common module¶
This module contains some common functions for both folium and ipyleaflet to interact with the Earth Engine Python API.
PlanetaryComputerEndpoint (TitilerEndpoint)
¶
This class contains the methods for the Microsoft Planetary Computer endpoint.
Source code in geemap/common.py
class PlanetaryComputerEndpoint(TitilerEndpoint):
"""This class contains the methods for the Microsoft Planetary Computer endpoint."""
def __init__(
self,
endpoint="https://planetarycomputer.microsoft.com/api/data/v1",
name="item",
TileMatrixSetId="WebMercatorQuad",
):
"""Initialize the PlanetaryComputerEndpoint object.
Args:
endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://planetarycomputer.microsoft.com/api/data/v1".
name (str, optional): The name to be used in the file path. Defaults to "item".
TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
"""
super().__init__(endpoint, name, TileMatrixSetId)
def url_for_stac_collection(self):
return f"{self.endpoint}/collection/{self.TileMatrixSetId}/tilejson.json"
def url_for_collection_assets(self):
return f"{self.endpoint}/collection/assets"
def url_for_collection_bounds(self):
return f"{self.endpoint}/collection/bounds"
def url_for_collection_info(self):
return f"{self.endpoint}/collection/info"
def url_for_collection_info_geojson(self):
return f"{self.endpoint}/collection/info.geojson"
def url_for_collection_pixel_value(self, lon, lat):
return f"{self.endpoint}/collection/point/{lon},{lat}"
def url_for_collection_wmts(self):
return f"{self.endpoint}/collection/{self.TileMatrixSetId}/WMTSCapabilities.xml"
def url_for_collection_lat_lon_assets(self, lng, lat):
return f"{self.endpoint}/collection/{lng},{lat}/assets"
def url_for_collection_bbox_assets(self, minx, miny, maxx, maxy):
return f"{self.endpoint}/collection/{minx},{miny},{maxx},{maxy}/assets"
def url_for_stac_mosaic(self, searchid):
return f"{self.endpoint}/mosaic/{searchid}/{self.TileMatrixSetId}/tilejson.json"
def url_for_mosaic_info(self, searchid):
return f"{self.endpoint}/mosaic/{searchid}/info"
def url_for_mosaic_lat_lon_assets(self, searchid, lon, lat):
return f"{self.endpoint}/mosaic/{searchid}/{lon},{lat}/assets"
__init__(self, endpoint='https://planetarycomputer.microsoft.com/api/data/v1', name='item', TileMatrixSetId='WebMercatorQuad')
special
¶
Initialize the PlanetaryComputerEndpoint object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
endpoint |
str |
The endpoint of the titiler server. Defaults to "https://planetarycomputer.microsoft.com/api/data/v1". |
'https://planetarycomputer.microsoft.com/api/data/v1' |
name |
str |
The name to be used in the file path. Defaults to "item". |
'item' |
TileMatrixSetId |
str |
The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad". |
'WebMercatorQuad' |
Source code in geemap/common.py
def __init__(
self,
endpoint="https://planetarycomputer.microsoft.com/api/data/v1",
name="item",
TileMatrixSetId="WebMercatorQuad",
):
"""Initialize the PlanetaryComputerEndpoint object.
Args:
endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://planetarycomputer.microsoft.com/api/data/v1".
name (str, optional): The name to be used in the file path. Defaults to "item".
TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
"""
super().__init__(endpoint, name, TileMatrixSetId)
TitilerEndpoint
¶
This class contains the methods for the titiler endpoint.
Source code in geemap/common.py
class TitilerEndpoint:
"""This class contains the methods for the titiler endpoint."""
def __init__(
self,
endpoint="https://titiler.xyz",
name="stac",
TileMatrixSetId="WebMercatorQuad",
):
"""Initialize the TitilerEndpoint object.
Args:
endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://titiler.xyz".
name (str, optional): The name to be used in the file path. Defaults to "stac".
TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
"""
self.endpoint = endpoint
self.name = name
self.TileMatrixSetId = TileMatrixSetId
def url_for_stac_item(self):
return f"{self.endpoint}/{self.name}/{self.TileMatrixSetId}/tilejson.json"
def url_for_stac_assets(self):
return f"{self.endpoint}/{self.name}/assets"
def url_for_stac_bounds(self):
return f"{self.endpoint}/{self.name}/bounds"
def url_for_stac_info(self):
return f"{self.endpoint}/{self.name}/info"
def url_for_stac_info_geojson(self):
return f"{self.endpoint}/{self.name}/info.geojson"
def url_for_stac_statistics(self):
return f"{self.endpoint}/{self.name}/statistics"
def url_for_stac_pixel_value(self, lon, lat):
return f"{self.endpoint}/{self.name}/point/{lon},{lat}"
def url_for_stac_wmts(self):
return (
f"{self.endpoint}/{self.name}/{self.TileMatrixSetId}/WMTSCapabilities.xml"
)
__init__(self, endpoint='https://titiler.xyz', name='stac', TileMatrixSetId='WebMercatorQuad')
special
¶
Initialize the TitilerEndpoint object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
endpoint |
str |
The endpoint of the titiler server. Defaults to "https://titiler.xyz". |
'https://titiler.xyz' |
name |
str |
The name to be used in the file path. Defaults to "stac". |
'stac' |
TileMatrixSetId |
str |
The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad". |
'WebMercatorQuad' |
Source code in geemap/common.py
def __init__(
self,
endpoint="https://titiler.xyz",
name="stac",
TileMatrixSetId="WebMercatorQuad",
):
"""Initialize the TitilerEndpoint object.
Args:
endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://titiler.xyz".
name (str, optional): The name to be used in the file path. Defaults to "stac".
TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
"""
self.endpoint = endpoint
self.name = name
self.TileMatrixSetId = TileMatrixSetId
add_crs(filename, epsg)
¶
Add a CRS to a raster dataset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The filename of the raster dataset. |
required |
epsg |
int | str |
The EPSG code of the CRS. |
required |
Source code in geemap/common.py
def add_crs(filename, epsg):
"""Add a CRS to a raster dataset.
Args:
filename (str): The filename of the raster dataset.
epsg (int | str): The EPSG code of the CRS.
"""
try:
import rasterio
except ImportError:
raise ImportError(
"rasterio is required for adding a CRS to a raster. Please install it using 'pip install rasterio'."
)
if not os.path.exists(filename):
raise ValueError("filename must exist.")
if isinstance(epsg, int):
epsg = f"EPSG:{epsg}"
elif isinstance(epsg, str):
epsg = "EPSG:" + epsg
else:
raise ValueError("epsg must be an integer or string.")
crs = rasterio.crs.CRS({"init": epsg})
with rasterio.open(filename, mode="r+") as src:
src.crs = crs
adjust_longitude(in_fc)
¶
Adjusts longitude if it is less than -180 or greater than 180.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_fc |
dict |
The input dictionary containing coordinates. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary containing the converted longitudes |
Source code in geemap/common.py
def adjust_longitude(in_fc):
"""Adjusts longitude if it is less than -180 or greater than 180.
Args:
in_fc (dict): The input dictionary containing coordinates.
Returns:
dict: A dictionary containing the converted longitudes
"""
try:
keys = in_fc.keys()
if "geometry" in keys:
coordinates = in_fc["geometry"]["coordinates"]
if in_fc["geometry"]["type"] == "Point":
longitude = coordinates[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][0] = longitude
elif in_fc["geometry"]["type"] == "Polygon":
for index1, item in enumerate(coordinates):
for index2, element in enumerate(item):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][index1][index2][0] = longitude
elif in_fc["geometry"]["type"] == "LineString":
for index, element in enumerate(coordinates):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["geometry"]["coordinates"][index][0] = longitude
elif "type" in keys:
coordinates = in_fc["coordinates"]
if in_fc["type"] == "Point":
longitude = coordinates[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][0] = longitude
elif in_fc["type"] == "Polygon":
for index1, item in enumerate(coordinates):
for index2, element in enumerate(item):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][index1][index2][0] = longitude
elif in_fc["type"] == "LineString":
for index, element in enumerate(coordinates):
longitude = element[0]
if longitude < -180:
longitude = 360 + longitude
elif longitude > 180:
longitude = longitude - 360
in_fc["coordinates"][index][0] = longitude
return in_fc
except Exception as e:
print(e)
return None
annual_NAIP(year, region)
¶
Create an NAIP mosaic of a specified year for a specified region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The specified year to create the mosaic for. |
required |
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def annual_NAIP(year, region):
"""Create an NAIP mosaic of a specified year for a specified region.
Args:
year (int): The specified year to create the mosaic for.
region (object): ee.Geometry
Returns:
object: ee.Image
"""
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
collection = (
ee.ImageCollection("USDA/NAIP/DOQQ")
.filterDate(start_date, end_date)
.filterBounds(region)
)
time_start = ee.Date(
ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
)
time_end = ee.Date(
ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
)
image = ee.Image(collection.mosaic().clip(region))
NDWI = ee.Image(image).normalizedDifference(["G", "N"]).select(["nd"], ["ndwi"])
NDVI = ee.Image(image).normalizedDifference(["N", "R"]).select(["nd"], ["ndvi"])
image = image.addBands(NDWI)
image = image.addBands(NDVI)
return image.set({"system:time_start": time_start, "system:time_end": time_end})
api_docs()
¶
Open a browser and navigate to the geemap API documentation.
Source code in geemap/common.py
def api_docs():
"""Open a browser and navigate to the geemap API documentation."""
import webbrowser
url = "https://geemap.org/geemap"
webbrowser.open_new_tab(url)
arc_active_map()
¶
Get the active map in ArcGIS Pro.
Returns:
| Type | Description |
|---|---|
arcpy.Map |
The active map in ArcGIS Pro. |
Source code in geemap/common.py
def arc_active_map():
"""Get the active map in ArcGIS Pro.
Returns:
arcpy.Map: The active map in ArcGIS Pro.
"""
if is_arcpy():
import arcpy
aprx = arcpy.mp.ArcGISProject("CURRENT")
m = aprx.activeMap
return m
else:
return None
arc_active_view()
¶
Get the active view in ArcGIS Pro.
Returns:
| Type | Description |
|---|---|
arcpy.MapView |
The active view in ArcGIS Pro. |
Source code in geemap/common.py
def arc_active_view():
"""Get the active view in ArcGIS Pro.
Returns:
arcpy.MapView: The active view in ArcGIS Pro.
"""
if is_arcpy():
import arcpy
aprx = arcpy.mp.ArcGISProject("CURRENT")
view = aprx.activeView
return view
else:
return None
arc_add_layer(url, name=None, shown=True, opacity=1.0)
¶
Add a layer to the active map in ArcGIS Pro.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL of the tile layer to add. |
required |
name |
str |
The name of the layer. Defaults to None. |
None |
shown |
bool |
Whether the layer is shown. Defaults to True. |
True |
opacity |
float |
The opacity of the layer. Defaults to 1.0. |
1.0 |
Source code in geemap/common.py
def arc_add_layer(url, name=None, shown=True, opacity=1.0):
"""Add a layer to the active map in ArcGIS Pro.
Args:
url (str): The URL of the tile layer to add.
name (str, optional): The name of the layer. Defaults to None.
shown (bool, optional): Whether the layer is shown. Defaults to True.
opacity (float, optional): The opacity of the layer. Defaults to 1.0.
"""
if is_arcpy():
m = arc_active_map()
if m is not None:
m.addDataFromPath(url)
if isinstance(name, str):
layers = m.listLayers("Tiled service layer")
if len(layers) > 0:
layer = layers[0]
layer.name = name
layer.visible = shown
layer.transparency = 100 - (opacity * 100)
arc_zoom_to_extent(xmin, ymin, xmax, ymax)
¶
Zoom to an extent in ArcGIS Pro.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
xmin |
float |
The minimum x value of the extent. |
required |
ymin |
float |
The minimum y value of the extent. |
required |
xmax |
float |
The maximum x value of the extent. |
required |
ymax |
float |
The maximum y value of the extent. |
required |
Source code in geemap/common.py
def arc_zoom_to_extent(xmin, ymin, xmax, ymax):
"""Zoom to an extent in ArcGIS Pro.
Args:
xmin (float): The minimum x value of the extent.
ymin (float): The minimum y value of the extent.
xmax (float): The maximum x value of the extent.
ymax (float): The maximum y value of the extent.
"""
if is_arcpy():
import arcpy
view = arc_active_view()
if view is not None:
view.camera.setExtent(
arcpy.Extent(
xmin,
ymin,
xmax,
ymax,
spatial_reference=arcpy.SpatialReference(4326),
)
)
# if isinstance(zoom, int):
# scale = 156543.04 * math.cos(0) / math.pow(2, zoom)
# view.camera.scale = scale # Not working properly
array_mean(arr)
¶
Calculates the mean of an array along the given axis.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
arr |
object |
Array to calculate mean. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def array_mean(arr):
"""Calculates the mean of an array along the given axis.
Args:
arr (object): Array to calculate mean.
Returns:
object: ee.Number
"""
total = ee.Array(arr).accum(0).get([-1])
size = arr.length()
return ee.Number(total.divide(size))
array_sum(arr)
¶
Accumulates elements of an array along the given axis.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
arr |
object |
Array to accumulate. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def array_sum(arr):
"""Accumulates elements of an array along the given axis.
Args:
arr (object): Array to accumulate.
Returns:
object: ee.Number
"""
return ee.Array(arr).accum(0).get([-1])
array_to_image(array, output=None, source=None, dtype=None, compress='deflate', transpose=True, cellsize=None, crs=None, driver='COG', **kwargs)
¶
Save a NumPy array as a GeoTIFF using the projection information from an existing GeoTIFF file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
array |
np.ndarray |
The NumPy array to be saved as a GeoTIFF. |
required |
output |
str |
The path to the output image. If None, a temporary file will be created. Defaults to None. |
None |
source |
str |
The path to an existing GeoTIFF file with map projection information. Defaults to None. |
None |
dtype |
np.dtype |
The data type of the output array. Defaults to None. |
None |
compress |
str |
The compression method. Can be one of the following: "deflate", "lzw", "packbits", "jpeg". Defaults to "deflate". |
'deflate' |
transpose |
bool |
Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True. |
True |
cellsize |
float |
The resolution of the output image in meters. Defaults to None. |
None |
crs |
str |
The CRS of the output image. Defaults to None. |
None |
driver |
str |
The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG". |
'COG' |
**kwargs |
Additional keyword arguments to be passed to the rasterio.open() function. |
{} |
Source code in geemap/common.py
def array_to_image(
array,
output: str = None,
source: str = None,
dtype: str = None,
compress: str = "deflate",
transpose: bool = True,
cellsize: float = None,
crs: str = None,
driver: str = "COG",
**kwargs,
) -> str:
"""Save a NumPy array as a GeoTIFF using the projection information from an existing GeoTIFF file.
Args:
array (np.ndarray): The NumPy array to be saved as a GeoTIFF.
output (str): The path to the output image. If None, a temporary file will be created. Defaults to None.
source (str, optional): The path to an existing GeoTIFF file with map projection information. Defaults to None.
dtype (np.dtype, optional): The data type of the output array. Defaults to None.
compress (str, optional): The compression method. Can be one of the following: "deflate", "lzw", "packbits", "jpeg". Defaults to "deflate".
transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
cellsize (float, optional): The resolution of the output image in meters. Defaults to None.
crs (str, optional): The CRS of the output image. Defaults to None.
driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
**kwargs: Additional keyword arguments to be passed to the rasterio.open() function.
"""
import numpy as np
import rasterio
import xarray as xr
if output is None:
return array_to_memory_file(
array, source, dtype, compress, transpose, cellsize, crs, driver, **kwargs
)
if isinstance(array, xr.DataArray):
coords = [coord for coord in array.coords]
if coords[0] == "time":
x_dim = coords[1]
y_dim = coords[2]
if array.dims[0] == "time":
array = array.isel(time=0)
array = array.rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
array = array.values
if array.ndim == 3 and transpose:
array = np.transpose(array, (1, 2, 0))
out_dir = os.path.dirname(os.path.abspath(output))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not output.endswith(".tif"):
output += ".tif"
if source is not None:
with rasterio.open(source) as src:
crs = src.crs
transform = src.transform
if compress is None:
compress = src.compression
else:
if cellsize is None:
raise ValueError("resolution must be provided if source is not provided")
if crs is None:
raise ValueError(
"crs must be provided if source is not provided, such as EPSG:3857"
)
if "transform" not in kwargs:
# Define the geotransformation parameters
xmin, ymin, xmax, ymax = (
0,
0,
cellsize * array.shape[1],
cellsize * array.shape[0],
)
transform = rasterio.transform.from_bounds(
xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
)
else:
transform = kwargs["transform"]
if dtype is None:
# Determine the minimum and maximum values in the array
min_value = np.min(array)
max_value = np.max(array)
# Determine the best dtype for the array
if min_value >= 0 and max_value <= 1:
dtype = np.float32
elif min_value >= 0 and max_value <= 255:
dtype = np.uint8
elif min_value >= -128 and max_value <= 127:
dtype = np.int8
elif min_value >= 0 and max_value <= 65535:
dtype = np.uint16
elif min_value >= -32768 and max_value <= 32767:
dtype = np.int16
else:
dtype = np.float64
# Convert the array to the best dtype
array = array.astype(dtype)
# Define the GeoTIFF metadata
metadata = {
"driver": driver,
"height": array.shape[0],
"width": array.shape[1],
"dtype": array.dtype,
"crs": crs,
"transform": transform,
}
if array.ndim == 2:
metadata["count"] = 1
elif array.ndim == 3:
metadata["count"] = array.shape[2]
if compress is not None:
metadata["compress"] = compress
metadata.update(**kwargs)
# Create a new GeoTIFF file and write the array to it
with rasterio.open(output, "w", **metadata) as dst:
if array.ndim == 2:
dst.write(array, 1)
elif array.ndim == 3:
for i in range(array.shape[2]):
dst.write(array[:, :, i], i + 1)
array_to_memory_file(array, source=None, dtype=None, compress='deflate', transpose=True, cellsize=None, crs=None, transform=None, driver='COG', **kwargs)
¶
Convert a NumPy array to a memory file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
array |
numpy.ndarray |
The input NumPy array. |
required |
source |
str |
Path to the source file to extract metadata from. Defaults to None. |
None |
dtype |
str |
The desired data type of the array. Defaults to None. |
None |
compress |
str |
The compression method for the output file. Defaults to "deflate". |
'deflate' |
transpose |
bool |
Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True. |
True |
cellsize |
float |
The cell size of the array if source is not provided. Defaults to None. |
None |
crs |
str |
The coordinate reference system of the array if source is not provided. Defaults to None. |
None |
transform |
tuple |
The affine transformation matrix if source is not provided. Defaults to None. |
None |
driver |
str |
The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG". |
'COG' |
**kwargs |
Additional keyword arguments to be passed to the rasterio.open() function. |
{} |
Returns:
| Type | Description |
|---|---|
rasterio.DatasetReader |
The rasterio dataset reader object for the converted array. |
Source code in geemap/common.py
def array_to_memory_file(
array,
source: str = None,
dtype: str = None,
compress: str = "deflate",
transpose: bool = True,
cellsize: float = None,
crs: str = None,
transform: tuple = None,
driver="COG",
**kwargs,
):
"""Convert a NumPy array to a memory file.
Args:
array (numpy.ndarray): The input NumPy array.
source (str, optional): Path to the source file to extract metadata from. Defaults to None.
dtype (str, optional): The desired data type of the array. Defaults to None.
compress (str, optional): The compression method for the output file. Defaults to "deflate".
transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
cellsize (float, optional): The cell size of the array if source is not provided. Defaults to None.
crs (str, optional): The coordinate reference system of the array if source is not provided. Defaults to None.
transform (tuple, optional): The affine transformation matrix if source is not provided. Defaults to None.
driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
**kwargs: Additional keyword arguments to be passed to the rasterio.open() function.
Returns:
rasterio.DatasetReader: The rasterio dataset reader object for the converted array.
"""
import rasterio
import numpy as np
import xarray as xr
if isinstance(array, xr.DataArray):
coords = [coord for coord in array.coords]
if coords[0] == "time":
x_dim = coords[1]
y_dim = coords[2]
if array.dims[0] == "time":
array = array.isel(time=0)
array = array.rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
array = array.values
if array.ndim == 3 and transpose:
array = np.transpose(array, (1, 2, 0))
if source is not None:
with rasterio.open(source) as src:
crs = src.crs
transform = src.transform
if compress is None:
compress = src.compression
else:
if cellsize is None:
raise ValueError("cellsize must be provided if source is not provided")
if crs is None:
raise ValueError(
"crs must be provided if source is not provided, such as EPSG:3857"
)
if "transform" not in kwargs:
# Define the geotransformation parameters
xmin, ymin, xmax, ymax = (
0,
0,
cellsize * array.shape[1],
cellsize * array.shape[0],
)
# (west, south, east, north, width, height)
transform = rasterio.transform.from_bounds(
xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
)
else:
transform = kwargs["transform"]
if dtype is None:
# Determine the minimum and maximum values in the array
min_value = np.min(array)
max_value = np.max(array)
# Determine the best dtype for the array
if min_value >= 0 and max_value <= 1:
dtype = np.float32
elif min_value >= 0 and max_value <= 255:
dtype = np.uint8
elif min_value >= -128 and max_value <= 127:
dtype = np.int8
elif min_value >= 0 and max_value <= 65535:
dtype = np.uint16
elif min_value >= -32768 and max_value <= 32767:
dtype = np.int16
else:
dtype = np.float64
# Convert the array to the best dtype
array = array.astype(dtype)
# Define the GeoTIFF metadata
metadata = {
"driver": driver,
"height": array.shape[0],
"width": array.shape[1],
"dtype": array.dtype,
"crs": crs,
"transform": transform,
}
if array.ndim == 2:
metadata["count"] = 1
elif array.ndim == 3:
metadata["count"] = array.shape[2]
if compress is not None:
metadata["compress"] = compress
metadata.update(**kwargs)
# Create a new memory file and write the array to it
memory_file = rasterio.MemoryFile()
dst = memory_file.open(**metadata)
if array.ndim == 2:
dst.write(array, 1)
elif array.ndim == 3:
for i in range(array.shape[2]):
dst.write(array[:, :, i], i + 1)
dst.close()
# Read the dataset from memory
dataset_reader = rasterio.open(dst.name, mode="r")
return dataset_reader
bands_to_image_collection(img)
¶
Converts all bands in an image to an image collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to convert. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def bands_to_image_collection(img):
"""Converts all bands in an image to an image collection.
Args:
img (object): The image to convert.
Returns:
object: ee.ImageCollection
"""
collection = ee.ImageCollection(img.bandNames().map(lambda b: img.select([b])))
return collection
bbox_coords(geometry, decimals=4)
¶
Get the bounding box coordinates of a geometry.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geometry |
ee.Geometry | ee.FeatureCollection |
The input geometry. |
required |
decimals |
int |
The number of decimals to round to. Defaults to 4. |
4 |
Returns:
| Type | Description |
|---|---|
list |
The bounding box coordinates in the form [west, south, east, north]. |
Source code in geemap/common.py
def bbox_coords(geometry, decimals=4):
"""Get the bounding box coordinates of a geometry.
Args:
geometry (ee.Geometry | ee.FeatureCollection): The input geometry.
decimals (int, optional): The number of decimals to round to. Defaults to 4.
Returns:
list: The bounding box coordinates in the form [west, south, east, north].
"""
if isinstance(geometry, ee.FeatureCollection):
geometry = geometry.geometry()
if geometry is not None:
if not isinstance(geometry, ee.Geometry):
raise ValueError("geometry must be an ee.Geometry.")
coords = geometry.bounds().coordinates().getInfo()[0]
x = [p[0] for p in coords]
y = [p[1] for p in coords]
west = round(min(x), decimals)
east = round(max(x), decimals)
south = round(min(y), decimals)
north = round(max(y), decimals)
return [west, south, east, north]
else:
return None
bbox_to_gdf(bbox, crs='EPSG:4326')
¶
Converts a bounding box to a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
bbox |
tuple |
A bounding box in the form of a tuple (minx, miny, maxx, maxy). |
required |
crs |
str |
The coordinate reference system of the bounding box to convert to. Defaults to "EPSG:4326". |
'EPSG:4326' |
Returns:
| Type | Description |
|---|---|
geopandas.GeoDataFrame |
A GeoDataFrame containing the bounding box. |
Source code in geemap/common.py
def bbox_to_gdf(bbox, crs="EPSG:4326"):
"""Converts a bounding box to a GeoDataFrame.
Args:
bbox (tuple): A bounding box in the form of a tuple (minx, miny, maxx, maxy).
crs (str, optional): The coordinate reference system of the bounding box to convert to. Defaults to "EPSG:4326".
Returns:
geopandas.GeoDataFrame: A GeoDataFrame containing the bounding box.
"""
check_package(name="geopandas", URL="https://geopandas.org")
from shapely.geometry import box
import geopandas as gpd
minx, miny, maxx, maxy = bbox
geometry = box(minx, miny, maxx, maxy)
d = {"geometry": [geometry]}
gdf = gpd.GeoDataFrame(d, crs="EPSG:4326")
gdf.to_crs(crs=crs, inplace=True)
return gdf
bbox_to_geojson(bounds)
¶
Convert coordinates of a bounding box to a geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
bounds |
list |
A list of coordinates representing [left, bottom, right, top]. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A geojson feature. |
Source code in geemap/common.py
def bbox_to_geojson(bounds):
"""Convert coordinates of a bounding box to a geojson.
Args:
bounds (list): A list of coordinates representing [left, bottom, right, top].
Returns:
dict: A geojson feature.
"""
return {
"geometry": {
"type": "Polygon",
"coordinates": [
[
[bounds[0], bounds[3]],
[bounds[0], bounds[1]],
[bounds[2], bounds[1]],
[bounds[2], bounds[3]],
[bounds[0], bounds[3]],
]
],
},
"type": "Feature",
}
blend(top_layer, bottom_layer=None, top_vis=None, bottom_vis=None, hillshade=True, expression='a*b', **kwargs)
¶
Create a blended image that is a combination of two images, e.g., DEM and hillshade. This function was inspired by Jesse Anderson. See https://github.com/jessjaco/gee-blend.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
top_layer |
ee.Image |
The top layer image, e.g., ee.Image("CGIAR/SRTM90_V4") |
required |
bottom_layer |
ee.Image |
The bottom layer image. If not specified, it will use the top layer image. |
None |
top_vis |
dict |
The top layer image vis parameters as a dictionary. Defaults to None. |
None |
bottom_vis |
dict |
The bottom layer image vis parameters as a dictionary. Defaults to None. |
None |
hillshade |
bool |
Flag to use hillshade. Defaults to True. |
True |
expression |
str |
The expression to use for the blend. Defaults to 'a*b'. |
'a*b' |
Returns:
| Type | Description |
|---|---|
ee.Image |
The blended image. |
Source code in geemap/common.py
def blend(
top_layer,
bottom_layer=None,
top_vis=None,
bottom_vis=None,
hillshade=True,
expression="a*b",
**kwargs,
):
"""Create a blended image that is a combination of two images, e.g., DEM and hillshade. This function was inspired by Jesse Anderson. See https://github.com/jessjaco/gee-blend.
Args:
top_layer (ee.Image): The top layer image, e.g., ee.Image("CGIAR/SRTM90_V4")
bottom_layer (ee.Image, optional): The bottom layer image. If not specified, it will use the top layer image.
top_vis (dict, optional): The top layer image vis parameters as a dictionary. Defaults to None.
bottom_vis (dict, optional): The bottom layer image vis parameters as a dictionary. Defaults to None.
hillshade (bool, optional): Flag to use hillshade. Defaults to True.
expression (str, optional): The expression to use for the blend. Defaults to 'a*b'.
Returns:
ee.Image: The blended image.
"""
from box import Box
if not isinstance(top_layer, ee.Image):
raise ValueError("top_layer must be an ee.Image.")
if bottom_layer is None:
bottom_layer = top_layer
if not isinstance(bottom_layer, ee.Image):
raise ValueError("bottom_layer must be an ee.Image.")
if top_vis is not None:
if not isinstance(top_vis, dict):
raise ValueError("top_vis must be a dictionary.")
elif "palette" in top_vis and isinstance(top_vis["palette"], Box):
try:
top_vis["palette"] = top_vis["palette"]["default"]
except Exception as e:
print("The provided palette is invalid.")
raise Exception(e)
if bottom_vis is not None:
if not isinstance(bottom_vis, dict):
raise ValueError("top_vis must be a dictionary.")
elif "palette" in bottom_vis and isinstance(bottom_vis["palette"], Box):
try:
bottom_vis["palette"] = bottom_vis["palette"]["default"]
except Exception as e:
print("The provided palette is invalid.")
raise Exception(e)
if top_vis is None:
top_bands = top_layer.bandNames().getInfo()
top_vis = {"bands": top_bands}
if hillshade:
top_vis["palette"] = ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"]
top_vis["min"] = 0
top_vis["max"] = 6000
if bottom_vis is None:
bottom_bands = bottom_layer.bandNames().getInfo()
bottom_vis = {"bands": bottom_bands}
if hillshade:
bottom_vis["bands"] = ["hillshade"]
top = top_layer.visualize(**top_vis).divide(255)
if hillshade:
bottom = ee.Terrain.hillshade(bottom_layer).visualize(**bottom_vis).divide(255)
else:
bottom = bottom_layer.visualize(**bottom_vis).divide(255)
if "a" not in expression or ("b" not in expression):
raise ValueError("expression must contain 'a' and 'b'.")
result = ee.Image().expression(expression, {"a": top, "b": bottom})
return result
bounds_to_xy_range(bounds)
¶
Convert bounds to x and y range to be used as input to bokeh map.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
bounds |
list |
A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax]. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (x_range, y_range). |
Source code in geemap/common.py
def bounds_to_xy_range(bounds):
"""Convert bounds to x and y range to be used as input to bokeh map.
Args:
bounds (list): A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax].
Returns:
tuple: A tuple of (x_range, y_range).
"""
if isinstance(bounds, tuple):
bounds = list(bounds)
elif not isinstance(bounds, list):
raise TypeError("bounds must be a list")
if len(bounds) == 4:
west, south, east, north = bounds
elif len(bounds) == 2:
south, west = bounds[0]
north, east = bounds[1]
xmin, ymin = lnglat_to_meters(west, south)
xmax, ymax = lnglat_to_meters(east, north)
x_range = (xmin, xmax)
y_range = (ymin, ymax)
return x_range, y_range
build_api_tree(api_dict, output_widget, layout_width='100%')
¶
Builds an Earth Engine API tree view.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_dict |
dict |
The dictionary containing information about each Earth Engine API function. |
required |
output_widget |
object |
An Output widget. |
required |
layout_width |
str |
The percentage width of the widget. Defaults to '100%'. |
'100%' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing two items: a tree Output widget and a tree dictionary. |
Source code in geemap/common.py
def build_api_tree(api_dict, output_widget, layout_width="100%"):
"""Builds an Earth Engine API tree view.
Args:
api_dict (dict): The dictionary containing information about each Earth Engine API function.
output_widget (object): An Output widget.
layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.
Returns:
tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.
"""
warnings.filterwarnings("ignore")
tree = Tree()
tree_dict = {}
names = api_dict.keys()
def handle_click(event):
if event["new"]:
name = event["owner"].name
values = api_dict[name]
with output_widget:
output_widget.outputs = ()
html_widget = widgets.HTML(value=values["html"])
display(html_widget)
for name in names:
func_list = ee_function_tree(name)
first = func_list[0]
if first not in tree_dict.keys():
tree_dict[first] = Node(first)
tree_dict[first].opened = False
tree.add_node(tree_dict[first])
for index, func in enumerate(func_list):
if index > 0:
if func not in tree_dict.keys():
node = tree_dict[func_list[index - 1]]
node.opened = False
tree_dict[func] = Node(func)
node.add_node(tree_dict[func])
if index == len(func_list) - 1:
node = tree_dict[func_list[index]]
node.icon = "file"
node.observe(handle_click, "selected")
return tree, tree_dict
build_repo_tree(out_dir=None, name='gee_repos')
¶
Builds a repo tree for GEE account.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_dir |
str |
The output directory for the repos. Defaults to None. |
None |
name |
str |
The output name for the repo directory. Defaults to 'gee_repos'. |
'gee_repos' |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes. |
Source code in geemap/common.py
def build_repo_tree(out_dir=None, name="gee_repos"):
"""Builds a repo tree for GEE account.
Args:
out_dir (str): The output directory for the repos. Defaults to None.
name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.
Returns:
tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.
"""
warnings.filterwarnings("ignore")
if out_dir is None:
out_dir = os.path.join(os.path.expanduser("~"))
repo_dir = os.path.join(out_dir, name)
if not os.path.exists(repo_dir):
os.makedirs(repo_dir)
URLs = {
# 'Owner': 'https://earthengine.googlesource.com/{ee_user_id()}/default',
"Writer": "",
"Reader": "https://github.com/gee-community/geemap",
"Examples": "https://github.com/giswqs/earthengine-py-examples",
"Archive": "https://earthengine.googlesource.com/EGU2017-EE101",
}
user_id = ee_user_id()
if user_id is not None:
URLs["Owner"] = f"https://earthengine.googlesource.com/{ee_user_id()}/default"
path_widget = widgets.Text(placeholder="Enter the link to a Git repository here...")
path_widget.layout.width = "475px"
clone_widget = widgets.Button(
description="Clone",
button_style="primary",
tooltip="Clone the repository to folder.",
)
info_widget = widgets.HBox()
groups = ["Owner", "Writer", "Reader", "Examples", "Archive"]
for group in groups:
group_dir = os.path.join(repo_dir, group)
if not os.path.exists(group_dir):
os.makedirs(group_dir)
example_dir = os.path.join(repo_dir, "Examples/earthengine-py-examples")
if not os.path.exists(example_dir):
clone_github_repo(URLs["Examples"], out_dir=example_dir)
left_widget, right_widget, tree_dict = file_browser(
in_dir=repo_dir,
add_root_node=False,
search_description="Filter scripts...",
use_import=True,
return_sep_widgets=True,
)
info_widget.children = [right_widget]
def handle_folder_click(event):
if event["new"]:
url = ""
selected = event["owner"]
if selected.name in URLs.keys():
url = URLs[selected.name]
path_widget.value = url
clone_widget.disabled = False
info_widget.children = [path_widget, clone_widget]
else:
info_widget.children = [right_widget]
for group in groups:
dirname = os.path.join(repo_dir, group)
node = tree_dict[dirname]
node.observe(handle_folder_click, "selected")
def handle_clone_click(b):
url = path_widget.value
default_dir = os.path.join(repo_dir, "Examples")
if url == "":
path_widget.value = "Please enter a valid URL to the repository."
else:
for group in groups:
key = os.path.join(repo_dir, group)
node = tree_dict[key]
if node.selected:
default_dir = key
try:
path_widget.value = "Cloning..."
clone_dir = os.path.join(default_dir, os.path.basename(url))
if url.find("github.com") != -1:
clone_github_repo(url, out_dir=clone_dir)
elif url.find("googlesource") != -1:
clone_google_repo(url, out_dir=clone_dir)
path_widget.value = "Cloned to {}".format(clone_dir)
clone_widget.disabled = True
except Exception as e:
path_widget.value = (
"An error occurred when trying to clone the repository " + str(e)
)
clone_widget.disabled = True
clone_widget.on_click(handle_clone_click)
return left_widget, info_widget, tree_dict
center_zoom_to_xy_range(center, zoom)
¶
Convert center and zoom to x and y range to be used as input to bokeh map.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
center |
tuple |
A tuple of (latitude, longitude). |
required |
zoom |
int |
The zoom level. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (x_range, y_range). |
Source code in geemap/common.py
def center_zoom_to_xy_range(center, zoom):
"""Convert center and zoom to x and y range to be used as input to bokeh map.
Args:
center (tuple): A tuple of (latitude, longitude).
zoom (int): The zoom level.
Returns:
tuple: A tuple of (x_range, y_range).
"""
if isinstance(center, tuple) or isinstance(center, list):
pass
else:
raise TypeError("center must be a tuple or list")
if not isinstance(zoom, int):
raise TypeError("zoom must be an integer")
latitude, longitude = center
x_range = (-179, 179)
y_range = (-70, 70)
x_full_length = x_range[1] - x_range[0]
y_full_length = y_range[1] - y_range[0]
x_length = x_full_length / 2 ** (zoom - 2)
y_length = y_full_length / 2 ** (zoom - 2)
south = latitude - y_length / 2
north = latitude + y_length / 2
west = longitude - x_length / 2
east = longitude + x_length / 2
xmin, ymin = lnglat_to_meters(west, south)
xmax, ymax = lnglat_to_meters(east, north)
x_range = (xmin, xmax)
y_range = (ymin, ymax)
return x_range, y_range
check_basemap(basemap)
¶
Check Google basemaps
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
basemap |
str |
The basemap name. |
required |
Returns:
| Type | Description |
|---|---|
str |
The basemap name. |
Source code in geemap/common.py
def check_basemap(basemap):
"""Check Google basemaps
Args:
basemap (str): The basemap name.
Returns:
str: The basemap name.
"""
if isinstance(basemap, str):
map_dict = {
"ROADMAP": "Google Maps",
"SATELLITE": "Google Satellite",
"TERRAIN": "Google Terrain",
"HYBRID": "Google Hybrid",
}
if basemap.upper() in map_dict.keys():
return map_dict[basemap.upper()]
else:
return basemap
else:
return basemap
check_cmap(cmap)
¶
Check the colormap and return a list of colors.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
cmap |
str | list | Box |
The colormap to check. |
required |
Returns:
| Type | Description |
|---|---|
list |
A list of colors. |
Source code in geemap/common.py
def check_cmap(cmap):
"""Check the colormap and return a list of colors.
Args:
cmap (str | list | Box): The colormap to check.
Returns:
list: A list of colors.
"""
from box import Box
from .colormaps import get_palette
if isinstance(cmap, str):
try:
palette = get_palette(cmap)
if isinstance(palette, dict):
palette = palette["default"]
return palette
except Exception as e:
try:
return check_color(cmap)
except Exception as e:
raise Exception(f"{cmap} is not a valid colormap.")
elif isinstance(cmap, Box):
return list(cmap["default"])
elif isinstance(cmap, list) or isinstance(cmap, tuple):
return cmap
else:
raise Exception(f"{cmap} is not a valid colormap.")
check_color(in_color)
¶
Checks the input color and returns the corresponding hex color code.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_color |
str or tuple |
It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple (e.g., (255, 127, 0)). |
required |
Returns:
| Type | Description |
|---|---|
str |
A hex color code. |
Source code in geemap/common.py
def check_color(in_color):
"""Checks the input color and returns the corresponding hex color code.
Args:
in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple (e.g., (255, 127, 0)).
Returns:
str: A hex color code.
"""
import colour
out_color = "#000000" # default black color
if isinstance(in_color, tuple) and len(in_color) == 3:
# rescale color if necessary
if all(isinstance(item, int) for item in in_color):
in_color = [c / 255.0 for c in in_color]
return colour.Color(rgb=tuple(in_color)).hex_l
else:
# try to guess the color system
try:
return colour.Color(in_color).hex_l
except Exception as e:
pass
# try again by adding an extra # (GEE handle hex codes without #)
try:
return colour.Color(f"#{in_color}").hex_l
except Exception as e:
print(
f"The provided color ({in_color}) is invalid. Using the default black color."
)
print(e)
return out_color
check_dir(dir_path, make_dirs=True)
¶
Checks if a directory exists and creates it if it does not.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dir_path |
[str |
The path to the directory. |
required |
make_dirs |
bool |
Whether to create the directory if it does not exist. Defaults to True. |
True |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the directory could not be found. |
TypeError |
If the input directory path is not a string. |
Returns:
| Type | Description |
|---|---|
str |
The path to the directory. |
Source code in geemap/common.py
def check_dir(dir_path, make_dirs=True):
"""Checks if a directory exists and creates it if it does not.
Args:
dir_path ([str): The path to the directory.
make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.
Raises:
FileNotFoundError: If the directory could not be found.
TypeError: If the input directory path is not a string.
Returns:
str: The path to the directory.
"""
if isinstance(dir_path, str):
if dir_path.startswith("~"):
dir_path = os.path.expanduser(dir_path)
else:
dir_path = os.path.abspath(dir_path)
if not os.path.exists(dir_path) and make_dirs:
os.makedirs(dir_path)
if os.path.exists(dir_path):
return dir_path
else:
raise FileNotFoundError("The provided directory could not be found.")
else:
raise TypeError("The provided directory path must be a string.")
check_file_path(file_path, make_dirs=True)
¶
Gets the absolute file path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
file_path |
[str |
The path to the file. |
required |
make_dirs |
bool |
Whether to create the directory if it does not exist. Defaults to True. |
True |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the directory could not be found. |
TypeError |
If the input directory path is not a string. |
Returns:
| Type | Description |
|---|---|
str |
The absolute path to the file. |
Source code in geemap/common.py
def check_file_path(file_path, make_dirs=True):
"""Gets the absolute file path.
Args:
file_path ([str): The path to the file.
make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.
Raises:
FileNotFoundError: If the directory could not be found.
TypeError: If the input directory path is not a string.
Returns:
str: The absolute path to the file.
"""
if isinstance(file_path, str):
if file_path.startswith("~"):
file_path = os.path.expanduser(file_path)
else:
file_path = os.path.abspath(file_path)
file_dir = os.path.dirname(file_path)
if not os.path.exists(file_dir) and make_dirs:
os.makedirs(file_dir)
return file_path
else:
raise TypeError("The provided file path must be a string.")
check_git_install()
¶
Checks if Git is installed.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Git is installed, otherwise returns False. |
Source code in geemap/common.py
def check_git_install():
"""Checks if Git is installed.
Returns:
bool: Returns True if Git is installed, otherwise returns False.
"""
import webbrowser
cmd = "git --version"
output = os.popen(cmd).read()
if "git version" in output:
return True
else:
url = "https://git-scm.com/downloads"
print(f"Git is not installed. Please download Git from {url} and install it.")
webbrowser.open_new_tab(url)
return False
check_install(package)
¶
Checks whether a package is installed. If not, it will install the package.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
package |
str |
The name of the package to check. |
required |
Source code in geemap/common.py
def check_install(package):
"""Checks whether a package is installed. If not, it will install the package.
Args:
package (str): The name of the package to check.
"""
import subprocess
try:
__import__(package)
# print('{} is already installed.'.format(package))
except ImportError:
print(f"{package} is not installed. Installing ...")
try:
subprocess.check_call(["python", "-m", "pip", "install", package])
except Exception as e:
print(f"Failed to install {package}")
print(e)
print(f"{package} has been installed successfully.")
check_titiler_endpoint(titiler_endpoint=None)
¶
Returns the default titiler endpoint.
Returns:
| Type | Description |
|---|---|
object |
A titiler endpoint. |
Source code in geemap/common.py
def check_titiler_endpoint(titiler_endpoint=None):
"""Returns the default titiler endpoint.
Returns:
object: A titiler endpoint.
"""
if titiler_endpoint is None:
if os.environ.get("TITILER_ENDPOINT") is not None:
titiler_endpoint = os.environ.get("TITILER_ENDPOINT")
if titiler_endpoint == "planetary-computer":
titiler_endpoint = PlanetaryComputerEndpoint()
else:
titiler_endpoint = "https://titiler.xyz"
elif titiler_endpoint in ["planetary-computer", "pc"]:
titiler_endpoint = PlanetaryComputerEndpoint()
return titiler_endpoint
classify(data, column, cmap=None, colors=None, labels=None, scheme='Quantiles', k=5, legend_kwds=None, classification_kwds=None)
¶
Classify a dataframe column using a variety of classification schemes.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
str | pd.DataFrame | gpd.GeoDataFrame |
The data to classify. It can be a filepath to a vector dataset, a pandas dataframe, or a geopandas geodataframe. |
required |
column |
str |
The column to classify. |
required |
cmap |
str |
The name of a colormap recognized by matplotlib. Defaults to None. |
None |
colors |
list |
A list of colors to use for the classification. Defaults to None. |
None |
labels |
list |
A list of labels to use for the legend. Defaults to None. |
None |
scheme |
str |
Name of a choropleth classification scheme (requires mapclassify). Name of a choropleth classification scheme (requires mapclassify). A mapclassify.MapClassifier object will be used under the hood. Supported are all schemes provided by mapclassify (e.g. 'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled', 'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced', 'JenksCaspallSampled', 'MaxP', 'MaximumBreaks', 'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean', 'UserDefined'). Arguments can be passed in classification_kwds. |
'Quantiles' |
k |
int |
Number of classes (ignored if scheme is None or if column is categorical). Default to 5. |
5 |
legend_kwds |
dict |
Keyword arguments to pass to :func: |
None |
classification_kwds |
dict |
Keyword arguments to pass to mapclassify. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
pd.DataFrame, dict |
A pandas dataframe with the classification applied and a legend dictionary. |
Source code in geemap/common.py
def classify(
data,
column,
cmap=None,
colors=None,
labels=None,
scheme="Quantiles",
k=5,
legend_kwds=None,
classification_kwds=None,
):
"""Classify a dataframe column using a variety of classification schemes.
Args:
data (str | pd.DataFrame | gpd.GeoDataFrame): The data to classify. It can be a filepath to a vector dataset, a pandas dataframe, or a geopandas geodataframe.
column (str): The column to classify.
cmap (str, optional): The name of a colormap recognized by matplotlib. Defaults to None.
colors (list, optional): A list of colors to use for the classification. Defaults to None.
labels (list, optional): A list of labels to use for the legend. Defaults to None.
scheme (str, optional): Name of a choropleth classification scheme (requires mapclassify).
Name of a choropleth classification scheme (requires mapclassify).
A mapclassify.MapClassifier object will be used
under the hood. Supported are all schemes provided by mapclassify (e.g.
'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled',
'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced',
'JenksCaspallSampled', 'MaxP', 'MaximumBreaks',
'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean',
'UserDefined'). Arguments can be passed in classification_kwds.
k (int, optional): Number of classes (ignored if scheme is None or if column is categorical). Default to 5.
legend_kwds (dict, optional): Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or `matplotlib.pyplot.colorbar`. Defaults to None.
Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or
Additional accepted keywords when `scheme` is specified:
fmt : string
A formatting specification for the bin edges of the classes in the
legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``.
labels : list-like
A list of legend labels to override the auto-generated labblels.
Needs to have the same number of elements as the number of
classes (`k`).
interval : boolean (default False)
An option to control brackets from mapclassify legend.
If True, open/closed interval brackets are shown in the legend.
classification_kwds (dict, optional): Keyword arguments to pass to mapclassify. Defaults to None.
Returns:
pd.DataFrame, dict: A pandas dataframe with the classification applied and a legend dictionary.
"""
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt
try:
import mapclassify
except ImportError:
raise ImportError(
'mapclassify is required for this function. Install with "pip install mapclassify".'
)
if isinstance(data, gpd.GeoDataFrame) or isinstance(data, pd.DataFrame):
df = data
else:
try:
df = gpd.read_file(data)
except Exception:
raise TypeError(
"Data must be a GeoDataFrame or a path to a file that can be read by geopandas.read_file()."
)
if df.empty:
warnings.warn(
"The GeoDataFrame you are attempting to plot is "
"empty. Nothing has been displayed.",
UserWarning,
)
return
columns = df.columns.values.tolist()
if column not in columns:
raise ValueError(
f"{column} is not a column in the GeoDataFrame. It must be one of {columns}."
)
# Convert categorical data to numeric
init_column = None
value_list = None
if np.issubdtype(df[column].dtype, np.object0):
value_list = df[column].unique().tolist()
value_list.sort()
df["category"] = df[column].replace(value_list, range(0, len(value_list)))
init_column = column
column = "category"
k = len(value_list)
if legend_kwds is not None:
legend_kwds = legend_kwds.copy()
# To accept pd.Series and np.arrays as column
if isinstance(column, (np.ndarray, pd.Series)):
if column.shape[0] != df.shape[0]:
raise ValueError(
"The dataframe and given column have different number of rows."
)
else:
values = column
# Make sure index of a Series matches index of df
if isinstance(values, pd.Series):
values = values.reindex(df.index)
else:
values = df[column]
values = df[column]
nan_idx = np.asarray(pd.isna(values), dtype="bool")
if cmap is None:
cmap = "Blues"
cmap = plt.cm.get_cmap(cmap, k)
if colors is None:
colors = [mpl.colors.rgb2hex(cmap(i))[1:] for i in range(cmap.N)]
colors = ["#" + i for i in colors]
elif isinstance(colors, list):
colors = [check_color(i) for i in colors]
elif isinstance(colors, str):
colors = [check_color(colors)] * k
allowed_schemes = [
"BoxPlot",
"EqualInterval",
"FisherJenks",
"FisherJenksSampled",
"HeadTailBreaks",
"JenksCaspall",
"JenksCaspallForced",
"JenksCaspallSampled",
"MaxP",
"MaximumBreaks",
"NaturalBreaks",
"Quantiles",
"Percentiles",
"StdMean",
"UserDefined",
]
if scheme.lower() not in [s.lower() for s in allowed_schemes]:
raise ValueError(
f"{scheme} is not a valid scheme. It must be one of {allowed_schemes}."
)
if classification_kwds is None:
classification_kwds = {}
if "k" not in classification_kwds:
classification_kwds["k"] = k
binning = mapclassify.classify(
np.asarray(values[~nan_idx]), scheme, **classification_kwds
)
df["category"] = binning.yb
df["color"] = [colors[i] for i in df["category"]]
if legend_kwds is None:
legend_kwds = {}
if "interval" not in legend_kwds:
legend_kwds["interval"] = True
if "fmt" not in legend_kwds:
if np.issubdtype(df[column].dtype, np.floating):
legend_kwds["fmt"] = "{:.2f}"
else:
legend_kwds["fmt"] = "{:.0f}"
if labels is None:
# set categorical to True for creating the legend
if legend_kwds is not None and "labels" in legend_kwds:
if len(legend_kwds["labels"]) != binning.k:
raise ValueError(
"Number of labels must match number of bins, "
"received {} labels for {} bins".format(
len(legend_kwds["labels"]), binning.k
)
)
else:
labels = list(legend_kwds.pop("labels"))
else:
# fmt = "{:.2f}"
if legend_kwds is not None and "fmt" in legend_kwds:
fmt = legend_kwds.pop("fmt")
labels = binning.get_legend_classes(fmt)
if legend_kwds is not None:
show_interval = legend_kwds.pop("interval", False)
else:
show_interval = False
if not show_interval:
labels = [c[1:-1] for c in labels]
if init_column is not None:
labels = value_list
elif isinstance(labels, list):
if len(labels) != len(colors):
raise ValueError("The number of labels must match the number of colors.")
else:
raise ValueError("labels must be a list or None.")
legend_dict = dict(zip(labels, colors))
df["category"] = df["category"] + 1
return df, legend_dict
clip_image(image, mask, output)
¶
Clip an image by mask.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
Path to the image file in GeoTIFF format. |
required |
mask |
str | list | dict |
The mask used to extract the image. It can be a path to vector datasets (e.g., GeoJSON, Shapefile), a list of coordinates, or m.user_roi. |
required |
output |
str |
Path to the output file. |
required |
Exceptions:
| Type | Description |
|---|---|
ImportError |
If the fiona or rasterio package is not installed. |
FileNotFoundError |
If the image is not found. |
ValueError |
If the mask is not a valid GeoJSON or raster file. |
FileNotFoundError |
If the mask file is not found. |
Source code in geemap/common.py
def clip_image(image, mask, output):
"""Clip an image by mask.
Args:
image (str): Path to the image file in GeoTIFF format.
mask (str | list | dict): The mask used to extract the image. It can be a path to vector datasets (e.g., GeoJSON, Shapefile), a list of coordinates, or m.user_roi.
output (str): Path to the output file.
Raises:
ImportError: If the fiona or rasterio package is not installed.
FileNotFoundError: If the image is not found.
ValueError: If the mask is not a valid GeoJSON or raster file.
FileNotFoundError: If the mask file is not found.
"""
try:
import fiona
import rasterio
import rasterio.mask
except ImportError as e:
raise ImportError(e)
if not os.path.exists(image):
raise FileNotFoundError(f"{image} does not exist.")
if not output.endswith(".tif"):
raise ValueError("Output must be a tif file.")
output = check_file_path(output)
if isinstance(mask, ee.Geometry):
mask = mask.coordinates().getInfo()[0]
if isinstance(mask, str):
if mask.startswith("http"):
mask = download_file(mask, output)
if not os.path.exists(mask):
raise FileNotFoundError(f"{mask} does not exist.")
elif isinstance(mask, list) or isinstance(mask, dict):
if isinstance(mask, list):
geojson = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {},
"geometry": {"type": "Polygon", "coordinates": [mask]},
}
],
}
else:
geojson = {
"type": "FeatureCollection",
"features": [mask],
}
mask = temp_file_path(".geojson")
with open(mask, "w") as f:
json.dump(geojson, f)
with fiona.open(mask, "r") as shapefile:
shapes = [feature["geometry"] for feature in shapefile]
with rasterio.open(image) as src:
out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True)
out_meta = src.meta
out_meta.update(
{
"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform,
}
)
with rasterio.open(output, "w", **out_meta) as dest:
dest.write(out_image)
clone_github_repo(url, out_dir)
¶
Clones a GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The link to the GitHub repository |
required |
out_dir |
str |
The output directory for the cloned repository. |
required |
Source code in geemap/common.py
def clone_github_repo(url, out_dir):
"""Clones a GitHub repository.
Args:
url (str): The link to the GitHub repository
out_dir (str): The output directory for the cloned repository.
"""
repo_name = os.path.basename(url)
# url_zip = os.path.join(url, 'archive/master.zip')
url_zip = url + "/archive/master.zip"
if os.path.exists(out_dir):
print(
"The specified output directory already exists. Please choose a new directory."
)
return
parent_dir = os.path.dirname(out_dir)
out_file_path = os.path.join(parent_dir, repo_name + ".zip")
try:
urllib.request.urlretrieve(url_zip, out_file_path)
except Exception:
print("The provided URL is invalid. Please double check the URL.")
return
with zipfile.ZipFile(out_file_path, "r") as zip_ref:
zip_ref.extractall(parent_dir)
src = out_file_path.replace(".zip", "-master")
os.rename(src, out_dir)
os.remove(out_file_path)
clone_google_repo(url, out_dir=None)
¶
Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The link to the Earth Engine repository |
required |
out_dir |
str |
The output directory for the cloned repository. Defaults to None. |
None |
Source code in geemap/common.py
def clone_google_repo(url, out_dir=None):
"""Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets
Args:
url (str): The link to the Earth Engine repository
out_dir (str, optional): The output directory for the cloned repository. Defaults to None.
"""
repo_name = os.path.basename(url)
if out_dir is None:
out_dir = os.path.join(os.getcwd(), repo_name)
if not os.path.exists(os.path.dirname(out_dir)):
os.makedirs(os.path.dirname(out_dir))
if os.path.exists(out_dir):
print(
"The specified output directory already exists. Please choose a new directory."
)
return
if check_git_install():
cmd = f'git clone "{url}" "{out_dir}"'
os.popen(cmd).read()
clone_repo(out_dir='.', unzip=True)
¶
Clones the geemap GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_dir |
str |
Output folder for the repo. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the repository. Defaults to True. |
True |
Source code in geemap/common.py
def clone_repo(out_dir=".", unzip=True):
"""Clones the geemap GitHub repository.
Args:
out_dir (str, optional): Output folder for the repo. Defaults to '.'.
unzip (bool, optional): Whether to unzip the repository. Defaults to True.
"""
url = "https://github.com/gee-community/geemap/archive/master.zip"
filename = "geemap-master.zip"
download_from_url(url, out_file_name=filename, out_dir=out_dir, unzip=unzip)
cog_bands(url, titiler_endpoint=None, timeout=300)
¶
Get band names of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of band names |
Source code in geemap/common.py
def cog_bands(url, titiler_endpoint=None, timeout=300):
"""Get band names of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A list of band names
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
r = requests.get(
f"{titiler_endpoint}/cog/info",
params={
"url": url,
},
timeout=timeout,
).json()
bands = [b[0] for b in r["band_descriptions"]]
return bands
cog_bounds(url, titiler_endpoint=None, timeout=300)
¶
Get the bounding box of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of values representing [left, bottom, right, top] |
Source code in geemap/common.py
def cog_bounds(url, titiler_endpoint=None, timeout=300):
"""Get the bounding box of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A list of values representing [left, bottom, right, top]
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
r = requests.get(
f"{titiler_endpoint}/cog/bounds", params={"url": url}, timeout=timeout
).json()
if "bounds" in r.keys():
bounds = r["bounds"]
else:
bounds = None
return bounds
cog_center(url, titiler_endpoint=None)
¶
Get the centroid of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple representing (longitude, latitude) |
Source code in geemap/common.py
def cog_center(url, titiler_endpoint=None):
"""Get the centroid of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
Returns:
tuple: A tuple representing (longitude, latitude)
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
bounds = cog_bounds(url, titiler_endpoint)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lat, lon)
return center
cog_info(url, titiler_endpoint=None, return_geojson=False, timeout=300)
¶
Get band statistics of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band info. |
Source code in geemap/common.py
def cog_info(url, titiler_endpoint=None, return_geojson=False, timeout=300):
"""Get band statistics of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band info.
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
info = "info"
if return_geojson:
info = "info.geojson"
r = requests.get(
f"{titiler_endpoint}/cog/{info}",
params={
"url": url,
},
timeout=timeout,
).json()
return r
cog_mosaic(links, titiler_endpoint=None, username='anonymous', layername=None, overwrite=False, verbose=True, timeout=300, **kwargs)
¶
Creates a COG mosaic from a list of COG URLs.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
links |
list |
A list containing COG HTTP URLs. |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
username |
str |
User name for the titiler endpoint. Defaults to "anonymous". |
'anonymous' |
layername |
[type] |
Layer name to use. Defaults to None. |
None |
overwrite |
bool |
Whether to overwrite the layer name if existing. Defaults to False. |
False |
verbose |
bool |
Whether to print out descriptive information. Defaults to True. |
True |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Exceptions:
| Type | Description |
|---|---|
Exception |
If the COG mosaic fails to create. |
Returns:
| Type | Description |
|---|---|
str |
The tile URL for the COG mosaic. |
Source code in geemap/common.py
def cog_mosaic(
links,
titiler_endpoint=None,
username="anonymous",
layername=None,
overwrite=False,
verbose=True,
timeout=300,
**kwargs,
):
"""Creates a COG mosaic from a list of COG URLs.
Args:
links (list): A list containing COG HTTP URLs.
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
username (str, optional): User name for the titiler endpoint. Defaults to "anonymous".
layername ([type], optional): Layer name to use. Defaults to None.
overwrite (bool, optional): Whether to overwrite the layer name if existing. Defaults to False.
verbose (bool, optional): Whether to print out descriptive information. Defaults to True.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Raises:
Exception: If the COG mosaic fails to create.
Returns:
str: The tile URL for the COG mosaic.
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if layername is None:
layername = "layer_" + random_string(5)
try:
if verbose:
print("Creating COG masaic ...")
# Create token
r = requests.post(
f"{titiler_endpoint}/tokens/create",
json={"username": username, "scope": ["mosaic:read", "mosaic:create"]},
).json()
token = r["token"]
# Create mosaic
requests.post(
f"{titiler_endpoint}/mosaicjson/create",
json={
"username": username,
"layername": layername,
"files": links,
# "overwrite": overwrite
},
params={
"access_token": token,
},
).json()
r2 = requests.get(
f"{titiler_endpoint}/mosaicjson/{username}.{layername}/tilejson.json",
timeout=timeout,
).json()
return r2["tiles"][0]
except Exception as e:
raise Exception(e)
cog_mosaic_from_file(filepath, skip_rows=0, titiler_endpoint=None, username='anonymous', layername=None, overwrite=False, verbose=True, **kwargs)
¶
Creates a COG mosaic from a csv/txt file stored locally for through HTTP URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
str |
Local path or HTTP URL to the csv/txt file containing COG URLs. |
required |
skip_rows |
int |
The number of rows to skip in the file. Defaults to 0. |
0 |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
username |
str |
User name for the titiler endpoint. Defaults to "anonymous". |
'anonymous' |
layername |
[type] |
Layer name to use. Defaults to None. |
None |
overwrite |
bool |
Whether to overwrite the layer name if existing. Defaults to False. |
False |
verbose |
bool |
Whether to print out descriptive information. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
str |
The tile URL for the COG mosaic. |
Source code in geemap/common.py
def cog_mosaic_from_file(
filepath,
skip_rows=0,
titiler_endpoint=None,
username="anonymous",
layername=None,
overwrite=False,
verbose=True,
**kwargs,
):
"""Creates a COG mosaic from a csv/txt file stored locally for through HTTP URL.
Args:
filepath (str): Local path or HTTP URL to the csv/txt file containing COG URLs.
skip_rows (int, optional): The number of rows to skip in the file. Defaults to 0.
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
username (str, optional): User name for the titiler endpoint. Defaults to "anonymous".
layername ([type], optional): Layer name to use. Defaults to None.
overwrite (bool, optional): Whether to overwrite the layer name if existing. Defaults to False.
verbose (bool, optional): Whether to print out descriptive information. Defaults to True.
Returns:
str: The tile URL for the COG mosaic.
"""
import urllib
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
links = []
if filepath.startswith("http"):
data = urllib.request.urlopen(filepath)
for line in data:
links.append(line.decode("utf-8").strip())
else:
with open(filepath) as f:
links = [line.strip() for line in f.readlines()]
links = links[skip_rows:]
# print(links)
mosaic = cog_mosaic(
links, titiler_endpoint, username, layername, overwrite, verbose, **kwargs
)
return mosaic
cog_pixel_value(lon, lat, url, bidx=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get pixel value from COG.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lon |
float |
Longitude of the pixel. |
required |
lat |
float |
Latitude of the pixel. |
required |
url |
str |
HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif' |
required |
bidx |
str |
Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band info. |
Source code in geemap/common.py
def cog_pixel_value(
lon,
lat,
url,
bidx=None,
titiler_endpoint=None,
timeout=300,
**kwargs,
):
"""Get pixel value from COG.
Args:
lon (float): Longitude of the pixel.
lat (float): Latitude of the pixel.
url (str): HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'
bidx (str, optional): Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band info.
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
kwargs["url"] = url
if bidx is not None:
kwargs["bidx"] = bidx
r = requests.get(
f"{titiler_endpoint}/cog/point/{lon},{lat}", params=kwargs, timeout=timeout
).json()
bands = cog_bands(url, titiler_endpoint)
# if isinstance(titiler_endpoint, str):
# r = requests.get(f"{titiler_endpoint}/cog/point/{lon},{lat}", params=kwargs).json()
# else:
# r = requests.get(
# titiler_endpoint.url_for_stac_pixel_value(lon, lat), params=kwargs
# ).json()
if "detail" in r:
print(r["detail"])
return None
else:
values = r["values"]
result = dict(zip(bands, values))
return result
cog_stats(url, titiler_endpoint=None, timeout=300)
¶
Get band statistics of a Cloud Optimized GeoTIFF (COG).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band statistics. |
Source code in geemap/common.py
def cog_stats(url, titiler_endpoint=None, timeout=300):
"""Get band statistics of a Cloud Optimized GeoTIFF (COG).
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band statistics.
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
r = requests.get(
f"{titiler_endpoint}/cog/statistics",
params={
"url": url,
},
timeout=timeout,
).json()
return r
cog_tile(url, bands=None, titiler_endpoint=None, timeout=300, proxies=None, **kwargs)
¶
Get a tile layer from a Cloud Optimized GeoTIFF (COG). Source code adapted from https://developmentseed.org/titiler/examples/notebooks/Working_with_CloudOptimizedGeoTIFF_simple/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif |
required |
titiler_endpoint |
str |
Titiler endpoint. Defaults to "https://titiler.xyz". |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
Proxies to use. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
tuple |
Returns the COG Tile layer URL and bounds. |
Source code in geemap/common.py
def cog_tile(
url,
bands=None,
titiler_endpoint=None,
timeout=300,
proxies=None,
**kwargs,
):
"""Get a tile layer from a Cloud Optimized GeoTIFF (COG).
Source code adapted from https://developmentseed.org/titiler/examples/notebooks/Working_with_CloudOptimizedGeoTIFF_simple/
Args:
url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): Proxies to use. Defaults to None.
Returns:
tuple: Returns the COG Tile layer URL and bounds.
"""
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
url = get_direct_url(url)
kwargs["url"] = url
band_names = cog_bands(url, titiler_endpoint)
if bands is None and "bidx" not in kwargs:
if len(band_names) >= 3:
kwargs["bidx"] = [1, 2, 3]
elif bands is not None and "bidx" not in kwargs:
if all(isinstance(x, int) for x in bands):
kwargs["bidx"] = bands
elif all(isinstance(x, str) for x in bands):
kwargs["bidx"] = [band_names.index(x) + 1 for x in bands]
else:
raise ValueError("Bands must be a list of integers or strings.")
if "palette" in kwargs:
kwargs["colormap_name"] = kwargs.pop("palette")
if "colormap" in kwargs:
kwargs["colormap_name"] = kwargs.pop("colormap")
if "rescale" not in kwargs:
stats = cog_stats(url, titiler_endpoint)
percentile_2 = min([stats[s]["percentile_2"] for s in stats])
percentile_98 = max([stats[s]["percentile_98"] for s in stats])
kwargs["rescale"] = f"{percentile_2},{percentile_98}"
TileMatrixSetId = "WebMercatorQuad"
if "TileMatrixSetId" in kwargs.keys():
TileMatrixSetId = kwargs["TileMatrixSetId"]
kwargs.pop("TileMatrixSetId")
r = requests.get(
f"{titiler_endpoint}/cog/{TileMatrixSetId}/tilejson.json",
params=kwargs,
timeout=timeout,
proxies=proxies,
).json()
return r["tiles"][0]
cog_validate(source, verbose=False)
¶
Validate Cloud Optimized Geotiff.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
A dataset path or URL. Will be opened in "r" mode. |
required |
verbose |
bool |
Whether to print the output of the validation. Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
ImportError |
If the rio-cogeo package is not installed. |
FileNotFoundError |
If the provided file could not be found. |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple containing the validation results (True is src_path is a valid COG, List of validation errors, and a list of validation warnings). |
Source code in geemap/common.py
def cog_validate(source, verbose=False):
"""Validate Cloud Optimized Geotiff.
Args:
source (str): A dataset path or URL. Will be opened in "r" mode.
verbose (bool, optional): Whether to print the output of the validation. Defaults to False.
Raises:
ImportError: If the rio-cogeo package is not installed.
FileNotFoundError: If the provided file could not be found.
Returns:
tuple: A tuple containing the validation results (True is src_path is a valid COG, List of validation errors, and a list of validation warnings).
"""
try:
from rio_cogeo.cogeo import cog_validate, cog_info
except ImportError:
raise ImportError(
"The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
)
if not source.startswith("http"):
source = check_file_path(source)
if not os.path.exists(source):
raise FileNotFoundError("The provided input file could not be found.")
if verbose:
return cog_info(source)
else:
return cog_validate(source)
column_stats(collection, column, stats_type)
¶
Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
FeatureCollection |
The input feature collection to calculate statistics. |
required |
column |
str |
The name of the column to calculate statistics. |
required |
stats_type |
str |
The type of statistics to calculate. |
required |
Returns:
| Type | Description |
|---|---|
dict |
The dictionary containing information about the requested statistics. |
Source code in geemap/common.py
def column_stats(collection, column, stats_type):
"""Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean,
sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Args:
collection (FeatureCollection): The input feature collection to calculate statistics.
column (str): The name of the column to calculate statistics.
stats_type (str): The type of statistics to calculate.
Returns:
dict: The dictionary containing information about the requested statistics.
"""
stats_type = stats_type.lower()
allowed_stats = ["min", "max", "mean", "median", "sum", "stdDev", "variance"]
if stats_type not in allowed_stats:
print(
"The stats type must be one of the following: {}".format(
",".join(allowed_stats)
)
)
return
stats_dict = {
"min": ee.Reducer.min(),
"max": ee.Reducer.max(),
"mean": ee.Reducer.mean(),
"median": ee.Reducer.median(),
"sum": ee.Reducer.sum(),
"stdDev": ee.Reducer.stdDev(),
"variance": ee.Reducer.variance(),
}
selectors = [column]
stats = collection.reduceColumns(
**{"selectors": selectors, "reducer": stats_dict[stats_type]}
)
return stats
connect_postgis(database, host='localhost', user=None, password=None, port=5432, use_env_var=False)
¶
Connects to a PostGIS database.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
database |
str |
Name of the database |
required |
host |
str |
Hosting server for the database. Defaults to "localhost". |
'localhost' |
user |
str |
User name to access the database. Defaults to None. |
None |
password |
str |
Password to access the database. Defaults to None. |
None |
port |
int |
Port number to connect to at the server host. Defaults to 5432. |
5432 |
use_env_var |
bool |
Whether to use environment variables. It set to True, user and password are treated as an environment variables with default values user="SQL_USER" and password="SQL_PASSWORD". Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If user is not specified. |
ValueError |
If password is not specified. |
Returns:
| Type | Description |
|---|---|
[type] |
[description] |
Source code in geemap/common.py
def connect_postgis(
database, host="localhost", user=None, password=None, port=5432, use_env_var=False
):
"""Connects to a PostGIS database.
Args:
database (str): Name of the database
host (str, optional): Hosting server for the database. Defaults to "localhost".
user (str, optional): User name to access the database. Defaults to None.
password (str, optional): Password to access the database. Defaults to None.
port (int, optional): Port number to connect to at the server host. Defaults to 5432.
use_env_var (bool, optional): Whether to use environment variables. It set to True, user and password are treated as an environment variables with default values user="SQL_USER" and password="SQL_PASSWORD". Defaults to False.
Raises:
ValueError: If user is not specified.
ValueError: If password is not specified.
Returns:
[type]: [description]
"""
check_package(name="geopandas", URL="https://geopandas.org")
check_package(
name="sqlalchemy",
URL="https://docs.sqlalchemy.org/en/14/intro.html#installation",
)
from sqlalchemy import create_engine
if use_env_var:
if user is not None:
user = os.getenv(user)
else:
user = os.getenv("SQL_USER")
if password is not None:
password = os.getenv(password)
else:
password = os.getenv("SQL_PASSWORD")
if user is None:
raise ValueError("user is not specified.")
if password is None:
raise ValueError("password is not specified.")
connection_string = f"postgresql://{user}:{password}@{host}:{port}/{database}"
engine = create_engine(connection_string)
return engine
convert_lidar(source, destination=None, point_format_id=None, file_version=None, **kwargs)
¶
Converts a Las from one point format to another Automatically upgrades the file version if source file version is not compatible with the new point_format_id
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str | laspy.lasdatas.base.LasBase |
The source data to be converted. |
required |
destination |
str |
The destination file path. Defaults to None. |
None |
point_format_id |
int |
The new point format id (the default is None, which won't change the source format id). |
None |
file_version |
str |
The new file version. None by default which means that the file_version may be upgraded for compatibility with the new point_format. The file version will not be downgraded. |
None |
Returns:
| Type | Description |
|---|---|
aspy.lasdatas.base.LasBase |
The converted LasData object. |
Source code in geemap/common.py
def convert_lidar(
source, destination=None, point_format_id=None, file_version=None, **kwargs
):
"""Converts a Las from one point format to another Automatically upgrades the file version if source file version
is not compatible with the new point_format_id
Args:
source (str | laspy.lasdatas.base.LasBase): The source data to be converted.
destination (str, optional): The destination file path. Defaults to None.
point_format_id (int, optional): The new point format id (the default is None, which won't change the source format id).
file_version (str, optional): The new file version. None by default which means that the file_version may be upgraded
for compatibility with the new point_format. The file version will not be downgraded.
Returns:
aspy.lasdatas.base.LasBase: The converted LasData object.
"""
try:
import laspy
except ImportError:
print(
"The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
)
return
if isinstance(source, str):
source = read_lidar(source)
las = laspy.convert(
source, point_format_id=point_format_id, file_version=file_version
)
if destination is None:
return las
else:
destination = check_file_path(destination)
write_lidar(las, destination, **kwargs)
return destination
coords_to_geojson(coords)
¶
Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords |
list |
A list of bbox coordinates representing [left, bottom, right, top]. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A geojson FeatureCollection. |
Source code in geemap/common.py
def coords_to_geojson(coords):
"""Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.
Args:
coords (list): A list of bbox coordinates representing [left, bottom, right, top].
Returns:
dict: A geojson FeatureCollection.
"""
features = []
for bbox in coords:
features.append(bbox_to_geojson(bbox))
return {"type": "FeatureCollection", "features": features}
copy_credentials_to_colab()
¶
Copies ee credentials from Google Drive to Google Colab.
Source code in geemap/common.py
def copy_credentials_to_colab():
"""Copies ee credentials from Google Drive to Google Colab."""
src = "/content/drive/My Drive/.config/earthengine/credentials"
dst = "/root/.config/earthengine/credentials"
wd = os.path.dirname(dst)
if not os.path.exists(wd):
os.makedirs(wd)
shutil.copyfile(src, dst)
copy_credentials_to_drive()
¶
Copies ee credentials from Google Colab to Google Drive.
Source code in geemap/common.py
def copy_credentials_to_drive():
"""Copies ee credentials from Google Colab to Google Drive."""
src = "/root/.config/earthengine/credentials"
dst = "/content/drive/My Drive/.config/earthengine/credentials"
wd = os.path.dirname(dst)
if not os.path.exists(wd):
os.makedirs(wd)
shutil.copyfile(src, dst)
create_code_cell(code='', where='below')
¶
Creates a code cell in the IPython Notebook.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
code |
str |
Code to fill the new code cell with. Defaults to ''. |
'' |
where |
str |
Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'. |
'below' |
Source code in geemap/common.py
def create_code_cell(code="", where="below"):
"""Creates a code cell in the IPython Notebook.
Args:
code (str, optional): Code to fill the new code cell with. Defaults to ''.
where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.
"""
import base64
import pyperclip
from IPython.display import Javascript, display
try:
pyperclip.copy(str(code))
except Exception as e:
pass
encoded_code = (base64.b64encode(str.encode(code))).decode()
display(
Javascript(
"""
var code = IPython.notebook.insert_cell_{0}('code');
code.set_text(atob("{1}"));
""".format(
where, encoded_code
)
)
)
create_colorbar(width=150, height=30, palette=['blue', 'green', 'red'], add_ticks=True, add_labels=True, labels=None, vertical=False, out_file=None, font_type='arial.ttf', font_size=12, font_color='black', add_outline=True, outline_color='black')
¶
Creates a colorbar based on the provided palette.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
width |
int |
Width of the colorbar in pixels. Defaults to 150. |
150 |
height |
int |
Height of the colorbar in pixels. Defaults to 30. |
30 |
palette |
list |
Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red']. |
['blue', 'green', 'red'] |
add_ticks |
bool |
Whether to add tick markers to the colorbar. Defaults to True. |
True |
add_labels |
bool |
Whether to add labels to the colorbar. Defaults to True. |
True |
labels |
list |
A list of labels to add to the colorbar. Defaults to None. |
None |
vertical |
bool |
Whether to rotate the colorbar vertically. Defaults to False. |
False |
out_file |
str |
File path to the output colorbar in png format. Defaults to None. |
None |
font_type |
str |
Font type to use for labels. Defaults to 'arial.ttf'. |
'arial.ttf' |
font_size |
int |
Font size to use for labels. Defaults to 12. |
12 |
font_color |
str |
Font color to use for labels. Defaults to 'black'. |
'black' |
add_outline |
bool |
Whether to add an outline to the colorbar. Defaults to True. |
True |
outline_color |
str |
Color for the outline of the colorbar. Defaults to 'black'. |
'black' |
Returns:
| Type | Description |
|---|---|
str |
File path of the output colorbar in png format. |
Source code in geemap/common.py
def create_colorbar(
width=150,
height=30,
palette=["blue", "green", "red"],
add_ticks=True,
add_labels=True,
labels=None,
vertical=False,
out_file=None,
font_type="arial.ttf",
font_size=12,
font_color="black",
add_outline=True,
outline_color="black",
):
"""Creates a colorbar based on the provided palette.
Args:
width (int, optional): Width of the colorbar in pixels. Defaults to 150.
height (int, optional): Height of the colorbar in pixels. Defaults to 30.
palette (list, optional): Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red'].
add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.
add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.
labels (list, optional): A list of labels to add to the colorbar. Defaults to None.
vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.
out_file (str, optional): File path to the output colorbar in png format. Defaults to None.
font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.
font_size (int, optional): Font size to use for labels. Defaults to 12.
font_color (str, optional): Font color to use for labels. Defaults to 'black'.
add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.
outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.
Returns:
str: File path of the output colorbar in png format.
"""
import decimal
# import io
import pkg_resources
from colour import Color
from PIL import Image, ImageDraw, ImageFont
warnings.simplefilter("ignore")
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
if out_file is None:
filename = "colorbar_" + random_string() + ".png"
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
out_file = os.path.join(out_dir, filename)
elif not out_file.endswith(".png"):
print("The output file must end with .png")
return
else:
out_file = os.path.abspath(out_file)
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
im = Image.new("RGBA", (width, height))
ld = im.load()
def float_range(start, stop, step):
while start < stop:
yield float(start)
start += decimal.Decimal(step)
n_colors = len(palette)
decimal_places = 2
rgb_colors = [Color(check_color(c)).rgb for c in palette]
keys = [
round(c, decimal_places)
for c in list(float_range(0, 1.0001, 1.0 / (n_colors - 1)))
]
heatmap = []
for index, item in enumerate(keys):
pair = [item, rgb_colors[index]]
heatmap.append(pair)
def gaussian(x, a, b, c, d=0):
return a * math.exp(-((x - b) ** 2) / (2 * c**2)) + d
def pixel(x, width=100, map=[], spread=1):
width = float(width)
r = sum(
[
gaussian(x, p[1][0], p[0] * width, width / (spread * len(map)))
for p in map
]
)
g = sum(
[
gaussian(x, p[1][1], p[0] * width, width / (spread * len(map)))
for p in map
]
)
b = sum(
[
gaussian(x, p[1][2], p[0] * width, width / (spread * len(map)))
for p in map
]
)
return min(1.0, r), min(1.0, g), min(1.0, b)
for x in range(im.size[0]):
r, g, b = pixel(x, width=width, map=heatmap)
r, g, b = [int(256 * v) for v in (r, g, b)]
for y in range(im.size[1]):
ld[x, y] = r, g, b
if add_outline:
draw = ImageDraw.Draw(im)
draw.rectangle(
[(0, 0), (width - 1, height - 1)], outline=check_color(outline_color)
)
del draw
if add_ticks:
tick_length = height * 0.1
x = [key * width for key in keys]
y_top = height - tick_length
y_bottom = height
draw = ImageDraw.Draw(im)
for i in x:
shape = [(i, y_top), (i, y_bottom)]
draw.line(shape, fill="black", width=0)
del draw
if vertical:
im = im.transpose(Image.ROTATE_90)
width, height = im.size
if labels is None:
labels = [str(c) for c in keys]
elif len(labels) == 2:
try:
lowerbound = float(labels[0])
upperbound = float(labels[1])
step = (upperbound - lowerbound) / (len(palette) - 1)
labels = [str(lowerbound + c * step) for c in range(0, len(palette))]
except Exception as e:
print(e)
print("The labels are invalid.")
return
elif len(labels) == len(palette):
labels = [str(c) for c in labels]
else:
print("The labels must have the same length as the palette.")
return
if add_labels:
default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")
if font_type == "arial.ttf":
font = ImageFont.truetype(default_font, font_size)
else:
try:
font_list = system_fonts(show_full_path=True)
font_names = [os.path.basename(f) for f in font_list]
if (font_type in font_list) or (font_type in font_names):
font = ImageFont.truetype(font_type, font_size)
else:
print(
"The specified font type could not be found on your system. Using the default font instead."
)
font = ImageFont.truetype(default_font, font_size)
except Exception as e:
print(e)
font = ImageFont.truetype(default_font, font_size)
font_color = check_color(font_color)
draw = ImageDraw.Draw(im)
w, h = draw.textsize(labels[0], font=font)
for label in labels:
w_tmp, h_tmp = draw.textsize(label, font)
if w_tmp > w:
w = w_tmp
if h_tmp > h:
h = h_tmp
W, H = width + w * 2, height + h * 2
background = Image.new("RGBA", (W, H))
draw = ImageDraw.Draw(background)
if vertical:
xy = (0, h)
else:
xy = (w, 0)
background.paste(im, xy, im)
for index, label in enumerate(labels):
w_tmp, h_tmp = draw.textsize(label, font)
if vertical:
spacing = 5
x = width + spacing
y = int(height + h - keys[index] * height - h_tmp / 2 - 1)
draw.text((x, y), label, font=font, fill=font_color)
else:
x = int(keys[index] * width + w - w_tmp / 2)
spacing = int(h * 0.05)
y = height + spacing
draw.text((x, y), label, font=font, fill=font_color)
im = background.copy()
im.save(out_file)
return out_file
create_contours(image, min_value, max_value, interval, kernel=None, region=None, values=None)
¶
Creates contours from an image. Code adapted from https://mygeoblog.com/2017/01/28/contour-lines-in-gee. Credits to MyGeoBlog.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
An image to create contours. |
required |
min_value |
float |
The minimum value of contours. |
required |
max_value |
float |
The maximum value of contours. |
required |
interval |
float |
The interval between contours. |
required |
kernel |
ee.Kernel |
The kernel to use for smoothing image. Defaults to None. |
None |
region |
ee.Geometry | ee.FeatureCollection |
The region of interest. Defaults to None. |
None |
values |
list |
A list of values to create contours for. Defaults to None. |
None |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The image must be an ee.Image. |
TypeError |
The region must be an ee.Geometry or ee.FeatureCollection. |
Returns:
| Type | Description |
|---|---|
ee.Image |
The image containing contours. |
Source code in geemap/common.py
def create_contours(
image, min_value, max_value, interval, kernel=None, region=None, values=None
):
"""Creates contours from an image. Code adapted from https://mygeoblog.com/2017/01/28/contour-lines-in-gee. Credits to MyGeoBlog.
Args:
image (ee.Image): An image to create contours.
min_value (float): The minimum value of contours.
max_value (float): The maximum value of contours.
interval (float): The interval between contours.
kernel (ee.Kernel, optional): The kernel to use for smoothing image. Defaults to None.
region (ee.Geometry | ee.FeatureCollection, optional): The region of interest. Defaults to None.
values (list, optional): A list of values to create contours for. Defaults to None.
Raises:
TypeError: The image must be an ee.Image.
TypeError: The region must be an ee.Geometry or ee.FeatureCollection.
Returns:
ee.Image: The image containing contours.
"""
if not isinstance(image, ee.Image):
raise TypeError("The image must be an ee.Image.")
if region is not None:
if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
pass
else:
raise TypeError(
"The region must be an ee.Geometry or ee.FeatureCollection."
)
if kernel is None:
kernel = ee.Kernel.gaussian(5, 3)
if isinstance(values, list):
values = ee.List(values)
elif isinstance(values, ee.List):
pass
if values is None:
values = ee.List.sequence(min_value, max_value, interval)
def contouring(value):
mycountour = (
image.convolve(kernel)
.subtract(ee.Image.constant(value))
.zeroCrossing()
.multiply(ee.Image.constant(value).toFloat())
)
return mycountour.mask(mycountour)
contours = values.map(contouring)
if region is not None:
if isinstance(region, ee.FeatureCollection):
return ee.ImageCollection(contours).mosaic().clipToCollection(region)
elif isinstance(region, ee.Geometry):
return ee.ImageCollection(contours).mosaic().clip(region)
else:
return ee.ImageCollection(contours).mosaic()
create_download_button(label, data, file_name=None, mime=None, key=None, help=None, on_click=None, args=None, **kwargs)
¶
Streamlit function to create a download button.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
label |
str |
A short label explaining to the user what this button is for.. |
required |
data |
str | list |
The contents of the file to be downloaded. See example below for caching techniques to avoid recomputing this data unnecessarily. |
required |
file_name |
str |
An optional string to use as the name of the file to be downloaded, such as 'my_file.csv'. If not specified, the name will be automatically generated. Defaults to None. |
None |
mime |
str |
The MIME type of the data. If None, defaults to "text/plain" (if data is of type str or is a textual file) or "application/octet-stream" (if data is of type bytes or is a binary file). Defaults to None. |
None |
key |
str |
An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key. Defaults to None. |
None |
help |
str |
An optional tooltip that gets displayed when the button is hovered over. Defaults to None. |
None |
on_click |
str |
An optional callback invoked when this button is clicked. Defaults to None. |
None |
args |
list |
An optional tuple of args to pass to the callback. Defaults to None. |
None |
kwargs |
dict |
An optional tuple of args to pass to the callback. |
{} |
Source code in geemap/common.py
def create_download_button(
label,
data,
file_name=None,
mime=None,
key=None,
help=None,
on_click=None,
args=None,
**kwargs,
):
"""Streamlit function to create a download button.
Args:
label (str): A short label explaining to the user what this button is for..
data (str | list): The contents of the file to be downloaded. See example below for caching techniques to avoid recomputing this data unnecessarily.
file_name (str, optional): An optional string to use as the name of the file to be downloaded, such as 'my_file.csv'. If not specified, the name will be automatically generated. Defaults to None.
mime (str, optional): The MIME type of the data. If None, defaults to "text/plain" (if data is of type str or is a textual file) or "application/octet-stream" (if data is of type bytes or is a binary file). Defaults to None.
key (str, optional): An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key. Defaults to None.
help (str, optional): An optional tooltip that gets displayed when the button is hovered over. Defaults to None.
on_click (str, optional): An optional callback invoked when this button is clicked. Defaults to None.
args (list, optional): An optional tuple of args to pass to the callback. Defaults to None.
kwargs (dict, optional): An optional tuple of args to pass to the callback.
"""
try:
import streamlit as st
import pandas as pd
if isinstance(data, str):
if file_name is None:
file_name = data.split("/")[-1]
if data.endswith(".csv"):
data = pd.read_csv(data).to_csv()
if mime is None:
mime = "text/csv"
return st.download_button(
label, data, file_name, mime, key, help, on_click, args, **kwargs
)
elif (
data.endswith(".gif") or data.endswith(".png") or data.endswith(".jpg")
):
if mime is None:
mime = f"image/{os.path.splitext(data)[1][1:]}"
with open(data, "rb") as file:
return st.download_button(
label,
file,
file_name,
mime,
key,
help,
on_click,
args,
**kwargs,
)
else:
return st.download_button(
label,
label,
data,
file_name,
mime,
key,
help,
on_click,
args,
**kwargs,
)
except ImportError:
print("Streamlit is not installed. Please run 'pip install streamlit'.")
return
except Exception as e:
raise Exception(e)
create_download_link(filename, title='Click here to download: ')
¶
Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The file path to the file to download |
required |
title |
str |
str. Defaults to "Click here to download: ". |
'Click here to download: ' |
Returns:
| Type | Description |
|---|---|
str |
HTML download URL. |
Source code in geemap/common.py
def create_download_link(filename, title="Click here to download: "):
"""Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578
Args:
filename (str): The file path to the file to download
title (str, optional): str. Defaults to "Click here to download: ".
Returns:
str: HTML download URL.
"""
import base64
from IPython.display import HTML
data = open(filename, "rb").read()
b64 = base64.b64encode(data)
payload = b64.decode()
basename = os.path.basename(filename)
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" style="color:#0000FF;" target="_blank">{title}</a>'
html = html.format(payload=payload, title=title + f" {basename}", filename=basename)
return HTML(html)
create_grid(ee_object, scale, proj=None)
¶
Create a grid covering an Earth Engine object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image | ee.Geometry | ee.FeatureCollection |
The Earth Engine object. |
required |
scale |
float |
The grid cell size. |
required |
proj |
str |
The projection. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The grid as a feature collection. |
Source code in geemap/common.py
def create_grid(ee_object, scale, proj=None):
"""Create a grid covering an Earth Engine object.
Args:
ee_object (ee.Image | ee.Geometry | ee.FeatureCollection): The Earth Engine object.
scale (float): The grid cell size.
proj (str, optional): The projection. Defaults to None.
Returns:
ee.FeatureCollection: The grid as a feature collection.
"""
if isinstance(ee_object, ee.FeatureCollection) or isinstance(ee_object, ee.Image):
geometry = ee_object.geometry()
elif isinstance(ee_object, ee.Geometry):
geometry = ee_object
else:
raise ValueError(
"ee_object must be an ee.FeatureCollection, ee.Image, or ee.Geometry"
)
if proj is None:
proj = geometry.projection()
grid = geometry.coveringGrid(proj, scale)
return grid
create_legend(title='Legend', labels=None, colors=None, legend_dict=None, builtin_legend=None, opacity=1.0, position='bottomright', draggable=True, output=None, style={})
¶
Create a legend in HTML format. Reference: https://bit.ly/3oV6vnH
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
title |
str |
Title of the legend. Defaults to 'Legend'. Defaults to "Legend". |
'Legend' |
colors |
list |
A list of legend colors. Defaults to None. |
None |
labels |
list |
A list of legend labels. Defaults to None. |
None |
legend_dict |
dict |
A dictionary containing legend items as keys and color as values. If provided, legend_keys and legend_colors will be ignored. Defaults to None. |
None |
builtin_legend |
str |
Name of the builtin legend to add to the map. Defaults to None. |
None |
opacity |
float |
The opacity of the legend. Defaults to 1.0. |
1.0 |
position |
str |
The position of the legend, can be one of the following: "topleft", "topright", "bottomleft", "bottomright". Defaults to "bottomright". |
'bottomright' |
draggable |
bool |
If True, the legend can be dragged to a new position. Defaults to True. |
True |
output |
str |
The output file path (*.html) to save the legend. Defaults to None. |
None |
style |
Additional keyword arguments to style the legend, such as position, bottom, right, z-index, border, background-color, border-radius, padding, font-size, etc. The default style is: style = { 'position': 'fixed', 'z-index': '9999', 'border': '2px solid grey', 'background-color': 'rgba(255, 255, 255, 0.8)', 'border-radius': '5px', 'padding': '10px', 'font-size': '14px', 'bottom': '20px', 'right': '5px' } |
{} |
Returns:
| Type | Description |
|---|---|
str |
The HTML code of the legend. |
Source code in geemap/common.py
def create_legend(
title="Legend",
labels=None,
colors=None,
legend_dict=None,
builtin_legend=None,
opacity=1.0,
position="bottomright",
draggable=True,
output=None,
style={},
):
"""Create a legend in HTML format. Reference: https://bit.ly/3oV6vnH
Args:
title (str, optional): Title of the legend. Defaults to 'Legend'. Defaults to "Legend".
colors (list, optional): A list of legend colors. Defaults to None.
labels (list, optional): A list of legend labels. Defaults to None.
legend_dict (dict, optional): A dictionary containing legend items as keys and color as values.
If provided, legend_keys and legend_colors will be ignored. Defaults to None.
builtin_legend (str, optional): Name of the builtin legend to add to the map. Defaults to None.
opacity (float, optional): The opacity of the legend. Defaults to 1.0.
position (str, optional): The position of the legend, can be one of the following:
"topleft", "topright", "bottomleft", "bottomright". Defaults to "bottomright".
draggable (bool, optional): If True, the legend can be dragged to a new position. Defaults to True.
output (str, optional): The output file path (*.html) to save the legend. Defaults to None.
style: Additional keyword arguments to style the legend, such as position, bottom, right, z-index,
border, background-color, border-radius, padding, font-size, etc. The default style is:
style = {
'position': 'fixed',
'z-index': '9999',
'border': '2px solid grey',
'background-color': 'rgba(255, 255, 255, 0.8)',
'border-radius': '5px',
'padding': '10px',
'font-size': '14px',
'bottom': '20px',
'right': '5px'
}
Returns:
str: The HTML code of the legend.
"""
import pkg_resources
from .legends import builtin_legends
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
legend_template = os.path.join(pkg_dir, "data/template/legend_style.html")
if draggable:
legend_template = os.path.join(pkg_dir, "data/template/legend.txt")
if not os.path.exists(legend_template):
raise FileNotFoundError("The legend template does not exist.")
if labels is not None:
if not isinstance(labels, list):
print("The legend keys must be a list.")
return
else:
labels = ["One", "Two", "Three", "Four", "etc"]
if colors is not None:
if not isinstance(colors, list):
print("The legend colors must be a list.")
return
elif all(isinstance(item, tuple) for item in colors):
try:
colors = [rgb_to_hex(x) for x in colors]
except Exception as e:
print(e)
elif all((item.startswith("#") and len(item) == 7) for item in colors):
pass
elif all((len(item) == 6) for item in colors):
pass
else:
print("The legend colors must be a list of tuples.")
return
else:
colors = [
"#8DD3C7",
"#FFFFB3",
"#BEBADA",
"#FB8072",
"#80B1D3",
]
if len(labels) != len(colors):
print("The legend keys and values must be the same length.")
return
allowed_builtin_legends = builtin_legends.keys()
if builtin_legend is not None:
if builtin_legend not in allowed_builtin_legends:
print(
"The builtin legend must be one of the following: {}".format(
", ".join(allowed_builtin_legends)
)
)
return
else:
legend_dict = builtin_legends[builtin_legend]
labels = list(legend_dict.keys())
colors = list(legend_dict.values())
if legend_dict is not None:
if not isinstance(legend_dict, dict):
print("The legend dict must be a dictionary.")
return
else:
labels = list(legend_dict.keys())
colors = list(legend_dict.values())
if all(isinstance(item, tuple) for item in colors):
try:
colors = [rgb_to_hex(x) for x in colors]
except Exception as e:
print(e)
allowed_positions = [
"topleft",
"topright",
"bottomleft",
"bottomright",
]
if position not in allowed_positions:
raise ValueError(
"The position must be one of the following: {}".format(
", ".join(allowed_positions)
)
)
if position == "bottomright":
if "bottom" not in style:
style["bottom"] = "20px"
if "right" not in style:
style["right"] = "5px"
if "left" in style:
del style["left"]
if "top" in style:
del style["top"]
elif position == "bottomleft":
if "bottom" not in style:
style["bottom"] = "5px"
if "left" not in style:
style["left"] = "5px"
if "right" in style:
del style["right"]
if "top" in style:
del style["top"]
elif position == "topright":
if "top" not in style:
style["top"] = "5px"
if "right" not in style:
style["right"] = "5px"
if "left" in style:
del style["left"]
if "bottom" in style:
del style["bottom"]
elif position == "topleft":
if "top" not in style:
style["top"] = "5px"
if "left" not in style:
style["left"] = "5px"
if "right" in style:
del style["right"]
if "bottom" in style:
del style["bottom"]
if "position" not in style:
style["position"] = "fixed"
if "z-index" not in style:
style["z-index"] = "9999"
if "background-color" not in style:
style["background-color"] = "rgba(255, 255, 255, 0.8)"
if "padding" not in style:
style["padding"] = "10px"
if "border-radius" not in style:
style["border-radius"] = "5px"
if "font-size" not in style:
style["font-size"] = "14px"
content = []
with open(legend_template) as f:
lines = f.readlines()
if draggable:
for index, line in enumerate(lines):
if index < 36:
content.append(line)
elif index == 36:
line = lines[index].replace("Legend", title)
content.append(line)
elif index < 39:
content.append(line)
elif index == 39:
for i, color in enumerate(colors):
item = f" <li><span style='background:{check_color(color)};opacity:{opacity};'></span>{labels[i]}</li>\n"
content.append(item)
elif index > 41:
content.append(line)
content = content[3:-1]
else:
for index, line in enumerate(lines):
if index < 8:
content.append(line)
elif index == 8:
for key, value in style.items():
content.append(
" {}: {};\n".format(key.replace("_", "-"), value)
)
elif index < 17:
pass
elif index < 19:
content.append(line)
elif index == 19:
content.append(line.replace("Legend", title))
elif index < 22:
content.append(line)
elif index == 22:
for index, key in enumerate(labels):
color = colors[index]
if not color.startswith("#"):
color = "#" + color
item = " <li><span style='background:{};opacity:{};'></span>{}</li>\n".format(
color, opacity, key
)
content.append(item)
elif index < 33:
pass
else:
content.append(line)
legend_text = "".join(content)
if output is not None:
with open(output, "w") as f:
f.write(legend_text)
else:
return legend_text
create_nlcd_qml(out_qml)
¶
Create a QGIS Layer Style (.qml) for NLCD data
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_qml |
str |
File path to the output qml. |
required |
Source code in geemap/common.py
def create_nlcd_qml(out_qml):
"""Create a QGIS Layer Style (.qml) for NLCD data
Args:
out_qml (str): File path to the output qml.
"""
import pkg_resources
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
qml_template = os.path.join(template_dir, "NLCD.qml")
out_dir = os.path.dirname(out_qml)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(qml_template, out_qml)
credentials_in_colab()
¶
Checks if the ee credentials file exists in Google Colab.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def credentials_in_colab():
"""Checks if the ee credentials file exists in Google Colab.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
credentials_path = "/root/.config/earthengine/credentials"
if os.path.exists(credentials_path):
return True
else:
return False
credentials_in_drive()
¶
Checks if the ee credentials file exists in Google Drive.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def credentials_in_drive():
"""Checks if the ee credentials file exists in Google Drive.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
credentials_path = "/content/drive/My Drive/.config/earthengine/credentials"
if os.path.exists(credentials_path):
return True
else:
return False
csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')
¶
Converts a csv file containing points (latitude, longitude) into a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv |
required |
out_shp |
str |
File path to the output shapefile. |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Source code in geemap/common.py
def csv_points_to_shp(in_csv, out_shp, latitude="latitude", longitude="longitude"):
"""Converts a csv file containing points (latitude, longitude) into a shapefile.
Args:
in_csv (str): File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv
out_shp (str): File path to the output shapefile.
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
"""
import whitebox
if in_csv.startswith("http") and in_csv.endswith(".csv"):
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
out_name = os.path.basename(in_csv)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
download_from_url(in_csv, out_dir=out_dir, verbose=False)
in_csv = os.path.join(out_dir, out_name)
wbt = whitebox.WhiteboxTools()
in_csv = os.path.abspath(in_csv)
out_shp = os.path.abspath(out_shp)
if not os.path.exists(in_csv):
raise Exception("The provided csv file does not exist.")
with open(in_csv, encoding="utf-8") as csv_file:
reader = csv.DictReader(csv_file)
fields = reader.fieldnames
xfield = fields.index(longitude)
yfield = fields.index(latitude)
wbt.csv_points_to_vector(in_csv, out_shp, xfield=xfield, yfield=yfield, epsg=4326)
csv_to_df(in_csv, **kwargs)
¶
Converts a CSV file to pandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
File path to the input CSV. |
required |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
pandas DataFrame |
Source code in geemap/common.py
def csv_to_df(in_csv, **kwargs):
"""Converts a CSV file to pandas dataframe.
Args:
in_csv (str): File path to the input CSV.
Returns:
pd.DataFrame: pandas DataFrame
"""
import pandas as pd
in_csv = github_raw_url(in_csv)
try:
return pd.read_csv(in_csv, **kwargs)
except Exception as e:
raise Exception(e)
csv_to_ee(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8', geodesic=True)
¶
Creates points for a CSV file and exports data as a GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The file path to the input CSV file. |
required |
latitude |
str |
The name of the column containing latitude coordinates. Defaults to "latitude". |
'latitude' |
longitude |
str |
The name of the column containing longitude coordinates. Defaults to "longitude". |
'longitude' |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. |
True |
Returns:
| Type | Description |
|---|---|
ee_object |
An ee.Geometry object |
Source code in geemap/common.py
def csv_to_ee(
in_csv, latitude="latitude", longitude="longitude", encoding="utf-8", geodesic=True
):
"""Creates points for a CSV file and exports data as a GeoJSON.
Args:
in_csv (str): The file path to the input CSV file.
latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.
Returns:
ee_object: An ee.Geometry object
"""
geojson = csv_to_geojson(
in_csv, latitude=latitude, longitude=longitude, encoding=encoding
)
fc = geojson_to_ee(geojson, geodesic=geodesic)
return fc
csv_to_gdf(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8')
¶
Creates points for a CSV file and converts them to a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The file path to the input CSV file. |
required |
latitude |
str |
The name of the column containing latitude coordinates. Defaults to "latitude". |
'latitude' |
longitude |
str |
The name of the column containing longitude coordinates. Defaults to "longitude". |
'longitude' |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
Returns:
| Type | Description |
|---|---|
object |
GeoDataFrame. |
Source code in geemap/common.py
def csv_to_gdf(in_csv, latitude="latitude", longitude="longitude", encoding="utf-8"):
"""Creates points for a CSV file and converts them to a GeoDataFrame.
Args:
in_csv (str): The file path to the input CSV file.
latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
Returns:
object: GeoDataFrame.
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
out_dir = os.getcwd()
out_geojson = os.path.join(out_dir, random_string() + ".geojson")
csv_to_geojson(in_csv, out_geojson, latitude, longitude, encoding)
gdf = gpd.read_file(out_geojson)
os.remove(out_geojson)
return gdf
csv_to_geojson(in_csv, out_geojson=None, latitude='latitude', longitude='longitude', encoding='utf-8')
¶
Creates points for a CSV file and exports data as a GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The file path to the input CSV file. |
required |
out_geojson |
str |
The file path to the exported GeoJSON. Default to None. |
None |
latitude |
str |
The name of the column containing latitude coordinates. Defaults to "latitude". |
'latitude' |
longitude |
str |
The name of the column containing longitude coordinates. Defaults to "longitude". |
'longitude' |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
Source code in geemap/common.py
def csv_to_geojson(
in_csv,
out_geojson=None,
latitude="latitude",
longitude="longitude",
encoding="utf-8",
):
"""Creates points for a CSV file and exports data as a GeoJSON.
Args:
in_csv (str): The file path to the input CSV file.
out_geojson (str): The file path to the exported GeoJSON. Default to None.
latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
"""
import pandas as pd
in_csv = github_raw_url(in_csv)
if out_geojson is not None:
out_geojson = check_file_path(out_geojson)
df = pd.read_csv(in_csv)
geojson = df_to_geojson(
df, latitude=latitude, longitude=longitude, encoding=encoding
)
if out_geojson is None:
return geojson
else:
with open(out_geojson, "w", encoding=encoding) as f:
f.write(json.dumps(geojson))
csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude', encoding='utf-8')
¶
Converts a csv file with latlon info to a point shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The input csv file containing longitude and latitude columns. |
required |
out_shp |
str |
The file path to the output shapefile. |
required |
latitude |
str |
The column name of the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
The column name of the longitude column. Defaults to 'longitude'. |
'longitude' |
Source code in geemap/common.py
def csv_to_shp(
in_csv, out_shp, latitude="latitude", longitude="longitude", encoding="utf-8"
):
"""Converts a csv file with latlon info to a point shapefile.
Args:
in_csv (str): The input csv file containing longitude and latitude columns.
out_shp (str): The file path to the output shapefile.
latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.
longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.
"""
import shapefile as shp
if in_csv.startswith("http") and in_csv.endswith(".csv"):
in_csv = github_raw_url(in_csv)
in_csv = download_file(in_csv, quiet=True, overwrite=True)
try:
points = shp.Writer(out_shp, shapeType=shp.POINT)
with open(in_csv, encoding=encoding) as csvfile:
csvreader = csv.DictReader(csvfile)
header = csvreader.fieldnames
[points.field(field) for field in header]
for row in csvreader:
points.point((float(row[longitude])), (float(row[latitude])))
points.record(*tuple([row[f] for f in header]))
out_prj = out_shp.replace(".shp", ".prj")
with open(out_prj, "w") as f:
prj_str = 'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.0174532925199433]] '
f.write(prj_str)
except Exception as e:
raise Exception(e)
csv_to_vector(in_csv, output, latitude='latitude', longitude='longitude', encoding='utf-8', **kwargs)
¶
Creates points for a CSV file and converts them to a vector dataset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
The file path to the input CSV file. |
required |
output |
str |
The file path to the output vector dataset. |
required |
latitude |
str |
The name of the column containing latitude coordinates. Defaults to "latitude". |
'latitude' |
longitude |
str |
The name of the column containing longitude coordinates. Defaults to "longitude". |
'longitude' |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
Source code in geemap/common.py
def csv_to_vector(
in_csv,
output,
latitude="latitude",
longitude="longitude",
encoding="utf-8",
**kwargs,
):
"""Creates points for a CSV file and converts them to a vector dataset.
Args:
in_csv (str): The file path to the input CSV file.
output (str): The file path to the output vector dataset.
latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
"""
gdf = csv_to_gdf(in_csv, latitude, longitude, encoding)
gdf.to_file(output, **kwargs)
date_sequence(start, end, unit, date_format='YYYY-MM-dd', step=1)
¶
Creates a date sequence.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
start |
str |
The start date, e.g., '2000-01-01'. |
required |
end |
str |
The end date, e.g., '2000-12-31'. |
required |
unit |
str |
One of 'year', 'quarter', 'month' 'week', 'day', 'hour', 'minute', or 'second'. |
required |
date_format |
str |
A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
step |
int |
The step size. Defaults to 1. |
1 |
Returns:
| Type | Description |
|---|---|
ee.List |
A list of date sequence. |
Source code in geemap/common.py
def date_sequence(start, end, unit, date_format="YYYY-MM-dd", step=1):
"""Creates a date sequence.
Args:
start (str): The start date, e.g., '2000-01-01'.
end (str): The end date, e.g., '2000-12-31'.
unit (str): One of 'year', 'quarter', 'month' 'week', 'day', 'hour', 'minute', or 'second'.
date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
step (int, optional): The step size. Defaults to 1.
Returns:
ee.List: A list of date sequence.
"""
def get_quarter(d):
return str((int(d[5:7]) - 1) // 3 * 3 + 1).zfill(2)
def get_monday(d):
date_obj = datetime.datetime.strptime(d, "%Y-%m-%d")
start_of_week = date_obj - datetime.timedelta(days=date_obj.weekday())
return start_of_week.strftime("%Y-%m-%d")
if unit == "year":
start = start[:4] + "-01-01"
elif unit == "month":
start = start[:7] + "-01"
elif unit == "quarter":
start = start[:5] + get_quarter(start) + "-01"
elif unit == "week":
start = get_monday(start)
start_date = ee.Date(start)
end_date = ee.Date(end)
if unit != "quarter":
count = ee.Number(end_date.difference(start_date, unit)).toInt()
num_seq = ee.List.sequence(0, count)
if step > 1:
num_seq = num_seq.slice(0, num_seq.size(), step)
date_seq = num_seq.map(
lambda d: start_date.advance(d, unit).format(date_format)
)
else:
unit = "month"
count = ee.Number(end_date.difference(start_date, unit)).divide(3).toInt()
num_seq = ee.List.sequence(0, count.multiply(3), 3)
date_seq = num_seq.map(
lambda d: start_date.advance(d, unit).format(date_format)
)
return date_seq
delete_shp(in_shp, verbose=False)
¶
Deletes a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
The input shapefile to delete. |
required |
verbose |
bool |
Whether to print out descriptive text. Defaults to False. |
False |
Source code in geemap/common.py
def delete_shp(in_shp, verbose=False):
"""Deletes a shapefile.
Args:
in_shp (str): The input shapefile to delete.
verbose (bool, optional): Whether to print out descriptive text. Defaults to False.
"""
from pathlib import Path
in_shp = os.path.abspath(in_shp)
in_dir = os.path.dirname(in_shp)
basename = os.path.basename(in_shp).replace(".shp", "")
files = Path(in_dir).rglob(basename + ".*")
for file in files:
filepath = os.path.join(in_dir, str(file))
try:
os.remove(filepath)
if verbose:
print(f"Deleted {filepath}")
except Exception as e:
if verbose:
print(e)
df_to_ee(df, latitude='latitude', longitude='longitude', **kwargs)
¶
Converts a pandas DataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pandas.DataFrame |
An input pandas.DataFrame. |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be pandas.DataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The ee.FeatureCollection converted from the input pandas DataFrame. |
Source code in geemap/common.py
def df_to_ee(df, latitude="latitude", longitude="longitude", **kwargs):
"""Converts a pandas DataFrame to ee.FeatureCollection.
Args:
df (pandas.DataFrame): An input pandas.DataFrame.
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Raises:
TypeError: The input data type must be pandas.DataFrame.
Returns:
ee.FeatureCollection: The ee.FeatureCollection converted from the input pandas DataFrame.
"""
import pandas as pd
if not isinstance(df, pd.DataFrame):
raise TypeError("The input data type must be pandas.DataFrame.")
geojson = df_to_geojson(df, latitude=latitude, longitude=longitude)
fc = geojson_to_ee(geojson)
return fc
df_to_geojson(df, out_geojson=None, latitude='latitude', longitude='longitude', encoding='utf-8')
¶
Creates points for a Pandas DataFrame and exports data as a GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pandas.DataFrame |
The input Pandas DataFrame. |
required |
out_geojson |
str |
The file path to the exported GeoJSON. Default to None. |
None |
latitude |
str |
The name of the column containing latitude coordinates. Defaults to "latitude". |
'latitude' |
longitude |
str |
The name of the column containing longitude coordinates. Defaults to "longitude". |
'longitude' |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
Source code in geemap/common.py
def df_to_geojson(
df,
out_geojson=None,
latitude="latitude",
longitude="longitude",
encoding="utf-8",
):
"""Creates points for a Pandas DataFrame and exports data as a GeoJSON.
Args:
df (pandas.DataFrame): The input Pandas DataFrame.
out_geojson (str): The file path to the exported GeoJSON. Default to None.
latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
"""
from geojson import Feature, FeatureCollection, Point
if out_geojson is not None:
out_dir = os.path.dirname(os.path.abspath(out_geojson))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
features = df.apply(
lambda row: Feature(
geometry=Point((float(row[longitude]), float(row[latitude]))),
properties=dict(row),
),
axis=1,
).tolist()
geojson = FeatureCollection(features=features)
if out_geojson is None:
return geojson
else:
with open(out_geojson, "w", encoding=encoding) as f:
f.write(json.dumps(geojson))
dict_to_csv(data_dict, out_csv, by_row=False, timeout=300, proxies=None)
¶
Downloads an ee.Dictionary as a CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_dict |
ee.Dictionary |
The input ee.Dictionary. |
required |
out_csv |
str |
The output file path to the CSV file. |
required |
by_row |
bool |
Whether to use by row or by column. Defaults to False. |
False |
timeout |
int |
Timeout in seconds. Defaults to 300 seconds. |
300 |
proxies |
dict |
Proxy settings. Defaults to None. |
None |
Source code in geemap/common.py
def dict_to_csv(data_dict, out_csv, by_row=False, timeout=300, proxies=None):
"""Downloads an ee.Dictionary as a CSV file.
Args:
data_dict (ee.Dictionary): The input ee.Dictionary.
out_csv (str): The output file path to the CSV file.
by_row (bool, optional): Whether to use by row or by column. Defaults to False.
timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
proxies (dict, optional): Proxy settings. Defaults to None.
"""
out_dir = os.path.dirname(out_csv)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not by_row:
csv_feature = ee.Feature(None, data_dict)
csv_feat_col = ee.FeatureCollection([csv_feature])
else:
keys = data_dict.keys()
data = keys.map(lambda k: ee.Dictionary({"name": k, "value": data_dict.get(k)}))
csv_feature = data.map(lambda f: ee.Feature(None, f))
csv_feat_col = ee.FeatureCollection(csv_feature)
ee_export_vector(csv_feat_col, out_csv, timeout=timeout, proxies=proxies)
display_html(src, width=950, height=600)
¶
Display an HTML file in a Jupyter Notebook.
Args src (str): File path to HTML file. width (int, optional): Width of the map. Defaults to 950. height (int, optional): Height of the map. Defaults to 600.
Source code in geemap/common.py
def display_html(src, width=950, height=600):
"""Display an HTML file in a Jupyter Notebook.
Args
src (str): File path to HTML file.
width (int, optional): Width of the map. Defaults to 950.
height (int, optional): Height of the map. Defaults to 600.
"""
if not os.path.isfile(src):
raise ValueError(f"{src} is not a valid file path.")
display(IFrame(src=src, width=width, height=height))
download_ee_image(image, filename, region=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, **kwargs)
¶
Download an Earth Engine Image as a GeoTIFF. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to be downloaded. |
required |
filename |
str |
Name of the destination file. |
required |
region |
ee.Geometry |
Region defined by geojson polygon in WGS84. Defaults to the entire image granule. |
None |
crs |
str |
Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band. |
None |
crs_transform |
list |
tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform. |
None |
scale |
float |
Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands. |
None |
resampling |
ResamplingMethod |
Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None. |
'near' |
dtype |
str |
Convert to this data type ( |
None |
overwrite |
bool |
Overwrite the destination file if it exists. Defaults to True. |
True |
num_threads |
int |
Number of tiles to download concurrently. Defaults to a sensible auto value. |
None |
max_tile_size |
int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB). |
None |
|
max_tile_dim |
int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000). |
None |
|
shape |
tuple of int, optional (height, width) dimensions to export (pixels). |
None |
|
scale_offset |
bool, optional Whether to apply any EE band scales and offsets to the image. |
False |
|
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
Source code in geemap/common.py
def download_ee_image(
image,
filename,
region=None,
crs=None,
crs_transform=None,
scale=None,
resampling="near",
dtype=None,
overwrite=True,
num_threads=None,
max_tile_size=None,
max_tile_dim=None,
shape=None,
scale_offset=False,
unmask_value=None,
**kwargs,
):
"""Download an Earth Engine Image as a GeoTIFF. Images larger than the `Earth Engine size limit are split and downloaded as
separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Args:
image (ee.Image): The image to be downloaded.
filename (str): Name of the destination file.
region (ee.Geometry, optional): Region defined by geojson polygon in WGS84. Defaults to the entire image granule.
crs (str, optional): Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are
re-projected to this CRS. Defaults to the CRS of the minimum scale band.
crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
List of 6 numbers specifying an affine transform in the specified CRS. In row-major order:
[xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to
this transform.
scale (float, optional): Resample image(s) to this pixel scale (size) (m). Where image bands have different scales,
all are resampled to this scale. Defaults to the minimum scale of image bands.
resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
or `float64`). Defaults to auto select a minimum size type that can represent the range of pixel values.
overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
max_tile_size: int, optional
Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).
max_tile_dim: int, optional
Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).
shape: tuple of int, optional
(height, width) dimensions to export (pixels).
scale_offset: bool, optional
Whether to apply any EE band scales and offsets to the image.
unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains
zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
"""
if os.environ.get("USE_MKDOCS") is not None:
return
try:
import geedim as gd
except ImportError:
raise ImportError(
"Please install geedim using `pip install geedim` or `conda install -c conda-forge geedim`"
)
if not isinstance(image, ee.Image):
raise ValueError("image must be an ee.Image.")
if unmask_value is not None:
if isinstance(region, ee.Geometry):
image = image.clip(region)
elif isinstance(region, ee.FeatureCollection):
image = image.clipToCollection(region)
image = image.unmask(unmask_value, sameFootprint=False)
if region is not None:
kwargs["region"] = region
if crs is not None:
kwargs["crs"] = crs
if crs_transform is not None:
kwargs["crs_transform"] = crs_transform
if scale is not None:
kwargs["scale"] = scale
if resampling is not None:
kwargs["resampling"] = resampling
if dtype is not None:
kwargs["dtype"] = dtype
if max_tile_size is not None:
kwargs["max_tile_size"] = max_tile_size
if max_tile_dim is not None:
kwargs["max_tile_dim"] = max_tile_dim
if shape is not None:
kwargs["shape"] = shape
if scale_offset:
kwargs["scale_offset"] = scale_offset
img = gd.download.BaseImage(image)
img.download(filename, overwrite=overwrite, num_threads=num_threads, **kwargs)
download_ee_image_collection(collection, out_dir=None, filenames=None, region=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, **kwargs)
¶
Download an Earth Engine ImageCollection as GeoTIFFs. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.ImageCollection |
The image collection to be downloaded. |
required |
out_dir |
str |
The directory to save the downloaded images. Defaults to the current directory. |
None |
filenames |
list |
A list of filenames to use for the downloaded images. Defaults to the image ID. |
None |
region |
ee.Geometry |
Region defined by geojson polygon in WGS84. Defaults to the entire image granule. |
None |
crs |
str |
Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band. |
None |
crs_transform |
list |
tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform. |
None |
scale |
float |
Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands. |
None |
resampling |
ResamplingMethod |
Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None. |
'near' |
dtype |
str |
Convert to this data type ( |
None |
overwrite |
bool |
Overwrite the destination file if it exists. Defaults to True. |
True |
num_threads |
int |
Number of tiles to download concurrently. Defaults to a sensible auto value. |
None |
max_tile_size |
int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB). |
None |
|
max_tile_dim |
int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000). |
None |
|
shape |
tuple of int, optional (height, width) dimensions to export (pixels). |
None |
|
scale_offset |
bool, optional Whether to apply any EE band scales and offsets to the image. |
False |
|
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
Source code in geemap/common.py
def download_ee_image_collection(
collection,
out_dir=None,
filenames=None,
region=None,
crs=None,
crs_transform=None,
scale=None,
resampling="near",
dtype=None,
overwrite=True,
num_threads=None,
max_tile_size=None,
max_tile_dim=None,
shape=None,
scale_offset=False,
unmask_value=None,
**kwargs,
):
"""Download an Earth Engine ImageCollection as GeoTIFFs. Images larger than the `Earth Engine size limit are split and downloaded as
separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Args:
collection (ee.ImageCollection): The image collection to be downloaded.
out_dir (str, optional): The directory to save the downloaded images. Defaults to the current directory.
filenames (list, optional): A list of filenames to use for the downloaded images. Defaults to the image ID.
region (ee.Geometry, optional): Region defined by geojson polygon in WGS84. Defaults to the entire image granule.
crs (str, optional): Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are
re-projected to this CRS. Defaults to the CRS of the minimum scale band.
crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
List of 6 numbers specifying an affine transform in the specified CRS. In row-major order:
[xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to
this transform.
scale (float, optional): Resample image(s) to this pixel scale (size) (m). Where image bands have different scales,
all are resampled to this scale. Defaults to the minimum scale of image bands.
resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
or `float64`). Defaults to auto select a minimum size type that can represent the range of pixel values.
overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
max_tile_size: int, optional
Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).
max_tile_dim: int, optional
Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).
shape: tuple of int, optional
(height, width) dimensions to export (pixels).
scale_offset: bool, optional
Whether to apply any EE band scales and offsets to the image.
unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
"""
if not isinstance(collection, ee.ImageCollection):
raise ValueError("ee_object must be an ee.ImageCollection.")
if out_dir is None:
out_dir = os.getcwd()
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
count = int(collection.size().getInfo())
print(f"Total number of images: {count}\n")
if filenames is not None:
if len(filenames) != count:
raise ValueError(
f"The number of filenames must match the number of image: {count}"
)
for i in range(0, count):
image = ee.Image(collection.toList(count).get(i))
if filenames is not None:
name = filenames[i]
if not name.endswith(".tif"):
name = name + ".tif"
else:
name = image.get("system:index").getInfo() + ".tif"
filename = os.path.join(os.path.abspath(out_dir), name)
print(f"Downloading {i + 1}/{count}: {name}")
download_ee_image(
image,
filename,
region,
crs,
crs_transform,
scale,
resampling,
dtype,
overwrite,
num_threads,
max_tile_size,
max_tile_dim,
shape,
scale_offset,
unmask_value,
**kwargs,
)
except Exception as e:
raise Exception(f"Error downloading image collection: {e}")
download_ee_image_tiles(image, features, out_dir=None, prefix=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, column=None, **kwargs)
¶
Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to be downloaded. |
required |
features |
ee.FeatureCollection |
The features to loop through to download image. |
required |
out_dir |
str |
The output directory. Defaults to None. |
None |
prefix |
str |
The prefix for the output file. Defaults to None. |
None |
crs |
str |
Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band. |
None |
crs_transform |
list |
tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform. |
None |
scale |
float |
Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands. |
None |
resampling |
ResamplingMethod |
Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None. |
'near' |
dtype |
str |
Convert to this data type ( |
None |
overwrite |
bool |
Overwrite the destination file if it exists. Defaults to True. |
True |
num_threads |
int |
Number of tiles to download concurrently. Defaults to a sensible auto value. |
None |
max_tile_size |
int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB). |
None |
|
max_tile_dim |
int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000). |
None |
|
shape |
tuple of int, optional (height, width) dimensions to export (pixels). |
None |
|
scale_offset |
bool, optional Whether to apply any EE band scales and offsets to the image. |
False |
|
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
column |
str |
The column name to use for the filename. Defaults to None. |
None |
Source code in geemap/common.py
def download_ee_image_tiles(
image,
features,
out_dir=None,
prefix=None,
crs=None,
crs_transform=None,
scale=None,
resampling="near",
dtype=None,
overwrite=True,
num_threads=None,
max_tile_size=None,
max_tile_dim=None,
shape=None,
scale_offset=False,
unmask_value=None,
column=None,
**kwargs,
):
"""Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as
separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Args:
image (ee.Image): The image to be downloaded.
features (ee.FeatureCollection): The features to loop through to download image.
out_dir (str, optional): The output directory. Defaults to None.
prefix (str, optional): The prefix for the output file. Defaults to None.
crs (str, optional): Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are
re-projected to this CRS. Defaults to the CRS of the minimum scale band.
crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
List of 6 numbers specifying an affine transform in the specified CRS. In row-major order:
[xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to
this transform.
scale (float, optional): Resample image(s) to this pixel scale (size) (m). Where image bands have different scales,
all are resampled to this scale. Defaults to the minimum scale of image bands.
resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
or `float64`). Defaults to auto select a minimum size type that can represent the range of pixel values.
overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
max_tile_size: int, optional
Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).
max_tile_dim: int, optional
Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).
shape: tuple of int, optional
(height, width) dimensions to export (pixels).
scale_offset: bool, optional
Whether to apply any EE band scales and offsets to the image.
unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
column (str, optional): The column name to use for the filename. Defaults to None.
"""
import time
start = time.time()
if os.environ.get("USE_MKDOCS") is not None:
return
if not isinstance(features, ee.FeatureCollection):
raise ValueError("features must be an ee.FeatureCollection.")
if out_dir is None:
out_dir = os.getcwd()
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if prefix is None:
prefix = ""
count = features.size().getInfo()
collection = features.toList(count)
if column is not None:
names = features.aggregate_array(column).getInfo()
else:
names = [str(i + 1).zfill(len(str(count))) for i in range(count)]
for i in range(count):
region = ee.Feature(collection.get(i)).geometry()
filename = os.path.join(
out_dir, "{}{}.tif".format(prefix, names[i].replace("/", "_"))
)
print(f"Downloading {i + 1}/{count}: {filename}")
download_ee_image(
image,
filename,
region,
crs,
crs_transform,
scale,
resampling,
dtype,
overwrite,
num_threads,
max_tile_size,
max_tile_dim,
shape,
scale_offset,
unmask_value,
**kwargs,
)
print(f"Downloaded {count} tiles in {time.time() - start} seconds.")
download_ee_image_tiles_parallel(image, features, out_dir=None, prefix=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, column=None, job_args={'n_jobs': -1}, **kwargs)
¶
Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to be downloaded. |
required |
features |
ee.FeatureCollection |
The features to loop through to download image. |
required |
out_dir |
str |
The output directory. Defaults to None. |
None |
prefix |
str |
The prefix for the output file. Defaults to None. |
None |
crs |
str |
Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band. |
None |
crs_transform |
list |
tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform. |
None |
scale |
float |
Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands. |
None |
resampling |
ResamplingMethod |
Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None. |
'near' |
dtype |
str |
Convert to this data type ( |
None |
overwrite |
bool |
Overwrite the destination file if it exists. Defaults to True. |
True |
num_threads |
int |
Number of tiles to download concurrently. Defaults to a sensible auto value. |
None |
max_tile_size |
int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB). |
None |
|
max_tile_dim |
int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000). |
None |
|
shape |
tuple of int, optional (height, width) dimensions to export (pixels). |
None |
|
scale_offset |
bool, optional Whether to apply any EE band scales and offsets to the image. |
False |
|
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
column |
str |
The column name in the feature collection to use as the filename. Defaults to None. |
None |
job_args |
dict |
The arguments to pass to joblib.Parallel. Defaults to {"n_jobs": -1}. |
{'n_jobs': -1} |
Source code in geemap/common.py
def download_ee_image_tiles_parallel(
image,
features,
out_dir=None,
prefix=None,
crs=None,
crs_transform=None,
scale=None,
resampling="near",
dtype=None,
overwrite=True,
num_threads=None,
max_tile_size=None,
max_tile_dim=None,
shape=None,
scale_offset=False,
unmask_value=None,
column=None,
job_args={"n_jobs": -1},
**kwargs,
):
"""Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as
separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574
Args:
image (ee.Image): The image to be downloaded.
features (ee.FeatureCollection): The features to loop through to download image.
out_dir (str, optional): The output directory. Defaults to None.
prefix (str, optional): The prefix for the output file. Defaults to None.
crs (str, optional): Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are
re-projected to this CRS. Defaults to the CRS of the minimum scale band.
crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
List of 6 numbers specifying an affine transform in the specified CRS. In row-major order:
[xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to
this transform.
scale (float, optional): Resample image(s) to this pixel scale (size) (m). Where image bands have different scales,
all are resampled to this scale. Defaults to the minimum scale of image bands.
resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
or `float64`). Defaults to auto select a minimum size type that can represent the range of pixel values.
overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
max_tile_size: int, optional
Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).
max_tile_dim: int, optional
Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).
shape: tuple of int, optional
(height, width) dimensions to export (pixels).
scale_offset: bool, optional
Whether to apply any EE band scales and offsets to the image.
unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
column (str, optional): The column name in the feature collection to use as the filename. Defaults to None.
job_args (dict, optional): The arguments to pass to joblib.Parallel. Defaults to {"n_jobs": -1}.
"""
import joblib
import time
start = time.time()
if os.environ.get("USE_MKDOCS") is not None:
return
if not isinstance(features, ee.FeatureCollection):
raise ValueError("features must be an ee.FeatureCollection.")
if out_dir is None:
out_dir = os.getcwd()
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if prefix is None:
prefix = ""
count = features.size().getInfo()
if column is not None:
names = features.aggregate_array(column).getInfo()
else:
names = [str(i + 1).zfill(len(str(count))) for i in range(count)]
collection = features.toList(count)
def download_data(index):
ee_initialize(opt_url="https://earthengine-highvolume.googleapis.com")
region = ee.Feature(collection.get(index)).geometry()
filename = os.path.join(
out_dir, "{}{}.tif".format(prefix, names[index].replace("/", "_"))
)
print(f"Downloading {index + 1}/{count}: {filename}")
download_ee_image(
image,
filename,
region,
crs,
crs_transform,
scale,
resampling,
dtype,
overwrite,
num_threads,
max_tile_size,
max_tile_dim,
shape,
scale_offset,
unmask_value,
**kwargs,
)
with joblib.Parallel(**job_args) as parallel:
parallel(joblib.delayed(download_data)(index) for index in range(count))
end = time.time()
print(f"Finished in {end - start} seconds.")
download_ee_video(collection, video_args, out_gif, timeout=300, proxies=None)
¶
Downloads a video thumbnail as a GIF image from Earth Engine.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
object |
An ee.ImageCollection. |
required |
video_args |
object |
Parameters for expring the video thumbnail. |
required |
out_gif |
str |
File path to the output GIF. |
required |
timeout |
int |
The number of seconds the request will be timed out. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use. Defaults to None. |
None |
Source code in geemap/common.py
def download_ee_video(collection, video_args, out_gif, timeout=300, proxies=None):
"""Downloads a video thumbnail as a GIF image from Earth Engine.
Args:
collection (object): An ee.ImageCollection.
video_args (object): Parameters for expring the video thumbnail.
out_gif (str): File path to the output GIF.
timeout (int, optional): The number of seconds the request will be timed out. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
"""
out_gif = os.path.abspath(out_gif)
if not out_gif.endswith(".gif"):
print("The output file must have an extension of .gif.")
return
if not os.path.exists(os.path.dirname(out_gif)):
os.makedirs(os.path.dirname(out_gif))
if "region" in video_args.keys():
roi = video_args["region"]
if not isinstance(roi, ee.Geometry):
try:
roi = roi.geometry()
except Exception as e:
print("Could not convert the provided roi to ee.Geometry")
print(e)
return
video_args["region"] = roi
if "dimensions" not in video_args:
video_args["dimensions"] = 768
try:
print("Generating URL...")
url = collection.getVideoThumbURL(video_args)
print(f"Downloading GIF image from {url}\nPlease wait ...")
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
if r.status_code != 200:
print("An error occurred while downloading.")
print(r.json()["error"]["message"])
return
else:
with open(out_gif, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
print(f"The GIF image has been saved to: {out_gif}")
except Exception as e:
print(e)
download_file(url=None, output=None, quiet=False, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False)
¶
Download a file from URL, including Google Drive shared URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
Google Drive URL is also supported. Defaults to None. |
None |
output |
str |
Output filename. Default is basename of URL. |
None |
quiet |
bool |
Suppress terminal output. Default is False. |
False |
proxy |
str |
Proxy. Defaults to None. |
None |
speed |
float |
Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None. |
None |
use_cookies |
bool |
Flag to use cookies. Defaults to True. |
True |
verify |
bool | str |
Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True. |
True |
id |
str |
Google Drive's file ID. Defaults to None. |
None |
fuzzy |
bool |
Fuzzy extraction of Google Drive's file Id. Defaults to False. |
False |
resume |
bool |
Resume the download from existing tmp file if possible. Defaults to False. |
False |
unzip |
bool |
Unzip the file. Defaults to True. |
True |
overwrite |
bool |
Overwrite the file if it already exists. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
str |
The output file path. |
Source code in geemap/common.py
def download_file(
url=None,
output=None,
quiet=False,
proxy=None,
speed=None,
use_cookies=True,
verify=True,
id=None,
fuzzy=False,
resume=False,
unzip=True,
overwrite=False,
):
"""Download a file from URL, including Google Drive shared URL.
Args:
url (str, optional): Google Drive URL is also supported. Defaults to None.
output (str, optional): Output filename. Default is basename of URL.
quiet (bool, optional): Suppress terminal output. Default is False.
proxy (str, optional): Proxy. Defaults to None.
speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
use_cookies (bool, optional): Flag to use cookies. Defaults to True.
verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.
id (str, optional): Google Drive's file ID. Defaults to None.
fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.
resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.
unzip (bool, optional): Unzip the file. Defaults to True.
overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.
Returns:
str: The output file path.
"""
import gdown
if output is None:
if isinstance(url, str) and url.startswith("http"):
output = os.path.basename(url)
if isinstance(url, str):
if os.path.exists(os.path.abspath(output)) and (not overwrite):
print(
f"{output} already exists. Skip downloading. Set overwrite=True to overwrite."
)
return os.path.abspath(output)
else:
url = github_raw_url(url)
if "https://drive.google.com/file/d/" in url:
fuzzy = True
output = gdown.download(
url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume
)
if unzip and output.endswith(".zip"):
with zipfile.ZipFile(output, "r") as zip_ref:
if not quiet:
print("Extracting files...")
zip_ref.extractall(os.path.dirname(output))
return os.path.abspath(output)
download_folder(url=None, id=None, output=None, quiet=False, proxy=None, speed=None, use_cookies=True, remaining_ok=False)
¶
Downloads the entire folder from URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
URL of the Google Drive folder. Must be of the format 'https://drive.google.com/drive/folders/{url}'. Defaults to None. |
None |
id |
str |
Google Drive's folder ID. Defaults to None. |
None |
output |
str |
String containing the path of the output folder. Defaults to current working directory. |
None |
quiet |
bool |
Suppress terminal output. Defaults to False. |
False |
proxy |
str |
Proxy. Defaults to None. |
None |
speed |
float |
Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None. |
None |
use_cookies |
bool |
Flag to use cookies. Defaults to True. |
True |
resume |
bool |
Resume the download from existing tmp file if possible. Defaults to False. |
required |
Returns:
| Type | Description |
|---|---|
list |
List of files downloaded, or None if failed. |
Source code in geemap/common.py
def download_folder(
url=None,
id=None,
output=None,
quiet=False,
proxy=None,
speed=None,
use_cookies=True,
remaining_ok=False,
):
"""Downloads the entire folder from URL.
Args:
url (str, optional): URL of the Google Drive folder. Must be of the format 'https://drive.google.com/drive/folders/{url}'. Defaults to None.
id (str, optional): Google Drive's folder ID. Defaults to None.
output (str, optional): String containing the path of the output folder. Defaults to current working directory.
quiet (bool, optional): Suppress terminal output. Defaults to False.
proxy (str, optional): Proxy. Defaults to None.
speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
use_cookies (bool, optional): Flag to use cookies. Defaults to True.
resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.
Returns:
list: List of files downloaded, or None if failed.
"""
import gdown
files = gdown.download_folder(
url, id, output, quiet, proxy, speed, use_cookies, remaining_ok
)
return files
download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True)
¶
Download a file shared via Google Drive (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gfile_url |
str |
The Google Drive shared file URL |
required |
file_name |
str |
The output file name to use. |
required |
out_dir |
str |
The output directory. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the output file if it is a zip file. Defaults to True. |
True |
verbose |
bool |
Whether to display or not the output of the function |
True |
Source code in geemap/common.py
def download_from_gdrive(gfile_url, file_name, out_dir=".", unzip=True, verbose=True):
"""Download a file shared via Google Drive
(e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)
Args:
gfile_url (str): The Google Drive shared file URL
file_name (str): The output file name to use.
out_dir (str, optional): The output directory. Defaults to '.'.
unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.
verbose (bool, optional): Whether to display or not the output of the function
"""
try:
from google_drive_downloader import GoogleDriveDownloader as gdd
except ImportError:
raise Exception(
"Please install the google_drive_downloader package using `pip install googledrivedownloader`"
)
file_id = gfile_url.split("/")[5]
if verbose:
print(f"Google Drive file id: {file_id}")
dest_path = os.path.join(out_dir, file_name)
gdd.download_file_from_google_drive(file_id, dest_path, True, unzip)
return
download_from_url(url, out_file_name=None, out_dir='.', unzip=True, verbose=True)
¶
Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The HTTP URL to download. |
required |
out_file_name |
str |
The output file name to use. Defaults to None. |
None |
out_dir |
str |
The output directory to use. Defaults to '.'. |
'.' |
unzip |
bool |
Whether to unzip the downloaded file if it is a zip file. Defaults to True. |
True |
verbose |
bool |
Whether to display or not the output of the function |
True |
Source code in geemap/common.py
def download_from_url(url, out_file_name=None, out_dir=".", unzip=True, verbose=True):
"""Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)
Args:
url (str): The HTTP URL to download.
out_file_name (str, optional): The output file name to use. Defaults to None.
out_dir (str, optional): The output directory to use. Defaults to '.'.
unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.
verbose (bool, optional): Whether to display or not the output of the function
"""
in_file_name = os.path.basename(url)
if out_file_name is None:
out_file_name = in_file_name
out_file_path = os.path.join(os.path.abspath(out_dir), out_file_name)
if verbose:
print(f"Downloading {url} ...")
try:
urllib.request.urlretrieve(url, out_file_path)
except Exception:
raise Exception("The URL is invalid. Please double check the URL.")
final_path = out_file_path
if unzip:
# if it is a zip file
if ".zip" in out_file_name:
if verbose:
print(f"Unzipping {out_file_name} ...")
with zipfile.ZipFile(out_file_path, "r") as zip_ref:
zip_ref.extractall(out_dir)
final_path = os.path.join(
os.path.abspath(out_dir), out_file_name.replace(".zip", "")
)
# if it is a tar file
if ".tar" in out_file_name:
if verbose:
print(f"Unzipping {out_file_name} ...")
with tarfile.open(out_file_path, "r") as tar_ref:
with tarfile.open(out_file_path, "r") as tar_ref:
def is_within_directory(directory, target):
abs_directory = os.path.abspath(directory)
abs_target = os.path.abspath(target)
prefix = os.path.commonprefix([abs_directory, abs_target])
return prefix == abs_directory
def safe_extract(
tar, path=".", members=None, *, numeric_owner=False
):
for member in tar.getmembers():
member_path = os.path.join(path, member.name)
if not is_within_directory(path, member_path):
raise Exception("Attempted Path Traversal in Tar File")
tar.extractall(path, members, numeric_owner=numeric_owner)
safe_extract(tar_ref, out_dir)
final_path = os.path.join(
os.path.abspath(out_dir), out_file_name.replace(".tar", "")
)
if verbose:
print(f"Data downloaded to: {final_path}")
return
download_ned(region, out_dir=None, return_url=False, download_args={}, **kwargs)
¶
Download the US National Elevation Datasets (NED) for a region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
str | list |
A filepath to a vector dataset or a list of bounds in the form of [minx, miny, maxx, maxy]. |
required |
out_dir |
str |
The directory to download the files to. Defaults to None, which uses the current working directory. |
None |
return_url |
bool |
Whether to return the download URLs of the files. Defaults to False. |
False |
download_args |
dict |
A dictionary of arguments to pass to the download_file function. Defaults to {}. |
{} |
Returns:
| Type | Description |
|---|---|
list |
A list of the download URLs of the files if return_url is True. |
Source code in geemap/common.py
def download_ned(region, out_dir=None, return_url=False, download_args={}, **kwargs):
"""Download the US National Elevation Datasets (NED) for a region.
Args:
region (str | list): A filepath to a vector dataset or a list of bounds in the form of [minx, miny, maxx, maxy].
out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
return_url (bool, optional): Whether to return the download URLs of the files. Defaults to False.
download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
Returns:
list: A list of the download URLs of the files if return_url is True.
"""
import geopandas as gpd
if out_dir is None:
out_dir = os.getcwd()
else:
out_dir = os.path.abspath(out_dir)
if isinstance(region, str):
if region.startswith("http"):
region = github_raw_url(region)
region = download_file(region)
elif not os.path.exists(region):
raise ValueError("region must be a path or a URL to a vector dataset.")
roi = gpd.read_file(region, **kwargs)
roi = roi.to_crs(epsg=4326)
bounds = roi.total_bounds
elif isinstance(region, list):
bounds = region
else:
raise ValueError(
"region must be a filepath or a list of bounds in the form of [minx, miny, maxx, maxy]."
)
minx, miny, maxx, maxy = [float(x) for x in bounds]
tiles = []
left = abs(math.floor(minx))
right = abs(math.floor(maxx)) - 1
upper = math.ceil(maxy)
bottom = math.ceil(miny) - 1
for y in range(upper, bottom, -1):
for x in range(left, right, -1):
tile_id = "n{}w{}".format(str(y).zfill(2), str(x).zfill(3))
tiles.append(tile_id)
links = []
filepaths = []
for index, tile in enumerate(tiles):
tif_url = f"https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/{tile}/USGS_13_{tile}.tif"
r = requests.head(tif_url)
if r.status_code == 200:
tif = os.path.join(out_dir, os.path.basename(tif_url))
links.append(tif_url)
filepaths.append(tif)
else:
print(f"{tif_url} does not exist.")
if return_url:
return links
else:
for index, link in enumerate(links):
print(f"Downloading {index + 1} of {len(links)}: {os.path.basename(link)}")
download_file(link, filepaths[index], **download_args)
dynamic_world(region=None, start_date='2020-01-01', end_date='2021-01-01', clip=False, reducer=None, projection='EPSG:3857', scale=10, return_type='hillshade')
¶
Create 10-m land cover composite based on Dynamic World. The source code is adapted from the following tutorial by Spatial Thoughts: https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
ee.Geometry | ee.FeatureCollection |
The region of interest. |
None |
start_date |
str | ee.Date |
The start date of the query. Default to "2020-01-01". |
'2020-01-01' |
end_date |
str | ee.Date |
The end date of the query. Default to "2021-01-01". |
'2021-01-01' |
clip |
bool |
Whether to clip the image to the region. Default to False. |
False |
reducer |
ee.Reducer |
The reducer to be used. Default to None. |
None |
projection |
str |
The projection to be used for creating hillshade. Default to "EPSG:3857". |
'EPSG:3857' |
scale |
int |
The scale to be used for creating hillshade. Default to 10. |
10 |
return_type |
str |
The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade". |
'hillshade' |
Returns:
| Type | Description |
|---|---|
ee.Image |
The image with the specified return_type. |
Source code in geemap/common.py
def dynamic_world(
region=None,
start_date="2020-01-01",
end_date="2021-01-01",
clip=False,
reducer=None,
projection="EPSG:3857",
scale=10,
return_type="hillshade",
):
"""Create 10-m land cover composite based on Dynamic World. The source code is adapted from the following tutorial by Spatial Thoughts:
https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1
Args:
region (ee.Geometry | ee.FeatureCollection): The region of interest.
start_date (str | ee.Date): The start date of the query. Default to "2020-01-01".
end_date (str | ee.Date): The end date of the query. Default to "2021-01-01".
clip (bool, optional): Whether to clip the image to the region. Default to False.
reducer (ee.Reducer, optional): The reducer to be used. Default to None.
projection (str, optional): The projection to be used for creating hillshade. Default to "EPSG:3857".
scale (int, optional): The scale to be used for creating hillshade. Default to 10.
return_type (str, optional): The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade".
Returns:
ee.Image: The image with the specified return_type.
"""
if return_type not in ["hillshade", "visualize", "class", "probability"]:
raise ValueError(
f"{return_type} must be one of 'hillshade', 'visualize', 'class', or 'probability'."
)
if reducer is None:
reducer = ee.Reducer.mode()
dw = ee.ImageCollection("GOOGLE/DYNAMICWORLD/V1").filter(
ee.Filter.date(start_date, end_date)
)
if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
dw = dw.filterBounds(region)
else:
raise ValueError("region must be an ee.FeatureCollection or ee.Geometry.")
# Create a Mode Composite
classification = dw.select("label")
dwComposite = classification.reduce(reducer)
if clip and (region is not None):
if isinstance(region, ee.Geometry):
dwComposite = dwComposite.clip(region)
elif isinstance(region, ee.FeatureCollection):
dwComposite = dwComposite.clipToCollection(region)
elif isinstance(region, ee.Feature):
dwComposite = dwComposite.clip(region.geometry())
dwVisParams = {
"min": 0,
"max": 8,
"palette": [
"#419BDF",
"#397D49",
"#88B053",
"#7A87C6",
"#E49635",
"#DFC35A",
"#C4281B",
"#A59B8F",
"#B39FE1",
],
}
if return_type == "class":
return dwComposite
elif return_type == "visualize":
return dwComposite.visualize(**dwVisParams)
else:
# Create a Top-1 Probability Hillshade Visualization
probabilityBands = [
"water",
"trees",
"grass",
"flooded_vegetation",
"crops",
"shrub_and_scrub",
"built",
"bare",
"snow_and_ice",
]
# Select probability bands
probabilityCol = dw.select(probabilityBands)
# Create a multi-band image with the average pixel-wise probability
# for each band across the time-period
meanProbability = probabilityCol.reduce(ee.Reducer.mean())
# Composites have a default projection that is not suitable
# for hillshade computation.
# Set a EPSG:3857 projection with 10m scale
proj = ee.Projection(projection).atScale(scale)
meanProbability = meanProbability.setDefaultProjection(proj)
# Create the Top1 Probability Hillshade
top1Probability = meanProbability.reduce(ee.Reducer.max())
if clip and (region is not None):
if isinstance(region, ee.Geometry):
top1Probability = top1Probability.clip(region)
elif isinstance(region, ee.FeatureCollection):
top1Probability = top1Probability.clipToCollection(region)
elif isinstance(region, ee.Feature):
top1Probability = top1Probability.clip(region.geometry())
if return_type == "probability":
return top1Probability
else:
top1Confidence = top1Probability.multiply(100).int()
hillshade = ee.Terrain.hillshade(top1Confidence).divide(255)
rgbImage = dwComposite.visualize(**dwVisParams).divide(255)
probabilityHillshade = rgbImage.multiply(hillshade)
return probabilityHillshade
dynamic_world_s2(region=None, start_date='2020-01-01', end_date='2021-01-01', clip=False, cloud_pct=0.35, reducer=None)
¶
Create Sentinel-2 composite for the Dynamic World Land Cover product.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
ee.Geometry | ee.FeatureCollection |
The region of interest. Default to None. |
None |
start_date |
str | ee.Date |
The start date of the query. Default to "2020-01-01". |
'2020-01-01' |
end_date |
str | ee.Date |
The end date of the query. Default to "2021-01-01". |
'2021-01-01' |
clip |
bool |
Whether to clip the image to the region. Default to False. |
False |
cloud_pct |
float |
The percentage of cloud cover to be used for filtering. Default to 0.35. |
0.35 |
reducer |
ee.Reducer |
The reducer to be used for creating image composite. Default to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.Image |
The Sentinel-2 composite. |
Source code in geemap/common.py
def dynamic_world_s2(
region=None,
start_date="2020-01-01",
end_date="2021-01-01",
clip=False,
cloud_pct=0.35,
reducer=None,
):
"""Create Sentinel-2 composite for the Dynamic World Land Cover product.
Args:
region (ee.Geometry | ee.FeatureCollection): The region of interest. Default to None.
start_date (str | ee.Date): The start date of the query. Default to "2020-01-01".
end_date (str | ee.Date): The end date of the query. Default to "2021-01-01".
clip (bool, optional): Whether to clip the image to the region. Default to False.
cloud_pct (float, optional): The percentage of cloud cover to be used for filtering. Default to 0.35.
reducer (ee.Reducer, optional): The reducer to be used for creating image composite. Default to None.
Returns:
ee.Image: The Sentinel-2 composite.
"""
s2 = (
ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
.filterDate(start_date, end_date)
.filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloud_pct * 100))
)
if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
s2 = s2.filterBounds(region)
else:
raise ValueError("region must be an ee.FeatureCollection or ee.Geometry.")
if reducer is None:
reducer = ee.Reducer.median()
image = s2.reduce(reducer).rename(s2.first().bandNames())
if clip and (region is not None):
if isinstance(region, ee.Geometry):
image = image.clip(region)
elif isinstance(region, ee.FeatureCollection):
image = image.clipToCollection(region)
return image
edit_download_html(htmlWidget, filename, title='Click here to download: ')
¶
Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
htmlWidget |
object |
The HTML widget to display the URL. |
required |
filename |
str |
File path to download. |
required |
title |
str |
Download description. Defaults to "Click here to download: ". |
'Click here to download: ' |
Source code in geemap/common.py
def edit_download_html(htmlWidget, filename, title="Click here to download: "):
"""Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058
Args:
htmlWidget (object): The HTML widget to display the URL.
filename (str): File path to download.
title (str, optional): Download description. Defaults to "Click here to download: ".
"""
# from IPython.display import HTML
# import ipywidgets as widgets
import base64
# Change widget html temporarily to a font-awesome spinner
htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'
# Process raw data
data = open(filename, "rb").read()
b64 = base64.b64encode(data)
payload = b64.decode()
basename = os.path.basename(filename)
# Create and assign html to widget
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
htmlWidget.value = html.format(
payload=payload, title=title + basename, filename=basename
)
# htmlWidget = widgets.HTML(value = '')
# htmlWidget
ee_api_to_csv(outfile=None, timeout=300, proxies=None)
¶
Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
outfile |
str |
The output file path to a csv file. Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
Proxy settings. Defaults to None. |
None |
Source code in geemap/common.py
def ee_api_to_csv(outfile=None, timeout=300, proxies=None):
"""Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.
Args:
outfile (str, optional): The output file path to a csv file. Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): Proxy settings. Defaults to None.
"""
import pkg_resources
from bs4 import BeautifulSoup
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
csv_file = os.path.join(template_dir, "ee_api_docs.csv")
if outfile is None:
outfile = csv_file
else:
if not outfile.endswith(".csv"):
print("The output file must end with .csv")
return
else:
out_dir = os.path.dirname(outfile)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
url = "https://developers.google.com/earth-engine/api_docs"
try:
r = requests.get(url, timeout=timeout, proxies=proxies)
soup = BeautifulSoup(r.content, "html.parser")
names = []
descriptions = []
functions = []
returns = []
arguments = []
types = []
details = []
names = [h2.text for h2 in soup.find_all("h2")]
descriptions = [h2.next_sibling.next_sibling.text for h2 in soup.find_all("h2")]
func_tables = soup.find_all("table", class_="blue")
functions = [func_table.find("code").text for func_table in func_tables]
returns = [func_table.find_all("td")[1].text for func_table in func_tables]
detail_tables = []
tables = soup.find_all("table", class_="blue")
for table in tables:
item = table.next_sibling
if item.attrs == {"class": ["details"]}:
detail_tables.append(item)
else:
detail_tables.append("")
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("code")]
else:
items = ""
arguments.append(items)
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("td")]
items = items[1::3]
else:
items = ""
types.append(items)
for detail_table in detail_tables:
if detail_table != "":
items = [item.text for item in detail_table.find_all("p")]
else:
items = ""
details.append(items)
with open(outfile, "w", encoding="utf-8") as csv_file:
csv_writer = csv.writer(csv_file, delimiter="\t")
csv_writer.writerow(
[
"name",
"description",
"function",
"returns",
"argument",
"type",
"details",
]
)
for i in range(len(names)):
name = names[i]
description = descriptions[i]
function = functions[i]
return_type = returns[i]
argument = "|".join(arguments[i])
argu_type = "|".join(types[i])
detail = "|".join(details[i])
csv_writer.writerow(
[
name,
description,
function,
return_type,
argument,
argu_type,
detail,
]
)
except Exception as e:
print(e)
ee_data_html(asset)
¶
Generates HTML from an asset to be used in the HTML widget.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset |
dict |
A dictionary containing an Earth Engine asset. |
required |
Returns:
| Type | Description |
|---|---|
str |
A string containing HTML. |
Source code in geemap/common.py
def ee_data_html(asset):
"""Generates HTML from an asset to be used in the HTML widget.
Args:
asset (dict): A dictionary containing an Earth Engine asset.
Returns:
str: A string containing HTML.
"""
try:
asset_title = asset.get("title", "Unknown")
asset_dates = asset.get("dates", "Unknown")
ee_id_snippet = asset.get("id", "Unknown")
asset_uid = asset.get("uid", None)
asset_url = asset.get("asset_url", "")
code_url = asset.get("sample_code", None)
thumbnail_url = asset.get("thumbnail_url", None)
asset_type = asset.get("type", "Unknown")
if asset_type == "image":
ee_id_snippet = "ee.Image('{}')".format(ee_id_snippet)
elif asset_type == "image_collection":
ee_id_snippet = "ee.ImageCollection('{}')".format(ee_id_snippet)
elif asset_type == "table":
ee_id_snippet = "ee.FeatureCollection('{}')".format(ee_id_snippet)
if not code_url and asset_uid:
coder_url = f"""https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2F{asset_uid}"""
else:
coder_url = code_url
## ee datasets always have a asset_url, and should have a thumbnail
catalog = (
bool(asset_url)
* f"""
<h4>Data Catalog</h4>
<p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','description')}" target="_blank">Description</a></p>
<p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','bands')}" target="_blank">Bands</a></p>
<p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','image-properties')}" target="_blank">Properties</a></p>
<p style="margin-left: 40px"><a href="{coder_url}" target="_blank">Example</a></p>
"""
)
thumbnail = (
bool(thumbnail_url)
* f"""
<h4>Dataset Thumbnail</h4>
<img src="{thumbnail_url}">
"""
)
## only community datasets have a code_url
alternative = (
bool(code_url)
* f"""
<h4>Community Catalog</h4>
<p style="margin-left: 40px">{asset.get('provider','Provider unknown')}</p>
<p style="margin-left: 40px">{asset.get('tags','Tags unknown')}</p>
<p style="margin-left: 40px"><a href="{coder_url}" target="_blank">Example</a></p>
"""
)
template = f"""
<html>
<body>
<h3>{asset_title}</h3>
<h4>Dataset Availability</h4>
<p style="margin-left: 40px">{asset_dates}</p>
<h4>Earth Engine Snippet</h4>
<p style="margin-left: 40px">{ee_id_snippet}</p>
{catalog}
{alternative}
{thumbnail}
</body>
</html>
"""
return template
except Exception as e:
print(e)
ee_data_thumbnail(asset_id, timeout=300, proxies=None)
¶
Retrieves the thumbnail URL of an Earth Engine asset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset_id |
str |
An Earth Engine asset id. |
required |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
Proxy settings. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
str |
An http url of the thumbnail. |
Source code in geemap/common.py
def ee_data_thumbnail(asset_id, timeout=300, proxies=None):
"""Retrieves the thumbnail URL of an Earth Engine asset.
Args:
asset_id (str): An Earth Engine asset id.
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): Proxy settings. Defaults to None.
Returns:
str: An http url of the thumbnail.
"""
import urllib
from bs4 import BeautifulSoup
asset_uid = asset_id.replace("/", "_")
asset_url = "https://developers.google.com/earth-engine/datasets/catalog/{}".format(
asset_uid
)
thumbnail_url = "https://mw1.google.com/ges/dd/images/{}_sample.png".format(
asset_uid
)
r = requests.get(thumbnail_url, timeout=timeout, proxies=proxies)
try:
if r.status_code != 200:
html_page = urllib.request.urlopen(asset_url)
soup = BeautifulSoup(html_page, features="html.parser")
for img in soup.findAll("img"):
if "sample.png" in img.get("src"):
thumbnail_url = img.get("src")
return thumbnail_url
return thumbnail_url
except Exception as e:
print(e)
ee_export_geojson(ee_object, filename=None, selectors=None, timeout=300, proxies=None)
¶
Exports Earth Engine FeatureCollection to geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection to export. |
required |
filename |
str |
Output file name. Defaults to None. |
None |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300 seconds. |
300 |
proxies |
dict |
Proxy settings. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_geojson(
ee_object, filename=None, selectors=None, timeout=300, proxies=None
):
"""Exports Earth Engine FeatureCollection to geojson.
Args:
ee_object (object): ee.FeatureCollection to export.
filename (str): Output file name. Defaults to None.
selectors (list, optional): A list of attributes to export. Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
proxies (dict, optional): Proxy settings. Defaults to None.
"""
if not isinstance(ee_object, ee.FeatureCollection):
print("The ee_object must be an ee.FeatureCollection.")
return
if filename is None:
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(out_dir, random_string(6) + ".geojson")
allowed_formats = ["geojson"]
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if not (filetype.lower() in allowed_formats):
print("The output file type must be geojson.")
return
if selectors is None:
selectors = ee_object.first().propertyNames().getInfo()
selectors = [".geo"] + selectors
elif not isinstance(selectors, list):
print("selectors must be a list, such as ['attribute1', 'attribute2']")
return
else:
allowed_attributes = ee_object.first().propertyNames().getInfo()
for attribute in selectors:
if not (attribute in allowed_attributes):
print(
"Attributes must be one chosen from: {} ".format(
", ".join(allowed_attributes)
)
)
return
try:
# print('Generating URL ...')
url = ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
# print('Downloading data from {}\nPlease wait ...'.format(url))
r = None
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
if r.status_code != 200:
print("An error occurred while downloading. \n Retrying ...")
try:
new_ee_object = ee_object.map(filter_polygons)
print("Generating URL ...")
url = new_ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
print(f"Downloading data from {url}\nPlease wait ...")
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
except Exception as e:
print(e)
with open(filename, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
if r is not None:
print(r.json()["error"]["message"])
return
with open(filename) as f:
geojson = f.read()
return geojson
ee_export_image(ee_object, filename, scale=None, crs=None, crs_transform=None, region=None, dimensions=None, file_per_band=False, format='ZIPPED_GEO_TIFF', unzip=True, unmask_value=None, timeout=300, proxies=None)
¶
Exports an ee.Image as a GeoTIFF.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image to download. |
required |
filename |
str |
Output filename for the exported image. |
required |
scale |
float |
A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None. |
None |
crs |
str |
A default CRS string to use for any bands that do not explicitly specify one. Defaults to None. |
None |
crs_transform |
list |
a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None. |
None |
region |
object |
A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None. |
None |
dimensions |
list |
An optional array of two integers defining the width and height to which the band is cropped. Defaults to None. |
None |
file_per_band |
bool |
Whether to produce a different GeoTIFF per band. Defaults to False. |
False |
format |
str |
One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY", filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array. |
'ZIPPED_GEO_TIFF' |
unzip |
bool |
Whether to unzip the downloaded file. Defaults to True. |
True |
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
timeout |
int |
The timeout in seconds for the request. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_image(
ee_object,
filename,
scale=None,
crs=None,
crs_transform=None,
region=None,
dimensions=None,
file_per_band=False,
format="ZIPPED_GEO_TIFF",
unzip=True,
unmask_value=None,
timeout=300,
proxies=None,
):
"""Exports an ee.Image as a GeoTIFF.
Args:
ee_object (object): The ee.Image to download.
filename (str): Output filename for the exported image.
scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
crs_transform (list, optional): a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.
region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
dimensions (list, optional): An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.
file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
format (str, optional): One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY",
filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.
unzip (bool, optional): Whether to unzip the downloaded file. Defaults to True.
unmask_value (float, optional): The value to use for pixels that are masked in the input image.
If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
timeout (int, optional): The timeout in seconds for the request. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
"""
if not isinstance(ee_object, ee.Image):
print("The ee_object must be an ee.Image.")
return
if unmask_value is not None:
ee_object = ee_object.selfMask().unmask(unmask_value)
if isinstance(region, ee.Geometry):
ee_object = ee_object.clip(region)
elif isinstance(region, ee.FeatureCollection):
ee_object = ee_object.clipToCollection(region)
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
filename_zip = filename.replace(".tif", ".zip")
if filetype != "tif":
print("The filename must end with .tif")
return
try:
print("Generating URL ...")
params = {"name": name, "filePerBand": file_per_band}
params["scale"] = scale
if region is None:
region = ee_object.geometry()
if dimensions is not None:
params["dimensions"] = dimensions
if region is not None:
params["region"] = region
if crs is not None:
params["crs"] = crs
if crs_transform is not None:
params["crs_transform"] = crs_transform
if format != "ZIPPED_GEO_TIFF":
params["format"] = format
try:
url = ee_object.getDownloadURL(params)
except Exception as e:
print("An error occurred while downloading.")
print(e)
return
print(f"Downloading data from {url}\nPlease wait ...")
# Need to initialize r to something because of how we currently handle errors
# We should aim to refactor the code such that only one try block is needed
r = None
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
if r.status_code != 200:
print("An error occurred while downloading.")
return
with open(filename_zip, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
if r is not None:
print(r.json()["error"]["message"])
return
try:
if unzip:
with zipfile.ZipFile(filename_zip) as z:
z.extractall(os.path.dirname(filename))
os.remove(filename_zip)
if file_per_band:
print(f"Data downloaded to {os.path.dirname(filename)}")
else:
print(f"Data downloaded to {filename}")
except Exception as e:
print(e)
ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, crs_transform=None, region=None, dimensions=None, file_per_band=False, format='ZIPPED_GEO_TIFF', unmask_value=None, filenames=None, timeout=300, proxies=None)
¶
Exports an ImageCollection as GeoTIFFs.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image to download. |
required |
out_dir |
str |
The output directory for the exported images. |
required |
scale |
float |
A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None. |
None |
crs |
str |
A default CRS string to use for any bands that do not explicitly specify one. Defaults to None. |
None |
crs_transform |
list |
a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None. |
None |
region |
object |
A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None. |
None |
dimensions |
list |
An optional array of two integers defining the width and height to which the band is cropped. Defaults to None. |
None |
file_per_band |
bool |
Whether to produce a different GeoTIFF per band. Defaults to False. |
False |
format |
str |
One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY", filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array. |
'ZIPPED_GEO_TIFF' |
unmask_value |
float |
The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None. |
None |
filenames |
list | int |
A list of filenames to use for the exported images. Defaults to None. |
None |
timeout |
int |
The timeout in seconds for the request. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_image_collection(
ee_object,
out_dir,
scale=None,
crs=None,
crs_transform=None,
region=None,
dimensions=None,
file_per_band=False,
format="ZIPPED_GEO_TIFF",
unmask_value=None,
filenames=None,
timeout=300,
proxies=None,
):
"""Exports an ImageCollection as GeoTIFFs.
Args:
ee_object (object): The ee.Image to download.
out_dir (str): The output directory for the exported images.
scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
crs_transform (list, optional): a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.
region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
dimensions (list, optional): An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.
file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
format (str, optional): One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY",
filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.
unmask_value (float, optional): The value to use for pixels that are masked in the input image.
If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.
filenames (list | int, optional): A list of filenames to use for the exported images. Defaults to None.
timeout (int, optional): The timeout in seconds for the request. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
"""
if not isinstance(ee_object, ee.ImageCollection):
print("The ee_object must be an ee.ImageCollection.")
return
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
count = int(ee_object.size().getInfo())
print(f"Total number of images: {count}\n")
if filenames is None:
filenames = ee_object.aggregate_array("system:index").getInfo()
elif isinstance(filenames, int):
filenames = [str(f + filenames) for f in range(0, count)]
if len(filenames) != count:
raise Exception(
"The number of filenames must be equal to the number of images."
)
filenames = [str(f) + ".tif" for f in filenames if not str(f).endswith(".tif")]
for i in range(0, count):
image = ee.Image(ee_object.toList(count).get(i))
filename = os.path.join(out_dir, filenames[i])
print(f"Exporting {i + 1}/{count}: {filename}")
ee_export_image(
image,
filename=filename,
scale=scale,
crs=crs,
crs_transform=crs_transform,
region=region,
dimensions=dimensions,
file_per_band=file_per_band,
format=format,
unmask_value=unmask_value,
timeout=timeout,
proxies=proxies,
)
print("\n")
except Exception as e:
print(e)
ee_export_image_collection_to_asset(ee_object, descriptions=None, assetIds=None, pyramidingPolicy=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, **kwargs)
¶
Creates a batch task to export an ImageCollection as raster images to Google Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
The image collection to export. |
required | |
descriptions |
A list of human-readable names of the tasks. |
None |
|
assetIds |
The destination asset ID. |
None |
|
pyramidingPolicy |
The pyramiding policy to apply to each band in the image, a dictionary keyed by band name. Values must be one of: "mean", "sample", "min", "max", or "mode". Defaults to "mean". A special key, ".default", may be used to change the default for all bands. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_image_collection_to_asset(
ee_object,
descriptions=None,
assetIds=None,
pyramidingPolicy=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
**kwargs,
):
"""Creates a batch task to export an ImageCollection as raster images to Google Drive.
Args:
ee_object: The image collection to export.
descriptions: A list of human-readable names of the tasks.
assetIds: The destination asset ID.
pyramidingPolicy: The pyramiding policy to apply to each band in the
image, a dictionary keyed by band name. Values must be
one of: "mean", "sample", "min", "max", or "mode".
Defaults to "mean". A special key, ".default", may be used to
change the default for all bands.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(ee_object, ee.ImageCollection):
raise ValueError("The ee_object must be an ee.ImageCollection.")
try:
count = int(ee_object.size().getInfo())
print(f"Total number of images: {count}\n")
if (descriptions is not None) and (len(descriptions) != count):
print("The number of descriptions is not equal to the number of images.")
return
if descriptions is None:
descriptions = ee_object.aggregate_array("system:index").getInfo()
if assetIds is None:
assetIds = descriptions
images = ee_object.toList(count)
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
for i in range(0, count):
image = ee.Image(images.get(i))
description = descriptions[i]
assetId = assetIds[i]
ee_export_image_to_asset(
image,
description,
assetId,
pyramidingPolicy,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
**kwargs,
)
except Exception as e:
print(e)
ee_export_image_collection_to_cloud_storage(ee_object, descriptions=None, bucket=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)
¶
Creates a batch task to export an ImageCollection as raster images to a Google Cloud bucket.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
The image collection to export. |
required | |
descriptions |
A list of human-readable names of the tasks. |
None |
|
bucket |
The name of a Cloud Storage bucket for the export. |
None |
|
fileNamePrefix |
Cloud Storage object name prefix for the export. Defaults to the name of the task. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
shardSize |
Size in pixels of the tiles in which this image will be computed. Defaults to 256. |
None |
|
fileDimensions |
The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize. |
None |
|
skipEmptyTiles |
If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false. |
None |
|
fileFormat |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'. |
None |
|
formatOptions |
A dictionary of string keys to format specific options. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_image_collection_to_cloud_storage(
ee_object,
descriptions=None,
bucket=None,
fileNamePrefix=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
shardSize=None,
fileDimensions=None,
skipEmptyTiles=None,
fileFormat=None,
formatOptions=None,
**kwargs,
):
"""Creates a batch task to export an ImageCollection as raster images to a Google Cloud bucket.
Args:
ee_object: The image collection to export.
descriptions: A list of human-readable names of the tasks.
bucket: The name of a Cloud Storage bucket for the export.
fileNamePrefix: Cloud Storage object name prefix for the export.
Defaults to the name of the task.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
shardSize: Size in pixels of the tiles in which this image will be
computed. Defaults to 256.
fileDimensions: The dimensions in pixels of each image file, if the
image is too large to fit in a single file. May specify a
single number to indicate a square shape, or a tuple of two
dimensions to indicate (width,height). Note that the image will
still be clipped to the overall image dimensions. Must be a
multiple of shardSize.
skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
image tiles. Defaults to false.
fileFormat: The string file format to which the image is exported.
Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
'GeoTIFF'.
formatOptions: A dictionary of string keys to format specific options.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(ee_object, ee.ImageCollection):
raise ValueError("The ee_object must be an ee.ImageCollection.")
try:
count = int(ee_object.size().getInfo())
print(f"Total number of images: {count}\n")
if (descriptions is not None) and (len(descriptions) != count):
print("The number of descriptions is not equal to the number of images.")
return
if descriptions is None:
descriptions = ee_object.aggregate_array("system:index").getInfo()
images = ee_object.toList(count)
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
for i in range(0, count):
image = ee.Image(images.get(i))
description = descriptions[i]
ee_export_image_to_cloud_storage(
image,
description,
bucket,
fileNamePrefix,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
shardSize,
fileDimensions,
skipEmptyTiles,
fileFormat,
formatOptions,
**kwargs,
)
except Exception as e:
print(e)
ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)
¶
Creates a batch task to export an ImageCollection as raster images to Google Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
The image collection to export. |
required | |
descriptions |
A list of human-readable names of the tasks. |
None |
|
folder |
The name of a unique folder in your Drive account to export into. Defaults to the root of the drive. |
None |
|
fileNamePrefix |
The Google Drive filename for the export. Defaults to the name of the task. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
shardSize |
Size in pixels of the tiles in which this image will be computed. Defaults to 256. |
None |
|
fileDimensions |
The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize. |
None |
|
skipEmptyTiles |
If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false. |
None |
|
fileFormat |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'. |
None |
|
formatOptions |
A dictionary of string keys to format specific options. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'. |
{} |
Source code in geemap/common.py
def ee_export_image_collection_to_drive(
ee_object,
descriptions=None,
folder=None,
fileNamePrefix=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
shardSize=None,
fileDimensions=None,
skipEmptyTiles=None,
fileFormat=None,
formatOptions=None,
**kwargs,
):
"""Creates a batch task to export an ImageCollection as raster images to Google Drive.
Args:
ee_object: The image collection to export.
descriptions: A list of human-readable names of the tasks.
folder: The name of a unique folder in your Drive account to
export into. Defaults to the root of the drive.
fileNamePrefix: The Google Drive filename for the export.
Defaults to the name of the task.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
shardSize: Size in pixels of the tiles in which this image will be
computed. Defaults to 256.
fileDimensions: The dimensions in pixels of each image file, if the
image is too large to fit in a single file. May specify a
single number to indicate a square shape, or a tuple of two
dimensions to indicate (width,height). Note that the image will
still be clipped to the overall image dimensions. Must be a
multiple of shardSize.
skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
image tiles. Defaults to false.
fileFormat: The string file format to which the image is exported.
Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
'GeoTIFF'.
formatOptions: A dictionary of string keys to format specific options.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.
"""
if not isinstance(ee_object, ee.ImageCollection):
raise ValueError("The ee_object must be an ee.ImageCollection.")
try:
count = int(ee_object.size().getInfo())
print(f"Total number of images: {count}\n")
if (descriptions is not None) and (len(descriptions) != count):
raise ValueError(
"The number of descriptions is not equal to the number of images."
)
if descriptions is None:
descriptions = ee_object.aggregate_array("system:index").getInfo()
images = ee_object.toList(count)
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
for i in range(0, count):
image = ee.Image(images.get(i))
description = descriptions[i]
ee_export_image_to_drive(
image,
description,
folder,
fileNamePrefix,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
shardSize,
fileDimensions,
skipEmptyTiles,
fileFormat,
formatOptions,
**kwargs,
)
except Exception as e:
print(e)
ee_export_image_to_asset(image, description='myExportImageTask', assetId=None, pyramidingPolicy=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, **kwargs)
¶
Creates a task to export an EE Image to an EE Asset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
The image to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportImageTask' |
|
assetId |
The destination asset ID. |
None |
|
pyramidingPolicy |
The pyramiding policy to apply to each band in the image, a dictionary keyed by band name. Values must be one of: "mean", "sample", "min", "max", or "mode". Defaults to "mean". A special key, ".default", may be used to change the default for all bands. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_image_to_asset(
image,
description="myExportImageTask",
assetId=None,
pyramidingPolicy=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
**kwargs,
):
"""Creates a task to export an EE Image to an EE Asset.
Args:
image: The image to be exported.
description: Human-readable name of the task.
assetId: The destination asset ID.
pyramidingPolicy: The pyramiding policy to apply to each band in the
image, a dictionary keyed by band name. Values must be
one of: "mean", "sample", "min", "max", or "mode".
Defaults to "mean". A special key, ".default", may be used to
change the default for all bands.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if isinstance(image, ee.Image) or isinstance(image, ee.image.Image):
pass
else:
raise ValueError("Input image must be an instance of ee.Image")
if isinstance(assetId, str):
if assetId.startswith("users/") or assetId.startswith("projects/"):
pass
else:
assetId = f"{ee_user_id()}/{assetId}"
task = ee.batch.Export.image.toAsset(
image,
description,
assetId,
pyramidingPolicy,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
**kwargs,
)
task.start()
ee_export_image_to_cloud_storage(image, description='myExportImageTask', bucket=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)
¶
Creates a task to export an EE Image to Google Cloud Storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
The image to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportImageTask' |
|
bucket |
The name of a Cloud Storage bucket for the export. |
None |
|
fileNamePrefix |
Cloud Storage object name prefix for the export. Defaults to the name of the task. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
shardSize |
Size in pixels of the tiles in which this image will be computed. Defaults to 256. |
None |
|
fileDimensions |
The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize. |
None |
|
skipEmptyTiles |
If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false. |
None |
|
fileFormat |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'. |
None |
|
formatOptions |
A dictionary of string keys to format specific options. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_image_to_cloud_storage(
image,
description="myExportImageTask",
bucket=None,
fileNamePrefix=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
shardSize=None,
fileDimensions=None,
skipEmptyTiles=None,
fileFormat=None,
formatOptions=None,
**kwargs,
):
"""Creates a task to export an EE Image to Google Cloud Storage.
Args:
image: The image to be exported.
description: Human-readable name of the task.
bucket: The name of a Cloud Storage bucket for the export.
fileNamePrefix: Cloud Storage object name prefix for the export.
Defaults to the name of the task.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
shardSize: Size in pixels of the tiles in which this image will be
computed. Defaults to 256.
fileDimensions: The dimensions in pixels of each image file, if the
image is too large to fit in a single file. May specify a
single number to indicate a square shape, or a tuple of two
dimensions to indicate (width,height). Note that the image will
still be clipped to the overall image dimensions. Must be a
multiple of shardSize.
skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
image tiles. Defaults to false.
fileFormat: The string file format to which the image is exported.
Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
'GeoTIFF'.
formatOptions: A dictionary of string keys to format specific options.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(image, ee.Image):
raise ValueError("Input image must be an instance of ee.Image")
try:
task = ee.batch.Export.image.toCloudStorage(
image,
description,
bucket,
fileNamePrefix,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
shardSize,
fileDimensions,
skipEmptyTiles,
fileFormat,
formatOptions,
**kwargs,
)
task.start()
except Exception as e:
print(e)
ee_export_image_to_drive(image, description='myExportImageTask', folder=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)
¶
Creates a batch task to export an Image as a raster to Google Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
The image to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportImageTask' |
|
folder |
The name of a unique folder in your Drive account to export into. Defaults to the root of the drive. |
None |
|
fileNamePrefix |
The Google Drive filename for the export. Defaults to the name of the task. |
None |
|
dimensions |
The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region. |
None |
|
scale |
The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified. |
None |
|
crs |
The coordinate reference system of the exported image's projection. Defaults to the image's default projection. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform. |
None |
|
maxPixels |
The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. |
None |
|
shardSize |
Size in pixels of the tiles in which this image will be computed. Defaults to 256. |
None |
|
fileDimensions |
The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize. |
None |
|
skipEmptyTiles |
If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false. |
None |
|
fileFormat |
The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'. |
None |
|
formatOptions |
A dictionary of string keys to format specific options. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'. |
{} |
Source code in geemap/common.py
def ee_export_image_to_drive(
image,
description="myExportImageTask",
folder=None,
fileNamePrefix=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
shardSize=None,
fileDimensions=None,
skipEmptyTiles=None,
fileFormat=None,
formatOptions=None,
**kwargs,
):
"""Creates a batch task to export an Image as a raster to Google Drive.
Args:
image: The image to be exported.
description: Human-readable name of the task.
folder: The name of a unique folder in your Drive account to
export into. Defaults to the root of the drive.
fileNamePrefix: The Google Drive filename for the export.
Defaults to the name of the task.
dimensions: The dimensions of the exported image. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the image's
region.
scale: The resolution in meters per pixel. Defaults to the
native resolution of the image asset unless a crsTransform
is specified.
crs: The coordinate reference system of the exported image's
projection. Defaults to the image's default projection.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported image's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image's native CRS transform.
maxPixels: The maximum allowed number of pixels in the exported
image. The task will fail if the exported region covers more
pixels in the specified projection. Defaults to 100,000,000.
shardSize: Size in pixels of the tiles in which this image will be
computed. Defaults to 256.
fileDimensions: The dimensions in pixels of each image file, if the
image is too large to fit in a single file. May specify a
single number to indicate a square shape, or a tuple of two
dimensions to indicate (width,height). Note that the image will
still be clipped to the overall image dimensions. Must be a
multiple of shardSize.
skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
image tiles. Defaults to false.
fileFormat: The string file format to which the image is exported.
Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
'GeoTIFF'.
formatOptions: A dictionary of string keys to format specific options.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.
"""
if not isinstance(image, ee.Image):
raise ValueError("Input image must be an instance of ee.Image")
task = ee.batch.Export.image.toDrive(
image,
description,
folder,
fileNamePrefix,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
shardSize,
fileDimensions,
skipEmptyTiles,
fileFormat,
formatOptions,
**kwargs,
)
task.start()
ee_export_map_to_cloud_storage(image, description='myExportMapTask', bucket=None, fileFormat=None, path=None, writePublicTiles=None, maxZoom=None, scale=None, minZoom=None, region=None, skipEmptyTiles=None, mapsApiKey=None, **kwargs)
¶
Creates a task to export an Image as a pyramid of map tiles.
Exports a rectangular pyramid of map tiles for use with web map viewers. The map tiles will be accompanied by a reference index.html file that displays them using the Google Maps API, and an earth.html file for opening the map on Google Earth.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
The image to export as tiles. |
required | |
description |
Human-readable name of the task. |
'myExportMapTask' |
|
bucket |
The destination bucket to write to. |
None |
|
fileFormat |
The map tiles' file format, one of 'auto', 'png', or 'jpeg'. Defaults to 'auto', which means that opaque tiles will be encoded as 'jpg' and tiles with transparency will be encoded as 'png'. |
None |
|
path |
The string used as the output's path. A trailing '/' is optional. Defaults to the task's description. |
None |
|
writePublicTiles |
Whether to write public tiles instead of using the bucket's default object ACL. Defaults to True and requires the invoker to be an OWNER of bucket. |
None |
|
maxZoom |
The maximum zoom level of the map tiles to export. |
None |
|
scale |
The max image resolution in meters per pixel, as an alternative to 'maxZoom'. The scale will be converted to the most appropriate maximum zoom level at the equator. |
None |
|
minZoom |
The optional minimum zoom level of the map tiles to export. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Map tiles will be produced in the rectangular region containing this geometry. Defaults to the image's region. |
None |
|
skipEmptyTiles |
If true, skip writing empty (i.e. fully-transparent) map tiles. Defaults to false. |
None |
|
mapsApiKey |
Used in index.html to initialize the Google Maps API. This removes the "development purposes only" message from the map. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_map_to_cloud_storage(
image,
description="myExportMapTask",
bucket=None,
fileFormat=None,
path=None,
writePublicTiles=None,
maxZoom=None,
scale=None,
minZoom=None,
region=None,
skipEmptyTiles=None,
mapsApiKey=None,
**kwargs,
):
"""Creates a task to export an Image as a pyramid of map tiles.
Exports a rectangular pyramid of map tiles for use with web map
viewers. The map tiles will be accompanied by a reference
index.html file that displays them using the Google Maps API,
and an earth.html file for opening the map on Google Earth.
Args:
image: The image to export as tiles.
description: Human-readable name of the task.
bucket: The destination bucket to write to.
fileFormat: The map tiles' file format, one of 'auto', 'png',
or 'jpeg'. Defaults to 'auto', which means that opaque tiles
will be encoded as 'jpg' and tiles with transparency will be
encoded as 'png'.
path: The string used as the output's path. A trailing '/'
is optional. Defaults to the task's description.
writePublicTiles: Whether to write public tiles instead of using the
bucket's default object ACL. Defaults to True and requires the
invoker to be an OWNER of bucket.
maxZoom: The maximum zoom level of the map tiles to export.
scale: The max image resolution in meters per pixel, as an alternative
to 'maxZoom'. The scale will be converted to the most appropriate
maximum zoom level at the equator.
minZoom: The optional minimum zoom level of the map tiles to export.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Map tiles will be
produced in the rectangular region containing this geometry.
Defaults to the image's region.
skipEmptyTiles: If true, skip writing empty (i.e. fully-transparent)
map tiles. Defaults to false.
mapsApiKey: Used in index.html to initialize the Google Maps API. This
removes the "development purposes only" message from the map.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(image, ee.Image):
raise TypeError("image must be an ee.Image")
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.map.toCloudStorage(
image,
description,
bucket,
fileFormat,
path,
writePublicTiles,
maxZoom,
scale,
minZoom,
region,
skipEmptyTiles,
mapsApiKey,
**kwargs,
)
task.start()
ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False, timeout=300, proxies=None)
¶
Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection to export. |
required |
filename |
str |
Output file name. |
required |
selectors |
list |
A list of attributes to export. Defaults to None. |
None |
verbose |
bool |
Whether to print out descriptive text. |
True |
keep_zip |
bool |
Whether to keep the downloaded shapefile as a zip file. |
False |
timeout |
int |
Timeout in seconds. Defaults to 300 seconds. |
300 |
proxies |
dict |
A dictionary of proxies to use. Defaults to None. |
None |
Source code in geemap/common.py
def ee_export_vector(
ee_object,
filename,
selectors=None,
verbose=True,
keep_zip=False,
timeout=300,
proxies=None,
):
"""Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.
Args:
ee_object (object): ee.FeatureCollection to export.
filename (str): Output file name.
selectors (list, optional): A list of attributes to export. Defaults to None.
verbose (bool, optional): Whether to print out descriptive text.
keep_zip (bool, optional): Whether to keep the downloaded shapefile as a zip file.
timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
proxies (dict, optional): A dictionary of proxies to use. Defaults to None.
"""
if not isinstance(ee_object, ee.FeatureCollection):
raise ValueError("ee_object must be an ee.FeatureCollection")
allowed_formats = ["csv", "geojson", "json", "kml", "kmz", "shp"]
# allowed_formats = ['csv', 'kml', 'kmz']
filename = os.path.abspath(filename)
basename = os.path.basename(filename)
name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if filetype == "shp":
filename = filename.replace(".shp", ".zip")
if not (filetype.lower() in allowed_formats):
raise ValueError(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
if selectors is None:
selectors = ee_object.first().propertyNames().getInfo()
if filetype == "csv":
# remove .geo coordinate field
ee_object = ee_object.select([".*"], None, False)
if filetype == "geojson":
selectors = [".geo"] + selectors
elif not isinstance(selectors, list):
raise ValueError(
"selectors must be a list, such as ['attribute1', 'attribute2']"
)
else:
allowed_attributes = ee_object.first().propertyNames().getInfo()
for attribute in selectors:
if not (attribute in allowed_attributes):
raise ValueError(
"Attributes must be one chosen from: {} ".format(
", ".join(allowed_attributes)
)
)
try:
if verbose:
print("Generating URL ...")
url = ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
if verbose:
print(f"Downloading data from {url}\nPlease wait ...")
r = None
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
if r.status_code != 200:
print("An error occurred while downloading. \n Retrying ...")
try:
new_ee_object = ee_object.map(filter_polygons)
print("Generating URL ...")
url = new_ee_object.getDownloadURL(
filetype=filetype, selectors=selectors, filename=name
)
print(f"Downloading data from {url}\nPlease wait ...")
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
except Exception as e:
print(e)
raise ValueError
with open(filename, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
except Exception as e:
print("An error occurred while downloading.")
if r is not None:
print(r.json()["error"]["message"])
raise ValueError(e)
try:
if filetype == "shp":
with zipfile.ZipFile(filename) as z:
z.extractall(os.path.dirname(filename))
if not keep_zip:
os.remove(filename)
filename = filename.replace(".zip", ".shp")
if verbose:
print(f"Data downloaded to {filename}")
except Exception as e:
raise ValueError(e)
ee_export_vector_to_asset(collection, description='myExportTableTask', assetId=None, maxVertices=None, **kwargs)
¶
Creates a task to export a FeatureCollection to Asset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The feature collection to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportTableTask' |
|
assetId |
The destination asset ID. |
None |
|
maxVertices |
Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated. |
{} |
Source code in geemap/common.py
def ee_export_vector_to_asset(
collection,
description="myExportTableTask",
assetId=None,
maxVertices=None,
**kwargs,
):
"""Creates a task to export a FeatureCollection to Asset.
Args:
collection: The feature collection to be exported.
description: Human-readable name of the task.
assetId: The destination asset ID.
maxVertices:
Max number of uncut vertices per geometry; geometries with more
vertices will be cut into pieces smaller than this size.
**kwargs: Holds other keyword arguments that may have been deprecated.
"""
if not isinstance(collection, ee.FeatureCollection):
raise ValueError("The collection must be an ee.FeatureCollection.")
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
if isinstance(assetId, str):
if assetId.startswith("users/") or assetId.startswith("projects/"):
pass
else:
assetId = f"{ee_user_id()}/{assetId}"
print(assetId)
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.table.toAsset(
collection,
description,
assetId,
maxVertices,
**kwargs,
)
task.start()
ee_export_vector_to_cloud_storage(collection, description='myExportTableTask', bucket=None, fileNamePrefix=None, fileFormat=None, selectors=None, maxVertices=None, **kwargs)
¶
Creates a task to export a FeatureCollection to Google Cloud Storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The feature collection to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportTableTask' |
|
bucket |
The name of a Cloud Storage bucket for the export. |
None |
|
fileNamePrefix |
Cloud Storage object name prefix for the export. Defaults to the name of the task. |
None |
|
fileFormat |
The output format: "CSV" (default), "GeoJSON", "KML", "KMZ", "SHP", or "TFRecord". |
None |
|
selectors |
The list of properties to include in the output, as a list of strings or a comma-separated string. By default, all properties are included. |
None |
|
maxVertices |
Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'outputBucket'. |
{} |
Source code in geemap/common.py
def ee_export_vector_to_cloud_storage(
collection,
description="myExportTableTask",
bucket=None,
fileNamePrefix=None,
fileFormat=None,
selectors=None,
maxVertices=None,
**kwargs,
):
"""Creates a task to export a FeatureCollection to Google Cloud Storage.
Args:
collection: The feature collection to be exported.
description: Human-readable name of the task.
bucket: The name of a Cloud Storage bucket for the export.
fileNamePrefix: Cloud Storage object name prefix for the export.
Defaults to the name of the task.
fileFormat: The output format: "CSV" (default), "GeoJSON", "KML", "KMZ",
"SHP", or "TFRecord".
selectors: The list of properties to include in the output, as a list
of strings or a comma-separated string. By default, all properties
are included.
maxVertices:
Max number of uncut vertices per geometry; geometries with more
vertices will be cut into pieces smaller than this size.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'outputBucket'.
"""
if not isinstance(collection, ee.FeatureCollection):
raise ValueError("The collection must be an ee.FeatureCollection.")
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp", "tfrecord"]
if not (fileFormat.lower() in allowed_formats):
raise ValueError(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.table.toCloudStorage(
collection,
description,
bucket,
fileNamePrefix,
fileFormat,
selectors,
maxVertices,
**kwargs,
)
task.start()
ee_export_vector_to_drive(collection, description='myExportTableTask', folder=None, fileNamePrefix=None, fileFormat=None, selectors=None, maxVertices=None, **kwargs)
¶
Creates a task to export a FeatureCollection to Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The feature collection to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportTableTask' |
|
folder |
The name of a unique folder in your Drive account to export into. Defaults to the root of the drive. |
None |
|
fileNamePrefix |
The Google Drive filename for the export. Defaults to the name of the task. |
None |
|
fileFormat |
The output format: "CSV" (default), "GeoJSON", "KML", "KMZ", "SHP", or "TFRecord". |
None |
|
selectors |
The list of properties to include in the output, as a list of strings or a comma-separated string. By default, all properties are included. |
None |
|
maxVertices |
Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'driveFolder' and 'driveFileNamePrefix'. |
{} |
Source code in geemap/common.py
def ee_export_vector_to_drive(
collection,
description="myExportTableTask",
folder=None,
fileNamePrefix=None,
fileFormat=None,
selectors=None,
maxVertices=None,
**kwargs,
):
"""Creates a task to export a FeatureCollection to Drive.
Args:
collection: The feature collection to be exported.
description: Human-readable name of the task.
folder: The name of a unique folder in your Drive account to
export into. Defaults to the root of the drive.
fileNamePrefix: The Google Drive filename for the export.
Defaults to the name of the task.
fileFormat: The output format: "CSV" (default), "GeoJSON", "KML",
"KMZ", "SHP", or "TFRecord".
selectors: The list of properties to include in the output, as a list
of strings or a comma-separated string. By default, all properties
are included.
maxVertices:
Max number of uncut vertices per geometry; geometries with more
vertices will be cut into pieces smaller than this size.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'driveFolder' and 'driveFileNamePrefix'.
"""
if not isinstance(collection, ee.FeatureCollection):
raise ValueError("The collection must be an ee.FeatureCollection.")
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp", "tfrecord"]
if not (fileFormat.lower() in allowed_formats):
raise ValueError(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.table.toDrive(
collection,
description,
folder,
fileNamePrefix,
fileFormat,
selectors,
maxVertices,
**kwargs,
)
task.start()
ee_export_vector_to_feature_view(collection, description='myExportTableTask', assetId=None, ingestionTimeParameters=None, **kwargs)
¶
Creates a task to export a FeatureCollection to a FeatureView.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The feature collection to be exported. |
required | |
description |
Human-readable name of the task. |
'myExportTableTask' |
|
assetId |
The destination asset ID. |
None |
|
ingestionTimeParameters |
The FeatureView ingestion time parameters. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated. |
{} |
Source code in geemap/common.py
def ee_export_vector_to_feature_view(
collection,
description="myExportTableTask",
assetId=None,
ingestionTimeParameters=None,
**kwargs,
):
"""Creates a task to export a FeatureCollection to a FeatureView.
Args:
collection: The feature collection to be exported.
description: Human-readable name of the task.
assetId: The destination asset ID.
ingestionTimeParameters: The FeatureView ingestion time parameters.
**kwargs: Holds other keyword arguments that may have been deprecated.
"""
if not isinstance(collection, ee.FeatureCollection):
raise ValueError("The collection must be an ee.FeatureCollection.")
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.table.toFeatureView(
collection,
description,
assetId,
ingestionTimeParameters,
**kwargs,
)
task.start()
ee_export_video_to_cloud_storage(collection, description='myExportVideoTask', bucket=None, fileNamePrefix=None, framesPerSecond=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, maxFrames=None, **kwargs)
¶
Creates a task to export an ImageCollection as a video to Cloud Storage.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The image collection to be exported. The collection must only contain RGB images. |
required | |
description |
Human-readable name of the task. |
'myExportVideoTask' |
|
bucket |
The name of a Cloud Storage bucket for the export. |
None |
|
fileNamePrefix |
Cloud Storage object name prefix for the export. Defaults to the task's description. |
None |
|
framesPerSecond |
A number between .1 and 120 describing the framerate of the exported video. |
None |
|
dimensions |
The dimensions of the exported video. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the first image's region. |
None |
|
scale |
The resolution in meters per pixel. |
None |
|
crs |
The coordinate reference system of the exported video's projection. Defaults to SR-ORG:6627. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported video's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image collection's native CRS transform. |
None |
|
maxPixels |
The maximum number of pixels per frame. Defaults to 1e8 pixels per frame. By setting this explicitly, you may raise or lower the limit. |
None |
|
maxFrames |
The maximum number of frames to export. Defaults to 1000 frames. By setting this explicitly, you may raise or lower the limit. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_video_to_cloud_storage(
collection,
description="myExportVideoTask",
bucket=None,
fileNamePrefix=None,
framesPerSecond=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
maxFrames=None,
**kwargs,
):
"""Creates a task to export an ImageCollection as a video to Cloud Storage.
Args:
collection: The image collection to be exported. The collection must
only contain RGB images.
description: Human-readable name of the task.
bucket: The name of a Cloud Storage bucket for the export.
fileNamePrefix: Cloud Storage object name prefix for the export.
Defaults to the task's description.
framesPerSecond: A number between .1 and 120 describing the
framerate of the exported video.
dimensions: The dimensions of the exported video. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the first
image's region.
scale: The resolution in meters per pixel.
crs: The coordinate reference system of the exported video's
projection. Defaults to SR-ORG:6627.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported video's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image collection's native CRS transform.
maxPixels: The maximum number of pixels per frame.
Defaults to 1e8 pixels per frame. By setting this explicitly,
you may raise or lower the limit.
maxFrames: The maximum number of frames to export.
Defaults to 1000 frames. By setting this explicitly, you may
raise or lower the limit.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(collection, ee.ImageCollection):
raise TypeError("collection must be an ee.ImageCollection")
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.video.toCloudStorage(
collection,
description,
bucket,
fileNamePrefix,
framesPerSecond,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
maxFrames,
**kwargs,
)
task.start()
ee_export_video_to_drive(collection, description='myExportVideoTask', folder=None, fileNamePrefix=None, framesPerSecond=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, maxFrames=None, **kwargs)
¶
Creates a task to export an ImageCollection as a video to Drive.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
The image collection to be exported. The collection must only contain RGB images. |
required | |
description |
Human-readable name of the task. |
'myExportVideoTask' |
|
folder |
The name of a unique folder in your Drive account to export into. Defaults to the root of the drive. |
None |
|
fileNamePrefix |
The Google Drive filename for the export. Defaults to the name of the task. |
None |
|
framesPerSecond |
A number between .1 and 120 describing the framerate of the exported video. |
None |
|
dimensions |
The dimensions of the exported video. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers. |
None |
|
region |
The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the first image's region. |
None |
|
scale |
The resolution in meters per pixel. |
None |
|
crs |
The coordinate reference system of the exported video's projection. Defaults to SR-ORG:6627. |
None |
|
crsTransform |
A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported video's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image collection's native CRS transform. |
None |
|
maxPixels |
The maximum number of pixels per frame. Defaults to 1e8 pixels per frame. By setting this explicitly, you may raise or lower the limit. |
None |
|
maxFrames |
The maximum number of frames to export. Defaults to 1000 frames. By setting this explicitly, you may raise or lower the limit. |
None |
|
**kwargs |
Holds other keyword arguments that may have been deprecated such as 'crs_transform'. |
{} |
Source code in geemap/common.py
def ee_export_video_to_drive(
collection,
description="myExportVideoTask",
folder=None,
fileNamePrefix=None,
framesPerSecond=None,
dimensions=None,
region=None,
scale=None,
crs=None,
crsTransform=None,
maxPixels=None,
maxFrames=None,
**kwargs,
):
"""Creates a task to export an ImageCollection as a video to Drive.
Args:
collection: The image collection to be exported. The collection must
only contain RGB images.
description: Human-readable name of the task.
folder: The name of a unique folder in your Drive account to
export into. Defaults to the root of the drive.
fileNamePrefix: The Google Drive filename for the export.
Defaults to the name of the task.
framesPerSecond: A number between .1 and 120 describing the
framerate of the exported video.
dimensions: The dimensions of the exported video. Takes either a
single positive integer as the maximum dimension or "WIDTHxHEIGHT"
where WIDTH and HEIGHT are each positive integers.
region: The lon,lat coordinates for a LinearRing or Polygon
specifying the region to export. Can be specified as a nested
lists of numbers or a serialized string. Defaults to the first
image's region.
scale: The resolution in meters per pixel.
crs: The coordinate reference system of the exported video's
projection. Defaults to SR-ORG:6627.
crsTransform: A comma-separated string of 6 numbers describing
the affine transform of the coordinate reference system of the
exported video's projection, in the order: xScale, xShearing,
xTranslation, yShearing, yScale and yTranslation. Defaults to
the image collection's native CRS transform.
maxPixels: The maximum number of pixels per frame.
Defaults to 1e8 pixels per frame. By setting this explicitly,
you may raise or lower the limit.
maxFrames: The maximum number of frames to export.
Defaults to 1000 frames. By setting this explicitly, you may
raise or lower the limit.
**kwargs: Holds other keyword arguments that may have been deprecated
such as 'crs_transform'.
"""
if not isinstance(collection, ee.ImageCollection):
raise TypeError("collection must be an ee.ImageCollection")
if os.environ.get("USE_MKDOCS") is not None: # skip if running GitHub CI.
return
print(
f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
)
task = ee.batch.Export.video.toDrive(
collection,
description,
folder,
fileNamePrefix,
framesPerSecond,
dimensions,
region,
scale,
crs,
crsTransform,
maxPixels,
maxFrames,
**kwargs,
)
task.start()
ee_function_tree(name)
¶
Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name |
str |
The name of the Earth Engine function |
required |
Returns:
| Type | Description |
|---|---|
list |
The list for parent functions. |
Source code in geemap/common.py
def ee_function_tree(name):
"""Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]
Args:
name (str): The name of the Earth Engine function
Returns:
list: The list for parent functions.
"""
func_list = []
try:
items = name.split(".")
if items[0] == "ee":
for i in range(2, len(items) + 1):
func_list.append(".".join(items[0:i]))
else:
for i in range(1, len(items) + 1):
func_list.append(".".join(items[0:i]))
return func_list
except Exception as e:
print(e)
print("The provided function name is invalid.")
ee_initialize(token_name='EARTHENGINE_TOKEN', auth_mode=None, service_account=False, auth_args={}, user_agent_prefix='geemap', **kwargs)
¶
Authenticates Earth Engine and initialize an Earth Engine session
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
token_name |
str |
The name of the Earth Engine token. Defaults to "EARTHENGINE_TOKEN". In Colab, you can also set a secret named "EE_PROJECT_ID" to initialize Earth Engine. |
'EARTHENGINE_TOKEN' |
auth_mode |
str |
The authentication mode, can be one of colab, notebook, localhost, or gcloud. See https://developers.google.com/earth-engine/guides/auth for more details. Defaults to None. |
None |
service_account |
bool |
If True, use a service account. Defaults to False. |
False |
auth_args |
dict |
Additional authentication parameters for aa.Authenticate(). Defaults to {}. |
{} |
user_agent_prefix |
str |
If set, the prefix (version-less) value used for setting the user-agent string. Defaults to "geemap". |
'geemap' |
kwargs |
dict |
Additional parameters for ee.Initialize(). For example, opt_url='https://earthengine-highvolume.googleapis.com' to use the Earth Engine High-Volume platform. Defaults to {}. |
{} |
Source code in geemap/common.py
def ee_initialize(
token_name="EARTHENGINE_TOKEN",
auth_mode=None,
service_account=False,
auth_args={},
user_agent_prefix="geemap",
**kwargs,
):
"""Authenticates Earth Engine and initialize an Earth Engine session
Args:
token_name (str, optional): The name of the Earth Engine token. Defaults to "EARTHENGINE_TOKEN".
In Colab, you can also set a secret named "EE_PROJECT_ID" to initialize Earth Engine.
auth_mode (str, optional): The authentication mode, can be one of colab, notebook, localhost, or gcloud.
See https://developers.google.com/earth-engine/guides/auth for more details. Defaults to None.
service_account (bool, optional): If True, use a service account. Defaults to False.
auth_args (dict, optional): Additional authentication parameters for aa.Authenticate(). Defaults to {}.
user_agent_prefix (str, optional): If set, the prefix (version-less) value used for setting the user-agent string. Defaults to "geemap".
kwargs (dict, optional): Additional parameters for ee.Initialize(). For example,
opt_url='https://earthengine-highvolume.googleapis.com' to use the Earth Engine High-Volume platform. Defaults to {}.
"""
import httplib2
from .__init__ import __version__
user_agent = f"{user_agent_prefix}/{__version__}"
if "http_transport" not in kwargs:
kwargs["http_transport"] = httplib2.Http()
if auth_mode is None:
if in_colab_shell():
from google.colab import userdata
try:
project_id = userdata.get("EE_PROJECT_ID")
auth_mode = "colab"
kwargs["project"] = project_id
except Exception:
auth_mode = "notebook"
else:
auth_mode = "notebook"
auth_args["auth_mode"] = auth_mode
if ee.data._credentials is None:
ee_token = os.environ.get(token_name)
if in_colab_shell():
from google.colab import userdata
try:
ee_token = userdata.get(token_name)
except Exception:
pass
if service_account:
try:
credential_file_path = os.path.expanduser(
"~/.config/earthengine/private-key.json"
)
if os.path.exists(credential_file_path):
with open(credential_file_path) as f:
token_dict = json.load(f)
else:
token_name = "EARTHENGINE_TOKEN"
ee_token = os.environ.get(token_name)
token_dict = json.loads(ee_token, strict=False)
service_account = token_dict["client_email"]
private_key = token_dict["private_key"]
credentials = ee.ServiceAccountCredentials(
service_account, key_data=private_key
)
ee.Initialize(credentials, **kwargs)
except Exception as e:
raise Exception(e)
else:
try:
if ee_token is not None:
credential_file_path = os.path.expanduser(
"~/.config/earthengine/credentials"
)
if not os.path.exists(credential_file_path):
os.makedirs(
os.path.dirname(credential_file_path), exist_ok=True
)
if ee_token.startswith("{") and ee_token.endswith(
"}"
): # deals with token generated by new auth method (earthengine-api>=0.1.304).
token_dict = json.loads(ee_token)
with open(credential_file_path, "w") as f:
f.write(json.dumps(token_dict))
else:
credential = (
'{"refresh_token":"%s"}' % ee_token
) # deals with token generated by old auth method.
with open(credential_file_path, "w") as f:
f.write(credential)
ee.Initialize(**kwargs)
except Exception:
ee.Authenticate(**auth_args)
ee.Initialize(**kwargs)
ee.data.setUserAgent(user_agent)
ee_join_table(ee_object, data, src_key, dst_key=None)
¶
Join a table to an ee.FeatureCollection attribute table.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
The ee.FeatureCollection to be joined by a table. |
required |
data |
str | pd.DataFraem | gpd.GeoDataFrame |
The table to join to the ee.FeatureCollection. |
required |
src_key |
str |
The key of ee.FeatureCollection attribute table to join. |
required |
dst_key |
str |
The key of the table to be joined to the ee.FeatureCollection. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The joined ee.FeatureCollection. |
Source code in geemap/common.py
def ee_join_table(ee_object, data, src_key, dst_key=None):
"""Join a table to an ee.FeatureCollection attribute table.
Args:
ee_object (ee.FeatureCollection): The ee.FeatureCollection to be joined by a table.
data (str | pd.DataFraem | gpd.GeoDataFrame): The table to join to the ee.FeatureCollection.
src_key (str): The key of ee.FeatureCollection attribute table to join.
dst_key (str, optional): The key of the table to be joined to the ee.FeatureCollection. Defaults to None.
Returns:
ee.FeatureCollection: The joined ee.FeatureCollection.
"""
import pandas as pd
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("The input ee_object must be of type ee.FeatureCollection.")
if not isinstance(src_key, str):
raise TypeError("The input src_key must be of type str.")
if dst_key is None:
dst_key = src_key
if isinstance(data, str):
data = github_raw_url(data)
if data.endswith(".csv"):
df = pd.read_csv(data)
elif data.endswith(".geojson"):
df = geojson_to_df(data)
else:
import geopandas as gpd
gdf = gpd.read_file(data)
df = gdf_to_df(gdf)
elif isinstance(data, pd.DataFrame):
if "geometry" in data.columns:
df = data.drop(columns=["geometry"])
elif "geom" in data.columns:
df = data.drop(columns=["geom"])
else:
df = data
else:
raise TypeError("The input data must be of type str or pandas.DataFrame.")
df[dst_key] = df[dst_key].astype(str)
df.set_index(dst_key, inplace=True)
df = df[~df.index.duplicated(keep="first")]
table = ee.Dictionary(df.to_dict("index"))
fc = ee_object.map(lambda f: f.set(table.get(f.get(src_key), ee.Dictionary())))
return fc
ee_num_round(num, decimal=2)
¶
Rounds a number to a specified number of decimal places.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
num |
ee.Number |
The number to round. |
required |
decimal |
int |
The number of decimal places to round. Defaults to 2. |
2 |
Returns:
| Type | Description |
|---|---|
ee.Number |
The number with the specified decimal places rounded. |
Source code in geemap/common.py
def ee_num_round(num, decimal=2):
"""Rounds a number to a specified number of decimal places.
Args:
num (ee.Number): The number to round.
decimal (int, optional): The number of decimal places to round. Defaults to 2.
Returns:
ee.Number: The number with the specified decimal places rounded.
"""
format_str = "%.{}f".format(decimal)
return ee.Number.parse(ee.Number(num).format(format_str))
ee_search(asset_limit=100)
¶
Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command: jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
asset_limit |
int |
The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100. |
100 |
Source code in geemap/common.py
def ee_search(asset_limit=100):
"""Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command:
jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000
Args:
asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.
"""
warnings.filterwarnings("ignore")
class Flags:
def __init__(
self,
repos=None,
docs=None,
assets=None,
docs_dict=None,
asset_dict=None,
asset_import=None,
):
self.repos = repos
self.docs = docs
self.assets = assets
self.docs_dict = docs_dict
self.asset_dict = asset_dict
self.asset_import = asset_import
flags = Flags()
search_type = widgets.ToggleButtons(
options=["Scripts", "Docs", "Assets"],
tooltips=[
"Search Earth Engine Scripts",
"Search Earth Engine API",
"Search Earth Engine Assets",
],
button_style="primary",
)
search_type.style.button_width = "100px"
search_box = widgets.Text(placeholder="Filter scripts...", value="Loading...")
search_box.layout.width = "310px"
tree_widget = widgets.Output()
left_widget = widgets.VBox()
right_widget = widgets.VBox()
output_widget = widgets.Output()
output_widget.layout.max_width = "650px"
search_widget = widgets.HBox()
search_widget.children = [left_widget, right_widget]
display(search_widget)
repo_tree, repo_output, _ = build_repo_tree()
left_widget.children = [search_type, repo_tree]
right_widget.children = [repo_output]
flags.repos = repo_tree
search_box.value = ""
def search_type_changed(change):
search_box.value = ""
output_widget.outputs = ()
tree_widget.outputs = ()
if change["new"] == "Scripts":
search_box.placeholder = "Filter scripts..."
left_widget.children = [search_type, repo_tree]
right_widget.children = [repo_output]
elif change["new"] == "Docs":
search_box.placeholder = "Filter methods..."
search_box.value = "Loading..."
left_widget.children = [search_type, search_box, tree_widget]
right_widget.children = [output_widget]
if flags.docs is None:
api_dict = read_api_csv()
ee_api_tree, tree_dict = build_api_tree(api_dict, output_widget)
flags.docs = ee_api_tree
flags.docs_dict = tree_dict
else:
ee_api_tree = flags.docs
with tree_widget:
tree_widget.outputs = ()
display(ee_api_tree)
right_widget.children = [output_widget]
search_box.value = ""
elif change["new"] == "Assets":
search_box.placeholder = "Filter assets..."
left_widget.children = [search_type, search_box, tree_widget]
right_widget.children = [output_widget]
search_box.value = "Loading..."
if flags.assets is None:
asset_tree, asset_widget, asset_dict = build_asset_tree(
limit=asset_limit
)
flags.assets = asset_tree
flags.asset_dict = asset_dict
flags.asset_import = asset_widget
with tree_widget:
tree_widget.outputs = ()
display(flags.assets)
right_widget.children = [flags.asset_import]
search_box.value = ""
search_type.observe(search_type_changed, names="value")
def search_box_callback(text):
if search_type.value == "Docs":
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.outputs = ()
display(flags.docs)
else:
tree_widget.outputs = ()
print("Searching...")
tree_widget.outputs = ()
sub_tree = search_api_tree(text.value, flags.docs_dict)
display(sub_tree)
elif search_type.value == "Assets":
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.outputs = ()
display(flags.assets)
else:
tree_widget.outputs = ()
print("Searching...")
tree_widget.outputs = ()
sub_tree = search_api_tree(text.value, flags.asset_dict)
display(sub_tree)
search_box.on_submit(search_box_callback)
ee_to_bbox(ee_object)
¶
Get the bounding box of an Earth Engine object as a list in the format [xmin, ymin, xmax, ymax].
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image | ee.Geometry | ee.Feature | ee.FeatureCollection |
The input Earth Engine object. |
required |
Returns:
| Type | Description |
|---|---|
list |
The bounding box of the Earth Engine object in the format [xmin, ymin, xmax, ymax]. |
Source code in geemap/common.py
def ee_to_bbox(ee_object):
"""Get the bounding box of an Earth Engine object as a list in the format [xmin, ymin, xmax, ymax].
Args:
ee_object (ee.Image | ee.Geometry | ee.Feature | ee.FeatureCollection): The input Earth Engine object.
Returns:
list: The bounding box of the Earth Engine object in the format [xmin, ymin, xmax, ymax].
"""
if (
isinstance(ee_object, ee.Image)
or isinstance(ee_object, ee.Feature)
or isinstance(ee_object, ee.FeatureCollection)
):
geometry = ee_object.geometry()
elif isinstance(ee_object, ee.Geometry):
geometry = ee_object
else:
raise Exception(
"The ee_object must be an ee.Image, ee.Feature, ee.FeatureCollection or ee.Geometry object."
)
bounds = geometry.bounds().getInfo()["coordinates"][0]
xmin = bounds[0][0]
ymin = bounds[0][1]
xmax = bounds[1][0]
ymax = bounds[2][1]
bbox = [xmin, ymin, xmax, ymax]
return bbox
ee_to_csv(ee_object, filename, columns=None, remove_geom=True, sort_columns=False, **kwargs)
¶
Downloads an ee.FeatureCollection as a CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection |
required |
filename |
str |
The output filepath of the CSV file. |
required |
columns |
list |
A list of attributes to export. Defaults to None. |
None |
remove_geom |
bool |
Whether to remove the geometry column. Defaults to True. |
True |
sort_columns |
bool |
Whether to sort the columns alphabetically. Defaults to False. |
False |
kwargs |
Additional arguments passed to ee_to_df(). |
{} |
Source code in geemap/common.py
def ee_to_csv(
ee_object,
filename,
columns=None,
remove_geom=True,
sort_columns=False,
**kwargs,
):
"""Downloads an ee.FeatureCollection as a CSV file.
Args:
ee_object (object): ee.FeatureCollection
filename (str): The output filepath of the CSV file.
columns (list, optional): A list of attributes to export. Defaults to None.
remove_geom (bool, optional): Whether to remove the geometry column. Defaults to True.
sort_columns (bool, optional): Whether to sort the columns alphabetically. Defaults to False.
kwargs: Additional arguments passed to ee_to_df().
"""
try:
if filename.lower().endswith(".csv"):
df = ee_to_df(ee_object, columns, remove_geom, sort_columns, **kwargs)
df.to_csv(filename, index=False)
else:
print("The filename must end with .csv")
except Exception as e:
print(e)
ee_to_df(ee_object, columns=None, remove_geom=True, sort_columns=False, **kwargs)
¶
Converts an ee.FeatureCollection to pandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
ee.FeatureCollection. |
required |
columns |
list |
List of column names. Defaults to None. |
None |
remove_geom |
bool |
Whether to remove the geometry column. Defaults to True. |
True |
sort_columns |
bool |
Whether to sort the column names. Defaults to False. |
False |
kwargs |
Additional arguments passed to ee.data.computeFeature. |
{} |
Exceptions:
| Type | Description |
|---|---|
TypeError |
ee_object must be an ee.FeatureCollection |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
pandas DataFrame |
Source code in geemap/common.py
def ee_to_df(
ee_object,
columns=None,
remove_geom=True,
sort_columns=False,
**kwargs,
):
"""Converts an ee.FeatureCollection to pandas dataframe.
Args:
ee_object (ee.FeatureCollection): ee.FeatureCollection.
columns (list): List of column names. Defaults to None.
remove_geom (bool): Whether to remove the geometry column. Defaults to True.
sort_columns (bool): Whether to sort the column names. Defaults to False.
kwargs: Additional arguments passed to ee.data.computeFeature.
Raises:
TypeError: ee_object must be an ee.FeatureCollection
Returns:
pd.DataFrame: pandas DataFrame
"""
if isinstance(ee_object, ee.Feature):
ee_object = ee.FeatureCollection([ee_object])
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("ee_object must be an ee.FeatureCollection")
try:
property_names = ee_object.first().propertyNames().sort().getInfo()
if remove_geom:
data = ee_object.map(
lambda f: ee.Feature(None, f.toDictionary(property_names))
)
else:
data = ee_object
kwargs["expression"] = data
kwargs["fileFormat"] = "PANDAS_DATAFRAME"
df = ee.data.computeFeatures(kwargs)
if isinstance(columns, list):
df = df[columns]
if remove_geom and ("geo" in df.columns):
df = df.drop(columns=["geo"], axis=1)
if sort_columns:
df = df.reindex(sorted(df.columns), axis=1)
return df
except Exception as e:
raise Exception(e)
ee_to_gdf(ee_object, columns=None, sort_columns=False, **kwargs)
¶
Converts an ee.FeatureCollection to GeoPandas GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
ee.FeatureCollection. |
required |
columns |
list |
List of column names. Defaults to None. |
None |
sort_columns |
bool |
Whether to sort the column names. Defaults to False. |
False |
kwargs |
Additional arguments passed to ee.data.computeFeature. |
{} |
Exceptions:
| Type | Description |
|---|---|
TypeError |
ee_object must be an ee.FeatureCollection |
Returns:
| Type | Description |
|---|---|
gpd.GeoDataFrame |
GeoPandas GeoDataFrame |
Source code in geemap/common.py
def ee_to_gdf(
ee_object,
columns=None,
sort_columns=False,
**kwargs,
):
"""Converts an ee.FeatureCollection to GeoPandas GeoDataFrame.
Args:
ee_object (ee.FeatureCollection): ee.FeatureCollection.
columns (list): List of column names. Defaults to None.
sort_columns (bool): Whether to sort the column names. Defaults to False.
kwargs: Additional arguments passed to ee.data.computeFeature.
Raises:
TypeError: ee_object must be an ee.FeatureCollection
Returns:
gpd.GeoDataFrame: GeoPandas GeoDataFrame
"""
if isinstance(ee_object, ee.Feature):
ee_object = ee.FeatureCollection([ee_object])
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("ee_object must be an ee.FeatureCollection")
try:
kwargs["expression"] = ee_object
kwargs["fileFormat"] = "GEOPANDAS_GEODATAFRAME"
crs = ee_object.first().geometry().projection().crs().getInfo()
gdf = ee.data.computeFeatures(kwargs)
if isinstance(columns, list):
gdf = gdf[columns]
if sort_columns:
gdf = gdf.reindex(sorted(gdf.columns), axis=1)
gdf.crs = crs
return gdf
except Exception as e:
raise Exception(e)
ee_to_geojson(ee_object, filename=None, indent=2, **kwargs)
¶
Converts Earth Engine object to geojson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
An Earth Engine object. |
required |
filename |
str |
The file path to save the geojson. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
GeoJSON object. |
Source code in geemap/common.py
def ee_to_geojson(ee_object, filename=None, indent=2, **kwargs):
"""Converts Earth Engine object to geojson.
Args:
ee_object (object): An Earth Engine object.
filename (str, optional): The file path to save the geojson. Defaults to None.
Returns:
object: GeoJSON object.
"""
try:
if (
isinstance(ee_object, ee.Geometry)
or isinstance(ee_object, ee.Feature)
or isinstance(ee_object, ee.FeatureCollection)
):
json_object = ee_object.getInfo()
if filename is not None:
filename = os.path.abspath(filename)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
with open(filename, "w") as f:
f.write(json.dumps(json_object, indent=indent, **kwargs) + "\n")
else:
return json_object
else:
print("Could not convert the Earth Engine object to geojson")
except Exception as e:
raise Exception(e)
ee_to_geotiff(ee_object, output, bbox=None, vis_params={}, zoom=None, resolution=None, crs='EPSG:3857', to_cog=False, quiet=False, **kwargs)
¶
Downloads an Earth Engine object as GeoTIFF.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image | ee.FeatureCollection |
The Earth Engine object to download. |
required |
output |
str |
The output path for the GeoTIFF. |
required |
bbox |
str |
The bounding box in the format [xmin, ymin, xmax, ymax]. Defaults to None, which is the bounding box of the Earth Engine object. |
None |
vis_params |
dict |
Visualization parameters. Defaults to {}. |
{} |
zoom |
int |
The zoom level to download the image at. Defaults to None. |
None |
resolution |
float |
The resolution in meters to download the image at. Defaults to None. |
None |
crs |
str |
The CRS of the output image. Defaults to "EPSG:3857". |
'EPSG:3857' |
to_cog |
bool |
Whether to convert the image to Cloud Optimized GeoTIFF. Defaults to False. |
False |
quiet |
bool |
Whether to hide the download progress bar. Defaults to False. |
False |
Source code in geemap/common.py
def ee_to_geotiff(
ee_object,
output,
bbox=None,
vis_params={},
zoom=None,
resolution=None,
crs="EPSG:3857",
to_cog=False,
quiet=False,
**kwargs,
):
"""Downloads an Earth Engine object as GeoTIFF.
Args:
ee_object (ee.Image | ee.FeatureCollection): The Earth Engine object to download.
output (str): The output path for the GeoTIFF.
bbox (str, optional): The bounding box in the format [xmin, ymin, xmax, ymax]. Defaults to None,
which is the bounding box of the Earth Engine object.
vis_params (dict, optional): Visualization parameters. Defaults to {}.
zoom (int, optional): The zoom level to download the image at. Defaults to None.
resolution (float, optional): The resolution in meters to download the image at. Defaults to None.
crs (str, optional): The CRS of the output image. Defaults to "EPSG:3857".
to_cog (bool, optional): Whether to convert the image to Cloud Optimized GeoTIFF. Defaults to False.
quiet (bool, optional): Whether to hide the download progress bar. Defaults to False.
"""
from box import Box
image = None
if (
not isinstance(ee_object, ee.Image)
and not isinstance(ee_object, ee.ImageCollection)
and not isinstance(ee_object, ee.FeatureCollection)
and not isinstance(ee_object, ee.Feature)
and not isinstance(ee_object, ee.Geometry)
):
err_str = "\n\nThe image argument in 'addLayer' function must be an instance of one of ee.Image, ee.Geometry, ee.Feature or ee.FeatureCollection."
raise AttributeError(err_str)
if (
isinstance(ee_object, ee.geometry.Geometry)
or isinstance(ee_object, ee.feature.Feature)
or isinstance(ee_object, ee.featurecollection.FeatureCollection)
):
features = ee.FeatureCollection(ee_object)
width = 2
if "width" in vis_params:
width = vis_params["width"]
color = "000000"
if "color" in vis_params:
color = vis_params["color"]
image_fill = features.style(**{"fillColor": color}).updateMask(
ee.Image.constant(0.5)
)
image_outline = features.style(
**{"color": color, "fillColor": "00000000", "width": width}
)
image = image_fill.blend(image_outline)
elif isinstance(ee_object, ee.image.Image):
image = ee_object
elif isinstance(ee_object, ee.imagecollection.ImageCollection):
image = ee_object.mosaic()
if "palette" in vis_params:
if isinstance(vis_params["palette"], Box):
try:
vis_params["palette"] = vis_params["palette"]["default"]
except Exception as e:
print("The provided palette is invalid.")
raise Exception(e)
elif isinstance(vis_params["palette"], str):
vis_params["palette"] = check_cmap(vis_params["palette"])
elif not isinstance(vis_params["palette"], list):
raise ValueError(
"The palette must be a list of colors or a string or a Box object."
)
map_id_dict = ee.Image(image).getMapId(vis_params)
url = map_id_dict["tile_fetcher"].url_format
if bbox is None:
bbox = ee_to_bbox(image)
if zoom is None and resolution is None:
raise ValueError("Either zoom level or resolution must be specified.")
tms_to_geotiff(output, bbox, zoom, resolution, url, crs, to_cog, quiet, **kwargs)
ee_to_numpy(ee_object, region=None, scale=None, bands=None, **kwargs)
¶
Extracts a rectangular region of pixels from an image into a numpy array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image |
The image to sample. |
required |
region |
ee.Geometry |
The region to sample. Defaults to None. |
None |
bands |
list |
The list of band names to extract. Defaults to None. |
None |
scale |
int |
A nominal scale in meters of the projection to sample in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
np.ndarray |
A 3D numpy array in the format of [row, column, band]. |
Source code in geemap/common.py
def ee_to_numpy(ee_object, region=None, scale=None, bands=None, **kwargs):
"""Extracts a rectangular region of pixels from an image into a numpy array.
Args:
ee_object (ee.Image): The image to sample.
region (ee.Geometry, optional): The region to sample. Defaults to None.
bands (list, optional): The list of band names to extract. Defaults to None.
scale (int, optional): A nominal scale in meters of the projection to sample in. Defaults to None.
Returns:
np.ndarray: A 3D numpy array in the format of [row, column, band].
"""
import numpy as np
if (region is not None) or (scale is not None):
ee_object = ee_object.clipToBoundsAndScale(geometry=region, scale=scale)
kwargs["expression"] = ee_object
kwargs["fileFormat"] = "NUMPY_NDARRAY"
if bands is not None:
kwargs["bandIds"] = bands
try:
struct_array = ee.data.computePixels(kwargs)
array = np.dstack(([struct_array[band] for band in struct_array.dtype.names]))
return array
except Exception as e:
raise Exception(e)
ee_to_shp(ee_object, filename, columns=None, sort_columns=False, **kwargs)
¶
Downloads an ee.FeatureCollection as a shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
ee.FeatureCollection |
required |
filename |
str |
The output filepath of the shapefile. |
required |
columns |
list |
A list of attributes to export. Defaults to None. |
None |
sort_columns |
bool |
Whether to sort the columns alphabetically. Defaults to False. |
False |
kwargs |
Additional arguments passed to ee_to_gdf(). |
{} |
Source code in geemap/common.py
def ee_to_shp(
ee_object,
filename,
columns=None,
sort_columns=False,
**kwargs,
):
"""Downloads an ee.FeatureCollection as a shapefile.
Args:
ee_object (object): ee.FeatureCollection
filename (str): The output filepath of the shapefile.
columns (list, optional): A list of attributes to export. Defaults to None.
sort_columns (bool, optional): Whether to sort the columns alphabetically. Defaults to False.
kwargs: Additional arguments passed to ee_to_gdf().
"""
try:
if filename.lower().endswith(".shp"):
gdf = ee_to_gdf(ee_object, columns, sort_columns, **kwargs)
gdf.to_file(filename)
else:
print("The filename must end with .shp")
except Exception as e:
print(e)
ee_to_xarray(dataset, drop_variables=None, io_chunks=None, n_images=-1, mask_and_scale=True, decode_times=True, decode_timedelta=None, use_cftime=None, concat_characters=True, decode_coords=True, crs=None, scale=None, projection=None, geometry=None, primary_dim_name=None, primary_dim_property=None, ee_mask_value=None, ee_initialize=True, **kwargs)
¶
Open an Earth Engine ImageCollection as an Xarray Dataset. This function is a wrapper for xee. EarthEngineBackendEntrypoint.open_dataset(). See https://github.com/google/Xee/blob/main/xee/ext.py#L886
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dataset |
An asset ID for an ImageCollection, or an ee.ImageCollection object. |
required | |
drop_variables |
optional |
Variables or bands to drop before opening. |
None |
io_chunks |
optional |
Specifies the chunking strategy for loading data
from EE. By default, this automatically calculates optional chunks based
on the |
None |
n_images |
optional |
The max number of EE images in the collection to open. Useful when there are a large number of images in the collection since calculating collection size can be slow. -1 indicates that all images should be included. |
-1 |
mask_and_scale |
optional |
Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). |
True |
decode_times |
optional |
Decode cf times (e.g., integers since "hours since 2000-01-01") to np.datetime64. |
True |
decode_timedelta |
optional |
If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. |
None |
use_cftime |
optional |
Only relevant if encoded dates come from a standard
calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not
specified). If None (default), attempt to decode times to
|
None |
concat_characters |
optional |
Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" |
True |
decode_coords |
optional |
bool or {"coordinates", "all"}, Controls which
variables are set as coordinate variables: - "coordinates" or True: Set
variables referred to in the |
True |
crs |
optional |
The coordinate reference system (a CRS code or WKT string). This defines the frame of reference to coalesce all variables upon opening. By default, data is opened with `EPSG:4326'. |
None |
scale |
optional |
The scale in the |
None |
projection |
optional |
Specify an |
None |
geometry |
optional |
Specify an |
None |
primary_dim_name |
optional |
Override the name of the primary dimension of the output Dataset. By default, the name is 'time'. |
None |
primary_dim_property |
optional |
Override the |
None |
ee_mask_value |
optional |
Value to mask to EE nodata values. By default, this is 'np.iinfo(np.int32).max' i.e. 2147483647. |
None |
request_byte_limit |
the max allowed bytes to request at a time from Earth Engine. By default, it is 48MBs. |
required | |
ee_initialize |
optional |
Whether to initialize ee with the high-volume endpoint. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
An xarray.Dataset that streams in remote data from Earth Engine. |
Source code in geemap/common.py
def ee_to_xarray(
dataset,
drop_variables=None,
io_chunks=None,
n_images=-1,
mask_and_scale=True,
decode_times=True,
decode_timedelta=None,
use_cftime=None,
concat_characters=True,
decode_coords=True,
crs=None,
scale=None,
projection=None,
geometry=None,
primary_dim_name=None,
primary_dim_property=None,
ee_mask_value=None,
ee_initialize=True,
**kwargs,
):
"""Open an Earth Engine ImageCollection as an Xarray Dataset. This function is a wrapper for
xee. EarthEngineBackendEntrypoint.open_dataset().
See https://github.com/google/Xee/blob/main/xee/ext.py#L886
Args:
dataset: An asset ID for an ImageCollection, or an
ee.ImageCollection object.
drop_variables (optional): Variables or bands to drop before opening.
io_chunks (optional): Specifies the chunking strategy for loading data
from EE. By default, this automatically calculates optional chunks based
on the `request_byte_limit`.
n_images (optional): The max number of EE images in the collection to
open. Useful when there are a large number of images in the collection
since calculating collection size can be slow. -1 indicates that all
images should be included.
mask_and_scale (optional): Lazily scale (using scale_factor and
add_offset) and mask (using _FillValue).
decode_times (optional): Decode cf times (e.g., integers since "hours
since 2000-01-01") to np.datetime64.
decode_timedelta (optional): If True, decode variables and coordinates
with time units in {"days", "hours", "minutes", "seconds",
"milliseconds", "microseconds"} into timedelta objects. If False, leave
them encoded as numbers. If None (default), assume the same value of
decode_time.
use_cftime (optional): Only relevant if encoded dates come from a standard
calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not
specified). If None (default), attempt to decode times to
``np.datetime64[ns]`` objects; if this is not possible, decode times to
``cftime.datetime`` objects. If True, always decode times to
``cftime.datetime`` objects, regardless of whether or not they can be
represented using ``np.datetime64[ns]`` objects. If False, always
decode times to ``np.datetime64[ns]`` objects; if this is not possible
raise an error.
concat_characters (optional): Should character arrays be concatenated to
strings, for example: ["h", "e", "l", "l", "o"] -> "hello"
decode_coords (optional): bool or {"coordinates", "all"}, Controls which
variables are set as coordinate variables: - "coordinates" or True: Set
variables referred to in the ``'coordinates'`` attribute of the datasets
or individual variables as coordinate variables. - "all": Set variables
referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as
coordinate variables.
crs (optional): The coordinate reference system (a CRS code or WKT
string). This defines the frame of reference to coalesce all variables
upon opening. By default, data is opened with `EPSG:4326'.
scale (optional): The scale in the `crs` or `projection`'s units of
measure -- either meters or degrees. This defines the scale that all
data is represented in upon opening. By default, the scale is 1° when
the CRS is in degrees or 10,000 when in meters.
projection (optional): Specify an `ee.Projection` object to define the
`scale` and `crs` (or other coordinate reference system) with which to
coalesce all variables upon opening. By default, the scale and reference
system is set by the the `crs` and `scale` arguments.
geometry (optional): Specify an `ee.Geometry` to define the regional
bounds when opening the data. When not set, the bounds are defined by
the CRS's 'area_of_use` boundaries. If those aren't present, the bounds
are derived from the geometry of the first image of the collection.
primary_dim_name (optional): Override the name of the primary dimension of
the output Dataset. By default, the name is 'time'.
primary_dim_property (optional): Override the `ee.Image` property for
which to derive the values of the primary dimension. By default, this is
'system:time_start'.
ee_mask_value (optional): Value to mask to EE nodata values. By default,
this is 'np.iinfo(np.int32).max' i.e. 2147483647.
request_byte_limit: the max allowed bytes to request at a time from Earth
Engine. By default, it is 48MBs.
ee_initialize (optional): Whether to initialize ee with the high-volume endpoint. Defaults to True.
Returns:
An xarray.Dataset that streams in remote data from Earth Engine.
"""
try:
import xee
except ImportError:
install_package("xee")
import xee
import xarray as xr
kwargs["drop_variables"] = drop_variables
kwargs["io_chunks"] = io_chunks
kwargs["n_images"] = n_images
kwargs["mask_and_scale"] = mask_and_scale
kwargs["decode_times"] = decode_times
kwargs["decode_timedelta"] = decode_timedelta
kwargs["use_cftime"] = use_cftime
kwargs["concat_characters"] = concat_characters
kwargs["decode_coords"] = decode_coords
kwargs["crs"] = crs
kwargs["scale"] = scale
kwargs["projection"] = projection
kwargs["geometry"] = geometry
kwargs["primary_dim_name"] = primary_dim_name
kwargs["primary_dim_property"] = primary_dim_property
kwargs["ee_mask_value"] = ee_mask_value
kwargs["engine"] = "ee"
if ee_initialize:
opt_url = "https://earthengine-highvolume.googleapis.com"
ee.Initialize(opt_url=opt_url)
if isinstance(dataset, str):
if not dataset.startswith("ee://"):
dataset = "ee://" + dataset
elif isinstance(dataset, ee.Image):
dataset = ee.ImageCollection(dataset)
elif isinstance(dataset, ee.ImageCollection):
pass
elif isinstance(dataset, list):
items = []
for item in dataset:
if isinstance(item, str) and not item.startswith("ee://"):
item = "ee://" + item
items.append(item)
dataset = items
else:
raise ValueError(
"The dataset must be an ee.Image, ee.ImageCollection, or a list of ee.Image."
)
if isinstance(dataset, list):
ds = xr.open_mfdataset(dataset, **kwargs)
else:
ds = xr.open_dataset(dataset, **kwargs)
return ds
ee_user_id()
¶
Gets Earth Engine account user id.
Returns:
| Type | Description |
|---|---|
str |
A string containing the user id. |
Source code in geemap/common.py
def ee_user_id():
"""Gets Earth Engine account user id.
Returns:
str: A string containing the user id.
"""
# ee_initialize()
roots = ee.data.getAssetRoots()
if len(roots) == 0:
return None
else:
root = ee.data.getAssetRoots()[0]
user_id = root["id"].replace("projects/earthengine-legacy/assets/", "")
return user_id
ee_vector_style(collection, column, labels=None, color='black', pointSize=3, pointShape='circle', width=2, fillColor=None, lineType='solid', neighborhood=5, return_fc=False)
¶
Create a vector style for a feature collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.FeatureCollection |
The input feature collection. |
required |
column |
str |
The name of the column to use for styling. |
required |
labels |
list |
A list of labels to use for styling. Defaults to None. |
None |
color |
str | list |
A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Supports opacity (e.g.: 'FF000088' for 50% transparent red). Defaults to "black". |
'black' |
pointSize |
int | list |
The default size in pixels of the point markers. Defaults to 3. |
3 |
pointShape |
str | list |
The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle". |
'circle' |
width |
int | list |
The default line width for lines and outlines for polygons and point shapes. Defaults to 2. |
2 |
fillColor |
str | list |
The color for filling polygons and point shapes. Defaults to 'color' at 0.66 opacity. Defaults to None. |
None |
lineType |
str | list |
The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid". |
'solid' |
neighborhood |
int |
If styleProperty is used and any feature has a pointSize or width larger than the defaults, tiling artifacts can occur. Specifies the maximum neighborhood (pointSize + width) needed for any feature. Defaults to 5. |
5 |
return_fc |
bool |
If True, return an ee.FeatureCollection with a style property. Otherwise, return a styled ee.Image. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection | ee.Image |
The styled Earth Engine FeatureCollection or Image. |
Source code in geemap/common.py
def ee_vector_style(
collection,
column,
labels=None,
color="black",
pointSize=3,
pointShape="circle",
width=2,
fillColor=None,
lineType="solid",
neighborhood=5,
return_fc=False,
):
"""Create a vector style for a feature collection.
Args:
collection (ee.FeatureCollection): The input feature collection.
column (str): The name of the column to use for styling.
labels (list, optional): A list of labels to use for styling. Defaults to None.
color (str | list, optional): A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Supports opacity (e.g.: 'FF000088' for 50% transparent red). Defaults to "black".
pointSize (int | list, optional): The default size in pixels of the point markers. Defaults to 3.
pointShape (str | list, optional): The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle".
width (int | list, optional): The default line width for lines and outlines for polygons and point shapes. Defaults to 2.
fillColor (str | list, optional): The color for filling polygons and point shapes. Defaults to 'color' at 0.66 opacity. Defaults to None.
lineType (str | list, optional): The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid".
neighborhood (int, optional): If styleProperty is used and any feature has a pointSize or width larger than the defaults, tiling artifacts can occur. Specifies the maximum neighborhood (pointSize + width) needed for any feature. Defaults to 5.
return_fc (bool, optional): If True, return an ee.FeatureCollection with a style property. Otherwise, return a styled ee.Image. Defaults to False.
Returns:
ee.FeatureCollection | ee.Image: The styled Earth Engine FeatureCollection or Image.
"""
if not isinstance(collection, ee.FeatureCollection):
raise ValueError("collection must be an ee.FeatureCollection.")
if not isinstance(column, str):
raise ValueError("column must be a string.")
prop_names = ee.Feature(collection.first()).propertyNames().getInfo()
if column not in prop_names:
raise ValueError(
f"{column} is not a property name of the collection. It must be one of {','.join(prop_names)}."
)
if labels is None:
labels = collection.aggregate_array(column).distinct().sort().getInfo()
elif isinstance(labels, list):
collection = collection.filter(ee.Filter.inList(column, labels))
elif not isinstance(labels, list):
raise ValueError("labels must be a list.")
size = len(labels)
if isinstance(color, str):
color = [color] * size
elif size != len(color):
raise ValueError("labels and color must be the same length.")
elif not isinstance(color, list):
raise ValueError("color must be a string or a list.")
if isinstance(pointSize, int):
pointSize = [pointSize] * size
elif not isinstance(pointSize, list):
raise ValueError("pointSize must be an integer or a list.")
if isinstance(pointShape, str):
pointShape = [pointShape] * size
elif not isinstance(pointShape, list):
raise ValueError("pointShape must be a string or a list.")
if isinstance(width, int):
width = [width] * size
elif not isinstance(width, list):
raise ValueError("width must be an integer or a list.")
if fillColor is None:
fillColor = color
elif isinstance(fillColor, str):
fillColor = [fillColor] * size
elif not isinstance(fillColor, list):
raise ValueError("fillColor must be a list.")
if not isinstance(neighborhood, int):
raise ValueError("neighborhood must be an integer.")
if isinstance(lineType, str):
lineType = [lineType] * size
elif not isinstance(lineType, list):
raise ValueError("lineType must be a string or list.")
style_dict = {}
for i, label in enumerate(labels):
style_dict[label] = {
"color": color[i],
"pointSize": pointSize[i],
"pointShape": pointShape[i],
"width": width[i],
"fillColor": fillColor[i],
"lineType": lineType[i],
}
style = ee.Dictionary(style_dict)
result = collection.map(lambda f: f.set("style", style.get(f.get(column))))
if return_fc:
return result
else:
return result.style(**{"styleProperty": "style", "neighborhood": neighborhood})
explode(coords)
¶
Explode a GeoJSON geometry's coordinates object and yield coordinate tuples. As long as the input is conforming, the type of the geometry doesn't matter. From Fiona 1.4.8
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords |
list |
A list of coordinates. |
required |
Yields:
| Type | Description |
|---|---|
[type] |
[description] |
Source code in geemap/common.py
def explode(coords):
"""Explode a GeoJSON geometry's coordinates object and yield
coordinate tuples. As long as the input is conforming, the type of
the geometry doesn't matter. From Fiona 1.4.8
Args:
coords (list): A list of coordinates.
Yields:
[type]: [description]
"""
for e in coords:
if isinstance(e, (float, int)):
yield coords
break
else:
for f in explode(e):
yield f
extract_pixel_values(ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False)
¶
Samples the pixels of an image, returning them as a ee.Dictionary.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.Image | ee.ImageCollection |
The ee.Image or ee.ImageCollection to sample. |
required |
region |
ee.Geometry |
The region to sample from. If unspecified, uses the image's whole footprint. |
required |
scale |
float |
A nominal scale in meters of the projection to sample in. Defaults to None. |
None |
projection |
str |
The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
tileScale |
int |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1. |
1 |
getInfo |
bool |
Whether to use getInfo with the results, i.e., returning the values a list. Default to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The image must be an instance of ee.Image. |
TypeError |
Region must be an instance of ee.Geometry. |
Returns:
| Type | Description |
|---|---|
ee.Dictionary |
The dictionary containing band names and pixel values. |
Source code in geemap/common.py
def extract_pixel_values(
ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False
):
"""Samples the pixels of an image, returning them as a ee.Dictionary.
Args:
ee_object (ee.Image | ee.ImageCollection): The ee.Image or ee.ImageCollection to sample.
region (ee.Geometry): The region to sample from. If unspecified, uses the image's whole footprint.
scale (float, optional): A nominal scale in meters of the projection to sample in. Defaults to None.
projection (str, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
tileScale (int, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
getInfo (bool, optional): Whether to use getInfo with the results, i.e., returning the values a list. Default to False.
Raises:
TypeError: The image must be an instance of ee.Image.
TypeError: Region must be an instance of ee.Geometry.
Returns:
ee.Dictionary: The dictionary containing band names and pixel values.
"""
if isinstance(ee_object, ee.ImageCollection):
ee_object = ee_object.toBands()
if not isinstance(ee_object, ee.Image):
raise TypeError("The image must be an instance of ee.Image.")
if not isinstance(region, ee.Geometry):
raise TypeError("Region must be an instance of ee.Geometry.")
dict_values = (
ee_object.sample(region, scale, projection, tileScale=tileScale)
.first()
.toDictionary()
)
if getInfo:
band_names = ee_object.bandNames().getInfo()
values_tmp = dict_values.getInfo()
values = [values_tmp[i] for i in band_names]
return dict(zip(band_names, values))
else:
return dict_values
extract_transect(image, line, reducer='mean', n_segments=100, dist_interval=None, scale=None, crs=None, crsTransform=None, tileScale=1.0, to_pandas=False, **kwargs)
¶
Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to extract transect from. |
required |
line |
ee.Geometry.LineString |
The LineString used to extract transect from an image. |
required |
reducer |
str |
The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean". |
'mean' |
n_segments |
int |
The number of segments that the LineString will be split into. Defaults to 100. |
100 |
dist_interval |
float |
The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
crs |
ee.Projection |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
crsTransform |
list |
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None. |
None |
tileScale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1. |
1.0 |
to_pandas |
bool |
Whether to convert the result to a pandas dataframe. Default to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
If the geometry type is not LineString. |
Exception |
If the program fails to compute. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The FeatureCollection containing the transect with distance and reducer values. |
Source code in geemap/common.py
def extract_transect(
image,
line,
reducer="mean",
n_segments=100,
dist_interval=None,
scale=None,
crs=None,
crsTransform=None,
tileScale=1.0,
to_pandas=False,
**kwargs,
):
"""Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.
Args:
image (ee.Image): The image to extract transect from.
line (ee.Geometry.LineString): The LineString used to extract transect from an image.
reducer (str, optional): The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean".
n_segments (int, optional): The number of segments that the LineString will be split into. Defaults to 100.
dist_interval (float, optional): The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (ee.Projection, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
crsTransform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None.
tileScale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
to_pandas (bool, optional): Whether to convert the result to a pandas dataframe. Default to False.
Raises:
TypeError: If the geometry type is not LineString.
Exception: If the program fails to compute.
Returns:
ee.FeatureCollection: The FeatureCollection containing the transect with distance and reducer values.
"""
try:
geom_type = line.type().getInfo()
if geom_type != "LineString":
raise TypeError("The geometry type must be LineString.")
reducer = eval("ee.Reducer." + reducer + "()")
maxError = image.projection().nominalScale().divide(5)
length = line.length(maxError)
if dist_interval is None:
dist_interval = length.divide(n_segments)
distances = ee.List.sequence(0, length, dist_interval)
lines = line.cutLines(distances, maxError).geometries()
def set_dist_attr(l):
l = ee.List(l)
geom = ee.Geometry(l.get(0))
distance = ee.Number(l.get(1))
geom = ee.Geometry.LineString(geom.coordinates())
return ee.Feature(geom, {"distance": distance})
lines = lines.zip(distances).map(set_dist_attr)
lines = ee.FeatureCollection(lines)
transect = image.reduceRegions(
**{
"collection": ee.FeatureCollection(lines),
"reducer": reducer,
"scale": scale,
"crs": crs,
"crsTransform": crsTransform,
"tileScale": tileScale,
}
)
if to_pandas:
return ee_to_df(transect)
return transect
except Exception as e:
raise Exception(e)
extract_values_to_points(in_fc, image, out_fc=None, scale=None, crs=None, crsTransform=None, tileScale=1, stats_type='FIRST', timeout=300, proxies=None, **kwargs)
¶
Extracts image values to points.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_fc |
object |
ee.FeatureCollection. |
required |
image |
object |
The ee.Image to extract pixel values. |
required |
out_fc |
object |
The output feature collection. Defaults to None. |
None |
scale |
ee.Projectoin |
A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used. |
None |
crs |
str |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
crsTransform |
list |
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. |
None |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. |
required |
stats_type |
str |
Statistic type to be calculated. Defaults to 'FIRST'. |
'FIRST' |
timeout |
int |
The number of seconds after which the request will be terminated. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use for each request. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def extract_values_to_points(
in_fc,
image,
out_fc=None,
scale=None,
crs=None,
crsTransform=None,
tileScale=1,
stats_type="FIRST",
timeout=300,
proxies=None,
**kwargs,
):
"""Extracts image values to points.
Args:
in_fc (object): ee.FeatureCollection.
image (object): The ee.Image to extract pixel values.
out_fc (object, optional): The output feature collection. Defaults to None.
scale (ee.Projectoin, optional): A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used.
crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
crsTransform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
stats_type (str, optional): Statistic type to be calculated. Defaults to 'FIRST'.
timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use for each request. Defaults to None.
Returns:
object: ee.FeatureCollection
"""
if "tile_scale" in kwargs:
tileScale = kwargs["tile_scale"]
if "crs_transform" in kwargs:
crsTransform = kwargs["crs_transform"]
allowed_stats = {
"FIRST": ee.Reducer.first(),
"MEAN": ee.Reducer.mean(),
"MAXIMUM": ee.Reducer.max(),
"MEDIAN": ee.Reducer.median(),
"MINIMUM": ee.Reducer.min(),
"STD": ee.Reducer.stdDev(),
"MIN_MAX": ee.Reducer.minMax(),
"SUM": ee.Reducer.sum(),
"VARIANCE": ee.Reducer.variance(),
}
if stats_type.upper() not in allowed_stats:
raise ValueError(
f"The statistics_type must be one of the following {', '.join(allowed_stats.keys())}"
)
if not isinstance(in_fc, ee.FeatureCollection):
try:
in_fc = shp_to_ee(in_fc)
except Exception as e:
print(e)
return
if not isinstance(image, ee.Image):
print("The image must be an instance of ee.Image.")
return
result = image.reduceRegions(
collection=in_fc,
reducer=allowed_stats[stats_type.upper()],
scale=scale,
crs=crs,
crsTransform=crsTransform,
tileScale=tileScale,
)
if out_fc is not None:
ee_export_vector(result, out_fc, timeout=timeout, proxies=proxies)
else:
return result
file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False, node_icon='file')
¶
Creates a simple file browser and text editor.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_dir |
str |
The input directory. Defaults to None, which will use the current working directory. |
None |
show_hidden |
bool |
Whether to show hidden files/folders. Defaults to False. |
False |
add_root_node |
bool |
Whether to add the input directory as a root node. Defaults to True. |
True |
search_description |
str |
The description of the search box. Defaults to None. |
None |
use_import |
bool |
Whether to show the import button. Defaults to False. |
False |
return_sep_widgets |
bool |
Whether to return the results as separate widgets. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
object |
An ipywidget. |
Source code in geemap/common.py
def file_browser(
in_dir=None,
show_hidden=False,
add_root_node=True,
search_description=None,
use_import=False,
return_sep_widgets=False,
node_icon="file",
):
"""Creates a simple file browser and text editor.
Args:
in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.
show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.
add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.
search_description (str, optional): The description of the search box. Defaults to None.
use_import (bool, optional): Whether to show the import button. Defaults to False.
return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.
Returns:
object: An ipywidget.
"""
import platform
if in_dir is None:
in_dir = os.getcwd()
if not os.path.exists(in_dir):
print("The provided directory does not exist.")
return
elif not os.path.isdir(in_dir):
print("The provided path is not a valid directory.")
return
sep = "/"
if platform.system() == "Windows":
sep = "\\"
if in_dir.endswith(sep):
in_dir = in_dir[:-1]
full_widget = widgets.HBox()
left_widget = widgets.VBox()
right_widget = widgets.VBox()
import_btn = widgets.Button(
description="import",
button_style="primary",
tooltip="import the content to a new cell",
disabled=True,
)
import_btn.layout.width = "70px"
path_widget = widgets.Text()
path_widget.layout.min_width = "400px"
# path_widget.layout.max_width = '400px'
save_widget = widgets.Button(
description="Save",
button_style="primary",
tooltip="Save edits to file.",
disabled=True,
)
info_widget = widgets.HBox()
info_widget.children = [path_widget, save_widget]
if use_import:
info_widget.children = [import_btn, path_widget, save_widget]
text_widget = widgets.Textarea()
text_widget.layout.width = "630px"
text_widget.layout.height = "600px"
right_widget.children = [info_widget, text_widget]
full_widget.children = [left_widget]
if search_description is None:
search_description = "Search files/folders..."
search_box = widgets.Text(placeholder=search_description)
search_box.layout.width = "310px"
tree_widget = widgets.Output()
tree_widget.layout.max_width = "310px"
tree_widget.overflow = "auto"
left_widget.children = [search_box, tree_widget]
tree = Tree(multiple_selection=False)
tree_dict = {}
def on_button_clicked(b):
content = text_widget.value
out_file = path_widget.value
out_dir = os.path.dirname(out_file)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
with open(out_file, "w") as f:
f.write(content)
text_widget.disabled = True
text_widget.value = "The content has been saved successfully."
save_widget.disabled = True
path_widget.disabled = True
if (out_file not in tree_dict.keys()) and (out_dir in tree_dict.keys()):
node = Node(os.path.basename(out_file))
tree_dict[out_file] = node
parent_node = tree_dict[out_dir]
parent_node.add_node(node)
save_widget.on_click(on_button_clicked)
def import_btn_clicked(b):
if (text_widget.value != "") and (path_widget.value.endswith(".py")):
create_code_cell(text_widget.value)
import_btn.on_click(import_btn_clicked)
def search_box_callback(text):
with tree_widget:
if text.value == "":
print("Loading...")
tree_widget.outputs = ()
display(tree)
else:
tree_widget.outputs = ()
print("Searching...")
tree_widget.outputs = ()
sub_tree = search_api_tree(text.value, tree_dict)
display(sub_tree)
search_box.on_submit(search_box_callback)
def handle_file_click(event):
if event["new"]:
cur_node = event["owner"]
for key in tree_dict.keys():
if (cur_node is tree_dict[key]) and (os.path.isfile(key)):
if key.endswith(".py"):
import_btn.disabled = False
else:
import_btn.disabled = True
try:
with open(key) as f:
content = f.read()
text_widget.value = content
text_widget.disabled = False
path_widget.value = key
path_widget.disabled = False
save_widget.disabled = False
full_widget.children = [left_widget, right_widget]
except Exception as e:
path_widget.value = key
path_widget.disabled = True
save_widget.disabled = True
text_widget.disabled = True
text_widget.value = (
"Failed to open {}.".format(cur_node.name) + "\n\n" + str(e)
)
full_widget.children = [left_widget, right_widget]
return
break
def handle_folder_click(event):
if event["new"]:
full_widget.children = [left_widget]
text_widget.value = ""
if add_root_node:
root_name = in_dir.split(sep)[-1]
root_node = Node(root_name)
tree_dict[in_dir] = root_node
tree.add_node(root_node)
root_node.observe(handle_folder_click, "selected")
for root, d_names, f_names in os.walk(in_dir):
if not show_hidden:
folders = root.split(sep)
for folder in folders:
if folder.startswith("."):
continue
for d_name in d_names:
if d_name.startswith("."):
d_names.remove(d_name)
for f_name in f_names:
if f_name.startswith("."):
f_names.remove(f_name)
d_names.sort()
f_names.sort()
if (not add_root_node) and (root == in_dir):
for d_name in d_names:
node = Node(d_name)
tree_dict[os.path.join(in_dir, d_name)] = node
tree.add_node(node)
node.opened = False
node.observe(handle_folder_click, "selected")
if (root != in_dir) and (root not in tree_dict.keys()):
name = root.split(sep)[-1]
dir_name = os.path.dirname(root)
parent_node = tree_dict[dir_name]
node = Node(name)
tree_dict[root] = node
parent_node.add_node(node)
node.observe(handle_folder_click, "selected")
if len(f_names) > 0:
parent_node = tree_dict[root]
parent_node.opened = False
for f_name in f_names:
node = Node(f_name)
node.icon = node_icon
full_path = os.path.join(root, f_name)
tree_dict[full_path] = node
parent_node.add_node(node)
node.observe(handle_file_click, "selected")
with tree_widget:
tree_widget.outputs = ()
display(tree)
if return_sep_widgets:
return left_widget, right_widget, tree_dict
else:
return full_widget
filter_HUC08(region)
¶
Filters HUC08 watersheds intersecting a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_HUC08(region):
"""Filters HUC08 watersheds intersecting a given region.
Args:
region (object): ee.Geometry
Returns:
object: ee.FeatureCollection
"""
USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08") # Subbasins
HUC08 = USGS_HUC08.filterBounds(region)
return HUC08
filter_HUC10(region)
¶
Filters HUC10 watersheds intersecting a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_HUC10(region):
"""Filters HUC10 watersheds intersecting a given region.
Args:
region (object): ee.Geometry
Returns:
object: ee.FeatureCollection
"""
USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10") # Watersheds
HUC10 = USGS_HUC10.filterBounds(region)
return HUC10
filter_NWI(HUC08_Id, region, exclude_riverine=True)
¶
Retrieves NWI dataset for a given HUC8 watershed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC8 watershed id. |
required |
region |
object |
ee.Geometry |
required |
exclude_riverine |
bool |
Whether to exclude riverine wetlands. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def filter_NWI(HUC08_Id, region, exclude_riverine=True):
"""Retrieves NWI dataset for a given HUC8 watershed.
Args:
HUC08_Id (str): The HUC8 watershed id.
region (object): ee.Geometry
exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.
Returns:
object: ee.FeatureCollection
"""
nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
nwi_asset_suffix = "_Wetlands"
nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
nwi_huc = ee.FeatureCollection(nwi_asset_path).filterBounds(region)
if exclude_riverine:
nwi_huc = nwi_huc.filter(
ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
)
return nwi_huc
filter_polygons(ftr)
¶
Converts GeometryCollection to Polygon/MultiPolygon
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ftr |
object |
ee.Feature |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Feature |
Source code in geemap/common.py
def filter_polygons(ftr):
"""Converts GeometryCollection to Polygon/MultiPolygon
Args:
ftr (object): ee.Feature
Returns:
object: ee.Feature
"""
# ee_initialize()
geometries = ftr.geometry().geometries()
geometries = geometries.map(
lambda geo: ee.Feature(ee.Geometry(geo)).set("geoType", ee.Geometry(geo).type())
)
polygons = (
ee.FeatureCollection(geometries)
.filter(ee.Filter.eq("geoType", "Polygon"))
.geometry()
)
return ee.Feature(polygons).copyProperties(ftr)
find_HUC08(HUC08_Id)
¶
Finds a HUC08 watershed based on a given HUC08 ID
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC08 ID. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_HUC08(HUC08_Id):
"""Finds a HUC08 watershed based on a given HUC08 ID
Args:
HUC08_Id (str): The HUC08 ID.
Returns:
object: ee.FeatureCollection
"""
USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08") # Subbasins
HUC08 = USGS_HUC08.filter(ee.Filter.eq("huc8", HUC08_Id))
return HUC08
find_HUC10(HUC10_Id)
¶
Finds a HUC10 watershed based on a given HUC08 ID
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC10_Id |
str |
The HUC10 ID. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_HUC10(HUC10_Id):
"""Finds a HUC10 watershed based on a given HUC08 ID
Args:
HUC10_Id (str): The HUC10 ID.
Returns:
object: ee.FeatureCollection
"""
USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10") # Watersheds
HUC10 = USGS_HUC10.filter(ee.Filter.eq("huc10", HUC10_Id))
return HUC10
find_NAIP(region, add_NDVI=True, add_NDWI=True)
¶
Create annual NAIP mosaic for a given region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
region |
object |
ee.Geometry |
required |
add_NDVI |
bool |
Whether to add the NDVI band. Defaults to True. |
True |
add_NDWI |
bool |
Whether to add the NDWI band. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def find_NAIP(region, add_NDVI=True, add_NDWI=True):
"""Create annual NAIP mosaic for a given region.
Args:
region (object): ee.Geometry
add_NDVI (bool, optional): Whether to add the NDVI band. Defaults to True.
add_NDWI (bool, optional): Whether to add the NDWI band. Defaults to True.
Returns:
object: ee.ImageCollection
"""
init_collection = (
ee.ImageCollection("USDA/NAIP/DOQQ")
.filterBounds(region)
.filterDate("2009-01-01", "2019-12-31")
.filter(ee.Filter.listContains("system:band_names", "N"))
)
yearList = ee.List(
init_collection.distinct(["system:time_start"]).aggregate_array(
"system:time_start"
)
)
init_years = yearList.map(lambda y: ee.Date(y).get("year"))
# remove duplicates
init_years = ee.Dictionary(
init_years.reduce(ee.Reducer.frequencyHistogram())
).keys()
years = init_years.map(lambda x: ee.Number.parse(x))
# years = init_years.map(lambda x: x)
# Available NAIP years with NIR band
def NAIPAnnual(year):
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
collection = init_collection.filterDate(start_date, end_date)
# .filterBounds(geometry)
# .filter(ee.Filter.listContains("system:band_names", "N"))
time_start = ee.Date(
ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
).format("YYYY-MM-dd")
time_end = ee.Date(
ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
).format("YYYY-MM-dd")
col_size = collection.size()
image = ee.Image(collection.mosaic().clip(region))
if add_NDVI:
NDVI = (
ee.Image(image)
.normalizedDifference(["N", "R"])
.select(["nd"], ["ndvi"])
)
image = image.addBands(NDVI)
if add_NDWI:
NDWI = (
ee.Image(image)
.normalizedDifference(["G", "N"])
.select(["nd"], ["ndwi"])
)
image = image.addBands(NDWI)
return image.set(
{
"system:time_start": time_start,
"system:time_end": time_end,
"tiles": col_size,
}
)
# remove years with incomplete coverage
naip = ee.ImageCollection(years.map(NAIPAnnual))
mean_size = ee.Number(naip.aggregate_mean("tiles"))
total_sd = ee.Number(naip.aggregate_total_sd("tiles"))
threshold = mean_size.subtract(total_sd.multiply(1))
naip = naip.filter(
ee.Filter.Or(ee.Filter.gte("tiles", threshold), ee.Filter.gte("tiles", 15))
)
naip = naip.filter(ee.Filter.gte("tiles", 7))
naip_count = naip.size()
naip_seq = ee.List.sequence(0, naip_count.subtract(1))
def set_index(index):
img = ee.Image(naip.toList(naip_count).get(index))
return img.set({"system:uid": ee.Number(index).toUint8()})
naip = naip_seq.map(set_index)
return ee.ImageCollection(naip)
find_NWI(HUC08_Id, exclude_riverine=True)
¶
Finds NWI dataset for a given HUC08 watershed.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
HUC08_Id |
str |
The HUC08 watershed ID. |
required |
exclude_riverine |
bool |
Whether to exclude riverine wetlands. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def find_NWI(HUC08_Id, exclude_riverine=True):
"""Finds NWI dataset for a given HUC08 watershed.
Args:
HUC08_Id (str): The HUC08 watershed ID.
exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.
Returns:
object: ee.FeatureCollection
"""
nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
nwi_asset_suffix = "_Wetlands"
nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
nwi_huc = ee.FeatureCollection(nwi_asset_path)
if exclude_riverine:
nwi_huc = nwi_huc.filter(
ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
)
return nwi_huc
find_files(input_dir, ext=None, fullpath=True, recursive=True)
¶
Find files in a directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
input_dir |
str |
The input directory. |
required |
ext |
str |
The file extension to match. Defaults to None. |
None |
fullpath |
bool |
Whether to return the full path. Defaults to True. |
True |
recursive |
bool |
Whether to search recursively. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
list |
A list of matching files. |
Source code in geemap/common.py
def find_files(input_dir, ext=None, fullpath=True, recursive=True):
"""Find files in a directory.
Args:
input_dir (str): The input directory.
ext (str, optional): The file extension to match. Defaults to None.
fullpath (bool, optional): Whether to return the full path. Defaults to True.
recursive (bool, optional): Whether to search recursively. Defaults to True.
Returns:
list: A list of matching files.
"""
from pathlib import Path
files = []
if ext is None:
ext = "*"
else:
ext = ext.replace(".", "")
ext = f"*.{ext}"
if recursive:
if fullpath:
files = [str(path.joinpath()) for path in Path(input_dir).rglob(ext)]
else:
files = [str(path.name) for path in Path(input_dir).rglob(ext)]
else:
if fullpath:
files = [str(path.joinpath()) for path in Path(input_dir).glob(ext)]
else:
files = [path.name for path in Path(input_dir).glob(ext)]
return files
find_landsat_by_path_row(landsat_col, path_num, row_num)
¶
Finds Landsat images by WRS path number and row number.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
landsat_col |
str |
The image collection id of Landsat. |
required |
path_num |
int |
The WRS path number. |
required |
row_num |
int |
the WRS row number. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def find_landsat_by_path_row(landsat_col, path_num, row_num):
"""Finds Landsat images by WRS path number and row number.
Args:
landsat_col (str): The image collection id of Landsat.
path_num (int): The WRS path number.
row_num (int): the WRS row number.
Returns:
object: ee.ImageCollection
"""
try:
if isinstance(landsat_col, str):
landsat_col = ee.ImageCollection(landsat_col)
collection = landsat_col.filter(ee.Filter.eq("WRS_PATH", path_num)).filter(
ee.Filter.eq("WRS_ROW", row_num)
)
return collection
except Exception as e:
print(e)
fishnet(data, h_interval=1.0, v_interval=1.0, rows=None, cols=None, delta=1.0, intersect=True, output=None, **kwargs)
¶
Create a fishnet (i.e., rectangular grid) based on an input vector dataset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
str | ee.Geometry | ee.Feature | ee.FeatureCollection |
The input vector dataset. It can be a file path, HTTP URL, ee.Geometry, ee.Feature, or ee.FeatureCollection. |
required |
h_interval |
float |
The horizontal interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0. |
1.0 |
v_interval |
float |
The vertical interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0. |
1.0 |
rows |
int |
The number of rows. Defaults to None. |
None |
cols |
int |
The number of columns. Defaults to None. |
None |
delta |
float |
The buffer distance in degrees. Defaults to 1.0. |
1.0 |
intersect |
bool |
If True, the output will be a feature collection of intersecting polygons. Defaults to True. |
True |
output |
str |
The output file path. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The fishnet as an ee.FeatureCollection. |
Source code in geemap/common.py
def fishnet(
data,
h_interval=1.0,
v_interval=1.0,
rows=None,
cols=None,
delta=1.0,
intersect=True,
output=None,
**kwargs,
):
"""Create a fishnet (i.e., rectangular grid) based on an input vector dataset.
Args:
data (str | ee.Geometry | ee.Feature | ee.FeatureCollection): The input vector dataset. It can be a file path, HTTP URL, ee.Geometry, ee.Feature, or ee.FeatureCollection.
h_interval (float, optional): The horizontal interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.
v_interval (float, optional): The vertical interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.
rows (int, optional): The number of rows. Defaults to None.
cols (int, optional): The number of columns. Defaults to None.
delta (float, optional): The buffer distance in degrees. Defaults to 1.0.
intersect (bool, optional): If True, the output will be a feature collection of intersecting polygons. Defaults to True.
output (str, optional): The output file path. Defaults to None.
Returns:
ee.FeatureCollection: The fishnet as an ee.FeatureCollection.
"""
if isinstance(data, str):
data = vector_to_ee(data, **kwargs)
if isinstance(data, ee.FeatureCollection) or isinstance(data, ee.Feature):
data = data.geometry()
elif isinstance(data, ee.Geometry):
pass
else:
raise ValueError(
"data must be a string, ee.FeatureCollection, ee.Feature, or ee.Geometry."
)
coords = data.bounds().coordinates().getInfo()
west = coords[0][0][0]
east = coords[0][1][0]
south = coords[0][0][1]
north = coords[0][2][1]
if rows is not None and cols is not None:
v_interval = (north - south) / rows
h_interval = (east - west) / cols
# west = west - delta * h_interval
east = east + delta * h_interval
# south = south - delta * v_interval
north = north + delta * v_interval
grids = latlon_grid(v_interval, h_interval, west, east, south, north)
if intersect:
grids = grids.filterBounds(data)
if output is not None:
ee_export_vector(grids, output)
else:
return grids
gdf_bounds(gdf, return_geom=False)
¶
Returns the bounding box of a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
gpd.GeoDataFrame |
A GeoDataFrame. |
required |
return_geom |
bool |
Whether to return the bounding box as a GeoDataFrame. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list | gpd.GeoDataFrame |
A bounding box in the form of a list (minx, miny, maxx, maxy) or GeoDataFrame. |
Source code in geemap/common.py
def gdf_bounds(gdf, return_geom=False):
"""Returns the bounding box of a GeoDataFrame.
Args:
gdf (gpd.GeoDataFrame): A GeoDataFrame.
return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.
Returns:
list | gpd.GeoDataFrame: A bounding box in the form of a list (minx, miny, maxx, maxy) or GeoDataFrame.
"""
bounds = gdf.total_bounds
if return_geom:
return bbox_to_gdf(bbox=bounds)
else:
return bounds
gdf_centroid(gdf, return_geom=False)
¶
Returns the centroid of a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
gpd.GeoDataFrame |
A GeoDataFrame. |
required |
return_geom |
bool |
Whether to return the bounding box as a GeoDataFrame. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list | gpd.GeoDataFrame |
A bounding box in the form of a list (lon, lat) or GeoDataFrame. |
Source code in geemap/common.py
def gdf_centroid(gdf, return_geom=False):
"""Returns the centroid of a GeoDataFrame.
Args:
gdf (gpd.GeoDataFrame): A GeoDataFrame.
return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.
Returns:
list | gpd.GeoDataFrame: A bounding box in the form of a list (lon, lat) or GeoDataFrame.
"""
warnings.filterwarnings("ignore")
centroid = gdf_bounds(gdf, return_geom=True).centroid
if return_geom:
return centroid
else:
return centroid.x[0], centroid.y[0]
gdf_geom_type(gdf, first_only=True)
¶
Returns the geometry type of a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
gpd.GeoDataFrame |
A GeoDataFrame. |
required |
first_only |
bool |
Whether to return the geometry type of the first feature in the GeoDataFrame. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
str |
The geometry type of the GeoDataFrame. |
Source code in geemap/common.py
def gdf_geom_type(gdf, first_only=True):
"""Returns the geometry type of a GeoDataFrame.
Args:
gdf (gpd.GeoDataFrame): A GeoDataFrame.
first_only (bool, optional): Whether to return the geometry type of the first feature in the GeoDataFrame. Defaults to True.
Returns:
str: The geometry type of the GeoDataFrame.
"""
if first_only:
return gdf.geometry.type[0]
else:
return gdf.geometry.type
gdf_to_df(gdf, drop_geom=True)
¶
Converts a GeoDataFrame to a pandas DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
gpd.GeoDataFrame |
A GeoDataFrame. |
required |
drop_geom |
bool |
Whether to drop the geometry column. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
A pandas DataFrame containing the GeoDataFrame. |
Source code in geemap/common.py
def gdf_to_df(gdf, drop_geom=True):
"""Converts a GeoDataFrame to a pandas DataFrame.
Args:
gdf (gpd.GeoDataFrame): A GeoDataFrame.
drop_geom (bool, optional): Whether to drop the geometry column. Defaults to True.
Returns:
pd.DataFrame: A pandas DataFrame containing the GeoDataFrame.
"""
import pandas as pd
if drop_geom:
df = pd.DataFrame(gdf.drop(columns=["geometry"]))
else:
df = pd.DataFrame(gdf)
return df
gdf_to_ee(gdf, geodesic=True, date=None, date_format='YYYY-MM-dd')
¶
Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
geopandas.GeoDataFrame |
The input geopandas.GeoDataFrame to be converted ee.FeatureCollection. |
required |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True. |
True |
date |
str |
Column name for the date column. Defaults to None. |
None |
date_format |
str |
Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be geopandas.GeoDataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame. |
Source code in geemap/common.py
def gdf_to_ee(gdf, geodesic=True, date=None, date_format="YYYY-MM-dd"):
"""Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Args:
gdf (geopandas.GeoDataFrame): The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True.
date (str, optional): Column name for the date column. Defaults to None.
date_format (str, optional): Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
Raises:
TypeError: The input data type must be geopandas.GeoDataFrame.
Returns:
ee.FeatureCollection: The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
if not isinstance(gdf, gpd.GeoDataFrame):
raise TypeError("The input data type must be geopandas.GeoDataFrame.")
out_json = os.path.join(os.getcwd(), random_string(6) + ".geojson")
gdf = gdf.to_crs(4326)
gdf.to_file(out_json, driver="GeoJSON")
fc = geojson_to_ee(out_json, geodesic=geodesic)
if date is not None:
try:
fc = fc.map(
lambda x: x.set(
"system:time_start",
ee.Date.parse(date_format, x.get(date)).millis(),
)
)
except Exception as e:
raise Exception(e)
os.remove(out_json)
return fc
gdf_to_geojson(gdf, out_geojson=None, epsg=None)
¶
Converts a GeoDataFame to GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
GeoDataFrame |
A GeoPandas GeoDataFrame. |
required |
out_geojson |
str |
File path to he output GeoJSON. Defaults to None. |
None |
epsg |
str |
An EPSG string, e.g., "4326". Defaults to None. |
None |
Exceptions:
| Type | Description |
|---|---|
TypeError |
When the output file extension is incorrect. |
Exception |
When the conversion fails. |
Returns:
| Type | Description |
|---|---|
dict |
When the out_json is None returns a dict. |
Source code in geemap/common.py
def gdf_to_geojson(gdf, out_geojson=None, epsg=None):
"""Converts a GeoDataFame to GeoJSON.
Args:
gdf (GeoDataFrame): A GeoPandas GeoDataFrame.
out_geojson (str, optional): File path to he output GeoJSON. Defaults to None.
epsg (str, optional): An EPSG string, e.g., "4326". Defaults to None.
Raises:
TypeError: When the output file extension is incorrect.
Exception: When the conversion fails.
Returns:
dict: When the out_json is None returns a dict.
"""
check_package(name="geopandas", URL="https://geopandas.org")
try:
if epsg is not None:
gdf = gdf.to_crs(epsg=epsg)
geojson = gdf.__geo_interface__
if out_geojson is None:
return geojson
else:
ext = os.path.splitext(out_geojson)[1]
if ext.lower() not in [".json", ".geojson"]:
raise TypeError(
"The output file extension must be either .json or .geojson"
)
out_dir = os.path.dirname(out_geojson)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
gdf.to_file(out_geojson, driver="GeoJSON")
except Exception as e:
raise Exception(e)
geocode(location, max_rows=10, reverse=False)
¶
Search location by address and lat/lon coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
Place name or address |
required |
max_rows |
int |
Maximum number of records to return. Defaults to 10. |
10 |
reverse |
bool |
Search place based on coordinates. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list |
Returns a list of locations. |
Source code in geemap/common.py
def geocode(location, max_rows=10, reverse=False):
"""Search location by address and lat/lon coordinates.
Args:
location (str): Place name or address
max_rows (int, optional): Maximum number of records to return. Defaults to 10.
reverse (bool, optional): Search place based on coordinates. Defaults to False.
Returns:
list: Returns a list of locations.
"""
import geocoder
if not isinstance(location, str):
print("The location must be a string.")
return None
if not reverse:
locations = []
addresses = set()
g = geocoder.arcgis(location, maxRows=max_rows)
for result in g:
address = result.address
if address not in addresses:
addresses.add(address)
locations.append(result)
if len(locations) > 0:
return locations
else:
return None
else:
try:
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return
g = geocoder.arcgis(latlon, method="reverse")
locations = []
addresses = set()
for result in g:
address = result.address
if address not in addresses:
addresses.add(address)
locations.append(result)
if len(locations) > 0:
return locations
else:
return None
except Exception as e:
print(e)
return None
geojson_to_df(in_geojson, encoding='utf-8', drop_geometry=True)
¶
Converts a GeoJSON object to a pandas DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_geojson |
str | dict |
The input GeoJSON file or dict. |
required |
encoding |
str |
The encoding of the GeoJSON object. Defaults to "utf-8". |
'utf-8' |
drop_geometry |
bool |
Whether to drop the geometry column. Defaults to True. |
True |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the input GeoJSON file could not be found. |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
A pandas DataFrame containing the GeoJSON object. |
Source code in geemap/common.py
def geojson_to_df(in_geojson, encoding="utf-8", drop_geometry=True):
"""Converts a GeoJSON object to a pandas DataFrame.
Args:
in_geojson (str | dict): The input GeoJSON file or dict.
encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".
drop_geometry (bool, optional): Whether to drop the geometry column. Defaults to True.
Raises:
FileNotFoundError: If the input GeoJSON file could not be found.
Returns:
pd.DataFrame: A pandas DataFrame containing the GeoJSON object.
"""
import pandas as pd
from urllib.request import urlopen
if isinstance(in_geojson, str):
if in_geojson.startswith("http"):
in_geojson = github_raw_url(in_geojson)
with urlopen(in_geojson) as f:
data = json.load(f)
else:
in_geojson = os.path.abspath(in_geojson)
if not os.path.exists(in_geojson):
raise FileNotFoundError("The provided GeoJSON file could not be found.")
with open(in_geojson, encoding=encoding) as f:
data = json.load(f)
elif isinstance(in_geojson, dict):
data = in_geojson
df = pd.json_normalize(data["features"])
df.columns = [col.replace("properties.", "") for col in df.columns]
if drop_geometry:
df = df[df.columns.drop(list(df.filter(regex="geometry")))]
return df
geojson_to_ee(geo_json, geodesic=False, encoding='utf-8')
¶
Converts a geojson to ee.Geometry()
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_json |
dict |
A geojson geometry dictionary or file path. |
required |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to False. |
False |
encoding |
str |
The encoding of characters. Defaults to "utf-8". |
'utf-8' |
Returns:
| Type | Description |
|---|---|
ee_object |
An ee.Geometry object |
Source code in geemap/common.py
def geojson_to_ee(geo_json, geodesic=False, encoding="utf-8"):
"""Converts a geojson to ee.Geometry()
Args:
geo_json (dict): A geojson geometry dictionary or file path.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to False.
encoding (str, optional): The encoding of characters. Defaults to "utf-8".
Returns:
ee_object: An ee.Geometry object
"""
try:
if isinstance(geo_json, str):
if geo_json.startswith("http") and geo_json.endswith(".geojson"):
geo_json = github_raw_url(geo_json)
out_geojson = temp_file_path(extension=".geojson")
download_file(geo_json, out_geojson)
with open(out_geojson, "r", encoding=encoding) as f:
geo_json = json.loads(f.read())
os.remove(out_geojson)
elif os.path.isfile(geo_json):
with open(os.path.abspath(geo_json), encoding=encoding) as f:
geo_json = json.load(f)
# geo_json["geodesic"] = geodesic
if geo_json["type"] == "FeatureCollection":
for feature in geo_json["features"]:
if feature["geometry"]["type"] != "Point":
feature["geometry"]["geodesic"] = geodesic
features = ee.FeatureCollection(geo_json)
return features
elif geo_json["type"] == "Feature":
geom = None
if "style" in geo_json["properties"]:
keys = geo_json["properties"]["style"].keys()
if "radius" in keys: # Checks whether it is a circle
geom = ee.Geometry(geo_json["geometry"])
radius = geo_json["properties"]["style"]["radius"]
geom = geom.buffer(radius)
elif geo_json["geometry"]["type"] == "Point":
geom = ee.Geometry(geo_json["geometry"])
else:
geom = ee.Geometry(geo_json["geometry"], "", geodesic)
elif (
geo_json["geometry"]["type"] == "Point"
): # Checks whether it is a point
coordinates = geo_json["geometry"]["coordinates"]
longitude = coordinates[0]
latitude = coordinates[1]
geom = ee.Geometry.Point(longitude, latitude)
else:
geom = ee.Geometry(geo_json["geometry"], "", geodesic)
return geom
else:
raise Exception("Could not convert the geojson to ee.Geometry()")
except Exception as e:
print("Could not convert the geojson to ee.Geometry()")
raise Exception(e)
geometry_type(ee_object)
¶
Get geometry type of an Earth Engine object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
An Earth Engine object. |
required |
Returns:
| Type | Description |
|---|---|
str |
Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon. |
Source code in geemap/common.py
def geometry_type(ee_object):
"""Get geometry type of an Earth Engine object.
Args:
ee_object (object): An Earth Engine object.
Returns:
str: Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.
"""
if isinstance(ee_object, ee.Geometry):
return ee_object.type().getInfo()
elif isinstance(ee_object, ee.Feature):
return ee_object.geometry().type().getInfo()
elif isinstance(ee_object, ee.FeatureCollection):
return ee.Feature(ee_object.first()).geometry().type().getInfo()
else:
raise TypeError(
"The ee_object must be one of ee.Geometry, ee.Feature, ee.FeatureCollection."
)
geopandas_to_ee(gdf, geodesic=True, date=None, date_format='YYYY-MM-dd')
¶
Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
gdf |
geopandas.GeoDataFrame |
The input geopandas.GeoDataFrame to be converted ee.FeatureCollection. |
required |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True. |
True |
date |
str |
Column name for the date column. Defaults to None. |
None |
date_format |
str |
Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be geopandas.GeoDataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame. |
Source code in geemap/common.py
def gdf_to_ee(gdf, geodesic=True, date=None, date_format="YYYY-MM-dd"):
"""Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.
Args:
gdf (geopandas.GeoDataFrame): The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True.
date (str, optional): Column name for the date column. Defaults to None.
date_format (str, optional): Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
Raises:
TypeError: The input data type must be geopandas.GeoDataFrame.
Returns:
ee.FeatureCollection: The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
if not isinstance(gdf, gpd.GeoDataFrame):
raise TypeError("The input data type must be geopandas.GeoDataFrame.")
out_json = os.path.join(os.getcwd(), random_string(6) + ".geojson")
gdf = gdf.to_crs(4326)
gdf.to_file(out_json, driver="GeoJSON")
fc = geojson_to_ee(out_json, geodesic=geodesic)
if date is not None:
try:
fc = fc.map(
lambda x: x.set(
"system:time_start",
ee.Date.parse(date_format, x.get(date)).millis(),
)
)
except Exception as e:
raise Exception(e)
os.remove(out_json)
return fc
geotiff_to_image(image, output)
¶
Converts a GeoTIFF file to a JPEG/PNG image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The path to the input GeoTIFF file. |
required |
output |
str |
The path to save the output JPEG/PNG file. |
required |
Returns:
| Type | Description |
|---|---|
None |
None |
Source code in geemap/common.py
def geotiff_to_image(image: str, output: str) -> None:
"""
Converts a GeoTIFF file to a JPEG/PNG image.
Args:
image (str): The path to the input GeoTIFF file.
output (str): The path to save the output JPEG/PNG file.
Returns:
None
"""
import rasterio
from PIL import Image
# Open the GeoTIFF file
with rasterio.open(image) as dataset:
# Read the image data
data = dataset.read()
# Convert the image data to 8-bit format (assuming it's not already)
if dataset.dtypes[0] != "uint8":
data = (data / data.max() * 255).astype("uint8")
# Convert the image data to RGB format if it's a single band image
if dataset.count == 1:
data = data.squeeze()
data = data.reshape((1, data.shape[0], data.shape[1]))
data = data.repeat(3, axis=0)
# Create a PIL Image object from the image data
image = Image.fromarray(data.transpose(1, 2, 0))
# Save the image as a JPEG file
image.save(output)
get_all_NAIP(start_year=2009, end_year=2019)
¶
Creates annual NAIP imagery mosaic.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
start_year |
int |
The starting year. Defaults to 2009. |
2009 |
end_year |
int |
The ending year. Defaults to 2019. |
2019 |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def get_all_NAIP(start_year=2009, end_year=2019):
"""Creates annual NAIP imagery mosaic.
Args:
start_year (int, optional): The starting year. Defaults to 2009.
end_year (int, optional): The ending year. Defaults to 2019.
Returns:
object: ee.ImageCollection
"""
try:
def get_annual_NAIP(year):
try:
collection = ee.ImageCollection("USDA/NAIP/DOQQ")
start_date = ee.Date.fromYMD(year, 1, 1)
end_date = ee.Date.fromYMD(year, 12, 31)
naip = collection.filterDate(start_date, end_date).filter(
ee.Filter.listContains("system:band_names", "N")
)
return ee.ImageCollection(naip)
except Exception as e:
print(e)
years = ee.List.sequence(start_year, end_year)
collection = years.map(get_annual_NAIP)
return collection
except Exception as e:
print(e)
get_annual_NAIP(year, RGBN=True)
¶
Filters NAIP ImageCollection by year.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year to filter the NAIP ImageCollection. |
required |
RGBN |
bool |
Whether to retrieve 4-band NAIP imagery only. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.ImageCollection |
Source code in geemap/common.py
def get_annual_NAIP(year, RGBN=True):
"""Filters NAIP ImageCollection by year.
Args:
year (int): The year to filter the NAIP ImageCollection.
RGBN (bool, optional): Whether to retrieve 4-band NAIP imagery only. Defaults to True.
Returns:
object: ee.ImageCollection
"""
try:
collection = ee.ImageCollection("USDA/NAIP/DOQQ")
start_date = str(year) + "-01-01"
end_date = str(year) + "-12-31"
naip = collection.filterDate(start_date, end_date)
if RGBN:
naip = naip.filter(ee.Filter.listContains("system:band_names", "N"))
return naip
except Exception as e:
print(e)
get_api_key(token_name, m=None)
¶
Retrieves an API key based on a system environmen variable.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
token_name |
str |
The token name. |
required |
m |
ipyleaflet.Map | folium.Map |
A Map instance. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
str |
The API key. |
Source code in geemap/common.py
def get_api_key(token_name, m=None):
"""Retrieves an API key based on a system environmen variable.
Args:
token_name (str): The token name.
m (ipyleaflet.Map | folium.Map, optional): A Map instance. Defaults to None.
Returns:
str: The API key.
"""
api_key = os.environ.get(token_name)
if m is not None and token_name in m.api_keys:
api_key = m.api_keys[token_name]
return api_key
get_bounds(geometry, north_up=True, transform=None)
¶
Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection. left, bottom, right, top not xmin, ymin, xmax, ymax If not north_up, y will be switched to guarantee the above. Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geometry |
dict |
A GeoJSON dict. |
required |
north_up |
bool |
. Defaults to True. |
True |
transform |
[type] |
. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
A list of coordinates representing [left, bottom, right, top] |
Source code in geemap/common.py
def get_bounds(geometry, north_up=True, transform=None):
"""Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.
left, bottom, right, top
*not* xmin, ymin, xmax, ymax
If not north_up, y will be switched to guarantee the above.
Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361
Args:
geometry (dict): A GeoJSON dict.
north_up (bool, optional): . Defaults to True.
transform ([type], optional): . Defaults to None.
Returns:
list: A list of coordinates representing [left, bottom, right, top]
"""
if "bbox" in geometry:
return tuple(geometry["bbox"])
geometry = geometry.get("geometry") or geometry
# geometry must be a geometry, GeometryCollection, or FeatureCollection
if not (
"coordinates" in geometry or "geometries" in geometry or "features" in geometry
):
raise ValueError(
"geometry must be a GeoJSON-like geometry, GeometryCollection, "
"or FeatureCollection"
)
if "features" in geometry:
# Input is a FeatureCollection
xmins = []
ymins = []
xmaxs = []
ymaxs = []
for feature in geometry["features"]:
xmin, ymin, xmax, ymax = get_bounds(feature["geometry"])
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
if north_up:
return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
else:
return min(xmins), max(ymaxs), max(xmaxs), min(ymins)
elif "geometries" in geometry:
# Input is a geometry collection
xmins = []
ymins = []
xmaxs = []
ymaxs = []
for geometry in geometry["geometries"]:
xmin, ymin, xmax, ymax = get_bounds(geometry)
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
if north_up:
return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
else:
return min(xmins), max(ymaxs), max(xmaxs), min(ymins)
elif "coordinates" in geometry:
# Input is a singular geometry object
if transform is not None:
xyz = list(explode(geometry["coordinates"]))
xyz_px = [transform * point for point in xyz]
xyz = tuple(zip(*xyz_px))
return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
else:
xyz = tuple(zip(*list(explode(geometry["coordinates"]))))
if north_up:
return min(xyz[0]), min(xyz[1]), max(xyz[0]), max(xyz[1])
else:
return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
# all valid inputs returned above, so whatever falls through is an error
raise ValueError(
"geometry must be a GeoJSON-like geometry, GeometryCollection, "
"or FeatureCollection"
)
get_census_dict(reset=False)
¶
Returns a dictionary of Census data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
reset |
bool |
Reset the dictionary. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of Census data. |
Source code in geemap/common.py
def get_census_dict(reset=False):
"""Returns a dictionary of Census data.
Args:
reset (bool, optional): Reset the dictionary. Defaults to False.
Returns:
dict: A dictionary of Census data.
"""
import pkg_resources
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
census_data = os.path.join(pkg_dir, "data/census_data.json")
if reset:
try:
from owslib.wms import WebMapService
except ImportError:
raise ImportError(
'The owslib package must be installed to use this function. Install with "pip install owslib"'
)
census_dict = {}
names = [
"Current",
"ACS 2021",
"ACS 2019",
"ACS 2018",
"ACS 2017",
"ACS 2016",
"ACS 2015",
"ACS 2014",
"ACS 2013",
"ACS 2012",
"ECON 2012",
"Census 2020",
"Census 2010",
"Physical Features",
"Decennial Census 2020",
"Decennial Census 2010",
"Decennial Census 2000",
"Decennial Physical Features",
]
links = {}
print("Retrieving data. Please wait ...")
for name in names:
if "Decennial" not in name:
links[name] = (
f"https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_{name.replace(' ', '')}/MapServer/WMSServer"
)
else:
links[name] = (
f"https://tigerweb.geo.census.gov/arcgis/services/Census2020/tigerWMS_{name.replace('Decennial', '').replace(' ', '')}/MapServer/WMSServer"
)
wms = WebMapService(links[name], timeout=300)
layers = list(wms.contents)
layers.sort()
census_dict[name] = {
"url": links[name],
"layers": layers,
# "title": wms.identification.title,
# "abstract": wms.identification.abstract,
}
with open(census_data, "w") as f:
json.dump(census_dict, f, indent=4)
else:
with open(census_data, "r") as f:
census_dict = json.load(f)
return census_dict
get_center(geometry, north_up=True, transform=None)
¶
Get the centroid of a GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geometry |
dict |
A GeoJSON dict. |
required |
north_up |
bool |
. Defaults to True. |
True |
transform |
[type] |
. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
[lon, lat] |
Source code in geemap/common.py
def get_center(geometry, north_up=True, transform=None):
"""Get the centroid of a GeoJSON.
Args:
geometry (dict): A GeoJSON dict.
north_up (bool, optional): . Defaults to True.
transform ([type], optional): . Defaults to None.
Returns:
list: [lon, lat]
"""
bounds = get_bounds(geometry, north_up, transform)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lat, lon)
return center
get_current_latlon()
¶
Get the current latitude and longitude based on the user's location.
Source code in geemap/common.py
def get_current_latlon():
"""Get the current latitude and longitude based on the user's location."""
import geocoder
g = geocoder.ip("me")
props = g.geojson["features"][0]["properties"]
lat = props["lat"]
lon = props["lng"]
return lat, lon
get_current_year()
¶
Get the current year.
Returns:
| Type | Description |
|---|---|
int |
The current year. |
Source code in geemap/common.py
def get_current_year():
"""Get the current year.
Returns:
int: The current year.
"""
today = datetime.date.today()
return today.year
get_direct_url(url)
¶
Get the direct URL for a given URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL to get the direct URL for. |
required |
Returns:
| Type | Description |
|---|---|
str |
The direct URL. |
Source code in geemap/common.py
def get_direct_url(url):
"""Get the direct URL for a given URL.
Args:
url (str): The URL to get the direct URL for.
Returns:
str: The direct URL.
"""
if not isinstance(url, str):
raise ValueError("url must be a string.")
if not url.startswith("http"):
raise ValueError("url must start with http.")
r = requests.head(url, allow_redirects=True)
return r.url
get_ee_token()
¶
Get Earth Engine token.
Returns:
| Type | Description |
|---|---|
dict |
The Earth Engine token. |
Source code in geemap/common.py
def get_ee_token():
"""Get Earth Engine token.
Returns:
dict: The Earth Engine token.
"""
credential_file_path = os.path.expanduser("~/.config/earthengine/credentials")
if os.path.exists(credential_file_path):
with open(credential_file_path, "r") as f:
credentials = json.load(f)
return credentials
else:
print("Earth Engine credentials not found. Please run ee.Authenticate()")
return None
get_geometry_coords(row, geom, coord_type, shape_type, mercator=False)
¶
Returns the coordinates ('x' or 'y') of edges of a Polygon exterior.
:param: (GeoPandas Series) row : The row of each of the GeoPandas DataFrame. :param: (str) geom : The column name. :param: (str) coord_type : Whether it's 'x' or 'y' coordinate. :param: (str) shape_type
Source code in geemap/common.py
def get_geometry_coords(row, geom, coord_type, shape_type, mercator=False):
"""
Returns the coordinates ('x' or 'y') of edges of a Polygon exterior.
:param: (GeoPandas Series) row : The row of each of the GeoPandas DataFrame.
:param: (str) geom : The column name.
:param: (str) coord_type : Whether it's 'x' or 'y' coordinate.
:param: (str) shape_type
"""
# Parse the exterior of the coordinate
if shape_type.lower() in ["polygon", "multipolygon"]:
exterior = row[geom].geoms[0].exterior
if coord_type == "x":
# Get the x coordinates of the exterior
coords = list(exterior.coords.xy[0])
if mercator:
coords = [lnglat_to_meters(x, 0)[0] for x in coords]
return coords
elif coord_type == "y":
# Get the y coordinates of the exterior
coords = list(exterior.coords.xy[1])
if mercator:
coords = [lnglat_to_meters(0, y)[1] for y in coords]
return coords
elif shape_type.lower() in ["linestring", "multilinestring"]:
if coord_type == "x":
coords = list(row[geom].coords.xy[0])
if mercator:
coords = [lnglat_to_meters(x, 0)[0] for x in coords]
return coords
elif coord_type == "y":
coords = list(row[geom].coords.xy[1])
if mercator:
coords = [lnglat_to_meters(0, y)[1] for y in coords]
return coords
elif shape_type.lower() in ["point", "multipoint"]:
exterior = row[geom]
if coord_type == "x":
# Get the x coordinates of the exterior
coords = exterior.coords.xy[0][0]
if mercator:
coords = lnglat_to_meters(coords, 0)[0]
return coords
elif coord_type == "y":
# Get the y coordinates of the exterior
coords = exterior.coords.xy[1][0]
if mercator:
coords = lnglat_to_meters(0, coords)[1]
return coords
get_image_collection_thumbnails(ee_object, out_dir, vis_params, dimensions=500, region=None, format='jpg', names=None, verbose=True, timeout=300, proxies=None)
¶
Download thumbnails for all images in an ImageCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.ImageCollection instance. |
required |
out_dir |
[str |
The output directory to store thumbnails. |
required |
vis_params |
dict |
The visualization parameters. |
required |
dimensions |
int |
(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500. |
500 |
region |
object |
Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None. |
None |
format |
str |
Either 'png' or 'jpg'. Default to 'jpg'. |
'jpg' |
names |
list |
The list of output file names. Defaults to None. |
None |
verbose |
bool |
Whether or not to print hints. Defaults to True. |
True |
timeout |
int |
The number of seconds after which the request will be terminated. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use for the request. Defaults to None. |
None |
Source code in geemap/common.py
def get_image_collection_thumbnails(
ee_object,
out_dir,
vis_params,
dimensions=500,
region=None,
format="jpg",
names=None,
verbose=True,
timeout=300,
proxies=None,
):
"""Download thumbnails for all images in an ImageCollection.
Args:
ee_object (object): The ee.ImageCollection instance.
out_dir ([str): The output directory to store thumbnails.
vis_params (dict): The visualization parameters.
dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
format (str, optional): Either 'png' or 'jpg'. Default to 'jpg'.
names (list, optional): The list of output file names. Defaults to None.
verbose (bool, optional): Whether or not to print hints. Defaults to True.
timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use for the request. Defaults to None.
"""
if not isinstance(ee_object, ee.ImageCollection):
print("The ee_object must be an ee.ImageCollection.")
raise TypeError("The ee_object must be an ee.Image.")
if format not in ["png", "jpg"]:
raise ValueError("The output image format must be png or jpg.")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
try:
count = int(ee_object.size().getInfo())
if verbose:
print(f"Total number of images: {count}\n")
if (names is not None) and (len(names) != count):
print("The number of names is not equal to the number of images.")
return
if names is None:
names = ee_object.aggregate_array("system:index").getInfo()
images = ee_object.toList(count)
for i in range(0, count):
image = ee.Image(images.get(i))
name = str(names[i])
ext = os.path.splitext(name)[1][1:]
if ext != format:
name = name + "." + format
out_img = os.path.join(out_dir, name)
if verbose:
print(f"Downloading {i+1}/{count}: {name} ...")
get_image_thumbnail(
image,
out_img,
vis_params,
dimensions,
region,
format,
timeout=timeout,
proxies=proxies,
)
except Exception as e:
print(e)
get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='jpg', crs='EPSG:3857', timeout=300, proxies=None)
¶
Download a thumbnail for an ee.Image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The ee.Image instance. |
required |
out_img |
str |
The output file path to the png thumbnail. |
required |
vis_params |
dict |
The visualization parameters. |
required |
dimensions |
int |
(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500. |
500 |
region |
object |
Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None. |
None |
format |
str |
Either 'png' or 'jpg'. Default to 'jpg'. |
'jpg' |
timeout |
int |
The number of seconds after which the request will be terminated. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use for the request. Defaults to None. |
None |
Source code in geemap/common.py
def get_image_thumbnail(
ee_object,
out_img,
vis_params,
dimensions=500,
region=None,
format="jpg",
crs="EPSG:3857",
timeout=300,
proxies=None,
):
"""Download a thumbnail for an ee.Image.
Args:
ee_object (object): The ee.Image instance.
out_img (str): The output file path to the png thumbnail.
vis_params (dict): The visualization parameters.
dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
format (str, optional): Either 'png' or 'jpg'. Default to 'jpg'.
timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
proxies (dict, optional): A dictionary of proxy servers to use for the request. Defaults to None.
"""
if not isinstance(ee_object, ee.Image):
raise TypeError("The ee_object must be an ee.Image.")
ext = os.path.splitext(out_img)[1][1:]
if ext not in ["png", "jpg"]:
raise ValueError("The output image format must be png or jpg.")
else:
format = ext
out_image = os.path.abspath(out_img)
out_dir = os.path.dirname(out_image)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if region is not None:
vis_params["region"] = region
vis_params["dimensions"] = dimensions
vis_params["format"] = format
vis_params["crs"] = crs
url = ee_object.getThumbURL(vis_params)
try:
r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
except Exception as e:
print("An error occurred while downloading.")
print(e)
if r.status_code != 200:
print("An error occurred while downloading.")
print(r.json()["error"]["message"])
else:
with open(out_img, "wb") as fd:
for chunk in r.iter_content(chunk_size=1024):
fd.write(chunk)
get_info(ee_object, layer_name='', opened=False, return_node=False)
¶
Print out the information for an Earth Engine object using a tree structure. The source code was adapted from https://github.com/google/earthengine-jupyter. Credits to Tyler Erickson.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
The Earth Engine object. |
required |
layer_name |
str |
The name of the layer. Defaults to "". |
'' |
opened |
bool |
Whether to expand the tree. Defaults to False. |
False |
return_node |
bool |
Whether to return the widget as ipytree.Node. If False, returns the widget as ipytree.Tree. Defaults to False. |
False |
Source code in geemap/common.py
def get_info(ee_object, layer_name="", opened=False, return_node=False):
"""Print out the information for an Earth Engine object using a tree structure. The source code
was adapted from https://github.com/google/earthengine-jupyter. Credits to Tyler Erickson.
Args:
ee_object (object): The Earth Engine object.
layer_name (str, optional): The name of the layer. Defaults to "".
opened (bool, optional): Whether to expand the tree. Defaults to False.
return_node (bool, optional): Whether to return the widget as ipytree.Node.
If False, returns the widget as ipytree.Tree. Defaults to False.
"""
def _order_items(item_dict, ordering_list):
"""Orders dictionary items in a specified order.
Adapted from https://github.com/google/earthengine-jupyter.
"""
list_of_tuples = [
(key, item_dict[key])
for key in [x for x in ordering_list if x in item_dict.keys()]
]
return dict(list_of_tuples)
def _process_info(info):
node_list = []
if isinstance(info, list):
for count, item in enumerate(info):
if isinstance(item, (list, dict)):
if "id" in item:
count = f"{count}: \"{item['id']}\""
if "data_type" in item:
count = f"{count}, {item['data_type']['precision']}"
if "crs" in item:
count = f"{count}, {item['crs']}"
if "dimensions" in item:
dimensions = item["dimensions"]
count = f"{count}, {dimensions[0]}x{dimensions[1]} px"
node_list.append(
Node(f"{count}", nodes=_process_info(item), opened=opened)
)
else:
node_list.append(Node(f"{count}: {item}", icon="file"))
elif isinstance(info, dict):
for k, v in info.items():
if isinstance(v, (list, dict)):
if k == "properties":
k = f"properties: Object ({len(v)} properties)"
elif k == "bands":
k = f"bands: List ({len(v)} elements)"
node_list.append(
Node(f"{k}", nodes=_process_info(v), opened=opened)
)
else:
node_list.append(Node(f"{k}: {v}", icon="file"))
else:
node_list.append(Node(f"{info}", icon="file"))
return node_list
if isinstance(ee_object, ee.FeatureCollection):
ee_object = ee_object.map(lambda f: ee.Feature(None, f.toDictionary()))
layer_info = ee_object.getInfo()
if not layer_info:
return None
props = layer_info.get("properties", {})
layer_info["properties"] = dict(sorted(props.items()))
ordering_list = []
if "type" in layer_info:
ordering_list.append("type")
ee_type = layer_info["type"]
else:
ee_type = ""
if "id" in layer_info:
ordering_list.append("id")
ee_id = layer_info["id"]
else:
ee_id = ""
ordering_list.append("version")
ordering_list.append("bands")
ordering_list.append("properties")
layer_info = _order_items(layer_info, ordering_list)
nodes = _process_info(layer_info)
if len(layer_name) > 0:
layer_name = f"{layer_name}: "
if "bands" in layer_info:
band_info = f' ({len(layer_info["bands"])} bands)'
else:
band_info = ""
root_node = Node(f"{layer_name}{ee_type} {band_info}", nodes=nodes, opened=opened)
# root_node.open_icon = "plus-square"
# root_node.open_icon_style = "success"
# root_node.close_icon = "minus-square"
# root_node.close_icon_style = "info"
if return_node:
return root_node
else:
tree = Tree()
tree.add_node(root_node)
return tree
get_local_tile_layer(source, port='default', debug=False, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, tile_format='ipyleaflet', layer_name='Local COG', return_client=False, quiet=False, **kwargs)
¶
Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG). If you are using this function in JupyterHub on a remote server and the raster does not render properly, try running the following two lines before calling this function:
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF. |
required |
port |
str |
The port to use for the server. Defaults to "default". |
'default' |
debug |
bool |
If True, the server will be started in debug mode. Defaults to False. |
False |
indexes |
int |
The band(s) to use. Band indexing starts at 1. Defaults to None. |
None |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
None |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
tile_format |
str |
The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
layer_name |
str |
The layer name to use. Defaults to None. |
'Local COG' |
return_client |
bool |
If True, the tile client will be returned. Defaults to False. |
False |
quiet |
bool |
If True, the error messages will be suppressed. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
ipyleaflet.TileLayer | folium.TileLayer |
An ipyleaflet.TileLayer or folium.TileLayer. |
Source code in geemap/common.py
def get_local_tile_layer(
source,
port="default",
debug=False,
indexes=None,
colormap=None,
vmin=None,
vmax=None,
nodata=None,
attribution=None,
tile_format="ipyleaflet",
layer_name="Local COG",
return_client=False,
quiet=False,
**kwargs,
):
"""Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG).
If you are using this function in JupyterHub on a remote server and the raster does not render properly, try
running the following two lines before calling this function:
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'
Args:
source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
port (str, optional): The port to use for the server. Defaults to "default".
debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
layer_name (str, optional): The layer name to use. Defaults to None.
return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
quiet (bool, optional): If True, the error messages will be suppressed. Defaults to False.
Returns:
ipyleaflet.TileLayer | folium.TileLayer: An ipyleaflet.TileLayer or folium.TileLayer.
"""
import rasterio
check_package(
"localtileserver", URL="https://github.com/banesullivan/localtileserver"
)
# Handle legacy localtileserver kwargs
if "cmap" in kwargs:
warnings.warn(
"`cmap` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
)
if "palette" in kwargs:
warnings.warn(
"`palette` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
)
if "band" in kwargs or "bands" in kwargs:
warnings.warn(
"`band` and `bands` are deprecated keyword arguments for get_local_tile_layer. Please use `indexes`."
)
if "projection" in kwargs:
warnings.warn(
"`projection` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
)
if "style" in kwargs:
warnings.warn(
"`style` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
)
if "max_zoom" not in kwargs:
kwargs["max_zoom"] = 30
if "max_native_zoom" not in kwargs:
kwargs["max_native_zoom"] = 30
if "cmap" in kwargs:
colormap = kwargs.pop("cmap")
if "palette" in kwargs:
colormap = kwargs.pop("palette")
if "band" in kwargs:
indexes = kwargs.pop("band")
if "bands" in kwargs:
indexes = kwargs.pop("bands")
# Make it compatible with binder and JupyterHub
if os.environ.get("JUPYTERHUB_SERVICE_PREFIX") is not None:
os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
f"{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}"
)
if is_studio_lab():
os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
f"studiolab/default/jupyter/proxy/{{port}}"
)
elif is_on_aws():
os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = "proxy/{port}"
elif "prefix" in kwargs:
os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = kwargs["prefix"]
kwargs.pop("prefix")
from localtileserver import (
get_leaflet_tile_layer,
get_folium_tile_layer,
TileClient,
)
# if "show_loading" not in kwargs:
# kwargs["show_loading"] = False
if isinstance(source, str):
if not source.startswith("http"):
if source.startswith("~"):
source = os.path.expanduser(source)
# else:
# source = os.path.abspath(source)
# if not os.path.exists(source):
# raise ValueError("The source path does not exist.")
else:
source = github_raw_url(source)
elif isinstance(source, TileClient) or isinstance(
source, rasterio.io.DatasetReader
):
pass
else:
raise ValueError("The source must either be a string or TileClient")
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
if layer_name is None:
if source.startswith("http"):
layer_name = "RemoteTile_" + random_string(3)
else:
layer_name = "LocalTile_" + random_string(3)
if isinstance(source, str) or isinstance(source, rasterio.io.DatasetReader):
tile_client = TileClient(source, port=port, debug=debug)
else:
tile_client = source
if quiet:
output = widgets.Output()
with output:
if tile_format == "ipyleaflet":
tile_layer = get_leaflet_tile_layer(
tile_client,
port=port,
debug=debug,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
name=layer_name,
**kwargs,
)
else:
tile_layer = get_folium_tile_layer(
tile_client,
port=port,
debug=debug,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attr=attribution,
overlay=True,
name=layer_name,
**kwargs,
)
else:
if tile_format == "ipyleaflet":
tile_layer = get_leaflet_tile_layer(
tile_client,
port=port,
debug=debug,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
name=layer_name,
**kwargs,
)
else:
tile_layer = get_folium_tile_layer(
tile_client,
port=port,
debug=debug,
indexes=indexes,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attr=attribution,
overlay=True,
name=layer_name,
**kwargs,
)
if return_client:
return tile_layer, tile_client
else:
return tile_layer
# center = tile_client.center()
# bounds = tile_client.bounds() # [ymin, ymax, xmin, xmax]
# bounds = (bounds[2], bounds[0], bounds[3], bounds[1]) # [minx, miny, maxx, maxy]
# if get_center and get_bounds:
# return tile_layer, center, bounds
# elif get_center:
# return tile_layer, center
# elif get_bounds:
# return tile_layer, bounds
# else:
# return tile_layer
get_palettable(types=None)
¶
Get a list of palettable color palettes.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
types |
list |
A list of palettable types to return, e.g., types=['matplotlib', 'cartocolors']. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
A list of palettable color palettes. |
Source code in geemap/common.py
def get_palettable(types=None):
"""Get a list of palettable color palettes.
Args:
types (list, optional): A list of palettable types to return, e.g., types=['matplotlib', 'cartocolors']. Defaults to None.
Returns:
list: A list of palettable color palettes.
"""
try:
import palettable
except ImportError:
raise ImportError(
"The palettable package is not installed. Please install it with `pip install palettable`."
)
if types is not None and (not isinstance(types, list)):
raise ValueError("The types must be a list.")
allowed_palettes = [
"cartocolors",
"cmocean",
"colorbrewer",
"cubehelix",
"lightbartlein",
"matplotlib",
"mycarta",
"scientific",
"tableau",
"wesanderson",
]
if types is None:
types = allowed_palettes[:]
if all(x in allowed_palettes for x in types):
pass
else:
raise ValueError(
"The types must be one of the following: " + ", ".join(allowed_palettes)
)
palettes = []
if "cartocolors" in types:
cartocolors_diverging = [
f"cartocolors.diverging.{c}"
for c in dir(palettable.cartocolors.diverging)[:-19]
]
cartocolors_qualitative = [
f"cartocolors.qualitative.{c}"
for c in dir(palettable.cartocolors.qualitative)[:-19]
]
cartocolors_sequential = [
f"cartocolors.sequential.{c}"
for c in dir(palettable.cartocolors.sequential)[:-41]
]
palettes = (
palettes
+ cartocolors_diverging
+ cartocolors_qualitative
+ cartocolors_sequential
)
if "cmocean" in types:
cmocean_diverging = [
f"cmocean.diverging.{c}" for c in dir(palettable.cmocean.diverging)[:-19]
]
cmocean_sequential = [
f"cmocean.sequential.{c}" for c in dir(palettable.cmocean.sequential)[:-19]
]
palettes = palettes + cmocean_diverging + cmocean_sequential
if "colorbrewer" in types:
colorbrewer_diverging = [
f"colorbrewer.diverging.{c}"
for c in dir(palettable.colorbrewer.diverging)[:-19]
]
colorbrewer_qualitative = [
f"colorbrewer.qualitative.{c}"
for c in dir(palettable.colorbrewer.qualitative)[:-19]
]
colorbrewer_sequential = [
f"colorbrewer.sequential.{c}"
for c in dir(palettable.colorbrewer.sequential)[:-41]
]
palettes = (
palettes
+ colorbrewer_diverging
+ colorbrewer_qualitative
+ colorbrewer_sequential
)
if "cubehelix" in types:
cubehelix = [
"classic_16",
"cubehelix1_16",
"cubehelix2_16",
"cubehelix3_16",
"jim_special_16",
"perceptual_rainbow_16",
"purple_16",
"red_16",
]
cubehelix = [f"cubehelix.{c}" for c in cubehelix]
palettes = palettes + cubehelix
if "lightbartlein" in types:
lightbartlein_diverging = [
f"lightbartlein.diverging.{c}"
for c in dir(palettable.lightbartlein.diverging)[:-19]
]
lightbartlein_sequential = [
f"lightbartlein.sequential.{c}"
for c in dir(palettable.lightbartlein.sequential)[:-19]
]
palettes = palettes + lightbartlein_diverging + lightbartlein_sequential
if "matplotlib" in types:
matplotlib_colors = [
f"matplotlib.{c}" for c in dir(palettable.matplotlib)[:-16]
]
palettes = palettes + matplotlib_colors
if "mycarta" in types:
mycarta = [f"mycarta.{c}" for c in dir(palettable.mycarta)[:-16]]
palettes = palettes + mycarta
if "scientific" in types:
scientific_diverging = [
f"scientific.diverging.{c}"
for c in dir(palettable.scientific.diverging)[:-19]
]
scientific_sequential = [
f"scientific.sequential.{c}"
for c in dir(palettable.scientific.sequential)[:-19]
]
palettes = palettes + scientific_diverging + scientific_sequential
if "tableau" in types:
tableau = [f"tableau.{c}" for c in dir(palettable.tableau)[:-14]]
palettes = palettes + tableau
return palettes
get_palette_colors(cmap_name=None, n_class=None, hashtag=False)
¶
Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
cmap_name |
str |
The name of the matplotlib colormap. Defaults to None. |
None |
n_class |
int |
The number of colors. Defaults to None. |
None |
hashtag |
bool |
Whether to return a list of hex colors. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list |
A list of hex colors. |
Source code in geemap/common.py
def get_palette_colors(cmap_name=None, n_class=None, hashtag=False):
"""Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.
Args:
cmap_name (str, optional): The name of the matplotlib colormap. Defaults to None.
n_class (int, optional): The number of colors. Defaults to None.
hashtag (bool, optional): Whether to return a list of hex colors. Defaults to False.
Returns:
list: A list of hex colors.
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap(cmap_name, n_class)
colors = [mpl.colors.rgb2hex(cmap(i))[1:] for i in range(cmap.N)]
if hashtag:
colors = ["#" + i for i in colors]
return colors
get_temp_dir()
¶
Returns the temporary directory.
Returns:
| Type | Description |
|---|---|
str |
The temporary directory. |
Source code in geemap/common.py
def get_temp_dir():
"""Returns the temporary directory.
Returns:
str: The temporary directory.
"""
import tempfile
return tempfile.gettempdir()
get_wms_layers(url, return_titles=False)
¶
Returns a list of WMS layers from a WMS service.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL of the WMS service. |
required |
return_titles |
bool |
If True, the titles of the layers will be returned. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list |
A list of WMS layers. |
Source code in geemap/common.py
def get_wms_layers(url, return_titles=False):
"""Returns a list of WMS layers from a WMS service.
Args:
url (str): The URL of the WMS service.
return_titles (bool, optional): If True, the titles of the layers will be returned. Defaults to False.
Returns:
list: A list of WMS layers.
"""
from owslib.wms import WebMapService
wms = WebMapService(url)
layers = list(wms.contents)
layers.sort()
if return_titles:
return layers, [wms[layer].title for layer in layers]
else:
return layers
github_raw_url(url)
¶
Get the raw URL for a GitHub file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The GitHub URL. |
required |
Returns:
| Type | Description |
|---|---|
str |
The raw URL. |
Source code in geemap/common.py
def github_raw_url(url):
"""Get the raw URL for a GitHub file.
Args:
url (str): The GitHub URL.
Returns:
str: The raw URL.
"""
if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
url = url.replace("github.com", "raw.githubusercontent.com").replace(
"blob/", "", 1
)
return url
has_transparency(img)
¶
Checks whether an image has transparency.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
a PIL Image object. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if it has transparency, False otherwise. |
Source code in geemap/common.py
def has_transparency(img):
"""Checks whether an image has transparency.
Args:
img (object): a PIL Image object.
Returns:
bool: True if it has transparency, False otherwise.
"""
if img.mode == "P":
transparent = img.info.get("transparency", -1)
for _, index in img.getcolors():
if index == transparent:
return True
elif img.mode == "RGBA":
extrema = img.getextrema()
if extrema[3][0] < 255:
return True
return False
hex_to_rgb(value='FFFFFF')
¶
Converts hex color to RGB color.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
value |
str |
Hex color code as a string. Defaults to 'FFFFFF'. |
'FFFFFF' |
Returns:
| Type | Description |
|---|---|
tuple |
RGB color as a tuple. |
Source code in geemap/common.py
def hex_to_rgb(value="FFFFFF"):
"""Converts hex color to RGB color.
Args:
value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.
Returns:
tuple: RGB color as a tuple.
"""
value = value.lstrip("#")
lv = len(value)
return tuple(int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3))
html_to_gradio(html, width='100%', height='500px', **kwargs)
¶
Converts the map to an HTML string that can be used in Gradio. Removes unsupported elements, such as attribution and any code blocks containing functions. See https://github.com/gradio-app/gradio/issues/3190
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
width |
str |
The width of the map. Defaults to '100%'. |
'100%' |
height |
str |
The height of the map. Defaults to '500px'. |
'500px' |
Returns:
| Type | Description |
|---|---|
str |
The HTML string to use in Gradio. |
Source code in geemap/common.py
def html_to_gradio(html, width="100%", height="500px", **kwargs):
"""Converts the map to an HTML string that can be used in Gradio. Removes unsupported elements, such as
attribution and any code blocks containing functions. See https://github.com/gradio-app/gradio/issues/3190
Args:
width (str, optional): The width of the map. Defaults to '100%'.
height (str, optional): The height of the map. Defaults to '500px'.
Returns:
str: The HTML string to use in Gradio.
"""
if isinstance(width, int):
width = f"{width}px"
if isinstance(height, int):
height = f"{height}px"
if isinstance(html, str):
with open(html, "r") as f:
lines = f.readlines()
elif isinstance(html, list):
lines = html
else:
raise TypeError("html must be a file path or a list of strings")
output = []
skipped_lines = []
for index, line in enumerate(lines):
if index in skipped_lines:
continue
if line.lstrip().startswith('{"attribution":'):
continue
elif "on(L.Draw.Event.CREATED, function(e)" in line:
for i in range(14):
skipped_lines.append(index + i)
elif "L.Control.geocoder" in line:
for i in range(5):
skipped_lines.append(index + i)
elif "function(e)" in line:
print(
f"Warning: The folium plotting backend does not support functions in code blocks. Please delete line {index + 1}."
)
else:
output.append(line + "\n")
return f"""<iframe style="width: {width}; height: {height}" name="result" allow="midi; geolocation; microphone; camera;
display-capture; encrypted-media;" sandbox="allow-modals allow-forms
allow-scripts allow-same-origin allow-popups
allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
allowpaymentrequest="" frameborder="0" srcdoc='{"".join(output)}'></iframe>"""
html_to_streamlit(filename, width=None, height=None, scrolling=False, replace_dict={})
¶
Renders an HTML file as a Streamlit component.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The filename of the HTML file. |
required |
width |
int |
Width of the map. Defaults to None. |
None |
height |
int |
Height of the map. Defaults to 600. |
None |
scrolling |
bool |
Whether to allow the map to scroll. Defaults to False. |
False |
replace_dict |
dict |
A dictionary of strings to replace in the HTML file. Defaults to {}. |
{} |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the filename does not exist. |
Returns:
| Type | Description |
|---|---|
streamlit.components |
components.html object. |
Source code in geemap/common.py
def html_to_streamlit(
filename, width=None, height=None, scrolling=False, replace_dict={}
):
"""Renders an HTML file as a Streamlit component.
Args:
filename (str): The filename of the HTML file.
width (int, optional): Width of the map. Defaults to None.
height (int, optional): Height of the map. Defaults to 600.
scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
replace_dict (dict, optional): A dictionary of strings to replace in the HTML file. Defaults to {}.
Raises:
ValueError: If the filename does not exist.
Returns:
streamlit.components: components.html object.
"""
import streamlit.components.v1 as components
if not os.path.exists(filename):
raise ValueError("filename must exist.")
f = open(filename, "r")
html = f.read()
for key, value in replace_dict.items():
html = html.replace(key, value)
f.close()
return components.html(html, width=width, height=height, scrolling=scrolling)
image_area(img, region=None, scale=None, denominator=1.0)
¶
Calculates the area of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
denominator |
float |
The denominator to use for converting size from square meters to other units. Defaults to 1.0. |
1.0 |
Returns:
| Type | Description |
|---|---|
object |
ee.Dictionary |
Source code in geemap/common.py
def image_area(img, region=None, scale=None, denominator=1.0):
"""Calculates the area of an image.
Args:
img (object): ee.Image
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.
Returns:
object: ee.Dictionary
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
pixel_area = (
img.unmask().neq(ee.Image(0)).multiply(ee.Image.pixelArea()).divide(denominator)
)
img_area = pixel_area.reduceRegion(
**{
"geometry": region,
"reducer": ee.Reducer.sum(),
"scale": scale,
"maxPixels": 1e12,
}
)
return img_area
image_area_by_group(img, groups=None, region=None, scale=None, denominator=1.0, out_csv=None, labels=None, decimal_places=4, verbose=True)
¶
Calculates the area of each class of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
groups |
object |
The groups to use for the area calculation. Defaults to None. |
None |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
denominator |
float |
The denominator to use for converting size from square meters to other units. Defaults to 1.0. |
1.0 |
out_csv |
str |
The path to the output CSV file. Defaults to None. |
None |
labels |
object |
The class labels to use in the output CSV file. Defaults to None. |
None |
decimal_places |
int |
The number of decimal places to use for the output. Defaults to 2. |
4 |
verbose |
bool |
If True, print the progress. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
object |
pandas.DataFrame |
Source code in geemap/common.py
def image_area_by_group(
img,
groups=None,
region=None,
scale=None,
denominator=1.0,
out_csv=None,
labels=None,
decimal_places=4,
verbose=True,
):
"""Calculates the area of each class of an image.
Args:
img (object): ee.Image
groups (object, optional): The groups to use for the area calculation. Defaults to None.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.
out_csv (str, optional): The path to the output CSV file. Defaults to None.
labels (object, optional): The class labels to use in the output CSV file. Defaults to None.
decimal_places (int, optional): The number of decimal places to use for the output. Defaults to 2.
verbose (bool, optional): If True, print the progress. Defaults to True.
Returns:
object: pandas.DataFrame
"""
import pandas as pd
values = []
if region is None:
region = ee.Geometry.BBox(-179.9, -89.5, 179.9, 89.5)
if groups is None:
groups = image_value_list(img, region, scale)
if not isinstance(groups, list):
groups = groups.getInfo()
groups.sort(key=int)
for group in groups:
if verbose:
print(f"Calculating area for group {group} ...")
area = image_area(img.eq(float(group)), region, scale, denominator)
values.append(area.values().get(0).getInfo())
d = {"group": groups, "area": values}
df = pd.DataFrame(data=d)
df = df.set_index("group")
df["percentage"] = df["area"] / df["area"].sum()
df = df.astype(float).round(decimal_places)
if isinstance(labels, list) and len(labels) == len(values):
df["labels"] = labels
if out_csv is not None:
df.to_csv(out_csv)
else:
return df
image_band_names(img)
¶
Gets image band names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image. |
required |
Returns:
| Type | Description |
|---|---|
ee.List |
The returned list of image band names. |
Source code in geemap/common.py
def image_band_names(img):
"""Gets image band names.
Args:
img (ee.Image): The input image.
Returns:
ee.List: The returned list of image band names.
"""
return img.bandNames()
image_bandcount(image, **kwargs)
¶
Get the number of bands in an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
int |
The number of bands in the image. |
Source code in geemap/common.py
def image_bandcount(image, **kwargs):
"""Get the number of bands in an image.
Args:
image (str): The input image filepath or URL.
Returns:
int: The number of bands in the image.
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return len(client.metadata()["bands"])
image_bounds(image, **kwargs)
¶
Get the bounds of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
list |
A list of bounds in the form of [(south, west), (north, east)]. |
Source code in geemap/common.py
def image_bounds(image, **kwargs):
"""Get the bounds of an image.
Args:
image (str): The input image filepath or URL.
Returns:
list: A list of bounds in the form of [(south, west), (north, east)].
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
bounds = client.bounds()
return [(bounds[0], bounds[2]), (bounds[1], bounds[3])]
image_cell_size(img)
¶
Retrieves the image cell size (e.g., spatial resolution)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
Returns:
| Type | Description |
|---|---|
float |
The nominal scale in meters. |
Source code in geemap/common.py
def image_cell_size(img):
"""Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.
"""
bands = img.bandNames()
scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
scale = ee.Algorithms.If(
scales.distinct().size().gt(1),
ee.Dictionary.fromLists(bands.getInfo(), scales),
scales.get(0),
)
return scale
image_center(image, **kwargs)
¶
Get the center of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (latitude, longitude). |
Source code in geemap/common.py
def image_center(image, **kwargs):
"""Get the center of an image.
Args:
image (str): The input image filepath or URL.
Returns:
tuple: A tuple of (latitude, longitude).
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return client.center()
image_client(image, **kwargs)
¶
Get a LocalTileserver TileClient from an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
TileClient |
A LocalTileserver TileClient. |
Source code in geemap/common.py
def image_client(image, **kwargs):
"""Get a LocalTileserver TileClient from an image.
Args:
image (str): The input image filepath or URL.
Returns:
TileClient: A LocalTileserver TileClient.
"""
image_check(image)
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
return client
image_convolution(image, kernel=None, resample=None, projection='EPSG:3857', **kwargs)
¶
Performs a convolution on an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image | ee.ImageCollection |
The image to convolve. |
required |
kernel |
ee.Kernel |
The kernel to convolve with. Defaults to None, a 7x7 gaussian kernel. |
None |
resample |
str |
The resample method to use. It can be either 'bilinear' or 'bicubic'". Defaults to None, which uses the image's resample method. |
None |
projection |
str |
The projection to use. Defaults to 'EPSG:3857'. |
'EPSG:3857' |
Returns:
| Type | Description |
|---|---|
ee.Image |
The convolved image. |
Source code in geemap/common.py
def image_convolution(
image, kernel=None, resample=None, projection="EPSG:3857", **kwargs
):
"""Performs a convolution on an image.
Args:
image (ee.Image | ee.ImageCollection): The image to convolve.
kernel (ee.Kernel, optional): The kernel to convolve with. Defaults to None, a 7x7 gaussian kernel.
resample (str, optional): The resample method to use. It can be either 'bilinear' or 'bicubic'". Defaults to None, which uses the image's resample method.
projection (str, optional): The projection to use. Defaults to 'EPSG:3857'.
Returns:
ee.Image: The convolved image.
"""
if isinstance(image, ee.ImageCollection):
image = image.mosaic()
elif not isinstance(image, ee.Image):
raise ValueError("image must be an ee.Image or ee.ImageCollection.")
if kernel is None:
kernel = ee.Kernel.gaussian(radius=3, sigma=2, units="pixels", normalize=True)
elif not isinstance(kernel, ee.Kernel):
raise ValueError("kernel must be an ee.Kernel.")
if resample is not None:
if resample not in ["bilinear", "bicubic"]:
raise ValueError("resample must be one of 'bilinear' or 'bicubic'")
result = image.convolve(kernel)
if resample is not None:
result = result.resample(resample)
return result.setDefaultProjection(projection)
image_count(collection, region=None, band=None, start_date=None, end_date=None, clip=False)
¶
Create an image with the number of available images for a specific region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.ImageCollection |
The collection to be queried. |
required |
region |
ee.Geometry | ee.FeatureCollection |
The region to be queried. |
None |
start_date |
str | ee.Date |
The start date of the query. |
None |
band |
str |
The band to be queried. |
None |
end_date |
str | ee.Date |
The end date of the query. |
None |
clip |
bool |
Whether to clip the image to the region. |
False |
Returns:
| Type | Description |
|---|---|
ee.Image |
The image with each pixel value representing the number of available images. |
Source code in geemap/common.py
def image_count(
collection, region=None, band=None, start_date=None, end_date=None, clip=False
):
"""Create an image with the number of available images for a specific region.
Args:
collection (ee.ImageCollection): The collection to be queried.
region (ee.Geometry | ee.FeatureCollection, optional): The region to be queried.
start_date (str | ee.Date, optional): The start date of the query.
band (str, optional): The band to be queried.
end_date (str | ee.Date, optional): The end date of the query.
clip (bool, optional): Whether to clip the image to the region.
Returns:
ee.Image: The image with each pixel value representing the number of available images.
"""
if not isinstance(collection, ee.ImageCollection):
raise TypeError("collection must be an ee.ImageCollection.")
if region is not None:
if isinstance(region, ee.Geometry) or isinstance(region, ee.FeatureCollection):
pass
else:
raise TypeError("region must be an ee.Geometry or ee.FeatureCollection.")
if (start_date is not None) and (end_date is not None):
pass
elif (start_date is None) and (end_date is None):
pass
else:
raise ValueError("start_date and end_date must be provided.")
if band is None:
first_image = collection.first()
band = first_image.bandNames().get(0)
if region is not None:
collection = collection.filterBounds(region)
if start_date is not None and end_date is not None:
collection = collection.filterDate(start_date, end_date)
image = (
collection.filter(ee.Filter.listContains("system:band_names", band))
.select([band])
.reduce(ee.Reducer.count())
)
if clip:
image = image.clip(region)
return image
image_date(img, date_format='YYYY-MM-dd')
¶
Retrieves the image acquisition date.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
date_format |
str |
The date format to use. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
str |
A string representing the acquisition of the image. |
Source code in geemap/common.py
def image_date(img, date_format="YYYY-MM-dd"):
"""Retrieves the image acquisition date.
Args:
img (object): ee.Image
date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.
Returns:
str: A string representing the acquisition of the image.
"""
return ee.Date(img.get("system:time_start")).format(date_format)
image_dates(img_col, date_format='YYYY-MM-dd')
¶
Get image dates of all images in an ImageCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img_col |
object |
ee.ImageCollection |
required |
date_format |
str |
A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
object |
ee.List |
Source code in geemap/common.py
def image_dates(img_col, date_format="YYYY-MM-dd"):
"""Get image dates of all images in an ImageCollection.
Args:
img_col (object): ee.ImageCollection
date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.
Returns:
object: ee.List
"""
dates = img_col.aggregate_array("system:time_start")
new_dates = dates.map(lambda d: ee.Date(d).format(date_format))
return new_dates
image_geotransform(image, **kwargs)
¶
Get the geotransform of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
list |
A list of geotransform values. |
Source code in geemap/common.py
def image_geotransform(image, **kwargs):
"""Get the geotransform of an image.
Args:
image (str): The input image filepath or URL.
Returns:
list: A list of geotransform values.
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return client.metadata()["GeoTransform"]
image_histogram(img, region=None, scale=None, x_label=None, y_label=None, title=None, width=None, height=500, plot_args={}, layout_args={}, return_df=False, **kwargs)
¶
Create a histogram of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The image to calculate the histogram. |
required |
region |
ee.Geometry | ee.FeatureCollection |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
x_label |
str |
Label for the x axis. Defaults to None. |
None |
y_label |
str |
Label for the y axis. Defaults to None. |
None |
title |
str |
Title for the plot. Defaults to None. |
None |
width |
int |
Width of the plot in pixels. Defaults to None. |
None |
height |
int |
Height of the plot in pixels. Defaults to 500. |
500 |
layout_args |
dict |
Layout arguments for the plot to be passed to fig.update_layout(), |
{} |
return_df |
bool |
If True, return a pandas dataframe. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
pandas DataFrame | plotly figure object |
A dataframe or plotly figure object. |
Source code in geemap/common.py
def image_histogram(
img,
region=None,
scale=None,
x_label=None,
y_label=None,
title=None,
width=None,
height=500,
plot_args={},
layout_args={},
return_df=False,
**kwargs,
):
"""Create a histogram of an image.
Args:
img (ee.Image): The image to calculate the histogram.
region (ee.Geometry | ee.FeatureCollection, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
x_label (str, optional): Label for the x axis. Defaults to None.
y_label (str, optional): Label for the y axis. Defaults to None.
title (str, optional): Title for the plot. Defaults to None.
width (int, optional): Width of the plot in pixels. Defaults to None.
height (int, optional): Height of the plot in pixels. Defaults to 500.
layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
return_df (bool, optional): If True, return a pandas dataframe. Defaults to False.
Returns:
pandas DataFrame | plotly figure object: A dataframe or plotly figure object.
"""
import pandas as pd
import plotly.express as px
hist = image_value_list(img, region, scale, return_hist=True, **kwargs).getInfo()
keys = sorted(hist, key=int)
values = [hist.get(key) for key in keys]
data = pd.DataFrame({"key": keys, "value": values})
if return_df:
return data
else:
labels = {}
if x_label is not None:
labels["key"] = x_label
if y_label is not None:
labels["value"] = y_label
try:
fig = px.bar(
data,
x="key",
y="value",
labels=labels,
title=title,
width=width,
height=height,
**plot_args,
)
if isinstance(layout_args, dict):
fig.update_layout(**layout_args)
return fig
except Exception as e:
raise Exception(e)
image_max_value(img, region=None, scale=None)
¶
Retrieves the maximum value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the maximum value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_max_value(img, region=None, scale=None):
"""Retrieves the maximum value of an image.
Args:
img (object): The image to calculate the maximum value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
max_value = img.reduceRegion(
**{
"reducer": ee.Reducer.max(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return max_value
image_mean_value(img, region=None, scale=None)
¶
Retrieves the mean value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the mean value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_mean_value(img, region=None, scale=None):
"""Retrieves the mean value of an image.
Args:
img (object): The image to calculate the mean value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
mean_value = img.reduceRegion(
**{
"reducer": ee.Reducer.mean(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return mean_value
image_metadata(image, **kwargs)
¶
Get the metadata of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of image metadata. |
Source code in geemap/common.py
def image_metadata(image, **kwargs):
"""Get the metadata of an image.
Args:
image (str): The input image filepath or URL.
Returns:
dict: A dictionary of image metadata.
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return client.metadata()
image_min_value(img, region=None, scale=None)
¶
Retrieves the minimum value of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the minimum value. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_min_value(img, region=None, scale=None):
"""Retrieves the minimum value of an image.
Args:
img (object): The image to calculate the minimum value.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
min_value = img.reduceRegion(
**{
"reducer": ee.Reducer.min(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return min_value
image_projection(image, **kwargs)
¶
Get the projection of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
str |
The projection of the image. |
Source code in geemap/common.py
def image_projection(image, **kwargs):
"""Get the projection of an image.
Args:
image (str): The input image filepath or URL.
Returns:
str: The projection of the image.
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return client.metadata()["Projection"]
image_props(img, date_format='YYYY-MM-dd')
¶
Gets image properties.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image. |
required |
date_format |
str |
The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'. |
'YYYY-MM-dd' |
Returns:
| Type | Description |
|---|---|
dd.Dictionary |
The dictionary containing image properties. |
Source code in geemap/common.py
def image_props(img, date_format="YYYY-MM-dd"):
"""Gets image properties.
Args:
img (ee.Image): The input image.
date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.
Returns:
dd.Dictionary: The dictionary containing image properties.
"""
if not isinstance(img, ee.Image):
print("The input object must be an ee.Image")
return
keys = img.propertyNames().remove("system:footprint").remove("system:bands")
values = keys.map(lambda p: img.get(p))
props = ee.Dictionary.fromLists(keys, values)
names = keys.getInfo()
bands = img.bandNames()
scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
scale = ee.Algorithms.If(
scales.distinct().size().gt(1),
ee.Dictionary.fromLists(bands.getInfo(), scales),
scales.get(0),
)
props = props.set("NOMINAL_SCALE", scale)
if "system:time_start" in names:
image_date = ee.Date(img.get("system:time_start")).format(date_format)
time_start = ee.Date(img.get("system:time_start")).format("YYYY-MM-dd HH:mm:ss")
# time_end = ee.Date(img.get('system:time_end')).format('YYYY-MM-dd HH:mm:ss')
time_end = ee.Algorithms.If(
ee.List(img.propertyNames()).contains("system:time_end"),
ee.Date(img.get("system:time_end")).format("YYYY-MM-dd HH:mm:ss"),
time_start,
)
props = props.set("system:time_start", time_start)
props = props.set("system:time_end", time_end)
props = props.set("IMAGE_DATE", image_date)
if "system:asset_size" in names:
asset_size = (
ee.Number(img.get("system:asset_size"))
.divide(1e6)
.format()
.cat(ee.String(" MB"))
)
props = props.set("system:asset_size", asset_size)
return props
image_reclassify(img, in_list, out_list)
¶
Reclassify an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to which the remapping is applied. |
required |
in_list |
list |
The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'. |
required |
out_list |
list |
The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def image_reclassify(img, in_list, out_list):
"""Reclassify an image.
Args:
img (object): The image to which the remapping is applied.
in_list (list): The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.
out_list (list): The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.
Returns:
object: ee.Image
"""
image = img.remap(in_list, out_list)
return image
image_resolution(image, **kwargs)
¶
Get the resolution of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
float |
The resolution of the image. |
Source code in geemap/common.py
def image_resolution(image, **kwargs):
"""Get the resolution of an image.
Args:
image (str): The input image filepath or URL.
Returns:
float: The resolution of the image.
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
return client.metadata()["GeoTransform"][1]
image_scale(img)
¶
Retrieves the image cell size (e.g., spatial resolution)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
ee.Image |
required |
Returns:
| Type | Description |
|---|---|
float |
The nominal scale in meters. |
Source code in geemap/common.py
def image_scale(img):
"""Retrieves the image cell size (e.g., spatial resolution)
Args:
img (object): ee.Image
Returns:
float: The nominal scale in meters.
"""
# bands = img.bandNames()
# scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
# scale = ee.Algorithms.If(scales.distinct().size().gt(1), ee.Dictionary.fromLists(bands.getInfo(), scales), scales.get(0))
return img.select(0).projection().nominalScale()
image_set_crs(image, epsg)
¶
Define the CRS of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath |
required |
epsg |
int |
The EPSG code of the CRS to set. |
required |
Source code in geemap/common.py
def image_set_crs(image, epsg):
"""Define the CRS of an image.
Args:
image (str): The input image filepath
epsg (int): The EPSG code of the CRS to set.
"""
from rasterio.crs import CRS
import rasterio
with rasterio.open(image, "r+") as rds:
rds.crs = CRS.from_epsg(epsg)
image_size(image, **kwargs)
¶
Get the size (width, height) of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath or URL. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (width, height). |
Source code in geemap/common.py
def image_size(image, **kwargs):
"""Get the size (width, height) of an image.
Args:
image (str): The input image filepath or URL.
Returns:
tuple: A tuple of (width, height).
"""
image_check(image)
if isinstance(image, str):
_, client = get_local_tile_layer(image, return_client=True, **kwargs)
else:
client = image
metadata = client.metadata()
return metadata["sourceSizeX"], metadata["sourceSizeY"]
image_smoothing(img, reducer, kernel)
¶
Smooths an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to be smoothed. |
required |
reducer |
object |
ee.Reducer |
required |
kernel |
object |
ee.Kernel |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def image_smoothing(img, reducer, kernel):
"""Smooths an image.
Args:
img (object): The image to be smoothed.
reducer (object): ee.Reducer
kernel (object): ee.Kernel
Returns:
object: ee.Image
"""
image = img.reduceNeighborhood(
**{
"reducer": reducer,
"kernel": kernel,
}
)
return image
image_stats(img, region=None, scale=None)
¶
Gets image descriptive statistics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The input image to calculate descriptive statistics. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.Dictionary |
A dictionary containing the description statistics of the input image. |
Source code in geemap/common.py
def image_stats(img, region=None, scale=None):
"""Gets image descriptive statistics.
Args:
img (ee.Image): The input image to calculate descriptive statistics.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
ee.Dictionary: A dictionary containing the description statistics of the input image.
"""
if not isinstance(img, ee.Image):
print("The input object must be an ee.Image")
return
stat_types = ["min", "max", "mean", "std", "sum"]
image_min = image_min_value(img, region, scale)
image_max = image_max_value(img, region, scale)
image_mean = image_mean_value(img, region, scale)
image_std = image_std_value(img, region, scale)
image_sum = image_sum_value(img, region, scale)
stat_results = ee.List([image_min, image_max, image_mean, image_std, image_sum])
stats = ee.Dictionary.fromLists(stat_types, stat_results)
return stats
image_stats_by_zone(image, zones, out_csv=None, labels=None, region=None, scale=None, reducer='MEAN', bestEffort=True, **kwargs)
¶
Calculate statistics for an image by zone.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to calculate statistics for. |
required |
zones |
ee.Image |
The zones to calculate statistics for. |
required |
out_csv |
str |
The path to the output CSV file. Defaults to None. |
None |
labels |
list |
The list of zone labels to use for the output CSV. Defaults to None. |
None |
region |
ee.Geometry |
The region over which to reduce data. Defaults to the footprint of zone image. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
reducer |
str | ee.Reducer |
The reducer to use. It can be one of MEAN, MAXIMUM, MINIMUM, MODE, STD, MIN_MAX, SUM, VARIANCE. Defaults to MEAN. |
'MEAN' |
bestEffort |
bool |
If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
str | pd.DataFrame |
The path to the output CSV file or a pandas DataFrame. |
Source code in geemap/common.py
def image_stats_by_zone(
image,
zones,
out_csv=None,
labels=None,
region=None,
scale=None,
reducer="MEAN",
bestEffort=True,
**kwargs,
):
"""Calculate statistics for an image by zone.
Args:
image (ee.Image): The image to calculate statistics for.
zones (ee.Image): The zones to calculate statistics for.
out_csv (str, optional): The path to the output CSV file. Defaults to None.
labels (list, optional): The list of zone labels to use for the output CSV. Defaults to None.
region (ee.Geometry, optional): The region over which to reduce data. Defaults to the footprint of zone image.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
reducer (str | ee.Reducer, optional): The reducer to use. It can be one of MEAN, MAXIMUM, MINIMUM, MODE, STD, MIN_MAX, SUM, VARIANCE. Defaults to MEAN.
bestEffort (bool, optional): If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. Defaults to True.
Returns:
str | pd.DataFrame: The path to the output CSV file or a pandas DataFrame.
"""
import pandas as pd
if region is not None:
if isinstance(region, ee.Geometry):
pass
elif isinstance(region, ee.FeatureCollection):
region = region.geometry()
else:
raise ValueError("region must be an ee.Geometry or ee.FeatureCollection")
if scale is None:
scale = image_scale(image)
allowed_stats = {
"MEAN": ee.Reducer.mean(),
"MAXIMUM": ee.Reducer.max(),
"MEDIAN": ee.Reducer.median(),
"MINIMUM": ee.Reducer.min(),
"MODE": ee.Reducer.mode(),
"STD": ee.Reducer.stdDev(),
"MIN_MAX": ee.Reducer.minMax(),
"SUM": ee.Reducer.sum(),
"VARIANCE": ee.Reducer.variance(),
}
if isinstance(reducer, str):
if reducer.upper() not in allowed_stats:
raise ValueError(
"reducer must be one of: {}".format(", ".join(allowed_stats.keys()))
)
else:
reducer = allowed_stats[reducer.upper()]
elif isinstance(reducer, ee.Reducer):
pass
else:
raise ValueError(
"reducer must be one of: {}".format(", ".join(allowed_stats.keys()))
)
values = image_value_list(zones, region=region)
values = values.map(lambda x: ee.Number.parse(x))
def get_stats(value):
img = image.updateMask(zones.eq(ee.Number(value)))
kwargs["reducer"] = reducer
kwargs["scale"] = scale
kwargs["geometry"] = region
kwargs["bestEffort"] = bestEffort
stat = img.reduceRegion(**kwargs)
return ee.Image().set({"zone": value}).set({"stat": stat.values().get(0)})
collection = ee.ImageCollection(values.map(lambda x: get_stats(x)))
keys = collection.aggregate_array("zone").getInfo()
values = collection.aggregate_array("stat").getInfo()
if labels is not None and isinstance(labels, list):
if len(labels) != len(keys):
warnings.warn("labels are not the same length as keys, ignoring labels.")
df = pd.DataFrame({"zone": keys, "stat": values})
else:
df = pd.DataFrame({"zone": keys, "label": labels, "stat": values})
else:
df = pd.DataFrame({"zone": keys, "stat": values})
if out_csv is not None:
check_file_path(out_csv)
df.to_csv(out_csv, index=False)
return out_csv
else:
return df
image_std_value(img, region=None, scale=None)
¶
Retrieves the standard deviation of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the standard deviation. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_std_value(img, region=None, scale=None):
"""Retrieves the standard deviation of an image.
Args:
img (object): The image to calculate the standard deviation.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
std_value = img.reduceRegion(
**{
"reducer": ee.Reducer.stdDev(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return std_value
image_sum_value(img, region=None, scale=None)
¶
Retrieves the sum of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to calculate the standard deviation. |
required |
region |
object |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def image_sum_value(img, region=None, scale=None):
"""Retrieves the sum of an image.
Args:
img (object): The image to calculate the standard deviation.
region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
Returns:
object: ee.Number
"""
if region is None:
region = img.geometry()
if scale is None:
scale = image_scale(img)
sum_value = img.reduceRegion(
**{
"reducer": ee.Reducer.sum(),
"geometry": region,
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return sum_value
image_to_cog(source, dst_path=None, profile='deflate', **kwargs)
¶
Converts an image to a COG file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str |
A dataset path, URL or rasterio.io.DatasetReader object. |
required |
dst_path |
str |
An output dataset path or or PathLike object. Defaults to None. |
None |
profile |
str |
COG profile. More at https://cogeotiff.github.io/rio-cogeo/profile. Defaults to "deflate". |
'deflate' |
Exceptions:
| Type | Description |
|---|---|
ImportError |
If rio-cogeo is not installed. |
FileNotFoundError |
If the source file could not be found. |
Source code in geemap/common.py
def image_to_cog(source, dst_path=None, profile="deflate", **kwargs):
"""Converts an image to a COG file.
Args:
source (str): A dataset path, URL or rasterio.io.DatasetReader object.
dst_path (str, optional): An output dataset path or or PathLike object. Defaults to None.
profile (str, optional): COG profile. More at https://cogeotiff.github.io/rio-cogeo/profile. Defaults to "deflate".
Raises:
ImportError: If rio-cogeo is not installed.
FileNotFoundError: If the source file could not be found.
"""
try:
from rio_cogeo.cogeo import cog_translate
from rio_cogeo.profiles import cog_profiles
except ImportError:
raise ImportError(
"The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
)
if not source.startswith("http"):
source = check_file_path(source)
if not os.path.exists(source):
raise FileNotFoundError("The provided input file could not be found.")
if dst_path is None:
if not source.startswith("http"):
dst_path = os.path.splitext(source)[0] + "_cog.tif"
else:
dst_path = temp_file_path(extension=".tif")
dst_path = check_file_path(dst_path)
dst_profile = cog_profiles.get(profile)
cog_translate(source, dst_path, dst_profile, **kwargs)
image_to_numpy(image)
¶
Converts an image to a numpy array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
A dataset path, URL or rasterio.io.DatasetReader object. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the provided file could not be found. |
Returns:
| Type | Description |
|---|---|
np.array |
A numpy array. |
Source code in geemap/common.py
def image_to_numpy(image):
"""Converts an image to a numpy array.
Args:
image (str): A dataset path, URL or rasterio.io.DatasetReader object.
Raises:
FileNotFoundError: If the provided file could not be found.
Returns:
np.array: A numpy array.
"""
import rasterio
from osgeo import gdal
gdal.UseExceptions()
gdal.PushErrorHandler("CPLQuietErrorHandler")
if not os.path.exists(image):
raise FileNotFoundError("The provided input file could not be found.")
with rasterio.open(image, "r") as ds:
arr = ds.read() # read all raster values
return arr
image_value_list(img, region=None, scale=None, return_hist=False, **kwargs)
¶
Get the unique values of an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
ee.Image |
The image to calculate the unique values. |
required |
region |
ee.Geometry | ee.FeatureCollection |
The region over which to reduce data. Defaults to the footprint of the image's first band. |
None |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
return_hist |
bool |
If True, return a histogram of the values. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
ee.List | ee.Dictionary |
A list of unique values or a dictionary containing a list of unique values and a histogram. |
Source code in geemap/common.py
def image_value_list(img, region=None, scale=None, return_hist=False, **kwargs):
"""Get the unique values of an image.
Args:
img (ee.Image): The image to calculate the unique values.
region (ee.Geometry | ee.FeatureCollection, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
return_hist (bool, optional): If True, return a histogram of the values. Defaults to False.
Returns:
ee.List | ee.Dictionary: A list of unique values or a dictionary containing a list of unique values and a histogram.
"""
if region is None:
geom = img.geometry().bounds()
region = ee.FeatureCollection([ee.Feature(geom)])
elif isinstance(region, ee.Geometry):
region = ee.FeatureCollection([ee.Feature(region)])
elif isinstance(region, ee.FeatureCollection):
pass
else:
raise ValueError("region must be an ee.Geometry or ee.FeatureCollection")
if scale is None:
scale = img.select(0).projection().nominalScale().multiply(10)
reducer = ee.Reducer.frequencyHistogram()
kwargs["scale"] = scale
kwargs["reducer"] = reducer
kwargs["collection"] = region
result = img.reduceRegions(**kwargs)
hist = ee.Dictionary(result.first().get("histogram"))
if return_hist:
return hist
else:
return hist.keys()
in_colab_shell()
¶
Tests if the code is being executed within Google Colab.
Source code in geemap/common.py
def in_colab_shell():
"""Tests if the code is being executed within Google Colab."""
import sys
if "google.colab" in sys.modules:
return True
else:
return False
install_from_github(url)
¶
Install a package from a GitHub repository.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL of the GitHub repository. |
required |
Source code in geemap/common.py
def install_from_github(url):
"""Install a package from a GitHub repository.
Args:
url (str): The URL of the GitHub repository.
"""
try:
download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
if not os.path.exists(download_dir):
os.makedirs(download_dir)
repo_name = os.path.basename(url)
zip_url = os.path.join(url, "archive/master.zip")
filename = repo_name + "-master.zip"
download_from_url(
url=zip_url, out_file_name=filename, out_dir=download_dir, unzip=True
)
pkg_dir = os.path.join(download_dir, repo_name + "-master")
pkg_name = os.path.basename(url)
work_dir = os.getcwd()
os.chdir(pkg_dir)
print(f"Installing {pkg_name}...")
cmd = "pip install ."
os.system(cmd)
os.chdir(work_dir)
print(f"{pkg_name} has been installed successfully.")
# print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")
except Exception as e:
print(e)
install_package(package)
¶
Install a Python package.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
package |
str | list |
The package name or a GitHub URL or a list of package names or GitHub URLs. |
required |
Source code in geemap/common.py
def install_package(package):
"""Install a Python package.
Args:
package (str | list): The package name or a GitHub URL or a list of package names or GitHub URLs.
"""
import subprocess
if isinstance(package, str):
packages = [package]
for package in packages:
if package.startswith("https"):
package = f"git+{package}"
# Execute pip install command and show output in real-time
command = f"pip install {package}"
process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)
# Print output in real-time
while True:
output = process.stdout.readline()
if output == b"" and process.poll() is not None:
break
if output:
print(output.decode("utf-8").strip())
# Wait for process to complete
process.wait()
is_arcpy()
¶
Check if arcpy is available.
Returns:
| Type | Description |
|---|---|
book |
True if arcpy is available, False otherwise. |
Source code in geemap/common.py
def is_arcpy():
"""Check if arcpy is available.
Returns:
book: True if arcpy is available, False otherwise.
"""
import sys
if "arcpy" in sys.modules:
return True
else:
return False
is_drive_mounted()
¶
Checks whether Google Drive is mounted in Google Colab.
Returns:
| Type | Description |
|---|---|
bool |
Returns True if Google Drive is mounted, False otherwise. |
Source code in geemap/common.py
def is_drive_mounted():
"""Checks whether Google Drive is mounted in Google Colab.
Returns:
bool: Returns True if Google Drive is mounted, False otherwise.
"""
drive_path = "/content/drive/My Drive"
if os.path.exists(drive_path):
return True
else:
return False
is_latlon_valid(location)
¶
Checks whether a pair of coordinates is valid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
A pair of latlon coordinates separated by comma or space. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Returns True if valid. |
Source code in geemap/common.py
def is_latlon_valid(location):
"""Checks whether a pair of coordinates is valid.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns True if valid.
"""
latlon = []
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return False
try:
lat, lon = float(latlon[0]), float(latlon[1])
if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
return True
else:
return False
except Exception as e:
print(e)
return False
is_on_aws()
¶
Check if the current notebook is running on AWS.
Returns:
| Type | Description |
|---|---|
bool |
True if the notebook is running on AWS. |
Source code in geemap/common.py
def is_on_aws():
"""Check if the current notebook is running on AWS.
Returns:
bool: True if the notebook is running on AWS.
"""
import psutil
output = psutil.Process().parent().cmdline()
on_aws = False
for item in output:
if item.endswith(".aws") or "ec2-user" in item:
on_aws = True
return on_aws
is_studio_lab()
¶
Check if the current notebook is running on Studio Lab.
Returns:
| Type | Description |
|---|---|
bool |
True if the notebook is running on Studio Lab. |
Source code in geemap/common.py
def is_studio_lab():
"""Check if the current notebook is running on Studio Lab.
Returns:
bool: True if the notebook is running on Studio Lab.
"""
import psutil
output = psutil.Process().parent().cmdline()
on_studio_lab = False
for item in output:
if "studiolab/bin" in item:
on_studio_lab = True
return on_studio_lab
is_tool(name)
¶
Check whether name is on PATH and marked as executable.
Source code in geemap/common.py
def is_tool(name):
"""Check whether `name` is on PATH and marked as executable."""
# from shutil import which
return shutil.which(name) is not None
jpg_to_gif(in_dir, out_gif, fps=10, loop=0)
¶
Convert a list of jpg images to gif.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_dir |
str |
The input directory containing jpg images. |
required |
out_gif |
str |
The output file path to the gif. |
required |
fps |
int |
Frames per second. Defaults to 10. |
10 |
loop |
bool |
controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0. |
0 |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
No jpg images could be found. |
Source code in geemap/common.py
def jpg_to_gif(in_dir, out_gif, fps=10, loop=0):
"""Convert a list of jpg images to gif.
Args:
in_dir (str): The input directory containing jpg images.
out_gif (str): The output file path to the gif.
fps (int, optional): Frames per second. Defaults to 10.
loop (bool, optional): controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.
Raises:
FileNotFoundError: No jpg images could be found.
"""
import glob
from PIL import Image
if not out_gif.endswith(".gif"):
raise ValueError("The out_gif must be a gif file.")
out_gif = os.path.abspath(out_gif)
out_dir = os.path.dirname(out_gif)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Create the frames
frames = []
imgs = list(glob.glob(os.path.join(in_dir, "*.jpg")))
imgs.sort()
if len(imgs) == 0:
raise FileNotFoundError(f"No jpg could be found in {in_dir}.")
for i in imgs:
new_frame = Image.open(i)
frames.append(new_frame)
# Save into a GIF file that loops forever
frames[0].save(
out_gif,
format="GIF",
append_images=frames[1:],
save_all=True,
duration=1000 / fps,
loop=loop,
)
jrc_hist_monthly_history(collection=None, region=None, start_date='1984-03-16', end_date=None, start_month=1, end_month=12, scale=None, frequency='year', reducer='mean', denominator=10000.0, x_label=None, y_label=None, title=None, width=None, height=None, layout_args={}, return_df=False, **kwargs)
¶
Create a JRC monthly history plot.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
ee.ImageCollection |
The image collection of JRC surface water monthly history. Default to ee.ImageCollection('JRC/GSW1_4/MonthlyHistory') |
None |
region |
ee.Geometry | ee.FeatureCollection |
The region to plot. Default to None. |
None |
start_date |
str |
The start date of the plot. Default to '1984-03-16'. |
'1984-03-16' |
end_date |
str |
The end date of the plot. Default to the current date. |
None |
start_month |
int |
The start month of the plot. Default to 1. |
1 |
end_month |
int |
The end month of the plot. Default to 12. |
12 |
scale |
float |
The scale to compute the statistics. Default to None. |
None |
frequency |
str |
The frequency of the plot. Can be either 'year' or 'month', Default to 'year'. |
'year' |
reducer |
str |
The reducer to compute the statistics. Can be either 'mean', 'min', 'max', 'median', etc. Default to 'mean'. |
'mean' |
denominator |
int |
The denominator to convert area from square meters to other units. Default to 1e4, converting to hectares. |
10000.0 |
x_label |
str |
Label for the x axis. Defaults to None. |
None |
y_label |
str |
Label for the y axis. Defaults to None. |
None |
title |
str |
Title for the plot. Defaults to None. |
None |
width |
int |
Width of the plot in pixels. Defaults to None. |
None |
height |
int |
Height of the plot in pixels. Defaults to 500. |
None |
layout_args |
dict |
Layout arguments for the plot to be passed to fig.update_layout(), |
{} |
return_df |
bool |
Whether to return the dataframe of the plot. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
Pandas dataframe of the plot. |
Source code in geemap/common.py
def jrc_hist_monthly_history(
collection=None,
region=None,
start_date="1984-03-16",
end_date=None,
start_month=1,
end_month=12,
scale=None,
frequency="year",
reducer="mean",
denominator=1e4,
x_label=None,
y_label=None,
title=None,
width=None,
height=None,
layout_args={},
return_df=False,
**kwargs,
):
"""Create a JRC monthly history plot.
Args:
collection (ee.ImageCollection, optional): The image collection of JRC surface water monthly history.
Default to ee.ImageCollection('JRC/GSW1_4/MonthlyHistory')
region (ee.Geometry | ee.FeatureCollection, optional): The region to plot. Default to None.
start_date (str, optional): The start date of the plot. Default to '1984-03-16'.
end_date (str, optional): The end date of the plot. Default to the current date.
start_month (int, optional): The start month of the plot. Default to 1.
end_month (int, optional): The end month of the plot. Default to 12.
scale (float, optional): The scale to compute the statistics. Default to None.
frequency (str, optional): The frequency of the plot. Can be either 'year' or 'month', Default to 'year'.
reducer (str, optional): The reducer to compute the statistics. Can be either 'mean', 'min', 'max', 'median', etc. Default to 'mean'.
denominator (int, optional): The denominator to convert area from square meters to other units. Default to 1e4, converting to hectares.
x_label (str, optional): Label for the x axis. Defaults to None.
y_label (str, optional): Label for the y axis. Defaults to None.
title (str, optional): Title for the plot. Defaults to None.
width (int, optional): Width of the plot in pixels. Defaults to None.
height (int, optional): Height of the plot in pixels. Defaults to 500.
layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
return_df (bool, optional): Whether to return the dataframe of the plot. Defaults to False.
Returns:
pd.DataFrame: Pandas dataframe of the plot.
"""
from datetime import date
import pandas as pd
import plotly.express as px
if end_date is None:
end_date = date.today().strftime("%Y-%m-%d")
if collection is None:
collection = ee.ImageCollection("JRC/GSW1_4/MonthlyHistory")
if frequency not in ["year", "month"]:
raise ValueError("frequency must be 'year' or 'month'.")
images = (
collection.filterDate(start_date, end_date)
.filter(ee.Filter.calendarRange(start_month, end_month, "month"))
.map(lambda img: img.eq(2).selfMask())
)
def cal_area(img):
pixel_area = img.multiply(ee.Image.pixelArea()).divide(denominator)
img_area = pixel_area.reduceRegion(
**{
"geometry": region,
"reducer": ee.Reducer.sum(),
"scale": scale,
"maxPixels": 1e12,
"bestEffort": True,
}
)
return img.set({"area": img_area})
areas = images.map(cal_area)
stats = areas.aggregate_array("area").getInfo()
values = [item["water"] for item in stats]
labels = areas.aggregate_array("system:index").getInfo()
months = [label.split("_")[1] for label in labels]
if frequency == "month":
area_df = pd.DataFrame({"Month": labels, "Area": values, "month": months})
else:
dates = [d[:4] for d in labels]
data_dict = {"Date": labels, "Year": dates, "Area": values}
df = pd.DataFrame(data_dict)
result = df.groupby("Year").agg(reducer)
area_df = pd.DataFrame({"Year": result.index, "Area": result["Area"]})
area_df = area_df.reset_index(drop=True)
if return_df:
return area_df
else:
labels = {}
if x_label is not None:
labels[frequency.title()] = x_label
if y_label is not None:
labels["Area"] = y_label
fig = px.bar(
area_df,
x=frequency.title(),
y="Area",
labels=labels,
title=title,
width=width,
height=height,
**kwargs,
)
fig.update_layout(**layout_args)
return fig
jslink_slider_label(slider, label)
¶
Link a slider and a label.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
slider |
ipywidgets.IntSlider | ipywidgets.FloatSlider |
The slider. |
required |
label |
ipywidgets.Label |
The label. |
required |
Source code in geemap/common.py
def jslink_slider_label(slider, label):
"""Link a slider and a label.
Args:
slider (ipywidgets.IntSlider | ipywidgets.FloatSlider): The slider.
label (ipywidgets.Label): The label.
"""
def update_label(change):
if change["name"]:
label.value = str(change["new"])
slider.observe(update_label, "value")
kml_to_ee(in_kml, **kwargs)
¶
Converts a KML to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def kml_to_ee(in_kml, **kwargs):
"""Converts a KML to ee.FeatureCollection.
Args:
in_kml (str): The file path to the input KML.
Raises:
FileNotFoundError: The input KML could not be found.
Returns:
object: ee.FeatureCollection
"""
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
out_json = os.path.join(os.getcwd(), "tmp.geojson")
check_package(name="geopandas", URL="https://geopandas.org")
kml_to_geojson(in_kml, out_json, **kwargs)
ee_object = geojson_to_ee(out_json)
os.remove(out_json)
return ee_object
kml_to_geojson(in_kml, out_geojson=None, **kwargs)
¶
Converts a KML to GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
out_geojson |
str |
The file path to the output GeoJSON. Defaults to None. |
None |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
TypeError |
The output must be a GeoJSON. |
Source code in geemap/common.py
def kml_to_geojson(in_kml, out_geojson=None, **kwargs):
"""Converts a KML to GeoJSON.
Args:
in_kml (str): The file path to the input KML.
out_geojson (str): The file path to the output GeoJSON. Defaults to None.
Raises:
FileNotFoundError: The input KML could not be found.
TypeError: The output must be a GeoJSON.
"""
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
if out_geojson is not None:
out_geojson = os.path.abspath(out_geojson)
ext = os.path.splitext(out_geojson)[1].lower()
if ext not in [".json", ".geojson"]:
raise TypeError("The output file must be a GeoJSON.")
out_dir = os.path.dirname(out_geojson)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
import fiona
# print(fiona.supported_drivers)
fiona.drvsupport.supported_drivers["KML"] = "rw"
gdf = gpd.read_file(in_kml, driver="KML", **kwargs)
if out_geojson is not None:
gdf.to_file(out_geojson, driver="GeoJSON", **kwargs)
else:
return gdf.__geo_interface__
kml_to_shp(in_kml, out_shp, **kwargs)
¶
Converts a KML to shapefile.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kml |
str |
The file path to the input KML. |
required |
out_shp |
str |
The file path to the output shapefile. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KML could not be found. |
TypeError |
The output must be a shapefile. |
Source code in geemap/common.py
def kml_to_shp(in_kml, out_shp, **kwargs):
"""Converts a KML to shapefile.
Args:
in_kml (str): The file path to the input KML.
out_shp (str): The file path to the output shapefile.
Raises:
FileNotFoundError: The input KML could not be found.
TypeError: The output must be a shapefile.
"""
warnings.filterwarnings("ignore")
in_kml = os.path.abspath(in_kml)
if not os.path.exists(in_kml):
raise FileNotFoundError("The input KML could not be found.")
out_shp = os.path.abspath(out_shp)
if not out_shp.endswith(".shp"):
raise TypeError("The output must be a shapefile.")
out_dir = os.path.dirname(out_shp)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
import fiona
# import fiona
# print(fiona.supported_drivers)
fiona.drvsupport.supported_drivers["KML"] = "rw"
df = gpd.read_file(in_kml, driver="KML", **kwargs)
df.to_file(out_shp, **kwargs)
kmz_to_ee(in_kmz, **kwargs)
¶
Converts a KMZ to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_kmz |
str |
The file path to the input KMZ. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The input KMZ could not be found. |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def kmz_to_ee(in_kmz, **kwargs):
"""Converts a KMZ to ee.FeatureCollection.
Args:
in_kmz (str): The file path to the input KMZ.
Raises:
FileNotFoundError: The input KMZ could not be found.
Returns:
object: ee.FeatureCollection
"""
in_kmz = os.path.abspath(in_kmz)
if not os.path.exists(in_kmz):
raise FileNotFoundError("The input KMZ could not be found.")
out_dir = os.path.dirname(in_kmz)
out_kml = os.path.join(out_dir, "doc.kml")
with zipfile.ZipFile(in_kmz, "r") as zip_ref:
zip_ref.extractall(out_dir)
fc = kml_to_ee(out_kml, **kwargs)
os.remove(out_kml)
return fc
landsat_scaling(image, thermal_bands=True, apply_fmask=False)
¶
Apply scaling factors to a Landsat image. See an example at https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The input Landsat image. |
required |
thermal_bands |
bool |
Whether to apply scaling to thermal bands. Defaults to True. |
True |
apply_fmask |
bool |
Whether to apply Fmask cloud mask. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
ee.Image |
The scaled Landsat image. |
Source code in geemap/common.py
def landsat_scaling(image, thermal_bands=True, apply_fmask=False):
"""Apply scaling factors to a Landsat image. See an example at
https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2
Args:
image (ee.Image): The input Landsat image.
thermal_bands (bool, optional): Whether to apply scaling to thermal bands. Defaults to True.
apply_fmask (bool, optional): Whether to apply Fmask cloud mask. Defaults to False.
Returns:
ee.Image: The scaled Landsat image.
"""
# Apply the scaling factors to the appropriate bands.
opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
if thermal_bands:
thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149)
if apply_fmask:
# Replace the original bands with the scaled ones and apply the masks.
# Bit 0 - Fill
# Bit 1 - Dilated Cloud
# Bit 2 - Cirrus
# Bit 3 - Cloud
# Bit 4 - Cloud Shadow
qaMask = image.select("QA_PIXEL").bitwiseAnd(int("11111", 2)).eq(0)
if thermal_bands:
return (
image.addBands(thermalBands, None, True)
.addBands(opticalBands, None, True)
.updateMask(qaMask)
)
else:
return image.addBands(opticalBands, None, True).updateMask(qaMask)
else:
if thermal_bands:
return image.addBands(thermalBands, None, True).addBands(
opticalBands, None, True
)
else:
return image.addBands(opticalBands, None, True)
latitude_grid(step=1.0, west=-180, east=180, south=-85, north=85)
¶
Create a latitude grid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
float |
The step size in degrees. Defaults to 1.0. |
1.0 |
west |
int |
The west boundary in degrees. Defaults to -180. |
-180 |
east |
int |
The east boundary in degrees. Defaults to 180. |
180 |
south |
int |
The south boundary in degrees. Defaults to -85. |
-85 |
north |
int |
The north boundary in degrees. Defaults to 85. |
85 |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
A feature collection of latitude grids. |
Source code in geemap/common.py
def latitude_grid(step=1.0, west=-180, east=180, south=-85, north=85):
"""Create a latitude grid.
Args:
step (float, optional): The step size in degrees. Defaults to 1.0.
west (int, optional): The west boundary in degrees. Defaults to -180.
east (int, optional): The east boundary in degrees. Defaults to 180.
south (int, optional): The south boundary in degrees. Defaults to -85.
north (int, optional): The north boundary in degrees. Defaults to 85.
Returns:
ee.FeatureCollection: A feature collection of latitude grids.
"""
values = ee.List.sequence(south, north - step, step)
def create_feature(lat):
return ee.Feature(
ee.Geometry.BBox(west, lat, east, ee.Number(lat).add(step))
).set(
{
"south": lat,
"west": west,
"north": ee.Number(lat).add(step),
"east": east,
}
)
features = ee.FeatureCollection(values.map(create_feature))
return features
latlon_from_text(location)
¶
Extracts latlon from text.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
location |
str |
A pair of latlon coordinates separated by comma or space. |
required |
Returns:
| Type | Description |
|---|---|
bool |
Returns (lat, lon) if valid. |
Source code in geemap/common.py
def latlon_from_text(location):
"""Extracts latlon from text.
Args:
location (str): A pair of latlon coordinates separated by comma or space.
Returns:
bool: Returns (lat, lon) if valid.
"""
latlon = []
try:
if "," in location:
latlon = [float(x) for x in location.split(",")]
elif " " in location:
latlon = [float(x) for x in location.split(" ")]
else:
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return None
lat, lon = latlon[0], latlon[1]
if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
return lat, lon
else:
return None
except Exception as e:
print(e)
print(
"The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
)
return None
latlon_grid(lat_step=1.0, lon_step=1.0, west=-180, east=180, south=-85, north=85)
¶
Create a rectangular grid of latitude and longitude.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lat_step |
float |
The step size in degrees. Defaults to 1.0. |
1.0 |
lon_step |
float |
The step size in degrees. Defaults to 1.0. |
1.0 |
west |
int |
The west boundary in degrees. Defaults to -180. |
-180 |
east |
int |
The east boundary in degrees. Defaults to 180. |
180 |
south |
int |
The south boundary in degrees. Defaults to -85. |
-85 |
north |
int |
The north boundary in degrees. Defaults to 85. |
85 |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
A feature collection of latitude and longitude grids. |
Source code in geemap/common.py
def latlon_grid(lat_step=1.0, lon_step=1.0, west=-180, east=180, south=-85, north=85):
"""Create a rectangular grid of latitude and longitude.
Args:
lat_step (float, optional): The step size in degrees. Defaults to 1.0.
lon_step (float, optional): The step size in degrees. Defaults to 1.0.
west (int, optional): The west boundary in degrees. Defaults to -180.
east (int, optional): The east boundary in degrees. Defaults to 180.
south (int, optional): The south boundary in degrees. Defaults to -85.
north (int, optional): The north boundary in degrees. Defaults to 85.
Returns:
ee.FeatureCollection: A feature collection of latitude and longitude grids.
"""
longitudes = ee.List.sequence(west, east - lon_step, lon_step)
latitudes = ee.List.sequence(south, north - lat_step, lat_step)
def create_lat_feature(lat):
def create_lon_features(lon):
return ee.Feature(
ee.Geometry.BBox(
lon, lat, ee.Number(lon).add(lon_step), ee.Number(lat).add(lat_step)
)
).set(
{
"south": lat,
"west": lon,
"north": ee.Number(lat).add(lat_step),
"east": ee.Number(lon).add(lon_step),
}
)
return ee.FeatureCollection(longitudes.map(create_lon_features))
return ee.FeatureCollection(latitudes.map(create_lat_feature)).flatten()
legend_from_ee(ee_class_table)
¶
Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_class_table |
str |
An Earth Engine class table with triple quotes. |
required |
Returns:
| Type | Description |
|---|---|
dict |
Returns a legend dictionary that can be used to create a legend. |
Source code in geemap/common.py
def legend_from_ee(ee_class_table):
"""Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page
such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Args:
ee_class_table (str): An Earth Engine class table with triple quotes.
Returns:
dict: Returns a legend dictionary that can be used to create a legend.
"""
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split("\n")[1:]
if lines[0] == "Value\tColor\tDescription":
lines = lines[1:]
legend_dict = {}
for _, line in enumerate(lines):
items = line.split("\t")
items = [item.strip() for item in items]
color = items[1]
key = items[0] + " " + items[2]
legend_dict[key] = color
return legend_dict
except Exception as e:
print(e)
list_vars(var_type=None)
¶
Lists all defined avariables.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
var_type |
object |
The object type of variables to list. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
list |
A list of all defined variables. |
Source code in geemap/common.py
def list_vars(var_type=None):
"""Lists all defined avariables.
Args:
var_type (object, optional): The object type of variables to list. Defaults to None.
Returns:
list: A list of all defined variables.
"""
result = []
for var in globals():
reserved_vars = [
"In",
"Out",
"get_ipython",
"exit",
"quit",
"json",
"getsizeof",
"NamespaceMagics",
"np",
"var_dic_list",
"list_vars",
"ee",
"geemap",
]
if (not var.startswith("_")) and (var not in reserved_vars):
if var_type is not None and isinstance(eval(var), var_type):
result.append(var)
elif var_type is None:
result.append(var)
return result
lnglat_to_meters(longitude, latitude)
¶
coordinate conversion between lat/lon in decimal degrees to web mercator
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
longitude |
float |
The longitude. |
required |
latitude |
float |
The latitude. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (x, y) in meters. |
Source code in geemap/common.py
def lnglat_to_meters(longitude, latitude):
"""coordinate conversion between lat/lon in decimal degrees to web mercator
Args:
longitude (float): The longitude.
latitude (float): The latitude.
Returns:
tuple: A tuple of (x, y) in meters.
"""
import numpy as np
origin_shift = np.pi * 6378137
easting = longitude * origin_shift / 180.0
northing = np.log(np.tan((90 + latitude) * np.pi / 360.0)) * origin_shift / np.pi
if np.isnan(easting):
if longitude > 0:
easting = 20026376
else:
easting = -20026376
if np.isnan(northing):
if latitude > 0:
northing = 20048966
else:
northing = -20048966
return (easting, northing)
load_GeoTIFF(URL)
¶
Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
URL |
str |
The Cloud Storage URL of the GeoTIFF to load. |
required |
Returns:
| Type | Description |
|---|---|
ee.Image |
an Earth Engine image. |
Source code in geemap/common.py
def load_GeoTIFF(URL):
"""Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats:
Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Args:
URL (str): The Cloud Storage URL of the GeoTIFF to load.
Returns:
ee.Image: an Earth Engine image.
"""
uri = URL.strip()
if uri.startswith("http"):
uri = get_direct_url(uri)
if uri.startswith("https://storage.googleapis.com/"):
uri = uri.replace("https://storage.googleapis.com/", "gs://")
elif uri.startswith("https://storage.cloud.google.com/"):
uri = uri.replace("https://storage.cloud.google.com/", "gs://")
if not uri.startswith("gs://"):
raise Exception(
f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
)
if not uri.lower().endswith(".tif"):
raise Exception(
f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
)
cloud_image = ee.Image.loadGeoTIFF(uri)
return cloud_image
load_GeoTIFFs(URLs)
¶
Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
URLs |
list |
A list of Cloud Storage URL of the GeoTIFF to load. |
required |
Returns:
| Type | Description |
|---|---|
ee.ImageCollection |
An Earth Engine ImageCollection. |
Source code in geemap/common.py
def load_GeoTIFFs(URLs):
"""Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats:
Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF
Args:
URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.
Returns:
ee.ImageCollection: An Earth Engine ImageCollection.
"""
if not isinstance(URLs, list):
raise Exception("The URLs argument must be a list.")
URIs = []
for URL in URLs:
uri = URL.strip()
if uri.startswith("http"):
uri = get_direct_url(uri)
if uri.startswith("https://storage.googleapis.com/"):
uri = uri.replace("https://storage.googleapis.com/", "gs://")
elif uri.startswith("https://storage.cloud.google.com/"):
uri = uri.replace("https://storage.cloud.google.com/", "gs://")
if not uri.startswith("gs://"):
raise Exception(
f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
)
if not uri.lower().endswith(".tif"):
raise Exception(
f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
)
URIs.append(uri)
URIs = ee.List(URIs)
collection = URIs.map(lambda uri: ee.Image.loadGeoTIFF(uri))
return ee.ImageCollection(collection)
local_tile_bands(source)
¶
Get band names from COG.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str | TileClient |
A local COG file path or TileClient |
required |
Returns:
| Type | Description |
|---|---|
list |
A list of band names. |
Source code in geemap/common.py
def local_tile_bands(source):
"""Get band names from COG.
Args:
source (str | TileClient): A local COG file path or TileClient
Returns:
list: A list of band names.
"""
check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
from localtileserver import TileClient
if isinstance(source, str):
tile_client = TileClient(source)
elif isinstance(source, TileClient):
tile_client = source
else:
raise ValueError("source must be a string or TileClient object.")
return tile_client.band_names
local_tile_pixel_value(lon, lat, tile_client, verbose=True, **kwargs)
¶
Get pixel value from COG.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lon |
float |
Longitude of the pixel. |
required |
lat |
float |
Latitude of the pixel. |
required |
url |
str |
HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif' |
required |
bidx |
str |
Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None. |
required |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
required |
verbose |
bool |
Print status messages. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
PointData |
rio-tiler point data. |
Source code in geemap/common.py
def local_tile_pixel_value(
lon,
lat,
tile_client,
verbose=True,
**kwargs,
):
"""Get pixel value from COG.
Args:
lon (float): Longitude of the pixel.
lat (float): Latitude of the pixel.
url (str): HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'
bidx (str, optional): Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
verbose (bool, optional): Print status messages. Defaults to True.
Returns:
PointData: rio-tiler point data.
"""
return tile_client.point(lon, lat, coord_crs="EPSG:4326", **kwargs)
local_tile_vmin_vmax(source, bands=None, **kwargs)
¶
Get vmin and vmax from COG.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str | TileClient |
A local COG file path or TileClient object. |
required |
bands |
str | list |
A list of band names. Defaults to None. |
None |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If source is not a TileClient object or a local COG file path. |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of vmin and vmax. |
Source code in geemap/common.py
def local_tile_vmin_vmax(
source,
bands=None,
**kwargs,
):
"""Get vmin and vmax from COG.
Args:
source (str | TileClient): A local COG file path or TileClient object.
bands (str | list, optional): A list of band names. Defaults to None.
Raises:
ValueError: If source is not a TileClient object or a local COG file path.
Returns:
tuple: A tuple of vmin and vmax.
"""
check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
from localtileserver import TileClient
if isinstance(source, str):
tile_client = TileClient(source)
elif isinstance(source, TileClient):
tile_client = source
else:
raise ValueError("source must be a string or TileClient object.")
bandnames = tile_client.band_names
stats = tile_client.reader.statistics()
if isinstance(bands, str):
bands = [bands]
elif isinstance(bands, list):
pass
elif bands is None:
bands = bandnames
if all(b in bandnames for b in bands):
vmin = min([stats[b]["min"] for b in bands])
vmax = max([stats[b]["max"] for b in bands])
else:
vmin = min([stats[b]["min"] for b in bandnames])
vmax = max([stats[b]["max"] for b in bandnames])
return vmin, vmax
longitude_grid(step=1.0, west=-180, east=180, south=-85, north=85)
¶
Create a longitude grid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
float |
The step size in degrees. Defaults to 1.0. |
1.0 |
west |
int |
The west boundary in degrees. Defaults to -180. |
-180 |
east |
int |
The east boundary in degrees. Defaults to 180. |
180 |
south |
int |
The south boundary in degrees. Defaults to -85. |
-85 |
north |
int |
The north boundary in degrees. Defaults to 85. |
85 |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
A feature collection of longitude grids. |
Source code in geemap/common.py
def longitude_grid(step=1.0, west=-180, east=180, south=-85, north=85):
"""Create a longitude grid.
Args:
step (float, optional): The step size in degrees. Defaults to 1.0.
west (int, optional): The west boundary in degrees. Defaults to -180.
east (int, optional): The east boundary in degrees. Defaults to 180.
south (int, optional): The south boundary in degrees. Defaults to -85.
north (int, optional): The north boundary in degrees. Defaults to 85.
Returns:
ee.FeatureCollection: A feature collection of longitude grids.
"""
values = ee.List.sequence(west, east - step, step)
def create_feature(lon):
return ee.Feature(
ee.Geometry.BBox(lon, south, ee.Number(lon).add(step), north)
).set(
{
"south": south,
"west": lon,
"north": north,
"east": ee.Number(lon).add(step),
}
)
features = ee.FeatureCollection(values.map(create_feature))
return features
meters_to_lnglat(x, y)
¶
coordinate conversion between web mercator to lat/lon in decimal degrees
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
float |
The x coordinate. |
required |
y |
float |
The y coordinate. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple of (longitude, latitude) in decimal degrees. |
Source code in geemap/common.py
def meters_to_lnglat(x, y):
"""coordinate conversion between web mercator to lat/lon in decimal degrees
Args:
x (float): The x coordinate.
y (float): The y coordinate.
Returns:
tuple: A tuple of (longitude, latitude) in decimal degrees.
"""
import numpy as np
origin_shift = np.pi * 6378137
longitude = (x / origin_shift) * 180.0
latitude = (y / origin_shift) * 180.0
latitude = (
180 / np.pi * (2 * np.arctan(np.exp(latitude * np.pi / 180.0)) - np.pi / 2.0)
)
return (longitude, latitude)
minimum_bounding_box(geojson)
¶
Gets the minimum bounding box for a geojson polygon.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geojson |
dict |
A geojson dictionary. |
required |
Returns:
| Type | Description |
|---|---|
tuple |
Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)). |
Source code in geemap/common.py
def minimum_bounding_box(geojson):
"""Gets the minimum bounding box for a geojson polygon.
Args:
geojson (dict): A geojson dictionary.
Returns:
tuple: Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)).
"""
coordinates = []
try:
if "geometry" in geojson.keys():
coordinates = geojson["geometry"]["coordinates"][0]
else:
coordinates = geojson["coordinates"][0]
lower_left = min([x[1] for x in coordinates]), min(
[x[0] for x in coordinates]
) # (lat, lon)
upper_right = max([x[1] for x in coordinates]), max(
[x[0] for x in coordinates]
) # (lat, lon)
bounds = (lower_left, upper_right)
return bounds
except Exception as e:
raise Exception(e)
mosaic(images, output, merge_args={}, verbose=True, **kwargs)
¶
Mosaics a list of images into a single image. Inspired by https://bit.ly/3A6roDK.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
images |
str | list |
An input directory containing images or a list of images. |
required |
output |
str |
The output image filepath. |
required |
merge_args |
dict |
A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}. |
{} |
verbose |
bool |
Whether to print progress. Defaults to True. |
True |
Source code in geemap/common.py
def mosaic(images, output, merge_args={}, verbose=True, **kwargs):
"""Mosaics a list of images into a single image. Inspired by https://bit.ly/3A6roDK.
Args:
images (str | list): An input directory containing images or a list of images.
output (str): The output image filepath.
merge_args (dict, optional): A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.
verbose (bool, optional): Whether to print progress. Defaults to True.
"""
from rasterio.merge import merge
import rasterio as rio
from pathlib import Path
output = os.path.abspath(output)
if isinstance(images, str):
path = Path(images)
raster_files = list(path.iterdir())
elif isinstance(images, list):
raster_files = images
else:
raise ValueError("images must be a list of raster files.")
raster_to_mosiac = []
if not os.path.exists(os.path.dirname(output)):
os.makedirs(os.path.dirname(output))
for index, p in enumerate(raster_files):
if verbose:
print(f"Reading {index+1}/{len(raster_files)}: {os.path.basename(p)}")
raster = rio.open(p, **kwargs)
raster_to_mosiac.append(raster)
if verbose:
print("Merging rasters...")
arr, transform = merge(raster_to_mosiac, **merge_args)
output_meta = raster.meta.copy()
output_meta.update(
{
"driver": "GTiff",
"height": arr.shape[1],
"width": arr.shape[2],
"transform": transform,
}
)
with rio.open(output, "w", **output_meta) as m:
m.write(arr)
netcdf_tile_layer(filename, variables=None, colormap=None, vmin=None, vmax=None, nodata=None, port='default', debug=False, attribution=None, tile_format='ipyleaflet', layer_name='NetCDF layer', return_client=False, shift_lon=True, lat='lat', lon='lon', **kwargs)
¶
Generate an ipyleaflet/folium TileLayer from a netCDF file. If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer), try adding to following two lines to the beginning of the notebook if the raster does not render properly.
1 2 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
File path or HTTP URL to the netCDF file. |
required |
variables |
int |
The variable/band names to extract data from the netCDF file. Defaults to None. If None, all variables will be extracted. |
None |
port |
str |
The port to use for the server. Defaults to "default". |
'default' |
colormap |
str |
The name of the colormap from |
None |
vmin |
float |
The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None. |
None |
vmax |
float |
The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None. |
None |
nodata |
float |
The value from the band to use to interpret as not valid data. Defaults to None. |
None |
debug |
bool |
If True, the server will be started in debug mode. Defaults to False. |
False |
projection |
str |
The projection of the GeoTIFF. Defaults to "EPSG:3857". |
required |
attribution |
str |
Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None. |
None |
tile_format |
str |
The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
layer_name |
str |
The layer name to use. Defaults to "NetCDF layer". |
'NetCDF layer' |
return_client |
bool |
If True, the tile client will be returned. Defaults to False. |
False |
shift_lon |
bool |
Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True. |
True |
lat |
str |
Name of the latitude variable. Defaults to 'lat'. |
'lat' |
lon |
str |
Name of the longitude variable. Defaults to 'lon'. |
'lon' |
Returns:
| Type | Description |
|---|---|
ipyleaflet.TileLayer | folium.TileLayer |
An ipyleaflet.TileLayer or folium.TileLayer. |
Source code in geemap/common.py
def netcdf_tile_layer(
filename,
variables=None,
colormap=None,
vmin=None,
vmax=None,
nodata=None,
port="default",
debug=False,
attribution=None,
tile_format="ipyleaflet",
layer_name="NetCDF layer",
return_client=False,
shift_lon=True,
lat="lat",
lon="lon",
**kwargs,
):
"""Generate an ipyleaflet/folium TileLayer from a netCDF file.
If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer),
try adding to following two lines to the beginning of the notebook if the raster does not render properly.
import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = f'{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}'
Args:
filename (str): File path or HTTP URL to the netCDF file.
variables (int, optional): The variable/band names to extract data from the netCDF file. Defaults to None. If None, all variables will be extracted.
port (str, optional): The port to use for the server. Defaults to "default".
colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
projection (str, optional): The projection of the GeoTIFF. Defaults to "EPSG:3857".
attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
layer_name (str, optional): The layer name to use. Defaults to "NetCDF layer".
return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
lon (str, optional): Name of the longitude variable. Defaults to 'lon'.
Returns:
ipyleaflet.TileLayer | folium.TileLayer: An ipyleaflet.TileLayer or folium.TileLayer.
"""
check_package(
"localtileserver", URL="https://github.com/banesullivan/localtileserver"
)
try:
import xarray as xr
except ImportError as e:
raise ImportError(e)
if filename.startswith("http"):
filename = download_file(filename)
if not os.path.exists(filename):
raise FileNotFoundError(f"{filename} does not exist.")
output = filename.replace(".nc", ".tif")
xds = xr.open_dataset(filename, **kwargs)
if shift_lon:
xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
xds = xds.sortby(xds.lon)
allowed_vars = list(xds.data_vars.keys())
if isinstance(variables, str):
if variables not in allowed_vars:
raise ValueError(f"{variables} is not a subset of {allowed_vars}.")
variables = [variables]
if variables is not None and len(variables) > 3:
raise ValueError("Only 3 variables can be plotted at a time.")
if variables is not None and (not set(variables).issubset(allowed_vars)):
raise ValueError(f"{variables} must be a subset of {allowed_vars}.")
xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
if variables is None:
if len(allowed_vars) >= 3:
band_idx = [1, 2, 3]
else:
band_idx = [1]
else:
band_idx = [allowed_vars.index(var) + 1 for var in variables]
tile_layer = get_local_tile_layer(
output,
port=port,
debug=debug,
indexes=band_idx,
colormap=colormap,
vmin=vmin,
vmax=vmax,
nodata=nodata,
attribution=attribution,
tile_format=tile_format,
layer_name=layer_name,
return_client=return_client,
)
return tile_layer
netcdf_to_ee(nc_file, var_names, band_names=None, lon='lon', lat='lat', decimal=2)
¶
Creates an ee.Image from netCDF variables band_names that are read from nc_file. Currently only supports variables in a regular longitude/latitude grid (EPSG:4326).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
nc_file |
str |
the name of the netCDF file to read |
required |
var_names |
str or list |
the name(s) of the variable(s) to read |
required |
band_names |
list |
if given, the bands are renamed to band_names. Defaults to the original var_names |
None |
lon |
str |
the name of the longitude variable in the netCDF file. Defaults to "lon" |
'lon' |
lat |
str |
the name of the latitude variable in the netCDF file. Defaults to "lat" |
'lat' |
decimal |
int |
the number of decimal places to round the longitude and latitude values to. Defaults to 2. |
2 |
Returns:
| Type | Description |
|---|---|
image |
An ee.Image |
Source code in geemap/common.py
def netcdf_to_ee(nc_file, var_names, band_names=None, lon="lon", lat="lat", decimal=2):
"""
Creates an ee.Image from netCDF variables band_names that are read from nc_file. Currently only supports variables in a regular longitude/latitude grid (EPSG:4326).
Args:
nc_file (str): the name of the netCDF file to read
var_names (str or list): the name(s) of the variable(s) to read
band_names (list, optional): if given, the bands are renamed to band_names. Defaults to the original var_names
lon (str, optional): the name of the longitude variable in the netCDF file. Defaults to "lon"
lat (str, optional): the name of the latitude variable in the netCDF file. Defaults to "lat"
decimal (int, optional): the number of decimal places to round the longitude and latitude values to. Defaults to 2.
Returns:
image: An ee.Image
"""
try:
import xarray as xr
except Exception:
raise ImportError(
"You need to install xarray first. See https://github.com/pydata/xarray"
)
import numpy as np
from collections import Counter
def most_common_value(lst):
counter = Counter(lst)
most_common = counter.most_common(1)
return float(format(most_common[0][0], f".{decimal}f"))
try:
if not isinstance(nc_file, str):
print("The input file must be a string.")
return
if band_names and not isinstance(band_names, (list, str)):
print("Band names must be a string or list.")
return
if not isinstance(lon, str) or not isinstance(lat, str):
print("The longitude and latitude variable names must be a string.")
return
ds = xr.open_dataset(nc_file)
data = ds[var_names]
lon_data = data[lon]
lat_data = data[lat]
dim_lon = np.unique(np.ediff1d(lon_data))
dim_lat = np.unique(np.ediff1d(lat_data))
dim_lon = [most_common_value(dim_lon)]
dim_lat = [most_common_value(dim_lat)]
# if (len(dim_lon) != 1) or (len(dim_lat) != 1):
# print("The netCDF file is not a regular longitude/latitude grid")
# return
try:
data = data.to_array()
# ^ this is only needed (and works) if we have more than 1 variable
# axis_for_roll will be used in case we need to use np.roll
# and should be 1 for the case with more than 1 variable
axis_for_roll = 1
except Exception:
axis_for_roll = 0
# .to_array() does not work (and is not needed!) if there is only 1 variable
# in this case, the axis_for_roll needs to be 0
data_np = np.array(data)
do_transpose = True # To do: figure out if we need to transpose the data or not
if do_transpose:
try:
data_np = np.transpose(data_np, (0, 2, 1))
except Exception:
data_np = np.transpose(data_np)
# Figure out if we need to roll the data or not
# (see https://github.com/gee-community/geemap/issues/285#issuecomment-791385176)
if np.max(lon_data) > 180:
data_np = np.roll(data_np, 180, axis=axis_for_roll)
west_lon = lon_data[0] - 180
else:
west_lon = lon_data[0]
transform = [dim_lon[0], 0, float(west_lon), 0, dim_lat[0], float(lat_data[0])]
if band_names is None:
band_names = var_names
image = numpy_to_ee(
data_np, "EPSG:4326", transform=transform, band_names=band_names
)
return image
except Exception as e:
print(e)
netcdf_to_tif(filename, output=None, variables=None, shift_lon=True, lat='lat', lon='lon', return_vars=False, **kwargs)
¶
Convert a netcdf file to a GeoTIFF file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
Path to the netcdf file. |
required |
output |
str |
Path to the output GeoTIFF file. Defaults to None. If None, the output file will be the same as the input file with the extension changed to .tif. |
None |
variables |
str | list |
Name of the variable or a list of variables to extract. Defaults to None. If None, all variables will be extracted. |
None |
shift_lon |
bool |
Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True. |
True |
lat |
str |
Name of the latitude variable. Defaults to 'lat'. |
'lat' |
lon |
str |
Name of the longitude variable. Defaults to 'lon'. |
'lon' |
return_vars |
bool |
Flag to return all variables. Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
ImportError |
If the xarray or rioxarray package is not installed. |
FileNotFoundError |
If the netcdf file is not found. |
ValueError |
If the variable is not found in the netcdf file. |
Source code in geemap/common.py
def netcdf_to_tif(
filename,
output=None,
variables=None,
shift_lon=True,
lat="lat",
lon="lon",
return_vars=False,
**kwargs,
):
"""Convert a netcdf file to a GeoTIFF file.
Args:
filename (str): Path to the netcdf file.
output (str, optional): Path to the output GeoTIFF file. Defaults to None. If None, the output file will be the same as the input file with the extension changed to .tif.
variables (str | list, optional): Name of the variable or a list of variables to extract. Defaults to None. If None, all variables will be extracted.
shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
lon (str, optional): Name of the longitude variable. Defaults to 'lon'.
return_vars (bool, optional): Flag to return all variables. Defaults to False.
Raises:
ImportError: If the xarray or rioxarray package is not installed.
FileNotFoundError: If the netcdf file is not found.
ValueError: If the variable is not found in the netcdf file.
"""
try:
import xarray as xr
except ImportError as e:
raise ImportError(e)
if filename.startswith("http"):
filename = download_file(filename)
if not os.path.exists(filename):
raise FileNotFoundError(f"{filename} does not exist.")
if output is None:
output = filename.replace(".nc", ".tif")
else:
output = check_file_path(output)
xds = xr.open_dataset(filename, **kwargs)
if shift_lon:
xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
xds = xds.sortby(xds.lon)
allowed_vars = list(xds.data_vars.keys())
if isinstance(variables, str):
if variables not in allowed_vars:
raise ValueError(f"{variables} is not a valid variable.")
variables = [variables]
if variables is not None and (not set(variables).issubset(allowed_vars)):
raise ValueError(f"{variables} must be a subset of {allowed_vars}.")
if variables is None:
xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
else:
xds[variables].rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
if return_vars:
return output, allowed_vars
else:
return output
num_round(num, decimal=2)
¶
Rounds a number to a specified number of decimal places.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
num |
float |
The number to round. |
required |
decimal |
int |
The number of decimal places to round. Defaults to 2. |
2 |
Returns:
| Type | Description |
|---|---|
float |
The number with the specified decimal places rounded. |
Source code in geemap/common.py
def num_round(num, decimal=2):
"""Rounds a number to a specified number of decimal places.
Args:
num (float): The number to round.
decimal (int, optional): The number of decimal places to round. Defaults to 2.
Returns:
float: The number with the specified decimal places rounded.
"""
return round(num, decimal)
numpy_to_cog(np_array, out_cog, bounds=None, profile=None, dtype=None, crs='epsg:4326')
¶
Converts a numpy array to a COG file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
np_array |
np.array |
A numpy array representing the image. |
required |
out_cog |
str |
The output COG file path. |
required |
bounds |
tuple |
The bounds of the image in the format of (minx, miny, maxx, maxy). Defaults to None. |
None |
profile |
str | dict |
File path to an existing COG file or a dictionary representing the profile. Defaults to None. |
None |
dtype |
str |
The data type of the output COG file. Defaults to None. |
None |
crs |
str |
The coordinate reference system of the output COG file. Defaults to "epsg:4326". |
'epsg:4326' |
Source code in geemap/common.py
def numpy_to_cog(
np_array, out_cog, bounds=None, profile=None, dtype=None, crs="epsg:4326"
):
"""Converts a numpy array to a COG file.
Args:
np_array (np.array): A numpy array representing the image.
out_cog (str): The output COG file path.
bounds (tuple, optional): The bounds of the image in the format of (minx, miny, maxx, maxy). Defaults to None.
profile (str | dict, optional): File path to an existing COG file or a dictionary representing the profile. Defaults to None.
dtype (str, optional): The data type of the output COG file. Defaults to None.
crs (str, optional): The coordinate reference system of the output COG file. Defaults to "epsg:4326".
"""
import numpy as np
import rasterio
from rasterio.io import MemoryFile
from rasterio.transform import from_bounds
from rio_cogeo.cogeo import cog_translate
from rio_cogeo.profiles import cog_profiles
warnings.filterwarnings("ignore")
if not isinstance(np_array, np.ndarray):
raise TypeError("The input array must be a numpy array.")
out_dir = os.path.dirname(out_cog)
check_dir(out_dir)
if profile is not None:
if isinstance(profile, str):
if not os.path.exists(profile):
raise FileNotFoundError("The provided file could not be found.")
with rasterio.open(profile) as ds:
bounds = ds.bounds
elif isinstance(profile, rasterio.profiles.Profile):
profile = dict(profile)
elif not isinstance(profile, dict):
raise TypeError("The provided profile must be a file path or a dictionary.")
if bounds is None:
bounds = (-180.0, -85.0511287798066, 180.0, 85.0511287798066)
if not isinstance(bounds, tuple) and len(bounds) != 4:
raise TypeError("The provided bounds must be a tuple of length 4.")
# Rasterio uses numpy array of shape of `(bands, height, width)`
if len(np_array.shape) == 3:
nbands = np_array.shape[0]
height = np_array.shape[1]
width = np_array.shape[2]
elif len(np_array.shape) == 2:
nbands = 1
height = np_array.shape[0]
width = np_array.shape[1]
np_array = np_array.reshape((1, height, width))
else:
raise ValueError("The input array must be a 2D or 3D numpy array.")
src_transform = from_bounds(*bounds, width=width, height=height)
if dtype is None:
dtype = str(np_array.dtype)
if isinstance(profile, dict):
src_profile = profile
src_profile["count"] = nbands
else:
src_profile = dict(
driver="GTiff",
dtype=dtype,
count=nbands,
height=height,
width=width,
crs=crs,
transform=src_transform,
)
with MemoryFile() as memfile:
with memfile.open(**src_profile) as mem:
# Populate the input file with numpy array
mem.write(np_array)
dst_profile = cog_profiles.get("deflate")
cog_translate(
mem,
out_cog,
dst_profile,
in_memory=True,
quiet=True,
)
numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None)
¶
Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
np_array |
np.array |
the 3D (or 2D) numpy array to add to an image |
required |
crs |
str |
The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string. |
None |
transform |
list |
The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'. |
None |
transformWkt |
str |
The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'. |
None |
band_names |
str or list |
The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc. |
None |
Returns:
| Type | Description |
|---|---|
image |
An ee.Image |
Source code in geemap/common.py
def numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None):
"""
Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.
Args:
np_array (np.array): the 3D (or 2D) numpy array to add to an image
crs (str): The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string.
transform (list): The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'.
transformWkt (str): The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'.
band_names (str or list, optional): The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc.
Returns:
image: An ee.Image
"""
import numpy as np
if not isinstance(np_array, np.ndarray):
print("The input must be a numpy.ndarray.")
return
if not len(np_array.shape) in [2, 3]:
print("The input must have 2 or 3 dimensions")
return
if band_names and not isinstance(band_names, (list, str)):
print("Band names must be a str or list")
return
try:
projection = ee.Projection(crs, transform, transformWkt)
coords = ee.Image.pixelCoordinates(projection).floor().int32()
x = coords.select("x")
y = coords.select("y")
s = np_array.shape
if len(s) < 3:
dimx = s[0]
dimy = s[1]
else:
dimx = s[1]
dimy = s[2]
dimz = s[0]
coord_mask = x.gte(0).And(y.gte(0)).And(x.lt(dimx)).And(y.lt(dimy))
coords = coords.updateMask(coord_mask)
def list_to_ee(a_list):
ee_data = ee.Array(a_list)
image = ee.Image(ee_data).arrayGet(coords)
return image
if len(s) < 3:
image = list_to_ee(np_array.tolist())
else:
image = list_to_ee(np_array[0].tolist())
for z in np.arange(1, dimz):
image = image.addBands(list_to_ee(np_array[z].tolist()))
if band_names:
image = image.rename(band_names)
return image
except Exception as e:
print(e)
nwi_add_color(fc)
¶
Converts NWI vector dataset to image and add color to it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
ee.FeatureCollection |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Image |
Source code in geemap/common.py
def nwi_add_color(fc):
"""Converts NWI vector dataset to image and add color to it.
Args:
fc (object): ee.FeatureCollection
Returns:
object: ee.Image
"""
emergent = ee.FeatureCollection(
fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Emergent Wetland"))
)
emergent = emergent.map(lambda f: f.set("R", 127).set("G", 195).set("B", 28))
# print(emergent.first())
forested = fc.filter(
ee.Filter.eq("WETLAND_TY", "Freshwater Forested/Shrub Wetland")
)
forested = forested.map(lambda f: f.set("R", 0).set("G", 136).set("B", 55))
pond = fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Pond"))
pond = pond.map(lambda f: f.set("R", 104).set("G", 140).set("B", 192))
lake = fc.filter(ee.Filter.eq("WETLAND_TY", "Lake"))
lake = lake.map(lambda f: f.set("R", 19).set("G", 0).set("B", 124))
riverine = fc.filter(ee.Filter.eq("WETLAND_TY", "Riverine"))
riverine = riverine.map(lambda f: f.set("R", 1).set("G", 144).set("B", 191))
fc = ee.FeatureCollection(
emergent.merge(forested).merge(pond).merge(lake).merge(riverine)
)
# base = ee.Image(0).mask(0).toInt8()
base = ee.Image().byte()
img = base.paint(fc, "R").addBands(
base.paint(fc, "G").addBands(base.paint(fc, "B"))
)
return img
open_github(subdir=None)
¶
Opens the GitHub repository for this package.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
subdir |
str |
Sub-directory of the repository. Defaults to None. |
None |
Source code in geemap/common.py
def open_github(subdir=None):
"""Opens the GitHub repository for this package.
Args:
subdir (str, optional): Sub-directory of the repository. Defaults to None.
"""
import webbrowser
url = "https://github.com/gee-community/geemap"
if subdir == "source":
url += "/tree/master/geemap/"
elif subdir == "examples":
url += "/tree/master/examples"
elif subdir == "tutorials":
url += "/tree/master/tutorials"
webbrowser.open_new_tab(url)
open_image_from_url(url, timeout=300, proxies=None)
¶
Loads an image from the specified URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
URL of the image. |
required |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
Dictionary of proxies. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
Image object. |
Source code in geemap/common.py
def open_image_from_url(url, timeout=300, proxies=None):
"""Loads an image from the specified URL.
Args:
url (str): URL of the image.
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): Dictionary of proxies. Defaults to None.
Returns:
object: Image object.
"""
from PIL import Image
# from io import BytesIO
# from urllib.parse import urlparse
try:
url = get_direct_url(url)
response = requests.get(url, timeout=timeout, proxies=proxies)
img = Image.open(io.BytesIO(response.content))
return img
except Exception as e:
print(e)
open_url(url)
¶
Opens the URL in a new browser tab.
Source code in geemap/common.py
def open_url(url):
"""Opens the URL in a new browser tab."""
if in_colab_shell():
display(
Javascript('window.open("{url}", "_blank", "noopener")'.format(url=url))
)
else:
import webbrowser
webbrowser.open_new_tab(url)
open_youtube()
¶
Opens the YouTube tutorials for geemap.
Source code in geemap/common.py
def open_youtube():
"""Opens the YouTube tutorials for geemap."""
import webbrowser
url = "https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3"
webbrowser.open_new_tab(url)
osm_to_ee(query, which_result=None, by_osmid=False, buffer_dist=None, geodesic=True)
¶
Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
str | dict | list |
Query string(s) or structured dict(s) to geocode. |
required |
which_result |
INT |
Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None. |
None |
by_osmid |
bool |
If True, handle query as an OSM ID for lookup rather than text search. Defaults to False. |
False |
buffer_dist |
float |
Distance to buffer around the place geometry, in meters. Defaults to None. |
None |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. |
True |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
An Earth Engine FeatureCollection. |
Source code in geemap/common.py
def osm_to_ee(
query, which_result=None, by_osmid=False, buffer_dist=None, geodesic=True
):
"""Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.
Args:
query (str | dict | list): Query string(s) or structured dict(s) to geocode.
which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.
Returns:
ee.FeatureCollection: An Earth Engine FeatureCollection.
"""
gdf = osm_to_gdf(query, which_result, by_osmid, buffer_dist)
fc = gdf_to_ee(gdf, geodesic)
return fc
osm_to_gdf(query, which_result=None, by_osmid=False, buffer_dist=None)
¶
Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
str | dict | list |
Query string(s) or structured dict(s) to geocode. |
required |
which_result |
INT |
Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None. |
None |
by_osmid |
bool |
If True, handle query as an OSM ID for lookup rather than text search. Defaults to False. |
False |
buffer_dist |
float |
Distance to buffer around the place geometry, in meters. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
GeoDataFrame |
A GeoPandas GeoDataFrame. |
Source code in geemap/common.py
def osm_to_gdf(
query,
which_result=None,
by_osmid=False,
buffer_dist=None,
):
"""Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.
Args:
query (str | dict | list): Query string(s) or structured dict(s) to geocode.
which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.
Returns:
GeoDataFrame: A GeoPandas GeoDataFrame.
"""
check_package(
"geopandas", "https://geopandas.org/getting_started.html#installation"
)
check_package("osmnx", "https://osmnx.readthedocs.io/en/stable/")
try:
import osmnx as ox
gdf = ox.geocode_to_gdf(query, which_result, by_osmid, buffer_dist)
return gdf
except Exception as e:
raise Exception(e)
osm_to_geojson(query, which_result=None, by_osmid=False, buffer_dist=None)
¶
Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
str | dict | list |
Query string(s) or structured dict(s) to geocode. |
required |
which_result |
INT |
Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None. |
None |
by_osmid |
bool |
If True, handle query as an OSM ID for lookup rather than text search. Defaults to False. |
False |
buffer_dist |
float |
Distance to buffer around the place geometry, in meters. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
An Earth Engine FeatureCollection. |
Source code in geemap/common.py
def osm_to_geojson(query, which_result=None, by_osmid=False, buffer_dist=None):
"""Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.
Args:
query (str | dict | list): Query string(s) or structured dict(s) to geocode.
which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.
Returns:
ee.FeatureCollection: An Earth Engine FeatureCollection.
"""
gdf = osm_to_gdf(query, which_result, by_osmid, buffer_dist)
return gdf.__geo_interface__
osm_to_geopandas(query, which_result=None, by_osmid=False, buffer_dist=None)
¶
Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query |
str | dict | list |
Query string(s) or structured dict(s) to geocode. |
required |
which_result |
INT |
Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None. |
None |
by_osmid |
bool |
If True, handle query as an OSM ID for lookup rather than text search. Defaults to False. |
False |
buffer_dist |
float |
Distance to buffer around the place geometry, in meters. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
GeoDataFrame |
A GeoPandas GeoDataFrame. |
Source code in geemap/common.py
def osm_to_gdf(
query,
which_result=None,
by_osmid=False,
buffer_dist=None,
):
"""Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.
Args:
query (str | dict | list): Query string(s) or structured dict(s) to geocode.
which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.
Returns:
GeoDataFrame: A GeoPandas GeoDataFrame.
"""
check_package(
"geopandas", "https://geopandas.org/getting_started.html#installation"
)
check_package("osmnx", "https://osmnx.readthedocs.io/en/stable/")
try:
import osmnx as ox
gdf = ox.geocode_to_gdf(query, which_result, by_osmid, buffer_dist)
return gdf
except Exception as e:
raise Exception(e)
pandas_to_ee(df, latitude='latitude', longitude='longitude', **kwargs)
¶
Converts a pandas DataFrame to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pandas.DataFrame |
An input pandas.DataFrame. |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Exceptions:
| Type | Description |
|---|---|
TypeError |
The input data type must be pandas.DataFrame. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The ee.FeatureCollection converted from the input pandas DataFrame. |
Source code in geemap/common.py
def df_to_ee(df, latitude="latitude", longitude="longitude", **kwargs):
"""Converts a pandas DataFrame to ee.FeatureCollection.
Args:
df (pandas.DataFrame): An input pandas.DataFrame.
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Raises:
TypeError: The input data type must be pandas.DataFrame.
Returns:
ee.FeatureCollection: The ee.FeatureCollection converted from the input pandas DataFrame.
"""
import pandas as pd
if not isinstance(df, pd.DataFrame):
raise TypeError("The input data type must be pandas.DataFrame.")
geojson = df_to_geojson(df, latitude=latitude, longitude=longitude)
fc = geojson_to_ee(geojson)
return fc
planet_biannual_tiles_tropical(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet bi-annual imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_biannual_tiles_tropical(
api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
"""Generates Planet bi-annual imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
tiles = {}
link = planet_biannual_tropical(api_key, token_name)
for url in link:
index = url.find("20")
name = "Planet_" + url[index : index + 15]
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
tiles[name] = tile
return tiles
planet_biannual_tropical(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet bi-annual imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the API key could not be found. |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_biannual_tropical(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet bi-annual imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: If the API key could not be found.
Returns:
list: A list of tile URLs.
"""
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
dates = [
"2015-12_2016-05",
"2016-06_2016-11",
"2016-12_2017-05",
"2017-06_2017-11",
"2017-12_2018-05",
"2018-06_2018-11",
"2018-12_2019-05",
"2019-06_2019-11",
"2019-12_2020-05",
"2020-06_2020-08",
]
link = []
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
for d in dates:
url = f"{prefix}{d}{subfix}{api_key}"
link.append(url)
return link
planet_by_month(year=2016, month=1, api_key=None, token_name='PLANET_API_KEY')
¶
Gets Planet global mosaic tile url by month. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year of Planet global mosaic, must be >=2016. Defaults to 2016. |
2016 |
month |
int |
The month of Planet global mosaic, must be 1-12. Defaults to 1. |
1 |
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
The Planet API key is not provided. |
ValueError |
The year is invalid. |
ValueError |
The month is invalid. |
ValueError |
The month is invalid. |
Returns:
| Type | Description |
|---|---|
str |
A Planet global mosaic tile url. |
Source code in geemap/common.py
def planet_by_month(
year=2016,
month=1,
api_key=None,
token_name="PLANET_API_KEY",
):
"""Gets Planet global mosaic tile url by month. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: The Planet API key is not provided.
ValueError: The year is invalid.
ValueError: The month is invalid.
ValueError: The month is invalid.
Returns:
str: A Planet global mosaic tile url.
"""
# from datetime import date
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
today = datetime.date.today()
year_now = int(today.strftime("%Y"))
month_now = int(today.strftime("%m"))
# quarter_now = (month_now - 1) // 3 + 1
if year > year_now:
raise ValueError(f"Year must be between 2016 and {year_now}.")
elif year == year_now and month >= month_now:
raise ValueError(f"Month must be less than {month_now} for year {year_now}")
if month < 1 or month > 12:
raise ValueError("Month must be between 1 and 12.")
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
m_str = str(year) + "_" + str(month).zfill(2)
url = f"{prefix}{m_str}{subfix}{api_key}"
return url
planet_by_quarter(year=2016, quarter=1, api_key=None, token_name='PLANET_API_KEY')
¶
Gets Planet global mosaic tile url by quarter. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year of Planet global mosaic, must be >=2016. Defaults to 2016. |
2016 |
quarter |
int |
The quarter of Planet global mosaic, must be 1-4. Defaults to 1. |
1 |
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
The Planet API key is not provided. |
ValueError |
The year is invalid. |
ValueError |
The quarter is invalid. |
ValueError |
The quarter is invalid. |
Returns:
| Type | Description |
|---|---|
str |
A Planet global mosaic tile url. |
Source code in geemap/common.py
def planet_by_quarter(
year=2016,
quarter=1,
api_key=None,
token_name="PLANET_API_KEY",
):
"""Gets Planet global mosaic tile url by quarter. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: The Planet API key is not provided.
ValueError: The year is invalid.
ValueError: The quarter is invalid.
ValueError: The quarter is invalid.
Returns:
str: A Planet global mosaic tile url.
"""
# from datetime import date
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
today = datetime.date.today()
year_now = int(today.strftime("%Y"))
month_now = int(today.strftime("%m"))
quarter_now = (month_now - 1) // 3 + 1
if year > year_now:
raise ValueError(f"Year must be between 2016 and {year_now}.")
elif year == year_now and quarter >= quarter_now:
raise ValueError(f"Quarter must be less than {quarter_now} for year {year_now}")
if quarter < 1 or quarter > 4:
raise ValueError("Quarter must be between 1 and 4.")
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
m_str = str(year) + "q" + str(quarter)
url = f"{prefix}{m_str}{subfix}{api_key}"
return url
planet_catalog(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_catalog(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Returns:
list: A list of tile URLs.
"""
quarterly = planet_quarterly(api_key, token_name)
monthly = planet_monthly(api_key, token_name)
return quarterly + monthly
planet_catalog_tropical(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_catalog_tropical(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Returns:
list: A list of tile URLs.
"""
biannual = planet_biannual_tropical(api_key, token_name)
monthly = planet_monthly_tropical(api_key, token_name)
return biannual + monthly
planet_monthly(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet monthly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the API key could not be found. |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_monthly(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet monthly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: If the API key could not be found.
Returns:
list: A list of tile URLs.
"""
# from datetime import date
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
today = datetime.date.today()
year_now = int(today.strftime("%Y"))
month_now = int(today.strftime("%m"))
link = []
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
for year in range(2016, year_now + 1):
for month in range(1, 13):
m_str = str(year) + "_" + str(month).zfill(2)
if year == year_now and month >= month_now:
break
url = f"{prefix}{m_str}{subfix}{api_key}"
link.append(url)
return link
planet_monthly_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_monthly_tiles(
api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
"""Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
tiles = {}
link = planet_monthly(api_key, token_name)
for url in link:
index = url.find("20")
name = "Planet_" + url[index : index + 7]
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
tiles[name] = tile
return tiles
planet_monthly_tiles_tropical(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_monthly_tiles_tropical(
api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
"""Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
tiles = {}
link = planet_monthly_tropical(api_key, token_name)
for url in link:
index = url.find("20")
name = "Planet_" + url[index : index + 7]
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
tiles[name] = tile
return tiles
planet_monthly_tropical(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the API key could not be found. |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_monthly_tropical(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: If the API key could not be found.
Returns:
list: A list of tile URLs.
"""
# from datetime import date
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
today = datetime.date.today()
year_now = int(today.strftime("%Y"))
month_now = int(today.strftime("%m"))
links = []
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
for year in range(2020, year_now + 1):
for month in range(1, 13):
m_str = str(year) + "-" + str(month).zfill(2)
if year == 2020 and month < 9:
continue
if year == year_now and month >= month_now:
break
url = f"{prefix}{m_str}{subfix}{api_key}"
links.append(url)
return links
planet_quarterly(api_key=None, token_name='PLANET_API_KEY')
¶
Generates Planet quarterly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the API key could not be found. |
Returns:
| Type | Description |
|---|---|
list |
A list of tile URLs. |
Source code in geemap/common.py
def planet_quarterly(api_key=None, token_name="PLANET_API_KEY"):
"""Generates Planet quarterly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
Raises:
ValueError: If the API key could not be found.
Returns:
list: A list of tile URLs.
"""
# from datetime import date
if api_key is None:
api_key = os.environ.get(token_name)
if api_key is None:
raise ValueError("The Planet API Key must be provided.")
today = datetime.date.today()
year_now = int(today.strftime("%Y"))
month_now = int(today.strftime("%m"))
quarter_now = (month_now - 1) // 3 + 1
link = []
prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="
for year in range(2016, year_now + 1):
for quarter in range(1, 5):
m_str = str(year) + "q" + str(quarter)
if year == year_now and quarter >= quarter_now:
break
url = f"{prefix}{m_str}{subfix}{api_key}"
link.append(url)
return link
planet_quarterly_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_quarterly_tiles(
api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
"""Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
tiles = {}
links = planet_quarterly(api_key, token_name)
for url in links:
index = url.find("20")
name = "Planet_" + url[index : index + 6]
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
tiles[name] = tile
return tiles
planet_tile_by_month(year=2016, month=1, name=None, api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year of Planet global mosaic, must be >=2016. Defaults to 2016. |
2016 |
month |
int |
The month of Planet global mosaic, must be 1-12. Defaults to 1. |
1 |
name |
str |
The layer name to use. Defaults to None. |
None |
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_tile_by_month(
year=2016,
month=1,
name=None,
api_key=None,
token_name="PLANET_API_KEY",
tile_format="ipyleaflet",
):
"""Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis
Args:
year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
name (str, optional): The layer name to use. Defaults to None.
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
url = planet_by_month(year, month, api_key, token_name)
if name is None:
name = "Planet_" + str(year) + "_" + str(month).zfill(2)
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
return tile
planet_tile_by_quarter(year=2016, quarter=1, name=None, api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
year |
int |
The year of Planet global mosaic, must be >=2016. Defaults to 2016. |
2016 |
quarter |
int |
The quarter of Planet global mosaic, must be 1-4. Defaults to 1. |
1 |
name |
str |
The layer name to use. Defaults to None. |
None |
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_tile_by_quarter(
year=2016,
quarter=1,
name=None,
api_key=None,
token_name="PLANET_API_KEY",
tile_format="ipyleaflet",
):
"""Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis
Args:
year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
name (str, optional): The layer name to use. Defaults to None.
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
import folium
import ipyleaflet
if tile_format not in ["ipyleaflet", "folium"]:
raise ValueError("The tile format must be either ipyleaflet or folium.")
url = planet_by_quarter(year, quarter, api_key, token_name)
if name is None:
name = "Planet_" + str(year) + "_q" + str(quarter)
if tile_format == "ipyleaflet":
tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
else:
tile = folium.TileLayer(
tiles=url,
attr="Planet",
name=name,
overlay=True,
control=True,
)
return tile
planet_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_tiles(api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"):
"""Generates Planet imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
catalog = {}
quarterly = planet_quarterly_tiles(api_key, token_name, tile_format)
monthly = planet_monthly_tiles(api_key, token_name, tile_format)
for key in quarterly:
catalog[key] = quarterly[key]
for key in monthly:
catalog[key] = monthly[key]
return catalog
planet_tiles_tropical(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')
¶
Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
api_key |
str |
The Planet API key. Defaults to None. |
None |
token_name |
str |
The environment variable name of the API key. Defaults to "PLANET_API_KEY". |
'PLANET_API_KEY' |
tile_format |
str |
The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet". |
'ipyleaflet' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the tile layer format is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary of TileLayer. |
Source code in geemap/common.py
def planet_tiles_tropical(
api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
"""Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf
Args:
api_key (str, optional): The Planet API key. Defaults to None.
token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".
Raises:
ValueError: If the tile layer format is invalid.
Returns:
dict: A dictionary of TileLayer.
"""
catalog = {}
biannul = planet_biannual_tiles_tropical(api_key, token_name, tile_format)
monthly = planet_monthly_tiles_tropical(api_key, token_name, tile_format)
for key in biannul:
catalog[key] = biannul[key]
for key in monthly:
catalog[key] = monthly[key]
return catalog
plot_raster(image, band=None, cmap='terrain', proj='EPSG:3857', figsize=None, open_kwargs={}, **kwargs)
¶
Plot a raster image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str | xarray.DataArray |
The input raster image, can be a file path, HTTP URL, or xarray.DataArray. |
required |
band |
int |
The band index, starting from zero. Defaults to None. |
None |
cmap |
str |
The matplotlib colormap to use. Defaults to "terrain". |
'terrain' |
proj |
str |
The EPSG projection code. Defaults to "EPSG:3857". |
'EPSG:3857' |
figsize |
tuple |
The figure size as a tuple, such as (10, 8). Defaults to None. |
None |
open_kwargs |
dict |
The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}. |
{} |
**kwargs |
Additional keyword arguments to pass to xarray.DataArray.plot(). |
{} |
Source code in geemap/common.py
def plot_raster(
image,
band=None,
cmap="terrain",
proj="EPSG:3857",
figsize=None,
open_kwargs={},
**kwargs,
):
"""Plot a raster image.
Args:
image (str | xarray.DataArray ): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
band (int, optional): The band index, starting from zero. Defaults to None.
cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
figsize (tuple, optional): The figure size as a tuple, such as (10, 8). Defaults to None.
open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
**kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().
"""
if os.environ.get("USE_MKDOCS") is not None:
return
if in_colab_shell():
print("The plot_raster() function is not supported in Colab.")
return
try:
import pvxarray
import rioxarray
import xarray
except ImportError:
raise ImportError(
"pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
)
if isinstance(image, str):
da = rioxarray.open_rasterio(image, **open_kwargs)
elif isinstance(image, xarray.DataArray):
da = image
else:
raise ValueError("image must be a string or xarray.Dataset.")
if band is not None:
da = da[dict(band=band)]
da = da.rio.reproject(proj)
kwargs["cmap"] = cmap
kwargs["figsize"] = figsize
da.plot(**kwargs)
plot_raster_3d(image, band=None, cmap='terrain', factor=1.0, proj='EPSG:3857', background=None, x=None, y=None, z=None, order=None, component=None, open_kwargs={}, mesh_kwargs={}, **kwargs)
¶
Plot a raster image in 3D.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str | xarray.DataArray |
The input raster image, can be a file path, HTTP URL, or xarray.DataArray. |
required |
band |
int |
The band index, starting from zero. Defaults to None. |
None |
cmap |
str |
The matplotlib colormap to use. Defaults to "terrain". |
'terrain' |
factor |
float |
The scaling factor for the raster. Defaults to 1.0. |
1.0 |
proj |
str |
The EPSG projection code. Defaults to "EPSG:3857". |
'EPSG:3857' |
background |
str |
The background color. Defaults to None. |
None |
x |
str |
The x coordinate. Defaults to None. |
None |
y |
str |
The y coordinate. Defaults to None. |
None |
z |
str |
The z coordinate. Defaults to None. |
None |
order |
str |
The order of the coordinates. Defaults to None. |
None |
component |
str |
The component of the coordinates. Defaults to None. |
None |
open_kwargs |
dict |
The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}. |
{} |
mesh_kwargs |
dict |
The keyword arguments to pass to pyvista.mesh.warp_by_scalar(). Defaults to {}. |
{} |
**kwargs |
Additional keyword arguments to pass to xarray.DataArray.plot(). |
{} |
Source code in geemap/common.py
def plot_raster_3d(
image,
band=None,
cmap="terrain",
factor=1.0,
proj="EPSG:3857",
background=None,
x=None,
y=None,
z=None,
order=None,
component=None,
open_kwargs={},
mesh_kwargs={},
**kwargs,
):
"""Plot a raster image in 3D.
Args:
image (str | xarray.DataArray): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
band (int, optional): The band index, starting from zero. Defaults to None.
cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
factor (float, optional): The scaling factor for the raster. Defaults to 1.0.
proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
background (str, optional): The background color. Defaults to None.
x (str, optional): The x coordinate. Defaults to None.
y (str, optional): The y coordinate. Defaults to None.
z (str, optional): The z coordinate. Defaults to None.
order (str, optional): The order of the coordinates. Defaults to None.
component (str, optional): The component of the coordinates. Defaults to None.
open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
mesh_kwargs (dict, optional): The keyword arguments to pass to pyvista.mesh.warp_by_scalar(). Defaults to {}.
**kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().
"""
if os.environ.get("USE_MKDOCS") is not None:
return
if in_colab_shell():
print("The plot_raster_3d() function is not supported in Colab.")
return
try:
import pvxarray
import pyvista
import rioxarray
import xarray
except ImportError:
raise ImportError(
"pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
)
if isinstance(background, str):
pyvista.global_theme.background = background
if isinstance(image, str):
da = rioxarray.open_rasterio(image, **open_kwargs)
elif isinstance(image, xarray.DataArray):
da = image
else:
raise ValueError("image must be a string or xarray.Dataset.")
if band is not None:
da = da[dict(band=band)]
da = da.rio.reproject(proj)
mesh_kwargs["factor"] = factor
kwargs["cmap"] = cmap
coords = list(da.coords)
if x is None:
if "x" in coords:
x = "x"
elif "lon" in coords:
x = "lon"
if y is None:
if "y" in coords:
y = "y"
elif "lat" in coords:
y = "lat"
if z is None:
if "z" in coords:
z = "z"
elif "elevation" in coords:
z = "elevation"
elif "band" in coords:
z = "band"
# Grab the mesh object for use with PyVista
mesh = da.pyvista.mesh(x=x, y=y, z=z, order=order, component=component)
# Warp top and plot in 3D
mesh.warp_by_scalar(**mesh_kwargs).plot(**kwargs)
png_to_gif(in_dir, out_gif, fps=10, loop=0)
¶
Convert a list of png images to gif.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_dir |
str |
The input directory containing png images. |
required |
out_gif |
str |
The output file path to the gif. |
required |
fps |
int |
Frames per second. Defaults to 10. |
10 |
loop |
bool |
controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0. |
0 |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
No png images could be found. |
Source code in geemap/common.py
def png_to_gif(in_dir, out_gif, fps=10, loop=0):
"""Convert a list of png images to gif.
Args:
in_dir (str): The input directory containing png images.
out_gif (str): The output file path to the gif.
fps (int, optional): Frames per second. Defaults to 10.
loop (bool, optional): controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.
Raises:
FileNotFoundError: No png images could be found.
"""
import glob
from PIL import Image
if not out_gif.endswith(".gif"):
raise ValueError("The out_gif must be a gif file.")
out_gif = os.path.abspath(out_gif)
out_dir = os.path.dirname(out_gif)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Create the frames
frames = []
imgs = list(glob.glob(os.path.join(in_dir, "*.png")))
imgs.sort()
if len(imgs) == 0:
raise FileNotFoundError(f"No png could be found in {in_dir}.")
for i in imgs:
new_frame = Image.open(i)
frames.append(new_frame)
# Save into a GIF file that loops forever
frames[0].save(
out_gif,
format="GIF",
append_images=frames[1:],
save_all=True,
duration=1000 / fps,
loop=loop,
)
points_from_xy(data, x='longitude', y='latitude', z=None, crs=None, **kwargs)
¶
Create a GeoPandas GeoDataFrame from a csv or Pandas DataFrame containing x, y, z values.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
str | pd.DataFrame |
A csv or Pandas DataFrame containing x, y, z values. |
required |
x |
str |
The column name for the x values. Defaults to "longitude". |
'longitude' |
y |
str |
The column name for the y values. Defaults to "latitude". |
'latitude' |
z |
str |
The column name for the z values. Defaults to None. |
None |
crs |
str | int |
The coordinate reference system for the GeoDataFrame. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
geopandas.GeoDataFrame |
A GeoPandas GeoDataFrame containing x, y, z values. |
Source code in geemap/common.py
def points_from_xy(data, x="longitude", y="latitude", z=None, crs=None, **kwargs):
"""Create a GeoPandas GeoDataFrame from a csv or Pandas DataFrame containing x, y, z values.
Args:
data (str | pd.DataFrame): A csv or Pandas DataFrame containing x, y, z values.
x (str, optional): The column name for the x values. Defaults to "longitude".
y (str, optional): The column name for the y values. Defaults to "latitude".
z (str, optional): The column name for the z values. Defaults to None.
crs (str | int, optional): The coordinate reference system for the GeoDataFrame. Defaults to None.
Returns:
geopandas.GeoDataFrame: A GeoPandas GeoDataFrame containing x, y, z values.
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
import pandas as pd
if crs is None:
crs = "epsg:4326"
data = github_raw_url(data)
if isinstance(data, pd.DataFrame):
df = data
elif isinstance(data, str):
if not data.startswith("http") and (not os.path.exists(data)):
raise FileNotFoundError("The specified input csv does not exist.")
else:
df = pd.read_csv(data, **kwargs)
else:
raise TypeError("The data must be a pandas DataFrame or a csv file path.")
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[x], df[y], z=z, crs=crs))
return gdf
postgis_to_ee(sql, con, geom_col='geom', crs=None, geodestic=False, **kwargs)
¶
Reads data from a PostGIS database and returns a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
sql |
str |
SQL query to execute in selecting entries from database, or name of the table to read from the database. |
required |
con |
sqlalchemy.engine.Engine |
Active connection to the database to query. |
required |
geom_col |
str |
Column name to convert to shapely geometries. Defaults to "geom". |
'geom' |
crs |
str | dict |
CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None. |
None |
geodestic |
bool |
Whether to use geodestic coordinates. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
[type] |
[description] |
Source code in geemap/common.py
def postgis_to_ee(sql, con, geom_col="geom", crs=None, geodestic=False, **kwargs):
"""Reads data from a PostGIS database and returns a GeoDataFrame.
Args:
sql (str): SQL query to execute in selecting entries from database, or name of the table to read from the database.
con (sqlalchemy.engine.Engine): Active connection to the database to query.
geom_col (str, optional): Column name to convert to shapely geometries. Defaults to "geom".
crs (str | dict, optional): CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.
geodestic (bool, optional): Whether to use geodestic coordinates. Defaults to False.
Returns:
[type]: [description]
"""
check_package(name="geopandas", URL="https://geopandas.org")
gdf = read_postgis(sql, con, geom_col, crs=crs, **kwargs)
fc = gdf_to_ee(gdf, geodesic=geodestic)
return fc
random_sampling(image, region=None, scale=None, projection=None, factor=None, numPixels=None, seed=0, dropNulls=True, tileScale=1.0, geometries=True, to_pandas=False)
¶
Samples the pixels of an image, returning them as a FeatureCollection. Each feature will have 1 property per band in the input image. Note that the default behavior is to drop features that intersect masked pixels, which result in null-valued properties (see dropNulls argument).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The image to sample. |
required |
region |
ee.Geometry |
The region to sample from. If unspecified, uses the image's whole footprint. Defaults to None. |
None |
scale |
float |
A nominal scale in meters of the projection to sample in.. Defaults to None. |
None |
projection |
ee.Projection |
The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.. Defaults to None. |
None |
factor |
float |
A subsampling factor, within (0, 1]. If specified, 'numPixels' must not be specified. Defaults to no subsampling. Defaults to None. |
None |
numPixels |
int |
The approximate number of pixels to sample. If specified, 'factor' must not be specified. Defaults to None. |
None |
seed |
int |
A randomization seed to use for subsampling. Defaults to True. Defaults to 0. |
0 |
dropNulls |
bool |
Post filter the result to drop features that have null-valued properties. Defaults to True. |
True |
tileScale |
float |
Post filter the result to drop features that have null-valued properties. Defaults to 1. |
1.0 |
geometries |
bool |
If true, adds the center of the sampled pixel as the geometry property of the output feature. Otherwise, geometries will be omitted (saving memory). Defaults to True. |
True |
to_pandas |
bool |
Whether to return the result as a pandas dataframe. Defaults to False. |
False |
Exceptions:
| Type | Description |
|---|---|
TypeError |
If the input image is not an ee.Image. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
Random sampled points. |
Source code in geemap/common.py
def random_sampling(
image,
region=None,
scale=None,
projection=None,
factor=None,
numPixels=None,
seed=0,
dropNulls=True,
tileScale=1.0,
geometries=True,
to_pandas=False,
):
"""Samples the pixels of an image, returning them as a FeatureCollection. Each feature will have 1 property per band in the input image. Note that the default behavior is to drop features that intersect masked pixels, which result in null-valued properties (see dropNulls argument).
Args:
image (ee.Image): The image to sample.
region (ee.Geometry, optional): The region to sample from. If unspecified, uses the image's whole footprint. Defaults to None.
scale (float, optional): A nominal scale in meters of the projection to sample in.. Defaults to None.
projection (ee.Projection, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.. Defaults to None.
factor (float, optional): A subsampling factor, within (0, 1]. If specified, 'numPixels' must not be specified. Defaults to no subsampling. Defaults to None.
numPixels (int, optional): The approximate number of pixels to sample. If specified, 'factor' must not be specified. Defaults to None.
seed (int, optional): A randomization seed to use for subsampling. Defaults to True. Defaults to 0.
dropNulls (bool, optional): Post filter the result to drop features that have null-valued properties. Defaults to True.
tileScale (float, optional): Post filter the result to drop features that have null-valued properties. Defaults to 1.
geometries (bool, optional): If true, adds the center of the sampled pixel as the geometry property of the output feature. Otherwise, geometries will be omitted (saving memory). Defaults to True.
to_pandas (bool, optional): Whether to return the result as a pandas dataframe. Defaults to False.
Raises:
TypeError: If the input image is not an ee.Image.
Returns:
ee.FeatureCollection: Random sampled points.
"""
if not isinstance(image, ee.Image):
raise TypeError("The image must be ee.Image")
points = image.sample(
**{
"region": region,
"scale": scale,
"projection": projection,
"factor": factor,
"numPixels": numPixels,
"seed": seed,
"dropNulls": dropNulls,
"tileScale": tileScale,
"geometries": geometries,
}
)
if to_pandas:
return ee_to_df(points)
else:
return points
random_string(string_length=3)
¶
Generates a random string of fixed length.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
string_length |
int |
Fixed length. Defaults to 3. |
3 |
Returns:
| Type | Description |
|---|---|
str |
A random string |
Source code in geemap/common.py
def random_string(string_length=3):
"""Generates a random string of fixed length.
Args:
string_length (int, optional): Fixed length. Defaults to 3.
Returns:
str: A random string
"""
import random
import string
# random.seed(1001)
letters = string.ascii_lowercase
return "".join(random.choice(letters) for i in range(string_length))
read_api_csv()
¶
Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.
Returns:
| Type | Description |
|---|---|
dict |
The dictionary containing information about each function, including name, description, function form, return type, arguments, html. |
Source code in geemap/common.py
def read_api_csv():
"""Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.
Returns:
dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html.
"""
import copy
import pkg_resources
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
csv_file = os.path.join(template_dir, "ee_api_docs.csv")
html_file = os.path.join(template_dir, "ee_api_docs.html")
with open(html_file) as f:
in_html_lines = f.readlines()
api_dict = {}
with open(csv_file, "r", encoding="utf-8") as f:
csv_reader = csv.DictReader(f, delimiter="\t")
for line in csv_reader:
out_html_lines = copy.copy(in_html_lines)
out_html_lines[65] = in_html_lines[65].replace(
"function_name", line["name"]
)
out_html_lines[66] = in_html_lines[66].replace(
"function_description", line.get("description")
)
out_html_lines[74] = in_html_lines[74].replace(
"function_usage", line.get("function")
)
out_html_lines[75] = in_html_lines[75].replace(
"function_returns", line.get("returns")
)
arguments = line.get("argument")
types = line.get("type")
details = line.get("details")
if "|" in arguments:
argument_items = arguments.split("|")
else:
argument_items = [arguments]
if "|" in types:
types_items = types.split("|")
else:
types_items = [types]
if "|" in details:
details_items = details.split("|")
else:
details_items = [details]
out_argument_lines = []
for index in range(len(argument_items)):
in_argument_lines = in_html_lines[87:92]
in_argument_lines[1] = in_argument_lines[1].replace(
"function_argument", argument_items[index]
)
in_argument_lines[2] = in_argument_lines[2].replace(
"function_type", types_items[index]
)
in_argument_lines[3] = in_argument_lines[3].replace(
"function_details", details_items[index]
)
out_argument_lines.append("".join(in_argument_lines))
out_html_lines = (
out_html_lines[:87] + out_argument_lines + out_html_lines[92:]
)
contents = "".join(out_html_lines)
api_dict[line["name"]] = {
"description": line.get("description"),
"function": line.get("function"),
"returns": line.get("returns"),
"argument": line.get("argument"),
"type": line.get("type"),
"details": line.get("details"),
"html": contents,
}
return api_dict
read_file_from_url(url, return_type='list', encoding='utf-8')
¶
Reads a file from a URL.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
The URL of the file. |
required |
return_type |
str |
The return type, can either be string or list. Defaults to "list". |
'list' |
encoding |
str |
The encoding of the file. Defaults to "utf-8". |
'utf-8' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
The return type must be either list or string. |
Returns:
| Type | Description |
|---|---|
str | list |
The contents of the file. |
Source code in geemap/common.py
def read_file_from_url(url, return_type="list", encoding="utf-8"):
"""Reads a file from a URL.
Args:
url (str): The URL of the file.
return_type (str, optional): The return type, can either be string or list. Defaults to "list".
encoding (str, optional): The encoding of the file. Defaults to "utf-8".
Raises:
ValueError: The return type must be either list or string.
Returns:
str | list: The contents of the file.
"""
from urllib.request import urlopen
if return_type == "list":
return [line.decode(encoding).rstrip() for line in urlopen(url).readlines()]
elif return_type == "string":
return urlopen(url).read().decode(encoding)
else:
raise ValueError("The return type must be either list or string.")
read_lidar(filename, **kwargs)
¶
Read a LAS file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
A local file path or HTTP URL to a LAS file. |
required |
Returns:
| Type | Description |
|---|---|
LasData |
The LasData object return by laspy.read. |
Source code in geemap/common.py
def read_lidar(filename, **kwargs):
"""Read a LAS file.
Args:
filename (str): A local file path or HTTP URL to a LAS file.
Returns:
LasData: The LasData object return by laspy.read.
"""
try:
import laspy
except ImportError:
print(
"The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
)
return
if (
isinstance(filename, str)
and filename.startswith("http")
and (filename.endswith(".las") or filename.endswith(".laz"))
):
filename = github_raw_url(filename)
filename = download_file(filename)
return laspy.read(filename, **kwargs)
read_netcdf(filename, **kwargs)
¶
Read a netcdf file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
File path or HTTP URL to the netcdf file. |
required |
Exceptions:
| Type | Description |
|---|---|
ImportError |
If the xarray or rioxarray package is not installed. |
FileNotFoundError |
If the netcdf file is not found. |
Returns:
| Type | Description |
|---|---|
xarray.Dataset |
The netcdf file as an xarray dataset. |
Source code in geemap/common.py
def read_netcdf(filename, **kwargs):
"""Read a netcdf file.
Args:
filename (str): File path or HTTP URL to the netcdf file.
Raises:
ImportError: If the xarray or rioxarray package is not installed.
FileNotFoundError: If the netcdf file is not found.
Returns:
xarray.Dataset: The netcdf file as an xarray dataset.
"""
try:
import xarray as xr
except ImportError as e:
raise ImportError(e)
if filename.startswith("http"):
filename = download_file(filename)
if not os.path.exists(filename):
raise FileNotFoundError(f"{filename} does not exist.")
xds = xr.open_dataset(filename, **kwargs)
return xds
read_postgis(sql, con, geom_col='geom', crs=None, **kwargs)
¶
Reads data from a PostGIS database and returns a GeoDataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
sql |
str |
SQL query to execute in selecting entries from database, or name of the table to read from the database. |
required |
con |
sqlalchemy.engine.Engine |
Active connection to the database to query. |
required |
geom_col |
str |
Column name to convert to shapely geometries. Defaults to "geom". |
'geom' |
crs |
str | dict |
CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
[type] |
[description] |
Source code in geemap/common.py
def read_postgis(sql, con, geom_col="geom", crs=None, **kwargs):
"""Reads data from a PostGIS database and returns a GeoDataFrame.
Args:
sql (str): SQL query to execute in selecting entries from database, or name of the table to read from the database.
con (sqlalchemy.engine.Engine): Active connection to the database to query.
geom_col (str, optional): Column name to convert to shapely geometries. Defaults to "geom".
crs (str | dict, optional): CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.
Returns:
[type]: [description]
"""
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
gdf = gpd.read_postgis(sql, con, geom_col, crs, **kwargs)
return gdf
remove_geometry(fc)
¶
Remove .geo coordinate field from a FeatureCollection
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
The input FeatureCollection. |
required |
Returns:
| Type | Description |
|---|---|
object |
The output FeatureCollection without the geometry field. |
Source code in geemap/common.py
def remove_geometry(fc):
"""Remove .geo coordinate field from a FeatureCollection
Args:
fc (object): The input FeatureCollection.
Returns:
object: The output FeatureCollection without the geometry field.
"""
return fc.select([".*"], None, False)
rename_bands(img, in_band_names, out_band_names)
¶
Renames image bands.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img |
object |
The image to be renamed. |
required |
in_band_names |
list |
The list of input band names. |
required |
out_band_names |
list |
The list of output band names. |
required |
Returns:
| Type | Description |
|---|---|
object |
The output image with the renamed bands. |
Source code in geemap/common.py
def rename_bands(img, in_band_names, out_band_names):
"""Renames image bands.
Args:
img (object): The image to be renamed.
in_band_names (list): The list of input band names.
out_band_names (list): The list of output band names.
Returns:
object: The output image with the renamed bands.
"""
return img.select(in_band_names, out_band_names)
reproject(image, output, dst_crs='EPSG:4326', resampling='nearest', **kwargs)
¶
Reprojects an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
str |
The input image filepath. |
required |
output |
str |
The output image filepath. |
required |
dst_crs |
str |
The destination CRS. Defaults to "EPSG:4326". |
'EPSG:4326' |
resampling |
Resampling |
The resampling method. Defaults to "nearest". |
'nearest' |
**kwargs |
Additional keyword arguments to pass to rasterio.open. |
{} |
Source code in geemap/common.py
def reproject(image, output, dst_crs="EPSG:4326", resampling="nearest", **kwargs):
"""Reprojects an image.
Args:
image (str): The input image filepath.
output (str): The output image filepath.
dst_crs (str, optional): The destination CRS. Defaults to "EPSG:4326".
resampling (Resampling, optional): The resampling method. Defaults to "nearest".
**kwargs: Additional keyword arguments to pass to rasterio.open.
"""
import rasterio as rio
from rasterio.warp import calculate_default_transform, reproject, Resampling
if isinstance(resampling, str):
resampling = getattr(Resampling, resampling)
image = os.path.abspath(image)
output = os.path.abspath(output)
if not os.path.exists(os.path.dirname(output)):
os.makedirs(os.path.dirname(output))
with rio.open(image, **kwargs) as src:
transform, width, height = calculate_default_transform(
src.crs, dst_crs, src.width, src.height, *src.bounds
)
kwargs = src.meta.copy()
kwargs.update(
{
"crs": dst_crs,
"transform": transform,
"width": width,
"height": height,
}
)
with rio.open(output, "w", **kwargs) as dst:
for i in range(1, src.count + 1):
reproject(
source=rio.band(src, i),
destination=rio.band(dst, i),
src_transform=src.transform,
src_crs=src.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=resampling,
**kwargs,
)
requireJS(lib_path=None, Map=None)
¶
Import Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL). For more info, visit https://www.open-geocomputing.org/OpenEarthEngineLibrary.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lib_path |
str |
A local file path or HTTP URL to a JavaScript library. It can also be in a format like 'users/gena/packages:grid'. Defaults to None. |
None |
Map |
geemap.Map |
An geemap.Map object. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
oeel object. |
Source code in geemap/common.py
def requireJS(lib_path=None, Map=None):
"""Import Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL).
For more info, visit https://www.open-geocomputing.org/OpenEarthEngineLibrary.
Args:
lib_path (str, optional): A local file path or HTTP URL to a JavaScript library. It can also be in a format like 'users/gena/packages:grid'. Defaults to None.
Map (geemap.Map, optional): An geemap.Map object. Defaults to None.
Returns:
object: oeel object.
"""
try:
from oeel import oeel
except ImportError:
raise ImportError(
"oeel is required for requireJS. Please install it using 'pip install oeel'."
)
ee_initialize()
if lib_path is None:
if Map is not None:
oeel.setMap(Map)
return oeel
elif isinstance(lib_path, str):
if lib_path.startswith("http"):
lib_path = get_direct_url(lib_path)
lib_path = change_require(lib_path)
if Map is not None:
oeel.setMap(Map)
return oeel.requireJS(lib_path)
else:
raise ValueError("lib_path must be a string.")
rgb_to_hex(rgb=(255, 255, 255))
¶
Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
rgb |
tuple |
RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255). |
(255, 255, 255) |
Returns:
| Type | Description |
|---|---|
str |
hex color code |
Source code in geemap/common.py
def rgb_to_hex(rgb=(255, 255, 255)):
"""Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.
Args:
rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).
Returns:
str: hex color code
"""
return "%02x%02x%02x" % rgb
save_colorbar(out_fig=None, width=4.0, height=0.3, vmin=0, vmax=1.0, palette=None, vis_params=None, cmap='gray', discrete=False, label=None, label_size=10, label_weight='normal', tick_size=8, bg_color='white', orientation='horizontal', dpi='figure', transparent=False, show_colorbar=True, **kwargs)
¶
Create a standalone colorbar and save it as an image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
out_fig |
str |
Path to the output image. |
None |
width |
float |
Width of the colorbar in inches. Default is 4.0. |
4.0 |
height |
float |
Height of the colorbar in inches. Default is 0.3. |
0.3 |
vmin |
float |
Minimum value of the colorbar. Default is 0. |
0 |
vmax |
float |
Maximum value of the colorbar. Default is 1.0. |
1.0 |
palette |
list |
List of colors to use for the colorbar. It can also be a cmap name, such as ndvi, ndwi, dem, coolwarm. Default is None. |
None |
vis_params |
dict |
Visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options. |
None |
cmap |
str |
Matplotlib colormap. Defaults to "gray". See https://matplotlib.org/3.3.4/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py for options. |
'gray' |
discrete |
bool |
Whether to create a discrete colorbar. Defaults to False. |
False |
label |
str |
Label for the colorbar. Defaults to None. |
None |
label_size |
int |
Font size for the colorbar label. Defaults to 12. |
10 |
label_weight |
str |
Font weight for the colorbar label, can be "normal", "bold", etc. Defaults to "normal". |
'normal' |
tick_size |
int |
Font size for the colorbar tick labels. Defaults to 10. |
8 |
bg_color |
str |
Background color for the colorbar. Defaults to "white". |
'white' |
orientation |
str |
Orientation of the colorbar, such as "vertical" and "horizontal". Defaults to "horizontal". |
'horizontal' |
dpi |
float | str |
The resolution in dots per inch. If 'figure', use the figure's dpi value. Defaults to "figure". |
'figure' |
transparent |
bool |
Whether to make the background transparent. Defaults to False. |
False |
show_colorbar |
bool |
Whether to show the colorbar. Defaults to True. |
True |
**kwargs |
Other keyword arguments to pass to matplotlib.pyplot.savefig(). |
{} |
Returns:
| Type | Description |
|---|---|
str |
Path to the output image. |
Source code in geemap/common.py
def save_colorbar(
out_fig=None,
width=4.0,
height=0.3,
vmin=0,
vmax=1.0,
palette=None,
vis_params=None,
cmap="gray",
discrete=False,
label=None,
label_size=10,
label_weight="normal",
tick_size=8,
bg_color="white",
orientation="horizontal",
dpi="figure",
transparent=False,
show_colorbar=True,
**kwargs,
):
"""Create a standalone colorbar and save it as an image.
Args:
out_fig (str): Path to the output image.
width (float): Width of the colorbar in inches. Default is 4.0.
height (float): Height of the colorbar in inches. Default is 0.3.
vmin (float): Minimum value of the colorbar. Default is 0.
vmax (float): Maximum value of the colorbar. Default is 1.0.
palette (list): List of colors to use for the colorbar. It can also be a cmap name, such as ndvi, ndwi, dem, coolwarm. Default is None.
vis_params (dict): Visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options.
cmap (str, optional): Matplotlib colormap. Defaults to "gray". See https://matplotlib.org/3.3.4/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py for options.
discrete (bool, optional): Whether to create a discrete colorbar. Defaults to False.
label (str, optional): Label for the colorbar. Defaults to None.
label_size (int, optional): Font size for the colorbar label. Defaults to 12.
label_weight (str, optional): Font weight for the colorbar label, can be "normal", "bold", etc. Defaults to "normal".
tick_size (int, optional): Font size for the colorbar tick labels. Defaults to 10.
bg_color (str, optional): Background color for the colorbar. Defaults to "white".
orientation (str, optional): Orientation of the colorbar, such as "vertical" and "horizontal". Defaults to "horizontal".
dpi (float | str, optional): The resolution in dots per inch. If 'figure', use the figure's dpi value. Defaults to "figure".
transparent (bool, optional): Whether to make the background transparent. Defaults to False.
show_colorbar (bool, optional): Whether to show the colorbar. Defaults to True.
**kwargs: Other keyword arguments to pass to matplotlib.pyplot.savefig().
Returns:
str: Path to the output image.
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from .colormaps import palettes, get_palette
if out_fig is None:
out_fig = temp_file_path("png")
else:
out_fig = check_file_path(out_fig)
if vis_params is None:
vis_params = {}
elif not isinstance(vis_params, dict):
raise TypeError("The vis_params must be a dictionary.")
if palette is not None:
if palette in ["ndvi", "ndwi", "dem"]:
palette = palettes[palette]
elif palette in list(palettes.keys()):
palette = get_palette(palette)
vis_params["palette"] = palette
orientation = orientation.lower()
if orientation not in ["horizontal", "vertical"]:
raise ValueError("The orientation must be either horizontal or vertical.")
if "opacity" in vis_params:
alpha = vis_params["opacity"]
if type(alpha) not in (int, float):
raise ValueError("The provided opacity value must be type scalar.")
else:
alpha = 1
if cmap is not None:
cmap = mpl.pyplot.get_cmap(cmap)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
if "palette" in vis_params:
hexcodes = to_hex_colors(vis_params["palette"])
if discrete:
cmap = mpl.colors.ListedColormap(hexcodes)
vals = np.linspace(vmin, vmax, cmap.N + 1)
norm = mpl.colors.BoundaryNorm(vals, cmap.N)
else:
cmap = mpl.colors.LinearSegmentedColormap.from_list(
"custom", hexcodes, N=256
)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
elif cmap is not None:
cmap = mpl.pyplot.get_cmap(cmap)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
else:
raise ValueError(
'cmap keyword or "palette" key in vis_params must be provided.'
)
fig, ax = plt.subplots(figsize=(width, height))
cb = mpl.colorbar.ColorbarBase(
ax, norm=norm, alpha=alpha, cmap=cmap, orientation=orientation, **kwargs
)
if label is not None:
cb.set_label(label=label, size=label_size, weight=label_weight)
cb.ax.tick_params(labelsize=tick_size)
if transparent:
bg_color = None
if bg_color is not None:
kwargs["facecolor"] = bg_color
if "bbox_inches" not in kwargs:
kwargs["bbox_inches"] = "tight"
fig.savefig(out_fig, dpi=dpi, transparent=transparent, **kwargs)
if not show_colorbar:
plt.close(fig)
return out_fig
screen_capture(filename, monitor=1)
¶
Takes a full screenshot of the selected monitor.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The output file path to the screenshot. |
required |
monitor |
int |
The monitor to take the screenshot. Defaults to 1. |
1 |
Source code in geemap/common.py
def screen_capture(filename, monitor=1):
"""Takes a full screenshot of the selected monitor.
Args:
filename (str): The output file path to the screenshot.
monitor (int, optional): The monitor to take the screenshot. Defaults to 1.
"""
try:
from mss import mss
except ImportError:
raise ImportError("Please install mss package using 'pip install mss'")
out_dir = os.path.dirname(filename)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not isinstance(monitor, int):
print("The monitor number must be an integer.")
return
try:
with mss() as sct:
sct.shot(output=filename, mon=monitor)
return filename
except Exception as e:
print(e)
search_api_tree(keywords, api_tree)
¶
Search Earth Engine API and return functions containing the specified keywords
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
keywords |
str |
The keywords to search for. |
required |
api_tree |
dict |
The dictionary containing the Earth Engine API tree. |
required |
Returns:
| Type | Description |
|---|---|
object |
An ipytree object/widget. |
Source code in geemap/common.py
def search_api_tree(keywords, api_tree):
"""Search Earth Engine API and return functions containing the specified keywords
Args:
keywords (str): The keywords to search for.
api_tree (dict): The dictionary containing the Earth Engine API tree.
Returns:
object: An ipytree object/widget.
"""
warnings.filterwarnings("ignore")
sub_tree = Tree()
for key in api_tree.keys():
if keywords.lower() in key.lower():
sub_tree.add_node(api_tree[key])
return sub_tree
search_ee_data(keywords, regex=False, source='ee', types=None, keys=['id', 'provider', 'tags', 'title'])
¶
Searches Earth Engine data catalog.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
keywords |
str | list |
Keywords to search for can be id, provider, tag and so on. Split by space if string, e.g. "1 2" becomes ['1','2']. |
required |
regex |
bool |
Allow searching for regular expressions. Defaults to false. |
False |
source |
str |
Can be 'ee', 'community' or 'all'. Defaults to 'ee'. For more details, see https://github.com/samapriya/awesome-gee-community-datasets/blob/master/community_datasets.json |
'ee' |
types |
list |
List of valid collection types. Defaults to None so no filter is applied. A possible filter ['image_collection'] |
None |
keys |
list |
List of metadata fields to search from. Defaults to ['id','provider','tags','title'] |
['id', 'provider', 'tags', 'title'] |
Returns:
| Type | Description |
|---|---|
list |
Returns a list of assets. |
Source code in geemap/common.py
def search_ee_data(
keywords,
regex=False,
source="ee",
types=None,
keys=["id", "provider", "tags", "title"],
):
"""Searches Earth Engine data catalog.
Args:
keywords (str | list): Keywords to search for can be id, provider, tag and so on. Split by space if string, e.g. "1 2" becomes ['1','2'].
regex (bool, optional): Allow searching for regular expressions. Defaults to false.
source (str, optional): Can be 'ee', 'community' or 'all'. Defaults to 'ee'. For more details, see https://github.com/samapriya/awesome-gee-community-datasets/blob/master/community_datasets.json
types (list, optional): List of valid collection types. Defaults to None so no filter is applied. A possible filter ['image_collection']
keys (list, optional): List of metadata fields to search from. Defaults to ['id','provider','tags','title']
Returns:
list: Returns a list of assets.
"""
if isinstance(keywords, str):
keywords = keywords.split(" ")
import re
from functools import reduce
def search_collection(pattern, dict_):
if regex:
if any(re.match(pattern, dict_[key]) for key in keys):
return dict_
elif any(pattern in dict_[key] for key in keys):
return dict_
return {}
def search_all(pattern):
# updated daily
a = "https://raw.githubusercontent.com/samapriya/Earth-Engine-Datasets-List/master/gee_catalog.json"
b = "https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/master/community_datasets.json"
sources = {"ee": [a], "community": [b], "all": [a, b]}
matches = []
for link in sources[source]:
r = requests.get(link)
catalog_list = r.json()
matches += [search_collection(pattern, x) for x in catalog_list]
matches = [x for x in matches if x]
if types:
return [x for x in matches if x["type"] in types]
return matches
try:
assets = list(
{json.dumps(match) for match in search_all(pattern=k)} for k in keywords
)
assets = sorted(list(reduce(set.intersection, assets)))
assets = [json.loads(x) for x in assets]
results = []
for asset in assets:
asset_dates = (
asset.get("start_date", "Unknown")
+ " - "
+ asset.get("end_date", "Unknown")
)
asset_snippet = asset["id"]
if "ee." in asset_snippet:
start_index = asset_snippet.index("'") + 1
end_index = asset_snippet.index("'", start_index)
asset_id = asset_snippet[start_index:end_index]
else:
asset_id = asset_snippet
asset["dates"] = asset_dates
asset["id"] = asset_id
asset["uid"] = asset_id.replace("/", "_")
results.append(asset)
return results
except Exception as e:
print(e)
search_qms(keyword, limit=10, list_only=True, add_prefix=True, timeout=300)
¶
Search for QMS tile providers from Quick Map Services.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
keyword |
str |
The keyword to search for. |
required |
limit |
int |
The maximum number of results to return. Defaults to 10. |
10 |
list_only |
bool |
If True, only the list of services will be returned. Defaults to True. |
True |
add_prefix |
bool |
If True, the prefix "qms." will be added to the service name. Defaults to True. |
True |
timeout |
int |
The timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of QMS tile providers. |
Source code in geemap/common.py
def search_qms(keyword, limit=10, list_only=True, add_prefix=True, timeout=300):
"""Search for QMS tile providers from Quick Map Services.
Args:
keyword (str): The keyword to search for.
limit (int, optional): The maximum number of results to return. Defaults to 10.
list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
add_prefix (bool, optional): If True, the prefix "qms." will be added to the service name. Defaults to True.
timeout (int, optional): The timeout in seconds. Defaults to 300.
Returns:
list: A list of QMS tile providers.
"""
QMS_API = "https://qms.nextgis.com/api/v1/geoservices"
services = requests.get(
f"{QMS_API}/?search={keyword}&type=tms&epsg=3857&limit={limit}", timeout=timeout
)
services = services.json()
if services["results"]:
providers = services["results"]
if list_only:
if add_prefix:
return ["qms." + provider["name"] for provider in providers]
else:
return [provider["name"] for provider in providers]
else:
return providers
else:
return None
search_xyz_services(keyword, name=None, list_only=True, add_prefix=True)
¶
Search for XYZ tile providers from xyzservices.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
keyword |
str |
The keyword to search for. |
required |
name |
str |
The name of the xyz tile. Defaults to None. |
None |
list_only |
bool |
If True, only the list of services will be returned. Defaults to True. |
True |
add_prefix |
bool |
If True, the prefix "xyz." will be added to the service name. Defaults to True. |
True |
Returns:
| Type | Description |
|---|---|
list |
A list of XYZ tile providers. |
Source code in geemap/common.py
def search_xyz_services(keyword, name=None, list_only=True, add_prefix=True):
"""Search for XYZ tile providers from xyzservices.
Args:
keyword (str): The keyword to search for.
name (str, optional): The name of the xyz tile. Defaults to None.
list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
add_prefix (bool, optional): If True, the prefix "xyz." will be added to the service name. Defaults to True.
Returns:
list: A list of XYZ tile providers.
"""
import xyzservices.providers as xyz
if name is None:
providers = xyz.filter(keyword=keyword).flatten()
else:
providers = xyz.filter(name=name).flatten()
if list_only:
if add_prefix:
return ["xyz." + provider for provider in providers]
else:
return [provider for provider in providers]
else:
return providers
set_proxy(port=1080, ip='http://127.0.0.1', timeout=300)
¶
Sets proxy if needed. This is only needed for countries where Google services are not available.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
port |
int |
The proxy port number. Defaults to 1080. |
1080 |
ip |
str |
The IP address. Defaults to 'http://127.0.0.1'. |
'http://127.0.0.1' |
timeout |
int |
The timeout in seconds. Defaults to 300. |
300 |
Source code in geemap/common.py
def set_proxy(port=1080, ip="http://127.0.0.1", timeout=300):
"""Sets proxy if needed. This is only needed for countries where Google services are not available.
Args:
port (int, optional): The proxy port number. Defaults to 1080.
ip (str, optional): The IP address. Defaults to 'http://127.0.0.1'.
timeout (int, optional): The timeout in seconds. Defaults to 300.
"""
try:
if not ip.startswith("http"):
ip = "http://" + ip
proxy = "{}:{}".format(ip, port)
os.environ["HTTP_PROXY"] = proxy
os.environ["HTTPS_PROXY"] = proxy
a = requests.get("https://earthengine.google.com/", timeout=timeout)
if a.status_code != 200:
print(
"Failed to connect to Earth Engine. Please double check the port number and ip address."
)
except Exception as e:
print(e)
setupJS()
¶
Install npm packages for Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL).
Source code in geemap/common.py
def setupJS():
"""Install npm packages for Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL)."""
try:
os.system("npm install @google/earthengine")
os.system("npm install zeromq@6.0.0-beta.6")
os.system("npm install request")
except Exception as e:
raise Exception(
f"Error installing npm packages: {e}. Make sure that you have installed nodejs. See https://nodejs.org/"
)
show_html(html)
¶
Shows HTML within Jupyter notebook.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
html |
str |
File path or HTML string. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the file does not exist. |
Returns:
| Type | Description |
|---|---|
ipywidgets.HTML |
HTML widget. |
Source code in geemap/common.py
def show_html(html):
"""Shows HTML within Jupyter notebook.
Args:
html (str): File path or HTML string.
Raises:
FileNotFoundError: If the file does not exist.
Returns:
ipywidgets.HTML: HTML widget.
"""
if os.path.exists(html):
with open(html, "r") as f:
content = f.read()
widget = widgets.HTML(value=content)
return widget
else:
try:
widget = widgets.HTML(value=html)
return widget
except Exception as e:
raise Exception(e)
show_image(img_path, width=None, height=None)
¶
Shows an image within Jupyter notebook.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img_path |
str |
The image file path. |
required |
width |
int |
Width of the image in pixels. Defaults to None. |
None |
height |
int |
Height of the image in pixels. Defaults to None. |
None |
Source code in geemap/common.py
def show_image(img_path, width=None, height=None):
"""Shows an image within Jupyter notebook.
Args:
img_path (str): The image file path.
width (int, optional): Width of the image in pixels. Defaults to None.
height (int, optional): Height of the image in pixels. Defaults to None.
"""
from IPython.display import display
try:
out = widgets.Output()
# layout={'border': '1px solid black'})
# layout={'border': '1px solid black', 'width': str(width + 20) + 'px', 'height': str(height + 10) + 'px'},)
out.outputs = ()
display(out)
with out:
if isinstance(img_path, str) and img_path.startswith("http"):
file_path = download_file(img_path)
else:
file_path = img_path
file = open(file_path, "rb")
image = file.read()
if (width is None) and (height is None):
display(widgets.Image(value=image))
elif (width is not None) and (height is not None):
display(widgets.Image(value=image, width=width, height=height))
else:
print("You need set both width and height.")
return
except Exception as e:
print(e)
show_youtube(id='h0pz3S6Tvx0')
¶
Displays a YouTube video within Jupyter notebooks.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
id |
str |
Unique ID of the video. Defaults to 'h0pz3S6Tvx0'. |
'h0pz3S6Tvx0' |
Source code in geemap/common.py
def show_youtube(id="h0pz3S6Tvx0"):
"""Displays a YouTube video within Jupyter notebooks.
Args:
id (str, optional): Unique ID of the video. Defaults to 'h0pz3S6Tvx0'.
"""
from IPython.display import YouTubeVideo, display
if "/" in id:
id = id.split("/")[-1]
try:
out = widgets.Output(layout={"width": "815px"})
# layout={'border': '1px solid black', 'width': '815px'})
out.outputs = ()
display(out)
with out:
display(YouTubeVideo(id, width=800, height=450))
except Exception as e:
print(e)
shp_to_ee(in_shp, **kwargs)
¶
Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
File path to a shapefile. |
required |
Returns:
| Type | Description |
|---|---|
object |
Earth Engine objects representing the shapefile. |
Source code in geemap/common.py
def shp_to_ee(in_shp, **kwargs):
"""Converts a shapefile to Earth Engine objects. Note that the CRS of the shapefile must be EPSG:4326
Args:
in_shp (str): File path to a shapefile.
Returns:
object: Earth Engine objects representing the shapefile.
"""
# ee_initialize()
try:
if "encoding" in kwargs:
json_data = shp_to_geojson(in_shp, encoding=kwargs.pop("encoding"))
else:
json_data = shp_to_geojson(in_shp)
ee_object = geojson_to_ee(json_data)
return ee_object
except Exception as e:
print(e)
shp_to_gdf(in_shp, **kwargs)
¶
Converts a shapefile to Geopandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
File path to the input shapefile. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The provided shp could not be found. |
Returns:
| Type | Description |
|---|---|
gpd.GeoDataFrame |
geopandas.GeoDataFrame |
Source code in geemap/common.py
def shp_to_gdf(in_shp, **kwargs):
"""Converts a shapefile to Geopandas dataframe.
Args:
in_shp (str): File path to the input shapefile.
Raises:
FileNotFoundError: The provided shp could not be found.
Returns:
gpd.GeoDataFrame: geopandas.GeoDataFrame
"""
warnings.filterwarnings("ignore")
in_shp = os.path.abspath(in_shp)
if not os.path.exists(in_shp):
raise FileNotFoundError("The provided shp could not be found.")
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
try:
return gpd.read_file(in_shp, **kwargs)
except Exception as e:
raise Exception(e)
shp_to_geojson(in_shp, filename=None, **kwargs)
¶
Converts a shapefile to GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
File path of the input shapefile. |
required |
filename |
str |
File path of the output GeoJSON. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
object |
The json object representing the shapefile. |
Source code in geemap/common.py
def shp_to_geojson(in_shp, filename=None, **kwargs):
"""Converts a shapefile to GeoJSON.
Args:
in_shp (str): File path of the input shapefile.
filename (str, optional): File path of the output GeoJSON. Defaults to None.
Returns:
object: The json object representing the shapefile.
"""
try:
import shapefile
# from datetime import date
in_shp = os.path.abspath(in_shp)
if filename is not None:
ext = os.path.splitext(filename)[1]
print(ext)
if ext.lower() not in [".json", ".geojson"]:
raise TypeError("The output file extension must the .json or .geojson.")
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
if not is_GCS(in_shp):
try:
import geopandas as gpd
except Exception:
raise ImportError(
"GeoPandas is required to perform reprojection of the data. See https://geopandas.org/install.html"
)
try:
in_gdf = gpd.read_file(in_shp)
out_gdf = in_gdf.to_crs(epsg="4326")
out_shp = in_shp.replace(".shp", "_gcs.shp")
out_gdf.to_file(out_shp)
in_shp = out_shp
except Exception as e:
raise Exception(e)
if "encoding" in kwargs:
reader = shapefile.Reader(in_shp, encoding=kwargs.pop("encoding"))
else:
reader = shapefile.Reader(in_shp)
out_dict = reader.__geo_interface__
# fields = reader.fields[1:]
# field_names = [field[0] for field in fields]
# # pyShp returns dates as `datetime.date` or as `bytes` when they are empty
# # This is not JSON compatible, so we keep track of them to convert them to str
# date_fields_names = [field[0] for field in fields if field[1] == "D"]
# buffer = []
# for sr in reader.shapeRecords():
# atr = dict(zip(field_names, sr.record))
# for date_field in date_fields_names:
# value = atr[date_field]
# # convert date to string, similar to pyShp writing
# # https://github.com/GeospatialPython/pyshp/blob/69c60f6d07c329f7d3ac2cba79bc03643bd424d8/shapefile.py#L1814
# if isinstance(value, date):
# value = "{:04d}{:02d}{:02d}".format(
# value.year, value.month, value.day
# )
# elif not value: # empty bytes string
# value = "0" * 8 # QGIS NULL for date type
# atr[date_field] = value
# geom = sr.shape.__geo_interface__
# buffer.append(dict(type="Feature", geometry=geom, properties=atr))
# out_dict = {"type": "FeatureCollection", "features": buffer}
if filename is not None:
# from json import dumps
with open(filename, "w") as geojson:
geojson.write(json.dumps(out_dict, indent=2) + "\n")
else:
return out_dict
except Exception as e:
raise Exception(e)
shp_to_geopandas(in_shp, **kwargs)
¶
Converts a shapefile to Geopandas dataframe.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_shp |
str |
File path to the input shapefile. |
required |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
The provided shp could not be found. |
Returns:
| Type | Description |
|---|---|
gpd.GeoDataFrame |
geopandas.GeoDataFrame |
Source code in geemap/common.py
def shp_to_gdf(in_shp, **kwargs):
"""Converts a shapefile to Geopandas dataframe.
Args:
in_shp (str): File path to the input shapefile.
Raises:
FileNotFoundError: The provided shp could not be found.
Returns:
gpd.GeoDataFrame: geopandas.GeoDataFrame
"""
warnings.filterwarnings("ignore")
in_shp = os.path.abspath(in_shp)
if not os.path.exists(in_shp):
raise FileNotFoundError("The provided shp could not be found.")
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
try:
return gpd.read_file(in_shp, **kwargs)
except Exception as e:
raise Exception(e)
stac_assets(url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get all assets of a STAC item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of assets. |
Source code in geemap/common.py
def stac_assets(
url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs
):
"""Get all assets of a STAC item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A list of assets.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/assets", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_assets(), params=kwargs, timeout=timeout
).json()
return r
stac_bands(url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get band names of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of band names |
Source code in geemap/common.py
def stac_bands(
url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs
):
"""Get band names of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A list of band names
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/assets", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_assets(), params=kwargs, timeout=timeout
).json()
return r
stac_bounds(url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A list of values representing [left, bottom, right, top] |
Source code in geemap/common.py
def stac_bounds(
url=None, collection=None, item=None, titiler_endpoint=None, timeout=300, **kwargs
):
"""Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A list of values representing [left, bottom, right, top]
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/bounds", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_bounds(), params=kwargs, timeout=timeout
).json()
bounds = r["bounds"]
return bounds
stac_center(url=None, collection=None, item=None, titiler_endpoint=None, **kwargs)
¶
Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
tuple |
A tuple representing (longitude, latitude) |
Source code in geemap/common.py
def stac_center(url=None, collection=None, item=None, titiler_endpoint=None, **kwargs):
"""Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
Returns:
tuple: A tuple representing (longitude, latitude)
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if isinstance(url, str):
url = get_direct_url(url)
bounds = stac_bounds(url, collection, item, titiler_endpoint, **kwargs)
center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2) # (lon, lat)
return center
stac_info(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get band info of a STAC item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
assets |
str | list |
The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"]. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band info. |
Source code in geemap/common.py
def stac_info(
url=None,
collection=None,
item=None,
assets=None,
titiler_endpoint=None,
timeout=300,
**kwargs,
):
"""Get band info of a STAC item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
assets (str | list): The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"].
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band info.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
if assets is not None:
kwargs["assets"] = assets
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/info", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_info(), params=kwargs, timeout=timeout
).json()
return r
stac_info_geojson(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get band info of a STAC item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
assets |
str | list |
The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"]. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band info. |
Source code in geemap/common.py
def stac_info_geojson(
url=None,
collection=None,
item=None,
assets=None,
titiler_endpoint=None,
timeout=300,
**kwargs,
):
"""Get band info of a STAC item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
assets (str | list): The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"].
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band info.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
if assets is not None:
kwargs["assets"] = assets
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/info.geojson", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_info_geojson(), params=kwargs, timeout=timeout
).json()
return r
stac_pixel_value(lon, lat, url=None, collection=None, item=None, assets=None, titiler_endpoint=None, verbose=True, timeout=300, **kwargs)
¶
Get pixel value from STAC assets.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lon |
float |
Longitude of the pixel. |
required |
lat |
float |
Latitude of the pixel. |
required |
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
assets |
str | list |
The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"]. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
verbose |
bool |
Print out the error message. Defaults to True. |
True |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of pixel values for each asset. |
Source code in geemap/common.py
def stac_pixel_value(
lon,
lat,
url=None,
collection=None,
item=None,
assets=None,
titiler_endpoint=None,
verbose=True,
timeout=300,
**kwargs,
):
"""Get pixel value from STAC assets.
Args:
lon (float): Longitude of the pixel.
lat (float): Latitude of the pixel.
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
assets (str | list): The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"].
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
verbose (bool, optional): Print out the error message. Defaults to True.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of pixel values for each asset.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
if assets is None:
assets = stac_assets(
url=url,
collection=collection,
item=item,
titiler_endpoint=titiler_endpoint,
)
assets = ",".join(assets)
kwargs["assets"] = assets
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/{lon},{lat}", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_pixel_value(lon, lat),
params=kwargs,
timeout=timeout,
).json()
if "detail" in r:
if verbose:
print(r["detail"])
return None
else:
values = [v[0] for v in r["values"]]
result = dict(zip(assets.split(","), values))
return result
stac_stats(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get band statistics of a STAC item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
assets |
str | list |
The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"]. |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
list |
A dictionary of band statistics. |
Source code in geemap/common.py
def stac_stats(
url=None,
collection=None,
item=None,
assets=None,
titiler_endpoint=None,
timeout=300,
**kwargs,
):
"""Get band statistics of a STAC item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
assets (str | list): The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"].
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
list: A dictionary of band statistics.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
if assets is not None:
kwargs["assets"] = assets
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/statistics", params=kwargs, timeout=timeout
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_statistics(), params=kwargs, timeout=timeout
).json()
return r
stac_tile(url=None, collection=None, item=None, assets=None, bands=None, titiler_endpoint=None, timeout=300, **kwargs)
¶
Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
url |
str |
HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json |
None |
collection |
str |
The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2. |
None |
item |
str |
The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1. |
None |
assets |
str | list |
The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"]. |
None |
bands |
list |
A list of band names, e.g., ["SR_B7", "SR_B5", "SR_B4"] |
None |
titiler_endpoint |
str |
Titiler endpoint, e.g., "https://titiler.xyz", "https://planetarycomputer.microsoft.com/api/data/v1", "planetary-computer", "pc". Defaults to None. |
None |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
Returns:
| Type | Description |
|---|---|
str |
Returns the STAC Tile layer URL. |
Source code in geemap/common.py
def stac_tile(
url=None,
collection=None,
item=None,
assets=None,
bands=None,
titiler_endpoint=None,
timeout=300,
**kwargs,
):
"""Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.
Args:
url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
collection (str): The Microsoft Planetary Computer STAC collection ID, e.g., landsat-8-c2-l2.
item (str): The Microsoft Planetary Computer STAC item ID, e.g., LC08_L2SP_047027_20201204_02_T1.
assets (str | list): The Microsoft Planetary Computer STAC asset ID, e.g., ["SR_B7", "SR_B5", "SR_B4"].
bands (list): A list of band names, e.g., ["SR_B7", "SR_B5", "SR_B4"]
titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "https://planetarycomputer.microsoft.com/api/data/v1", "planetary-computer", "pc". Defaults to None.
timeout (int, optional): Timeout in seconds. Defaults to 300.
Returns:
str: Returns the STAC Tile layer URL.
"""
if url is None and collection is None:
raise ValueError("Either url or collection must be specified.")
if collection is not None and titiler_endpoint is None:
titiler_endpoint = "planetary-computer"
if url is not None:
url = get_direct_url(url)
kwargs["url"] = url
if collection is not None:
kwargs["collection"] = collection
if item is not None:
kwargs["item"] = item
if "palette" in kwargs:
kwargs["colormap_name"] = kwargs["palette"]
del kwargs["palette"]
if isinstance(bands, list) and len(set(bands)) == 1:
bands = bands[0]
if isinstance(assets, list) and len(set(assets)) == 1:
assets = assets[0]
titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
if "expression" in kwargs and ("asset_as_band" not in kwargs):
kwargs["asset_as_band"] = True
if isinstance(titiler_endpoint, PlanetaryComputerEndpoint):
if isinstance(bands, str):
bands = bands.split(",")
if isinstance(assets, str):
assets = assets.split(",")
if assets is None and (bands is not None):
assets = bands
else:
kwargs["bidx"] = bands
kwargs["assets"] = assets
# if ("expression" in kwargs) and ("rescale" not in kwargs):
# stats = stac_stats(
# collection=collection,
# item=item,
# expression=kwargs["expression"],
# titiler_endpoint=titiler_endpoint,
# )
# kwargs[
# "rescale"
# ] = f"{stats[0]['percentile_2']},{stats[0]['percentile_98']}"
# if ("asset_expression" in kwargs) and ("rescale" not in kwargs):
# stats = stac_stats(
# collection=collection,
# item=item,
# expression=kwargs["asset_expression"],
# titiler_endpoint=titiler_endpoint,
# )
# kwargs[
# "rescale"
# ] = f"{stats[0]['percentile_2']},{stats[0]['percentile_98']}"
if (
(assets is not None)
and ("asset_expression" not in kwargs)
and ("expression" not in kwargs)
and ("rescale" not in kwargs)
):
stats = stac_stats(
collection=collection,
item=item,
assets=assets,
titiler_endpoint=titiler_endpoint,
)
if "detail" not in stats:
try:
percentile_2 = min([stats[s]["percentile_2"] for s in stats])
percentile_98 = max([stats[s]["percentile_98"] for s in stats])
except:
percentile_2 = min(
[
stats[s][list(stats[s].keys())[0]]["percentile_2"]
for s in stats
]
)
percentile_98 = max(
[
stats[s][list(stats[s].keys())[0]]["percentile_98"]
for s in stats
]
)
kwargs["rescale"] = f"{percentile_2},{percentile_98}"
else:
print(stats["detail"]) # When operation times out.
else:
if isinstance(bands, str):
bands = bands.split(",")
if isinstance(assets, str):
assets = assets.split(",")
if assets is None and (bands is not None):
assets = bands
else:
kwargs["asset_bidx"] = bands
kwargs["assets"] = assets
TileMatrixSetId = "WebMercatorQuad"
if "TileMatrixSetId" in kwargs.keys():
TileMatrixSetId = kwargs["TileMatrixSetId"]
kwargs.pop("TileMatrixSetId")
if isinstance(titiler_endpoint, str):
r = requests.get(
f"{titiler_endpoint}/stac/{TileMatrixSetId}/tilejson.json",
params=kwargs,
timeout=timeout,
).json()
else:
r = requests.get(
titiler_endpoint.url_for_stac_item(), params=kwargs, timeout=timeout
).json()
return r["tiles"][0]
str_to_num(in_str)
¶
Converts a string to an ee.Number.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_str |
str |
The string to convert to a number. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.Number |
Source code in geemap/common.py
def str_to_num(in_str):
"""Converts a string to an ee.Number.
Args:
in_str (str): The string to convert to a number.
Returns:
object: ee.Number
"""
return ee.Number.parse(str)
summarize_by_group(collection, column, group, group_name, stats_type, return_dict=True)
¶
Calculates summary statistics by group.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
object |
The input feature collection |
required |
column |
str |
The value column to calculate summary statistics. |
required |
group |
str |
The name of the group column. |
required |
group_name |
str |
The new group name to use. |
required |
stats_type |
str |
The type of summary statistics. |
required |
return_dict |
bool |
Whether to return the result as a dictionary. |
True |
Returns:
| Type | Description |
|---|---|
object |
ee.Dictionary or ee.List |
Source code in geemap/common.py
def summarize_by_group(
collection, column, group, group_name, stats_type, return_dict=True
):
"""Calculates summary statistics by group.
Args:
collection (object): The input feature collection
column (str): The value column to calculate summary statistics.
group (str): The name of the group column.
group_name (str): The new group name to use.
stats_type (str): The type of summary statistics.
return_dict (bool): Whether to return the result as a dictionary.
Returns:
object: ee.Dictionary or ee.List
"""
stats_type = stats_type.lower()
allowed_stats = ["min", "max", "mean", "median", "sum", "stdDev", "variance"]
if stats_type not in allowed_stats:
print(
"The stats type must be one of the following: {}".format(
",".join(allowed_stats)
)
)
return
stats_dict = {
"min": ee.Reducer.min(),
"max": ee.Reducer.max(),
"mean": ee.Reducer.mean(),
"median": ee.Reducer.median(),
"sum": ee.Reducer.sum(),
"stdDev": ee.Reducer.stdDev(),
"variance": ee.Reducer.variance(),
}
selectors = [column, group]
stats = collection.reduceColumns(
**{
"selectors": selectors,
"reducer": stats_dict[stats_type].group(
**{"groupField": 1, "groupName": group_name}
),
}
)
results = ee.List(ee.Dictionary(stats).get("groups"))
if return_dict:
keys = results.map(lambda k: ee.Dictionary(k).get(group_name))
values = results.map(lambda v: ee.Dictionary(v).get(stats_type))
results = ee.Dictionary.fromLists(keys, values)
return results
summary_stats(collection, column)
¶
Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
collection |
FeatureCollection |
The input feature collection to calculate summary statistics. |
required |
column |
str |
The name of the column to calculate summary statistics. |
required |
Returns:
| Type | Description |
|---|---|
dict |
The dictionary containing information about the summary statistics. |
Source code in geemap/common.py
def summary_stats(collection, column):
"""Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean,
sample standard deviation, sample variance, total standard deviation and total variance of the selected property.
Args:
collection (FeatureCollection): The input feature collection to calculate summary statistics.
column (str): The name of the column to calculate summary statistics.
Returns:
dict: The dictionary containing information about the summary statistics.
"""
stats = collection.aggregate_stats(column).getInfo()
return eval(str(stats))
system_fonts(show_full_path=False)
¶
Gets a list of system fonts
1 2 3 4 | |
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
show_full_path |
bool |
Whether to show the full path of each system font. Defaults to False. |
False |
Returns:
| Type | Description |
|---|---|
list |
A list of system fonts. |
Source code in geemap/common.py
def system_fonts(show_full_path=False):
"""Gets a list of system fonts
# Common font locations:
# Linux: /usr/share/fonts/TTF/
# Windows: C:/Windows/Fonts
# macOS: System > Library > Fonts
Args:
show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.
Returns:
list: A list of system fonts.
"""
try:
import matplotlib.font_manager
font_list = matplotlib.font_manager.findSystemFonts(
fontpaths=None, fontext="ttf"
)
font_list.sort()
font_names = [os.path.basename(f) for f in font_list]
font_names.sort()
if show_full_path:
return font_list
else:
return font_names
except Exception as e:
print(e)
temp_file_path(extension)
¶
Returns a temporary file path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
extension |
str |
The file extension. |
required |
Returns:
| Type | Description |
|---|---|
str |
The temporary file path. |
Source code in geemap/common.py
def temp_file_path(extension):
"""Returns a temporary file path.
Args:
extension (str): The file extension.
Returns:
str: The temporary file path.
"""
import tempfile
import uuid
if not extension.startswith("."):
extension = "." + extension
file_id = str(uuid.uuid4())
file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{extension}")
return file_path
tif_to_jp2(filename, output, creationOptions=None)
¶
Converts a GeoTIFF to JPEG2000.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The path to the GeoTIFF file. |
required |
output |
str |
The path to the output JPEG2000 file. |
required |
creationOptions |
list |
A list of creation options for the JPEG2000 file. See
https://gdal.org/drivers/raster/jp2openjpeg.html. For example, to specify the compression
ratio, use |
None |
Source code in geemap/common.py
def tif_to_jp2(filename, output, creationOptions=None):
"""Converts a GeoTIFF to JPEG2000.
Args:
filename (str): The path to the GeoTIFF file.
output (str): The path to the output JPEG2000 file.
creationOptions (list): A list of creation options for the JPEG2000 file. See
https://gdal.org/drivers/raster/jp2openjpeg.html. For example, to specify the compression
ratio, use ``["QUALITY=20"]``. A value of 20 means the file will be 20% of the size in comparison
to uncompressed data.
"""
from osgeo import gdal
gdal.UseExceptions()
if not os.path.exists(filename):
raise Exception(f"File {filename} does not exist")
if not output.endswith(".jp2"):
output += ".jp2"
in_ds = gdal.Open(filename)
gdal.Translate(output, in_ds, format="JP2OpenJPEG", creationOptions=creationOptions)
in_ds = None
tms_to_geotiff(output, bbox, zoom=None, resolution=None, source='OpenStreetMap', crs='EPSG:3857', to_cog=False, quiet=False, **kwargs)
¶
Download TMS tiles and convert them to a GeoTIFF. The source is adapted from https://github.com/gumblex/tms2geotiff. Credits to the GitHub user @gumblex.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
output |
str |
The output GeoTIFF file. |
required |
bbox |
list |
The bounding box [minx, miny, maxx, maxy], e.g., [-122.5216, 37.733, -122.3661, 37.8095] |
required |
zoom |
int |
The map zoom level. Defaults to None. |
None |
resolution |
float |
The resolution in meters. Defaults to None. |
None |
source |
str |
The tile source. It can be one of the following: "OPENSTREETMAP", "ROADMAP", "SATELLITE", "TERRAIN", "HYBRID", or an HTTP URL. Defaults to "OpenStreetMap". |
'OpenStreetMap' |
crs |
str |
The coordinate reference system. Defaults to "EPSG:3857". |
'EPSG:3857' |
to_cog |
bool |
Convert to Cloud Optimized GeoTIFF. Defaults to False. |
False |
quiet |
bool |
Suppress output. Defaults to False. |
False |
**kwargs |
Additional arguments to pass to gdal.GetDriverByName("GTiff").Create(). |
{} |
Source code in geemap/common.py
def tms_to_geotiff(
output,
bbox,
zoom=None,
resolution=None,
source="OpenStreetMap",
crs="EPSG:3857",
to_cog=False,
quiet=False,
**kwargs,
):
"""Download TMS tiles and convert them to a GeoTIFF. The source is adapted from https://github.com/gumblex/tms2geotiff.
Credits to the GitHub user @gumblex.
Args:
output (str): The output GeoTIFF file.
bbox (list): The bounding box [minx, miny, maxx, maxy], e.g., [-122.5216, 37.733, -122.3661, 37.8095]
zoom (int, optional): The map zoom level. Defaults to None.
resolution (float, optional): The resolution in meters. Defaults to None.
source (str, optional): The tile source. It can be one of the following: "OPENSTREETMAP", "ROADMAP",
"SATELLITE", "TERRAIN", "HYBRID", or an HTTP URL. Defaults to "OpenStreetMap".
crs (str, optional): The coordinate reference system. Defaults to "EPSG:3857".
to_cog (bool, optional): Convert to Cloud Optimized GeoTIFF. Defaults to False.
quiet (bool, optional): Suppress output. Defaults to False.
**kwargs: Additional arguments to pass to gdal.GetDriverByName("GTiff").Create().
"""
import io
import math
import itertools
import concurrent.futures
import numpy
from PIL import Image
from osgeo import gdal, osr
gdal.UseExceptions()
try:
import httpx
SESSION = httpx.Client()
except ImportError:
import requests
SESSION = requests.Session()
xyz_tiles = {
"OpenStreetMap": {
"url": "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
"attribution": "OpenStreetMap",
"name": "OpenStreetMap",
},
"ROADMAP": {
"url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Street_Map/MapServer/tile/{z}/{y}/{x}",
"attribution": "Esri",
"name": "Esri.WorldStreetMap",
},
"SATELLITE": {
"url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
"attribution": "Esri",
"name": "Esri.WorldImagery",
},
"TERRAIN": {
"url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Topo_Map/MapServer/tile/{z}/{y}/{x}",
"attribution": "Esri",
"name": "Esri.WorldTopoMap",
},
"HYBRID": {
"url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
"attribution": "Esri",
"name": "Esri.WorldImagery",
},
}
if isinstance(source, str) and source.upper() in xyz_tiles:
source = xyz_tiles[source.upper()]["url"]
elif isinstance(source, str) and source.startswith("http"):
pass
else:
raise ValueError(
'source must be one of "OpenStreetMap", "ROADMAP", "SATELLITE", "TERRAIN", "HYBRID", or a URL'
)
def resolution_to_zoom_level(resolution):
"""
Convert map resolution in meters to zoom level for Web Mercator (EPSG:3857) tiles.
"""
# Web Mercator tile size in meters at zoom level 0
initial_resolution = 156543.03392804097
# Calculate the zoom level
zoom_level = math.log2(initial_resolution / resolution)
return int(zoom_level)
if isinstance(bbox, list) and len(bbox) == 4:
west, south, east, north = bbox
else:
raise ValueError(
"bbox must be a list of 4 coordinates in the format of [xmin, ymin, xmax, ymax]"
)
if zoom is None and resolution is None:
raise ValueError("Either zoom or resolution must be provided")
elif zoom is not None and resolution is not None:
raise ValueError("Only one of zoom or resolution can be provided")
if resolution is not None:
zoom = resolution_to_zoom_level(resolution)
EARTH_EQUATORIAL_RADIUS = 6378137.0
Image.MAX_IMAGE_PIXELS = None
web_mercator = osr.SpatialReference()
web_mercator.ImportFromEPSG(3857)
WKT_3857 = web_mercator.ExportToWkt()
def from4326_to3857(lat, lon):
xtile = math.radians(lon) * EARTH_EQUATORIAL_RADIUS
ytile = (
math.log(math.tan(math.radians(45 + lat / 2.0))) * EARTH_EQUATORIAL_RADIUS
)
return (xtile, ytile)
def deg2num(lat, lon, zoom):
lat_r = math.radians(lat)
n = 2**zoom
xtile = (lon + 180) / 360 * n
ytile = (1 - math.log(math.tan(lat_r) + 1 / math.cos(lat_r)) / math.pi) / 2 * n
return (xtile, ytile)
def is_empty(im):
extrema = im.getextrema()
if len(extrema) >= 3:
if len(extrema) > 3 and extrema[-1] == (0, 0):
return True
for ext in extrema[:3]:
if ext != (0, 0):
return False
return True
else:
return extrema[0] == (0, 0)
def paste_tile(bigim, base_size, tile, corner_xy, bbox):
if tile is None:
return bigim
im = Image.open(io.BytesIO(tile))
mode = "RGB" if im.mode == "RGB" else "RGBA"
size = im.size
if bigim is None:
base_size[0] = size[0]
base_size[1] = size[1]
newim = Image.new(
mode, (size[0] * (bbox[2] - bbox[0]), size[1] * (bbox[3] - bbox[1]))
)
else:
newim = bigim
dx = abs(corner_xy[0] - bbox[0])
dy = abs(corner_xy[1] - bbox[1])
xy0 = (size[0] * dx, size[1] * dy)
if mode == "RGB":
newim.paste(im, xy0)
else:
if im.mode != mode:
im = im.convert(mode)
if not is_empty(im):
newim.paste(im, xy0)
im.close()
return newim
def finish_picture(bigim, base_size, bbox, x0, y0, x1, y1):
xfrac = x0 - bbox[0]
yfrac = y0 - bbox[1]
x2 = round(base_size[0] * xfrac)
y2 = round(base_size[1] * yfrac)
imgw = round(base_size[0] * (x1 - x0))
imgh = round(base_size[1] * (y1 - y0))
retim = bigim.crop((x2, y2, x2 + imgw, y2 + imgh))
if retim.mode == "RGBA" and retim.getextrema()[3] == (255, 255):
retim = retim.convert("RGB")
bigim.close()
return retim
def get_tile(url):
retry = 3
while 1:
try:
r = SESSION.get(url, timeout=60)
break
except Exception:
retry -= 1
if not retry:
raise
if r.status_code == 404:
return None
elif not r.content:
return None
r.raise_for_status()
return r.content
def draw_tile(
source, lat0, lon0, lat1, lon1, zoom, filename, quiet=False, **kwargs
):
x0, y0 = deg2num(lat0, lon0, zoom)
x1, y1 = deg2num(lat1, lon1, zoom)
x0, x1 = sorted([x0, x1])
y0, y1 = sorted([y0, y1])
corners = tuple(
itertools.product(
range(math.floor(x0), math.ceil(x1)),
range(math.floor(y0), math.ceil(y1)),
)
)
totalnum = len(corners)
futures = []
with concurrent.futures.ThreadPoolExecutor(5) as executor:
for x, y in corners:
futures.append(
executor.submit(get_tile, source.format(z=zoom, x=x, y=y))
)
bbox = (math.floor(x0), math.floor(y0), math.ceil(x1), math.ceil(y1))
bigim = None
base_size = [256, 256]
for k, (fut, corner_xy) in enumerate(zip(futures, corners), 1):
bigim = paste_tile(bigim, base_size, fut.result(), corner_xy, bbox)
if not quiet:
print("Downloaded image %d/%d" % (k, totalnum))
if not quiet:
print("Saving GeoTIFF. Please wait...")
img = finish_picture(bigim, base_size, bbox, x0, y0, x1, y1)
imgbands = len(img.getbands())
driver = gdal.GetDriverByName("GTiff")
if "options" not in kwargs:
kwargs["options"] = [
"COMPRESS=DEFLATE",
"PREDICTOR=2",
"ZLEVEL=9",
"TILED=YES",
]
gtiff = driver.Create(
filename,
img.size[0],
img.size[1],
imgbands,
gdal.GDT_Byte,
**kwargs,
)
xp0, yp0 = from4326_to3857(lat0, lon0)
xp1, yp1 = from4326_to3857(lat1, lon1)
pwidth = abs(xp1 - xp0) / img.size[0]
pheight = abs(yp1 - yp0) / img.size[1]
gtiff.SetGeoTransform((min(xp0, xp1), pwidth, 0, max(yp0, yp1), 0, -pheight))
gtiff.SetProjection(WKT_3857)
for band in range(imgbands):
array = numpy.array(img.getdata(band), dtype="u8")
array = array.reshape((img.size[1], img.size[0]))
band = gtiff.GetRasterBand(band + 1)
band.WriteArray(array)
gtiff.FlushCache()
if not quiet:
print(f"Image saved to {filename}")
return img
try:
draw_tile(source, south, west, north, east, zoom, output, quiet, **kwargs)
if crs.upper() != "EPSG:3857":
reproject(output, output, crs, to_cog=to_cog)
elif to_cog:
image_to_cog(output, output)
except Exception as e:
raise Exception(e)
to_hex_colors(colors)
¶
Convert a GEE color palette into hexadecimal color codes. can handle mixin formats
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
colors |
list |
A list of hex color codes. |
required |
Returns:
| Type | Description |
|---|---|
list |
A list of hex color codes prefixed with #. |
Source code in geemap/common.py
def to_hex_colors(colors):
"""Convert a GEE color palette into hexadecimal color codes. can handle mixin formats
Args:
colors (list): A list of hex color codes.
Returns:
list: A list of hex color codes prefixed with #.
"""
return [check_color(c) for c in colors]
update_package()
¶
Updates the geemap package from the geemap GitHub repository without the need to use pip or conda. In this way, I don't have to keep updating pypi and conda-forge with every minor update of the package.
Source code in geemap/common.py
def update_package():
"""Updates the geemap package from the geemap GitHub repository without the need to use pip or conda.
In this way, I don't have to keep updating pypi and conda-forge with every minor update of the package.
"""
try:
download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
if not os.path.exists(download_dir):
os.makedirs(download_dir)
clone_repo(out_dir=download_dir)
pkg_dir = os.path.join(download_dir, "geemap-master")
work_dir = os.getcwd()
os.chdir(pkg_dir)
if shutil.which("pip") is None:
cmd = "pip3 install ."
else:
cmd = "pip install ."
os.system(cmd)
os.chdir(work_dir)
print(
"\nPlease comment out 'geemap.update_package()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output"
)
except Exception as e:
raise Exception(e)
upload_to_imgur(in_gif)
¶
Uploads an image to imgur.com
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_gif |
str |
The file path to the image. |
required |
Source code in geemap/common.py
def upload_to_imgur(in_gif):
"""Uploads an image to imgur.com
Args:
in_gif (str): The file path to the image.
"""
import subprocess
pkg_name = "imgur-uploader"
if not is_tool(pkg_name):
check_install(pkg_name)
try:
IMGUR_API_ID = os.environ.get("IMGUR_API_ID", None)
IMGUR_API_SECRET = os.environ.get("IMGUR_API_SECRET", None)
credentials_path = os.path.join(
os.path.expanduser("~"), ".config/imgur_uploader/uploader.cfg"
)
if (
(IMGUR_API_ID is not None) and (IMGUR_API_SECRET is not None)
) or os.path.exists(credentials_path):
proc = subprocess.Popen(["imgur-uploader", in_gif], stdout=subprocess.PIPE)
for _ in range(0, 2):
line = proc.stdout.readline()
print(line.rstrip().decode("utf-8"))
# while True:
# line = proc.stdout.readline()
# if not line:
# break
# print(line.rstrip().decode("utf-8"))
else:
print(
"Imgur API credentials could not be found. Please check https://pypi.org/project/imgur-uploader/ for instructions on how to get Imgur API credentials"
)
return
except Exception as e:
print(e)
use_mkdocs()
¶
Test if the current notebook is running in mkdocs.
Returns:
| Type | Description |
|---|---|
bool |
True if the notebook is running in mkdocs. |
Source code in geemap/common.py
def use_mkdocs():
"""Test if the current notebook is running in mkdocs.
Returns:
bool: True if the notebook is running in mkdocs.
"""
if os.environ.get("USE_MKDOCS") is not None:
return True
else:
return False
vec_area(fc)
¶
Calculate the area (m2) of each each feature in a feature collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
The feature collection to compute the area. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def vec_area(fc):
"""Calculate the area (m2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection
"""
return fc.map(lambda f: f.set({"area_m2": f.area(1).round()}))
vec_area_ha(fc)
¶
Calculate the area (hectare) of each each feature in a feature collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
The feature collection to compute the area. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def vec_area_ha(fc):
"""Calculate the area (hectare) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection
"""
return fc.map(lambda f: f.set({"area_ha": f.area(1).divide(1e4).round()}))
vec_area_km2(fc)
¶
Calculate the area (km2) of each each feature in a feature collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
The feature collection to compute the area. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def vec_area_km2(fc):
"""Calculate the area (km2) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection
"""
return fc.map(lambda f: f.set({"area_km2": f.area(1).divide(1e6).round()}))
vec_area_mi2(fc)
¶
Calculate the area (square mile) of each each feature in a feature collection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fc |
object |
The feature collection to compute the area. |
required |
Returns:
| Type | Description |
|---|---|
object |
ee.FeatureCollection |
Source code in geemap/common.py
def vec_area_mi2(fc):
"""Calculate the area (square mile) of each each feature in a feature collection.
Args:
fc (object): The feature collection to compute the area.
Returns:
object: ee.FeatureCollection
"""
return fc.map(lambda f: f.set({"area_mi2": f.area(1).divide(2.59e6).round()}))
vector_centroids(ee_object)
¶
Returns the centroids of an ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
ee.FeatureCollection |
The ee.FeatureCollection to get the centroids of. |
required |
Exceptions:
| Type | Description |
|---|---|
TypeError |
If the ee_object is not an ee.FeatureCollection. |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The centroids of the ee_object. |
Source code in geemap/common.py
def vector_centroids(ee_object):
"""Returns the centroids of an ee.FeatureCollection.
Args:
ee_object (ee.FeatureCollection): The ee.FeatureCollection to get the centroids of.
Raises:
TypeError: If the ee_object is not an ee.FeatureCollection.
Returns:
ee.FeatureCollection: The centroids of the ee_object.
"""
if not isinstance(ee_object, ee.FeatureCollection):
raise TypeError("The input must be an Earth Engine FeatureCollection.")
centroids = ee_object.map(
lambda f: ee.Feature(f.geometry().centroid(0.001), f.toDictionary())
)
centroids = centroids.map(
lambda f: f.set(
{
"longitude": f.geometry().coordinates().get(0),
"latitude": f.geometry().coordinates().get(1),
}
)
)
return centroids
vector_styling(ee_object, column, palette, color='000000', colorOpacity=1.0, pointSize=3, pointShape='circle', width=1, lineType='solid', fillColorOpacity=0.66)
¶
Add a new property to each feature containing a stylying dictionary.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_object |
object |
An ee.FeatureCollection. |
required |
column |
str |
The column name to use for styling. |
required |
palette |
list | dict |
The palette (e.g., list of colors or a dict containing label and color pairs) to use for styling. |
required |
color |
str |
A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Defaults to "black". |
'000000' |
colorOpacity |
float |
Opacity between 0-1 of the features. Defaults to 1 |
1.0 |
pointSize |
int |
The default size in pixels of the point markers. Defaults to 3. |
3 |
pointShape |
str |
The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle". |
'circle' |
width |
int |
The default line width for lines and outlines for polygons and point shapes. Defaults to 1. |
1 |
lineType |
str |
The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid". |
'solid' |
fillColorOpacity |
float |
Opacity between 0-1 of the fill. Defaults to 0.66. Color of the fill is based on the column name or index in the palette. |
0.66 |
Exceptions:
| Type | Description |
|---|---|
ValueError |
The provided column name is invalid. |
TypeError |
The provided palette is invalid. |
TypeError |
The provided ee_object is not an ee.FeatureCollection. |
Returns:
| Type | Description |
|---|---|
object |
An ee.FeatureCollection containing the styling attribute. |
Source code in geemap/common.py
def vector_styling(
ee_object,
column,
palette,
color="000000",
colorOpacity=1.0,
pointSize=3,
pointShape="circle",
width=1,
lineType="solid",
fillColorOpacity=0.66,
):
"""Add a new property to each feature containing a stylying dictionary.
Args:
ee_object (object): An ee.FeatureCollection.
column (str): The column name to use for styling.
palette (list | dict): The palette (e.g., list of colors or a dict containing label and color pairs) to use for styling.
color (str, optional): A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Defaults to "black".
colorOpacity (float, optional): Opacity between 0-1 of the features. Defaults to 1
pointSize (int, optional): The default size in pixels of the point markers. Defaults to 3.
pointShape (str, optional): The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle".
width (int, optional): The default line width for lines and outlines for polygons and point shapes. Defaults to 1.
lineType (str, optional): The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid".
fillColorOpacity (float, optional): Opacity between 0-1 of the fill. Defaults to 0.66. Color of the fill is based on the column name or index in the palette.
Raises:
ValueError: The provided column name is invalid.
TypeError: The provided palette is invalid.
TypeError: The provided ee_object is not an ee.FeatureCollection.
Returns:
object: An ee.FeatureCollection containing the styling attribute.
"""
from box import Box
if isinstance(ee_object, ee.FeatureCollection):
prop_names = ee.Feature(ee_object.first()).propertyNames().getInfo()
arr = ee_object.aggregate_array(column).distinct().sort()
if column not in prop_names:
raise ValueError(f"The column name must of one of {', '.join(prop_names)}")
if isinstance(palette, Box):
try:
palette = list(palette["default"])
except Exception as e:
print("The provided palette is invalid.")
raise Exception(e)
elif isinstance(palette, tuple):
palette = list(palette)
elif isinstance(palette, dict):
values = list(arr.getInfo())
labels = list(palette.keys())
if not all(elem in values for elem in labels):
raise ValueError(
f"The keys of the palette must contain the following elements: {', '.join(values)}"
)
else:
colors = [palette[value] for value in values]
palette = colors
if not isinstance(palette, list):
raise TypeError("The palette must be a list.")
colors = ee.List(
[
color.strip() + str(hex(int(fillColorOpacity * 255)))[2:].zfill(2)
for color in palette
]
)
fc = ee_object.map(lambda f: f.set({"styleIndex": arr.indexOf(f.get(column))}))
step = arr.size().divide(colors.size()).ceil()
fc = fc.map(
lambda f: f.set(
{
"style": {
"color": color + str(hex(int(colorOpacity * 255)))[2:].zfill(2),
"pointSize": pointSize,
"pointShape": pointShape,
"width": width,
"lineType": lineType,
"fillColor": colors.get(
ee.Number(
ee.Number(f.get("styleIndex")).divide(step)
).floor()
),
}
}
)
)
return fc
else:
raise TypeError("The ee_object must be an ee.FeatureCollection.")
vector_to_ee(filename, bbox=None, mask=None, rows=None, geodesic=True, **kwargs)
¶
Converts any geopandas-supported vector dataset to ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO). |
required |
bbox |
tuple | GeoDataFrame or GeoSeries | shapely Geometry |
Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with mask. Defaults to None. |
None |
mask |
dict | GeoDataFrame or GeoSeries | shapely Geometry |
Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. Defaults to None. |
None |
rows |
int or slice |
Load in specific rows by passing an integer (first n rows) or a slice() object.. Defaults to None. |
None |
geodesic |
bool |
Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. |
True |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
Earth Engine FeatureCollection. |
Source code in geemap/common.py
def vector_to_ee(
filename,
bbox=None,
mask=None,
rows=None,
geodesic=True,
**kwargs,
):
"""Converts any geopandas-supported vector dataset to ee.FeatureCollection.
Args:
filename (str): Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO).
bbox (tuple | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with mask. Defaults to None.
mask (dict | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. Defaults to None.
rows (int or slice, optional): Load in specific rows by passing an integer (first n rows) or a slice() object.. Defaults to None.
geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.
Returns:
ee.FeatureCollection: Earth Engine FeatureCollection.
"""
geojson = vector_to_geojson(
filename, bbox=bbox, mask=mask, rows=rows, epsg="4326", **kwargs
)
return geojson_to_ee(geojson, geodesic=geodesic)
vector_to_geojson(filename, out_geojson=None, bbox=None, mask=None, rows=None, epsg='4326', **kwargs)
¶
Converts any geopandas-supported vector dataset to GeoJSON.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO). |
required |
out_geojson |
str |
The file path to the output GeoJSON. Defaults to None. |
None |
bbox |
tuple | GeoDataFrame or GeoSeries | shapely Geometry |
Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with mask. Defaults to None. |
None |
mask |
dict | GeoDataFrame or GeoSeries | shapely Geometry |
Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. Defaults to None. |
None |
rows |
int or slice |
Load in specific rows by passing an integer (first n rows) or a slice() object.. Defaults to None. |
None |
epsg |
str |
The EPSG number to convert to. Defaults to "4326". |
'4326' |
Exceptions:
| Type | Description |
|---|---|
ValueError |
When the output file path is invalid. |
Returns:
| Type | Description |
|---|---|
dict |
A dictionary containing the GeoJSON. |
Source code in geemap/common.py
def vector_to_geojson(
filename, out_geojson=None, bbox=None, mask=None, rows=None, epsg="4326", **kwargs
):
"""Converts any geopandas-supported vector dataset to GeoJSON.
Args:
filename (str): Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO).
out_geojson (str, optional): The file path to the output GeoJSON. Defaults to None.
bbox (tuple | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with mask. Defaults to None.
mask (dict | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. Defaults to None.
rows (int or slice, optional): Load in specific rows by passing an integer (first n rows) or a slice() object.. Defaults to None.
epsg (str, optional): The EPSG number to convert to. Defaults to "4326".
Raises:
ValueError: When the output file path is invalid.
Returns:
dict: A dictionary containing the GeoJSON.
"""
warnings.filterwarnings("ignore")
check_package(name="geopandas", URL="https://geopandas.org")
import geopandas as gpd
import fiona
if not filename.startswith("http"):
filename = os.path.abspath(filename)
else:
filename = download_file(github_raw_url(filename))
ext = os.path.splitext(filename)[1].lower()
if ext == ".kml":
fiona.drvsupport.supported_drivers["KML"] = "rw"
df = gpd.read_file(
filename, bbox=bbox, mask=mask, rows=rows, driver="KML", **kwargs
)
else:
df = gpd.read_file(filename, bbox=bbox, mask=mask, rows=rows, **kwargs)
gdf = df.to_crs(epsg=epsg)
if out_geojson is not None:
if not out_geojson.lower().endswith(".geojson"):
raise ValueError("The output file must have a geojson file extension.")
out_geojson = os.path.abspath(out_geojson)
out_dir = os.path.dirname(out_geojson)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
gdf.to_file(out_geojson, driver="GeoJSON")
else:
return gdf.__geo_interface__
view_lidar(filename, cmap='terrain', backend='pyvista', background=None, **kwargs)
¶
View LiDAR data in 3D.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename |
str |
The filepath to the LiDAR data. |
required |
cmap |
str |
The colormap to use. Defaults to "terrain". cmap currently does not work for the open3d backend. |
'terrain' |
backend |
str |
The plotting backend to use, can be pyvista, ipygany, panel, and open3d. Defaults to "pyvista". |
'pyvista' |
background |
str |
The background color to use. Defaults to None. |
None |
Exceptions:
| Type | Description |
|---|---|
FileNotFoundError |
If the file does not exist. |
ValueError |
If the backend is not supported. |
Source code in geemap/common.py
def view_lidar(filename, cmap="terrain", backend="pyvista", background=None, **kwargs):
"""View LiDAR data in 3D.
Args:
filename (str): The filepath to the LiDAR data.
cmap (str, optional): The colormap to use. Defaults to "terrain". cmap currently does not work for the open3d backend.
backend (str, optional): The plotting backend to use, can be pyvista, ipygany, panel, and open3d. Defaults to "pyvista".
background (str, optional): The background color to use. Defaults to None.
Raises:
FileNotFoundError: If the file does not exist.
ValueError: If the backend is not supported.
"""
if in_colab_shell():
print("The view_lidar() function is not supported in Colab.")
return
warnings.filterwarnings("ignore")
filename = os.path.abspath(filename)
if not os.path.exists(filename):
raise FileNotFoundError(f"{filename} does not exist.")
backend = backend.lower()
if backend in ["pyvista", "ipygany", "panel"]:
try:
import pyntcloud
except ImportError:
print(
"The pyvista and pyntcloud packages are required for this function. Use pip install geemap[lidar] to install them."
)
return
try:
if backend == "pyvista":
backend = None
if backend == "ipygany":
cmap = None
data = pyntcloud.PyntCloud.from_file(filename)
mesh = data.to_instance("pyvista", mesh=False)
mesh = mesh.elevation()
mesh.plot(
scalars="Elevation",
cmap=cmap,
jupyter_backend=backend,
background=background,
**kwargs,
)
except Exception as e:
print("Something went wrong.")
print(e)
return
elif backend == "open3d":
try:
import laspy
import open3d as o3d
import numpy as np
except ImportError:
print(
"The laspy and open3d packages are required for this function. Use pip install laspy open3d to install them."
)
return
try:
las = laspy.read(filename)
point_data = np.stack([las.X, las.Y, las.Z], axis=0).transpose((1, 0))
geom = o3d.geometry.PointCloud()
geom.points = o3d.utility.Vector3dVector(point_data)
# geom.colors = o3d.utility.Vector3dVector(colors) # need to add colors. A list in the form of [[r,g,b], [r,g,b]] with value range 0-1. https://github.com/isl-org/Open3D/issues/614
o3d.visualization.draw_geometries([geom], **kwargs)
except Exception as e:
print("Something went wrong.")
print(e)
return
else:
raise ValueError(f"{backend} is not a valid backend.")
vis_to_qml(ee_class_table, out_qml)
¶
Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ee_class_table |
str |
An Earth Engine class table with triple quotes. |
required |
out_qml |
str |
File path to the output QGIS Layer Style (.qml). |
required |
Source code in geemap/common.py
def vis_to_qml(ee_class_table, out_qml):
"""Create a QGIS Layer Style (.qml) based on an Earth Engine class table from the Earth Engine Data Catalog page
such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Args:
ee_class_table (str): An Earth Engine class table with triple quotes.
out_qml (str): File path to the output QGIS Layer Style (.qml).
"""
import pkg_resources
pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
data_dir = os.path.join(pkg_dir, "data")
template_dir = os.path.join(data_dir, "template")
qml_template = os.path.join(template_dir, "NLCD.qml")
out_dir = os.path.dirname(out_qml)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
with open(qml_template) as f:
lines = f.readlines()
header = lines[:31]
footer = lines[51:]
entries = []
try:
ee_class_table = ee_class_table.strip()
lines = ee_class_table.split("\n")[1:]
if lines[0] == "Value\tColor\tDescription":
lines = lines[1:]
for line in lines:
items = line.split("\t")
items = [item.strip() for item in items]
value = items[0]
color = items[1]
label = items[2]
entry = ' <paletteEntry alpha="255" color="#{}" value="{}" label="{}"/>\n'.format(
color, value, label
)
entries.append(entry)
out_lines = header + entries + footer
with open(out_qml, "w") as f:
f.writelines(out_lines)
except Exception as e:
print(e)
widget_template(widget=None, opened=True, show_close_button=True, widget_icon='gear', close_button_icon='times', widget_args={}, close_button_args={}, display_widget=None, m=None, position='topright')
¶
Create a widget template.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
widget |
ipywidgets.Widget |
The widget to be displayed. Defaults to None. |
None |
opened |
bool |
Whether to open the toolbar. Defaults to True. |
True |
show_close_button |
bool |
Whether to show the close button. Defaults to True. |
True |
widget_icon |
str |
The icon name for the toolbar button. Defaults to 'gear'. |
'gear' |
close_button_icon |
str |
The icon name for the close button. Defaults to "times". |
'times' |
widget_args |
dict |
Additional arguments to pass to the toolbar button. Defaults to {}. |
{} |
close_button_args |
dict |
Additional arguments to pass to the close button. Defaults to {}. |
{} |
display_widget |
ipywidgets.Widget |
The widget to be displayed when the toolbar is clicked. |
None |
m |
geemap.Map |
The geemap.Map instance. Defaults to None. |
None |
position |
str |
The position of the toolbar. Defaults to "topright". |
'topright' |
Source code in geemap/common.py
def widget_template(
widget=None,
opened=True,
show_close_button=True,
widget_icon="gear",
close_button_icon="times",
widget_args={},
close_button_args={},
display_widget=None,
m=None,
position="topright",
):
"""Create a widget template.
Args:
widget (ipywidgets.Widget, optional): The widget to be displayed. Defaults to None.
opened (bool, optional): Whether to open the toolbar. Defaults to True.
show_close_button (bool, optional): Whether to show the close button. Defaults to True.
widget_icon (str, optional): The icon name for the toolbar button. Defaults to 'gear'.
close_button_icon (str, optional): The icon name for the close button. Defaults to "times".
widget_args (dict, optional): Additional arguments to pass to the toolbar button. Defaults to {}.
close_button_args (dict, optional): Additional arguments to pass to the close button. Defaults to {}.
display_widget (ipywidgets.Widget, optional): The widget to be displayed when the toolbar is clicked.
m (geemap.Map, optional): The geemap.Map instance. Defaults to None.
position (str, optional): The position of the toolbar. Defaults to "topright".
"""
name = "_" + random_string() # a random attribute name
if "value" not in widget_args:
widget_args["value"] = False
if "tooltip" not in widget_args:
widget_args["tooltip"] = "Toolbar"
if "layout" not in widget_args:
widget_args["layout"] = widgets.Layout(
width="28px", height="28px", padding="0px 0px 0px 4px"
)
widget_args["icon"] = widget_icon
if "value" not in close_button_args:
close_button_args["value"] = False
if "tooltip" not in close_button_args:
close_button_args["tooltip"] = "Close the tool"
if "button_style" not in close_button_args:
close_button_args["button_style"] = "primary"
if "layout" not in close_button_args:
close_button_args["layout"] = widgets.Layout(
height="28px", width="28px", padding="0px 0px 0px 4px"
)
close_button_args["icon"] = close_button_icon
toolbar_button = widgets.ToggleButton(**widget_args)
close_button = widgets.ToggleButton(**close_button_args)
toolbar_widget = widgets.VBox()
toolbar_widget.children = [toolbar_button]
toolbar_header = widgets.HBox()
if show_close_button:
toolbar_header.children = [close_button, toolbar_button]
else:
toolbar_header.children = [toolbar_button]
toolbar_footer = widgets.VBox()
if widget is not None:
toolbar_footer.children = [
widget,
]
else:
toolbar_footer.children = []
def toolbar_btn_click(change):
if change["new"]:
close_button.value = False
toolbar_widget.children = [toolbar_header, toolbar_footer]
if display_widget is not None:
widget.outputs = ()
with widget:
display(display_widget)
else:
toolbar_widget.children = [toolbar_button]
toolbar_button.observe(toolbar_btn_click, "value")
def close_btn_click(change):
if change["new"]:
toolbar_button.value = False
if m is not None:
control = getattr(m, name)
if control is not None and control in m.controls:
m.remove_control(control)
delattr(m, name)
toolbar_widget.close()
close_button.observe(close_btn_click, "value")
toolbar_button.value = opened
if m is not None:
import ipyleaflet
toolbar_control = ipyleaflet.WidgetControl(
widget=toolbar_widget, position=position
)
if toolbar_control not in m.controls:
m.add_control(toolbar_control)
setattr(m, name, toolbar_control)
else:
return toolbar_widget
write_lidar(source, destination, do_compress=None, laz_backend=None)
¶
Writes to a stream or file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
source |
str | laspy.lasdatas.base.LasBase |
The source data to be written. |
required |
destination |
str |
The destination filepath. |
required |
do_compress |
bool |
Flags to indicate if you want to compress the data. Defaults to None. |
None |
laz_backend |
str |
The laz backend to use. Defaults to None. |
None |
Source code in geemap/common.py
def write_lidar(source, destination, do_compress=None, laz_backend=None):
"""Writes to a stream or file.
Args:
source (str | laspy.lasdatas.base.LasBase): The source data to be written.
destination (str): The destination filepath.
do_compress (bool, optional): Flags to indicate if you want to compress the data. Defaults to None.
laz_backend (str, optional): The laz backend to use. Defaults to None.
"""
try:
import laspy
except ImportError:
print(
"The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
)
return
if isinstance(source, str):
source = read_lidar(source)
source.write(destination, do_compress=do_compress, laz_backend=laz_backend)
xee_to_image(xds, filenames=None, out_dir=None, crs=None, nodata=None, driver='COG', time_unit='D', quiet=False, **kwargs)
¶
Convert xarray Dataset to georeferenced images.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
xds |
xr.Dataset |
The xarray Dataset to convert to images. |
required |
filenames |
Union[str, List[str]] |
Output filenames for the images. If a single string is provided, it will be used as the filename for all images. If a list of strings is provided, the filenames will be used in order. Defaults to None. |
None |
out_dir |
str |
Output directory for the images. Defaults to current working directory. |
None |
crs |
str |
Coordinate reference system (CRS) of the output images. If not provided, the CRS is inferred from the Dataset's attributes ('crs' attribute) or set to 'EPSG:4326'. |
None |
nodata |
float |
The nodata value used for the output images. Defaults to None. |
None |
driver |
str |
Driver used for writing the output images, such as 'GTiff'. Defaults to "COG". |
'COG' |
time_unit |
str |
Time unit used for generating default filenames. Defaults to 'D'. |
'D' |
quiet |
bool |
If True, suppresses progress messages. Defaults to False. |
False |
**kwargs |
Additional keyword arguments passed to rioxarray's |
{} |
Returns:
| Type | Description |
|---|---|
None |
None |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the number of filenames doesn't match the number of time steps in the Dataset. |
Source code in geemap/common.py
def xee_to_image(
xds,
filenames: Optional[Union[str, List[str]]] = None,
out_dir: Optional[str] = None,
crs: Optional[str] = None,
nodata: Optional[float] = None,
driver: str = "COG",
time_unit: str = "D",
quiet: bool = False,
**kwargs,
) -> None:
"""
Convert xarray Dataset to georeferenced images.
Args:
xds (xr.Dataset): The xarray Dataset to convert to images.
filenames (Union[str, List[str]], optional): Output filenames for the images.
If a single string is provided, it will be used as the filename for all images.
If a list of strings is provided, the filenames will be used in order. Defaults to None.
out_dir (str, optional): Output directory for the images. Defaults to current working directory.
crs (str, optional): Coordinate reference system (CRS) of the output images.
If not provided, the CRS is inferred from the Dataset's attributes ('crs' attribute) or set to 'EPSG:4326'.
nodata (float, optional): The nodata value used for the output images. Defaults to None.
driver (str, optional): Driver used for writing the output images, such as 'GTiff'. Defaults to "COG".
time_unit (str, optional): Time unit used for generating default filenames. Defaults to 'D'.
quiet (bool, optional): If True, suppresses progress messages. Defaults to False.
**kwargs: Additional keyword arguments passed to rioxarray's `rio.to_raster()` function.
Returns:
None
Raises:
ValueError: If the number of filenames doesn't match the number of time steps in the Dataset.
"""
import numpy as np
try:
import rioxarray
except ImportError:
install_package("rioxarray")
import rioxarray
if crs is None and "crs" in xds.attrs:
crs = xds.attrs["crs"]
if crs is None:
crs = "EPSG:4326"
if out_dir is None:
out_dir = os.getcwd()
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if isinstance(filenames, str):
filenames = [filenames]
if isinstance(filenames, list):
if len(filenames) != len(xds.time):
raise ValueError(
"The number of filenames must match the number of time steps"
)
coords = [coord for coord in xds.coords]
x_dim = coords[1]
y_dim = coords[2]
for index, time in enumerate(xds.time.values):
if nodata is not None:
# Create a Boolean mask where all three variables are zero (nodata)
mask = (xds == nodata).all(dim="time")
# Set nodata values based on the mask for all variables
xds = xds.where(~mask, other=np.nan)
if not quiet:
print(f"Processing {index + 1}/{len(xds.time.values)}: {time}")
image = xds.sel(time=time)
# transform the image to suit rioxarray format
image = (
image.rename({y_dim: "y", x_dim: "x"})
.transpose("y", "x")
.rio.write_crs(crs)
)
if filenames is None:
date = np.datetime_as_string(time, unit=time_unit)
filename = f"{date}.tif"
else:
filename = filenames.pop()
output_path = os.path.join(out_dir, filename)
image.rio.to_raster(output_path, driver=driver, **kwargs)
xy_to_points(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8')
¶
Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_csv |
str |
File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv |
required |
latitude |
str |
Column name for the latitude column. Defaults to 'latitude'. |
'latitude' |
longitude |
str |
Column name for the longitude column. Defaults to 'longitude'. |
'longitude' |
Returns:
| Type | Description |
|---|---|
ee.FeatureCollection |
The ee.FeatureCollection containing the points converted from the input csv. |
Source code in geemap/common.py
def xy_to_points(in_csv, latitude="latitude", longitude="longitude", encoding="utf-8"):
"""Converts a csv containing points (latitude and longitude) into an ee.FeatureCollection.
Args:
in_csv (str): File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv
latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.
Returns:
ee.FeatureCollection: The ee.FeatureCollection containing the points converted from the input csv.
"""
geojson = csv_to_geojson(in_csv, None, latitude, longitude, encoding)
fc = geojson_to_ee(geojson)
return fc
zonal_statistics(in_value_raster, in_zone_vector, out_file_path=None, stat_type='MEAN', scale=None, crs=None, tile_scale=1.0, return_fc=False, verbose=True, timeout=300, proxies=None, **kwargs)
¶
Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_value_raster |
object |
An ee.Image or ee.ImageCollection that contains the values on which to calculate a statistic. |
required |
in_zone_vector |
object |
An ee.FeatureCollection that defines the zones. |
required |
out_file_path |
str |
Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz |
None |
stat_type |
str |
Statistical type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps. |
'MEAN' |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
crs |
str |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0. |
1.0 |
verbose |
bool |
Whether to print descriptive text when the programming is running. Default to True. |
True |
return_fc |
bool |
Whether to return the results as an ee.FeatureCollection. Defaults to False. |
False |
timeout |
int |
Timeout in seconds. Default to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use for the request. Default to None. |
None |
Source code in geemap/common.py
def zonal_stats(
in_value_raster,
in_zone_vector,
out_file_path=None,
stat_type="MEAN",
scale=None,
crs=None,
tile_scale=1.0,
return_fc=False,
verbose=True,
timeout=300,
proxies=None,
**kwargs,
):
"""Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An ee.Image or ee.ImageCollection that contains the values on which to calculate a statistic.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
stat_type (str, optional): Statistical type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0.
verbose (bool, optional): Whether to print descriptive text when the programming is running. Default to True.
return_fc (bool, optional): Whether to return the results as an ee.FeatureCollection. Defaults to False.
timeout (int, optional): Timeout in seconds. Default to 300.
proxies (dict, optional): A dictionary of proxy servers to use for the request. Default to None.
"""
if isinstance(in_value_raster, ee.ImageCollection):
in_value_raster = in_value_raster.toBands()
if not isinstance(in_value_raster, ee.Image):
print("The input raster must be an ee.Image.")
return
if not isinstance(in_zone_vector, ee.FeatureCollection):
print("The input zone data must be an ee.FeatureCollection.")
return
if out_file_path is None:
out_file_path = os.path.join(os.getcwd(), "zonal_stats.csv")
if "statistics_type" in kwargs:
stat_type = kwargs.pop("statistics_type")
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp"]
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
# name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if not (filetype in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
return
# Parameters for histogram
# The maximum number of buckets to use when building a histogram; will be rounded up to a power of 2.
max_buckets = None
# The minimum histogram bucket width, or null to allow any power of 2.
min_bucket_width = None
# The number of values to accumulate before building the initial histogram.
max_raw = None
hist_min = 1.0 # The lower (inclusive) bound of the first bucket.
hist_max = 100.0 # The upper (exclusive) bound of the last bucket.
hist_steps = 10 # The number of buckets to use.
if "max_buckets" in kwargs.keys():
max_buckets = kwargs["max_buckets"]
if "min_bucket_width" in kwargs.keys():
min_bucket_width = kwargs["min_bucket"]
if "max_raw" in kwargs.keys():
max_raw = kwargs["max_raw"]
if isinstance(stat_type, str):
if (
stat_type.upper() == "FIXED_HIST"
and ("hist_min" in kwargs.keys())
and ("hist_max" in kwargs.keys())
and ("hist_steps" in kwargs.keys())
):
hist_min = kwargs["hist_min"]
hist_max = kwargs["hist_max"]
hist_steps = kwargs["hist_steps"]
elif stat_type.upper() == "FIXED_HIST":
print(
"To use fixedHistogram, please provide these three parameters: hist_min, hist_max, and hist_steps."
)
return
allowed_statistics = {
"COUNT": ee.Reducer.count(),
"MEAN": ee.Reducer.mean(),
"MEAN_UNWEIGHTED": ee.Reducer.mean().unweighted(),
"MAXIMUM": ee.Reducer.max(),
"MEDIAN": ee.Reducer.median(),
"MINIMUM": ee.Reducer.min(),
"MODE": ee.Reducer.mode(),
"STD": ee.Reducer.stdDev(),
"MIN_MAX": ee.Reducer.minMax(),
"SUM": ee.Reducer.sum(),
"VARIANCE": ee.Reducer.variance(),
"HIST": ee.Reducer.histogram(
maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw
),
"FIXED_HIST": ee.Reducer.fixedHistogram(hist_min, hist_max, hist_steps),
"COMBINED_COUNT_MEAN": ee.Reducer.count().combine(
ee.Reducer.mean(), sharedInputs=True
),
"COMBINED_COUNT_MEAN_UNWEIGHTED": ee.Reducer.count().combine(
ee.Reducer.mean().unweighted(), sharedInputs=True
),
}
if isinstance(stat_type, str):
if not (stat_type.upper() in allowed_statistics.keys()):
print(
"The statistics type must be one of the following: {}".format(
", ".join(list(allowed_statistics.keys()))
)
)
return
reducer = allowed_statistics[stat_type.upper()]
elif isinstance(stat_type, ee.Reducer):
reducer = stat_type
else:
raise ValueError("statistics_type must be either a string or ee.Reducer.")
if scale is None:
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
if verbose:
print("Computing statistics ...")
result = in_value_raster.reduceRegions(
collection=in_zone_vector,
reducer=reducer,
scale=scale,
crs=crs,
tileScale=tile_scale,
)
if return_fc:
return result
else:
ee_export_vector(result, filename, timeout=timeout, proxies=proxies)
except Exception as e:
raise Exception(e)
zonal_statistics_by_group(in_value_raster, in_zone_vector, out_file_path=None, stat_type='SUM', decimal_places=0, denominator=1.0, scale=None, crs=None, crs_transform=None, best_effort=True, max_pixels=10000000.0, tile_scale=1.0, return_fc=False, verbose=True, timeout=300, proxies=None, **kwargs)
¶
Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_value_raster |
object |
An integer Image that contains the values on which to calculate area/percentage. |
required |
in_zone_vector |
object |
An ee.FeatureCollection that defines the zones. |
required |
out_file_path |
str |
Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz |
None |
stat_type |
str |
Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'. |
'SUM' |
decimal_places |
int |
The number of decimal places to use. Defaults to 0. |
0 |
denominator |
float |
To convert area units (e.g., from square meters to square kilometers). Defaults to 1.0. |
1.0 |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
crs |
str |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
crs_transform |
list |
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. |
None |
best_effort |
bool |
If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. |
True |
max_pixels |
int |
The maximum number of pixels to reduce. Defaults to 1e7. |
10000000.0 |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0. |
1.0 |
verbose |
bool |
Whether to print descriptive text when the programming is running. Default to True. |
True |
return_fc |
bool |
Whether to return the results as an ee.FeatureCollection. Defaults to False. |
False |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxies to use. Defaults to None. |
None |
Source code in geemap/common.py
def zonal_stats_by_group(
in_value_raster,
in_zone_vector,
out_file_path=None,
stat_type="SUM",
decimal_places=0,
denominator=1.0,
scale=None,
crs=None,
crs_transform=None,
best_effort=True,
max_pixels=1e7,
tile_scale=1.0,
return_fc=False,
verbose=True,
timeout=300,
proxies=None,
**kwargs,
):
"""Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
stat_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.
decimal_places (int, optional): The number of decimal places to use. Defaults to 0.
denominator (float, optional): To convert area units (e.g., from square meters to square kilometers). Defaults to 1.0.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
crs_transform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
best_effort (bool, optional): If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
max_pixels (int, optional): The maximum number of pixels to reduce. Defaults to 1e7.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0.
verbose (bool, optional): Whether to print descriptive text when the programming is running. Default to True.
return_fc (bool, optional): Whether to return the results as an ee.FeatureCollection. Defaults to False.
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): A dictionary of proxies to use. Defaults to None.
"""
if isinstance(in_value_raster, ee.ImageCollection):
in_value_raster = in_value_raster.toBands()
if not isinstance(in_value_raster, ee.Image):
print("The input raster must be an ee.Image.")
return
if out_file_path is None:
out_file_path = os.path.join(os.getcwd(), "zonal_stats_by_group.csv")
if "statistics_type" in kwargs:
stat_type = kwargs.pop("statistics_type")
band_count = in_value_raster.bandNames().size().getInfo()
band_name = ""
if band_count == 1:
band_name = in_value_raster.bandNames().get(0)
else:
print("The input image can only have one band.")
return
band_types = in_value_raster.bandTypes().get(band_name).getInfo()
band_type = band_types.get("precision")
if band_type != "int":
print("The input image band must be integer type.")
return
if not isinstance(in_zone_vector, ee.FeatureCollection):
print("The input zone data must be an ee.FeatureCollection.")
return
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp"]
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
# name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:]
if not (filetype.lower() in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
return
out_dir = os.path.dirname(filename)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
allowed_statistics = ["SUM", "PERCENTAGE"]
if not (stat_type.upper() in allowed_statistics):
print(
"The statistics type can only be one of {}".format(
", ".join(allowed_statistics)
)
)
return
if scale is None:
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
if verbose:
print("Computing ... ")
geometry = in_zone_vector.geometry()
hist = in_value_raster.reduceRegion(
ee.Reducer.frequencyHistogram(),
geometry=geometry,
scale=scale,
crs=crs,
crsTransform=crs_transform,
bestEffort=best_effort,
maxPixels=max_pixels,
tileScale=tile_scale,
)
class_values = (
ee.Dictionary(hist.get(band_name))
.keys()
.map(lambda v: ee.Number.parse(v))
.sort()
)
class_names = class_values.map(
lambda c: ee.String("Class_").cat(ee.Number(c).format())
)
# class_count = class_values.size().getInfo()
dataset = ee.Image.pixelArea().divide(denominator).addBands(in_value_raster)
init_result = dataset.reduceRegions(
**{
"collection": in_zone_vector,
"reducer": ee.Reducer.sum().group(
**{
"groupField": 1,
"groupName": "group",
}
),
"scale": scale,
}
)
# def build_dict(input_list):
# decimal_format = '%.{}f'.format(decimal_places)
# in_dict = input_list.map(lambda x: ee.Dictionary().set(ee.String('Class_').cat(
# ee.Number(ee.Dictionary(x).get('group')).format()), ee.Number.parse(ee.Number(ee.Dictionary(x).get('sum')).format(decimal_format))))
# return in_dict
def get_keys(input_list):
return input_list.map(
lambda x: ee.String("Class_").cat(
ee.Number(ee.Dictionary(x).get("group")).format()
)
)
def get_values(input_list):
decimal_format = "%.{}f".format(decimal_places)
return input_list.map(
lambda x: ee.Number.parse(
ee.Number(ee.Dictionary(x).get("sum")).format(decimal_format)
)
)
def set_attribute(f):
groups = ee.List(f.get("groups"))
keys = get_keys(groups)
values = get_values(groups)
total_area = ee.List(values).reduce(ee.Reducer.sum())
def get_class_values(x):
cls_value = ee.Algorithms.If(
keys.contains(x), values.get(keys.indexOf(x)), 0
)
cls_value = ee.Algorithms.If(
ee.String(stat_type).compareTo(ee.String("SUM")),
ee.Number(cls_value).divide(ee.Number(total_area)),
cls_value,
)
return cls_value
full_values = class_names.map(lambda x: get_class_values(x))
attr_dict = ee.Dictionary.fromLists(class_names, full_values)
attr_dict = attr_dict.set("Class_sum", total_area)
return f.set(attr_dict).set("groups", None)
final_result = init_result.map(set_attribute)
if return_fc:
return final_result
else:
ee_export_vector(final_result, filename, timeout=timeout, proxies=proxies)
except Exception as e:
raise Exception(e)
zonal_stats(in_value_raster, in_zone_vector, out_file_path=None, stat_type='MEAN', scale=None, crs=None, tile_scale=1.0, return_fc=False, verbose=True, timeout=300, proxies=None, **kwargs)
¶
Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_value_raster |
object |
An ee.Image or ee.ImageCollection that contains the values on which to calculate a statistic. |
required |
in_zone_vector |
object |
An ee.FeatureCollection that defines the zones. |
required |
out_file_path |
str |
Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz |
None |
stat_type |
str |
Statistical type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps. |
'MEAN' |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
crs |
str |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0. |
1.0 |
verbose |
bool |
Whether to print descriptive text when the programming is running. Default to True. |
True |
return_fc |
bool |
Whether to return the results as an ee.FeatureCollection. Defaults to False. |
False |
timeout |
int |
Timeout in seconds. Default to 300. |
300 |
proxies |
dict |
A dictionary of proxy servers to use for the request. Default to None. |
None |
Source code in geemap/common.py
def zonal_stats(
in_value_raster,
in_zone_vector,
out_file_path=None,
stat_type="MEAN",
scale=None,
crs=None,
tile_scale=1.0,
return_fc=False,
verbose=True,
timeout=300,
proxies=None,
**kwargs,
):
"""Summarizes the values of a raster within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An ee.Image or ee.ImageCollection that contains the values on which to calculate a statistic.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
stat_type (str, optional): Statistical type to be calculated. Defaults to 'MEAN'. For 'HIST', you can provide three parameters: max_buckets, min_bucket_width, and max_raw. For 'FIXED_HIST', you must provide three parameters: hist_min, hist_max, and hist_steps.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0.
verbose (bool, optional): Whether to print descriptive text when the programming is running. Default to True.
return_fc (bool, optional): Whether to return the results as an ee.FeatureCollection. Defaults to False.
timeout (int, optional): Timeout in seconds. Default to 300.
proxies (dict, optional): A dictionary of proxy servers to use for the request. Default to None.
"""
if isinstance(in_value_raster, ee.ImageCollection):
in_value_raster = in_value_raster.toBands()
if not isinstance(in_value_raster, ee.Image):
print("The input raster must be an ee.Image.")
return
if not isinstance(in_zone_vector, ee.FeatureCollection):
print("The input zone data must be an ee.FeatureCollection.")
return
if out_file_path is None:
out_file_path = os.path.join(os.getcwd(), "zonal_stats.csv")
if "statistics_type" in kwargs:
stat_type = kwargs.pop("statistics_type")
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp"]
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
# name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:].lower()
if not (filetype in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
return
# Parameters for histogram
# The maximum number of buckets to use when building a histogram; will be rounded up to a power of 2.
max_buckets = None
# The minimum histogram bucket width, or null to allow any power of 2.
min_bucket_width = None
# The number of values to accumulate before building the initial histogram.
max_raw = None
hist_min = 1.0 # The lower (inclusive) bound of the first bucket.
hist_max = 100.0 # The upper (exclusive) bound of the last bucket.
hist_steps = 10 # The number of buckets to use.
if "max_buckets" in kwargs.keys():
max_buckets = kwargs["max_buckets"]
if "min_bucket_width" in kwargs.keys():
min_bucket_width = kwargs["min_bucket"]
if "max_raw" in kwargs.keys():
max_raw = kwargs["max_raw"]
if isinstance(stat_type, str):
if (
stat_type.upper() == "FIXED_HIST"
and ("hist_min" in kwargs.keys())
and ("hist_max" in kwargs.keys())
and ("hist_steps" in kwargs.keys())
):
hist_min = kwargs["hist_min"]
hist_max = kwargs["hist_max"]
hist_steps = kwargs["hist_steps"]
elif stat_type.upper() == "FIXED_HIST":
print(
"To use fixedHistogram, please provide these three parameters: hist_min, hist_max, and hist_steps."
)
return
allowed_statistics = {
"COUNT": ee.Reducer.count(),
"MEAN": ee.Reducer.mean(),
"MEAN_UNWEIGHTED": ee.Reducer.mean().unweighted(),
"MAXIMUM": ee.Reducer.max(),
"MEDIAN": ee.Reducer.median(),
"MINIMUM": ee.Reducer.min(),
"MODE": ee.Reducer.mode(),
"STD": ee.Reducer.stdDev(),
"MIN_MAX": ee.Reducer.minMax(),
"SUM": ee.Reducer.sum(),
"VARIANCE": ee.Reducer.variance(),
"HIST": ee.Reducer.histogram(
maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw
),
"FIXED_HIST": ee.Reducer.fixedHistogram(hist_min, hist_max, hist_steps),
"COMBINED_COUNT_MEAN": ee.Reducer.count().combine(
ee.Reducer.mean(), sharedInputs=True
),
"COMBINED_COUNT_MEAN_UNWEIGHTED": ee.Reducer.count().combine(
ee.Reducer.mean().unweighted(), sharedInputs=True
),
}
if isinstance(stat_type, str):
if not (stat_type.upper() in allowed_statistics.keys()):
print(
"The statistics type must be one of the following: {}".format(
", ".join(list(allowed_statistics.keys()))
)
)
return
reducer = allowed_statistics[stat_type.upper()]
elif isinstance(stat_type, ee.Reducer):
reducer = stat_type
else:
raise ValueError("statistics_type must be either a string or ee.Reducer.")
if scale is None:
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
if verbose:
print("Computing statistics ...")
result = in_value_raster.reduceRegions(
collection=in_zone_vector,
reducer=reducer,
scale=scale,
crs=crs,
tileScale=tile_scale,
)
if return_fc:
return result
else:
ee_export_vector(result, filename, timeout=timeout, proxies=proxies)
except Exception as e:
raise Exception(e)
zonal_stats_by_group(in_value_raster, in_zone_vector, out_file_path=None, stat_type='SUM', decimal_places=0, denominator=1.0, scale=None, crs=None, crs_transform=None, best_effort=True, max_pixels=10000000.0, tile_scale=1.0, return_fc=False, verbose=True, timeout=300, proxies=None, **kwargs)
¶
Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
in_value_raster |
object |
An integer Image that contains the values on which to calculate area/percentage. |
required |
in_zone_vector |
object |
An ee.FeatureCollection that defines the zones. |
required |
out_file_path |
str |
Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz |
None |
stat_type |
str |
Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'. |
'SUM' |
decimal_places |
int |
The number of decimal places to use. Defaults to 0. |
0 |
denominator |
float |
To convert area units (e.g., from square meters to square kilometers). Defaults to 1.0. |
1.0 |
scale |
float |
A nominal scale in meters of the projection to work in. Defaults to None. |
None |
crs |
str |
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None. |
None |
crs_transform |
list |
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. |
None |
best_effort |
bool |
If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. |
True |
max_pixels |
int |
The maximum number of pixels to reduce. Defaults to 1e7. |
10000000.0 |
tile_scale |
float |
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0. |
1.0 |
verbose |
bool |
Whether to print descriptive text when the programming is running. Default to True. |
True |
return_fc |
bool |
Whether to return the results as an ee.FeatureCollection. Defaults to False. |
False |
timeout |
int |
Timeout in seconds. Defaults to 300. |
300 |
proxies |
dict |
A dictionary of proxies to use. Defaults to None. |
None |
Source code in geemap/common.py
def zonal_stats_by_group(
in_value_raster,
in_zone_vector,
out_file_path=None,
stat_type="SUM",
decimal_places=0,
denominator=1.0,
scale=None,
crs=None,
crs_transform=None,
best_effort=True,
max_pixels=1e7,
tile_scale=1.0,
return_fc=False,
verbose=True,
timeout=300,
proxies=None,
**kwargs,
):
"""Summarizes the area or percentage of a raster by group within the zones of another dataset and exports the results as a csv, shp, json, kml, or kmz.
Args:
in_value_raster (object): An integer Image that contains the values on which to calculate area/percentage.
in_zone_vector (object): An ee.FeatureCollection that defines the zones.
out_file_path (str): Output file path that will contain the summary of the values in each zone. The file type can be: csv, shp, json, kml, kmz
stat_type (str, optional): Can be either 'SUM' or 'PERCENTAGE' . Defaults to 'SUM'.
decimal_places (int, optional): The number of decimal places to use. Defaults to 0.
denominator (float, optional): To convert area units (e.g., from square meters to square kilometers). Defaults to 1.0.
scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
crs_transform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
best_effort (bool, optional): If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
max_pixels (int, optional): The maximum number of pixels to reduce. Defaults to 1e7.
tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.0.
verbose (bool, optional): Whether to print descriptive text when the programming is running. Default to True.
return_fc (bool, optional): Whether to return the results as an ee.FeatureCollection. Defaults to False.
timeout (int, optional): Timeout in seconds. Defaults to 300.
proxies (dict, optional): A dictionary of proxies to use. Defaults to None.
"""
if isinstance(in_value_raster, ee.ImageCollection):
in_value_raster = in_value_raster.toBands()
if not isinstance(in_value_raster, ee.Image):
print("The input raster must be an ee.Image.")
return
if out_file_path is None:
out_file_path = os.path.join(os.getcwd(), "zonal_stats_by_group.csv")
if "statistics_type" in kwargs:
stat_type = kwargs.pop("statistics_type")
band_count = in_value_raster.bandNames().size().getInfo()
band_name = ""
if band_count == 1:
band_name = in_value_raster.bandNames().get(0)
else:
print("The input image can only have one band.")
return
band_types = in_value_raster.bandTypes().get(band_name).getInfo()
band_type = band_types.get("precision")
if band_type != "int":
print("The input image band must be integer type.")
return
if not isinstance(in_zone_vector, ee.FeatureCollection):
print("The input zone data must be an ee.FeatureCollection.")
return
allowed_formats = ["csv", "geojson", "kml", "kmz", "shp"]
filename = os.path.abspath(out_file_path)
basename = os.path.basename(filename)
# name = os.path.splitext(basename)[0]
filetype = os.path.splitext(basename)[1][1:]
if not (filetype.lower() in allowed_formats):
print(
"The file type must be one of the following: {}".format(
", ".join(allowed_formats)
)
)
return
out_dir = os.path.dirname(filename)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
allowed_statistics = ["SUM", "PERCENTAGE"]
if not (stat_type.upper() in allowed_statistics):
print(
"The statistics type can only be one of {}".format(
", ".join(allowed_statistics)
)
)
return
if scale is None:
scale = in_value_raster.projection().nominalScale().multiply(10)
try:
if verbose:
print("Computing ... ")
geometry = in_zone_vector.geometry()
hist = in_value_raster.reduceRegion(
ee.Reducer.frequencyHistogram(),
geometry=geometry,
scale=scale,
crs=crs,
crsTransform=crs_transform,
bestEffort=best_effort,
maxPixels=max_pixels,
tileScale=tile_scale,
)
class_values = (
ee.Dictionary(hist.get(band_name))
.keys()
.map(lambda v: ee.Number.parse(v))
.sort()
)
class_names = class_values.map(
lambda c: ee.String("Class_").cat(ee.Number(c).format())
)
# class_count = class_values.size().getInfo()
dataset = ee.Image.pixelArea().divide(denominator).addBands(in_value_raster)
init_result = dataset.reduceRegions(
**{
"collection": in_zone_vector,
"reducer": ee.Reducer.sum().group(
**{
"groupField": 1,
"groupName": "group",
}
),
"scale": scale,
}
)
# def build_dict(input_list):
# decimal_format = '%.{}f'.format(decimal_places)
# in_dict = input_list.map(lambda x: ee.Dictionary().set(ee.String('Class_').cat(
# ee.Number(ee.Dictionary(x).get('group')).format()), ee.Number.parse(ee.Number(ee.Dictionary(x).get('sum')).format(decimal_format))))
# return in_dict
def get_keys(input_list):
return input_list.map(
lambda x: ee.String("Class_").cat(
ee.Number(ee.Dictionary(x).get("group")).format()
)
)
def get_values(input_list):
decimal_format = "%.{}f".format(decimal_places)
return input_list.map(
lambda x: ee.Number.parse(
ee.Number(ee.Dictionary(x).get("sum")).format(decimal_format)
)
)
def set_attribute(f):
groups = ee.List(f.get("groups"))
keys = get_keys(groups)
values = get_values(groups)
total_area = ee.List(values).reduce(ee.Reducer.sum())
def get_class_values(x):
cls_value = ee.Algorithms.If(
keys.contains(x), values.get(keys.indexOf(x)), 0
)
cls_value = ee.Algorithms.If(
ee.String(stat_type).compareTo(ee.String("SUM")),
ee.Number(cls_value).divide(ee.Number(total_area)),
cls_value,
)
return cls_value
full_values = class_names.map(lambda x: get_class_values(x))
attr_dict = ee.Dictionary.fromLists(class_names, full_values)
attr_dict = attr_dict.set("Class_sum", total_area)
return f.set(attr_dict).set("groups", None)
final_result = init_result.map(set_attribute)
if return_fc:
return final_result
else:
ee_export_vector(final_result, filename, timeout=timeout, proxies=proxies)
except Exception as e:
raise Exception(e)
zoom_level_resolution(zoom, latitude=0)
¶
Returns the approximate pixel scale based on zoom level and latutude. See https://blogs.bing.com/maps/2006/02/25/map-control-zoom-levels-gt-resolution
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
zoom |
int |
The zoom level. |
required |
latitude |
float |
The latitude. Defaults to 0. |
0 |
Returns:
| Type | Description |
|---|---|
float |
Map resolution in meters. |
Source code in geemap/common.py
def zoom_level_resolution(zoom, latitude=0):
"""Returns the approximate pixel scale based on zoom level and latutude.
See https://blogs.bing.com/maps/2006/02/25/map-control-zoom-levels-gt-resolution
Args:
zoom (int): The zoom level.
latitude (float, optional): The latitude. Defaults to 0.
Returns:
float: Map resolution in meters.
"""
import math
resolution = 156543.04 * math.cos(latitude) / math.pow(2, zoom)
return abs(resolution)