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J@~_+q_%F!Hvnc=o literal 0 HcmV?d00001 diff --git a/docs/getting_started/tutorial/70_Reprojecting_Seismic_Data.ipynb b/docs/getting_started/tutorial/70_Reprojecting_Seismic_Data.ipynb new file mode 100644 index 00000000..f69ae074 --- /dev/null +++ b/docs/getting_started/tutorial/70_Reprojecting_Seismic_Data.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "86f44ca3", + "metadata": {}, + "source": [ + "# 70 Reprojecting Seismic Data and extracting path and CDP points from Seismic Data\n", + "\n", + "This notebook illustrates how to reproject 2D seismic data from the original coordinate system to a new coordinate system. In this tutorial, we are making use of the `segysak` package (https://segysak.readthedocs.io/en/latest/) to load the seismic data in combination with `GeoPandas` (https://geopandas.org/en/stable/) to perform the coordinate reprojection. Further, it is shown how to export the path of the seismic data as LineString, as CDP points and as points every n-th meter along the line.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "e7147456", + "metadata": {}, + "source": [ + "## Set File Paths and download Tutorial Data\n", + "\n", + "If you downloaded the latest `GemGIS` version from the Github repository, append the path so that the package can be imported successfully. Otherwise, it is recommended to install `GemGIS` via `pip install gemgis` and import `GemGIS` using `import gemgis as gg`. In addition, the file path to the folder where the data is being stored is set. The tutorial data is downloaded using Pooch (https://www.fatiando.org/pooch/latest/index.html) and stored in the specified folder. Use `pip install pooch` if Pooch is not installed on your system yet." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "eb8af06a", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# import gemgis as gg\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "from typing import Union\n", + "import pyproj\n", + "import numpy as np\n", + "import shapely\n", + "from shapely.geometry import LineString" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4128de8", + "metadata": {}, + "outputs": [], + "source": [ + "file_path ='data/70_reprojecting_seismic_data/'\n", + "# gg.download_gemgis_data.download_tutorial_data(filename=\"68_creating_finite_faults_with_gemgis.zip\", dirpath=file_path)" + ] + }, + { + "cell_type": "markdown", + "id": "fee09444-505a-4834-b669-2cd2a1223f60", + "metadata": {}, + "source": [ + "## Defining functions\n", + "\n", + "As there may be conflicts between the dependencies of GemGIS and the `segysak` package, we define the functions that are needed to reproject the seismic data and for the further contents of the notebook. However, they are also implemented in the GemGIS `utils` module. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7bbd5398-7730-40c8-b29b-b5d072ed9642", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_crs_seismic_data(path_in: str,\n", + " path_out: str,\n", + " crs_in: Union[str, pyproj.crs.crs.CRS],\n", + " crs_out: Union[str, pyproj.crs.crs.CRS],\n", + " cdpx: int = 181,\n", + " cdpy: int = 185,\n", + " vert_domain: str = 'TWT'):\n", + " \"\"\"Convert CDP coordinates of seismic data to a new CRS.\n", + "\n", + " Parameters\n", + " __________\n", + " path_in : str\n", + " Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n", + " path_out : str\n", + " Path to the converted seismic data, e.g. ``path_out='seismic_converted.sgy'``.\n", + " crs_in : str, pyproj.crs.crs.CRS\n", + " Coordinate reference system of the original seismic data.\n", + " crs_out : str, pyproj.crs.crs.CRS\n", + " Coordinate reference system of the converted seismic data.\n", + " cdpx : int\n", + " Byte position for the X coordinates, default is ``cdpx=181``.\n", + " cdpy : int\n", + " Byte position for the Y coordinates, default is ``cdpx=185``.\n", + " vert_domain : str\n", + " Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n", + "\n", + " .. versionadded:: 1.1.1\n", + "\n", + " \"\"\"\n", + " # Trying to import segysak but returning error if segysak is not installed\n", + " try:\n", + " from segysak.segy import segy_loader, segy_writer\n", + " except ModuleNotFoundError:\n", + " raise ModuleNotFoundError(\n", + " 'segysak package is not installed. Use pip install segysak to install the latest version')\n", + "\n", + " # Checking that path_in is of type string\n", + " if not isinstance(path_in, str):\n", + " raise TypeError('path_in must be provided as string')\n", + "\n", + " # Checking that path_out is of type string\n", + " if not isinstance(path_out, str):\n", + " raise TypeError('path_out must be provided as string')\n", + "\n", + " # Checking that crs_in is of type string or pyproj CRS\n", + " if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n", + " raise TypeError('crs_in must be provided as string or pyproj CRS')\n", + "\n", + " # Checking that crs_out is of type string or pyproj CRS\n", + " if not isinstance(crs_out, (str, pyproj.crs.crs.CRS)):\n", + " raise TypeError('crs_out must be provided as string or pyproj CRS')\n", + "\n", + " # Checking that vert_domain is of type str\n", + " if not isinstance(vert_domain, str):\n", + " raise TypeError('vert_domain must be provided as string')\n", + "\n", + " # Loading seismic data\n", + " seismic = segy_loader(path_in,\n", + " vert_domain=vert_domain,\n", + " cdpx=cdpx,\n", + " cdpy=cdpy)\n", + "\n", + " # Converting Seismic to DataFrame\n", + " df_seismic = seismic.to_dataframe()\n", + "\n", + " # Checking that the CDP coordinates are in the DataFrame\n", + " if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n", + " raise ValueError(\n", + " 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n", + "\n", + " # Extracting the length of the samples to reduce computing time\n", + " samples = len(df_seismic.index.get_level_values(1).unique())\n", + "\n", + " # Resample DataFrame\n", + " df_seismic_resampled = df_seismic[::samples]\n", + "\n", + " # Reprojecting Coordinates\n", + " df_seismic_reprojected = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values,\n", + " y=df_seismic_resampled['cdp_y'].values),\n", + " crs=crs_in).to_crs(crs_out)\n", + "\n", + " # Extracting reprojected coordinates\n", + " x = df_seismic_reprojected.geometry.x.values\n", + " y = df_seismic_reprojected.geometry.y.values\n", + "\n", + " # Assigning new coordinates\n", + " seismic['cdp_x'][:] = x\n", + " seismic['cdp_y'][:] = y\n", + "\n", + " # Saving reprojected seismic data to file\n", + " segy_writer(seismic,\n", + " path_out,\n", + " trace_header_map=dict(cdp_x=181,\n", + " cdp_y=185))\n", + "\n", + " print('Seismic data was successfully reprojected and saved to file')\n", + "\n", + "\n", + "def get_cdp_linestring_of_seismic_data(path_in: str,\n", + " crs_in: Union[str, pyproj.crs.crs.CRS],\n", + " cdpx: int = 181,\n", + " cdpy: int = 185,\n", + " vert_domain: str = 'TWT'):\n", + " \"\"\"Extracting the path of the seismic data as LineString.\n", + "\n", + " Parameters\n", + " __________\n", + " path_in : str\n", + " Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n", + " crs_in : str, pyproj.crs.crs.CRS\n", + " Coordinate reference system of the original seismic data.\n", + " cdpx : int\n", + " Byte position for the X coordinates, default is ``cdpx=181``.\n", + " cdpy : int\n", + " Byte position for the Y coordinates, default is ``cdpx=185``.\n", + " vert_domain : str\n", + " Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n", + "\n", + " Returns\n", + " _______\n", + " gpd.GeoDataFrame\n", + " GeoDataFrame containing the surface path of the seismic data as LineString.\n", + "\n", + " .. versionadded:: 1.1.1\n", + "\n", + " \"\"\"\n", + " # Trying to import segysak but returning error if segysak is not installed\n", + " try:\n", + " from segysak.segy import segy_loader, segy_writer\n", + " except ModuleNotFoundError:\n", + " raise ModuleNotFoundError(\n", + " 'segysak package is not installed. Use pip install segysak to install the latest version')\n", + "\n", + " # Checking that path_in is of type string\n", + " if not isinstance(path_in, str):\n", + " raise TypeError('path_in must be provided as string')\n", + "\n", + " # Checking that crs_in is of type string or pyproj CRS\n", + " if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n", + " raise TypeError('crs_in must be provided as string or pyproj CRS')\n", + "\n", + " # Checking that vert_domain is of type str\n", + " if not isinstance(vert_domain, str):\n", + " raise TypeError('vert_domain must be provided as string')\n", + "\n", + " # Loading seismic data\n", + " seismic = segy_loader(path_in,\n", + " vert_domain=vert_domain,\n", + " cdpx=cdpx,\n", + " cdpy=cdpy)\n", + "\n", + " # Converting Seismic to DataFrame\n", + " df_seismic = seismic.to_dataframe()\n", + "\n", + " # Checking that the CDP coordinates are in the DataFrame\n", + " if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n", + " raise ValueError(\n", + " 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n", + "\n", + " # Extracting the length of the samples to reduce computing time\n", + " samples = len(df_seismic.index.get_level_values(1).unique())\n", + "\n", + " # Resample DataFrame\n", + " df_seismic_resampled = df_seismic[::samples]\n", + "\n", + " # Creating LineString from coordinates\n", + " linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values,\n", + " df_seismic_resampled['cdp_y'].values)])\n", + "\n", + " # Reprojecting Coordinates\n", + " gdf_seismic = gpd.GeoDataFrame(geometry=[linestring],\n", + " crs=crs_in)\n", + "\n", + " return gdf_seismic\n", + "\n", + "\n", + "def get_cdp_points_of_seismic_data(path_in: str,\n", + " crs_in: Union[str, pyproj.crs.crs.CRS],\n", + " cdpx: int = 181,\n", + " cdpy: int = 185,\n", + " vert_domain: str = 'TWT',\n", + " filter: int = None,\n", + " n_meter: Union[int, float] = None):\n", + " \"\"\"Extracting the path of the seismic data as LineString.\n", + "\n", + " Parameters\n", + " __________\n", + " path_in : str\n", + " Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n", + " crs_in : str, pyproj.crs.crs.CRS\n", + " Coordinate reference system of the original seismic data.\n", + " cdpx : int\n", + " Byte position for the X coordinates, default is ``cdpx=181``.\n", + " cdpy : int\n", + " Byte position for the Y coordinates, default is ``cdpx=185``.\n", + " vert_domain : str\n", + " Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n", + " filter : int\n", + " Filtering the points to only return every n-th point, e.g. ``filter=100`` to return only every 100-th point.\n", + " n_meter : int, float\n", + " Parameter to select a point along the line every n-th meter.\n", + " \n", + " Returns\n", + " _______\n", + " gpd.GeoDataFrame\n", + " GeoDataFrame containing the CDPs as Points.\n", + "\n", + " .. versionadded:: 1.1.1\n", + "\n", + " \"\"\"\n", + " # Trying to import segysak but returning error if segysak is not installed\n", + " try:\n", + " from segysak.segy import segy_loader, segy_writer\n", + " except ModuleNotFoundError:\n", + " raise ModuleNotFoundError(\n", + " 'segysak package is not installed. Use pip install segysak to install the latest version')\n", + "\n", + " # Checking that path_in is of type string\n", + " if not isinstance(path_in, str):\n", + " raise TypeError('path_in must be provided as string')\n", + "\n", + " # Checking that crs_in is of type string or pyproj CRS\n", + " if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n", + " raise TypeError('crs_in must be provided as string or pyproj CRS')\n", + "\n", + " # Checking that vert_domain is of type str\n", + " if not isinstance(vert_domain, str):\n", + " raise TypeError('vert_domain must be provided as string')\n", + "\n", + " # Loading seismic data\n", + " seismic = segy_loader(path_in,\n", + " vert_domain=vert_domain,\n", + " cdpx=cdpx,\n", + " cdpy=cdpy)\n", + "\n", + " # Converting Seismic to DataFrame\n", + " df_seismic = seismic.to_dataframe()\n", + "\n", + " # Checking that the CDP coordinates are in the DataFrame\n", + " if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n", + " raise ValueError(\n", + " 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n", + "\n", + " # Extracting the length of the samples to reduce computing time\n", + " samples = len(df_seismic.index.get_level_values(1).unique())\n", + "\n", + " # Resample DataFrame\n", + " df_seismic_resampled = df_seismic[::samples]\n", + "\n", + " if n_meter:\n", + "\n", + " # Creating LineString from coordinates\n", + " linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values,\n", + " df_seismic_resampled['cdp_y'].values)])\n", + "\n", + " # Defining number of samples\n", + " samples = np.arange(0, round(linestring.length/n_meter)+1, 1)\n", + "\n", + " # Getting points every n_meter\n", + " points = [shapely.line_interpolate_point(linestring, n_meter*sample) for sample in samples]\n", + "\n", + " # Creating GeoDataFrame from points\n", + " gdf_seismic = gpd.GeoDataFrame(geometry=points,\n", + " crs=crs_in)\n", + "\n", + " # Appending distance\n", + " gdf_seismic['distance'] = samples*n_meter\n", + "\n", + " else:\n", + " # Creating Points from coordinates\n", + " gdf_seismic = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values,\n", + " y=df_seismic_resampled['cdp_y'].values),\n", + " data=df_seismic_resampled,\n", + " crs=crs_in).reset_index().drop(['twt', 'data'], axis=1)\n", + " \n", + " # Returning only every nth point\n", + " if filter:\n", + " gdf_seismic = gdf_seismic[::filter]\n", + "\n", + " return gdf_seismic" + ] + }, + { + "cell_type": "markdown", + "id": "0219a63e", + "metadata": {}, + "source": [ + "## Reprojecting Seismic Data\n", + "\n", + "The seismic data used here can be freely downloaded from NLOG.nl and is provided in the Coordinate References System (CRS) with `EPSG:28992`. To reproject the data, we are using the function `convert_crs_seismic_data(...)`. The output path and the output CRS are defined, optionally, the byte positions for the X and Y coordinates of each CDP and the vertical domain can be defined. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "796e20c6-f719-4608-9603-2332154dc8c7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "db001bd6faad4998b6837086a708a99f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/2.70k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    geometry
    0LINESTRING (194105.422 382534.062, 194115.391 ...
    \n", + "" + ], + "text/plain": [ + " geometry\n", + "0 LINESTRING (194105.422 382534.062, 194115.391 ..." + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_linestring = get_cdp_linestring_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n", + " crs_in='EPSG:28992')\n", + "gdf_linestring" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9682d223-8247-461e-baec-d1f973d9a5d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(10,10))\n", + "\n", + "gdf_linestring.plot(ax=ax)\n", + "\n", + "ax.set_ylim(380000,384000)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "id": "f38397c5-933e-4f0f-9408-876b2421b30f", + "metadata": {}, + "source": [ + "## Extracting the CDP points of the seismic data\n", + "\n", + "The CDP points of the seismic data can be extracted using the function `get_cdp_points_of_seismic_data`. By setting a `filter`, only the n-th point can be selected. A GeoDataFrame containing points will be returned. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8e184c39-fe78-494d-99f5-8265263abc9b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c3aa451d36945cc93f36d79a7216b20", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/2.70k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    cdpcdp_xcdp_ygeometry
    01194105.421875382534.06250POINT (194105.422 382534.062)
    250251196602.859375382433.34375POINT (196602.859 382433.344)
    500501199099.406250382333.03125POINT (199099.406 382333.031)
    750751201554.859375381880.46875POINT (201554.859 381880.469)
    10001001204022.796875381520.40625POINT (204022.797 381520.406)
    12501251206520.484375381599.18750POINT (206520.484 381599.188)
    15001501209018.203125381503.37500POINT (209018.203 381503.375)
    17501751211512.828125381340.65625POINT (211512.828 381340.656)
    20002001213993.937500381043.43750POINT (213993.938 381043.438)
    22502251216479.656250380814.06250POINT (216479.656 380814.062)
    25002501218918.343750381326.12500POINT (218918.344 381326.125)
    \n", + "" + ], + "text/plain": [ + " cdp cdp_x cdp_y geometry\n", + "0 1 194105.421875 382534.06250 POINT (194105.422 382534.062)\n", + "250 251 196602.859375 382433.34375 POINT (196602.859 382433.344)\n", + "500 501 199099.406250 382333.03125 POINT (199099.406 382333.031)\n", + "750 751 201554.859375 381880.46875 POINT (201554.859 381880.469)\n", + "1000 1001 204022.796875 381520.40625 POINT (204022.797 381520.406)\n", + "1250 1251 206520.484375 381599.18750 POINT (206520.484 381599.188)\n", + "1500 1501 209018.203125 381503.37500 POINT (209018.203 381503.375)\n", + "1750 1751 211512.828125 381340.65625 POINT (211512.828 381340.656)\n", + "2000 2001 213993.937500 381043.43750 POINT (213993.938 381043.438)\n", + "2250 2251 216479.656250 380814.06250 POINT (216479.656 380814.062)\n", + "2500 2501 218918.343750 381326.12500 POINT (218918.344 381326.125)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_points = get_cdp_points_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n", + " crs_in='EPSG:28992',\n", + " filter=250)\n", + "gdf_points" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6a061760-4109-4639-8295-450aa32c18cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uQyAiIiJqNxo0bPLWrVsIDQ1FTk4OrKys4O3tjcOHD2PQoEEAgAMHDmD+/PkYNmwY7t69Czc3N2zbtk2678rMzAx79+7F4sWLUVhYCEdHRwwePBgLFiyQhitaWVnhyJEjCA8Ph6+vL+zt7bFo0SJMnjxZ6kffvn0RExODBQsW4J133kHXrl0RFxeH7t27SzFz585FYWEhJk+ejLy8PDz//PM4dOgQTExMHvmkke4qLy/HzJkz8de//lWWL2PHjoWLiwucnJyQmpqKefPmITMzE3v37m3B3jaPbt264ejRo9JzQ8P/fizMmjUL33//PXbv3g0rKytERERgxIgR+PHHH2VtTJw4EbGxscjNzW22fhMRERG1Nw0q3jZv3lzr/q5du2LPnj017vfy8sLx48frfB9vb2+cOXOm1phRo0Zh1KhRNe7X09PDsmXLZPcwtXV3795FVlaW9Pzq1atQq9WwtbWFs7NzC/as9QgPD0d6ejp++OEH2fbKPw54eXnB0dERL730ErKzs9GlS5fm7mazMjQ0lO4TrSw/Px+bN29GTEwMBg4cCADYsmULnnnmGZw7d06a2XTDhg0oKSnB2bNnWbwRERERPUaPfM8btR5JSUnw8fGBj48PAGD27Nnw8fHBokWLWrhnrUNERAT279+PEydOoFOnTrXG+vn5AYCsGG6rrly5AicnJzz11FMYN24crl+/DqBiqYmSkhLZeokeHh5wdnZGQkJCS3WXiIiIqN1q9CLd1LzKygXOX83FrTtF6NjBBM+52sJAX760woABAyCEaKEetl5CCEyfPh2xsbE4efJklXUEq6NWqwFANltpW+Tn54etW7fC3d0dOTk5WLp0Kfr164f09HRoNBooFApYW1vLXlPXeolERERE9HiweNMBh9JzsPS7S8jJL5K2OVqZYPEwTwzu3raLi6YQHh6OmJgYfPvtt+jQoYNUeFhZWcHU1BTZ2dmIiYnB0KFDYWdnh9TUVMyaNQv9+/eHt7e31E5WVhbu3r0LjUaD+/fvSwWep6cnFApFSxzaIxsyZIj0Z29vb/j5+cHFxQW7du2CqalpC/aMiIiIiB7G4q2VO5Seg2nbL+Lh62ma/CJM234RUa/1YgFXh6ioKAAVVyYr27JlCyZOnAiFQoGjR4/io48+QmFhITp37oyRI0diwYIFsvg33ngDp06dkp5rh6devXoVTz755GM9huZibW2Np59+GllZWRg0aBAePHiAvLw82dW3ymsqEhEREVHzYfHWipWVCyz97lKVwg0ABAA9AEu/u4RBnsoqQyjbk7qGlNY1lLRz586yoqwmJ0+efNSutnp3795FdnY2xo8fD19fXxgZGeHYsWMYOXIkACAzMxPXr1/neolERERELYDFWyt2/mqubKjkwwSAnPwinL+aC1UXu+brWCvCIaWP5u2338awYcPg4uKC33//HYsXL4aBgQHGjBkDKysrhIWFYfbs2bC1tYWlpSWmT58OlUolzTQJVAwnvX37NvLy8trMcFIiIiKi1ojFWyt2607NhVtj4toaDil9dDdu3MCYMWPw559/4oknnsDzzz+Pc+fO4YknngAArFu3Dvr6+hg5ciSKi4sRGBiITz75RNZGexhOSkRERNQasHhrxTp2qN+C4vWNa0s4pLR+6hpSumPHjlpfb2Jigk2bNmHTpk01xpw8eRIlJSU4cOAAhg4dCiMjoybrPxERERH9F4u3Vuw5V1s4WplAk19UbZGiB0BpVfGFvL3hkNK6cUgpERERUdvCRbpbMQN9PSwe5gmgolCrTPt88TDPdnlliUNKa6cdUvpwgasdUnooPaeFekZEREREjcXirZUb3N0RUa/1gtJKPjRSaWXSru/p4pDSmtU1pBSoGFJaVs4F3Wvy5JNPQk9Pr8ojPDwcQMWyEw/vmzp1qqyNGTNmwNfXF8bGxujZs2cLHAURERG1NRw2qQMGd3fEIE9lrfcutTccUlozDil9dBcuXEBZWZn0PD09HYMGDcKoUaOkbZMmTcKyZcuk52ZmZlXaef3115GYmIjU1NTH22EiIiJqF1i86QgDfT1+0a5EO6R02vaL0ANkBRyHlHJI6aPSzraptXLlSnTp0gUvvPCCtM3MzKzWxco3bNgAAPjjjz9YvBEREVGT4LBJ0lkcUlo9DiltWg8ePMD27dvx+uuvQ0/vvz8GfPXVV7C3t0f37t0RGRmJe/futWAviYiIqD3glTfSaRxSWhWHlDatuLg45OXlYeLEidK2sWPHwsXFBU5OTkhNTcW8efOQmZmJvXv3tlxHiYiIqM1j8UY6j0NK5TiktGlt3rwZQ4YMgZOTk7Rt8uTJ0p+9vLzg6OiIl156CdnZ2ejSpUtLdJOIiIjaAQ6bJGqDOKS0afz66684evQo3njjjVrj/Pz8AABZWVnN0S0iIiJqp3jljaiN4pDSR7dlyxZ07NgRQUFBtcap1WoAgKMji2IiIiJ6fBp05S0qKgre3t6wtLSEpaUlVCoVDh48KO3XaDQYP348lEolzM3N0atXL+zZs0faf+3aNYSFhcHV1RWmpqbo0qULFi9ejAcPHsjeJzU1Ff369YOJiQk6d+6M1atXV+nL7t274eHhARMTE3h5eeHAgQOy/UIILFq0CI6OjjA1NYW/vz+uXLnSkMMl0nnaIaXDe/4Fqi52LNwaoLy8HFu2bMGECRNgaPjf37mys7OxfPlyJCcn49q1a9i3bx9CQ0PRv39/eHt7S3FZWVlQq9XQaDS4f/8+1Go11Gp1lc87ahtOnz6NYcOGwcnJCXp6eoiLi5P2lZSUYN68efDy8oK5uTmcnJwQGhqK33//XdZGdesLrly5UtpfVFSEiRMnwsvLC4aGhggODm6moyMiotaiQcVbp06dsHLlSiQnJyMpKQkDBw7E8OHDkZGRAQAIDQ1FZmYm9u3bh7S0NIwYMQIhISFISUkBAFy+fBnl5eX49NNPkZGRgXXr1iE6OhrvvPOO9B4FBQUICAiAi4sLkpOTsWbNGixZsgSfffaZFHP27FmMGTMGYWFhSElJQXBwMIKDg5Geni7FrF69Ghs2bEB0dDQSExNhbm6OwMBAFBVxenQiqtvRo0dx/fp1vP7667LtCoUCR48eRUBAADw8PPDWW29h5MiR+O6772Rxb7zxBnx8fPDpp5/if//3f+Hj4wMfH58qX9ipbSgsLESPHj2wadOmKvvu3buHixcvYuHChbh48SL27t2LzMxMvPzyy1Vily1bhpycHOkxffp0aV9ZWRlMTU0xY8YM+Pv7P9bjISKi1klPCFHdhHT1ZmtrizVr1iAsLAwWFhaIiorC+PHjpf12dnZYtWpVjfeMrFmzBlFRUfjll18AVFzde/fdd6HRaKBQKAAA8+fPR1xcHC5fvgwAGD16NAoLC7F//36pnT59+qBnz56Ijo6GEAJOTk5466238PbbbwMA8vPz4eDggK1bt+LVV1+tti/FxcUoLi6WnhcUFKBz5874z3/+A0tLy0c4S7qjpKQE8fHxGDRoEIyMjFq6O6QjdClvysoFkn+9jf/cLYa9hTF8XWx4RbIBzpw5g7Vr1yIlJQU5OTnYvXs3hg8fLu0XQmDp0qX48ssvkZeXh759++Ljjz9G165dpZjc3FzMnDkT33//PcrKyvDKK6/go48+goWFBYCKK0zh4eG4ePEiLl++jKFDh8pGcbR2CoWiynl5WFJSEvr27YusrCw4OzsDALp27Yrp06djxowZdb5HWFgY8vLydOq8NBVd+ryh1oN5Q43xcN4UFBTA3t4e+fn5LVYbNPqet7KyMuzevRuFhYVQqVQAgL59+2Lnzp0ICgqCtbU1du3ahaKiIgwYMKDGdvLz82Fr+98pyxMSEtC/f3+pcAOAwMBArFq1Crdv34aNjQ0SEhIwe/ZsWTuBgYHSMJWrV69Co9HIfpm0srKCn58fEhISaizeVqxYgaVLl1bZfuTIEZiZmdV5TtqS+Pj4lu4C6SBdy5v/ADj8c0v3QrckJyfD3NwcEyZMkEZiVP4itHfvXnzzzTd488034eDggJiYGAwcOBAff/yx9Lm+bNky5ObmYuHChSgtLcXHH3+M4cOH46233gJQUbz98ccfeOGFF6BQKHDz5s0qQ+Nbu4fPy8N++ukn6Onp4fz589KokXv37uG9997D0qVLYW9vj/79++Pll1+GgYFBldffuHEDhYWFOnFeMjIyEBsbi+zsbNy+fRvz589Hnz59pP3r16/HiRMnZK/x8fHB4sWLped37tzB559/jgsXLkBPTw8qlQr379+HqakpgIr1GKOiopCdnY0bN26gd+/eslE9RFq69u8UtQ7avGkNa7o2uHhLS0uDSqVCUVERLCwsEBsbC09PTwDArl27MHr0aNjZ2cHQ0BBmZmaIjY2Fm5tbtW1lZWXh448/xgcffCBt02g0cHV1lcU5ODhI+2xsbKDRaKRtlWM0Go0UV/l11cVUJzIyUlYUaq+8BQQE8MobUS10IW+O/nwTs3aqq6x9p73mtm50T/g/4/Dwy+ghQ4cOlf68cuVK+Pr6StuEEJg6dSoWLVokfZaOGzcOnTp1QnFxMYKDg/Hzzz/j4sWLSEhIgLe3N+Lj4/HJJ59gxIgR+J//+R9pSYYRI0YA+O8Vpsrvqwsqn5eHFRUVYenSpRg9ejReeeUVafucOXPg4+MDGxsbnDt3DgsWLIC1tTXWrFlTpY09e/bozHnR19fHnTt3MHfuXISEhFQ5N3v27EFgYCA+//xzaZuxsTFsbGyk58OGDUNeXh7i4+Nx//59hIaGYu/evfjqq68AVAxbPXr0KEJCQhAbGwtjY2OdODfUfHTh3ylqfaq78tbSGly8ubu7Q61WIz8/H9988w0mTJiAU6dOwdPTEwsXLkReXh6OHj0Ke3t7xMXFISQkBGfOnIGXl5esnX//+98YPHgwRo0ahUmTJjXZAT0KY2NjGBsbV9luZGTU7v5Hb4/HTI+uteZNWbnAsu8zUVRW/fBIPQDLvs9EQPe/cAhlAxkaGkp/57/88gs0Gg0CAwOlbfb29vDz88OFCxfw2muvISkpCdbW1ujTpw9KSkoAVIyc0NfXx8WLF+Hi4iJrX19fH/r6+q0yr2pT+bxUVlJSgnHjxgEAPv30U1nMnDlzpD/7+vrC1NQUU6ZMwapVq6r826RL52XYsGEYNmyY9Pzhc6Ovry9NUFadn3/+GYcPH8aFCxfQu3dvlJSUYNKkSVi+fDk++ugjODk5wdraGp9++ikA4Ny5c8jLy9OJc0PNr7X+O0WtmzZvWkPuNHidN4VCATc3N/j6+mLFihXo0aMH1q9fj+zsbGzcuBFffvklXnrpJfTo0QOLFy9G7969q9zA/fvvv+PFF19E3759ZRORAIBSqcTNmzdl27TPlUplrTGV91d+XXUxRNR+nL+ai5z8micrEgBy8otw/mpu83WqDarPqAeNRoOOHTvK9hsaGsLW1rbWkRFtQUlJCUJCQvDrr78iPj6+zhEdfn5+KC0txbVr15qngy3o5MmT6NixI9zd3TFt2jT8+eef0r6EhARYW1ujd+/e0rYePXpAX18fiYmJLdFdIqIW88iLdJeXl6O4uFgaA6qvL2/SwMAA5eXl0vN///vfGDBgAHx9fbFly5Yq8SqVCqdPn5Z+kQUqxpm6u7tLQyhUKhWOHTsme118fLx0752rqyuUSqUspqCgAImJiVIMEbUft+7Ub5bZ+sYRNZS2cLty5QqOHj0KOzu7Ol+jVquhr69fpdhtawYPHox//etfOHbsGFatWoVTp05hyJAhKCsrA1B9wW9gYNAmCv7alpgAUGXpCO2j8lBaLjFB1L40qHiLjIzE6dOnce3aNaSlpSEyMhInT57EuHHj4OHhATc3N0yZMgXnz59HdnY21q5di/j4eOmDQlu4OTs744MPPsAff/wBjUYj+/AdO3YsFAoFwsLCkJGRgZ07d2L9+vWye9HefPNNHDp0CGvXrsXly5exZMkSJCUlISIiAkDFh93MmTPx3nvvScsWhIaGwsnJiR9aRO1Qxw4mTRpH1avPqAelUolbt27J9peWliI3N1enR0bcvXtXWssPqJg4S61W4/r16ygpKcErr7yCpKQkfPXVVygrK5P+7dOu+5eQkICPPvoIP/30E3755Rd89dVXmDVrFl577TXZvV+XLl2CWq1Gbm4u8vPzZe+pq1599VW8/PLL8PLyQnBwMPbv348LFy7g5MmTLd21x662JSYAyJaNyMnJwZdffgk9PT2MHDlSFsclJqg9W7FiBZ599ll06NABHTt2RHBwMDIzM2UxAwYMqPIjx9SpU2Ux169fR1BQEMzMzNCxY0fMmTMHpaWl0v6cnByEhYUBAKytrTFz5szHfmzVadA9b7du3UJoaChycnJgZWUFb29vHD58GIMGDQIAHDhwAPPnz8ewYcNw9+5duLm5Ydu2bdJNw/Hx8cjKykJWVhY6deoka1u7YoGVlRWOHDmC8PBw+Pr6wt7eHosWLcLkyZOl2L59+yImJgYLFizAO++8g65duyIuLg7du3eXYubOnYvCwkJMnjwZeXl5eP7553Ho0CGYmPDLGVF785yrLRytTKDJL6oyYQlQcc+b0soEz7naVrOX6qvyqIeePXsC+O+oh2nTpgGoGDmRl5eH5ORkaVHzEydOoLy8HH5+fi3V9UeWlJSEF198UXqu/cFxwoQJWLJkCfbt2wcA0nnROnHiBAYMGABjY2Ps2LEDS5YsQXFxMVxdXTFr1qwqMysPHToUv/76q/Tcx8cHwH//DW0LnnrqKdjb2yMrKwsvvfRStQV/WVmZzhf8ADBkyBAMGTKkxv0PH9+3336LF198EU899ZRse4cOHWo8F+bm5oiKigIA/Pjjj8jLy3u0ThO1MqdOnUJ4eDieffZZlJaW4p133kFAQAAuXboEc3NzKW7SpElYtmyZ9LzyTPJlZWUICgqCUqnE2bNnkZOTg9DQUOjr66Nv374AKpYUs7e3BwBZzdHcGlS8bd68udb9Xbt2rXXNmYkTJ2LixIl1vo+3tzfOnDlTa8yoUaMwatSoGvfr6elh2bJlsr8kImqfDPT1sHiYJ6Ztvwg9QFbAaacnWTzMk5OV1MPdu3eRlZUlPddeYbK1tYWzs7M06qFr165wdXXFwoULZaMennnmGQwePBiTJk3Cxo0b8fPPP2Pz5s149dVXpZkmgYorTA8ePEBubi7u3LkjXV16uPhpDmXlAuev5uLWnSJ07FBR5D+cKwMGDKi1gKqruOrVqxfOnTtXZ1/aw/1vN27cwJ9//glHR0cA8oLf19cXAJCamqrzBX9D3bx5E99//z22bdtWZd/KlSuxfPlyODs7Y+zYsZg1axYMDRu9GhSRTjl06JDs+datW9GxY0ckJyejf//+0nYzM7Maf+Q4cuQILl26hKNHj8LBwQE9e/bE8uXLMW/ePDz77LMAKoYor1q1CtHR0bCysnp8B1QH/p9NRO3C4O6OiHqtF5Z+d0k2eYnSygSLh3licHfHFuyd7qjtCtPWrVvrNerhq6++QkREBAIDA1FeXo5Ro0Zh48aNsvdpLVeYDqXnVMkZR+ZMg9RW8Nva2mLp0qUYOXIklEolsrOzMXfuXLi5uSEwMBCAvOCPjo7G/fv38fnnnyMkJKTVFvyPw7Zt29ChQwdpGQ2tGTNmoFevXrC1tcXZs2cRGRmJnJwcfPjhhy3UU6KWlZ+fDwCydaSBin97tm/fDqVSiWHDhmHhwoXS1beEhAR4eXnJJtwKDAzEtGnT8NtvvzVf5+uBxRsRtRuDuztikKeyzqso7VF9ri4BdV9hqs+oB1tbW8TExKCkpAQHDhzA0KFDq0y/3BquMB1Kz8G07RerDLXV5Bdh2vaLiHqtFwu4eqit4I+KikJqaiq2bduGvLw8ODk5ISAgAMuXL5ctj6At+F966SXo6+vj2WeflYYCarWWgv9x+fLLLzFu3Lgqt39UHlrr7e0NhUKBKVOmYMWKFdUuf0TUlpWXl2PmzJn461//KhvaOHbsWLi4uMDJyQmpqamYN28eMjMzsXfvXgCocQ1pALh9+3bzHUA9sHgjonbFQF8Pqi51z/TXnvDqUlVl5QJLv7tU7T2SAhXDbZd+dwmDPJXtuvhviiGlhw8frvN9tAU/AKnot7CwkMW0hoL/cTlz5gwyMzOxc+fOOmMrLzHh7u7eDL0jaj3Cw8ORnp6OH374Qba98twZXl5ecHR0xEsvvYTs7Gx06dKlubv5SFi8ERG1Y7y6VL2GrA3YXn8MYNHffDZv3gxfX1/06NGjztj2ssQE0cMiIiKwf/9+nD59usrEiA/T3i+blZWFLl26QKlU4vz587IY7czJlWf8bQ0eeZ03IiLSTXVdXQIqri6VlbeNYWcNwbUBa6ct+h8ucLVF/6H0nBbqmW6pbYkJrYKCAuzevRtvvPFGlde35yUmiLSEEIiIiEBsbCyOHz8OV1fXOl+jzf/KEyOlpaXJZraNj4+HpaUlOnfu/Fj63Vgs3oiI2qmGXF1qb7g2YM1Y9DedpKQk+Pj4SPfnzZ49Gz4+Pli0aJEUs2PHDgghMGbMmCqv1y4x8cILL6Bbt254//33MWvWLHz22WeyuKFDh8LHxwffffcdTp48KXvP1qg+63YVFRUhPDwcdnZ2sLCwwMiRI6usMVl53a6//OUv2Lp1a5V1u8aOHYunn34a+vr6LbZuFz2a8PBwbN++HTExMejQoYO0jub9+/cBANnZ2Vi+fDmSk5Nx7do17Nu3D6Ghoejfv7+0ZE1AQAA8PT0xfvx4/PTTTzh8+DAWLFiAqVOnyu7JTk1NBVDxw8sff/wBtVqNS5cuNevxsngjImqneHWpZtq1AWu6m00PFUME2+PagCz666esXCAh+098q/43ErL/rLaY1d4P+PBj69atUszkyZNx7969aqcm1y4xkZeXh/v37+PSpUuIjIysMlHJtWvXqn2f1kq7bte5c+cQHx+PkpISBAQEoLCwUIqZNWsWvvvuO+zevRunTp3C77//LpuJU7tu14MHD3D27Fls3rwZx48fx5IlS6SY4uJiPPHEE1iwYEG9hqRS6xQVFYX8/HwMGDAAjo6O0kN7j6hCocDRo0cREBAADw8PvPXWWxg5ciS+++47qQ0DAwPs378fBgYGUKlUeO211xAaGirLFwDo168fgIordzExMfDx8ZHWs24uvOeNiKid4tWlmnFtwJqx6K8b7wd8NHWt25Wfn4/NmzcjJiYGAwcOBABs2bIFzzzzDM6dO4c+ffpUWberW7duGDt2LKKjo7F8+XIoFAo8+eSTWL9+PYCK2Typ9anPpEh1/RDRuXNnnDp1qs73cnFxwYEDB2TbSkpKZM/z8/NhZWWF/Px8WFpa1vMomhavvBERtVO8ulQ77dqASit58aq0Mmm3E7kALPrrwvsBm97D63YlJyejpKQE/v7+UoyHhwecnZ2RkJAAoPp1u3x8fFBQUICMjIxm7D011qH0HDy/6jjGfH4Ob+5QY8zn5/D8quPt/v8hXnkjImqneHWpblwbsCpt0a/JL6r2vjc9VBS47bHo5xITTa+6dbs0Gg0UCgWsra1lsQ4ODtBoNFLMw+t2aeO1MdR6cSbkmvHKGxFRO8arS3XTrg04vOdfoOpi1+6/dGuLfgBVrtq296Kf9wM2Pe26XTt27GjprlAz4aRItWPxRkTUzg3u7ogf5g3E15P6YP2rPfH1pD74Yd5AFm5UIxb91eP9gE1Lu27XiRMnZOt2KZVKPHjwAHl5ebL4mzdvQqlUSjEPzz6pjdfG6KK6ZuLMzc3F9OnT4e7uDlNTUzg7O2PGjBnS0FMtPT29Ko/KBXJLzsTJH0Fqx2GTREQkXV0iqi8OKa2K9wM2DSEEpk+fjtjYWJw8ebLKul2+vr4wMjLCsWPHMHLkSABAZmYmrl+/DpVKBaBi3a73338ft27dkhYsV6vVsLS0hKenZ/MeUBPSzsT57LPPorS0FO+88w4CAgJw6dIlmJub4/fff8fvv/+ODz74AJ6envj1118xdepU/P777/jmm29kbW3ZsgWDBw+Wnlcehlp5Js5169Y11+EB4I8gdWHxRkRERI3Col+O9wM2jfDwcMTExODbb7+V1u0CACsrK5iamsLKygphYWGYPXs2bG1tYWlpienTp0OlUqFPnz4A5Ot2rV69Gjdu3EBMTAymTp0qW0pBu1hz5XW7FApFqy3w6pqJs3v37tizZ4+0v0uXLnj//ffx2muvobS0FIaG//3qb21tXeNVyJaciZM/gtSOwyaJiIiImgDvB2wada3bBQDr1q3D3/72N4wcORL9+/eHUqnE3r17pf0Pr9s1ceJEDBgwoMq6XdoFy5OTk1ts3a5H8fBMnDXFWFpaygo3oKJItre3x3PPPYcvv/yy1az9x5mQa8crb0RERERNRHs/4MPrvCm5zhuAplm3CwBMTEywadMmbNq0qcaYyut2lZSU4MCBA1UKmNZSsDRGdTNxPuw///kPli9fjsmTJ8u2L1u2DAMHDoSZmRmOHDmCf/zjH7h79y5mzJjRHF2vFWdCrh2LNyIiIqImxPsBq8fFy5uWdibOH374odr9BQUFCAoKgqenZ5UrjgsXLpT+7OPjg8LCQqxZs6ZVFG8AfwSpTYOGTUZFRcHb2xuWlpawtLSESqXCwYMHpf0ajQbjx4+HUqmEubk5evXqJRt3CwDvv/8++vbtCzMzsyrrc2hdv34dQUFBMDMzQ8eOHTFnzhyUlpbKYk6ePIlevXrB2NgYbm5u2Lp1a5V2Nm3ahCeffBImJibw8/PD+fPnG3K4RERERI3CJSbkuHh506ppJk6tO3fuYPDgwejQoQNiY2NhZGRUa3t+fn64ceMGiouLH1eXG4wzIVevQcVbp06dsHLlSiQnJyMpKQkDBw7E8OHDpZXqQ0NDkZmZiX379iEtLQ0jRoxASEgIUlJSpDYePHiAUaNGYdq0adW+R1lZGYKCgvDgwQOcPXsW27Ztw9atW7Fo0SIp5urVqwgKCsKLL74ItVqNmTNn4o033sDhw4elmJ07d2L27NlYvHgxLl68iB49eiAwMBC3bt1q0AkiIiIiosbjul1NRwiBiIgIxMbG4vjx41Vm4gQqrrgFBARAoVBg3759MDGpe2IPtVoNGxsb2WQurQF/BKmqQcXbsGHDMHToUHTt2hVPP/003n//fVhYWODcuXMAgLNnz2L69Ol47rnn8NRTT2HBggWwtrZGcnKy1MbSpUsxa9YseHl5VfseR44cwaVLl7B9+3b07NkTQ4YMwfLly7Fp0yY8ePAAABAdHQ1XV1esXbsWzzzzDCIiIvDKK6/IpjL98MMPMWnSJPz973+Hp6cnoqOjYWZm1uwz5hARERG1Z1y3q+mEh4dj+/btiImJkWbi1Gg0uH//PoD/Fm6FhYXYvHkzCgoKpJiysjIAwHfffYcvvvgC6enpyMrKQlRUFP75z39i+vTpsvdSq9VQq9WymTgvXbrU7MdMco2+562srAy7d+9GYWGhtKZG3759sXPnTgQFBcHa2hq7du1CUVERBgwYUO92ExIS4OXlBQcHB2lbYGAgpk2bhoyMDPj4+CAhIQH+/v6y1wUGBkoLCD548ADJycmIjIyU9uvr68Pf3x8JCQk1vndxcbHscnFBQQGAiptcS0pK6n0Mukx7nO3leKlpMG+oMZg31BjMG91zK78QxgZ1X1W7lV+IkhLLx9KHtpI3UVFRAFDlu/UXX3yB0NBQnD9/HomJiQAANzc3Wcz//u//4sknn4Senh42btyIWbNmQQiBLl26YM2aNQgLC5OdHx8fH+nP2tk4XVxccOXKlcd0dK3Pw3nTGvKnwcVbWloaVCoVioqKYGFhgdjYWGktjF27dmH06NGws7ODoaEhzMzMEBsbWyV5aqPRaGSFGwDpuXadj5piCgoKcP/+fdy+fRtlZWXVxly+fLnG916xYgWWLl1aZfuRI0dgZmZW72NoC+Lj41u6C6SDmDfUGMwbagzmjW5Z/Vw9gn5LwYHfUuqOewS6njdxcXE17tPOrFlTzKVLl6QrZ9V93314Dbma2tG+T3uizZt79+61cE8aUby5u7tDrVYjPz8f33zzDSZMmIBTp07B09MTCxcuRF5eHo4ePQp7e3vExcUhJCQEZ86cqXGYZGsSGRmJ2bNnS88LCgrQuXNnBAQEwNLy8fwS1NqUlJQgPj4egwYNqvPmViIt5g01BvOGGoN5o3vKygUCPzqNmwU1L17uYGmCwzP7P7Z7mlp73hz9+SZWHrwMTUGlmRUtTTB/iAf8n3Go5ZX0OD2cN9pReS2pwcWbQqGQrqT5+vriwoULWL9+PebOnYuNGzciPT0d3bp1AwD06NEDZ86cwaZNmxAdHV2v9pVKZZVZIW/evCnt0/5Xu61yjKWlJUxNTWFgYAADA4NqY2paSR4AjI2Nq71R08jIqFX+j/44tcdjpkfHvKHGYN5QYzBvdIcRgMigbpi2/SKA6tftigzqBhNjxePvSyvMm0PpOfhHzE//d17+W7xev12Mf8T8hKjXerX7GRZbmjZvWkPuNGjCkuqUl5ejuLhYuoyory9v0sDAAOXl5fVuT6VSIS0tTTYrZHx8PCwtLaXhmSqVCseOHZO9Lj4+Xrr3TqFQwNfXVxZTXl6OY8eOSTFERERE1Dy063YpreQzHyqtTNp1ccKZOKmhGnTlLTIyEkOGDIGzszPu3LmDmJgYnDx5EocPH4aHhwfc3NwwZcoUfPDBB7Czs0NcXBzi4+Oxf/9+qY3r168jNzcX169fR1lZGdRqNYCKmyotLCwQEBAAT09PjB8/HqtXr4ZGo8GCBQsQHh4uXRWbOnUqNm7ciLlz5+L111/H8ePHsWvXLnz//ffS+8yePRsTJkxA79698dxzz+Gjjz5CYWEh/v73vzfBaSMiIiKihuDi5VU1ZCZOVRe75usYtVoNKt5u3bqF0NBQ5OTkwMrKCt7e3jh8+DAGDRoEoOIGxvnz52PYsGG4e/cu3NzcsG3bNgwdOlRqY9GiRdi2bZv0XDuTzYkTJzBgwAAYGBhg//79mDZtGlQqFczNzTFhwgQsW7ZMeo2rqyu+//57zJo1C+vXr0enTp3wxRdfIDAwUIoZPXo0/vjjDyxatAgajQY9e/bEoUOHqkxiQkRERETNQ7tuF1W4dafmwq0xcdT2Nah427x5c637u3btij179tQas3XrVmzdurXWGBcXlzpnshkwYIBs8e/qREREICIiotYYIiIiIqKW0LFD3QtoNySO2r5HvueNiIiIiIga7jlXWzhamaCmgaN6ABytKoaXEgEs3oiIiIiIWoSBvh4WD6uYkO/hAk77fPEwz3Z9XyDJsXgjIiIiImohnImTGqLB67wREREREVHT4UycVF8s3oiIiIiIWhhn4qT64LBJIiIiIiIiHcDijYiIiIiISAeweCMiIiIiItIBLN6IiIiIiIh0AIs3IiIiIiIiHcDijYiIiIiISAeweCMiIiIiItIBLN6IiIiIiIh0AIs3IiIiIiIiHcDijYiIiIiISAeweCMiIiIiItIBLN6IiIiIiIh0AIs3IiIiIiIiHdCg4i0qKgre3t6wtLSEpaUlVCoVDh48KO3XaDQYP348lEolzM3N0atXL+zZs0fWRm5uLsaNGwdLS0tYW1sjLCwMd+/elcWkpqaiX79+MDExQefOnbF69eoqfdm9ezc8PDxgYmICLy8vHDhwQLZfCIFFixbB0dERpqam8Pf3x5UrVxpyuERERERERK1Gg4q3Tp06YeXKlUhOTkZSUhIGDhyI4cOHIyMjAwAQGhqKzMxM7Nu3D2lpaRgxYgRCQkKQkpIitTFu3DhkZGQgPj4e+/fvx+nTpzF58mRpf0FBAQICAuDi4oLk5GSsWbMGS5YswWeffSbFnD17FmPGjEFYWBhSUlIQHByM4OBgpKenSzGrV6/Ghg0bEB0djcTERJibmyMwMBBFRUWNPllEREREREQtRU8IIR6lAVtbW6xZswZhYWGwsLBAVFQUxo8fL+23s7PDqlWr8MYbb+Dnn3+Gp6cnLly4gN69ewMADh06hKFDh+LGjRtwcnJCVFQU3n33XWg0GigUCgDA/PnzERcXh8uXLwMARo8ejcLCQuzfv196nz59+qBnz56Ijo6GEAJOTk5466238PbbbwMA8vPz4eDggK1bt+LVV1+t9liKi4tRXFwsPc/Pz4ezszOuXr2KDh06PMpp0hklJSU4ceIEXnzxRRgZGbV0d0hHMG+oMZg31BjMG2oM5g01xsN5c+fOHbi6uiIvLw9WVlYt0ynRSKWlpeLrr78WCoVCZGRkCCGEGDRokAgKChJ//vmnKCsrE19//bUwMzMTV65cEUIIsXnzZmFtbS1rp6SkRBgYGIi9e/cKIYQYP368GD58uCzm+PHjAoDIzc0VQgjRuXNnsW7dOlnMokWLhLe3txBCiOzsbAFApKSkyGL69+8vZsyYUeMxLV68WADggw8++OCDDz744IMPPvio9vHbb781qG5qSoZooLS0NKhUKhQVFcHCwgKxsbHw9PQEAOzatQujR4+GnZ0dDA0NYWZmhtjYWLi5uQGouCeuY8eOsvYMDQ1ha2sLjUYjxbi6uspiHBwcpH02NjbQaDTStsoxlduo/LrqYqoTGRmJ2bNnS8/Ly8uRm5sLOzs76Onp1e8E6biCggJ07twZv/32GywtLVu6O6QjmDfUGMwbagzmDTUG84Ya4+G8EULgzp07cHJyarE+Nbh4c3d3h1qtRn5+Pr755htMmDABp06dgqenJxYuXIi8vDwcPXoU9vb2iIuLQ0hICM6cOQMvL6/H0f8mZWxsDGNjY9k2a2vrlulMC9NOSkPUEMwbagzmDTUG84Yag3lDjVE5b1psuOT/aXDxplAopCtpvr6+uHDhAtavX4+5c+di48aNSE9PR7du3QAAPXr0wJkzZ7Bp0yZER0dDqVTi1q1bsvZKS0uRm5sLpVIJAFAqlbh586YsRvu8rpjK+7XbHB0dZTE9e/Zs6CE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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(10,10))\n", + "\n", + "for label, x, y in zip(gdf_points['cdp'], gdf_points['cdp_x'], gdf_points['cdp_y']):\n", + " plt.text(x+200,y+200,label)\n", + " \n", + "gdf_points.plot(ax=ax)\n", + "\n", + "ax.set_ylim(380000,384000)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "id": "3dac0da3-8a30-4bf6-98fe-8a25d8fce938", + "metadata": {}, + "source": [ + "## Extracting the CDP Points every n-th meter" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6f1def53-97e4-4cea-9b11-333f1b1c7bc0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c59ab66ff3c648c2b26f0fedf394aa3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/2.70k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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    3POINT (206020.565 381595.751)12000
    4POINT (210016.378 381442.716)16000
    \n", + "" + ], + "text/plain": [ + " geometry distance\n", + "0 POINT (194105.422 382534.062) 0\n", + "1 POINT (198102.572 382412.468) 4000\n", + "2 POINT (202044.081 381777.218) 8000\n", + "3 POINT (206020.565 381595.751) 12000\n", + "4 POINT (210016.378 381442.716) 16000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_points = get_cdp_points_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n", + " crs_in='EPSG:28992',\n", + " n_meter=4000)\n", + "gdf_points.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a2b2492a-4922-48d7-8c6a-2cd47c0e6a58", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(10,10))\n", + "\n", + "for label, x, y in zip(gdf_points['distance'], gdf_points.geometry.x, gdf_points.geometry.y):\n", + " plt.text(x+200,y+200,str(label) + ' m')\n", + " \n", + "gdf_points.plot(ax=ax)\n", + "\n", + "ax.set_ylim(380000,384000)\n", + "ax.grid()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/getting_started/tutorial/index.rst b/docs/getting_started/tutorial/index.rst index 989bdc35..e4fff30d 100644 --- a/docs/getting_started/tutorial/index.rst +++ b/docs/getting_started/tutorial/index.rst @@ -106,4 +106,5 @@ Each set of functions of GemGIS is collected in a different module. The function 65_displaying_seismic_horizons_and_faults 66_generating_voronoi_polygons 67_rotating_gempy_input_data - 68_Creating_Finite_Faults_with_GemGIS \ No newline at end of file + 68_Creating_Finite_Faults_with_GemGIS + 70_Reprojecting_Seismic_Data \ No newline at end of file diff --git a/gemgis/utils.py b/gemgis/utils.py index 768d4d63..7f39e883 100644 --- a/gemgis/utils.py +++ b/gemgis/utils.py @@ -2494,3 +2494,284 @@ def open_mpk(path_in: str): return dict_vector_data, dict_raster_data + +def convert_crs_seismic_data(path_in: str, + path_out: str, + crs_in: Union[str, pyproj.crs.crs.CRS], + crs_out: Union[str, pyproj.crs.crs.CRS], + cdpx: int = 181, + cdpy: int = 185, + vert_domain: str = 'TWT'): + """Convert CDP coordinates of seismic data to a new CRS. + + Parameters + __________ + path_in : str + Path to the original seismic data, e.g. ``path_in='seismic.sgy'``. + path_out : str + Path to the converted seismic data, e.g. ``path_out='seismic_converted.sgy'``. + crs_in : str, pyproj.crs.crs.CRS + Coordinate reference system of the original seismic data. + crs_out : str, pyproj.crs.crs.CRS + Coordinate reference system of the converted seismic data. + cdpx : int + Byte position for the X coordinates, default is ``cdpx=181``. + cdpy : int + Byte position for the Y coordinates, default is ``cdpx=185``. + vert_domain : str + Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``. + + .. versionadded:: 1.1.1 + + """ + # Trying to import segysak but returning error if segysak is not installed + try: + from segysak.segy import segy_loader, segy_writer + except ModuleNotFoundError: + raise ModuleNotFoundError( + 'segysak package is not installed. Use pip install segysak to install the latest version') + + # Checking that path_in is of type string + if not isinstance(path_in, str): + raise TypeError('path_in must be provided as string') + + # Checking that path_out is of type string + if not isinstance(path_out, str): + raise TypeError('path_out must be provided as string') + + # Checking that crs_in is of type string or pyproj CRS + if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)): + raise TypeError('crs_in must be provided as string or pyproj CRS') + + # Checking that crs_out is of type string or pyproj CRS + if not isinstance(crs_out, (str, pyproj.crs.crs.CRS)): + raise TypeError('crs_out must be provided as string or pyproj CRS') + + # Checking that vert_domain is of type str + if not isinstance(vert_domain, str): + raise TypeError('vert_domain must be provided as string') + + # Loading seismic data + seismic = segy_loader(path_in, + vert_domain=vert_domain, + cdpx=cdpx, + cdpy=cdpy) + + # Converting Seismic to DataFrame + df_seismic = seismic.to_dataframe() + + # Checking that the CDP coordinates are in the DataFrame + if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns): + raise ValueError( + 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored') + + # Extracting the length of the samples to reduce computing time + samples = len(df_seismic.index.get_level_values(1).unique()) + + # Resample DataFrame + df_seismic_resampled = df_seismic[::samples] + + # Reprojecting Coordinates + df_seismic_reprojected = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values, + y=df_seismic_resampled['cdp_y'].values), + crs=crs_in).to_crs(crs_out) + + # Extracting reprojected coordinates + x = df_seismic_reprojected.geometry.x.values + y = df_seismic_reprojected.geometry.y.values + + # Assigning new coordinates + seismic['cdp_x'][:] = x + seismic['cdp_y'][:] = y + + # Saving reprojected seismic data to file + segy_writer(seismic, + path_out, + trace_header_map=dict(cdp_x=181, + cdp_y=185)) + + print('Seismic data was successfully reprojected and saved to file') + + +def get_cdp_linestring_of_seismic_data(path_in: str, + crs_in: Union[str, pyproj.crs.crs.CRS], + cdpx: int = 181, + cdpy: int = 185, + vert_domain: str = 'TWT'): + """Extracting the path of the seismic data as LineString. + + Parameters + __________ + path_in : str + Path to the original seismic data, e.g. ``path_in='seismic.sgy'``. + crs_in : str, pyproj.crs.crs.CRS + Coordinate reference system of the original seismic data. + cdpx : int + Byte position for the X coordinates, default is ``cdpx=181``. + cdpy : int + Byte position for the Y coordinates, default is ``cdpx=185``. + vert_domain : str + Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``. + + Returns + _______ + gpd.GeoDataFrame + GeoDataFrame containing the surface path of the seismic data as LineString. + + .. versionadded:: 1.1.1 + + """ + # Trying to import segysak but returning error if segysak is not installed + try: + from segysak.segy import segy_loader, segy_writer + except ModuleNotFoundError: + raise ModuleNotFoundError( + 'segysak package is not installed. Use pip install segysak to install the latest version') + + # Checking that path_in is of type string + if not isinstance(path_in, str): + raise TypeError('path_in must be provided as string') + + # Checking that crs_in is of type string or pyproj CRS + if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)): + raise TypeError('crs_in must be provided as string or pyproj CRS') + + # Checking that vert_domain is of type str + if not isinstance(vert_domain, str): + raise TypeError('vert_domain must be provided as string') + + # Loading seismic data + seismic = segy_loader(path_in, + vert_domain=vert_domain, + cdpx=cdpx, + cdpy=cdpy) + + # Converting Seismic to DataFrame + df_seismic = seismic.to_dataframe() + + # Checking that the CDP coordinates are in the DataFrame + if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns): + raise ValueError( + 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored') + + # Extracting the length of the samples to reduce computing time + samples = len(df_seismic.index.get_level_values(1).unique()) + + # Resample DataFrame + df_seismic_resampled = df_seismic[::samples] + + # Creating LineString from coordinates + linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values, + df_seismic_resampled['cdp_y'].values)]) + + # Reprojecting Coordinates + gdf_seismic = gpd.GeoDataFrame(geometry=[linestring], + crs=crs_in) + + return gdf_seismic + + +def get_cdp_points_of_seismic_data(path_in: str, + crs_in: Union[str, pyproj.crs.crs.CRS], + cdpx: int = 181, + cdpy: int = 185, + vert_domain: str = 'TWT', + filter: int = None, + n_meter: Union[int, float] = None): + """Extracting the path of the seismic data as LineString. + + Parameters + __________ + path_in : str + Path to the original seismic data, e.g. ``path_in='seismic.sgy'``. + crs_in : str, pyproj.crs.crs.CRS + Coordinate reference system of the original seismic data. + cdpx : int + Byte position for the X coordinates, default is ``cdpx=181``. + cdpy : int + Byte position for the Y coordinates, default is ``cdpx=185``. + vert_domain : str + Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``. + filter : int + Filtering the points to only return every n-th point, e.g. ``filter=100`` to return only every 100-th point. + n_meter : int, float + Parameter to select a point along the line every n-th meter. + + Returns + _______ + gpd.GeoDataFrame + GeoDataFrame containing the CDPs as Points. + + .. versionadded:: 1.1.1 + + """ + # Trying to import segysak but returning error if segysak is not installed + try: + from segysak.segy import segy_loader, segy_writer + except ModuleNotFoundError: + raise ModuleNotFoundError( + 'segysak package is not installed. Use pip install segysak to install the latest version') + + # Checking that path_in is of type string + if not isinstance(path_in, str): + raise TypeError('path_in must be provided as string') + + # Checking that crs_in is of type string or pyproj CRS + if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)): + raise TypeError('crs_in must be provided as string or pyproj CRS') + + # Checking that vert_domain is of type str + if not isinstance(vert_domain, str): + raise TypeError('vert_domain must be provided as string') + + # Loading seismic data + seismic = segy_loader(path_in, + vert_domain=vert_domain, + cdpx=cdpx, + cdpy=cdpy) + + # Converting Seismic to DataFrame + df_seismic = seismic.to_dataframe() + + # Checking that the CDP coordinates are in the DataFrame + if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns): + raise ValueError( + 'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored') + + # Extracting the length of the samples to reduce computing time + samples = len(df_seismic.index.get_level_values(1).unique()) + + # Resample DataFrame + df_seismic_resampled = df_seismic[::samples] + + if n_meter: + + # Creating LineString from coordinates + linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values, + df_seismic_resampled['cdp_y'].values)]) + + # Defining number of samples + samples = np.arange(0, round(linestring.length / n_meter) + 1, 1) + + # Getting points every n_meter + points = [shapely.line_interpolate_point(linestring, n_meter * sample) for sample in samples] + + # Creating GeoDataFrame from points + gdf_seismic = gpd.GeoDataFrame(geometry=points, + crs=crs_in) + + # Appending distance + gdf_seismic['distance'] = samples * n_meter + + else: + # Creating Points from coordinates + gdf_seismic = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values, + y=df_seismic_resampled['cdp_y'].values), + data=df_seismic_resampled, + crs=crs_in).reset_index().drop(['twt', 'data'], axis=1) + + # Returning only every nth point + if filter: + gdf_seismic = gdf_seismic[::filter] + + return gdf_seismic