjkVRp2ziWZwzDxqMnQGlfUypZhDGA0rbodgOfo3YMpmE2KMcPP//pLVXJ+tpZ7636cDAJ5p9GFRdac9fuas+/Dz5oEAgO9sejkm23Pcj47qfr5CWb/MnS3lXT6/RcJdov+IQJmrake4EBDoMS0U9TXbrQgs0zJThofXzMf33ns9AF3UImkZj/NOiJfN2YrRhm7D1GkcPDRFBOqhDdGhQFfOyT4jZZjqVF0bc47ueefHqBrbKk+dVohgcwHYZirkBC772gFXr4AcTJ7TQen5wIYqlajLezlpuMQ54SRHZ5YZ646jf8OS0WbzdK8g56C1F2OfFKqFp4oQhSLSYgEw3r+dQR1PmC2uzEVWgLirKkMBgXJhETqRt1vgWMps/JaIXQhDHkuuXIEnP9Ft23ni/F3bepqzuK0ysiuoWmZ4cy/97IFJrG0MYOuUplpLYYID5+pMLn9YrxefzMs0GUzBbKozgpjUlFR5C0Onz1E7f7jkY7oQ+nPg4ItXIq2RXnyhacks+ftjF06njW2R4lBBmpCgsMFQewTYYbLA1NYIa7NtziMbO8sTLcBnP6AXuPEDQpuGTxZhC4gDwOJrV0AOZ7XNCAd883IAQDBI6EyVbTUe0XaZkZIaabt3FhGQCCuwiAsdA5ptoHjOqzd0YTKItTDOBFu4NXIF3Au6v1jF3Lvh4pPTPmnpRr410lWBMhNELoYVUnsPZ/ZXHgPtITfnZEV28VGZ8EqkwOY/zAIAqFI/IAjcVBBSzQDZap1WdbhN2mMWRh1sqM/LpbiMD2whjCTi0aLt4yxrz+DAFOrNAiomMbwCs5WAeqI23vWLM/GKQW2cftP9H8HOlh7Enx93JV7RsxYAsLRSxH9uOgxlUw5w1fZXY21du8Vv2N6vq0wVdPsX/dsVWPuef7bP/NjbXJjM7sCLKVQsbL+K8cC+x7LCgN6kKzNTFq6iUmZt3bIZAG2386Q1FSDL8FWOsaVRsxuLvoEGCiaN4HhUQmfM1Yhk7VxsZy7tJOtwBA1nI6daCmZSh0rJoTKFYGsBPHG0f14Qq0hnh5oJ2JKLoULPoPbfaDSKkBOhLfDenucWSyqRrVKVtAMteM27zIjp0KGXQJyqp389PDw8PDxmCPukpppHvq5oaWOQy9GKLgeezCOUJZpysYHtCbNetzz7Lpelae1HplNu7FkbxQOuWoHAaC+Z92eGuAb84dLdOwaxrICxZFAmz+2W0V5s4zXXyKp2DgEATOlyZlkGIdXm1uOO150W0pyjy3hllzjwihU2oHtPaM9NDQVoyneFZLXA6W0PUNymO7l9UNsl769KBK3Qeh4uvnZFV5IFGzdqqOtsnKJJQnOu/vuJ887CQZetcEW0i7D1MINiAmX4WLWtiNK4ixWNe135MhUAUM7LWUWErB6Y6OhxzOKcoZxmIMtkY0OzzFM2gT8jV5tX6vhN6/RWkbZoQTguEJmSeZ0+Aleu2APB5fCVRa3IZHSzLkmXSyQgCMwkTH/Nj85Bb6SpmFaOXk8TAdUWVouCyFGfJQVRkHj6fY4utLRf0WkWQhAKhQTDi7XTUSxzGhsxHDZ3M3oN3bm+0Q9hXpKxThmlIEHbZLaIpcBQSWs95/32/0dvoOeRJI45lUk8ul0XXXjt8OM2x24l7OCRLXPQHDNOQNUXnkBANQOwhIP3Z7GdEsJoktLQ1yVTNzVJBOKmeRk6AlJ3I4JJAVlWzmFwbmqdiub01aGIITW0eij+L3vvHWbXVd4L/9ba/fTpozozKpYrxgU7AS65DoH4I/kuJKQQbkLCJYRcQuLYNFeKjausUQKE3gNcQghJboAQSgiQQAIugJskq8yoT585dde1vj/etdc+ZzQjyUbmw+i8z6NHM3PO2WfXtdb7vr+SaNH8KDB1NmcuGuAR2SQCStRBAdvSVo3Wz+0RkMocPLWOAwDbB+xa1oLY8/Zs/BCVCJP/c3XlsScU6tpX+usaaVwNDMBLEKt70C4FEMpOL26a5KEKQijbbohAPXiSCxqv2NM/Uz0rJ9VUdcZoZUhSYQOtkQi2QtqmNy6g3DjScmmZnOzTay+cTFou9cPUlJrV6gDL5hirzlbtV55sQgXaSjyCESUGpF6TSK57LAYX2L+fBiMYEvaskS0eBINUA1zSF0Goz5jHbDU4t5HQ1d2yacc4mMwGbxazbKFQAdxCAEOZInMm0RymLzvnc7fCUyW6KDHABywYquRrHHMylaCI45G7rsW2t1G52Q0Z/AFFF2LQIhTgpNxy+PnpwiJb0Gy5ZxxJQWphb2lLXZqLIq4h/94MecNKvZhiuvxr1RnCMs8I6jEyJGNA90XE0gmYXFoAgC9lF1g4Sg5Qtwug35d4kmgGdiqcwTtEI9KWg3CIwpSWvYUlwXRNECQuobYhrWwSBQBuChQKNJn5sYlvPv/d+rVN/+cO2l5ggJkSXDm0JO2+sfzEgc5Sxu9hzdZ0LJ4XiCIT04oilfdCWKqnWvFaqEcOBl2acIuWDz+h9+VMH0JyHK4RSlZIhrLr6++aDkv673N+XpeQ3/ed/64XScwUEKGRYRui02z+t0Wq8JMoSplZjPRkAMHADIlQCRPYTowwdVJgEixJjSUkSSGq82haCTwlwBALjrwVwrHp4jd9G3G6n3MOoEQ0GGhRxtocYFajtWz+zO1YM0Syn1OLRcSK2hO3OKw6y7yK22Ly968/8Y+nGSldTXoJDDeGW1Hqa2aiF+2l3gbCKJtWOBcoFmnVMSOKEMo0A00DoWFlMoURp3Ky/8Sv3U9bnJWTauJIko6zobMBq2aCR5nrSHs2ySMg7MkmEGnJDqut9AHgEdFONMfPknrVqvlbACZv6AQJ7Lr12iyjWhbbbt2pe5Hb3r4TUUlg/7VZ9tv+c6rZKQBAMC0PVnQCjF5A6kr3f3sbeMQQqR6ZtKUGFSDkGWd12cQvTeiBO6WmQPVHMGvDVPZrsUVGz5euJa7e4XoFXA3KGyqLmjv7wPH1GBmcx+ESZRdxJYGhwDBmg2PzveOw1GQWlqVWgGFNI5MKlHQt0wWQZMgmgwRgJrQtXMwEeK/STPYN2PNKzUaoiU4lKHFBakqNMIlD2gFeU4ceu5TtW4oNwmKm7x0gW1AZLQBOBijhYVsmKemfezyzUkuBUOlKH8gWeIbO1jNlJ3AgKSXgOTVTxxymm1JZiA8ZKBWfKO4csPb/DmUsV3/zGuw9PgBD3Z9GUeDx37wFgFL/GsxQexf841uxsZ+4l85QjD3HqC+bgsyKeeovcgZtfr1Y8zA2MI/7pkle0jVjeCZtM+dEeGRmGP0Fui+eUTmCPuUzNxfl4SkPvUga2H9wMDOQB8AWaPssAVBKdOZ0YFkmNvIxpTGdTlRtYJi0j8x7gaSWlWJi39SLCcYlkpDrfmtjKa+t2hIn0z82QgbDi7RFWqXURK9HE8pS4GJithdDFTp3Ld/Si96kJ9SUt6iUAK4g9SFAA9D0sXzoHq1WNDRUx2yNSiKP/+YtGvC2/9rXU6/+DDupSXV/uMUAOTdAUS1wLCNBPaSbf3PvLK7smcCkT/3u3UuDODRLCybGAJ7SkmKlLuZm9/Hka96IarWK8rWn7oH/NEe3p9qNbnSjG93oxhmKszJTjUsSwiU/UF2iVcwPrZErMtcRYWdC6lJRNXJHlIlwWWZiBIzQo+nn4hyZEwPkzyhVqXDkA9vBmzxTnzEkWA+9tvmecSrbqAX542/JUMHC6izDAdCC8cKUQEq8j0jLNS3nRYmBiSVycpGWBF9iuoQqfUDYbWhDtXoWpnJX0bXsjFJjz3FIA4iK6rBF5rfJl0zIfIh6RPVUy0gQqf7aTDMPW9U+E8FxeK4Cto1SPdayIGJFP+gJgYgjcTN6gzmnhCB8Bncuuxa735Jl6ltvH9fZhDRVVSF9kVFZHCBRfq59Y+lv6f63n2dp0DlIkbtmKyvT8wiw6pmSUWJn5ydxskw1FZAwUvEENytJ85iKuJrSZGYZbVIQWhzECBlY1NbHjzMzA2FKKp0pT0xnTbMD7fq8r70RC026MRpNR6OeK6Um7n8R/ewaES5cdxTHGlRq/a9fvkt/XuYT3X8GgEde/HZc9iVyM/GsCOUilYJLqmSbViViwXWmalkJjlZLukQ42juHXfPUjmBMYnPvLBYCagP829EtOK+PkOobvXkUFRz/M/svw9Dwot6mKDE88uK30/b+6l4YVUN76Y5+4i5IlZVahRB8kbYtiklHlspi1qHTa5ZCfb9YVqIRyoFvQQYG2Kzy4x0IYUyn1YXMg1gaQCwZoYABzMmCPh9RwmHbMaaXCuq49SMO2TI7aHbnvCOrWun2Tvp73YDooU9Oz5S0eE26LwBw7i07sfsUbaMnE5OvfhMAYOxTd6Ccb0GoSlIrsnQ/3k8s/M2BS9EKlA7x8ZzeLwiGAyfROf9ZibNyUmUx/TPCDOCROJKoK2kbpa0PBmQSdDwiHmNqD9YOWtq7ggTa6LupD4FilPULDAHRRpljAdd9NrPJOsA47fH4jSvckOluDAW6hLR24xzma3mEPv3+rV/art9+wb/uhN8vibIBAlekFA+ylkopIzTR2Ko/GBcFvKO0//YSUN0itKxg0heBq5JULh/gvIEp3TOba+S0M0XBDtGMldJKZCDnhrrsFyUGgkHafjOwUa+7EOp4nOMWTAUGoV6uOo1Z643230J2zVQ7WFNeTEnG8yDT+dRUmUcMiZ3JLPIom0SlsrtLJ+qwlNGDeABEBaavm8xarxC21NSYJC/IyFpJLVq1zFg6sYnywlJucCyzxRuTYDyd+CWskMHfTDs58Yrrce7naUIJfAsyNHQfbzl9xDISGEr83XUj5FTJLi1LAsA/PPc9eMm/v1YDZ9pj8pWdrYrxx14AP3o2AOjtAkAjtMFYxkXtzzVQVZqfm3vm8Oj0sAazbPQWUC/SquDyykF88egFeuH1zIGj+MWexwAAj7XW4lhI4vtRYqA338Q5PTThlsxAnwPpeEjyUtNLZNNA/8ZFOj+xgZpqHfCqSaYRqlQuI65pSzAlkjlH9y/HPn2HohYBRiHqsDAzjtt6AdUu3ylMiXjR1hPkyEfuxkKNHvRwwYVV8WGkCzQzm7RRCjH6CVrITLziepKvLKX3S3buR997L8yQQ6q+rzNr6AXZ6HvuxcSbT24p+eNEO02pXG4ib4fwlY/gzEIRieorTwUVMN+ATGk7jgBTtLmfAbGk04pu+bcb3ehGN7rRjTMUZ2Wmmuq5tgZFVl5R/8VKRNuoG5mdWcKQKFg7D9XKNC0lxtnPK8XE61ZfPaZ2SUy06QyXFXJQWZOd+9ad2PX2zlLOpnECJCR9EVCkD16w7rgurcbSgG0kqAcnimY/ck/ntrQdGtRKMi1NBhyG36mdq0ufsUTuGMcjd7eBpD54DwDALjdwsNqDKIXRCw7Xpoxl/7F+iDb91SQ2UMlR+TBnhVhqUfmx2XRIcLyqMgU/+24pob1gl2sAC1tqpSuAjLe16XeT65+dOQaVAJE5gJv5QTKRCX0wACzKqgbCA6SViiAwuMcNyFRn10CG8DWgl+XWggEeMQ2EEo7sAFPxgEOUU+oG1+efRRwinyrRcIQbI1gefdm2v7tVf1mh4MM2E9SaKwukB7GJRotei/zscb/v/7mj433/8Nz3rPj55XHdeV/FY41XAAC+fXAzNvRQRuiYMWLBEShqTM4MUatT2bmad3HuwBQmVQtiT30Q12/4Eu2TNPCDwnrMtKgs6hgxPnTwuQAIrXxJ/xE6Dt/CwFAdlkIBfufYKPqLyoS70EQiOAKFOl2cK+jzEfoWuKK1SEtRxmRWjkyD2WRFNvZpOi+mlSBK6SqBAVaMIFS7QwYGrFkFLCpKTYfjIeso47arFm17+074jgWoazhUbmCpRb2HRsKxv81fdtety553pXuNoQhYcvTY4A/FGfPgDGaBWz57GwBg72/dokv9uaKlvY9Heqo4tFjJKEGLDqxF1eYaikjEQWX2RovDXqCdO5V2+c9KnJWTquEz8sa0ZActBAyw5+nmMBtMl0KFTU4h9DMpKKW0T2lkaN9tt+5E7GX0hgPXrCwLl0asDMB5wPWgLpkqVaYlyBVku8QgjdCmKSBU2XXXkWGMDFGz8fK+gwCALx4gf9JnfuFmtO4jNJ6wZId6k+RSUzpoZ9KeMoe9xMBUPZK8PumlB9/XWYYe+ejd8PqoFjtUqGPIq2EuIFTio0eGIZRJubRktn1bIAkMTC3RSLWxdwFlT/XluECUGIjqiuzJMmUqbyY7H2Gx87zwdmoP1ISVUk1sAWtBlQeNrI9pBMT/FNpwPZtEDZ9DOplQvnCFVoqxaqxD5SjOZVwqyZEhRwWViFNUr2hHWwvo/irtMLS6lRRMK+uwhOFA26B7+T/fSM4vAB78lduxPF74b38OAJiqFWGZJnIu3S/VyNA8yScbz/vaG+EYdC9xLjWK90WDD+Hzxy7R4vh+bGFskO7HnBliIcjBNLKL896pXwQABImJhSCHq4cfpW0ygTFvBgDw7bmtmKjTRDzUW8WCn8MCqJxacEIkanKstlzEsQFfqRKxkIOr8qmMONiSKj+6tMiSQdrwZvpayIDOjaFkFsOqo8vJrBzBcmPkPSq/twIb8rjqjcZMq1n56xKMvH87cdkBoBLBVIuwZAuHYUjEQdaSGf1rkmSUp7gmGttxyEHYm2hEu1k3sO8N9Cym3OEzEWrYw5bP3gZT8YQZk9jSR9fzaL2E6tEi1n9NtYYuNhCso3uMcUmdl2bWc26O0Aka+dA9evKffNWbztj+/rTFWTmppmYIkmcavnaVepkpwCRx2kQG4qynGuU7+3rCkroR//gNT2wlxtXDLVlGV+EB9dw0YIqRtRJAk5pwJa24QSAbqTLCUm9D9zjum9sIx4h1z2ZhqgTFdMDut3VOiNIR+kZnPoepHobcUQazKREpQ+TYk1o/dHlMvvLNGPmooi30ATN+AQu+IuJbCfycyrhMqa3lpCA5x5akgXBvMKi1YqNpDxN/8gZsfVhl8jGDVU/3o43GIhVQq811SvdtUgqLGvAEyybcR+6+FlvuGtfnkAeZtCQTaJscCahk1dJ+upH1Sm3qcxoN9XVJtv2wJ+Mus4R6tlrSjcus3xQrPmiaTecjvd9mOdB9KhEYGP3rO2G5dDCFHFtxMk3jaJUy/of/x6247Es3aUPtUrGle9hPNiwj0TKCvfkmdk8TpeaHu38F5209ogFxL934ID47cRkAoMdt4sh8GcOVrEGYZrRHG2UM5mrY1SD5zEfmhrGlQi4v63OL+n17jg7BcUNU8lTZiBIDTQWGaSx4KD1spzRtLJ0fI1I0ImYJwhsAmPydGzHy8btgqDcSt1VNqjHxVNNKCq8ZWq/7/Bt2orGZI2zQ903+QZaBbr19HP5adX+7CRBxrW/r5ULk1YJmdqpE8oBLWXklxSSk40Aa59+wE61hVRkLGPa1gRW33jGu7609t2Tjzcks2k43tv3drQhaFmSdnkm7z8dgiR62pZaLIzUq78wergAcOHKV+iBPtAMQcyNMvLyT0pTiSmRe6Mn2Zzm6PdVudKMb3ehGN85QnJWZKg8YDEZlOS1JZ1OWk/ZR27NHgPqIABGrN98zrlGlvA3SNvYXO1Y0zl410ozIETAUmVzYJGaQCgmEAzGMGr3mTXMEvRLJeiVM7ht6WeQHFnlCAmAtctsQBcpszHyER+/sLLeMvodWj5Ovzf6+efs4krRcyhjCUuYba4QM576FFI6W93xGPngPDCXsP9/KYb6aQ14pyQRH8yiNkFh6veZqcQmjzpEUhBb35lxoRZh01Z1mp85Cdh1YAjTWpRQGSb6XaqUvjKynmngCLGbwjqfC/p2nXpeJOR1r6krEI6bpLxBKOSnKPpOihk2fw52V+vy3nEzqMLGl3vewL6ESYbu4eepAwgAUYsgUWcqgBQE8L0RdZQz5nhYC34Kt1HhOlqUClKGmkdJmAOCSL96EZ/VOnvSzq8U7Hv5VAEDZ3oA3jnwZAPCh48/TFBG7HGCqXkCv6pF/Zep8PKP/KADgm/u24qote8BVn0RIjopF6OMrKgfQTBxEqml+1C3jMz//AQDkUJPiBBw31GVJAPAjU2fdjXkPtYsD3SrhSyZiSw1tCYNQ99im8R3ghgXZ3waCSNWynASibgFt7k0XXE/3+6N3rV6Bevym67JMjHFAMLgV1Qop17RcI/MN8Dmr0/1qiV5bzhoQNrQ0Ybs/rX7NyqhDaZ+9kPOxuEjtkuXiFyeLkQ9sh6l8jIuFEEHD1sjpVmihYFOW7xgx9j1A4h3cJFqYtLPz6PTQMcfLBEZGPrAdk6870ZXrZzmY1CKfP/tRrVZRLpex6S23g7su9crUeBaWBPHYFDUBLUMrtPCG0TFZbt6eTaqSAfveSA/FyAdVz0ANjKeyV0ofRpYwTWtJtYNT3qTwBEz18HlTJKuoVXdKAoZyyJBo0/80JNyKrwFC9174t/iTT76GtmetXsbdctd4m3oQO8GMfNutNMgk9urb2PSZ22Hu9fTx8Bi6lJXkBHp+pNwr+hhaIxF6hqp0LIKjNkeDQm6vDXdeZk5ABsMP35XtS6ocI2zqPetSa8T0z0YIOPNtJdlyZuQtLGiqkLb7S5WS8m2yk1DXIgWsiax/brQY7FrGcw16JCLVI2ciKydDAKKQdFJ90vvKTiBirsu65WJLl+wtI8G8UsuJIwNeLuiYLH/ScctDLwEA1GMH/7TnIgDAC7fswlEFLts1PYRN/XPoVYpZU80S1udpcN6cm8GuxpCmU/3hmm8jUSfEQoKvVi9EWWlD1hIXlrpoi1FOT777G/3YNTeIgpPp+h6a7AcAMFuAmQLlEr1Xti10/dBCoHR6ZUS2db0DdM/Nz5Q0oGZ4ZA5T02UUlLymZSQaaBWHBiZ+7wbdA213wBn71B3IPaR4wOcHMI7bwEbaxkXrj2Dep2tYC2zUGi72/tYtpzzXF75hJ5prUwFxBkcBfVrrElJJawMn5QbofAvBER5Rk6qy+EsV1gCAebHeXr6c3WdCcN0eMJhEo+loW7u8E2rA4cKBHm2LlyqBiYJ6uEwJS+kiM0b91+gw7Yu0VpdZXCnSMXppaQmlUum0P/fTFN3ybze60Y1udKMbZyjOyvJvqookbJmp2ZRjMENqxxa4AnyRTs/yku6+N16X0WFkpmqEHgJGnG7u3063SY22seyzIjI02CYqKr9PBZoSFkMChaxlUiOX5eYmhGDYUKFMYSIcgFDKRfHMsnpSW7Ck09d16+3jugyd5AWKGj27On4/lwtQL7soLGbAnxTIZdVMNJWXdDvIAqDSnKEyjKBPgCdcZ6r+oNBi3gDAWNuKmQOy7aSlmSQPOYJKJ9I5Vaox24wUUmH8FN0NlpV7E5dQwWnJl9ScMsBUlEenmpYypJcGMmBSPoZhibbMgGm0Z6FIJbMU9RwmhqaFxAlHQaFNA8tA7dgyqPNTEM/7GpXprhrag7df9I8AgC/tvxD7wkHsrpMhuMkERgbJLHyi0Ytf7N8NADi3OIVzvOP42vz5AIAjS2X8t4G9AID19hw2OdP47NTlAICPTT1Hl0VtI8GgU8NkixDFQ05VK/VwJrDFnaLt+RUM5BuoR20Cy6oMioYJwSSatlKVsmPEKsPya44GFo1+8k4lXEAXOOxpotWiz9RaLjaumcfaPLUqplrFjjL73Y9ejYnfo7L3a+//XRz31fWY2ZI9s0wiHozQU6BMdd98P4ouXcNTlezbQ3LAqmUi/X5arpZAcbCuS6yGIbRKlR9aQB991zmfuxVB3UF7vTwFNHI7QX3RQ7GHsvpmzUGuSJ9jXEBIhmadxpQ4NmCaKc0ha2HQMw0wJaYSjvkwVYUvDFLTX3VKotXHip/VOCsn1diT4J6E0WJIFC8VEQcaDEi5gQ2jk+6gYsvd4yTZp3qPcTmBU1b9hIYNGfAO9ZXTDv1gqkk/5WWyrO9rNmlSTdQzlmyK0NOn0Hl7e3Q/0V90YPf6CBQa+LNHL9dlpxR6nzqUpKLJjIrzAAAgAElEQVTqANlFdQRrk9szJfwB2slU2en536D/9x0e0CUxU5WSUgnDYDABKysh+2ln1Z7z/uvaOK8fvgdhwPD4Daqs/v7tWTmVQ1vc8ZZCWvKs1KrR26pkm5as2w3So2JGe0p81kHx40GbFGHMIJiEM684dwEQpUpY6ty0U3hSNSQwCZbaxVkcgkuw1FiHSwwOk7NIK7SQsyNtvN2K8ogV968v30QzypCiRqmtGfcUhWPQ6u1oUNZ/e9GmhwEAP7rvFfpvz+qlSTUQJiaUcPpv9H4f7zt2Ffoduh+vXDuJQ34PAMAXFiJpYGOejvtoq6wVtziTWOcs6pIvADhqVXMk6NGT9KOzQ6jWPT3IBwuupsO4Qw3EMdcOMJxJtBo0MXCr7QJJoL/QwKYioYsP2T2Yd+iCtkILfmzi/sPUNxwbmMNL/v21dOyH1sP1fg7N5MUAgK/sfZZ2n/n5n9uFB4+tAwCYe0oQJlBaT5NUu1XdcuP0k0U7l3zkY3frEjWLGUwu0FuiSbsZWWj4Sg7QtyAaSuYzNICYgbWhitMun+QShi0QKnR0X18dSzVV5p53wX2mx5EgJ+Cr8ZAnbTKZQqHmE9UWYRKtWTqPVjnA3t++GSMfJt46VqAE/qzHWTmp8oiBGwRUsuboFJBXJpAoaLh0BLgClKQTKUA9wtgDEtU/K/Q30Kiqxppg1C9rm1NTUQRIhsk/Wr1h3w7+GXnfds1Fs+o8Aw9xILFZhx1cKtVmhAzCyMQqzhue0sCQffP9mtQuLQtXfPkGSHli5lM/J9QZuDQAbmSTBgsZ4kLnImNiigZUZ5+r//aDX33HCdsd/aTi47knqmSk4hOiGGsyvjVrQhpEmAcArMkyv8k/eqOmGPGQQVo8WxWHTGeqxDPOssyxv9gBS6a8T6kdXyjTzTJcMJk5FcWU0RpK0S8qZDKCiSthL3I90Jg1pif0uJJoGz5EHLJpajeUUjGTByx7PjwzQqS0Mk0jAVdZSNV3kFMZ1dxCAQdefvrgk9XidQ+8HEdbNGF+/jnvPeH1QwvkJrKxsIB7H/1lAEBT2EgkR8Gga+zwGHWF+lqMPPxy7yMAABsJqpGL0RxxGf/j2BhKKktzemJcXjiAYxFNsqEw8YwSiTpYLMFS7GmN3FZiYVGtXBqJrbPWnB0hyXEMFmnSnnNzqDdonwpegDjh8NT5qvmOvpeE4HqA557EviMDWOqjz+WsSC8Cy56P6aWCdudZHlf+y/X4t2nK1p+x/oheFBxplNGcUyutwQj5nkwT9792b0JO9Wi9QnDCNlNMBQ+4lsk8QYo04BqIZzYY6k1HL7yChypaItHeUkfQzndtGZnwScggIzU2lEIIwRAqi77ZuqP3MRYExEzpZrkjBpi6N6OCRDCQ8eqlJZGkIEOm6GAAoqpNY408cWoZ+djdHXSkn9Xo9lS70Y1udKMb3ThDcZZmqpSFtXufsphRyTDt00SZiwyrM12CTdxM7QgAGjU368MGHLAkJv63EuV+5w4oARgwN5P7O2V4CZJUUclEJprfZB3LoOd97Y3wHEqrmq2szMIihn3zfVhbIpRjf6Ghxc+bto3vXX0nRt5HyMDRd+2ALGiFBBgKXGnVGaI89LHGhQSO6r1c8I9vhd+yIY6rTOHQyZvIE7+7etkrNd625+0T0MbnfI7Qrmwm19FrTpWqNo3vACR0qZVHGdo3sUn8Pu3LWvXMfDxmyJCMUOYJbdvXqGNbdtBo4lwb0lhQOV67yqRm5ABYy9AuKeAAuNSiAmFsanSq4Ui0Ygu2cpN2jASxKm+uKVZxRIk4WHaMX/32n2p6yUpZ5unEuy/9NK7+5jUrvrb1b2+D/X36vscKQ1gIqST4zPJhbHGnMB8TdebTBy/HS9b/CABweeEApiLKfP+jSlnct6a2ACDVoV8bofctxR6ORT2YjWgbRdPHtJLDWucsYiYsopVkpe7UQ/XC4lHsqlMTvuK2YHKBw/MV/b61fdT/ZEzCj01dxvdbNiF9gY7yo1ywMfEnb8AVX6b7MUiMDkee1eJF3/ozjJV81GPK0JdCDy1Vmu/3GiT6AAAS8OyIMmUAphsjUOVZkTBc+H/fAkdd39mjZcCmfdv/utWdW1jE9DOStkN0/I/sx4v/6RYIdU/HkQHWE0Is0HdLkfkuj773XkhDgqtKGBdAq0HXxWxwbHvbTnCFmXj0jux5PP/GnSnbCJJLWP2tjl1JqWB+zInapMZRvmTpShUzMiEbe4mET5Y/8z8LcVZOqokryRyXZZOqVD+nkmbCy6y3JG8bMBPlJKN6EnvfdBK5LQldhpKtk5/q825SdBUXwJps5hROxsOEpAXBlrupPBP1DMKeVbJ5AZWlAQADAXpyLfzW2vsAAPfXRwHVJpsN8rjgH98Ko6Ie9ohrwIE7ZWg3mMQBos0t4u4BQMgRTdDDF1oSwhGYfCKc3FVCy7Cu4Mqz5zfIcaWdF7z19nENPkoKAkbNQPEAbaS5Rur37bmZ3nP+DXReJe/UCk4BajxUvVN1fYVJVCUAMJe4VlNKI86ntBypWwEA3Tvp4CdNIEndfjwBc8FE3EMTYqPmIqdKgZFFZt2OSa999aqdq56nV37vlR0TzxOJX/g6LfLICcZd8T3P2nAQ8Xq6zxYDD6Gqcx8NKvjewqguOY6UFjAfK5pI4mIqoIk4EAZ2HR1CWZW3hWBYb1Pvtd+0cDDow1RAE+lYbg6X5Igv+/36GFqJhQeOrgcAbBmY1W0LgABDAGkYJ5KhT+n9tiJT95wXlvLghoBI9aartp7opOAn9PWmDlEZ2ihGGhfw9avGsTzGPkUtk+EBD/XQQb+SYPzCVe/q6LdK5aZk9fioNR0ES0ou0Um0TKEUHGFo6pL45B++SU8wo39176q0k5Nph7fH4pFSJn8pSbs8xSmxmGkaGuMM4FmSwGNovAIPU5oafW7z9uycxBsTrVwmPYFCztf2bnHMkaSl58BAYbiGpirNi55QK0fBFroF5LsCMKTGeJwJRaifluiWf7vRjW50oxvdOENxVmaqqSF5e0iLnErSbIaE0FWpT0KDV3is1JZWyKyWx6kE9Tt2qQ0oY86b4Kk8rCdhV2ntYy9StpVmRNbB7PJJnokb5As+DC7woYnnACBAxv59hEKEIWF4CSyFlAwirleqwpIIaBFPwt0MHeAqre8b8Q6BhCcTm3bugNFksFXZ+rF3rF4G2reCTy2AFYFfW++g1fWWu8ZJ9L7tNY1kZtDlXrquUp/TMNdZoWAy89wVFiD6qDTJZ2y0BqUuN5utzKwaATSoy1wwwTY20JsnhLjBJS7qOwYAyJsBhOR4vDqw6rGn8dErPrrqa9f/8KWw1A3DITUdBiBx/RcMEbUlkgYeXNyg/z45Rzq9w5UqyrajM8Ra6MBV2XMrsfCc3n345iyVd+f8PO6b3AgAePG2H2nB+1hQ2S81GL+0dBA1xVM6Fpaxqzak9+lAsw/HVYa7v9aPqVoBm/oJ4HRJ5RDWWEQF+87SZjy3fx8AYHd9CMeaJczUVLUkaLv3JUO44GplINhC+/sKJvXPiW9i9K/vRK6fssUk4ZhW21seF//TLZCSejeLDQ9xbGC6mr334SNr6YfjDpDPMuvQtzrE4q/8FzJCmF0oQiQcTWWyftW/vh6FMcr4HzoDoh7utNlmdk9VOJmKnSBjE0z86etx6WvG4ffSvRqVpL5vmaBMVd/vbc8CkyzLVJkSzkiHwITBKtK5N4oR6kteZhIgMkEWKx8iMui62V6EcCanW2yjn7gLuZKPpHkioOvpFmflpCpsATgCRsPIeI2CQVoSMk+DCTMlEpGa63IIJZItAw6etAm3/xiRTgA8ZEh62iQRk4xGYzAyxAYUEtdGx4SQIoN5DJibqTzlWjEaoY25eRoERNOEodxVsMaH54Vo7adBzQwyo21ngaG6TYm27zMhzM7bY7lh9Y8T1hKH2SJx+zMVY3+5AybLZOYSL1Nl4kmGjgbLrh8T9HuUz+QY7cV0kUH/0nMcFwQp5oAGHxZkZbSwnE3MwpLaeDzujeAaErbqpZXsQPcrjzTL4Exq84Etn70NV2wkhyHOBD555YdPecxXf/Ma5MxBjZ5tj7c+9GJc2mPiaEB9yL21fgx6JGrPIweVApVq5xo5zNQK6M3T764ZY7RApduK1URT2BqhHCUGSoqH+fkfXIoN62kynK3lsW39FM4tHAcArLUW8IW5iwFQCbfHaWIpoOPcWpzBbJBZvW3pndWT1MHFCp45RMjgWBjYXafJuB47qPoOHGV8LgRDq6a0J2MOno8QLypHlUKszRkcN9H0GkhAhgZaNSpN9g9UMTPZs+J5XZwrgClT9SCwkLTMjvt/72/fvOLnlsd8lSZOy47hWDFCRXM7NFs5LXWl9th87zjMZiYd2t5jbaeMjf3FDnLPauOIOnN0P27aMY6eGHDn0meB6RZG4sgOmjxvMc1ygCG1xCjsttkVpGglVHsgSemEaU9bQMsZRr4JW8kqBvMeWD4mgAMAO5cpZT3do1v+7UY3utGNbnTjDMVZmalaVQM8NJSZNP2NSSDxEr3MkL6RlQ5ZhvqUhgRvMZiNH289smnHOFhqH2dKXe5NATVxPjUrz4TanQUJIwQUiBJmg2m1oqgk8ZwNEwCoBLhrcRCGUjkRtoCwFEdTMNRn87DTEnKNdaD8Lv0jxVPlEs21TPNWVyvBPtlgCYF8Ui7q7rf++BkrizOkZGrvl/qkIsw4psKRWpWKQGikE5z+rqsQApBWJiRhNLm+TogZzCbTPq8sadNrdoU2ko7LEjk3K2kt+J72HAVILCOnypaj5Xksqiz2vNJx/P73/hcAYN9SP/zYxGCePscZoYYBIIgdcCbx7N79AIDpKOMfB8LEw9W1WgTE4AIHa1SutYwEdYVSbSx5kAnTPM+rhvfozBQANtpzuN+gku/h+QouXkuZZFiZx4EF4io/Z8MBODxGTrmx7w8G0Ws31P4KODzBWo/QupPNXr3t80rHUTACFCw6R4thDi/u+wEAYPveF6I/l1nVDeQbmlf6yNH1GphjFUIwLpFkmhUatORHRoYEBgAmteLZwlJem4pv+extiENTly2t4xbitUrPFsgyrycYqZJWb6EJz4pWBEQtj8v/+UZdeRCSofZ9ag/EQwnMpqnLsKuFyAlCxKsbWdrytFC2m3bugFXNOLN+H7RONZgES5XFbAJgaU3lpqn5/TAkDaaphjoHHAXMi2MDkSrbs1wM6Rt6/A19C7EhIVqnOLinQZyVk2riEfqXxapXAEUdaStx8BbXE1ZcTPT7AGDPGUC97n99NkltvnccSIXsQ4ZgIAZXZZQ4J7V/6A/eoxCtN2YoUU3/YMDji/TwbatQXytFHkZ1G1Ipo2DOgREDpX3qXLQ58Zx38054aVlU0mSfO/bj1bk33zOuFyrOHMOjd9LDnYrTP3b7mSv/EpKRfk4KElFFQKrByZ02ocZ7iCgTwmcCMFvtPe2s/wQGxG6mvsRiIMlnE6ywMycdYdJ9AgBGX4C4qCQdWwYW5oowHdqxTUOzqFhUPj3SoPLvN35xxwnH8mcP/g7WODQJrRlcwvfnR+AqqsmBxV40WjQhFnMBntl3GDdf+AX92bc+RMo/Xz50EUYr89hYIiWj6aCgJ8vZeh7nDZAE4GMYQrPhaOPzrxw9F7GSEdzaM4N/Png+qlWa7NcMLIGrkXB9bhEXlcmJZiYsomRmNIvpqIjdS1S63VyahcdDOKrPYPMY5+TpHv32zBZsK0/hmcXDAIDvLGzC3oA+94kLPo4fBFQW/uzUs3CkVcbsAi0amCOQK1Gf2jISRImhJ1IhmKa59fY00FJ9TN+3IAXTeALHjjUyOu95WJrz9GQQ9SYaNSwly0qep4gtn70NnhKkd6wYjk1D7FwtjyjKFipjn7pD4zpsJ8bul75Fv1ZrunoyDgMLKCrB+waHOwv88J0nX+CaSwaEIyFKqpXVyr538/ZxiHW+pnihZWh2wYFrX4+ff9kOMEHfPVPgSJK2AUYhqpOIU3k9HRokdBkXhgSrWiT8D8CohKR+BWDy1aszJUY+eA+c/iYS8dQrhz3VcVa61Gy86x3kUhO2mYFzBVRKV2MNrgdXkROQKRct4idVRloe296mKB2m1L0LqYzNUyeXuCAyhR+k9mPqF5kBbOKC7OijnHfTTjS3KICAF+tel8ElFqs5eDlaIfotG0zxyIyHCxj5+3m0NtLg9K0vZDf6+TfuhOFn+/7Q+LU472ba/5MBiU4V7dnupnE1gUg6xhO4d08iUgUYgLJ3gJx8hCu0Hq+1xKAMT+D3Sj2pGgE5zpjquBMnyzghFYUmlRgMWAdHWdikqgTQ+4TbplqjJOPgJDCdRA+S6/oX0ePQjtQi96SZS+oMc9tF/4BnfuFmnN9Pk+AjM8P6Pa3A0tQjAHjJv78Waz3iJx/3i3h8bgCJ6ncZXHToDM8tUskjiTn6euuwFThJSIZnDVBvd3+9H5MLPfq1l43djw0W9VsXk5z+3h81NmDArmFJ3eTzYQ6OSusrVhPzYR77quQqM5yv6on5hX2P4EAwAF8hBA81e7DGpcXEYpSDUCP3gWofJg/3a6uzqGZrNyirEMJxYq2JG4UmklqKOJRt9BoGxiWcHG0j5wZ6cSIEQ7TU6Q/IUhyFYDCcBEmVtslitioF5Iov36CvtZQM07OEXeAGZchC2RsiZmA+XZd22symHeNIignMRXpfPBAR/x0ZWCitkok2GVVpZgtKgFTVkoLQ32WqsUYoXerUWo5FTNP+wCR4lMl0inwCrvj1zJBZ9iw4pGg7ljawozQV1z+dtJnMtu8mehxaLtk48v7txOdu+Th8zVu7LjXd6EY3utGNbnTjLC3/GgEDZ4zQbmo1yrwYct7WJY24J86WHBLIVVorb+wUEaVCAg0OrvQ3eYshKknE/YqeYSXAJKVOkgMCMhOJR6a/azQZLn7dTp1lcQPa5DuXC1BV/o8i4pAtE80UncckYp9WlbkFYObnexC79NozrtmJH/0lZaHtvdU0nmiGOvbpO3Dg5Tdi9BOkVCMFAyp0IkfffS8mrstW5VvvXD1Lu+LLN2gvx/mj5ZOWjrypTPwh7Gk3oM6M5qXFECiEddiXZL6rAWkHN9eorMRAZqTASK0p7dOyBEh4RrNiPQEipbIjXKFbBIxD0zviwMBQb1W7sgBAzqTrXrZ9XP3Na7ClOAOAFI/a47aL/gEAiTcY3MGnf+6DJxz7JV+8CS/4xrU4XqPKQ08urzOKnBnigoHjeFAJKwjBtC6tyYXWIS65AWyeaIeWPYsD+NE8icQfXyxSH0z18XfV12CbogRNhv1aCCJvBMjxEDOC9qNitdBQ5+ZAow85M8R1Y18BALgswqGIerGTQT8ONPtQVb3klww9qFWafrCwXqOQhWS4ctt+HKoRkvlYWIFhZdc6STiChkL/miLTXg65ptQwkBOLZVH2VWu4WiAhWnIAW+jfZcJQrND5iWIDrUVXq3DZCyvnIpvGdyAp9KJ3HVGC6k0Xsk5DbGJIMEfoTA22xMQf0j098oHMMIKvCwDfhDTU/dKm/SsNCe+YgaikzCByWSUGIpPblZwUwnianboS0UBqdcUpw0/LujzLcFnCwATTTAfmc8hQqTIVInA7MydghtSVAgC6ksdUmTztY5sLJmlhA+ALlkbLL4/J17wRm3buAPONFV9/OsVZOalGPTG4F4NFPCsNxUocXauQcBiqzCIZ0OT00J/KeHx5pO4ro+++t80CjdPgn/YhFh1dctn75utw7i07wVZp4QR92UMgDWhgRc6OMKTExoVkOPDYGuQUN7I2XcCkeoDxe09o91eNc94xrtWGHrn7Wmz57G0AANOivhJTg4IMTBg1dXBMapUWHpLN3Lm3pOVxaIBE7AHxlqJ20Bl914k9x/Z4aCdN/Jt27kCiHGyYpFKtqQbAoDfRzkLMi8FnaLAwG8RNTku30pTwjqiB0JYQFpWOAer/btpB+58/zLBUMWGvo4E3bGTlSJEOXABYzcQxq4y+Hro2jhFrMFLBChAkJqaV0tCrvv8H+O+VXQCAry2cj36bPnNk9hkdxtsAcNmXbtI/H5zv0UbTlpFoNSQRMTRjG9sGqX8ZS45qQCuyVmSRXRhIOjFJOBrKVm2p6WFTH1Fl5qwchGC6/zqWm8XegMrPD1Y3aKUlziSqtotLClQ23tVag1qclVMdnuCf5i8BAJyTP44Rm5xi1tvzGLKWsMmmfWxKB99c3Eb7LxmKFt3Dz+w7DM6kNjqfcQqwFGbAMAQ2VhYh+ugc7ZvuB9RrobQh1DPCmIRpAJGy17OsJOtzWhLcFLqkyQyJQKmmiYQDMde4h6TNfW70E3dhaIgmUckHwNwEi0t59X3IFuZMTTSqfdAhLG8JSNXjETEHfK4Xg2atjfYXcvgDQi/6zLqhS8A8ygwdRF5g8o87y9OpcpG6WBprAGRmFZIzyHyE0sNKKSkHNEdoAWi7sQYZ6QNKy7pcggXqeY/VMar7Py63TcQSWUl6WYx8YDvgAeJUKKynQXTLv93oRje60Y1unKE4KzNVWAKwBJweX4tQp5ZPI+8noXkedOq+7nr9jyd84B0zOpCuW+4eh1SZsBFmWrUAIVFTsEyifF/pBWVppq5aVMzoAZcPHMLeGgFB9h8bhLQkavO0YtZZqorLXjWO5ppM4GAlOsvou3bAm+Irln+33D0Oq8X0in3kA9thlRXoKuIw7YTKQwCQML0Sthe5Vn2KShLnvmUnpMKT+BtD9A9W9XfMzWXqNRN/ujraeuwvdmiwkOwBEcpBJH/W4poOI9tQzmze1iW8qCix9/rrtD2YW/Gx648I+DPyoXvAmwbiNgBIitoe/cRdsL0oy5ZKPgwlFpACZgBgaHgeOStEyaaMqxq6SoMX2FMdgMEl8spPdc7P47ucBOnzRoicQqs9Y/0RzPl5vOAbdC2+etVO3P+izPR6y9+8A+sHKVvyzAg5kz7nxxYSyTX9xuACkcosZw9WMDBCqGDOJLZUZnH/EVJbCpoWpl06/6/c+p/419ltON4g0Mg5hWn8qEql4UGnjopCf1VjF88u7sVkSPdgv1XHF3dfSOdqiLLeX+h9HABwZW4vJiJ631KSQ69ZR01Q9n4o6sXrhr4OAHikshZ/cM536TP/cj2CyNTWgs/+ypuRaDNziQG3jgeOU5k7alkwVKnSLQbwlUhECrqJVXa672UZwGvko3ejUmmg3qRMPqzbsBVKmAFkCajO3b6XZVWC/oEqZhQiWeQEZW+qtOrkQzgKPJhIhnrVy8q/7cHQUUplEdeCCTLMMtVgOAJvGaetB5zGpp07IHuyjJFbiQbuipaZKSNZ1E7y+1SbZCizYwznXMDJzEY6AEiWoJKzOk/tmudoq/4leaEBWCecgohRKfpMqOr8/xxn5aRa6W3AyMVYPFTRjjJpTL7m9JG9q8W5b0nF8aX2R3zs9mtxzm3097ggwBhbkfs58vG7YJVsPfmIfIKkoPpgVQN+QWgEqswnWLuGBsbHloZwZJ56UcI3wLwY3u4TxdMv/tOdCNYxTdN5aPxaLex94JrXY/Q9VCZy5jMO2fLY++brMPbOHfpBOhUaOlWOivISqrqJ2iiDEQD1dTRwFXsbmJ1RdImmCaPG9Xls95rd9radeqLMHWMwBzKFGcNniBVC0+gLIByuJ3eWMN3nEbaAMUqTQRKa5HmrtuFXs5Ll8sVIezBTwDQTJKrva1kZ+tRzQu2F6scmKk7Wj9871Y9yQZXl6x6SiGuT6OUqPSlPNWeGWGA5zDYJbXvZl25C3qaJ0zUjrOs3OwzNDy1S39GxYlSbLtb1UK+0EbhYrNM2etYvaaoJYxLfeXCb7ot5ZR+byjQRfm3mXLRiS0/GgTDR7xB3NJIcQxYthFKP1ZSnOuH34c+fSZPjdxc3o2K1MKucbnxpYdAgZSfXiTBo1PDp+Z+jcxK7iFQv5M3nf1kfUxCZGK3M69+XWi4C5Qm6ZXgGrcRCq5XVZVPeZNwydS/QNBMYhkDSVvpMHVQADssQWNtL5yqpcGwtU697f60Phxq9kCtUJvtzDeSUylO14NA1VSVk00zww//3thM/tCzsfIhYfUb4Joz+QCONjTCTyUTCwCKGsb9Qz+tpUvuEK8GaqqwdMQiPZxQhU/FK1fZFy0Q4qBYTttCvyajNmMCQYHaiaWJRywJXUqqimAAR66QqKprSyVpnE3/yBmz7u1uRNP1V3/N0ibNyUv2Pq285Y3Dt0U/cpeXMRNOEWYyw79abVnzvnltODfqZ/P3rsfWO8UxbN2FgypotZhIwJSxFCTCtBJbqpc3U8wibalCJOcxpa8UsU3LAqtNkmkYKxBn58D2kiwgg6E9O6hzRu2UeDd9e9fU0Rt63HehX31PnEMruavABgWPPYXqF3my20RkEAA6ElWwU23YrTbDFK2cwO0HiAS3BYfiZAEPcH8FYVLf0EQ9s2NcDIeOyQ1oyrVBICfB8rAEqScg7FhmrhWklKOV8PaA2QhuRoSYlK0bFpYm0YAU4VKtgqUkTZ9SyMJtSNzhQHKjj4VW0Xz9+xUf0zy/61p9hWk1KeSdEr0sT27FGCX1eE5NK5m/zhmnkPkmLq+lfCyAEQy2g76s2XfzypscAAA/MbtB2gAeP94KVQlTKtNBYW6pq2k4QmvCcCJcPH6JdZhL/dZyEIPpzTcRqxK9YTTzcWq+ddNJJFiArtz67DkeBAWwkmJO04Bu15nEormCDSxPmQ7V1WIjy+rOvvf936fg3NvHw0lq88nuvBACUvEFA0YOqgYtmZKOgMAROJdY96LrvZLaHTQci4YhVb3DkI3fDyNFrpUoDw/kajjdU1ikZ/uvoCADqUychB4Isy0r5revyTQ3+MnICA/kGYtXcXIl/vFJYVoKwTs8Sd2OIeVsLW0RFoQFSLGYQrnjCji7STWDMqwWUBA5cc/JFcFq1mdBkT14AACAASURBVHjF9Rj9a1p0GF4C26F7PY4M7P3tm7H1b2nBUOxpwvfUoi42IJsOpEfPwskWpstj90vfQrRH3HnqN/8UR7en2o1udKMb3ejGGYqzUvzhTBKLRz9xF6Bg8xOvPb0V5Oh77j3pezeN79AOOZJDU2+cQoDBch2eomRM1wuaaF5vOogaqZWOBKuZq/ZeLv9fGZXlvo9cpzMz4QlwRUh3p/gZUzvS7jaCgSthf3eGw64CixeohqWbKdikZaIUadsuuMBDBkcJ3j96x7UkHt6nBDCOOxoByaRyEkp1HCypV88QGXKxOFwjwXRVxk18E/AzSUdpSk2PEYLpjDafJyegFP1qcqFLvoO5GgZcqq8favTgWDW71wYL9ZP6pq4Wv/Xd1yCveqytxEKoMsSFIIcj31kHdh59X84N8OCvUL/1ki/eBNtMYKh7pOT4hIwFsHlwVm97cr4HRS/A+qKigkQODs6T0HzQsnDZ2EHsU3KEW3tn8fi86pvmmnhmLykhLUae/h4AON4qYvfMIADgsrWHMNUsaX/fddYCcsolYlewFo8216IWUea63luApThku+rDGiktJMOlPYd0afjx2oCmDu2eHUQYmroH2pdvYmqJMk7OBRxFoXGsGBYX+nMlx9do6OOLRfQUm1q6MQxNlFTmG8QGags5MOWbKvOxFje4eNtBNBQiebpWgAS0Z2qv10SfKpXvWhjE3GJBqzk99mtv0+fqnM/dCs/JlIRqdQ9JqoIUc1jzqnQrlRd0mxlEirdggp1QCRv5ONHaWMMEVLVLJuy0jTG23jmOcI0Syii3tClEo2VDCq4rA42WDUM9o37dhjFnQagxa+IV15/0O0Y+RFkxLIHJ37/+KRmjf9JxVpZ/z2SsdNOkerZgne4R+jOnmHx5zBApbhfzYliqdzFcqWFreQYPTBNQZHG6CKY4eNwSMFIZsZaRcS1XiPs+ch1eeGVWcnwiFnWnG1o5CYAdKI1QnunoCgsoHEmQ3oJL27KJbuud4xBGuztf9lPiSTS97NgO/Pnrda+6PeKCIA3equpVcWCiraez7e/o+EtugGk/60caTqKdNqQXwysGeuECAM1FVcZ1DBQLgeaf5u1QA5CGvRqChI7ryFIZnh1hSpWsf/jHp+6xpfHcr1LprBVZuP9F7+947cYf/ToA4IXrHsK91tU4vEQl35eO/BDnfI6OzbENrCnWNFVmql7Qk+lju9fjyovIEu68wSmEwsTBKk2ktaare8v5nhb2LfTpiXrkQ/dg3Ubqt44W59BvUW/08doAbCPRgCbLSLChhyZpiwmUnRZ+OU/ft94s4O0z59N5lAaEZHhBzyMAgK8vno+DddqPdPEIAJEwMNHsw0yLSuBBYqJg0+Rlm9TPThdGxxZKGnh40f/NwEhziwWYVoJIUWWOcoHBCi1G1vUuIREcjVQecMHFguqRi0WbwDiqD+nkQz0JTi72nNA3Tfu0R6JenDNKrj1DuTo2l+d0abg9wqatQW6+6gtrvicD9l7fWa5NwZTMS2Dtp+uU5E583id//8SxaeTjd2HkY3dnC9hXnVie3Xo7LWbD4Vi3tpqLHprqMyzkpxzDRt63/aSvnxDBygCmp2N0y7/d6EY3utGNbpyh6GaqTzLS0go3hQZFmHYM140A0EqbJaTPC1CWtuu2TkrN3jcT+nfkg/do/03e4hB50eYOAcQqk6r6Du5rbUBNKSdxN4bwFUGdSy2gjyUHhQmOy15FK877P7yCvu5TXPVPFWL2vek6bLmb9qPd2D0qCjSGOWIlH8sDlkHvHYkkLzQSe+ud49o8XnqJXsWncToAsOWRkv6PzZYh6pZ2/mBtJV4pGVpzHrii6RiGAMJMtL1gB9o1pRVbGpzUSixdEjS4wNTRir6+ALmQAIQc/fIv/OWq+9hQ6NwoOXEVf8czPq9/fvfRP9ZUkz++//c0CGu4WMOmwuwJSk0A8LfrLsNjPoGbHljcAJvH2pgcOV9nczdf8CXsDwbx7K9Qlj+8gWnDBs+IsKdBgKacGWFrfhoPT60BAJw7MIVBVQL/7tFRXDR4FI+G9FzMJnW8ovI9OjYwfG7pUhyKetU2QxQV/eiR42v0fjhuiFrgaHOAMDHhq2qAlAwbexdQDR39+/O+RvdOb16ioVSBhGSQkmFNHyF8I8HRUkIQ04sFuG6kEdy8xZGY6rw7CbiTgKtnMvQthOqZtJxYg3lSPVvzMO1H1JPgwAwd10m9U5lEc049CCkVRSFm05Jze7QzFNKq2N7TdJFiXEKGXN/v7VmrPW1i+LsJ1ihB/YNXc0yskMmeTqTuP6d83xMAMj1dottTfZKRIuS4G4Op8TJpmiSUrXp3MuTwDtHDZzYzucGwDPgjIfJK+rB5rKDVmxKX0H6p4DUvRBBNerDcHr+jZ7Y80rKT9A3YM6Y22z6TRuDtsRLl5YnEtlt3IiqmhsfQk6pwJSDIyBxQUo/lrBS4XIz7x43R994LFGn7MuZa6Srf34QQDKbqJTWbjlbckQnD0JpFTT1pxjZMJSB/QekY5kIqU86HOUzWejBfI0Rr0LQgW8r+yk1O2nO6sK10uRpCeHn82YO/g28c2goAKLgBvvvCu1d836vve4V2jdm9NISi7Wu1oj3HBiHVQHvBxmNY4y3hq7vOAwBcsXkCIzlC6h7zS7qPGgkDDo8xUVfWcjzBlX0TAIB/OXIefm5oAud6JG8YSROWQgIfiypotkkURdLA4SZRgj7/nPfqv//Gd/4YBxb7YCqE9fRcCbk8lX/zTgjXjDXtp+Y7sNT7hOB6UdKY98BMCXcfTXr+YCZXKfIJcr1N3X9dmM0s9MAkLC9CkpaD6xacXnp248iESN1sQt5BGxl75w7dhpFesmovc+wvd2SqRo4ATLFi6RYANn3mdq2e9URNzgGaRFndQGWUSvN+aCFUC5fENwEm4Ryi6xFWxBPmxP44MfbpOyCaPg6++randU+1W/7tRje60Y1udOMMRbf8+yRi5P3bwdSq+MCrbtR/P/etOxHlJWIFGHIqPuKiQmwuOXCP0un218QYGFrC3AJlMxP/+w2kfQkAhoSMGKk+gfiQrJIpwUTWyg19Qtim4p8MwshE4gHg0ldTCfaBD545s3F/iI5z084d2H/tqcFOY+/cofWP973hOiS27LCM0t6wloDRoXnKgFlVwnOfgsJKzCBVNQBOAqsUZi/FHL0F4m+uKWYApG/90na89v7fRUNlWX5iYn2OVv+P1Yaxe5aQr/2FBpaanq62e4UAsdsm0XSSON3stD2EZLh4iEzEjzQqeP0Pfgv9Sunjhgu+hJd9948AAP/5+PnwiorXacVY8D3MHK6ojWT84QGnjr3VAb39aujii1MXAACetfYg9igP3+n5ElwvxBVrSPu336lrQ4BE/jrueMbn8Y6HfxUAYDCBRPFUHRZjQeTw9clzAACmIfCQOu6r/vX1WJenc/q5Z3/4hGMd+/QdABSPVHDNRy25AeaVyEUYmigqlKpbDuA5IX6gqgMjH7lba2czSyDvhvBTXWAv0mIco391L/K9DfQXCMm75LtoBgpQxGJdok7FMzZ/RlWS+hhMVcZ13NV9Qp8IUJAziahlnfqNq0SH5rCKdOwZ2LCAufkCgiHV7ij+ZL1NRc2CaCWnfuNPeXTLv08iRt63HUaTHsZUMD+NLXeNZ64yPiM/TgDRQAyuhOWlJYFS1FHG3PI31BMzTAG/5sBQLhumlQkTGIZAq2Gjr5cGyVrT1YO1SDi4QuoFcx7seUO7t0SFH88PdbVIEX4sWd1fcuydO8BTlxcB7L2eJvW0dGwrZcLmsIRznup1RQbiQ3lNF0hVqZ7KSMv5zEmQV5ONbcZoBTZG+6jcucaranGDx2aH0Jtvwo9pQL2k74h+LRDmio4yP6l43QMvBwA0Ygf9Tl27xbznsk/izx78HQDAP95/CXqG6eR7doRj0xUUS0pST3Dtn7q+vKRpQwAJLQzn6XO10NXUnvlGDmM989r/9Av/7V36u462yvjcs9/XsY8v+ffXAqBe7MFaD/79BXT+n/+N6zBVUz6vCUcpR9ciTgwsVHNIqjSZOb2tzGEmNOF6od7nnB3pfa42XY15iCIDnAtdNn3uV9+k0dtLDQ+hb2rxfeu4rUVR/LURjGKEoV46bj8yEarrzpjU2+dMQkimKTst30KcTrgRP20qy/JIKWncJmPw1IB94nfPbBvkpyG6lJqzNE7WhPdmGEJiNyDxpFYAc49YWs9XcEAEBsnjAYBk8PpVH1Yy6uktKL6cZcPop94RYxJgwNw8DToy4XpSzZV8uIonGcCD4Wf7YTw517qTxugn7wS4GjBW0DPdfK/imLoSST6d+du0lJf1YbfeOa7pBEIwCE9k7uxPcYx96g5Yyj2jWGjpAdM2E3hWE8/qnQQA/OfsmKbXcC4w4NV1H/I/jo5hUekVr5QNPJVx/Q9fqvujAgyBoMGolVjYfvHf6kn25f/5apxboAXZr192v/78f86MYs3gIuaq1Pct5gKU3EytaLaeR0FxL4PIxGFBGa2QDEWH/r61bwZzfh4tJZf4sT0/jxwnIFQ9ons5zeD2vewm/MNz3wMAeOG//TlyVlYZ+PpV4xpkNJirYdCh/f3GwS0Y7q1i1qR9FAmHrahmVt5HGJqo5LLMKjVmN81E31eOE4FziXM//3YAgGvbqNYoo00CA7xqgvfR8UQ9CUQtBS0Z2P/qN2kFocd/8xZNW5KPF8DPoX0s51tYqOU0PYZzqe8XMxdhy9+84wQpyjTSscAqhdj72zdnlBRbwMjFansCnEvd2z3deP43rsOST5WBasNFUHcwNEwVgO9d/fRWL/ppjG5PtRvd6EY3utGNMxTdTPUMx0M7MuF8yTNdXTDyEAWAxCEoeUqtaPi2LiH5dRu8bmj6icwn6FME9bWFJRxrlLDUIEpNHHNItSJv1Vy0mNJbNST8fgGetu5KT9z5YfRdO8AjaETu8p6pbJhZCXyFfmraHzV8rsvhLM6MySUjA/FEaa+iD+AqkzWtBJG0TyitP1Vx4H/eiCv/hfpsjpHAyVEPtey08IND6/F/Fi8DAHhuhLLSm+3NtfDDo+uw+6VvWXmjP6F43+5fwBrbwlJC98RUUNF6vL12E8//xnWwOPU9Xzr8AP5+ijxN51o5lB06lh63hV1HhpEog+grNkziSEOJ8hsx+gsNBKpM2ptv6vJmj9PEQkCZ3myrgIrTwqYioaGn4jIOtggJ3O/WcfU3r4Hr0Tav/uY1bUfAsOfwkP7tvL9/G35hIwkmHG2W8e0jmwAAaytVHK8WtehCo+noKo1lJBAm16VcgwvESgjCMROEqYDBfA7D6+dRUGYEx5ZKSFLKSqoFPa+eIUsgUVgGGBJjn7oDhpXtY6RoOnyshaipfGmdEIYhdHYqJTk2pZEKUqwUKeUqzWTzw9S/bSx6utwLxmAYCfb8Bt1zo+++F+dceFid4waacdZrbcY2InUf1AIX88rjVQqGnv4ammH23rFPUW9aLtmnrQp3puJXv/2nAIAgNsGYRNwIfqLf/1REt6d6GjH2lzsge05PdgsANt9DE4fVYFB+0VQKVvNH4koILwFPpcj6Ak3VcA7bEJbMFFL6AvzSNrJ2eWxhGJaR4OA0DVYSQFJLCZwMLFCFB4MoKUZLKQOZ8rSARAC0A4YRMEACVl0ZdC/ryY589G7tfCFtkdU8DAljzgJPxxKBzPg9wf/H3nsHSHZVd8K/+/KrXNXVeWa6JyuiAEIIsDCrRZb5ANs4YS82BtbY+AMMtkHYYoVARgiEpF3DYhs+rwNYnzHYBBNMspCEJSQklGc0owk9qadzxVcv37t/nPtudWtmNCMhwkCdv2a6ql69euncc84vqOMhTAE+FPcdMgRTdl1CMPAV65SFw8//Ij2IttSWjpndnUq89Pa3oCbl5Bb8IsZzNDu758gGBD0L+/9bH4yWzQIB4JLavjVOKj+qeMUdb8LLRx4EAKykeezoUtvVYBwPLE4qN5rMeBwAHpsdRaVEiweNCfQiExsq1BK0tARctt6boQvGBCx5Qr3YWmMWkInJO3qMiBu4/zDZr71syyPqu1biPBphTlnSPTg3odSWzqsewaOtcSz16KG/pbKkpAmPdooIZTs5TTXs+uWrVQtZCIZSkfbfDy2kKVPWbFMjK5hvEyUmSTQ1e60VPXQDWy0K2vsqfScmDeAOV9Q2PhQrd5s0MMA0otUAlByzRIRFW9FhzBEfpXywxpbvySIDWkEAPDNLT9lxVY4Aad7RMJWN4cwb/0Q9a7DBV4L3UWhi+8Q89i7W5bFjiJvSsUoTKI501SKec6Y425xrYIA6riei7j3deM5X/kwBweLYgGkmGMrTd7V8B0P5HhIvxO0v/+hpPVMdtH8HMYhBDGIQg3iGYlCpnkJM/c0HT7h6nPrYDWSwCwCMdH2n/zd5kuqeplRr2Sp0utCpWs2WNGabqXaps8wQVch6DQDc8S5+ZcsDAICvHD4LzY7bh9SHOjTZgrUbmgJCJQVacRsSPRsOpwpMpFcipC0LZoNWpxka93hx1p/dDOXgdU4HqSS5O06MbsuFltFcjH6bW2iyCpUC3kg06BLwkeZTaBlMPztkcr8MI1UADKZxJEvuD60VdcGXrsKoBPA8mcLRj1O8++FfAAB88dA5+N3N34bDqAqcT8rY0yM6z38e3oRXbbkPn9p7IQAyT89JwFGrnQeXdCY7F6OYC1CW4KRebKrqNmfFqLk9RRfqJhZsWbVqjOOQRypJlpZia3FB7d8Rv4IDHXptSArLZ1Z2L/z6O2BLpG7RDLDkF1TFu67YxN27qOXrlgIlTJBRSQxJR3LcCNWcrJitEIeaFfQ8ulh511SKRNAFNElP44EO55CFYJy2YbT0/miCA3GVA5nJPWfQl6VmtS6I8iUFWcAZmCuvbwbVOjbsBMWCD01e26dasa6O6Y9+iJ4jq3yeVxtLGD2mqmvDZwjqEhQVM6SFPoWuMNlGz6PqNG2ZSqHNLEUo5IM16OVel46bk4swXPSw1KWuwY5fvOYp7//Gf7wOlYqn/r+yVESuRNfVVK2hAGS6xjHXKfYrZsFQzflIvBB3/eKHT+tKdZBUn2Zk8mCJKxR3UuiCzLC1fktThSGU2wqLGbSIwexkvFLA6vTfGhcAf730TC3E2DRKIujtyEajk0O0SHMso6PBbEs4fwz0JqUItyFgL+oIJvsJ7ESLgpNFJsEGAKZ8oBlGSsLy2cOkYayRIARIoYZ+q6YoNYKt+nukgVUiZSIOAFzOlVjDAsQPRuj/Jy0u+dqVeO/Wz+NQTC4ytzW3466vngsAyF2wjJFCV83W8maETQW6lvZ0hrHjAEkK2rkYtaKnZAqDxFAPv7IdoB3ZeHadZncPrUyoBDhRaGFLnoy8f7lyL77jb0ZL6k7u6I4r8XhLS1AyQtw9T/6ky8sFaFJpyHYiOFYMRyoZxamOULYIg9CEnc1QOw4KxUDNSi0jVbzRIcfDol/Avj0kmaj1tFVKRhxmgRYcccsGCzU1hlmNeUhGItj5CLH0WsWiDWmIQ4vTaU/RY9Ax++5HVl9SVHcTlAq+kox8KpHJnuZKAXb+0jU463PX0HH8xWtw5mfp372GC71hqpFM9rwBgCQvkAzRsWImhwh1uAckxavOwUt0fN1SgFqhPxdfbBcUz3ZqdPkYD1iFchZMLeZnfvuduORrVyov50MLVWjymVfIB6gXPHTlzHnI7am2fy+xMOrQg64VO9CYUHzu7kwZ5oSHtBdg32veP0iqp0s8ozKFq1wYDFmJJeWUbrBsThhr0OScUzApQQaSHtQ0ATFLK0mj15+9mh3SwPXHKPkMbWooM/CCG0IIho409GYMSPYRjcNsM/Ww4BZJjCkbNE7fAVCFfKrzVUBa24FccNJMICFlJM6Q8XEjqJubcbrBUzn3gdZfWwg3VRKAcNM1DzEe6cpgXIvZKWuZAsDbH/xVAEAncXB5pT/LW0hK+P3tt53ydn4S4vXf/R08skLJ5e6fux5vu//XlT7xgV4Nz6vsAwB85tCFaEubs3XlFgyN4+Fd6wEAVjlEuUBV4NlDc7jnyAb14J2sN9GVpue6xmFJOcBvv+SD+P37fgujkni8t1dXgKnHG8Mo2KHi9C7sGkZuit6XphrqRU850nRjC7Mz0tXe4jBz9EBmDMg5EaYqpP3LwVCxaB6nM4HlMK/2X2/riuMcDfeBQnpHB89xtbjNbA4BwN3QOaYy23yDnFeu92GYKcKFnPwcU90ptgozAAZom7oKSPTE2PaZ96r5rmGkapapaQJ+g+bIpeEuothAJO8Lw0wRSUtH56AFLerjG7a+7yZw2UlKVs2AwYjTngGozHyEZIG2L/Ip8lVfSR16XQeuPMZJoiGODGVPF3ZsFIdo4RKGprpX60Md1NxVidkrKOENPzLhBybKBQnoc3so23QtZdQqgDpCG2+5DsYBegZGQymsagDeC7Dvd647rZPqYKY6iEEMYhCDGMQzFANKzdOIre+/CSAALvRyBLRpFchCDdCEQgOyhJH7CoCkmijpQd42IQSD3ZbtTgFw2a5inGauGXJ3eaWAglS60TVyxDGlG013MQ/H76+YswKZJWQCnjm7pK6A3svU6iU6Wf43qSTQJIr3eO3W1WjnjA4DAEaXqfYY0NdpEJI6lBRWCYRnvRBDgEmlKJFqiHwTQrbzWNdQ29vztqemoHTrLAnIm3qKVJbFu5qjuP2/PkVPx2coLv3G27GxREjbbI74g46X3v4WAICjuyhJn9Fnf/kqnFPP484DGwEAF6w7jE8dJHpQx3eUKMIe6bhSGpFG53aE0Rz9W4PAizbsxV89+xNP+v2vuusNGHci3LlE89AVP4fRArX6GBNoBzamylRljl7QVa3hBa+ApU4eiVQycpwYupSJzEQUAMCyEoSxAV/SRqpOT71WNn3EXEd9ghS5lvQS0rK8L0JNSWEaPpAKDUlJiu27KaY30Rw45cfWF3vfTtfhuV+4Gp2Wq67jNM+RH6EKTgggJ1vUeStCO7Dx3H//U7XNrF0dJToY639HHOtKwpAxAU2OVpJUg2GkMGU7PE01cPnarqvXiopwC+ATVBGuH26qtnzDc9Fbya0Z+Ux/hHAeTilEzo7QyCg2KVtlPsAwXm9hdl7SqYohuvLZphsc5bLsDGgcqdAQpv3fs7hMaOuRehuWkWJjha5/S0sV1SfmOopmoD6zfqSBdkmK3Bwpo17uIjFC7DvmTJxeMUiqTyOi4QS5IbrA4lhXrU7GQSAGeRPzSozEkhfeKtNwlkshUgYm22OGD1R2S8BOmIKlQG9Ycsw2EigIAMbXdbB/YQhsH7Wh7BQwMkwAAzKzD13yYf1Ruml1nyGqyXmrDvBc2reWS5hqh62OF/7SDfj2Z9cqR6VqdkwtWrZKwjYzH2cc4Dr6Jumr2+GC9UEjHRNC11RyN0Z7T8t1A4AC2HzzxTet+fsVt/3hjwR4dFZ1DsOrh+Q/hMicWL586V/gtfe8FgCw0C0g5rpq5TaCHKakdVrbcTKHQjx2aAyFko/JMiWlduigFVFb7oLKIbzn3M8f9ztfe89r8bfP/VsAgKGleKg5iTlJZTH1FHsWqI1rmik0JpDIVRMXDLNtau0lXEPBDdHp0fcNFzwEEky1sFJSdobdpovJ8QbOqcwCAFJomAtoG7vbI2iFDobztBA4/5wjuH1mMwAyG880eZPYBM9xFMfo3OSsWClAzc9Vjvl9r7jjTQCAh1/xETz/a1diJU/3XeibakFiWokCXXHBoGtCJTfG+opKus5hm4k6T2FgIpJJybQSuHm6aYtugJbnrlFlKsrzlyXrbPbNJ6ooS1pUJ7DR7tBzYv9v9ilgKqTLk6Zx+JGJepWOgRBMJUQ3H6HhuWpek8SG0kYuVbuoSI62F1loBQ5yJm1zpZFfQzsDaEEHEAc/lIu2J+7X3HfGwbfS/tfWNdH2HaQZr/80jkH7dxCDGMQgBjGIZygGlerTCGPFgDgghXXzIpPABbeozZpIaLs5ZyGZoFW38A0FjBCmAJxUAX3qD4awv/2o2r44czO4QQAk559yaGyl1e2RYhnJsgNHtnJXF0NGT6BNnTekLpDkhAJGJVbfNFwbCgHfUMbEzE2P60aROGvXW5tuuhE8Ax/pQFhLYS/JysMWqlLlxYRQwRmlIelXo+BMCaIzBsAQCpmZRIYiw/NIBzP4KQmGv+TWt+GbL775uK/9oKvUi7/6TuUneucq39KTtUpf/B/UZg8S44R+p08WL/z6O1SFtbGyjEW/gNsu6x8DW6dK5hXTD+Px7ogi3P/M+j1YiKgq4YKhFVJlYzkx4thQx2vjLdepCujTey/Ae849/n7kjRCvvvv1AIBxp41eYmHFoG1qDBivEhhpU3EZj66MYVEKPJgaR0lWozXHQ8EMsZKn13JGhAcOTwIAkqalcG35UQ+2kWDSJmrPTDCkRPNjriOIDUwWaJuuHvVbxyaHLitVbgicNT2rhCaWGkXYjmzrMIGpv78emrxuh2pdbKrQfXHFbX8I2zAQSMCQiDVY0n0qTTQckcCq4lgHOStWlWrKmfJnNbiGe3/+uuMfSADb/4VQto1ODrt/5Wql8DXk9jDfpXPGmEDLc8Fd+p26maLVll0rJyYq0QniiR7Emb7y0UYJpbvpnEVFF8FZvlJwSgMdOen5/FSEIF511xvQ9UkEJFhy4Q7TtXTuF65WDkQAsPtdf6TEZlYcG7lRD5yf/pXqAP37NGPrdbLVKABN3vmpK1Gvq9CAGZRdb/RnhtzhYE6Kyc/Tw06POKwm3cDmkRWkR+bATHotfP6ZaGynuUPrzBTgQGmPVDLSoHik3ARSm07l41fRLGjq//ug2g+WqRVxBq1l9k3Qfe24s9Tz3nIzHvyLvorSlutvQjzST75a10Bulh5q3ekEyJDNVgrDStWNGTfsfqsZAJPtJKEJigAbCwAAIABJREFUmlHJ3K3lYyXpxlMNiW88bVePH1Y88SFxKvGib/4JJvPUZjW1dM3M9bX3vBYVkx5AN1/wKfX3l93xZhSMEK5Oxz9LmgBw/9IkHCPBQpsWYVdM71SozMwxZb9HdJup3AqGTJoXPNKZwLxPD+uUa1js5HH2KMkDGowro4CI6yhZARZ92n7RDPGCoT0AgG7qKDP2fd0hOHqM3ZIisaHaQDeii/PQoSGU6p5qLzt6DEf+hp1LozizPo8VKXfYCh1Fqel6jnJeqpc8XDGxAyMmJeqluIgdXaIEtWMHZ5bmlBuPl1rKks6StnAA8VkB4FCTWr29ro0NY+RANLNnFDA5aqPSgcdzMCLlQUt2gJjr0ORQdd98HY4rk+qquSJjApxrCllbdEN0/D5Sv17w0Oi56tzkbdpGx7cVJSVNNXDOMFKm7x7Lt9W58GILZStQrflDi1Xkc/Sb2vMFxQfn9Rhukag5x4vzv/guNKT5gzlnofYIfff8ixPi9TZkch4Oj2nrPtU463PXKDOGk4n3T3/y/eB+gENveO9pjf4dVKpPM+yGTBp5KElBrgOiHilvTiUbCCCtxUDU9ztlKyaCiqweUw3OPK0I+XIDOG+bwvY0ttsIJMPAqPtIEx2GJHXHBaaSKkvJIWd1HPjvJ+emZkIVT4zVCRUgkYjMggqcAFjdjfRgZG4KQzqGpImGODDUXEYrxeB+n4qTkdqnP/oh5Ca6ajZVLXuqokoSHXtfddVJ9/1HEb921+8BAO7ZO42ReqwcYD5y4S2n9Pm8GWHSpWprKSzgD+57NT767E8CABbDgppRAlBVoAYXtp7gnqMbANCD92enKLGdUV3AvUfXY/swAW4O9GrYmCeQyOrEDNCcKy91b1eDuC752pUYLnqK8jDfLWA4T8m3ZlOSH3bpIX/vgQ145Aglsyu27sRdc8Q91RhQcX3kZKIYd9sA5Q+4RoyI65hZJnRfrdBTUoeT5RaWg7wCy9h6irnDEgWoCUDeQ20rwd7eMHak9N0R19GRyWVdvom62YEOSmZ3Hp1GQe5H1e6pWe5ct4hGK6/k/IRgmP8WVcXYGGFy3QqWpFNPEphqn1KuoWgGipokAHSXaRHATK641rxrgsWMqGMgvEXco+u7VPMw1yyq5GkYKc6szQMAGmFOAbf2Ltbh2v1qF6BFDkB83ITragHF4z4QqjTahSu57R3fhrEK5AVAzdm/O7ceneWi4pjvubIPCpz+xPsx81t/ii3XU8EQ+99/engqAhKGlYAnp+Y1/OMcg5nqIAYxiEEMYhDPUAzav08zzng3zbCikkBalqsrkwOBDnuxzzUJR6SkWz6G5fSruTTRMPxVqgxyczGc+ySQ3DDAp0YRjNAyvzdiIKjRqlJ/0Qp6D1WRk+bjYRVIzqYKQszkldLKE71Kn25suf4mRZXRUvQpOnkOs6FByIUs2+gpx5C46ZBilPSl5IkGw8p+t74GATj9D9dTNQIAAihXqCoyjfRJ508/qrj8W29VsmrtyMbdP3f9ST/zpu/9Jj5y4S2qwtUgUJOiBbvaI9AgMCHbwcthXs0dS1aIr8tZ8Sv/843ggqFsSfRlYmE5oPc1fQedrovnTpHnq60l2JanCmjcbOB3tt2FX7nz9wEAD81OHFeY4LJb/wh7jwwrc/b1laZSwSmaIRaDAkqSCvFPl3xszedCKegwkusQqrdLWIOObyvR/LrjIREadi6SG41rxdhWpcp6vlciD1i5neVuDkFW3ZV8dLp0H6SyyzO9jhShXjC8D13ZpjFZirLhoxFT9fgfh7epFvKm+vKaqi9nRPjeA4QMNuoBYjnjH13fgK5xNKUDlKYJ1Z61VlG1AODoQgWarE7TSFNSm8I3oPmaUg0zCrESUijlAiw1C2rWaxgpNtepo9AMXNUW7nk2hmudNaIaT4zz/o1Q8q1GHkxuj8f6Kfn4Tn/i/dDnbKW+lK/1zZbLOR9lO1Cz9Yu/+k5EUjrUDy089sp3n3T7328MTMp/iuOx9/QT19T/kW3RSAPjDFqmBcyB3AE6xL0pIJUgCN3I9DrpbUwAwqeLm7ku4rKNoEoXc5xnKpk98vJrgZcff3+m//eH4K5K5ieKbdfeTGpLUpv3wGuvVDep17PXmCgnExHQlZeIt7apEY3FalZqpBpsR7bUShElniNSwaWYYs8JAEczv/1OJYPGOVM3cHOxcNLfcaLIaBCGluJfX/CXT3s7Wbzsjjfjiz/zYQD0cG2H1HIMohODQlaHn1p49d2vR8asqlk9jJiEMHsgnsRIroO6RQujWa+MjSWa8R3sVJVUXb1QwES+hcWAjsuehTo2DdMDueIGqLgB2jHt144jY/jbV1EL+dJvvB0fm7kSd15Ozj3P/9rah26mlgU2DG3JQiCvS6PGlRpSKhgONitYJ+ehQB9UUylYCqi1LteEn5p4YIZUjZ6/ZZ9KZvN+Ed3IIq4ngNpED0vyt7QjG46RoCPVnap5H8tyBtrxHHAJqEOsQevpOGxLHtAwmbADZNH3SGcCexs0J9leX1Az4IIZqgVImBjYVFhW9JLV1/rWT18Lnmo4bz3JMVYtXx1vDoYFr6BmsxMjTaKeAGAu4MtFgFPvIfAs6Ga/TZxFo5MjJyqzn5x3zdH82bFjNQZhmsBKOwdrlWXc6iDJQqnEZHJAqrJZQV/l7cDvv/24nwUIsDT1dx9AXgKQyjlfLSTavoP55bJ679xcBboln1X68fdnEMfGoP07iEEMYhCDGMQzFIP275NEpiD0+J8eq/Az9TFaFbKUKT1bYUtZo8ygu633Eb+lRL1P7+gwpzyMf1y6Q3xvP7ovlGT1ggYt6evl9sY0PHzjU2vnnvE/bgbjwM73Hf9zUx//oPqCA2/or2o3/dP7sE8ChLZ86s/BD+dUe1aLmEIX73/ribWDN/7jdWsQg2d//t149Bfec0r7fdmtdJyfKOLwVCKrVM8qHUU3tU8ZQPRk+5SXrdADzapqA9pGekouJJd/662o2at0Uv0Cdj9Ofqdnbj+MUbeDhxYJfDNVbqi2X16PlPrMnXMbUXV8BZwJU0MhUUdzbext1TG3SBXG8FAHy02qsC6enkGQGnjkKG1f1/lxEaFTf/cBuPstnH/FTvpuI0JLVr69xEI3srEi25MXjx/EIWlgvq20gKpEK5d1H53UwYMtAv6cUZzHv+w+HwAQeRam1y/itss+pI7poWXahmUlyFkxmt1+2zVD1CaxDt6WHQFdgLkp2LKktegCkJSXDMGbAXgYE/APE7J5fPsCXjd1JwBgzGxiJhrGve1pAMB3ZzfAk+YUMAV0O8VEnVrWc40ixqRAwli+jZlWbc0xi2VXpes5SKURhJ2Loetc7X+aMnWfmVYCv2srUFMmapFF9hld59B1Dl/6n2odA2NnUqu80c3hsVe+G2f863vUserN0m8WufSU2r8AoX9/e/PdAIBbZi5CKkFLYWyid7QAoxaofQ6l7njaM/Arz74XANBOHHz8Of9wSt/1VGPQ/v0JjqmP3QBHzmXOuPrmY+aURku2YGKoPk80Koj/mc1bagmYbKm5uxwleF88xFH4ognrUZqDeZdsRnuDRAynJD+m+7SNzqan3nYJxtInNfg+8LvHRwXvW4W43fPr78LUx26A0aTfabUYdlz31OQDATxpQn3h19+BiYJU8YkcbJfSflfc9ofIGdHTat9ms8BmksP5hYNP+fNPjPFcS6Fix0ttZdb9hZ/5yDHv3ZQZaKcMtSqhZwu2gXbsYNmnh/dKOw8meZOLXgEHG1WFmJ3tlhU6NzBD3L9ACWrb0CI0CEWt8AVD3pRSfkzgvKFZXD5ORvbfbUzhFeseBgA81JmEpaX4rxt3AwDuX55Ujic5O0ZbosjXT3aw+awlPLJEyfdZ9VnVPgWIwpOp6WiM44X1vQCATurAlrJaE2YD90XTKJg0S//KwbOwUbaotRGBlGsqGdhmHjxLIkzACy2ES5RUzWqo6CpxYChDCrPDEA1p0CLJ0W5qiKSUZzKioeb2wMr9+sDbQtv41fXfw0XuDADAYSkORUN4dJkMB3zPUgvgiYkVLDYLmF2iZP/c6RnsXBqVWyvhjNo8dsrPdXp9CowQDHaOkrtrR0i5tkYoP5NfjEJTuTwBQBLpKsFaVoLRCiXwbmhBCIbRqUW1/WyxkM00swQchTqQccxX8Tun/uqGJ20B56wYD3fWqf9PlohGlI05VsfUx+VM1xT47GPnAQDOmTx6wm0PYtD+HcQgBjGIQQziGYtB+/dJYvt7CX256+pj26gZ+lewvu5tPCK5qHK4b+YjtaJ9+ZZH8G9ffB4AwPAY6o/EaE1Taysq9UFLqU3auuGQFNsWgDUp7Zd65jHKKD8p8fYHfxV1CeC5v7VhDcr0RxUXf/WduPvnrlcKSAbjWPQkj5Fr6KzklWm2AJTgxZbxRcx1qP24ubaETuzgSIPas/6Ki+FJajE6RoJUMHQlSIcLptDFF44fwoIUZwCAbmQr8YKa3VMgmrLlY3NhCbcd3QIAGMl3UTDofXfvm8aZ6+fgxVThBomBZpcq5nLehxfS3ydKbYzm2kpcwtVjPLhCVfLBuRrO3nBUmZRvy83haETV3A3nfRq3PP5cAMAZ1hwunOp3Bn7trt9DI5DVuZ8DF0CrI9V/7BhRSNd+0jXJc7dClWW55KFxkMBIwuJg0uzB6DGY7T5oz+j10ejBCEdhY0v5q5YtX3FKp3IrmHYIMVzUfHBouOHRlwCgat2V+rV1t4vDnQpWpND85tElZWf27y/6X9j0T++DI8F4vY6tqs5ywVfdhXZgo9N1wSWgSYGsQAAkXefqNcNMYEtkcL3gqfHAQqdAgK6WNEg3hbKZm3kTdZ82y45IEhiK+85Wc8A/8X4YVoq4K8FPVkoesAB0T4PVZIhL9H1jF84pzvDxKtUt/3wtACCN9e9bCOJUYtD+/QmP4yVTANj4v26EGOsbjmcOMHrDQJrvk8EhGC6cPAQAGLHaOOfFjwMAHr5tK1a2m0q4QeiAFINBXOAkPF+nByNjAsEytcaMto7t77lZGYI//mdPvR374xp+auJAQuhNjQmc/8V3wbXoIeYYyTHmyc90bPr/icKTBjpqI9QOa6xQIpyXIvHkLEKtW8eKMTmxgp5EAa8rt1Ty2j0zBlMKpB/uVLDSLEDPPlcNlHi8p3E4VgxDzkrruR7aUoVoKSigGfTVd+JER82l+WWWUAFgOciTOo98sC/28vAt2qetkwvoRjZyslUMAJVC34M0k4SrOj3cc2hKLQD9hRymttAc78z1cyiZAe44TBqYD7kTSkAeAD6zIF1vIgdfn1p7TDMfzUbgQgNDVUofDue7SipwMSxB6+gqAXU8B8hLab95izyKAYhARzDClYpXXBLKI9hZ0NCu5FRSjbiBSI5a2omDpZjO3xFeRTPJKW/YfNFTx8bRE/iRqc7v44+sw8yb+9iBtG3Bl8dramIZS10puainygvWzifodF0lfRhHhpqdGkZKCF/Wr2GyRBokBnpygZOkGrRFSy0YAAFW7Z8/AGuEUTL3GWEJlWydfAJ/2QULpPJawtRCPzEEkjLgHqFjcFH9wDEiIasjltKMWttQ35Ul90EcPwZJ9WnE/j/8YwVUErqAJi9ec8qDCA2ct4Fg+cNOF/ccpSfNPQemUP6WtFEaZvBHBHRpC6clfbeZqARgJIAuH7TmQwU4F1GV4OUciHkb+/749EqmGU9yNV8w4saalfG+bl3RVbhgqOZ8Jcf3w3CauWwrzSRvP7AFK3OUTA+8/h10nm3aL6YJ1KTlVxgbsI1EJbqcEanf55RC5U7S6TlwcyF8SZkQXMCVM8MgNNGSQBAA0DWhqqNW6GBEuq40AxdhbGDBo2Q6lu+Ay2GgF1tohDllHJ6kGiblnHpjfhl7u3V87oUfPenvv/ir74Qtr7nbX/luvPvhX6Bt2Av4RuMslbDaga0qXAAnnHv3Ektp84axgbIbYCRHnQhHT7DzcalkxMnw25il/be2hUqFiNsCzhAl5oA50DwdXD6xdJ8pLIMeAaxnKOWhXbOj4PN0zsJnGSjotED1uYXDvYpKbAUrVPtoaCkcM1GdAt5jSm1saGMDB97QB6VtvOU6WFJBbH65jEWdzksa6zDtBKGswnWdK4eWILUgQh16IVavld2+DVoWYWhCjIZrVNn4qSgb6QKJ/EzasKUFpVxZaAK6XKiIRRtGl6lO2Ge/cxFuvuBJthtnsqJQwLBBPHkMZqqDGMQgBjGIQTxDMahUTzGmP0ztR91n4LaApqSGgKQiVZM4g2GmSlT8gfYGoE2HWBuKcP9fUYV5zp/cjMeuXVttnnkVzWh5jsPUOdzv0Oq3NyHA99NsQRsLYG9q/wB/5TMTF3/1nTBlxcUFQ8WhyuPR3evwGxcRlP++lQ24/FtvxZ6jdKyet9HHsEOVWSYyn9FjnhiZIXczcPGG6dvR4dQBePMZ//GU9/UDO67AUlzEckit5/MmjmCnSajP6Q/fCK0Ww3Zle89M0Jat217LhZe3UcpTtZGk2hrVnXKeKqxeaMH3LaSBvNWcROm1pqkGIQAeUqdjca4MUyJJa2VPqSt1AxuWkWB+niron79wB+5aJuPxpW4eAn1EqGPF2LtCv+W+3dP48mV/gZfeLgXqI/uExu1TpQbuO0Dawq+957WwdbrmetzCEa+Cc6vkY/rt2U2o5ak6f9Vdb8C9h0jsAYIhnwuVKEYSjyEnxd4LTggvMvHgMiFOq2VPVWwAkOY0JYhi6immpGoSYwIzM3R9WIsGhNHHL4hVOiepI2DWfWVcbpgpAllV7T00ohx9LhvfhR2NMSQhHY9DzQo2VomO041t2EaCld1EneHVFEabvmRlTw1bPngT9ryD7lnnURe9Kar8tHwMQ5pJ8FRHFJiqykxXtXrBGRlsRFLUReOKpsRAyHIAKDohZpfLYJlBRTFeg8pfc84+dgOY7FjobV1V7tylOaww+2OoVF5jjAHxhhCmrLQf/9UTexhvu/ZmsLqc5775j7H9Pcd3gzrnC1crERAuGAw9RU/iBHb98rEKXj/pMQAqnWJkaiVaoEHogCjJNo6dolam1pilp3DNGHuPkEOG6PbXLFqkYd036Ua54/MnhrtvvOU6kj2TSikQTFFx9r799Gr7PjG2fvpavGIr0T24YLhveYOiC5w1OqeoK1/72f+55nMvvf0tylkkTnRsGaaH7kuHH0aHO7ivRS32f77kr/HeR0hyytZiXHnWv6/ZzuXfeisAYDzXRl7vz6naiY1I9hV7iaW+q7mSB9MECmWZIPeUwev9ZDAx1lDtw5RraHXpnBXzAXzZIo0iA7Ydo7dACRIM0GRCEYmGctVTlJrFRh+YFPsmNKmq47gRem1HgWMK+QAvWb8LAPC9lfU40ijDldso2pGa9/VmC8gd0hE8i/Z/89iiaksfbZdOyWHn1Xe/Hlz0G1oPzE2qf7tWjJ+dIJzAvzx0Ic3uJIWkONZRMn/dwAa/pwJ/OyVZzeSoynum1XER9/rqVLmKrygpcdtWfzeLEQFvMsvCXKKSyMhwG2U7wJ6jdN/xuL+/dj7Cc9YRruGIV0Y3tNGT7VnHirGxQkm1EeZQNEPsnKMFVRwYEHKsw0IN+UM6/FG6Eff90Vqe9nO+QgCeZjsHzjVwyaVlKQN35c2b4Szk4oExKLqd4AxCJr1crQe/4yiDilrZU+4ur7779fjkxX+jvnfTjTdBMrzIsSonv0tI3qp0eZr62A3QpSIatwVm/uCZnYm+5Na34fwajbzuXd6AIcfDZ57/V09rWz8JQKVB+3cQgxjEIAYxiGcoBu1fGRv/540wO5oC53FDIK7IlR8DDrxxbXU5/UlaPZpmisVDRAFwjxiwG4Ax0W9RZdsbekgoPd8z/sfNeOza4yOLNU0gjTVomXyww5/xleWPKp6s1QT0bdWyuERq1Ta6Q3CsrC3aQ5TScexxG3t6I7igRJXI5/aeh2/OE11iMt/CW+7/Ddy9QFVszoxRlgjZmunhkF9V35NwTdmD5YwIm2tUCT8cWIjaNvzHqHKFDlg52kYpH8ALLZgSLeqaMfwHqO3ae7bAuhqBy4YcDw/OTiq/WQj0VYIcDtNIMexS1bbYLJB5O4B8xVct3TgyMDHewOwstSYbTRufXiHULZhAtdZFY5mq3KjsKz9Pc9jHhrOXFABsqZeDJ/09K4W+kPqTRZCaWPQLGHJoH39zy714pEOKUHft3oTbGFF5RKYWJsUIOrNFeMV+VS/WpTAPSXUeB1jKqsCeDi1hpJAEoJfmlDgGc1KYB2l/40ADK8cYHSEQ1mKjiHKR2r3rik1oTCix+uFaB45EKBesEBtcqkZved7HccGXrlLXUpLq2N+kYyoEw4HOEHiD9tEa6YHl+vv/6JuO38bc9pn3IoklUCnUAQ5ACurD16FJgQpeSlCo9eC16VyIVIOQpuJaoMHw6fgFXhFgQJyjzy2vGik8ujS29ss1gbjW1/Ce/mjfxpF5OrZ8gJTJ7FhXTAMMxdh4y3VrjC2+34i4joebdE0sdArY9/gYtn5LqqIxIC7SeTkZanjTjTeBB8eCt063+KlPqhmKVxcauCmUGL7hMQhDKrbkOTZ/kC4SLQWSqQAbJ0gtZv+jE8gflQg5BiQOqSIBxKPL6C+tzRqk+MwJEypAcPlNN92o3GHc2ZOL5P+kxD9f8tdr/n/X5R9Q/87UiqLEwM9NkZxeCgZXj/GfKyTx+F1tCr+x7rsAgIW4hC32vOIo7u6NIZYi8d3UVupEGhNohw5avmzdOiFqcjbn2DGSbk612KJKirRF71vskOTc1CRtf1t5AXglmXx3Ykchlx+Yn0TYM+GW5Ow10aBLjmA5T3zKTAVK1wUS2fqMOENBzmtL5RBBYihOLLeYor9USj0sL/XbxheMHcZjK9TC7B4u4YBRVV6rE24btiREx+LUrqsxp2+SDQA644oKMjzSVm5CZ/3ZzYiLAvaKTJwWoB4vDAjqXHEjmYCSv2Qpg7PQX8z21gMI6OIf2tTAsuRrspgByxYWlmWL1+FYkbPLihugF5uYlBKDZ1fm4Mr2vp9a+NSjtAC59YM3ovMLjlq4JJEO/Qidz8QVYAL9c72YAyvRNpgmTTMyTmisQVjZgtvtHyyTQ7NTcNkCZ+VIoYndXATHTBDL+XzUs5TgvtAFkoJUV2poiKockM+hxDcw/ZeULHPjJp795auUPGZaTDE63lRf/8TF99TfkBqSUwmQLkg5Rs/A/jc+M4v0zAzDMApKQjNTUMsSPIv6Uo1PFtMf+RBgA5zzk773xz1+Kmeq62+4FprrQDj9E2jNGQCDsk9LbdDFDXlNGFID12cwpzxER2hulZvVFB0mdYAkB0ivZMRFkamgwegwJYmmJUBYETC9zOhcYK8EQUz9/fVEM/DooXe6csIyMFHF8pWowN9c9Hff93anPnaDolkksY6t4wsoSUu0RpDDO6a/AgD4cvM8GBrHEZ+qzF5iYsIlMMjhXgXzPaouqo6PXtynf7QDG35P0lO6JvSO3rf2Y1BiBBCkh7zlQslDdjuomLRfO1tj2HNQAmxyMSwrQa9L2zSsFJYk/eftCHGqqQdvGBvwO7KS8XVF+j///H3Y3xiCJ2k5hsFVhdztOOChjmkpa3fF2A78497nAAA6jRzcYohrn/UFAMB93rRKiNc961/xZw+9EgDw3eUprMs3Ecq5sq0lqrorGz3EQsddK8RT1ZjAUY9mXbOzNRRrkmIUmuD784o7CgBC3jN6wJDkBeSaBmmRwx0jUJo/WwCLGcyOrOhMoTia3OX9Gaqg7ZgtSUOLAX9UdoTGAqwfbaAtwTHryi3F8V3xcvBWJCAo1CF0Aeb2ebaa3EfbidBbzKu/gxMOApC0uYghzcsb2+Jq3q0fdBBLoCI0mpe7spuRJBpykrM6nPfAwRTt6shKWSV3w0jhHaGFkRYycEdQMgLomTRBx1gIhqhjKUlCFmvqGjHbDDmpHuitA+IyR30jncPVNorT/3A9ipUePClRue83+hXri//jj7HYzSOW8qyj5Q5ieV/M7qsr6cPpT7wfTBeKgxt1LBx43Vrd4c0foiJk758cHwdy9jtuhrcxVQsX0bYw88Y/GcxUBzGIQQxiEIMYRD9+Ktu/LCHCubAJ2ZeFljDVqRC6gB7IqtUSyGpOq6XBvq0IR1WjgB7I1W5LIDUZemOyhez3Z0WPXfs2nP0OgqQXZjl2XPfHiqazWrmFdQwIQ6ypon9cI5t5DrveMQLzmal13ghxoFs75rNPNbLKd3RDv5rgguGxh9ejurEBgIyUb6+fseb1IKVLfM9KHUsuVaeNnqvEDApmiBU/h6Idqs9l9ABP44hhw8xTpc25Blv6UOo6h6FxHFim3zYjakqpRzdSuEXanmtH0DUBqyIpGAwIpcOJYyRIuQVLSuXFqQ5XGoXbtUQhdXuJhV5gwZaI0LIbKD/PUslH3o5QlBKGf3r2l/H3u6SJQagj6OSxZzu1g79y8Cw1b32s80Y4+hAA4FnVIzgalNU5qxg9zIZE35nxh7BzZbQvZuE72DREow+/bqInxSsYE2BTPQSSOqQ1TYV8NVo6hCHAsrlrV0P0uKSJmQJpOUFkSjnCrgZdjtW4Q10d+reAPdVWFT8WbXAnk/JkOLxUUVX4/lRHt+mqY4DM39RJgZRBZGIKBgdkxd+bzwNuCt2m/6ehrjyH0TahxQxahXYsbtsw5Nw0LnJkD4N8xYcQUJSjIDHUPo3m2jjUrSp6TxSYyhTeMhJ48rvGtjSw8MCoou9s/5f3IufQue14DqGrM9ELrz9SiosCbVdW8et6OGdiHhWL9gMgJyIAKNV6a1Dfb7n/N3D7ERqfJDyPMDSpGgYQFnrIyWvTbOnYcj1Vn2JIh1ZIYNuyle1Z2PKpP6d92pUHNwXSwpM3QFMHcI7qYIdlF4EPZqqndTz8B29GqVTClutvgh4FfqgXAAAgAElEQVRlrRRpFi5zbFzsz1dFLYY+TxeauyDAEuB7H3/q9JZsjNWe0ohjJvs/mz90k2qTaPUQImVwdron2syPJM787DUYkhJ3Y/k2WqGLqSIlpuPp9H6/2r3nfuFqFOXDZCTXwZcvPVYV6KW3vwViE4MlQSmXjB3AN49uBwBsKDZQs3qoWpQEK26AIKHL/eXTj2Bfj0BFzdBFxfHRlfKAhsZRydFnwtiAPeypeZGhc5V8m74DIRiCjvyckyCXp9dKboBFab9m5VNoTKArXWrqRU8l1YRrKNihevD6eqqUkcKwL5mXpDrSVMPGGrXzDJaqedzquXMWmZsJAFzwpauwJIE0RSdEV1J95rwSpqUh+t2L0zg8X0W5TOeXC6aO/excFVYuUq1K00wU8AlAfz4Z67DsWLU39YlYzYetYQ+9xbxKqmZLQ5mYOGhtZhDMUD0zbtAME6D2evZDhUmWarpsOfLREMgAX5xBpAxMzjnDwESxKhObbyGR3FDTSXDe5BEE0tx8x5ExJZ24nBYAAaQy4W6Znsee3eTaA0MgXRfAkL+1Nt5CJK+lyOIwszlpaCBNNfgy2bhmjAvrNB4wWQqTcXSkHulYvoMlnxaIncBGoUL70ey5yB/ut9D1+4pojMuFZDmGXoyRSqoetwR02QrOWu0AELds7L9vE6IK/W3qoQ8ClrSc01PctPMl6r3jlonL1pGL0YFeDd99dBNY0ncQ2reXFmT5Zn98ZbVNsIs9NSZBypRKWHuDCa1lgA31F6nHi6jKaSyQk7N1CVbLipDTOQbt30EMYhCDGMQgnqH4qaxUs+C2UMuKtCxgtTTknk1ozlEnVMIEALDzt9/5fX/fjvevRf1m2qJJtQ+cSNsWdE/Dzj9/asbkP+iYqjVUFXKwXUU3sJUTy8kiE/pmqxRm9vz6u570M0FoYn2FkI2ZMMMT41CzgslySzmScDC8RPqKPtyewMFeFdN5alWeWZ1TnzscVJTQxKFGBTm7T50o5Dz0pDC+a0dIUh2tFlWZlYoHL6LXinYExgSGp6haP9Iqq5Zy0QoVpaNohti9OKxoHLrGFXhtpZtDveghb1PLa5HnocuVernQA5eHK4hMiJTBT6RaEbdRkELwL7vjzce4i7zkVrp2Dt26ATjfUg42WcUNAHXXQ1sakResEJWK16++Ih1XSIT1o844lv2cUkoqu4FSAgoCU1WqupEqBxaAxBN02XbdXF9Gu+ip8z8zMwJfonrj9SGEgDKhEE1TeaZyA4qTprkJ4shAuiSrIwboshoyzRS6zhFlakU9E7Evq9hIA5NgpyjUcX+6HoknX2MCS21Jl7I4dDsF79I2Dq9UoBX7Ih081hDLCq4Z6cAi7QcbitTvtp0IFSdSms0HmxVF6Xp2/TAOelU0fTp2y0tFnDFFyKINxQa+c982+i5DYONj/Spv3ddbYAkdR3+igPmLbGy4M3udo7Et48roqO6mv3cnLbS2iuMabrz67tdjzGip/5ssxaGARhit0IVZipS6VZgYMBt0TcQFgcev6m9v8z+9b42w/+rYet1NiA3ruK+tjrjIwWp0HaeeAaQMXPz4j71OFj+V6N8MWbblAzchtaW8ViVBvuLDl2LeWLBhNelm2XXNDybBbfwLmqnyYqLUT37c4rJb6UbigqEjW5NBbOCRU1DjyeKCL9HNd///876TvPOpxeXfeitG3A6O9mj+N+R4iv4x7rZwpFdRdJUgNZXsnxda6CzSgskuB6iXPPhxP3Hrku6RcgbHTDAqheANjavW4YJXAGMCy7LNq+kcZ41R4s4ZsTL5fnzHJNyjOvIvIHSuF1jK9qwouaLZ99lGoji4lp5iqd2fH1tWgmGZtIPEQFkmYkPjx7XsyuKsz12jFJuqTl/Kb7ZbVpZfw3YXITcwnaMFyB5vWInTz7Rr2FRewsEOPXgXO3mVRNJE66shRRr0coSS/E2ebyOW89UN4ysIU13tcyo07JnJDMABtxyoeXTiG9CktCdLiTZCB4CvmSciYdCkqpkhk2rWbo67llIuQqT13VqcFEYxhinR12mqIZL7zzoGGGcqoadjIQwrs3cLFI0FALZfczPCUblfhVhRRkRPh1GJUK/S9TKa6yrLvHZiY6Y9hDmpmqXrAqFM/KJrYP3X5Ob+cz94owHo0uh80wbwnJxbJxwQAuyghPlyAb6dkrZgQPMMKW06xsD1vqRjOBmjNETXTjZPfdE3iVUw7HYRSdyBl1j45otvwqab6LnkLGkIa7SNE6F4nxhnXnUz/DHe5/uXY9TqdDwaB6owJMo7KXEgBRjvOw1FJQEeBJh591UD9O8gBjGIQQxiEIP4KW3/Xvr/fgS65cAcY9jzvrUrsAyR6yxrsBs/2P3gGYE8/PFd24y6tMo82ispz0drlZ/mqcTKIq04pz/5fsy8+pkzWX+iRjAAZSh+IK2hE9logtptCdewKFWH0MeBwLVjzM5VFe/wieLlv3bX76lWcSc2sKlI4wFHj3GwXUVFAl2qjo+ObKeuBHnF73Mnuhg7s4NU/t/UU3Sl8k/CNTzyiveqbsCh5QpC6V/pFCKlEJSmGiZKbQVoKpqhsn4LEwPP/9qVqrV7ZLmsWsiMCYxXOmhKYYvZdgm/NH4/AGCHPaF4qcshVcRZe3/E7ihucTe2Md8rYb5Fxy6cz0HvyUp1NEROoqF7i3kYe1ykDWqVx1tSJZAwe+84hA7Mj1F7cmpiGUVZOUWRgdJnC+iN0TYLRzgWL5TCB/UYuhS8SAMDLNQUIIclDEKqH8XlGKnBlVg9VoNdBIMz2VXHI/At5CV3dHX1+cS44EtXYVKK3I+7rTWvRRWOynqqQDtdF3xBWgMK6lhk56kVOZjI0W8pGBHG8m3MrdC9EPYMMKkRfMZfLoD5dGwEAG37ZrAV+k5xdAEspmPAXPk9bh/EqPWkhnWSYug++nfnlUMIhlOIHF0T+eqx6lm3XdZXX3r2l+maX9lTw6YHblTaxtuvuVnpjp9qJHkB4aTQMuR0qGPlCLXYGQPiMSl0YqdIewa0Fl2DQqfW92rA1ekaP9Xt30GcPF511xsAAM3IxWSObvQXlB9HUQuwLyJ1m5wW4WBI9Iwbzvv0MdvIWk0rXu6URNwv/uo74YXWKbeYM0HznBmjIxN/3opxdLkMS85Lk0RXRPyiEyrf0qrdgxfb+PqLj+/AsTre+eAv485FEkFIuAbbSFC1Jco0NfHlS/8CALWlVyTad6zQUeblAOBFlpoBl+xAzUkBoOU7eOBlf67+n5k4wOEo1jwMSarGC4b34e7laQBAN7IQJboSkGiuFDCzav7/mntep8wDdrZGMV0gxO+u5ojaj15souqsffBGUqlBCIaYa/Az9xmuIdgpzQ2qKfSOpML0GPQAMFZtRpPPey7HfokEDYd1DmuKEl2SaIg7Nqx5+XA1SMEMAIx6gGRRzn03tNHbW0aaCdRrUMnTyMdIfANIsgGvUK/lSgFKOSkO0qFzsvtXTu6c8pp7Xoefrz0EADgQ1fGvh85Xi8rNtSXcv3MagFRXkonAaOtISikgaTn5cqDmyKaeYqLUhiU5Qg/s2wB9gba37cOHILpS4GH9KILxAuwlOpDs0b3H7BvbIE0NhAC0DBSS4ujlhFZub+UQGpSfqjUUqAXaamT4Mxkv/Po7AABHHxhbg+oVuoC9LOU2iwL2trbcXY1a/nN0cXBTAIKB+wEOXfmu0/oZ/VNZqQ7i1GPepwrFiyxVyXwjPUvxPwFp9yQf0E900gBINg4AeOXkcmUAAXPWlVt42R1vBgBEqY6WpHEIwfBfxokCcNCv4ZbnfVxZqS2FeeVw0mzmwSMdSZMeXMNTDcSSyjLfLGLzCFWc+xtDCCJT8WCzxHi82Nerq2Q8lm9jvldUVWzMdfzmd34XALBr71ZMrqf55N6lIYyUujiyTHNf14kVAOvRnesxNr2MzWV6b3YMs6hM0gNofaWJTYUl3HaYAEe7uyPQ5HDRCy1EkYFnTZA1285Ux9ZPXwsAqJU85MyqSvwG4/BIPxDrik31gF8J82gELsbz9H0LvaKi3owVO9g9O4qS1NntzBWRa2ecbQOhpG2YHtAb5zA7fZnPTK4TAgjqAtGQ1PRNGPwlSnDMSQFdgG2jJBu1beiupBKFBoSkgnTmC8hv7CCStm1pqqnKNGlbNHPNlIYiTQ22etztu960bFTGOmuO8aabqTMlpJpaRnsT2jk4eBHpQx9ersC2Y7VwuX/PlFJ6EoZQrkOJJmAU4lULOQ221e/qXFQ9gCmbrrv7d2zE8H0y8QQhkEre61AO7r4VsFSC73QdIpEcZ9sGdB08L1cnOqPECmD5vBKa59L7zKZOFaF06zHNFMNFOr5nf/7dePQX3oMzP3sNbVMT6Eolqdp465RwD6+553Uom756Hvz3+h14+eTZAIB/6LnozhdgNiWNqdHnHZseQ2eM9n3r+nksdAtok/AYGADumRA/ATKFP759x0EMYhCDGMQgTrMYVKo/pJj62A0wyzQ3WT/cwK3/5cYf8R6dWpxoP3/x23+gZkcb88tKoP1wr7LmfZd/6604u0wr98WogNfc8zoAwKTTxLfmtipjb8dIMJmn9vJzxmJ4iaXmhst+BbacGa70XNy1RAbdhsbxijveBNsk1KPn24i6stXKGcxiCHcocyTprx8dO0bBpHORrbYfmyU06icefx5+a+t3jvub//mSv8al3yD907weYcjx1Oc/98KP4nfv/W0AwIHXv2PN5879wtXY82vHcei5FHjub92IpdcHa/blibHj8DiSCQ1TFRry541IaRU7poucFWPXEi35vY6DoRpVJRtLK7D1BL5ELJdtH4FsNy/6eaXCVLN72L9cw0XDBwEAE24Lsz5V1p3IAU8YGvPUitM7Omq76Fz4Qxr0ULqr1AT0sI+eTfIC2fDaH+fgbgoWZ4L0DEY309XVoU15SGQXoVT30F6QVDbOoEttWNNMsa2+gJlmX52ruULvM6sBklhXFCTBtL5vaddAkjniCCAVTFXyUdNGbRtdc6OFLg41K+jKmTDzdOzfRe1UoQlEmg1T7ovWNlYZgnPoUqSDGzoYE6hKg/rlTh6dJm3PKYQo6gE+PvNC2n7CUHmIOhSII6QdqqCtHYeBSgnclYhffRRaR7aG4xjMNJEW6DV/1MLi+bK1uiFEXZ53tkGQcIlJlWsvtLDYoWM1Wurg1Xe/Hr0WUXgQaTjwhhP7O6+OjKq10J1E2Q1Qsel3PlYaRVmnf//65u/h1uI2HHiAXGu4xZSRghYyMNmG3jtfR9q0VEWrJUSh4sHpbyAymKk+zTjvzTSDCyuA/hxq56WpdszMYvt76H3BVISSFB839BQr8yVocvbCPfOUL+zTIVY7aZz/xXdhXZkeXM3AxboCHauiGeCRlXE0uvTQmR5aQUWqH9Vtejh05BBu1iurGd+R5TJcp88rBYC2fBAi1qDl6EHi5iKkqab4oY4Vr5kbZrPMhXYBOTvG5iq15Z7olLM6zv78u1F2KQHeeRwlo9VxMkHxk8Ur//ON2LtCqk8J11ByA5V0NSZWY63ghRZaDWrhjY81FHXFT0xcNroLdyxS2/jgSlVZ0s3MD6mW7nDeQyo0jMuZ+d5WXc0Pa/ketpcXFKf17jvOhDtP3945KwLz5UOQgVqicgHlPG4TDxw0bw2rArqkV0ZToaKhMIMDAmAL1EZPK8ka825L2q8RD9VAPkcbiWKjT0npGdAKMRyp6tNrOyhIAJV3qM+ldhbJms2fltePJlCo0TE4e2QOhzsVRWMKOzYMKby/91VXkeOLpD5NTK5gQQKOBCjhA0QxOmdyFnvkeet5jjIsF8UErGvAbFMSLD8ODP3zg2rfhAQjaa4D6DpYnq5pUcqrFq84PIf03E1IclQLHXiZifx0Sx3+jDqVpDryVqTAZUmsY3yI3hdzDWFsKHnJyDcx81unBh48799oYbixugxLT9WsXlvFP18MCthxdBTGo5JqFvVV6pKcQLQuA1ZpgCbApDqU4TFAA3gQYP+AUjOIQQxiEIMYxCCAQfv3aceDH6ZWyOYbbgK7h1qeRgjglWvfl2lZamYKT2pllgo+2YjJ1/Ij3g9np38I8Yvf/gNsrHBlOL6ubKFgUHXh5GPV0l0MCmj7jkJHznWKCOUKfE+zDi+wMFKiijVvRjgq1Zs419BpEaVgZLiNhYUytKzVt2RByOqCMYGCG6qWYBgb0CQZ3k9MBTrZ+UvXnPQ3ve3+XwcAVHNT+PZLPnhKx+HpVqhZVK2+2fhIvgdTS7FvniqgQj6AKyvwKDHg+RY0sw/w2LlrHQDysvw/u8ZgjVA19qyJWVXtOm6kDMs7noO8G6EoBfVzZqwUocZzbcx0a9h9lNrL/+2K2/HJ//gZAIDWMSCqsjIthAg8S4nVx2WhLBCNHmC1GLjszGuLFkypRDVa7iBMdcz1qK2rOym49GRlGhBJ+zVYHEwXCuS11MtjXrb6WS4B04QCJBUqPs4dIYGEu5ouDryG0NAZXY7JYzUx1lCVaTtySHxDVr9mLlYiF5tuvhE6N5BKU4SjCxV13TJdIJVKSxduOIQ5r4RAWvSZj7vKoFs0TRg9honbpS7w/obSseC9HphJn+F+AAgOTQKX4PXQez51GlxTx4GX5hDV6TVrkaGzTPs/ObmiaFUroYVOr4i8S/edJ2x0pC1e0QkhjBTlKoHSguKpp4BOl+67GVbDOcNHlY7xZ57/Vyf8zBlX36wQ4WFdqHuVC7K3zB+W51cA/hgH/wmg1AyS6vcZe9/+5A/P3e869vWzPncNEGvgJl1Q4+X2D2LXfiRx7YYvoMltxIIurc81L1QygzHXUZJ32CG/iq1DizjcoQVJPechlIjisUIHR3gJB44STYdpAmXJB01bFvQytZDm5yo0KFuRsPxCqlovmVnyiSJTebrgS1edFPF48wWfOuXf/0wFFwzPGqbEULe6eLQ1Dku61AzleyoBHmhVYZopejIZHN0zDKNGD9NCPkB7fwXFrGWaGmqW/KJ1ezFk0aJlsz2PDeYKPEEP9s8sXaQM3ROhYX2+gb17SbnnloOXAnafK8olx3rDhgZ2tcdVIk1KqZprpkUd7lgXua9SOy/6+Taq0rSgE1oIIhO5et9RxS9LZG2kUZsQgGZQUs2MDyYKLaUI9fjsCNJQh1mgpDJW6qjFW5ZQAUDYtD+mPI5L7bziBe+OR+G4EepS/Wf1qODxpXUwuwBbzjiVhnLPSR2BDLD6iD0Oy0gQt2gfhSuQ5uhF97CB8e+EsOfomIvDR8EM6eiTy6nvYjkXwg+AcVrEpGUXQVWOPi6tgqWALuUT9XoPBTmjjFIdR5doDu64ESwrQSzVuUwzVc45hxar2DK2qPjnqWCK2z2ea+OW530cq+OcLxD9qHuwhJk39b1Xz/jX9+DidQdwsgjGUuie5DVXEphZUvVMCIvDl45e3BJwxj2kvdPfpWYwU/0RxTlfuFqtaF03OiX+5ukez//alRiTtI1EPrQPtelBMJz3FG1mYakEHuuo1vv0h2wGZJopIilpZxgp8k6EpSWqYkWqKa3Y1Q/T0zEu/cbbMSGBWxoTmGnX0JSau4wJVWnn7GiN9OFQpYufGSNu45f3n4UwNJGGdKzP3XQEWwsLAIBuamNLjv69EJUw7SzhjsZWei22sb04DwCIhY4vPvIsxakEJ0vDLKKt0gWo7KHiBljqUuVk6KmqbP4ve28eL9lVlgs/a+25ds2nztjDOX26090JSQgJEIKA5iL8lA+VDyfA6eKE8uOicAUvKhFBI4QkeEEBEfVeJ5BPNKA/5IKIIoOEzGN3ejqn+8x1Ts1Ve17r++Ndtao7SScIuZIO9f7T1aeqdq29a9da633e532eNDLx0kvv0e+5tzmnpR8f2plEc6MIw1c1RSNDMhh9lqFq5JwL2HaGgqpph7GlkR8wCdMUWuqQcYE0VP2/XRNT+4kQ1AsdlHMBVk9Sxm92DF2bTucisB0buQW6P3sbeVhlJcggGbKmA0OTqySEQj3c6T6CoQl6wun/ql0IXMJU2rnTtwrkT/aoDQaAsd2B7A9TuEjLDbYO+rAGEr1dqjXsyliLw5ilGIaZ6e9+utxFVclOCjAsNakFqOhG6Efnau/GigjGuYRtprAVuUpKhkhJdKaC4yULD+BUnzazG/0inlWjhfMTR56OTLkCLb/mP8b/eNr/IF5JbzHV/cMs4tRHa6n/Bwb8uS6yQYSjr3rXk2KO/kZjXFMdxzjGMY5xjOMJijH8+y2I/TfeDMlLWg7s2ym68ciLs5/YsBR8td4p6haAcrmPVsvXu2khGSLlWxr3DZQXqK5mcAnLyOAXKXuZKvSelK1Kb7/v+3DdpX9/3uef/WliX15c3dSiDpdUUmwr+UATZIg+FJ4wzhKJyARDwYtQKA8RAK4zjaliD0tnJnWmc6pR1a49AHB3Zw8AoGr3sRTWtDThd5RPYCmkY3xlYx+MTRtOU7XK5EYsXmFBQ9Ke+u6GcoxntweZboLtKK/NDsLUxG1LlJmJhIMPDEDJ1aW2HJJsIVwB0VSsYC6RWAKBq2qPA5OEIwAs/fhbMP/hG+AoE3EhGEHHAGBJnbkDwIOdGayp1p78CkN/TjFrUw6nxR+1bHDo42+HDEfernaTQxKKi7BgayUnaZKov6lab8rFAaIpyhDXd7mY+KcieKIMPC4toP5sVTc1Jaxtdf5zMcAlZKKEMgwBf4ay0UHPQZZYun6+3fU187be9cHVhWv2PTA2uk/ixERZeSFngmMQ2RioGqvnxNqF6fCeDeR4jHZEmXcmGb6gHI5MK4UzRee18Oe/Cxkbo6zTECPjdy6x/LNvxqW/Qtmp3ZHIFMQLAe3rKv2U2OLqmprVEEliIEsu/DxvvKh+C+KbJbFcSPG6O16Fz58hWHG+EuA51VMAgJrZxT39Pbhtiyb2znoBbk1BifkBHC/B5prSDI0MQMGA0hHa1eWxtFu/lfHa238c1xSPAwCOh9P4rcvOv6C+7IuvxYEyLWYlK8B7n/ER/dwPffkXAACNKIc5v60t16LE1NJ7AJC3R32rjjFS8GkHLp5xYBkPqR7WXsvD0SL14x4qbeJkQDDoSq+MfcUdPLO4BAD486Wr0WjTgp50bJiAditx60wr5AgLiJSK0RUTq1gLimhZtGmyzewcc3cBhs0BtVm4Zoqhw5cUDNLLYCldYCa47vtMExMnXzmq4+17702QfTVl5TK9cC68/0bAZIh6yo6tb8CapHsp6dm4p7lLH6MTO9pAu+WaGn5EzHH0N8/vRiVsCbehNgw2tCaudcZBPKWuOZdIE0OPqyl97VizuKeB9o+58JVlX96K0F4hQpnjJOiadL0NS2Cu1sLKJkG5jpfoHl7HS4gMptScOJeYUjD6RquAYE3BzoUUpptAKPWsLDQQKmLbfLmJh4KRFaFjpZo8dOT0DI5vTGJOtd9kgqPdpXuOG0Lb/DFlJu6sEBycudD3BI8YFn7/RhhKy3mwSyLz1QbBzZArju7bQcfVG4TJCuljZ+yxzc0vhLjwtwXjGMc4xjGOcTxJYpypjuMxY6gSdCi3iTmLFH1u7y/gE0cvf1yjcQDwjAQ/tHgXACLHDKOR5nFHfbeGqA4cXIdnqjaRzMCEN8BxQZlUPLB1k/jSa3/lCTqz/3txsjeBo0rU9PLy6nlf96p//zl0kwJued77H/X5tR6RuNY2y5iY6GltYN+JtU4vAAjJYasMNRUc28rL9fDEFnqpMzInTzgeOk2ZajBnaTWrODWx0i8DWNDHdJTAhhAcxuZommAZtD4uTwF5kjKsr+TmcfXMae3Uc/XEEr67cD8AoCNcfGLnSq2KtXRiGk6VMknTj1H1B+grwf44NdHbpmN6lQD7/up6+rBtB0bIkBTpGLxrgCvY9cSvvBH7330zMuUIwzKGNBrCkcDxdTJ+ED0LLOLwdlN2FzHotqVUOfkcvk6JukwIoEZZ01RtgK3MR6ZE4oUBeFv0Wb39AqZP1ypLOdC0MUTn98zX8QNzJPDQTH18rTGv2cu92MFclTLCfmwjKSiFL8GwWh+17FQLfW1mYJtE0huK4wvB8MDWDI0/NcBDlUkzE5khIJQZe2Gmq4UhWpGHPZWWblF7Rm0V93FSjoozAwYX2pTi8sl1tPN0Xdb7RWxuKuSoReb00Qzdc0bb1L6oRgzklwwIxRPr78vAAxqXUYoghv6pdgJnoqtRpz2FFsLUQgIlDnEBx3hRHcdjRj2kCXrR28ZnmySa/c93X4zln3vzY71Nx9muNS/+l1/WpuHdxMWt3/O7533foY+/HfMTtIgHJevr7g/9VsbCX9D5XLGQ4LIyCdy3Eu8Rrxu2KRyuGViuV857vI6qe120m1i6vmqHKdmhrr2uDkqo9334Cs4zuMAgoRltkNpohZ5eOCAYrHWaMNd2pnHt8+8FAJStAW7b2Yv7tmlybTR9CGUwPpRcPPg3xE7v7ni6lpY/biGZps9tnq7gHitFQylkfT65CCf7tCkSYNjoF7HeJDYnH3DEqjZqFkOs75R0LW16qo2BS+d95OW/iYU/eyeN3ZJI8imgoFUmCZIdhhEBaUX1hKYMULKUbGDolhcecOTWOXq2Mn93MxQnqNbYig1c9Ls3QygBJuFnsFYUG33HAY8Yqf6ozxrMqc8yJLKh5RyTsFsc6QHaMMzmOritvQAACDMTUWbqDc6wJQoAbJ7BVn/vhA6y1MB0jRZc10zPUc/aP72tXY+6oYNeb8RREENDd0PCsjNIBet21wpgpBqIODHx0v33oeRQX/A/HT9Epu4AYAo4fozFSaq7f3VlfsRktgVYj87TX2ij1/Zgbqp6ty8QFlTP/ZIJIyQDBQDgfgJh0L00Ve7hkgqxyu/dmUWYmBio++yBZBpCcGSDMfw7jnGMYxzjGMc4VIwz1XE8ZgzFAm5ZuRxFJcD+cPusX7j9J9BTzM7b1/acV6Vo+X65UJYAACAASURBVAvz4JfRDnxQ94Frz/+50VYOgVJUCtML4za9dJ6yU84k1kPKygapjVff+mptBP7J5/8+FivkadqOPKTxo5/by7/0izhcG2ViZ7plneWc7lXQj0d9iL6daLZtEDlwlBpSI8yBMwmuUh2rHCIRKrMxJb68sgAAKPsBNndKuHQ3jf/q6WVsRYRQvOjzb4BjpjhIqDHuC+Y0uYSlFrwTStx9f4z1I1PIKy3aVt9DWxGrLp1eR9kJsGXQMePSKPvq13OkLqYYyhvBBKCOv/+Gm8FNyrr5rgCFfKBVoKKWq83mF953E8pXNFE5S0B+oHpYUwFtCWd1OKyuhL2trOAqDO0WIQVGzJAUBQwFVZotU/u62jMDRE0XqRKlcDdMSEWs4tsOhMrcjbaBNCdxeBdZHZ5sT2AuT9cjlQaSzNCWeoFtIa9+TwJMs3iLboQ4NrUC0nqrggnVr+1ZCfqJrZWYMsHh5Qj5iWMDUv0GJZfIMg5HWdAtHt7G8Q2CwC0rw/3tWay0lRWhF+t7hwEo+iFOq35X08wwvZfu1a16Ee4cZdfdeh68ZyAtKYKWLWDW1XlNCRgR03q/xooLtpfeN5nraxUmgwtIyTSU3Wt7kKEBEVz4ed6FMVuN41sSr/jKz+MrR8nNYnK6je0BQUGXTa2d87pMMtxXJ+jw4qnN8x7v6HXnZ1eeHYc+/nZM7Quw1aZJ+OsxlX4yRKQW/65w0Oa0oLRDF92Bi2sXjgEA3nT3D2OtR2zoRisPwzzXP3Lo4jPhpNgMaGGedHuoTKzpemXRCnHbNrGm09iEaafYN0mTn2Vk6EU0cfl2jP3Fbe1SU/fyWLfVYn+moMVHuoZA1rW0EIdrJrC5Mtq2Iqx0yyi5NDFese8M7l+n77o3L7QBdfFeG8G0RGeTvrPCdA8LFYLvD/h1JNLAoSLdG/+0chAtxS6WoQG7HCJdp3uLxwyZQszT6VgL7++ptdAJHc2EZRGHqRi+wueY8Aca9u5sFHTNNhs4kMrYXJoSwmTw6nR8nhnaTFvYEnwiwolXkNLWvr+8HoUCnXNnxwdzBCyPFqnQcMB2FNNYEsQMAFk1hbFjoRXSCVS9AcLhte/n4ZgpJn0lz5jrYH1A13u9XYRjDd2UDAjB0FtVrkCVCG0F8e7EeUzVRrV0xiSKSgyjkfngSo7ScxL0+i6CLr0vKRmoFEeKVWdaZQwUU7pc7iNr031g9AzUO7Y2Wc+XAy0MwS2BaIW+WyNh4AlgzNKmwDAkkoYqojISxw8WaLFnAwNcGZE/yGa0icNw/MMwLAGvFDwl4N/xojqO88ZHr/kQcM35n7/qUzQBmcYcYrWg/O13fOCb/tyrdp1BI8phxu8+/oufJPG8z74Zh5Q1Wzd1ULNp8jzJqd9zaAh+R2OPlsCrzAT47LXv0cd43R2vgm+o1hiewMtTbWtowzbMZjwjwbWLtEhvBkU8VJ9ESdlwxWeZx/djG83YO8c1ZdiuUt7XROsMEU8yL0Zlro3GBn3OVzdK+N4rqN5atfvY5+/gRI8ynbVeCULVK3kC2OoriktA5khM76FrUHYDdJRC1scevBJgEq4iP933/W/HvvcqHV4uIZZ9MEVYyQqZXkjL1b5WAlo6MQ2zbWj93epCEwVFvlmpV3B6u4Kcq0gupoCpNiuRKcG7dIx4JkGSN+HWVTbaA4bcOQ4G08pw1c+Su9CpD/+aJklZfozDs1tY7dBC11rNwW4N22uk1uK1NiwkUwkCtRB5loGT66quPDAxvbupVbJ2Il/X0y0r0/3aaWZQjVNl61nKoUqSYEyi3fcQqzqkbNoIarQglgoDNFbp+0yLBmQ2slnbaBcQKcWtrGfBKsR42l6SwGwEI4lEYUt4tYF2/+nt5LSTlrnsgqlWnriSQRoMfJk2RmKhj8oh2tTtLNM55RSCIRxo6cp4mul5Iu9GKDgxTq/R78PxYySJiSwZtYRdqHHh59rjGMc4xjGOcTxJYpypjuMbjrxDUM1WJw/TEI/z6sePl3/pFwEAE+q4Dxf3/s+ON9z5o2gkvs4yb12f1yILWcYR9hws/SRpDHtmoutFvhFjO1ZqSFxgd6GFh1SLTWPgoeiOrtvZ8c+nD+KSKarH7fGa+u8FM8Jur4m7W7v18YeQbivy4FgpZlyCBU/3q1pgwDFSuEaKwzViD9+bzGnIrdXIw2zTuQQyj4EptRKQBNCKCcKsOT30Mkeztmu5PnbuUy0qnkA4QcfLrTGE03SuACkqDbMgIThyfoi54lnGEep2YWBI8xl4kbJYLgHPp8/qDxykdcp2WTmB8BMs/9hIDGIY8398A07+zK9h4QM30h/cDCUl2C+ngYFyNWI9AzKXwXomZYvdVg5SmWLXdrdgmykqP0m+us/8x18DU1lmZkm0Kh4OVQk3/iqqWvxBWABs+k9SBZgtMFug9P051VOoOAR3rvZK2JVvYzMgevH2wEfSonOTHQNbSq3IPm3DZtDtO3Y10JrG+UKI+UpT10NZuY9A1Wgbm0VtYOA4CZLY1KxesxBCKM9XbzKGyQXOtCir7XQ8rXG8q9ZCJji4MhV3zQRnmvS6fc/bwMltyiply4VkgFDoQtFJNKIguUSwJ9Fog10JdduVc8TDy3/4VgDAn33pO2CUY339w8yBU4jwVFCiHy+q4/iGo9GnSdOx0nPqI99oPBHQ8dnxki+8HlWHYNi/uPqPz3nuN+/9AQBAzoiwGlXw2aVDAIAfOnAXNqKhkLcDkwn01WLJucAuNWHaRoZuycGLPk914ulcB5PK9eVMUNEwbNEK0UlcDd1ePrmuJ9acIpI88x9/Tf1/BN9+tb6gjdBff+crsRJUsDtHC9bRzhROrNDC5uUjpKmBPS7Bb3kjwpEusYqSzMG9W7PaWN00M/09mes2MpceW22OY295IxY/qhSqJNNjHKQ2XHNEhCpaIZ529UkAQD3wNaEm7lfAg1GPrGOkCBUMKhKOQd/FcUUe2vd7N8Hq0fGi6RQs5VqtR0oi3QBA0nTAhiSaHRvSlFj4A1o4vbkeBjsKuhQM8390A5Z/kdp/Fv7sndpE3LQybaQuC7Q5GloHspYF6SrJv7US5vbu4OgaXTv3rhwKQynCGkPN6+H+OvWEGhHTtVhzsQepYFCWMshijKsqpwEAHBKXF6lPecrpYS0ooqnqrfX1km5NEnMhHCX3yC6JkJwsIFP9uHFk6YVmptBFfeAjr2DvRj+HxRqVCB4MZjRxa9D0wGwBrmwQc06MeCianxqw3RS13EA/N3Sz2e75qOQCPG+KZB1XwzIyxTg6cjvJSgIAN4BTrz9XYnX+Q+8GADhbJpKCQH6RNi5BaIGpTh8mgI/84wvocU6g4IdoK4cp5AQ8J0GWjeHfcYxjHOMYxzjGoWJs/TaOp2xc/Hdv02xkm2dY6xNs9ordX8NKTM3vrSQHz4jRSijr6aaO9nidy7cx43Y0dJtJjklXGaebBMGe7ipihhXjQIGgw/tbM3oM/diGZWRY31JG9laGS+YI4k0lx1KjisuniU19sj2h2ZZT+d45JKaz4/v/7XWY9ghK7acOOokLU6UDOTNBR7GEz7TK2FNuIVaZSOtP9mgB8/bL+ojWfH1MM2DIlE9qbqGDXoOux/yebeSsWKs5mSzDRQWCQYVkuKNBLOTlu+cwd+kmig5lxb3YwYoi4mRtC2bXGLryQRjExAVG/7KKgj+dFGFHZS8pG2nzMgnGJdgwu4sNIFGpWcZgNw19bsIGhHqf5IDMj7IfbmcjpaFjJvp7RszgSy9bRlcpHm1+YZdWjspcCeuijoaREXGtIFTY1UG3RdfKzsWIWi7MPCEQe6caWCxQJlk0A9zXmsPyDl2TeGCTSAXI7F0or9e5xW1sNorYP033Uiq5bilzzRSd0EU6FLZgUpddBpGNSNnd2U6Csh9oUlCcGmeZ2lNrz9A+0XUSfN/CfQCAS70VfHTj2TjZpN9GNRdgdVuR2VIOtq1admoxln78LTg7Fm8m4pnd5IgvCZDz6T5YrDRw32376BiFDEZHWdAlDEklAx8M7fRI11sEIVZ++boLeo4ew7/jeMrGZKGPnZAWjk7oaIblR1eepV+Ts2KcaZXxkoUHAAAnuxNYr9Pi61sxbJ7p2mB9rQy+SKzJTVnALr+lF5F+YuPWrb0AyDlmCKkdnKjjVKuKKxbOAADCzIKp2lXCxIRtpjiyQ4v2XS/9bT2ua//5v+MlX3g9AKAZevjKi9+F7/wcSTQWbAZDrVAX+Vv48vYi7l2iemu+OkCS0GcPewDrfboGwct68BVDNlwvjZR6KjESywRTk3yvmUNtuqOvj5BMq/ic2S4jnqFpo2wHuma4sdDFVjsPQ6ka9WMbrjesjZpgErrvUxpytFgaEgg5yqrlI2cnWFfsVjhAzqd6X3CiCGlAt/BEE0IvREbAISxdxoPwBH0GAJZLYSombS4Xodd1teduVJYQChI2dywc/cI+2G1lml0AMuWZmuYzZIENruqSIuawJ2m8ErRQAwStsoRDKJZsK3Bxa4/uiTTjqPgBDk9TfbsR5nBmdUKPdyjlt35sErwa48Swr9ROtSKWbWawzLMMx7fL2nuWcQm/NDJWbw88pMrg3bZTDJTUYe9MEeX5FmI19XdbOdRjguyP82m8sHYEp5rPpWMELrBCGwl7oYe4Suf18AV14Q9uhKyoa7M3gqjn0FXGB61coI3a7VIESzGlByt5+EumZl9HiyF409aOShdyjBfVb5MYSuN163ktPfdUjy9897txxT+QPnFzq6D/nqsE2lbO4AJ7yi2dcQ31hwFajDa6BTx9mupiBSfSz3/y+b9/zmfN/8m78F8uPQIASCTXJuzL3Qqm8z3d1iIle1R7sYdHN3Iwm6OFzVSsmKGtGgD4yn9tJaxg0uvhIbVG7Sm3cHSV6oLXHDiOu7fm9PsMQ6DRogXWLkaIMeq1/IGr78AnvnYlva5poleg5yZyfbRCD75NC+RkuacX2EOFTfx7fQEAOY44/iijdcxUa9b6JyyEExKWakMBB+JqpseRtnNoLSviTDWGVFmgaWfnvVb7b7wZXJmvyz0h5IYDe55QhKDrwNxULR0hB5uhxSbNOBgfCU3EswmYIvekhQwsxxDPDQuA0G0+5pYNVg2RqAza6HH4d9JCdOcHRo5TV7z2ZrQuEXr83Z6nrfGyjGOjUUTfVyIJkaUXd1hiyNuCUUggWjZ4ma63aWboqww5cYkAtHsX1da7BQf1oaF720IwbMMJDYBLLdKxOLmDDaX1a9ZCZIIj2RgZqf/zceITTFc72NgpwVdZZqfrQaoFMavn9EZi8SPXIwsM8I5aPgwGrxTq86zsber2rK1OHs+/gn4XXzq+H8d++K36el38G+8Z1fVXHKSe1AS2Czku/G3BOMYxjnGMYxxPkhhnqt8mcd/3v/1bPYRvSQxrlLlKoNm2rU5OQ6P92EZiGNho0k4+abra1zWn6lCrfcqiKs4AU6qm+vDYtbuBr21Qtnuguq3Zvklm4MFTc5jfTTWyqyeXvq5xV70BljpU2yo6IRY/cj1OvnJkKvDf7/oRAMCZfhnH16dgKL9ZkwtIBfsFmYVuz9MqNpYhdNYT9yzkp+lcpGT41ENP0/Cv5EBymjLaEyfyqFy2je2eahEyMs2srXfzuo6HyMCuPW3sKEZ4b+CAHaP3ZDUBYQFZWflqOplWkjLNDDJgEBZ9dhYY+lyyjGsBhlOvOreVRphSC8jn7/aQWUA8q+qEpxwkSuCdTYfascbzYgg7hSqnA00btVliqZbcEMtbVZ1Z9us5QMHQaSEDq3uaocx3BfAvIxThh778C/ib534QAHDX+9+I+Q+9G1xBzwdm6pj2iC3+Lw8eBG9baBuU7UpXkEk3AAiAqbqvaNiQfgahvoswGNVek64DZAx39hb0dRyGGTCwJdV+ZADYGyCvMs5EoSaAgpOZ1AIYqS/hKxnBmteHNZUR7AvK1GVhhNwMHWbM4x64I2EqBnfmSCSKLZ6WMkRWBkM594RrPv7i5Yp5f/U5XyEGe1JAGc0v/xS1pnU6HZTe8vjuV0/mGC+q43hKR6jgMTcXawhTFIDegCaOODbh2Knu6WMJw6TSHO6GDqYLXd2jKSTDhpo8XvKF1+NTL3iv/pyhWTQANKMcAiWZ1wscuIVIk3aWVyfw7qc//rhdI0Ve1RPvWZ07x6z7F27/CWSSxj+b62AzX4CjdG8bQQ7791Dd7v6tGTxt1zpWuwTFtfsepqZoEdlcrSA8qpSaMiCppXDrqpWlKJBVaVLMPeSg/8VJBDOqHuqnYKovMzxVgLFbyc5JoN7z0d0o6P8bqiZZWOJoPSPWrS0IODLlMDOQNnhOQqjWFsPL9Ibn2CtGUOHDo3xRAy0FZfcWGKyWAfsBWtDTvISYomtncgFbLVgGF4hDUxOVeCVGLdfX1820MvQbtOkwmyZU6RuSG0iLAvDpOI6bYFURz774Y6ONzqWfvA4w8piaoAX3O2vH8PHTV9Bnb9uw2wxxmc6N12IkLdXXPNNHoO5TkcsANoJu085I49kqRcg2PUgFS0sJTc5KipnGHY0eRxaaYHnVkxx4aNbpe5mZa2Jru4j8UAnrkgAF1TfdCHO4uLKBrwVUBy7mA8yoFrIgtfSGqTuVwt0wYT5TuUhtFGC2VB1/wDFYz8NRkolHX3d+q8bl17zpvM9dyDGGf8cxjnGMYxzjeIJi3FIzjqdUvOyLrwUA3PK89+N5n30zVtYJ6zPsDKkitrj5GHlvJNzNmERXZa6mmeGQUiA60aiBMYndJcruNnoF3Pa9139d43jeZ4kMttkqgHOps69dpbZ2kTm2NQnXTnD3973jEe+/+cEXIVJOz2952qdw8d+9TYvJ76014ShT8iNr0zAMoQkgh9/6Hhx5BwlSHPjYO1AujBihtVwf9T4RbNr3TcA+SBlVFFo48Ypfx6G3UQuPGQL9XSpzjBjMwUjsIJ2MYajWCpYBYhdBhxPVHlwzxXaXskfLzBDeq7Ro8xKl/U3d4jHoOlqoAF0LMpdqKJT1TUiVESLlOhMbZjXzf/Ku0UVSHqwsZeAR0y0wws+0dq7hZMhUtmeVIiR9S2fapp2ioCDSxmoZLJcCbbrmTt04J+WIDwSYUR6nncA9h0D1rJ8iveDmJcDMlRt48eyDNDxp4C/ueTaNw8pweHYL9x4nljYyBqbGb08OEHUVDVYwOmc1fmvd1obfciaE6I9g+96WD0Nl4VJCE6Scky7C6RRcQbf5fIhOcySUMT3b0spXx3YmkVeZapyaEBKaJR+nBtodet/URAezPt0vrpHizrXdKPl0b+0utHDHCRKHMN0EScuFqVpnzAH7uo00gKfGHD1eVMfxlImf+dp/1Y/P9MuQkqEdjUychxPJaq+k/yYlQ5wa6Cm1H8dJsUstoq3Qg5BMW64lmQFDsVv3l3bQSxxs9AlWO5/h+qWfvA6OmSFTE97ZbTOPFq+741UAgEiYWOpSy8Vnr30PvudffwlFmxaA+zZndX0rCizMTra1+tK+v7wetksT7dEfvA4H/+btSFV9VNgSvDbaTBinCOrMFkLk/BDdHXodb5uwumpFUbXhTF3GtJrAqtMsL0zgymseAgA8p3wKADAQtID9+YPPRrpKEzLLGORMqGuNjEl9XCk40r6lnV6W/tu5Sj3DWPizdwJ9U7fKmG0D2bSSVYw5YEpYygbNsjJdPx9EFoJV2kiwSowXXHRcy05+8cwiAuXWgo4FaUk4W8oFRzBt6Za/gnpNhwL+7cDVClPpVyv62kSzCQw/1S01MuUoVgle3ldp4MjmFKK+kg50MmTbqr5qSkD16y7s20Kjn9OM7f72SPAegmHidgN3/CExjg++4z1I9tPCxjYciEl1/m6KLON632KYma4HDzd3L1u8BwBwR3OP3mgFsYVB1wFrjKDoIVN6/uINvGSW+lkf6M3h9o3dWm6z2fe0Iw56JqQjwFSL0NJZ8O/C798I6asa6qt/FQ+PH/nKa5D0Y9zyov91Qc/R45rqOJ4y8cfP+l+6l7Mf26jl+jhQJoLQ8VZNS+gNIls3wy+UGnhG8QxuWbkcALWtDBfiYQvN0G1lX7EBz6D3XVVYwpFgVvecni8GfRcDQPdsLt50MwncAjjwjDP4zHf93jmv34pokf7YNX94zt/LdoBZlxb7o8YUYpWWnXqYFq4YmEi2aLI+8Ne/DdsRSKbVQtpwIJr0nL1tIJpWGeHARGRaYEOf1Izpup3VZ4hLEs7OUEfQglD10NLBhpZVXI3K+M7iEQiV3onsObDbw4UZCHsWjEqox2moBStsWzC6Bk6+8dEX02HIhINlpBUMkENOGo4IOLnSAFyNOUkM2AoNSDOOsErXfs9UEyfaNb1Jmiz0cVotqtKQqO5qoeHQ9ectC7JC37WUDAaXeiGt5fs4vU018mR3htn9JIaRCQ7PSnTtMRg4ul55fKeGqOGBKWJOGprIryj3nIJEqohVy+sTcHMx5LDp1pA6q2cBR+MKgSt/njJjPs0glW2bmEgosweQJga8XKx1pOtBXreCnW5WUMoFWA0JRTjTKmtS3WA9j9pCA/VMaQt3TTgNtTiemcTdPhHxBqmFKLIg1bk9fWYNt6f0XDww4KybiHar39cHbgSL1SajlI3aiFQs/D7JTtb2N+CaJaT9C9/6bVxTHcc4xjGOcYzjCYox/DuOCyredPcP497WHADgsvIaTvUncGSbFIkWKw1wdm73uFCC4BNOH/2MdvWD1MZGjzKSw9VNGEziqytUE6rkB1qtyDFT+GaMWLUkHCjUkaoMkUNiLSjilue9/3HHvPiR68FVdnT8R96K+Q+S+LhRSgAmkala79UHT+n3bA4KmFaM4qV2FQUn0m0tSWagv0WPc5N9PPj/vk2/75Jb3oYHXkb/n//wDZTlDH/hhtQMXLPLtZTfo2WJF/0uZUNOgyGzoZmwYU0irak2i4zpto6pWgcGF2j2KEuT9xa1kLqwgGgqhVFSfqeSIYtVPbRvAgxY+sXzs0QBymiMkGspRavDkFRG33VurqeF+MuFAFWP2KcmF1pasp/auGdtTkPnlpVpo/aFmR0s5Bu4Z5vurVYnp5m3e/bVEWeGZo87Rop15a0axtY5bPE4NfCMWRILuXV5Hn6OMi/XTtDq5c757CHTGILBLqm6ZtMl9m5eoQYxg1TKUbnaAIOOq9tPDrzrZl1HTotn1ZHdDDk/1NmuZWTnCIcEgQ3PGzkBZQMFWGYM7CwJRh4xpCVlVj/V19l/nJpa5QkAfu6iL+ODR54HAIhOFiEcoWUnRWjCKoyyz0yJTrhbHMFsBl4dylMmMM0M2SDCkVfecEHP0WP4dxxPSAxrgak08MGr/vwJPfbr73wl7tgmeOlQeaRX++nlizFb7GChQtT+vBVp3VsOCc6Eboc52prS5tFFN0KjRVDwljfA8Y1JZAE957sxLq+RFu9d9Tm0uYuFEjnA9FMHiVqkp50ucmepL50v5j98A8z8yKD7klveBqtKM2G1OMBiaQehglBbkaff149tbEiaVObybZxo1HQ9d1epjVX1uuECirP+P1TPYkkRrBhDKNlCbmfgqh6auVLr7i584EawhEEo0gsLDTBfyfUZgNVjYGpezLyzNi0Z0/2mBSfCRcU6/rlzEQAgrmUw+iNdVxgSmYIqIaENr1k5huha579+f6xaVjwgdQV4T0Gm06neLJiFBDknhm3S5/UCBzmLrvec3wbHKG+IOo6GSRNbAGpx50wikRzXzNDGZmZvBwWD4Oq7unTvHW1N6ePkVH214I5UtvLFCMfWp3D7Kr0+aTto9YcsI4a5PTva1SdnJ4jzdC6WlSHLRjXsrJrCL1OtdLCWH7nPbOcAS+LAX1Ndvngih52r1XcWc5hKhYlziW4zp+uolpeglKfjtXsekr6FdEf9TioxuIKkRc8CGGAryDdzpYar+w0Pk/N1ff71bh6DDh3jw8eei0Fd/S4LGYwe1w48uepAvycMbCKRAQgOJmSkrjawR17+m7jsk9eNpDMv4BjDv+MYxzjGMY5xPEExzlTH8YTE6T61rszl2k/YMff9JbWvyOZVmL+YSBeeEePioiJg9PPE3lXi9cIcPY4yE9O5LoTa+xadEDuKTQtAu2gcXZ4BAkO3H2xvFuGrbOWle+/HLacux9ceIpcNpAwsfCSr8bHCrw0wW+pokksndDFdJFjXZAKD1NZkp6NLs1jcS+08FTfQQv4AMFXo6YxopV3SJtzXfOZX0ej6mtVJBCwF8U4E8L0YfcVsTro2nL5S6ikIQJ2zHJjg5VgLSKQ5A1JlkmLbQZgTMFXWaTc5rN00/kouwMoGEXbaoYvTRgWLk8SUPRqZyEoq7+ha4AODhA0AwJRgLeW1GpowgnP39vtvIOj5xJvfOIKuBQNsoVWUAGjCl5QM9c0SoL4buAKiQKzb462ahoI5k2CmhFQotNEwkSl48/jpKQSzFp45SV6oA2HjthaJIAxSG3krwoxqKXloZxJ9xXY17QxFdS+lGYcQDNG6Yj0bAFOqTMIVaPU9uIogZxuZJmsFKwVI1ebjTQ5QyQ90+9FPfdc/wVAX4X1f/G4wI9NKSXd86DfwtE/8Jn12auAZcysAgAe3pxEHFqQStTcLIVwF3dZbDnLLlm7TSSrQGS1MAWfDhlAQO8sAqVjZsAXq3by+9P22C65QCiEZqkqPuHmyCiNiiBV0Xl7o6Oxzte5riNrNRwi6DnIKHt/3V9dDpjmI4MLP88aL6ji+6Tj08bfj6A+eKzA/NO9OJYdv0Sy20SugH9KPzTAEFisN1AOCjb7y4nfh4TE7SQt0cXeoBcFvTeaxWKKJ2zFTNIKchuzWghJsVbu0jQy+EetF9VivBlstGp6VotmkCaJYGSDOm4gGNMtYuQSfOnopALIsgyvAFOSIUoJcbQRnnR3P/Mdf06zSs88lSQx87tqbsfB+Yjka1Qj7K8RIXuuV0IkdytoF2AAAIABJREFUbG4R27JSG6ky+VaEIKUxzfgdhKmFdkwT+aTf172uncjFRKGPfkTXtd31dB2v4Ifo9jwN0aIYISnQT16aQv/dneojDC29ODTbPvyCEkj3YxS8UI+RbzlITxIs3SuPFv3t7QLad9bwwy/9IgDa8GyvqvfEDMITum4IAMlQXYlJnHzDuTXdE28eidQPa8A8AYTLNGTNA65l8pi04AQMg710TS7et4anlchNaHlQRcmic/nyygLQMfUknzvQ1o+T1MDqUg3NvnJlOatfOWf2MUgt3HXnfnoxl0CRFsdk08NOkb4nZgrIto38Mo05mJLaoQWGxGDTx0DVR1tOppm7APRiE3QdVPMDvGD2OADgSzsHsJCn+/3lz7oNf3vXlWg16Tez8IEbAd/Tnz10ZEoyAzIyNMQerOexWla9xaFB/cWq71hkDPt3Eaw7SCxstqaQVRQrXDBtdC7b9qgGDMBbshHsoWsQGBK2cvuRuQxxDnoztL5VxuwULbhGn0O4qs47kSF2Mm2eLgITyz/zZuK9/MLjG048mePC3xaMYxzjGMc4xvEkiXGmOo5vOvZPbj/ib0PGrJQMD6zMAgDmai2UypQ1OEaKjX5BMyqv+tSvY1eBMtNLiuvoZQ4AInyU7QCZT/s/34p0v6Zvxvjq+l404hF5ablJcKRnJ+CQONOhbKkfOBrmGuWDpAc7WewhK9CT2+08uKKtZq6B2lQH+w5SpjAUTh/Giz7/BkSKZFRyM7RDFw+PodLR0msJLl78yPUYpJQ1hKmJRttHudJXY5GY9IhJuhP62qt1J/TBIWErmHjS62lyUyI4DCY1rDgIR9lPGFs4/qMjcfL5D98APkfHtAyhrc0AIOtZqHfUtTsrG88yjp22rwUBWAbwcNizaoKrHkThSAgT+OvPEAs0m4m0SpK0JIxCglQRpuRZSkm8a2LhfTfBiNRrGaBc7ZCUxLnQsJAa8hWOQKpIY3IixtRkBy9V2V0/dXBPcxcA4FS9CqnEL8wBAypCj7l3uggUFdHHJE/TKKKsU0qGjT5l5N3IRpyaGqI1OwZSQ4n3zw40EzaOLIhSjKSgskdB5w6QlrDZZ4hL9NrqwQ46yp+VMXmOOINnJXiwQ0b3eSvCJ+4m/WBuZTC9FJ5CIrJ8BPPf6P7Of88Gjq0SkUpGBuwtE0LN7jwFEnV/Sz+F/R1d7M7TfVZ2AhzKbwIAbjl1OeR0BCgWtVcJtJm5PTXy6c1CE8KWMJv0HCtFmPTpHt51sINGkEOmyh2OkWGzTSiTs9DVjOS8G2niHQCE2SgLvtBjvKiO45uOZujhms+QQkq9lcelc+v41xc+stXke/71l9CLVX0vM1D1BshbNEFcVlnD1+pUw/o/ncOo5gJtrHy8VcOUT5NAzkxwokcGzqfbZUwXeljt08QSZwZ6PcVqLEhsB76uTVUKA8RK5i9OTbzw0FEAwL8tL2KxsoN716iVwvci7Wzz4svuxT8euQS9wHnEubzsi6+FZZh6YvfNGJ/7XqoF7nvvTbp+uPyzb8a+v7wepjKyzmILD6zShJnLRZipdtAa0IRyxcyqXiw5JHZUTfVwZQsFM8S9TTVGM9IiFKkwEKQWwpCOITKu67fsYVTK5Z99My76/0gSMefG+jzTlAOCwSjSBqeaH2h4+Uy9Av9LPqyuEgiYYRh2LQ32hShX6Hvp9F3IY3lAIYf8tAM5tNvc30O86o9qqlwCw8c9Azxhur3H6vCR8ESbI1Wi8MIVJByg4F/mZvDnlJoQAN+OdavVILFx+ih5ypp9DkvBxNIEjJBpBrO0JCpVGr9nJ6gbBVwyR/X6MDN1Tbvd8ck1ZsiSDTlyZxSMvlbAYB9dN2YKsKaNuKgu0FSkIVjZNRBOpyjM0ZZOSGCyRJ/d6OW0oXjY8XB8ywdUq5LhZHDy9Bux7RQ5O8HmBgk3sIEBXEmbpF1OiE1Vo53es4M1XtUyjrXdLexs08I2PddCvVHQEPJfPf+PsKHEHu6s7MG927vBVItXaDhgDfoNRtYI8kbCEFdGG56k66BVpPtvu1mA68WYLNC5ZYLj7KbN4aIaxBaa60WwIfs6ZTj09vcgC0cCIRdqjBfV/4txds/g2fH0v6fsZUgnfzzpuidrvPTf/hsAYNZPdd1q0uuft3fz09/5P/Xjw3/7W4gCCxNqUgszCyWVmTG4OLEyiXyJJk0pmV4o7t6cQ6B6C6ulPgwmkKhmvUxwagsAcGjfMna5Lf15/7J2AFwtMhP+AJbK+q7ctYJYGHj6LmpS6SUOZj0ipPQzGy+46LjORuc/fIPOlJZ/+vz9qZJLLe8GkOrRwvtuonPjEpcuEqGkHbvwzETXYoVkqNh0ziYX8NWGo2b3EGQ2FgrU2rPUnUDJodflrQjt2EWnT5uJrOmgrz6XgTSI6x2qHwvBkGzS5NeNc1oZSboCLOIQDdo8rDUmgaGmbGCgt1eCK3KP0xiJ4siMYaehjt2zYLgSmSISsYhrK7njP3gdDnzsHZDb9NnSy8DbikQzYIAAEuXeYoTQWWtSAHRKyyRm9jawrSz6spSjtzySm+y4edhlul5JZOq3ib0B2L20OGauhNVjsFSNXFgSTZuO1zJIOvH4DpnJe3aCgc5aAW4JVIqUwXd9B4PCSKuXKWUnHphwdjiEeiqLXaSq31TOhLh6cRkFUyksdWpoKOWlNDF0Fg8u4U/1z6lfDhei7kYBXUlOMABg9hkilx4fW5/CZJXu23orTwtuiRbBXuBAqgX2sol1HDNTnD5Cm44/3X4eXly+HwD1csOQWgry4vl1PBiSVrHZNpAqtGioWZyW1A7KkDrr3D3ZRCK4dmmKUwOJynaj0NDoRRTnwFOmVbeiaobEl1oa8kKOcU11HOMYxzjGMY4nKMaZ6hMcL/z8iLkoRAnPVbDol1/8Lhz8GzIKNwwDc+UOPnftzd+SMT5R8Q/Pf983/N5gxwNLOIwa7XDvPz2LGcX2tbiAYQndEO/nQg0Tm1xorVLOJOTZgveCY2E/1YdI/EHqLDaMLVxUo9rvA+vTuKhEjMdD+U3c3d6lRSKaUQ77cvS6durh/5w+jEC1pEzuan1dLjXmgCNT7i2HPv527K024cxRvZJzgWPblA1JyRBHFkpF5enZn0HJo/dN5brY65OoRSd1EQsTDzYouwhiS59zzAQWCztYWiZIHIbUrizLP0/OLovvoSxZWFJDdpknIFWmymJOqj3DpDDmkEPWbUQs22F2mjnQsK4cmFo8we4yJHsi2GeGIvHA8bNYvMd/5K2Y/0NSkkI6wqUzV0K4EixRsOCM0ApCENDb/uWfezMu++R1SJX6D++a4CqjFbaE0TSRqIzR3dVDquqVk5UuxAsIct1YqcLcMTV8LRYCQCEb1roF43AX/ZYyGShwXSuVgpE4vkvfzUWVOk4VqvqYZ5+PcKCh7MyWGspmAB6oT49eJ7hWV+KG0HVMbgrE0WhazgYmMpUZgkuwmMMIhpm8hLlN40+rDGmRjrFroo0NQ2q2u5AM+RrdY6d7FZy5fwbGNJ3L8c4k/n3t/wEA9HouWN9EfjdlvO3IBVdtSnaLQXLFct4VIA1NYIjGxGzEolbKTUODifkPvVvfhwduuFn76EoDsLqEUgAExctsJEhyIce35aL6yq+8EZZvY6Vb/rqtvL7e+Ny1N+OqT/06AMCxUpx9i9TU5Lm2VUZpKniUd1/4ceCvfxtCcC29t/xfH+lGAQCl+y2437OFq2oEhd7FduGKCYJg72/NoJAP9AITpibu2yCy09lQ8CC2UHYDTDgEyzmGA1fVGg/lN3G0N427N6jONlnoY7lFC5vIDDzYpAmuOBlg0u1hPSAo0TMTbET0OJEcByfq6BVpodjsFnTt+NFagIYxecUm6kqxKerbOI3KWSQPR0Ng4BKTM22kavNQ9UcEIc4ktkKCJo81J7GzXIE3Q1B50HewrYhJW9tFLP34WwByGMP8B9+t4blhDFtWLv3kdbjv+2ljt/iem5DlaeGRgsPocQh3yB6SGrqVhkS4O9H9ubk1A9lZs8ZQipAJQPZNJLuVc0zEccktb6NrcLwIlgIWp2uQFoQm8Ag3o3pu76yDqgWdSSCp0sKw8IEbYU+aWg1JGhKZagsZ9gwv/AG1Lbl2grkakcvWuwUNn1Zn2qguBjixQhsQtuFqqC7NSchjBXBlrJ7lYkRrdP2tHgOPGU6mRARqTnnaDnDLLUKq+qfcthFNZho+zU32IdQiE6/46PYsuDX63dtWClu1NGWCa4iUc0ktUYqM1Ou7SJUqEws4eHRWjdgAkilV52TQJYDuwEGaGrp3OY5Mjdlv9fLg0yHMhwh6bvghEsU1wLYDnkFvIrsNH84czVlR6kMOTdBPeXAyQBkSIXMkthQsn4QmuCkw/0dKCcsezYDmgS7kCXodi4DUw6jObgvIXAoZxLjQYwz/jmMc4xjHOMbxBMW3ZaY663ZhexYe2p56/Bd/A3H7S37nUf/+5cfIbp4qkWXULmE4j22J1nlmiH4rj6RG+7qFYgPHOpRBTOe6aA08dJROav+OGnAxQXh7JlqIFKPXMxOEqYUdtYW2eIYpJZ5eNftIJdfi5g5PsalIKafPUmE61SeVpaG9Wzey8dDmpP6sILGwvk2Zq2EITBdoHD9160+jHbuPSsqa8AbIK8GLExuT+r0AtJA8AJSn6FhDJaP1ZhGe8gF1jBSnG5RZ+26MqX075/VsXfjAjaM2l9woe1z4gxvh7+6ip8T3IQoaRSkdBBpniEUKDhIpGA6NjTRfZcbhLtkI91Hm9MD15ypJ7X83lTCi6RQs5ZBDOzZToL+hDMszIMuNCDAQGLXiSAM85JqRa3U5UpUt2m0OY12xTycyxPWcJukIb6QINf/hG2A1TMgSva/b89BR2rxBZGtLuNlSB1FqoqAIcJ2Ew1MtXuKBAmWrCn5MYxOGuqbODjGew91KFavnYV0dc2ayjYES3mgFBoyOqQlgcWQhU7Bubr4LxqQm2aWJgVShOdwSIwUrAO2mrzNLJ5cglZb+noyYIHgASH0Br0TjNwyh7Qy7Axd+LtKIznMWTiFVLW63nd6LtGODK6RgENqanSscATCg8gWCwJtXpkjP0HfobXD09itiUt+ANICp2xQ8zhk2r6b3mAmDd7gFoUhNQd/Gvr9SymiZDfgjowAA8IdsaMGQxCZE+tjzxoUQ35aL6lK/AhMOovD8Qt7/2bHwFzRhyj4xFw0lju04CcLTtBic+qXH9px8MkQ+H8KzE92+ctWnfv1RNxn5YoDuVh63rs/rv3UVfHVwdgu+E+uFL9kfAKrOtNXN414FYV7xD7+BSi7QtUfPSDBp04/0nt5uCMnQipVCDk81i9dgI7biAxszsO1Uw6IAtPTb0uYE0o6t+zbDdR/z+4mBe2d99yNKB6/6958DAPSTonaUKRX78KxUC6knsadZme12jiZTVVuz8zHYp2khPXplAdY2jXe7lAGW1PCmtCSgoMPln/5V2NuGnsiFy7RziTkZIEkMrQIVRZZWOVrcv4mGqya4oRRdOnxfpGFLv9RH302x/Ipfx8Nj4c/eiaU3kWPKwgduBIsYuBz2rTK9iKa7aEEeLhyIDGSKXYzIQGan4AryTXZH4MoPNprINEw8VByCqidCYLSAS4bMlmA5OuZFs1u6HWa61EUylK5MTVxUqqPrO+o8RypEwdV9nFmuaZjbdhPw/fQbDOc50sgEU+fm5yINtU6Xu+h0VF9qxAlSV2NdmN5BRZUmHtiaIUF55WcruQRU36vom6NNTNsG8okW0Q+7jlY1Kk4H6NdsxKqUkPNjBANapF0vRsWlzYJvx+jHtoZ1O7Gn1bnmJtpYOzULu6lYuBJIFSsbuQxXXbSEB1YPAgDyxyz0LlbtQqs2mK8Y4eUIaWRidULJYbY4uEKhjQFDt54Hd1U9t2fp83SLEUK1kZAWUJ1rj+BxMfLGvdDj23JRfWhlGtxzYXuP7zLynxFX/MNvwDDpxk4t2i0OXU3i2IAYUte/znjh598Ix0jxqRe89wkf6/nixf/yywCAIKghik38l33H6P+ZhTfc+aMAqE44ZRMJ4hWLJtrzHv5phX7AzWaeiC8A2lUXrplq8QODC7SVfFxw1kZo2Io0JIctFrZxu9JrDVILG50Cnjl7BgAtqg01gZa8EBst2qhEOx4ees2bzjmX+3/g8WXShud7dgwzgyg1dS3ds1Kkguv+WXtmVDeNt3LI7+qit03jylIDvRfQ8yw2kJSVpFvLAFvsQ9KQYRgSR3/wOn2czJHI5hSRy05JQQFAlhiAKWAONVpTDm9CuZ8kFmyfJsw4c8igfGjy3bEBtZh1kxxYw8L8nxLKwkyBpZ94CwBg6Sf/hx6DNCRkXuh6rlkNtTG7Y2aIUgPd+kg7dohkVKc6eO7MKUxZtBn6yImr0B9qS4QGeF8hCq95ExbedxPkjFqgEw5Hjd+xU3RbOd1zanKhrc7WGiVqsQFgmAK9swzq81aMfmKrMabgfUOTtwDAMlWrk9r06M+zUq2nfGZ1AoZaQMzpAHjIx97nkkRilJroMPrec06M/nYOtqqpOk4CxxyJP7S6amF2U8Q9G+5pW3+3SY0+PzcZo+oP0I3os9OMw1EbtKIb4cQmEeBMM0POjVHLUz00SC2stmkzFZ4qwIoYDFW6TDdzI2JYSoIXh19Ev90TjRqwQTdd93CiuQBpy4VRi5AOM1yXj0hjloS5YyKdHFoISd1yFEoHVlEJVyQGeoORWArnEklgQYQXfqY6rqmOYxzjGMc4xvEExbdlpoq2BcQWHlI77m91PJr4w4GPkfKNbWfIkq9v93b4bynDmioaaAXfnOzXj3zlNfjYNX+ohfGjzMSzassAgJuu+NgjXt9VSklQkmt37xDrNskM7C6QCEMsTHwtJrh3b6GBpc4EItUkDsE09f7h8f3/9joNJ9/58nOh5Od+5ldRVYjD/Y1ZzUK07BRhYON/P/tPHvM85z/07sd8/nzxme/6vUf87aPXfOiRx//f74TlJdgzRRB1N7JRVqzmrh9ip5HXQhF829VsTm5nw4QTmSchN3JaJi9h0AL90hbABJ0vAIgzvm5/EVWCEQc9+m64KbScIWcSUmVgSBgkOIy2qvHFDElZ3XP5FLKa6CywWS+Mzu0P3w1LvccwOYQFSGvkXDI0ey97lJ0NW6SSxNBIjGVkuL81i65PWUsY2HDU9xkmnGqnABZvvgnwJNgmnQuzJCKVOdm1FM+56CQuKVCGeKQ3gy1OWbHjJIj7lPVJUyCMLa0klXkcc3li8a50y7B393XtOxjY8D+XV9eNIbosRUEZHuwutDCTp8enGlVdRkpXc+A2cPwYMdUXD2ygpZSuBpFNWZu6Jr2OB7NMmWRzu6hbYwSXYJMxhKrtJpMpCpN07Wf9DlZ7Je04c6i2hXt36LN6kYNaWbHDlRLZMKPNBIej/GWtAy30AxudLSXEkR8JcQhPYPVkDfnDlE3uKrURKgGMuO3ArKusvsHQ900NUbOpkFpsAECZnNuqFp7mJYRH95KZGyFuIjYQdy04KnNPlvIwALCnQKbKpDxbROqpHZ1OB6VS6UnnKv/0v38r7v6+d5zztyGhpBc4o3aMvgXeMyCUXunyq89tV9F12ZjDLsSaoFHIhdr+yjYytEIPX3zRDY85phd+/o3wzET3b3LIRywanz55iX5cz+h63tGbx5mggvs3SYrPtRO9iAwSC47qr9xX3EE7drEd0MQ1levib7/jA485pseLxY/+jq5ZGZUIppWdA5M+GeKaz/yqdtJZWqmBty3k5gkSF4JpacH4SBFygSYc6/4c4orQbS6ZP3psDhiSooA3RxPqYCena3AiNMHdFEzVNkXKYavFbKLY120/SWDBdDIsTFMbysm1GoSqfRm5lCzTTipVooUQYtiywySMjWFfqoTk0PCpdDP4FRr/D+6/C7vsJv506RoAQJoZ2DlFtWNpSTjVADmX7rMoMRFs0LjsbUPbkEkOZFOxrqOyjAGqxleb7OC2771e94TXvL6We6z3fTS26N7kbROiksBScO2+qR30FPybCY7tZkG3grGBAV6hMYmMQSYcTz9ApYSTzaruE/asFDtdgu/DHQ/2tqFr2ml+1DrE/QSGlSHpqT6UlOvaK7cz8DWliOUJoJjCVdKEvhvr+r/BBXqho0sL/Z6rN1OWlY1gaitF0Y0QqVry5mYZTG0WFmZ3cHqrCrHhjq6jWuitHge7tKP1fh0nQcGjcezcO6kJcGlRwJoMEHfVuYiRJaL0BLX+JCM4mKlxyWoMU/UPp1sepJtprLQw2UO3mYMIQqz84m896ebo/0iM4d9xjGMc4xjHOJ6g+PaEf79F8epbXw0AONmdQJwZCJPh5X/k3iYbqq1wgYJP2UUrNElYXGUKix/9Ha1HGqcG9u+ibKUdukgzrpve04xjZ0C76X5oY2+1+bhjtXiGVIzG1Uudc9ShACBUVP96WkQjo+NfkltDPc5jUplENwceJlwletGcw7ULRIL49P1PO4fo8kTEyUdhqD4R8dZ7X6Yf12OCPz941Z//h45x2SeHGbODvEu7f2ZIyGqMwRLtyKUhYSktVCtgEEcoOzzyjjc84niH3vYeAKRIZPY4BnXV+lAbaHF2vxCiu+NrdjFzMkwpEfeyG2hGsljNIbd/oIlWe2caWD5CSIOxYYGnDEevozEs3nwTMEUZnHXa0RlJ6gGiILSCELhEqGDRvzt1OaLIgqPahbr1vBbUZ4ZE1Hbhq4xICI5LnkZG4adbZe0aw5mETExkQ91hQJsUdPouXvj5NyLOVFtU4mCxQFn3qfqEhil5wsC3bBQuIeh2e5CDr+DwRKqMbdjyJJWfLgBeSMCcDHcfJRKcVYwwUAK//kQbWTpURaBsfajYxCOGTJGAZMZh5WIYylM27DqE4wMQwoRQ19RoWDC8RItBdAXTWXynl///2XvzeMuyskpw7X3mO9/75hfDexGRQ+REkswoIlQrpTgU2l2WbWn9irK1LYpCERmUTMViSBrITLWEn2PZpXQ5VaktFhRKiQKKCZlAkmRGZkZkxHsR8eY7T2fce/cf+zv7RGQmmGAghn2/f+LFe/eee86+5+y9v/V9ay3YtkBMcL7tZ2bceueaKB3W361rC0jFTMmkVIswGeixOb/fQjZwYRF07nUZEmJWRcsZ3EeqII0OTOccJD19b4qSNOpK3tIU9XKIETVaCcGR7Op71RpaeNbzT+O+s7rMo9IiI/dLhbBD6klqqFPme2/NjyCmMS7i6o7ZonoF4sUf0bXA89tzcILU1I6eOMlvjLW02fndFpRk8Er6gb5hafdJx7y0znrid3QdkXEF5UpYBO/lkxQANEuhEYk/WuthnHrY7OjPU0oLxwPAvefWcaHXMAtkww+NCtHpzoKhq7hcoOaGeISk8W6Z2zFt+Tf84Vtw6rvegs1kwXz+G2/8H+bn3/jTNxqOaRw7uO9x/YBZjsQnt9f1WPwtC+rL/uLHjByhzaXhpjKmnrKe+dWM02PNZ2650y97Mc0jN2PmXKIz1AugCi09EVN3t9W1DVQWLovLbc+eEClZiElfAdUUjBaAwEswSnW9zLMFpr6AGOm/Lc4PjfuMzQRKtKBEh6bwnMx0zJ7fbZkJ9PRP6c7q3BCAMwA9gv0UDKeUAUAlBRuSCH1kGfsvqxzBsiSmRENhMQena5OeBEoCw4c1XzhbTHDB0bP88WYX22M9qXd6FbinA4AcYNRSbKBPzhXO7c5DEDTsHupikOrPchyBhKDybC6FV4uRESy6XB0Z+HccerBtAUFQ+dzhPtp7BD92PKhGoSolxiVgQS9me59ahk2drygruEOG6RGixzAFi+qVVgyEh3X3NACwiQV7geqJoQNnR5+HM2LIpmVki/rahGchpgWRJRxJNQWnzUS1EsInjrM1H2PSIYH+OofILNxyWHO0l4MRzjb0+D56Wvc65KpMp95ebNhe/JHXY1MtGslI/6KDjFSrvOUpXnT0LACdFMSZjWZZn/9utwZV1udUWxjh06fX4Z2nDZunkNZJRtSWRe3VlXD2HaRz+vzjyIHFJYS4+qk1M/h3FrOYxSxmMYsrFLNM9e8YL/3z1yEi0+lyPcSxVtco8jwxPvpP7nrK3+ddu08Va7/2rkJk0xPwazHiqc4GGq3Q2KUNY9/Ad+eHTUwiF9WS/lvepAQAMrQRKWBKO1ybS0yYPv4LVjfwQ/f9KwDAc5o9bMcN3Dq/DQDoJQGOV7XQ/DMaegf8ozd85CnPOc5sZAQ9nVhsX2b59nRjnHgmUxWKYUyZL2cK3/vJHwag9X3/Yv9aQ+6/EopVv/ToN5qfU2XhM8M1jDPdOGNzYT674YY4P2maRq4nGpg/Mb5Uw9Tx39YiEjx1kJHijAok8k37yZ+5B9IGHrujyCrEJco0KrYMnOpYEhnxMtvTmk4hCTIdTAJc09Df4S3VLdyLdfP7/ihAJ9XXybouzr6OuL93acUkThhq3o0LAGlDwR5RxukA3jkfgvSDeQLEq/q108hFpRQhIqEC5UrIHMYtCzBLmiYYABiRmMKg7JuGoCOLPVwAoPp0jMhC1KEmqWoGrxKjXNb3e5zZxmC8VZ7iuasaTt4Lq2hPy5hcYuSeC8GHfR9W3wanNKNXKheiGNDCFdY8QdQZh0WNRcoBJPV6Ck9pKJWupXWsj8EjczQeDLztQDSpA7YksEjdursXlmCTqIUVA0kD4PTZlaWx4Tir2IVKCjh1MChhRJl1tRJisK9fl07KYBlwpqR5q6Oqb57dwVEfuxdbqDxX///FH3k99skacLEG+Lu2MVLPSso0XVmWxCjT4z3nT7A/rcKzcqRAghPqNhwH8Dc92PmUM2ZICV7Oxg6cqn5eso4PlgJWnzx9BcNYMMhiqrpqY7aofgXx7A+++ZKOPNssbNc02jjVWTQ1kKcb0cTFcYJ4xcQBm1pQpDjDPGm6N5WUuu1KAAAgAElEQVRkyFILS4uaBuDbhUrQKPKw29UTCQOQ9V2oRRKej12cTfTDXZ6bIgpdAy/f8IdvwRrVWP/q4nGst7Ri0CAN8Oz6Ji5EGkK+v3cYjwoNg962fBHf/NHX4s9ees9TXk97qw6bJOS+kgUVuNzV59LxvH5+Hw3yHB1mPnb7NdMdfSXCgjT1YQsKB3EF62Vdn3OYQIX8MMeZh29aOIWf//jLAABrj/xf2Pw3T20eAABr/4kWfAZTEwdXeoKk+pnyL3HpEAyMYMXSrsJovTjWsf/nHXo2h+66ZVPLmHfv7TQMxLh2pIPuNEBK0PPaXNeYqn9g6xZzv8iMw7IlJMHELFcxAnD2dT+OtV95NzjJA0IwcFoXpK+QVWlxF0zDhsTnT5oSbKI/152bwncy03kcR5aBC3PDhdzBho1sI5Cx3a0bOgxSpnE1uk4rEIZ2YjkSzeoUCyVduz+Ylg3MnUmOlNR6xomH7qBsqEQXVAO2nRunA1iNDA1IKlZ07kYczJEGRgeArFXQQ/LF1x0wqOsnuHZeP0OZ4ugtkdjBkQwWAxTJFnqPBdjf16UVb1iMaVLXpd28u3sy8cGoTq2UPs9SlXxjL7nv+/0y0NDja3sZ5HaA8UBvTqp+jINIL5xC6muJadNrcdv0Xuz/5SriRQlFXdW87RiRiPB8FQc1XYveG1axUB3jaEXPG4/vzaNKG5rhKIBigKSVJWko8Io+LyUYWnVyZAJg7ZRhxTSGPRvTawWkuPrB09mi+hWEkAwBtYYHdooz2zozra5HiBLHZGlf96dvfFrZU7UeGqeSsOdBVTOwSW5abEHRTen4GUp+IUe2M6yZLC2JbZPZOW6G5trEPCyDsa/VdQBMYxvlWmQ+O45cPLZDGsjbPh4ic+Qfet7H8en+usl++5sNU+N7PykO/cj9PwAAWHKHxmLtHc/4A2z+0BuezjD+rfHY//bU2d2rP/N9AAABDpHxK9qg9Hi0iB1yqdme1NENS1gJ9CYGHDgf6k3GIPHRT0tYO74PAPjYNxV81/X332moH+6+jWQ1hdWjBUsBWZ6tMKXpHbmwTKeEPHeSnjLZ6GTFBhPKUKYYR0FTKGXwF6cID6hRxBewaKHoTQMIwU3N/vHuPM7FenOlJDP3yzdcdxofP3uiSNsyWrgB2Bs+eJUXzTcJg6CFVFnKSPdBaj6t28+pFMzU5iZbVWRLFqrkvJJdKBvnnDw2/8+n5ijnsfaf36l/oE0BY8rUFsXUxu5OE/uu3iQ06hN0M30fL1XG5h5uj8qQiWU2HVnGTVOX5QmIsQNOij++l8Gt6OdMKYbBhfolFJ4RDlX1PdGPA1xs61RMhgHUZhmniQO6dE0bFn1WOvTAfGE2Fo/87Gtx8qf1pjSrKFO7dPsc0oIxe7d2vULGMgTUkCMiStDaYhcX6LMZV5ib04vewcUGUBM4fkhnowfjMvqkSCYlB4Y2xiM9VuNqZpS02JwETxgcqpkLX8G7SXPMa7bA2U2aJwRDmlqwKbFYW+yarJUxhd6CA0bIiUo57Iv6Bs9qAoNz+hjpkQQ2h8mE47rUik25lOVVHFf/tmAWs5jFLGYxi38gMctUv4L43Le/zezkVddD6YjeIZ7aX0KWWVDnNHzYiSrAy576GDf+0VswoazQ6jVMDSuP/PgytI3CTLUU49rmAcZU2+BcQhK0pxRDlXbWJTcFZwon6nqnet5vIsoKmPhFh86Zz6lWQvS39K7Vm3Aorl/3193j2B7WTEeoNRdjvqmv85l/cjvmylMcLuvs6/RkEf/lBb/65Qzh3ykmdP3nJ01zzU8V3/nxVxuh7m5YMko6TxTaAIDvv/cHAQArPjddq1uDOhxL4MJECxW0vKk5nmsJPNhZwfhvqH7+TcWxcg1jAEgWMrDQKlSObK2NCgDZQgqpmDEV574qxBNspYn5AKIjKZxKgptXCwP22gl93RZTON1fQEiuLL1eBau/pzODg9sqSOoSpykTqQWR8ahdrQywOdTX9cnz65BdDw6p22R7JdRqBLGvU1ZLdbxs4pjz51lhWC48haQlIB1SZUoZGDWnCwZEQ89QT3L3l0tj/bfIgYcBil7HXWGEJtjEBgQzik3iwEd1TWeLpeYInCksBBpajIRtyiLjxDMORI4tENvSaBLPVabY6WhUgjEFq5zBolLLzUs7uG9Dd607pwO4DMhIDOKGuV2MU30P1rzIwMnpvFawyrvz99s1gOq+8CRUZCGlG2H9ve/Bxn/Qjj9rv/zuQkC/JGCNLNg1/VnCt57UKZ/Tqc4OXWMioAQrKHqOwi0nLmKzp7/f8YVCRIGlDIzDGJ2L1EHjWg1XD+0SSveWMD5GkH4GIwThWMLU5sGA+frYUO4WgjEGsZ7LAjcFDg8wHFNmPLGNgAfLGKJVuikkQ7iWmO5oZSl42w7EPwJFpdmi+hVGXuc89++/MueY6FwVQVfflE/kIT7/w2+CpFpmZWFiYJbB2Menh2uQNNk2GoWsWgKgQhOrxSUCO0Uv1pCgVAwOHeO6uQMAMA1JJ1plPEiNG1mDo3y/fs/Dcyuo1IuWfeFzdIkK4nkpBpGPOV8vql/feNw08GxP6pdBoV+N+I3n/QYArfpkcfVFX/fH3/CL5ucX/dkbUPXiJ73mRX/2Bnh2hgVfLwb7cRUrvlY4+vrDZyEVx4Qa0R4fzJn6+cV+A1IyWM/S8Nj1/+0/aDNoAIBjpAetiEFUpIF8+diCRbVSZ8NFWnXMRG5FDCnV8eyhBe+aQmlpsTY2ziucKcx5ZB4tbRyvt3Gqo3mlkAxJVX923JS6lEATeZTacOh+2Rw2DY9RCq4VkTbIqu3YBCs1/dnzlQnO7c4bo2woIJvXEyMLLQPxMgGAAxnRJ/xd28gs8pCBDxxYZJFmXfI1rP/iezSUbdHxq5nhijJPoNHS14kWMDrTMAL7TBWm7mHqoOpFONvTz0zJTVF29bMwTh0EJNHn2gLL8wPz2fuDCjiNh2VJBF6iKUMAHtg+hGPLelO65dURHZTQoA1cYKWIhD7fQRTgmhX9TOWG6BOqZTZaY/SiYorlQYZz3/dTeGJUV0aYjPTC758KwFNg3KD3scJezz9gcEcKWKH7p2sho/sMnsSoRyWAno0HHz1iGppYNTNmAPHEBe84ELTQ2SHDlJoAbzm8hYfOXQObHA2SloBFX2J3rwbk1n0pg5AcihdNawuBbrqyeAmpsFCjsUqDBJMh1TdGjmnAkoGAU0tNewESruvyM/h3FrOYxSxmMYtZ5DHT/v0HGDf/8U/jcJ06fK0UPjUB/M4LfwXHf/sdBr4SghvlFcYUXnJCqxV14hK6URklMsoeJ56hJthM4ubGNrqpzkpOdZfQHeodbuCnaJb0DvNErY15b4zTIw1vXhw1MCKrpqOtHnpRYI45F0yxQVDTpb6kX8t4xSdehaYbGsFxpRhqPgnZx96TvFBzAY9McpPZdCZ6XHKRhMBJ0Z3qLGQ0DrQYRw799TxYlEWJigCPC3EDljIwgg4r9wUIOkQ1WeQY3ZTA6jrmfXnXpwoErFIh8mFb0qBvNpdYJrP0eX+MSDh4pK0bQPifNzG4VX9WUI8QtkuAl1uMwRhSM65MF/nemXnwVgJBsHVjcYS1hu7sTKSFc+05RB193TzikCTsb4Xc+LgqW2l4kLIZf9uBTUnm5KhAsGsZZpi0lfmZZVoj1z/QYxceS4zSEAsEHI+6eHdKcFYnkGdJr7iVYfP/eOqGuB/89L/G+bG+Hyepa+5TzhQcLjAgONizhKGT+G4KITliep7idgCnpe+XdOrimrU9A3fu9muQj5LYfspg3arHMbeUa2/X6UIZrBHdE1UB2NJ0iH/zR19rkIdjjY6hAO1+ZhlWyIw/aVZWSOapKXLLRngoM9kcS5kRq+flTHuXArBrCRw3Q4XUllYrQ7g0h1TtGJ/YPI70gAT1LYXSov6iGAOmF6oGGmaAoXiVD48w2dJqYrwVg3OJMs1DZS8xxgTDxEdnWjbsCN/OzBgn56qmo1q1EgTlGNMe3VcjG/aYQUYRzr7tzf/g5+gvFTP49x9gfKmFyfVSc8M6ljDwb6MUGhUZm0ukkuMieSjaXKJG0ngCHLF0YFM7p5DcPBzjiW+O/flkFbYlLukgDrDa0g/O2b15ZLGF647qGt+ffMN/vHIX/2XEj9z/A0ilhTLRXM6O57E71g/+0Zqmw+R1VM/J0A/1+Fhc4YWkKBUmDmxLok4m0YcrfVM3DVMHw8jDwUBPCpVSZLqtgyDBcLcKt001xIrSEpLQkx1PclNQDa3mdBVvoDC4Rk/O0VIGq2dDEL3kUk4pFDPmAKH09OS3pCc/pZihFQVWCqm4adwd3JhheVUviJwphL3AqBytXL9v1JxKfoKbW7or2D+ZoTMpIeJ6pWNMmUl4krlwnQzphEAtyRBQTRUKSBp0H9kKbs9CWtEXkDQl3L5+T+tBDnsqoQhbHZ5gSGu0GMQcXscyx0HCsbymKUwWU0/qnr+Z5B4X/CdD+Xl85P6bUF3Vm45Ru5BprC6OEU49I+peLUWGUnNybh8r/gAf3rwBABBzmI75SnOKMHWwvac7bd1Nz4w3kzASgJO0hOriGIzg6/rDFpypHo/2cxg2frigXPWiAJ0NvfDXTkZo+RrKvngoRppy2FSXrVZCzNNizdaBra0WQOclywLzK/qZDBMH4QGpGE19JBJo+/q75scVbmjpZ/XR/qKukVNdW/pAuK3vb2UrwJOwVvS9FXcDsyGrBxEmNYKo/RRJbJuyQndcMopnQjIEboqE/l/zIkx8fV+FVWE2BGpqYxpaZqwA4PSbf1wnPm/76siN/n3FbFG9ymKlMcT+iMQILIElqivV3dBQJDYGLfh2donVlo1JqB+4NLaxWBqZmtD1rX3shXohGg6Dy+QRX/YXP4YpybgpybDxuObVwda6nV+Mp/rVjFfd//2o2fqhX/US7CdV7Ef6/BNhGWrSKPHR9Ke4fl5TXrbHdXDiNW536jhGjiw7ew1wRxqT6D2/ghXKAjujMtLUAqeNRq9XQbOpa0dR4sCqpkjymq5gRtpPetLUrJQnAQkzmbSfI6Eou7CCDMJ2YA2p+anHUT9LziWZZWqSkxVNaZkO9c49XUpw2tEIQpTaWK6NjAwibIneiJAHL4Vbi5GQTm1nWDac38XKGA/39PdZdWPYXGKOxAg8S5jMqeQkGHbLAGUsUADdLrCHFuwpach2bGQlZerFpS2GB+95smYxAFzzrrux8WrdpHPiXXcjXhBgDSJEphy7O3qxyTmseaz98rsBRy8UI1HBFwurkRhDdGtoQRFXdLRbRWlhgmmfsjSlZQwB4GVzD+Fi0kJIAhVwJSRxSsOzNcSHbLjUMDj/vL5BMwaRj4TcfiRXGA8C2NTwxVQZgxP6cBuv+onLznHw2XlskATksZ+7C9aqfo4bjQmmkWvOC4CxenO4AHcFAnL/SVML7dyKL7YA2pw5tRjHFzs4va3Ri+P1Dn50SQu1vHb0PRB7gRGbgGKwiGGX1hTAFNIt6p04NEFAUqienZmGyUYpRB8BxmTUnvZ9ZA2aa3o+1OoQEdnPnYnmzcafCQaHNlpMAMKD2UTyYxNc97a7IaKC7ne1xqymOotZzGIWs5jFFYpZpnqVxe6gisMNDflU3Qg7lFEEto2Nvu5+nIQeHKeAbjmXiGlXySyFz2wehaLE49y/fHI3Yh5/38L1Tyfe9+z3m59/6vPfjVC4OD/Smc00cUyt8UspOa2//06c3dYSbosLQySZhQHVlY82Doz04G2rE3xu5xA86oBmpRjqg1o8IX5RBM9PEFL7osoYJHVbIuVQRH6HApxGjLRLHZCuRHNBn2OvU4HTtg3FZO5hgb/6r5dnNABw0xvu0ZQc2tXXPudhUqPvMwMu+A3Eh6nzu29DkqvRJHShJAfPa7NuhsFAX2cqLCNSsrI8RKUZ47GOzn4tP8aAoPLEs9CYG2PQ1++bnx9h8Dk9dmmlkLGTDsBTGJWjB+8pKGJrv/xu+Ds2Hn2LzlzPvKH4m3K0SQRyp5eUw60/NbTLMgZeKzK43PlHQWeh1WU9rqJfBa/pMZARB2vTWHGFcKcCENRf8hNTLx+JAI9NFiFJBYp5EvY2Za0cSCeuGX+7MUJg6/ftpRWISU5lkeC2NJ6pTBrhK1z7+2/FUmNkfIxzowJzbZQ4vmBlE59tH8Jt81oKVFxi9p5KC5t+C5P9cvFGqm+7B5ZxDIrWFVJpGSWmvzl9HG+Kv1uf76AKKMC9Wc8hk4tViDJRXhSDNSpKCcl+CbmWWb9UwfXr2gT+mmobH4uOI6V7pLY8Qkr3EmtFmEw9Q3UbjEookeiH1YwBQgmEr72ATd29HKAQj7y6Y7aoXkXx8o+9Bos117jFJNJC1dU37LlOCzFRY1qNMW6a28XfXFgHAMQXK5i7RnPRXDvDzm4TTpA++QOusjg7mcc0c40jDsPTl0V0zuqHG3MjlNwU3pymkGz2mnjeitaKvThpIPASdHd0bdqpxUifS+M2dJByadR5VGpj8wd148zxe+4CVjSMZdsCzeoUHdrgLDRHaFONltsSZ954+eR6adz8eoLXLSCuF9Dqg/e8trB+KyvYEwa7o++JrCLQrOoJba40QcWJDbVqp18zfMjxQRlBS78uErrGnm/Cti7MgdEGIXJdKMVg0f/v+9Z34Lm/qykeijH0T9IpxgxeF3js9idfjzXRdeVbflyf84N3vxY33K5/VotA5bSD8XHauCQc9UP6vL794//eqFl14jLs+UPIcmk+ycxEHtGCl8vy8YgDib4nvEMTo5WdSwTmlJ3edh1DaiT6pcmLoD5bh0eNV0lDIl3RSwqb2nD2HWT0/2Hk4fyO3sBajjTNWasrPewe1M11Mwm0vkAL4tkytl/y1MCgLEmkRMf62IUTaJRCPDrQ0O2zWhfM6/7gkWcCbc/IEfK+A28r53kCzpiOt+XgbLRsJB0hGbaov+KZK1t4xF00/HMAYDkXWGnVI5vGxOlxA+8DFtKj+vfnp02MB4FpsBs7vmnY45ZCrTo11KQF4rYDwNFGHw+Fh/R/BAPLbDhDUovqcV3uuPoZNTP4dxazmMUsZjGLKxWzTPUqig+++BcAaKEBQGdBJ1q64eZwY2BUZCquzk7yHfrqyX0D5+W0Epe8KI/93F0492NfmYDFlxs/f+qbcDHRUO2SM8RD41WkpOX6/uf/+pd9vPsuHIFlKUMTyelFf1tUP+tjvFa0/APA1uOka8qAh33dwHPvP33nZe9b+6V3A9Th6zRiZLENlZPVbYnb/rvuWhSlhnmwcv3i3DBhGPpYaeqseLVcCBE8VYxO6oyk/nkHtsWQNPWFXnvn3UhWNITpdi3dYELBY25ciZRiONdvGSPuaxbaGFEGdzCq4PicvndKdoKNQQthWABwuXYxD1J4XmqgYgAYHdXXHLek0QROmwKP/vTl+r25SD4qgKgAj91e/P3U2zQUfMvr7sGDd70W17wrz36Bdls333S6FcSreiQHsQ8wpU2vAYDB6B2zhKNydGiOHe57yG1wkgtlqBKdpCuBrEiF7L5lxCqm7RLsmkLWIqGIaoKlBjWsjUuIpzXY5Hk6dEqGjjSNXYTUyLbbrmN9pYOqo9Gj9uEytvd1x/DG9//k5WPza+8CH5OJeNtGeIh0kjOOKHTxPTfeDwB4SfUU7pseBwDdQV4RYER9UrzovvbaHOESCTpMtflATudhgmHs6LH65N61cOoxUhKlYPUU6BNGLZlWfsp9fFOG6RH9fJTP2zj3qKan8WYMfuAaOhUGLppHdcf5aBygHkRwyds5FjbmSRhimrlF6WOnBiu55LuYME2zSq/+VHW2qF6FkduIXfv7b8XWiGgzlkCNoOAwc3Cx0zAm6ACMKtPZ08uAIzHdJEebv0cFk1PTFYxJnSgUDibCxakDvYC98lOvxB518faiAJ98GkYE5SDBWqNnaqDbk/qTXvPSP9cbhjl/YoTVWy8vQ1AHdZTZ+NS33IkXKQ3dbu03sLvbMO9f/813GuF2uwGjZpURlJgrazlBqsXKAQQLU8Rh4WjyLX/5o1idI3pM6hjJyHvPruPYuXeYehpjytSfwsiB39DHHl7LwVMGe0z1W65g1YmrPPFhxcxAwydesoEKTeqP9+bR3a/BWdGqT1vDGgbUGSzGDpKG/n0/qmMceRC5+k/KwEg8PkstiMwCI1H0a++8G+QUiNaDDO3nU8dp38Kxn7sLMl/AuALLZQmnHIoXRuf+fiHQny4o7YLjFaCZc1GPVRYoPM51nbdZn6BeiTCiOmGa2GBE9+BcwncyCIIg4yMTBCRc/+Al9LS1X30XWMp1RzYAsRKDk3i84grCl3jJzY/qYwgbP7LyUQDAr+y+BN1WD6fOaOjy2657GJ/c1RKGK7UhHt3Vv3caEc7vty5TacolF1/xiVdhlPr4ny+9m75smLEKTyY4tlqI39tc4sxEX/fLal/Abz36PH38VoQ0dKBo4eGKGfUj6cKMaWlXIWnwQtFKwsCquayhTWOXjRwwGvrSkSGWayO0x7pmOzzXgE0Lf9xSBQ2n7cGJWO5tgPp6z3TMS8lRdWMcLvXNOE6EHuNUWugRh7d2yjZ1+Pz8w8MpZHj1l6Vmi+pVGMd+QU9OwVGBaawnb9fmxr3mQqeBE4ttjCkr6Ye+4alOGjFExgtN1dwx5e8YN/7RWwAAK/UhLnYLS62H/lnhFcuh4NEOtpuUcd/mUdx8SDc/7EVVPHxe74Tr9adnqjhfmSCRFjhl6PPBGC//2GsAFFl93lAyTHxTVwK09B+gm5tOvPtuiGWdQavQxuYPFxnVxr96k/a0hZaZy2tHQSVGsxxip62PKQTHkHxAZWwhaGhE4KY33oPx9avG7q1MXFMAWF4YwLWKxptUWMayrNxMcDDVk1v2uQoUB/onqSmlx8GJcpFYQLDLjJjCQ+dWcdMx7YErJENzYWSy8ap3CU2jlZkaqlQMJS9B5OmDyIkF1sm9TxVkSYDRAuDsW0iremHLSgwe1WWDz1cxPgrIcpE128RtZalu2PF2C+9Vnp8KB+BI+At6XDwnQ79LlDE/NYtjmDhIUwvN6tT8f0rNd46bodOtwCb0ZbExRtkp7ALX3/ce/br5GFnkIKjQxmXkwVqk2rcjkMQ2dqZ6s3mk3Mc7Nr4NAFBzI9xQ28Xprl5I/2r7GJZoERnEPtymPkbS98ASjoxsG0u1CEvLenH5oxe9D5dFxsy9EE5ds+GbK0+hFMP+VG8wbz/9CqOZnKUWEFtgdPxz3/+TuO6tujYtXYWsor+j7jMV3I5l7NcUg3F/OXyoh/aojPgCjXHCzIKbZRba4zL62yS64EpI6q9jWfE63kjgH40Mpz1wU8N7X6yM4fLM2O1JMFSJRz61XSOdaCUK0mEY3EKLKFewAgHGr8x89LWMWU11FrOYxSxmMYsrFLNM9SqMc695cg305R97DR46q2Eoy8/w2PYSOGVBxxa62BkRSZxpab1gQe/4n24d8tJ45adeiZY7wSf2dK2nMygD0DvTnUENQjBEnYKc/3WkXtQKmqamJyTHMw5v4XMP6mOgml5Wd1p/73vgkVm1bQuUySzgU99y52Xn0nBDrPi6vnXPbb+Lb/yfmpLynR9/NTLFjSBGPwrMzxaXxq1FKAbrZA/jCW3J955seH6pHF6u6KMUwyjy8JJrtDTkvdtriCONGgStENFZPd52VcHqOeBHdFaSxDayuJCWZEyZjPFQvaiLx5mNPeokZc9QsCIGGdD5n+ib+qdzaIKkXTWZKiQzqk81P4ZvpzhZ12o6oXCwUiJlJy4QkAN1O67gkc4iankW6GbIzlGH8oTB2rMg/KIrFss68+jPWahRdsi/tQ37/nk450ly8RljpCSGAcWgPAlF94jfZYhalO3WBSqtQqTj4qgwWb9u+QBn9jV9p+QnUAo4XNWZ3yjxcY7GO44dMKYM/N6fBki8S75HyuzEbgk8ZXjmtWcBAA/Zy0aUfxx7CC2JI2V9/D9/7DrYBPunkY1PZ8eART1enEtTctga1E1/QhrYWhieIOrpQRlTqdGGtV97F6xyhrP/u6awua3IKBK1GhOEaVEuCBMHLiE9UsHAyWqgn50SZdprv/xuuBZdp9KSgwAAS/uz5qUdWZLGM3Xr4SVITwIBiYwsFRl9PHU0rJ5LHzrSzCFSWBCkzCViC9W5GGFaOF8NSL7TsQRkhaEd6vun5kWYkICMzSR88qvt3cZQecyBt6uPkazHEEMHMrz6XWpm2r//iCOHQmNh49ye5leKgQswGI3ZJyq9PJ249QN34Hkrm/hCV8O1JSfFHtUob17YxV5YxUf/yV3m9a/97L8wP3+mewQA0J2UMBoGUMTxu9Sk+js//moMEt8sgoGd4kJf1zkf/BISjq9/4J+jm5DiTuZhe1zHXl8vbuvzXZw70DSI5eYICS08e1tNQAGMuItqbMPt6L+dfvMXp7sc/523a23UgZ4wKstjszhOx555nZragCOx+cpCHWjtP+l6MZvYYHMxPII4b17ewZmuXkROzu3jINJjev6vD4NdP4agZiHXS5E8ru9faSuUdjnIkxvqphGec1jTMM6Pmmj4oZGQi4WNYzXdnFS1IzhMX/NOVMenNtaNchQApGRHxxKua6JEzxCBNPAjt5QpIwDakBp0CKeWmLpz/h177XwBYPD02oXMB+SzRoYq8+y18wZKPAgruNDT37tSDEIwLJLqU9lJIAmPPPvAIchmCoQkK7gyRkrHi3s+Nn9Ib4que9vdkC6QzunzbyyOYNOCNfjCHCBhbMpEK0WtqRfcCcHMgpx6WMzROKov4HizY/i9434JzJZwvaIencX6PGxPgFuyoAQdeLBJRSmNbbNQLlTHGEYePvttb8cXi7Vf1+UIp1M4ATFRGH6D6e9J5bKZrkD+Qu5IyL4L0No1f6KLg209xtbQAlYis0lwbGGuHYB5Hj0/hUXQ4ToAACAASURBVGMJQ8uJExsevUdIjvVW17y25kTYGBJ/PnbNeLcvNuDvXmKRWNO/l1GE8z95+1U9R8/g31nMYhazmMUsrlDM4N9/xNEhF4y9g3pB/eAKdt+GKMsv8c4vHSdabfzFuWuNwP40deARXHVjVTce5YL1APDJl/3uUx5n/b3vwea/KzLlb/6oplmsV4cYJD48EnXvRcGXzFDzuK9zFJOEBDCCKaLMNrqjm52med0o8jAiE2UAYBHHpXhNRqLw6++/E9wuNGBxiXfr2koPibCQ1PQjNJz4SEhJp9QIMSUPSSYYEF/+mOVOJU+Ma99+N/xbdAb0aHfB+Fy6Nw+QJDYEZYWRcCFbhadpuCghqEnFERyf2T4MAGhWptifVNCna62VI5zq6m5r384M1QHQgiHzJQ1RD2If05K+lge+462aGkOZKvezgqC/65kZxG9zhCuZyWLP/IvbcfIO3UST1hWs9TEkaeTaE5jMWtoAe6CK5gu1J+nGoIXFsj4vCWbKE1lka4iTGrPDzMFuT6MQz37eaWyN69gh+sr0XA2yQWISUQEDZyUFrEYIKKsaDgMszmsqTvWmDlxbGAOGTHJMqCTALYFsPwCnJjX30MS87vHuvIGdm3MjjKc+EuqiPvcvfwo3/b8/AwC4dWkb926sAyR67/Y50CN94pJCdlx/nzu9Gmxb4jkf0jDxE92U9ADq7zpdSMGmhRhG3gmsO8UtxAv0ekei0tBZ8VJ1jJ2ghoigcyEZqgt6vEeiCowcJOTtKmIGsUDj6Eq4lE3XgghSMRzQ+CvJIElz++TqHsLMgUOdaHthFd2xnoeirYrpvOaVFPF8IezfepBhcB3A4qufUjODf/9/Fuu/dScwdozk2lcC/578g5+FbQv806OPAAA+tHEjnruqVYj+5sI6ksg2ll05TxMAnv3BN+P+lz81rPX8D7/JwEnXNQ/AmUSF6laf3jtqOg2TzLpM9H/9t+40tS/LlqarVynd3p+bJcepbaCnZik0tm7jnYpeJGjRsEoZ6jW9uKTCQhQ54LSYWpbElFR7mvMjLFbGiEjdanNzoTBxVsVkzlL2lDXwp4pr33634e6FxxKj/ANPgNuF4pGKLaN4hImNYHlsJvZo5JmJVpUEgloEhzY8aWahVdGT6067jiOLmlt4tNpFwwnxUF/D+ef3WxC9SyBsV8Kp6O8iHXjGgF15EuUNgpabCnI1Ajr6faqRGk6pU41xzVIbZ/Y0tM0YCusxX8DbdpAe0zXu29Yv4GxPlypOzu3jCwfafH08DBCUYwPrVssRxmRFuNQY4cLZhctKCNf+/lsBAMnIhTUgXudSjHIlMmM13a5cJifJbIm5Ob3AJJllOnInQx9gMPf0SnNo6qHb3bq5PxqVKXa3mnBIJtIPEjyf1LkW3BE+dP5GjE6T002P45G30ibyve/Bxr+7/DnMec1KMtRIIYsxhdHDLbC1ojs+r88ryUwt19pzwQSQN9LylOkNBYBrXrCJkp3gNEHWw+2qgeUTUu3KF2cowCX67/CWBLU5/Vy4toBnZ9g6r78niKIzGFzBqSbI2tSjUEvNefEdHxnJTPpzIaJOgOBCYcae1hRkFGHjjqvb+m0G/85iFrOYxSxmcYVilqnO4suOHKbNd+uPnlsxHp65ClHehZsKy8A/npPhge9469P6jNd97nuwF2t4aT+smm5Lm0uc6+sdcvugCm4XMLZlS6MUU2mEqAUR+hOdEU2HPlwSw6hXQoRkTTUZ+vDLCTLiArquMKbWYay7IXP41wlSYxBf9WNUnMSoIn1m9zDGBJupngsrzDmZCspTQFV/tprYaB3WEG/ejHLbj2hBAOkyMKHHdHQMxpIrOZoAIxuo62M45z0ky7mLNUdteYRhj9SFJrZBIZStUF6aGL9P2xMmk2/+pY/JCukRf/2OEaQAgE6vAjksulGdVgSfmqlG21VYU31toixhkwhFtRKi4iXoEgIQTlzMtXTWt17v4r9+3S+ZzulWKTSfx5jC/qkFsGV9sceWOkbvNpMce7F+Tv/q4WvAJjb4vIYgW42x4d92JyVEoQuRZ/YZ1zA1AMsRSMnOza/GSBMbgrxtna4F6tVC2pJg9QQWZfVZ5MDP+awDX/OM81vNUWgt6+89FZYR5V8sj/Hw1jIENSfVmlN8/aFzAICL0wYeb89huqcb6ayJpY3LAfBLGsE2Xv0TeP6H32TgZYsrw12eJg5qfoytdiFO8lSTNz8fgGUw2aM1ZUbYP16PtZgJZeFiYsPf1n9MywqiKuBv6892R2TPBkA8e2RKKUoB4diDvUWa20qbqQNknL6QGD43sySadB/0z7TgHqZs18kwbJf1/QpoY/ZMNyqdfevVnanOaqpXeeQL3OlTh74iKPcriT976T34wU//a2xNNeXjlmsuPsmoPBdd2NyZg0ULH2PKOIssV0dkhk0LWOaY94wSDwtBC3WHiPlcokR/e2BvFcs1Tbz3VjLEmY0O0Xf8IEFAi54C4HBp4ML5hRGGVCMbjAMkQ5otUoZo7EDldSoGOFVSK8o4ZGSbGpZlS4Ny5UYGuVj9ofoAF+lvqhJrWBmAM7QgAHDqCFaHI3RJVeblH3sN9sZV9L5Obx5Y1zHYkSwLCIJx7R0XigE2QWo8BezHiVIzAdwjGVrzJP/GK+AEDTPoxc0+0K/N5lM4e3oC7dwmjGqSVAwHFxuYP6QXCsuSua834EpkiY3RRB/D7VpI6zQetdTQPY7W+yjZCRokQNIvFxSmQUyiGH+j69qNb+1iYunjBXaK3WoL1pZ+zQbmcOaMhnyXj3Zx0NUT68LKAAcXG7pzFcDQ8ZELE2apDZFw+ARRM6aMopV1qoJ0XX9XUd/XnbAUoqTMYqY8Ac4ARdAwd4pSgh1kEDYHaDGGm2GYK1MlFmIyp+j0KxCRDYtENuYrE3Ba9uLMxnTog9PmCvXUbN68pcwont36gTtQ9hR6JNW4driNo1VthnHf1lEMRyXY5JrEuULYJSpL1za0J6xNkQw8cNrY2WOGlOhYrOdABBasGrkalTNkvl4GFm7ex+7GnOkpyCoFTSewhXmWSn4Cuy4xyTvC48IRh3UcqNAy/QcK3FC8grUR5it6UW2Py3D3HAM1WzGQlmHUoq7mmC2qV3G84hOvwkZb18H+vt0dPJ6ZSXPBG+PVn/k+AMAk8/Abz/uNy9xirnmnzsSmSzYa83rXGmYOGn6I/YlefOpehLMdnYEu1Ua4MGrAbxyYY5RJlcW1C8WV/jSA56Smpjqd+DixrN+TSAs2l6beVfFik1m321VYVPcKguQy1af1974HWc5xZNC1S3pfPHXAaEeuFMPOqIqlKu3Cw8DUPLPMAqghI20qBOcdoyA0mbNMlnC2PYd6OYQielNpfWToOI36FP02cX2nDqSvwMZUU2WAQ2W1pAYMN1qoH9YLopIMgugw1oRDeQrZPDU1cYV0njK4vo0m0ULizIbXjDCJLjHfovNnIxvKk7BIIpFnDD5xeUPbgU88xwvDOspuarKqzrhk6s8AcOy/vAPXfLP+blwrw7VVzUu9EDbRWBxhEOuNRvBgCdk6LVIPLEIs6J/7wxKaK0P0N3WWFo88IxGplK7T1st6QR9OffgPF59dqunNWTjxdMMeLXruXAhBDXwiteB6qblHGIOhGE1iG4wrSOJvMsBktCKxkHRos1NLYfuZ2WgsBGOsBVp+8K931gHFjCQg2/KRUn3RXZxgcF5fv3IU+q40WfE0dXCqozcZUjKIyDJcV0D3IgDAnlc3KljsTAmsIaFImzdcEVj5BG0QphL9EzbwUr0JywTHdEmf1P4jC2gc72OwQRSbkOHsTxT9AMd+XtPk+i0HzdYYzTotkAdVgDJOxQC3bRnetAgkRtDZuVNKcH5X02tY14U/Lmq907UMlTMO2FO7/l1VMaupzmIWs5jFLGZxhWKWqV7F8Ucvep8REtj8t38/0G8egZVgvaxhKZsL7EZklm6lePFHXo+PfdO7zWuzKmU9oYXBRb0jf+Df/gR+6vPfjQ8NbwCgM9fpls7Mzg19rCz30Cbxg8e2l7Bd1ccvualp179xcRfn+nMmM6hTlyQATBIX/VFgtGM3t+dMhqhibugpleYIt37gDvR3qH5TluCUAcmxA15OTQaKoQNJUN8g9iEEx+NbuovSdjPcvKrpRJuDptFrFRlHtMqhSMic29KYZEvJsHuhBbtCpuK9wHRY9versLuFG4k9ZkgJlnPGhdZvPCfBaglEfo6SgVEHsWhmsPzMgBjZ1AYjSFDMF0o6h6oD9N0AGUGfCkCPXE2yvSpUaGHpPj3Go0OAJOQ82LYRkUbt1FPo+gKtJQ3KlrwUEQlvSF9C2dKoeg0TD1MyVqg5EeYrE2SHqHt5Ukf5nIZZpysSjAQdUsEQOQLOov6OL+12TWMbK4t9rJT1Z19EA/sn9fGr9RDWh3XmFd4kYTUTOESpifdLhsLBE4ao6prviY1seCQCcvRFW7C5RNPT8MDpzgIGPZ19KQWAMljHzeC5mVGHqtqxEdgYPdJC5doBBFFPokUOd4vMJaIqVJ4FZxzehotoWZ/HaOojIDUxKTjY1DKdwWe/982ouPpv9/7A5S44x++6Gw5Bq14HiMnJ6OBZFphQSKgGz3sOGAlesIzBczKovCwgiuXhhjffA6tKSAwA38mw/3miZ02YgYmFB6RNCbebmz9wiKo+XvODhbl6OM+Q1mBKCW4jxvTWDHIa4WqP2aJ6lUfOeVz7z++Evacf0sff8GQloLVf1SosubrM3yW+95M/jJbrokEY5JnJgqGW9KLSZQsqgCfRBfL4062TWKvrBqeKE2O7pSc/MbYhJDeSfavzfWx39GLcm9TQrusHL/DSy/4VkiEhIe/+KEDa88H39cKsljJTRyo1pqaelWQW4tQGC/SDr7JCZo5XUqiBC3tIgubVwkwaFU3ZyV1IpGUZabaymwKFSiMmXoaEanxKMqiQaCi2BWQMGf2fuwLDDkFlBzasKOcdAtGSNPWnrFxI0DEBYN9DuKMhSIuhaIBxBWTKzefZtQQZTX7NubFxdQkzBxU3xtm2ht+zjCMlzq2zPkU2cvHXv6fvmxd/x7uw9eJcqR2Fok/KoHxgEpLIvSOMc45sCnBL4Si54txU38HmVMOANhdYCkYFxakmIF2i4owYMv1r8GaGsF0ytTrmSjzjmK5iZ4rjaLlnjL33tpqGcjTslLH53uJ5eM4r78bgOv2z5QCc9hZZRcHfsSAdff9kJYV4nswfJiUoaIgfAJ6zfAGP+vqzxrFrJC6l4Kj4MS6M9H089CL0Ev2epWfsYXvvEucoprDyXL0J2+1XEVNt1B5yREcT1D+vx3/g+IiyAsrefMKztN3Xm8Hv/Pir8cff8Ivm98pR8Dp6/EfPD1H6HBm4JwrS1YspoA0TFI2pqGeYJg44bfowKpYHESiII/q5u+7QPh67sASPDMyThoQk43Rnx9UuSmG+lVMAmZkPTjCktMF2BwyKKzC6B1eaQ3hWhmwS4zyu7pjBv7OYxSxmMYtZXKGYZapf5XjV/d8PAFj1+rj95j+54se/4Q/fAgCoNSVGl9AgnhhXIkM1xxo1kZQtk6n+3gt/+bK/f92fvhEvXjoDAHjnrf/tKY9x4nfeDsf1YBP889fnjpuGoJuuv4itQd2IQXTaVWOaDQAJmVM7qyMoxQwdZr42wXZXZ7SuK5AFAmmN7McyZjqDa36MCnXvRpmDwbCMxQUNHaaCG7i3166CJwwpGVnbYwtsLm9ecVHyYzjUiRlFDsYxNYowZQTRe6OS1uylzlHWSMApYxZj6vYlKoiaWHAH5EEaMdiXIGH8Ev9K6xLVmdoZhqzEEC5S046vCnGGxIU1H8Mh0fiVxhDntrUAQ2+/iuuP60ypG5Zg8aLb9cz33IHv/PirAWgE4bM7h8znXXypBWdEvqWLAvZcLkwANKtTdEg1SZ2uQFDHKXckyuUIw1hndB/avAHhY6TpezhEszY1Cld2PYFICV9WDMqhzBTADddfxEWy7xvtVPHAWa0c9cLrzmp96615el9hss5KmRY8gdZhtm8o7iMRSGRNMvnescEEYJz4GBCTxd2wXYYVCExzCpmzaIQhhORIp/TcpRx7iuHEkm5Osrk0IgsWl7BdYaBcx8nQJRH6JHKM9rQ4kgJDB9MXkkXg2AWn8oDrZ1h//53GeGL9P94FVdbH+OPvKrLU9d+6E8qzMXgmNaXt+IjnSRgi1Bli7uXKUmZES5grEUUO5Fhfj50yYx6fNSRADXBndo+Cr0aIqataSQbElzT3KYbpMX3OfGpBEvwrStw0VKqR9nvlZL3nWAKBnSK9pBHxao3ZovpVjphMDX/78Wfj9puv/PFPfddbrvxBv0jkkmuTYQODaoCHdnXn8TueUbzm2R98M1aqU/QJt3vFJ16FCU2S49TFN9Jie3jhGJZKI4zpbydX93Dqgu5ybE/LCGOnMO+2JVguq8hgeKNJYqNeCZH8hZ64dl9gQRDlImEAXInyFol+zysExCfc71cw8PQEXwsiWLYwVJDOtIwoy6XfAFkRpg6Zzaew8rqsYhhPfSM+PlcvfFKHUx+J4WECoucZ5R4V2ijP6UWOl2JEkQNBVAXV9Qx2FB5PDP3FnjLdwZvzXDoFNGwlwOfe91rz2cd+7i5s/FjRsXnyD34WHi38DheYn9Ndn+PQw6OP0WLpSFTnJkYUHYCBEt/3yEvQcieGCnXzc7sYJHrstu9dhSJRHSk4Dh6bR+0xfQGD66WpzfF9D+N9D8MSUYcyBuRm5j0P/QsBBDnffMuND+Nj/gk9HoeAbLNwV9rsNs09AQUzkT+wuwrLkoZKJDIO5pJso5fBp/JA6DlIXRdOmdxmtkpQRPdgkmlFIYKDk7UYzo6+N+s399EblE23+06nbjpfJxPfqGcppnBiqW24yxcnDVTIxzjJbJRLsalbT0Y+ZC6un3BY5D1rH5kg5o6pybOIQ3kEs26VsfGa15kuXBVIWMGTXV28sz6iw6nx8GWHQqS0UPqLY6jERr61iHs+eK7A5UpIyfVCC02nyXmqrFVweFPf1eYFRvoUhjDLhObdyiU95lIyM95nXnmHOcdjP3cXrNWpGYPHv3AI9mI4q6nO4m+PX3/u//21PoUrErd+4A489M++uHBDLvZwpDbFnDeFSxppNpcIqd56srGPIdWHjlR6GCaB0Sd2aCcPAPvtms42aBGpNafGLYMBqJGU3HxlAocLbFmUDVjSZBB8yiBshehZegETXQ8HFzRPkmUMmNMPr1UKsT7fvcSwG6beyn0BxhUCauqRkqFEE/QkcpFGNhwyCxeSG7cPHmS49aiu97XDCnqfqmKi/a1RWi30dufKU+yLiuFGZtUM3pqerO17m0ia1DDlKrDYwvy6bgwbHMxrcj+AyQrT2rxEgakdLxZ3QJunj3KeqGJGJMK6xLCcTSx84ZWXayuvv19nd6XKS/D81U3curQFADhW6uAjO9cDAJStZQsBgAUCPAMy6kVpnGIYvZjcWiQDi7hBIuz5qNhI9F1YEQMjCcDPd1aNLq088OFQ3S7teEimAcQKcS4cBZdsxKTksC1pvgslmbl3ssTCiOrg1VqIxYZ2gQGAUVVoZxYAma8ADjx2u66/3viT9xjXl96gjFI5Mg1fw3YZ7Z6uCStPwJ3XGzLOFXpRYNxzLnQamCPJy1YwxTDxDO9Wjh0wQii8Lkd6XXEMuBKKssIn9iNc+/a7IRdyKyAJmT2ZSxctZ2CeuMxK8W1f+HYAwH/fvglueYqL96/qz/MU3B41r0UupK3g0pgH+wzZ/6Lr4L6TYTDOHcsBZKwoHnIFThuLZF6gujxCRNaEqc0ht0rmPHJ6nVzMgMwyWbGVMKSTfxzWb7Oa6ixmMYtZzGIWVyhmMoVXcZz4nbfj8e9989f6NAAURuTrtS5W/AH+x6amytSCCBZlKDc0d83PHAq7URWbA73jH13i2ygF13KDcVEXy8XMS36CKkFq3UkJaWrBo+xRKYb4UTL2znR7fy71pyxAHKXs1BZGdm8y8rGyMMCUZAunkWt8S0Xb09KAeTIwcMCaiTmGkhyiq8/bW5oadxJ0PCN/x1YieH5qoOdMcGO4Poo8VPwY3ZFO7+KdEvxVndk88t0/g+f8G72rbz9X6g5NOg/3wDLwL5OAdArx9OiG0AgMiKkNvxEh3tOZguIKLmXorpuZcxpNfTz6vxbGB8/8k9uNCEJ3pw7mCyM5+PrrPoyPDU4CAD70V7cZKoWqZHD2XNNNK23AuV7XqePIhRg4YASBM7sQ5RBtDzxhELmrjCuhKINjsQWrqb9rthEgncvgUD0aTMF1C2pSEjuQeUc0UyZTnZsb40RT1zg/v7OKZmWKkqOv+8zpFVi5wtGOB+kWCkuwFTh1hPulBEJwc194foqQFKaUZKYD3K/FcJ1iXA86VQQEfTqWwHAUmNeqlINP9PFkLUOLavrzpSke21hGiRSz0qqCIMoLTxh4WtBQVFmg3NQZbhS65juzHQF1qgKqPOHMm37ceLBef2Ib09TFxccW6YtiplPXjhiko5DM50r8MGpiXjnRoiag8svAgcq7hBWDNdJ/4ykg1yIjBQloOUhA36ucMuu0KsFXQ0hS0pK+0hKHUYTzb7q6/VRni+pVGLmKyij08fAr3vK1PRloR42zlyzu67/5Tti0aK0t9HBTQzfEjFLf1JgdLhALG2f6urkkSm0Duw5HmsKQr2WMKWPkvVobourqhWGU+GBMFbVBwCj/SE9BlQWsXqH0ktNQ5EJidF3r5RC9Uck06aRTF2ySW70BEDB6syKywWiSUYLBPnDNoh2vZAbOY/UEkiYVXk1Rq4ZGHckOMgiqCXNXgAE4vKBpRZ6VwSe7u61RHd2+XmyPLnWxP6zgSFNDcWc/dRSC1HKckYZVqxv6PEbr+lwAIGiGCDtBUYtVgL+gJ+EkttGgumD/TAs8ZSBvcKSLKUDNTizhUJUMPkGt4nTFNExli2mhh6sYwJThlSpLIVik2vGna8iePTK0IgycwjQ71d+LOhKacc0hQfCCs1N53Ia0geQWfUzbEXCpVmxxiTi1EZHGb70+NY5EmeA4XNc1zp1RDTU/MrJ5k8RBnJIG9H4ZbjPC6X9e1P3yeMUnXgXOFC4SVUZIbr4bJRk84i43KlNMYxfDrv6bu+PAPqkXS8aA6cgzdV/PS02tezAsGbN3ZikEj/hI6gT9ewqS7PRq8xMkaVGxc5wMqzVajP0JHthbNX8b71ZgD0izesQQU4OdrFOdO9+QRLaBwFnGIEoSqOnrKVULeaModI2mMQst2ANuZBHtkBlqFRgt/vkmb1EYBS7pFhSapCEh66mpkfO2Vg2TYYQLP3HHVT1Hz+DfWcxiFrOYxSyuUMwala7CyJ1gvpZx6wfuMD6dNx4aXfa3cj1EQubSZ3fmcbisM6xQOJh3dXY0zDxUncgYUj+ytYxyWad9np9CSmYgwjhyTCPRwaSM/bHO+iahi2MLXSMcLivCmHWzjAERL7I0W0GQsTfvOggpEzvUHKDUSjGKqUPZlggVGYxTVnGEMslx7KE31FCq7QuIfbcQP0g47JY+/3ToGpoC5wr9g4rxOBWXuOooweD4GfYGusO1Xg4RUbbeCMLLIHEhOB7d0N3Wll2IPyhbIa0oDK4t4OD8s8N2Cc3VAfqUVbGuayBqvuNjvKGvMxgzTFcFrIiypakFRu4zMrVgtx2kfZoqXIWFm7SG7zjyEFNTUTp1dHZL23SecKgHa+ackqmrdXcBsFoGjMkJpW0haUgIOi/LzwDq3GVTyzjiZGVAWgoZZbHC4xBukRMcW+zgwNHXWfMvybCYja2hPo+ql0BIbtSnGkGEPcpUnUaENLKx/pv62bJcgQrdj2WvCscSSAj+VIohKOnxce2s0HwWFpLPNHOBJdghMOqT4H05Qb0xNe4zaWoVutSVCCkdOxz4mB7NDCyteq7pZH7B6gY8nuH8RJdM2mEZ875+nrpxCc2Szva3O3XUVkYYSX3d2bwAJyEHNrXAWwkE0e/soQVB+s1KarQh7xqOIwcZZaf8Eg1s5Uqky8Lc00kg4RLEa48K6hcAeB0LyQ36vGTK4W3oezrY4eAXPEyOFM+rM2CQ0dWf580W1SsQx35Bt7g/XTPqfwzxpSzcjrW6hpMIADbhilIxXJxqCO3m+jbOTBbw/7H3ptG2bFd52Lea6nbt/jS3e7d9naSHhIUEMtj0A4cwCIM0jtOMmPyIMzABE5pALDBgGplGCJwE44CdAI4BhxAMwxhChi170IVuIAk17z3dd9/tzz3dPrutfq2VH2vWrHMREkJDNlzrzF/n3nN27apVVWutOefX3Jv7f1/enbFtWLaOgFXApdxmt0Kv5yfK84MVo4kHcYmHyyH0RV8SFMKx4pF8EPvyMa1hTeI6Pui0RkST4v6qjzedv48Xa99j0tqw6P8gLvGG6UP8/rEvLxe1xmjgv6sxCpvLOWoq5TojmAahBjX3bKtKQfUaBPdJV/BYodyl0luv8SLpRL8pggZr0gC8NFrg6pZfzBNd41D3UbfoVq1JHw+oRsYbPBPtQq0U0xtkJjGfp3A0MepznYxj3TcIqOxX7FhgVMNt02AdRZCk0ORSC5PYjl8YWpYDXAQJbN//Yp4lmO8NIVeEJB0YlMSdDS4t4WYpUyvqeQxJtI06dbAakGQkbtcKgtR5xMSg0bSIpgLhTHoUMeDdcwhBrLTF4Sblkq91AhvqkVsrmQNbpwqBMoip7Fo2mlGqvbTAYLzGwz2PELdGsFJSK6x/Qu5C00tziFbS0QmcG/hN5SzvoUkdwjlxWCNgskOOSkEDY2W3MFuJ6n3+eLIWngID4PxTM7xh6yEaUgZ758vPedcXAC8vdnGut8J+5jeVs2WKFSGZl0cp+kTVCgKD9SpmpSQRWNjUPzuqX3u1qylZ3JUdMldFBtYI3kw2ixCSxtuFgze9zgAAIABJREFUjjdrCLx8pKb300QOsuzK+eW2Y243Isvv3fA9EYot6qWPqfR/ypWmOl/D5jWe9HjytwVncRZncRZncRZ/RuIsU/04xJOeoT7/s56jWCwjBum4vsGd//obP+xnvuCdX8M6uzcGxwxAur8e4y+df5XVo77gnV+Dq8kxAOBlu4vdyGeBD4oxllXMhPrZpgfdytk4D3RhJCbAiMqjLOVS2WLeg44aaCqpBtqg2vMlQJNaBHPJqj5OOy4rukZwRntte4ZNE6KgzGacdOTzSDf49YfXOQPaSjOUplPkiaIarSx9swkQU/bbNIqzG9OEcPMQ5Q55Zx6oDjWZK9jIMFJyvgy43HnwrMEWKSE1TiLUDUrl/66JLAM+0lcC5OcsbEzf1zcsRmAntSfoU8Yy6OeYkU8nAofmIiFrpcOfv34bDdXt3h+fR7Zsjc0bRHGN0alxefnIZ/WXRgssSbwj0Aai18C212YFo3hXewOoYc3oWbVSrJRkEgcIBxt195oeCZzfXgAkkjTfJMiShIFnplCsQjQeZihqzaXVQnWgN60sCzUkQY3KKEZb95MSQ6o8nBwNkMcRA6Nsrvk+ncz6WAYWwdyf//pwCxXdT5nWmB/6zFGUCm63QkPA2rc89yreNLoDAPid+TW8+8ElVIQaju6FbHvmlPNGvgAe3Z/iLbt3cDv3pdt0UMBRNaB9z+Zrn13Wpe6y9bnGuiHB6cBCxQb98/5dC3XDYiRXJycojcbyx70a1cGfN+iPWlUsx76uADCfj/m5CmfKV3sA2B4QzCXzeKOZhKKKu9Meed/b9WOulMWaLACXzzZI9uj+xQ46Ewx2anZrBL0KVnRGD09qnKF/P8HjmX/ynWwiXh70uC9o9hJsPX/Mxt5VFuI1Vz2K9+ajHdz8K9+MT/tlTy43VrIXZ1VpDPs5y/RNk4y/66RIENFkp6RFbRQqWqSyKujoNtJitU58bxKAXmimbgBgqoAwACTgpqSQoxyCDxJEP3AQjUA97Ho2uhUA3zJ8vJ0rJ4h1g7xuF9Uc64omvtZEmhC5ia4xJyGFO3d2EI0KVlRyTrADidYGmxWVv48i9O9IZBepFGeBekzUktjAZQrBokXMAoYmsehChnHfj90kzvFgMcLykV8Q09uae7k2ACCA4I2+VJxGFVY53bNKkYcnLWBGMtXErgMu7ekLGbQ2eHbbU09ePZkip7JoEDbecYYWrDwPWbUqPb/hxddlCsGk7CT7qlNFMO0AC5ZPDOcSJZU7k2GBfBlzyVonDS5t+x788+N93Fn7/uHBuo/1Joa9T+bgowbnnvLXbKxE2Sgu62ZliE1r3p3WCImOVZVe1ajdQJWbEGGvNaRX/h62cpKlhNjyK4WUDtYIVvKCQLfyFwq7v+n//+BzK4hMQxINaDTIcH3sBTsKE+D9dy4ANHYuNpDkiBO/P0E8o+N98TEWqx73n1Vg8BeuvQoAmAYb7BUj/Nata/4ebgJoohg16wCq33kEJ2H9mD9uVfrrurI7wyAouYVyfz7ie4hSIZwUGFIvebGOYdtNpHDo/a4f+2LbebEGwi+YScM922ApvLzka/yCHkU14yvqhynifWrp9Bxk0zkvmVHjJQ2LAne/8cmm1Jxlqp/gEScVVnudfFw7Kapzpd/9L/wLF48L9LR/gS9tz/HmX3ornhp4qsLD9YjpHidFguOTPgM5Yt1w1rDYJOgnfsI5WaRMLQF8RlSvaBJwXjNUEG3ESce9F1UKntBM6Bei8Oap/m1LcZkC9cjAUXYaHmpYOh4GNcZjv2AdHQ88544yp+N4AEV0g9deeoRlGTP/b1HFqKmXNtxZY3ewxt0j34ObDje8mK3vjPgcw5WnqoQntKBPus1BlFYoRQDQJBwEBiF97uJ4iUHgL+b+aoy6UZhe8mO8PtriHT7gJ7U+bWJWedxNkisNuVWyabmMG7gT/3M8U7C0sWjQQ9Vv8L7SA6EubC1wfeIXg00TYl2FSOi81sc9JA/9tLHRPaYR6Y2EPOpBUfZl+pYXUbUUnMkAfkKN7vvnrJgoCAcE5ARUX7bY0KYmEBYzUtzKyxDWStgdymQKhSPSGQ6IXtNmqtkqgqA+shqVODf0fc2sDjBfd/QpAFyxCKIGQgCvec0jAB44J1tlLekgZGcwLqVFU3ZUrd/5Ma/C9Kb/5h2Yvd5B3PcL+kkSYRZ4zEAwKuEqBUHVF9lrcO2Cr+DsvfgU5p9JHOpNDDxIMH2t/10aVlhRNaA0GndXE94YAYB9RKC60MEe+p/zHQ+E6sV+rE4eDRnsNApzLKoEjwgclz/sI5zTZmclkJ3v4ajvv+/85RmWmT9m/fIQ2QXKVHdLpOMM1aukILbUTMdyyr+v9h5l02vJlRIZAzVRhfRKQFWAeL2/N6M0x/4r249tnp/UOOupnsVZnMVZnMVZfJziLFM9FX/x//0GLkf+9hf+nT/ls/k3Gzd+6m0AALPuc7bRu7xiioS1Aut5l4kMegUqqrvuLwZwTmAR+R35dm+DB+Qecm6wQl4FXAp9NB+gXPmdb2+c4/DAl3SitPJqRC01pAiB1ge0kJCNYF0BWQvWupUV0FBbUJUCTjtoqjCHS4dqRPSGvoVLDCRp3Fbb6ITDc41NSJq1gsj1LTK4UIiGPmt45WgLxkiklF1nRQRD53t+ssI8T7hPWNa6BeRCODCtwERAvuu49CwsGJFc1wo6atBQeUxpC00Z56v7W125UTr0+iVmD3xmEIpOmSZ6fgEpHC4MyBxcV3i/9ObRGAOrRwOM3+OPv76qmXJUXKghWnWfwAKN5Awu0g0jTI8XKaKoweYeVTOcQJNSj/alAJtLVF43PmuTZJBua6/+04aJuoqp3khGpiYPqfx/mUT/Q4NhTH65quL2waYIobTh7FGlFWLqsy+XCcpNR9mRC+3RqhRMl8ojNLWCoL7h9s4Sa/J/VcoizyKsK3pW0wI9Ov7Jqod6GXXuP7WApuusx4ZNw81bAohCoRlSeT9p8PzlfQDAZ23fxKNqiF97eAOAf17uHvjSdnOtBsjz1mqJi6/fxyjyY6ClxSsz31i2TiDLOgGJoJejXvn7ZAMDScIOZhnCAKhTQsKvFRx5w77r9mVIbSFvk0frpRIV9WzD90UYvSxgEvJGHvcwINT9+pk1v6tpXHsxE3KfEZlCQPrBNvDZaosWL68XrCKFWrK6kkkc8isNUnoONmWI0eUFTHaqpPGExtmieirWZfgYx+3jEa18X2UUDvdJQk/bx8Su/zTi1n/+Vv65XWCtFexE0WxCqMiwkfWmCHGr9JYk036GBw+m2FdUQro7wPhpXy483KQYJgVy6ls1ViIg+H6ehyw9WDYx7nzF42LhN97hqUmmb2Gl6xZcKVn2r55Y/v+glQKk+dPEnSpQ/5ZC8xkb3iQEs65EVXXVYoxGGbIiROlocj1R2Bz70lU4LBGGDQICKg16BdLQl9QWeYxhXCLc8hPL3sEYSUrPznYJHPvj6dxLv5VU9jXTms+/7cfuknn3lcEJ9jLidgJIiFqipYVxAhvnATbVlkE09eCSNKqwLiIcZqTw4/oMtmqN3ZfPEEArclxeaxcIAEgvZNicJN29t7KzcLvTw2ZgGUDltENDC3MWCFz8VX+8g08RqKcWspW8W0v+OzgHKHSlPdspO2VHCfRKQZKKz2iQYyfx/bijqo9juq66VkjiGhlxd7fGSyQkN9hYiez2EDrvjNtrWlTLTYhiSfc2NiRh6K/lcH/EG5zt80vkIsSCSviBMjg4IMrLcQCpqF8Iv3HQxE4SjUJFADKZNti5fAJFi3Y/qBj4szAJElUz1gAAMgLwBMea++yf/Nx9LKoYQ1IN08JyDzgOa4RRzf3RutKwu/6ZS9KKQUtiWMEsQwh1ql9Ji5ddBzAOUE97INH4X6VYfSapbE0cyi2gnpAZRqOYKy0EkJJ1YlEFgBXdJiO0aJJ2g+bgphXLio56BV/z/sEI+jm/SbJWYLtXMjjx0cMJ5ELDFk++S81Z+fcszuIszuIszuLjFGeZ6qlYLHtsjP2H45PIT1JLi3d98Xd+1MfcO/K73dEww/auL9G15a0/zXjdP/02AB50sT3xO/7FJoFSbTonEQ0LpK1vZBFic+Czho3qQQSWdYev/+TbMGvLuoMSq00M1WZEwnFZ0SxDVj+Sp0jfAHDth94OISkTKCTsoGF0pF0Hj2VYrTdps9XArgMoKp0pCRQ7rfg44GrF2qJWgyk6sl9jNOiEEILAoKTyrw07eoNzPkMK6fsi3WCfym3OCSzyGIsTyhALhXxOIK/8VGbdt3DSqz35kxZcdq1vDRDcWGFD5ubHQYpDOn6ehVhSCXawu4YSjmkKkzTvMqA8Rl0rrBDTGFgcrnwFwZYKkI4zedcIDF8m5Z7zDg151CZhjWYg0RDQ5+7+FJYUd0Tf+myOXgvRCCDqwEi//rP/AwDgjV/+DswuGViibtjQcnkwHeXI1hGjcJtG4ampz84XvQLrPEJOaN2FSpAN/Hjcmm/h6BE9V6Q93FJgKqPw5p27AIDnhwf4DXWNxRlkIaEJ+NRYjZBELyaDDLNlr9MdDgzSgX8XszKAAJBTFtjUGtEtAgjtGLjIolI0CK3yEOBVqCgLHA5yDKISkqDMpdGMsn33ySX0g5Kz64ODEeskNwOLZNtfV2E0NlWIE+mrJcsqwqDnz3ES51jXIfbW/rycFehRq6Iqg8eAf/1za6yp4iIyxdrCsl9DSIf60I/3u37oa/HRxF/6V/89aqLQ3S/GEIFFOOrMGdaNv08uNpDS4U1P3QMAzKsE+6SANhjlbIahpUWgDI9VdPkIjwYDmDM/1X+34tX/4q0f9nctgrU5xVP8aGJ3yy+k14czzCv/IP/yZ//dj/EMP7a48VNv417Uzf/UC4b/UUL8b/6ltzKCEkagrjQKWmTrWnG55/ZXfSgvtzXUriP/UtVtf0sZ9s6EBburyD8knHL7v/t6PPtd3pWl2m285F3YUU+6L7JMEbGFglopRvWaBFzOc8p7aTpacOWFgmkb6aAr48a6xkuvXsD5qx5tuX9rG7JdYK0EnMODQ9/LHI82TPvJygCbUkOc+Ot2PQPXcu4iC7X036s3EiZy7KAC5QAy7zYp0BwniM57BGQalBhQ6VZJC0V0oLZ81grBL/KYlYCs9ZJ5DS3UlVVs/CwyhWRPMVe3t9f1eutztZdxBLBYJWhOTcjqUcQoVUNm4g1tCmQlvXMPAK0tbzZ7X1rjsjL4tS/wbijXfvjtvNikUYV+XPKztd7EfC2xbpDJkEviTd3RrADg3EW/+F7sL2CdZG70g8UIv/C+N/D9zDadpCO2StRx55JSk5D//skUMpf4rE9/HwBgVUe4u/To7dwF2BqvsdhQr1E4FJfpIdUWcAKaFmDrBPOrlbYYU8/98vAE1klc6/tn6eZqhxfVdRXh4XKIkhZtHdeoiU8cjAumnWR1iFAZnm9GUYFLPY+yf2W5jbwK4AivIEPDEomDfs5ylKNRhk0edo4U45p/tBsNEVkEO92m8qOJX/mcH+SNeJ0HiPslEnJbWq56EKSo5AKBa+ePmYZ2lKVIqTddNA57h37jYyuFS5dm6IeEfFcG5SqCzZ989O/ZovpRxLWf+G685qrv83ykBfH6P34bXriyxy/Sv/jcd/wb1el99me+g3slp6272mhFHZQWDKh5+qe/C02u0Rv7lyp/0Mftr/S9zdki5Uz1zpf7DKTloirhMO/7ifyZ734HmpHF7b/uP+eOI5he17PRk4KPE4c1Az72MII8JA1SA1z/u9+PV7/aL9BPv/0dUJTRBjONetItpG2mA5DVVqshWyiIUwt1Nezs0Zq+BZYB+ndICCHQsMTYCd68wZCAID1d4cbVA9x6lcA9gYVT/iCTfo7zgxVOCj/RNkbhU877Hfh+McB771yEuuAzDHU7hb1CE5UTCPYo02uAeuh4gYEV0JSxCenQKMUG7OG2wVt2vVjAB+bnkWg/bncXYwjhmBpirMRKUxZvJUZJwZOYhcCtd3tZRaeA8vUZLNnTVROwFvJ4a+0nXvhJcjjJ2MDcRo5J/1C+t33xuuewHi36mFC2GCqDiM5jEBR45f9+Fs8t/DMnqhQ9WoRi3SDWNe6RRGW9iHAQUEZ+nHgeK22c4l6F0vhn5Hx/hQuJX1ByE2BWpvy7LIvgMvr5aOjdV4hvbctTG1/h4FoHGCNg+4aPcVykmK/8vb2xe4zjLMV0sOFzLinrnhEN58LEb5AfHI+Yv1mvAtR1931bvQ0mhJwLZYMPHu8A8JaF016OmjK142XKHNOt0QYHxz7Tu783hQoN3vDsQwA+232h73+WwuFX5zcg6N26cm6GGwO/gP/B8QU4WqTn+QAiNojIZSYMGxYSuXcwfQxP8cdF64jlw1dD0lHubRaJ26204U3XYHeNw3XKICx7EEPu+uegWYbde+A8N/30EppOcpjoyQcqnfVUz+IszuIszuIsPk5xlql+FHH7r/6PnLF9pHj1v/zwO8C/9rt/FT/65p/4uJxPiygOgoSRhld/7HsQ085UKev9D9ckCJ42TBh3pUYwKFFTuckNGlz7e28HAIiRQEWelK//hW/BH3zJt7PwgXGCS3T12EDUEtf+F/85iK6sKGIDYySXEy9NFpxxmW2BfZYCEnBW4Orf/z5/LVax+pEd1xhOMi6xaWWxrkn4u5Ls0CILAVl5ugadBtNaZCFgUovNVRKhTwy2z/tMY5zk2NT+OhsrcbhOAaKoILIQZLi8XPUQBQ2LOtS1wvu1z2jXRYTBMGeVI/lUgenQZwPHsz6q1khawqvRUM9MSseSf49mQ+8dSo4kl+I5Sut3/4muMSv8NW+lGVJdcQVk04TISEjdGIGy1owIFaLL1mUD2JOIj++MxGTbZ7vrLOJs65krB7i9v8Vo0eipNfI5QaQbCWiLkMqRF6cLzopDZdgQ4Vj0sPm0DIpte4D8pi/1xW9aYDveYJ36c86WMfdQ4QRELtG76CtB/bjEhZ6/T8s6xqbxn5HCIlY19td+vE9nOLIREHMNQz1iRAYhZcl1qTmjhfFuQr9794r/ZyNZ0KE0Gus8grX+uqOo5r51FDSIgob/fXl7jlu3/HMgSglHDj7HjcRbzt3Bw5J6u8Lxe7A2MbSyCCmzT5OSS+CRMuj3qT+pDZ6dHLKTTu0kHpR+jO9txggCw0YLV/onsO3fNQogqo3YaOhhBUtl4iIPcUR/d9o8/I+LN/7iN2EY+/PN64BbDjvTJY7nfW7DVIsIiloaGSmwGeoXi8BBEH0nEOg8ewce1dwQhkUJ543g2zbJExxni+pHGafl9j6W+GgX1Oe+4wcgKyDyVS+8+3/6msd+//RPfxeSnp9oRr0cPQI+LB8OIGkiz5YxIBw0TabNOugk0SY5iocptm74F/OoGCA656+tn5QoqUy8oZejLV+//he+hV8cOwtZeg6Ar3eQRBpWAdROjenEl9HOJSs8nXqrsA+o81jTYpBtImAWMuionjr0d/xnhok3km5l1rIiRErlar9ZIDBMoBCsOilCq0lxCZ4vJxLTAUq0Y3pQXge8YK/KCOtlAknn76zA4Kof/CyLcHQyYKpJmpTY2/cTXH+UI9KGVY4uDFZ48cF5f4xScQ8rPpTIr9TYIsnBSDdQrWtPpaA2Eo54lD//L9+C+Gm/oLSgMAAIJ14OseX+TvsZTz72fg/mcgZDpT+50FAth3LaID5VirdWICdQVL0J2Y1knidoVgEmF/x3h9qgeEA6stJBpAa37/oyZjrJ0aNeWlFr5FS6NrMIwULyMyESh1tf50EwV3/0e/GSduhP/Riko4KpTk2hIVLD19YCeQDgYNPn0vtnn7uJedNjziYAtkdrhPOygi3FwwKW+MmuVHxOspTeGWXuNytBI7jf/ECNIAT4PAJlcIkMwJdljEAZXEnJNUjVuJN6epk67Bx3iqHEL7779QhSf151of2mBN715mgVICYZ0IuTBZeXV2WIKZVnR2GBxkmWZyyaABd2/Hl88uQBerrCrPDPxnHZ45+FcAhoAWykQ72IEIw70M/6Ed3PP8GaVVSd5eK7vvg7mY+7fziCKxRz2CEdb2bdSQgX2w7sFxvoPXJJyoDVs2SS3q8ZBwAAcVKisRLilDXikxpn5d+zOIuzOIuzOIuPU5xlqh9l/NtC7IrXrVDVCi/+Z9/0R/7+0vYcD2ce1FBVGo/u0041dGgoy+wNC2xOEgYqSGVxZdvvsu/Pxug9tcb1sQc4/N4XfRcf+/Pf+bXYW/hjm+rx/dZylkKGnYsMHFgvVw0rFjKYnptjniXd5+oYH1j5DG5ddwhNW0sgtqx4ZCvFpbEl0UQCUneYDDJkRICP4goZAVFc0MAuAwYqIXLe+5PCWQFFAJher2S3mUg3OFr7cbPOA5/kkT+3/rNzLi1GcY1sHXmABQAz7AAp5rcmOHqmgkpo5y26klc8LlCQ0XkROkSPNPZO/BjoXKAeUGl44GWIBCVnpm863V5tEY/8/SsOE7hpgYQ8Wh2AhjK921/7dbjxju9H0CoZRR1VBtqhOEw6H8zQYkrl32IVMUF/dpIiGHXlSAAIzhN1ZRNCHkQQhGw27x7h6IbPgGRgGQQVHSj0Hjlk57tKQRt3/to34OnvfQeylX+2gosbFs3vBRX2131GOA/CEmsq+c5XCV/zbx5dx7Lonp+nzx3hIZmP96dLHHxgB3ZMsluFREOob1hAkfOSKoGm71jrVpaEGIf3Nx0MMs7kAeD9970W8mS0gZIWt53PHjdVCLPwF7j9xkMuC+/PhjBN1/qI0orbLHAAjiNUpBl873DC+sHbO0vWO55nCWqjGJR2MV3g1Y3Pih+sRyyqAvjsdLfn/+5wlfrMGABKXwGpJT3TOxtYEtfopQWu/vh3486XeQDStb/39i57dcDtr/h6PPN/eq/kpoghhl222zovpcMSemIwOyTBe+FY63uzDnybqHWYkg4vfntXbbv6D76XPkO3itpNgTYoSw1bNnjS42xR/Qhx/Se90lDSq5CTZZOQ3rnj/NiXZNKg4r7Ar3zOD/6Jjv/pv9JZq7VUjV4cIEj/6BLIG3/xmyBFyIowZaahz5OrzFGEauFfoqbXYOf8Ag2Vsk7TZ77kV78SdxdjvDDwjjNf+K+/mjcMD+dDfOA/7P72dET3AzQkX+ZiC6ccU12UsiiJtpCFAaS0eHTPT0CH6ZDpJDpsENLCPJhkqGuNgs45OAgwD/1C1+uXSKKay4yjqICSfrF5uDfxdBvA95AE2CC57jvmRrpcQQYGA3LcCLThvqB1AnnR9iABFVrYc8SfU4YneCkchHBoEiqtSotnL3kU7Hv1RYha4ilaHACg2vbl67wIIGmxFSlQigDhMXESY4f4up8IN7MENrIdB9QIxH1/HtuDDQ6XvmSnRjVG/QLPTw8AALeXUzx3xQu/f/qvfCNMfxuCnG6EEXAt19jBT27ULxaZxvyD/r4gtqgV0Y20Q50HTD3pj3PEkZ+Ez41X6F8v8dK7fB/SKQd5QCo7DnAkyVfuGshGdejrcYfeft1bfwChEHAr/8vSpVjQ8Wsr8fz0EAUhck/KHh6QEtTWaMPtjaLRmPZy7MS+93pY9Jl2kpUh7MBAU/nTaMlIWAj4MQZgRhYyaVBO6CQXAffnxYMEs1GAYECLg40QhK3rTQClLGp6n5R0+LxPeT8A4IOLHVZhEtJCabAMonOnnW0cVAOAzN/rYQPZ9iHLENWLfoFqLlboDQtuT+znA94AOgAXh0tMI7/hWTcR3v3AI70ngwxPjcngYjlEnkVoXdzyPESP3oOiCAAn8Nqf+zZ/78tBt8l44wJf8/t/BU355/wHpUP1sj+vp9/+DtjQl803OyWketwa8toPv729TG9i0S6qpWLshQsdl4yt03BVCNfem77EeJDDqCcf/Xu2qH6ESPud/mZBUmQCwPnxknfN8yzBu/+D7/gTHff6P34bdGigtd8mPzVeMDVhGmfsa/mHY7HqwWyCDpZeSxiimohJBUfQ/jipsFgn+OBf/lsfcoz9rI8oaPC3X//zH/K77DDFtf+DNI+XAW6fkhF8+Zu/Fld/xIOK7vy3nm5z9cd9v9VZAUVZ5clx3wsutIIPlYQgoEjjAtw8dczX/dNv6yzCpLeyAgCXVjBO4JCk8uZBwllmf5xjQ9mcIMcVdp8RDo4W3NZvdE294TCqAXLtOJj3mWLkjADmISY3vMyitRJzGlMpLdK4wqztG5YKD+Muk1eRYaDYbNNjuzQI11msbTSQGNTtRD6ssdmn6oIRcKGDWtM5XyzwhguePvFoM+SN297JEFWjcJj78bg2nPEY7i2HfmGgwzvleKFwAhCRgWu98mrAtpm8AC88tpZQpWCgT56HePqc3zxM4gz3VmMW32hScDXgxnOPcHvPZ1E4jFDs2k6KUFtc/Yc+KwkHGsFSMKUpXEjM9z3g6PprbgMAfv/mVf/LSjLgxgxyaOo/f/a5m/jS8e/hZ+efCsBXQJjrGjSIxwVKcljSoUEyJBnELEJCUn7WSuSbkAE2YruEoaqHILGLtqdaZwHiYbvRskjCGrupX9C1MNgjv9PDZR8lbbhdpjC8uMKaAIJRXEPSeyEkkD6zQET2dI2RnBXndwdoawQ6rlEUAW5l2/Q5xxWb9nn4wBGBpIRjmk+kGmzFflO3rKLHeMHDuORMuNEKtQOyuZ97tBEot/wYyyLAP7v5SZAtR3nVUV6E6WQaX/4bb8WzP/P4nNdqOUN4MJizXaYK2cpHCi8eAmB0dYWsCDkRyJYxMhHB5h89kOrPapz1VM/iLM7iLM7iLD5OcZapfoRoe5S10zDUo+mNc+R1wCXCdaHxJb/6lQCAi70F3ju7wKoyHy6UtpgOu9JW4ySSUySBnq7wzD/xUojPnD9kKsVT2wJHccq9x6rRyB6SbN4pYnV2kGJ4YYVr/8hnnWFSs7cioOGcYBH9x4hxL72hAAAgAElEQVTgkQFox97KCbbx3He+A2Lqd5nPvu0dqHYagNRtbGgZfYpSQi0VzEW/yxfSIbnblia74139se+BjgPe1tVj478ffgfunGCpwzissVr7nXW97tR3ZEMQ/VZ2rhYwIe2KSwGbayjKNupaYdF0vV5L2XM8KVCuA1ajSdIKxZr8R08CZLFFsuvLbbs7JzihfrEMLGwjOQPIipB7ZFFaMUNHDSukvRJrTdn1QQRHpeZWpMCMfaYwTgvORvthiWVJBuBOYJNFeEBl/7wJ2Ne1H5e49Lp7ePn3fHlWNAKKkJdGAi5B5zggBBC1nAYB1Sc0txEwpUJMaNG60rg780pDN4sd2EKzK46TAEgYomw0Lp/zvfo7m12fIdNXBUcBTKt0FTvYAGi2/POukgYxZV+bOsJ77l9i1KczAunEZ5mzeR8LquZc6Z/gdr2NC6Evt79z8yy/B8dHAySDAtepNJ/omlHDVa35PRbCYXd7iXM9KiHnKR6RjKiTvjRpaUoMhiWjppcnPdSDEleG/loXZcLvpJSWsQYiNOjHJc4PfHlfSYv75N7knIBWhl1xjJEsgP/q3+gUyl74+W+FEA6rhz6Tb0VYAF/ZqSrFCmU3Lh7h/syj0UdpzlUTJRzKRmND39WPKv5dUyvIyLAMpb1YdBKG8EIRG3qO9VzhlW/4o2UMP6QK1raCQgt73PWloR1c0z4UDq4tmhiF6iSGJFUvV0m4wHZl+yc4zhbVjxDbpK6ydzziF6fIQjy/c4D7K/8wD5OCbbJeOthlaHkbrEgkLdNJTBNj/3CE8dgfvxfWmFCfZDde46XFLr7wWd+zOSgH+MBtD5gQ0nMQe1t+gTTWQoz8z27R6eransXqwbDjXiY1livfD3GO+oi0Kbj+k2/jyV2ffhn+0HXISgA0CVeJeIy6YQrZVnigcgGngCjxE2i5CdFWHxF6NSYAEAOFpu40VBFaXhyrSqMXd8o6XPYGIArJhuXC+pKiIQNmO2x4kbYWXsKwT2NyGMMlNPlFBinxRrN1BKQNczQ3Byk7wJjIAFagoHLww0aiJseTeFLAWoH9u75HKfs1Lwx1reC4d+QVkK6c9yVbecGhICrF06MjVFYjUX6s9vMBL6Q7ybpbGDYhBtMN1kv/73wTQtF3TUcb72hykVRr1sFj7iGw6CzeYgNFfcfetGTJPJNFHT0KfqN19X/7Hhpv5SfGtsRuBMsbHi1TBqi10ZYBo5nA5pP85mE8WeOF7UeYlX7jooWFlqSgpBqkT5Xc7waA/cwvKPt2wFZ4N5fbuLX6bAT0uTSscPvWLgAgGFa4OF7y72qj+Hh1pbnEi0oiCxIsx9QS0KYD2BQKcq1ZTevidImLqe9R/tbmGsoiwD165wPSrQWAQVKyBd04KVA0mls5WnRUoVAbxEGDndS/8/fnY+aRAp0W9/u/9G8DAI//jZ/+Ll7ctfbvbpL6c15XIc6N/ALeWMkL/cODMXTQjeeDwzHrLptGwpYKF2/4DYgSDhsCP63WCdbzhPWyTy+o137o7QxMRGAR9GrUVG6XSw19zn/fC5f2cDPZRkZ2j0m/RK5JDa1UkATmy+73gdRAkKqULRWm2yuYrMR9PNlxVv49i7M4i7M4i7P4OMVZpvoR4srAl3ukcCz8/PxgH6+sdxgEsMpijFKf2cRhjShoGNXr/RR99pcENY6XRONYBxBWYEWlrclujqzxf/f33/SPAABv+ueeUnN0MGSwg7MCQdRgtiCgCwDbEqgTAzklo+e7CeppA7Q0iEY9Rs4W0wr1vv9uFzgGhjR9A01qQq2Qeht138G1ma8REOdK2Id+x28Ty5mtmzaQyqEieL+rJeoBucM0QENCDU47QDsIojs4Cc6WoqhGVoRe0J6uuy1ti6YD5ajCZ0QtmrOxulNbany52dylcq0C0KpIaYWMsuKwV6Ha78EkdNDQIiFkqogrjJICdwiMY/Z6AIF0yk0IFRrOCoNpw5lHU2ouUSeTEtnDPu5PaVevLFMfJnGOG/0jHuO0X+E91UUAwAcOz3HJLuhV6IU1tkhn+HDVZ2rGwc0t7OtpR5uRYEENf+MkZwO9tCsDbjZxl8EpB5kYRmJf/V+/DyD2yu2v+Hpc/ZHvg96i0nAWMLisnCUo2+8VzpuU03jUfYfwVf98nDjgIB0w9UQKy8+7FA5aWqTKn2PtJL7ownsBAO/Uz2OW+wpLXgfQ0kJStrQsIlap2h2uURvFKPxBWLL+7nIY4eSYXO2TBpPpGkp2ZZjWNaUsAjSNZLeYp4dH+NThqwCAOzsT7B2MWWx/2Cswjv07f+dg2gl9BA0CaTljbGxHAZo/GqDeydAP/HUGyrRSunj+Z78dFYGWrv/k2zAaZgj6LVjOsQBD0yhU8wiazvF43ueKhWkkOy8laYkoaDC/4zNrF1m0zR9nJIbbG1we+DL6y8c7XK6WAjhajhhdf/V//x6u4EBq7Fz28+Hh/TGi30tRv9YfVRjg3NQDpvpBiUujBfakH/Oq0jw3iMhwQSQ80KhiyyCsOPZes+ZUxeJJjbNF9SNE6yozjAr0tH+A3jO/hHmesFmyVJbt4qpGIQoaLKjvFg4MV1EbK/Hyf/KhovcfLo4OaSKoJOQR9T+0QxXrri9mOqNgCMCRM4rKBESt4WjOrHfBpVW1VcIZwehYVAKClGlUv8LNL/9QxDDgbb64l9lrIJXBzVYx5x98byeNpxyiuOPn2dNrswMfo3VqcbRIyaTpeoxGolqH3FsTwqGJ/O8KKxiWr3IJYf0CCgDRsWS5xHLHwmnHVBadgY3Cw7nEi199WigcjHpuxdcBX5a/83CLry25umLx9KZWGPRznJDsm9YWm1Z6T1v+TDZPgNCiWRAlK20QUmlcC4vGKaxpgamsZocWYyWjiZta4Wjex7Dvx0NKi7RHi8FFg3wVccnuNPJYZBoY1MxHzbOQ+2fJoIRpqUPSeccRoh2WtUbKPXjvotKn78uU5XJkVQZMl5J7MWTd8VPrvoNsD7EKcHc2QZ/cXAZRye/MSZGgF9R4btvThWJZY21iHoN2A1mvQ6i05sl7nBR4RGXLvZMhrBVMNWsyzWpRzkgkxLXMZwk2ecTI77dcu83XOI0zvNddRH7kF/EX+7tclg+khQ4Mb54bo3g+eO7CAdYVmaBLy1QYwNvTMU/VCmwOUuSknHSaMXDjp96GrcmarllwKwXwKl6MEl5FkL2Gn0EhHSOeg7jBgswBnBNoGoWo5RqXmp+JsFdh0su5xL5cJzg58j9H9wNg1yAgq7xeXCGgZ2R5Zwf1+0lVawAEG8ebK7NVd4bxTiLRNd5ywdvyfXCxg33hj1/MY353q4nB+NyKaXMP72xhoxPY/Mm3fjsr/57FWZzFWZzFWXyc4hMyU/2MX/pOqF4E4wTe+yXf/mH/7phKT8ZKHJ8QypY4gZLKIn8UF7QVvI9Ug3/9+W//mM4xfEAarSOLcN6SEAXypxxbn8EK3haplUSwbvlgQHHeICKbNdVrOEPZHq9hncBxuxtehJzlhKHBCz//rf4Ywis0tSAdFwv22PwQ4wDZibgDQL6KGbSjBjUaOl+Vd4bOrVB7m1HYXHP5t8hDCOVYXUiFhoEXIjQseN/0HJwWEJSpCgf+Wa8FmhQMhLKBgCVz7epKzkAQtdAwowaKNGChDBtVS2kRpxWDTaKg5izBrgIsDifoX/OZ0/nhCvda8/FKc9bdG+fIH/ShM/pdaBEO/Uk+P9xHYyXevfAl31AZHM78rl5pw2VisQwQzCRy+OexuNTp48qkAaxgZadmE3SesoFFFNdcPlTKIk58ZjDsFTgmHrCgakubY8Vh95nXf+0PYOvf2+DLrv5/AAADiR981+f771qGUFRaNrF3Mm9GdAMCB/3Aj2N8R2EjUuSxv5/ZJGff2KwMUVQBXo496KgyGldSD+o6KRIWYLCpQK9Xcvl0U4eMwC+sRBLXWBGQS2jXcVGlYSBRrmKUswTPP/cAAPBgM+LKwKqIkPRKrAkV/nBvgn1SLovjGjowyChjTMKay9ehNHwM7QR2eyscEYL7ZN3jFkYwKdBUmgU9rv3P34/0in92IEL87r/v0fjP/sx34IN/+W/xe7haJ2iO/bgFSwmTODRTPwY6NOwX/MH/6m8yb1xqiyYPPcgMgFpL5iD3CByZ0jiemy6x98gjvYUR0MMKKVUUNnnEQLHySgmnSFkscdhctejdI+u9y8CMqnPzPEFWBjhMfYXhYDbkdzeZ5FzlSMKaPY0BYPvSAus8gtFn4g9PZCznPcgyho7rD/s3vi9KpS3hMCIHkqIKUBbBR/QkbEsh7cT0scQH30ql1R/5PhQ7VCKtBYKZhLDdotL6XprUwpCRi8olZClhqf9k5iEmT3kk48X+AgfZgMtNR3bAC24hgq60lwWI+xUUoZ7dKc/jZ3/mO+BeTVGTmo6XmaLzqCRgBSIq71aFhqTyspEB/x0EoGYBL3RQnaemLZRfwGlBbFYBbOq//TVXHuEDxi9CVSow/EDAJcfsUic+oDfS91/pHhS7Bmrbv7BRXHs5NRo3GRmWYLMPEjhC4M4uRFCRQUKlz+vjGf4gu8j3SF/MsN33k9T+qt+V+gAMt/z/a2WQxbbVVYdQjnuLr262UJiAnUbuPpryYuyyEJrQ3P07AuUUKAhhqfo1o26bWiEdFDx5N+uAhTdEr0FTa3YaUtowvaSsNSso1XmAJqkZIZo3CrtD/3yUEjicDfG9H/xiAL5n2jqNyMhBT2nSDR1c5bgdIfKu9201EMwkahJz2hyk2Aj/sMpMIb6ywnse+nFNohrvf+glHc1+wmpI6bkNBMCORwBYyahHfVHZbuSURUWCDKgkqpTkAM8tkVcBMnIoqozCnKha1grEcY3JOb/QNVbyfcqKEJNBxu9zEtS8kIbKYEOLbVlqzLOEn4kkqpDTxtAqAXEKayC3S0YQB/2czd6BAE//9HdhQP6k8aDBUYtPWIfQG4HG0YY76ZSLrv7D7+XZXIcG/fEGMyrrNqGEyDsUci+o+JkbhCWWIzKreN5gPMzYeKIudScVurXG1hVyWoLDug7xUJGpQL/GmDZJZaOhlcGS7g0A9qhVwnFPX0uLeZ5gduIXX6WtRyYXT/6S9ORfwccQ0601VK/GOo9YGeTidAEpHPMOtfQLKACkUQVNtI1QG+T6IzfT/8XnvuND/u/P/bNvZi6kDrx7iqZJ4MX/6Fs/5O+v/mjLdRXcJ3TSQTjBPUSdA9T29QvIlp9cmsDLr9X9Tqv35IB6KC9NYRML12riTnJWK3IA1ouOh9mLS6zJCssa6VWJAJRlAPdUgYA+1xzGcK39GlEV24wujBvu+8ACckW91kEDM63RJ/eZ5t1j6I3/3fp6AwcL2fYrcwF3QhPj02v0Rm1v0WEpUu7Til5ncVcHCsIIhI+o/+QU6i1aYPOwk+6zfvKvibKjrUDTZq3CYWe6ZCrUcZGyhvLN1TmEYYPZhqTbVjH3Lp0T3HNvSuVdPOj5UYHlHlllNF56tMtasUHUoKLeq97N0dAiPR9pyH7NAJvT/b3DkwFWjwYdBUq6DjSWadjE8ObRNIo1cat1x2tWoYVUFnnWSnE6HNN1yc86gdnvI2hVnyxYGUlWAtWef6an7xOYvbmBIMpEsJAod2nhTyXsqGFQWvJQ8zPc9PwieJHUgm6+eo6dXZAY5tICPrtp5QwTXfNmwVqJTzl3Hycj/+xmTYjwvH++783HXHlY1jGaWuE+LXQ6ajijbTnRF8mZJlY19jY+Uz15MMLMCTy15cE9ozBHRTyxVRVhvU8Zfy0gdnMGTE17Odap/65+r0Aa1nj0e37DUO80WJP0Zvw7KdZX/fNx+6u+Dm/8xW9Cw711z3sGAHutRtMoOBofFVgGICptENHzFyiDqtG4dME/q5VRXGnLqwA2FtwTjlWDh8Jfp1QW1nUqZDgJkdP4vOH6Q1zreb3w+8UYr9x6ml2rpLSPKWtBAAFJUjYXGhwp/92fevku379bJ1texpHmCa/KZjvA3RMcZz3VsziLsziLsziLj1N8QmaqZaOgGgUpLa5MfVk0bwIYgFGJ1gnuxZSNfszN5U8SrTJSvRwiINcRUyv0+iWbVb/xF7+JsxdjJCaDDOPzJHxwd9QdTHhxhezqKcJ9a0y80pBEcUHkECwkmvRUNvaUz2z0qPSZI5UZqzJgFGi13+PSIRwwyzVE60t5yvtQKQsLyeVZWQtY1ZV1hXAs1mDziEWzkZiO7uH8Dnx97DOiXvm4swmc4NK2CwQLN9xabOO1u/sAvGtHlkTQVKKuNiHUjDLh2GH8XokgoxJyLNiHVTTSi00AcDXgNppRifLqhneaUVSjahRnRPcOJ/jsGzcBADfVLvIsYlpKb5Rz77VaRCyoj1Ih2c46Ar+0fN8XVYzJIMMBuX1URSe8X6YBZ/HrTR84imAmPrtoco1Wxl9KC2wkXKtvW2jIDT3DiYUzgh1tIBz3uqV2XPZ3FigXcYfMduDeIqyAzBTrK8tKcM/WRg7Dm7I7dmSYdlWNwSh1qx1gBKNRzbKP+rq/tk++fB+NVXhxf7e793SO+ihAuN35GMe6wW7i6R+vLLa5qrK1tcZ+MWAt4E0dMuXl0miBgw31OBcp7CqAIsGU6WjD97ZqFK6PZyiMH6sPHJzjkno4LWAaxf6nB82Az6mxkr14lTZwVmDv0L+zSVrhhQveuEJLi1dOttD02/fEsaLSe9/eubhc+4nvRm8YYEPavNu7S6b9DKMC9xcjLkNbKxilHSqDnGh+g6jCbm+FuBUVyYbYJGF7a3Gw7mOLUMgnpcSNie9hP9oMMFv2WNRBl4IFat48usPn+BuHN6CDBhW1D5xTbPbuBFUwRn5cg0HJ1R0AnOFvpRlWZYTxlm8z5GWAcpbA5k++n+on5KL6W1/0TRgO/UTWLnqjQY40rFBRzykJay7BNOZjT+hb6bpzl0/YtqmUvq922sqqha6fG62gpcURgUgwaIBle5sEyqmDICWjcC6h2nLeyEGTBVoVOZjE8SIVrMG9HGsE2Y3RxGsF0IrL9xsWv0cpIOOG1VucAwOH5EkMOzCdIXXcAZV0QCoyLaVBdRO5W2vucaLXoFxGUAv/ffl526kr0STcTsqulFBz/3cP5ATZlr+wqtHQoUG1onpkI2FGtGjXAsungZgQWfXAdXKGmQQmJKN4N4G5UDL464ULe48ZPz+aD1m2ELXEv/yD1/rfBRbWCASkyJMd9TA+7yd8vW2xOWob3N7pZkgLqQC4xbDKIkRhg4vn/BL56HiE5tmMPiZYhk8Oashl7EvJ8ILldUxyemGDZrtCdNNPwsW5Bv17/t4uX2ggtGWakNBe9B0AjHYdaEwID2abkw3XXKImJSqdCeh1hw2wIVCeJx5vYvCer/aKYS/8/LfC7fUhqCSYDgusD/04hvsa9bSbLJ/9C7dxb+45lKsqxroOUZICj1xrz2GGp3G11mAvXNmDhGNVpl5QY4sm5CSocbDps4zgpg4ZZNgLau5dhlENOfUSoQAQKYMVbWYbo/D7dy5z+yBJS1yc+A33JMrwweMdGHp+dnobrIhGk9ea8RZK+nvd9g1PVj2899ee8deSONjUQFNfP4xq9Kid8sW/+lV43z2vmnb9qWPcPx7jhRseTHXraAuKOOPGSjgn+HoaIbHdI4WmxQgp0VOGUQEtLF5ZeFH+2SpFcUw0v2OFLHbAs/5ejJICi4paPE4gDA0iUneTW46dkX57cY37yLGusT3c4ND5OUpKB0PvTx1ZYLvrz5erCK9UnorzCnbgqD0gew12tlaMZSg3oZ8zmid/UT0r/57FWZzFWZzFWXyc4hMyUz0d9Ynfca5fSnF0pWLgghtnvPNT0uLNv+TRvq2C0m9/4d/5Y4997YffDqcIbi8tI+6EAC4MlzjK/K47rwJG3D03OkAgLK71fUnm1+9dR0O7vnodQq4VggUhPROg3Pa/U+MKBZlpi1pA5RJ6449Zbhmo4xbtKiGHNdBqbtYSgkqhYq07b08H2FmEjOzVXGw5s3EB+SK21A3lAMqG6lr6n0+R4NnXSpyyiKqkL/G2T+C46iynTonzA4BeKi8+AS9osOkRzccKXwprM9C0hiVwEyRgtitkEdGKCgmZEio7l9Cv+J17M/BiCc+/xmcGPV1DJj4DevHQW2z1SDxASue1ggEuOddUiehtZwxsM41EMOyoAdZKHC38rt4YycINUdhgu7/BIfllmlKdKs9aDNuyX1xiciXjDPr+0Zh9YrXyqMniGmXeAli/qW6HwBsHtInmRnFpFcohoCqEEEAQGGxaM4VaICC2h868vnK5RW2AwEFQKX5A9moAYH53jOSNS1yi7O5onUJSViysgFMkMAHv97khMMytF8eITgSoOgkngeIyUS0E8JqnfKk/VjUGusStlQfEDMISZUBVGqPQC2qmslRG8fu0KUPUVBbdGmzw5p272JAJ+t31BCv4Ly7KAEHYoDwh2o+JYQi1GkqD7f4GD2a+rLvYJNwack5wtvXs5BBSOAbjAMDsaRr8WmFnskZNVaG8DHCu3+n2DkkNaVOF6PcK7K1IkCFo2McVAF6/u4ebc5+BDuOSxSZ2+htktb9/j9YDTBPFhh3xZIE7BHpr+j6L/IwLtwEAbx68in9+9Hp/7MkS97Mx04XmRYI/OPAZtAO4ZLzIYyyWPa7uTKZrpDQeb3j6JeyEKywbP46/cXAd89/071Fy6NCQctn6usT+eorBBT8Gra65+HcAqPQJv6iedoG48VNvY16cOrUoxLphV5A4rB97yD9S3P7rX89uMAfzvndYAZBOczxaDbg3EiiDhpoSgbC4td7Ci/c8SlDqTsqrNgJ21KAmZaDesEB5k/w9Uwk97HhfZRBCk3E1hg1iWhiyvT7cPGRjbxE42LYfortrtlstktX/W2w6uTFIL84uqXQrC8k+pqoUMOEpAfZBDXlIvqeneqYuNp6nSuLs4iCCIf4mlOvMAOi82pK37RumgkA4/xLSi+iMRLjVKbLUeQBBnD5jBEAbBJ0LkGgP+q9K1J/ZLYB72ZCdaIyR6Ccl992Wm5g9XxEar0SUdp9tpQOFAE+0YdSgaSRTPJJhgQ0hg6OoxqYKEZPHphnlHaeyDBlZPIhLrOqYN2UA+Bj1JoCMDCNmRdzwZOcazwuW1Be3Sj3mBlPTOQnl+3utY0jdt8xrrCILNAKaetXCUokPwPrWqFOiuqyBox7unFKcajdhJnGQmWQE+tUrRzg5pvbG0CBYa1RDx/c6nXZKWi395XDTR155KUE/Vmt+J8taY7bs8dhMhxmbMTwzPcJLh75fm9caldUoiZJWWYXPueh75OejBX5nfg0HY3+Osa69UQGARNVIgwoRzQ2hNijqU/SpqHvvhkGB/dyXO58eHWMr8QtRaTQCabC/9tdtrcCrx55jFIc1LhDqeBgWuL2YMtWnF1eMsr93MsamH2JO1xpPl7h54BdYKR2PTRzXkD2HCz3fVnj3wUWe15KowlaaQRIvamUSDIgbOqtSfGD/PJ7f9SXftowN+KSgNf3I6wDDQY4VbYyqRjNnWEuDgSpwXPsN4Osm+3jXp/rxnu2NIHv+PLYna3bsAXyP32TqMUWzJzU+4RfV02GPIhQ92tGlnZZo3N8wCKKxEs4ovPbnvg0AMEm73XrbI20FJV77c9+G6di/cLN5n2W9Nsc92Ek3gTZWMn/wfjbGyw/P8WRtjOS/613ykmVt/9UBaHboha4lJDmQGCOhhhUa4ReAMDlljA3AJQZGn8oCSSpQrRXMgI7d2ni12JVTCy5CCxU3sJxRgvmhVvuJl7WG8842TGfdwtpoCacM7MCfs9goBsoExxrCeam79kLbvp5cBLD0XfVVWtBoQZdrhXqX7OLSCvVSM1c3OtCoB6SluxZoUgKo/EUvlTaOCMglLN75ed8PAHju//p2zJc9jKln9rrzj/DBwE+Y2TpCGHeScVI6dv6BE7wA5lmI0TBj+lT2sI+QrOScEyhrjfks5c/liuy6RjmqVl8aflI7aLPdVcDOPKilrza09AbVTa5Bzz83rYiEOrXgyuOQ74VsPF+31TUGwBs0B7/457lfbPRGILrXmbGb89TvMwLb12bYFNSjPI752Wl1pAOS27zjdjo92LRBNemqKlYL5Hf9d7lp5elPdB47kxULMBSN5iytqAI0RcDOQIe14krCqydbyKi3W48L/D8vvZa/+/zOAoeVH9MHxRhXe535+3GRYkm9xuMiRRpU0NTLXG5iljqMk4qxEsdFipdmO7xZro1CTLxaJSxCafDp5z3g5z2zi7x5W6w6kYhXqy3uTwIe29E+S1UZ4KAesN5vomtsUX94/3gETZuzNPI81PsbsoVLCgbH5XWAxkq8b+4z0HcdP4Xnx34RvZTMMb2+wf2sc+NpZQSHYYlnB4d+3JIVHmYjPFA+cz+6N0Y58c/Yr5sbOJr2cX/tjyGEw7m+r/w0u4qpZkWtobU55ZSkydD8yc9Un/xtwVmcxVmcxVmcxZ+ROMtUT8Xtr/o6FoNIkxIVQegPV30uAUrh4ITz7gsATjYJq8+cG6yxKruSRrafohr7nVjSK+FIIi5bR8hPEjSDztVEEVr2ME8x6OeYt+Ux1ykzaW2wPdjg4ZHfBaZpAUmfe/XLPlTh6doPe4nEah4xZURnAk0joLNWOlDAtQre0rFRuDwJvD9pK5JgBByNgcgV3FKzLGKTdsha0QjvfEM9Lb3oeq/lhQYgL1S1UbDOI00BTxY3JGRRXKmASnYSeKWCoBJyNa0Q9P3/J4GBVhZLQkCbrRqTid+5r9ZJR+UBEKyAartVWwLyS0S2/4+/BZ/2y38To8Bnqj/65p/gz6RJidnhEPN7vo/3ls+7g83Q399F5MuxE6JuLMr4sRJkiyJ1VqCxEklLz5qW7GYThRW0shhOSK2rDFgWTgmHVjtotUhQVprlJPWgxoUt37s8Jim8ttxcZIZQ4HAAACAASURBVCH0PinujBVkWmN7x/etjmd9NoEWseP+thkaRGnlkeAA3CnJQjuLkMUashUWQYRknzKnEVhMAr0GmyJk6pAsBBs62NhCVBLmKUJAL0KoFZVxz1uYSQMzoIsT4IoL2p45fOVjsUm4SrMpQkbPTgcbiMEGJ+QiU5bBY77G20/5MmgaVp7u1UoHKsMUmmUV4/2H5xhHsS4inB/6cTNW4tbxFiLqUUrpULfUoUpxtWKTR6hOYtx45hEA4FI6R0Qer5smxLxKkNP3vXbyCHbsz+PWaovpOmWtASeR0Fzxpu17mNcJ/Xwfd7MJNnXne9xmya38JAAcL1Kswvj/Z+9NYzXb0vKwZ621528886np1nDr3tsj3dAO3SIOjgE5g2IpshAKjjwgO8KRFUc0ARqjYIcAgm4GQ5QE23GIE2EwsuNYUZARsrGNsd0YSEP37b7zrbnO+M17XkN+rHe/+xSDFSn9I+V7ltTqqnu++s4e11rv8z4DXtjybOiiDXFKaoK7Bz4VqXOLmukMmtxlTuoR3jc4wp3UV6T/4PiDqAmhK3WIN9cepWlsgFWdcOU+PNxgM/PP/oZQpI/teEP9x9UU91feBrEsI8RJ34uuq74flG17xyoTPv+G+sK53xmr/W/uWK1WmEwmWC6XLKn51433/e//DQDf8+hch7rmf0fFjwLDkVDOCQzTmtMi2iLkcPPJpOCJ9cm9Xd8j7FCecYv9Pd9TabTCYj6AmPnvj29sEBOsszvMsShTnN/zD+nWzTkWb/u+jDCA2fKfu/+nvwt3fvYHYco+2ov1p9JBFAEvkMFGQW91k1gfaI1RC1cE3Je1kYMqCdbaNlC5hJlSj+lpCEmLZXVFQ41amGXX9wTbDwoBhujCWeBDawbdJCxZlyobAVUKmDt+wXJGwJHcIzooen0cOTDFu/5zW6MC79vyUNar54dYrDK0C78IDu4HyO/4658+CFFeo2v1rb6nfvOnyQt4EeCdb/t2dOP2X/lRKJIqfeIPv4qzyk9OifLEGPaOLWPuK7VGoWkuwMLKYkR+qrPlgMlwbUnPBy0cplIISN86HPS913YZQ41aONoj7O+uMCTf1MfzCeoqhF31E1QHBb90/QSDsMa89hPek9mkt+/Tgi3uVGxYYwl4mU4nn0KtcPelp7j/r677Y4x6bWq1BzQv+WvfeTcH8773qkedztgBFsgO/IanXCcI4l5r3a4j/nd6bFhHahvVa6EDB+fANo4isPjDr7zhfyYczusMb577Sd9c4DzEoWYpTF2HMFpB0mJwdXvF5B4pHBab1Es7aAS0wdyZbrCb5YjIBuq4GOF0ThaAdcAbW1MrwAIfvOtJb1+3+zpm2kPPv3p6B/ef7CAhctjLe6f4wmMPwV7fXfBxnB5NgFbi6i2/+N0YLRj+LXSEQFjkRCQahRUOSbf7+nIfJ2v/bLatQhBcCFJPekvESgeo2oDbWXvDnHulidJobMcqBDZtjIg2BaUOubAwTqK+EMb+1r0DtuUcJjVCaRn2rk2AWPnPPVmNuc0lpUOoDPYJGj5aj/x8UNR47Zs//f96jv7/47iEfy/H5bgcl+NyXI4v07iEf3+f8Qd/6Ttxd8/vwtdNjA3BuqfrAco8ZqeRxSZDNaNdfWQZCgKA4XaBkqoN5wQzBmUhkZxK1NvkOnLY9sYQTeDJPcR5qR8NMbjrCRRnmwGME8ge+d+xKnZgyd/3/p/7Dtz63zwT8+bf/CGoWdwTT2RPPLFGwEYWguQOeqftGaFWMJFFnUaAAyfkmEQw6SecK7RbBsEpkQxiHxgOADExN+2Qview0GQo4YzgSlinDqoWDP+qSiA5J0gwFhAWKEEIQAD+3U0ZInjsr6lwgHsxZ8jRWImnhd/dTpIKRR3BTWiXf0MgJAZreauByi64UgE9s/lKhZf/zvfx78K2gKHrs2gyTCPy3K2Gfqed+UrhXbPNOZjGCShFbkXOp9YUdIwqsExEkaFndnd/d3EP4w3ihj1rRaZhGomQYNEk0D15pQngtERK5CdrBYesWwicFCOsiUDXVgHC9AJ5qe6E+A2adQRBf29D2+f0AnhwtsV/DnLh3ZIA1LdrHOyQAb1ROHs6YbN91Qh0V1iNmz5ZCcBgUqLI/TFZ8kbuyHKyljAkDVOjlmHtjr1aS7omAF5feFZvGrYYBA1DmsZIfiaAnslvjITVAnbun6sHdYCD/SV/Jo40GjKbcFpwZdZdb+16E/3uPhVWcqasig1MqfDFez4c4NV3r0IRoUxKi+lWzjKpLGj4GIdRzaTIINWIty6Qk0w/n5yXGbKwRUVQ8SBoOPO51gFLXjrIdr72CEUc9uqFo8dbmO5tULzpb+K70QSjj77rz8UJBNLitTN/Xb/pzv+Nr87eBgD8WvEiXtt4RcK91TaHhgCAjPrc6FWZIAo0zkgm1hmYdKOD7wNlsDfI8dZTuoe/laIZOdjq+Yd/LxfVf83oHuxlmTB0O4ganAmwubkxgiUdMrCAExhdCJOuBAUM1yFkF0dlBNSFhKM6j+CoHTfKauiHA4ZTdWqZQq9X3nVEXDX8PR2E/OJnfgz3vuO7+Ttv/tRnnjGn7uKXXABo1TugQPTB3KJQiGcErxnP5JX07gQF0NJCqYcO6aMAghZ+GwLldZIitV2iBv1/riCy3l1JUgRaPBdoxw7RkhbttP9dJgV04j8DeHTUJAQv7wi89SlK8PnpH4Y4T2HJHUlrhbNzspBzwPXDOR4uyOg7tDDXKDLrSQJLx/fiz/0A0qyGCGkBNwJ13idsHFyb8+R6Xmb4/MMbAIDBTgGtJZ4Iz4DcHuWYb/x9Opis8Zj63lJZtFXAzlrZuAJJlxEFvm9vTA+JdX2mMtDYGvlJ8qQZIx3U3AaY5RlLMEaDCnJkGc672Nd/cLaFZhH30L/wkhsA2N5dYz7r5B0S0JJN/2WiIYh5bVYRdKtg9+jmWAFLet80bvHS1PffLPXlzlpacReKcTDbKB8vR7BscZL2rQ8yUe8ShPQmRDylxJM8gnrk70X5Qo39vRV2hhS8bRROCIKdjEo8qccoyPJyuJfz+xoqw9dGwLPCB+TEtNwkzCbeHebQkURzwUHsGlmYGishhcNe7P/dNCrYFUsNSzagN62EqBVcS/PBsOW5IFAWjVYsU2msYoP+iz3S6bhArRXCrkdu+lSddZlgVSTcSniyGfP3KeH4OY2VwaPTLf77fJ1hd+KPHY3E8tEEIFtLVyv89rse2v+qF+/jrdkuS7yWOsUvLD8CwKsSXhx6SHrVJlhUKS/ucdqioudW1wHEpMQ0I91tHfX6fPRzqrUStekTlKzyrHxT95uv53Vcwr+X43JcjstxOS7Hl2lcVqq/z2iM4l3VYj5ASZo/YwSclbzD0k2AkHbu1np4qRNF20Yx+UPbgOE1CaAdgatV+TSCvU2i/yaE3WsAYru62LDna7aXw1qJ17+ly14E7vy411SazPKfZS0ghoK1pdODNR9vUUUIQ4N4z8PXxemAoc9wKVkDGq89BNvFzkGC47rCFcGXXaUag/VlUlqowEJMqdKxMVenAFgHq2sF0XroGABG9xxWL3ZMY58V22lT25FjtrKYhbj9E/480xsVdKawM/W7cCkc7/CXZeJhTzo3eSGoPby9gaGd9WhY4tZ0huNkxNf/c//p9/PxfvwXP8WkoLfeOuyrLyvQFBG7Lb08PUU98q/TeTXAkByPjJWII83wmHMCNZtEePJNFzsmw7Yncog+3EEpC+d6ODLPEzygz12ZrrCqEiaiPFn05I5mRX66ZPhu1yFXLOsqRjr0D2BVREh3CzSdZnAV9ujLsEVwP+k1w9OGIwCvT5cYk3HAWTPA2aMpB1c7BbQ7vZY2TFtv2g+K6KOWQEKaV/OKfx5FYtgHWC0CKKpczFmEehowKRAAG9Iv1ymStIEgQlwSanYu0qZnXg8HFZKoxTjyx7xYZf07XiYIlWXjg2FYs0HCrB5gVScYhl2FOGE9bqAs65ivjZZ482yXc3X3t9ZM5llVCeomwNHSP2eD3QajsIer9hJ/Xx5stpiN2w32Lg40WqNwRhX6aFiicoSARC0b6mujYCqFeOS/PwgsAwMQADQYKXj/S49Z7zsKa+wPN/z3zy+uwtCxSLieuTw+wttyF0cUjTfOKg4cf+etQ6zLEQY3/d9vTBccaNBohc3SownpoEGsND9LTlKb598A2uzlovo7RhcWHCqJI7IKu/cnvhu3/5Z3RhICgHDIO3bkOkSb0oMXG6TDumeBJophETuLER9Qz6MM0QSRz0CFZ9bKLh3HWsRpizoi44ajkAPLX/4734c3vvF7PbQL30e9yFTtxoc/+eMopYQgO8PqyTYbHwS58L2LTm4i0AvxXb/IlfsO4UrwwmliMNxrEoc3/+In8dIP+tzYZk8jogSetgqgG8WMSIxbmIQmCSN6aU8uEK2BmtDCzQ2BxCOJ0BnQDoHyBTJubyVcSH2rSqAlt6e6CuGMwNFRJzRHb4/oBFRkEJH8ZpD2fat8nSDueotG4a3ZLvI1ZWxGz2blhhfsJWEFhvt+8tvMMySjmuG3L5xd4b747rBPP+lE991YVzEqOg4hHIQQnI06iSsO4c51D5sl2xpxoHnBNKWC7ozIKRT6fNmFAIB/N6RDMNAsYyiM7M0TqhAD6guKQYO2VXjnm70s6+b/8sPPJNbozHFK0GhS8iQ/iUteeH7z4XXEJ4plNHBAds9fj+LFBkFg0HQ2l0Yi3CLrx30D5wTSTirjYoinXTgv0H7AvzOfuHUPn713q5f6nCYApfZIZdG2ATLaJJRNiPICi9eMyE2siLHepMhpw5OkDW5MPQQ7r1KMoxqzyk/6lQmwaXp53KJIcbwg68C45XccAC8olQmQRC3D9A493JlXEdomQE0b5NfafRxu+X58Fjb40txb+U2TErHQeLr293pTxiyzqqsQ9sKGvmpChGHfs+xMFqRweHWecX94vLfihTLarrA72fDm86u2HuKfnbwIALiSrjAMa86RBcCOUIlqWdpjncQf2n4T/0LeAQA8WG/hlYnfjDycbKFdxDid+e9wTuBw4M/zuBjy+5VGLY7XI97IyczPN65XBj2343JRpXH3b38/dB3g3p/8vt/z5+/+cT/hvPJ3vw9COJSUbB8UEjruSCgGWve6NSGAiAgWONTcpDdGwJo+JDpcC7TCv8DmwPY2fADaSU+4eOMbqUK9oL985hx+yC9yYgfehYgkMOEaCChkut5xcIFDsKHZz4EXVRv2a1J8LtCOHEd5hRugOKT+B/3+brG/+VOfQUOeqQgtUEuYrp9rBEATcjyufbwcgGbLodp3yB53ZsMAqQ9gQjp22oUL563uAE+AQSe/SAzUcQxDUhxMW07BgBNwkXlmcTsgUtG9aNvrAeEX1XydYGfbT0i/M+JvXcVMlhFaIH/gJwu1W0Op3nFmthrwfXtUhdii6sU64StoWoiMExAd+UYZQBkm2Ezikn1Xz/MMH9l/AgD4yOghPr++jrcfe8kIJHBttwt/A4oywZS8Y5eblPWmAGAahaarRC74WRdVhCVVDc4IbO+uWVYk1wGHCVkHxNdyXNnyPdwnswkq58/zcTjBKfntAkC9b3qdc2BZ/hIOGxSrhJOGzNigpWrUDVroWsEQEmTPY4wfUy9dAXjZL5SPNlOvYyQZFQaaK/y2CSCk60mCrte3Al7nC/h+alOH7F40yUosa0peEg7vHO9yD/QijyKvI0hp4cio2lrJkjQpHJOAQiehjcJN0ofuxDme5L7nLoSDlA6ONpu6CfDwse/3B4lmzsM0KfFfXv8l/MPVBwEAp80I//zRLX8iwmE0LPnZBfoqdhA1vAmwTmC8VWCz8ec2Xw6Qkt/0i/tneHl8gi8tPOnojc0+u4l97vQa+xED1IOm+LhRUGNAqIR1Er98/jKWRJIKpGVbyDsHZ7intnE4pYV0OWJbV+cEkrjnpsyLFIbQIzO2aLYdbPk7CITP4bjsqV6Oy3E5LsfluBxfpnFZqdJI0wa5lbj1P3gXovhM4fXv/bbf9TnnAKN7Y3I91RC0+2zXsQ/1pkphMCmZSVc2IUNBe9MNTgFmA9frmHfxzTqCyjRSChWXwuHjv/gpAEBRR9hsEoiih7Yujq7nKWvfD1UEpZQHDuHa/+54LiCMhM78705PJCfFCAOWRMD5yszG1Mfb6vuagMTtv/Kj/FkRCTiSRKSTCtXRoO/FRpbNDuqzlHs7emwA6ZDfpHCAx4r/TVgA9ZbDgCqW8sCxN2w7cFxOB09iCA2EZFBRRo7Dx6EBqyXuXPGw1B/Z/yL2Ar97/pnm42hjko8YhQ/deAu/8NmPAvBypPt/yl/vu5/+MSTvEyg74+9xy0k6gBfZnxBUZsuA036u7ixZClHrADtZjkXV9wITgmOjwCAJNCZk3L4fb/BO66uXLGq5ygGAJ/mkh6ybgJmXobTYH27w1tEef7aTrwy3C9R1wD611gnMiKFsjYSjShKtwPx8xDIrVQkYuu9qFqBpMhzTPYzjFmvyKn4y24Uk6Y3baqFGbc81qHquQVuGgBMwE3pAtURI7QKpLNzjFCA2bboRUHXHkgeWr3lof3WQ8XPUfYchV6aQ2MKWrnkY6T4/OA+519o2AXa3en7BMGy40guExZNyG2v096nry6ZxgygwuDLyz09tAob9Kx0wlLoVF9BW4vHKPxNlFuLRjFjg0npDD/IylhfORQiwvGbTxPirT/9dfGziHYmO63Hf624UCoH+fraSf7adlcip/WCJrdzlvH7s4BFimhxS1SA3MSZUnb493+HcaCkc3jrZxT713dOgZSTixI3w5HzC9/P9t59gj9KcVk2Cp6U/5yerMfYnG77GSlksV/55ca4/b02e5n/olTf9eZYjDMMabd7gEZ7vcbmo0qibAEnaIOzgn3GGuz/vLQsv2rYJKA/ldJNroBke1HniH5wuiWGYY0kwcagMw79VG+Dm3hznlEJSz1KGYIPzEFoAdae5O8qwpt7RvT/x3bj51z/N5I2L48XP/BgskUnEwHm5DX2nHhk0PswCspII1gLx3E9I0cKhIS2ncL2sxUk/qXULnWwE2nGve4Xt88ZdaKFoAtVaYnh9xYvnenVhMdkrnzEL102AYEQr/+MB93Nl6zcGFR0z3IXjMOBedJALVPsW9sIe4+DQw6KLTYo40gzv/driNj4y9q/rpokZsmu0wq88vsMm9/uTDW7+9U/765EqrE+GLPFwjYLpyGWZ9nDeBaeqKUHIizLBZuHv7c7uGtO45EU1i1oUNPltJSVKHWLd+hP/humr+PvmKwEAZ8UA2vpjn5UZpmmJw6mHYMs2ZAhwYxQenu7yRk6NGyahFJvYp+rQpq9wPgQAAPa3VzBkk3dyOvaQKcHobWz7PrtRcJFl8k0QGKguuEGHsLTQq9BCyr51EaYtZKcVdcLbTq4VXyud+T+7PAR2G4CiCYNcoDikc6kBQzrs8ChCOzV8XCpXMNsXFm0tIdPfDR2KTGMw6vva22nBXImNcDg784vG9jjHzRtnrK+cZCVvgp0TMFbyAryVFOyudFYNsUsko88dX8N21m+kH88niOjP3SI8JBjWXLCutE7g6tDfWyksGhPgV859uHneRqyJLR3Bu/T8ywv9/3ePdlkvCwfs7q65jzoNC0wCP68NVYV/NruLNYUFrDcpx18iNRhtFchC//sWVYrTTqJ2FrPjmTACtQn4+mRBg4Z25u/bPcFpOeS5ramD/ngDB03RjOYsBq5t8I9ffQUAcOuFU7z++AC2eP51qpfw7+W4HJfjclyOy/FlGu/JSvXDP/8jkFmCwbDiiiIeeHPsovI7OFcp2AtOQ1Hc54tqrZgKbi+YfmPUeoYrQRwPT7YxJgG/thLrZW8qIAWwKXp2YQezYtQClUK7ICJUI3CRj3r/P/vO3/OcbOg8iQfen9XGDgMiAW1G4HBqO7YwJuDoM0Ay1CeMYJN1PXCIlpKdjJwEVCn5d7nIwXWB2qXiY4x2SlgrOVt0f3eFnBinW1mJw4Hfkc/rDLUO8PCBL0cD1bsyVbueCRgTF6cZC2jKZw1K0QdmSwmnXJ/TqhyTInQbwFqB3aGXamx0jM+vrwEA4kBjTH6krVHYtBFXEg8f7LKbkDD+3nfEn2hcM/GsrkLvE0vVQDjoWZ+r2ZgNNZbrFJthjP2Br2buz7eYaXztygP8xvF19j/9ldXL7NML9KzS1iiMwgrXKR/z/3rtQ0xsaTaRP96xfx63xgUaggc36wTxdslB6jYPEVLVtpfmGJM71HydIQjMM0Sf7hluwqh3m4Kv2jo2qst6By5ngbYKuS0yGpZI6XqcWIEWEey+/3uc9TCxiaV/p0JySrLg3+cEEKyIWa8pt5eeAxe4PiZMS8hcAUS+qwYB+xpv7624clxXMUodso/3+WqA9swjCOeOckg7SVaRsoG+tRKBsiypCYRlo/xr2ZLzWZ0TyJuI3YYOJmtmc3eVXdcWKJveSzeSFi8MvGvavzy+hXWRYIcC0hujmEGehBrrImaISCnLsHH9YIgu/tQd1N7fl9CMz57ewlOCbl88PEVtAq7IL5IihXS4u33GzlHHD7YRrHozmI6B72ILYyX7An949BhrCih+K99D2YZMiGuaAAEhSWnaYD33MHE8UyiSjO/1w9+6Apc6uPL3JmE+T+M9uah2oyhihu+qTQS5DJGc0oTqgMKb50BNGjbads4zGzuNXBRrnpB1q/xiTBo8WGBJD83O1oZlODK0MFZAk9xmsJ8jP/IP+XQrx+J0yDZ6OlZsJ3fzr30GkA73/+zvXlhd4ICrlNN5mvoQ8q7nmRlm8aKWcAp9j3LU9yubiYXNun/k4NYSIfWtmomFvTChucAy5ChaARH6z5WrBCo2yEi20BrJsoWj9Qj3tA8AyMIWj0+mfjIEOT0Rw1c2fpNh65693EzoZ0uB+Iz6oSMLYQSCa/68t0YFL6r2PIKzAjOCzq7srdgs/Gt238GD0h/Hm4s9nM5HaNf05lvBE7fYajzDlCaxF/ZmbFoO4XgBAfzkNNynhJOsxoakQ0ZLvHO2w72kg/Ea+yNy9GkTTNIKT6gH11r1DOS4oNbBdlZiVg0QEfx4ZXfJbkJCOWTXNrzwz1cZL3RCAtNhiTz057apAmyIvfzbi4zh6izxx93l9tZtyAuKjltvdE7Xta4D7l3Cgdsgo2GJONQeSoZfiAYEI7aLBCLVyEad85XkjagxPg/WZdT3HAmIl/xxNVr5fis8M12VAi19ziUOMbkf6Tbwmzq6H9m4Yri60QEqgtuLWfYMnB+GBtkN/2yuNynqOsQ25ZOuioRZwlncoDWK81WnUYk3l76H3cm0/HkpINQMn66b3pD+D+w8wKvLK4jJvjIJND685dnd1+I5Hlb+edyUsZfeUM+8qENIclBKwxa5jHjO6nqrAGCnPfSdJBpV2UuyjhcjhuIfLydIo5Y3GsHOBotjgngF8PlH13oXqHEDEHPaCQBDymsdVwiVwYbaFverHexFvt+8qFMvaaLe8WhYsjUjAGQv+muz86ECj5cTvn5uT0BKC1NcsJp7Tsd7clG1VQCIAMGg5YDreKdEoxzKMb0kWiA8oz7SnkYnn0pTr+nrRPpNHaDNaZddKiC2EG1vr9ftJE8f9v6pYVYii1rUVDUYI3Fwy+9UF5sUMjaw5JGrIssVoQoNrJW4/TNeM3uxX4ZQ9rvOxPokmu6dywMIMgBApeCk69NolOPFUjYCitJVbOjQjiwsEZAuhny71ECUCju3vHTAuj6ebrXOoIJeJjJ7PMWAjC3iC5q6+w93ISrFEp4g7yVAqgRM4q0RAW8u0RlZOAE0084nVsDuNsBbfkNyvJMg2fETgthuYFvJVaB1Ap+Yeo/TtUkwCvy1PxyscLYaICHdZP1kwJpMqyWEAUKavAdBgyFNELvDHEfLEYqFn3RErvB47quBcp5CdFV95FC2CqPtnI+jSyTRViJvIibAhMqg1LTRihqecHaSHNtRwdduHmaMnLRliHyecrqNncUs20i2K6yKhBeY+GmA+qCrFhuu2JwTOBiu8c6ZJ0k1TYBi4yfMbFijrkIOYG/zCKAqHJHlSn29SbGyAKhntqhHPKmH0wrTUQlNC3NTJ5iwBCiBjC2SCd23vZ4gZKVl6YrJLIxyTHCyVvJGYjwqsJEJ/76q6BeeG3tz3pwUwmG0l7NH7qaOMZuTvlc5bI9zrOmzg6RhTW+jAxwM+x7lo82USUFF1aNNUnorwk7a0hoFHdAiKlsEwuLx2j8jdRtgRajYOKk4ySWJWlTHA5zPaRMfWQjiFoTKa3q5lyocxwiq2CCmzZG10iMNnc92q9g7mUlacc33PrnRx7GtioQRNNMoONoohmmLIRHsPnrwGFeSFe4XfiPw+nIfJ7HfbJ4XA6xmAyiSVmmjeHO1aWI2roiVQdWEGGWkVxYO1gmY8FJSczkux+W4HJfjclwOGu/JSjXbLqEyi3yZQI4I8pon3nCcYLvpCwvMhYeyIukwJkgkCgxsJJgJ+PRkylIKsQngjPDh3gAgHDP3LgZJj7IK5+sBQ8oyMtjQjrcpQ6jQQlMFbWoFQTvTJGl9Koki60MB7n3F05bp9bpWEBZox9SHXEtoEuyrQiJcCTZTsImDpWI3KASyp7Tb3xUejhV9T8tRf1XGBi+8cIojcvgZZRWuDr35+FnU4vG9XYwJ/k13Czw+9bKCOGmZAR0OGrQuggs6k34JQcV0M3JkBtHLaCJKs6nfVzK87iKJ8FGEdtqdgGUphVQOCPtMyVS1+FLu8ys/NrqH48Yfe2V80EF3XHqvgumyUM9CmJ2+/1eZgEPJ8zZCHGqUZEDgEovynJjORnBAejip4azkyj1WGqOoh7hmeYaPXfXyiVhqvJn71I6zasD/5igfo9ARM4jjC4xzUSjA9Ek68UHBAd1ZUqOsIxbYIwIzg+t1jJqqlw5BSC/0xTB4swAAIABJREFUwdKBP8Y7O+c4LzMcnZK8p73A4hUSZof6jlrAFQFk0zFQJQqqeMJIY1PGKIknIALHOaNBZNDWAaqOlyAc7GPfVw7XEu0NCkE4D+Gkg076KcsQRLo48dfl4CVv+H46G8M2fb3w8UN/ff9xfRfr2QBFZyM6i/h6uHGL9QXDh/3BBtGIjOWbFJHUeHfuK/m8jNh4Igw1p+BUTYj1OoEjl7C2DTBJ/Hvw+eVVNFZxtmsYGO6ZGysZAfnK/RXW22d49dibMzgn+nstHHSr2CFLt4rbUC4PUHbVLT37gtAdQTaXgJ+HNmXMqIGSjo+jaENMBiVX67oOOGWnXSQIdol3sNnCvdUO92l1HkJR79UMLLauLtlxarFJ8UaXRJM2fOwPnuzgq196l60OlbR4/+QYzabF5/B8j/fkoqpbCdsqCOXY9QXKQaUGhlx8AmWRUO9llFZM/lDSW6LNl2TDlQcMwcr9CgHAUFkat1gcebgjGLXsB1u3AYyWSAn+LTcxcqKgR+P6mViu5GmAep9gnEGD5jiD3PYvgQoM2g3ZzhnhFxKAe6mG2oRmaJAcE1lFOejM8aLqQgeVdwtnn3oTlOSwRP+hmVpOMdmZ5jhajFkeEymDLz71NmthaJDt5Xy9toYF2rSPzDqiF9FZ4MMvPcKaoLIH2TbczP85KARs5KBpw2DHGuKMIPbjGKqbgKUnKQmayF0o4LqeqvD62PkFfWhAwtovqqvsAPNkNcbBZI2Hpx6et43iIHWTWYy2Cob6H86nnBCyzFNIadn7N1+kvbVfZPj+wQkfAE7HfF4MuAf/wniOYVLjhfgcAPCo2UZIPbg7o3N8YeY3AWnQ4rQYsBfwvEgZnnXKQW3X7GtsjGC3ImsFwtAAFGDublReagW/GasJFnYA7s22eQMiAJ4Us6DBSibY3vLw9TJI0dpO+wS2o3SWnp1ubdxqeEPZNAGaWdIvYNJCUcuhrX10naTJtilDqCv+mlbjkKFmpxyS22vmHoiBxtVrvmWyKhMPUdPi5iyQvusf/nfzK3gn9AsUQgu0Eq5LbIosBEH9QWiwPSg4ym/ZpJwekzcRWqNYxqRPUmCPZFYuYPKa0RIyMryZsFriaOXf/3OVYSsrmcAzjnqLy6erMY7IAjGN/caT20vrCHtX/IZ1uUk9JAvwfQq7NoAVPXGru2adhjgUEPS5zr6x2ygKARiCyss6wt54w/10k0m2QSzRb7pao7AoUibLmSbm6D5Ih3/r8AGOSbc6X2fcz83zhPu1w6mH4F+ZHvuf6RiLNmXo/3kel/Dv5bgcl+NyXI7L8WUa78lK1RoJGInJpEB+IURcCIfrt72r+8lqyBKDSVwhI7LKrBpAhBqzBe34MsNkDXseQ+3UuLvvYSjrBBaP/Y5Nr0NoEn6v5xnQSKQHZGiu3AVpgoAKLQwltOjU4d5//l8BAG7+zz8MqQVXwrpRPfSsHO8I490S7SiAfEJwWyNR71HSxZmCcA6Cvl9YyUQlEzlIYmTJUMAGHh4GADvUvAVbrlOYo5QN9p80Ae9apXQIpMWI4Kx1FTO0OttkLEUKA4PzMuPqK4gMxBVfDb3xjd+Lm3/j05AEM8pVAFXR9ZE+z9Ufk4EILVeWCBxXTrcPz5AGfdjzw8WUiSbaSbx/fOSP74vbuPU1byE68Mf4eDnpd/MSyDcJ0qyruBTOak/IkMKhMQG71mBasoxIKoutQ5JSGYV1ETPpKAwMXt7yLk+fO76GUVLjb97/BADgK3ae4MHGV8zGSrww8kSws2qAs/mo9wwOe5GVaCRMEeDrPvQaACA3ET77ujc6b5cxMG4Q7PnqIElalllUmwh14Z+PNtUIIoMrEwoct5I9jVdNggdH2ywRUolBtEPM7jKELWkKUd7NanzFf8cgbvDkeMrHCYE+ySg0uL7nz601CssyYbaoDFz/jEQa5QWP4HyZYHDonxElLROJ6ipEnLS9W5SWjMTIWsISdOOE9FIbqpjTnZJZzQCQNyHeqT3E+4kr9/GomPK9aG0ffD65ucTiKZnORxbo7I+Fg5LOZ9gCQNDn4yLx55oEvRlES2z0OOxD5zv5jCKjlaMi5EScpiCTC6o6pbJM1nJWsFl9mDVomgCGSGOwYHN9CIcgMsxGD5XBB3Z9tRhKg1xHqBIyuR/3Wio9lYz6aCuRn2fclsJAA/QOhuMa99Y7OCv8vWjLEOFjSiI6bLEWCR/vF+wVDjH/2MEjvLPegb5k/z6fI4g0VKSxXGYQM5pA9ys4K1GTXCCJWnz8+n0AXupwb+WZboOwwbIcQk0IIlmFEANyULozg5KWYaMnswlPQEI4Xmg2mzGCvZIX9CA0aNv+oVfKQhMspZXzUhoAiL3dYBeP5KzgCC1XSzRdL7eRXmpS9AuRzHsno3AjoDvJrOydkYQD0pl/mY9fJA0oHYeMDb+0TR4haPvvbw4cT07WCgSh5d7RIG4wp7guYyReOfQLyrqJ8U++/kfwkf/zv/afS2vMz/3sdOfnfgD3/8z3cAqOSR3qPWIvtpL7lZDOG+h3DGgjeCIpdkLESnPs1HqVIiKIMQsa7IaeFfwffMOv4588usvXRkoLRYtWu44hGomKJqAkaZGf+nsW7xYYhhrrDZ1bK+E6RnUrcXZvSMduAQnU1PMsRy1+vXzBn+fuOV57csC/+1VxhXWMUjgclx4SbI3CeFQgpEm90Qol6QIReJbnP/rcB/wx7pS9nV/ipV8JbQrubM3wcOXh9yRq+flr1xGMcuh8w7RWHLh+fD6BKwKIjl3cSpimS9nxgeYAMaWl49jD/IKeUixCuMQi3faLe9sEDLePBhWkcNw3NK1CxQsAGF43RuLfefkdjh97sN7C2ZqgYOlQlhEMsfDT+yEij5ii3gHaa10EmkFThqyN/KqrvSHe67N9rIuYNb3/sHy5D8OAT1UZ04K3rmJMafMAAC9u+030o7VfhM8uLNSK7nsUaEjhONRh08Ysm+HrBTB0nNEGfHdvjQ3ZZCbDBrpVHDWXBJod2+K4P9bNIvXPQO8qymxoETiowLKkpmoDtjD86OghfnXxIlbkQhYrzS2B8zK7kDwDDHaK3qqxDtFQG0efpXhjdeUZDXHXhpLLAJYCL5x0KB1Qwv/wrXQXlQ5g2kud6nM5qnUCqROEaQu771+Uu4enCKTFdkzV0nwfby89l3033WAc+8+9dbyLILAwS9oFBg4He9TzyFPsjnI8OPILsJDgXXc5S3FKL1uwWyEILCpK3LCzmF+AQdpACIeaSC/hQqE9pB5qZOFeyWF136/r+1QOcknfl1jIojd1kLqPbVMVoJPebEKYXsrSTC3++c/7qvhr/+incf8/hpcdwW8KOvLRrFUw+w4BVe+xcLxjtlZCyT6/cbbJuJ8YhgZ3hn4C+smv/Fn8wV/6Tryf0la+cHrIi4HVEnf/9vfD3u4SbByid2jxShzMsM/pFE1vIYm474N/Yu8eZu2Ae6fLOPXRcAAerLbYWzWWGqujkdfkAdia5FhTHxwA4lOFhvSJRRQjoP6zezTB/EaL+IheoQgsYbJ7DbopTg5aCAmYDSW0rCLIfSJrLKaYjApsqGKcJiXOii7CzbHVW6Asbk7mbD7wcLOF1al/xt759k/i1n//Ixhc95P1OK0gyDjg8YMdiMiiod7pu/NtbMh4Ih000LSAxNMKUaRxmvvfLWVPbAkjDTF2z/TxDG0eJpNe5rM4H3pi11bXXxRsogEHILLPJMd0Vd9qnSKMNNv3bYoYLeXAIrQoyKQA0uHX4xtc8VflhWi38xiiFUiWfeJRtdebmHRmFUYrCOnYQ/k3n1zHgGQo3YLaLYJbo4JRlKIOUbUBMuo1fu21d7iKXbe9FnUQNZDCYRUlfI5dYg3g+/e7FBNZmwAlSauqNuBFtKJ4t9WZP+/hTsHXyloBqSyTjM6rjLW0zvl+LOA3O05LKNrsOwvgnPq8Q41GBNjZ8f3oZZ1grf3PzvQQk7DEXJHFoPXGKN34iht+EyKFw7pJ2O6x2UQQhGTISviYyK7XLgBc89fYtRKOkI1wriDOFcwd/74eL0colwlseWlTeDkux+W4HJfjclwOGu/JSnW6s4HKvK1ctxtdNzGMlSy+j5TBjCqF1kqmmVujUBYR7v+57wAA3PzpH0ZMEEkYGDw63oKjqkRkBhUleohJy+YMg2mJ7UGBhoTPx/k2Q8hVE6KpA0QzEsAHgOgqWtnCNL2tGNwF1yQrgF2/2xVOwA1b2CNycDJgqDYoPKvXtj00zBaJDvjon/eQ6/IbFOJJztVp148EgP2dFfezAC/W73bTe6Mc+oL5eNerBnz19ZNf+bP879KgxaunnpmpdR9sbmoFDYWvfvEeAOA37r+AdkTMzqg3ew8nNfa31mxQvylibHdVWjXF/dUWhyw3mwiK0kr29zd4I/ewayAN1LiBJrbu+WyIkK69bCmUYI/6PIsIepeE8tc1sA45BAAA1Av+d9/eneN+xyY2CraVEARZD6cFapI+7U1KDMIG25mHRTVV+YBPJOog9CgwiC442JxtBkjvenTk7s//t8Ao5N7d3Ejul4nEIIg0dib+uA6yDR7IKd+XzpEoDDWKIuZq6Wuu3+Pw8V967f3+/jGU6BhRaI3CNhkp5FkE+SiEGdEHlbuAonhzgq7CTbMaJcGcQjqMspplHVnUoqRnrtEBs2VbI5GEmu/nST7sDUaiDPXDIUTX/Zi6Pqte9f1EGXpov5P2QPQs2DjSjGQA/r93/b4o0MjClpnZb672uL+4lZQ4r/w7XusADj1LFvDuS4CHq6PAsISkMQpFJ4HTCjFVmUnS+neHwBJrZW9/WSQwWnHL4SKrXErHZh7xwMPEnXl9MGphaW5QAiy5AjwL+aTwv+woH2MY1ew8thUXqAhuP0jXeCnzrZtM1bjHaRe+D/zwbe8wZRMgnlTMXk7HFc8d60WGYEHzWuTg4j5RSSkLEVoIfQn/PpdDSU8o0Eah4rSPGOVJhuzAT0CddALwurd3vtmHlN/+yR8FBuaZ77tPYcODSYWXrx/jbYrh0ouok3n6h4eel6YOsFYxluTmEmx6/VotgOBRDEWLYGiAnAgDcdqiPkuhpqRvqxUvxlYGAOkrs2kJpSzW1GMKnwYcC6cH8PZydAo6AsfABbnAynNcYKctnBO8eOaLFAnZzG2PchyO1kxGKKuQ+2fLMsFyPuBeWNsqpCSt2KLFAwBu/60fRDYY88un235hdqGAbRR+7e1b/Hk36HLtPNEF8DDk6XIITecdpy1DdH9097fwq9FLWFCQcjho8FUEX71/eIR/8OT9ADzl3ywiJsBMshJmx0OpZ1/chdSC5SrCCES0EEWRxqYKmOQlDDiofetagQ++9BQA8MZqH28d7eH2vpfNzKuUe+uHgxWyoIGlCJ5Yavx2cdVf7yLmCXNnUGBeZQzP1lUftC2EQzKsMRn4a7suExRkh5kMa8ShZu/l+80WE5AWecqT/zipMU5q7n1/7vQaarovMrB8/v5GgFNf7m6fIaeFPo418u1+w4NWsgsWQv8dllatdZUxsUWNWpwdjZETbL89LNB2EW7KYL72z9iA3sdTgscbrWAkQcFPBggqgfIFf+9lpnut8TpgIqGUFmnSYkDP42qd8oIVBQZlFfLC2mjF1/jmdI7XT/b5EtzcmWFEjkR5G2FNGvMuzoz77EbiQUTEMycwiBpcyXwv9rgcseZ5WSfsFyyFQ2sUR/vVrULREZU2EWRkYAjORwomCDrXbxCypEYlQ94kxaHmvqxuPQTePUsHww0EkTSWRYrGKEyT/j3djv0m5rQa9j1+q1DrABt6ruZnI9b7R4lGXYYILvSju2sst3IYktJslukzHtAAMBqXMMHzT1S6hH8vx+W4HJfjclyOL9N4T1aqswdTyDSBGDfePxegMNF+VzWKaxw/IMJR2+NCwVrgzb/QG9rf/5bvwn/0K/8FAA/p1CZg15Bgz6AicwbUCoh7SDQvIza510PTs4lrBRs7qDmZZifgpIjGZpDjhuEUK8AVYrRVsRn40dHU//fOlKIBNHkg2NAhWgsOJnfKwR1SxqO8wMLcRGhmCVpi/4pcoabKdB1qFHWE1ZrMzlvJXp8AIALvVgV40kSX+1xdYDu++8f/Ir7+lz/JaRmlA/uYunkE2Qq4HV9RyJMYZqvl7+4MO2Ri8P7DY3S0qHdn2zgmw/ufeOvroI3EatObP3TM2n96ehcvTnzl+Jt5CjHsd9XLIkV+QuSYoYVN9TMCe/OAApdfXAFawNFxBY8iOKqc/tWX7mD70MOzobLY21ozqWN3mPP9y4IGmzbGjODDog0ZcoRwuE4krq24wBtn+yzKb6ugN5rQEuGuxphcmqRwDMlK4XC8HKHqSG/TCmV9IZWEquxZkaKqQgRE0lkXMXboWQqVwTpP2LzdaYnrE39u17MFciK53DvbBkYtBEGOLnAeAga8w1Sl2PC+Y6cDgCl8uPtrf+wvAfDMb3Qm+qlFSHKgxb0pVru9yQWcgFiQ9MPAV8ida5CWHPYAACExlMPQQBvJ6MhF+LRoQkQX8knXecKM2jdO9xAEBpu5f/5P4iGjLo1RCFTncyuhZO+iFkjL1VygLFqjkFOw8d3pGcOsADj39+pwib1kg/PaPxPLJmUDCTWxGGc9kWeZp0wQ7M4N8C2kahMjoHmoLCOuaIV0PhWoUzmolr9ve1DAOYEpBZhHUuNK4ivrWZ3xM9xds26u3N1fMbJRFDGwDKEltbquL7kiPysy5MQOj9IWQWBYNRAFGtO0ghbPf6X6nlxUnXD+f+uQa/Vs1/e6OjjCOtEvgjFw58d/FIDXb978qc9wTxUAU9DPNgMYI9k6bDTsQ7n1KgTI+UcDPpalwwlCB0sWY+FKIsgFh3I76RmvADHrUsUPtWtlH+lmJDu+xIMG9aqXNJjUcbA3pF9gu+9vXmhYOjAdlpwy4j/reOEXI82QblVGZJ/Ww3sXx2BYcTLFMKrx+rveGejVb/ku/sx/+E//AqwLGY6MQ43TGblPrSX00GJEJut6WLMpfBhpVBScHDwN8Xg6wY2xZ1gmoeYJTgqH2XIAs/TXNdvP8YUn/jjaeYKTQ7/4ai2BdYhy1qXUgO3dXGawu7vGgjYPMjZwyh/HKK1QphEc3TcbAhHFlFVjjYY2EGnoe0pdr7TWAWJaOI+LMeZVitNjL3MRgcX1A38uh1tr3oS8udlDqAy7HO3urbFYZXw92ibAg5mHGaW0+MihTz+5kizx944+isE+mflb8cyzOSBLvtb6ya2TslyE0U/nI7SLGOke6W5bhS/d89fxtdULAG0G01EFLRXcsDdu77StIvHG++6CpjWlNouUDkXeN6bf+U++h//8wb//l3jSNbs1nOm1mKNhCT2liLtNAjxMEJ3T5nPfcWC5Vb3pfGMFjJFsLi8luO0CUAIVPdNp0vKzNJuN/SbG9otJF4owXw64VxxEGqUMWa4ymw+eCcaYjHN2Pfvi2QHet3PC1+rWxLNxB6pBaUKOAAylYevE5SbFfJ3x5lkIh+s7foPzdDHGiqDyNKsRxJo3Qvyewvcu6zLELm3YrJPPJCNpK1Fo/xxoKfH2xm8CWqu4Z1u1AXMmAO/I1G1UBABMWkial+o24Dl1djQBqEAJt2rISMPSz+o2xFETwhQ9V+N5HZfw7+W4HJfjclyOy/FlGu/JSjXcqiAzH2MlaOdY1yHcUYIN7cZ2swLDqa+UintjZsi+823f/sx3vfJ3vw9C9FVPu4qxTV6djQ7gOh3ZuO0hu1ZCppp9aoOTkHWksN4xSBIqY0Ow2YGJATUPeo3pqOVqbrNJsFz6napQBL1V/vvbkYMiVMUqANaxK5Ezgh16TmcjPiYYARFb9ie+sbXgc368nMBawcQirRVrAbO4QaMDZuTGgcYrtz1p5+t/+ZO8K86bAdZl/MwuumMCmtQBCmio8p4MShyfejhMmwTygHxXDx1m8wGzKKsiwv5uL8pXyiKhiqipA/7dSAxXE0Uee9jyhCrO2HHg91fefohIGXz22LO37v/p78L7/95fBgAMowbHF7JXDSxsREzMVYCcGJX5NMFgVDFTtTGKwwekcHjr6R7v3pGHeKiJ9DYt8QJpHDt2ed74Y1TSYnfLk6nyOkJdhqhWBHMLh9/QN+j8r2F/d8VknyaPEBAUmoQa86KHxrt7CfhqtIsiO9xeYRZn7Lb0ZDGGPk26X+W9rwFUQQTbKIiKYOLQsk7SGjIp6dy/coUpEavmv3KI4Cs2uDhe/Lkf8McU9OQ1U3nCkaVKe+UEa8DtOkRwgTt4/8/07ZnbP/OD7Lmdl5EPvOhC5yPDpOY0ap/RVwNg4tZ0d+Od0QjBWBUJV/VSuZ5BHHuDhw4KjRLNVWwYaeRljGvb/t7vDxq+xpUOAYJc502Kw3SNu2Pv7BZLjXDiT+7tzR60652NwsDgCcUN1kWIdEgmF9KiBmA71UB04d6WgY+o9GADLASM6zJeW0TK9BF6JsQ4pOxZabAuev2tDAyjL4ESrIWW0iKMLSsdtLS4f+7baDLREANC3ZSFA7j6tU6gWCew5QVq8nM63pOL6uf+2HdhPB7j5l/9DJMV3SKEU0B75Cegd98dQHeB3YFDsOsf+lv/44+wbSAA1EcZp7f4LwKWK+q7WWBIi1KOGOZCL9C2EqMtD6mVkYE5pl7DXEKHDs02TUBaICQaerAREKaHbs1SYkWOSmrc+tQNeBmBsOghXwG0Y+qpOAEzctzTkpHB5tyfs4wNQnLfaWcJnPMSGQA42QwZfmxbhSDorQhXZcKLRqTMM/KDp6sxDigvtGhDDClbUQjnjSJoYimKmAOv3V4NgV6aMFsOICkP1s0jmA310kb+5yXBh0Gk2ci/NQrDrGJIzFzss2mJs1O/6N194QRv3TtgO8bJjSX3JANpcVoOuV/8sV/4HpTk0PT206uUSyv4PimC94ONYAmQjhXKIMIg6/Mrd8lg5N5m2/ecO7zI9czmr73xNrrxpcUBNnWfLKKNZIZs2yoMRhUa6v81x5k3r6f7LvYcL0zZpOT7uapi7t92so0P3PF2dYGwKEhaNsszCOE4nL04HSC54q9PGGqsz+lZX4WQ4xa22zg6Mh0AACsgyAoRANSo5sB1/eENdBnim/7Ft/prstzG9T1/r0dRzWxfYyTqVQxJcjUkuoccBxqtFrj35/v3shviJIahdxBOQJ5FaFMyxrcCcqtnpudNhL2BX+Dvz7YYes5bhb2bc5RdhuomZi6GGLVs1bhpFcbbOb8Lg2HBvcs40NhOCxwk/l0oTYglmYrcHp9joPxxPCnHKE2I3cgfx1ZQoCVXkXFU4sOjx3i39OqC3zq/iifU51WxYVtVayXaTcSZyc4KDsOAFcCot+/M24jvbagMhnHDkPjToy2IjpG/iHz6DQA1bTBKK4SyXxA7mFsEPtQBdJ9aLYAhmdeEvT3ieFBhN8vZqvE8zxBnLQz6Hu/zOt6Ti2o37n/rd/Cu2LTer+/en/wU//zjv+j/fHw8he48PA1w+yd+lCOu3Nhx7/X+n/oUbv61z8CQFVfyKEKlSN867isg20qgVuwK45zgB7YdOe+ARJN1NJfsxxsQ0737e2MFbEiRS63wWlUA1/6RxcN/HzxZy1JAdb2pgfVVLE1+tgww2CVii+zdd5AapKMa9+/5F1jmigPLx4drNG3AL5W5QP7YykosyhTXRn5H/tGDx3iU9x6qnQxnsRjAOcHxd65QTNaC80Hy68d+hxvPBDpXPhs6xB/yVVNdh/5lrrsFRiKd+sno/TsnSFWLzxpvCbgzKDhy7Wgz4kk9DVqoxMDRhF81IcJR35e9kq1g9/21muUZV2ZCACpXfRyeFrxxcULBdv99oJEkLXu6vjg5Q0CwxNF6BJMHkBn1IQf9AliaEP/y4S0AwMFkjUHUYE2kl07qAfiUl7YMPREIgDQCkhAKM9XYGRb40LZHCn7r/CoTw4yRXEW1RYhw0PCGpNHBBYcsX9l1MhTRCCY+VaFlOzox0LCbsCcjCTChTNQKTjnI+37zU+0bBNse+diZ5FhHCR6Sxd/10QINsehefXCFz1NIPCPTMa2EXpHFaC0Rz6WXuwHAbo1bh56I5vZ3OOpNCAe3XwO0+KBUvAk4zzNYK/BuSUHtVYCIqvp/+9a7yHWE335ylY8lJstFayQuCuwu6rmHUQNFet/3TY5RmhCnlV/ATooRezuXJuTFpTIh9uSGA8DrJGD3pnuLbbx6dogbY/9uXRsuIegSrauY3bIA70fezSm4cEzjvQ1ub80wJv3scTHm0PZxXGFVJ3j01P9u10iA0KJgIxlNa4cSVRPChfRerDLesOo8RDBoYUhiI5Xrnx3Ra2nvTM9R6ZCv1/5wg7c3e4xEPM/jsqd6OS7H5bgcl+NyfJnGe7pSBcDJH+ppire/85PP/Oyz/94P8Z+7XbAqBN761Cdx678jNnAtmSF763/9ISDoWYLVvkawoR5TZCHW/eV2ifUpIoBnz3Y78MjAWLCXpg0dOkW6WjjYQEC1BJMq0afKNBLZEcE23+RzXXUHDc/C3pc2FHADy7vYdLvk3shmk7B4fzCpUGxibB36qnCTJ0gJYtwd5tiKC/zGG7cAAEGqmVFZ6wDbaYFF7auZZZNgU5PvqAP3Mq/sLXG2GrCBuZOql0vkAdIjxX1gnYGPSw8cQqrU2irwLMM5OcdcqxjOl8LiYT5lR54PTo9wGPsd/lE2wRfmV/h4pTK4uufP8/HZlOUNiyqFEA7HJxTQvQ655+YCXwkwedQB2UPqYcVARX2wKDSoyghLusbnyQDboYdgr45XnGEJ+JQUSd/3q/duQxNq8LDawnSaM/xe1P1uPgi9LKQLtXeBg6PqMdsu0BiFXz/xPdZ1kfDvaozyFQYAtBLmKMNKE1S+0/Zm+NIhHVW9aURqEZ7644pWAfKb5C8bWY/YdJ7BWcNZv044QDqWdUH1cPLhaI0Fciq9AAAgAElEQVRVkWBB/cvz1aBPUIk1M9ObJoCJFRTl6upEIpx3zldAdbXFaJ+u4yZhdu4r149xj3p6zgnUOmRHKCd6xEYIhzg0HOoQZS0qcl76VXMbo0HFLGKpyJifro9oOv9di7IOmZ0/Saued2AV3lnvPmOi31Vpx0XvNT0Kazwuprg/92zu18Q+o0A3thbQTuKLT7wL2e39c4ZqW624sk6iFsu6z2SGFYyemVRiJ845mKA2Afd2A2Hw0uQUu6m/jq+f7aN60z/7qhZoqR/qigD71zaYldQ2kg6KkB5dKuhaYTDpgxC6kAVnBQZD/987hvELA1+t+4SaoE89eo7H838G/x9H56DS7v3+WP7N/+nTiK7QDP+Wh8/CNT3MU8t2bPIshLxW8kuly+BZyv6wj2bSTcCuI+0m6jED5YDY8sLsZL9wmsTb5hmCXaIlhRPDz2UNqWHCVzMyEqd/lzpeVDFpIZwACP6siggNQXZOS7YKLIsIcdqygXlwIW4sbyKsqgQpvThJ1HKPb56ncAPfjwGA6fYGednF6wGjbb94FW3ICyoATkEBABcbNGPJ0KoqBdpd0hqeBX1QtRawQwN16I9jPKhYE7dpY+wmF/pbQY3bsSd/XI9mOGv8d9xfbeHazrKHobZXOFv6ibVZR975p4NyG+GD3AGYwxo27Pu0ch2g2qM+uOnN5OO4RSMcL4gfGj/BkHYLH5w8xcPFlO0wpbR4Yeph0Ve2T7Ad+cX38/OrWFUxh3BfGa+wILLKrFEeAqfHLD5V0DT5NXUINSjRUC+wbRWTkyT1tPl4NZ4JKsh2PCQ4Smv8gb2HuJf7hWmxXUAf+Qet2rWId/z1VsqiKqN+wbIS413q354MAeVgD/x5R7FmOFw738/d0MSrLzwTybBm6UahYwxejy5IzQIMHtI7OAbe+LPfyRtfR25ggNd/dgtbXQfelaygVk5s+XirMsL+1hprauVkScPyI2slNkWCQ3p2zzcXjOyNgKN3Jhi1GA8q7n2/PDlhvemjYopFmWJITkyxtHic+wUrkobJTp1Nage/ax345xDAm8U+rh/M+V18+3gXYwr9FsJx6yYjrkN3XFAOks5rkpU+FYh+3zQuoTuiktIoTcjwexRo5HTPdCt5A3Lrzgm06y0140ijU5ea1EBFvQxoGNfcctBaMdQs4fDS6ASfm1/3592GSCY1TPj861Qv4d/LcTkux+W4HJfjyzTe05XqB/6Pv4yWoElo8ft+ThYKDYhhqjxRSd0lBqTwMVEAcL4YQrcKGVHbdRkgWBNUuaMhcvK5LVJ84EMP8OrrfpcWTmqYblfcEW86X4XNs8flpPeNALzEpiVpDCQgayKXxA7CAO2ECDCxZfP05tEAwUZAEztVaMBs0+cuOCo5JxCGms3et5OcjbdD5SHHThxvrGQ5TxhrlE2Iq4ce1hlGNWqqSgJp2WheV6EnUGw6Bx7bB7vS6PyPzY2KZT86Mhzd9f+w9+bRlmV3edi3hzPf+U31anpVXVU90BISVgwWgxeQWLaz4jjGZmWFEOKYOFmQGDMIxKAoAiFwa2gCeAoBFkkcGxub2DKLLMwQBgPCMkgNanWru7prfvO78z3z3jt/7H1+51WjlvGy/nCl316rV1fVu+/ec8+w9/59v2+oSglMfUh3vtfiFUWn9YMcHa/AlcSK6re8OVbaXsOZivBlw5fdabuOm9N1TGb297Rm8F9ykoXIoOqrNjNUaiIqmUKAnXKRKs8b8HkDZRusX7dEmSdHBwi4wud3rO9wzAvkxn7nj06uUqADYP1Qp67K+Pk/+SP4qt/8BgCWRLOcxqidb2pRS6oSPE+hWvqQk0Y+YVBvOLalYfCFwt5kQMfc+PH4QUUZm2LFoTZLCrkHa8lQR+Mu/t/sOoZOApMeJBAuAFz1FE0gjFmHosZwAAwoXOUUjTIwZoilXU5C7DdEKMOszKWRnoQ1KldJloVH0Wxq5aEYGbCm+MoYcsuhI1JYE2SPsKIw8EDUD3kJx36FI+2ybhWnlkMnyTHPA6oCs8Jr2xGaQQuDQyedK05J8UwuCMnodjJkZUu+eX68jSOHelS5BOMGo/MOAfBzyl/d6C0xL0+RqQwj56RFFqJ2QRBCKhzNO1RBq1xiw0nG7o6H9OwulpF9lppQ+ECRXO1g3ENZSzwxssYTvqiRuVYN9w0SWRDz+1Jvhq6D/fcmPYLlJdcoaomqIT9q3j6fnn5objCmlTAxZrA/tSjHiRfjKEuwndjq/3DRQVVK6PLRX5Ie/W/w7zCMaWHNm1/7Xa/7Or6ZW2YjgHpYu2BotwjOPRyc6o12hyliB32seEiMUKhTtHZp7ILq+qjVNHyINclCBTFt5TFNb5FXADRQuRaMClu3JW/JiOQnVwzFSBNcY3IB/cA+3HqkgFTQAlz3FdD04wJN8FvUzRF6NWKvTdxoMmX3F10slhFNwuXSx/qWfTgCaYONm6zIZRmQPeB8FdL7e25hLB1rVXYr6stm8xDBWCLbdudOM1ROF+gnJQYO8iprgdSvUSzt+f/0+AL8oct8nSUo1wXWQjvp/OIv/DFc+5I7AKxJeOKMu/fSHrLSg2h0tn6N+TnXo10IhIcS2vXuXv6uh3vuj/3Qhy2bGgBPOeE+3pxBuYlkWQXY7h7huLbnf10Cn1jYHufNo3UoxSmhhAlDcOfXfPSv4pyDsn8/Pw8mNRYzO/klvRzrndZVp64FymaBTGpcP2cza4dBigfLPmRjs1hxqIk9V+JCgThxm780AnxFbFrZK+F5rTOS1hyHU3v8iBTWr9iNSqU4waxlLVGtfKBZiLiBEk3uL4CaQzRZtAJQHQc5RiXqWthNFR62YNQVx/Ge62dLDSMNmLtvq0slbQq8fXu9br6GE9Fco/CqlbEIrtHxC5imRZ4HBJnayZ9j0HEG8uMeuQIZxZH0Mwop94dz6kMeLLqYO40wYwZJUJLZ/uGig7KxKS055DAnqHVZBQT5fvp4k65nWnkYhhn67lnLSw86bFtTSnFKn2GhwtyFg58bzAlmXU7awAJ7/JrcrFQGTAzDfd8u6NowktcwlsCLFe7N7cY3rySdH89T6IQtNBt7Jf3eRrJC6nJXm01Bs7EQXJPUpy4kuWzxocb+UR8Hz7ugAgPoyEBnZyk1j/RIZxG4a7Dv/MQH0NtYPhTG3Iw6TcDcgih8ZcXsjTXZUqBuFs5CYLUIkXvupl9JeBNnkbZVt9UwB8SiJZtoD2BOBsFL20PV7lms41YLqQVQd4Gq76wUpYF2WlpVC0qb0Jdy6EIguOfsxhJNi6h/IqA9UIQZ9xUl6URxgczJfKpS4mgS4di3K3gQVfRwqIUHHteoG3s5AzJgmM5jMN6Kv+NeTkL/KKjaWDJHr+9dsJPHIMrwygNXepQc6aWaPHn9sO2RcW7IznDQXyEKKhRjN6nVrJ3EDHAcJnj7+i0AQPiVNcbO+m1cxOS72vULyJ6mQPDIr3DhKUtoevGTl6A6gHT2g0+9+4fwwvd/C123V7/l2/D499uovDoCuq+6Cb8D6iP7I4XDogvfxQR9fHoJ92d2Vi9LaUO/3Y7/y6+/jFnlAsu9DL/wsk3SGfRSHO/26R5cnsRI3UZC5xJep3xIFrVw6EsgaowXCV0L1O1EG3o19f6yjoZaeCS9qFOJJn783GiOtPSwdFFtqPhDmuSpi0Q0Bgi6RUs804yq+uBY2P63My0pz5ekoUzn4cMIRd16VoMZREN77xS5Z/2gm56qZtQPvfmdDy+mj//j70OxsMfLNxXpcRkzqLQgOdXF/ozINpXiCLgmWZExaHW1ooIva1zq2n73yE/xwtRGB073ehRw7wkNbRiWzqTDABCOK6CMRBKVpN8ORY2paav1g8bfl2uSqgG2f96YPaSFj36S4dqluwDsfdwc/ywL28QXZbXtTQVvEoNky5KPODPQhmHmrlvoV+i42D1jGO4uh1i4a61qQUUH55rev+9nWAtWuLdyCTyao3LIRuxXiLwKw6D1n5aOa3C47BBq0I1ypJMIzi4bImNWX1+dnnkfzXHWUz0bZ+NsnI2zcTY+R+MNXalyT5FAHdLuMptdbVb4ZL0X9AprUA9AxApBqOl1Cz/CnVMm4KfH1R/5MCrnjISakYuRCTVUR5NInylAu4xWM9AWinNws+xUyBq3kmMfOtZgrsdiNCOYx5u1fVJzGACBRt1pd7xV31UXU45yo6KekF559H7LSUzWhuJEgPUMvINmNx3AXHEs260lqlogd/Cb9BViB2H24hx56SF2JgxH0w4Ox7aP8vSFPaoQtWFW4qAadxveZjD6NTYGS+wf2YpO1YKg4c3ekmCn+y9uAcJAjFzKTi7pfKxfnEJwjd85uQIA2I7nlMgxKWLqvR49GMAbSyhXRWUXV2Qmz4Yl9NIjZnh9QePpf2bTVJZHCeRUWlMPWCs4w+x3We3UGLpe4MhPsZv2aed+MZ4So7ppEzSQ7x+cbJOxOgBsO7P0rPIgOy08vt5dUYZn6vvg3BCKICVaiVQZoMxbaQWLa6o85quQmO/at7KQ4Fxb7TZs5aySmE4SazMIAMz25QBrP9iI9eO+zfBtYGijWji8uFCBZYKkPmwlIVJ3Dh4wlEMgP+eqScXIzhCstU8UQkNcW7a2hcuA+oTNePJnvxeAtR8Ug4bx24ZkJH6FjWiJOw7e9LkiqHaaRdamsEkQSlqoc3kSw1tXeHlszeV9qbB05x++ptbBiYPIm7934wLCWSRWobDtCgeTVlqQQ5PkrTvZIMywqnzqtc+KEG8a7QOwVoF3V0PcX1roNhA1LrswiXkZ4qVPXaRzqD0D7qq+eu6jcFB2J8lhNCcThrTwkbvjKGuB0K/ISrFSAvsTW0HXVYDAfa9lFeDufEi2hd04x8plviZhidu3N5FdsMc1XbZJOkZz9Bz/pB/k4BfH2D+2z3g99RFspVBpm8LzqI439KJ667/87of+vvOTzzzUE2oWm7hbUO9PjTtgK4bJjp007/zld+H1xq1van2Cb7z/WcKUX3Z2ao3W1XQU5Nj1PDoatddOSKriMLTw22NqSEf6pQ4RmmQG+HPXx7sMcMMJVRMlQ+V0k6UB4tsezNtsDzRnp2QQmUdJHzJjkCkjD+I6AbSbWBd1jKSfka0gF7ql1wuFWghaEKOkRF23coFI2jdcVAE4M9iMbb8rFBX1P0/yBHktIdwCMOqlFOI8XsVtD2itgOfXiN0CxroG4wM74S/SwE7yp2wLm99bj1Z0HNNehEoYuu5aM4ra0qVA79yCeplmJbGCnUh6m0uUQ4ncbbagGZZX3AKQctI77vbtgtpsHroyx3poNxxKc+wvurg6tAtpqQQ+uW/1s1908Q4tnIFXo5Pk8Nyk1kB3ALDRX6JUgpJ0GANNVKZ2GmrZbq4aFyVtmN2EAJAZtwQ4dy/lmY/6jv0M5QOmV4O7vrvutQtzZSTZKkqhsVyE1BZBxXE6sJxn3OpfYYkzV56yfd9ffuezuP7Ms/CPHOwqAe0WRD+oSJrBmcFiFrXe1Kd6hld+9MPwpxyG2+tWdA106HrdSUWkq1kWYryKKULv7nJI4e60mLqf9YMcN/ddO0IYZLmHjYFd+H77Hc/YiDqAbPya4y2KNkJPcI1e6Eh6zD4jzX1wOgYxKzyCRQdhhiudMV5dWGenRR7guWPr5DQMM8zLVlolOMeDpb3WHa8ktylmADVUROAzizbwvlmsm5QjYxjZpzJuMDiX0bFtuWcTsGk+DSy9Hc/R8Qq8VNnzc3RvSC2qzACs3xLFpNQ2CQpAkqR0LaZ5hHka4qmLdsPwoNuHFBrKtByOR3Wcwb9n42ycjbNxNs7G52i8oSvV1454mCGduh3/UoI7MlFxEOD2O/8ws/CPMt7yz/8nAEB5MYE8sBDJlb/5IciUI5q5/M0N0C4TAMJRTnBeEFSoQ/vn0aUV5mmIzJFUWGIgnQmFCgHnyY3+TaAYcBRDJ30IDVUNqqNRXUyhnJfx9taUsj9PTAeeo+gLrpF9aoDBS/b3xAODg43GOcYK6ZtRla3E43iZQOtTuZdRjgNXtb78wgWY2P77lUtHUJpj1+20x8uYpALSU6hrTjKjSnH4oq2wGs9kITQYAybOHJ/lgqBDVVvT/2Znvns8gHSM1qySBG9qJfD4lX28dLP1mb2xZk0iOucK/Patx9pKPtCEGiwXoXWwaSDNpYRcOajcB7a2Lfw1DFJ0ZY4nE0tyUYZjWtl7LJIVvvqxj+OO85t9YX8Lbz6/S8fRQIKcGZR1CxfmmY+uY0AbWMJQE6i9mkVUcaIQlvTj4LftzSkOTuz51qUg1nfdVWBJTbKIqpDEKteBAc9EyyUqeevXrBiZCijNEXcKrOauihaGSIBGMyipH/KgbZyF3vyR90Bclyj2nDF8xqFd1VPudjE+b7+zmnuAaNEjU3K61nzFUQ40TNhKnKTznlU1x3TPohfhroSRBqtte64eu3pAVdnUOTkljrRTKGnJWwCgGCrNkCftPd/kvl776fdTZerLGoFXE7yvzcPw9INZn5KXjAHqqvGRNohc+8TnNe6vBsSY3+ouW8P7RKEf5Lg/HdDvNaSxtPCJ+a5q62ncEPjEkoMd2u+y2gFdZwDQU58ui0lqdLwSTVbPOE/Q8dvkm8aY4lefe9LKAOu2rdA8I19+/WV0vZwcmw7n18gRapUFBAVPjzqQcY2bhxZSL5aBZQBnZ/Dv/6/GC3/hvfTnnR//AJm43/mr3/GZf+F1xs6PfdD+gRnAQVJiKskGcefHPgilDUGyciEgnSYzj2tUdxOaxNKBpJSU8TwhGQFgNabaPfd8AVS2TYjwBPDnBpVTQciMI3d2cl5cwmiO0JlcPz44wq9+8gl7jBMP6cwttjeOoJ+aQd9yfc2AYf1j7hjXBZZXOeAmLukrXHBxZss8QFn4BDedzBLcPiVXesevfjMA4EI8w4O0T+HanmwTLAZxhsf6x/jdXSs98U5BbJ5QqGhB5KgqAebYooYBaFRLnkJZCijXw4qSkvrgQGsR98WPvYKXpxuInDNQ9WoXe702qP3y5pgWgFIJzJ3zTzGOUAKt/nchKT6o7ih89aXfs+cUBgsV4tXMTh4P0gF25/b9I7/Cv8YOxrldUKRU2FvZn828iqDxzXCJ3bSP48xe4CQoafLvegVyJbFyZvtMavB9BxuPLYvac5NtVnrUly2NJEP0ncvHKJSgzQ8b+1BNQlOowE88oFHH+BqDkYWv89KjCLTIr5CVHkzpJtqcQ0VNYg0DX7Zsd7NeEIS9mEXwwhpy0wXS5x6EO/66r6jfnGznEFy3qUOnFmgtbaA8WQ5GNcm9jGGAk/LowEBfzQAnHUqr1lJQK45unBGbOc0D8Ca6LpPojFro8ul/9j8TXBxGjDbA653CHmNu3yOvJL3/KErhS0UJSF2/wEsHFj7d7Lc8gbT2USiJE5d0lYUloejzIrQSGJoDGC2qpxfwICxt2ETVbPIMuWWFnkIUlMiZ04fnIW0GzdTHqweXaIOvPWDtmtN5dxa4dWA3f2IhoOcJtIPzo15OMPqkjPDidJNaF1UpyWoSAIpJm6BUz33UTf+8YmA1p+jAR3mcwb9n42ycjbNxNs7G52icVaqvM+78t3+06vTK3/tBYkbyjMMMKzBX2fCcQzkNq+rVuPK3PmT/fVjBX6vJsLteq1D3HMM3rqGC1ssVNYOZul3fWgnvXgDuHG2YZnDyR/BTlpnaA3gNhCeOPLDVGod7nkKeCnLIub8aUEaljjX5hS7zAKu7PfDEVadrhpyceAn4myntOrujJSZOA1orjrXBkqqeMK7xtp+3UJk1hrBv8sp8HZXmKJyGsjMqUDmYaDNeIOAtRDWeJUSwYcJAOSE7EwZ+XEI6cbwxVncHWEcgKTTGE7vjrypBAvv1aIkXDq3O8FWso6wFkSnqfo2xqxKkVFjy1iCgE+d46S+9B4AzfvAE3nTlHgBgv9fD7suO2NKpIVx9cTtfw1IF+KXffIs9rlBTNuxk5eFklCCJ7MUrch8HjkU5GizJQGNWRvC4IrLWogjILzgPbARfQ3Rh3CAYO6ebAACzrEsAtspjjU4YkC5y7niZIFv5gEMpRM5ghLsBFxJGAsbljl46N6HPLgsP3LHIj+8PEN+VCBrY2AeqhiwU1jAlh3GfN+ynBMsbzVCmHkHUYiKhXMQgqxhw216LRWTARgV9HnwN5u5bXgGqZi2ZSksIhyAYzVCNHBRccnSiErXXsnU3Xdh7v5uhrAXFpwVRRezo3saS2hsAcHU0xqHL1R3PEqpGY69EodpWiFIeAgfhl1pgEGXou8i1d6x/Cn939mUALDnppNGbFiHGi4Squ2Gc4cHYtUi0zbYdOoOK0+CygSW0ARaR6HdTbJ+3+bgv3D8HTJrYQIbJcZfgWm7amEkdGfAKpEooB5oITYJrgo3ztQp85iHqWQTkK3dehnTP66/cu4E891pnLWOjCwFgmkaQTi9bFNb7u3FHMwao73RIffAojzfkovr0T/4weBSCKYbb39gGG7/pI++hG1VpjtS5EH2m8ONmcKlhmgDtiEF4GsrJLF79xu/Czk98wL7Q04icQD89SlDMfISb9u/5NKRMVsYM/G5BD5UnFE2YWnOU1xT0sbM0q049EB4gnWJfS4bVBYOmkVrHhqz2pNC4fv6IpC03726is+OSaCYxwq6dBJYLCwulX+geYK6pxwlPIQkqxNt24gpP9ZGMYwk2DwtjBid3LMS7ebVN1diKl/jU7jmakIAWwro1WcPHd6+S4xR4259ThQBrnG4KgZJ7xHLc6K4wCOykNS0iVJpjbdSK3kPH+D1MuzRhPrizBtGtSKrxWqj/yo9+GHALyse/+v3074YBUT9H7npHnBmga8/x45f3cb+03/lSOMavn9zAjbfYxffVwzWwVxxOr4GCh9hZs/3XNA9IxlUrTlDzztoEPlfoOLPx3WkPXbcQV0pgeSpwvOQC6UX7Z15wMMVQO5Zv0ClQOrgWKwnWb0T/1l6ysoeMusvhPbCv8+YM6dM5/tSTLwAA7q2G2HN92WF/RQuIWNPY9UeQR84QPjBgmVvkhIBccmhnEDKbDGHWm4xNAMyAuaQhI9p7OjrkdmMAa3QCYQj65J5GcNhChZViqF0OLhhos8a4TZwBABVwLCcxuXk9vn1I0qqs9FCW7fuVpSSIdzGNYTTD4zuWqXoxnmJvYWH6jeECa5HLI/Vy3F8OMHOJO/lJRL37QZxhWfpIpP3en1xdwNBZgN6f9ZEVTbayPSdNutJ2PCdjCMYMLvRneLxnLQYXdUi9y4O0S+zc+SqE1oxCF8KoRNpI9KRCWTHAScj0Zgns2POx1klRVJLmm0goOq6s9NCNm+QZhqrmdH40GI6LU9aPSlB76W3n72E7tPPLzzz3NrCpe15KBrNR0nt04wL86QxqVeAOHu3xhlxUwex/cj3D1b//AwBsD04IgZHbBXpC4cHGvxnfV4V4KCaLcY0wrk+9wM3eSpD3qZwL1GsVEUo66yuSDgzDDLcO13Cxb3uUrx6toczbmKk4KQDbnkM9DhHdaXd2LrkLdWK1avUN+10k17SILGYR/sT52/Qwdh8rELsH/V9nl5G76pP5GtVIYWfdEmzuHQ4xGtpKr6wF+lFO8VqzNKJJnXODSFb0sKQHCZjrvc5WEdnAPVj0kcQFnvtz7wMAvO3nv4f6nFLYak6fiq4yM9czVIwWW6YsWWblKhb/Yk3OSBf6M1RlgMKRQSK/eihAunEx6m0twZnBypGfXjuYbqVEp0d8dQ5f1uh5dqK53j3CwNkKZrWHj9x8MwBg0Elx+OIGaQbRhhrZyl8avHTXVs1i7CEfuHunZjh32fazEq+A5Bq3pran1RA/AGAzWWJ33iPHI8bQbkZy4PY3vJM2dlozsolD1PYgq9L2/syJe4+1AqoxphoaxJ0CB5ldRDgzJKn5ku1X8dLc2swdrTrY2JpBbTgiys0R4ELnxbEP7Rnc+mYrMbv2oWdRuygyr1cCzECxxpazdRBLt7VNbYLt5XrMWL9nAHwqwer2GsHY5woAdGgAxyfQFYdpisyTAOjVRIh7aX+D+vicG+tK5j6PAbThM4WVuA1De+8qw0gqA4A2lKUWuHcwtJ8DQNTtz9483MXvjy/g5ol9eG+erLf6zVN90roUWBstcbVnr/395YAkQJ7QkFxjUdtn9EI4xVFpv+hchhg5LXShBE6mHYyNfRayRQBv4JzLkgyhXyF1PU+lGHyv8Um2RKgm+rBWgvrFxcrHonl+GOB1StqI7qZ9sl9sNkmFQ1xeHG/hk2rbfU/QvVmfSuICbP+5FxbAKUTgUR2Pfq19Ns7G2TgbZ+Ns/Hsy3pCVamdrCRFXiP0Kk4ZNqGzSwjxvsj8ZfAc5Xvvp9yNwfbsit+brBKMZhrjvDN4LD3UpUU3b0yqWDhbdKnBuw1afe3oI4SvqSV65sYtLTnIB2MrkzsxicTtrE7x6YHe3W6M5ODO47/o+JlTIzjkP4pSR8T5Ym9wB2LzWkTO8z6WHX3zhKVzetjthxgzuOYr+O268iI8dXAYAnNweQqYcv/4ffZDep6nqGTe4NjpGXlsY8PJwQj2yOydD3DpcI/Ylqxk533SiAl+0acEdjykclx08/o+/DwDw0l96P576v99Ln9XrZZjfcs4xR22PWfsG2ndOTkuGumNQbdlKu/HbBYBX9jegJgE8119cjDSW2/bn3Sin3usqDazJP2/PVzM+75++F/EVg8xVgW/+yHsIsltO1hF2CzJWr7Ugf99PH2+CvWgriIP1GF7KqPqquprOx6vf9q24+iMfptxYbOeEDFSak4C+4xXoyQKTyN6rRS0IYqwUx3wRW8kJAAgDzxmJNDm8VJFO2hD6eJSSNKs+Cu11cvApA6Dcn8UwR7bycYvbPNVOWCB3vro/9+k3kezEHxTYHs6JrWuuTdBxEOasH2J+1MHVH3ZmJ7EBcxWzOnWUHd8AACAASURBVIggtjKwLVtJ6UJA7jtUQoOCzU2iLfvW9dyMb6AaboFiqAcKrHR92ZRDjZ3370YOc2ifF+1bWU9TrSsm4MWt2QDjBl3ngFTWgioxE1untSZM/IXVFvX/OTfE6N2b96BXHnwncys3FM71bD/xUjjGR6sr6DY5sqpFG5Rm5GoUdCtc7k0wdx7Qq9KnkG/OjZ2vLLCBa/ER3t57BQDgsask1RqGGQ5Vj57BpJ9TtVspgbz0qDo1Hkjmsyp9HMy61ILwhGodrLSkKp4LgyQuKLg+V5Kq29VhYuF8J286Pu5SEhV8TZK6oFPA89ocWcEMilpCqZZL8aiON+SiKpiBYAaLLMD20C02tcTxpIt+r9FpGjIcL1l7mrRi0FpaSAgg2j0AvPIau8Kr/9cPwKzb1/lBjd27Fr6TnQp+UMH/l3aSPDqfEDRZKNvTuNSzC/DuskfWXgbWFSZ0E4FSHKaRdNxLKCau6miYcwVCp32TcUEPcVUJmELg7r6dJONOQTD0/XSAP3/59wEAvxFdx+6895AtH3OQDis4dgd9smU72B/g+o4lRRTLAMP1BcFeweU5nUfGDFa1nexWtY/7ywERfwDQJF94GrriFOXlrYA/eLY1sn/zt/2QvRYCyC4onDtnF6JFFmI1cxPQ1EMwbRdjVjGa0AQzmO7bCfLr3/4bmFQxfugL/iFeO9567gFuzUcoHPxelBL3Fq7xqK09215qYVFlODacU1IclijdBie675yCHCbUQKDNOO26BQD/wf9jXb6WWYDLI9trHXgZAl5jK7IT9KIIcHjfBYWLCKxs+1u3//tvx86PW7i3IdtxZwnIK0bhD3nmk+E9EgUZV+TUA8PQOWc/izODlfGxcJ839zSEg/NhGOlBy6WPO7ONFtuWGlnH3puRm3x1xy3USQXlJlrWrxFFJeZH9l7iyzb2EAYt6SrzrRG/gwyDIwGZuY9KgWKTEe5W9xW9zvMU+CX7XVLnjNX05C9tTaiPrwzDfuZTuyDwanLjUoqjLCXpuZufN6PpK0uh8cTjDzC9bD9nkQXYju1zfCdbx8XulOITw6DA4dx+52GS0aIKAKWWdFxSKIx69vnXhiH2Kmo5TKoYk8rOIRv+An92+BwA4Hs//efAWBt03gkL+i6LNEA+CbF+3h6XL2vM3LNa1AKca+rnVooTVA7FSFbV61p5ULORPlh0MZu63pOnwVJBlxCGQQ7twyBku0jnqY/iOAIcma07WsFjpg0FeITHGfx7Ns7G2TgbZ+NsfI7GG7JSXS5DcB3CGODI/Vvg1QAzJC4HQK412jCK2oJmxEQFbGM+P0Vy2flfP0jG+TwLkdywVZTWHA06W8996A6D+UpbJUslyK0kkhXmeYiXlxbyrSqBc472n1UeAq8m2n/SzUn8PemGMM51yKyVYAZIDx3LVBowd8wysHmwwu3WPaGoqrw3HeCV47cBsG5FkmtMD114q6cBRy65+JY9FEoQJN7tZLh7bCu4tz/xCqZlhJsOsg7DiuQ7q8InT1PJNAZhhs/7p+8FYHfTcdfBkTXH2miOcezkMNcf3r2m284parOE8BURSjyhyBNWpsyynq9Y5CEQGhux/fPPfdmP4przbn3Pm/45ABA0iY0Ct77GVotN1mSVn3pMGvnEMENeSdzLLUSdhCX5CVdKoHiTY03fj+DNGF58X1tpv97Y+alnEHbttS0LSbFet8UaBn6KlyZWslNUkqo5nnHoroKYtXDi68nBjLDG+QBgxj6Fa0MzVJMAzH236NyS0IXAq20OppNaiW5FXrHC120AtTCAp4nE5Pk1MV9nsxjhICdYsaoElJNWmXGAxdy3xCm4YAnm3j9nyC47FnmobEtA2XtCPZWhOHLh2iUDzClXMmbIPEXK9v5oTP9TB6funvRx3oUWjFcxPL+mqjrNg9bXueLw+gVB275UGDupCWMtaz3yKxSqrTIvDmY4zOzzc2B6OFolEK7NMIpSrEI7b6wKH9mJy8rdSDEvQozTiK5b6KpiwTU+f/QAnxdb163fmT+GxNmorXtH9Pr1eEUVNwA8NTrA3eXQHa+B6nLk7ueTeUzXM+yUMAZIXdW8OOi0hhqGAc0cIjS6QYFpZo9xetIBHBGPVRzGMxTtJ4YFrmyd0LHszVwYQ2XD3Rvv6OujY2jDUbFH3/v3DbmoekEFEQgUdzt44Zu/+3Vf9yd/6dsB4KGFFgB0zalnoGsGuH7Wzt+1/ccmjUMNK6yW9gEWQoPHTa5jY01mb6DFKsRu4Yyx4xy+VHDPG5TiFBS+LHwIDty44Cj1ZdA6qfga3E2szFeoT0J4rp9bbZUUVO15CqLb9sUWCKFdj0l4GrXTgF64MMY0jeA5CC+JC8AixghEjaNFQg4zinNi0/7eg4vg3JA2sqokJu5ngySjHmTPz7G77OPKyPZ2JdO4517XdRPsabPz//jXvwmAhSMfe5s9xmXlI5Q1End+9pddWijkk3OUhUfH9dS5Azz3/I79Al9mJUKAXcj4VML4TahA+0jspT2sCh/cyQB0rMCCRoPIkWc+tHt9amIUm/bP2/05FqE9v//qdRKMPtO485ffhcf+ge1be36N1MkZ7qs+FlFAZu/TeUzM2lun3Kpeb2in+wRvF1UwELNWJq4dIVtZVzN6YYGFF9GmzA9qlA1D0wV7A4CMKmjFETkpRa04pscW3gx7BXbWxthzTlJaM4jm+VlKyBlH5a657FTQLoO0nvqUISs9hbKS8BLHhPUU9Mi1QWYeWMXbzWwF0lRnaQDlXHqCboH6VOKR1gz7U5fC4uDRyC1g290FdgN7vLFfYbKKMF9G9NnNqm1gZS6AleHdOhqhmtlrf+T38NSVPfszw8CYwZrb2G0ES3LqunW4RvNJWQrsT7vE+Jeeont4rZPi7mqEjx1Z3sONwREq11d4OdvCCytrvH+c2nZSE8BwUiTE1OfMyoCae8vzFPX4i5UPGdpQAAAIH3jEzSg3FG26RlGKWRHSxss78CAy9x6bCqLXLoydJKd83+kyImvGK5eOILk+ZenI4YsaTJxSTjyi4w25qKpKwFQCYjv7rK87TdJpxpM/+72oSklmBLdfM6nt/NQzCJxuslgGtGDpTNIk5sUlqtSH59I4wrCiVlSaB9B+Be4mLsZshBdgheDDIMXv3rSLQ5CUrbyi5MAlZ/V2EiI4Esgv2Ztb+NqScQD0OznSwgNbOMMHqSlZpNaMemK7hwP4YUXV6CoN2oSJow78fQ/8iSX9rDneJCpQKUE9JytVsb/XD3L0fNsPurcYwOMaLzurtk6c047c4xpHi4TOaS/OKaj5YNwjq8bRYIVh2F7DUNZIBvbv6TyEF1XoOG2dBiNyDGDt8OxF4sBGayqgUonr//D7AQDGDOH5NRGJ5Eyi7js9KPPseXMbqOHGgqLcxmmE7W6b8PFvGlf/F1slMw2w8+2C9qn/7L0ArMHIdNwhe8BqFuDOf/ftf+T3bxZSf8rhucNa7Siwxre3FKi4pntJKU7nOCt8cNFGohkDiCY2UAORMzdYS1LM8wAbiV009hddso+sKwFjGDrutfP9LpGKjK9Ryxb96XYySvhBoig9qDISVcUhHIehKiVpIWuvRjGOiBTIagblCDGhX9PrONcoCg+lexZ0KWwoOmCfTc1o4wgAf/ycDQP/5HgbUmjSerJOuynbHCxR6RYl0Lr1JPbCGvuLJnzcgLPWHrPr5Vg5C80kLpA4LfSNwREqLfCpY8tGWqYBIT0dv0BWe7jWt5XfQdrDRUdwvF8kuL+wqElWeuDMUKwdh6H+5yINkERoSUalB89tuI1hqAoJNnYLrsBDDhNNL/rW0RqkVCS3UaGxaAEsGuJ5ykYOApgvojYKL8nRcxwWnyvMy4B4GVl/BV8o1KtTLjaP6DjrqZ6Ns3E2zsbZOBufo/GGrFTjuISIGYpKkqRDKw69G+HVb/m2z/q7dc0RxwXmB3aHtfOTzyAaNE4jHKgCksqwkhOFHKbd6dWlRNApqB+63V3gaNVWZsowwO0su3FOiRW9IEfXKzBccxViFrTGE1GNemV30sn5JaIrFXxXeQReCyXmlURReDBBY2TdVlsP9YtPApTCA7Zsb7Ba+OArJyNYL1ANBbqusqwrQQzFshZgAPV0pNQEX82KkCpVXyjMspCsz3phgY3Ifq/DtIvQr5C4yg8ADmcOSgwrcr4pa4FlGVD/aXHQITSASYMqlxhPncXbUQ/RsT2Ox/7BD8BzcGNvfYHxJKFKWxcClavseMmR9WpK8aiHNVV98oFE501jPDa0VcOiDElWYAyjyvqPNNzW1my0u3St2v3ua9GQzzSaalcPK9z5r7+T/v3ah56FbEwGpEHunBTlet4yO6ceqlxQ7ipLBYJjd46H2vZiHdSqpcJa31ajh8c9urYHtYDv1Xj5gTWDkJ4ilronFW7ubUC7Si/ZXBHjnDFgdRyj07H3xUbSGqHs7o4oIIGtJKAB5RIkTFIhdQiOyS1j2DReIUFrGqE1A3POJ/24wCDKce+O7ffzpYB2yAOTGlh42N23vcdsTeL22Jatq+MYLOdNqxcA6BxwZshwpIHPGza6YAbrkX1dVntk4gEAMS8xck5M69GKbP7uLofo+xmuDGxb5MDvohfYc5NWPnpBjqPMPgvHaYy5OwexV1GoAmBb7o2d5LFJCKoF7HzTfLemzw0Ay+PEnm9XnRZbbQqTnErU7v6Q0Sk3LADiXAZccNW5stajTbVruEGyM3PXgrepUcsESnGyS1SaY1kLqPLRZ/++IRfVS/0pvMTHiwebKJz/rnci4eUMN37gWQDAy9/9+lFvgmtcumJDlkslaAFZVQG8Xom6sTvLRRt6rgG564gPBqh3NDL3e0+P9nC5Y+UTe1kPz79yAU9fewDAxkUtV23qxQsvXgTcgshWghZH0akIbmPMYDxJ0Onah5EzY/VjAM5fOUYSlDhxljOMmTa4uhLAkT1GHWv4RwKF52QIJaPgZ+9eCHMlI31bFBek9wWAMKja9A/NSNrT9QuCqPbHPSRxQYsZZwZpbR/EReEjK3xKp2HMYM1JnQCg4+z11sIV9tIeuU/dzNp0HFVx3P6v2sVo58c+iOyC69esJIRLCJotQzBh0IntgjYtJfixPY54nyOrJSW23Pn6lgD02IefhTFtSkjXb+G8KGo3A9d++v0PSa12fuyDD0G3V3/4wzBrzi5w5VFfUP9bGMvs/MQH4F+wx19XAlf+zx8EAIi9AHVPQbqEGO0ZKLeIYNH2K437f3P/oGdQuAmT5xxGGLovkDEcefZe0l2FcN/eA8VIoRyVbcSg4vAdrFjVAlFcQrm/57lH+tZoLQMLFBbHzuN3nFAkHaShHjmLS+iFR/IgU/uk/TWxja7TzXQmDDn3bA/ntCjdngxRPjdEx+7dUPYNShdIZCqOYDOlnp8vFSbjxpaJ2Z67W1TUqTZCL8gp2SZPPbz10n1yKNtL+2QHGsgaufLwdMf2WD2uMPDtonpvOaT76EIyRU8W+MTJBfqM0xu0VeXT5nNZ+WQpGIiaXM2ywkfg1fSzRhIGAHUu8Qpbh+e1kG/Tdkku2Dmokd9Mxx3Aya4MNw8FwwOgFsGgl9Jzt8wC1EchvLnT6o4MyYWE0JRONIwzZJUH5d6jrG3L6HTo+6M6zuDfs3E2zsbZOBtn43M03pCV6oNFD8I5dZPEQAAqMrj5HZ89jNzcTTCTMZbnna+u1MTirXKbfeo58/qqz9o+/0KSIFrmVqYxcYGnv8cuUQB1tvIR9XOS2DBmUE9tBbQsLczFHAwLBvLP1DUnE/uqEmCntktFJRGMLNlhb3+IJ3f2sO5YiIerDsYvWpkLC0y7zWIG1VDj3I6FN2erCJljDNfnFczCw/ply3pZFT4lreSlB20Yibw5bx1bZkVIu+B+N0Xk1cSA3IiWuDO30JvgNomjMQhflT5mjkV9feuYPE63Q1uhNtXAqL9C5WAuXyq8+SPvIdiLJz7BWlFcot84I+UBstwjIs7m+hyHxpYvxfkakhvgnq2iHv/+Z/HSu9v7Y7EKcduzMNr1wTECVyn0/YxSe25sH+JtP/89JGHobOGhoRMFOG9hphhuf0Mb3tC0JhoCyfA37fn/+N95zT3KDcF4YVCRGcFs7FuJQ+NGtV4CjRGH1DBOIsVqS1DjDdIBQDklldYeTKThuYqUGVCAOc84audqJDcsnNzc+3UpCKbfGCxR1hLHB/a8+rsedN9+VnYcgykGf+KeQwlUI1dNG5BUQzcSkaaCLhiUM5OAMDY83v0sGOSIQyeNqTxivjIA1bUMpXuevAlvqy/Pev825i9FJXHtopWp7M+7KEtJiAtnbfV1c7GOwLGJ+0mGw7RL5984SRYAzAuJXpDjD6aWobssgzantxbkynRvOcRWvCAU6z+5/Dw+NrbExKy2z1bu5oY3jfYwKe19dph2CX4exTMsSx/KwQ29bkqoz/G0gyLzCH7f7C/xxODQfS+N544vUIXLPQ3lNXmtnNJr6lo8lOs8X4UEBedHEZhoc55NfMo1iWuaA+/vDx8i+jEG6H4GVT76leobclG1LDxAvdqxCwmAV9752RfTZtSDGl63ROU0bKzf9sG4p6FWHlRjoi819TwRamsiDYD1CiS+Qu5gmenzaxQizHOGPPRx545dcEXKIB5z5t17AYy00g4A4GkL/26uz+nmPZrYGTFz7x+GFUUxcU9jkkfU25ivQqjEfXbauhgZycELRi47WjPSqRoOgBnq4QyiHAdOmsC5lRg0CRmTRYwnzrmHFgYPFrbHuZEsEcsStVsQOQylfUim0fEKvH1gLdg+svcWCuW+EM+wFVgG4SemF1FrjtTBrpXiJIlYj1a4V/chRautaxb3TlgQa9jjGlIoFG7iLSqg62wnO2GBvJKYBC7WDu1QgxpcMYK9b/ERLZx5Len8/tp/+CF8tiFmkuL7qrWH5QSn3aYAAF/7md8j7BXE3I2DCuOp688bwCQ1hYUHUYVq10G3kSKI1DjmazNN+kGF3Fn7MWVN142bJJVnSKKzc+2QjsETCnuzHlZTl4zSLfDmbaunHPopnju+AJa2m8HwyP45u1jBSAPDnTwrNLRR9KYCddxA1AbhRoaNnoU+7+2O6H4Uc2ljC10ftSokSgfVBl7eSpEW9tikYwnXXUMLhQ5r1JnEgjsz+UJSbzf0aizHMemVpa/IArAqJKT7rONJF2uDJZarthXSLJaAXUg7vp0vlGG0CQOAg5l9fnpxjpfH62RNqAzHUz2bjlMZAW04Dl0izKIKMSvcc5ZG9FmJLNH1crxS2N7xYhUicX3QfjfF+KiHct8e44NC4u2btwAAT0W7uDnfwDxtuAEgON8kNbauTel7GoA2zoyBrsv9lQ+TCtRuTmHcUO/emDbQ3Whm77lT90Tu+9DZo7+onsG/Z+NsnI2zcTbOxudovCEr1UXqQyCA9g1u/fXPzvZtRkP+YCFQTQMKmg6DCrwpTD0FHtRQL7tswUtVGxvGQOYP1SRExQDWRMRFGswJpk0RgOcM3rwJmjZgD9zuuavs7zhYTXdqCPceyzwg7Vmzg3zLBUt22l32W3F7UD7ktlKmfnscmY9q1FTBHEYAuweWWMSEIfF3vLlEP85w7Bi5944T2p55cYnbxyMUi9bcvnEG2oxb7WYsS9xfDIgBeTAfYf+BhX+/7o//Nv5gdh6/Pb0GwELDDeNxUQeYlBZDXVU+ODPkZLTIA0wdE1gZBsENsZ4DryIT9MkixuEtC3lvXj3BKM5w68AFK6QS3Ok31zsrG4U1dNXd2Mf1D1giGwYMxjCcH1kI+mQVE+FjGGbErnzrz70bgVfjd/7038Brx85PPgNPeeCO18SKf/s97lt/7t3wPU4wY1Z6pMvMeQAIA+GYznUlLTMW1vnGmzpv6zX3c3ev1kuPErjkkkNcTFEa56lccCIIHc47ePvF2wDsddlDj4gmvldjPbBQ6nGRYDyPbeg4AG/BiKkLzeCfCIrG8xYM2Tl3PpSFlQGACw3ONe4fOO/lXIB1WhMHIwzdxzqXWLmKqCwkVu58dOICeekh7zhmqjT0HPPDwELb7tp3+xmhDYJreElpAwkAVJoRA1/5devT6ykcnXTJEEQkNZRrMzBY2LhQ9mdKc3CHomgDCh6/1J1if9WjOeVjJzt46+g+AKCoPTwR7+OxyH63TywuksvTdm9OWcIjP0WmPNIFC64xdeiFUQzhXR/KmZ3odYPnJi0pCrDuYICtLGPHct7utn7eZsBwPOlSBZp0cpwsncOU0DABg2nCDhZWXwwA1anoN+4piyy4882FgSqFJUs+4uMNuah6nobwFL7kiz9J/3b9H70Po16Kf/VnfvAPvX7np55B46nPTnyYWMHM7YM5zyX1fXjBoSON8IZ94DyuUTloqJoFxIYsYYXnxtmDsV4NHLsMxpxB5Az5trMm61VkqA8A82VEtmLMMwTZdcKCHu5ekqNWHM8f2NmpF+eUQ9mLc1SKU0/IaIY7X9dKME6PK3/nQ5QwoQMFb+SMFDRDxysxc3KYqhC0KUiiEvN5BHlif09tFW3yD+wEAgB350Mc3R9A7jjWY2eGA+lSaViNJ7sHqNzMOy4TWji1YZiVduEslUBa+BR8DoDYxMs8QF0LgsQHUU7GBJ++tY2dGzYAIK8l0srDuZGFlB/kI5KCFLVEIGqyq4zXZgRRryofRd0GOr9pY5/com7ELSz6dH8PryzX8eTPfq89V6Vsrf08BlaBskvjBwI3ftAu2vpqBuU2T7dfc33+zK/99VPZsD1wZmjD4EmFza69//YYsJqFtMiWhQQfOvnXXkxMYwQaXlSRPEZXHNItDLwCit2YAthNXJPrVF0J3F/Za3a47IAxg9iF3MdBiV+/ZzdFZSlQH8QkaTKn9g7JbQntWSYuAIgCBIcDdgML2P5tMaghjx0OrYG66ccBgDCQrrcZ9jIyKjEGbVAAgGwWQrheI0s5zMptXiMNZhhid+0jv6L+f5r7GPVSHDorQXOK/at0G2agFIOQmvgLw07a5gtwDW0YzjnmrtKcmLCzSUKuRou1ANdHx1i4zeDuvIfaWAeljWiJSIzw5T0bGL9QIdkg7s17yGN7bl6druGxwQmlHPlCYbJr2y6sZjDc0DUw+yEWI3uOn59to6hl28ZYBKQM2OoscW8ycN+T26SuWROA4ZE0jnGAzyT8WQvnm6wJdADqy/b+k76ClK1bVF1JmIo/dG4f1fGGXFSZS0N4fnyO/s1ojsOjHq78H7aiuP1130nVKSoB4/oppqOst6hbHcRMwHc7/uxSBfgaHUfamc5j1I1vrAHUzY57Dw3GbXQbfb7XutRUI0UEoVDWyFy/b56G6CY5Vm7hqKYhhT0vgoDizJ5e38ers3WMnPvMncMRrm05CZAWWBUJXvgL7wUAfPG/eBe+9BetVORf/qkPPHSejN/q/cJuQQ9Y4NUotaAHIjgSqB2RaKYZsPDIqtELa6yd6is1hKzZMgSLFHY/bXWNv/XXngG+5DNdLeCbPv5fkB0bYKU0AHCy2sAq86EbHW83ReYmo7KQYAwI3K47ryUCt6nprq1wd89WkowbDAYrqgwAUN+n4xdQmmNnYKUGPT9D7sLd95ddfOHmXWg3be5nXXRdeshHJ1ex55x07k0HGMYZchcVyKUmmzzPU1huc8RD5408jkm2gUIgcgvUl/7id2CWhei6ymNV9GjzUCkBX9ZkSVdWErtTSwj6wgt38eur68ju2mPR/QrCyWf8iyuK/6qWIarUg3fgNlDrNSrn9qVCtwC5CkMGNVSjBeYGL91yz5AwEL4mAsxqHJFkBwbAsEQGew5EziiWTuQMdWJa396S0WJvTlkpMg2IE48oCuZCTsgJQgU/rImcpAxDtrKfpWuOeMMiJEUtwDwNfsHej90kx6yRgi08sIwTzyFLfdLVyl6J2SrC+hWrHV2kIXn4AkCWtZ/FhYEQrYTsjw3vAQA+Nt5BJCvsxPY9vmrjd/GR4y8AALwkN2gh64UFxnlCm7XVLMItl64zXQtxFHXwsWNLXDpaJBQGrgpBnAHONW7PRug7KZEnFS30qhYoQkkELV5wzN3i3g9yHM06KN25CzoFkbBefP4S5HrrXlatfCTrKX1es3FZ5QIcFmEDLAG0cSHDqTjNTpwjK3zqR5c5Aw9rQD/6NoWP/rbgbJyNs3E2zsbZ+PdkvCEr1dUiBK9DiL7Bf/ob/yMAYG3QRTco8Mtf8Sy9rsmbZJqRM1J4x4f2DckK9HqFrNcYPFhG28lt2/cxgba7L1jxdNVUpoWAnApg0cgITJv7qRggNfbvuUoq58DA7nyF1ODcENRSRzWxi9c6KfWbXuRbWGYBVczBH8S4I+3PtOKQnsJbf+7dAIDL/RXuzgaf8Twla2nrNKQ5sZqLysKeDYwJaVA7GUQYV8hrDuWCxHtR65T0yf1tqm6rowjG17jz12xPe+fHP/C66SqAzZgEgGUVUG6pFBpCGGh3WqVo5U3dOMdklpAofbqKMHF9n7rmMGXDSAQG2znJLlAzkgfsznsYRDm6zgXKYxqjyPZQa83xiZMLeGpoYeSOV+Clma26b7+y1fo8dwss5hH5q1aFROXOo5QaMq7JJzkPa+opcU9h5Pps5zszVJoT21oKTaJ5X9Yoa4nn//z30rl6+794FwDg1166AZNKyqz0ooqCDgAQu7UKBV78mu/Gjffbez/c9ZBfsD8zpYBMGaomRlYJ65kLF2Di0IvwvoRhoOBwUQMqdtWKZ2B8TdWu4Qy6abm7RJmGUSyPJJzaDeVIwT9yPUjfBae7yqbXzTEfO5azBsoyoHtrazTHloPAd6c9LF14vZQKjBuqliaTDvihM/o44Tbw3rVh5GaGNz+1S+fq3ryPhWPFFmkrSalPhZnrus0NBmwPddO3bYUvXX8FhZb096O6h2VlP+tid4qV+/OsCFEqgYXL+2WhIic2wLYkGrmNZlu12gAAIABJREFUFBrabyu73MGxo605rg+OcZzb83PrZISeC1+fLiJ7HZwBvh5UhMxM8gg3to6wFdrK/vnxOYxnrhcbKugHjncwquB3Svo9zjmWrrr1911bqZkSPUNJN2DtuclLD5wbCk8vPQl1EAH5maPSIzlMyWEEx+xuH/dOw2iihWOv/O0PtWfHWOgJAKqOgbdgKLccuyQXkC4dRnU0+EpCNRPEWEL7bvIO9Cn6voa3ZG1v6RT2aLgBO/JQ99xEOOcILzsCQpLC5wqJZxfLcTchtxjJFBZ9139iBsYwzFyqhn5zitrd9Iwb3PzP302f90W/8J0PQVnNuP6P3gffByVW1KWw8V4AqpJj76hPCxYulmQ3ls8DsFyQS08/yimYua5E6zbVq2hCBv5wXNk7fvWbyWXmrYMCfc+eg76XEdQ0ilJ4QrWJG0KR9CavJYRUtGDNprG1swPs5qf5zr7G0TJp9cSaEQGjLgVGcYaLsZUSeKy9P3yh8MTg8KFIraZ3bC+Cff/HNk+gDMetfStvMIUgx6DVygPvVLS4lUlJEYOdbo7fesczAICv+s1veIhcVitOQdLnOzPcdfreZjQSkuFwiVUYoMzcvZv6JGGoY0XRZnf+m3e5Y7b/K/oK8S2n7XSqsLIJhlhKCg7nZav9zc8pBEeC3KeMr9uwccXgnUjUTjamfQPWBOcUzEaFOUP2qmdIRgMA5bq9fnwlAE8jcJD4/CR52AwfwKUNC9OvSh8P3AYq9CtMJ3ZhyMsQMEDlNqVmKaEdYccGnjOU244nsAjwlqcsQWhZBwhlRWS528XaQykyJC0JK5SZR/ctAHS5s18MF/jt+XW8mtr74P5iQPajp8cqt25I8YZtccRBRU5JsVdhK1rg5tS+xyoLSCrn+TVkc6/PEzxXnycS0zDJqH9rw0Q4bRxRCKxv28+62LX3+W/cfgwAUB9GRCALLq3gr7sYRamQFh49W5USMK4XbeADGmTtaRlazZ8NcTvg1+jGBYqGQBmVqC/VUOmZof7ZOBtn42ycjbNxNtx4Q1aq/q4PEfooBxrjI2f+qYFP/pXvo9fc/sZ3YucnLHHHeBqvOqebt/389+DklRGYIwjd/h/eiSf+if29V/7ie3D9A89CNdp7aciowTuRqIaOIVdyVB1DuzleAef+hPUE3R33oOqWGVyeU5AO1jpZxchzD6GLYytLCTFyIeVpRNXMYhVCnQrWDjpFS46RCjd+5n0omwBmPmwrlVPDsicVwVx+WKPrYtSm8xib6wscnthzJ7xWDF+kEUykiBGdVR4WDn4LoxKLpfNTVQw8aT1yXzs4M8RmPio72Msse7Hn5YgcEeTp/h7uZUN8KrVkmfxUNbfIQgw6GZ2Tbj9DETmf2pXfVsmGYTmPwBsz+bKFf+OOhYV/6c4TACxSMApPifmrgIg6LBPtFjVSWN+0MF/oGMFNFRGuleQ0pCoBU3PsORP3qJvDNIb9QuFrf+fr7XFI4HJ/ilsT2xKoFcdmp82sPM1Y/8bf/VpwWNf8ybiDbj/7wyYSrzMa9OXOX3kX3vytP2Q/KwLUFywQu+ubHsco19zXvC/IOSfcFdZbuTkHovWKZRV/yKnssQ8/S0YNRgA6MGRiogsO4dii9bCGHDuoM2XIk/baBN0ChbuH+dJCz/eO7HkMw4paE5P7/Zbl7BnA01B37QPKAk1GEMXQVVfksKTx0eOr9qtwjXvTAVZzZ4pQCHi9tqJqkA0/qOH1ciLm+Vxhoe3v7FUD3E2HWHMyoxuDI7w0tdfJF4qOXQiNsvAoWm6c+ogSe62HowyRqAhRG3QyIv5xZlzMom1p9KMcA2dw8srROlXTm+tzyzx2KFZVSOwd22drM17gOOugGjvjD2avDQCUuUekpaIWqErZuijlHgLnNpVu2GvWmNmYQJPsT1dtpGBdC2Slh8/fshD7Ud7Bzdtb0NkpxuAjOt6Qi2rd19ChhvE1PAdbVOMQ1376/Vgf2n7C8aQL6RZHIRWu/n0bHt3pCPjbK7pJAeDTf7GdtOpYU7KGSjQlu9Q9TTo9f8rBNFAnrveSMtz7lJ2c5YJBXyzB566fWzNUR/bByV0fF1dtXy8OS2zHdvK+0hnjVz71JADg0oUTeELBc8kXD2Z9MtUuC2vJR/aMzOCxD9temhrUhNucvzjHZBkTO68TFQT3hlGJ6TJuEyZqjjddsA/H79cXcG40x96RfVAXWYCeW4wnixj+qcmIfwbYuRn7iy7B0j5XOErtxSh8iePUQnuRV+O33vEMbvzM+wBY1m7uWLD5vS5WfkLQYDDM0XWm+VUpkQztxJfnHqKwIuelzK/pe1ZuQW5gtEGYoeO1x/+J/Qs0YWAmoZunqWKYuF6U0gy15jQhLZchpIMfcRQAwrrVAHbBIi2zEvjonSsAgJ2NCX75K57FV/yK7T9zZsiNp+nLNeNvv+3vPfT3K3/rszs6AVY6dfsb3knnaud//xu482wr43nin3wfspXrNU4kgmnLzm1CrF9+77fiyt/8EIW9M6kp2FynEjs/+QyFS0gjCO5l2kHADgY00tB78qUgDS+vrI63cFC2kBqe21iouYSJNclSykJSi2Pnf/tA29P7/9h782jLsrM+7Lf3PuOd75tq7qrqqu5GaiEhkEQImGUtGYizQMEEszDEJsuAgQTHQVgKNBGSAY2twTgEELYhMYYlvIxBYJtBLITNrAhLanWru6u75qpXb77zPdMe8sf+znde9QA46X/K/fZavbqq3n33nnvOHr7hNwBAKRlpDye4zQIA6Gio+ppLhev7/qALQ4OiCCCpJ6ySgufu8cGUudHLPMJ6f84o8K/f+DS2tV8Hn9h5EMN4iVndOy1TbmNE0qBFc3O5SOAsYKntIgPL/q83xgPcHDf4hzjUTYXdCV6rOg9RGoUb9Fopm/c4qNpQyjY9VtOCJorUYzdPo9ddQnb9TRfKwWSN21T9WdZ6d5lajnGlv2A50OjYDGPZaYJW5dCmz3IA972X+y0ssgB/vH/Rf1Ymke4qmOLeV1R6WR6qtmWA1CAdZrxIZa+C20wwfZwg9gMHPSBbqK7jBZsXIcp59ILZ3fkf/yAQgV1B9FoFMSOhhlTz4nZSwgX+5wCgO5INtKueQ7QZNfqqGtz3kaXXaJ2R7dzaqQluzQ+BjKjHdGtrCMxDNm02xwu0ew3H1FoJQwtH7kcwbcqgC4kHX+n7SLcnfWS7LQTkCLN7kAKkA3rm5AH2PnUC5ox/j+P3HbD7zPpwhtEiZYecQBkm0dcAK8D3IENl8A1/+D0AgH/z5T+F1/z62wF4IJSUAevzfvbSfRycbHX1Xff+3M+/B9F1OnBPVwhIYhBrhadJUAYUBJY5sv3ekg9slTr00xwVXWMaauyQcbIuAqTDEhdXPB3JOsGByqRMPG1jnzi4sYMLmw2hrgwI4ZBnEW8mcI04R+vCGJPr/buk2hxJCj7+5qZqUo9Z4T/rdRs3cXnq+2off+OH73rNq976YSg69ycPGoQziXP/h7eFS/YkAkq0H3/0+/h3ar3h+GZDeanHxfd/CDpt83eTplHedMpr9QI++5RSsKiAyxWvGWEFoD3HFQBMJaAIXwABqExwxis1GOQmKyDZp3W35iCsgCBgUXR+xlq3U7R8IFs/i7K5/s7GAguipEALyNjAhsTzDlzTZxcOYVKxheH2tRUICqovrOyjtIoNx4VwLPQRSYOsaFxgsirAtbGvKPya+CI8s++z0TSqUBnFAiihNDjd9z3MzUWfsQtBqFEVAVdObB4AtJ/MwwQuNdxXrihjBIBhf4GQKFdvvPhZ/NatV6BFvfqiarJKrSW0lgzeMpXkfUMFXqe35mnnWcSSkUI2Fo7OCXRaOd//eR6jqI3frUTYrljSUQYW01GL/1xnqiI2cEZy4OWkQ5VJ2ODFA+17ZRz1VI/G0TgaR+NoHI2XaLwsM9XWSgbVsrBWMNWhmMdA26IOoLFRcMRxGC37mu/9MJ75ie/DCw2b+BJvjf4VcwV7zEePUjqWL6vOFsAk5L5stC9RdZsITZaCVXaEAeJ96jG1HMoNzb9XaoU48D/bOuhxVOkKhZMXdplC4soAvdRnjgLA5uV1JMfIPHkRNOjllsXTl7xkWdgvIErJvdJT9+1zmevm7VWIDQNF2W6sDBJCISdKY1FEWJAg+Nlj+3jjxiUAwKXFBu4sfTksDSrsZy1cn/io/is+/jZskGTctb0VZNMIi1FKz2uJJ7/9nS94z6/97R/EhVo6sJQssgABhMMcikqa51YOWCLu+s4Kl7mPD2dYSRbYnPvr8lly8yzmiwSTtr+OUBlsEJ0nTStsr0+wufQNxnAUoBoQjWaQ46ENr6pUWoWDvS6LLlz5lke4Bz+dpQiW3rgAAOxDcza/PjzO/8K7cfVbH2EVpY9/4rUvagDhFJCREbnKpSfh0++VPcfz6sIHPgQ9pGxrK8AzP/QWFKTiJeeKr9GcCoBpxMh1EwGuX1NjAN0jmbnMyxfWcwlo3GxcbCGMYAS9sIKzXaGFL/fWmVTquAcqHLA87vjPLnSwJNQeBQanyEfX3C8wv9Hj9oo5hB7OlnEDRdUKcj9EPKcPs42xeX66QpJUGFOvUZYSOfUWP5ef5H0CAL741C1+FtemK4zA7bS9oEHdz718sMa/U1TeZKF2L9qbt1GRmcQkTxDV7YG9Nn1ZugeZhBtSVYn9kv17tpMSGc3Vogpw/8ALxlxbrsI6oKB+6yDNMSNkepGHcAAEwa9XV+dYFiG/73TWgibRC9nS3K5xziORAd9TLXWABWW7dfULANqtAkpa6JT66WjM24VwnCFney0gtMwoUKGGOW5hs3sf/fuyPFS1FnBaPg/Ace5fvofLb24ashn44fHZFzlQAQCRhZMCgviocieGvOUnUbV6qPyrJBA6Ls86Cd5YAaC4kAMTkvnrOui8BnwIXz6kZv50q4uZ8WAh0Stx8QEPduqGOfpRjv+46/sVK8MFtp7yHEqsFXCBhaaeh+xUcLXxc1c3tnV5ACQG59f9Qq1sY+XVXVkgOaYxrc3TdYCdqS+Z9lo5iiJkJaPRMsWnRl4B5iBv4eYVv+O3jy2wnMXcpzLdJS98ZwVQSIh2XUJ98VsOAMe+yHNFA2m5z3NjZwVSOrSoJDYtEgY+Ma0HwN6sjawKsbdLLjuBhaxBNLRZ1ObjqMAcPsADTOrIy8QOEUkAxnGFJ26f8P9eSW9yfWgqHe7Bv9i4+L4P4dn/jQ5OAbziV96JbE7POnrxElm27ljmz0YO0YFksIn/N///5x7K537iAxADmrejgMuKNg+8wyAFiul6A9TKFxHfKzlNEMwFtyqcbMzPAe++VB8UKgfKISnpxECwkKywZCNAUVzhAvBh++zb3oJz/+K9CKhcWGqFS3f8nHY3WlAOMF36vLbGuX9J4C0nmo27V0KtGFYeMtOQsQWilAikxWzs57Qy4OsNIgPnBHMstZOIiF5lnOBSf1YQ97KWS4wq7ptubg/gjETnjJ+PF1b2sEcUnZOdKS4f+OBMtTSStGQd78k85ZKpCix0pZgfmvYq7O/5edsfLvDZTR8QW+vLsye6fq5Oy5j55s4JQLi7AseTA4/L0FZiy/QgqM+cJBVrhmfjBHu1nrdwCNsVFJWow9CwilQaVWhHJdvatcOSKYCP3z7Jh68oBWRP4xjJg06zBDkiiOre76kelX+PxtE4GkfjaByNl2i8LDNVZySskTj/C++Gq02blYUQYL1SlVZMOzn/Tz4It+Ijx+B2jGpgmNgeHip3ia4CjGC3DHc8Z+oAphFkTZc4UIimAov7CGmYOrSvE6ApAJarCu1Tvsy4nMWw9JjCTEH2ShZFP9Gfs9D8rXGfgSxb064Xk9/yUff+KGKyfXglQbFhYMl4O8gBfd6/36m1CQsYVMsQEMDT14kysggghzWAQWC5SBBROWg8T3GK3Fpu7KxASMslH20lHrvmI+hWt0Cbys6nB2MMjmV4hkpkk2mbI/5qGQKh5ZJbv5Xhm//47wEArk5XsLPnM7ar3/oIADB1QFvJxPsHTuzchZS8s9/H4SEOSCM49BSGGq3opGsE+gsFU0kMTvn3P9Uao0MooMop7M46OHbKCw5YJ1jlKAk1I4eD0KAqA1z5W4/wZ5//cQ8ceiGHpIvv9aXsGhkOAFe/5RF86W/9ACNwa0Pvw+PszzwKAEinASNmTS6gW64ph0pAkYD8uf/zAwjWCLy2mQIxuH2g10tgTujzUsJFFi3SeW3FFWbLQ4jjGkirHHSrUVSCcAxsE0uFcCL5O9kQ7HQjtIDuWiiqlgQzAUk/s+LujPza3/kBRuHny4if//l//EGPIqb3MC1AjKmkaQRst3Gz0TJoUonAwaGmUkksspjR19FKxoj5KNIMTAKAZw/W0Kd2CgDWXY4C41sy9NqzvRGe2PHrJ0w0ykWEhwfeG3VcpXj92nUAwAPpNv7p1AtfFzLEchYjozaGUpYzQqO9GlptSH/z6jrCAbV1hENYizFUAWaLpFmDRjF1UIYGqysL9hPeXbQxyf0+MZq0YfZirg7MRcr7l7ACcljw/RCHns2w3WgCA15847AAxhf2PTPg8sEaZreIwih9JWjz2ho/JzW890u/wMv0UDVGwhmJKNHMw2wlJRZZzJyzsgi5XGvbBoLKsUL7CVDz6vSpgpF6bhz5hToj2H+/ghkRFWEhEc6o5xM5L8VGPUnMQ+51OQnACDZ79vxHUn15YAatJVZ7/mCa5THmoll8O7RwaheRGmxrYgtHiEoxDRDvKFQdUkcaNDJiW/t9YNMvsM6uQDFwMGdo400MLCGU5WaC41+8zQtne9Jlnl2rVWB60EZr4IOCblSg6vjrlcKxfdS8jLGsIpRUJkrShr+pEgMpLdoks1gZhQPqRc2yhEuA9RiRIXqkDCaEQtze7SMIDXRA/WItce1/+EEAXi2rVrMSd8iknQ4AVyq2YBPw5tg1jaYX5NxLe/zgJD7/9e/ka3jz738vntr25cjxuA278HOgtb5gl5t6/Hl2g6w09BwK7yuH29h93L//0+94fgtCkUtQNVYsTq8KAZM6tM/4MmA7Lps5UjaGCO0LE8wnKdSWn6t6oBmN7lIDWIHlrg/Czj50i1Wrro/Xm6CxZb0MHR2ILjVI+37uZDJBHjmEByRrVwn+nk4ALhQwJKUorISl+f7MDz6/b1yL3ItDCF9I7zIj6bkdPz7GPj0LWQIlcbkvnN5FZRTujPw96LQaQX2lvBnAYT63bNXuMxKFUHywaquwKP26293uIyE0bjcpWDYS8IHWf3XSH5z7RQuv7G1hu/CfHUuNrxt8BgCQuxAFyWRWixCiUHCELwhTjQEdWv04x/a8w5Svhx68jVnpn9nuqMv/Xv+/5mhHQcObLrSCsQK7iza/xtjm0Fb53aV4ve7/ErYqXo+BsizZCADjZcr7qFIeJVwj3ENlsF/6MrcUjoM6vaKhpINLG7F9M4phs3sf/fuyPFTdQQyXxTBZwtJpbl3cZUUklGNeVrQT8GZhEi+p5gLqc2zG0MfIx3QpEZ+f8YSa7Xa4L6iVgtDNplIOLGfJCC2yM03ErGYKxrdKoGYSq6/eBeBdak6uTJHTop3NU44Y14YzzKcUBSYW0ILl3oJuBU1WdWXPwsWNLJyoBNLPk09kxwEPUkProkZb2sYto0g5+q/WNW5vriAiUroxknVPZ0ULcOD+6GSZcg/otadvIdcke+gknrx1HL2u3zCWecSarGudKaZ5zFScRRbjU3/93fz8vvb3/z4A4I2/+/0YLVM457/bQRnyve/3l1hpL1l3+F992Uf499VSQjWJhu9xU9863pPckzQxkJ+02Fr4+7rUEfdXt6ddHB7Xx0MUB/5wj7cDlORL63nBwMMfewcAYLW9xH/8az6rvPDRd+HyN//QXe/zzCMvDEDKTAicyl7wZwD4sK8Gh2hLViGcCpY0TMOKrfwgHFSX6AxOIEorSBI3COchilVaFwK+j0/9/6ceu4/5rEgMa72WGxpypuCofyvCpm+PUkJ2KlTknRluRQhqsJDwHqp14lO1G1GUB3/0wxwgPPODb8HZjzzKWZSshLcmBAAJ9E7MuA/5wGAX2y2fAclQME5gf9HCdJ7yXO2tFHcdRK24bNxWyvAuO72yCrj6UFUKsxmJmDiBla5fMxutGaRweGrX+/1+7s5JnF7xtJnT7THmJoamaE7C4anC993/7c6rsZjQIaUl0KmYA54tYu5DVkZhrbVk3rS2kjPOrB1iSiArk3s3nhn9fW04YznAbBmjUBbrfb8uWlGF0YIs7YheVFG/Ww4LrBHFSEnL69E5gVZSsH1kVSnu0db/X6dAujIK/+4zr/bvMQ5genWkL2CNAMbk3rRWwBXi7mDpHh1HPdWjcTSOxtE4GkfjJRovy0xVFQJSCKhMwFFcofvU06zdSyrJ5apwJtg5w7S8tFldTtXHSiiKwN1x35etI0sYAUdi+cIIRmEKR240RaM6IlL/HqpbQSoLs19nDQLbt31pVSYam7rXsO8BGCK876JBW6qWhjWCDX8dwHSD1Yf2PaLwqi//VAODBRmFh8MCfVI/KbTCdJoyDej6d7yN+3aQXoJOk1kA9mLIDVJN2U4gHDCrfCYX9Eq85owXlBgXKZeMCxPg4TN3MIh8JDyvYvYqNVZCyQhFLYhRBGzy/dQ3vAP7mS/ZlTqAc4JLyk//9z+Msz/nRegfvnAbgyh7nuIQAEQXpqiepqxeAFf/l+/HuZ/wWU9+sYDaIrm30FMbbu7UovlgdRhjJM79i/eygbhxAtf/3lsBeFN7RdmRzr2w/9lV33vNdCMUECcVLv7Sj0HvkvxdanH925/v1PONf/TduNjex0e/+Wee9zMA+MrfeSs6bf8988+3uXxXdR2kFti95LM229WoPQGCTAFDPyfacYm9Z9dx5Ud9WfncT32A/XBlbCASoP2kv+7FGcs9WjUOGtGGhXezqTuPYhlDJ9STnEiUseF1onsWNiRk7VKg6jpWOXKBQziq1x3w5NubzF0Y0ZQPN0qAMivZq1CUAWZjPy+2bq4gpNfZ0KEkakwZRzh+YgRN1ZftSZf7k8ZIhIHCsO0zuFke889acYm1zgJ7c1LJMgJ1PUB1K87gljpCpAxOD3x2emfaY5zDDTdEoddxX9fPg41khmcyn9GuJ3N89cOfBwDsF210w4LpNuMyxbUDTzuLlIGSFgkhba0UOCAnmqyI2BRCBBYuU5AkXqONuksNrZMUaId+khQmYKrM0np2QbDqs9/D9K5QWiyosiYF/ceKagpO1VUPhTA07Hm81BFCchoSDixkYbrGo5oHNFnveHUx3PuJKoRz7t4vYv8lx3Q6Rb/fx/l/9C7IJKHTxv+sWtE4dnqEOZUtlbRYEiDDbSZeFQYNBy4kZ3vddkjv8z2r2sbo4A6BYrTgsvHhflOwFCg3NLrrfgEr4TDe8+WkwdocUlpMpn6xWCOYtpCk3sVE7lLJpBSwZ/0C6LZzBFT6LLVCFBgckDvHYLDg9wtCjTA0XJLVVQBDoJT2+hILKmHK2EBIQNJiieMGrGGd57OJ6/61+kSjQ+u0gCgUA1jWzh/geGdGvycQUDPt5rQPbRQ6RHlRopE9c05gWYUM/CmqEEsqTcqwscBotQpUVcD9HKMVl+wFvGn5wZZ/FocPq6/+vf8VV3Y8haEaJbj+XW/F4XH/R9/l7wf1h2sdYxgBUPAD69V5YtJhtlYwRevCR9/FvbmN3hzWCXYkCaTFSuoDiUyHWFYhDmYU4FQK/a7/WWUUHlzd5fvWj3L832/4WQDA637jEWzQ5g/4nnJdbq+qAOZZP5dULqAKz08FvDynSw71o2lTXFmbYTJrwVBpeOXEhA+e2eTu0qCaK9hVsiLcjrg3LbUH2dXvKUuBYFmDkYB8wyJY1JQyML4gO2l8ibfGDXQqXxYE4CZRY15O3M1wTMGvIis4kF6zEQ1mIWnUygBwm0II5wF89O+mVGyrFkYag07Gc3BZRJjO/HdXgYEumvxDhhaWsBJyWGBIJdJekqPQAT/rSiskpGqUlyGME6xyNFmkrKU9mrTR6zZUpb92+hK+dfgnAIB/NX49/v2NV9L7F7gz6nFvs9RBo9QFMFBpttcmdxh/744dm/DBHwUaxkruixc64EB3Z9qBEI4BTmudBSStqNIqbjvlZejXMu0N1kpMrxIocK3AxRO7+BsnPg0AuFGs4vNTX+a+Ph7iAZrTX7P6BC7nG3hs4kGM2/MurAPMssDnvumDmEwm6PV6uBfHUfn3aByNo3E0jsbReInGy7L8Gx0IqFggO26baDc2yMoQa9Rgv7G1wibl0SHnBJMA8b5iDVVhgOVNX+pcxBZqplhX1gZAcMy/0FQKvZ7PKkutEKsmE1bKYrjus7miCtBODE6TN+SNzVXOJEtloDZjj1YFYFcruInPWqdGQFCI1O8tMEgz7N0a0HXFDGhyTqCTFDCRf/HeXheStGcX223IGnUYawgBrPd8RlQahREhJY1WiOIK+hzdhEpB7FPJdFBB9Er+rtpIjnbHRYrjbY9CnC8TVHnAmWWoDELKtBMSi69RlGURcNStAsOOGIG0iNOCxbzHiwjDVX+9n/naH8Nzx9f/wf8EAPjtv/qTbNL+mW96+12vufhLP4YhXfup7gRZd4abI38frRUYEJL3YNJGlQfItX+GdRkY8DSaWlVnl7LQIvf3J20VTNnZHXVh8gAdEvfvpAXrE4fKYFb67Lwb5bg0XucSuHMJK9MkUQVtJZf3pLQoWk3xSRWCS761xi/gTeFrCs1k2ka/t0Ae+0x1NOqwbi+sQNQp2U9VLQVSQgkHS4Cq/MjXrTeQIIcZGzm4WkM48NfBxtVBQxkSlfDZM1VjorhisY90uODvmY8TwBwCYinX6MYigJMWRa8BSdU0FDjBakVFEdzl3oRCwhE+qDQhjh/bwbjw2els0RgfKGXhrGWYE20ZAAAgAElEQVQKWVmECNcyehZgilEaVsiqgMF9y2XM2W67k6OqAkxq4Qkj+JmtDWdsQA8ADyZbWNqmTVBnjp2owLHBjOdPVkT+vgAIOyXKwv+5t7bAbJoy2Gmex16nGt5RKjqEnr+/v485zeG85VWfUqq4STjMCZiXVwFfb6As5lmMjChH1ko2ERF3EtxK+/ioeb2/B1XzPd587nNoUW/i4fg2Prs4g288/mcAgE/O7sfTkw1oe+/Tal6Wh+onf+R/Rq/Xw/kf/yAEoT6dFpjaFqa7hOoTDqoWxm87KEKlBQsJOCBfp35C7Jj+EI4C6NTBnfQTI460txkD0B005R1jJNpJCRP7zz4zHDPEXUqHyTxF2PcT/+ypPRwsCPYvLeaujdXz/sAdPbWCK2/x9IyzP/MoH4gHOz2MozYEwdXjSKOiBXZmSCLehBQerswxi2rB8aZw4VVZChaaj5ThHspwOMP21gCCRO6FEexOIkMLpyWLjMeRRkJc2lZY4oltz9sbdpcokoDLY7MswZwOdykd+u2MA4FOO2+QhmXA4uO59eow/Y4vo/WGS2SHNqfnjl/9ip/kP7/QoQv4Uusg9RvmpEwQycboPA41bzhGe6UkHBLRr0cUal82BiAjA7mZcFCg5ym21yiQ65cQgWU0p3Xg6w8Cgzsk4L4jOkiiCidI+eba1mpTXu8uECqDHUKjVmXAdK8qBmwoeY4cHte/42143d/1nNhsLcT4i9C45wCMmLVOeAWqmscbNtJ+y5MOVb/eTdGYksPPCX3ocBfmEF3IeDlCwNPVkn6BTtpspnUJ9mDagr1NBhc1ZecQDoExCS3jFbhqDIEDgkNl+fowEAL+oKF5JXsGhrjjtbLXIPbP/o7qMZJcKQujLLIZ9b5zxc89SDViamHsTDqspgR44+06uBIATg4n2Jr4Z5pEhgMobRTioFGK+vCTb2IzibwKcGHoVc2+qHcLSxvh97Ye8M8o1CjogBTSoUe/M52lgAMHRsZIbiGlSQXrBNvHAUAn8Ne/5brIq4Dv/zhLGPHcSQustHzCsbtoo8yDhj9bKP98aAgBNkWX0nKS8cx8AyuEofgV/SXYKzr4Y3sBAPDJO2cxGbVhs0Ow/Ht0HJV/j8bROBpH42gcjZdovCwz1XqEU8mi33q9ugtV+9oHr7M25+0nj3HZKdkMEC7AmasTAuQGhmzdoXtNYEllu3zNICIE8bSUWDnhVYesFWiFFYMF9pdtzlCKLIQzEsvE//3M2g6rJt2Z9ho9WADnx+9mRG44yFGNG0K2TDWX1OazhDmxz8yPYbg6Y6umw8ooUjlYUmGqWhZlZDClkm+clCwwno8SBJ0KyefpmlccNJXe3CSCS5qstp/mjaqMMpw1zLIYQgB9IozOAFRkw+ec/+9+0h2OpMEVMuiuyoBRmUUeIQg1R/w1r/D/zzh/bJ91S0ujUEmFLmUif/BV78dXfcIjZIX0KMu6hHp4fO6Qbdu5n/wAVCYg3CGecy2QYCTEQYhxrZ/QLdgQoNPLGp1UadGPc9yeeNCVCpr7uyhDOBdxtpvZmFV2dBHAhJLnSI1OrkfVqYFzAHZjVFRtCFZy1klmqy7Keqq+QVULVVk0YXnkAWS1N6xeNkYNwgLBQrDakm5buJa/3jOn9yGF4zLh3l6X1a7CqQSiWoVJAMrzGQHALANIyqYFXUqNuHbwptqAn98LWhfBWMENDAtl2N0EcrXge/r47RMM1kqGOVLivbajCoOVjIUWrl0+BkGZqgoOZfehgQOY62oBzhCHrQzb0y4/Uykr/nMSl2hRBeTK5hrsLERO3Gi7XuIyvf9jmyfxyuNbXCqOA4PuMV95WhYRiz1IWetENYPbP/T3BVV+Pr15Gjlp+srIQCnLer/ONXaNZ/sHuEqWdvNlDBUaNquotGz0zkNfeamrO2utBaOVt7Murkw8QHCQZNhdtJv9YEHVnD/HY/leGS9L9G+NLLv/Qx9Eske0jY5DeaLC/We9u8iJ1hSXaQJs3RkyAjQYB1C5QEA8fKGBeEKl4FAAsnHWgAPK1ZrD4BrSvBVAYNm5xCyanmFvbYFT/QmeeuIMAKBzeup9DUFKUFawgpMpFAYrC/puaSM2Pou8VyR93PrGlBecNhJSOhzv+R7unUkPy7lfVIPBAqPbfuNO1jKURQBLfeVwFCAmb0sngeyEZdpF+4ZiakXZdzAnC9x3/ACALwPVpdso1NyHMVphY3XaiBYYhd1N2q2NwIMPbOJk2wchm4s+duY+wCmqgE3Px/MUWis2eI4Dw44bRRHiyjf/EM7/gheNSNslS79J6Ri1W6sifeXv+ANnmsdYbS/53hgjGVWphMNnv84bor/pE2/BJE/QT/y1dMMck5JcfPYGcOTxahKHwfkRRjvUfHQC0TYp7pBgfOshvzHO58ldm3Qd5HXaOe7rj3F97Kk9xgne+ILAYEalZgC4/j82XrMPf+wdODsc4cmbPqhJn0jRvU5zJxIo+xQQOCDbcI3E4Mm8QchW3pC6Fkw4HHhCC5b1dNrPfVZuMKKhR1TSC6bU5uCJgVgSXmF9CecEymXIr617/MIAloLecCThFFCt0UFaSKaJOekVnEJyVDFacTAQhIZ7i7pUWBku+IDZu7wCRYe0kBa6DFgac6W3QEn3WBsFbSQfgvk44e+Z9IumTEyGDjXSVh9C5vZaOUrdUFv866kkG1bokcvT9dEQi80u9yghfJsAAFqdAp2kYGT2ie4UPTJEf3bcOOJMFimqMoDd8+taWPJiBqD7BiLVkHuEgVCA7Wj+LgB43yizkO9d7bEKANks8W0eep2UjlXDNjpzHGQtDhLu6x5gSutCO8n0oGwZQUjH618qi26rgFkW+OzfvLfRvy/LTPVVv/RByDSBMnHDP42BqFOiS5J0txd9HBANJWyVDKl3gYJJvKILAKQHDv/pIy+sgnPxvR9i6TSbNC41MtEQ0iufAD6rrPmx090O5tf6nP0aI+8y/HVaeucTAJCO1XIWwWE9VgG5G0Ke8QduEmi8YeMGAODfPfaFCFKNW9ofYFWl4GiDK9rBXT3ClcEC85jMjA86KMnarFzTUDMFS1KH5cBB05/DkwsEArh+yy9yFTfyhlWkfP+FrnE8b3HWeXizgQUuXT2Ok6/yh2ocaO69tuKS+0G2LZCVIdMK9vbafI9F3QOkn2WLiKUDrQM0AbzO/+K7oZRFv+v/Ph61Mb5Jh3unQruXY4V6VUlQ4Rv/6LsBAKuJw33tEfYLP0dGRYs3iDjWmBPtRCYGG505NjoeQPX05ZOo7qPNq1CQc4WCHGFUYGBpwxTC8QGbRhV+7a/8BJu4n+5POGsFfIZRz59zP/8enD3pM/y1juch1hq5APCGv+N1h4PCoSBEkEm9tqslqUA3CzkAHBDta0GBXbWImIbiKgVXSzpqCbmUHGw6CRjSlHbTwIOUVG2JGPABWy4jBLHmAFPsRixZ6AKgJoSaGIBwnCE6gFXNRCkgSolqRJWayEISJ1YqC3vocMurABEB4Vxquec56OZIBhp9OtwKE6BDlaTNcQ9SOhi6FhEZJIfUxATdRyV9cFi/Z5FHTK1aFL6aUAdsWRXigKwZe0nOXOtemiMbRnxQS+mwSopNhQ5wtjfivuS4THFtusLfbfsa/Tl0EIXk3np8J/AWgAAQWO+AVTvfVICjdZ3vp2itN9xUoRwfqkUe+eAfAIyAyRVjODrtnAOVXIeIlMFfP/EEv8+VzO8FV2ZrXFHprk6xmi5ZYnRv0sF0kcA20JN7dhz1VI/G0TgaR+NoHI2XaLwsM1V1J4ZMYkhzyEiZyqWf3/SlMlxvQRN1Bb2KDYJN2yKYKpBZCSvDvNCwZ3M46g/FnQJV3cdUzpdmqK9ktYScULaSC1SDphe7PGhBkXNGGGmYsMlmzDLAnc974XB1otGFHR6bYrIYQu9ROXKUsJPLuft2Mc1jjPZ9OfLV52/hMe0J2FUZIKDPKvMAc4ANBmziWK81uR2i6lu0bpAW6qvnuErUlFf92g8jVIb9ON1ODEcuIaYIIersfK1AWTTCDeUygqDsAnkIFAq3Fj5j1FZyn6o0Ct3I3/zV3gK5CXGjdqPR0vf2ADjncPafvR+iqEutTS8TQCMq4LyyUY3qdVmAgHRutQog+o6j69/+q/8Yb/qEr0oM4yWkcIgUOYMYxSo1a50FU7MAT/156rafV731OYaU+W7u91GZmCkkUWRQUpXNWYkhCQL88Ve/D1/x8bfhs1/3fgBe87h+/+1pF0pZWNHExzWlQwqHNKg4w51dGmKDwKlFX8DUcrNth/Js0Tg2BRbBIY/QKNJIqFxeZSEbNojE8LqwlYQrQq4QmI6DoQoIuhqdpyKUpOBUdRxE/T2FhI0OU9Ys0KNy+07Egv0udLD9CmrPVxuuvOX7G+1f4d13ahR+1ZVNdWQZcz84KID5cdW0Yer/AzjRneFUa4xnp97vd1mFGFMmCQCtuMDJnkdfX95aR0ZiJEGiue+4WMawWqLdJdWtQiFtNahmbRQ7wphDqOSbu0NEVHYuiwBSWrTIj3ilveTqWSANSqPwW0+/wl9+YKDrdVYoRKREZUPAnsg5G7WBYuQ1tIQwgvWt4fx9BrwpgdaKv0+SlkwFM8vAt7DgK2tRornKpK1Eu0bIK41YeRcnAPjNzVdijyp+g07G+syhtLg5HjBdCGiwFPf6eFkeqlL7/3TbMXdOFQJ6J+WNV6GxydIt2bhxAIAD5vfTrHwBI/Oz/9RvfhABb5jFQepLL/BWWPGeQtWlDz+Vof+A73GOn1mB7FU4cdH3dm/tDZiX+fCxLfw/j19gLmDn3BRFl3qeoUF22/cdgwsZolOLBoAwDnCLyoVRYJCXIeS+35yevH0/QKVbrVzzPUOLwki4jDZoAK07VNZaAVqbEotTVKKyEm/4Te8Ao0SA0ZUVLj0htpDEs7WJ5X8PQo213gI7Y3/NrpAID6jMXQHFhsE20URaUcUw/7XWAoXxr5uUKULZgJ9Ur2yk2lIHXSqE1Cd65m/ezUc9+8/8MwpjjSjQuEkWVHIpWTVLJAazvTZmWz4AOXfzvQgS39e8otfw2rM3MYz8AWnbAgkdsJdG66yatDntoUoUqwTN5wnOD32/edGNEA1ndymzZSF9t2kbf/o17+V//4Ovej//eWvS5Xnl6RKWe52uEiyQLoSnedSbpN0o8Ce/6J/T2Y88yhaFEL5nlpGaVtIr0aZNfZFHyPMQQa08lFZ49tsaTu4FUp9SkYE4WaEyRCUyXsbQv79DdsKyTKdaSF/aBeCGGgKetwkA6Gnuh7rTGZdBhXCoikYW8f4PfxBC1VxaL0doWs0B3LQxJKIxzds167EGFNhBOWys+oOyMgpXZmssr9ePJPYn/rtEkcHrj91AN/CH5a1xH0viH0f9Cim1JpS0yIuQLePSXo6MOKy9ru8d1+DEOvAB/JqsP0uXCoPhgtXRtiZdzBJax6Lm8lIgVwYNUM4C5ZBARRd3fOC86de8DcH9baF9md/W3SIBhGQlmYbG044OcdqDWkUtlFz2F9JBCMdl7kBZVmW6ORogX0a4dNmrKIlCwrUJRBdpVlDLtafu1DzkchoDlYB9cc+Ie2YclX+PxtE4GkfjaByNl2i8LDNVWI+Ii/ckR7fCADYSCKnEaSPg8tteGIB04f0fgiJE3pXnWHf5X36+3q9raxYAd22Dqmp0gd1mgvw8kdUTCywDzAg2byuJjEThPzU7h+7xWaNJ7AQDqFZ6S3QvkomwMlCdJQyhZPfDDkbb5CG5tkAcamRrBLRYHpoC0jUEeiHhpMXws1RSigQyQjUnewLB0iHboOsHsL3lS7BiFiA9PUOXyPy7B10uxbV6OWce54YjbypOXpejJMWiG9ePB9IIRluWWiGgrGRaJFxmXVQRhp0lR/CtVsFZ32IZI0z08zLUetSl5qoM8Olv/GFc+ACZg/cOoW+XylNh6J8cFHQdxWcBHrt9krPA1564jUHow+yLgz1WqZmNWpjLlEumTjrcnPoM4kxvgoWO8MUrNwEAe0UHt5f+Z924QVsCwGt+/e1shr3erZiCkpUho7frUQO3rBWQlFUAnrLzLX/ynQCAh1+RYnvuM3Bjhc9qqEjx6hObiKg++JntU9CVQlmXGY1go/BWu8DqoNHVnSxSZJSV2Eqie4VKwYlAvubgCJ2upUNAwiqQHkxUo7GjQDONS6BpP0SxhjMSjpCqLjJs9iAqqiRRdhq2Km90D8D2NBskBEuBMlFsx+i0xDoJGpRGoRsUDBh6Zn+dS6thaPC7Vx9gU4TlflMWXo5TuH6tZuXQaeV8/UUWQtH8mM1TDHpLbl0EwuAO0WbyMmQ6nDAC8yhBmjbPf0l7QZ6HSJKKQXu6arSLO4Ml7qcKyOO3T0BdTRHSLTaxg201lmsQQDjw19Fp5zg38L93fTJEUYVN5UdZXoNhWjHgy5QK+SHdZCkdU2iEcN7Io26vlICgZ70Y9VFO/XsU5wu0ejlXKDprC1grYJZHikr35BDGC3B3bjt86mf9wfngj34YKmtQvZf+9+cbQdfjxQ7bcz/1AQQTCRU2pZ1aIs7GCslaU9vonxhjl/qaEOBDqFpROLs+wum2p1l81p1k5OiV7TVfiiRE3vIgRkwyiO2oZEcM6wRiZTCixTgYLLjc1IoqVEZC1RvQ+pK5kUGkGx6dcFBPthHkVHLaM4jHdWHDQSfgvrK+nQIr1JPsVciXEZYkxK5GAaLT/hqldKzQcpC18MBgF7vCl3hDZTBc8yXveeUNzFeoFLeXdbC5TxxNZXGbVKpEYHH79gpLPA5bGW4fUJk70neJjT93xC3qEVYKZ3/2fQhAvMbEsPpVWQXQVVO6dYsQjnqGsuN5zfVnXJuu8GF/obeH3DSIbf9BtPGWEtORp2o9+4BHLt+Z0Oaah0wPqhHH9aipPPWoKUB5Ffjgjcr0TjmU9Dyd8RKDdT/6wfVthAQrH0QZnp56lxQ9DxGM1V3zui7nn+5PcNMNMD+gg6SSjBANpGXOZKgMkqhCSYdst51jeZw8fEfevILVy2LbmJI7Py/q8n4aVRiRyhEqyWXLvFCewkOGBrZUkFS+dtb7sdZl42oWNZu6A+bnqMyd+h5wbXQu2xU+d/m0v45Z4Lmz9KzDfoG1VWrJTFsQ0rGXbtgroHdqFTKFTPp11+7n6CUFelTiPFi02DlHht5I4XTLr+vL0zUOBscHbW7puMihWoRc4jVGopg3PNI4rHgtp62CA+z5NMVjU4+NwDSEVA1eRGzkDRqe7u35DY8QtxBYavIjvtNHPGgUjcKwKfEeRqPrRQgRWkYGWyPuMkS3vYIxFe2VBaabfp+78r3/EBff74NXMQqBXo6Ngd/bzvUO0AtzlPMSz+DeHi/LQ7V700FFzYEKAJfe/uKH6F80/uvfJm6gXYdpOSTbtCkI78IBAAgcim2/wMJ1L8H38H13AABXD1a4B7e734USlkE6bzp9Cb/8R28A4B1fDpzAF5zcBgBEUuMqbdDr6ZxdJLSV2GjNMFr6hb/IYo4+jZFY7rcQMSdNIDok6VYvdOsEqvMFdJtkFq82QBBVkewcZdrpDpAJ0jRd0QgiA0cRugkNU4LKSQxFWcLOqAspHJPQJ7MWrgtPCeh3M5Ra8UHaaec4uerpNVkV4qC257MCg7U5XrtxGwDwc2/4OTz4r73wglKWBRKeO77qE9+HIPAHWVkEEKGFSf2mHrVK5hlWZeApQLVLjYPf6OEzMXuIfmS6DT1gK+zi8oHv0QaRF2rIWwQuU9KbyAPQ43pjpvdXDgvaxB5/84/ggff4DSgaCzzxvmZ+fu3v/30U1Lt0TsBWEqJLD6dsel+DlSWOd2d4dttfy/XJCr78xBUAwL996gshbpNurAXCicCDP+Y/rzimAeF7xye/YIJXrG/jM7k/fKyVbBWWlSGDuEodIM9DpkwtROwPN/pe0XbAsoUuAffWXalQWMEAFQdwRSfaU42LVNfBxbbRJDYClqosskWUtPpxBK4JYlqacQGYB4j2FfLjlFXtxKjDXycAOVEIz/qA4fzaPtv0LeMIxkiW3ju7PsJVmiOmUEwHCpXBeJnyAZPGJcLU36skrrA57bGWblaGLPgilINtN+5HInCs1euMREgcUWslxpM2H2ZRpFnXWMQO2YIqWNKxuXw9auCZSh2O9WcYJn6/+dzWCbZBDAKgCOLm/QdLBiNJ2QD2yjyEKyUMrV0ox2Ih1gnoSvHzzLKInYVe+ciHURvUyVLgq77ySfzK418EwPPDu0kBvbj3M9WjnurROBpH42gcjaPxEo2XZab6H37ye19StY7NZzwMP1hI2LAJD4uhY+Pw1soSGZVxqu0UW6MEw4d8ie8LN+5AEwz5gVM7mJWxL+sB+A/5RZy86D0IO1GBvApYIefMsREL11sn0KHy49asi2GU4ctPXQUAHJQtnEl9P+gzo9PYko57ZEZLFsZe7S+4x3R5bxXZXszRbr4qEPu3wPI4EC4bgXQTAf2HfF9mdNBBFGlWgYoSzX3U5aQDG1MpVUncvLbGJTwIsIk14Eu5dfQbKMtZ9xP/3T+6696/5tffjicO/P340t/6AVz6xvfiLxp3Zl0uZ4btElHLIKPoXAgw7L+qlBfoOCRMIfNaWYh6v22fiSyKiIX+p4uE0bJBYGCMhCIZN1NJzqJEanz/iSHLgKUs7ez/9T6I00TeP2jkJwFga95lAYwk1DDdgvutxkp0U1/CO9aaY1ImGHT9PBtN2vh48ZB/3TSEIDRuNGmUiQAgGAWM1P30pbPori1QTahvKx32qO/L6kmAFxxJGjF5vZ+gd9oja5eXBqhOlwhvk4BE2AgToBJwWsGyk0zIdDJVCl5PshQwsc/qAMBVYAUlXQReGIJ2s8HKHNM5+cBuJWhR5cjEQH6mwmDDl3VP9Se4r+0n9W8+8TBUUkFSGTrTIcY054yRkNIyFWoYLxGe8Pf7yu4qNK3VybUBpG4MB8JzE/Q7tZuNv8e10tA8j/mz4rRCXgsrKAcRWEZ3O+dQUTk/apc4uT7BjW1f0dFaokPtAiEceyTbvgPmCopcgoSy6LX9nIgDjVZY4pPPnvP3dSeGbR96jrlE+0Tj1VvSnO60c/4OcVqhsBFcvRbCRkSjqhSMlmh1/HUtttoI6WVlzzGDoOoCv/7xL4UbUgY9cJjlMcy9n6i+PA/V1/zrRyFbCfQ0ep4e6n/uuPD+D0GJGtzjF1T2Cj+Bk1bJ5ZPZQRtiRmCP2AKB5XLhnWmPpfzedO4SPr1/CgUBUaJA3yV3FgcGK8f8RqCtZMPi2/M+dkfkgJGW+JPbZ3F2xb9uWiS4NvELcW/cwbGVKXYL38u0VkLTgt7Xkm3X4lCjWs8ZKKJzBfU6f+BWT/ehu4AmN57gVozsT9fo+oDFSsg80PTkDOvEqbzaS7jvJQMHBO4utZ/D4xv+8HtwobcHAHh6vIHd7SH/7BW/8k4AQCuucGYwxdVa+ix7cYcaAFwaBgLenI1WyLTiPlteKaYiCem8GTkpxzgtGwyaFhBVY7dnjGQgRxAaBgdprVBMYwaviUIC/cZAGwJ8OEXtkst5y0XM4J3HH/3Bu77HPIsZGPKFG5tQwmFU+CBhXKQoqOdWWoXtSRcZgWDUJEBh/ZwLTHOW67YvF1ZDOujamst3YhZgfrMHtBs3FOYSasmALxlYmENKTHDAdETuSqczoFRelhFA67aCE40ucHbcopIRX2NNXSwHFjZqNnyRGO5pwwo+bISyCGIDIRv6TY8Os+kxAUd87apvsXZigjM9vy6skxiThN4bHriKW7NBY/ZuFPoUnNTAoDcdf9q/p05wE34+SulYNalaA8wsRPe4P7Q3us3hdGNvCCkdFsT7zMYJ928ROqA2jy+UB2AdkntMV/13qcoAq8kCp+73rZCr0xVMDh38tRavtQ66YyAI1DU4ZI9Y6hh3ntxgxTa7XiKI68DWoZrFWBAHN+0U7FwkpeU9ifXC6/87geUe9Y4XCvJYgeUdv79EI8lORuFEIic1RScdqoHhoDpQFnkWw1RNYHavjqPy79E4GkfjaByNo/ESjZdlpqonEWQZ4fp3Pz9LPfvPPcm+uz7H/IYvESc7Ck/9iAeKnP3Io7j+XW/FuZ9/DwDA9QLIsvaadJBLCZBYdaVjZOsEja8EXA1sCS3CVoWtGz7DErFlxOkfbp6HlJZRpYsqxrGBj3wnWcIoScALq9eKJLujLhKC4c9v9YCOxm7so8WDcQNuEAA2r6xhcMqX5qazlPVbkThcu+wRoYgsWv2M37NMAr6m+19/E7fGfTgqDR32m626FiqXXBqeJe0GURxaNly38wDB8u6Y7sKjRGsZajx4IcHJ1EfkoTIc/b/uNx7Bk3/j3Xf93pf8e09rWs5jFiO4/M0/hFf92g9zNva5N/8Il+nCSHN0nqSlv181WvSQ6tLJ+w5QGsXAE+sEjp/x17E17WIxSZmSlDswdcgaAUfV0nK7hXRbsVi9DQFdazcLB5SSQ9s4rphEL6VDkTUGzwDwbZ/8uwCAb7g4xkHlgUrTKkFuFAJKPRZlhBEJCWxXAy90cMjI4cr3N+C8i+/197uusLi4AcvUWYiLLRAdKkeWEnJRq2xZqHYtCGA986h+Xds0ZXMnMFiZY5GSjnTWgaCExMYecV+XD03XwNSdmUog2m9KwbkV/DxdaP29A4BSQcfWZ3wADrIQAX2XbifDfEDenpWAsQLXyG2llxTYXnb4fpRacUbXiQpU1n/2mDxQf/nqawD4knttpKBUUxbOqhBFW+FE18+RC709SOqfXN9ZQVk1Qv+QQEBiJ3q95Psb9AuowKIsGj3oU0O/DpZViHkVY04Uu4NZu7lvRqJLJd5WUqBMS55LRRVwGdcBsKlFe4OoREXAmXZRhIh6BQMLs0XcmL0f6kAUeYggqWAjUuACYEjEIVgIaAsGjem246pVebzCax+8DgD49BPnIQXj4ioAABTcSURBVDsVOuSFXGiF6loHNr/3j6R7/xv8fxhPfPs/ePGeat3vAnD6IU/xOP7FUz5sETuc//EPQhoqPRnBvSgnANuy3Ed1SwWwgDwaDqgE1FNt2If8QWqmEffxOlGJZ68fwyvObwLwpdu6tzif+/Lp2PlNU4UWD5zw13j+vgNcJXFtc2KOKDA40fUH5zyLuQw93+5AdDTG236j6G3MsSRKQL+TY0GLKF9GkNLhwVXfz93P25gTZefGwdBLDI7pHlgvAwgA4azmJlIvbBJADKnUOg2hemRx9bcewdmffvSuW3/5rc2G/w1/+D24sfDfZ1FGvFEBwP10cJplACiH69/2LrzQWG0vcXPHv8eFj76LNy5dKVw47r/XzrwD0S/hqPwLK9jGamu/D1NKpsU4KwBy9+kkBULVWOO57RgY+M3p4sld3ByRdKLzzj02PaS8RQdW0NJwqUCbNsON7hw39ob1r8HS3Dn7z98PERk47fuh68cnjBavN/7aHjANK+zV3GPnqT91yVQ9p19V902DpWBDBADe/LsOACOLVi/ncnNlFOaCXHG0YKtAYQEROYD6eK5lWEHM5Aqi5zmcAHBwPEE4ataFrIBwjwKeeRPUVD0HTf0+G5MSWkF9vYMAVbe5nyaSTYvXeYlQwMv8jTt+rQstMLrdZ455ZRQfPNYJzOdJE5iWMUtXlkUAW0nkFCnlrRInO35tteOSA93agrBGyR6ULT4Ak7SE1ooD09KEDSc6VwBRhapJjOT4jG3n1tsLTMlyzliJedlwklmFCp5LW5trhMrgXPcOdnMfMFzaWufSeCupMJ1GWBDyPGyVjAVIkgp5FnFrxJYKMfVG8zLkni0cEAQWgviCVaXg6PrLU8a3BUia9MTxEZbU2lptL3Fr5tfF8NQEpQ7YtaqaxIiXAiZ/cdnXe2UclX+PxtE4GkfjaByNl2i8LDPVFxtf+Ttvhej4zGa63cHU+VC481CBHllgTQ/asG3DZPt42kT5wdIjFEPSPNWpg6OAXxjR6N72ShQtDbHjo/yHXn0TG6nPgJ4Zr+OvvOISexButGYMQgkjjQeP7WJa+N8bxBmXr860RzhLAIzHsxOIQ41zbU/wvpP2GE2cri2RTRN86cPe+jhVFf7gygUAwGwZM1I3SipEgcZ+TmXGPOYybnWjDRcc4hoGDpriM1l5AYJaRCOcSpj/5KNTObSgW4rz/+SDwHNwRed+0gukv/nL/gzWDWGp2LfII0wX/jtHkX5hFSsar/zVdwLwPqmTLGGfR+MahaY4rFg/eJlHnuMYHMrUCFBmlIOwAm1CsSrhWGDDWIlAWS6d5T2FiOzALm+vcVlYGgHIpgQGgO3LzG6C5NSc9VBnReyFEgAPgqqrp/T69tB/l2URsSF7HGpURuLANio/cb8pTQbKNiIDxQvH0LrtYGOLlIj/cahhqDpSlgGWBy0UBCgxuWoANs5XZngo15hMVxKo1ZBWc6y3F5xltdYX0Ptkwq0AlQnOop0Al8rrUiXghSxkLnhe2bBBnz/9Dl/hqNsHTjlYMrIY9xL0Tvq1NT1oozNcIqGse7W1wPV9v96d86XWOhvLqgAdUj+679gB9hctzHb9WhMCWE/8fjCMl7w+K6O8+AN5nLaDEtvLLr2/gBBNtl7FFSPkq0nMXFphBU50Zzz3R3nKz3q2SCClhas5slqyb3ErbBSYVuIlUlUhoBsUx5pL1DdvriKYKJiUKgq7bahzvhScZxFMpiBqP9tBhZI44dZK9tQV0kEpywwCaxQkAZqCyKDaaqFFbZJBkvFc2pu3GeRUC1jUqlUInPdn/i/ApPxleah+9e++HUE7vkukHACu317FcNUvllHRQ3vVT7ZlFWE+o039Tgh7PoOhfk6xZtgAWFYCKhegipwndNcUAC2ZfqFnIcJeiYuv8R6ngyhjibs3Hn8GhQ1wOvHKK88u1rG/8Iu2XEaYlTFubPmNYL+bM1Lvd0ddnCCBhDyLkGcRfq+8CAB41foWHt8ll5RWjk5a4LE7J/l7D3q+lLgsQu69mFKhmMcY1YdsWqGoUbGR8+U9poI4b1IN8l9fCEZ6msShpI1XrhZNT8kCvUt3T7/hY/6efix6LYbHp0yOz/dTLwQAYHa8wp83FtuNYXcrqhATmtkeosXcGfUwIGSnEPALuT4orGApPJRe/q42BP+S09dxaezpU97AWnBZfeXYFGnYIBdnLX+vplUXohKIdgmRe1xzf1uu57BWYEwC+FUZ8ByxlULSJdNtI/DsNzVyiw/98o+wOECUaJjLHW5B6L5G75ifw2lUYTRrsdvN9e98G7/H2Z9+FOjXtBYJ1W9E9A9GbaZE1N+9nu+He86I7V33TZTy7p/T9ywnMZ6pNjgAaSUllqu0CU8VdOoax5yWa1TIUsuo2LBVoVyGELox137mkecom9WXkjhceIVvn6RBhYDe8BknsJikqEhMoZfkiOiZZXmIahIj7JEq1nAfufFr4Qv627gUbGBE5dX9cQefvHMfAKCf5uwAZZzAA90DfM3wcX+/yzU8M/HzZa2zgLGSS8O782aeBr3SmyLAm6MD3u0FAN548hn87u0H/fV2MhgrWZzkvuEI23MynQhLnGr59X9zMcCz4zVUplFH2yfHnaCloQHv6ARAxxpBLWu5F6N9ZoaU1tho0oam8myQVJBhI7RfFAGjr1FKhIMmkDPrOQcnz+6ssQhLr5UzFWx77N2V6rXgrE9ObHB0qN6TQwrvOH/+F98Ndcuv5mpF4/p3/MBf8Jt+vPoffBiP/XijcPPwx94BwFM61oczzAmWP9/u3LXJ1DqgcpJApwZPXvUH27HjY2xHPqL9srWrOKjaDHDQViKniX3s2Bhb4x6ryvTTnLOQ6aiFnQktsHaO2W4Hs4X/+5PiGGLa/JW06IdNH2i6TGBqw+JDmZcKDHQVNBZat9tYe8BHxXs7PQhlGwswI6BW/CZsjUAl4oaH6NBkgZsJHPVasVZiOry7f/Lpn35h+cezP/Moqp5/v9oG78XG9e9qwGe9OOfD1DmBj7/xw897/cMfewdUbFgJSBQS155Ds/qCf+O5sX984xxbci3zCEFgefMIpGU6xlpnAUeHxDRuwyUOZUCRViUA4ivrloHrgsEmgHfNAYDSCuYxRpSp1iCsXrdg2k+5H8O1LTdyrn/n2/C63/A0pU/+N+/Bwx97B9+Dcz//Hlz7256eI4xotKgDB5MH2Ft4/mmwH2BBSlroaojQej4tAAQOQjXZI2cWzkvsocZWWcFgFbnwZubhaT9HsiKCSP33lMMc5X7S6GB3DAStkygynLnr0CLplCjrPu1BjHM/4Ssb1773H+KVv/pO6PX6ZJbscPTK9W3W840CjapV8ia/OeojrHvF4wSyXXFlIxCWZQn/dOcstnf7DNpptQo+2KRweJJs/TZWpgiExR/NfDB7ULb5sztRASMltqZ+nbfjkgOWbJIgoPuxN+pi1/VYEnB73mXwVFEF6LVypr1lOkSb6D7LKsKfbXvVq1IHZA1I1oxGMcdU5x6HgDqxDX2QDwBypWBsB+ArY5qoT/IQlUpJz0tls3rXZJzWWpgsQNRv6Eg10M9YiVtXvAIcuhXCbsG8bDUO7qrm3MvjqKd6NI7G0TgaR+NovERDOPdfgi3sX25Mp1P0+31MJhNG/9YCz8++iEj+X2bU9BohHR44tYMbBx7BmS8itKiEd6w3w0N9j9T91M4ZzLOYKRNSOR9BAnj9g1fxcPcOnpz76LcfZmycHEqDTljg09fOAPDqS08/5UW0k/UMHRKC0EZiNk9x/rgXT9BWMsoR8KWlw36OWzMfPadRhQmVIss8wCHfa5hF4Mt7AIK1DHo/RUgC+zp1UCd9idFZCaMlgk2fSamlQEgc+Pn9hjNYmXiN4FowQU8jVm86nG2+0Ki1lgsdYLpI2InmK3/nrTjf833k7ayLQFrcmfnn/Gf/7QsjhAHvf3u4NPqir/vIo03JO7SQhxSg2p2cqwHtqOQe17Nb67BWci8pCA2K/7e9+wmRK6njAP6tqvf635vunp4/ySSbkGxiwuKuiBdXvXgRr7LiRZAFwYPeRMQ9iKKXyAqCB48iIqKevIgeBK+7Bw/LroSYZHczM5nM/5me6c7r1+9P1fNQ1dWTgyto6Rry/ZxmoKe7p/t1/6p+VfX7zeq6GuFnfQDQutdC2be3+8Rn7uHuoW1An6Yt9Hspxq5KUBRr3yTbH8txz2vja9/x12N/cYKVhRSZO7Q/TNuYHLi0Y2TQW0n9/dcGfje3LAR6N+z6/M3lA7x/soxj1/dWvd9CseKyHv3Cd+lRyqDbnuJy1y5b3N5bQ+aWTOqpeiJjIWKDldWRf60eHS7OMwpGwIzPHCVyO+m7yymuDoZ+P8HGe+d88/Hm+QmKaQTpllqkMr4Hcbc13/J8NEp8oXcAEDst6P6sdq5G1NC4vGr/734j82ulSVzA1MKnV5Mox2ZqP+OH2YJP/x5MErx8bgPKTdHLWqGqZ/2Ia2xOBri3Zz/LjUbld+uOJq35sksl0WiVvqhL0ih9yrjUCmkRo6hms0LhZ7viTHWrqlJY6qc+A/Vcb4T7+/Zxy40EpllDLtv7b7XmvYqneYwyn7+OZ+9z9niAvf6MlrY62Oy1nBXXzyKIUqK9Zj/0d175AT7+B/v5PNnt+tu3lqYQova7oXWlIJWGmUzx4Ku3nviOfto880H1P3X11z/y608rCyk2d5ew/hWbYvv0n1/Dm59/3d921lnkYJxgctJGdGA/SOraY1t1x7l2dd93PHk06uF515ppqmP04inuHs/W9SJM0nn1n7PJ1JXB2JccNLXA37ft+dNBb4Lnuqc4zOyXa1ZGON61ab/WmQ4VjbhCUUY+BTnZT/yXYm/1MbSRKG/PX8PyiivQnynbmNjddmE98mcSs7Ua+ryrwtTQELL2FZtUpKHdz/+syhJgA+rOHRtsTKKhkgpmaIPB+euHPs01HHdseblNt145qBC5ovPnl0Z448z78vxvbvm13tl7N/PC7384/8J7PB9YQAB1W/u0XQ34ov9pEWOl49YxjwYo9jr+mBXq+RlQ0daoz7Tek1PpC6uL2Phi7EZLRLFG4QZedS3836luCRXpeYeWNPbBPm5WuPel7+Ozf/k2ANs1ZbTnlghWJljr280kW0eLKIYtKLeeOLtGAGBvsgAlal+yr7jb8wG8upAjcu3ckk4OKQ1iN0jQRuJw310fhXxyE5MRiN1g86MX9nA87WBrx+4TwGk8PxqzkmNpMXX3J+ygzz2PyeOm7yKjpMHRaeKvH6nmHVWEwBODzZODBf/6mCyal8msgXrUgBzY264Oxojdtb877KLZrLDmjlPlVYQTV1x/mjX8noRYaQxaGU5cpbTkzOahG70D7OcLeGvTDoi7C5lvbt5t5Nh3a6N+Q5MLuGvJCC1lb/f23nMoysinrJuR9oFTG4mpD8wKrXbhg2CvM8WxO7tstjrQXe0b1LeT+aBDShvkzi5HzAYhtRG+ulIUaxRFBO2uR5Eq2xgerupVpnw3ofVX50tqV37+Y39USwigP5h3zzJG4Hx/jCrN8cYXfvZUB1Wmf4mIiAJ5pjYqzSblo5FNPX35zW/hnft2F9/tV7/5gX/70u9+AgB4+fo6NkYDHLp0mBQ1xtt2pqQXI9TTDFdet6nGv33ju/6xPvnHWyjcRpZOZ4JPXXkP7/bsjPOl5W38tboCACjeWcRRV2DDNTCWqsbYjfAfnbbxsXNDHN2xI2GxWKDr0m2nu10o1wxYaGB0s8b2u67QphZoDuyI/+BOB+bqFGO3bf7q8hGOZsdLdiWUm3n1B2MkqvZHT9TixI8qJ0MJqUrk5+xsRogancjN1tdSNFWFu+sX7G1vVrjkUmoia/uNG+lJG+1uDuVSeAo1it3kiffnxV/+FAAQLZSoXNPpOIkB9z8PehmSRo6tDZsq31lvIenbVNz0oAaMAZr2/xZDiXJkX8eHww5e+K0dQRfTCELUePuLNqV89Rff8zN+qQzeeuU1vPgr+zxQV76ABzQQtaco3PEp1dDYcTMFIWscbrim7amErObN03W/8scPzMTOSmYpcKMVImFnKFUaAZU7xpJFqJoaxrVYlZmCadqsgowKRJ1ivmFKCZ96vrgwwuf+9HU03IN/pHOErRWblYilQU/bFN2ldoaxamLo0sutaoy7D22qbnLYRmspgzHueSFDlM76sWWIXNGJ9MQeuei4ZtidRobrqzbDsnk8QCPWGLjZe15FfoPdg502Si1xY3Xdvh9LyqerY6WRun6hx8cJVCQgXNojigpEuX1vH20uIxopSDfpEpVEfs3+oiKDzNWTTcdNmCxHo+E2Iy0YJI35bHLvdBGLkTsKUo/xwBUOyccaumHwwDVhKPNo3p5ukCGaXWOixv39BHJW6Kllm58DwHC4irRoIDL2Nc8fG+jZRrROiWzkKkC1pii0wq7LvuyYZb8jOD3VADR6fft7V+U4dkfedKXQFPaaMEKgGCufvj0aS9S126FcCqgdAe0Kf6TLtW/9qFSNqpS+l6sAfJeFfBpDClcD27V9lO6YQ2ViwCW5RG3QTHKfMZp9lgHAlBlQuZrg3RzNKkPPbboa5h1sPWrAZLMdxk9vAvWZSv9ubW3h8uXLH/bTICKiD/Dw4UNcunTpw34a/5ZnKqgaY7C9vY1utwshxL/+AyIi+p+p6xrj8RgXL16ElE/n6uQzFVSJiIj+m57OoQAREdH/IQZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgoEAZVIiKiQBhUiYiIAmFQJSIiCoRBlYiIKBAGVSIiokAYVImIiAJhUCUiIgqEQZWIiCgQBlUiIqJAGFSJiIgCYVAlIiIKhEGViIgokH8AAtW9bvriMVMAAAAASUVORK5CYII="></td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2019-02-28</td>
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>
+      <td>(-7546437.526804707, 993342.4642358534, -7427829.471373559, 1111950.519667)</td>
+      <td><img 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"></td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2019-02-28</td>
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>
+      <td>(-7427829.47137356, 993342.4642358534, -7309221.415942413, 1111950.519667)</td>
       <td><img 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"></td>\
-    </tr>\
-    <tr>\
-      <th>4</th>\
-      <td>2019-02-28</td>\
-      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>\
-      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>\
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2019-02-28</td>
+      <td>(+proj=sinu +lon_0=0.0 +x_0=0.0 +y_0=0.0 +a=6371007.181 +b=6371007.181 +units=m ,)</td>
+      <td>(-7309221.415942414, 993342.4642358534, -7190613.360511266, 1111950.519667)</td>
       <td><img 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"></td>\
-    </tr>\
-  </tbody>\
+    </tr>
+  </tbody>
 </table>

From c19a2a020eefbbfb58aed51d89eb357eab068ce8 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 10:02:26 -0400
Subject: [PATCH 321/380] Little tweaks to wording and code style for nodata
 page

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/nodata-handling.pymd | 42 +++++++++++--------
 1 file changed, 25 insertions(+), 17 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 68e3102b6..6eb4151d6 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -148,7 +148,7 @@ display(sample[1])
 
 ## NoData and Local Arithmatic
 
-Let's now explore how the presence of NoData affects local arithmatic operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0. 
+Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0. 
 
 
 ```python
@@ -161,11 +161,14 @@ y = Tile(np.zeros((tile_size, tile_size), dtype='int16'))
 rf = spark.createDataFrame([Row(x=x, y=y)])
 print('x')
 display(x)
+```
+
+```python
 print('y')
 display(y)
 ```
 
-Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for arithmatic operations, Data + NoData = NoData. To see more information about possible operations on Tile columns, see the @ref:[local map algebra](local-algebra.md) doc.
+Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for local algebra operations, Data + NoData = NoData. 
 
 ```python
 masked_rf = rf.withColumn('x_nd', rf_mask_by_value('x', 'x', lit(1)) )
@@ -173,15 +176,19 @@ masked_rf = masked_rf.withColumn('x_nd_y_sum', rf_local_add('x_nd', 'y'))
 row = masked_rf.collect()[0]
 print('x with NoData')
 display(row.x_nd)
-print('x with NoData and y sum')
+```
+
+```python
+print('x with NoData plus y')
 display(row.x_nd_y_sum)
 ```
+To see more information about possible operations on Tile columns, see the @ref:[local map algebra](local-algebra.md) page and @ref:[function reference](reference.md#local-map-algebra).
 
 ## Changing a Tile's NoData Values
 
-One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a rasterframe from a tile with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 Tile with columns of 0, 1, 2, and 3.
+One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a DataFrame from a tile with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 Tile with vertical bands containing values 0, 1, 2, and 3.
 
-```python create_dummy_tile
+```python create_dummy_tile, caption='Dummy Tile'
 tile_size = 100
 x = np.zeros((tile_size, tile_size), dtype='int16')
 
@@ -225,7 +232,7 @@ display(collected[0].tile_nd_2)
 
 ## Combining Tiles with Different Data Types
 
-RasterFrames supports having Tile columns with multiple cell types in a single RasterFrame. It is important to understand how these different cell types interact. 
+RasterFrames supports having Tile columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact. 
 
 Let's first create a RasterFrame that has columns of `float` and `int` cell type. 
 
@@ -234,24 +241,25 @@ x = Tile((np.ones((100, 100))*2).astype('float'))
 y = Tile((np.ones((100, 100))*3.0).astype('int32'))
 rf = spark.createDataFrame([Row(x=x, y=y)])
 
-rf.select(rf_cell_type('y'), rf_cell_type('x')).distinct().show()
+rf.select(rf_cell_type('x'), rf_cell_type('y')).distinct().show()
 ```
 
-When performing a local operation between tile columns with cell types `int` and type `float`, the resulting tile cell type will be `float`. If two tiles of different sized integer cell types are added together in the same way, the resulting cell type will be the same as the largest of the two integer cell types.
+When performing a local operation between tile columns with cell types `int` and type `float`, the resulting tile cell type will be `float`. In local algebra over two tiles of different "sized" cell types, the resulting cell type will be the largest of the two input tiles' cell types.
 
 ```python
-rf_added = rf.withColumn('xy_sum', rf_local_add('y', 'x'))
-rf_added.select(rf_cell_type('xy_sum'), rf_cell_type('y'), rf_cell_type('x')).distinct().show()
+rf.select(
+    rf_cell_type('x'), 
+    rf_cell_type('y'),
+    rf_cell_type(rf_local_add('x', 'y').alias('xy_sum')), 
+    ).show(1)
 ```
 
 Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 tile columns: one with a NoData value of 1, and one with a NoData value of 2.
 
 ```python
-x = Tile((np.ones((100, 100))*3).astype('int16'))
-rf = spark.createDataFrame([Row(x=x)])
-
-rf_nd = rf.withColumn('x_nd_1', rf_convert_cell_type('x', get_nodata_ct(1))) \
-          .withColumn('x_nd_2', rf_convert_cell_type('x', get_nodata_ct(2)))
+x_nd_1 = Tile((np.ones((100, 100))*3), get_nodata_ct(1))
+x_nd_2 = Tile((np.ones((100, 100))*3), get_nodata_ct(2))
+rf_nd = spark.createDataFrame([Row(x_nd_1=x_nd_1, x_nd_2=x_nd_2)])
 ```
 
 Let's try adding the tile columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1. 
@@ -268,9 +276,9 @@ rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x_nd_2'))
 rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 ```
 
-## Nodata Values in Aggregation
+## NoData Values in Aggregation
 
-Let's use the same tile as before to demonstrate how NoData values affect tile aggregations
+Let's use the same tile as before to demonstrate how NoData values affect tile aggregations.
 
 ```python
 tile_size = 100

From 693c0479da9594b2bac81ed527f4d3eaaaeb4fcb Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 7 Aug 2019 10:59:55 -0400
Subject: [PATCH 322/380] remove print dtypes?

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/raster-write.pymd | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 4bda0a78e..db56a6a22 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -45,7 +45,6 @@ pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
                     rf_extent('proj_raster').alias('extent'),
                     rf_tile('proj_raster').alias('tile'),
                 ).limit(5).toPandas()
-pandas_df.dtypes
 ```
 
 Viewing the DataFrame in Jupyter looks like this.

From 57cde08afcf552c0ad5999b726e8ade3971a863e Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Wed, 7 Aug 2019 11:01:33 -0400
Subject: [PATCH 323/380] add white image explanation

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/raster-write.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index db56a6a22..aab0f323a 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -15,7 +15,7 @@ spark = pyrasterframes.get_spark_session()
 
 When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_.
 
-In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata.
+In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata. In the example below, the displayed tile is mostly white due to cloud cover.
 
 ```python tile_sample
 def scene(band):

From e8e250b8c663d123811cf1eaf0846467efe5204a Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 11:20:34 -0400
Subject: [PATCH 324/380] Revert "add white image explanation"

This reverts commit 57cde08afcf552c0ad5999b726e8ade3971a863e.
---
 pyrasterframes/src/main/python/docs/raster-write.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index aab0f323a..db56a6a22 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -15,7 +15,7 @@ spark = pyrasterframes.get_spark_session()
 
 When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_.
 
-In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata. In the example below, the displayed tile is mostly white due to cloud cover.
+In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata.
 
 ```python tile_sample
 def scene(band):

From 3bbdae4097baa03a6e7a62151b21d15e9dcb4e58 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 12:05:26 -0400
Subject: [PATCH 325/380] Write singleband for example, fix path to uri in
 writing

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-write.pymd    | 32 +++++++------------
 .../python/docs/static/df-samples-output.md   | 22 ++++++-------
 2 files changed, 22 insertions(+), 32 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index db56a6a22..c0613208f 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -8,6 +8,8 @@ However, there are times in any analysis where writing a representative sample o
 import pyrasterframes
 from pyrasterframes.rasterfunctions import *
 from IPython.display import display
+import os.path
+
 spark = pyrasterframes.get_spark_session()
 ```
 
@@ -19,10 +21,10 @@ In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an im
 
 ```python tile_sample
 def scene(band):
+    b = str(band).zfill(2) # converts int 2 to '02'
     return 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
-             'MCD43A4.A2019059.h11v08.006.2019072203257_B{}.TIF'.format(band)
-raster_url = scene('02')
-spark_df = spark.read.raster(raster_url)
+             'MCD43A4.A2019059.h11v08.006.2019072203257_B{}.TIF'.format(b)
+spark_df = spark.read.raster(scene(2), tile_dimensions=(128, 128))
 tile = spark_df.select(rf_tile('proj_raster').alias('tile')).first()['tile']
 tile
 ```
@@ -40,8 +42,7 @@ In the example below, notice the result is limited to a small subset. For more d
 ```python to_pandas, evaluate=True
 import pyrasterframes.rf_ipython
 
-pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
-                .select(
+pandas_df = spark_df.select(
                     rf_extent('proj_raster').alias('extent'),
                     rf_tile('proj_raster').alias('tile'),
                 ).limit(5).toPandas()
@@ -55,41 +56,32 @@ pandas_df
 
 @@include[df-samples-output.md](static/df-samples-output.md)
 
-
 ## GeoTIFFs
 
 GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTIFF.
 
 One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
 
-Fortunately, we can use the cluster computing capability to downsample the data (using nearest-neighbor) into a more manageable size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
+Fortunately, we can use the cluster computing capability to downsample the data into a more manageable size. For sake of example, let's render an overview our scene's red band as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)). 
 
 ```python write_geotiff
-import os.path
-from docs import docs_dir
-cat = """
-red,green,blue
-{},{},{}
-""".format(scene('01'), scene('04'), scene('03'))
-outfile = os.path.join(docs_dir(), 'geotiff-overview.tif')
-rf = spark.read.raster(catalog=cat, catalog_col_names=['red', 'green', 'blue'])
-rf.write.geotiff(outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
+outfile = os.path.join('/tmp', 'geotiff-overview.tif')
+spark_df.write.geotiff('file://' + outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
 ```
 
 View it with `rasterio` to check the results:
 
 ```python view_geotiff
 import rasterio
-import numpy as np
 from rasterio.plot import show, show_hist
 
 with rasterio.open(outfile) as src:
-    cells = np.clip(src.read(), 0, 1800).astype('float32')
-    show(cells)
-    show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.3,
+    show(src, adjust='linear')
+    show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.6,
         histtype='stepfilled', title="Overview Histogram")
 ```
 
+If there are many tile or projected raster columns in the DataFrame, the GeoTIFF writer will write each one as a separate band in the file. Each band in the output will be tagged the input column names for reference.
 
 ## GeoTrellis Layers
 
diff --git a/pyrasterframes/src/main/python/docs/static/df-samples-output.md b/pyrasterframes/src/main/python/docs/static/df-samples-output.md
index a7f56faf2..7cbfaf1ae 100644
--- a/pyrasterframes/src/main/python/docs/static/df-samples-output.md
+++ b/pyrasterframes/src/main/python/docs/static/df-samples-output.md
@@ -1,5 +1,3 @@
-
-
 <div>
 <style scoped="">
     .dataframe tbody tr th:only-of-type {
@@ -25,28 +23,28 @@
   <tbody>
     <tr>
       <th>0</th>
-      <td>(-7783653.637667, 1082298.5058092135, -7754001.623809213, 1111950.519667)</td>
-      <td><img 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" alt="Image" class=""></td>
+      <td>(-7783653.637667, 1052646.4919514267, -7724349.609951426, 1111950.519667)</td>
+      <td><img 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" alt="Image" class=""></td>
     </tr>
     <tr>
       <th>1</th>
-      <td>(-7754001.623809214, 1082298.5058092135, -7724349.609951426, 1111950.519667)</td>
-      <td><img 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fkDe5EJmFrgTZuE/dkk7E+D3VYIBeXRJi8ePMiYtUxLeUxFVaLEoB07GFLRv7nGhlKNY8sDnD4xlFnRCjFJxwI3U29bKx54KVvH53nx8EGOdof55vRG4rsrxEWN3xIw0WWir0YtyjyFPndyN74RXpSjSyZ1wN3GJ2/82AXznHrdGwG48UtvhVigygnHXv/Wc2krAx5xt0TzmpTcWRO7BVElI71yGJymwogUWgimni+REeSWFIkjCEuC3j1VPl/cjSkVZbvLj1eOMJhv88zqafavrGO6ViIpSMq5gN17Zrh3bj0TlTp5K+T+2VEsMyVODOLI5ORdY3zmegNTKlpni1iuRiaw/hWn6MQ2YWrynUObub5+C/99S5FGnLtQ04HLEKluuOEG/d3vfvcx7+09/EK+3djE2VaZ7eUF/vIZf7mmOq97/b5MDlRQPJuiBXzr797Elg/sJfE0zrLECDMRp3okofmaJuVcwFi+zvPKR4m1wZeXd9DndLguP0Wf0eaL9V2UrABHJlTNDq3U5XS3j6Wez/RKiSQxSE7lEWNd4p6J40foB/M4dcG1rzjCTLuE0oLp6Wq2hWMpxkdqPPjav2D+0IXFgcuypz4eVpIcO/JzAGsi9CHSTv/RG9ny/r2YXUFtm0FvSLH1fXtRNmBojCjTxZUFKxtNwsiiVK4TKZMD3fWc6VQwpeLLR7dzdGiQfVs+ydaheQ6E65mKquRkRCt1eVrxDPNekVOLfaRnfNJiilhyuenph1gOfQ6d9Rl99hlsmbCrOstCL89iPk/UsdGByeSZfpqRe9F2XVFSP37Xs6gONXna4PQF823aezsyEcTFFJFmHz0upQx/B3KTHSZfVgIgNwsr20CZ4C1ompsgzkPRC5EiU1PvWhwniCzue+mt/Kr1Knb4syyrHL6IuLO1ia+f3Uxn3mf4XyXdAYlb0+iXdGC0h5x3UJUYiebIzBBDOxcY8xt4RsQuf5q79QSbB5c4fGI0m5e9FBVeXLW6oqROvvqta86bjIYU9rscvC2THiZ/883wmw/Pc93r9zH0b4LE1SgTtJlta0+UalTsgILZY+Z0P1YpWzyeXz7EbmcGgAeiYb50dAfMOhhAfbtAJNDarDCBF11ziG8VNjBeanCi2U+11OGdWz7PdFyhpy2OBUPMBiUOH19HYaCNITSN5fya2nZFSV0r9HBIf6XN0u4LayqjX5iFRpPg+o0oS9LaZBCXFE8pTWGJlDvmrgVL4XkRb97/nxiyKqRIfqmwzP84+3QQGjHaQwPplEeSTzCbBjeMneX3hv+Fb5SHuLO9ieOdAQ7OjdBULgUj4CtL2/nO6Q3IEx6mDR3PJZ/vkT9sf08jvACuGKk3/Ne9LN6YPmyf5/GgE8niTDkzmlwIQnDHwkcf48Yb2Xv4hVScLqdDSXOuwBf0tfifKrL7dw7w+/szP2EBmSlSgL11BSk06bBkk7/Ep9tbub89xkKYZ7pd4kfHT/D3CzfwwPwwTx2ZJmnY2IAY77JlcJm5VoHegAYvvWj7rgipW/72Vp7xG5P8cukUcHFShZnJiqxceMv3jgfff+762T/3Qb75998zFH9mZg87y3PsjzJDTXvJJ6/gu3+1h6N/kk0p296zj8LTF3nx+kN8bX4reTubJiyRMh1V+PKpa5BSE6y4zD44iBEIBPDNFY/yuiYNO49npRydHGbssxI2CtZiBbgsUp/1pbfiWxEf3fIJfube13D8F95zSeVP/eKj94UuhIk//wD29Q9/1dOTA0zdO8qpN72BjX90O3QNwlfW2f/SWwHY9In38qqXf5sRq8HXGtdQ63rM1IoMV1qEyiRvhNw4fpq759Zn7kmlmCQnGV5fA6Dk9OjPd5hcrLDpbzQyjhlcjjnWuwoL1dHWWV5UaPDAwjCbx2Y5cKFQgysEv7/LwMb2ud+bbt9L34PWOUv8qde/8VFlTr7yFgDecf/LcWSKAAbLbZZaPv3r2izFeVYil6Dr4KyaAIWh2FxaZqpdRmvBiSOjeFMGzplZ0uOnSH/saWt638uwUiUMu00++dMX1qquJLYPzHP/9Ch7PvdOViZLjNwlKN0zv6ayf3Ddp/mnU9sBOLA8Qt4L+d/Hn0mz5jM6WsM8kiPxNd7WJkoJmrFLrCSJshn9Fyg8WGP6p0YQyQipBxy++DMv2Z6aanlOF76aePk3fwvI1Noji0O4bkwvsvDHWoy97ij8z0fr4LcefCmfO7n7YWkfP3ojm8wVhpwmcSrpz3Vot1xQMDNbIdzco7xzmf58h0q+y4k7NrHYyLPQyDP9As3x/1KhuTuiPaHojmQbmBfDJffUTf4EZ1pVJj56G0jWtNpfDnYWZ9nxu/s4fOv7H5a+6RPv5TuHNjP5q295VJnvNsbZ4s49LO3d//hzjO+aZant067nWFds8pIdB/nSyaz3CgETpRr3nRlDLduYRY15ME/qaXLtzJPRcFLSnMRqGIiLL/6Xt1B9/QW3wQsup+Ta8cXpHVSeO/eodCk0RjFk8217SSoJhp9gHfN48F038w/P+QhvvO/neeV5+U+97o3c8I/voNtxIBa4RsxXp7ZQyIUoDY0Vn6lWGTHtIkyNjASFSU2SyzQ5oSRx00UVNamnM9/Zi+AJ60l954veR7/XYcct+86lbbp9L3HHIqq56LEAo2mglhw23Lb/XJ7bn/LJR9WVs2JKxS4yl7DFX2R9aYVebFKbKqOXHRp3DSLSzAvRXoHYzxzxZKIJK5poNEb0hQzvWEDYF5f+fyga1Vpx6h82Y0ff+52WE8yahZaabR+oo5st7lj8WBY2dgF8/QW3AXD9F25hqlcmSCyiyCR3xiSsapKtXXK5kPBAme6oOufWbXYk6UiP3RtmaIYujpFk/lsXwROa1AO3P8LtRmjSoWyBUiWfL5744EXreOrnb+Hel7yXDR/5ILt2Nzm8PMzSQhFSQTKoEEM9HCemc7qEHQuikRgEbBhfZMBrE6UmeSvElgmtyF1TtOwTmtTzsf3T7wbDQYcGdinkyG+uzbgRpwYb/uoPoag5sdTH+vIKSxQxcgnKSRkot5mfrmCGAmVnvv3WgoWzMSFRkpl2kW5oU/B6a37XHyipGz90O8Vj8lH7WmtBeDaPqEa84obv0kkdPnr9Xz/s/nP+6S1ML5RRnWw/a9u2aXaWZrl2oMx36hupVtpYhqIW5LD9iKjhICJJzc5hNA2EBrMriGJJfmeNqUaZ7nSe8TsU8UaLxvMkrh1f3F7BD3ih0hLUT9Qvq6zyU/r7WzzYGuLbsxNM/NltD7t/9mwfKjDZsHmen7j+AGN+g1OdPo4uDyBaJrW5EkuH+hkr1omXPIxCjIwExuE85lgHuy4wg8w9qdnKER0pUjhhkHiS0smY5ESeZsvDDC7+rj/Qnmq1BAdedml2Ash6Ya4vR6Pl0eq6xJGJ199l44dup3xY0OsXsFUjWyZnFys0uh47+ufppRZ9fpdWr8qGnTP8zMh93NOcQISCwjdzNJ4S425s0Zn3CTcmTHxGM3BfipYSZ2aJpOoTDDooS1CYBONBl6Xo4pPqD4zUTbfvpTx5eWXLbkAQW6xEJskJH+UreiXB4LYluhsthvMdZuoltB9h2wmNmk+z5LKzNMs35jdx0/PuZ8JbxhIpnTQL5GiPawoD7czqJGDwWwaIBKMTkxRs0pJH6hgYoUJGir5DMWHFIs4/QVb/DX/zPoRhc8/HHm34WAsOTo7ieDFx3cEJBUlZUywGmFKRppKp5TIA0YrDyMYFrulbpGIH9Ftt8nbE6XaVIafJHYs7ue/0GGJbgJDQOV1CJEAppTMqUKZJeySPP5+S5AyUJUgtgRFL6iMGYRX03Vehp84Gx9ec9/ov3AKf6uP0X1x+6P/EX3wAvy+k4PXoWAmdgkul1CVVklo7RziVR7kKq9zL/Fel4t7TY5RKXb48tZN1m5b41k+8n48cuYmvdLciDY1q2BhNiZaQVBPsORN7RWP2NEak6VUMrK5CppqwJIkLkvaeHtLQSOviwv8lL1Qj3hY+cuSmNeUtfTCPP59c6iMehly1S86JWOl4dDsuKjBptnK0mh7BUg53XmK0JcmyxzXXzDBVK6PaFvWpEiKXUu943HzvL/Cd5iaeN3ScSqkDgJoI0Ot7+H3dLH5LkfXKUOMtJfjTASKFzjpBakMuHzLct4LWV2n4399ZmxH1xC8ZlPevLaDrsbD9XftAlFhclyC8BB0ZGPkY204IVlyEl6CeFpAueiDh2PQgnh9iV3skkYGKDYSAxShPO3aQaJZPVRCxgFkXZWmCCpiuRkawsgWClonV1KTXWPQGdObMtiBYmcoTpAUILt4PL4vUR8qIj4fHsiRdClJPE1VTrHKPuOFiNg0SqelFBlYuZl1/A60Fs0qQ+1qe9nMiDEPRXfCRgWR4xyKG0ETKpNbzWQryoLO4ry1/tYTK2XTG8zS2CpaemVBd16A+WcGbMTIfVgvsFYERaYa/KeiMyDWN7Se0RpXkNEZHkigPGWdD1C2G2FZCp5t59Y0XatS6HhtfeYzJlQqNhs/GT6UYcYz+bInZZ7nMDA3irW/R69poM7M2Hf/lfobvTJl5eYRf6DHm9Ui1QPsJYZ/AWZaYHUEwkpK6kvLRLEZ2LVaqJySpN37prdRaPqrgQCLBVmgpUQ6QClpdD7Fsc7ozyEyxRJpKTssqwT19OClAyPRzPHrbejxlwzGk0Nw/PYp7yCN1IVwXASZTP5vQV+mw0napKUmn5iE6Ju5iFm0TF9Q5b8PE5ZwX+MXwhCR1ZrYCPQPcFFY91mUos4AMmUW2yFRg1kzktEVaUHQ7HnLVgDz9YzZmBywn4cjCEJaZoqZzlE4qwpLA3B3gDMUM+B2Wuz4A3ek8mBozECQ5jTJAJILcnMRbVkSFVUKNi7P6hLKnbv7gXjb+8e2gBSIViLaJUAJigbY09voO5XymJ4o0O1Qh8TRWMxuqytIkOU1cVhy+9WaKfo+g5RDur3Dy5jeSOoLaDQmdhkdtrkQ3tunFJulcDmtFIoPMwzC1M58BbWv8aYXZ0ziNbOiLNZB6yT31VOcy1aILYNun3kOv6SAtMzu1IspOsFA5hdkwEEqS+BrXjrM4qVyC1bYxellouxFmLu/RUIJwUnQq2PXZd6GUhZuPSC0bgMZLOhScmE7LBQumF8uIeQenITGC7JQNLTNf2jivsZcNZKpQhiDxstixq2JPjdQlxL6sEQ++4l1A5oVtNg2SUuabj9TZlkkzC2mPEoOJap2alVD3PLQp6I3GoARWPQtEM2ad7OCFKZvE1+hqzMZnZA5zRb/H8okqRpARI1S2witLk7qZZ2HqgNXKji6RSWavTnKZfcHskvkIXASXTOq2wvpLLbJ2iEzDEV0D5aUYKyaI7EwALTVByyUtS3w7Yr4vBUPjlnu4XyugTKgezPIv7xLE1ezokUq1zXi+zgu/cjNBVIJSjE4sZChICgrtKIyemXmnpBmxZhfMribxBPXtMguZb0JnZ49C/uJmqifMQrXj//4eIuegg9Udy56BUKth8LEg7kvQkeTY9CD5/S6+Cd3xNOvlr4Br37GPzohEWeBtrzNeblDveSw28hxrDFB2g+yslTkrO5TB1BhdiTVnYHUgKmaEygSsTjZvtic0qa+wBgLCmRzjIzXuW8NIvaKe1N8vtr9rH3FBk5SyMKCHtoPtuiQYjxE9A28u09nTnCYaTDD9mCQwQYM9a5G64Gxq0l3xIBUIO8V0EuK6m1n1GwbWSjY/pnYWBWOsnjqUehqzLcjNaeKiIKxoEj8LZpNRVqb+p++ncWThB+dJ/f3ikaHgG/7kdrSX0vM0WBpiTXdTnIk1iQBz1bihgVQQVxRGR9Jd9M/5I2z40w+SCLCXDaL+9Bx5WkI6GpKs+kY5s5k6LVPOEWoEWdh8XFSYHUGyLsSRF7dlPKFIfQhPf9Ve7vr4G6gcEvSqFsoCbRikribVWXCZSARaZscyIQGVyZVosOoG29+1j6ii0L6Grok2IH/cJBhRiFQSDia4XkxsaNSyDUJjtQRaQJwDdU2HqGehNci6RbA+4dlbT/LlNfj9PeFI3foHezHXC7a+dy9iVCBjKJ7SmbXIyWKxIDs7QC4ZpJ4mLqc4fQH2YEJrtoCIsvhSqykzv/5QYrUEysq0ImVr3DmTHh5Gy7CBVsEAAAKgSURBVMCrZZHhSU6jPI0oRmwaqJMoyfRSmcQxMVsGd359Bytd76JteEII/1vevzf7/7e3okzoDapsznM1wWhCZ52guz7NNCYji8y22gKnAev/OWLLJ2Kqn/YxhKa6voHoC0lc/bBI7bAvq9OuC4rHwZvXiFCC0PSGFMZIF726/a21QGnBgNemXOxCkn1cZ1kgoifYxt9jYdO+29EyM0Yf/4Xf5cRb3oAqJChHoyyNXTMyxzCZaUvSTUBk8mRuXvHVL70Na65F0CdpzBSJEpOcH6LsbE4UlspO/5EglCA/o8gtpShL4J+VVB7IjCeWlaJTiQgFumvSiWzunxmlvuLjLkpklMmpP1D39MtF6itkPmbyfEdgkamg7rwBAqKhBK8SELQcXDdBrI8Z/z8CeTIT6s/+9CBagNk0CDpF0koC5YR0NGKk3GZGVbHmLGJfkzgCp66RkcZdzvb5UxfSUwWECWIohEjSChxMU6FO5LGb2WvZzSe4lWrLJ3+f4z//zsf2GhQaq26QOqsHMcaCsGdBYKCm8hy75Q3w8u9ltxua7ojAmxcEg5l+Lq3sgMXFegGjnh13V712iSWvSrNurp74I+k7GJE4FmYIyhTUr3WhP8YwFNcOzHNIKlaGfOxpi7Aq4J8u3rYfGqnHf/6dj0rb87l30mp7EFrYK2L1lAqV9VwlsvP/hhMm/tf7cYohR38uU2876wRH33kz171uX6Zqtk1SS6FiG1GOUDmF8BPGi3XiMYN41MCRil5UZuk6O5NLfUGcFyR9EYaT0qrnOOuWKXs9Wmke5UAq1+afesnCvxBiEbjyVpV/P5jQWg9cKMOTB31fBfzQV///iHiS1KuAJ0m9CniS1KuAJ0m9CniS1KuAJ0m9CniS1KuAJ0m9Cvj/V93s2dRQgWIAAAAASUVORK5CYII=" alt="Image"></td>
+      <td>(-7724349.609951427, 1052646.4919514267, -7665045.582235852, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
     </tr>
     <tr>
       <th>2</th>
-      <td>(-7724349.609951427, 1082298.5058092135, -7694697.59609364, 1111950.519667)</td>
-      <td><img 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" alt="Image"></td>
+      <td>(-7665045.582235853, 1052646.4919514267, -7605741.554520279, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
     </tr>
     <tr>
       <th>3</th>
-      <td>(-7694697.596093641, 1082298.5058092135, -7665045.582235852, 1111950.519667)</td>
-      <td><img 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bHNErWrCbvSpzl3/ANIpx21s+pyrvW6gioTQdFwiHFM7aZyGCr0XOLtghOc7U8ySCPEUGM1yEJgIoeTDpkLrANbtZ5cK18yKYeBDykrhubykEpYsPbEHCoVmKolGPPX9FyDMPfLuWhZ9E7AVjEJwNTeDrVKzmCPJP5KFRCks45k3jue3rLEasimnPf8BejEO6ds0mErFjFU45KLw1QdwUBSVkBlgrzlsOEVYHS1oDrtNaesOpjOiCoFrmow6xWK3ZiyUKyuTlEaiauVlBWH1VBMWEzLXKYtQUfBTMbMwTbV2SHLy1vEUymt1ohmnGGdQM8lRId77D24TRiUmI0KrScF8c4zJsFUfMQjMkl7q0ktyjG5omg6TGX8nKVf1kXdL3nhfGJIjyDa9eNzyoe3ohSEu55XCyPoHzbeF8Azye5XkGvy/rIQlBVDFJdMNYbY+oj1C1NQCtzY6KejENXxl7eRw9VK5O44pb6YEddSAN6y93ECYfijrUNkwxClLFv9GmWpKIYBRaqphAX9XoXmSYlOHO3XWEzTEK0GmIoveSx+Ada+VZGVGnrjaTnQfUF103v7dM7iJOjEa67VMFwUJEcyZGiQgJMBwRDkrsBUwCpFdcNzWWF9LuP6g+pADwWtpxRFvUH4nT1ubm2yWOtRDzL+XOs0v7l2Jycf30NtVXq+OWHAePsV3dplpu6J+mKlh3WCr/f3sNZp4lLFaFiHeoGzgpsPrFPRBSc/eRNywVLWIFnwmld/MiDsOub/80mefPdB1ucBA+1OzZdNhj5OFw5MIHDKL+HmU56N5E3Ipv2UJqf7DJMI9dUGTvpAIOpZ0glJOiOIuhYnJXEOKr0BoArrHUT3ZiincsIs4ktry/R6FY7s2+BfbH8j7QsTqNQ/VScACbKvMRXH0kSHQR4Rq4LEBDzc28OpnWlqcU7eCLClwFnBkeV1nnh8L8QWudci83HpO/DVB28rHe37Vpj7E8HWX8wQ2yHqXIwwYGIIu1A0oIy9uZA5JHOen9rAkU+O4/onprABTLYdOgGVOZJpSd4U1C9a8oakqInL7OCV5JqaKUzsmL9jgz372mxfbCGE48i+Df7c1BnefvPv+1q5GgOgfGpNlAIXOi52WwzzgLPdKU5sLrLWb/INe86hlcGWAh2VUEhOP7CfoKsQypddwo5gtMdiIodKJNVVwfJnCoLJlM1v9MTTx/gCWQhUCsMle9nhXAovTcXhlFcMlUiibUXtgqR2UWIigRPQX5KUFd8DAB5kG+Dp1I2I/a0CPT/COMHWboNwWzNoxnzfkc9SOMWT6QJqKIl2JGXNF9Js4BCTGXPTfXZOzGKqltpSn8FGnaSnGH7TDndMr/L5fpWsG6O6mupFgYmhv/BMuk2WXvsvaeyp/1shMgeBI4wLslgB0mfBFATjpLMNwSrPTPIpgx5I5r4E6SSk06BHjrQiyOecDxLaPmwNRg5Zem6rUxDGa/t1B5XAMTsxIMkDTKYwcyVT9YRPrN9DPcg4159EjbwNy2YMrmII6zmLkz3OXpxGRp5CZWnAocNr3D11nvOjSX73kduI6zlIh53LyNsxo8M5aifwFCf2Szba8UA5jdcaJ8AIsk6MHCqc8AGDLEH2/JDTGd8kUbQcaiSJtwTVjYxgpJGFYrDsGY0aCcADKgzoxBK1c3orMaJ0VNruxmgqFqwTnnDHJQfnt/n+vV/klnCNf7n9zZR1yVa5SFF3uKqBXCIfqdO7JwMnsLHlTXc9BsBMOCCzmnP9SXRkSLcr3kzUnS+jWE/Si6anYrqvPKAKHxVJR+WJiNFy6Y+NHNmMJd6UBAOHU4J01rf/FC2Hk17Lw55juCdkuCDJpj1dinYEeuTzAOAIBhBtZxStkGROUluzCOuwN4RSWUGkDNXGgC1R41BjG4XldVHIWzeW6Tw6jXZgFjKq9Qx7ooXKoHtqkupSn8lawt64w288eSdSOm5fWCMrNWY7QmXSe+umQ0clYWgY5p5k1s74thwfy/sGB4xgdKBAFBIXGcjVM31dSlDWfChrJ3zgEfQklU1BNuUzYCr35iwYiMt8tbruNbW2npHNRIxmFfGOY7DXBwHu8zcAVGHg7NlZ4omU5ek2Z4ZTfGNTcqx9kLxUlDMFZV1Sb6bEQcnOtCHoSvRAcNfiRV7bPM961qIsNGao+Vq5F1MoXGRRXU+6y0RDVFKWEpQjXNfEO17TkjmvUWVVcOjf5BStgD/65DsBuOkjxwgGHlUbXiLrvqQTbyiijiOb9A9GZZBP+BqVMGP7WfjAoLKVY7Vk6w5NslT61aMclVV94zL/wbbGNASrvSaHprbpmCoHo02sE+zd0yYpNMMkwjmBmMrRe711f2RrgYvDFmu7Taq1lEwHFJlGrUVI4e2kE4ByFIMQGRtEaClXUoZHSpSylE82aa6DvuAQ1qFH3out/PrP41oKPdIIfEoPIBh4G6xTfFHQjJM0IeAEeugBxoFOfL9VOh1eXhUIh5wsMCNN1ObG1KiE8+m+6XqKVobSKXbLGp/ZvJ1k5Ov3/c06OMiMoLYwJE1ClDakSUhnvcHSyjaHW1tcHLU4vztBWUQII8jmS5oLfXqrDYSVWOV81gmoVzKMFYwWM9q1gMqaYuNbQvb+nvcczkG4oy7nPIWBYOh/O4nP8Nd8NiqfcKjEx/7qUnEiFAwXFSqFuGNoH9FkM75HzHQDZCovU8XrDuol2e1VadYTvn5yH/UjGa+bPMfGoMEgiRCZRE7lmIFmuFFDjSTlfEa1ltGc6dJJYipTBc0wJQpK0pqjsi4oUkn/fJOwKykaPvdqc4VLFV3pIwm9FqH7grADy7/yJOyZ9wOygukTjsE+HyardAxkxZdyjBakM46yYYm2fEU1bzrAmxOnoLJtqV0YkSxUfOowdqh6gTxboXkK6qvFFS3/qyb/NoDpA7vMT/WohgU6Lnnb4meJRMlNk9sEQYmLLc5BdWYEymEaBnUhZjSIWF2dot+u8VuPHuXE6h76gwpR23PLsCOJ1xUqEZz5kXcgAoszwiekH6njzlcxoSPqwNyv/Alu3yK//fCHADjwcahfSAl73k7GHUvrVI6N8PnV2Gtt5aIi3vL2FumplImgjAXxVkYyX6G7olGJQHelbxHFVzuq/+3UFXn/a4qotDJs7TbY3G2gA8P9F/8Sf7h1M09szzEaRczu6eBKiT3RonomACso6xa2I4LNAKEtUjuUslgrSPYXDJYtZcWRzVrSw35Nnvnr7yXYCog3JEEfTr7z7diK9cnryUl+58TPXh7T5377PXz2v/4kRV0Q71jKWLB1Z4QTII1f5pUNT9HK2ti2Jj76CnveUdlQEe1kFHV/zajtO7idBJ06MNbz41fC52oBFYFl/cIUIpdQK7HWsjZssrHTwlmwqaaoKaZn+mQTI+wDk4hKidyMkIWfWG9O4qTxVdTJAYM4omg3qK4Ksml48m0/fvl+xVwBKzmjXsRNHzlGWCge+9C98KHnjuvoe46jh0AEowXpbZ+DsOt8T2rgTUIyD0UTwo4gGPhzF/9wm3yuTvdARNGILudYlz69hRgkfObscW5/13F6b70V8S8+88oYXe3mtOrhRbf/F95KWShajYTtzSYIh+gFPncZOOL5IX9h+Sk+d/4Qo/MN9Mg7iEu1/HTO9zexN0FKi71YpXZeErd9D1Oy4LDaIUqBvmlAnmrsMODsD73rZcd25w8fQ5aeQdhAkDe9raxuOF8mkZDOCl+ITAVB3+dTwZuL7kGf2Q97jtpaycbrA2QBj33Q934d+vljXPjYcdKL569vz39VF8w0hihpubm1ye+u3o5IlS/CTRlUo6AsJdt5jeFmDZ0KVCIwE5baG7dJ8gCGEaYXoM9VPM0ZR0gm9GWLcjFj5g8isimB22qQHi6vrDXEeS9eVvE/44R03vQZLgTjqMmHvCAoa4LqhiWZ8XY9GDiirqW/X/sOPwe3vP84soRA3qB8qhaWwkq+YeYsJzp7UbUSm4beOgcWKS1lpvnSyRXUQGJDRyEBCYMkwhhJ+GSFdE9JOVNAKdE9X8JIpwUmdtANMJFfuoMl8RwNveWnjpNNWU7/6DueM65b3n8cMemroZeiKpV5MIUdc2A5rkVNGUQhULmi/qT1GTXtewEmTmaM5kLcuJU97DjSmXHLj+XGdKgI4dhb7/K13X2cWZ/G5op4cYS1ArFdoRxo6mf9U88noKg7ahcFyRuH3DK3wVM7swznDXIkcVMG2ZGUdYOpCGrnFKZmiLYV8a7lgY+/8wX3T/YXRK2Ub/rd99BLI6yVJOca6Irv7RcGwu64jj/j03w2giff93aO/Mxxyooj3PYPMZsyJNMKG0LzjCEYGIqar6vlLZ8dy6YFOvH3tpor6k+9au/fHtX4+uoe7pk+h000ejOAhxvIx+pULihaT/lkhsoc2bSlbBm6t5UUqWZt2CRNQqaXdxHzGa6Q2NjbTmEEyZxDDRXBQNB4esDBf/9cb3Tbb95H2MwQjzbYODGPMZKbZzaxkSWfNpiGwdS85qWzvsUINwb0p4+jEq9pKh97/V2FqUBt1RLvFETtjKidUV/NiXe8g9Oj54J5JU0q15SlCsOS33j4LkQmfUjYtOiBRCfj8M8Kn25bHAL++NEgIvvPc8g3D9nZqUM3IBhIwp5PDF+ydft/9XEAHvuFg4jScvt/+QBZFqCUxRiFO12juur46j/2/a4H/s2HIbBEzYziQm0cWvpUn+lrnIbDHz5GdUugCud7qho+H6CHgsbZZ5a/HOW4RsRgb4jKfepvuOiZhHDPFBGvP6jKMVivIzOJLC+VKQQIX1xTuc8i2dCR9yJkpeTWhQ0eThaRJcz/u5gL312iEklZcwRHetiHWnz9uPew3/arf4enf3kfsjA4IylLhXNQFoogLEn3pQxMfHk4p7/vJy7/ffjDxxDGg2b1uNVH+rTeaK8j7ArySYOw/kGqRBAMLbU/PQOtBmhFUQ+wSnheOrarZQz9A4bqqroxTb8wriqW0DjjWxMvOQGroWg4gr7AHExoVDPcFya5+AcH2bdRYgPD2hsULvUt5C6y5E83qfbh25d+FJcXnPpHe6lFOZ1RHRUZ8lxjS4nLFGWkcKXke7/zj/nxh77Axx95HbYfEExkVCo5NpxA5r7S4EJHtO5zAdIA1tf2wS9lVQicdvT3KcrKAcqKn4sNBZUte9mpBQPHYMlTxSuJpq4JVKUsopUjpyyDskrYFcRtzwOzSQHlWFO2YvphiFgx7P2DAatvamHCcZLDaZ5+j1++N91/jK9/9F74KNz9Q8eoxm2yQiO0D3WDwJClmqCeUxYKcsnARDzdn0VKx9RSh+4gJss1pmYpJ8ecRzmCMavIm94clFWf+behI94S4w4Z6EwqshnrG+V2ASSVLYuJBGXsAwVhFFFn/IBeSemulvxXDu1xc+/9MSgFwa4i7ArCnl/6RdMPdrDs7dSlMLC67uNxEwlE6fjar3hAD338gzz9V993Rfd9zb3HmfvyiM7NFSa+7wKNIOWrT6yAcMStDGsFZa6YmBjS61dxqzG1i5J0xj2zU896ZlBWYemzCSotWfumBmV13FcVQ7ztV2FZ9Z3eyeJz9ysIIb7snLvn5cZ6Tctf9ZSvFY2bYGUO2bKjmCqxWl/eSS2cL/wN9/jJmNjTnDs/9T5KKymz6ive65Jcsrn/xx++nZPn55BbIdWDPZwTJLsVVK1ASNht13FDTTT0y9mEgPCpvkuOprbq2Ly7gk4dUcenBL02+hxAWRn3Vyn8DuurlKuvpuZ+l7QP+cYti1VBZR30MECUeCo1ZdC1ArMbUSqHLDXpPr+ZYfDoFGFHcObD93LkP/4Meya73Nra4PbaBQySH7nlD17y/t86+xRnN6cIb0pJ0wAz0lRPBcjXD0lGES7R6L7CVBxFy2FDnxMNOxoTO181GJefVeYjOae8ZsY73m4O9zgEPnhQ+RV2+j5LrilLdYlWCDPeB6/9oIqGNyV65CucYVQQzCQQWNzRvq+bW3wCe9Kx/E8+4o8XlgujCf75qTfymc3bOfjvPsydn3pxs/CB2z9JvZYybFcwnZBDv2bQqd/RYlIFgSXoCcqm8c1vcDlik6VfLZf3ADQFg5VLSWwY7oHuEef7DHZ9SBp2rh6fq7ap0fKSW/rhe4m2BUh84rdpEOclVX4AAAcxSURBVEagBpKw68vT6YIhaEtsBOVESTSRko9CRCfAVgxBW1PuyWi0EkajCGsFYVSSdiNkXyMsRCt9sjTEbUYvCEu/5Tt/ke07AkZLhnhdUdlytO8uQUDzkeDy/nynHfGG9juhRz7qap729fzhgi/mhT3IW95USeOT1vGWj8a+8k+fu//rxthUB+VihjDR5S3caiC9trYMYVdT1ByuYgDf7Vw2xjbKCFyzRLW1zxplitEoIq7kpElIlgToaols5Dz1ve8H4LWffD97964D8Prffi9JHtDvVlhSPpycO7jDZr1FWQ05+7ffDcCRnz5O0NbI3GeiTAVwHtB4y3ec5E3BaK/1gcqCxWlH8wkPh0r9Bo5o9wpc/YvIVS9/YUFuh741vWF8Nn1TIjOB7iryCb/9u/pUSLztc5ayUTDZGPlXaqTSk2/tSxUTzRHTtRFlpnAj7fdQWf8Qfurr38VUbcRsPODnHvkO/t7Bz6GVwZWSi2+WJAdyth6fofFQRGXrGdtnQ1+eaZxxVLZ9V4lOfPtk3LEM90gfxQ0kpm5wgW9RH+3xDGC4zxcOs8YVEtPnyTVpqswEbiVBrsfULgqyFpi6xQmHzCUqHTfUpp6/2kSzfn6KylRCKkPCC36XSpFWGBwx7PaqkCluPryKkpanH1jm9nceR3zLaxkMYi6GLU5UFrljdo32hQl0K8d2NWd/4D0vGN6Rnz5OdUtgg3HKz+B7YgsIBzCal/Rv8mVzdaRHZAX5Wg0XWEzVku7LMCONMCFl7Rpc/7WAKiyowwPSfsTy7xvSCRgsCf/ymIkSVzVUvhYRDB2dI47ZL8NtHzjPo+/fT54FuERjQwh6PvZOkgA30hBYnl6bRUhHeHOf4DU5nW6NvbMd0lKzvd3gyWCWs3/nXaz8yv2c+bF3vOj4wq7XxqwpLvPhZ8vdP3QMOZnjZgzqS03SfcY7pJmEhYk+Zy/MvOjDuiqMrtpR7VtyR7/9Xp+0HVnyumT3Nt/eaKuGiYcC+jf56MSGfpNXvO03Lzxx370vuN6RnzlOUbfY2Zx6K2E4iLGFhFyidzX1W3e5Z+E8D64v8frFs/yze379Jcf22rcdR2WOMhac+AcvvNf1kBvjqBTs3u6wdd/Ie/ATOTuv02Bh4qFgnL906IHfdbL/dRdpD6sMz7de9HImdpi65ez3+/ctrvzrnyPYCFGJL3s04oxIlrxmbvVlAQV46B++EMjb33Wchz9yYwB+Kbkmm+oubbuxcPbbQhAWVzeoQiJH0Hpc+feQGFiptzmzNs2Zv/fchPPhDx0j2hU8ff8zS3T5136eYCPyW8RzH2ev7rTYHVVYnty9ouG9/m98FBMKRvOCxz50L4P9llv/03089t33XfVUr1WunvwL59/uoy1n3/ouv6m2K6mcDhnNC9pHfW9AvA3R0Q5/YfIxfuKez/Dnf+/dvOa/fODyZUzs7fNN9x9j5Zfv57afOE7tiYiw46Mdp6Dck3Hq//1JzIMT/Na3/MOXHdY3/M1j3PnDx+gelHz5V9/uu0uAMz/yDpJhxPKv/iIr///PXfV0r0WuAVQQhUS1PUm9tOsv2VOS7CmxscUq6Lym4Iv3/BqpDfj6aB93TK3SiDMOffyDHPi3H8ZWLCbySZfqBd8iGXZ80y1WUDT8q5huuv8Y6R7Dyi/dz63vOw7Ayj+6/wXDUoXfKyULn1eVueC1n3w/f/NLP0C17vsIas0raNi/DnJNyx8Hp+59B7e/8zhyEhhJwsN+X3/3fItHfvFefuLE9/D+jTdw/K6PA3D4Ez9LvZoilUUHBhYKuNAk6oybyQQYJ8gmfQUUASymWCtwiQYjSBYNN91/jNmvS1735WOozBEkji/8xjtJJy4lbLyG1s5JhmKSz5+ZYPJgm7SnsKcn4LuuF3QvLdfwsi9B7YwnxdmUo6z594/MNX1ngu5Jlv/xR8is5lNP3375NFMqOt0a1ThHCEd2vo5KfPSiEzz/zaG6KqisC1QiMf2A8OkKe39H0npEc+aH34mZy7GBf6lBMivJa5LX/eAxeocco9tSTt37Dp/x13DPNz/O5ME2d86u8pff/KDv/PsfIFdNqe655x734IMPvuDzdz30vXz69FGGmzUm93RxTvDQ//VMW87dP3SM4T5BdiCl1kzJH24R9nyThbDj/OXQRz1FDaYfMXzhP76wmnr4Ez+L/lqdsupj+cc+9OKe/a1f/n60MKwmLb5yaj86Knn6/3n/Vc31xeRKKNV1A/VaZOWX7qey5juV7Xiv02DZEnQlT9x3L0fuO07RtFQ2/M7oN7zpEQZFxInz+ziwsM257Ula9YR9jQ4fWf5P/NTFv0yviPnUN3/suff59Z/nzN/48ZcYxdXJDQFVCLEFnP2zDOx/cVl2zs2+3AGvvuj7Bsirb/q9AfIqqDdAXgX1BsiroN4AeRXUGyCvgnoD5FVQb4C8CuoNkFdBvQHy3wETfluoosBpuQAAAABJRU5ErkJggg==" alt="Image"></td>
+      <td>(-7605741.55452028, 1052646.4919514267, -7546437.526804706, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
     </tr>
     <tr>
       <th>4</th>
-      <td>(-7665045.582235853, 1082298.5058092135, -7635393.568378066, 1111950.519667)</td>
-      <td><img 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" alt="Image"></td>
+      <td>(-7546437.526804707, 1052646.4919514267, -7487133.499089133, 1111950.519667)</td>
+      <td><img 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" alt="Image"></td>
     </tr>
   </tbody>
 </table>

From 6a57e060aeac3939ccee5420e89ed60c84057368 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 12:52:33 -0400
Subject: [PATCH 326/380] Unit tests around implicit file:// for schemeless
 paths with leading /

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/ref/RasterSourceSpec.scala   |  6 +++++
 .../main/python/tests/GeoTiffWriterTests.py   | 23 +++++++++++++++++--
 .../main/python/tests/PyRasterFramesTests.py  |  7 ++++++
 3 files changed, 34 insertions(+), 2 deletions(-)

diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index d3963dedd..4d2f1a02f 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -106,6 +106,12 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       val src = RasterSource(localSrc)
       assert(!src.extent.isEmpty)
     }
+    it("should interpret no scheme as file://"){
+      val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString()
+      val schemelessUri = new URI(localSrc)
+      val src = RasterSource(schemelessUri)
+      assert(!src.extent.isEmpty)
+    }
   }
 
   if(GDALRasterSource.hasGDAL) {
diff --git a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
index 9ddc8b65b..8fa30a7b2 100644
--- a/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
+++ b/pyrasterframes/src/main/python/tests/GeoTiffWriterTests.py
@@ -33,11 +33,14 @@ def _tmpfile():
 
     def test_identity_write(self):
         rf = self.spark.read.geotiff(self.img_uri)
+        rf_count = rf.count()
+        self.assertTrue(rf_count > 0)
 
         dest = self._tmpfile()
         rf.write.geotiff(dest)
 
-        rf2 = self.spark.read.geotiff('file://' + dest)
+        rf2 = self.spark.read.geotiff(dest)
+
         self.assertEqual(rf2.count(), rf.count())
 
         os.remove(dest)
@@ -47,7 +50,7 @@ def test_unstructured_write(self):
         dest_file = self._tmpfile()
         rf.write.geotiff(dest_file, crs='EPSG:32616')
 
-        rf2 = self.spark.read.raster('file://' + dest_file)
+        rf2 = self.spark.read.raster(dest_file)
         self.assertEqual(rf2.count(), rf.count())
 
         with rasterio.open(self.img_uri) as source:
@@ -58,6 +61,22 @@ def test_unstructured_write(self):
 
         os.remove(dest_file)
 
+    def test_unstructured_write_schemeless(self):
+        # should be able to write a projected raster tile column to path like '/data/foo/file.tif'
+        from pyrasterframes.rasterfunctions import rf_agg_stats, rf_crs
+        rf = self.spark.read.raster(self.img_uri)
+        max = rf.agg(rf_agg_stats('proj_raster').max.alias('max')).first()['max']
+        crs = rf.select(rf_crs('proj_raster').crsProj4.alias('c')).first()['c']
+
+        dest_file = self._tmpfile()
+        self.assertTrue(not dest_file.startswith('file://'))
+        rf.write.geotiff(dest_file, crs=crs)
+
+        with rasterio.open(dest_file) as src:
+            self.assertEqual(src.read().max(), max)
+
+        os.remove(dest_file)
+
     def test_downsampled_write(self):
         rf = self.spark.read.raster(self.img_uri)
         dest = self._tmpfile()
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index af14f5ade..eba050cd0 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -470,6 +470,13 @@ def l8path(b):
         print(path_count.toPandas())
         self.assertTrue(path_count.count() == 3)
 
+    def test_raster_source_reader_schemeless(self):
+        import os.path
+        path = os.path.join(self.resource_dir, "L8-B8-Robinson-IL.tiff")
+        self.assertTrue(not path.startswith('file://'))
+        df = self.spark.read.raster(path)
+        self.assertTrue(df.count() > 0)
+
     def test_raster_source_catalog_reader(self):
         import pandas as pd
 

From cdef62354de9d19310b31bbe343ee014ef20caa0 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 12:59:14 -0400
Subject: [PATCH 327/380] Remove file:// scheme from docs geotiff write

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-write.pymd | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index c0613208f..309eea89e 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -66,7 +66,7 @@ Fortunately, we can use the cluster computing capability to downsample the data
 
 ```python write_geotiff
 outfile = os.path.join('/tmp', 'geotiff-overview.tif')
-spark_df.write.geotiff('file://' + outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
+spark_df.write.geotiff(outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
 ```
 
 View it with `rasterio` to check the results:
@@ -83,6 +83,10 @@ with rasterio.open(outfile) as src:
 
 If there are many tile or projected raster columns in the DataFrame, the GeoTIFF writer will write each one as a separate band in the file. Each band in the output will be tagged the input column names for reference.
 
+```python, echo=False
+os.remove(outfile)
+```
+
 ## GeoTrellis Layers
 
 [GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames write data from an appropriate DataFrame into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both reading and writing of GeoTrellis layers.

From 4ffbd9ce7395355883f8e5d0202cc0a34c84eaf8 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 14:50:08 -0400
Subject: [PATCH 328/380] Handle null URI scheme in RasterSource

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../locationtech/rasterframes/ref/RasterSource.scala  | 11 ++++++++---
 .../rasterframes/ref/RasterSourceSpec.scala           | 10 ++++++++++
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index 6aa9ba441..ea33cf240 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -129,13 +129,18 @@ object RasterSource extends LazyLogging {
       } else false
 
     /** Extractor for determining if a scheme indicates GDAL preference.  */
-    def unapply(source: URI): Boolean =
-      gdalOnly(source) || ((preferGdal || source.getScheme.startsWith("gdal")) && GDALRasterSource.hasGDAL)
+    def unapply(source: URI): Boolean = {
+      lazy val schemeIsGdal = Option(source.getScheme())
+        .exists(_.startsWith("gdal"))
+
+      gdalOnly(source) || ((preferGdal || schemeIsGdal) && GDALRasterSource.hasGDAL)
+    }
   }
 
   object IsDefaultGeoTiff {
     def unapply(source: URI): Boolean = source.getScheme match {
-      case "file" | "http" | "https" | "s3" | "" => true
+      case "file" | "http" | "https" | "s3" => true
+      case null | ""                        ⇒ true
       case _                                => false
     }
   }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index 4d2f1a02f..a1390521b 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -109,6 +109,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     it("should interpret no scheme as file://"){
       val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString()
       val schemelessUri = new URI(localSrc)
+      schemelessUri.getScheme should be (null)
       val src = RasterSource(schemelessUri)
       assert(!src.extent.isEmpty)
     }
@@ -138,6 +139,15 @@ class RasterSourceSpec extends TestEnvironment with TestData {
 
         gdal.bandCount should be (3)
       }
+
+      it("should interpret no scheme as file://") {
+        val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString()
+        val schemelessUri = new URI(localSrc)
+        val gdal = GDALRasterSource(schemelessUri)
+        val jvm = JVMGeoTiffRasterSource(schemelessUri)
+        gdal.extent should be (jvm.extent)
+        gdal.cellSize should be(jvm.cellSize)
+      }
     }
   }
 

From 66acf9e414740d0ed3c25698367e91c9cbeeb7d9 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 15:28:14 -0400
Subject: [PATCH 329/380] Update docs raster-read to discuss geotrellis

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-read.pymd     | 55 ++++++++++++++-----
 .../src/main/python/docs/raster-write.pymd    |  2 +-
 2 files changed, 41 insertions(+), 16 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 91350dbb8..33c3110f9 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -8,11 +8,13 @@ from pyrasterframes.rasterfunctions import *
 spark = create_rf_spark_session()
 ```
 
-RasterFrames registers a DataSource named `raster` that enables reading of GeoTIFFs (and other formats when @ref:[GDAL is installed](getting-started.md#installing-gdal)) from arbitrary URIs. In the examples that follow we'll be reading from a Sentinel-2 scene stored in an AWS S3 bucket.
+RasterFrames registers a DataSource named `raster` that enables reading of GeoTIFFs (and other formats when @ref:[GDAL is installed](getting-started.md#installing-gdal)) from arbitrary URIs. The `raster` DataSource operates on either a single raster file location or another DataFrame, called a _catalog_, containing pointers to many raster file locations.
+
+RasterFrames can also read from @ref:[GeoTrellis catalogs and layers](raster-read.md#geotrellis).
 
 ## Single Raster
 
-The simplest form is reading a single raster from a single URI.
+The simplest way to use the `raster` reader is with a single raster from a single URI or file. In the examples that follow we'll be reading from a Sentinel-2 scene stored in an AWS S3 bucket.
 
 ```python read_one_uri
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
@@ -56,19 +58,19 @@ RasterFrames relies on three different IO drivers, selected based on a combinati
 
 | Prefix              | GDAL        | Java I/O | Hadoop   |
 | ------------------- | ----------- | -------- | -------- |
-| `gdal://<vsidrv>//` | +    | -        | -        |
-| `file://`           | +    | + | -        |
-| `http://`           | +    | + | -        |
-| `https://`          | +    | + | -        |
-| `ftp://`            | `/vsicurl/` | + | -        |
-| `hdfs://`           | `/vsihdfs/` |-         | + |
-| `s3://`             |  `/vsis3/`  | + | -        |
-| `s3n://`            |  -          | -        | + |
-| `s3a://`            |  -          | -        | + |
-| `wasb://`           |  `/vsiaz/`  | -        | + |
-| `wasbs://`          |  -          | -        | + |
+| `gdal://<vsidrv>//` | +           | -        | -        |
+| `file://`           | +           | +        | -        |
+| `http://`           | +           | +        | -        |
+| `https://`          | +           | +        | -        |
+| `ftp://`            | `/vsicurl/` | +        | -        |
+| `hdfs://`           | `/vsihdfs/` | -        | + |
+| `s3://`             | `/vsis3/`   | +        | -        |
+| `s3n://`            | -           | -        | + |
+| `s3a://`            | -           | -        | + |
+| `wasb://`           | `/vsiaz/`   | -        | + |
+| `wasbs://`          | -           | -        | + |
 
-Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. See the GDAL documentation for the format of the URIs after the `gdal:/` prefix (which is stripped off before passing the rest of the path to GDAL).
+Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. Another example would be a MRF file in an S3 bucket on AWS: `gdal://vsis3/my-bucket/prefix/to/raster.mrf`. See the GDAL documentation for the format of the URIs after the `gdal:/` scheme. The `gdal:/` scheme is stripped off before passing the rest of the path to GDAL.
 
 
 ## Raster Catalogs
@@ -127,7 +129,7 @@ Observe that the schema of the resulting DataFrame has a projected raster struct
 rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')).show(3, False)
 ```
 
-### Lazy Raster Reads
+## Lazy Raster Reads
 
 By default the raster reads are delayed as long as possible. The DataFrame will contain metadata and pointers to the appropriate portion of the data until
 
@@ -180,6 +182,29 @@ mb2 = spark.read.raster(catalog=spark.createDataFrame(mb_cat),
 mb2.printSchema()
 ```
 
+## GeoTrellis
+
+### GeoTrellis Catalogs
+
+[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. RasterFrames provides a DataSource that supports both reading and @ref:[writing](raster-write.md#geotrellis-layers) with GeoTrellis.
+
+A GeoTrellis catalog is a set of GeoTrellis layers. We can read a dataframe giving details of the content of a catalog using the following. The scheme is typically `hdfs` or a cloud storage provider like `s3` or `wasb`.
+
+```python, evaluate=False
+gt_cat = spark.read.format('geotrellis-catalog').load('scheme://path-to-gt-catalog')
+```
+
+### GeoTrellis Layers
+
+The catalog will give details on the particular layers available for query. We can read the layer with the same URI to the catalog, the layer name, and the desired zoom level.
+
+```python, evaluate=False
+gt_layer = spark.read.geotrellis(path='scheme://path-to-gt-catalog', layer=layer_name, zoom=zoom_level)
+```
+
+This will return a RasterFrame with additional metadata inherited from the GeoTrellis TileLayerMetadata, such as the SpatialKey. The TileLayerMetadata is also stored as json in the metadata of the tile column.
+
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
 [Tile]: concepts.md#tile
+[GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index c0613208f..acc84ee80 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -85,7 +85,7 @@ If there are many tile or projected raster columns in the DataFrame, the GeoTIFF
 
 ## GeoTrellis Layers
 
-[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames write data from an appropriate DataFrame into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both reading and writing of GeoTrellis layers.
+[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames writes data from an appropriate DataFrame into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both @ref:[reading](raster-read.md#geotrellis) and writing of GeoTrellis layers.
 
 > An example is forthcoming.
 

From 530ddcdacce636a5a3aa4c8238d56ce9576608e9 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Wed, 7 Aug 2019 16:25:03 -0400
Subject: [PATCH 330/380] Update raster read docs page to fix  #250 and #89

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/raster-read.pymd     | 36 ++++++++++---------
 .../src/main/python/docs/reference.pymd       |  2 +-
 2 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 33c3110f9..28ee2a997 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -58,17 +58,17 @@ RasterFrames relies on three different IO drivers, selected based on a combinati
 
 | Prefix              | GDAL        | Java I/O | Hadoop   |
 | ------------------- | ----------- | -------- | -------- |
-| `gdal://<vsidrv>//` | +           | -        | -        |
-| `file://`           | +           | +        | -        |
-| `http://`           | +           | +        | -        |
-| `https://`          | +           | +        | -        |
-| `ftp://`            | `/vsicurl/` | +        | -        |
-| `hdfs://`           | `/vsihdfs/` | -        | + |
-| `s3://`             | `/vsis3/`   | +        | -        |
-| `s3n://`            | -           | -        | + |
-| `s3a://`            | -           | -        | + |
-| `wasb://`           | `/vsiaz/`   | -        | + |
-| `wasbs://`          | -           | -        | + |
+| `gdal://<vsidrv>//` | yes         | no       | no       |
+| `file://`           | yes         | yes      | no       |
+| `http://`           | yes         | yes      | no       |
+| `https://`          | yes         | yes      | no       |
+| `ftp://`            | `/vsicurl/` | yes      | no       |
+| `hdfs://`           | `/vsihdfs/` | no       | yes      |
+| `s3://`             | `/vsis3/`   | yes      | no       |
+| `s3n://`            | no          | no       | yes      |
+| `s3a://`            | no          | no       | yes      |
+| `wasb://`           | `/vsiaz/`   | no       | yes      |
+| `wasbs://`          | no          | no       | yes      |
 
 Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. Another example would be a MRF file in an S3 bucket on AWS: `gdal://vsis3/my-bucket/prefix/to/raster.mrf`. See the GDAL documentation for the format of the URIs after the `gdal:/` scheme. The `gdal:/` scheme is stripped off before passing the rest of the path to GDAL.
 
@@ -131,9 +131,9 @@ rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('ni
 
 ## Lazy Raster Reads
 
-By default the raster reads are delayed as long as possible. The DataFrame will contain metadata and pointers to the appropriate portion of the data until
+By default the raster reads are delayed as long as possible. The DataFrame will contain metadata and pointers to the appropriate portion of the data until reading of the source raster data is absolutely necessary. This can save a lot of computation and I/O time for two reasons. One is that a _catalog_ may contain millions of rows. Second is that the `raster` DataSource attempts to ensure filters are processed before reading raster data.
 
-Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the realized tile is queried right away.
+Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the option to force the raster to be fully read right away.
 
 ```python lazy_demo
 uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
@@ -144,15 +144,17 @@ spark.read.raster(uri, lazy_tiles=False) \
     .select('proj_raster.tile').show(1, False)
 ```
 
-In the initial examples on this page, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized tile from the lazy representation.
+In the initial examples on this page, you may have noticed that the realized (non-lazy) tiles are shown, but we did not change `lazy_tiles`. Instead, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized tile from the lazy representation.
 
 ## Multiband Rasters
 
-A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements. 
 
-When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
+Multiband rasters files have a strictly ordered set of bands, which are typically indexed from 1. Some files have metadata tags associated with each band. Some files have a color interpetation metadata tag indicating how to interpret the bands.
 
-For example we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting DataFrame still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
+When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, **indexed from zero**, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
+
+For example, we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting DataFrame still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
 
 ```python Multiband
 mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 81f20cba8..6508fda26 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -76,7 +76,7 @@ Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_conv
 
     Tile rf_tile(ProjectedRasterTile proj_raster)
 
-Get the `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
+Get the fully realized (non-lazy) `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
 
 ### rf_extent
 

From c9e9652dc34ebfd7223f217f717dbd9302e509f0 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 8 Aug 2019 08:04:54 -0400
Subject: [PATCH 331/380] Add band to concepts, address issue 248 items on
 catalog doc page

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/concepts.md          | 10 ++-
 .../src/main/python/docs/raster-catalogs.pymd | 66 +++++++++++--------
 .../src/main/python/docs/raster-read.pymd     |  2 +-
 3 files changed, 45 insertions(+), 33 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
index f367a1310..8375d8283 100644
--- a/pyrasterframes/src/main/python/docs/concepts.md
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -45,6 +45,10 @@ A "NoData" (or N/A) value is a specifically identified value for a cell type use
 
 A scene (or granule) is a discrete instance of EO @ref:[raster data](concepts.md#raster) with a specific extent (region), date-time, and map projection (or CRS).
 
+## Band
+
+A @ref:[scene](concepts.md#scene) frequently defines many different measurements captured a the same date-time, over the same extent, and meant to be processed together. These different measurements are referred to as bands. The name comes from the varying bandwidths of light and electromagnetic radiation measured in many EO datasets. 
+
 ## Coordinate Reference System (CRS)
 
 A [coordinate reference system (or spatial reference system)][CRS] is a set of mathematical constructs used to translate locations on the three-dimensional surface of the earth to the two dimensional raster grid. A CRS typically accompanies any EO data so it can be precisely located. 
@@ -57,10 +61,10 @@ An extent (or bounding box) is a rectangular region specifying the geospatial co
 
 A tile (sometimes called a "chip") is a rectangular subset of a @ref:[scene](concepts.md#scene). As a scene is a raster, a tile is also a raster. A tile can conceptually be thought of as a two-dimensional array. 
 
-Some EO data has many bands or channels. Tiles in this context are conceptually a three-dimensional array, with the extra dimension representing the bands.
+Some EO data has many @ref:[bands](concepts.md#band) or channels. Within RasterFrames, this third dimension is handled across columns of the DataFrame, such that the tiles within DataFrames are all two-dimensional arrays.
 
 Tiles are often square and the dimensions are some power of two, for example 256 by 256.
 
-The tile is the primary discretization unit used in RasterFrames. Each band of a scene is in a separate column. The scene's overall @ref:[extent](concepts.md#extent) is carved up into smaller extents for each tile. Each row of the DataFrame contains a two-dimensional tile per band column.
+The tile is the primary discretization unit used in RasterFrames. The scene's overall @ref:[extent](concepts.md#extent) is carved up into smaller extents and spread across rows. 
 
-[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
\ No newline at end of file
+[CRS]: https://en.wikipedia.org/wiki/Spatial_reference_system
diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 325ca0dd8..233d430a6 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,15 +1,15 @@
 # Raster Catalogs
 
-While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _Catalogs_ of raster data are to be processed. In its simplest form, a _Catalog_ is a listing of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This listing can be manifested as a DataFrame, CSV file or CSV string. The _Catalog_ is input into the `raster` DataSource, described in the next page.
+While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.pymd), which creates tiles from the rasters at the referenced URLs.
 
-A _Catalog_ can have one or two dimensions:
+A _catalog_ can have one or two dimensions:
 
-* One-D: A single column containing one or many URLs across the rows. All referenced rasters represent the same content type. For example, a column of URLs to Landsat 8 NIR rasters covering Europe. Each row represents different places and times.
-* Two-D: Many columns containing raster URLs. Each column references the same content type, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row represents a single spatiotemporal location (or scene) with the same dimensions, extent, [_CRS_][CRS], etc across the row.
+* One-D: A single column contains raster URLs across the rows. All referenced rasters represent the same @ref:[band](concepts.md#band). For example, a column of URLs to Landsat 8 near-infrared rasters covering Europe. Each row represents different places and times.
+* Two-D: Many columns containing raster URLs. Each column references the same band, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row represents a single @ref:[scene](concepts.md#scene) with the same resolution, extent, [_CRS_][CRS], etc across the row.
 
 ## Creating a Catalog
 
-This section will provide some examples of creating your own _Catalogs_, as well as introduce some experimental _Catalogs_ built into RasterFrames. Reading raster data represented by a _Catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
+This section will provide some examples of creating your own _catalogs_, as well as introduce some experimental _catalogs_ built into RasterFrames. Reading raster data represented by a _catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
 
 ```python, echo=False
 from IPython.display import display
@@ -25,30 +25,37 @@ A single URL is the simplest form of a catalog.
 
 ```python
 from pyspark.sql import Row
-my_cat = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
-# or
-my_cat_df = spark.createDataFrame([Row(B01=my_cat)])
+
+file_uri = "/data/raster/myfile.tif"
+# Pandas DF
+my_cat = pd.DataFrame({'B01': [file_uri]})
+
+# equivalent Spark DF
+my_cat = spark.createDataFrame([Row(B01=file_uri)])
+
+#equivalent CSV string
+my_cat = "B01\n{}".format(file_uri)
 ```
 
-A single column represents the same content type with different observations along the rows. In this example it is band 1 of MODIS, which is visible red. In the example the location of the images is the same (h04v09) but the dates differ.
+A single column represents the same content type with different observations along the rows. In this example it is band 1 of MODIS surface reflectance, which is visible red. In the example the location of the images is the same, indicated by the granule identifier `h04v09`, but the dates differ: 2018185 (July 4, 2018) and 2018188 (July 7, 2018).
 
 ```python
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 
-# As CSV string
-my_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
-# or
-my_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
-my_cat_df.printSchema()
-# or
-pandas_cat = pd.DataFrame([{'B01': scene1_B01}, {'B01': scene2_B01}])
-my_cat = spark.createDataFrame(pandas_cat)
+# a pandas DF
+one_d_cat = pd.DataFrame({'B01': [scene1_B01, scene2_B01]})
+
+# equivalent spark DF
+one_d_cat = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
+
+# equivalent CSV string
+one_d_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
 ```
 
 ### Two-D
 
-Example of a multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same. The first column is band 1 and the second is band 2, near infrared.
+Example of a multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same: granule id `h04v09` on July 4 or July 7, 2018. The first column is band 1, red,  and the second is band 2, near infrared.
 
 ```python
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
@@ -68,7 +75,7 @@ my_cat_df.printSchema()
 
 ## Using External Catalogs
 
-The simplest example of an external _Catalog_ is a DataFrame (or a transformation of) in one of the formats above. Here's an extended example of reading an external CSV file of MODIS scenes and transforming it into a _Catalog_. The metadata describing the content of each URL is an important aspect of processing raster data. This example includes some minimal metadata.
+The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading an cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data. 
 
 ```python
 from pyspark import SparkFiles
@@ -76,28 +83,29 @@ from pyspark.sql import functions as F
 
 spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/2018-07-04_scenes.txt")
 
-# The scenes list file has index URIs in the download_url column, for example:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/index.html
-# Image URIs take the form:
-#    https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF
-
-modis_catalog = spark.read \
+scene_list = spark.read \
     .format("csv") \
     .option("header", "true") \
-    .load(SparkFiles.get("2018-07-04_scenes.txt")) \
+    .load(SparkFiles.get("2018-07-04_scenes.txt")) 
+scene_list.head(3)
+```
+
+Observe the scenes list file has URIs to `index.html` files in the download_url column. The image URI's are in the same directory. The filenames are of the form `${gid}_B${band}.TIF`. The next code chunk builds these URIs, which completes our catalog.
+
+```python
+modis_catalog = scene_list
     .withColumn('base_url',
         F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
     ) \
     .withColumn('B01' , F.concat('base_url', F.lit("_B01.TIF"))) \
     .withColumn('B02' , F.concat('base_url', F.lit("_B02.TIF"))) \
     .withColumn('B03' , F.concat('base_url', F.lit("_B03.TIF")))
-# ... and so on.
-modis_catalog.printSchema()
+modis_catalog.head(3)
 ```
 
 ## Using Built-in Experimental Catalogs
 
-RasterFrames comes with two experimental catalogs over the AWS PDS [Landsat 8][Landsat] and [MODIS][MODIS] repositories. They are created by downloading the latest scene lists and transforming as in the prior example.
+RasterFrames comes with two experimental catalogs over the AWS PDS [Landsat 8][Landsat] and [MODIS][MODIS] repositories. They are created by downloading the latest scene lists and building up the appropriate band URI columns as in the prior example.
 
 > Note: The first time you run these may take some time, as the catalogs are large. However, they are cached and subsequent invocations should be faster.
 
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 91350dbb8..807392d63 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -146,7 +146,7 @@ In the initial examples on this page, we used @ref:[`rf_tile`](reference.md#rf-t
 
 ## Multiband Rasters
 
-A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different @ref:[bands](concepts.md#band). The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
 
 When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
 

From 9fb6f13c33a5497b1fd1b5ae7ac4ee372b4773f5 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 8 Aug 2019 08:12:12 -0400
Subject: [PATCH 332/380] Fix indent error in raster catalogs doc code chunk

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-catalogs.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 233d430a6..fc1e67be0 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -93,7 +93,7 @@ scene_list.head(3)
 Observe the scenes list file has URIs to `index.html` files in the download_url column. The image URI's are in the same directory. The filenames are of the form `${gid}_B${band}.TIF`. The next code chunk builds these URIs, which completes our catalog.
 
 ```python
-modis_catalog = scene_list
+modis_catalog = scene_list \
     .withColumn('base_url',
         F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
     ) \

From 5789f101f0077dff17e5ebcb326f33086b7faaf9 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 8 Aug 2019 09:18:03 -0400
Subject: [PATCH 333/380] Disabled nightly staticAnalysis until tooling issue
 can be figured out.

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 8697bb425..67b1e0a72 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -204,4 +204,4 @@ workflows:
       - it
       - itWithoutGdal
       - docs
-      - staticAnalysis
+#      - staticAnalysis

From 28d0205f862f82e5b00a510a783289c16e166c36 Mon Sep 17 00:00:00 2001
From: Tom Schroeder <tschroeder@astraea.io>
Date: Thu, 8 Aug 2019 09:38:22 -0400
Subject: [PATCH 334/380] Update LD_LIBRARY_PATH to ensure access to GDAL in
 rasterframes notebook

Signed-off-by: Tom Schroeder <tschroeder@astraea.io>
---
 rf-notebook/src/main/docker/Dockerfile | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/rf-notebook/src/main/docker/Dockerfile b/rf-notebook/src/main/docker/Dockerfile
index 3b62ac84f..ad430a9f7 100644
--- a/rf-notebook/src/main/docker/Dockerfile
+++ b/rf-notebook/src/main/docker/Dockerfile
@@ -2,7 +2,8 @@ FROM s22s/pyspark-notebook:spark-2.3.3-hadoop-2.7
 
 MAINTAINER Astraea, Inc.
 
-ENV RF_LIB_LOC /usr/local/rasterframes
+ENV RF_LIB_LOC=/usr/local/rasterframes \
+    LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/opt/conda/lib"
 
 USER root
 
@@ -14,11 +15,6 @@ EXPOSE 4040 4041 4042 4043 4044
 RUN conda install --quiet --yes \
     anaconda sphinx nbsphinx shapely numpy folium geopandas geojsonio
 
-RUN apt-get update && \
-    apt-get install -y gdal-bin && \
-    apt autoremove && \
-    apt-get clean all
-
 # Cleanup pip residuals
 RUN rm -rf /home/$NB_USER/.local && \
     fix-permissions /home/$NB_USER

From 48e636600bb849b5c66350c15d898583922dbd7e Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 8 Aug 2019 12:59:20 -0400
Subject: [PATCH 335/380] Python extension method for geotrellis_catalog

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/raster-read.pymd      | 2 +-
 pyrasterframes/src/main/python/pyrasterframes/__init__.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 28ee2a997..2018a1aee 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -193,7 +193,7 @@ mb2.printSchema()
 A GeoTrellis catalog is a set of GeoTrellis layers. We can read a dataframe giving details of the content of a catalog using the following. The scheme is typically `hdfs` or a cloud storage provider like `s3` or `wasb`.
 
 ```python, evaluate=False
-gt_cat = spark.read.format('geotrellis-catalog').load('scheme://path-to-gt-catalog')
+gt_cat = spark.read.geotrellis_catalog('scheme://path-to-gt-catalog')
 ```
 
 ### GeoTrellis Layers
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index e431b4e7a..3898f045f 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -223,4 +223,5 @@ def set_dims(parts):
 DataFrameReader.geotiff = lambda df_reader, path: _layer_reader(df_reader, "geotiff", path)
 DataFrameWriter.geotiff = _geotiff_writer
 DataFrameReader.geotrellis = lambda df_reader, path: _layer_reader(df_reader, "geotrellis", path)
+DataFrameReader.geotrellis_catalog = lambda df_reader, path: _aliased_reader(df_reader, "geotrellis-catalog", path)
 DataFrameWriter.geotrellis = lambda df_writer, path: _aliased_writer(df_writer, "geotrellis", path)

From 867d309d21e4bbe9ebb0b1862d958f0c2f573464 Mon Sep 17 00:00:00 2001
From: Jason T Brown <jason@astraea.earth>
Date: Thu, 8 Aug 2019 13:10:56 -0400
Subject: [PATCH 336/380] Docs: known issue for 188  (#264)

* Doc known issue 188 and incorporate work around by reference

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/src/main/paradox/release-notes.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/src/main/paradox/release-notes.md b/docs/src/main/paradox/release-notes.md
index 89b52c718..81fe395b0 100644
--- a/docs/src/main/paradox/release-notes.md
+++ b/docs/src/main/paradox/release-notes.md
@@ -39,6 +39,10 @@
 * Introduced `LocalCellStatistics` class to wrap together results from `LocalStatsAggregate`.
 * _Breaking_: `TileDimensions` moved from `astraea.spark.rasterframes` to `org.locationtech.rasterframes.model`.
 * _Breaking_: Renamed `RasterFrame.withBounds` to `RasterFrameLayer.withGeometry` for consistency with DataSource schemas.
+
+#### Known issues
+
+* [#188](https://github.com/locationtech/rasterframes/issues/188): Error on deserialization of a `Tile` with a `bool` cell type to the Python side; see issue description for work around.
    
 ## 0.7.x
 

From 1faa5ff89d6116d7e7c26eb140b3280bec85c055 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 8 Aug 2019 14:14:57 -0400
Subject: [PATCH 337/380] Clarify pyrasterframes/package sbt log statement re
 Maven python sources artifact

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/README.md                  | 1 -
 project/PythonBuildPlugin.scala | 2 +-
 pyrasterframes/README.md        | 4 ++--
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index d89d0ea60..877ff50d3 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -54,7 +54,6 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
    [info] Running 'python setup.py build bdist_wheel' in '<src-root>/pyrasterframes/target/python'
    ... more noise ...
    [info] Python .whl file written to '<src-root>/pyrasterframes/target/python/dist/pyrasterframes-0.8.0.dev0-py2.py3-none-any.whl'
-   [info] Maven Python .zip artifact written to '<src-root>/pyrasterframes/target/scala-2.11/pyrasterframes-python-0.8.0-SNAPSHOT.zip' 
     [success] Total time: 83 s, completed Jul 5, 2019 12:25:48 PM
     sbt:RasterFrames>
     ```
diff --git a/project/PythonBuildPlugin.scala b/project/PythonBuildPlugin.scala
index 545fbcb75..7cc33fda3 100644
--- a/project/PythonBuildPlugin.scala
+++ b/project/PythonBuildPlugin.scala
@@ -77,7 +77,7 @@ object PythonBuildPlugin extends AutoPlugin {
     val pyDest = (packageBin / artifactPath).value
     val whl = pyWhl.value
     IO.copyFile(whl, pyDest)
-    log.info(s"Maven Python .zip artifact written to '$pyDest'")
+    log.info(s"Maven Python artifact written to '$pyDest'")
     pyDest
   }.dependsOn(pyWhl)
 
diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index 1961dd93b..ee0541407 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -133,7 +133,7 @@ sbt pyrasterframes/package # alias 'pyBuild'
 
 Observe the output messages such as:
 
-    [info] Python .whl file written to '/Users/monty/rasterframes/pyrasterframes/target/python/dist/pyrasterframes-0.8.0.dev0-py2.py3-none-any.whl'
+    [info] Python .whl file written to '/Users/monty/rasterframes/pyrasterframes/target/python/dist/pyrasterframes-${VERSION}-py2.py3-none-any.whl'
     
 This wheel is suitable for publishing to a package index. See [packaging.python.org](https://packaging.python.org/tutorials/packaging-projects/#uploading-the-distribution-archives).
 
@@ -162,4 +162,4 @@ Or to build a specific document with desired output format:
 sbt 'pySetup pweave -f notebook -s docs/numpy-pandas.pymd'
 ```
 
-*Note: You may need to run `sbt pyrasterframes/package` at least once for certain `pySetup` commands to work.*
+*Note: You may need to run `sbt pyrasterframes/package` and/or `sbt pyTest` at least once for certain `pySetup` commands to work.*

From edee9651c92d0898854d2d79c5ee36abb8aaee68 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 13:54:24 -0400
Subject: [PATCH 338/380] squashed changes

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     |   6 +-
 .../src/main/python/docs/getting-started.pymd |  16 +++--
 .../src/main/python/docs/local-algebra.pymd   |   4 +-
 .../src/main/python/docs/nodata-handling.pymd |  60 ++++++++++--------
 .../src/main/python/docs/numpy-pandas.pymd    |  14 ++--
 ...sentinel-2-scene-classification-labels.png | Bin 0 -> 48070 bytes
 .../main/python/docs/supervised-learning.pymd |  38 +++++------
 .../src/main/python/docs/time-series.pymd     |  28 ++++----
 .../python/docs/unsupervised-learning.pymd    |  27 ++++----
 .../src/main/python/docs/vector-data.pymd     |  24 +++----
 10 files changed, 110 insertions(+), 107 deletions(-)
 create mode 100644 pyrasterframes/src/main/python/docs/static/sentinel-2-scene-classification-labels.png

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 13143d280..51d81184c 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -28,19 +28,19 @@ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
 rf.select("id", rf_render_matrix("tile")).show(10, False)
 ```
 
-In this code block we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
+In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
 ```python
 rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
 ```
 
-In this code block we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
+In this code block, we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
 ```python
 rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
-In this code block we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
 
 To compute an element-wise local aggregate, tiles need have the same dimensions as in the example below where both tiles have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal tile dimensions, we would get a runtime error.
 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index ac0c07bdc..6233dfd29 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -28,7 +28,7 @@ from pyspark.sql.functions import lit
 # Read a MODIS surface reflectance granule
 df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
 
-# Add 3 element-wise, show some rows of the dataframe
+# Add 3 element-wise, show some rows of the DataFrame
 df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
 ```
 
@@ -52,9 +52,7 @@ You can also use RasterFrames in the following environments:
  1. Install [docker](https://docs.docker.com/install/)
  1. Pull the image: `docker pull s22s/rasterframes-notebook`
  1. Run a container with the image, for example: 
-
-      docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest
-
+      `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest`
  1. In a browser, open `localhost:8808` in the example above. 
 
 See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
@@ -94,7 +92,11 @@ SparkSession available as 'spark'.
 
 Now you have the configured SparkSession with RasterFrames enabled.
 
-## Installing GDAL 
+```python, echo=False
+spark.stop()
+```
+
+## Installing GDAL
 
 GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below. Version 2.4.1 or greater is required.
 
@@ -111,7 +113,7 @@ brew install gdal
 Using [`apt-get`](https://wiki.debian.org/Apt):
 
 ```bash
-sudo apt-get update 
+sudo apt-get update
 sudo apt-get install gdal-bin
 ```
 
@@ -133,4 +135,4 @@ from pyrasterframes.utils import gdal_version
 print(gdal_version())
 ```
 
-This will print out something like "GDAL x.y.z, released 20yy/mm/dd". If it reports `not available`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
\ No newline at end of file
+This will print out something like "GDAL x.y.z, released 20yy/mm/dd". If it reports `not available`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index f5f215974..8f50ad93b 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -51,7 +51,7 @@ RasterFrames provides a wide variety of local map algebra functions. There are s
  * A function on a Tile and a scalar is a binary operation; example: @ref:[rf_local_less](reference.md#rf-local-less); or
  * A function on many Tiles is a n-ary operation; example: @ref:[rf_agg_local_min](reference.md#rf-agg-local-min)
 
-We can express the normalized difference with a combination of `rf_local_divide`, `rf_local_subtract`, and `rf_local_add`. Since the normalized difference is so common there is a convenience method `rf_normalized_difference` which we use in this example. We will append a new column to the DataFrame, which will apply the map alegbra function to each row.
+We can express the normalized difference with a combination of `rf_local_divide`, `rf_local_subtract`, and `rf_local_add`. Since the normalized difference is so common, there is a convenience method `rf_normalized_difference`, which we use in this example. We will append a new column to the DataFrame, which will apply the map alegbra function to each row.
 
 ```python
 df = df.withColumn('ndvi', rf_normalized_difference(df.nir, df.red))
@@ -70,4 +70,4 @@ We continue examining NDVI in the @ref:[time series](time-series.md) section.
 
 ```python, echo=False
 spark.stop()
-```
\ No newline at end of file
+```
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 6eb4151d6..ead17b2e7 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -2,9 +2,9 @@
 
 ## What is NoData?
 
-In raster operations, the preservation and correct processing of missing observations is very important. In [most dataframes and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value. 
+In raster operations, the preservation and correct processing of missing observations is very important. In [most DataFrames and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value.
 
-RasterFrames provides a variety of functions to inspect and manage NoData within `tile`s. 
+RasterFrames provides a variety of functions to inspect and manage NoData within `tile`s.
 
 ## Cell Types
 
@@ -40,7 +40,7 @@ spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif
 
 ### Understanding Cell Types and NoData
 
-Use the methods on the `CellType` class to learn more about a specific cell type. Take for example the cell type of our sample data above.
+We can use the methods on the `CellType` class to learn more about a specific cell type. Let's consider the cell type of our sample data above.
 
 ```python
 ct = CellType('uint16raw')
@@ -55,13 +55,13 @@ ct = CellType('uint16')
 ct, ct.is_floating_point(), ct.has_no_data(), ct.no_data_value()
 ```
 
-In this case, the minimum value of 0 is designated as the NoData value. For integral valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with a `ud`. 
+In this case, the minimum value of 0 is designated as the NoData value. For integral-valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with a `ud`.
 
 ```python
 CellType.uint16().with_no_data_value(99).cell_type_name
 ```
 
-Floating point types by default have `NaN` as the NoData value. However a user-defined NoData can be set. 
+Floating point types have `NaN` as the NoData value by default. However, a user-defined NoData can be set.
 
 ```python float_ud
 print(CellType.float32().no_data_value())
@@ -70,9 +70,13 @@ print(CellType.float32().with_no_data_value(-99.9).no_data_value())
 
 ## Masking
 
-Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no defined NoData. The quality information is in a separate file called the scene classification (SCL), which delineates areas of missing data and probable clouds. For much more information on that, see the [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm). Figure 3 tells us how to interpret the scene classification. For this example, we will exclude NoData, defective pixels, probable clouds, and cirrus clouds: values 0, 1, 8, 9, and 10.
+Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no defined NoData. The quality information is in a separate file called the scene classification (SCL), which delineates areas of missing data and probable clouds. For more information on that, see the [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm). Figure 3 tells us how to interpret the scene classification. For this example, we will exclude NoData, defective pixels, probable clouds, and cirrus clouds: values 0, 1, 8, 9, and 10.
 
-The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog and now the blue band and SCL tiles are aligned across rows.
+![Sentinel-2 Scene Classification Values](static/sentinel-2-scene-classification-labels.png)
+
+Credit: [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm)
+
+The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog, so the blue band and SCL tiles are aligned across rows.
 
 ```python blue_scl_cat
 from pyspark.sql import Row
@@ -85,7 +89,7 @@ unmasked.printSchema()
 unmasked.select(rf_cell_type('blue'), rf_cell_type('scl')).distinct().show()
 ```
 
-Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create a new tile column containing our indicator of unwanted pixels, as defined above.
+Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new tile columns that are indicators of unwanted pixels, as defined above. Since the mask column is bit type, the addition is equivalent to a logical or, so the true values are 1.
 
 ```python def_mask
 from pyspark.sql.functions import lit
@@ -94,7 +98,7 @@ mask_part = unmasked.withColumn('nodata', rf_local_equal('scl', lit(0))) \
                     .withColumn('defect', rf_local_equal('scl', lit(1))) \
                     .withColumn('cloud8', rf_local_equal('scl', lit(8))) \
                     .withColumn('cloud9', rf_local_equal('scl', lit(9))) \
-                    .withColumn('cirrus', rf_local_equal('scl', lit(10))) 
+                    .withColumn('cirrus', rf_local_equal('scl', lit(10)))
 
 one_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
                     .withColumn('mask', rf_local_add('mask', 'cloud8')) \
@@ -104,13 +108,13 @@ one_mask = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
 one_mask.select(rf_cell_type('mask')).distinct().show()
 ```
 
-Now we will use the @ref:[`rf_mask_by_value`](reference.md#rf-mask-by-value) to designate the cloudy and other unwanted pixels as NoData in the blue column. Because there is not a NoData already defined, we will choose one. Note that in this particular example the minimum value is greater than zero, so we can use 0 as the NoData value.
+Because there is not a NoData already defined, we will choose one. In this particular example, the minimum value is greater than zero, so we can use 0 as the NoData value.
 
 ```python pick_nd
 one_mask.agg(rf_agg_stats('blue').min.alias('blue_min')).show()
 ```
 
-We can now construct the cell type string for our blue band's cell type, but designating 0 as NoData.
+We can now construct the cell type string for our blue band's cell type, designating 0 as NoData.
 
 ```python get_ct_string
 blue_ct = one_mask.select(rf_cell_type('blue')).distinct().first()[0][0]
@@ -118,16 +122,16 @@ masked_blue_ct = CellType(blue_ct).with_no_data_value(0)
 masked_blue_ct.cell_type_name
 ```
 
-Convert the cell type and apply the mask. Since the mask column is bit type, the addition done above was equivalent to a logical or. So the true values are 1.
+Now we will use the @ref:[`rf_mask_by_value`](reference.md#rf-mask-by-value) to designate the cloudy and other unwanted pixels as NoData in the blue column by converting the cell type and applying the mask.
 
-```python mask_blu 
-with_nd = rf_convert_cell_type('blue', masked_blue_ct.cell_type_name)
-masked = one_mask.withColumn('blue_masked', 
+```python mask_blu
+with_nd = rf_convert_cell_type('blue', masked_blue_ct)
+masked = one_mask.withColumn('blue_masked',
                              rf_mask_by_value(with_nd, 'mask', lit(1))) \
                  .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
 ```
 
-We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` tile.
+We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` tile to ensure our logic is correct.
 
 ```python
 masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
@@ -148,7 +152,7 @@ display(sample[1])
 
 ## NoData and Local Arithmatic
 
-Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0. 
+Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0.
 
 
 ```python
@@ -168,7 +172,7 @@ print('y')
 display(y)
 ```
 
-Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for local algebra operations, Data + NoData = NoData. 
+Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for local algebra operations, Data + NoData = NoData.
 
 ```python
 masked_rf = rf.withColumn('x_nd', rf_mask_by_value('x', 'x', lit(1)) )
@@ -207,7 +211,7 @@ First, we mask the value of 1 by making a new column with the user defined cell
 def get_nodata_ct(nd_val):
 	return CellType('uint16').with_no_data_value(nd_val)
 
-masked_rf = rf.withColumn('tile_nd_1', 
+masked_rf = rf.withColumn('tile_nd_1',
                            rf_convert_cell_type('tile', get_nodata_ct(1))) \
               .withColumn('tile_nd_2',
                           rf_convert_cell_type('tile_nd_1', get_nodata_ct(2))) \
@@ -217,7 +221,7 @@ masked_rf = rf.withColumn('tile_nd_1',
 collected = masked_rf.collect()
 ```
 
-Let's look at the new Tiles we created. The tile named `tile_nd_1` has the 1 values masked out as expected. 
+Let's look at the new Tiles we created. The tile named `tile_nd_1` has the 1 values masked out as expected.
 
 ```python
 display(collected[0].tile_nd_1)
@@ -232,9 +236,9 @@ display(collected[0].tile_nd_2)
 
 ## Combining Tiles with Different Data Types
 
-RasterFrames supports having Tile columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact. 
+RasterFrames supports having Tile columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact.
 
-Let's first create a RasterFrame that has columns of `float` and `int` cell type. 
+Let's first create a RasterFrame that has columns of `float` and `int` cell type.
 
 ```python
 x = Tile((np.ones((100, 100))*2).astype('float'))
@@ -248,9 +252,9 @@ When performing a local operation between tile columns with cell types `int` and
 
 ```python
 rf.select(
-    rf_cell_type('x'), 
+    rf_cell_type('x'),
     rf_cell_type('y'),
-    rf_cell_type(rf_local_add('x', 'y').alias('xy_sum')), 
+    rf_cell_type(rf_local_add('x', 'y').alias('xy_sum')),
     ).show(1)
 ```
 
@@ -262,14 +266,14 @@ x_nd_2 = Tile((np.ones((100, 100))*3), get_nodata_ct(2))
 rf_nd = spark.createDataFrame([Row(x_nd_1=x_nd_1, x_nd_2=x_nd_2)])
 ```
 
-Let's try adding the tile columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1. 
+Let's try adding the tile columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1.
 
 ```python
 rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_2', 'x_nd_1'))
 rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 ```
 
-Reversing the order of the sum changes the NoData value of the resulting column to 2.  
+Reversing the order of the sum changes the NoData value of the resulting column to 2.
 
 ```python
 rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x_nd_2'))
@@ -291,10 +295,10 @@ rf = spark.createDataFrame([Row(tile=x)])
 display(x)
 ```
 
-First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked. 
+First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked.
 
 ```python
-masked_rf = rf.withColumn('tile_nd_1', 
+masked_rf = rf.withColumn('tile_nd_1',
                            rf_convert_cell_type('tile', get_nodata_ct(1))) \
               .withColumn('tile_nd_2',
                           rf_convert_cell_type('tile_nd_1', get_nodata_ct(2)))
diff --git a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
index 7edd65802..a279fe86c 100644
--- a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
+++ b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
@@ -1,6 +1,6 @@
 # NumPy and Pandas
 
-In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will recieve this data a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame. 
+In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will recieve this data a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame.
 
 ## Performance Considerations
 
@@ -8,7 +8,7 @@ When working with large, distributed datasets in Spark care is required when inv
 
 When many _actions_ are invoked, a lot of data can flow from executors to the driver. In `pyspark` the data then has to move from the driver JVM to the Python process running the driver. When that happens if there are any _tiles_ in the data, they will be converted to a Python [`Tile`](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/pyrasterframes/rf_types.py) object. In practical work with Earth observation data, the _tiles_ are frequently 256 by 256 arrays, which may be 100kb or more each. Individually they are small, a DataFrame can easily have dozens of such tile columns and millions of rows.
 
-All of this discussion reinforces important principle for working with Spark: understanding the cost of an _action_ is important; and use of @ref:[aggreates](aggregation.md), summaries, or samples to manage the cost of _actions_. 
+All of this discussion reinforces important principle for working with Spark: understanding the cost of an _action_ is important; and use of @ref:[aggreates](aggregation.md), summaries, or samples to manage the cost of _actions_.
 
 ## The `Tile` Class
 
@@ -30,7 +30,7 @@ t.cells.shape, t.cells.nbytes
 
 ## DataFrame `toPandas`
 
-As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samples), a pretty display of Pandas DataFrame containing _tiles_ is available by importing the `rf_ipython` submodule. In addition, as discussed in the @ref:[vector data chapter](vector-data.md), any geometry type in the Spark dataframe will be converted into a Shapely geometry. Taken together, we can can easily get the spatial information and raster data as a NumPy array, all within a Pandas DataFrame.
+As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samples), a pretty display of Pandas DataFrame containing _tiles_ is available by importing the `rf_ipython` submodule. In addition, as discussed in the @ref:[vector data chapter](vector-data.md), any geometry type in the Spark DataFrame will be converted into a Shapely geometry. Taken together, we can can easily get the spatial information and raster data as a NumPy array, all within a Pandas DataFrame.
 
 ```python spark_session,  echo=False
 import pyrasterframes
@@ -49,7 +49,7 @@ cat = spark.read.format('aws-pds-modis-catalog').load() \
                     (col('granule_id') == 'h11v04') &
                     (col('acquisition_date') > lit('2018-02-19')) &
                     (col('acquisition_date') < lit('2018-02-22'))
-                ) 
+                )
 
 spark_df = spark.read.raster(catalog=cat, catalog_col_names=['B01']) \
                 .select(
@@ -106,9 +106,9 @@ import pandas as pd
 from shapely.geometry import Point
 
 pandas_df = pd.DataFrame([
-        { 
-            'tile': Tile(np.random.randn(100, 100)), 
-            'geom': Point(-90 + 90 * np.random.random((2, 1))) 
+        {
+            'tile': Tile(np.random.randn(100, 100)),
+            'geom': Point(-90 + 90 * np.random.random((2, 1)))
         }  for _ in range(10)
     ])
 
diff --git a/pyrasterframes/src/main/python/docs/static/sentinel-2-scene-classification-labels.png b/pyrasterframes/src/main/python/docs/static/sentinel-2-scene-classification-labels.png
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HcmV?d00001

diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index dc38ace05..044a8f72e 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -6,7 +6,7 @@ In this example we will demonstrate how to fit and score a supervised learning m
 from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
-from pyspark.sql.functions import lit 
+from pyspark.sql.functions import lit
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -19,9 +19,9 @@ spark = create_rf_spark_session()
 
 ## Create and Read Raster Catalog
 
-The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
+The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
-The imagery for feature data will come from [eleven bands of 60 meter resolution Sentinel 2](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) imagery. We also will use the [scene classification (SCL)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm) data to identify high quality, non-cloudy pixels.
+The imagery for feature data will come from [eleven bands of 60 meter resolution Sentinel-2](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) imagery. We also will use the [scene classification (SCL)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm) data to identify high quality, non-cloudy pixels.
 
 ```python, read_bands, term=True
 uri_base = 's3://s22s-test-geotiffs/luray_snp/{}.tif'
@@ -29,10 +29,10 @@ bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', '
 cols = ['SCL'] + bands
 
 catalog_df = pd.DataFrame([
-    {b: uri_base.format(b) for b in cols} 
+    {b: uri_base.format(b) for b in cols}
 ])
 
-df = spark.read.raster(catalog=catalog_df, 
+df = spark.read.raster(catalog=catalog_df,
 					   catalog_col_names=cols,
 					   tile_dimensions=(128, 128)
 					   ).repartition(100)
@@ -60,11 +60,11 @@ df.printSchema()
 
 ### Label Data
 
-The land classification labels are based on a small set of hand drawn polygons in the geojson file [here](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/test/resources/luray-labels.geojson). The property `id` indicates the type of land cover in each area. For these integer values 1 is forest, 2 is cropland and 3 is developed areas.
+The land classification labels are based on a small set of hand drawn polygons in the GeoJSON file [here](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/test/resources/luray-labels.geojson). The property `id` indicates the type of land cover in each area. For these integer values, 1 is forest, 2 is cropland, and 3 is developed areas.
 
-We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive, but in this case both datasets are quite small. To speed up the join for the small vector DataFrame, we put the `broadcast` hint on it. Spark will put a copy of it on each Spark executor. 
+We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive, but in this case both datasets are quite small. To speed up the join for the small vector DataFrame, we put the `broadcast` hint on it, which will tell Spark to put a copy of it on each Spark executor.
 
-After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tile_ cells are the `id` property of the geojson; which we will use as labels in our supervised learning task. In areas where the geometry does not intersect, the cells will contain a NoData.
+After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tile_ cells are the `id` property of the GeoJSON; which we will use as labels in our supervised learning task. In areas where the geometry does not intersect, the cells will contain a NoData.
 
 ```python join_and_rasterize
 crses = df.select('crs.crsProj4').distinct().collect()
@@ -75,7 +75,7 @@ label_df = spark.read.geojson(os.path.join(resource_dir_uri(), 'luray-labels.geo
 					 .select('id', st_reproject('geometry', lit('EPSG:4326'), lit(crs)).alias('geometry')) \
 					 .hint('broadcast')
 
-df_joined = df.join(label_df, st_intersects(st_geometry('extent'), 'geometry')) 
+df_joined = df.join(label_df, st_intersects(st_geometry('extent'), 'geometry'))
 
 df_joined.createOrReplaceTempView('df_joined')
 df_labeled = spark.sql("""
@@ -84,7 +84,7 @@ FROM df_joined
 """)
 ```
 
-## Masking Poor Quality Cells 
+## Masking Poor Quality Cells
 
 To filter only for good quality pixels, we follow roughly the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually setting NoData values in the unwanted cells of any of the imagery bands, we will just on filter out the mask cell values later in the process.
 
@@ -95,7 +95,7 @@ mask_part = df_labeled.withColumn('nodata', rf_local_equal('scl', lit(0))) \
               .withColumn('defect', rf_local_equal('scl', lit(1))) \
               .withColumn('cloud8', rf_local_equal('scl', lit(8))) \
               .withColumn('cloud9', rf_local_equal('scl', lit(9))) \
-              .withColumn('cirrus', rf_local_equal('scl', lit(10))) 
+              .withColumn('cirrus', rf_local_equal('scl', lit(10)))
 
 df_mask_inv = mask_part.withColumn('mask', rf_local_add('nodata', 'defect')) \
                    .withColumn('mask', rf_local_add('mask', 'cloud8')) \
@@ -125,7 +125,7 @@ from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 from pyspark.ml import Pipeline
 ```
 
-SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the tiles into a single row per cell or pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). If a _tile_ cell contains a NoData it will become a null value after the exploder stage. Then we filter out any rows that missing or null values, which will cause an error during training. Finally we use the SparkML `VectorAssembler` to create that `Vector`. 
+SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the _tiles_ into a single row per cell or pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). If a _tile_ cell contains a NoData it will become a null value after the exploder stage. Then we filter out any rows that missing or null values, which will cause an error during training. Finally we use the SparkML `VectorAssembler` to create that `Vector`.
 
 It is worth discussing a couple of interesting things about the `NoDataFilter`. First, we filter out missing values in the mask column. Recall above we set undesirable pixels to NoData, so they will be removed at this stage. The other column for the `NoDataFilter` is the `label` column. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
 
@@ -155,7 +155,7 @@ pipeline.getStages()
 
 ## Train the Model
 
-Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model. We filter the dataframe for only tiles intersecting the label raster, because the label shapes are relatively sparse over the imagery. It would be logically equivalent to either include or exclude this, but it is more efficient to do this filter because it will mean less data going into the pipeline. 
+Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model. We filter the DataFrame for only _tiles_ intersecting the label raster, because the label shapes are relatively sparse over the imagery. It would be logically equivalent to either include or exclude this, but it is more efficient to do this filter because it will mean less data going into the pipeline.
 
 ```python, train
 model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
@@ -163,7 +163,7 @@ model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
 
 ## Model Evaluation
 
-To view the model's performance, we first call the pipeline's `transform` method on the training dataset. This transformed dataset will have the model's prediction included in each row. We next construct an evaluator and pass it the transformed dataset to easily compute the performance metric. We can also create custom metrics using a variety of DataFrame or SQL transformations. 
+To view the model's performance, we first call the pipeline's `transform` method on the training dataset. This transformed dataset will have the model's prediction included in each row. We next construct an evaluator and pass it the transformed dataset to easily compute the performance metric. We can also create custom metrics using a variety of DataFrame or SQL transformations.
 
 ```python eval
 prediction_df = model.transform(df_mask) \
@@ -190,17 +190,17 @@ prediction_df.groupBy(classifier.getPredictionCol()) \
 
 ## Visualize Prediction
 
-Because the pipeline included a `TileExploder`, we will recreate the tiled data structure. The explosion transformation includes metadata enabling us to recreate the _tiles_. See the @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile) function documentation for more details. In this case, the pipeline is scoring on all areas, regardless of whether they intersect the label polygons. This is simply done by removing the `label` column, as @ref:[discussed above](supervised-learning.md#create-ml-pipeline). 
+Because the pipeline included a `TileExploder`, we will recreate the tiled data structure. The explosion transformation includes metadata enabling us to recreate the _tiles_. See the @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile) function documentation for more details. In this case, the pipeline is scoring on all areas, regardless of whether they intersect the label polygons. This is simply done by removing the `label` column, as @ref:[discussed above](supervised-learning.md#create-ml-pipeline).
 
 ```python assemble_prediction
 scored = model.transform(df_mask.drop('label'))
 scored.createOrReplaceTempView('scored')
 
 retiled = spark.sql("""
-SELECT extent, crs, 
+SELECT extent, crs,
     rf_assemble_tile(column_index, row_index, prediction, 128, 128) as prediction,
     rf_assemble_tile(column_index, row_index, B04, 128, 128) as red,
-    rf_assemble_tile(column_index, row_index, B03, 128, 128) as grn, 
+    rf_assemble_tile(column_index, row_index, B03, 128, 128) as grn,
     rf_assemble_tile(column_index, row_index, B02, 128, 128) as blu
 FROM scored
 GROUP BY extent, crs
@@ -209,7 +209,7 @@ GROUP BY extent, crs
 retiled.printSchema()
 ```
 
-Take a look at a sample of the resulting output and the corresponding area's red-green-blue composite image. 
+Take a look at a sample of the resulting output and the corresponding area's red-green-blue composite image.
 
 ```python display_rgb
 sample = retiled.select('prediction', 'red', 'grn', 'blu') \
@@ -226,7 +226,7 @@ mins = np.nanmin(sample_rgb, axis=(0,1))
 plt.imshow((sample_rgb -  mins)/ (np.nanmax(sample_rgb, axis=(0,1)) - mins))
 ```
 
-Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow). 
+Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow).
 
 ```python display_prediction
 display(sample_prediction)
diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
index 6edd61ed1..ddb0eaf9c 100644
--- a/pyrasterframes/src/main/python/docs/time-series.pymd
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -11,14 +11,14 @@ import pyrasterframes.rf_ipython
 spark = pyrasterframes.get_spark_session()
 ```
 
-In this example we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will start with our @ref:[built-in MODIS data catalog](raster-catalogs.md#using-built-in-experimental-catalogs).
+In this example, we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will start with our @ref:[built-in MODIS data catalog](raster-catalogs.md#using-built-in-experimental-catalogs).
 
 ```python catalog
 cat = spark.read.format('aws-pds-modis-catalog').load().repartition(200)
 cat.printSchema()
 ```
 
-We will summarize the change in NDVI over 2018 in the Cuyahoga Valley National Park in Ohio USA. First, we will retrieve open vector data delineating the park boundary from the US National Park Service's LandsNet.
+We will summarize the change in NDVI over 2018 in the Cuyahoga Valley National Park in Ohio, USA. First, we will retrieve open vector data delineating the park boundary from the US National Park Service's LandsNet.
 
 ```python get_park_boundary
 import requests
@@ -35,48 +35,48 @@ park_geo = park_df[park_df.UNIT_CODE=='CUVA'].geometry[0]
 park_geo.wkt[:100]
 ```
 
-The entire park boundary is contained in MODIS granule h11 v4. We will simply filter on this granule, rather than using a proper @ref:[spatial relation](vector-data.md#geomesa-functions-and-spatial-relations). The time period selected should show the change in plant vigor as leaves emerge over the spring into early summer.
+The entire park boundary is contained in MODIS granule h11 v4. We will simply filter on this granule, rather than using a @ref:[spatial relation](vector-data.md#geomesa-functions-and-spatial-relations). The time period selected should show the change in plant vigor as leaves emerge over the spring and into early summer.
 
 ```python query_catalog
 from pyspark.sql.functions import lit
 park_cat = cat.filter(
                     (cat.granule_id == 'h11v04') &
                     (cat.acquisition_date > lit('2018-02-19')) &
-                    (cat.acquisition_date < lit('2018-07-01')) 
+                    (cat.acquisition_date < lit('2018-07-01'))
             )
 park_cat.printSchema()
 ```
 
 ## Catalog Read
 
-Now we have a catalog with several months of MODIS data for a single granule. However the granule is larger than our park boundary. We will combine the park geometry with the catalog, and read only the bands of interest to compute NDVI, which we discussed @ref:[earlier](local-algebra.md#computing-ndvi).
+Now we have a catalog with several months of MODIS data for a single granule. However, the granule is larger than our park boundary. We will combine the park geometry with the catalog, and read only the bands of interest to compute NDVI, which we discussed in a @ref:[previous section](local-algebra.md#computing-ndvi).
 
 ```python read_catalog
 raster_cols = ['B01', 'B02',] # red and near-infrared respectively
-park_rf = spark.read.raster(catalog=park_cat.select(['acquisition_date', 'granule_id'] + raster_cols), 
+park_rf = spark.read.raster(catalog=park_cat.select(['acquisition_date', 'granule_id'] + raster_cols),
                             catalog_col_names=raster_cols,
                            ) \
-                    .withColumn('park', st_geomFromWKT(lit(park_geo.wkt))) 
+                    .withColumn('park', st_geomFromWKT(lit(park_geo.wkt)))
 park_rf.createOrReplaceTempView('catalog_read')
 park_rf.printSchema()
 ```
 
 ## Vector and Raster Data Interaction
 
-Now we have the vector representation of the park boundary alongside the tiles of red and near infrared bands. Next we need to create a tile representation of the park to allow us to limit the time series analysis to pixels within the park. This is similar to the masking operation demonstrated in @ref:[NoData handling](nodata-handling.md#masking).
+Now we have the vector representation of the park boundary alongside the _tiles_ of red and near infrared bands. Next, we need to create a _tile_ representation of the park to allow us to limit the time series analysis to pixels within the park. This is similar to the masking operation demonstrated in @ref:[NoData handling](nodata-handling.md#masking).
 
-We do this using two SQL transformation. This demonstrates that we can use any `rf_` or `st_` function in SQL. The first one will reproject the park boundary from coordinates to the MODIS sinusoidal projection. The second one will create a new tile aligned with the imagery containing value 1 where the pixels are contained within the park and NoData elsewhere.
+We do this using two SQL transformations. The first one will reproject the park boundary from coordinates to the MODIS sinusoidal projection. The second one will create a new _tile_ aligned with the imagery containing a value of 1 where the pixels are contained within the park and NoData elsewhere. The code demonstrates that we can use any `rf_` or `st_` function in SQL. For a more thorough demonstration of how Python and SQL codes compare in RasterFrames, see the @ref:[language support section](languages.md).
 
 ```python burn_in
 cr_1 = spark.sql("""
-SELECT *, 
+SELECT *,
     st_reproject(park, 'EPSG:4326', rf_crs(B01).crsProj4) as park_native
 FROM catalog_read
 """)
 cr_1.createOrReplaceTempView('cr_1')
 
 cr_2 = spark.sql("""
-SELECT *, 
+SELECT *,
     rf_rasterize(park_native, rf_geometry(B01), 1, rf_dimensions(B01).cols, rf_dimensions(B01).rows) as park_tile
 FROM cr_1
 """).where(rf_tile_sum('park_tile') > 0)
@@ -85,7 +85,7 @@ cr_2.printSchema()
 
 ## Create Time Series
 
-Next we will compute NDVI as the normalized difference of near infrared (band 2) and red (band 1). The tiles are masked by the `park_tile`. We will then aggregate across the remaining values to arrive at an average NDVI for each week of the year. Note that the computation is creating a weighted average, weighting by the number of valid observations per week.
+Next, we will compute NDVI as the normalized difference of near infrared (band 2) and red (band 1). The _tiles_ are masked by the `park_tile`. We will then aggregate across the remaining values to arrive at an average NDVI for each week of the year. Note that the computation is creating a weighted average, which is weighted by the number of valid observations per week.
 
 ```python ndvi_time_series
 from pyspark.sql.functions import col, year, weekofyear, month
@@ -93,7 +93,7 @@ from pyspark.sql.functions import sum as sql_sum
 
 rf_ndvi = cr_2.withColumn('ndvi', rf_normalized_difference('B02', 'B01')) \
               .withColumn('ndvi_masked', rf_mask('ndvi', 'park_tile'))
-                  
+
 time_series = rf_ndvi \
         .withColumn('ndvi_wt', rf_tile_sum('ndvi_masked')) \
         .withColumn('wt', rf_data_cells('ndvi_masked')) \
@@ -103,7 +103,7 @@ time_series = rf_ndvi \
 
 ```
 
-Finally we will take a look at the NDVI over time.
+Finally, we will take a look at the NDVI over time.
 
 ```python time_series_display
 import matplotlib.pyplot as plt
diff --git a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index 13dd58a44..f258fc5c8 100644
--- a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -1,6 +1,6 @@
 # Unsupervised Machine Learning
 
-In this example we will demonstrate how to fit and score an unsupervised learning model with a [sample of Landsat 8 data](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/src/test/resources).
+In this example, we will demonstrate how to fit and score an unsupervised learning model with a [sample of Landsat 8 data](https://github.com/locationtech/rasterframes/tree/develop/pyrasterframes/src/test/resources).
 
 ## Imports and Data Preparation
 
@@ -16,7 +16,7 @@ spark = create_rf_spark_session()
 import pandas as pd
 ```
 
-We import various Spark components that we need to construct our Pipeline.
+We import various Spark components that we need to construct our `Pipeline`.
 
 ```python, echo=True
 from pyrasterframes import TileExploder
@@ -28,7 +28,7 @@ from pyspark.ml import Pipeline
 
 ```
 
-The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from [the I/O page](raster-io.html#Single-Scene--Multiple-Bands). In the catalog, each row represents a distinct area and time; and each column is the URI to a band's image product. The function `resource_dir` gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
+The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from [the I/O page](raster-io.html#Single-Scene--Multiple-Bands). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. The function `resource_dir_uri` gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
 
 ```python, term=True
@@ -53,15 +53,13 @@ df.printSchema()
 
 ## Create ML Pipeline
 
-SparkML requires that each observation be in its own row, and features for each
-observation be packed into a single `Vector`. For this unsupervised learning problem, we will treat each _pixel_ as an observation and use one feature per band. The first step is to
-"explode" the tiles into a single row per pixel. In the scala API a pixel is called a `cell`.
+SparkML requires that each observation be in its own row, and features for each observation be packed into a single `Vector`. For this unsupervised learning problem, we will treat each _pixel_ as an observation and each band as a feature. The first step is to "explode" the _tiles_ into a single row per pixel. In RasterFrames, generally a pixel is called a @ref:[`cell`](concepts.md#cell).
 
 ```python
 exploder = TileExploder()
 ```
 
-To "vectorize" the the band columns we use the SparkML `VectorAssembler`. We use each of the seven bands for a different feature.
+To "vectorize" the the band columns, we use the SparkML `VectorAssembler`. Each of the seven bands is a different feature.
 
 ```python
 assembler = VectorAssembler() \
@@ -69,13 +67,13 @@ assembler = VectorAssembler() \
     .setOutputCol("features")
 ```
 
-Configure our clustering algorithm.
+For this problem, we will use the K-means clustering algorithm and configure our model to have 5 clusters.
 
 ```python
 kmeans = KMeans().setK(5).setFeaturesCol('features')
 ```
 
-Combine the stages into a single pipeline.
+We can combine the above stages into a single _pipeline_.
 
 ```python
 pipeline = Pipeline().setStages([exploder, assembler, kmeans])
@@ -83,27 +81,26 @@ pipeline = Pipeline().setStages([exploder, assembler, kmeans])
 
 ## Fit the Model and Score
 
-Fit the pipeline; this actually executes exploding the tiles, assembling the `Vector`s, and fitting the k-means clustering.
+Fitting the _pipeline_ actually executes exploding the _tiles_, assembling the features _vectors_, and fitting the K-means clustering model.
 
 ```python
 model = pipeline.fit(df)
 ```
 
-Score the training data in the fitted pipeline model. This will add a column called `prediction` with the closest cluster identifier.
+We can use the `transform` function to score the training data in the fitted _pipeline_ model. This will add a column called `prediction` with the closest cluster identifier.
 
 ```python
 clustered = model.transform(df)
 clustered.show(8)
 ```
 
-If we want to inspect the model statistics, the SparkML API requires us to go
-through this unfortunate contortion:
+If we want to inspect the model statistics, the SparkML API requires us to go through this unfortunate contortion:
 
 ```python
 cluster_stage = model.stages[2]
 ```
 
-Compute sum of squared distances of points to their nearest center. This is elemental to most of the cluster quality metrics.
+We can then compute the sum of squared distances of points to their nearest center, which is elemental to most cluster quality metrics.
 
 ```python
 metric = cluster_stage.computeCost(clustered)
@@ -126,7 +123,7 @@ retiled.printSchema()
 retiled.show()
 ```
 
-Take a look at the resulting output.
+The resulting output is shown below.
 
 ```python
 display(retiled.select('prediction').first()['prediction'])
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
index 2fb2faa4e..1f268ff89 100644
--- a/pyrasterframes/src/main/python/docs/vector-data.pymd
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -1,6 +1,6 @@
 # Vector Data
 
-RasterFrames provides a variety of ways to work with spatial vector (points, lines, and polygons) data alongside raster data. There is a convenience DataSource for geojson format, as well as the ability to convert from [GeoPandas][GeoPandas] to Spark. Representation of vector geometries in pyspark is through [Shapely][Shapely] which provides a great deal of flexibility. RasterFrames also provides access to Spark functions for working with geometries.
+RasterFrames provides a variety of ways to work with spatial vector (points, lines, and polygons) data alongside raster data. There is a convenience DataSource for GeoJSON format, as well as the ability to convert from [GeoPandas][GeoPandas] to Spark. Representation of vector geometries in pyspark is through [Shapely][Shapely] which provides a great deal of flexibility. RasterFrames also provides access to Spark functions for working with geometries.
 
 ## GeoJSON DataSource
 
@@ -18,11 +18,11 @@ df = spark.read.geojson(SparkFiles.get('admin1-us.geojson'))
 df.printSchema()
 ```
 
-The properties of each feature are available as columns of the dataframe, along with the geometry. 
+The properties of each feature are available as columns of the DataFrame, along with the geometry.
 
 ## GeoPandas and RasterFrames
 
-You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as `df` defined above by the geojson reader. Note that in a geopandas dataframe there can be heterogeneous geometry types in the column, but this may fail Spark's schema inference. 
+You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as `df` defined above by the GeoJSON reader. Note that in a GeoPandas DataFrame there can be heterogeneous geometry types in the column, but this may fail Spark's schema inference.
 
 ```python
 import geopandas
@@ -43,20 +43,20 @@ df2.printSchema()
 
 ## Shapely Geometry Support
 
-The geometry column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working on the Python side. This means that when the data is collected to the driver, it will be a Shapely geometry object. 
+The `geometry` column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working on the Python side. This means that when the data is collected to the driver, it will be a Shapely geometry object.
 
 ```python
 the_first = df.first()
 print(type(the_first['geometry']))
 ```
 
-Since it is a geometry we can do things like this.
+Since it is a geometry we can do things like this:
 
 ```python
 the_first['geometry'].wkt
 ```
 
-You can also write user-defined functions that input geometries, output geometries, or both, via user defined types in the [geomesa_pyspark.types](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/geomesa_pyspark/types.py) module. Here is a simple example using both a geometry input and output. The example is repeated below with a built-in function.
+You can also write user-defined functions that take geometries as input, output, or both, via user defined types in the [geomesa_pyspark.types](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/geomesa_pyspark/types.py) module. Here is a simple example of a user-defined function that uses both a geometry input and output to compute the centroid of a geometry.
 
 ```python
 from pyspark.sql.functions import udf
@@ -70,7 +70,7 @@ df = df.withColumn('naive_centroid', get_centroid(df.geometry))
 df.printSchema()
 ```
 
-We can take a look at a sample of the data. Notice the geometry columns print as well known text.
+We can take a look at a sample of the data. Notice the geometry columns print as well known text (wkt).
 
 ```python
 df.show(4)
@@ -79,16 +79,16 @@ df.show(4)
 
 ## GeoMesa Functions and Spatial Relations
 
-As documented in the @ref:[function reference](reference.md), the various user-defined functions already available through GeoMesa are available for use. The example above is logically equivalent but less efficient than this.
+As documented in the @ref:[function reference](reference.md), various user-defined functions implemented by GeoMesa are also available for use. The example below uses a GeoMesa user-defined function to compute the centroid of a geometry. It is logically equivalent to the example above, but more efficient.
 
 
 ```python
-from pyrasterframes.rasterfunctions import st_centroid 
+from pyrasterframes.rasterfunctions import st_centroid
 df = df.withColumn('centroid', st_centroid(df.geometry))
 df.select('name', 'geometry', 'naive_centroid', 'centroid').show(4)
 ```
 
-The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example we use the @ref:[built-in MODIS catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter only those within approximately 500 km of a selected point.
+The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example, we use the @ref:[built-in Landsat catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter only those within approximately 500 km of a selected point.
 
 ```python, evaluate=True
 from pyrasterframes.rasterfunctions import st_geometry, st_bufferPoint, st_intersects, st_point
@@ -102,11 +102,11 @@ l8_filtered = l8.filter(st_intersects(l8.geom, st_bufferPoint(l8.paducah, lit(50
 ```
 
 ```python, evaluate=False, echo=False
-# suppressed due to run time. 
+# suppressed due to run time.
 l8_filtered.count()
 ```
 
 
 [GeoPandas]: http://geopandas.org
 [OGR]: https://gdal.org/drivers/vector/index.html
-[Shapely]: https://shapely.readthedocs.io/en/latest/manual.html
\ No newline at end of file
+[Shapely]: https://shapely.readthedocs.io/en/latest/manual.html

From 6da2b4ecdab70bea415e1a2ab01a4d861a93885a Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 15:12:25 -0400
Subject: [PATCH 339/380] numpy page edits

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/numpy-pandas.pymd     | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
index a279fe86c..32249b924 100644
--- a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
+++ b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
@@ -1,14 +1,14 @@
 # NumPy and Pandas
 
-In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will recieve this data a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame.
+In the Python Spark API, the work of distributed computing over the DataFrame is done on many executors (the Spark term for workers) inside Java virtual machines (JVM). Most calls to `pyspark` are passed to a Java process via the `py4j` library. The user can also ask for data inside the JVM to be brought over to the Python driver (the Spark term for the client application). When dealing with _tiles_, the driver will receive this data as a lightweight wrapper object around a NumPy ndarray. It is also possible to write lambda functions against NumPy arrays and evaluate them in the Spark DataFrame.
 
 ## Performance Considerations
 
-When working with large, distributed datasets in Spark care is required when invoking _actions_ on the data. In general _transformations_ are lazily evaluated in Spark, meaning the code runs fast and it doesn't move any data around. But _actions_ cause the evaluation to happen, meaning all the lazily planned _transformations_ are going to be computed and data is going to be processed and moved around. In general, if a [`pyspark` function](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html) returns a DataFrame, it is probably a _transformation_ and otherwise it is an _action_.
+When working with large, distributed datasets in Spark, attention is required when invoking _actions_ on the data. In general, _transformations_ are lazily evaluated in Spark, meaning the code runs fast and it doesn't move any data around. But _actions_ cause the evaluation to happen, meaning all the lazily planned _transformations_ are going to be computed and data is going to be processed and moved around. In general, if a [`pyspark` function](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html) returns a DataFrame, it is probably a _transformation_, and if not, it is an _action_.
 
-When many _actions_ are invoked, a lot of data can flow from executors to the driver. In `pyspark` the data then has to move from the driver JVM to the Python process running the driver. When that happens if there are any _tiles_ in the data, they will be converted to a Python [`Tile`](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/pyrasterframes/rf_types.py) object. In practical work with Earth observation data, the _tiles_ are frequently 256 by 256 arrays, which may be 100kb or more each. Individually they are small, a DataFrame can easily have dozens of such tile columns and millions of rows.
+When many _actions_ are invoked, a lot of data can flow from executors to the driver. In `pyspark`, the data then has to move from the driver JVM to the Python process running the driver. When that happens, if there are any _tiles_ in the data, they will be converted to a Python [`Tile`](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/pyrasterframes/rf_types.py) object. In practical work with Earth observation data, the _tiles_ are frequently 256 by 256 arrays, which may be 100kb or more each. Individually they are small, but a DataFrame can easily have dozens of such _tile_ columns and millions of rows.
 
-All of this discussion reinforces important principle for working with Spark: understanding the cost of an _action_ is important; and use of @ref:[aggreates](aggregation.md), summaries, or samples to manage the cost of _actions_.
+All of this discussion reinforces two important principles for working with Spark: understanding the cost of an _action_ and using @ref:[aggreates](aggregation.md), summaries, or samples to manage the cost of _actions_.
 
 ## The `Tile` Class
 
@@ -30,7 +30,7 @@ t.cells.shape, t.cells.nbytes
 
 ## DataFrame `toPandas`
 
-As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samples), a pretty display of Pandas DataFrame containing _tiles_ is available by importing the `rf_ipython` submodule. In addition, as discussed in the @ref:[vector data chapter](vector-data.md), any geometry type in the Spark DataFrame will be converted into a Shapely geometry. Taken together, we can can easily get the spatial information and raster data as a NumPy array, all within a Pandas DataFrame.
+As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samples), a pretty display of Pandas DataFrame containing _tiles_ is available by importing the `rf_ipython` submodule. In addition, as discussed in the @ref:[vector data chapter](vector-data.md), any geometry type in the Spark DataFrame will be converted into a Shapely geometry. Taken together, we can easily get the spatial information and raster data as a NumPy array, all within a Pandas DataFrame.
 
 ```python spark_session,  echo=False
 import pyrasterframes
@@ -65,9 +65,9 @@ pandas_df.iloc[0].apply(lambda v: type(v))
 
 ## User Defined Functions
 
-As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/s22s/raster-frames) if you have an idea for a function that should be included.
+As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, and then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/s22s/raster-frames) if you have an idea for a function that should be included.
 
-We will demonstrate creating a UDF that is logically equivalent to a built in function. We'll quickly show that the resulting _tiles_ are approximately equivalent. The reason they are not exactly the same is because one is computed in Python and the other is computed in Java.
+We will demonstrate an example of creating a UDF that is logically equivalent to a built-in function. We'll quickly show that the resulting _tiles_ are approximately equivalent. The reason they are not exactly the same is that one is computed in Python and the other is computed in Java.
 
 ```python udf
 from pyrasterframes.rf_types import TileUDT
@@ -89,7 +89,7 @@ print(type(diff))
 np.abs(diff.cells).max()
 ```
 
-We can also inspect an image of the difference between the two _tiles_; it is just random noise. Both tiles have the same structure of NoData, the white areas.
+We can also inspect an image of the difference between the two _tiles_, which is just random noise. Both _tiles_ have the same structure of NoData, as exhibited by the white areas.
 
 ```python udf_diff_noise_tile
 display(diff)
@@ -99,7 +99,7 @@ display(diff)
 
 You can also create a Spark DataFrame with a column full of `Tile` objects or Shapely geomtery objects.
 
-The example below will create a Pandas DataFrame with ten rows of noise tiles and random Points. We will then create a Spark DataFrame from it.
+The example below will create a Pandas DataFrame with ten rows of noise _tiles_ and random `Point`s. We will then create a Spark DataFrame from it.
 
 ```python create_spark_df
 import pandas as pd

From 90b73a1f64095adc66550b21e5d5cea8139c8b0a Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 15:25:47 -0400
Subject: [PATCH 340/380] small edits on languages page

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/languages.pymd       | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index dc7e93065..7c37f9a5d 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -1,6 +1,6 @@
 # API Languages
 
-One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This manual is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program (computing average NDVI per month for a single tile in Tanzania).
+One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This tutorial is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single tile in Tanzania.
 
 ```python, imports, echo=False
 from pyspark.sql.functions import *
@@ -25,9 +25,9 @@ modis = spark.read.format('aws-pds-modis-catalog').load()
 ```python, step_2_python
 red_nir_monthly_2017 = modis \
     .select(
-        col('granule_id'), 
-        month('acquisition_date').alias('month'), 
-        col('B01').alias('red'), 
+        col('granule_id'),
+        month('acquisition_date').alias('month'),
+        col('B01').alias('red'),
         col('B02').alias('nir')
     ) \
     .where((year('acquisition_date') == 2017) & (dayofmonth('acquisition_date') == 15) & (col('granule_id') == 'h21v09'))
@@ -37,7 +37,7 @@ red_nir_monthly_2017 = modis \
 
 ```python, step_3_python
 red_nir_tiles_monthly_2017 = spark.read.raster(
-    catalog=red_nir_monthly_2017, 
+    catalog=red_nir_monthly_2017,
     catalog_col_names=['red', 'nir'],
     tile_dimensions=(256, 256)
 )
@@ -48,7 +48,7 @@ red_nir_tiles_monthly_2017 = spark.read.raster(
 ```python, step_4_python
 result = red_nir_tiles_monthly_2017 \
     .where(st_intersects(
-        st_reproject(rf_geometry(col('red')), rf_crs(col('red')).crsProj4, rf_mk_crs('EPSG:4326')), 
+        st_reproject(rf_geometry(col('red')), rf_crs(col('red')).crsProj4, rf_mk_crs('EPSG:4326')),
         st_makePoint(lit(34.870605), lit(-4.729727)))
     ) \
     .groupBy('month') \
@@ -60,7 +60,7 @@ result.show()
 
 ## SQL
 
-For convenience we're going to evaluate SQL from the Python environment. The SQL fragments should work in the `spark-sql` shell just the same.
+For convenience, we're going to evaluate SQL from the Python environment. The SQL fragments should work in the `spark-sql` shell just the same.
 
 ```python, sql_setup
 def sql(stmt):
@@ -82,17 +82,17 @@ SELECT granule_id, month(acquisition_date) as month, B01 as red, B02 as nir
 FROM modis
 WHERE year(acquisition_date) = 2017 AND day(acquisition_date) = 15 AND granule_id = 'h21v09'
 """)
-sql('DESCRIBE red_nir_monthly_2017').show() 
+sql('DESCRIBE red_nir_monthly_2017').show()
 ```
 
 ### Step 3: Read tiles
 
 ```python, step_3_sql
 sql("""
-CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017 
-USING raster 
+CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017
+USING raster
 OPTIONS (
-    catalogTable='red_nir_monthly_2017', 
+    catalogTable='red_nir_monthly_2017',
     catalogColumns='red,nir',
     tileDimensions='256,256'
     )
@@ -165,4 +165,4 @@ val result = red_nir_tiles_monthly_2017
   .agg(rf_agg_stats(rf_normalized_difference($"nir", $"red")) as "ndvi_stats")
   .orderBy("month")
   .select("month", "ndvi_stats.*")
-``` 
\ No newline at end of file
+```

From ead0d79852b6c3bef36c5ac7be2f80bf8e115f23 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 15:33:29 -0400
Subject: [PATCH 341/380] make tile italicized when appropriate

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/aggregation.pymd     | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 51d81184c..983a9a693 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -10,11 +10,11 @@ import os
 spark = create_rf_spark_session()
 ```
 
-There are 3 types of aggregate functions: tile aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[tile aggregate functions](reference.md#tile-statistics), we are computing a statistical summary per row of a `tile` column in a DataFrame. In the @ref:[DataFrame aggregate functions](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the @ref:[element-wise local aggregate functions](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of tiles.
+There are 3 types of aggregate functions: _tile_ aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[tile aggregate functions](reference.md#tile-statistics), we are computing a statistical summary per row of a _tile_ column in a DataFrame. In the @ref:[DataFrame aggregate functions](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the @ref:[element-wise local aggregate functions](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of _tiles_.
 
 ## Tile Mean Example
 
-We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 tiles where the first tile is composed of 25 values of 1.0 and the second tile is composed of 25 values of 3.0.
+We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 _tiles_ where the first _tile_ is composed of 25 values of 1.0 and the second _tile_ is composed of 25 values of 3.0.
 
 ```python
 import pyspark.sql.functions as F
@@ -28,7 +28,7 @@ SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
 rf.select("id", rf_render_matrix("tile")).show(10, False)
 ```
 
-In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the tile aggregate mean of cells in each row of column `tile`. The mean of each tile is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
+In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the _tile_ aggregate mean of cells in each row of column `tile`. The mean of each _tile_ is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
 ```python
 rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
@@ -40,9 +40,9 @@ In this code block, we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-me
 rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
-In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the `tile`.
+In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the _tile_.
 
-To compute an element-wise local aggregate, tiles need have the same dimensions as in the example below where both tiles have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal tile dimensions, we would get a runtime error.
+To compute an element-wise local aggregate, _tiles_ need have the same dimensions as in the example below where both _tiles_ have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal _tile_ dimensions, we would get a runtime error.
 
 ```python
 rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(10, False)
@@ -50,7 +50,7 @@ rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_ma
 
 ## Cell Counts Example
 
-We can also count the total number of data and NoData cells over all the tiles in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
+We can also count the total number of data and NoData cells over all the _tiles_ in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
 ```python
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
@@ -61,9 +61,9 @@ stats.show(5, False)
 
 ## Statistical Summaries
 
-The statistical summary functions return a summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance, which can be computed as a tile aggregate, a DataFrame aggregate, or an element-wise local aggregate.
+The statistical summary functions return a summary of cell values: number of data cells, number of NoData cells, minimum, maximum, mean, and variance, which can be computed as a _tile_ aggregate, a DataFrame aggregate, or an element-wise local aggregate.
 
-The @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) function computes summary statistics separately for each row in a `tile` column as shown below.
+The @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) function computes summary statistics separately for each row in a _tile_ column as shown below.
 
 ```python
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
@@ -73,7 +73,7 @@ stats.printSchema()
 stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
 ```
 
-The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the tiles in a DataFrame and returns a statistical summary of all cell values as shown below.
+The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the _tiles_ in a DataFrame and returns a statistical summary of all cell values as shown below.
 
 ```python
 rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
@@ -81,7 +81,7 @@ rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
     .show(10, False)
 ```
 
-The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal tile dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
+The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal _tile_ dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
 
 ```python
 rf = spark.sql("""
@@ -101,7 +101,7 @@ for r in agg_local_stats:
 
 ## Histogram
 
-The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of tile and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of `tile` in this DataFrame, but this histogram is just computed for the `tile` in the first row.
+The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of _tile_ and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of _tile_ in this DataFrame, but this histogram is just computed for the _tile_ in the first row.
 
 ```python
 import matplotlib.pyplot as plt
@@ -119,7 +119,7 @@ plt.hist(values, weights=counts, bins=100)
 plt.show()
 ```
 
-The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of `tile` in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
+The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of _tile_ in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
 
 ```python
 bins_list = rf.agg(

From adf96ae783abe581e1316f3a2f3d99b8e175ec2b Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 15:48:31 -0400
Subject: [PATCH 342/380] make tile italicized when appropriate

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/description.md       |  6 ++--
 .../src/main/python/docs/languages.pymd       |  2 +-
 .../src/main/python/docs/local-algebra.pymd   | 12 +++----
 .../src/main/python/docs/nodata-handling.pymd | 34 +++++++++----------
 .../src/main/python/docs/raster-catalogs.pymd |  6 ++--
 .../src/main/python/docs/raster-read.pymd     |  8 ++---
 .../src/main/python/docs/raster-write.pymd    |  4 +--
 7 files changed, 36 insertions(+), 36 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 7e5017821..974441633 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -25,15 +25,15 @@ RasterFrames builds upon several other LocationTech projects:
 
 ![LocationTech Stack](static/rasterframes-locationtech-stack.png)
 
-RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called @ref:[_tiles_](concepts.md#tile). Each tile contains a 2-D matrix of @ref:[_cell_](concepts.md#tile) or pixel values along with information on how to numerically interpret those cells. 
+RasterFrames introduces georectified raster imagery to Spark SQL. It quantizes scenes into chunks called @ref:[_tiles_](concepts.md#tile). Each _tile_ contains a 2-D matrix of @ref:[_cell_](concepts.md#cell) or pixel values along with information on how to numerically interpret those _cells_.
 
-As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type @ref:[`tile`](concepts.md#tile). A `tile` column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other raster spatial data. Along with `tile` columns there is typically an @ref:[`extent`](concepts.md#extent) specifying the geographic location of the data, the map projection of that geometry (@ref:[`crs`](concepts.md#coordinate-reference-system--crs-)), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering.
+As shown in the figure below, a "RasterFrame" is a Spark DataFrame with one or more columns of type @ref:[_tile_](concepts.md#tile). A _tile_ column typically represents a single frequency band of sensor data, such as "blue" or "near infrared", but can also be quality assurance information, land classification assignments, or any other raster spatial data. Along with _tile_ columns there is typically an @ref:[`extent`](concepts.md#extent) specifying the geographic location of the data, the map projection of that geometry (@ref:[`crs`](concepts.md#coordinate-reference-system--crs-)), and a `timestamp` column representing the acquisition time. These columns can all be used in the `WHERE` clause when filtering.
 
 @@include[RasterFrame Example](static/rasterframe-sample.md)
 
 RasterFrames also includes support for working with vector data, such as [GeoJSON][GeoJSON]. RasterFrames vector data operations let you filter with geospatial relationships like contains or intersects, mask cells, convert vectors to rasters, and more.
 
-Raster data can be read from a @ref:[number of sources](raster-io.md). Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of imagery (including Cloud Optimized GeoTIFFs or [COGS][COGS]), [GeoTrellis Layers][GTLayer], and from catalogs of large datasets like Landsat 8 and MODIS data sets on the @ref:[AWS Public Data Set (PDS)](raster-catalogs.md#using-external-catalogs). 
+Raster data can be read from a @ref:[number of sources](raster-io.md). Through the flexible Spark SQL DataSource API, RasterFrames can be constructed from collections of imagery (including Cloud Optimized GeoTIFFs or [COGS][COGS]), [GeoTrellis Layers][GTLayer], and from catalogs of large datasets like Landsat 8 and MODIS data sets on the @ref:[AWS Public Data Set (PDS)](raster-catalogs.md#using-external-catalogs).
 
 [R]:https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/data.frame
 [Pandas]:https://pandas.pydata.org/
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index 7c37f9a5d..d0513ea89 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -1,6 +1,6 @@
 # API Languages
 
-One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This tutorial is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single tile in Tanzania.
+One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This tutorial is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single _tile_ in Tanzania.
 
 ```python, imports, echo=False
 from pyspark.sql.functions import *
diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index 8f50ad93b..e1a0ddf10 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -12,7 +12,7 @@ import os
 spark = create_rf_spark_session()
 ```
 
-[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single `tile`,  between a `tile` and a scalar, between two `tile`s, or among many `tile`s. These operations are common in processing of earth observation and other image data.
+[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single _tile_,  between a _tile_ and a scalar, between two _tiles_, or among many _tiles_. These operations are common in processing of earth observation and other image data.
 
 <img src="https://gisgeography.com/wp-content/uploads/2015/05/Local-Operation-Raster.png" alt="local op" style="width:200px;"/>
 
@@ -44,12 +44,12 @@ Observe how the bands we need to use to compute the index are arranged as column
 
 ### Local Map Algebra
 
-RasterFrames provides a wide variety of local map algebra functions. There are several different broad categories, based on how many tiles the function takes as input:
+RasterFrames provides a wide variety of local map algebra functions. There are several different broad categories, based on how many _tiles_ the function takes as input:
 
- * A function on a single Tile is a unary operation; example: @ref:[rf_log](reference.md#rf-log);
- * A function on two Tiles is a binary operation; example: @ref:[rf_local_multiply](reference.md#rf-local-multiply);
- * A function on a Tile and a scalar is a binary operation; example: @ref:[rf_local_less](reference.md#rf-local-less); or
- * A function on many Tiles is a n-ary operation; example: @ref:[rf_agg_local_min](reference.md#rf-agg-local-min)
+ * A function on a single _tile_ is a unary operation; example: @ref:[rf_log](reference.md#rf-log);
+ * A function on two _tiles_ is a binary operation; example: @ref:[rf_local_multiply](reference.md#rf-local-multiply);
+ * A function on a _tile_ and a scalar is a binary operation; example: @ref:[rf_local_less](reference.md#rf-local-less); or
+ * A function on many _tiles_ is a n-ary operation; example: @ref:[rf_agg_local_min](reference.md#rf-agg-local-min)
 
 We can express the normalized difference with a combination of `rf_local_divide`, `rf_local_subtract`, and `rf_local_add`. Since the normalized difference is so common, there is a convenience method `rf_normalized_difference`, which we use in this example. We will append a new column to the DataFrame, which will apply the map alegbra function to each row.
 
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index ead17b2e7..ebd4e5636 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -4,7 +4,7 @@
 
 In raster operations, the preservation and correct processing of missing observations is very important. In [most DataFrames and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value.
 
-RasterFrames provides a variety of functions to inspect and manage NoData within `tile`s.
+RasterFrames provides a variety of functions to inspect and manage NoData within _tiles_.
 
 ## Cell Types
 
@@ -76,7 +76,7 @@ Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no
 
 Credit: [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm)
 
-The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog, so the blue band and SCL tiles are aligned across rows.
+The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog, so the blue band and SCL _tiles_ are aligned across rows.
 
 ```python blue_scl_cat
 from pyspark.sql import Row
@@ -89,7 +89,7 @@ unmasked.printSchema()
 unmasked.select(rf_cell_type('blue'), rf_cell_type('scl')).distinct().show()
 ```
 
-Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new tile columns that are indicators of unwanted pixels, as defined above. Since the mask column is bit type, the addition is equivalent to a logical or, so the true values are 1.
+Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new _tile_ columns that are indicators of unwanted pixels, as defined above. Since the mask column is bit type, the addition is equivalent to a logical or, so the true values are 1.
 
 ```python def_mask
 from pyspark.sql.functions import lit
@@ -131,7 +131,7 @@ masked = one_mask.withColumn('blue_masked',
                  .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
 ```
 
-We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` tile to ensure our logic is correct.
+We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` _tile_ to ensure our logic is correct.
 
 ```python
 masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
@@ -152,7 +152,7 @@ display(sample[1])
 
 ## NoData and Local Arithmatic
 
-Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0.
+Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two _tiles_. One _tile_ will have values of 0 and 1, and the other will have values of just 0.
 
 
 ```python
@@ -186,11 +186,11 @@ display(row.x_nd)
 print('x with NoData plus y')
 display(row.x_nd_y_sum)
 ```
-To see more information about possible operations on Tile columns, see the @ref:[local map algebra](local-algebra.md) page and @ref:[function reference](reference.md#local-map-algebra).
+To see more information about possible operations on _tile_ columns, see the @ref:[local map algebra](local-algebra.md) page and @ref:[function reference](reference.md#local-map-algebra).
 
 ## Changing a Tile's NoData Values
 
-One way to mask a tile is to make a new tile with a user defined NoData value. We will explore this method below. First, lets create a DataFrame from a tile with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 Tile with vertical bands containing values 0, 1, 2, and 3.
+One way to mask a _tile_ is to make a new _tile_ with a user defined NoData value. We will explore this method below. First, lets create a DataFrame from a _tile_ with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 _tile_ with vertical bands containing values 0, 1, 2, and 3.
 
 ```python create_dummy_tile, caption='Dummy Tile'
 tile_size = 100
@@ -205,7 +205,7 @@ rf = spark.createDataFrame([Row(tile=x)])
 display(x)
 ```
 
-First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a tile with the cell type 'uint16ud2'.
+First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a _tile_ with the cell type 'uint16ud2'.
 
 ```python
 def get_nodata_ct(nd_val):
@@ -221,13 +221,13 @@ masked_rf = rf.withColumn('tile_nd_1',
 collected = masked_rf.collect()
 ```
 
-Let's look at the new Tiles we created. The tile named `tile_nd_1` has the 1 values masked out as expected.
+Let's look at the new _tiles_ we created. The _tile_ named `tile_nd_1` has the 1 values masked out as expected.
 
 ```python
 display(collected[0].tile_nd_1)
 ```
 
-And the tile named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the tile by setting a new user defined NoData value to `tile_nd_1` the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
+And the _tile_ named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the _tile_ by setting a new user defined NoData value to `tile_nd_1` the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
 
 ```python
 display(collected[0].tile_nd_2)
@@ -236,7 +236,7 @@ display(collected[0].tile_nd_2)
 
 ## Combining Tiles with Different Data Types
 
-RasterFrames supports having Tile columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact.
+RasterFrames supports having _tile_ columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact.
 
 Let's first create a RasterFrame that has columns of `float` and `int` cell type.
 
@@ -248,7 +248,7 @@ rf = spark.createDataFrame([Row(x=x, y=y)])
 rf.select(rf_cell_type('x'), rf_cell_type('y')).distinct().show()
 ```
 
-When performing a local operation between tile columns with cell types `int` and type `float`, the resulting tile cell type will be `float`. In local algebra over two tiles of different "sized" cell types, the resulting cell type will be the largest of the two input tiles' cell types.
+When performing a local operation between _tile_ columns with cell types `int` and type `float`, the resulting _tile_ cell type will be `float`. In local algebra over two _tiles_ of different "sized" cell types, the resulting cell type will be the largest of the two input _tiles'_ cell types.
 
 ```python
 rf.select(
@@ -258,7 +258,7 @@ rf.select(
     ).show(1)
 ```
 
-Combining tile columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 tile columns: one with a NoData value of 1, and one with a NoData value of 2.
+Combining _tile_ columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 _tile_ columns: one with a NoData value of 1, and one with a NoData value of 2.
 
 ```python
 x_nd_1 = Tile((np.ones((100, 100))*3), get_nodata_ct(1))
@@ -266,7 +266,7 @@ x_nd_2 = Tile((np.ones((100, 100))*3), get_nodata_ct(2))
 rf_nd = spark.createDataFrame([Row(x_nd_1=x_nd_1, x_nd_2=x_nd_2)])
 ```
 
-Let's try adding the tile columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1.
+Let's try adding the _tile_ columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1.
 
 ```python
 rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_2', 'x_nd_1'))
@@ -282,7 +282,7 @@ rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 
 ## NoData Values in Aggregation
 
-Let's use the same tile as before to demonstrate how NoData values affect tile aggregations.
+Let's use the same _tile_ as before to demonstrate how NoData values affect _tile_ aggregations.
 
 ```python
 tile_size = 100
@@ -295,7 +295,7 @@ rf = spark.createDataFrame([Row(tile=x)])
 display(x)
 ```
 
-First we create the two new masked tile columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked.
+First we create the two new masked _tile_ columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked.
 
 ```python
 masked_rf = rf.withColumn('tile_nd_1',
@@ -304,7 +304,7 @@ masked_rf = rf.withColumn('tile_nd_1',
                           rf_convert_cell_type('tile_nd_1', get_nodata_ct(2)))
 ```
 
-The results of `rf_tile_sum` vary on the tiles that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
+The results of `rf_tile_sum` vary on the _tiles_ that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
 
 ```python
 masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index fc1e67be0..4dc7180e1 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,6 +1,6 @@
 # Raster Catalogs
 
-While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.pymd), which creates tiles from the rasters at the referenced URLs.
+While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.pymd), which creates _tiles_ from the rasters at the referenced URLs.
 
 A _catalog_ can have one or two dimensions:
 
@@ -75,7 +75,7 @@ my_cat_df.printSchema()
 
 ## Using External Catalogs
 
-The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading an cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data. 
+The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading an cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data.
 
 ```python
 from pyspark import SparkFiles
@@ -86,7 +86,7 @@ spark.sparkContext.addFile("https://modis-pds.s3.amazonaws.com/MCD43A4.006/2018-
 scene_list = spark.read \
     .format("csv") \
     .option("header", "true") \
-    .load(SparkFiles.get("2018-07-04_scenes.txt")) 
+    .load(SparkFiles.get("2018-07-04_scenes.txt"))
 scene_list.head(3)
 ```
 
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 3c28dafea..9ca4336f7 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -44,7 +44,7 @@ You can also see that the single raster has been broken out into many arbitrary
 rf.groupby(rf.proj_raster_path).count().show()
 ```
 
-Let's select a single tile and view it. The tile preview image as well as the string representation provide some basic information about the tile: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the tile. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
+Let's select a single _tile_ and view it. The _tile_ preview image as well as the string representation provide some basic information about the _tile_: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the _tile_. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
 
 ```python show_tile_sample
 tile = rf.select(rf_tile("proj_raster")).first()[0]
@@ -144,7 +144,7 @@ spark.read.raster(uri, lazy_tiles=False) \
     .select('proj_raster.tile').show(1, False)
 ```
 
-In the initial examples on this page, you may have noticed that the realized (non-lazy) tiles are shown, but we did not change `lazy_tiles`. Instead, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized tile from the lazy representation.
+In the initial examples on this page, you may have noticed that the realized (non-lazy) _tiles_ are shown, but we did not change `lazy_tiles`. Instead, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized _tile_ from the lazy representation.
 
 ## Multiband Rasters
 
@@ -204,9 +204,9 @@ The catalog will give details on the particular layers available for query. We c
 gt_layer = spark.read.geotrellis(path='scheme://path-to-gt-catalog', layer=layer_name, zoom=zoom_level)
 ```
 
-This will return a RasterFrame with additional metadata inherited from the GeoTrellis TileLayerMetadata, such as the SpatialKey. The TileLayerMetadata is also stored as json in the metadata of the tile column.
+This will return a RasterFrame with additional metadata inherited from the GeoTrellis `TileLayerMetadata`, such as the `SpatialKey`. The `TileLayerMetadata` is also stored as JSON in the metadata of the _tile_ column.
 
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
 [Tile]: concepts.md#tile
-[GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
\ No newline at end of file
+[GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index e1efeae94..2d8f84a76 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -62,7 +62,7 @@ GeoTIFF is one of the most common file formats for spatial data, providing flexi
 
 One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
 
-Fortunately, we can use the cluster computing capability to downsample the data into a more manageable size. For sake of example, let's render an overview our scene's red band as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)). 
+Fortunately, we can use the cluster computing capability to downsample the data into a more manageable size. For sake of example, let's render an overview our scene's red band as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)).
 
 ```python write_geotiff
 outfile = os.path.join('/tmp', 'geotiff-overview.tif')
@@ -81,7 +81,7 @@ with rasterio.open(outfile) as src:
         histtype='stepfilled', title="Overview Histogram")
 ```
 
-If there are many tile or projected raster columns in the DataFrame, the GeoTIFF writer will write each one as a separate band in the file. Each band in the output will be tagged the input column names for reference.
+If there are many _tile_ or projected raster columns in the DataFrame, the GeoTIFF writer will write each one as a separate band in the file. Each band in the output will be tagged the input column names for reference.
 
 ```python, echo=False
 os.remove(outfile)

From c1dc85e348fd9099c7f0d0d434e6a150bfc9eee5 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 8 Aug 2019 16:14:32 -0400
Subject: [PATCH 343/380] Document and improve use of CellType in
 rasterfunctions args

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../rasterframes/ref/RasterSourceIT.scala     |  1 -
 .../src/main/python/docs/reference.pymd       | 24 ++++++++++++-------
 .../python/docs/unsupervised-learning.pymd    |  4 +++-
 .../python/pyrasterframes/rasterfunctions.py  | 16 ++++++-------
 .../main/python/pyrasterframes/rf_types.py    |  6 ++++-
 .../main/python/tests/PyRasterFramesTests.py  |  4 ++++
 .../main/python/tests/RasterFunctionsTests.py | 20 ++++++++++++++--
 7 files changed, 54 insertions(+), 21 deletions(-)

diff --git a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
index 58fae9e0f..ae8b0b1d4 100644
--- a/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
+++ b/core/src/it/scala/org/locationtech/rasterframes/ref/RasterSourceIT.scala
@@ -24,7 +24,6 @@ package org.locationtech.rasterframes.ref
 import java.lang.Math.ceil
 import java.net.URI
 
-import com.azavea.gdal.GDALWarp
 import org.locationtech.rasterframes
 import org.locationtech.rasterframes.util.time
 import org.locationtech.rasterframes.{NOMINAL_TILE_SIZE, TestData, TestEnvironment}
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 6508fda26..341100943 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -126,26 +126,29 @@ Functions to create a new Tile column, either from scratch or from existing data
 ### rf_make_zeros_tile
 
 ```
-Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, String cell_type_name)
+Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, [CellType cell_type])
+Tile rf_make_zeros_tile(Int tile_columns, Int tile_rows, [String cell_type_name])
 ```
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of zeros, with the optional cell type; default is float64. See @ref:[this discussion](nodata-handling.md#cell-types) on cell types for info on the `cell_type` argument. All arguments are literal values and not column expressions.
 
 ### rf_make_ones_tile
 
 ```
-Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, String cell_type_name)
+Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, [CellType cell_type])
+Tile rf_make_ones_tile(Int tile_columns, Int tile_rows, [String cell_type_name])
 ```
 
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of ones, with the optional cell type; default is float64. See @ref:[this discussion](nodata-handling.md#cell-types) on cell types for info on the `cell_type` argument. All arguments are literal values and not column expressions.
 
 ### rf_make_constant_tile
 
-    Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  String cell_type_name)
+    Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  [CellType cell_type])
+    Tile rf_make_constant_tile(Numeric constant, Int tile_columns, Int tile_rows,  [String cell_type_name])
 
 
-Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the specified cell type. See function @ref:[`rf_cell_types`](reference.md#rf-cell-types) for valid values. All arguments are literal values and not column expressions.
+Create a `tile` of shape `tile_columns` by `tile_rows` full of `constant`, with the optional cell type; default is float64. See @ref:[this discussion](nodata-handling.md#cell-types) on cell types for info on the `cell_type` argument. All arguments are literal values and not column expressions.
 
 
 ### rf_rasterize
@@ -182,9 +185,14 @@ Python only. Create a `tile` from a Spark SQL [Array](http://spark.apache.org/do
 
 ### rf_assemble_tile
 
-    Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, String cellType)
+    Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, [CellType cell_type])
+    Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows, [String cell_type_name])
 
-Python only. Create a Tile from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  The `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. Valid values for `cellType` can be found with function @ref:[`rf_cell_types`](reference.md#rf-cell-types).
+SQL:  `Tile rf_assemble_tile(Int colIndex, Int rowIndex, Numeric cellData, Int numCols, Int numRows)`
+
+Create `tile`s of dimension `numCols` by `numRows` from  a column of cell data with location indices. This function is the inverse of @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles). Intended use is with a `groupby`, producing one row with a new `tile` per group.  In Python, the `numCols`, `numRows` and `cellType` arguments are literal values, others are column expressions. 
+See @ref:[this discussion](nodata-handling.md#cell-types) on cell types for info on the optional `cell_type` argument. The default is float64.
+SQL implementation does not accept a cell_type argument. It returns a float64 cell type `tile` by default.
 
 ## Masking and NoData
 
diff --git a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index f258fc5c8..3e0221b76 100644
--- a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -112,11 +112,13 @@ print("Within set sum of squared errors: %s" % metric)
 We can recreate the tiled data structure using the metadata added by the `TileExploder` pipeline stage.
 
 ```python
+from pyrasterframes.rf_types import CellType
+
 tile_dims = df.select(rf_dimensions(df.b1).alias('dims')).first()['dims']
 retiled = clustered.groupBy('extent', 'crs') \
     .agg(
         rf_assemble_tile('column_index', 'row_index', 'prediction',
-            tile_dims['cols'], tile_dims['rows'], 'int8').alias('prediction')
+            tile_dims['cols'], tile_dims['rows'], CellType.int8()).alias('prediction')
 )
 
 retiled.printSchema()
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 68e30f6ec..546463b8d 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -53,12 +53,12 @@ def rf_cell_types():
     return [CellType(str(ct)) for ct in _context_call('rf_cell_types')]
 
 
-def rf_assemble_tile(col_index, row_index, cell_data_col, num_cols, num_rows, cell_type_str):
+def rf_assemble_tile(col_index, row_index, cell_data_col, num_cols, num_rows, cell_type):
     """Create a Tile from  a column of cell data with location indices"""
     jfcn = RFContext.active().lookup('rf_assemble_tile')
     return Column(
         jfcn(_to_java_column(col_index), _to_java_column(row_index), _to_java_column(cell_data_col), num_cols, num_rows,
-             _parse_cell_type(cell_type_str)))
+             _parse_cell_type(cell_type)))
 
 
 def rf_array_to_tile(array_col, num_cols, num_rows):
@@ -73,22 +73,22 @@ def rf_convert_cell_type(tile_col, cell_type):
     return Column(jfcn(_to_java_column(tile_col), _parse_cell_type(cell_type)))
 
 
-def rf_make_constant_tile(scalar_value, num_cols, num_rows, cell_type):
+def rf_make_constant_tile(scalar_value, num_cols, num_rows, cell_type=CellType.float64()):
     """Constructor for constant tile column"""
     jfcn = RFContext.active().lookup('rf_make_constant_tile')
-    return Column(jfcn(scalar_value, num_cols, num_rows, cell_type))
+    return Column(jfcn(scalar_value, num_cols, num_rows, _parse_cell_type(cell_type)))
 
 
-def rf_make_zeros_tile(num_cols, num_rows, cell_type='float64'):
+def rf_make_zeros_tile(num_cols, num_rows, cell_type=CellType.float64()):
     """Create column of constant tiles of zero"""
     jfcn = RFContext.active().lookup('rf_make_zeros_tile')
-    return Column(jfcn(num_cols, num_rows, cell_type))
+    return Column(jfcn(num_cols, num_rows, _parse_cell_type(cell_type)))
 
 
-def rf_make_ones_tile(num_cols, num_rows, cell_type='float64'):
+def rf_make_ones_tile(num_cols, num_rows, cell_type=CellType.float64()):
     """Create column of constant tiles of one"""
     jfcn = RFContext.active().lookup('rf_make_ones_tile')
-    return Column(jfcn(num_cols, num_rows, cell_type))
+    return Column(jfcn(num_cols, num_rows, _parse_cell_type(cell_type)))
 
 
 def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index d4c3f1c25..0875ec783 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -261,7 +261,11 @@ def to_numpy_dtype(self):
     def with_no_data_value(self, no_data):
         if self.has_no_data() and self.no_data_value() == no_data:
             return self
-        return CellType(self.base_cell_type_name() + 'ud' + str(no_data))
+        if self.is_floating_point():
+            no_data = str(float(no_data))
+        else:
+            no_data = str(int(no_data))
+        return CellType(self.base_cell_type_name() + 'ud' + no_data)
 
     def __eq__(self, other):
         if type(other) is type(self):
diff --git a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
index eba050cd0..f682e2609 100644
--- a/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
+++ b/pyrasterframes/src/main/python/tests/PyRasterFramesTests.py
@@ -66,6 +66,10 @@ def test_cell_type_no_data(self):
         self.assertTrue(np.isnan(CellType.float32().no_data_value()))
 
         self.assertEqual(CellType("float32ud-98").no_data_value(), -98.0)
+        self.assertEqual(CellType("float32ud-98").no_data_value(), -98)
+        self.assertEqual(CellType("int32ud-98").no_data_value(), -98.0)
+        self.assertEqual(CellType("int32ud-98").no_data_value(), -98)
+
         self.assertTrue(math.isnan(CellType.float64().no_data_value()))
         self.assertEqual(CellType.uint8().no_data_value(), 0)
 
diff --git a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
index 7989b5405..fb0e0870d 100644
--- a/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
+++ b/pyrasterframes/src/main/python/tests/RasterFunctionsTests.py
@@ -35,7 +35,6 @@ def test_setup(self):
                          "org.apache.spark.serializer.KryoSerializer")
         print("GDAL version", gdal_version())
 
-
     def test_identify_columns(self):
         cols = self.rf.tile_columns()
         self.assertEqual(len(cols), 1, '`tileColumns` did not find the proper number of columns.')
@@ -48,9 +47,11 @@ def test_identify_columns(self):
         print("Temporal key column: ", col)
 
     def test_tile_creation(self):
+        from pyrasterframes.rf_types import CellType
+
         base = self.spark.createDataFrame([1, 2, 3, 4], 'integer')
         tiles = base.select(rf_make_constant_tile(3, 3, 3, "int32"), rf_make_zeros_tile(3, 3, "int32"),
-                            rf_make_ones_tile(3, 3, "int32"))
+                            rf_make_ones_tile(3, 3, CellType.int32()))
         tiles.show()
         self.assertEqual(tiles.count(), 4)
 
@@ -249,3 +250,18 @@ def test_exists_for_all(self):
 
         self.assertTrue(df.select(rf_for_all(df.should_exist).alias('se')).take(1)[0].se)
         self.assertTrue(not df.select(rf_for_all(df.should_not_exist).alias('se')).take(1)[0].se)
+
+    def test_cell_type_in_functions(self):
+        from pyrasterframes.rf_types import CellType
+        ct = CellType.float32()
+        ct_str = CellType.float32().with_no_data_value(-999).cell_type_name  # NB int ND into float celltype
+
+        df = self.rf.withColumn('ct_str', rf_convert_cell_type('tile', ct_str)) \
+                    .withColumn('ct', rf_convert_cell_type('tile', ct)) \
+                    .withColumn('make', rf_make_constant_tile(99, 3, 4, CellType.int8()))
+
+        result = df.select('ct', 'ct_str', 'make').first()
+
+        self.assertEqual(result['ct'].cell_type, ct)
+        self.assertEqual(result['ct_str'].cell_type, CellType(ct_str))
+        self.assertEqual(result['make'].cell_type, CellType.int8())

From 26e9426fedf32b6eba8c5a8ad5ee220534ca0823 Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Thu, 8 Aug 2019 16:40:44 -0400
Subject: [PATCH 344/380] small edits

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 .../src/main/python/docs/getting-started.pymd        | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 6233dfd29..0bc959a38 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -19,7 +19,7 @@ import pyrasterframes
 spark = pyrasterframes.get_spark_session()
 ```
 
-Then you can read a raster and work with it in a Spark DataFrame.
+Then, you can read a raster and work with it in a Spark DataFrame.
 
 ```python
 from pyrasterframes.rasterfunctions import rf_local_add
@@ -51,13 +51,13 @@ You can also use RasterFrames in the following environments:
 
  1. Install [docker](https://docs.docker.com/install/)
  1. Pull the image: `docker pull s22s/rasterframes-notebook`
- 1. Run a container with the image, for example: 
+ 1. Run a container with the image, for example:
       `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest`
- 1. In a browser, open `localhost:8808` in the example above. 
+ 1. In a browser, open `localhost:8808` in the example above.
 
 See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
 
-### `pyspark` shell 
+### `pyspark` shell
 
 You can use RasterFrames in a `pyspark` shell. To set up the `pyspark` environment, prepare your call with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. For RasterFrames support you need to pass arguments pointing to the various Java dependencies. You will also need the Python source zip, even if you have pip installed the package. You can download the source zip here: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip.
 
@@ -67,10 +67,10 @@ The `pyspark` shell command will look something like this.
    pyspark \
     --master local[*] \
     --py-files pyrasterframes_2.11-${VERSION}-python.zip \
-    --packages org.locationtech.rasterframes:rasterframes_2.11:${VERSION},org.locationtech.rasterframes:pyrasterframes_2.11:${VERSION},org.locationtech.rasterframes:rasterframes-datasource_2.11:${VERSION}
+    --packages org.locationtech.rasterframes:rasterframes_2.11:${VERSION},org.locationtech.rasterframes:pyrasterframes_2.11:${VERSION},org.locationtech.rasterframes:rasterframes-datasource_2.11:${VERSION} \
     --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \ # these configs improve serialization performance
     --conf spark.kryo.registrator=org.locationtech.rasterframes.util.RFKryoRegistrator \
-    --conf spark.kryoserializer.buffer.max=500m 
+    --conf spark.kryoserializer.buffer.max=500m
 ```
 
 Then in the `pyspark` shell, import the module and call `withRasterFrames` on the SparkSession.

From 3d281e6315e57cd1e0af343310f042897c0652d6 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 9 Aug 2019 09:11:55 -0400
Subject: [PATCH 345/380] Fix broken ref, attempt trigger circle build for
 docs/* branches

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .circleci/config.yml                                     | 1 +
 pyrasterframes/src/main/python/docs/raster-catalogs.pymd | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 67b1e0a72..a2ade65ad 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -191,6 +191,7 @@ workflows:
             branches:
               only:
                 - /feature\/.*docs.*/
+                - /docs\/.*/
 
   nightly:
     triggers:
diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index fc1e67be0..995322401 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,6 +1,6 @@
 # Raster Catalogs
 
-While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.pymd), which creates tiles from the rasters at the referenced URLs.
+While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.md), which creates tiles from the rasters at the referenced URLs.
 
 A _catalog_ can have one or two dimensions:
 

From 021b7bcd2745b385e127478eb404392a1f676ecc Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 9 Aug 2019 09:24:59 -0400
Subject: [PATCH 346/380] Update docs readme

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 docs/README.md | 30 ++++++++++++++++++++++++------
 1 file changed, 24 insertions(+), 6 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index d89d0ea60..86f3e563b 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -25,6 +25,21 @@ The build constructs in `<src-root>/docs` are (due to legacy reasons) the top-le
 }
 ```
 
+## Building the docs
+
+To build the static site locally:
+
+    sbt makeSite
+    
+The site will be at `<src-root>/docs/target/site/index.html`.
+    
+    
+To start an interactive server running the docs:
+
+    sbt previewSite
+    
+The sbt server log a message with an address to view the site.
+
 ## Content Development Process
 
 Start with one of the existing files in `<src-root>/pyrasterframes/src/main/python/docs` as a template. [`local-algebra.pymd`](../pyrasterframes/src/main/python/docs/local-algebra.pymd) is a good example. If the content will have code blocks you want evaluated an results injected into the output, use the file extension `.pymd`. If the content doesn't use evaluatable code blocks, use `.md`.
@@ -44,9 +59,9 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     [info] Set current project to RasterFrames (in build file:<src-root>/)
     sbt:RasterFrames>    
     ```  
-2. The first time you check out the code, or whenever RasterFrames code is updated, you need to build the project artifacts so they are available for Pweave.
+2. The first time you check out the code, or whenever RasterFrames code is updated, you need to build the project artifacts so they are available for Pweave. Some docs also refer to test resources, so the easiest way to do it is to run the unit tests.
     ```
-    sbt:RasterFrames> pyrasterframes/package
+    sbt:RasterFrames> pyrasterframes/test
     [info] Compiling 4 Scala sources to <src-root>/core/target/scala-2.11/classes ...
    ... lots of noise ...
    [info] PyRasterFrames assembly written to '<src-root>/pyrasterframes/target/python/deps/jars/pyrasterframes-assembly-0.8.0-SNAPSHOT.jar'
@@ -67,7 +82,7 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     sbt:RasterFrames> pyrasterframes/doc
     ```
     There's a command alias for this last step: `pyDocs`.
-4. To evaluate a single `.pymd` file, you pass the `-s` option and the filename relative to the `python` directory:
+4. To evaluate a single `.pymd` file, you pass the `-s` option and the filename relative to the `pyraterframes/src/main/python` directory. You can also specify the output [format](http://mpastell.com/pweave/formats.html) with the `-f` argument.
     ```
     sbt:RasterFrames> pyrasterframes/pySetup pweave -s docs/getting-started.pymd
     [info] Synchronizing 44 files to '<src-root>/pyrasterframes/target/python'
@@ -83,7 +98,7 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
     Weaved docs/getting-started.pymd to docs/getting-started.md
     ```
 5. The _output_ Markdown files are written to `<src-root>/pyrasterframes/target/python/docs`. _Note_: don't edit any files in the `pyrasterframes/target` directory... they will get overwritten each time `sbt` runs a command.
-6. During content development it's sometimes helpful to see the output rendered as basic HTML. To do this, add the `-d html` option to the pweave command:
+6. During content development it's sometimes helpful to see the output rendered as basic HTML. To do this, add the `-f html` option to the pweave command:
     ```
     sbt:RasterFrames> pyrasterframes/pySetup pweave -f html -s docs/getting-started.pymd
     [info] Synchronizing 54 files to '<src-roog>/pyrasterframes/target/python'
@@ -104,8 +119,11 @@ To set up an environment whereby you can easily test/evaluate your code blocks d
 
 ## Notebooks
 
-The `rf-notebooks` sub-project creates a Docker image with Jupyter Notebooks pre-configured with RasterFrames. Any `.pymd` file under `.../python/docs/` is converted to an evaluated Jupyter Notebook and included as a part of the build (an additional bonus of the Pweave tool). 
+The `rf-notebooks` sub-project creates a Docker image with Jupyter Notebooks pre-configured with RasterFrames. Any `.pymd` file under `.../python/docs/` is converted to an evaluated Jupyter Notebook and included as a part of the build.
 
 ## Submission Process
 
-Submit new and updated documentation as a PR against locationtech/rasterframes. Make sure you've signed the Eclipse Foundation ECA and you ["Signed-off-by:"](https://stackoverflow.com/questions/1962094/what-is-the-sign-off-feature-in-git-for) each commit in the PR. The "Signed-off-by" address needs to be the exact same one as registered with the [Eclipse Foundation](https://wiki.eclipse.org/Development_Resources/Contributing_via_Git).
+Submit new and updated documentation as a PR against locationtech/rasterframes. Make sure you've signed the Eclipse Foundation ECA and you ["Signed-off-by:"](https://stackoverflow.com/questions/1962094/what-is-the-sign-off-feature-in-git-for) each commit in the PR. The "Signed-off-by" email address needs to be the exact same one as registered with the [Eclipse Foundation](https://wiki.eclipse.org/Development_Resources/Contributing_via_Git).
+
+If you are using circle CI, the circle configuration is set up to build the docs with `sbt makeSite` for branch names matching `feature/.*docs.*` or `docs/.*`
+

From ff65c87003ebfab016fb3f2213a577b647051ea2 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Fri, 9 Aug 2019 09:42:03 -0400
Subject: [PATCH 347/380] In docs, prefer python CellType to srings or implied
 numpy dype

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 .../src/main/python/docs/nodata-handling.pymd         | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index ead17b2e7..ac2fca493 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -150,11 +150,10 @@ And the original SCL data. The bright yellow is a cloudy region in the original
 display(sample[1])
 ```
 
-## NoData and Local Arithmatic
+## NoData and Local Arithmetic
 
 Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two tiles. One tile will have values of 0 and 1, and the other will have values of just 0.
 
-
 ```python
 tile_size = 100
 x = np.zeros((tile_size, tile_size), dtype='int16')
@@ -241,8 +240,8 @@ RasterFrames supports having Tile columns with multiple cell types in a single D
 Let's first create a RasterFrame that has columns of `float` and `int` cell type.
 
 ```python
-x = Tile((np.ones((100, 100))*2).astype('float'))
-y = Tile((np.ones((100, 100))*3.0).astype('int32'))
+x = Tile((np.ones((100, 100))*2), CellType.float64())
+y = Tile((np.ones((100, 100))*3), CellType.int32())
 rf = spark.createDataFrame([Row(x=x, y=y)])
 
 rf.select(rf_cell_type('x'), rf_cell_type('y')).distinct().show()
@@ -286,10 +285,10 @@ Let's use the same tile as before to demonstrate how NoData values affect tile a
 
 ```python
 tile_size = 100
-x = np.zeros((tile_size, tile_size), dtype='int16')
+x = np.zeros((tile_size, tile_size))
 for i in range(4):
     x[:, i*tile_size//4:(i+1)*tile_size//4] = i
-x = Tile(x)
+x = Tile(x, CellType.int16())
 
 rf = spark.createDataFrame([Row(tile=x)])
 display(x)

From ee2ec6d5feeee6184a86785fe2401e65ff01440b Mon Sep 17 00:00:00 2001
From: Courtney Whalen <cwhalen@astraea.io>
Date: Fri, 9 Aug 2019 11:39:54 -0400
Subject: [PATCH 348/380] wrong link to raster-reads

Signed-off-by: Courtney Whalen <cwhalen@astraea.io>
---
 pyrasterframes/src/main/python/docs/raster-catalogs.pymd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 4dc7180e1..ddddfb399 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,6 +1,6 @@
 # Raster Catalogs
 
-While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.pymd), which creates _tiles_ from the rasters at the referenced URLs.
+While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.md), which creates _tiles_ from the rasters at the referenced URLs.
 
 A _catalog_ can have one or two dimensions:
 

From 0a512be9da919b42afa78fd378808adba4de9ef4 Mon Sep 17 00:00:00 2001
From: Eric Culbertson <eculbertson@astraea.io>
Date: Fri, 9 Aug 2019 12:31:34 -0400
Subject: [PATCH 349/380] changed ipython Tile render to not scale with Tile
 size. Font size now does scale with render size. Smaller render size for
 Tiles within a dataframe

Signed-off-by: Eric Culbertson <eculbertson@astraea.io>
---
 .../main/python/pyrasterframes/rf_ipython.py  | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index 141e4aad4..b6f019dd3 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -21,7 +21,7 @@
 import pyrasterframes.rf_types
 
 
-def tile_to_png(tile):
+def tile_to_png(tile, fig_size=None):
     """ Provide image of Tile."""
     if tile.cells is None:
         return None
@@ -31,10 +31,11 @@ def tile_to_png(tile):
     from matplotlib.figure import Figure
 
     # Set up matplotlib objects
-    [width, height] = tile.dimensions()
-    dpi = 300
-    nominal_size = 5.5  # approx full size for a 256x256 tile
-    fig = Figure(figsize=(nominal_size * width / dpi,   nominal_size * height / dpi))
+    nominal_size = 4  # approx full size for a 256x256 tile
+    if fig_size is None:
+        fig_size = (nominal_size, nominal_size)
+
+    fig = Figure(figsize=fig_size)
     canvas = FigureCanvas(fig)
     axis = fig.add_subplot(1, 1, 1)
 
@@ -43,18 +44,19 @@ def tile_to_png(tile):
     axis.xaxis.set_ticks([])
     axis.yaxis.set_ticks([])
 
-    axis.set_title('{}, {}'.format(tile.dimensions(), tile.cell_type.__repr__()))  # compact metadata as title
+    axis.set_title('{}, {}'.format(tile.dimensions(), tile.cell_type.__repr__()),
+                   fontsize=fig_size[0]*4)  # compact metadata as title
 
     with io.BytesIO() as output:
         canvas.print_png(output)
         return output.getvalue()
 
 
-def tile_to_html(tile):
+def tile_to_html(tile, fig_size=None):
     """ Provide HTML string representation of Tile image."""
     import base64
     b64_img_html = '<img src="data:image/png;base64,{}" />'
-    png_bits = tile_to_png(tile)
+    png_bits = tile_to_png(tile, fig_size)
     b64_png = base64.b64encode(png_bits).decode('utf-8').replace('\n', '')
     return b64_img_html.format(b64_png)
 
@@ -76,7 +78,7 @@ def pandas_df_to_html(df):
 
     def _safe_tile_to_html(t):
         if isinstance(t, pyrasterframes.rf_types.Tile):
-            return tile_to_html(t)
+            return tile_to_html(t, fig_size=(2, 2))
         else:
             # handles case where objects in a column are not all Tile type
             return t.__repr__()

From cc8b0967ffa37c9de9ce8b0436692c06e9b272d1 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 9 Aug 2019 15:06:56 -0400
Subject: [PATCH 350/380] Fixes #242.

---
 .../model/FixedRasterExtent.scala             | 278 ++++++++++++++++++
 .../rasterframes/ref/RasterRef.scala          |   2 +-
 .../rasterframes/ref/RasterSource.scala       |   4 +-
 .../rasterframes/ref/RasterRefSpec.scala      |  52 +++-
 .../rasterframes/ref/RasterSourceSpec.scala   |  31 +-
 .../geotiff/GeoTiffDataSource.scala           |   2 +-
 .../geotiff/GeoTiffDataSourceSpec.scala       |  22 +-
 .../raster/RasterSourceDataSourceSpec.scala   |  49 ++-
 project/RFDependenciesPlugin.scala            |   2 +-
 9 files changed, 391 insertions(+), 51 deletions(-)
 create mode 100644 core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala

diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala
new file mode 100644
index 000000000..8eefa76fc
--- /dev/null
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala
@@ -0,0 +1,278 @@
+/*
+ * Copyright 2016 Azavea
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.locationtech.rasterframes.model
+
+
+import geotrellis.raster._
+import geotrellis.vector._
+
+import scala.math.{ceil, max, min}
+
+/**
+  * This class is a copy of the GeoTrellis 2.x `RasterExtent`,
+  * with [GT 3.0 fixes](https://github.com/locationtech/geotrellis/pull/2953/files) incorporated into the
+  * new `GridExtent[T]` class. This class should be removed after RasterFrames is upgraded to GT 3.x.
+  */
+case class FixedRasterExtent(
+  override val extent: Extent,
+  override val cellwidth: Double,
+  override val cellheight: Double,
+  cols: Int,
+  rows: Int
+) extends GridExtent(extent, cellwidth, cellheight) with Grid {
+  import FixedRasterExtent._
+
+  if (cols <= 0) throw GeoAttrsError(s"invalid cols: $cols")
+  if (rows <= 0) throw GeoAttrsError(s"invalid rows: $rows")
+
+  /**
+    * Convert map coordinates (x, y) to grid coordinates (col, row).
+    */
+  final def mapToGrid(x: Double, y: Double): (Int, Int) = {
+    val col = floorWithTolerance((x - extent.xmin) / cellwidth).toInt
+    val row = floorWithTolerance((extent.ymax - y) / cellheight).toInt
+    (col, row)
+  }
+
+  /**
+    * Convert map coordinate x to grid coordinate column.
+    */
+  final def mapXToGrid(x: Double): Int = floorWithTolerance(mapXToGridDouble(x)).toInt
+
+  /**
+    * Convert map coordinate x to grid coordinate column.
+    */
+  final def mapXToGridDouble(x: Double): Double = (x - extent.xmin) / cellwidth
+
+  /**
+    * Convert map coordinate y to grid coordinate row.
+    */
+  final def mapYToGrid(y: Double): Int = floorWithTolerance(mapYToGridDouble(y)).toInt
+
+  /**
+    * Convert map coordinate y to grid coordinate row.
+    */
+  final def mapYToGridDouble(y: Double): Double = (extent.ymax - y ) / cellheight
+
+  /**
+    * Convert map coordinate tuple (x, y) to grid coordinates (col, row).
+    */
+  final def mapToGrid(mapCoord: (Double, Double)): (Int, Int) = {
+    val (x, y) = mapCoord
+    mapToGrid(x, y)
+  }
+
+  /**
+    * Convert a point to grid coordinates (col, row).
+    */
+  final def mapToGrid(p: Point): (Int, Int) =
+    mapToGrid(p.x, p.y)
+
+  /**
+    * The map coordinate of a grid cell is the center point.
+    */
+  final def gridToMap(col: Int, row: Int): (Double, Double) = {
+    val x = col * cellwidth + extent.xmin + (cellwidth / 2)
+    val y = extent.ymax - (row * cellheight) - (cellheight / 2)
+
+    (x, y)
+  }
+
+  /**
+    * For a give column, find the corresponding x-coordinate in the
+    * grid of the present [[FixedRasterExtent]].
+    */
+  final def gridColToMap(col: Int): Double = {
+    col * cellwidth + extent.xmin + (cellwidth / 2)
+  }
+
+  /**
+    * For a give row, find the corresponding y-coordinate in the grid
+    * of the present [[FixedRasterExtent]].
+    */
+  final def gridRowToMap(row: Int): Double = {
+    extent.ymax - (row * cellheight) - (cellheight / 2)
+  }
+
+  /**
+    * Gets the GridBounds aligned with this FixedRasterExtent that is the
+    * smallest subgrid of containing all points within the extent. The
+    * extent is considered inclusive on it's north and west borders,
+    * exclusive on it's east and south borders.  See [[FixedRasterExtent]]
+    * for a discussion of grid and extent boundary concepts.
+    *
+    * The 'clamp' flag determines whether or not to clamp the
+    * GridBounds to the FixedRasterExtent; defaults to true. If false,
+    * GridBounds can contain negative values, or values outside of
+    * this FixedRasterExtent's boundaries.
+    *
+    * @param     subExtent      The extent to get the grid bounds for
+    * @param     clamp          A boolean
+    */
+  def gridBoundsFor(subExtent: Extent, clamp: Boolean = true): GridBounds = {
+    // West and North boundaries are a simple mapToGrid call.
+    val (colMin, rowMin) = mapToGrid(subExtent.xmin, subExtent.ymax)
+
+    // If South East corner is on grid border lines, we want to still only include
+    // what is to the West and\or North of the point. However if the border point
+    // is not directly on a grid division, include the whole row and/or column that
+    // contains the point.
+    val colMax = {
+      val colMaxDouble = mapXToGridDouble(subExtent.xmax)
+      if(math.abs(colMaxDouble - floorWithTolerance(colMaxDouble)) < FixedRasterExtent.epsilon) colMaxDouble.toInt - 1
+      else colMaxDouble.toInt
+    }
+
+    val rowMax = {
+      val rowMaxDouble = mapYToGridDouble(subExtent.ymin)
+      if(math.abs(rowMaxDouble - floorWithTolerance(rowMaxDouble)) < FixedRasterExtent.epsilon) rowMaxDouble.toInt - 1
+      else rowMaxDouble.toInt
+    }
+
+    if(clamp) {
+      GridBounds(math.min(math.max(colMin, 0), cols - 1),
+        math.min(math.max(rowMin, 0), rows - 1),
+        math.min(math.max(colMax, 0), cols - 1),
+        math.min(math.max(rowMax, 0), rows - 1))
+    } else {
+      GridBounds(colMin, rowMin, colMax, rowMax)
+    }
+  }
+
+  /**
+    * Combine two different [[FixedRasterExtent]]s (which must have the
+    * same cellsizes).  The result is a new extent at the same
+    * resolution.
+    */
+  def combine (that: FixedRasterExtent): FixedRasterExtent = {
+    if (cellwidth != that.cellwidth)
+      throw GeoAttrsError(s"illegal cellwidths: $cellwidth and ${that.cellwidth}")
+    if (cellheight != that.cellheight)
+      throw GeoAttrsError(s"illegal cellheights: $cellheight and ${that.cellheight}")
+
+    val newExtent = extent.combine(that.extent)
+    val newRows = ceil(newExtent.height / cellheight).toInt
+    val newCols = ceil(newExtent.width / cellwidth).toInt
+
+    FixedRasterExtent(newExtent, cellwidth, cellheight, newCols, newRows)
+  }
+
+  /**
+    * Returns a [[RasterExtent]] with the same extent, but a modified
+    * number of columns and rows based on the given cell height and
+    * width.
+    */
+  def withResolution(targetCellWidth: Double, targetCellHeight: Double): FixedRasterExtent = {
+    val newCols = math.ceil((extent.xmax - extent.xmin) / targetCellWidth).toInt
+    val newRows = math.ceil((extent.ymax - extent.ymin) / targetCellHeight).toInt
+    FixedRasterExtent(extent, targetCellWidth, targetCellHeight, newCols, newRows)
+  }
+
+  /**
+    * Returns a [[FixedRasterExtent]] with the same extent, but a modified
+    * number of columns and rows based on the given cell height and
+    * width.
+    */
+  def withResolution(cellSize: CellSize): FixedRasterExtent =
+    withResolution(cellSize.width, cellSize.height)
+
+  /**
+    * Returns a [[FixedRasterExtent]] with the same extent and the given
+    * number of columns and rows.
+    */
+  def withDimensions(targetCols: Int, targetRows: Int): FixedRasterExtent =
+    FixedRasterExtent(extent, targetCols, targetRows)
+
+  /**
+    * Adjusts a raster extent so that it can encompass the tile
+    * layout.  Will resample the extent, but keep the resolution, and
+    * preserve north and west borders
+    */
+  def adjustTo(tileLayout: TileLayout): FixedRasterExtent = {
+    val totalCols = tileLayout.tileCols * tileLayout.layoutCols
+    val totalRows = tileLayout.tileRows * tileLayout.layoutRows
+
+    val resampledExtent = Extent(extent.xmin, extent.ymax - (cellheight*totalRows),
+      extent.xmin + (cellwidth*totalCols), extent.ymax)
+
+    FixedRasterExtent(resampledExtent, cellwidth, cellheight, totalCols, totalRows)
+  }
+
+  /**
+    * Returns a new [[FixedRasterExtent]] which represents the GridBounds
+    * in relation to this FixedRasterExtent.
+    */
+  def rasterExtentFor(gridBounds: GridBounds): FixedRasterExtent = {
+    val (xminCenter, ymaxCenter) = gridToMap(gridBounds.colMin, gridBounds.rowMin)
+    val (xmaxCenter, yminCenter) = gridToMap(gridBounds.colMax, gridBounds.rowMax)
+    val (hcw, hch) = (cellwidth / 2, cellheight / 2)
+    val e = Extent(xminCenter - hcw, yminCenter - hch, xmaxCenter + hcw, ymaxCenter + hch)
+    FixedRasterExtent(e, cellwidth, cellheight, gridBounds.width, gridBounds.height)
+  }
+}
+
+/**
+  * The companion object for the [[FixedRasterExtent]] type.
+  */
+object FixedRasterExtent {
+  final val epsilon = 0.0000001
+
+  /**
+    * Create a new [[FixedRasterExtent]] from an Extent, a column, and a
+    * row.
+    */
+  def apply(extent: Extent, cols: Int, rows: Int): FixedRasterExtent = {
+    val cw = extent.width / cols
+    val ch = extent.height / rows
+    FixedRasterExtent(extent, cw, ch, cols, rows)
+  }
+
+  /**
+    * Create a new [[FixedRasterExtent]] from an Extent and a [[CellSize]].
+    */
+  def apply(extent: Extent, cellSize: CellSize): FixedRasterExtent = {
+    val cols = (extent.width / cellSize.width).toInt
+    val rows = (extent.height / cellSize.height).toInt
+    FixedRasterExtent(extent, cellSize.width, cellSize.height, cols, rows)
+  }
+
+  /**
+    * Create a new [[FixedRasterExtent]] from a [[CellGrid]] and an Extent.
+    */
+  def apply(tile: CellGrid, extent: Extent): FixedRasterExtent =
+    apply(extent, tile.cols, tile.rows)
+
+  /**
+    * Create a new [[FixedRasterExtent]] from an Extent and a [[CellGrid]].
+    */
+  def apply(extent: Extent, tile: CellGrid): FixedRasterExtent =
+    apply(extent, tile.cols, tile.rows)
+
+
+  /**
+    * The same logic is used in QGIS: https://github.com/qgis/QGIS/blob/607664c5a6b47c559ed39892e736322b64b3faa4/src/analysis/raster/qgsalignraster.cpp#L38
+    * The search query: https://github.com/qgis/QGIS/search?p=2&q=floor&type=&utf8=%E2%9C%93
+    *
+    * GDAL uses smth like that, however it was a bit hard to track it down:
+    * https://github.com/OSGeo/gdal/blob/7601a637dfd204948d00f4691c08f02eb7584de5/gdal/frmts/vrt/vrtsources.cpp#L215
+    * */
+  def floorWithTolerance(value: Double): Double = {
+    val roundedValue = math.round(value)
+    if (math.abs(value - roundedValue) < epsilon) roundedValue
+    else math.floor(value)
+  }
+}
+
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
index f191f4188..b0aabcc48 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterRef.scala
@@ -48,7 +48,7 @@ case class RasterRef(source: RasterSource, bandIndex: Int, subextent: Option[Ext
   def cellType: CellType = source.cellType
   def tile: ProjectedRasterTile = ProjectedRasterTile(RasterRefTile(this), extent, crs)
 
-  protected lazy val grid: GridBounds = source.rasterExtent.gridBoundsFor(extent)
+  protected lazy val grid: GridBounds = source.rasterExtent.gridBoundsFor(extent, true)
   protected def srcExtent: Extent = extent
 
   protected lazy val realizedTile: Tile = {
diff --git a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
index ea33cf240..0f73f85cf 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/ref/RasterSource.scala
@@ -33,7 +33,7 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.rf.RasterSourceUDT
-import org.locationtech.rasterframes.model.{TileContext, TileDimensions}
+import org.locationtech.rasterframes.model.{FixedRasterExtent, TileContext, TileDimensions}
 import org.locationtech.rasterframes.{NOMINAL_TILE_DIMS, rfConfig}
 
 import scala.concurrent.duration.Duration
@@ -68,7 +68,7 @@ trait RasterSource extends ProjectedRasterLike with Serializable {
 
   protected def readBounds(bounds: Traversable[GridBounds], bands: Seq[Int]): Iterator[Raster[MultibandTile]]
 
-  def rasterExtent = RasterExtent(extent, cols, rows)
+  def rasterExtent = FixedRasterExtent(extent, cols, rows)
 
   def cellSize = CellSize(extent, cols, rows)
 
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
index aeffdcd66..f34c89859 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterRefSpec.scala
@@ -161,28 +161,52 @@ class RasterRefSpec extends TestEnvironment with TestData {
         val data = buf.toByteArray
         val in = new ObjectInputStream(new ByteArrayInputStream(data))
         val recovered = in.readObject()
-        assert(subRaster === recovered)
+        subRaster should be (recovered)
       }
     }
   }
 
-  describe("CreateRasterRefs") {
-    it("should convert and expand RasterSource") {
-      new Fixture {
-        import spark.implicits._
-        val df = Seq(src).toDF("src")
-        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), Seq(0), $"src"))
-        assert(refs.count() > 1)
+  describe("RasterRef creation") {
+    it("should realize subiles of proper size") {
+      val src = RasterSource(remoteMODIS)
+      val dims = src
+        .layoutExtents(NOMINAL_TILE_DIMS)
+        .map(e => RasterRef(src, 0, Some(e)))
+        .map(_.dimensions)
+        .distinct
+
+      forEvery(dims) { d =>
+        d._1 should be <= NOMINAL_TILE_SIZE
+        d._2 should be <= NOMINAL_TILE_SIZE
       }
     }
+  }
 
-    it("should work with tile realization") {
-      new Fixture {
-        import spark.implicits._
-        val df = Seq(src).toDF("src")
-        val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), Seq(0), $"src"))
-        assert(refs.count() > 1)
+  describe("RasterSourceToRasterRefs") {
+    it("should convert and expand RasterSource") {
+      val src = RasterSource(remoteMODIS)
+      import spark.implicits._
+      val df = Seq(src).toDF("src")
+      val refs = df.select(RasterSourceToRasterRefs(None, Seq(0), $"src"))
+      refs.count() should be (1)
+    }
+
+    it("should properly realize subtiles") {
+      val src = RasterSource(remoteMODIS)
+      import spark.implicits._
+      val df = Seq(src).toDF("src")
+      val refs = df.select(RasterSourceToRasterRefs(Some(NOMINAL_TILE_DIMS), Seq(0), $"src") as "proj_raster")
+
+      refs.count() shouldBe > (1L)
+
+
+      val dims = refs.select(rf_dimensions($"proj_raster")).distinct().collect()
+      forEvery(dims) { r =>
+        r.cols should be <=NOMINAL_TILE_SIZE
+        r.rows should be <=NOMINAL_TILE_SIZE
       }
+
+      dims.foreach(println)
     }
   }
 }
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
index a1390521b..1e62b95f5 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ref/RasterSourceSpec.scala
@@ -23,10 +23,9 @@ package org.locationtech.rasterframes.ref
 
 import java.net.URI
 
-import org.locationtech.rasterframes.TestData
+import org.locationtech.rasterframes._
 import geotrellis.vector.Extent
 import org.apache.spark.sql.rf.RasterSourceUDT
-import org.locationtech.rasterframes.TestEnvironment
 import org.locationtech.rasterframes.model.TileDimensions
 
 
@@ -57,7 +56,20 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       val dims = TileDimensions(63, 63)
       val ext = rs.layoutExtents(dims).head
       val bounds = rs.layoutBounds(dims).head
-      rs.rasterExtent.gridBoundsFor(ext, false) should be (bounds)
+      rs.rasterExtent.gridBoundsFor(ext) should be (bounds)
+    }
+    it("should compute layout extents from scene with fractional gsd") {
+
+      val rs = RasterSource(remoteMODIS)
+
+      val dims = rs.layoutExtents(NOMINAL_TILE_DIMS)
+        .map(e => rs.rasterExtent.gridBoundsFor(e, false))
+        .map(b => (b.width, b.height))
+        .distinct
+      forEvery(dims) { d =>
+        d._1 should be <= NOMINAL_TILE_SIZE
+        d._2 should be <= NOMINAL_TILE_SIZE
+      }
     }
   }
 
@@ -80,9 +92,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       }
       withClue("remoteCOGMultiband") {
         val src = RasterSource(remoteCOGMultiband)
-        //println("CoG size", src.size, src.dimensions)
         val raster = src.read(sub(src.extent))
-        //println("Subtile size", raster.size, raster.dimensions)
         assert(raster.size > 0 && raster.size < src.size)
       }
     }
@@ -107,7 +117,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       assert(!src.extent.isEmpty)
     }
     it("should interpret no scheme as file://"){
-      val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString()
+      val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString
       val schemelessUri = new URI(localSrc)
       schemelessUri.getScheme should be (null)
       val src = RasterSource(schemelessUri)
@@ -141,7 +151,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       }
 
       it("should interpret no scheme as file://") {
-        val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString()
+        val localSrc = geotiffDir.resolve("LC08_B7_Memphis_COG.tiff").toString
         val schemelessUri = new URI(localSrc)
         val gdal = GDALRasterSource(schemelessUri)
         val jvm = JVMGeoTiffRasterSource(schemelessUri)
@@ -151,7 +161,7 @@ class RasterSourceSpec extends TestEnvironment with TestData {
     }
   }
 
-  describe("RasterSourceToTiles Expression") {
+  describe("RasterSource tile construction") {
     it("should read all tiles") {
       val src = RasterSource(remoteMODIS)
 
@@ -167,11 +177,6 @@ class RasterSourceSpec extends TestEnvironment with TestData {
       val totalCells = subrasters.map(_.size).sum
 
       assert(totalCells === src.size)
-
-//      subrasters.zipWithIndex.foreach{case (r, i) ⇒
-//        // TODO: how to test?
-//        GeoTiff(r, src.crs).write(s"target/$i.tiff")
-//      }
     }
   }
 }
diff --git a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
index 5ec4ffb36..77781a781 100644
--- a/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
+++ b/datasource/src/main/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSource.scala
@@ -144,7 +144,7 @@ class GeoTiffDataSource
     }
 
     val tags = Tags(
-      RFBuildInfo.toMap.filter(_._1.startsWith("rf")).mapValues(_.toString),
+      RFBuildInfo.toMap.filter(_._1.toLowerCase().contains("version")).mapValues(_.toString),
       tileCols.map(c => Map("RF_COL" -> c.columnName)).toList
     )
 
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
index 266fdb8d6..eb5e55b0c 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotiff/GeoTiffDataSourceSpec.scala
@@ -23,7 +23,6 @@ package org.locationtech.rasterframes.datasource.geotiff
 import java.nio.file.Paths
 
 import geotrellis.proj4._
-import geotrellis.raster.ProjectedRaster
 import geotrellis.raster.io.geotiff.{MultibandGeoTiff, SinglebandGeoTiff}
 import geotrellis.vector.Extent
 import org.locationtech.rasterframes._
@@ -117,8 +116,10 @@ class GeoTiffDataSourceSpec
 
       val crs = df.select(rf_crs($"proj_raster")).first()
 
+      val out = Paths.get("target", "unstructured.tif").toString
+
       noException shouldBe thrownBy {
-        df.write.geotiff.withCRS(crs).save("unstructured.tif")
+        df.write.geotiff.withCRS(crs).save(out)
       }
 
       val (inCols, inRows) = {
@@ -128,7 +129,7 @@ class GeoTiffDataSourceSpec
       inCols should be (774)
       inRows should be (500) //from gdalinfo
 
-      val outputTif = SinglebandGeoTiff("unstructured.tif")
+      val outputTif = SinglebandGeoTiff(out)
       outputTif.imageData.cols should be (inCols)
       outputTif.imageData.rows should be (inRows)
 
@@ -156,13 +157,16 @@ class GeoTiffDataSourceSpec
           max($"ext.xmax").alias("xmax"),
           max($"ext.ymax").alias("ymax")
         ).first()
+
       val dfExtent = Extent(totalExtentRow.getDouble(0), totalExtentRow.getDouble(1), totalExtentRow.getDouble(2), totalExtentRow.getDouble(3))
       logger.info(s"Dataframe extent: ${dfExtent.toString()}")
 
-      dfExtent shouldBe (resourceExtent)
+      dfExtent shouldBe resourceExtent
+
+      val out = Paths.get("target", "unstructured_cog.tif").toString
 
       noException shouldBe thrownBy {
-        df.write.geotiff.withCRS(crs).save("target/unstructured_cog.tif")
+        df.write.geotiff.withCRS(crs).save(out)
       }
 
       val (inCols, inRows, inExtent, inCellType) = {
@@ -174,12 +178,11 @@ class GeoTiffDataSourceSpec
       inRows should be (754) //from gdalinfo
       inExtent should be (resourceExtent)
 
-      val outputTif = SinglebandGeoTiff("target/unstructured_cog.tif")
+      val outputTif = SinglebandGeoTiff(out)
       outputTif.imageData.cols should be (inCols)
       outputTif.imageData.rows should be (inRows)
       outputTif.extent should be (resourceExtent)
       outputTif.cellType should be (inCellType)
-
     }
 
     it("should write GeoTIFF without layer") {
@@ -228,12 +231,13 @@ red,green,blue
 ${s(1)},${s(4)},${s(3)}
 """
       val scene = spark.read.raster.fromCSV(cat, "red", "green", "blue").load()
+      val out = Paths.get("target", "geotiff-overview.tif").toString
       scene.write.geotiff
         .withCRS(LatLng)
         .withDimensions(256, 256)
-        .save("geotiff-overview.tif")
+        .save(out)
 
-      val outTif = MultibandGeoTiff("geotiff-overview.tif")
+      val outTif = MultibandGeoTiff(out)
       outTif.bandCount should be (3)
     }
   }
diff --git a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
index 5f9716116..a02c858e1 100644
--- a/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
+++ b/datasource/src/test/scala/org/locationtech/rasterframes/datasource/raster/RasterSourceDataSourceSpec.scala
@@ -21,7 +21,7 @@
 
 package org.locationtech.rasterframes.datasource.raster
 import geotrellis.raster.Tile
-import org.apache.spark.sql.functions.{lit, udf}
+import org.apache.spark.sql.functions.{lit, udf, round}
 import org.locationtech.rasterframes.{TestEnvironment, _}
 import org.locationtech.rasterframes.datasource.raster.RasterSourceDataSource.{RasterSourceCatalog, _}
 import org.locationtech.rasterframes.model.TileDimensions
@@ -250,12 +250,12 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
     val modis_df = spark.read.raster
       .withTileDimensions(128, 128)
       .withLazyTiles(true)
-      .load(remoteMODIS.toASCIIString())
+      .load(remoteMODIS.toASCIIString)
 
     val l8_df = spark.read.raster
       .withTileDimensions(32, 33)
       .withLazyTiles(true)
-      .load(remoteL8.toASCIIString())
+      .load(remoteL8.toASCIIString)
 
     ignore("should have at most four tile dimensions reading MODIS; ignore until fix #242") {
       val dims = modis_df.select(rf_dimensions($"proj_raster")).distinct().collect()
@@ -269,17 +269,46 @@ class RasterSourceDataSourceSpec extends TestEnvironment with TestData {
       dims.length should be <= (4)
     }
 
-    ignore("should have consistent tile resolution reading MODIS; ignore until fix #242") {
-      val res = modis_df.select((rf_extent($"proj_raster").getField("xmax") - rf_extent($"proj_raster").getField("xmin")) /
-        rf_dimensions($"proj_raster").getField("cols")).distinct().collect()
-      res.length should be (1)
+    it("should provide MODIS tiles with requested size") {
+      val res = modis_df
+        .withColumn("dims", rf_dimensions($"proj_raster"))
+        .select($"dims".as[TileDimensions]).distinct().collect()
+
+      forEvery(res) { r =>
+        r.cols should be <=128
+        r.rows should be <=128
+      }
+    }
+
+    it("should provide Landsat tiles with requested size") {
+      val dims = l8_df
+        .withColumn("dims", rf_dimensions($"proj_raster"))
+        .select($"dims".as[TileDimensions]).distinct().collect()
+
+      forEvery(dims) { d =>
+        d.cols should be <=32
+        d.rows should be <=33
+      }
+    }
+
+    it("should have consistent tile resolution reading MODIS") {
+      val res = modis_df
+        .withColumn("ext", rf_extent($"proj_raster"))
+        .withColumn("dims", rf_dimensions($"proj_raster"))
+        .select(round(($"ext.xmax" - $"ext.xmin") / $"dims.cols", 5))
+        .distinct().collect()
+      withClue(res.mkString("(", ", ", ")")) {
+        res.length should be(1)
+      }
     }
 
     it("should have consistent tile resolution reading Landsat") {
-      val res = l8_df.select((rf_extent($"proj_raster").getField("xmax") - rf_extent($"proj_raster").getField("xmin")) /
-        rf_dimensions($"proj_raster").getField("cols")).distinct().collect()
+      val res = l8_df
+        .withColumn("ext", rf_extent($"proj_raster"))
+        .withColumn("dims", rf_dimensions($"proj_raster"))
+        .select(($"ext.xmax" - $"ext.xmin") / $"dims.cols")
+        .distinct().collect()
       res.length should be (1)
     }
-
   }
 }
diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index f351f2e3a..aa3269869 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -46,7 +46,7 @@ object RFDependenciesPlugin extends AutoPlugin {
     val `geotrellis-contrib-vlm` = "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0"
     val `geotrellis-contrib-gdal` = "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0"
 
-    val scaffeine = "com.github.blemale" %% "scaffeine" % "2.5.0"
+    val scaffeine = "com.github.blemale" %% "scaffeine" % "2.6.0"
   }
   import autoImport._
 

From 7a789647322b3b7d44031ce97624c09d1a96f2da Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 11:18:05 -0400
Subject: [PATCH 351/380] Updated gitter links.

---
 CONTRIBUTING.md                                          | 2 +-
 README.md                                                | 2 +-
 pyrasterframes/src/main/python/README.md                 | 4 ++--
 pyrasterframes/src/main/python/docs/getting-started.pymd | 2 +-
 pyrasterframes/src/main/python/docs/index.md             | 2 +-
 pyrasterframes/src/main/python/docs/numpy-pandas.pymd    | 2 +-
 6 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 756d4cb8a..65d76ea3f 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -71,6 +71,6 @@ and respond as necessary.
 
 Help, questions and community dialog are supported via Gitter:
 
- * https://gitter.im/s22s/raster-frames
+ * https://gitter.im/locationtech/rasterframes
 
 Commercial support is available by writing to info@astraea.earth
diff --git a/README.md b/README.md
index 77795f2b2..f994b61ce 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <img src="docs/src/main/paradox/_template/images/RasterFramesLogo.png" width="300px"/><sup style="vertical-align: top;">&reg;</sup>
 
- [![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+ [![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 _RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
 
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index 16d6163ff..bb309124f 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -30,13 +30,13 @@ df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 
 ## Support
 
-Reach out to us on [gitter](https://gitter.im/s22s/raster-frames)!
+Reach out to us on [gitter](https://gitter.im/s22s/locationtech/rasterframes)!
 
 Issue tracking is through [github](https://github.com/locationtech/rasterframes/issues). 
 
 ## Contributing
 
-Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/s22s/raster-frames) so the community can help you get started!
+Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
 
 
 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 0bc959a38..2ce64a7b6 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -52,7 +52,7 @@ You can also use RasterFrames in the following environments:
  1. Install [docker](https://docs.docker.com/install/)
  1. Pull the image: `docker pull s22s/rasterframes-notebook`
  1. Run a container with the image, for example:
-      `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest`
+      `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/jovyan/work rasterframes-notebook:latest`
  1. In a browser, open `localhost:8808` in the example above.
 
 See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 25d6548ea..aabea7932 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -24,7 +24,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 ## Related Links
 
-* [Gitter Channel](https://gitter.im/s22s/raster-frames)
+* [Gitter Channel](https://gitter.im/locationtech/rasterframes)
 * [Scala API Documentation](latest/api/index.html)
 * [GitHub Repository](https://github.com/locationtech/rasterframes)
 * [Astraea, Inc.](http://www.astraea.earth/), the company behind RasterFrames
diff --git a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
index 32249b924..537ffe3df 100644
--- a/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
+++ b/pyrasterframes/src/main/python/docs/numpy-pandas.pymd
@@ -65,7 +65,7 @@ pandas_df.iloc[0].apply(lambda v: type(v))
 
 ## User Defined Functions
 
-As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, and then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/s22s/raster-frames) if you have an idea for a function that should be included.
+As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, and then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/locationtech/rasterframes) if you have an idea for a function that should be included.
 
 We will demonstrate an example of creating a UDF that is logically equivalent to a built-in function. We'll quickly show that the resulting _tiles_ are approximately equivalent. The reason they are not exactly the same is that one is computed in Python and the other is computed in Java.
 

From c069c9925355b63efff301517ed21bd6c0fea6b4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 11:18:15 -0400
Subject: [PATCH 352/380] Updated gdal-contrib.

---
 project/RFDependenciesPlugin.scala | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/project/RFDependenciesPlugin.scala b/project/RFDependenciesPlugin.scala
index aa3269869..85fa87923 100644
--- a/project/RFDependenciesPlugin.scala
+++ b/project/RFDependenciesPlugin.scala
@@ -43,8 +43,8 @@ object RFDependenciesPlugin extends AutoPlugin {
     val scalatest = "org.scalatest" %% "scalatest" % "3.0.3" % Test
     val shapeless = "com.chuusai" %% "shapeless" % "2.3.2"
     val `jts-core` = "org.locationtech.jts" % "jts-core" % "1.16.0"
-    val `geotrellis-contrib-vlm` = "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.11.0"
-    val `geotrellis-contrib-gdal` = "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.11.0"
+    val `geotrellis-contrib-vlm` = "com.azavea.geotrellis" %% "geotrellis-contrib-vlm" % "2.12.0"
+    val `geotrellis-contrib-gdal` = "com.azavea.geotrellis" %% "geotrellis-contrib-gdal" % "2.12.0"
 
     val scaffeine = "com.github.blemale" %% "scaffeine" % "2.6.0"
   }
@@ -60,8 +60,8 @@ object RFDependenciesPlugin extends AutoPlugin {
 
     // NB: Make sure to update the Spark version in pyrasterframes/python/setup.py
     rfSparkVersion := "2.3.3",
-    rfGeoTrellisVersion := "2.2.0",
+    rfGeoTrellisVersion := "2.3.1",
     rfGeoMesaVersion := "2.2.1",
-    dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.58d4965"
+    //dependencyOverrides += "com.azavea.gdal" % "gdal-warp-bindings" % "33.58d4965"
   )
 }

From 65203c02826fbf69d7053c00816a04918402a8bf Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 13:24:53 -0400
Subject: [PATCH 353/380] Documentation tweaks.

---
 CONTRIBUTING.md                               | 26 +++-------------
 README.md                                     | 30 ++++++++++++-------
 .../src/main/python/docs/languages.pymd       |  5 ++++
 3 files changed, 28 insertions(+), 33 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 65d76ea3f..1be2dcdf7 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,19 +1,10 @@
 # Contributing to RasterFrames
 
-Thanks for your interest in this project.
+Community contributions are always welcome. To get started, please review this document, 
+the [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and reach out to 
+us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
 
-## Project Description
-
-LocationTech RasterFrames brings the power of Spark DataFrames to geospatial
-raster data, empowered by the map algebra and tile layer operations of
-GeoTrellis. The underlying purpose of RasterFrames is to allow data scientists
-and software developers to process and analyze geospatial-temporal raster data
-with the same flexibility and ease as any other Spark Catalyst data type. At its
-core is a user-defined type (UDT) called TileUDT, which encodes a GeoTrellis
-Tile in a form the Spark Catalyst engine can process. Furthermore, we extend the
-definition of a DataFrame to encompass some additional invariants, allowing for
-geospatial operations within and between RasterFrames to occur, while still
-maintaining necessary geo-referencing constructs.
+The official home of RasterFrames under the Eclipse Foundation may be found here:
 
  * https://projects.eclipse.org/projects/locationtech.rasterframes
  
@@ -58,15 +49,6 @@ commands are as follows:
 * Build documentation: `sbt makeSite`
 * Spark shell with RasterFrames initialized: `sbt console`
 
- 
-## Contribution Process
-
-RasterFrames uses GitHub pull requests (PRs) for accepting contributions. 
-Please fork the repository, create a branch, and submit a PR based off the `master` branch.
-During the PR review process comments may be attached. Please look out for comments
-and respond as necessary.
- 
-
 ## Contact
 
 Help, questions and community dialog are supported via Gitter:
diff --git a/README.md b/README.md
index f994b61ce..bd1b86d14 100644
--- a/README.md
+++ b/README.md
@@ -2,31 +2,39 @@
 
  [![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
-_RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
+RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
+
+RasterFrames provides a DataFrame-centric view over arbitrary raster data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is both accessible to general analysts and scalable along with the rapidly growing data footprint.
 
 <img src="docs/src/main/paradox/RasterFramePipeline.png" width="600px"/>
 
 Please see the [Getting Started](http://rasterframes.io/getting-started.html) section of the Users' Manual to start using RasterFrames.
 
-## Documentation
+## User Resources
+
+* [RasterFrames Users' Manual](http://rasterframes.io/)
+* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/)
+* [Gitter Channel](https://gitter.im/locationtech/rasterframes)
+* [Submit an Issue](https://github.com/locationtech/rasterframes/issues) 
+
+
+## Contributing
 
-* [Users' Manual](http://rasterframes.io/)
-* [API Documentation](http://rasterframes.io/latest/api/index.html) 
-* [List of available UDFs](http://rasterframes.io/latest/api/index.html#org.locationtech.rasterframes.RasterFunctions)
-* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/) 
+Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and reach out to us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
 
-## Build instruction
+RasterFrames is part of the LocationTech Stack.
 
-First, you will need [sbt](https://www.scala-sbt.org/).
+<img src ="pyrasterframes/src/main/python/docs/static/rasterframes-locationtech-stack.png" width="600px" />
 
-Download the source from GitHub.
+It is written in Scala, but with Python bindings. If you wish to contribute to the development of RasterFrames, or you
+wish to build it from scratch, you will need [sbt](https://www.scala-sbt.org/). Then clone the repository from GitHub.
 
 ```bash
 git clone https://github.com/locationtech/rasterframes.git
 cd rasterframes
 ```
 
-To publish to hyou Ivy local repository:
+To publish to your local repository:
 
 ```bash
 sbt publishLocal
@@ -50,7 +58,7 @@ The documentation may be built with
 sbt makeSite
 ```
 
-The `pyrasterframes` build instructions are located at [pyrasterframes/src/main/python/README.md](pyrasterframes/src/main/python/README.md)
+Additional, Python sepcific build instruction may be found at [pyrasterframes/src/main/python/README.md](pyrasterframes/src/main/python/README.md)
 
 ## Copyright and License
 
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index d0513ea89..f7a45b0c1 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -115,6 +115,11 @@ SELECT month, ndvi_stats.* FROM (
 
 ## Scala
 
+The latest Scala API documentation is available here:
+
+* @ref:[Scala API Documentation](http://rasterframes.io/latest/api/index.html) 
+
+
 ### Step 1: Load the catalog
 
 ```scala

From baa375bcc54477aaa973678ea32b1ebb69a7b14f Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 13:50:10 -0400
Subject: [PATCH 354/380] Experimental implementation for #279.

---
 pyrasterframes/src/main/python/setup.py | 52 +++++++++++++++----------
 1 file changed, 31 insertions(+), 21 deletions(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index f2f3ba76f..0138382d0 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -53,7 +53,8 @@ class PweaveDocs(distutils.cmd.Command):
     user_options = [
         # The format is (long option, short option, description).
         ('files=', 's', 'Specific files to pweave. Defaults to all in `docs` directory.'),
-        ('format=', 'f', 'Output format type. Defaults to `markdown`')
+        ('format=', 'f', 'Output format type. Defaults to `markdown`'),
+        ('quick=', 'q', 'Check to see if the source file is newer than existing output before building. Defaults to `False`.')
     ]
 
     def initialize_options(self):
@@ -64,6 +65,7 @@ def initialize_options(self):
             glob(path.join(here, 'docs', '*.pymd'))
         )
         self.format = 'markdown'
+        self.quick = False
 
     def finalize_options(self):
         """Post-process options."""
@@ -73,6 +75,11 @@ def finalize_options(self):
             # `html` doesn't do quite what one expects... only replaces code blocks, leaving markdown in place
             if self.format is 'html':
                 self.format = 'pandoc2html'
+        if isinstance(self.quick, str):
+            self.quick = self.quick == 'True' or self.quick == 'true'
+
+    def dest_file(self, src_file):
+        return path.splitext(src_file)[0] + '.md'
 
     def run(self):
         """Run pweave."""
@@ -82,26 +89,27 @@ def run(self):
 
         for file in self.files:
             name = path.splitext(path.basename(file))[0]
-            print(_divided('Running %s' % name))
-            try:
-                pweave.weave(
-                    file=str(file),
-                    doctype=self.format
-                )
-                if self.format == 'markdown':
-                    dest = path.splitext(file)[0] + '.md'
-                    if not path.exists(dest):
-                        raise FileNotFoundError("Markdown file '%s' didn't get created as expected" % dest)
-                    with open(dest, "r") as result:
-                        for (n, line) in enumerate(result):
-                            for word in bad_words:
-                                if word in line:
-                                    raise ChildProcessError("Error detected on line %s in %s:\n%s" % (n + 1, dest, line))
-
-            except Exception:
-                print(_divided('%s Failed:' % file))
-                print(traceback.format_exc())
-                exit(1)
+            dest = self.dest_file(file)
+
+            if (not self.quick) or (not path.exists(dest)) or (path.getmtime(dest) < path.getmtime(file)):
+                print(_divided('Running %s' % name))
+                try:
+                    pweave.weave(file=str(file), doctype=self.format)
+                    if self.format == 'markdown':
+                        if not path.exists(dest):
+                            raise FileNotFoundError("Markdown file '%s' didn't get created as expected" % dest)
+                        with open(dest, "r") as result:
+                            for (n, line) in enumerate(result):
+                                for word in bad_words:
+                                    if word in line:
+                                        raise ChildProcessError("Error detected on line %s in %s:\n%s" % (n + 1, dest, line))
+
+                except Exception:
+                    print(_divided('%s Failed:' % file))
+                    print(traceback.format_exc())
+                    exit(1)
+            else:
+                print(_divided('Skipping %s' % file))
 
 
 class PweaveNotebooks(PweaveDocs):
@@ -109,6 +117,8 @@ def initialize_options(self):
         super().initialize_options()
         self.format = 'notebook'
 
+    def dest_file(self, src_file):
+        return path.splitext(src_file)[0] + '.ipynb'
 
 setup(
     name='pyrasterframes',

From 400b83242dfc117e7e56be9b78888429912f0acc Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 14:10:42 -0400
Subject: [PATCH 355/380] Put lower bounds on major library versions to cut
 down on build errors.

---
 pyrasterframes/src/main/python/requirements.txt |  5 +++--
 pyrasterframes/src/main/python/setup.py         | 16 ++++++++--------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/pyrasterframes/src/main/python/requirements.txt b/pyrasterframes/src/main/python/requirements.txt
index 84a79d5c8..536636f33 100644
--- a/pyrasterframes/src/main/python/requirements.txt
+++ b/pyrasterframes/src/main/python/requirements.txt
@@ -1,7 +1,8 @@
 pytz
-shapely
+Shapely>=1.6.0
 pyspark==2.3.3  # See issue # 154
 numpy>=1.7
-pandas
+pandas>=0.25.0
 matplotlib<3.0.0  # no python 2.7 support after v2.x.x
 ipython==6.2.1
+rasterio>=1.0.0
\ No newline at end of file
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 0138382d0..084ce37bf 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -136,18 +136,18 @@ def dest_file(self, src_file):
     },
     install_requires=[
         'pytz',
-        'Shapely',
+        'Shapely>=1.6.0',
         'pyspark<2.4',
         'numpy>=1.7',
-        'pandas',
+        'pandas>=0.25.0',
     ],
     setup_requires=[
         'pytz',
-        'Shapely',
+        'Shapely>=1.6.0',
         'pyspark<2.4',
         'numpy>=1.7',
         'matplotlib<3.0.0',
-        'pandas',
+        'pandas>=0.25.0',
         'geopandas',
         'requests',
         'pytest-runner',
@@ -156,15 +156,15 @@ def dest_file(self, src_file):
         'ipykernel==4.8.0',
         'Pweave==0.30.3',
         'fiona==1.8.6',
-        'rasterio',  # for docs
+        'rasterio>=1.0.0',  # for docs
     ],
     tests_require=[
         'pytest==3.4.2',
         'pypandoc',
         'numpy>=1.7',
-        'Shapely',
-        'pandas',
-        'rasterio',
+        'Shapely>=1.6.0',
+        'pandas>=0.25.0',
+        'rasterio>=1.0.0',
         'boto3',
     ],
     packages=[

From f6fdcd4a15f3b2ad6db6416248f6b730a0223c33 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Mon, 12 Aug 2019 16:08:38 -0400
Subject: [PATCH 356/380] Misc docs tweaking.

---
 build.sbt                                     |  7 +++--
 .../model/FixedRasterExtent.scala             |  2 +-
 .../src/main/python/docs/aggregation.pymd     | 22 ++++++++--------
 pyrasterframes/src/main/python/docs/index.md  |  2 +-
 .../src/main/python/docs/languages.pymd       |  6 ++---
 .../python/docs/unsupervised-learning.pymd    | 26 +++++++++----------
 .../src/main/python/docs/vector-data.pymd     | 16 ++++++------
 7 files changed, 42 insertions(+), 39 deletions(-)

diff --git a/build.sbt b/build.sbt
index 365ccb73f..fb711ea64 100644
--- a/build.sbt
+++ b/build.sbt
@@ -141,9 +141,12 @@ lazy val docs = project
     ),
     paradoxNavigationExpandDepth := Some(3),
     paradoxTheme := Some(builtinParadoxTheme("generic")),
-    makeSite := makeSite.dependsOn(Compile / unidoc).dependsOn(Compile / paradox).value,
+    makeSite := makeSite
+      .dependsOn(Compile / unidoc)
+      .dependsOn((Compile / paradox)
+        .dependsOn(pyrasterframes / doc)
+      ).value,
     Compile / paradox / sourceDirectories += (pyrasterframes / Python / doc / target).value,
-    Compile / paradox := (Compile / paradox).dependsOn(pyrasterframes / doc).value
   )
   .settings(
     addMappingsToSiteDir(ScalaUnidoc / packageDoc / mappings, ScalaUnidoc / siteSubdirName)
diff --git a/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala b/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala
index 8eefa76fc..cdce274bb 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/model/FixedRasterExtent.scala
@@ -19,7 +19,7 @@ package org.locationtech.rasterframes.model
 import geotrellis.raster._
 import geotrellis.vector._
 
-import scala.math.{ceil, max, min}
+import scala.math.ceil
 
 /**
   * This class is a copy of the GeoTrellis 2.x `RasterExtent`,
diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 983a9a693..398e36bfc 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -1,6 +1,6 @@
 # Aggregation
 
-```python, echo=False
+```python, setup, echo=False
 from docs import *
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
@@ -16,7 +16,7 @@ There are 3 types of aggregate functions: _tile_ aggregate, DataFrame aggregate,
 
 We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 _tiles_ where the first _tile_ is composed of 25 values of 1.0 and the second _tile_ is composed of 25 values of 3.0.
 
-```python
+```python, sql_dataframe
 import pyspark.sql.functions as F
 
 rf = spark.sql("""
@@ -30,13 +30,13 @@ rf.select("id", rf_render_matrix("tile")).show(10, False)
 
 In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the _tile_ aggregate mean of cells in each row of column `tile`. The mean of each _tile_ is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
-```python
+```python, tile_mean
 rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
 ```
 
 In this code block, we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
-```python
+```python, agg_mean
 rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
 ```
 
@@ -44,7 +44,7 @@ In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-
 
 To compute an element-wise local aggregate, _tiles_ need have the same dimensions as in the example below where both _tiles_ have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal _tile_ dimensions, we would get a runtime error.
 
-```python
+```python, local_mean
 rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(10, False)
 ```
 
@@ -52,7 +52,7 @@ rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_ma
 
 We can also count the total number of data and NoData cells over all the _tiles_ in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
-```python
+```python, cell_counts
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
 stats = rf.agg(rf_agg_data_cells('proj_raster'), rf_agg_no_data_cells('proj_raster'))
 
@@ -65,7 +65,7 @@ The statistical summary functions return a summary of cell values: number of dat
 
 The @ref:[`rf_tile_stats`](reference.md#rf-tile-stats) function computes summary statistics separately for each row in a _tile_ column as shown below.
 
-```python
+```python, tile_stats
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 stats = rf.select(rf_tile_stats('proj_raster').alias('stats'))
 
@@ -75,7 +75,7 @@ stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10,
 
 The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the _tiles_ in a DataFrame and returns a statistical summary of all cell values as shown below.
 
-```python
+```python, agg_stats
 rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
     .select('stats.min', 'stats.max', 'stats.mean', 'stats.variance') \
     .show(10, False)
@@ -83,7 +83,7 @@ rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
 
 The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal _tile_ dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
 
-```python
+```python, agg_local_stats
 rf = spark.sql("""
 SELECT 1 as id, rf_make_ones_tile(5, 5, 'float32') as tile
 UNION
@@ -103,7 +103,7 @@ for r in agg_local_stats:
 
 The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of _tile_ and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of _tile_ in this DataFrame, but this histogram is just computed for the _tile_ in the first row.
 
-```python
+```python, tile_histogram
 import matplotlib.pyplot as plt
 
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
@@ -121,7 +121,7 @@ plt.show()
 
 The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of _tile_ in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
 
-```python
+```python, agg_histogram
 bins_list = rf.agg(
     rf_agg_approx_histogram('proj_raster')['bins'].alias('bins')
     ).collect()
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index aabea7932..4d4053fe5 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -16,7 +16,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 ## Detailed Contents
 
-@@ toc { depth=2 }
+@@ toc { depth=3 }
 
 @@@
 
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index f7a45b0c1..ff79a93e6 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -69,7 +69,7 @@ def sql(stmt):
 
 ### Step 1: Load the catalog
 
-```python, step_1_sql
+```python, step_1_sql, results=hidden
 sql("CREATE OR REPLACE TEMPORARY VIEW modis USING `aws-pds-modis-catalog`")
 ```
 
@@ -87,7 +87,7 @@ sql('DESCRIBE red_nir_monthly_2017').show()
 
 ### Step 3: Read tiles
 
-```python, step_3_sql
+```python, step_3_sql, results=hidden
 sql("""
 CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017
 USING raster
@@ -117,7 +117,7 @@ SELECT month, ndvi_stats.* FROM (
 
 The latest Scala API documentation is available here:
 
-* @ref:[Scala API Documentation](http://rasterframes.io/latest/api/index.html) 
+* [Scala API Documentation](https://rasterframes.io/latest/api/index.html) 
 
 
 ### Step 1: Load the catalog
diff --git a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index 3e0221b76..d7d2aede6 100644
--- a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -4,7 +4,7 @@ In this example, we will demonstrate how to fit and score an unsupervised learni
 
 ## Imports and Data Preparation
 
-```python, echo=False
+```python, setup, echo=False
 from IPython.core.display import display
 from docs import resource_dir_uri
 from pyrasterframes.utils import create_rf_spark_session
@@ -18,7 +18,7 @@ import pandas as pd
 
 We import various Spark components that we need to construct our `Pipeline`.
 
-```python, echo=True
+```python, imports, echo=True
 from pyrasterframes import TileExploder
 from pyrasterframes.rasterfunctions import rf_assemble_tile, rf_crs, rf_extent, rf_tile, rf_dimensions
 
@@ -31,7 +31,7 @@ from pyspark.ml import Pipeline
 The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from [the I/O page](raster-io.html#Single-Scene--Multiple-Bands). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. The function `resource_dir_uri` gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
 
-```python, term=True
+```python, catalog, term=True
 filenamePattern = "L8-B{}-Elkton-VA.tiff"
 catalog_df = pd.DataFrame([
     {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
@@ -55,13 +55,13 @@ df.printSchema()
 
 SparkML requires that each observation be in its own row, and features for each observation be packed into a single `Vector`. For this unsupervised learning problem, we will treat each _pixel_ as an observation and each band as a feature. The first step is to "explode" the _tiles_ into a single row per pixel. In RasterFrames, generally a pixel is called a @ref:[`cell`](concepts.md#cell).
 
-```python
+```python, exploder
 exploder = TileExploder()
 ```
 
 To "vectorize" the the band columns, we use the SparkML `VectorAssembler`. Each of the seven bands is a different feature.
 
-```python
+```python, assembler
 assembler = VectorAssembler() \
     .setInputCols(list(catalog_df.columns)) \
     .setOutputCol("features")
@@ -69,13 +69,13 @@ assembler = VectorAssembler() \
 
 For this problem, we will use the K-means clustering algorithm and configure our model to have 5 clusters.
 
-```python
+```python, kmeans
 kmeans = KMeans().setK(5).setFeaturesCol('features')
 ```
 
 We can combine the above stages into a single _pipeline_.
 
-```python
+```python, pipeline
 pipeline = Pipeline().setStages([exploder, assembler, kmeans])
 ```
 
@@ -83,26 +83,26 @@ pipeline = Pipeline().setStages([exploder, assembler, kmeans])
 
 Fitting the _pipeline_ actually executes exploding the _tiles_, assembling the features _vectors_, and fitting the K-means clustering model.
 
-```python
+```python, fit
 model = pipeline.fit(df)
 ```
 
 We can use the `transform` function to score the training data in the fitted _pipeline_ model. This will add a column called `prediction` with the closest cluster identifier.
 
-```python
+```python, transform
 clustered = model.transform(df)
 clustered.show(8)
 ```
 
 If we want to inspect the model statistics, the SparkML API requires us to go through this unfortunate contortion:
 
-```python
+```python, cluster_stats
 cluster_stage = model.stages[2]
 ```
 
 We can then compute the sum of squared distances of points to their nearest center, which is elemental to most cluster quality metrics.
 
-```python
+```python, distance
 metric = cluster_stage.computeCost(clustered)
 print("Within set sum of squared errors: %s" % metric)
 ```
@@ -111,7 +111,7 @@ print("Within set sum of squared errors: %s" % metric)
 
 We can recreate the tiled data structure using the metadata added by the `TileExploder` pipeline stage.
 
-```python
+```python, assemble
 from pyrasterframes.rf_types import CellType
 
 tile_dims = df.select(rf_dimensions(df.b1).alias('dims')).first()['dims']
@@ -127,6 +127,6 @@ retiled.show()
 
 The resulting output is shown below.
 
-```python
+```python, viz
 display(retiled.select('prediction').first()['prediction'])
 ```
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
index 1f268ff89..0df549689 100644
--- a/pyrasterframes/src/main/python/docs/vector-data.pymd
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -4,13 +4,13 @@ RasterFrames provides a variety of ways to work with spatial vector (points, lin
 
 ## GeoJSON DataSource
 
-```python, echo=False
+```python, setup, echo=False
 import pyrasterframes
 from pyrasterframes.utils import create_rf_spark_session
 spark = create_rf_spark_session()
 ```
 
-```python
+```python, read_geojson
 from pyspark import SparkFiles
 spark.sparkContext.addFile('https://raw.githubusercontent.com/datasets/geo-admin1-us/master/data/admin1-us.geojson')
 
@@ -24,7 +24,7 @@ The properties of each feature are available as columns of the DataFrame, along
 
 You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as `df` defined above by the GeoJSON reader. Note that in a GeoPandas DataFrame there can be heterogeneous geometry types in the column, but this may fail Spark's schema inference.
 
-```python
+```python, read_and_normalize
 import geopandas
 from shapely.geometry import MultiPolygon
 
@@ -45,20 +45,20 @@ df2.printSchema()
 
 The `geometry` column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working on the Python side. This means that when the data is collected to the driver, it will be a Shapely geometry object.
 
-```python
+```python, show_geom
 the_first = df.first()
 print(type(the_first['geometry']))
 ```
 
 Since it is a geometry we can do things like this:
 
-```python
+```python, show_wkt
 the_first['geometry'].wkt
 ```
 
 You can also write user-defined functions that take geometries as input, output, or both, via user defined types in the [geomesa_pyspark.types](https://github.com/locationtech/rasterframes/blob/develop/pyrasterframes/src/main/python/geomesa_pyspark/types.py) module. Here is a simple example of a user-defined function that uses both a geometry input and output to compute the centroid of a geometry.
 
-```python
+```python, add_centroid
 from pyspark.sql.functions import udf
 from geomesa_pyspark.types import PointUDT
 
@@ -72,7 +72,7 @@ df.printSchema()
 
 We can take a look at a sample of the data. Notice the geometry columns print as well known text (wkt).
 
-```python
+```python, show_centroid
 df.show(4)
 ```
 
@@ -82,7 +82,7 @@ df.show(4)
 As documented in the @ref:[function reference](reference.md), various user-defined functions implemented by GeoMesa are also available for use. The example below uses a GeoMesa user-defined function to compute the centroid of a geometry. It is logically equivalent to the example above, but more efficient.
 
 
-```python
+```python, native_centroid
 from pyrasterframes.rasterfunctions import st_centroid
 df = df.withColumn('centroid', st_centroid(df.geometry))
 df.select('name', 'geometry', 'naive_centroid', 'centroid').show(4)

From 7cd8ec6b9675922d0a72c489f87f30db22d54b59 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 13 Aug 2019 10:34:48 -0400
Subject: [PATCH 357/380] pymd code chunk options are string literals

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/languages.pymd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index ff79a93e6..8e0b96c1d 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -69,7 +69,7 @@ def sql(stmt):
 
 ### Step 1: Load the catalog
 
-```python, step_1_sql, results=hidden
+```python, step_1_sql, results='hidden'
 sql("CREATE OR REPLACE TEMPORARY VIEW modis USING `aws-pds-modis-catalog`")
 ```
 
@@ -87,7 +87,7 @@ sql('DESCRIBE red_nir_monthly_2017').show()
 
 ### Step 3: Read tiles
 
-```python, step_3_sql, results=hidden
+```python, step_3_sql, results='hidden'
 sql("""
 CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017
 USING raster

From 7b39434c5d37b58789c15a313c9c1c554e400b7f Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 13 Aug 2019 10:57:18 -0400
Subject: [PATCH 358/380] Fix a few links in READMEs

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 README.md                                | 4 ++--
 pyrasterframes/src/main/python/README.md | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index bd1b86d14..2b3bcb43f 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <img src="docs/src/main/paradox/_template/images/RasterFramesLogo.png" width="300px"/><sup style="vertical-align: top;">&reg;</sup>
 
- [![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+ [![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/locationtech/rasterframes?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
 
@@ -13,7 +13,7 @@ Please see the [Getting Started](http://rasterframes.io/getting-started.html) se
 ## User Resources
 
 * [RasterFrames Users' Manual](http://rasterframes.io/)
-* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/)
+* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebook/)
 * [Gitter Channel](https://gitter.im/locationtech/rasterframes)
 * [Submit an Issue](https://github.com/locationtech/rasterframes/issues) 
 
diff --git a/pyrasterframes/src/main/python/README.md b/pyrasterframes/src/main/python/README.md
index bb309124f..22a6f30c7 100644
--- a/pyrasterframes/src/main/python/README.md
+++ b/pyrasterframes/src/main/python/README.md
@@ -30,14 +30,14 @@ df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 
 ## Support
 
-Reach out to us on [gitter](https://gitter.im/s22s/locationtech/rasterframes)!
+Reach out to us on [gitter][gitter]!
 
 Issue tracking is through [github](https://github.com/locationtech/rasterframes/issues). 
 
 ## Contributing
 
-Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
-
+Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter][gitter] so the community can help you get started!
 
 
 
+[gitter]: https://gitter.im/locationtech/rasterframes

From 56478b24aca6dff67f5853949e5e85d22d38eb47 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 13 Aug 2019 11:50:40 -0400
Subject: [PATCH 359/380] Deterministic pymd file order

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index f2f3ba76f..9bfd9517b 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -80,7 +80,7 @@ def run(self):
         import pweave
         bad_words = ["Error"]
 
-        for file in self.files:
+        for file in sorted(self.files):
             name = path.splitext(path.basename(file))[0]
             print(_divided('Running %s' % name))
             try:

From 8024ffc47114f509656db39334cac1268f2e414b Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Tue, 13 Aug 2019 11:59:43 -0400
Subject: [PATCH 360/380] reverse sort

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index b7b25b848..c3e1f46ad 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -87,7 +87,7 @@ def run(self):
         import pweave
         bad_words = ["Error"]
 
-        for file in sorted(self.files):
+        for file in sorted(self.files, reverse=True):
             name = path.splitext(path.basename(file))[0]
             dest = self.dest_file(file)
 

From 9ca8d6a0514914f405294a040093f3bb39f247cb Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 13 Aug 2019 18:09:15 -0400
Subject: [PATCH 361/380] Initial pass at better documentation rendering of
 `show` output.

---
 .../expressions/accessors/RealizeTile.scala   |  2 +-
 .../rasterframes/util/package.scala           | 22 ++++++
 .../rasterframes/ExtensionMethodSpec.scala    | 13 +++-
 docs/build.sbt                                |  1 -
 .../awspds/MODISCatalogDataSource.scala       | 44 ++++++++++-
 .../src/main/python/docs/__init__.py          |  3 +-
 .../src/main/python/docs/aggregation.pymd     | 33 ++++----
 .../src/main/python/docs/getting-started.pymd | 23 +++---
 .../src/main/python/docs/languages.pymd       | 10 +--
 .../src/main/python/docs/nodata-handling.pymd | 78 ++++++++++---------
 .../src/main/python/docs/raster-catalogs.pymd | 20 ++---
 .../src/main/python/docs/raster-read.pymd     | 42 ++++++----
 .../main/python/docs/supervised-learning.pymd | 15 ++--
 .../python/docs/unsupervised-learning.pymd    |  8 +-
 .../src/main/python/docs/vector-data.pymd     |  5 +-
 .../main/python/pyrasterframes/rf_ipython.py  | 15 +++-
 pyrasterframes/src/main/python/setup.py       |  6 +-
 .../rasterframes/py/PyRFContext.scala         |  5 ++
 18 files changed, 234 insertions(+), 111 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala
index 8e7cee24f..d8c9f0ba6 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/expressions/accessors/RealizeTile.scala
@@ -42,7 +42,7 @@ import org.locationtech.rasterframes.model.TileContext
 case class RealizeTile(child: Expression) extends UnaryRasterOp with CodegenFallback {
   override def dataType: DataType = TileType
 
-  override def nodeName: String = "rf_realize_tile"
+  override def nodeName: String = "rf_tile"
   implicit val tileSer = TileUDT.tileSerializer
 
   override protected def eval(tile: Tile, ctx: Option[TileContext]): Any =
diff --git a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
index 99bf80d96..e94869986 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/util/package.scala
@@ -36,6 +36,7 @@ import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpression}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.rf._
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.sql._
@@ -183,6 +184,27 @@ package object util {
     }
   }
 
+  implicit class DFWithPrettyPrint(val df: Dataset[_]) extends AnyVal {
+    def toMarkdown(numRows: Int = 5, truncate: Boolean = false): String = {
+      import df.sqlContext.implicits._
+      val cols = df.columns
+      val header = cols.mkString("| ", " | ", " |") + "\n" + ("|---" * cols.length) + "|\n"
+      val stringifiers = cols
+        .map(c => s"`$c`")
+        .map(c => df.col(c).cast(StringType))
+        .map(c => if (truncate) substring(c, 1, 40) else c)
+      val cat = concat_ws(" | ", stringifiers: _*)
+      val body = df
+        .select(cat).limit(numRows)
+        .as[String]
+        .collect()
+        .map(_.replaceAll("\\[", "\\\\["))
+        .map(_.replace('\n', '↩'))
+        .mkString("| ", " |\n| ", " |")
+      header + body
+    }
+  }
+
   object Shims {
     // GT 1.2.1 to 2.0.0
     def toArrayTile[T <: CellGrid](tile: T): T =
diff --git a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
index 606794bbf..eeea68544 100644
--- a/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
+++ b/core/src/test/scala/org/locationtech/rasterframes/ExtensionMethodSpec.scala
@@ -67,7 +67,7 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
       // Not sure why implicit resolution isn't handling this properly.
       implicit val enc = Encoders.tuple(crsEncoder, Encoders.STRING, crsEncoder, Encoders.scalaDouble)
       val df = Seq((pe.crs, "fred", pe.crs, 34.0)).toDF("c1", "s", "c2", "n")
-      df.crsColumns.size should be (2)
+      df.crsColumns.size should be(2)
     }
 
     it("should split TileLayout") {
@@ -92,10 +92,10 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
 
     it("should split key") {
       val s1 = SpatialKey(0, 0).subdivide(2)
-      assert(s1 === Seq(SpatialKey(0,0), SpatialKey(1,0), SpatialKey(0,1), SpatialKey(1,1)))
+      assert(s1 === Seq(SpatialKey(0, 0), SpatialKey(1, 0), SpatialKey(0, 1), SpatialKey(1, 1)))
 
       val s2 = SpatialKey(2, 3).subdivide(3)
-      assert(s2 === Seq(SpatialKey(6,9), SpatialKey(7,9), SpatialKey(8,9), SpatialKey(6,10), SpatialKey(7,10), SpatialKey(8,10), SpatialKey(6,11), SpatialKey(7,11), SpatialKey(8,11)))
+      assert(s2 === Seq(SpatialKey(6, 9), SpatialKey(7, 9), SpatialKey(8, 9), SpatialKey(6, 10), SpatialKey(7, 10), SpatialKey(8, 10), SpatialKey(6, 11), SpatialKey(7, 11), SpatialKey(8, 11)))
     }
 
     it("should split TileLayerMetadata[SpatialKey]") {
@@ -107,7 +107,12 @@ class ExtensionMethodSpec extends TestEnvironment with TestData with SubdivideSu
 
       val divided = tlm.subdivide(2)
 
-      assert(divided.tileLayout.tileDimensions === (tileSize/2, tileSize/2))
+      assert(divided.tileLayout.tileDimensions === (tileSize / 2, tileSize / 2))
+    }
+
+    it("should render Markdown") {
+      import org.locationtech.rasterframes.util._
+      rf.toMarkdown().count(_ == '|') shouldBe >=(3 * 5)
     }
   }
 }
diff --git a/docs/build.sbt b/docs/build.sbt
index 8e2325fa0..201402da7 100644
--- a/docs/build.sbt
+++ b/docs/build.sbt
@@ -8,7 +8,6 @@ makePDF := {
 
   // Get the python source directory configured in the root project.
   val base = (Compile / paradox / sourceDirectories).value.find(_.toString.contains("python")).head
-  println(base)
 
   // Hard coded lacking any simple  way of determining order.
   val files = Seq(
diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
index c4498c63a..31860f46a 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
@@ -75,9 +75,49 @@ object MODISCatalogDataSource extends LazyLogging with ResourceCacheSupport {
   final val MCD43A4_BASE = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/"
   override def maxCacheFileAgeHours: Int = Int.MaxValue
 
-  // List of missing days
+  // List of missing days in PDS
   private val blacklist = Seq[String](
-    //"2018-05-06"
+    "2018-02-27",
+    "2018-02-28",
+    "2018-03-01",
+    "2018-03-02",
+    "2018-03-03",
+    "2018-03-04",
+    "2018-03-05",
+    "2018-03-06",
+    "2018-03-07",
+    "2018-03-08",
+    "2018-03-09",
+    "2018-03-10",
+    "2018-03-11",
+    "2018-03-12",
+    "2018-03-13",
+    "2018-03-14",
+    "2018-05-16",
+    "2018-05-17",
+    "2018-05-18",
+    "2018-05-19",
+    "2018-05-20",
+    "2018-05-21",
+    "2018-06-01",
+    "2018-06-04",
+    "2018-07-29",
+    "2018-08-03",
+    "2018-08-04",
+    "2018-08-05",
+    "2018-10-01",
+    "2018-10-02",
+    "2018-10-03",
+    "2018-10-22",
+    "2018-10-23",
+    "2018-11-12",
+    "2018-12-19",
+    "2018-12-20",
+    "2018-12-21",
+    "2018-12-22",
+    "2018-12-23",
+    "2018-12-24",
+    "2019-03-18"
   )
 
   private def sceneFiles(start: LocalDate, end: LocalDate, useBlacklist: Boolean) = {
diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index d1a64afb9..617ed41e7 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -19,7 +19,8 @@
 #
 
 import os
-
+from pyspark.sql import DataFrame
+from pyrasterframes import RFContext
 
 def docs_dir():
     return os.path.dirname(os.path.realpath(__file__))
diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 398e36bfc..310868209 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -1,6 +1,7 @@
 # Aggregation
 
 ```python, setup, echo=False
+from pyrasterframes import rf_ipython
 from docs import *
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
@@ -16,7 +17,7 @@ There are 3 types of aggregate functions: _tile_ aggregate, DataFrame aggregate,
 
 We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 _tiles_ where the first _tile_ is composed of 25 values of 1.0 and the second _tile_ is composed of 25 values of 3.0.
 
-```python, sql_dataframe
+```python, sql_dataframe, results='raw'
 import pyspark.sql.functions as F
 
 rf = spark.sql("""
@@ -25,38 +26,39 @@ UNION
 SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
 """)
 
-rf.select("id", rf_render_matrix("tile")).show(10, False)
+rf.select("id", rf_render_matrix("tile")).show(truncate=False)
 ```
 
+
 In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the _tile_ aggregate mean of cells in each row of column `tile`. The mean of each _tile_ is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
-```python, tile_mean
-rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(10, False)
+```python, tile_mean, results='raw'
+rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(truncate=False)
 ```
 
 In this code block, we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
-```python, agg_mean
-rf.agg(rf_agg_mean(F.col('tile'))).show(10, False)
+```python, agg_mean, results='raw'
+rf.agg(rf_agg_mean(F.col('tile'))).show()
 ```
 
 In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the _tile_.
 
 To compute an element-wise local aggregate, _tiles_ need have the same dimensions as in the example below where both _tiles_ have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal _tile_ dimensions, we would get a runtime error.
 
-```python, local_mean
-rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(10, False)
+```python, local_mean, results='raw'
+rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(truncate=False)
 ```
 
 ## Cell Counts Example
 
 We can also count the total number of data and NoData cells over all the _tiles_ in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
-```python, cell_counts
+```python, cell_counts, results='raw'
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
 stats = rf.agg(rf_agg_data_cells('proj_raster'), rf_agg_no_data_cells('proj_raster'))
 
-stats.show(5, False)
+stats.show()
 ```
 
 ## Statistical Summaries
@@ -70,15 +72,18 @@ rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0
 stats = rf.select(rf_tile_stats('proj_raster').alias('stats'))
 
 stats.printSchema()
-stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, False)
+```
+
+```python, show_stats, results='raw'
+stats.select('stats.min', 'stats.max', 'stats.mean', 'stats.variance').show(10, truncate=False)
 ```
 
 The @ref:[`rf_agg_stats`](reference.md#rf-agg-stats) function aggregates over all of the _tiles_ in a DataFrame and returns a statistical summary of all cell values as shown below.
 
-```python, agg_stats
+```python, agg_stats, results='raw'
 rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
     .select('stats.min', 'stats.max', 'stats.mean', 'stats.variance') \
-    .show(10, False)
+    .show()
 ```
 
 The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal _tile_ dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
@@ -103,6 +108,7 @@ for r in agg_local_stats:
 
 The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of _tile_ and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of _tile_ in this DataFrame, but this histogram is just computed for the _tile_ in the first row.
 
+
 ```python, tile_histogram
 import matplotlib.pyplot as plt
 
@@ -121,6 +127,7 @@ plt.show()
 
 The @ref:[`rf_agg_approx_histogram`](reference.md#rf-agg-approx-histogram) function computes a count of cell values across all of the rows of _tile_ in a DataFrame or group. In the example below, the range of the y-axis is significantly wider than the range of the y-axis on the previous histogram since this histogram was computed for all cell values in the DataFrame.
 
+
 ```python, agg_histogram
 bins_list = rf.agg(
     rf_agg_approx_histogram('proj_raster')['bins'].alias('bins')
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 2ce64a7b6..388459cad 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -14,22 +14,29 @@ $ python3 -m pip install pyrasterframes
 
 Then in a python interpreter of your choice, you can get a [`pyspark` `SparkSession`](https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.SparkSession) using the [`local[*]` master](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls).
 
-```python
+
+```python, setup, echo=False
+from pyrasterframes import rf_ipython
+```
+
+```python, version 
 import pyrasterframes
 spark = pyrasterframes.get_spark_session()
 ```
 
 Then, you can read a raster and work with it in a Spark DataFrame.
 
-```python
-from pyrasterframes.rasterfunctions import rf_local_add
+```python, local_add, results='raw'
+from pyrasterframes.rasterfunctions import *
 from pyspark.sql.functions import lit
 
 # Read a MODIS surface reflectance granule
 df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')
 
 # Add 3 element-wise, show some rows of the DataFrame
-df.select(rf_local_add(df.proj_raster, lit(3))).show(5, False)
+df.withColumn('added', rf_local_add(df.proj_raster, lit(3))) \
+  .select(rf_crs('added'), rf_extent('added'), rf_tile('added')) \
+  .show(3)
 ```
 
 This example is extended in the [getting started Jupyter notebook](https://nbviewer.jupyter.org/github/locationtech/rasterframes/blob/develop/rf-notebook/src/main/notebooks/Getting%20Started.ipynb).
@@ -75,7 +82,7 @@ The `pyspark` shell command will look something like this.
 
 Then in the `pyspark` shell, import the module and call `withRasterFrames` on the SparkSession.
 
-```python, evaluate=False
+```python, shell, evaluate=False
 Welcome to
       ____              __
      / __/__  ___ _____/ /__
@@ -92,10 +99,6 @@ SparkSession available as 'spark'.
 
 Now you have the configured SparkSession with RasterFrames enabled.
 
-```python, echo=False
-spark.stop()
-```
-
 ## Installing GDAL
 
 GDAL provides a wide variety of drivers to read data from many different raster formats. If GDAL is installed in the environment, RasterFrames will be able to @ref:[read](raster-read.md) those formats.  If you are using the @ref:[Jupyter Notebook image](getting-started.md#jupyter-notebook), GDAL is already installed for you. Otherwise follow the instructions below. Version 2.4.1 or greater is required.
@@ -130,7 +133,7 @@ To support GeoTIFF and JPEG2000 formats, you should look for the following drive
 
 Do the following to see if RasterFrames was able to find GDAL:
 
-```python, evaluate=False
+```python, gdal_version, evaluate=False
 from pyrasterframes.utils import gdal_version
 print(gdal_version())
 ```
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index ff79a93e6..624428c2a 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -45,7 +45,7 @@ red_nir_tiles_monthly_2017 = spark.read.raster(
 
 ### Step 4: Compute aggregates
 
-```python, step_4_python
+```python, step_4_python, results='raw'
 result = red_nir_tiles_monthly_2017 \
     .where(st_intersects(
         st_reproject(rf_geometry(col('red')), rf_crs(col('red')).crsProj4, rf_mk_crs('EPSG:4326')),
@@ -69,13 +69,13 @@ def sql(stmt):
 
 ### Step 1: Load the catalog
 
-```python, step_1_sql, results=hidden
+```python, step_1_sql, results='hidden'
 sql("CREATE OR REPLACE TEMPORARY VIEW modis USING `aws-pds-modis-catalog`")
 ```
 
 ### Step 2: Down-select data by month
 
-```python, step_2_sql
+```python, step_2_sql, results='raw'
 sql("""
 CREATE OR REPLACE TEMPORARY VIEW red_nir_monthly_2017 AS
 SELECT granule_id, month(acquisition_date) as month, B01 as red, B02 as nir
@@ -87,7 +87,7 @@ sql('DESCRIBE red_nir_monthly_2017').show()
 
 ### Step 3: Read tiles
 
-```python, step_3_sql, results=hidden
+```python, step_3_sql, results='hidden'
 sql("""
 CREATE OR REPLACE TEMPORARY VIEW red_nir_tiles_monthly_2017
 USING raster
@@ -101,7 +101,7 @@ OPTIONS (
 
 ### Step 4: Compute aggregates
 
-```python, step_4_sql
+```python, step_4_sql, results='raw'
 sql("""
 SELECT month, ndvi_stats.* FROM (
     SELECT month, rf_agg_stats(rf_normalized_difference(nir, red)) as ndvi_stats
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index 09ecc7dd4..c55243a41 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -24,16 +24,21 @@ spark = pyrasterframes.get_spark_session()
 
 The `CellType` class from the `rf_types` submodule allows us to create a representation of any valid cell type. There are convenience methods to create instances for a variety of basic types.
 
-```python celltype_ctors
+```python, celltype_ctors, results='hidden'
 from pyrasterframes.rf_types import CellType
-import inspect
-
-[c[0] for c in inspect.getmembers(CellType, inspect.ismethod)]
+CellType.bool()
+CellType.int8()
+CellType.uint8()
+CellType.int16()
+CellType.uint16()
+CellType.int32()
+CellType.float32()
+CellType.float64()
 ```
 
 We can also inspect the cell type of a given _tile_ or `proj_raster` column.
 
-```python ct_from_sen
+```python, ct_from_sen, results='raw'
 spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif') \
     .select(rf_cell_type('proj_raster')).distinct().show()
 ```
@@ -42,14 +47,14 @@ spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif
 
 We can use the methods on the `CellType` class to learn more about a specific cell type. Let's consider the cell type of our sample data above.
 
-```python
+```python, raw_example
 ct = CellType('uint16raw')
 ct, ct.is_floating_point(), ct.has_no_data()
 ```
 
 We can see that for the above data source, there is no defined NoData value. This means that each value is interpreted as a valid observation. Often such data is meant to be combined with another band indicating the quality of observations at each location. The lack of NoData is indicated by the `raw` at the end of the type name. Consider also the `uint16` cell type.
 
-```python
+```python, non_raw_example
 from pyrasterframes.rf_types import CellType
 ct = CellType('uint16')
 ct, ct.is_floating_point(), ct.has_no_data(), ct.no_data_value()
@@ -57,13 +62,13 @@ ct, ct.is_floating_point(), ct.has_no_data(), ct.no_data_value()
 
 In this case, the minimum value of 0 is designated as the NoData value. For integral-valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with a `ud`.
 
-```python
+```python, cell_type_name
 CellType.uint16().with_no_data_value(99).cell_type_name
 ```
 
 Floating point types have `NaN` as the NoData value by default. However, a user-defined NoData can be set.
 
-```python float_ud
+```python, float_ud
 print(CellType.float32().no_data_value())
 print(CellType.float32().with_no_data_value(-99.9).no_data_value())
 ```
@@ -78,7 +83,7 @@ Credit: [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/techn
 
 The first step is to create a catalog with our band of interest and the SCL band. We read the data from the catalog, so the blue band and SCL _tiles_ are aligned across rows.
 
-```python blue_scl_cat
+```python, blue_scl_cat
 from pyspark.sql import Row
 
 blue_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
@@ -86,12 +91,15 @@ scl_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/SCL.tif'
 cat = spark.createDataFrame([Row(blue=blue_uri, scl=scl_uri),])
 unmasked = spark.read.raster(catalog=cat, catalog_col_names=['blue', 'scl'])
 unmasked.printSchema()
+```
+
+```python, show_cell_types, results='raw'
 unmasked.select(rf_cell_type('blue'), rf_cell_type('scl')).distinct().show()
 ```
 
 Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new _tile_ columns that are indicators of unwanted pixels, as defined above. Since the mask column is bit type, the addition is equivalent to a logical or, so the true values are 1.
 
-```python def_mask
+```python, def_mask, results='raw'
 from pyspark.sql.functions import lit
 
 mask_part = unmasked.withColumn('nodata', rf_local_equal('scl', lit(0))) \
@@ -110,13 +118,13 @@ one_mask.select(rf_cell_type('mask')).distinct().show()
 
 Because there is not a NoData already defined, we will choose one. In this particular example, the minimum value is greater than zero, so we can use 0 as the NoData value.
 
-```python pick_nd
+```python, pick_nd, results='raw'
 one_mask.agg(rf_agg_stats('blue').min.alias('blue_min')).show()
 ```
 
 We can now construct the cell type string for our blue band's cell type, designating 0 as NoData.
 
-```python get_ct_string
+```python, get_ct_string
 blue_ct = one_mask.select(rf_cell_type('blue')).distinct().first()[0][0]
 masked_blue_ct = CellType(blue_ct).with_no_data_value(0)
 masked_blue_ct.cell_type_name
@@ -124,7 +132,7 @@ masked_blue_ct.cell_type_name
 
 Now we will use the @ref:[`rf_mask_by_value`](reference.md#rf-mask-by-value) to designate the cloudy and other unwanted pixels as NoData in the blue column by converting the cell type and applying the mask.
 
-```python mask_blu
+```python, mask_blu
 with_nd = rf_convert_cell_type('blue', masked_blue_ct)
 masked = one_mask.withColumn('blue_masked',
                              rf_mask_by_value(with_nd, 'mask', lit(1))) \
@@ -133,20 +141,20 @@ masked = one_mask.withColumn('blue_masked',
 
 We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` _tile_ to ensure our logic is correct.
 
-```python
+```python, show_masked, results='raw'
 masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
 ```
 
 It's also nice to view a sample. The white regions are areas of NoData.
 
-```python, caption='Blue band masked against selected SCL values'
+```python, display_blu, caption='Blue band masked against selected SCL values'
 sample = masked.orderBy(-rf_no_data_cells('blue_masked')).select(rf_tile('blue_masked'), rf_tile('scl')).first()
 display(sample[0])
 ```
 
 And the original SCL data. The bright yellow is a cloudy region in the original image.
 
-```python, caption='SCL tile for above'
+```python, display_scl, caption='SCL tile for above'
 display(sample[1])
 ```
 
@@ -154,7 +162,7 @@ display(sample[1])
 
 Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two _tiles_. One _tile_ will have values of 0 and 1, and the other will have values of just 0.
 
-```python
+```python, local_arith
 tile_size = 100
 x = np.zeros((tile_size, tile_size), dtype='int16')
 x[:,tile_size//2:] = 1
@@ -166,14 +174,14 @@ print('x')
 display(x)
 ```
 
-```python
+```python, display_1
 print('y')
 display(y)
 ```
 
 Now, let's create a new column from `x` with the value of 1 changed to NoData. Then, we will add this new column with NoData to the `y` column. As shown below, the result of the sum also has NoData (represented in white). In general for local algebra operations, Data + NoData = NoData.
 
-```python
+```python, display_2
 masked_rf = rf.withColumn('x_nd', rf_mask_by_value('x', 'x', lit(1)) )
 masked_rf = masked_rf.withColumn('x_nd_y_sum', rf_local_add('x_nd', 'y'))
 row = masked_rf.collect()[0]
@@ -181,7 +189,7 @@ print('x with NoData')
 display(row.x_nd)
 ```
 
-```python
+```python, display_3
 print('x with NoData plus y')
 display(row.x_nd_y_sum)
 ```
@@ -191,7 +199,7 @@ To see more information about possible operations on _tile_ columns, see the @re
 
 One way to mask a _tile_ is to make a new _tile_ with a user defined NoData value. We will explore this method below. First, lets create a DataFrame from a _tile_ with values of 0, 1, 2, and 3. We will use numpy to create a 100x100 _tile_ with vertical bands containing values 0, 1, 2, and 3.
 
-```python create_dummy_tile, caption='Dummy Tile'
+```python, create_dummy_tile, caption='Dummy Tile'
 tile_size = 100
 x = np.zeros((tile_size, tile_size), dtype='int16')
 
@@ -206,7 +214,7 @@ display(x)
 
 First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a _tile_ with the cell type 'uint16ud2'.
 
-```python
+```python, masked_rf
 def get_nodata_ct(nd_val):
 	return CellType('uint16').with_no_data_value(nd_val)
 
@@ -216,19 +224,19 @@ masked_rf = rf.withColumn('tile_nd_1',
                           rf_convert_cell_type('tile_nd_1', get_nodata_ct(2))) \
 ```
 
-```python
+```python, masked_collected
 collected = masked_rf.collect()
 ```
 
 Let's look at the new _tiles_ we created. The _tile_ named `tile_nd_1` has the 1 values masked out as expected.
 
-```python
+```python, display_4
 display(collected[0].tile_nd_1)
 ```
 
 And the _tile_ named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the _tile_ by setting a new user defined NoData value to `tile_nd_1` the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
 
-```python
+```python, display_5
 display(collected[0].tile_nd_2)
 ```
 
@@ -239,7 +247,7 @@ RasterFrames supports having _tile_ columns with multiple cell types in a single
 
 Let's first create a RasterFrame that has columns of `float` and `int` cell type.
 
-```python
+```python, show_1, results='raw'
 x = Tile((np.ones((100, 100))*2), CellType.float64())
 y = Tile((np.ones((100, 100))*3), CellType.int32())
 rf = spark.createDataFrame([Row(x=x, y=y)])
@@ -249,17 +257,17 @@ rf.select(rf_cell_type('x'), rf_cell_type('y')).distinct().show()
 
 When performing a local operation between _tile_ columns with cell types `int` and type `float`, the resulting _tile_ cell type will be `float`. In local algebra over two _tiles_ of different "sized" cell types, the resulting cell type will be the largest of the two input _tiles'_ cell types.
 
-```python
+```python, show_2, results='raw'
 rf.select(
     rf_cell_type('x'),
     rf_cell_type('y'),
-    rf_cell_type(rf_local_add('x', 'y').alias('xy_sum')),
+    rf_cell_type(rf_local_add('x', 'y')).alias('xy_sum'),
     ).show(1)
 ```
 
 Combining _tile_ columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 _tile_ columns: one with a NoData value of 1, and one with a NoData value of 2.
 
-```python
+```python, create_ones
 x_nd_1 = Tile((np.ones((100, 100))*3), get_nodata_ct(1))
 x_nd_2 = Tile((np.ones((100, 100))*3), get_nodata_ct(2))
 rf_nd = spark.createDataFrame([Row(x_nd_1=x_nd_1, x_nd_2=x_nd_2)])
@@ -267,14 +275,14 @@ rf_nd = spark.createDataFrame([Row(x_nd_1=x_nd_1, x_nd_2=x_nd_2)])
 
 Let's try adding the _tile_ columns with different NoData values. When there is an inconsistent NoData value in the two columns, the NoData value of the right-hand side of the sum is kept. In this case, this means the result has a NoData value of 1.
 
-```python
+```python, show_3, results='raw'
 rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_2', 'x_nd_1'))
 rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 ```
 
 Reversing the order of the sum changes the NoData value of the resulting column to 2.
 
-```python
+```python, show_4, results='raw'
 rf_nd_sum = rf_nd.withColumn('x_nd_sum', rf_local_add('x_nd_1', 'x_nd_2'))
 rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 ```
@@ -283,7 +291,7 @@ rf_nd_sum.select(rf_cell_type('x_nd_sum')).distinct().show()
 
 Let's use the same _tile_ as before to demonstrate how NoData values affect _tile_ aggregations.
 
-```python
+```python, display_6
 tile_size = 100
 x = np.zeros((tile_size, tile_size))
 for i in range(4):
@@ -296,7 +304,7 @@ display(x)
 
 First we create the two new masked _tile_ columns as before. One with only the value of 1 masked, and the other with and values of 1 and 2 masked.
 
-```python
+```python, cell_type_2
 masked_rf = rf.withColumn('tile_nd_1',
                            rf_convert_cell_type('tile', get_nodata_ct(1))) \
               .withColumn('tile_nd_2',
@@ -305,6 +313,6 @@ masked_rf = rf.withColumn('tile_nd_1',
 
 The results of `rf_tile_sum` vary on the _tiles_ that were masked. This is because any cells with NoData values are ignored in the aggregation. Note that `tile_nd_2` has the lowest sum, since it has the fewest amount of data cells.
 
-```python
+```python, show_5, results='raw'
 masked_rf.select(rf_tile_sum('tile'), rf_tile_sum('tile_nd_1'), rf_tile_sum('tile_nd_2')).show()
 ```
diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index ddddfb399..35e695dd2 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -11,10 +11,10 @@ A _catalog_ can have one or two dimensions:
 
 This section will provide some examples of creating your own _catalogs_, as well as introduce some experimental _catalogs_ built into RasterFrames. Reading raster data represented by a _catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
 
-```python, echo=False
-from IPython.display import display
+```python, setup, echo=False
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
+import pyrasterframes.rf_ipython
 import pandas as pd
 spark = create_rf_spark_session()
 ```
@@ -23,7 +23,7 @@ spark = create_rf_spark_session()
 
 A single URL is the simplest form of a catalog.
 
-```python
+```python, oned_onerow_catalog
 from pyspark.sql import Row
 
 file_uri = "/data/raster/myfile.tif"
@@ -39,7 +39,7 @@ my_cat = "B01\n{}".format(file_uri)
 
 A single column represents the same content type with different observations along the rows. In this example it is band 1 of MODIS surface reflectance, which is visible red. In the example the location of the images is the same, indicated by the granule identifier `h04v09`, but the dates differ: 2018185 (July 4, 2018) and 2018188 (July 7, 2018).
 
-```python
+```python, oned_tworow_catalog
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 
@@ -57,7 +57,7 @@ one_d_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
 
 Example of a multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same: granule id `h04v09` on July 4 or July 7, 2018. The first column is band 1, red,  and the second is band 2, near infrared.
 
-```python
+```python, twod_catalog
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
 scene1_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B02.TIF"
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
@@ -77,7 +77,7 @@ my_cat_df.printSchema()
 
 The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading an cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data.
 
-```python
+```python, remote_csv, results='raw'
 from pyspark import SparkFiles
 from pyspark.sql import functions as F
 
@@ -87,12 +87,12 @@ scene_list = spark.read \
     .format("csv") \
     .option("header", "true") \
     .load(SparkFiles.get("2018-07-04_scenes.txt"))
-scene_list.head(3)
+scene_list.show(4, truncate=False)
 ```
 
 Observe the scenes list file has URIs to `index.html` files in the download_url column. The image URI's are in the same directory. The filenames are of the form `${gid}_B${band}.TIF`. The next code chunk builds these URIs, which completes our catalog.
 
-```python
+```python, show_remote_catalog, results='raw'
 modis_catalog = scene_list \
     .withColumn('base_url',
         F.concat(F.regexp_replace('download_url', 'index.html$', ''), 'gid',)
@@ -100,7 +100,7 @@ modis_catalog = scene_list \
     .withColumn('B01' , F.concat('base_url', F.lit("_B01.TIF"))) \
     .withColumn('B02' , F.concat('base_url', F.lit("_B02.TIF"))) \
     .withColumn('B03' , F.concat('base_url', F.lit("_B03.TIF")))
-modis_catalog.head(3)
+modis_catalog.show(4, truncate=True)
 ```
 
 ## Using Built-in Experimental Catalogs
@@ -144,6 +144,8 @@ The Landsat 8 catalog includes a richer set of metadata describing the contents
 ```python, evaluate=False
 l8 = spark.read.format('aws-pds-l8-catalog').load()
 l8.printSchema()
+```
+```
 root
  |-- product_id: string (nullable = false)
  |-- entity_id: string (nullable = false)
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 9ca4336f7..9a8905a8b 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -2,6 +2,7 @@
 
 ```python, echo=False
 from IPython.display import display
+import pyrasterframes.rf_ipython
 import pandas as pd
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
@@ -16,7 +17,7 @@ RasterFrames can also read from @ref:[GeoTrellis catalogs and layers](raster-rea
 
 The simplest way to use the `raster` reader is with a single raster from a single URI or file. In the examples that follow we'll be reading from a Sentinel-2 scene stored in an AWS S3 bucket.
 
-```python read_one_uri
+```python, read_one_uri
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 rf.printSchema()
 ```
@@ -25,7 +26,7 @@ The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https:/
 
 Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a @ref:[_CRS_][CRS], a spatial @ref:[_extent_][Extent] measured in that CRS, and a two-dimensional array of numeric values called a @ref:[_tile_][Tile].
 
-```python unpack_schema
+```python, raster_parts
 crs = rf.select(rf_crs("proj_raster").alias("value")).first()
 
 parts = rf.select(
@@ -34,14 +35,16 @@ parts = rf.select(
 )
 
 print("CRS", crs.value.crsProj4)
-parts.show(5, False)
+```
 
+```python,  show_parts, results='raw'
+parts.show(5, truncate=False)
 ```
 
 You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on manageable amounts of data per task.
 
-```python count_by_uri
-rf.groupby(rf.proj_raster_path).count().show()
+```python count_by_uri, results='raw'
+rf.groupby(rf.proj_raster_path).count().show(truncate=False)
 ```
 
 Let's select a single _tile_ and view it. The _tile_ preview image as well as the string representation provide some basic information about the _tile_: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the _tile_. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
@@ -98,8 +101,10 @@ modis_catalog = spark.read \
 modis_catalog.printSchema()
 
 print("Available scenes: ", modis_catalog.count())
+```
 
-modis_catalog.show(5, truncate=False)
+```python, show_catalog, results='raw'
+modis_catalog.show(5, truncate=True)
 ```
 
 MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using `(h,v)` grid coordinates.
@@ -108,14 +113,15 @@ MODIS data products are delivered on a regular, consistent grid, making identifi
 
 For example, MODIS data right above the equator is all grid coordinates with `v07`.
 
-```python catalog_filtering
-equator = modis_catalog.where(F.col('gid').like('%v07%'))
-equator.select('date', 'gid').show(10, False)
+```python catalog_filtering, results='raw'
+equator = modis_catalog.where(F.col('gid').like('%v07%')) 
+equator.select('date', 'gid') \
+    .show(10, truncate=False)
 ```
 
 Now that we have prepared our catalog, we simply pass the DataFrame or CSV string to the `raster` DataSource to load the imagery. The `catalog_col_names` parameter gives the columns that contain the URI's to be read.
 
-```python read_catalog
+```python, read_catalog
 rf = spark.read.raster(
     catalog=equator,
     catalog_col_names=['red', 'nir'],
@@ -125,8 +131,9 @@ rf.printSchema()
 
 Observe that the schema of the resulting DataFrame has a projected raster struct for each column passed in `catalog_col_names`. For reference, the URI is now in a column appended with `_path`. We can take a quick look at the representation of the data. We see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent from within that coverage.
 
-```python cat_read_sample
-rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')).show(3, False)
+```python, cat_read_sample, results='raw'
+rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')) \
+    .show(3, truncate=True)
 ```
 
 ## Lazy Raster Reads
@@ -135,13 +142,18 @@ By default the raster reads are delayed as long as possible. The DataFrame will
 
 Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the option to force the raster to be fully read right away.
 
-```python lazy_demo
+```python, lazy_demo_1, results='raw'
 uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
 spark.read.raster(uri) \
-    .select('proj_raster.tile').show(1, False)
+    .select('proj_raster.tile') \
+    .show(1, truncate=False)
+
+```
 
+```python, lazy_demo_2, results='raw'
 spark.read.raster(uri, lazy_tiles=False) \
-    .select('proj_raster.tile').show(1, False)
+    .select('proj_raster.tile') \
+    .show(1, truncate=False)
 ```
 
 In the initial examples on this page, you may have noticed that the realized (non-lazy) _tiles_ are shown, but we did not change `lazy_tiles`. Instead, we used @ref:[`rf_tile`](reference.md#rf-tile) to explicitly request the realized _tile_ from the lazy representation.
diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index 044a8f72e..46ba4f6c7 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -6,6 +6,7 @@ In this example we will demonstrate how to fit and score a supervised learning m
 from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
+import pyrasterframes.rf_ipython
 from pyspark.sql.functions import lit
 import pandas as pd
 import numpy as np
@@ -23,7 +24,7 @@ The first step is to create a Spark DataFrame of our imagery data. To achieve th
 
 The imagery for feature data will come from [eleven bands of 60 meter resolution Sentinel-2](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) imagery. We also will use the [scene classification (SCL)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm) data to identify high quality, non-cloudy pixels.
 
-```python, read_bands, term=True
+```python, read_bands
 uri_base = 's3://s22s-test-geotiffs/luray_snp/{}.tif'
 bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12']
 cols = ['SCL'] + bands
@@ -88,7 +89,7 @@ FROM df_joined
 
 To filter only for good quality pixels, we follow roughly the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually setting NoData values in the unwanted cells of any of the imagery bands, we will just on filter out the mask cell values later in the process.
 
-```python make_mask
+```python, make_mask
 from pyspark.sql.functions import lit
 
 mask_part = df_labeled.withColumn('nodata', rf_local_equal('scl', lit(0))) \
@@ -181,18 +182,18 @@ print("\nAccuracy:", accuracy)
 
 As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. The categories down the rows are the predictions, and the truth labels are across the columns.
 
-```python confusion_mtrx
+```python, confusion_mtrx, results='raw'
 prediction_df.groupBy(classifier.getPredictionCol()) \
     .pivot(classifier.getLabelCol()) \
     .count() \
-	.sort(classifier.getPredictionCol()).show(20, False)
+	.sort(classifier.getPredictionCol()).show(truncate=False)
 ```
 
 ## Visualize Prediction
 
 Because the pipeline included a `TileExploder`, we will recreate the tiled data structure. The explosion transformation includes metadata enabling us to recreate the _tiles_. See the @ref:[`rf_assemble_tile`](reference.md#rf-assemble-tile) function documentation for more details. In this case, the pipeline is scoring on all areas, regardless of whether they intersect the label polygons. This is simply done by removing the `label` column, as @ref:[discussed above](supervised-learning.md#create-ml-pipeline).
 
-```python assemble_prediction
+```python, assemble_prediction
 scored = model.transform(df_mask.drop('label'))
 scored.createOrReplaceTempView('scored')
 
@@ -211,7 +212,7 @@ retiled.printSchema()
 
 Take a look at a sample of the resulting output and the corresponding area's red-green-blue composite image.
 
-```python display_rgb
+```python, display_rgb
 sample = retiled.select('prediction', 'red', 'grn', 'blu') \
     .sort(-rf_tile_sum(rf_local_equal('prediction', lit(3.0)))) \
     .first()
@@ -228,6 +229,6 @@ plt.imshow((sample_rgb -  mins)/ (np.nanmax(sample_rgb, axis=(0,1)) - mins))
 
 Recall the label coding: 1 is forest (purple), 2 is cropland (green) and 3 is developed areas(yellow).
 
-```python display_prediction
+```python, display_prediction
 display(sample_prediction)
 ```
diff --git a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index d7d2aede6..a89d3826f 100644
--- a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -7,6 +7,7 @@ In this example, we will demonstrate how to fit and score an unsupervised learni
 ```python, setup, echo=False
 from IPython.core.display import display
 from docs import resource_dir_uri
+import pyrasterframes.rf_ipython
 from pyrasterframes.utils import create_rf_spark_session
 
 import os
@@ -31,7 +32,7 @@ from pyspark.ml import Pipeline
 The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create a catalog DataFrame using the pattern from [the I/O page](raster-io.html#Single-Scene--Multiple-Bands). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. The function `resource_dir_uri` gives a local file system path to the sample Landsat data. The resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
 
-```python, catalog, term=True
+```python, catalog
 filenamePattern = "L8-B{}-Elkton-VA.tiff"
 catalog_df = pd.DataFrame([
     {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
@@ -89,7 +90,7 @@ model = pipeline.fit(df)
 
 We can use the `transform` function to score the training data in the fitted _pipeline_ model. This will add a column called `prediction` with the closest cluster identifier.
 
-```python, transform
+```python, transform, results='raw'
 clustered = model.transform(df)
 clustered.show(8)
 ```
@@ -122,6 +123,9 @@ retiled = clustered.groupBy('extent', 'crs') \
 )
 
 retiled.printSchema()
+```
+
+```python, results='raw'
 retiled.show()
 ```
 
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
index 0df549689..8a145568f 100644
--- a/pyrasterframes/src/main/python/docs/vector-data.pymd
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -6,6 +6,7 @@ RasterFrames provides a variety of ways to work with spatial vector (points, lin
 
 ```python, setup, echo=False
 import pyrasterframes
+import pyrasterframes.rf_ipython
 from pyrasterframes.utils import create_rf_spark_session
 spark = create_rf_spark_session()
 ```
@@ -72,7 +73,7 @@ df.printSchema()
 
 We can take a look at a sample of the data. Notice the geometry columns print as well known text (wkt).
 
-```python, show_centroid
+```python, show_centroid, results='raw'
 df.show(4)
 ```
 
@@ -82,7 +83,7 @@ df.show(4)
 As documented in the @ref:[function reference](reference.md), various user-defined functions implemented by GeoMesa are also available for use. The example below uses a GeoMesa user-defined function to compute the centroid of a geometry. It is logically equivalent to the example above, but more efficient.
 
 
-```python, native_centroid
+```python, native_centroid, results='raw'
 from pyrasterframes.rasterfunctions import st_centroid
 df = df.withColumn('centroid', st_centroid(df.geometry))
 df.select('name', 'geometry', 'naive_centroid', 'centroid').show(4)
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index b6f019dd3..39ae535c2 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -31,7 +31,7 @@ def tile_to_png(tile, fig_size=None):
     from matplotlib.figure import Figure
 
     # Set up matplotlib objects
-    nominal_size = 4  # approx full size for a 256x256 tile
+    nominal_size = 2  # approx full size for a 256x256 tile
     if fig_size is None:
         fig_size = (nominal_size, nominal_size)
 
@@ -106,7 +106,18 @@ def _safe_tile_to_html(t):
     # modifications to currently running ipython session, if we are in one; these enable nicer visualization for Pandas
     if get_ipython() is not None:
         import pandas
-        html_formatter = get_ipython().display_formatter.formatters['text/html']
+        import pyspark.sql
+        ip = get_ipython()
+        html_formatter = ip.display_formatter.formatters['text/html']
         html_formatter.for_type(pandas.DataFrame, pandas_df_to_html)
+
+        # See if we're in documentation mode and register a custom show implementation.
+        if ip.__class__.__name__ == 'InProcessInteractiveShell':
+            def _df_show_markdown(df, numRows=5, truncate=True, vertical=False):
+                from pyrasterframes import RFContext
+                print(RFContext.active().call("_dfToMarkdown", df._jdf, numRows, truncate))
+
+            pyspark.sql.DataFrame.show = _df_show_markdown
+
 except ImportError:
     pass
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 084ce37bf..834ddfbb1 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -73,7 +73,8 @@ def finalize_options(self):
         if isinstance(self.files, str):
             self.files = filter(lambda s: len(s) > 0, re.split(',', self.files))
             # `html` doesn't do quite what one expects... only replaces code blocks, leaving markdown in place
-            if self.format is 'html':
+            print("format.....", self.format)
+            if self.format == 'html':
                 self.format = 'pandoc2html'
         if isinstance(self.quick, str):
             self.quick = self.quick == 'True' or self.quick == 'true'
@@ -86,6 +87,7 @@ def run(self):
         import traceback
         import pweave
         bad_words = ["Error"]
+        pweave.rcParams["chunk"]["defaultoptions"].update({'wrap': False, 'dpi': 100})
 
         for file in self.files:
             name = path.splitext(path.basename(file))[0]
@@ -109,7 +111,7 @@ def run(self):
                     print(traceback.format_exc())
                     exit(1)
             else:
-                print(_divided('Skipping %s' % file))
+                print(_divided('Skipping %s' % name))
 
 
 class PweaveNotebooks(PweaveDocs):
diff --git a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
index 4ae5c1231..f950cf2f3 100644
--- a/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
+++ b/pyrasterframes/src/main/scala/org/locationtech/rasterframes/py/PyRFContext.scala
@@ -231,4 +231,9 @@ class PyRFContext(implicit sparkSession: SparkSession) extends RasterFunctions
     val ref = RasterRef(src, bandIndex, Some(extent))
     ref.tile.toArrayTile().toBytes()
   }
+
+  def _dfToMarkdown(df: DataFrame, numRows: Int, truncate: Boolean): String = {
+    import rasterframes.util.DFWithPrettyPrint
+    df.toMarkdown(numRows, truncate)
+  }
 }

From bac4f07503ff7fea0cc41082f1d7cc3b2ed5b4d4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 13 Aug 2019 19:30:05 -0400
Subject: [PATCH 362/380] Tweaked pweave markdown generator.

---
 pyrasterframes/src/main/python/setup.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 834ddfbb1..21657dfef 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -47,6 +47,16 @@ def _divided(msg):
     return divider + '\n' + msg + '\n' + divider
 
 
+from pweave import PwebPandocFormatter
+
+class PegdownMarkdownFormatter(PwebPandocFormatter):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    # Pegdown doesn't support the width and label options.
+    def make_figure_string(self, figname, width, label, caption=""):
+        return "![%s](%s)" % (caption, figname)
+
 class PweaveDocs(distutils.cmd.Command):
     """A custom command to run documentation scripts through pweave."""
     description = 'Pweave PyRasterFrames documentation scripts'
@@ -88,6 +98,11 @@ def run(self):
         import pweave
         bad_words = ["Error"]
         pweave.rcParams["chunk"]["defaultoptions"].update({'wrap': False, 'dpi': 100})
+        if self.format == 'markdown':
+            pweave.PwebFormats.formats['markdown'] = {
+                'class': PegdownMarkdownFormatter,
+                'description': 'Pegdown compatible markdown'
+            }
 
         for file in self.files:
             name = path.splitext(path.basename(file))[0]

From e69c5dfceccced5bd936404b8b88b16e38f9d041 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Tue, 13 Aug 2019 21:26:27 -0400
Subject: [PATCH 363/380] Tweaks to time-series.pymd to lower memory pressure
 and remove SQL.

---
 .../rasterframes/RasterFunctions.scala        |  5 +++
 .../src/main/python/docs/time-series.pymd     | 40 +++++++++----------
 .../main/python/pyrasterframes/__init__.py    |  4 +-
 .../python/pyrasterframes/rasterfunctions.py  |  6 +--
 .../src/main/python/pyrasterframes/utils.py   |  4 +-
 5 files changed, 31 insertions(+), 28 deletions(-)

diff --git a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
index 0ea7f785e..20b11c679 100644
--- a/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
+++ b/core/src/main/scala/org/locationtech/rasterframes/RasterFunctions.scala
@@ -280,6 +280,11 @@ trait RasterFunctions {
       udf(F.rasterize(_: Geometry, _: Geometry, _: Int, cols, rows)).apply(geometry, bounds, value)
     )
 
+  def rf_rasterize(geometry: Column, bounds: Column, value: Column, cols: Column, rows: Column): TypedColumn[Any, Tile] =
+    withTypedAlias("rf_rasterize", geometry)(
+      udf(F.rasterize).apply(geometry, bounds, value, cols, rows)
+    )
+
   /** Reproject a column of geometry from one CRS to another.
     * @param sourceGeom Geometry column to reproject
     * @param srcCRS Native CRS of `sourceGeom` as a literal
diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
index ddb0eaf9c..68bda9017 100644
--- a/pyrasterframes/src/main/python/docs/time-series.pymd
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -7,8 +7,8 @@ from IPython.display import display
 import pyrasterframes
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
-
-spark = pyrasterframes.get_spark_session()
+# This job is more memory bound, so reduce the concurrent tasks.
+spark = pyrasterframes.get_spark_session("local[4]")
 ```
 
 In this example, we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will start with our @ref:[built-in MODIS data catalog](raster-catalogs.md#using-built-in-experimental-catalogs).
@@ -35,6 +35,7 @@ park_geo = park_df[park_df.UNIT_CODE=='CUVA'].geometry[0]
 park_geo.wkt[:100]
 ```
 
+
 The entire park boundary is contained in MODIS granule h11 v4. We will simply filter on this granule, rather than using a @ref:[spatial relation](vector-data.md#geomesa-functions-and-spatial-relations). The time period selected should show the change in plant vigor as leaves emerge over the spring and into early summer.
 
 ```python query_catalog
@@ -53,34 +54,30 @@ Now we have a catalog with several months of MODIS data for a single granule. Ho
 
 ```python read_catalog
 raster_cols = ['B01', 'B02',] # red and near-infrared respectively
-park_rf = spark.read.raster(catalog=park_cat.select(['acquisition_date', 'granule_id'] + raster_cols),
-                            catalog_col_names=raster_cols,
-                           ) \
-                    .withColumn('park', st_geomFromWKT(lit(park_geo.wkt)))
-park_rf.createOrReplaceTempView('catalog_read')
+park_rf = spark.read.raster(
+    catalog=park_cat.select(['acquisition_date', 'granule_id'] + raster_cols),
+    catalog_col_names=raster_cols
+    ) \
+    .withColumn('park', st_geomFromWKT(lit(park_geo.wkt)))
 park_rf.printSchema()
+park_rf.persist()                    
 ```
 
 ## Vector and Raster Data Interaction
 
 Now we have the vector representation of the park boundary alongside the _tiles_ of red and near infrared bands. Next, we need to create a _tile_ representation of the park to allow us to limit the time series analysis to pixels within the park. This is similar to the masking operation demonstrated in @ref:[NoData handling](nodata-handling.md#masking).
 
-We do this using two SQL transformations. The first one will reproject the park boundary from coordinates to the MODIS sinusoidal projection. The second one will create a new _tile_ aligned with the imagery containing a value of 1 where the pixels are contained within the park and NoData elsewhere. The code demonstrates that we can use any `rf_` or `st_` function in SQL. For a more thorough demonstration of how Python and SQL codes compare in RasterFrames, see the @ref:[language support section](languages.md).
+We do this using two transformations. The first one will reproject the park boundary from coordinates to the MODIS sinusoidal projection. The second one will create a new _tile_ aligned with the imagery containing a value of 1 where the pixels are contained within the park and NoData elsewhere. 
 
 ```python burn_in
-cr_1 = spark.sql("""
-SELECT *,
-    st_reproject(park, 'EPSG:4326', rf_crs(B01).crsProj4) as park_native
-FROM catalog_read
-""")
-cr_1.createOrReplaceTempView('cr_1')
-
-cr_2 = spark.sql("""
-SELECT *,
-    rf_rasterize(park_native, rf_geometry(B01), 1, rf_dimensions(B01).cols, rf_dimensions(B01).rows) as park_tile
-FROM cr_1
-""").where(rf_tile_sum('park_tile') > 0)
+cr_1 = park_rf.withColumn('park_native', st_reproject('park', lit('EPSG:4326'), rf_crs('B01')))
+
+cr_2 = cr_1 \
+    .withColumn('dims', rf_dimensions('B01')) \
+    .withColumn('park_tile', rf_rasterize('park_native', rf_geometry('B01'), lit(1), 'dims.cols', 'dims.rows')) \
+    .where(rf_tile_sum('park_tile') > 0)
 cr_2.printSchema()
+cr_2.persist()
 ```
 
 ## Create Time Series
@@ -100,7 +97,8 @@ time_series = rf_ndvi \
         .groupby(year('acquisition_date').alias('year'), weekofyear('acquisition_date').alias('week')) \
         .agg(sql_sum('ndvi_wt').alias('ndvi_wt'), sql_sum('wt').alias('wt')) \
         .withColumn('ndvi', col('ndvi_wt') / col('wt'))
-
+time_series.printSchema()
+time_series.persist()
 ```
 
 Finally, we will take a look at the NDVI over time.
diff --git a/pyrasterframes/src/main/python/pyrasterframes/__init__.py b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
index 3898f045f..1fa5e91cf 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/__init__.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/__init__.py
@@ -55,11 +55,11 @@ def _kryo_init(builder):
     return builder
 
 
-def get_spark_session():
+def get_spark_session(master="local[*]"):
     """ Create a SparkSession with pyrasterframes enabled and configured. """
     from pyrasterframes.utils import create_rf_spark_session
 
-    return create_rf_spark_session()
+    return create_rf_spark_session(master)
 
 
 def _convert_df(df, sp_key=None, metadata=None):
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
index 546463b8d..6545b5a72 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rasterfunctions.py
@@ -91,11 +91,11 @@ def rf_make_ones_tile(num_cols, num_rows, cell_type=CellType.float64()):
     return Column(jfcn(num_cols, num_rows, _parse_cell_type(cell_type)))
 
 
-def rf_rasterize(geometry_col, bounds_col, value_col, num_cols, num_rows):
+def rf_rasterize(geometry_col, bounds_col, value_col, num_cols_col, num_rows_col):
     """Create a tile where cells in the grid defined by cols, rows, and bounds are filled with the given value."""
     jfcn = RFContext.active().lookup('rf_rasterize')
-    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(bounds_col), _to_java_column(value_col), num_cols,
-                       num_rows))
+    return Column(jfcn(_to_java_column(geometry_col), _to_java_column(bounds_col),
+                       _to_java_column(value_col), _to_java_column(num_cols_col),  _to_java_column(num_rows_col)))
 
 
 def st_reproject(geometry_col, src_crs, dst_crs):
diff --git a/pyrasterframes/src/main/python/pyrasterframes/utils.py b/pyrasterframes/src/main/python/pyrasterframes/utils.py
index d18e1fb37..e1ffd4074 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/utils.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/utils.py
@@ -76,12 +76,12 @@ def find_pyrasterframes_assembly():
     return jarpath[0]
 
 
-def create_rf_spark_session():
+def create_rf_spark_session(master="local[*]"):
     """ Create a SparkSession with pyrasterframes enabled and configured. """
     jar_path = find_pyrasterframes_assembly()
 
     spark = (SparkSession.builder
-             .master("local[*]")
+             .master(master)
              .appName("RasterFrames")
              .config('spark.jars', jar_path)
              .withKryoSerialization()

From a905b5b2a637f9230b54197553fd99cec348af19 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 14 Aug 2019 09:46:29 -0400
Subject: [PATCH 364/380] Edits: index.md & description.md

---
 pyrasterframes/src/main/python/docs/description.md | 8 ++++----
 pyrasterframes/src/main/python/docs/index.md       | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/description.md b/pyrasterframes/src/main/python/docs/description.md
index 974441633..8db6347af 100644
--- a/pyrasterframes/src/main/python/docs/description.md
+++ b/pyrasterframes/src/main/python/docs/description.md
@@ -1,16 +1,16 @@
 # Overview
 
-RasterFrames® provides a DataFrame-centric view over arbitrary Earth-observation (EO) data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of [Apache Spark](https://spark.apache.org/docs/latest/) [ML](https://spark.apache.org/docs/latest/ml-guide.html) algorithms. It provides APIs in @ref:[Python, SQL, and Scala](languages.md), and can scale from a laptop to a large distributed cluster, enabling _global_ analysis with satellite imagery in a wholly new, flexible and convenient way.
+RasterFrames® provides a DataFrame-centric view over arbitrary Earth-observation (EO) data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of [Apache Spark](https://spark.apache.org/docs/latest/) [ML](https://spark.apache.org/docs/latest/ml-guide.html) algorithms. It provides APIs in @ref:[Python, SQL, and Scala](languages.md), and can scale from a laptop computer to a large distributed cluster, enabling _global_ analysis with satellite imagery in a wholly new, flexible, and convenient way.
 
 ## Context
 
-We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent attributes and measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and is the mental model employed by many data scientists across diverse forms of modeling and analysis.
+We have a millennia-long history of organizing information in tabular form. Typically, rows represent independent events or observations, and columns represent attributes and measurements from the observations. The forms have evolved, from hand-written agricultural records and transaction ledgers, to the advent of spreadsheets on the personal computer, and on to the creation of the _DataFrame_ data structure as found in [R Data Frames][R] and [Python Pandas][Pandas]. The table-oriented data structure remains a common and critical component of organizing data across industries, and—most importantly—it is the mental model employed by data scientists across diverse forms of modeling and analysis.
 
 The evolution of the DataFrame form has continued with [Spark SQL](https://spark.apache.org/docs/latest/sql-programming-guide.html), which brings DataFrames to the big data distributed compute space. Through several novel innovations, Spark SQL enables data scientists to work with DataFrames too large for the memory of a single computer. As suggested by the name, these DataFrames are manipulatable via standard SQL, as well as the more general-purpose programming languages Python, R, Java, and Scala.
 
-RasterFrames, an incubating Eclipse Foundation LocationTech project, brings together EO data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. According to a World Bank document on assets for post-disaster situation awareness[^1]:
+RasterFrames, an incubating Eclipse Foundation LocationTech project, brings together EO data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity and a huge challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. According to a World Bank document on assets for post-disaster situation awareness[^1]:
 
-> Of the 1,738 operational satellites currently orbiting the earth (as of 9/[20]17), 596 are earth observation satellites and 477 of these are non-military assets (ie available to civil society including commercial entities and governments for earth observation, according to the Union of Concerned Scientists). This number is expected to increase significantly over the next ten years. The 200 or so planned remote sensing satellites have a value of over 27 billion USD (Forecast International). This estimate does not include the burgeoning fleets of smallsats as well as micro, nano and even smaller satellites... All this enthusiasm has, not unexpectedly, led to a veritable fire-hose of remotely sensed data which is becoming difficult to navigate even for seasoned experts.
+> Of the 1,738 operational satellites currently orbiting the earth (as of 9/[20]17), 596 are earth observation satellites and 477 of these are non-military assets (i.e. available to civil society including commercial entities and governments for earth observation, according to the Union of Concerned Scientists). This number is expected to increase significantly over the next ten years. The 200 or so planned remote sensing satellites have a value of over 27 billion USD (Forecast International). This estimate does not include the burgeoning fleets of smallsats as well as micro, nano and even smaller satellites... All this enthusiasm has, not unexpectedly, led to a veritable fire-hose of remotely sensed data which is becoming difficult to navigate even for seasoned experts.
 
 ## Benefit
 
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index 4d4053fe5..cc51e414c 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -1,6 +1,6 @@
 # RasterFrames
 
-RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
+RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity and a huge challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger. 
 
 RasterFrames provides a DataFrame-centric view over arbitrary raster data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is both accessible to general analysts and scalable along with the rapidly growing data footprint.
 

From c1ae43070809fee3a271806247d08acce9346a33 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 14 Aug 2019 10:10:56 -0400
Subject: [PATCH 365/380] Edits: concepts.md & getting-started.md.

---
 pyrasterframes/src/main/python/docs/concepts.md        |  5 +++--
 .../src/main/python/docs/getting-started.pymd          | 10 +++++-----
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/concepts.md b/pyrasterframes/src/main/python/docs/concepts.md
index 8375d8283..dda457055 100644
--- a/pyrasterframes/src/main/python/docs/concepts.md
+++ b/pyrasterframes/src/main/python/docs/concepts.md
@@ -8,7 +8,7 @@ There are a number of Earth-observation (EO) concepts that crop up in the discus
 
 ## Raster
 
-A raster is a regular grid of numeric values. A raster can be thought of as an image, as is the case if the values in the grid represent brightness along a greyscale. More generally a raster can measure many different phenomena or encode a variety of different discrete classifications.
+A raster is a regular grid of numeric values. A raster can be thought of as an image, as is the case if the values in the grid represent brightness along a greyscale. More generally, a raster can measure many different phenomena or encode a variety of different discrete classifications.
 
 ## Cell
 
@@ -17,6 +17,7 @@ A cell is a single row and column intersection in the raster grid. It is a singl
 ## Cell Type
 
 A numeric cell value may be encoded in a number of different computer numeric formats. There are typically three characteristics used to describe a cell type:
+
 * word size (bit-width)
 * signed vs unsigned
 * integral vs floating-point
@@ -47,7 +48,7 @@ A scene (or granule) is a discrete instance of EO @ref:[raster data](concepts.md
 
 ## Band
 
-A @ref:[scene](concepts.md#scene) frequently defines many different measurements captured a the same date-time, over the same extent, and meant to be processed together. These different measurements are referred to as bands. The name comes from the varying bandwidths of light and electromagnetic radiation measured in many EO datasets. 
+A @ref:[scene](concepts.md#scene) frequently defines many different measurements captured at the same date-time, over the same extent, and meant to be processed together. These different measurements are referred to as bands. The name comes from the varying bandwidths of light and electromagnetic radiation measured in many EO datasets. 
 
 ## Coordinate Reference System (CRS)
 
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 388459cad..576994f6c 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -52,19 +52,19 @@ You can also use RasterFrames in the following environments:
 1. Jupyter Notebook
 1. `pyspark` shell
 
-### Jupyter Notebook
+### Using Jupyter Notebook
 
  RasterFrames provides a Docker image for a Jupyter notebook server whose default kernel is already set up for running RasterFrames. To use it:
 
- 1. Install [docker](https://docs.docker.com/install/)
+ 1. Install [Docker](https://docs.docker.com/install/)
  1. Pull the image: `docker pull s22s/rasterframes-notebook`
  1. Run a container with the image, for example:
       `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/jovyan/work rasterframes-notebook:latest`
  1. In a browser, open `localhost:8808` in the example above.
 
-See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
+See the [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.
 
-### `pyspark` shell
+### Using `pyspark` shell
 
 You can use RasterFrames in a `pyspark` shell. To set up the `pyspark` environment, prepare your call with the appropriate `--master` and other `--conf` arguments for your cluster manager and environment. For RasterFrames support you need to pass arguments pointing to the various Java dependencies. You will also need the Python source zip, even if you have pip installed the package. You can download the source zip here: https://repo1.maven.org/maven2/org/locationtech/rasterframes/pyrasterframes_2.11/${VERSION}/pyrasterframes_2.11-${VERSION}-python.zip.
 
@@ -138,4 +138,4 @@ from pyrasterframes.utils import gdal_version
 print(gdal_version())
 ```
 
-This will print out something like "GDAL x.y.z, released 20yy/mm/dd". If it reports `not available`, then GDAL isn't installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving it.
+This will print out something like "GDAL x.y.z, released 20yy/mm/dd". If it reports "not available", then GDAL is not installed in a place where the RasterFrames runtime was able to find it. Please [file an issue](https://github.com/locationtech/rasterframes/issues) to get help resolving this problem.

From 73028775a4e59e2bceb4f769bbd9c3fceb8998c4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Wed, 14 Aug 2019 11:07:37 -0400
Subject: [PATCH 366/380] Fixes to PDF generation code.

---
 docs/build.sbt                   | 7 +++----
 docs/src/main/latex/header.latex | 9 ++++++++-
 2 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/docs/build.sbt b/docs/build.sbt
index 201402da7..59f734a48 100644
--- a/docs/build.sbt
+++ b/docs/build.sbt
@@ -41,9 +41,9 @@ makePDF := {
   work.mkdirs()
 
   val prepro = files.zipWithIndex.map { case (f, i) ⇒
-    val dest = work / f"$i%02d.md"
+    val dest = work / f"$i%02d-${f.getName}%s"
     // Filter cross links and add a newline
-    (Seq("sed", "-e", """s/@ref:\[\([^]]*\)\](.*)/_\1_/g;s/@@.*//g""", f.toString) #> dest).!
+    (Seq("sed", "-e", """s/@ref://g;s/@@.*//g""", f.toString) #> dest).!
     // Add newline at the end of the file so as to make pandoc happy
     ("echo" #>> dest).!
     ("echo \\pagebreak" #>> dest).!
@@ -55,7 +55,7 @@ makePDF := {
   val header = (Compile / sourceDirectory).value / "latex" / "header.latex"
 
   val args = "pandoc" ::
-    "--from=markdown" ::
+    "--from=markdown+pipe_tables" ::
     "--to=pdf" ::
     "-t" :: "latex" ::
     "-s" ::
@@ -64,7 +64,6 @@ makePDF := {
     "-V" :: "author:Astraea, Inc." ::
     "-V" :: "geometry:margin=0.75in" ::
     "-V" :: "papersize:letter" ::
-    "-V" :: "links-as-notes" ::
     "--include-in-header" :: header.toString ::
     "-o" :: output.toString ::
     prepro.map(_.toString).toList
diff --git a/docs/src/main/latex/header.latex b/docs/src/main/latex/header.latex
index a6fca2586..50a53b5da 100644
--- a/docs/src/main/latex/header.latex
+++ b/docs/src/main/latex/header.latex
@@ -1,2 +1,9 @@
 \DeclareUnicodeCharacter{2218}{$\circ$}
-\DeclareUnicodeCharacter{2714}{$\checkmark}
+\DeclareUnicodeCharacter{2714}{$\checkmark$}
+\DeclareUnicodeCharacter{21A9}{$\newline$}
+\hypersetup{
+    colorlinks=true,
+    linkcolor=blue,
+    allbordercolors={0 0 0},
+    pdfborderstyle={/S/U/W 1}
+}
\ No newline at end of file

From 57b4644b3297e8efb4fddfde9f4e13c40c105a90 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 10:00:02 -0400
Subject: [PATCH 367/380] Edits: raster-io.md, raster-catalogs.pymd,
 raster-read.pymd.

---
 .../src/main/python/docs/raster-catalogs.pymd | 35 ++++++++------
 .../src/main/python/docs/raster-io.md         |  4 +-
 .../src/main/python/docs/raster-read.pymd     | 46 ++++++++++---------
 pyrasterframes/src/main/python/setup.py       |  1 +
 4 files changed, 48 insertions(+), 38 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index 35e695dd2..ef9b130f3 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -1,15 +1,15 @@
 # Raster Catalogs
 
-While much interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource, described in the @ref:[next page](raster-read.md), which creates _tiles_ from the rasters at the referenced URLs.
+While interesting processing can be done on a @ref:[single raster file](raster-read.md#single-raster), RasterFrames shines when _catalogs_ of raster data are to be processed. In its simplest form, a _catalog_ is a list of @ref:[URLs referencing raster files](raster-read.md#uri-formats). This list can be a Spark DataFrame, Pandas DataFrame, CSV file or CSV string. The _catalog_ is input into the `raster` DataSource described in the @ref:[next page](raster-read.md), which creates _tiles_ from the rasters at the referenced URLs.
 
 A _catalog_ can have one or two dimensions:
 
 * One-D: A single column contains raster URLs across the rows. All referenced rasters represent the same @ref:[band](concepts.md#band). For example, a column of URLs to Landsat 8 near-infrared rasters covering Europe. Each row represents different places and times.
-* Two-D: Many columns containing raster URLs. Each column references the same band, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row represents a single @ref:[scene](concepts.md#scene) with the same resolution, extent, [_CRS_][CRS], etc across the row.
+* Two-D: Many columns contain raster URLs. Each column references the same band, and each row represents the same place and time. For example, red-, green-, and blue-band columns for scenes covering Europe. Each row represents a single @ref:[scene](concepts.md#scene) with the same resolution, extent, [_CRS_][CRS], etc across the row.
 
 ## Creating a Catalog
 
-This section will provide some examples of creating your own _catalogs_, as well as introduce some experimental _catalogs_ built into RasterFrames. Reading raster data represented by a _catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
+This section will provide some examples of _catalogs_ creation, as well as introduce some experimental _catalogs_ built into RasterFrames. Reading raster data represented by a _catalog_ is covered in more detail in the @ref:[next page](raster-read.md).
 
 ```python, setup, echo=False
 from pyrasterframes.utils import create_rf_spark_session
@@ -24,13 +24,12 @@ spark = create_rf_spark_session()
 A single URL is the simplest form of a catalog.
 
 ```python, oned_onerow_catalog
-from pyspark.sql import Row
-
 file_uri = "/data/raster/myfile.tif"
 # Pandas DF
 my_cat = pd.DataFrame({'B01': [file_uri]})
 
 # equivalent Spark DF
+from pyspark.sql import Row
 my_cat = spark.createDataFrame([Row(B01=file_uri)])
 
 #equivalent CSV string
@@ -55,7 +54,7 @@ one_d_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
 
 ### Two-D
 
-Example of a multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same: granule id `h04v09` on July 4 or July 7, 2018. The first column is band 1, red,  and the second is band 2, near infrared.
+In this example, multiple columns representing multiple content types (bands) across multiple scenes. In each row, the scene is the same: granule id `h04v09` on July 4 or July 7, 2018. The first column is band 1, red,  and the second is band 2, near infrared.
 
 ```python, twod_catalog
 scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43A4.A2018185.h04v09.006.2018194032851_B01.TIF"
@@ -63,19 +62,25 @@ scene1_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B02.TIF"
 
+# Pandas DF
+my_cat = pd.DataFrame([
+    {'B01': [scene1_B01], 'B02': [scene1_B02]},
+    {'B01': [scene2_B01], 'B02': [scene2_B02]}
+])     
 
-# As CSV string
-my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
 # or
 my_cat_df = spark.createDataFrame([
     Row(B01=scene1_B01, B02=scene1_B02),
-    Row(B01=scene2_B01, B02=scene2_B02)])
-my_cat_df.printSchema()
+    Row(B01=scene2_B01, B02=scene2_B02)
+])
+    
+# As CSV string
+my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
 ```
 
 ## Using External Catalogs
 
-The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading an cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data.
+The concept of a _catalog_ is much more powerful when we consider examples beyond constructing the DataFrame, and instead read the data from an external source. Here's an extended example of reading a cloud-hosted CSV file containing MODIS scene metadata and transforming it into a _catalog_. The metadata describing the content of each URL is an important aspect of processing raster data.
 
 ```python, remote_csv, results='raw'
 from pyspark import SparkFiles
@@ -103,17 +108,17 @@ modis_catalog = scene_list \
 modis_catalog.show(4, truncate=True)
 ```
 
-## Using Built-in Experimental Catalogs
+## Using Built-in Catalogs
 
 RasterFrames comes with two experimental catalogs over the AWS PDS [Landsat 8][Landsat] and [MODIS][MODIS] repositories. They are created by downloading the latest scene lists and building up the appropriate band URI columns as in the prior example.
 
-> Note: The first time you run these may take some time, as the catalogs are large. However, they are cached and subsequent invocations should be faster.
+> Note: The first time you run these may take some time, as the catalogs are large and have to be downloaded. However, they are cached and subsequent invocations should be faster.
 
 ### MODIS
 
 ```python, evaluate=False
-modis_catalog2 = spark.read.format('aws-pds-modis-catalog').load()
-modis_catalog2.printSchema()
+modis_catalog = spark.read.format('aws-pds-modis-catalog').load()
+modis_catalog.printSchema()
 ```
 ```
 root
diff --git a/pyrasterframes/src/main/python/docs/raster-io.md b/pyrasterframes/src/main/python/docs/raster-io.md
index 9f6edc824..68696397c 100644
--- a/pyrasterframes/src/main/python/docs/raster-io.md
+++ b/pyrasterframes/src/main/python/docs/raster-io.md
@@ -11,11 +11,13 @@ The standard mechanism by which any data is brought in and out of a Spark Datafr
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
     - `geotrellis`: for reading a [GeoTrellis layer][GTLayer]
 * @ref:[Raster Writers](raster-write.md)
-    - You can write @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
     - @ref:[`geotiff`](raster-write.md#geotiffs): beta writer to GeoTiff file format
     - @ref:[`geotrellis`](raster-write.md#geotrellis-layers): creating a [GeoTrellis layer][GTLayer]
     - @ref:[`parquet`](raster-write.md#parquet): general purpose writer for [Parquet][Parquet]
 
+
+Furthermore, when in a Jupyter Notebook environment, you can view @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples.
+
 There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.
 
 @@@ index
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 9a8905a8b..18228e414 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -1,8 +1,8 @@
 # Reading Raster Data
 
 ```python, echo=False
-from IPython.display import display
 import pyrasterframes.rf_ipython
+from IPython.display import display
 import pandas as pd
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
@@ -22,7 +22,7 @@ rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0
 rf.printSchema()
 ```
 
-The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.
+The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to non-COG GeoTIFFs.
 
 Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a @ref:[_CRS_][CRS], a spatial @ref:[_extent_][Extent] measured in that CRS, and a two-dimensional array of numeric values called a @ref:[_tile_][Tile].
 
@@ -41,22 +41,24 @@ print("CRS", crs.value.crsProj4)
 parts.show(5, truncate=False)
 ```
 
-You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on manageable amounts of data per task.
+You can also see that the single raster has been broken out into many arbitrary non-overlapping regions. Doing so takes advantage of parallel in-memory reads from the cloud hosted data source and allows Spark to work on manageable amounts of data per task. The following code fragment shows us how many subtiles were created from a single source image.
 
-```python count_by_uri, results='raw'
+```python, count_by_uri, results='raw'
 rf.groupby(rf.proj_raster_path).count().show(truncate=False)
 ```
 
-Let's select a single _tile_ and view it. The _tile_ preview image as well as the string representation provide some basic information about the _tile_: it's dimensions as number of columns and rows, and the cell type, or data type of all the cells in the _tile_. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
+Let's select a single _tile_ and view it. The _tile_ preview image as well as the string representation provide some basic information about the _tile_: its dimensions as numbers of columns and rows and the cell type, or data type of all the cells in the _tile_. For more about cell types, refer to @ref:[this discussion](nodata-handling.md#cell-types).
 
-```python show_tile_sample
+```python, show_tile_sample
 tile = rf.select(rf_tile("proj_raster")).first()[0]
 display(tile)
 ```
 
 ## URI Formats
 
-RasterFrames relies on three different IO drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used first if a compatible version of GDAL (>= 2.4) is installed. It is the only driver that can read non-GeoTIFF files. If GDAL is not available, either the _Java I/O_ or _Hadoop_ driver will be selected, depending on scheme.
+RasterFrames relies on three different I/O drivers, selected based on a combination of scheme, file extentions, and library availability. GDAL is used by default if a compatible version of GDAL (>= 2.4) is installed, and if GDAL supports the specified scheme. If GDAL is not available, either the _Java I/O_ or _Hadoop_ driver will be selected, depending on scheme.
+
+Note: The GDAL driver is the only one that can read non-GeoTIFF files. 
 
 
 | Prefix              | GDAL        | Java I/O | Hadoop   |
@@ -73,14 +75,14 @@ RasterFrames relies on three different IO drivers, selected based on a combinati
 | `wasb://`           | `/vsiaz/`   | no       | yes      |
 | `wasbs://`          | no          | no       | yes      |
 
-Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. Another example would be a MRF file in an S3 bucket on AWS: `gdal://vsis3/my-bucket/prefix/to/raster.mrf`. See the GDAL documentation for the format of the URIs after the `gdal:/` scheme. The `gdal:/` scheme is stripped off before passing the rest of the path to GDAL.
+Specific [GDAL Virtual File System drivers](https://gdal.org/user/virtual_file_systems.html) can be selected using the `gdal://<vsidrv>//` syntax. For example If you have a `archive.zip` file containing a GeoTiff named `my-file-inside.tif`, you can address it with `gdal://vsizip//path/to/archive.zip/my-file-inside.tif`. Another example would be a MRF file in an S3 bucket on AWS: `gdal://vsis3/my-bucket/prefix/to/raster.mrf`. See the GDAL documentation for the format of the URIs after the `gdal:/` scheme.
 
 
 ## Raster Catalogs
 
-Considering the definition of a @ref:[_Catalog_](raster-catalogs.md) previously discussed, let's read the raster data contained in the catalog URIs. We will start with the @ref:[external catalog](raster-catalogs.md#using-external-catalogs) of MODIS surface reflectance that we previously demonstrated.
+Consider the definition of a @ref:[_Catalog_](raster-catalogs.md) previously discussed, let's read the raster data contained in the catalog URIs. We will start with the @ref:[external catalog](raster-catalogs.md#using-external-catalogs) of MODIS surface reflectance.
 
-```python catalog_prep
+```python, catalog_prep
 from pyspark import SparkFiles
 from pyspark.sql import functions as F
 
@@ -107,7 +109,7 @@ print("Available scenes: ", modis_catalog.count())
 modis_catalog.show(5, truncate=True)
 ```
 
-MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using `(h,v)` grid coordinates.
+MODIS data products are delivered on a regular, consistent grid, making identification of a specific area over time easy using [`(h,v)`](https://modis-land.gsfc.nasa.gov/MODLAND_grid.html) grid coordinates (see below).
 
 ![MODIS Grid](https://modis-land.gsfc.nasa.gov/images/MODIS_sinusoidal_grid1.gif)
 
@@ -129,18 +131,18 @@ rf = spark.read.raster(
 rf.printSchema()
 ```
 
-Observe that the schema of the resulting DataFrame has a projected raster struct for each column passed in `catalog_col_names`. For reference, the URI is now in a column appended with `_path`. We can take a quick look at the representation of the data. We see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent from within that coverage.
+Observe the schema of the resulting DataFrame has a projected raster struct for each column passed in `catalog_col_names`. For reference, the URI is now in a column appended with `_path`. Taking a quick look at the representation of the data, we see again each row contains an arbitrary portion of the entire scene coverage. We also see that for two-D catalogs, each row contains the same spatial extent for all tiles in that row.
 
 ```python, cat_read_sample, results='raw'
 rf.select('gid', rf_extent('red'), rf_extent('nir'), rf_tile('red'), rf_tile('nir')) \
     .show(3, truncate=True)
 ```
 
-## Lazy Raster Reads
+## Lazy Raster Reading
 
-By default the raster reads are delayed as long as possible. The DataFrame will contain metadata and pointers to the appropriate portion of the data until reading of the source raster data is absolutely necessary. This can save a lot of computation and I/O time for two reasons. One is that a _catalog_ may contain millions of rows. Second is that the `raster` DataSource attempts to ensure filters are processed before reading raster data.
+By default, reading raster pixel values is delayed until it is absolutely needed. The DataFrame will contain metadata and pointers to the appropriate portion of the data until reading of the source raster data is absolutely necessary. This can save a significant of computation and I/O time for two reasons. First, a catalog may contain a large number of rows. Second, the `raster` DataSource attempts to apply spatial preciates (e.g. `where`/`WHERE` clauses with `st_intersects`, et al.) at row creation, reducing the chance of unneeded data being fetched.
 
-Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the option to force the raster to be fully read right away.
+Consider the following two reads of the same data source. In the first, the lazy case, there is a pointer to the URI, extent and band to read. This will not be evaluated until the cell values are absolutely required. The second case shows the option to force the raster to be fully loaded right away.
 
 ```python, lazy_demo_1, results='raw'
 uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
@@ -160,7 +162,7 @@ In the initial examples on this page, you may have noticed that the realized (no
 
 ## Multiband Rasters
 
-A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different @ref:[bands](concepts.md#band). The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated for different spectral @ref:[bands](concepts.md#band). The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could measure other phenomena such as time, quality indications, or additional gas concentrations, etc.
 
 Multiband rasters files have a strictly ordered set of bands, which are typically indexed from 1. Some files have metadata tags associated with each band. Some files have a color interpetation metadata tag indicating how to interpret the bands.
 
@@ -175,11 +177,11 @@ mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.
 mb.printSchema()
 ```
 
-If a band is passed into `band_index` that exceeds the number of bands in the raster, a projected raster column will still be generated in the schema but it will be full of `null` values.
+If a band is passed into `band_indexes` that exceeds the number of bands in the raster, a projected raster column will still be generated in the schema but the column will be full of `null` values.
 
 You can also pass a `catalog` and `band_indexes` together into the `raster` reader. This will create a projected raster column for the combination of all items passed into `catalog_col_names` and `band_indexes`. Again if a band in `band_indexes` exceeds the number of bands in a raster, it will have a `null` value for the corresponding column.
 
-Here is a trivial example with a _Catalog_ over multiband rasters. We specify two columns containing URIs and two bands, resulting in four projected raster columns.
+Here is a trivial example with a _catalog_ over multiband rasters. We specify two columns containing URIs and two bands, resulting in four projected raster columns.
 
 ```python multiband_catalog
 import pandas as pd
@@ -200,9 +202,9 @@ mb2.printSchema()
 
 ### GeoTrellis Catalogs
 
-[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. RasterFrames provides a DataSource that supports both reading and @ref:[writing](raster-write.md#geotrellis-layers) with GeoTrellis.
+[GeoTrellis][GeoTrellis] is one of the key libraries upon which RasterFrames is built. It provides a Scala language API for working with geospatial raster data.  GeoTrellis defines a [tile layer storage](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html) format for persisting imagery mosaics. RasterFrames provides a DataSource supporting both @ref:[reading](raster-read.md#geotrellis-layers) and @ref:[writing](raster-write.md#geotrellis-layers) GeoTrellis layers.
 
-A GeoTrellis catalog is a set of GeoTrellis layers. We can read a dataframe giving details of the content of a catalog using the following. The scheme is typically `hdfs` or a cloud storage provider like `s3` or `wasb`.
+A GeoTrellis catalog is a set of GeoTrellis layers. We can read a DataFrame giving details of the content of a catalog using the syntax below. The scheme is typically `hdfs` or `file`, or a cloud storage provider like `s3`.
 
 ```python, evaluate=False
 gt_cat = spark.read.geotrellis_catalog('scheme://path-to-gt-catalog')
@@ -210,13 +212,13 @@ gt_cat = spark.read.geotrellis_catalog('scheme://path-to-gt-catalog')
 
 ### GeoTrellis Layers
 
-The catalog will give details on the particular layers available for query. We can read the layer with the same URI to the catalog, the layer name, and the desired zoom level.
+The catalog will give details on the individual layers available for query. We can read each layer with the URI to the catalog, the layer name, and the desired zoom level.
 
 ```python, evaluate=False
 gt_layer = spark.read.geotrellis(path='scheme://path-to-gt-catalog', layer=layer_name, zoom=zoom_level)
 ```
 
-This will return a RasterFrame with additional metadata inherited from the GeoTrellis `TileLayerMetadata`, such as the `SpatialKey`. The `TileLayerMetadata` is also stored as JSON in the metadata of the _tile_ column.
+This will return a RasterFrame with additional [GeoTrellis-specific metadata](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html#layouts-and-tile-layers), inherited from GeoTrellis, stored as JSON in the metadata of the _tile_ column.
 
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 21657dfef..95e7d3fe5 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -183,6 +183,7 @@ def dest_file(self, src_file):
         'pandas>=0.25.0',
         'rasterio>=1.0.0',
         'boto3',
+        'Pweave==0.30.3',
     ],
     packages=[
         'pyrasterframes',

From 240f8787cb3fa058084d5995bb3462411ca1f156 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 10:38:39 -0400
Subject: [PATCH 368/380] Python SetupTools + TravisCI == Painful Combo.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 9df6d0290..4ab034f02 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -24,7 +24,7 @@ addons:
       - pandoc
 
 install:
-  - pip install rasterio shapely pandas numpy
+  - pip install rasterio shapely pandas numpy pweave
   - wget -O - https://piccolo.link/sbt-1.2.8.tgz | tar xzf -
 
 script:

From c5e59b9d92881ad1ca4106015f67b9ee33269e5c Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 10:42:51 -0400
Subject: [PATCH 369/380] Edits: raster-write.pymd & vector-data.pymd.

---
 pyrasterframes/README.md                      |  2 +-
 .../src/main/python/docs/raster-write.pymd    | 22 ++++++++++---------
 .../src/main/python/docs/vector-data.pymd     | 10 ++++-----
 3 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/pyrasterframes/README.md b/pyrasterframes/README.md
index ee0541407..5c4096a80 100644
--- a/pyrasterframes/README.md
+++ b/pyrasterframes/README.md
@@ -62,7 +62,7 @@ To manually initialize PyRasterFrames in a `pyspark` shell, prepare to call pysp
    
 ```
 
-Then in the pyspark shell or app, import the module and call `withRasterFrames` on the SparkSession.
+Then in the PySpark shell or script, import the module and call `withRasterFrames` on the SparkSession.
 
 ```python
 from pyrasterframes.utils import create_rf_spark_session
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 2d8f84a76..61491d007 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -1,8 +1,8 @@
 # Writing Raster Data
 
-RasterFrames is oriented toward large scale analyses of spatial data. The primary output of these analyses could be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set.
+RasterFrames is oriented toward large scale analyses of spatial data. The primary output of these analyses could be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input dataset.
 
-However, there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the current state of the process and results.
+However, there are times in any analysis where writing a representative sample of the work in progress provides valuable feedback on the current state of the process and results.
 
 ```python imports, echo=False
 import pyrasterframes
@@ -15,7 +15,7 @@ spark = pyrasterframes.get_spark_session()
 
 ## Tile Samples
 
-When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_.
+We have some convenience methods to quickly visualize _tile_s (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) when inspecting a subset of the data in a Notebook.
 
 In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata.
 
@@ -37,7 +37,7 @@ display(tile) # IPython.display function
 
 Within an IPython or Jupyter interpreter, a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
 
-In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
+In the example below, notice the result is limited to a small subset. For more information about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
 ```python to_pandas, evaluate=True
 import pyrasterframes.rf_ipython
@@ -60,23 +60,25 @@ pandas_df
 
 GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTIFF.
 
-One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
+One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF, all the data to be encoded has to be in the memory of one computer (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you must either specify the dimensions of the output raster, or deliberately limit the size of the collected data.
 
-Fortunately, we can use the cluster computing capability to downsample the data into a more manageable size. For sake of example, let's render an overview our scene's red band as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)).
+Fortunately, we can use the cluster computing capability to downsample the data into a more manageable size. For sake of example, let's render an overview of a scene's red band as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)).
 
 ```python write_geotiff
 outfile = os.path.join('/tmp', 'geotiff-overview.tif')
 spark_df.write.geotiff(outfile, crs='EPSG:4326', raster_dimensions=(256, 256))
 ```
 
-View it with `rasterio` to check the results:
+We can view the written file with `rasterio`:
 
 ```python view_geotiff
 import rasterio
 from rasterio.plot import show, show_hist
 
 with rasterio.open(outfile) as src:
+    # View raster
     show(src, adjust='linear')
+    # View data distribution
     show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.6,
         histtype='stepfilled', title="Overview Histogram")
 ```
@@ -89,13 +91,13 @@ os.remove(outfile)
 
 ## GeoTrellis Layers
 
-[GeoTrellis][GeoTrellis] is one of the key libraries that RasterFrames builds upon. It provides a Scala language API to working with large raster data with Apache Spark. Ingesting raster data into a Layer is one of the key concepts for creating a dataset for processing on Spark. RasterFrames writes data from an appropriate DataFrame into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both @ref:[reading](raster-read.md#geotrellis) and writing of GeoTrellis layers.
+[GeoTrellis][GeoTrellis] is one of the key libraries upon which RasterFrames is built. It provides a Scala language API for working with geospatial raster data.  GeoTrellis defines a [tile layer storage](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html) format for persisting imagery mosaics. RasterFrames can write data from a `RasterFrameLayer` into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both @ref:[reading](raster-read.md#geotrellis-layers) and @ref:[writing](raster-write.md#geotrellis-layers) GeoTrellis layers.
 
-> An example is forthcoming.
+> An example is forthcoming. In the mean time referencing the [`GeoTrellisDataSourceSpec` test code](https://github.com/locationtech/rasterframes/blob/develop/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) may help
 
 ## Parquet
 
-You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format.
+You can write a RasterFrame to the [Apache Parquet][Parquet] format. This format is designed to efficiently persist and query columnar data in distributed file system, such as HDFS. It also provides benefits when working in single node (or "local") mode, such as tailoring organization for defined query patterns.
 
 ```python write_parquet, evaluate=False
 spark_df.withColumn('exp', rf_expm1('proj_raster')) \
diff --git a/pyrasterframes/src/main/python/docs/vector-data.pymd b/pyrasterframes/src/main/python/docs/vector-data.pymd
index 8a145568f..1ec42d522 100644
--- a/pyrasterframes/src/main/python/docs/vector-data.pymd
+++ b/pyrasterframes/src/main/python/docs/vector-data.pymd
@@ -1,6 +1,6 @@
 # Vector Data
 
-RasterFrames provides a variety of ways to work with spatial vector (points, lines, and polygons) data alongside raster data. There is a convenience DataSource for GeoJSON format, as well as the ability to convert from [GeoPandas][GeoPandas] to Spark. Representation of vector geometries in pyspark is through [Shapely][Shapely] which provides a great deal of flexibility. RasterFrames also provides access to Spark functions for working with geometries.
+RasterFrames provides a variety of ways to work with spatial vector data (points, lines, and polygons) alongside raster data. There is a convenience DataSource for the GeoJSON format, as well as the ability to convert from [GeoPandas][GeoPandas] to Spark. Representation of vector geometries in PySpark is through [Shapely][Shapely], providing a great deal of interoperability. RasterFrames also provides access to Spark functions for working with geometries.
 
 ## GeoJSON DataSource
 
@@ -19,11 +19,11 @@ df = spark.read.geojson(SparkFiles.get('admin1-us.geojson'))
 df.printSchema()
 ```
 
-The properties of each feature are available as columns of the DataFrame, along with the geometry.
+The properties of each discrete geometry are available as columns of the DataFrame, along with the geometry itself.
 
 ## GeoPandas and RasterFrames
 
-You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as `df` defined above by the GeoJSON reader. Note that in a GeoPandas DataFrame there can be heterogeneous geometry types in the column, but this may fail Spark's schema inference.
+You can also convert a [GeoPandas][GeoPandas] GeoDataFrame to a Spark DataFrame, preserving the geometry column. This means that any vector format that can be read with [OGR][OGR] can be converted to a Spark DataFrame. In the example below, we expect the same schema as the DataFrame defined above by the GeoJSON reader. Note that in a GeoPandas DataFrame there can be heterogeneous geometry types in the column, which may fail Spark's schema inference.
 
 ```python, read_and_normalize
 import geopandas
@@ -44,7 +44,7 @@ df2.printSchema()
 
 ## Shapely Geometry Support
 
-The `geometry` column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working on the Python side. This means that when the data is collected to the driver, it will be a Shapely geometry object.
+The `geometry` column will have a Spark user-defined type that is compatible with [Shapely][Shapely] when working with Python via PySpark. This means that when the data is collected to the driver, it will be a Shapely geometry object.
 
 ```python, show_geom
 the_first = df.first()
@@ -89,7 +89,7 @@ df = df.withColumn('centroid', st_centroid(df.geometry))
 df.select('name', 'geometry', 'naive_centroid', 'centroid').show(4)
 ```
 
-The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example, we use the @ref:[built-in Landsat catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter only those within approximately 500 km of a selected point.
+The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example, we use the @ref:[built-in Landsat catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter to those within approximately 500 km of a selected point.
 
 ```python, evaluate=True
 from pyrasterframes.rasterfunctions import st_geometry, st_bufferPoint, st_intersects, st_point

From 3f3682ddba439b5598ac8bd74a54de1c35ccea19 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 12:51:54 -0400
Subject: [PATCH 370/380] Edits: local-algebra.pymd, nodata-handling.pymd.
 Rendering tweaks.

---
 .../src/main/python/docs/local-algebra.pymd   | 33 +++++++++----------
 .../src/main/python/docs/nodata-handling.pymd | 26 +++++++--------
 .../src/main/python/docs/raster-read.pymd     | 24 +++++++-------
 .../main/python/pyrasterframes/rf_ipython.py  | 24 ++++++++++----
 .../main/python/pyrasterframes/rf_types.py    |  3 +-
 5 files changed, 57 insertions(+), 53 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/local-algebra.pymd b/pyrasterframes/src/main/python/docs/local-algebra.pymd
index e1a0ddf10..8707196e3 100644
--- a/pyrasterframes/src/main/python/docs/local-algebra.pymd
+++ b/pyrasterframes/src/main/python/docs/local-algebra.pymd
@@ -1,7 +1,7 @@
 # Local Map Algebra
 
-```python, echo=False
-from IPython.core.display import display, HTML
+```python, setup, echo=False
+from IPython.core.display import display
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import rf_normalized_difference, rf_tile, rf_dimensions, rf_extent, rf_tile_mean
 import pyrasterframes.rf_ipython
@@ -12,7 +12,7 @@ import os
 spark = create_rf_spark_session()
 ```
 
-[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single _tile_,  between a _tile_ and a scalar, between two _tiles_, or among many _tiles_. These operations are common in processing of earth observation and other image data.
+[Local map algebra](https://gisgeography.com/map-algebra-global-zonal-focal-local/) raster operations are element-wise operations on a single _tile_,  between a _tile_ and a scalar, between two _tiles_, or among many _tiles_. These operations are common in processing of earth observation imagery and other image data.
 
 <img src="https://gisgeography.com/wp-content/uploads/2015/05/Local-Operation-Raster.png" alt="local op" style="width:200px;"/>
 
@@ -21,15 +21,19 @@ Credit: [GISGeography](https://gisgeography.com)
 
 ## Computing NDVI
 
-Here’s an example of computing the Normalized Differential Vegetation Index (NDVI). NDVI is a vegetation index which emphasizes differences in relative biomass and vegetation health. The term _index_ in Earth observation means a combination of many raster bands into a single band that highlights a phenomenon of interest. Various indexes have proven useful visual tools and frequently appear as features in machine learning models using Earth observation data.
+Here is an example of computing the Normalized Differential Vegetation Index (NDVI). NDVI is a vegetation index which emphasizes differences in relative biomass and vegetation health. The term _index_ in Earth observation means a combination of many raster bands into a single band that highlights a phenomenon of interest. Various indices have proven useful visual tools and frequently appear as features in machine learning models using Earth observation data.
 
 > NDVI is often used worldwide to monitor drought, monitor and predict agricultural production, assist in predicting hazardous fire zones, and map desert encroachment. The NDVI is preferred for global vegetation monitoring because it helps to compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors (Lillesand. _Remote sensing and image interpretation_. 2004)
 
-### Create DataFrame
+We will apply the @ref:[catalog pattern](raster-catalogs.md) for defining the data we wish to process. To compute NDVI we need to compute local algebra on the *red* and *near infrared* (nir) bands: 
 
-We will use the @ref:[catalog pattern](raster-io.md) for data access. To compute NDVI we need to compute local algebra on the *red* and *near infrared* bands: `(n - r) / (n + r)`. This form of `(x - y) / (x + y)` is common in remote sensing and is called a normalized difference. It is used with other band pairs to highlight water, snow, and other phenomena.
+           nir - red
+    NDVI = ---------
+           nir + red
 
-```python, term=True
+This form of `(x - y) / (x + y)` is common in remote sensing and is called a normalized difference. It is used with other band pairs to highlight water, snow, and other phenomena.
+
+```python, read_rasters
 bands = {4: 'red', 8: 'nir'}
 uri_pattern = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B0{}.tif'
 catalog_df = pd.DataFrame([
@@ -42,8 +46,6 @@ df.printSchema()
 
 Observe how the bands we need to use to compute the index are arranged as columns of our resulting DataFrame. The rows or observations are various spatial extents within the entire coverage area.
 
-### Local Map Algebra
-
 RasterFrames provides a wide variety of local map algebra functions. There are several different broad categories, based on how many _tiles_ the function takes as input:
 
  * A function on a single _tile_ is a unary operation; example: @ref:[rf_log](reference.md#rf-log);
@@ -53,21 +55,16 @@ RasterFrames provides a wide variety of local map algebra functions. There are s
 
 We can express the normalized difference with a combination of `rf_local_divide`, `rf_local_subtract`, and `rf_local_add`. Since the normalized difference is so common, there is a convenience method `rf_normalized_difference`, which we use in this example. We will append a new column to the DataFrame, which will apply the map alegbra function to each row.
 
-```python
+```python, compute_ndvi
 df = df.withColumn('ndvi', rf_normalized_difference(df.nir, df.red))
 df.printSchema()
 ```
 
 We can inspect a sample of the data. Yellow indicates very healthy vegetation, and purple represents bare soil or impervious surfaces.
 
-```python
-
-display(df.select(rf_tile('ndvi').alias('ndvi')).first()['ndvi'])
+```python, show_tile
+t = df.select(rf_tile('ndvi').alias('ndvi')).first()['ndvi']
+display(t)
 ```
 
 We continue examining NDVI in the @ref:[time series](time-series.md) section.
-
-
-```python, echo=False
-spark.stop()
-```
diff --git a/pyrasterframes/src/main/python/docs/nodata-handling.pymd b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
index c55243a41..568da4a75 100644
--- a/pyrasterframes/src/main/python/docs/nodata-handling.pymd
+++ b/pyrasterframes/src/main/python/docs/nodata-handling.pymd
@@ -2,13 +2,13 @@
 
 ## What is NoData?
 
-In raster operations, the preservation and correct processing of missing observations is very important. In [most DataFrames and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. A great deal of raster data is stored for space efficiency. This typically leads to use of integral values and a "sentinel" value to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value.
+In raster operations, the preservation and correct processing of missing observations is very important. In [most DataFrames and scientific computing](https://www.oreilly.com/learning/handling-missing-data), the idea of missing data is expressed as a `null` or `NaN` value. However, a great deal of raster data is stored for space efficiency, which typically leads to use of integral values with a ["sentinel" value](https://en.wikipedia.org/wiki/Sentinel_value) designated to represent missing observations. This sentinel value varies across data products and is usually called the "NoData" value.
 
 RasterFrames provides a variety of functions to inspect and manage NoData within _tiles_.
 
 ## Cell Types
 
-To understand how NoData is handled in RasterFrames, we first need to understand the different underlying types of data called cell types. The cell types are GeoTrellis `CellType`s, so the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values) is a valuable resource on how these are defined.
+To understand how NoData is handled in RasterFrames, we first need to understand the different underlying types of data called cell types. RasterFrames cell types are GeoTrellis `CellType`s, so the [GeoTrellis documentation](https://geotrellis.readthedocs.io/en/latest/guide/core-concepts.html?#working-with-cell-values) is a valuable resource on how these are defined.
 
 ```python setup, echo=False
 import pyrasterframes
@@ -52,7 +52,7 @@ ct = CellType('uint16raw')
 ct, ct.is_floating_point(), ct.has_no_data()
 ```
 
-We can see that for the above data source, there is no defined NoData value. This means that each value is interpreted as a valid observation. Often such data is meant to be combined with another band indicating the quality of observations at each location. The lack of NoData is indicated by the `raw` at the end of the type name. Consider also the `uint16` cell type.
+We can see that for the above data source, there is no defined NoData value. This means that each value is interpreted as a valid observation. Often such data is meant to be combined with another band indicating the quality of observations at each location. The lack of NoData is indicated by the `raw` at the end of the type name. Consider for contrast the `uint16` cell type.
 
 ```python, non_raw_example
 from pyrasterframes.rf_types import CellType
@@ -60,7 +60,7 @@ ct = CellType('uint16')
 ct, ct.is_floating_point(), ct.has_no_data(), ct.no_data_value()
 ```
 
-In this case, the minimum value of 0 is designated as the NoData value. For integral-valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with a `ud`.
+In this case, the minimum value of 0 is designated as the NoData value. For integral-valued cell types, the NoData is typically zero, the maximum, or the minimum value for the underlying data type. The NoData value can also be a user-defined value. In that case the value is designated with `ud`.
 
 ```python, cell_type_name
 CellType.uint16().with_no_data_value(99).cell_type_name
@@ -75,7 +75,7 @@ print(CellType.float32().with_no_data_value(-99.9).no_data_value())
 
 ## Masking
 
-Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no defined NoData. The quality information is in a separate file called the scene classification (SCL), which delineates areas of missing data and probable clouds. For more information on that, see the [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm). Figure 3 tells us how to interpret the scene classification. For this example, we will exclude NoData, defective pixels, probable clouds, and cirrus clouds: values 0, 1, 8, 9, and 10.
+Let's continue the example above with Sentinel-2 data. Band 2 is blue and has no defined NoData. The quality information is in a separate file called the scene classification (SCL), which delineates areas of missing data and probable clouds. For more information on this, see the [Sentinel-2 algorithm overview](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm). Figure 3 tells us how to interpret the scene classification. For this example, we will exclude NoData, defective pixels, probable clouds, and cirrus clouds: values 0, 1, 8, 9, and 10.
 
 ![Sentinel-2 Scene Classification Values](static/sentinel-2-scene-classification-labels.png)
 
@@ -97,7 +97,7 @@ unmasked.printSchema()
 unmasked.select(rf_cell_type('blue'), rf_cell_type('scl')).distinct().show()
 ```
 
-Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new _tile_ columns that are indicators of unwanted pixels, as defined above. Since the mask column is bit type, the addition is equivalent to a logical or, so the true values are 1.
+Drawing on @ref:[local map algebra](local-algebra.md) techniques, we will create new _tile_ columns that are indicators of unwanted pixels, as defined above. Since the mask column is an integer type, the addition is equivalent to a logical or, so the boolean true values are 1.
 
 ```python, def_mask, results='raw'
 from pyspark.sql.functions import lit
@@ -139,7 +139,7 @@ masked = one_mask.withColumn('blue_masked',
                  .drop('nodata', 'defect', 'cloud8', 'cloud9', 'cirrus', 'blue')
 ```
 
-We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the bit-type `mask` _tile_ to ensure our logic is correct.
+We can verify that the number of NoData cells in the resulting `blue_masked` column matches the total of the boolean `mask` _tile_ to ensure our logic is correct.
 
 ```python, show_masked, results='raw'
 masked.select(rf_no_data_cells('blue_masked'), rf_tile_sum('mask')).show(10)
@@ -160,7 +160,7 @@ display(sample[1])
 
 ## NoData and Local Arithmetic
 
-Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behaviour, lets create two _tiles_. One _tile_ will have values of 0 and 1, and the other will have values of just 0.
+Let's now explore how the presence of NoData affects @ref:[local map algebra](local-algebra.md) operations. To demonstrate the behavior, lets create two _tiles_. One _tile_ will have values of 0 and 1, and the other will have values of just 0.
 
 ```python, local_arith
 tile_size = 100
@@ -212,7 +212,7 @@ rf = spark.createDataFrame([Row(tile=x)])
 display(x)
 ```
 
-First, we mask the value of 1 by making a new column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a _tile_ with the cell type 'uint16ud2'.
+First, we mask the value of 1 by making a new _tile_ column with the user defined cell type 'uint16ud1'. Then, we mask out the value of two by making a _tile_ column with the cell type 'uint16ud2'.
 
 ```python, masked_rf
 def get_nodata_ct(nd_val):
@@ -234,7 +234,7 @@ Let's look at the new _tiles_ we created. The _tile_ named `tile_nd_1` has the 1
 display(collected[0].tile_nd_1)
 ```
 
-And the _tile_ named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the _tile_ by setting a new user defined NoData value to `tile_nd_1` the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
+And the _tile_ named `tile_nd_2` has the values of 1 and 2 masked out. This is because we created the _tile_ by setting a new user defined NoData value to `tile_nd_1`, and the values previously masked out in `tile_nd_1` stayed masked when creating `tile_nd_2`.
 
 ```python, display_5
 display(collected[0].tile_nd_2)
@@ -243,7 +243,7 @@ display(collected[0].tile_nd_2)
 
 ## Combining Tiles with Different Data Types
 
-RasterFrames supports having _tile_ columns with multiple cell types in a single DataFrame. It is important to understand how these different cell types interact.
+RasterFrames supports having _tile_ columns with different cell types in a single DataFrame. It is important to understand how these different cell types interact.
 
 Let's first create a RasterFrame that has columns of `float` and `int` cell type.
 
@@ -255,7 +255,7 @@ rf = spark.createDataFrame([Row(x=x, y=y)])
 rf.select(rf_cell_type('x'), rf_cell_type('y')).distinct().show()
 ```
 
-When performing a local operation between _tile_ columns with cell types `int` and type `float`, the resulting _tile_ cell type will be `float`. In local algebra over two _tiles_ of different "sized" cell types, the resulting cell type will be the largest of the two input _tiles'_ cell types.
+When performing a local operation between _tile_ columns with cell types `int` and  `float`, the resulting _tile_ cell type will be `float`. In local algebra over two _tiles_ of different "sized" cell types, the resulting cell type will be the larger of the two input _tiles'_ cell types.
 
 ```python, show_2, results='raw'
 rf.select(
@@ -265,7 +265,7 @@ rf.select(
     ).show(1)
 ```
 
-Combining _tile_ columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create 2 _tile_ columns: one with a NoData value of 1, and one with a NoData value of 2.
+Combining _tile_ columns of different cell types gets a little trickier when user defined NoData cell types are involved. Let's create two _tile_ columns: one with a NoData value of 1, and one with a NoData value of 2 (using our previously defined `get_nodata_ct` function).
 
 ```python, create_ones
 x_nd_1 = Tile((np.ones((100, 100))*3), get_nodata_ct(1))
diff --git a/pyrasterframes/src/main/python/docs/raster-read.pymd b/pyrasterframes/src/main/python/docs/raster-read.pymd
index 18228e414..3a505993d 100644
--- a/pyrasterframes/src/main/python/docs/raster-read.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-read.pymd
@@ -1,8 +1,8 @@
 # Reading Raster Data
 
-```python, echo=False
-import pyrasterframes.rf_ipython
+```python, setup, echo=False
 from IPython.display import display
+import pyrasterframes.rf_ipython
 import pandas as pd
 from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
@@ -26,18 +26,16 @@ The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https:/
 
 Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a @ref:[_CRS_][CRS], a spatial @ref:[_extent_][Extent] measured in that CRS, and a two-dimensional array of numeric values called a @ref:[_tile_][Tile].
 
-```python, raster_parts
+```python, select_crs
 crs = rf.select(rf_crs("proj_raster").alias("value")).first()
+print("CRS", crs.value.crsProj4)
+```
 
+```python, raster_parts, results='raw'
 parts = rf.select(
     rf_extent("proj_raster").alias("extent"),
     rf_tile("proj_raster").alias("tile")
 )
-
-print("CRS", crs.value.crsProj4)
-```
-
-```python,  show_parts, results='raw'
 parts.show(5, truncate=False)
 ```
 
@@ -115,7 +113,7 @@ MODIS data products are delivered on a regular, consistent grid, making identifi
 
 For example, MODIS data right above the equator is all grid coordinates with `v07`.
 
-```python catalog_filtering, results='raw'
+```python, catalog_filtering, results='raw'
 equator = modis_catalog.where(F.col('gid').like('%v07%')) 
 equator.select('date', 'gid') \
     .show(10, truncate=False)
@@ -170,7 +168,7 @@ When reading a multiband raster or a _catalog_ describing multiband rasters, you
 
 For example, we can read a four-band (red, green, blue, and near-infrared) image as follows. The individual rows of the resulting DataFrame still represent distinct spatial extents, with a projected raster column for each band specified by `band_indexes`.
 
-```python Multiband
+```python, multiband
 mb = spark.read.raster('s3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
                        band_indexes=[0, 1, 2, 3],
                       )
@@ -183,7 +181,7 @@ You can also pass a `catalog` and `band_indexes` together into the `raster` read
 
 Here is a trivial example with a _catalog_ over multiband rasters. We specify two columns containing URIs and two bands, resulting in four projected raster columns.
 
-```python multiband_catalog
+```python, multiband_catalog
 import pandas as pd
 mb_cat = pd.DataFrame([
     {'foo': 's3://s22s-test-geotiffs/naip/m_3807863_nw_17_1_20160620.tif',
@@ -206,7 +204,7 @@ mb2.printSchema()
 
 A GeoTrellis catalog is a set of GeoTrellis layers. We can read a DataFrame giving details of the content of a catalog using the syntax below. The scheme is typically `hdfs` or `file`, or a cloud storage provider like `s3`.
 
-```python, evaluate=False
+```python, geotrellis_cat, evaluate=False
 gt_cat = spark.read.geotrellis_catalog('scheme://path-to-gt-catalog')
 ```
 
@@ -214,7 +212,7 @@ gt_cat = spark.read.geotrellis_catalog('scheme://path-to-gt-catalog')
 
 The catalog will give details on the individual layers available for query. We can read each layer with the URI to the catalog, the layer name, and the desired zoom level.
 
-```python, evaluate=False
+```python, geotrellis, evaluate=False
 gt_layer = spark.read.geotrellis(path='scheme://path-to-gt-catalog', layer=layer_name, zoom=zoom_level)
 ```
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index 39ae535c2..09a8992f1 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -100,24 +100,34 @@ def _safe_tile_to_html(t):
     pd.set_option('display.max_colwidth', default_max_colwidth)
     return return_html
 
+def spark_df_to_markdown(df, num_rows=5, truncate=True, vertical=False):
+    from pyrasterframes import RFContext
+    return RFContext.active().call("_dfToMarkdown", df._jdf, num_rows, truncate)
 
 try:
     from IPython import get_ipython
+    from IPython.display import display_png, display_markdown, display
     # modifications to currently running ipython session, if we are in one; these enable nicer visualization for Pandas
     if get_ipython() is not None:
         import pandas
         import pyspark.sql
+        from pyrasterframes.rf_types import Tile
         ip = get_ipython()
+
         html_formatter = ip.display_formatter.formatters['text/html']
         html_formatter.for_type(pandas.DataFrame, pandas_df_to_html)
 
-        # See if we're in documentation mode and register a custom show implementation.
-        if ip.__class__.__name__ == 'InProcessInteractiveShell':
-            def _df_show_markdown(df, numRows=5, truncate=True, vertical=False):
-                from pyrasterframes import RFContext
-                print(RFContext.active().call("_dfToMarkdown", df._jdf, numRows, truncate))
+        markdown_formatter = ip.display_formatter.formatters['text/markdown']
+        html_formatter.for_type(pyspark.sql.DataFrame, spark_df_to_markdown)
 
-            pyspark.sql.DataFrame.show = _df_show_markdown
+        Tile.show = lambda t: display_png(t._repr_png_(), raw=True)
+
+        # See if we're in documentation mode and register a custom show implementation.
+        if 'InProcessInteractiveShell' in ip.__class__.__name__:
+            pyspark.sql.DataFrame._repr_markdown_ = spark_df_to_markdown
+            pyspark.sql.DataFrame.show = lambda df, num_rows=5, truncate=True: display_markdown(spark_df_to_markdown(df, num_rows, truncate), raw=True)
 
-except ImportError:
+except ImportError as e:
+    print(e)
+    raise e
     pass
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
index 0875ec783..60b99cecb 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_types.py
@@ -308,7 +308,7 @@ def __eq__(self, other):
             return False
 
     def __str__(self):
-        return "Tile(dimensions={}, cell_type={}, cells={})" \
+        return "Tile(dimensions={}, cell_type={}, cells=\n{})" \
             .format(self.dimensions(), self.cell_type, self.cells)
 
     def __repr__(self):
@@ -358,7 +358,6 @@ def dimensions(self):
         """ Return a list of cols, rows as is conventional in GeoTrellis and RasterFrames."""
         return [self.cells.shape[1], self.cells.shape[0]]
 
-
     def _repr_png_(self):
         """Provide default PNG rendering in IPython and Jupyter"""
         from pyrasterframes.rf_ipython import tile_to_png

From 07ee2332d751a3c8b407b8ed576030543ff2f9b4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 14:09:25 -0400
Subject: [PATCH 371/380] Edits: aggregation.pymd, supervised-learning.pymd,
 unsupervised-learning.pymd, time-series.pymd

---
 .../awspds/MODISCatalogDataSource.scala       |  1 +
 .../src/main/python/docs/aggregation.pymd     | 37 ++++++++++---------
 .../main/python/docs/supervised-learning.pymd | 17 ++++-----
 .../src/main/python/docs/time-series.pymd     |  6 +--
 .../python/docs/unsupervised-learning.pymd    |  8 ++--
 pyrasterframes/src/main/python/setup.py       |  2 +-
 6 files changed, 37 insertions(+), 34 deletions(-)

diff --git a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
index 31860f46a..347d82d29 100644
--- a/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
+++ b/experimental/src/main/scala/org/locationtech/rasterframes/experimental/datasource/awspds/MODISCatalogDataSource.scala
@@ -93,6 +93,7 @@ object MODISCatalogDataSource extends LazyLogging with ResourceCacheSupport {
     "2018-03-12",
     "2018-03-13",
     "2018-03-14",
+    "2018-03-15",
     "2018-05-16",
     "2018-05-17",
     "2018-05-18",
diff --git a/pyrasterframes/src/main/python/docs/aggregation.pymd b/pyrasterframes/src/main/python/docs/aggregation.pymd
index 310868209..586458bcf 100644
--- a/pyrasterframes/src/main/python/docs/aggregation.pymd
+++ b/pyrasterframes/src/main/python/docs/aggregation.pymd
@@ -11,13 +11,13 @@ import os
 spark = create_rf_spark_session()
 ```
 
-There are 3 types of aggregate functions: _tile_ aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[tile aggregate functions](reference.md#tile-statistics), we are computing a statistical summary per row of a _tile_ column in a DataFrame. In the @ref:[DataFrame aggregate functions](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the @ref:[element-wise local aggregate functions](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of _tiles_.
+There are three types of aggregate functions: _tile_ aggregate, DataFrame aggregate, and element-wise local aggregate. In the @ref:[tile aggregate functions](reference.md#tile-statistics), we are computing a statistical summary per row of a _tile_ column in a DataFrame. In the @ref:[DataFrame aggregate functions](reference.md#aggregate-tile-statistics), we are computing statistical summaries over all of the cell values *and* across all of the rows in the DataFrame or group. In the @ref:[element-wise local aggregate functions](reference.md#tile-local-aggregate-statistics), we are computing the element-wise statistical summary across a DataFrame or group of _tiles_.
 
 ## Tile Mean Example
 
-We can illustrate these differences in computing an aggregate mean. First, we create a sample DataFrame of 2 _tiles_ where the first _tile_ is composed of 25 values of 1.0 and the second _tile_ is composed of 25 values of 3.0.
+We can illustrate aggregate differences by computing an aggregate mean. First, we create a sample DataFrame of 2 _tiles_ where the first _tile_ is composed of 25 values of 1.0 and the second _tile_ is composed of 25 values of 3.0.
 
-```python, sql_dataframe, results='raw'
+```python, sql_dataframe
 import pyspark.sql.functions as F
 
 rf = spark.sql("""
@@ -26,33 +26,36 @@ UNION
 SELECT 2 as id, rf_local_multiply(rf_make_ones_tile(5, 5, 'float32'), 3) as tile
 """)
 
-rf.select("id", rf_render_matrix("tile")).show(truncate=False)
+tiles = rf.select("tile").collect()
+print(tiles[0]['tile'].cells)
+print(tiles[1]['tile'].cells)
 ```
 
-
-In this code block, we are using the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the _tile_ aggregate mean of cells in each row of column `tile`. The mean of each _tile_ is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
+We use the @ref:[`rf_tile_mean`](reference.md#rf-tile-mean) function to compute the _tile_ aggregate mean of cells in each row of column `tile`. The mean of each _tile_ is computed separately, so the first mean is 1.0 and the second mean is 3.0. Notice that the number of rows in the DataFrame is the same before and after the aggregation.
 
 ```python, tile_mean, results='raw'
-rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show(truncate=False)
+rf.select(F.col('id'), rf_tile_mean(F.col('tile'))).show()
 ```
 
-In this code block, we are using the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
+We use the @ref:[`rf_agg_mean`](reference.md#rf-agg-mean) function to compute the DataFrame aggregate, which averages 25 values of 1.0 and 25 values of 3.0, across the fifty cells in two rows. Note that only a single row is returned since the average is computed over the full DataFrame.
 
 ```python, agg_mean, results='raw'
 rf.agg(rf_agg_mean(F.col('tile'))).show()
 ```
 
-In this code block, we are using the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. In this example it is computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the _tile_.
+We use the @ref:[`rf_agg_local_mean`](reference.md#rf-agg-local-mean) function to compute the element-wise local aggregate mean across the two rows. For this aggregation, we are computing the mean of one value of 1.0 and one value of 3.0 to arrive at the element-wise mean, but doing so twenty-five times, one for each position in the _tile_.
 
-To compute an element-wise local aggregate, _tiles_ need have the same dimensions as in the example below where both _tiles_ have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal _tile_ dimensions, we would get a runtime error.
+To compute an element-wise local aggregate, _tiles_ need to have the same dimensions. In this case, both _tiles_ have 5 rows and 5 columns. If we tried to compute an element-wise local aggregate over the DataFrame without equal _tile_ dimensions, we would get a runtime error.
 
-```python, local_mean, results='raw'
-rf.agg(rf_agg_local_mean(F.col('tile')).alias("local_mean")).select(rf_render_matrix("local_mean")).show(truncate=False)
+```python, local_mean
+t = rf.agg(rf_agg_local_mean(F.col('tile')).alias('local_mean')) \
+    .collect()[0]['local_mean']
+print(t.cells)    
 ```
 
 ## Cell Counts Example
 
-We can also count the total number of data and NoData cells over all the _tiles_ in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are 3,842,290 data cells and 1,941,734 NoData cells in this DataFrame. See section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
+We can also count the total number of data and NoData cells over all the _tiles_ in a DataFrame using @ref:[`rf_agg_data_cells`](reference.md#rf-agg-data-cells) and @ref:[`rf_agg_no_data_cells`](reference.md#rf-agg-no-data-cells). There are ~3.8 million data cells and ~1.9 million NoData cells in this DataFrame. See the section on @ref:["NoData" handling](nodata-handling.md) for additional discussion on handling missing data.
 
 ```python, cell_counts, results='raw'
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/MCD43A4.006/11/05/2018233/MCD43A4.A2018233.h11v05.006.2018242035530_B02.TIF')
@@ -86,7 +89,7 @@ rf.agg(rf_agg_stats('proj_raster').alias('stats')) \
     .show()
 ```
 
-The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks, has unequal _tile_ dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
+The @ref:[`rf_agg_local_stats`](reference.md#rf-agg-local-stats) function computes the element-wise local aggregate statistical summary as shown below. The DataFrame used in the previous two code blocks has unequal _tile_ dimensions, so a different DataFrame is used in this code block to avoid a runtime error.
 
 ```python, agg_local_stats
 rf = spark.sql("""
@@ -106,7 +109,7 @@ for r in agg_local_stats:
 
 ## Histogram
 
-The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of _tile_ and outputs a `bins` array with the schema below. In the graph below, we have plotted `value` on the x-axis and `count` on the y-axis to create the histogram. There are 100 rows of _tile_ in this DataFrame, but this histogram is just computed for the _tile_ in the first row.
+The @ref:[`rf_tile_histogram`](reference.md#rf-tile-histogram) function computes a count of cell values within each row of _tile_ and outputs a `bins` array with the schema below. In the graph below, we have plotted each bin's `value` on the x-axis and `count` on the y-axis for the _tile_ in the first row of the DataFrame.
 
 
 ```python, tile_histogram
@@ -118,8 +121,8 @@ hist_df = rf.select(rf_tile_histogram('proj_raster')['bins'].alias('bins'))
 hist_df.printSchema()
 
 bins_row = hist_df.first()
-values = [int(row['value']) for row in bins_row.bins]
-counts = [int(row['count']) for row in bins_row.bins]
+values = [int(bin['value']) for bin in bins_row.bins]
+counts = [int(bin['count']) for bin in bins_row.bins]
 
 plt.hist(values, weights=counts, bins=100)
 plt.show()
diff --git a/pyrasterframes/src/main/python/docs/supervised-learning.pymd b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
index 46ba4f6c7..e3f73ace4 100644
--- a/pyrasterframes/src/main/python/docs/supervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/supervised-learning.pymd
@@ -20,7 +20,7 @@ spark = create_rf_spark_session()
 
 ## Create and Read Raster Catalog
 
-The first step is to create a Spark DataFrame of our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
+The first step is to create a Spark DataFrame containing our imagery data. To achieve that we will create @ref:[a catalog DataFrame](raster-catalogs.md#creating-a-catalog). In the catalog, each row represents a distinct area and time, and each column is the URI to a band's image product. In this example our catalog just has one row. After reading the catalog, the resulting Spark DataFrame may have many rows per URI, with a column corresponding to each band.
 
 The imagery for feature data will come from [eleven bands of 60 meter resolution Sentinel-2](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) imagery. We also will use the [scene classification (SCL)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm) data to identify high quality, non-cloudy pixels.
 
@@ -65,7 +65,7 @@ The land classification labels are based on a small set of hand drawn polygons i
 
 We will create a very small Spark DataFrame of the label shapes and then join it to the raster DataFrame. Such joins are typically expensive, but in this case both datasets are quite small. To speed up the join for the small vector DataFrame, we put the `broadcast` hint on it, which will tell Spark to put a copy of it on each Spark executor.
 
-After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tile_ cells are the `id` property of the GeoJSON; which we will use as labels in our supervised learning task. In areas where the geometry does not intersect, the cells will contain a NoData.
+After the raster and vector data are joined, we will convert the vector shapes into _tiles_ using the @ref:[`rf_rasterize`](reference.md#rf-rasterize) function. This procedure is sometimes called "burning in" a geometry into a raster. The values in the resulting _tile_ cells are the `id` property of the GeoJSON, which we will use as labels in our supervised learning task. In areas where the geometry does not intersect, the cells will contain NoData.
 
 ```python join_and_rasterize
 crses = df.select('crs.crsProj4').distinct().collect()
@@ -87,7 +87,7 @@ FROM df_joined
 
 ## Masking Poor Quality Cells
 
-To filter only for good quality pixels, we follow roughly the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually setting NoData values in the unwanted cells of any of the imagery bands, we will just on filter out the mask cell values later in the process.
+To filter only for good quality pixels, we follow roughly the same procedure as demonstrated in the @ref:[quality masking](nodata-handling.md#masking) section of the chapter on NoData. Instead of actually setting NoData values in the unwanted cells of any of the imagery bands, we will just filter out the mask cell values later in the process.
 
 ```python, make_mask
 from pyspark.sql.functions import lit
@@ -126,9 +126,9 @@ from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 from pyspark.ml import Pipeline
 ```
 
-SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the _tiles_ into a single row per cell or pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). If a _tile_ cell contains a NoData it will become a null value after the exploder stage. Then we filter out any rows that missing or null values, which will cause an error during training. Finally we use the SparkML `VectorAssembler` to create that `Vector`.
+SparkML requires that each observation be in its own row, and those observations be packed into a single [`Vector`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.linalg) object. The first step is to "explode" the _tiles_ into a single row per cell or pixel with the `TileExploder` (see also @ref:[`rf_explode_tiles`](reference.md#rf_explode_tiles)). If a _tile_ cell contains a NoData it will become a null value after the exploder stage. Then we use the `NoDataFilter` to filter out any rows that missing or null values, which will cause an error during training. Finally we use the SparkML `VectorAssembler` to create that `Vector`.
 
-It is worth discussing a couple of interesting things about the `NoDataFilter`. First, we filter out missing values in the mask column. Recall above we set undesirable pixels to NoData, so they will be removed at this stage. The other column for the `NoDataFilter` is the `label` column. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
+Recall above we set undesirable pixels to NoData, so the `NoDataFilter` will remove them at this stage. We apply the filter to the `mask` column and the `label` column, the latter being used during training. When it is time to score the model, the pipeline will ignore the fact that there is no `label` column on the input DataFrame.
 
 ```python, transformers
 exploder = TileExploder()
@@ -156,7 +156,7 @@ pipeline.getStages()
 
 ## Train the Model
 
-Push the "go button"! This will actually run each step of the Pipeline we created including fitting the decision tree model. We filter the DataFrame for only _tiles_ intersecting the label raster, because the label shapes are relatively sparse over the imagery. It would be logically equivalent to either include or exclude this, but it is more efficient to do this filter because it will mean less data going into the pipeline.
+The next step is to actually run each step of the Pipeline we created, including fitting the decision tree model. We filter the DataFrame for only _tiles_ intersecting the label raster because the label shapes are relatively sparse over the imagery. It would be logically equivalent to either include or exclude thi step, but it is more efficient to filter because it will mean less data going into the pipeline.
 
 ```python, train
 model = pipeline.fit(df_mask.filter(rf_tile_sum('label') > 0).cache())
@@ -173,14 +173,13 @@ prediction_df.printSchema()
 
 eval = MulticlassClassificationEvaluator(predictionCol=classifier.getPredictionCol(),
 										 labelCol=classifier.getLabelCol(),
-										 metricName='accuracy',
-)
+										 metricName='accuracy')
 
 accuracy = eval.evaluate(prediction_df)
 print("\nAccuracy:", accuracy)
 ```
 
-As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. The categories down the rows are the predictions, and the truth labels are across the columns.
+As an example of using the flexibility provided by DataFrames, the code below computes and displays the confusion matrix. 
 
 ```python, confusion_mtrx, results='raw'
 prediction_df.groupBy(classifier.getPredictionCol()) \
diff --git a/pyrasterframes/src/main/python/docs/time-series.pymd b/pyrasterframes/src/main/python/docs/time-series.pymd
index 68bda9017..4c6e7cfc2 100644
--- a/pyrasterframes/src/main/python/docs/time-series.pymd
+++ b/pyrasterframes/src/main/python/docs/time-series.pymd
@@ -95,8 +95,8 @@ time_series = rf_ndvi \
         .withColumn('ndvi_wt', rf_tile_sum('ndvi_masked')) \
         .withColumn('wt', rf_data_cells('ndvi_masked')) \
         .groupby(year('acquisition_date').alias('year'), weekofyear('acquisition_date').alias('week')) \
-        .agg(sql_sum('ndvi_wt').alias('ndvi_wt'), sql_sum('wt').alias('wt')) \
-        .withColumn('ndvi', col('ndvi_wt') / col('wt'))
+        .agg(sql_sum('ndvi_wt').alias('ndvi_wt_wk'), sql_sum('wt').alias('wt_wk')) \
+        .withColumn('ndvi', col('ndvi_wt_wk') / col('wt_wk'))
 time_series.printSchema()
 time_series.persist()
 ```
@@ -114,4 +114,4 @@ plt.ylabel('NDVI')
 plt.title('Cuyahoga Valley NP Green-up')
 ```
 
-We can see two fairly clear elbows in the curve at week 17 and week 21, indicating the start and end of the green up period. Estimation of such parameters is one technique [phenology](https://en.wikipedia.org/wiki/Phenology) researchers use to monitor changes in climate and environment!
+We can see two fairly clear elbows in the curve at week 17 and week 21, indicating the start and end of the green up period. Estimation of such parameters is one technique [phenology](https://en.wikipedia.org/wiki/Phenology) researchers use to monitor changes in climate and environment.
diff --git a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
index a89d3826f..c93492fa3 100644
--- a/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
+++ b/pyrasterframes/src/main/python/docs/unsupervised-learning.pymd
@@ -14,12 +14,12 @@ import os
 
 spark = create_rf_spark_session()
 
-import pandas as pd
 ```
 
-We import various Spark components that we need to construct our `Pipeline`.
+We import various Spark components needed to construct our `Pipeline`.
 
 ```python, imports, echo=True
+import pandas as pd
 from pyrasterframes import TileExploder
 from pyrasterframes.rasterfunctions import rf_assemble_tile, rf_crs, rf_extent, rf_tile, rf_dimensions
 
@@ -37,7 +37,7 @@ filenamePattern = "L8-B{}-Elkton-VA.tiff"
 catalog_df = pd.DataFrame([
     {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
 ])
-df = spark.read.raster(catalog=catalog_df.to_csv(index=None), catalog_col_names=catalog_df.columns)
+df = spark.read.raster(catalog=catalog_df, catalog_col_names=catalog_df.columns)
 df = df.select(
     rf_crs(df.b1).alias('crs'),
     rf_extent(df.b1).alias('extent'),
@@ -95,7 +95,7 @@ clustered = model.transform(df)
 clustered.show(8)
 ```
 
-If we want to inspect the model statistics, the SparkML API requires us to go through this unfortunate contortion:
+If we want to inspect the model statistics, the SparkML API requires us to go through this unfortunate contortion to access the clustering results:
 
 ```python, cluster_stats
 cluster_stage = model.stages[2]
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 95e7d3fe5..43a11ebae 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -97,7 +97,7 @@ def run(self):
         import traceback
         import pweave
         bad_words = ["Error"]
-        pweave.rcParams["chunk"]["defaultoptions"].update({'wrap': False, 'dpi': 100})
+        pweave.rcParams["chunk"]["defaultoptions"].update({'wrap': False, 'dpi': 175})
         if self.format == 'markdown':
             pweave.PwebFormats.formats['markdown'] = {
                 'class': PegdownMarkdownFormatter,

From e62f5fed78c93883cfbfe08062e833890c1448a0 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 15 Aug 2019 14:46:46 -0400
Subject: [PATCH 372/380] Fix setup.py dependency, fix rf_rasterize signature
 regression in unit test

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/setup.py       | 20 ++++++++--------
 .../src/main/python/tests/VectorTypesTests.py | 23 +++++++++++++++----
 2 files changed, 28 insertions(+), 15 deletions(-)

diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index 21657dfef..d900b88d2 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -47,16 +47,6 @@ def _divided(msg):
     return divider + '\n' + msg + '\n' + divider
 
 
-from pweave import PwebPandocFormatter
-
-class PegdownMarkdownFormatter(PwebPandocFormatter):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    # Pegdown doesn't support the width and label options.
-    def make_figure_string(self, figname, width, label, caption=""):
-        return "![%s](%s)" % (caption, figname)
-
 class PweaveDocs(distutils.cmd.Command):
     """A custom command to run documentation scripts through pweave."""
     description = 'Pweave PyRasterFrames documentation scripts'
@@ -67,6 +57,16 @@ class PweaveDocs(distutils.cmd.Command):
         ('quick=', 'q', 'Check to see if the source file is newer than existing output before building. Defaults to `False`.')
     ]
 
+    from pweave import PwebPandocFormatter
+
+    class PegdownMarkdownFormatter(PwebPandocFormatter):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+
+        # Pegdown doesn't support the width and label options.
+        def make_figure_string(self, figname, width, label, caption=""):
+            return "![%s](%s)" % (caption, figname)
+
     def initialize_options(self):
         """Set default values for options."""
         # Each user option must be listed here with their default value.
diff --git a/pyrasterframes/src/main/python/tests/VectorTypesTests.py b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
index ba70f7520..e31f26b43 100644
--- a/pyrasterframes/src/main/python/tests/VectorTypesTests.py
+++ b/pyrasterframes/src/main/python/tests/VectorTypesTests.py
@@ -97,7 +97,7 @@ def some_point(g):
             pandas_df_out.poly_len.values
         )
 
-    def test_rasterize(self):
+    def test_geometry_udf(self):
         from geomesa_pyspark.types import PolygonUDT
         # simple test that raster contents are not invalid
 
@@ -117,12 +117,25 @@ def area(g):
         area_result = area.collect()
         self.assertTrue(all([r[0] for r in area_result]))
 
-        cols = 194
-        rows = 250
-        with_raster = with_poly.withColumn('rasterized', rf_rasterize('poly', 'geometry', lit(16), cols, rows))
-        # expect a 4 by 4 cell
+    def test_rasterize(self):
+        from geomesa_pyspark.types import PolygonUDT
+
+        @udf(PolygonUDT())
+        def buffer(g, d):
+            return g.buffer(d)
+
+        # start with known polygon, the tile extents, **negative buffered**  by 10 cells
+        buf_cells = 10
+        with_poly = self.rf.withColumn('poly', buffer(self.rf.geometry, lit(-15 * buf_cells)))  # cell res is 15x15
+
+        # rasterize value 16 into buffer shape.
+        cols = 194  # from dims of tile
+        rows = 250  # from dims of tile
+        with_raster = with_poly.withColumn('rasterized',
+                                          rf_rasterize('poly', 'geometry', lit(16), lit(cols), lit(rows)))
         result = with_raster.select(rf_tile_sum(rf_local_equal_int(with_raster.rasterized, 16)),
                                     rf_tile_sum(with_raster.rasterized))
+        #
         expected_burned_in_cells = (cols - 2 * buf_cells) * (rows - 2 * buf_cells)
         self.assertEqual(result.first()[0], float(expected_burned_in_cells))
         self.assertEqual(result.first()[1], 16. * expected_burned_in_cells)

From 61ad464e92cdb28e240b312b91467a0fa1705585 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 14:55:56 -0400
Subject: [PATCH 373/380] Edits: languages.pymd & reference.pymd

---
 .../src/main/python/docs/languages.pymd       |  4 ++-
 .../src/main/python/docs/reference.pymd       | 28 +++++++++----------
 2 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index 624428c2a..b76a9cc9d 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -1,6 +1,6 @@
 # API Languages
 
-One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This tutorial is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single _tile_ in Tanzania.
+One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This documentation focuses on Python because it is the most commonly used language in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single _tile_ in Tanzania.
 
 ```python, imports, echo=False
 from pyspark.sql.functions import *
@@ -170,4 +170,6 @@ val result = red_nir_tiles_monthly_2017
   .agg(rf_agg_stats(rf_normalized_difference($"nir", $"red")) as "ndvi_stats")
   .orderBy("month")
   .select("month", "ndvi_stats.*")
+  
+result.show()  
 ```
diff --git a/pyrasterframes/src/main/python/docs/reference.pymd b/pyrasterframes/src/main/python/docs/reference.pymd
index 341100943..bc4ae3413 100644
--- a/pyrasterframes/src/main/python/docs/reference.pymd
+++ b/pyrasterframes/src/main/python/docs/reference.pymd
@@ -1,6 +1,6 @@
 # Function Reference
 
-RasterFrames provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module.
+RasterFrames provides a rich set of columnar function for processing geospatial raster data. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module.
 
 The convention in this document will be to define the function signature as below, with its return type, the function name, and named arguments with their types.
 
@@ -30,7 +30,7 @@ become available for use with DataFrames. You can view all of the available func
 
 ## Vector Operations
 
-Various LocationTech GeoMesa user-defined functions (UDFs) to deal with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
+Various LocationTech GeoMesa user-defined functions (UDFs) dealing with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
 
 RasterFrames provides some additional functions for vector geometry operations.
 
@@ -39,7 +39,7 @@ RasterFrames provides some additional functions for vector geometry operations.
     Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
 
-Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
+Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
 
 
 ### st_extent
@@ -76,7 +76,7 @@ Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_conv
 
     Tile rf_tile(ProjectedRasterTile proj_raster)
 
-Get the fully realized (non-lazy) `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
+Get the fully realized (non-lazy) `tile` from a `ProjectedRasterTile` struct column.
 
 ### rf_extent
 
@@ -96,7 +96,7 @@ Fetch CRS structure representing the coordinate reference system of a `Projected
 
     Struct rf_mk_crs(String crsText)   
 
-Construct a CRS structure from one of its string representations. Three froms are supported:
+Construct a CRS structure from one of its string representations. Three forms are supported:
 
 * [EPSG code](https://www.epsg-registry.org/): `EPSG:<integer>`
 * [Proj4 string](https://proj.org/): `+proj <proj4 parameters>`
@@ -256,7 +256,7 @@ The binary local map algebra functions have similar variations in the Python API
 
 The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
-Local map algebra operations for more than two tiles are implemented to work across rows in the data frame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
+Local map algebra operations for more than two `tile`s are implemented to work across rows in the DataFrame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
 
 ### rf_local_add
 
@@ -546,43 +546,43 @@ Aggregates over all of the rows in DataFrame of `tile` and returns a count of ea
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
-When these functions encounter a NoData in a cell location, it will be ignored. 
+When these functions encounter NoData in a cell location, it will be ignored. 
 
 ### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)
 
-Compute the cell-local maximum operation over Tiles in a column.
+Compute the cell-local maximum operation over `tile`s in a column.
 
 ### rf_agg_local_min
 
     Tile rf_agg_local_min(Tile tile)
 
-Compute the cell-local minimum operation over Tiles in a column.
+Compute the cell-local minimum operation over `tile`s in a column.
 
 ### rf_agg_local_mean
 
     Tile rf_agg_local_mean(Tile tile)
 
-Compute the cell-local mean operation over Tiles in a column.
+Compute the cell-local mean operation over `tile`s in a column.
 
 ### rf_agg_local_data_cells
 
     Tile rf_agg_local_data_cells(Tile tile)
 
-Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
+Compute the cell-local count of data cells over `tile`s in a column. Returned `tile` has a cell type of `int32`.
 
 ### rf_agg_local_no_data_cells
 
     Tile rf_agg_local_no_data_cells(Tile tile)
 
-Compute the cell-local count of NoData cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
+Compute the cell-local count of NoData cells over `tile`s in a column. Returned `tile` has a cell type of `int32`.
 
 ### rf_agg_local_stats
 
     Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
 
-Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of Tiles. Returns a struct of five `tile`s.
+Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of `tile`s. Returns a struct of five `tile`s.
 
 
 ## Converting Tiles
@@ -599,7 +599,7 @@ Create a row for each cell in `tile` columns. Many `tile` columns can be passed
 
     Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
 
-Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0.
+Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the `rf_explode-tiles`, but allows a random subset of the data to be selected. Parameter `sample_frac` should be between 0.0 and 1.0.
 
 ### rf_tile_to_array_int
 

From a40be5f5544a4e52b776edd370ea8b41beced0e2 Mon Sep 17 00:00:00 2001
From: "Jason T. Brown" <jason@astraea.earth>
Date: Thu, 15 Aug 2019 15:04:58 -0400
Subject: [PATCH 374/380] Move PegdownMarkdownFormatter to docs class

Signed-off-by: Jason T. Brown <jason@astraea.earth>
---
 pyrasterframes/src/main/python/docs/__init__.py | 11 +++++++++++
 pyrasterframes/src/main/python/setup.py         | 11 ++---------
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/pyrasterframes/src/main/python/docs/__init__.py b/pyrasterframes/src/main/python/docs/__init__.py
index 617ed41e7..e0aee3a26 100644
--- a/pyrasterframes/src/main/python/docs/__init__.py
+++ b/pyrasterframes/src/main/python/docs/__init__.py
@@ -21,6 +21,8 @@
 import os
 from pyspark.sql import DataFrame
 from pyrasterframes import RFContext
+from pweave import PwebPandocFormatter
+
 
 def docs_dir():
     return os.path.dirname(os.path.realpath(__file__))
@@ -38,3 +40,12 @@ def resource_dir():
 
 def resource_dir_uri():
     return 'file://' + resource_dir()
+
+
+class PegdownMarkdownFormatter(PwebPandocFormatter):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    # Pegdown doesn't support the width and label options.
+    def make_figure_string(self, figname, width, label, caption=""):
+        return "![%s](%s)" % (caption, figname)
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index d900b88d2..ee044b0b3 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -57,15 +57,6 @@ class PweaveDocs(distutils.cmd.Command):
         ('quick=', 'q', 'Check to see if the source file is newer than existing output before building. Defaults to `False`.')
     ]
 
-    from pweave import PwebPandocFormatter
-
-    class PegdownMarkdownFormatter(PwebPandocFormatter):
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-
-        # Pegdown doesn't support the width and label options.
-        def make_figure_string(self, figname, width, label, caption=""):
-            return "![%s](%s)" % (caption, figname)
 
     def initialize_options(self):
         """Set default values for options."""
@@ -96,6 +87,8 @@ def run(self):
         """Run pweave."""
         import traceback
         import pweave
+        from docs import PegdownMarkdownFormatter
+
         bad_words = ["Error"]
         pweave.rcParams["chunk"]["defaultoptions"].update({'wrap': False, 'dpi': 100})
         if self.format == 'markdown':

From 186e82a61c1a91c5dbfb856059cb516df0237a84 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 16:40:40 -0400
Subject: [PATCH 375/380] Final documentation tweaks.

---
 docs/src/main/paradox/_template/page.st       |  4 +++-
 .../src/main/python/docs/getting-started.pymd | 10 +++++++-
 pyrasterframes/src/main/python/docs/index.md  |  2 +-
 .../src/main/python/docs/languages.pymd       |  7 +++++-
 .../src/main/python/docs/raster-catalogs.pymd | 24 ++++++++++++++-----
 .../src/main/python/docs/raster-write.pymd    |  2 +-
 .../main/python/pyrasterframes/rf_ipython.py  |  7 +++++-
 7 files changed, 44 insertions(+), 12 deletions(-)

diff --git a/docs/src/main/paradox/_template/page.st b/docs/src/main/paradox/_template/page.st
index c264d9e7c..2a32dd170 100644
--- a/docs/src/main/paradox/_template/page.st
+++ b/docs/src/main/paradox/_template/page.st
@@ -31,6 +31,8 @@
     .md-left { float: left; }
     .md-right { float: right; }
     .md-clear { clear: both; }
+    table { font-size: 80%; }
+    code { font-size: 0.75em !important; }
   </style>
 </head>
 
@@ -132,7 +134,7 @@
               <div class="row site-footer-content">
                 <div class="small-12 text-center large-9 column">
                   <div class="copyright">
-                    <span class="text">&copy; $page.properties.("date.year")$
+                    <span class="text">Copyright &copy; $page.properties.("date.year")$
                     <a href="http://www.astraea.earth/">Astraea, Inc.</a></span>
                   </div>
                 </div>
diff --git a/pyrasterframes/src/main/python/docs/getting-started.pymd b/pyrasterframes/src/main/python/docs/getting-started.pymd
index 576994f6c..4051286ae 100644
--- a/pyrasterframes/src/main/python/docs/getting-started.pymd
+++ b/pyrasterframes/src/main/python/docs/getting-started.pymd
@@ -1,6 +1,14 @@
 # Getting Started
 
-There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell.  To get started you will need:
+@@@ note
+
+If you are new to Earth-observing imagery, you might consider looking at the [Concepts](concepts.md) section first.
+
+@@@
+
+There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it.  
+
+The simplest way to get started with RasterFrames is via the the Python shell.  To get started you will need:
 
 1. [Python](https://www.python.org/) installed. Version 3.6 or greater is recommended.
 1. [`pip`](https://pip.pypa.io/en/stable/installing/) installed. If you are using Python 3, `pip` may already be installed.
diff --git a/pyrasterframes/src/main/python/docs/index.md b/pyrasterframes/src/main/python/docs/index.md
index cc51e414c..d5f27a20b 100644
--- a/pyrasterframes/src/main/python/docs/index.md
+++ b/pyrasterframes/src/main/python/docs/index.md
@@ -39,8 +39,8 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 @@@ index
 * [Overview](description.md)
-* [Concepts](concepts.md)
 * [Getting Started](getting-started.md)
+* [Concepts](concepts.md)
 * [Raster Data I/O](raster-io.md)
 * [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)
diff --git a/pyrasterframes/src/main/python/docs/languages.pymd b/pyrasterframes/src/main/python/docs/languages.pymd
index b76a9cc9d..5111b7b74 100644
--- a/pyrasterframes/src/main/python/docs/languages.pymd
+++ b/pyrasterframes/src/main/python/docs/languages.pymd
@@ -30,7 +30,12 @@ red_nir_monthly_2017 = modis \
         col('B01').alias('red'),
         col('B02').alias('nir')
     ) \
-    .where((year('acquisition_date') == 2017) & (dayofmonth('acquisition_date') == 15) & (col('granule_id') == 'h21v09'))
+    .where(
+        (year('acquisition_date') == 2017) & 
+        (dayofmonth('acquisition_date') == 15) & 
+        (col('granule_id') == 'h21v09')
+    )
+red_nir_monthly_2017.printSchema()    
 ```
 
 ### Step 3: Read tiles
diff --git a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
index ef9b130f3..15baddab9 100644
--- a/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-catalogs.pymd
@@ -43,13 +43,19 @@ scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 
 # a pandas DF
-one_d_cat = pd.DataFrame({'B01': [scene1_B01, scene2_B01]})
+one_d_cat_pd = pd.DataFrame({'B01': [scene1_B01, scene2_B01]})
 
 # equivalent spark DF
-one_d_cat = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
+one_d_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
 
 # equivalent CSV string
-one_d_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
+one_d_cat_csv = '\n'.join(['B01', scene1_B01, scene2_B01])
+```
+
+This is what it looks like in DataFrame form:
+
+```python, view_spark_cat_1
+one_d_cat_df.show(truncate=False)
 ```
 
 ### Two-D
@@ -63,19 +69,25 @@ scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43
 scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B02.TIF"
 
 # Pandas DF
-my_cat = pd.DataFrame([
+two_d_cat_pd = pd.DataFrame([
     {'B01': [scene1_B01], 'B02': [scene1_B02]},
     {'B01': [scene2_B01], 'B02': [scene2_B02]}
 ])     
 
 # or
-my_cat_df = spark.createDataFrame([
+two_d_cat_df = spark.createDataFrame([
     Row(B01=scene1_B01, B02=scene1_B02),
     Row(B01=scene2_B01, B02=scene2_B02)
 ])
     
 # As CSV string
-my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
+tow_d_cat_csv = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
+```
+
+This is what it looks like in DataFrame form:
+
+```python, view_spark_cat_1
+two_d_cat_df.show(truncate=False)
 ```
 
 ## Using External Catalogs
diff --git a/pyrasterframes/src/main/python/docs/raster-write.pymd b/pyrasterframes/src/main/python/docs/raster-write.pymd
index 61491d007..df68c5e8e 100644
--- a/pyrasterframes/src/main/python/docs/raster-write.pymd
+++ b/pyrasterframes/src/main/python/docs/raster-write.pymd
@@ -93,7 +93,7 @@ os.remove(outfile)
 
 [GeoTrellis][GeoTrellis] is one of the key libraries upon which RasterFrames is built. It provides a Scala language API for working with geospatial raster data.  GeoTrellis defines a [tile layer storage](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html) format for persisting imagery mosaics. RasterFrames can write data from a `RasterFrameLayer` into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both @ref:[reading](raster-read.md#geotrellis-layers) and @ref:[writing](raster-write.md#geotrellis-layers) GeoTrellis layers.
 
-> An example is forthcoming. In the mean time referencing the [`GeoTrellisDataSourceSpec` test code](https://github.com/locationtech/rasterframes/blob/develop/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) may help
+> An example is forthcoming. In the mean time referencing the [`GeoTrellisDataSourceSpec` test code](https://github.com/locationtech/rasterframes/blob/develop/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) may help.
 
 ## Parquet
 
diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index 09a8992f1..d13eb0e4f 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -29,6 +29,8 @@ def tile_to_png(tile, fig_size=None):
     import io
     from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
     from matplotlib.figure import Figure
+    import matplotlib.colors as colors
+    from matplotlib import cm
 
     # Set up matplotlib objects
     nominal_size = 2  # approx full size for a 256x256 tile
@@ -39,7 +41,10 @@ def tile_to_png(tile, fig_size=None):
     canvas = FigureCanvas(fig)
     axis = fig.add_subplot(1, 1, 1)
 
-    axis.imshow(tile.cells)
+    data = tile.cells
+    norm = colors.LogNorm(data.min(), data.max())
+
+    axis.imshow(data, norm=norm, cmap=cm.get_cmap("RdBu"))
     axis.set_aspect('equal')
     axis.xaxis.set_ticks([])
     axis.yaxis.set_ticks([])

From 9a066afda2410089994352ea13640921ba9853a8 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 20:58:09 -0400
Subject: [PATCH 376/380] Additional CI + Python dependency attempts.

---
 .circleci/config.yml | 4 +---
 .travis.yml          | 1 +
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index a2ade65ad..4b86bc633 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -29,9 +29,6 @@ _save_cache: &save_cache
     - ~/.ivy2/cache
     - ~/.sbt
 
-before_script:
-  - ulimit -c unlimited -S
-
 jobs:
   test:
     <<: *defaults
@@ -68,6 +65,7 @@ jobs:
           <<: *restore_cache
 
       - run: ulimit -c unlimited -S
+      - run: pip3 install --user -r pyrasterframes/src/main/python/requirements.txt
       - run:
           command: cat /dev/null | sbt makeSite
           no_output_timeout: 30m
diff --git a/.travis.yml b/.travis.yml
index 4ab034f02..12cad75b7 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,6 +10,7 @@ cache:
     - $HOME/.ivy2/cache
     - $HOME/.sbt/boot
     - $HOME/.rf_cache
+    - $HOME/.cache/coursier
 
 scala:
   - 2.11.11

From 16097917e1e008cf30c01ecdb376effa8fc5a627 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 21:07:52 -0400
Subject: [PATCH 377/380] Quiet down pip; cache .local.

---
 .circleci/config.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 4b86bc633..e9d3844bf 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -28,6 +28,7 @@ _save_cache: &save_cache
     - ~/.cache/coursier
     - ~/.ivy2/cache
     - ~/.sbt
+    - ~/.local
 
 jobs:
   test:
@@ -65,7 +66,7 @@ jobs:
           <<: *restore_cache
 
       - run: ulimit -c unlimited -S
-      - run: pip3 install --user -r pyrasterframes/src/main/python/requirements.txt
+      - run: pip3 install --quiet --user -r pyrasterframes/src/main/python/requirements.txt
       - run:
           command: cat /dev/null | sbt makeSite
           no_output_timeout: 30m

From e11d9455c5fba23bdf85592bdce240c09ebeccc4 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Thu, 15 Aug 2019 21:17:08 -0400
Subject: [PATCH 378/380] Removed tile display contrast fiddling.

---
 pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
index d13eb0e4f..c1fe857d4 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py
@@ -29,8 +29,6 @@ def tile_to_png(tile, fig_size=None):
     import io
     from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
     from matplotlib.figure import Figure
-    import matplotlib.colors as colors
-    from matplotlib import cm
 
     # Set up matplotlib objects
     nominal_size = 2  # approx full size for a 256x256 tile
@@ -42,9 +40,8 @@ def tile_to_png(tile, fig_size=None):
     axis = fig.add_subplot(1, 1, 1)
 
     data = tile.cells
-    norm = colors.LogNorm(data.min(), data.max())
 
-    axis.imshow(data, norm=norm, cmap=cm.get_cmap("RdBu"))
+    axis.imshow(data)
     axis.set_aspect('equal')
     axis.xaxis.set_ticks([])
     axis.yaxis.set_ticks([])

From 2560bd47bed230193d8be871104a960789f5aa25 Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 16 Aug 2019 09:49:55 -0400
Subject: [PATCH 379/380] Set version to 0.8.0.

---
 pyrasterframes/src/main/python/pyrasterframes/version.py | 2 +-
 version.sbt                                              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyrasterframes/src/main/python/pyrasterframes/version.py b/pyrasterframes/src/main/python/pyrasterframes/version.py
index ce58048ae..273686206 100644
--- a/pyrasterframes/src/main/python/pyrasterframes/version.py
+++ b/pyrasterframes/src/main/python/pyrasterframes/version.py
@@ -20,4 +20,4 @@
 #
 
 # Translating Java version from version.sbt to PEP440 norms
-__version__ = '0.8.0.dev0'
+__version__ = '0.8.0'
diff --git a/version.sbt b/version.sbt
index d8a83a96c..c3945bb4f 100644
--- a/version.sbt
+++ b/version.sbt
@@ -1 +1 @@
-version in ThisBuild := "0.8.0-SNAPSHOT"
+version in ThisBuild := "0.8.0"

From ddf668af599fc08f3daa176069ce8406434840ae Mon Sep 17 00:00:00 2001
From: "Simeon H.K. Fitch" <fitch@astraea.io>
Date: Fri, 16 Aug 2019 12:11:27 -0400
Subject: [PATCH 380/380] Tweaks to get .whl file under the default size limit
 on PyPi.

---
 build.sbt                               | 2 ++
 project/RFAssemblyPlugin.scala          | 3 ++-
 pyrasterframes/src/main/python/setup.py | 2 +-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/build.sbt b/build.sbt
index fb711ea64..5aa2e6009 100644
--- a/build.sbt
+++ b/build.sbt
@@ -21,6 +21,7 @@
 
 addCommandAlias("makeSite", "docs/makeSite")
 addCommandAlias("previewSite", "docs/previewSite")
+addCommandAlias("ghpagesPushSite", "docs/ghpagesPushSite")
 addCommandAlias("console", "datasource/console")
 
 // Prefer our own IntegrationTest config definition, which inherits from Test.
@@ -36,6 +37,7 @@ lazy val root = project
 lazy val `rf-notebook` = project
   .dependsOn(pyrasterframes)
   .enablePlugins(RFAssemblyPlugin, DockerPlugin)
+  .settings(publish / skip := true)
 
 lazy val core = project
   .enablePlugins(BuildInfoPlugin)
diff --git a/project/RFAssemblyPlugin.scala b/project/RFAssemblyPlugin.scala
index 8f69776f6..06246d3ae 100644
--- a/project/RFAssemblyPlugin.scala
+++ b/project/RFAssemblyPlugin.scala
@@ -45,7 +45,8 @@ object RFAssemblyPlugin extends AutoPlugin {
   override def projectSettings = Seq(
     test in assembly := {},
     autoImport.assemblyExcludedJarPatterns := Seq(
-      "scalatest.*".r
+      "scalatest.*".r,
+      "junit.*".r
     ),
     assemblyShadeRules in assembly := {
       val shadePrefixes = Seq(
diff --git a/pyrasterframes/src/main/python/setup.py b/pyrasterframes/src/main/python/setup.py
index bfc09efc1..2e5b7d4ed 100644
--- a/pyrasterframes/src/main/python/setup.py
+++ b/pyrasterframes/src/main/python/setup.py
@@ -104,7 +104,7 @@ def run(self):
             if (not self.quick) or (not path.exists(dest)) or (path.getmtime(dest) < path.getmtime(file)):
                 print(_divided('Running %s' % name))
                 try:
-                    pweave.weave(file=str(file), doctype=self.format)
+                    pweave.weave(file=str(file), doctype=self.format, cache=True)
                     if self.format == 'markdown':
                         if not path.exists(dest):
                             raise FileNotFoundError("Markdown file '%s' didn't get created as expected" % dest)