common module

This module contains some common functions for both folium and ipyleaflet.

PlanetaryComputerEndpoint

Bases: TitilerEndpoint

This class contains the methods for the Microsoft Planetary Computer endpoint.

Source code in leafmap/stac.py

class PlanetaryComputerEndpoint(TitilerEndpoint):
    """This class contains the methods for the Microsoft Planetary Computer endpoint."""

    def __init__(
        self,
        endpoint: Optional[str] = "https://planetarycomputer.microsoft.com/api/data/v1",
        name: Optional[str] = "item",
        TileMatrixSetId: Optional[str] = "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__(endpoint='https://planetarycomputer.microsoft.com/api/data/v1', name='item', TileMatrixSetId='WebMercatorQuad')

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 leafmap/stac.py

def __init__(
    self,
    endpoint: Optional[str] = "https://planetarycomputer.microsoft.com/api/data/v1",
    name: Optional[str] = "item",
    TileMatrixSetId: Optional[str] = "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)

The_national_map_USGS

The national map is a collection of topological datasets, maintained by the USGS.

It provides an API endpoint which can be used to find downloadable links for the products offered. - Full description of datasets available can retrieved. This consists of metadata such as detail description and publication dates. - A wide range of dataformats are available

This class is a tiny wrapper to find and download files using the API.

More complete documentation for the API can be found at https://apps.nationalmap.gov/tnmaccess/#/

Source code in leafmap/common.py

class The_national_map_USGS:
    """
    The national map is a collection of topological datasets, maintained by the USGS.

    It provides an API endpoint which can be used to find downloadable links for the products offered.
        - Full description of datasets available can retrieved.
          This consists of metadata such as detail description and publication dates.
        - A wide range of dataformats are available

    This class is a tiny wrapper to find and download files using the API.

    More complete documentation for the API can be found at
        https://apps.nationalmap.gov/tnmaccess/#/
    """

    def __init__(self):
        self.api_endpoint = r"https://tnmaccess.nationalmap.gov/api/v1/"
        self.DS = self.datasets_full

    @property
    def datasets_full(self) -> list:
        """
        Full description of datasets provided.
        Returns a JSON or empty list.
        """
        link = f"{self.api_endpoint}datasets?"
        try:
            return requests.get(link).json()
        except Exception:
            print(f"Failed to load metadata from The National Map API endpoint\n{link}")
            return []

    @property
    def prodFormats(self) -> list:
        """
        Return all datatypes available in any of the collections.
        Note that "All" is only peculiar to one dataset.
        """
        return set(i["displayName"] for ds in self.DS for i in ds["formats"])

    @property
    def datasets(self) -> list:
        """
        Returns a list of dataset tags (most common human readable self description for specific datasets).
        """
        return set(y["sbDatasetTag"] for x in self.DS for y in x["tags"])

    def parse_region(self, region, geopandas_args={}) -> list:
        """

        Translate a Vector dataset to its bounding box.

        Args:
            region (str | list): an URL|filepath to a vector dataset to a polygon
            geopandas_reader_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
        """
        import geopandas as gpd

        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, **geopandas_args)
            roi = roi.to_crs(epsg=4326)
            return roi.total_bounds
        return region

    def download_tiles(
        self, region=None, out_dir=None, download_args={}, geopandas_args={}, API={}
    ) -> None:
        """

        Download the US National Elevation Datasets (NED) for a region.

        Args:
            region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
                Alternatively you could use API parameters such as polygon or bbox.
            out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
            download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
            geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
            API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
                Exposes most of the documented API. Defaults to {}.

        Returns:
            None
        """

        if os.environ.get("USE_MKDOCS") is not None:
            return

        if out_dir is None:
            out_dir = os.getcwd()
        else:
            out_dir = os.path.abspath(out_dir)

        tiles = self.find_tiles(
            region, return_type="list", geopandas_args=geopandas_args, API=API
        )
        T = len(tiles)
        errors = 0
        done = 0

        for i, link in enumerate(tiles):
            file_name = os.path.basename(link)
            out_name = os.path.join(out_dir, file_name)
            if i < 5 or (i < 50 and not (i % 5)) or not (i % 20):
                print(f"Downloading {i+1} of {T}: {file_name}")
            try:
                download_file(link, out_name, **download_args)
                done += 1
            except KeyboardInterrupt:
                print("Cancelled download")
                break
            except Exception:
                errors += 1
                print(f"Failed to download {i+1} of {T}: {file_name}")

        print(
            f"{done} Downloads completed, {errors} downloads failed, {T} files available"
        )
        return

    def find_tiles(self, region=None, return_type="list", geopandas_args={}, API={}):
        """
        Find a list of downloadable files.

        Args:
            region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
                Alternatively you could use API parameters such as polygon or bbox.
            out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
            return_type (str): list | dict. Defaults to list. Changes the return output type and content.
            geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
            API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
                Exposes most of the documented API parameters. Defaults to {}.

        Returns:
            list: A list of download_urls.
            dict: A dictionary with urls and related metadata
        """
        assert region or API, "Provide a region or use the API"

        if region:
            API["bbox"] = self.parse_region(region, geopandas_args)

        results = self.find_details(**API)
        if return_type == "list":
            return [i["downloadURL"] for i in results.get("items")]
        return results

    def find_details(
        self,
        bbox: List[float] = None,
        polygon: List[Tuple[float, float]] = None,
        datasets: str = None,
        prodFormats: str = None,
        prodExtents: str = None,
        q: str = None,
        dateType: str = None,
        start: str = None,
        end: str = None,
        offset: int = 0,
        max: int = None,
        outputFormat: str = "JSON",
        polyType: str = None,
        polyCode: str = None,
        extentQuery: int = None,
    ) -> Dict:
        """
        Possible search parameters (kwargs) support by API

        Parameter               Values
            Description
        ---------------------------------------------------------------------------------------------------
        bbox                    'minx, miny, maxx, maxy'
            Geographic longitude/latitude values expressed in  decimal degrees in a comma-delimited list.
        polygon                 '[x,y x,y x,y x,y x,y]'
            Polygon, longitude/latitude values expressed in decimal degrees in a space-delimited list.
        datasets                See: Datasets (Optional)
            Dataset tag name (sbDatasetTag)
            From https://apps.nationalmap.gov/tnmaccess/#/product
        prodFormats             See: Product Formats (Optional)
            Dataset-specific format

        prodExtents             See: Product Extents (Optional)
            Dataset-specific extent
        q                       free text
            Text input which can be used to filter by product titles and text descriptions.
        dateType                dateCreated | lastUpdated | Publication
            Type of date to search by.
        start                   'YYYY-MM-DD'
            Start date
        end                     'YYYY-MM-DD'
            End date (required if start date is provided)
        offset                  integer
            Offset into paginated results - default=0
        max                     integer
            Number of results returned
        outputFormat            JSON | CSV | pjson
            Default=JSON
        polyType                state | huc2 | huc4 | huc8
            Well Known Polygon Type. Use this parameter to deliver data by state or HUC
            (hydrologic unit codes defined by the Watershed Boundary Dataset/WBD)
        polyCode                state FIPS code or huc number
            Well Known Polygon Code. This value needs to coordinate with the polyType parameter.
        extentQuery             integer
            A Polygon code in the science base system, typically from an uploaded shapefile
        """

        try:
            # call locals before creating new locals
            used_locals = {k: v for k, v in locals().items() if v and k != "self"}

            # Parsing
            if polygon:
                used_locals["polygon"] = ",".join(
                    " ".join(map(str, point)) for point in polygon
                )
            if bbox:
                used_locals["bbox"] = str(bbox)[1:-1]

            if max:
                max += 2

            # Fetch response
            response = requests.get(f"{self.api_endpoint}products?", params=used_locals)
            if response.status_code // 100 == 2:
                return response.json()
            else:
                # Parameter validation handled by API endpoint error responses
                print(response.json())
            return {}
        except Exception as e:
            print(e)
            return {}

datasets property

Returns a list of dataset tags (most common human readable self description for specific datasets).

datasets_full property

Full description of datasets provided. Returns a JSON or empty list.

prodFormats property

Return all datatypes available in any of the collections. Note that "All" is only peculiar to one dataset.

download_tiles(region=None, out_dir=None, download_args={}, geopandas_args={}, API={})

Download the US National Elevation Datasets (NED) for a region.

Parameters:

Name	Type	Description	Default
region	str | list	An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy]. Alternatively you could use API parameters such as polygon or bbox.	None
out_dir	str	The directory to download the files to. Defaults to None, which uses the current working directory.	None
download_args	dict	A dictionary of arguments to pass to the download_file function. Defaults to {}.	{}
geopandas_args	dict	A dictionary of arguments to pass to the geopandas.read_file() function. Used for reading a region URL|filepath.	{}
API	dict	A dictionary of arguments to pass to the self.find_details() function. Exposes most of the documented API. Defaults to {}.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def download_tiles(
    self, region=None, out_dir=None, download_args={}, geopandas_args={}, API={}
) -> None:
    """

    Download the US National Elevation Datasets (NED) for a region.

    Args:
        region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
            Alternatively you could use API parameters such as polygon or bbox.
        out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
        download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
            Exposes most of the documented API. Defaults to {}.

    Returns:
        None
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if out_dir is None:
        out_dir = os.getcwd()
    else:
        out_dir = os.path.abspath(out_dir)

    tiles = self.find_tiles(
        region, return_type="list", geopandas_args=geopandas_args, API=API
    )
    T = len(tiles)
    errors = 0
    done = 0

    for i, link in enumerate(tiles):
        file_name = os.path.basename(link)
        out_name = os.path.join(out_dir, file_name)
        if i < 5 or (i < 50 and not (i % 5)) or not (i % 20):
            print(f"Downloading {i+1} of {T}: {file_name}")
        try:
            download_file(link, out_name, **download_args)
            done += 1
        except KeyboardInterrupt:
            print("Cancelled download")
            break
        except Exception:
            errors += 1
            print(f"Failed to download {i+1} of {T}: {file_name}")

    print(
        f"{done} Downloads completed, {errors} downloads failed, {T} files available"
    )
    return

find_details(bbox=None, polygon=None, datasets=None, prodFormats=None, prodExtents=None, q=None, dateType=None, start=None, end=None, offset=0, max=None, outputFormat='JSON', polyType=None, polyCode=None, extentQuery=None)

Possible search parameters (kwargs) support by API

Parameter Values Description

---

bbox 'minx, miny, maxx, maxy' Geographic longitude/latitude values expressed in decimal degrees in a comma-delimited list. polygon '[x,y x,y x,y x,y x,y]' Polygon, longitude/latitude values expressed in decimal degrees in a space-delimited list. datasets See: Datasets (Optional) Dataset tag name (sbDatasetTag) From https://apps.nationalmap.gov/tnmaccess/#/product prodFormats See: Product Formats (Optional) Dataset-specific format

Product Extents (Optional)

Dataset-specific extent

q free text Text input which can be used to filter by product titles and text descriptions. dateType dateCreated | lastUpdated | Publication Type of date to search by. start 'YYYY-MM-DD' Start date end 'YYYY-MM-DD' End date (required if start date is provided) offset integer Offset into paginated results - default=0 max integer Number of results returned outputFormat JSON | CSV | pjson Default=JSON polyType state | huc2 | huc4 | huc8 Well Known Polygon Type. Use this parameter to deliver data by state or HUC (hydrologic unit codes defined by the Watershed Boundary Dataset/WBD) polyCode state FIPS code or huc number Well Known Polygon Code. This value needs to coordinate with the polyType parameter. extentQuery integer A Polygon code in the science base system, typically from an uploaded shapefile

Source code in leafmap/common.py

def find_details(
    self,
    bbox: List[float] = None,
    polygon: List[Tuple[float, float]] = None,
    datasets: str = None,
    prodFormats: str = None,
    prodExtents: str = None,
    q: str = None,
    dateType: str = None,
    start: str = None,
    end: str = None,
    offset: int = 0,
    max: int = None,
    outputFormat: str = "JSON",
    polyType: str = None,
    polyCode: str = None,
    extentQuery: int = None,
) -> Dict:
    """
    Possible search parameters (kwargs) support by API

    Parameter               Values
        Description
    ---------------------------------------------------------------------------------------------------
    bbox                    'minx, miny, maxx, maxy'
        Geographic longitude/latitude values expressed in  decimal degrees in a comma-delimited list.
    polygon                 '[x,y x,y x,y x,y x,y]'
        Polygon, longitude/latitude values expressed in decimal degrees in a space-delimited list.
    datasets                See: Datasets (Optional)
        Dataset tag name (sbDatasetTag)
        From https://apps.nationalmap.gov/tnmaccess/#/product
    prodFormats             See: Product Formats (Optional)
        Dataset-specific format

    prodExtents             See: Product Extents (Optional)
        Dataset-specific extent
    q                       free text
        Text input which can be used to filter by product titles and text descriptions.
    dateType                dateCreated | lastUpdated | Publication
        Type of date to search by.
    start                   'YYYY-MM-DD'
        Start date
    end                     'YYYY-MM-DD'
        End date (required if start date is provided)
    offset                  integer
        Offset into paginated results - default=0
    max                     integer
        Number of results returned
    outputFormat            JSON | CSV | pjson
        Default=JSON
    polyType                state | huc2 | huc4 | huc8
        Well Known Polygon Type. Use this parameter to deliver data by state or HUC
        (hydrologic unit codes defined by the Watershed Boundary Dataset/WBD)
    polyCode                state FIPS code or huc number
        Well Known Polygon Code. This value needs to coordinate with the polyType parameter.
    extentQuery             integer
        A Polygon code in the science base system, typically from an uploaded shapefile
    """

    try:
        # call locals before creating new locals
        used_locals = {k: v for k, v in locals().items() if v and k != "self"}

        # Parsing
        if polygon:
            used_locals["polygon"] = ",".join(
                " ".join(map(str, point)) for point in polygon
            )
        if bbox:
            used_locals["bbox"] = str(bbox)[1:-1]

        if max:
            max += 2

        # Fetch response
        response = requests.get(f"{self.api_endpoint}products?", params=used_locals)
        if response.status_code // 100 == 2:
            return response.json()
        else:
            # Parameter validation handled by API endpoint error responses
            print(response.json())
        return {}
    except Exception as e:
        print(e)
        return {}

find_tiles(region=None, return_type='list', geopandas_args={}, API={})

Find a list of downloadable files.

Parameters:

Name	Type	Description	Default
region	str | list	An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy]. Alternatively you could use API parameters such as polygon or bbox.	None
out_dir	str	The directory to download the files to. Defaults to None, which uses the current working directory.	required
return_type	str	list | dict. Defaults to list. Changes the return output type and content.	'list'
geopandas_args	dict	A dictionary of arguments to pass to the geopandas.read_file() function. Used for reading a region URL|filepath.	{}
API	dict	A dictionary of arguments to pass to the self.find_details() function. Exposes most of the documented API parameters. Defaults to {}.	{}

Returns:

Name	Type	Description
list		A list of download_urls.
dict		A dictionary with urls and related metadata

Source code in leafmap/common.py

def find_tiles(self, region=None, return_type="list", geopandas_args={}, API={}):
    """
    Find a list of downloadable files.

    Args:
        region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
            Alternatively you could use API parameters such as polygon or bbox.
        out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
        return_type (str): list | dict. Defaults to list. Changes the return output type and content.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
            Exposes most of the documented API parameters. Defaults to {}.

    Returns:
        list: A list of download_urls.
        dict: A dictionary with urls and related metadata
    """
    assert region or API, "Provide a region or use the API"

    if region:
        API["bbox"] = self.parse_region(region, geopandas_args)

    results = self.find_details(**API)
    if return_type == "list":
        return [i["downloadURL"] for i in results.get("items")]
    return results

parse_region(region, geopandas_args={})

Translate a Vector dataset to its bounding box.

Parameters:

Name	Type	Description	Default
region	str | list	an URL|filepath to a vector dataset to a polygon	required
geopandas_reader_args	dict	A dictionary of arguments to pass to the geopandas.read_file() function. Used for reading a region URL|filepath.	required

Source code in leafmap/common.py

def parse_region(self, region, geopandas_args={}) -> list:
    """

    Translate a Vector dataset to its bounding box.

    Args:
        region (str | list): an URL|filepath to a vector dataset to a polygon
        geopandas_reader_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
    """
    import geopandas as gpd

    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, **geopandas_args)
        roi = roi.to_crs(epsg=4326)
        return roi.total_bounds
    return region

TitilerEndpoint

This class contains the methods for the titiler endpoint.

Source code in leafmap/stac.py

class TitilerEndpoint:
    """This class contains the methods for the titiler endpoint."""

    def __init__(
        self,
        endpoint: Optional[str] = None,
        name: Optional[str] = "stac",
        TileMatrixSetId: Optional[str] = "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__(endpoint=None, name='stac', TileMatrixSetId='WebMercatorQuad')

Initialize the TitilerEndpoint object.

Parameters:

Name	Type	Description	Default
endpoint	str	The endpoint of the titiler server. Defaults to "https://titiler.xyz".	None
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 leafmap/stac.py

def __init__(
    self,
    endpoint: Optional[str] = None,
    name: Optional[str] = "stac",
    TileMatrixSetId: Optional[str] = "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

WhiteboxTools

Bases: WhiteboxTools

This class inherits the whitebox WhiteboxTools class.

Source code in leafmap/common.py

class WhiteboxTools(whitebox.WhiteboxTools):
    """This class inherits the whitebox WhiteboxTools class."""

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

__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 leafmap/common.py

def __install_from_github(url: str) -> None:
    """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("Installing {}...".format(pkg_name))
        cmd = "pip install ."
        os.system(cmd)
        os.chdir(work_dir)
        print("{} has been installed successfully.".format(pkg_name))
        # print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")

    except Exception as e:
        raise Exception(e)

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 leafmap/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

add_image_to_gif(in_gif, out_gif, in_image, xy=None, image_size=(80, 80), circle_mask=False)

Adds an image logo to a GIF image.

Parameters:

Name	Type	Description	Default
in_gif	str	Input file path to the GIF image.	required
out_gif	str	Output file path to the GIF image.	required
in_image	str	Input file path to the image.	required
xy	tuple	Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	None
image_size	tuple	Resize image. Defaults to (80, 80).	(80, 80)
circle_mask	bool	Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.	False

Source code in leafmap/common.py

def add_image_to_gif(
    in_gif, out_gif, in_image, xy=None, image_size=(80, 80), circle_mask=False
):
    """Adds an image logo to a GIF image.

    Args:
        in_gif (str): Input file path to the GIF image.
        out_gif (str): Output file path to the GIF image.
        in_image (str): Input file path to the image.
        xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        image_size (tuple, optional): Resize image. Defaults to (80, 80).
        circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.
    """
    import io

    from PIL import Image, ImageDraw, ImageSequence

    warnings.simplefilter("ignore")

    in_gif = os.path.abspath(in_gif)

    is_url = False
    if in_image.startswith("http"):
        is_url = True

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if (not is_url) and (not os.path.exists(in_image)):
        print("The provided logo file does not exist.")
        return

    out_dir = check_dir((os.path.dirname(out_gif)))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    try:
        gif = Image.open(in_gif)
    except Exception as e:
        print("An error occurred while opening the image.")
        print(e)
        return

    logo_raw_image = None
    try:
        if in_image.startswith("http"):
            logo_raw_image = open_image_from_url(in_image)
        else:
            in_image = os.path.abspath(in_image)
            logo_raw_image = Image.open(in_image)
    except Exception as e:
        print(e)

    logo_raw_size = logo_raw_image.size

    ratio = max(
        logo_raw_size[0] / image_size[0],
        logo_raw_size[1] / image_size[1],
    )
    image_resize = (int(logo_raw_size[0] / ratio), int(logo_raw_size[1] / ratio))
    image_size = min(logo_raw_size[0], image_size[0]), min(
        logo_raw_size[1], image_size[1]
    )

    logo_image = logo_raw_image.convert("RGBA")
    logo_image.thumbnail(image_size, Image.ANTIALIAS)

    gif_width, gif_height = gif.size
    mask_im = None

    if circle_mask:
        mask_im = Image.new("L", image_size, 0)
        draw = ImageDraw.Draw(mask_im)
        draw.ellipse((0, 0, image_size[0], image_size[1]), fill=255)

    if has_transparency(logo_raw_image):
        mask_im = logo_image.copy()

    if xy is None:
        # default logo location is 5% width and 5% height of the image.
        delta = 10
        xy = (gif_width - image_resize[0] - delta, gif_height - image_resize[1] - delta)
        # xy = (int(0.05 * gif_width), int(0.05 * gif_height))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
        return
    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < gif_width) and (y > 0) and (y < gif_height):
            pass
        else:
            print(
                "xy is out of bounds. x must be within [0, {}], and y must be within [0, {}]".format(
                    gif_width, gif_height
                )
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = int(float(x.replace("%", "")) / 100.0 * gif_width)
                y = int(float(y.replace("%", "")) / 100.0 * gif_height)
                xy = (x, y)
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )

    else:
        raise Exception(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )

    try:
        frames = []
        for _, frame in enumerate(ImageSequence.Iterator(gif)):
            frame = frame.convert("RGBA")
            frame.paste(logo_image, xy, mask_im)

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)
            frames.append(frame)

        frames[0].save(out_gif, save_all=True, append_images=frames[1:])
    except Exception as e:
        print(e)

add_mask_to_image(image, mask, output, color='red')

Overlay a binary mask (e.g., roads, building footprints, etc) on an image. Credits to Xingjian Shi for the sample code.

Parameters:

Name	Type	Description	Default
image	str	A local path or HTTP URL to an image.	required
mask	str	A local path or HTTP URL to a binary mask.	required
output	str	A local path to the output image.	required
color	str	Color of the mask. Defaults to 'red'.	'red'

Raises:

Type	Description
ImportError	If rasterio and detectron2 are not installed.

Source code in leafmap/common.py

def add_mask_to_image(image, mask, output, color="red"):
    """Overlay a binary mask (e.g., roads, building footprints, etc) on an image. Credits to Xingjian Shi for the sample code.

    Args:
        image (str): A local path or HTTP URL to an image.
        mask (str): A local path or HTTP URL to a binary mask.
        output (str): A local path to the output image.
        color (str, optional): Color of the mask. Defaults to 'red'.

    Raises:
        ImportError: If rasterio and detectron2 are not installed.
    """
    try:
        import rasterio
        from detectron2.utils.visualizer import Visualizer
        from PIL import Image
    except ImportError:
        raise ImportError(
            "Please install rasterio and detectron2 to use this function. See https://detectron2.readthedocs.io/en/latest/tutorials/install.html"
        )

    ds = rasterio.open(image)
    image_arr = ds.read()

    mask_arr = rasterio.open(mask).read()

    vis = Visualizer(image_arr.transpose((1, 2, 0)))
    vis.draw_binary_mask(mask_arr[0] > 0, color=color)

    out_arr = Image.fromarray(vis.get_output().get_image())

    out_arr.save(output)

    if ds.crs is not None:
        numpy_to_cog(output, output, profile=image)

add_progress_bar_to_gif(in_gif, out_gif, progress_bar_color='blue', progress_bar_height=5, duration=100, loop=0)

Adds a progress bar to a GIF image.

Parameters:

Name	Type	Description	Default
in_gif	str	The file path to the input GIF image.	required
out_gif	str	The file path to the output GIF image.	required
progress_bar_color	str	Color for the progress bar. Defaults to 'white'.	'blue'
progress_bar_height	int	Height of the progress bar. Defaults to 5.	5
duration	int	controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation. Defaults to 100.	100
loop	int	controls how many times the animation repeats. The default, 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

Source code in leafmap/common.py

def add_progress_bar_to_gif(
    in_gif,
    out_gif,
    progress_bar_color="blue",
    progress_bar_height=5,
    duration=100,
    loop=0,
):
    """Adds a progress bar to a GIF image.

    Args:
        in_gif (str): The file path to the input GIF image.
        out_gif (str): The file path to the output GIF image.
        progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
        progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
        duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation. Defaults to 100.
        loop (int, optional): controls how many times the animation repeats. The default, 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.

    """
    import io

    from PIL import Image, ImageDraw, ImageSequence

    warnings.simplefilter("ignore")

    in_gif = os.path.abspath(in_gif)
    out_gif = os.path.abspath(out_gif)

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    progress_bar_color = check_color(progress_bar_color)

    try:
        image = Image.open(in_gif)
    except Exception as e:
        raise Exception("An error occurred while opening the gif.")

    count = image.n_frames
    W, H = image.size
    progress_bar_widths = [i * 1.0 / count * W for i in range(1, count + 1)]
    progress_bar_shapes = [
        [(0, H - progress_bar_height), (x, H)] for x in progress_bar_widths
    ]

    try:
        frames = []
        # Loop over each frame in the animated image
        for index, frame in enumerate(ImageSequence.Iterator(image)):
            # Draw the text on the frame
            frame = frame.convert("RGB")
            draw = ImageDraw.Draw(frame)
            # w, h = draw.textsize(text[index])
            draw.rectangle(progress_bar_shapes[index], fill=progress_bar_color)
            del draw

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)

            frames.append(frame)
        # https://www.pythoninformer.com/python-libraries/pillow/creating-animated-gif/
        # Save the frames as a new image

        frames[0].save(
            out_gif,
            save_all=True,
            append_images=frames[1:],
            duration=duration,
            loop=loop,
            optimize=True,
        )
    except Exception as e:
        raise Exception(e)

add_text_to_gif(in_gif, out_gif, xy=None, text_sequence=None, font_type='arial.ttf', font_size=20, font_color='#000000', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, duration=100, loop=0)

Adds animated text to a GIF image.

Parameters:

Name	Type	Description	Default
in_gif	str	The file path to the input GIF image.	required
out_gif	str	The file path to the output GIF image.	required
xy	tuple	Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	None
text_sequence	(int, str, list)	Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None.	None
font_type	str	Font type. Defaults to "arial.ttf".	'arial.ttf'
font_size	int	Font size. Defaults to 20.	20
font_color	str	Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff'). Defaults to '#000000'.	'#000000'
add_progress_bar	bool	Whether to add a progress bar at the bottom of the GIF. Defaults to True.	True
progress_bar_color	str	Color for the progress bar. Defaults to 'white'.	'white'
progress_bar_height	int	Height of the progress bar. Defaults to 5.	5
duration	int	controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100.	100
loop	int	controls how many times the animation repeats. The default, 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

Source code in leafmap/common.py

def add_text_to_gif(
    in_gif,
    out_gif,
    xy=None,
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="#000000",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    duration=100,
    loop=0,
):
    """Adds animated text to a GIF image.

    Args:
        in_gif (str): The file path to the input GIF image.
        out_gif (str): The file path to the output GIF image.
        xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        text_sequence (int, str, list, optional): Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None.
        font_type (str, optional): Font type. Defaults to "arial.ttf".
        font_size (int, optional): Font size. Defaults to 20.
        font_color (str, optional): Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff').  Defaults to '#000000'.
        add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.
        progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
        progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
        duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100.
        loop (int, optional): controls how many times the animation repeats. The default, 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.

    """
    import io

    import importlib.resources
    from PIL import Image, ImageDraw, ImageFont, ImageSequence

    warnings.simplefilter("ignore")
    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.py")
    default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")

    in_gif = os.path.abspath(in_gif)
    out_gif = os.path.abspath(out_gif)

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if font_type == "arial.ttf":
        font = ImageFont.truetype(default_font, font_size)
    elif font_type == "alibaba.otf":
        default_font = os.path.join(pkg_dir, "data/fonts/alibaba.otf")
        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)

    color = check_color(font_color)
    progress_bar_color = check_color(progress_bar_color)

    try:
        image = Image.open(in_gif)
    except Exception as e:
        print("An error occurred while opening the gif.")
        print(e)
        return

    count = image.n_frames
    W, H = image.size
    progress_bar_widths = [i * 1.0 / count * W for i in range(1, count + 1)]
    progress_bar_shapes = [
        [(0, H - progress_bar_height), (x, H)] for x in progress_bar_widths
    ]

    if xy is None:
        # default text location is 5% width and 5% height of the image.
        xy = (int(0.05 * W), int(0.05 * H))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
        return
    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < W) and (y > 0) and (y < H):
            pass
        else:
            print(
                f"xy is out of bounds. x must be within [0, {W}], and y must be within [0, {H}]"
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = int(float(x.replace("%", "")) / 100.0 * W)
                y = int(float(y.replace("%", "")) / 100.0 * H)
                xy = (x, y)
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )
    else:
        print(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )
        return

    if text_sequence is None:
        text = [str(x) for x in range(1, count + 1)]
    elif isinstance(text_sequence, int):
        text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
    elif isinstance(text_sequence, str):
        try:
            text_sequence = int(text_sequence)
            text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
        except Exception:
            text = [text_sequence] * count
    elif isinstance(text_sequence, list) and len(text_sequence) != count:
        print(
            f"The length of the text sequence must be equal to the number ({count}) of frames in the gif."
        )
        return
    else:
        text = [str(x) for x in text_sequence]

    try:
        frames = []
        # Loop over each frame in the animated image
        for index, frame in enumerate(ImageSequence.Iterator(image)):
            # Draw the text on the frame
            frame = frame.convert("RGB")
            draw = ImageDraw.Draw(frame)
            # w, h = draw.textsize(text[index])
            draw.text(xy, text[index], font=font, fill=color)
            if add_progress_bar:
                draw.rectangle(progress_bar_shapes[index], fill=progress_bar_color)
            del draw

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)

            frames.append(frame)
        # https://www.pythoninformer.com/python-libraries/pillow/creating-animated-gif/
        # Save the frames as a new image

        frames[0].save(
            out_gif,
            save_all=True,
            append_images=frames[1:],
            duration=duration,
            loop=loop,
            optimize=True,
        )
    except Exception as e:
        print(e)

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:

Name	Type	Description
dict		A dictionary containing the converted longitudes

Source code in leafmap/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

arc_active_map()

Get the active map in ArcGIS Pro.

Returns:

Type	Description
	arcpy.Map: The active map in ArcGIS Pro.

Source code in leafmap/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  # pylint: disable=E0401

        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 leafmap/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  # pylint: disable=E0401

        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 leafmap/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_bounds(bounds)

Zoom to a bounding box.

Parameters:

Name	Type	Description	Default
bounds	list	The bounding box to zoom to in the form [xmin, ymin, xmax, ymax] or [(ymin, xmin), (ymax, xmax)].	required

Raises:

Type	Description
ValueError	description

Source code in leafmap/common.py

def arc_zoom_to_bounds(bounds):
    """Zoom to a bounding box.

    Args:
        bounds (list): The bounding box to zoom to in the form [xmin, ymin, xmax, ymax] or [(ymin, xmin), (ymax, xmax)].

    Raises:
        ValueError: _description_
    """

    if len(bounds) == 4:
        xmin, ymin, xmax, ymax = bounds
    elif len(bounds) == 2:
        (ymin, xmin), (ymax, xmax) = bounds
    else:
        raise ValueError("bounds must be a tuple of length 2 or 4.")

    arc_zoom_to_extent(xmin, ymin, xmax, ymax)

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 leafmap/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  # pylint: disable=E0401

        view = arc_active_view()
        if view is not None:
            view.camera.setExtent(
                arcpy.Extent(
                    xmin,
                    ymin,
                    xmax,
                    ymax,
                    spatial_reference=arcpy.SpatialReference(4326),
                )
            )

array_to_image(array, output=None, source=None, dtype=None, compress='deflate', transpose=True, cellsize=None, crs=None, transform=None, driver='COG', colormap=None, **kwargs)

Save a NumPy array as a GeoTIFF using the projection information from an existing GeoTIFF file.

Parameters:

Name	Type	Description	Default
array	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	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
transform	tuple	The affine transformation matrix, can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361). Defaults to None.	None
driver	str	The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".	'COG'
colormap	dict	A dictionary defining the colormap (value: (R, G, B, A)).	None
**kwargs		Additional keyword arguments to be passed to the rasterio.open() function.	{}

Source code in leafmap/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,
    transform: tuple = None,
    driver: str = "COG",
    colormap: dict = None,
    **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.
        transform (tuple, optional): The affine transformation matrix, can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361).
            Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        colormap (dict, optional): A dictionary defining the colormap (value: (R, G, B, A)).
        **kwargs: Additional keyword arguments to be passed to the rasterio.open() function.
    """

    import numpy as np
    import rasterio
    import xarray as xr
    import rioxarray
    from rasterio.transform import Affine

    if output is None:
        return array_to_memory_file(
            array,
            source,
            dtype,
            compress,
            transpose,
            cellsize,
            crs=crs,
            transform=transform,
            driver=driver,
            colormap=colormap,
            **kwargs,
        )

    if isinstance(array, xr.DataArray):
        if (
            hasattr(array, "rio")
            and (array.rio.crs is not None)
            and (array.rio.transform() is not None)
        ):

            if "latitude" in array.dims and "longitude" in array.dims:
                array = array.rename({"latitude": "y", "longitude": "x"})
            elif "lat" in array.dims and "lon" in array.dims:
                array = array.rename({"lat": "y", "lon": "x"})

            if array.ndim == 2 and ("x" in array.dims) and ("y" in array.dims):
                array = array.transpose("y", "x")
            elif array.ndim == 3 and ("x" in array.dims) and ("y" in array.dims):
                dims = list(array.dims)
                dims.remove("x")
                dims.remove("y")
                array = array.transpose(dims[0], "y", "x")
            if "long_name" in array.attrs:
                array.attrs.pop("long_name")

            array.rio.to_raster(
                output, driver=driver, compress=compress, dtype=dtype, **kwargs
            )
            if colormap:
                write_image_colormap(output, colormap, output)
            return output

    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)

    ext = os.path.splitext(output)[-1].lower()
    if ext == "":
        output += ".tif"
        driver = "COG"
    elif ext == ".png":
        driver = "PNG"
    elif ext == ".jpg" or ext == ".jpeg":
        driver = "JPEG"
    elif ext == ".jp2":
        driver = "JP2OpenJPEG"
    elif ext == ".tiff":
        driver = "GTiff"
    else:
        driver = "COG"

    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 is None:
            # 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]
            )
        elif isinstance(transform, Affine):
            pass
        elif isinstance(transform, (tuple, list)):
            transform = Affine(*transform)

        kwargs["transform"] = 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 and (driver in ["GTiff", "COG"]):
        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)
            if colormap:
                dst.write_colormap(1, colormap)
        elif array.ndim == 3:
            for i in range(array.shape[2]):
                dst.write(array[:, :, i], i + 1)
                if colormap:
                    dst.write_colormap(i + 1, colormap)
    return output

array_to_memory_file(array, source=None, dtype=None, compress='deflate', transpose=True, cellsize=None, crs=None, transform=None, driver='COG', colormap=None, **kwargs)

Convert a NumPy array to a memory file.

Parameters:

Name	Type	Description	Default
array	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. Can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361). Defaults to None.	None
driver	str	The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".	'COG'
colormap	dict	A dictionary defining the colormap (value: (R, G, B, A)).	None
**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 leafmap/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",
    colormap: dict = None,
    **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.
            Can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361). Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        colormap (dict, optional): A dictionary defining the colormap (value: (R, G, B, A)).
        **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
    from rasterio.transform import Affine

    if isinstance(array, xr.DataArray):
        coords = [coord for coord in array.coords]
        if coords[0] == "time":
            x_dim = coords[1]
            y_dim = coords[2]
            array = (
                array.isel(time=0).rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
            )
        if hasattr(array, "rio"):
            if hasattr(array.rio, "crs"):
                if array.rio.crs is not None:
                    crs = array.rio.crs
            if transform is None and hasattr(array.rio, "transform"):
                transform = array.rio.transform()
        elif source is None:
            if hasattr(array, "encoding"):
                if "source" in array.encoding:
                    source = array.encoding["source"]
        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 crs is None:
            raise ValueError(
                "crs must be provided if source is not provided, such as EPSG:3857"
            )

        if transform is None:
            if cellsize is None:
                raise ValueError("cellsize must be provided if source is not provided")
            # 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]
            )
        elif isinstance(transform, Affine):
            pass
        elif isinstance(transform, (tuple, list)):
            transform = Affine(*transform)

        kwargs["transform"] = 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)
        if colormap:
            dst.write_colormap(1, colormap)
    elif array.ndim == 3:
        for i in range(array.shape[2]):
            dst.write(array[:, :, i], i + 1)
            if colormap:
                dst.write_colormap(i + 1, colormap)

    dst.close()
    # Read the dataset from memory
    dataset_reader = rasterio.open(dst.name, mode="r")

    return dataset_reader

assign_continuous_colors(df, column, cmap=None, colors=None, labels=None, scheme='Quantiles', k=5, legend_kwds=None, classification_kwds=None, to_rgb=True, return_type='array', return_legend=False)

Assigns continuous colors to a DataFrame column based on a specified scheme.

Parameters:

Name	Type	Description	Default
df		A pandas DataFrame.	required
column	str	The name of the column to assign colors.	required
cmap	str	The name of the colormap to use.	None
colors	list	A list of custom colors.	None
labels	list	A list of custom labels for the legend.	None
scheme	str	The scheme for classifying the data. Default is 'Quantiles'.	'Quantiles'
k	int	The number of classes for classification.	5
legend_kwds	dict	Additional keyword arguments for configuring the legend.	None
classification_kwds	dict	Additional keyword arguments for configuring the classification.	None
to_rgb	bool	Whether to convert colors to RGB values. Default is True.	True
return_type	str	The type of the returned values. Default is 'array'.	'array'
return_legend	bool	Whether to return the legend. Default is False.	False

Returns:

Type	Description
Union[ndarray, Tuple[ndarray, dict]]	The assigned colors as a numpy array or a tuple containing the colors and the legend, depending on the value of return_legend.

Source code in leafmap/common.py

def assign_continuous_colors(
    df,
    column: str,
    cmap: str = None,
    colors: list = None,
    labels: list = None,
    scheme: str = "Quantiles",
    k: int = 5,
    legend_kwds: dict = None,
    classification_kwds: dict = None,
    to_rgb: bool = True,
    return_type: str = "array",
    return_legend: bool = False,
) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
    """Assigns continuous colors to a DataFrame column based on a specified scheme.

    Args:
        df: A pandas DataFrame.
        column: The name of the column to assign colors.
        cmap: The name of the colormap to use.
        colors: A list of custom colors.
        labels: A list of custom labels for the legend.
        scheme: The scheme for classifying the data. Default is 'Quantiles'.
        k: The number of classes for classification.
        legend_kwds: Additional keyword arguments for configuring the legend.
        classification_kwds: Additional keyword arguments for configuring the classification.
        to_rgb: Whether to convert colors to RGB values. Default is True.
        return_type: The type of the returned values. Default is 'array'.
        return_legend: Whether to return the legend. Default is False.

    Returns:
        The assigned colors as a numpy array or a tuple containing the colors and the legend, depending on the value of return_legend.
    """
    import numpy as np

    data = df[[column]].copy()
    new_df, legend = classify(
        data, column, cmap, colors, labels, scheme, k, legend_kwds, classification_kwds
    )
    values = new_df["color"].values.tolist()

    if to_rgb:
        values = [hex_to_rgb(check_color(color)) for color in values]
        if return_type == "array":
            values = np.array(values, dtype=np.uint8)

    if return_legend:
        return values, legend
    else:
        return values

assign_discrete_colors(df, column, cmap, to_rgb=True, return_type='array')

Assigns unique colors to each category in a categorical column of a dataframe.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input dataframe.	required
column	str	The name of the categorical column.	required
cmap	dict	A dictionary mapping categories to colors.	required
to_rgb	bool	Whether to convert the colors to RGB values. Defaults to True.	True
return_type	str	The type of the returned values. Can be 'list' or 'array'. Defaults to 'array'.	'array'

Returns:

Name	Type	Description
list		A list of colors for each category in the categorical column.

Source code in leafmap/common.py

def assign_discrete_colors(df, column, cmap, to_rgb=True, return_type="array"):
    """
    Assigns unique colors to each category in a categorical column of a dataframe.

    Args:
        df (pandas.DataFrame): The input dataframe.
        column (str): The name of the categorical column.
        cmap (dict): A dictionary mapping categories to colors.
        to_rgb (bool): Whether to convert the colors to RGB values. Defaults to True.
        return_type (str): The type of the returned values. Can be 'list' or 'array'. Defaults to 'array'.

    Returns:
        list: A list of colors for each category in the categorical column.
    """
    import numpy as np

    # Copy the categorical column from the original dataframe
    category_column = df[column].copy()

    # Map colors to the categorical values
    category_column = category_column.map(cmap)

    values = category_column.values.tolist()

    if to_rgb:
        values = [hex_to_rgb(check_color(color)) for color in values]
        if return_type == "array":
            values = np.array(values, dtype=np.uint8)

    return values

basemap_xyz_tiles()

Returns a dictionary containing a set of basemaps that are XYZ tile layers.

Returns:

Name	Type	Description
dict		A dictionary of XYZ tile layers.

Source code in leafmap/common.py

def basemap_xyz_tiles():
    """Returns a dictionary containing a set of basemaps that are XYZ tile layers.

    Returns:
        dict: A dictionary of XYZ tile layers.
    """
    from .leafmap import basemaps

    layers_dict = {}
    keys = dict(basemaps).keys()
    for key in keys:
        if isinstance(basemaps[key], ipyleaflet.WMSLayer):
            pass
        else:
            layers_dict[key] = basemaps[key]
    return layers_dict

bbox_to_gdf(bbox, crs='epsg:4326')

Convert a bounding box to a GeoPandas GeoDataFrame.

Parameters:

Name	Type	Description	Default
bbox	list	A bounding box in the format of [minx, miny, maxx, maxy].	required
crs	str	The CRS of the bounding box. Defaults to 'epsg:4326'.	'epsg:4326'

Returns:

Name	Type	Description
GeoDataFrame		A GeoDataFrame with a single polygon.

Source code in leafmap/common.py

def bbox_to_gdf(bbox, crs="epsg:4326"):
    """Convert a bounding box to a GeoPandas GeoDataFrame.

    Args:
        bbox (list): A bounding box in the format of [minx, miny, maxx, maxy].
        crs (str, optional): The CRS of the bounding box. Defaults to 'epsg:4326'.

    Returns:
        GeoDataFrame: A GeoDataFrame with a single polygon.
    """
    import geopandas as gpd
    from shapely.geometry import Polygon

    return gpd.GeoDataFrame(
        geometry=[Polygon.from_bounds(*bbox)],
        crs=crs,
    )

bbox_to_geojson(bounds)

Convert coordinates of a bounding box to a geojson.

Parameters:

Name	Type	Description	Default
bounds	list | tuple	A list of coordinates representing [left, bottom, right, top] or m.bounds.	required

Returns:

Name	Type	Description
dict		A geojson feature.

Source code in leafmap/common.py

def bbox_to_geojson(bounds):
    """Convert coordinates of a bounding box to a geojson.

    Args:
        bounds (list | tuple): A list of coordinates representing [left, bottom, right, top] or m.bounds.

    Returns:
        dict: A geojson feature.
    """

    if isinstance(bounds, tuple) and len(bounds) == 2:
        bounds = [bounds[0][1], bounds[0][0], bounds[1][1], bounds[1][0]]

    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",
    }

bbox_to_polygon(bbox)

Convert a bounding box to a shapely Polygon.

Parameters:

Name	Type	Description	Default
bbox	list	A bounding box in the format of [minx, miny, maxx, maxy].	required

Returns:

Name	Type	Description
Polygon		A shapely Polygon.

Source code in leafmap/common.py

def bbox_to_polygon(bbox):
    """Convert a bounding box to a shapely Polygon.

    Args:
        bbox (list): A bounding box in the format of [minx, miny, maxx, maxy].

    Returns:
        Polygon: A shapely Polygon.
    """
    from shapely.geometry import Polygon

    return Polygon.from_bounds(*bbox)

blend_images(img1, img2, alpha=0.5, output=False, show=True, figsize=(12, 10), axis='off', **kwargs)

Blends two images together using the addWeighted function from the OpenCV library.

Parameters:

Name	Type	Description	Default
img1	ndarray	The first input image on top represented as a NumPy array.	required
img2	ndarray	The second input image at the bottom represented as a NumPy array.	required
alpha	float	The weighting factor for the first image in the blend. By default, this is set to 0.5.	0.5
output	str	The path to the output image. Defaults to False.	False
show	bool	Whether to display the blended image. Defaults to True.	True
figsize	tuple	The size of the figure. Defaults to (12, 10).	(12, 10)
axis	str	The axis of the figure. Defaults to "off".	'off'
**kwargs		Additional keyword arguments to pass to the cv2.addWeighted() function.	{}

Returns:

Type	Description
	numpy.ndarray: The blended image as a NumPy array.

Source code in leafmap/common.py

def blend_images(
    img1,
    img2,
    alpha=0.5,
    output=False,
    show=True,
    figsize=(12, 10),
    axis="off",
    **kwargs,
):
    """
    Blends two images together using the addWeighted function from the OpenCV library.

    Args:
        img1 (numpy.ndarray): The first input image on top represented as a NumPy array.
        img2 (numpy.ndarray): The second input image at the bottom represented as a NumPy array.
        alpha (float): The weighting factor for the first image in the blend. By default, this is set to 0.5.
        output (str, optional): The path to the output image. Defaults to False.
        show (bool, optional): Whether to display the blended image. Defaults to True.
        figsize (tuple, optional): The size of the figure. Defaults to (12, 10).
        axis (str, optional): The axis of the figure. Defaults to "off".
        **kwargs: Additional keyword arguments to pass to the cv2.addWeighted() function.

    Returns:
        numpy.ndarray: The blended image as a NumPy array.
    """
    import numpy as np
    import matplotlib.pyplot as plt

    try:
        import cv2
    except ImportError:
        raise ImportError("The blend_images function requires the OpenCV library.")

    # Resize the images to have the same dimensions
    if isinstance(img1, str):
        if img1.startswith("http"):
            img1 = download_file(img1)

        if not os.path.exists(img1):
            raise ValueError(f"Input path {img1} does not exist.")

        img1 = cv2.imread(img1)

    if isinstance(img2, str):
        if img2.startswith("http"):
            img2 = download_file(img2)

        if not os.path.exists(img2):
            raise ValueError(f"Input path {img2} does not exist.")

        img2 = cv2.imread(img2)

    if img1.dtype == np.float32:
        img1 = (img1 * 255).astype(np.uint8)

    if img2.dtype == np.float32:
        img2 = (img2 * 255).astype(np.uint8)

    if img1.dtype != img2.dtype:
        img2 = img2.astype(img1.dtype)

    img1 = cv2.resize(img1, (img2.shape[1], img2.shape[0]))

    # Blend the images using the addWeighted function
    beta = 1 - alpha
    blend_img = cv2.addWeighted(img1, alpha, img2, beta, 0, **kwargs)

    if output:
        array_to_image(blend_img, output, img2)

    if show:
        plt.figure(figsize=figsize)
        plt.imshow(blend_img)
        plt.axis(axis)
        plt.show()
    else:
        return blend_img

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	Union[List[Union[Tuple[float, float], float]], Tuple[float, float, float, float]]	A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax].	required

Returns:

Type	Description
Tuple[Tuple[float, float], Tuple[float, float]]	Tuple[Tuple[float, float], Tuple[float, float]]: A tuple of (x_range, y_range).

Source code in leafmap/common.py

def bounds_to_xy_range(
    bounds: Union[
        List[Union[Tuple[float, float], float]], Tuple[float, float, float, float]
    ],
) -> Tuple[Tuple[float, float], Tuple[float, float]]:
    """
    Convert bounds to x and y range to be used as input to bokeh map.

    Args:
        bounds (Union[List[Union[Tuple[float, float], float]], Tuple[float, float, float, float]]):
            A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax].

    Returns:
        Tuple[Tuple[float, float], Tuple[float, float]]: A tuple of (x_range, y_range).
    """
    if isinstance(bounds, tuple):
        if len(bounds) != 4:
            raise ValueError(
                "Tuple bounds must have exactly 4 elements (xmin, ymin, xmax, ymax)."
            )
        west, south, east, north = bounds
    elif isinstance(bounds, list):
        if len(bounds) == 2 and all(
            isinstance(coord, tuple) and len(coord) == 2 for coord in bounds
        ):
            (south, west), (north, east) = bounds
        elif len(bounds) == 4 and all(
            isinstance(coord, (int, float)) for coord in bounds
        ):
            west, south, east, north = bounds
        else:
            raise ValueError(
                "List bounds must be in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax]."
            )
    else:
        raise TypeError("bounds must be a list or tuple")

    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

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:

Name	Type	Description
tuple		A tuple of (x_range, y_range).

Source code in leafmap/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

cesium_to_streamlit(html, width=800, height=600, responsive=True, scrolling=False, token_name=None, token_value=None, **kwargs)

Renders an cesium HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

Parameters:

Name	Type	Description	Default
html	str	The HTML file to render. It can a local file path or a URL.	required
width	int	Width of the map. Defaults to 800.	800
height	int	Height of the map. Defaults to 600.	600
responsive	bool	Whether to make the map responsive. Defaults to True.	True
scrolling	bool	Whether to allow the map to scroll. Defaults to False.	False
token_name	str	The name of the token in the HTML file to be replaced. Defaults to None.	None
token_value	str	The value of the token to pass to the HTML file. Defaults to None.	None

Returns:

Type	Description
	streamlit.components: components.html object.

Source code in leafmap/common.py

def cesium_to_streamlit(
    html,
    width=800,
    height=600,
    responsive=True,
    scrolling=False,
    token_name=None,
    token_value=None,
    **kwargs,
):
    """Renders an cesium HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

    Args:
        html (str): The HTML file to render. It can a local file path or a URL.
        width (int, optional): Width of the map. Defaults to 800.
        height (int, optional): Height of the map. Defaults to 600.
        responsive (bool, optional): Whether to make the map responsive. Defaults to True.
        scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
        token_name (str, optional): The name of the token in the HTML file to be replaced. Defaults to None.
        token_value (str, optional): The value of the token to pass to the HTML file. Defaults to None.

    Returns:
        streamlit.components: components.html object.
    """
    if token_name is None:
        token_name = "your_access_token"

    if token_value is None:
        token_value = os.environ.get("CESIUM_TOKEN")

    html_to_streamlit(
        html, width, height, responsive, scrolling, token_name, token_value
    )

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:

Name	Type	Description
list		A list of colors.

Source code in leafmap/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:
            return get_palette(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 or list	It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple (e.g., (255, 127, 0)).	required

Returns:

Name	Type	Description
str	str	A hex color code.

Source code in leafmap/common.py

def check_color(in_color: Union[str, Tuple]) -> str:
    """Checks the input color and returns the corresponding hex color code.

    Args:
            in_color (str or tuple or list): 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) or isinstance(in_color, list)) 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

Raises:

Type	Description
FileNotFoundError	If the directory could not be found.
TypeError	If the input directory path is not a string.

Returns:

Name	Type	Description
str		The path to the directory.

Source code in leafmap/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

Raises:

Type	Description
FileNotFoundError	If the directory could not be found.
TypeError	If the input directory path is not a string.

Returns:

Name	Type	Description
str		The absolute path to the file.

Source code in leafmap/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_html_string(html_string)

Check if an HTML string contains local images and convert them to base64.

Parameters:

Name	Type	Description	Default
html_string	str	The HTML string.	required

Returns:

Name	Type	Description
str		The HTML string with local images converted to base64.

Source code in leafmap/common.py

def check_html_string(html_string):
    """Check if an HTML string contains local images and convert them to base64.

    Args:
        html_string (str): The HTML string.

    Returns:
        str: The HTML string with local images converted to base64.
    """
    import re
    import base64

    # Search for img tags with src attribute
    img_regex = r'<img[^>]+src\s*=\s*["\']([^"\':]+)["\'][^>]*>'

    for match in re.findall(img_regex, html_string):
        with open(match, "rb") as img_file:
            img_data = img_file.read()
            base64_data = base64.b64encode(img_data).decode("utf-8")
            html_string = html_string.replace(
                'src="{}"'.format(match),
                'src="data:image/png;base64,' + base64_data + '"',
            )

    return html_string

check_titiler_endpoint(titiler_endpoint=None)

Returns the default titiler endpoint.

Returns:

Name	Type	Description
object		A titiler endpoint.

Source code in leafmap/stac.py

def check_titiler_endpoint(titiler_endpoint: Optional[str] = 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

check_url(url)

Check if an HTTP URL is working.

Parameters:

Name	Type	Description	Default
url	str	The URL to check.	required

Returns:

Name	Type	Description
bool	bool	True if the URL is working (returns a 200 status code), False otherwise.

Source code in leafmap/common.py

def check_url(url: str) -> bool:
    """Check if an HTTP URL is working.

    Args:
        url (str): The URL to check.

    Returns:
        bool: True if the URL is working (returns a 200 status code), False otherwise.
    """
    try:
        response = requests.get(url)
        if response.status_code == 200:
            return True
        else:
            return False
    except requests.exceptions.RequestException:
        return False

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 | DataFrame | 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: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.	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 leafmap/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)
        or isinstance(data, pd.Series)
    ):
        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.object_):
        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"
    try:
        cmap = plt.get_cmap(cmap, k)
    except:
        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, to_cog=True)

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
to_cog	bool	Flags to indicate if you want to convert the output to COG. Defaults to True.	True

Raises:

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 leafmap/common.py

def clip_image(image, mask, output, to_cog=True):
    """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.
        to_cog (bool, optional): Flags to indicate if you want to convert the output to COG. Defaults to True.

    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 json
        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, 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)

    if to_cog:
        image_to_cog(output, output)

cog_bands(url, titiler_endpoint=None)

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

Returns:

Name	Type	Description
list	List	A list of band names

Source code in leafmap/stac.py

def cog_bands(
    url: str,
    titiler_endpoint: Optional[str] = None,
) -> List:
    """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".

    Returns:
        list: A list of band names
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    r = requests.get(
        f"{titiler_endpoint}/cog/info",
        params={
            "url": url,
        },
    ).json()

    bands = [b[0] for b in r["band_descriptions"]]
    return bands

cog_bounds(url, titiler_endpoint=None)

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

Returns:

Name	Type	Description
list	List	A list of values representing [left, bottom, right, top]

Source code in leafmap/stac.py

def cog_bounds(
    url: str,
    titiler_endpoint: Optional[str] = None,
) -> List:
    """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".

    Returns:
        list: A list of values representing [left, bottom, right, top]
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    r = requests.get(f"{titiler_endpoint}/cog/bounds", params={"url": url}).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:

Name	Type	Description
tuple	Tuple	A tuple representing (longitude, latitude)

Source code in leafmap/stac.py

def cog_center(
    url: str,
    titiler_endpoint: Optional[str] = None,
) -> Tuple:
    """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)
    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)

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

Returns:

Name	Type	Description
list	List	A dictionary of band info.

Source code in leafmap/stac.py

def cog_info(
    url: str,
    titiler_endpoint: Optional[str] = None,
    return_geojson: Optional[bool] = False,
) -> List:
    """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".

    Returns:
        list: A dictionary of band info.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    info = "info"
    if return_geojson:
        info = "info.geojson"

    r = requests.get(
        f"{titiler_endpoint}/cog/{info}",
        params={
            "url": url,
        },
    ).json()

    return r

cog_mosaic(links, titiler_endpoint=None, username='anonymous', layername=None, overwrite=False, verbose=True, **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

Raises:

Type	Description
Exception	If the COG mosaic fails to create.

Returns:

Name	Type	Description
str	str	The tile URL for the COG mosaic.

Source code in leafmap/stac.py

def cog_mosaic(
    links: List,
    titiler_endpoint: Optional[str] = None,
    username: Optional[str] = "anonymous",
    layername=None,
    overwrite: Optional[bool] = False,
    verbose: Optional[bool] = True,
    **kwargs,
) -> str:
    """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.

    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_X"

    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",
        ).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:

Name	Type	Description
str	str	The tile URL for the COG mosaic.

Source code in leafmap/stac.py

def cog_mosaic_from_file(
    filepath: str,
    skip_rows: Optional[int] = 0,
    titiler_endpoint: Optional[str] = None,
    username: Optional[str] = "anonymous",
    layername=None,
    overwrite: Optional[bool] = False,
    verbose: Optional[bool] = True,
    **kwargs,
) -> str:
    """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, titiler_endpoint=None, 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.	None
verbose	bool	Print status messages. Defaults to True.	True

Returns:

Name	Type	Description
list	List	A dictionary of band info.

Source code in leafmap/stac.py

def cog_pixel_value(
    lon: float,
    lat: float,
    url: str,
    bidx: Optional[str],
    titiler_endpoint: Optional[str] = None,
    verbose: Optional[bool] = True,
    **kwargs,
) -> List:
    """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:
        list: A dictionary of band info.
    """

    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).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:
        if verbose:
            print(r["detail"])
        return None
    else:
        values = r["values"]
        result = dict(zip(bands, values))
        return result

cog_stats(url, titiler_endpoint=None)

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

Returns:

Name	Type	Description
list	List	A dictionary of band statistics.

Source code in leafmap/stac.py

def cog_stats(
    url: str,
    titiler_endpoint: Optional[str] = None,
) -> List:
    """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".

    Returns:
        list: A dictionary of band statistics.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    r = requests.get(
        f"{titiler_endpoint}/cog/statistics",
        params={
            "url": url,
        },
    ).json()

    return r

cog_tile(url, bands=None, titiler_endpoint=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
bands	list	List of bands to use. Defaults to None.	None
titiler_endpoint	str	TiTiler endpoint. Defaults to "https://titiler.xyz".	None
**kwargs		Additional arguments to pass to the titiler endpoint. For more information about the available arguments, see https://developmentseed.org/titiler/endpoints/cog/#tiles. For example, to apply a rescaling to multiple bands, use something like rescale=["164,223","130,211","99,212"].	{}

Returns:

Name	Type	Description
tuple	Tuple	Returns the COG Tile layer URL and bounds.

Source code in leafmap/stac.py

def cog_tile(
    url,
    bands: str = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> Tuple:
    """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
        bands (list, optional): List of bands to use. Defaults to None.
        titiler_endpoint (str, optional): TiTiler endpoint. Defaults to "https://titiler.xyz".
        **kwargs: Additional arguments to pass to the titiler endpoint. For more information about the available arguments, see https://developmentseed.org/titiler/endpoints/cog/#tiles.
            For example, to apply a rescaling to multiple bands, use something like `rescale=["164,223","130,211","99,212"]`.

    Returns:
        tuple: Returns the COG Tile layer URL and bounds.
    """
    import json

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    kwargs["url"] = url

    band_names = cog_bands(url, titiler_endpoint)

    if isinstance(bands, str):
        bands = [bands]

    if bands is None and "bidx" not in kwargs:
        if len(band_names) >= 3:
            kwargs["bidx"] = [1, 2, 3]
    elif isinstance(bands, list) and "bidx" not in kwargs:
        if all(isinstance(x, int) for x in bands):
            if len(set(bands)) == 1:
                bands = bands[0]
            kwargs["bidx"] = bands
        elif all(isinstance(x, str) for x in bands):
            if len(set(bands)) == 1:
                bands = bands[0]
            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["palette"].lower()
        del kwargs["palette"]

    if "bidx" not in kwargs:
        kwargs["bidx"] = [1]
    elif isinstance(kwargs["bidx"], int):
        kwargs["bidx"] = [kwargs["bidx"]]

    if "rescale" not in kwargs and ("colormap" not in kwargs):
        stats = cog_stats(url, titiler_endpoint)

        if "message" not in stats:
            try:
                rescale = []
                for i in band_names:
                    rescale.append(
                        "{},{}".format(
                            stats[i]["percentile_2"],
                            stats[i]["percentile_98"],
                        )
                    )
                kwargs["rescale"] = rescale
            except Exception as e:
                pass

    if "colormap" in kwargs and isinstance(kwargs["colormap"], dict):
        kwargs["colormap"] = json.dumps(kwargs["colormap"])

    TileMatrixSetId = "WebMercatorQuad"
    if "TileMatrixSetId" in kwargs.keys():
        TileMatrixSetId = kwargs["TileMatrixSetId"]
        kwargs.pop("TileMatrixSetId")

    if "default_vis" in kwargs.keys() and kwargs["default_vis"]:
        kwargs = {"url": url}

    r = requests.get(
        f"{titiler_endpoint}/cog/{TileMatrixSetId}/tilejson.json", params=kwargs
    ).json()
    return r["tiles"][0]

cog_tile_vmin_vmax(url, bands=None, titiler_endpoint=None, percentile=True, **kwargs)

Get a tile layer from a Cloud Optimized GeoTIFF (COG) and return the minimum and maximum values.

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
bands	list	List of bands to use. Defaults to None.	None
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
percentile	bool	Whether to use percentiles or not. Defaults to True.	True

Source code in leafmap/stac.py

def cog_tile_vmin_vmax(
    url: str,
    bands: Optional[List] = None,
    titiler_endpoint: Optional[str] = None,
    percentile: Optional[bool] = True,
    **kwargs,
) -> Tuple:
    """Get a tile layer from a Cloud Optimized GeoTIFF (COG) and return the minimum and maximum values.

    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
        bands (list, optional): List of bands to use. Defaults to None.
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        percentile (bool, optional): Whether to use percentiles or not. Defaults to True.
    Returns:
        tuple: Returns the minimum and maximum values.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    stats = cog_stats(url, titiler_endpoint)

    if isinstance(bands, str):
        bands = [bands]

    if bands is not None:
        stats = {s: stats[s] for s in stats if s in bands}

    if percentile:
        vmin = min([stats[s]["percentile_2"] for s in stats])
        vmax = max([stats[s]["percentile_98"] for s in stats])
    else:
        vmin = min([stats[s]["min"] for s in stats])
        vmax = max([stats[s]["max"] for s in stats])

    return vmin, vmax

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

Raises:

Type	Description
ImportError	If the rio-cogeo package is not installed.
FileNotFoundError	If the provided file could not be found.

Returns:

Name	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 leafmap/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)

color_code_dataframe(data, legend_dict)

Converts values in a dataframe to color codes based on a legend dictionary.

This function takes a dataframe (or path to a dataframe) and a legend dictionary and returns a new dataframe with values replaced by their corresponding color codes. It supports both numeric range legends and categorical legends.

Parameters:

Name	Type	Description	Default
data	Union[str, DataFrame, GeoDataFrame]	Input data source, can be: - Path to a CSV file or geospatial file - pandas DataFrame - geopandas GeoDataFrame	required
legend_dict	Dict[str, str]	Dictionary mapping values to colors, can be: - Numeric ranges ("[ 100000, 200000]") mapped to color codes - Categorical values ("low", "medium") mapped to color codes - Can include a "Nodata" key for None/NaN values	required

Returns:

Type	Description
Union[DataFrame, GeoDataFrame]	A new dataframe with values replaced by color codes, preserving the
Union[DataFrame, GeoDataFrame]	input data type (DataFrame or GeoDataFrame)

Raises:

Type	Description
TypeError	If the input data type is not supported
ValueError	If the file format is not supported

Examples:

>>> # Example with numeric ranges
>>> range_legend = {
...     "[ 0, 200000]": "#daeaf6",
...     "(200001, 400000]": "#9ecae1",
...     "Nodata": "#f0f0f0"
... }
>>> color_df = color_code_dataframe("housing_data.csv", range_legend)

>>> # Example with categorical values
>>> cat_legend = {
...     "low": "#daeaf6",
...     "medium": "#9ecae1",
...     "high": "#2171b5",
...     "Nodata": "#f0f0f0"
... }
>>> df = pd.DataFrame({"Risk": ["low", "medium", "high", None]})
>>> color_df = color_code_dataframe(df, cat_legend)

Source code in leafmap/common.py

def color_code_dataframe(
    data: Union[str, pd.DataFrame, "gpd.GeoDataFrame"], legend_dict: Dict[str, str]
) -> Union[pd.DataFrame, "gpd.GeoDataFrame"]:
    """Converts values in a dataframe to color codes based on a legend dictionary.

    This function takes a dataframe (or path to a dataframe) and a legend dictionary
    and returns a new dataframe with values replaced by their corresponding color codes.
    It supports both numeric range legends and categorical legends.

    Args:
        data: Input data source, can be:
            - Path to a CSV file or geospatial file
            - pandas DataFrame
            - geopandas GeoDataFrame
        legend_dict: Dictionary mapping values to colors, can be:
            - Numeric ranges ("[ 100000, 200000]") mapped to color codes
            - Categorical values ("low", "medium") mapped to color codes
            - Can include a "Nodata" key for None/NaN values

    Returns:
        A new dataframe with values replaced by color codes, preserving the
        input data type (DataFrame or GeoDataFrame)

    Raises:
        TypeError: If the input data type is not supported
        ValueError: If the file format is not supported

    Examples:
        >>> # Example with numeric ranges
        >>> range_legend = {
        ...     "[ 0, 200000]": "#daeaf6",
        ...     "(200001, 400000]": "#9ecae1",
        ...     "Nodata": "#f0f0f0"
        ... }
        >>> color_df = color_code_dataframe("housing_data.csv", range_legend)

        >>> # Example with categorical values
        >>> cat_legend = {
        ...     "low": "#daeaf6",
        ...     "medium": "#9ecae1",
        ...     "high": "#2171b5",
        ...     "Nodata": "#f0f0f0"
        ... }
        >>> df = pd.DataFrame({"Risk": ["low", "medium", "high", None]})
        >>> color_df = color_code_dataframe(df, cat_legend)
    """
    import re

    # Handle different input types
    if isinstance(data, str):
        # Input is a path to a file
        path = Path(data)
        if path.suffix.lower() == ".csv":
            df = pd.read_csv(data)
        elif path.suffix.lower() in [".geojson", ".shp"]:
            df = gpd.read_file(data)
        else:
            raise ValueError(f"Unsupported file format: {path.suffix}")
    elif isinstance(data, pd.DataFrame):
        # Input is already a pandas DataFrame
        df = data.copy()
    elif isinstance(data, gpd.GeoDataFrame):
        # Input is a GeoDataFrame
        df = data.copy()
    else:
        raise TypeError("Input must be a file path, pandas DataFrame, or GeoDataFrame")

    # Determine legend type: numeric ranges or categorical
    # Exclude "Nodata" key from this check
    legend_without_nodata = {
        k: v
        for k, v in legend_dict.items()
        if k != "Nodata" and not isinstance(k, str) or k.lower() != "nodata"
    }
    is_range_legend = any(
        "[" in key or "(" in key for key in legend_without_nodata.keys()
    )

    # Get the "Nodata" color if provided
    nodata_keys = ["Nodata", "nodata", "NODATA", "NoData"]
    nodata_color = None
    for key in nodata_keys:
        if key in legend_dict:
            nodata_color = legend_dict[key]
            break

    # Function to get color based on numeric value and range legend
    def get_color_for_numeric(value: Any) -> Optional[str]:
        """Maps a numeric value to a color based on the range legend.

        Args:
            value: The value to map to a color

        Returns:
            The corresponding color code or None if no match is found
        """
        if pd.isna(value) or value is None:
            return nodata_color

        if not isinstance(value, (int, float)):
            return nodata_color

        for range_str, color in legend_dict.items():
            # Skip the Nodata entry
            if isinstance(range_str, str) and range_str.lower() == "nodata":
                continue

            # Parse the range string like "[ 182913, 357522]" or "( 357522, 415584]"
            match = re.search(r"[\[\(]\s*(\d+),\s*(\d+)[\]\)]", range_str)
            if not match:
                continue

            lower_bound = int(match.group(1))
            upper_bound = int(match.group(2))
            lower_inclusive = range_str.startswith("[")
            upper_inclusive = range_str.endswith("]")

            # Check if the value is within the range
            above_lower = (
                value >= lower_bound if lower_inclusive else value > lower_bound
            )
            below_upper = (
                value <= upper_bound if upper_inclusive else value < upper_bound
            )

            if above_lower and below_upper:
                return color

        return None

    # Function to get color based on categorical value
    def get_color_for_categorical(value: Any) -> Optional[str]:
        """Maps a categorical value to a color.

        Args:
            value: The value to map to a color

        Returns:
            The corresponding color code or None if no match is found
        """
        if pd.isna(value) or value is None:
            return nodata_color

        # Convert to string for comparison
        str_value = str(value).lower()

        # Try direct matching
        if str_value in legend_dict:
            return legend_dict[str_value]

        # Try case-insensitive matching
        for cat, color in legend_dict.items():
            if isinstance(cat, str) and cat.lower() == "nodata":
                continue

            if isinstance(cat, str) and cat.lower() == str_value:
                return color

        return None

    # Select appropriate color mapping function
    get_color = get_color_for_numeric if is_range_legend else get_color_for_categorical

    # Identify columns to process
    if is_range_legend:
        # For numeric ranges, look for numeric columns and date-formatted columns
        columns_to_process: List[str] = []
        for col in df.columns:
            # Check if column name matches date pattern
            if isinstance(col, str) and re.match(r"^\d{4}-\d{2}-\d{2}$", col):
                columns_to_process.append(col)
                continue

            # Check if column contains numbers
            if pd.api.types.is_numeric_dtype(df[col]):
                # Exclude columns that are likely IDs or categorical codes
                if not (
                    col.lower().endswith("id")
                    or "code" in col.lower()
                    or "fips" in col.lower()
                ):
                    columns_to_process.append(col)
    else:
        # For categorical legend, look for object or category columns
        columns_to_process: List[str] = []
        for col in df.columns:
            if pd.api.types.is_object_dtype(
                df[col]
            ) or pd.api.types.is_categorical_dtype(df[col]):
                columns_to_process.append(col)
            # Also check if numeric columns might contain discrete categories
            elif pd.api.types.is_numeric_dtype(df[col]) and df[col].nunique() < 10:
                columns_to_process.append(col)

    # Replace each value with its corresponding color
    for col in columns_to_process:
        df[col] = df[col].apply(get_color)

    return df

connect_points_as_line(gdf, sort_column=None, crs='EPSG:4326', single_line=True)

Connects points in a GeoDataFrame into either a single LineString or multiple LineStrings based on a specified sort column or the index if no column is provided. The resulting GeoDataFrame will have the specified CRS.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoDataFrame containing point geometries.	required
sort_column	Optional[str]	Column name to sort the points by (e.g., 'timestamp'). If None, the index is used for sorting. Defaults to None.	None
crs	str	The coordinate reference system (CRS) for the resulting GeoDataFrame. Defaults to "EPSG:4326".	'EPSG:4326'
single_line	bool	If True, generates a single LineString connecting all points. If False, generates multiple LineStrings, each connecting two consecutive points. Defaults to True.	True

Returns:

Name	Type	Description
GeoDataFrame	GeoDataFrame	A new GeoDataFrame containing either a single LineString or multiple LineString geometries based on the single_line parameter, with the specified CRS.

Example

line_gdf = connect_points_as_line(gdf, 'timestamp', crs="EPSG:3857", single_line=True) line_gdf = connect_points_as_line(gdf, single_line=False) # Uses index and defaults to EPSG:4326

Source code in leafmap/common.py

def connect_points_as_line(
    gdf: "GeoDataFrame",
    sort_column: Optional[str] = None,
    crs: str = "EPSG:4326",
    single_line: bool = True,
) -> "GeoDataFrame":
    """
    Connects points in a GeoDataFrame into either a single LineString or multiple LineStrings
    based on a specified sort column or the index if no column is provided. The resulting
    GeoDataFrame will have the specified CRS.

    Args:
        gdf (GeoDataFrame): A GeoDataFrame containing point geometries.
        sort_column (Optional[str]): Column name to sort the points by (e.g., 'timestamp').
                                     If None, the index is used for sorting. Defaults to None.
        crs (str): The coordinate reference system (CRS) for the resulting GeoDataFrame.
                   Defaults to "EPSG:4326".
        single_line (bool): If True, generates a single LineString connecting all points.
                            If False, generates multiple LineStrings, each connecting two consecutive points.
                            Defaults to True.

    Returns:
        GeoDataFrame: A new GeoDataFrame containing either a single LineString or multiple LineString geometries
                      based on the single_line parameter, with the specified CRS.

    Example:
        >>> line_gdf = connect_points_as_line(gdf, 'timestamp', crs="EPSG:3857", single_line=True)
        >>> line_gdf = connect_points_as_line(gdf, single_line=False)  # Uses index and defaults to EPSG:4326
    """
    from shapely.geometry import LineString
    import geopandas as gpd

    # Sort the GeoDataFrame by the specified column or by index if None
    gdf_sorted = gdf.sort_values(by=sort_column) if sort_column else gdf.sort_index()

    if single_line:
        # Create a single LineString connecting all points
        line = LineString(gdf_sorted.geometry.tolist())
        line_gdf = gpd.GeoDataFrame(geometry=[line], crs=crs)
    else:
        # Generate LineStrings for each consecutive pair of points
        lines = [
            LineString([gdf_sorted.geometry.iloc[i], gdf_sorted.geometry.iloc[i + 1]])
            for i in range(len(gdf_sorted) - 1)
        ]
        line_gdf = gpd.GeoDataFrame(geometry=lines, crs=crs)

    return line_gdf

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

Raises:

Type	Description
ValueError	If user is not specified.
ValueError	If password is not specified.

Returns:

Type	Description

Source code in leafmap/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

construct_bbox(*args, buffer=0.001, crs='EPSG:4326', return_gdf=False)

Construct a bounding box (bbox) geometry based on either a centroid point or bbox.

Parameters:

Name	Type	Description	Default
*args	Union[float, Tuple[float, float, float, float]]	Coordinates for the geometry. - If 2 arguments are provided, it is interpreted as a centroid (x, y) with a buffer. - If 4 arguments are provided, it is interpreted as a bbox (minx, miny, maxx, maxy).	()
buffer	float	The buffer distance around the centroid point (default is 0.01 degrees).	0.001
crs	str	The coordinate reference system (default is "EPSG:4326").	'EPSG:4326'
return_gdf	bool	Whether to return a GeoDataFrame (default is False).	False

Returns:

Type	Description
Union[Polygon, GeoDataFrame]	shapely.geometry.Polygon: The constructed bounding box (Polygon).

Source code in leafmap/common.py

def construct_bbox(
    *args: Union[float, Tuple[float, float, float, float]],
    buffer: float = 0.001,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
) -> Union["Polygon", "gpd.GeoDataFrame"]:
    """
    Construct a bounding box (bbox) geometry based on either a centroid point or bbox.

    Args:
        *args: Coordinates for the geometry.
            - If 2 arguments are provided, it is interpreted as a centroid (x, y) with a buffer.
            - If 4 arguments are provided, it is interpreted as a bbox (minx, miny, maxx, maxy).
        buffer (float): The buffer distance around the centroid point (default is 0.01 degrees).
        crs (str): The coordinate reference system (default is "EPSG:4326").
        return_gdf (bool): Whether to return a GeoDataFrame (default is False).

    Returns:
        shapely.geometry.Polygon: The constructed bounding box (Polygon).
    """
    from shapely.geometry import box

    if len(args) == 2:
        # Case 1: Create a bounding box around the centroid point with a buffer
        x, y = args
        minx, miny = x - buffer, y - buffer
        maxx, maxy = x + buffer, y + buffer
        geometry = box(minx, miny, maxx, maxy)

    elif len(args) == 4:
        # Case 2: Create a bounding box directly from the given coordinates
        geometry = box(args[0], args[1], args[2], args[3])

    else:
        raise ValueError(
            "Provide either 2 arguments for centroid (x, y) or 4 arguments for bbox (minx, miny, maxx, maxy)."
        )

    if return_gdf:
        return gpd.GeoDataFrame(geometry=[geometry], columns=["geometry"], crs=crs)
    else:
        return geometry

convert_coordinates(x, y, source_crs, target_crs='epsg:4326')

Convert coordinates from the source EPSG code to the target EPSG code.

Parameters:

Name	Type	Description	Default
x	float	The x-coordinate of the point.	required
y	float	The y-coordinate of the point.	required
source_crs	str	The EPSG code of the source coordinate system.	required
target_crs	str	The EPSG code of the target coordinate system. Defaults to '4326' (EPSG code for WGS84).	'epsg:4326'

Returns:

Name	Type	Description
tuple		A tuple containing the converted longitude and latitude.

Source code in leafmap/common.py

def convert_coordinates(x, y, source_crs, target_crs="epsg:4326"):
    """Convert coordinates from the source EPSG code to the target EPSG code.

    Args:
        x (float): The x-coordinate of the point.
        y (float): The y-coordinate of the point.
        source_crs (str): The EPSG code of the source coordinate system.
        target_crs (str, optional): The EPSG code of the target coordinate system.
            Defaults to '4326' (EPSG code for WGS84).

    Returns:
        tuple: A tuple containing the converted longitude and latitude.
    """
    import pyproj

    # Create the transformer
    transformer = pyproj.Transformer.from_crs(source_crs, target_crs, always_xy=True)

    # Perform the transformation
    lon, lat = transformer.transform(x, y)  # pylint: disable=E0633

    # Return the converted coordinates
    return lon, lat

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 | 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 leafmap/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

convert_to_gdf(data, geometry=None, lat=None, lon=None, crs='EPSG:4326', included=None, excluded=None, obj_to_str=False, open_args=None, **kwargs)

Convert data to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
data	Union[DataFrame, str]	The input data, either as a DataFrame or a file path.	required
geometry	Optional[str]	The column name containing geometry data. Defaults to None.	None
lat	Optional[str]	The column name containing latitude data. Defaults to None.	None
lon	Optional[str]	The column name containing longitude data. Defaults to None.	None
crs	str	The coordinate reference system to use. Defaults to "EPSG:4326".	'EPSG:4326'
included	Optional[List[str]]	List of columns to include. Defaults to None.	None
excluded	Optional[List[str]]	List of columns to exclude. Defaults to None.	None
obj_to_str	bool	Whether to convert object dtype columns to string. Defaults to False.	False
open_args	Optional[Dict[str, Any]]	Additional arguments for file opening functions. Defaults to None.	None
**kwargs	Any	Additional keyword arguments for GeoDataFrame creation.	{}

Returns:

Type	Description
GeoDataFrame	gpd.GeoDataFrame: The converted GeoDataFrame.

Raises:

Type	Description
ValueError	If the file format is unsupported or required columns are not provided.

Source code in leafmap/common.py

def convert_to_gdf(
    data: Union[pd.DataFrame, str],
    geometry: Optional[str] = None,
    lat: Optional[str] = None,
    lon: Optional[str] = None,
    crs: str = "EPSG:4326",
    included: Optional[List[str]] = None,
    excluded: Optional[List[str]] = None,
    obj_to_str: bool = False,
    open_args: Optional[Dict[str, Any]] = None,
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """Convert data to a GeoDataFrame.

    Args:
        data (Union[pd.DataFrame, str]): The input data, either as a DataFrame or a file path.
        geometry (Optional[str], optional): The column name containing geometry data. Defaults to None.
        lat (Optional[str], optional): The column name containing latitude data. Defaults to None.
        lon (Optional[str], optional): The column name containing longitude data. Defaults to None.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:4326".
        included (Optional[List[str]], optional): List of columns to include. Defaults to None.
        excluded (Optional[List[str]], optional): List of columns to exclude. Defaults to None.
        obj_to_str (bool, optional): Whether to convert object dtype columns to string. Defaults to False.
        open_args (Optional[Dict[str, Any]], optional): Additional arguments for file opening functions. Defaults to None.
        **kwargs (Any): Additional keyword arguments for GeoDataFrame creation.

    Returns:
        gpd.GeoDataFrame: The converted GeoDataFrame.

    Raises:
        ValueError: If the file format is unsupported or required columns are not provided.
    """
    import geopandas as gpd
    from shapely.geometry import Point, shape

    if open_args is None:
        open_args = {}

    if not isinstance(data, pd.DataFrame):
        if isinstance(data, str):
            if data.endswith(".parquet"):
                data = pd.read_parquet(data, **open_args)
            elif data.endswith(".csv"):
                data = pd.read_csv(data, **open_args)
            elif data.endswith(".json"):
                data = pd.read_json(data, **open_args)
            elif data.endswith(".xlsx"):
                data = pd.read_excel(data, **open_args)
            else:
                raise ValueError(
                    "Unsupported file format. Only Parquet, CSV, JSON, and Excel files are supported."
                )

    # If include_cols is specified, filter the DataFrame to include only those columns
    if included:
        if geometry:
            included.append(geometry)
        elif lat and lon:
            included.append(lat)
            included.append(lon)
        data = data[included]

    # Exclude specified columns if provided
    if excluded:
        data = data.drop(columns=excluded)

    # Convert 'object' dtype columns to 'string' if obj_to_str is True
    if obj_to_str:
        data = data.astype(
            {col: "string" for col in data.select_dtypes(include="object").columns}
        )

    # Handle the creation of geometry
    if geometry:

        def convert_geometry(x):
            if isinstance(x, str):
                try:
                    # Parse the string as JSON and then convert to a geometry
                    return shape(json.loads(x))
                except (json.JSONDecodeError, TypeError) as e:
                    print(f"Error converting geometry: {e}")
                    return None
            return x

        data = data[data[geometry].notnull()]
        data[geometry] = data[geometry].apply(convert_geometry)
    elif lat and lon:
        # Create a geometry column from latitude and longitude
        data["geometry"] = data.apply(lambda row: Point(row[lon], row[lat]), axis=1)
        geometry = "geometry"
    else:
        raise ValueError(
            "Either geometry_col or both lat_col and lon_col must be provided."
        )

    # Convert the DataFrame to a GeoDataFrame
    gdf = gpd.GeoDataFrame(data, geometry=geometry, **kwargs)

    # Set CRS (assuming WGS84 by default, modify as needed)
    gdf.set_crs(crs, inplace=True)

    return gdf

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:

Name	Type	Description
dict		A geojson FeatureCollection.

Source code in leafmap/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}

coords_to_vector(coords, output=None, crs='EPSG:4326', **kwargs)

Convert a list of coordinates to a GeoDataFrame or a vector file.

Parameters:

Name	Type	Description	Default
coords	list	A list of coordinates in the format of [(x1, y1), (x2, y2), ...].	required
output	str	The path to the output vector file. Defaults to None.	None
crs	str	The CRS of the coordinates. Defaults to "EPSG:4326".	'EPSG:4326'

Returns:

Type	Description
	gpd.GeoDataFraem: A GeoDataFrame of the coordinates.

Source code in leafmap/common.py

def coords_to_vector(coords, output=None, crs="EPSG:4326", **kwargs):
    """Convert a list of coordinates to a GeoDataFrame or a vector file.

    Args:
        coords (list): A list of coordinates in the format of [(x1, y1), (x2, y2), ...].
        output (str, optional): The path to the output vector file. Defaults to None.
        crs (str, optional): The CRS of the coordinates. Defaults to "EPSG:4326".

    Returns:
        gpd.GeoDataFraem: A GeoDataFrame of the coordinates.
    """
    import geopandas as gpd
    from shapely.geometry import Point

    if not isinstance(coords, (list, tuple)):
        raise TypeError("coords must be a list of coordinates")

    if isinstance(coords[0], int) or isinstance(coords[0], float):
        coords = [(coords[0], coords[1])]

    # convert the points to a GeoDataFrame
    geometry = [Point(xy) for xy in coords]
    gdf = gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326")
    gdf.to_crs(crs, inplace=True)

    if output is not None:
        gdf.to_file(output, **kwargs)
    else:
        return gdf

coords_to_xy(src_fp, coords, coord_crs='epsg:4326', request_payer='bucket-owner', env_args={}, open_args={}, **kwargs)

Converts a list of coordinates to pixel coordinates, i.e., (col, row) coordinates.

Parameters:

Name	Type	Description	Default
src_fp	str	The source raster file path.	required
coords	list	A list of coordinates in the format of [[x1, y1], [x2, y2], ...]	required
coord_crs	str	The coordinate CRS of the input coordinates. Defaults to "epsg:4326".	'epsg:4326'
request_payer		Specifies who pays for the download from S3. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	'bucket-owner'
env_args		Additional keyword arguments to pass to rasterio.Env.	{}
open_args		Additional keyword arguments to pass to rasterio.open.	{}
**kwargs		Additional keyword arguments to pass to rasterio.transform.rowcol.	{}

Returns:

Type	Description
list	A list of pixel coordinates in the format of [[x1, y1], [x2, y2], ...]

Source code in leafmap/common.py

def coords_to_xy(
    src_fp: str,
    coords: list,
    coord_crs: str = "epsg:4326",
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs,
) -> list:
    """Converts a list of coordinates to pixel coordinates, i.e., (col, row) coordinates.

    Args:
        src_fp: The source raster file path.
        coords: A list of coordinates in the format of [[x1, y1], [x2, y2], ...]
        coord_crs: The coordinate CRS of the input coordinates. Defaults to "epsg:4326".
        request_payer: Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args: Additional keyword arguments to pass to rasterio.Env.
        open_args: Additional keyword arguments to pass to rasterio.open.
        **kwargs: Additional keyword arguments to pass to rasterio.transform.rowcol.

    Returns:
        A list of pixel coordinates in the format of [[x1, y1], [x2, y2], ...]
    """
    import numpy as np
    import rasterio

    if isinstance(coords, np.ndarray):
        coords = coords.tolist()

    if len(coords) == 4 and all([isinstance(c, (int, float)) for c in coords]):
        coords = [[coords[0], coords[1]], [coords[2], coords[3]]]

    xs, ys = zip(*coords)
    with rasterio.Env(AWS_REQUEST_PAYER=request_payer, **env_args):
        with rasterio.open(src_fp, **open_args) as src:
            width = src.width
            height = src.height
            if coord_crs != src.crs:
                xs, ys = transform_coords(
                    xs, ys, coord_crs, src.crs, **kwargs
                )  # pylint: disable=E0633
            rows, cols = rasterio.transform.rowcol(src.transform, xs, ys, **kwargs)
        result = [[col, row] for col, row in zip(cols, rows)]

        result = [
            [x, y] for x, y in result if x >= 0 and y >= 0 and x < width and y < height
        ]
        if len(result) == 0:
            print("No valid pixel coordinates found.")
        elif len(result) < len(coords):
            print("Some coordinates are out of the image boundary.")

        return result

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 leafmap/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

    # try:
    #     import pyperclip
    # except ImportError:
    #     install_package("pyperclip")
    #     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_download_link(filename, title='Click here to download: ', basename=None)

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:

Name	Type	Description
str		HTML download URL.

Source code in leafmap/common.py

def create_download_link(filename, title="Click here to download: ", basename=None):
    """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()
    if basename is None:
        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_legend(title='Legend', labels=None, colors=None, legend_dict=None, builtin_legend=None, opacity=1.0, position='bottomright', draggable=True, output=None, style={}, shape_type='rectangle')

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:

Name	Type	Description
str		The HTML code of the legend.

Source code in leafmap/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={},
    shape_type="rectangle",
):
    """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 importlib.resources
    from .legends import builtin_legends

    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.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 shape_type == "circle":
        legend_text = legend_text.replace("width: 30px", "width: 16px")
        legend_text = legend_text.replace(
            "border: 1px solid #999;",
            "border-radius: 50%;\n      border: 1px solid #999;",
        )
    elif shape_type == "line":
        legend_text = legend_text.replace("height: 16px", "height: 3px")

    if output is not None:
        with open(output, "w") as f:
            f.write(legend_text)
    else:
        return legend_text

create_mosaicjson(images, output)

Create a mosaicJSON file from a list of images.

Parameters:

Name	Type	Description	Default
images	str | list	A list of image URLs or a URL to a text file containing a list of image URLs.	required
output	str	The output mosaicJSON file path.	required

Source code in leafmap/stac.py

def create_mosaicjson(images, output):
    """Create a mosaicJSON file from a list of images.

    Args:
        images (str | list): A list of image URLs or a URL to a text file containing a list of image URLs.
        output (str): The output mosaicJSON file path.

    """
    try:
        from cogeo_mosaic.mosaic import MosaicJSON
        from cogeo_mosaic.backends import MosaicBackend
    except ImportError:
        raise ImportError(
            "cogeo-mosaic is required to use this function. "
            "Install with `pip install cogeo-mosaic`."
        )

    if isinstance(images, str):
        if images.startswith("http"):
            import urllib.request

            with urllib.request.urlopen(images) as f:
                file_contents = f.read().decode("utf-8")
                images = file_contents.strip().split("\n")
        elif not os.path.exists(images):
            raise FileNotFoundError(f"{images} does not exist.")

    elif not isinstance(images, list):
        raise ValueError("images must be a list or a URL.")

    mosaic = MosaicJSON.from_urls(images)
    with MosaicBackend(output, mosaic_def=mosaic) as f:
        f.write(overwrite=True)

create_timelapse(images, out_gif, ext='.tif', bands=None, size=None, bbox=None, fps=5, loop=0, add_progress_bar=True, progress_bar_color='blue', progress_bar_height=5, add_text=False, text_xy=None, text_sequence=None, font_type='arial.ttf', font_size=20, font_color='black', mp4=False, quiet=True, reduce_size=False, clean_up=True, **kwargs)

Creates a timelapse gif from a list of images.

Parameters:

Name	Type	Description	Default
images	list | str	The list of images or input directory to create the gif from. For example, '/path/to/images/*.tif' or ['/path/to/image1.tif', '/path/to/image2.tif', ...]	required
out_gif	str	File path to the output gif.	required
ext	str	The extension of the images. Defaults to '.tif'.	'.tif'
bands	list	The bands to use for the gif. For example, [0, 1, 2] for RGB, and [0] for grayscale. Defaults to None.	None
size	tuple	The size of the gif. For example, (500, 500). Defaults to None, using the original size.	None
bbox	list	The bounding box of the gif. For example, [xmin, ymin, xmax, ymax]. Defaults to None, using the original bounding box.	None
fps	int	The frames per second of the gif. Defaults to 5.	5
loop	int	The number of times to loop the gif. Defaults to 0, looping forever.	0
add_progress_bar	bool	Whether to add a progress bar to the gif. Defaults to True.	True
progress_bar_color	str	The color of the progress bar, can be color name or hex code. Defaults to 'blue'.	'blue'
progress_bar_height	int	The height of the progress bar. Defaults to 5.	5
add_text	bool	Whether to add text to the gif. Defaults to False.	False
text_xy	tuple	The x, y coordinates of the text. For example, ('10%', '10%'). Defaults to None, using the bottom left corner.	None
text_sequence	list	The sequence of text to add to the gif. For example, ['year 1', 'year 2', ...].	None
font_type	str	The font type of the text, can be 'arial.ttf' or 'alibaba.otf', or any system font. Defaults to 'arial.ttf'.	'arial.ttf'
font_size	int	The font size of the text. Defaults to 20.	20
font_color	str	The color of the text, can be color name or hex code. Defaults to 'black'.	'black'
mp4	bool	Whether to convert the gif to mp4. Defaults to False.	False
quiet	bool	Whether to print the progress. Defaults to False.	True
reduce_size	bool	Whether to reduce the size of the gif using ffmpeg. Defaults to False.	False
clean_up	bool	Whether to clean up the temporary files. Defaults to True.	True

Source code in leafmap/common.py

def create_timelapse(
    images: Union[List, str],
    out_gif: str,
    ext: str = ".tif",
    bands: Optional[List] = None,
    size: Optional[Tuple] = None,
    bbox: Optional[List] = None,
    fps: int = 5,
    loop: int = 0,
    add_progress_bar: bool = True,
    progress_bar_color: str = "blue",
    progress_bar_height: int = 5,
    add_text: bool = False,
    text_xy: Optional[Tuple] = None,
    text_sequence: Optional[List] = None,
    font_type: str = "arial.ttf",
    font_size: int = 20,
    font_color: str = "black",
    mp4: bool = False,
    quiet: bool = True,
    reduce_size: bool = False,
    clean_up: bool = True,
    **kwargs,
):
    """Creates a timelapse gif from a list of images.

    Args:
        images (list | str): The list of images or input directory to create the gif from.
            For example, '/path/to/images/*.tif' or ['/path/to/image1.tif', '/path/to/image2.tif', ...]
        out_gif (str): File path to the output gif.
        ext (str, optional): The extension of the images. Defaults to '.tif'.
        bands (list, optional): The bands to use for the gif. For example, [0, 1, 2] for RGB, and [0] for grayscale. Defaults to None.
        size (tuple, optional): The size of the gif. For example, (500, 500). Defaults to None, using the original size.
        bbox (list, optional): The bounding box of the gif. For example, [xmin, ymin, xmax, ymax]. Defaults to None, using the original bounding box.
        fps (int, optional): The frames per second of the gif. Defaults to 5.
        loop (int, optional): The number of times to loop the gif. Defaults to 0, looping forever.
        add_progress_bar (bool, optional): Whether to add a progress bar to the gif. Defaults to True.
        progress_bar_color (str, optional): The color of the progress bar, can be color name or hex code. Defaults to 'blue'.
        progress_bar_height (int, optional): The height of the progress bar. Defaults to 5.
        add_text (bool, optional): Whether to add text to the gif. Defaults to False.
        text_xy (tuple, optional): The x, y coordinates of the text. For example, ('10%', '10%').
            Defaults to None, using the bottom left corner.
        text_sequence (list, optional): The sequence of text to add to the gif. For example, ['year 1', 'year 2', ...].
        font_type (str, optional): The font type of the text, can be 'arial.ttf' or 'alibaba.otf', or any system font. Defaults to 'arial.ttf'.
        font_size (int, optional): The font size of the text. Defaults to 20.
        font_color (str, optional): The color of the text, can be color name or hex code. Defaults to 'black'.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.
        quiet (bool, optional): Whether to print the progress. Defaults to False.
        reduce_size (bool, optional): Whether to reduce the size of the gif using ffmpeg. Defaults to False.
        clean_up (bool, optional): Whether to clean up the temporary files. Defaults to True.

    """

    import glob
    import tempfile

    if isinstance(images, str):
        if not images.endswith(ext):
            images = os.path.join(images, f"*{ext}")
        images = list(glob.glob(images))

    if not isinstance(images, list):
        raise ValueError("images must be a list or a path to the image directory.")

    images.sort()

    temp_dir = os.path.join(tempfile.gettempdir(), "timelapse")
    if not os.path.exists(temp_dir):
        os.makedirs(temp_dir)

    if bbox is not None:
        clip_dir = os.path.join(tempfile.gettempdir(), "clip")
        if not os.path.exists(clip_dir):
            os.makedirs(clip_dir)

        if len(bbox) == 4:
            bbox = bbox_to_geojson(bbox)

    else:
        clip_dir = None

    output = widgets.Output()

    if "out_ext" in kwargs:
        out_ext = kwargs["out_ext"].lower()
    else:
        out_ext = ".jpg"

    try:
        for index, image in enumerate(images):
            if bbox is not None:
                clip_file = os.path.join(clip_dir, os.path.basename(image))
                with output:
                    clip_image(image, mask=bbox, output=clip_file, to_cog=False)
                image = clip_file

            if "add_prefix" in kwargs:
                basename = (
                    str(f"{index + 1}").zfill(len(str(len(images))))
                    + "-"
                    + os.path.basename(image).replace(ext, out_ext)
                )
            else:
                basename = os.path.basename(image).replace(ext, out_ext)
            if not quiet:
                print(f"Processing {index+1}/{len(images)}: {basename} ...")

            # ignore GDAL warnings
            with output:
                numpy_to_image(
                    image, os.path.join(temp_dir, basename), bands=bands, size=size
                )
        make_gif(
            temp_dir,
            out_gif,
            ext=out_ext,
            fps=fps,
            loop=loop,
            mp4=mp4,
            clean_up=clean_up,
        )

        if clip_dir is not None:
            shutil.rmtree(clip_dir)

        if add_text:
            add_text_to_gif(
                out_gif,
                out_gif,
                text_xy,
                text_sequence,
                font_type,
                font_size,
                font_color,
                add_progress_bar,
                progress_bar_color,
                progress_bar_height,
                1000 / fps,
                loop,
            )
        elif add_progress_bar:
            add_progress_bar_to_gif(
                out_gif,
                out_gif,
                progress_bar_color,
                progress_bar_height,
                1000 / fps,
                loop,
            )

        if reduce_size:
            reduce_gif_size(out_gif)
    except Exception as e:
        print(e)

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/opengeos/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 leafmap/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/opengeos/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'.

    """

    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)
        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 leafmap/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

    try:
        return pd.read_csv(in_csv, **kwargs)
    except Exception as e:
        raise Exception(e)

csv_to_gdf(in_csv, latitude='latitude', longitude='longitude', geometry=None, crs='EPSG:4326', encoding='utf-8', **kwargs)

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'
geometry	str	The name of the column containing geometry. Defaults to None.	None
crs	str	The coordinate reference system. Defaults to "EPSG:4326".	'EPSG:4326'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Returns:

Name	Type	Description
object		GeoDataFrame.

Source code in leafmap/common.py

def csv_to_gdf(
    in_csv,
    latitude="latitude",
    longitude="longitude",
    geometry=None,
    crs="EPSG:4326",
    encoding="utf-8",
    **kwargs,
):
    """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".
        geometry (str, optional): The name of the column containing geometry. Defaults to None.
        crs (str, optional): The coordinate reference system. Defaults to "EPSG:4326".
        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
    import pandas as pd
    from shapely import wkt

    out_dir = os.getcwd()

    if geometry is None:
        out_geojson = os.path.join(out_dir, random_string() + ".geojson")
        csv_to_geojson(in_csv, out_geojson, latitude, longitude, encoding=encoding)

        gdf = gpd.read_file(out_geojson)
        os.remove(out_geojson)
    else:
        df = pd.read_csv(in_csv, encoding=encoding)
        df["geometry"] = df[geometry].apply(wkt.loads)
        gdf = gpd.GeoDataFrame(df, geometry="geometry", crs=crs, **kwargs)
    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 leafmap/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 leafmap/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', geometry=None, crs='EPSG:4326', 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'
geometry	str	The name of the column containing geometry. Defaults to None.	None
crs	str	The coordinate reference system. Defaults to "EPSG:4326".	'EPSG:4326'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'
**kwargs		Additional keyword arguments to pass to gdf.to_file().	{}

Source code in leafmap/common.py

def csv_to_vector(
    in_csv,
    output,
    latitude="latitude",
    longitude="longitude",
    geometry=None,
    crs="EPSG:4326",
    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".
        geometry (str, optional): The name of the column containing geometry. Defaults to None.
        crs (str, optional): The coordinate reference system. Defaults to "EPSG:4326".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".
        **kwargs: Additional keyword arguments to pass to gdf.to_file().

    """
    gdf = csv_to_gdf(in_csv, latitude, longitude, geometry, crs, encoding)
    gdf.to_file(output, **kwargs)

d2s_tile(url, titiler_endpoint=None, **kwargs)

Generate a D2S tile URL with optional API key.

Parameters:

Name	Type	Description	Default
url	str	The base URL for the tile.	required
titiler_endpoint	str	The endpoint for the titiler service. Defaults to "https://tt.d2s.org".	None
**kwargs	Any	Additional keyword arguments to pass to the cog_stats function.	{}

Returns:

Name	Type	Description
str	str	The modified URL with the API key if required, otherwise the original URL.

Raises:

Type	Description
ValueError	If the API key is required but not set in the environment variables.

Source code in leafmap/common.py

def d2s_tile(url: str, titiler_endpoint: str = None, **kwargs: Any) -> str:
    """Generate a D2S tile URL with optional API key.

    Args:
        url (str): The base URL for the tile.
        titiler_endpoint (str, optional): The endpoint for the titiler service.
            Defaults to "https://tt.d2s.org".
        **kwargs (Any): Additional keyword arguments to pass to the cog_stats function.

    Returns:
        str: The modified URL with the API key if required, otherwise the original URL.

    Raises:
        ValueError: If the API key is required but not set in the environment variables.
    """

    if titiler_endpoint is None:
        titiler_endpoint = os.environ.get("TITILER_ENDPOINT", "https://titiler.xyz")

    stats = cog_stats(url, titiler_endpoint=titiler_endpoint, **kwargs)
    if "detail" in stats:
        api_key = get_api_key("D2S_API_KEY")
        if api_key is None:
            raise ValueError("Please set the D2S_API_KEY environment variable.")
        else:
            return f"{url}?API_KEY={api_key}"
    else:
        return url

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 True.	False

Source code in leafmap/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 True.
    """
    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))
        os.remove(filepath)
        if verbose:
            print(f"Deleted {filepath}")

df_to_gdf(df, geometry='geometry', src_crs='EPSG:4326', dst_crs=None, **kwargs)

Converts a pandas DataFrame to a GeoPandas GeoDataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The pandas DataFrame to convert.	required
geometry	str	The name of the geometry column in the DataFrame.	'geometry'
src_crs	str	The coordinate reference system (CRS) of the GeoDataFrame. Default is "EPSG:4326".	'EPSG:4326'
dst_crs	str	The target CRS of the GeoDataFrame. Default is None	None

Returns:

Type	Description
	geopandas.GeoDataFrame: The converted GeoPandas GeoDataFrame.

Source code in leafmap/common.py

def df_to_gdf(df, geometry="geometry", src_crs="EPSG:4326", dst_crs=None, **kwargs):
    """
    Converts a pandas DataFrame to a GeoPandas GeoDataFrame.

    Args:
        df (pandas.DataFrame): The pandas DataFrame to convert.
        geometry (str): The name of the geometry column in the DataFrame.
        src_crs (str): The coordinate reference system (CRS) of the GeoDataFrame. Default is "EPSG:4326".
        dst_crs (str): The target CRS of the GeoDataFrame. Default is None

    Returns:
        geopandas.GeoDataFrame: The converted GeoPandas GeoDataFrame.
    """
    import geopandas as gpd
    from shapely import wkt

    # Convert the geometry column to Shapely geometry objects
    df[geometry] = df[geometry].apply(lambda x: wkt.loads(x))

    # Convert the pandas DataFrame to a GeoPandas GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs=src_crs, **kwargs)
    if dst_crs is not None and dst_crs != src_crs:
        gdf = gdf.to_crs(dst_crs)

    return gdf

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	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 leafmap/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".

    """

    import json
    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_json(data, file_path, indent=4)

Writes a dictionary to a JSON file.

Parameters:

Name	Type	Description	Default
data	dict	A dictionary.	required
file_path	str	The path to the JSON file.	required
indent	int	The indentation of the JSON file. Defaults to 4.	4

Raises:

Type	Description
TypeError	If the input data is not a dictionary.

Source code in leafmap/common.py

def dict_to_json(data, file_path, indent=4):
    """Writes a dictionary to a JSON file.

    Args:
        data (dict): A dictionary.
        file_path (str): The path to the JSON file.
        indent (int, optional): The indentation of the JSON file. Defaults to 4.

    Raises:
        TypeError: If the input data is not a dictionary.
    """
    import json

    file_path = check_file_path(file_path)

    if isinstance(data, dict):
        with open(file_path, "w") as f:
            json.dump(data, f, indent=indent)
    else:
        raise TypeError("The provided data must be a dictionary.")

disjoint(input_features, selecting_features, output=None, **kwargs)

Find the features in the input_features that do not intersect the selecting_features.

Parameters:

Name	Type	Description	Default
input_features	str | GeoDataFrame	The input features to select from. Can be a file path or a GeoDataFrame.	required
selecting_features	str | GeoDataFrame	The features in the Input Features parameter will be selected based on their relationship to the features from this layer.	required
output	are	The output path to save the GeoDataFrame in a vector format (e.g., shapefile). Defaults to None.	None

Returns:

Type	Description
	str | GeoDataFrame: The path to the output file or the GeoDataFrame.

Source code in leafmap/common.py

def disjoint(input_features, selecting_features, output=None, **kwargs):
    """Find the features in the input_features that do not intersect the selecting_features.

    Args:
        input_features (str | GeoDataFrame): The input features to select from. Can be a file path or a GeoDataFrame.
        selecting_features (str | GeoDataFrame): The features in the Input Features parameter will be selected based
            on their relationship to the features from this layer.
        output (are, optional): The output path to save the GeoDataFrame in a vector format (e.g., shapefile). Defaults to None.

    Returns:
        str | GeoDataFrame: The path to the output file or the GeoDataFrame.
    """
    import geopandas as gpd

    if isinstance(input_features, str):
        input_features = gpd.read_file(input_features, **kwargs)
    elif not isinstance(input_features, gpd.GeoDataFrame):
        raise TypeError("input_features must be a file path or a GeoDataFrame")

    if isinstance(selecting_features, str):
        selecting_features = gpd.read_file(selecting_features, **kwargs)
    elif not isinstance(selecting_features, gpd.GeoDataFrame):
        raise TypeError("selecting_features must be a file path or a GeoDataFrame")

    selecting_features = selecting_features.to_crs(input_features.crs)

    input_features["savedindex"] = input_features.index
    intersecting = selecting_features.sjoin(input_features, how="inner")["savedindex"]
    results = input_features[~input_features.savedindex.isin(intersecting)].drop(
        columns=["savedindex"], axis=1
    )

    if output is not None:
        results.to_file(output, **kwargs)
    else:
        return results

display_html(html, width='100%', height=500)

Displays an HTML file or HTML string in a Jupyter Notebook.

Parameters:

Name	Type	Description	Default
html	Union[str, bytes]	Path to an HTML file or an HTML string.	required
width	str	Width of the displayed iframe. Default is '100%'.	'100%'
height	int	Height of the displayed iframe. Default is 500.	500

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def display_html(
    html: Union[str, bytes], width: str = "100%", height: int = 500
) -> None:
    """
    Displays an HTML file or HTML string in a Jupyter Notebook.

    Args:
        html (Union[str, bytes]): Path to an HTML file or an HTML string.
        width (str, optional): Width of the displayed iframe. Default is '100%'.
        height (int, optional): Height of the displayed iframe. Default is 500.

    Returns:
        None
    """
    from IPython.display import IFrame, display

    if isinstance(html, str) and html.startswith("<"):
        # If the input is an HTML string
        html_content = html
    elif isinstance(html, str):
        # If the input is a file path
        with open(html, "r") as file:
            html_content = file.read()
    elif isinstance(html, bytes):
        # If the input is a byte string
        html_content = html.decode("utf-8")
    else:
        raise ValueError("Invalid input type. Expected a file path or an HTML string.")

    display(IFrame(src=html_content, width=width, height=height))

download_data_catalogs(out_dir=None, quiet=True, overwrite=False)

Download geospatial data catalogs from https://github.com/giswqs/geospatial-data-catalogs.

Parameters:

Name	Type	Description	Default
out_dir	str	The output directory. Defaults to None.	None
quiet	bool	Whether to suppress the download progress bar. Defaults to True.	True
overwrite	bool	Whether to overwrite the existing data catalog. Defaults to False.	False

Returns:

Name	Type	Description
str	str	The path to the downloaded data catalog.

Source code in leafmap/stac.py

def download_data_catalogs(
    out_dir: Optional[str] = None,
    quiet: Optional[bool] = True,
    overwrite: Optional[bool] = False,
) -> str:
    """Download geospatial data catalogs from https://github.com/giswqs/geospatial-data-catalogs.

    Args:
        out_dir (str, optional): The output directory. Defaults to None.
        quiet (bool, optional): Whether to suppress the download progress bar. Defaults to True.
        overwrite (bool, optional): Whether to overwrite the existing data catalog. Defaults to False.

    Returns:
        str: The path to the downloaded data catalog.
    """
    import tempfile
    import gdown
    import zipfile

    if out_dir is None:
        out_dir = tempfile.gettempdir()
    elif not os.path.exists(out_dir):
        os.makedirs(out_dir)

    url = "https://github.com/giswqs/geospatial-data-catalogs/archive/refs/heads/master.zip"

    out_file = os.path.join(out_dir, "geospatial-data-catalogs.zip")
    work_dir = os.path.join(out_dir, "geospatial-data-catalogs-master")

    if os.path.exists(work_dir) and not overwrite:
        return work_dir
    else:
        gdown.download(url, out_file, quiet=quiet)
        with zipfile.ZipFile(out_file, "r") as zip_ref:
            zip_ref.extractall(out_dir)
        return work_dir

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, subfolder=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
subfolder	bool	Create a subfolder with the same name as the file. Defaults to False.	False

Returns:

Name	Type	Description
str		The output file path.

Source code in leafmap/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,
    subfolder=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.
        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.

    Returns:
        str: The output file path.
    """
    try:
        import gdown
    except ImportError:
        print(
            "The gdown package is required for this function. Use `pip install gdown` to install it."
        )
        return

    if output is None:
        if isinstance(url, str) and url.startswith("http"):
            output = os.path.basename(url)

    out_dir = os.path.abspath(os.path.dirname(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    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:
        if output.endswith(".zip"):
            with zipfile.ZipFile(output, "r") as zip_ref:
                if not quiet:
                    print("Extracting files...")
                if subfolder:
                    basename = os.path.splitext(os.path.basename(output))[0]

                    output = os.path.join(out_dir, basename)
                    if not os.path.exists(output):
                        os.makedirs(output)
                    zip_ref.extractall(output)
                else:
                    zip_ref.extractall(os.path.dirname(output))
        elif output.endswith(".tar.gz") or output.endswith(".tar"):
            if output.endswith(".tar.gz"):
                mode = "r:gz"
            else:
                mode = "r"

            with tarfile.open(output, mode) as tar_ref:
                if not quiet:
                    print("Extracting files...")
                if subfolder:
                    basename = os.path.splitext(os.path.basename(output))[0]
                    output = os.path.join(out_dir, basename)
                    if not os.path.exists(output):
                        os.makedirs(output)
                    tar_ref.extractall(output)
                else:
                    tar_ref.extractall(os.path.dirname(output))

    return os.path.abspath(output)

download_file_lite(url, output=None, binary=False, overwrite=False, **kwargs) async

Download a file using Pyodide. This function is only available on JupyterLite. Call the function with await, such as await download_file_lite(url).

Parameters:

Name	Type	Description	Default
url	str	The URL of the file.	required
output	str	The local path to save the file. Defaults to None.	None
binary	bool	Whether the file is binary. Defaults to False.	False
overwrite	bool	Whether to overwrite the file if it exists. Defaults to False.	False

Source code in leafmap/common.py

async def download_file_lite(url, output=None, binary=False, overwrite=False, **kwargs):
    """Download a file using Pyodide. This function is only available on JupyterLite. Call the function with await, such as await download_file_lite(url).

    Args:
        url (str): The URL of the file.
        output (str, optional): The local path to save the file. Defaults to None.
        binary (bool, optional): Whether the file is binary. Defaults to False.
        overwrite (bool, optional): Whether to overwrite the file if it exists. Defaults to False.
    """
    import sys
    import pyodide  # pylint: disable=E0401

    if "pyodide" not in sys.modules:
        raise ValueError("Pyodide is not available.")

    if output is None:
        output = os.path.basename(url)

    output = os.path.abspath(output)

    ext = os.path.splitext(output)[1]

    if ext in [".png", "jpg", ".tif", ".tiff", "zip", "gz", "bz2", "xz"]:
        binary = True

    if os.path.exists(output) and not overwrite:
        print(f"{output} already exists, skip downloading.")
        return output

    if binary:
        response = await pyodide.http.pyfetch(url)
        with open(output, "wb") as f:
            f.write(await response.bytes())

    else:
        obj = pyodide.http.open_url(url)
        with open(output, "w") as fd:
            shutil.copyfileobj(obj, fd)

    return output

download_files(urls, out_dir=None, filenames=None, quiet=False, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False, multi_part=False)

Download files from URLs, including Google Drive shared URL.

Parameters:

Name	Type	Description	Default
urls	list	The list of urls to download. Google Drive URL is also supported.	required
out_dir	str	The output directory. Defaults to None.	None
filenames	list	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
subfolder	bool	Create a subfolder with the same name as the file. Defaults to False.	False
multi_part	bool	If the file is a multi-part file. Defaults to False.	False

files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
base_url = "https://github.com/opengeos/datasets/releases/download/models/"
urls = [base_url + f for f in files]
leafmap.download_files(urls, out_dir="models", multi_part=True)

Source code in leafmap/common.py

def download_files(
    urls,
    out_dir=None,
    filenames=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    verify=True,
    id=None,
    fuzzy=False,
    resume=False,
    unzip=True,
    overwrite=False,
    subfolder=False,
    multi_part=False,
):
    """Download files from URLs, including Google Drive shared URL.

    Args:
        urls (list): The list of urls to download. Google Drive URL is also supported.
        out_dir (str, optional): The output directory. Defaults to None.
        filenames (list, 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.
        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.
        multi_part (bool, optional): If the file is a multi-part file. Defaults to False.

    Examples:

        files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
        base_url = "https://github.com/opengeos/datasets/releases/download/models/"
        urls = [base_url + f for f in files]
        leafmap.download_files(urls, out_dir="models", multi_part=True)
    """

    if out_dir is None:
        out_dir = os.getcwd()

    if filenames is None:
        filenames = [None] * len(urls)

    filepaths = []
    for url, output in zip(urls, filenames):
        if output is None:
            filename = os.path.join(out_dir, os.path.basename(url))
        else:
            filename = os.path.join(out_dir, output)

        filepaths.append(filename)
        if multi_part:
            unzip = False

        download_file(
            url,
            filename,
            quiet,
            proxy,
            speed,
            use_cookies,
            verify,
            id,
            fuzzy,
            resume,
            unzip,
            overwrite,
            subfolder,
        )

    if multi_part:
        archive = os.path.splitext(filename)[0] + ".zip"
        out_dir = os.path.dirname(filename)
        extract_archive(archive, out_dir)

        for file in filepaths:
            os.remove(file)

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:

Name	Type	Description
list		List of files downloaded, or None if failed.

Source code in leafmap/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.
    """

    try:
        import gdown
    except ImportError:
        print(
            "The gdown package is required for this function. Use `pip install gdown` to install it."
        )
        return

    files = gdown.download_folder(
        url, id, output, quiet, proxy, speed, use_cookies, remaining_ok
    )
    return files

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/opengeos/whitebox-python/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 leafmap/common.py

def download_from_url(
    url: str,
    out_file_name: Optional[str] = None,
    out_dir: Optional[str] = ".",
    unzip: Optional[bool] = True,
    verbose: Optional[bool] = True,
):
    """Download a file from a URL (e.g., https://github.com/opengeos/whitebox-python/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)
    out_dir = check_dir(out_dir)

    if out_file_name is None:
        out_file_name = in_file_name
    out_file_path = os.path.join(out_dir, out_file_name)

    if verbose:
        print("Downloading {} ...".format(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("Unzipping {} ...".format(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("Unzipping {} ...".format(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(".tart", "")
            )

    if verbose:
        print("Data downloaded to: {}".format(final_path))

download_google_buildings(location, out_dir=None, merge_output=None, head=None, keep_geojson=False, overwrite=False, quiet=False, **kwargs)

Download Google Open Building dataset for a specific location. Check the dataset links from https://sites.research.google/open-buildings.

Parameters:

Name	Type	Description	Default
location	str	The location name for which to download the dataset.	required
out_dir	Optional[str]	The output directory to save the downloaded files. If not provided, the current working directory is used.	None
merge_output	Optional[str]	Optional. The output file path for merging the downloaded files into a single GeoDataFrame.	None
head	Optional[int]	Optional. The number of files to download. If not provided, all files will be downloaded.	None
keep_geojson	bool	Optional. If True, the GeoJSON files will be kept after converting them to CSV files.	False
overwrite	bool	Optional. If True, overwrite the existing files.	False
quiet	bool	Optional. If True, suppresses the download progress messages.	False
**kwargs		Additional keyword arguments to be passed to the gpd.to_file function.	{}

Returns:

Type	Description
List[str]	A list of file paths of the downloaded files.

Source code in leafmap/common.py

def download_google_buildings(
    location: str,
    out_dir: Optional[str] = None,
    merge_output: Optional[str] = None,
    head: Optional[int] = None,
    keep_geojson: bool = False,
    overwrite: bool = False,
    quiet: bool = False,
    **kwargs,
) -> List[str]:
    """
    Download Google Open Building dataset for a specific location. Check the dataset links from
        https://sites.research.google/open-buildings.

    Args:
        location: The location name for which to download the dataset.
        out_dir: The output directory to save the downloaded files. If not provided, the current working directory is used.
        merge_output: Optional. The output file path for merging the downloaded files into a single GeoDataFrame.
        head: Optional. The number of files to download. If not provided, all files will be downloaded.
        keep_geojson: Optional. If True, the GeoJSON files will be kept after converting them to CSV files.
        overwrite: Optional. If True, overwrite the existing files.
        quiet: Optional. If True, suppresses the download progress messages.
        **kwargs: Additional keyword arguments to be passed to the `gpd.to_file` function.

    Returns:
        A list of file paths of the downloaded files.

    """

    import pandas as pd
    import geopandas as gpd
    from shapely import wkt

    building_url = "https://sites.research.google/open-buildings/tiles.geojson"
    country_url = (
        "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
    )

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    building_gdf = gpd.read_file(building_url)
    country_gdf = gpd.read_file(country_url)

    country = country_gdf[country_gdf["NAME"] == location]

    if len(country) == 0:
        country = country_gdf[country_gdf["NAME_LONG"] == location]
        if len(country) == 0:
            raise ValueError(f"Could not find {location} in the Natural Earth dataset.")

    gdf = building_gdf[building_gdf.intersects(country.geometry.iloc[0])]
    gdf.sort_values(by="size_mb", inplace=True)

    print(f"Found {len(gdf)} links for {location}.")
    if head is not None:
        gdf = gdf.head(head)

    if len(gdf) > 0:
        links = gdf["tile_url"].tolist()
        download_files(links, out_dir=out_dir, quiet=quiet, **kwargs)
        filenames = [os.path.join(out_dir, os.path.basename(link)) for link in links]

        gdfs = []
        for filename in filenames:
            # Read the CSV file into a pandas DataFrame
            df = pd.read_csv(filename)

            # Create a geometry column from the "geometry" column in the DataFrame
            df["geometry"] = df["geometry"].apply(wkt.loads)

            # Convert the pandas DataFrame to a GeoDataFrame
            gdf = gpd.GeoDataFrame(df, geometry="geometry")
            gdf.crs = "EPSG:4326"
            if keep_geojson:
                gdf.to_file(
                    filename.replace(".csv.gz", ".geojson"), driver="GeoJSON", **kwargs
                )
            gdfs.append(gdf)

        if merge_output:
            if os.path.exists(merge_output) and not overwrite:
                print(f"File {merge_output} already exists, skip merging...")
            else:
                if not quiet:
                    print("Merging GeoDataFrames ...")
                gdf = gpd.GeoDataFrame(
                    pd.concat(gdfs, ignore_index=True), crs="EPSG:4326"
                )
                gdf.to_file(merge_output, **kwargs)

    else:
        print(f"No buildings found for {location}.")

download_mapillary_image(image_id, output=None, resolution='original', access_token=None, quiet=True, **kwargs)

Downloads a Mapillary image.

Parameters:

Name	Type	Description	Default
image_id	str	The ID of the Mapillary image.	required
output	str	The output file path. Defaults to None.	None
resolution	str	The resolution of the image. Can be 256, 1024, 2048, or original. Defaults to "original".	'original'
access_token	str	The access token for the Mapillary API. Defaults to None.	None
quiet	bool	Whether to suppress output. Defaults to True.	True
**kwargs	Any	Additional keyword arguments for the download.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def download_mapillary_image(
    image_id: str,
    output: Optional[str] = None,
    resolution: str = "original",
    access_token: Optional[str] = None,
    quiet: bool = True,
    **kwargs: Any,
) -> None:
    """
    Downloads a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        output (str, optional): The output file path. Defaults to None.
        resolution (str): The resolution of the image. Can be 256, 1024, 2048, or original.
            Defaults to "original".
        access_token (str, optional): The access token for the Mapillary API. Defaults to None.
        quiet (bool): Whether to suppress output. Defaults to True.
        **kwargs: Additional keyword arguments for the download.

    Returns:
        None
    """

    image_url = get_mapillary_image_url(
        image_id, resolution=resolution, access_token=access_token
    )
    if output is None:

        output = f"{image_id}.jpg"
    download_file(image_url, output, quiet=quiet, **kwargs)

download_mapillary_images(image_ids, output_dir=None, resolution='original', **kwargs)

Downloads multiple Mapillary images.

Parameters:

Name	Type	Description	Default
image_ids	List[str]	A list of Mapillary image IDs.	required
output_dir	str	The directory to save the images. Defaults to the current working directory.	None
resolution	str	The resolution of the images. Defaults to "original".	'original'
**kwargs	Any	Additional keyword arguments for the download.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def download_mapillary_images(
    image_ids: List[str],
    output_dir: Optional[str] = None,
    resolution: str = "original",
    **kwargs: Any,
) -> None:
    """
    Downloads multiple Mapillary images.

    Args:
        image_ids (List[str]): A list of Mapillary image IDs.
        output_dir (str, optional): The directory to save the images. Defaults
            to the current working directory.
        resolution (str): The resolution of the images. Defaults to "original".
        **kwargs: Additional keyword arguments for the download.

    Returns:
        None
    """
    if output_dir is None:
        output_dir = os.getcwd()

    for index, image_id in enumerate(image_ids):
        output = os.path.join(output_dir, f"{image_id}.jpg")
        print(f"Downloading {index + 1}/{len(image_ids)}: {image_id}.jpg ...")
        download_mapillary_image(
            image_id=image_id, output=output, resolution=resolution, **kwargs
        )

download_ms_buildings(location, out_dir=None, merge_output=None, head=None, quiet=False, **kwargs)

Download Microsoft Buildings dataset for a specific location. Check the dataset links from https://minedbuildings.blob.core.windows.net/global-buildings/dataset-links.csv.

Parameters:

Name	Type	Description	Default
location	str	The location name for which to download the dataset.	required
out_dir	Optional[str]	The output directory to save the downloaded files. If not provided, the current working directory is used.	None
merge_output	Optional[str]	Optional. The output file path for merging the downloaded files into a single GeoDataFrame.	None
head		Optional. The number of files to download. If not provided, all files will be downloaded.	None
quiet	bool	Optional. If True, suppresses the download progress messages.	False
**kwargs		Additional keyword arguments to be passed to the gpd.to_file function.	{}

Returns:

Type	Description
List[str]	A list of file paths of the downloaded files.

Source code in leafmap/common.py

def download_ms_buildings(
    location: str,
    out_dir: Optional[str] = None,
    merge_output: Optional[str] = None,
    head=None,
    quiet: bool = False,
    **kwargs,
) -> List[str]:
    """
    Download Microsoft Buildings dataset for a specific location. Check the dataset links from
        https://minedbuildings.blob.core.windows.net/global-buildings/dataset-links.csv.

    Args:
        location: The location name for which to download the dataset.
        out_dir: The output directory to save the downloaded files. If not provided, the current working directory is used.
        merge_output: Optional. The output file path for merging the downloaded files into a single GeoDataFrame.
        head: Optional. The number of files to download. If not provided, all files will be downloaded.
        quiet: Optional. If True, suppresses the download progress messages.
        **kwargs: Additional keyword arguments to be passed to the `gpd.to_file` function.

    Returns:
        A list of file paths of the downloaded files.

    """

    import pandas as pd
    import geopandas as gpd
    from shapely.geometry import shape

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    dataset_links = pd.read_csv(
        "https://minedbuildings.blob.core.windows.net/global-buildings/dataset-links.csv"
    )
    country_links = dataset_links[dataset_links.Location == location]

    if not quiet:
        print(f"Found {len(country_links)} links for {location}")
    if head is not None:
        country_links = country_links.head(head)

    filenames = []
    i = 1

    for _, row in country_links.iterrows():
        if not quiet:
            print(f"Downloading {i} of {len(country_links)}: {row.QuadKey}.geojson")
        i += 1
        filename = os.path.join(out_dir, f"{row.QuadKey}.geojson")
        filenames.append(filename)
        if os.path.exists(filename):
            print(f"File {filename} already exists, skipping...")
            continue
        df = pd.read_json(row.Url, lines=True)
        df["geometry"] = df["geometry"].apply(shape)
        gdf = gpd.GeoDataFrame(df, crs=4326)
        gdf.to_file(filename, driver="GeoJSON", **kwargs)

    if merge_output is not None:
        if os.path.exists(merge_output):
            print(f"File {merge_output} already exists, skip merging...")
            return filenames
        merge_vector(filenames, merge_output, quiet=quiet)

    return filenames

download_ned(region, out_dir=None, return_url=False, download_args={}, geopandas_args={}, query={})

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 {}.	{}
geopandas_args	dict	A dictionary of arguments to pass to the geopandas.read_file() function. Used for reading a region URL|filepath.	{}
query	dict	A dictionary of arguments to pass to the The_national_map_USGS.find_details() function. See https://apps.nationalmap.gov/tnmaccess/#/product for more information.	{}

Returns:

Name	Type	Description
list	Union[None, List]	A list of the download URLs of the files if return_url is True.

Source code in leafmap/common.py

def download_ned(
    region,
    out_dir=None,
    return_url=False,
    download_args={},
    geopandas_args={},
    query={},
) -> Union[None, List]:
    """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 {}.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        query (dict, optional): A dictionary of arguments to pass to the The_national_map_USGS.find_details() function.
            See https://apps.nationalmap.gov/tnmaccess/#/product for more information.

    Returns:
        list: A list of the download URLs of the files if return_url is True.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if not query:
        query = {
            "datasets": "National Elevation Dataset (NED) 1/3 arc-second",
            "prodFormats": "GeoTIFF",
        }

    TNM = The_national_map_USGS()
    if return_url:
        return TNM.find_tiles(region=region, geopandas_args=geopandas_args, API=query)
    return TNM.download_tiles(
        region=region,
        out_dir=out_dir,
        download_args=download_args,
        geopandas_args=geopandas_args,
        API=query,
    )

download_nlcd(years, out_dir=None, quiet=False, **kwargs)

Downloads NLCD (National Land Cover Database) files for the specified years.

Parameters:

Name	Type	Description	Default
years	List[int]	A list of years for which to download the NLCD files.	required
out_dir	str	The directory where the downloaded files will be saved. Defaults to the current working directory.	None
quiet	bool	If True, suppresses download progress messages. Defaults to False.	False
**kwargs	Any	Additional keyword arguments to pass to the download_file function.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def download_nlcd(
    years: List[int], out_dir: str = None, quiet: bool = False, **kwargs: Any
) -> None:
    """
    Downloads NLCD (National Land Cover Database) files for the specified years.

    Args:
        years (List[int]): A list of years for which to download the NLCD files.
        out_dir (str, optional): The directory where the downloaded files will be saved.
            Defaults to the current working directory.
        quiet (bool, optional): If True, suppresses download progress messages. Defaults to False.
        **kwargs (Any): Additional keyword arguments to pass to the download_file function.

    Returns:
        None
    """

    allow_years = list(range(1985, 2024, 1))
    url = "https://s3-us-west-2.amazonaws.com/mrlc/Annual_NLCD_LndCov_{}_CU_C1V0.tif"
    if out_dir is None:
        out_dir = os.getcwd()
    elif not os.path.exists(out_dir):
        os.makedirs(out_dir)
    for year in years:
        if year not in allow_years:
            print(f"Year {year} is not available. Skipping...")
            continue
        year_url = url.format(year)
        basename = os.path.basename(year_url)
        filepath = os.path.join(out_dir, basename)
        download_file(year_url, filepath, quiet=quiet, **kwargs)

download_tnm(region=None, out_dir=None, return_url=False, download_args={}, geopandas_args={}, API={})

Download the US National Elevation Datasets (NED) for a region.

Parameters:

Name	Type	Description	Default
region	str | list	An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy]. Alternatively you could use API parameters such as polygon or bbox.	None
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 {}.	{}
geopandas_args	dict	A dictionary of arguments to pass to the geopandas.read_file() function. Used for reading a region URL|filepath.	{}
API	dict	A dictionary of arguments to pass to the The_national_map_USGS.find_details() function. Exposes most of the documented API. Defaults to {}	{}

Returns:

Name	Type	Description
list	Union[None, List]	A list of the download URLs of the files if return_url is True.

Source code in leafmap/common.py

def download_tnm(
    region=None,
    out_dir=None,
    return_url=False,
    download_args={},
    geopandas_args={},
    API={},
) -> Union[None, List]:
    """Download the US National Elevation Datasets (NED) for a region.

    Args:
        region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
            Alternatively you could use API parameters such as polygon or bbox.
        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 {}.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        API (dict, optional): A dictionary of arguments to pass to the The_national_map_USGS.find_details() function.
            Exposes most of the documented API. Defaults to {}

    Returns:
        list: A list of the download URLs of the files if return_url is True.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    TNM = The_national_map_USGS()
    if return_url:
        return TNM.find_tiles(region=region, geopandas_args=geopandas_args, API=API)
    return TNM.download_tiles(
        region=region,
        out_dir=out_dir,
        download_args=download_args,
        geopandas_args=geopandas_args,
        API=API,
    )

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 leafmap/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
    )

ee_tile_url(ee_object=None, vis_params={}, asset_id=None, ee_initialize=False, project_id=None, **kwargs)

Adds a Google Earth Engine tile layer to the map based on the tile layer URL from https://github.com/opengeos/ee-tile-layers/blob/main/datasets.tsv.

Parameters:

Name	Type	Description	Default
ee_object	object	The Earth Engine object to display.	None
vis_params	dict	Visualization parameters. For example, {'min': 0, 'max': 100}.	{}
asset_id	str	The ID of the Earth Engine asset.	None
ee_initialize	bool	Whether to initialize the Earth Engine	False

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def ee_tile_url(
    ee_object=None,
    vis_params={},
    asset_id: str = None,
    ee_initialize: bool = False,
    project_id=None,
    **kwargs,
) -> None:
    """
    Adds a Google Earth Engine tile layer to the map based on the tile layer URL from
        https://github.com/opengeos/ee-tile-layers/blob/main/datasets.tsv.

    Args:
        ee_object (object): The Earth Engine object to display.
        vis_params (dict): Visualization parameters. For example, {'min': 0, 'max': 100}.
        asset_id (str): The ID of the Earth Engine asset.
        ee_initialize (bool, optional): Whether to initialize the Earth Engine

    Returns:
        None
    """
    import pandas as pd

    if isinstance(asset_id, str):
        df = pd.read_csv(
            "https://raw.githubusercontent.com/opengeos/ee-tile-layers/main/datasets.tsv",
            sep="\t",
        )

        asset_id = asset_id.strip()

        if asset_id in df["id"].values:
            url = df.loc[df["id"] == asset_id, "url"].values[0]
            return url
        else:
            print(f"The provided EE tile layer {asset_id} does not exist.")
            return None
    elif ee_object is not None:
        try:
            import geemap
            from geemap.ee_tile_layers import _get_tile_url_format

            if ee_initialize:
                geemap.ee_initialize(project=project_id, **kwargs)
            url = _get_tile_url_format(ee_object, vis_params)
            return url
        except Exception as e:
            print(e)
            return None

evaluate_model(df, y_col='y', y_pred_col='y_pred', metrics=None, drop_na=True, filter_nonzero=True)

Evaluates the model performance on the given dataframe with customizable options.

Parameters:

Name	Type	Description	Default
df	DataFrame	A pandas DataFrame with columns for actual and predicted values.	required
y_col	str	Column name for the actual values.	'y'
y_pred_col	str	Column name for the predicted values.	'y_pred'
metrics	list	A list of metrics to calculate. Available options: - 'r2': R-squared - 'r': Pearson correlation coefficient - 'rmse': Root Mean Squared Error - 'mae': Mean Absolute Error - 'mape': Mean Absolute Percentage Error Defaults to all metrics if None.	None
drop_na	bool	Whether to drop rows with NaN in the actual values column.	True
filter_nonzero	bool	Whether to filter out rows where actual values are zero.	True

Returns:

Type	Description
dict	A dictionary of the selected performance metrics.

Source code in leafmap/common.py

def evaluate_model(
    df: pd.DataFrame,
    y_col: str = "y",
    y_pred_col: str = "y_pred",
    metrics: list = None,
    drop_na: bool = True,
    filter_nonzero: bool = True,
) -> dict:
    """
    Evaluates the model performance on the given dataframe with customizable options.

    Args:
        df: A pandas DataFrame with columns for actual and predicted values.
        y_col: Column name for the actual values.
        y_pred_col: Column name for the predicted values.
        metrics: A list of metrics to calculate. Available options:
            - 'r2': R-squared
            - 'r': Pearson correlation coefficient
            - 'rmse': Root Mean Squared Error
            - 'mae': Mean Absolute Error
            - 'mape': Mean Absolute Percentage Error
            Defaults to all metrics if None.
        drop_na: Whether to drop rows with NaN in the actual values column.
        filter_nonzero: Whether to filter out rows where actual values are zero.

    Returns:
        A dictionary of the selected performance metrics.
    """

    import math

    try:
        from sklearn import metrics as skmetrics
    except ImportError:
        raise ImportError(
            "The scikit-learn package is required for this function. Install it using 'pip install scikit-learn'."
        )

    # Default metrics if none are provided
    if metrics is None:
        metrics = ["r2", "r", "rmse", "mae", "mape"]

    # Data preprocessing
    if drop_na:
        df = df.dropna(subset=[y_col])
    if filter_nonzero:
        df = df[df[y_col] != 0]

    # Metric calculations
    results = {}
    if "r2" in metrics:
        results["r2"] = skmetrics.r2_score(df[y_col], df[y_pred_col])
    if "r" in metrics:
        results["r"] = df[y_col].corr(df[y_pred_col])
    if "rmse" in metrics:
        results["rmse"] = math.sqrt(
            skmetrics.mean_squared_error(df[y_col], df[y_pred_col])
        )
    if "mae" in metrics:
        results["mae"] = skmetrics.mean_absolute_error(df[y_col], df[y_pred_col])
    if "mape" in metrics:
        results["mape"] = skmetrics.mean_absolute_percentage_error(
            df[y_col], df[y_pred_col]
        )

    return results

execute_maplibre_notebook_dir(in_dir, out_dir, delete_html=True, replace_api_key=True, recursive=False, keep_notebook=False, index_html=True, ignore_files=None)

Executes Jupyter notebooks found in a specified directory, optionally replacing API keys and deleting HTML outputs.

Parameters:

Name	Type	Description	Default
in_dir	str	The input directory containing Jupyter notebooks to be executed.	required
out_dir	str	The output directory where the executed notebooks and their HTML outputs will be saved.	required
delete_html	bool	If True, deletes any existing HTML files in the output directory before execution. Defaults to True.	True
replace_api_key	bool	If True, replaces the API key in the output HTML. Defaults to True. set "MAPTILER_KEY" and "MAPTILER_KEY_PUBLIC" to your MapTiler API key and public key, respectively.	True
recursive	bool	If True, searches for notebooks in the input directory recursively. Defaults to False.	False
keep_notebook	bool	If True, keeps the executed notebooks in the output directory. Defaults to False.	False
index_html	bool	If True, generates an index.html file in the output directory listing all files. Defaults to True.	True
ignore_files	list	A list of notebook files to ignore during execution. Defaults to None.	None

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def execute_maplibre_notebook_dir(
    in_dir: str,
    out_dir: str,
    delete_html: bool = True,
    replace_api_key: bool = True,
    recursive: bool = False,
    keep_notebook: bool = False,
    index_html: bool = True,
    ignore_files: Optional[List[str]] = None,
) -> None:
    """
    Executes Jupyter notebooks found in a specified directory, optionally replacing API keys and deleting HTML outputs.

    Args:
        in_dir (str): The input directory containing Jupyter notebooks to be executed.
        out_dir (str): The output directory where the executed notebooks and their HTML outputs will be saved.
        delete_html (bool, optional): If True, deletes any existing HTML files in the output directory before execution. Defaults to True.
        replace_api_key (bool, optional): If True, replaces the API key in the output HTML. Defaults to True.
            set "MAPTILER_KEY" and "MAPTILER_KEY_PUBLIC" to your MapTiler API key and public key, respectively.
        recursive (bool, optional): If True, searches for notebooks in the input directory recursively. Defaults to False.
        keep_notebook (bool, optional): If True, keeps the executed notebooks in the output directory. Defaults to False.
        index_html (bool, optional): If True, generates an index.html file in the output directory listing all files. Defaults to True.
        ignore_files (list, optional): A list of notebook files to ignore during execution. Defaults to None.

    Returns:
        None
    """
    import shutil

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if replace_api_key:
        os.environ["MAPTILER_REPLACE_KEY"] = "True"

    if delete_html:
        html_files = find_files(out_dir, "*.html", recursive=recursive)
        for file in html_files:
            os.remove(file)

    if ignore_files is not None:
        ignore_files = [os.path.join(in_dir, f) for f in ignore_files]

    files = find_files(in_dir, "*.ipynb", recursive=recursive)
    for index, file in enumerate(files):
        print(f"Processing {index + 1}/{len(files)}: {file} ...")

        if ignore_files is not None and file in ignore_files:
            print(f"Skipping {file} ...")
            continue

        basename = os.path.basename(file)
        out_file = os.path.join(out_dir, basename)
        shutil.copy(file, out_file)

        with open(out_file, "r") as f:
            lines = f.readlines()

        out_lines = []
        for line in lines:
            if line.strip() == '"m"':
                title = os.path.splitext(basename)[0].replace("_", " ")
                out_lines.append(line.replace("m", f"m.to_html(title='{title}')"))
            else:
                out_lines.append(line)

        with open(out_file, "w") as f:
            f.writelines(out_lines)

        out_html = os.path.basename(out_file).replace(".ipynb", ".html")
        os.environ["MAPLIBRE_OUTPUT"] = out_html
        execute_notebook(out_file)

    if not keep_notebook:
        all_files = find_files(out_dir, "*", recursive=recursive)
        for file in all_files:
            if not file.endswith(".html"):
                os.remove(file)

    if index_html:
        generate_index_html(out_dir)

execute_notebook(in_file)

Executes a Jupyter notebook and save output cells

Parameters:

Name	Type	Description	Default
in_file	str	Input Jupyter notebook.	required

Source code in leafmap/common.py

def execute_notebook(in_file):
    """Executes a Jupyter notebook and save output cells

    Args:
        in_file (str): Input Jupyter notebook.
    """
    # command = 'jupyter nbconvert --to notebook --execute ' + in_file + ' --inplace'
    command = 'jupyter nbconvert --to notebook --execute "{}" --inplace'.format(in_file)
    print(os.popen(command).read().rstrip())

execute_notebook_dir(in_dir)

Executes all Jupyter notebooks in the given directory recursively and save output cells.

Parameters:

Name	Type	Description	Default
in_dir	str	Input folder containing notebooks.	required

Source code in leafmap/common.py

def execute_notebook_dir(in_dir):
    """Executes all Jupyter notebooks in the given directory recursively and save output cells.

    Args:
        in_dir (str): Input folder containing notebooks.
    """
    from pathlib import Path

    in_dir = os.path.abspath(in_dir)

    files = list(Path(in_dir).rglob("*.ipynb"))
    files.sort()
    count = len(files)
    if files is not None:
        for index, file in enumerate(files):
            in_file = str(file)
            print(f"Processing {index + 1}/{count}: {file} ...")
            execute_notebook(in_file)

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

Source code in leafmap/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_archive(archive, outdir=None, **kwargs)

Extracts a multipart archive.

This function uses the patoolib library to extract a multipart archive. If the patoolib library is not installed, it attempts to install it. If the archive does not end with ".zip", it appends ".zip" to the archive name. If the extraction fails (for example, if the files already exist), it skips the extraction.

Parameters:

Name	Type	Description	Default
archive	str	The path to the archive file.	required
outdir	str	The directory where the archive should be extracted.	None
**kwargs		Arbitrary keyword arguments for the patoolib.extract_archive function.	{}

Returns:

Type	Description
None	None

Raises:

Type	Description
Exception	An exception is raised if the extraction fails for reasons other than the files already existing.

files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
base_url = "https://github.com/opengeos/datasets/releases/download/models/"
urls = [base_url + f for f in files]
leafmap.download_files(urls, out_dir="models", multi_part=True)

Source code in leafmap/common.py

def extract_archive(archive, outdir=None, **kwargs) -> None:
    """
    Extracts a multipart archive.

    This function uses the patoolib library to extract a multipart archive.
    If the patoolib library is not installed, it attempts to install it.
    If the archive does not end with ".zip", it appends ".zip" to the archive name.
    If the extraction fails (for example, if the files already exist), it skips the extraction.

    Args:
        archive (str): The path to the archive file.
        outdir (str): The directory where the archive should be extracted.
        **kwargs: Arbitrary keyword arguments for the patoolib.extract_archive function.

    Returns:
        None

    Raises:
        Exception: An exception is raised if the extraction fails for reasons other than the files already existing.

    Example:

        files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
        base_url = "https://github.com/opengeos/datasets/releases/download/models/"
        urls = [base_url + f for f in files]
        leafmap.download_files(urls, out_dir="models", multi_part=True)

    """
    try:
        import patoolib
    except ImportError:
        install_package("patool")
        import patoolib

    if not archive.endswith(".zip"):
        archive = archive + ".zip"

    if outdir is None:
        outdir = os.path.dirname(archive)

    try:
        patoolib.extract_archive(archive, outdir=outdir, **kwargs)
    except Exception as e:
        print("The unzipped files might already exist. Skipping extraction.")
        return

extract_parquet_by_bbox(input_parquet, bbox, output_file, geometry='geometry', driver='PARQUET')

Extract buildings that intersect with a specific bounding box in San Diego.

Uses DuckDB with spatial extension to query buildings that intersect with a bounding box and saves the results to a Parquet file.

Parameters:

Name	Type	Description	Default
input_parquet_pattern	str	Pattern for input Parquet files (e.g. '*.parquet')	required
output_parquet_path	str	Output file path for resulting Parquet file	required

Returns:

Name	Type	Description
None		The function writes the results to the output_parquet_path

Source code in leafmap/common.py

def extract_parquet_by_bbox(
    input_parquet, bbox, output_file, geometry="geometry", driver="PARQUET"
):
    """
    Extract buildings that intersect with a specific bounding box in San Diego.

    Uses DuckDB with spatial extension to query buildings that intersect with
    a bounding box and saves the results to a Parquet file.

    Args:
        input_parquet_pattern (str): Pattern for input Parquet files (e.g. '*.parquet')
        output_parquet_path (str): Output file path for resulting Parquet file

    Returns:
        None: The function writes the results to the output_parquet_path
    """
    import duckdb

    # Connect to DuckDB
    conn = duckdb.connect()

    # Install and load spatial extension
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    if driver.upper() == "PARQUET":
        fmt = "FORMAT PARQUET"
    else:
        fmt = f"FORMAT GDAL, DRIVER '{driver}'"

    # Run the query
    query = f"""
    COPY (
        WITH bbox AS (
            SELECT ST_MakeEnvelope({bbox[0]}, {bbox[1]}, {bbox[2]}, {bbox[3]}) AS geom2
        )

        SELECT * FROM '{input_parquet}'
        WHERE
            ST_Intersects(
                {geometry},
                (SELECT geom2 FROM bbox)
            )
    ) TO '{output_file}' ({fmt})
    """

    # Execute the query
    conn.execute(query)

    # Close the connection
    conn.close()

filter_bounds(data, bbox, within=False, align=True, **kwargs)

Filters a GeoDataFrame or GeoSeries by a bounding box.

Parameters:

Name	Type	Description	Default
data	str | GeoDataFrame	The input data to filter. Can be a file path or a GeoDataFrame.	required
bbox	list | GeoDataFrame	The bounding box to filter by. Can be a list of 4 coordinates or a file path or a GeoDataFrame.	required
within	bool	Whether to filter by the bounding box or the bounding box's interior. Defaults to False.	False
align	bool	If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.	True

Returns:

Name	Type	Description
GeoDataFrame		The filtered data.

Source code in leafmap/common.py

def filter_bounds(data, bbox, within=False, align=True, **kwargs):
    """Filters a GeoDataFrame or GeoSeries by a bounding box.

    Args:
        data (str | GeoDataFrame): The input data to filter. Can be a file path or a GeoDataFrame.
        bbox (list | GeoDataFrame): The bounding box to filter by. Can be a list of 4 coordinates or a file path or a GeoDataFrame.
        within (bool, optional): Whether to filter by the bounding box or the bounding box's interior. Defaults to False.
        align (bool, optional): If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.

    Returns:
        GeoDataFrame: The filtered data.
    """
    import geopandas as gpd

    if isinstance(data, str):
        data = gpd.read_file(data, **kwargs)
    elif not isinstance(data, (gpd.GeoDataFrame, gpd.GeoSeries)):
        raise TypeError("data must be a file path or a GeoDataFrame or GeoSeries")

    if isinstance(bbox, list):
        if len(bbox) != 4:
            raise ValueError("bbox must be a list of 4 coordinates")
        bbox = bbox_to_gdf(bbox)
    elif isinstance(bbox, str):
        bbox = gpd.read_file(bbox, **kwargs)

    if within:
        result = data[data.within(bbox.unary_union, align=align)]
    else:
        result = data[data.intersects(bbox.unary_union, align=align)]

    return result

filter_date(data, start_date=None, end_date=None, date_field='date', date_args={}, **kwargs)

Filters a DataFrame, GeoDataFrame or GeoSeries by a date range.

Parameters:

Name	Type	Description	Default
data	str | DataFrame | GeoDataFrame	The input data to filter. Can be a file path or a DataFrame or GeoDataFrame.	required
start_date	str	The start date, e.g., 2023-01-01. Defaults to None.	None
end_date	str	The end date, e.g., 2023-12-31. Defaults to None.	None
date_field	str	The name of the date field. Defaults to "date".	'date'
date_args	dict	Additional arguments for pd.to_datetime. Defaults to {}.	{}

Returns:

Name	Type	Description
DataFrame		The filtered data.

Source code in leafmap/common.py

def filter_date(
    data, start_date=None, end_date=None, date_field="date", date_args={}, **kwargs
):
    """Filters a DataFrame, GeoDataFrame or GeoSeries by a date range.

    Args:
        data (str | DataFrame | GeoDataFrame): The input data to filter. Can be a file path or a DataFrame or GeoDataFrame.
        start_date (str, optional): The start date, e.g., 2023-01-01. Defaults to None.
        end_date (str, optional): The end date, e.g., 2023-12-31. Defaults to None.
        date_field (str, optional): The name of the date field. Defaults to "date".
        date_args (dict, optional): Additional arguments for pd.to_datetime. Defaults to {}.

    Returns:
        DataFrame: The filtered data.
    """

    import datetime
    import pandas as pd
    import geopandas as gpd

    if isinstance(data, str):
        data = gpd.read_file(data, **kwargs)
    elif not isinstance(
        data, (gpd.GeoDataFrame, gpd.GeoSeries, pd.DataFrame, pd.Series)
    ):
        raise TypeError("data must be a file path or a GeoDataFrame or GeoSeries")

    if date_field not in data.columns:
        raise ValueError(f"date_field must be one of {data.columns}")

    new_field = f"{date_field}_temp"
    data[new_field] = pd.to_datetime(data[date_field], **date_args)

    if end_date is None:
        end_date = datetime.datetime.now().strftime("%Y-%m-%d")

    if start_date is None:
        start_date = data[new_field].min()

    mask = (data[new_field] >= start_date) & (data[new_field] <= end_date)
    result = data.loc[mask]
    return result.drop(columns=[new_field], axis=1)

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:

Name	Type	Description
list		A list of matching files.

Source code in leafmap/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)]

    files.sort()
    return files

flatten_dict(my_dict, parent_key=False, sep='.')

Flattens a nested dictionary.

Parameters:

Name	Type	Description	Default
my_dict	dict	The dictionary to flatten.	required
parent_key	bool	Whether to include the parent key. Defaults to False.	False
sep	str	The separator to use. Defaults to '.'.	'.'

Returns:

Name	Type	Description
dict		The flattened dictionary.

Source code in leafmap/stac.py

def flatten_dict(my_dict, parent_key=False, sep="."):
    """Flattens a nested dictionary.

    Args:
        my_dict (dict): The dictionary to flatten.
        parent_key (bool, optional): Whether to include the parent key. Defaults to False.
        sep (str, optional): The separator to use. Defaults to '.'.

    Returns:
        dict: The flattened dictionary.
    """

    flat_dict = {}
    for key, value in my_dict.items():
        if not isinstance(value, dict):
            flat_dict[key] = value
        else:
            sub_dict = flatten_dict(value)
            for sub_key, sub_value in sub_dict.items():
                if parent_key:
                    flat_dict[parent_key + sep + sub_key] = sub_value
                else:
                    flat_dict[sub_key] = sub_value

    return flat_dict

gdb_layer_names(gdb_path)

Get a list of layer names in a File Geodatabase (GDB).

Parameters:

Name	Type	Description	Default
gdb_path	str	The path to the File Geodatabase (GDB).	required

Returns:

Type	Description
List[str]	List[str]: A list of layer names in the GDB.

Source code in leafmap/common.py

def gdb_layer_names(gdb_path: str) -> List[str]:
    """Get a list of layer names in a File Geodatabase (GDB).

    Args:
        gdb_path (str): The path to the File Geodatabase (GDB).

    Returns:
        List[str]: A list of layer names in the GDB.
    """

    from osgeo import ogr

    # Open the GDB
    gdb_driver = ogr.GetDriverByName("OpenFileGDB")
    gdb_dataset = gdb_driver.Open(gdb_path, 0)

    # Get the number of layers in the GDB
    layer_count = gdb_dataset.GetLayerCount()
    # Iterate over the layers
    layer_names = []
    for i in range(layer_count):
        layer = gdb_dataset.GetLayerByIndex(i)
        feature_class_name = layer.GetName()
        layer_names.append(feature_class_name)

    # Close the GDB dataset
    gdb_dataset = None
    return layer_names

gdb_to_vector(gdb_path, out_dir, layers=None, filenames=None, gdal_driver='GPKG', file_extension=None, overwrite=False, quiet=False, **kwargs)

Converts layers from a File Geodatabase (GDB) to a vector format.

Parameters:

Name	Type	Description	Default
gdb_path	str	The path to the File Geodatabase (GDB).	required
out_dir	str	The output directory to save the converted files.	required
layers	Optional[List[str]]	A list of layer names to convert. If None, all layers will be converted. Default is None.	None
filenames	Optional[List[str]]	A list of output file names. If None, the layer names will be used as the file names. Default is None.	None
gdal_driver	str	The GDAL driver name for the output vector format. Default is "GPKG".	'GPKG'
file_extension	Optional[str]	The file extension for the output files. If None, it will be determined automatically based on the gdal_driver. Default is None.	None
overwrite	bool	Whether to overwrite the existing output files. Default is False.	False
quiet	bool	If True, suppress the log output. Defaults to False.	False

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def gdb_to_vector(
    gdb_path: str,
    out_dir: str,
    layers: Optional[List[str]] = None,
    filenames: Optional[List[str]] = None,
    gdal_driver: str = "GPKG",
    file_extension: Optional[str] = None,
    overwrite: bool = False,
    quiet=False,
    **kwargs,
) -> None:
    """Converts layers from a File Geodatabase (GDB) to a vector format.

    Args:
        gdb_path (str): The path to the File Geodatabase (GDB).
        out_dir (str): The output directory to save the converted files.
        layers (Optional[List[str]]): A list of layer names to convert. If None, all layers will be converted. Default is None.
        filenames (Optional[List[str]]): A list of output file names. If None, the layer names will be used as the file names. Default is None.
        gdal_driver (str): The GDAL driver name for the output vector format. Default is "GPKG".
        file_extension (Optional[str]): The file extension for the output files. If None, it will be determined automatically based on the gdal_driver. Default is None.
        overwrite (bool): Whether to overwrite the existing output files. Default is False.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        None
    """
    from osgeo import ogr

    # Open the GDB
    gdb_driver = ogr.GetDriverByName("OpenFileGDB")
    gdb_dataset = gdb_driver.Open(gdb_path, 0)

    # Get the number of layers in the GDB
    layer_count = gdb_dataset.GetLayerCount()

    if isinstance(layers, str):
        layers = [layers]

    if isinstance(filenames, str):
        filenames = [filenames]

    if filenames is not None:
        if len(filenames) != len(layers):
            raise ValueError("The length of filenames must match the length of layers.")

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    ii = 0
    # Iterate over the layers
    for i in range(layer_count):
        layer = gdb_dataset.GetLayerByIndex(i)
        feature_class_name = layer.GetName()

        if layers is not None:
            if feature_class_name not in layers:
                continue

        if file_extension is None:
            file_extension = get_gdal_file_extension(gdal_driver)

        # Create the output file path
        if filenames is not None:
            output_file = os.path.join(out_dir, filenames[ii] + "." + file_extension)
            ii += 1
        else:
            output_file = os.path.join(
                out_dir, feature_class_name + "." + file_extension
            )

        if os.path.exists(output_file) and not overwrite:
            print(f"File {output_file} already exists. Skipping...")
            continue
        else:
            if not quiet:
                print(f"Converting layer {feature_class_name} to {output_file}...")

        # Create the output driver
        output_driver = ogr.GetDriverByName(gdal_driver)
        output_dataset = output_driver.CreateDataSource(output_file)

        # Copy the input layer to the output format
        output_dataset.CopyLayer(layer, feature_class_name)

        output_dataset = None

    # Close the GDB dataset
    gdb_dataset = None

gdf_bounds(gdf, return_geom=False)

Returns the bounding box of a GeoDataFrame.

Parameters:

Name	Type	Description	Default
gdf	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 leafmap/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	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 leafmap/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	GeoDataFrame	A GeoDataFrame.	required
first_only	bool	Whether to return the geometry type of the f irst feature in the GeoDataFrame. Defaults to True.	True

Returns:

Name	Type	Description
str		The geometry type of the GeoDataFrame, such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon. For more info, see https://shapely.readthedocs.io/en/stable/manual.html

Source code in leafmap/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 f
            irst feature in the GeoDataFrame. Defaults to True.

    Returns:
        str: The geometry type of the GeoDataFrame, such as Point, LineString,
            Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import geopandas as gpd

    if first_only:
        return gdf.geometry.type[0]
    else:
        return gdf.geometry.type

gdf_to_bokeh(gdf)

Function to convert a GeoPandas GeoDataFrame to a Bokeh ColumnDataSource object.

:param: (GeoDataFrame) gdf: GeoPandas GeoDataFrame with polygon(s) under the column name 'geometry.'

:return: ColumnDataSource for Bokeh.

Source code in leafmap/common.py

def gdf_to_bokeh(gdf):
    """
    Function to convert a GeoPandas GeoDataFrame to a Bokeh
    ColumnDataSource object.

    :param: (GeoDataFrame) gdf: GeoPandas GeoDataFrame with polygon(s) under
                                the column name 'geometry.'

    :return: ColumnDataSource for Bokeh.
    """
    from bokeh.plotting import ColumnDataSource

    shape_type = gdf_geom_type(gdf)

    gdf_new = gdf.drop("geometry", axis=1).copy()
    gdf_new["x"] = gdf.apply(
        get_geometry_coords,
        geom="geometry",
        coord_type="x",
        shape_type=shape_type,
        mercator=True,
        axis=1,
    )

    gdf_new["y"] = gdf.apply(
        get_geometry_coords,
        geom="geometry",
        coord_type="y",
        shape_type=shape_type,
        mercator=True,
        axis=1,
    )

    return ColumnDataSource(gdf_new)

gdf_to_df(gdf, drop_geom=True)

Converts a GeoDataFrame to a pandas DataFrame.

Parameters:

Name	Type	Description	Default
gdf	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 leafmap/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_geojson(gdf, out_geojson=None, epsg=None, tuple_to_list=False, encoding='utf-8')

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
tuple_to_list	bool	Whether to convert tuples to lists. Defaults to False.	False
encoding	str	The encoding to use for the GeoJSON. Defaults to "utf-8".	'utf-8'

Raises:

Type	Description
TypeError	When the output file extension is incorrect.
Exception	When the conversion fails.

Returns:

Name	Type	Description
dict		When the out_json is None returns a dict.

Source code in leafmap/common.py

def gdf_to_geojson(
    gdf, out_geojson=None, epsg=None, tuple_to_list=False, encoding="utf-8"
):
    """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.
        tuple_to_list (bool, optional): Whether to convert tuples to lists. Defaults to False.
        encoding (str, optional): The encoding to use for the GeoJSON. Defaults to "utf-8".

    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")

    def listit(t):
        return list(map(listit, t)) if isinstance(t, (list, tuple)) else t

    try:
        if epsg is not None:
            if gdf.crs is not None and gdf.crs.to_epsg() != epsg:
                gdf = gdf.to_crs(epsg=epsg)
        geojson = gdf.__geo_interface__

        if tuple_to_list:
            for feature in geojson["features"]:
                feature["geometry"]["coordinates"] = listit(
                    feature["geometry"]["coordinates"]
                )

        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", encoding=encoding)
    except Exception as e:
        raise Exception(e)

gedi_download_file(url, filename=None, username=None, password=None)

Downloads a file from the given URL and saves it to the specified filename. If no filename is provided, the name of the file from the URL will be used.

Parameters:

Name	Type	Description	Default
url	str	The URL of the file to download. e.g., https://daac.ornl.gov/daacdata/gedi/GEDI_L4A_AGB_Density_V2_1/data/GEDI04_A_2019298202754_O04921_01_T02899_02_002_02_V002.h5	required
filename	str	The name of the file to save the downloaded content to. Defaults to None.	None
username	str	Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None. Create an account at https://urs.earthdata.nasa.gov	None
password	str	Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.	None

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def gedi_download_file(
    url: str, filename: str = None, username: str = None, password: str = None
) -> None:
    """
    Downloads a file from the given URL and saves it to the specified filename.
    If no filename is provided, the name of the file from the URL will be used.

    Args:
        url (str): The URL of the file to download.
            e.g., https://daac.ornl.gov/daacdata/gedi/GEDI_L4A_AGB_Density_V2_1/data/GEDI04_A_2019298202754_O04921_01_T02899_02_002_02_V002.h5
        filename (str, optional): The name of the file to save the downloaded content to. Defaults to None.
        username (str, optional): Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None.
            Create an account at https://urs.earthdata.nasa.gov
        password (str, optional): Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.

    Returns:
        None
    """
    import requests
    from tqdm import tqdm
    from urllib.parse import urlparse

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        raise ValueError(
            "Username and password must be provided. Create an account at https://urs.earthdata.nasa.gov."
        )

    with requests.Session() as session:
        r1 = session.request("get", url, stream=True)
        r = session.get(r1.url, auth=(username, password), stream=True)
        print(r.status_code)

        if r.status_code == 200:
            total_size = int(r.headers.get("content-length", 0))
            block_size = 1024  # 1 KB

            # Use the filename from the URL if not provided
            if not filename:
                parsed_url = urlparse(url)
                filename = parsed_url.path.split("/")[-1]

            progress_bar = tqdm(total=total_size, unit="B", unit_scale=True)

            with open(filename, "wb") as file:
                for data in r.iter_content(block_size):
                    progress_bar.update(len(data))
                    file.write(data)

            progress_bar.close()

gedi_download_files(urls, outdir=None, filenames=None, username=None, password=None, overwrite=False)

Downloads files from the given URLs and saves them to the specified directory. If no directory is provided, the current directory will be used. If no filenames are provided, the names of the files from the URLs will be used.

Parameters:

Name	Type	Description	Default
urls	List[str]	The URLs of the files to download. e.g., ["https://example.com/file1.txt", "https://example.com/file2.txt"]	required
outdir	str	The directory to save the downloaded files to. Defaults to None.	None
filenames	str	The names of the files to save the downloaded content to. Defaults to None.	None
username	str	Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None. Create an account at https://urs.earthdata.nasa.gov	None
password	str	Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.	None
overwrite	bool	Whether to overwrite the existing output files. Default is False.	False

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def gedi_download_files(
    urls: List[str],
    outdir: str = None,
    filenames: str = None,
    username: str = None,
    password: str = None,
    overwrite: bool = False,
) -> None:
    """
    Downloads files from the given URLs and saves them to the specified directory.
    If no directory is provided, the current directory will be used.
    If no filenames are provided, the names of the files from the URLs will be used.

    Args:
        urls (List[str]): The URLs of the files to download.
            e.g., ["https://example.com/file1.txt", "https://example.com/file2.txt"]
        outdir (str, optional): The directory to save the downloaded files to. Defaults to None.
        filenames (str, optional): The names of the files to save the downloaded content to. Defaults to None.
        username (str, optional): Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None.
            Create an account at https://urs.earthdata.nasa.gov
        password (str, optional): Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.
        overwrite (bool): Whether to overwrite the existing output files. Default is False.

    Returns:
        None
    """

    import requests
    from tqdm import tqdm
    from urllib.parse import urlparse
    import geopandas as gpd

    if isinstance(urls, gpd.GeoDataFrame):
        urls = urls["granule_url"].tolist()

    session = requests.Session()

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        print("Username and password must be provided.")
        return

    if outdir is None:
        outdir = os.getcwd()

    if not os.path.exists(outdir):
        os.makedirs(outdir)

    for index, url in enumerate(urls):
        print(f"Downloading file {index+1} of {len(urls)}...")

        if url is None:
            continue

        # Use the filename from the URL if not provided
        if not filenames:
            parsed_url = urlparse(url)
            filename = parsed_url.path.split("/")[-1]
        else:
            filename = filenames.pop(0)

        filepath = os.path.join(outdir, filename)
        if os.path.exists(filepath) and not overwrite:
            print(f"File {filepath} already exists. Skipping...")
            continue

        r1 = session.request("get", url, stream=True)
        r = session.get(r1.url, auth=(username, password), stream=True)

        if r.status_code == 200:
            total_size = int(r.headers.get("content-length", 0))
            block_size = 1024  # 1 KB

            progress_bar = tqdm(total=total_size, unit="B", unit_scale=True)

            with open(filepath, "wb") as file:
                for data in r.iter_content(block_size):
                    progress_bar.update(len(data))
                    file.write(data)

            progress_bar.close()

    session.close()

gedi_search(roi, start_date=None, end_date=None, add_roi=False, return_type='gdf', output=None, sort_filesize=False, **kwargs)

Searches for GEDI data using the Common Metadata Repository (CMR) API. The source code for this function is adapted from https://github.com/ornldaac/gedi_tutorials. Credits to ORNL DAAC and Rupesh Shrestha.

Parameters:

Name	Type	Description	Default
roi		A list, tuple, or file path representing the bounding box coordinates in the format (min_lon, min_lat, max_lon, max_lat), or a GeoDataFrame containing the region of interest geometry.	required
start_date	Optional[str]	The start date of the temporal range to search for data in the format 'YYYY-MM-DD'.	None
end_date	Optional[str]	The end date of the temporal range to search for data in the format 'YYYY-MM-DD'.	None
add_roi	bool	A boolean value indicating whether to include the region of interest as a granule in the search results. Default is False.	False
return_type	str	The type of the search results to return. Must be one of 'df' (DataFrame), 'gdf' (GeoDataFrame), or 'csv' (CSV file). Default is 'gdf'.	'gdf'
output	Optional[str]	The file path to save the CSV output when return_type is 'csv'. Optional and only applicable when return_type is 'csv'.	None
sort_filesize	bool	A boolean value indicating whether to sort the search results.	False
**kwargs		Additional keyword arguments to be passed to the CMR API.	{}

Returns:

Type	Description
Union[DataFrame, None]	The search results as a pandas DataFrame (return_type='df'), geopandas GeoDataFrame
Union[DataFrame, None]	(return_type='gdf'), or a CSV file (return_type='csv').

Raises:

Type	Description
ValueError	If roi is not a list, tuple, or file path.

Source code in leafmap/common.py

def gedi_search(
    roi,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    add_roi: bool = False,
    return_type: str = "gdf",
    output: Optional[str] = None,
    sort_filesize: bool = False,
    **kwargs,
) -> Union[pd.DataFrame, None]:
    """
    Searches for GEDI data using the Common Metadata Repository (CMR) API.
    The source code for this function is adapted from https://github.com/ornldaac/gedi_tutorials.
    Credits to ORNL DAAC and Rupesh Shrestha.

    Args:
        roi: A list, tuple, or file path representing the bounding box coordinates
            in the format (min_lon, min_lat, max_lon, max_lat), or a GeoDataFrame
            containing the region of interest geometry.
        start_date: The start date of the temporal range to search for data
            in the format 'YYYY-MM-DD'.
        end_date: The end date of the temporal range to search for data
            in the format 'YYYY-MM-DD'.
        add_roi: A boolean value indicating whether to include the region of interest
            as a granule in the search results. Default is False.
        return_type: The type of the search results to return. Must be one of 'df'
            (DataFrame), 'gdf' (GeoDataFrame), or 'csv' (CSV file). Default is 'gdf'.
        output: The file path to save the CSV output when return_type is 'csv'.
            Optional and only applicable when return_type is 'csv'.
        sort_filesize: A boolean value indicating whether to sort the search results.
        **kwargs: Additional keyword arguments to be passed to the CMR API.

    Returns:
        The search results as a pandas DataFrame (return_type='df'), geopandas GeoDataFrame
        (return_type='gdf'), or a CSV file (return_type='csv').

    Raises:
        ValueError: If roi is not a list, tuple, or file path.

    """

    import requests
    import datetime as dt
    import pandas as pd
    import geopandas as gpd
    from shapely.geometry import MultiPolygon, Polygon, box
    from shapely.ops import orient

    # CMR API base url
    cmrurl = "https://cmr.earthdata.nasa.gov/search/"

    doi = "10.3334/ORNLDAAC/2056"  # GEDI L4A DOI

    # Construct the DOI search URL
    doisearch = cmrurl + "collections.json?doi=" + doi

    # Send a request to the CMR API to get the concept ID
    response = requests.get(doisearch)
    response.raise_for_status()
    concept_id = response.json()["feed"]["entry"][0]["id"]

    # CMR formatted start and end times
    if start_date is not None and end_date is not None:
        dt_format = "%Y-%m-%dT%H:%M:%SZ"
        start_date = dt.datetime.strptime(start_date, "%Y-%m-%d")
        end_date = dt.datetime.strptime(end_date, "%Y-%m-%d")
        temporal_str = (
            start_date.strftime(dt_format) + "," + end_date.strftime(dt_format)
        )
    else:
        temporal_str = None

    # CMR formatted bounding box
    if isinstance(roi, list) or isinstance(roi, tuple):
        bound_str = ",".join(map(str, roi))
    elif isinstance(roi, str):
        roi = gpd.read_file(roi)
        roi.geometry = roi.geometry.apply(orient, args=(1,))  # make counter-clockwise
    elif isinstance(roi, gpd.GeoDataFrame):
        roi.geometry = roi.geometry.apply(orient, args=(1,))  # make counter-clockwise
    else:
        raise ValueError("roi must be a list, tuple, or a file path.")

    page_num = 1
    page_size = 2000  # CMR page size limit

    granule_arr = []

    while True:
        # Define CMR search parameters
        cmr_param = {
            "collection_concept_id": concept_id,
            "page_size": page_size,
            "page_num": page_num,
        }

        if temporal_str is not None:
            cmr_param["temporal"] = temporal_str

        if kwargs:
            cmr_param.update(kwargs)

        granulesearch = cmrurl + "granules.json"

        if isinstance(roi, list) or isinstance(roi, tuple):
            cmr_param["bounding_box[]"] = bound_str
            response = requests.get(granulesearch, params=cmr_param)
            response.raise_for_status()
        else:
            cmr_param["simplify-shapefile"] = "true"
            geojson = {
                "shapefile": (
                    "region.geojson",
                    roi.geometry.to_json(),
                    "application/geo+json",
                )
            }
            response = requests.post(granulesearch, data=cmr_param, files=geojson)

        # Send a request to the CMR API to get the granules
        granules = response.json()["feed"]["entry"]

        if granules:
            for index, g in enumerate(granules):
                granule_url = ""
                granule_poly = ""

                # Read file size
                granule_size = float(g["granule_size"])

                # Read bounding geometries
                if "polygons" in g:
                    polygons = g["polygons"]
                    multipolygons = []
                    for poly in polygons:
                        i = iter(poly[0].split(" "))
                        ltln = list(map(" ".join, zip(i, i)))
                        multipolygons.append(
                            Polygon(
                                [
                                    [float(p.split(" ")[1]), float(p.split(" ")[0])]
                                    for p in ltln
                                ]
                            )
                        )
                    granule_poly = MultiPolygon(multipolygons)

                # Get URL to HDF5 files
                for links in g["links"]:
                    if (
                        "title" in links
                        and links["title"].startswith("Download")
                        and links["title"].endswith(".h5")
                    ):
                        granule_url = links["href"]

                granule_id = g["id"]
                title = g["title"]
                time_start = g["time_start"]
                time_end = g["time_end"]

                granule_arr.append(
                    [
                        granule_id,
                        title,
                        time_start,
                        time_end,
                        granule_size,
                        granule_url,
                        granule_poly,
                    ]
                )

            page_num += 1
        else:
            break

    # Add bound as the last row into the dataframe
    if add_roi:
        if isinstance(roi, list) or isinstance(roi, tuple):
            b = list(roi)
            granule_arr.append(
                ["roi", None, None, None, 0, None, box(b[0], b[1], b[2], b[3])]
            )
        else:
            granule_arr.append(["roi", None, None, None, 0, None, roi.geometry.item()])

    # Create a pandas dataframe
    columns = [
        "id",
        "title",
        "time_start",
        "time_end",
        "granule_size",
        "granule_url",
        "granule_poly",
    ]
    l4adf = pd.DataFrame(granule_arr, columns=columns)

    # Drop granules with empty geometry
    l4adf = l4adf[l4adf["granule_poly"] != ""]

    if sort_filesize:
        l4adf = l4adf.sort_values(by=["granule_size"], ascending=True)

    if return_type == "df":
        return l4adf
    elif return_type == "gdf":
        gdf = gpd.GeoDataFrame(l4adf, geometry="granule_poly")
        gdf.crs = "EPSG:4326"
        return gdf
    elif return_type == "csv":
        columns.remove("granule_poly")
        return l4adf.to_csv(output, index=False, columns=columns)
    else:
        raise ValueError("return_type must be one of 'df', 'gdf', or 'csv'.")

gedi_subset(spatial=None, start_date=None, end_date=None, out_dir=None, collection=None, variables=['all'], max_results=None, username=None, password=None, overwrite=False, **kwargs)

Subsets GEDI data using the Harmony API.

Parameters:

Name	Type	Description	Default
spatial	Union[str, GeoDataFrame, List[float]]	Spatial extent for subsetting. Can be a file path to a shapefile, a GeoDataFrame, or a list of bounding box coordinates [minx, miny, maxx, maxy]. Defaults to None.	None
start_date	str	Start date for subsetting in 'YYYY-MM-DD' format. Defaults to None.	None
end_date	str	End date for subsetting in 'YYYY-MM-DD' format. Defaults to None.	None
out_dir	str	Output directory to save the subsetted files. Defaults to None, which will use the current working directory.	None
collection	Collection	GEDI data collection. If not provided, the default collection with DOI '10.3334/ORNLDAAC/2056' will be used. Defaults to None.	None
variables	List[str]	List of variable names to subset. Defaults to ['all'], which subsets all available variables.	['all']
max_results	int	Maximum number of results to return. Defaults to None, which returns all results.	None
username	str	Earthdata username. Defaults to None, which will attempt to read from the 'EARTHDATA_USERNAME' environment variable.	None
password	str	Earthdata password. Defaults to None, which will attempt to read from the 'EARTHDATA_PASSWORD' environment variable.	None
overwrite	bool	Whether to overwrite existing files in the output directory. Defaults to False.	False
**kwargs		Additional keyword arguments to pass to the Harmony API request.	{}

Raises:

Type	Description
ImportError	If the 'harmony' package is not installed.
ValueError	If the 'spatial', 'start_date', or 'end_date' arguments are not valid.

Returns:

Name	Type	Description
None		This function does not return any value.

Source code in leafmap/common.py

def gedi_subset(
    spatial=None,
    start_date=None,
    end_date=None,
    out_dir=None,
    collection=None,
    variables=["all"],
    max_results=None,
    username=None,
    password=None,
    overwrite=False,
    **kwargs,
):
    """
    Subsets GEDI data using the Harmony API.

    Args:
        spatial (Union[str, gpd.GeoDataFrame, List[float]], optional): Spatial extent for subsetting.
            Can be a file path to a shapefile, a GeoDataFrame, or a list of bounding box coordinates [minx, miny, maxx, maxy].
            Defaults to None.
        start_date (str, optional): Start date for subsetting in 'YYYY-MM-DD' format.
            Defaults to None.
        end_date (str, optional): End date for subsetting in 'YYYY-MM-DD' format.
            Defaults to None.
        out_dir (str, optional): Output directory to save the subsetted files.
            Defaults to None, which will use the current working directory.
        collection (Collection, optional): GEDI data collection. If not provided,
            the default collection with DOI '10.3334/ORNLDAAC/2056' will be used.
            Defaults to None.
        variables (List[str], optional): List of variable names to subset.
            Defaults to ['all'], which subsets all available variables.
        max_results (int, optional): Maximum number of results to return.
            Defaults to None, which returns all results.
        username (str, optional): Earthdata username.
            Defaults to None, which will attempt to read from the 'EARTHDATA_USERNAME' environment variable.
        password (str, optional): Earthdata password.
            Defaults to None, which will attempt to read from the 'EARTHDATA_PASSWORD' environment variable.
        overwrite (bool, optional): Whether to overwrite existing files in the output directory.
            Defaults to False.
        **kwargs: Additional keyword arguments to pass to the Harmony API request.

    Raises:
        ImportError: If the 'harmony' package is not installed.
        ValueError: If the 'spatial', 'start_date', or 'end_date' arguments are not valid.

    Returns:
        None: This function does not return any value.
    """

    try:
        import harmony  # pylint: disable=E0401
    except ImportError:
        install_package("harmony-py")

    import requests as re
    import geopandas as gpd
    from datetime import datetime
    from harmony import (
        BBox,
        Client,
        Collection,
        Environment,
        Request,
    )  # pylint: disable=E0401

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if collection is None:
        # GEDI L4A DOI
        doi = "10.3334/ORNLDAAC/2056"

        # CMR API base url
        doisearch = f"https://cmr.earthdata.nasa.gov/search/collections.json?doi={doi}"
        concept_id = re.get(doisearch).json()["feed"]["entry"][0]["id"]
        concept_id
        collection = Collection(id=concept_id)

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        raise ValueError("username and password must be provided.")

    harmony_client = Client(auth=(username, password))

    if isinstance(spatial, str):
        spatial = gpd.read_file(spatial)

    if isinstance(spatial, gpd.GeoDataFrame):
        spatial = spatial.total_bounds.tolist()

    if isinstance(spatial, list) and len(spatial) == 4:
        bounding_box = BBox(spatial[0], spatial[1], spatial[2], spatial[3])
    else:
        raise ValueError(
            "spatial must be a list of bounding box coordinates or a GeoDataFrame, or a file path."
        )

    if isinstance(start_date, str):
        start_date = datetime.strptime(start_date, "%Y-%m-%d")

    if isinstance(end_date, str):
        end_date = datetime.strptime(end_date, "%Y-%m-%d")

    if start_date is None or end_date is None:
        print("start_date and end_date must be provided.")
        temporal_range = None
    else:
        temporal_range = {"start": start_date, "end": end_date}

    request = Request(
        collection=collection,
        variables=variables,
        temporal=temporal_range,
        spatial=bounding_box,
        ignore_errors=True,
        max_results=max_results,
        **kwargs,
    )

    # submit harmony request, will return job id
    subset_job_id = harmony_client.submit(request)

    print(f"Processing job: {subset_job_id}")

    print(f"Waiting for the job to finish")
    results = harmony_client.result_json(subset_job_id, show_progress=True)

    print(f"Downloading subset files...")
    futures = harmony_client.download_all(
        subset_job_id, directory=out_dir, overwrite=overwrite
    )
    for f in futures:
        # all subsetted files have this suffix
        if f.result().endswith("subsetted.h5"):
            print(f"Downloaded: {f.result()}")

    print(f"Done downloading files.")

generate_index_html(directory, output='index.html')

Generates an HTML file named 'index.html' in the specified directory, listing all files in that directory as clickable links.

Parameters:

Name	Type	Description	Default
directory	str	The path to the directory for which to generate the index.html file.	required
output	str	The name of the output HTML file. Defaults to "index.html".	'index.html'

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def generate_index_html(directory: str, output: str = "index.html") -> None:
    """
    Generates an HTML file named 'index.html' in the specified directory, listing
    all files in that directory as clickable links.

    Args:
        directory (str): The path to the directory for which to generate the index.html file.
        output (str, optional): The name of the output HTML file. Defaults to "index.html".

    Returns:
        None
    """
    # Get a list of files in the directory
    files = sorted(
        [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
    )

    # Start the HTML content
    html_content = """<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Index of {directory}</title>
</head>
<body>
    <h1>Index of {directory}</h1>
    <ul>
""".format(
        directory=directory
    )

    # Add each file to the HTML list
    for file in files:
        html_content += '        <li><a href="{file}">{file}</a></li>\n'.format(
            file=file
        )

    # Close the HTML content
    html_content += """    </ul>
</body>
</html>"""

    # Write the HTML content to index.html in the specified directory
    with open(os.path.join(directory, output), "w") as f:
        f.write(html_content)

geojson_bounds(geojson)

Calculate the bounds of a GeoJSON object.

This function uses the shapely library to calculate the bounds of a GeoJSON object. If the shapely library is not installed, it will print a message and return None.

Parameters:

Name	Type	Description	Default
geojson	dict	A dictionary representing a GeoJSON object.	required

Returns:

Name	Type	Description
list	Optional[list]	A list of bounds (minx, miny, maxx, maxy) if shapely is installed, None otherwise.

Source code in leafmap/common.py

def geojson_bounds(geojson: dict) -> Optional[list]:
    """
    Calculate the bounds of a GeoJSON object.

    This function uses the shapely library to calculate the bounds of a GeoJSON object.
    If the shapely library is not installed, it will print a message and return None.

    Args:
        geojson (dict): A dictionary representing a GeoJSON object.

    Returns:
        list: A list of bounds (minx, miny, maxx, maxy) if shapely is installed, None otherwise.
    """
    try:
        import shapely
    except ImportError:
        print("shapely is not installed")
        return

    if isinstance(geojson, str):
        geojson = json.loads(geojson)

    return list(shapely.bounds(shapely.from_geojson(json.dumps(geojson))))

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

Raises:

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 leafmap/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 json
    import pandas as pd
    from urllib.request import urlopen

    if isinstance(in_geojson, str):
        if in_geojson.startswith("http"):
            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_gdf(in_geojson, encoding='utf-8', **kwargs)

Converts a GeoJSON object to a geopandas GeoDataFrame.

Parameters:

Name	Type	Description	Default
in_geojson	str | dict	The input GeoJSON file or GeoJSON object as a dict.	required
encoding	str	The encoding of the GeoJSON object. Defaults to "utf-8".	'utf-8'

Returns:

Type	Description
	geopandas.GeoDataFrame: A geopandas GeoDataFrame containing the GeoJSON object.

Source code in leafmap/common.py

def geojson_to_gdf(in_geojson, encoding="utf-8", **kwargs):
    """Converts a GeoJSON object to a geopandas GeoDataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or GeoJSON object as a dict.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".

    Returns:
        geopandas.GeoDataFrame: A geopandas GeoDataFrame containing the GeoJSON object.
    """

    import geopandas as gpd

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, encoding=encoding, **kwargs)
    return gdf

geojson_to_gpkg(in_geojson, out_gpkg, **kwargs)

Converts a GeoJSON object to GeoPackage.

Parameters:

Name	Type	Description	Default
in_geojson	str | dict	The input GeoJSON file or dict.	required
out_gpkg	str	The output GeoPackage path.	required

Source code in leafmap/common.py

def geojson_to_gpkg(in_geojson, out_gpkg, **kwargs):
    """Converts a GeoJSON object to GeoPackage.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        out_gpkg (str): The output GeoPackage path.
    """
    import geopandas as gpd
    import json

    ext = os.path.splitext(out_gpkg)[1]
    if ext.lower() != ".gpkg":
        out_gpkg = out_gpkg + ".gpkg"
    out_gpkg = check_file_path(out_gpkg)

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, **kwargs)
    name = os.path.splitext(os.path.basename(out_gpkg))[0]
    gdf.to_file(out_gpkg, layer=name, driver="GPKG")

geojson_to_mbtiles(input_file, output_file, layer_name=None, options=None, quiet=False)

Converts vector data to .mbtiles using Tippecanoe.

Parameters:

Name	Type	Description	Default
input_file	str	Path to the input vector data file (e.g., .geojson).	required
output_file	str	Path to the output .mbtiles file.	required
layer_name	Optional[str]	Optional name for the layer. Defaults to None.	None
options	Optional[List[str]]	List of additional arguments for tippecanoe. For example '-zg' for auto maxzoom. Defaults to None.	None
quiet	bool	If True, suppress the log output. Defaults to False.	False

Returns:

Type	Description
Optional[str]	Optional[str]: Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

Raises:

Type	Description
CalledProcessError	If there's an error executing the tippecanoe command.

Source code in leafmap/common.py

def geojson_to_mbtiles(
    input_file: str,
    output_file: str,
    layer_name: Optional[str] = None,
    options: Optional[List[str]] = None,
    quiet: bool = False,
) -> Optional[str]:
    """
    Converts vector data to .mbtiles using Tippecanoe.

    Args:
        input_file (str): Path to the input vector data file (e.g., .geojson).
        output_file (str): Path to the output .mbtiles file.
        layer_name (Optional[str]): Optional name for the layer. Defaults to None.
        options (Optional[List[str]]): List of additional arguments for tippecanoe. For example '-zg' for auto maxzoom. Defaults to None.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        Optional[str]: Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

    Raises:
        subprocess.CalledProcessError: If there's an error executing the tippecanoe command.
    """

    import subprocess
    import shutil

    # Check if tippecanoe exists
    if shutil.which("tippecanoe") is None:
        print("Error: tippecanoe is not installed.")
        print("You can install it using conda with the following command:")
        print("conda install -c conda-forge tippecanoe")
        return None

    command = ["tippecanoe", "-o", output_file]

    # Add layer name specification if provided
    if layer_name:
        command.extend(["-L", f"{layer_name}:{input_file}"])
    else:
        command.append(input_file)

    # Append additional arguments if provided
    if options:
        command.extend(options)

    try:
        process = subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
        )

        if not quiet:
            for line in process.stdout:
                print(line, end="")

        exit_code = process.wait()
        if exit_code != 0:
            raise subprocess.CalledProcessError(exit_code, command)

    except subprocess.CalledProcessError as e:
        print(f"\nError executing tippecanoe: {e}")
        return None

    return "Tippecanoe process completed successfully."

geojson_to_pmtiles(input_file, output_file=None, layer_name=None, projection='EPSG:4326', overwrite=False, options=None, quiet=False)

Converts vector data to PMTiles using Tippecanoe.

Parameters:

Name	Type	Description	Default
input_file	str	Path to the input vector data file (e.g., .geojson).	required
output_file	str	Path to the output .mbtiles file.	None
layer_name	Optional[str]	Optional name for the layer. Defaults to None.	None
projection	Optional[str]	Projection for the output PMTiles file. Defaults to "EPSG:4326".	'EPSG:4326'
overwrite	bool	If True, overwrite the existing output file. Defaults to False.	False
options	Optional[List[str]]	List of additional arguments for tippecanoe. Defaults to None. To reduce the size of the output file, use '-zg' or '-z max-zoom'.	None
quiet	bool	If True, suppress the log output. Defaults to False.	False

Returns:

Type	Description
Optional[str]	Optional[str]: Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

Raises:

Type	Description
CalledProcessError	If there's an error executing the tippecanoe command.

Source code in leafmap/common.py

def geojson_to_pmtiles(
    input_file: str,
    output_file: Optional[str] = None,
    layer_name: Optional[str] = None,
    projection: Optional[str] = "EPSG:4326",
    overwrite: bool = False,
    options: Optional[List[str]] = None,
    quiet: bool = False,
) -> Optional[str]:
    """
    Converts vector data to PMTiles using Tippecanoe.

    Args:
        input_file (str): Path to the input vector data file (e.g., .geojson).
        output_file (str): Path to the output .mbtiles file.
        layer_name (Optional[str]): Optional name for the layer. Defaults to None.
        projection (Optional[str]): Projection for the output PMTiles file. Defaults to "EPSG:4326".
        overwrite (bool): If True, overwrite the existing output file. Defaults to False.
        options (Optional[List[str]]): List of additional arguments for tippecanoe. Defaults to None.
            To reduce the size of the output file, use '-zg' or '-z max-zoom'.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        Optional[str]: Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

    Raises:
        subprocess.CalledProcessError: If there's an error executing the tippecanoe command.
    """

    import subprocess
    import shutil

    # Check if tippecanoe exists
    if shutil.which("tippecanoe") is None:
        print("Error: tippecanoe is not installed.")
        print("You can install it using conda with the following command:")
        print("conda install -c conda-forge tippecanoe")
        return None

    if output_file is None:
        output_file = os.path.splitext(input_file)[0] + ".pmtiles"

    if not output_file.endswith(".pmtiles"):
        raise ValueError("Error: output file must be a .pmtiles file.")

    command = ["tippecanoe", "-o", output_file]

    # Add layer name specification if provided
    if layer_name:
        command.extend(["-L", f"{layer_name}:{input_file}"])
    else:
        command.append(input_file)

    command.extend(["--projection", projection])

    if options is None:
        options = []

    if overwrite:
        command.append("--force")

    # Append additional arguments if provided
    if options:
        command.extend(options)

    try:
        process = subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
        )

        if not quiet:
            for line in process.stdout:
                print(line, end="")

        exit_code = process.wait()
        if exit_code != 0:
            raise subprocess.CalledProcessError(exit_code, command)

    except subprocess.CalledProcessError as e:
        print(f"\nError executing tippecanoe: {e}")
        return None

    return "Tippecanoe process completed successfully."

geojson_to_shp(in_geojson, out_shp, **kwargs)

Converts a GeoJSON object to GeoPandas GeoDataFrame.

Parameters:

Name	Type	Description	Default
in_geojson	str | dict	The input GeoJSON file or dict.	required
out_shp	str	The output shapefile path.	required

Source code in leafmap/common.py

def geojson_to_shp(in_geojson, out_shp, **kwargs):
    """Converts a GeoJSON object to GeoPandas GeoDataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        out_shp (str): The output shapefile path.
    """
    import geopandas as gpd
    import json

    ext = os.path.splitext(out_shp)[1]
    if ext != ".shp":
        out_shp = out_shp + ".shp"
    out_shp = check_file_path(out_shp)

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, **kwargs)
    gdf.to_file(out_shp)

geojsonl_to_parquet_batch(input_dir, output_dir, batch_size=50, file_ext='.json', filename_predix='batch_', **kwargs)

Convert JSON Lines files to multiple GeoParquet files, with each GeoParquet file containing data from a specified number of JSON Lines files.

Parameters:

Name	Type	Description	Default
input_dir	str	Directory containing JSON Lines files to convert	required
output_dir	str	Directory for output GeoParquet files	required
batch_size	int	Number of JSON Lines files to combine in each GeoParquet file. Defaults to 50.	50
file_ext	str	File extension of the input files. Defaults to ".json".	'.json'
filename_predix	str	Prefix for the output GeoParquet files. Defaults to "batch_".	'batch_'
**kwargs		Additional keyword arguments to pass to the to_parquet function of GeoDataFrame.	{}

Source code in leafmap/common.py

def geojsonl_to_parquet_batch(
    input_dir,
    output_dir,
    batch_size=50,
    file_ext=".json",
    filename_predix="batch_",
    **kwargs,
):
    """
    Convert JSON Lines files to multiple GeoParquet files, with each GeoParquet file
    containing data from a specified number of JSON Lines files.

    Args:
        input_dir (str): Directory containing JSON Lines files to convert
        output_dir (str): Directory for output GeoParquet files
        batch_size (int, optional): Number of JSON Lines files to combine in each GeoParquet file.
                                    Defaults to 50.
        file_ext (str, optional): File extension of the input files. Defaults to ".json".
        filename_predix (str, optional): Prefix for the output GeoParquet files. Defaults to "batch_".
        **kwargs: Additional keyword arguments to pass to the `to_parquet` function of GeoDataFrame.

    """
    import geopandas as gpd
    from shapely.geometry import shape
    import glob
    import math

    if not os.path.exists(input_dir):
        raise FileNotFoundError(f"Input directory not found: {input_dir}")

    # Get all JSON files
    json_files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"))

    if not json_files:
        raise ValueError(f"No JSON files found in {input_dir}")

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Calculate number of output files
    num_files = len(json_files)
    num_batches = math.ceil(num_files / batch_size)

    print(
        f"Processing {num_files} JSON Lines files into {num_batches} GeoParquet files"
    )

    # Track statistics
    processed_files = 0
    processed_records = 0
    failed_files = 0
    successful_parquets = 0

    # Process files in batches
    for batch_num in range(num_batches):
        print(f"\nProcessing batch {batch_num+1}/{num_batches}")

        # Generate output filename
        output_file = os.path.join(
            output_dir, f"{filename_predix}{batch_num+1:04d}.parquet"
        )
        if os.path.exists(output_file):
            print(f"Output file already exists: {output_file}")
            continue

        start_idx = batch_num * batch_size
        end_idx = min(start_idx + batch_size, num_files)
        batch_files = json_files[start_idx:end_idx]

        records = []

        for file_path in batch_files:
            try:
                file_records = 0

                # Process the file line by line (JSON Lines format)
                with open(file_path, "r", encoding="utf-8") as f:
                    for line in f:
                        if line.strip():  # Skip empty lines
                            try:
                                data = json.loads(line)

                                # Create a record with properties and geometry
                                record = {}

                                # Extract all properties except geometry
                                for key, value in data.items():
                                    if key != "geometry":
                                        record[key] = value

                                # Handle geometry
                                if "geometry" in data:
                                    # Convert the geometry to a Shapely object
                                    record["geometry"] = shape(data["geometry"])
                                else:
                                    continue  # Skip records without geometry

                                records.append(record)
                                file_records += 1
                            except json.JSONDecodeError as e:
                                print(
                                    f"Error decoding JSON in file {file_path}: {str(e)}"
                                )
                                continue  # Skip invalid JSON lines
                            except Exception as e:
                                print(
                                    f"Error processing record in file {file_path}: {str(e)}"
                                )
                                continue  # Skip problematic records

                # print(f"Processed {file_records} records from {file_path}")
                processed_records += file_records
                processed_files += 1

            except Exception as e:
                print(f"Error processing file {file_path}: {str(e)}")
                failed_files += 1

        if not records:
            print(f"No valid records found in batch {batch_num+1}")
            continue

        # Create a GeoDataFrame with the correct CRS
        gdf = gpd.GeoDataFrame(records, geometry="geometry", crs="EPSG:4326")

        # Write to GeoParquet
        gdf.to_parquet(output_file, index=False, **kwargs)
        successful_parquets += 1

        # Print summary for this batch
        print(f"Created GeoParquet file {batch_num+1}/{num_batches}: {output_file}")
        print(f"  - Number of features: {len(gdf)}")
        print(f"  - Columns: {list(gdf.columns)}")
        if len(gdf) > 0:
            print(
                f"  - First record attributes: {[k for k in gdf.iloc[0].keys() if k != 'geometry']}"
            )

    # Print final summary
    print(f"\nSummary:")
    print(f"Total files processed: {processed_files} of {num_files}")
    print(f"Failed files: {failed_files}")
    print(f"Total records processed: {processed_records}")
    print(f"GeoParquet files created: {successful_parquets}")

geom_type(in_geojson, encoding='utf-8')

Returns the geometry type of a GeoJSON object.

Parameters:

Name	Type	Description	Default
in_geojson	dict	A GeoJSON object.	required
encoding	str	The encoding of the GeoJSON object. Defaults to "utf-8".	'utf-8'

Returns:

Name	Type	Description
str		The geometry type of the GeoJSON object, such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon. For more info, see https://shapely.readthedocs.io/en/stable/manual.html

Source code in leafmap/common.py

def geom_type(in_geojson, encoding="utf-8"):
    """Returns the geometry type of a GeoJSON object.

    Args:
        in_geojson (dict): A GeoJSON object.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".

    Returns:
        str: The geometry type of the GeoJSON object, such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import json

    try:
        if isinstance(in_geojson, str):
            if in_geojson.startswith("http"):
                data = requests.get(in_geojson).json()
            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
        else:
            raise TypeError("The input geojson must be a type of str or dict.")

        return data["features"][0]["geometry"]["type"]

    except Exception as e:
        raise Exception(e)

geometry_bounds(geometry, decimals=4)

Returns the bounds of a geometry.

Parameters:

Name	Type	Description	Default
geometry	dict	A GeoJSON geometry.	required
decimals	int	The number of decimal places to round the bounds to. Defaults to 4.	4

Returns:

Name	Type	Description
list		A list of bounds in the form of [minx, miny, maxx, maxy].

Source code in leafmap/common.py

def geometry_bounds(geometry, decimals=4):
    """Returns the bounds of a geometry.

    Args:
        geometry (dict): A GeoJSON geometry.
        decimals (int, optional): The number of decimal places to round the bounds to. Defaults to 4.

    Returns:
        list: A list of bounds in the form of [minx, miny, maxx, maxy].
    """
    if isinstance(geometry, dict):
        if "geometry" in geometry:
            coords = geometry["geometry"]["coordinates"][0]
        else:
            coords = geometry["coordinates"][0]

    else:
        raise ValueError("geometry must be a GeoJSON-like dictionary.")

    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]

get_3dep_dem(geometry, resolution=30, src_crs=None, output=None, dst_crs='EPSG:5070', to_cog=False, overwrite=False, **kwargs)

Get DEM data at any resolution from 3DEP.

Parameters:

Name	Type	Description	Default
geometry	Polygon | MultiPolygon | tuple	It can be a polygon or a bounding box of form (xmin, ymin, xmax, ymax).	required
resolution	int	arget DEM source resolution in meters. Defaults to 30.	30
src_crs	str	The spatial reference system of the input geometry. Defaults to "EPSG:4326".	None
output	str	The output GeoTIFF file. Defaults to None.	None
dst_crs	str	The spatial reference system of the output GeoTIFF file. Defaults to "EPSG:5070".	'EPSG:5070'
to_cog	bool	Convert to Cloud Optimized GeoTIFF. Defaults to False.	False
overwrite	bool	Whether to overwrite the output file if it exists. Defaults to False.	False

Returns:

Type	Description
	xarray.DataArray: DEM at the specified resolution in meters and CRS.

Source code in leafmap/common.py

def get_3dep_dem(
    geometry,
    resolution=30,
    src_crs=None,
    output=None,
    dst_crs="EPSG:5070",
    to_cog=False,
    overwrite=False,
    **kwargs,
):
    """Get DEM data at any resolution from 3DEP.

    Args:
        geometry (Polygon | MultiPolygon | tuple): It can be a polygon or a bounding
            box of form (xmin, ymin, xmax, ymax).
        resolution (int): arget DEM source resolution in meters. Defaults to 30.
        src_crs (str, optional): The spatial reference system of the input geometry. Defaults to "EPSG:4326".
        output (str, optional): The output GeoTIFF file. Defaults to None.
        dst_crs (str, optional): The spatial reference system of the output GeoTIFF file. Defaults to "EPSG:5070".
        to_cog (bool, optional): Convert to Cloud Optimized GeoTIFF. Defaults to False.
        overwrite (bool, optional): Whether to overwrite the output file if it exists. Defaults to False.

    Returns:
        xarray.DataArray: DEM at the specified resolution in meters and CRS.
    """

    try:
        import py3dep
    except ImportError:
        print("py3dep is not installed. Installing py3dep...")
        install_package("py3dep")
        import py3dep

    import geopandas as gpd

    if output is not None and os.path.exists(output) and not overwrite:
        print(f"File {output} already exists. Set overwrite=True to overwrite it")
        return

    if isinstance(geometry, gpd.GeoDataFrame):
        if src_crs is None:
            src_crs = geometry.crs
        geometry = geometry.geometry.unary_union

    if src_crs is None:
        src_crs = "EPSG:4326"

    dem = py3dep.get_dem(geometry, resolution=resolution, crs=src_crs)
    dem = dem.rio.reproject(dst_crs)

    if output is not None:
        if not output.endswith(".tif"):
            output += ".tif"
        dem.rio.to_raster(output, **kwargs)

        if to_cog:
            try:
                image_to_cog(output, output)
            except Exception as e:
                print(e)

    return dem

get_api_key(name=None, key=None)

Retrieves an API key. If a key is provided, it is returned directly. If a name is provided, the function attempts to retrieve the key from user data (if running in Google Colab) or from environment variables.

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name of the key to retrieve. Defaults to None.	None
key	Optional[str]	The key to return directly. Defaults to None.	None

Returns:

Type	Description
Optional[str]	Optional[str]: The retrieved key, or None if no key was found.

Source code in leafmap/common.py

def get_api_key(name: Optional[str] = None, key: Optional[str] = None) -> Optional[str]:
    """
    Retrieves an API key. If a key is provided, it is returned directly. If a
    name is provided, the function attempts to retrieve the key from user data
    (if running in Google Colab) or from environment variables.

    Args:
        name (Optional[str], optional): The name of the key to retrieve. Defaults to None.
        key (Optional[str], optional): The key to return directly. Defaults to None.

    Returns:
        Optional[str]: The retrieved key, or None if no key was found.
    """
    if key is not None:
        return key
    if name is not None:
        try:
            if _in_colab_shell():
                from google.colab import userdata  # pylint: disable=E0611

                return userdata.get(name)
        except Exception:
            pass
        return os.environ.get(name)
    return None

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:

Name	Type	Description
list		A list of coordinates representing [left, bottom, right, top]

Source code in leafmap/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:

Name	Type	Description
dict		A dictionary of Census data.

Source code in leafmap/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 json
    import importlib.resources

    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.py")
    census_data = os.path.join(pkg_dir, "data/census_data.json")

    if reset:
        try:
            from owslib.wms import WebMapService
        except ImportError:
            raise ImportError("Please install owslib using '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:

Name	Type	Description
list		[lon, lat]

Source code in leafmap/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_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:

Name	Type	Description
str		The direct URL.

Source code in leafmap/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_gdal_drivers()

Get a list of available driver names in the GDAL library.

Returns:

Type	Description
List[str]	List[str]: A list of available driver names.

Source code in leafmap/common.py

def get_gdal_drivers() -> List[str]:
    """Get a list of available driver names in the GDAL library.

    Returns:
        List[str]: A list of available driver names.
    """
    from osgeo import ogr

    driver_list = []

    # Iterate over all registered drivers
    for i in range(ogr.GetDriverCount()):
        driver = ogr.GetDriver(i)
        driver_name = driver.GetName()
        driver_list.append(driver_name)

    return driver_list

get_gdal_file_extension(driver_name)

Get the file extension corresponding to a driver name in the GDAL library.

Parameters:

Name	Type	Description	Default
driver_name	str	The name of the driver.	required

Returns:

Type	Description
Optional[str]	Optional[str]: The file extension corresponding to the driver name, or None if the driver is not found or does not have a specific file extension.

Source code in leafmap/common.py

def get_gdal_file_extension(driver_name: str) -> Optional[str]:
    """Get the file extension corresponding to a driver name in the GDAL library.

    Args:
        driver_name (str): The name of the driver.

    Returns:
        Optional[str]: The file extension corresponding to the driver name, or None if the driver is not found or does not have a specific file extension.
    """
    from osgeo import ogr

    driver = ogr.GetDriverByName(driver_name)
    if driver is None:
        drivers = get_gdal_drivers()
        raise ValueError(
            f"Driver {driver_name} not found. Available drivers: {drivers}"
        )

    metadata = driver.GetMetadata()
    if "DMD_EXTENSION" in metadata:
        file_extension = driver.GetMetadataItem("DMD_EXTENSION")
    else:
        file_extensions = driver.GetMetadataItem("DMD_EXTENSIONS")
        if file_extensions == "json geojson":
            file_extension = "geojson"
        else:
            file_extension = file_extensions.split()[0].lower()

    return file_extension

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 leafmap/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_geometry_type(in_geojson)

Get the geometry type of a GeoJSON file.

Parameters:

Name	Type	Description	Default
in_geojson	str | dict	The path to the GeoJSON file or a GeoJSON dictionary.	required

Returns:

Name	Type	Description
str	str	The geometry type. Can be one of "Point", "LineString", "Polygon", "MultiPoint", "MultiLineString", "MultiPolygon", "GeometryCollection", or "Unknown".

Source code in leafmap/common.py

def get_geometry_type(in_geojson: Union[str, Dict]) -> str:
    """Get the geometry type of a GeoJSON file.

    Args:
        in_geojson (str | dict): The path to the GeoJSON file or a GeoJSON dictionary.

    Returns:
        str: The geometry type. Can be one of "Point", "LineString", "Polygon", "MultiPoint",
            "MultiLineString", "MultiPolygon", "GeometryCollection", or "Unknown".
    """

    import geojson

    try:
        if isinstance(in_geojson, str):  # If input is a file path
            with open(in_geojson, "r") as geojson_file:
                geojson_data = geojson.load(geojson_file)
        elif isinstance(in_geojson, dict):  # If input is a GeoJSON dictionary
            geojson_data = in_geojson
        else:
            return "Invalid input type. Expected file path or dictionary."

        if "type" in geojson_data:
            if geojson_data["type"] == "FeatureCollection":
                features = geojson_data.get("features", [])
                if features:
                    first_feature = features[0]
                    geometry = first_feature.get("geometry")
                    if geometry and "type" in geometry:
                        return geometry["type"]
                    else:
                        return "No geometry type found in the first feature."
                else:
                    return "No features found in the FeatureCollection."
            elif geojson_data["type"] == "Feature":
                geometry = geojson_data.get("geometry")
                if geometry and "type" in geometry:
                    return geometry["type"]
                else:
                    return "No geometry type found in the Feature."
            else:
                return "Unsupported GeoJSON type."
        else:
            return "No 'type' field found in the GeoJSON data."
    except Exception as e:
        raise e

get_google_map(map_type='HYBRID', show=True, api_key=None, backend='ipyleaflet', **kwargs)

Gets Google basemap tile layer.

Parameters:

Name	Type	Description	Default
map_type	str	Can be one of "ROADMAP", "SATELLITE", "HYBRID" or "TERRAIN". Defaults to 'HYBRID'.	'HYBRID'
show	bool	Whether to add the layer to the map. Defaults to True.	True
api_key	str	The Google Maps API key. Defaults to None.	None
**kwargs		Additional arguments to pass to ipyleaflet.TileLayer().	{}

Source code in leafmap/common.py

def get_google_map(
    map_type="HYBRID", show=True, api_key=None, backend="ipyleaflet", **kwargs
):
    """Gets Google basemap tile layer.

    Args:
        map_type (str, optional): Can be one of "ROADMAP", "SATELLITE", "HYBRID" or "TERRAIN". Defaults to 'HYBRID'.
        show (bool, optional): Whether to add the layer to the map. Defaults to True.
        api_key (str, optional): The Google Maps API key. Defaults to None.
        **kwargs: Additional arguments to pass to ipyleaflet.TileLayer().
    """

    allow_types = ["ROADMAP", "SATELLITE", "HYBRID", "TERRAIN"]
    if map_type not in allow_types:
        print("map_type must be one of the following: {}".format(allow_types))
        return

    if api_key is None:
        api_key = os.environ.get("GOOGLE_MAPS_API_KEY", "YOUR-API-KEY")

    if api_key == "":
        MAP_TILES = {
            "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",
            },
        }

        print(
            "Google Maps API key is required to use Google Maps. You can generate one from https://bit.ly/3sw0THG and use geemap.set_api_key(), defaulting to Esri basemaps."
        )

    else:
        MAP_TILES = {
            "ROADMAP": {
                "url": f"https://mt1.google.com/vt/lyrs=m&x={{x}}&y={{y}}&z={{z}}&key={api_key}",
                "attribution": "Google",
                "name": "Google Maps",
            },
            "SATELLITE": {
                "url": f"https://mt1.google.com/vt/lyrs=s&x={{x}}&y={{y}}&z={{z}}&key={api_key}",
                "attribution": "Google",
                "name": "Google Satellite",
            },
            "TERRAIN": {
                "url": f"https://mt1.google.com/vt/lyrs=p&x={{x}}&y={{y}}&z={{z}}&key={api_key}",
                "attribution": "Google",
                "name": "Google Terrain",
            },
            "HYBRID": {
                "url": f"https://mt1.google.com/vt/lyrs=y&x={{x}}&y={{y}}&z={{z}}&key={api_key}",
                "attribution": "Google",
                "name": "Google Hybrid",
            },
        }

    if "max_zoom" not in kwargs:
        kwargs["max_zoom"] = 24

    if backend == "ipyleaflet":
        import ipyleaflet

        layer = ipyleaflet.TileLayer(
            url=MAP_TILES[map_type]["url"],
            name=MAP_TILES[map_type]["name"],
            attribution=MAP_TILES[map_type]["attribution"],
            visible=show,
            **kwargs,
        )
    elif backend == "folium":
        import folium

        layer = folium.TileLayer(
            tiles=MAP_TILES[map_type]["url"],
            name=MAP_TILES[map_type]["name"],
            attr=MAP_TILES[map_type]["attribution"],
            overlay=True,
            control=True,
            show=show,
            **kwargs,
        )
    else:
        raise ValueError("backend must be either 'ipyleaflet' or 'folium'")

    return layer

get_image_colormap(image, index=1)

Retrieve the colormap from an image.

Parameters:

Name	Type	Description	Default
image	(str, DatasetReader, DataArray)	The input image. It can be: - A file path to a raster image (string). - A rasterio dataset. - A rioxarray DataArray.	required
index	int	The band index to retrieve the colormap from (default is 1).	1

Returns:

Name	Type	Description
dict		A dictionary representing the colormap (value: (R, G, B, A)), or None if no colormap is found.

Raises:

Type	Description
ValueError	If the input image type is unsupported.

Source code in leafmap/common.py

def get_image_colormap(image, index=1):
    """
    Retrieve the colormap from an image.

    Args:
        image (str, rasterio.io.DatasetReader, rioxarray.DataArray):
            The input image. It can be:
            - A file path to a raster image (string).
            - A rasterio dataset.
            - A rioxarray DataArray.
        index (int): The band index to retrieve the colormap from (default is 1).

    Returns:
        dict: A dictionary representing the colormap (value: (R, G, B, A)), or None if no colormap is found.

    Raises:
        ValueError: If the input image type is unsupported.
    """
    import rasterio
    import rioxarray
    import xarray as xr

    dataset = None

    if isinstance(image, str):  # File path
        with rasterio.open(image) as ds:
            return ds.colormap(index) if ds.count > 0 else None
    elif isinstance(image, rasterio.io.DatasetReader):  # rasterio dataset
        dataset = image
    elif isinstance(image, xr.DataArray) or isinstance(image, xr.Dataset):
        source = image.encoding.get("source")
        if source:
            with rasterio.open(source) as ds:
                return ds.colormap(index) if ds.count > 0 else None
        else:
            raise ValueError(
                "Cannot extract colormap: DataArray does not have a source."
            )
    else:
        raise ValueError(
            "Unsupported input type. Provide a file path, rasterio dataset, or rioxarray DataArray."
        )

    if dataset:
        return dataset.colormap(index) if dataset.count > 0 else None

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', client_args={'cors_all': False}, 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}'

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 matplotlib to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.	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'
client_args	dict	Additional arguments to pass to the TileClient. Defaults to {}.	{'cors_all': False}
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 leafmap/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",
    client_args={"cors_all": False},
    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.
        client_args (dict, optional): Additional arguments to pass to the TileClient. Defaults to {}.
        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 nodata is None:
        nodata = get_api_key("NODATA")
        if isinstance(nodata, str):
            nodata = float(nodata)

    if isinstance(colormap, str):
        colormap = colormap.lower()

    if quiet:
        output = widgets.Output()
        with output:
            if tile_format == "ipyleaflet":
                tile_layer = get_leaflet_tile_layer(
                    source,
                    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(
                    source,
                    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(
                source,
                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(
                source,
                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_layer.tile_server
    else:
        return tile_layer

get_mapillary_image_url(image_id, resolution='original', access_token=None, **kwargs)

Retrieves the URL of a Mapillary image.

Parameters:

Name	Type	Description	Default
image_id	str	The ID of the Mapillary image.	required
resolution	str	The resolution of the image. Can be 256, 1024, 2048, or original. Defaults to "original".	'original'
access_token	str	The access token for the Mapillary API. Defaults to None.	None
**kwargs	Any	Additional keyword arguments for the request.	{}

Raises:

Type	Description
ValueError	If no access token is provided.

Returns:

Type	Description
Optional[str]	Optional[str]: The URL of the Mapillary image, or None if an error occurs.

Source code in leafmap/common.py

def get_mapillary_image_url(
    image_id: str,
    resolution: str = "original",
    access_token: Optional[str] = None,
    **kwargs: Any,
) -> Optional[str]:
    """
    Retrieves the URL of a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        resolution (str): The resolution of the image. Can be 256, 1024, 2048, or original.
            Defaults to "original".
        access_token (str, optional): The access token for the Mapillary API. Defaults to None.
        **kwargs: Additional keyword arguments for the request.

    Raises:
        ValueError: If no access token is provided.

    Returns:
        Optional[str]: The URL of the Mapillary image, or None if an error occurs.
    """
    if access_token is None:
        access_token = get_api_key("MAPILLARY_API_KEY")

    if access_token is None:
        raise ValueError(
            "Mapillary API access token is required. Set it using the 'access_token' parameter."
        )

        # API URL
    url = f"https://graph.mapillary.com/{image_id}"

    # Fields to retrieve
    fields = f"thumb_{resolution}_url"

    # Request parameters
    params = {"fields": fields, "access_token": access_token}

    # Fetch the data
    response = requests.get(url, params=params, **kwargs)

    # Check the response
    if response.status_code == 200:
        data = response.json()
        image_url = data.get(fields)
        return image_url
    else:
        print(f"Error {response.status_code}: {response.text}")
        return None

get_mapillary_image_widget(image_id, style='photo', width=800, height=600, frame_border=0, **kwargs)

Creates an iframe widget to display a Mapillary image.

Parameters:

Name	Type	Description	Default
image_id	str	The ID of the Mapillary image.	required
style	str	The style of the image. Can be "photo", "classic", "split". Defaults to "photo".	'photo'
width	int	The width of the iframe. Defaults to 800.	800
height	int	The height of the iframe. Defaults to 600.	600
frame_border	int	The frame border of the iframe. Defaults to 0.	0
**kwargs	Any	Additional keyword arguments for the widget.	{}

Returns:

Type	Description
HTML	widgets.HTML: An iframe widget displaying the Mapillary image.

Source code in leafmap/common.py

def get_mapillary_image_widget(
    image_id: str,
    style: str = "photo",
    width: int = 800,
    height: int = 600,
    frame_border: int = 0,
    **kwargs: Any,
) -> widgets.HTML:
    """
    Creates an iframe widget to display a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        style (str): The style of the image. Can be "photo", "classic", "split". Defaults to "photo".
        width (int): The width of the iframe. Defaults to 800.
        height (int): The height of the iframe. Defaults to 600.
        frame_border (int): The frame border of the iframe. Defaults to 0.
        **kwargs: Additional keyword arguments for the widget.

    Returns:
        widgets.HTML: An iframe widget displaying the Mapillary image.
    """

    content = f"""
    <iframe
        src="https://www.mapillary.com/embed?image_key={image_id}&style={style}"
        height="{height}"
        width="{width}"
        frameborder="{frame_border}">
    </iframe>
    """

    # Create an iframe widget
    iframe = widgets.HTML(value=content, placeholder="Mapillary Image", **kwargs)

    return iframe

get_max_pixel_coords(geotiff_path, band_idx=1, roi=None, dst_crs='EPSG:4326', output=None, return_gdf=True, **kwargs)

Find the geographic coordinates of the maximum pixel value in a GeoTIFF.

Parameters:

Name	Type	Description	Default
geotiff_path	str	Path to the GeoTIFF file.	required
band_idx	int	Band index to use (default is 1).	1
roi	str	Path to a vector dataset containing the region of interest (default is None).	None
dst_crs	str	Desired output coordinate system in EPSG format (e.g., "EPSG:4326").	'EPSG:4326'
output	str	Path to save the output GeoDataFrame (default is None).	None
return_gdf	bool	Whether to return a GeoDataFrame (default is True).	True

Returns:

Name	Type	Description
dict		Maximum pixel value and its geographic coordinates in the specified CRS.

Source code in leafmap/common.py

def get_max_pixel_coords(
    geotiff_path,
    band_idx=1,
    roi=None,
    dst_crs="EPSG:4326",
    output=None,
    return_gdf=True,
    **kwargs,
):
    """
    Find the geographic coordinates of the maximum pixel value in a GeoTIFF.

    Args:
        geotiff_path (str): Path to the GeoTIFF file.
        band_idx (int): Band index to use (default is 1).
        roi (str): Path to a vector dataset containing the region of interest (default is None).
        dst_crs (str): Desired output coordinate system in EPSG format (e.g., "EPSG:4326").
        output (str): Path to save the output GeoDataFrame (default is None).
        return_gdf (bool): Whether to return a GeoDataFrame (default is True).

    Returns:
        dict: Maximum pixel value and its geographic coordinates in the specified CRS.
    """
    import rasterio
    import numpy as np
    import geopandas as gpd
    from rasterio.warp import transform
    from rasterio.mask import mask
    from rasterio.warp import transform, transform_geom

    with rasterio.open(geotiff_path) as dataset:
        # If ROI is provided, handle potential CRS differences
        if roi:
            if isinstance(roi, str):
                gdf = gpd.read_file(roi)
            elif isinstance(roi, gpd.GeoDataFrame):
                gdf = roi
            elif isinstance(roi, dict):
                gdf = gpd.GeoDataFrame.from_features([roi])
            else:
                raise ValueError(
                    "Invalid ROI input. Must be a file path or a GeoDataFrame."
                )
            roi_geojson = gdf.__geo_interface__

            # Reproject ROI to match the raster's CRS if necessary
            roi_crs = gdf.crs
            if roi_crs is None:
                roi_crs = "EPSG:4326"
            if roi_crs != dataset.crs.to_string():
                roi_geojson["features"][0]["geometry"] = transform_geom(
                    roi_crs,
                    dataset.crs.to_string(),
                    roi_geojson["features"][0]["geometry"],
                )

            # Mask the raster using the transformed ROI geometry
            clipped_band, clipped_transform = mask(
                dataset, [roi_geojson["features"][0]["geometry"]], crop=True
            )
            band = clipped_band[
                band_idx - 1
            ]  # Mask returns a 3D array (bands, rows, cols), so select the first band
            transform_to_use = clipped_transform
        else:
            # Use the entire raster
            band = dataset.read(band_idx)
            transform_to_use = dataset.transform

        # Find the maximum value and its index
        max_value = band.max()
        max_index = np.unravel_index(band.argmax(), band.shape)

        # Convert pixel coordinates to the raster's CRS coordinates
        original_coords = transform_to_use * (max_index[1], max_index[0])

        # Transform coordinates to the desired CRS
        src_crs = dataset.crs
        x, y = transform(src_crs, dst_crs, [original_coords[0]], [original_coords[1]])

        if return_gdf:
            x_coords = [x[0]]
            y_coords = [y[0]]
            # Create a DataFrame
            df = pd.DataFrame({"x": x_coords, "y": y_coords})

            # Convert the DataFrame to a GeoDataFrame
            gdf = gpd.GeoDataFrame(
                df, geometry=gpd.points_from_xy(df.x, df.y), crs=dst_crs
            )

            if output:
                gdf.to_file(output, **kwargs)

        else:
            return {"max_value": max_value, "coordinates": (x[0], y[0]), "crs": dst_crs}

get_nhd(geometry, geo_crs=4326, xy=True, buffer=0.001, dataset='wbd08', predicate='intersects', sort_attr=None, **kwargs)

Fetches National Hydrography Dataset (NHD) data based on the provided geometry.

Parameters:

Name	Type	Description	Default
geometry	Union[GeoDataFrame, str, List[float], Tuple[float, float, float, float]]	The geometry to query the NHD data. It can be a GeoDataFrame, a file path, or coordinates.	required
geo_crs	int	The coordinate reference system (CRS) of the geometry (default is 4326).	4326
xy	bool	Whether to use x, y coordinates (default is True).	True
buffer	float	The buffer distance around the centroid point (default is 0.001 degrees).	0.001
dataset	str	The NHD dataset to query (default is "wbd08").	'wbd08'
predicate	str	The spatial predicate to use for the query (default is "intersects").	'intersects'
sort_attr	Optional[str]	The attribute to sort the results by (default is None).	None
**kwargs		Additional keyword arguments to pass to the WaterData.bygeom method.	{}

Returns:

Type	Description
Optional[GeoDataFrame]	Optional[gpd.GeoDataFrame]: The fetched NHD data as a GeoDataFrame, or None if an error occurs.

Raises:

Type	Description
ImportError	If the pynhd package is not installed.
ValueError	If the geometry type is unsupported.

Source code in leafmap/common.py

def get_nhd(
    geometry: Union[
        "gpd.GeoDataFrame", str, List[float], Tuple[float, float, float, float]
    ],
    geo_crs: int = 4326,
    xy: bool = True,
    buffer: float = 0.001,
    dataset: str = "wbd08",
    predicate: str = "intersects",
    sort_attr: Optional[str] = None,
    **kwargs,
) -> Optional["gpd.GeoDataFrame"]:
    """
    Fetches National Hydrography Dataset (NHD) data based on the provided geometry.

    Args:
        geometry (Union[gpd.GeoDataFrame, str, List[float], Tuple[float, float, float, float]]):
            The geometry to query the NHD data. It can be a GeoDataFrame, a file path, or coordinates.
        geo_crs (int): The coordinate reference system (CRS) of the geometry (default is 4326).
        xy (bool): Whether to use x, y coordinates (default is True).
        buffer (float): The buffer distance around the centroid point (default is 0.001 degrees).
        dataset (str): The NHD dataset to query (default is "wbd08").
        predicate (str): The spatial predicate to use for the query (default is "intersects").
        sort_attr (Optional[str]): The attribute to sort the results by (default is None).
        **kwargs: Additional keyword arguments to pass to the WaterData.bygeom method.

    Returns:
        Optional[gpd.GeoDataFrame]: The fetched NHD data as a GeoDataFrame, or None if an error occurs.

    Raises:
        ImportError: If the pynhd package is not installed.
        ValueError: If the geometry type is unsupported.
    """
    try:
        import pynhd
    except ImportError:
        print("The pynhd package is required for this function. Installing...")
        install_package("pynhd")

    import geopandas as gpd
    from pynhd import WaterData

    if isinstance(geometry, (list, tuple)):
        crs = f"EPSG:{geo_crs}"
        geometry = construct_bbox(*geometry, buffer=buffer, crs=crs, return_gdf=False)
    elif isinstance(geometry, gpd.GeoDataFrame):
        geometry = geometry.unary_union
    elif isinstance(geometry, str):
        geometry = gpd.read_file(geometry).unary_union

    water_data = WaterData(dataset)

    try:
        gdf = water_data.bygeom(geometry, geo_crs, xy, predicate, sort_attr, **kwargs)
    except Exception as e:
        print(e)
        gdf = None

    return gdf

get_nhd_basins(feature_ids, fsource='nwissite', split_catchment=False, simplified=True, **kwargs)

Get NHD basins for a list of station IDs.

Parameters:

Name	Type	Description	Default
feature_ids	str | list	Target feature ID(s).	required
fsource	str	The name of feature(s) source, defaults to nwissite. The valid sources are: * 'comid' for NHDPlus comid. * 'ca_gages' for Streamgage catalog for CA SB19 * 'gfv11_pois' for USGS Geospatial Fabric V1.1 Points of Interest * 'huc12pp' for HUC12 Pour Points * 'nmwdi-st' for New Mexico Water Data Initiative Sites * 'nwisgw' for NWIS Groundwater Sites * 'nwissite' for NWIS Surface Water Sites * 'ref_gage' for geoconnex.us reference gauges * 'vigil' for Vigil Network Data * 'wade' for Water Data Exchange 2.0 Sites * 'WQP' for Water Quality Portal	'nwissite'
split_catchment	bool	If True, split basins at their outlet locations	False
simplified	bool	If True, return a simplified version of basin geometries. Default to True.	True

Raises:

Type	Description
ImportError	If pynhd is not installed.

Returns:

Type	Description
	geopandas.GeoDataFrame: NLDI indexed basins in EPSG:4326. If some IDs don't return any features a list of missing ID(s) are returned as well.

Source code in leafmap/common.py

def get_nhd_basins(
    feature_ids,
    fsource="nwissite",
    split_catchment=False,
    simplified=True,
    **kwargs,
):
    """Get NHD basins for a list of station IDs.

    Args:
        feature_ids (str | list): Target feature ID(s).
        fsource (str, optional): The name of feature(s) source, defaults to ``nwissite``.
            The valid sources are:
            * 'comid' for NHDPlus comid.
            * 'ca_gages' for Streamgage catalog for CA SB19
            * 'gfv11_pois' for USGS Geospatial Fabric V1.1 Points of Interest
            * 'huc12pp' for HUC12 Pour Points
            * 'nmwdi-st' for New Mexico Water Data Initiative Sites
            * 'nwisgw' for NWIS Groundwater Sites
            * 'nwissite' for NWIS Surface Water Sites
            * 'ref_gage' for geoconnex.us reference gauges
            * 'vigil' for Vigil Network Data
            * 'wade' for Water Data Exchange 2.0 Sites
            * 'WQP' for Water Quality Portal
        split_catchment (bool, optional): If True, split basins at their outlet locations
        simplified (bool, optional): If True, return a simplified version of basin geometries.
            Default to True.

    Raises:
        ImportError: If pynhd is not installed.

    Returns:
        geopandas.GeoDataFrame: NLDI indexed basins in EPSG:4326. If some IDs don't return any features
            a list of missing ID(s) are returned as well.
    """

    try:
        from pynhd import NLDI
    except ImportError:
        raise ImportError("pynhd is not installed. Install it with pip install pynhd")

    return NLDI().get_basins(
        feature_ids, fsource, split_catchment, simplified, **kwargs
    )

get_nwi(geometry, inSR='4326', outSR='3857', spatialRel='esriSpatialRelIntersects', return_geometry=True, outFields='*', output=None, **kwargs)

Query the NWI (National Wetlands Inventory) API using various geometry types. https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Wetlands/FeatureServer

Parameters:

Name	Type	Description	Default
geometry	dict	The geometry data (e.g., point, polygon, polyline, multipoint, etc.).	required
inSR	str	The input spatial reference (default is EPSG:4326).	'4326'
outSR	str	The output spatial reference (default is EPSG:3857).	'3857'
spatialRel	str	The spatial relationship (default is "esriSpatialRelIntersects").	'esriSpatialRelIntersects'
return_geometry	bool	Whether to return the geometry (default is True).	True
outFields	str	The fields to be returned (default is "*").	'*'
output	str	The output file path to save the GeoDataFrame (default is None).	None
**kwargs	Any	Additional keyword arguments to pass to the API.	{}

Returns:

Type	Description
Union[GeoDataFrame, DataFrame, Dict[str, str]]	gpd.GeoDataFrame: The queried NWI data as a GeoDataFrame.

Source code in leafmap/common.py

def get_nwi(
    geometry: Dict[str, Any],
    inSR: str = "4326",
    outSR: str = "3857",
    spatialRel: str = "esriSpatialRelIntersects",
    return_geometry: bool = True,
    outFields: str = "*",
    output: Optional[str] = None,
    **kwargs: Any,
) -> Union["gpd.GeoDataFrame", "pd.DataFrame", Dict[str, str]]:
    """
    Query the NWI (National Wetlands Inventory) API using various geometry types.
    https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Wetlands/FeatureServer

    Args:
        geometry (dict): The geometry data (e.g., point, polygon, polyline, multipoint, etc.).
        inSR (str): The input spatial reference (default is EPSG:4326).
        outSR (str): The output spatial reference (default is EPSG:3857).
        spatialRel (str): The spatial relationship (default is "esriSpatialRelIntersects").
        return_geometry (bool): Whether to return the geometry (default is True).
        outFields (str): The fields to be returned (default is "*").
        output (str): The output file path to save the GeoDataFrame (default is None).
        **kwargs: Additional keyword arguments to pass to the API.

    Returns:
        gpd.GeoDataFrame: The queried NWI data as a GeoDataFrame.
    """

    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Polygon

    def detect_geometry_type(geometry):
        """
        Automatically detect the geometry type based on the structure of the geometry dictionary.
        """
        if "x" in geometry and "y" in geometry:
            return "esriGeometryPoint"
        elif (
            "xmin" in geometry
            and "ymin" in geometry
            and "xmax" in geometry
            and "ymax" in geometry
        ):
            return "esriGeometryEnvelope"
        elif "rings" in geometry:
            return "esriGeometryPolygon"
        elif "paths" in geometry:
            return "esriGeometryPolyline"
        elif "points" in geometry:
            return "esriGeometryMultipoint"
        else:
            raise ValueError("Unsupported geometry type or invalid geometry structure.")

    # Convert GeoDataFrame to a dictionary if needed
    geometry_type = None
    if isinstance(geometry, gpd.GeoDataFrame):
        geometry_dict = _convert_geodataframe_to_esri_format(geometry)[0]
        geometry_type = detect_geometry_type(geometry_dict)
    elif isinstance(geometry, dict):
        geometry_type = detect_geometry_type(geometry)
        geometry_dict = geometry
    elif isinstance(geometry, str):
        geometry_dict = geometry
    else:
        raise ValueError(
            "Invalid geometry input. Must be a GeoDataFrame or a dictionary."
        )

    # Convert geometry to a JSON string (required by the API)
    if isinstance(geometry_dict, dict):
        geometry_json = json.dumps(geometry_dict)
    else:
        geometry_json = geometry_dict
    # API URL for querying wetlands
    url = "https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Wetlands/MapServer/0/query"

    # Construct the query parameters
    params = {
        "geometry": geometry_json,  # The geometry as a JSON string
        "geometryType": geometry_type,  # Geometry type (automatically detected)
        "inSR": inSR,  # Spatial reference system (default is WGS84)
        "spatialRel": spatialRel,  # Spatial relationship (default is intersects)
        "outFields": outFields,  # Which fields to return (default is all fields)
        "returnGeometry": str(
            return_geometry
        ).lower(),  # Whether to return the geometry
        "f": "json",  # Response format
    }

    for key, value in kwargs.items():
        params[key] = value

    # Make the GET request
    response = requests.get(url, params=params)

    # Check if the request was successful
    if response.status_code == 200:
        data = response.json()  # Return the data as a Python dictionary
    else:
        return {"error": f"Request failed with status code {response.status_code}"}

    # Extract the features
    features = data["features"]

    # Prepare the attribute data and geometries
    attributes = [feature["attributes"] for feature in features]

    # Create a DataFrame for attributes
    df = pd.DataFrame(attributes)
    df.rename(
        columns={
            "Shape__Length": "Shape_Length",
            "Shape__Area": "Shape_Area",
            "WETLAND_TYPE": "WETLAND_TY",
        },
        inplace=True,
    )

    if return_geometry:
        geometries = [Polygon(feature["geometry"]["rings"][0]) for feature in features]
        # Create a GeoDataFrame by combining the attributes and geometries
        gdf = gpd.GeoDataFrame(
            df,
            geometry=geometries,
            crs=f"EPSG:{data['spatialReference']['latestWkid']}",
        )
        if outSR != "3857":
            gdf = gdf.to_crs(outSR)

        if output is not None:
            gdf.to_file(output)

        return gdf
    else:
        return df

get_nwi_by_huc8(huc8=None, geometry=None, out_dir=None, quiet=True, layer='Wetlands', **kwargs)

Fetches National Wetlands Inventory (NWI) data by HUC8 code.

Parameters:

Name	Type	Description	Default
huc8	Optional[str]	The HUC8 code to query the NWI data. It must be a string of length 8.	None
geometry	Optional[Union[GeoDataFrame, str]]	The geometry to derive the HUC8 code. It can be a GeoDataFrame or a file path.	None
out_dir	Optional[str]	The directory to save the downloaded data. Defaults to a temporary directory.	None
quiet	bool	Whether to suppress download progress messages. Defaults to True.	True
layer	str	The layer to fetch from the NWI data. It can be one of the following: Wetlands, Watershed, Riparian_Project_Metadata, Wetlands_Historic_Map_Info. Defaults to "Wetlands".	'Wetlands'
**kwargs		Additional keyword arguments to pass to the download_file function.	{}

Returns:

Type	Description
GeoDataFrame	gpd.GeoDataFrame: The fetched NWI data as a GeoDataFrame.

Raises:

Type	Description
ValueError	If the HUC8 code is invalid or the layer is not allowed.

Source code in leafmap/common.py

def get_nwi_by_huc8(
    huc8: Optional[str] = None,
    geometry: Optional[Union["gpd.GeoDataFrame", str]] = None,
    out_dir: Optional[str] = None,
    quiet: bool = True,
    layer: str = "Wetlands",
    **kwargs,
) -> "gpd.GeoDataFrame":
    """
    Fetches National Wetlands Inventory (NWI) data by HUC8 code.

    Args:
        huc8 (Optional[str]): The HUC8 code to query the NWI data. It must be a
            string of length 8.
        geometry (Optional[Union[gpd.GeoDataFrame, str]]): The geometry to derive
            the HUC8 code. It can be a GeoDataFrame or a file path.
        out_dir (Optional[str]): The directory to save the downloaded data.
            Defaults to a temporary directory.
        quiet (bool): Whether to suppress download progress messages. Defaults to True.
        layer (str): The layer to fetch from the NWI data. It can be one of the following:
            Wetlands, Watershed, Riparian_Project_Metadata, Wetlands_Historic_Map_Info.
            Defaults to "Wetlands".
        **kwargs: Additional keyword arguments to pass to the download_file function.

    Returns:
        gpd.GeoDataFrame: The fetched NWI data as a GeoDataFrame.

    Raises:
        ValueError: If the HUC8 code is invalid or the layer is not allowed.
    """
    import tempfile
    import geopandas as gpd

    if geometry is not None:
        wbd = get_wbd(geometry, return_geometry=False)
        huc8 = wbd["huc8"].values[0]

    if isinstance(huc8, str) and len(huc8) == 8:
        pass
    else:
        raise ValueError("Invalid HUC8 code. It must be a string of length 8.")

    if out_dir is None:
        out_dir = tempfile.gettempdir()

    allowed_layers = [
        "Wetlands",
        "Watershed",
        "Riparian_Project_Metadata",
        "Wetlands_Historic_Map_Info",
        "Wetlands_Project_Metadata",
    ]
    if layer not in allowed_layers:
        raise ValueError(f"Invalid layer. Allowed values are {allowed_layers}")

    url = f"https://documentst.ecosphere.fws.gov/wetlands/downloads/watershed/HU8_{huc8}_Watershed.zip"

    filename = os.path.join(out_dir, f"HU8_{huc8}_Watershed.zip")

    download_file(url, filename, quiet=quiet, **kwargs)

    data_dir = os.path.join(out_dir, f"HU8_{huc8}_Watershed")

    filepath = os.path.join(data_dir, f"HU8_{huc8}_{layer}.shp")

    gdf = gpd.read_file(filepath)
    return gdf

get_overlap(img1, img2, overlap, out_img1=None, out_img2=None, to_cog=True)

Get overlapping area of two images.

Parameters:

Name	Type	Description	Default
img1	str	Path to the first image.	required
img2	str	Path to the second image.	required
overlap	str	Path to the output overlap area in GeoJSON format.	required
out_img1	str	Path to the cropped image of the first image.	None
out_img2	str	Path to the cropped image of the second image.	None
to_cog	bool	Whether to convert the output images to COG.	True

Returns:

Name	Type	Description
str		Path to the overlap area in GeoJSON format.

Source code in leafmap/common.py

def get_overlap(img1, img2, overlap, out_img1=None, out_img2=None, to_cog=True):
    """Get overlapping area of two images.

    Args:
        img1 (str): Path to the first image.
        img2 (str): Path to the second image.
        overlap (str): Path to the output overlap area in GeoJSON format.
        out_img1 (str, optional): Path to the cropped image of the first image.
        out_img2 (str, optional): Path to the cropped image of the second image.
        to_cog (bool, optional): Whether to convert the output images to COG.

    Returns:
        str: Path to the overlap area in GeoJSON format.
    """
    import json
    from osgeo import gdal, ogr, osr
    import geopandas as gpd

    extent = gdal.Info(img1, format="json")["wgs84Extent"]
    poly1 = ogr.CreateGeometryFromJson(json.dumps(extent))
    extent = gdal.Info(img2, format="json")["wgs84Extent"]
    poly2 = ogr.CreateGeometryFromJson(json.dumps(extent))
    intersection = poly1.Intersection(poly2)
    gg = gdal.OpenEx(intersection.ExportToJson())
    ds = gdal.VectorTranslate(
        overlap,
        srcDS=gg,
        format="GeoJSON",
        layerCreationOptions=["RFC7946=YES", "WRITE_BBOX=YES"],
    )
    ds = None

    d = gdal.Open(img1)
    proj = osr.SpatialReference(wkt=d.GetProjection())
    epsg = proj.GetAttrValue("AUTHORITY", 1)

    gdf = gpd.read_file(overlap)
    gdf.to_crs(epsg=epsg, inplace=True)
    gdf.to_file(overlap)

    if out_img1 is not None:
        clip_image(img1, overlap, out_img1, to_cog=to_cog)

    if out_img2 is not None:
        clip_image(img2, overlap, out_img2, to_cog=to_cog)

    return overlap

get_overture_data(overture_type, bbox=None, columns=None, output=None)

Fetches overture data and returns it as a GeoDataFrame.

Parameters:

Name	Type	Description	Default
overture_type	str	The type of overture data to fetch.It can be one of the following: address|building|building_part|division|division_area|division_boundary|place| segment|connector|infrastructure|land|land_cover|land_use|water	required
bbox	Tuple[float, float, float, float]	The bounding box to filter the data. Defaults to None.	None
columns	List[str]	The columns to include in the output. Defaults to None.	None
output	str	The file path to save the output GeoDataFrame. Defaults to None.	None

Returns:

Type	Description
GeoDataFrame	gpd.GeoDataFrame: The fetched overture data as a GeoDataFrame.

Raises:

Type	Description
ImportError	If the overture package is not installed.

Source code in leafmap/common.py

def get_overture_data(
    overture_type: str,
    bbox: Tuple[float, float, float, float] = None,
    columns: List[str] = None,
    output: str = None,
) -> "gpd.GeoDataFrame":
    """Fetches overture data and returns it as a GeoDataFrame.

    Args:
        overture_type (str): The type of overture data to fetch.It can be one of the following:
            address|building|building_part|division|division_area|division_boundary|place|
            segment|connector|infrastructure|land|land_cover|land_use|water
        bbox (Tuple[float, float, float, float], optional): The bounding box to
            filter the data. Defaults to None.
        columns (List[str], optional): The columns to include in the output.
            Defaults to None.
        output (str, optional): The file path to save the output GeoDataFrame.
            Defaults to None.

    Returns:
        gpd.GeoDataFrame: The fetched overture data as a GeoDataFrame.

    Raises:
        ImportError: If the overture package is not installed.
    """

    try:
        from overturemaps import core
    except ImportError:
        install_package("overturemaps")
        from overturemaps import core

    gdf = core.geodataframe(overture_type, bbox=bbox)
    if columns is not None:
        gdf = gdf[columns]

    gdf.crs = "EPSG:4326"
    if output is not None:
        gdf.to_file(output)

    return gdf

get_overture_latest_release(patch=False)

Retrieves the value of the 'latest' key from the Overture Maps release JSON file.

Parameters:

Name	Type	Description	Default
patch	bool	If True, returns the full version string (e.g., "2025-02-19.0").	False

Returns:

Name	Type	Description
str	str	The value of the 'latest' key from the releases.json file.

Raises:

Type	Description
RequestException	If there's an issue with the HTTP request.
KeyError	If the 'latest' key is not found in the JSON data.
JSONDecodeError	If the response cannot be parsed as JSON.

Source code in leafmap/common.py

def get_overture_latest_release(patch=False) -> str:
    """
    Retrieves the value of the 'latest' key from the Overture Maps release JSON file.

    Args:
        patch (bool): If True, returns the full version string (e.g., "2025-02-19.0").

    Returns:
        str: The value of the 'latest' key from the releases.json file.

    Raises:
        requests.RequestException: If there's an issue with the HTTP request.
        KeyError: If the 'latest' key is not found in the JSON data.
        json.JSONDecodeError: If the response cannot be parsed as JSON.
    """
    url = "https://labs.overturemaps.org/data/releases.json"

    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for HTTP errors

        data = response.json()
        if patch:
            latest_release = data.get("latest")
        else:
            latest_release = data.get("latest").split(".")[
                0
            ]  # Extract the version number

        if latest_release is None:
            raise KeyError("The 'latest' key was not found in the releases.json file")

        return latest_release

    except requests.RequestException as e:
        print(f"Error making the request: {e}")
        raise
    except json.JSONDecodeError as e:
        print(f"Error parsing JSON response: {e}")
        raise
    except KeyError as e:
        print(f"Key error: {e}")
        raise

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:

Name	Type	Description
list		A list of palettable color palettes.

Source code in leafmap/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(
            "Please install the palettable package using '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:

Name	Type	Description
list		A list of hex colors.

Source code in leafmap/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

    try:
        cmap = plt.get_cmap(cmap_name, n_class)
    except:
        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_parquet_geometry_column(input_parquet, db_con=None)

Retrieves the geometry column name from a Parquet file.

This function checks for the presence of a geometry column in the input Parquet file. It looks for columns named "geometry" or "geom" and returns the first match.

Parameters:

Name	Type	Description	Default
input_parquet	str	The path to the input Parquet file.	required
db_con	Connection	An existing DuckDB connection. If None, a new connection will be created.	None

Returns:

Name	Type	Description
str	str	The name of the geometry column ("geometry" or "geom").

Raises:

Type	Description
ValueError	If no recognized geometry column is found in the input Parquet file.

Example

geometry_column = get_parquet_geometry_column("data.parquet") print(geometry_column) "geometry"

Source code in leafmap/common.py

def get_parquet_geometry_column(input_parquet: str, db_con=None) -> str:
    """
    Retrieves the geometry column name from a Parquet file.

    This function checks for the presence of a geometry column in the input Parquet file.
    It looks for columns named "geometry" or "geom" and returns the first match.

    Args:
        input_parquet (str): The path to the input Parquet file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        str: The name of the geometry column ("geometry" or "geom").

    Raises:
        ValueError: If no recognized geometry column is found in the input Parquet file.

    Example:
        >>> geometry_column = get_parquet_geometry_column("data.parquet")
        >>> print(geometry_column)
        "geometry"
    """
    column_names = get_vector_column_names(input_parquet, db_con=db_con)
    if "geometry" in column_names:
        return "geometry"
    elif "geom" in column_names:
        return "geom"
    else:
        raise ValueError(
            f"The input vector file does not contain a recognized geometry column. "
            f"Available columns: {column_names}. Please ensure the vector file has a 'geometry' or 'geom' column."
        )

get_solar_data(lat, lon, radiusMeters=50, view='FULL_LAYERS', requiredQuality='HIGH', pixelSizeMeters=0.1, api_key=None, header=None, out_dir=None, basename=None, quiet=False, **kwargs)

Retrieve solar data for a specific location from Google's Solar API https://developers.google.com/maps/documentation/solar. You need to enable Solar API from https://console.cloud.google.com/google/maps-apis/api-list.

Parameters:

Name	Type	Description	Default
lat	float	Latitude of the location.	required
lon	float	Longitude of the location.	required
radiusMeters	int	Radius in meters for the data retrieval (default is 50).	50
view	str	View type (default is "FULL_LAYERS"). For more options, see https://bit.ly/3LazuBi.	'FULL_LAYERS'
requiredQuality	str	Required quality level (default is "HIGH").	'HIGH'
pixelSizeMeters	float	Pixel size in meters (default is 0.1).	0.1
api_key	str	Google API key for authentication (if not provided, checks 'GOOGLE_API_KEY' environment variable).	None
header	dict	Additional HTTP headers to include in the request.	None
out_dir	str	Directory where downloaded files will be saved.	None
basename	str	Base name for the downloaded files (default is generated from imagery date).	None
quiet	bool	If True, suppress progress messages during file downloads (default is False).	False
**kwargs	Any	Additional keyword arguments to be passed to the download_file function.	{}

Returns:

Type	Description
Dict[str, str]	Dict[str, str]: A dictionary mapping file names to their corresponding paths.

Source code in leafmap/common.py

def get_solar_data(
    lat: float,
    lon: float,
    radiusMeters: int = 50,
    view: str = "FULL_LAYERS",
    requiredQuality: str = "HIGH",
    pixelSizeMeters: float = 0.1,
    api_key: Optional[str] = None,
    header: Optional[Dict[str, str]] = None,
    out_dir: Optional[str] = None,
    basename: Optional[str] = None,
    quiet: bool = False,
    **kwargs: Any,
) -> Dict[str, str]:
    """
    Retrieve solar data for a specific location from Google's Solar API https://developers.google.com/maps/documentation/solar.
    You need to enable Solar API from https://console.cloud.google.com/google/maps-apis/api-list.

    Args:
        lat (float): Latitude of the location.
        lon (float): Longitude of the location.
        radiusMeters (int, optional): Radius in meters for the data retrieval (default is 50).
        view (str, optional): View type (default is "FULL_LAYERS"). For more options, see https://bit.ly/3LazuBi.
        requiredQuality (str, optional): Required quality level (default is "HIGH").
        pixelSizeMeters (float, optional): Pixel size in meters (default is 0.1).
        api_key (str, optional): Google API key for authentication (if not provided, checks 'GOOGLE_API_KEY' environment variable).
        header (dict, optional): Additional HTTP headers to include in the request.
        out_dir (str, optional): Directory where downloaded files will be saved.
        basename (str, optional): Base name for the downloaded files (default is generated from imagery date).
        quiet (bool, optional): If True, suppress progress messages during file downloads (default is False).
        **kwargs: Additional keyword arguments to be passed to the download_file function.

    Returns:
        Dict[str, str]: A dictionary mapping file names to their corresponding paths.
    """

    if api_key is None:
        api_key = os.environ.get("GOOGLE_API_KEY", "")

    if api_key == "":
        raise ValueError("GOOGLE_API_KEY is required to use this function.")

    url = "https://solar.googleapis.com/v1/dataLayers:get"
    params = {
        "location.latitude": lat,
        "location.longitude": lon,
        "radiusMeters": radiusMeters,
        "view": view,
        "requiredQuality": requiredQuality,
        "pixelSizeMeters": pixelSizeMeters,
        "key": api_key,
    }

    solar_data = requests.get(url, params=params, headers=header).json()

    links = {}

    for key in solar_data.keys():
        if "Url" in key:
            if isinstance(solar_data[key], list):
                urls = [url + "&key=" + api_key for url in solar_data[key]]
                links[key] = urls
            else:
                links[key] = solar_data[key] + "&key=" + api_key

    if basename is None:
        date = solar_data["imageryDate"]
        year = date["year"]
        month = date["month"]
        day = date["day"]
        basename = f"{year}_{str(month).zfill(2)}_{str(day).zfill(2)}"

    filenames = {}

    for link in links:
        if isinstance(links[link], list):
            for i, url in enumerate(links[link]):
                filename = (
                    f"{basename}_{link.replace('Urls', '')}_{str(i+1).zfill(2)}.tif"
                )
                if out_dir is not None:
                    filename = os.path.join(out_dir, filename)
                download_file(url, filename, quiet=quiet, **kwargs)
                filenames[link.replace("Urls", "") + "_" + str(i).zfill(2)] = filename
        else:
            name = link.replace("Url", "")
            filename = f"{basename}_{name}.tif"
            if out_dir is not None:
                filename = os.path.join(out_dir, filename)
            download_file(links[link], filename, quiet=quiet, **kwargs)
            filenames[name] = filename

    return filenames

get_stac_collections(url, **kwargs)

Retrieve a list of STAC collections from a URL. This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L31. Credits to Mykola Kozyr.

Parameters:

Name	Type	Description	Default
url	str	A URL to a STAC catalog.	required
**kwargs		Additional keyword arguments to pass to the pystac Client.open() method. See https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.open	{}

Returns:

Name	Type	Description
list		A list of STAC collections.

Source code in leafmap/common.py

def get_stac_collections(url, **kwargs):
    """Retrieve a list of STAC collections from a URL.
    This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L31.
    Credits to Mykola Kozyr.

    Args:
        url (str): A URL to a STAC catalog.
        **kwargs: Additional keyword arguments to pass to the pystac Client.open() method.
            See https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.open

    Returns:
        list: A list of STAC collections.
    """
    from pystac_client import Client

    # Expensive function. Added cache for it.

    # Empty list that would be used for a dataframe to collect and visualize info about collections
    root_catalog = Client.open(url, **kwargs)
    collections_list = []
    # Reading collections in the Catalog
    collections = list(root_catalog.get_collections())
    print(collections)
    for collection in collections:
        id = collection.id
        title = collection.title
        # bbox = collection.extent.spatial.bboxes # not in use for the first release
        # interval = collection.extent.temporal.intervals # not in use for the first release
        description = collection.description

        # creating a list of lists of values
        collections_list.append([id, title, description])
    return collections_list

get_stac_items(url, collection, limit=None, bbox=None, datetime=None, intersects=None, ids=None, open_args=None, **kwargs)

Retrieve a list of STAC items from a URL and a collection. This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L49. Credits to Mykola Kozyr. Available parameters can be found at https://github.com/radiantearth/stac-api-spec/tree/master/item-search

Parameters:

Name	Type	Description	Default
url	str	A URL to a STAC catalog.	required
collection	str	A STAC collection ID.	required
limit	int	The maximum number of results to return (page size). Defaults to None.	None
bbox	tuple	Requested bounding box in the format of (minx, miny, maxx, maxy). Defaults to None.	None
datetime	str	Single date+time, or a range ('/' separator), formatted to RFC 3339, section 5.6. Use double dots .. for open date ranges.	None
intersects	dict	A dictionary representing a GeoJSON Geometry. Searches items by performing intersection between their geometry and provided GeoJSON geometry. All GeoJSON geometry types must be supported.	None
ids	list	A list of item ids to return.	None
open_args	dict	A dictionary of arguments to pass to the pystac Client.open() method. Defaults to None.	None
**kwargs		Additional keyword arguments to pass to the Catalog.search() method.	{}

Returns:

Type	Description
	GeoPandas.GeoDataFraem: A GeoDataFrame with the STAC items.

Source code in leafmap/common.py

def get_stac_items(
    url,
    collection,
    limit=None,
    bbox=None,
    datetime=None,
    intersects=None,
    ids=None,
    open_args=None,
    **kwargs,
):
    """Retrieve a list of STAC items from a URL and a collection.
    This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L49.
    Credits to Mykola Kozyr.
    Available parameters can be found at https://github.com/radiantearth/stac-api-spec/tree/master/item-search

    Args:
        url (str): A URL to a STAC catalog.
        collection (str): A STAC collection ID.
        limit (int, optional): The maximum number of results to return (page size). Defaults to None.
        bbox (tuple, optional): Requested bounding box in the format of (minx, miny, maxx, maxy). Defaults to None.
        datetime (str, optional): Single date+time, or a range ('/' separator), formatted to RFC 3339, section 5.6. Use double dots .. for open date ranges.
        intersects (dict, optional): A dictionary representing a GeoJSON Geometry. Searches items by performing intersection between their geometry and provided GeoJSON geometry. All GeoJSON geometry types must be supported.
        ids (list, optional): A list of item ids to return.
        open_args (dict, optional): A dictionary of arguments to pass to the pystac Client.open() method. Defaults to None.
        **kwargs: Additional keyword arguments to pass to the Catalog.search() method.

    Returns:
        GeoPandas.GeoDataFraem: A GeoDataFrame with the STAC items.
    """

    import itertools
    import geopandas as gpd
    from shapely.geometry import shape
    from pystac_client import Client

    # Empty list that would be used for a dataframe to collect and visualize info about collections
    items_list = []

    if open_args is None:
        open_args = {}

    root_catalog = Client.open(url)

    if limit:
        kwargs["limit"] = limit
    if bbox:
        kwargs["bbox"] = bbox
    if datetime:
        kwargs["datetime"] = datetime
    if intersects:
        kwargs["intersects"] = intersects
    if ids:
        kwargs["ids"] = ids

    if kwargs:
        try:
            catalog = root_catalog.search(collections=collection, **kwargs)
        except NotImplementedError:
            catalog = root_catalog
    else:
        catalog = root_catalog

    iterable = catalog.get_all_items()
    items = list(
        itertools.islice(iterable, limit)
    )  # getting first 25000 items. To Do some smarter logic
    if len(items) == 0:
        try:
            catalog = root_catalog.get_child(collection)
            iterable = catalog.get_all_items()
            items = list(itertools.islice(iterable, limit))
        except Exception as _:
            print("Ooops, it looks like this collection does not have items.")
            return None
    # Iterating over items to collect main information
    for item in items:
        id = item.id
        geometry = shape(item.geometry)
        datetime = (
            item.datetime
            or item.properties["datetime"]
            or item.properties["end_datetime"]
            or item.properties["start_datetime"]
        )
        links = item.links
        for link in links:
            if link.rel == "self":
                self_url = link.target
        assets_list = []
        assets = item.assets
        for asset in assets:
            assets_list.append(asset)

        # creating a list of lists of values
        items_list.append([id, geometry, datetime, self_url, assets_list])

    if limit is not None:
        items_list = items_list[:limit]
    items_df = gpd.GeoDataFrame(items_list)
    items_df.columns = ["id", "geometry", "datetime", "self_url", "assets_list"]

    items_gdf = items_df.set_geometry("geometry")
    items_gdf["datetime"] = items_gdf["datetime"].astype(
        str
    )  # specifically for KeplerGL. See https://github.com/keplergl/kepler.gl/issues/602
    # items_gdf["assets_list"] = items_gdf["assets_list"].astype(str) #specifically for KeplerGL. See https://github.com/keplergl/kepler.gl/issues/602
    items_gdf.set_crs(epsg=4326, inplace=True)
    return items_gdf

get_vector_column_names(input_vector, db_con=None)

Retrieves the column names from a DuckDB table.

Parameters:

Name	Type	Description	Default
input_vecotr	str	The path to the input vector file (e.g., a Parquet or GeoPackage file).	required
db_con	Connection	An existing DuckDB connection. If None, a new connection will be created.	None

Returns:

Type	Description
	List[str]: A list of column names from the specified table.

Raises:

Type	Description
CatalogException	If the table does not exist.

Source code in leafmap/common.py

def get_vector_column_names(input_vector, db_con=None):
    """
    Retrieves the column names from a DuckDB table.

    Args:
        input_vecotr (str): The path to the input vector file (e.g., a Parquet or GeoPackage file).
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        List[str]: A list of column names from the specified table.

    Raises:
        duckdb.CatalogException: If the table does not exist.
    """
    import duckdb

    if db_con is None:
        db_con = duckdb.connect()

    db_con.execute("INSTALL spatial;")
    db_con.execute("LOAD spatial;")

    if not os.path.exists(input_vector):
        raise ValueError(f"Input vector file does not exist: {input_vector}")

    if input_vector.endswith(".parquet"):
        query = f"SELECT * FROM '{input_vector}' LIMIT 0"
    else:
        query = f"SELECT * FROM ST_Read('{input_vector}') LIMIT 0"
    db_con.execute(query)
    return [desc[0] for desc in db_con.description]

get_vector_crs(input_vector, db_con=None, return_epsg=False)

Retrieves the Coordinate Reference System (CRS) of a vector file.

This function extracts the CRS information from the metadata of the input vector file.

Parameters:

Name	Type	Description	Default
input_vector	str	The path to the input vector file.	required
db_con	Connection	An existing DuckDB connection. If None, a new connection will be created.	None
return_epsg	bool	Whether to return the EPSG code of the CRS. Defaults to False.	False

Returns:

Type	Description
	Union[dict, int]: The CRS information as a dictionary or the EPSG code as an integer.

Raises:

Type	Description
ValueError	If the CRS information is not available in the input vector file.

Example

crs = get_vector_crs("data.gpkg", return_epsg=True) print(crs) 4326

Source code in leafmap/common.py

def get_vector_crs(input_vector, db_con=None, return_epsg=False):
    """
    Retrieves the Coordinate Reference System (CRS) of a vector file.

    This function extracts the CRS information from the metadata of the input vector file.

    Args:
        input_vector (str): The path to the input vector file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.
        return_epsg (bool): Whether to return the EPSG code of the CRS. Defaults to False.

    Returns:
        Union[dict, int]: The CRS information as a dictionary or the EPSG code as an integer.

    Raises:
        ValueError: If the CRS information is not available in the input vector file.

    Example:
        >>> crs = get_vector_crs("data.gpkg", return_epsg=True)
        >>> print(crs)
        4326
    """
    metadata = get_vector_metadata(input_vector, db_con=db_con)
    crs = metadata["geometry_fields"][0]["crs"]
    if return_epsg:
        if crs["auth_name"] == "EPSG" and len(crs["auth_code"]) > 0:
            return int(crs["auth_code"])  # Return the EPSG code if available
        else:
            raise ValueError(
                f"CRS information is not available in the input vector file: {input_vector}. "
            )
    else:
        return crs

get_vector_metadata(input_vector, db_con=None)

Retrieves metadata for a vector file.

This function uses DuckDB with the spatial extension to extract metadata about the layers in the input vector file.

Parameters:

Name	Type	Description	Default
input_vector	str	The path to the input vector file.	required
db_con	Connection	An existing DuckDB connection. If None, a new connection will be created.	None

Returns:

Name	Type	Description
dict		A dictionary containing metadata about the vector file.

Raises:

Type	Description
ValueError	If the input vector file does not exist.

Example

metadata = get_vector_metadata("data.gpkg") print(metadata) {'geometry_fields': [{'name': 'geom', 'crs': {'auth_name': 'EPSG', 'auth_code': '4326'}}], ...}

Source code in leafmap/common.py

def get_vector_metadata(input_vector, db_con=None):
    """
    Retrieves metadata for a vector file.

    This function uses DuckDB with the spatial extension to extract metadata
    about the layers in the input vector file.

    Args:
        input_vector (str): The path to the input vector file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        dict: A dictionary containing metadata about the vector file.

    Raises:
        ValueError: If the input vector file does not exist.

    Example:
        >>> metadata = get_vector_metadata("data.gpkg")
        >>> print(metadata)
        {'geometry_fields': [{'name': 'geom', 'crs': {'auth_name': 'EPSG', 'auth_code': '4326'}}], ...}
    """
    import duckdb

    if db_con is None:
        db_con = duckdb.connect()

    db_con.execute("INSTALL spatial;")
    db_con.execute("LOAD spatial;")

    if not os.path.exists(input_vector):
        raise ValueError(f"Input vector file does not exist: {input_vector}")

    query = f"SELECT * FROM ST_Read_Meta('{input_vector}')"
    df = db_con.execute(query).fetch_df()
    meta = df["layers"][0][0]
    return meta

get_wbd(geometry=None, searchText=None, inSR='4326', outSR='3857', digit=8, spatialRel='esriSpatialRelIntersects', return_geometry=True, outFields='*', output=None, **kwargs)

Query the WBD (Watershed Boundary Dataset) API using various geometry types or a GeoDataFrame. https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer

Parameters:

Name	Type	Description	Default
geometry	Union[GeoDataFrame, Dict]	The geometry data (GeoDataFrame or geometry dict).	None
inSR	str	The input spatial reference (default is EPSG:4326).	'4326'
outSR	str	The output spatial reference (default is EPSG:3857).	'3857'
digit	int	The digit code for the WBD layer (default is 8).	8
spatialRel	str	The spatial relationship (default is "esriSpatialRelIntersects").	'esriSpatialRelIntersects'
return_geometry	bool	Whether to return the geometry (default is True).	True
outFields	str	The fields to be returned (default is "*").	'*'
output	Optional[str]	The output file path to save the GeoDataFrame (default is None).	None
**kwargs	Any	Additional keyword arguments to pass to the API.	{}

Returns:

Type	Description
Union[GeoDataFrame, DataFrame, Dict[str, str]]	gpd.GeoDataFrame or pd.DataFrame: The queried WBD data as a GeoDataFrame or DataFrame.

Source code in leafmap/common.py

def get_wbd(
    geometry: Union["gpd.GeoDataFrame", Dict[str, Any]] = None,
    searchText: Optional[str] = None,
    inSR: str = "4326",
    outSR: str = "3857",
    digit: int = 8,
    spatialRel: str = "esriSpatialRelIntersects",
    return_geometry: bool = True,
    outFields: str = "*",
    output: Optional[str] = None,
    **kwargs: Any,
) -> Union["gpd.GeoDataFrame", "pd.DataFrame", Dict[str, str]]:
    """
    Query the WBD (Watershed Boundary Dataset) API using various geometry types or a GeoDataFrame.
    https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer

    Args:
        geometry (Union[gpd.GeoDataFrame, Dict]): The geometry data (GeoDataFrame or geometry dict).
        inSR (str): The input spatial reference (default is EPSG:4326).
        outSR (str): The output spatial reference (default is EPSG:3857).
        digit (int): The digit code for the WBD layer (default is 8).
        spatialRel (str): The spatial relationship (default is "esriSpatialRelIntersects").
        return_geometry (bool): Whether to return the geometry (default is True).
        outFields (str): The fields to be returned (default is "*").
        output (Optional[str]): The output file path to save the GeoDataFrame (default is None).
        **kwargs: Additional keyword arguments to pass to the API.

    Returns:
        gpd.GeoDataFrame or pd.DataFrame: The queried WBD data as a GeoDataFrame or DataFrame.
    """

    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Polygon

    def detect_geometry_type(geometry):
        """
        Automatically detect the geometry type based on the structure of the geometry dictionary.
        """
        if "x" in geometry and "y" in geometry:
            return "esriGeometryPoint"
        elif (
            "xmin" in geometry
            and "ymin" in geometry
            and "xmax" in geometry
            and "ymax" in geometry
        ):
            return "esriGeometryEnvelope"
        elif "rings" in geometry:
            return "esriGeometryPolygon"
        elif "paths" in geometry:
            return "esriGeometryPolyline"
        elif "points" in geometry:
            return "esriGeometryMultipoint"
        else:
            raise ValueError("Unsupported geometry type or invalid geometry structure.")

    allowed_digit_values = [2, 4, 6, 8, 10, 12, 14, 16]
    if digit not in allowed_digit_values:
        raise ValueError(
            f"Invalid digit value. Allowed values are {allowed_digit_values}"
        )

    layer = allowed_digit_values.index(digit) + 1

    # Convert GeoDataFrame to a dictionary if needed
    if isinstance(geometry, gpd.GeoDataFrame):
        geometry_dict = _convert_geodataframe_to_esri_format(geometry)[0]
        geometry_type = detect_geometry_type(geometry_dict)
    elif isinstance(geometry, dict):
        geometry_type = detect_geometry_type(geometry)
        geometry_dict = geometry
    elif isinstance(geometry, str):
        geometry_dict = geometry
    elif searchText is None:
        raise ValueError(
            "Invalid geometry input. Must be a GeoDataFrame or a dictionary."
        )
    else:
        geometry_dict = None

    if geometry_dict is not None:
        # Convert geometry to a JSON string (required by the API)
        if isinstance(geometry_dict, dict):
            geometry_json = json.dumps(geometry_dict)
        else:
            geometry_json = geometry_dict

        # Construct the query parameters
        params = {
            "geometry": geometry_json,
            "geometryType": geometry_type,
            "inSR": inSR,
            "spatialRel": spatialRel,
            "outFields": outFields,
            "returnGeometry": str(return_geometry).lower(),
            "f": "json",
        }
        # API URL for querying the WBD
        url = f"https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/{layer}/query"
    else:
        # Construct the query parameters
        params = {
            "searchText": searchText,
            "contains": "true",
            "layers": str(layer),
            "inSR": inSR,
            "outFields": outFields,
            "returnGeometry": str(return_geometry).lower(),
            "f": "json",
        }
        url = f"https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/find"

    # Add additional keyword arguments
    for key, value in kwargs.items():
        params[key] = value

    # Make the GET request
    response = requests.get(url, params=params)

    if response.status_code != 200:
        return {"error": f"Request failed with status code {response.status_code}"}

    data = response.json()

    if geometry_dict is not None:
        # Extract features from the API response
        features = data.get("features", [])
        crs = f"EPSG:{data['spatialReference']['latestWkid']}"
    else:
        features = data.get("results", [])
        crs = f"EPSG:{data['results'][0]['geometry']['spatialReference']['latestWkid']}"

    # Prepare attribute data and geometries
    attributes = [feature["attributes"] for feature in features]
    df = pd.DataFrame(attributes)
    df.rename(
        columns={"Shape__Length": "Shape_Length", "Shape__Area": "Shape_Area"},
        inplace=True,
    )

    # Handle geometries
    if return_geometry:
        geometries = [
            (
                Polygon(feature["geometry"]["rings"][0])
                if "rings" in feature["geometry"]
                else None
            )
            for feature in features
        ]
        gdf = gpd.GeoDataFrame(
            df,
            geometry=geometries,
            crs=crs,
        )
        if outSR != "3857":
            gdf = gdf.to_crs(outSR)

        if output is not None:
            gdf.to_file(output)

        return gdf
    else:
        return df

get_wms_layers(url)

Returns a list of WMS layers from a WMS service.

Parameters:

Name	Type	Description	Default
url	str	The URL of the WMS service.	required

Returns:

Name	Type	Description
list		A list of WMS layers.

Source code in leafmap/common.py

def get_wms_layers(url):
    """Returns a list of WMS layers from a WMS service.

    Args:
        url (str): The URL of the WMS service.

    Returns:
        list: A list of WMS layers.
    """
    try:
        from owslib.wms import WebMapService
    except ImportError:
        raise ImportError("Please install owslib using 'pip install owslib'.")

    wms = WebMapService(url)
    layers = list(wms.contents)
    layers.sort()
    return layers

gif_fading(in_gif, out_gif, duration=1, verbose=True)

Fade in/out the gif.

Parameters:

Name	Type	Description	Default
in_gif	str	The input gif file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"	required
out_gif	str	The output gif file.	required
duration	float	The duration of the fading. Defaults to 1.	1
verbose	bool	Whether to print the progress. Defaults to True.	True

Raises:

Type	Description
FileNotFoundError	Raise exception when the input gif does not exist.
Exception	Raise exception when ffmpeg is not installed.

Source code in leafmap/common.py

def gif_fading(in_gif, out_gif, duration=1, verbose=True):
    """Fade in/out the gif.

    Args:
        in_gif (str): The input gif file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"
        out_gif (str): The output gif file.
        duration (float, optional): The duration of the fading. Defaults to 1.
        verbose (bool, optional): Whether to print the progress. Defaults to True.

    Raises:
        FileNotFoundError: Raise exception when the input gif does not exist.
        Exception: Raise exception when ffmpeg is not installed.
    """
    import glob
    import tempfile

    current_dir = os.getcwd()

    if isinstance(in_gif, str) and in_gif.startswith("http"):
        ext = os.path.splitext(in_gif)[1]
        file_path = temp_file_path(ext)
        download_from_url(in_gif, file_path, verbose=verbose)
        in_gif = file_path

    in_gif = os.path.abspath(in_gif)
    if not in_gif.endswith(".gif"):
        raise Exception("in_gif must be a gif file.")

    if " " in in_gif:
        raise Exception("The filename cannot contain spaces.")

    out_gif = os.path.abspath(out_gif)
    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    basename = os.path.basename(in_gif).replace(".gif", "")
    temp_dir = os.path.join(tempfile.gettempdir(), basename)
    if os.path.exists(temp_dir):
        shutil.rmtree(temp_dir)

    gif_to_png(in_gif, temp_dir, verbose=verbose)

    os.chdir(temp_dir)

    images = list(glob.glob(os.path.join(temp_dir, "*.png")))
    count = len(images)

    files = []
    for i in range(1, count + 1):
        files.append(f"-loop 1 -t {duration} -i {i}.png")
    inputs = " ".join(files)

    filters = []
    for i in range(1, count):
        if i == 1:
            filters.append(
                f"\"[1:v][0:v]blend=all_expr='A*(if(gte(T,3),1,T/3))+B*(1-(if(gte(T,3),1,T/3)))'[v0];"
            )
        else:
            filters.append(
                f"[{i}:v][{i-1}:v]blend=all_expr='A*(if(gte(T,3),1,T/3))+B*(1-(if(gte(T,3),1,T/3)))'[v{i-1}];"
            )

    last_filter = ""
    for i in range(count - 1):
        last_filter += f"[v{i}]"
    last_filter += f'concat=n={count-1}:v=1:a=0[v]" -map "[v]"'
    filters.append(last_filter)
    filters = " ".join(filters)

    cmd = f"ffmpeg -y -loglevel error {inputs} -filter_complex {filters} {out_gif}"

    os.system(cmd)
    try:
        shutil.rmtree(temp_dir)
    except Exception as e:
        print(e)

    os.chdir(current_dir)

gif_to_mp4(in_gif, out_mp4)

Converts a gif to mp4.

Parameters:

Name	Type	Description	Default
in_gif	str	The input gif file.	required
out_mp4	str	The output mp4 file.	required

Source code in leafmap/common.py

def gif_to_mp4(in_gif, out_mp4):
    """Converts a gif to mp4.

    Args:
        in_gif (str): The input gif file.
        out_mp4 (str): The output mp4 file.
    """
    from PIL import Image

    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    out_mp4 = os.path.abspath(out_mp4)
    if not out_mp4.endswith(".mp4"):
        out_mp4 = out_mp4 + ".mp4"

    if not os.path.exists(os.path.dirname(out_mp4)):
        os.makedirs(os.path.dirname(out_mp4))

    if not is_tool("ffmpeg"):
        print("ffmpeg is not installed on your computer.")
        return

    width, height = Image.open(in_gif).size

    if width % 2 == 0 and height % 2 == 0:
        cmd = f"ffmpeg -loglevel error -i {in_gif} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
        os.system(cmd)
    else:
        width += width % 2
        height += height % 2
        cmd = f"ffmpeg -loglevel error -i {in_gif} -vf scale={width}:{height} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
        os.system(cmd)

    if not os.path.exists(out_mp4):
        raise Exception(f"Failed to create mp4 file.")

gif_to_png(in_gif, out_dir=None, prefix='', verbose=True)

Converts a gif to png.

Parameters:

Name	Type	Description	Default
in_gif	str	The input gif file.	required
out_dir	str	The output directory. Defaults to None.	None
prefix	str	The prefix of the output png files. Defaults to None.	''
verbose	bool	Whether to print the progress. Defaults to True.	True

Raises:

Type	Description
FileNotFoundError	Raise exception when the input gif does not exist.
Exception	Raise exception when ffmpeg is not installed.

Source code in leafmap/common.py

def gif_to_png(in_gif, out_dir=None, prefix="", verbose=True):
    """Converts a gif to png.

    Args:
        in_gif (str): The input gif file.
        out_dir (str, optional): The output directory. Defaults to None.
        prefix (str, optional): The prefix of the output png files. Defaults to None.
        verbose (bool, optional): Whether to print the progress. Defaults to True.

    Raises:
        FileNotFoundError: Raise exception when the input gif does not exist.
        Exception: Raise exception when ffmpeg is not installed.
    """
    import tempfile

    in_gif = os.path.abspath(in_gif)
    if " " in in_gif:
        raise Exception("in_gif cannot contain spaces.")
    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    basename = os.path.basename(in_gif).replace(".gif", "")
    if out_dir is None:
        out_dir = os.path.join(tempfile.gettempdir(), basename)
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)
    elif isinstance(out_dir, str) and not os.path.exists(out_dir):
        os.makedirs(out_dir)
    elif not isinstance(out_dir, str):
        raise Exception("out_dir must be a string.")

    out_dir = os.path.abspath(out_dir)
    cmd = f"ffmpeg -loglevel error -i {in_gif} -vsync 0 {out_dir}/{prefix}%d.png"
    os.system(cmd)

    if verbose:
        print(f"Images are saved to {out_dir}")

github_delete_asset(username, repository, asset_id, access_token=None)

Deletes an asset from a GitHub release.

Parameters:

Name	Type	Description	Default
username	str	GitHub username or organization name.	required
repository	str	Name of the GitHub repository.	required
asset_id	int	ID of the asset to delete.	required
access_token	str	Personal access token for authentication.	None

Source code in leafmap/common.py

def github_delete_asset(username, repository, asset_id, access_token=None):
    """
    Deletes an asset from a GitHub release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        asset_id (int): ID of the asset to delete.
        access_token (str): Personal access token for authentication.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    url = f"https://api.github.com/repos/{username}/{repository}/releases/assets/{asset_id}"
    headers = {
        "Authorization": f"token {access_token}",
        "Accept": "application/vnd.github.v3+json",
    }

    response = requests.delete(url, headers=headers)

    if response.status_code == 204:
        print(f"Successfully deleted asset ID: {asset_id}")
    else:
        print(f"Error: Unable to delete asset (Status code: {response.status_code})")

github_get_release_assets(username, repository, release_id, access_token=None)

Fetches the assets for a given release.

Parameters:

Name	Type	Description	Default
username	str	GitHub username or organization name.	required
repository	str	Name of the GitHub repository.	required
release_id	int	ID of the release to fetch assets for.	required
access_token	str	Personal access token for authentication.	None

Returns:

Name	Type	Description
list		List of assets if successful, None otherwise.

Source code in leafmap/common.py

def github_get_release_assets(username, repository, release_id, access_token=None):
    """
    Fetches the assets for a given release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        release_id (int): ID of the release to fetch assets for.
        access_token (str): Personal access token for authentication.

    Returns:
        list: List of assets if successful, None otherwise.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    url = f"https://api.github.com/repos/{username}/{repository}/releases/{release_id}/assets"
    headers = {
        "Authorization": f"token {access_token}",
        "Accept": "application/vnd.github.v3+json",
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: Unable to fetch assets (Status code: {response.status_code})")
        return None

github_get_release_id_by_tag(username, repository, tag_name, access_token=None)

Fetches the release ID by tag name for a given GitHub repository.

Parameters:

Name	Type	Description	Default
username	str	GitHub username or organization name.	required
repository	str	Name of the GitHub repository.	required
tag_name	str	Tag name of the release.	required
access_token	str	Personal access token for authentication. Defaults to None.	None

Returns:

Name	Type	Description
int		The release ID if found, None otherwise.

Source code in leafmap/common.py

def github_get_release_id_by_tag(username, repository, tag_name, access_token=None):
    """
    Fetches the release ID by tag name for a given GitHub repository.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        tag_name (str): Tag name of the release.
        access_token (str, optional): Personal access token for authentication. Defaults to None.

    Returns:
        int: The release ID if found, None otherwise.
    """

    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")

    # GitHub API URL for fetching releases
    url = (
        f"https://api.github.com/repos/{username}/{repository}/releases/tags/{tag_name}"
    )

    # Headers for authentication (optional)
    headers = {"Authorization": f"token {access_token}"} if access_token else {}

    # Make the request to the GitHub API
    response = requests.get(url, headers=headers)

    # Check if the request was successful
    if response.status_code == 200:
        release_info = response.json()
        return release_info.get("id")
    else:
        print(
            f"Error: Unable to fetch release info for tag {tag_name} (Status code: {response.status_code})"
        )
        return None

github_raw_url(url)

Get the raw URL for a GitHub file.

Parameters:

Name	Type	Description	Default
url	str	The GitHub URL.	required

Source code in leafmap/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/", ""
        )
    return url

github_upload_asset_to_release(username, repository, release_id, asset_path, quiet=False, access_token=None)

Uploads an asset to a GitHub release.

Parameters:

Name	Type	Description	Default
username	str	GitHub username or organization name.	required
repository	str	Name of the GitHub repository.	required
release_id	int	ID of the release to upload the asset to.	required
asset_path	str	Path to the asset file.	required
access_token	str	Personal access token for authentication.	None

Returns:

Name	Type	Description
dict		The response JSON from the GitHub API if the upload is successful.
None		If the upload fails.

Source code in leafmap/common.py

def github_upload_asset_to_release(
    username, repository, release_id, asset_path, quiet=False, access_token=None
):
    """
    Uploads an asset to a GitHub release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        release_id (int): ID of the release to upload the asset to.
        asset_path (str): Path to the asset file.

        access_token (str): Personal access token for authentication.

    Returns:
        dict: The response JSON from the GitHub API if the upload is successful.
        None: If the upload fails.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    # GitHub API URL for uploading release assets
    url = f"https://uploads.github.com/repos/{username}/{repository}/releases/{release_id}/assets"

    # Extract the filename from the asset path
    asset_name = os.path.basename(asset_path)

    # Set the headers for the upload request
    headers = {
        "Authorization": f"token {access_token}",
        "Content-Type": "application/octet-stream",
    }

    # Set the parameters for the upload request
    params = {"name": asset_name}

    # Check if the asset already exists
    assets = github_get_release_assets(username, repository, release_id, access_token)
    if assets:
        for asset in assets:
            if asset["name"] == asset_name:
                github_delete_asset(username, repository, asset["id"], access_token)
                break

    # Open the asset file in binary mode
    with open(asset_path, "rb") as asset_file:
        # Make the request to upload the asset
        response = requests.post(url, headers=headers, params=params, data=asset_file)

    # Check if the request was successful
    if response.status_code == 201:
        print(f"Successfully uploaded asset: {asset_name}")
        if not quiet:
            return response.json()
        else:
            return None
    else:
        print(f"Error: Unable to upload asset (Status code: {response.status_code})")
        if not quiet:
            print(response.json())
        return None

google_buildings_csv_to_vector(filename, output=None, **kwargs)

Convert a CSV file containing Google Buildings data to a GeoJSON vector file.

Parameters:

Name	Type	Description	Default
filename	str	The path to the input CSV file.	required
output	str	The path to the output GeoJSON file. If not provided, the output file will have the same name as the input file with the extension changed to '.geojson'.	None
**kwargs		Additional keyword arguments that are passed to the to_file method of the GeoDataFrame.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def google_buildings_csv_to_vector(
    filename: str, output: Optional[str] = None, **kwargs
) -> None:
    """
    Convert a CSV file containing Google Buildings data to a GeoJSON vector file.

    Args:
        filename (str): The path to the input CSV file.
        output (str, optional): The path to the output GeoJSON file. If not provided, the output file will have the same
            name as the input file with the extension changed to '.geojson'.
        **kwargs: Additional keyword arguments that are passed to the `to_file` method of the GeoDataFrame.

    Returns:
        None
    """
    import pandas as pd
    import geopandas as gpd
    from shapely import wkt

    df = pd.read_csv(filename)

    # Create a geometry column from the "geometry" column in the DataFrame
    df["geometry"] = df["geometry"].apply(wkt.loads)

    # Convert the pandas DataFrame to a GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry="geometry")
    gdf.crs = "EPSG:4326"

    if output is None:
        output = os.path.splitext(filename)[0] + ".geojson"

    gdf.to_file(output, **kwargs)

h5_keys(filename)

Retrieve the keys (dataset names) within an HDF5 file.

Parameters:

Name	Type	Description	Default
filename	str	The filename of the HDF5 file.	required

Returns:

Type	Description
List[str]	List[str]: A list of dataset names present in the HDF5 file.

Raises:

Type	Description
ImportError	Raised if h5py is not installed.

Example

keys = h5_keys('data.h5') print(keys) [

Source code in leafmap/common.py

def h5_keys(filename: str) -> List[str]:
    """
    Retrieve the keys (dataset names) within an HDF5 file.

    Args:
        filename (str): The filename of the HDF5 file.

    Returns:
        List[str]: A list of dataset names present in the HDF5 file.

    Raises:
        ImportError: Raised if h5py is not installed.

    Example:
        >>> keys = h5_keys('data.h5')
        >>> print(keys)
        [
    """
    try:
        import h5py
    except ImportError:
        raise ImportError(
            "h5py must be installed to use this function. Please install it with 'pip install h5py'."
        )

    with h5py.File(filename, "r") as f:
        keys = list(f.keys())

    return keys

h5_to_gdf(filenames, dataset, lat='lat_lowestmode', lon='lon_lowestmode', columns=None, crs='EPSG:4326', nodata=None, **kwargs)

Read data from one or multiple HDF5 files and return as a GeoDataFrame.

Parameters:

Name	Type	Description	Default
filenames	str or List[str]	The filename(s) of the HDF5 file(s).	required
dataset	str	The dataset name within the H5 file(s).	required
lat	str	The column name representing latitude. Default is 'lat_lowestmode'.	'lat_lowestmode'
lon	str	The column name representing longitude. Default is 'lon_lowestmode'.	'lon_lowestmode'
columns	List[str]	List of column names to include. If None, all columns will be included. Default is None.	None
crs	str	The coordinate reference system code. Default is "EPSG:4326".	'EPSG:4326'
**kwargs		Additional keyword arguments to be passed to the GeoDataFrame constructor.	{}

Returns:

Type	Description
	geopandas.GeoDataFrame: A GeoDataFrame containing the data from the H5 file(s).

Raises:

Type	Description
ImportError	Raised if h5py is not installed.
ValueError	Raised if the provided filenames argument is not a valid type or if a specified file does not exist.

Example

gdf = h5_to_gdf('data.h5', 'dataset1', 'lat', 'lon', columns=['column1', 'column2'], crs='EPSG:4326') print(gdf.head()) column1 column2 lat lon geometry 0 10 20 40.123456 -75.987654 POINT (-75.987654 40.123456) 1 15 25 40.234567 -75.876543 POINT (-75.876543 40.234567) ...

Source code in leafmap/common.py

def h5_to_gdf(
    filenames: str,
    dataset: str,
    lat: str = "lat_lowestmode",
    lon: str = "lon_lowestmode",
    columns: Optional[List[str]] = None,
    crs: str = "EPSG:4326",
    nodata=None,
    **kwargs,
):
    """
    Read data from one or multiple HDF5 files and return as a GeoDataFrame.

    Args:
        filenames (str or List[str]): The filename(s) of the HDF5 file(s).
        dataset (str): The dataset name within the H5 file(s).
        lat (str): The column name representing latitude. Default is 'lat_lowestmode'.
        lon (str): The column name representing longitude. Default is 'lon_lowestmode'.
        columns (List[str], optional): List of column names to include. If None, all columns will be included. Default is None.
        crs (str, optional): The coordinate reference system code. Default is "EPSG:4326".
        **kwargs: Additional keyword arguments to be passed to the GeoDataFrame constructor.

    Returns:
        geopandas.GeoDataFrame: A GeoDataFrame containing the data from the H5 file(s).

    Raises:
        ImportError: Raised if h5py is not installed.
        ValueError: Raised if the provided filenames argument is not a valid type or if a specified file does not exist.

    Example:
        >>> gdf = h5_to_gdf('data.h5', 'dataset1', 'lat', 'lon', columns=['column1', 'column2'], crs='EPSG:4326')
        >>> print(gdf.head())
           column1  column2        lat        lon                    geometry
        0        10       20  40.123456 -75.987654  POINT (-75.987654 40.123456)
        1        15       25  40.234567 -75.876543  POINT (-75.876543 40.234567)
        ...

    """
    try:
        import h5py
    except ImportError:
        install_package("h5py")
        import h5py

    import glob
    import pandas as pd
    import geopandas as gpd

    if isinstance(filenames, str):
        if os.path.exists(filenames):
            files = [filenames]
        else:
            files = glob.glob(filenames)
            if not files:
                raise ValueError(f"File {filenames} does not exist.")
            files.sort()
    elif isinstance(filenames, list):
        files = filenames
    else:
        raise ValueError("h5_file must be a string or a list of strings.")

    out_df = pd.DataFrame()

    for file in files:
        h5 = h5py.File(file, "r")
        try:
            data = h5[dataset]
        except KeyError:
            print(f"Dataset {dataset} not found in file {file}. Skipping...")
            continue
        col_names = []
        col_val = []

        for key, value in data.items():
            if columns is None or key in columns or key == lat or key == lon:
                col_names.append(key)
                col_val.append(value[:].tolist())

        df = pd.DataFrame(map(list, zip(*col_val)), columns=col_names)
        out_df = pd.concat([out_df, df])
        h5.close()

    if nodata is not None and columns is not None:
        out_df = out_df[out_df[columns[0]] != nodata]

    gdf = gpd.GeoDataFrame(
        out_df, geometry=gpd.points_from_xy(out_df[lon], out_df[lat]), crs=crs, **kwargs
    )

    return gdf

h5_variables(filename, key)

Retrieve the variables (column names) within a specific key (dataset) in an H5 file.

Parameters:

Name	Type	Description	Default
filename	str	The filename of the H5 file.	required
key	str	The key (dataset name) within the H5 file.	required

Returns:

Type	Description
List[str]	List[str]: A list of variable names (column names) within the specified key.

Raises:

Type	Description
ImportError	Raised if h5py is not installed.

Example

variables = h5_variables('data.h5', 'dataset1') print(variables) ['var1', 'var2', 'var3']

Source code in leafmap/common.py

def h5_variables(filename: str, key: str) -> List[str]:
    """
    Retrieve the variables (column names) within a specific key (dataset) in an H5 file.

    Args:
        filename (str): The filename of the H5 file.
        key (str): The key (dataset name) within the H5 file.

    Returns:
        List[str]: A list of variable names (column names) within the specified key.

    Raises:
        ImportError: Raised if h5py is not installed.

    Example:
        >>> variables = h5_variables('data.h5', 'dataset1')
        >>> print(variables)
        ['var1', 'var2', 'var3']
    """
    try:
        import h5py
    except ImportError:
        raise ImportError(
            "h5py must be installed to use this function. Please install it with 'pip install h5py'."
        )

    with h5py.File(filename, "r") as f:
        cols = list(f[key].keys())

    return cols

has_transparency(img)

Checks whether an image has transparency.

Parameters:

Name	Type	Description	Default
img	object	a PIL Image object.	required

Returns:

Name	Type	Description
bool	bool	True if it has transparency, False otherwise.

Source code in leafmap/common.py

def has_transparency(img) -> bool:
    """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:

Name	Type	Description
tuple	Tuple[int, int, int]	RGB color as a tuple.

Source code in leafmap/common.py

def hex_to_rgb(value: Optional[str] = "FFFFFF") -> Tuple[int, int, int]:
    """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:

Name	Type	Description
str		The HTML string to use in Gradio.

Source code in leafmap/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(html, width=800, height=600, responsive=True, scrolling=False, token_name=None, token_value=None, **kwargs)

Renders an HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

Parameters:

Name	Type	Description	Default
html	str	The HTML file to render. It can a local file path or a URL.	required
width	int	Width of the map. Defaults to 800.	800
height	int	Height of the map. Defaults to 600.	600
responsive	bool	Whether to make the map responsive. Defaults to True.	True
scrolling	bool	Whether to allow the map to scroll. Defaults to False.	False
token_name	str	The name of the token in the HTML file to be replaced. Defaults to None.	None
token_value	str	The value of the token to pass to the HTML file. Defaults to None.	None

Returns:

Type	Description
	streamlit.components: components.html object.

Source code in leafmap/common.py

def html_to_streamlit(
    html,
    width=800,
    height=600,
    responsive=True,
    scrolling=False,
    token_name=None,
    token_value=None,
    **kwargs,
):
    """Renders an HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

    Args:
        html (str): The HTML file to render. It can a local file path or a URL.
        width (int, optional): Width of the map. Defaults to 800.
        height (int, optional): Height of the map. Defaults to 600.
        responsive (bool, optional): Whether to make the map responsive. Defaults to True.
        scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
        token_name (str, optional): The name of the token in the HTML file to be replaced. Defaults to None.
        token_value (str, optional): The value of the token to pass to the HTML file. Defaults to None.

    Returns:
        streamlit.components: components.html object.
    """

    try:
        import streamlit as st  # pylint: disable=E0401
        import streamlit.components.v1 as components  # pylint: disable=E0401

        if isinstance(html, str):
            temp_path = None
            if html.startswith("http") and html.endswith(".html"):
                temp_path = temp_file_path(".html")
                out_file = os.path.basename(temp_path)
                out_dir = os.path.dirname(temp_path)
                download_from_url(html, out_file, out_dir)
                html = temp_path

            elif not os.path.exists(html):
                raise FileNotFoundError("The specified input html does not exist.")

            with open(html) as f:
                lines = f.readlines()
                if (token_name is not None) and (token_value is not None):
                    lines = [line.replace(token_name, token_value) for line in lines]
                html_str = "".join(lines)

            if temp_path is not None:
                os.remove(temp_path)

            if responsive:
                make_map_responsive = """
                <style>
                [title~="st.iframe"] { width: 100%}
                </style>
                """
                st.markdown(make_map_responsive, unsafe_allow_html=True)
            return components.html(
                html_str, width=width, height=height, scrolling=scrolling
            )
        else:
            raise TypeError("The html must be a string.")

    except Exception as e:
        raise Exception(e)

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:

Name	Type	Description
int		The number of bands in the image.

Source code in leafmap/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:

Name	Type	Description
list		A list of bounds in the form of [(south, west), (north, east)].

Source code in leafmap/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_center(image, **kwargs)

Get the center of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
tuple		A tuple of (latitude, longitude).

Source code in leafmap/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:

Name	Type	Description
TileClient		A LocalTileserver TileClient.

Source code in leafmap/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_comparison(img1, img2, label1='1', label2='2', width=704, show_labels=True, starting_position=50, make_responsive=True, in_memory=True, out_html=None)

Create a comparison slider for two images. The source code is adapted from https://github.com/fcakyon/streamlit-image-comparison. Credits to the GitHub user @fcakyon. Users can also use https://juxtapose.knightlab.com to create a comparison slider.

Parameters:

Name	Type	Description	Default
img1	str	Path to the first image. It can be a local file path, a URL, or a numpy array.	required
img2	str	Path to the second image. It can be a local file path, a URL, or a numpy array.	required
label1	str	Label for the first image. Defaults to "1".	'1'
label2	str	Label for the second image. Defaults to "2".	'2'
width	int	Width of the component in pixels. Defaults to 704.	704
show_labels	bool	Whether to show labels on the images. Default is True.	True
starting_position	int	Starting position of the slider as a percentage (0-100). Default is 50.	50
make_responsive	bool	Whether to enable responsive mode. Default is True.	True
in_memory	bool	Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.	True
out_html	str	Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.	None

Source code in leafmap/common.py

def image_comparison(
    img1: str,
    img2: str,
    label1: str = "1",
    label2: str = "2",
    width: int = 704,
    show_labels: bool = True,
    starting_position: int = 50,
    make_responsive: bool = True,
    in_memory: bool = True,
    out_html: str = None,
):
    """Create a comparison slider for two images. The source code is adapted from
        https://github.com/fcakyon/streamlit-image-comparison. Credits to the GitHub user @fcakyon.
        Users can also use https://juxtapose.knightlab.com to create a comparison slider.

    Args:
        img1 (str): Path to the first image. It can be a local file path, a URL, or a numpy array.
        img2 (str): Path to the second image. It can be a local file path, a URL, or a numpy array.
        label1 (str, optional): Label for the first image. Defaults to "1".
        label2 (str, optional): Label for the second image. Defaults to "2".
        width (int, optional): Width of the component in pixels. Defaults to 704.
        show_labels (bool, optional): Whether to show labels on the images. Default is True.
        starting_position (int, optional): Starting position of the slider as a percentage (0-100). Default is 50.
        make_responsive (bool, optional): Whether to enable responsive mode. Default is True.
        in_memory (bool, optional): Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.
        out_html (str, optional): Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.

    """

    from PIL import Image
    import base64
    import io
    import os
    import uuid
    from typing import Union
    import requests
    import tempfile
    import numpy as np
    from IPython.display import HTML, display

    TEMP_DIR = os.path.join(tempfile.gettempdir(), random_string(6))
    os.makedirs(TEMP_DIR, exist_ok=True)

    def exif_transpose(image: Image.Image):
        """
        Transpose a PIL image accordingly if it has an EXIF Orientation tag.
        Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
        :param image: The image to transpose.
        :return: An image.
        """
        exif = image.getexif()
        orientation = exif.get(0x0112, 1)  # default 1
        if orientation > 1:
            method = {
                2: Image.FLIP_LEFT_RIGHT,
                3: Image.ROTATE_180,
                4: Image.FLIP_TOP_BOTTOM,
                5: Image.TRANSPOSE,
                6: Image.ROTATE_270,
                7: Image.TRANSVERSE,
                8: Image.ROTATE_90,
            }.get(orientation)
            if method is not None:
                image = image.transpose(method)
                del exif[0x0112]
                image.info["exif"] = exif.tobytes()
        return image

    def read_image_as_pil(
        image: Union[Image.Image, str, np.ndarray], exif_fix: bool = False
    ):
        """
        Loads an image as PIL.Image.Image.
        Args:
            image : Can be image path or url (str), numpy image (np.ndarray) or PIL.Image
        """
        # https://stackoverflow.com/questions/56174099/how-to-load-images-larger-than-max-image-pixels-with-pil
        Image.MAX_IMAGE_PIXELS = None

        if isinstance(image, Image.Image):
            image_pil = image.convert("RGB")
        elif isinstance(image, str):
            # read image if str image path is provided
            try:
                image_pil = Image.open(
                    requests.get(image, stream=True).raw
                    if str(image).startswith("http")
                    else image
                ).convert("RGB")
                if exif_fix:
                    image_pil = exif_transpose(image_pil)
            except:  # handle large/tiff image reading
                try:
                    import skimage.io
                except ImportError:
                    raise ImportError(
                        "Please run 'pip install -U scikit-image imagecodecs' for large image handling."
                    )
                image_sk = skimage.io.imread(image).astype(np.uint8)
                if len(image_sk.shape) == 2:  # b&w
                    image_pil = Image.fromarray(image_sk, mode="1").convert("RGB")
                elif image_sk.shape[2] == 4:  # rgba
                    image_pil = Image.fromarray(image_sk, mode="RGBA").convert("RGB")
                elif image_sk.shape[2] == 3:  # rgb
                    image_pil = Image.fromarray(image_sk, mode="RGB")
                else:
                    raise TypeError(
                        f"image with shape: {image_sk.shape[3]} is not supported."
                    )
        elif isinstance(image, np.ndarray):
            if image.shape[0] < 5:  # image in CHW
                image = image[:, :, ::-1]
            image_pil = Image.fromarray(image).convert("RGB")
        else:
            raise TypeError("read image with 'pillow' using 'Image.open()'")

        return image_pil

    def pillow_to_base64(image: Image.Image) -> str:
        """
        Convert a PIL image to a base64-encoded string.

        Parameters
        ----------
        image: PIL.Image.Image
            The image to be converted.

        Returns
        -------
        str
            The base64-encoded string.
        """
        in_mem_file = io.BytesIO()
        image.save(in_mem_file, format="JPEG", subsampling=0, quality=100)
        img_bytes = in_mem_file.getvalue()  # bytes
        image_str = base64.b64encode(img_bytes).decode("utf-8")
        base64_src = f"data:image/jpg;base64,{image_str}"
        return base64_src

    def local_file_to_base64(image_path: str) -> str:
        """
        Convert a local image file to a base64-encoded string.

        Parameters
        ----------
        image_path: str
            The path to the image file.

        Returns
        -------
        str
            The base64-encoded string.
        """
        file_ = open(image_path, "rb")
        img_bytes = file_.read()
        image_str = base64.b64encode(img_bytes).decode("utf-8")
        file_.close()
        base64_src = f"data:image/jpg;base64,{image_str}"
        return base64_src

    def pillow_local_file_to_base64(image: Image.Image, temp_dir: str):
        """
        Convert a Pillow image to a base64 string, using a temporary file on disk.

        Parameters
        ----------
        image : PIL.Image.Image
            The Pillow image to convert.
        temp_dir : str
            The directory to use for the temporary file.

        Returns
        -------
        str
            A base64-encoded string representing the image.
        """
        # Create temporary file path using os.path.join()
        img_path = os.path.join(temp_dir, str(uuid.uuid4()) + ".jpg")

        # Save image to temporary file
        image.save(img_path, subsampling=0, quality=100)

        # Convert temporary file to base64 string
        base64_src = local_file_to_base64(img_path)

        return base64_src

    # Prepare images
    img1_pillow = read_image_as_pil(img1)
    img2_pillow = read_image_as_pil(img2)

    img_width, img_height = img1_pillow.size
    h_to_w = img_height / img_width
    height = int((width * h_to_w) * 0.95)

    if in_memory:
        # Convert images to base64 strings
        img1 = pillow_to_base64(img1_pillow)
        img2 = pillow_to_base64(img2_pillow)
    else:
        # Create base64 strings from temporary files
        os.makedirs(TEMP_DIR, exist_ok=True)
        for file_ in os.listdir(TEMP_DIR):
            if file_.endswith(".jpg"):
                os.remove(os.path.join(TEMP_DIR, file_))
        img1 = pillow_local_file_to_base64(img1_pillow, TEMP_DIR)
        img2 = pillow_local_file_to_base64(img2_pillow, TEMP_DIR)

    # Load CSS and JS
    cdn_path = "https://cdn.knightlab.com/libs/juxtapose/latest"
    css_block = f'<link rel="stylesheet" href="{cdn_path}/css/juxtapose.css">'
    js_block = f'<script src="{cdn_path}/js/juxtapose.min.js"></script>'

    # write html block
    htmlcode = f"""
        <html>
        <head>
        <style>body {{ margin: unset; }}</style>
        {css_block}
        {js_block}
        <div id="foo" style="height: {height}; width: {width or '100%'};"></div>
        <script>
        slider = new juxtapose.JXSlider('#foo',
            [
                {{
                    src: '{img1}',
                    label: '{label1}',
                }},
                {{
                    src: '{img2}',
                    label: '{label2}',
                }}
            ],
            {{
                animate: true,
                showLabels: {'true' if show_labels else 'false'},
                showCredits: true,
                startingPosition: "{starting_position}%",
                makeResponsive: {'true' if make_responsive else 'false'},
            }});
        </script>
        </head>
        </html>
        """

    if out_html is not None:
        with open(out_html, "w") as f:
            f.write(htmlcode)

    shutil.rmtree(TEMP_DIR)

    display(HTML(htmlcode))

image_filesize(region, cellsize, bands=1, dtype='uint8', unit='MB', source_crs='epsg:4326', dst_crs='epsg:3857', bbox=False)

Calculate the size of an image in a given region and cell size.

Parameters:

Name	Type	Description	Default
region	list	A bounding box in the format of [minx, miny, maxx, maxy].	required
cellsize	float	The resolution of the image.	required
bands	int	Number of bands. Defaults to 1.	1
dtype	str	Data type, such as unit8, float32. For more info, see https://numpy.org/doc/stable/user/basics.types.html. Defaults to 'uint8'.	'uint8'
unit	str	The unit of the output. Defaults to 'MB'.	'MB'
source_crs	str	The CRS of the region. Defaults to 'epsg:4326'.	'epsg:4326'
dst_crs	str	The destination CRS to calculate the area. Defaults to 'epsg:3857'.	'epsg:3857'
bbox	bool	Whether to use the bounding box of the region to calculate the area. Defaults to False.	False

Returns:

Name	Type	Description
float		The size of the image in a given unit.

Source code in leafmap/common.py

def image_filesize(
    region,
    cellsize,
    bands=1,
    dtype="uint8",
    unit="MB",
    source_crs="epsg:4326",
    dst_crs="epsg:3857",
    bbox=False,
):
    """Calculate the size of an image in a given region and cell size.

    Args:
        region (list): A bounding box in the format of [minx, miny, maxx, maxy].
        cellsize (float): The resolution of the image.
        bands (int, optional): Number of bands. Defaults to 1.
        dtype (str, optional): Data type, such as unit8, float32. For more info,
            see https://numpy.org/doc/stable/user/basics.types.html. Defaults to 'uint8'.
        unit (str, optional): The unit of the output. Defaults to 'MB'.
        source_crs (str, optional): The CRS of the region. Defaults to 'epsg:4326'.
        dst_crs (str, optional): The destination CRS to calculate the area. Defaults to 'epsg:3857'.
        bbox (bool, optional): Whether to use the bounding box of the region to calculate the area. Defaults to False.

    Returns:
        float: The size of the image in a given unit.
    """
    import numpy as np
    import geopandas as gpd

    if bbox:
        if isinstance(region, gpd.GeoDataFrame):
            region = region.to_crs(dst_crs).total_bounds.tolist()
        elif isinstance(region, str) and os.path.exists(region):
            region = gpd.read_file(region).to_crs(dst_crs).total_bounds.tolist()
        elif isinstance(region, list):
            region = (
                bbox_to_gdf(region, crs=source_crs)
                .to_crs(dst_crs)
                .total_bounds.tolist()
            )
        else:
            raise ValueError("Invalid input region.")

        bytes = (
            np.prod(
                [
                    int((region[2] - region[0]) / cellsize),
                    int((region[3] - region[1]) / cellsize),
                    bands,
                ]
            )
            * np.dtype(dtype).itemsize
        )
    else:
        if isinstance(region, list):
            region = bbox_to_gdf(region, crs=source_crs)

        bytes = (
            vector_area(region, crs=dst_crs)
            / pow(cellsize, 2)
            * np.dtype(dtype).itemsize
            * bands
        )

    unit = unit.upper()

    if unit == "KB":
        return bytes / 1024
    elif unit == "MB":
        return bytes / pow(1024, 2)
    elif unit == "GB":
        return bytes / pow(1024, 3)
    elif unit == "TB":
        return bytes / pow(1024, 4)
    elif unit == "PB":
        return bytes / pow(1024, 5)
    else:
        return bytes

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:

Name	Type	Description
list		A list of geotransform values.

Source code in leafmap/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_metadata(image, **kwargs)

Get the metadata of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
dict		A dictionary of image metadata.

Source code in leafmap/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_max(image, bands=None)

Computes the minimum and maximum pixel values of an image.

This function opens an image file using xarray and rasterio, optionally selects specific bands, and then computes the minimum and maximum pixel values in the image.

Parameters:

Name	Type	Description	Default
image	str	The path to the image file.	required
bands	int or list	The band or list of bands to select. If None, all bands are used.	None

Returns:

Type	Description
Tuple[float, float]	Tuple[float, float]: The minimum and maximum pixel values in the image.

Source code in leafmap/common.py

def image_min_max(
    image: str, bands: Optional[Union[int, list]] = None
) -> Tuple[float, float]:
    """
    Computes the minimum and maximum pixel values of an image.

    This function opens an image file using xarray and rasterio, optionally
        selects specific bands, and then computes the minimum and maximum pixel
        values in the image.

    Args:
        image (str): The path to the image file.
        bands (int or list, optional): The band or list of bands to select. If
            None, all bands are used.

    Returns:
        Tuple[float, float]: The minimum and maximum pixel values in the image.
    """

    import rioxarray
    import xarray as xr

    dataset = xr.open_dataset(image, engine="rasterio")

    if bands is not None:
        dataset = dataset.sel(band=bands)

    vmin = dataset["band_data"].min().values.item()
    vmax = dataset["band_data"].max().values.item()

    return vmin, vmax

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:

Name	Type	Description
str		The projection of the image.

Source code in leafmap/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_resolution(image, **kwargs)

Get the resolution of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
float		The resolution of the image.

Source code in leafmap/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_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 leafmap/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:

Name	Type	Description
tuple		A tuple of (width, height).

Source code in leafmap/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_to_cog(source, dst_path=None, profile='deflate', BIGTIFF=None, **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'
BIGTIFF	str	Create a BigTIFF file. Can be "IF_SAFER" or "YES". Defaults to None.	None

Raises:

Type	Description
ImportError	If rio-cogeo is not installed.
FileNotFoundError	If the source file could not be found.

Source code in leafmap/common.py

def image_to_cog(source, dst_path=None, profile="deflate", BIGTIFF=None, **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".
        BIGTIFF (str, optional): Create a BigTIFF file. Can be "IF_SAFER" or "YES". Defaults to None.

    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)
    if "dst_kwargs" in kwargs:
        dst_profile.update(kwargs.pop("dst_kwargs"))

    if BIGTIFF is not None:
        dst_profile.update({"BIGTIFF": BIGTIFF})
    cog_translate(source, dst_path, dst_profile, **kwargs)

image_to_geotiff(image, dst_path, dtype=None, to_cog=True, **kwargs)

Converts an image to a GeoTIFF file.

This function takes an image in the form of a rasterio.io.DatasetReader object, and writes it to a GeoTIFF file at the specified destination path. The data type of the output GeoTIFF can be specified. Additional keyword arguments can be passed to customize the GeoTIFF profile.

Parameters:

Name	Type	Description	Default
image	DatasetReader	The input image as a rasterio.io.DatasetReader object.	required
dst_path	str	The destination path where the GeoTIFF file will be saved.	required
dtype	Optional[str]	The data type for the output GeoTIFF file. If None, the data type of the input image will be used. Defaults to None.	None
to_cog	bool	Whether to convert the output GeoTIFF to a Cloud Optimized GeoTIFF (COG). Defaults to True.	True
**kwargs		Additional keyword arguments to be included in the GeoTIFF profile.	{}

Raises:

Type	Description
ValueError	If the input image is not a rasterio.io.DatasetReader object.

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def image_to_geotiff(image, dst_path, dtype=None, to_cog=True, **kwargs) -> None:
    """
    Converts an image to a GeoTIFF file.

    This function takes an image in the form of a rasterio.io.DatasetReader object, and writes it to a GeoTIFF file
    at the specified destination path. The data type of the output GeoTIFF can be specified. Additional keyword
    arguments can be passed to customize the GeoTIFF profile.

    Args:
        image (DatasetReader): The input image as a rasterio.io.DatasetReader object.
        dst_path (str): The destination path where the GeoTIFF file will be saved.
        dtype (Optional[str]): The data type for the output GeoTIFF file. If None, the data type of the input image
            will be used. Defaults to None.
        to_cog (bool): Whether to convert the output GeoTIFF to a Cloud Optimized GeoTIFF (COG). Defaults to True.
        **kwargs: Additional keyword arguments to be included in the GeoTIFF profile.

    Raises:
        ValueError: If the input image is not a rasterio.io.DatasetReader object.

    Returns:
        None
    """
    import rasterio
    from rasterio.enums import Resampling

    if not isinstance(image, rasterio.io.DatasetReader):
        raise ValueError("The input image must be a rasterio.io.DatasetReader object.")

    dst_path = check_file_path(dst_path)

    profile = image.profile
    if dtype is not None:
        profile["dtype"] = dtype

    for key, value in kwargs.items():
        profile[key] = value

    with rasterio.open(dst_path, "w", **profile) as dst:
        dst.write(image.read())

    if to_cog:
        image_to_cog(dst_path, dst_path)

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

Raises:

Type	Description
FileNotFoundError	If the provided file could not be found.

Returns:

Type	Description
	np.array: A numpy array.

Source code in leafmap/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

    # ... and suppress errors
    gdal.PushErrorHandler("CPLQuietErrorHandler")

    try:
        with rasterio.open(image, "r") as ds:
            arr = ds.read()  # read all raster values
        return arr
    except Exception as e:
        raise Exception(e)

images_to_tiles(images, names=None, **kwargs)

Convert a list of images to a dictionary of ipyleaflet.TileLayer objects.

Parameters:

Name	Type	Description	Default
images	str | list	The path to a directory of images or a list of image paths.	required
names	list	A list of names for the layers. Defaults to None.	None
**kwargs		Additional arguments to pass to get_local_tile_layer().	{}

Returns:

Name	Type	Description
dict	Dict[str, TileLayer]	A dictionary of ipyleaflet.TileLayer objects.

Source code in leafmap/common.py

def images_to_tiles(
    images: Union[str, List[str]], names: List[str] = None, **kwargs
) -> Dict[str, ipyleaflet.TileLayer]:
    """Convert a list of images to a dictionary of ipyleaflet.TileLayer objects.

    Args:
        images (str | list): The path to a directory of images or a list of image paths.
        names (list, optional): A list of names for the layers. Defaults to None.
        **kwargs: Additional arguments to pass to get_local_tile_layer().

    Returns:
        dict: A dictionary of ipyleaflet.TileLayer objects.
    """

    tiles = {}

    if isinstance(images, str):
        images = os.path.abspath(images)
        images = find_files(images, ext=".tif", recursive=False)

    if not isinstance(images, list):
        raise ValueError("images must be a list of image paths or a directory")

    if names is None:
        names = [os.path.splitext(os.path.basename(image))[0] for image in images]

    if len(names) != len(images):
        raise ValueError("names must have the same length as images")

    for index, image in enumerate(images):
        name = names[index]
        try:
            if image.startswith("http") and image.endswith(".tif"):
                url = cog_tile(image, **kwargs)
                tile = ipyleaflet.TileLayer(url=url, name=name, **kwargs)
            elif image.startswith("http"):
                url = stac_tile(image, **kwargs)
                tile = ipyleaflet.TileLayer(url=url, name=name, **kwargs)
            else:
                tile = get_local_tile_layer(image, layer_name=name, **kwargs)
            tiles[name] = tile
        except Exception as e:
            print(image, e)

    return tiles

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 leafmap/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]
    elif isinstance(package, list):
        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:

Name	Type	Description
book		True if arcpy is available, False otherwise.

Source code in leafmap/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_array(x)

Test whether x is either a numpy.ndarray or xarray.DataArray

Source code in leafmap/common.py

def is_array(x):
    """Test whether x is either a numpy.ndarray or xarray.DataArray"""
    import sys

    if isinstance(x, sys.modules["numpy"].ndarray):
        return True
    if "xarray" in sys.modules:
        if isinstance(x, sys.modules["xarray"].DataArray):
            return True
    return False

is_jupyterlite()

Check if the current notebook is running on JupyterLite.

Returns:

Name	Type	Description
book		True if the notebook is running on JupyterLite.

Source code in leafmap/common.py

def is_jupyterlite():
    """Check if the current notebook is running on JupyterLite.

    Returns:
        book: True if the notebook is running on JupyterLite.
    """
    import sys

    if "pyodide" in sys.modules:
        return True
    else:
        return False

is_on_aws()

Check if the current notebook is running on AWS.

Returns:

Name	Type	Description
bool		True if the notebook is running on AWS.

Source code in leafmap/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

    on_aws = False
    try:
        output = psutil.Process().parent().cmdline()

        for item in output:
            if item.endswith(".aws") or "ec2-user" in item:
                on_aws = True
    except:
        pass
    return on_aws

is_studio_lab()

Check if the current notebook is running on Studio Lab.

Returns:

Name	Type	Description
bool		True if the notebook is running on Studio Lab.

Source code in leafmap/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

    on_studio_lab = False

    try:
        output = psutil.Process().parent().cmdline()

        for item in output:
            if "studiolab/bin" in item:
                on_studio_lab = True
    except:
        pass
    return on_studio_lab

is_tool(name)

Check whether name is on PATH and marked as executable.

Source code in leafmap/common.py

def is_tool(name):
    """Check whether `name` is on PATH and marked as executable."""

    return shutil.which(name) is not None

json_to_geojson(input_path, output_path)

Converts a JSON file to a GeoJSON file.

Parameters:

Name	Type	Description	Default
input_path	str	The file path to the input JSON file.	required
output_path	str	The file path to save the output GeoJSON file.	required

Raises:

Type	Description
FileNotFoundError	If the input file does not exist.
ValueError	If the input JSON file is not properly formatted.

Source code in leafmap/common.py

def json_to_geojson(input_path: str, output_path: str) -> None:
    """
    Converts a JSON file to a GeoJSON file.

    Args:
        input_path (str): The file path to the input JSON file.
        output_path (str): The file path to save the output GeoJSON file.

    Raises:
        FileNotFoundError: If the input file does not exist.
        ValueError: If the input JSON file is not properly formatted.
    """
    # Load the input JSON file
    with open(input_path, "r") as f:
        data = json.load(f)

    # Wrap each item as a GeoJSON feature
    features = []
    for item in data:
        feature = {
            "type": "Feature",
            "geometry": item.get("geometry"),
            "properties": item.get("properties", {}),
            "id": item.get("id"),
        }
        features.append(feature)

    # Build the GeoJSON FeatureCollection
    geojson = {"type": "FeatureCollection", "features": features}

    # Save the GeoJSON file
    with open(output_path, "w") as f:
        json.dump(geojson, f, indent=2)

kml_to_geojson(in_kml, out_geojson=None)

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

Raises:

Type	Description
FileNotFoundError	The input KML could not be found.
TypeError	The output must be a GeoJSON.

Source code in leafmap/common.py

def kml_to_geojson(in_kml, out_geojson=None):
    """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

    # import fiona
    # print(fiona.supported_drivers)
    fiona.drvsupport.supported_drivers["KML"] = "rw"
    gdf = gpd.read_file(in_kml, driver="KML")

    if out_geojson is not None:
        gdf.to_file(out_geojson, driver="GeoJSON")
    else:
        return gdf.__geo_interface__

kml_to_shp(in_kml, out_shp)

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

Raises:

Type	Description
FileNotFoundError	The input KML could not be found.
TypeError	The output must be a shapefile.

Source code in leafmap/common.py

def kml_to_shp(in_kml, out_shp):
    """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

    # print(fiona.supported_drivers)
    fiona.drvsupport.supported_drivers["KML"] = "rw"
    df = gpd.read_file(in_kml, driver="KML")
    df.to_file(out_shp)

line_to_points(data)

Converts a LineString geometry in a GeoDataFrame into individual points.

Parameters:

Name	Type	Description	Default
line_gdf	GeoDataFrame	A GeoDataFrame containing a LineString geometry.	required

Returns:

Name	Type	Description
GeoDataFrame	GeoDataFrame	A new GeoDataFrame where each vertex of the LineString is a Point geometry.

Source code in leafmap/common.py

def line_to_points(data: str) -> "GeoDataFrame":
    """
    Converts a LineString geometry in a GeoDataFrame into individual points.

    Args:
        line_gdf (GeoDataFrame): A GeoDataFrame containing a LineString geometry.

    Returns:
        GeoDataFrame: A new GeoDataFrame where each vertex of the LineString is a Point geometry.
    """
    import geopandas as gpd
    from shapely.geometry import Point, LineString
    from geopandas import GeoDataFrame

    if isinstance(data, str):
        line_gdf = gpd.read_file(data)
    elif isinstance(data, GeoDataFrame):
        line_gdf = data
    else:
        raise ValueError("Invalid input. Must be a file path or a GeoDataFrame.")

    # Ensure there is a LineString in the GeoDataFrame
    if not all(line_gdf.geometry.type == "LineString"):
        raise ValueError("Input GeoDataFrame must contain only LineString geometries.")

    # Extract the first (and only) LineString from the GeoDataFrame
    line = line_gdf.geometry.iloc[0]

    # Convert each point in the LineString to a Point geometry
    points = [Point(coord) for coord in line.coords]

    # Create a new GeoDataFrame with these points
    points_gdf = gpd.GeoDataFrame(geometry=points, crs=line_gdf.crs)

    return points_gdf

list_palettes(add_extra=False, lowercase=False)

List all available colormaps. See a complete lost of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

Returns:

Name	Type	Description
list		The list of colormap names.

Source code in leafmap/common.py

def list_palettes(add_extra=False, lowercase=False):
    """List all available colormaps. See a complete lost of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

    Returns:
        list: The list of colormap names.
    """
    import matplotlib.pyplot as plt

    result = plt.colormaps()
    if add_extra:
        result += ["dem", "ndvi", "ndwi"]
    if lowercase:
        result = [i.lower() for i in result]
    result.sort()
    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:

Name	Type	Description
tuple		A tuple of (x, y) in meters.

Source code in leafmap/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)

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:

Name	Type	Description
list		A list of band names.

Source code in leafmap/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:

Name	Type	Description
PointData		rio-tiler point data.

Source code in leafmap/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

Raises:

Type	Description
ValueError	If source is not a TileClient object or a local COG file path.

Returns:

Name	Type	Description
tuple		A tuple of vmin and vmax.

Source code in leafmap/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

make_gif(images, out_gif, ext='jpg', fps=10, loop=0, mp4=False, clean_up=False)

Creates a gif from a list of images.

Parameters:

Name	Type	Description	Default
images	list | str	The list of images or input directory to create the gif from.	required
out_gif	str	File path to the output gif.	required
ext	str	The extension of the images. Defaults to 'jpg'.	'jpg'
fps	int	The frames per second of the gif. Defaults to 10.	10
loop	int	The number of times to loop the gif. Defaults to 0.	0
mp4	bool	Whether to convert the gif to mp4. Defaults to False.	False

Source code in leafmap/common.py

def make_gif(images, out_gif, ext="jpg", fps=10, loop=0, mp4=False, clean_up=False):
    """Creates a gif from a list of images.

    Args:
        images (list | str): The list of images or input directory to create the gif from.
        out_gif (str): File path to the output gif.
        ext (str, optional): The extension of the images. Defaults to 'jpg'.
        fps (int, optional): The frames per second of the gif. Defaults to 10.
        loop (int, optional): The number of times to loop the gif. Defaults to 0.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.

    """
    import glob
    from PIL import Image

    ext = ext.replace(".", "")

    if isinstance(images, str) and os.path.isdir(images):
        images = list(glob.glob(os.path.join(images, f"*.{ext}")))
        if len(images) == 0:
            raise ValueError("No images found in the input directory.")
    elif not isinstance(images, list):
        raise ValueError("images must be a list or a path to the image directory.")

    images.sort()

    frames = [Image.open(image) for image in images]
    frame_one = frames[0]
    frame_one.save(
        out_gif,
        format="GIF",
        append_images=frames,
        save_all=True,
        duration=int(1000 / fps),
        loop=loop,
    )

    if mp4:
        if not is_tool("ffmpeg"):
            print("ffmpeg is not installed on your computer.")
            return

        if os.path.exists(out_gif):
            out_mp4 = out_gif.replace(".gif", ".mp4")
            cmd = f"ffmpeg -loglevel error -i {out_gif} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
            os.system(cmd)
            if not os.path.exists(out_mp4):
                raise Exception(f"Failed to create mp4 file.")
    if clean_up:
        for image in images:
            os.remove(image)

map_tiles_to_geotiff(output, bbox, zoom=None, resolution=None, source='OpenStreetMap', crs='EPSG:3857', to_cog=False, quiet=False, **kwargs)

Download map 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] coordinates in EPSG:4326, 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 leafmap/common.py

def map_tiles_to_geotiff(
    output,
    bbox,
    zoom=None,
    resolution=None,
    source="OpenStreetMap",
    crs="EPSG:3857",
    to_cog=False,
    quiet=False,
    **kwargs,
):
    """Download map 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] coordinates in EPSG:4326, 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 re
    import io
    import math
    import itertools
    import concurrent.futures

    import numpy
    from PIL import Image

    try:
        from osgeo import gdal, osr
    except ImportError:
        raise ImportError("GDAL is not installed. Install it with pip install GDAL")

    try:
        import httpx

        SESSION = httpx.Client()
    except ImportError:
        import requests

        SESSION = requests.Session()

    SESSION.headers.update(
        {
            "Accept": "*/*",
            "Accept-Encoding": "gzip, deflate",
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; rv:91.0) Gecko/20100101 Firefox/91.0",
        }
    )

    xyz_tiles = {
        "OPENSTREETMAP": {
            "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
            "attribution": "OpenStreetMap",
            "name": "OpenStreetMap",
        },
        "ROADMAP": {
            "url": "https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Maps",
        },
        "SATELLITE": {
            "url": "https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Satellite",
        },
        "TERRAIN": {
            "url": "https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Terrain",
        },
        "HYBRID": {
            "url": "https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Satellite",
        },
    }

    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
    elif isinstance(source, str):
        tiles = basemap_xyz_tiles()
        if source in tiles:
            source = tiles[source].url
    else:
        raise ValueError(
            'source must be one of "OpenStreetMap", "ROADMAP", "SATELLITE", "TERRAIN", "HYBRID", or a URL'
        )

    # Web Mercator tile size in meters at zoom level 0
    MERCATOR_ZOOM_0_RESOLUTION_M = 156543.03392804097

    def resolution_to_zoom_level(resolution):
        """
        Convert map resolution in meters to zoom level for Web Mercator (EPSG:3857) tiles.
        """
        initial_resolution = MERCATOR_ZOOM_0_RESOLUTION_M

        # Calculate the zoom level
        zoom_level = math.log2(initial_resolution / resolution)

        return int(zoom_level)

    def zoom_level_to_resolution(zoom):
        """
        Convert map zoom level to resolution in meters for Web Mercator (EPSG:3857) tiles.
        """
        initial_resolution = MERCATOR_ZOOM_0_RESOLUTION_M

        # Calculate resolution
        resolution_m = initial_resolution / (2**zoom)

        return resolution_m

    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")

    elif (zoom is None) and (resolution is not None):
        zoom = resolution_to_zoom_level(resolution)
    else:
        # condition: (resolution is None) and (zoom is not None):
        resolution = zoom_level_to_resolution(zoom)

    EARTH_EQUATORIAL_RADIUS = 6378137.0

    Image.MAX_IMAGE_PIXELS = None

    gdal.UseExceptions()
    web_mercator = osr.SpatialReference()
    try:
        web_mercator.ImportFromEPSG(3857)
    except RuntimeError as e:
        # https://github.com/PDAL/PDAL/issues/2544#issuecomment-637995923
        if "PROJ" in str(e):
            pattern = r"/[\w/]+"
            match = re.search(pattern, str(e))
            if match:
                file_path = match.group(0)
                os.environ["PROJ_LIB"] = file_path
                os.environ["GDAL_DATA"] = file_path.replace("proj", "gdal")
                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",
            ]

        kwargs.pop("overwrite", None)
        gtiff = driver.Create(
            filename,
            img.size[0],
            img.size[1],
            imgbands,
            gdal.GDT_Byte,
            **kwargs,
        )

        gtiff.SetMetadata({"ZOOM_LEVEL": str(zoom), "RESOLUTION_M": str(resolution)})

        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 = np.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(image=output, output=output, dst_crs=crs, to_cog=to_cog)
        elif to_cog:
            image_to_cog(source=output, dst_path=output)
    except Exception as e:
        raise Exception(e)

maxar_all_items(collection_id, return_gdf=True, assets=['visual'], verbose=True, **kwargs)

Retrieve STAC items from Maxar's public STAC API.

Parameters:

Name	Type	Description	Default
collection_id	str	The collection ID, e.g., Kahramanmaras-turkey-earthquake-23 Use maxar_collections() to retrieve all available collection IDs.	required
return_gdf	bool	If True, return a GeoDataFrame. Defaults to True.	True
assets	list	A list of asset names to include in the GeoDataFrame. It can be "visual", "ms_analytic", "pan_analytic", "data-mask". Defaults to ['visual'].	['visual']
verbose	bool	If True, print progress. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the pystac Catalog.from_file() method.	{}

Returns:

Type	Description
	GeoDataFrame | pystac.ItemCollection: If return_gdf is True, return a GeoDataFrame.

Source code in leafmap/stac.py

def maxar_all_items(
    collection_id: str,
    return_gdf: Optional[bool] = True,
    assets: Optional[List] = ["visual"],
    verbose: Optional[bool] = True,
    **kwargs,
):
    """Retrieve STAC items from Maxar's public STAC API.

    Args:
        collection_id (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23
            Use maxar_collections() to retrieve all available collection IDs.
        return_gdf (bool, optional): If True, return a GeoDataFrame. Defaults to True.
        assets (list, optional): A list of asset names to include in the GeoDataFrame.
            It can be "visual", "ms_analytic", "pan_analytic", "data-mask". Defaults to ['visual'].
        verbose (bool, optional): If True, print progress. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the pystac Catalog.from_file() method.

    Returns:
        GeoDataFrame | pystac.ItemCollection: If return_gdf is True, return a GeoDataFrame.
    """

    child_ids = maxar_child_collections(collection_id, **kwargs)
    for index, child_id in enumerate(child_ids):
        if verbose:
            print(
                f"Processing ({str(index+1).zfill(len(str(len(child_ids))))} out of {len(child_ids)}): {child_id} ..."
            )
        items = maxar_items(collection_id, child_id, return_gdf, assets, **kwargs)
        if return_gdf:
            if child_id == child_ids[0]:
                gdf = items
            else:
                gdf = pd.concat([gdf, items], ignore_index=True)
        else:
            if child_id == child_ids[0]:
                items_all = items
            else:
                items_all.extend(items)

    if return_gdf:
        return gdf
    else:
        return items_all

maxar_child_collections(collection_id, return_ids=True, **kwargs)

Get a list of Maxar child collections.

Parameters:

Name	Type	Description	Default
collection_id	str	The collection ID, e.g., Kahramanmaras-turkey-earthquake-23 Use maxar_collections() to retrieve all available collection IDs.	required
return_ids	bool	Whether to return the collection ids. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the pystac Catalog.from_file() method.	{}

Returns:

Name	Type	Description
list	List	A list of Maxar child collections.

Source code in leafmap/stac.py

def maxar_child_collections(
    collection_id: str, return_ids: Optional[bool] = True, **kwargs
) -> List:
    """Get a list of Maxar child collections.

    Args:
        collection_id (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23
            Use maxar_collections() to retrieve all available collection IDs.
        return_ids (bool, optional): Whether to return the collection ids. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the pystac Catalog.from_file() method.

    Returns:
        list: A list of Maxar child collections.
    """

    import tempfile
    from pystac import Catalog

    file_path = os.path.join(tempfile.gettempdir(), f"maxar-{collection_id}.txt")
    if return_ids:
        if os.path.exists(file_path):
            with open(file_path, "r") as f:
                return [line.strip() for line in f.readlines()]

    if "MAXAR_STAC_API" in os.environ:
        url = os.environ["MAXAR_STAC_API"]
    else:
        url = "https://maxar-opendata.s3.amazonaws.com/events/catalog.json"

    root_catalog = Catalog.from_file(url, **kwargs)

    collections = root_catalog.get_child(collection_id).get_collections()

    if return_ids:
        collection_ids = [collection.id for collection in collections]
        with open(file_path, "w") as f:
            f.write("\n".join(collection_ids))
        return collection_ids

    else:
        return collections

maxar_collection_url(collection, dtype='geojson', raw=True)

Retrieve the URL to a Maxar Open Data collection.

Parameters:

Name	Type	Description	Default
collection	str	The collection ID, e.g., Kahramanmaras-turkey-earthquake-23. Use maxar_collections() to retrieve all available collection IDs.	required
dtype	str	The data type. It can be 'geojson' or 'tsv'. Defaults to 'geojson'.	'geojson'
raw	bool	If True, return the raw URL. Defaults to True.	True

Returns:

Name	Type	Description
str		The URL to the collection.

Source code in leafmap/stac.py

def maxar_collection_url(collection, dtype="geojson", raw=True):
    """Retrieve the URL to a Maxar Open Data collection.

    Args:
        collection (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23.
            Use maxar_collections() to retrieve all available collection IDs.
        dtype (str, optional): The data type. It can be 'geojson' or 'tsv'. Defaults to 'geojson'.
        raw (bool, optional): If True, return the raw URL. Defaults to True.

    Returns:
        str: The URL to the collection.
    """
    collections = maxar_collections()
    if collection not in collections:
        raise ValueError(
            f"Invalid collection name. Use maxar_collections() to retrieve all available collection IDs."
        )

    if dtype not in ["geojson", "tsv"]:
        raise ValueError(f"Invalid dtype. It can be 'geojson' or 'tsv'.")

    if raw:
        url = f"https://raw.githubusercontent.com/giswqs/maxar-open-data/master/datasets/{collection}.{dtype}"
    else:
        url = f"https://github.com/giswqs/maxar-open-data/blob/master/datasets/{collection}.{dtype}"
    return url

maxar_collections(return_ids=True, **kwargs)

Get a list of Maxar collections.

Parameters:

Name	Type	Description	Default
return_ids	bool	Whether to return the collection ids. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the pystac Catalog.from_file() method.	{}

Returns:

Name	Type	Description
list	List	A list of Maxar collections.

Source code in leafmap/stac.py

def maxar_collections(return_ids: Optional[bool] = True, **kwargs) -> List:
    """Get a list of Maxar collections.

    Args:
        return_ids (bool, optional): Whether to return the collection ids. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the pystac Catalog.from_file() method.

    Returns:
        list : A list of Maxar collections.
    """

    import tempfile
    from pystac import Catalog
    import pandas as pd

    if return_ids:
        url = "https://raw.githubusercontent.com/giswqs/maxar-open-data/master/datasets.csv"
        df = pd.read_csv(url)
        return df["dataset"].tolist()

    file_path = os.path.join(tempfile.gettempdir(), "maxar-collections.txt")
    if return_ids:
        if os.path.exists(file_path):
            with open(file_path, "r") as f:
                return [line.strip() for line in f.readlines()]

    if "MAXAR_STAC_API" in os.environ:
        url = os.environ["MAXAR_STAC_API"]
    else:
        url = "https://maxar-opendata.s3.amazonaws.com/events/catalog.json"

    root_catalog = Catalog.from_file(url, **kwargs)

    collections = root_catalog.get_collections()

    # if return_ids:
    #     collection_ids = [collection.id for collection in collections]
    #     with open(file_path, "w") as f:
    #         f.write("\n".join(collection_ids))

    #     return collection_ids
    # else:
    return collections

maxar_download(images, out_dir=None, quiet=False, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, overwrite=False)

Download Mxar Open Data images.

Parameters:

Name	Type	Description	Default
images	str | images	The list of image links or a file path to a geojson or tsv containing the Maxar download links.	required
out_dir	str	The output directory. Defaults to None.	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
overwrite	bool	Overwrite the file if it already exists. Defaults to False.	False

Source code in leafmap/stac.py

def maxar_download(
    images,
    out_dir=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    verify=True,
    id=None,
    fuzzy=False,
    resume=False,
    overwrite=False,
):
    """Download Mxar Open Data images.

    Args:
        images (str | images): The list of image links or a file path to a geojson or tsv containing the Maxar download links.
        out_dir (str, optional): The output directory. Defaults to None.
        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.
        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.

    """
    import gdown

    if out_dir is None:
        out_dir = os.getcwd()

    if isinstance(images, str):
        if images.endswith(".geojson"):
            import geopandas as gpd

            data = gpd.read_file(images)
            images = data["visual"].tolist()
        elif images.endswith(".tsv"):
            import pandas as pd

            data = pd.read_csv(images, sep="\t")
            images = data["visual"].tolist()
        else:
            raise ValueError(f"Invalid file type. It can be 'geojson' or 'tsv'.")

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    for index, image in enumerate(images):
        items = image.split("/")
        file_name = items[7] + ".tif"
        dir_name = items[-1].split("-")[0]
        if not os.path.exists(os.path.join(out_dir, dir_name)):
            os.makedirs(os.path.join(out_dir, dir_name))
        out_file = os.path.join(out_dir, dir_name, file_name)
        if os.path.exists(out_file) and (not overwrite):
            print(f"{out_file} already exists. Skipping...")
            continue
        if not quiet:
            print(
                f"Downloading {str(index+1).zfill(len(str(len(images))))} out of {len(images)}: {dir_name}/{file_name}"
            )

        gdown.download(
            image, out_file, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume
        )

maxar_items(collection_id, child_id, return_gdf=True, assets=['visual'], **kwargs)

Retrieve STAC items from Maxar's public STAC API.

Parameters:

Name	Type	Description	Default
collection_id	str	The collection ID, e.g., Kahramanmaras-turkey-earthquake-23 Use maxar_collections() to retrieve all available collection IDs.	required
child_id	str	The child collection ID, e.g., 1050050044DE7E00 Use maxar_child_collections() to retrieve all available child collection IDs.	required
return_gdf	bool	If True, return a GeoDataFrame. Defaults to True.	True
assets	list	A list of asset names to include in the GeoDataFrame. It can be "visual", "ms_analytic", "pan_analytic", "data-mask". Defaults to ['visual'].	['visual']
**kwargs		Additional keyword arguments to pass to the pystac Catalog.from_file() method.	{}

Returns:

Type	Description
	GeoDataFrame | pystac.ItemCollection: If return_gdf is True, return a GeoDataFrame.

Source code in leafmap/stac.py

def maxar_items(
    collection_id: str,
    child_id: str,
    return_gdf: Optional[bool] = True,
    assets: Optional[List] = ["visual"],
    **kwargs,
):
    """Retrieve STAC items from Maxar's public STAC API.

    Args:
        collection_id (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23
            Use maxar_collections() to retrieve all available collection IDs.
        child_id (str): The child collection ID, e.g., 1050050044DE7E00
            Use maxar_child_collections() to retrieve all available child collection IDs.
        return_gdf (bool, optional): If True, return a GeoDataFrame. Defaults to True.
        assets (list, optional): A list of asset names to include in the GeoDataFrame.
            It can be "visual", "ms_analytic", "pan_analytic", "data-mask". Defaults to ['visual'].
        **kwargs: Additional keyword arguments to pass to the pystac Catalog.from_file() method.

    Returns:
        GeoDataFrame | pystac.ItemCollection: If return_gdf is True, return a GeoDataFrame.
    """

    import pickle
    import tempfile
    from pystac import Catalog, ItemCollection

    file_path = os.path.join(
        tempfile.gettempdir(), f"maxar-{collection_id}-{child_id}.pkl"
    )

    if os.path.exists(file_path):
        with open(file_path, "rb") as f:
            items = pickle.load(f)
        if return_gdf:
            import geopandas as gpd

            gdf = gpd.GeoDataFrame.from_features(
                pystac.ItemCollection(items).to_dict(), crs="EPSG:4326"
            )
            # convert bbox column type from list to string
            gdf["proj:bbox"] = [",".join(map(str, l)) for l in gdf["proj:bbox"]]
            if assets is not None:
                if isinstance(assets, str):
                    assets = [assets]
                elif not isinstance(assets, list):
                    raise ValueError("assets must be a list or a string.")

                for asset in assets:
                    links = []
                    for item in items:
                        if asset in item.get_assets():
                            link = item.get_assets()[asset].get_absolute_href()
                            links.append(link)
                        else:
                            links.append("")

                    gdf[asset] = links

            return gdf
        else:
            return items

    if "MAXAR_STAC_API" in os.environ:
        url = os.environ["MAXAR_STAC_API"]
    else:
        url = "https://maxar-opendata.s3.amazonaws.com/events/catalog.json"

    root_catalog = Catalog.from_file(url, **kwargs)

    collection = root_catalog.get_child(collection_id)
    child = collection.get_child(child_id)

    items = ItemCollection(child.get_all_items())

    with open(file_path, "wb") as f:
        pickle.dump(items, f)

    if return_gdf:
        import geopandas as gpd

        gdf = gpd.GeoDataFrame.from_features(
            pystac.ItemCollection(items).to_dict(), crs="EPSG:4326"
        )
        # convert bbox column type from list to string
        gdf["proj:bbox"] = [",".join(map(str, l)) for l in gdf["proj:bbox"]]
        if assets is not None:
            if isinstance(assets, str):
                assets = [assets]
            elif not isinstance(assets, list):
                raise ValueError("assets must be a list or a string.")

            for asset in assets:
                links = []
                for item in items:
                    if asset in item.get_assets():
                        link = item.get_assets()[asset].get_absolute_href()
                        links.append(link)
                    else:
                        links.append("")

                gdf[asset] = links

        return gdf
    else:
        return items

maxar_refresh()

Refresh the cached Maxar STAC items.

Source code in leafmap/stac.py

def maxar_refresh():
    """Refresh the cached Maxar STAC items."""
    import tempfile

    temp_dir = tempfile.gettempdir()
    for f in os.listdir(temp_dir):
        if f.startswith("maxar-"):
            os.remove(os.path.join(temp_dir, f))

    print("Maxar STAC items cache has been refreshed.")

maxar_search(collection, start_date=None, end_date=None, bbox=None, within=False, align=True)

Search Maxar Open Data by collection ID, date range, and/or bounding box.

Parameters:

Name	Type	Description	Default
collection	str	The collection ID, e.g., Kahramanmaras-turkey-earthquake-23. Use maxar_collections() to retrieve all available collection IDs.	required
start_date	str	The start date, e.g., 2023-01-01. Defaults to None.	None
end_date	str	The end date, e.g., 2023-12-31. Defaults to None.	None
bbox	list | GeoDataFrame	The bounding box to filter by. Can be a list of 4 coordinates or a file path or a GeoDataFrame.	None
within	bool	Whether to filter by the bounding box or the bounding box's interior. Defaults to False.	False
align	bool	If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.	True

Returns:

Name	Type	Description
GeoDataFrame		A GeoDataFrame containing the search results.

Source code in leafmap/stac.py

def maxar_search(
    collection, start_date=None, end_date=None, bbox=None, within=False, align=True
):
    """Search Maxar Open Data by collection ID, date range, and/or bounding box.

    Args:
        collection (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23.
            Use maxar_collections() to retrieve all available collection IDs.
        start_date (str, optional): The start date, e.g., 2023-01-01. Defaults to None.
        end_date (str, optional): The end date, e.g., 2023-12-31. Defaults to None.
        bbox (list | GeoDataFrame): The bounding box to filter by. Can be a list of 4 coordinates or a file path or a GeoDataFrame.
        within (bool, optional): Whether to filter by the bounding box or the bounding box's interior. Defaults to False.
        align (bool, optional): If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.

    Returns:
        GeoDataFrame: A GeoDataFrame containing the search results.
    """
    import datetime
    import pandas as pd
    import geopandas as gpd
    from shapely.geometry import Polygon

    collections = maxar_collections()
    if collection not in collections:
        raise ValueError(
            f"Invalid collection name. Use maxar_collections() to retrieve all available collection IDs."
        )

    url = f"https://raw.githubusercontent.com/giswqs/maxar-open-data/master/datasets/{collection}.geojson"
    data = gpd.read_file(url)

    if bbox is not None:
        bbox = gpd.GeoDataFrame(
            geometry=[Polygon.from_bounds(*bbox)],
            crs="epsg:4326",
        )
        if within:
            data = data[data.within(bbox.unary_union, align=align)]
        else:
            data = data[data.intersects(bbox.unary_union, align=align)]

    date_field = "datetime"
    new_field = f"{date_field}_temp"
    data[new_field] = pd.to_datetime(data[date_field])

    if end_date is None:
        end_date = datetime.datetime.now().strftime("%Y-%m-%d")

    if start_date is None:
        start_date = data[new_field].min()

    mask = (data[new_field] >= start_date) & (data[new_field] <= end_date)
    result = data.loc[mask]
    return result.drop(columns=[new_field], axis=1)

maxar_tile_url(collection, tile, dtype='geojson', raw=True)

Retrieve the URL to a Maxar Open Data tile.

Args:

collection (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23.
    Use maxar_collections() to retrieve all available collection IDs.
tile (str): The tile ID, e.g., 10300500D9F8E600.
dtype (str, optional): The data type. It can be 'geojson', 'json' or 'tsv'. Defaults to 'geojson'.
raw (bool, optional): If True, return the raw URL. Defaults to True.

Returns:

Name	Type	Description
str		The URL to the tile.

Source code in leafmap/stac.py

def maxar_tile_url(collection, tile, dtype="geojson", raw=True):
    """Retrieve the URL to a Maxar Open Data tile.

    Args:

        collection (str): The collection ID, e.g., Kahramanmaras-turkey-earthquake-23.
            Use maxar_collections() to retrieve all available collection IDs.
        tile (str): The tile ID, e.g., 10300500D9F8E600.
        dtype (str, optional): The data type. It can be 'geojson', 'json' or 'tsv'. Defaults to 'geojson'.
        raw (bool, optional): If True, return the raw URL. Defaults to True.

    Returns:
        str: The URL to the tile.
    """

    collections = maxar_collections()
    if collection not in collections:
        raise ValueError(
            f"Invalid collection name. Use maxar_collections() to retrieve all available collection IDs."
        )

    if dtype not in ["geojson", "json", "tsv"]:
        raise ValueError(f"Invalid dtype. It can be 'geojson', 'json' or 'tsv'.")

    if raw:
        url = f"https://raw.githubusercontent.com/giswqs/maxar-open-data/master/datasets/{collection}/{tile}.{dtype}"
    else:
        url = f"https://github.com/giswqs/maxar-open-data/blob/master/datasets/{collection}/{tile}.{dtype}"

    return url

mbtiles_to_pmtiles(input_file, output_file, max_zoom=99)

Converts mbtiles to pmtiles using the pmtiles package.

Parameters:

Name	Type	Description	Default
input_file	str	Path to the input .mbtiles file.	required
output_file	str	Path to the output .pmtiles file.	required
max_zoom	int	Maximum zoom level for the conversion. Defaults to 99.	99

Returns:

Name	Type	Description
None	Optional[None]	The function returns None either upon successful completion or when the pmtiles package is not installed.

Source code in leafmap/common.py

def mbtiles_to_pmtiles(
    input_file: str, output_file: str, max_zoom: int = 99
) -> Optional[None]:
    """
    Converts mbtiles to pmtiles using the pmtiles package.

    Args:
        input_file (str): Path to the input .mbtiles file.
        output_file (str): Path to the output .pmtiles file.
        max_zoom (int): Maximum zoom level for the conversion. Defaults to 99.

    Returns:
        None: The function returns None either upon successful completion or when the pmtiles package is not installed.

    """

    import pmtiles.convert as convert

    convert.mbtiles_to_pmtiles(input_file, output_file, maxzoom=max_zoom)

merge_gifs(in_gifs, out_gif)

Merge multiple gifs into one.

Parameters:

Name	Type	Description	Default
in_gifs	str | list	The input gifs as a list or a directory path.	required
out_gif	str	The output gif.	required

Raises:

Type	Description
Exception	Raise exception when gifsicle is not installed.

Source code in leafmap/common.py

def merge_gifs(in_gifs, out_gif):
    """Merge multiple gifs into one.

    Args:
        in_gifs (str | list): The input gifs as a list or a directory path.
        out_gif (str): The output gif.

    Raises:
        Exception:  Raise exception when gifsicle is not installed.
    """
    import glob

    try:
        if isinstance(in_gifs, str) and os.path.isdir(in_gifs):
            in_gifs = glob.glob(os.path.join(in_gifs, "*.gif"))
        elif not isinstance(in_gifs, list):
            raise Exception("in_gifs must be a list.")

        in_gifs = " ".join(in_gifs)

        cmd = f"gifsicle {in_gifs} > {out_gif}"
        os.system(cmd)

    except Exception as e:
        print(
            "gifsicle is not installed. Run 'sudo apt-get install -y gifsicle' to install it."
        )
        print(e)

merge_rasters(input_dir_or_files, output, input_pattern='*.tif', output_format='GTiff', output_nodata=None, output_options=None, **kwargs)

Merge a directory of rasters or a list of file paths into a single raster.

Parameters:

Name	Type	Description	Default
input_dir_or_files	Union[str, List[str]]	The path to the input directory or a list of file paths.	required
output	str	The path to the output raster.	required
input_pattern	str	The glob pattern to match input files if a directory is provided. Defaults to "*.tif".	'*.tif'
output_format	str	The output raster format. Defaults to "GTiff".	'GTiff'
output_nodata	float	The nodata value for the output raster. Defaults to None.	None
output_options	list	A list of creation options for the output raster. Defaults to ["COMPRESS=DEFLATE"].	None
**kwargs		Additional arguments to pass to gdal.WarpOptions.	{}

Raises:

Type	Description
ImportError	If GDAL is not installed.
ValueError	If no input files are found.

Source code in leafmap/common.py

def merge_rasters(
    input_dir_or_files: Union[str, List[str]],
    output: str,
    input_pattern: str = "*.tif",
    output_format: str = "GTiff",
    output_nodata: float = None,
    output_options: list = None,
    **kwargs,
):
    """
    Merge a directory of rasters or a list of file paths into a single raster.

    Args:
        input_dir_or_files (Union[str, List[str]]): The path to the input directory or a list of file paths.
        output (str): The path to the output raster.
        input_pattern (str, optional): The glob pattern to match input files if a directory is provided. Defaults to "*.tif".
        output_format (str, optional): The output raster format. Defaults to "GTiff".
        output_nodata (float, optional): The nodata value for the output raster. Defaults to None.
        output_options (list, optional): A list of creation options for the output raster. Defaults to ["COMPRESS=DEFLATE"].
        **kwargs: Additional arguments to pass to gdal.WarpOptions.

    Raises:
        ImportError: If GDAL is not installed.
        ValueError: If no input files are found.
    """
    import glob

    if output_options is None:
        output_options = ["COMPRESS=DEFLATE"]

    try:
        from osgeo import gdal  # Ensure GDAL is available
    except ImportError as e:
        raise ImportError(
            "GDAL is required to use this function. Install it with `conda install gdal -c conda-forge`"
        ) from e

    # Get a list of input files
    if isinstance(input_dir_or_files, str):  # It's a directory
        input_files = glob.glob(os.path.join(input_dir_or_files, input_pattern))
    elif isinstance(input_dir_or_files, list):  # It's a list of file paths
        input_files = input_dir_or_files
    else:
        raise ValueError(
            "input_dir_or_files must be a directory path (str) or a list of file paths (List[str])."
        )

    # Check if files are found
    if not input_files:
        raise ValueError(
            "No input files found. Please check the directory or file paths provided."
        )

    # Configure warp options
    warp_options = gdal.WarpOptions(
        format=output_format,
        dstNodata=output_nodata,
        creationOptions=output_options,
        **kwargs,
    )

    # Perform the raster merge
    print(f"Merging {len(input_files)} rasters into '{output}'...")
    result = gdal.Warp(
        destNameOrDestDS=output,
        srcDSOrSrcDSTab=input_files,
        options=warp_options,
    )

    # Check for success
    if result is None:
        raise RuntimeError(
            "Raster merge failed. Please check your inputs and parameters."
        )
    print(f"Raster merge completed successfully: {output}")

merge_vector(files, output=None, crs=None, ext='geojson', recursive=False, quiet=False, return_gdf=False, **kwargs)

Merge vector files into a single GeoDataFrame.

Parameters:

Name	Type	Description	Default
files	Union[str, List[str]]	A string or a list of file paths to be merged.	required
output	str	The file path to save the merged GeoDataFrame.	None
crs	str	Optional. The coordinate reference system (CRS) of the output GeoDataFrame.	None
ext	str	Optional. The file extension of the input files. Default is 'geojson'.	'geojson'
recursive	bool	Optional. If True, search for files recursively in subdirectories. Default is False.	False
quiet	bool	Optional. If True, suppresses progress messages. Default is False.	False
return_gdf	bool	Optional. If True, returns the merged GeoDataFrame. Default is False.	False
**kwargs		Additional keyword arguments to be passed to the gpd.read_file function.	{}

Returns:

Type	Description
Optional[GeoDataFrame]	If return_gdf is True, returns the merged GeoDataFrame. Otherwise, returns None.

Raises:

Type	Description
TypeError	If files is not a list of file paths.

Source code in leafmap/common.py

def merge_vector(
    files: Union[str, List[str]],
    output: str = None,
    crs: str = None,
    ext: str = "geojson",
    recursive: bool = False,
    quiet: bool = False,
    return_gdf: bool = False,
    **kwargs,
) -> Optional["gpd.GeoDataFrame"]:
    """
    Merge vector files into a single GeoDataFrame.

    Args:
        files: A string or a list of file paths to be merged.
        output: The file path to save the merged GeoDataFrame.
        crs: Optional. The coordinate reference system (CRS) of the output GeoDataFrame.
        ext: Optional. The file extension of the input files. Default is 'geojson'.
        recursive: Optional. If True, search for files recursively in subdirectories. Default is False.
        quiet: Optional. If True, suppresses progress messages. Default is False.
        return_gdf: Optional. If True, returns the merged GeoDataFrame. Default is False.
        **kwargs: Additional keyword arguments to be passed to the `gpd.read_file` function.

    Returns:
        If `return_gdf` is True, returns the merged GeoDataFrame. Otherwise, returns None.

    Raises:
        TypeError: If `files` is not a list of file paths.

    """

    import pandas as pd
    import geopandas as gpd

    if isinstance(files, str):
        files = find_files(files, ext=ext, recursive=recursive)

    if not isinstance(files, list):
        raise TypeError("files must be a list of file paths")

    gdfs = []
    for index, filename in enumerate(files):
        if not quiet:
            print(f"Reading {index+1} of {len(files)}: {filename}")
        gdf = gpd.read_file(filename, **kwargs)
        if crs is None:
            crs = gdf.crs
        else:
            gdf = gdf.to_crs(crs)
        gdfs.append(gdf)

    if not quiet:
        print("Merging GeoDataFrames ...")
    gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True), crs=crs)

    if output is not None:
        if not quiet:
            print(f"Saving merged file to {output} ...")
        gdf.to_file(output)
        print(f"Saved merged file to {output}")

    if return_gdf:
        return gdf

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:

Name	Type	Description
tuple		A tuple of (longitude, latitude) in decimal degrees.

Source code in leafmap/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.
    """

    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)

mosaic(images, output, ext='tif', recursive=True, merge_args={}, to_cog=True, 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
ext	str	The file extension of the images. Defaults to 'tif'.	'tif'
recursive	bool	Whether to recursively search for images in the input directory. Defaults to True.	True
merge_args	dict	A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.	{}
to_cog	bool	Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.	True
verbose	bool	Whether to print progress. Defaults to True.	True

Source code in leafmap/common.py

def mosaic(
    images,
    output,
    ext="tif",
    recursive=True,
    merge_args={},
    to_cog=True,
    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.
        ext (str, optional): The file extension of the images. Defaults to 'tif'.
        recursive (bool, optional): Whether to recursively search for images in the input directory. Defaults to True.
        merge_args (dict, optional): A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.
        to_cog (bool, optional): Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.
        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):
        raster_files = find_files(images, ext=ext, recursive=recursive)
    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)

    if to_cog:
        if verbose:
            print("Converting to COG...")
        image_to_cog(output, output)

    if verbose:
        print(f"Saved mosaic to {output}")

mosaic_bounds(url, titiler_endpoint=None, **kwargs)

Get the bounding box of a MosaicJSON.

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a MosaicJSON.	required
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.	None

Returns:

Name	Type	Description
list		A list of values representing [left, bottom, right, top]

Source code in leafmap/common.py

def mosaic_bounds(url, titiler_endpoint=None, **kwargs):
    """Get the bounding box of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.

    Returns:
        list: A list of values representing [left, bottom, right, top]
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/bounds",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r["bounds"]

mosaic_info(url, titiler_endpoint=None, **kwargs)

Get the info of a MosaicJSON.

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a MosaicJSON.	required
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.	None

Returns:

Name	Type	Description
dict		A dictionary containing bounds, center, minzoom, maxzoom, and name as keys.

Source code in leafmap/common.py

def mosaic_info(url, titiler_endpoint=None, **kwargs):
    """Get the info of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.

    Returns:
        dict: A dictionary containing bounds, center, minzoom, maxzoom, and name as keys.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/info",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r

mosaic_info_geojson(url, titiler_endpoint=None, **kwargs)

Get the info of a MosaicJSON.

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a MosaicJSON.	required
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.	None

Returns:

Name	Type	Description
dict		A dictionary representing a dict of GeoJSON.

Source code in leafmap/common.py

def mosaic_info_geojson(url, titiler_endpoint=None, **kwargs):
    """Get the info of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.

    Returns:
        dict: A dictionary representing a dict of GeoJSON.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/info.geojson",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r

mosaic_tile(url, titiler_endpoint=None, **kwargs)

Get the tile URL from a MosaicJSON.

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a MosaicJSON.	required
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.	None

Returns:

Name	Type	Description
str		The tile URL.

Source code in leafmap/common.py

def mosaic_tile(url, titiler_endpoint=None, **kwargs):
    """Get the tile URL from a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz". Defaults to None.

    Returns:
        str: The tile URL.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/tilejson.json",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r["tiles"][0]

nasa_data_download(granules, out_dir=None, provider=None, threads=8)

Downloads NASA Earthdata granules.

Parameters:

Name	Type	Description	Default
granules	List[dict]	The granules to download.	required
out_dir	str	The output directory where the granules will be downloaded. Defaults to None (current directory).	None
provider	str	The provider of the granules.	None
threads	int	The number of threads to use for downloading. Defaults to 8.	8

Source code in leafmap/common.py

def nasa_data_download(
    granules: List[dict],
    out_dir: Optional[str] = None,
    provider: Optional[str] = None,
    threads: int = 8,
) -> None:
    """Downloads NASA Earthdata granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be downloaded. Defaults to None (current directory).
        provider (str, optional): The provider of the granules.
        threads (int, optional): The number of threads to use for downloading. Defaults to 8.
    """
    import earthaccess

    if os.environ.get("USE_MKDOCS") is not None:
        return

    earthaccess.download(
        granules, local_path=out_dir, provider=provider, threads=threads
    )

nasa_data_granules_to_gdf(granules, crs='EPSG:4326', output=None, **kwargs)

Converts granules data to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
granules	List[dict]	A list of granules.	required
crs	str	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	'EPSG:4326'
output	str	The output file path to save the GeoDataFrame as a file. Defaults to None.	None
**kwargs		Additional keyword arguments for the gpd.GeoDataFrame.to_file() function.	{}

Returns:

Type	Description
	gpd.GeoDataFrame: The resulting GeoDataFrame.

Source code in leafmap/common.py

def nasa_data_granules_to_gdf(
    granules: List[dict], crs: str = "EPSG:4326", output: str = None, **kwargs
):
    """Converts granules data to a GeoDataFrame.

    Args:
        granules (List[dict]): A list of granules.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        output (str, optional): The output file path to save the GeoDataFrame as a file. Defaults to None.
        **kwargs: Additional keyword arguments for the gpd.GeoDataFrame.to_file() function.

    Returns:
        gpd.GeoDataFrame: The resulting GeoDataFrame.
    """
    import pandas as pd
    import geopandas as gpd
    from shapely.geometry import box, Polygon

    df = pd.json_normalize([dict(i.items()) for i in granules])
    df.columns = [col.split(".")[-1] for col in df.columns]
    df = df.drop("Version", axis=1)

    def get_bbox(rectangles):
        xmin = min(rectangle["WestBoundingCoordinate"] for rectangle in rectangles)
        ymin = min(rectangle["SouthBoundingCoordinate"] for rectangle in rectangles)
        xmax = max(rectangle["EastBoundingCoordinate"] for rectangle in rectangles)
        ymax = max(rectangle["NorthBoundingCoordinate"] for rectangle in rectangles)

        bbox = (xmin, ymin, xmax, ymax)
        return bbox

    def get_polygon(coordinates):
        # Extract the points from the dictionary
        points = [
            (point["Longitude"], point["Latitude"])
            for point in coordinates[0]["Boundary"]["Points"]
        ]

        # Create a Polygon
        polygon = Polygon(points)
        return polygon

    if "BoundingRectangles" in df.columns:
        df["bbox"] = df["BoundingRectangles"].apply(get_bbox)
        df["geometry"] = df["bbox"].apply(lambda x: box(*x))
    elif "GPolygons" in df.columns:
        df["geometry"] = df["GPolygons"].apply(get_polygon)

    gdf = gpd.GeoDataFrame(df, geometry="geometry")

    gdf.crs = crs

    if output is not None:
        for column in gdf.columns:
            if gdf[column].apply(lambda x: isinstance(x, list)).any():
                gdf[column] = gdf[column].apply(lambda x: str(x))

        gdf.to_file(output, **kwargs)

    return gdf

nasa_data_login(strategy='all', persist=True, **kwargs)

Logs in to NASA Earthdata.

Parameters:

Name	Type	Description	Default
strategy	str	The authentication method. "all": (default) try all methods until one works "interactive": enter username and password. "netrc": retrieve username and password from ~/.netrc. "environment": retrieve username and password from $EARTHDATA_USERNAME and $EARTHDATA_PASSWORD.	'all'
persist	bool	Whether to persist credentials in a .netrc file. Defaults to True.	True
**kwargs		Additional keyword arguments for the earthaccess.login() function.	{}

Source code in leafmap/common.py

def nasa_data_login(strategy: str = "all", persist: bool = True, **kwargs) -> None:
    """Logs in to NASA Earthdata.

    Args:
        strategy (str, optional): The authentication method.
            "all": (default) try all methods until one works
            "interactive": enter username and password.
            "netrc": retrieve username and password from ~/.netrc.
            "environment": retrieve username and password from $EARTHDATA_USERNAME and $EARTHDATA_PASSWORD.
        persist (bool, optional): Whether to persist credentials in a .netrc file. Defaults to True.
        **kwargs: Additional keyword arguments for the earthaccess.login() function.
    """
    try:
        import earthaccess
    except ImportError:
        install_package("earthaccess")
        import earthaccess

    try:
        earthaccess.login(strategy=strategy, persist=persist, **kwargs)
    except:
        print(
            "Please login to Earthdata first. Register at https://urs.earthdata.nasa.gov"
        )

nasa_data_search(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs)

Searches for NASA Earthdata granules.

Parameters:

Name	Type	Description	Default
count	int	The number of granules to retrieve. Defaults to -1 (retrieve all).	-1
short_name	str	The short name of the dataset.	None
bbox	List[float]	The bounding box coordinates [xmin, ymin, xmax, ymax].	None
temporal	str	The temporal extent of the data.	None
version	str	The version of the dataset.	None
doi	str	The Digital Object Identifier (DOI) of the dataset.	None
daac	str	The Distributed Active Archive Center (DAAC) of the dataset.	None
provider	str	The provider of the dataset.	None
output	str	The output file path to save the GeoDataFrame as a file.	None
crs	str	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	'EPSG:4326'
return_gdf	bool	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	False
**kwargs		Additional keyword arguments for the earthaccess.search_data() function.	{}

Returns:

Type	Description
Union[List[dict], tuple]	Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in leafmap/common.py

def nasa_data_search(
    count: int = -1,
    short_name: Optional[str] = None,
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    version: Optional[str] = None,
    doi: Optional[str] = None,
    daac: Optional[str] = None,
    provider: Optional[str] = None,
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA Earthdata granules.

    Args:
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset.
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        version (str, optional): The version of the dataset.
        doi (str, optional): The Digital Object Identifier (DOI) of the dataset.
        daac (str, optional): The Distributed Active Archive Center (DAAC) of the dataset.
        provider (str, optional): The provider of the dataset.
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """
    try:
        import earthaccess
    except ImportError:
        install_package("earthaccess")

    if short_name is not None:
        kwargs["short_name"] = short_name
    if bbox is not None:
        kwargs["bounding_box"] = bbox
    if temporal is not None:
        kwargs["temporal"] = temporal
    if version is not None:
        kwargs["version"] = version
    if doi is not None:
        kwargs["doi"] = doi
    if daac is not None:
        kwargs["daac"] = daac
    if provider is not None:
        kwargs["provider"] = provider

    granules = earthaccess.search_data(
        count=count,
        **kwargs,
    )

    if output is not None:
        nasa_data_granules_to_gdf(granules, crs=crs, output=output)

    if return_gdf:
        gdf = nasa_data_granules_to_gdf(granules, crs=crs)
        return granules, gdf
    else:
        return granules

nasa_datasets(keyword=None, df=None, return_short_name=False)

Searches for NASA datasets based on a keyword in a DataFrame.

Parameters:

Name	Type	Description	Default
keyword	str	The keyword to search for. Defaults to None.	None
df	DataFrame	The DataFrame to search in. If None, it will download the NASA dataset CSV from GitHub. Defaults to None.	None
return_short_name	bool	If True, only returns the list of short names of the matched datasets. Defaults to False.	False

Returns:

Type	Description
	Union[pd.DataFrame, List[str]]: Filtered DataFrame if return_short_name is False, otherwise a list of short names.

Source code in leafmap/common.py

def nasa_datasets(keyword=None, df=None, return_short_name=False):
    """
    Searches for NASA datasets based on a keyword in a DataFrame.

    Args:
        keyword (str, optional): The keyword to search for. Defaults to None.
        df (pd.DataFrame, optional): The DataFrame to search in. If None, it will download the NASA dataset CSV from GitHub. Defaults to None.
        return_short_name (bool, optional): If True, only returns the list of short names of the matched datasets. Defaults to False.

    Returns:
        Union[pd.DataFrame, List[str]]: Filtered DataFrame if return_short_name is False, otherwise a list of short names.

    """
    import pandas as pd

    if df is None:
        url = "https://github.com/opengeos/NASA-Earth-Data/raw/main/nasa_earth_data.tsv"
        df = pd.read_csv(url, sep="\t")

    if keyword is not None:
        # Convert keyword and DataFrame values to lowercase
        keyword_lower = keyword.lower()
        df_lower = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)

        # Use boolean indexing to filter the DataFrame
        filtered_df = df[
            df_lower.astype(str).apply(lambda x: keyword_lower in " ".join(x), axis=1)
        ].reset_index(drop=True)

        if return_short_name:
            return filtered_df["ShortName"].tolist()
        else:
            return filtered_df
    else:
        if return_short_name:
            return df["ShortName"].tolist()
        else:
            return df

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}}'

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 matplotlib to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.	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 leafmap/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_tif(filename, output=None, variables=None, shift_lon=True, lat='lat', lon='lon', lev='lev', level_index=0, time=0, crs='epsg:4326', 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'
lev	str	Name of the level variable. Defaults to 'lev'.	'lev'
level_index	int	Index of the level dimension. Defaults to 0'.	0
time	int	Index of the time dimension. Defaults to 0'.	0
crs	str	The coordinate reference system. Defaults to 'epsg:4326'.	'epsg:4326'
return_vars	bool	Flag to return all variables. Defaults to False.	False

Raises:

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 leafmap/common.py

def netcdf_to_tif(
    filename,
    output=None,
    variables=None,
    shift_lon=True,
    lat="lat",
    lon="lon",
    lev="lev",
    level_index=0,
    time=0,
    crs="epsg:4326",
    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'.
        lev (str, optional): Name of the level variable. Defaults to 'lev'.
        level_index (int, optional): Index of the level dimension. Defaults to 0'.
        time (int, optional): Index of the time dimension. Defaults to 0'.
        crs (str, optional): The coordinate reference system. Defaults to 'epsg:4326'.
        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:
        ext = os.path.splitext(filename)[1].lower()
        if ext not in [".nc", ".nc4"]:
            raise TypeError(
                "The output file must be a netCDF with extension .nc or .nc4."
            )
        output = filename.replace(ext, ".tif")
    else:
        output = check_file_path(output)

    xds = xr.open_dataset(filename, **kwargs)

    coords = list(xds.coords.keys())
    if "time" in coords:
        xds = xds.isel(time=time, drop=True)

    if lev in coords:
        xds = xds.isel(lev=level_index, drop=True)

    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.write_crs(crs).rio.to_raster(
            output
        )
    else:
        xds[variables].rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.write_crs(
            crs
        ).rio.to_raster(output)

    if return_vars:
        return output, allowed_vars
    else:
        return output

numpy_to_cog(np_array, out_cog, bounds=None, profile=None, dtype=None, dst_crs=None, coord_crs=None)

Converts a numpy array to a COG file.

Parameters:

Name	Type	Description	Default
np_array	array	A numpy array representing an image or an HTTP URL to an 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
dst_crs	str	The coordinate reference system of the output COG file. Defaults to "epsg:4326".	None
coord_crs	str	The coordinate reference system of bbox coordinates. Defaults to None.	None

Source code in leafmap/common.py

def numpy_to_cog(
    np_array,
    out_cog,
    bounds=None,
    profile=None,
    dtype=None,
    dst_crs=None,
    coord_crs=None,
):
    """Converts a numpy array to a COG file.

    Args:
        np_array (np.array): A numpy array representing an image or an HTTP URL to an 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.
        dst_crs (str, optional): The coordinate reference system of the output COG file. Defaults to "epsg:4326".
        coord_crs (str, optional): The coordinate reference system of bbox coordinates. Defaults to None.

    """

    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 isinstance(np_array, str):
        with rasterio.open(np_array, "r") as ds:
            np_array = ds.read()

    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 profile.startswith("http")) and (not os.path.exists(profile)):
                raise FileNotFoundError("The provided file could not be found.")
            with rasterio.open(profile) as ds:
                dst_crs = ds.crs
                if bounds is None:
                    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:
        print(
            "warning: bounds is not set. Using the default bounds (-180.0, -85.0511, 180.0, 85.0511)"
        )
        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.")

    if coord_crs is not None and dst_crs is not None:
        bounds = transform_bbox_coords(bounds, coord_crs, dst_crs)

    src_transform = from_bounds(*bounds, width=width, height=height)
    if dtype is None:
        dtype = str(np_array.dtype)

    if dst_crs is None:
        dst_crs = "epsg:4326"

    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=dst_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_image(np_array, filename, transpose=True, bands=None, size=None, resize_args=None, **kwargs)

Converts a numpy array to an image in the specified format, such as JPG, PNG, TIFF, etc.

Parameters:

Name	Type	Description	Default
np_array	ndarray	A numpy array or a path to a raster file.	required
filename	str	The output filename.	required
transpose	bool	Whether to transpose the array from (bands, rows, cols) to (rows, cols, bands). Defaults to True.	True
bands	int | list	The band(s) to use, starting from 0. Defaults to None.	None

Source code in leafmap/common.py

def numpy_to_image(
    np_array,
    filename: str,
    transpose: bool = True,
    bands: Union[int, list] = None,
    size: Tuple = None,
    resize_args: dict = None,
    **kwargs,
) -> None:
    """Converts a numpy array to an image in the specified format, such as JPG, PNG, TIFF, etc.

    Args:
        np_array (np.ndarray): A numpy array or a path to a raster file.
        filename (str): The output filename.
        transpose (bool, optional): Whether to transpose the array from (bands, rows, cols) to (rows, cols, bands). Defaults to True.
        bands (int | list, optional): The band(s) to use, starting from 0. Defaults to None.

    """

    import numpy as np
    from PIL import Image

    warnings.filterwarnings("ignore")

    if isinstance(np_array, str):
        np_array = image_to_numpy(np_array)

    if not isinstance(np_array, np.ndarray):
        raise TypeError("The provided input must be a numpy array.")

    if np_array.dtype == np.float64 or np_array.dtype == np.float32:
        # Convert the array to uint8
        # np_array = (np_array * 255).astype(np.uint8)
        np.interp(np_array, (np_array.min(), np_array.max()), (0, 255)).astype(np.uint8)
    else:
        # The array is already uint8
        np_array = np_array

    if np_array.ndim == 2:
        img = Image.fromarray(np_array)
    elif np_array.ndim == 3:
        if transpose:
            np_array = np_array.transpose(1, 2, 0)
        if bands is None:
            if np_array.shape[2] < 3:
                np_array = np_array[:, :, 0]
            elif np_array.shape[2] > 3:
                np_array = np_array[:, :, :3]

        elif isinstance(bands, list):
            if len(bands) == 1:
                np_array = np_array[:, :, bands[0]]
            else:
                np_array = np_array[:, :, bands]
        elif isinstance(bands, int):
            np_array = np_array[:, :, bands]
        img = Image.fromarray(np_array)
    else:
        raise ValueError("The provided input must be a 2D or 3D numpy array.")

    if isinstance(size, tuple):
        try:
            from skimage.transform import resize
        except ImportError:
            raise ImportError(
                "The scikit-image package is not installed. Please install it with `pip install scikit-image` \
                  or `conda install scikit-image -c conda-forge`."
            )
        if resize_args is None:
            resize_args = {}
        if "preserve_range" not in resize_args:
            resize_args["preserve_range"] = True
        np_array = resize(np_array, size, **resize_args).astype("uint8")
        img = Image.fromarray(np_array)

    img.save(filename, **kwargs)

oam_search(bbox=None, start_date=None, end_date=None, limit=100, return_gdf=True, **kwargs)

Search OpenAerialMap (https://openaerialmap.org) and return a GeoDataFrame or list of image metadata.

Parameters:

Name	Type	Description	Default
bbox	list | str	The bounding box [xmin, ymin, xmax, ymax] to search within. Defaults to None.	None
start_date	str	The start date to search within, such as "2015-04-20T00:00:00.000Z". Defaults to None.	None
end_date	str	The end date to search within, such as "2015-04-21T00:00:00.000Z". Defaults to None.	None
limit	int	The maximum number of results to return. Defaults to 100.	100
return_gdf	bool	If True, return a GeoDataFrame, otherwise return a list. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the API. See https://hotosm.github.io/oam-api/	{}

Returns:

Type	Description
	GeoDataFrame | list: If return_gdf is True, return a GeoDataFrame. Otherwise, return a list.

Source code in leafmap/stac.py

def oam_search(
    bbox=None, start_date=None, end_date=None, limit=100, return_gdf=True, **kwargs
):
    """Search OpenAerialMap (https://openaerialmap.org) and return a GeoDataFrame or list of image metadata.

    Args:
        bbox (list | str, optional): The bounding box [xmin, ymin, xmax, ymax] to search within. Defaults to None.
        start_date (str, optional): The start date to search within, such as "2015-04-20T00:00:00.000Z". Defaults to None.
        end_date (str, optional): The end date to search within, such as "2015-04-21T00:00:00.000Z". Defaults to None.
        limit (int, optional): The maximum number of results to return. Defaults to 100.
        return_gdf (bool, optional): If True, return a GeoDataFrame, otherwise return a list. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the API. See https://hotosm.github.io/oam-api/

    Returns:
        GeoDataFrame | list: If return_gdf is True, return a GeoDataFrame. Otherwise, return a list.
    """

    from shapely.geometry import Polygon
    import geopandas as gpd

    url = "https://api.openaerialmap.org/meta"
    if bbox is not None:
        if isinstance(bbox, str):
            bbox = [float(x) for x in bbox.split(",")]
        if not isinstance(bbox, list):
            raise ValueError("bbox must be a list.")
        if len(bbox) != 4:
            raise ValueError("bbox must be a list of 4 numbers.")
        bbox = ",".join(map(str, bbox))
        kwargs["bbox"] = bbox

    if start_date is not None:
        kwargs["acquisition_from"] = start_date

    if end_date is not None:
        kwargs["acquisition_to"] = end_date

    if limit is not None:
        kwargs["limit"] = limit

    try:
        r = requests.get(url, params=kwargs).json()
        if "results" in r:
            results = []
            for result in r["results"]:
                if "geojson" in result:
                    del result["geojson"]
                if "projection" in result:
                    del result["projection"]
                if "footprint" in result:
                    del result["footprint"]
                result = flatten_dict(result)
                results.append(result)

            if not return_gdf:
                return results
            else:
                df = pd.DataFrame(results)

                polygons = [Polygon.from_bounds(*bbox) for bbox in df["bbox"]]
                gdf = gpd.GeoDataFrame(geometry=polygons, crs="epsg:4326")

                return pd.concat([gdf, df], axis=1)

        else:
            print("No results found.")
            return None

    except Exception as e:
        return None

open_image_from_url(url)

Loads an image from the specified URL.

Parameters:

Name	Type	Description	Default
url	str	URL of the image.	required

Returns:

Name	Type	Description
object		Image object.

Source code in leafmap/common.py

def open_image_from_url(url: str):
    """Loads an image from the specified URL.

    Args:
        url (str): URL of the image.

    Returns:
        object: Image object.
    """
    from PIL import Image

    from io import BytesIO

    # from urllib.parse import urlparse

    try:
        response = requests.get(url)
        img = Image.open(BytesIO(response.content))
        return img
    except Exception as e:
        print(e)

overlay_images(image1, image2, alpha=0.5, backend='TkAgg', height_ratios=[10, 1], show_args1={}, show_args2={})

Overlays two images using a slider to control the opacity of the top image.

Parameters:

Name	Type	Description	Default
image1	str | ndarray	The first input image at the bottom represented as a NumPy array or the path to the image.	required
image2	_type_	The second input image on top represented as a NumPy array or the path to the image.	required
alpha	float	The alpha value of the top image. Defaults to 0.5.	0.5
backend	str	The backend of the matplotlib plot. Defaults to "TkAgg".	'TkAgg'
height_ratios	list	The height ratios of the two subplots. Defaults to [10, 1].	[10, 1]
show_args1	dict	The keyword arguments to pass to the imshow() function for the first image. Defaults to {}.	{}
show_args2	dict	The keyword arguments to pass to the imshow() function for the second image. Defaults to {}.	{}

Source code in leafmap/common.py

def overlay_images(
    image1,
    image2,
    alpha=0.5,
    backend="TkAgg",
    height_ratios=[10, 1],
    show_args1={},
    show_args2={},
):
    """Overlays two images using a slider to control the opacity of the top image.

    Args:
        image1 (str | np.ndarray): The first input image at the bottom represented as a NumPy array or the path to the image.
        image2 (_type_): The second input image on top represented as a NumPy array or the path to the image.
        alpha (float, optional): The alpha value of the top image. Defaults to 0.5.
        backend (str, optional): The backend of the matplotlib plot. Defaults to "TkAgg".
        height_ratios (list, optional): The height ratios of the two subplots. Defaults to [10, 1].
        show_args1 (dict, optional): The keyword arguments to pass to the imshow() function for the first image. Defaults to {}.
        show_args2 (dict, optional): The keyword arguments to pass to the imshow() function for the second image. Defaults to {}.

    """
    import sys
    import matplotlib
    import matplotlib.pyplot as plt
    import matplotlib.widgets as mpwidgets

    if "google.colab" in sys.modules:
        backend = "inline"
        print(
            "The TkAgg backend is not supported in Google Colab. The overlay_images function will not work on Colab."
        )
        return

    matplotlib.use(backend)

    if isinstance(image1, str):
        if image1.startswith("http"):
            image1 = download_file(image1)

        if not os.path.exists(image1):
            raise ValueError(f"Input path {image1} does not exist.")

    if isinstance(image2, str):
        if image2.startswith("http"):
            image2 = download_file(image2)

        if not os.path.exists(image2):
            raise ValueError(f"Input path {image2} does not exist.")

    # Load the two images
    x = plt.imread(image1)
    y = plt.imread(image2)

    # Create the plot
    fig, (ax0, ax1) = plt.subplots(2, 1, gridspec_kw={"height_ratios": height_ratios})
    img0 = ax0.imshow(x, **show_args1)
    img1 = ax0.imshow(y, alpha=alpha, **show_args2)

    # Define the update function
    def update(value):
        img1.set_alpha(value)
        fig.canvas.draw_idle()

    # Create the slider
    slider0 = mpwidgets.Slider(ax=ax1, label="alpha", valmin=0, valmax=1, valinit=alpha)
    slider0.on_changed(update)

    # Display the plot
    plt.show()

pandas_to_geojson(df, coordinates=['lng', 'lat'], geometry_type='Point', properties=None, output=None)

Convert a DataFrame to a GeoJSON format.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input DataFrame containing the data.	required
coordinates	list	A list of two column names representing the longitude and latitude coordinates.	['lng', 'lat']
geometry_type	str	The type of geometry for the GeoJSON features (e.g., "Point", "LineString", "Polygon").	'Point'
properties	list	A list of column names to include in the properties of each GeoJSON feature. If None, all columns except the coordinate columns are included.	None
output	str	The file path to save the GeoJSON output. If None, the GeoJSON is not saved to a file.	None

Returns:

Name	Type	Description
dict	dict	A dictionary representing the GeoJSON object.

Source code in leafmap/common.py

def pandas_to_geojson(
    df,
    coordinates=["lng", "lat"],
    geometry_type: str = "Point",
    properties: list = None,
    output: Optional[str] = None,
) -> dict:
    """
    Convert a DataFrame to a GeoJSON format.

    Args:
        df (pd.DataFrame): The input DataFrame containing the data.
        coordinates (list): A list of two column names representing the
            longitude and latitude coordinates.
        geometry_type (str): The type of geometry for the GeoJSON features
            (e.g., "Point", "LineString", "Polygon").
        properties (list): A list of column names to include in the properties
            of each GeoJSON feature. If None, all columns except the coordinate
            columns are included.
        output (str, optional): The file path to save the GeoJSON output. If None,
            the GeoJSON is not saved to a file.

    Returns:
        dict: A dictionary representing the GeoJSON object.
    """

    import pandas as pd

    if isinstance(df, str):
        if df.endswith(".csv"):
            df = pd.read_csv(df)
        elif df.endswith(".json"):
            df = pd.read_json(df)
        else:
            raise ValueError("The input file must be a CSV or JSON file.")

    geojson = {"type": "FeatureCollection", "features": []}

    if properties is None:
        properties = [col for col in df.columns if col not in coordinates]

    for _, row in df.iterrows():
        feature = {
            "type": "Feature",
            "properties": {},
            "geometry": {"type": geometry_type, "coordinates": []},
        }
        feature["geometry"]["coordinates"] = list(row[coordinates])
        for prop in properties:
            feature["properties"][prop] = row[prop]

        geojson["features"].append(feature)

    if output:
        with open(output, "w") as f:
            json.dump(geojson, f, indent=4)

    return geojson

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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the API key could not be found.

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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'

Raises:

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:

Name	Type	Description
str		A Planet global mosaic tile url.

Source code in leafmap/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 = 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'

Raises:

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:

Name	Type	Description
str		A Planet global mosaic tile url.

Source code in leafmap/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 = 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:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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'

Raises:

Type	Description
ValueError	If the API key could not be found.

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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 = 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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the API key could not be found.

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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 = 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'

Raises:

Type	Description
ValueError	If the API key could not be found.

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in leafmap/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 = 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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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.
    """

    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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in leafmap/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_actual_vs_predicted(df, x_col='y', y_col='y_pred', xlim=None, ylim=None, title='Actual vs. Predicted Values', x_label='Actual Values', y_label='Predicted Values', marker_size=6, marker_opacity=0.7, marker_color='blue', line_color='red', line_dash='dash', width=800, height=800, showlegend=True, template='plotly_white', square_aspect=True, return_figure=False, **kwargs)

Plots a customizable scatter plot with a reference line for actual vs. predicted values.

Parameters:

Name	Type	Description	Default
df	DataFrame	A pandas DataFrame with columns for actual and predicted values.	required
x_col	str	Column name for the x-axis (actual values).	'y'
y_col	str	Column name for the y-axis (predicted values).	'y_pred'
xlim	tuple	A tuple (min, max) for x-axis limits.	None
ylim	tuple	A tuple (min, max) for y-axis limits.	None
title	str	Title of the plot.	'Actual vs. Predicted Values'
x_label	str	Label for the x-axis.	'Actual Values'
y_label	str	Label for the y-axis.	'Predicted Values'
marker_size	int	Size of the scatter plot markers.	6
marker_opacity	float	Opacity of the scatter plot markers.	0.7
marker_color	str	Color of the scatter plot markers.	'blue'
line_color	str	Color of the reference line.	'red'
line_dash	str	Dash style of the reference line ('dash', 'dot', etc.).	'dash'
width	int	Width of the plot in pixels.	800
height	int	Height of the plot in pixels.	800
showlegend	bool	Whether to show the legend.	True
template	str	Plotly template for styling.	'plotly_white'
square_aspect	bool	Whether to enforce a square aspect ratio.	True
return_figure	bool	Whether to return the Plotly figure object.	False
**kwargs		Additional keyword arguments for Plotly figure.	{}

Returns:

Type	Description
	A Plotly figure object.

Source code in leafmap/common.py

def plot_actual_vs_predicted(
    df: pd.DataFrame,
    x_col: str = "y",
    y_col: str = "y_pred",
    xlim: tuple = None,
    ylim: tuple = None,
    title: str = "Actual vs. Predicted Values",
    x_label: str = "Actual Values",
    y_label: str = "Predicted Values",
    marker_size: int = 6,
    marker_opacity: float = 0.7,
    marker_color: str = "blue",
    line_color: str = "red",
    line_dash: str = "dash",
    width: int = 800,
    height: int = 800,
    showlegend: bool = True,
    template: str = "plotly_white",
    square_aspect: bool = True,
    return_figure: bool = False,
    **kwargs,
):
    """
    Plots a customizable scatter plot with a reference line for actual vs. predicted values.

    Args:
        df: A pandas DataFrame with columns for actual and predicted values.
        x_col: Column name for the x-axis (actual values).
        y_col: Column name for the y-axis (predicted values).
        xlim: A tuple (min, max) for x-axis limits.
        ylim: A tuple (min, max) for y-axis limits.
        title: Title of the plot.
        x_label: Label for the x-axis.
        y_label: Label for the y-axis.
        marker_size: Size of the scatter plot markers.
        marker_opacity: Opacity of the scatter plot markers.
        marker_color: Color of the scatter plot markers.
        line_color: Color of the reference line.
        line_dash: Dash style of the reference line ('dash', 'dot', etc.).
        width: Width of the plot in pixels.
        height: Height of the plot in pixels.
        showlegend: Whether to show the legend.
        template: Plotly template for styling.
        square_aspect: Whether to enforce a square aspect ratio.
        return_figure: Whether to return the Plotly figure object.
        **kwargs: Additional keyword arguments for Plotly figure.

    Returns:
        A Plotly figure object.

    """
    import plotly.graph_objects as go

    # Default x and y limits if not provided
    x_min, x_max = xlim if xlim else (df[x_col].min(), df[x_col].max())
    y_min, y_max = ylim if ylim else (df[y_col].min(), df[y_col].max())

    # Scatter plot for actual vs predicted
    scatter = go.Scatter(
        x=df[x_col],
        y=df[y_col],
        mode="markers",
        marker=dict(size=marker_size, opacity=marker_opacity, color=marker_color),
        name="Predicted vs Actual",
    )

    # Reference line y = x
    ref_line = go.Scatter(
        x=[x_min, x_max],
        y=[x_min, x_max],
        mode="lines",
        line=dict(color=line_color, dash=line_dash),
        name="Reference Line",
    )

    # Layout settings
    layout = go.Layout(
        title=title,
        xaxis=dict(title=x_label, range=[x_min, x_max]),
        yaxis=dict(title=y_label, range=[y_min, y_max]),
        template=template,
        showlegend=showlegend,
        height=height,
        width=width,
    )

    # Ensure square aspect ratio if specified
    if square_aspect:
        layout.update(xaxis_scaleanchor="y")

    # Create the figure
    fig = go.Figure(data=[scatter, ref_line], layout=layout, **kwargs)
    if return_figure:
        return fig
    else:
        fig.show()

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 | 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 leafmap/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

    try:
        import pvxarray
        import rioxarray
        import xarray
    except ImportError:
        print(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )
        return

    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 | 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 leafmap/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().
    """
    import sys

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if "google.colab" in sys.modules:
        print("This function is not supported in Google Colab.")
        return

    try:
        import pvxarray
        import pyvista
        import rioxarray
        import xarray
    except ImportError:
        print(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )
        return

    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)

pmtiles_header(input_file)

Fetch the header information from a local or remote .pmtiles file.

This function retrieves the header from a PMTiles file, either local or hosted remotely. It deserializes the header and calculates the center and bounds of the tiles from the given metadata in the header.

Parameters:

Name	Type	Description	Default
input_file	str	Path to the .pmtiles file, or its URL if the file is hosted remotely.	required

Returns:

Name	Type	Description
dict		A dictionary containing the header information, including center and bounds.

Raises:

Type	Description
ImportError	If the pmtiles library is not installed.
ValueError	If the input file is not a .pmtiles file or if it does not exist.

Example

header = pmtiles_header("https://example.com/path/to/tiles.pmtiles") print(header["center"]) [52.5200, 13.4050]

Note

If fetching a remote PMTiles file, this function only downloads the first 127 bytes of the file to retrieve the header.

Source code in leafmap/common.py

def pmtiles_header(input_file: str):
    """
    Fetch the header information from a local or remote .pmtiles file.

    This function retrieves the header from a PMTiles file, either local or hosted remotely.
    It deserializes the header and calculates the center and bounds of the tiles from the
    given metadata in the header.

    Args:
        input_file (str): Path to the .pmtiles file, or its URL if the file is hosted remotely.

    Returns:
        dict: A dictionary containing the header information, including center and bounds.

    Raises:
        ImportError: If the pmtiles library is not installed.
        ValueError: If the input file is not a .pmtiles file or if it does not exist.

    Example:
        >>> header = pmtiles_header("https://example.com/path/to/tiles.pmtiles")
        >>> print(header["center"])
        [52.5200, 13.4050]

    Note:
        If fetching a remote PMTiles file, this function only downloads the first 127 bytes
        of the file to retrieve the header.
    """

    import requests
    from urllib.parse import urlparse

    try:
        from pmtiles.reader import Reader, MmapSource
        from pmtiles.tile import deserialize_header
    except ImportError:
        print(
            "pmtiles is not installed. Please install it using `pip install pmtiles`."
        )
        return
    if not urlparse(input_file).path.endswith(".pmtiles"):
        raise ValueError("Input file must be a .pmtiles file.")

    if input_file.startswith("http"):
        # Fetch only the first 127 bytes
        headers = {"Range": "bytes=0-127"}
        response = requests.get(input_file, headers=headers)
        header = deserialize_header(response.content)

    else:
        if not os.path.exists(input_file):
            raise ValueError(f"Input file {input_file} does not exist.")

        with open(input_file, "rb") as f:
            reader = Reader(MmapSource(f))
            header = reader.header()

    header["center"] = [header["center_lat_e7"] / 1e7, header["center_lon_e7"] / 1e7]
    header["bounds"] = [
        header["min_lon_e7"] / 1e7,
        header["min_lat_e7"] / 1e7,
        header["max_lon_e7"] / 1e7,
        header["max_lat_e7"] / 1e7,
    ]

    return header

pmtiles_metadata(input_file)

Fetch the metadata from a local or remote .pmtiles file.

This function retrieves metadata from a PMTiles file, whether it's local or hosted remotely. If it's remote, the function fetches the header to determine the range of bytes to download for obtaining the metadata. It then reads the metadata and extracts the layer names.

Parameters:

Name	Type	Description	Default
input_file	str	Path to the .pmtiles file, or its URL if the file is hosted remotely.	required

Returns:

Name	Type	Description
dict	Dict[str, Union[str, int, List[str]]]	A dictionary containing the metadata information, including layer names.

Raises:

Type	Description
ImportError	If the pmtiles library is not installed.
ValueError	If the input file is not a .pmtiles file or if it does not exist.

Example

metadata = pmtiles_metadata("https://example.com/path/to/tiles.pmtiles") print(metadata["layer_names"]) ['buildings', 'roads']

Note

If fetching a remote PMTiles file, this function may perform multiple requests to minimize the amount of data downloaded.

Source code in leafmap/common.py

def pmtiles_metadata(input_file: str) -> Dict[str, Union[str, int, List[str]]]:
    """
    Fetch the metadata from a local or remote .pmtiles file.

    This function retrieves metadata from a PMTiles file, whether it's local or hosted remotely.
    If it's remote, the function fetches the header to determine the range of bytes to download
    for obtaining the metadata. It then reads the metadata and extracts the layer names.

    Args:
        input_file (str): Path to the .pmtiles file, or its URL if the file is hosted remotely.

    Returns:
        dict: A dictionary containing the metadata information, including layer names.

    Raises:
        ImportError: If the pmtiles library is not installed.
        ValueError: If the input file is not a .pmtiles file or if it does not exist.

    Example:
        >>> metadata = pmtiles_metadata("https://example.com/path/to/tiles.pmtiles")
        >>> print(metadata["layer_names"])
        ['buildings', 'roads']

    Note:
        If fetching a remote PMTiles file, this function may perform multiple requests to minimize
        the amount of data downloaded.
    """

    import json
    import requests
    from urllib.parse import urlparse

    try:
        from pmtiles.reader import Reader, MmapSource, MemorySource
    except ImportError:
        print(
            "pmtiles is not installed. Please install it using `pip install pmtiles`."
        )
        return

    # ignore uri parameters when checking file suffix
    if not urlparse(input_file).path.endswith(".pmtiles"):
        raise ValueError("Input file must be a .pmtiles file.")

    header = pmtiles_header(input_file)
    metadata_offset = header["metadata_offset"]
    metadata_length = header["metadata_length"]

    if input_file.startswith("http"):
        headers = {"Range": f"bytes=0-{metadata_offset + metadata_length}"}
        response = requests.get(input_file, headers=headers)
        content = MemorySource(response.content)
        metadata = Reader(content).metadata()
    else:
        with open(input_file, "rb") as f:
            reader = Reader(MmapSource(f))
            metadata = reader.metadata()
            if "json" in metadata:
                metadata["vector_layers"] = json.loads(metadata["json"])[
                    "vector_layers"
                ]

    vector_layers = metadata["vector_layers"]
    layer_names = [layer["id"] for layer in vector_layers]

    if "tilestats" in metadata:
        geometries = [layer["geometry"] for layer in metadata["tilestats"]["layers"]]
        metadata["geometries"] = geometries

    metadata["layer_names"] = layer_names
    metadata["center"] = header["center"]
    metadata["bounds"] = header["bounds"]
    return metadata

pmtiles_style(url, layers=None, cmap='Set3', n_class=None, opacity=0.5, circle_radius=5, line_width=1, attribution='PMTiles', **kwargs)

Generates a Mapbox style JSON for rendering PMTiles data.

Parameters:

Name	Type	Description	Default
url	str	The URL of the PMTiles file.	required
layers	str or list[str]	The layers to include in the style. If None, all layers will be included. Defaults to None.	None
cmap	str	The color map to use for styling the layers. Defaults to "Set3".	'Set3'
n_class	int	The number of classes to use for styling. If None, the number of classes will be determined automatically based on the color map. Defaults to None.	None
opacity	float	The fill opacity for polygon layers. Defaults to 0.5.	0.5
circle_radius	int	The circle radius for point layers. Defaults to 5.	5
line_width	int	The line width for line layers. Defaults to 1.	1
attribution	str	The attribution text for the data source. Defaults to "PMTiles".	'PMTiles'

Returns:

Name	Type	Description
dict		The Mapbox style JSON.

Raises:

Type	Description
ValueError	If the layers argument is not a string or a list.
ValueError	If a layer specified in the layers argument does not exist in the PMTiles file.

Source code in leafmap/common.py

def pmtiles_style(
    url: str,
    layers: Optional[Union[str, List[str]]] = None,
    cmap: str = "Set3",
    n_class: Optional[int] = None,
    opacity: float = 0.5,
    circle_radius: int = 5,
    line_width: int = 1,
    attribution: str = "PMTiles",
    **kwargs,
):
    """
    Generates a Mapbox style JSON for rendering PMTiles data.

    Args:
        url (str): The URL of the PMTiles file.
        layers (str or list[str], optional): The layers to include in the style. If None, all layers will be included.
            Defaults to None.
        cmap (str, optional): The color map to use for styling the layers. Defaults to "Set3".
        n_class (int, optional): The number of classes to use for styling. If None, the number of classes will be
            determined automatically based on the color map. Defaults to None.
        opacity (float, optional): The fill opacity for polygon layers. Defaults to 0.5.
        circle_radius (int, optional): The circle radius for point layers. Defaults to 5.
        line_width (int, optional): The line width for line layers. Defaults to 1.
        attribution (str, optional): The attribution text for the data source. Defaults to "PMTiles".

    Returns:
        dict: The Mapbox style JSON.

    Raises:
        ValueError: If the layers argument is not a string or a list.
        ValueError: If a layer specified in the layers argument does not exist in the PMTiles file.
    """

    if cmap == "Set3":
        palette = [
            "#8dd3c7",
            "#ffffb3",
            "#bebada",
            "#fb8072",
            "#80b1d3",
            "#fdb462",
            "#b3de69",
            "#fccde5",
            "#d9d9d9",
            "#bc80bd",
            "#ccebc5",
            "#ffed6f",
        ]
    elif isinstance(cmap, list):
        palette = cmap
    else:
        from .colormaps import get_palette

        palette = ["#" + c for c in get_palette(cmap, n_class)]

    n_class = len(palette)

    metadata = pmtiles_metadata(url)
    layer_names = metadata["layer_names"]

    style = {
        "version": 8,
        "sources": {
            "source": {
                "type": "vector",
                "url": "pmtiles://" + url,
                "attribution": attribution,
            }
        },
        "layers": [],
    }

    if layers is None:
        layers = layer_names
    elif isinstance(layers, str):
        layers = [layers]
    elif isinstance(layers, list):
        for layer in layers:
            if layer not in layer_names:
                raise ValueError(f"Layer {layer} does not exist in the PMTiles file.")
    else:
        raise ValueError("The layers argument must be a string or a list.")

    for i, layer_name in enumerate(layers):
        layer_point = {
            "id": f"{layer_name}_point",
            "source": "source",
            "source-layer": layer_name,
            "type": "circle",
            "paint": {
                "circle-color": palette[i % n_class],
                "circle-radius": circle_radius,
            },
            "filter": ["==", ["geometry-type"], "Point"],
        }

        layer_stroke = {
            "id": f"{layer_name}_stroke",
            "source": "source",
            "source-layer": layer_name,
            "type": "line",
            "paint": {
                "line-color": palette[i % n_class],
                "line-width": line_width,
            },
            "filter": ["==", ["geometry-type"], "LineString"],
        }

        layer_fill = {
            "id": f"{layer_name}_fill",
            "source": "source",
            "source-layer": layer_name,
            "type": "fill",
            "paint": {
                "fill-color": palette[i % n_class],
                "fill-opacity": opacity,
            },
            "filter": ["==", ["geometry-type"], "Polygon"],
        }

        style["layers"].extend([layer_point, layer_stroke, layer_fill])

    return style

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

Raises:

Type	Description
FileNotFoundError	No png images could be found.

Source code in leafmap/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,
    )

point_to_gdf(x, y, point_crs='EPSG:4326', to_crs='EPSG:4326', **kwargs)

Convert a point to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
x	float	X coordinate of the point.	required
y	float	Y coordinate of the point.	required
point_crs	str	Coordinate Reference System of the point.	'EPSG:4326'

Returns:

Type	Description
	gpd.GeoDataFrame: GeoDataFrame containing the point.

Source code in leafmap/common.py

def point_to_gdf(x, y, point_crs="EPSG:4326", to_crs="EPSG:4326", **kwargs):
    """
    Convert a point to a GeoDataFrame.

    Args:
        x (float): X coordinate of the point.
        y (float): Y coordinate of the point.
        point_crs (str): Coordinate Reference System of the point.

    Returns:
        gpd.GeoDataFrame: GeoDataFrame containing the point.
    """
    import geopandas as gpd
    from shapely.geometry import Point

    # Create a Point object
    point = Point(x, y)

    # Convert the Point to a GeoDataFrame
    gdf = gpd.GeoDataFrame([{"geometry": point}], crs=point_crs)

    if to_crs != point_crs:
        gdf = gdf.to_crs(to_crs)

    return gdf

points_from_xy(data, x=None, y=None, 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 | DataFrame	A csv or Pandas DataFrame containing x, y, z values.	required
x	str	The column name for the x values. Defaults to "longitude".	None
y	str	The column name for the y values. Defaults to "latitude".	None
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 leafmap/common.py

def points_from_xy(data, x=None, y=None, 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"

    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.")

    columns = df.columns

    if x is None:
        if "longitude" in columns:
            x = "longitude"
        elif "x" in columns:
            x = "x"
        elif "lon" in columns:
            x = "lon"
        else:
            raise ValueError("The x column could not be found.")

    if y is None:
        if "latitude" in columns:
            y = "latitude"
        elif "y" in columns:
            y = "y"
        elif "lat" in columns:
            y = "lat"
        else:
            raise ValueError("The y column could not be found.")

    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[x], df[y], z=z, crs=crs))

    return gdf

points_to_line(data, src_lat, src_lon, dst_lat, dst_lon, crs='EPSG:4326', **kwargs)

Converts source and destination coordinates into a GeoDataFrame with LineString geometries.

Parameters:

Name	Type	Description	Default
data	Union[str, DataFrame, GeoDataFrame]	Input data which can be a file path or a DataFrame.	required
src_lat	str	Column name for source latitude.	required
src_lon	str	Column name for source longitude.	required
dst_lat	str	Column name for destination latitude.	required
dst_lon	str	Column name for destination longitude.	required
crs	str	Coordinate reference system. Defaults to "EPSG:4326".	'EPSG:4326'
**kwargs	Any	Additional arguments passed to the file reading functions.	{}

Returns:

Type	Description
GeoDataFrame	gpd.GeoDataFrame: A GeoDataFrame with LineString geometries.

Source code in leafmap/common.py

def points_to_line(
    data: Union[str, pd.DataFrame],
    src_lat: str,
    src_lon: str,
    dst_lat: str,
    dst_lon: str,
    crs: str = "EPSG:4326",
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """
    Converts source and destination coordinates into a GeoDataFrame with LineString geometries.

    Args:
        data (Union[str, pd.DataFrame, gpd.GeoDataFrame]): Input data which can be a file path or a DataFrame.
        src_lat (str): Column name for source latitude.
        src_lon (str): Column name for source longitude.
        dst_lat (str): Column name for destination latitude.
        dst_lon (str): Column name for destination longitude.
        crs (str, optional): Coordinate reference system. Defaults to "EPSG:4326".
        **kwargs (Any): Additional arguments passed to the file reading functions.

    Returns:
        gpd.GeoDataFrame: A GeoDataFrame with LineString geometries.
    """
    import geopandas as gpd
    from shapely.geometry import LineString

    if isinstance(data, str):
        if data.endswith(".parquet"):
            gdf = pd.read_parquet(data, **kwargs)
        elif data.endswith(".csv"):
            gdf = pd.read_csv(data, **kwargs)
        elif data.endswith(".json"):
            gdf = pd.read_json(data, **kwargs)
        elif data.endswith(".xlsx"):
            gdf = pd.read_excel(data, **kwargs)
        else:
            gdf = gpd.read_file(data, **kwargs)

    elif isinstance(data, pd.DataFrame) or isinstance(data, gpd.GeoDataFrame):
        gdf = data.copy()
    else:
        raise ValueError(
            "Unsupported data type. Please provide a file path or a DataFrame."
        )

    # Assuming you have a GeoDataFrame 'gdf' with the source and destination coordinates
    def create_polyline(row):
        source_point = (row[src_lon], row[src_lat])
        dst_point = (row[dst_lon], row[dst_lat])
        return LineString([source_point, dst_point])

    # Apply the function to create the polyline geometry
    gdf["geometry"] = gdf.apply(create_polyline, axis=1)

    # Set the GeoDataFrame's geometry column to the newly created geometry column
    gdf = gdf.set_geometry("geometry")
    gdf.crs = crs
    return gdf

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:

Name	Type	Description
str	str	A random string

Source code in leafmap/common.py

def random_string(string_length: Optional[int] = 3) -> str:
    """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))

raster_to_vector(source, output, simplify_tolerance=None, dst_crs=None, open_args={}, **kwargs)

Vectorize a raster dataset.

Parameters:

Name	Type	Description	Default
source	str	The path to the tiff file.	required
output	str	The path to the vector file.	required
simplify_tolerance	float	The maximum allowed geometry displacement. The higher this value, the smaller the number of vertices in the resulting geometry.	None

Source code in leafmap/common.py

def raster_to_vector(
    source, output, simplify_tolerance=None, dst_crs=None, open_args={}, **kwargs
):
    """Vectorize a raster dataset.

    Args:
        source (str): The path to the tiff file.
        output (str): The path to the vector file.
        simplify_tolerance (float, optional): The maximum allowed geometry displacement.
            The higher this value, the smaller the number of vertices in the resulting geometry.
    """
    import rasterio
    import shapely
    import geopandas as gpd
    from rasterio import features

    with rasterio.open(source, **open_args) as src:
        band = src.read()

        mask = band != 0
        shapes = features.shapes(band, mask=mask, transform=src.transform)

    fc = [
        {"geometry": shapely.geometry.shape(shape), "properties": {"value": value}}
        for shape, value in shapes
    ]
    if simplify_tolerance is not None:
        for i in fc:
            i["geometry"] = i["geometry"].simplify(tolerance=simplify_tolerance)

    gdf = gpd.GeoDataFrame.from_features(fc)
    if src.crs is not None:
        gdf.set_crs(crs=src.crs, inplace=True)

    if dst_crs is not None:
        gdf = gdf.to_crs(dst_crs)

    gdf.to_file(output, **kwargs)

read_file(data, **kwargs)

Reads a file and returns a DataFrame or GeoDataFrame.

Parameters:

Name	Type	Description	Default
data	str	The file path or a DataFrame/GeoDataFrame.	required
**kwargs	Any	Additional arguments passed to the file reading function.	{}

Returns:

Type	Description
Union[DataFrame, GeoDataFrame]	Union[pd.DataFrame, gpd.GeoDataFrame]: The read data as a DataFrame or GeoDataFrame.

Raises:

Type	Description
ValueError	If the data type is unsupported.

Source code in leafmap/common.py

def read_file(data: str, **kwargs: Any) -> Union[pd.DataFrame, "gpd.GeoDataFrame"]:
    """
    Reads a file and returns a DataFrame or GeoDataFrame.

    Args:
        data (str): The file path or a DataFrame/GeoDataFrame.
        **kwargs (Any): Additional arguments passed to the file reading function.

    Returns:
        Union[pd.DataFrame, gpd.GeoDataFrame]: The read data as a DataFrame or GeoDataFrame.

    Raises:
        ValueError: If the data type is unsupported.
    """
    import geopandas as gpd

    if isinstance(data, str):
        if data.endswith(".parquet"):
            df = pd.read_parquet(data, **kwargs)
        elif data.endswith(".csv"):
            df = pd.read_csv(data, **kwargs)
        elif data.endswith(".json"):
            df = pd.read_json(data, **kwargs)
        elif data.endswith(".xlsx"):
            df = pd.read_excel(data, **kwargs)
        else:
            df = gpd.read_file(data, **kwargs)
    elif isinstance(data, dict) or isinstance(data, list):
        df = pd.DataFrame(data, **kwargs)

    elif isinstance(data, pd.DataFrame) or isinstance(data, gpd.GeoDataFrame):
        df = data
    else:
        raise ValueError(
            "Unsupported data type. Please provide a file path or a DataFrame."
        )

    return df

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'

Raises:

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 leafmap/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_geojson(data, **kwargs)

Fetches and parses a GeoJSON file from a given URL.

Parameters:

Name	Type	Description	Default
data	str	The URL of the GeoJSON file.	required
**kwargs	Any	Additional keyword arguments to pass to the requests.get() method.	{}

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: The parsed GeoJSON data.

Source code in leafmap/common.py

def read_geojson(data: str, **kwargs: Any) -> Dict[str, Any]:
    """
    Fetches and parses a GeoJSON file from a given URL.

    Args:
        data (str): The URL of the GeoJSON file.
        **kwargs (Any): Additional keyword arguments to pass to the requests.get() method.

    Returns:
        Dict[str, Any]: The parsed GeoJSON data.
    """

    return requests.get(data, **kwargs).json()

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:

Name	Type	Description
LasData		The LasData object return by laspy.read.

Source code in leafmap/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

Raises:

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 leafmap/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_parquet(source, geometry=None, columns=None, exclude=None, db=None, table_name=None, sql=None, limit=None, src_crs=None, dst_crs=None, return_type='gdf', **kwargs)

Read Parquet data from a source and return a GeoDataFrame or DataFrame.

Parameters:

Name	Type	Description	Default
source	str	The path to the Parquet file or directory containing Parquet files.	required
geometry	str	The name of the geometry column. Defaults to None.	None
columns	str or list	The columns to select. Defaults to None (select all columns).	None
exclude	str or list	The columns to exclude from the selection. Defaults to None.	None
db	str	The DuckDB database path or alias. Defaults to None.	None
table_name	str	The name of the table in the DuckDB database. Defaults to None.	None
sql	str	The SQL query to execute. Defaults to None.	None
limit	int	The maximum number of rows to return. Defaults to None (return all rows).	None
src_crs	str	The source CRS (Coordinate Reference System) of the geometries. Defaults to None.	None
dst_crs	str	The target CRS to reproject the geometries. Defaults to None.	None
return_type	str	The type of object to return: - 'gdf': GeoDataFrame (default) - 'df': DataFrame - 'numpy': NumPy array - 'arrow': Arrow Table - 'polars': Polars DataFrame	'gdf'
**kwargs		Additional keyword arguments that are passed to the DuckDB connection.	{}

Returns:

Type	Description
	Union[gpd.GeoDataFrame, pd.DataFrame, np.ndarray]: The loaded data.

Raises:

Type	Description
ValueError	If the columns or exclude arguments are not of the correct type.

Source code in leafmap/common.py

def read_parquet(
    source: str,
    geometry: Optional[str] = None,
    columns: Optional[Union[str, list]] = None,
    exclude: Optional[Union[str, list]] = None,
    db: Optional[str] = None,
    table_name: Optional[str] = None,
    sql: Optional[str] = None,
    limit: Optional[int] = None,
    src_crs: Optional[str] = None,
    dst_crs: Optional[str] = None,
    return_type: str = "gdf",
    **kwargs,
):
    """
    Read Parquet data from a source and return a GeoDataFrame or DataFrame.

    Args:
        source (str): The path to the Parquet file or directory containing Parquet files.
        geometry (str, optional): The name of the geometry column. Defaults to None.
        columns (str or list, optional): The columns to select. Defaults to None (select all columns).
        exclude (str or list, optional): The columns to exclude from the selection. Defaults to None.
        db (str, optional): The DuckDB database path or alias. Defaults to None.
        table_name (str, optional): The name of the table in the DuckDB database. Defaults to None.
        sql (str, optional): The SQL query to execute. Defaults to None.
        limit (int, optional): The maximum number of rows to return. Defaults to None (return all rows).
        src_crs (str, optional): The source CRS (Coordinate Reference System) of the geometries. Defaults to None.
        dst_crs (str, optional): The target CRS to reproject the geometries. Defaults to None.
        return_type (str, optional): The type of object to return:
            - 'gdf': GeoDataFrame (default)
            - 'df': DataFrame
            - 'numpy': NumPy array
            - 'arrow': Arrow Table
            - 'polars': Polars DataFrame
        **kwargs: Additional keyword arguments that are passed to the DuckDB connection.

    Returns:
        Union[gpd.GeoDataFrame, pd.DataFrame, np.ndarray]: The loaded data.

    Raises:
        ValueError: If the columns or exclude arguments are not of the correct type.

    """
    import duckdb

    if isinstance(db, str):
        con = duckdb.connect(db)
    else:
        con = duckdb.connect()

    con.install_extension("httpfs")
    con.load_extension("httpfs")

    con.install_extension("spatial")
    con.load_extension("spatial")

    if columns is None:
        columns = "*"
    elif isinstance(columns, list):
        columns = ", ".join(columns)
    elif not isinstance(columns, str):
        raise ValueError("columns must be a list or a string.")

    if exclude is not None:
        if isinstance(exclude, list):
            exclude = ", ".join(exclude)
        elif not isinstance(exclude, str):
            raise ValueError("exclude_columns must be a list or a string.")
        columns = f"{columns} EXCLUDE {exclude}"

    result = None
    if return_type in ["df", "numpy", "arrow", "polars"]:
        if sql is None:
            sql = f"SELECT {columns} FROM '{source}'"
        if limit is not None:
            sql += f" LIMIT {limit}"

        if return_type == "df":
            result = con.sql(sql, **kwargs).df()
        elif return_type == "numpy":
            result = con.sql(sql, **kwargs).fetchnumpy()
        elif return_type == "arrow":
            result = con.sql(sql, **kwargs).arrow()
        elif return_type == "polars":
            result = con.sql(sql, **kwargs).pl()

        if table_name is not None:
            con.sql(f"CREATE OR REPLACE TABLE {table_name} AS FROM result", **kwargs)

    elif return_type == "gdf":
        if geometry is None:
            geometry = "geometry"
        if sql is None:
            # if src_crs is not None and dst_crs is not None:
            #     geom_sql = f"ST_AsText(ST_Transform(ST_GeomFromWKB({geometry}), '{src_crs}', '{dst_crs}', true)) AS {geometry}"
            # else:
            geom_sql = f"ST_AsText(ST_GeomFromWKB(ST_AsWKB({geometry}))) AS {geometry}"
            sql = f"SELECT {columns} EXCLUDE {geometry}, {geom_sql} FROM '{source}'"
        if limit is not None:
            sql += f" LIMIT {limit}"

        df = con.sql(sql, **kwargs).df()
        if table_name is not None:
            con.sql(f"CREATE OR REPLACE TABLE {table_name} AS FROM df", **kwargs)
        result = df_to_gdf(df, geometry=geometry, src_crs=src_crs, dst_crs=dst_crs)

    con.close()
    return result

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	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

Source code in leafmap/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

read_raster(source, window=None, return_array=True, coord_crs=None, request_payer='bucket-owner', env_args={}, open_args={}, **kwargs)

Read a raster from S3.

Parameters:

Name	Type	Description	Default
source	str	The path to the raster on S3.	required
window	tuple	The window (col_off, row_off, width, height) to read. Defaults to None.	None
return_array	bool	Whether to return a numpy array. Defaults to True.	True
coord_crs	str	The coordinate CRS of the input coordinates. Defaults to None.	None
request_payer	str	Specifies who pays for the download from S3. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	'bucket-owner'
env_args	dict	Additional arguments to pass to rasterio.Env(). Defaults to {}.	{}
open_args	dict	Additional arguments to pass to rasterio.open(). Defaults to {}.	{}

Returns:

Type	Description
	np.ndarray: The raster as a numpy array.

Source code in leafmap/common.py

def read_raster(
    source,
    window=None,
    return_array=True,
    coord_crs=None,
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs,
):
    """Read a raster from S3.

    Args:
        source (str): The path to the raster on S3.
        window (tuple, optional): The window (col_off, row_off, width, height) to read. Defaults to None.
        return_array (bool, optional): Whether to return a numpy array. Defaults to True.
        coord_crs (str, optional): The coordinate CRS of the input coordinates. Defaults to None.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args (dict, optional): Additional arguments to pass to rasterio.Env(). Defaults to {}.
        open_args (dict, optional): Additional arguments to pass to rasterio.open(). Defaults to {}.

    Returns:
        np.ndarray: The raster as a numpy array.
    """
    import rasterio
    from rasterio.windows import Window

    with rasterio.Env(AWS_REQUEST_PAYER=request_payer, **env_args):
        src = rasterio.open(source, **open_args)
        if not return_array:
            return src
        else:
            if window is None:
                window = Window(0, 0, src.width, src.height)
            else:
                if isinstance(window, list):
                    coords = coords_to_xy(
                        source,
                        window,
                        coord_crs,
                        env_args=env_args,
                        open_args=open_args,
                    )
                    window = xy_to_window(coords)
                window = Window(*window)

            array = src.read(window=window, **kwargs)
            return array

read_rasters(sources, window=None, coord_crs=None, request_payer='bucket-owner', env_args={}, open_args={}, **kwargs)

Read a raster from S3.

Parameters:

Name	Type	Description	Default
sources	str	The list of paths to the raster files.	required
window	tuple	The window (col_off, row_off, width, height) to read. Defaults to None.	None
coord_crs	str	The coordinate CRS of the input coordinates. Defaults to None.	None
request_payer	str	Specifies who pays for the download from S3. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	'bucket-owner'
env_args	dict	Additional arguments to pass to rasterio.Env(). Defaults to {}.	{}
open_args	dict	Additional arguments to pass to rasterio.open(). Defaults to {}.	{}

Returns:

Type	Description
	np.ndarray: The raster as a numpy array.

Source code in leafmap/common.py

def read_rasters(
    sources,
    window=None,
    coord_crs=None,
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs,
):
    """Read a raster from S3.

    Args:
        sources (str): The list of paths to the raster files.
        window (tuple, optional): The window (col_off, row_off, width, height) to read. Defaults to None.
        coord_crs (str, optional): The coordinate CRS of the input coordinates. Defaults to None.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args (dict, optional): Additional arguments to pass to rasterio.Env(). Defaults to {}.
        open_args (dict, optional): Additional arguments to pass to rasterio.open(). Defaults to {}.

    Returns:
        np.ndarray: The raster as a numpy array.
    """
    import numpy as np

    if not isinstance(sources, list):
        sources = [sources]

    array_list = []

    for source in sources:
        array = read_raster(
            source,
            window,
            True,
            coord_crs,
            request_payer,
            env_args,
            open_args,
            **kwargs,
        )
        array_list.append(array)

    result = np.concatenate(array_list, axis=0)
    return result

read_vector(source, layer=None, **kwargs)

Reads vector data from various formats including GeoParquet.

This function dynamically determines the file type based on extension and reads it into a GeoDataFrame. It supports both local files and HTTP/HTTPS URLs.

Parameters:

Name	Type	Description	Default
source		String path to the vector file or URL.	required
layer		String or integer specifying which layer to read from multi-layer files (only applicable for formats like GPKG, GeoJSON, etc.). Defaults to None.	None
**kwargs		Additional keyword arguments to pass to the underlying reader.	{}

Returns:

Type	Description
	geopandas.GeoDataFrame: A GeoDataFrame containing the vector data.

Raises:

Type	Description
ValueError	If the file format is not supported or source cannot be accessed.

Examples:

Read a local shapefile

>>> gdf = read_vector("path/to/data.shp")
>>>
Read a GeoParquet file from URL
>>> gdf = read_vector("https://example.com/data.parquet")
>>>
Read a specific layer from a GeoPackage
>>> gdf = read_vector("path/to/data.gpkg", layer="layer_name")

Source code in leafmap/common.py

def read_vector(source, layer=None, **kwargs):
    """Reads vector data from various formats including GeoParquet.

    This function dynamically determines the file type based on extension
    and reads it into a GeoDataFrame. It supports both local files and HTTP/HTTPS URLs.

    Args:
        source: String path to the vector file or URL.
        layer: String or integer specifying which layer to read from multi-layer
            files (only applicable for formats like GPKG, GeoJSON, etc.).
            Defaults to None.
        **kwargs: Additional keyword arguments to pass to the underlying reader.

    Returns:
        geopandas.GeoDataFrame: A GeoDataFrame containing the vector data.

    Raises:
        ValueError: If the file format is not supported or source cannot be accessed.

    Examples:
        Read a local shapefile
        >>> gdf = read_vector("path/to/data.shp")
        >>>
        Read a GeoParquet file from URL
        >>> gdf = read_vector("https://example.com/data.parquet")
        >>>
        Read a specific layer from a GeoPackage
        >>> gdf = read_vector("path/to/data.gpkg", layer="layer_name")
    """

    import urllib.parse

    import fiona
    import geopandas as gpd

    # Determine if source is a URL or local file
    parsed_url = urllib.parse.urlparse(source)
    is_url = parsed_url.scheme in ["http", "https"]

    # If it's a local file, check if it exists
    if not is_url and not os.path.exists(source):
        raise ValueError(f"File does not exist: {source}")

    # Get file extension
    _, ext = os.path.splitext(source)
    ext = ext.lower()

    # Handle GeoParquet files
    if ext in [".parquet", ".pq", ".geoparquet"]:
        return gpd.read_parquet(source, **kwargs)

    # Handle common vector formats
    if ext in [".shp", ".geojson", ".json", ".gpkg", ".gml", ".kml", ".gpx"]:
        # For formats that might have multiple layers
        if ext in [".gpkg", ".gml"] and layer is not None:
            return gpd.read_file(source, layer=layer, **kwargs)
        return gpd.read_file(source, **kwargs)

    # Try to use fiona to identify valid layers for formats that might have them
    # Only attempt this for local files as fiona.listlayers might not work with URLs
    if layer is None and ext in [".gpkg", ".gml"] and not is_url:
        try:
            layers = fiona.listlayers(source)
            if layers:
                return gpd.read_file(source, layer=layers[0], **kwargs)
        except Exception:
            # If listing layers fails, we'll fall through to the generic read attempt
            pass

    # For other formats or when layer listing fails, attempt to read using GeoPandas
    try:
        return gpd.read_file(source, **kwargs)
    except Exception as e:
        raise ValueError(f"Could not read from source '{source}': {str(e)}")

reduce_gif_size(in_gif, out_gif=None)

Reduces a GIF image using ffmpeg.

Parameters:

Name	Type	Description	Default
in_gif	str	The input file path to the GIF image.	required
out_gif	str	The output file path to the GIF image. Defaults to None.	None

Source code in leafmap/common.py

def reduce_gif_size(in_gif, out_gif=None):
    """Reduces a GIF image using ffmpeg.

    Args:
        in_gif (str): The input file path to the GIF image.
        out_gif (str, optional): The output file path to the GIF image. Defaults to None.
    """

    try:
        import ffmpeg
    except ImportError:
        print("ffmpeg is not installed on your computer. Skip reducing gif size.")
        return

    warnings.filterwarnings("ignore")

    if not is_tool("ffmpeg"):
        print("ffmpeg is not installed on your computer. Skip reducing gif size.")
        return

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if out_gif is None:
        out_gif = in_gif
    elif not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if in_gif == out_gif:
        tmp_gif = in_gif.replace(".gif", "_tmp.gif")
        shutil.copyfile(in_gif, tmp_gif)
        stream = ffmpeg.input(tmp_gif)
        stream = ffmpeg.output(stream, in_gif, loglevel="quiet").overwrite_output()
        ffmpeg.run(stream)
        os.remove(tmp_gif)

    else:
        stream = ffmpeg.input(in_gif)
        stream = ffmpeg.output(stream, out_gif, loglevel="quiet").overwrite_output()
        ffmpeg.run(stream)

regularize(source, output=None, crs='EPSG:4326', **kwargs)

Regularize a polygon GeoDataFrame.

Parameters:

Name	Type	Description	Default
source	str | GeoDataFrame	The input file path or a GeoDataFrame.	required
output	str	The output file path. Defaults to None.	None

Returns:

Type	Description
	gpd.GeoDataFrame: The output GeoDataFrame.

Source code in leafmap/common.py

def regularize(source, output=None, crs="EPSG:4326", **kwargs):
    """Regularize a polygon GeoDataFrame.

    Args:
        source (str | gpd.GeoDataFrame): The input file path or a GeoDataFrame.
        output (str, optional): The output file path. Defaults to None.


    Returns:
        gpd.GeoDataFrame: The output GeoDataFrame.
    """
    import geopandas as gpd

    if isinstance(source, str):
        gdf = gpd.read_file(source)
    elif isinstance(source, gpd.GeoDataFrame):
        gdf = source
    else:
        raise ValueError("The input source must be a GeoDataFrame or a file path.")

    polygons = gdf.geometry.apply(lambda geom: geom.minimum_rotated_rectangle)
    result = gpd.GeoDataFrame(geometry=polygons, data=gdf.drop("geometry", axis=1))

    if crs is not None:
        result.to_crs(crs, inplace=True)
    if output is not None:
        result.to_file(output, **kwargs)
    else:
        return result

remove_port_from_string(data)

Removes the port number from all URLs in the given string.

Args:: data (str): The input string containing URLs.

Returns:

Name	Type	Description
str	str	The string with port numbers removed from all URLs.

Source code in leafmap/common.py

def remove_port_from_string(data: str) -> str:
    """
    Removes the port number from all URLs in the given string.

    Args::
        data (str): The input string containing URLs.

    Returns:
        str: The string with port numbers removed from all URLs.
    """
    import re

    # Regular expression to match URLs with port numbers
    url_with_port_pattern = re.compile(r"(http://[\d\w.]+):\d+")

    # Function to remove the port from the matched URLs
    def remove_port(match):
        return match.group(1)

    # Substitute the URLs with ports removed
    result = url_with_port_pattern.sub(remove_port, data)

    return result

replace_hyphens_in_keys(d)

Recursively replaces hyphens with underscores in dictionary keys.

Parameters:

Name	Type	Description	Default
d	Union[Dict, List, Any]	The input dictionary, list or any other data type.	required

Returns:

Type	Description
Union[Dict, List, Any]	Union[Dict, List, Any]: The modified dictionary or list with keys having hyphens replaced with underscores,
Union[Dict, List, Any]	or the original input if it's not a dictionary or list.

Source code in leafmap/common.py

def replace_hyphens_in_keys(d: Union[Dict, List, Any]) -> Union[Dict, List, Any]:
    """
    Recursively replaces hyphens with underscores in dictionary keys.

    Args:
        d (Union[Dict, List, Any]): The input dictionary, list or any other data type.

    Returns:
        Union[Dict, List, Any]: The modified dictionary or list with keys having hyphens replaced with underscores,
        or the original input if it's not a dictionary or list.
    """
    if isinstance(d, dict):
        return {k.replace("-", "_"): replace_hyphens_in_keys(v) for k, v in d.items()}
    elif isinstance(d, list):
        return [replace_hyphens_in_keys(i) for i in d]
    else:
        return d

replace_top_level_hyphens(d)

Replaces hyphens with underscores in top-level dictionary keys.

Parameters:

Name	Type	Description	Default
d	Union[Dict, Any]	The input dictionary or any other data type.	required

Returns:

Type	Description
Union[Dict, Any]	Union[Dict, Any]: The modified dictionary with top-level keys having hyphens replaced with underscores,
Union[Dict, Any]	or the original input if it's not a dictionary.

Source code in leafmap/common.py

def replace_top_level_hyphens(d: Union[Dict, Any]) -> Union[Dict, Any]:
    """
    Replaces hyphens with underscores in top-level dictionary keys.

    Args:
        d (Union[Dict, Any]): The input dictionary or any other data type.

    Returns:
        Union[Dict, Any]: The modified dictionary with top-level keys having hyphens replaced with underscores,
        or the original input if it's not a dictionary.
    """
    if isinstance(d, dict):
        return {k.replace("-", "_"): v for k, v in d.items()}
    return d

replace_underscores_in_keys(d)

Recursively replaces underscores with hyphens in dictionary keys.

Parameters:

Name	Type	Description	Default
d	Union[Dict, List, Any]	The input dictionary, list or any other data type.	required

Returns:

Type	Description
Union[Dict, List, Any]	Union[Dict, List, Any]: The modified dictionary or list with keys having underscores replaced with hyphens,
Union[Dict, List, Any]	or the original input if it's not a dictionary or list.

Source code in leafmap/common.py

def replace_underscores_in_keys(d: Union[Dict, List, Any]) -> Union[Dict, List, Any]:
    """
    Recursively replaces underscores with hyphens in dictionary keys.

    Args:
        d (Union[Dict, List, Any]): The input dictionary, list or any other data type.

    Returns:
        Union[Dict, List, Any]: The modified dictionary or list with keys having underscores replaced with hyphens,
        or the original input if it's not a dictionary or list.
    """
    if isinstance(d, dict):
        return {
            k.replace("_", "-"): replace_underscores_in_keys(v) for k, v in d.items()
        }
    elif isinstance(d, list):
        return [replace_underscores_in_keys(i) for i in d]
    else:
        return d

reproject(image, output, dst_crs='EPSG:4326', resampling='nearest', to_cog=True, **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'
to_cog	bool	Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to rasterio.open.	{}

Source code in leafmap/common.py

def reproject(
    image, output, dst_crs="EPSG:4326", resampling="nearest", to_cog=True, **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".
        to_cog (bool, optional): Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.
        **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,
                )

    if to_cog:
        image_to_cog(output, output)

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:

Name	Type	Description
str	str	hex color code

Source code in leafmap/common.py

def rgb_to_hex(rgb: Optional[Tuple[int, int, int]] = (255, 255, 255)) -> str:
    """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

s3_download_file(filename=None, bucket=None, key=None, outfile=None, **kwargs)

Download a file from S3.

Parameters:

Name	Type	Description	Default
filename	str	The full path to the file. Defaults to None.	None
bucket	str	The name of the bucket. Defaults to None.	None
key	str	The key of the file. Defaults to None.	None
outfile	str	The name of the output file. Defaults to None.	None

Source code in leafmap/common.py

def s3_download_file(filename=None, bucket=None, key=None, outfile=None, **kwargs):
    """Download a file from S3.

    Args:
        filename (str, optional): The full path to the file. Defaults to None.
        bucket (str, optional): The name of the bucket. Defaults to None.
        key (str, optional): The key of the file. Defaults to None.
        outfile (str, optional): The name of the output file. Defaults to None.
    Raises:
        ImportError: If boto3 is not installed.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    client = boto3.client("s3", **kwargs)

    if filename is not None:
        bucket = filename.split("/")[2]
        key = "/".join(filename.split("/")[3:])

    if outfile is None:
        outfile = key.split("/")[-1]

    if not os.path.exists(outfile):
        client.download_file(bucket, key, outfile)
    else:
        print(f"File already exists: {outfile}")

s3_download_files(filenames=None, bucket=None, keys=None, outdir=None, quiet=False, **kwargs)

Download multiple files from S3.

Parameters:

Name	Type	Description	Default
filenames	list	A list of filenames. Defaults to None.	None
bucket	str	The name of the bucket. Defaults to None.	None
keys	list	A list of keys. Defaults to None.	None
outdir	str	The name of the output directory. Defaults to None.	None
quiet	bool	Suppress output. Defaults to False.	False

Raises:

Type	Description
ValueError	If neither filenames or keys are provided.

Source code in leafmap/common.py

def s3_download_files(
    filenames=None, bucket=None, keys=None, outdir=None, quiet=False, **kwargs
):
    """Download multiple files from S3.

    Args:
        filenames (list, optional): A list of filenames. Defaults to None.
        bucket (str, optional): The name of the bucket. Defaults to None.
        keys (list, optional): A list of keys. Defaults to None.
        outdir (str, optional): The name of the output directory. Defaults to None.
        quiet (bool, optional): Suppress output. Defaults to False.

    Raises:
        ValueError: If neither filenames or keys are provided.
    """

    if keys is None:
        keys = []

    if filenames is not None:
        if isinstance(filenames, list):
            for filename in filenames:
                bucket = filename.split("/")[2]
                key = "/".join(filename.split("/")[3:])
                keys.append(key)
    elif filenames is None and keys is None:
        raise ValueError("Either filenames or keys must be provided")

    for index, key in enumerate(keys):
        if outdir is not None:
            if not os.path.exists(outdir):
                os.makedirs(outdir)
            outfile = os.path.join(outdir, key.split("/")[-1])
        else:
            outfile = key.split("/")[-1]

        if not quiet:
            print(f"Downloading {index+1} of {len(keys)}: {outfile}")
        s3_download_file(bucket=bucket, key=key, outfile=outfile, **kwargs)

s3_get_object(bucket, key, output=None, chunk_size=1024 * 1024, request_payer='bucket-owner', quiet=False, client_args={}, **kwargs)

Download a file from S3.

Parameters:

Name	Type	Description	Default
bucket	str	The name of the bucket.	required
key	key	The key of the file.	required
output	str	The name of the output file. Defaults to None.	None
chunk_size	int	The chunk size in bytes. Defaults to 1024 * 1024.	1024 * 1024
request_payer	str	Specifies who pays for the download from S3.	'bucket-owner'
quiet	bool	Suppress output. Defaults to False. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	False
client_args	dict	Additional arguments to pass to boto3.client(). Defaults to {}.	{}
**kwargs		Additional arguments to pass to boto3.client().get_object().	{}

Source code in leafmap/common.py

def s3_get_object(
    bucket,
    key,
    output=None,
    chunk_size=1024 * 1024,
    request_payer="bucket-owner",
    quiet=False,
    client_args={},
    **kwargs,
):
    """Download a file from S3.

    Args:
        bucket (str): The name of the bucket.
        key (key): The key of the file.
        output (str, optional): The name of the output file. Defaults to None.
        chunk_size (int, optional): The chunk size in bytes. Defaults to 1024 * 1024.
        request_payer (str, optional): Specifies who pays for the download from S3.
        quiet (bool, optional): Suppress output. Defaults to False.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.
        **kwargs: Additional arguments to pass to boto3.client().get_object().
    """

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    # Set up the S3 client
    s3 = boto3.client("s3", **client_args)

    if output is None:
        output = key.split("/")[-1]

    out_dir = os.path.dirname(os.path.abspath(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    # Set up the progress bar
    def progress_callback(bytes_amount):
        # This function will be called by the StreamingBody object
        # to report the number of bytes downloaded so far
        total_size = int(response["ContentLength"])
        progress_percent = int(bytes_amount / total_size * 100)
        if not quiet:
            print(f"\rDownloading: {progress_percent}% complete.", end="")

    # Download the file
    response = s3.get_object(
        Bucket=bucket, Key=key, RequestPayer=request_payer, **kwargs
    )

    # Save the file to disk
    with open(output, "wb") as f:
        # Use the StreamingBody object to read the file in chunks
        # and track the download progress
        body = response["Body"]
        downloaded_bytes = 0
        for chunk in body.iter_chunks(chunk_size=chunk_size):
            f.write(chunk)
            downloaded_bytes += len(chunk)
            progress_callback(downloaded_bytes)

s3_get_objects(bucket, keys=None, out_dir=None, prefix=None, limit=None, ext=None, chunk_size=1024 * 1024, request_payer='bucket-owner', quiet=True, client_args={}, **kwargs)

Download multiple files from S3.

Parameters:

Name	Type	Description	Default
bucket	str	The name of the bucket.	required
keys	list	A list of keys. Defaults to None.	None
out_dir	str	The name of the output directory. Defaults to None.	None
prefix	str	Limits the response to keys that begin with the specified prefix. Defaults to None.	None
limit	int	The maximum number of keys returned in the response body.	None
ext	str	Filter by file extension. Defaults to None.	None
chunk_size	int	The chunk size in bytes. Defaults to 1024 * 1024.	1024 * 1024
request_payer	str	Specifies who pays for the download from S3. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	'bucket-owner'
quiet	bool	Suppress output. Defaults to True.	True
client_args	dict	Additional arguments to pass to boto3.client(). Defaults to {}.	{}
**kwargs		Additional arguments to pass to boto3.client().get_object().	{}

Source code in leafmap/common.py

def s3_get_objects(
    bucket,
    keys=None,
    out_dir=None,
    prefix=None,
    limit=None,
    ext=None,
    chunk_size=1024 * 1024,
    request_payer="bucket-owner",
    quiet=True,
    client_args={},
    **kwargs,
):
    """Download multiple files from S3.

    Args:
        bucket (str): The name of the bucket.
        keys (list, optional): A list of keys. Defaults to None.
        out_dir (str, optional): The name of the output directory. Defaults to None.
        prefix (str, optional): Limits the response to keys that begin with the specified prefix. Defaults to None.
        limit (int, optional): The maximum number of keys returned in the response body.
        ext (str, optional): Filter by file extension. Defaults to None.
        chunk_size (int, optional): The chunk size in bytes. Defaults to 1024 * 1024.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        quiet (bool, optional): Suppress output. Defaults to True.
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.
        **kwargs: Additional arguments to pass to boto3.client().get_object().

    """

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    if out_dir is None:
        out_dir = os.getcwd()

    if keys is None:
        fullpath = False
        keys = s3_list_objects(
            bucket, prefix, limit, ext, fullpath, request_payer, client_args, **kwargs
        )

    for index, key in enumerate(keys):
        print(f"Downloading {index+1} of {len(keys)}: {key}")
        output = os.path.join(out_dir, key.split("/")[-1])
        s3_get_object(
            bucket, key, output, chunk_size, request_payer, quiet, client_args, **kwargs
        )

s3_list_objects(bucket, prefix=None, limit=None, ext=None, fullpath=True, request_payer='bucket-owner', client_args={}, **kwargs)

List objects in a S3 bucket

Parameters:

Name	Type	Description	Default
bucket	str	The name of the bucket.	required
prefix	str	Limits the response to keys that begin with the specified prefix. Defaults to None.	None
limit	init	The maximum number of keys returned in the response body.	None
ext	str	Filter by file extension. Defaults to None.	None
fullpath	bool	Return full path. Defaults to True.	True
request_payer	str	Specifies who pays for the download from S3. Can be "bucket-owner" or "requester". Defaults to "bucket-owner".	'bucket-owner'
client_args	dict	Additional arguments to pass to boto3.client(). Defaults to {}.	{}

Returns:

Name	Type	Description
list		List of objects.

Source code in leafmap/common.py

def s3_list_objects(
    bucket,
    prefix=None,
    limit=None,
    ext=None,
    fullpath=True,
    request_payer="bucket-owner",
    client_args={},
    **kwargs,
):
    """List objects in a S3 bucket

    Args:
        bucket (str): The name of the bucket.
        prefix (str, optional): Limits the response to keys that begin with the specified prefix. Defaults to None.
        limit (init, optional): The maximum number of keys returned in the response body.
        ext (str, optional): Filter by file extension. Defaults to None.
        fullpath (bool, optional): Return full path. Defaults to True.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.

    Returns:
        list: List of objects.
    """
    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    client = boto3.client("s3", **client_args)

    if prefix is not None:
        kwargs["Prefix"] = prefix

    files = []
    kwargs["RequestPayer"] = request_payer
    if isinstance(limit, int) and limit < 1000:
        kwargs["MaxKeys"] = limit
        response = client.list_objects_v2(Bucket=bucket, **kwargs)
        for obj in response["Contents"]:
            files.append(obj)
    else:
        paginator = client.get_paginator("list_objects_v2")
        pages = paginator.paginate(Bucket=bucket, **kwargs)

        for page in pages:
            files.extend(page.get("Contents", []))

    if ext is not None:
        files = [f for f in files if f["Key"].endswith(ext)]

    if fullpath:
        return [f"s3://{bucket}/{r['Key']}" for r in files]
    else:
        return [r["Key"] for r in files]

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:

Name	Type	Description
str		Path to the output image.

Source code in leafmap/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 "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.colormaps[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

save_data(data, file_ext=None, file_name=None)

Save data in the memory to a file.

Parameters:

Name	Type	Description	Default
data	object	The data to be saved.	required
file_ext	str	The file extension of the file.	None
file_name	str	The name of the file to be saved. Defaults to None.	None

Returns:

Name	Type	Description
str		The path of the file.

Source code in leafmap/common.py

def save_data(data, file_ext=None, file_name=None):
    """Save data in the memory to a file.

    Args:
        data (object): The data to be saved.
        file_ext (str): The file extension of the file.
        file_name (str, optional): The name of the file to be saved. Defaults to None.

    Returns:
        str: The path of the file.
    """
    import tempfile
    import uuid

    try:
        if file_ext is None:
            if hasattr(data, "name"):
                _, file_ext = os.path.splitext(data.name)
        else:
            if not file_ext.startswith("."):
                file_ext = "." + file_ext

        if file_name is not None:
            file_path = os.path.abspath(file_name)
            if not file_path.endswith(file_ext):
                file_path = file_path + file_ext
        else:
            file_id = str(uuid.uuid4())
            file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_ext}")

        with open(file_path, "wb") as file:
            file.write(data.getbuffer())
        return file_path
    except Exception as e:
        print(e)
        return None

screen_capture(outfile, monitor=1)

Takes a full screenshot of the selected monitor.

Parameters:

Name	Type	Description	Default
outfile	str	The output file path to the screenshot.	required
monitor	int	The monitor to take the screenshot. Defaults to 1.	1

Source code in leafmap/common.py

def screen_capture(outfile, monitor=1):
    """Takes a full screenshot of the selected monitor.

    Args:
        outfile (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 using 'pip install mss'")

    out_dir = os.path.dirname(outfile)
    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=outfile, mon=monitor)
            return outfile

    except Exception as e:
        raise Exception(e)

search_mapillary_images(lon=None, lat=None, radius=5e-05, bbox=None, limit=2000, access_token=None)

Retrieves Mapillary image IDs near the specified test point within a bounding box.

Parameters:

Name	Type	Description	Default
lon	float	Longitude of the test point. Defaults to None.	None
lat	float	Latitude of the test point. Defaults to None.	None
radius	float	Radius to create the bounding box. Defaults to 0.00005.	5e-05
bbox	Union[str, List[float]]	Bounding box coordinates. Defaults to None.	None
limit	int	Maximum number of image IDs to retrieve. Defaults to 2000.	2000
access_token	str	Mapillary API access token. Defaults to None.	None

Returns:

Type	Description
List[str]	List[str]: JSON response from the Mapillary API containing image IDs.

Source code in leafmap/common.py

def search_mapillary_images(
    lon: Optional[float] = None,
    lat: Optional[float] = None,
    radius: float = 0.00005,
    bbox: Optional[Union[str, List[float]]] = None,
    limit: int = 2000,
    access_token: Optional[str] = None,
) -> List[str]:
    """
    Retrieves Mapillary image IDs near the specified test point within a bounding box.

    Args:
        lon (float, optional): Longitude of the test point. Defaults to None.
        lat (float, optional): Latitude of the test point. Defaults to None.
        radius (float, optional): Radius to create the bounding box. Defaults to 0.00005.
        bbox (Union[str, List[float]], optional): Bounding box coordinates. Defaults to None.
        limit (int, optional): Maximum number of image IDs to retrieve. Defaults to 2000.
        access_token (str, optional): Mapillary API access token. Defaults to None.

    Returns:
        List[str]: JSON response from the Mapillary API containing image IDs.
    """

    if access_token is None:
        access_token = get_api_key("MAPILLARY_API_KEY")

    if access_token is None:
        raise ValueError(
            "Mapillary API access token is required. Set it using the 'access_token' parameter."
        )

    metadata_endpoint = "https://graph.mapillary.com"
    headers = {"Authorization": f"OAuth {access_token}"}

    if bbox is None:
        if lon is None or lat is None:
            raise ValueError("Longitude and latitude are required.")
        bbox = f"{lon - radius},{lat - radius},{lon + radius},{lat + radius}"
    else:
        if isinstance(bbox, list):
            bbox = ",".join(str(x) for x in bbox)

    # Construct the bounding box for the API call
    url_imagesearch = f"{metadata_endpoint}/images?fields=id&bbox={bbox}&limit={limit}"

    try:
        response = requests.get(url_imagesearch, headers=headers)
        response.raise_for_status()  # Raise an HTTPError for bad responses
        return [image["id"] for image in response.json()["data"]]
    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return []

search_qms(keyword, limit=10, list_only=True, add_prefix=True)

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

Returns:

Name	Type	Description
list		A list of QMS tile providers.

Source code in leafmap/common.py

def search_qms(keyword, limit=10, list_only=True, add_prefix=True):
    """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.

    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}"
    )
    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:

Name	Type	Description
list		A list of XYZ tile providers.

Source code in leafmap/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

select_largest(source, column, count=1, output=None, **kwargs)

Select the largest features in a GeoDataFrame based on a column.

Parameters:

Name	Type	Description	Default
source	str | GeoDataFrame	The path to the vector file or a GeoDataFrame.	required
column	str	The column to sort by.	required
count	int	The number of features to select. Defaults to 1.	1
output	str	The path to the output vector file. Defaults to None.	None

Returns:

Name	Type	Description
str		The path to the output vector file.

Source code in leafmap/common.py

def select_largest(source, column, count=1, output=None, **kwargs):
    """Select the largest features in a GeoDataFrame based on a column.

    Args:
        source (str | gpd.GeoDataFrame): The path to the vector file or a GeoDataFrame.
        column (str): The column to sort by.
        count (int, optional): The number of features to select. Defaults to 1.
        output (str, optional): The path to the output vector file. Defaults to None.

    Returns:
        str: The path to the output vector file.
    """

    import geopandas as gpd

    if isinstance(source, str):
        gdf = gpd.read_file(source, **kwargs)
    else:
        gdf = source

    if not isinstance(gdf, gpd.GeoDataFrame):
        raise TypeError("source must be a GeoDataFrame or a file path")

    gdf = gdf.sort_values(column, ascending=False).head(count)

    if output is not None:
        gdf.to_file(output)

    else:
        return gdf

set_api_key(key, name='GOOGLE_MAPS_API_KEY')

Sets the Google Maps API key. You can generate one from https://bit.ly/3sw0THG.

Parameters:

Name	Type	Description	Default
key	str	The Google Maps API key.	required
name	str	The name of the environment variable. Defaults to "GOOGLE_MAPS_API_KEY".	'GOOGLE_MAPS_API_KEY'

Source code in leafmap/common.py

def set_api_key(key: str, name: str = "GOOGLE_MAPS_API_KEY"):
    """Sets the Google Maps API key. You can generate one from https://bit.ly/3sw0THG.

    Args:
        key (str): The Google Maps API key.
        name (str, optional): The name of the environment variable. Defaults to "GOOGLE_MAPS_API_KEY".
    """
    os.environ[name] = key

set_proj_lib_path(verbose=False)

Set the PROJ_LIB and GDAL_DATA environment variables based on the current conda environment.

This function attempts to locate and set the correct paths for PROJ_LIB and GDAL_DATA by checking multiple possible locations within the conda environment structure.

Parameters:

Name	Type	Description	Default
verbose	bool	If True, print additional information during the process.	False

Returns:

Name	Type	Description
bool		True if both paths were set successfully, False otherwise.

Source code in leafmap/common.py

def set_proj_lib_path(verbose=False):
    """
    Set the PROJ_LIB and GDAL_DATA environment variables based on the current conda environment.

    This function attempts to locate and set the correct paths for PROJ_LIB and GDAL_DATA
    by checking multiple possible locations within the conda environment structure.

    Args:
        verbose (bool): If True, print additional information during the process.

    Returns:
        bool: True if both paths were set successfully, False otherwise.
    """
    import sys

    try:
        from rasterio.env import set_gdal_config

        # Get conda environment path
        conda_env_path = os.environ.get("CONDA_PREFIX") or sys.prefix

        # Define possible paths for PROJ_LIB
        possible_proj_paths = [
            os.path.join(conda_env_path, "share", "proj"),
            os.path.join(conda_env_path, "Library", "share", "proj"),
            os.path.join(conda_env_path, "Library", "share"),
        ]

        # Define possible paths for GDAL_DATA
        possible_gdal_paths = [
            os.path.join(conda_env_path, "share", "gdal"),
            os.path.join(conda_env_path, "Library", "share", "gdal"),
            os.path.join(conda_env_path, "Library", "data", "gdal"),
            os.path.join(conda_env_path, "Library", "share"),
        ]

        # Set PROJ_LIB environment variable
        proj_set = False
        for proj_path in possible_proj_paths:
            if os.path.exists(proj_path) and os.path.isdir(proj_path):
                # Verify it contains projection data
                if os.path.exists(os.path.join(proj_path, "proj.db")):
                    os.environ["PROJ_LIB"] = proj_path
                    if verbose:
                        print(f"PROJ_LIB set to: {proj_path}")
                    proj_set = True
                    break

        # Set GDAL_DATA environment variable
        gdal_set = False
        for gdal_path in possible_gdal_paths:
            if os.path.exists(gdal_path) and os.path.isdir(gdal_path):
                # Verify it contains the header.dxf file or other critical GDAL files
                if os.path.exists(
                    os.path.join(gdal_path, "header.dxf")
                ) or os.path.exists(os.path.join(gdal_path, "gcs.csv")):
                    os.environ["GDAL_DATA"] = gdal_path
                    if verbose:
                        print(f"GDAL_DATA set to: {gdal_path}")
                    gdal_set = True
                    break

        # If paths still not found, try a last-resort approach
        if not proj_set or not gdal_set:
            # Try a deep search in the conda environment
            for root, dirs, files in os.walk(conda_env_path):
                if not gdal_set and "header.dxf" in files:
                    os.environ["GDAL_DATA"] = root
                    if verbose:
                        print(f"GDAL_DATA set to: {root} (deep search)")
                    gdal_set = True

                if not proj_set and "proj.db" in files:
                    os.environ["PROJ_LIB"] = root
                    if verbose:
                        print(f"PROJ_LIB set to: {root} (deep search)")
                    proj_set = True

                if proj_set and gdal_set:
                    break

        set_gdal_config("PROJ_LIB", os.environ["PROJ_LIB"])
        set_gdal_config("GDAL_DATA", os.environ["GDAL_DATA"])

    except Exception as e:
        print(f"Error setting projection library paths: {e}")
        return

set_proxy(port=1080, ip='http://127.0.0.1')

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'

Source code in leafmap/common.py

def set_proxy(
    port: Optional[int] = 1080, ip: Optional[str] = "http://127.0.0.1"
) -> None:
    """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'.
    """

    if not ip.startswith("http://") and not ip.startswith("https://"):
        ip = f"http://{ip}"
    proxy = f"{ip}:{port}"

    os.environ["HTTP_PROXY"] = proxy
    os.environ["HTTPS_PROXY"] = proxy

    try:
        response = requests.get("https://google.com")
        response.raise_for_status()
    except requests.exceptions.RequestException as e:
        print(
            "Failed to connect to Google Services. "
            "Please double check the port number and IP address."
        )
        print(f"Error: {e}")

show_html(html)

Shows HTML within Jupyter notebook.

Parameters:

Name	Type	Description	Default
html	str	File path or HTML string.	required

Raises:

Type	Description
FileNotFoundError	If the file does not exist.

Returns:

Type	Description
	ipywidgets.HTML: HTML widget.

Source code in leafmap/common.py

def show_html(html: str):
    """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 leafmap/common.py

def show_image(
    img_path: str, width: Optional[int] = None, height: Optional[int] = 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:
            file = open(img_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_video(url, width=800, height=450, allow_autoplay=False, **kwargs)

Displays a Youtube video in a Jupyter notebook.

Parameters:

Name	Type	Description	Default
url	string	a link to a Youtube video.	required
width	int	the width of the video. Defaults to 800.	800
height	int	the height of the video. Defaults to 600.	450
allow_autoplay	bool	whether to allow autoplay. Defaults to False.	False
**kwargs		further arguments for IPython.display.YouTubeVideo	{}

Returns:

Name	Type	Description
YouTubeVideo		a video that is displayed in your notebook.

Source code in leafmap/common.py

def show_youtube_video(url, width=800, height=450, allow_autoplay=False, **kwargs):
    """
    Displays a Youtube video in a Jupyter notebook.

    Args:
        url (string): a link to a Youtube video.
        width (int, optional): the width of the video. Defaults to 800.
        height (int, optional): the height of the video. Defaults to 600.
        allow_autoplay (bool, optional): whether to allow autoplay. Defaults to False.
        **kwargs: further arguments for IPython.display.YouTubeVideo

    Returns:
        YouTubeVideo: a video that is displayed in your notebook.
    """
    import re
    from IPython.display import YouTubeVideo

    if not isinstance(url, str):
        raise TypeError("URL must be a string")

    match = re.match(
        r"^https?:\/\/(?:www\.)?youtube\.com\/watch\?(?=.*v=([^\s&]+)).*$|^https?:\/\/(?:www\.)?youtu\.be\/([^\s&]+).*$",
        url,
    )
    if not match:
        raise ValueError("Invalid YouTube video URL")

    video_id = match.group(1) if match.group(1) else match.group(2)

    return YouTubeVideo(
        video_id, width=width, height=height, allow_autoplay=allow_autoplay, **kwargs
    )

shp_to_gdf(in_shp)

Converts a shapefile to Geopandas dataframe.

Parameters:

Name	Type	Description	Default
in_shp	str	File path to the input shapefile.	required

Raises:

Type	Description
FileNotFoundError	The provided shp could not be found.

Returns:

Type	Description
	gpd.GeoDataFrame: geopandas.GeoDataFrame

Source code in leafmap/common.py

def shp_to_gdf(in_shp):
    """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)
    except Exception as e:
        raise Exception(e)

shp_to_geojson(in_shp, output=None, encoding='utf-8', crs='EPSG:4326', **kwargs)

Converts a shapefile to GeoJSON.

Parameters:

Name	Type	Description	Default
in_shp	str	File path of the input shapefile.	required
output	str	File path of the output GeoJSON. Defaults to None.	None

Returns:

Name	Type	Description
object		The json object representing the shapefile.

Source code in leafmap/common.py

def shp_to_geojson(in_shp, output=None, encoding="utf-8", crs="EPSG:4326", **kwargs):
    """Converts a shapefile to GeoJSON.

    Args:
        in_shp (str): File path of the input shapefile.
        output (str, optional): File path of the output GeoJSON. Defaults to None.

    Returns:
        object: The json object representing the shapefile.
    """
    try:
        import geopandas as gpd

        gdf = gpd.read_file(in_shp, **kwargs)
        gdf.to_crs(crs, inplace=True)
        if output is None:
            return gdf.__geo_interface__
        else:
            gdf.to_file(output, driver="GeoJSON")
    except Exception as e:
        raise Exception(e)

skip_mkdocs_build()

Skips the MkDocs build if the USE_MKDOCS environment variable is set.

Returns:

Name	Type	Description
bool		Whether to skip the MkDocs build.

Source code in leafmap/common.py

def skip_mkdocs_build():
    """Skips the MkDocs build if the USE_MKDOCS environment variable is set.

    Returns:
        bool: Whether to skip the MkDocs build.
    """
    if os.environ.get("USE_MKDOCS") is not None:
        return True
    else:
        return False

split_parquet_by_geometries(input_parquet, output_dir, input_vector, column, filename_prefix='', filename_suffix='', driver='PARQUET', verbose=True)

Split a Parquet file containing geometries based on intersection with features from a vector file.

Parameters:

Name	Type	Description	Default
input_parquet	str	Path to the input Parquet file containing geometries.	required
output_dir	str	Directory to save the split files.	required
input_vector	str	Path to the vector file containing geometries to split by.	required
column	str	Column name in the vector file to use for splitting.	required
filename_prefix	str	Prefix for output filenames.	''
filename_suffix	str	Suffix for output filenames.	''
driver	str	Output format driver (e.g., "PARQUET", "GPKG").	'PARQUET'
verbose	bool	Whether to print progress information.	True
**kwargs		Additional arguments.	required

Raises:

Type	Description
ParserException	If there's a syntax error in the SQL query.

Source code in leafmap/common.py

def split_parquet_by_geometries(
    input_parquet,
    output_dir,
    input_vector,
    column,
    filename_prefix="",
    filename_suffix="",
    driver="PARQUET",
    verbose=True,
):
    """
    Split a Parquet file containing geometries based on intersection with features from a vector file.

    Args:
        input_parquet (str): Path to the input Parquet file containing geometries.
        output_dir (str): Directory to save the split files.
        input_vector (str): Path to the vector file containing geometries to split by.
        column (str): Column name in the vector file to use for splitting.
        filename_prefix (str): Prefix for output filenames.
        filename_suffix (str): Suffix for output filenames.
        driver (str): Output format driver (e.g., "PARQUET", "GPKG").
        verbose (bool): Whether to print progress information.
        **kwargs: Additional arguments.

    Raises:
        ParserException: If there's a syntax error in the SQL query.
    """
    import duckdb

    # Connect to DuckDB
    con = duckdb.connect()

    # Load spatial extension
    con.execute("INSTALL spatial;")
    con.execute("LOAD spatial;")

    if driver.upper() == "PARQUET":
        fmt = "FORMAT PARQUET"
    else:
        fmt = f"FORMAT GDAL, DRIVER '{driver}'"

    os.makedirs(output_dir, exist_ok=True)

    if input_vector.endswith(".parquet"):
        read_str = f"'{input_vector}'"
    else:
        read_str = f"ST_Read('{input_vector}')"

    # Get all state IDs from the parquet file
    state_ids = con.execute(
        f"""
        SELECT {column} FROM {read_str}
        """
    ).fetchall()

    state_ids.sort()

    geometry = "geometry"

    column_names = get_vector_column_names(input_vector, db_con=con)
    if "geometry" not in column_names:
        if "geom" in column_names:
            geometry = "geom"  # Fallback to geom if geometry is not available
        else:
            raise ValueError(
                f"The input vector file does not contain a geometry column. Available columns: {column_names}"
            )

    # Loop through each state and save buildings
    for index, (state_id,) in enumerate(state_ids):
        if verbose:
            print(
                f"Processing {index + 1}/{len(state_ids)}: Extracting geometries for '{state_id}'..."
            )

        output_path = os.path.join(
            output_dir, f"{filename_prefix}{state_id}{filename_suffix}.{driver.lower()}"
        )

        # Use proper path formatting
        query = f"""
        COPY (
            WITH bbox AS (
                SELECT {geometry} AS geom2
                FROM {read_str}
                WHERE {column} = '{state_id}'
            )
            SELECT * FROM '{input_parquet}'
            WHERE ST_Intersects({geometry}, (SELECT geom2 FROM bbox))
        ) TO '{output_path}' ({fmt});
        """

        con.execute(query)

    con.close()
    print("Done!")

split_raster(filename, out_dir, tile_size=256, overlap=0, prefix='tile')

Split a raster into tiles.

Parameters:

Name	Type	Description	Default
filename	str	The path or http URL to the raster file.	required
out_dir	str	The path to the output directory.	required
tile_size	int | tuple	The size of the tiles. Can be an integer or a tuple of (width, height). Defaults to 256.	256
overlap	int	The number of pixels to overlap between tiles. Defaults to 0.	0
prefix	str	The prefix of the output tiles. Defaults to "tile".	'tile'

Raises:

Type	Description
ImportError	Raised if GDAL is not installed.

Source code in leafmap/common.py

def split_raster(filename, out_dir, tile_size=256, overlap=0, prefix="tile"):
    """Split a raster into tiles.

    Args:
        filename (str): The path or http URL to the raster file.
        out_dir (str): The path to the output directory.
        tile_size (int | tuple, optional): The size of the tiles. Can be an integer or a tuple of (width, height). Defaults to 256.
        overlap (int, optional): The number of pixels to overlap between tiles. Defaults to 0.
        prefix (str, optional): The prefix of the output tiles. Defaults to "tile".

    Raises:
        ImportError: Raised if GDAL is not installed.
    """

    try:
        from osgeo import gdal
    except ImportError:
        raise ImportError(
            "GDAL is required to use this function. Install it with `conda install gdal -c conda-forge`"
        )

    if isinstance(filename, str):
        if filename.startswith("http"):
            output = filename.split("/")[-1]
            download_file(filename, output)
            filename = output

    # Open the input GeoTIFF file
    ds = gdal.Open(filename)

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if isinstance(tile_size, int):
        tile_width = tile_size
        tile_height = tile_size
    elif isinstance(tile_size, tuple):
        tile_width = tile_size[0]
        tile_height = tile_size[1]
    else:
        raise ValueError("tile_size must be an integer or a tuple of (width, height)")

    # Get the size of the input raster
    width = ds.RasterXSize
    height = ds.RasterYSize

    # Calculate the number of tiles needed in both directions, taking into account the overlap
    num_tiles_x = (width - overlap) // (tile_width - overlap) + int(
        (width - overlap) % (tile_width - overlap) > 0
    )
    num_tiles_y = (height - overlap) // (tile_height - overlap) + int(
        (height - overlap) % (tile_height - overlap) > 0
    )

    # Get the georeferencing information of the input raster
    geotransform = ds.GetGeoTransform()

    # Loop over all the tiles
    for i in range(num_tiles_x):
        for j in range(num_tiles_y):
            # Calculate the pixel coordinates of the tile, taking into account the overlap and clamping to the edge of the raster
            x_min = i * (tile_width - overlap)
            y_min = j * (tile_height - overlap)
            x_max = min(x_min + tile_width, width)
            y_max = min(y_min + tile_height, height)

            # Adjust the size of the last tile in each row and column to include any remaining pixels
            if i == num_tiles_x - 1:
                x_min = max(x_max - tile_width, 0)
            if j == num_tiles_y - 1:
                y_min = max(y_max - tile_height, 0)

            # Calculate the size of the tile, taking into account the overlap
            tile_width = x_max - x_min
            tile_height = y_max - y_min

            # Set the output file name
            output_file = f"{out_dir}/{prefix}_{i}_{j}.tif"

            # Create a new dataset for the tile
            driver = gdal.GetDriverByName("GTiff")
            tile_ds = driver.Create(
                output_file,
                tile_width,
                tile_height,
                ds.RasterCount,
                ds.GetRasterBand(1).DataType,
            )

            # Calculate the georeferencing information for the output tile
            tile_geotransform = (
                geotransform[0] + x_min * geotransform[1],
                geotransform[1],
                0,
                geotransform[3] + y_min * geotransform[5],
                0,
                geotransform[5],
            )

            # Set the geotransform and projection of the tile
            tile_ds.SetGeoTransform(tile_geotransform)
            tile_ds.SetProjection(ds.GetProjection())

            # Read the data from the input raster band(s) and write it to the tile band(s)
            for k in range(ds.RasterCount):
                band = ds.GetRasterBand(k + 1)
                tile_band = tile_ds.GetRasterBand(k + 1)
                tile_data = band.ReadAsArray(x_min, y_min, tile_width, tile_height)
                tile_band.WriteArray(tile_data)

            # Close the tile dataset
            tile_ds = None

    # Close the input dataset
    ds = None

st_download_button(label, data, file_name=None, mime=None, key=None, help=None, on_click=None, args=None, csv_sep=',', **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 leafmap/common.py

def st_download_button(
    label,
    data,
    file_name=None,
    mime=None,
    key=None,
    help=None,
    on_click=None,
    args=None,
    csv_sep=",",
    **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 key is None:
            key = random_string(6)

        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(sep=csv_sep, index=False)
                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,
                    )
        elif isinstance(data, pd.DataFrame):
            if file_name is None:
                file_name = "data.csv"

            data = data.to_csv(sep=csv_sep, index=False)
            if mime is None:
                mime = "text/csv"
            return st.download_button(
                label, data, file_name, mime, key, help, on_click, args, **kwargs
            )

        else:
            # if mime is None:
            #     mime = "application/pdf"
            return st.download_button(
                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)

stac_assets(url=None, collection=None, item=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A list of assets.

Source code in leafmap/stac.py

def stac_assets(
    url: str = None,
    collection: str = None,
    item: str = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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).json()
    else:
        r = requests.get(titiler_endpoint.url_for_stac_assets(), params=kwargs).json()

    return r

stac_bands(url=None, collection=None, item=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A list of band names

Source code in leafmap/stac.py

def stac_bands(
    url: str = None,
    collection: str = None,
    item: str = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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).json()
    else:
        r = requests.get(titiler_endpoint.url_for_stac_assets(), params=kwargs).json()

    return r

stac_bounds(url=None, collection=None, item=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A list of values representing [left, bottom, right, top]

Source code in leafmap/stac.py

def stac_bounds(
    url: str = None,
    collection: str = None,
    item: str = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        kwargs["url"] = url
        response = requests.get(url)
        r = response.json()
        if "mosaicjson" in r:
            if "bounds" in r:
                return r["bounds"]

    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).json()
    else:
        r = requests.get(titiler_endpoint.url_for_stac_bounds(), params=kwargs).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:

Name	Type	Description
tuple	Tuple[float, float]	A tuple representing (longitude, latitude)

Source code in leafmap/stac.py

def stac_center(
    url: str = None,
    collection: str = None,
    item: str = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> Tuple[float, float]:
    """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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    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_client(url, headers=None, parameters=None, ignore_conformance=False, modifier=None, request_modifier=None, stac_io=None, return_col_id=False, get_root=True, **kwargs)

Get the STAC client. It wraps the pystac.Client.open() method. See https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.open

Parameters:

Name	Type	Description	Default
url	str	The URL of a STAC Catalog.	required
headers	dict	A dictionary of additional headers to use in all requests made to any part of this Catalog/API. Defaults to None.	None
parameters	dict	Optional dictionary of query string parameters to include in all requests. Defaults to None.	None
ignore_conformance	bool	Ignore any advertised Conformance Classes in this Catalog/API. This means that functions will skip checking conformance, and may throw an unknown error if that feature is not supported, rather than a NotImplementedError. Defaults to False.	False
modifier	function	A callable that modifies the children collection and items returned by this Client. This can be useful for injecting authentication parameters into child assets to access data from non-public sources. Defaults to None.	None
request_modifier	function	A callable that either modifies a Request instance or returns a new one. This can be useful for injecting Authentication headers and/or signing fully-formed requests (e.g. signing requests using AWS SigV4). The callable should expect a single argument, which will be an instance of requests.Request. If the callable returns a requests.Request, that will be used. Alternately, the callable may simply modify the provided request object and return None.	None
stac_io	stac_io	A StacApiIO object to use for I/O requests. Generally, leave this to the default. However in cases where customized I/O processing is required, a custom instance can be provided here.	None
return_col_id	bool	Return the collection ID. Defaults to False.	False
get_root	bool	Get the root link of the STAC object. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the pystac.Client.open() method.	{}

Returns:

Type	Description
	pystac.Client: The STAC client.

Source code in leafmap/stac.py

def stac_client(
    url: str,
    headers: Optional[Dict] = None,
    parameters: Optional[Dict] = None,
    ignore_conformance: Optional[bool] = False,
    modifier: Optional[Callable] = None,
    request_modifier: Optional[Callable] = None,
    stac_io=None,
    return_col_id: Optional[bool] = False,
    get_root: Optional[bool] = True,
    **kwargs,
):
    """Get the STAC client. It wraps the pystac.Client.open() method. See
        https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.open

    Args:
        url (str): The URL of a STAC Catalog.
        headers (dict, optional):  A dictionary of additional headers to use in all requests
            made to any part of this Catalog/API. Defaults to None.
        parameters (dict, optional): Optional dictionary of query string parameters to include in all requests.
            Defaults to None.
        ignore_conformance (bool, optional): Ignore any advertised Conformance Classes in this Catalog/API.
            This means that functions will skip checking conformance, and may throw an unknown error
            if that feature is not supported, rather than a NotImplementedError. Defaults to False.
        modifier (function, optional): A callable that modifies the children collection and items
            returned by this Client. This can be useful for injecting authentication parameters
            into child assets to access data from non-public sources. Defaults to None.
        request_modifier (function, optional): A callable that either modifies a Request instance or returns
            a new one. This can be useful for injecting Authentication headers and/or signing fully-formed
            requests (e.g. signing requests using AWS SigV4). The callable should expect a single argument,
            which will be an instance of requests.Request. If the callable returns a requests.Request, that
            will be used. Alternately, the callable may simply modify the provided request object and
            return None.
        stac_io (pystac.stac_io, optional): A StacApiIO object to use for I/O requests. Generally, leave
            this to the default. However in cases where customized I/O processing is required, a custom
            instance can be provided here.
        return_col_id (bool, optional): Return the collection ID. Defaults to False.
        get_root (bool, optional): Get the root link of the STAC object. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the pystac.Client.open() method.

    Returns:
        pystac.Client: The STAC client.
    """
    from pystac_client import Client

    collection_id = None

    if not get_root:
        return_col_id = False

    try:
        if get_root:
            root = stac_root_link(url, return_col_id=return_col_id)
        else:
            root = url

        if return_col_id:
            client = Client.open(
                root[0],
                headers,
                parameters,
                ignore_conformance,
                modifier,
                request_modifier,
                stac_io,
                **kwargs,
            )
            collection_id = root[1]
            return client, collection_id
        else:
            client = Client.open(
                root,
                headers,
                parameters,
                ignore_conformance,
                modifier,
                request_modifier,
                stac_io,
                **kwargs,
            )
            return client, client.id

    except Exception as e:
        print(e)
        return None

stac_collections(url, return_ids=False, get_root=True, **kwargs)

Get the collection IDs of a STAC catalog.

Parameters:

Name	Type	Description	Default
url	str	The STAC catalog URL.	required
return_ids	bool	Return collection IDs. Defaults to False.	False
get_root	bool	Get the root link of the STAC object. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the stac_client() method.	{}

Returns:

Name	Type	Description
list	List	A list of collection IDs.

Source code in leafmap/stac.py

def stac_collections(
    url: str, return_ids: Optional[bool] = False, get_root=True, **kwargs
) -> List:
    """Get the collection IDs of a STAC catalog.

    Args:
        url (str): The STAC catalog URL.
        return_ids (bool, optional): Return collection IDs. Defaults to False.
        get_root (bool, optional): Get the root link of the STAC object. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the stac_client() method.

    Returns:
        list: A list of collection IDs.
    """
    try:
        client, _ = stac_client(url, get_root=get_root, **kwargs)
        collections = client.get_all_collections()

        if return_ids:
            return [c.id for c in collections]
        else:
            return collections

    except Exception as e:
        print(e)
        return None

stac_info(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A dictionary of band info.

Source code in leafmap/stac.py

def stac_info(
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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).json()
    else:
        r = requests.get(titiler_endpoint.url_for_stac_info(), params=kwargs).json()

    return r

stac_info_geojson(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A dictionary of band info.

Source code in leafmap/stac.py

def stac_info_geojson(
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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).json()
    else:
        r = requests.get(
            titiler_endpoint.url_for_stac_info_geojson(), params=kwargs
        ).json()

    return r

stac_min_max(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, **kwargs)

Get the min and max values 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

Returns:

Name	Type	Description
list	List	A dictionary of band statistics.

Source code in leafmap/stac.py

def stac_min_max(
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """Get the min and max values 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.

    Returns:
        list: A dictionary of band statistics.
    """

    stats = stac_stats(url, collection, item, assets, titiler_endpoint, **kwargs)

    values = stats.values()

    try:
        min_values = [v["min"] for v in values]
        max_values = [v["max"] for v in values]

        return min(min_values), max(max_values)
    except Exception as e:
        return None, None

stac_object_type(url, **kwargs)

Get the STAC object type.

Parameters:

Name	Type	Description	Default
url	str	The STAC object URL.	required
**kwargs		Keyword arguments for pystac.STACObject.from_file(). Defaults to None.	{}

Returns:

Name	Type	Description
str	str	The STAC object type, can be catalog, collection, or item.

Source code in leafmap/stac.py

def stac_object_type(url: str, **kwargs) -> str:
    """Get the STAC object type.

    Args:
        url (str): The STAC object URL.
        **kwargs: Keyword arguments for pystac.STACObject.from_file(). Defaults to None.

    Returns:
        str: The STAC object type, can be catalog, collection, or item.
    """
    try:
        obj = pystac.STACObject.from_file(url, **kwargs)

        if isinstance(obj, pystac.Collection):
            return "collection"
        elif isinstance(obj, pystac.Item):
            return "item"
        elif isinstance(obj, pystac.Catalog):
            return "catalog"

    except Exception as e:
        print(e)
        return None

stac_pixel_value(lon, lat, url=None, collection=None, item=None, assets=None, titiler_endpoint=None, verbose=True, **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

Returns:

Name	Type	Description
list		A dictionary of pixel values for each asset.

Source code in leafmap/stac.py

def stac_pixel_value(
    lon: float,
    lat: float,
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    titiler_endpoint: Optional[str] = None,
    verbose: Optional[bool] = True,
    **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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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,
        )
    kwargs["assets"] = assets

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    if isinstance(titiler_endpoint, str):

        r = requests.get(
            f"{titiler_endpoint}/stac/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:
        if verbose:
            print(r["detail"])
        return None
    else:
        values = r["values"]
        if isinstance(assets, str):
            assets = assets.split(",")
        result = dict(zip(assets, values))
        return result

stac_root_link(url, return_col_id=False, **kwargs)

Get the root link of a STAC object.

Parameters:

Name	Type	Description	Default
url	str	The STAC object URL.	required
return_col_id	bool	Return the collection ID if the STAC object is a collection. Defaults to False.	False
**kwargs		Keyword arguments for pystac.STACObject.from_file(). Defaults to None.	{}

Returns:

Name	Type	Description
str	str	The root link of the STAC object.

Source code in leafmap/stac.py

def stac_root_link(url: str, return_col_id: Optional[bool] = False, **kwargs) -> str:
    """Get the root link of a STAC object.

    Args:
        url (str): The STAC object URL.
        return_col_id (bool, optional): Return the collection ID if the STAC object is a collection. Defaults to False.
        **kwargs: Keyword arguments for pystac.STACObject.from_file(). Defaults to None.

    Returns:
        str: The root link of the STAC object.
    """
    collection_id = None
    try:
        obj = pystac.STACObject.from_file(url, **kwargs)
        if isinstance(obj, pystac.Collection):
            collection_id = obj.id
        href = obj.get_root_link().get_href()

        if not url.startswith(href):
            href = obj.get_self_href()

        if return_col_id:
            return href, collection_id
        else:
            return href

    except Exception as e:
        print(e)
        if return_col_id:
            return None, None
        else:
            return None

stac_search(url, method='POST', max_items=None, limit=100, ids=None, collections=None, bbox=None, intersects=None, datetime=None, query=None, filter=None, filter_lang=None, sortby=None, fields=None, get_collection=False, get_items=False, get_assets=False, get_links=False, get_gdf=False, get_info=False, get_root=True, **kwargs)

Search a STAC API. The function wraps the pysatc_client.Client.search() method. See https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.search

Parameters:

Name	Type	Description	Default
url	str	The STAC API URL.	required
method	str	The HTTP method to use when making a request to the service. This must be either "GET", "POST", or None. If None, this will default to "POST". If a "POST" request receives a 405 status for the response, it will automatically retry with "GET" for all subsequent requests. Defaults to "POST".	'POST'
max_items	init	The maximum number of items to return from the search, even if there are more matching results. This client to limit the total number of Items returned from the items(), item_collections(), and items_as_dicts methods(). The client will continue to request pages of items until the number of max items is reached. This parameter defaults to 100. Setting this to None will allow iteration over a possibly very large number of results.. Defaults to None.	None
limit	int	A recommendation to the service as to the number of items to return per page of results. Defaults to 100.	100
ids	list	List of one or more Item ids to filter on. Defaults to None.	None
collections	list	List of one or more Collection IDs or pystac.Collection instances. Only Items in one of the provided Collections will be searched. Defaults to None.	None
bbox	list | tuple	A list, tuple, or iterator representing a bounding box of 2D or 3D coordinates. Results will be filtered to only those intersecting the bounding box. Defaults to None.	None
intersects	str | dict	A string or dictionary representing a GeoJSON geometry, or an object that implements a geo_interface property, as supported by several libraries including Shapely, ArcPy, PySAL, and geojson. Results filtered to only those intersecting the geometry. Defaults to None.	None
datetime	str	Either a single datetime or datetime range used to filter results. You may express a single datetime using a datetime.datetime instance, a RFC 3339-compliant timestamp, or a simple date string (see below). Instances of datetime.datetime may be either timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp before being passed to the endpoint. Timezone unaware instances are assumed to represent UTC timestamps. You may represent a datetime range using a "/" separated string as described in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances. For open-ended ranges, use either ".." ('2020-01-01:00:00:00Z/..', ['2020-01-01:00:00:00Z', '..']) or a value of None (['2020-01-01:00:00:00Z', None]). If using a simple date string, the datetime can be specified in YYYY-mm-dd format, optionally truncating to YYYY-mm or just YYYY. Simple date strings will be expanded to include the entire time period. Defaults to None.	None
query	list	List or JSON of query parameters as per the STAC API query extension. such as {"eo:cloud_cover":{"lt":10}}. Defaults to None.	None
filter	dict	JSON of query parameters as per the STAC API filter extension. Defaults to None.	None
filter_lang	str	Language variant used in the filter body. If filter is a dictionary or not provided, defaults to ‘cql2-json’. If filter is a string, defaults to cql2-text. Defaults to None.	None
sortby	str | list	A single field or list of fields to sort the response by. such as [{ 'field': 'properties.eo:cloud_cover', 'direction': 'asc' }]. Defaults to None.	None
fields	list	A list of fields to include in the response. Note this may result in invalid STAC objects, as they may not have required fields. Use items_as_dicts to avoid object unmarshalling errors. Defaults to None.	None
get_collection	bool	True to return a pystac.ItemCollection. Defaults to False.	False
get_items	bool	True to return a list of pystac.Item. Defaults to False.	False
get_assets	bool	True to return a list of pystac.Asset. Defaults to False.	False
get_links	bool	True to return a list of links. Defaults to False.	False
get_gdf	bool	True to return a GeoDataFrame. Defaults to False.	False
get_info	bool	True to return a dictionary of STAC items. Defaults to False.	False
get_root	bool	Get the root link of the STAC object. Defaults to True.	True
**kwargs		Additional keyword arguments to pass to the stac_client() function.	{}

Returns:

Type	Description
List	list | pystac.ItemCollection : The search results as a list of links or a pystac.ItemCollection.

Source code in leafmap/stac.py

def stac_search(
    url: str,
    method: Optional[str] = "POST",
    max_items: Optional[int] = None,
    limit: Optional[int] = 100,
    ids: Optional[List] = None,
    collections: Optional[List] = None,
    bbox: Optional[Union[List, Tuple]] = None,
    intersects: Optional[Union[str, Dict]] = None,
    datetime: Optional[str] = None,
    query: Optional[List] = None,
    filter: Optional[Dict] = None,
    filter_lang: Optional[str] = None,
    sortby: Optional[Union[List, str]] = None,
    fields: Optional[List] = None,
    get_collection: Optional[bool] = False,
    get_items: Optional[bool] = False,
    get_assets: Optional[bool] = False,
    get_links: Optional[bool] = False,
    get_gdf: Optional[bool] = False,
    get_info: Optional[bool] = False,
    get_root: Optional[bool] = True,
    **kwargs,
) -> List:
    """Search a STAC API. The function wraps the pysatc_client.Client.search() method. See
        https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.search

    Args:
        url (str): The STAC API URL.
        method (str, optional): The HTTP method to use when making a request to the service.
            This must be either "GET", "POST", or None. If None, this will default to "POST".
            If a "POST" request receives a 405 status for the response, it will automatically
            retry with "GET" for all subsequent requests. Defaults to "POST".
        max_items (init, optional): The maximum number of items to return from the search,
            even if there are more matching results. This client to limit the total number of
            Items returned from the items(), item_collections(), and items_as_dicts methods().
            The client will continue to request pages of items until the number of max items
            is reached. This parameter defaults to 100. Setting this to None will allow iteration
            over a possibly very large number of results.. Defaults to None.
        limit (int, optional): A recommendation to the service as to the number of items to
            return per page of results. Defaults to 100.
        ids (list, optional): List of one or more Item ids to filter on. Defaults to None.
        collections (list, optional): List of one or more Collection IDs or pystac.Collection instances.
            Only Items in one of the provided Collections will be searched. Defaults to None.
        bbox (list | tuple, optional): A list, tuple, or iterator representing a bounding box of 2D
            or 3D coordinates. Results will be filtered to only those intersecting the bounding box.
            Defaults to None.
        intersects (str | dict, optional):  A string or dictionary representing a GeoJSON geometry, or
            an object that implements a __geo_interface__ property, as supported by several
            libraries including Shapely, ArcPy, PySAL, and geojson. Results filtered to only
            those intersecting the geometry. Defaults to None.
        datetime (str, optional): Either a single datetime or datetime range used to filter results.
            You may express a single datetime using a datetime.datetime instance, a RFC 3339-compliant
            timestamp, or a simple date string (see below). Instances of datetime.datetime may be either
            timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp
            before being passed to the endpoint. Timezone unaware instances are assumed to represent
            UTC timestamps. You may represent a datetime range using a "/" separated string as described
            in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances.
            For open-ended ranges, use either ".." ('2020-01-01:00:00:00Z/..', ['2020-01-01:00:00:00Z', '..'])
            or a value of None (['2020-01-01:00:00:00Z', None]). If using a simple date string,
            the datetime can be specified in YYYY-mm-dd format, optionally truncating to
            YYYY-mm or just YYYY. Simple date strings will be expanded to include the entire
            time period. Defaults to None.
        query (list, optional): List or JSON of query parameters as per the STAC API query extension.
            such as {"eo:cloud_cover":{"lt":10}}. Defaults to None.
        filter (dict, optional): JSON of query parameters as per the STAC API filter extension. Defaults to None.
        filter_lang (str, optional): Language variant used in the filter body. If filter is a dictionary
            or not provided, defaults to ‘cql2-json’. If filter is a string, defaults to cql2-text. Defaults to None.
        sortby (str | list, optional): A single field or list of fields to sort the response by.
            such as [{ 'field': 'properties.eo:cloud_cover', 'direction': 'asc' }]. Defaults to None.
        fields (list, optional): A list of fields to include in the response. Note this may result in
            invalid STAC objects, as they may not have required fields. Use items_as_dicts to avoid object
            unmarshalling errors. Defaults to None.
        get_collection (bool, optional): True to return a pystac.ItemCollection. Defaults to False.
        get_items (bool, optional): True to return a list of pystac.Item. Defaults to False.
        get_assets (bool, optional): True to return a list of pystac.Asset. Defaults to False.
        get_links (bool, optional): True to return a list of links. Defaults to False.
        get_gdf (bool, optional): True to return a GeoDataFrame. Defaults to False.
        get_info (bool, optional): True to return a dictionary of STAC items. Defaults to False.
        get_root (bool, optional): Get the root link of the STAC object. Defaults to True.
        **kwargs: Additional keyword arguments to pass to the stac_client() function.

    Returns:
        list | pystac.ItemCollection : The search results as a list of links or a pystac.ItemCollection.
    """

    client, collection_id = stac_client(
        url, return_col_id=True, get_root=get_root, **kwargs
    )

    if client is None:
        return None
    else:
        if isinstance(intersects, dict) and "geometry" in intersects:
            intersects = intersects["geometry"]

        if collection_id is not None and collections is None:
            collections = [collection_id]

        search = client.search(
            method=method,
            max_items=max_items,
            limit=limit,
            ids=ids,
            collections=collections,
            bbox=bbox,
            intersects=intersects,
            datetime=datetime,
            query=query,
            filter=filter,
            filter_lang=filter_lang,
            sortby=sortby,
            fields=fields,
        )

        if get_collection:
            return search.item_collection()
        elif get_items:
            return list(search.items())
        elif get_assets:
            assets = {}
            for item in search.items():
                assets[item.id] = {}
                for key, value in item.get_assets().items():
                    assets[item.id][key] = value.href
            return assets
        elif get_links:
            return [item.get_self_href() for item in search.items()]
        elif get_gdf:
            import geopandas as gpd

            gdf = gpd.GeoDataFrame.from_features(
                search.item_collection().to_dict(), crs="EPSG:4326"
            )
            return gdf
        elif get_info:
            items = search.items()
            info = {}
            for item in items:
                info[item.id] = {
                    "id": item.id,
                    "href": item.get_self_href(),
                    "bands": list(item.get_assets().keys()),
                    "assets": item.get_assets(),
                }
            return info
        else:
            return search

stac_search_to_df(search, **kwargs)

Convert STAC search result to a DataFrame.

Parameters:

Name	Type	Description	Default
search	item_search	The search result returned by leafmap.stac_search().	required
**kwargs		Additional keyword arguments to pass to the DataFrame.drop() function.	{}

Returns:

Name	Type	Description
DataFrame	DataFrame	A Pandas DataFrame object.

Source code in leafmap/stac.py

def stac_search_to_df(search, **kwargs) -> pd.DataFrame:
    """Convert STAC search result to a DataFrame.

    Args:
        search (pystac_client.item_search): The search result returned by leafmap.stac_search().
        **kwargs: Additional keyword arguments to pass to the DataFrame.drop() function.

    Returns:
        DataFrame: A Pandas DataFrame object.
    """
    gdf = stac_search_to_gdf(search)
    return gdf.drop(columns=["geometry"], **kwargs)

stac_search_to_dict(search, **kwargs)

Convert STAC search result to a dictionary.

Parameters:

Name	Type	Description	Default
search	item_search	The search result returned by leafmap.stac_search().	required

Returns:

Name	Type	Description
dict	Dict	A dictionary of STAC items, with the stac item id as the key, and the stac item as the value.

Source code in leafmap/stac.py

def stac_search_to_dict(search, **kwargs) -> Dict:
    """Convert STAC search result to a dictionary.

    Args:
        search (pystac_client.item_search): The search result returned by leafmap.stac_search().

    Returns:
        dict: A dictionary of STAC items, with the stac item id as the key, and the stac item as the value.
    """

    items = list(search.item_collection())
    info = {}
    for item in items:
        info[item.id] = {
            "id": item.id,
            "href": item.get_self_href(),
            "bands": list(item.get_assets().keys()),
            "assets": item.get_assets(),
        }
        links = {}
        assets = item.get_assets()
        for key, value in assets.items():
            links[key] = value.href
        info[item.id]["links"] = links
    return info

stac_search_to_gdf(search, **kwargs)

Convert STAC search result to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
search	item_search	The search result returned by leafmap.stac_search().	required
**kwargs		Additional keyword arguments to pass to the GeoDataFrame.from_features() function.	{}

Returns:

Name	Type	Description
GeoDataFrame		A GeoPandas GeoDataFrame object.

Source code in leafmap/stac.py

def stac_search_to_gdf(search, **kwargs):
    """Convert STAC search result to a GeoDataFrame.

    Args:
        search (pystac_client.item_search): The search result returned by leafmap.stac_search().
        **kwargs: Additional keyword arguments to pass to the GeoDataFrame.from_features() function.

    Returns:
        GeoDataFrame: A GeoPandas GeoDataFrame object.
    """
    import geopandas as gpd

    gdf = gpd.GeoDataFrame.from_features(
        search.item_collection().to_dict(), crs="EPSG:4326", **kwargs
    )
    return gdf

stac_search_to_list(search, **kwargs)

Convert STAC search result to a list.

Parameters:

Name	Type	Description	Default
search	item_search	The search result returned by leafmap.stac_search().	required

Returns:

Name	Type	Description
list	List	A list of STAC items.

Source code in leafmap/stac.py

def stac_search_to_list(search, **kwargs) -> List:
    """Convert STAC search result to a list.

    Args:
        search (pystac_client.item_search): The search result returned by leafmap.stac_search().

    Returns:
        list: A list of STAC items.
    """

    return search.item_collections()

stac_stats(url=None, collection=None, item=None, assets=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
list	List	A dictionary of band statistics.

Source code in leafmap/stac.py

def stac_stats(
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> List:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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).json()
    else:
        r = requests.get(
            titiler_endpoint.url_for_stac_statistics(), params=kwargs
        ).json()

    return r

stac_tile(url=None, collection=None, item=None, assets=None, bands=None, titiler_endpoint=None, **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

Returns:

Name	Type	Description
str	str	Returns the STAC Tile layer URL.

Source code in leafmap/stac.py

def stac_tile(
    url: str = None,
    collection: str = None,
    item: str = None,
    assets: Union[str, List] = None,
    bands: list = None,
    titiler_endpoint: Optional[str] = None,
    **kwargs,
) -> str:
    """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.

    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 isinstance(url, pystac.Item):
        try:
            url = url.self_href
        except Exception as e:
            print(e)

    if url is not None:
        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"].lower()
        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

    mosaic_json = False

    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 (
            (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:
        data = requests.get(url).json()
        if "mosaicjson" in data:
            mosaic_json = True

        if isinstance(bands, str):
            bands = bands.split(",")
        if isinstance(assets, str):
            assets = assets.split(",")

        if assets is None:
            if bands is not None:
                assets = bands
            else:
                bnames = stac_bands(url)
                if isinstance(bnames, list):
                    if len(bnames) >= 3:
                        assets = bnames[0:3]
                    else:
                        assets = bnames[0]
                else:
                    assets = None

        else:
            kwargs["asset_bidx"] = bands
        if assets is not None:
            kwargs["assets"] = assets

        if (
            (assets is not None)
            and ("asset_expression" not in kwargs)
            and ("expression" not in kwargs)
            and ("rescale" not in kwargs)
        ):
            stats = stac_stats(
                url=url,
                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.

    TileMatrixSetId = "WebMercatorQuad"
    if "TileMatrixSetId" in kwargs.keys():
        TileMatrixSetId = kwargs["TileMatrixSetId"]
        kwargs.pop("TileMatrixSetId")

    if mosaic_json:
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/{TileMatrixSetId}/tilejson.json",
            params=kwargs,
        ).json()
    else:
        if isinstance(titiler_endpoint, str):
            r = requests.get(
                f"{titiler_endpoint}/stac/{TileMatrixSetId}/tilejson.json",
                params=kwargs,
            ).json()
        else:
            r = requests.get(titiler_endpoint.url_for_stac_item(), params=kwargs).json()

    return r["tiles"][0]

start_server(directory=None, port=8000, background=True, quiet=True)

Start a simple web server to serve files from the specified directory with directory listing and CORS support. Optionally, run the server asynchronously in a background thread.

Parameters:

Name	Type	Description	Default
directory	str	The directory from which files will be served.	None
port	int	The port on which the web server will run. Defaults to 8000.	8000
background	bool	Whether to run the server in a separate background thread. Defaults to True.	True
quiet	bool	If True, suppress the log output. Defaults to True.	True

Raises:

Type	Description
ImportError	If required modules are not found.
Exception	Catches other unexpected errors during execution.

Returns:

Type	Description
None	None. The function runs the server indefinitely until manually stopped.

Source code in leafmap/common.py

def start_server(
    directory: str = None, port: int = 8000, background: bool = True, quiet: bool = True
) -> None:
    """
    Start a simple web server to serve files from the specified directory
    with directory listing and CORS support. Optionally, run the server
    asynchronously in a background thread.

    Args:
        directory (str): The directory from which files will be served.
        port (int, optional): The port on which the web server will run. Defaults to 8000.
        background (bool, optional): Whether to run the server in a separate background thread.
                                     Defaults to True.
        quiet (bool, optional): If True, suppress the log output. Defaults to True.

    Raises:
        ImportError: If required modules are not found.
        Exception: Catches other unexpected errors during execution.

    Returns:
        None. The function runs the server indefinitely until manually stopped.
    """

    # If no directory is specified, use the current working directory
    if directory is None:
        directory = os.getcwd()

    def run_flask():
        try:
            from flask import Flask, send_from_directory, render_template_string
            from flask_cors import CORS

            app = Flask(__name__, static_folder=directory)
            CORS(app)  # Enable CORS for all routes

            if quiet:
                # This will disable Flask's logging
                import logging

                log = logging.getLogger("werkzeug")
                log.disabled = True
                app.logger.disabled = True

            @app.route("/<path:path>", methods=["GET"])
            def serve_file(path):
                return send_from_directory(directory, path)

            @app.route("/", methods=["GET"])
            def index():
                # List files and directories under the specified directory
                items = os.listdir(directory)
                items.sort()
                # Generate an HTML representation of the directory listing
                listing_template = """
                <h2>Directory listing for /</h2>
                <hr>
                <ul>
                    {% for item in items %}
                        <li><a href="{{ item }}">{{ item }}</a></li>
                    {% endfor %}
                </ul>
                """
                return render_template_string(listing_template, items=items)

            print(f"Server is running at http://127.0.0.1:{port}/")
            app.run(port=port)

        except ImportError as e:
            print(f"Error importing module: {e}")
        except Exception as e:
            print(f"An error occurred: {e}")

    if background:
        import threading

        # Start the Flask server in a new background thread
        t = threading.Thread(target=run_flask)
        t.start()
    else:
        # Run the Flask server in the main thread
        run_flask()

streamlit_legend(html, width=None, height=None, scrolling=True)

Streamlit function to display a legend.

Parameters:

Name	Type	Description	Default
html	str	The HTML string of the legend.	required
width	str	The width of the legend. Defaults to None.	None
height	str	The height of the legend. Defaults to None.	None
scrolling	bool	Whether to allow scrolling in the legend. Defaults to True.	True

Source code in leafmap/common.py

def streamlit_legend(html, width=None, height=None, scrolling=True):
    """Streamlit function to display a legend.

    Args:
        html (str): The HTML string of the legend.
        width (str, optional): The width of the legend. Defaults to None.
        height (str, optional): The height of the legend. Defaults to None.
        scrolling (bool, optional): Whether to allow scrolling in the legend. Defaults to True.

    """

    try:
        import streamlit.components.v1 as components

        components.html(html, width=width, height=height, scrolling=scrolling)

    except ImportError:
        print("Streamlit is not installed. Please run 'pip install streamlit'.")
        return

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

Parameters:

Name	Type	Description	Default
show_full_path	bool	Whether to show the full path of each system font. Defaults to False.	False

Returns:

Name	Type	Description
list	List	A list of system fonts.

Source code in leafmap/common.py

def system_fonts(show_full_path: Optional[bool] = False) -> List:
    """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:
        raise Exception(e)

temp_file_path(extension)

Returns a temporary file path.

Parameters:

Name	Type	Description	Default
extension	str	The file extension.	required

Returns:

Name	Type	Description
str		The temporary file path.

Source code in leafmap/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 ["QUALITY=20"]. A value of 20 means the file will be 20% of the size in comparison to uncompressed data.	None

Source code in leafmap/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.

    """

    if not os.path.exists(filename):
        raise Exception(f"File {filename} does not exist")

    if not output.endswith(".jp2"):
        output += ".jp2"

    from osgeo import gdal

    in_ds = gdal.Open(filename)
    gdal.Translate(output, in_ds, format="JP2OpenJPEG", creationOptions=creationOptions)
    in_ds = None

to_hex_colors(colors)

Adds # to a list of hex color codes.

Parameters:

Name	Type	Description	Default
colors	list	A list of hex color codes.	required

Returns:

Name	Type	Description
list		A list of hex color codes prefixed with #.

Source code in leafmap/common.py

def to_hex_colors(colors):
    """Adds # to a list of hex color codes.

    Args:
        colors (list): A list of hex color codes.

    Returns:
        list: A list of hex color codes prefixed with #.
    """
    result = all([len(color.strip()) == 6 for color in colors])
    if result:
        return ["#" + color.strip() for color in colors]
    else:
        return colors

transform_bbox_coords(bbox, src_crs, dst_crs, **kwargs)

Transforms the coordinates of a bounding box [x1, y1, x2, y2] from one CRS to another.

Parameters:

Name	Type	Description	Default
bbox	list | tuple	The bounding box [x1, y1, x2, y2] coordinates.	required
src_crs	str	The source CRS, e.g., "EPSG:4326".	required
dst_crs	str	The destination CRS, e.g., "EPSG:3857".	required

Returns:

Name	Type	Description
list		The transformed bounding box [x1, y1, x2, y2] coordinates.

Source code in leafmap/common.py

def transform_bbox_coords(bbox, src_crs, dst_crs, **kwargs):
    """Transforms the coordinates of a bounding box [x1, y1, x2, y2] from one CRS to another.

    Args:
        bbox (list | tuple): The bounding box [x1, y1, x2, y2] coordinates.
        src_crs (str): The source CRS, e.g., "EPSG:4326".
        dst_crs (str): The destination CRS, e.g., "EPSG:3857".

    Returns:
        list: The transformed bounding box [x1, y1, x2, y2] coordinates.
    """
    x1, y1, x2, y2 = bbox

    x1, y1 = transform_coords(
        x1, y1, src_crs, dst_crs, **kwargs
    )  # pylint: disable=E0633
    x2, y2 = transform_coords(
        x2, y2, src_crs, dst_crs, **kwargs
    )  # pylint: disable=E0633

    return [x1, y1, x2, y2]

transform_coords(x, y, src_crs, dst_crs, **kwargs)

Transform coordinates from one CRS to another.

Parameters:

Name	Type	Description	Default
x	float	The x coordinate.	required
y	float	The y coordinate.	required
src_crs	str	The source CRS, e.g., "EPSG:4326".	required
dst_crs	str	The destination CRS, e.g., "EPSG:3857".	required

Returns:

Name	Type	Description
dict		The transformed coordinates in the format of (x, y)

Source code in leafmap/common.py

def transform_coords(x, y, src_crs, dst_crs, **kwargs):
    """Transform coordinates from one CRS to another.

    Args:
        x (float): The x coordinate.
        y (float): The y coordinate.
        src_crs (str): The source CRS, e.g., "EPSG:4326".
        dst_crs (str): The destination CRS, e.g., "EPSG:3857".

    Returns:
        dict: The transformed coordinates in the format of (x, y)
    """
    import pyproj

    transformer = pyproj.Transformer.from_crs(
        src_crs, dst_crs, always_xy=True, **kwargs
    )
    return transformer.transform(x, y)

update_package()

Updates the leafmap package from the leafmap 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 leafmap/common.py

def update_package() -> None:
    """Updates the leafmap package from the leafmap 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.

    """

    download_dir = Path.home() / "Downloads"
    download_dir.mkdir(parents=True, exist_ok=True)
    _clone_repo(out_dir=str(download_dir))
    pkg_dir = download_dir / "leafmap-master"
    work_dir = Path.cwd()

    os.chdir(pkg_dir)
    try:
        if shutil.which("pip"):
            cmd = ["pip", "install", "."]
        else:
            cmd = ["pip3", "install", "."]
        subprocess.run(cmd, check=True)
    except subprocess.CalledProcessError as error:
        print(f"Failed to install the package: {error}")
    finally:
        os.chdir(work_dir)

    print(
        "\nPlease comment out 'leafmap.update_package()' and restart kernel to take effect:\n"
        "Jupyter menu -> Kernel -> Restart & Clear Output"
    )

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 leafmap/common.py

def upload_to_imgur(in_gif: str):
    """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:
        raise Exception(e)

vector_area(vector, unit='m2', crs='epsg:3857')

Calculate the area of a vector.

Parameters:

Name	Type	Description	Default
vector	str	A local path or HTTP URL to a vector.	required
unit	str	The unit of the area, can be 'm2', 'km2', 'ha', or 'acres'. Defaults to 'm2'.	'm2'

Returns:

Name	Type	Description
float		The area of the vector.

Source code in leafmap/common.py

def vector_area(vector, unit="m2", crs="epsg:3857"):
    """Calculate the area of a vector.

    Args:
        vector (str): A local path or HTTP URL to a vector.
        unit (str, optional): The unit of the area, can be 'm2', 'km2', 'ha', or 'acres'. Defaults to 'm2'.

    Returns:
        float: The area of the vector.
    """
    import geopandas as gpd

    if isinstance(vector, str):
        gdf = gpd.read_file(vector)
    elif isinstance(vector, gpd.GeoDataFrame):
        gdf = vector
    else:
        gdf = None
        raise ValueError("Invalid input vector.")

    area = gdf.to_crs(crs).area.sum()

    if unit == "m2":
        return area
    elif unit == "km2":
        return area / 1000000
    elif unit == "ha":
        return area / 10000
    elif unit == "acres":
        return area / 4046.8564224
    else:
        raise ValueError("Invalid unit.")

vector_col_names(filename, **kwargs)

Retrieves the column names of a vector attribute table.

Parameters:

Name	Type	Description	Default
filename	str	The input file path.	required

Returns:

Name	Type	Description
list		The list of column names.

Source code in leafmap/common.py

def vector_col_names(filename, **kwargs):
    """Retrieves the column names of a vector attribute table.

    Args:
        filename (str): The input file path.

    Returns:
        list: The list of column names.
    """

    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)
    ext = os.path.splitext(filename)[1].lower()
    if ext == ".kml":
        fiona.drvsupport.supported_drivers["KML"] = "rw"
        gdf = gpd.read_file(filename, driver="KML", **kwargs)
    else:
        gdf = gpd.read_file(filename, **kwargs)
    col_names = gdf.columns.values.tolist()
    return col_names

vector_geom_type(data, first_only=True, **kwargs)

Returns the geometry type of a vector dataset.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoDataFrame.	required
first_only	bool	Whether to return the geometry type of the first feature in the GeoDataFrame. Defaults to True.	True
kwargs		Additional keyword arguments to pass to the geopandas.read_file function.	{}

Returns:

Name	Type	Description
str		The geometry type of the GeoDataFrame, such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon. For more info, see https://shapely.readthedocs.io/en/stable/manual.html

Source code in leafmap/common.py

def vector_geom_type(data, first_only=True, **kwargs):
    """Returns the geometry type of a vector dataset.

    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.
        kwargs: Additional keyword arguments to pass to the geopandas.read_file function.


    Returns:
        str: The geometry type of the GeoDataFrame, such as Point, LineString,
            Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import geopandas as gpd

    if isinstance(data, str) or isinstance(data, dict):
        gdf = gpd.read_file(data, **kwargs)

    if first_only:
        return gdf.geometry.type[0]
    else:
        return gdf.geometry.type

vector_set_crs(source, output=None, crs='EPSG:4326', **kwargs)

Set CRS of a vector file.

Parameters:

Name	Type	Description	Default
source	str | GeoDataFrame	The path to the vector file or a GeoDataFrame.	required
output	str	The path to the output vector file. Defaults to None.	None
crs	str	The CRS to set. Defaults to "EPSG:4326".	'EPSG:4326'

Returns:

Type	Description
	gpd.GeoDataFrame: The GeoDataFrame with the new CRS.

Source code in leafmap/common.py

def vector_set_crs(source, output=None, crs="EPSG:4326", **kwargs):
    """Set CRS of a vector file.

    Args:
        source (str | gpd.GeoDataFrame): The path to the vector file or a GeoDataFrame.
        output (str, optional): The path to the output vector file. Defaults to None.
        crs (str, optional): The CRS to set. Defaults to "EPSG:4326".


    Returns:
        gpd.GeoDataFrame: The GeoDataFrame with the new CRS.
    """

    import geopandas as gpd

    if isinstance(source, str):
        source = gpd.read_file(source, **kwargs)

    if not isinstance(source, gpd.GeoDataFrame):
        raise TypeError("source must be a GeoDataFrame or a file path")

    gdf = source.set_crs(crs)

    if output is not None:
        gdf.to_file(output)
    else:
        return gdf

vector_to_geojson(filename, out_geojson=None, bbox=None, mask=None, rows=None, epsg='4326', encoding='utf-8', **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'
encoding	str	The encoding of the input file. Defaults to "utf-8".	'utf-8'

Raises:

Type	Description
ValueError	When the output file path is invalid.

Returns:

Name	Type	Description
dict		A dictionary containing the GeoJSON.

Source code in leafmap/common.py

def vector_to_geojson(
    filename,
    out_geojson=None,
    bbox=None,
    mask=None,
    rows=None,
    epsg="4326",
    encoding="utf-8",
    **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".
        encoding (str, optional): The encoding of the input file. Defaults to "utf-8".


    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)
        if filename.endswith(".zip"):
            filename = "zip://" + 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",
            encoding=encoding,
            **kwargs,
        )
    else:
        df = gpd.read_file(
            filename, bbox=bbox, mask=mask, rows=rows, encoding=encoding, **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__

vector_to_geojson_batch(input_dir, output_dir=None, file_ext='.shp', **kwargs)

Converts all vector files in a directory to GeoJSON format in batch.

Parameters:

Name	Type	Description	Default
input_dir	str	The directory containing the input vector files.	required
output_dir	str	The directory to save the converted GeoPackage files. If not provided, the input directory will be used. Defaults to None.	None
file_ext	str	The file extension of the input vector files (e.g., ".shp"). Defaults to ".shp".	'.shp'
**kwargs		Additional keyword arguments to be passed to the glob.glob function for file matching.	{}

Returns:

Type	Description
	None

Example

vector_to_geojson_batch("input_directory", "output_directory", ".shp")

Source code in leafmap/common.py

def vector_to_geojson_batch(input_dir, output_dir=None, file_ext=".shp", **kwargs):
    """
    Converts all vector files in a directory to GeoJSON format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted GeoPackage files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".shp"). Defaults to ".shp".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_geojson_batch("input_directory", "output_directory", ".shp")
    """
    import glob
    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        gpkg_file = os.path.join(output_dir, f"{file_name_without_ext}.geojson")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to GeoJSON...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{gpkg_file}' (FORMAT GDAL, DRIVER 'GeoJSON')
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")

vector_to_gif(filename, out_gif, colname, vmin=None, vmax=None, step=1, facecolor='black', figsize=(10, 8), padding=3, title=None, add_text=True, xy=('1%', '1%'), fontsize=20, add_progress_bar=True, progress_bar_color='blue', progress_bar_height=5, dpi=300, fps=10, loop=0, mp4=False, keep_png=False, verbose=True, open_args={}, plot_args={})

Convert a vector to a gif. This function was inspired by by Johannes Uhl's shapefile2gif repo at https://github.com/johannesuhl/shapefile2gif. Credits to Johannes Uhl.

Parameters:

Name	Type	Description	Default
filename	str	The input vector file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"	required
out_gif	str	The output gif file.	required
colname	str	The column name of the vector that contains numerical values.	required
vmin	float	The minimum value to filter the data. Defaults to None.	None
vmax	float	The maximum value to filter the data. Defaults to None.	None
step	float	The step to filter the data. Defaults to 1.	1
facecolor	str	The color to visualize the data. Defaults to "black".	'black'
figsize	tuple	The figure size. Defaults to (10, 8).	(10, 8)
padding	int	The padding of the figure tight_layout. Defaults to 3.	3
title	str	The title of the figure. Defaults to None.	None
add_text	bool	Whether to add text to the figure. Defaults to True.	True
xy	tuple	The position of the text from the lower-left corner. Defaults to ("1%", "1%").	('1%', '1%')
fontsize	int	The font size of the text. Defaults to 20.	20
add_progress_bar	bool	Whether to add a progress bar to the figure. Defaults to True.	True
progress_bar_color	str	The color of the progress bar. Defaults to "blue".	'blue'
progress_bar_height	int	The height of the progress bar. Defaults to 5.	5
dpi	int	The dpi of the figure. Defaults to 300.	300
fps	int	The frames per second (fps) of the gif. Defaults to 10.	10
loop	int	The number of loops of the gif. Defaults to 0, infinite loop.	0
mp4	bool	Whether to convert the gif to mp4. Defaults to False.	False
keep_png	bool	Whether to keep the png files. Defaults to False.	False
verbose	bool	Whether to print the progress. Defaults to True.	True
open_args	dict	The arguments for the geopandas.read_file() function. Defaults to {}.	{}
plot_args	dict	The arguments for the geopandas.GeoDataFrame.plot() function. Defaults to {}.	{}

Source code in leafmap/common.py

def vector_to_gif(
    filename,
    out_gif,
    colname,
    vmin=None,
    vmax=None,
    step=1,
    facecolor="black",
    figsize=(10, 8),
    padding=3,
    title=None,
    add_text=True,
    xy=("1%", "1%"),
    fontsize=20,
    add_progress_bar=True,
    progress_bar_color="blue",
    progress_bar_height=5,
    dpi=300,
    fps=10,
    loop=0,
    mp4=False,
    keep_png=False,
    verbose=True,
    open_args={},
    plot_args={},
):
    """Convert a vector to a gif. This function was inspired by by Johannes Uhl's shapefile2gif repo at
            https://github.com/johannesuhl/shapefile2gif. Credits to Johannes Uhl.

    Args:
        filename (str): The input vector file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"
        out_gif (str): The output gif file.
        colname (str): The column name of the vector that contains numerical values.
        vmin (float, optional): The minimum value to filter the data. Defaults to None.
        vmax (float, optional): The maximum value to filter the data. Defaults to None.
        step (float, optional): The step to filter the data. Defaults to 1.
        facecolor (str, optional): The color to visualize the data. Defaults to "black".
        figsize (tuple, optional): The figure size. Defaults to (10, 8).
        padding (int, optional): The padding of the figure tight_layout. Defaults to 3.
        title (str, optional): The title of the figure. Defaults to None.
        add_text (bool, optional): Whether to add text to the figure. Defaults to True.
        xy (tuple, optional): The position of the text from the lower-left corner. Defaults to ("1%", "1%").
        fontsize (int, optional): The font size of the text. Defaults to 20.
        add_progress_bar (bool, optional): Whether to add a progress bar to the figure. Defaults to True.
        progress_bar_color (str, optional): The color of the progress bar. Defaults to "blue".
        progress_bar_height (int, optional): The height of the progress bar. Defaults to 5.
        dpi (int, optional): The dpi of the figure. Defaults to 300.
        fps (int, optional): The frames per second (fps) of the gif. Defaults to 10.
        loop (int, optional): The number of loops of the gif. Defaults to 0, infinite loop.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.
        keep_png (bool, optional): Whether to keep the png files. Defaults to False.
        verbose (bool, optional): Whether to print the progress. Defaults to True.
        open_args (dict, optional): The arguments for the geopandas.read_file() function. Defaults to {}.
        plot_args (dict, optional): The arguments for the geopandas.GeoDataFrame.plot() function. Defaults to {}.

    """
    import geopandas as gpd
    import matplotlib.pyplot as plt

    out_dir = os.path.dirname(out_gif)
    tmp_dir = os.path.join(out_dir, "tmp_png")
    if not os.path.exists(tmp_dir):
        os.makedirs(tmp_dir)

    if isinstance(filename, str):
        gdf = gpd.read_file(filename, **open_args)
    elif isinstance(filename, gpd.GeoDataFrame):
        gdf = filename
    else:
        raise ValueError(
            "filename must be a string or a geopandas.GeoDataFrame object."
        )

    bbox = gdf.total_bounds

    if colname not in gdf.columns:
        raise Exception(
            f"{colname} is not in the columns of the GeoDataFrame. It must be one of {gdf.columns}"
        )

    values = gdf[colname].unique().tolist()
    values.sort()

    if vmin is None:
        vmin = values[0]
    if vmax is None:
        vmax = values[-1]

    options = range(vmin, vmax + step, step)

    W = bbox[2] - bbox[0]
    H = bbox[3] - bbox[1]

    if xy is None:
        # default text location is 5% width and 5% height of the image.
        xy = (int(0.05 * W), int(0.05 * H))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        raise Exception("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")

    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < W) and (y > 0) and (y < H):
            pass
        else:
            print(
                f"xy is out of bounds. x must be within [0, {W}], and y must be within [0, {H}]"
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = float(x.replace("%", "")) / 100.0 * W
                y = float(y.replace("%", "")) / 100.0 * H
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )
    else:
        raise Exception(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )

    x = bbox[0] + x
    y = bbox[1] + y

    for index, v in enumerate(options):
        if verbose:
            print(f"Processing {index+1}/{len(options)}: {v}...")
        yrdf = gdf[gdf[colname] <= v]
        fig, ax = plt.subplots()
        ax = yrdf.plot(facecolor=facecolor, figsize=figsize, **plot_args)
        ax.set_title(title, fontsize=fontsize)
        ax.set_axis_off()
        ax.set_xlim([bbox[0], bbox[2]])
        ax.set_ylim([bbox[1], bbox[3]])
        if add_text:
            ax.text(x, y, v, fontsize=fontsize)
        fig = ax.get_figure()
        plt.tight_layout(pad=padding)
        fig.savefig(tmp_dir + os.sep + "%s.png" % v, dpi=dpi)
        plt.clf()
        plt.close("all")

    png_to_gif(tmp_dir, out_gif, fps=fps, loop=loop)

    if add_progress_bar:
        add_progress_bar_to_gif(
            out_gif,
            out_gif,
            progress_bar_color,
            progress_bar_height,
            duration=1000 / fps,
            loop=loop,
        )

    if mp4:
        gif_to_mp4(out_gif, out_gif.replace(".gif", ".mp4"))

    if not keep_png:
        shutil.rmtree(tmp_dir)

    if verbose:
        print(f"Done. The GIF is saved to {out_gif}.")

vector_to_gpkg_batch(input_dir, output_dir=None, file_ext='.geojson', **kwargs)

Converts all vector files in a directory to GeoPackage format in batch.

Parameters:

Name	Type	Description	Default
input_dir	str	The directory containing the input vector files.	required
output_dir	str	The directory to save the converted GeoPackage files. If not provided, the input directory will be used. Defaults to None.	None
file_ext	str	The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".	'.geojson'
**kwargs		Additional keyword arguments to be passed to the glob.glob function for file matching.	{}

Returns:

Type	Description
	None

Example

vector_to_gpkg_batch("input_directory", "output_directory", ".geojson")

Source code in leafmap/common.py

def vector_to_gpkg_batch(input_dir, output_dir=None, file_ext=".geojson", **kwargs):
    """
    Converts all vector files in a directory to GeoPackage format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted GeoPackage files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_gpkg_batch("input_directory", "output_directory", ".geojson")
    """
    import glob
    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        gpkg_file = os.path.join(output_dir, f"{file_name_without_ext}.gpkg")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to GeoPackage...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{gpkg_file}' (FORMAT GDAL, DRIVER 'GPKG')
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")

vector_to_mbtiles(source_path, target_path, max_zoom=5, name=None, **kwargs)

Convert a vector dataset to MBTiles format using the ogr2ogr command-line tool.

Parameters:

Name	Type	Description	Default
source_path	str	The path to the source vector dataset (GeoPackage, Shapefile, etc.).	required
target_path	str	The path to the target MBTiles file to be created.	required
max_zoom	int	The maximum zoom level for the MBTiles dataset. Defaults to 5.	5
name	str	The name of the MBTiles dataset. Defaults to None.	None
**kwargs		Additional options to be passed as keyword arguments. These options will be used as -dsco options when calling ogr2ogr. See https://gdal.org/drivers/raster/mbtiles.html for a list of options.	{}

Returns:

Type	Description
None	None

Raises:

Type	Description
CalledProcessError	If the ogr2ogr command fails to execute.

Example

source_path = "countries.gpkg" target_path = "target.mbtiles" name = "My MBTiles" max_zoom = 5 vector_to_mbtiles(source_path, target_path, name=name, max_zoom=max_zoom)

Source code in leafmap/common.py

def vector_to_mbtiles(
    source_path: str, target_path: str, max_zoom: int = 5, name: str = None, **kwargs
) -> None:
    """
    Convert a vector dataset to MBTiles format using the ogr2ogr command-line tool.

    Args:
        source_path (str): The path to the source vector dataset (GeoPackage, Shapefile, etc.).
        target_path (str): The path to the target MBTiles file to be created.
        max_zoom (int, optional): The maximum zoom level for the MBTiles dataset. Defaults to 5.
        name (str, optional): The name of the MBTiles dataset. Defaults to None.
        **kwargs: Additional options to be passed as keyword arguments. These options will be used as -dsco options
                  when calling ogr2ogr. See https://gdal.org/drivers/raster/mbtiles.html for a list of options.

    Returns:
        None

    Raises:
        subprocess.CalledProcessError: If the ogr2ogr command fails to execute.

    Example:
        source_path = "countries.gpkg"
        target_path = "target.mbtiles"
        name = "My MBTiles"
        max_zoom = 5
        vector_to_mbtiles(source_path, target_path, name=name, max_zoom=max_zoom)
    """
    import subprocess

    command = [
        "ogr2ogr",
        "-f",
        "MBTILES",
        target_path,
        source_path,
        "-dsco",
        f"MAXZOOM={max_zoom}",
    ]

    if name:
        command.extend(["-dsco", f"NAME={name}"])

    for key, value in kwargs.items():
        command.extend(["-dsco", f"{key.upper()}={value}"])

    try:
        subprocess.run(command, check=True)
    except subprocess.CalledProcessError as e:
        raise e

vector_to_parquet(source, output, crs=None, overwrite=False, **kwargs)

Convert a GeoDataFrame or a file containing vector data to Parquet format.

Parameters:

Name	Type	Description	Default
source	Union[GeoDataFrame, str]	The source data to convert. It can be either a GeoDataFrame or a file path to the vector data file.	required
output	str	The file path where the Parquet file will be saved.	required
crs	str	The coordinate reference system (CRS) to use for the output file. Defaults to None.	None
overwrite	bool	Whether to overwrite the existing output file. Default is False.	False
**kwargs		Additional keyword arguments to be passed to the to_parquet function of GeoDataFrame.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def vector_to_parquet(
    source: str, output: str, crs=None, overwrite=False, **kwargs
) -> None:
    """
    Convert a GeoDataFrame or a file containing vector data to Parquet format.

    Args:
        source (Union[gpd.GeoDataFrame, str]): The source data to convert. It can be either a GeoDataFrame
            or a file path to the vector data file.
        output (str): The file path where the Parquet file will be saved.
        crs (str, optional): The coordinate reference system (CRS) to use for the output file. Defaults to None.
        overwrite (bool): Whether to overwrite the existing output file. Default is False.
        **kwargs: Additional keyword arguments to be passed to the `to_parquet` function of GeoDataFrame.

    Returns:
        None
    """

    import geopandas as gpd

    if os.path.exists(output) and not overwrite:
        print(f"File {output} already exists. Skipping...")
        return

    if isinstance(source, gpd.GeoDataFrame):
        gdf = source
    else:
        gdf = gpd.read_file(source)

    if crs is not None:
        gdf = gdf.to_crs(crs)

    out_dir = os.path.dirname(os.path.abspath(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    gdf.to_parquet(output, **kwargs)

vector_to_parquet_batch(input_dir, output_dir=None, file_ext='.geojson', **kwargs)

Converts all vector files in a directory to Parquet format in batch.

Parameters:

Name	Type	Description	Default
input_dir	str	The directory containing the input vector files.	required
output_dir	str	The directory to save the converted Parquet files. If not provided, the input directory will be used. Defaults to None.	None
file_ext	str	The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".	'.geojson'
**kwargs		Additional keyword arguments to be passed to the glob.glob function for file matching.	{}

Returns:

Type	Description
	None

Example

vector_to_parquet_batch("input_directory", "output_directory", ".geojson")

Source code in leafmap/common.py

def vector_to_parquet_batch(input_dir, output_dir=None, file_ext=".geojson", **kwargs):
    """
    Converts all vector files in a directory to Parquet format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted Parquet files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_parquet_batch("input_directory", "output_directory", ".geojson")
    """
    import glob
    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        parquet_file = os.path.join(output_dir, f"{file_name_without_ext}.parquet")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to Parquet...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{parquet_file}' (FORMAT PARQUET)
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")

vector_to_pmtiles(source_path, target_path, max_zoom=15, name=None, **kwargs)

Converts a vector file to PMTiles format.

Parameters:

Name	Type	Description	Default
source_path	str	Path to the source vector file.	required
target_path	str	Path to the target PMTiles file.	required
max_zoom	int	Maximum zoom level for the PMTiles. Defaults to 5.	15
name	str	Name of the PMTiles dataset. Defaults to None.	None
**kwargs		Additional keyword arguments to be passed to the underlying conversion functions.	{}

Raises:

Type	Description
ValueError	If the target file does not have a .pmtiles extension.

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def vector_to_pmtiles(
    source_path: str, target_path: str, max_zoom: int = 15, name: str = None, **kwargs
) -> None:
    """
    Converts a vector file to PMTiles format.

    Args:
        source_path (str): Path to the source vector file.
        target_path (str): Path to the target PMTiles file.
        max_zoom (int, optional): Maximum zoom level for the PMTiles. Defaults to 5.
        name (str, optional): Name of the PMTiles dataset. Defaults to None.
        **kwargs: Additional keyword arguments to be passed to the underlying conversion functions.

    Raises:
        ValueError: If the target file does not have a .pmtiles extension.

    Returns:
        None
    """
    if not target_path.endswith(".pmtiles"):
        raise ValueError("Error: target file must be a .pmtiles file.")
    import subprocess

    command = [
        "ogr2ogr",
        "-skipfailures",
        "-f",
        "PMTiles",
        "-dsco",
        f"MAXZOOM={max_zoom}",
        target_path,
        source_path,
    ]

    if name:
        command.extend([f"NAME={name}"])

    for key, value in kwargs.items():
        command.extend([f"{key.upper()}={value}"])

    try:
        subprocess.run(command, check=True)
    except subprocess.CalledProcessError as e:
        raise e

vector_to_raster(vector, output, field='FID', assign='last', nodata=True, cell_size=None, base=None, callback=None, verbose=False, to_epsg=None)

Convert a vector to a raster.

Parameters:

Name	Type	Description	Default
vector	str | GeoDataFrame	The input vector data, can be a file path or a GeoDataFrame.	required
output	str	The output raster file path.	required
field	str	Input field name in attribute table. Defaults to 'FID'.	'FID'
assign	str	Assignment operation, where multiple points are in the same grid cell; options include 'first', 'last' (default), 'min', 'max', 'sum', 'number'. Defaults to 'last'.	'last'
nodata	bool	Background value to set to NoData. Without this flag, it will be set to 0.0.	True
cell_size	float	Optionally specified cell size of output raster. Not used when base raster is specified	None
base	str	Optionally specified input base raster file. Not used when a cell size is specified. Defaults to None.	None
callback	fuct	A callback function to report progress. Defaults to None.	None
verbose	bool	Whether to print progress to the console. Defaults to False.	False
to_epsg	integer	Optionally specified the EPSG code to reproject the raster to. Defaults to None.	None

Source code in leafmap/common.py

def vector_to_raster(
    vector,
    output,
    field="FID",
    assign="last",
    nodata=True,
    cell_size=None,
    base=None,
    callback=None,
    verbose=False,
    to_epsg=None,
):
    """Convert a vector to a raster.

    Args:
        vector (str | GeoPandas.GeoDataFrame): The input vector data, can be a file path or a GeoDataFrame.
        output (str): The output raster file path.
        field (str, optional): Input field name in attribute table. Defaults to 'FID'.
        assign (str, optional): Assignment operation, where multiple points are in the same grid cell; options
            include 'first', 'last' (default), 'min', 'max', 'sum', 'number'. Defaults to 'last'.
        nodata (bool, optional): Background value to set to NoData. Without this flag, it will be set to 0.0.
        cell_size (float, optional): Optionally specified cell size of output raster. Not used when base raster is specified
        base (str, optional): Optionally specified input base raster file. Not used when a cell size is specified. Defaults to None.
        callback (fuct, optional): A callback function to report progress. Defaults to None.
        verbose (bool, optional): Whether to print progress to the console. Defaults to False.
        to_epsg (integer, optional): Optionally specified the EPSG code to reproject the raster to. Defaults to None.

    """
    import geopandas as gpd
    import whitebox

    output = os.path.abspath(output)

    if isinstance(vector, str):
        gdf = gpd.read_file(vector)
    elif isinstance(vector, gpd.GeoDataFrame):
        gdf = vector
    else:
        raise TypeError("vector must be a file path or a GeoDataFrame")

    if to_epsg is None:
        to_epsg = 3857

    if to_epsg == 4326:
        raise ValueError("to_epsg cannot be 4326")

    if gdf.crs.is_geographic:
        gdf = gdf.to_crs(epsg=to_epsg)
        vector = temp_file_path(extension=".shp")
        gdf.to_file(vector)
    else:
        to_epsg = gdf.crs.to_epsg()

    wbt = whitebox.WhiteboxTools()
    wbt.verbose = verbose

    goem_type = gdf.geom_type[0]

    if goem_type == "LineString":
        wbt.vector_lines_to_raster(
            vector, output, field, nodata, cell_size, base, callback
        )
    elif goem_type == "Polygon":
        wbt.vector_polygons_to_raster(
            vector, output, field, nodata, cell_size, base, callback
        )
    else:
        wbt.vector_points_to_raster(
            vector, output, field, assign, nodata, cell_size, base, callback
        )

    image_set_crs(output, to_epsg)

view_lidar(filename, cmap='terrain', backend='pyvista', background=None, eye_dome_lighting=False, **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
eye_dome_lighting	bool	Whether to use eye dome lighting. Defaults to False.	False

Raises:

Type	Description
FileNotFoundError	If the file does not exist.
ValueError	If the backend is not supported.

Source code in leafmap/common.py

def view_lidar(
    filename,
    cmap="terrain",
    backend="pyvista",
    background=None,
    eye_dome_lighting=False,
    **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.
        eye_dome_lighting (bool, optional): Whether to use eye dome lighting. Defaults to False.

    Raises:
        FileNotFoundError: If the file does not exist.
        ValueError: If the backend is not supported.
    """

    import sys

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if "google.colab" in sys.modules:
        print("This function is not supported in Google 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 leafmap[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,
                eye_dome_lighting=eye_dome_lighting,
                **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.")

whiteboxgui(verbose=True, tree=False, reset=False, sandbox_path=None)

Shows the WhiteboxTools GUI.

Parameters:

Name	Type	Description	Default
verbose	bool	Whether to show progress info when the tool is running. Defaults to True.	True
tree	bool	Whether to use the tree mode toolbox built using ipytree rather than ipywidgets. Defaults to False.	False
reset	bool	Whether to regenerate the json file with the dictionary containing the information for all tools. Defaults to False.	False
sandbox_path	str	The path to the sandbox folder. Defaults to None.	None

Returns:

Name	Type	Description
object	dict	A toolbox GUI.

Source code in leafmap/common.py

def whiteboxgui(
    verbose: Optional[bool] = True,
    tree: Optional[bool] = False,
    reset: Optional[bool] = False,
    sandbox_path: Optional[str] = None,
) -> dict:
    """Shows the WhiteboxTools GUI.

    Args:
        verbose (bool, optional): Whether to show progress info when the tool is running. Defaults to True.
        tree (bool, optional): Whether to use the tree mode toolbox built using ipytree rather than ipywidgets. Defaults to False.
        reset (bool, optional): Whether to regenerate the json file with the dictionary containing the information for all tools. Defaults to False.
        sandbox_path (str, optional): The path to the sandbox folder. Defaults to None.

    Returns:
        object: A toolbox GUI.
    """
    import whiteboxgui

    return whiteboxgui.show(verbose, tree, reset, sandbox_path)

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	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	Widget	The widget to be displayed when the toolbar is clicked.	None
m	Map	The geemap.Map instance. Defaults to None.	None
position	str	The position of the toolbar. Defaults to "topright".	'topright'

Source code in leafmap/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_image_colormap(image, colormap, output_path=None)

Apply or update a colormap to a raster image.

Parameters:

Name	Type	Description	Default
image	(str, DatasetReader, DataArray)	The input image. It can be: - A file path to a raster image (string). - A rasterio dataset. - A rioxarray DataArray.	required
colormap	dict	A dictionary defining the colormap (value: (R, G, B, A)).	required
output_path	str	Path to save the updated raster image. If None, the original file is updated in-memory.	None

Returns:

Name	Type	Description
str		Path to the updated raster image.

Raises:

Type	Description
ValueError	If the input image type is unsupported.

Source code in leafmap/common.py

def write_image_colormap(image, colormap, output_path=None):
    """
    Apply or update a colormap to a raster image.

    Args:
        image (str, rasterio.io.DatasetReader, rioxarray.DataArray):
            The input image. It can be:
            - A file path to a raster image (string).
            - A rasterio dataset.
            - A rioxarray DataArray.
        colormap (dict): A dictionary defining the colormap (value: (R, G, B, A)).
        output_path (str, optional): Path to save the updated raster image.
            If None, the original file is updated in-memory.

    Returns:
        str: Path to the updated raster image.

    Raises:
        ValueError: If the input image type is unsupported.
    """
    import rasterio
    import rioxarray
    import xarray as xr

    dataset = None
    src_profile = None
    src_data = None

    if isinstance(image, str):  # File path
        with rasterio.open(image) as ds:
            dataset = ds
            src_profile = ds.profile
            src_data = ds.read(1)  # Assuming single-band
    elif isinstance(image, rasterio.io.DatasetReader):  # rasterio dataset
        dataset = image
        src_profile = dataset.profile
        src_data = dataset.read(1)  # Assuming single-band
    elif isinstance(image, xr.DataArray):  # rioxarray DataArray
        source = image.encoding.get("source")
        if source:
            with rasterio.open(source) as ds:
                dataset = ds
                src_profile = ds.profile
                src_data = ds.read(1)  # Assuming single-band
        else:
            raise ValueError("Cannot apply colormap: DataArray does not have a source.")
    else:
        raise ValueError(
            "Unsupported input type. Provide a file path, rasterio dataset, or rioxarray DataArray."
        )

    # Ensure the dataset is single-band
    if dataset.count != 1:
        raise ValueError(
            "Colormaps can only be applied to single-band raster datasets."
        )

    # Update the profile and colormap
    src_profile.update(dtype=src_data.dtype, count=1)

    if not output_path:
        output_path = "output_with_colormap.tif"

    # Write the updated dataset with the colormap
    with rasterio.open(output_path, "w", **src_profile) as dst:
        dst.write(src_data, 1)
        dst.write_colormap(1, colormap)

    return output_path

write_lidar(source, destination, do_compress=None, laz_backend=None)

Writes to a stream or file.

Parameters:

Name	Type	Description	Default
source	str | 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 leafmap/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)

xarray_to_raster(dataset, filename, **kwargs)

Convert an xarray Dataset to a raster file.

Parameters:

Name	Type	Description	Default
dataset	Dataset	The input xarray Dataset to be converted.	required
filename	str	The output filename for the raster file.	required
**kwargs	Dict[str, Any]	Additional keyword arguments passed to the rio.to_raster() method. See https://corteva.github.io/rioxarray/stable/examples/convert_to_raster.html for more info.	{}

Returns:

Type	Description
None	None

Source code in leafmap/common.py

def xarray_to_raster(dataset, filename: str, **kwargs: Dict[str, Any]) -> None:
    """Convert an xarray Dataset to a raster file.

    Args:
        dataset (xr.Dataset): The input xarray Dataset to be converted.
        filename (str): The output filename for the raster file.
        **kwargs (Dict[str, Any]): Additional keyword arguments passed to the `rio.to_raster()` method.
            See https://corteva.github.io/rioxarray/stable/examples/convert_to_raster.html for more info.

    Returns:
        None
    """
    import rioxarray

    dims = list(dataset.dims)

    new_names = {}

    if "lat" in dims:
        new_names["lat"] = "y"
        dims.remove("lat")
    if "lon" in dims:
        new_names["lon"] = "x"
        dims.remove("lon")
    if "lng" in dims:
        new_names["lng"] = "x"
        dims.remove("lng")
    if "latitude" in dims:
        new_names["latitude"] = "y"
        dims.remove("latitude")
    if "longitude" in dims:
        new_names["longitude"] = "x"
        dims.remove("longitude")

    dataset = dataset.rename(new_names)
    dataset.transpose(..., "y", "x").rio.to_raster(filename, **kwargs)

xy_to_window(xy)

Converts a list of coordinates to a rasterio window.

Parameters:

Name	Type	Description	Default
xy	list	A list of coordinates in the format of [[x1, y1], [x2, y2]]	required

Returns:

Name	Type	Description
tuple		The rasterio window in the format of (col_off, row_off, width, height)

Source code in leafmap/common.py

def xy_to_window(xy):
    """Converts a list of coordinates to a rasterio window.

    Args:
        xy (list): A list of coordinates in the format of [[x1, y1], [x2, y2]]

    Returns:
        tuple: The rasterio window in the format of (col_off, row_off, width, height)
    """

    x1, y1 = xy[0]
    x2, y2 = xy[1]

    left = min(x1, x2)
    right = max(x1, x2)
    top = min(y1, y2)
    bottom = max(y1, y2)

    width = right - left
    height = bottom - top

    return (left, top, width, height)

zonal_stats(vectors, raster, layer=0, band_num=1, nodata=None, affine=None, stats=None, all_touched=False, categorical=False, category_map=None, add_stats=None, raster_out=False, prefix=None, geojson_out=False, gdf_out=False, dst_crs=None, open_vector_args={}, open_raster_args={}, **kwargs)

This function wraps rasterstats.zonal_stats and performs reprojection if necessary. See https://pythonhosted.org/rasterstats/rasterstats.html.

Parameters:

Name	Type	Description	Default
vectors	str | list | GeoDataFrame	path to an vector source or geo-like python objects.	required
raster	str | ndarray	ndarray or path to a GDAL raster source.	required
layer	int	If vectors is a path to an fiona source, specify the vector layer to use either by name or number. Defaults to 0	0
band_num	int | str	If raster is a GDAL source, the band number to use (counting from 1). defaults to 1.	1
nodata	float	If raster is a GDAL source, this value overrides any NODATA value specified in the file’s metadata. If None, the file’s metadata’s NODATA value (if any) will be used. defaults to None.	None
affine	Affine	required only for ndarrays, otherwise it is read from src. Defaults to None.	None
stats	str | list	Which statistics to calculate for each zone. It can be ['min', 'max', 'mean', 'count']. For more, see https://pythonhosted.org/rasterstats/manual.html#zonal-statistics Defaults to None.	None
all_touched	bool	Whether to include every raster cell touched by a geometry, or only those having a center point within the polygon. defaults to False	False
categorical	bool	If True, the raster values will be treated as categorical.	False
category_map	dict	A dictionary mapping raster values to human-readable categorical names. Only applies when categorical is True	None
add_stats	dict	with names and functions of additional stats to compute. Defaults to None.	None
raster_out	bool	Include the masked numpy array for each feature?. Defaults to False.	False
prefix	str	add a prefix to the keys. Defaults to None.	None
geojson_out	bool	Return list of GeoJSON-like features (default: False) Original feature geometry and properties will be retained with zonal stats appended as additional properties. Use with prefix to ensure unique and meaningful property names.. Defaults to False.	False
gdf_out	bool	Return a GeoDataFrame. Defaults to False.	False
dst_crs	str	The destination CRS. Defaults to None.	None
open_vector_args	dict	Pass additional arguments to geopandas.open_file(). Defaults to {}.	{}
open_raster_args	dict	Pass additional arguments to rasterio.open(). Defaults to {}.	{}

Returns:

Type	Description
	dict | list | GeoDataFrame: The zonal statistics results

Source code in leafmap/common.py

def zonal_stats(
    vectors,
    raster,
    layer=0,
    band_num=1,
    nodata=None,
    affine=None,
    stats=None,
    all_touched=False,
    categorical=False,
    category_map=None,
    add_stats=None,
    raster_out=False,
    prefix=None,
    geojson_out=False,
    gdf_out=False,
    dst_crs=None,
    open_vector_args={},
    open_raster_args={},
    **kwargs,
):
    """This function wraps rasterstats.zonal_stats and performs reprojection if necessary.
        See https://pythonhosted.org/rasterstats/rasterstats.html.

    Args:
        vectors (str | list | GeoDataFrame): path to an vector source or geo-like python objects.
        raster (str | ndarray): ndarray or path to a GDAL raster source.
        layer (int, optional): If vectors is a path to an fiona source, specify the vector layer to
            use either by name or number. Defaults to 0
        band_num (int | str, optional): If raster is a GDAL source, the band number to use (counting from 1). defaults to 1.
        nodata (float, optional): If raster is a GDAL source, this value overrides any NODATA value
            specified in the file’s metadata. If None, the file’s metadata’s NODATA value (if any)
            will be used. defaults to None.
        affine (Affine, optional): required only for ndarrays, otherwise it is read from src. Defaults to None.
        stats (str | list, optional): Which statistics to calculate for each zone.
            It can be ['min', 'max', 'mean', 'count']. For more, see https://pythonhosted.org/rasterstats/manual.html#zonal-statistics
            Defaults to None.
        all_touched (bool, optional): Whether to include every raster cell touched by a geometry, or only those having
            a center point within the polygon. defaults to False
        categorical (bool, optional): If True, the raster values will be treated as categorical.
        category_map (dict, optional):A dictionary mapping raster values to human-readable categorical names.
            Only applies when categorical is True
        add_stats (dict, optional): with names and functions of additional stats to compute. Defaults to None.
        raster_out (bool, optional): Include the masked numpy array for each feature?. Defaults to False.
        prefix (str, optional): add a prefix to the keys. Defaults to None.
        geojson_out (bool, optional): Return list of GeoJSON-like features (default: False)
            Original feature geometry and properties will be retained with zonal stats
            appended as additional properties. Use with prefix to ensure unique and
            meaningful property names.. Defaults to False.
        gdf_out (bool, optional): Return a GeoDataFrame. Defaults to False.
        dst_crs (str, optional): The destination CRS. Defaults to None.
        open_vector_args (dict, optional): Pass additional arguments to geopandas.open_file(). Defaults to {}.
        open_raster_args (dict, optional): Pass additional arguments to rasterio.open(). Defaults to {}.

    Returns:
        dict | list | GeoDataFrame: The zonal statistics results
    """

    import geopandas as gpd
    import rasterio
    import xarray as xr

    try:
        import rasterstats
    except ImportError:
        raise ImportError(
            "rasterstats is not installed. Install it with pip install rasterstats"
        )
    try:
        if isinstance(raster, str):
            with rasterio.open(raster, **open_raster_args) as src:
                affine = src.transform
                nodata = src.nodata
                array = src.read(band_num, masked=True)
                raster_crs = src.crs
        elif isinstance(raster, rasterio.io.DatasetReader):
            affine = raster.transform
            nodata = raster.nodata
            array = raster.read(band_num, masked=True)
            raster_crs = raster.crs
        elif isinstance(raster, xr.DataArray):
            array = raster
            raster_crs = raster.rio.crs
        else:
            array = raster

        if isinstance(vectors, str):
            gdf = gpd.read_file(vectors, **open_vector_args)
        elif isinstance(vectors, list):
            gdf = gpd.GeoDataFrame.from_features(vectors)
        else:
            gdf = vectors

        vector_crs = gdf.crs

        if gdf.crs.is_geographic:
            if not raster_crs.is_geographic:
                gdf = gdf.to_crs(raster_crs)
        elif gdf.crs != raster_crs:
            if not raster_crs.is_geographic:
                gdf = gdf.to_crs(raster_crs)
            else:
                raise ValueError("The vector and raster CRSs are not compatible")

        if gdf_out is True:
            geojson_out = True

        result = rasterstats.zonal_stats(
            gdf,
            array,
            layer=layer,
            band_num=band_num,
            nodata=nodata,
            affine=affine,
            stats=stats,
            all_touched=all_touched,
            categorical=categorical,
            category_map=category_map,
            add_stats=add_stats,
            raster_out=raster_out,
            prefix=prefix,
            geojson_out=geojson_out,
            **kwargs,
        )

        if gdf_out is True:
            if dst_crs is None:
                dst_crs = vector_crs

            out_gdf = gpd.GeoDataFrame.from_features(result)
            out_gdf.crs = raster_crs
            return out_gdf.to_crs(dst_crs)
        else:
            return result

    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:

Name	Type	Description
float		Map resolution in meters.

Source code in leafmap/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)