Convert JavaScript dataset examples to Python

examples/AAFC/AAFC_ACI.py

@@ -0,0 +1,7 @@
+dataset = ee.ImageCollection('AAFC/ACI')
+crop_2016 = dataset.filter(ee.Filter.date('2016-01-01', '2016-12-31')).first()
+
+m = geemap.Map()
+m.set_center(-103.8881, 53.0372, 10)
+m.add_layer(crop_2016, {}, '2016 Canada AAFC Annual Crop Inventory')
+m

examples/AHN/AHN_AHN2_05M_INT.py

@@ -0,0 +1,8 @@
+dataset = ee.Image('AHN/AHN2_05M_INT')
+elevation = dataset.select('elevation')
+elevation_vis = {'min': -5.0, 'max': 30.0}
+
+m = geemap.Map()
+m.set_center(5.76583, 51.855276, 16)
+m.add_layer(elevation, elevation_vis, 'Elevation')
+m

examples/ASTER/ASTER_AST_L1T_003.py

@@ -0,0 +1,11 @@
+dataset = ee.ImageCollection('ASTER/AST_L1T_003').filter(
+    ee.Filter.date('2018-01-01', '2018-08-15')
+)
+
+false_color = dataset.select(['B3N', 'B02', 'B01'])
+false_color_vis = {'min': 0.0, 'max': 255.0}
+
+m = geemap.Map()
+m.set_center(-122.0272, 39.6734, 11)
+m.add_layer(false_color.median(), false_color_vis, 'False Color')
+m

examples/BIOPAMA/BIOPAMA_GlobalOilPalm_v1.py

@@ -0,0 +1,30 @@
+# Import the dataset a collection of composite granules from 2019.
+dataset = ee.ImageCollection('BIOPAMA/GlobalOilPalm/v1')
+
+# Select the classification band.
+op_class = dataset.select('classification')
+
+# Mosaic all of the granules into a single image.
+mosaic = op_class.mosaic()
+
+# Define visualization parameters.
+classification_vis = {
+    'min': 1,
+    'max': 3,
+    'palette': ['ff0000', 'ef00ff', '696969'],
+}
+
+# Create a mask to add transparency to non-oil palm plantation class pixels.
+mask = mosaic.neq(3)
+mask = mask.where(mask.eq(0), 0.6)
+
+# Display the data on the map.
+m = geemap.Map()
+m.add_layer(
+    mosaic.updateMask(mask),
+    classification_vis,
+    'Oil palm plantation type',
+    True,
+)
+m.set_center(-3.0175, 5.2745, 12)
+m

examples/COPERNICUS/COPERNICUS_CORINE_V20_100m.py

@@ -0,0 +1,7 @@
+dataset = ee.Image('COPERNICUS/CORINE/V20/100m/2012')
+land_cover = dataset.select('landcover')
+
+m = geemap.Map()
+m.set_center(16.436, 39.825, 6)
+m.add_layer(land_cover, {}, 'Land Cover')
+m

examples/COPERNICUS/COPERNICUS_DEM_GLO30.py

@@ -0,0 +1,13 @@
+dataset = ee.ImageCollection('COPERNICUS/DEM/GLO30')
+elevation = dataset.select('DEM')
+
+elevation_vis = {
+    'min': 0.0,
+    'max': 1000.0,
+    'palette': ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff'],
+}
+
+m = geemap.Map()
+m.set_center(-6.746, 46.529, 4)
+m.add_layer(elevation, elevation_vis, 'DEM')
+m

examples/COPERNICUS/COPERNICUS_S2.py

@@ -0,0 +1,41 @@
+def mask_s2_clouds(image):
+  """Masks clouds using the Sentinel-2 QA band.
+
+  Args:
+      image (ee.Image): Sentinel-2 image.
+
+  Returns:
+      ee.Image: Cloud masked Sentinel-2 image.
+  """
+  qa = image.select('QA60')
+
+  # Bits 10 and 11 are clouds and cirrus, respectively.
+  cloud_bit_mask = 1 << 10
+  cirrus_bit_mask = 1 << 11
+
+  # Both flags should be set to zero, indicating clear conditions.
+  mask = (
+      qa.bitwiseAnd(cloud_bit_mask)
+      .eq(0)
+      .And(qa.bitwiseAnd(cirrus_bit_mask).eq(0))
+  )
+
+  return image.updateMask(mask).divide(10000)
+
+
+# Load Sentinel-2 TOA reflectance data.
+dataset = (
+    ee.ImageCollection('COPERNICUS/S2')
+    .filterDate('2018-01-01', '2018-01-31')
+    # Pre-filter to get less cloudy granules.
+    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
+    .map(mask_s2_clouds)
+)
+
+rgb_vis = {'min': 0.0, 'max': 0.3, 'bands': ['B4', 'B3', 'B2']}
+
+# Create and display the map.
+m = geemap.Map()
+m.set_center(-9.1695, 38.6917, 12)
+m.add_layer(dataset.median(), rgb_vis, 'RGB')
+m

examples/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.py

@@ -0,0 +1,58 @@
+s2_sr = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
+s2_clouds = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
+
+start_date = ee.Date('2019-01-01')
+end_date = ee.Date('2019-03-01')
+max_cloud_probability = 65
+region = ee.Geometry.Rectangle(
+    coords=[-76.5, 2.0, -74, 4.0], geodesic=False, proj='EPSG:4326'
+)
+
+
+def maskClouds(img):
+  clouds = ee.Image(img.get('cloud_mask')).select('probability')
+  is_not_cloud = clouds.lt(max_cloud_probability)
+  return img.updateMask(is_not_cloud)
+
+
+# The masks for the 10m bands sometimes do not exclude bad data at
+# scene edges, so we apply masks from the 20m and 60m bands as well.
+# Example asset that needs this operation:
+# COPERNICUS/S2_CLOUD_PROBABILITY/20190301T000239_20190301T000238_T55GDP
+def maskEdges(s2_img):
+  return s2_img.updateMask(
+      s2_img.select('B8A').mask().updateMask(s2_img.select('B9').mask())
+  )
+
+
+# Filter input collections by desired data range and region.
+criteria = ee.Filter.And(
+    ee.Filter.bounds(region), ee.Filter.date(start_date, end_date)
+)
+s2_sr = s2_sr.filter(criteria).map(maskEdges)
+s2_clouds = s2_clouds.filter(criteria)
+
+# Join S2 SR with cloud probability dataset to add cloud mask.
+s2_sr_with_cloud_mask = ee.Join.saveFirst('cloud_mask').apply(
+    primary=s2_sr,
+    secondary=s2_clouds,
+    condition=ee.Filter.equals(
+        leftField='system:index', rightField='system:index'
+    ),
+)
+
+s_2_cloud_masked = (
+    ee.ImageCollection(s2_sr_with_cloud_mask).map(maskClouds).median()
+)
+
+rgb_vis = {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}
+
+m = geemap.Map()
+m.set_center(-75, 3, 12)
+m.add_layer(
+    s_2_cloud_masked,
+    rgb_vis,
+    'S2 SR masked at ' + str(max_cloud_probability) + '%',
+    True,
+)
+m

examples/COPERNICUS/COPERNICUS_S3_OLCI.py

@@ -0,0 +1,17 @@
+dataset = ee.ImageCollection('COPERNICUS/S3/OLCI').filterDate(
+    '2018-04-01', '2018-04-04'
+)
+
+# Select bands for visualization and apply band-specific scale factors.
+rgb = (
+    dataset.select(['Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance'])
+    .median()
+    .multiply(ee.Image([0.00876539, 0.0123538, 0.0115198]))
+)  # Convert to radiance units.
+
+vis_params = {'min': 0, 'max': 6, 'gamma': 1.5}
+
+m = geemap.Map()
+m.set_center(46.043, 1.45, 5)
+m.add_layer(rgb, vis_params, 'RGB')
+m

examples/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.py

@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')
+    .select('NO2_column_number_density')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.0002,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P N02')
+m.set_center(65.27, 24.11, 4)
+m

examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.py

@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')
+    .select('absorbing_aerosol_index')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': -1,
+    'max': 2.0,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P Aerosol')
+m.set_center(-118.82, 36.1, 5)
+m

examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.py

@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')
+    .select('CH4_column_volume_mixing_ratio_dry_air')
+    .filterDate('2019-06-01', '2019-07-16')
+)
+
+band_viz = {
+    'min': 1750,
+    'max': 1900,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P CH4')
+m.set_center(0.0, 0.0, 2)
+m

examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.py

@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')
+    .select('CO_column_number_density')
+    .filterDate('2019-06-01', '2019-06-11')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.05,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P CO')
+m.set_center(-25.01, -4.28, 4)
+m

examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.py

@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')
+    .select('tropospheric_NO2_column_number_density')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.0002,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P N02')
+m.set_center(65.27, 24.11, 4)
+m

examples/ECMWF/ECMWF_ERA5_LAND_HOURLY.py

@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY').filter(
+    ee.Filter.date('2020-07-01', '2020-07-02')
+)
+
+visualization = {
+    'bands': ['temperature_2m'],
+    'min': 250.0,
+    'max': 320.0,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(22.2, 21.2, 0)
+m.add_layer(dataset, visualization, 'Air temperature [K] at 2m height')
+m

examples/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.py

@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR').first()
+
+visualization = {
+    'bands': ['temperature_2m'],
+    'min': 250,
+    'max': 320,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(70, 45, 3)
+m.add_layer(
+    dataset, visualization, 'Air temperature [K] at 2m height', True, 0.8
+)
+m

examples/ECMWF/ECMWF_ERA5_MONTHLY.py

@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5/MONTHLY')
+
+visualization = {
+    'bands': ['mean_2m_air_temperature'],
+    'min': 250.0,
+    'max': 320.0,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(22.2, 21.2, 0)
+m.add_layer(
+    dataset, visualization, 'Monthly average air temperature [K] at 2m height'
+)
+m

examples/EO1/EO1_HYPERION.py

@@ -0,0 +1,11 @@
+dataset = ee.ImageCollection('EO1/HYPERION').filter(
+    ee.Filter.date('2016-01-01', '2017-03-01')
+)
+
+rgb = dataset.select(['B050', 'B023', 'B015'])
+rgb_vis = {'min': 1000.0, 'max': 14000.0, 'gamma': 2.5}
+
+m = geemap.Map()
+m.set_center(162.0044, -77.3463, 9)
+m.add_layer(rgb.median(), rgb_vis, 'RGB')
+m

examples/ESA/ESA_CCI_FireCCI_5_1.py

@@ -0,0 +1,24 @@
+# Visualize FireCCI51 for one year
+dataset = ee.ImageCollection('ESA/CCI/FireCCI/5_1').filterDate(
+    '2020-01-01', '2020-12-31'
+)
+
+burned_area = dataset.select('BurnDate')
+max_ba = burned_area.max()
+
+# Use a circular palette to assign colors to date of first detection
+ba_vis = {
+    'min': 1,
+    'max': 366,
+    'palette': [
+        'ff0000', 'fd4100', 'fb8200', 'f9c400', 'f2ff00', 'b6ff05',
+        '7aff0a', '3eff0f', '02ff15', '00ff55', '00ff99', '00ffdd',
+        '00ddff', '0098ff', '0052ff', '0210ff', '3a0dfb', '7209f6',
+        'a905f1', 'e102ed', 'ff00cc', 'ff0089', 'ff0047', 'ff0004'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(0, 18, 2.1)
+m.add_layer(max_ba, ba_vis, 'Burned Area')
+m

examples/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3.py

@@ -0,0 +1,7 @@
+dataset = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3')
+landcover = dataset.select('landcover')
+
+m = geemap.Map()
+m.set_center(-88.6, 26.4, 3)
+m.add_layer(landcover, {}, 'Landcover')
+m

examples/ESA/ESA_WorldCover_v200.py

@@ -0,0 +1,8 @@
+dataset = ee.ImageCollection('ESA/WorldCover/v200').first()
+
+visualization = {'bands': ['Map']}
+
+m = geemap.Map()
+m.center_object(dataset)
+m.add_layer(dataset, visualization, 'Landcover')
+m